May 2002

Exposure Analysis for Dioxins,

Dibenzofurans, and CoPlanar

Polychlorinated Biphenyls 

in Sewage Sludge

Technical Background Document

DRAFT

Prepared for

ICF Consulting, Inc.

The Office of Water, U.S. Environmental Protection Agency

401M Street, SW (5307W)

Washington, DC 20460
Exposure Analysis for Dioxins,

Dibenzofurans, and CoPlanar

Polychlorinated Biphenyls 

in Sewage Sludge

Technical Background Document

DRAFT

May 2002

Prepared for

ICF Consulting, Inc.

Office of Water, U.S. Environmental Protection Agency

401 M Street, SW (5307W)

Washington, DC 20460

Prepared by

Center for Environmental Analysis

RTI

Research Triangle Park, NC 27709
May 2002

iii

Table of Contents

Section Page

Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

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

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.2 Summary of the Risk Assessment Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

1.3 Overview of Risk Assessment Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2

1.4 Document Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3

1.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5

2.0 Hazard Identification/Dose-Response Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1 Adverse Effects in Humans and Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1.1 Mechanism of Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2.1.2 Epidemiologic Studies—Cancer Endpoint . . . . . . . . . . . . . . . . . . . . . . 2-3

2.1.3 Animal Studies—Cancer Endpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

2.2 Risk Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

2.3 Dose-Response and Slope Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

2.3.1 Human and Animal Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

2.3.2 Toxicity Equivalency Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10

3.0 Risk Assessment Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 Human Health Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1.1 Application of Biosolids to Agricultural Land . . . . . . . . . . . . . . . . . . . 3-1

3.1.2 Constituents of Concern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.1.3 Site Configuration and Environmental Setting . . . . . . . . . . . . . . . . . . . 3-2

3.1.4 Exposure Point Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3.1.5 Assessing Human Exposures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5

3.1.6 Toxicity Assessment and Risk Characterization . . . . . . . . . . . . . . . . . . 3-6

3.2 Probabilistic Method for Determining Exposure Point Concentrations . . . . . . 3-6

3.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7
May 2002

iv

Table of Contents (continued)

Section Page

4.0 Input Data Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1 Input Data Development Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2 Characterization of Biosolids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.2.1 Concentrations of Dioxin and Furan Congeners . . . . . . . . . . . . . . . . . . 4-2

4.2.2 Agricultural Application of Biosolids . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4.3 Site Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4.3.1 Conceptual Site Layouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

4.3.2 Regional Environmental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-4

4.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21

5.0 Estimating Exposure Point Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1 Source Partition Modeling of Constituent Releases . . . . . . . . . . . . . . . . . . . . . 5-2

5.1.1 Land Application Unit Partitioning Model Used for

Agricultural Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-2

5.2 Fate and Transport Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-11

5.2.1 Dispersion and Deposition Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 5-12

5.2.2 Estimation of Soil and Sediment Concentrations . . . . . . . . . . . . . . . . 5-19

5.3 Calculation of Food Chain Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . 5-25

5.3.1 Terrestrial Food Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-25

5.3.2 Aquatic Food Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-39

5.4 Infant Breast Milk Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-40

5.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-44

6.0 Human Exposure Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1 Receptors and Exposure Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6.1.1 Childhood Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

6.1.2 Infant Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

6.1.3 Exposure Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

6.2 Exposure Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6.2.1 Intake Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

6.2.2 Other Exposure Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-22

6.3 Dose Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-25

6.3.1 Average Daily Dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-26

6.3.2 Lifetime Average Daily Dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-26

6.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27
May 2002

v

Table of Contents (continued)

Section Page

7.0 Human Health Risk Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.1 Human Health Risk Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.1.1 Lifetime Excess Cancer Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.1.2 Total Lifetime Excess Cancer Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2

7.1.3 Risk Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2

7.2 Multipathway Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

8.0 Analysis of Variability and Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

8.1 Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1

8.1.1 Source Characterization and Emissions Modeling . . . . . . . . . . . . . . . . 8-2

8.1.2 Fate and Transport Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-3

8.2 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9

8.2.1 Scenario Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9

8.2.2 Model Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11

8.2.3 Variable Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-14

8.3 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-15

9.0 Screening Ecological Risk Assessment of Dioxins and PCBs in Land-Applied 

Biosolids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1

9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1

9.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2

9.2.1 Assessment Endpoint Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-2

9.2.2 Development of Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4

9.2.3 Analysis Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-15

9.3 Analysis Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-15

9.3.1 Phase 1 – Maximum Potential Risk . . . . . . . . . . . . . . . . . . . . . . . . . 9-16

9.3.2 Phase 2 – Deterministic Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-20

9.4 Results and Risk Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-27

9.4.1 Interpreting Results from the SERA . . . . . . . . . . . . . . . . . . . . . . . . . 9-28

9.4.2 Silvicultural and Reclamation Site Applications . . . . . . . . . . . . . . . . . 9-32

9.4.3 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-33

9.5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-36

Appendix A 2001 National Sewage Sludge Survey – Congener Concentration Data . . . . . . A-1

Appendix B 2001 National Sewage Sludge Survey – Sample Selection Strategy . . . . . . . . . B-1

Appendix C Agricultural Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Appendix D Congener-Specific Parameters for Source Partitioning and Fate and 

Transport Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

Appendix E Site Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1
May 2002

vi

Table of Contents (continued)

Section Page

Appendix F Source Model for Land Application Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-1

Appendix G Air Dispersion and Deposition Modeling Input Files . . . . . . . . . . . . . . . . . . . . G-1

Appendix H Direct and Indirect Exposure Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H-1

Appendix I Variables for Aboveground Fate and Transport . . . . . . . . . . . . . . . . . . . . . . . . I-1

Appendix J Human Exposure Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J-1

Appendix K Sensitivity Analysis Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K-1

Appendix L Screening Ecological Risk Assessment Data . . . . . . . . . . . . . . . . . . . . . . . . . . L-1

Appendix M Climate Region Selections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M-1
May 2002

vii

List of Figures

Number Page

3-1 Agricultural Application Conceptual Site Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

3-2 Map of 41 Climatic Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3

5-1 Biosolids Application to Agricultural Field Source Module . . . . . . . . . . . . . . . . . . . . 5-3

5-2 Emissions Mechanisms in the Local Watershed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-3

5-3 Example Depth-Averaged Soil Concentration Annual Time Series . . . . . . . . . . . . . . 5-10

5-4 Biosolids Application to Agricultural Fields Media Concentration Module . . . . . . . . 5-26

6-1 Human Exposure Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6-2 Distribution of Exposed Fruit Consumption Rates by Age Group . . . . . . . . . . . . . . . . 6-9

6-3 Distribution of Exposed Vegetable Consumption Rates by Age Group . . . . . . . . . . . 6-10

6-4 Distribution of Root Vegetable Consumption Rates by Age Group . . . . . . . . . . . . . . 6-12

6-5 Distribution of Beef Consumption Rates by Age Group . . . . . . . . . . . . . . . . . . . . . . 6-13

6-6 Distribution of Milk Consumption Rates by Age Group . . . . . . . . . . . . . . . . . . . . . . 6-15

6-7 Distribution of Poultry Consumption Rates by Age Group . . . . . . . . . . . . . . . . . . . . 6-17

6-8 Distribution of Egg Consumption Rates by Age Group . . . . . . . . . . . . . . . . . . . . . . . 6-18

6-9 Distribution of Adult Fish Consumption Rates by Age Group . . . . . . . . . . . . . . . . . . 6-20

6-10 Distribution of Breast Milk Consumption Rates by Age Group . . . . . . . . . . . . . . . . . 6-21

6-11 Distribution of Inhalation Rates by Age Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-22

6-12 Distribution of Body Weights by Age Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-23

6-13 Distribution of Exposure Duration for Child and Adult . . . . . . . . . . . . . . . . . . . . . . . 6-24

8-1 Convergence analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-13
May 2002

viii

List of Figures (continued)

Number Page

9-1 Conceptual model for the biosolids SERA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5

9-2 Terrestrial food web, including example receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-9

9-3 interface between terrestrial receptors and aquatic food web, including

 Example receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-10

 
May 2002

ix

List of Tables

Number Page

2-1 Summary of Combined Cohort and Selected Industrial Cohort Studies with

High Exposure Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5

2-2 Toxic Equivalency Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

4-1 Physical Characteristics of Biosolids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

4-2 Median Farm Size for Each Climatic Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

4-3 Relation between Anderson Land Use codes and PCRAMMET Land Use Codes . . . . 4-8

4-4 Daytime Bowen Ratio by Land Use Season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10

4-5 Minimum Monin-Obukhov Length (Stable Conditions) . . . . . . . . . . . . . . . . . . . . . . 4-10

4-6 Albedo Values of Natural Ground Covers for Land Use Types and Seasons . . . . . . . 4-11

4-7 Surface Roughness Length for Land Use Types and Seasons (Meters) . . . . . . . . . . . 4-11

4-8 Summary of Soi8ld Properties Collected for Biosolids Risk Analysis . . . . . . . . . . . . 4-14

4-9 Hydrological Soil Parameters Correlated to Soil Texture . . . . . . . . . . . . . . . . . . . . . 4-16

4-10 Depth to Root Zone Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17

4-11 Field Capacity (FC) and Wilting Point (WP) Values . . . . . . . . . . . . . . . . . . . . . . . . . 4-17

4-12 SCS Curve Number Values by SCS Hydrologic Soil Group . . . . . . . . . . . . . . . . . . . 4-18

4-13 Default Flow Lengths by Slope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-20

5-1 Calculated TCDD Half-Lives for Selected Risk Distribution Percentiles . . . . . . . . . . 5-10

5-2 Soil Half-Life Data Reported in the Draft Dioxin Reassessment Document . . . . . . . . 5-11

5-3 TCDD-TEQ Media Concentration for Ambient Air Variable Concentrations . . . . . . 5-18

5-4 TCDD-TEQ Media Concentration for Soil in Buffer, Cropland, Pasture, and

Sediment Variable Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-22

5-5 Terrestrial Food Chain Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-27

5-6 TCDD-TEQ Media Concentratin for Exposed Fruits and Vegetables

Variable Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-30

5-7 TCDD-TEQ Media Concentration for Belowground Vegetables 

Variable Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-31

5-8 TCDD-TEQ Media Concentrations by Percentile for Beef

Variable Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-35

5-9 TCDD-TEQ Media Concentrations by Percentile for Milk

Variable Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-35

5-10 TCDD-TEQ Media Concentrations for Poultry Thigh Meat 

Variable Concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-38

5-11 TCDD-TEQ Media Concentrations for Eggs Variable Concentrations . . . . . . . . . . . 5-38

6-1 Receptors and Exposure Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6-2 Human Exposure Factor Input Parameters and Data Sources . . . . . . . . . . . . . . . . . . . 6-6

6-3 Soil Ingestion Rates Used in this Risk Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

6-4 Exposed Fruit Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8
May 2002

x

List of Tables (continued)

Number Page

6-5 Exposed Vegetable Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . 6-10

6-6 Root Vegetable Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . 6-11

6-7 Beef Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-13

6-8 Dairy Products (Milk) Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . 6-14

6-9 Poultry Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-16

6-10 Egg Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-18

6-11 Fish Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-19

6-12 Breast Milk Consumption Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-20

6-13 Inhalation Rate Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-21

6-14 Body Weight Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-23

6-15 Exposure Duration Data and Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-24

7-1 Percentile Risk for Soil Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

7-2 Percentile Risk for Exposed Produce Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . 7-4

7-3 Percentile Risk for Belowground Vegetable Ingestion Pathway . . . . . . . . . . . . . . . . . 7-5

7-4 Percentile Risk for Poultry Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

7-5 Percentile Risk for Egg Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

7-6 Percentile Risk for Beef Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7

7-7 Percentile Risk for Milk Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7

7-8 Percentile Risk for Fish Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-8

7-9 Percentile Risk for Air Inhalation Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

7-10 Percentile Risk for Breast Milk Ingestion Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9

7-11 Multipathway Risks and Associated LADD for Adult and Child Farm Family

Members—Baseline All Samples from 2001 NSSS . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10

7-12 Multipathway Risks for Adult and Child Farm Family Members—300 TEQ

Cutoff Limit for Samples from 2001 NSSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-10

7-13 Multipathway Risks for Adult and Child Farm Family Members—100 TEQ

Cutoff Limit for Samples from 2001 NSSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-11

8-1 Results of Sensitivity Analysis by Pathway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-10

9-1 Assessment Endpoints for the Ecological Receptors of Concern . . . . . . . . . . . . . . . . . 9-3

9-2 Dioxin Congeners Assessed in the SERA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5

9-3 Wildlife Receptors for the Biosolids SERA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-12

9-4 Values and Assumptions for the SERA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-17

9-5 Phase 1 Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-17

9-6 Selected Diet Items for Phase 1 Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-19

9-7 Receptors Evaluated in Phase 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-23

9-8 Phase 1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-28

9-9 Screening Results from Phase 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-29
May 2002

xi

Acronyms and Abbreviations

ADD Average daily dose

Ah Aryl hydrocarbon

AhR Ah receptor

AHF Altered hepatocellular foci

AML Arc Macro Language

BAF Bioaccumulation factor

BEF Bioaccumulation equivalence factor

BSAF Biota-sediment accumulation factor

CI Confidence intervals

CSF Cancer slope factor

CV Coefficients of variation

CWA Clean Water Act

DW Dry weight

ED Effective dose

EFH Exposure factors handbook

EGF Epidermal growth factor

EPA U.S. Environmental Protection Agency

ER Estrogen receptor

FC Field capacity

foc Fraction organic carbon

GIRAS Geographic retrieval and analysis system

GIS Geographic information systems

GSCM Generic soil column model

HQ Hazard quotient

HUC Hydrological unit code

I-P Initiation-promotion

IARC International agency for research on cancer

ISCST3 Industrial source complex, short-term model, version 3

IUPAC International Union of Pure and Applied Chemists

LADD Lifetime average daily dose

LOAEL Lowest observed adverse effects level

LOEC Lowest observed effects concentration

MAF Moisture adjustment factor

MATC Maximum allowable toxicant concentration

NOAEL No observed adverse effects level
May 2002

xii

Acronyms and Abbreviations (continued)

NIOSH National Institute of Occupational Safety and Health

NSSS National sewage sludge survey

PCB Polychlorinated biphenyl

PCDD Polychlorinated dibenzo-p-dioxin

PCDF Polychlorinated dibenzofuran

POTW Publicly owned treatment works

QC Quality control

RSD Relative standard deviations

SAMSON Solar and meteorological surface observation network

SCRAM Support center for regulatory air models

SCS Soil conservation service

SERA Screening ecological risk assessment

SMR Standardized mortality ratio

STATSGO State soil geographic

T3 Trophic level 3

T4 Trophic level 4

TCDD Tetrachlorodibenzo-p-dioxin

TEF Toxic equivalency factor

TEQ Toxic equivalency quotient

UAC Unit air concentration

UDPGT Uridine diphosphate-glucuronyltransferases

USDA U.S. Department of Agricultural

USGS U.S. Geological Survey

USLE Universal soil loss equation

WHO World health organization

WP Wilting point

WW Wet weight

WWTP Wastewater treatment plant
Section 1.0 May 2002

1-1

1.0 Introduction

1.1 Background

In February 1993, the U.S. Environmental Protection Agency (EPA) published the

Standards for the Use or Disposal of Sewage Sludge (40 CFR Part 503). This regulation lists

management practices and pollutant limits that protect public health and the environment from

the reasonably anticipated adverse effects of pollutants in municipal biosolids (formerly referred

to as “sewage sludge”) when the biosolids are land-applied, placed on a surface disposal site, or

fired in a biosolids incinerator. The Part 503 rule published in February 1993 is known as the

Round One Biosolids Regulation. Section 405 of the Clean Water Act (CWA) requires EPA to

publish a Round Two Biosolids Regulation, which will contain limits for pollutants not regulated

in Round One.

Pollutants considered but not regulated under Round One were again considered under

Round Two for potential regulation. Subsequently, EPA conducted preliminary exposure

analyses in a Comprehensive Hazard Identification exercise to determine which of the 31

pollutants should be on the final pollutant list for potential regulation under Round Two (U.S.

EPA, 1996). Based on the results of those analyses, three groups of pollutants were placed on the

pollutant list for Round Two: polychlorinated dibenzo-p-dioxins (PCDDs, or dioxins),

polychlorinated dibenzofurans (PCDFs, or furans), and coplanar polychlorinated biphenyls

(PCBs).

This document describes the risk assessment conducted to determine the concentrations

of dioxins, furans, and PCBs that can be present in biosolids and remain “protective” (below a

specified level of risk) of human health. This risk-based concentration limit was generated by

evaluating cancer risks for individuals (receptors) who may be exposed to these constituents if

biosolids are applied to agricultural fields. The goal of this risk assessment was to estimate a

national distribution of the incremental increase in individual lifetime risk of developing cancer

due to exposure to dioxins, furans, and PCBs potentially present in the biosolids for farm

families who apply biosolids as fertilizer or soil conditioner.

1.2 Summary of the Risk Assessment Process

For risk assessments, human health risks are generally assessed using a four-step process,

as outlined in NRC (1994): 

1. Hazard Identification. Identify the hazard posed by a pollutant by determining

whether a pollutant may cause health hazards, quantifying environmental

concentrations of the pollutant, describing the toxicity that may be caused by the
Section 1.0 May 2002

1-2

pollutant, and evaluating the conditions under which toxicity might be expressed

in humans. Sources for this information include environmental monitoring data,

as well as epidemiologic and animal studies.

2. Dose-Response Assessment. Establish the relationship between pollutant doses

and the health effects in humans through data analysis (most often data from

animal studies and occasionally from human studies) and modeling. 

Mathematical models may help determine the quantitative relationship between

the dose of the pollutant and toxic responses; in particular, the potencies of

suspected carcinogens have frequently been evaluated using such models. 

3. Exposure Assessment. Use available data on constituent concentrations in

materials of concern to estimate concentrations of constituents in environmental

media and human contact with those media. Exposure assessments should

consider fate and transport of material in the environment, routes of exposure, and

pharmacokinetics of material once in the body. Data limitations on the

environmental concentrations of interest often require the use of environmental

modeling to provide relevant estimates of exposure, as they did in this risk

assessment.

4. Risk Characterization. Integrate information from Steps 1, 2, and 3 to estimate

the likelihood that any of the hazards associated with the pollutant will be

manifested in exposed persons. In addition, EPA emphasizes the importance of

clearly describing uncertainties in the risk assessment when characterizing risks.

The Draft Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-pDioxin (TCDD) and Related Compounds (U.S. EPA, 2000) describes Steps 1 and 2 of the

process for this risk assessment. This document is referred to here as the Draft Dioxin

Reassessment Document. The current document focuses on the last two steps of the

process—exposure assessment and risk characterization. 

1.3 Overview of Risk Assessment Methodology

The purpose of this analysis was to estimate the total concentrations of dioxins, furans,

and PCBs that can be present in biosolids and remain protective of human health when biosolids

are applied to agricultural land. The two final steps of the risk assessment process—exposure

assessment and risk characterization—were conducted to arrive at the estimates.

Steps in the exposure assessment included

 Characterizing the management practices associated with the agricultural uses of

biosolids, 

 Describing the environmental settings where agricultural uses of biosolids may

occur,
Section 1.0 May 2002

1-3

 Identifying scenarios under which contaminants in biosolids may be transported

through the environment and/or the food chain to a human receptor, and

  Quantifying an individual’s exposure to the contaminants resulting from the

agricultural use of biosolids in the environment. 

Steps in the risk characterization phase included

 Describing the individual’s predicted risk from exposure to concentrations of

constituents in environmental media, and

 Determining the risk-based concentrations for dioxins in biosolids that are

protective of individual health when biosolids are applied to agricultural land.

EPA estimated protective constituent concentrations using a probabilistic analysis. A

probabilistic risk analysis produces a distribution of risks for each receptor by allowing some of

the parameters in the analysis to have more than one value. This type of analysis was ideal for

this risk assessment because biosolids are generated nationwide and, therefore, may be used on

agricultural fields anywhere in the United States. The probabilistic analysis not only captures the

nationwide variability in biosolid application practices, it also captures the differences in the

environmental settings (e.g., soils, meteorology) in which biosolids may be land-applied. 

1.4 Document Organization

This document is organized into the following sections:

 Section 2, Hazard Identification/Dose-Response Assessment, summarizes the

toxicological data supporting the health benchmark used in this analysis and the

toxic equivalency factors (TEFs) used for the congeners evaluated in this risk

assessment. These data are based on the Draft Dioxin Reassessment Document

(U.S. EPA, 2000).

 Section 3, Risk Assessment Overview, describes the conceptual framework for the

biosolids risk assessment. This section presents the conceptual framework for the

human health risk assessment, including a description of biosolids and biosolids

management practices, fate and transport modeling, exposure assessment, and

calculation of protective biosolids concentrations, as well as a detailed

explanation of the framework for the probabilistic analysis.

 Section 4, Input Data Characterization, presents the methodologies used to

characterize the environmental setting, including delineation of the site layout and

environmental setting (e.g., meteorology, climate, and soils). It also describes

how the agricultural fields were characterized.

 Section 5, Estimating Exposure Point Concentrations, describes the models and

methods used for source partition modeling, air dispersion and deposition
Section 1.0 May 2002

1-4

modeling, watershed and waterbody modeling, terrestrial food chain modeling,

and aquatic food chain modeling.

 Section 6, Human Exposure Assessment, presents an overview of the human

receptors, selected exposure pathways, and exposure scenarios considered for this

assessment. It also presents exposure factors (i.e., values needed to calculate

human exposure) used in the analysis and methods used to estimate dose,

including lifetime average daily dose (LADD). 

 Section 7, Human Health Risk Results, presents the methods used to characterize

the risk posed to an individual. It describes the calculation methods used to

generate risk-based constituent concentrations that are protective of human health.

 Section 8, Analysis of Variability and Uncertainty, discusses the methods that

were used to account for variability and uncertainty in the risk assessment. 

 Section 9, Screening Ecological Risk Assessment of Dioxins in Land-Applied

Biosolids, describes the screening ecological risk assessment that was performed

to investigate the potential for adverse ecological effects from dioxins in landapplied biosolids.

The following appendices provide supplemental technical information and supporting data:

 Appendix A, 2001 National Sewage Sludge Survey—Congener Concentration

Data

 Appendix B, 2001 National Sewage Sludge Survey—Sample Selection Strategy

 Appendix C, Agricultural Parameters

 Appendix D, Congener-Specific Parameters for Source Partitioning and Fate and

Transport Models

 Appendix E, Site Data

 Appendix F, Source Model for Land Application Units

 Appendix G, Air Dispersion and Deposition Modeling Input Files

 Appendix H, Direct and Indirect Exposure Equations

 Appendix I, Variables for Aboveground Fate and Transport

 Appendix J, Human Exposure Factors

 Appendix K, Sensitivity Analysis
Section 1.0 May 2002

1-5

 Appendix L, Ecological Assessment

 Appendix M, Climate Region Selections

1.5 References

NRC. 1994. Science and Judgement in Risk Assessment. National Research Council,

Committee on Risk Assessment of Hazardous Air Pollutants. Washington, DC: National

Academy Press.

U.S. EPA (Environmental Protection Agency). 1996. Technical Support Document for the

Round Two Sewage Sludge Pollutants. EPA-822-R-96-003. Washington, DC: U.S.

Government Printing Office. 

U.S. EPA (Environmental Protection Agency). 2000. Exposure and Human Health

Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds. 

EPA/600/P-00/001Bg. Washington, DC: National Center for Environmental

Assessment, Office of Research and Development. September. 
x
Section 2.0 May 2002

1 This section summarizes and draws heavily from the material presented in Part II: Health Assessment for

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and Related Compounds (U.S. EPA, 2000).

2-1

2.0 Hazard Identification/Dose-Response

Assessment1

The constituents evaluated in this risk assessment are dioxins, furans, and PCBs

contained in biosolids managed as a beneficial use on agricultural fields. All of these

constituents were evaluated in the Draft Dioxin Reassessment Document (U.S. EPA, 2000),

which concluded that “based on all available information, dioxins are potent animal toxicants

with potential to produce a broad spectrum of adverse effects in humans.” This risk assessment

focuses on the potential of these biosolid constituents to act as human carcinogens. EPA

characterizes 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) as a Human Carcinogen based on

weight of evidence and characterizes other dioxins, furans, and PCBs as Likely Human

Carcinogens. The toxicity of all of the dioxin, furan, and PCB congeners considered in this

analysis is based on the toxicity of the most highly characterized congener, 2,3,7,8-TCDD

(U.S. EPA, 2000).

The cancer slope factor (CSF) for 2,3,7,8-TCDD used by EPA in this risk assessment is

1.56×105

 (mg/kg-d)-1 (U.S. EPA, 1997). The CSF is defined as the upper bound on the slope of

the dose-response curve in the low-dose region and is generally assumed to be linear. It is

expressed as a lifetime excess cancer risk per unit exposure. The same slope factor is used to

estimate cancer risks for both child and adult resident receptors. However, significant

uncertainties exist concerning the estimation of lifetime cancer risks in children. This facter

differs from the more recent CSF for TCDD proposed in the Draft Dioxin Reassessment

Document (U.S. EPA, 2000). At the time this risk assessment was conducted, the decision was

made to use the older value until a concensus was reached on a new value.

2.1 Adverse Effects in Humans and Animals

2,3,7,8-TCDD and related compounds have been reported to produce a wide variety of

adverse effects in humans and animals, including cancer, reproductive and developmental effects,

immunotoxicity, chloracne, diabetes, and several other less common health effects. This

assessment will evaluate risk based only on the cancer endpoint because this is the only endpoint

for which there are sufficient data to adequately support the assessment for all the dioxin-like

congeners (U.S. EPA, 2000).
Section 2.0 May 2002

2-2

2.1.1 Mechanism of Action

The mechanisms of toxicity for dioxins are not completely understood but have been

studied extensively, particularly for 2,3,7,8-TCDD. Many dioxins, furans, coplanar PCBs, and

other structurally related halogenated aromatic hydrocarbons are believed to share a common

mechanism of action related to similarities in their structures. The extraordinary potency of

2,3,7,8-TCDD in evoking a dose-related induction response, and the tissue specificity of enzyme

induction led Poland and Glover (U.S. EPA, 2000, citing Poland and Glover, 1973) to postulate

the existence of an induction receptor. This receptor, the Ah receptor (Ah for aromatic

hydrocarbon), was identified in the cytosol of mouse liver cells (U.S. EPA, 2000, citing Poland et

al., 1976) and in hepatic and extrahepatic tissues of a variety of laboratory animals, mammalian

cell cultures, human organs and cell cultures, and tissues of nonmammalian species (U.S. EPA,

2000, citing Okey et al., 1994). 2,3,7,8-TCDD and structurally related compounds induce a wide

range of biological responses, including alterations in metabolic pathways, body weight loss,

thymic atrophy, impaired immune responses, hepatotoxicity, chloracne and related skin lesions,

developmental and reproductive effects, and neoplasia. These responses are thought to be

initiated by the binding of individual congeners (or ligands) with the aryl hydrocarbon (Ah)

receptor. Of the many adverse responses observed both in humans and experimental animals

after exposure to 2,3,7,8-TCDD, the ones that appear at the lowest dose (more sensitive) are

developmental and reproductive effects, alterations in the immune response, and neoplasia. 

Much evidence indicates that 2,3,7,8-TCDD acts via the intracellular protein, AhR, that

functions in partnership with a second protein (known as the Ah receptor nuclear translocator,

Arnt) to alter gene expression. In addition, receptor binding may result in release of cytoplasmic

proteins, which alter the activity of cell regulatory proteins. Comparative data from animal and

human cells and tissues suggest a strong similarity in response to dioxin-like chemicals across

species. Biochemical and biological responses to dioxin exposure are sometimes considered

adaptive, or reflective, of exposure but are within normal homeostatic limits and thus may not be

considered adverse. However, many of these biochemical changes are potentially on a continuum

of dose-response relationships, which leads to adverse responses. Given the possible mechanism

of action, there are constraints on the possible models that account for dioxin’s biological effects

and on the assumptions used during the risk assessment. The linear relationship expected

between ligand concentration and receptor binding may or may not be reflective of dose-response

relationships for downstream events that require complex interactions. Biochemical and genetic

analyses of these mechanisms suggest a novel regulatory system whereby a chemical signal can

alter cellular regulatory processes.

The ability of 2,3,7,8-TCDD and other dioxin-like compounds to modulate a number of

biochemical parameters is well recognized. Despite the ever-expanding list of these responses

over the past 20 years and the work on the molecular mechanisms mediating some of these, there

is still a considerable gap between our knowledge of the biochemical changes and the degree to

which they are related to the more complex biological and toxicological endpoints.

TCDD-elicited activation of the Ah receptor has been clearly shown to mediate altered

transcription oncogenes (cancer genes) and genes encoding growth factors, receptors, hormones,

and drug-metabolizing enzymes. Based on the cumulative evidence available, it is presumed that
Section 2.0 May 2002

2-3

all of these processes are mediated by the binding of 2,3,7,8-TCDD to the AhR. The dioxin

induces certain drug-metabolizing enzymes such as CYP1A1, CYP1A2, and CYP1B1 in

different animal species including humans at body burdens as low as 1 to 10 ng TCDD/kg. 

These and other enzymes are responsible for the metabolism of a variety of exogenous and

endogenous compounds. Several lines of experimental evidence suggest that these enzymes may

be responsible for either enhancing or protecting against the toxic effects of a variety of agents,

including known carcinogens, as well as endogenous substrates such as hormones. These effects

are dependent upon the compounds and the experimental system examined. Several reports

(U.S. EPA, 2000, citing Kadlubar et al., 1992; Esteller et al., 1997; Ambrosone et al., 1995;

Kawajiri et al., 1993) provide evidence that higher levels of enzyme activity are associated with

increased susceptibility to colorectal, endometrial, breast, and lung tumors. Changes in these

enzymes by dioxin may play a role in chemical carcinogenesis. However, the exact relationship

between the induction of these enzymes and any toxic endpoint observed following dioxin

exposure has not been clearly established. Animal evidence supports the understanding that AhR

plays a key role in tumor production. 

The role of the epidermal growth factor (EGF) receptor in 2,3,7,8-TCDD-induced

carcinogenicity has also been examined. EGF is a mitogen that stimulates the generation of

mitotic signals in both normal and neoplastic cells, and its receptor and ligands have a variety of

functions involved in cell transformation and tumorigenesis. It has been shown that

2,3,7,8-TCDD decreases the binding capacity of the plasma membrane EGF receptor for its

ligand without changing the affinity constant (U.S. EPA, 2000, citing Abbott and Birnbaum,

1990; Hudson et al., 1985; Lin et al., 1991; Madhukar et al., 1984). The effects of 2,3,7,8-TCDD

on the EGF receptor have been shown to require the Ah receptor (U.S. EPA, 2000, citing Lin et

al., 1991). 

The possible role of uridine diphosphate-glucuronyltransferases (UDPGT) on the

carcinogenicity of 2,3,7,8-TCDD has also been studied. UDPGTs are thought to be a

deactivation pathway for many environmental chemicals by increasing their water solubility,

thereby facilitating excretion. 2,3,7,8-TCDD induces synthesis of at least one UDPGT isozyme

(U.S. EPA, 2000, citing Lucier et al., 1986) by an Ah receptor-mediated mechanism (U.S. EPA,

2000, citing Bock, 1991). The results of Kohn et al. (1996) (U.S. EPA, 2000, citing Kohn et al.,

1996) provide further support to the hypothesis that induction of UDPGT is an early event in the

generation of thyroid tumors by 2,3,7,8-TCDD in the rat.

There is evidence that some carcinogenic responses to 2,3,7,8-TCDD are related to

effects of 2,3,7,8-TCDD on the estrogen receptor (ER) and on estrogen metabolism. The

responses appear to be tissue-specific. In rats, 2,3,7,8-TCDD increases liver tumor incidence, but

decreases tumor incidence in mammary glands, the uterus, and the pituitary gland (U.S. EPA,

2000, citing Kociba et al., 1978a). 

2.1.2 Epidemiologic Studies—Cancer Endpoint

Numerous studies have provided support for an association between exposure to dioxin

and dioxin-like compounds and several types of cancer. Since the last formal EPA review of the

human database relating to the carcinogenicity of TCDD and related compounds in 1988, a
Section 2.0 May 2002

2-4

number of new follow-up mortality studies have been completed. Among the most important of

these are 

 Studies of 5,172 U.S. chemical manufacturing workers by Fingerhut et al.

(U.S. EPA, 2000, citing Fingerhut et al., 1991a) and Steenland et al. (U.S. EPA,

2000, citing Steenland et al., 1999) from the National Institute of Occupational

Safety and Health (NIOSH) and an independent study by Aylward et al.

(U.S. EPA, 2000, citing Aylward et al., 1996) 

 A study of 2,479 German workers involved in the production of phenoxy

herbicides and chlorophenols by Becher et al. (U.S. EPA, 2000, citing Becher et

al., 1996, 1998) and by others in separate publications (U.S. EPA, 2000, citing

Manz et al., 1991; Nagel et al., 1994; Flesch-Janys et al., 1995, 1998)

 A study of more than 2,000 Dutch workers in two plants involved in the synthesis

and formulation of phenoxy herbicides and chlorophenols (U.S. EPA, 2000, citing

Bueno de Mesquita et al., 1993) and subsequent follow-up and expansion by

Hooiveld et al. (U.S. EPA, 2000, citing Hooiveld et al., 1998) 

 A smaller study of 247 workers involved in a chemical accident cleanup by Zober

et al. (U.S. EPA, 2000, citing Zober et al., 1990) and subsequent follow-up

(U.S. EPA, 2000, citing Ott and Zober, 1996b) 

 An international study of more than 18,000 workers exposed to phenoxy

herbicides and chlorophenols by Saracci et al. (U.S. EPA, 2000, citing Saracci et

al., 1991), with subsequent follow-up and expansion by Kogevinas et al.

(U.S. EPA, 2000, citing Kogevinas et al., 1997). 

Although uncertainty remains in interpreting these studies, because not all potential

confounders have been ruled out, all indicate a potential association between exposure to dioxin

and related compounds and increased cancer mortality. One of the strengths of these studies is

that each has some exposure information that permits an assessment of dose response (U.S. EPA,

2000).

Results from several epidemiologic studies are summarized in Table 2-1. Observed

numbers of cases, standardized mortality ratios (SMRs), and 95 percent confidence intervals (CI)

are given for all cancers and for lung cancer, specifically. Although uncertainty remains

concerning potential confounders in the studies, there is a strong inference regarding the

carcinogenic potential of these constituents and the increased cancer mortality. Some of these

studies have been judged adequate for use for fitting the dose-response models in the dioxin

reassessment (U.S. EPA, 2000). In studies reviewed for the International Agency for Research

on Cancer (IARC) monograph (U.S. EPA, 2000, citing IARC, 1997), the working group focused

on the most exposed subcohorts with adequate latency and found that the most exposed groups

had the highest incidence for all cancers combined and for lung cancer mortality. Although the

increase was generally low (20 to 50 percent), it was highest in subcohorts with presumed
Section 2.0 May 2002

2-5

Table 2-1. Summary of Combined Cohort and Selected Industrial Cohort Studies

with High Exposure Levelsa

Referencea

All Cancers Lung Cancer

Obs. SMR 95% CI Obs. SMR 95% CI

International cohort

Kogevinas et al. (1997) b 394 1.2 1.1–1.3 127 1.2 1.0–1.4

Industrial populations (high-exposure subcohorts)

Fingerhut et al. (1991a) c

 (USA) 114 1.5 1.2–1.8 40 1.4 1.0–1.9

Becher et al. (1996) d

 (Germany) 105 [1.3] [1.0–1.5] 33 [1.4] [1.0–2.0]

Hooiveld et al. (1998) e

 (Netherlands) 51 1.5 1.1–1.9 14 1 0.5–1.7

Ott and Zober (1996b) f

(BASF accident)

18 1.9 1.1–3.0 7 2.4 1.0–5.0

Total g 288 [1.4] [1.2–1.6] [94] [1.4] [1.1–1.7]

p value <0.001 <0.01

CI = Confidence intervals.

Obs = Observed number of cases.

SMR = Standardized mortality ratios.

Adapted from IARC; Table 38 (U.S. EPA, 2000, citing IARC; Table 38, 1997); non-Hodgkin’s

lymphoma, soft tissue sarcoma, and gastrointestinal results not shown.

a All references are as cited in U.S. EPA (2000).

b U.S. EPA, 2000 citing Kogevinas et al. (1997): men and women >20 years since first exposure.

These data include the cohorts of Fingerhut et al. (1991a,b), Becher et al. (1996), Hooiveld et al.,

(1998), the original IARC cohort (Saracci et al., 1991), and other cohorts. c Fingerhut et al. (1991a): men 20 years latency and 1 year exposure. d Becher et al. (1996): men, Cohort I and II, summed (Boehringer-Ingelheim, Bayer-Uerdingen

cohorts).

e Hooiveld et al. (1998): men and women, Factory A.

f Ott and Zober (1996b): men, chloracne subgroup, 20 years latency. Data presented for lung

cancer are all respiratory tract cancers combined.

g Totals in square brackets are those calculated by the IARC Working Group.
Section 2.0 May 2002

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heaviest exposure. This outcome is unlikely due to chance, and the increase in lung cancer is not

explained by confounding exposure due to smoking. Positive dose-response trends in the

German studies and increased risk in the longer-duration U.S. subcohort and the most heavily

exposed Dutch workers support this view. These results are further substantiated by the

increased mortality found in the Japanese rice oil poisoning accident where high levels of

exposure to furans and PCBs were observed and were associated with increased incidence of

lung and liver cancers. Although increases in cancer incidence at other sites (e.g., non-Hodgkin’s

lymphoma, soft tissue sarcoma, gastrointestinal cancer) have been reported, the data to associate

them with exposure to dioxin-like chemicals are less compelling because of the limited numbers

of observed tumors at any specific site (U.S. EPA, 2000).

2,3,7,8-TCDD and, by inference from more limited data, other dioxin-like compounds are

potentially Multisite Carcinogens in the more highly exposed human populations that have been

studied, primarily in adult males. 2,3,7,8-TCDD cancer experience for women may differ from

that for men. Animal and mechanistic studies suggest different responses in males and females,

but there are no data to adequately support this. Although the epidemiologic data are not

sufficient by themselves to infer a causal association between exposure to TCDD and other

dioxin-like chemicals and increased cancer in humans (U.S. EPA, 2000, citing IARC, 1997, and

ATSDR, 1998) and although uncertainty remains, the epidemiologic data are generally consistent

with results from studies of multiple laboratory animal species where dioxin-like compounds

have clearly been identified as Multisite Carcinogens and Tumor Promoters. In addition, the

findings of increased cancer incidence at multiple sites in occupationally exposed workers appear

to be plausible given what is known about mechanisms of dioxin action. The epidemiological

data, however, are insufficient to establish the shape of the dose-response curve below the range

of observation in these occupationally exposed populations.

2.1.3 Animal Studies—Cancer Endpoint

Many animal studies have shown that 2,3,7,8-TCDD is a carcinogen based on long-term

bioassays conducted in numerous species, including both sexes of rats and mice. According to

the Draft Dioxin Reassessment Document, “TCDD is a nongenotoxic carcinogen because it is

negative in most assays for DNA damage; however, it is a potent “promoter” and a weak initiator

or noninitiator in two-stage initiation-promotion (I-P) models for liver and skin” (U.S. EPA,

2000). Multiple I-P studies show that induction of altered hepatocellular foci (AHF) is dosedependent, exposure duration-dependent, and partially reversible. AHF induction is associated

with liver cancer in rodents. 

In addition to liver effects, TCDD is a potent cancer promoter in mouse skin (source of

the CSF used). It is also characterized as a Multisite Carcinogen because it increases the

incidence of tumors at sites distant from treatment sites. This association is substantiated by the

fact that all long-term cancer bioassays have been positive in both sexes of both rats and mice.

2.2 Risk Characterization

Characterization of dioxin risks is based on an extensive amount of data. 

Characterization of the health hazard, modes of action, dose-response, and exposure all
Section 2.0 May 2002

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contribute to the dioxin risk. Subpopulations and developmental stages are included in this

characterization.

EPA drew several solid conclusions regarding carcinogenicity based on its analysis (U.S.

EPA, 2000):

 “Dioxin and related compounds can produce a wide variety of effects in animals

and might produce many of the same effects in humans”

 “Dioxin and related compounds are structurally related and elicit their effects

through a common mode of action”

 “EPA and the international scientific community have adopted toxic equivalency

of dioxin and related compounds as prudent science policy”

 “Complex mixtures of dioxin and related compounds are highly potent, likely

carcinogens.”

Adequate evidence supports the belief that humans are likely to respond to exposure to

dioxin with a broad spectrum of effects. These effects appear to begin with biochemical changes

at or near background levels of exposure (concentrations measured in the ambient environment),

increasing in severity as body burdens increase. Enzyme induction, changes in hormone levels,

and altered cellular function may represent effects of unknown significance at the lowest

exposure levels. Adverse effects, including cancer, may not be detectable until exposure reaches

10 to 100 times background levels. Humans most likely fall into the middle of the range of

sensitivity among mammals, neither extremely sensitive nor extremely insensitive to the effects

of dioxin.

Currently, there have been few cohorts with dioxin exposure high enough to raise body

burdens significantly over background levels. In those studies, few clinically significant

noncancer effects were detected.

Most, if not all, observed effects of dioxin can be described in a series of common

biological steps. The initial step and the single largest determinant of toxicity, including tumor

development, is binding of dioxin and related compounds to the AhR. Dioxin and dioxin-related

compounds exist as complex mixtures in nature, and the biological activity of the mixture can be

estimated using relative potency values, coupled with an assumption of dose additivity. This

exposure has evolved to the use of Toxic Equivalency Quotients (TEQs) in risk assessment. 

With this approach, cumulative exposures of AhR-mediated chemicals can be translated with

increasing confidence to human responses.

A weight-of-evidence evaluation concluded that mixtures of dioxin and related

compounds are Strong Cancer Promoters and likely pose a cancer hazard to humans. The data

for complex mixtures of dioxins, furans, and coplanar PCBs constitute “strong evidence” of

carcinogenicity (U.S. EPA, 2000) and include epidemiological cancer observations and

unequivocal positive responses in both sexes, multiple species, multiple sites, and different
Section 2.0 May 2002

2-8

routes. Laboratory evidence supports the epidemiological results, suggesting dioxin exposure

contributes to carcinogenic response, but it is insufficient to confirm a causal relationship. 

Human studies alone cannot demonstrate this causal relationship.

2.3 Dose-Response and Slope Factors

Current knowledge of the mechanisms of action of dioxin, receptor theory, and the

available dose-response data are insufficient to establish a nonlinear procedure for estimating

cancer potency. Both cancer and noncancer effects appear to result from qualitatively similar

modes of action; thus, the potential for either type of effect is considered equal. A common

metric for comparison is the effective dose (ED). In the observable range of 1 percent excess

response, quantitative differences between cancer and noncancer EDs are relatively small.

2.3.1 Human and Animal Studies

Dioxins and other xenobiotics that operate through receptor-binding mechanisms will,

according to theory, follow a linear dose-response binding. This theory is supported by empirical

findings. The biochemical and transcription reactions for dioxins may also follow linear doseresponse kinetics. More distal toxic effects could be linear or threshold (sublinear) depending on

(1) the toxic mechanism, (2) the location on the dose-response curve, and (3) interactions with

other processes. Too much data variability exists to clearly distinguish statistically between

dose-response curve options and to determine whether dose-response follows linear,

supra/sublinear, power curve, or threshold kinetics. Toxic effects at higher doses may be more

likely to result from multiple cellular changes and thus be less likely to follow linear

relationships. Empirical dose-response data from cancer studies—both epidemiological and

bioassays—do not provide consistent or compelling support to either threshold or supralinear

models. Thus, the default linear extrapolation policy is used.

 Current human body burdens are already substantially along the dose-response curve. 

Margins of exposure between population levels of background exposure and the empirical

1 percent effect levels due to additional exposure for a number of biochemical and toxic effects

are on the order of less than 1 to 2 orders of magnitude. Therefore, the extrapolation between

observed effects and background levels is not large. 

Because human data were available for cancer dose-response analysis and because EPA

wanted to stay within the estimated range of responses, EPA chose a 1 percent excess risk as a

point of departure (U.S. EPA, 2000). Restricting the analysis to log-linear models, human cancer

effective doses at the 1 percent excess risk level (ED01s) were estimated to range from 5.7 to 250

ng/kg. In similar estimates based on animal studies, most ED01s ranged from 14 to 500 ng/kg.

Calculations of a CSF based on the extrapolation of lower ED01 to background response

rates based on human data yielded a CSF estimate of approximately 1 × 10-3 per pg TCDD per kg

body weight per day. Based on animal data, a similar CSF of 1.4 × 10-3 per pg TCDD/kg body

weight/day was estimated (U.S. EPA, 2000). “The Agency, although fully recognizing the range

and the public health conservative nature of slope factors that make up the range, suggests the
Section 2.0 May 2002

2-9

use of the 1 × 10-3 per pg TEQ/kgBW/day as an estimator of upper bound cancer risk for both

background intakes and incremental intakes above background” (U.S. EPA, 2000). 

For this risk assessment, however, the current EPA-sanctioned CSF was used because of

the draft nature of the most recent dioxin risk assessment document. EPA has used a CSF for

2,3,7,8-TCDD of 1.56 × 10-1 (pg/kg-d)-1 and unit risk estimates of 3.3 × 10-5 (pg/m3)

-1 for

inhalation exposure and 4.4 × 10-3 (pg/L)-1 for drinking water exposure. These values are now

under review and are subject to change; they are based on an oral study in which rats were

exposed to 2,3,7,8-TCDD in their diet for 720 days, resulting in tumors of the respiratory system

and liver (U.S. EPA, 2000; U.S. EPA, 2000, citing Kociba et al., 1978). The inhalation unit risk

estimate was based on route-to-route extrapolation from the oral CSF, assuming 75 percent

absorption (U.S. EPA, 1997, 2000).

2.3.2 Toxicity Equivalency Factors

Over the past decade, the scientific community, led by the World Health Organization

(WHO), has developed a system of TEFs that relate the toxicity of each dioxin, furan, and PCB

congener to the toxicity of 2,3,7,8-TCDD. The TEFs used in this analysis are those developed by

the WHO in 1998 (U.S. EPA, 2000, citing Van den Berg et al., 1998) and recommended in the

Draft Dioxin Reassessment Document (U.S. EPA, 2000).

These TEFs, presented in Table 2-2, were multiplied by the CSF of 1.56 × 105

 (mg/kg-d)-1

currently recommended by EPA to determine the congener-specific CSF that was used to

estimate congener-specific risks.

Table 2-2. Toxic Equivalency Factors

Congener WHO—98

Polychlorinated dibenzodioxins

2,3,7,8-TCDD 1

1,2,3,7,8- PeCDD 1

1,2,3,4,7,8-HxCDD 0.1

1,2,3,7,8,9-HxCDD 0.1

1,2,3,6,7,8-HxCDD 0.1

1,2,3,4,6,7,8-HpCDD 0.01

1,2,3,4,6,7,8,9-OCDD 0.0001

Polychlorinated dibenzofurans

2,3,7,8-TCDF 0.1

1,2,3,7,8-PeCDF 0.05

2,3,4,7,8-PeCDF 0.5

1,2,3,4,7,8-HxCDF 0.1

(continued)
Section 2.0 May 2002

2-10

Table 2-2. (continued)

Polychlorinated dibenzofurans

1,2,3,7,8,9-HxCDF 0.1

1,2,3,6,7,8-HxCDF 0.1

2,3,4,6,7,8-HxCDF 0.1

1,2,3,4,6,7,8-HpCDF 0.01

1,2,3,4,7,8,9-HpCDF 0.01

1,2,3,4,6,7,8,9-OCDD 0.0001

Polychlorinated biphenyls

IUPAC # Structure WHO—98

77 3,3’,4,4’-TCB 0.0001

81 3,4,4’,5-TCB 0.0001

105 2,3,3’,4,4’-PeCB 0.0001

114 2,3,4,4’,5-PeCB 0.0005

118 2,3’,4,4’,5-PeCB 0.0001

123 2’,3,4,4’,5-PeCB 0.0001

126 3,3’,4,4’,5-PeCB 0.1

156 2,3,3’,4,4’,5-HxCB 0.0005

157 2,3,3’,4,4’,5’-HxCB 0.0005

167 2,3’,4,4’,5,5’-HxCB 0.00001

169 3,3’,4,4’,5,5’-HxCB 0.01

170 2,2’,3,3’,4,4’,5-HpCB -

180 2,2’,3,,4,4’,5,5’-HpCB -

189 2,3,3’,4,4’,5,5’-HpCB 0.0001

2.4 References

ATSDR. 1998. Toxicological Profile for Chlorinated Dibenzo-p-dioxins. Agency for Toxic

Substances and Disease Registry, Department of Human and Health Services. Atlanta,

GA.

U.S. Environmental Protection Agency (EPA). 1997. Chapter 8. Dose-response modeling for

2,3,7,8-TCDD. January 1997 Workshop Review Draft. EPA/600/P-92/001C8.

U.S. Environmental Protection Agency (EPA). 2000. Exposure and Human Health

Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds. 

EPA/600/P-00/001Bg. Washington, DC: National Center for Environmental

Assessment, Office of Research and Development. September.
Section 3.0 May 2002

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3.0 Risk Assessment Overview

This section describes the conceptual framework for the risk assessment conducted for

dioxins, furans, and coplanar PCBs in biosolids. Section 3.1 presents the conceptual framework

for the human health risk assessment. This includes a description of biosolids and the

agricultural practices, fate and transport modeling, exposure assessment, and calculation of riskbased concentrations of these constituents in biosolids. Section 3.2 describes the framework for

the probabilistic analysis. 

3.1 Human Health Risk Assessment

The human health risk assessment for the evaluation of dioxins, furans, and coplanar

PCBs is intended to evaluate nationwide risk to farmers and the children of farmers who apply

biosolids to their crop lands and pastures and consume home-produced foods. The

concentrations of the 29 dioxin, furan, and PCB congeners used in this risk assessment were

derived from the 2001 National Sewage Sludge Survey (NSSS, see Appendix A).

Biosolids are solid, semisolid, or liquid residue generated during the treatment of

domestic sewage in municipal wastewater treatment works. When biosolids are land-applied,

surface-disposed, or fired in a biosolids incinerator, the applicable requirements in Part 503 of

the CWA must be met. Part 503 contains both risk-based requirements and technology-based

requirements. The EPA risk assessment approach used in this analysis was designed to produce

a scientifically defensible evaluation of the concentrations of dioxins in biosolids that are

protective of human health when biosolids are applied to agricultural land.

3.1.1 Application of Biosolids to Agricultural Land

Biosolids may be applied to agricultural land that may be used as crop land or as pasture

for cattle. These applications occur nationwide; therefore, a probabilistic risk assessment was

structured to capture the variability in climate, soil, and agricultural practices throughout the

United States. The 48 contiguous states were subdivided into 41 climatic regions assumed to be

sufficiently uniform to be represented adequately by climate data from any reporting

meteorologic station within the bounds of the region. These geographic regions were also used

as the basis for identifying a representative farm size and a distribution of soil types on the farms. 

The data sources for characterizing the distribution of agricultural field sizes are 

 U.S. DOC (Department of Commerce). 1989. 1987 Census of Agriculture,

Volume 1, Geographic Area Series State and County Data. 
Section 3.0 May 2002

1

 A third-order stream is defined as the joining of two second-order channels. Second-order channels are

formed from the joining of two first-order channels. First-order channels are the smallest finger-tip tributaries in a

watershed. Thus, the actual dimensions of streams of each order may vary.

3-2

 U.S. DOC (Department of Commerce). 1994. 1992 Census of Agriculture

Volume 1, Geographic Area Series State and County Data. Bureau of the Census,

Washington, DC.

3.1.2 Constituents of Concern

Constituents of concern for the Round Two Biosolids Regulation are dioxins, furans, and

coplanar PCBs. These pollutants are similar in many respects, including fate and transport and

toxicology. The human health benchmarks for these constituents are related by a system of TEFs

to the health benchmark for the most well-characterized of these compounds: 2,3,7,8-TCDD. 

Table 2-2 presents the constituents of concern evaluated in this risk assessment.

3.1.3 Site Configuration and Environmental Setting

A single conceptual site layout was used to define the relationship between the

agricultural site and the human receptors evaluated in this risk assessment. The same site layout

was used for the 41 geographical regions. The environmental characteristics of the regions

provide the data used for the environmental characteristics of the sites. 

3.1.3.1 Conceptual Site Layout. Figure 3-1 depicts the conceptual site layout for the

agricultural application of biosolids. Farmers are assumed to apply biosolids to crop land where

exposed fruits, vegetables, and root crops are produced and pasture land where beef and dairy

cattle are grazed. The farmers are assumed to live on a small strip of land (the buffer area)

between the crop or pasture land and the stream. The farmer raises free-range chickens in a yard

that is also located in the buffer area. Beyond the buffer area is a third-order stream.1 This ordersize stream was chosen because it is the smallest size stream that is assumed to be fishable. The

farmer, his lactating wife, their infant, and older children may come in contact with dioxin

congeners via several routes of exposure. 

3.1.3.2 Regional Environmental Setting. Biosolids are produced and managed in all

states in the continental United States; therefore, environmental settings used in this risk

assessment are developed to be representative of each geographical region in the United States. 

The primary objective in characterizing a regional environmental setting is to represent the

variation in environmental conditions that results from the geographic diversity in the United

States. Within each of the 41 representative climatic regions, a meteorological station was

identified to represent the climatic and meteorological conditions for that geographic area. The

41 climatic regions used for modeling are shown in 

Figure 3-2.
Section 3.0 May 2002

3-3

Figure 3-1. Agricultural application conceptual site model.

Figure 3-2. Map of 41 climatic regions.
Section 3.0 May 2002

3-4

The following characteristics are assumed to be associated with the 41 regions:

 Soil characteristics for land having agricultural use (crops or pasture)

 Representative meteorological and climatic data 

 Agricultural field sizes (medium farm size for the region).

3.1.4 Exposure Point Estimates

A series of models was used to estimate concentrations of congeners in the environment

with which individuals may come into contact. A source partition model was used to estimate

environmental releases of each congener from the crop land, or pasture, where biosolids are

applied. These estimated environmental releases provide input to the fate and transport models

to estimate media concentrations for dioxins, furans, and coplanar PCBs in air, soil, above- and

belowground produce, and surface water. A farm food chain model was used to estimate

environmental concentrations of these congeners in home-produced produce, poultry, eggs, beef,

and dairy products. These models are discussed in detail in Section 5.0. Aquatic

bioconcentration factors were used to estimate concentrations in home-caught fish. All

concentrations were estimated as congener-specific concentrations and TEQ concentrations (both

congener-specific and total).

3.1.4.1 Source Partition Modeling. Biosolids application to pastures is assumed to

differ from biosolids application to crop land, and the differences affect the behavior of

constituents in the environment. The source partition model requires information on farm area,

biosolids characteristics (e.g., moisture content, congener concentrations), and environmental

setting (e.g., precipitation, temperature, soil characteristics) to estimate environmental releases. 

Crop Land. Biosolids applied to crop land are tilled into the soil; thus, the dioxins are

thoroughly mixed with the top 20 cm of soil. The congeners are released to the air from the soil

as vapors and particulates; the crops take the congeners in through the air; and congeners bound

to the soil particles are eroded onto and through the residential property and chicken yard and

into the nearby stream.

Pasture. Biosolids applied to the pasture are not tilled and, thus, are not actively mixed

with the soil. However, over time the congeners penetrate into the soil and are assumed to be

mixed in the top 2 cm of soil. The congeners are released to the air from the soil surface; grasses

take up the congeners through air-to-plant transfer. The congeners bound to the biosolids and to

the soil are eroded onto and through the residential property and chicken yard and into the nearby

stream (where they are mixed with the soil estimated to be eroded from the adjacent crop land). 

3.1.4.2 Fate and Transport Modeling. Fate and transport algorithms describe the

mechanism by which the congeners move from the source through the environment. As

described above, a source partition model was used to determine the amount and nature of

congener released from the agricultural field. A multimedia approach was used to characterize

the movement of the dioxins through the environment. This approach considered atmospheric

concentrations, atmospheric deposition, soil concentrations, and sediment concentrations in the

waterbody.
Section 3.0 May 2002

3-5

3.1.4.3 Farm Food Chain Model. A farm food chain model was used to estimate the

concentration of congeners in aboveground produce, belowground produce, poultry, eggs, beef,

and dairy products. Aboveground produce is affected via vapor transfer and deposition of

dioxins in the air. Belowground produce is affected only by uptake of dioxins from tilled soil. 

The concentration of dioxins was also estimated for the forage and silage consumed by cattle. 

Forage is assumed to be grown on the untilled pasture, whereas silage is assumed to be harvested

from the tilled fields. Agricultural field size was estimated as the median agricultural field size 

for each of the 41 climatic regions modeled, and it varied among the climatic regions. Dioxins

that are ingested by animals were partitioned to the lipid fraction of each animal product.

3.1.4.4 Aquatic Food Chain Model. An aquatic food chain model was used to estimate

the concentration of dioxins, furans, and coplanar PCBs in fish populations. These congeners are

eroded from the agricultural fields where they are managed into the sediment of the adjacent

stream where they can contaminate fish. The uptake into fish from the sediment is represented

by congener-specific bioaccumulation constants called biota sediment accumulation factors

(BSAFs). Trophic level 3 (T3) and 4 (T4) fish were considered in this analysis. Trophic level 3

fish are those that consume invertebrates and plankton. Trophic level 4 fish are those that

consume other fish. Most of the fish that humans consume are T4 fish (e.g., salmon, trout,

walleye, bass) and medium to large T3 fish (e.g., carp, smelt, perch, catfish, sucker bullhead,

sauger).

3.1.5 Assessing Human Exposures 

Individuals may come into contact with dioxins in biosolids applied to agricultural fields

through a variety of pathways. 

3.1.5.1 Human Receptors. Four individual receptors were evaluated in this assessment: 

 Adult farmer (members of the farm family who begin exposure as adults)

 Child of farmer (members of the farm family who begin exposure in childhood)

 Infant of farmer (infant born to the farm family during the exposure period)

 Fisher (adult member of the farm family who fishes in the stream adjacent to the

farm where biosolids are applied).

These receptors reflect the range of possible individual exposures for direct and indirect

exposure pathways. Child exposures were evaluated based on an initial start age of 1 to 6 years. 

This age range was selected because this represents the highest consumption rate (intake/body

weight) for most of the exposure pathways evaluated in this risk assessment. The child was

assumed to age through a selected exposure duration; thus, because consumption rates vary over

time, childhood exposures reflect a time-weighted consumption rate for the selected exposure

duration.
Section 3.0 May 2002

3-6

3.1.5.2 Exposure Pathways. Environmental media and exposure pathways were

modeled in this assessment for agricultural and fisher scenarios. Exposure pathways are either

direct, such as inhalation of ambient air, or indirect, such as the farm food chain pathways. The

exposure pathways considered in this assessment were

 Inhalation of ambient air 

 Incidental ingestion of soil in the buffer 

 Ingestion of above- and belowground produce grown on the crop land 

 Ingestion of beef and dairy products from the pasture 

 Ingestion of home-produced poultry and eggs from the buffer 

 Ingestion of fish from the nearby waterbody.

3.1.6 Toxicity Assessment and Risk Characterization

The single risk characterization endpoint used in this risk assessment was the incremental

individual lifetime risk of developing cancer. To characterize this risk from human exposure to

dioxins, furans, and PCBs, TEFs were used with the CSFs developed for 2,3,7,8-TCDD and the

congener-specific exposure assessment results. The toxicity of all other dioxin, furan, and PCB

congeners was determined based on the relationship of each congener to the toxicity of 2,3,7,8-

TCDD. The series of TEFs used in this analysis was developed by the WHO and published in

1998 and are recommended for use by the Draft Dioxin Reassessment Document (U.S. EPA,

2000). These TEFs are applied to the CSF for TCDD (1.56 × 105

) to determine the congenerspecific health benchmarks used in this risk analysis. 

3.2 Probabilistic Method for Determining Exposure Point Concentrations 

The primary objective of this assessment was to estimate risk using a probabilistic

(Monte Carlo) approach. The probabilistic analysis produces a nationwide distribution of risk for

each receptor type by varying parameter values over multiple iterations of the model. 

An overview of the probabilistic analysis follows, and this analysis method is discussed

in greater detail throughout this document. The results of this analysis are presented in

Section 7.0.

The probabilistic analysis was performed using a Monte Carlo simulation. In a Monte

Carlo simulation, the models are run for a fixed number of iterations, each producing a single

result (e.g., a single estimate of cancer risk). For this assessment, 3,000 iterations were run in the

Monte Carlo simulation; therefore, the output of the probabilistic analysis was a distribution of

3,000 values. This distribution represents the distribution of possible outcomes, which reflects

the underlying variability in the data used in the analysis. These results were then used to

identify risk at various percentile levels (e.g., 90th percentile risk value).

Some model input parameters used in the Monte Carlo simulation were drawn from

statistical distributions. For others, variability was associated with variable locations; thus,

location variability was explicitly considered in the setup of the data used for the probabilistic

analysis. For location-dependent parameters, locations were first selected at random with equal
Section 3.0 May 2002

3-7

probability of occurrence based on the 41 climatic regions. These regions defined a set of related

environmental conditions (e.g., soil type, hydrogeologic environment) that characterized the

environmental setting. All location-specific parameters (e.g., rainfall) thus remained correlated

while allowing variability within and among locations. Location-dependent parameters are

discussed in Section 4.3.

3.3 References

U.S. DOC (Department of Commerce). 1989. 1987 Census of Agriculture, Volume 1,

Geographic Area Series State and County Data. Bureau of Census, Washington, DC.

U.S. DOC (Department of Commerce). 1994. 1992 Census of Agriculture Volume 1,

Geographic Area Series State and County Data. Bureau of the Census, Washington, DC.

 U.S. EPA (Environmental Protection Agency). 2000. Exposure and Human Health

Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds. 

EPA/600/P-00/001Bg. National Center for Environmental Assessment, Office of

Research and Development, Washington, DC. September. 
x
Section 4.0 May 2002

4-1

4.0 Input Data Characterization

This risk assessment provides a national characterization of biosolids applied to

agricultural fields. How this practice is characterized in terms of the physical dimensions of the

farms, agricultural application practices, and climatic region is fundamental to the construction of

scenarios for modeling. 

The foundation for the Monte Carlo simulation is the data describing the scenario that

defines each of the Monte Carlo iterations. Specifically for this analysis, 3,000 iterations were

completed to define a distribution of risk for each pathway in the agricultural application

scenario. Compiling the source data for this analysis required characterizing the environmental

setting in which biosolids application to agricultural fields is likely to occur.

Section 4.1 presents an overview of the source data development procedure. Section 4.2

summarizes development of the biosolids management scenarios evaluated in this risk

assessment. Section 4.3 presents the methodologies used to characterize the environmental

setting, including delineation of the environmental setting (e.g., meteorology, climate, and soils). 

Section 4.4 describes the characterization of the agricultural field size. 

4.1 Input Data Development Procedure

To capture the national variation in agricultural practices for the Monte Carlo analysis, a

database of representations of agricultural practices was developed that contains all of the

parameters needed to describe the application of biosolids to crop land or pasture. These source

data, which provide the input data for the fate and transport modeling, are organized into two

source data files, one for pastures and one for crop lands. The source data files contain

information on climatic region and biosolid characteristics and descriptions of agricultural

practices. Agricultural application rates, frequencies, and duration for the use of biosolids on

crop land and pastures were selected to be consistent with common agronomic practices.

4.2 Characterization of Biosolids

Biosolids in this risk assessment were assumed to be characterized by a single set of

physical and chemical parameter values. Thus, the physical characteristics of biosolids (e.g., bulk

density, percent solids, and fraction organic carbon) required to estimate emissions using the

source models used the biosolids characteristics provided by EPA. If biosolid-specific physical

characteristics were not available from EPA for a specific parameter, silt soil parameters were

used to represent biosolids. Table 4-1 provides the biosolids characteristics used in this analysis.
Section 4.0 May 2002

4-2

Table 4-1. Physical Characteristics of Biosolids

Characteristic Parameter Value Units Source

Dry bulk density (BD) 1.6 g/cm3 Technical Support Document for

Land Application of Sewage

Sludge (U.S. EPA, 1992)

Fraction organic carbon (foc) 0.4 Unitless

Percent solid Variable Volume

percent

2001 NSSS (U.S. EPA, 2001)

Porosity 0.4 Unitless Based on Carsel & Parrish

(1988)

Silt content 2.2 to 21 

Uniform distribution

Mass

percent

Table 13.2.2-1 AP-42

(U.S. EPA, 1995a)

4.2.1 Concentrations of Dioxin and Furan Congeners

 The concentrations of dioxin and furan congeners in biosolids were obtained from the

NSSS 2001 (U.S. EPA, 2001). This survey analyzed more than 100 samples of biosolids for the

17 dioxin and furan congeners and 12 PCBs of concern in this risk assessment. These analytical

results are presented in Appendix A of this document. The biosolids samples were obtained

from 94 municipal wastewater treatment facilities. The following steps outline how the variable

concentrations were selected for use in the variable concentration Monte Carlo risk analysis:

1. Identify one representative sample for each wastewater treatment plant

(WWTP) in the NSSS. The 100 analyzed samples in the NSSS represented 94 

WWTPs. For the facilities with multiple types of biosolids, multiple samples

were taken and analyzed. These multiple samples were combined based on the

percent of the total biosolids volume represented by each sample to produce a

single weighted average concentration for each congener. This process produced

a single representative sample for each congener for each facility.

2. Select samples from the distribution. The frequency with which a facility was

selected from the distribution of sample data was weighted according to the

quantity of biosolids produced by the facility. The facilities were placed into one

of four strata depending on the quantity of biosolids produced at that facility. The

strata were given weights of 0.0035, 0.03902, 0.23027, or 0.71921. The

weighting method is the same as that used for the 1988 NSSS samples and is

described in detail in Appendix B.

3. Use the concentrations in sample selected for all congeners in the sample.

When a facility was selected, that facility’s sample was used. The concentrations

for each congener in the sample were thus kept correlated throughout the analysis.
Section 4.0 May 2002

4-3

4. For congener concentrations below the analytical detection limit, use a value

of one-half the analytical detection limit. When the congener concentrations

were below the minimum detection limit, a concentration equal to one-half of the

detection limit was assumed. 

Each iteration of the Monte Carlo analysis evaluated one sample from a single facility from the

distribution of dioxin, furan, and PCB samples. Thus, each total TEQ risk result represents the

total risk from all 29 congeners (17 dioxin and furan congeners and 12 PCBs). For each

iteration, the concentration of dioxins in the biosolids was assumed to remain constant for the

entire period of application. Thus, some iterations in this analysis represented the repeated

application of biosolids containing up to 700 ng/kg of dioxin TEQ to agricultural land. 

Comparisons of samples for the same facility from the EPA 1988 NSSS and the EPA 2001 NSSS

indicate that high dioxin TEQ concentrations in sewage sludge (i.e., > 100 ppt TEQ) do not

appear to remain constant over time. For this reason, the use of a constant high dioxin TEQ

concentration in biosolids may somewhat overestimate the risk for those iterations.

4.2.2 Agricultural Application of Biosolids

Biosolids were assumed to be applied to agricultural land at appropriate agronomic rates. 

Agronomic rates vary according to soil type, crop type, biosolid characteristics, and climatic

conditions. Currently, Section 503 rules limit application of biosolids based on loading of metals

to the soil. For this risk assessment, the following assumptions were made about the application

of biosolids. These assumptions reflect a distribution of agricultural practices common

throughout the United States:

 Biosolids are applied at a rate of 5 to 10 metric tons per hectare per application

(uniform distribution).

 Applications occur once every 2 years.

 Application continues for up to 40 years (20 applications).

 Crop land is tilled to a depth of 20 cm multiple times during the year.

 Pasture land is not tilled; thus, biosolids are assumed to be incorporated into only

the top 2 cm of soil.

Application rates for biosolids were not varied with location in this analysis and were assumed to

be uniform nationwide.

4.3 Site Characterization

The site characteristics used in this analysis were based on one conceptual site layout and

regional characterization of environmental parameters. The conceptual site layout defines the

area in the immediate vicinity of the farm applying biosolids and defines the geographic

relationship among important features, such as the crop land, pasture, residence, chicken yard,
Section 4.0 May 2002

4-4

and stream. A single conceptual site layout was evaluated at each of the 41 climatic regions in

the analysis. 

4.3.1 Conceptual Site Layouts

This risk assessment was based on a conceptual site layout rather than on site-specific

layouts. The conceptual site layout was designed to capture possible relationships between

management practices for biosolids and individual receptors.

The conceptual or general site layouts are shown in Figure 3-1, which shows the

agricultural field, the buffer area (i.e., an area between the agricultural field and the stream or the

monofill), and the residence. 

The agricultural field area was assumed to be the median area for farms in each climatic

region. The agricultural field sizes were taken from the county-level data provided in the Census

of Agriculture. The Census of Agriculture (U.S. DOC, 1989, 1994) provides periodic and

comprehensive statistics about agricultural operations, production, operators, and land use. It is

conducted every 5 years for years ending in 2 and 7. Its coverage includes all operators of U.S.

farms or ranches (Division A, SIC 01-02) that sold or normally would have sold at least $1,000

worth of agricultural products during the census year. In 1992, approximately 1.9 million

operators produced $162 billion in crops and livestock. Data for 1987 and 1992 were averaged. 

The median farm size was determined for all counties in each of the 41 climatic regions. From

this distribution, the median farm size for each climatic region was determined. The agricultural

field sizes used in this analysis are presented in Table 4-2. The agricultural field size was

important in this analysis for the air dispersion and deposition and soil erosion pathways. The

larger the source, the greater the off-site concentrations due to air deposition and erosion.

Adjacent to the agricultural field is a waterbody that is assumed to be 5.5 m wide and

0.21 m deep. These values are typical of a third-order stream (van der Leeden et al., 1990). The

stream length is determined by the width of the agricultural field. Surface area of the stream is,

therefore, determined by the fixed width (5.5 m) and the size of the agricultural field, which

varies by climatic region as described above. The fishing scenario estimated risks to adult fishers

who caught and consumed fish on a recreational basis from this waterbody. 

4.3.2 Regional Environmental Setting

The regional environmental setting approach was developed as a way to include the

variability associated with geographic locations throughout the United States. The boundaries of

the climatic regions used in this analysis were drawn to circumscribe areas that could be

represented by a single set of climatic data. The boundaries considered geographic boundaries,

such as mountains, and other parameters that differentiate meteorological conditions (rainfall,

temperature, windspeed). A description of the selection of the climatic regions and the

representative meteorological stations is presented in Appendix M. However, once the

boundaries of the climatic regions were drawn, other data associated with geographic location

were linked to the climatic region designations. For example, soil characteristics also vary by
Section 4.0 May 2002

4-5

Table 4-2. Median Farm Size for Each Climatic Region

Climatic Region Name

(Selected Met. Station) Median Farm Size (Acres)

Seattle 40.10

Boise 194.40

Billings 1241.70

Burlington 159.20

Portland 98.20

Bismarck 923.80

Minneapolis 208.60

Salem 44.60

Muskegon 117.10

Chicago 177.60

Cleveland 109.20

Winnemucca 162.30

Casper 829.60

Hartford 50.00

San Francisco 39.80

Williamsport 127.10

Salt Lake City 143.50

Fresno 46.80

Lincoln 282.20

Philadelphia 39.00

Denver 738.00

Harrisburg 102.80

Norfolk 97.50

Huntington 86.70

Raleigh-Durham 85.40

Nashville 94.40

Asheville 55.40

Las Vegas 97.60

Little Rock 159.10

Tulsa 184.00

Albuquerque 464.30

Los Angeles 24.20

Charleston 80.40

Atlanta 105.90

Phoenix 339.70

Meridian 123.00

Shreveport 110.90

New Orleans 90.90

Houston 123.50

Miami 39.60

Tampa 67.00
Section 4.0 May 2002

4-6

geographic location, and this variability is reflected using a regional environmental setting

approach. Another variable that is associated with location, but not directly linked to climate or 

soil conditions, is farm size. Farms in the more densely populated eastern part of the United

States are much smaller than farms and ranches in the less densely populated western potion of

the country. This variation was also included by using the regional environmental setting

approach, which keeps correlated the conditions that are likely to occur together and prevents

implausible combinations from being chosen during a random selection process. Using this

approach, the climatic region was randomly selected, but all other data were selected to be

consistent with conditions in that geographic location.

A meteorological station was selected to represent each of the 41 climatic regions. All

meteorological stations within each climatic region were assumed to be representative of the

entire region. The selected meteorological stations are listed in Table 4-2. Each climatic region

was equally weighted in the probabilistic analysis.

4.3.2.1 Meteorological Data. Five years of representative meteorological data were

processed for this analysis. The data gathered included surface data, upper-air data, and

precipitation data. These observational data were used as Industrial Source Complex, ShortTerm Model, version 3 (ISCST3), inputs.

Surface Data. Hourly surface meteorological data used in air dispersion modeling were

processed from the Solar and Meteorological Surface Observation Network (SAMSON) CDROM (U.S. DOC and U.S. DOE, 1993). Variables included

 Temperature 

 Pressure

 Wind direction

 Windspeed

 Opaque cloud cover

 Ceiling height

 Current weather

 Hourly precipitation. 

Upper-Air Data. Twice-daily mixing-height data were calculated from upper-air data

contained in the radiosonde data of the North America CD-ROM set (NCDC, 1997). This set

contains upper-air data from 1946 through 1996 for most upper-air stations in the United States. 

The upper-air data were combined with the SAMSON data to create the mixing-height files. 

EPA’s Support Center for Regulatory Air Models (SCRAM) bulletin board was also used to

obtain mixing-height data (if available) when mixing-height data could not be successfully

calculated from the radiosonde data.

Filling Missing Data. Missing surface data were identified using a program called

SQAQC, which searched for incidents of missing data on the observation indicator, opaque cloud

cover, temperature, station pressure, wind direction and speed, and ceiling height. Years that

were missing 10 percent or more of the data were discarded (Atkinson and Lee, 1992). 

Verification (quality control or QC) checks were performed on the SQAQC program by applying
Section 4.0 May 2002

4-7

it to station data where the missing data were known and by intentionally degrading surface

meteorological files and then running SQAQC to detect the missing values. 

Missing surface data were filled in by a program called METFIX. This program fills in up

to 5 consecutive hours of data for cloud cover, ceiling height, temperature, pressure, wind

direction, and windspeed. For single missing values, the program follows the objective

procedures developed by Atkinson and Lee (1992). For two to five consecutive missing values,

other rules were developed because the subjective methods provided by Atkinson and Lee (1992)

rely on professional judgment and could not be programmed. The METFIX program flagged

files where missing data exceeded five consecutive values. In the few cases where this occurred

and the missing data did not constitute 10 percent of the file, they were filled manually according

to procedures set forth in Atkinson and Lee (1992). If more than 10 percent of the data were

missing, the station was discarded and another station in the climatic region was selected.

All upper-air files were checked for missing data using a program called QAQC. QAQC

produces a log file containing occurrences of missing mixing height. Verification (QC) checks

were performed on the QAQC program by applying it to station data where the missing data were

known and by intentionally degrading existing mixing height files and then running QAQC to

detect the missing values. 

Missing mixing heights were filled in by running the files through another program

written to interpolate one to five consecutive missing values. According to Atkinson and Lee

(1992), if there are one to five consecutive missing values, the values should be filled in

subjectively using professional judgment. Again, programming these subjective procedures was

not feasible, and the program used simple linear interpolation to fill in these values

automatically. Information from Atkinson and Lee (1992) was used to determine which files

should be discarded (i.e., files missing more than five consecutive missing values or missing

10 percent or more of the data). After the missing mixing heights were filled in for all upper-air

files, they were checked once more for missing data using the QAQC program.

Other Meteorological Data. In addition to the surface and upper-air data, air modeling

requires the input of the following meteorological parameters (U.S. EPA, 1995b):

 Minimum Monin-Obukhov length (m)

 Anemometer height (m)

 Roughness length (m), surface meteorological station

 Roughness length (m), area around facility

 Noontime albedo

 Bowen ratio

 Anthropogenic heat flux (W/m2)

 Fraction net radiation absorbed by the ground.

Anemometer height was collected from local climatic data summaries (NOAA, 1983). 

When anemometer height was not available, the station was assigned the most common

anemometer height from the other stations. This value was 6.1 m.
Section 4.0 May 2002

4-8

Land use information is required for determining a number of inputs. To obtain this

information, a geographic information system (GIS) was used to determine the land use within a

3 km radius around each meteorological station by using Geographic Retrieval and Analysis

System (GIRAS) spatial data with Anderson land use codes (Anderson et al., 1976). Table 4-3

shows how the Anderson land use codes were related to PCRAMMET land use codes.

A weighted average, based on the land use percentages for a 3 km radius around each

meteorological station, was used to estimate the Bowen ratio, minimum Monin-Obukhov length,

the noontime albedo, the roughness height at the meteorological station, and the fraction of net

radiation absorbed by the ground. 

The Bowen ratio is a measure of the amount of moisture at the surface around a

meteorological station. The wetness of a location was determined based on the annual average

precipitation amount. The range of values is provided in Table 4-4 as a function of land use type,

season, and moisture condition. For this analysis, the annual average values were applied. 

The minimum Monin-Obukhov length, a measure of the atmospheric stability at a

meteorological station, was correlated with the land use classification, as shown in Table 4-5.

 

Noontime albedo values also were correlated with land use around a meteorological

station, as shown in Table 4-6. 

The surface roughness length is a measure of the height of obstacles to the wind flow. It

is not equal to the physical dimensions of the obstacles but is generally proportional to them. 

Surface roughness length data are shown in Table 4-7 along with their corresponding land use. 

The roughness height was assumed to be the same at the meteorological station and at the farm

site.

Table 4-3. Relation between Anderson Land Use Codes and PCRAMMET

Land Use Codes

Anderson Code and Descriptiona PCRAMMET Type and Descriptionb

51 Streams and canals 1 Water surface

52 Lakes 1 Water surface

53 Reservoirs 1 Water surface

54 Bays and estuaries 1 Water surface

41 Deciduous forest land 2 Deciduous forest

61 Forested wetland 2 Deciduous forest

42 Evergreen forest land 3 Coniferous forest

43 Mixed forest land 4 Mixed forest

(continued)
Section 4.0 May 2002

4-9

Table 4-3. (continued)

Anderson Code and Descriptiona RAMMET Type and Descriptionb

62 Nonforested wetland 5 Swamp (nonforested)

84 Wet tundra 5 Swamp (nonforested)

21 Cropland and pasture 6 Agricultural

22 Orchards-groves-vineyards-nurseries-ornamental 6 Agricultural

23 Confined feeding operations 6 Agricultural

24 Other agricultural land 6 Agricultural

31 Herbaceous rangeland 7 Rangeland (grassland)

32 Shrub and brush rangeland 7 Rangeland (grassland)

33 Mixed rangeland 7 Rangeland (grassland)

11 Residential 9 Urban

12 Commercial and services 9 Urban

13 Industrial 9 Urban

14 Transportation-communication-utilities 9 Urban

15 Industrial and commercial complexes 9 Urban

16 Mixed urban or built-up land 9 Urban

17 Other urban or built-up land 9 Urban

71 Dry salt flats 10 Desert shrubland

72 Beaches 10 Desert shrubland

73 Sandy areas not beaches 10 Desert shrubland

74 Bare exposed rock 10 Desert shrubland

75 Strip mines-quarries-gravel pits 10 Desert shrubland

76 Transitional areas 10 Desert shrubland

81 Shrub and brush tundra 10 Desert shrubland

82 Herbaceous tundra 10 Desert shrubland

83 Bare ground 10 Desert shrubland

85 Mixed tundra 10 Desert shrubland

91 Perennial snowfields 10 Desert shrubland

92 Glaciers 10 Desert shrubland

a

 Anderson codes from Anderson et al. (1976).

b

 PCRAMMET codes from U.S. EPA (1995b).
Section 4.0 May 2002

4-10

Table 4-4. Daytime Bowen Ratio by Land Use and Season

Land Use Type

Spring Summer Autumn Winter

Annual 

Average

Dry Wet Avg. Dry Wet Avg. Dry Wet Avg. Dry Wet Avg. Dry Wet Avg.

Water surface 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 2.0 0.3 1.5 0.575 0.15 0.45

Deciduous forest 1.5 0.3 0.7 0.6 0.2 0.3 2.0 0.4 1.0 2.0 0.5 1.5 1.53 0.35 0.875

Coniferous forest 1.5 0.3 0.7 0.6 0.2 0.3 1.5 0.3 0.8 2.0 0.3 1.5 1.4 0.275 0.825

Swamp 0.2 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.1 2.0 0.5 1.5 0.65 0.2 0.45

Cultivated land

(agricultural) 1.0 0.2 0.3 1.5 0.3 0.5 2.0 0.4 0.7 2.0 0.5 1.5 1.63 0.35 0.75

Grassland 1.0 0.3 0.4 2.0 0.4 0.8 2.0 0.5 1.0 2.0 0.5 1.5 1.75 0.425 0.825

Urban 2.0 0.5 1.0 4.0 1.0 2.0 4.0 1.0 2.0 2.0 0.5 1.5 3.0 0.75 1.6

Desert shrubland 5.0 1.0 3.0 6.0 5.0 4.0 10.0 2.0 6.0 10.0 2.0 6.0 7.75 2.5 4.75

 Source: U.S. EPA, 1995b. Averages were computed for this effort.

Table 4-5. Minimum Monin-Obukhov Length

(Stable Conditions)

Urban Land Use Classification Length (m)

Agriculture (open) 2

Residential 25

Compact residential/industrial 50

Commercial (19–40 story buildings)

 (> 40 story buildings)

100

150

Source: U.S. EPA, 1995b.
Section 4.0 May 2002

4-11

Table 4-6. Albedo Values of Natural Ground Covers for Land Use Types and Seasons

Land Use Type Spring Summer Autumn Winter Annual Average

Water surface 0.12 0.1 0.14 0.2 0.14

Deciduous forest 0.12 0.12 0.12 0.5 0.22

Coniferous forest 0.12 0.12 0.12 0.35 0.18

Swamp 0.12 0.14 0.16 0.3 0.18

Cultivated land (agricultural) 0.14 0.2 0.18 0.6 0.28

Grassland 0.18 0.18 0.20 0.6 0.29

Urban 0.14 0.16 0.18 0.35 0.21

Desert shrubland 0.3 0.28 0.28 0.45 0.33

Source: U.S. EPA, 1995b. Average values were computed for this analysis.

Table 4-7. Surface Roughness Length for Land Use Types and Seasons

(meters)

Land Use Type Spring Summer Autumn Winter Annual Average

Water surface 0.0001 0.0001 0.0001 0.0001 0.0001

Deciduous forest 1.0 1.3 0.8 0.5 0.9

Coniferous forest 1.3 1.3 1.3 1.3 1.3

Swamp 0.2 0.2 0.2 0.05 0.16

Cultivated land (agricultural) 0.03 0.2 0.05 0.01 0.07

Grassland 0.05 0.2 0.01 0.001 0.04

Urban 1.0 1.0 1.0 1.0 1.0

Desert shrubland 0.3 0.3 0.3 0.15 0.26

Source: U.S. EPA, 1995b. Average values were computed for this analysis.

During daytime hours, the heat flux into the ground is parameterized as a fraction of the

net radiation incident on the ground. This fraction varies based on land use. A value of 0.15 was

used for rural locations. Suburban and urban locations were given values of 0.22 and 0.27,

respectively (U.S. EPA, 1995b).
Section 4.0 May 2002

4-12

Anthropogenic heat flux for a meteorological station can usually be neglected in areas

outside of highly urbanized locations; however, in areas with high population densities or energy

use, such as an industrial facility, this flux may not always be negligible (U.S. EPA, 1995b). For

this analysis, anthropogenic heat flux was assumed to be zero for all meteorological stations. 

4.3.2.2 Meteorological Data. Meteorological stations selected for purposes of air

dispersion modeling also provided long-term climatic data that were necessary for fate and

transport modeling. For each of the 41 stations, the following data were compiled:

 Mean annual wind direction 

 Mean annual windspeed

 Average temperature

 Average annual runoff

 Universal Soil Loss Equation (USLE) rainfall/erosivity factor. 

4.3.2.3 Soil Characterization. The fate and transport models used in the biosolids risk

assessment require surface soil properties to model erosion and overland transport and properties

of the entire soil column. A regional approach was also used to compile soil data for these

modeling requirements. All land with agricultural use was used to characterize the soils within

the 41 climatic regions. This regional characterization of soil types captured variability in soils

in a manner that is generally representative of agricultural lands across the United States. A GIS

was used to compile soil texture and other soil data within each climatic region. Then, database

programs processed these data to create a distribution of input variables required by the models. 

Data Sources. The primary data source for soil properties is the State Soil Geographic

(STATSGO) database. STATSGO is a repository of nationwide soil properties primarily

compiled by the U.S. Department of Agriculture (USDA) from county soil survey data (USDA,

1994). STATSGO includes a 1:250,000-scale GIS coverage that delineates soil map units, and an

associated database containing soil data for each STATSGO map unit. (Map units are areas used

to spatially represent soils in the database.)

In addition, two compilations of STATSGO data, each keyed to the STATSGO map unit

GIS coverage, were used in the analysis as a convenient source of average soil properties:

 USSOILS. USSOILS (Schwarz and Alexander, 1995) averages STATSGO data

over the entire soil column for each map unit. 

 CONUS. CONUS (Miller and White, 1998) provides average STATSGO data

by map unit and a set of 11 standardized soil layers. 

 GIRAS. The GIRAS land use database (U.S. EPA, 1994) provides

comprehensive land use data, in digital GIS format, for the contiguous 48 states.

Soil properties derived directly from STATSGO, CONUS, or USSOILS data include

organic matter content, USLE K (erodibility) and S (slope) factors, and pH. A complete set of
Section 4.0 May 2002

1 Hydrologic soil properties required for modeling include bulk density, saturated water content, residual

water content, field moisture content, wilting point, saturated hydraulic conductivity, soil moisture coefficient b, and

soil moisture retention parameters alpha and beta.

4-13

hydrologic soil properties1 was not available from STATSGO. To ensure consistent and realistic

values, it was necessary to rely on established, nationwide relationships between hydrologic

properties and soil texture or hydrologic soil group, both of which are available from STATSGO.

Sources for these relationships include Carsel and Parrish (1988), Carsel et al. (1988), and Clapp

and Hornberger (1978). These peer-reviewed references provide a consistent set of correlated

hydrologic properties for each soil texture or hydrologic group.

Finally, two parameters—root zone depth and Soil Conservation Service (SCS) curve

number (used for recharge calculations)—required site-based land use data, as well as soil

texture or hydrologic soil group. The land use data were obtained for each of the 41 climatic

regions from the GIRAS land use database (U.S. EPA, 1994). GIRAS provides comprehensive

land use data, in digital GIS format, for the contiguous 48 states. Land use/land cover

information in GIRAS was mapped and coded using the Anderson classification system

(Anderson et al., 1976), which is a hierarchical system of land use characterizations. This

nationwide coverage is based on late-1970s to early-1980s satellite images and aerial

photography. The relationships used to convert the land use and soil data were obtained from

Dunne and Leopold (1978) for root zone depth and USDA (1986) for the SCS curve number.

Methodology. The soil data collection methodology begins with GIS programs (in Arc

Macro Language (AML)) that overlay the boundaries of the 41 climatic regions on the

STATSGO map unit coverage to determine the STATSGO map units and their area within the

regions. These data are then passed to data processing programs that derive predominant soil

properties within each climatic region, either through direct calculations or by applying

established relationships in lookup tables. In deriving soil model inputs, the biosolids soil data

processing effort bases all collected soil properties on the predominant soil type (texture and

hydrologic group) for the STATSGO map units having agricultural land use within each climatic

region. Depending on modeling requirements, soil properties were derived for surface soils (top

20 cm), the entire soil column (to represent the vadose zone), or both, as shown in Table 4-8. 

To ensure consistent, realistic properties, the soil data processing effort bases all collected

soil properties on the predominant soil texture for each STATSGO map unit. For each

STATSGO map unit within a meteorological station region, predominant texture was determined

both for surface soils (top 20 cm) and the entire soil column (to represent the vadose zone) from

CONUS data. For surface soils, the predominant texture is the thickest, weighted by depth, soil

texture for the top three CONUS layers (20 cm). Where there was a tie, the texture of the top

two layers was used as the predominant soil texture for that map unit. Twelve common soil

textures were collected to develop hydrologic properties (Table 4-4). Map units that did not have

one of the 12 common soil textures (e.g., those with water or organic matter) were excluded from

the analysis. Soil column texture was obtained in a similar manner, except that all CONUS layers

were used.
Section 4.0 May 2002

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Table 4-8. Summary of Soil Properties Collected for Biosolids Risk Analysis

Soil Variable Units Data Source

Properties Derived from Soil Texture

USDA soil texture Unitless CONUS/STATSGO

Saturated hydraulic conductivity cm/h Relationship from Carsel and Parrish (1988)

Saturated water content L/L Relationship from Carsel and Parrish (1988)

Soil moisture coefficient b Unitless Relationship from Clapp and Hornberger (1988)

Soil bulk density mg/L Calculated from saturated water content

Root zone depth cm Relationship (with land use) from Dunne and Leopold

(1978)

Properties Derived from Soil Hydrologic Class

SCS hydrologic class Unitless CONUS/STATSGO

Field capacity % (vol.) Relationship from Carsel et al. (1988)

Wilting point % (vol.) Relationship from Carsel et al. (1988)

SCS curve number Unitless Relationship (with land use) from USDA (1986)

Properties Obtained Directly from STATSGO

Fraction organic carbon g/g STATSGO

Silt content % (wt.) STATSGO

USLE erodibility factor (K) kg/m2 STATSGO

USLE slope (S) Degrees STATSGO

Properties Derived from Slope

USLE slope length (L) m Relationship from Lightle and Weesies (1998)

USLE length/slope factor (LS) Unitless Calculated from L and S per Williams and Berndt (1977)

To limit data collection to agricultural soils, GIS programs (in AML) were used to

overlay the STATSGO map unit GIS coverage with the GIRAS land use GIS coverage and then

determine the map units (and their respective areas) that occur in crop land and pasture land use

(i.e., Anderson land use code 21) within each meteorological region. These data were then

processed to create a set of the 12 soil textures, ranked by percentage of crop land and pasture

land with each texture, for each region. These textures were used to derive soil properties for this

analysis for each region/texture combination as described in the next section. These properties

were then passed on to the model in Access database tables indexed by meteorological station

and soil texture.
Section 4.0 May 2002

4-15

Because certain soil properties were derived from SCS hydrologic soil groups, it was

necessary to develop a hydrologic soil group that would be consistent with the soils of each

texture within a region. To do so, a table of hydrologic soil groups by STATSGO map unit was

created using STATSGO data for hydrologic soil groups by the component soils within the map

unit. Based on the predominant texture for each map unit, hydrologic soil groups for the

component soils with the same texture were averaged across each map unit (weighted by

component percent) using the numeric conversion: group A = 1, group B = 2, group C = 3, and

group D = 4. These values were then averaged again (weighted by map unit area) for each soil

texture occurring in a region. After this regional average by texture was calculated, the numbers

were converted back to letters using the same conversion, resulting in a hydrologic soil group for

each texture occurring within a meteorologic region. Note that hydrologic soil group applies to

the entire soil column and is not layer-specific. 

Development of Soil Properties. Once the distribution of soil textures and their related

hydrologic class was determined for each meteorological region, average soil properties were

determined for each soil texture present in a region by relationships with soil texture or

hydrologic class or by extracting the data for soils of each texture directly from STATSGO. 

Soil Properties Based on Relationship with Predominant Texture—Several soil

hydrologic properties were derived directly from predominant texture using database lookup

tables relating mean properties to texture class (see Table 4-9). Tables 4-9 through 4-11

summarize the relationships used, which are described below.

 Saturated hydraulic conductivity (cm/h) was determined for both surface soil

(Ksat_top20) and the entire soil column (VadSATK) using a national relationship

from Carsel and Parrish (1988) (Table 4-9).

 Saturated water content (unitless) was determined for both surface soil

(WCS_top20) and the entire soil column (VadWCS) using a relationship from

Carsel and Parrish (1988) (Table 4-9).

 Bulk density (g/cm3) was calculated for surface soil (BD_top20) from saturated

water content using the equation 

'b = 2.65(1 - 1) (4-1)

where 

'b = bulk density of the soil (U.S. EPA, 1997)

2.65 = particle density in g/cm3 (assumed to be quartz)

1 = saturated water content.

 Soil moisture coefficient (unitless) was determined for both the surface soil

(SMb_top20) and the entire soil column (SMb_sub) using a relationship from

Clapp and Hornberger (1978) (Table 4-9).
Section 4.0 May 2002

4-16

Table 4-9. Hydrological Soil Parameters Correlated to Soil Texture

Soil Texture

Saturated Hydralic

Conductivity

Ksata (cm/h)

Storated Water

Content WCSa

(L/L)

Bulk Density

RHOBb

(g/cm3)

Soil Moisture

Coefficient b

SMbc

Clay (C) 0.20 0.38 1.643 11.4

Clay loam (CL) 0.26 0.41 1.5635 8.52

Loam (L) 1.04 0.43 1.5105 5.39

Loamy sand (LS) 14.59 0.41 1.5635 4.38

Silt (SI) 0.25 0.46 1.431 --

Silt loam (SIL) 0.45 0.45 1.4575 5.30

Silty clay (SIC) 0.02 0.36 1.696 10.4

Silty clay loam (SICL) 0.07 0.43 1.5105 7.75

Sand (S) 29.70 0.43 1.5105 4.05

Sandy clay (SC) 0.12 0.38 1.643 10.4

Sandy clay loam (SCL) 1.31 0.39 1.6165 7.12

Sandy loam (SL) 4.42 0.41 1.5635 4.90

a

 Carsel and Parrish (1988).

b Calculated from WCS using equation from U.S. EPA (1997). c

 Clapp and Hornberger (1978).

Depth to root zone (cm) was determined using a Dunne and Leopold (1978) table of

rooting depth by vegetation type and soil texture (Table 4-10). For each soil texture, a minimum

and a maximum root zone depth (for shallow and deep-rooted crops) were used to represent the

range across crop land and pasture land use. Because Dunne and Leopold included only five soil

textures, these five textures were mapped across the 12 basic textures used in this analysis as

shown in Table 4-10.

Soil Parameters Based on Relationship with Hydrologic Group—The following soil

parameters are all based on the average hydrologic soil group for each texture within a

meteorological region. Mean values by hydrologic group were obtained using the following

relationships:

 Soil moisture field capacity (volume %). A single field capacity value (SMFC)

was obtained by hydrologic soil group by averaging the layered property values

from Carsel et al. (1988). Table 4-11 presents the mean value for field capacity by

hydrologic soil group and layer, as well as the average values used in this analysis.

 Soil moisture wilting point (volume %). A single wilting point value (SMWP)

was obtained by hydrologic soil group by averaging the layered property values

from Carsel et al. (1988). Table 4-11 lists the mean value for wilting point by

hydrologic soil group and layer, as well as the average values used in this analysis.
Section 4.0 May 2002

4-17

Table 4-10. Depth to Root Zone Values

USDA Soil Texture

Dunne & Leopold

Texture

Shallow-Rooted Crops

(DRZ_Min, cm)

Deep-Rooted Crops

(DRZ_Max, cm)

Sand Fine sand 50 100

Loamy sand Fine sandy loam 50 100 Sandy loam

Silt

Silt loam Silt loam 62 125

Loam

Sandy clay loam

Silty clay loam Clay loam 40 100

Clay loam

Sandy clay

Silty clay Clay 25 67

Clay

Source: Dunne and Leopold (1978).

Table 4-11. Field Capacity (FC) and Wilting Point (WP) Values

Hydrologic Group Layer FC WP

A 1 9.4 3.1

2 8.1 2.3

3 5.9 2.1

4 5.8 1.9

Avg. 7.3 2.4

B 1 19.1 8.7

2 18.8 9.3

3 18.7 8.9

4 17.5 8.4

Avg. 18.5 8.8

(continued)
Section 4.0 May 2002

4-18

Table 4-11. (continued)

Hydrologic Group Layer FC WP

C 1 22.5 10.4

2 23.2 12.1

3 22.9 11.9

4 21.3 11.5

Avg. 22.5 11.5

D 1 24.2 13.8

2 26.3 17.0

3 25.6 16.3

4 24.4 15.1

Avg. 25.1 15.6

Source: Carsel et al. (1988).

 SCS curve number (unitless). Minimum and maximum SCS curve number

values (CN_min and CN_max) were determined for each regional soil texture

based on a USDA (1986) table of curve numbers by cover type and hydrologic

soil group, assuming a good condition pasture land use for CN_min and poor

condition crop land use for CN_max. A lookup table (Table 4-12) with minimum

and maximum SCS curve numbers by hydrologic soil group was used to assign

the appropriate value for each regional soil texture according to its hydrologic soil

group.

Table 4-12. SCS Curve Number Values

by SCS Hydrologic Soil Group

SCS Hydrologic

Soil Group

SCS Curve Number

CN_Min

(Pasture)

CN_Max

(Crop Land)

A 39 72

B 61 81

C 74 88

D 80 91

Source: Derived from USDA (1986).
Section 4.0 May 2002

4-19

Parameters Collected Directly from STATSGO-Based Data Sources—Several variables

were obtained directly from STATSGO (Schwarz and Alexander, 1995). Although they are not

derived from soil texture, they were extracted and averaged based only on soil map units with the

predominant texture to ensure consistent soil properties.

 USLE erodibility factor—top 20 cm (ton/acre). An area-weighted average

erodibility factor for the top 20 cm of soil (K_top20) was calculated from

STATSGO data by layer and component. STATSGO layer data were translated

into K values using standardized CONUS layers and calculating a depth-weighted

average value. Further, a component percent-weighted average K was calculated

for each CONUS layer across all components contained in each map unit. The

resulting table contains K values by map unit and standardized CONUS layer. To

get one value for K by map unit for the top 20 cm of soil, a depth-weighted

average for the top three CONUS layers was calculated. The final K value by

meteorological region and soil texture was obtained by averaging the map units

for each surface soil texture present within the meteorological region. 

 Fraction organic carbon—top 20 cm (mass fraction). An area-weighted average

fraction organic carbon for surface soils (foc_top20) was calculated for each

region and soil texture using only the map units with the predominant surface soil

texture of interest within the region. Percent organic matter for the top 20 cm of

soil was obtained from STATSGO organic matter data by layer and component

(Schwarz and Alexander, 1995) and converted to fraction organic carbon by

dividing by 174 (100 × 1.74 g organic matter/g organic carbon) (U.S. EPA, 1997).

Percent organic matter values were translated from STATSGO layer and

component into standardized CONUS layers using the same methodology

described for the USLE erodibility factor K. Then, a depth-weighted average

percent organic matter was calculated for the top three CONUS layers (top 20 cm

of soil).

 Silt content—top 20 cm (weight percent). An area-weighted average silt content

for surface soils (Ss_top20) was derived from STATSGO data for each region and

soil texture in the same manner described for USLE erodibility factor.

The USLE’s length slope factor (LS) was derived from STATSGO slope data. Percent

slope (Theta) was obtained by region and soil texture by using only the map units with the

predominant texture of interest. An area-weighted average slope was calculated for each texture

occurring in a region. Length (Length, ft) was then obtained from a Lightle and Weesies (1998)

lookup table of default flow lengths by slope, using slope values rounded to the nearest integer

(Table 4-13). All slopes less than 0.5 were given the length corresponding to 0.5 and all slopes

greater than 24 were given the length corresponding to 24. The USLE length/slope factor LS

(unitless) was then calculated using the equation from Williams and Berndt (1977):

LS = (L/72.6)m(0.065 + 0.0454S + 0.0065S2

) (4-2)
Section 4.0 May 2002

4-20

where

L = flow length

S = slope in percent

and

m = 0.2 for slope <1 percent

m = 0.3 for slope 1 percent and <3 percent

m = 0.4 for slope 3 percent and <5 percent

m = 0.5 for slope 5 percent.

One basic type of source was evaluated in this assessment: land application of biosolids

to crop land or pastures. It was necessary to determine the physical characteristics of the farm

where biosolids were assumed to be applied and the operating parameters used for that

application for the air dispersion modeling and source partition modeling. First, representative

agricultural field sizes were identified. To identify a representative farm size for each climatic

region, the median farm size in each county within a climatic region was determined from the

Census of Agriculture (U.S. DOC, 1989, 1994). Then the median farm sizes for each county

within each of the 41 climatic regions were ranked and the median farm size for each climate

region was selected. The farm was assumed to devote one-half of its area to raising crops and

one-half to pasturing cattle. 

Table 4-13. Default Flow Lengths by Slope

Slope

Length

(ft) Slope

Length

(ft)

0.5 100 13 90

1 200 14 80

2 300 15 70

3 200 16 60

4 180 17 60

5 160 18 50

6 150 19 50

7 140 20 50

8 130 21 50

9 125 22 50

10 120 23 50

11 110 24 50

12 100

Source: Lightle and Weesies, 1998.
Section 4.0 May 2002

4-21

Next 3,000-record source data files were constructed for use in the probabilistic analysis. 

These files were constructed by combining the environmental setting data, agricultural practice

data, and the biosolids characterization data using the following steps: 

 Select one of the 41 climatic regions (each region was assumed to be equally

likely)

 Select data associated with the selected climatic region (farm size, soil data,

meteorologic data)

 Select agricultural practice data independent of climatic region (application rate,

frequency, and number of applications)

 Select biosolids characteristics (independent of climatic region and agricultural

practice).

Two source data files were generated in this manner: one for crop land and one for pastures. 

Each of the 3,000 records in each of the source data files was identified by a model run

identification number.

4.4 References

Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover

classification system for use with remote sensor data. Geological Survey Professional

Paper 964. In: U.S. Geological Survey Circular 671. U.S. Geological Survey,

Washington, DC.

Atkinson, D., and R.F. Lee. 1992. Procedures for Substituting Values for Missing NWS

Meteorological Data for Use in Regulatory Air Quality Models. U.S. Environmental

Protection Agency, Research Triangle Park, NC. 

Carsel, R.F., and R.S. Parrish. 1988. Developing joint probability distributions of soil water 

retention characteristics. Water Resources Research 24(5):755-769.

Carsel, R.F., R.S. Parrish, R.L. Jones, J.L. Hansen, and R.L. Lamb. 1988. Characterizing the

uncertainty of pesticide leaching in agricultural soils. Journal of Contaminant Hydrology

2:111-124.

Clapp, R.B., and G.M. Hornberger. 1978. Empirical equations for some soil hydraulic

properties. Water Resources Research 14:601-604.

Dunne, T., and L.B. Leopold. 1978. Water in Environmental Planning. New York: W. H.

Freeman and Company.
Section 4.0 May 2002

4-22

Lightle, D.T., and G. Weesies. 1998. Default slope parameters. Memorandum to Scott Guthrie

(RTI) from D.T. Lightle and G. Weesies (USDA, Natural Resources Conservation

Service), West Lafayette, IN. June 8.

Miller, D. A., and R. A. White. 1998. A Conterminous United States Multilayer Soil

Characteristics Dataset for Regional Climate and Hydrology Modeling. Website at

http://www.essc.psu.edu/soil_info/index.cgi?soil_data&index.html.

NCDC (National Climatic Data Center and Forecast Systems Laboratory). 1997. Radiosonde

Data of North America, 1946-1996, Version 1.0, June 1997 (Updated).

NOAA (National Oceanic and Atmospheric Administration). 1983. Local Climatological Data.

Annual Summaries for 1982: Part I - ALA - MONT and Part II - NEB - WYO. National

Climatic Data Center, National Environmental Satellite, Data, and Information Service,

Asheville, NC.

Schwarz, G.E., and R.B. Alexander. 1995. State Soil Geographic (STATSGO) Data Base for the

Conterminous United States. Edition 1.1 . Open-File Report 95-449. U.S. Geological

Survey, Reston, VA. Website at

http://water.usgs.gov/GIS/metadata/usgswrd/ussoils.html. September 1.

USDA (U.S. Department of Agriculture). 1986. Urban Hydrology for Small Watersheds. TR-55

(210-VI-TR-55). Engineering Division, Soil Conservation Service, Washington, DC.

pp. 2-5. June.

USDA (U.S. Department of Agriculture). 1994. State Soil Geographic (STATSGO) Data Base. 

Data Use Information. Miscellaneous Publication No. 1492. Natural Resources

Conservation Service, Fort Worth, TX. Available online:

http://www.ftw.nrcs.usda.gov/stat_data.html.

U.S. DOC (Department of Commerce). 1989. 1987 Census of Agriculture, Volume 1,

Geographic Area Series State and County Data. Bureau of Census, Washington, DC.

U.S. DOC (Department of Commerce). 1994. 1992 Census of Agriculture Volume 1,

Geographic Area Series State and County Data. Bureau of the Census, Washington, DC.

U.S. DOC and U.S. DOE (U.S. Department of Commerce National Climatic Data Center and

U.S. Department of Energy National Renewable Energy Laboratory). 1993. Solar and

Meteorological Surface Observation Network (SAMSON) 1961-1990. Version 1.0.

U.S. EPA (Environmental Protection Agency). 1992. Technical Support Document for Land

Application of Sewage Sludge. Office of Water, Office of Science and Technology. EPA

822/R-93-001a. November.

U.S. EPA (Environmental Protection Agency). 1994. 1:250,000 Scale Quadrangles of

Landuse/Landcover GIRAS Spatial Data in the Conterminous United States: Metadata.
Section 4.0 May 2002

4-23

Office of Information Resources Management, Washington, DC. Website at

http://www.epa.gov/ngispgm3/nsdi/projects/giras.htm.

U.S. EPA (Environmental Protection Agency). 1995a. Compilation of Air Pollutant Emission

Factors, Volume 1: Stationary Point and Area Sources. 5th Edition. AP-42. PB95-

196028INZ. Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1995b. PCRAMMET User’s Guide (Draft). 

Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1997. EPA’s Composite Model for Leachate

Migration with Transformation Products. EPACMTP: User’s Guide. Office of Solid

Waste, Washington, DC.

U.S. EPA, 2001. 2001 National Sewage Sludge Survey.

van der Leeden, F., F.L. Troise, and D.K. Todd. 1990. The Water Encyclopedia. 2nd edition.

Chelsea, Michigan: Lewis Publishers. p. 176.

Williams, J.R., and H.D. Berndt. 1977. Determining the universal soil loss equation’s lengthslope factor for watersheds. In: A National Conference on Soil Erosion - Soil Erosion:

Prediction and Control, May 24-26, 1976, Perdue University, West Lafayette, IN, pp.

217-225, Soil Conservation Society of America, Ankeny, IA.
x
Section 5.0 May 2002

5-1

5.0 Estimating Exposure Point Concentrations

Exposure point concentrations are constituent concentrations at the location in the

environment at which an individual may be exposed. To determine constituent concentrations in

environmental media (e.g., air or soil) with which a receptor comes in contact, several computerbased models and sets of equations are used:

 Source partition models 

 Fate and transport models 

 Farm food chain equations

 Aquatic food chain equations.

The agricultural application of biosolids evaluated in this risk assessment is described in

Section 4.0. Dioxins, furans, and coplanar PCBs found in biosolids are released from these

agricultural applications into the environment. Releases to the atmosphere occur through

volatilization or wind erosion of particles. Releases from the agricultural field may also occur

through erosion of soil particles onto the residential plot and, subsequently, into a nearby stream. 

The constituents may move into the human food chain by contaminating fruits, vegetables,

poultry, eggs, beef, milk, and fish consumed by humans.

This risk analysis was performed in a probabilistic format. Section 3.0 explains the risk

assessment framework, including the structure of the probabilistic analysis. The current section

describes the models and algorithms used for the risk analysis. In the probabilistic analysis,

specified model input parameter values were varied in each of 3,000 iterations to generate a

distribution of media concentrations.

The following subsections describe the models and equations used in this risk assessment

and their application. Section 5.1 describes the source partition models used to predict

environmental releases of constituents from the biosolids. Section 5.2 discusses the air

dispersion and deposition modeling and methodologies used to estimate concentrations of

constituent releases used in the human health risk analysis. Section 5.3 discusses the

methodology for calculating food chain concentrations based on air, soil, and water

concentrations.

Greater detail on the modeling performed for this risk analysis is provided in appendices

to this document:

 Appendix F, Source Model for Land Application Unit. This appendix explains

the source partition model used in this risk assessment.
Section 5.0 May 2002

5-2

 Appendix G, Air Dispersion and Deposition Modeling. This appendix provides

details, including all input data files, used in the air dispersion and deposition

modeling for this risk assessment.

 Appendix H, Direct and Indirect Exposure Equations. This appendix documents

the algorithms used to calculate exposure point concentrations for the surface

water sediment, terrestrial food chain, and aquatic food chain.

 Appendix I, Variables for Aboveground Fate and Transport. This appendix

presents and references the input values or distributions used in the algorithms

presented in Appendix H.

5.1 Source Partition Modeling of Constituent Releases

5.1.1 Land Application Unit Partitioning Model Used for Agricultural Fields

Fate and transport of chemicals within the agricultural fields and chemical emissions

from these fields to surrounding media were simulated using adaptations of the Land Application

Unit (LAU) model. The LAU model was used in two slightly different versions for this biosolids

risk assessment—one version representing the crop agricultural field, and the second representing

the pasture agricultural field. An overview of the LAU model is presented in the following

sections, including a description of the LAU’s “local watershed” concept, the important

assumptions inherent in the LAU methodology, its hydrology and soil erosion methodologies, its

fundamental fate and transport algorithmic “engine”—the Generic Soil Column Model (GSCM),

its particulate emissions to the atmosphere estimation methods, and the differences between the

LAU (crop) and LAU (pasture) versions and other modifications required to execute the LAU

model for purposes of this analysis. The LAU module is described in Appendix F. 

A complete listing of input parameters for both the crop and pasture source partition

models is provided in Appendix C.

Figure 5-1 shows the data flow into and out of the source model.

5.1.1.1 Local Watershed. The agricultural land where biosolids are applied, whether a

crop land or a pasture land, is considered an integral part of a “local watershed,” as illustrated in

Figure 5-2. A local watershed is defined here as that drainage area that contains only the

agricultural field and its downslope contiguous land areas in which runoff occurs as overland

flow (sheet flow) only. Thus, a local watershed extends downslope only to the point that runoff

flows and eroded soil loads would enter a well-defined drainage channel, e.g., a ditch, stream,

lake, or some other waterbody. The sheet-flow-only restriction is based on the assumption that

any area downstream of the agricultural field is subject to contamination from the application of

biosolids through overland runoff and soil erosion.

The “buffer” illustrated in Figure 5-2 shows the area where the farm family is assumed to

live. For simplicity, it is assumed that the agricultural field extends from the drainage divide of

the local
Section 5.0 May 2002

5-3

GIS

Processing

Air Model

Source

Model

GIS

Processing

Land Use

STATSGO

(soil data)

Agricultural

Census

Site Data

(soils)

Farm Size

Met Data

Regional

Watershed

Area

Agricultural

Practice

Biosolids

Data

Runoff and erosion

from RWS

Soil Concentration

Air Concentration

Location Independent

Input Data

Location Dependent

Input Data

Model Component

Modeled Media

Concentrations

Legend

Figure 5-1. Biosolids application to agricultural field source module.

Figure 5-2. Emissions mechanisms in the local watershed.
Section 5.0 May 2002

5-4

watershed downslope to the boundary with the buffer (where the family resides). The buffer,

which is part of the local watershed, is also simulated by the LAU model; that is, the LAU model

simulates the dynamic fate and transport of constituents within the agricultural field, from the

agricultural field to the buffer, within the buffer, and from the buffer to the waterbody.

Also illustrated in Figure 5-2 are the mechanisms by which constituent emissions to

surrounding media occur. In the agricultural field itself, emissions to the atmosphere occur via

volatilization of gaseous phases and wind/vehicular erosion of particulate phases. Runoff and

erosion processes transport surficial constituents downslope to contaminate the buffer area and

the contiguous waterbody. Fate and transport processes are simulated in the buffer, similar to

and concurrent with the agricultural field simulation. 

5.1.1.2 LAU Assumptions. A number of assumptions inherent in the LAU model

pertain primarily to how the computational engine, the GSCM, is applied to simulate application

of biosolids to agricultural land. The GSCM is discussed in greater detail in Section 5.1.1.3. 

The LAU assumptions are summarized as follows:

 Biosolids are applied to the soil surface periodically at set intervals (e.g.,

biennially) and then either tilled into the soil to a depth of 20 cm in the case of

crop agricultural field or mixed with the top 2 cm of soil in the case of pasture.

 Whether the biosolids are tilled into the soil or remain near the surface, the

constituent concentration in the zone occupied by the biosolids is assumed to be

uniformly distributed (completely mixed) after each application.

 The contaminant mass is concentrated in the solids portion of the biosolids and is

repartitioned among the solid, aqueous, and gas phases in the soil column.

 Biosolids applications do not result in any buildup of the soil surface, nor does

erosion significantly degrade the soil surface (i.e., the distance from the site

surface [z = 0] to a fixed point below the surface is constant). As a result, there is

no naturally occurring limit to the modeled total soil concentration. In other

words, the modeled constituent concentration in the agricultural field could

exceed the constituent concentration in the waste. Indeed, this is physically

possible for highly immobile constituents if the waste matrix is organic (as is the

case for biosolids) and decomposes, leaving behind the constituent to concentrate

over multiple applications.

 The first-order loss rate due to wind erosion and other surface disturbances is

applied to the surface layer of the soil only and is calculated each year as an

annual average with consideration of losses from an active agricultural field due

to wind erosion, vehicular activity, and tilling operations (for the crop field). The

particulate emission loss rate from an inactive agricultural field or pasture

includes wind erosion only. 
Section 5.0 May 2002

5-5

(5-1)

5.1.1.3 Generic Soil Column Model. The GSCM solves the following partial

differential equation in space and time

where

CT = total (dissolved plus sorbed plus vapor-phase) chemical concentration (M/L3)

t = time (T-1)

DE = effective diffusivity (L2/T)

z = depth in the soil column (L)

VE = effective solute advection velocity (L/T)

k = overall first-order loss rate constant (T-1).

The term on the left side of Equation 5-1 represents changes in CT over time. The first

term on the right side represents vertical transport due to diffusion. The second term represents

vertical transport due to bulk advective movement of water passing downward through the soil

column. The last term represents the cumulative loss of chemical due to decay, hydrolysis,

volatilization, or erosion losses from the surface. The vertical extent over which Equation 5-1 is

solved is the depth from the soil surface down to a depth of 20 cm. Boundary conditions are CT

= 0 at the soil surface and a zero-gradient boundary condition at the lower soil column boundary

(20 cm). A zero-gradient boundary condition implies that the concentration, whatever it may be,

on either side of the boundary is identical on both sides. Initial conditions reflect the

concentration profile over the soil column depth, z, at the time the simulation begins. 

The solution technique used for solving Equation 5-1 over space and time is a hybrid of

analytical and numerical methods developed to achieve a balance between simulation accuracy

and execution speed. It is more fully described in Appendix F. The GSCM operates on a daily

time step, with outputs aggregated to annual average values. The output of the GSCM is a time

series of annual average values. A brief narrative description of how the GSCM is used to

simulate fate and transport of chemical in the crop and pasture agricultural fields follows.

At the start of the simulation (time 0), the soil column is clean; that is, the chemical

concentration is 0 throughout the soil column modeled depth. (Both the crop and pasture

modeled soil column depths are 20 cm.) The initial waste application then occurs, introducing

chemical mass into the soil column. This mass is introduced by calculating the total chemical

mass associated with the waste application, “mixing” that mass into a specific depth of the soil

column, and calculating the resulting uniform chemical concentration over that mixed depth. For

the crop model, the depth over which waste is mixed at the time of application is the full soil

column depth, 20 cm, under the assumption that the waste is mechanically tilled into that depth. 

For the pasture model, the mixing depth is 2 cm, under the assumption that cattle activity and/or

other bioturbation processes effectively mix the surface-applied biosolids to that depth.
Section 5.0 May 2002

5-6

(5-2)

Following incorporation of newly added waste into the soil column, the dynamic solution

of Equation 5-1 then proceeds over space (the vertical soil column is disaggregated into 20 1-cm

thick layers for purposes of simulating vertical gradients—concentration is uniform within a

layer, but can vary among layers) and time. As previously mentioned, the fundamental time step

is daily, so that fate and transport of chemical in the surficial soil layer can respond to daily

rainfall and runoff events. For example, on a dry day, chemical in the surficial soil layer (and

elsewhere) is lost or transported via the mechanisms of vertical advection (long-term average

infiltration/leaching), vertical diffusion, and, as appropriate for the chemical being analyzed,

volatilization, hydrolysis, and biodegradation. On a day with precipitation and runoff (not all

precipitation events lead to runoff), in addition to these processes, chemical is lost from the

surface layer to downslope land areas (the buffer and waterbody) due to lateral advective

transport of any dissolved chemical that has diffused from the soil pore water into the runoff

water and due to erosion of chemical sorbed to eroded soil particles.

This dynamic solution of Equation 5-1 on a daily time step continues until the time of the

next waste application. At that time, the residual chemical concentration profile (just prior to the

new application time) is retrieved, and the concentrations in the soil layers receiving waste

(20 cm in the crop field, 2 cm in the pasture field) are then increased by the newly added

chemical mass, and the simulation is begun again from this new initial condition. At the end of

the agricultural field operating life, when biosolids cease to be added, the simulation continues to

simulate depuration or reduction of chemical. The simulation ultimately terminates when either

99 percent of the peak chemical mass has been removed via the various fate and transport

processes or 200 years has elapsed (from the first application), whichever comes first. (For

dioxins, 200 years comes first because of the persistence of the chemicals.) 

The following major assumptions were used in the development of the GSCM:

The contaminant partitions to three phases: sorbed (solid), dissolved (liquid), and

gaseous. The total contaminant concentration in soil is calculated as follows:

where 

CT = total contaminant concentration in soil

'b = soil dry bulk density (M/L3)

Cs = sorbed-phase contaminant concentration in soil (M/M of dry soil)

w = soil volumetric water content (L3 soil water/L3 soil)

CL = aqueous-phase contaminant concentration soil (M/L3 of soil water)

a = soil volumetric air content (L3 soil air/L3 soil)

CG = gas-phase contaminant concentration in soil (M/L3

 of soil air).

 The contaminant undergoes reversible, linear equilibrium partitioning between the

adsorbed and dissolved phases. The sorbed-phase contaminant concentration in

soil is calculated as follows:
Section 5.0 May 2002

5-7

(5-3)

d Koc K = foc × (5-4)

(5-5)

where Kd is the linear equilibrium partitioning coefficient (L3

/M). For organic

contaminants:

where foc is the organic carbon fraction in soil and Koc is the equilibrium partition

coefficient, normalized to organic carbon. (It is implicit in this linear equilibrium

partitioning assumption that the sorptive capacity of the soil column solids is

considered to be infinite with respect to the total mass of contaminant over the

duration of the simulation, i.e., the soil column sorptive capacity does not become

exhausted. This condition is assumed true for this case.)

 The contaminant in the dissolved and gaseous phases is assumed to be in

equilibrium and to follow Henry’s law. The gas-phase contaminant concentration

in soil is calculated as follows:

where H
 is the dimensionless Henry’s law coefficient.

 Material in the soil column (including bulk waste) can be approximated as

unconsolidated homogeneous porous media whose basic properties ('b, foc, w, a,

and , where  is the total soil porosity) are average annual values, constant in

space. 

 Contaminant mass may be lost from the soil column due to one or more first-order

loss processes. 

 The total chemical flux is the sum of the vapor flux and the flux of the dissolved

solute, diffusive losses of the dissolved phase from the surficial soil pore water

into overlying runoff water during a runoff event, loss of sorbed phase due to

wind and water erosion from the surficial soil, and internal sinks due to

biochemical decay and hydrolysis.

 The chemical is transported in one dimension through the soil column. 

 The modeled soil column remains constant in volume and fixed in space with

respect to the water table.
Section 5.0 May 2002

5-8

(5-6)

5.1.1.4 Hydrology and Soil Erosion. The hydrology model used in the agricultural field

model provides estimates of daily soil moisture, runoff, potential evapotranspiration, actual

evapotranspiration, and infiltration. The hydrology model is based on a daily soil moisture water

balance performed on the root zone depth of the soil column. At the end of a given day, t, the

soil moisture in the root zone of an arbitrary local watershed subarea, i, is updated as

where

SMi,t = soil moisture (L) in root zone at end of day t for subarea i

SMi,t-1 = soil moisture (L) in root zone at end of previous day for subarea i

Pt = total precipitation depth (L) on day t

ROi-1,t = storm runoff depth (L) on day t coming onto subarea i from i-1

ROi,t = storm runoff depth (L) on day t leaving subarea i

ETi,t = evapotranspiration (L) from root zone on day t for subarea i

INi,t = infiltration (groundwater recharge) on day t (L) for subarea i.

Precipitation is undifferentiated between rainfall and frozen precipitation; that is, frozen

precipitation is treated as rainfall. 

Runoff is calculated as a function of soil type, soil cover, precipitation, and antecedent

soil moisture using the SCS “curve number” method (USDA, 1986). Potential evapotranspiration

is estimated as a function of air temperature, latitude (solar declination), and day-of-year using

the Hargreaves equation (Shuttleworth, 1993). Actual evapotranspiration is estimated as a

function of potential evapotranspiration, soil moisture, soil wilting point, and soil field capacity

(Dunne and Leopold, 1978). Finally, infiltration is estimated as that day’s residual soil moisture

(net of runoff and evapotranspiration) in excess of the soil’s field capacity. Maximum

infiltration rates are limited to the saturated hydraulic conductivity of the soil. If the calculated

infiltration exceeds the saturated hydraulic conductivity, a feedback loop is triggered that

increases runoff and/or evapotranspiration (if less than potential evapotranspiration) to maintain

the daily water balance. 

Soil erosion is estimated based on the USLE methodology (Wischmeier and Smith,

1978), modified for application of a daily storm event. The daily application of the USLE is

achieved by linearly distributing the USLE’s long-term average rainfall factor (R) to storm-eventspecific R values. This allocation is made based on the fraction of the long-term R value that is

contributed by each individual storm event, as measured by the storm event’s contribution to

long-term average precipitation depths. A sediment delivery factor is also used in the soil

erosion methodology. The sediment delivery factor accounts for the empirical observation that

less eroded soil leaves a watershed per unit surface area as the size of the watershed increases;

that is, soil mobilized by precipitation and runoff may be trapped in surface depressions before it

can exit the watershed.
Section 5.0 May 2002

5-9

5.1.1.5 Particulate Emissions. Wind erosion, vehicular activity, tilling operations, or

other surface disturbances may result in suspension of surficial soil particles into the atmosphere. 

To the extent that those soil particles contain sorbed chemical, this process becomes a source of

particulate chemical flux into the atmosphere. The agricultural field model includes equations to

estimate particulate emissions of chemical sorbed onto particles of 30 m diameter or smaller. 

These equations are based on empirical relationships developed by EPA in 1986 (updated, U.S.

EPA, 1995a) and Cowherd et al. (1985), which are summarized in U.S. EPA (1999b). These

empirical relationships estimate emission fluxes of surficial soil particles resulting from various

surface-disturbing activities. The contemporaneous sorbed chemical concentration on surficial

soils, estimated by the GSCM, then provides the chemical concentration also sorbed onto the

airborne particles. No chemical “enrichment” is assumed to occur; that is, the sorbed

concentration on the 30-m particles is the same concentration as on the surficial soils. Landdisturbing activities for the agricultural fields (crops and pastures) are wind erosion, vehicular

activity, and spreading (pasture) or tilling (crop).

5.1.1.6 Effective Soil Half-life. Although the source model used in this risk assessment

to simulate chemical releases from and soil concentrations within the agricultural fields receiving

biosolids has been extensively verified, it has not been validated. Verification is the process of

confirming, through testing, sensitivity analysis, or benchmarking against other models, for

example, that a model performs as it was intended by the modelers; that is, its functionality is

verified. Validation is the more rigorous process of confirming that a model’s predictions are in

fact in reasonable agreement with phenomena observed in nature. Model validation requires

extensive and appropriate data on observed emission rates and soil concentrations, as well as

model calibration activities, neither of which were feasible for this modeling study primarily for

the practical reason that there are no data available for all the components of this modeling effort.

Although strict validation to actual site emission rates and soil concentrations was not

feasible, an analysis was performed to estimate overall chemical half-lives at selected percentiles

of the risk distribution built up by the 3,000 probabilistic simulations. A chemical half-life is

simply the period of time that it takes for a chemical to depurate (be reduced) from some initial

concentration to one-half of that concentration. Thus, the half-life is an overall measure of the

various mechanisms by which a chemical may be reduced: physical mechanisms such as

runoff/erosion or leaching or biochemical mechanisms such as decay/degradation or

volatilization. Chemicals that are “quick,” i.e., highly volatile or quickly degraded by

biochemical processes, have correspondingly short half-lives—on the order of weeks, days, or

even smaller time spans. In contrast, other chemicals have exceedingly long half-lives; for

example, some radioactive isotopes have half-lives on the order of thousands of years. A

chemical’s half-life may also be affected by the environment in which the chemical exists. For

example, some chemicals may be rapidly degraded by aerobic biochemical processes, but much

more slowly degraded by anaerobic processes. Thus, the same chemical would have a much

longer half-life in an anaerobic landfill than it would in an oxygen-rich environment, such as a

surficial soil.

An example analysis of half-life was performed for TCDD for the LAU (pasture) model

for selected iterations of the risk distribution. The runs selected corresponded to the runs that

produced beef risk values at the 10th, 25th, 50th, 75th, 90th, 95th, and 99th percentiles. Each of these
Section 5.0 May 2002

5-10

T C D D , 9 5th P e rcen tile R u n

H a lf-L ife = 4 8 years

0.00E+00

1.00E-08

2.00E-08

3.00E-08

4.00E-08

0 50 100 150 200 250

Ye a rs

CTda (ug/g)

Figure 5-3. Example depth-averaged soil concentration

annual time series.

specific model runs, out of the 3,000 probabilistic runs, is defined by its own set of input

parameters. The sets of input parameters corresponding to each of these runs were retrieved from

the larger database, and the source model used to represent the LAU (pasture) was executed for

each set. For each of these half-life runs, the time series of annual average depth-averaged (20

cm) soil concentrations was then analyzed. This analysis first determined the year that the soil

concentration reached its peak (the last year of biosolids application to the pasture), and then

simply counted the number of years required for the depth-averaged soil concentration to reach

half that peak value. The time series of soil concentrations for the 95th percentile analysis is

shown in Figure 5-3 as an example. In this example, TCDD concentration reaches its peak at year

34 and reaches one-half that peak concentration in year 82, for a half-life of 48 years. Calculated

half-lives for all the selected percentiles are shown in Table 5-1.

Table 5-1. Calculated TCDD Half-Lives for Selected Risk Distribution Percentiles

Risk Distribution Percentile

Calculated TCDD Half-Life

(years)

10th 20

20th 38

50th 39

75th 26

90th 35

95th 48

99th 37
Section 5.0 May 2002

5-11

Because each of the 3,000 sets of input parameters represents a somewhat different

“environment” for TCDD, it was expected that the resulting half-lives for the selected percentiles

would be variable, as indeed they are. They do not vary a great deal, however, and the

conclusion of the analysis is that TCDD’s half-life for this risk assessment is within the

approximate range of 20 to 50 years. The Draft Dioxin Reassessment Document presents a

review of the literature on the persistence of dioxin in soil (U.S. EPA, 2000; Section 2.6.1.3,

Transport Mechanisms in Soil). These studies are summarized in Table 5-2. These observed

half-lives seem to corroborate the range of half-lives resulting from the source model runs,

thereby affording a measure of credibility to the modeled results.

Table 5-2. Soil Half-Life Data Reported in the Draft Dioxin Reassessment Document

Soil Half-Life Study Parameters Reference

25 to 100 years

9 to 15 years

Subsurface soil

Top 0.1 cm

Paustenbach, D.J., R.J. Wenning, V. Lau, N.W.

Harrington, D.K. Rennix, and A.H. Parsons,

1992. Recent developments on the hazards

posed by 2,3,7,8-tetrachlorobenzo-p-dioxin in

soil: implications for setting risk-based cleanup

levels at residential and industrial sites. J.

Toxicol. and Environ. Health 36:103-149.

Approximately

20 years

Sludge-amended soil sampled

from a long-term field

experiment

McLachlan, M.S., A.P. Sewart, I.R. Bacon, and

K.C. Jones. 1996. Persistence of PCDD/Fs in

a sludge-amended soil. Environ. Sci. Technol.

30(8):2567-2571.

10 to 12 years Field studies on a military test

area aerially sprayed with

2,4,5-T. Data for 2,3,7,8-TCDD

incorporated in the soil

Young, A.L. 1983. Long-term studies on the

persistence and movement of TCDD in a

natural ecosystem. In: Human and

environmental risks of chlorinated

dibenzodioxins and related compounds.

Tucker, R.E., A.L. Young, A.P. Gray. Eds.

Plenum Press.

5.2 Fate and Transport Modeling

This section describes the methodology and the models that were used to predict the fate

and transport of chemical constituents in the environment. The methodology is based on the

methodology used in the Draft Dioxin Reassessment (U.S. EPA, 2000).

Once dioxin congeners are released, they can move through the air, soil, and food chain

by natural processes. The purpose of the fate and transport modeling performed for this

assessment is to estimate the concentration of dioxins in environmental media (i.e., air, soil, and

food items) to which individuals may be exposed. To predict a contaminant’s movement through

these different media, several media-specific fate and transport models are employed. Fate and
Section 5.0 May 2002

5-12

transport models typically used by EPA are either a series of computer-based algorithms or sets

of equations that predict chemical movement due to natural forces. These fate and transport

models integrate information on a site’s geology, hydrology, and meteorology with chemical,

physical, and biological processes that can take place in the environment. The result is a

simulation of chemical movement in the environment and a prediction of the concentration of a

constituent at a certain point called the “exposure point.” The following fate and transport

models were used for this analysis: 

 Air dispersion and deposition model

 Watershed model

 Food chain model.

These three models and the general framework for performing the fate and transport

modeling are described in the following sections. Section 5.2.1 discusses the air dispersion and

deposition modeling. Section 5.2.2 describes the watershed model used to determine soil and

water constituent concentrations. Detailed descriptions of the models and a comprehensive list

of the input values used in them can be found in Appendices G and H, respectively. The

calculations of the food chain model are based on these media concentrations and are presented

in Section 5.3. 

5.2.1 Dispersion and Deposition Modeling

Dispersion modeling is a computer-based set of calculations used to estimate ambient

ground-level constituent concentrations associated with constituent releases from biosolids

management practice. The dispersion model uses information on meteorology (e.g., windspeed

and wind direction, temperature) to estimate the movement of constituents through the

atmosphere. Movement downwind is largely determined by windspeed and wind direction. 

Dispersion around the centerline of the contaminant plume is estimated by empirically derived

dispersion coefficients that account for movement of constituents in the horizontal and vertical

directions. In addition, constituent movement from the atmosphere to the ground is also modeled

to account for deposition processes driven by gravitational settling and removal by precipitation.

The air dispersion and deposition modeling conducted for this analysis produced output

data that were used to calculate environmental media concentrations and food chain

concentrations (see Section 5.3). The dispersion model outputs included air concentration of

vapors and particles, wet deposition of vapors and particles, and dry deposition of particles. Dry

deposition of vapors was also calculated, but outside the dispersion model.

5.2.1.1 Industrial Source Complex Short-Term Dispersion Model. A number of

dispersion models are available for estimating the transport of constituent through the

atmosphere, several of which are available on EPA’s SCRAM Bulletin Board

(http://www.epa.gov/scram001/). These dispersion models were developed for a variety of

applications and each has its own strengths and weaknesses. The ISCST3 model was selected for

air dispersion modeling in this analysis. Because this assessment required a model with the

capability to model ground-level area sources, ambient air concentrations and deposition fluxes,

vapors and particulates, and annual averaging times, ISCST3 was an appropriate model to use. 
Section 5.0 May 2002

5-13

In addition, ISCST3 is supported by EPA’s Office of Air Quality Planning and Standards and has

been used extensively in regulatory applications.

ISCST3 (U.S. EPA, 1999a), a recommended dispersion model in EPA’s Guideline on Air

Quality Models (U.S. EPA, 1999c), is a steady-state, Gaussian plume dispersion model. A

steady-state model is one in which the model inputs and outputs are constant with respect to

time. That is, the system being modeled is assumed to be unchanging over time. The term

“Gaussian plume” refers to the kind of mathematical solution used to solve the air dispersion

equations. It essentially means that the constituent concentration is dispersed within the plume

laterally and vertically according to a Gaussian distribution, which is another name for to a

normal distribution. These assumptions and solutions hold for each hour modeled. The results

for each hour are then processed to provide values for different averaging times depending on the

user’s needs (e.g., annual average). 

ISCST3 is capable of simulating dispersion of pollutants from a variety of source types,

including point, area, volume, and line sources. ISCST3 can account for both long- and shortterm air concentration of particles and vapor and wet and dry deposition of particles and vapor. 

In addition to deposition, wet and dry plume depletion can be selected to account for removal of

matter by deposition processes and to maintain mass balance. Receptor locations can be

specified in polar or cartesian arrays or can be set to discrete points as needed. Flat or rolling

terrain may be modeled, but only flat terrain may be used for area sources. ISCST3 considers

effects on dispersion of environmental setting by allowing the user to choose dispersion

parameters representing either an urban or rural setting.

5.2.1.2 Configuration of ISCST3 for Air Dispersion and Deposition Modeling.

Results of air dispersion and deposition modeling represent the initial step in the fate and

transport of vapor and particle emissions in the environment. The ISCST3 model was used to

estimate

 Air concentration of vapors

 Air concentration of particles

 Wet deposition of vapors and particles

 Dry deposition of particles.

Dry deposition of vapors was calculated outside of ISCST3, as explained below.

All air concentrations and deposition values developed by ISCST3 were unit values based

on modeling default unit emission rates. Later in the exposure modeling process, the unit air

concentrations [(g/m3

) per (unit emission rate of 1 g/s-m2)] and deposition rates [(g/m2) per

(unit emission rate of 1 g/ s-m2

)] were multiplied by chemical-specific emission rates to produce

values used to calculate environmental media concentrations.

Modeling was conducted using 5 years of data obtained from each of 41 meteorological

stations assumed to be representative of the climatic regions throughout the country (see

Section 4.3 for a discussion of meteorological site selection). Modeling was conducted using the

median farm size area in each of the 41 climatic regions as the source area.
Section 5.0 May 2002

5-14

Summary of ISCST3 Modeling

 The wet and dry depletion option was activated

in the dispersion modeling for particles. Wet

depletion was considered for vapors.

 Area source was modeled for biosolids

management.

 Modeling was conducted using unit emission

rates.

 The rural option was used in the ISCST3

modeling because the agricultural management

practices being assessed are typically in

nonurban areas.

 Flat terrain was assumed. 

Air Concentrations of Vapor and Particles. ISCST3 estimates air concentrations of

particles and vapors based on a number of variables, including wet and dry deposition and plume

depletion. The model accounts for downwind movement of the plume containing airborne

vapors and particles. It also accounts for dispersion of vapors and particles around the centerline

of the plume as the plume travels in a downwind direction. Removal of constituent mass from

the plume occurs as a result of wet and dry deposition. Wet and dry deposition are important

processes in indirect exposure modeling because they account for the movement of constituent

mass from the atmosphere to soil, water, and vegetation. Deposition is discussed below. There

is, however, a closely related process, known as depletion, that affects the calculation of air

concentrations. 

Depletion is essentially the mirror of

deposition. That is, while deposition

accounts for the amount of constituent that

moves to the ground, depletion accounts for

the amount of mass removed from the

atmosphere by deposition. The ISCST3

model allows the user to model depletion and

deposition separately (i.e., the user may select

depletion, deposition, or both depletion and

deposition). When depletion is included, the

mass deposited on the ground due to wet or

dry deposition is removed from the plume,

thereby avoiding double counting (U.S. EPA,

1995d). In this analysis, air concentration of

particles was modeled with both wet and dry

depletion activated. For vapors, ISCST3 was

used to model only wet deposition and

depletion, and dry deposition of vapors was

calculated outside the model. As a result of calculating dry deposition/depletion of vapors outside

ISCST3, the mass balance for vapors was not maintained and uncertainty was introduced into the

air modeling calculation, which would tend to overpredict vapor air concentrations.

Wet Deposition of Particles and Vapor. Wet deposition is the deposition of material on

a surface from a plume as a result of precipitation. The amount of material removed by wet

deposition from the plume is a function of the scavenging rate coefficient, which is based on

particle size (U.S. EPA, 1995d). To perform these calculations, wet deposition, wet depletion,

and dry depletion were all selected in the input run-stream file. Precipitation data from the

SAMSON CD-ROM (U.S. DOC and U.S. DOE, 1993) were required to process the

meteorological inputs for this analysis. 

Dry Deposition of Particles. Dry deposition refers to the deposition of material on a

surface (e.g., ground, vegetation) from a plume of material as a result of processes such as

gravitational settling, turbulent diffusion, and molecular diffusion. Dry deposition is calculated

as the product of air concentration and dry deposition velocity. To calculate dry deposition,
Section 5.0 May 2002

5-15

ISCST3 requires mass mean diameter, particle density, and mass fraction to be input into the

source pathway for deposition calculations (U.S. EPA, 1995b). Dry deposition calculations also

require the meteorological input file to contain surface friction velocity, hourly Monin-Obukhov

length, and surface roughness length. Surface friction velocity and hourly Monin-Obukhov

length were calculated in the PCRAMMET preprocessor (U.S. EPA, 1995c). More detail on the

PCRAMMET preprocessor is provided in Appendix G.

Dry Deposition of Vapors. Dry deposition of vapors was calculated using a step

external to the ISCST3 model because chemical-specific dry deposition modeling within ISCST3

was precluded by time considerations. Using a dry deposition algorithm for particles (from the

ISCST3 user’s manual), dry deposition of vapor was calculated by multiplying the vapor air

concentration by a default deposition velocity of 0.5 cm/s (Koester and Hites, 1992). This

approach assumes that vapors behave as fine aerosols and, therefore, are amenable to modeling

using the dry deposition algorithm for particles. 

To calculate the weighted dry deposition velocity, land use was obtained from 1:250,000-

scale quadrangles of land use and GIRAS spatial data obtained from the EPA website and placed

in an ARC-INFO format (U.S. EPA, 1994). Land use was based on data from the mid-1970s to

the early 1980s. The fraction of time in each stability class was based on 5-year hourly

meteorological files used in ISCST3 modeling.

Averaging Time. For the dioxins in the biosolids risk assessment, all human health risks

were evaluated based on benchmarks for chronic, long-term exposure. Therefore, the air

concentrations and deposition values required for the human health and ecological risk

assessment were long-term averages. Long-term averages calculated by the ISCST3 model were

annual averages. However, because the ISCST3 model was run using 5 years of meteorological

data, it actually averages the hourly concentrations over the entire 5-year period.

Rural vs. Urban. The rural vs. urban setting in ISCST3 allows the user to account for

differences between rural and urban environments. In urban environments, the built environment

(e.g., buildings, roads, and parking lots) alters the dispersion character of the atmosphere,

particularly at night because of building-induced turbulence and reduced nighttime cooling. 

Thus, there is greater nighttime mixing of constituents in urban areas compared with rural areas. 

For purposes of ISCST3 modeling, the urban classification applies mainly to large cities; even

small cities and suburban areas are classified as rural for ISCST3 purposes. For this analysis, the

rural setting was used.

Placements of Points Where Air Concentrations Were Calculated. A grid of points

where air concentration and deposition values were calculated was established using a Cartesian

grid. Air concentration and deposition values were produced for each point on the grid (i.e., x, y

coordinate) at fixed distances ranging from 0 to 20,000 m from the edge of the management site. 

For the agricultural application, the receptors were placed on the field and at the following

distances from the edge of the field: 100, 200, 500, 1,000, 1,500, 2,000, 3,000, 5,000, 7,500,

10,000, 15,000, and 20,000 m.
Section 5.0 May 2002

5-16

Flat vs. Elevated Terrain. The ISCST3 model allows the user to account for elevated

terrain by specifying an elevation for each point on the grid where air concentrations and

deposition values are calculated. This feature, however, is not available for use with area

sources. Because all sources modeled in this analysis were area sources, elevated terrain was not

considered.

TOXICS vs. Regulatory Mode. The most recent version of ISCST3 (99155, U.S. EPA,

1999a) allows the user to select a regulatory default option or to select a TOXICS option. The

regulatory default option uses the Romberg numeric integration solution to estimate air

concentration from an area source. Based on the results of validation tests performed by EPA,

EPA concluded that the Romberg algorithm performs very well in terms of efficiency and

reasonableness (U.S. EPA, 1992). However, this algorithm takes a significant amount of time to

execute for large area sources. To improve model run times, the TOXICS option was added to

the area source model by EPA. The TOXICS option also uses a Romberg numeric integration

solution to estimate air concentrations and deposition rates near the management site. Farther

from the site, however, the TOXICS option uses a two-point Gaussian Quadrature routine instead

of the Romberg solution to estimate air concentration and deposition. The two-point Gaussian

Quadrature solution is computationally more efficient, which accounts for the shorter model run

time. For this study, a sensitivity analysis was conducted to compare the estimated air

concentrations calculated using the regulatory option with those calculated using the TOXICS

mode. This analysis showed small differences between results obtained using either option (see

Appendix G). Given the benefit of reduced run times, the TOXICS option was selected for this

analysis. 

Source Shape. Agricultural land was modeled as a ground-level area source. The

ISCST3 model allows the user to model area sources as polygonal sources with 3 to 20 sides

(U.S. EPA, 1999a). The ISCST3 was set up in this analysis to model an area source as a square. 

This option was chosen because there are no actual data on the shape of sources, and a square

source is assumed most like agricultural land. 

5.2.1.3 Preparing ISCST3 Input Files. Two types of input files are required to run

ISCST3, a run-stream file and a meteorological file. The run-stream file is an ASCII file that

contains the model option settings, source parameters, and receptor locations. The

meteorological file contains hourly values of windspeed, wind direction, stability class, mixing

height, ambient air temperature, and precipitation type and amount. 

 ISCST Run-Stream Files. The ISCST3 run-stream file is composed of six pathways

that drive different model functions: the Control Pathway, Source Pathway, Receptor Pathway,

Meteorology Pathway, Terrain Grid Pathway, and Output Pathway. Each of these pathways is

described in Appendix G. The Terrain Grid Pathway is not presented because it is used only with

point sources (i.e., for facilities with stacks), which were not modeled in this analysis.

Meteorological Files. The meteorological file was generated using the meteorological

preprocessor PCRAMMET (U.S. EPA, 1995c). The preprocessor pairs hourly surface

observations with upper-air measurements. For each of the 41 meteorological stations modeled,

5 years of surface and upper-air data were used. The preprocessor creates a file in binary format
Section 5.0 May 2002

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Key Meteorological Data for

 the ISCST3 Model

Wind Direction determines the wind direction of the

greatest impacts.

Windspeed is inversely proportional to ground-level

air concentration, so the lower the windspeed, the

higher the concentration.

Stability Class affects the rate of lateral and vertical

diffusion. The more unstable the air, the greater the

diffusion.

Mixing Height determines the height to which

chemical constituents can be diffused vertically. 

that contains hourly windspeed, wind

direction, atmospheric stability class,

temperature, and mixing height. Land use

data also were required by PCRAMMET in

the vicinity of each meteorological station to

derive air model inputs, such as Bowen ratio,

surface roughness height, minimum MoninObukhov length, noontime albedo, and the

fraction of net radiation absorbed by the

ground. Appendix G discusses the derivation

of each of these model inputs.

5.2.1.4 Source Areas Modeled. In

the modeling analysis, application to

agricultural land was considered. Because the

ISCST3 model is sensitive to the size of the

area source, the relationship between air concentrations and size of the area source was analyzed. 

For relatively small area sources, air concentrations increase significantly as the size of the area

source increases. For large area sources, this increase in air concentrations is not as significant. 

The median farm size for each climatic region was modeled.

5.2.1.5 Spatial Averaging of Air Concentrations and Deposition. A GIS model was

used to calculate air concentration and deposition rates for the buffer area, agricultural field,

regional watershed, and waterbodies. This crucial step combines the spatial characterization of

the buffer area, agricultural field, and waterbody in the site layout with air modeling outputs for

each climatic region/management practice combination.

In an automated batch program, the ASCII files produced by ISCST3 were converted

from a Cartesian array of values into an evenly spaced grid of concentration values distributed

around the center of the site layout in the form of a GIS point coverage. To calculate the point

estimate for a location, the program estimates the air concentration of vapors and particles from

this GIS point coverage by selecting the grid point nearest that location. These values are used

directly to determine human inhalation exposures.

To calculate the spatial averages for the buffer area, agricultural field, and waterbody

polygons, the program individually overlays these areas with this point coverage and averaged

the overlapping points. These mean concentration values and their associated identifiers are the

output of the program and represent the average air concentrations and deposition values used in

subsequent modeling steps to predict soils, water, and food chain concentrations. The

distribution of average ambient air concentration estimated in the buffer using all sample

concentrations used to estimate inhalation risks are presented in Table 5-3. This distribution also

includes variability across the 41 climatic regions, as well as variability in agricultural practices.
Section 5.0 May 2002

5-18

Table 5-3. TCDD-TEQ Media Concentration

for Ambient Air Variable Concentrations

Percentile

TCDD-TEQ

Concentration in

Ambient Air

(ng/m3

)

Rural Air

Background

50th 2.3E-7 1.7E-5

75th 4.8E-7

90th 9.5E-7

95th 1.4E-6

99th 3.1E-6

The air concentration of dioxin congeners was influenced by several factors in the risk

analysis that were ranked by the statistically based sensitivity analysis according to percentage of

variation accounted for by the variable. This analysis has shown that the most important factors

influencing the air concentrations of dioxins are

 Agricultural application rate (how many tons of biosolids are applied to the land

per unit area)

 Number of years biosolids are applied to the land.

Other factors that are also important in this analysis are linked to the geographic location of the

modeled farm:

 Meteorological conditions (temperature, rainfall, windspeed, etc.) 

Soil conditions (soil foc, soil bulk density, etc.)

Area of the farm where biosolids are assumed to be applied. 

All of the factors linked to location influence the release of dioxin congeners to the air to

varying degrees. The location that is linked to 26 of the 30 iterations in the highest 1 percent of

the air concentrations is Phoenix, AZ. This is not unexpected in that this is an extremely hot

location with large farm areas. The other locations represented in the highest 1 percent of air

concentrations are Tampa, FL, and Fresno, CA, also hot locations. Other locations associated

with the top 10 percent of air concentrations are mostly more southern areas (Houston, TX;

Meridian, MS; Atlanta, GA; Charleston, SC; Shreveport, LA; and Las Vegas, NV) or areas with

very large average farm sizes (i.e., western climate regions; Bismark, ND; Boise, ID; Boulder,

CO; Casper, WY). This indicates that ambient temperature is an important climatic parameter.
Section 5.0 May 2002

1 Aerial deposition from a crop or pasture back onto itself is not considered, nor is aerial deposition from

the crop onto the pasture, or vice versa; only deposition onto the buffer is considered. 

5-19

5.2.2 Estimation of Soil and Sediment Concentrations

This section describes the components that make up the waterbody model and those

portions of the watershed model that simulate fate and transport of chemicals that have been

aerially deposited and eroded. There are two different types of watersheds—local watersheds,

which contain the agricultural land where biosolids are applied that are subject to sheet flow

runoff and erosion directly from the field, and regional watersheds. The regional watershed is the 

drainage area upstream of the modeled waterbody. Fate and transport of eroded chemical from

the agricultural field downslope across the buffer area and into the waterbody is simulated by the

source model as described in Section 5.1. The watershed models discussed below consider only

chemical that is airborne from either the crop or pasture and subsequently deposited onto the

residential buffer area of the local watershed1 and the regional watershed. Thus, eroded chemical

contaminating the buffer area or entering the waterbody from the local watershed consists of two

components: (1) direct erosion from agricultural field and (2) aerially deposited and subsequently

eroded. These two components are summed to determine total chemical in the buffer soils and

load entering the waterbody. For the regional watershed, only the aerially deposited component

is relevant. Discussions in this section are general in nature. Two appendices support the

discussions with more detailed information: Appendix H contains the full set of equations used

to calculate media concentrations, and Appendix D lists the physical/chemical properties used;

the parameter values selected for fate, transport, and exposure modeling; and citations for the

parameter values selected.

5.2.2.1 Predicting Soil Concentrations. Soil concentrations due to aerially deposited

chemical were calculated for the buffer area of the local watershed and the entire regional

watershed. Soil concentrations are determined by the deposition flux of chemical and loss

mechanisms of that chemical from the soil. Soil losses accounted for in this analysis include

only erosion. Other losses, such as biodegradation, volatilization, leaching, and dissolved loss in

surface runoff, were assumed to be negligible for the chemicals considered in this analysis.

Soil concentrations in the regional watershed were calculated using the solution to a

differential equation that expresses soil concentrations over time as a function of loadings and

first-order losses, as presented in Equation 5-7. This equation is based on the soil concentration

equation presented in the Draft Dioxin Reassessment Document (U.S. EPA, 2000) and was

modified for this application to include aerially deposited loads of congeners as inputs. One of

the fundamental underlying assumptions of the approach used for the regional watershed is that

the soil compartment can be modeled as completely mixed. No losses other than erosion losses

were assumed in this application, i.e., Ks = 0. 
Section 5.0 May 2002

5-20

(5-7)

Csoil_t

Parameter Definition Value

Csoil_t Total soil concentration Calculated

Csoil_i Initial soil concentration (mg/kg) Calculated

Dep Deposition term for soil (mg/yr) Calculated in Table H-2.8

Load Mass of contaminant loaded to soil (mg/yr) Calculated in Table H-2.17

Soil R Mass of soil removed from site (kg/yr) Calculated in Table H-2.22

Ks Soil loss constant (1/yr) Calculation

Mass Mass of soil (kg) Calculated in Table H-2.20

T Time for which soil concentration is being calculated (yr)

Source: Based on U.S. EPA (2000) with values for deposition load added into the equation.

Note: Depending on the value of T, this equation is used to calculate Csoil t1, Csoil t2, Csoil td. The value for T

is determined in either Csoil 1F or Csoil 2F.

The USLE was used to estimate soil erosion losses (Xe) as shown in Equation 5-8. The

USLE is an empirically derived equation originally developed by the SCS of the USDA to

estimate soil erosion losses from agricultural fields during soil conservation planning. The

USLE is applied in the context of the Gross Erosion Sediment-Delivery Ratio method outlined in

USDA (1978) and described in greater detail in the SCS National Engineering Handbook

(USDA, 1971). Gross erosion is defined as the summation of erosion from all sources within a

watershed, as estimated for sheet and rill erosion by USLE. The sediment delivery ratio adjusts

gross erosion rates to account for terrain and cover features, which effectively reduce sediment

erosion. 

Constituent loadings to soil in the buffer area and the regional watershed area due to

aerial deposition of vapors and particles were calculated using Equation 5-9.

Soil constituent concentration changes with each year of application of biosolids to the

agricultural land. During the application period, the dioxin concentrations in soils resulting from

aerial deposition steadily increase for such a persistent chemical. This temporal change,

combined with the assumption that a receptor can begin his or her exposure duration at any time

during the facility operation period, is accounted for in the soil concentration model by

dynamically estimating the soil concentration at the beginning of the exposure duration and the

soil concentration at the end of the exposure duration and determining the average concentration

over the exposure period. 
Section 5.0 May 2002

5-21

(5-9)

Dep Q Area F D D F D D = ×× × × + +− × + 1000 1 [ ] v ydv ywv v ydp ywp ( ) ( ) ( )

X R K LS C P e =×× ×××

90718

4047

. (5-8)

D CV

ydv yv dv

= ×× 0 31536 .

Xe

Parameter Definition Value

Xe Loss due to erosion (kg/m2

/yr) Calculated

R USLE rainfall/erosivity factor (1/yr) See Appendix E

K USLE soil erodibility factor (short tons/acre) See Appendix E

LS USLE length-slope factor (unitless) Calculated in Table H-2.20

C USLE cover management factor (unitless) See Appendix C

P USLE supporting practice factor (unitless) See Appendix C

907.18 Conversion factor (kg/short tons)

4047 Conversion factor (m2

/acres)

Source: U.S. EPA (1998).

Dep

Parameter Definition Value

Dep Deposition term for soil (mg/yr) Calculated

0.31536 Unit conversion factor (m-g-s/cm-g-yr)

Cyv Normalized vapor-phase air concentration (g-s-m2

/gm3) See Appendix G

Vdv Dry deposition velocity (cm/s) See Appendix D

1000 Unit conversion (mg/g)

Q Emission rate from source (g/s-m2

) Calculated by

source model

Area Area of deposition (m2

) See Appendix E

Fv Fraction of air concentration in vapor phase (unitless) See Appendix D

Dydv Normalized annual average dry deposition from vapor phase (s-m2

/m2-yr) Calculated

Dywv Normalized annual average wet deposition from vapor phase (s-m2

/m2-yr) See Appendix G

Dydp Normalized annual average dry deposition from particle phase (s-m2

/m2-yr) See Appendix G

Dywp Normalized annual average wet deposition from particle phase (s-m2

/m2-yr) See Appendix G

Source: U.S. EPA (1998).
Section 5.0 May 2002

5-22

Table 5-4 presents the soil concentrations estimated in the buffer area, tilled cropland,

pasture, and stream sediment using all the samples of dioxins, furans, and PCBs as variable

congener concentrations in the model.

The individual congeners in each biosolids sample are modeled individually in the source

partition model. However, the congeners remained linked throughout the modeling by their

sample number. Thus, all media concentrations of the dioxin congeners can be summed using

the TEF system to produce a single TEQ media concentration resulting from the application of

biosolids represented by a specific sample. Therefore, the results of the probabilistic source

partition model may be expressed as a distribution of TEQ soil concentration in the crop, pasture,

and buffer soils. Table 5-4 presents TEQ soil concentrations that show specific percentiles from

this distribution. The background concentration for rural soil is 2.5 ng/kg TCDD-TEQ, and the

national average background concentration for sediment is 5.8 ng/kg TCDD-TEQ. 

Table 5-4. TCDD-TEQ Media Concentration for Soil

in Buffer, Crop Land, Pasture, and Sediment

Variable Concentrations

Percentile

TCDD-TEQ Concentration

(ng/kg)

Buffer

Crop

Land Pasture Sediment

Applied

Biosolids

50th 0.6 0.3 1.5 0.04 24

75th 1.0 0.6 2.6 0.08 35

90th 1.6 0.9 4.2 0.15 55

95th 2.3 1.4 5.7 0.25 74

99th 8.4 3.4 21.4 0.7 453

The dioxin congener concentrations in the soils of the crop land, pasture, and residential

area are influenced by the following factors in the risk analysis. These factors were identified by

the statistically based sensitivity analysis described in detail in Section 8.1.2.6 and are ranked

according to the percentage of variation they account for in the estimation of the soil

concentrations:

The year during biosolids application that the farm family moves to the farm

Agricultural application rate (tons of biosolids applied to the land per unit area)

Number of years biosolids are applied to the agricultural area.

Other less important factors in this analysis as ranked by the sensitivity analysis are linked to the

geographic location of the modeled farm:
Section 5.0 May 2002

5-23

Soil conditions (soil foc, soil bulk density, etc.)

Meteorological conditions (temperature, rainfall, windspeed, etc.) 

Area of the farm where biosolids are assumed to be applied.

The greater the loading of biosolids to the soil during the period of time the farm family is

exposed, the greater the soil media concentration to which the family is exposed. The farm

family is assumed to move to the farm where biosolids are applied during the period of biosolids

application. The later the farm family moves to the farm, the greater their estimated exposure

because dioxins are persistent and accumulate during the period of application and then remain at

high levels for many years after applications cease. 

All of the factors linked to location influence the concentration of dioxin congeners in the

soil. The locations linked to the highest 1 percent of the soil concentrations are more varied than 

the locations associated with the highest 1 percent of air concentrations. The highest 1 percent of

soil concentrations are linked to cooler locations with smaller average farm sizes. There is no

single location that is linked to more than 5 of the top 30 soil concentrations. The locations and

the number of iterations in the top 30 for the location are Little Rock, AR (5); Burlington, VT

(4); Salem, OR (4); Seattle, WA (4); Muskegon, MI (3); Boise, ID (3); Bismark, ND (2);

Winnemucca, WI (2); Minneapolis, MN (1); Salt Lake City, UT (1); and Chicago, IL (1).

5.2.2.2 Predicting Surface Water Concentrations. The waterbody in this analysis is a

stream located downslope of the waste management unit. For modeling purposes, the stream is

shaped as a rectangle 5.5 m wide and as long as the width of the agricultural fields. It was

assumed that the stream is 5.5 m because this width is the median of a third-order fishable stream

(van der Leeden et al., 1990). A third-order stream refers to a type of stream segment

classification. In this classification scheme, a first-order stream segment is one with no

tributaries. That is, a first-order stream segment receives all of its flow from runoff from the

surrounding watershed soils. A second-order stream segment is produced when two first-order

stream segments come together. A third-order stream segment occurs when two second-order

segments come together, but not when a second-order and a first-order stream segment combine. 

The third-order steam segment, therefore, has the combined flow of at least two second-order

stream segments. The third-order stream was selected because it reasonably represents the

smallest waterbody that would routinely support recreational fishing of consumable fish. 

Constituents can enter the waterbody by one of four pathways: 

 Constituents in the air above the waterbody can be deposited directly onto the

waterbody’s surface. This occurs for airborne particles via dry and wet deposition

due to gravitational settling and scavenging by precipitation, respectively. 

 Vapors can also deposit directly onto the waterbody’s surface via scavenging by

precipitation (i.e., wet deposition). 

 Constituents on the soils in the local watershed can enter the waterbody through

runoff and erosion. 
Section 5.0 May 2002

5-24

 Constituents on the soils in the upstream regional watershed can also enter the

waterbody through runoff and erosion. 

Thus, the total chemical load to the waterbody is the sum of 

a. Direct atmospheric inputs 

b. Eroded load from the local watershed (which itself is the sum of chemical eroded

directly from the agricultural fields plus aerially deposited and eroded load from

the buffer) 

c. Eroded load from the regional watershed.

Once in the waterbody, constituents are assumed to be uniformly mixed in a single stream

segment. There is water flow in and out of the stream segment, which is predicted by the

Regional Watershed Model as described next. Water flowing into the upstream boundary of the

waterbody is assumed to have a constituent concentration determined by the application of the

soil concentration algorithms described in Section 5.2.2.1 applied to the soils in the regional

watershed. The waterbody is modeled based on the waterbody model described in the Draft

Dioxin Reassessment Document (U.S. EPA, 2000). The equations used are presented in

Appendix H; they partition the chemical mass into chemical sorbed to suspended solids in the

water column and chemical sorbed to sediment solids. The soluble fractions are assumed to be

zero.

Regional Watershed Model and Waterbody Streamflow. Because the chemicals of

concern in this analysis have a strong tendency to be both persistent and accumulative in soils

and sediments, it was considered essential to include in the analysis inputs to the waterbody that

result from aerial deposition over the upstream watershed and subsequent erosion. As discussed,

that upstream watershed is termed here the regional watershed to distinguish it from the local

watershed, i.e., the hillside area containing the agricultural fields or landfill. Chemicals

deposited onto the regional watershed will be transported in their particulate form on eroded soils

into the waterbody network that drains the regional watershed and hence downstream into the

modeled waterbody where fishing is assumed to occur. (Not all soils that are eroded from the

regional watershed complete the journey downstream to the modeled waterbody. A sediment

delivery ratio is included in the calculations that estimates the fraction of mobilized soil that

actually arrives at the modeled waterbody as a function of regional watershed area.) A schematic

diagram illustrating the regional watershed and its relationship to the agricultural fields and the

local watershed is shown in Figure 3-1.

The regional watershed was modeled to provide estimates of two inputs to the modeled

waterbody: streamflow and chemical loads associated with eroded soil. Suspended solids

concentrations in the waterbody were not modeled, but were assumed to be a constant of 10

mg/L, in accordance with the Draft Dioxin Reassessment Document (U.S. EPA, 2000). 

Chemical loads associated with eroded soils were estimated using the same equations used for

aerially deposited chemical in the local watershed, as discussed in Section 5.2.2.1. Methods used

for estimating streamflow are discussed below.
Section 5.0 May 2002

5-25

Streamflow in the modeled waterbody consists of surface runoff from the upstream

regional watershed, a baseflow component, and surface runoff from the local watershed (for

agricultural fields only). Surface runoff from the upstream regional watershed was estimated

using the hydrology algorithm from the source partition model. Because the hydrology algorithm

is not stand-alone, executing it required running the source partition model in a mode termed the

“LAU as Regional Watershed” (see Appendix F). In this mode, the model is run to estimate the

surface water runoff. 

Baseflow represents the component of streamflow that is not direct surface runoff. 

Baseflow was estimated as a function of regional watershed area and U.S. Geological Survey

(USGS) Hydrological Unit Code (HUC) number using regional regression models. These

regression models predict HUC-specific 30Q2 low flows as a function of watershed area. The

30Q2 flow is a statistical estimate of the 30-day average low flow expected to occur, on average,

every other year (2-year return period). The 30Q2 low flow was assumed (for this analysis) to be

a reasonable representation of stream baseflow. 

The third component of streamflow is direct surface runoff from the local watershed, i.e.,

the tributary hillside containing the agricultural fields. This runoff is a modeled output of the

model used for the agricultural fields; however, the monofill is assumed to have no runoff. Thus,

surface runoff for the agricultural field local watershed was available and was used as a

contributor to total streamflow. For the landfill (monofill) scenario, streamflow is composed

only of surface runoff from the upstream regional watershed and baseflow.

5.3 Calculation of Food Chain Concentrations

Constituents can pass from contaminated air, soil, and surface water to reach individuals

through the food chain. For example, constituents that are entrained in air may be deposited on

plants growing in the agricultural field or home garden. Constituents from the air and soil may

accumulate in fruits and vegetables that are consumed by people. In addition, beef and dairy

cattle may feed on forage and silage that are grown in biosolids-amended soil. The beef and

dairy products may be subsequently consumed by people. Free-range chickens may also consume

contaminated soil. Similarly, constituents that erode into surface water may accumulate in fish,

which are subsequently consumed by a recreational fisher.

This section presents the methodology used to calculate contaminant concentrations for

each of the food chain pathways considered. An approach was developed for a terrestrial food

chain to calculate concentrations of produce, poultry, eggs, beef, and milk that are consumed by

the adult and child farmer evaluated in this assessment. In addition, an approach was developed

for an aquatic food chain to calculate concentrations in fish that may be consumed by a

recreational fisher. 

5.3.1 Terrestrial Food Chain

The terrestrial food chain is designed to predict the accumulation of a contaminant in the

edible parts of aboveground vegetation from direct deposition of contaminants in air. 

Concentrations are predicted for three main categories of food crops presumed to be eaten by
Section 5.0 May 2002

5-26

Media

Concentration

Model

Site Data

(soils)

Met Data

Chemical

Specific

Transfer

Factors

Regional

Watershed

Area

Soil Concentration

Air Concentration

Aboveground

Vegetable

Concentration

Location Independent

Input Data

Location Dependent Input

Data

Model Component Modeled Media

Concentrations

Legend

Surface Water

Concentration

Fruit

Concentration

Root

Vegetable

Concentration

Egg

Concentration

Poultry

Concentration

MIlk

Concentration

Beef

Concentration

Figure 5-4. Biosolids application to agricultural fields media concentration module.

humans: exposed fruits, exposed vegetables, and root vegetables. The term “exposed” refers to

the fact that the edible portion of the produce is exposed to the atmosphere. Examples of the

three categories include tomatoes (exposed vegetable), apples (exposed fruit), and potatoes (root

vegetables). Figure 5-4 shows the data flow into and out of the food chain model.

In addition, the terrestrial food chain estimates the contaminant concentration in farm

crops for cattle. Vegetation consumed by cattle includes grain, forage, and silage. Forage is

considered exposed vegetation. Silage is calculated as exposed vegetation; however, an

empirical correction factor for silage takes into account that silage is partly protected and partly

exposed. 

Table 5-5 summarizes the mechanisms by which vegetation can be exposed to

contaminants. The two mechanisms are deposition of particle-bound contaminants to exposed

plant tissues and vapor-phase deposition of contaminants to exposed plant tissues. Exposed

vegetation is subject to contamination via particulate deposition and vapor-phase deposition,

while protected vegetation is not contaminated because the edible portion of the vegetation is not

in direct contact with air. 
Section 5.0 May 2002

5-27

Table 5-5. Terrestrial Food Chain Vegetation

Type of Vegetation

Particulate

Deposition

Vapor-Phase

Deposition

Human ingestion

Exposed vegetables 7 7

Exposed fruit 7 7

Beef and dairy cow ingestion

Forage 7 7

Silage 7 7

5.3.1.1 Aboveground Vegetation. Aboveground vegetation is subject to contamination

via deposition of particle-bound contaminants and vapor transfer of contaminants. Equation 5-10

is used to calculate the concentration of contaminant in aboveground vegetation.

Deposition of Particle-Bound Contaminants. Airborne particle-bound contaminants

are deposited by wet and dry deposition; thus they affect only exposed vegetation. As described

earlier, the air dispersion model ISCST3 was used to calculate the wet and dry deposition rates

for the particle-bound contaminants. Not all airborne particles will settle on a plant’s edible 

surface. Some will fall to the ground; others will fall on other surfaces that will undergo

weathering processes, such as wind removal, water removal, and growth dilution; and most will

end up in the soil or eroded soil. Thus, only a fraction of the total deposition rate per area is used

to estimate the amount of airborne particles that contacts the edible portion of the plant. 

The calculation of vegetative concentration due to deposition also takes into account the

length of time plants are exposed to contaminants. One determination of the length of exposure

is the growing season. For instance, the time from when a tomato begins to grow until it is

harvested equals its length of exposure to deposition. The productivity level of the plant or

biomass is also a factor. The biomass is determined by the amount of standing crop for the

average farm. The biomass is needed to take into account the dilution of constituent by biomass

growth. Equation 5-11 is used to calculate the concentration of congeners due to direct

deposition.
Section 5.0 May 2002

5-28

( )

( ) P

D R

Y K Par

d

p p

p p

= ×

×

(5-11)

( ) P PPP

MAF

vegww dvr veg veg veg

= ++ 



 



 ×

100−

100

P PPP veg d v r DW veg veg veg= ++ 



 





Pveg

(5-10)

Parameter Definition Value

Pveg Vegetation concentration (mg/kg) Calculated

Pveg_ww Vegetation concentration [wet weight (mg/kg-WW)] Calculated

Pveg_DW Vegetation concentration [dry weight (mg/kg-DW)] Calculated

Pd_veg Vegetative concentration due to direct deposition (mg/kg - DW) Calculated in Table H-3.12

Pv_veg Vegetative concentration due to air-to-plant transfer (mg/kg - DW) Calculated in Table H-3.14

Pr_veg Aboveground vegetation concentration due to root uptake, zero for

this analysis (mg/kg - DW)

0

MAF Plant tissue-specific moisture adjustment factor to convert DW

concentration into WW (percent)

Source: U.S. EPA (1998).

Note: For exposed vegetation, MAF is 92; for exposed fruit, MAF is 85. Dry weight (DW) is used for silage

and feed. Wet weight (WW) is used for exposed vegetation and exposed fruit.

Pd

Parameter Definition Value

Pd Vegetation concentration due to air deposition (mg/kg DW) Calculated

Dp Deposition term for plants (mg/m2

-yr) Calculated

Rp Interception fraction - aboveground vegetables (fraction) See Appendix I 

Exposed fruits and vegetables 0.48

Forage 0.35

Feed 0.62

Yp Crop yield (kg DW/m2

) See Appendix I

Exposed fruits and vegetables 1.17

Forage 0.15

Feed 0.63

KpPar Plant surface loss coefficient, particulate (1/yr) 18.07

Source: U.S. EPA (2000).
Section 5.0 May 2002

5-29

( ) P

C B VG

v

vapor v ag

= ×× × 1000

1200

(5-12)

Vapor-Phase Transfer of Contaminants. The concentration of contaminants due to

vapor-phase transfer depends on the constituent being considered. Evidence shows that wet

deposition is negligible and contact of vapor phase with the plant surface is the primary

mechanism of plant uptake; therefore, a different equation is used based on the vapor-phase air

concentration of the constituent. Equation 5-12 is used to calculate the concentration of

congeners in aboveground vegetation due to air-to-plant transfer.

Vapor-phase transfer for high log Kow constituents, such as dioxins, furans, and coplanar

PCBs, uses a congener-specific air-to-plant biotransfer factor to estimate the concentration of

contaminants in vegetation. The air-to-plant biotransfer factor is defined as the ratio of

contaminant concentration in exposed plant parts to the vapor-phase concentration of

contaminant in air. The biotransfer factors have been measured for these constituents (U.S. EPA,

2000). In addition, an empirical correction factor (VGAG) is recommended by EPA (U.S. EPA,

1997) to be applied to the calculation of concentrations in each type of vegetation. The factor is

used to adjust the air-to-plant bioconcentration factors that are developed using the different

types of vegetation considered in this analysis. This factor also is applied to take into account the

difference between outer-surface and whole-plant concentrations. This is important for lipophilic

organic chemicals that tend to remain on the outer portion of the plant surface because washing

and peeling fruits and vegetables reduces the outer surface residues. Because silage is assumed

to be partly protected and partly exposed, the correction factor for silage takes into account that

some of the vegetation is not contaminated as a result of vapor deposition onto plant surfaces. 

Table 5-6 presents the percentile concentrations estimated in aboveground fruits and vegetables

using all biosolids samples of dioxins, furans, and PCBs as variable congener concentrations in

the model.

Pv

Parameter Definition Value

Pv Plant concentration due to vapor (mg/kg DW) Calculated

Cvapor Concentration of vapor (mg/m3

) Calculated

Bv Air-to-plant biotransfer factor (g/g DW plant/g/g air) Constituent-specific

VGAG Empirical correction factor for aboveground vegetables

(unitless)

Exposed fruits and vegetables 0.1

Forage 1.00

Feed 0.5

1000 Conversion factor (g/kg) 1,000

1200 Rho - the density of air (g/m3

) 1,200

Source: U.S. EPA (1998).
Section 5.0 May 2002

5-30

( )

( ) P

C RCF VG

Kd r

soil bg

bg

= × × (5-13)

Table 5-6. TCDD-TEQ Media Concentration for

Exposed Fruits and Vegetables 

Variable Concentrations

Percentile

TCDD-TEQ Concentration

(ng/kg) 

Fruits Vegetables

50th 0.00010 0.00005

75th 0.00024 0.00013

90th 0.00046 0.00024

95th 0.00069 0.00037

99th 0.0018 0.00096

The exposed aboveground vegetation media concentrations of dioxins are driven by the

air concentrations of vapors; therefore, the factors that increase the vapor concentrations increase

the aboveground vegetation concentrations also. 

5.3.1.2 Belowground Vegetation. In belowground plants, roots can take in

contaminants from the soil that may accumulate in the edible portion of the plant. For organdies,

the calculation is a function of the root concentration factor, which is used to estimate the amount

of constituent moving from the soil into the root vegetable. Equation 5-13 gives the equation for

calculating the concentration of congeners in root vegetables.

Pr_bg

Parameter Definition Value

Pr_bg Concentration in root vegetables (mg/kg) Calculated

Csoil Concentration of contaminant in soil (mg/kg) Calculated

RCF Root concentration factor (g/g - WW plant)/(g/mL soil water) Congener-specific

VGbg Empirical correction factor for belowground vegetables (unitless) 0.25

Kd Soil water partition coefficient Calculated

Source: U.S. EPA (1998).
Section 5.0 May 2002

5-31

In addition, an empirical correction factor (VGBG) is applied to the concentrations in

belowground vegetables. The correction factor is applied to adjust the root concentration factor

so that it is appropriate for bulky belowground root crops. This factor adjusts for the

concentration gradient from the outside of the root vegetable to the center. Another factor also

accounts for constituent losses due to cleaning and cooking and the tendency of lipophilic

contaminants to remain in the outer portions of the root (U.S. EPA, 1997). Table 5-7 presents

the percentile concentrations estimated in belowground fruits and vegetables using all biosolids

samples of dioxins, furans, and PCBs as variable congener concentrations in the model.

Table 5-7. TCDD-TEQ Media Concentration

for Belowground Vegetables

Variable Concentrations

Percentile

TCDD-TEQ Concentration in

Belowground Vegetables

(ng/kg)

50th 0.0071

75th 0.014

90th 0.028

95th 0.040

99th 0.090

The dioxin congener concentrations in root crops are influenced by the following factors

in the risk analysis. These factors were identified by the statistically based sensitivity analysis

(see Appendix K) and are presented according to the percentage of variation they account for in

the estimation of the root concentrations:

Soil foc

The year during biosolids application that the farm family moves to the farm

Agricultural application rate (tons of biosolids applied to the land per unit area)

Number of years biosolids are applied to the agricultural area.

Another factor important in this analysis is linked to the geographic location of the modeled

farm:

Soil bulk density.

The greater the loading of biosolids to the soil during the period of time the farm family is

exposed, the greater the soil media concentration. The farm family is assumed to move to the

farm where biosolids are applied during the period of biosolids application. The later the farm

family moves to the farm, the greater their estimated exposure because dioxins are persistent and
Section 5.0 May 2002

5-32

accumulate during the period of application and then remain at high levels for many years after

applications cease. 

All of the factors linked to location influence the behavior of dioxin congeners in soil to

varying degrees. The locations that are linked to the highest 30 estimations of the root

concentrations are Las Vegas, NV (21), and Phoenix, AZ (9). The soils in these locations are

very arid and have low organic content. These are the properties identified by the sensitivity

analysis.

5.3.1.3 Animal Tissue Concentration. The animal products considered in this risk

analysis are beef and milk from beef and dairy cattle, respectively, and poultry and eggs. The

contaminant concentrations in beef tissue and milk were estimated based on the amount of

contaminant the cattle were assumed to have consumed through ingestion. Specifically, the diet

for cattle was assumed to comprise a specific fraction of soil, forage, and silage. The animals

were assumed to ingest soil, with which they come in contact during grazing or other activities

on untilled soils. Different diet fractions were used for beef and dairy cattle, depending on the

amount of feed they consume and the activity patterns of the animals. For example, beef cattle

are assumed to spend more time grazing and, therefore, have a higher incidental ingestion rate of

soil and forage. The animal concentrations also depend on biotransfer factors, which are the ratio

of the contaminant concentration in animal tissue to the daily intake of contaminant by the

animal. Congener-specific biotransfer factors derived for milk were suggested for use for both

milk and beef in the Draft Dioxin Reassessment Document (U.S. EPA, 2000). Chemical

concentrations in feed and soil are multiplied by their respective diet fraction and by constituentspecific biotransfer factors and then summed to obtain the concentration of individual

constituents in tissue. Equation 5-14 is used to calculate the concentration of congeners in beef.

Equation 5-15 is used to calculate the concentration in milk.

Table 5-8 presents the percentile concentrations estimated in beef using all biosolids

samples of dioxins, furans, and PCBs as the variable congener concentrations in the model. 

Table 5-9 presents the percentile concentrations estimated in milk using all biosolids samples of

dioxins, furans, and PCBs as the variable congener concentrations in the model.
Section 5.0 May 2002

5-33

A C beef fat = × 0 2.

C BCF FF DF B C DF P DF P fat cattle beef s soil beef forage beef feed ( ) ( ) soil forage feed = × × ×× + × + ×

Abeef

(5-14)

Parameter Definition Value

Abeef Concentration in beef (mg/kg) Calculated

Cfat Concentration of dioxin (2,3,7,8-TCDD) in beef fat

(mg/kg)

Calculated

0.2 Fraction of fat in beef (unitless)

BCFcattle Bioconcentration ratio of contaminant as determined

from cattle vegetative intake (pasture grass or feed)

See Appendix D

Congener-specific

FF Feedlot factor for beef fat calculation (<=1 for beef fat

and = 1 for milk fat) (unitless)

See Appendix I

1.0

DFbeef soil Fraction of cattle diet that is soil (unitless) 0.04

Bs Bioavailability of contaminant on the soil vehicle

relative to the vegetative vehicle (unitless)

0.65

Csoil Average contaminant soil concentration (mg/kg) Calculated in Tables H-2.3, H-2.4

DFbeef_forage Fraction of cattle diet that is pasture grass (unitless) 0.48

Pforage Average concentration of contaminant on pasture grass

(mg/kg)

Calculated

DFbeef_feed Fraction of cattle diet that is feed (unitless) 0.48

Pfeed Average concentration of contaminant in feed (mg/kg) Calculated

Source: U.S. EPA (2000).
Section 5.0 May 2002

5-34

A C milk fat = × 0 04 .

C BCF FF DF B C DF P DF P fat cattle dairy s soil dairy forage dairy feed ( ) ( ) soil forage feed = × × ×× + × + ×

Amilk

(5-15)

Parameter Definition Value

Amilk Concentration in milk (mg/kg) Calculated

Cfat Concentration of dioxin (2,3,7,8-TCDD) in milk fat

(mg/kg)

Calculated

0.04 Fraction of fat in milk (unitless)

BCFcattle Bioconcentration ratio of contaminant as determined

from cattle vegetative intake (pasture grass or feed)

(unitless)

See Appendix D

Congener-specific

FF Feedlot factor for beef fat calculation (1 for beef fat

and = 1 for milk fat) (unitless)

1.0

DFdairy_ soil Fraction of cattle diet that is soil (unitless) 0.02

Bs Bioavailability of contaminant on the soil vehicle

relative to the vegetative vehicle (unitless)

See Appendix I

0.65

Csoil Average contaminant soil concentration (mg/kg) Calculated in Tables H-2.3, H-2.4

DFdairy _forage Fraction of cattle diet that is pasture grass (unitless) 0.08

Pforage Average concentration of contaminant on pasture grass

(mg/kg)

Calculated

DFdairy _feed Fraction of cattle diet that is feed (unitless) 0.90

Pfeed Average concentration of contaminant in feed (mg/kg) Calculated

Source: U.S. EPA (2000).
Section 5.0 May 2002

5-35

Table 5-8. TCDD-TEQ Media Concentration by Percentile for Beef

Variable Concentrations

Percentile

TCDD-TEQ

Concentration

in Beef

(ng/kg)

50th 0.088

75th 0.16

90th 0.28

95th 0.40

99th 0.86

Background 0.29

Table 5-9. TCDD-TEQ Media Concentration by Percentile for Milk

Variable Concentrations

Percentile

TCDD-TEQ

Concentration

in Milk

(ng/kg)

50th 0.0081

75th 0.015

90th 0.027

95th 0.038

99th 0.094

Background 0.047

The contaminant concentrations in poultry and eggs were estimated based on the amount

of contaminant the chickens were assumed to have consumed through ingestion. Specifically,

the diet for chickens was assumed to comprise a specific fraction of soil and feed. The animals

were assumed to ingest soil with which they came in contact during free-range activities in the

contaminated chicken yard. The chicken diet was assumed to contain 20 percent soil. The

chicken feed was assumed to be uncontaminated. Bioaccumulation factors specific for chickens

and eggs and based on ingestion of contaminated soils are from the Draft Dioxin Reassessment
Section 5.0 May 2002

5-36

A C poultry fat = × 01.

C BCF DF B C DF P DF P fat poultry poultry s soil poultry forage poultry feed ( ) soil forage feed = × ×× + × + ×

Document (U.S. EPA, 2000). Equations 5-16 and 5-17 are used to calculate the concentration of

congeners in poultry and eggs, respectively. 

Table 5-10 presents the percentile concentrations estimated in poultry using all biosolids

samples of dioxins, furans, and PCBs as variable congener concentrations in the model. 

Table 5-11 presents the percentile concentrations estimated in eggs using all biosolids samples of

dioxins, furans, and PCBs as variable congener concentrations in the model.

Apoultry

(5-16)

Parameter Definition Value

Apoultry Concentration in poultry (mg/kg) Calculated

Cfat Concentration of dioxin (2,3,7,8-TCDD) in chicken fat

(mg/kg)

Calculated

0.1 Fraction of fat in poultry (unitless)

BCFpoultry Bioconcentration ratio of contaminant developed for

chicken vegetative intake (unitless) 

See Appendix D

Congener-specific

DFpoultry _soil Fraction of chicken diet that is soil (unitless) See Appendix I

0.05

Bs Bioavailability of contaminant on the soil vehicle

relative to the vegetative vehicle (unitless)

See Appendix I

0.65

Csoil Average contaminant soil concentration (mg/kg) Calculated in Tables H-2.3, H-2.4

DFpoultry_forage Fraction of chicken diet that is incidental vegetation

while free ranging (unitless)

See Appendix I

0.05

Pforage Average concentration of contaminant on free-range

vegetation (mg/kg)

Calculated

DFpoultry_feed Fraction of chicken diet that is feed (unitless) See Appendix I

0.85

Pfeed Average concentration of contaminant in feed (mg/kg) 0

Source: U.S. EPA (2000).
Section 5.0 May 2002

5-37

A C eggs fat = × 01.

C BCF DF B C DF P DF P fat egg poultry s soil poultry forage poultry feed ( ) soil forage feed= × ×× + × + ×

Aeggs

(5-17)

Parameter Definition Value

Aeggs Concentration in eggs (mg/kg) Calculated

Cfat Concentration of dioxin (2,3,7,8-TCDD) in egg fat

(mg/kg)

Calculated

0.1 Fraction of fat in eggs (unitless)

BCFegg Bioconcentration ratio of contaminant developed for

chicken vegetative intake (unitless)

See Appendix D

Congener-specific

DFpoultry _soil Fraction of chicken diet that is soil (unitless) See Appendix I

0.10

Bs Bioavailability of contaminant on the soil vehicle

relative to the vegetative vehicle (unitless)

See Appendix I

0.65

Csoil Average contaminant soil concentration (mg/kg) Calculated in Tables H-2.3, H-2.4

DFpoultry_forage Fraction of chicken diet that is incidental vegetation

while free ranging (unitless)

See Appendix I

0.05

Pforage Average concentration of contaminant on free-range

vegetation (mg/kg)

Calculated

DFpoultry_feed Fraction of chicken diet that is feed (unitless) See Appendix I

0.85

Pfeed Average concentration of contaminant in feed (mg/kg) 0

Source: U.S. EPA (2000).
Section 5.0 May 2002

5-38

Table 5-10. TCDD-TEQ Media Concentration for Poultry Thigh Meat

Variable Concentrations

Percentile

TCDD-TEQ

Concentration in

Poultry Thigh Meat

(ng/kg)

50th 0.021

75th 0.036

90th 0.060

95th 0.088

99th 0.18

Background 0.16

Table 5-11. TCDD-TEQ Media Concentration for Eggs

Variable Concentrations

Percentile

TCDD-TEQ

Concentration

in Eggs

(ng/kg)

50th 0.026

75th 0.046

90th 0.075

95th 0.11

99th 0.32

Background 0.13

All of the factors linked to location for the poultry and egg ingestion pathway influence

the soil concentration of dioxin congeners in the residential buffer. The ingestion of soil in the

buffer area is the exposure pathway for the free-range chickens raised and eaten by the farm

family. The geographic locations linked to the highest 30 estimations of dioxin concentrations in

poultry meat and eggs are linked to colder locations with soils with higher soil foc, which binds

dioxins to the soil particles. These locations are not confined to a single area of the country, but

are more dispersed as indicated by the following locations associated with the top 1 percent of
Section 5.0 May 2002

5-39

the poultry and egg concentration estimates (listed in order of decreasing frequency of

occurrence):

 Burlington, VT (6)

 Salem, OR (6)

 Chicago, IL (5)

 Little Rock, AR (2)

 Portland, ME (2)

 Williamsport, PA (2)

 Muskegon, MI (2)

 Minneapolis, MN (2)

 Atlanta, GA (1)

 Cleveland, OH (1)

 Seattle, WA (1).

5.3.2 Aquatic Food Chain

An aquatic food chain model was used to estimate the concentration of constituent that

may accumulate in fish. It is assumed for this analysis that fish is a food source for a recreational

fisher. T3 and T4 fish were considered in this analysis. T3 fish are those that consume

invertebrates and plankton. T4 fish are those that consume other fish. Most of the fish that

humans eat are T4 fish (e.g., salmon, trout, walleye, bass) and medium to large T3 fish (e.g.,

carp, smelt, perch, catfish, sucker, bullhead, sauger). 

The concentration of constituent that accumulates in fish is calculated using the

concentration calculated for the sediment in the waterbody adjacent to the buffer. Fish tissue

concentrations are dependent on a BSAF. These factors are used to estimate the amount of

constituent being transferred from the sediment into the fish tissue. Specifically, they reflect the

ratio between the tissue concentration in fish and the appropriate sediment concentration. BSAFs

only take into account partitioning from the sediment to the fish and do not consider

accumulation through the food chain. The fish concentrations calculated for human receptors are

generally lower than whole fish concentrations. Human receptors usually consume only the filet

portion of the fish, which has a lower lipid content. Because constituents tend to accumulate in

the fatty tissue, the concentration in the filet portion of the fish is lower than the concentration in

the whole fish. Equation 5-18 is used to calculate the concentration of congeners in fish. 

Because the variation in fish concentration is dependent only on the variation in the sediment

concentration, the discussion on the variation in the sediment concentration also applies to fish

concentration.
Section 5.0 May 2002

5-40

C C LF fish fishlipid= ×

C BASF C fish sed lipid= ×

Cfish

(5-18)

Parameter Definition Value

Cfish Concentration in fish (mg/kg) Calculated

Cfish_lipid Concentration of contaminant in fish lipid (mg/kg) Calculated

LF Lipid fraction (unitless) T3, 0.0182

T4, 0.031

BASF Biota sediment accumulation factor (unitless) See Appendix D

Congener-specific

Csed Concentration in sediment settling to bottom (mg/kg) Calculated in Table H-2.2

Source: U.S. EPA (1998).

5.4 Infant Breast Milk Exposure

The concentrations of dioxins in breastmilk are modeled using a steady-state first-order

kinetics model obtained from U.S. EPA (1998). This approach allows infant exposures to both

lipophilic and nonlipophilic constituents to be modeled based on projected constituent

concentrations in maternal breast milk. Lipophilic compounds, such as dioxins, are assumed to

accumulate in the lipid fraction of breast milk, and the concentrations in breast milk are equal to

concentrations in maternal body fat. Nonlipophilic constituents are assumed to accumulate in the

aqueous phase of breast milk and to be proportional to the concentrations in maternal blood

plasma. Dioxins are assumed to accumulate exclusively in the lipid phase of breast milk. The

equation for estimating the dioxin concentration in milk fat is presented in Equation 5-19.

The concentration of dioxins in maternal milk fat is dependent on the maternal exposure

and the biological half-life for the contaminant. A range of 5–7 years was identified for

biological half-life (U.S. EPA, 1998); for this analysis, an upper bound of the range (7 years) was

used. This assumption will result in a longer time to steady state and in a higher dioxin

concentration in maternal fat and, thus, breast milk. This is a protective assumption.

In this risk assessment, the maternal body burdens are assumed to have reached steady

state. Reductions in maternal body burden resulting from losses from breast-feeding are not

considered. These assumptions may introduce error if the constituent being modeled has a

relatively long half-life
Section 5.0 May 2002

5-41

(5-19)

Cmilkfat

Parameter Definition Value

Cmilkfat Concentration in maternal milk fat (mg/kg) Calculated

ADDmat Average daily dose consumed by the mother (mg/kg-day) Calculated

fam Fraction of ingested contaminant absorbed by the mother

(unitless)

1.0

ff Fraction of contaminant stored in maternal fat (unitless) 0.9

t1/2

b Biological half-life of contaminant in lactating women

(days) (used to calculate biological elimination constant

for the contaminant in nonlactating women)

2,555 days (7 y)

ffm Fraction of mother’s weight that is fat (unitless) 0.3

Source: U.S. EPA (1998).

and the maternal exposure duration used for the mother prior to the start of lactation is relatively

short. In this analysis, maternal body burdens approach steady-state concentrations; thus, the

amount of error introduced by not considering losses due to breast-feeding are expected to be

small. These losses have been shown to be greatest during the initial stages of maternal

exposure, when body burden levels are low and the breast milk loss mechanism is more

significant.

5.5 References

Cowherd, C., G.E. Muleski, P.J. Englehart, and D.A. Gillette. 1985. Rapid Assessment of

Exposure to Particulate Emissions from Surface Contamination Sites. EPA/600/8-

85/002. U.S. Environmental Protection Agency, Office of Research and Development,

Office of Health and Environmental Assessment, Washington, DC. February.

Dunne, Thomas, and Luna B. Leopold. 1978. Water in Environmental Planning. W.H. Freeman

and Company, New York.

Koester, C.J., and R.A. Hites. 1992. Wet and dry deposition of chlorinated dioxins and furans. 

Environmental Science and Technology 26:1375-1382.

Paustenbach, D.J., R.J. Wenning, V. Lau, N.W. Harrington, D.K. Rennix, and A.H. Parsons.

1992. Recent developments on the hazards posed by 2,3,7,8-tetrachlorobenzo-p-dioxin in
Section 5.0 May 2002

5-42

soil: implications for setting risk-based cleanup levels at residential and industrial sites. J.

Toxicol. and Environ. Health 36:103-149..

McLachlan, M.S., A.P. Sewart, I.R. Bacon, and K.C. Jones. 1996. Persistence of PCDD/Fs in a

sludge-amended soil. Environ. Sci. Technol. 30(8):2567-2571.

Shuttleworth, W. James. 1993. Chapter 4: Evaporation. In: Handbook of Hydrology, David R.

Maidment (ed.). McGraw-Hill, Inc., New York, NY. pp. 4-4.

USDA (Department of Agriculture). 1971. Chapter 6: Sediment sources, yields, and delivery

ratios. In: National Engineering Handbook, Section 3: Sedimentation, Soil

Conservation Service, Washington, DC. pp. 6-1 to 6-14.

USDA (Department of Agriculture). 1978. Predicting Rainfall Erosion Losses: A Guide to

Conservation Planning. Agriculture Handbook No. 537. Science and Education

Administration, Washington, DC.

USDA (Department of Agriculture). 1986. Urban Hydrology for Small Watersheds. TR-55.

U.S. Department of Agriculture, Engineering Division, Soil Conservation Service,

Washington, DC. pp. 2-5. June.

U.S. DOC and U.S. DOE (U.S. Department of Commerce National Climatic Data Center and

U.S. Department of Energy National Renewable Energy Laboratory). 1993. Solar and

Meteorological Surface Observation Network (SAMSON) 1961-1990. Version 1.0.

U.S. EPA (Environmental Protection Agency). 1992. Comparison of a Revised Area Source

Algorithm for the Industrial Source Complex Short Term Model and Wind Tunnel Data. 

EPA Publication No. EPA-454/R-92-014. Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1994. 1:250,000 Scale Quadrangles of

Landuse/Landcover GIRAS Spatial Data in the Conterminous United States: Metadata. 

National GIS Program, Office of Information Resources Management, Washington, DC.

Website at http://www.epa.gov/ngispgm3/nsdi/projects/giras.htm

U.S. EPA (Environmental Protection Agency). 1995a. Compilation of Air Pollutant Emission

Factors, Volume 1: Stationary Point and Area Sources. 5th Edition. AP-42. PB95-

196028INZ. Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1995b. Draft User’s Guide for the Industrial

Source Complex (ISC3) Dispersion Models. Volume I: User Instructions (Revised).

EPA-454/B-95-003a. Emissions, Monitoring, and Analysis Division, Office of Air

Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1995c. PCRAMMET User’s Guide (Draft). 

Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1995d. User’s Guide for the Industrial Source

Complex (ISC3) Dispersion Models. Volume II: Description of Model Algorithms. 
Section 5.0 May 2002

5-43

EPA-454/B-95-003b. Emissions, Monitoring, and Analysis Division, Office of Air

Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1997. The Parameter Guidance Document. A

Companion Document to the Methodology for Assessing Health Risks Associated with

Multiple Pathways Exposure to Combustion Emissions. (Internal draft) NCEA-2038. 

National Center for Environmental Assessment, Cincinnati, OH.

U.S. EPA (Environmental Protection Agency). 1998. Methodology for Assessing Health Risks

Associated with Multiple Pathways of Exposure to Combustor Emissions. Update to

Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor

Emissions. EPA-600/R-98/137. National Center for Environmental Assessment,

Cincinnati, OH.

U.S. EPA (Environmental Protection Agency). 1999a. Addendum. User’s Guide for the

Industrial Source Complex (ISC3) Dispersion Models. Volume I: User Instructions for

the Revised ISCST3 Model (Dated 99155). Office of Air Quality Planning and Standards,

Research Triangle Park, NC.

U.S. EPA (Environmental Protection Agency). 1999b. Source Modules for Nonwastewater

Waste Management Units (Land Application Units, Waste Piles, and Landfills):

Background and Implementation for the Multimedia, Multipathway, and Multireceptor

Risk Assessment (3MRA) for HWIR99. Office of Solid Waste, Washington, DC. 

October.

U.S. EPA (Environmental Protection Agency). 1999c. Appendix W to Part 51--Guideline on

Air Quality Models. Code of Federal Regulations 40 CFR 51.

U.S. EPA (Environmental Protection Agency). 2000. Exposure and Human Health

Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds. 

EPA/600/P-00/001Bg. National Center for Environmental Assessment, Office of

Research and Development, Washington, DC. September. 

van der Leeden, F., F.L. Troise, and D.K. Todd. 1990. The Water Encyclopedia. 2nd edition. 

Chelsea, Michigan: Lewis Publishers. p. 176.

Wischmeier, W. H., and D. D. Smith. 1978. Predicting rainfall erosion losses. A guide to

conservation planning. In: Agricultural Handbook. 537 Edition. U.S. Department of

Agriculture, Washington, DC.

Young, A.L. 1983. Long-term studies on the persistence and movement of TCDD in a

natural ecosystem. In: Human and environmental risks of chlorinated dibenzodioxins and

related compounds. Tucker, R.E., A.L. Young, A.P. Gray. Eds. Plenum Press.
x
Section 6.0 May 2002

6-1

Figure 6-1. Human exposure pathways.

6.0 Human Exposure Assessment

This section describes the human exposure assessment that was conducted for this risk

assessment. An exposure assessment is the determination or estimation of the magnitude,

frequency, duration, and route of exposure to contaminants that an individual may experience. 

The term “exposure,” as defined by EPA’s Guidelines for Exposure Assessment (U.S. EPA,

1992), is the condition that occurs when a contaminant comes into contact with the outer

boundary of the body. The exposure of an individual to a contaminant is what completes an

exposure pathway (i.e., the course a constituent takes from the agricultural land amended with

biosolids to an exposed individual). Once the body is exposed, the constituent can cross the

outer boundary and enter the body. The amount of contaminant that crosses and is available for

adsorption at internal exchange boundaries is referred to as the “dose” (U.S. EPA, 1992). Each

exposure pathway, as illustrated in Figure 6-1, includes an exposure point and exposure route. 

The biosolids agricultural application risk assessment evaluated the risk to farmers and

their families and adult fishers.
Section 6.0 May 2002

6-2

Although all fishers and farmers are exposed to dioxins in biosolids, not all individuals

experience the same exposure. Different individuals will have a different magnitude, frequency,

duration, and route of exposure. Steps were taken in this analysis to capture the variability in

individual exposures by taking into account differences in physiological characteristics and daily

activity patterns. One step was to vary the values (i.e., exposure factors) used to calculate

exposure/intake for fishers, farmers, and their infants and children. Section 6.1 presents an

overview of the selected exposure pathways and exposure scenarios considered for this

assessment. Section 6.2 presents particular exposure factors (i.e., values needed to calculate

human exposure) used in the analysis. Section 6.3 describes the methods used to estimate dose,

including average daily dose (ADD) and LADD. 

6.1 Receptors and Exposure Pathways

Four types of human receptors were assumed to be representative of the individuals who

might be exposed to dioxin-like compounds in biosolids: an infant of a farmer, a child of a

farmer, an adult farmer, and an adult recreational fisher. These receptors reflect the range of

possible individual exposures for direct and indirect exposure pathways. The routes of exposure

differ for the farmer and fisher. For example, for this assessment, it is assumed that a farmer

consumes produce grown on the farm, as well as animal products (i.e., beef, dairy, poultry, and

eggs).

Table 6-1 lists each receptor along with the specific exposure pathways that apply to that

receptor. The adult and child farmer are exposed via the inhalation of air and the ingestion of

soil, homegrown above- and belowground produce, beef, dairy, poultry, and egg products. The

fisher is assumed to be a recreational angler who catches and consumes fish from the nearby

waterbody. Infants of farmers are exposed via the ingestion of breast milk only. For very

lipophilic constituents that have low volatility, such as dioxins and PCBs, infant exposures from

breast milk were assumed to be much greater than exposures through other potential infant

pathways, i.e., inhalation or incidental soil ingestion. Therefore, only the breast milk pathway

was evaluated in this risk analysis for infants. Infants were considered separately from other

childhood exposures.

Table 6-1. Receptors and Exposure Pathways

Receptor

Inhalation

of

Ambient

Air

Ingestion

of Soil

Ingestion

of Aboveand

Belowground

Produce

Ingestion

of Beef

and Dairy

Products

Ingestion

of Poultry

and Egg

Products

Ingestion

of Fish

Ingestion

of Breast

Milk

Adult farmer 7 7 7 77

Child farmer 7 7 7 77

Infant farmer 7

Adult fisher 7
Section 6.0 May 2002

6-3

(6-1)

6.1.1 Childhood Exposure

Children are an important subpopulation to consider in a risk assessment. They are likely

to be more susceptible to exposures, compared with adults, because they may eat more food and

drink more fluids per unit of body weight. This higher intake-rate-to-body-weight ratio can result

in a higher ADD than adults experience. 

As children mature, however, their physical characteristics and behavior patterns change. 

To capture these changes in the analysis, the life of a child was divided into several age ranges: 

ages 1 to 5, 6 to 11, 12 to 19, and 20 to 70 (adult). Each age range has distributions of the values,

called “exposure parameters,” that are required to calculate exposure to an individual. The

exposure parameter distributions for each age range reflect the physical characteristics and

behavior patterns of that age range. Data from the Exposure Factors Handbook (EFH) were used

to derive distributions appropriate for each age range (U.S. EPA, 1997a,b,c). The distributions

for the 20- to 70-yr-old cohort were used for adult receptors.

Development of the child exposure parameters consisted of three steps:

 Define the start age of the child.

 Select the exposure duration of the child.

 Calculate time-weighted exposure parameters.

To capture the higher intake-rate-to-body-weight ratio of children, a start age between the ages of

1 and 6 was selected for all children. For the probabilistic analysis, a start age between these

ages was selected randomly for each iteration. 

To select the exposure duration for each of the 3,000 iterations in the analysis, a

distribution was chosen to define the exposure duration based on the start age. For example, if

the start age was 2, the distribution for cohort 1 (children between ages 1 and 5) was used to

define exposure duration. However, if the start age was 6, the distribution developed for cohort 2

(children between ages 6 and 11) was used to define exposure duration. 

After the start age and the exposure duration were defined for a given iteration, all the

other exposure parameters needed to calculate exposure to a child were developed using the

distributions associated with each of the age groups through which the child would age. In this

process, an exposure parameter selected from each age group was time-weighted and combined

with values from the other age groups to create a single time-weighted exposure parameter. For

example, the beef ingestion rates selected from each age group were time-weighted according to

the number of years the child remained in the age group and were combined to generate a single

time-adjusted beef ingestion rate for the child. The same was done for all the parameters (e.g.,

body weight, inhalation rate, fruit ingestion rate) required to assess exposure. Equation 6-1 is

used to combine each child’s exposure parameters into one time-weighted exposure parameter: 
Section 6.0 May 2002

6-4

where

EPTW = time-weighted exposure parameter (e.g., ingestion rate of milk, body

weight)

EP1 = exposure parameter for ages 1 to 5

ED1 = time spent in age group 1

EP2 = exposure parameter for ages 6 to 11

ED2 = time spent in age group 2

EP3 = exposure parameter for ages 12 to 19

ED3 = time spent in age group 3

EP4 = exposure parameter for ages 20 to 70

ED4 = time spent in age group 4

ED = total exposure duration of the receptor (sum of ED1 + ED2 +ED3 + ED4).

In some cases, the time-weighted exposure parameter methodology resulted in a higher

ADD for children than for adults. However, even in those cases where the ADD was higher for

children than for adults, the LADD (used for assessing long-term cumulative endpoints, such as

cancer) was lower for children than for adults. The reason for this is that total exposure duration

is usually shorter for children than for adults, while the same 70-year average lifetime is assumed

for averaging the LADD for both children and adults.

6.1.2 Infant Exposure

Infants are an important subpopulation to consider in this risk assessment because they

may be exposed to dioxin-like compounds via the ingestion of breast milk. The characterization

of risks to infants of farmers and home gardeners was considered separately from the

characterization of risks to older children (i.e., aged 1 year or older).

6.1.3 Exposure Pathways

Human receptors may come into contact with dioxins, furans, and PCBs present in

environmental media by a variety of pathways. In general, exposure pathways are either direct,

such as inhalation of ambient air, or indirect, such as the farm food chain pathways. The

exposure pathways considered in this assessment were inhalation of ambient air and ingestion of

soil, aboveground produce, belowground produce (i.e., root crops), beef, dairy products, poultry,

eggs, fish, and breast milk (infants only). 

6.1.3.1 Inhalation of Ambient Air. Both vapors and particles can be inhaled in ambient

air by a receptor. Both adults and children (except infants) were affected via direct inhalation.

6.1.3.2 Ingestion of Soil. Both adults and children (except infants) were exposed to soil

based on incidental ingestion, mostly due to hand-to-mouth behavior. Soil ingested was modeled

as the top 1 cm of soil, untilled for children. The adult farmer was assumed to ingest soil from

the tilled crop land.
Section 6.0 May 2002

6-5

6.1.3.3 Ingestion of Above- and Belowground Produce. Ingestion of the following

categories of produce was used in this risk assessment: exposed fruit, exposed vegetables, and

root vegetables. For aboveground produce, the term “exposed” refers to the fact that the edible

portion of the plant is exposed to the atmosphere. It was assumed that farmers grow a portion of

their fruits and vegetables on land amended with biosolids and that these fruits and vegetables

become contaminated via soil and air. Belowground produce refers to root crops grown by the

farmer. The soil root crops were grown in was assumed to be tilled, so dioxins, furans, and PCBs

were mixed throughout the root zone.

6.1.3.4 Ingestion of Beef and Dairy Products. Beef and dairy cattle were assumed to

be exposed to dioxins, furans, and PCBs via differing intake rates of contaminated soil, forage,

and feed. Adult and child farmer receptors were assumed to consume beef and drink milk from

cattle that grazed in the pasture amended with biosolids.

6.1.3.5 Ingestion of Poultry and Egg Products. Chickens were assumed to be exposed

to dioxins, furans, and PCBs via intake rates of contaminated soil while free-range feeding. 

Adult and child farmer receptors were assumed to consume poultry and eggs from the chicken.

6.1.3.6 Ingestion of Fish. Fish are exposed to dioxins, furans, and PCBs via uptake of

contaminants from surface waters. Adult fishers were assumed to consume fish caught in local

waterbodies.

6.1.3.7 Ingestion of Breast Milk. Adult women farmers were assumed to be exposed to

dioxins, furans, and PCBs via the consumption of contaminated food items and soil and

inhalation of contaminated ambient air until they reach a steady-state concentration. Infants of

farmer receptors were assumed to consume breast milk from exposed adult receptors for the first

year of life.

6.2 Exposure Factors

Table 6-2 lists the exposure factors used in this risk assessment, along with their data

sources and whether they were represented by a distribution or a fixed value in the Monte Carlo

analysis. Exposure factors are used to calculate the dose of a chemical based on contact with

contaminated media or food, the duration of that contact, and the body weight of the exposed

individuals. The primary data source of human exposure model inputs used in this risk

assessment was EPA’s EFH (U.S. EPA, 1997a,b,c). The EFH summarizes data on human

behaviors and characteristics related to human exposure from relevant key studies and provides

recommendations and associated confidence estimates on the values of exposure factors. EPA

carefully reviewed and evaluated the quality of the data before their inclusion in the EFH. EPA’s

evaluation criteria included peer review, reproducibility, pertinence to the United States,

currency, adequacy of the data collection period, validity of the approach, representativeness of

the population, characterization of the variability, lack of bias in study design, and measurement

error (U.S. EPA, 1997a,b,c).
Section 6.0 May 2002

6-6

Table 6-2. Human Exposure Factor Input Parameters and Data Sources

Parameter Variable Type Data Source

Body weight (adult, child, infant) Distribution U.S. EPA (1997a)

Inhalation rate (adult, child) Distribution U.S. EPA (1997a)

Ingestion rate: soil (adult, child) Fixed (constant) U.S. EPA (1997a)

Consumption rate for farmer: exposed vegetables (adult, child) Distribution U.S. EPA (1997b)

Consumption rate for farmer: root vegetables (adult, child) Distribution U.S. EPA (1997b)

Consumption rate for farmer: exposed fruit (adult, child) Distribution U.S. EPA (1997b)

Consumption rate for recreational fisher: fish (adult) Distribution U.S. EPA (1997b)

Consumption rate for farmer: beef (adult, child) Distribution U.S. EPA (1997b)

Consumption rate for farmer: milk (adult, child) Distribution U.S. EPA (1997b)

Consumption rate for farmer: poultry (adult, child) Distribution U.S. EPA (1997b)

Consumption rate for farmer: eggs (adult, child) Distribution U.S. EPA (1997b)

Consumption rate for farmer: breast milk (infant) Distribution U.S. EPA (1997b)

Exposure duration (adult, child) Distribution U.S. EPA (1997c)

Exposure frequency (adult, child) Fixed (constant) U.S. EPA policy

Fraction contaminated: soil Fixed (constant) U.S. EPA policy

Fraction contaminated for recreational fisher: fish Fixed (constant) U.S. EPA (1997b)

Fraction homegrown for farmer: exposed vegetables Fixed (constant) U.S. EPA (1997b)

Fraction homegrown for farmer: root vegetables Fixed (constant) U.S. EPA (1997b)

Fraction homegrown for farmer: exposed fruit Fixed (constant) U.S. EPA (1997b)

Fraction contaminated (home-raised) for farmer: beef Fixed (constant) U.S. EPA (1997b)

Fraction contaminated (home-raised) for farmer: dairy Fixed (constant) U.S. EPA (1997b)

Fraction contaminated (home-raised) for farmer: poultry Fixed (constant) U.S. EPA (1997b)

Fraction contaminated (home-raised) for farmer: eggs Fixed (constant) U.S. EPA (1997b)

Fraction of T3 fish consumed Fixed (constant) U.S. EPA (1997b)

Fraction of T4 fish consumed Fixed (constant) U.S. EPA (1997b)

Food preparation and cooking losses: exposed vegetables Fixed (constant) U.S. EPA (1997b)

Food preparation and cooking losses: root vegetables Fixed (constant) U.S. EPA (1997b)

Food preparation and cooking losses: exposed fruit Fixed (constant) U.S. EPA (1997b)

Food preparation and cooking losses: beef Fixed (constant) U.S. EPA (1997b)

Food preparation and cooking losses: poultry Fixed (constant) U.S. EPA (1997b)

Human lifetime (used in carcinogenic risk calculation) Fixed (constant) U.S. EPA policy
Section 6.0 May 2002

6-7

 For probabilistic risk analyses, probability distribution functions were developed from the

values in the EFH (U.S. EPA, 1997a,b,c). Appendix K presents the exposure factors used in the

probabilistic analysis. Appendix K also describes the rationale and data used to select the

parametric models (i.e., gamma, lognormal, and Weibull) for those exposure factors that were

varied and the maximum and minimum exposure parameter values used in the analysis.

6.2.1 Intake Factors

This section presents the basis for the intake rates used for soil and food items in the

probabilistic analysis. Adult and child receptor intake rates for soil and food items were derived

from data in the EFH (U.S. EPA, 1997a,b).

6.2.1.1 Soil Ingestion. Ingestion of contaminated soil is a pathway common to all

receptors. Because most available data are from studies measuring soil ingestion in children

under the ages of 5 or 6, the adult soil ingestion rate was used for children older than age 5. 

Thus, soil ingestion rates used in the probabilistic analysis were not varied for any age group. 

The constant rates used for soil ingestion in this analysis are presented in Table 6-3.

Table 6-3. Soil Ingestion Rates Used

in This Risk Analysis

Receptor

Soil Intake Rate

(mg/d)

Child 100 

Adult 50

6.2.1.2 Fruit and Vegetable Ingestion. Ingestion of contaminated homegrown fruits

and vegetables is a potential pathway of exposure for adult farmers and home gardeners and their

children. Consumption rate data of homegrown exposed fruit, exposed vegetables, and root

vegetables by these receptors were obtained from the EFH. Examples of exposed fruits are

apples, peaches, pears, and berries. Aboveground exposed vegetables include tomatoes, green

leafy vegetables (e.g., lettuce, cabbage, kale), cucumber, summer squash, peppers, broccoli, okra,

and snap beans. Common root vegetables include carrots, onions, potatoes, and beets (U.S. EPA,

1997b). 

Because farmers grow much but generally not all of their food, the fraction of the

farmers’ diets that may be contaminated was considered. Specifically, the EFH provides

recommendations on the percent of the total diet of farmers that is homegrown. In addition,

produce consumption rate data were adjusted to account for food preparation and cooking losses.

 Table 6-4 presents exposed fruit consumption data used in the Monte Carlo analysis. 

Data for consumption of homegrown exposed fruit were obtained from Table 13-61 of the EFH

(U.S. EPA, 1997b). Data (in g WW/kg-d) were presented by age groups and for farmers and
Section 6.0 May 2002

6-8

home gardeners (adults). For the 1- to 5-yr-old age group, data were only available for those

aged 3 to 5 years; therefore, these data were used for the entire 1- to 5-yr-old age group. 

Percentile data were used to fit parametric models (gamma, lognormal, and Weibull) using

maximum likelihood estimation. Measures of goodness of fit were used to select the most

appropriate model. The fraction of exposed fruit intake that is home-produced is 0.328 for

households that farm and 0.116 for households that garden (Table 13-71, U.S. EPA, 1997b). 

Figure 6-2 presents these distributions graphically. The distributions were truncated at the

maximum value shown in the table and graph.

Table 6-4. Exposed Fruit Consumption Data and Distributions

Age

Cohort N

EFH Data (g WW/kg-d) Distributions

Data

Mean

Data

SDev P01 P05 P10 P25 P50 P75 P90 P95 P99 Distribution

PopEstd

Mean

PopEstd

SDev Max

1-5 49 2.6 3.947 0.373 1 1.82 2.64 5.41 6.07 Gamma 2.25 1.89 16

6-11 68 2.52 3.496 0.171 0.373 0.619 1.11 2.91 6.98 11.7 Lognormal 2.78 5.12 36

12-19 50 1.33 1.457 0.123 0.258 0.404 0.609 2.27 3.41 4.78 Lognormal 1.54 2.44 18

Adult

Farmer 112 2.32 2.646 0.072 0.276 0.371 0.681 1.3 3.14 5 6.12 15.7 Lognormal 2.36 3.33 31

N = Number of samples; P01-P99 = Percentiles; Pop-Estd = Population-estimated; SDev = Standard deviation;

Minimum is assumed = 0 
Section 6.0 May 2002

6-9

Exposed Vegetable Consumption. Table 6-5 presents exposed vegetable consumption

data and distribution. Data for consumption of homegrown exposed vegetables were obtained

from Table 13-63 of the EFH (U.S. EPA, 1997b). Data (in g WW/kg/d) were presented for those

aged 1 to 2, 3 to 5, 6 to 11, 12 to 19, 20 to 39, and 40 to 69 years, as well as farmers and home

gardeners. Weighted averages of percentiles, means, and standard deviations were calculated for

the 1- to 5-yr-old age group (combining groups of those aged 1 to 2 years and 3 to 5 years). 

Percentile data were used to fit parametric models (gamma, lognormal, and Weibull) using

maximum likelihood estimation. Measures of goodness of fit were used to select the most

appropriate model. The fraction of exposed vegetable intake that is home-produced is 0.42 for

households that farm and 0.233 for households that garden (Table 13-71, U.S. EPA, 1997b). 

Figure 6-3 presents these distributions graphically. The distributions were truncated at the

maximum value shown in the table and graph.

Figure 6-2. Distribution of exposed fruit consumption rates by age group.
Section 6.0 May 2002

6-10

Table 6-5. Exposed Vegetable Consumption Data and Distributions

Age

Cohort N

EFH Data (g WW/kg-d) Distributions

Data

Mean

Data

SDev P01 P05 P10 P25 P50 P75 P90 P95 P99 Distribution

PopEstd

Mean

PopEstd

SDev MAX

1-5 105 2.453 2.675 0.102 0.37 0.833 1.459 3.226 6.431 8.587 Gamma 2.55 2.58 21

6-11 134 1.39 2.037 0.044 0.094 0.312 0.643 1.6 3.22 5.47 13.3 Lognormal 1.64 3.95 27

12-19 143 1.07 1.128 0.029 0.142 0.304 0.656 1.46 2.35 3.78 5.67 Gamma 1.08 1.13 11

Adult

farmer 207 2.17 2.316 0.184 0.372 0.647 1.38 2.81 6.01 6.83 10.3 Lognormal 2.38 3.5 26

N = Number of samples; P01-P99 = Percentiles; Pop-Estd = Population-estimated; SDev = Standard deviation; Minimum is assumed = 0

Root Vegetable Consumption. Table 6-6 presents root vegetable consumption rates and

distributions. Homegrown root vegetable consumption data were obtained from Table 13-65 of

the EFH (U.S. EPA, 1997b). Data (in g WW/kg/d) were presented for those aged 1 to 2, 3 to 5,

6 to 11, 12 to 19, 20 to 39, 40 to 69 years, and adult farmers and home gardeners. Weighted

averages of percentiles, means, and standard deviations were calculated for the 1- to 5-yr-old age

group (combining groups of those aged 1 to 2 and 3 to 5 years). Percentile data were used to fit

Figure 6-3. Distribution of exposed vegetable consumption rates by age group.
Section 6.0 May 2002

6-11

parametric models (gamma, lognormal, and Weibull) using maximum likelihood estimation. 

Measures of goodness of fit were used to select the most appropriate model. The fraction of root

vegetable intake that is home-produced is 0.173 for households that farm and 0.106 for

households that garden (Table 13-71, U.S. EPA, 1997b). Figure 6-4 presents these distributions

graphically. The distributions were truncated at the maximum value shown in the table and

graph.

6.2.1.3 Beef and Dairy Ingestion. The farmer (adult and child) is assumed to ingest

beef and dairy products from cattle raised on pastures amended with biosolids. As with fruits

and vegetables, it was necessary to consider the fraction of the total beef and dairy in the farmer’s

diet that consists of products raised on the amended pasture. In addition, beef consumption rate

data were adjusted to account for food preparation and cooking losses.

Table 6-6. Root Vegetable Consumption Data and Distributions

Age

Cohort N

EFH Data (g WW/kg-d) Distributions

Data

Mean

Data

SDev P01 P05 P10 P25 P50 P75 P90 P95 P99 Distribution

PopEstd

Mean

PopEstd

SDev MAX

1-5 45 1.886 2.371 0.081 0.167 0.291 0.686 2.653 5.722 7.502 Lognormal 2.31 6.05 41

6-11 67 1.32 1.752 0.014 0.036 0.232 0.523 1.63 3.83 5.59 Weibull 1.38 2.07 15

12-19 76 0.937 1.037 0.008 0.068 0.269 0.565 1.37 2.26 3.32 Weibull 0.99 1.19 9

Adult

farmer 136 1.39 1.469 0.111 0.158 0.184 0.365 0.883 1.85 3.11 4.58 7.47 Lognormal 1.45 2.06 15

N = Number of samples; P01-P99 = Percentiles; Pop-Estd = Population-estimated; SDev = Standard deviation; Minimum is assumed = 0
Section 6.0 May 2002

6-12

 Beef Consumption. Table 6-7 presents beef consumption data and distributions. 

Home-produced beef consumption data were obtained from Table 13-36 of the EFH (U.S. EPA,

1997b). Data (in g WW/kg-d) were presented for farmers and those aged 6 to 11, 12 to 19, 20 to

39, and 40 to 69. Percentile data were used to fit parametric models (gamma, lognormal, and

Weibull) using maximum likelihood estimation. Measures of goodness of fit were used to select

the most appropriate model. 

Data were not available for those aged 1 to 2 and 3 to 5. For beef consumption for 1- to

5-yr-olds, the lognormal model was used because, among the other age groups, it was the bestfitted model in all but one case. The population-estimated mean and standard deviation for 6- to

11-yr-olds were used for 1- to 5-yr-olds for the analysis (normalized for body weight) and are

supported by data in Table 11-3 of the EFH (per capita intake for beef, including store-bought

products), which indicate that those aged 1 to 2, 3 to 5, and 6 to 11 have the highest consumption

rate of beef on a gram/kilogram/ day basis. Figure 6-5 presents these data graphically. The

distribution of beef consumption rates was truncated at the maximum value indicated in the table

and graph. The fraction of beef intake that is home-produced is 0.485 for households that farm

(Table 13-71, U.S. EPA, 1997b).

Figure 6-4. Distribution of root vegetable consumption rates by age group.
Section 6.0 May 2002

6-13

Table 6-7. Beef Consumption Data and Distributions

Age

Cohort N

EFH Data (g WW/kg-d) Distributions

Data

Mean

Data

SDev P01 P05 P10 P25 P50 P75 P90 P95 P99

Distribution

PopEstd

Mean

PopEstd

SDev MAX

1-5 ND ND Lognormal 3.88 4.71 36

6-11 38 3.77 3.662 0.663 0.753 1.32 2.11 4.43 11.4 12.5 Lognormal 3.88 4.71 36

12-19 41 1.72 1.044 0.478 0.513 0.896 1.51 2.44 3.53 3.57 Gamma 1.77 1.12 10

Adult

farmer 182 2.63 2.644 0.27 0.394 0.585 0.896 1.64 3.25 5.39 7.51 11.3 Lognormal 2.5 2.69 23

N = Number of samples; P01-P99 = Percentiles; Pop-Estd = Population-estimated; SDev = Standard deviation;

Minimum is assumed = 0

Figure 6-5. Distribution of beef consumption rates by age group.
Section 6.0 May 2002

6-14

Beef consumption rate data were adjusted to account for food preparation and cooking

losses. A mean net cooking loss of 27 percent accounts for dripping and volatile losses during

cooking (averaged over various cuts and preparation methods). A mean net postcooking loss of

24 percent accounts for losses from cutting, shrinkage, excess fat, bones, scraps, and juices. 

These data were obtained from Table 13-5 of the EFH (U.S. EPA, 1997b).

Dairy Products (Milk) Consumption. Table 6-8 presents summary statistics on

consumption of dairy products. Home-produced dairy product consumption rate data were

obtained from Table 13-28 of the EFH (U.S. EPA, 1997b) for farmers, all ages combined, and

individual age groups. No age-specific data for children were available for home-produced dairy

products consumption. Per capita intake data for dairy products (including store-bought

products), however, were available from the EFH and from CSFII (USDA, 1997) for those aged

1 to 2, 3 to 5, 6 to 11, and 12 to 19; the data in the EFH were based on the 1989–1991 CSFII, so

the more recent 1994–1996 CSFII raw data were used. Therefore, data for the general population

were used to calculate adjustment factors to develop distributions for the nonadult age groups for

consumption of home-produced dairy products. Figure 6-6 presents these distributions

graphically. The distributions were truncated at the maximum value as shown in the table and

graph.

Table 6-8. Dairy Products (Milk) Consumption Data and Distributions

Source

Age

Cohort

Data (g WW/kg-d) Distributions

Data

Mean

Data

SDev P05 P10 P25 P50 P75 P90 P95 Distribution

PopEstd

Shape

PopEstd

Scale Max

CSFII (gen) All 6.81 10.8 0.199 0.392 1.14 3.25 7.59 16.9 26.1

CSFII (gen) 1-5 27.4 22.3 1.12 4.39 12.2 22.3 37.1 55.9 70.1

CSFII (gen) 6-11 14 10 0.826 2.16 6.48 12.3 19.2 27.3 33.5

CSFII (gen) 12-19 6.2 5.87 0.264 0.484 1.88 4.55 8.88 13.5 17.8

CSFII (gen) 20-69 3.23 3.3 0.162 0.303 0.854 2.22 4.48 7.45 9.88

HP 1-5 Gamma 0.961 61.80 482

HP 6-11 Gamma 0.961 31.40 245

HP 12-19 Gamma 0.961 13.90 109

EFH (HP) 20_39 7.41 6.12 0.396 0.446 1.89 6.46 12.1 15.4 19.5 Gamma 0.961 8.01

EFH (HP) All 14 15.28 0.446 0.508 3.18 10.2 19.5 34.2 44 Gamma 0.78 18.26

EFH (HP) Adult farmer 17.1 15.8 0.736 3.18 9.06 12.1 20.4 34.9 44 Gamma 1.38 11.85 116

CSFII = CSFII (USDA, 1997); gen = general population data; EFH = U.S. EPA (1997b); HP = home-produced

data; P05-P95 = Percentiles; Sdev = standard deviation; Pop-Estd = population-estimated; Minimum is assumed = 0
Section 6.0 May 2002

6-15

Percentile data (USDA, 1997) were used to fit parametric models (gamma, lognormal,

and Weibull) using maximum likelihood estimation. Measures of goodness of fit were used to

select gamma as the most appropriate model in all cases. Tables J-19 and J-20 (Appendix J)

provide the data used to develop the distributions and adjustment factors. It was assumed that

the relative standard deviations (RSD) for consumption rates were the same for all age groups;

the similarity of coefficients of variation (CV) suggests that this is a reasonable approximation

for the general population. The other assumption used to develop distributions for the child age

groups for the consumption of home-produced dairy products was that the mean intake rates have

the same fixed ratio for all the age groups of a given food type. That is, the ratio of the mean

amount consumed of home-produced dairy products divided by the mean amount of dairy

products consumed in the general population is the same for any two age groups. These two

assumptions, of constant RSD and constant mean ratio, were used to infer the parameters of the

gamma distributions for the home-produced foods from those of the general population (i.e.,

mean, standard deviation, shape, and scale).

The fraction of dairy product intake that is home-produced is 0.254 for households that

farm (Table 13-71, U.S. EPA, 1997b).

6.2.1.4 Poultry and Egg Ingestion. The farmer (adult and child) is assumed to ingest

poultry and egg products from chickens raised on the farm using biosolids as a soil amendment. 

As with fruits and vegetables, it was necessary to consider the fraction of the total poultry and

eggs in the farmer’s diet that consists of products raised on the farm. In addition, poultry

consumption rate data were adjusted to account for food preparation and cooking losses.

Figure 6-6. Distribution of milk consumption rates by age group.
Section 6.0 May 2002

6-16

 Poultry Consumption. Table 6-9 presents summary statistics on consumption of

poultry. Home-produced poultry consumption rate data were obtained from Table 13-55 of the

EFH (U.S. EPA, 1997b) for farmers, all ages combined, and individual age groups 20 to 39 and

40 to 69; statistics for the 20- to 69-yr-old age group were calculated as simple averages of the

statistics for the 20- to 39- and 40- to 69-yr-old age groups. No age-specific data for children

were available for home-produced poultry consumption. Per capita intake data for poultry

(including store-bought products), however, were available for those aged 1 to 2, 3 to 5, 6 to 11,

and 12 to 19 years old from the EFH and from CSFII (USDA, 1997); the data in the EFH were

based on the 1989-1991 CSFII, so the more recent 1994–1996 CSFII raw data were used. 

Therefore, data for the general population were used to calculate adjustment factors to develop

distributions for the nonadult age groups for consumption of home-produced poultry. Figure 6-7

presents these distributions graphically. The distributions for poultry consumption were

trancated at the maximum value indicated in the table and graph.

Table 6-9. Poultry Consumption Data and Distributions

Source

Age 

Cohort

Data (g WW/kg-d) Distributions

Data

Mean

Data

SDev P05 P10 P25 P50 P75 P90 P95 Distribution

PopEstd

Shape

PopEstd

Scale MAX

CSFII (gen) All 0.688 0.942 0.018 0.034 0.111 0.334 0.917 1.76 2.47

CSFII (gen) 1-5 1.43 1.73 0.025 0.056 0.192 0.736 2.2 3.63 4.66

CSFII (gen) 6-11 0.884 1.15 0.019 0.036 0.116 0.365 1.29 2.42 3.22

CSFII (gen) 12-19 0.645 0.795 0.019 0.034 0.103 0.346 0.896 1.71 2.23

CSFII (gen) 20-69 0.57 0.712 0.017 0.032 0.105 0.303 0.804 1.4 1.92

HP 1-5 Gamma 1.69 1.92 21

HP 6-11 Gamma 1.69 1.21 14

HP 12-19 Gamma 1.69 0.87 10

EFH (HP) 20-69 1.34 1.088 0.299 0.352 0.524 0.962 2.03 2.545 3.765 Gamma 1.69 0.80

EFH (HP) All 1.57 1.178 0.303 0.418 0.637 1.23 2.19 3.17 3.83 Gamma 1.83 0.85

EFH (HP) Adult

farmer

1.54 1.375 0.228 0.303 0.595 1.06 2.18 3.47 4.83 Gamma 1.38 1.16 11

CSFII = (USDA, 1997); gen = general population data; EFH = U.S. EPA (1997b); HP = home-produced data;

P05-P95 = Percentiles; Sdev = standard deviation; Pop-Estd = population-estimated; Minimum is assumed = 0
Section 6.0 May 2002

6-17

Percentile data (USDA, 1997) were used to fit parametric models (gamma, lognormal,

and Weibull) using maximum likelihood estimation. Measures of goodness of fit were used to

select gamma as the most appropriate model in all cases. Tables J-19 and J-20 (see Appendix J)

provide the data used to develop the distributions and adjustment factors. Constant RSD and

constant mean ratio were assumed, and these data were used to infer the parameters of the

gamma distributions for the home-produced foods from those of the general population (i.e.,

mean, standard deviation, shape, and scale). The fraction of poultry intake that is home-produced

is 0.156 for households that farm (Table 13-71, U.S. EPA, 1997b). 

 Egg Consumption. Table 6-10 presents summary statistics on consumption of eggs. 

Home-produced egg consumption rate data were obtained from Table 13-43 of the EFH (U.S.

EPA, 1997b) for farmers, all ages combined, and individual age groups 20 - 39 and 40 - 69;

statistics for the 20- to 69-yr-old age group were calculated as simple averages of the statistics for

the 20- to 39- and 40- to 69-yr-old age groups. No age-specific data for children were available

for home-produced egg consumption. Per capita intake data for eggs (including store-bought

products), however, were available from the EFH and from CSFII (USDA, 1997) for those aged

1 to 2, 3 to 5, 6 to 11, and 12 to 19; the data in the EFH were based on the 1989–1991 CSFII, so

the more recent 1994–1996 CSFII raw data were used. Therefore, data for the general population

were used to calculate adjustment factors to develop distributions for the nonadult age groups for

consumption of home-produced eggs. Figure 6-8 presents these distributions graphically. The

distribution of egg consumption rates was truncated at the maximum value shown in the table

and graph.

Figure 6-7. Distribution of poultry consumption rates by age group.
Section 6.0 May 2002

6-18

Table 6-10. Egg Consumption Data and Distributions

Source

Age

Cohort

Data (g WW/kg-d) Distributions

Data

Mean

Data

SDev P05 P10 P25 P50 P75 P90 P95 Distribution

PopEstd

Shape

PopEstd

Scale MAX

CSFII (gen) All 1.01 1.04 0.133 0.253 0.422 0.724 1.22 1.99 2.82

CSFII (gen) 1-5 2.41 1.94 0.101 0.328 1.16 1.88 3.23 5.03 6.15

CSFII (gen) 6-11 1.44 1.25 0.125 0.302 0.641 1.08 1.87 2.95 3.45

CSFII (gen) 12-19 0.962 0.708 0.092 0.328 0.469 0.821 1.22 1.71 2.24

CSFII (gen) 20-69 0.792 0.663 0.145 0.248 0.389 0.633 1.01 1.52 1.88

HP 1-5 Gamma 1.88 0.839 10

HP 6-11 Gamma 1.88 0.493 6

HP 12-19 Gamma 1.88 0.334 4

EFH (HP) 20-69 0.611 0.442 0.106 0.183 0.308 0.465 0.829 1.31 1.645 Gamma 1.88 0.336

EFH (HP) All 0.731 1.114 0.15 0.175 0.268 0.466 0.902 1.36 1.69 Gamma 1.81 0.357

EFH (HP) Adult farmer 0.898 1.128 0.165 0.177 0.272 0.666 1.19 1.65 1.85 Gamma 1.64 0.488 13

CSFII = CSFII (USDA, 1997); gen = general population data; EFH = U.S. EPA (1997b); HP = home-produced data; Sdev = standard deviation; Pop-Estd =

population-estimated; Minimum is assumed = 0

Figure 6-8. Distribution of egg consumption rates by age group.
Section 6.0 May 2002

6-19

Percentile data (USDA, 1997) were used to fit parametric models (gamma, lognormal,

and Weibull) using maximum likelihood estimation. Measures of goodness of fit were used to

select gamma as the most appropriate model in all cases. Tables J-19 and J-20 (see Appendix J)

provide the data used to develop the distributions and adjustment factors. It was assumed that

the relative standard deviations for consumption rates were the same for all age groups; the

similarity of coefficients of variation suggests that this is a reasonable approximation for the

general population. The other assumption used to develop distributions for the child age groups

for the consumption of home-produced eggs was that the mean intake rates have the same fixed

ratio for all the age groups of a given food type. That is, the ratio of the mean amount consumed

of home-produced eggs divided by the mean amount of eggs consumed in the general population

is the same for any two age groups. These two assumptions, of constant RSD and constant mean

ratio, were used to infer the parameters of the gamma distributions for the home-produced foods

from those of the general population (i.e., mean, standard deviation, shape, and scale).

The fraction of egg intake that is home-produced is 0.146 for households that farm

(Table 13-71, U.S. EPA, 1997b).

6.2.1.5 Fish Ingestion. Fish ingestion rates were based on an adult recreational angler

who catches and eats some fish from a stream affected by contaminants released from biosolids. 

All fish are assumed to be home-caught and contaminated for households that fish.

Fish Consumption. Table 6-11 presents fish consumption data and distribution. Fish

consumption data were obtained from Table 10-64 of the EFH (U.S. EPA, 1997b). Data (in g/d)

were available for adult freshwater anglers in Maine. The Maine fish consumption study was one

of four recommended freshwater angler studies in the EFH (U.S. EPA, 1997b). The other

recommended fish consumption studies (i.e., Michigan and New York) had large percentages of

anglers who fished from the Great Lakes, which is not consistent with the modeling scenarios

used in this risk analysis. The anglers in the Maine study fished from streams, rivers, and ponds;

these data are more consistent with modeling scenarios for this risk analysis. Although the

Maine data have a lower mean than the Michigan data, the Maine data compared better with a

national USDA study. Also, the Maine study had percentile data available, which were necessary

to develop a distribution. Figure 6-9 presents fish consumption rate distribution for adults. The

distribution of fish consumption rates was truncated at the maximum value shown in the table

and graph.

Table 6-11. Fish Consumption Data and Distributions

Age

Cohort N

EFH Data (g/d) Distributions

Data

Mean

Data

SDev P50 P66 P75 P90 P95 Distribution

Pop-Estd

Mean

Pop-Estd

SDev MAX

Adult 1,053 6.4 2 4 5.8 13 26 Lognormal 6.48 19.9 1500

N = Number of samples; P50-P95 = Percentiles; Pop-Estd = Population-estimated; SDev = Standard deviation.
Section 6.0 May 2002

6-20

Figure 6-9. Distribution of adult fish

consumption rates.

Percentile data were used to fit parametric models (gamma, lognormal, and Weibull) and

measures of goodness of fit were used to select lognormal as the most appropriate model. The

fraction of fish intake that is locally caught is 0.325 for adult fishers (Table 13-71, U.S. EPA,

1997b). The fractions of consumed T3 and T4 fish were 0.36 and 0.64, respectively (Table 10-

66, U.S. EPA, 1997b).

6.2.1.6 Breast Milk Ingestion. Ingestion of contaminated breast milk is a potential

pathway of exposure for infants of farmers. Consumption rate data were obtained from the EFH. 

 Breast Milk Consumption. Table 6-12 presents breast milk consumption data for

infants. The data mean and upper percentile for breast milk consumption in 1- to 12-month-olds

were 688 and 980 mL/d, respectively (Table 14-16, U.S. EPA, 1997b). The triangular model was

used for breast milk consumption (12-month-olds) because no percentile or related data were

available; other distributions (e.g., lognormal) resulted in overestimation of the upper percentile. 

Figure 6-10 presents this distribution graphically. The EFH population mean for breast milk

consumption was 688 mL/d and was assumed to equal the mode. The distribution of breast milk

consumption rates was truncated at the maximum value shown in the table and graph.

Table 6-12. Breast Milk Consumption Data and Distribution

Age

Cohort

Data Mean

(mL/d)

Data

SDev

Upper

Percentile Distribution

Pop-Estd

Mode

(mL/d)

Pop-Estd

SDev

(mL/d) Max

<1 688 ND 980 Triangular 688 688 1380

Pop-Estd = population-estimated; SDev = Standard deviation; ND = No data.
Section 6.0 May 2002

6-21

Figure 6-10. Distribution of breast milk

consumption rates.

6.2.1.7 Inhalation Rates. The EFH reports inhalation values by age, gender, activity

pattern, and outdoor workers; however, it does not provide high-end values in most cases. The

inhalation rate is the same for all adults, whether farmer, or fisher, while child receptors use a

single child inhalation rate.

 Inhalation Rate. Table 6-13 presents inhalation rate data and distribution. No

percentile data were available for the inhalation rate, and the default lognormal model was

assumed. In an analysis of inhalation data, Myers et al. (U.S. EPA, 2000) found that, for those

younger than 3 years, CV was close to 70 percent; for other age groups, it was close to

30 percent. The lognormal distribution was fitted by using CV = 50 percent [(30+70)/2] for the

1- to 5-yr-old age group and CV = 30 percent for the 6- to 11-yr-olds, 12- to 19-yr-olds, and adult

age groups. Figure 6-11 presents this distribution graphically. The distribution of inhalation

rates was truncated at the minimum and maximum values shown in the table and graph.

Table 6-13. Inhalation Rate Data and Distribution

Age

Cohort Distribution

PopulationEstimated

Mean

 (m3

/d)

PopulationEstimated

SDev

(m3/d) Min Max

1-5 Lognormal 7.55 3.78 1 40

6-11 Lognormal 11.75 3.53 1 45

12-19 Lognormal 14.0 4.2 1 55

Adult Lognormal 13.3 3.99 1 50

SDev = Standard deviation.
Section 6.0 May 2002

6-22

6.2.2 Other Exposure Factors

6.2.2.1 Body Weights. Distributions of body weight were developed for adult (farmer

and fisher), child (farmer), and infant (farmer) receptors based on data from the EFH. 

Table 6-14 presents body weight data and distribution. Body weight data were obtained

from Tables 7-2 through 7-7 of the EFH (U.S. EPA, 1997a). Data (in kg) were presented by age

and gender. Weighted averages of percentiles, means, and standard deviations were calculated

for infants (<1 year old), 1- to 5-yr-olds, 6- to 11-yr-olds, 12- to 19-year olds, and adult age

groups; male and female data were weighted and combined for each age group. These percentile

data were used as the basis for fitting distributions. These data were analyzed to fit parametric

models (gamma, lognormal, and Weibull) using maximum likelihood estimation. Measures of

goodness of fit were used to select the most appropriate model. Figure 6-12 presents these

distributions graphically. The body weight distributions are truncated at the maximum values

shown in the table and graph.

Figure 6-11. Distribution of inhalation rates by age group.
Section 6.0 May 2002

6-23

Table 6-14. Body Weight Data and Distributions

Age

Cohort N

EFH Data (kg) Distributions

Data

Mean

Data

SDev P05 P10 P15 P25 P50 P75 P85 P90 P95 Distribution

PopEstd

Mean

PopEstd

SDev Min Max

<1 356 9.102 1.287 7.053 7.451 7.852 8.252 9.151 9.752 10.4 10.65 11.15 Gamma 9.09 1.23 2 26

1-5 3,762 15.52 3.719 12.5 13.1 13.45 14.03 15.26 16.67 17.58 18.32 19.45 Lognormal 15.5 2.05 4 50

6-11 1,725 30.84 9.561 22.79 24.05 25.07 26.44 29.58 33.44 36.82 39.66 43.5 Lognormal 30.7 5.96 6 200

12-19 2,615 58.45 13.64 43.84 46.52 48.31 50.94 56.77 63.57 68.09 71.98 79.52 Lognormal 58.2 10.2 13 300

20+ 12,504 71.41 15.45 52.86 55.98 58.21 61.69 69.26 78.49 84.92 89.75 97.64 Lognormal 71.2 13.3 15 300

N = Number of samples; P05-P95 = Percentiles; Pop-Estd = Population-estimated; SDev = Standard deviation.

Figure 6-12. Distribution of body weights by age group.
Section 6.0 May 2002

6-24

6.2.2.2 Exposure Duration. Exposure duration refers to the amount of time that a

receptor is exposed to a contaminant source. For this risk analysis, exposure duration was

assumed to correspond to the receptor’s residence time in the same house. Exposure durations

were determined using data on residential occupancy from the EFH (U.S. EPA, 1997c). Separate

distributions were developed for both adult and child adult farmers. Children of farmers were

assumed to have the same exposure duration as rural resident children because no age-specific

data were available for residential occupancy for farmers. 

Exposure duration for all adult and child receptors was capped at a total lifetime of 100

years.

 Table 6-15 presents exposure duration data and distributions. Exposure duration was

assumed to be equivalent to the average residence time for each receptor. Exposure durations for

adult residents and children (resident and farmer) were determined using data on residential

occupancy from the EFH, Table 15-168 (U.S. EPA, 1997c). The data represent the total time a

person is expected to live at a single location, based on age. The table presented male and female

data combined. For adult residents, age groups from 21-yr-olds to 90-yr-olds were pooled. For

children, the 3-yr-old age group was used for the 1- to 5-yr-olds. Figure 6-13 represents these

distributions graphically.

Table 6-15. Exposure Duration Data and Distributions

EFH Data Distributions

Age Cohort

Data Mean

(yr) Distribution

Pop-Estd Shape

(yr)a

Pop-Estd Scale

(yr) Min Max

Child (1- to 5-yr-olds) 6.5 Weibull 1.32 7.059 1 100

Adult farmer 18.75 Gamma 0.607 29.76 1 100

Pop-Estd = Population-estimated.

a

 Distributions used in risk assessment.

Figure 6-13. Distribution of exposure duration for child and adult.
Section 6.0 May 2002

6-25

In an analysis of residential occupancy data, Myers et al. (U.S. EPA, 2000) found that the

data, for most ages were best fit by a Weibull distribution. The Weibull distribution as

implemented in Crystal Ball® is characterized by three parameters: location, shape, and scale. 

Location is the minimum value and, in this case, was presumed to be 0. Shape and scale were

determined by fitting a Weibull distribution to the pooled data, as follows: to pool residential

occupancy data for the age cohorts, an arithmetic mean of data means was calculated for each age

group. Then, assuming a Weibull distribution, the variance within each age group (e.g., 6-yrolds) was calculated in the age cohort. These variances in turn were pooled over the age cohort

using equal weights. This is not the usual type of pooled variance, which would exclude the

variation in the group means. However, this way the overall variance reflected the variance of

means within the age groups (e.g., within the 6-yr-old age group). The standard deviation was

estimated as the square root of the variance. The coefficient of variation was calculated as the

ratio of the standard deviation divided by the Weibull mean. For each cohort, the populationestimated parameter uncertainty information (e.g., shape and scale) was calculated based on a

Weibull distribution, the calculated data mean for the age cohort, and the CV.

Exposure duration for adult farmers was determined using data on residential occupancy

from the EFH, Tables 15-163 and 15-164 (U.S. EPA, 1997c). The data represent the total time a

person is expected to live at a single location, based on household type. Age-specific data were

not provided. For residence duration of farmers (U.S. EPA 1997c, Tables 15-163 and 15-164),

the gamma model was used because it was the best-fitted model in five age groups and was the

second-best-fitted model in two cases (based on data in U.S. EPA 1997c, Tables 15-167 and 

15-168). A population mean of 18.07 years and a population standard deviation of 23.19 years

were calculated for adult farmers.

6.2.2.3 Exposure Frequency. Exposure frequency is the frequency at which the

receptor is exposed to the contaminated source during the exposure duration. Exposure

frequency is not expected to vary much, so distributions were not developed. All receptors were

assumed to be exposed to the contaminant source 350 d/yr. This value is based on an assumption

that individuals are away from their homes (e.g., on vacation) approximately 2 weeks out of the

year.

6.2.2.4 Lifetime and Averaging Time. Averaging time is the period of time over which

a receptor’s dose is averaged. When evaluating carcinogens, total dose is averaged over the

lifetime of the individual, assumed to be 70 years for exposure durations of equal to or less than

50 years. For exposures greater than a lifetime of 70 years, the lifetime averaging time was

assumed to be the lifetime of the individual evaluated in the risk assessment. For example, if an

adult is assumed to have an exposure duration of 70 years (adult exposure period starts at

age 20), that person is assumed to have a total lifetime (averaging time) of 90 years.

6.3 Dose Estimates

The purpose of the exposure assessment is to estimate the dose to each receptor by

combining intake values with media concentrations. Estimates of exposure are based on the

potential dose (e.g., the dose ingested or inhaled) rather than the applied dose (e.g., the dose

delivered to the gastrointestinal tract) or the internal dose (e.g., the dose delivered to the target
Section 6.0 May 2002

6-26

(6-2)

(6-3)

organ). This is generally consistent with the exposure metric used in most epidemiologic and

toxicologic studies that serve as the basis for establishing the toxicological benchmarks used for

risk assessment (see Section 9.2).

Doses from individual pathways (e.g., soil, exposed vegetables) were calculated by

multiplying the contaminant concentration with the respective intake rate on a per kilogram body

weight basis. Doses received from the various ingestion pathways (e.g., soil, food) were then

summed over the period of time in which exposure occurred, resulting in an ADD received from

ingestion exposure. The ADD was used for the calculation of maternal body burden. For cancer

effects, where the biological response is described in terms of lifetime probabilities, even though

exposure may not occur over the entire lifetime, dose is presented as an LADD. The LADD was

used to assess cancer risks from each exposure route (i.e., inhalation and ingestion).

6.3.1 Average Daily Dose

For the purposes of this risk analysis, ADD was defined as

where

ADD = average daily dose (mass constituent/body weight mass/time)

C = concentration (mass/volume or mass/mass)

IR = intake rate (mass/body weight mass/time or volume/body weight

mass/time).

Contaminant concentration represents the concentration of a chemical in a medium that

contacts the body. Intake rate for the respective ingestion pathway was applied. For several food

parameters, intake rates were provided in milligram per kilogram body weight per day. 

However, intake rates for fish and soil were adjusted by body weight in order to be on a

milligram per kilogram body weight per day basis.

Pathway-specific ADDs, designated as ADDi

s, were calculated for individual ingestion

pathways (e.g., soil, exposed vegetables). The summation of the ADDi

s results in an ADD for

the ingestion pathway (ADDingest), which was used to calculate maternal body burdens and assess

risk to infants of farmers and home gardeners resulting from the ingestion of breast milk.

6.3.2 Lifetime Average Daily Dose

The LADD, used for assessing risks for carcinogenic effects, was defined as 
Section 6.0 May 2002

6-27

where

LADD = lifetime average daily dose (mass constituent/body weight mass/time)

C = average concentration (mass/mass or mass/volume)

IR = intake rate (mass/body weight mass/time or volume/body weight

mass/time)

ED = exposure duration (yr)

EF = exposure frequency (d/yr)

AT = averaging time (yr)

365 = unit conversion factor (d/yr).

The contaminant concentration represents the concentration of a chemical in a medium

that contacts the body. Intake rate depends on the route of exposure; for example, it might be an

inhalation rate or an ingestion rate. Exposure frequency is the number of days per year the

receptor is exposed to the contaminated source during the exposure duration. 

For cancer effects, biological responses are described in terms of lifetime probabilities,

even though exposure may not be lifelong. Here, the exposure duration (the length of time of

contact with a contaminant) was used to average the ADD over a lifetime (70 years or more). 

The media concentrations used in the analysis for assessing the LADD (e.g., soil concentration)

were generally averaged explicitly over the duration of exposure. This provides a more exact

estimate of the LADD. An LADDingest was calculated for ingestion exposures and an LADDinh

was calculated for inhalation exposures.

6.4 References

USDA (U.S. Department of Agriculture). 1997. 1994-96 Continuing Survey of Food Intakes by

Individuals, CD-ROM. U.S. Department of Agriculture, Agricultural Research Service,

Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1992. Guidelines for exposure assessment. 

Final guidelines. Federal Register 57 FR 22888-22893. Washington, DC. May 29.

U.S. EPA (Environmental Protection Agency). 1997a. Exposure Factors Handbook, Volume I,

General Factors. EPA/600/P-95/002Fa. Office of Research and Development,

Washington, DC. August. 

U.S. EPA (Environmental Protection Agency). 1997b. Exposure Factors Handbook, Volume II,

Food Ingestion Factors. EPA/600/P-95/002Fa. Office of Research and Development,

Washington, DC. August. 
Section 6.0 May 2002

6-28

U.S. EPA (Environmental Protection Agency). 1997c. Exposure Factors Handbook, Volume III,

Activity Factors. EPA/600/P-95/002Fa. Office of Research and Development,

Washington, DC. August.

U.S. EPA (Environmental Protection Agency). 2000. Development of Statistical Distributions

for Exposure Factors.
Section 7.0 May 2002

7-1

Lifetime excess cancer risk LADD x CSF = (7-1)

7.0 Human Health Risk Results

The final step of the risk assessment process is to characterize the risk posed to receptors

(e.g., farmers and fishers). In this step, the preceding components of the risk assessment—

estimates of toxicity (the health benchmarks) and exposure assessments—are summarized and

integrated into quantitative expressions of risk. For this risk assessment, estimates of dose and

toxicity were used to calculate individual excess lifetime carcinogenic risk estimates for all

dioxin, furan, and PCB congeners in biosolids as a total TEQ. Section 7.1 describes the risk

calculations completed for this analysis. Section 7.2 describes multipathway risks.

7.1 Human Health Risk Characterization

The goal of this risk assessment was to estimate a national distribution of the incremental

increase in individual lifetime risk of developing cancer due to exposure to dioxins, furans, and

PCBs potentially present in the biosolids for farm families who apply biosolids as fertilizer or

soil conditioner. The probabilistic analysis showed that biosolids are equally likely to be applied

to farms nationwide (i.e., no region is more likely to have this practice than another). The farmer

is assumed to apply biosolids at agronomic rates once every other year for a maximum of

40 years (maximum 20 additions). The biosolids are assumed tilled into crop land but not tilled

into pasture land The farmer is assumed to consume a significant portion of his diet from

homegrown items produced on the biosolids-amended land. This scenario does not represent the

general population but intentionally reflects the risks to highly exposed individuals within the

subpopulation of farmers who apply biosolids. 

7.1.1 Lifetime Excess Cancer Risk

Cancer risk was characterized using lifetime excess cancer risk estimates to represent the

excess probability of an individual developing cancer over a lifetime as a result of exposure to

dioxins, furans, or PCBs in biosolids. Lifetime excess cancer risk estimates use the LADD as the

measure of exposure and are the product of the LADD, expressed as a toxicity equivalent of

2,3,7,8-TCDD for a specific receptor (i.e., adult farmer), and the CSF for 2,3,7,8-TCDD, as

shown in Equation 7-1. Lifetime excess cancer risk estimates are calculated independently for

each route of exposure and for the receptor, assuming multipathway exposures:
Section 7.0 May 2002

7-2

where

LADD = lifetime average daily dose (mg/kg BW/d)

CSF = cancer slope factor (mg/kg BW/d)-1.

7.1.2 Total Lifetime Excess Cancer Risk

Congener-specific individual incremental increases in lifetime excess cancer risks were

generated for each receptor for each inhalation and ingestion pathway exposure. These pathwayspecific lifetime excess cancer risks for each congener were then summed to generate a total risk

due to exposure to all dioxin, furans, and PCBs in biosolids; this total risk for all congeners

combined is presented in this section. This total risk is estimated for multipathway exposures, as

well as for individual exposure pathways for each of the 3,000 iterations in the probabilistic

analysis. 

7.1.3 Risk Results

The results of the risk analysis yielded distributions of risk for each receptor for each

potential exposure pathway individually and a distribution of risk for each receptor considering

multipathway exposures. When each pathway is considered individually, the percentiles of the

risk distribution describe the risk from only that single pathway. For example, the risk

distribution for the aboveground vegetable ingestion pathway considers only the factors that are

included in that pathway, including all factors that increase the concentration of dioxin-like

congeners in aboveground vegetation and ingestion rates for this dietary item. The inputs to the

risk analysis iteration that yield the 90th percentile risk from the ingestion of aboveground

vegetation pathway are highly unlikely to occur in the same iteration as the set of inputs

(individual) that yield the 90th percentile risk for the beef ingestion pathway. In addition, the

multipathway analysis considered the risk from all pathways simultaneously; thus, the individual

with the 90th percentile multipathway risk may not be, and frequently is not, the same individual

with the 90th percentile risk for a single pathway. 

Risk represents the combination of the exposure point media concentration and the

receptor-dependent exposure factors. The distributions of media concentrations used in the risk

calculations and the representative percentiles from the distributions are presented in Section 5.0

The distributions of the exposure factors used in this risk assessment are presented in Section 6.0. 

 A statistical sensitivity analysis was performed using the inputs and outputs to the

probabilistic analysis risk to identify and rank the most influential factors in calculating the risk

for each pathway. For all pathways, exposure duration and consumption rate are the two most

important factors in the risk calculation. Of the factors that affect the loading of dioxins in

biosolids to the soil, the most important factors appear to be the number of applications of

biosolids made to the soil prior to the start of the exposure and the rate at which the biosolids are

applied. The later in the period of application the family lives on the farm, the higher the average

soil concentration during the exposure period because the total soil loadings to which the farmers

are exposed are higher. The number of additions of biosolids and the rates at which those

additions are made determine the total loading of dioxins in biosolids to the soil. The sensitivity
Section 7.0 May 2002

7-3

LADD I C ED =× ××× soil soil

− 365 10 6 (7-2)

analysis used to identify these parameters is described in detail in Section 8.1.2.6. The following

sections present the percentiles for the incremental increase in individual lifetime risk to the adult

and child farmers for each pathway in the probabilistic analysis. All dioxin-like congener

concentrations were modeled individually in the exposure analysis.

7.1.3.1 Soil Ingestion. Soil is assumed ingested incidentally by adult farmers and their

children. The adult farmer is assumed to ingest soil from the crop land where he is assumed to

have the greatest opportunity for exposure. The children are assumed to be exposed to soil

concentrations at the residence location (buffer area). The buffer soil is assumed to receive

erosion, runoff, and air deposition from both the crop land (tilled) and pasture (untilled) areas. 

Thus, the soil concentration in the buffer is higher than the soil concentration in the crop area,

but lower than the concentration modeled for the pasture area. All soil assumed ingested by the

adult farmer and child is assumed to be from their own farm where biosolids are applied

(contaminated fraction = 1). The data on soil ingestion rates for both adults and children are

limited; therefore, the soil ingestion rate is assumed constant in this analysis. From age 1 to 7,

the child is assumed to ingest 100 mg/d (this does not include pica behavior), and all individuals

over age 7 (older children and all adults) are assumed to ingest 50 mg/d. Because ingestion rates

are assumed constant for soil, risks are driven by the exposure duration and soil concentration. 

The LADD for soil ingestion is presented below:

where

LADD = lifetime average daily dose (mg constituent/kgBW/d)

Isoil = intake of soil (mgsoil/day)

Csoil = concentration of in soil (mg/kg soil) 

ED = exposure duration (yr)

365 = d/yr

1×10-6 = kg/mg

BW = body weight (kg).

The most important factor in the soil ingestion risk is the length of time that the individual is

exposed. The soil concentration to which the individual is exposed is driven by the total loading

of the soil with dioxins at the time the individual is exposed. The factors that affect this

exposure concentration are the number of applications that have occurred before or during the

time of exposure and the rate at which the biosolids are applied to the land. Of these factors, the

most important factor, as shown by the sensitivity analysis, is the start year of exposure, which is

an indication of how many applications have occurred before the individual moves to the farm

(i.e., the later in the process the farmer starts his exposure, the higher the average concentration

to which he is exposed, because even if additions of biosolids cease, concentrations fall slowly

due to the extended half-life of dioxin in soil). Table 7-1 presents the risks for the soil ingestion

pathway for adult farmers and their offspring who begin their exposure in childhood. 
Section 7.0 May 2002

7-4

Table 7-1. Percentile Risk for Soil Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 8E-9 2E-8

75th 2E-8 4E-8

90th 5E-8 7E-8

95th 8E-8 1E-7

99th 2E-7 2E-7

7.1.3.2 Exposed Produce. Exposed produce is assumed to be grown on tilled crop land

that is amended with biosolids. The fruits and vegetables receive exposure to dioxin-like

congeners only through the air pathway (there is no root uptake of dioxin-like congeners to

aboveground vegetation). The concentrations in the produce result primarily from vapor uptake

through the leaves and fruit as represented by the air-to-plant transfer factors. The homeproduced exposed vegetables are assumed to represent a fraction (fruit = 0.328; vegetables =

0.42) of the total amount of exposed produce the farmer and his offspring consume throughout

the year. The exposure factors (exposure duration and intake rate) are the major driving

components in the risk equation for this pathway as well. In addition, because this pathway is

driven by the air-to-plant transfer of dioxins, the factors that increase the tendency of constituents

to volatilize are also important. These factors include the soil texture, as represented by the soil

moisture retention factor b and climate factors, such as ambient temperature as noted in

Section 5. Table 7-2 presents the risks for the exposed produce ingestion pathway.

Table 7-2. Percentile Risk for Exposed Produce Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 1E-9 8E-10

75th 5E-9 2E-9

90th 2E-8 6E-9

95th 3E-8 1E-8

99th 1E-7 3E-8

7.1.3.3 Belowground Vegetables. Belowground vegetables are assumed produced on

tilled crop land that is amended with biosolids. The farm family is assumed to consume these
Section 7.0 May 2002

7-5

home-produced root vegetables as a fraction (0.173) of their total intake of root vegetables. Root

vegetables absorb dioxin-like congeners directly from the soil. The exposure factors (exposure

duration and intake rate) are most important to the risk calculation; however, for root vegetables,

soil parameters are more important than for other pathways. The soil parameter that is identified

in the sensitivity analysis is the soil foc, which is a measure of the soil organic carbon content. 

Because the concentration in the soil is the media concentration that drives this pathway, the

factors that reduce losses of dioxins to the environment through volatilization or erosion increase

the concentration in the soil. Table 7-3 presents the risks for the belowground vegetable

ingestion pathway.

Table 7-3. Percentile Risk for Belowground Vegetable Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 2E-8 1E-8

75th 6E-8 3E-8

90th 2E-7 9E-8

95th 4E-7 2E-7

99th 1E-6 4E-7

7.1.3.4 Poultry. Free-range chickens are assumed to be raised by the farm family near

the residence. The chickens are assumed to be confined to the buffer area and, thus, are assumed

to consume soil only from that area. The chicken feed is assumed to be purchased from an

uncontaminated source. The concentration of dioxin-like congeners in poultry is calculated

based on the lipid concentration of chicken thigh meat. The farm family is assumed to consume

homegrown chickens as a fraction (0.156) of their total poultry consumption. The exposure

factors (exposure duration and intake rate) are again the most important factors in the risk

calculation. The media concentration that drives this pathway is the soil concentration in the

buffer area. This soil receives dioxins predominantly from erosion from the crop land and

pasture amended with biosolids with some contribution from air deposition from these areas.

Thus, the factors that lead to higher concentrations in the soil also lead to higher concentrations

in the poultry. These factors include higher application rates and a greater number of

applications at the time of exposure. Table 7-4 presents the risks for the poultry ingestion

pathway. 

7.1.3.5 Eggs. The free-range chicken scenario used in the poultry scenario was also

assumed for the egg ingestion scenario. The farm family is assumed to consume home-produced

eggs as a fraction (0.146) of their total egg consumption. The factors important to the poultry

ingestion pathway are identical to the egg ingestion pathway. Table 7-5 presents the risks for the

egg ingestion pathway.
Section 7.0 May 2002

7-6

Table 7-4. Percentile Risk for Poultry Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 3E-8 3E-8

75th 1E-7 8E-8

90th 3E-7 2E-7

95th 5E-7 2E-7

99th 1E-6 6E-7

Table 7-5. Percentile Risk for Egg Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 4E-8 4E-8

75th 1E-7 9E-8

90th 3E-7 2E-7

95th 6E-7 2E-7

99th 2E-6 7E-7

7.1.3.6 Beef. Beef cattle are assumed to be raised on the pasture that is top-dressed with

biosolids. The cattle are assumed to graze in the amended pasture obtaining 48 percent of their

diet from forage, 48 percent from silage, and 4 percent from incidental ingestion of surficial soil

while grazing in the pasture. The cattle consumed by the farm family are assumed not finished in

a feed lot. Exposure factors (exposure duration and intake rate) and soil loading factors

(application rate and number of applications) also drive this pathway. The majority of the

concentration in the beef is due to the concentration of dioxins in the pasture grass where the

cattle forage. The concentration in the forage is due to air-to-plant transfer of vapors. Thus,

higher loadings of biosolids to the soil, especially in areas where it is hot and dry to promote

volatilization of constituents from the soil, increase the risk from the beef ingestion pathway. The

farm family that raises beef cattle is assumed to obtain 49 percent of the beef it consumes from

home-raised cattle. Table 7-6 presents the risks for the beef ingestion pathway.
Section 7.0 May 2002

7-7

Table 7-6. Percentile Risk for Beef Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 6E-7 6E-7

75th 2E-6 1E-6

90th 6E-6 5E-6

95th 1E-5 5E-6

99th 3E-5 1E-5

7.1.3.7 Milk. Dairy cattle are also assumed to be raised on a pasture that is top-dressed

biennially with biosolids. The cattle are assumed to graze in the amended pasture; however,

dairy cattle are assumed to obtain only 8 percent of their diet from forage, 90 percent from silage,

and 2 percent from incidental ingestion of surficial soil in the pasture. The same factors that

drive the risk from the beef pathway also drive the risks from the dairy pathway, although the

dairy cattle are assumed to eat less forage. The silage that dairy cattle are assumed to consume is

grown on crop land that is amended with biosolids. Thus, the silage is assumed to receive

dioxins through air-to-plant transfer to the nongrain portion of the silage (0.5). The

concentration of dioxins in silage is less than that in forage; however, it is still a significant

source of dioxin in milk. The farm family that raises dairy cattle is assumed to obtain 25 percent

of its total milk consumption from home-raised cattle. Table 7-7 presents the risks for the milk

ingestion pathway.

Table 7-7. Percentile Risk for Milk Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 3E-7 5E-7

75th 1E-6 1E-6

90th 3E-6 3E-6

95th 6E-6 5E-6

99th 2E-5 1E-5

7.1.3.8 Fish. Edible fish are assumed to be caught from a stream adjacent to the farm

where biosolids are applied. The stream, therefore, receives runoff, erosion, and air deposition

from the amended crop land and pasture. The eroded soil from the farm is transported across the

buffer directly to the stream. The stream also receives direct air deposition of particles and
Section 7.0 May 2002

7-8

vapors from the crop land and pasture. In addition, particles and vapors from the crop land and

pasture are transported and deposited on the much larger area of the regional watershed from

which they are also eroded to the modeled stream. The fish that live in the stream are assumed to

include T3 and T4 fish (i.e., edible species). These fish are assumed to be caught and consumed

by a recreational fisher. This fisher may also be the farmer who applies the biosolids or an

individual from a nearby town who has no other pathways of exposure. The recreational fisher is

assumed to catch all the home-caught fish he consumes from this single stream adjacent to the

biosolids-amended field. Table 7-8 presents the risks for the fish ingestion pathway. 

Table 7-8. Percentile Risk for Fish Ingestion Pathway

Percentile

Lifetime Individual Risk

Adult

50th 8E-10

75th 4E-9

90th 2E-8

95th 4E-8

99th 2E-7

7.1.3.9 Ambient Air. The ambient air concentration that the farm family is assumed to

breathe is the average air concentration estimated over the residential buffer. The vapor and

particulate air concentrations are estimated independently in the air modeling, but are summed in

the inhalation risk estimates. The risks for this pathway are driven predominantly by exposure

factors (exposure duration and inhalation rate), also. Other factors that influence this pathway

are the loading to the soil and the soil and climate properties that lead to greater air emissions

from the amended soil. Table 7-9 presents the risks for the air inhalation pathway.

7.1.3.10 Breast Milk. The lactating woman is an adult member of the farm family and,

therefore, is assumed to consume all types of home-produced food with the consumption rates

and fractions homegrown presented in the preceding sections for each of the following dietary

items: exposed produce, root vegetables, poultry, eggs, beef, and milk. The mother is assumed to

have reached a steady-state concentration of dioxins in lipids before lactation begins. The

maternal concentrations of dioxins are then modeled to partition each congener to the lipid

fraction of breast milk, which is subsequently assumed ingested by an infant. The infant is

assumed to consume no homegrown dietary items directly and, thus, obtains exposures only

through the ingestion of breast milk during the first year of life. Table 7-10 presents the risks for

the breast milk ingestion pathway.
Section 7.0 May 2002

7-9

Table 7-9. Percentile Risk for Air Inhalation Pathway

Percentile

Lifetime Individual Risk

Adult Child

50th 7E-10 9E-10

75th 2E-9 2E-9

90th 7E-9 5E-9

95th 1E-8 8E-9

99th 3E-8 2E-8

Table 7-10. Percentile Risk for Breast Milk Ingestion Pathway

Percentile

Lifetime Individual Risk

Infant

50th 2E-9

75th 4E-9

90th 8E-9

95th 1E-8

99th 3E-8

7.2 Multipathway Risks

The structure of the probabilistic analysis is based on the modeling of 3,000 individual

exposure scenarios for adults and children. This means that for each individual (iteration) in the

analysis, a set of intake rates is chosen from the distribution for each pathway for an adult

receptor and a child receptor. The intake rates for dietary items are not correlated in any way. 

Insufficient data are available to enable correlation. This process allows the evaluation of risk

from each pathway independently, and it also allows the evaluation of multipathway risks. The

distribution of multipathway risks is the distribution of the sum of the risks across pathways for

each of the 3,000 individual adults and children in the analysis. Thus, for example, the 90th

percentile multipathway risk to an adult receptor may not correspond to the 90th percentile risk

for any single pathway, but it is selected from the distribution of 3,000 risks summed across all

pathways. Multipathway risks were also evaluated for the adult farmer and his child. The

multipathway results presented in Tables 7-11 through 7-13 are from the distribution of the total

risk. Table 7-11 presents the multipathway risks to the adult and child members of the farm

family and includes the lifetime average daily dose (LADD) that produced these risks.
Section 7.0 May 2002

7-10

Table 7-11. Multipathway Risks and Associated LADD for

 Adult and Child Farm Family Members—

Baseline All Samples from 2001 NSSS

Percentile

Adult Child

Risk

Daily Exposure,

pg TEQ/kg-d Risk

Daily Exposure,

pg TEQ/kg-d

50th 1 × 10-6 0.009 1 × 10-6 0.009

75th 4 × 10-6 0.03 3 × 10-6 0.02

90th 1 × 10-5 0.06 7 × 10-6 0.04

95th 2 × 10-5 0.1 1 × 10-5 0.06

99th 4 × 10-5 0.3 2 × 10-5 0.1

In order to see the effect of setting a cutoff limit based upon the TEQ of the biosolids

sample, several additional evaluations of multipathway risk were made with biosolids samples

having TEQ values above a specified value removed. The cutoff limits examined in this way

were 300 ng/kg TEQ and 100 ng/kg TEQ. Table 7-12 presents the multipathway risks to the

adult and child members of the farm family using only the biosolid samples with a total TEQ

concentration below 300 ng/kg TEQ. Table 7-13 presents the multipathway risks to the adult and

child members of the farm family using only the biosolid samples with a total TEQ concentration

below 100 ng/kg TEQ.

No decrease in total risk is observed with the elimination of the samples with the highest

concentrations. This is because there are so few samples in these concentration ranges.

Table 7-12. Multipathway Risks for Adult and Child Farm Family Members—

300 TEQ Cutoff Limit for Samples from 2001 NSSS

Percentile Adult Child

50th 1E-6 1E-6

75th 4E-6 3E-6

90th 1E-5 7E-6

95th 2E-5 1E-5

99th 4E-5 2E-5
Section 7.0 May 2002

7-11

Table 7-13. Multipathway Risks for Adult and Child Farm Family Members—

100 TEQ Cutoff Limit for Samples from 2001 NSSS

Percentile Adult Child

50th 1E-6 1E-6

75th 4E-6 3E-6

90th 1E-5 6E-6

95th 2E-5 1E-5

99th 4E-5 2E-5
x
Section 8.0 May 2002

8-1

Variability arises from true heterogeneity in

characteristics, such as body weight differences

within a population or differences in contaminant

levels in the environment.

Uncertainty represents lack of knowledge about

factors, such as the nature of adverse effects from

exposure to constituents, that may be reduced with

additional research.

8.0 Analysis of Variability and Uncertainty

This section discusses the methods

that were used in the risk assessment for

dioxins, furans, and PCBs in biosolids to

account for variability and uncertainty. 

Variability and uncertainty are fundamentally

different. Variability represents true

heterogeneity in characteristics such as body

weight differences within a population or

differences in contaminant levels in the

environment. It accounts for the distribution

of risk within the exposed population. 

Uncertainty, on the other hand, represents lack of knowledge about factors, such as adverse

effects from contaminant exposure, that may be reduced with additional research to improve data

or models. 

This discussion describes the treatment of variability and uncertainty in reference to some

parameters used to describe human exposures and risk. Treatment of variability using a Monte

Carlo simulation forms the basis for the human health risk distributions, which in turn are the

basis for calculating a protective concentration for dioxins, furans, and PCBs in biosolids. 

Previous sections of this document describe how distributions were generated and point values

estimated for input parameters. They also describe how these values were used in the models

and in calculations to produce a national-level TEQ concentration in biosolids that is protective

of human health. Uncertainty necessitated the use of assumptions and default values in this

study. This discussion focuses on how this treatment of variability and uncertainty affects the

results. 

8.1 Variability

Variability is often used interchangeably with the term uncertainty, but the two are not

synonymous. Variability is tied to variations in physical, chemical, and biological processes and

cannot be reduced with additional research or information. Although variability may be known

with great certainty (e.g., age distribution of a population may be known and represented by the

mean age and its standard deviation), it cannot be eliminated and needs to be treated explicitly in

the analysis. Spatial and temporal variability in parameter values used to model exposure and

risk account for the distribution of risk in the exposed population.

For example, the meteorological parameters used in dispersion modeling, such as

windspeed and wind direction, are measured hourly by the National Weather Service at many
Section 8.0 May 2002

8-2

locations throughout the United States, and statistics about these parameters are well

documented. Although the distributions of these parameters may be well known, their actual

values vary spatially and temporally and cannot be predicted exactly. Thus, the concentration

calculated by a dispersion model for a particular receptor for a particular time period will provide

information on average conditions that may over- or underpredict actual concentrations. Much of

the temporal variation is accounted for by using models such as ISCST3 that calculate

concentrations hourly and sum these hourly values to provide annual concentration estimates. 

Additionally, using meteorological data from multiple monitoring stations located throughout the

United States can account for some but not all spatial variability.

In planning this analysis, it was important to specifically address as much of the

variability as possible, either directly in the Monte Carlo analysis or through disaggregation of

the data into discrete elements of the analysis. For example, use of a refined receptor grid

accounts for spatial variability in concentrations on and around the agricultural field where

biosolids are applied. Variability in agricultural practices is accounted for by using distributions

that represent the range of possible agricultural practices. 

Spatial variability in environmental setting was accounted for by using 41 different

climatic regions throughout the contiguous 48 states. Because biosolids are generated

nationwide, the application of biosolids to agricultural fields may occur nationwide; thus, this

analysis characterized environmental conditions that influence the fate and transport of

constituents in the environment using regional data based on climatic conditions. 

The risk assessment components discussed include

 Source characterization and emissions modeling

 Fate and transport modeling

 Exposure modeling. 

8.1.1 Source Characterization and Emissions Modeling

The specific agricultural fields where biosolids were applied were not known; however,

EPA assumed that biosolids could be applied to any agricultural land. For this analysis,

agricultural field areas were varied according to climatic regions. The median farm size for each

climatic region was used to represent the regional variability of farm size. However, uncertainty

about farm size within a climatic region remained. Distributions were used to capture

nationwide variability in agricultural practices. The variation in median farm size based on

regions and the nationwide distribution of agricultural practice parameters was used in the

probabilistic analysis to characterize the national variation in farm areas and operating

characteristics.

Source partition modeling was performed for 41 different climatic regions, which allowed

variation in location-dependent parameters (e.g., soil, temperature, precipitation) to be considered

explicitly in the modeling. Variation in these parameters influenced variation in predicted air

emissions rates. These meteorological data sets were combined with the surface area of the
Section 8.0 May 2002

8-3

agricultural field to provide unit air concentrations (UACs), which were used with emissions data

to estimate air concentrations for crop land and pastures.

In the Monte Carlo analysis, the agricultural field characteristics, environmental

conditions from 41 climatic regions, and parameter values for biosolids characteristics were

combined to produce the 3,000 iterations of the source partition model calculations. The source

model calculations generated the distribution of environmental releases used in the fate and

transport modeling.

8.1.2 Fate and Transport Modeling 

The parameter values required to model contaminant fate and transport were obtained

from regional databases. The treatment of regional variation in location-dependent parameters

used in fate and transport modeling is discussed in the following sections.

8.1.2.1 Air Dispersion Modeling. To capture geographic variation, dispersion modeling

was conducted using meteorological data sets from 41 different meteorological stations

throughout the contiguous 48 states. This provided regional representation of the variability in

meteorological data. Obviously, 41 meteorological stations do not represent every site-specific

condition that could exist in the 48 states. However, in selecting the climatic regions,

consideration was given to representing different Bailey’s ecological regions and to not

excluding from the analysis those areas with unique dispersion characteristics (e.g., coastal

areas). Thus, it is believed that these 41 climatic regions are a reasonable representation of the

variability in meteorological conditions for the United States. 

8.1.2.2 Soil and Water Modeling. Soil characteristics were based on the location of the

41 climatic regions used in the modeling. Soil characteristics for all nonurban soil within the

climatic region were used to determine the soil characteristics for watershed modeling. This

approach captured the national distribution of soil types and accounted for regional variation in

soil characteristics.

Waterbody characteristics were not varied in the fate and transport modeling. However,

in addition to variation in soil type and precipitation, watershed modeling also took into account

regional variation in agricultural field size and regional watershed size, which can affect

constituent loading to the waterbody via runoff and erosion. Otherwise, regional variations in

waterbody were not accounted for in this analysis. 

8.1.2.3 Terrestrial and Aquatic Food Chain. To the extent that agricultural field size

and variation in regional watershed areas affects runoff and erosion of constituents into the

waterbodies modeled in this assessment, the variation had an effect on runoff and erosion

loadings to the waterbody. Otherwise, no regional variations were considered for the aquatic

food chain modeling.

8.1.2.4 Exposure Modeling. Individual physical characteristics, activities, and behavior

are quite different. As such, the exposure factors that influence the exposure of an individual,

including inhalation rate, ingestion rate, body weight, and exposure duration, are quite variable. 
Section 8.0 May 2002

8-4

To include this variability explicitly in the analysis, statistical distributions for these variables

were used for each receptor in the analysis: adult, child, and infant in the farm family and a

recreational fisher. For adults, a single exposure factor distribution was used for males and

females. For child exposures, one age group (ages 1 to 6) was used to represent the age at the

start of exposure, because this age group is considered to be most sensitive for most health

effects. The infant was evaluated only for breast milk ingestion during the first year of life. 

Exposure parameter data from the EFH (U.S. EPA, 1997a, b, c) were used to establish statistical

distributions of values for each exposure parameter for each receptor. 

8.1.2.5 Summary of Variability Considerations. In summary, a protective biosolids

concentration was developed that includes specific consideration of the variability in

 Agricultural field size and biosolids characteristics

 Agricultural practices

 Regional-specific environmental conditions 

 Exposure factors for each receptor.

Taken together, these provide nationally applicable risk-specific TEQ concentration for dioxins,

furans, and PCBs in biosolids.

8.1.2.6 Sensitivity Analysis. A statistically based sensitivity analysis was performed to

rank the variable parameters in the analysis according to their contribution to the variability of

the resulting risk for each pathway. This methodology is referred to as a response surface

regression approach because it uses models characteristic of those used in a response surface

experiment. Response surface methodology involves a statistical approach to designing

experiments and an associated model estimation methodology. The terminology “response

surface” derives from the fact that a regression model involving a number of continuous

independent variables can be viewed as providing an estimated surface of the results in space. 

Often a goal of response surface experimentation is to ascertain the combination(s) of input

variable values that will yield a minimum or a maximum response. The complexity of the model

(e.g., whether it contains only first- and second-order terms or terms of higher degree) determines

the general shape of the contours and the degree to which the “true” surface can be

approximated. 

In this analysis, a regression analysis was applied to a linear equation to estimate the

relative change in the output of a probabilistic simulation relative to the changes in the input

variable values. This methodology is one of the recommended methods for conducting a

sensitivity analysis based on the results of a Monte Carlo analysis described in Appendix B of

RAGS 3A - Process For Conducting Probabilistic Risk Assessment - Draft (1999) (U.S. EPA,

1999).

Sensitivity analyses historically were conducted by evaluating how much change in risk

occurred as a result of varying an individual input variable from a median or mean value to a 90th

percentile or high-end value. When the risk depends on the aggregate impact of a number of

input variables, however, such an approach may not necessarily identify the most important one. 

This may occur for several reasons:
Section 8.0 May 2002

8-5

 The ranges chosen for the various input variables may not be defined consistently.

 Various input variables may interact with one another (i.e., the effect of input X1

on an outcome Y depends on the level of other inputs X2, X3, etc., so that the

observed effect of X1 depends on what values were chosen for the other variables

as well). 

 Nonlinear effects may obscure the effect of the input variable (e.g., if only low

and high values of an input variable are examined but the relationship between the

risk and the input variable is of a quadratic nature, then the importance of the

input variable may be overlooked). 

To address such issues, statistical regression methods were used to perform the sensitivity

analyses. Although regression methods have distinct advantages over previous approaches,

certain limitations remain. Regression methods are not capable of determining the sensitivity of

model results to input variables that are not varied in the analysis (e.g., assumptions) or are not

otherwise included within the scope of the analysis (e.g., model-derived variables). If, for some

reason, the most important variables are not varied or their variability is improperly

characterized, the sensitivity analysis may not identify them as being important.

The sensitivity analysis was conducted on a data set generated during modeling of each

pathway. For example, a set of input variables (X1, X2, ..., Xp) was used in the modeling

simulation. 

The result of interest is the individual risk calculated for each pathway as a result of

exposure to all dioxin-like congeners as expressed as a TEQ. In this case, the Xs are parameters

associated with agricultural practices, site, environmental conditions, and exposure parameters.

The regression approach uses the various combinations of X values that were used during

the simulation and the resulting risk values as input data to a regression model. Functions of the

results variables (denoted as Ys) were treated as dependent variables; for example, Y denoted the

logarithm of the risk. Functions of the Xs were treated as independent variables. The goals of

the approach were 

1. To determine a fairly simple polynomial approximation to the simulation results

that expressed the Ys as functions of the Xs

2. To optimize this “response surface” and assess the importance of the various Xs

by performing statistical tests on the model parameters

3. To rank the Xs based on their relative contribution (in terms of risk) to the final

response surface regression model. 

These goals were realized using a second-order regression model. Such a model takes the

following form:
Section 8.0 May 2002

8-6

(8-1)

where the s are the least squares regression estimates of the model parameters.

The statistical significance of the parameters associated with the first-order, squared, and

cross-product terms were tested and all nonsignificant terms were removed from the model. The

parameters in this reduced model were then reestimated and the process of testing was repeated. 

This was done to capture the most important independent variables (Xs) that influence the

dependent variables (Ys).

Once the final regression model was developed, the input parameters (Xs) were ranked

based on percentage of risk accounted for by that parameter. The percent risk was calculated

using the following equation: 

(8-2)

where 

FMSS = model sum of squares for the final model 

RMSS = model sum of squares for a model in which all terms involving xu are removed

(i.e., a reduced model) 

ERSS = model error sum of squares.

The two parameters responsible for the largest percentage of the risk are the two

parameters set to high-end values in the deterministic analysis.

The major steps in the sensitivity analysis are identified below, along with details on the

reasons for these steps.

 Perform any necessary manipulations to the data set. To perform the

sensitivity analysis, the data set must contain only one record for each Monte

Carlo iteration, and all variables in the data set must be numeric.

 Remove any variables that are constants. Any variable that was constant across

all Monte Carlo iterations does not have any effect on the resulting risk and was

removed from the data set prior to the start of the regression analysis.

 Perform transformations (log, square root, etc.) to the continuous input

variables, if necessary, so that all input variables will have approximately

symmetric distributions. Transforming the input variables so that each one has
Section 8.0 May 2002

8-7

an approximately symmetric distribution is necessary to make the standardization

of the variables meaningful (i.e., so the mean is near the midpoint of the extremes,

and the mean and standard deviation are not highly related).

 Check the correlations of the transformed input variables. Remove any

input variables that are highly correlated with other input variables in the

data set. Regression analysis measures the linear relationship between the terms

in the model and the response variable. If two or more input variables are highly

correlated with one another, then there is a strong linear relationship between

those input variables. Keeping all highly correlated variables in the model will

reduce the significance of each of the correlated input variables because each one

is essentially explaining the same linear relationship with the response variable

(i.e., the effect of one such variable may mask the effect of another).

 Standardize the transformed variables. Standardizing the input variables (i.e.,

subtracting the mean and dividing by the standard deviation) allows the regression

results to be independent of the magnitude of the value of the input variables. The

larger value input variables could cause the regression results to seriously

underestimate the effects of the smaller value input variables on the changes in

environmental concentration and risk. The combination of transforming and

standardizing the input variables creates more optimal conditions for regression

analysis.

 Use response surface regression methods to test for the main effects, squared

terms, and cross products that have the greatest effect on the

log(environmental concentration). Develop a model for log(environmental

concentration) based on the results of the regression analysis. After the

response surface regression results are obtained, the significance of each term on

environmental concentration is evaluated. First, any second-order terms that are

determined not to have a significant effect on the environmental concentration are

dropped from the model. Any first-order term that is part of a significant secondorder term will remain in the model, regardless of the level of significance of that

first-order term. For example, if the second- order term X1 × X2 has a significant

effect on the environmental concentration and remains in the model, then both of

the first-order terms, X1 and X2, will also remain in the model. Any first-order

terms that are determined not to be significant and not to have any significant

second-order terms are dropped from the model. The regression analysis is then

conducted on the reduced model. This process is repeated until all of the secondorder terms in the model have significant effects on the environmental

concentration and no more terms can be removed. The iterative process of

dropping insignificant terms and reevaluating the model allows only the input

variables with the most effect on the environmental concentration to remain in the

model.

 Use the model for log(environmental concentration) as part of the model for

the log(risk). The equation that must be evaluated is
Section 8.0 May 2002

8-8

(8-3)

(8-4)

(8-5)

Taking the log of both sides of the above equation results in

The log(environmental concentration) in the above equation is replaced with the

final model of input variables from the regression analysis in the previous analysis

step. Regression analysis is performed on the new model for log(risk).

 Test for the effect of each variable on log(risk) and use the p-values to rank

the variables by the amount of effect each variable has on log(risk). Because

the final model will most likely contain first- and second-order terms involving

the same input variables, F-tests need to be performed to evaluate the effect of

each input variable in the final model on the log(risk). The F-tests of each

variable will be of the form

where 

FMSS = model sum of squares for full model

containing all significant terms

RMSS and RMDF = model sum of squares and degrees of

freedom, respectively, for reduced model 

FMDF = model degrees of freedom for full model

FRSS and FRDF = residual sum of squares and degrees of

freedom, respectively, for full model.

The full model refers to the model containing all significant terms in the final

log(risk) model. The reduced model refers to the full model minus all terms

containing the input variable X whose significance is being tested. The F-tests

evaluate the effect of variable X on the risk by evaluating the differences when

variable X is in the regression model (full model) and when all model terms

containing variable X are removed (reduced model). If a substantial increase in

the residuals results from ignoring terms involving the variable X, then F will be

“large,” implying that these factors can be considered important, in the sense that
Section 8.0 May 2002

8-9

they require different regression coefficients for the Xs. The ordering of the

p-values from such tests can then be used to rank the importance of the various

factors on the risk. The most important four parameters for each pathway

identified by the sensitivity analysis are presented in Table 8-1. Detailed results

of the sensitivity analysis are presented in Appendix K.

8.2 Uncertainty

Uncertainty is a description of the imperfection in knowledge of the true value of a

particular parameter. In contrast to variability, uncertainty is reducible by additional

information-gathering or analysis activities (e.g., better data, better models). EPA typically

classifies the major areas of uncertainty in risk assessments as scenario uncertainty, model

uncertainty, and parameter uncertainty. Scenario uncertainty refers to missing or incomplete

information needed to fully define exposure and dose. Model uncertainty is a measure of how

well the model simulates reality. Parameter uncertainty is the lack of knowledge regarding the

true value of a parameter used in the analysis.

Although some aspects of uncertainty were directly addressed in this analysis, much of

the uncertainty associated with this analysis could only be addressed qualitatively. Significant

sources of uncertainty are presented in this section. If the analysis directly addressed uncertainty,

the approach used is described. If the analysis did not directly address uncertainty, a qualitative

discussion of its importance is provided.

8.2.1 Scenario Uncertainty

Sources of scenario uncertainty include the assumptions and modeling decisions that are

made to represent an exposure scenario. The entire hypothetical farm scenario is a source of

uncertainty in this analysis. The analysis is based on a single conceptual site model that assumes

biosolids are applied to a farm that is half crop land and half pasture and that the farm family

lives adjacent to those areas where biosolids are applied. These are reasonable assumptions;

however, much uncertainty is associated with the scenario. The lack of information or resources

to define and model actual exposure conditions introduced uncertainty into this analysis. 

Professional judgment, data availability, and, in some cases, an evaluation of the results

of a sensitivity analysis are used to decide which parameters to include in describing exposure

conditions and behaviors. Scenario uncertainties that are important to understand in interpreting

the results of this study are discussed in the following subsections.

8.2.1.1 Biosolids Characteristics. Few data were available on the physical and

chemical characteristics of biosolids. To address this lack, assumptions on specific biosolids

characteristics were based on general knowledge of biosolids. In this analysis, except for

constituent concentration, which was measured, general biosolids characteristics, including

default assumptions for bulk density, moisture, and porosity, were used. 
Section 8.0 May 2002

8-10

Table 8-1. Results of Sensitivity Analysis by Pathway

Pathway Sensitivity Variables

Percent of Risk Accounted

for by Variable

Air Exposure duration 66

Inhalation rate 4

Soil moisture retention exponent b 2

Application rate 2

Soil Exposure duration 78

Average year that the farm family moves in 3

Application rate 2

Body weight 1.5

Aboveground vegetables and fruit Exposure duration Fruit 56, Veg. 59

Consumption rate Fruit 11, Veg. 6

Average year that the farm family moves in Fruit 2, Veg. 3

Soil moisture retention exponent b Fruit 2, Veg. 2

Root vegetable Exposure duration 49

Consumption rate 30

Soil fraction organic carbon (foc) 10

Average year that the farm family moves in 4

Poultry Exposure duration 55

Consumption rate 33

Average year that the farm family moves in 2

Application rate 1

Egg Exposure duration 60

Consumption rate 28

Average year that the farm family moves in 2

Application rate 1

Beef

Exposure duration 60

Consumption rate 26

Application rate 1

Average year that the farm family moves in 1

Milk Exposure duration 54

Consumption rate 32

Average year that the farm family moves in 1

Application rate 1

Fish Consumption rate 47

Exposure duration 34

Average year that the farm family moves in 1

Application rate 1
Section 8.0 May 2002

8-11

8.2.1.2 Characteristics and Location of Waterbodies. One aspect of the site layout of

particular relevance to aquatic food chain modeling is the location and characteristics of the

waterbodies. The size of the waterbody impacts constituent concentration predicted for that

waterbody. The waterbody characteristics selected were for a third-order stream, intended to

represent a small but fishable waterbody. This small size would tend to ensure that calculated

waste concentrations would be protective of routes of exposure from surface water. The location

of the waterbody was assumed to be at the edge of the agricultural field.

8.2.1.3 Receptor Populations Evaluated. The land use for the application of biosolids

to agricultural fields is assumed to be agricultural. As such, human receptors evaluated include

an adult farmer, the child and infant of the farmer, and a resident who is a recreational fisher at a

nearby waterbody. Risk estimates presented in this document address hypothetical chronic

exposures for these receptors and are designed to provide a realistic range of potential scenarios.

8.2.1.4 Exposure Uncertainty. Exposure modeling relies heavily on default

assumptions concerning population activity patterns, mobility, dietary habits, body weights, and

other factors. As described earlier in the variability section, the probabilistic analysis for the

adult and child exposure scenario addressed the possible variability in the exposure modeling by

using distributions of values for exposure factors. There are some uncertainties, however, in the

data that are used. Although it is possible to study various populations to determine various

exposure parameters (e.g., age-specific soil ingestion rates or intake rates for food) or to assess

past exposures (epidemiological studies) or current exposures, risk assessment is about

prediction. Therefore, long-term exposure monitoring in this context is infeasible. The EFH

(U.S. EPA, 1997a,b,c) provides the current state-of-the-science concerning exposure

assumptions, and it is used throughout this document. To the extent that actual exposure

scenarios vary from the assumptions in this risk assessment, risks could be underestimated or

overestimated. For example, there could be farmers and children who have higher exposures

than those predicted; however, it is more likely that actual exposures for most of these

individuals would fall within the predicted range and, moreover, would be similar to what was

modeled.

8.2.1.5 Natural Background Exposures. Dioxins are present in the environment as a

result of the application of biosolids and from other sources. Thus, receptors potentially receive

a “background” exposure that may be greater than the exposure resulting from release of dioxins

from biosolids. For national analyses such as this assessment, the inclusion of background

concentrations as part of the analysis is not feasible because of the variability of background

concentrations nationwide and the lack of data on national background concentrations for each

constituent. Not including the exposure an individual may already have to a constituent of

concern (i.e., exposure to background concentrations) does not change the “incremental” increase

in risk to an individual due to possible exposures to constituents in biosolids. 

8.2.2 Model Uncertainty

Model uncertainty is associated with all models used in all phases of a risk assessment

because models and their mathematical expressions are simplifications of reality that are used to

approximate real-world conditions and processes and their relationships. Computer models are
Section 8.0 May 2002

8-12

simplifications of reality, requiring exclusion of some variables that influence predictions but

cannot be included in models either because of their complexity or because data are lacking on a

particular parameter. Models do not include all parameters or equations necessary to express

reality because of the inherent complexity of the natural environment and the lack of sufficient

data to describe the natural environment. Because this is a probabilistic assessment that predicts

what may occur with the management of biosolids under assumed scenarios, it is not possible to

compare the results of these models (sometimes referred to as model validation) to any specific

situation that may exist. The risk assessor needs to consider the importance of excluded

variables on a case-by-case basis, because a given variable may be important in some instances

and not in others. A similar problem can occur when a model that is applicable under average

conditions is used for conditions that differ from the average. In addition, in some instances,

choosing the correct model form is difficult when conflicting theories seem to explain a

phenomenon equally well. In other instances, EPA does not have established model forms from

which to choose to address certain phenomena, such as facilitated transport. 

Models used in this risk assessment were selected based on science, policy, and

professional judgment. These models were selected because they provide the information needed

for this analysis and because they are generally considered to be state-of-the-science. Even

though the models used in the risk analyses are used widely and have been accepted for

numerous applications, they each retain significant sources of uncertainty. Evaluated as a whole,

the sources of model uncertainty in this analysis could result in either an overestimation or

underestimation of risk. For example, exposure modeling relies heavily on default assumptions

concerning population activity patterns, mobility, dietary habits, body weights, and other factors. 

There are some uncertainties associated with some of the data used for these parameters. 

Although it is possible to study various populations to determine various exposure parameters

(e.g., age-specific soil ingestion rates or intake rates for food) or to assess past exposures

(epidemiological studies) or current exposures, risk assessment is about prediction. Therefore,

long-term exposure monitoring in this context is infeasible. The EFH (U.S. EPA, 1997a,b,c),

which provides the current state-of-the-science concerning exposure assumptions, was used in

this risk assessment. To the extent that actual exposure factors vary from the assumptions in this

risk assessment, risks could be underestimated or overestimated. 

Another issue in model uncertainty is the number of iterations necessary to achieve

convergence of the analysis, especially at the higher ends of the distribution. In order to

determine the convergence of this analysis, the results from various portions of the iterations

were selected from the total number of iterations. The percentile values for the 90th, 95th, and 99th

percentiles of the smaller number of iterations were compared to the 90th, 95th, and 99th

percentiles for 10,000 iterations. Convergence at the 95th percentile was achieved with 2,500 to

3,000 iterations. Thus, for this analysis, 3,000 iterations was assumed to be sufficient to estimate

a reliable distribution of risks, including risks at and above the 95th percentile. Figure 8-1

presents the convergence analysis of risk values.

8.2.2.1 Air Dispersion Modeling. The ISCST3 model was used to calculate the

dispersion of particle and vapor emissions from a waste management unit. This model has many

capabilities needed for this assessment, such as the ability to model area sources. For dispersion

modeling of this type, ISCST3 is considered a fairly accurate model with error within about a 
Section 8.0 May 2002

8-13

Conv ergence Analysis of Risk Values

0.0E+00

5.0E-04

1.0E-03

1.5E-03

2.0E-03

2.5E-03

3.0E-03

0 2000 4000 6000 8000 10000 12000

Number of Runs

Total Risk

 Risk at 99%

Risk at 95%

Risk at 90%

Figure 8-1. Convergence analysis.

factor of 2. It does not include photochemical reactions or degradation of a chemical in the air,

which results in additional model uncertainty. Deposition and associated plume depletion are

important for particulates and vapors and were explicitly incorporated into this analysis. 

Currently, algorithms specifically designed to model the dry deposition of gases have not been

verified for the specific compounds in question (primarily volatile organics). In place of

algorithms, a transfer coefficient was used to model the dry deposition of gases. A concern with

this approach is that the deposition is calculated outside of the model. As a result, the mass is

deposited on the ground from the plume and is not subtracted from the air concentrations

estimated by ISCST3. This results in a slight nonconservation of mass in the system.

Other uncertainties introduced into the analysis in dispersion modeling are related to

agricultural field shape. A square shape was selected because it minimizes the error introduced

by not knowing the orientation of the agricultural field to wind direction. 

8.2.2.2 Human Health Benchmarks. Toxicological benchmarks are designed to be

conservative (that is, to potentially overestimate risk) because of the uncertainties and challenges

associated with condensing toxicity data into a single quantitative expression. 

Cancer Slope Factor. The CSF for TCDD was derived as the 95 percent upper

confidence limit of the slope of the dose-response curve using a linear, no-threshold,

dose-response model. The CSF, is, therefore, an upper-bound estimate of the cancer risk per unit

dose and, for this reason, may overstate the magnitude of the risk. In addition, the use of CSFs in

projecting excess individual cancer risk introduces uncertainty stemming from a number of

factors, including 

 Limited understanding of cancer biology

 Variability in the response of animal models

 Differential response in animal models versus humans

 Difference between animal dosing protocols and human exposure patterns. 
Section 8.0 May 2002

8-14

A key step in CSF development is high- to low-dose extrapolation. Depending on the

model used to fit the data, extrapolations to the low-dose range can vary by several orders of

magnitude, reflecting the potential uncertainty associated with the CSF. In addition, uncertainty

is introduced in the analysis of dioxins, furans, and PCBs because the TEF scheme is used to

relate the toxicity of all congeners to the toxicity of TCDD. There are no other data for use for

congener-specific toxicity endpoints.

Human Health Benchmarks and Children. EPA recognizes that significant

uncertainties exist regarding the estimation of lifetime cancer risks in children. EPA estimated

the risk of developing cancer from the estimated LADD and the slope of the dose-response curve. 

A CSF is derived from either human or animal data and is taken as the upper bound on the slope

of the dose-response curve in the low-dose region, generally assumed to be linear, expressed as a

lifetime excess cancer risk per unit exposure. Individuals exposed to carcinogens in the first few

years of life may be at increased risk of developing cancer.

8.2.3 Variable Uncertainty

Variable uncertainty occurs when (1) there is a lack of data about the values used in the

equations, (2) the data that are available are not representative of the particular instance being

modeled, or (3) variable values cannot be measured precisely and/or accurately because of

limitations in measurement technology. Random, or sample, errors are a common source of

parameter uncertainty that is especially critical for small sample sizes. More difficult to

recognize are nonrandom or systematic errors that result from bias in sampling, experimental

design, or choice of assumptions. 

8.2.3.1 Agricultural Field Variables. Source characterization required making

assumptions about agricultural practices on farms where biosolids may be applied. There is

much uncertainty associated with the actual practices employed on farms where biosolids are

actually employed. It is not known what area is amended with biosolids and what crops or

animals are raised on the amended land or what specific practices are employed. The variables

used in this analysis represent the data available on potential agricultural practices. For this

reason, substantial uncertainty concerning the variable values for agricultural practices remains.

8.2.3.2 Watershed Universal Soil Loss Equation (USLE) Variables. A combination

of region-specific and national default variables was used along with USLE to model soil erosion

losses from watersheds to waterbodies. The USLE calculations are particularly sensitive to sitespecific values; thus, uncertainty is associated with using regional and national parameter values. 

Many of the ULSE parameters were based on the regional meteorological and regional soil data

used in other parts of the analysis. These include soil erodibility factor (K), rainfall erosivity, and

slope. Other variables were based on national default values (e.g., cover and management

factors) or default relationships with other factors (e.g., length was determined as a function of

slope). 

8.2.3.3 Exposure Factors. For most exposure factors addressed, data analyses involved

fitting distributions of data summaries from the EFH (U.S. EPA, 1997a, b, c), in most cases by

fitting distributions to selected percentiles. It is assumed that little information is lost by fitting
Section 8.0 May 2002

8-15

to percentiles versus fitting to raw data. However, some believe that such analyses should always

be based on raw data, synthesizing all credible sources.

Three standard two-parameter probability statistical distributions (gamma, lognormal, and

Weibull) were used for this analysis. These distributions are special cases of a three-parameter

distribution (generalized gamma) that allows for a likelihood ratio test of the fit of the

two-parameter models. Other statistical distributions are possible (e.g., U.S. EPA, 2000), but the

technique used in this analysis offered considerable improvement over using a lognormal model

in all cases, and it was appropriate for this analysis. In support of this conclusion, a comparison

of results showed that the three-parameter generalized gamma distribution did not significantly

improve on goodness of fit over the two-parameter distributional forms in 58 of 59 cases at the

5 percent level of significance.

Although they offer significant improvement in objectivity over visual estimation,

goodness-of-fit tests used to determine which statistical distribution to use for a particular

parameter are themselves subject to some uncertainty that should be considered in their

application to exposure factors. One area of concern is uncertainty about how the survey

statistics in the EFH (U.S. EPA, 1997a, b, c) were calculated. All of the statistics that have been

used to assess goodness of fit assume a random sample, which may or may not be a valid

assumption for EFH data. Specifically, many of the EFH data sources are surveys that, in many

cases, do not involve purely random samples. Rather, they use clustering and stratification,

primarily for economic reasons.

8.3 References

U.S. EPA (Environmental Protection Agency). 1994. Methods for Derivation of Inhalation

Reference Concentrations and Application of Inhalation Dosimetry. EPA/600/8-90-

066F. Washington, DC: U.S. Government Printing Office.

U.S. EPA (Environmental Protection Agency). 1997a. Exposure Factors Handbook, Volume I, 

General Factors. EPA/600/P-95/002Fa. Washington, DC: U.S. Government Printing

Office. 

U.S. EPA (Environmental Protection Agency). 1997b. Exposure Factors Handbook, Volume II,

Food Ingestion Factors. EPA/600/P-95/002Fa. Washington, DC: U.S. Government

Printing Office.

U.S. EPA (Environmental Protection Agency). 1997c. Exposure Factors Handbook, Volume III,

Activity Factors. EPA/600/P-95/002Fa. Washington, DC: U.S. Government Printing

Office.

U.S. EPA (Environmental Protection Agency). 1999. Risk Assessment Guidance for Superfund,

Volume 3, Part A, Process for Conducting Probabilistic Risk Assessment. EPA Office of

Solid Waste and Emergency Response.
Section 8.0 May 2002

8-16

U.S. Environmental Protection Agency (EPA). 2000. Options for Development of Parametric

Probability Distributions for Exposure Factors. EPA/600/R-00/058. Washington, DC: 

U.S. Government Printing Office.
Section 9.0 May 2002

9-1

9.0 Screening Ecological Risk Assessment of

Dioxins and PCBs in Land-Applied Biosolids

9.1 Introduction

This section describes the screening ecological risk assessment (SERA) that was

performed to investigate the potential for adverse ecological effects from dioxins in land-applied

biosolids. Screening-level ecological risk assessments are designed to provide a high level of

confidence in determining a low probability of adverse effects to ecological receptors (U.S. EPA,

2001a). The SERA was not designed or intended to provide definitive estimates of risk; rather,

the SERA provides insight into the potential for ecological risk. The SERA was designed to be

consistent with EPA’s Guidelines for Ecological Risk Assessment (U.S. EPA, 1998).

The SERA was conducted in two phases. In Phase 1, an initial screen was conducted to

determine whether the dioxin concentrations in land-applied biosolids warranted an ecological

risk assessment. The purpose of this screen was not to identify ecological receptors for further

analysis; rather, the screen was designed to provide a simple, efficient indicator of the potential

for adverse ecological effects at a high-end exposure. In Phase 2, a deterministic screening

assessment was performed on a representative suite of ecological receptors that are typical of

terrestrial and waterbody margin habitats. 

The risk metric chosen for the SERA is the hazard quotient (HQ), the ratio of the

exposure (in units of dose) to an ecological benchmark. Media concentrations (e.g., sediment,

soil) from the human health risk assessment modeling simulations were used to predict exposure

doses, and HQs were calculated on a TCDD TEQ basis. Calculation of HQs has a binary

outcome: either the dose is below the protective ecological benchmark (HQ<1), or it is equal to

or greater than the benchmark (HQ>1). However, the screening HQ results should be interpreted

within the context of the SERA design. For example, a high level of conservatism built into the

SERA may support a conclusion of low potential for adverse ecological effects at an HQ between

0.1 and 1. Conversely, an HQ that is within a factor of 10 of the target HQ of 1.0 may provide

sufficient justification for further analysis. The HQ results presented in this section are intended

only to provide useful information for the decision-making process; the screening HQ results

cannot be used to predict the probability or ecological significance of adverse effects.

 Sections 9.2, 9.3, and 9.4 describe the SERA methodology as suggested in EPA’s

guidelines: problem formulation, analysis, and risk characterization.
Section 9.0 May 2002

1

 Populations of reptiles and amphibians (i.e., herpetofauna) were not included in the SERA because

suitable data on chronic toxicity could not be identified.

9-2

9.2 Problem Formulation

 The problem formulation process consists of (1) selection of assessment endpoints,

(2) development of a conceptual model, and (3) development of an analysis plan (U.S. EPA,

1998). The selection of endpoints and development of the conceptual model are discussed in

Sections 9.2.1 and 9.2.2, and development of the analysis plan is briefly described in 9.2.3. 

9.2.1 Assessment Endpoint Selection

Assessment endpoints are defined as “explicit expressions of the actual environmental

value that is to be protected” (U.S. EPA, 1998). The assessment endpoints serve as critical links

between the ecological risk assessment and the management goal. For the biosolids SERA, the

management goal was the following:

 Evaluate and characterize the potential for adverse ecological effects on wildlife

that may be affected by land application of biosolids.

The assessment endpoints (the values to be protected) are viable wildlife populations and

ecological communities. Specifically, the assessment endpoints were defined in terms of

characteristics relevant to population viability—reproductive and developmental success—for

which toxicity data were identified for the mammalian and avian species1

 included in the SERA. 

However, population-level risks were not directly assessed. A population-level assessment

would require information on a variety of parameters, such as survival, fecundity, immigration,

and predator-prey relations. Although models were identified to evaluate the effects of chemical

stressors on wildlife species populations, the data needed to support them are not readily

available for a national-level assessment, and such an approach was considered beyond the scope

of this screening-level analysis. Consequently, the SERA evaluated organism-level endpoints

considered highly relevant to the variability of wildlife populations. 

Although the assessment endpoints shown in Table 9-1 include both species populations

and communities, the ecotoxicological data were considered insufficient to develop

environmental quality criteria relevant to the soil, sediment, and surface water communities. The

receptors selected under each assessment endpoint reflect the desire to represent:

 Significance of the receptor to the ecosystem

 Position of the receptor along a continuum of trophic levels

 Susceptibility of the receptor through media and food exposure pathways

 Toxicological sensitivity of the receptor to dioxins and PCBs.
9-3

Table 9-1. Assessment Endpoints for the Biosolids SERA Ecological Significance Assessment Endpoint Representative Receptors Characteristic(s) 

 Includes upper trophic level consumers  Socially valued (e.g., endangered species)  Top recipients of bioaccumulative chemicals  Represents species with large foraging ranges  Represents species with longer life spans Viable mammalian wildlife populations e.g., deer mouse, meadow vole, red fox Reproductive and developmental success Chr

NOdevrep

Viable avian wildlife populations e.g., red-tailed hawk, belted kingfisher Reproductive and developmental success Chr

NOdevrep

Viable amphibian and reptile wildlife populations e.g., green frog, eastern newt, northern water snake, eastern box turtle Reproductive and developmental success Chr

NOdevrep

 Represents base food web in terrestrial systems  Habitat vital to decomposers and soil aerators  Proper soil community function related to nutrient cycling Sustainable soil community structure and function e.g., nematodes, soils mites, springtails, annelids, arthropods Growth, survival, and reproductive success Soi Highly exposed receptors from constant contact with contaminated media  Act as vectors to transfer contaminants to terrestrial species Sustainable aquatic community structure and function e.g., fish (salmonids), aquatic invertebrates (daphnids) Growth, survival, and reproductive success Wa Provide habitat for reproductive life stages (e.g., eggs, larval forms)  Habitat for key invertebrate species  Act to process nutrients and decompose organic matter Sustainable benthic community structure and function e.g., protozoa, flat worms, ostracods Growth, survival, reproductive success Sed

NOAEL - No observed adverse effects level MATL - Maximum allowable toxicant level
Section 9.0 May 2002

9-4

This approach assumes that, if individuals are protected from adverse reproductive and

developmental effects associated with dioxins and PCBs in biosolids, protection at a higher level

of organization (in this case, wildlife populations) may be inferred. In the case of mammals and

birds, studies identifying reproductive and developmental effects in laboratory species were

extrapolated to benchmarks for representative wildlife species. As noted above, these endpoints

do not reflect true population benchmarks because they do not consider other factors relevant to

population dynamics, such as emigration, immigration, carrying capacity, and predator-prey

interactions. Nevertheless, the selection of endpoints on reproductive fitness allows for inference

on the possible impacts on wildlife populations.

9.2.2 Development of Conceptual Model

The conceptual model for the assessment describes the exposure scenarios and the

relationships between the ecological receptors and the stressors of concern. The conceptual

model is developed through analysis of the (1) environmental behavior of constituents, (2)

identification of exposure pathways of concern, (3) identification of habitats and receptors of

concern, and (4) characterization of ecological effects. Therefore, the conceptual model

integrates information related to the constituents to be modeled (e.g., environmental behavior

such as bioaccumulation), ecotoxicological effects data for constituents of concern, receptors and

ecosystems potentially at risk, and relevant pathways of exposure. Because land application of

biosolids may occur throughout the United States, virtually any type of ecosystem and ecological

receptor may be exposed to dioxins and PCBs in biosolids. For screening purposes, the

conceptual model included ecological receptors that are representative of either waterbody

margin habitats in freshwater systems (e.g., streams, lakes, or ponds) or terrestrial habitats (e.g.,

forests, crop lands). Previous sections of this report provide extensive details on the agricultural

application of biosolids and how these exposure scenarios are developed. To avoid duplicating

the discussions in Sections 3.0 through 5.0, the description of the conceptual model is

intentionally brief with respect to the exposure scenario, application rates of biosolids, and other

pertinent information on the site layout. As appropriate, references to previous sections have

been provided to allow the reader to quickly identify additional details on the fate and transport

modeling of chemical constituents in biosolids.

Because dioxins and PCBs are persistent, bioaccumulative organics, the conceptual

model includes both direct and indirect (i.e., food chain) exposures for ecological receptors. 

Constituents released from an agricultural application of biosolids may be transported to surface

waterbodies through erosion and runoff and, frequently, are buried in the bed sediment. In

addition, constituents may be dispersed and deposited directly onto plants, soils, and surface

waterbodies by wet and dry deposition mechanisms. Soils and sediments have been shown to be

sinks for environmental releases of dioxin and dioxin-like compounds; therefore, direct contact

with these contaminated media may pose potential risks to ecological receptors (e.g., benthic

dwellers). The dioxin-like constituents in biosolids have been shown to bioaccumulate in the

food chain, and receptors in higher trophic levels may be particularly at risk through food chain

exposures. Figure 9-1 presents a graphic representation of the conceptual model.
Section 9.0 May 2002

9-5

Figure 9-1. Conceptual model for the biosolids SERA.

9.2.2.1 Environmental Behavior of Chemicals of Concern. The SERA addresses the

29 dioxin and PCB congeners modeled in the human health risk assessment, shown in Table 9-2.

 

Table 9-2. Dioxin Congeners Assessed in the SERA

Polychlorinated dibenzodioxins

CAS # Congener

1746016 2,3,7,8-TCDD

40321764 1,2,3,7,8- PeCDD

39227286 1,2,3,4,7,8-HxCDD

19408743 1,2,3,7,8,9-HxCDD

35822469 1,2,3,6,7,8-HxCDD

3268879 1,2,3,4,6,7,8-HpCDD

51207319 1,2,3,4,6,7,8,9-OCDD

(continued)
Section 9.0 May 2002

9-6

Table 9-2. (continued)

Polychlorinated dibenzofurans

CAS # Congener

51207319 2,3,7,8-TCDF

57117416 1,2,3,7,8-PeCDF

57117314 2,3,4,7,8-PeCDF

70648269 1,2,3,4,7,8-HxCDF

57117449 1,2,3,7,8,9-HxCDF

72918219 1,2,3,6,7,8-HxCDF

60851345 2,3,4,6,7,8-HxCDF

67562394 1,2,3,4,6,7,8-HpCDF

55673897 1,2,3,4,7,8,9-HpCDF

39001020 1,2,3,4,6,7,8,9-OCDF

Polychlorinated biphenyls

IUPAC # CAS # Structure

77 32598133 3,3’,4,4’-TCB

81 70362504 3,4,4’,5-TCB

105 32598144 2,3,3’,4,4’-PeCB

114 74472370 2,3,4,4’,5-PeCB

118 31508006 2,3’,4,4’,5-PeCB

123 65510443 2’,3,4,4’,5-PeCB

126 57465288 3,3’,4,4’,5-PeCB

156 38380084 2,3,3’,4,4’,5-HxCB*

157 52663726 2,3,3’,4,4’,5’-HxCB*

167 32774166 2,3’,4,4’,5,5’-HxCB

169 39635319 3,3’,4,4’,5,5’-HxCB

189 70362504 2,3,3’,4,4’,5,5’-HpCB

* These two congeners are co-eluting and are therefore modeled as a single congener.
Section 9.0 May 2002

9-7

Overall, the mobility and fate of dioxins and PCBs is closely tied to the movement of

sediments, particulates, and soils via erosion. For example, in surface water, dioxin is associated

primarily with suspended organic matter, which eventually settles into sediments. Concentrations

in sediments range from 6.0E-05 to 7.6E-03 mg/kg sediment, with the latter being related to

sediments in areas of high industrial activity. In addition to the movement of dioxin via abiotic

means, dioxin is also mobile through biotic means. Concentrations in fish range from below

detection, 5.0E-07, to 1.0E-04 mg/kg fish tissue (whole body, wet weight). Over time,

concentrations in sediment and biota decrease as dioxins and PCBs are slowly metabolized or

transported elsewhere through sediment movement. Similar chemical behavior is observed in

terrestrial systems; however, dioxin is adsorbed to organic content in the soil and is somewhat

less mobile (Eisler, 1986). The accumulation of dioxins from the soil into plants has been shown

to be negligible (U.S. EPA, 2000). 

The environmental behavior of chemical contaminants in biosolids is, to some degree,

determined by application and management practices. For example, concentration profiles for

dioxins and PCBs applied on a daily basis would likely be very different than the profiles

developed for biannual applications. In Section 3.1.3, Figure 3-1 is accompanied by a detailed

explanation of the conceptual site model used in the model simulations, and Section 4.0 provides

a complete characterization of agriculturally applied biosolids, from the physical characteristics

of the biosolids to the properties of the environmental setting (e.g., soil properties). Because

biosolids applications occur nationwide, the model simulations produce distributions of media

concentrations that capture the variability in climate, soil, and agricultural practices across the

contiguous United States. To support the modeling simulations described in Sections 4.0 and

5.0, the 48 states were subdivided into 41 climatic regions (see Figure 3-2), and each region was

represented by climate data from any reporting meteorological station within the bounds of the

region. This implicitly assumes that the meteorological conditions in any region are sufficiently

uniform so as to be represented by a single station. As described in Section 4.3.2, these

geographic regions were also used as the basis for identifying a representative farm size and a

distribution of soil types on the farm. For convenience, several key assumptions on common

agricultural practices are presented below. 

 Biosolids are applied at a rate of 5 to 10 metric tons per hectare per application

(application rates for biosolids were assumed to be uniform nationwide).

 Applications occur once every 2 years.

 Application continues for up to 40 years (20 applications).

 Crop land is tilled to a depth of 20 cm multiple times during the year.

 Pasture land is not tilled; thus, biosolids are assumed to be incorporated into only

the top 2 cm of soil.

9.2.2.2 Habitats Potentially at Risk. For agricultural application of biosolids, the SERA

addresses two generalized habitat types: a terrestrial habitat and a water body margin habitat

associated with freshwater systems (e.g., streams, ponds). These habitat types provide a
Section 9.0 May 2002

9-8

framework for identifying exposure pathways of concern and define the context for receptor

species selection.

The terrestrial habitat consists of crop fields and pastures where biosolids are applied. 

The conceptual layout described in detail in Section 4.3.1 is based on the assumption that farmers

apply biosolids to adjacent crop fields and pasture. Thus, ecological receptors may be exposed to

contaminants in plants, prey, and soil in the crop field and pasture by feeding and foraging in

these areas. The waterbody margin habitat consists of nearby surface waterbodies and their

adjacent terrestrial margin. The waterbodies receive chemical loads through runoff and erosion

from the agricultural field. The buffer area shown in Figure 3-1 is located between the fields and

a nearby surface waterbody; for the purposes of the SERA, it constitutes the terrestrial margin

associated with the waterbody. Receptors may be exposed to terrestrial plants and prey and to

soil in the buffer area as part of the margin habitat; in addition, receptors may take fish, other

aquatic biota, sediment, and drinking water from the receiving waterbody. 

In summary, the representative terrestrial and margin habitats in the SERA are intended to

capture the key elements of freshwater and agricultural field systems. However, the actual

exposures received by wildlife will be strongly influenced by a variety of habitat characteristics. 

In margin habitats, the waterbody size, flow rate, bed sediment composition, and the presence of

aquatic vegetation will significantly affect the ecological exposures. Similarly, in terrestrial

systems, factors such as regional location, vegetative cover type, soil characteristics, and

adequacy of food sources will determine the applied dose to wildlife. Although these habitat

characteristics are not explicitly addressed in the SERA, receptors assigned to the two

representative habitats are intended to address significant exposure pathways and represent

scenarios appropriate for a screening-level analysis.

Figures 9-2 and 9-3 show simplified food webs for exposure in terrestrial and margin

habitats. The trophic levels and feeding guilds shown in the figures are defined as follows:

Trophic Levels Feeding Guilds

T1: Species is prey to other receptors,

but is not a predator.

Herbivore: Consumes only plant matter.

T2: Species is both predator and prey

to other receptors.

Omnivore: Can be expected to consume both

plant and animal matter, although can

at times feed exclusively on one or the

other.

T3: Top predators; species are

generally assumed not to be prey to

other receptors.

Carnivore: Feeds exclusively on animals.
9-9

Figure 9-2. Terrestrial food web, including example receptors.
9-10

Figure 9-3. Interface between terrestrial receptors and aquatic food web, including example r
Section 9.0 May 2002

9-11

The food webs were developed based on generally accepted concepts about food webs

and natural community dynamics (Anderson, 1997; Begon and Mortimer, 1981; Caduto, 1990;

Davis and Simon, 1995; Kadlec and Knight, 1996; Sample et al., 1997; Schoener, 1989;

Schoenly and Cohen, 1991; Suter, 1993; Tanner, 1978; U.S. EPA, 1993a, 1994). Speciesspecific information was taken from the references listed in Appendix L, Table L-5. The food

webs serve two purposes: they illustrate potential exposure pathways, and they facilitate the

selection of receptor species for each habitat type. 

9.2.2.3 Selection of Receptors of Concern. Ecological receptors typical of the

terrestrial and margin habitats were considered on the basis of (1) trophic levels, taxa, and

feeding guilds (e.g., herbivores, carnivores), (2) potential for exposure to dioxins in land-applied

biosolids, (3) toxicological sensitivity and (4) geographical distribution (e.g., avoid narrow

ecological niches). Receptors with a high potential for exposure were defined as those

documented to feed and forage in agricultural fields or in margin habitats. Because dioxin

congeners are known to bioaccumulate in fish, small mammals, and soil and sediment

invertebrates, receptors whose diets include these items were also assumed to have a high

potential for exposure. 

Of the representative receptors considered for inclusion in the SERA, adequate

ecotoxicological data were identified only for mammals and birds. Therefore, the only

assessment endpoints in Table 9-1 that were quantitatively screened were mammalian and avian

wildlife populations. The primary exposure route of interest for mammals and birds is ingestion,

and exposure is expressed in terms of ingestion dose. The wildlife species included in the SERA

are shown in Table 9-3. The SERA did not include aquatic and terrestrial plants, aquatic

invertebrates, or amphibians, because of their demonstrated tolerance to TCDD in laboratory

studies (U.S. EPA, 2001b). 

The representative species selected for the Phase 2 analysis were not limited to keystone

or indicator species. Indicator species imply that a level of significance to total ecosystem

structure or function can be ascertained; however, in a screening-level assessment, this can not be

determined with a high level of confidence. The receptors were selected because (1) these

species represent a full range of trophic levels and feeding guilds relevant to dioxin exposures

through the food web; (2) life-history data, such as dietary habitats and distribution in the

contiguous United States were available; and (3) toxicological data were identified, suggesting

that the species was sensitive to dioxins and PCBs (e.g., mammals are highly sensitive to

dioxins). Although this approach tends to “overrepresent” certain taxa, such as Mustella, the

SERA was intended to provide information across a number of receptors, not just the most highly

exposed and sensitive species.

9.2.2.4 Identification of Exposure Pathways of Concern. Dioxin and PCB congeners

are persistent, bioaccumulative, and hydrophobic compounds that have been shown to

biomagnify in the food web. Typically, these congeners are stored in the fat tissues of organisms

and are minimally metabolized over time. Consequently, animals foraging in the terrestrial and
Section 9.0 May 2002

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Table 9-3. Wildlife Receptors for the Biosolids SERA

Species Scientific Name

Feeding

Guild1

Trophic

Level2 Habitats

American kestrel Falco sparverius C T2 terrestrial

American robin Turdus migratorius O T2 terrestrial

American woodcock Scolopax minor O T2 terrestrial

Bald eagle Haliaeetus leucocephalus C T3 margin 

Beaver Castor canadensis H T1 margin

Belted kingfisher Ceryle alcyon O T2 margin

Black bear Ursus americanus O T3 terrestrial

Canada goose Branta canadensis H T1 terrestrial

Cooper’s hawk Accipiter cooperi C T3 terrestrial

Coyote Canis latrans O T3 terrestrial

Deer mouse Peromyscus maniculatus O T2 terrestrial

Eastern cottontail

rabbit

Sylvilagus floridanus H T1 terrestrial

Great blue heron Ardea herodias O T2 margin

Green heron Butorides virescens O T2 margin

Herring gull Larus argentatus O T2 margin

Least weasel Mustela nivalis C T2 terrestrial

Lesser scaup Aythya affinis O T2 margin

Little brown bat Myotis lucifugus I T2 terrestrial

Long-tailed weasel Mustela frenata C T2 terrestrial

Mallard Anas platyrhynchos O T2 margin

Meadow vole Microtus pennsylvanicus H T1 terrestrial

Mink Mustela vison C T2 margin 

Muskrat Ondatra zibethicus H T1 margin

Northern bobwhite Colinus virginianus O T2 terrestrial

Osprey Pandion haliaetus C T3 margin

Prairie vole Microtus ochrogaster H T1 terrestrial

(continued)
Section 9.0 May 2002

9-13

Table 9-3. (continued)

Species Scientific Name

Feeding

Guild1

Trophic

Level2 Habitats

Raccoon Procyon lotor O T2 terrestrial, margin

Red fox Vulpes vulpes O T3 terrestrial

Red-tailed hawk Buteo jamaicensis C T3 terrestrial

River otter Lutra canadensis C T2 margin

Short-tailed shrew Blarina brevicauda O T2 terrestrial

Short-tailed weasel Mustela erminea C T2 terrestrial

Tree swallow Tachycineta bicolor O T2 terrestrial

Western meadowlark Sturnella neglecta O T2 terrestrial

White-tailed deer Odocoileus virginianus H T1 terrestrial

1 Feeding guild: C = carnivore, H = herbivore, I = insectivore, O = omnivore.

2 Trophic level: T1 = prey, not a predator; T2 = both a predator and prey; T3 = a top predator, not prey.

margin habitats may be exposed through the food chain, as well as through direct ingestion of

contaminated soil, surface water, and sediment. Inhalation was not considered to be a significant

route of exposure for dioxins and PCBs and was not included in the SERA.

Receptor species are exposed through the ingestion of

 Aquatic prey, such as fish, mussels, and snails

 Terrestrial prey from the waterbody margin or field, such as vegetation and small

mammals

 Soil from the contaminated field or buffer

 Water from contaminated waterbodies

 Sediment from contaminated waterbodies. 

In addition, receptors that live in close contact with contaminated media (e.g., benthic

invertebrates) may receive significant exposures to dioxins and PCBs. The primary routes of

exposure for these receptors include ingestion of contaminated plants and prey, as well as direct

contact. However, as indicated in Section 9.2.1, sufficient toxicological data were not identified

to develop environmental quality criteria for soil, sediment, or surface water. As a result, direct

contact with contaminated media was not included in the SERA.

9.2.2.5 Characterization of Ecological Effects. As indicated in the previous section,

the focus for the SERA is on mammalian and avian receptors. Therefore, the effects

characterization in this section discusses the relevant studies reviewed in selecting the most

appropriate toxicological data to develop the ecological benchmarks for mammals and birds. 
Section 9.0 May 2002

9-14

The effects characterization is based on a review of recently published sources, other literature

citations, and EPA publications. In particular, the Dose-Response Assessment from Recently

Published Research of the Toxicity of 2,3,7,8-Tetrachlorodibenzo-p-dioxin and Related

Compounds to Aquatic Wildlife-Laboratory Studies was reviewed to identify appropriate

benchmark studies (NCEA, 2001). 

9.2.2.5.1 Mammals. TCDD exposures have been associated with a variety of

reproductive and developmental effects in mammals. For example, Khera and Ruddick (1973)

assessed the postnatal effect of TCDD on pregnant Wistar rats and observed a dose-related

decrease in the average litter size and pup weight at birth in all but the 0.125 ug/kg-day dose. 

Bowman et al. (1989a, 1989b) studied the reproductive effects of Rhesus monkeys exposed to

diets containing 5 ppt and 25 ppt TCDD for 7 and 24 months. The female monkeys exposed to

25 ppt had a significantly lower Index of Overall Reproductive Success (IORS), while the 5 ppt

group did not differ from the control. Hochstein et al. (1988) administered TCDD dietary

concentrations of 0, 0.001, 0.01, 0.1, 1.0, 10, and 100 ppb to mink for 125 days. While no

significant adverse effects were observed on mink fed dietary concentrations of 0.1 ppb or less,

mortality was noted in groups fed 1 and 10 ppb.

Murray et al. (1979) exposed three generations of Sprague-Dawley rats to diets containing

0, 0.001, 0.01, or 0.1 g TCDD/kg-day. At the 0.01 g/kg-day dose, Murray et al. (1979)

observed no effect on fertility among the f0 rats, but a significant reduction in fertility was

observed among the f1 and f2 rats. Thus, through three successive generations, the reproductive

capacity of rats ingesting TCDD was clearly affected at dose levels of 0.01 and 0.1 g/kg-day,

but not at 0.001 g/kg-day. This study was selected for benchmark derivation because it consists

of a multigenerational exposure scenario that demonstrates a clear dose-response for reproductive

effects attributable to TCDD. 

The 125-day test performed by Hochstein et al. (1988) was not considered appropriate for

deriving a benchmark because the study was subchronic rather than chronic and the perceived

endpoints focus more on mortality than reproductive effects. The Murray et al. (1979) study was

chosen over the Khera and Ruddick study (as cited in U.S. EPA, 1995) because of a lower

reported NOAEL for rats. The reproduction study by Bowman et al. (1989a, 1989b) on Rhesus

monkeys (which produced a lower NOAEL) was not selected because the Murray et al. (1979)

study incorporated a multigenerational exposure regime and contained stronger dose-response

information.

9.2.2.5.2 Birds. TCDD toxicity has been demonstrated in the embryos of many bird

species, including domestic chickens (Brunstrom and Lund, 1988), great blue herons (Hart et al.,

1991), ring-necked pheasants (Nosek et al., 1993) and double-crested cormorants (Powell et al.,

1997). Sublethal responses include subcutaneous edema (Hart et al., 1991), induction of hepatic

microsomal ethoxyresorufin-O-dealkylase, depressed embryonic growth, brain asymmetry

(Custer et al., 1997), short beaks, fatty liver, heart abnormalities, and poorly developed stomachs

(Henshel et al., 1997). Egg mortality has also been found in many studies (e.g., Nosek et al.,

1993; Powell et al., 1997). Exposure in these studies was usually by injection, either into the

yolk, albumin, or air cell.
Section 9.0 May 2002

9-15

Effects on adult birds appear to have been much less studied. Nosek et al. (1992) injected

ring-necked pheasants (Phasianus colchicus) with various doses of TCDD, once a week for 10

weeks. Mortality, egg production, and embryo mortality were recorded. Embryo mortality was

increased by exposure of adults to TCDD, with 100 percent egg mortality at a cumulative dose of

10 g kg-1 body weight. However, even at the highest dose, some eggs were produced. Adult

mortality only occurred at the highest dose. The weekly dose to the pheasants for 10 weeks by

intraperitoneal (ip) injection is at an equivalent rate of 0.14, 0.014, and 0.0014 g TCDD/kg-day

(weekly dose was divided by 7 for the equivalent daily dose and adjusted for intake and body

weight). Cumulative egg production was significantly reduced among pheasants exposed to 0.14

g TCDD/kg-day, but not among those pheasants exposed to the two lower doses. Based the

limited data on adult birds, the study by Nosek was considered to be the most appropriate

benchmark study because the endpoint is highly relevant to population viability and because

there is substantial support in EPA publications to use these data to evaluate ecological risks to

birds. Assuming 100 percent absorption from ip injection, the ip exposure route may

overestimate the absorption rate of TCDD via oral ingestion by a factor of 1 to 5 depending upon

diet composition (Abt, 1993). 

9.2.3 Analysis Plan

The analysis plan is the third critical product of the problem-formulation phase. In

essence, the analysis plan provides a blueprint for evaluating the potential for adverse ecological

effects for the assessment endpoints, receptors, and exposure pathways of concern. The analysis

plan can be broken down into two sections: an exposure analysis and an ecological response

analysis. As summarized in the introduction, the analysis consisted of a two-phased approach. 

Phase 1 was designed as a bounding analysis to assess the potential for ecological effects at highend exposures. Therefore, the exposure analysis is based on an evaluation of the 50th and 90th

percentile, and maximum TEQ concentrations in biosolids. This phase was a highly conservative

“trigger” that is based on the lowest available ecological benchmarks (i.e., NOAELs) and

considers only a few highly exposed ecological receptors (e.g., American robin eating

100 percent diet of earthworms). Phase 2 was designed to provide a conservative screen of the

potential hazard to an expanded list of mammalian and avian receptors intended to represent

general terrestrial and waterbody margin habitats. For the exposure analysis, fate and transport

algorithms described in detail in Section 5.0 provide an appropriate tool to estimate

concentrations of dioxins and PCBs in the environmental media and terrestrial plants attributed

to each habitat. For the ecological response analysis, the critical ecotoxicological data presented

above are used to estimate less conservative ecological benchmarks (i.e., the geometric mean of

the NOAEL and lowest observed adverse effects level (LOAEL)) for receptors representing the

terrestrial and margin habitats. The exposure dose predicted using the modeled concentrations of

dioxins and PCBs is compared to the benchmark to generate HQs for the entire list of mammals

and birds in the generalized terrestrial and margin habitats.

9.3 Analysis Methods 

The analysis phase of the SERA began with a highly conservative approach to determine

whether any of the habitats, receptor categories, and exposure routes might be of concern. In this

phase, the concentrations of dioxins and PCBs in biosolids were used to predict maximum
Section 9.0 May 2002

9-16

possible exposure doses to several highly exposed receptors. Risks were estimated using

conservative ecological benchmarks. The congener concentrations in biosolids were obtained

from the NSSS 2001 (U.S. EPA, 2001c). As previously suggested, the intent of the Phase 1

“trigger” was simply to determine whether any further ecological risk analysis was warranted. 

Phase 2 consisted of a less conservative analysis based on representative exposure scenarios. 

The values and data sources used for the key input variables in the SERA are shown in Table 9-4

for both phases of the SERA. 

The concentrations of dioxins and PCBs in various media were derived using a

conceptual site model (see Figure 3-1) that simulates the application of biosolids based on the

available data on biosolids management. The release, fate and transport, and estimation of media

concentrations are presented in detail in Section 5.0. The following sections present the methods

used in each phase of the analysis.

9.3.1 Phase 1 – Maximum Potential Risk 

The Phase 1 analysis was a highly conservative assessment of the maximum possible

risks for highly exposed receptors. Exposure was based on the 50th and 90th percentiles and

maximum concentrations in biosolids; HQs were calculated using NOAELs for reproductive

endpoints on individual organisms. The Phase 1 receptors shown in Table 9-5 were selected to

maximize exposure. The ingestion route of exposure was assessed using receptor species whose

diets consist largely of animals known to accumulate dioxins and PCBs from soil and sediments. 

These receptors are widely distributed across a large portion of the United States and, based on

their diet, represent high-end exposures for birds and mammals in terrestrial and waterbody

margin habitats.

Phase 1 risk estimates were generated using a simple spreadsheet model with the

following steps. These steps are further discussed below. 

1. For each receptor, select diet item to maximize exposure.

2. Calculate congener-specific concentrations in diet items.

3. Calculate congener-specific exposure dose for each receptor.

4. Apply TEFs to congener-specific dose estimates; sum to obtain TCDD TEQ.

5. Calculate HQ using receptor-specific TCDD benchmark.

9.3.1.1 Development of Benchmarks for Phase 1. For the SERA, exposure for all 29

congeners in the assessment was expressed in terms of 2,3,7,8-TCDD toxicity equivalence, and

risk estimates were based on NOAELs. Benchmark studies for TCDD for mammals and birds

were identified in the literature, and species-specific scaled benchmarks were calculated for each

mammal and bird receptor. In identifying appropriate studies to develop benchmarks, several

study selection criteria were adopted to ensure that (1) the endpoint was highly relevant to the

viability of populations, (2) the dose-response information was sufficient to support development

of a MATL, and (3) the study had been reviewed and approved by other EPA and federal

agencies.
Section 9.0 May 2002

9-17

Table 9-4. Values and Assumptions for the SERA

Parameter

Phase 1 – Maximum

Potential Risk

Phase 2 – Deterministic

Screening

Congeners addressed All All

Receptors Four highly exposed

mammals and birds 

35 representative mammals and

birds

Dietary composition Diets reflecting maximum

exposure

Representative diets

Biouptake factors Fixed values Fixed values

Percentage of diet taken from

contaminated area

100% 100%

Ecological benchmarks NOAELs MATL, calculated as the

geometric means of NOAELs

and LOAELs

Media concentrations used to

estimate exposure

50th and 90th percentiles

and maximum biosolids

concentrations

90th percentile modeled media

concentrations (see Section 5.0)

Table 9-5. Phase 1 Receptors1

Receptor Description Pathway Habitat

Osprey Piscivorous bird that

uses variety of margin

habitats (e.g., wetlands,

streams); diet consists

entirely of fish. 

Ingestion Margin

American robin Bird found in variety of

terrestrial habitats; diet

consists largely of

earthworms.

Ingestion Terrestrial

Belted kingfisher Bird that primarily uses

small ponds and

streams; diet consists

largely of fish.

Ingestion Margin

Mink Mammal that uses

variety of margin

habitats; diet consists

largely of fish and

invertebrates.

Ingestion Margin

1

 Sources for species-specific dietary composition data are listed in Appendix L, Table L-5.
Section 9.0 May 2002

2

 For example, although the robin’s diet could consist of anywhere from less than 10 percent to 100 percent soil

invertebrates, it was conservatively assumed that the robin’s entire diet consists of earthworms for Phase 1. 

9-18

Using the benchmark study identified during the problem formulation (see Section

9.2.2.5), a scaled benchmark was calculated for each receptor species. For mammals, a scaling

factor of 1/4 was used in accordance with the default methodology proposed by EPA for

carcinogenicity assessments and reportable quantity documents for adjusting animal data to an

equivalent human dose (U.S. EPA, 1992). For birds, research suggests that the cross-species

scaling equation used for mammals is not appropriate (Mineau et al., 1996). Using a database

that characterized acute toxicity of pesticides to avian receptors of various body weights, Mineau

et al. (1996) concluded that applying mammalian scaling equations may not predict sufficiently

protective doses for avian species. Mineau et al. recommended that a scaling factor of 1

provided a better dose estimate for birds. Therefore, a scaling factor of 1 was applied for avian

receptors 

Appendix L (page L-3) provides a detailed description of the development of ecological

benchmarks. Table L-3 presents the benchmark values used in Phase 1.

9.3.1.2 Estimating Exposure for Phase 1. Exposure was estimated as an applied dose

based on species-specific body weights, ingestion rates, and dietary composition. For Phase 1, 

exposure was maximized by assuming that

 Environmental concentrations are equal to biosolids concentrations

 Each receptor’s diet consists entirely of a single food item that significantly

bioaccumulates TCDD2

 The entire diet comes from contaminated media. 

The dietary item for the ingestion pathway for each receptor is shown in Table 9-6.

9.3.1.2.1 Concentrations in Diet Items. The first step in estimating exposure dose is the

calculation of congener-specific concentrations in each receptor’s selected diet item. 

Concentrations in worms are a function of the soil concentration, the soil-to-worm

bioaccumulation factor (BAF), and the congener-specific bioaccumulation equivalency factor

(BEF), as given in Equation 9-1. Concentrations in terrestrial prey items (e.g., worms) were

calculated using a BAF for TCDD. The soil-to-worm BAF for TCDD was identified in the

literature; all BAFs and their respective sources are presented in Appendix L, Table L-1.

Congener-specific BEFs were not available for soil-based uptake; therefore, a default BEF of 1

was assumed for all congeners. 
Section 9.0 May 2002

9-19

(9-1)

(9-2)

Table 9-6. Selected Diet Items for Phase 1 Receptors

Receptor

Diet Item Selected to Maximize

Exposure

Osprey Fish

American robin Worms

Belted kingfisher Fish

Mink Fish

where

Cworm i = Total concentration of congener i in earthworms (mg/kg WW)

Csoil i = Soil concentration for congener i (mg/kg)

BAF = Bioaccumulation factor for TCDD reflecting biouptake from soil into worms 

( mg/kg WWworm / mg/kg soil)

BEFi = Bioaccumulation equivalence factor for congener i (unitless; default value of

1 was used)

 The concentration in fish was calculated as a function of sediment concentration

normalized for organic carbon and a congener-specific BSAFs for uptake of dioxins from

sediment to fish, as shown in Equation 9-2. Congener-specific BSAFs were recommended in

EPA’s Draft Dioxin Reassessment Document (U.S. EPA, 2000); all BSAFs and their respective

sources are presented in Appendix L, Table L-2.

where

Cfish li = Lipid-based concentration of congener i in fish (mg/kgl

)

Coc_sediment i = Sediment concentration normalized for organic carbon for congener i

(mg/kgoc)

BSAFli = Biota-sediment accumulation factor for congener i reflecting biouptake

from sediment into fish lipid (kgoc/kgl

).
Section 9.0 May 2002

9-20

(9-3)

(9-4)

9.3.1.2.2 Receptor Dose. As given by Equation 9-3, the ingestion exposure dose for

Phase 1 receptors was calculated based on species-specific body weights and ingestion rates. 

where

Dosei = Phase 1 exposure dose for congener i (mg/kg-d)

Cdiet i = Concentration of congener i in fish or earthworms (mg/kg WW)

IRdiet = Species-specific ingestion rate (kg WW/d)

BW = Species-specific adult body weight (kg).

Body weights and ingestion rates were taken from the EPA’s Wildlife Exposure Factors

Handbook (U.S. EPA, 1993a). Average adult body weights and adult ingestion rates were used

throughout the assessment.

Congener-specific exposure estimates were multiplied by their respective TCDD TEFs to

derive a total dose for each receptor in terms of TCDD equivalence (i.e., TEQs). The summation

of congener-specific doses is given by Equation 9-4:

where

DoseTEQ = Total dose in toxicity equivalence (mg/kg-d)

Dosei = Dose for congener i (mg/kg-d)

TEFi = Toxicity equivalence factor for congener i. 

The TEFs were taken from the WHO consensus TEFs for mammals, fish, and birds (U.S. EPA,

2001b), and are presented in Appendix L, Table L-9. 

9.3.1.3 Risk Calculations for Phase 1. The risk metric for the Phase 1 screen was the

HQ, calculated as the ratio of the TEQ exposure dose to the species-specific ecological

benchmarks based on allometric scaling of the NOAELs. The exposure doses were calculated

using the 50th, 90th, and maximum TEQ concentrations in biosolids. The toxicological studies

used in benchmark derivation are described in Section 9.2.2.5 on effects characterization. The

assumptions, scaling equations, and factors (i.e., factor of 1/4 for mammals and 1 for birds) are

presented in Appendix L.

9.3.2 Phase 2 – Deterministic Screening

The second phase of the analysis was a deterministic screening of an expanded list of

mammals and birds intended to represent a broad range of feeding guilds and trophic levels that

are typical of the terrestrial and margin habitats. Phase 2 included all receptors shown in

Table 9-3 and addressed receptors typical of crop fields, pastures, and surface waterbodies. The
Section 9.0 May 2002

9-21

dietary preferences for these receptors were based on information presented in the Wildlife

Exposure Factors Handbook (U.S. EPA, 1993a), as well as information from the open literature. 

Consequently, the receptor diet is intended to reflect the documented variability in dietary

preferences rather than to maximize exposure (as in Phase 1). The receptor-specific benchmarks

were calculated as the geometric mean of the LOAEL and NOAEL—referred to as the

MATL—to provide a less conservative benchmark for adverse ecological effects. The exposure

and potential ecological risk (expressed as an HQ) is estimated for each receptor in each habitat

type assuming that 100 percent of the diet originates on the contaminated area. The analysis

consisted of the following major steps:

1. Assign representative receptors to each habitat type.

2. Establish dietary composition for each receptor based on habitat assignment.

3. Calculate congener-specific concentrations in each diet item.

4. Calculate total exposure dose for each congener for each receptor based on the

ingestion of contaminated food and media.

5. Apply TEFs to congener-specific exposure doses to derive total TCDD equivalent

exposure.

6. Calculate HQs for each receptor in each habitat.

9.3.2.1 Development of Benchmarks for Phase 2. Appropriate studies (e.g., on

reproductive fitness) were identified in the literature and in EPA sources, and species-specific

benchmarks were calculated using the scaling algorithms described in Appendix L. Although the

same studies were used to derive benchmarks in both phases of the SERA, the Phase 2 screen

used a less conservative measure of effect, the MATL. As the geometric mean between the

NOAEL and LOAEL, the MATL is intended to represent a de minimis level of effect for a

wildlife species population. Because the benchmarks are based on effects to individual

organisms, a less conservative measure was considered appropriate for the assessment endpoint

of population viability. A NOAEL is highly conservative in that it suggests that any

toxicological response to a chemical stressor is considered unacceptable. Given the conservative

nature of the Phase 2 screen (e.g., 100 percent of diet is contaminated), making inferences about

a wildlife population based on a NOAEL for individual organisms would have been overly

conservative and inconsistent with management goals for the SERA. Appendix L includes a

detailed description of the benchmark development methods and presents the species-specific

benchmark values used in Phase 2.

9.3.2.2 Characterization of Exposure. The 90th percentile congener-specific

concentrations in soil, sediment, surface water and terrestrial plants were used to calculate

exposure doses. The 90th percentile concentrations were derived from the fate and transport

modeling described in Section 5.0 by (1) adjusting the concentrations predicted by the model by

the TEFs for mammals and birds, respectively, (2) arranging the TEQ concentrations for

mammals and birds in rank order by the soil concentration in the field and pasture for terrestrial
Section 9.0 May 2002

9-22

habitats, and the sediment concentration for margin habitats, and (3) selecting the 90th percentile

set of concentrations based on the TEQ rank order for mammals and birds, respectively, for the

terrestrial habitat (driven by soil TEQ concentration) and the margin habitat (driven by sediment

concentration). 

The concentration profiles (for media and plants) generated in the model simulations are

maximum annual average concentrations. To derive these concentrations, the source model (see

Section 5.1) generated 3000 Monte Carlo realizations of a 200-year time series, and the

maximum annual average concentration was picked off of these distributions and rank ordered as

described in the preceding paragraph. Consequently, “the 90th percentile” represents the 90th

percentile from a distribution of maximum annual average concentrations and provides a

conservative upper bound of the modeled concentrations.

Exposures were calculated for receptors assigned to the terrestrial (i.e., field/pasture) and

margin (i.e., pond/lake/stream) habitats depending on foraging and feeding habits indicated in the

ecological exposure factor database. Table 9-7 presents the list of receptors according to their

feeding guild, trophic level, and habitat that were evaluated in the Phase 2 screen. Appendix L,

Tables L-4 and L-5 show the data sources used for habitat assignments, as well as other exposure

factor data for each receptor.

9.3.2.2.1 Estimation of Exposure Dose. Exposure doses were estimated in Phase 2

using the same basic approach that was used in Phase 1; however, the receptor diet in Phase 2

was constructed from the exposure factor database, and modeled concentrations in environmental

media and terrestrial plants were used rather than the congener concentrations in biosolids. 

Ingestion exposure doses were calculated in three steps: (1) development of species-specific

diets, (2) calculation of concentrations in each category of food (e.g., vegetation, small mammals,

small birds), and (3) summation of total exposure dose. For the terrestrial habitat, incidental

ingestion of soil (e.g., associated with the ingestion of terrestrial prey, preening, and other

behaviors) was assumed to come from the agricultural field and pasture. For the margin habitat,

the incidental ingestion of sediment—rather than soil—was evaluated because wildlife assigned

to this habitat consume primarily aquatic biota (e.g., fish, sediment invertebrates). These steps

are described in the following sections.

Receptor Diets

Dietary composition for Phase 2 was based on species-specific data on foraging and

feeding behavior and reflected a year-round adult diet. The receptor diets were constructed to

represent variability in feeding habits, rather than to artificially maximize exposure using the

range (defined by the minimum and maximum) for each item in the diet. Diet items are grouped

in 17 categories, including different types of vegetation (e.g., fruits, forage, grain, roots) and

several categories of prey (e.g., small birds, small mammals, invertebrates, fish). For example,

the American robin’s dietary percentage ranges are as follows (Terres, 1980; U.S. EPA, 1993a;

Stokes and Stokes, 1996):
Section 9.0 May 2002

9-23

Table 9-7. Receptors Evaluated in Phase 2

Species

Feeding

Guild

Trophic

Level

Terrestrial

Habitat

Margin

Habitat

American kestrel C T2 

American robin O T2 

American woodcock O T2 

Bald eagle C T3 

Beaver H T1 

Belted kingfisher O T2 

Black bear O T3 

Canada goose H T1 

Cooper’s hawk C T3 

Coyote O T3 

Deer mouse O T2 

Eastern cottontail rabbit H T1 

Great blue heron O T2 

Green heron O T2 

Herring gull O T2 

Least weasel C T2 

Lesser scaup O T2 

Little brown bat I T2 

Long-tailed weasel C T2 

Mallard O T2 

Meadow vole H T1 

Mink C T2 

Muskrat H T1 

Northern bobwhite O T2 

Osprey C T3 

(continued)
Section 9.0 May 2002

9-24

Table 9-7. (continued)

Species

Feeding

Guild

Trophic

Level

Terrestrial

Habitat

Margin

Habitat

Prairie vole H T1 

Raccoon O T2  

Red fox O T3 

Red-tailed hawk C T3 

River otter C T2 

Short-tailed shrew O T2 

Short-tailed weasel C T2 

Tree swallow O T2 

Western meadowlark O T2 

White-tailed deer H T1 

Feeding guild: C = carnivore, H = herbivore, I = insectivore, O = omnivore.

Trophic level: T1 = prey, not a predator; T2 = both a predator and prey; T3 = a top predator, not prey.

Diet Item Dietary Percentage Range

Soil invertebrates (other than earthworms) 8 to 93

Fruits 7 to 92

Earthworms 15 to 27 

Forage 0 to 24

For the Phase 2 analysis, each receptor’s diet was constructed using the midpoint of dietary

percentages for each diet item, beginning with the item with highest midpoint value and

proceeding through the diet items until a full diet (100 percent) was accumulated. Thus, the

robin’s diet would consist of 50.5 percent soil invertebrates and 49.5 percent fruits, based on the

following dietary percentage midpoints:

Diet Item Dietary Percentage Midpoint

Soil invertebrates 50.5

Fruits 49.5

Worms 21

Forage 12

The dietary composition used for each receptor species is presented in Appendix L, Tables L-6

and L-7. 
Section 9.0 May 2002

3

 Because soil-to-plant uptake of dioxin-like congeners is negligible, plant concentrations were taken from

the multimedia modeling simulation and used directly to calculate Cdiet i

.

9-25

(9-5)

Concentrations in Diet Items

Dietary concentrations were calculated separately for terrestrial-based food items (e.g.,

soil invertebrates, small mammals) and for aquatic-based food items (e.g., fish, sediment

invertebrates). 

Terrestrial items. Terrestrial items in the diet include vegetation and small prey, and the

prey concentrations for each congener are based on soil-to-organism BAFs and the soil

concentration. Concentrations in vegetation occur through particle deposition and vapor transfer

(U.S. EPA, 2000), and plant concentrations were calculated based on these two transport

mechanisms using the methods described in Section 5.0 (U.S. EPA, 2001a). For the ecological

assessment, concentrations in all types of vegetation were calculated on a wet weight (WW)

basis, as described in Appendix H, Table H3.16. 

Concentrations in prey items (e.g., small mammals and birds) were calculated as

described in Section 9.3.1.2.1 for Phase 1. BAFs for terrestrial prey items are empirical values

that reflect prey tissue concentrations as a function of soil concentrations. That is, the BAF does

not represent a biotransfer from one compartment in the food chain to another. In addition, a diet

fraction variable was added to the calculation to account for each diet item’s contribution to the

total diet (Equation 9-5).3

where

Cdiet i = Total concentration of congener i in diet (mg/kg WW)

Csoil i = Soil concentration for congener i (mg/kg) 

BAFij = Bioaccumulation factor for congener i for food item j (mg/kg WW/

mg/kg soil)

BEFi = Bioaccumulation equivalence factor for congener i (unitless; default

value of 1 was used)

DietFracj = Fraction of item j in diet.

Aquatic items. Aquatic items in the diet include T3 and T4 fish, aquatic plants, and

benthic invertebrates, primarily filter feeders. Concentrations in these items were calculated as

described in Section 9.3.1.2.1 for Phase 1. The dietary fraction was added to the calculation (as

for Phase 2 terrestrial prey) to account for each diet item’s contribution to the diet (Equation 9-6).
Section 9.0 May 2002

4

 Concentrations in lipid tissue of aquatic organisms were converted to whole-body concentrations by

adjusting for lipid content in prey.

5

 The assimilation efficiency for dioxin-like chemicals was conservatively assumed to be 1.0 for each

congener. Therefore, it was not shown explicitly in this equation.

9-26

(9-6)

(9-7)

where

Cdiet li = Lipid-based concentration of congener i in diet (mg/kg)

Coc_sediment i = Sediment concentration normalized for organic carbon for congener i

(mg/kgoc).

BSAFlij = Biota-sediment accumulation factor reflecting biouptake from sediment

into lipid tissue of item j (kgoc/kgl)

DietFracj = Fraction of item j in diet (unitless).

Total Exposure Dose

Each receptor’s exposure dose was calculated as a function of its respective ingestion

rate, body weight, and the concentrations in the various diet items.4

 In addition to prey and plant

items, soil and sediment ingestion, as a fraction of total diet, were also accounted for in both the

terrestrial and margin habitats.5

 In addition, exposure through drinking water ingestion was

included in predicting the exposure dose for receptors in the margin habitat. For completeness,

Equation 9-7 presents the total exposure dose calculation for mammals and birds assigned to the

margin habitat. For the terrestrial habitat, the last term representing water ingestion is simply

omitted from the equation.

where

Dosei = Exposure dose for congener i (mg/kg-d)

IRdiet = Species-specific ingestion rate (kg WW/d)

Cdiet i = Total concentration of congener i in diet (mg/kg WW)

Csoil/sed i = Concentration of congener i in soil or sediment (mg/kg)

Sfrac = Fraction of soil or sediment in the diet (unitless)

Cwater i = Concentration of congener i in surface water (mg/L)

IRwater = Species-specific water ingestion rate (L/d)

BW = Species-specific average adult body weight. 
Section 9.0 May 2002

9-27

(9-8)

Congener-specific doses were summed to derive a single TEQ dose for each receptor in

each habitat, as shown in Equation 9-8:

where

DoseTEQ = Total dose in toxicity equivalence (mg/kg-d)

Dosei = Dose for congener i (mg/kg-d)

TEFi = Toxicity equivalence factor for congener i. 

TEFs were taken from the WHO consensus TEFs for mammals, fish, and birds (U.S. EPA,

2001b) and are presented in Appendix L, Table L-9. 

9.3.2.3 Risk Calculations for Phase 2. As with Phase 1, the risk metric for the Phase 2

screen was the HQ, calculated as the ratio of the TEQ exposure dose to the species-specific

ecological benchmarks based on allometric scaling of the MATLs. The exposure doses were

calculated using the 90th percentile TEQ concentrations in environmental media for mammals

and birds, respectively. The toxicological studies used in deriving the species-specific MATLs

were described in Section 9.2.2.5 on effects characterization. The assumptions, scaling

equations, and factors (i.e., factor of 1/4 for mammals and 1 for birds) are presented in

Appendix L.

9.4 Results and Risk Characterization

The two phases of the SERA were designed to provide insight into the potential for

adverse ecological effects, and the results from each phase support different conclusions and

decisions. Phase 1 was a highly conservative screen intended to serve as the “trigger” for a more

refined screening assessment. The HQ results from Phase 1 were used only to indicate that

further analysis was warranted. Phase 2 of the SERA was a less conservative screen of the

potential for adverse effects on wildlife associated with terrestrial and waterbody margin habitats

that may be affected by the agricultural application of biosolids. Although both phases of the

SERA were deterministic, the Phase 2 risk estimates were based on less conservative

assumptions regarding the environmental media concentrations, receptor-specific dietary

preferences, and ecological benchmarks. The HQ results from Phase 2 are point estimates of risk

to a wide variety of mammals and birds, and were intended to inform the ongoing assessment of

the ecological risks associated with the agricultural application of biosolids. 

In the Phase 1 analysis, the HQ values varied from a low of 2 (osprey) for the 50th

percentile concentration, to a high of 209 (mink) for the maximum concentration. The highest

HQs are associated with biosolids concentrations that were used as a surrogate for sediment (i.e.,

exposures in margin habitats for osprey, belted kingfisher, and mink). As suggested in the

problem formulation, a target HQ of 1 for the Phase 1 screen was used as a “trigger” to determine

whether further analysis was warranted. Simply put, HQs greater than 1 in the first phase of the
Section 9.0 May 2002

6

 For the mink, the 50th percentile TEQ biosolids concentration results in an HQ that is 1.4 times higher than

the HQ for the 90th percentile TEQ biosolids concentration. Although the 90th percentile biosolids are more toxic to

mammals than the 50th percentile biosolids, the congeners in the 50th percentile biosolids include higher

concentrations of more bioaccumulative congeners. As a result, the predicted hazard associated with fish ingestion is

actually higher for the less toxic sludge. That is, the applied dose in fish reflects a stronger potential to

bioaccumulate the 50th percentile congener mixture than the 90th percentile congener mixture.

9-28

SERA indicated that the second phase of the SERA was necessary. The results of the Phase 1

analysis6 are presented in Table 9-8. 

Table 9-8. Phase 1 Results

Receptor

HQ

50th percentile [TEQ]

in biosolids

90th percentile [TEQ]

in biosolids

Maximum [TEQ] 

in biosolids

Osprey 2 11 31

American robin 5 15 166

Belted kingfisher 4 25 72

Mink 36 26 209

As shown in Table 9-9, no HQ values exceeded the target HQ of 1; values range from a

minimum of 0.0035 (Canada goose) to a maximum of 0.36 (short-tailed shrew). The median HQ

for the receptors assigned to margin habitats was 0.015, and the median HQ for receptors

assigned to terrestrial habitats was 0.044, suggesting that the potential risks to terrestrial

receptors may be slightly higher than risks to receptors in margin habitats. Although the risk

results from Phase 2 did not exceed the target HQ of 1, the HQ values for 8 receptors were within

a factor of 10 of the target HQ (7 terrestrial receptors and 1 margin receptor).

9.4.1 Interpreting Results from the SERA

As described in Section 9.2, the SERA was designed to evaluate the potential for adverse

effects to mammals and birds selected to represent species in general terrestrial and waterbody

margin habitats. By inference from the measures of effect (e.g., reproductive fitness), the SERA

is intended to provide insight into the potential effects on wildlife populations, capturing the

most significant exposure pathways associated with dioxin and PCB releases into the

environment. Consequently, the SERA addresses only wildlife species of mammals and birds.

For Phase 1 of the SERA, the exceedances of the target HQ clearly indicated that Phase 2

should be conducted. Although the HQ results from Phase 2 are suggestive of a low potential for

adverse ecological effects, these results are intended only to inform the ongoing evaluation of

potential ecological risks associated with biosolids application. The conservative assumptions
Section 9.0 May 2002

9-29

Table 9-9. Screening Results from Phase 2

Receptor Species Terrestrial Habitats Margin habitats

American kestrel 3.5E-02 not assigned

American robin 1.2E-02 not assigned

American woodcock 1.8E-01 not assigned

Bald eagle not assigned 0.0028

Beaver not assigned 0.025

Belted kingfisher not assigned 0.009

Black bear 8.1E-02 not assigned

Canada goose 0.0035 not assigned

Cooper’s hawk 2.9E-02 not assigned

Coyote 2.2E-01 not assigned

Deer mouse 3.0E-02 not assigned

Eastern cottontail rabbit 0.044 not assigned

Great blue heron not assigned 0.0035

Green heron not assigned 0.0063

Herring gull not assigned 0.0088

Least weasel 1.6E-01 not assigned

Lesser scaup not assigned 0.021

Little brown bat 6.2E-02 not assigned

Long-tailed weasel 2.2E-01 not assigned

Mallard not assigned 0.01

Meadow vole 0.017 not assigned

Mink not assigned 0.023

Muskrat not assigned 0.081

Northern bobwhite 1.3E-02 not assigned

Osprey not assigned 0.0036

Prairie vole 2.3E-02 not assigned

(continued)
Section 9.0 May 2002

9-30

Table 9-9. (continued)

Receptor Species Terrestrial Habitats Margin Habitats

Raccoon 4.4E-02 0.13

Red fox 1.7E-01 not assigned

Red-tailed hawk 1.9E-02 not assigned

River otter not assigned 0.026

Short-tailed shrew 3.6E-01 not assigned

Short-tailed weasel 1.8E-01 not assigned

Tree swallow 2.8E-02 not assigned

Western meadowlark 1.7E-02 not assigned

White-tailed deer 0.061 not assigned

(e.g., 100 percent of the diet is contaminated) built into the SERA do provide some basis for

interpreting the HQ results; however, the Phase 2 screening analysis was not intended to replace

a formal ecological risk assessment. Rather, it represents one step in the evaluation of ecological

risk. For example, threatened and endangered species and habitats were not included in the

analysis because a more site-specific approach would be required to address the co-occurrence of

these receptors and their critical habitat with biosolids application sites. Consequently, the

screening results do not indicate whether endangered species are at risk. In addition, the

potential for adverse ecological effects (as indicated by the HQ results) should not be confused

with the ecological significance. Screening results can only suggest the potential for ecological

damage; they do not demonstrate actual ecological effects, nor do they indicate whether those

effects will have significant implications for ecosystems and their components.

The results from the Phase 2 screening were compared to the results from an ecological

risk assessment of TCDD in pulp and paper sludge (Meyn et al., 1997). The assessment

conducted by Meyn et al. evaluated many of the same receptors (e.g., red-tailed hawk, shrew) as

those considered in the biosolids SERA, and included four scenarios: agricultural fields (row

crops), pasture, silviculture, and mine reclamation. The authors used a Monte Carlo approach to

characterize the potential risks to wildlife, and determined that shrews were the wildlife species

associated with the highest risks from exposure to TCDD in sludge. This finding is consistent

with the results from the biosolids SERA; however, the hazard quotients predicted in Meyn et al.

were substantially higher than those predicted in the biosolids SERA. The 50th percentile hazard

quotients for row crops and pastures were 60 and 200, respectively, for shrews.

The difference between the results presented by Meyn et al and those presented in Phase 2

can be attributed primarily to differences in the soil concentrations predicted by the respective

models, as well as the choice of benchmarks. The 90th percentile TCDD concentrations for the
Section 9.0 May 2002

7

 The factor of 25 was estimated by calculating a simple arithmetic average for the agricultural field and

pasture for Meyn study (63 ng/kg) and the biosolids SERA (~2.5 ng/kg), respectively, and dividing.

9-31

row crop and pasture scenarios in the Meyn study were 54 ng/kg and 72 ng/kg, respectively, as

compared to the 90th percentile TEQ soil concentrations predicted for the biosolids SERA shown

in Table 5-4 (0.9 ng/kg and 4.2, respectively). The benchmarks used by Meyn et al., were

NOAELs divided by an interspecies uncertainty factor (UF) of 10; the MATLs used in the SERA

were not adjusted to address interspecies differences in sensitivity. Considering for the

differences in soil concentrations and benchmarks, the risk results from the biosolids SERA for

are consistent with risk estimates presented by Meyn et al., (1997). For example, increasing the

HQ results by a factor of 25 to account for differences in soil concentrations7, and a factor of 10

to account for the interspecies uncertainty factor, would result in an HQ of approximately 90 for

the shrew. This HQ is between the HQ values at the 50th percentile calculated for the row crop

and pasture scenarios for shrews presented in Meyn et al., (1997). It should be noted that the 50th

percentile hazard quotients for the silviculture and mine reclamation scenarios were also 60 and

200. 

As indicated in the problem formulation, the toxicological data identified during the

effects characterization were considered inadequate to evaluate risks to certain receptors, such as

reptiles. In addition, the toxicity data were insufficient to develop environmental quality criteria

for water column, sediment, and soil communities. However, substantial data were available on

adverse effects to fish exposed to dioxin and PCBs. Therefore, adverse effects concentrations for

dioxins were compared with the 90th percentile TEF-adjusted surface water concentration (3.5E12 mg/L) to investigate the potential risks to fish populations. A variety of sources were

reviewed to identify appropriate effects concentrations for comparison, as well as to determine

which fish species and endpoints were considered most sensitive based on the available data. 

The Dose-Response Assessment from Recently Published Research of the Toxicity of 2,3,7,8-

Tetrachlorodibenzo-p-dioxin and Related Compounds to Aquatic Life–Laboratory Studies

(NCEA, 2001) was reviewed along with other relevant reports, such as

 Workshop Report on the Application of 2,3,7,8-TCDD Toxicity Equivalence

Factors to Fish and Wildlife (U.S. EPA, 2001b)

 A Compendium of Environmental Quality Benchmarks (MacDonald et al., 1999)

 Screening Level Ecological Risk Assessment Protocol for Hazardous Waste

Combustion Facilities (U.S. EPA, 1999)

 Toxicological Benchmarks for Screening Potential Contaminants of Concern for

Effects on Aquatic Biota: 1996 Revision (Suter and Tsao, 1996)

 Interim Report on Data and Methods for Assessment of 2,3,7,8-

Tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and Associated Wildlife (U.S.

EPA, 1993b).
Section 9.0 May 2002

9-32

The environmental quality criteria and effects concentrations for 2,3,7,8-TCDD for fish

and aquatic life range from 2.0E-11 mg/L from a proposed water quality criterion for Ontario,

Canada, (MacDonald et al., 1999) to a value of 3.8E-09 mg/L proposed as a screening-level

benchmark for fish in the ecological risk assessment protocol for Hazardous Waste Combustion

facilities (U.S .EPA, 1999). The Canadian value likely reflects wildlife exposures, as well, so it

was of limited value in the SERA because wildlife exposures were estimated in the Phase 2

screening. Suter and Tsao (1996) presented a screening value of 1.0E-08 mg/L for fish, based on

EPA Region IV Water Management Division, Water Quality Standards Unit’s Screening List. 

EPA proposed a low-risk water concentration of 6E-10 mg/L and a high-risk water concentration

of 1.0E-09 mg/L for fish (U.S. EPA, 1993b); the follow-on dose-response report (NCEA, 2001)

provided additional data in support of EPA’s proposed benchmark concentrations.

The preponderance of studies on adverse effects to fish report data on reproductive and

developmental endpoints. However, EPA determined that the critical life stage for several fish

species was embryo development, that salmonid fish were the most sensitive group tested, and

that lake trout were the most sensitive species in that group (NCEA, 2001). Toxicity was not

observed in adult female lake trout exposed to 2,3,7,8-TCDD dissolved in water even at

concentrations at which the oocytes were nonviable. The environmental concentration for low

risk to fish proposed in the Interim Report on Data and Methods for Assessment of 2,3,7,8-

Tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and Associated Wildlife (U.S. EPA, 1993b)

was based on the survival of lake trout sac fry exposed as eggs. This concentration reflects data

on the most sensitive fish species (lake trout) at the most sensitive life stage and, therefore, was

considered to be an appropriate benchmark for screening purposes. EPA’s analysis of

ecotoxicological data suggests that fish are more sensitive to TCDD than are aquatic

invertebrates or amphibians (U.S. EPA, 2001b). 

The comparison of the low-risk-effects concentration (6E-10 mg/L) with the 90th

percentile TEF-adjusted surface water concentration for 2,3,7,8-TCDD (3.5E-12 mg/L) resulted

in a screening HQ of 0.0058. Subsequent research (NCEA, 2001) indicates that the observations

regarding the most sensitive species (salmonids) and endpoint (survival of sac fry) presented by

EPA (U.S. EPA, 1993b) are still valid. Based on these results, fish were not considered to be

among the most sensitive receptors evaluated in the SERA.

9.4.2 Silvicultural and Reclamation Site Applications

In addition to agricultural applications, biosolids are applied as a soil amendment to

silvicultural operations and to land reclamation projects. In general, reclamation applications of

biosolids are not well characterized. These applications can consist of spreading biosolids on

reformed land surfaces as an amendment to support revegetation or as fill material deposited in

excavations. In the former case, some tilling may occur with landscaping operations; for the

latter case, tilling is unlikely. In either case, the dioxins and PCBs would be expected to bind to

soil particles and exhibit fate and transport behavior similar to that in pastures; that is, the

biosolids will not be tilled into the soil. While the application rates and frequency are not

necessarily comparable, ecological exposures are likely to occur in a manner similar to that for

agricultural fields. The terrestrial vertebrates evaluated in Phase 2 of the SERA are likely to be

similar to receptors found at reclamation sites for terrestrial and margin habitats.
Section 9.0 May 2002

9-33

For silvicultural application of biosolids, the application rates and frequency are not well

characterized; however, it appears that biosolids are probably applied once per site. The

concentration profile for soils may be similar to pastures with the exception of reforestation

projects where site preparation for new plantings could include tilling of biosolids into the soil. 

The concentration profile for reforestation projects would tend to be more similar to the

agricultural field applications evaluated in the SERA that involve tilling. Twelve of the avian and

mammalian species listed in Table 9-3 for the terrestrial habitat are also be expected to feed and

forage in forests; therefore, the screening results for the generalized terrestrial habitat are

considered relevant to forest habitats. Although there are forest species that are not represented

in the agricultural scenario, the major trophic elements are substantially represented.

The HQ results from Phase 2 of the SERA have limited applicability to silvicultural and

reclamation site applications. The application of biosolids to the surface may form a litter layer

with substantially higher concentrations of dioxins and PCBs than the concentrations estimated

in the model simulations for agricultural fields and pastures. The invertebrate community

feeding on the biosolids layer may accumulate relatively high congener concentrations resulting

in exposures for mammals and birds that are similar to those evaluated in the Phase 1 screen. 

Extrapolating from the information presented in Phases 1 and 2 of the SERA, the hazards to

receptors feeding on soil invertebrates in a silvicultural application could potentially fall within a

range of concern (i.e., an HQ above 1). Although the biosolids SERA provides some indication

of the potential for adverse ecological effects associated with the silvicultural and reclamation

scenarios, further evaluation will be required to characterize the potential ecological risks.

9.4.3 Uncertainty

In discussing the uncertainties associated with Phase 2 of the SERA, it is important to

consider the management goal as the context for identifying key uncertainties and deciding

whether these uncertainties are acceptable. Uncertainties do not necessarily diminish the value of

the information presented in the SERA. For example, given the goals of the Phase 2 screening

assessment, uncertainties that tend to bias the risk results to produce more conservative estimates

of the potential for adverse ecological effects may be considered acceptable. Consequently, this

discussion is focused on the most significant sources of uncertainty and describes the most likely

impact of those uncertainties on the screening risk estimates. The results of the SERA are not

intended to provide a final or conclusive statement regarding the ecological risks associated with

the agricultural application of biosolids.

 Ecological effects associated with background concentrations of dioxins and

PCBs are not considered. The screening results reflect the incremental risk to

ecological receptors from exposure to dioxins and PCBs in biosolids. However,

there is some evidence to suggest that ecological damages may be associated with

background concentrations of dioxin-like compounds. Table 5.4 presents soil and

sediment concentrations of TCDD-TEQs (based on human health TEFs) for

comparison with background concentrations in rural soil (2.5 ng/kg) and sediment

(5.8 ng/kg). Based on this information, the aggregate risk (i.e., background and

biosolids-related) from TCDD exposure may be higher than the incremental risk

attributable to biosolids application.
Section 9.0 May 2002

9-34

 The SERA evaluates only dioxins and PCBs, implying that other stressors

are insignficant. The SERA did not address other chemical constituents in

biosolids, nor did it address other potential stressors (chemical or other) to which

wildlife may be exposed. As a result, the predicted screening risks (as represented

by the HQ results) may underestimate the potential for adverse ecological effects

in a multistressor environment.

 The agricultural application of biosolids does not adequately represent the

silviculture and reclamation scenarios. As discussed above, there is

considerable uncertainty in extrapolating from the agricultural application of

biosolids to other scenarios. This uncertainty suggests that further evaluation of

silvicultural and reclamation practices may be required to evaluate the potential

for adverse ecological effects in those scenarios.

 The temporal scale for the assessment is driven by the modeling system and

is based on annual concentrations averaged across the area of interest.

Because annual concentrations are used in calculating exposure doses, potentially

significant peaks in exposure are not explicitly addressed (e.g., the concentration

profile following an application of biosolids). To some degree, the data on

bioaccumulation and toxicity support the use of annual averages because they are

based on long-term, steady-state situations. Nevertheless, risks to wildlife may be

underestimated if peak exposures occur at sensitive lifestages.

 The spatial scale of the assessment assumes that 100 percent of the diet

originates from the contaminated area. For certain receptors (e.g., deer mouse),

this assumption is consistent with the relationship between the species home range

and the size of the agricultural field and pasture in the conceptual site model. 

However, for species with much larger home ranges (e.g., coyote), this

assumption tends to overestimate the potential hazard associated with biosolids

application.

 Margin habitats are broadly defined in terms of streams, ponds, and lakes.

Defining the margin habitat broadly, rather than simply modeling a small farm

pond, has implications with regard to receptor selection, as well as the applicabilty

of exposure estimates. For example, concentrations of dioxins and PCBs in a lake

ecosystem attributable to biosolids are likely to be very small and, possibly,

negligible. In contrast, the concentration in sediment of a small farm pond may

increase substantially from erosion and runoff of soil-bound congeners. The

surface water model does not distinguish between these types of waterbodies, and,

as a result, the sediment and surface water concentrations would likely

overestimate potential exposures for lakes and moderate-sized streams. 

 The measure of effect is at the level of the individual organism; therefore,

effects at the population level must be inferred from the endpoint. Although

the endpoints chosen for benchmark development for mammals and birds are

highly relevant to population viability, they cannot be used to directly evaluate the
Section 9.0 May 2002

9-35

potential risks to wildlife species populations. Population-level models have been

used by ecologists for decades to evaluate population impacts associated with a

variety of stressors. However, parameterizing a population model requires a

substantial investment in resources, and there are many difficult decisions to be

made regarding the appropriate level of effect for the population (e.g., is a 10

percent reduction in the reproductive fitness of the shrew population acceptable?). 

Currently, it is not possible to determine whether inference to populations from

endpoints relevant to population viability tends to over- or underestimate the

potential for adverse ecological effects.

 Ecological benchmarks in Phase 2 are based on a statistical rather than

biological derivation, and data are insufficient to provide defensible

adjustment factors to account for interspecies variability. Although it is

widely recognized that using NOAELs in screening analyses tends to produce

results that are difficult to interpret, there is no consensus on the most appropriate

measure of effect for a SERA. Moreover, the available toxicological data provide

little support to develop adjustment factors to account for interspecies variability. 

Indeed, there is some evidence to suggest that the ring-necked pheasant may be

one of the more tolerant avian species (Giesy et al., 1995). As a result, there is

significant uncertainty associated with deriving the species-specific ecological

benchmarks.

 It is not possible to verify that reproductive and developmental endpoints

are, in all cases, sufficient to protect the assessment endpoints for wildlife

populations. The endpoints for certain wildlife populations (i.e., mammals,

birds) were almost exclusively taken from reproductive and developmental

studies. Although reproductive and developmental endpoints have been

recognized by the SAB as relevant to population viability, they are not always the

critical effect associated with a chemical stressor. The assumption that effects

that are relevant to population viability do not occur at lower environmental

concentrations limits confidence in the screening HQ results.

 Uncertainty is inherent in the TEF/TEQ methodology. Although EPA has

determined that the TEF/TEQ methodology used in this analysis reduces the

uncertainty associated with risk estimates for AhR agonists relative to those based

on a single compound (e.g., TCDD), there may be effects associated with these

chemicals that are unrelated to AhR and, therefore, are not accounted for (U.S.

EPA, 2001b). Furthermore, EPA points out that use of the TEFs is most

appropriate for taxa and endpoints used in developing the TEF values. 

Uncertainties are introduced with increasing taxonomic and endpoint

extrapolation. 

 The selection of terrestrial BAFs is based on regression analyses and

empirical data and does not include all of the prey categories. The terrestrial

BAFs were based on empirical data and regression analyses (see Appendix L), and

information on small mammals was used to represent terrestrial vertebrates of
Section 9.0 May 2002

9-36

different sizes. In addition, measures of central tendency—rather than high-end

values—were considered appropriate in selecting input values. Although the

uncertainty associated with representing terrestrial vertebrates using data on small

mammals has not been quantified, using a high-end value for bioaccumulation

may have produced HQ results that exceeded the target HQ of 1.

 The default BEF of 1 assumes that all congeners are accumulated in

terrestrial prey at a rate similar to 2,3,7,8-TCDD. Lacking congener-specific

adjustment factors for bioaccumulation, no adjustment was made to account for

differences in congener-specific accumulation in terrestrial animals. Based on the

BEFs for bioaccumulation in aquatic organisms, the default factor likely

overestimates the tissue concentrations in terrestrial prey.

 The congener-specific BSAFs were recommended for use in risk assessment

in the Dioxin Reassessment (U.S. EPA, 2000). The U.S. EPA Office of

Research and Development recommends congener-specific values for BSAFs. 

These recommendations were based on a review of numerous studies from

different types of waterbodies and many species of fish. Although there is some

uncertainty in applying empirical values to estimate tissue concentrations in fish,

this is considered to be a relatively minor source of uncertainty given the

exhaustive review conducted by EPA.

9.5 References

Abt (Abt Associates, Inc.). 1993. Revision of Assessment of Risks to Terrestrial Wildlife from

TCDD and TCDF in Pulp and Paper Sludge. Prepared for U.S. Environmental Protection

Agency, Office of Pollution Prevention and Toxics, by Abt Associates, Inc., Bethesda,

MD.

Anderson, A.N. 1997. Using ants as bioindicators: Multiscale issues in ant community ecology. 

Conservation Ecology 1:8.

Begon, M., and M. Mortimer. 1981. Population Ecology: A Unified Study of Animals and

Plants. Sunderland, MA: Sinauer Assoc., Inc.

Bowman, R.E., S.L. Schantz, M.L. Gross, and S.A. Ferguson. 1989a. Behavioral effects in

monkeys exposed to 2,3,7,8-TCDD transmitted maternally during gestation and four

months of nursing. Chemosphere 18(1-6):235-242.

Bowman, R.E., S.L. Schantz, N.C.A. Weerasinghe, M.L. Gross, and D.A. Barsotti. 1989b. 

Chronic dietary intake of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) at 5 or 25 parts per

trillion in the monkey: TCDD kinetics and dose-effect estimate of reproductive toxicity. 

Chemosphere 18(1-6):243-252.

Brunstrom, B., and J. Lund. 1988. Differences between chick and turkey embryos in sensitivity

to 3,3',4,4'-tetrachlorobiphenyl and in concentration/affinity of the hepatic receptor for
Section 9.0 May 2002

9-37

2,3,7,8-tetrachlorodibenzo-p-dioxin. Comparative Biochemistry and Physiology C

91:507-512.

Caduto, M.J. 1990. Pond and Brook. Hanover, NH: University Press of New England.

Custer, T.W., R.K. Hines, M.J. Melancon, D.J. Hoffman, J.K. Wickliffe, J.W. Brickham, J.W.

Martin, and D.S. Henshel. 1997. Contaminant concentrations and biomarker responses

in great blue heron eggs from 10 colonies on the Upper Mississippi River, USA. 

Environmental Toxicology and Chemistry 16:260-271.

Davis, W.S., and T.P. Simon (eds). 1995. Biological Assessment and Criteria: Tools for Water

Resource Planning and Decision Making. Boca Raton, FL: Lewis Publishers.

Eisler, R. 1986. Dioxin hazards to fish, wildlife, and invertebrates: a synoptic review. In

Contaminant Hazard Reviews, Report No. 8. U.S. Fish and Wildlife Service, U.S.

Department of the Interior, Laurel, MD.

Giesy, J.P., W.W. Bowerman, M.A. Mora, D.A. Verbrugge, R.A. Othoudt, J.L. Newsted, C.L.

Summer, R.J. Aulerich, S.J. Bursian, J.P. Ludwig, G.A. Dawson, T.J. Kubiak, D.A. Best,

and D.E. Tillitt. 1995. Contaminants in fishes from Great Lakes-influenced sections and

above dams of three Michigan rivers: Implications for health of bald eagles. Archives of

Environmental Contamination and Toxicology 29:309-321.

Hart, L.E., K.M. Cheng, P.E. Whitehead, R.M. Shah, R.J. Lewis, S.R. Ruschkowski, R.W. Blair,

D.C. Bennett, S.M. Bandiera, R.J. Norstrom, and G.D. Bellward. 1991. Dioxin

contamination and growth and development in great blue heron embryos. Journal of

Toxicology and Environmental Health 32:331-344.

Henshel, D.S., B. Hehn, R. Wagey, M. Vo, and J.D. Steeves. 1997. The relative sensitivity of

chicken embryos to yolk- or air-cell-injected 2,3,7,8-tetrachlorodibenzo-p-dioxin. 

Environmenal Toxicology and Chemistry 16:725-732.

Hochstein, J.R., R.J. Aulerich, and S.J. Bursian. 1988. Acute toxicity of 2,3,7,8-

tetrachlorodibenzo-p-dioxin to mink. Archives of Environmental Contamination and

Toxicology 17:33-37.

Kadlec, R.H., and R.L. Knight. 1996. Treatment Wetlands. CRC Press, Boca Raton, Florida

Khera, K.S., and J.A. Ruddick. 1973. Polychlorodibenz-p-dioxins: perinatal effects and the

dominant lethal test in wistar rats. In Chlorodioxins - Origin and Fate. A Symposium

Sponsored by the Division of Pesticide Chemistry at the 162nd Meeting of the American

Chemical Society, E.H. Blair (ed.), 8th Edition. pp. 70-84. American Chemical Society,

Washington, DC. September 16-17, 1971.
Section 9.0 May 2002

9-38

MacDonald, D.D., T. Berger, K. Wood, J. Brown, T. Johnsen, M.L. Haines, K. Brydges, M.J.

MacDonald, S.L. Smith, and D.P. Shaw. 1999. A Compendium of Environmental

Quality Benchmarks. Prepared for Environment Canada, Vancouver, B.C., Canada.

Meyn, O., M. Zeeman, M.J. Wise, and S.E. Keane. 1997. Terrestrial wildlife risk assessment fo

TCDD in land-applied pulp and paper mill sludge. Environmental Toxicology and

Chemistry 16:1789-1801.

Mineau, P., B.T. Collins, and A. Baril. 1996. On the use of scaling factors to improve

interspecies extrapolation of acute toxicity in birds. Regul. Toxicol. and Pharmacol.

24:24-29.

Murray, F.J., F.A. Smith, K.D. Nitschke, C.G. Humiston, R.J. Kociba, and B.A. Schwetz. 1979. 

Three-generation reproduction study of rats given 2,3,7,8-tetrachlorodibenzo-p-dioxin

(TCDD) in the diet. Toxicology and Applied Pharmacology 50:241-252.

NCEA (National Center for Exposure Analysis). 2001. “Dose-Response Assessment from

Recently Published Research of the Toxicity of 2,3,7,8-Tetrachlorodibenzo-p-dioxin and

Related Compounds to Aquatic Wildlife—Laboratory Studies.” NCEA-C-0649. July

2001.

Nosek, J.A., S.R. Craven, J.R. Sullivan, S.S. Hurley, and R.E. Peterson. 1992. Toxicity and

reproductive effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin in ring-necked pheasant hens. 

Journal of Toxicology and Environmental Health 35:187-198.

Nosek, J.A., J.R. Sullivan, S.R. Craven, A. Gendron-Fitzpatrick, and R.E. Peterson. 1993. 

Embryotoxocity of 2,3,7,8-tetrachlorodibenzo-p-dioxin in the ring-necked pheasant. 

Environmental Toxicology and Chemistry 12:1215-1222.

Powell, D.C., R.J. Aulerich, J.C. Meadows, D.E. Tillitt, J.F. Powell, J.C. Restum, K.L.

Stromborg, J.P. Giesy, and S.J. Bursian. 1997. Effects of 3,3',4,4',5-pentachlorobiphenyl

(PCB 126), 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), or an extract derived from

field-collected cormorant eggs injected into double-crested cormorant (Phalacrocorax

auritus) eggs. Environmental Toxicology and Chemistry 16:1450-1455.

Sample, B.E., M.S. Alpin, R.A. Efroymson, G.W. Suter, and C.J.E. Welsh. 1997. Methods and

Tools for Estimation of the Exposure of Terrestrial Wildlife to Contaminants. Prepared

for the U.S. Department of Energy, Office of Environmental Policy and Assistance,

Air, Water, and Radiation Division. Prepared by Oak Ridge National Laboratory, Oak

Ridge, TN.

Schoener, T.W. 1989. Food webs from small to large. Ecology 70(6):1559-1589.

Schoenly, K., and J. Cohen. 1991. Temporal variation in food web structure: 16 empirical

cases. Ecological Monographs 61(3):267-298.
Section 9.0 May 2002

9-39

Stokes, D.W., and L.Q. Stokes. 1996. Stokes Field Guide to Birds. Boston, MA: Little, Brown,

& Co.

Suter, G.W., II. 1993. Ecological Risk Assessment. Chelsea, MI: Lewis Publishers.

Suter, G.W., II, and C.L. Tsao. 1996. Toxicological Benchmarks for Screening Potential

Contaminants of Concern for Effects on Aquatic Biota: 1996 Revision. ES/ER/TM96/R2. Prepared for the U.S. Department of Energy, Washington, DC.

Tanner, J.T. 1978. Guide to Study of Animal Populations. Knoxville, TN: University of

Tennessee.

Terres, J.K. 1980. The Audubon Society Encyclopedia of North American Birds. New York: 

Alfred A. Knopf.

U.S. EPA (Environmental Protection Agency). 1992. Draft Report: A Cross-Species Scaling

Factor for Carcinogen Risk Assessment Based on Equivalence of mg/kg3/4/day. Federal

Register 57 FR 24152, June 5, 1992.

U.S. EPA (Environmental Protection Agency). 1993a. Wildlife Exposure Factors Handbook. 

EPA/60/P-92-003C. Office of Research and Development, U.S. Environmental

Protection Agency, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1993b. Interim Report on Data and Methods for

Assessment of 2,3,7,8-Tetrachlorodibenzo-p-dioxin Risks to Aquatic Life and Associated

Wildlife. EPA/600/R-93/055. Office of Research and Development, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1994. Application of Trophic Level Concept to

Analysis of Environmental Contaminant Transfer through Terrestrial Food Webs: Issues

and Comparisons with Aquatic Trophic Levels. Submitted by M. McVey, ICF Inc.,

Fairfax, VA, to Office of Science and Technology, Office of Water, U.S. Environmental

Protection Agency, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1995. Great Lakes Water Quality Initiative 

Criteria Documents for the Protection of Wildlife: DDT, Mercury, 2,3,7,8-TCDD, and

PCBs. EPA-820-B-95-008. Office of Water, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1998. Risk Assessment Forum. Guidelines for

Ecological Risk Assessment – Final. EPA/630/R-95/002F.

U.S. EPA (Environmental Protection Agency). 1999. Screening-Level Ecological Risk

Assessment Protocol for Hazardous Waste Combustion Facilities. Office of Solid Waste,

Washington, DC.

U.S. EPA (Environmental Protection Agency). 2000. Exposure and Human Health Reassessment

of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds. EPA/600/P-
Section 9.0 May 2002

9-40

00/001Bb. Exposure Assessment and Risk Characterization Group, National Center for

Environmental Assessment, Office of Research and Development, Washington, DC. 

September.

U.S. EPA (Environmental Protection Agency). 2001a. “The Role of Screening-Level Risk

Assessments and Refining Contaminants of Concern in Baseline Ecological

Assessments.” EPA ECO Update, Publication 9345.0-14. EPA/540/F-01/014. Office of

Solid Waste and Emergency Response, U.S. EPA, Washington, DC. June.

U.S. EPA (Environmental Protection Agency). 2001b. “Workshop Report on the Application of

2,3,7,8-TCDD Toxicity Equivalence Factors to Fish and Wildlife.” EPA/630/R-01/002. 

Risk Assessment Forum, U.S. EPA, Washington, DC. August.

U.S. EPA (Environmental Protection Agency). 2001c. 2001 National Sewage Sludge Survey.