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, Work Assignment 0-4 

Zachary Pekar, Work Assignment Manager 

Nancy Riley, Project Officer

ICF International i DRAFT – December 2006 

DISCLAIMER 

This report is being furnished to the U.S. Environmental Protection Agency (EPA) by ICF 

International in partial fulfillment of Contract No. EP-D-06-115, Work Assignment No. 0-4. 

Any opinions, findings, conclusions, or recommendations are those of the authors and do not 

necessarily reflect the views of the EPA or ICF International. 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 should be addressed to Zachary Pekar, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, 

Research Triangle Park, North Carolina 27711 (email: pekar.zachary@epa.gov). 

ICF International ii DRAFT – December 2006 

PREFACE

This document has been prepared in support of the U.S. Environmental Protection Agency’s 

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

document describes the pilot phase of t the human exposure, health 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 insights into their performance and to solicit 

comments from the Clean Air Scientific Advisory Committee (CASAC) and the public on the 

use of these methodologies in the full-scale analysis. Accordingly, the exposure 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 particularly the case for 

models not previously employed for 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 .................................................................................................................3-20 
ICF 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 Sediment 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 Soil 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 Assessment and Blood Pb Modeling Approach.......5-1 
ICF 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 Exposure 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 Population 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 Parameters 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 Water 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 Geometric Standard Deviation..............................................6-22 
ICF 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...............................................................................................................................8-1 
ICF International vii DRAFT – December 2006 

LIST OF EXHIBITS 

Exhibit Page

2-1 Conceptual Model for Pb Human Exposure 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 Pilot Phase Exposure Assessment.....................................2-10 

2-5 Conceptual Model for Pb Ecological Risk Assessment.................................................2-18 

2-6 Overview of Ecological Risk 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 Species in Pike County, Alabama....................................3-18 
ICF 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 Sources for Near Roadway Urban Case 

Study.................................................................................................................. 3-20 

3-15 Summary of Environmental Data Sources 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 Sources 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: Modeled Air Concentrations and Deposition.............4-6 

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

4-4 Primary Pb Smelter Case Study Air Concentration Modeled Results Compared to 

Monitor Values ....................................................................................................4-9 

4-5 Primary Pb Smelter Case Study Deposition Modeled Results Compared 

to Measured Values............................................................................................4-10 

4-6 Wind Rose of Meteorological Data used 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 Measurements 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 Analysis Results (ELM 2005) ......................... 4-14 
ICF International ix DRAFT – December 2006 

4-11 Primary Pb Smelter Case Study: Modeled 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: Summary of Modeled Air Concentrations and 

Deposition.......................................................................................................... 4-25 

4-17 Secondary Pb Smelter Case Study: Concentration 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 Studies in the United States .............................................4-28 

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

4-22 Summary of Secondary Pb Smelter Case Study Soil Concentrations with 

Distance from Source Compared to Measurements .......................................... 4-30 

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

4-24 Enhancement Ratios and Air Concentrations for Near Roadway Urban 

Scenario..............................................................................................................4-34 

4-25 Estimated Inhalation Exposure Concentrations 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 Concentrations in Pacoima, California .......................4-37 
ICF 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: Modeled Indoor Dust Pb Concentrations...............4-39 

4-30 Pb Concentrations in Surface Soil, Corpus 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 Concentrations 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 Biokinetic 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 Exposure Concentrations for the Three 

Case Studies.......................................................................................................5-16 

5-8 Population Distribution of House Dust Exposure Concentrations for the 

Primary Pb Smelter 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 Dietary Pb Intake Estimates...................................................5-19 

5-11 Input Parameter Values for the IEUBK Model..............................................................5-20 
ICF 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-Relevant 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) for 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 Primary Pb Smelter Attainment Scenario ........................6-5 

6-3 Average Exposure Pathway Contributions 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 Uptake, Secondary Pb Smelter Case Study.............6-9 

6-7 Projected IQ Losses from the Near 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 the Leggett and IEUBK Blood Pb Models ........6-18
ICF 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 Drinking Water GI Absorption Fractions on 

Estimated Blood Pb Distributions......................................................................6-20 

6-15 Impacts of Changing Diet and Drinking Water GI Absorption Fractions on 

Estimated IQ Loss Distributions........................................................................6-20 

6-16 Effects of Von Lindern et al. (2003) Soil and Dust Intake 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 Intake Parameters on Estimated 

IQ Loss Distributions, Primary Pb Smelter Case Study ....................................6-22 

6-18 Effects of Varying Individual Variability Estimates on Predicted Blood Pb 

Distributions.......................................................................................................6-23 

6-19 Effect of Changing the GSD for Individual Variability on Predicted IQ Loss 

Distributions.......................................................................................................6-24 

6-20 Effects of Alternative Blood Pb-IQ Models 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 Cutoff Vlaues for IQ Estimation ....................................6-28 

6-24 Impacts of Alternative Models and 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 Bodies 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......................7-10 
ICF International xiii DRAFT – December 2006 

7-6 HQs Calculated for Sediments in Surface 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-Urban 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 Screen –HQs for Measured Dissolved Pb 

Concentrations Compared to Chronic AWQC (i.e., Criterion Continuous 

Concentration or CCC) for the Protection of Aquatic Life for Pb.....................7-15

7-11 Results of Acute Aquatic Risk Screening Assessment –HQs for Measured 

Dissolved Pb Concentrations Compared to Acute AWQC (i.e., Criterion 

Maximum Concentration or CMC) for the Protection of 

Aquatic Life for Pb ............................................................................................7-17 

7-12 Results of Aquatic Risk Screening Assessment – Mining Sites at which 

Dissolved Pb Measurements Exceed AWQC....................................................7-19 

7-13 Results of Aquatic Risk Screening Assessment - 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 Stations at which Dissolved Pb 

Measurements Exceed AWQC, Excluding Mining Sites ..................................7-22 

7-16 Concentrations of Total Pb in Sediments at Locations Near or Co-Located with 

the 15 Sites at which Dissolved Pb Concentrations Exceeded the AWQC, 

Excluding Mining Sites......................................................................................7-24 

 Introduction 

ICF International 1-1 DRAFT – December 2006 

1 1. Introduction 

2 

3 Sections 108 and 109 of the Clean Air Act (CAA) govern the establishment and periodic review 

4 of the National Ambient Air Quality Standards (NAAQS) by the U.S. Environmental Protection 

5 Agency (EPA). These standards are established for pollutants that may reasonably be anticipated 

6 to endanger public health and welfare, and whose presence in the ambient air results from 

7 numerous or diverse mobile or stationary sources. The NAAQS are to be based on air quality 

8 criteria, which are to accurately reflect the latest scientific knowledge useful in indicating the 

9 type and extent of identifiable effects on public health or welfare that may be expected from the 

10 presence of the pollutant in ambient air. The EPA Administrator is to promulgate and 

11 periodically review “primary” (health-based) and “secondary” (welfare-based) NAAQS for such 

12 pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator 

13 is to make revisions in the criteria and standards, and promulgate any new standards, as may be 

14 appropriate. The CAA also requires that an independent scientific review committee advise the 

15 Administrator as part of this NAAQS review process, a function performed by the Clean Air 

16 Scientific Advisory Committee (CASAC). 

17 

18 The EPA is presently conducting a review of the NAAQS for lead (Pb). The EPA’s overall plan 

19 and schedule for this NAAQS review is presented in the Plan for the Review of the National 

20 Ambient Air Quality Standards for Lead (USEPA 2006a). This plan discusses the preparation of 

21 two key documents in the NAAQS review process: an Air Quality Criteria Document (CD) and 

22 a Staff Paper. The CD provides a critical assessment of the latest available scientific information 

23 upon which the NAAQS is to be based (USEPA 2006b). The Staff Paper evaluates the policy 

24 implications of this information and presents staff conclusions and recommendations for 

25 standard-setting options for the Administrator to consider. 

26 

27 In conjunction with preparation of the first draft of the Staff Paper for Pb, EPA’s Office of Air 

28 Quality Planning and Standards (OAQPS) has conducted various policy-relevant assessments, 

29 including pilot-scale quantitative human exposure and health risk assessments and an ecological 

30 risk assessment for selected areas. The methods and results of these assessments are summarized 

31 in Chapters 4 and 6 of the draft Staff Paper (USEPA 2006c) and are described in detail in this 

32 draft document. 

33 

34 Chapter 2 of this document provides an overview of the methods and data used for the pilot 

35 phase human exposure and health risk assessments and ecological risk assessment. An overview 

36 of the selected case studies, including descriptions of the locations, populations in the 

37 surrounding areas, and available emissions estimates and environmental measurements, is 

38 provided in Chapter 3. The methods and data used to estimate Pb concentrations in 

39 environmental media for each case study are described in Chapter 4. Chapters 5 and 6 describe 

40 the human exposure assessment and health risk characterization, respectively, including 

41 descriptions of the methods, models, and data used. The methods and results of the ecological 

42 risk assessment are provided in Chapter 7. The references for this document are provided in 

43 Chapter 8.
 
 Overview of Risk Assessment 

ICF International 2-1 DRAFT – December 2006 

1 2. Overview of Risk Assessment 

2 

3 The human exposure and health risk assessments and ecological risk assessment being conducted 

4 in support of U.S. EPA’s review of the Pb NAAQS focus on the estimation of risk resulting from 

5 exposure to Pb released into ambient air. The preliminary design of these assessments was 

6 described in the draft Analysis Plan (USEPA 2006d). As described in the draft Analysis Plan, 

7 these assessments are being conducted in two phases: the pilot and the full-scale phases. The 

8 pilot phase, described in this report, employed a case study based approach to estimate human 

9 exposure and health risks and ecological risks associated with the following two air quality 

10 scenarios. 

11 

12 • Current Conditions. This scenario estimated risks associated with current conditions at 

13 each case study location.

14 

15 • Attainment of Current NAAQS. This scenario, referred to as “attainment” in this 

16 report, estimated risks assuming attainment of the current NAAQS at the case study 

17 location that is currently not in attainment. 

18 

19 For both air quality scenarios, human and ecological exposures to Pb in air, as well as exposures 

20 to Pb in other media (e.g., soil, indoor dust) resulting from atmospheric deposition of Pb, were 

21 assessed. The sources associated with these exposures are referred to in this report as “policy22 relevant” sources. This assessment also considers policy-relevant “background” Pb exposures, 

23 which include exposures that are not linked to Pb released to air, such as ingestion of drinking 

24 water, ingestion of Pb paint, and other non-air-related exposures. 

25 

26 For the ecological risk assessment, the case study approach was supplemented by a national27 scale screening assessment of Pb concentration measurements in surface waters and sediments 

28 across the United States. 

29 

30 The remainder of this chapter provides an overview of the selected case studies (Section 2.1) and 

31 summaries of the approaches taken for the human exposure and health risk assessments (Section 

32 2.2) and the ecological risk assessment (Section 2.3). 

33 2.1 Case Studies 

34 

35 The selection of case study locations reflected consideration of factors presented in the draft 

36 Analysis Plan, as well as comments received during consultation with CASAC (Henderson 

37 2006). The selection criteria included, but were not limited to, the following: 

38 

39 • Coverage for a variety of ambient air sources of Pb emissions ranging from large 

40 industrial point sources (e.g., primary and secondary Pb smelters) to areas with 

41 potentially significant reentrainment of historically deposited Pb; 

42 

43 • Availability of site-specific measurements for key environmental media (i.e., air, soil, and 

44 indoor dust) and biomonitoring of Pb exposure (i.e., blood Pb levels); and 
 Overview of Risk Assessment 

ICF International 2-2 DRAFT – December 2006 

1 

2 • Consideration of demographics and socioeconomic factors related to exposure (e.g., areas 

3 with higher density of children in likely higher risk locations). 

4 

5 Five case studies were selected for the pilot phase. Some of the selected case studies were 

6 “shared” by the human and ecological assessments (e.g., with one characterization of 

7 environmental conditions serving the purposes of both assessments), while other case studies 

8 were specific to one of the assessments. A brief description of each selected location is provided 

9 below. 

10 

11 • Primary Pb Smelter Location. This case study involved a location associated with a 

12 primary Pb smelting facility. This facility is located in Herculaneum, Missouri, and has 

13 been in operation for more than 100 years. Recent measured air concentrations from the 

14 surrounding area and a robust data set of recent facility configuration information, 

15 emissions estimates, measured media concentrations (soil, indoor dust, surface water, and 

16 sediment), and exposure (i.e., blood Pb) measurements were available for this location. 

17 This case study was used for both the human exposure and health risk assessments and 

18 the ecological risk assessment. 

19 

20 • Secondary Pb Smelter Location. This case study focused on a location associated with 

21 a secondary Pb smelting facility in Troy, Alabama. Recent measured air concentrations 

22 from the area surrounding the facility and facility characterization data (including 

23 emission estimates) were readily available for this location. This case study was used for 

24 both the human exposure and health risk assessments and the ecological risk assessment. 

25 

26 • Near Roadway Urban Location. This location in Houston, Texas, was selected to 

27 provide a case study that reflected the influence of historically deposited Pb near 

28 roadways in urban areas. It covers approximately 2.5 kilometers (km) of roadway near 

29 an air monitoring site and includes residences within 200 meters (m) of this section of 

30 roadway. The location is in a relatively dry region of the country where resuspension is 

31 likely to occur, there are air measurements available from a monitor located close to a 

32 roadway, there are numerous homes along the roadways near this monitor, and there is a 

33 lack of identified point sources of Pb emissions in the surrounding area. This case study 

34 was used for the human exposure and health risk assessments. 

35 

36 • Near Roadway Non-Urban Locations. Two locations were selected to evaluate the 

37 ecological risks of historically deposited Pb in soils near roads in less developed areas, 

38 where exposures to ecological receptors are expected to occur. One location was in 

39 Corpus Christi, Texas, and the other was in Atlee, Virginia, north of Richmond, both near 

40 highly traveled interstates, but less developed than the near roadway urban location. 

41 

42 • Ecologically Vulnerable Location. The Hubbard Brook Experimental Forest, located in 

43 the White Mountain National Forest, near North Woodstock, New Hampshire, was 

44 selected as an ecological case study location. This location was selected because: (1) it 

45 is located in an acidified watershed and is therefore expected to have higher 

46 bioavailability of Pb; (2) there are no identified point sources of Pb in the surrounding 
 Overview of Risk Assessment 

ICF International 2-3 DRAFT – December 2006 

1 area, which allows for an evaluation of impacts of regional background Pb 

2 concentrations; (3) it is located in an elevated area, which is subject to comparatively 

3 higher deposition of Pb due to wind speed and precipitation; and (4) there are available 

4 data on trends (temporal, elevation, etc.) in Pb concentrations in various environmental 

5 media. This assessment only focused on identifying this site and did not involve 

6 characterization of potential impacts associated with measured Pb concentrations. 

7 

8 The specific approach for each case study was tailored to the location, based on the available 

9 data, the characteristics of the emission sources, and the potentially affected populations. 

10 

11 For the ecological risk assessment, these case studies were supplemented by a national-scale 

12 ecological screening risk assessment, consisting of two parts: the surface water portion and the 

13 sediment portion. This screening assessment was fundamentally different from the case study 

14 analyses because it analyzed data from locations across the United States, instead of focusing on 

15 one (or two) specific location(s). An overview of this screening assessment is provided below. 

16 

17 • National-Scale Screening Risk Assessment, Consisting of the Surface Water and the 

18 Sediment Portions. This analysis utilized Pb concentration measurements for 

19 freshwaters across the United States collected by the U.S. Geological Survey as part of 

20 their National Ambient Water Quality Assessment (NAQWA) program (USGS 2004). 

21 Information on land use, Pb air emission sources, and surface water point source 

22 discharges for a subset of these locations also played a role in this analysis. 

23 2.2 Human Exposure and Health Risk Assessments 

24 

25 The pilot phase of the human exposure and health risk assessments was designed to estimate 

26 neurological effects in young children (in terms of IQ loss) associated with exposures to Pb 

27 emitted into ambient air for three case studies: primary Pb smelter, secondary Pb smelter, and 

28 near roadway urban location. This section provides an overview of the approach implemented 

29 for these assessments, beginning with a description of the conceptual model (Section 2.2.1) and a 

30 summary of the spatial scale of the analysis (Section 2.2.2), followed by brief descriptions of the 

31 exposure assessment (Section 2.2.3), risk assessment (Section 2.2.4), and uncertainty/variability 

32 analysis (Section 2.2.5).

33 2.2.1 Conceptual Model 

34 

35 This section presents the conceptual model (Exhibit 2-1) intended to illustrate the elements 

36 pertinent to assessment of public health risks associated with environmental Pb exposures. 

37 Elements included in the pilot phase are indicated with gray background and solid borders. 

38 Exposure pathways involving Pb released into ambient air are shown in bold text and pathways 

39 involving background sources are shown in non-bold (regular) text. A description of how the 

40 different elements of the conceptual model were addressed in the pilot phase, including any 

41 differences from what was presented in the draft Analysis Plan, is provided below: 

42 

43 • Sources. All sources in the conceptual model were addressed, either explicitly or 

44 implicitly, in the pilot phase. As mentioned previously, the pilot phase estimated effects 
 Overview of Risk Assessment 

ICF International 2-4 DRAFT – December 2006 

1 for two categories of sources: policy-relevant sources (which include stationary and 

2 mobile sources and outdoor soil which contains historically deposited Pb) and 

background (which includes diet,1 3 drinking water, indoor Pb paint, and other 

4 miscellaneous sources). To the extent possible, estimates of blood Pb levels were 

5 differentiated by the contributing exposure pathways. 

6 

7 • Pathways. Exhibit 2-1 is intended to generally illustrate the many pathways by which Pb 

8 emitted into the environment becomes available for human exposure. For the purposes of 

9 this assessment, the pathways that pass through ambient air are of particular interest; all 

10 other pathways are considered background. For the pilot phase, the contribution of 

11 reentrained outdoor soil to ambient air concentrations was not estimated separately from 

12 the contribution from other emission sources.

13 

14 • Routes. The ingestion and inhalation routes are considered the primary routes of human 

15 exposure to environmental Pb, with the ingestion route expected to be the more 

16 significant. Both routes were included in the pilot phase. 

17 

18 • Exposed Populations. For the purposes of the pilot phase, population groups were 

19 identified based primarily on age or life stage. Exposures were characterized in the pilot 

20 phase for young children (ages 0 to 7). 

21 

22 • Internal Disposition. While Pb is distributed throughout the body, bone is an 

23 established site of internal accumulation of Pb and blood is an established internal dose 

24 metric for purposes of both exposure and risk assessment. The pilot phase relied on 

25 blood Pb with corresponding dose-response functions to estimate risks. The biokinetic 

26 models used in this analysis recognize the role of bone as a reservoir with the potential to 

27 act as a source and storage site. 

28 

29 • Endpoints. As illustrated in Exhibit 2-1, numerous health endpoints are recognized in 

30 the Air Quality Criteria Document (CD) as associated with Pb exposures. The endpoints 

31 of interest for this assessment are those associated with the range of exposures expected 

32 to closely reflect current levels, and for which there is adequate information to develop 

33 quantitative risk assessments. Recognizing this, the pilot phase focused on neurological 

34 effects in children. 

35 

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

37 

 

1

 Note that exposure to Pb through the diet likely reflects 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 exposure was treated as purely 

background. 
 Overview of Risk Assessment 

ICF International 2-5 DRAFT – December 2006 

Exhibit 2-1. Conceptual Model for Pb Human Exposure and Health Risk Assessmentsa 1 

2 
 Overview of Risk Assessment 

ICF International 2-6 DRAFT – December 2006 

1 An overview of the approach developed to implement the selected elements of the conceptual 

2 model for the pilot phase is provided in Exhibit 2-2. This exhibit shows the key types of 

3 information and models involved in each part of the pilot phase and how they are related to each 

4 other and to the other phases of the analysis. Exhibit 2-3 summarizes the use of these 

5 information types and models for each case study. As indicated in these exhibits, the specific 

6 approach for each case study differed based on the nature of the case study (e.g., type of source, 

7 locations of populations) and the site-specific measurements available. In cases where the 

8 available measurements were not sufficient to characterize the study area (e.g., due to 

9 insufficient spatial coverage), these data were used for performance evaluation. The following 

10 sections describe each part of the pilot phase in more detail. 
 Overview of Risk Assessment 

ICF International 2-7 DRAFT – December 2006 

1 Exhibit 2-2. Overview of Human Exposure and Health Risk Assessments’ 

2 Approach for the Pilot Phase 

3 
 Overview of Risk Assessment 

ICF International 2-8 DRAFT – December 2006 

Exhibit 2-3. Models and Measurements Used 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 

1 2.2.2 Spatial Scale 

2 

3 To capture the spatial variability in media and exposure concentrations, a GIS-based “spatial 

4 template” was created for each case study. This template defines the outer boundary and the 

5 spatial units within this boundary for each case study. Due to the inherent differences between 

6 the case studies, such as the environmental setting, locations of populations, source types, and 

7 availability of measurements, the spatial template for each case study was different. Detailed 

8 descriptions of the templates are provided in Chapter 4. The spatial template for each case study 

9 location is summarized below: 

10 

11 • Primary Pb Smelter Location. The outer boundary of this study area was set to 10 km, 

12 which was expected to capture the population experiencing the most significant impacts 

13 of the facility’s emissions. To provide sufficient spatial resolution to capture 

14 concentration gradients, while minimizing the number of computations required for 

15 estimating soil concentrations, blood Pb levels, and risks, the study area was divided into 

16 a combination of U.S. Census blocks and block groups. This was done by first modeling 

17 air concentrations at the centroid of each U.S. Census block within the study area. Based 

on these estimated concentrations, locations with higher gradients in air concentrations2 18 

19 used blocks as the spatial unit. All other locations used block groups. Air concentrations 

20 for block groups were estimated by calculating the area-weighted average of the block 

21 values. Soil concentrations were estimated for the same combination of blocks and block 

22 groups as the air concentrations. 

23 

24 • Secondary Pb Smelter Location. The outer boundary of this study area was also set to 

25 10 km, which was expected to capture the population experiencing the most significant 

26 impacts of the facility’s emissions. Air concentrations and deposition were modeled for 

27 the centroid of each U.S. Census block within the study area. These estimated 

28 concentrations indicated that relatively high gradients exist across the blocks in nearly all 

29 of the block groups in the study area, and thus all locations used blocks as the spatial unit. 

30 Soil concentrations were estimated for the same spatial units (i.e., blocks) as the air 

31 concentrations.

32 

33 • Near Roadway Urban Location. The boundaries of this study area were set based on 

34 the presence of an approximately 2.5 km stretch of roadway adjacent to the selected air 

35 monitoring site. This section of roadway was selected based on data availability for 

36 roadway traffic volume and the assumption that the measured air concentrations at the 

37 monitoring site were a reasonable representation of concentrations along that length of 

38 roadway. The “depth” of the study area (i.e., how far the study area extends from the 

39 roadway) was set based on published data (ICF 2005) for other locations suggesting that 

40 emissions on roadways can contribute to ambient concentrations adjacent to the roadway 

41 out to about 200 m. This study area was then subdivided into three zones (i.e., 0 to 12 m, 

42 12 to 75 m, and 75 to 200 m) on either side of the roadway, based on locations of soil 

 

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 

1 measurements and factors relating ambient concentrations in these regions to 

2 concentrations directly over the roadway.

3 

4 2.2.3 Exposure Assessment 

5 

6 In the exposure assessment, blood Pb levels were estimated by first estimating Pb concentrations 

7 in environmental media (i.e., air, soil, indoor dust) and then modeling blood Pb levels for the 

8 populations exposed to these media. A summary of the timeframe associated with estimated 

9 media concentrations and population data used in the pilot phase is provided in Exhibit 2-4. 

10 

11 Exhibit 2-4. Timeframe Reflected in the Pilot Phase Exposure Assessment 

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 

12 The remainder of this section describes how media concentrations were estimated for each case 

13 study and how blood Pb modeling was used in the assessment. 

14 2.2.3.1 Estimation of Media Concentrations 

15 

16 This section summarizes the approach for estimating media concentrations for each case study in 

17 the pilot phase. In order to model the exposure pathways identified in Exhibit 2-1, Pb 

18 concentrations in the following media are required: 

19 

20 • Air, 

21 • Outdoor soil, and 

22 • Indoor dust. 

23 

24 For the pilot phase, concentrations in these media were estimated for both the current conditions 

25 and attainment air quality scenarios. The approach for estimating media concentrations for these 

26 scenarios depended on the case study. 
 Overview of Risk Assessment 

ICF International 2-11 DRAFT – December 2006 

1 Primary Pb Smelter: For the primary Pb smelter case study, a combination of measurements 

2 and modeling was used to characterize media concentrations. Substantial analysis has been 

3 performed for this location by U.S. EPA Region 7 and the facility. As a result, a robust data set 

4 of ambient air monitoring, outdoor soil measurements, surface water and sediment 

5 measurements, and indoor dust measurements, as well as air modeling input values, were 

6 available. Ambient air concentrations for the current conditions scenario were characterized 

7 using the Industrial Source Complex – Plume Risk Model Enhancements (ISC-Prime) model 

8 (Schulman et al. 1997). This was the only case study with estimated ambient air concentrations 

9 that exceeded the current NAAQS standard, thus requiring an additional assessment of ambient 

10 air concentrations for the attainment scenario. Ambient air concentrations for the attainment 

11 scenario were estimated by first calculating quarterly average concentrations for the current 

12 conditions scenario, replacing any estimated concentrations that exceeded the NAAQS with the 

current standard (1.5 μg/m3 13 ), and then calculating annual average concentrations from these 

14 quarterly averages. 

15 

16 Inhalation exposure concentrations were estimated from these ambient air concentrations to 

17 account for variability in daily mobility for the population of interest (young children). In this 

18 context, mobility refers to the time spent in different locations, such as outside at home, inside at 

19 school, and in vehicles. These concentrations were estimated using ratios for Pb of estimated 

20 exposure concentrations to ambient air concentrations (derived from the exposure and ambient 

21 air concentrations estimated as part of the 1999 National-Scale Air Toxics Assessment [USEPA 

22 2006e]). These ratios, which are age- and census-tract-specific, were applied to estimated 

23 ambient concentrations for the current condition and attainment scenarios to calculate inhalation 

24 exposure concentrations specific to age and location of the study population. 

25 

26 Part of this study area has undergone substantial soil monitoring and remediation activities, 

27 resulting in very different spatial coverage of available soil measurements within the remediation 

28 zone compared to outside of it. For locations within the soil remediation zone, which extends 

29 approximately 1.5 km from the facility, soil concentrations were estimated using available 

30 measurements for the soil currently in those locations. Concentrations of Pb in soil outside of 

31 the soil remediation zone were estimated using a statistical regression equation based on 

32 available pre-remediation measurements. The same soil concentrations were used for the current 

33 conditions and attainment scenarios. 

34 

35 As with soil concentrations, indoor dust concentrations of Pb were estimated using different 

36 approaches for houses within the soil remediation zone than for those outside of the zone. For 

37 residences within the soil remediation zone, an empirical model, developed from air monitoring 

38 data and post-remediation indoor dust measurements available within the zone, was used. For 

39 locations outside the remediation zone, indoor dust concentrations were estimated using a pooled 

40 analysis model, based on data from a variety of industrial and urban/industrial studies (USEPA 

41 1989, Appendix B). Both models estimated indoor dust concentrations for both the current 

42 conditions and attainment scenarios based on ambient air and outdoor soil concentrations, 

43 estimated as described above. 

44 
 Overview of Risk Assessment 

ICF International 2-12 DRAFT – December 2006 

1 Secondary Pb Smelter: For this case study, ambient air concentrations were estimated using 

2 the American Meteorology Society/Environmental Protection Agency Regulatory Model 

3 (AERMOD) (USEPA 2004a). Based on these ambient air concentrations, inhalation exposure 

4 concentrations were estimated using the same approach as for the primary Pb smelter case study 

5 (described above). Outdoor soil concentrations were estimated using the soil modeling approach 

6 from EPA’s Multiple Pathways of Exposure (MPE) methodology (USEPA 1998) and deposition 

7 outputs from AERMOD. Indoor dust concentrations were estimated using a pooled analysis 

8 model, based on data from a variety of industrial and urban/industrial studies (USEPA 1989, 

9 Appendix B), which relates ambient air concentrations to indoor dust levels (outdoor soil was not 

10 considered explicitly as an independent variable). 

11 

12 Near Roadway Urban Location: For this case study, air monitoring data collected adjacent to 

13 a roadway in Houston, Texas, were used to define the ambient air concentrations for the site. 

14 These air monitoring data were combined with data from the literature characterizing the spatial 

15 gradient in particulate matter concentrations near roadways to estimate air concentrations in two 

16 distance bands from the roadway: 0 to 75 m and 75 to 200 m (ICF 2005). Based on these 

17 ambient air concentrations, inhalation exposure concentrations were calculated using the same 

18 approach used for the primary Pb smelter case study (described above). No adequate site19 specific outdoor soil measurements were available for this site or elsewhere in Houston, and thus 

20 Pb concentrations in outdoor soil were based on measurements obtained adjacent to a similar 

21 roadway in Corpus Christi, Texas, taking into consideration other soil Pb measurements reported 

22 in the literature and values obtained from EPA’s SPECIATE data base. Based on these data and 

23 the distance bands used to characterize air concentrations, soil concentrations were estimated for 

24 three distance bands from the roadway: 0 to 12 m, 12 to 75 m, and 75 to 200 m. Indoor dust 

25 concentrations were estimated using the pooled analysis model used for the outer zone of the 

26 primary Pb smelter case study that relates ambient air and outdoor soil concentrations to indoor 

27 dust levels (USEPA 1989, Appendix B). 

28 2.2.3.2 Blood Pb Modeling 

29 Blood Pb levels were predicted for a child population at each case study location using two 

30 different biokinetic models: the Integrated Exposure Uptake Biokinetic Model for Children 

31 (IEUBK) (USEPA 2005a) and the International Commission for Radiation Protection model 

(Leggett 1993).3 32 Inputs to these models included the estimated exposure media (air, soil, and 

33 indoor dust) concentrations of Pb for each case study, as well as values for other parameters that 

34 influence blood Pb levels. 

35 The same fundamental approach was used to estimate population distributions of blood Pb levels 

36 at each of the three case study locations. This approach involved two steps. The first step 

37 involved applying the IEUBK and Leggett models to predict central tendency blood Pb levels for 

38 children within the study area for each case study location. The estimated blood Pb levels were 

39 then parsed into the fraction associated with policy-relevant background and the fraction 

40 associated with policy-relevant sources, with the latter category being further subdivided into 

 

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 

1 contributions from inhalation, soil ingestion and indoor dust ingestion exposure pathways. For 

2 the purposes of this pilot phase assessment, drinking water and diet exposure pathways are 

3 categorized as policy-relevant background, although it is recognized that a portion of Pb in both 

4 pathways may derive from policy-relevant sources. 

5 The second step involved implementing a probabilistic model to generate population 

6 distributions of blood Pb levels for children in each case study location based on consideration of 

7 three key factors: 

8 1. The central tendency blood Pb levels generated by the blood Pb model for each U.S. 

9 Census block and block group in the study area (from the first step described above); 

10 2. Demographic data (i.e., distribution of children 0 to 7 years of age across the blocks and 

11 block groups comprising a given study area); and 

12 3. A geometric standard deviation characterizing inter-individual variability in blood Pb 

13 level (e.g., reflecting differences in behavior and biokinetics related to Pb). 

14 

15 This approach produced a distribution of modeled children’s blood Pb levels for each case study 

16 location. Each of the individual modeled blood Pb levels was further differentiated to show the 

17 relative contribution of policy-relevant background and sources to total blood Pb. 

18 2.2.4 Risk Assessment 

19 

20 As discussed above, IQ impacts in exposed children have been selected as the endpoint for risk 

21 quantification in the pilot phase assessment, on the basis of a large body of evidence establishing 

22 the occurrence of the IQ effects at low exposure and blood Pb levels. The characterization of the 

23 IQ loss impacts for the pilot phase assessment involved generating a distribution of IQ loss 

24 estimates for the set of children simulated in the exposure assessment using the estimated blood 

25 Pb levels for these children and a blood Pb concentration-response relationship for IQ loss. The 

26 selected concentration-response relationships are the log-normal concentration-response 

functions for concurrent4 27 and lifetime average blood Pb concentrations from a pooled analysis of 

28 epidemiology studies focusing on IQ loss in children (Lanphear et al. 2005). 

29 

30 For the pilot phase, cutpoints of 2.4 μg/dL (concurrent exposure metric) and 6.1 μg/dL (lifetime 

31 average exposure metric) were selected as blood Pb levels below which IQ loss was not to be 

32 predicted. The selection of these cutpoints is based on consideration for the blood Pb level at 

33 which confidence in being able to characterize the shape of the concentration-response functions 

34 diminishes significantly. For example, for the concurrent blood Pb concentration response 

35 function, less than five percent of the children from the pooled analysis had concurrent blood Pb 

36 levels below 2.4 μg /dL (Lanphear et al. 2005), which suggests an increased uncertainty in the 

37 functional form below this level of exposure. 

38 

 

4

 In the epidemiological studies, “concurrent” 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 more 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 

1 As with the exposure analysis, the contributions of policy-relevant sources and policy-relevant 

2 background exposure pathways to overall IQ loss are differentiated. These results are presented 

3 using two primary risk metrics, as described below: 

4 • Population risk percentiles. This metric characterizes the degree of IQ loss associated 

with policy-relevant sources for specific percentiles of the child population (e.g., the 50th 5 , 

90th, 95th, 99th 6 percentile modeled child). This category of metric provides perspective on 

7 the distribution of IQ loss resulting from policy-relevant sources in each study area, 

ranging from the typical or average child (50th 8 percentile or mean) to children 

experiencing higher exposures (e.g., 90th and 99th 9 percentiles). 

10 • Child frequency counts associated with specific risk percentiles. This metric 

11 characterizes the number of children, for a given study area, associated with each of the 

12 population percentiles (e.g., 25 children in a particular study area population are 

predicted to have risk levels at or above the 99th 13 percentile). It provides a perspective on 

14 the number of children associated with various degrees of IQ loss for a particular case 

15 study. 

16 2.2.5 Uncertainty/Variability Analysis 

17 For the pilot phase assessment, sensitivity analysis techniques were used to examine the issue of 

18 uncertainty and its impact on exposure and risk estimates. This sensitivity analysis consisted of a 

19 series of “one-at-a-time” analyses conducted to evaluate the impacts of modeling methods and 

20 parameter inputs on estimated blood Pb and IQ loss distributions. The general approach 

21 employed was to evaluate changes in risk metrics associated with changing single models or 

22 input parameter values away from those used in a “baseline” analysis. 

23 The baseline risk analysis focused on the current conditions scenario for the primary Pb smelter 

24 case study. Note that while the baseline models and parameter values are believed to provide a 

25 credible estimate of blood Pb and IQ changes, it is not suggested that the baseline model is 

26 uniquely the “best,” or that it provides demonstrably “central tendency” risk estimates. 

27 The sensitivity analysis completed for the pilot phase focused on those modeling elements 

28 (including input datasets and modeling steps) believed to have the greatest potential for 

29 significantly impacting exposure and risk modeling, including the following: 

30 • Indoor dust prediction approach. Given its notable contribution to blood Pb levels, the 

31 sensitivity of the blood Pb and IQ change estimates to the selected approach used to 

32 estimate indoor dust concentrations was analyzed. This analysis evaluated the effect of 

33 using different regression models on distributions of estimated indoor dust Pb 

34 concentrations. 

35 • Blood Pb model selection. Blood Pb metrics developed using IEUBK were compared to 

36 those based on the Leggett biokinetic model, as well as an “empirical” blood Pb model 

37 developed by Lanphear et al. (1998). 
 Overview of Risk Assessment 

ICF International 2-15 DRAFT – December 2006 

1 • Exposure, intake, and update value selection. Two variations in gastrointestinal 

2 absorption and soil and dust ingestion parameters were evaluated. In one set of 

3 sensitivity runs, the gastrointestinal absorption fraction for dietary and drinking water 

4 ingestion was varied above and below the central tendency value used in the baseline 

5 assessment. For the second set of sensitivity runs, an alternative set of inputs for soil and 

6 dust absorption and relative ingestion amounts of soil versus dust were evaluated for their 

7 impacts on estimated blood Pb and IQ loss distributions. These values for the second set 

8 of runs were derived by Von Lindern et al. (2003) from a statistical analysis of soil, dust, 

9 and blood Pb distributions at a large mining/smelting “Superfund” site. 

10 • Blood Pb GSD selection. The probabilistic exposure model was run with two alternative 

11 values of the blood Pb GSD and the results were compared to the results using the 

12 baseline blood Pb GSD. 

13 • Concentration-response function for IQ loss. Three alternative concentration-response 

14 functions for IQ loss were evaluated. The first two consisted of the upper and lower 95 

15 percent confidence limits on the log-linear regression model, which were derived based 

16 on a reanalysis of the Lanphear et al. model results (USEPA 2006a). The third 

17 alternative consisted of the piecewise linear version of the Lanphear et al. (2005) model.

18 • Blood Pb metrics. This analysis evaluated the magnitude of impact on estimated IQ 

19 losses associated with using the following alternative blood Pb metrics: lifetime average 

20 and peak (defined as the highest annual average). These results were compared to the 

21 results estimated using concurrent blood Pb level distributions. 

22 • Blood Pb cutpoint for IQ loss. The sensitivity of estimated IQ change to 50 percent 

23 reductions in the blood Pb cutpoints for the concurrent and lifetime average blood Pb 

24 levels was evaluated. 

25 Ultimately, this sensitivity analysis serves two primary purposes: 

26 

27 1. Determining which of the modeling elements included in the sensitivity analysis has the 

28 greatest impact on risk modeling, which may help guide future efforts to refine the 

29 overall assessment; and 

30 2. Obtaining a semi-qualitative feel for the potential magnitude of overall uncertainty in the 

31 risk results. 

32 

33 This type of sensitivity analysis does not, however, allow for a rigorous assessment of 

34 probabilistically-defined confidence in specific risk percentiles to be quantified (that only being 

35 possible with a formal uncertainty analysis). 
 Overview of Risk Assessment 

ICF International 2-16 DRAFT – December 2006 

1 2.3 Ecological Risk Assessment 

2 

3 The ecological risk assessment involved four case studies and a national-scale screening 

4 assessment. These analyses 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 location. Activities for the fourth 

7 case study (ecologically vulnerable location) focused on identification and description and did 

8 not include risk analyses. 

9 

10 This section provides an overview of the approaches implemented for these analyses, beginning 

11 with a description of the conceptual model (Section 2.3.1) and a summary of the spatial scale of 

12 the analysis (Section 2.3.2), followed by brief descriptions of the exposure assessment (Section 

13 2.3.3), effects assessment (Section 2.3.4), risk characterization (Section 2.3.5), and uncertainty 

14 and variability assessment (Section 2.3.6).

15 2.3.1 Conceptual Model 

16 

17 This section presents the conceptual model (Exhibit 2-5) intended to illustrate the elements 

18 pertinent to assessment of ecological risks associated with environmental Pb exposures. 

19 Elements addressed either explicitly or implicitly in the pilot phase are indicated with black text. 

20 Components with gray text were not addressed in the pilot phase due to uncertainty regarding 

21 available data and modeling tools. A description of how the different elements of the conceptual 

22 model were addressed in the pilot phase, including any differences from what was presented in 

23 the draft Analysis Plan, is provided below: 

24 

25 Sources. Sources associated with Pb released into ambient air were addressed, either explicitly 

26 or implicitly, in this assessment. These sources are indicated in black text in Exhibit 2-5. When 

27 measurements were used, the analysis attempted to focus on those pertaining to Pb initially 

28 released into ambient air, isolating contributions from policy-relevant background (i.e., non-air) 

29 sources to the extent possible. 

30 

31 Pathways. This assessment focused on policy-relevant exposure pathways involving Pb 

32 released to ambient air and subsequently transported to soil, surface water, and sediment. Direct 

33 inhalation exposures for birds and mammals were not addressed because ecological exposures 

34 are expected to be primarily through ingestion of Pb in and on their foods and through incidental 

35 ingestion of Pb in soils. Because Pb accumulates in soils, the ingestion exposures are expected 

36 to be much higher than any inhalation exposures. In addition, the human exposure and health 

37 risk assessments are expected to be protective of inhalation exposures for wildlife because the 

38 critical effect under evaluation for humans (i.e., IQ loss) is more sensitive to Pb exposure than 

39 the effects of Pb on growth, reproduction, or survival that are relevant to wildlife population 

40 risks. 

41 

42 Organisms. Potential ecological impacts for both terrestrial receptors (e.g., populations of birds, 

43 mammals, soil invertebrates, plants) and freshwater aquatic organisms (e.g., benthic 

44 communities, water-column communities) were considered in the pilot phase. 

45 
 Overview of Risk Assessment 

ICF International 2-17 DRAFT – December 2006 

1 Assessment Endpoints. The assessment endpoints for assessing ecological risks associated with 

2 Pb deposition from air are adverse effects on the survival, growth, and reproduction of exposed 

3 ecological receptors. 

4 

5 Risk Metrics. Assessment of ecological risk for this assessment involves assessing the potential 

6 for Pb exposures to be toxic to ecological receptors at the organism level and to adversely affect 

7 growth and reproductive rates at the population level. These effects will be evaluated in each 

8 case study (and in the national-scale screening assessment) using the hazard quotient (HQ) 

9 approach, in which HQs are calculated by dividing environmental medium-specific exposure 

10 estimates by the appropriate ecotoxicity screening values. 

11 An overview of the approach developed to implement the selected elements of the conceptual 

12 model for the ecological risk assessment is provided in Exhibit 2-6. This exhibit shows the key 

13 types of information and models involved in each part of the assessment and how they are related 

14 to each other and to the other parts of the analysis. Exhibit 2-7 summarizes the use of these 

15 information types and models for each case study. As indicated in these exhibits, the specific 

16 approach for each case study differed based on the nature of the case study (e.g., type of source, 

17 locations of populations) and the site-specific measurements available. In cases where the 

18 available measurements were not sufficient to characterize the study area (e.g., due to

19 insufficient spatial coverage), these data were used for performance evaluation. The following 

20 sections describe each of the parts of the assessment in more detail. 

21 
 Overview of Risk Assessment 

ICF International 2-18 DRAFT – December 2006 

Exhibit 2-5. Conceptual Model for Pb Ecological Risk Assessmenta, b 1 

2 
 Overview of Risk Assessment 

ICF International 2-19 DRAFT – December 2006 

1 Exhibit 2-6. Overview of Ecological Risk Assessment Approach 

2 
 Over

ICF International 2-20 D

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

Primary Pb Smelter Case 

Study 

Secondary Pb 

Smelter Case 

Study 

Near Roadway Non-Urban 

Case Study 

Ecologically 

Vulnerable Ca

Study 

Location Herculaneum, Missouri Troy, Alabama Corpus Christi, Texas Atlee, Virginia Hubbard Broo

Experimental Fo

New Hampsh

Spatial extent and resolution 

Approximately 6 km 

diameter, centered on point 

source 

U.S. Census 

blocks 

• Corpus Christi: single 

transect perpendicular to road; 0.5 to 4 m from road 

• Atlee: 140 m section of 

road; 2 to 30 m from road 

oblong basin abo

km long by 5 km 

Exposure Assessment: Estimating Media Concentrations 

Models n/a AERMODa Deposition n/a n/a 

to soil Measurements n/a n/a n/a 12 samples 

Models n/a MPEb n/a n/a 

Soil conc. 

Measurements 

Site-specific conc. of total 

Pb in soil samples 

(26 locations) 

n/a 

Site-specific conc. of total 

Pb in soil samples 

(Corpus Christi: 

2 locations; 

Atlee: 26 locations) 

Site-specific con

total Pb in so

samples 

(124 locations

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

Surface water conc. Measurements 

Site-specific conc. of 

dissolved Pb in water 

column samples from eight 

water bodies/drainage areas 

(30 locations) 

n/a n/a 

Site-specific con

dissolved Pb in w

column sampl

(locations no

reported) 

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

Sediment conc. Measurements 

Site-specific conc. of total 

Pb in sediment samples 

from five water 

bodies/drainage areas (69 

locations) 

n/a n/a n/a 
 Over

ICF International 2-21 D

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

Primary Pb Smelter Case 

Study 

Secondary Pb 

Smelter Case 

Study 

Near Roadway Non-Urban 

Case Study 

Ecologically 

Vulnerable Ca

Study 

Ecological Risk Assessment 

Soil Soil screening values developed based on U.S. EPA Superfund Ecological Soil Screening Level (Eco-SSL) methodology c n/a 

Freshwater – 

water column 

U.S. EPA Pb freshwater 

AWQC for aquatic life 

derived based on sitespecific measured water 

hardness (conc. of CaCO3)

c

n/a n/a n/a 

Ecotoxicity Screening Levels 

Freshwater – 

sediment 

Sediment screening values 

based on MacDonald et al. 

(2000) sediment quality 

assessment guidelines c

n/a n/a n/a 

a 1 American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) (USEPA 2004a) b 2 Multiple Pathways of Exposure (MPE) (USEPA 1998) c 3 These values are based on measured ecotoxicity data and other types of measured data (e.g., soil screening levels evaluate measured f

4 rates for birds and mammals).
 Overview of Risk Assessment 

ICF International 2-22 DRAFT – December 2006 

1 2.3.2 Spatial Scale 

2 

3 To capture the spatial variability in media and exposure concentrations, a GIS-based “spatial 

4 template” was created for each case study. This template defines the outer boundary and the 

5 spatial units within this boundary for each case study. Due to the inherent differences between 

6 the case studies, such as the environmental setting, locations of populations, source types, and 

7 availability of measurements, the spatial template for each case study was different. Detailed 

8 descriptions of the templates are provided in Chapter 4. The spatial template for each case study 

9 location is summarized below: 

10 

11 • Primary Pb Smelter Location. Overall, the spatial template for this case study is the 

12 same as that used for the human exposure and health risk assessments of this location. 

13 For the ecological risk assessment, however, the spatial template also included a surface 

14 water body within 2.5 km of the facility.

15 

16 • Secondary Pb Smelter Location. The spatial template for this case study is the same as 

17 that used for the human exposure and health risk assessments of this location.

18 

19 • Near Roadway Non-Urban Locations. The characterization of the two locations 

20 selected for this case study relied upon available soil measurements. For the Corpus 

21 Christi, Texas location, the template includes a single transect of soil perpendicular to the 

22 road extending approximately 4 m from the edge of the road. For the Atlee, Virginia 

23 location, the template includes a band of soil approximately 30 m wide located adjacent 

24 to a 140 m section of I-95.

25 

26 • Ecologically Vulnerable Location. This location was selected and described as part of 

27 the assessment, but not subjected to any further analysis. Thus, no spatial template was 

28 developed.

29 

30 • National-Scale Ecological Screening Assessment: Surface Water and Sediment 

31 Portions. The spatial scale of this analysis included parts of watershed basins from all 

32 regions of the United States, accounting for drainage from approximately 50 percent of 

33 the United States land base. 

34 2.3.3 Exposure Assessment 

35 

36 The measures of exposure for the pilot phase are total Pb concentrations in soil, dissolved Pb 

37 concentrations in fresh surface waters (water column), and total Pb concentrations in freshwater 

38 sediments. Exposure concentrations were estimated for the three case studies and the national39 scale screening assessment as described below. 

40 

41 • For the primary Pb smelter case study, measured concentrations of total Pb in soil, 

42 dissolved Pb in surface waters, and total Pb in sediment were used to develop point 

43 estimates for locations with Pb thought to be associated with atmospheric Pb deposition, 

44 rather than with non-air sources, such as runoff from waste storage piles. 
 Overview of Risk Assessment 

ICF International 2-23 DRAFT – December 2006 

1 

2 • For the secondary Pb smelter case study, soil concentrations of Pb were estimated using 

3 fate and transport modeling based on EPA’s MPE methodology (USEPA 1998). The 

4 pilot phase of the assessment was limited to soil pathways for this case study. 

5 

6 • For the near roadway non-urban case study, soil concentration measurements collected 

7 for two locations adjacent to interstate highways, one in an area of fairly high-density 

8 development (Corpus Christi, Texas) and another in an area of medium-density 

9 development (Atlee, Virginia), were used to develop point estimates of Pb associated 

10 with historical deposition. The pilot phase of the assessment was limited to soil pathways 

11 for this case study. 

12 

13 • In the national-scale surface water and sediment screening assessment, measurements of 

14 dissolved Pb concentrations in surface water and total Pb concentrations in sediments for 

15 locations across the United States were used. Air emissions, water discharge, and land 

16 use data for the areas surrounding these locations were assessed to identify locations 

17 where atmospheric Pb deposition may be expected to contribute to potential ecological 

18 impacts. The exposure assessment focused on these locations. 

19 2.3.4 Effects Assessment 

20 

21 The tools used in the ecological risk assessment for soils, fresh surface water bodies, and 

22 freshwater sediments included the following screening-level ecotoxicity values. 

23 

24 • For terrestrial organisms, soil screening values were developed for the NAAQS review 

25 based on the methodology for Ecological Soil Screening Levels (Eco-SSLs) developed 

26 by EPA’s Office of Solid Waste and Emergency Response (USEPA 2005b; USEPA 

27 2005c) 

28 

29 • For aquatic organisms in the water column, EPA-recommended ambient water quality 

30 criteria (AWQC), expressed as concentrations of Pb in the water column, were used. 

31 

32 • For benthic organisms, the sediment screening values used were sediment quality 

33 assessment guidelines developed by MacDonald et al. (2000; reported in MacDonald et 

34 al. 2003). 

35 2.3.5 Risk Characterization 

36 

37 The HQ approach was used for three case studies (primary Pb smelter, secondary Pb smelter, and 

38 near roadway non-urban) and the national-scale ecological screening risk assessment, consisting 

39 of the surface water portion and the sediment portion. Measures of exposure for each location 

40 were divided by ecotoxicity screening values for each environmental medium (i.e., point 

41 estimates of Pb concentrations in soil, surface water, and sediment were divided by the 

42 corresponding ecotoxicity screening value). For each case study, as well as for the national-scale 

43 ecological screening assessment’s surface water and sediment portions, HQ values were 

44 calculated where estimates of media Pb concentrations were available. 
 Overview of Risk Assessment 

ICF International 2-24 DRAFT – December 2006 

1 2.3.6 Uncertainty/Variability Analysis 

2 

3 For the ecological risk assessment, a qualitative evaluation of uncertainties was conducted and is 

4 described in Section 7.3. 
 Case Study Descriptions 

ICF International 3-1 DRAFT – December 2006 

1 3. Case Study Descriptions 

2 

3 As discussed in Chapter 2, a case study-based approach was employed for the pilot phase to 

4 estimate human exposure and health risks and ecological risks. This chapter, in sections 3.1 

5 through 3.5, provides descriptions of each of the case study locations, accompanied by an 

6 overview of the available information relevant to and considered in the human exposure and 

7 health risk assessments and the ecological risk assessment. The surface water and sediment data 

8 used for the national-scale screening assessment are discussed in Section 3.6. Exhibit 3-1 

9 provides a list of the case study and national-scale evaluations performed for the pilot phase 

10 analysis and indicates the applicability of each to the human exposure and health risk 

11 assessments and/or the ecological risk assessment. 

12 

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

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 

15 3.1 Primary Pb Smelter Case Study 

16 

17 The Herculaneum Lead Smelter (HLS) is one of the largest primary Pb smelters in the world and 

18 is the only currently operating Pb smelter in the United States. The HLS facility represents a 

19 relatively large point source that has been active for over a century and has a large amount of 

20 site-specific data characterizing both media concentrations (soil, indoor dust, and ambient air) 

21 and human blood Pb levels. Pb contaminant conditions for the area surrounding this facility are 

22 dominated by emissions from this facility, with older historical auto and other point source 

23 emissions being of relatively lesser importance. Environmental sampling conducted around the 

24 primary Pb smelter has shown Pb contamination throughout the community surrounding the 

25 smelter. Available environmental data are discussed in Section 3.1.5 and presented in Appendix 

26 A. 

27 

28 3.1.1 Description of Case Study Location 

29 

30 The HLS facility is located in Herculaneum, Missouri. The city of Herculaneum is in Jefferson 

31 County, about 42 kilometers (km) [26 miles (mi)] southwest of St. Louis, and its approximate 

area is 9 km2 32 . As of 2000, there were estimated to be 37,562 people living within a 10-km radius 

33 of the HLS facility (2,064 within 2 km; 14,237 between 2 and 5 km; and 21,261 between 5 and 

34 10 km). Of this population in 2000, 3,880 were children ages 7 years old and younger (171 

35 within 2 km; 1,545 between 2 and 5 km; and 2,164 between 5 and 10 km) (U.S. Census Bureau 

36 2005). 

37 
 Case Study Descriptions 

ICF International 3-2 DRAFT – December 2006 

1 3.1.2 Description of Primary Pb Smelter 

2 

3 The HLS facility is located at 881 Main Street in Herculaneum (see Exhibit 3-2). The HLS 

4 property is 52 acres and consists of three main areas: (1) the smelter plant, which is located on 

5 the east side of Main Street; (2) office buildings located on the west side of Main Street; and (3) 

6 a 40- to 50-feet high slag storage pile that covers 24 acres. The facility is bordered on the east by 

7 the Mississippi River, on the southeast by Joachim Creek, on the west and north-northwest by 

8 residential areas, and on the south-southwest by the slag pile. A large part of the slag pile is 

9 located in the floodplain wetlands of Joachim Creek and the Mississippi River. 

10 

11 The principal processing occurring at the facility includes: (1) sintering, smelting, and refining 

12 of Pb ore; (2) sulfuric acid production from waste sulfur-containing gases generated by the 

13 sintering operation; and (3) wastewater treatment. Sources at the facility include various stacks 

14 and vents from plant processes, fugitive emissions from ore handling operations, wind erosion 

15 from the slag pile, and fugitive emissions from transport of Pb concentrate over local roads. A 

16 Pb ore concentrate, consisting of approximately 80 percent Pb sulfide, is processed at the 

17 smelter. The ore is transported by truck from eight Pb mines near Viburnum, Missouri, 

18 approximately 42 km (75 mi) south-southwest of Herculaneum. The smelting operation 

19 generates a molten slag, 20 percent of which is sent to the slag storage pile as waste. Stack and 

20 fugitive emissions from the facility and deposition of these emissions to soil and surface water 

21 have resulted in elevated Pb concentrations in the surrounding areas (Missouri DNR 1999, as 

22 cited in ATSDR 2003). 
 Case Study Descriptions 

ICF International 3-3 DRAFT – December 2006 

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

2 
 Case Study Descriptions 

ICF International 3-4 DRAFT – December 2006 

1 3.1.3 Human Exposure Measurements 

2 

3 Blood Pb levels at or above 10 μg/dL have been recorded for Herculaneum residents, including 

4 children less than 72 months of age (ATSDR 2002; ATSDR 2003). The U.S. Department of 

5 Health and Human Services (DHSS) and the Jefferson County Health Department (JCHD), in 

6 cooperation with the Agency for Toxic Substances and Disease Registry (ATSDR), have offered 

7 blood Pb (PbB) testing to the residents of Herculaneum and surrounding communities. Results 

8 of two such testing events conducted in 2001 and 2002 have been documented in DHSS/ATSDR 

9 health consultation reports (ATSDR 2002; ATSDR 2003) and are summarized here. 

10 

11 A total of 935 Herculaneum residents were tested in 2001. A summary of PbB results by age 

12 group is provided in Exhibit 3-3. Of the children less than 72 months old that were tested in 

13 2001, 33 (28 percent) had PbBs of 10 μg/dL or greater. In the area closest to the smelter, 30 out 

14 of 67 (45 percent) of the children under 72 months of age who were tested in 2001 had PbBs 

15 equal to or above 10 μg/dL (ATSDR 2002). 

16 

17 Exhibit 3-3. Summary of 2001 PbB Measurements 

18 for Herculaneum Residents 

PbB (μg/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 

19 Source: ATSDR 2002 

20 

21 In September 2002, DHSS and JCHD conducted a voluntary community-wide PbB testing event, 

22 and a total of 340 Herculaneum residents were tested. Results by age group for Herculaneum 

23 residents are summarized in Exhibit 3-4. As shown in Exhibit 3-4, of the children less than 72 

24 months old that were tested in 2002, 8 (14 percent) had PbBs 10 μg/dL or higher. 

25 
 Case Study Descriptions 

ICF International 3-5 DRAFT – December 2006 

1 Exhibit 3-4. Summary of 2002 PbB Measurements 

2 for Herculaneum Residents 

PbB (μg/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 

3 Source: ATSDR 2003 (Tables 1-3) 

4 

5 3.1.4 Emissions 

6 

7 The Pb emissions estimates used for the primary Pb smelter case study were obtained from EPA 

8 Region 7 and reflect the 2000 Revision of the State Implementation Plan (SIP) developed for the 

9 facility (MDNR 2000). Cumulative Pb emissions from processes at the facility, fugitive 

10 emissions from transferring of materials, fugitive emissions from storage at the slag pile, and 

11 emissions associated with dust from roadways in the vicinity of the smelter totaled 266 

12 tons/year. Particle sizes for emissions from road segment emission points around the primary Pb 

13 smelter ranged from 44 to 210 μm. Particle sizes for emissions from all other emission points at 

14 the primary Pb smelter ranged from 6.3 to 45.5 μm. EPA Region 7 is currently in the process of 

15 reviewing source characterization data to enhance modeling conducted as part of the SIP 

16 planning. Consequently, the dispersion model runs completed for the pilot phase using these 

17 emissions should be considered illustrative only. Emissions and release parameters, particle size 

18 inputs, and other inputs used for fate and transport modeling of the primary Pb smelter are 

19 provided in Appendix B. 

20 

21 3.1.5 Summary of Environmental Data 

22 

23 The environmental data sets available for the primary Pb smelter case study are summarized in 

24 Exhibit 3-5. 

25 
 Case Study Descriptions 

ICF International 3-6 DRAFT – December 2006 

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

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 2 Several data sources existed, including analyses conducted by EPA, the facility, ATSDR, Missouri Department of 

3 Natural Resources (DNR), and various consultants. Aside from EPA’s AQS air monitoring data, the data 

4 represented in this table were obtained electronically from EPA Region 7 (2006a). These EPA data are the only 

5 environmental data discussed and summarized for the primary Pb smelter in this chapter and in the associated 

6 appendices. Attempts were made to obtain environmental data from sources outside EPA (such as those listed 

7 above), but no additional data were received within the time available for this assessment. 

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

c 9 Some sampling locations were excluded from the ecological risk assessment because of their close proximity to the 

10 slag storage area; it was assumed that these areas had significant Pb contributions from pathways other than the air 

11 (e.g., runoff and flooding).

d 12 The sampling locations were from eight water bodies/drainage areas including Joachim Creek, Mississippi River, 

13 U-shaped pond approximately 1.7 km NW of the facility. 

e 14 The sampling locations were from five water bodies/drainage areas including Joachim Creek, Mississippi River, U15 shaped pond approximately 1.7 km NW of the facility. 
 Case Study Descriptions 

ICF International 3-7 DRAFT – December 2006 

1 

2 Air Monitoring: As shown in Exhibit 3-5, there are two air monitoring data sets available from 

3 EPA for the primary Pb smelter. Appendix A, Exhibit A-1 shows the locations of the 13 air 

4 monitoring locations relative to the facility. Air monitoring data for the four EPA high-volume 

5 sites are provided by year in Exhibit A-2. In general, average annual Pb concentrations in 

6 ambient air declined from 2001 to 2003 at these locations. Most notable are the reductions in 

7 measurements at the monitor with Pilot Analysis ID 102. The maximum Pb concentration (65 

μg/m3 8 ) occurred at this monitor in 2001. In 2003, the maximum at this monitor decreased to 10 

μg/m3 9 (a decrease of 85 percent). Likewise, the average annual Pb concentration at the monitor 

10 decreased 84 percent from 2001 to 2003. 

11 

12 Air monitoring data for the nine AirData monitors are provided by year in Exhibit A-3. Based 

13 on these data, decreases in average annual Pb concentrations have been observed since 2001. 

14 The largest difference was observed for Monitor ID 290990005, where average annual Pb 

concentrations decreased from 2.11 μg/m3 in 2001 to 0.278 μg/m3 15 in 2005 (a decrease of 87 

16 percent). 

17 

18 For comparison purposes, the average annual Pb concentrations for the year 2005 from AirData 

19 monitors located around the primary Pb smelter were compared to AirData monitor results 

20 across the United States. Exhibit 3-6 shows the distribution of average annual Pb concentrations 

21 in total suspended particulate matter (TSP) for 208 monitors across the United States (with 

22 average annual monitored Pb concentrations sorted in ascending order). The 2005 monitor 

23 results for the nine AirData monitors located in the vicinity of the primary Pb smelter are 

24 indicated using a solid square (■). The average monitored Pb concentrations detected at the 208 

monitors ranged from 0.001 to 1.56 μg/m3

. The 1.56 μg/m3 25 maximum is associated with 

26 Monitor 290990015, one of the monitors identified within 10 km of the primary Pb smelter. Of 

27 the 208 locations, the nine monitors located within 10 km of the primary Pb smelter all fall in the 

28 top 30 percent of all monitored ambient levels for these 208 monitors, with four of the nine 

29 monitors in the top 10 percent. 

30 

31 Exhibit 3-6. Distribution of Pb TSP Measurements

32 Across the United States Relative to Monitors Near the Primary Pb Smelter 

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/m 3)

■- Monitor Around Hercualneum

33 

34 
 Case Study Descriptions 

ICF International 3-8 DRAFT – December 2006 

1 Soil: As shown in Exhibit 3-5, there are three soil data sets available from EPA for the primary 

2 Pb smelter: pre-excavation, post-excavation, and recontamination assessment data. Pre3 excavation soil samples were collected from residential locations around the smelter prior to soil 

4 removal activities. Pre-excavation soil sample results for over 900 residential locations around 

5 the primary Pb smelter are presented in Appendix A, Exhibit A-4. Average soil concentrations 

6 at these sampling locations ranged from 53 to 23,350 mg/kg. 

7 

8 Based on pre-excavation sampling results, Pb-contaminated soil in residential yards near the 

9 smelter was removed, replaced with clean backfill, and re-seeded with grass. Post-excavation 

10 soil data were available for over 300 residential locations. It is unclear from the available 

11 information whether post-excavation soil samples were collected immediately following 

12 excavation or after the yards were backfilled. Post-excavation results are presented in Appendix 

13 A, Exhibit A-5. Average soil concentrations at these properties ranged from 70 to 2,757 mg/kg. 

14 

15 The EPA has recently conducted post-remediation residential yard soil sampling at 31 locations 

16 within approximately a 1.6 km (1 mi) radius of the primary Pb smelter to determine whether 

17 residential soils previously excavated are becoming recontaminated. Results for the 

18 recontamination assessment samples are provided in Appendix A, Exhibit A-6. For most of the 

19 31 recontamination assessment locations, average Pb concentrations in the replacement “clean” 

20 soil increased between 2002 and 2006. Refer to Appendix A, Exhibit A-7 for a summary of the 

21 pre-excavation, post-excavation, and recontamination assessment data for these 31 residential 

22 locations. 

23 

24 Indoor Dust: The interiors of 17 of the 31 residential properties identified for the soil 

25 recontamination assessment were also assessed for Pb levels in indoor dust. Interior dust 

26 removal (in which areas inside homes were wiped and/or vacuumed) was performed at these 

27 residences prior to recontamination sampling. Appendix A, Exhibit A-8 provides a summary of 

28 recontamination indoor dust sample results for these 17 properties. Carpet dust samples 

29 collected during recontamination sampling events at these residences contained Pb 

30 concentrations that ranged from 122 to 4,350 mg/kg. Pb loadings in window sill wipe samples 

ranged from 5.6 to 1,385 μg/ft2 31 . There were no general patterns identified at homes during 

32 successive sampling events. Pb concentrations and/or loadings may have increased, decreased, 

33 or remained generally the same (see Appendix A, Exhibit A-8). This lack of pattern may be 

34 attributed in part to inconsistent house cleaning protocols within the homes. 

35 

Deposition: As shown in Exhibit 3-5, soil boxes1 36 were set up at 10 locations around the primary 

37 Pb smelter. Deposition monitoring locations are shown in Appendix A, Exhibit A-9. From 2003 

38 to 2004, samples were collected monthly to measure Pb deposition on soil; results for these 

39 locations are presented in Appendix A, Exhibit A-10. Maximum concentrations at the nine 

40 locations (excluding the control site) ranged from 25 to 406 mg/kg in 2003 and from 25.3 to 527 

41 mg/kg in 2004. The overall average Pb concentration in these soil boxes across all nine locations 

42 increased from 49 mg/kg in 2003 to 96.5 mg/kg in 2004, an increase of almost 100 percent. 

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 

1 Air deposition monitoring data were available for the same 10 locations around the primary Pb 

2 smelter for which soil box monitoring data were available (see Appendix A, Exhibit A-9). Dry 

3 deposition samples were collected monthly at two levels (1 foot and 10 feet) above the ground 

4 surface from April 2003 through April 2004. Data collected at each level for these locations are 

5 presented in Appendix A, Exhibit A-11. The annual Pb deposition rates at a height of 1 foot for 

the nine monitoring locations (excluding the control site) ranged from 0.34 to 22 mg/ft2 6 , and the 

overall average Pb deposition rate across all nine locations at the height of 1 foot was 4.8 mg/ft2 7 . 

8 The annual Pb deposition rates at a height of 10 feet for the nine monitoring locations ranged 

from 0.26 to 33 mg/ft2 9 , and the overall average Pb deposition rate across all nine locations at the 

height of 10 feet was 5.0 mg/ft2 10 . The average annual Pb deposition rates at each level by 

11 location are provided in Exhibit A-11. 

12 

13 3.1.6 Ecological Characterization 

14 

15 3.1.6.1 Description of Site and Surroundings 

16 

17 Ecologically relevant features near the primary Pb smelter facility include the Mississippi River, 

18 Joachim Creek, emergent and scrub-shrub wetlands, and successional and mature bottomland 

19 hardwood forest tracts (ELM 2005). Bottomland hardwood forests and agricultural fields are 

20 present to the west, south, and east of the characterization area between the smelter’s slag storage 

21 area (SSA) and Joachim Creek. The most mature bottomland hardwood forest is adjacent to 

22 Joachim Creek. Immediately south of the facility is a mixture of floodplain forest, emergent 

23 marsh, and scrub-shrub wetland habitat that is populated by willow (Salix spp.) trees (ELM 

24 2005). 

25 

26 The Missouri Department of Conservation (MDC) lists the plant and animal species that are 

27 endangered, vulnerable, or imperiled by county in the Heritage Database (MDC 2006). Exhibit 

28 3-7 lists the species of concern for Jefferson County; species that have acquired the state or 

29 federal status of “endangered” are identified. As shown in Exhibit 3-7, the pink mucket, 

30 scaleshell, gray bat, Indiana bat, and pallid sturgeon are on both the state and federal endangered 

31 lists. 

32 

Exhibit 3-7. Species of Concern Occurring in Jefferson County, Missouria

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 debilis 
 Case Study Descriptions 

ICF International 3-10 DRAFT – December 2006 

Exhibit 3-7. Species of Concern Occurring in Jefferson County, Missouria

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 1 Abbreviations: SE = state endangered status; FE = federally endangered status; FCE = federal candidate for 

2 endangered status; Source: MDC (2006). 

3 

4 Throughout much of the year, migratory birds such as the red-tailed hawk, belted kingfisher, and 

5 great blue herons utilize the habitat near the primary Pb smelter. The state and federally 

6 endangered bald eagle has been spotted on-site at the primary Pb smelter facility, which is 

7 known to be within the habitat for the bird. The facility is also within the habitat of the Indiana 

8 bat, which, as mentioned above, is also on the state and federal endangered species lists. None 

9 of these species, however, feed on soil invertebrates. In addition, the state and federally 

10 endangered pallid sturgeon has been identified in the Mississippi River adjacent to and 

11 downstream of the facility (USEPA Region 7 2000). 

12 

13 3.1.6.2 Description of Available Pb Environmental Data 

14 

15 As shown in Exhibit 3-5, the data set for the ecological risk assessment includes Pb 

16 concentration data in surface soil, surface water, and sediment collected from non-residential 

17 locations near the primary Pb smelter (ELM 2005). The data set sampling locations include 
 Case Study Descriptions 

ICF International 3-11 DRAFT – December 2006 

1 areas within a 2.1 km (1.5 mi) radius of the facility and two “reference areas” that are 6 to 7 km 

2 south of the facility (ELM 2005). 

3 

4 Soil: Total Pb concentration data are available for soil samples occurring in six clusters of 

5 locations in forested areas within 0.3 km of Joachim Creek that are remote from residential 

6 development (ELM 2005, Figure 7): the western and southern slopes of the slag storage area 

7 (SSA); sites along central, east, and west transects from the SSA; and sites located along the 

8 historic railroad grade on the west side of Joachim Creek. Soil samples near Joachim Creek may 

9 reflect Pb contribution from runoff from the SSA, and flooding from Joachim Creek (ELM 

10 2005). The soil sampling locations within a 2.1 km radius were all in areas that might have been 

11 subject to Pb inputs from Joachim Creek during flooding events. As such, the stations might not 

12 represent the concentrations of Pb in soils that result from direct air emissions from the smelter. 

13 The reference soil samples were taken from areas approximately 5.6 and 6.5 km south of the 

14 facility. Soil samples were collected from 0 to 3 inches below the surface (ELM 2003). 

15 

16 The average total Pb soil concentration for the entire ELM (2005) data set was 269 mg/kg. The 

17 minimum concentration detected was 20.4 mg/kg, and the maximum was 752 mg/kg. 

18 

19 Surface water and sediment: Dissolved Pb concentration data in surface water samples and 

20 total Pb concentration data for sediment samples are available for sites along the Mississippi 

21 River, including one reference station more than 2.1 km upstream of the facility, and sampling 

22 stations within approximately 2.5 km upstream and downstream of the confluence with Joachim 

23 Creek. Data are also available for sites in Joachim Creek along the approximately 4.4 km of 

24 Joachim Creek that lie within 2.1 km of the smelter, as well as two locations farther upstream. 

25 Pb measurements are also available in nearby wetlands (ELM 2005). 

26 

27 Dissolved Pb was not detected by ELM in samples from surface water locations included in the 

28 pilot phase, and the detection limit for the analysis was 3 μg/L. Total Pb concentrations in 

29 surface water for the full data set averaged 14.5 μg/L and ranged from below the detection limit 

30 to 105 μg/L. Total Pb concentrations in the sediment samples for the full data set averaged 263 

31 mg/kg and ranged from 6.2 to 3,040 mg/kg. 

32 

33 3.2 Secondary Pb Smelter Case Study 

34 

35 The secondary Pb smelter case study focused on the impacts of emissions from a smaller point 

36 source (compared to the primary Pb smelter) located in Alabama. There were less site-specific 

37 data characterizing media concentrations and human exposure levels available for this study area 

38 than for the primary Pb smelter case study. However, recent air concentration data from the area 

39 surrounding the facility and facility characterization data (including emission estimates) were 

40 readily available. 

41 

42 3.2.1 Description of Case Study Location 

43 

44 The secondary Pb smelter case study location is in Troy, Alabama. Troy is a city located in Pike 

County, positioned in the south central portion of the state, and its approximate area is 68 km2 45 . 

46 As of 2002, there were estimated to be 17,910 people living within a 10-kilometer radius of the 
 Case Study Descriptions 

ICF International 3-12 DRAFT – December 2006 

1 facility (2,186 within 2 kilometers; 10,634 between 2 and 5 km; and 5,090 between 5 and 10 

2 km). Of this population, 1,672 are children ages 7 years and under (187 within 2 km; 896 

3 between 2 and 5 km; and 589 between 5 and 10 km) (U.S. Census Bureau 2005). 

4 

5 3.2.2 Description of Secondary Pb Smelter

6 

7 This facility is one of 15 secondary Pb smelters currently operating in the United States (ECR 

8 2006). US-231 borders the facility to the north-northeast and a railroad line and Henderson 

9 Highway run along the north-northwest and west boundaries of the facility. The area located 

10 directly west of Henderson Highway is forested. To the south and south-southwest are other 

11 industries and businesses. Big Creek appears to be the closest major water body, located 

12 approximately 0.8 km (0.5 mi) south-southeast from the center of the facility. The City of Troy 

13 is located north and east of the facility and north of US-231 (see Exhibit 3-8). 

14 

15 Secondary Pb smelters produce Pb from scrap and provide the primary means for recycling Pb16 acid automotive batteries. Approximately 95 percent of all Pb-acid batteries are recycled at 

17 secondary Pb smelters. Secondary Pb smelters perform three basic unit operations: battery 

18 breaking, smelting, and refining and alloying. Battery breaking is accomplished by either 

19 crushing or cutting battery cases into pieces. The plastic, spent acid, and Pb-bearing materials 

20 are then separated. Pb-bearing materials are processed in one of three types of smelting 

21 furnaces: blast, reverberatory, or rotary. Molten Pb from these furnaces is further processed in 

22 refining kettles and subsequently cast into molds. The waste stream from the furnaces (i.e., slag) 

23 is either returned to the primary smelting furnace or treated in a separate furnace dedicated to 

24 slag cleaning to recover additional Pb. There are three types of emission sources at secondary Pb 

25 facilities – process sources, process fugitive sources, and fugitive dust sources. The types of 

26 sources at the secondary Pb smelter analyzed in these assessments include: blast furnace, 

27 agglomeration furnace, alloying kettles and heating system, flue dust storage bins, and slag 

28 treatment furnace. Stack emissions from the facility and fugitive emissions associated with 

29 materials storage and handling and roadway dust have resulted in releases of Pb to the air and 

30 soil (ECR 2006). 

31 

32 
 Case Study Descriptions 

ICF International 3-13 DRAFT – December 2006 

1 

2 Exhibit 3-8. Facility Location Map – Secondary Pb Smelter 

3 

4 
 Case Study Descriptions 

ICF International 3-14 DRAFT – December 2006 

1 3.2.3 Human Exposure Measurements 

2 

3 No information on children’s PbBs specific to the area around the secondary Pb smelter was 

4 identified. However, the Lead Poisoning Prevention Branch of the CDC collected PbB 

5 surveillance data for children less than 72 months of age in Pike County, Alabama in 2003. Of 

6 the 407 children tested by the CDC, there were 33 (approximately 8 percent) confirmed cases of 

7 elevated PbB (i.e., PbB above 10 μg/dL). For children less than 72 months of age in the state of 

8 Alabama and in the United States as a whole, the confirmed elevated PbBs as a percent of 

9 children tested in 2003 was 2.4 percent and 1.9 percent, respectively (CDC 2003). Note, 

10 however, that the statistics for children in Pike County do not necessarily represent PbBs for 

11 children living in Troy, Alabama. 

12 

13 3.2.4 Emissions 

14 

15 As of June 9, 1994, when EPA proposed the secondary Pb smelter MACT standard (59 FR 

16 63941), there were 23 secondary Pb smelters operating in the United States. As of 2002, there 

17 were 15 operating facilities in 11 states. Of these 15 facilities, the secondary Pb smelter 

18 analyzed in this study is the third highest emitter of Pb (ECR 2006). 

19 

20 The estimates for process emissions for the secondary Pb smelter analyzed in this study were 

21 calculated from Pb emissions measured during stack tests performed December 1997, November 

22 1999, and February 2000 (ECR 2006). Fugitive emissions were estimated by comparing the 

23 modeled concentrations from the stack emissions to background Pb concentrations and 

24 monitored concentrations. The cumulative Pb emissions from this facility, including facility 

25 process and fugitive emissions, were estimated to be 4.56 tons/year. Particle sizes for emissions 

26 from point sources at the facility ranged from 0.5 to 10 μm, and particle sizes for emissions from 

27 area sources at the facility ranged from 1.25 to 22.5 μm. Emissions and release parameters, 

28 particle size inputs, and other inputs for fate and transport modeling for the facility are provided 

29 in Appendix C. 

30 

31 3.2.5 Summary of Environmental Data 

32 

33 The environmental data sets available for the secondary Pb smelter case study are summarized in 

34 Exhibit 3-9. 
 Case Study Descriptions 

ICF International 3-15 DRAFT – December 2006 

1 

2 Exhibit 3-9. Summary of Environmental Data Sources for 

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 4 In general, site characterization information was lacking for this secondary Pb smelter. Data, with the exception of 

5 limited air monitoring data, were not available based on information from EPA Region 4. Information from the 

6 Alabama Department of Environmental Management (ADEM) indicates there may be relevant soil data available 

7 from the facility (ADEM 2006); however, no data have been obtained to date. 

b 8 AirData monitor values from 1997 - 2000 were obtained for the purpose of comparing monitored values to 

9 modeled air concentrations (see Chapter 4). Because emissions data for the secondary Pb smelter were based on 

10 stack tests for 1997, 1999, and 2000, monitoring results from similar years were used. 

11 

12 Air Monitoring: As shown in Exhibit 3-9, average annual Pb concentrations in the vicinity of 

13 the secondary Pb smelter were available from EPA’s AQS/AirData network (USEPA 2006f) for 

14 two air monitors located near the facility (see Appendix A, Exhibit A-12). Data from these two 

15 air monitoring sites for 1997 through 2000 are presented in Appendix A, Exhibit A-13. Over this 

16 period, average annual Pb concentrations at the monitor closer to the facility ranged from 0.383 

to 0.474 μg/m3 17 , with the lowest average annual concentration in year 2000. Average annual Pb 

concentrations at the second monitor ranged from 0.132 to 0.198 μg/m3 18 . 

19 

20 For comparison purposes, the average annual Pb concentrations for the year 2005 from AirData 

21 monitors located around the secondary Pb smelter case study location were compared to AirData 

22 monitor results across the United States Exhibit 3-10 shows the distribution of average annual 

23 Pb concentrations in total suspended particulate matter (TSP) for 208 monitors across the United 

24 States (with average annual monitored Pb concentrations sorted in ascending order). The 2005 

25 monitor results for the two AirData monitors located in the vicinity of the secondary Pb smelter 

26 are indicated using a solid square (■). The average monitored Pb concentrations from the 208 

monitors ranged from 0.001 to 1.56 μg/m3 27 . Both of the monitors located near the secondary Pb 

28 smelter fall into the top 15 percent of the 208 locations. 
 Case Study Descriptions 

ICF International 3-16 DRAFT – December 2006 

1 

2 Exhibit 3-10. Distribution of Pb TSP Measurements Across the 

3 United States Relative to Monitors Near the Secondary Pb Smelter 

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 

6 Soil: No soil measurement data for Pb were identified in the vicinity of the secondary Pb 

7 smelter case study location. For the human exposure and health risk assessments, soil 

8 concentrations were estimated using two methods – a model-only approach and a hybrid 

9 approach using model results and surrogate measurements from a similar facility. See Section 

10 4.2.3 for details.

11 

12 Indoor Dust: No indoor dust data for Pb were available from homes located in the vicinity of 

13 the secondary Pb smelter. Indoor dust concentrations were estimated using an empirical model 

14 that relates ambient air concentrations to indoor dust concentrations, as discussed in Chapter 4.

15 

16 Deposition: No Pb deposition monitoring data were identified in the vicinity of the secondary 

17 Pb smelter case study location. For the human exposure and health risk assessments and the 

18 ecological risk assessment, Pb deposition arising from emissions from the secondary Pb facility 

19 was modeled using EPA’s AERMOD air dispersion model, as discussed in Chapter 4.

20 

21 3.2.6 Ecological Characterization 

22 

23 3.2.6.1 Description of Site and Surroundings 

24 

25 The secondary Pb smelter location falls within the Alabama Coastal Plain in Pike County, 

26 Alabama. It is located in an area of disturbed forests, and is less than 1.6 km (1 mi) from Big 

27 Creek, which is part of the Pea River watershed. Big Creek is located approximately 0.8 km (0.5 

28 mi) south-southeast from the center of the facility. The surrounding area includes emergent and 

29 scrub-shrub wetlands, forests, freshwater creeks, ponds, rivers, croplands, pastureland, and 

30 developed urban areas. The Pea River watershed drains into the Gulf of Mexico. The watershed 

31 is underlain by coastal plain sediments, including sand, clay, and limestone; and the topography 

32 can be characterized as gentle to moderate rolling hills (CPYRWMA 2006). 

33 
 Case Study Descriptions 

ICF International 3-17 DRAFT – December 2006 

1 Diversity of terrestrial and aquatic animal species is relatively high. The Choctawhatee and Pea 

2 River basins, in which the secondary Pb smelter is located, contain 43 species of marine, 

3 estuarine, and freshwater fish species (Cook and Kopaska-Merkel 1996). Anadromous fish 

4 species (i.e., saltwater fish that must spawn in freshwater) found in the Pea River basin include 

5 the following: the threatened Gulf sturgeon (Acipenser oxyrhynchus desotoi), Alabama shad 

6 (Alosa alabamae), striped bass (Morone saxatalis), and skipjack herring (Alosa chrysochloris). 

7 The Pea River basin also provides habitat for 20 species of freshwater mussels (Cook and 

8 Kopaska-Merkel 1996), as well as numerous species of snails, snakes, and other invertebrates. 

9 

10 Terrestrial species supported in this region include a variety of birds, mammals, invertebrates, 

11 and vascular plants. A recent floristic survey of the Pike County Pocosin Nature Preserve 

12 indicated that there were a total of 348 species of vascular flora (Alvin et al. 2002). The preserve 

13 is approximately 11 km (7 mi) northeast of the secondary Pb smelter facility. In a recent survey 

14 of birds spotted by citizens of Troy, Alabama, 29 species were reported, including American 

15 robins (which consume soil invertebrates), the northern cardinal, American goldfinch, and 

16 various sparrows, doves, woodpeckers, and warblers (GBBC 2003). Other terrestrial fauna 

17 found in the region include small mammals and invertebrate species. A total of 34 vascular flora 

18 from Pike County are listed by the Alabama Natural Heritage Inventory Program as endangered, 

19 threatened, or of special concern in the state (Alabama Natural Heritage Inventory 2001). 

20 

21 Exhibit 3-11 displays the current listing of threatened and endangered species for Pike County, 

22 Alabama (NatureServe 2006). According to NatureServe and the USFWS, no species in Pike 

23 County are on the state or federal endangered species list (Outdoor Alabama 2003). A few 

24 species, however, are candidates for the federal list, as designated in Exhibit 3-11. 

25 
 Case Study Descriptions 

ICF International 3-18 DRAFT – December 2006 

Exhibit 3-11. Threatened and Endangered Species in Pike County, Alabamaa 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 2 Abbreviation: FCE = Federal candidate for endangered status 

3 Source: NatureServe (2006) 

4 

5 3.2.6.2 Description of Available Data 

6 

7 Pb data in environmental media other than air were not available around the vicinity of the 

8 secondary Pb smelter facility. Therefore, for estimates of Pb concentrations in outdoor soils, the 

9 ecological risk screening assessment for the secondary Pb smelter case study relied on fate and 

10 transport modeling and input data collected for the human exposure and health risk assessments. 

11 

12 3.3 Near Roadway Urban Case Study 

13 

14 The location for the near roadway case study was selected primarily based on the availability of 

15 suitable air monitoring data. Specifically, measurements were sought that were inclusive of Pb 

16 concentrations and particle sizes, reported for an urban site adjacent to a roadway, and located in 

17 a dry climate where there was historical use of leaded gasoline. Several days of air monitoring 

18 data for ambient measurements of particle-bound Pb were available for an urban site adjacent to 

19 a roadway in Houston, Texas (ICF 2006a). This location was selected for the pilot phase, and 

20 the section of land adjacent to a segment of a well-traveled road near the monitoring site was 

21 defined as the specific study area. This road has been in use as a thoroughfare since at least the 

22 1950s; therefore, it seems likely that the historical use of leaded gasoline in automobiles during 

23 this time has resulted in an accumulation of Pb in the near roadway soil. 

24 
 Case Study Descriptions 

ICF International 3-19 DRAFT – December 2006 

1 3.3.1 Description of Case Study Location 

2 

3 The setting for the near roadway case study location is a small area of mixed residential and 

4 commercial use about 14 km northwest of downtown Houston (see Exhibit 3-12). The study 

5 area is the narrow strip of land that extends 200 meters from either side of a well-traveled 

6 roadway that is located south of the selected air monitoring station. The area is focused on a 2.3 

7 km stretch of this street that runs roughly east-west, with the monitoring station situated about 

8 115 m north of the street. This length was defined as the segment for the case study based on the 

9 subdivision of the street into “links.” Roadways are typically divided into links for the purposes 

10 of organizing data by departments of transportation and other municipal offices; the study area 

11 comprises two links of this street. Relevant vehicle traffic data for these links are presented in 

12 Exhibit 3-13. 

13 

14 The land adjacent to the segment of road that makes up the study area was subdivided into three 

15 zones for this case study: one extending from the edge of the roadway out to 12 meters; one that 

16 includes the region between 12 to 75 meters from the roadway edge; and one that includes the 

17 region between 75 to 200 meters from the roadway edge (see Exhibit 3-12). Additional 

18 information regarding the spatial characteristics of the study area and the rationale for 

19 subdividing the area into three zones is included in Section 4.3.1. 

20 

21 

22 Exhibit 3-12. Study Area for Near Roadway Case Study, Houston, Texas 

23 
 Case Study Descriptions 

ICF International 3-20 DRAFT – December 2006 

1 

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

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 3 Length and vehicle throughput from Highway Performance Monitoring System (U.S. Department of 

4 Transportation 2006); data are representative of 2002 and 2003. 

5 

6 As of 2000, about 1,950 people resided in 620 households within the boundaries of this study 

7 area, including about 320 children between the ages of 0 and 7 years. Demographic data for the 

8 case study were estimated using information from the U.S. Census by area-weighting population 

9 data for census blocks overlapping with the study area. More information on these calculations 

10 and relevant demographics for each of the three zones in the study area is included in Appendix 

11 D. 

12 

13 3.3.2 Human Exposure Measurements 

14 

15 No human exposure measurements were identified for the near roadway urban study area. 

16 

17 3.3.3 Emissions 

18 

19 No emissions data were collected for the near roadway case study. Soil and air Pb 

20 concentrations were derived from measurements; and no modeling of emissions was conducted 

21 for this case study. 

22 

23 3.3.4 Summary of Environmental Data 

24 

25 The environmental data sets identified for the near roadway case study are summarized in 

26 Exhibit 3-14. 

27 

28 Exhibit 3-14. Summary of Environmental Data Sources for 

29 Near Roadway Urban Case Study 

Media Data Set Timeframe Location Comments 

DRI: 1 monitor (PM2.5 

and PM10) 

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 (PM2.5, PM10, 

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

ICF International 3-21 DRAFT – December 2006 

1 

2 Two air monitoring data sets were identified for locations in the vicinity of the road segment that 

3 is the focus of this case study. Air monitoring of Pb was conducted by Desert Research Institute 

4 (DRI) at a single monitor located about 115 m north of the road segment. Daily average air 

5 concentrations of Pb in the PM10 fraction collected over three days in February 2001 ranged from 

0.0045 to 0.0056 μg/m3 6 . In addition, EPA’s AirData database includes measurements of Pb for 

7 three air monitors located in Houston. None of these monitors is located within 15 km of the 

8 road segment of interest, and the distance to the nearest roadway was not specified for any 

monitor. Mean concentrations of Pb in the TSP fraction ranged from 0.001 to 0.02 μg/m3 9 for 

10 1996 to 2006 across the three monitors. Mean concentrations of Pb in the PM10 fraction ranged 

from 0.006 to 0.012 μg/m3 11 for 1999 to 2006 for the one monitor with PM10 measurements. 

12 

13 Although no suitable soil Pb measurements were identified for locations in the vicinity of the 

14 road segment of interest or elsewhere in Houston, one set of near roadway soil measurements in 

15 Corpus Christi was identified in the literature. Turer and Maynard (2003) reported 

16 concentrations of Pb in surface soil samples taken adjacent to an interstate entrance ramp; 

17 concentrations ranged from 214 mg/kg at 12 m from the roadway to 766 mg/kg at 3 m from the 

18 roadway. See Appendix D for additional detail on the air and soil data sets. No measurements 

19 of Pb in dust inside residences or measurements of deposition were identified in the vicinity of 

20 the near roadway urban case study location. 

21 

22 3.4 Near Roadway Non-Urban Case Study 

23 

24 From the literature search for studies of Pb in near roadway soils (described in Section 3.3.4), 

25 two non-urban sites for which soil Pb levels are available were identified for use in the 

26 ecological risk assessment. These locations are: (1) Interstate 37 near oil refineries in Corpus 

27 Christi, Texas (Turer and Maynard 2003) and (2) Interstate 95 north of Richmond, Virginia, at 

28 Exit 86 which connects to a moderately traveled, two-lane road (Speiran 1998). 

29 

30 Land cover data from 1992 within 1.6 km (1 mi) of the Corpus Christi, Texas study location 

31 showed 59 percent industrial, 10 percent low intensity residential, and 25 percent high intensity 

32 residential (Vogelmann et al. 2001). The remaining 5 percent of the surrounding area included 

33 shrubland, row crops, pasture, grasses, and forested upland, including evergreen forest and 

34 deciduous forest. The 1992 land cover data within approximately 1.6 km (1 mi) of the Atlee, 

35 Virginia, study locations was 26 percent developed: 2 percent low-intensity residential and 

36 commercial and 24 percent industrial and transportation. The remaining 74 percent included 25 

37 percent deciduous forest, 14 percent woody wetlands, and 12 percent pasture (Vogelmann et al. 

38 2001). Smaller proportions of mixed forest, evergreen forest, row crops, and transitional 

39 (barren) areas were also found. 

40 

41 Exhibit 3-15 lists the environmental data sources for the near roadway non-urban case study. 

42 
 Case Study Descriptions 

ICF International 3-22 DRAFT – December 2006 

1 Exhibit 3-15. Summary of Environmental Data Sources for 

2 Near Roadway Non-Urban Case Study 

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 3 Sample data for soil confirmed by Maynard (2006). 

4 

5 The Pb soil data for the Corpus Christi, Texas location 2 meters from the roadway ranged from 

6 15 mg/kg at 20-cm depths to 650 mg/kg at 10-cm depths. The Pb soil concentrations at the 

7 Atlee, Virginia location ranged from 17 mg/kg 15 meters from the roadway to 540 mg/kg 2 

8 meters from the roadway; both of these Atlee samples were collected from 7.5-to 15-cm soil 

9 depths. 

10 

11 3.5 Ecologically Vulnerable Case Study 

12 

13 The Hubbard Brook Experimental Forest (HBEF) is located in central New Hampshire within 

14 the White Mountain National Forest. HBEF is approximately 210 km north of Boston, MA, and 

15 vehicle access is limited. HBEF soils are mostly well-drained Spodosols with pH levels at or 

16 below 4.5 and feature the Berkshire, Skerry, Becket, and Lyman soil series (Hubbard Brook 

17 Information Oversight Committee 2001). A unique characteristic of the Spodosol soil order is 

18 that the soils horizons are not necessarily parallel to the soil surface, as is the case in most orders 

19 (Soil Survey Staff 1999). An important feature of HBEF surface topography is the soil layer 

20 movement caused by the uprooting of trees, which creates mixtures of the lower mineral surface 

21 soil layers as well as buried horizons where mixture does not occur. This surface disturbance 

22 influences weathering and biogeochemical cycles. HBEF is completely forested and consists of 

23 80 to 90 percent hardwoods and 10 to 20 percent conifers. Sugar maple (Acer saccharum), 

24 beech (Fagus grandifolia), and yellow birch (Betula allegheniensis) are prevalent at low to mid 

25 elevations, while red spruce (Picea rubens), balsam fir (Abies balsamea), and white birch (Betula 

26 papyrifera var. cordifolia) are predominant at higher elevations. Commercial logging at HBEF 

27 ended between 1915 and 1917; the present forest is an even-aged, second-growth forest. 

28 (Hubbard Brook Information Oversight Committee 2001). HBEF consists of a number of study 

29 watersheds that have been subject to experimental manipulation; Watershed 6 (W6) serves as the 

30 reference watershed and has not been experimentally disturbed. 

31 
 Case Study Descriptions 

ICF International 3-23 DRAFT – December 2006 

1 Pb concentration data in forest soils, surface water, and bulk precipitation are available for 

2 HBEF; Pb bulk deposition data are also available (see Exhibit 3-16). Ambient air Pb 

3 concentration data were not located. The presented data largely focus on W6 and areas 

4 proximate to W6, although data from other areas of HBEF are also included. 

5 

6 Exhibit 3-16. Summary of Environmental Data Sources for HBEF

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 floor2 8 soil samples (this data set also includes samples from 

9 Watershed 1 [W1]) and reported a 32 percent decline in forest floor Pb concentrations at HBEF 

10 from 1976 to 2000. Pb concentrations for this data set ranged from about 38 mg/kg collected in 

11 2000 to 115 mg/kg collected in 1978. Soil profile analysis shows that Pb has become more 

12 concentrated at lower depths over time. Johnson et al. (1995) also noted a downward movement 

13 of Pb in soil layers. Wang et al. (1995) reported stream dissolved Pb concentrations ranging 

14 from about 5 to 15 parts per trillion (ppt) for samples collected in 1993. 

15 

16 For the pilot phase, the ecologically vulnerable case study assessment was limited to identifying 

17 the case study location (i.e., HBEF) and identifying associated available media concentration 

18 data sets (as described further in Appendix E). 

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 

1 3.6 National-Scale Surface Water and Sediment Screening Assessment 

2 

3 The geographic coverage of the national-scale screening assessment includes locations in 

4 freshwater watershed basins from all regions of the United States. The assessment was limited to 

5 freshwaters. Monitoring data reviewed by EPA’s Office of Research and Development indicated 

6 that Pb concentrations in large lakes and oceans are lower than in smaller freshwater bodies 

7 (USEPA 2006b). Exhibit 3-17 lists the data sets used in this screening assessment. Exhibit 3-18 

8 lists the 47 basins for which the National Water Quality Assessment (NAWQA) database 

9 provided dissolved Pb surface water concentration data. Approximately 50 percent of the land 

10 base of the United States is covered by these 47 basins. The basins for which sediment Pb 

11 concentration were evaluated are listed in Exhibit 3-19. 

12 

13 Exhibit 3-17. Summary of Environmental Data Sources for 

14 National-Scale Surface Water and Sediment Screening Assessment 

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 

15 
 Case Study Descriptions 

ICF International 3-25 DRAFT – December 2006 

Exhibit 3-18. USGS NAWQA Study Unitsa 1 Included in the National-Scale 

2 Surface Water Column Screening Assessment 

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

4 
 Case Study Descriptions 

ICF International 3-26 DRAFT – December 2006 

1 Exhibit 3-19. USGS NAWQA Study Units 

2 Included in National-Scale Sediment Screening Assessment 

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 

4 There are temporal trends associated with whether dissolved Pb was measured at given station. 

5 Most notably, at many stations for which multiple years of data are available, if the first year of 

6 sampling indicated all dissolved Pb measurements to be less than the quantitation limit (QL) of 1 

7 μg/L, no further measurements of dissolved Pb were attempted (e.g., dissolved Pb measured in 

8 1999 less than 1 μg/L; no further measurements in 2000 through 2004). 

9 

10 Most dissolved Pb concentrations reported in the NAWQA database are below the QL. There 

11 are only 2,558 samples analyzed for dissolved Pb in surface waters for the NAWQA database 

from 1994 to 2004.3 12 Of the 2,558 samples examined, 83 percent (2,116) were below the QL 

13 (USEPA 2006b). Of the 442 dissolved Pb concentrations reported in the data set, 17 percent (74 

of 442) were estimated.4 14 

15 

16 The NAWQA database had only one measurement of total Pb sediment concentrations for each 

17 of the 12 locations evaluated in the sediment portion of the screening assessment. Of these 12 

18 total measurements, seven total Pb concentrations were between 23 and 68 mg/kg; two were at 

19 239 and 240 mg/kg, and three were between 1,620 and 2,900 mg/kg. 

 

3

 Note that EPA/ORD’s review of the NAWQA database identified 3,445 samples; 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 

1 4. Estimates of Media Concentrations 

2 

3 This chapter discusses methods, results, limitations, and uncertainties associated with the media 

4 concentrations used for exposure assessment in the human exposure and health risk assessments 

5 and ecological risk assessment. The context for these media concentrations within the risk 

6 assessments is described in Chapter 2 and illustrated in Exhibits 2-2 and 2-3 for the human 

7 exposure and health risk assessments and in Exhibits 2-5 and 2-6 for the ecological risk 

8 assessment. 

9 

10 4.1 Primary Pb Smelter 

11 

12 The media concentrations for the primary Pb smelter case study were estimated using a 

13 combination of modeling approaches and monitoring data. The timeframe of the data used in the 

14 study is from 2000 to 2005. The emissions inputs are representative of potential emissions in 

15 2001, and the soil sample data used was collected from 2000 to 2005. Recent air monitoring 

16 data indicated that this facility is not in attainment with the current Pb NAAQS; therefore, both a 

17 current conditions and an attainment scenario were considered. The inhalation exposure and 

18 indoor dust concentrations are different between the two scenarios for the modeled locations 

19 where the current conditions air concentrations exceed the current Pb NAAQS. This is because 

20 the inhalation exposure and indoor dust concentrations are calculated from the modeled air 

21 concentrations. For the pilot phase, the same soil concentrations of Pb were used for both 

22 scenarios. 

23 

24 4.1.1 Spatial Template 

25 

26 As discussed in Section 2.2.2, the outer boundary of the study area for the primary Pb smelter 

27 case study was set to approximately 10 kilometers (km), which was expected to capture the 

28 population experiencing the most significant impacts of the facility’s emissions. Modeled air 

29 concentrations of Pb from the primary Pb smelter were compared to estimated background Pb 

30 concentrations from the Interagency Monitoring of Protected Visual Environments (IMPROVE) 

31 database (IMPROVE 2006) to determine at what distance the air concentrations resulting from 

32 the facility emissions reached approximately 50 percent of the background air concentration. 

33 From this comparison, it was found that a radius greater than 50 km would be required to reach 

34 air concentrations that would be 50 percent of background. Due to model run times, this would 

35 not be feasible, so the decision was made to model the facility’s impacts in a study area that 

36 extended 10 km from the facility. This smaller study area is expected to capture the highest 

37 exposures associated with the facility’s emissions. 

38 

39 A combination of U.S. Census blocks and block groups (U.S. Census Bureau 2005) were used as 

40 the spatial units at which to model air concentrations. The 29 block groups that are 

41 predominantly within 10 km of the facility were selected to define the spatial extent of the study 

42 area. Because of the irregular shape of block groups, not all of the block groups with area within 

43 10 km were included, and some that were included have area outside 10 km. The selection was 

44 made to create a coverage area that would include block groups whose average concentration 

45 would predominantly come from air within 10 km of the source. All census block centroids 

46 within these 29 block groups were included as receptors in the ISC model runs (i.e., air 
 Estimates of Media Concentrations 

ICF International 4-2 DRAFT – December 2006 

1 concentrations and deposition fluxes were modeled at each block centroid). There are 1,321 

2 blocks within these block groups. Of these blocks, 14 were identified as falling within facility 

boundaries, or adjacent to the facility in the Mississippi River.1 3 These 14 blocks were removed 

4 from the assessment; other zero population blocks within the study area were still included. A 

5 total of 1,307 census block centroids were included as receptors in the air dispersion model 

6 simulations, including blocks within the study area with zero population. The blocks with zero 

7 population were included in the modeling simulations to aid in understanding the patterns of air 

8 concentrations in the study area. These locations were not included in the exposure assessment 

9 and are not included in exhibits summarizing modeling results (with the exception of isopleths 

10 diagrams). The elevation of each block centroid was generated using USGS digital elevation 

11 model files and the AERMAP preprocessor model. 

12 

13 In order to limit the locations modeled for the exposure assessment, a combination of block and 

14 block group-level results were used for the exposure assessment. The average concentration and 

15 deposition in each block group was calculated by spatially-weighting the values at the block 

16 level. The area of each block was obtained from the U.S. Census (Census Bureau 2005). The 

17 decision of whether to use the block or block group was made by considering the range of block18 level concentrations within a block group. If the ratio of the maximum block-level air 

19 concentration in the block group to the average air concentration in the block group was greater 

20 than 2.0, the blocks in the block group were modeled individually. All other block groups were 

21 modeled as units. This method generally resulted in blocks near the facility, as well as some 

22 blocks far from the facility that fall within very large block groups, being modeled individually 

23 (see Exhibit 4-1). 

24 

25 Six census blocks near the facility for which there are non-zero population counts for the year 

26 2000 were removed from the analysis because current information indicates that as of 2004 they 

27 have zero population (Gradient Corporation 2004). A total of 22 census block groups and 201 

28 census blocks (all with non-zero population counts) in other block groups were included in the 

29 exposure assessment. 

 

1

 All territory in the United States is delineated into Census 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 

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

3 
 Estimates of Media Concentrations 

ICF International 4-4 DRAFT – December 2006 

1 4.1.2 Air 

2 

3 The air concentrations and deposition of Pb resulting from emissions at the primary Pb smelter 

4 facility were estimated using the ISC-PRIME (Schulman et al. 1997; USEPA 1995) air 

5 dispersion model and compared to the air concentration and deposition measurements from 

6 nearby monitors. The emissions used for the air quality modeling are described in Section 3.1.4. 

7 The air concentration and deposition monitoring data used for comparison are described in 

8 Section 3.1.5. 

9 

10 4.1.2.1 Study Area and Air Dispersion Modeling

11 

12 The air dispersion model ISC-PRIME was used for the primary Pb smelter air quality modeling. 

13 The meteorological data used for the model simulations included 25 consecutive months (April 

14 1, 1997 to April 30, 1999) of on-site data. These meteorological data were also used for an 

15 analysis by U.S. EPA Region 7 (2006b) of the facility. Meteorological data are currently being 

16 collected at the facility, but problems due to data gaps prevented their use in this analysis 

17 (USEPA Region 7 2006b). As described in Section 3.1.4, emissions, release parameters, particle 

18 size parameters, and building downwash inputs were all provided by Region 7 (in the form of an 

19 input runstream file). The emissions and source parameters represent the 2000 Revision of the 

20 SIP developed for the facility (MDNR 2000). 

21 

22 Hourly air concentrations and deposition were output from the dispersion model at each receptor 

23 (i.e., block or block group, as described in Section 4.1.1) and monitor location. The hourly air 

24 concentration and deposition model results were averaged over the modeling time period to 

25 obtain annual average air concentration and annual average deposition for the current conditions 

26 scenario. The concentrations were also averaged quarterly to determine whether any of the 

27 current conditions scenario’s air quarterly average concentrations were greater than the current 

28 Pb NAAQS. Modeled Pb air concentrations in some blocks exceeded the NAAQS for at least 

29 one quarter. For the current attainment scenario, these concentrations were set to the current 

NAAQS value (1.5 μg/m3 30 ), assuming attainment, and the annual averages were then recalculated 

31 using the revised quarterly averages. All other modeled concentrations for the current attainment 

scenario were set to the same values as the current conditions scenario.2 32 

33 

34 4.1.2.2 Inhalation Exposure Concentrations 

35 

36 Inhalation exposure concentrations of Pb were estimated for the population of interest (young 

37 children) from the estimated ambient air concentrations using age group- and location-specific 

38 relationships for Pb developed from modeling performed for U.S. EPA’s 1999 National-Scale 

39 Air Toxics Assessment (USEPA 2006e), one of U.S. EPA’s National Air Toxics Assessment 

40 (NATA) activities. These relationships account for air concentration differences indoors and 

41 outdoors and mobility or time spent in different locations (e.g., outdoors at home, inside at home, 

42 etc.) for the population of interest. 

 

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 

1 The NATA national-scale assessment produced air concentrations of Pb (and other hazardous air 

2 pollutants) for each U.S. Census tract (using the Assessment System for Population Exposure 

3 Nationwide model, ASPEN), and corresponding exposure concentrations of Pb for each of five 

4 age-groups at each U.S. Census tract. The relationships (or ratios) between ambient Pb 

5 concentration and Pb exposure concentration from the NATA national-scale assessment for the 0 

6 to 4 age group (the closest age group for which outputs are available to the age group of interest 

7 for this assessment) ranged from 0.37 to 0.42 for the U.S. Census tracts within the study area for 

8 the primary Pb smelter location. The ratios are presented in Appendix F, Exhibit F-1. 

9 

10 Use of ratios for the 0 to 4 age group (rather than for 0 to 7) contributes some uncertainty in the 

11 estimate of children inhalation exposure concentrations. In addition, there is some uncertainty in 

12 the magnitude of the air concentrations generated using the ASPEN model for the NATA 

13 assessment. In a comparison to monitoring data across the country, the ASPEN modeled air 

14 concentrations generally underestimate monitored concentrations (see section on Comparison to 

15 Monitored Values, USEPA 2006e). However, the relationship between ambient concentrations 

16 and exposure concentrations (which is the comparison used here) is not expected to be affected 

17 by underestimated ambient concentrations from the NATA study. 

18 

19 4.1.2.3 Air and Deposition Results 

20 

21 Annual average Pb air concentrations and deposition fluxes were calculated at the centroid of 

22 each census block group, block, and monitor receptor point for the two years of meteorological 

23 data. To take into account variations in meteorological data, the annual average concentrations 

24 and depositions for each of the two years were averaged to generate one set of representative 

25 annual average concentration and deposition results for the current conditions scenario. For the 

26 attainment scenario, concentration estimates were adjusted at the eight census blocks at which 

27 one or more quarterly average air concentrations exceeded the current NAAQS. At these blocks 

28 any quarterly averages that exceeded the NAAQS were set equal to the NAAQS and the annual 

29 average was calculated from the quarterly averages. All other block and block groups for which 

30 a quarterly average air concentration did not exceed the current NAAQS were kept at the 

31 modeled values for the attainment scenario. Using this method, it is likely that the 

32 concentrations at blocks that did not exceed the current NAAQS are slightly overestimated for 

33 the attainment scenario. However, because there may be multiple methods that the facility 

34 would use to obtain attainment (i.e., different controls on different processes), the exact pattern 

35 of concentration reductions could vary depending upon which location in the facility has reduced 

36 emissions. Thus, it is likely to underestimate reductions in ambient air concentrations for the 

37 current NAAQS attainment scenario relative to the current conditions scenario. 

38 

39 All concentration, deposition, and inhalation exposure estimates for the census blocks and block 

40 groups modeled are presented in Appendix F, Exhibits F-2 and F-3, for both scenarios. Exhibit 

41 4-2 presents the range of air concentration, inhalation exposure, and deposition values generated 

42 from the modeling for both the current conditions and current attainment scenarios. The values 

43 are summarized from the model results at the 223 blocks and block groups with non-zero 

44 population selected for further analysis in the exposure assessment. Exhibit 4-3 shows the 

45 isopleths of the block-level modeled air concentration results. 

46 
 Estimates of Media Concentrations 

ICF International 4-6 DRAFT – December 2006 

1 Exhibit 4-2. Primary Pb Smelter Case Study: 

Modeled Air Concentrations and Deposition a 2 

 Current Conditions Current Attainment 

Statisticb

Average 

Annual Pb Air 

Concentratio

n (μg/m3

) 

Children 0 to 4 

Average Annual 

Pb Air Exposure 

Concentration 

(μg/m3

) 

Average 

Annual Pb Air 

Concentration 

(μg/m3

) 

Children 0 to 4 

Average Annual Pb 

Air Exposure 

Concentration 

(μg/m3

) 

Dry 

Deposition 

(g/m2

/year) 

Maximum 2.73 1.14 1.50 0.628 5.20 

95th Percentile 0.662 0.277 0.662 0.277 1.28 

Median 0.0221 0.00895 0.0221 0.00895 0.0938 

5th Percentile 0.00845 0.00329 0.00845 0.00329 0.0266 

Minimum 0.00541 0.00210 0.00541 0.00210 0.0163 a 3 The 223 blocks and block groups with non-zero population selected for analysis were used to create this summary. 

4 Note that in some of these blocks the 2000 U.S. Census indicates there are no children. b

 The statistic (e.g., 95th 5 percentile, median) may not be at the same location for each of the data results presented 

6 here. 

7 
 Estimates of Media Concentrations 

ICF International 4-7 DRAFT – December 2006 

1 Exhibit 4-3. Primary Pb Smelter Case Study: Concentration and Deposition Isopleths 

2 

3 
 Estimates of Media Concentrations 

ICF International 4-8 DRAFT – December 2006 

1 4.1.2.4 Air Modeling Performance Assessment 

2 

3 As described in Section 3.1.5, nine air monitors from AQS/AirData (USEPA 2006f) and four 

4 EPA high-volume air monitoring stations that are not in the AQS/AirData database (USEPA 

5 Region 7 2006a) were identified within 10 km of the primary Pb smelter facility. Pb 

6 concentrations measured at all of these monitors are for Pb in total suspended particulates (TSP). 

7 Modeled air concentrations were generated from ISC-PRIME at each of these locations and are 

8 presented in Exhibit 4-4. There is no specific year of monitoring data that can be directly 

9 correlated with the emissions characterization because the emissions represent those from the 

10 2000 Revision of the SIP developed for the facility, rather than a specific year’s emissions 

11 (MDNR 2000). Therefore, the most recent 5 years of monitoring data were compared to the 

12 modeled air concentrations. The range of the ratios of monitored to modeled concentrations is 

13 presented for each monitor location in Exhibit 4-4. A more detailed comparison is presented in 

14 Appendix F, in Exhibit F-4. 

15 

16 As presented in Exhibit 4-4, it varies whether the model or monitor Pb air concentration is higher 

17 at each monitoring point. It appears that roughly with increasing distance from the facility, the 

18 ratio of monitored-to-modeled concentrations increases. This suggests that the model may be 

19 overestimating concentrations at close distances, and underestimating concentrations with 

20 increasing distance. This may be related to deposition parameters, emissions estimates at certain 

21 locations at the facility (the facility spans approximately 1 km), or meteorological data. 

22 

23 Exhibit 4-5 presents a comparison of the deposition modeled at 10 locations for which dry 

24 deposition measurements are available (USEPA Region 7 2006a). Measured deposition was 

25 available for 2003-04 at two heights (1 foot and 10 feet). The modeled deposition fluxes at those 

26 same locations are compared to the all measured values and a range of the ratio of monitored to 

27 modeled deposition is presented in Exhibit 4-5. A more detailed comparison table is provided in 

28 Appendix F, Exhibit F-5. For most locations, the predicted dry deposition is less than the 

29 measured dry deposition. 

30 

31 A wind rose created from the on-site meteorological data at the primary Pb smelter (Exhibit 4-6) 

32 shows that the predominant wind directions are to the east and north. The air monitors and 

33 facility are mapped in Exhibit 4-7 to show the relative locations. The shaded area on the map 

34 indicates the area with modeled emission release points, and the red circle is the main stack at the 

35 facility. Because the Mississippi River forms a natural boundary on the eastern side of the 

36 facility, the air monitors are all to the north, west, and south. There are no close-by air monitors 

37 to the east, in the predominant wind direction (the closest monitor in an eastern direction is more 

38 than 40 km to the northeast). However, because of the width of the river, the monitor network is 

39 expected to capture the high-end human inhalation exposure values from the facility emissions. 

40 In addition, the land on the eastern side of the river is farmland, and not as densely populated as 

41 the area with the monitors (see Exhibit 3-2). The highest monitored values are at monitors 102, 

42 290990015, 290990004, and 290990005, which are all north (and slightly west) of the facility. 

43 This corresponds with what may be expected from the wind rose. Of the modeling results, one 

44 of the highest concentrations was modeled at monitor 101 to the southwest of the main stack. 

45 This likely is a result of the wide spread of emission sources modeled. Monitor 101 is located 

46 amongst modeled emission sources, resulting in the high modeled concentration. Overall, the 
 Estimates of Media Concentrations 

ICF International 4-9 DRAFT – December 2006 

1 modeled concentrations and depositions are fairly representative of conditions over the last 5 

2 years based on comparison with the monitors. 

3 

4 Exhibit 4-4. Primary Pb Smelter Case Study Air Concentration Modeled Results 

5 Compared to Monitor Values 

Pilot Phase 

Analysis ID 

or Monitor ID 

Distance to Main 

Stack (km)a

2-year Average 

Modeled Air Conc 

(μg/m3

) 

Range of Monitored to 

Modeled Ratio 

for 2001 to 2005b

U.S. EPA Region 7 Database HiVols Valuesc

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 Valuesd

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 6 The facility spans roughly 0.2 by 1 km. Therefore, some monitors will be closer to other emission release 

7 points than they are to the main stack. 

b 8 The number indicated in parentheses after each range indicates how many annual average monitored 

9 concentrations are compared to the modeled results (i.e., 2, 3, or 5 annual average values are available from 

10 the monitor). 

c 11 The Pb air concentration detections (which appear to be TSP Pb) reported in the HiVols data set of the 

12 U.S. EPA Region 7 database were averaged for each year (see Exhibit A-2). All non-detects were assigned 

13 half of the detection limit for averaging. Averages for 2001 are from measurements taken between October 

14 and December; averages for 2002 are from measurements taken all year; and averages for 2003 are from 

15 measurements taken between January and July. 

d 16 AirData values are for TSP Pb and are averages obtained from the AirData website monitor reports. In 

17 some cases, there is more than one monitor at a site. 

18 
 Estimates of Media Concentrations 

ICF International 4-10 DRAFT – December 2006 

1 Exhibit 4-5. Primary Pb Smelter Case Study Deposition Modeled Results 

2 Compared to Measured Values 

Pilot Phase 

Analysis ID 

Distance to Main 

Stack (km)a Modeled Average Pb 

Deposition (g/m2/yr) 

Range of Monitored to Modeled 

Deposition Ratio for 2003-04b

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 3 The facility spans roughly 0.2 by 1 km. Therefore, some monitors will be closer to other 

4 emission release points than they are to the main stack. b 5 For each set of ratios, four sets of averaged deposition values were compared – deposition 

6 measurements at 1 ft and 10 ft in 2003 and 2004. See Exhibit A-11 for individual deposition 

7 measurements. 

8 

Exhibit 4-6. Wind Rose of Meteorological Data used for Primary Pb Smelter Case Study 

(Direction in which Wind is Blowing)a

a

 Wind rose for 18,240 hours of data 
 Estimates of Media Concentrations 

ICF International 4-11 DRAFT – December 2006 

1 Exhibit 4-7. Air Monitor Locations near Primary Pb Smelter 

2 

3 
 Estimates of Media Concentrations 

ICF International 4-12 DRAFT – December 2006 

1 4.1.3 Outdoor Surface Soil 

2 

3 For the human exposure and health risk assessments, outdoor surface soil concentrations were 

4 estimated for each spatial unit (i.e., census blocks and block groups) in the study area from the 

5 soil sample measurements in the area. The extent and types of soil data sets used for the 

6 calculations are described in Section 3.1.5. Many of the yards within 1.5 km of the primary Pb 

7 smelter facility have been excavated and filled with clean soil in the last 10 years. EPA has 

8 taken soil samples from 31 of these sites since 2002. These measurements are called 

9 “recontamination” samples. The EPA database also contains soil samples for more than 900 

10 locations labeled as “pre-excavation.” These samples were taken from November 2000 to 

11 August 2004. Not all of the locations with pre-excavation samples have been filled with clean 

12 soil, only select locations within 1.5 km of the facility. Documentation available for the 

13 “recontamination” soil samples indicates that samples were taken to a depth of less than an inch 

14 (EPA 2001), and it is likely the “pre-excavation” samples were taken the same way. Depending 

15 on the location of the modeled block or block group in this study (within or outside of the soil 

16 cleanup area), the soil concentrations for this assessment were calculated using either the 

17 recontamination or pre-excavation data set. 

18 

19 All census blocks within the soil cleanup area (approximately 1.5 km) were identified from the 

20 Gradient Corporation report (2004). For these 28 blocks with population, soil concentrations 

21 were estimated from the recontamination soil samples taken in 2005. For blocks for which there 

22 were one or more soil measurements available, the block soil concentration was set to the 

23 average (arithmetic mean) of those measurements. For blocks for which there were no 

24 measurements, but for which there were nearby measurements (i.e., across the street), the soil 

25 concentration was set to the nearby measurement average. For other blocks, the average of all of 

26 the recontamination soil measurements within 500 m was calculated and set as the value for the 

27 block. 

28 

29 Outside the soil cleanup area, soil concentrations were estimated using a regression equation of 

30 the pre-excavation soil concentrations. The distance of each pre-excavation soil sample to the 

31 main stack was measured using a geographical information system (GIS). The measurements 

32 were grouped according to distance from the main stack, with separate groups for each 500 meter 

33 (m) increment. The arithmetic mean for each group was calculated, resulting in five arithmetic 

34 mean average values for soil concentration which were plotted versus distance. A regression 

power equation (R2 35 of 0.92) was calculated from the samples (see Exhibit 4-8). Note that pre36 excavation soil samples taken within 1.5 km of the facility were included to develop the 

37 regression equation; however, the equation was not used to estimate soil concentrations at blocks 

38 within the 1.5 km soil clean-up area (as indicated in Exhibit 4-8). The distance of each census 

39 block and block group centroid from the main stack was measured in GIS. Soil concentrations 

40 for the blocks and block groups outside the soil cleanup area were then calculated using the 

41 regression equations based on distance from the stack. 

42 
 Estimates of Media Concentrations 

ICF International 4-13 DRAFT – December 2006 

1 Exhibit 4-8. Average Pre-excavation Soil Measurements and Best-fit Trend Line 

y = 2E+07x-1.4417

R2 = 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 

4 All calculated soil concentrations at the block and block group locations are summarized in 

5 Appendix F, Exhibit F-2 and F-3, with an indication of which method was used to calculate the 

6 values. A summary of the soil concentrations is presented in Exhibit 4-9. Note that because of 

7 the soil cleanup within 1.5 km of the stack, the calculated soil concentrations, as well as the 

8 measured concentrations, near the facility are in many cases lower than the soil concentrations 

9 calculated or measured in locations without soil cleanup. Soil concentrations throughout the 

10 modeled region range from 16 to 976 mg/kg. 

11 

12 Exhibit 4-9. Summary of Primary Pb Smelter Case Study Soil Concentrations 

13 Extrapolated from Measurements 

Statistic Calculated Average Soil 

Concentration (mg/kg) 

Distance from Main Stack 

(m)a

Maximum 976 980 

95th Percentile 426 1,700 

Median 84.0 5,400 

5th Percentile 23.6 13,000 a

Minimum 15.9 17,000 a

a 14 Receptors at blocks greater than 10 km from the facility were included because of the irregular shape 

15 of block groups (see Section 4.1.1 and Exhibit 4-1). 

16 

17 For the ecological risk screening assessment, surface soil concentration data were extracted from 

18 data sets provided by ELM (2005) in its Characterization Area Investigation Report for the 
 Estimates of Media Concentrations 

ICF International 4-14 DRAFT – December 2006 

1 primary Pb smelter facility in Herculaneum, Missouri. Sample results were aggregated into 

2 geographic clusters to average media concentrations that will be used in the ecological risk 

3 characterization. To ensure that data reflect contributions primarily via air deposition, the 

4 sample locations were reviewed to exclude samples designed to assess contributions from other 

5 pathways (e.g., surface drainage pathways and groundwater flow pathways). The samples on the 

6 central, east, and west transects of the smelter’s slag storage area (SSA) were excluded from this 

7 assessment because the ELM (2005) report states that the floodwaters of Joachim Creek have 

8 been visually observed to come into contact with the SSA, thereby depositing slag materials into 

9 the floodplains as the water flow subsides. The soil measurements for the west and southern 

10 slope of the SSA were also excluded because the sampling transects were located close to water 

11 seeps from the SSA. 

12 

13 Soil samples farthest away from the SSA, where the possibility of direct contact with Pb from 

14 the SSA during flooding events was the lowest, were chosen in this analysis. This set of 

15 sampling sites was along the historic railroad grade on the west side of Joachim Creek, where 

16 each site was at a progressively greater distance from the SSA. Also included in this analysis 

17 were samples from the two “reference” areas. ELM collected samples from approximately 5.6 

18 km and 6.5 km south of the facility, respectively, to measure “background” concentrations of Pb 

19 in the soil and to establish a baseline for comparison to the measurements closer to the facility. 

20 The closer reference area was south of Crystal City and the farther reference area was south of 

21 the Festus Memorial Airport. ELM chose these areas because they have a similar habitat to areas 

22 surrounding the facility in terms of past and current land use and regional geology. There are no 

23 known industrial sources of Pb near these references sites. 

24 

25 To develop soil concentration estimates for the ecological risk screening assessment, the surface 

26 soil data was grouped into three geographic clusters. Cluster 1 comprised of ten samples taken 

27 from the west side of Joachim Creek (approximately 1 km west of the facility); Cluster 2 

28 contained six samples taken from Crystal City (approximately 5.6 km south of the facility); and 

29 Cluster 3 contained ten samples taken from near the Festus Memorial Airport (approximately 6.5 

30 km south of the facility). Soil samples were taken from depths of 0 to 3 inches (ELM 2003). 

31 The soil sampling results for the three soil sample clusters are summarized in Exhibit 4-10 and 

32 presented in more detail in Appendix F. 

33 

34 Exhibit 4-10. Soil Sample Clusters Used to Estimate Ecological Receptor Exposure 

35 via Soil and Corresponding Pb Analysis Results (ELM 2005) 

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 

37 
 Estimates of Media Concentrations 

ICF International 4-15 DRAFT – December 2006 

1 4.1.4 Indoor Dust 

2 

3 For estimating indoor dust concentrations for residences at the primary Pb smelting facility, two 

4 dust prediction models were used: 

5 

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

7 • For receptors more than 1.5 km away from the facility: a pooled analysis model (referred 

8 to as the “AGG” or “aggregate” model) identified from the literature which predicts Pb 

9 dust concentrations given soil and ambient air Pb levels based on data from a variety of 

10 industrial and urban and industrial studies (USEPA 1989). 

11 

12 The site-specific model is based on data collected within the residential remediation zone 

13 characterizing yard soil Pb levels (post-remediation) and indoor dust levels. The AGG, or non14 site-specific model, was selected for zones outside of the remediation area because available soil 

15 and indoor dust data did not extend to these more distant areas and the site-specific model 

16 derived for the remediated zone was deemed not representative for the non-remediated zone. 

17 

18 The site-specific regression model was developed for predicting indoor dust levels within the 

19 remediation zone immediately adjacent to the primary Pb smelter. The data set used to “fit” the 

20 model was based on indoor dust samples (dependent variable) collected for 17 houses within the 

21 remediation zone. Independent variables included: 

22 

23 • Modeled annual-average air Pb concentrations from census block centroids located 

24 within 200 m of each of the 17 houses; 

25 

• Road dust measurements located within 300 m of each house3 26 ; and 

27 

28 • Post-remediation residential soil Pb measurements collected from the yard of each house. 

29 

30 The pre-remediation soil Pb concentrations collected during 2001 were not included in 

31 regression modeling because they might not be representative of current conditions at the site 

32 (i.e., it was felt that soil samples collected post-remediation would be more representative of 

33 current conditions given the level of remediation within this zone and ongoing recontamination 

34 which was occurring). Because the focus is on predicting indoor dust Pb concentrations 

35 reflecting longer-term trends, multiple samples taken over time for a given medium at a specific 

36 house were averaged to produce a “temporally-averaged” value reflecting longer-term trends in 

37 Pb concentrations. For a more detailed summary of the regression analysis see Appendix F. 

38 

39 Multiple regression models were tested for this analysis, which are listed in Exhibit F-17 and F40 18 of Appendix F. During the testing, the road dust samples were found to not have significant 

41 predictive power for indoor dust Pb. This may reflect the fact that the indoor dust sample data 

42 set does not provide significant coverage for homes located near to the truck haul routes, 

43 resulting in a poor correlation between road dust levels and indoor dust levels. Yard soil samples 

 

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 

1 were found to be slightly (but statistically significantly) negatively correlated with indoor dust 

2 levels. This counter-intuitive finding may reflect the process of yard remediation which has 

3 likely resulted in a patchwork of remediated yards with varying degrees of recontamination by 

4 air-deposited Pb (i.e., remediation activity has interfered with any correlation between yard soil 

5 Pb levels, ambient air Pb levels, and indoor dust Pb levels that might have existed prior to 

6 remediation efforts). Because of this negative correlation, the relationship to soil Pb levels was 

7 excluded from the selected regression model, resulting in a model that only uses air 

8 concentrations as input but has a fairly large intercept, which likely reflects a number of factors 

9 not correlated with ambient air or distance from the facility, including generalized soil Pb 

10 contamination in the area as well as contributions of indoor Pb paint to indoor dust. 

11 

12 Although multiple regression models were tested, ultimately, the H6 model was selected for use 

in the pilot phase because it has one of the highest goodness-of-fit measures (R2 13 is 0.701), it 

14 tracks non-linearities of the data even at low levels, and fits longer-term trends better than other 

15 models. The H6 indoor dust model presented as follows relates the natural log of indoor house 

16 dust to the natural log of ambient air Pb: 

17 

18 ln(Pbdust) = 8.3884 + 0.73639 × ln(Pbair) 

19 

20 CCC = exp{1.273 [ln(hardness)] – 4.705} × CF

21 CMC = exp{1.273 [ln(hardness)] – 1.460} × CF

22 where: 

23 

24 CF = conversion factor = 1.46203 -[ ln(hardness) × 0.145712] hardness is 

25 expressed as mg/L of calcium carbonate (CaCO3) 

26 Pbdust = concentration of Pb in indoor dust (mg/kg) 

27 Pbair = concentration of Pb modeled in the ambient air (μg/m3). 

28 

29 For the remainder of the study area, the pooled analysis AGG model based on data collected 

30 during the 1970s and 1980s at a number of active primary Pb smelters, including the primary 

31 smelter analyzed here, was used (USEPA 1989). The AGG model predicts indoor dust Pb based 

32 on both outdoor soil and ambient air Pb levels. The model is appropriate for the non-remediation 

33 portion of the primary Pb smelter case study area because this area has not been subjected to 

34 extensive remediation and is therefore likely to resemble the locations included in the pooled 

35 analysis used in deriving this model (i.e., areas not having undergone extensive yard soil 

36 remediation). Furthermore, because the non-remediation portion of the study area is likely to 

37 have soil Pb gradients reflecting long-term atmospheric deposition of Pb, it is likely that indoor 

38 dust would be partially dependent on soil Pb. Therefore, the AGG model presented here 

39 (including both the soil and air factors) was selected for this portion of the study area: 

40 

41 Pbdust = 31.3 + (638 × Pbair) + (0.364 × Pbsoil) 

42 

43 where: 

44 

45 Pbsoil = concentration of Pb in outdoor soil (mg/kg). 

46 
 Estimates of Media Concentrations 

ICF International 4-17 DRAFT – December 2006 

1 Exhibit 4-11 presents a summary of the Pb indoor dust concentrations generated in the primary 

2 Pb smelting study area for the current conditions and current attainment scenarios. Indoor dust 

3 concentrations were only estimated for census blocks with children. Exhibit 4-11 shows the 

4 number of census block or block groups and children modeled with increasing levels of Pb house 

5 dust. All estimated indoor dust Pb concentrations for residences in the primary Pb smelter case 

6 study are presented in Appendix F, Exhibit F-14. 

7 

8 

9 

Exhibit 4-11. Primary Pb Smelter Case Study: Modeled Indoor Dust Pb Concentrations a,b 10 

Current Conditions Current Attainment 

House dust 

concentration 

(μg/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 11 The 137 blocks and block groups with children aged 0 to 7 in the 2000 U.S. Census (U.S. Census Bureau 2005) 

12 were used to develop this summary. Note that Exhibits 4-2 used all blocks with population, even blocks without 

13 children. 

b 14 Number of children ages 0 to 7 from U.S. Census 2000 were used in this analysis (U.S. Census Bureau 2005). 

15 

16 Studies summarized in the 1990 review of the Pb NAAQS contained measurements of house 

17 dust ranging from 10 to 35,000 ppm. A high value of 100,000 ppm was measured in one home 

18 within 2 km of a Pb smelting facility (USEPA 1989). The Pb indoor dust concentrations for this 

19 primary Pb smelter case study fall within the range presented by U.S. EPA (1989), but they are 

20 not in the high-end of the range. Within a distance of 2 km from the modeled primary Pb 

21 smelting facility, the maximum dust concentration at a receptor location is 5,300 parts per 

22 million (ppm) at 300 m from the main stack. In a study of Pb concentrations in household dust 

23 near a facility that has operated as a secondary Pb smelter since 1972 and as a primary smelter 

24 for the previous 200 years in the Czech Republic, Rieuwerts et al. (1999) measured household 

25 dust Pb concentrations in houses in neighborhood adjacent to the facility (the neighborhood 

26 ranges from approximately 0 to 500 m away from the facility according to a figure). Measured 

27 Pb concentrations in household dust from 14 homes ranged from 861 to 5890 ppm with a 

28 geometric mean of 1668 ppm. Indoor dust concentrations in the primary Pb smelter pilot phase 

29 assessment are similar, ranging from 1,500 to 5,300 ppm out to 500 m from the facility, with a 

30 geometric mean of 3,100 ppm. 

31 

32 4.1.5 Surface Water and Sediment 

33 

34 Measurements of sediment and surface water Pb concentrations were obtained from ELM’s 

35 Characterization Area Investigation Report (2005). Sediment samples chosen for this 

36 assessment were co-located with surface water samples at all locations. ELM sampled Joachim 
 Estimates of Media Concentrations 

ICF International 4-18 DRAFT – December 2006 

1 Creek, the Mississippi River, a U-shaped ephemeral pond approximately 1.7 km northwest of the 

2 primary Pb smelter facility, and drainage areas in the watershed. 

3 

4 4.1.5.1 Surface Water 

5 

6 Results of all surface water samples collected from the Joachim Creek, Mississippi River, and 

7 the nearby pond and backwater flow areas showed that levels of dissolved Pb were below the 

8 detection limit for Pb of 3.0 μg/L (note that the detection limit for dissolved Pb in surface waters 

9 for the U.S. Geological Survey since 2001 is 1.0 μg/L). The surface water sample data analyzed 

10 by ELM (2005) was reviewed and aggregated, and the results for sampling locations used in this 

11 assessment are presented in Appendix F. 

12 

13 ELM (2005) collected surface water samples from four backwater and low flow areas within a 

14 1.6 km radius to characterize impacts of aerial deposition of historical smelter emissions. ELM 

15 collected samples from two locations on Joachim Creek and its feeder streams (CHRDDP and 

16 DAMUP), based on the assumption that even though most of the direct deposition of particles 

17 from the emissions would be in the direction of the prevailing winds, minor gusts and winds 

18 along with aerodynamic effects may take the contaminants in any direction. Additional samples 

19 were taken from the unnamed pond north of Joachim Creek (UPOND) and from a slow flow area 

20 along the western shore of the Mississippi downstream of the facility (RRDP). 

21 

22 The other ELM (2005) surface water samples were reviewed and the ones that indicated likely 

23 contributions of Pb from sources other than air deposition because they bordered the storage slag 

24 area (SSA) were excluded. Sampling sites were chosen from among those taken along Joachim 

25 Creek to represent sections of the creek approximately 800 m, 1.6 km, and 3.2 km to the west 

26 and southwest of the primary Pb smelter based on aerial maps showing the sampling locations. 

27 Transects were established by ELM in the area of point bars, backwaters, and low flow areas for 

28 sediment sampling as well as locations perpendicular to stream flow at each sampling location. 

29 ELM collected three surface water samples at each transect, one at the thalweg (i.e., the deepest 

30 water area of the creek, generally in the middle) and the other two equidistant between the 

31 thalweg and the banks of the Joachim Creek. The water samples were collected in the center of 

32 the vertical water column (i.e., equidistant from the water surface and the top of the sediment) at 

33 each of the three locations of each transect. 

34 

35 The sampling locations identified in the Mississippi River were approximately 2.7 km upstream 

36 from the facility, in the immediate vicinity of the facility, and approximately 2.5 km downstream 

37 from the facility (data for sampling stations further downstream on the Mississippi River were 

38 not reported). ELM chose the farther downstream locations to assess the potential impact on the 

39 Mississippi River surface waters from the facility’s SSA downstream, as well as deposition of 

40 smelter emissions. 

41 

42 Data were aggregated for the ecological risk screening assessment as follows: 

43 

44 • Joachim Creek. Samples were clustered geographically and then averaged. Cluster 1 

45 corresponds to the set collected farthest upstream from the facility, while Cluster 5 was 

46 nearest upstream from the facility, and the remaining clusters fill the range between. 
 Estimates of Media Concentrations 

ICF International 4-19 DRAFT – December 2006 

1 Results are provided in Exhibit F-7. Although total Pb concentrations were typically 

2 above the detection limit of 3 μg/L, with values up to 106 μg total Pb per liter of water, 

dissolved Pb concentrations were not above detection limits.4 3 Therefore, all measured 

4 surface water concentrations of dissolved Pb at the sampling locations of interest were 

5 below the detection limits used in the study.

6 

7 • Mississippi River. Samples from upstream, at the facility, and downstream of the 

8 facility are presented and will be evaluated separately. Sample results are presented in 

9 Exhibit F-8. Although some of the measured total Pb concentrations exceeded the 

10 detection limit, all measured dissolved Pb concentrations were below the detection limit. 

11 

12 • Other surface water samples from U-shaped pond and drainage areas. Sample 

13 results are presented in Exhibit F-9. Again, all measured concentrations of dissolved Pb 

14 in these surface waters were below the detection limit of 3 μg/L. 

15 

16 4.1.5.2 Sediment 

17 

18 Sediment sample data were extracted and aggregated into geographic clusters based on ELM’s 

19 Characterization Area Investigation Report (2005) at the same locations as the surface water 

20 concentrations presented in the previous section. Mean Pb concentrations in dried sediment 

21 sample clusters for Joachim Creek ranged from 34.5 to 78.7 mg/kg; for the Mississippi River 

22 from 12.1 mg/kg downstream to 30.1 mg/kg near the facility; and in the U-shaped pond mean 

23 sediment concentrations were 170 mg/kg. Exhibits 4-12 through 4-14 present a summary of the 

24 clustered sediment concentrations considered in this analysis. More detailed tables showing 

25 measured sediment concentrations at each location are presented in Appendix F. 

26 

27 Sediment samples to 10 cm in depth from the Mississippi River were collected from the back28 eddies located behind wing dams and jetties, because ELM expected significant sedimentation in 

29 these areas due to the engineering of the Mississippi River by the Army Corps of Engineers. In 

30 Joachim Creek, ELM focused sediment sampling locations at point bars and other depositional 

31 areas. Joachim Creek exhibited sinuous meanders in the vicinity of the facility, which lies within 

32 one of the large loops of one of the meanders of the creek. Since the series of meanders lead to a 

33 slower and steadier current of the water, one would expect inorganic constituents associated with 

34 facility-related operations to sorb onto silt and organic matter particles and mobilized facility35 related constituents to collect at the point bars and other depositional zones. ELM collected 

36 sediment samples from these areas to assess the potential for impacts from deposition of smelter 

37 emissions, possibly from both slag piles and air emissions, on the sediments of Joachim Creek. 

38 

39 ELM also collected sediment samples from backwater and low flow pond areas, ephemeral 

40 ponds, and drainage pathways to characterize impacts of air deposition of historical smelter 

41 emissions. They collected surface water and sediment samples from the lowest points in the 

42 watershed and drainage pathways within 2.4 km from the facility. This choice of four sampling 

 

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 present the measured constituent concentration results for the 

characterization area surface water samples. The Appendix C data tables all indicate 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 

1 areas represented the areas of direct deposition from smelter emissions and deposition of 

2 impacted particles by surface water run-offs. 

3 

4 Given the focus of this assessment on impacts from air emissions from the smelter, sediment 

5 samples that were taken very close to the water seeps at the toe of the SSA were excluded, 

6 because the ELM reports states that the waters from Joachim Creek, on occasion, had been 

7 observed to come into contact with the SSA during flooding events. 

8 

9 Data were aggregated for the risk screening assessment as follows: 

10 

11 • Joachim Creek. Samples were clustered geographically and then averaged. Cluster 1 

12 corresponded to the samples collected furthest upstream from the facility, while Cluster 5 

13 was nearest upstream from the facility. Measurements are presented in Exhibit 4-12 and 

14 Exhibit F-10. 

15 

16 • Mississippi River. Samples from upstream, at the facility, and downstream of the 

17 facility are presented and will be evaluated separately. Measurements are listed in 

18 Exhibit 4-13 and Exhibit F-11. 

19 

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

21 Measurements are presented in Exhibit 4-14 and Exhibit F-12. 

22 

23 Exhibit 4-12. Joachim Creek Measured Sediment Pb Concentration Data 

24 for the Primary Pb Smelter Case Study (ELM 2005) 

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 

25 
 Estimates of Media Concentrations 

ICF International 4-21 DRAFT – December 2006 

1 Exhibit 4-13. Mississippi River Measured Sediment Pb Concentration Data 

2 for the Primary Pb Smelter Case Study (ELM 2005) 

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 

4 Exhibit 4-14. Other Surface Water—Drainage Areas and U-Shaped Pond—Measured 

5 Sediment Pb Concentration Data for the Primary Pb Smelter Case Study (ELM 2005) 

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 

7 4.2 Secondary Pb Smelter 

8 

9 The media concentrations for the secondary Pb smelter case study were estimated using 

10 modeling and then compared to available measured values. For the time-period represented by 

11 the emissions (1997 to 2000), air monitoring data near the facility did not indicate quarterly 

average air concentrations exceeding the current NAAQS5 12 , and modeled quarterly air 

13 concentrations were also all less than the current NAAQS (USEPA 2006f). Therefore, only a 

14 current conditions scenario was performed for this case study. 

15 

16 4.2.1 Spatial Template 

17 

18 The study area extent was defined out to the distance where the concentrations associated with 

19 the secondary Pb smelter facility emissions reached approximately 50 percent of the background 

20 concentration of Pb in the area. Air concentrations generated from a preliminary AERMOD run 

21 with a radial grid of receptors placed at seven distances out to 50 km were compared to 

22 background Pb concentrations from the Interagency Monitoring of Protected Visual 

23 Environments (IMPROVE) database (IMPROVE 2006). The average (arithmetic mean) of all 

24 background Pb measurements taken between 2000 and 2004 at two nearby sites that were used 

for comparison to the secondary Pb smelter results were 0.0012 μg/m3 25 (from 411 samples) and 

0.0019 μg/m3 26 (from 481 samples). Results of the preliminary air quality model run showed that 

27 at a radius of 10 km, estimated air concentrations were closest to 50 percent of the background 

28 average air concentrations. The deposition gradient was not considered for selecting the extent 

29 of the modeling region because background deposition measurements were not available. 

 

5

 In 2003, the quarterly average Pb air concentration at one of the monitors near the secondary Pb smelter exceeded 

the current NAAQS (measured concentration was 1.9 μg/m3

) (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 

2 Using GIS software, the 12 census block groups that are predominantly within 10 km of the 

3 facility were identified. Because of the irregular shape of block groups, not all of the block 

4 groups with area within 10 km were included, and some that were included have area outside 10 

5 km. The selection was made to include block groups whose average exposure would 

6 predominantly come from air within 10 km of the source. Model receptors were placed at all 

7 block centroids within the 12 block groups of interest. This resulted in 665 block centroid points 

8 being modeled. Some of these blocks had zero population in the 2000 U.S. Census. Two air 

9 monitors from AQS/AirData (USEPA 2006f) were identified between 400 and 700 m of the 

10 facility. The locations of these 2 monitors were modeled as discrete receptors, for direct 

11 comparison purposes between model results and monitoring data. 

12 

13 There are 435 census blocks with non-zero population in the 2000 U.S. Census within the model 

14 region, and air dispersion model results were generated at all of these blocks. An attempt was 

15 made to limit the locations modeled for the exposure assessment, as was done for the primary Pb 

16 smelting case study (see Section 4.1.1). The average concentration and deposition in each of the 

17 12 block groups was calculated by spatially-weighting the values at the block level. The area of 

18 each block was obtained from the U.S. Census (Census Bureau 2005). The range of block-level 

19 concentrations within each block group was considered by calculating the ratio of the maximum 

20 block-level air concentration in the block group to the average air concentration in the block 

21 group. For the primary Pb smelter case study, when these results were analyzed, the blocks 

22 groups with ratios greater than 2.0 were modeled at the block level and others modeled as block 

23 groups. For the secondary Pb smelter, although five block groups had maximum to average 

24 ratios less than 2.0, U.S. EPA and ICF decided to model these block groups as blocks because of 

25 the small size of the block groups and their proximity to the facility (see Exhibit 4-15). A total 

26 of 435 blocks with non-zero population were included in the assessment. 

27 
 Estimates of Media Concentrations 

ICF International 4-23 DRAFT – December 2006 

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

2 

3 
 Estimates of Media Concentrations 

ICF International 4-24 DRAFT – December 2006 

1 4.2.2 Air 

2 

3 The air concentrations and deposition of Pb from the secondary Pb smelter facility were modeled 

4 with AERMOD and compared to the air concentrations from nearby monitors. The emissions 

5 used for the air quality modeling are described in Section 3.2.4 and represent emissions from 

6 December 1997, November 1999, and February 2000. 

7 

8 4.2.2.1 Air Dispersion Modeling 

9 

10 The air dispersion model AERMOD was used for the secondary Pb smelter modeling (USEPA 

11 2004a; USEPA 2004b). The meteorological data used includes five consecutive years (1990 to 

12 1994) of nearby measurements. Surface-level and upper air meteorological data were obtained 

13 for weather stations located in Montgomery, Alabama (NOAA 1997a) and Centerville, Alabama 

14 (NOAA 1997b), respectively, and processed using the meteorological pre-processor, AERMET 

15 (USEPA 2002a). These stations represent locations close in proximity and geography to Troy, 

16 Alabama, and for which five consecutive years of surface and upper air meteorological data were 

17 available. Obtaining five consecutive years of weather observations for use in AERMOD was 

18 desirable because it allowed for the natural variability in weather conditions be captured in the 

19 air modeling. 

20 

21 AERMOD was configured to model the same emissions for the five years of meteorological data 

22 (1990 to 1994). Hourly air concentration and deposition results were averaged over this time 

23 period to obtain average air concentration and annual average deposition. As described in 

24 Section 3.2.4, emissions, release parameter, particle size parameters, and building downwash 

25 inputs were all obtained from the residual risk study of the facility (ECR 2006). Receptors and 

26 the study area boundary were selected specifically for this model application. Due to the 

27 relatively flat terrain in Alabama, terrain calculations were not used in this application. 

28 

29 4.2.2.2 Inhalation Exposure Concentrations 

30 

31 As in the primary Pb smelting case study, inhalation exposure concentrations of Pb were 

32 estimated for the population of interest (young children) from the estimated ambient air 

33 concentrations using age group- and location-specific relationships for Pb developed from 

34 modeling performed for U.S. EPA’s 1999 National-Scale Air Toxics Assessment (USEPA 

35 2006c), one of EPA’s NATA activities. See Section 4.1.2.2 for more information. 

36 

37 The relationships (or ratios) between ambient Pb concentration and Pb exposure concentration 

38 from the NATA national-scale assessment for the 0 to 4 age group (the closest age group for 

39 which outputs are available to the age group of interest for this assessment) ranged from 0.44 to 

40 0.46 for the census tracts in the study area for the secondary Pb smelter location. The ratios are 

41 presented in Appendix G, Exhibit G-1. Use of ratios for the 0 to 4 age group (rather than for 0 to 

42 7) contributes some uncertainty in the estimate of children’s inhalation exposure concentrations. 

43 Uncertainty related to the magnitude of the ASPEN modeled air concentrations is also discussed 

44 in Section 4.1.2.2. 

45 
 Estimates of Media Concentrations 

ICF International 4-25 DRAFT – December 2006 

1 4.2.2.3 Air and Deposition Results 

2 

3 Annual average Pb air concentrations and deposition fluxes were calculated at each block and 

4 monitor receptor point for the five years of meteorological data. To take into account variations 

5 in meteorological data, the annual average concentrations and depositions for each of the five 

6 years were averaged to generate one set of representative annual average concentration and 

7 deposition results for the current conditions scenario. 

8 

9 All concentration, deposition, and inhalation exposure estimates for the blocks modeled are 

10 presented in Appendix G, Exhibit G-2. In addition, Exhibit 4-16 presents the range of air 

11 concentration, inhalation exposure, and deposition values generated from the modeling. The 

12 values are summarized from the model results at the 435 blocks with non-zero population. 

13 Exhibit 4-17 shows isopleths of the block-level modeled air concentrations. 

14 

15 Exhibit 4-16. Secondary Pb Smelter Case Study: 

Summary of Modeled Air Concentrations and Depositiona 16 

 Current Conditions 

Statisticb

Average Annual Pb 

Air Concentration 

(μg/m3

) 

Children Ages 0 to 4 

Years Average Annual 

Pb Air Exposure 

Concentration (μg/m3

) 

Total 

Deposition 

(g/m2

/year) 

Maximum 0.536 0.238 0.435 

95th Percentile 0.0178 0.008 0.0066 

Median 0.0047 0.0021 0.0013 

5th Percentile 0.0009 0.0004 0.0002 

Minimum 0.0005 0.0002 0.0001 

a 17 The 435 blocks with non-zero population selected for analysis were used to create this summary. Note 

18 that in some of these blocks the 2000 U.S. Census indicates there are no children. b

 The statistic (e.g., 95th 19 percentile, median) may not be at the same location for each of the data results 

20 presented here. 

21 

22 
 Estimates of Media Concentrations 

ICF International 4-26 DRAFT – December 2006 

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

2 

3 
 Estimates of Media Concentrations 

ICF International 4-27 DRAFT – December 2006 

1 4.2.2.4 Air Modeling Performance Assessment 

2 

3 The monitoring data at the two air monitor locations near the facility were compared to modeled 

4 concentrations at the same locations (see Exhibit 4-18). Overall, the modeled concentrations at 

5 the monitor locations are approximately three times lower than the monitor values. Both of the 

6 monitors are located to the northwest of the facility. Because the meteorological data is not site7 specific, there is likely some uncertainty with its use in estimating air concentrations at specific 

8 points. It is possible that the local predominant wind direction is different from that of the 

9 meteorological data. Therefore, the monitored air concentrations were also compared to 

10 modeled air concentrations within similar distances to the facility, in all directions modeled on a 

11 radial grid (Exhibit 4-18). When compared to concentrations in all directions, the monitored 

12 values fall within the range of modeled results. A wind rose created from the five years of 

13 Montgomery, Alabama, wind data (Exhibit 4-19) shows that the predominant wind direction 

14 used in the model runs is directly to the west. The northwest (the direction of the monitors) is 

15 not a predominant wind direction in the metrological data set used for modeling; however, it is 

16 unknown whether this is the predominant wind direction at this site. 

17 

18 The potential difference between actual site meteorological data and the meteorological data 

19 used may help explain why the modeled concentrations are not closer to the monitored 

20 concentrations at the exact monitor locations, but modeled concentrations in all directions are 

21 within the range of monitored concentrations at similar distances. Because the monitors are both 

22 to the northwest of the facility (see Exhibit A-12), it is unknown whether modeled concentrations 

23 are underestimated in all directions. Thus, there is the potential that all air and deposition 

24 concentrations are underestimated from the modeling or that the degree of over- or under25 prediction of concentrations from modeling is dependent upon direction. A directional 

26 difference between modeled and actual air concentrations can impact risk results (either under27 or over-predicting) because the number of modeled children varies by census block and each 

28 census block is in a specific direction from the facility. 

29 

30 Exhibit 4-18. Secondary Pb Smelter Case Study Air Concentration 

31 Modeled Results Compared to Monitor Values 

 Modeled Results 

U.S. EPA 

AirData 

Monitor 

Distance 

from 

Midpoint of 

Facility (m) 

Range of Monitor 

Air Concentrations 

from AirData 1997 to 

2000 (μg/m3

) 

Range of 

Modeled 

Distances for 

Comparison 

Range of Modeled 

Concentrations a

(μg/m3

) 

Modeled 

Concentration at 

Monitor Location 

(μg/m3

) 

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 32 The modeled concentrations presented here were generated from a model run with a radial receptor grid. This 

33 summary is not from census block centroid results.

34 

35 No local measurements of Pb deposition were found to compare to the modeled results. Studies 

36 were summarized in the U.S. EPA Pb criteria document (USEPA 2006b) that provided ranges of 

37 Pb dry deposition fluxes in various locations across the United States. None of these are 

38 specifically for deposition near a secondary Pb smelter, but they provide a range of deposition 

39 values for comparison. Exhibit 4-20 summarizes these deposition values. 
 Estimates of Media Concentrations 

ICF International 4-28 DRAFT – December 2006 

1 

2 The maximum annual estimated deposition flux from the secondary Pb smelter emissions at a 

census block centroid (0.435 g/m2 3 /year) is higher than any of the deposition fluxes presented in 

4 Exhibit 4-20. This is expected because none of those studies measured deposition directly next 

to a facility. The median estimated deposition flux (0.0013 g/m2 5 /year) does fall within the range 

6 of some of the measurements in Exhibit 4-20 (i.e., New York City, Detroit, and sites near Lake 

7 Michigan). The lower estimated deposition fluxes are at the low end of most of the measured 

8 ranges, which is expected because the locations of those fluxes could be described as urban 

9 background. Comparison of the modeled deposition fluxes to these measurements throughout 

10 the country provides some confidence that the modeled deposition is within the expected range. 

11 

Exhibit 4-19. Wind Rose of Meteorological Data Used for Secondary 

Pb Smelter Case Study (Direction in which Wind is Blowing)a

a

 Wind rose for 43,824 hours of data 

12 

13 Exhibit 4-20. Dry Deposition Fluxes from Studies in the United States 

Location Mean Value or Range 

(g/m2

/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 area 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 

14 
 Estimates of Media Concentrations 

ICF International 4-29 DRAFT – December 2006 

1 4.2.3 Outdoor Surface Soil 

2 

3 Outdoor soil concentrations were estimated using two methods for the secondary Pb smelter 

4 facility – a model-only approach and a hybrid approach using model results and surrogate 

5 measurements. The modeled soil concentrations were calculated for each census block analyzed 

6 for this case study using the AERMOD deposition estimates and EPA’s Multiple Pathways of 

7 Exposure (MPE) methodology (USEPA 1998). The MPE methodology represents the update of 

8 the Indirect Exposure Methodology, or IEM (USEPA 1990a). MPE consists of a set of 

9 multimedia fate and transport algorithms developed by EPA’s Office of Research and 

10 Development, including a soil mixing model which was used in this assessment to calculate the 

11 soil concentrations resulting from deposition at the secondary Pb smelter location. 

12 

13 In the MPE algorithms, cumulative soil concentration is calculated as a function of particle 

14 deposition, soil mixing depth, bulk density, and a soil loss constant. The soil loss constant (in 

15 this case) was set up to be a function of loss due to leaching, erosion, and runoff. Concentration 

16 in the soil was calculated in the top 1 cm of soil assuming constant deposition of Pb for the entire 

17 operating period of the facility (37 years). Although it is likely that the deposition has changed 

18 in the last 37 years, possibly even declining with reduced emissions, this method was used due to 

19 lack of soil samples and historical emissions data. With this assumption in mind, other 

20 assumptions in the soil concentration calculations were made conservatively. All input 

21 parameters used for the soil mixing model are listed in Appendix G, Exhibit G-4. Site-specific 

22 input parameters were used when feasible, but assumptions were made for some parameters, in 

23 many cases based on suggested values in the Human Health Risk Assessment Protocol 

24 (HHRAP), which contains a database of input parameters for the MPE equations (USEPA 

25 2006g).

26 

27 Soil concentrations estimated using the modeling approach for each census block are 

28 summarized in Exhibit 4-21, and presented in more detail in Appendix G, Exhibit G-2. Across 

29 the census blocks in the study area, the modeled top 1 cm soil concentrations from the facility’s 

30 deposition ranged from 0.05 to 368 mg/kg. A background soil concentration of 15 mg/kg was 

31 added to these values for use in the exposure assessment (based on Gustavsson et al. 2001) to 

32 account for natural soil concentrations of Pb and general anthropogenic background. 

33 

34 As described in the next section (Section 4.2.3.1), a set of soil measurements at a similar facility 

35 was used to develop the hybrid soil concentration result set for the secondary Pb smelter case 

36 study (using modeled and surrogate data). Soil measurements taken in surface soil from 100 to 

37 1000 m from a similar facility (Kimbrough and Suffet 1995) were compared to the modeled soil 

38 concentrations from this assessment. The modeled soil concentrations at the census block 

39 receptors are approximately three times lower than the measured concentrations. Thus, the 

40 modeled results were scaled up three times to account for the potential under-prediction of the 

41 soil model when the results were compared to the surrogate measurements. The hybrid soil 

42 concentrations are presented in Exhibit 4-21 along with the modeled soil concentrations. 

43 Consideration of these two approaches for characterizing soil Pb represents an important 

44 sensitivity analysis consideration for this case study and consequently, parallel sets of risk 

45 estimates (using each approach) are presented. 

46 
 Estimates of Media Concentrations 

ICF International 4-30 DRAFT – December 2006 

 Exhibit 4-21. Summary of Secondary Pb Smelter Case Study Soil Concentrationsa 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 

95th Percentile 6.16 15 21.2 63.5 

Median 1.23 15 16.2 48.7 

5th Percentile 0.15 15 15.2 45.5 

Minimum 0.05 15 15.0 45.2 a 2 Surface soil concentrations were calculated to a depth of 1 cm. 

3 

4 4.2.3.1 Performance Evaluation for Soil Concentration Estimates 

5 

6 The modeled soil concentrations were compared to measured soil concentrations near another 

7 secondary Pb smelter because soil measurements are not available near the secondary smelter 

8 used in this assessment. Kimbrough and Suffet (1995) measured Pb in soil samples out to from 

9 100 to 1000 m from a secondary Pb smelter. Personal communication with Kimbrough indicated 

10 that the soil samples taken were from surface soil (information on the specific depth was not 

11 available) and in an urban area. Exhibit 4-22 presents the comparison of modeled soil 

12 concentrations at similar distances from the facility as presented in the Kimbrough and Suffet 

13 (1995) study. 

14 

15 Exhibit 4-22. Summary of Secondary Pb Smelter Case Study Soil Concentrations 

with Distance from Source Compared to Measurementsa 16 

Kimbrough and Suffet (1995) Secondary Pb Smelter Case Study 

Distance from 

Facility (m) 

Measured 

concentrations 

 (mg/kg) 

Number of 

measured 

locations 

comparedb

Modeled 

concentration + 

background 

(mg/kg) 

Number of 

modeled locations 

comparedc

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 17 Soil concentrations were modeled to a depth of 1 cm, and the measurements represent surface soil (according to the 

18 study’s author).

b 19 Kimbrough and Suffet (1995) measured Pb concentrations in soil at different directions from the facility. c 20 Modeled soil concentrations compared here were generated from deposition estimates at census block centroid and 

21 air monitor locations. Some of the census blocks near the facility had zero population in 2000 and are therefore not 

22 included in other parts of this study. 

23 
 Estimates of Media Concentrations 

ICF International 4-31 DRAFT – December 2006 

1 The comparison between the modeled soil concentrations and the Kimbrough and Suffet (1995) 

2 soil concentrations varies, mostly as a result of direction from the source. On average, the 

3 measured concentrations are approximately three times higher than the modeled results, 

4 suggesting an underprediction of soil concentrations from the model results for this case study. 

5 Because of this potentially significant impact (downward bias) on exposure and risk results, an 

6 alternative soil concentration for the study area was calculated by combining the modeling 

7 results (used to characterize spatial variability in soil levels across the study area) with surrogate 

8 data from Kimbrough and Suffet (1995) to adjust the absolute soil Pb levels for the study area. 

9 The result is the “hybrid” soil concentration set presented in Exhibit 4-21, in which the modeled 

10 soil concentrations are scaled up three times. 

11 

12 4.2.4 Indoor Dust 

13 

14 Indoor dust sampling data were not available for the secondary Pb smelter case study, 

15 necessitating the use of modeling to characterize indoor dust Pb levels within the study area. A 

16 version of the AGG pooled analysis (USEPA 1989) model that uses ambient air Pb levels for 

17 predicting dust levels was chosen. This is a similar model as used for the primary Pb smelter 

18 case study at distances greater than 1.5 km from the source (see Section 4.1.4); however, in the 

19 case of the secondary Pb smelter, an “air-only” version of the model was employed reflecting the 

20 reduced overall confidence associated with soil characterization at this case study. 

21 

22 The air-only AGG model does reflect (implicitly) some consideration for the soil-to-indoor dust 

23 mechanism in the air signal. Specifically, the larger air factor for the air-only model (relative to 

24 the air plus soil dust model's air factor) reflects the fact that, in this version of the model, air 

25 measurements are used to cover both the direct loading of indoor dust from air and the loading of 

26 outdoor soil by air with subsequent impacts of that soil on indoor dust through such mechanisms 

27 as indoor tracking of soil (USEPA 1989). The AGG model used for the secondary smelter was 

28 based on a number of studies focusing mainly on primary Pb smelters. This basis carries an 

29 uncertainty into indoor dust predictions generated using this model related to the potential for 

30 differences between primary and secondary Pb smelters that may relate to indoor dust loading 

31 (e.g., particle size profiles and nature of the entrained Pb compounds). The air-only AGG model 

32 used in this analysis is presented below: 

33 

34 Pbdust = 60 + (844 × Pbair)

35 

36 where: 

37 Pbdust = concentration of Pb in indoor dust (mg/kg), and Pbair is the concentration of Pb 

modeled in the ambient air (μg/m3 38 ). 

39 

40 Exhibit 4-23 presents a summary of the Pb indoor dust concentrations generated in the secondary 

41 Pb smelting study area for the current conditions scenario. Indoor dust concentrations were only 

42 estimated for census blocks with children. The numbers of census block or block groups and 

43 children modeled with increasing levels of Pb house dust are presented in Exhibit 4-23. All 

44 estimated Pb dust concentrations for the secondary Pb smelter facility are presented in Appendix 

45 G, Exhibit G-3. 

46 
 Estimates of Media Concentrations 

ICF International 4-32 DRAFT – December 2006 

1 Exhibit 4-23. Secondary Pb Smelter Case Study: Modeled Indoor Dust Pb 

Concentrations a 2 

 Current Conditions 

House dust 

concentration (μg/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 3 The 296 blocks with children aged 0 to 7 in the 2000 U.S. Census were used to develop this 

4 summary. Note that Exhibits 4-21 includes all blocks with population, even blocks without 

5 children. 

b 6 Number of children ages 0 to 7 from the 2000 U.S. Census were used in this analysis (U.S. 

7 Census Bureau 2005). 

8 

9 Studies summarized in the 1990 review of the Pb NAAQS contained measurements of house 

10 dust ranging from 10 to 35,000 ppm. A high value of 100,000 ppm was measured in one home 

11 within 2 km of a Pb smelting facility (USEPA 1989). The Pb indoor dust concentrations for this 

12 case study fall within the range presented by U.S. EPA (1989), but they are at the low-end of the 

13 range. In addition, all of the concentrations are much lower than the 100,000 ppm concentration 

14 cited within 2 km of a Pb smelter. The fact that this facility is a secondary, and not primary, Pb 

15 smelter may explain some of the difference. In the study mentioned in Section 4.1.4, household 

16 dust Pb concentrations were measured at 14 homes within approximately 500 m (approximated 

17 from a figure) from a secondary Pb smelter in the Czech Republic (Rieuwerts et al. 1999). 

18 Measured Pb concentrations ranged from 861 to 5890 ppm with a geometric mean of 1668 ppm. 

19 Indoor dust concentrations in this secondary Pb smelter pilot phase assessment were not 

20 estimated within 500 m. At 590 m from the facility in this pilot phase of the analysis, the 

21 estimated indoor dust concentration is 130 ppm. In addition to the close distance, the secondary 

22 Pb smelter studied by Rieuwerts et al. (1999) used to be a primary Pb smelter, perhaps 

23 explaining why the indoor dust concentrations were higher than in this pilot phase of the 

24 analysis. 

25 

26 4.3 Near Roadway Urban Location Scenario 

27 

28 4.3.1 Spatial Template 

29 

30 As described in Section 3.3.1, the location of the study area for the near roadway urban case 

31 study was selected based on the availability of air monitoring data. Of the streets in the vicinity 

32 of the near roadway monitoring site in Houston, Texas, a thoroughfare to the south was selected 

33 as the street around which the scenario was designed, due to its proximity to the monitoring site 

and the length of the road links for that street.6 34 The distance from the monitoring site to another 

35 major road is about the same as the distance from the site to the street that was selected (i.e., 100 

 

6

 Although it was not a strict requirement that the selected roadway section used for this 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 

1 m vs. 115 m for the selected roadway), but the roadway links for the selected street are shorter in 

2 this area. A shorter roadway segment was judged to be more desirable because it was assumed 

3 that less uncertainty would be associated with the assumption that the measured air 

4 concentrations are representative of conditions along the entire length of the roadway segment. 

5 

6 The subdivision of the study area into smaller regions for characterizing risks (i.e., the 

7 development of the spatial template) was based in part on research conclusions regarding the 

8 impacts of roadway proximity on the spatial gradient of pollutant concentrations near roads. 

9 Cohen et al. (2005) have conducted analyses using the CALPUFF non-steady state Gaussian puff 

10 model to estimate the dimensions of a “zone of influence” for the near-ground-level ambient air 

11 concentrations of several air pollutants, including diesel PM, in the vicinity of roadways. The 

12 zone of influence was defined as the area near a roadway in which ambient air concentrations are 

13 affected (i.e., enhanced) by emissions that occur in the roadway. Their results suggest that a 

14 steep spatial gradient exists adjacent to roadways, with a zone of influence existing out to 

15 somewhere between 200 and 400 m from the roadway. 

16 

17 In a related analysis of the data published by Cohen et al. (2005) as well as other air modeling 

18 results, ICF (2005) calculated the enhancement ratio (i.e., the ratio of near-roadway 

19 concentrations to concentrations more distant from the roadway) for several air pollutants, 

including diesel PM.7 20 These enhancement ratios were developed for a 0 to 75 m zone and a 75 

21 to 200 m zone adjacent to the roadway. Therefore, these same intervals formed the basis for the 

22 spatial template for air concentrations. 

23 

24 The inner 0 to 75 m zone was further divided into two areas, with one comprising the area 0 to 

25 12 m from the roadway and the other encompassing the area between 12 and 75 m from the road. 

26 The inner region at 0 to 12 m was developed to facilitate the use of soil measurements available 

27 for a surrogate location (see below) that included measurements at and within 12 m of the 

28 roadway. This allowed an apparently steep initial decrease in near-roadway soil Pb levels to be 

29 captured by the exposure modeling for this case study. Aerial photographs of the area indicated 

30 that relatively small residential yards are situated immediately adjacent to the road, suggesting 

31 that this initial 12 m band may serve as a play area for children. Consequently, the spatial 

32 template for this case study included exposure bands parallel to the road and on either side, 

33 extending from 0 to 12 m, 12 to 75 m, and 75 to 200 m. Although six separate zones could be 

34 defined – the exposure bands are located on both sides of the road – only three sets of media 

35 concentrations were necessary for the blood Pb modeling, as soil and air concentrations were 

36 assumed to be the same on both sides of the road. 

37 

38 4.3.2 Air 

39 

40 4.3.2.1 Ambient Air Concentrations 

41 

For this scenario, 0.005 μg/m3 42 , the average value for Pb in the PM10 fraction available from the 

43 measurements collected for this location (ICF 2006b), was assumed to be a reasonable estimate 

 

7

 Literature reviews did not identify any research specifically focused on spatial gradients associated with Pb sorbed 

to particulates near roadways or reentrained roadside dust. 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 1 To derive media concentrations 

2 for the zones adjacent to the roadway, geometric means of the enhancement ratios for near3 roadway concentrations of diesel PM from ICF (2005) were used to adjust the concentration at 

4 the monitoring station. As described in the previous description of the spatial template, 

5 enhancement ratios were derived for the intervals of 0 to 75 m from the road and 75 to 200 m 

6 from the road. Because the near roadway monitoring station in Houston was located about 115 

7 m from the selected street, the average measured Pb concentration at this station was assumed to 

8 be representative of ambient Pb in the 75 to 200 m zone. Using the geometric mean of the 

9 enhancement ratios, the concentration for the 0 to 75 m band was then estimated to be 

approximately 0.008 μg/m3 10 . Ambient air concentrations of Pb are summarized in Exhibit 4-24. 

11 

12 Exhibit 4-24. Enhancement Ratios and Air Concentrations for 

13 Near Roadway Urban Scenario 

Zone (Distance 

from Roadway) 

Enhancement 

Ratioa

Estimated Ambient 

Pb Concentration, 

μg/m3

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 14 Geometric means of enhancement ratios for diesel PM; from ICF (2005). 

15 

16 A comparison of the ambient air concentrations used for this scenario to other data can help 

17 evaluate the validity of the selected media concentrations. Although the average Pb 

18 concentration at the monitoring station was estimated based on only three days of measurements, 

the average value of 0.005 μg/m3 19 is reasonably consistent with the ambient Pb measurements for 

20 Houston reported in AQS (USEPA 2006f). Specifically, the average Pb measurements for PM10

at the industrial/suburban site identified in AQS were 0.006 and 0.007 μg/m3 21 for 1999 and 2005 

22 (see Section 3.3.4). The average of the Houston DRI Pb measurements is also near the middle of 

23 the range of averages of ambient measurements obtained by DRI (ICF 2006b) for the other five 

24 near-roadway monitoring locations included in their study, which range from 0.0024 to 0.0164 

μg/m3 25 . 

26 

27 4.3.2.2 Inhalation Exposure Concentrations 

28 

29 As in the primary Pb smelting case study, inhalation exposure concentrations of Pb were 

30 estimated for the population of interest (young children) from the estimated ambient air 

31 concentrations using age group- and location-specific relationships for Pb developed from 

 

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 criteria document (USEPA 2006b), reentrained Pb in surface soil 

is generally associated with smaller particle size fractions; thus, it seems likely that the PM10 Pb measurements 

presented here represent the bulk 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 monitor in Houston (labeled as commercial, urban/center city 

location), mean annual TSP Pb measured between 1996 and 2006 ranged from 0.02 μg/m3

 to 0.005 μg/m3. 
 Estimates of Media Concentrations 

ICF International 4-35 DRAFT – December 2006 

1 modeling performed for U.S. EPA’s 1999 National-Scale Air Toxics Assessment (USEPA 

2 2006e), one of U.S. EPA’s NATA activities. See Section 4.1.2.2 for more information. 

3 

4 For the near roadway urban scenario, the study area was encompassed by one census tract (FIPS 

5 ID 48201520500). The relationships (or ratios) between ambient Pb concentration and Pb 

6 exposure concentration from the NATA national-scale assessment for the 0 to 4 age group (the 

7 closest age group to that of interest for this assessment for which outputs are available) is 0.4603 

8 for this census tract. The exposure-to-concentration ratios for this tract for children 0 to 4 years 

9 old were used to calculate inhalation exposure concentrations for this case study (see Exhibit 4-

10 25). Note that the use of ratios for the 0 to 4 age group (rather than for 0 to 7) contributes some 

11 uncertainty in the estimate of children inhalation exposure concentrations. Uncertainty related to 

12 the magnitude of the ASPEN modeled air concentrations is also discussed in Section 4.1.2.2 

13 

14 Exhibit 4-25. Estimated Inhalation Exposure Concentrations for 

15 the Near Roadway Urban Scenario 

Zone Ambient 

Concentration 

(μg/m3

) 

Age Group Exposure-toconcentration 

Ratio 

Inhalation Exposure 

Concentration 

(μg/m3) 

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 

17 4.3.3 Soil 

18 

19 As described in Section 3.3.4, although no measurements of Pb in surface soil were identified in 

20 the vicinity of the case study location in Houston, a range of data collected at roadsides and 

21 similar sites at other locales were identified in the literature. Using these data, surrogate soil Pb 

22 values were developed for the Houston DRI monitoring site to estimate soil concentrations for 

23 this case study. The soil concentrations derived for this scenario are intended to represent Pb 

24 levels in surface soil that are primarily attributed to Pb emitted from vehicles during the 

25 historical use of leaded gasoline and deposited to the near roadway soils (rather than attributed to 

26 other urban sources of Pb, such as deposition of Pb from industrial sources or Pb paint). As 

27 such, the near roadway case study is intended to address the concern described in the criteria 

28 document (USEPA 2006b) that Pb deposited in surface soil over time derived from exhaust from 

29 automobile traffic burning leaded gasoline represents a potential source via direct exposure to the 

30 soil and through resuspension of dust to ambient air. 

31 

32 Measurements of Pb in roadside surface soil collected by Turer and Maynard (2003) in Corpus 

33 Christi, Texas, were identified as the most useful measurements for this scenario, primarily 

34 because of their proximity and geographic similarity to the case study location. Corpus Christi, 

35 located about 300 km southwest of Houston, is closer to the coast than Houston, but has roughly 

36 the same annual average air temperature and precipitation (NCDC 2006). Turer and Maynard 

37 collected samples at two locations, including a site adjacent to the entrance ramp to Interstate 37. 

38 The AADT for that site in 1998 was reported to be 48,000 vehicles per day. This is a higher 

39 traffic volume than the volume reported for the selected street in Houston near the case study 

40 (AADT about 20,000). This difference in traffic volume was judged to be acceptable for the 
 Estimates of Media Concentrations 

ICF International 4-36 DRAFT – December 2006 

1 pilot phase. Although elevated Pb concentrations in soil have been observed to be statistically 

2 correlated with increased traffic volume on adjacent roadways in many cases (see, for example, 

3 Sanchez-Martin et al. (2000) and Fakayode and Olu-Owolabi (2003)), there appear to be other 

4 confounding variables that can result in weak correlations between traffic counts and Pb levels in 

5 near roadway soil in some cases (Hafen and Brinkman 1996). In other words, Pb is generally 

6 more elevated in soils near heavily-traveled roadways, but the exact impact of traffic volume can 

7 vary between locations and under different conditions. Total Pb concentrations in surface soil 

8 for three samples taken near Interstate 37 are summarized in Exhibit 4-26. 

9 

Exhibit 4-26. Pb Concentrations in Surface Soil, Corpus Christi, Texasa 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 11 From Turer and Maynard (2003); sampling site near I-37 and Martin Luther King Drive at 

12 North Port Avenue close to downtown. 

13 

14 Before using the measurements presented by Turer and Maynard (2003) to develop media 

15 concentrations for this case study, other relevant data from the literature were reviewed. In their 

16 discussion, Turer and Maynard refer to Pb measurements by Harrison (1987) at the “edges of 

17 roadways and freeways in Corpus Christi” with concentrations of 250 mg/kg; no description of 

18 the specific sampling locations or distance from the roadway was provided. Other relevant data 

19 include records in EPA’s SPECIATE database of four samples in Houston. Although these 

20 samples are in the same town as this case study, they are judged to have greater uncertainty 

21 because it is not known when the samples were taken or where the sampling occurred, especially 

22 with regard to proximity to roadways. The sampling is described as “reentrained soil dust” (no 

23 sampling depth was specified). All four of these SPECIATE samples were reported to be 0.04 

24 percent Pb by weight, or 400 mg/kg. 

25 

26 In another recent study, Lejano and Ericson (2005) sampled surface soil for Pb in Pacoima, 

27 California (near Los Angeles), collecting a total of 176 measurements across eight sampling 

28 categories. Four of these categories include exclusively samples taken within 150 m of a major 

29 roadway, with each of the four categories focusing on a different road in the Pacoima area. 

30 Mean total Pb concentrations for each of these four sampling categories are reported here (see 

31 Exhibit 4-27). Traffic volume at each location as reported by Lejano and Ericson is also 

32 presented. Specific sampling locations (i.e., distance from the roadway) were not reported; 

33 however, mean Pb concentrations for three of the four roads were above 100 mg/kg. These 

34 results suggest that the near-roadway concentrations of Pb in soil within 50 m of the road could 

35 be substantially higher than the reported means. 

36 
 Estimates of Media Concentrations 

ICF International 4-37 DRAFT – December 2006 

Exhibit 4-27. Near Roadway Surface Soil Pb Concentrations in Pacoima, Californiaa 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 2 From Lejano and Ericson (2005). 

3 

4 There is substantial evidence that Pb concentrations in surface soil decrease rapidly with distance 

5 from the roadway. Sutherland and Tolosa (2001) reported that the relationship is approximately 

6 linear when the log of concentration is plotted against the log of distance from the roadway. 

7 Similarly, Filipelli et al. (2005) have reported an exponential decay in Pb concentration with 

8 increasing distance from the roadway. Hafen and Brinkmann (1996) surveyed results from 

9 several other studies and observed a generally exponential decrease in Pb concentration with 

10 distance from the road. In general, based on the conclusions of these studies, it appears that Pb 

11 concentrations adjacent to a road decrease down to the local background level within a distance 

12 of about 50 m from the roadway. 

13 

14 Local background in an urban environment, however, can vary depending on proximity to 

15 current and previous sources of Pb emissions, impacts from Pb-based paint, and other factors. 

16 No representative “urban measurements” for Houston were identified, but several studies 

17 reporting other relevant measurements were identified as follows: 

18 

19 • Mean concentration of total Pb in surface soil for “random” non-vehicular-impacted 

20 locations in Pacoima, California was reported as 111 mg/kg (Lejano and Ericson 2005). 

21 

22 • Soils at parks, schools, and roadside embankments in Corpus Christi were sampled in 

23 1984. The arithmetic mean surface soil concentration of Pb across all 485 samples was 

24 208 mg/kg (Harrison 1987, cited in Battelle 1996). 

25 

26 • Median Pb concentration in surface soil in samples from a range of land-use categories in 

27 Miami, Florida was reported to be 98 mg/kg (Chinreje et al. 2004). 

28 

29 • An approximate level of “background” for Pb in US soils is about 15 mg/kg, taking into 

30 account natural soil concentrations of Pb and general anthropogenic contributions 

31 (Gustavsson et al. 2001). 

32 

33 Based on the available data and considering the relevant details of the near roadway urban 

34 scenario, the measurements near I-37 in California reported by Turer and Maynard were selected 

35 as the primary data source for developing surrogate soil concentrations for this scenario. As 

36 described in Section 4.3.1, the zone extending out to 75 m was subdivided into two zones, a 

37 near-roadway zone from 0 to 12 m and another zone encompassing the area between 12 and 75 

38 m. These boundaries were defined based on the availability of Pb measurements in surface soil 

39 from Turer and Maynard (2003) out to 12 m. For estimating the average concentration within 
 Estimates of Media Concentrations 

ICF International 4-38 DRAFT – December 2006 

1 the 0 to 12 m zone, the relationship between log of Pb concentration and log of distance from the 

2 roadway within this near roadway range was assumed to be linear (Sutherland and Tolosa 2001). 

3 Based on this relationship and using the data points for samples taken 3 and 12 m from the road, 

4 intermediate concentrations between 3 and 12 m were estimated. The mean concentration for 

5 this zone was calculated using these intermediate values to be about 388 mg/kg. 

6 

7 For the study area between 12 and 75 m from the roadway, it seemed reasonable that Pb 

8 concentrations in surface soil would decrease more slowly until reaching local background 

9 levels. Based on the available literature (and especially Lejano and Ericson 2005), a surrogate 

10 background level of 100 mg/kg was defined for the case study location. For simplicity and 

11 consistency with the spatial template, it was assumed that the soil concentration at 75 m and 

12 beyond was at this level. In the absence of other, more specific data on near-road soil 

13 concentration patterns, the Pb surface soil levels for this case study were assumed to decrease 

14 linearly between 12 and 75 m. Given the soil concentrations defined for 12 and 75 m, the 

15 average concentration for this interval was calculated to be 157 mg/kg. Estimated soil 

16 concentrations for this case study are summarized in Exhibit 4-28. 

17 

18 Exhibit 4-28. Estimated Soil Exposure Concentrations 

19 for the Near Roadway Urban Scenario 

Zone Surface Soil 

Concentration (mg/kg) 

0 to 12 m 388 

12 to 75 m 157 

75 to 200 m 100 

20 

21 4.3.4 Indoor Dust 

22 

23 Because indoor dust sampling data were not available for the near roadway urban case study, an 

24 empirical model was used to describe the relationship between Pb concentrations in indoor dust 

25 and in outdoor soil. Specifically, a pooled analysis model (the AGG model) identified relates 

26 indoor dust levels to ambient air and soil Pb levels was used (USEPA 1989). In this case, unlike 

27 the secondary Pb smelter case study, soil Pb levels were considered because surrogate empirical 

28 data on Pb levels near roadways were available, thereby increasing confidence in the 

29 characterization of soil Pb levels for this case study. The same version of the “AGG” model 

30 (soil plus air regression model) (USEPA 1989) used for the primary Pb smelter was also used for 

31 the near roadway urban case study: 

32 

33 Pbdust = 31.3 + (638 × Pbair) + (0.364 × Pbsoil) 

34 

35 where: 

36 

37 Pbdust = concentration of Pb in indoor dust (mg/kg) 

Pbair = concentration of Pb modeled in the ambient air (μg/m3 38 ) 

39 Pbsoil = concentration of Pb in outdoor soil (mg/kg). 

40 
 Estimates of Media Concentrations 

ICF International 4-39 DRAFT – December 2006 

1 Exhibit 4-29 presents a summary of the Pb indoor dust concentrations generated in the near 

2 roadway urban study area for the current conditions and current attainment scenarios. Indoor 

3 dust concentrations were only estimated for census blocks with children. In addition, because 

4 only three sets of soil and air concentrations were estimated for the near roadway urban case 

5 study (corresponding to the three zones surrounding the road of interest), only three indoor dust 

6 concentration estimates were derived. Exhibit 4-29 shows the number of census blocks and 

7 children modeled with increasing levels of Pb house dust. Estimated Pb dust concentrations for 

8 all census blocks included in or partially overlapping with the near roadway study area are 

9 presented in Appendix D. 

10 

11 Exhibit 4-29. Near Roadway Urban Case Study: 

Modeled Indoor Dust Pb Concentrations a 12 

House Dust Concentration 

(μg/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 13 Number of children ages 0 to 7 from U.S. Census 2000 were used in this analysis (U.S. 

14 Census Bureau 2005). 

15 

16 As noted in Section 4.3.2.1, Pb measurements in outdoor air were only identified for Pb 

17 associated with PM10 and not TSP. Because concentrations of Pb in indoor dust were estimated 

18 using outdoor ambient Pb measurements that (for this case study) were reflective of the PM10

19 fraction only, modeled indoor dust levels may be underestimated. The degree to which the 

20 modeled indoor dust levels are underpredicted (if at all) would depend on: (1) the extent that the 

21 PM10 fraction represents total Pb in ambient air, and (2) the sensitivity of predicted indoor dust 

22 Pb concentrations to outdoor ambient Pb concentrations (especially with respect to the relative 

23 influence of soil concentration inputs on this estimate). 

24 

25 4.4 Near Roadway Non-Urban Locations 

26 

27 For the ecological risk screening assessment, measured soil concentrations reported in the 

28 literature were extracted for use in the risk characterization for this scenario. In contrast to the 

29 human exposure and health risk assessments, data were sought for areas not located in downtown 

30 areas. Less developed locations were evaluated to increase the likelihood of proximity to 

31 ecological receptors. Soil concentrations for two non-urban locations near roadways are 

32 summarized in this section. 

33 

34 4.4.1 Soil Concentrations at Corpus Christi, Texas Site 

35 

36 Turer and Maynard (2003) analyzed samples for Pb from roadside surface soil collected in 

37 Corpus Christi, Texas. The data summarized here are from an industrial site at I-37 and Upriver 

38 Road, which had a traffic volume of 45,000 vehicles per day. Exhibit 4-30 shows the 

39 concentrations of Pb measured at various distances and sample depths from the roadside. 

40 

41 

42 
 Estimates of Media Concentrations 

ICF International 4-40 DRAFT – December 2006 

1 

2 

3 Exhibit 4-30. Pb Concentrations in Surface Soil, Corpus Christi, Texas, 

for Ecological Risk Screening Assessmenta 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 5 From Turer and Maynard (2003); sampling site near I-37 and Upriver Road in refinery operations area. 

6 

7 4.4.2 Soil Concentrations at Atlee, Virginia Site 

8 

9 As described in Speiran (1998), the Virginia study site is located at the northbound Atlee/Elmont 

10 exit (exit 86) from Interstate 95 (I-95), about 14.5 km north of Richmond, Virginia. The 

11 northbound exit ramp at exit 86 is separated from the highway lanes by a triangular-shaped 

12 grassy area. The grassed area has been mowed periodically, but there has been no historical 

13 application of pesticides or fertilizers. Chemical characteristics of the soil in the grassed area 

14 were investigated in 1994, prior to planned construction modifications to Interstate travel lanes 

15 and drainage ways and installation of a detention basin. Soil samples in the grassed area were 

16 collected at various depth intervals and analyzed for heavy metals and other constituents. 

17 

18 Samples from seven soil sampling locations were initially collected in the grassy area in June 

19 1994. Samples were collected at 3-inch intervals up to a depth of 12 inches and were analyzed 

20 for metals. An additional 11 soil locations were sampled in the grassy area in October 1994 and 

21 were analyzed in the same manner as the June 1994 samples. Five of these 11 sampling 

22 locations were sampled at the 0 to 3 and 9 to 12-inch intervals. Two locations were sampled at 

23 the 0 to 3 and 3 to 6-inch intervals. The remaining four locations were sampled at the 0 to 3 inch 

24 interval only. 

25 

26 Samples used for this ecological screening risk assessment were limited to those located at a 

27 distance 2 m or greater from I-95 and the exit ramp at exit 86 and collected at a depth of 20 

28 centimeters or less. Therefore, only samples collected at 0 to 3 inches and 3 to 6 inches and 

29 samples that appear to be at least 2 m from either I-95 or the exit ramp (using figures provided in 

30 Speiran [1998]) were used in the evaluation of potential ecological risks for this near roadway 

31 non-urban scenario. Samples at these locations were considered to be representative of potential 

32 ecological exposures. Pb analytical results for these samples are presented in Exhibit 4-31. Pb 

33 concentrations at these locations and depths ranged from 17 to 540 mg/kg, with an average 

34 concentration of 123 mg/kg. 

35 

36 

37 

38 

39 
 Estimates of Media Concentrations 

ICF International 4-41 DRAFT – December 2006 

1 

2 

3 Exhibit 4-31. Pb Concentrations in Soil Collected from Potential 

Ecological Exposure Area, I-95 Interchange, Atlee, VAa 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 5 From Speiran 1998 (see Figures 2 & 3 and Table 1). 

6 

7 

8 4.5 National-Scale Surface Water and Sediment Ecological Risk Screening Assessment 

9 

10 This section provides a description of the media concentrations used in the national-scale surface 

11 water and sediment screening assessment. Section 4.5.1 describes the selection of a data set to 

12 represent Pb concentrations in freshwaters across the United States resulting from atmospheric 

13 sources of Pb. Section 4.5.2 provides information on the selected data set for dissolved Pb water 

14 column concentration data that were used in the surface water portion of the screening 

15 assessment. Section 4.5.3 describes the freshwater sediment data set compiled for the sediment 

16 portion of this screening assessment. These sediment data were selected based on results from 

17 the surface water portion of the screening assessment. 

18 

19 4.5.1 Assessment of Available Data Sources for Surface Water Data 

20 

21 To identify measured dissolved Pb concentrations in surface waters of the United States for use 

22 in the exposure analysis of the national-scale surface water and sediment screening assessment, 

23 readily available summaries of Pb concentrations in surface waters and database compilations of 

24 trace metal concentrations measured in surface waters were examined. The most promising 

25 databases are those developed and maintained on-line by U.S. EPA and the U.S. Geologic 

26 Survey (USGS). They include U.S. EPA’s STORET database, the USGS National Water 

27 Information System (NWIS) database, and the USGS National Water-Quality Assessment 

28 (NAWQA) Data Warehouse. Each of these databases was evaluated to select the one most 

29 appropriate for the surface water portion of the national-scale surface water and sediment 

30 screening assessment. The evaluation is briefly summarized below. 

31 

32 STORET: The STORET database from the EPA’s Office of Water includes the water quality 

33 sampling data that states develop to determine compliance with water quality standards and that 

34 states report to EPA annually. STORET also includes sampling data from Superfund sites and 
 Estimates of Media Concentrations 

ICF International 4-42 DRAFT – December 2006 

1 other sources (e.g., U.S. Army Corps of Engineers). The EPA BASINS interactive software 

2 program uses the data from STORET. The Legacy Data Center (LDC) part of STORET is a 

3 static, archived database that contains historical water quality data dating back to the early part of 

4 the 20th century and collected up to the end of 1998. The main STORET database is an 

5 operational system actively being populated with water quality data. It contains data collected 

6 beginning in 1999, along with older data that have been properly documented and migrated from 

7 the LDC. Both the LDC and STORET contain raw biological, chemical, and physical data on 

8 surface and ground water collected by federal, state, and local agencies, Indian Tribes, volunteer 

9 groups, academics, and others. All 50 states, territories, and jurisdictions of the United States are 

10 represented; however, few stations are present in some states (e.g., Texas). For an analysis of 

11 current water conditions, the operational STORET system is more appropriate than the LDC. 

12 

13 In a download of surface water concentrations for Pb from STORET for the months January 

14 2004 to May 2006, it was observed that detection or reporting limits appeared to range from less 

15 than 0.04 to 10 μg/L based on the values reported by different states, with a quantitation limit of 

16 1 μg/L being common. A total of 30,993 samples were found for Pb from the United States’ 

17 surface waters over that time period. Of the total reported Pb concentrations included in that 

18 download, only 32 percent (9,910 of 30,993) were for dissolved Pb; the remaining concentrations 

19 were for total recoverable Pb. Of the 9,910 samples analyzed for dissolved Pb, only 21.5 percent 

20 (2,134) had dissolved Pb concentrations above the detection or reporting limit. Thus, only 7 

21 percent of all samples analyzed for Pb (2,134 of 30,993) provided dissolved Pb measurements 

22 above the quantitation limit. 

23 

24 Limitations of STORET data include differences in sampling density per unit area or length of 

25 stream, differences in frequency of sampling, differences in detection and quantitation limits 

26 across states, and inconsistencies in how samples are collected, analyzed, and reported across 

27 states, particularly when estimating concentrations between the detection and quantitation limits. 

28 For Pb, another limitation is that many states do not attempt to measure dissolved Pb 

29 concentrations, only total Pb concentrations, because measurement of dissolved Pb generally 

30 results in non-detects. 

31 

32 USGS Databases: USGS maintains water quality data in two separate data sets: the National 

33 Water Information System (NWIS) Network and the National Water Quality Assessment 

34 (NAWQA) Data Warehouse (USGS 2004). The NWIS system was originally developed for 

35 water flow data, and includes data from 1.5 million sites across all 50 states, the District of 

36 Columbia, and Puerto Rico. Chemical quality data have been added to it in recent years, but still 

37 are limited. The data have been compiled through 2004 from a variety of projects ranging from 

38 national-level studies to small watershed projects; therefore, the sampling methods, density of 

39 sampling sites, and detection limits are variable across the data set. Data for dissolved Pb are 

40 available from 1972 to 2001; however, the vast majority of records are from the 1980s when the 

41 quantitation limit for Pb at most locations was 5 μg/L. 
 Estimates of Media Concentrations 

ICF International 4-43 DRAFT – December 2006 

1 The NAWQA Data Warehouse includes the sampling data from the NAWQA program that 

2 started systematically collecting chemical, biological, and physical water quality data from a 

3 large number of water basins and aquifers—referred to by NAWQA as “Study Units”—across 

4 the nation in 1991. As of July 2006, data have been compiled for samples collected through 

5 September 30, 2004, and cover 47 Study Units. Basins from all regions of the United States are 

6 included; however, only approximately 50 percent of the land base is covered by these basins 

7 (see Exhibit 4-32). NAWQA Study Unit boundaries frequently cross state boundaries, and their 

8 size usually exceeds 10,000 square kilometers (about 3,900 square miles). 

9 

10 Pb is one of the analytes in the program, but similar to the case for STORET, dissolved Pb 

11 measurements are available for fewer than 20 percent of the sampling locations. In an analysis 

12 of the data in NAWQA, U.S. EPA found a total of 3,445 measurements of dissolved Pb in 

13 surface waters, for which 86 percent were nondetects (see Exhibit 4-33) (USEPA 2006b). When 

14 looking at a subset of those data determined to be “natural” (i.e., forest or rangeland) or 

15 “ambient” (background) areas, 88 percent of the 430 samples for dissolved Pb in surface waters 

16 were non-detects. A mean concentration of 0.66 μg/L was calculated for all sites. A mean 

17 concentration of 0.52 μg/L was calculated for samples considered to represent natural areas. The 

relatively low dissolved Pb concentrations associated with the 95th 18 percentile values (Exhibit 4-

19 33) indicate that relatively few water samples are likely to exceed the initial aquatic screening 

Exhibit 4-32. Map of NAWQA Study Units 
 Estimates of Media Concentrations 

ICF International 4-44 DRAFT – December 2006 

1 value of 1.2 μg/L. Data for dissolved Pb from NAWQA from 1994 to 2004 was examined and it 

2 was found that the quantitation limit for dissolved Pb changed for this program from 1 μg/L to 

3 0.08 μg/L in the fall of 2000. 

4 

5 Exhibit 4-33. Distribution of Dissolved Pb Concentrations in Freshwater Surface Waters 

6 from the USGS NAWQA Database (USEPA 2006b) 

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 

95th Percentile (μg/L) 1.1 0.50 

Maximum (μg/L) 29.78 8.40 

7 

8 It was concluded that of the three available surface water quality databases, the NAWQA data set 

9 is most appropriate for the surface water portion of the national-scale surface water and sediment 

10 screening assessment for several reasons. The inclusion of dissolved Pb as an analyte is limited 

11 in all of the databases (total Pb is measured more often). None of the databases provide the co12 located measurements of water hardness in the same records as the measurements of dissolved 

13 Pb. STORET and NWIS include samples from more locations in the United States than does the 

14 NAWQA data set, but the sampling and reporting protocols represented in STORET and NWIS 

15 are less consistent from site to site. Data for dissolved Pb in NWIS are predominantly from the 

16 1980s, and therefore do not represent current conditions. The NAWQA data set, on the other 

17 hand, provides representative (even though not complete) coverage of the United States, with 

18 samples through 2004 included in the database. The NAWQA data set also provides a consistent 

19 approach to sampling and analysis of the elements using consistent quantitation limits across the 

20 country. Given the sampling design for NAWQA and the consistency of the data across the 

21 country, it is considered to be more appropriate for a national-scale surface water and sediment 

22 screening assessment than the other two data sets. 

23 

24 Even with using the NAWQA database, for which the quantitation limit for dissolved Pb was 

25 1 μg/L from 1994 to 2000, a difficulty for the assessment is that the chronic Ambient Water 

26 Quality Criterion (AWQC) for the protection of aquatic life in soft waters (i.e., for waters with 

27 CaCO3 concentrations less than 45 mg/L), is less than 1 μg/L, thus the detection limit is too high 

28 to resolve the chronic AWQC for soft water. The most recent quantitation limit of 0.08 μg/L 

29 (starting in the fall of 2000) is adequate to identify concentrations of dissolved Pb that exceed the 

30 chronic AWQC benchmark in water as soft as 4.7 mg/L CaCO3. Also, since the fall of 2000, 

31 dissolved Pb concentrations between the detection (0.04 μg/L) and quantitation (0.08 μg/L) 

32 limits were estimated in some cases. 

33 
 Estimates of Media Concentrations 

ICF International 4-45 DRAFT – December 2006 

1 4.5.2 Dissolved Pb Surface Water Concentrations in the NAWQA Database 

2 

3 To download dissolved Pb concentrations from the USGS NAWQA Data Warehouse, the 

4 NAWQA Study Units, trace elements in water, and the years 1994 to 2004 were specified (see 

5 Appendix H) to avoid earlier years when leaded gasoline in some locations might have still 

6 influenced air Pb concentrations. Once the data were downloaded, the values for dissolved Pb 

7 (analyte # 01049 - Pb, water, filtered, micrograms per liters) were examined. 

8 

9 The 49 Study Units for which sampling data currently are included in the USGS NAWQA Data 

10 Warehouse database are listed in Appendix H, Exhibit H-1. The different sampling stations 

11 within the unit were identified with an unique station ID number. Some units contained some 

12 recent series of sampling stations very close to each other along a river or stream (evident from 

13 the latitude and longitude reported for the stations); those were considered to be separate units. 

14 

15 It is important to note that the intensity of sampling at different locations within a Study Unit 

16 varied substantially. Sampling at some Study Units began in 1991, while sampling of others did 

17 not begin until 1999. In general, a few of the locations were sampled at least monthly for several 

18 years. In contrast, a single sample had been taken at many other locations, perhaps as an 

19 exploratory exercise. Some locations were sampled a few times a year at three year intervals, 

20 while other locations were sampled for only a couple years. 

21 

22 The percentage of the Study Unit sampling stations for which dissolved Pb was an analyte during 

23 one or more sampling times from 1994 to 2004 was 16 percent overall (Exhibit H-1), which is 

24 quite low. As is true for EPA’s STORET database, measurements of total Pb are much more 

25 common than measurements of dissolved Pb. However, because the AWQC for Pb are 

26 expressed as dissolved Pb, total Pb measurements were not evaluated. The distribution of 

27 dissolved Pb measurements was not uniform over the Study Units or across sampling stations 

28 within Study Units. Dissolved Pb was not measured for any of the stations for some of the Study 

29 Units, while for other Units, dissolved Pb was measured at least once at a majority of the 

30 Sampling Stations. 

31 

32 There are temporal trends in whether dissolved Pb was measured at given station. Most notably, 

33 at many stations for which multiple years of data are available, if the first year of sampling 

34 indicated all dissolved Pb measurements to be less than the quantitation limit (QL) of 1 μg/L, no 

35 further measurements of dissolved Pb were attempted (e.g., dissolved Pb measured in 1999 less 

36 than 1 μg/L; no further measurements in 2000 through 2004). 

37 

38 Most dissolved Pb concentrations reported in the NAWQA database are below the QL. There 

39 are only 2,558 samples analyzed for dissolved Pb in surface waters for the NAWQA database 

40 from 1994 to 2004. The 3,445 samples included in EPA’s review of the database (Exhibit 4-33) 

41 include samples from 1991 forward (USEPA 2006b). Of the 2,558 samples examined, 83 

42 percent (2,116) were below the QL. Of the 442 dissolved Pb concentrations reported in the data 

set, 17 percent (74 of 442) were estimated.9 43 Exhibit 4-34 summarizes the number of samples 

44 that were below the QL for dissolved Pb according to the reported QL. 

 

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 

2 Exhibit 4-34. Number of Dissolved Pb Samples 

3 Less than the QL 

QL Number of Sample Less 

Than QLa

2 11 

1 2010 

0.5 1 

0.2 10 

0.16 1 

0.08 83 

Total 2,116 a 4 A total of 2,558 samples were examined, of which 2,116 were 

5 below the QL. 

6 

7 Limiting the exposure assessment to the year 2000 to present was considered, given that fall 

8 2000 was when the QL for dissolved Pb dropped to 0.08 μg/L, low enough to determine if the 

9 chronic surface water screening value is exceeded for most waters (i.e., for waters with hardness 

10 > 4.7 mg/L as CaCO3). However, less than 14 percent of the total samples for dissolved Pb 

11 between 1994 and 2004 were analyzed with the lower dissolved Pb QL of 0.08 μg/L. To 

12 determine that percentage, the number of samples reported as “< 0.08” μg/L (N = 83), the 

13 number reported as estimated (E) with a value less than 0.08 μg/L (N = 68), and the number for 

14 which the measured value was equal to or greater than 0.08 but less than 1.0 μg/L (N = 205) 

15 were added. A few of the last group might have been measured with a QL between 0.08 and 1.0 

16 (see Exhibit 4-34); however, no attempts were made to adjust for that. Thus, the percentage of 

17 samples analyzed for dissolved Pb in the NAWQA data set between 1994 to 2004 for which the 

18 QL limit was 0.08 μg/L is approximately 14 percent (i.e., 356 of 2,558). Thus, limiting the 

19 surface water and sediment water screening assessment to the years for which the QL was 0.08 

20 μg/L would have severely limited the exposure data for the assessment. 

21 

22 Further descriptions of the dissolved Pb concentrations from 1994 to 2004 in the NAWQA Data 

23 Warehouse for the surface water portion of the screening assessment are provided in Section 

24 7.2.4 (Risk Characterization) as they relate to the aquatic screening levels (AWQC). 

25 

26 4.5.3 Total Pb Sediment Concentrations from NAWQA Database 

27 

28 The surface water portion of this national-scale surface water and sediment ecological screening 

29 assessment identified 15 locations at which at least one measurement of dissolved Pb in the 

30 water column exceeded the water-hardness-specific Pb chronic AWQC. These locations are 

31 likely to represent a fraction of locations that actually exceed the Pb chronic AWQC. 

32 Nonetheless, those 15 locations were made the focus of the sediment portion of the screening 

33 assessment. 

34 

35 The NAWQA database also includes total Pb sediment concentration data measured for water 

36 bodies across the United States. Sediment sampling locations in the NAWQA database co37 located with the surface water samples evaluated for the water column screening assessment 

38 were identified. Co-located sediment and surface water data were available for 9 of the 15 

39 locations. There were an additional three “near-matches,” with a sediment sampling station on 
 Estimates of Media Concentrations 

ICF International 4-47 DRAFT – December 2006 

1 the same creek or river, but somewhat upstream or downstream of the surface water sampling 

2 location. For the sediment sampling sites that were categorized as near matches, however, it is 

3 important to note that the surface water samples at the same location had not exceeded the 

4 chronic AWQC in the surface water column portion of the screening assessment. Therefore, the 

5 value of the “near-match” locations is limited. Finally, there were three locations for which even 

6 a near-match was not found. 

7 

8 The sediment total Pb concentration data are presented in Chapter 7. Exhibit 7-16 summarizes 

9 the total Pb sediment concentrations measured at the nine locations with co-located sediment and 

10 surface water data and the three “near-match” locations. The NAWQA database appears to have 

11 only one measurement of Pb sediment concentrations for each of these 12 locations. Several of 

12 those samples were taken in the early 1990s, earlier than the bulk of the surface water Pb 

13 concentrations used in the surface water screening assessment. The concentrations ranged from 

14 23 to 2,900 mg Pb/kg dry sediment. The high concentration was detected at a location that had 

15 an upstream NPDES permit for metals; thus the Pb contamination may have come from a source 

16 other than air. 

 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-1 DRAFT – December 2006 

1 5. Human Exposure Assessment and Blood Pb Estimation 

2 

3 This chapter describes the approaches and methods that were used to evaluate children’s Pb 

4 exposures to policy-relevant sources and policy-relevant background pathways, and to predict 

5 the changes in individual blood Pb levels and population blood Pb distributions associated with 

6 these exposures. Section 5.1 provides a description of the human exposure assessment and blood 

7 Pb modeling approaches, and presents the derivation of the model inputs. Section 5.2 reviews 

8 the results of the blood Pb model performance evaluation. Section 5.3 discusses the limitations 

9 and uncertainties associated with the human exposure assessment and blood Pb estimation, and 

10 Section 5.4 summarizes the results of the blood Pb modeling as applied to the primary Pb 

11 smelter, secondary Pb smelter, and near roadway urban case studies. 

12 

13 5.1 Blood Pb Modeling Methods 

14 

15 5.1.1 Overview of Human Exposure Assessment and Blood Pb Modeling Approach 

16 

17 Exhibit 5-1 (which is an excerpt from Exhibit 2-2) summarizes the human exposure assessment 

18 and blood Pb modeling methods used in this assessment. Exposure media (air, soil, and indoor 

19 dust) concentrations and Pb exposure and intake assumptions associated with both policy20 relevant sources and policy-relevant background pathways serve as inputs to two biokinetic 

21 blood Pb models (discussed in Section 5.1.2). For the purposes of the pilot phase assessment, 

22 drinking water and diet pathways are categorized as policy-relevant background, although it is 

23 recognized that a portion of Pb in both pathways may derive from policy-relevant sources. 

24 

25 Exhibit 5-1. Schematic Representation of the Human Exposure 

26 Assessment and Blood Pb Modeling Process 

27 

28 Exposure concentrations are the primary inputs to the blood Pb modeling process. In each 

29 exposure pathway, the relationship between exposure concentration and Pb uptake (absorbed 

30 dose) is defined by a range of factors related to physiological processes and to the chemical and 

31 physical properties of the exposure media. These factors include respiratory volume, soil and 

32 dust ingestion rates, and gastrointestinal (GI) absorption fractions for diet, water, and soil/dust, 

33 which determine how much Pb is absorbed from each medium. Values for these factors differ 

34 with age and across exposure media, as discussed in Section 5.1.4.3. 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-2 DRAFT – December 2006 

1 

2 As discussed in Chapter 4, exposure concentrations in air, soil, and indoor dust have been 

3 estimated for census blocks and block groups within the case study areas. Application of a 

4 specific set of exposure concentrations and the physiological and behavioral inputs to the blood 

5 Pb models yielded a lifetime (birth to 84 months [or 7 years] of age) blood Pb profile for the 

6 child receiving that combination of exposures, from which two blood Pb metrics have been 

7 derived. The first metric is the lifetime average (age six to 84 months) and the second is an 

8 estimate of “concurrent” blood Pb concentration, which has been defined as the average over 

ages 73 to 84 months). 1 9 The blood Pb models yield central tendency estimates of a child’s blood 

10 Pb concentrations for specified simulation periods (with the temporal precision varying 

11 depending on the specific model) and for specific patterns of exposure. Estimates for a single 

12 child (representing central tendency exposure) do not provide information about how individual 

13 responses to Pb exposure might vary among the exposed children or how changes in individual 

14 blood Pb levels would affect the population exposure for a given case study. Thus, a 

15 probabilistic approach has been implemented to capture both the effects of inter-individual 

16 variability in blood Pb levels and the population distribution of exposures on the resultant 

17 population distribution of blood Pb statistics. 

18 

19 Development of population distributions of blood Pb levels for each of the case studies involved 

20 the following steps: 

21 

22 1. Blood Pb model was used to generate central tendency estimates of blood Pb per census 

23 block or block group. 

24 2. The number of children (0 to 7 years of age) residing in each block and block group was 

25 determined from the U.S. Census Bureau data (U.S. Census Bureau 2005). 

26 3. A lognormal distribution reflecting inter-individual variability in both behavior and 

27 biokinetics related to Pb exposure was developed using a geometric standard deviation 

28 (GSD) obtained from the literature which was centered on a value of 1.0. Values selected 

29 from this distribution represent "adjustment factors" reflecting specific patterns of 

30 behavior and biokinetics in children related to Pb exposure. 

31 4. Population-weighted random sampling was then used to first select a blood Pb level from 

32 the results of Step 1 above. The probability for sampling each census block or block 

33 group is set proportional to the number of young children (0 to 7 years of age) in 

34 residence (obtained in Step 2). The data set generated in Step 1 was sampled 10,000 

35 times in this way. Each of these 10,000 blood Pb levels was then multiplied by an 

36 adjustment factor selected from the lognormal distribution (described in Step 3) to 

37 produce a blood Pb level for a simulated child. These 10,000 simulated child blood Pb 

38 levels were then used to characterize (via percentiles) the distribution of blood Pb levels 

39 in the population. 

40 

41 The predicted blood Pb distribution in the exposed population reflects variability contributed by 

42 both the population-weighted distributions of exposure concentrations and by the inter-individual 

43 responses to Pb exposures. 

 

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 

2 The following sections discuss in detail the blood Pb models used for this assessment, the 

3 selected model inputs, and how the models were implemented to estimate case study-specific 

4 blood Pb levels for children 0 to 7 years of age. 

5 

6 5.1.2 Selection of Blood Pb Models 

7 

8 Two biokinetic models were applied to predict blood Pb levels in the pilot phase assessment. 

9 These two models were the Integrated Exposure Uptake Biokinetic (IEUBK) Model for Children 

10 (hereafter referred to as the IEUBK model) and the International Commission for Radiation 

11 Protection (ICRP) model (hereafter referred to as the Leggett model). Both are well12 documented, widely used, and have been subject to a range of testing and calibration exercises 

13 (see Section 4.4 of USEPA 2006b). A third “empirical” (regression-based) model developed by 

14 Lanphear et al. (1998) was included in the sensitivity analysis, as described in Chapter 6 and 

15 Appendix K. 

16 

17 5.1.2.1 The IEUBK Model 

18 

19 This section presents a general overview of EPA’s IEUBK model, the primary blood Pb 

20 estimation tool in the pilot phase assessment. The IEUBK Lead Model Version 1.0 Build 263 

21 (USEPA 2005a) was used to model blood Pb levels for all three case studies. The IEUBK model 

22 consists of three main modules: the exposure module, the uptake module, and the biokinetic 

23 module (see Exhibit 5-2). The exposure module accepts inputs related to six exposure media: 

24 air, food (excluding water), water, soil, indoor dust, and other. Exposure, intake, and uptake 

25 factors were combined with exposure concentrations in the various media to estimate Pb uptake 

26 through two pathways: inhalation and ingestion. The IEUBK model provides default values for 

27 the various model input parameters that can be adjusted by the user for a specific application. 

28 These parameters include those used by the model to estimate Pb uptake, including absorption 

29 and inhalation rates, water intake, dietary intakes of specific food classes, and soil and dust 

30 ingestion rates. The selection of model input parameter values for the pilot phase assessment is 

31 discussed in Section 5.1.4. 

32 

33 The exposure module also includes default age-specific estimates of time spent outdoors, as well 

34 as along with age-specific estimates of outdoor and indoor air Pb concentrations, inhalation rate, 

35 and respiratory tract absorption fraction, all of which are used to estimate age-specific Pb 

36 inhalation uptakes. The absorption fraction implicitly reflects both deposition of inhaled Pb in 

37 the respiratory tract and absorption of deposited Pb, either from the respiratory tract or from the 

38 GI tract. The model also contains an option for calculating indoor dust Pb concentrations based 

39 on an empirical relationship between air, soil, and dust Pb levels (a variation of the AGG models 

40 discussed in Chapter 4). Ingestion uptake is calculated using absorption fractions that are 

41 specific to the ingested media (food, water, soil, or dust). Total GI Pb uptake is modeled as 

42 being composed of a saturable and an unsaturable component. 

43 

44 In the biokinetic component of the model, absorbed Pb (from ingestion and inhalation) is 

45 assumed to appear immediately in the plasma-extracellular fluid (ECF) compartment. The 

46 plasma-ECF compartment constitutes the central compartment in the biokinetic model from 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-4 DRAFT – December 2006 

1 which exchange to all the other compartments occurs. Cortical and trabecular bone (which are 

2 not directly coupled in the IEUBK model) constitute the main long-term storage compartments, 

3 with the estimated turnover in other compartments being more rapid. The binding capacity of 

4 the red blood cell compartment is modeled as being saturable, simulating the limited capacity of 

5 ALAD and other Pb binding proteins. Pb excretion occurs through a urine pathway (distinct 

6 from the kidney compartment); hepatobiliary secretion is coupled with the liver compartment, 

7 with a minor component of excretion from “other soft tissues” (i.e., skin, hair, and nails). The 

8 model is termed “biokinetic,” rather than “pharmacokinetic,” because transfers between 

9 compartments are controlled by first-order transfer coefficients (equivalent to first-order rate 

10 constants), rather than being perfusion-controlled. A more complete description of the derivation 

11 and structure of the IEUBK model can be found in USEPA (1994) and White et al. (1998). 

12 

13 Exhibit 5-2. Structure of the IEUBK Model 

14 (USEPA 1994; White et al. 1998) 

15 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-5 DRAFT – December 2006 

1 The structure of the IEUBK model is deterministic. As configured, the model generates point 

2 estimates of blood Pb concentrations in response to a given set of exposure parameters. Using 

3 the batch mode, the user may calculate the impacts of changing Pb concentrations in various 

4 media, and the model also allows the user to plot the impact of continuously changing the Pb 

5 concentration in a specified exposure medium between two values. The user can specify the age 

6 at the start of exposure, an initial blood Pb level, and the age at which the simulation ends. For 

7 each case study in the pilot phase assessment, the batch mode of the IEUBK model was used to 

8 generate blood Pb levels for the different age intervals for children living in each census block 

9 and block group. 

10 

11 The IEUBK model also allows the user to calculate blood Pb distributions based on the 

12 assumption of lognormality (USEPA 2002b). In this mode, the model uses the point estimate of 

13 blood Pb as the GM of a lognormal distribution with a defined GSD. Graphs of the projected 

14 blood Pb distributions can be generated, along with estimates of the proportions of exposed 

15 children with blood Pb levels above user-specified levels of concern. 

16 

17 5.1.2.2 The Leggett Model 

18 

19 Leggett (1993) published a sophisticated biokinetic model for Pb exposures, intended primarily 

20 for use in radiological dosimetry, that has since been adopted as the standard model for this 

21 purpose by the International Commission for Radiation Protection (ICRP). The model predicts 

22 changes in blood Pb levels for exposed individuals over their entire lifespan (birth to 90 years 

23 old). The compartmental structure of the Leggett model (Exhibit 5-3) is more complex than that 

24 of the IEUBK model, and was patterned after similar models developed by the ICRP to model 

25 the age-specific biokinetics of calcium-like radionuclides (Leggett 1993). 

26 

27 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-6 DRAFT – December 2006 

1 Exhibit 5-3. Structure of the Leggett Biokinetic Model (Leggett 1993) 

2 

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

4 

5 • The published version of the Leggett model lacks the multipathway exposure module of 

6 the IEUBK. The Leggett model accepts total respiratory and ingestion intakes 

7 (administered doses) as inputs and calculates Pb uptake using age-specific absorption 

8 factors. 

9 

10 • The Leggett model lacks a “probabilistic” component; all predictions are deterministic 

11 for a single individual receiving a given set of exposures, with no capability for 

12 generating graphical outputs. 

13 

14 • The central exchange compartment in the Leggett model is “diffusible plasma,” rather 

15 than the plasma-ECF compartment used in the IEUBK model. Extra-vascular fluid, red 

16 blood cells (RBCs), and a bound plasma fraction are the other blood/fluid compartments 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-7 DRAFT – December 2006 

1 that exchange directly with plasma in the Leggett model, with different transfer rates 

2 reflecting differences in estimated exchange rates. 

3 

4 • The trabecular and cortical bone compartments in the Leggett model are divided into two 

5 subcompartments each, one exchangeable, and one “non-exchangeable.” Pb in the “non6 exchangeable” compartments of both types of bone can be remobilized, but only 

7 relatively slowly as a result of bone remodeling. 

8 

9 • Urinary excretion is modeled in the Leggett model as part of an integrated kidney 

10 compartment, rather than separately as in the IEUBK model. 

11 

12 • In the Leggett model, the liver is modeled as two compartments with rapid and 

13 moderately rapid Pb exchange, respectively. Other soft tissues are modeled as having 

14 three compartments with differing exchange rates. Pb in brain tissue is explicitly 

15 modeled. 

16 

17 On the whole, the Leggett model is somewhat more technically sophisticated than the IEUBK 

18 model, but lacks a built-in facility to convert exposure concentrations into Pb uptake and to 

19 integrate uptakes from multiple exposure media. 

20 

21 Like the IEUBK model, the Leggett model is biokinetic, with exchange between compartments 

22 modeled using first-order “transfer rates” (equivalent to rate constants). The values for the 

23 transfer rates were estimated using a range of data from adult human radioactive tracer studies, 

24 autopsy data from adults and children, and data from animal studies related to the absorption, 

25 deposition, and excretion of Pb and chemically similar elements (Leggett 1993). The Leggett 

26 model differs from the IEUBK model in that data from short-term studies (on the time-scale of 

27 hours to days) are used to estimate parameter values for the most rapid of the uptake and 

28 exchange processes and thus the time resolution of the Leggett model is much finer than that of 

29 the IEUBK model. The user may specify any step length, depending on the degree of time 

30 resolution required in the blood Pb predictions. Like the IEUBK model, the binding capacity of 

31 Pb in RBCs is assumed to be saturable. 

32 

33 Leggett developed estimates of biokinetic parameters for six age categories: newborn (0 to 100 

34 days), 1 year, 5 years, 10 years, 15 years, and 25 years and older, with age-specific transfer 

35 parameters for children estimated by interpolation between the nearest values. Transfer factors 

36 for children were adjusted to take into account the more rapid bone turnover (calcium/Pb 

37 addition and resorption) in children compared with adults. 

38 

39 Predictions from the Leggett model have been compared with the deterministic predictions of 

40 blood Pb levels generated by the IEUBK model, using the IEUBK default inputs (Pounds and 

41 Leggett 1998). In that comparison, the Leggett model predictions were substantially higher than 

those of the IEUBK model.2 42 

 

2

 Blood Pb levels generated using the Leggett model for this exposure analysis (Section 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 

2 5.1.3 Application of Blood Pb Models 

3 

4 5.1.3.1 Key Modeling Assumptions 

5 

6 For all of the case studies, exposure concentrations were assumed to be constant throughout the 

7 seven-year duration of the exposure scenario. Exposure concentrations associated with policy8 relevant sources were estimated and modeled based on data from the recent past (1997 to 2005), 

9 as summarized in Exhibit 2-4. 

10 

11 Data from the U.S. Census provided estimates for the year 2000 of the numbers of children 

12 living in each block or block group (U.S. Census Bureau 2005; see Chapter 3); the numbers of 

13 exposed children were assumed to be constant through the entire exposure period (to 7 years). 

14 In- and out-migration to and from the case study areas were not considered. To the extent that 

15 the case study exposures were higher than might occur at typical residences outside the study 

16 areas, this assumption may have overestimated blood Pb impacts compared to those that might 

17 actually occur, and vice versa. 

18 

19 As discussed further below, maternal blood Pb levels during pregnancy were assumed to be 

20 identical for all children at a level consistent with nationally representative values for women of 

21 childbearing age (see Section 5.1.4.3). Thus, all children were assumed to start with the same 

22 body burden of Pb at birth. Similarly, all exposed children were assumed to receive the same 

23 pattern of nationally representative policy-relevant background (non-air related) exposures 

24 throughout the exposure period. 

25 

26 5.1.3.2 Adaptation of IEUBK Model 

27 

28 As discussed above, the IEUBK model was used in batch mode to generate blood Pb estimates at 

29 different ages for children exposed from birth in each block or block group for each case study 

30 location. Inputs to the IEUBK model included exposure parameters and intake and uptake factor 

31 values (see Section 5.1.4 and Appendix I) and the exposure concentrations for each pathway. 

32 The input data also include age-specific Pb exposure concentrations for policy-relevant 

33 background pathways (e.g., drinking water and non-water diet), which were assumed to be the 

34 same for children at all locations. 

35 

36 As described in Section 5.1.1, for the pilot phase assessment, blood Pb metrics were calculated to 

37 match the lifetime average and concurrent blood Pb estimates used in deriving the concentration38 response (blood Pb-IQ loss) functions that are used in this analysis (described in Section 6.1.2). 

39 For this analysis, lifetime average is derived as the average blood Pb level for the exposure 

40 interval 6 months through 84 months, and concurrent is derived as the average blood Pb level for 

the exposure interval 73 through 84 months.3 41 To derive these metrics, first IEUBK blood 

42 estimates were generated for nine specific ages (see Exhibit 5-4) for each block or block group; 

43 these estimates represented the central-tendency blood Pb levels experienced by children of those 

 

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 

1 ages in each block or block group. The lifetime average blood Pb metric is derived as the time2 weighted average of the blood Pb values for the nine ages. The concurrent blood Pb metric is 

3 derived as the average of the last two ages (6 years 4 months and 6 years 10 months). 

4 

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

Ages (Months) for Output IEUBK 

Blood Pb Estimates 

Age Period (Months) Represented by 

IEUBK Blood Pb Estimatea

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 6 Modeling periods run from the first day of the first month to the last day of the last month. 

7 

8 The nine age periods for which the point estimates were obtained using IEUBK were selected to 

9 capture those periods of childhood exposure expected to produce significant variability in blood 

10 Pb (i.e., exposures occurring under 2 years of age). Consequently, exposure intervals covering 

11 the first two years of life (i.e., 7 to 12 months, 13 to 8 months, and 19 to 24 months) are six 

12 months long, while the remainder of the simulation period, up to the last year (see below), are 

13 simulated with year-long exposure intervals. 

14 

15 The lifetime average and concurrent (average during age 6) estimates were stored in spreadsheets 

16 to serve as inputs to the probabilistic population blood Pb model (see below). Each time the 

17 Monte Carlo sampling algorithm chose a particular block or block group, the appropriate lifetime 

18 average and concurrent blood Pb levels served as the GM values for that block or block group 

19 from which the individual blood Pb estimates were derived. 

20 

21 5.1.3.3 Adaptation of Leggett Model 

22 

23 For the pilot phase assessment, rather than using the existing FORTRAN-based Leggett model, a 

24 version of the Leggett model implemented in Visual Basic® was developed. The need for this 

25 new version of the Leggett model was based on two factors: 

26 

27 1. The need for a version of the model capable of batch-mode runs to support estimating 

28 blood Pb levels for a relatively large number of blocks and block groups associated with 

29 each case study location (see Section 5.1.1), and 

30 2. The need for the ability to efficiently link the Leggett model (by coding the Leggett 

31 model in Visual Basic®) to the multipathway intake and uptake module that was 

32 developed for tracking pathway-specific contributions to total Pb uptake. 

33 

34 The development of a Visual Basic® version of the Leggett model involved contacting Dr. Joel 

35 Pounds (Pounds 2005) and obtaining the FORTRAN code for the Leggett model (DOS version) 

36 together with input files containing the biokinetic parameter values. The biokinetic algorithms of 

37 the model were recoded in Visual Basic® and a user interface and multipathway intake and 

38 uptake module were added (as noted above). The intake and uptake module was designed to 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-10 DRAFT – December 2006 

1 accept the same exposure, intake, and uptake factors as the IEUBK, and to calculate inhalation 

2 and ingestion Pb uptake in the same way as the IEUBK model. 

3 

4 Inhalation pathway uptake was calculated as follows: 

5 

6 BRTCRN = RSVOL × BRETH × AFLNG 

7 

8 where: 

9 

10 BRTCRN = Pb uptake by inhalation (μg/day) 

RSVOL = age-specific respiratory volume (m3 11 /day) 

BRETH = modeled exposure concentration (μg/m3 12 ) 

13 AFLNG = inhalation absorption fraction (unitless) 

14 

15 Ingestion pathway Pb uptake was calculated as: 

16 

17 EATCRN = FOODUP + H2OUP + SOILUP + DUSTUP 

18 FOODUP = EAT × FI 

19 H2OUP = H2OCNS × H2OCONC × H2OA 

20 SOILUP = SLING × SOILPB × SLAF 

21 DUSTUP = DSTING × DUSTPB × DSTAF 

22 

23 where: 

24 

25 EATCRN = total uptake through ingestion (μg/day) 

26 FOODUP = uptake from food (μg/day) 

27 EAT = daily Pb intake from non-water diet (μg/day) 

28 FI = GI absorption fraction for diet (unitless) 

29 H2OUP = uptake from drinking (μg/day) 

30 H2OCNS = direct residential drinking water consumption (l/day) 

31 H2OCONC = average Pb concentration in drinking water (μg/day) 

32 H2OA = GI absorption fraction for water (unitless) 

33 SOILUP = daily Pb uptake from soil (μg/day) 

34 SLING = daily soil ingestion (g/day) 

35 SOILPB = average soil Pb concentration (μg/g) 

36 SLAF = absolute GI absorption fraction for soil Pb (unitless) 

37 DUSTUP = daily Pb uptake from indoor dust (μg/day) 

38 DSTING = daily indoor dust ingestion (g/day) 

39 DUSTPB = average indoor dust Pb concentration (μg/g) 

40 DSTAF = absolute GI absorption fraction for dust Pb (unitless) 

41 

42 Model code was tested for logical correctness against the code supplied in Pounds (2005). The 

43 model algorithms were tested for numerical stability and precision, and the impacts of different 

44 iteration time step durations on predicted blood Pb levels were evaluated. In addition, the results 

45 of test exposure scenarios were compared to population blood Pb statistics (as discussed in 

46 Section 5.2). Age-specific parameter values used to estimate Pb input parameters were specified 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-11 DRAFT – December 2006 

1 to conform as closely as possible to those used in the IEUBK modeling; that is, so that they 

2 yielded the same daily Pb uptake (absorbed dose) from each pathway as went into the IEUBK 

3 biokinetic component. Although comprehensive model evaluation of the version of the Leggett 

4 model developed for the pilot phase assessment was not completed in time for this report, the 

5 Leggett-based exposure analysis has been included here to allow consideration for the use of this 

particular blood Pb model as part of the full-scale assessment.4 6 The results (both exposure and 

7 risk) generated using Leggett should, as is the case with all of the results in the pilot phase 

8 assessment, be considered provisional and subject to updating as part of the full-scale assessment 

9 and in light of additional model evaluation findings. 

10 

11 For the case study blood Pb modeling, exposure concentrations for each block or block group 

12 and exposure scenario were input to the model in batch mode. Lifetime (6 to 84 months) blood 

13 Pb profiles were generated from the daily Pb model outputs, and lifetime average and concurrent 

14 blood Pb estimates were calculated using the appropriate averaging periods (i.e., averaging the 

15 individual daily values from 6 to 84 months for the lifetime average metric and averaging daily 

16 values for the last year (months 73 to 84) for the concurrent metric). The model iteration time 

17 was set at 0.1 day throughout the modeling period. Test runs indicated that modeled daily, 

18 concurrent, and lifetime average blood Pb concentrations from age six months onward were 

19 identical to those obtained using much shorter time steps. 

20 

21 Outputs from the blood Pb modeling (lifetime average and concurrent blood Pb estimates for 

22 each census block or block group) were saved in spreadsheets (as were the IEUBK results) and 

23 used as inputs to the probabilistic model, as described in Section 5.1.3.4. 

24 

25 5.1.3.4 Implementation of the Probabilistic Population Blood Pb Model 

26 

27 The probabilistic population blood Pb and health risk model was implemented in an Excel® 

28 spreadsheet using spreadsheet functions and Visual Basic for Applications® code. The input 

29 sheets for the case studies and exposure scenarios contained one row per block or block group 

30 having one or more resident children. The data elements in each row included: the block or 

31 block group number; the number of resident children 0 to 7 years old; the exposure 

32 concentrations in air, soil, and indoor dust; and the estimated lifetime average and concurrent 

33 GM blood Pb concentrations for each block or block group, as derived using either the IEUBK 

34 or Leggett models described above. Also included in the table was a weighting factor value for 

35 each block or block group that indicated the cumulative proportion of exposed children residing 

36 in the block or block groups and all of the blocks or block groups in the preceding rows of the 

37 spreadsheet. The model used the cumulative weights to select census blocks or block groups in a 

38 population-weighted fashion during the sampling iterations, as discussed below. 

39 

40 The model included a spreadsheet and code algorithm that calculated the approximate 

41 proportions of total Pb uptake (absorbed dose) from each pathway for each census block or block 

42 group. This algorithm was run before the main simulation and the results were placed in the 

43 appropriate rows of the input sheet. Proportional contributions to total Pb uptake were calculated 

44 for inhalation, soil ingestion, indoor dust ingestion, and combined policy-relevant background 

 

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 

1 pathways. The model calculated indoor dust contributions separately for the intercept of the 

2 indoor dust model (assumed to be policy-relevant background from Pb paint and other exposure 

3 sources not explicitly modeled for the pilot phase), and for air- and soil-related terms in the AGG 

4 model, as appropriate. The source contributions to total Pb uptake were calculated 

5 deterministically for each block or block group using the same intake and uptake factors as used 

6 in the blood Pb models. It is important to note that the model calculates the contributions of the 

7 various exposure pathways to total Pb uptake for each block or block group, not for individuals. 

8 Thus, the results discussed in Section 5.3.1 represent only approximate central tendency 

9 estimates of pathway-specific contributions associated with various blood Pb percentile estimates 

10 from the probabilistic model. 

11 

12 The probabilistic blood Pb/IQ loss model was implemented by sampling blood Pb levels 10,000 

13 times in a population-weighted manner from the blocks and block groups included in each case 

14 study, adding a lognormally distributed term representing individual variability to each of the 

15 10,000 GM lifetime average and concurrent blood Pb values, and extracting population blood Pb 

16 statistics from the resulting individual blood Pb estimates. Individual variability was modeled by 

17 sampling from a 0 to 1 uniform variate and using the Excel® LOGINV function to generate a 

18 lognormal variate with the appropriate block or block group GM, and a defined population GSD 

19 (see Section 5.1.4.3). 

20 

21 The pathway contributions to total Pb uptake associated with each block or block group were 

22 also carried through the analysis. Each iteration of the model was implemented on a single row 

23 of a spreadsheet, and standard spreadsheet functions were used to extract population blood Pb 

24 statistics (percentile values) after the sampling was complete. For the primary Pb smelter and 

25 near roadway case studies, diet and drinking water were the only policy-relevant background 

26 pathways. In the secondary Pb smelter case study, diet, water, and an estimated 15 μg/g natural 

27 soil exposure were identified as policy-relevant background pathways. As noted in Chapter 4, 

28 the “AGG” models used to estimate house dust concentrations include intercepts that presumably 

29 represent the contribution to house dust from paint and other indoor sources of exposure. When 

30 the AGG (Air-only) model is used, the intercept contributes 60 μg/g to the total estimated house 

31 dust Pb levels. When the AGG (Air + Soil) version was used, the house dust intercept 

32 concentration was 30.5 μg/g. In all of the case studies, the “intercept” contributions were 

33 included as being associated with policy-relevant sources because it was not possible to 

34 unconditionally rule out these elements of the house dust Pb models as being indirectly related to 

35 air Pb levels. 

36 

37 5.1.4 Inputs to the Blood Pb Models 

38 

39 5.1.4.1 Exposure Concentration Estimates for Policy-Relevant Sources 

40 

41 Exposure concentrations in media impacted by policy-relevant sources were estimated for each 

42 census block or block group in each of the three case studies as described in Chapter 4. In the 

43 primary Pb smelter case study, two scenarios were evaluated: the current conditions scenario, 

44 representing estimates of exposure based on recent site-related data and modeling; and the 

45 attainment scenario, wherein all of the exposure concentration estimates were the same as under 

46 current conditions, with the exception of seven census blocks near the facility, where all 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-13 DRAFT – December 2006 

estimated quarterly averaged ambient air concentrations above the NAAQS (1.5 μg/m3 1 ) were 

2 reduced to the level of standard and annual average air concentrations then derived from these 

3 adjusted quarterly averages. Exposure concentrations that were estimated from the ambient air 

4 concentrations (for example, indoor dust Pb concentrations) were likewise reduced in these 

5 seven high-exposure blocks. In the secondary Pb smelter case study, two sets of soil and indoor 

6 dust exposure concentrations were developed, one based on EPA’s MPE methodology (see 

7 Section 4.2.3) and one based on a hybrid method in which the MPE estimates were scaled to be 

8 more congruent with environmental sampling data from similar facilities. 

9 

10 For the inhalation exposure modeling, as described above, each block or block group was 

11 characterized by an estimated inhalation exposure concentration and by an estimated number of 

12 resident children. The inhalation exposure concentrations together with exposure concentrations 

13 for other key media (e.g., soil, indoor dust, diet) and numbers of children exposed in each block 

14 or block group define the distribution of population exposures that go into the calculation of 

15 individual blood Pb levels in the probabilistic model. Exhibit 5-5 shows the cumulative 

16 population distribution of inhalation pathway exposure concentrations for the primary Pb smelter 

case study under the current conditions and attainment scenarios.5 17 Important features of this 

18 graph include the wide range of exposures (note that the y-axis is logarithmic) and the fact that 

19 the great majority of the exposed populations experience relatively low inhalation exposures 

(less than approximately 0.015 μg/m3 20 ). Only a small “tail” of the distribution (a small proportion 

21 of the exposed children) experiences much higher exposures. Note that the cumulative exposure 

22 distributions are identical for the two exposure scenarios, except for the seven highest exposure 

23 blocks, as discussed above. 

 

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 

2 Exhibit 5-5. Population Distribution of Inhalation Exposure Concentrations 

3 for the Primary Pb Smelter Case Study 

0.001

0.010

0.100

1.000

10.000

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

Cumulative Proportion of Population Exposed

Inhalation Exposure Concentration,

μg Pb/m3

Primary Pb Smelter,

Current Conditions

Primary Pb Smelter,

Attainment

4 

5 

6 Exhibit 5-6 shows the population distributions of inhalation exposure concentrations for the 

7 secondary Pb smelter and near roadway urban case studies. For the secondary Pb smelter case 

8 study, the basic shape is the same, although less extreme, than that seen for the primary Pb 

9 smelter. Estimated inhalation exposures are very low in the secondary Pb smelter case study, 

10 and increase gradually, for most of the population. There is a small “hook” at the high end of the 

11 curve representing a relatively small proportion of the population in census blocks near the 

12 facility experiencing higher exposures. In contrast, the exposure distribution for the near 

13 roadway urban case study is a step function; this pattern shows the single-value estimated 

14 inhalation concentrations for census blocks in the 75 to 200 meters (m) and 0 to 75 m bands, 

15 respectively. 

16 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-15 DRAFT – December 2006 

1 Exhibit 5-6. Population Distribution of Inhalation Exposure Concentrations 

2 for the Secondary Pb Smelter and Near Roadway Urban Case Studies 

0.0001

0.0010

0.0100

0.1000

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

Cumulative Proportion of Population Exposed

Inhalation Pb Exposure, 

μg Pb/m3

Secondary Pb

Smelter

Near Roadway

Urban

3 

4 

5 The distribution of soil exposure concentrations for all three case studies (including both the 

6 MPE and hybrid approaches for soil for the secondary Pb smelter case study) is shown in Exhibit 

7 5-7. In this case, it is the exposure concentration distributions for the secondary Pb smelter case 

8 study that are flatter, with more extreme high-end “tails” than the primary Pb smelter. The 

9 distribution of exposure concentrations for the near roadway urban case study is again a step 

10 function, in this case with three “steps” representing the soil concentrations in the 75 to 200 m, 

11 12 to 75 m, and 0 to 12 m exposure “bands,” respectively. 

12 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-16 DRAFT – December 2006 

1 Exhibit 5-7. Population Distribution of Soil Exposure Concentrations 

2 for the Three Case Studies 

10

100

1,000

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

Cumulative Proportion of Population Exposed

Soil Pb Concentration, μg Pb/g

Near Roadway Urban

Secondary Pb Smelter, MPE

Secondary Pb Smelter, Hybrid

Primary Pb Smelter

3 

4 

5 The population distributions of indoor dust exposure concentrations are shown for the primary 

6 Pb smelter case study in Exhibit 5-8, and for the secondary Pb smelter and near roadway urban 

7 case studies in Exhibit 5-9. The pattern of indoor dust exposure concentrations closely matches 

8 the pattern seen for inhalation exposure concentrations, a consequence of the fact that the air Pb 

9 concentration in air is a major determinant of indoor dust Pb concentrations estimates by the 

10 AGG models (described in Chapter 4 and 6). The estimated indoor dust concentrations for the 

11 secondary Pb smelter and near roadway urban case studies also follow the general patterns seen 

12 for the other media, again because of the correlation introduced between air, soil, and indoor dust 

13 exposure by the use of the AGG model. The skewed nature of the exposure concentration 

14 distributions for the primary and secondary Pb smelter case studies strongly affects the nature of 

15 the estimated blood Pb distributions, as will be discussed in Section 5.4. 

16 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-17 DRAFT – December 2006 

1 Exhibit 5-8. Population Distribution of House Dust Exposure Concentrations for the 

2 Primary Pb Smelter Case Study 

10

100

1,000

10,000

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

Cumulative Proportion of Population Exposed

House Dust Pb Concentration, μg Pb/

g

Primary Pb Smelter,

Current Conditions

Primary Pb Smelter,

Attainment

3 

4 

5 Exhibit 5-9. Population Distribution of House Dust Exposure Concentrations for the 

6 Secondary Pb Smelter and Near-Roadway Urban Case Studies 

0

50

100

150

200

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

Cumulative Proportion of Population Exposed

House Dust Pb Concentration, μg Pb/g

Near Roadway Urban

Secondary Pb Smelter,

MPE

Secondary Pb Smelter,

Hybrid

7 

8 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-18 DRAFT – December 2006 

1 5.1.4.2 Policy-Relevant Background Exposure Pathway Concentrations and 

2 Pb Intake Estimates 

3 

4 As noted above, the exposure concentrations and Pb intake from policy-relevant background 

5 pathways (drinking water and non-water diet) were also parameter inputs to the blood Pb model. 

6 For the purposes of the pilot phase assessment, as stated in Section 5.1.1, exposure through these 

7 pathways was categorized as policy-relevant background, and all of the exposed children were 

8 assigned the same age-specific estimates. 

9 

10 All of the exposed populations were assigned the same Pb concentration in drinking water. 

11 While there is a rather large amount of data in the literature, in many cases, the data are from 

12 “first-draw” samples, non-random (“priority”) samples, or from communities where Pb levels 

13 were known to be elevated. After reviewing the literature, the average drinking water 

14 concentration was estimated to be 4.61 μg/L, based on data from two recent studies of residential 

15 water concentrations in homes and apartments in the United States and Canada (Moir et al. 1996, 

16 Clayton et al. 1999). The range of values seen in these studies (0.84 to 16 μg/L) was considered 

17 to be representative of randomly sampled residential water in houses constructed since Pb pipe 

18 and solder were banned for residential use. The selected value is close to the “default” value (4.0 

19 μg/L) provided with the IEUBK model (USEPA 1994). Much higher values have been 

20 encountered in homes with Pb piping and/or very corrosive water. 

21 

22 In addition to drinking water, it is expected that young children will be exposed to Pb in the 

23 foods they consume. In this assessment, all exposed children were assumed to receive the age24 specific estimates of dietary Pb intake developed by EPA’s Office of Solid Waste and 

25 Emergency Response (USEPA 2006h). EPA developed these estimates by analyzing food 

26 consumption data from the third National Health and Nutrition Examination Survey (NHANES 

27 III) conducted by the National Center for Health Statistics (CDC 1997), and food residue data 

28 from the U.S. Food and Drug Administration’s (FDA) Total Dietary Study (USFDA 2001). The 

29 daily intake values shown in Exhibit 5-10 are considerably lower than those developed using the 

30 same methodology in the 1980s and 1990s. Pb concentrations in food have decreased 

31 dramatically since the prohibition of Pb solder in food containers in 1982 (USEPA 2006b, 

32 Section 3.4). 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-19 DRAFT – December 2006 

1 

2 Exhibit 5-10. Summary of Non-Water Dietary Pb Intake Estimates 

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 

4 There is some potential for double-counting of water and dietary Pb intake because some food 

5 categories (e.g., baby formula, soup) are prepared using domestic water. As discussed in Section 

6 5.1.4.3, this double-counting is minimized by limiting the estimated intake of domestic water to 

7 “direct ingestion” (i.e., consumption directly from the tap). 

8 

9 5.1.4.3 Factors Determining Pb Exposure, Intake, and Uptake 

10 

11 As discussed previously, there is a range of model inputs that govern how the exposure 

12 concentrations are converted to absorbed Pb dose (uptake). These factors represent the 

13 physiological and behavioral characteristics of the exposed population and the chemical and 

14 physical properties of the exposure media that govern exposure and absorption by inhalation and 

15 ingestion. 

16 

17 Because substantial data have become available since the IEUBK default values were last 

18 updated, a literature review was conducted to identify and evaluate recent information related to 

19 Pb exposures, absorption, and bioavailability (USEPA 2006b). Experts in OSWER and OAQPS 

20 were also consulted in an effort to derive exposure, intake, and uptake values for the pilot phase 

21 assessment. Exhibit 5-11 presents the parameter values that were selected as inputs to the 

22 IEUBK in the pilot phase assessment. 

23 

24 A number of the values in Exhibit 5-11 differ from the suggested default values in the most 

25 current version of the IEUBK (USEPA 2005a). Children’s daily ventilation rate estimates were 

26 based on the ICRP Publication 89 (ICRP 2002). Pending a re-analysis pertaining to the child 

27 respiratory absorption fraction values, the values used in the pilot phase for the primary and 

28 secondary Pb smelter case studies were those developed for point source locations for the 

29 assessment described in the 1990 Staff Paper (USEPA 1990b; USEPA 1989). For the near 

30 roadway urban case study, the value used in the pilot phase assessment was 0.32, which falls 

31 within the range of 0.25 to 0.45 estimated in the past for areas not influenced by point sources 

32 (USEPA 1989), and is the IEUBK default. 

33 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-20 DRAFT – December 2006 

1 Exhibit 5-11. Input Parameter Values for the IEUBK Model 

Parameter Value 

IEUBK Default Age Ranges (Years)

Parameter 

IEUBK 

Parameter 

Name 

0.5 to 1 

1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 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 

(μg/L) 

Pb 

concentration 

in drinking 

water (μg/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 

1 Exhibit 5-11. Input Parameter Values for the IEUBK Model 

Parameter Value 

IEUBK Default Age Ranges (Years)

Parameter 

IEUBK 

Parameter 

Name 

0.5 to 1 

1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Basis/Derivation 

Diet 

Dietary Pb 

intake (μg/day) 

Daily Pb 

intake 

(μg/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 delta 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 bioavailability (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 

1 Exhibit 5-11. Input Parameter Values for the IEUBK Model 

Parameter Value 

IEUBK Default Age Ranges (Years)

Parameter 

IEUBK 

Parameter 

Name 

0.5 to 1 

1 to 2 2 to 3 3 to 4 4 to 5 5 to 6 6 to 7 

Basis/Derivation 

Other 

Maternal blood 

Pb (μg/dL) 

Maternal 

blood Pb 

concentration 

at childbirth, 

μg/dL 

1.94 NHANES IV, national GM for adult women 

– all nationalities (CDC 2004). 

2 

3 Estimated children’s direct water ingestion values were interpolated from values in EPA’s 

4 Children-Specific Exposure Factors Handbook (USEPA 2002c); the GI absorption fraction of Pb 

5 from water (and diet) was retained at the IEUBK default value of 50 percent, and is consistent 

6 with OAQPS previous analyses of Pb uptake (USEPA 1989). As noted in Section 5.1.4.2, age7 specific dietary intake values for Pb were revised to reflect the latest analyses of FDA and 

8 NHANES III data on food consumption pattern and Pb residue levels. (USEPA 2006h). 

9 

10 Age-specific soil and indoor dust ingestion rates were left at the IEUBK default values. 

11 Similarly, the weighting factor for soil and indoor dust ingestion was also left at 45 percent soil, 

12 despite limited data supporting this specific value (USEPA 1989; USEPA 1994). The impacts of 

13 changes in the weighting factor and other variables related to soil and dust ingestion were 

14 investigated through sensitivity analyses, as discussed in Section 6.3. 

15 

16 Casteel et al. (2005) evaluated the GI absorption of Pb and other metals from soil samples taken 

17 from the primary Pb smelter study area in juvenile swine. Results of these experiments were 

18 used to derive estimates of absolute GI absorption fractions (the IEUBK inputs are called 

19 “Percent Available”) of 0.48 (48 percent) for soil and 0.26 (26 percent) for indoor dusts. For the 

20 other case studies, the IEUBK generic default value for GI absorption of Pb from soil and indoor 

21 dust (0.30, or 30 percent) was used. This value is generally consistent with more recently 

22 reported values, although estimates vary widely. As was the case with the soil-indoor dust 

23 weighting factor, the impacts of changes in absorption fractions for soil and indoor dust were 

24 investigated in sensitivity analyses. 

25 

26 For the case study blood Pb modeling, the IEUBK default value for maternal blood Pb level was 

27 updated using data from the most recent NHANES survey. NHANES IV data from 2002 to 

28 2004 indicate that the GM blood Pb value for reproductive age women has dropped to about 1.94 

29 μg/dL (Maddaloni et al. 2005). 

30 

31 5.1.4.4 Inter-Individual Variability 

32 

33 The last major input to the probabilistic blood Pb model is the GSD reflecting individual 

34 variability in response to Pb exposures. A GSD value of 1.6 has been used as the central 

35 tendency estimate for individual blood Pb variability in the pilot phase assessment. This value 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-23 DRAFT – December 2006 

1 was provided by EPA (USEPA 1994) based on the distribution of blood Pb levels measured in 

2 children exposed to smelter emissions at the Midvale, UT, smelter (White et al. 1998). 

3 Subsequent studies suggest that this is a reasonable central tendency GSD for populations of 

4 children living in relatively small, defined areas where the sources and relative importance of 

5 different exposure pathways and media are similar across the exposed population. The selection 

6 of this value is supported by the fact that exposure concentrations used in this analysis are 

7 expected to be relatively consistent within each block and block group. Similarly, using a GSD 

8 reflecting the distribution of blood Pb levels for children residing near a primary Pb smelter (i.e., 

9 a dominant Pb source such as the Midvale, UT, smelter) seems appropriate for modeling blood 

10 Pb levels near primary and secondary Pb smelters. It is also worth noting that the pre11 remediation blood Pb GSD for children participating in the Baltimore Urban Pb Soil Abatement 

12 Project was estimated at 1.5 (White et al. 1998), which adds support to using the GSD of 1.6 for 

13 the near roadway urban case study, which can be interpreted as being somewhat similar to a 

14 general urban scenario in terms of Pb exposure. 

15 

16 Recent surveys of blood Pb levels in children at the national-level (NHANES IV data for years 

17 1999 to 2002) have found GSD values in the range of 2.03 to 2.23 (CDC 2004). These GSDs, 

18 which are considerably larger than the values used in the pilot phase assessment, likely reflect 

19 the fact that, while blood Pb levels for the majority of children in the United States have 

20 decreased significantly over the last one to two decades, a small fraction of children still retain 

21 relatively elevated blood Pb levels due to continued exposure to Pb paint and other artifact 

22 sources. Consequently, as the median and mean blood Pb levels have dropped, the extreme 

23 upper tail of the distribution is still somewhat anchored by these high-exposure children, 

24 resulting in an increased GSD for the overall population (CDC 2004). It is expected that the 

25 variation in exposure concentrations within the case study blocks or block groups is much 

26 smaller than that seen in these national population surveys, further supporting the use of the 

27 smaller GSD values. 

28 

29 The small amount of publicly available blood Pb data from children living near the primary Pb 

30 smelter site (see Exhibits 3-3 and 3-4), while not sufficient by itself to establish reliable GSD 

31 estimates for this group, appear to be consistent with a low to moderate GSD value (less than 2). 

32 This conclusion was based on a review of summary blood Pb data from the site (numbers of 

33 samples within specified concentration ranges); individual blood Pb measurements were not 

34 available. The impact of assuming different GSD values has a major impact on the blood Pb 

35 distribution results; the results of sensitivity analysis involving this variable are provided in 

36 Section 6.4. 

37 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-24 DRAFT – December 2006 

1 5.2 Blood Pb Model Performance Evaluation 

2 

3 5.2.1 Evaluation Versus General Population and Site-Specific Blood Pb 

4 Measurements 

5 

6 As noted previously, the IEUBK and Leggett blood Pb models were evaluated with regard to 

7 their predictions relative to each other and relative to population blood Pb statistics from well8 studied populations believed to have experienced exposures similar to those estimated for 

9 populations in the case studies. In addition, the upper percentile blood Pb estimates from the 

10 primary Pb smelter case study were compared to children’s blood Pb sampling results taken near 

11 the facility in 2001. Exhibit 5-12 provides a summary of these analyses. 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-25 DRAFT – December 2006 

1 

2 Exhibit 5-12. Summary of Blood Pb Model Evaluation 

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 μg/dL 

(average 1.7) for the three case 

studies. Median concurrent blood 

Pb modeled with IEUBK ranged 

from 1.0 to 1.4 μg/dL (average 

1.2) for the three case studies. 

Leggett median lifetime average 

blood Pb ranged from 0.8 to 1.2 

μg/dL (average 1.0) for the three 

case studies Median concurrent 

blood Pb ranged from 0.5 to 0.8 

μg/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 to 

the 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 actually 

lower 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 μg/dL 

> 90th is 10 to 19 μg/dL 

> 50th is 0 to 9 μg/dL 

Percentile results generated 

from our site-specific modeling 

(Leggett and IEUBK 

combined): 

> 99.9th% is 13 to 29 μg/dL 

> 99.5th% is 6 to 17 μg/dL 

> 99th% is 4 to 11 μg/dL 

> 95th% approaches 5 μg/dL 

It should be noted that the blood Pb 

levels measured at the primary Pb 

smelter location correspond 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 

3 With regard to the median blood Pb levels, the IEUBK lifetime average blood Pb predictions 

4 from the three case studies rather closely match the GM estimate from the NHANES survey. As 

5 expected, the IEUBK concurrent blood Pb estimates are somewhat lower than the NHANES GM 

6 values, likely because the former data are derived from younger children, who are expected to 

7 have somewhat higher blood Pb levels. Median blood Pb estimates from the Leggett model are 

systematically lower than those from the IEUBK, and lower than the NHANES GM.6 8 The 

9 reason for this difference is not clear, although it is possible that the NHANES population may 

10 have experienced lower exposures than the exposed populations in the case studies; this would 

11 imply that the IEUBK values were slight overestimates and would support the argument that the 

12 Leggett model “underestimates” were not as large as they appear. 

13 

14 The upper percentile blood Pb estimates from the IEUBK and Leggett models appear to be in the 

15 same general range as the upper percentile data from the blood Pb samples taken near the facility 

16 in 2001. This suggests that the models are not dramatically overestimating blood Pb levels for 

17 this subpopulation that would be most strongly affected by Pb exposures from policy-relevant 

18 sources. 

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 

1 5.2.2 Stability of Percentile Blood Pb Estimates 

2 

3 In addition to the performance evaluation of the blood Pb model described above, the stability of 

4 the various blood Pb percentile estimates was also evaluated. Because these estimates are 

5 probabilistic, they can be expected to vary as the model is run multiple times or with different 

6 numbers of iterations. 

7 

8 To examine the variability in the percentile blood Pb estimates, the probabilistic blood Pb model 

9 for the primary Pb smelter current conditions scenario was run 100 times, with 10,000 iterations 

10 per run. The distributions of the blood Pb percentile estimates are summarized in Exhibit 5-13. 

11 

12 Exhibit 5-13. Distributions of Blood Pb Percentile Estimates, 

13 100 Repetitions of the Probabilistic Blood Pb Model 

Blood Pb 

Percentile 

Mean Estimate 

of Statistic, 

μg/dL 

Standard 

Deviation 

Coefficient 

of Variation 

Average 

Differencea

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 14 Average difference = expected value of difference between two successive simulation estimates (10,000 iterations). 

15 

16 The results indicate that the precision of the model is quite good for the mid-range blood Pb 

17 percentile estimates; the coefficient of variation (the standard deviation divided by the mean, 

multiplied by 100) is one percent or less for the median and all percentiles between the 25th 18 and 

the 90th, inclusive. Above the 95th 19 percentile, the precision of the estimates drops rapidly, such 

that the coefficient of variation is about five percent for the 99th 20 percentile, 8 percent for the 

99.5th percentile, and 10 percent for the 99.9th 21 percentile. These statistics should be kept in mind 

22 when interpreting the blood Pb distribution results presented in Section 5.4. 

23 

24 5.3 Limitations and Uncertainties in the Human Exposure Assessment and Blood Pb 

25 Modeling

26 

27 A number of factors affect the degree of uncertainty associated with the human exposure 

28 assessment and blood Pb modeling. These factors, discussed in the following subsections, 

29 include the estimated exposure concentrations associated with policy-relevant sources and 

30 policy-relevant background, the exposure, intake, and uptake factor values, the differences in the 

31 blood Pb model themselves, the approach used to characterize individual variability, and the 

32 demographics of the exposed population. 

33 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-28 DRAFT – December 2006 

1 5.3.1 Exposure Concentrations and Intake Estimates 

2 

3 Exposure concentration estimates have a varying affect on the results of the blood Pb modeling, 

4 depending on the extent to which they contribute to total Pb uptake (absorbed dose). Exhibit 5-

5 14 summarizes the general pattern of how policy-relevant sources and policy-relevant 

6 background pathways contribute to total Pb intake for different percentile estimates of blood Pb 

7 for the three case studies. As discussed in Section 5.1.3.4, the values in the exhibit are the Pb 

8 uptake contribution estimates for the block or block group whose geometric mean blood Pb was 

9 used to estimate the specified individual blood Pb percentiles. In addition, the values represent 

10 the averages of the pathway contributions calculated from the distributions of lifetime average 

11 and concurrent blood Pb percentile values, and the averages of the percentile values calculated 

12 from the different exposure scenarios for each case study location. For the most part, the 

13 pathway contributions to Pb uptake do not differ substantially across the exposure scenarios or 

14 blood Pb metrics. Exceptions to this pattern are discussed in Sections 6.1 and 6.2. Detailed 

15 tables of pathway contributions are provided in Appendix J. 

16 

17 Exhibit 5-14. Average Contributions of Policy-Relevant Sources and Policy-Relevant 

18 Background Pathways to Overall Pb Uptake 

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% 

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

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

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

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

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

75th 45% 55% 67% 33% 44% 56% 

Median 56% 44% 68% 32% 35% 65% 

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

1st 54% 46% 68% 32% 44% 56% 

19 

20 As illustrated in Exhibit 5-14, the policy-relevant background pathways tend to contribute a 

21 relatively large proportion of total Pb uptake for individuals with lower blood Pb levels, and the 

22 importance of policy-relevant background pathways then drops with increasing blood Pb. This 

23 pattern is especially pronounced for the primary Pb smelter because air, soil, and indoor dust 

24 exposures are relatively low for the bulk of the exposed population, and then become extremely 

25 high for the highest exposure blocks and block groups. For the secondary Pb smelter case study, 

26 the contribution of policy-relevant background exposure pathways (diet, water, and background 

27 soil) remains above 50 percent for all of the exposure percentiles. In the near roadway urban 

28 case study, the contribution from policy-relevant background pathways is lower for less exposed 

29 groups than for the other case studies, but it is noted that 100 μg/g “urban background” soil Pb is 

30 included as “policy-related” in this tabulation. 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-29 DRAFT – December 2006 

1 

2 Overall, among the pathways for which individual contributions were derived (see Appendix J), 

3 the inhalation pathway contributes the least to total Pb uptake, ranging from 0.01 percent in the 

4 lowest exposure blocks for the secondary Pb smelter case study to 4.3 percent in the highest 

5 exposure blocks for the primary Pb smelter. As noted previously, however, the impact of 

6 ambient air is assumed to be amplified by its impact on indoor dust Pb concentrations through 

7 the AGG models. The proportional contribution from indoor dust to total Pb intake is 

8 consistently in the range of 25 to 35 percent in the secondary Pb smelter and near roadway case 

9 studies; however, for the primary Pb smelter case study, it ranges from about 10 percent in the 

10 low-exposure blocks and block groups to 87 to 88 percent in the highest exposure block groups. 

11 As noted previously, the contribution from house dust includes an “intercept” which accounts for 

12 a large, sometimes dominant, proportion of the total dust contribution in census blocks with low 

13 air and/or soil concentrations. 

14 

15 Soil exposure contributes about 6 to 11 percent of total Pb intake in the secondary Pb smelter 

16 case study, 30 to 53 percent in the near roadway urban case study, and from about 7 to 63 

17 percent in the primary Pb smelter case study. Because of the relatively widespread soil 

18 contamination at the primary Pb smelter location, soil contributions to total Pb uptake tend to be 

19 greater for individuals in the lower blood Pb percentiles, whose air and indoor dust exposure 

20 concentrations are relatively low. 

21 

22 The factors that contribute to uncertainties in the exposure concentrations are discussed below 

23 for the primary Pb smelter, the secondary Pb smelter, and the near roadway urban case study 

24 locations: 

25 

26 Primary Pb Smelter 

27 

28 • Emissions estimates from the facility processes were based on maximum permitted 

29 emissions. This may not appropriately represent current emissions (could be higher or 

30 lower). In addition, source characteristics of area and volume sources are uncertain, and 

31 emissions from roadways not directly adjacent to the facility were not included. 

32 

33 • The particle size distributions of the emissions from the primary Pb smelter are uncertain. 

34 These distributions impact air and dust concentrations (but not soil, as soil concentrations 

35 were estimated from measurements for this case study). 

36 

37 • Inhalation exposure concentrations for the 0 to 7 age group were estimated from the pilot 

38 phase assessment ambient air concentrations using location-specific ratios of ambient 

39 concentration to exposure concentrations for the 0 to 4 age group from the NATA 

40 national-scale assessment (see Section 4.1.2.2). This contributes to uncertainty, 

41 particularly because the 5 to 7 age group is school-aged and may have different activity 

42 patterns from the 0 to 4 group. 

43 

44 • Soil concentration measurements were available out to approximately 2.5 km from the 

45 facility. Beyond that distance, the regression equation used to calculate soil 

46 concentration was extrapolated, leading to some uncertainty. 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-30 DRAFT – December 2006 

1 

2 • To generate air concentrations for the attainment scenario, all quarterly average air 

concentrations exceeding the NAAQS were set to the attainment level (1.5 μg/m3 3 ). All 

4 other air concentrations were kept at the current conditions modeled concentrations. This 

5 is a simplification of how air concentrations would be affected by emission changes 

6 necessary to meet the NAAQS, and likely results in an overestimation of air 

7 concentrations for the attainment scenario. 

8 

9 • Soil concentration estimates for the attainment scenario are the same as those used for the 

10 current conditions scenario. This may contribute to a more conservative soil 

11 concentration, although soil remediation activities make this difference difficult to 

12 predict. 

13 

14 Secondary Pb Smelter 

15 

16 • Facility Pb emissions estimates were based on stack tests from 1997, 1999, and 2000. 

17 Current emissions may be different. In addition, estimating annual emissions from 

18 single-day stack tests may not appropriately account for true trends in emissions over an 

19 entire year. 

20 

21 • The particle size distributions of the emissions from the secondary Pb smelter are 

22 uncertain and were based on general process information from AP-42. These 

23 distributions lead to uncertainties in air, soil, and dust concentrations. 

24 

25 • The meteorological data used for the air dispersion modeling were not local, but from a 

26 nearby station. The use of these data result in less precision in directional estimates of 

27 air, soil, and dust concentrations, which is consistent with the results of the comparison to 

28 air monitoring data at specific locations (see Section 4.2.2.4). 

29 

30 • Inhalation exposure concentrations for the 0 to 7 age group were estimated from the pilot 

31 phase analysis ambient air concentrations using location-specific ratios of ambient 

32 concentration to exposure concentrations for the 0 to 4 age group from the NATA 

33 national-scale assessment (see Section 4.1.2.2). This leads to uncertainty, particularly 

34 because the 5 to 7 age group is school-aged and may have different activity patterns from 

35 the 0 to 4 group. 

36 

37 • Soil concentrations of Pb were calculated assuming the deposition (derived from the 

38 recent emissions estimates [see Appendix L]) has been constant for the last 37 years 

39 (facility operating time). This is likely not true, and it is quite possible this results in an 

40 underestimation of soil concentrations. In addition, because of lack of site-specific data, 

41 modeled soil concentrations are uncertain. In a comparison to measured soil 

42 concentrations at a similar facility, the modeled results were much lower (Section 

43 4.2.3.1). Thus, a second soil scenario was created with soil concentrations scaled up by a 

44 factor of three. There is still uncertainty over whether this scaled-up set of Pb soil 

45 concentrations is an appropriate upper-bound because the measurements were not taken 

46 at the same location. 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-31 DRAFT – December 2006 

1 

2 Near Roadway Urban Case Study 

3 

4 • Compared to the other case studies, there were relatively little site-specific data available 

5 upon which to base estimates of air, soil, and indoor dust concentrations. These estimates 

6 are therefore very uncertain, and only indicative of potential exposure magnitudes. 

7 

8 5.3.2 Exposure, Intake, and Uptake Parameters and Individual Variability 

9 

10 The exposure, intake, and uptake factors used in this analysis, particularly those affecting 

11 exposure pathways accounting for a large proportion of total Pb uptake, may strongly affect the 

12 results of the blood Pb modeling for specific individuals. Section 6.4 presents the results of a 

13 sensitivity analysis in which the variables evaluated were selected based on the relative 

14 importance of the various pathways. The variables evaluated included dietary and drinking 

15 water GI absorption fractions, soil and dust ingestion rates, and soil and GI absorption fractions. 

16 As discussed in Chapter 6, variation of these values over credible ranges tended to have much 

17 less impact on the blood Pb distributions than differences in the blood Pb models that were used, 

18 and between the blood Pb metrics (lifetime average, concurrent, and peak). 

19 

20 The single variable with by far the largest impact on blood Pb distributions was, not 

21 unexpectedly, the GSD estimate representing individual responses to Pb exposures. 

22 

23 5.4 Blood Pb Distribution Estimates for the Three Case Studies 

24 

25 5.4.1 Primary Pb Smelter 

26 

27 The results of the blood Pb modeling for the primary Pb smelter case study are summarized in 

28 Exhibit 5-15. The median concurrent blood Pb estimates are 1.3 μg/dL and 0.7 μg/dL, 

29 respectively, when estimated with the IEUBK and Leggett models. The median lifetime average 

30 blood Pb estimates from the two models are 2.7 and 1.6 μg/dL, respectively. Comparing the two 

31 sets of estimates, it can be seen that the reduction of exposure in the highest exposure blocks 

32 under the attainment scenario has essentially no impact on the estimated blood Pb distributions 

except for the very highest percentiles. The 99.9th 33 percentile values for the four blood Pb metrics 

34 are reduced between about eight percent and 18 percent under the attainment scenario, compared 

35 to the current conditions estimates. 

36 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-32 DRAFT – December 2006 

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

Concurrent Blood Pb Lifetime Average Blood Pb 

Statistic IEUBK Leggett IEUBK Leggett 

Average Proportion 

from PolicyRelevant Sources a

Current Conditions Exposure Scenario 

99.9th 22 14 29 23 98% 

99.5th 12 6.7 17 11 95% 

99th 7.4 4.2 11 6.8 91% 

95th 3.7 2 5.3 3.1 67% 

90th 2.9 1.5 4.1 2.3 70% 

75th 2.0 1.0 2.7 1.6 59% 

Median 1.3 0.7 1.8 1.1 54% 

25th 0.9 0.5 1.2 0.7 49% 

1st 0.4 0.2 0.5 0.3 46% 

Attainment Exposure Scenario 

99.9th 18 13 25 21 96% 

99.5th 11 6.4 16 11 93% 

99th 7.9 4.3 11 7.1 93% 

95th 3.7 2 5.4 3.1 76% 

90th 2.9 1.5 4.1 2.3 68% 

75th 2.0 1.0 2.8 1.6 58% 

Median 1.4 0.7 1.9 1.1 52% 

25th 0.9 0.5 1.2 0.7 45% 

1st 0.4 0.2 0.5 0.3 44% a 2 Pathways impacted by policy-relevant sources include inhalation, soil and indoor dust ingestion (and 

3 exclude policy-relevant background sources, such as diet and drinking water). 

4 

5 The last column of Exhibit 5-15 summarizes the estimated average proportions of Pb uptake 

6 contributed by policy-relevant sources at the various percentile blood Pb estimates. Under both 

7 exposure scenarios, it can be seen that the proportion of Pb uptake from policy-relevant sources 

8 increases from around 40 percent for the lowest blood Pb percentiles, to around 98 percent for 

9 the very highest percentiles. The contributions of individual exposure pathways to total blood 

10 uptake in the three case studies are discussed in more detail in Section 6.2 and tabulated in 

11 Appendix J. 

12 

13 5.4.2 Secondary Pb Smelter 

14 

15 Exhibit 5-16 summarizes the blood Pb modeling results for the secondary Pb smelter case study. 

16 The median blood Pb levels (and the higher percentile estimates) are lower than those calculated 

17 for the primary Pb smelter, for both sets of soil estimates. Median concurrent estimates are 0.9 

18 μg/dL and 0.5 μg/dL, based on the IEUBK and Leggett models, respectively. The median 

19 lifetime average blood Pb estimates are 1.1 and 0.7 μg/dL. It can be seen from the data in 

20 Exhibit 5-16 that the higher soil concentrations estimated using the hybrid approach result in 

21 increases in both the blood percentile estimates and in the estimated proportions of Pb uptake 

22 from policy-relevant sources. 

23 
 Human Exposure Assessment and Blood Pb Estimation 

ICF International 5-33 DRAFT – December 2006 

1 Exhibit 5-16. Projected Blood Pb Levels (μg/dL) for 

2 Secondary Pb Smelter Case Study

Concurrent Blood Pb Lifetime Average Blood Pb 

Statistic IEUBK Leggett IEUBK Leggett 

Average Proportion 

from PolicyRelevant Sources a

MPE Approach for Characterizing Soil Concentrations 

99.9th 3.7 2.1 4.7 3.0 25% 

99.5th 3.0 1.6 3.9 2.3 27% 

99th 2.7 1.4 3.5 1.9 24% 

95th 1.9 1.0 2.5 1.4 28% 

90th 1.7 0.9 2.1 1.2 23% 

75th 1.2 0.6 1.6 0.9 24% 

Median 0.9 0.5 1.1 0.7 25% 

25th 0.7 0.3 0.8 0.5 24% 

1st 0.3 0.2 0.4 0.2 24% 

Hybrid Approach (Model + Surrogate Data) for Characterizing Soil Concentrations 

99.9th 4.7 2.3 6.3 3.3 38% 

99.5th 3.7 1.9 4.9 2.7 39% 

99th 3.3 1.7 4.4 2.5 40% 

95th 2.4 1.2 3.2 1.8 39% 

90th 2.0 1.0 2.7 1.5 37% 

75th 1.5 0.8 2.0 1.1 39% 

Median 1.1 0.6 1.5 0.8 38% 

25th 0.8 0.4 1.1 0.6 38% 

1st 0.4 0.2 0.5 0.3 37% a 3 Pathways impacted by policy-relevant sources include inhalation, soil and indoor dust ingestion (and 

4 exclude policy-relevant background sources, such as diet and drinking water). 

5 

6 5.4.3 Near Roadway Urban Case Study 

7 

8 The near roadway urban case study blood Pb estimates are shown in Exhibit 5-17. As discussed 

9 in Section 5.2.1, the median blood Pb estimates generated by the IEUBK model fall in the same 

10 range as those for the primary Pb smelter case study. However, the upper percentile estimates 

11 are substantially lower than for the primary Pb smelter because of the lower maximum exposures 

12 in the near roadway case. The estimated proportion of Pb uptake from policy-relevant sources 

13 stays relatively constant across the blood Pb percentiles. 

14 

15 Exhibit 5-17. Projected Blood Pb Levels (μg/dL) for Near Roadway Urban Case Study 

Concurrent Blood Pb Lifetime Average Blood Pb 

Statistic IEUBK Leggett IEUBK Leggett 

Average Proportion 

from PolicyRelevant Sources a

99.9th 6.5 4.2 9.1 6.9 62% 

99.5th 5.0 3.1 7.0 4.9 56% 

99th 4.4 2.7 6.2 4.1 58% 

95th 3.1 1.9 4.4 2.9 56% 

90th 2.6 1.6 3.6 2.4 58% 

75th 1.9 1.2 2.7 1.7 56% 

Median 1.4 0.8 1.9 1.2 63% 

25th 1.0 0.6 1.4 0.9 60% 

1st 0.4 0.3 0.6 0.4 56% a 16 Pathways impacted by policy-relevant sources include inhalation, soil and indoor dust ingestion (and 

17 exclude policy-relevant background sources, such as diet and drinking water). 

18 

 Estimation of Human Health Risk 

ICF International 6-1 DRAFT – December 2006 

1 6. Estimation of Human Health Risk 

2 

3 This chapter presents the methods and results for the characterization of health impacts 

4 associated with Pb exposures for the three human health case studies. As discussed in Chapter 2, 

5 IQ changes in exposed children have been selected as the endpoint for risk quantification in the 

6 pilot phase assessment, on the basis of a large body of evidence establishing the occurrence of 

7 the IQ effects at low exposure and blood Pb levels. In addition, the estimation of these impacts 

8 is facilitated by the availability of recent high-quality studies of the relationship between 

9 children’s blood Pb and IQ loss. It is noted, however, that low-level Pb exposures have been 

10 found to be associated with a wide range of adverse effects in children and adults beyond those 

11 quantified here (USEPA 2006b). 

12 

13 6.1 Methods 

14 

15 6.1.1 Overview of Approach 

16 

17 The estimation of IQ loss is performed using the distribution of blood Pb estimates for exposed 

18 populations derived via the probabilistic model described in Section 5.1.3.4. This model 

19 estimates a population-weighted distribution of estimated individual blood Pb concentrations by 

20 first taking 10,000 population-weighed random samples from the GM blood Pb concentrations 

21 estimated for each block or block group in the case studies. Each of these 10,000 GM blood Pb 

22 levels is then combined with an adjustment factor (sampled from a lognormal distribution 

23 reflecting inter-individual variability in behavior and biokinetics related to Pb exposure) to 

24 generate a blood Pb level for a simulated child. This procedure ultimately generates blood Pb 

25 levels for 10,000 simulated children for each case study location. 

26 

27 To calculate the IQ loss impacts of the case study Pb exposures, exposure-response functions for 

28 IQ loss differentiated for the two blood metrics considered in the pilot phase (i.e., concurrent and 

29 lifetime average) are used to convert each of the blood Pb levels for the 10,000 simulated 

30 children into resulting IQ loss. As described below, the blood Pb-IQ relationships are derived 

31 from the results of a recent analysis of pooled epidemiological data by Lanphear et al. (2005). 

32 Individual IQ loss estimates are calculated for each of the lifetime average and concurrent blood 

33 Pb estimates using two forms of the blood Pb-IQ model from Lanphear et al. (2005) matched to 

34 these blood Pb metrics. The outputs of the risk assessment are the distributions of estimated IQ 

35 loss across the exposed populations, characterized as percentile values. 

36 

37 6.1.2 Blood Pb-IQ Relationships 

38 

39 As noted above, the blood Pb-IQ models were derived from the results of a large epidemiological 

40 analysis by Lanphear et al. (2005), in which the relationships between blood Pb concentrations 

41 and IQ test results in seven populations were analyzed. The 1,333 subjects in the study 

42 population included subjects of four studies in the United States, and one each from Mexico, 

43 Australia, and Kosovo. A number of statistical techniques were used to characterize the 

44 relationships between several blood Pb metrics and test results for individual cohorts and for the 

45 pooled study population. For the pilot phase assessment, model forms were selected that related 

46 IQ change to two blood Pb metrics, lifetime average and concurrent. These metric were meant to 
 Estimation of Human Health Risk 

ICF International 6-2 DRAFT – December 2006 

1 be analogous to the metrics defined in the Lanphear et al. (2005) study, where lifetime average 

2 was defined as the average of all blood Pb measurements taken from a given individual, and 

3 concurrent as the blood Pb measurement taken closest to the time of IQ testing. For purposes of 

4 the pilot phase of the assessment, lifetime average blood Pb was the estimated time-weighted 

5 average blood Pb concentration from age 6 months to 84 months, calculated as described in 

6 Section 5.1.3, using either the IEUBK or Leggett model. Concurrent blood Pb was estimated, for 

7 purposes of the pilot phase assessment, as the average modeled blood Pb concentration from 

8 ages 73 to 84 months, inclusive (see Section 5.1.3), the rational being a desire to have a blood Pb 

9 metric matching as closely as possible to the average age at IQ testing (6.9 years) in the 

10 Lanphear et al. study. 

11 

12 The log-linear model for IQ loss derived from the Lanphear et al. pooled analysis was adapted 

13 for use in the pilot phase assessment. The coefficients in the model are different for lifetime 

14 average and concurrent blood Pb, as shown below: 

15 

16 Concurrent: IQ = 99.3 – 2.70 × ln (Concurrent Blood Pb) 

17 

18 Lifetime Average: IQ = 100.9 – 3.04 × ln (Lifetime Average Blood Pb) 

19 

20 The form of these models is consistent with the observed greater slope of the blood Pb-IQ 

21 relationship seen at low blood Pb levels. Lanphear et al. (2005) also investigated piecewise 

22 linear models to capture this relationship and found that the slope of the blood Pb-IQ relationship 

23 at blood Pb concentrations less than 7.5 μg/dL was significantly greater than the linear slope 

24 estimated above that level. 

25 

26 Applying the models shown above to the estimation of IQ changes involves a degree of 

27 uncertainty. First, because of the log linear form of the models, they predict very large changes 

28 in IQ for small changes in blood Pb at low concentrations. Thus, it is important to stay within 

29 the range of the exposure data used in fitting the functions when predicting IQ loss for 

30 hypothetical exposed populations. 

31 

32 In addition, the constant terms in the models represent central tendency estimates of 

33 “background” (low exposure) IQ distributions for the study populations. These values may not 

34 be applicable to the population for which IQ is being estimated. Thus, applying the models “as 

35 is” to the case study population would involve making possibly unjustifiable assumptions about 

36 baseline IQ levels. 

37 

38 For these reasons, the Lanphear et al. (2005) models were adjusted in the following manner for 

39 application in the pilot phase assessment. For each model, a blood Pb “cutpoint” was derived 

that corresponded to the approximate 5th 40 percentile blood Pb levels seen in the Lanphear et al. 

41 pooled study population. Adverse effects were not estimated for predicted individual blood Pb 

42 levels below the cutpoints. The cutpoints vary for the blood Pb metrics; for concurrent blood Pb, 

43 the cutpoint is 2.4 μg/dL, while for lifetime average blood Pb, the cutpoint is 6.1 μg/dL 

44 (Lanphear et al. 2005, Table 4). 

45 
 Estimation of Human Health Risk 

ICF International 6-3 DRAFT – December 2006 

1 The use of the blood Pb cutpoints in IQ loss estimation is not intended to imply that these 

2 concentrations are thresholds below which adverse effects do not occur. Rather, they are 

3 intended to reflect practical limits on IQ prediction based on the low numbers of Lanphear et al. 

4 (2005) study subjects in these concentration ranges, and the corresponding large uncertainties in 

5 the estimates of the blood Pb-IQ relationships below the cutpoints. 

6 

7 Changes in IQ associated with predicted individual blood Pb levels above the cutpoints were 

8 estimated relative to the corresponding cutpoint as: 

9 

10 Concurrent: IQ loss (relative to cutpoint) = - 2.70 × ln (concurrent blood Pb / 2.4) 

11 

12 Lifetime Average: IQ loss (relative to cutpoint) = - 3.04 × ln (lifetime average blood Pb / 6.1) 

13 

14 The outputs of the IQ modeling were thus estimates of changes in IQ relative to an individual 

15 exposed at the cutpoint level, rather than absolute IQ loss estimates. 

16 

17 6.2 Risk Assessment Results 

18 

19 As noted in Section 6.1, the primary outputs of the pilot phase risk assessment are population 

20 distributions of estimated blood Pb concentrations and IQ loss, along with evaluations of the 

21 proportional contribution of individual exposure pathways to total lifetime Pb uptake. This 

22 section presents the results for each of the three case studies. Detailed results of the risk 

23 assessment can be found in Appendix J. 

24 

25 Risk results presented in this section and in Appendix J reflect both IEUBK- and Leggett-based 

26 blood Pb modeling. As noted earlier in Chapter 5 (see Sections 5.1.2.2 and 5.1.3.3) and as 

27 evident in the risk results exhibits presented in this section, modeling completed for the pilot 

28 phase has consistently shown a trend of Leggett-derived blood Pb levels being lower than 

29 IEUBK-derived levels for the same exposure scenario. This trend is the opposite of findings 

30 presented in Pounds and Leggett (1998). This apparent discrepancy elevates the importance of 

31 model evaluation focused on IEUBK and Leggett (as applied in the pilot phase) which is 

32 currently ongoing. It also emphasizes the fact that results of the pilot phase should be considered 

33 provisional and subject to update in the full-scale assessment. Note that the results of model 

34 evaluation, once completed, will be conveyed by separate cover. 

35 

36 6.2.1 Primary Pb Smelter Case Study 

37 

38 Exhibit 6-1 summarizes the blood Pb and IQ loss distributions for the primary Pb smelter current 

39 conditions scenario, calculated using the IEUBK and Leggett blood Pb models, with IQ loss 

40 values based on lifetime average and concurrent values. All other input assumptions and values 

41 used in the assessment are described in Chapter 5. The first column of this exhibit labels the 

42 percentile values of the outputs from the probabilistic model, and the second column shows the 

43 number of children who would have blood Pb and IQ loss values above the percentile value, 

44 given the estimated population at risk in the study area. 

45 

46 
 Estimation of Human Health Risk 

ICF International 6-4 DRAFT – December 2006 

1 Exhibit 6-1. Projected IQ Losses from the 

2 Primary Pb Smelter Current Conditions Scenario 

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) 

99.9th 4 21.9 6.0 28.6 4.7 13.9 4.7 22.9 4.0 

99.5th 19 12.4 4.4 16.9 3.1 6.7 2.8 11.1 1.8 

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

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

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

75th 970 2.0 -- 2.7 -- 1.0 -- 1.6 -- 

Median 1,940 1.3 -- 1.8 -- 0.7 -- 1.1 -- 

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

1st 3,841 0.4 -- 0.5 -- 0.2 -- 0.3 -- 

3 

4 It can be seen that the large majority of exposed children are predicted to have blood Pb levels 

5 below both the concurrent and lifetime average cutpoint values (i.e., no IQ loss associated with 

Pb exposure is estimated). No IQ loss is predicted for the median or 75th 6 percentile child by 

7 either the IEUBK or Leggett models, based on either the lifetime average or concurrent blood Pb 

8 values. IQ losses are predicted for the largest proportion of children by the IEUBK model 

9 (because it predicted higher blood Pb concentration than Leggett) when the concurrent blood Pb 

10 metric is used as the input to the blood Pb-IQ model. Estimated IQ loss values range from less 

than1 for the 90th percentile child to 6.0 for the 99.9th 11 percentile. Under the three other modeling 

options, IQ losses are predicted starting between the 95th and 99th 12 percentiles and range from less 

13 than 1 to 4.7. 

14 

15 Predicted blood Pb and IQ loss distributions for the primary Pb smelter attainment scenario are 

16 essentially identical (Exhibit 6-2) to those for the current conditions scenario, except for the very 

17 highest percentiles. This is as expected because the exposure concentrations change between the 

18 two air quality scenarios only for the seven highest exposure blocks (see Section 5.1.4), where 

19 only about 0.5 percent of the exposed children reside. Thus, the estimated IQ losses are slightly 

smaller for the 99.5th and 99.9th 20 percentile children under the attainment scenario, while the 

21 remaining estimates are the same, within simulation error, to estimates derived for the current 

22 conditions scenario. 

23 
 Estimation of Human Health Risk 

ICF International 6-5 DRAFT – December 2006 

1 Exhibit 6-2. Projected IQ Losses from the Primary Pb Smelter Attainment Scenario 

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) 

99.9th 4 18.4 5.5 24.9 4.3 12.7 4.5 20.7 3.7 

99.5th 19 11.2 4.2 15.5 2.8 6.4 2.6 10.6 1.7 

99th 39 7.9 3.2 11.3 1.9 4.3 1.6 7.1 0.5 

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

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

75th 970 2.0 -- 2.8 -- 1.0 -- 1.6 -- 

Median 1,940 1.4 -- 1.9 -- 0.7 -- 1.1 -- 

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

1st 3,841 0.4 -- 0.5 -- 0.2 -- 0.3 -- 

2 

3 In addition to calculating blood Pb and IQ loss distributions, the probabilistic model also records 

4 the contribution of each exposure pathway to total lifetime average Pb uptake at each percentile 

5 of the blood distributions. Pb uptake is defined as the total amount of Pb that is absorbed into the 

6 blood compartments of the biokinetic models. The probabilistic model calculates pathway7 specific Pb uptakes for each census block and block group independently of the biokinetic 

8 models, using the same uptake calculations equations as used in the IEUBK and Leggett models 

9 (Section 5.1.3.3). When a census block or block group is chosen in the simulation model, the 

10 uptake values are saved. When percentiles of the blood Pb/IQ loss distributions are calculated, 

11 the associated pathway contributions for the block group are retrieved. These values give an 

12 approximate picture of the importance of specific exposure pathways at specific percentile 

13 values; they do not, however, reflect individual variability in Pb uptake within the blocks or 

14 block groups. 

15 

16 The average proportion of total Pb uptake contributed by policy-relevant sources and policy17 relevant background pathways differs widely across the blood Pb/IQ loss percentiles (Exhibit 6-

18 3). At the lowest percentiles, policy-relevant background (in this case study, they are diet and 

19 drinking water) contributes the majority (about 54 percent) of total Pb uptake. The proportional 

20 contribution from policy-relevant background then decreases rapidly until it accounts for only 

about two percent of the total Pb uptake at the 99.9th 21 percentile of the simulation blood Pb. 

22 

23 Among the exposure pathways associated with policy-relevant sources, inhalation exposure 

24 accounts for a very small proportion (less than five percent) of Pb uptake across all blood Pb 

25 percentiles. Ingestion of soil provides about 30 percent of total uptake for the low percentiles, 

increasing to just more than half of the total (59 percent) at the 90th 26 percentile, then decreasing 

27 for the highest blood Pb percentiles. As might be expected, the blocks and block groups with the 
 Estimation of Human Health Risk 

ICF International 6-6 DRAFT – December 2006 

highest percentile (99th 1 and above) individual blood Pb and IQ estimates appear to have very 

2 high house dust Pb levels, which accounts for the bulk of total Pb uptake. 

3 

4 The proportions graphed in Exhibit 6-3 represent the average of the contributions from all eight 

5 exposure scenario/blood Pb model/blood Pb metric combinations for the primary Pb smelter case 

study.1 6 The estimated contributions from the individual pathways for specific percentile blood 

7 Pb values differ only slightly (generally, a few percent) across the various modeling approaches. 

8 

9 Exhibit 6-3. Average Exposure Pathway Contributions to Total Pb Uptake, 

Primary Pb Smelter Case Studya 10 

11 

0%

25%

50%

75%

100%

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

Blood Pb/IQ Loss Percentile

Pathway Contribution

Inhalation

Soi Ingestion

House Dust Ingestion

Policy-Relevant

Background

Policy-Relevant Sources

12 a 13 ” Policy-Relevant Sources” exposures include air, soil, and house dust. Diet and water exposures constitute 

14 “Policy-Relevant Background” in this case study. 

15 

16 6.2.2 Secondary Pb Smelter Case Study 

17 

18 Exhibit 6-4 summarizes the estimated blood Pb and IQ loss distributions for the secondary Pb 

19 smelter case study when the modeled (MPE) estimates of soil concentrations are used as inputs. 

20 In this scenario, as well as when the hybrid soil concentration model is used, the AGG (Air + 

21 Soil) model (as described further in Section 4.1) was used to estimate house dust Pb 

22 concentrations. 

23 

24 As expected, due to the lower exposure concentrations, the estimated blood Pb levels and IQ 

25 losses are smaller than those estimated for the primary Pb smelter. Based on the concurrent 

26 blood Pb distributions obtained from the IEUBK model, IQ losses are predicted only for children 

 

1

 The eight estimates come from two exposure scenarios (current conditions and attainment), two blood Pb models 

(IEUBK and Leggett), and two blood Pb metrics (concurrent and lifetime average). 
 Estimation of Human Health Risk 

ICF International 6-7 DRAFT – December 2006 

above the 95th 1 percentile of the individual blood Pb distribution. Based on the estimated number 

2 of resident children near the secondary Pb smelter, approximately 84 children would be at the 

95th 3 percentile blood Pb or above. 

4 

5 Exhibit 6-4. Projected IQ Losses from the Secondary Pb Smelter – 

6 Modeled (MPE) Soil Concentration Estimates 

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) 

99.9th 2 3.7 1.8 4.7 -- 2.1 -- 3.0 -- 

99.5th 8 3.0 1.1 3.9 -- 1.6 -- 2.3 -- 

99th 17 2.7 <1 3.5 -- 1.4 -- 1.9 -- 

95th 84 1.9 <1 2.5 -- 1.0 -- 1.4 -- 

90th 167 1.7 -- 2.1 -- 0.9 -- 1.2 -- 

75th 418 1.2 -- 1.6 -- 0.6 -- 0.9 -- 

Median 836 0.9 -- 1.1 -- 0.5 -- 0.7 -- 

25th 1,254 0.7 -- 0.8 -- 0.3 -- 0.5 -- 

1st 1,655 0.3 -- 0.4 -- 0.2 -- 0.2 -- 

7 

For this case study, predicted IQ changes range from less than 1 point for the 99th 8 percentile to 

1.8 points for the 99.9th 9 percentile child. No IQ losses are predicted using the lifetime blood Pb 

10 distribution from the IEUBK model or using either the lifetime average or concurrent metric 

generated by the Leggett model. All of the 99.9th 11 percentile blood Pb estimates are less than the 

12 lifetime average (6.1 μg/dL) or concurrent (2.4 μg/dL) cutpoints. 

13 

14 Exhibit 6-5 shows the blood Pb and IQ distributions for the secondary Pb smelter case study 

15 when the hybrid soil Pb estimation model is used. As discussed in Section 5.1.4, hybrid soil 

16 concentration estimates are three times the MPE modeled values. Because of the higher soil 

17 concentrations, it can be seen that the estimated Pb and IQ loss percentiles are slightly higher 

18 than when the modeled (MPE) soil concentrations are used. IQ losses are predicted for children 

above about the 90th 19 percentile (corresponding to 167 children); based on the IEUBK concurrent 

blood Pb estimates, IQ losses range from less than 1 at the 95th percentile to 2.3 at the 99.9th 20 

21 percentile. A very small IQ loss (0.1 point) is also predicted by the IEUBK lifetime average 

22 blood Pb metric. As was the case for the modeled soil estimates, no IQ losses are predicted 

23 when the Leggett model is used to predict either lifetime average or concurrent blood Pb 

24 distributions. 

25 

26 
 Estimation of Human Health Risk 

ICF International 6-8 DRAFT – December 2006 

1 Exhibit 6-5. Projected IQ Losses from the Secondary Pb Smelter Case Study – 

2 Hybrid Soil Concentration Estimates 

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) 

99.9th 2 4.7 2.3 6.3 <1 2.3 -- 3.3 -- 

99.5th 8 3.7 1.4 4.9 -- 1.9 -- 2.7 -- 

99th 17 3.3 1.0 4.4 -- 1.7 -- 2.5 -- 

95th 84 2.4 <1 3.2 -- 1.2 -- 1.8 -- 

90th 167 2.0 -- 2.7 -- 1.0 -- 1.5 -- 

75th 418 1.5 -- 2.0 -- 0.8 -- 1.1 -- 

Median 836 1.1 -- 1.5 -- 0.6 -- 0.8 -- 

25th 1,254 0.8 -- 1.1 -- 0.4 -- 0.6 -- 

1st 1,655 0.4 -- 0.5 -- 0.2 -- 0.3 -- 

3 

4 The proportional contribution of the individual exposure pathways to total Pb uptake in the 

5 secondary Pb smelter case study is very different from that seen for the primary Pb smelter case 

6 study. Unlike the primary Pb smelter case study, the average pathway contributions remain 

7 approximately constant across the blood Pb/IQ loss percentiles, all varying less than about two 

8 percent around their mean values. This is due primarily to the relatively flat exposure 

9 distributions; most census blocks have very low ambient air and house dust Pb concentrations, 

10 and policy-relevant background exposure pathways (diet, water, and naturally occurring soil Pb) 

11 contribute a large percentage of total Pb uptake across all the blood Pb percentiles (Exhibit 6-6). 

12 

13 As expected, the relative pathway contributions differ when the two different sets of soil 

14 concentration estimates are used as inputs to the blood Pb/IQ loss models. The soil pathway 

15 contribution, averaged across the four combinations of exposure scenario and blood Pb model, 

16 increases from 7.8 percent to about 19 percent when hybrid, rather than modeled, soil 

17 concentration estimates are used. The contributions from the other pathways decrease in 

18 compensation for the higher soil Pb levels. 

19 
 Estimation of Human Health Risk 

ICF International 6-9 DRAFT – December 2006 

1 Exhibit 6-6. Pathway Contributions to Total Pb Uptake, 

Secondary Pb Smelter Case Studya 2 

0%

10%

20%

30%

40%

50%

60%

70%

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

Blood Pb/IQ Loss Percentile

Pathway Contribution

Diet

Inhalation

Soil Ingestion (M PE Estimate)

House Dust Ingestion

Policy-Relevant Background

Policy-Relevant Sources

Soil Ingestion (Hybrid

Estmate)

3 a 4 “Policy-Relevant Background” includes diet and water intake, plus a contribution from naturally occurring Pb in 

5 soil of 15 μg/g). 

6 

7 6.2.3 Near Roadway Urban Case Study 

8 

9 Exhibit 6-7 summarizes the results of the pilot phase risk assessment for the near roadway urban 

case study. IQ losses are predicted to occur for children above the 75th 10 percentile IEUBK 

concurrent blood Pb estimate, ranging from less than 1 point at the 90th 11 percentile to 2.7 points at 

the 99.9th 12 percentile. Based on the estimated 319 children living in the census blocks included in 

this case study, 32 children are predicted to be exposed at or above the 90th 13 percentile blood Pb 

level, and approximately three children above the 99th 14 percentile blood/IQ level. IQ losses are 

15 predicted for smaller proportions of children when the IEUBK model is used to estimate lifetime 

16 average blood Pb values and for both blood Pb metrics when estimated with the Leggett model. 

17 
 Estimation of Human Health Risk 

ICF International 6-10 DRAFT – December 2006 

1 Exhibit 6-7. Projected IQ Losses from the Near Roadway Urban Case Study 

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)

99.9th 0 6.5 2.7 9.1 1.2 4.2 1.5 6.9 <1 

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

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

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

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

75th 80 1.9 -- 2.7 -- 1.2 -- 1.7 -- 

Median 159 1.4 -- 1.9 -- 0.8 -- 1.2 -- 

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

1st 316 0.4 -- 0.6 -- 0.3 -- 0.4 -- 

2 

3 As was the case for the secondary Pb smelter case study, the contribution of the individual 

4 exposure pathways is relatively constant across blood Pb/IQ loss percentiles (Exhibit 6-8). In 

5 contrast to the secondary Pb smelter, however, policy-relevant sources contribute the majority of 

6 total Pb uptake (56 to 63 percent) across the blood Pb percentile distribution. Policy-relevant 

7 background exposures (diet and drinking water, for this case study) contribute between 37 and 

8 44 percent to total Pb uptake. 

9 

10 
 Estimation of Human Health Risk 

ICF International 6-11 DRAFT – December 2006 

1 Exhibit 6-8. Average Exposure Pathway Contributions to Total Pb Uptake, 

Near Roadway Urban Case Studya 2 

0%

10%

20%

30%

40%

50%

60%

70%

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

Blood Pb/IQ Loss Percentile

Pathway Contribution

Inhalation

Soil Ingestion

House Dust Ingestion

Policy-Relevant

Background

Policy-Relevant Sources

3 a 4 “Policy-Relevant Sources” exposures include air, soil, and house dust. Diet and water are the “Policy-Relevant 

5 Background” exposures in this case study.

6 

7 6.3 Uncertainty and Variability in the IQ Loss Estimates 

8 

9 6.3.1 Sources of Uncertainty 

10 

11 There are two sets of factors that contribute the overall uncertainty in the IQ loss estimates. The 

12 first, discussed in Section 5.3, are those factors which affect the blood Pb estimates from which 

13 the IQ loss distributions are calculated. These include the choice of the model used to estimate 

14 blood Pb levels, the GSD describing the individual variability in response to Pb exposures, 

15 uncertainties in exposure concentration estimates, and uncertainties in the exposure, intake, and 

16 uptake factors used to estimates of Pb uptake (absorbed dose). 

17 

18 The second set of factors that needs to be considered includes those that are associated 

19 specifically with the estimation of IQ loss from the individual blood Pb estimates. In the 

20 sensitivity analysis described in Section 6.4, the impact of using two different models (log-linear 

21 and piecewise linear) derived from the Lanphear et al. (2005) epidemiological study are 

22 examined. Blood Pb-IQ models have been developed from a number of other high-quality 

23 studies (Canfield et al. 2003, for example), and such models could also be applied to the 

24 individual blood Pb levels calculated in the probabilistic model. 

25 

26 The nature of the blood Pb metric used to estimate IQ loss also strongly affects the results of the 

27 analysis. Both the slope parameters and the cutpoints are very different in the concurrent and 

28 lifetime average blood Pb-IQ model equations summarized above. In the sensitivity analyses, 
 Estimation of Human Health Risk 

ICF International 6-12 DRAFT – December 2006 

1 described in the following section, the effect of using these two metrics, as well as models for the 

2 “peak” blood Pb, on the modeled IQ loss distributions was examined. 

3 

4 The decision to use the blood Pb cutpoints also introduces uncertainty into the assessment. 

5 Using different cutpoints (i.e., making different judgments about the predictive power of the 

6 Lanphear et al. (2005) blood Pb-IQ models at low blood Pb concentrations) changes the both the 

7 numbers and proportions of children for whom adverse effects (IQ loss) are predicted to occur, 

8 and the magnitude of the predicted IQ changes. The impact of different assumptions about the 

9 blood Pb cutpoints is also investigated in the sensitivity analysis described below. 

10 

11 6.3.2 Sensitivity Analysis Methodology 

12 

13 For the pilot phase risk assessment, a series of “one-at-a-time” analyses has been conducted to 

14 evaluate the impacts of individual modeling methods and parameter inputs on estimated blood 

15 Pb and IQ loss distributions. The general approach employed was to evaluate changes in risk 

16 metrics associated with varying single models or input parameter values away from those used in 

17 a “baseline” analysis. The set of models and input parameter values associated with the baseline 

18 analysis are summarized in Exhibit 6-9. This exhibit also indicates the analytical steps on which 

19 a sensitivity analysis was performed and the alternate model or parameter values that were 

20 examined in the analysis. 

21 

22 The baseline risk analysis focused on the current conditions scenario for the primary Pb smelter 

23 case study. In this analysis, inhalation and soil exposure concentrations were estimated as 

24 described in Section 4.1. House dust Pb concentrations were derived based on the H6 site25 specific regression model for the census block and block groups near the facility, and with the 

26 AGG (air + soil) model for the remainder of the exposed population. The baseline individual 

27 blood Pb concentrations were derived using the IEUBK model estimates for each census block or 

28 block group, assuming individual blood Pb concentrations were lognormally distributed with a 

29 GSD of 1.6. Individual IQ loss was estimated based on concurrent blood Pb concentration using 

30 the Lanphear et al. (2005) log-linear model with IQ loss not predicted for individuals with blood 

31 Pb concentrations below the cutpoint value of 2.4 μg/dL. Note that while we believe the 

32 baseline models and parameter values are believed to provide a credible estimate of blood Pb and 

33 IQ changes, it is not suggested that the baseline model is uniquely the “best,” or that it provides 

34 demonstrably “central tendency” risk estimates. 

35 

36 The bottom row of Exhibit 6-9 summarizes the models and parameters that were varied 

37 (individually) during the sensitivity analysis. Changes in soil and air exposure concentrations 

38 were not changed in the course of the sensitivity analysis. In contrast, the impact of different 

39 models for estimating house dust concentrations (AGG [air only] and AGG [air + soil]) was 

40 evaluated. GM blood Pb concentrations for the blocks and block groups were calculated based 

41 on the Leggett biokinetic model, as well as an “empirical” blood Pb model developed by 

42 Lanphear et al. (1998) (see Appendix K), and the impacts of using the different models on 

43 predicted changes in IQ was also evaluated. Variations in parameters related to Pb exposure, 

44 intake, and uptake estimation were also evaluated. In one set of sensitivity runs, the GI 

45 absorption fractions for dietary and drinking water ingestion were varied above and below their 

46 estimated central tendency values used in the baseline assessment. For the second set of 
 Estimation of Human Health Risk 

ICF International 6-13 DRAFT – December 2006 

1 sensitivity runs, alternative estimates of daily soil and dust ingestion and soil and dust absorption 

2 fractions for soil and dust were evaluated for their impacts on estimated blood Pb and IQ loss 

3 distributions. These values were derived by Von Lindern et al. (2003) from a systematic 

4 statistical analysis of soil, dust, and blood Pb distributions at a large mining/smelting 

5 “Superfund” site. In addition, the probabilistic model was run with two alternative values of the 

6 blood Pb GSD. 

7 
 Estimatio

ICF International 6-14 D

Exhibit 6-9. Baseline and Sensitivity Analysis Blood Pb and Risk Estimation Models and Assu

Modeling 

Element 

Case 

Study/ 

Scenario 

Air, Soil 

Exposure 

Conc. 

House 

Dust Pb 

Conc. 

Model 

Blood 

Estimation 

Model 

Exposure/ 

Intake/Uptake 

Factors 

Individual 

Blood Pb 

Variability 

(GSD) 

Blood PbIQ Model B

Baseline Estimate 

Primary Pb Smelter/ Current Conditions 

Modeled (ISCPRIME), regression (soil) 

Regression (H6 and AGG [Air + Soil]) 

IEUBK (Batch mode age profile) 

Baseline (Exhibit 5-11) 

1.6 Lanphear 

et al. (2005), loglinear 

Co(agmo

Sensitivity Analysis 

Unchanged Unchanged Exclusively 

AGG (Air + Soil), AGG (Air) 

Leggett, Lanphear et al. (1998) 

Low, high dietary absorption; Von Lindern et al. (2003) soil, dust exposure factor estimates 

1.3, 1.9; lower and upper bound values on GSD for children around exposure sources 

Lanphear et al. (2005), piece-wise linear, upper and lower confidence limit slope parameter estimates 

Life- 8mope(hianave
 Estimation of Human Health Risk 

ICF International 6-15 DRAFT – December 2006 

1 Alternative approaches to IQ loss estimation were also investigated. The results obtained from 

2 the piecewise linear form of the Lanphear et al. (2005) model were compared to the baseline 

3 (log-linear) modeling approach. Alternative estimates of IQ loss were also derived using the 

4 upper and lower confidence limit slope parameters from the Lanphear et al. (2005) model. 

5 

6 As noted above, the distribution of IQ loss was also estimated based on lifetime average and 

7 peak individual blood Pb values (instead of the baseline concurrent metric). Finally, the effect of 

8 reducing the Pb cutpoint value for estimating IQ loss was also tested. Detailed descriptions of 

9 the changes in models and parameter values are provided in Section 6.4. 

10 

11 6.4 Sensitivity Analysis Results 

12 

13 As discussed in 6.3, sensitivity analyses were conducted by varying one model or set of 

14 parameter values at a time from the “baseline” assumptions and examining the impact of the 

15 changes on blood Pb and IQ loss distributions. This section summarizes the results of these 

16 sensitivity analyses. In all of the following exhibits, the sensitivity analysis models differ from 

17 the baseline only with regard to the single model or input parameter value that is being varied. 

18 

19 6.4.1 Alternative House Dust Pb Concentration Models 

20 

21 As shown in Section 6.2.1, house dust Pb exposures can contribute a large proportion of total Pb 

22 uptake, particularly for the highly exposed blocks and block groups in the primary Pb smelter 

23 case study. Thus, the impacts of employing alternative models for estimating house dust Pb 

24 concentrations were examined as part of the sensitivity analysis. 

25 

26 In the baseline assessment, house dust Pb concentrations were estimated using two different 

27 models. The “H6” site-specific regression model was used for blocks and block groups within 

28 1.5 kilometers of the facility, while the AGG (Air + Soil) model was used to estimate house dust 

29 Pb levels for areas more distant from the smelter (see Section 4.1.4). In the sensitivity analysis, 

30 two variants of the AGG model were employed (Exhibit 6-10), one in which the Air + Soil 

31 variant was applied to all of the blocks and block groups, and the other where the Air Only form 

32 of the model was used. In all cases, the IEUBK model was used to estimate blood Pb 

33 distributions. The resultant blood Pb distributions are only mildly affected by the choice of the 

34 house dust Pb model – the median values are nearly the same (between 1.4 and 1.5 μg/dL). At 

35 the higher percentile values, the model estimates diverge more significantly because the H6 

36 model predicts much higher house dust Pb concentrations for the blocks and block groups near 

37 the facility, relative to the other models. 

38 
 Estimation of Human Health Risk 

ICF International 6-16 DRAFT – December 2006 

1 Exhibit 6-10. Effects of Alternative House Dust Pb Concentration Models 

on Estimated Blood Pb Distributions a,b 2 

House Dust Pb Model Change versus Baseline 

Percentile 

Baseline 

(Composite 

Model), 

μg/dL 

AGG Model 

(Air Only), 

μg/dLc

AGG Model 

(Air + Soil), 

μg/dLd

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

b 4 Concurrent blood Pb estimates from the IEUBK model and the log-linear form of the Lanphear et al. (2005) blood 

5 Pb-IQ model are used for all estimates.

c

 AGG Model (Air Only) House Dust (μg/g) = 60 + 844 * Ambient Air Pb (μg/m3 6 ) d

 AGG Model (Air +Soil)House Dust (μg/g) = 31.1 + 638 * Ambient Air Pb (μg/m3 7 ) + 0.364 * Soil Pb (μg/g) 

8 

9 This pattern is reflected in the estimated IQ loss distributions shown in Exhibit 6-11. Again, the 

10 decreasing slope of the log-linear model reduces the differences in estimated IQ loss at the 

11 higher percentiles. 

12 

13 Exhibit 6-11. Effects of Alternative House Dust Pb Models 

on Estimated IQ Distributionsa 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 15 Concurrent blood Pb estimates from the IEUBK model and the log-linear form of the Lanphear et al. (2005) 

16 blood Pb-IQ model are used for all estimates. 

17 
 Estimation of Human Health Risk 

ICF International 6-17 DRAFT – December 2006 

1 6.4.2 Alternative Blood Pb Models 

2 

3 To evaluate the sensitivity of IQ loss estimates to the selection of a blood Pb model, the IEUBK 

4 model was selected as the baseline and used to generate the concurrent baseline blood Pb 

5 distribution and IQ loss distributions, using as inputs the media concentration estimates from the 

6 primary Pb smelter current conditions scenario. The IEUBK input parameter values for this 

7 model run are presented in Exhibit 5-11. The results from this baseline IEUBK simulation were 

8 compared to results generated using two alternative blood Pb models. 

9 

First, the Leggett model was applied, with the results summarized in Section 6.2.1.2 10 In addition, 

11 an “empirical” blood Pb model developed by Lanphear et al. (1998) was applied, which predicts 

12 blood Pb levels for 16 month-old children. This model was derived by statistical analyses of the 

13 relationships between multimedia Pb exposures (air, soil, house dust Pb loading, drinking water, 

14 and presence/absence of Pb paint), socioeconomic variables, and blood Pb measurement in 

15 young children obtained as part of a national survey conducted in 1997. The best fitting models 

16 from the analysis predict children’s long-term blood Pb levels as a function of soil Pb 

17 concentrations and house dust Pb loading (the amount of Pb present per unit area of household 

18 surfaces; in this case, on the floor.) To apply the Lanphear et al. (1998) model, it was necessary 

19 to convert the estimated house dust exposure concentrations in the primary Pb smelter case study 

20 into estimates of dust Pb loading. As discussed in Appendix K, this was done by conducting a 

21 regression analysis of house dust Pb loading and concentration measurements from 305 housing 

22 units from the 1997 survey. The resulting relationship between house dust loading and 

23 concentration was then used to generate equivalent dust loading estimates for each block and 

24 block group, which were then used as inputs to the Lanphear et al. (1998) model (Appendix K). 

25 

26 The blood Pb distribution estimates generated by the IEUBK and Leggett models are 

27 summarized in Exhibit 6-12. As illustrated in this exhibit, the median blood Pb estimates from 

28 the models differ substantially, with the Leggett median estimate (0.7 μg/dL) being about one29 half that from the baseline IEUBK model (1.3 μg/dL). The higher percentiles also differ greatly, 

30 although on a proportional basis, the estimates converge slightly across the three blood Pb 

31 models. The Lanphear et al. (1998) model predicts blood Pb levels for 16 month-old children, 

32 when exposures and blood Pb levels tend to be considerably higher than for older children, and 

33 thus the blood Pb levels that it predicts (which are considerably higher than the IEUBK 

34 concurrent estimates) at all percentiles are not directly comparable to the values presented in 

35 Exhibit 6-12. The IQ loss estimates predicted based on the Lanphear et al. (1998) model, 

36 however, should be roughly comparable, since they predict the same persistent, possibly 

37 permanent, decrements in function due to exposures at different ages. 

38 

 

2

 As noted in Section 6.2, modeling completed for the pilot 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 (including 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 

1 Exhibit 6-12. Blood Pb Distributions Generated by the 

2 Leggett and IEUBK Models 

Blood Pb Model 

Change 

versus 

Baseline 

Percentile Leggett 

(1993) 

Modela,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 3 Exposure factor values for the Leggett model were selected so as to produce equivalent Pb uptake for 

4 each exposure pathway as those used in the IEUBK model, concurrent blood Pb (6-7 years). 

b. 5 Blood Pb estimate for 16-month old child, based on soil and dust pathways only. 

6 

7 Exhibit 6-13 summarizes the differences in estimated IQ losses when the three different blood Pb 

models are used. IQ losses are predicted above the 95th 8 percentile by the Leggett model, above 

the 75th percentile by the IEUBK model, and by the Lanphear et al. (1998) model.3 9 The 

maximum (99.9th 10 percentile) IQ losses predicted are not that different across the three models 

11 because the slope of the log-linear IQ model decreases rapidly with increased blood Pb. 

12 

 

3

 The IQ loss blood Pb cutoff (4.0 μg/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 

1 Exhibit 6-13. IQ Loss Estimates for Primary Pb Smelter Current Conditions Derived 

Using the Leggett, IEUBK, and Lanphear et al. (1998) Blood Pb Modelsa 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 3 Log-linear model IQ loss estimates are reported throughout. 

4 

5 6.4.3 Alternative Diet and Water Absorption Fractions 

6 

7 As noted in Section 6.2, policy-relevant background pathways (specifically, diet and drinking 

8 water) contribute a substantial proportion of total Pb uptake in all of the case studies. Under the 

9 primary Pb smelter current conditions scenario, diet and drinking water account for more than 

10 half of total Pb uptake at the low end of the blood Pb distribution, and lesser proportions at 

11 higher percentiles as the amount of uptake from other pathways increases. 

12 

13 Dietary and drinking water Pb uptake is determined by several sets of model inputs, including 

14 age-specific dietary intakes, drinking water Pb concentrations, and drinking water consumption. 

15 In addition, the GI uptake fractions (i.e., absorption fraction for dietary Pb [AFDiet] and 

16 absorption fraction for Pb in drinking water [AFWater]) determine the proportion of ingested Pb 

17 that is absorbed. For the pilot phase, the simplest approach to varying these policy-relevant 

18 background pathway Pb uptakes was determined to be adjusting AFDiet and AFWater values in 

19 parallel for both pathways. In the baseline analysis, AFDiet and AFWater are both 0.5 (50 

20 percent). For the sensitivity analysis, the values were varied downward to 40 percent and 

21 upward to 60 percent. These values were thought to be reasonable lower- and upper-end 

22 estimates based on a review of recent literature (USEPA 2006b; Maddaloni et al. 2005). 

23 

24 Increasing or decreasing the drinking water and dietary absorption fractions to these values has 

25 moderate impacts on the blood Pb distributions (Exhibit 6-14). Median concurrent blood Pb 

26 estimates for the low, baseline, and high estimates are 1.0, 1.3, and 1.5 μg/dL, respectively. For 

27 the higher blood Pb percentiles, the estimates derived with the low AFDiet and AFWater values 

28 are about 70 percent of those estimated using the baseline values, and the estimates derived with 

29 the high-end values are approximately 15 percent higher than the baseline. 

30 
 Estimation of Human Health Risk 

ICF International 6-20 DRAFT – December 2006 

1 Exhibit 6-14. Impacts of Changing Diet and Drinking Water GI Absorption Fractions 

on Estimated Blood Pb Distributionsa 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 3 All estimates are for the primary Pb smelter current conditions scenario using IEUBK concurrent blood 

4 Pb metrics. 

5 

6 Exhibit 6-15 shows the corresponding impacts of variations to AFDiet and AFWater on IQ loss 

7 distributions for the primary Pb smelter current conditions scenarios, based on concurrent blood 

8 Pb estimates from the IEUBK model. When low AF values are used, IQ loss is predicted for 

children above the 90th 9 percentile. When the baseline and higher values are used, IQ losses are 

predicted for children above the 75th percentile. The 99.9th 10 percentile IQ losses range from 4.4 

11 (low AF values) to 5.9 points (high AF values). 

12 

13 Exhibit 6-15. Impacts of Changing Diet and Drinking Water GI 

Absorption Fractions on Estimated IQ Loss Distributionsa 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 15 All estimates are derived using IEUBK concurrent blood Pb distributions, log-linear blood Pb-IQ model. 

16 
 Estimation of Human Health Risk 

ICF International 6-21 DRAFT – December 2006 

1 6.4.4 Alternative Values for Soil and House Dust Ingestion and for Soil and Dust 

2 Absorption Fractions 

3 

4 Soil and house dust exposures play an important role in determining Pb uptake and blood Pb 

5 distributions in the primary Pb smelter case study, particularly in the highly-exposed blocks and 

6 block groups near the facility. Thus, alternative assumptions relating to soil and dust ingestion 

7 rates and GI absorption fractions were examined to determine their impacts on blood Pb and IQ 

8 loss distributions. 

9 

10 Von Lindern et al. (2003) developed a combination of parameter estimates related to soil and 

11 dust Pb contributions to blood Pb based on a comprehensive statistical analyses of environmental 

12 and blood Pb measurements taken over a period of 12 years near a large mining and smelting 

13 “Superfund” site in Idaho. Based on their analyses, Von Lindern et al. estimated that dust 

14 ingestion accounted for about 42 percent and soil ingestion for approximately 58 percent of the 

15 total soil plus dust influence on blood Pb levels. Therefore, the “soil dust weighting factor” input 

16 to the IEUBK model was adjusted to 58 percent from the IEUBK default value. Von Lindern et 

17 al. distinguished between “community,” “neighborhood,” and “yard soil” contributions to total 

18 Pb intake. No such distinction was made in this assessment – the estimated soil and dust 

19 concentrations for each block and block group were assumed to be representative of all three 

20 types of contributions. 

21 

22 Von Lindern et al. (2003) also estimated that the average (absolute) GI absorption fraction for Pb 

23 uptake from soil and dust was 18 percent more than the period covered by their analysis. In the 

24 sensitivity analysis, this value (18 percent) was substituted for both the site-specific soil and 

25 house dust GI absorption fraction values used in the baseline modeling, which were 26 percent 

26 and 48 percent, respectively. 

27 

28 Exhibit 6-16 illustrates that using the alternative soil and dust intake and uptake factors from 

29 Von Lindern et al. (2003) results in lower blood Pb estimates than the when the baseline values 

30 are used. While the higher proportion of house dust ingestion might be expected to result in 

31 higher Pb uptake, especially in the most highly exposed block groups, this potential increase is 

32 offset by the lower estimated soil and dust ingestion fractions (18 percent for both media versus 

33 48 and 26 percent, respectively, in the baseline analysis). The median blood Pb estimate is thus 

34 reduced to 1.0 μg/dL, compared to 1.4 for the baseline parameter values, and the blood Pb 

35 percentile estimates above the median are 25 to 40 percent lower than the baseline estimates. 

36 

37 Use of the Von Lindern et al. (2003) soil-dust weighting factor and GI absorption fractions has a 

38 corresponding effect on the estimated IQ loss distributions (Exhibit 6-17). IQ losses are 

predicted above the 90th 39 percentile when the Von Lindern et al. parameters are used. When the 

baseline parameter estimates are used, IQ losses are predicted for children above the 75th 40 

percentile. The 99.9th 41 percentile IQ change under the baseline is 5.3 points, whereas, when the 

42 alternative values for the soil-dust weighting factor and absorption fraction are used, the 

43 corresponding IQ loss estimate is 4.4 points. 

44 
 Estimation of Human Health Risk 

ICF International 6-22 DRAFT – December 2006 

1 Exhibit 6-16. Effects of Von Lindern et al. (2003) Soil and Dust Intake Parameters on 

2 Estimated Blood Pb Distributions, Primary Pb Smelter Case Study 

Soil, House Dust Exposure 

Factor Values 

Change 

from 

Baseline 

Percentile 

Baseline 

Exposure 

Factorsa

Von Lindern 

et al. 

Exposure 

Factorsb

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

b 4 Soil-Dust Weighting factor = 58%, AFSoil = AFDust = 18 % 

5 

6 Exhibit 6-17. Effects of Von Lindern et al. (2003) Soil and Dust Intake Parameters on 

Estimated IQ Loss Distributions, Primary Pb Smelter Case Studya 7 

Soil, House Dust Exposure 

Factor Values 

Change from 

Baseline 

Percentile IQ Change 

Derived from 

Baseline 

Exposure 

Factorsa,b

IQ Change 

Derived from 

Von Lindern 

et al. 

Exposure 

Factorsa,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 8 Log-linear blood Pb-IQ loss model 

b 9 See notes to Exhibit 6-16 

10 

11 6.4.5 Alternative Blood Pb Geometric Standard Deviations 

12 

13 To this point, the sensitivity analysis has addressed models and parameters that affect the GM 

14 blood Pb estimates for the individual blocks and block groups. The parameter to be evaluated 

15 next, the GSD for individual variability, affects the distribution of individual blood Pb values 

16 derived from the block and block group GM values. 

17 
 Estimation of Human Health Risk 

ICF International 6-23 DRAFT – December 2006 

1 The baseline (central tendency) estimate for the blood Pb GSD used in all three case studies is 

2 1.6 (see Section 5.1.4.4). Based on a review of the literature conducted for this assessment, GSD 

3 values for children in limited geographical areas were found to range from approximately 1.3 to 

4 1.9. Larger variability has been seen in national and regional data (CDC 2004; Maddaloni 2005), 

5 but it is likely that the children in these populations faced more varied exposures than those in 

6 smaller populations. The values 1.3 and 1.9 were thus considered to be plausible “lower” and 

7 “upper” estimates of the GSD for individual variability, with no implication intended that they 

8 represent absolute limits or defined percentiles of actual population variability. 

9 

10 As expected, changing the GSD value leaves the median blood Pb estimates unchanged (Exhibit 

11 6-18) and reducing the GSD value to 1.3 results in the upper percentile values that are lower than 

12 the corresponding baseline estimates, with the proportional decrease increasing toward the “tail” 

of the analysis. The pattern is clear up to the 99th percentile, but the 99.5 and 99.9th 13 percentile 

14 blood Pb estimates calculated with the lower GSD value are similar to, or greater than, those 

15 calculated with the baseline values. This is certainly an artifact due to simulation uncertainty 

16 (see Exhibit 5-13). 

17 

18 Exhibit 6-18. Effect of Varying Individual Variability Estimates 

on Predicted Blood Pb Distributionsa 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 20 Concurrent blood Pb estimates are calculated for the primary Pb smelter current conditions scenario 

21 using the IEUBK model. 

22 

23 Blood Pb estimates above the median are consistently elevated compared to the baseline when 

24 the higher estimate for population variability (1.9) is used to calculate individual blood Pb levels. 

25 The estimates for the upper percentiles derived using the upper GSD estimate are on the order of 

26 30 to 35 percent higher than the corresponding baseline values. 

27 

28 Exhibit 6-19 shows the differences in estimates of IQ loss when the alternative GSD values are 

used. For the low GSD estimate, IQ losses are predicted for children above the 90th 29 percentile, 

while for the baseline and high-end GSDs, IQ losses are predicted for children above the 75th 30 

31 percentile. The highest predicted IQ losses derived using the three GSD estimates are quite 

32 similar (5.2, 5.3, and 6.1, respectively), the closeness of the estimates again reflecting the 

33 shallow slope of the log-linear blood Pb-IQ loss model at high blood Pb levels. 

34 
 Estimation of Human Health Risk 

ICF International 6-24 DRAFT – December 2006 

1 Exhibit 6-19. Effect of Changing the GSD for Individual Variability 

on Predicted IQ Loss Distributionsa 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 3 IQ loss estimates are derived based on concurrent IEUBK blood Pb estimates for the primary Pb 

4 smelter current conditions scenario. 

5 

6 6.4.5 Alternative Blood Pb-IQ Models and Uncertainty Related to Statistical Fit of 

7 Models 

8 

9 To this point, all of the IQ loss estimates have been calculated based on the log-linear version of 

10 the Lanphear et al. (2005) model. For the sensitivity analysis, three alternative parameterizations 

11 of the blood Pb-IQ models were used to calculate IQ changes. The first two sets of estimates 

12 were derived using the estimated upper and lower confidence limits on the log linear slope 

13 parameters from the concurrent blood Pb-IQ model derived by Lanphear et al. (2005). The 

14 equations for these model were: 

15 

16 IQ loss (relative to cutpoint) = - 3.74 × ln (concurrent blood Pb / 2.4) 

17 

18 IQ loss (relative to cutpoint) = - 1.66 × ln (concurrent blood Pb / 2.4) 

19 

20 with the first being the “upper” confidence limit (the value that predicts the largest change in 

21 IQ), and the second being the lower confidence limit. 

22 

23 The third alternative was the piecewise linear form of the model presented in Lanphear et al. 

24 (2005): 

25 

26 Concurrent Blood Pb < 2.4 μg/dL: IQ change not calculated 

27 

28 Concurrent Blood Pb < 10 μg/dL: IQ = 97.2 – 0.80 × Blood Pb 

29 

30 Concurrent Blood Pb > 10 μg/dL: IQ = 89.2 – 0.13 × Blood Pb 

31 

32 The intercept in the third equation is simply the IQ loss estimated from the second equation at a 

33 blood Pb concentration of 10 μg/dL. Thus, the form of this model is a “hockey-stick” with a 
 Estimation of Human Health Risk 

ICF International 6-25 DRAFT – December 2006 

1 slope of -0.80 below 10 μg/dL, and a slope of -0.13 above 10 μg/dL. As for the log-linear 

2 model, IQ changes were not estimated if individual blood Pb levels were below the concurrent 

3 blood Pb cutpoint discussed above. 

4 

5 Exhibit 6-20 summarizes the IQ loss distributions derived using the alternative blood Pb-IQ 

6 models with the concurrent blood Pb metric. In all cases, block and block group blood Pb 

7 concentrations were calculated with IEUBK, and a GSD value of 1.6 was used to describe 

8 individual variability in response to Pb exposures. Of the alternatives evaluated, the predictions 

generated by the piecewise linear model differ the most from the baseline estimates. The 90th 9 

and 95th percentile IQ losses predicted by the two models are quite similar, but the 99th 10 and 

11 higher percentile IQ loss predictions from the piecewise model are on the order of twice those 

12 from the log-linear model. This is due to difference in form and the much higher slope of the 

13 piecewise linear model in the range from the blood Pb cutpoint to 10 μg/dL. 

14 

15 The lower and upper confidence limit model predictions parallel the predictions from the 

16 baseline model, as expected. The lower confidence limit (LCL) model predicts IQ losses for 

children above the 75th percentile, as high as 1.2 points at the 99.9th 17 percentile. The upper 

18 confidence limit (UCL) model, like the baseline estimate, predicts IQ losses for children above 

the 75th percentile, but the maximum (99.9th 19 percentile) IQ loss is predicted to be on the order of 

20 8.1 points, compared to about 5.3 points for the baseline model. 

21 

22 Exhibit 6-20. Effects of Alternative Blood Pb-IQ Models on Predicted IQ Distributions 

Blood Pb-IQ Model Change Relative to Baseline 

Percentile 

95% LCL 

on LogLinear 

Model 

(1.66) 

Baseline 

(LogLinear 

Model)a

95% UCL 

on LogLinear 

Model 

(3.72) 

Piecewise 

Linear 

Model 

95% LCL 95% UCL Piecewise 

Linear 

99.9th -1.2 -5.3 -8.1 -8.9 -78% 53% 69% 

99.5th -0.8 -4.0 -5.9 -8.1 -80% 49% 103% 

99th -0.6 -3.0 -4.3 -3.9 -80% 44% 30% 

95th -0.2 -1.1 -1.6 -1.0 -77% 48% -12% 

90th -0.1 -0.5 -0.7 -0.4 -76% 49% -23% 

75th -- -- -- -- -- -- -- 

Median -- -- -- -- -- -- -- 

25th -- -- -- -- -- -- -- 

1st -- -- -- -- -- -- -- a 23 All IQ Loss estimates are for the primary Pb smelter current conditions scenario, based on IEUBK concurrent blood 

24 Pb estimates. 

25 
 Estimation of Human Health Risk 

ICF International 6-26 DRAFT – December 2006 

1 6.4.7 Alternative Blood Pb Metrics 

2 

3 In Section 6.2, the concurrent and lifetime average blood Pb distributions, and IQ loss estimates 

4 generated from these distributions, were presented for each of the case studies. In addition, the 

5 impact of using the “peak” blood Pb estimates (the highest annual average, which was found to 

6 occur at ages 6 through 18 months) on the magnitude of IQ loss predictions was also evaluated. 

7 The Lanphear et al. (2005) log-linear model form was used to derive all of these results. The 

8 concurrent, lifetime average, and peak blood Pb-based models, while sharing the same 

9 mathematical form, differ in two respects. First, they have different slope parameters (2.7, 3.04, 

10 and 2.85, respectively for the concurrent, lifetime average, and peak blood Pb metrics). In 

11 addition, the cutpoint values below which IQ losses are not calculated varies across the models 

12 (see Section 6.1.2). The cutpoint for concurrent blood Pb is 2.4 μg/dL, the cutpoint for lifetime 

13 average blood Pb is 6.1 μg/dL, and the cutpoint for peak exposures is 4.0 μg/dL, corresponding 

to the 5th 14 percentile blood Pb values measured in the Lanphear et al. (2005) pooled cohort. 

15 

16 Exhibit 6-21 shows that both the lifetime average and peak blood Pb values generated using the 

17 IEUBK model are substantially greater than the baseline (concurrent) estimates. This is because 

18 the lifetime average metric contains a substantial contribution from younger ages, when blood Pb 

19 is estimated to be higher than for the concurrent children (aged 6 to 7 years). The peak (highest 

20 annual) blood Pb is, by definition, taken from the exposure period when blood Pb levels are 

21 highest. Across the percentiles, the lifetime average blood Pb estimates are approximately 35 to 

22 40 percent higher than the corresponding baseline values, and the peak estimates are about 

23 80 percent higher. These observations are consistent with published findings in the literature 

24 regarding varying blood Pb levels with age (USEPA 2006a). 

25 

26 Exhibit 6-21. Distributions of Concurrent, Lifetime Average, and Peak Blood Pb 

27 Distributions from the Primary Pb Smelter Case Study 

Blood Pb Metric 

Percentile 

Lifetime 

Average 

Blood Pb, 

μg/dL 

Baseline 

(Concurrent 

Blood Pb), 

μg/dLa

Peak 

(Highest 

Annual) 

Blood Pb, 

μg/dLb

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

b 29 IEUBK highest 12-month average (age 6 to 18 months) blood Pb estimates. 

30 
 Estimation of Human Health Risk 

ICF International 6-27 DRAFT – December 2006 

1 These differences in blood Pb predictions strongly affect the magnitude of predicted IQ losses 

2 (Exhibit 6-22), as do the differences in the model parameters. Although the lifetime average 

3 blood Pb percentiles are higher than the baseline concurrent estimates, the magnitude of 

4 predicted IQ loss is lower. When interpreting the results in Exhibit 6-22, it is important to 

5 remember that IQ changes are calculated using different blood Pb cutpoints and slope factors for 

6 each blood Pb metric. 

7 

8 When the lifetime average blood Pb distribution is input into the lifetime average blood Pb-IQ 

model, only children above the 95th 9 percentile are predicted to experience IQ changes, and the 

greatest (99.9th 10 percentile) estimate is an IQ loss of 4.1 points. These IQ changes are 

11 significantly lower than the baseline predictions based on the concurrent blood Pb distributions. 

12 The primary reason for this pattern is the relatively high lifetime average blood Pb cutpoint (6.1 

13 μg/g), compared to the concurrent cutpoint of 2.4 μg/dL. 

14 

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

Blood Pb Metric Difference from Concurrent 

Percentile 

IQ Change 

Based on 

Lifetime 

Average 

Blood Pba

IQ Change 

Based on 

Concurrent 

Blood Pbb

IQ Change 

Based on 

Highest 

Annual 

Blood Pbc

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

17 blood Pb levels. 

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

c 19 Derived using Lanphear et al. (2005) parameter values for peak blood Pb exposures. 

20 

21 In contrast, the IQ losses predicted based on the distribution of peak blood Pb levels are 

22 considerably higher than the baseline estimates. IQ losses are predicted for children above the 

50th percentile blood Pb, and range from 1.1 points (75th percentile) to 7.5 points (99.9th 23 

24 percentile). In the case of the peak blood Pb values, the difference between the blood Pb cutoff 

25 values is not great enough to overcome the effect of much greater peak blood Pb estimates. 

26 

27 6.4.8 Alternative Blood Pb Cutpoint for IQ Loss Estimation 

28 

29 Because of the impact on IQ loss estimates of the blood Pb cutoff values used as the “floor” for 

30 estimating IQ loss, the impact of changing these values was also investigated. The data available 

31 from the Lanphear et al. (2005) study does not provide a foundation for alternative cutpoints for 

32 use in sensitivity analysis. For the pilot phase, the simple expedient of reducing the cutpoints for 
 Estimation of Human Health Risk 

ICF International 6-28 DRAFT – December 2006 

1 concurrent and lifetime average blood Pb by 50 percent was adopted. The slope parameters for 

2 the two models remained unchanged. 

3 

Exhibit 6-23. Effect of Reducing Blood Pb Cutoff Values for IQ Estimationa 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 5 All IQ loss estimates are derived from IEUBK blood Pb estimates for the primary Pb smelter 

6 current conditions scenario. 

7 

8 As shown in Exhibit 6-23, when the concurrent blood Pb cutpoint is reduced from 2.4 to 

1.2 μg/dL, children above the 25th (rather than the 75th 9 ) percentile in the primary Pb smelter case 

study are predicted to experience IQ loss, and the maximum (99.9th 10 percentile) IQ loss using 

11 IEUBK and concurrent blood Pb blood Pb-IQ function is increased from 5.3 to 8.3 points. A 

12 change in the cutpoint for the lifetime blood Pb metric would be expected to have similar 

13 impacts on the IQ changes. 

14 

15 6.4.9 Summary of Sensitivity Analysis 

16 

17 The preceding discussion demonstrates a wide range of impacts on blood Pb and IQ loss 

18 distributions associated with alternative model selection and parameter values. Exhibit 6-24 

19 summarizes the impacts of the variations in models and input assumptions on predicted blood Pb 

and IQ distributions. Changes in the median 90th, 95th, and 99th 20 percentile values relative to the 

21 corresponding baseline estimates are displayed for each set of models and parameter values that 

22 were varied. 

23 

24 As expected, the predicted changes in blood Pb and IQ values in Exhibit 6-24 tend to move in 

25 parallel, although with a reverse sign. However, variations in models or parameter values affect 

26 the predicted blood Pb or IQ to varying degrees owing to the non-linearity of the statistical blood 

27 Pb-IQ models and variations in the blood Pb-IQ loss cutpoints when different blood Pb metrics 

28 (concurrent, lifetime average, or peak) are used. Many changes in assumptions that result in 

29 significant decreases in predicted blood Pb levels accordingly result in a lesser predicted IQ 

30 change, or no predicted IQ change at some percentiles. Some relatively small changes in blood 

31 Pb associated with the sensitivity analysis abolish the estimated IQ loss entirely (identified as “-

32 100%” changes in Exhibit 6-24) because the predicted blood Pb falls below the cutpoint. In such 

33 cases, both the numbers of children for which IQ losses are predicted and the magnitude of IQ 
 Estimation of Human Health Risk 

ICF International 6-29 DRAFT – December 2006 

1 losses among the affected children are reduced. It is also important to remember that the 

2 proportional changes displayed in the exhibit apply to much smaller absolute blood Pb and IQ 

loss values at the median and 90th percentile than at the 95th and 99th 3 , owing to the “skewed” 

4 nature of the blood Pb and IQ loss distributions. 

5 

6 The factors most strongly affecting the magnitude of IQ loss estimates are the nature of the blood 

7 Pb metric from which they are estimated and assumptions related to the blood Pb cutpoint for IQ 

8 loss estimation. The estimated GSD values for individual variability in response to Pb exposures 

9 also strongly affect the predicted IQ loss distributions, particularly at the upper percentiles. 

10 Among the factors related to blood Pb modeling, the selection of blood Pb model has the most 

11 impact on estimated IQ changes. Relatively modest changes in GI absorption changes for diet 

12 and water Pb surprisingly produce relatively large proportional changes in the predicted blood Pb 

13 and IQ distributions, as do changes in soil/dust exposure factor values. As noted, above, 

14 however, the highest proportional changes associated with these modeling alternatives are seen 

15 at the lower percentile values, where the absolute magnitudes of the change from the baseline 

16 can be rather small. 

17 
 Estimatio

ICF International 6-30 D

Exhibit 6-24. Impacts of Alternative Models and Parameters on Blood Pb and IQ Loss Estimates in the S

Change Versus Baseline 

Percentile 

House Dust Pb 

Concentration 

Model 

Blood Pb 

Estimation 

Model 

Water, Diet 

Absorption 

Fractions 

Soil, Dust 

Exposure 

Intake/Uptake 

Factors 

Individual 

Blood Pb 

Variability 

(GSD) 

Blood Pb-IQ 

Model Blood 

Blood Pb 

99th -15% -42% to +58% -38% to +17% -38% -6% to +35% -- +43% 95th -5% to -7% -46% to 84% -30% to +11% -30% -19% to +31% -- +42% 90th -8% to -7% -47% to 84% -28% to +9% -28% -18% to +23% -- +41% 

Median -12% to -10% -46% to +108% -23% to +12% -23% 0% to 3% -- +37% 

IQ Loss 

99th -14% to -15% -50% to -2% -44% to +14% -44% -6% to +27% -80% to +44% -46%95th 12% to 17% -100% to +27% -88% to +25% -88% -51% to +66% -77% to +48% -100% 90th 43% to 46% -100% to +55% -100% to +52% -100% -100% to +124% -76% to +49% -100% 

Median -- -- -- -- -- -- 
 Ecological Risk Assessment

ICF International 7-1 DRAFT – December 2006 

1 7. Ecological Risk Assessment 

2 

3 The ecological risk assessment for this NAAQS review consisted of three case study screening 

4 assessments and a national-scale surface water and sediment screening assessment. The case 

5 study screening assessments were designed to estimate the potential for ecological risks 

6 associated with exposures to Pb emitted into ambient air for three case studies: primary Pb 

7 smelter, secondary Pb smelter, and near roadway non-urban location. The national-scale 

8 screening assessment evaluated the potential for ecological risks associated with the atmospheric 

9 deposition of Pb released into ambient air at surface water and sediment monitoring locations 

10 across the United States. This chapter describes the methods applied for this assessment (Section 

11 7.1), presents the exposure and risk results (Section 7.2), and discusses the uncertainties and 

12 limitations of the assessment (Section 7.3). 

13 

14 7.1 Methods 

15 

16 This section describes the screening methods used to determine the potential for risks of adverse 

17 effects on ecological receptors associated with exposures of Pb emitted into ambient air and 

18 subsequently deposited to other environmental media. Section 7.1.1 summarizes the risk 

19 characterization methods used to characterize ecological risks for the case studies; Section 7.1.2 

20 describes the methods used to conduct the national-scale surface water and sediment screening 

21 assessment; and Section 7.1.3 describes the ecological effects characterization methods and the 

22 ecotoxicity screening values used in this assessment. 

23 

24 7.1.1 Case Studies 

25 

26 The Hazard Quotient (HQ) approach was used to compare estimated media concentrations with 

27 ecotoxicity screening values in soils, surface waters, and sediments around the primary Pb 

28 smelter, and for soils only around the secondary Pb smelter case study location and the near 

29 roadway non-urban case study locations. The HQ is calculated as the ratio of the media 

30 concentration to the ecotoxicity screening value. The HQ is represented by the following 

31 equation: 

32 

33 HQ = (estimated media concentration) / (ecotoxicity screening value) 

34 

35 For each case study, HQ values were calculated for each location where either modeled or 

36 measured media concentrations were available. Separate soil HQ values were calculated for 

37 each ecological receptor group for which an ecotoxicity screening value has been developed (i.e., 

38 birds, mammals, soil invertebrates, and plants). HQ values less than 1.0 suggest that Pb 

39 concentrations in a specific medium are unlikely to pose significant risks to ecological receptors 

40 whereas HQ values greater than 1.0 indicate that the expected exposure exceeds the ecotoxicity 

41 screening value. 

42 

43 7.1.2 National-Scale Surface Water and Sediment Screening Assessment 

44 

45 The HQ method was used to characterize risk for the surface water portion of the national-scale 

46 surface water and sediment screening assessment, where measures of dissolved Pb 
 Ecological Risk Assessment

ICF International 7-2 DRAFT – December 2006 

1 concentrations in surface waters at monitoring locations across the United States were compared 

2 with both acute and chronic water column (surface water) ecotoxicity screening values. To 

3 identify which sites might pose a risk to aquatic communities, the frequencies with which 

4 dissolved Pb concentrations exceeded the selected chronic and acute surface water ecotoxicity 

5 screening values for the protection of aquatic life (described in Section 7.1.3.2) were examined. 

6 Because of uneven sampling frequency across sampling locations, a screen was implemented to 

7 exclude from further analysis sites expected to pose negligible risks to aquatic communities. 

8 Locations were eliminated if the frequency with which a sample exceeds the chronic surface 

9 water screening value for dissolved Pb was 5 percent or less of available data, and the acute 

10 screening value was never exceeded. The sampling locations that were dropped from the 

11 assessment using this screen are identified in Appendix H with the code “na” in the column 

12 indicating the number of Pb measurements that exceeded the screening value out of the total 

13 number of samples analyzed for dissolved Pb. 

14 

15 As described in Section 7.1.3.2, a review of the data on water hardness in the NAWQA data set 

16 indicated that the initial chronic screening value was too high to identify all locations where the 

17 chronic surface water screening value for the protection of aquatic life was exceeded. As a 

18 result, a second screen was conducted. In the second screen, for each sampling location with one 

19 or more measured dissolved Pb concentrations above the QL but less than 1.2 μg/L (the initial 

20 screening value assuming a water hardness of 50 μg/L CaCO3), measurements of CaCO3 were 

21 used to calculate a location-specific chronic surface water screening value. If none of the 

22 dissolved Pb measurements at a particular location exceeded this location-specific value, the 

23 location was not considered further. If one or more of the dissolved Pb measurements at a 

24 particular location did exceed the location-specific value, the location was retained for further 

25 analysis. 

26 

27 Where dissolved Pb concentrations were found to exceed the chronic surface water screening 

28 values at a frequency of greater than 5 percent of available data or where the acute surface water 

29 screening value was exceeded by at least one measurement, the next step involved identifying 

30 the most likely sources of Pb input to the surface waters to focus the screen on those locations for 

31 which air is a major contributor to the Pb concentrations in the water column. The following 

32 considerations were used to exclude sites for which air contributions could not be apportioned. 

33 

34 • Where the surrounding land use was for mining, the Pb in the surface water could not be 

35 apportioned between runoff/erosion and air emissions. Therefore, these sites were 

36 categorized separately. 

37 

38 • If there is a major permitted effluent discharge upstream of a sampling location with Pb 

39 specified in the permit, it is possible that this discharge was responsible for some of the 

40 Pb measured in the surface water. Therefore, air contributions could not be readily 

41 apportioned. 

42 

43 • If there were no facilities known to emit Pb to the air within a 50-km radius of the 

44 sampling location, the Pb in the surface water was assumed to come largely from non-air 

45 sources. 

46 
 Ecological Risk Assessment

ICF International 7-3 DRAFT – December 2006 

1 Based on this screen of monitoring locations, 15 non-mining surface water locations were 

2 identified with elevated Pb concentrations possibly attributable to policy-relevant sources. Risk 

3 characterization for the sediment portion of the screening assessment was conducted for these 15 

4 locations. This risk characterization was performed using the HQ method, where measures of 

5 total Pb concentrations in sediments were compared with the sediment ecotoxicity screening 

6 values for the protection of benthic communities. Concentrations that exceeded the sediment 

7 screening values were identified and possible sources of Pb input to these sediments were 

8 examined. The first step involved identifying sediment sampling locations in the NAWQA 

9 database that were co-located with the surface water samples identified for the surface water 

10 portion of the screening assessment. Three of the 15 sites did not have co-located sediment and 

11 surface water results. In these cases, sediment sampling results from nearby sampling locations 

12 were identified on the basis of latitude, longitude, and name of the site location. For the 

13 sediment sampling sites that were categorized as near matches, it is important to note that the 

14 surface water samples at the same location had not exceeded the chronic surface water screening 

15 value in the surface water portion of the screening assessment. Therefore, the value of the “near16 match” locations is limited. 

17 

18 7.1.3 Ecotoxicity Screening Values 

19 

20 The sections that follow describe the ecotoxicity screening values used in this ecological risk 

21 assessment to evaluate the soil, surface water, and sediment concentrations of Pb identified in the 

22 case studies and in the national-scale screening assessment. Section 7.1.3.1 describes the soil 

23 ecotoxicity screening values developed for this assessment. Section 7.1.3.2 describes the surface 

24 water quality screening values, and Section 7.1.3.3 describes the sediment screening values. 

25 

26 7.1.3.1 Soil Screening Values 

27 

28 In identifying soil screening values (SSVs) for this assessment, the Ecological Soil Screening 

29 Levels (Eco-SSLs) developed by EPA’s Superfund program (USEPA 2003; USEPA 2005c) 

30 were considered. These values are defined as “concentrations of contaminants in soil that are 

31 protective of ecological receptors that commonly come in contact with soil or ingest biota that 

32 live in or on soil.” They were derived separately for four categories of ecological receptors: 

33 plants, soil invertebrates, birds, and mammals. For the purposes of Superfund screening level 

34 ecological risk assessments at individual hazardous waste sites, Eco-SSLs are used to exclude 

35 from further investigation those receptor/exposure pathway combinations that pose negligible 

36 risks. Eco-SSLs are therefore derived using conservative assumptions and parameter values. 

37 The development of Eco-SSLs for each receptor category involved the assessment of several 

38 species within each category. Available toxicity test data for these species were evaluated. In 

39 the case of plants and soil invertebrates, these data are expressed as contaminant concentrations 

40 in soil (mg contaminant/kg soil), are evaluated with consideration of characteristics affecting 

41 bioavailability (e.g., pH, organic content, etc), and a single Eco-SSL is derived for each category. 

42 Unlike Eco-SSLs for plants and soil invertebrates, those for the avian and mammalian wildlife 

43 categories involve a two step process: (1) derivation of a toxicity reference value (TRV) in mg 

44 contaminant per kg body weight per day, and (2) application of the TRV and exposure parameter 

45 values to derive an Eco-SSL in mg contaminant per kg soil for three different species in each 

46 category. In the first step, a single toxicity reference value (TRV) was developed (e.g., the 
 Ecological Risk Assessment

ICF International 7-4 DRAFT – December 2006 

1 reference dose for the most sensitive of the adverse effects on birds that might cause population2 level effects) that applied to all species in each wildlife category. In the second step, however, 

3 species-specific exposure assumptions regarding incidental soil ingestion, diet composition, and 

4 contaminant uptake from soil by prey generally resulted in different Eco-SSL values, expressed 

5 as soil concentrations, for the different species in each receptor category. The receptor category 

6 Eco-SSL was then set equal to the lowest species-specific Eco-SSL. 

7 

8 In developing SSVs for use in the Pb NAAQS review, the exposure assumptions and parameter 

9 values used in the second step of the derivation of the Superfund Eco-SSLs for birds and 

10 mammals were examined, and as appropriate, augmented or replaced with alternatives 

11 considered more appropriate for the Pb NAAQS review (see Appendix L). SSVs for birds and 

12 mammals for this assessment were derived using the Eco-SSL methodology and TRVs and 

13 alternative values for some of the exposure parameters. For purposes of this assessment, the 

14 SSV for avian wildlife of 38 mg/kg dry weight was developed using data for the woodcock, the 

15 species on which the avian Eco-SSL was based (USEPA 2003; USEPA 2005c). A more 

16 nationally representative value was used for woodcock diet composition (i.e., 75 percent 

17 earthworms, 25 percent soil arthropods), and alternative values, supported by available data, 

18 were used for food ingestion rate (i.e., 0.20 kg food dry weight/kg-day), incidental soil ingestion 

19 rate (i.e., 11.4 percent of the dry weight diet), and absorption of ingested Pb (i.e., 50 percent). 

20 The same 50 percent absorption efficiency was used to calculate an SSV for mammals of 56 

21 mg/kg dry weight. This absorption efficiency assumption was based on studies that suggest that 

22 the oral bioavailability of Pb in soils or food for mammals is between 3 and 50 percent of its oral 

23 bioavailability when added as soluble Pb acetate to food or water (ATSDR 2005; Ruby et al. 

24 1999). The mammalian SSV developed for the Pb NAAQS review remains somewhat more 

25 conservative than the avian SSV because the proportion of earthworms in the diet of shrews (the 

26 mammalian species with the highest exposure) is likely less than the assumed 100 percent. 

27 Shrews also ingest arthropods, and an arthropod model for Pb uptake from soil predicts lower 

28 concentrations in the arthropods than in earthworms at the same soil concentrations. 

29 

30 The SSVs developed for this assessment are shown in Exhibit 7-1. For plants and soil 

31 invertebrates, the Eco-SSLs were adopted as the SSVs for the Pb NAAQS review. 

32 

33 Exhibit 7-1. Soil Screening Values (SSVs) for Pb 

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 

35 
 Ecological Risk Assessment

ICF International 7-5 DRAFT – December 2006 

1 7.1.3.2 Water Quality Criteria 

2 

3 The AWQC were developed by U.S. EPA to provide guidance to states and authorized tribes to 

4 use in adopting water quality standards and are based on toxicity testing in aquatic organisms, 

5 including fish, invertebrates and algae. AWQC values for chronic exposures are called the 

6 criterion continuous concentration (CCC) and for acute exposures are called the criterion 

7 maximum concentration (CMC), and they are available for freshwater and marine environments. 

8 For a CCC to be exceeded, a 4-day average water concentration must exceed the CCC more than 

9 once every three years (USEPA 1984). 

10 

11 The freshwater AWQC, both the CCC and CMC, for Pb are derived to vary with water hardness, 

12 and when initially developed pertained to total recoverable Pb. During a re-evaluation of 

13 AWQC for metals in 1993, EPA concluded that it would be more accurate to base the AWQC on 

14 that portion of the metal in the water column that is readily bioavailable, that is, the dissolved 

15 fraction (USEPA 1993). As a result, EPA examined the relationship of total to dissolved 

16 concentrations for a series of metals to develop a conversion factor for each metal. For Pb, the 

17 conversion varies with water hardness and is the same for both the CCC and CMC (USEPA 

18 1993). The current equations/values may be reissued based on pH at the conclusion of the 

19 current revisions to the Pb AWQC scheduled to be completed in 2007. The CCC and CMC are 

20 calculated using the following formulas (USEPA 1984): 

21 

22 CCC = exp{1.273 [ln(hardness)] – 4.705} x CF

23 CMC = exp{1.273 [ln(hardness)] – 1.460} x CF

24 

25 where: 

26 

27 CF = conversion factor = 1.46203 -[ln(hardness) x 0.145712] 

28 Hardness = mg/L of calcium carbonate (CaCO3). 

29 

30 For the primary Pb smelter case study, freshwater AWQC were calculated based on water body31 specific measured hardness values for the water bodies located in the vicinity of the facility that 

32 were sampled and analyzed for Pb (ELM 2005). Exhibit 7-2 lists these site-specific AWQC. 
 Ecological Risk Assessment

ICF International 7-6 DRAFT – December 2006 

1 

2 Exhibit 7-2. Calculated Pb AWQC for Water Bodies Analyzed 

3 for the Primary Pb Smelter Case Study 

Sample Cluster or Area Hardnessa 

(mg CaCO3/L) 

Chronic AWQC for 

Pb - CCC (μg/L) 

Acute AWQC for Pb - 

CMC (μg/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 4 Hardness measures were taken from ELM (2005). 

5 CCC = criterion continuous concentration (chronic criterion), U.S. EPA’s National AWQC for Pb 

6 CMC = criterion maximum concentration (acute criterion), U.S. EPA’s National AWQC for Pb 

7 

8 To simplify the national-scale surface water screening assessment, a more conservative 

9 freshwater CCC of 1.2 μg/L dissolved Pb associated with a low water hardness of 50 mg/L 

10 CaCO3 was used as an initial screening step. At sampling locations where that value was 

11 exceeded, the next step was to find co-located measurements of water hardness (expressed as 

12 mg/L of CaCO3) and to use those values to calculate a location-specific CCC and CMC for the 

13 identified sampling location. 

14 

15 Location-specific CCC and CMC values for the national-scale screening assessment were 

16 derived based on water hardness measurements available for those locations. The number of 

17 water hardness measurements at a given sampling station in the NAWQA data set ranged from 

18 zero to more than 60. Where there were two or more measurements of water hardness, the 

19 arithmetic mean of the available measurements across all years was used to represent the water 

20 hardness at the sampling location. Where there were no analyses of water hardness at a given 

21 sampling location, the next closest sampling station (from station ID number and latitude and 

22 longitude) was identified and water hardness associated with this station was used for the 

23 sampling location in question. Where there was a single value reported for water hardness, that 

24 value was used without comparing it to values for any next nearest sampling locations. 

25 

26 Rather than discard any sampling stations from the screening assessment, the use of single or 

27 nearby water hardness measurements to calculate a CCC and CMC for a location was deemed 

28 acceptable. For sampling stations with more than two measurements of water hardness, the 
 Ecological Risk Assessment

ICF International 7-7 DRAFT – December 2006 

1 arithmetic mean of the measurements is the measure most consistent with the limited data at 

2 other stations. 

3 

4 A review of the data on water hardness in the NAWQA data set for 1994 to 2004 indicated that 

5 the initial screening value of 1.2 μg/L was too high to identify all locations for which dissolved 

6 Pb concentrations exceeded the CCC for the protection of aquatic life. Many waters in the 

7 United States are softer than originally thought (i.e., measured CaCO3 concentrations were as 

8 low as 1 mg/L). A second screening was therefore conducted in which dissolved Pb 

9 measurements greater than the quantitation limit (QL) but less than 1.2 μg/L were reviewed. 

10 

11 In the second screen, for each sampling location with one or more measured dissolved Pb 

12 concentrations above the QL but less than 1.2 μg/L, measurements of CaCO3 were used to 

calculate a location-specific CCC as described above.1 13 If none of the dissolved Pb 

14 measurements at a particular location exceeded its location-specific CCC, the location was not 

15 considered further. If one or more of the dissolved Pb measurements at a particular location did 

16 exceed the location-specific CCC, the location was retained for further analysis. The Pb 

17 measurements for the additional locations identified in the second screening are provided in 

18 Appendix H. 

19 

20 7.1.3.3 Sediment Screening Values 

21 

22 A number of sediment ecotoxicity values were considered in identifying chronic sediment 

23 screening values for this assessment. These included several types of criteria for freshwater 

24 sediments derived using different approaches; Exhibit 7-3 lists the candidate sediment screening 

25 values; Appendix M reviews the details of their derivation. The EPA approach for sediment 

26 quality criteria (i.e., equilibrium partitioning sediment benchmarks) (USEPA 2005d) utilizes 

27 EPA’s AWQC with site-specific information on acid volatile sulfides and organic carbon. As 

28 such site-specific information was not available for the case study location at which sediment 

29 was assessed, or for the national-scale screening assessment, sediment quality assessment 

30 guidelines developed by MacDonald et al. (2000; reported in MacDonald et al. 2003) were 

31 identified for use in this ecological risk assessment at all case study locations and in the national32 scale screening assessment. 

33 

34 The MacDonald et al. (2000; 2003) guidelines include two values for Pb: (1) a threshold effect 

35 concentration (TEC) of 35.8 mg/kg and (2) a probable effect concentration (PEC) of 128 mg/kg. 

36 The TEC was derived as the geometric mean of literature values described as being at the lower 

37 end of the effects continuum. The PEC was derived as the geometric mean of literature values 

38 described as more prevalently associated with adverse effects. Appendix M provides more 

39 details on their basis. These values are considered to be applicable to the United States (and 

40 Canada). 

41 

 

1

 For each sampling site, the arithmetic mean of all CaCO3 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 

1 Exhibit 7-3. Pb Sediment Screening Values Evaluated for Use in 

2 This Ecological Risk Assessment 

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

3 Abbreviations: 

4 TNRCC = Texas National Resource Conservation Commission; TEC = Threshold Effect Concentration; 

5 Consensus PEC = Probable effect concentration (sediment concentration above which harmful effects are 

6 likely to be observed, MacDonald et al. [2000]); Consensus TEC = Threshold effect concentration 

7 (sediment concentration below which harmful effects are unlikely to be observed, MacDonald et al. [2000]) 

8 

9 The consensus-based TEC for Pb was calculated as the geometric mean of five literature values, 

10 ranging from a threshold effect level of 35 mg/kg from Smith et al. (1996) to a minimal effect 

11 threshold of 42 mg/kg from Environment Canada (EC) and Ministere de l’Environnement du 

12 Quebec (MENVIQ) (EC and MENVIQ 1992). The consensus-based PEC for Pb was calculated 

13 as the geometric mean of five values, ranging from a probable effect level of 91.3 mg/kg (Smith 

14 et al. 1996) to a severe effect level of 250 mg/kg (Persaud et al. 1993). Moreover, they were 

15 derived via collaboration of many partners in the Freshwater Sediment Quality Assessment 

16 Initiative (MacDonald et al. 2003) which included review of a wide range of options before 

17 selecting an approach. 

18 

19 Note that there are some limitations to using the TEC and PEC values for assessing risk to the 

20 benthic community. A major limitation is that site-specific differences in the bioavailability of 

21 Pb are not taken into account. 

22 

23 7.2 Exposure and Risk Estimates 

24 

25 Chapter 4 presents the methods and data used to develop the media concentrations used as the 

26 measures of exposure in the ecological risk assessment. These media concentrations of Pb are 

27 summarized and the associated risk HQs are presented in the sections that follow for the case 

28 studies: primary Pb smelter (Section 7.2.1), secondary Pb smelter (Section 7.2.2), and near 
 Ecological Risk Assessment

ICF International 7-9 DRAFT – December 2006 

1 roadway non-urban (Section 7.2.3). The national-scale surface water and sediment screening 

2 assessment Pb concentrations and HQs are presented in Section 7.2.4. 

3 

4 7.2.1 Primary Pb Smelter Case Study 

5 

6 Sampling clusters based on likely exposure patterns were created for this case study location by 

7 pooling empirical data collected from various sampling locations within the case study location. 

8 Section 4.1.3 summarizes the total Pb soil concentrations, and Section 4.1.5 discusses the surface 

9 water concentrations and sediment Pb concentrations for the primary Pb smelter case study. 

10 Appendix F presents a detailed description of the aggregates that form each cluster. Soil, surface 

11 water, and sediment average concentrations and HQ results are presented in Exhibits 7-4, 7-5, 

12 and 7-6, respectively. 

13 

Exhibit 7-4. HQs Calculated for Soils for Primary Pb Smelter Case Studya 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 15 HQ values greater than 1.0 are highlighted in bold type. 

16 

17 As shown in Exhibit 7-4, all of the soil sampling clusters had HQs that exceeded 1.0 for birds, 

18 including those “control” locations outside the assumed impact area of the primary Pb smelter. 

19 The west bank of the Joachim Creek samples also had HQs greater than 1.0 for plants and 

20 mammals. The west bank of Joachim Creek, which is inside the zone of influence of the primary 

21 smelter, had HQ values that were substantially greater than those for the control areas. 

22 

23 As discussed in Section 3.1.6, there is wildlife habitat in the vicinity of Joachim Creek, and birds 

24 and mammals often use riparian (stream-side) areas for feeding and for moving between habitats. 

25 
 Ecological Risk Assessment

ICF International 7-10 DRAFT – December 2006 

1 Exhibit 7-5. HQs Calculated for Surface Waters 

2 for Primary Pb Smelter Case Study 

Sample Location 

and Cluster ID 

Dissolved Pb 

Concentration

HQ using CCC 

(Chronic) 

HQ using 

CMC 

(Acute) 

Joachim Creek

Cluster 1 NDa

 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 3 ND = not detected; method detection limit was 3 μg/L; assumed for this assessment 

4 that surface water concentration level was at the detection limit. 

5 

6 Exhibit 7-6. HQs Calculated for Sediments in Surface Waters 

7 for Primary Pb Smelter Case Study 

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 8 HQ values greater than 1.0 are highlighted in bold type. 

9 

10 
 Ecological Risk Assessment

ICF International 7-11 DRAFT – December 2006 

1 The surface water sampling clusters, which assumed a Pb concentration equal to the detection 

2 level, had HQs less than 1.0. However, three sediment sample clusters in Joachim Creek (1, 2, 

3 and 3) had HQs ranging from 1.0 to 2.2, and the U-shaped ephemeral pond and one drainage area 

4 had sediment HQs greater than 3 but less than 5.

5 

6 7.2.2 Secondary Pb Smelter Case Study 

7 

8 For the secondary Pb smelter case study, as described in Section 4.2.3, two sets of modeled 

9 average Pb soil concentrations were developed for the human exposure and health risk 

10 assessments and also used as exposure estimates for the ecological risk assessment. The 

11 averages for 1-, 5-, or 10-km interval distances from the secondary Pb smelter facility are 

12 summarized in Exhibit 7-7. The associated soil HQs calculated for each interval are also 

13 presented in Exhibit 7-7. 

14 

Exhibit 7-7. HQs Calculated for Soils for Secondary Pb Smelter Case Studya 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 16 HQ values greater than 1.0 are highlighted in bold type. 

17 

18 The modeled soil concentrations within 1 km of the facility showed HQs of greater than 1.0 for 

19 avian wildlife. All soil concentrations for locations greater that 1 km from the facility were 

20 associated with HQs less than 1.0 for this data set. The scaled soil concentration data set resulted 

21 in avian HQs greater than 1.0 for all distance intervals evaluated, including the farthest interval 

22 modeled, 10 to 20 km from the facility. The scaled soil concentrations within 1 km of the 

23 facility also showed HQs greater than 1.0 for plants, birds, and mammals. 
 Ecological Risk Assessment

ICF International 7-12 DRAFT – December 2006 

1 As for the previous case study, there is wildlife habitat near the secondary Pb smelter facility. 

2 However, as discussed in Section 3.2.6, none of the species residing in the county where this 

3 facility is located are currently on state or federal threatened and endangered species lists. 

4 

5 7.2.3 Near Roadway Non-Urban Case Study 

6 

7 Average Pb soil concentrations and risk characterization results for the two non-urban case study 

8 locations—Corpus Christi, Texas and Atlee, Virginia—are presented in this section. Exhibit 7-8 

9 presents the average Pb soil concentrations (which are detailed in Section 4.4.1) and HQ 

10 calculated for the Corpus Christi, Texas near roadside soil concentration data. 

11 

12 Exhibit 7-8. Soil HQs Calculated for the Non-Urban Near Roadway Case Study Location 

in Corpus Christi, Texas a 13 

Sample Soil HQs 

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 nab 0.13 

4 2.5 140 1.17 0.082 3.68 1.25 a 14 HQ values greater than 1.0 are highlighted in bold type. 

b 15 na = not applicable. Woodcock are specialized for feeding on earthworms by probing into soils, and may 

16 be able to reach a depth of 6 to 7 cm, which is the approximate length of the bill (Keppie and Whiting 1994). 

17 

18 HQs for birds and mammals were greater than 1.0 at both 2 and 4 meters (m) from the road. 

19 Plants also had HQs ranging from 2.83 to 5.42 at the 2-m distance, depending on depth. 

20 

21 Exhibit 7-9 presents the location-specific Pb soil concentration data and HQs calculated for the 

22 ecological exposure area in Atlee, Virginia, for the four ecological receptor groups. The soil 

23 concentration data for this location are described in Section 4.4.2. The HQs associated with the 

24 overall average Pb concentration for the potential ecological exposure area are also presented. 

25 
 Ecological Risk Assessment

ICF International 7-13 DRAFT – December 2006 

1 Exhibit 7-9. Soil HQs Associated with Non-Urban Near Roadway 

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

b 4 Sampling depths of 0 to 15 inches. 

c 5 Sampled soils in a surface water runoff detention basin. 

6 

7 HQs for birds were greater than 1.0 at all locations except one (Location ID 3) at 30 meters from 

8 the interstate. Plant and mammal HQs were greater than 1.0 at two of the nine locations 

9 (Location IDs 6 and 16) within 2 and 10 meters of the road, respectively, although the HQs 

10 associated with Location ID 16, 10 m from the interstate, were only slightly above 1.0. HQs for 

11 the soil invertebrate receptor group were below 1.0 at all locations. 

12 

13 7.2.4 National-Scale Surface Water and Sediment Screening Assessment 

14 

15 This section presents the Pb exposure measures and risk characterization results of the national16 scale surface water and sediment screening assessment. The water column and sediment Pb 

17 concentration data used to estimate ecological exposures are described in Section 4.5.2 and 4.5.3, 

18 respectively. Section 7.2.4.1 presents the surface water dissolved Pb concentrations and 

19 associated risk HQs, and Section 7.2.4.2 presents the total Pb sediment concentrations and 

20 associated risk HQs. 

21 

22 7.2.4.1 Surface Water Column Portion of the Screening Assessment 

23 

24 The initial screen of dissolved Pb concentrations for measurements equal to or greater than 

25 1.2 μg/L identified 42 sampling locations for which one or more measurements exceeded that 

26 screening value. Data for each measurement of dissolved Pb at these locations are provided in 

27 Appendix H. The number of samples analyzed for dissolved Pb at each station ranged from 1 to 

28 60, and the number of measurements that exceeded 1.2 μg/L ranged from 1 to 50. For purposes 

29 of this assessment, given the limited analyses for dissolved Pb, all 42 sampling locations were 

30 retained for analysis. Eight additional locations were added during a second screen, which 

31 identified sampling locations with one or more measured dissolved Pb concentrations above the 

32 QL, but less than 1.2 μg/L, and that exceed site specific CCC values (see Appendix H). 

33 
 Ecological Risk Assessment

ICF International 7-14 DRAFT – December 2006 

1 Chronic Risks: Exhibit 7-10 presents the chronic HQs for the locations identified in the initial 

2 screen (i.e., those locations not specified as “na” in Appendix H). This exhibit is organized in 

3 order of increasing water hardness, and therefore, increasing values of the site-specific CCCs. 

4 The land use is as reported in the NAWQA database. 

5 

6 For the measures of CaCO3 at each sampling location, the mean (± one standard deviation) 

7 concentration and sample size are presented. For stations for which there were no water 

8 hardness data (ND), an endnote to the exhibit describes how the CCC was estimated. 

9 

10 The dissolved Pb measurements for each location were characterized in four ways: 

11 

12 1. The number of measured dissolved Pb concentrations that exceeded the CCC out of the 

13 total number of samples analyzed for dissolved Pb; 

14 2. The number of samples that were less than the QL where the QL is greater than the CCC 

15 for dissolved Pb (i.e., the number of samples that might or might not exceed the CCC); 

16 3. The mean of the measured Pb concentrations that exceeded the CCC; and 

17 4. The maximum measured dissolved Pb concentration.

18 

19 Two types of HQs are presented in Exhibit 7-10. The first type of HQ represents an average HQ 

20 for the measurements that exceeded the CCC. This HQ answers the question “when the CCC is 

21 exceeded, on average, what is the magnitude of the HQ?” The second HQ represents a 

22 maximum HQ based on the maximum dissolved Pb concentration. This HQ assumes that the 

23 maximum value represents at least a four-day average concentration. For many sampling sites, 

24 both HQs are the same because only a single measurement of dissolved Pb exceeded the CCC. 

25 Exhibit 7-10 illustrates that many of the larger chronic HQ values are associated with mining 

26 sites. 

27 

28 
Ecol

ICF International 7-15 DR

Exhibit 7-10. Results of Chronic Aquatic Risk Screening Assessment –HQs for Measured Dissolved Pb Con

to Chronic AWQC (i.e., Criterion Continuous Concentration or CCC) for the Protection of Aquati

Dissolved Pb Measurements Calcium Carbonate 

Basin 

ID

State Station ID Land Use Mean ± 

SD 

(mg/L) N 

Pb 

CCC 

(μg/L) 

No. 

> CCC 

/ Total 

N

No. < 

QL, 

which 

is > CCC 

Mean of 

[Pb]s 

> CCC 

(μg/L) 

Max 

(μg

44 UCOL CO 393557105512400 Mining 1 (b) 1 0.013 1/1 0 1.26 1.59 COOK AK 600715152572800 Reference 6 1 0.11 1/1 0 1.64 1.46 NROK ID 12392155 Forest 8.8 ± 2.8 18 0.17 4/17 10 0.224 0.258 OAHU HA 16212700 Mixed 9 2 0.17 1/2 1 0.337 0.3

2 CONN CT 1119375 Mixed 9.5 ± 2.9 19 0.18 5/20 13 0.302 0.32 CONN CT 1124000 Mixed 14.7 ± 4.9 30 0.30 11/23 9 0.759 1 

44 UCOL CO 375732107394000 Mining ND (c) 0 0.35 2/2 0 21.6 244 UCOL CO 385437107015600 Mining 17 1 0.35 1/1 0 6.02 6.

2 CONN CT 1127000 Mixed 17.4 ± 8.7 29 0.36 3/22 14 0.408 0.4

46 NROK ID 12419000 Mixed 17.9 ± 1.5 29 0.37 2/26 16 0.995 1.5

1 NECB RI 1112900 Mixed 21 ± 10 13 0.44 3/3 0 1.103 1.5

46 NROK ID 12413470 Mining 25 ± 7.2 34 0.54 33/33 0 4.62 1144 UCOL CO 9046530 Mining 31.6 ± 3.3 62 0.70 53/60 7 3.39 8 44 UCOL CO 392907106013900 Mining 35.3 ± 4.0 3 0.80 2/3 1 1.50 2 46 NROK ID 12413123 Mining 36.3 1 0.82 1/1 0 29.78 2944 UCOL CO 385240106583600 Other/Mixed 39 2 0.89 1/4 2 0.971 0.944 UCOL CO 392947106024500 Mining 41.3 ± 4.0 3 0.95 2/3 1 1.50 2 46 NROK WA 12422500 Urban 43 ± 27 20 0.99 4/28 24 1.131 1.246 NROK WA 12422000 Urban 43.1 ± 25.7 18 0.99 2/20 18 1.158 1.146 NROK ID 12413150 Mining 50.2 1 1.2 1/1 0 10.518 10.44 UCOL CO 9146200 Mining 84.7 ± 28 52 2.1 0/3 0 2 2 31 OZRK AR 7050500 Mixed 102 ± 39 13 2.6 1/8 0 9 9 45 RIOG NM 8331000 Mixed 113 ± 18 37 2.9 1/12 0 3 3 

50- 

51 CCYK WA 461850120005800 Other/Mixed ND (d) 0 3.3 0/1 0 1.2 1

31 OZRK MO 7061155 Mining 136 1 3.5 0/1 0 3 3 31 OZRK MO 7018100 Forest 142 1 3.7 1/2 0 7 7 31 OZRK MO 7061135 Mining 160 1 4.2 1/1 0 11 155 NVBR NV 94196783 Urban 175 ± 57 106 4.6 0/5 0 2.2 3 
Ecol

ICF International 7-16 DR

Exhibit 7-10. Results of Chronic Aquatic Risk Screening Assessment –HQs for Measured Dissolved Pb Con

to Chronic AWQC (i.e., Criterion Continuous Concentration or CCC) for the Protection of Aquati

Dissolved Pb Measurements Calcium Carbonate 

Basin 

ID

State Station ID Land Use Mean ± 

SD 

(mg/L) N 

Pb 

CCC 

(μg/L) 

No. 

> CCC 

/ Total 

N

No. < 

QL, 

which 

is > CCC 

Mean of 

[Pb]s 

> CCC 

(μg/L) 

Max 

(μg

40 SPLT CO 6752260 Mixed ND (e) 0 4.9 0/4 0 4 4 40 SPLT CO 6752270 Mixed ND (e) 0 4.9 0/4 0 2 2 40 SPLT CO 6752280 Mixed ND (e) 0 4.9 0/4 0 3 3 49 CAZB AZ 9517000 Agriculture 190 ± 36 48 5.0 0/1 0 2 2 47 GRSL UT 404026111273001 Rangeland 195 1 5.2 0/5 0 2.34 2.847 GRSL UT 403938111300201 Urban ND (f) 0 5.2 0/2 0 1.432 1.445 RIOG NM 8317200 Urban 196 ± 68 14 5.2 0/3 0 2.33 3 47 GRSL UT 404431111282901 Urban ND (g) 0 5.3 0/2 0 3.754 4.247 GRSL UT 404847111240501 Rangeland ND (h) 0 5.8 0/2 0 5.495 8.4

1 Abbreviations: SD = standard deviation; N = Total number of samples analyzed for specified analyte; [Pb] = dissolved Pb concentration2 (where QL = 0.08 μg/L dissolved Pb, some measured concentrations that were greater than the detection limit, but less than the QL wer3 considered ≥ QL for purposes of this exhibit); No. = Number of samples analyzed for dissolved Pb; Basin ID four-letter codes are descr4 codes = state postal abbreviations; ND = no data 

a 5 Records are presented in order of increasing mean water hardness (mg/L as CaCO3) and hardness-specific CCC. HQ values greater t

6 bold type. 

b 7 This measurement of water hardness seems unreasonably low. c 8 No water hardness data for this sampling station or nearby stations; used CCC determined for nearest station (i.e., 385437107015600)d 9 No water hardness data for this sampling station; used CCC based on a value of 128 mg/L as CaCO3 from nearest station (i.e., 461517e 10 No water hardness data for this sampling station; used CCC based on a value of 187 mg/L as CaCO3 from nearest station (i.e., 675400f 11 No data for this station; used CCC based on water hardness value for the other single value for Silver Creek (station No. 40402611127g 12 No 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 1

13 analysis therefore assumed a water hardness of 200 mg/L to estimate the CCC for this location. 

h 14 No water hardness data for this station; used CCC based on a value of 219 mg/L as CaCO3 from nearest station (i.e., 40475111142350
 Ecological Risk Assessment 

ICF International 7-17 DRAFT –December 2006 

1 Acute Risks: Exhibit 7-11 presents the acute aquatic risk HQs for the locations identified in the 

2 initial screen (i.e., those locations not specified as “na” in Appendix H). This exhibit is 

3 organized in order of increasing water hardness, and therefore, increasing values of the site4 specific CMCs. In this exhibit, dissolved Pb is characterized in two ways: 

5 

6 1. The number of measured dissolved Pb concentrations that exceed the CMC out of the 

7 total number of samples analyzed for dissolved Pb; and 

8 2. The maximum measured dissolved Pb concentration. 

9 

10 Given that the CMCs are between 20 and 25 times higher than the CCCs, the only HQ 

11 considered here is the maximum dissolved Pb concentration compared with the CMC. Exhibit 

12 7-11 shows that only three sampling sites had acute HQ values that exceeded 1.0 (in bold), and 

13 all three of these are located near mining sites. 

14 

Exhibit 7-11. Results of Acute Aquatic Risk Screening Assessment –HQs for 

Measured Dissolved Pb Concentrations Compared to Acute AWQC (i.e., Criterion 

Maximum Concentration or CMC) for the Protection of Aquatic Life for Pba

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 Forest 4.2 0/17 0.261 0.06 

58 OAHU HA 16212700 Mixed 4.4 0/2 0.337 0.08 

2 CONN CT 1119375 Mixed 4.6 0/20 0.377 0.08 

2 CONN CT 1124000 Mixed 7.6 0/23 1 0.13 

44 UCOL CO 375732107394000 Mining 9.0 0/1 6.02 0.67 

44 UCOL CO 385437107015600 Mining 9.0 2/2 23 2.56 

2 CONN CT 1127000 Mixed 9.2 0/22 0.472 0.05 

46 NROK ID 12419000 Mixed 9.5 0/26 1.579 0.17 

1 NECB RI 1112900 Mixed 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 Other/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 Mixed 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 0.10 
 Ecological 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 Compared to Acute AWQC (i.e., Criterion 

Maximum Concentration or CMC) for the Protection of Aquatic Life for Pba

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 Urban 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 

1 Abbreviations: Same as Exhibit 7-10. 

a 2 Records are presented in order of increasing hardness-specific CMC. HQ values greater than 1.0 are 

3 highlighted in bold type. 

4 

5 Aquatic Risks at Mining Sites Compared with other Sites: The locations of interest for the 

6 Pb NAAQS review are those locations for which deposition from air might be a significant input 

7 of Pb into the surface water. It may be assumed that air inputs are not easily distinguishable 

8 from other non-air inputs near mining sites. Therefore, the results for the mining sites (Exhibit 

9 7-12) were separated from the other types of locations (Exhibit 7-13). Locations for which the 

10 HQ was less than 1.0 were eliminated to summarize the results for the aquatic chronic risk 

11 screen. 

12 

13 Exhibit 7-12 summarizes the two chronic and one acute risk HQs for the 10 mining sites for 

14 which the chronic HQ exceeded 1.0. The exhibit is presented in order of increasing HQ. 

15 
 Ecological Risk Assessment 

ICF International 7-19 DRAFT –December 2006 

1 Exhibit 7-12. Results of Aquatic Risk Screening Assessment – Mining Sites at which 

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 

3 Abbreviations: Same as for Exhibits 7-10; CCC = criterion continuous concentration (or chronic AWQC); CMC = 

4 criterion maximum concentration (or acute AWQC). 

a 5 In order of increasing Hazard Quotient for the CCC aquatic toxicity benchmark. HQ values greater than 1.0 are 

6 highlighted in bold type. 

7 

8 Many of the sampling sites near mining land uses are characterized by soft water (i.e., CaCO3

9 less than 50 mg/L and CCC less than 1.2 μg/L). Note that some of the largest HQs are 

10 associated with sites that had one or two samples for dissolved Pb and no or few samples for 

11 CaCO3. Thus, the uncertainty associated with the chronic HQ values for these sites is higher 

12 than for the two locations that have been well characterized (i.e., with 33 and 60 samples 

13 analyzed for dissolved Pb). For the mining sites, it was assumed that drainage from the mines 

14 provides the majority of the Pb input to the surface waters, and these sites were not examined 

15 further. 

16 

17 Exhibit 7-13 summarizes the HQs for the 15 non-mining sites for which the chronic HQs exceed 

18 1.0 in order of increasing HQ. These locations are in areas classified in the NAWQA database as 

19 urban and mixed, but also include forest, rangeland, and a “reference” site in Alaska. The 

20 highest HQ is for the Alaska reference site and is based on one measurement of dissolved Pb and 

21 one measurement of CaCO3, which was extremely low (i.e., 6 mg/L). Thus, the uncertainty 

22 associated with this HQ is high. 

23 
 Ecological Risk Assessment 

ICF International 7-20 DRAFT –December 2006 

1 Exhibit 7-13. Results of Aquatic Risk Screening Assessment - Locations at which Dissolved 

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 

3 Abbreviations: Same as Exhibit 7-11; CCC = criterion continuous concentration (or chronic AWQC); CMC = 

4 criterion maximum concentration (or acute AWQC). 

 a 5 In order of increasing Hazard Quotient for the CCC aquatic toxicity benchmark. HQ values greater than 1.0 are 

6 highlighted in bold type. Additional information characterizing these locations is provided in Appendix H. 

7 

8 Contribution of Air to Pb Concentration at Non-Mining Site Locations: For the sites in 

9 Exhibit 7-13, three additional sources of information were examined in an attempt to determine 

10 whether air deposition is likely to be a significant source of Pb for the surface water. These 

11 sources are as follows: 

12 

13 1. Land Use. More extensive data on the land uses surrounding the sampling sites were 

14 obtained from the 1992 National Land Cover Database for land uses within 20 km of 

15 each sampling site, if available. 

16 2. Air Pb Emissions. The number of facilities that emit Pb to air, and the total annual 

17 quantity of Pb emitted to air, within 20 km and 50 km of the sampling site were 

18 determined from the 2002 National Emissions Inventory (USEPA 2006i). Facility 

19 emissions to the air within 20 km are likely to be much more important than the 

20 emissions between 20 and 50 km away. Data on non-point and mobile emissions of Pb at 

21 the county level were obtained from the same source. 

22 3. NPDES. For each sampling site, the watershed was examined to identify facilities in the 

23 National Pollutant Discharge Elimination System (NPDES) database upstream of the 

24 sampling site for which Pb was a constituent identified for the facility. For each facility 

25 identified, available data were examined on permitted limits for Pb releases and on 

26 measured Pb releases. 

27 

28 The detailed results of these analyses are presented in Appendix H, and the result highlights are 

29 presented in Exhibit 7-14. 
 Ecological Risk Assessment 

ICF International 7-21 DRAFT –December 2006 

1 

2 Exhibit 7-14. Results of National-Scale Surface Water Risk Screening Assessment - 

Locations at which Dissolved Pb Measurements 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 0.0011 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 

4 Abbreviations: Same as Exhibit 7-10; CCC = Criterion Continuous Concentration (or chronic AWQC); CMC = 

5 Criterion Maximum Concentration (or acute AWQC). 

a 6 In order of increasing Hazard Quotient (HQ) for the CCC aquatic toxicity benchmark. HQ values greater than 1.0 

7 are highlighted in bold type. Additional information characterizing these locations is provided in Appendix H.

8 

9 Of the 15 sampling locations in Exhibit 7-14, only three appear to be within 20 km of facilities 

10 emitting relatively large quantities of Pb to the atmosphere (i.e., more than 1 ton per year): one is 

11 in Oahu, Hawaii, one in Jewett City, Connecticut, and one in Manville, Rhode Island (see bold 

12 entries in column “Facilities < 20 km”). Two additional sampling locations appear to be within 

13 50 km of facilities emitting relatively large quantities of Pb, both in Connecticut; however, 

14 whether these facilities are close enough to influence the Pb concentrations in the water column 

15 at these sampling sites is unknown. 

16 

17 Of the three sampling locations within 20 km of facilities emitting more than 1 ton of Pb per 

18 year, the location in Rhode Island might also be receiving a large fraction of its Pb from 

19 upstream discharges from metal ore processing facilities (i.e., there are six such discharges out of 

20 fourteen NPDES permitted facilities upstream of this sampling location). 

21 

22 7.2.4.2 Sediment Portion of the Screening Assessment 

23 

24 The sediment sampling locations used to represent each of the 15 non-mining sites are identified 

25 in Exhibit 7-15. Co-located sediment and surface water data were available for 9 of the 15 sites. 

26 In addition, there were three “near matches,” with a sediment sampling station on the same creek 

27 or river, but somewhat upstream or downstream of the surface water sampling location. 
 Ecological Risk Assessment 

ICF International 7-22 DRAFT –December 2006 

1 

Exhibit 7-15. Results of Sediment 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, 

2 CONN CT Connecticut 

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 

2 Abbreviations: SW = surface water sampling station; Sed = sediment sampling station. 

 a 3 Locations listed in order of increasing Hazard Quotient for the CCC aquatic toxicity benchmark. 

4 

5 Measured Total Pb Sediment Concentrations: Exhibit 7-16 summarizes the total Pb sediment 

6 concentrations measured at 12 of the locations in Exhibit 7-15: nine locations with co-located 

7 sediment and surface water data and the three “near-match” locations in Exhibit 7-15. The 
 Ecological Risk Assessment 

ICF International 7-23 DRAFT –December 2006 

1 NAWQA database appears to have only one measurement of Pb sediment concentrations for 

2 each of these 12 locations. Several of those samples were taken in the early 1990s, earlier than 

3 most of the surface water Pb concentrations that were summarized in the water column risk 

4 screen. Nonetheless, assuming that sediment concentrations change slowly over time, which 

5 may be one reason for the single samples in the NAWQA database, these data are of some value. 

6 For comparison purposes, Exhibit 7-16 also provides the surface water HQ for Pb estimated by 

7 dividing the maximum dissolved Pb concentration by the CCC. For the 12 sites in Exhibit 7-16, 

8 the total Pb concentrations in sediments seemed to be trimodal. Seven total Pb concentrations 

9 were between 23 and 68 mg/kg; two were at 239 and 240 mg/kg, and three were between 1620 

10 and 2900 mg/kg. 

11 

12 Chronic Risks to Benthic Communities and Contribution of Air Pb Emissions to those 

13 Risks: This section presents the HQs associated with measured total Pb concentrations in 

14 sediments at the 12 sites presented in Exhibit 7-16. The HQs were calculated by dividing 

15 potential Pb exposure concentrations by the TEC and PEC benchmarks for benthic communities 

16 (MacDonald et al. 2000; 2003). The possible contributions of air Pb emissions to risks at these 

17 locations are also discussed. 

18 

19 As shown in Exhibit 7-16, 9 of 12 sites had TEC-based HQs that exceeded 1.0; TEC-based HQs 

20 at the remaining three sites were less than 1.0. The three sites with HQs less than 1.0 are 

21 unlikely to pose risks to benthic aquatic communities based on the available data. None of these 

22 three sites were those likely to be affected by air emissions of Pb from point sources (i.e., Pb 

23 emissions were less than 0.07 tons per year at all three sites). 

24 

25 PEC-based HQs at five sites exceeded 1.0, indicating probable adverse effects to ecological 

26 receptors at these sites. Three of these five exceeded a PEC-based HQ of 10, indicating a very 

27 high probability of adverse effects to ecological receptors at these sites, and possibly higher 

28 severity of effects than at the locations with lower HQ values. It is believed that none of these 

29 three sites were likely to be affected, however, by air emissions. One in Idaho was downstream 

30 from several NPDES-permitted discharges of metals to surface waters (station ID 12419000). 

31 The other two sites are located in Utah and Montana, suggesting possible historic sediment 

32 contamination from mining operations.
 Eco

ICF International 7-24 D

1 Exhibit 7-16. Concentrations of Total Pb in Sediments at Locations Near or Co-Located with the 15 Sites 

Pb Concentrations Exceeded the AWQC, Excluding Mining Sites a 2 

Pb Emissions 

(tons/year) 

Basin ID State Station ID Land Use Match Date 

Total [Pb] 

(mg/kg 

dry 

sediment) 

SW 

 HQ b : 

max 

[Pb]/CCC 

Fac < 

20 km 

Fac < 

50 km 

No. 

Upstream 

NPDES 

permits 

for 

metalsb

45 RIOG NM SW + Sed 8331000 Mixed Yes 02/25/93 23 1.03 0.068 0.095 0 

2 CONN CT Sed 1126850 Mixed Near 07/20/93 68 1.13 6.1 7.0 0 

46 NROK WA Sed 12424500 Urban Near 08/05/98 47.3 1.14 0.39 0.43 0 46 NROK ID SW + Sed 12392155 Forest Yes 07/29/98 24.9 1.32 0.0 0.0 1 47 GRSL UT 

SW + Sed 

4048471-

11240501 

Rangeland Yes 07/22/99 2900 1.45 0.0 0.36 1 

31 OZRK MO SW + Sed 7018100 Forest Yes 08/25/92 2300 1.89 0.0 0.34 ND 58 OAHU HA SW + Sed 16212700 Mixed Yes 07/12/00 59 1.98 4.9 4.9 ND 

1 NECB RI SW + Sed 1112900 Mixed Yes 06/30/99 240 2.51 4.1 11.7 6 2 CONN CT Sed 1126850 Mixed Near 07/20/93 68 2.53 0.081 11.3 0 

46 NROK ID SW + Sed 12419000 Mixed Yes 08/03/98 1620 2.69 0.34 0.43 4 31 OZRK AR SW + Sed 7050500 Mixed Yes 11/02/93 28 3.46 0.0 0.01 0 31 COOK AK 

SW + Sed 

6007151-

52572800 

Reference Yes 07/25/00 239 14.91 0.0 0.0 0 

a 3 Exhibited in increasing order of the surface water (SW) column risk hazard quotient (HQ). HQs exceeding 1.0 are highlighted in bold b 4 Data from Exhibit 7-14 for corresponding locations. 

5 Abbreviations:[Pb] = total Pb concentration in sediments (mg/kg dry sediment). CCC = Criterion Continuous Concentration (or chroni6 Basin IDs, see Appendix H. TEC = threshold-effect concentration, and PEC = probable-effect concentration, both from the consensus-7 criteria approach published by MacDonald et al. (2000; 2003).
 Ecological Risk Assessment 

ICF International 7-25 DRAFT – December 2006 

1 Of the three locations in Exhibit 7-16 for which air emissions of Pb from point sources might be 

contributing to ongoing Pb contamination of surface water and sediments (i.e., the 2nd, 7th 2 , and 

8th 3 rows for locations in Connecticut, Hawaii, and Rhode Island, respectively), only one, the 

4 Blackstone River in Manville, Rhode Island, is likely also to receive significant Pb inputs from 

5 upstream NPDES-permitted sites. That site also might have received historic inputs of Pb from 

6 the use of leaded gasoline and subsequent erosion of soils to the river. The other two, the 

7 Quinebaug River in Connecticut (a near match between the Jewett City and Clayville locations) 

8 and the water body at Waikakalaua Street near Wahiawa, Oahu, Hawaii, had no other obvious 

9 inputs of Pb in our assessment other than the point sources of air emissions within 20 km. Both 

10 of those sites, however, are in “mixed” urbanized areas, and therefore could quite possibly also 

11 have historic Pb deposition from leaded gasoline and ongoing inputs of Pb to sediments from 

12 erosion of soils contaminated by leaded gasoline. 

13 

14 7.3 Limitations and Uncertainties 

15 

16 This section summarizes primary limitations and uncertainties for each of the case studies and 

17 the national-scale screening assessment. Section 7.3.1 addresses uncertainties and limitations 

18 associated with the primary Pb smelter case study; Section 7.3.2 describes them for the 

19 secondary Pb smelter facility case study; Section 7.3.3 discusses them for the near roadway non20 urban case studies; and Section 7.3.4 addresses limitations and uncertainties associated with the 

21 national-scale screening assessment for risks to aquatic organisms from Pb deposition from air to 

22 surface waters. 

23 

24 Uncertainties that apply across the case studies and national-scale screening assessment include 

25 but are not limited to the following: 

26 

27 • The case study analyses and national-scale surface water and sediment screening 

28 assessment are limited to specific case study locations and other locations nationwide for 

29 which dissolved Pb data in surface water were available. Efforts were made to ensure 

30 that the exposure estimates were attributable to background Pb concentrations and air 

31 emissions of Pb; however, it is uncertain whether other sources might have actually 

32 contributed to the Pb exposure estimates. 

33 

34 • The selected ecotoxicity screening values for surface waters and sediments might not be 

35 sufficient to identify risks to some threatened or endangered species or unusually 

36 sensitive aquatic ecosystems. 

37 

38 • The database supporting the current AWQC for Pb is over 20 years old. There are data to 

39 indicate that Pb bioconcentrates to some extent in invertebrates (e.g., bioconcentration 

40 factors, or BCFs, of 500 to 1,700), and, to a lesser extent, in fish (e.g., BCFs of 42 to 45 

41 in two species) in freshwater ecosystems. However, in 1984, data were insufficient to 

42 estimate Final Tissue Residue Levels associated with adverse effects in fish, and thus the 

43 BCFs did not influence the CCC value (USEPA 1984). Also, EPA is evaluating whether 

44 pH may also be an indicator of bioavailability in addition to water hardness. 

45 
 Ecological Risk Assessment 

ICF International 7-26 DRAFT – December 2006 

1 • No adjustments were made for sediment-specific characteristics that might affect the 

2 bioavailability of Pb in sediments in the derivation of the sediment quality criteria used 

3 for this ecological risk assessment. Similarly, characteristics of soils for the case study 

4 locations were not evaluated for measures of bioavailability. 

5 

6 • Although the SSV calculated for birds is based on realistic parameter values for average 

diet composition, 90th 7 percentile soil and diet ingestion rates, and a high-end Pb 

8 assimilation efficiency, a recent site-specific determination of a soil concentration 

9 protective of soil-invertebrate-consuming birds suggested that the value of 38 mg/kg dry 

soil is still overly conservative. Use of 90th 10 percentile values for two parameters and the 

11 maximum Pb assimilation efficiency suggested by available data may be overly 

12 conservative. 

13 

14 7.3.1 Primary Pb Smelter Case Study 

15 

16 The ELM Sampling and Analysis Plan was designed to investigate possible ecological risks from 

17 all sources of Pb (and other contaminants) attributable to the primary Pb smelter without a need 

18 to attribute the source of Pb in ecologically sensitive areas (ELM 2003; ELM 2005). For 

19 purposes of the Pb NAAQS review, it is important to distinguish areas impacted primarily by air 

20 deposition of Pb from areas impacted primarily by Pb from other sources (e.g., soil runoff, 

21 groundwater discharge to surface water). 

22 

23 The soil sampling locations within a 2.1-km radius were all in areas that might have been subject 

24 to Pb inputs from Joachim Creek during flooding events. As such, the stations might not 

25 represent the concentrations of Pb in soils that result from direct air emissions from the smelter. 

26 This limitation may overstate the risks from deposition of Pb emitted from the facility. 

27 

28 7.3.2 Secondary Pb Smelter Case Study 

29 

30 The ecological risk assessment used modeled rather than soil concentration measurements to 

31 estimate exposure because measured data were not available. Fate and transport modeling 

32 limitations and uncertainties are described in Section 4.2.3.1. As discussed in this section, the 

33 modeled soil concentrations were compared to measured soil concentrations from areas 

34 surrounding another secondary Pb smelter and were found to be approximately three times lower 

35 than the measured concentrations. Therefore, the modeled results were scaled up three times to 

36 address the potential under-prediction of the soil model when the results were compared to the 

37 surrogate measurements. 

38 

39 7.3.3 Near Roadway Non-Urban Case Study 

40 

41 Few measurements were available to evaluate ecological risk associated with contaminated soils 

42 near roadways in less developed areas where presence of ecological receptors may be 

43 anticipated. It is also uncertain how representative of other roadways these data are. 

44 
 Ecological Risk Assessment 

ICF International 7-27 DRAFT – December 2006 

1 It is unlikely that there is significant avian and mammalian use of habitats within 2 meters of 

2 heavily traveled roads; therefore, HQs above 1.0 at locations at 2 meters might not be associated 

3 with ecological impacts beyond those associated with traffic on the road. 

4 

5 For the Atlee, Virginia location, soil concentrations were measured at sampling locations 

6 between 2 to 30 meters of intensely traveled roads. This assessment did not evaluate avian and 

7 mammalian wildlife habitat use within 30 meters of roadways. Without this evaluation, the 

8 Atlee, Virginia assessment is likely to overestimate the ecological risks of Pb in roadway soils. 

9 

10 The assessment did not address surface water ecosystem impacts of Pb from near roadway runoff 

11 of Pb contaminated soils. This may underestimate risks to aquatic receptors via this exposure 

12 pathway. 

13 

14 7.3.4 National-Scale Surface Water and Sediment Screening Assessment 

15 

16 7.3.4.1 Surface Water Column Screen 

17 

18 The analysis revealed only two or three NAWQA sampling locations nationwide where there 

19 appear to be potential risks to the aquatic community from Pb that might have originated from 

20 atmospheric deposition. However, this is likely to be a large underestimate of the true number of 

21 such sites for several reasons: 

22 

23 • The NAWQA Study Units cover less than 50 percent of the land area of the United 

24 States. 

25 • Dissolved Pb was an analyte at only 16 percent of all NAWQA sampling locations. 

26 • Dissolved Pb was measured only once or twice at many locations. 

27 • For waters with a hardness of less than 47 mg/L as CaCO3, the CCC for dissolved Pb is 

28 less than the quantitation limit for dissolved Pb that was used until the fall of 2000 (i.e., 

29 1 μg/L). 

30 • Fewer than 15 percent of samples analyzed for dissolved Pb between 1994 and 2004 were 

31 assessed with the lower quantitation limit of 0.08 μg/L, which is a value that is 

32 sufficiently low to match the CCC for waters with a hardness as low as 4.7 mg/L CaCO3. 

33 

34 The first two bullet points alone suggest that the number of such sites nationwide might easily be 

35 at least ten times higher than what was represented in the NAWQA database. In addition, where 

36 the land use around a sampling location was classified as “mining,” no investigation was 

37 conducted to determine whether air emissions from a nearby smelter might also be contributing 

38 to the Pb in the water. 

39 

40 There are many sources of uncertainty in the results presented for the sampling locations for 

41 which there were some data, including the following: 

42 

43 • Many sampling locations are represented by only one or two measurements of dissolved 

44 Pb. 

45 • The water hardness for some sampling locations was not measured or is represented by 

46 only one or two measurements. 
 Ecological Risk Assessment 

ICF International 7-28 DRAFT – December 2006 

1 • Where there are multiple measures of both dissolved Pb and water hardness at a given 

2 location, no attempt was made to match sampling dates and times to develop timespecific CCC values.2 3 

4 • The water hardness measured at some locations was less than the lowest value of 

5 20 mg/L of CaCO3 used to develop the equation to calculate a CCC. The CCC equation 

6 is not necessarily valid at values less than 20 mg/L CaCO3. 

7 • It is not known how quickly dissolved Pb concentrations changed at any of the locations. 

8 • The database supporting the current AWQC for Pb is over 20 years old; new AWQC for 

9 Pb should be available in 2007. 

10 

11 7.3.4.2 Sediment Screen 

12 

13 Results of this analysis cannot conclusively link any of the locations with probable adverse 

14 effects of Pb in sediments on benthic communities to ongoing air emissions of Pb. There is no 

15 clear correlation between HQs for the surface water column and HQs for the sediments. Some 

16 locations with the highest Pb concentrations in sediments have no obvious air or NPDES17 permitted discharge sources of Pb. Some locations (e.g., Utah and Montana) suggest historical 

18 drainage of Pb from mining activities. Other locations with high HQs for benthic communities 

19 (e.g., near or in cities) suggest historical deposition of Pb from the use of leaded gasoline. 

20 Erosion of Pb-contaminated soils from near roadways to these surface waters may be a 

21 significant ongoing source of Pb input in such areas. 

22 

23 This analysis was limited to those 15 locations from the NAWQA database at which dissolved 

24 concentrations of Pb in surface waters exceeded the chronic AWQC for Pb. Those 15 locations 

25 are believed to represent a small fraction of surface waters in the United States for reasons 

26 described in Section 7.3.4.1. 

27 

28 An additional limitation is that where the land use around a sampling location was classified as 

29 “mining,” no investigation was conducted to determine whether air emissions from a nearby 

30 smelter might also be contributing to the Pb in sediments. It was assumed that direct runoff and 

31 erosion from the mining sites to the surface waters would have contributed the bulk of the Pb in 

32 sediments. 

33 

34 Further limitations accrue from the sediment sampling data. There were only nine exact matches 

35 and three near matches between the 15 surface water sampling locations of interest and locations 

36 at which sediment samples also were analyzed. Furthermore, there was a single sediment sample 

37 at each of the locations of interest, some of which were taken in the early 1990s. 

38 

39 Finally, no adjustments were made for sediment-specific characteristics that might affect the 

40 bioavailability of Pb in sediments in the derivation of the sediment quality criteria used for this 

41 risk screen. 

 

2

 The coefficient of variation (i.e., the standard deviation divided by the mean – see Appendix H for standard 

deviation and mean values for CaCO3 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 

ICF International DRAFT - December 2006 8-1

8. References 

Agency for Toxic Substances and Disease Registry (ATSDR). (2002) Health Consultation; 

Public Health Implications from Attending or Working at Herculaneum Schools; Herculaneum 

Lead Smelter Site; Herculaneum, Jefferson County, Missouri. June. 

Agency for Toxic Substances and Disease Registry (ATSDR). (2003) Health Consultation; 

Blood Lead Results for 2002 Calendar Year; Herculaneum Lead Smelter Site; Herculaneum, 

Jefferson County, Missouri, EPA facility ID MOD006266373. August. 

Agency for Toxic Substances and Disease Registry (ATSDR). (2005) Draft Toxicological 

Profile for Lead. Atlanta, GA: U.S. Department of Health and Human Services, Pubic Health 

Service. September. 

Alabama Department of Environmental Management (ADEM). (2006) Conversations with 

representative of ADEM regarding available measured data from media in and around the 

secondary Pb smelter in Troy, AL. October 31 and November 1, 2006. 

Alabama Natural Heritage Inventory. (2001) Alabama Inventory List: The Rare, Threatened and 

Endangered Species, Animals and Natural Communities of Alabama. Montgomery, AL: 

Privately printed by the Alabama Natural Heritage Program. 

Alexander, F.W., Clayton, B.E., and Delves, H.T. (1974) Mineral and trace-metal balances in 

children receiving normal and synthetic diets. Q. J. Med. 43: 89-111. 

Alvin, R., Diamond, J.R., Woods, M., Hall, J.A., and Martin, B.H. (2002) The vascular flora for 

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