EPA 600/R-98/137

METHODOLOGY FOR ASSESSING HEALTH RISKS

ASSOCIATED WITH MULTIPLE PATHWAYS OF EXPOSURE

TO COMBUSTOR EMISSIONS

Update to EPA/600/6-90/003

Methodology for Assessing Health Risks

Associated With Indirect Exposure

to Combustor Emissions

National Center for Environmental Assessment

U.S. Environmental Protection Agency

26 W. Martin Luther King Drive

Cincinnati, OH 45268
Front Matter

Disclaimer

MPE ii December 1998

DISCLAIMER

Mention of trade names or commercial products does not constitute

endorsement or recommendation for use.
Front Matter

Preface

MPE iii December 1998

PREFACE

The National Center for Environmental Assessment has prepared this

methodology in response to the 1993 Combustion Strategy for Hazardous Waste

Incinerators released by the Administrator of EPA. Among other mandates regarding

the regulation of this industrial sector, the strategy involved the requirement of EPA

and/or the regulated community to conduct indirect exposure assessments to evaluate

the impact of stack emissions. Although this mandate was specific to hazardous waste

incinerators, the importance of the indirect pathways has extended to all combustors. 

The available methodologies at that time to conduct an indirect risk assessment were a

1990 ORD document titled, “Methodology for Assessing Health Risks Associated with

Indirect Exposure to Combustor Emissions” (IEM) and the 1992 NCEA dioxin exposure

reassessment document, “Estimating Exposure to Dioxin-Like Compounds”. In order to

develop a response to the Administrator’s’s strategy, a workgroup comprised of ORD,

OSWER, OAQPS, and others, convened. The result of this workgroup was an

“Addendum” document to the 1990 Indirect Exposure document, released in November

of 1993. This document described updates and other changes to the 1990 document;

the two documents, together with other relevant information such as that from the dioxin

exposure document or the Exposure Factors Handbook, would provide guidance on

conducting indirect risk assessments.

The purpose of this document is to update the 1990 IEM. Most of the revisions

provided in this document are based on SAB and public comments to the 1993

Addendum. Earlier versions of this document have undergone internal Agency and

external peer review.
Front Matter

Foreword

MPE iv December 1998

FOREWORD

The Cincinnati Office of the National Center for Environmental Assessment, in

partnership with NCEA offices in Washington, DC and Research Triangle Park, is a

center for leadership in the evolution of risk assessment research, conducting key risk

assessments, and providing expert advice and support. We strive to provide our

clients with the highest quality information and methods, resulting in the protection of

human health and preservation of ecological integrity on a social cost-effective basis.

This methodology has been developed by NCEA scientists and engineers in

Cincinnati and Washington. In addition, significant contributions to the methodology

were provided by Office of Research and Development’s National Exposure Research

Laboratory in Athens, Georgia. The purpose of this methodology is to provide a set of

procedures for estimating exposures resulting from atmospheric pollutants that are

emitted from a stationary combustion source, transferred through the atmosphere, and

deposited downwind to environmental media and biota.

Moreover, the methodology provides procedures to estimate indirect exposures

that may result from uptake and transfer of atmospheric agents through the terrestrial

or aquatic food chains. This multiple pathway exposure approach to environmental

stressors facilitates reaching goals for aggregate exposure and estimates of cumulative

risks.

This document is dedicated to our pursuit to improve exposure assessment as it

relates to combustion risk assessment and other multiple pathways of exposures within

the environment.

We commend all contributors and reviewers for creating this improved tool for

completing risk assessments that result in more informed risk intervention decisions

and improved communications for all.

Michael Callahan Terry Harvey, D.V.M.

Division Director Division Director

National Center for Environmental National Center for Environmental

 Assessment Assessment

Washington Office Cincinnati Office
Front Matter

Table of Contents

MPE v December 1998

TABLE OF CONTENTS

Page

1. INTRODUCTION ............................................... 1-1

1.1. BACKGROUND ........................................... 1-1

1.2. PURPOSE ............................................... 1-1

1.3. STATIONARY COMBUSTORS ............................... 1-2

1.4. SCOPE ................................................. 1-3

1.5. USE .................................................... 1-5

1.6. REFERENCES ........................................... 1-7

2. HUMAN EXPOSURE ASSESSMENT ............................... 2-1

2.1. INTRODUCTION .......................................... 2-1

2.2. OVERVIEW OF EXPOSURE ASSESSMENT .................... 2-1

2.3. CREATING EXPOSURE SCENARIOS ......................... 2-2

2.3.1. Identify Emission Source ............................. 2-4

2.3.2. Defining Source Characteristics ....................... 2-4

2.3.3. Defining the Study Area .............................. 2-6

2.3.4. Potential Pathways of Human Exposure ................. 2-8

2.3.5. Factors Related to Human Exposures .................. 2-12

2.4. DEFINING HUMAN EXPOSURE ............................ 2-17

2.4.1. Approaches Used to Describe Exposure/Risk Descriptors . . 2-17

2.4.2. Study Population .................................. 2-19

2.4.3. Defining the Exposed Individual(s) and Developing

Exposure Estimates ................................ 2-20

2.4.4. Commodity Shed Approach .......................... 2-29

2.5. REFERENCES ......................................... 2-34

3. AIR DISPERSION AND DEPOSITION MODELING OF THE POLLUTANT

STACK EMISSIONS ............................................ 3-1

3.1. INTRODUCTION .......................................... 3-1

3.2. SELECTING TOXIC AIR POLLUTANTS ........................ 3-3
Front Matter

Table of Contents

MPE vi December 1998

TABLE OF CONTENTS cont.

Page

3.2.1. Consider the Feed Material Being Combusted ............ 3-3

3.2.2. Consider the Availability of Appropriate Toxicological

Information ....................................... 3-10

3.2.3. Narrow the List with a Quantitative Ranking Scheme ...... 3-10

3.3. ESTIMATING THE STACK EMISSION RATE OF THE TOXIC

AIR POLLUTANTS ....................................... 3-11

3.3.1. Operational Facilities ............................... 3-12

3.3.2. Planned Facilities ................................. 3-13

3.3.3. Planned Facility with no Comparable Existing Facility ..... 3-14

3.4. INCREASES IN EMISSIONS DURING EQUIPMENT

MALFUNCTIONS; STARTUP; AND SHUTDOWN ............... 3-15

3.5. AIR DISPERSION/DEPOSITION MODELING OF STACK

GAS EMISSIONS ........................................ 3-18

3.5.1. Basic Physical Principles Used to Estimate Atmospheric 

Dispersion and Deposition of Stack Emissions ........... 3-19

3.5.2. Estimation of Dry Surface Deposition Flux .............. 3-22

3.5.3. The Particle Size Distribution in the Stack Emissions

and Estimation of Contaminant Particle-Bound

Emission Rate .................................... 3-30

3.5.4. Estimation of Wet Deposition Flux ..................... 3-36

3.5.5. Estimation of the Phase Distribution of Semivolatile

Organic Emissions ................................. 3-39

3.5.6. Recommendations for Regulatory Use ................. 3-43

3.5.7. Dispersion Parameters ............................. 3-44

3.5.8. Treatment of Terrain ............................... 3-44

3.5.9. Stack-Tip Downwash ............................... 3-46

3.5.10. Transitional/Final Rise .............................. 3-46

3.5.11. Buoyancy Induced Dispersion ........................ 3-48

3.5.12. Calms Processing ................................. 3-49

3.5.13. Building Wake Effects .............................. 3-49
Front Matter

Table of Contents

MPE vii December 1998

TABLE OF CONTENTS cont.

Page

3.6. MODEL PARAMETER REQUIREMENTS ...................... 3-49

3.6.1 Control Inputs .................................... 3-50

3.6.2. Source Inputs ..................................... 3-53

3.6.3. Meteorological Inputs ............................... 3-53

3.6.4. Receptor Inputs ................................... 3-59

3.7. OUTPUT ............................................... 3-61

3.8. ISC3 MODEL APPLICATIONS .............................. 3-61

3.8.1. Calculation of Amount Deposited ..................... 3-63

3.9. FUGITIVE DUST/EMISSIONS .............................. 3-65

3.10. MODELING INHALATION EXPOSURE ....................... 3-66

3.10.1. Estimating Exposure and Dose from Contaminant

Concentrations in the Air ............................ 3-66

3.10.2. Inhalation Doses .................................. 3-67

3.11. TREATMENT OF UNCERTAINTY ........................... 3-69

3.11.1. Overview of Model Uncertainty ....................... 3-69

3.11.2. Studies of Model Accuracy .......................... 3-70

3.11.3. Use of Uncertainty in Decision-Making ................. 3-71

3.12. REFERENCES ......................................... 3-73

4. SOIL CONCENTRATION AND EXPOSURE PATHWAYS ............... 4-1

4.1. INTRODUCTION .......................................... 4-1

4.2. OVERVIEW OF SOIL CONCENTRATION CALCULATIONS ........ 4-1

4.2.1. Calculating Cumulative Soil Concentration ............... 4-3

4.2.2. Calculating Deposition Rates ......................... 4-4

4.2.3. Calculating the Soil Loss Constant (ks) .................. 4-9

4.3 DESCRIPTION OF INPUT VARIABLES ....................... 4-13

4.3.1. Dry and Wet Deposition Rates (Dyd and Dyw) ........... 4-13

4.3.2. Diffusive Loading (LDIF) ............................. 4-13
Front Matter

Table of Contents

MPE viii December 1998

TABLE OF CONTENTS cont.

Page

4.3.3. Time Period of Deposition (tD) ....................... 4-14

4.3.4. Soil Depth (Zs) .................................... 4-14

4.3.5. Bulk Density of the Soil (BD) ......................... 4-16

4.3.6. Soil Loss Constant (ks) ............................. 4-16

4.3.7. Contaminant Losses by Physical Processes ............. 4-17

4.3.8. Losses by Chemical and Biological Processes ........... 4-21

4.4. SOIL EXPOSURE PATHWAYS ............................. 4-23

4.4.1. Soil Contaminant Concentration (Cs) ................... 4-24

4.4.2. Soil Ingestion — Human Daily Intake (DIs) ............... 4-24

4.4.3. Daily Dermal Intake From Soil ........................ 4-30

4.5. REFERENCES ......................................... 4-46

5. DUST RESUSPENSION ......................................... 5-1

5.1. INTRODUCTION ........................................... 5-1

5.2. OVERVIEW OF DUST RESUSPENSION ........................ 5-2

5.2.1. Sources of Dust .................................... 5-2

5.2.2. Environmental Conditions of Dust Generation ............. 5-3

5.2.3. Location of Dust Sources Relative to Receptor ............ 5-4

5.3. GENERATION OF AGRICULTURAL DUST ...................... 5-5

5.3.1. Agricultural Wind Erosion ............................ 5-5

5.3.2. Agricultural Tilling ................................ 5-10

5.3.3. Contaminant Emissions in Agricultural Areas ............ 5-11

5.4. VEHICULAR TRAFFIC .................................... 5-12

5.4.1. Unpaved Roads ................................... 5-12

5.4.2. Paved Roads ..................................... 5-16

5.4.3. Contaminant Emissions Due to Vehicular Traffic .......... 5-17

5.5. ESTIMATING EXPOSURES TO RESUSPENDED DUST

EMISSIONS ............................................ 5-19
Front Matter

Table of Contents

MPE ix December 1998

TABLE OF CONTENTS cont.

Page

5.5.1. Air Dispersion Modeling of Resuspended Dust Emissions . . 5-20

5.5.2. Alternative to Air Dispersion Modeling of Resuspended

Dust Emissions ................................... 5-22

5.6. REFERENCES .......................................... 5-28

6. DETERMINING EXPOSURE THROUGH THE TERRESTRIAL

FOOD CHAIN ................................................. 6-1

6.1. INTRODUCTION .......................................... 6-1

6.2. OVERVIEW OF TERRESTRIAL FOOD CHAIN .................. 6-1

6.3. CALCULATING CONTAMINANT CONCENTRATION IN

PLANTS ................................................ 6-3

6.3.1 Plant Contaminant Concentration Due to Root Uptake (Pri) . . 6-8

6.3.2. Plant Concentration Due to Deposition of Particle-Bound

Contaminants (Pdi) ................................ 6-18

6.3.3. Plant Concentration Due to Deposition of Vapor-Phase

Contaminants (Pvi) ................................. 6-24

6.4. CALCULATING CONTAMINANT CONCENTRATION IN

ANIMAL TISSUES ........................................ 6-36

6.5. CALCULATING HUMAN DAILY INTAKE ...................... 6-44

6.5.1. An Overview of Terrestrial Food Consumption Rates ...... 6-45

6.5.2. The Impact of Cooking on Food Concentrations .......... 6-52

6.5.3. Calculating Daily Intake from Contaminated Plants ........ 6-55

6.5.4. Calculating Daily Intake from Animal Tissue ............. 6-57

6.6. ALTERNATIVE MODELING APPROACHES FOR TERRESTRIAL

FOOD CHAIN ........................................... 6-59

6.7. REFERENCES ......................................... 6-63

7. WATER CONCENTRATION AND EXPOSURE PATHWAYS ............. 7-1

7.1. INTRODUCTION .......................................... 7-1

7.2. WATER CONCENTRATIONS OVERVIEW ..................... 7-2
Front Matter

Table of Contents

MPE x December 1998

TABLE OF CONTENTS cont.

Page

7.2.1. The Framework .................................... 7-2

7.2.2. Chemical Mass Balance Equations ..................... 7-9

7.2.3. Sediment Mass Balance Equations .................... 7-11

7.2.4. Loads to the Water Body ............................ 7-13

7.2.5. Advective Flow from the Water Body ................... 7-19

7.2.6. Chemical Dissipation Within the Water Body ............ 7-19

7.2.7. Parameter Guidance ............................... 7-33

7.3. PRECIPITATION ......................................... 7-45

7.4. GROUND WATER ....................................... 7-46

7.5. DAILY INTAKE FROM WATER (DI) .......................... 7-46

7.5.1. Water Contaminant Concentration (Cw) ................. 7-47

7.5.2. Water Consumption Rate (Crw) ....................... 7-48

7.5.3. Body Weight (BW) ................................. 7-49

7.6. DERMAL EXPOSURE FROM WATER ........................ 7-49

7.6.1. Daily Dermal Intake from Water ....................... 7-49

7.6.2. Estimating the Dermally Absorbed Dose per Event

(Devent) ........................................... 7-51

7.6.3. Skin Surface Area (A) .............................. 7-59

7.6.4. Body Weight (BW) ................................. 7-59

7.7. UNCERTAINTY IN THE AQUEOUS DERMAL EXPOSURE

MODEL ................................................ 7-59

7.8. REFERENCES ......................................... 7-62

8. DETERMINING EXPOSURE FROM INTAKE OF AQUATIC

ORGANISMS .................................................. 8-1

8.1. INTRODUCTION .......................................... 8-1

8.2. CALCULATING DAILY HUMAN INTAKE FROM FISH ............. 8-2

8.2.1. Total Water Column Contaminant Concentration (Cwctot)

and Benthic Sediment Concentration (Cbs) ............... 8-3

8.2.2. Bioaccumulation Factor (BAF) ......................... 8-4

8.2.3. Fish Ingestion (Dlf) ................................. 8-21

8.2.4. Body Weight ..................................... 8-24
Front Matter

Table of Contents

MPE xi December 1998

TABLE OF CONTENTS cont.

Page

8.3. OTHER AQUATIC MODELS ................................ 8-25

8.4. SPECIAL CONSIDERATIONS FOR FARM-RAISED FISH ......... 8-27

8.5. FISH PREPARATION FACTOR ............................. 8-27

8.6. REFERENCES ......................................... 8-29

9. BREAST MILK PATHWAY ........................................ 9-1

9.1. INTRODUCTION .......................................... 9-1

9.2. CHEMICAL CONTAMINANTS IN BREAST MILK ARISING FROM

COMBUSTION ........................................... 9-1

9.2.1. Residues of Highly Lipophilic Organic Contaminants in

Human Milk ....................................... 9-3

9.2.2. Compounds Partitioning into both Lipid and Aqueous

Phases ........................................... 9-8

9.2.3. Residues of Inorganic and Organometallic Contaminants

in Human Milk .................................... 9-10

9.2.4. Factors Influencing Transfer of Contaminants into Human

Milk ............................................ 9-12

9.3. AVERAGE DAILY DOSE OF CONTAMINANT TO THE 

NURSING INFANT ....................................... 9-12

9.3.1. Contaminant Concentration in Breast Milk Fat (Cmilkfat):

First Order Kinetics Approach ........................ 9-13

9.3.2. Contaminant Concentration in Breast Milk Fat (Cmilkfat):

Biotransfer Factor Approach ......................... 9-23

9.3.3. Fraction of Fat in Breast Milk (fmbm) .................... 9-25

9.3.4. Contaminant Concentration in the Aqueous Phase of

Breast Milk (Caqueous) ................................ 9-25

9.3.5. Fraction of Ingested Contaminant that is Absorbed by the

Infant (fai) ........................................ 9-28

9.3.6. Ingestion Rate of Breast Milk (IRmilk) ................... 9-28

9.3.7. Body Weight of Infant (BWINF) ........................ 9-29

9.3.8. Exposure Duration (ED) ............................. 9-29

9.3.9. Averaging Time (AT) ............................... 9-30
Front Matter

Table of Contents

MPE xii December 1998

TABLE OF CONTENTS cont.

Page

9.4. UNCERTAINTY IN THE BREAST MILK PATHWAY EXPOSURE

TO THE INFANT ......................................... 9-31

9.5. REFERENCES ......................................... 9-36

10. RISK ASSESSMENT ........................................... 10-1

10.1. INTRODUCTION ......................................... 10-1

10.2. EXPOSURE ASSESSMENT ................................ 10-3

10.2.1. Estimation of Individual Total Exposure ................. 10-4

10.2.2. Population Exposure .............................. 10-16

10.3. TOXICITY ASSESSMENT ................................ 10-16

10.3.1. Hazard Identification for Carcinogens ................. 10-19

10.3.2. Hazard Identification for Noncarcinogens .............. 10-19

10.3.3. Dose-Response Assessment for Carcinogens .......... 10-20

10.3.4. Dose-Response Assessment for Noncarcinogens ........ 10-22

10.4. RISK CHARACTERIZATION ............................... 10-26

10.4.1. Introduction ..................................... 10-26

10.4.2. Risk Estimation .................................. 10-27

10.5. UNCERTAINTY AND VARIABILITY IN THE RISK

CHARACTERIZATION ................................... 10-40

10.5.1. Sources of Uncertainty ............................ 10-41

10.5.2. Quantitative Evaluation of Uncertainty ................ 10-42

10.5.3. Mass Balance Checks ............................. 10-48

10.6. THE COMPLETED RISK CHARACTERIZATION ............... 10-50

10.7. REFERENCES ......................................... 10-53

Appendix A: MODEL VALIDATION ..................................... A-1

Appendix B: DERIVATION OF KINETIC BREAST MILK EXPOSURE MODEL

INCORPORATING BREAST FEEDING LOSSES ................ B-1

Appendix C: GLOSSARY ............................................. C-1
Front Matter

List of Tables

MPE xiii December 1998

LIST OF TABLES

Page

2-1 Example Table for Media Levels .................................. 2-23

2-2 Example Exposure Parameters for An Isopleth Ring Within the

Study Area ................................................... 2-25

3-1 Initial List of Dioxin-Like Compounds Identified in Emissions from

the Incineration and Combustion of Anthropogenic Wastes .............. 3-4

3-2 Initial List of Organic Compounds Identified in Emissions from the

Incineration and Combustion of Anthropogenic Wastes ................. 3-5

3-3 Initial List of Inorganic Contaminants Identified in Emissions from

the Incineration and Combustion of Anthropogenic Wastes .............. 3-9

3-4 Parameters Used to Calculate Pasquill-Gifford Fy..................... 3-22

3-5 Parameters Used to Calculate Pasquill-Gifford Fz..................... 3-23

3-6 Factors That Influence the Dry Deposition Removal Rate in 

the Atmosphere ............................................... 3-27

3-7 Summary of Dry Deposition Velocities for Particles .................... 3-30

3-8 Generalized Particles Size Distribution (μm), and Proportion of Available

Surface Area, in Particulate Emissions from Incineration ............... 3-31

3-9 Unit Wet Deposition Scavenging Coefficients Per Particle Diameter

Category (micrometers) Used In the Example ISC3 Analysis, Expressed

as 1/(s/mm/r) ................................................. 3-39

3-10 Values of 2, VT, and TSP in Different Air Regimes .................... 3-42

3-11 Determination of the Types of Urban Land Use ....................... 3-51

3-12 Classes of Atmospheric Stability and Associated Vertical Temperature 

Distribution ................................................... 3-58

3-13 Meteorological Conditions Defining Pasquill Turbulence Types .......... 3-58

3-14 Surface Roughness Heights for Land Use Types and Seasons

(meters) ..................................................... 3-60

4-1 Studies of Soil Intake in Children .................................. 4-26

4-2 Soil Consumption by Children as Measured by Various Tracer

Elements in Key Studies ........................................ 4-27

4-3 Body Weights of Adults and Children .............................. 4-31

4-4 Recommended Percentages of Applied Dose Absorbed for Soil

Applications of 0.2-1.0 mg/cm2 and Exposure Less than or Equal

to 24 Hours .................................................. 4-36

4-5 Mean Soil Adherence by Activity and Body Region .................... 4-39

4-6 Soil Adherence Values ......................................... 4-41

4-7 Assumptions of Frequency of Exposure to Soil ....................... 4-41

4-8 Total Surface Area of Adults and Children of Various Ages ............. 4-43

5-1 Soil Erodibility for Various Soil Textural Classes ....................... 5-7

5-2 Typical Silt Content Values of Surface Material on Industrial and Rural

Unpaved Roads ............................................... 5-14
Front Matter

List of Tables

MPE xiv December 1998

LIST OF TABLES cont.

Page

5-3 Ranges of Source Conditions for Unpaved Roads .................... 5-15

5-4 Typical Silt Content and Loading Values for Paved Roads at Industrial

Facilities ..................................................... 5-18

5-5 Ranges of Source Conditions for Paved Roads ...................... 5-19

5-6 Particle Size Distribution for Resuspended Soils and Road Dusts ........ 5-21

6-1 Uptake Mechanisms Based on Plant Morphology Assumed in this

Methodology .................................................. 6-6

6-2 Common Items from Each Category of Produce Consumed

by Humans .................................................... 6-7

6-3 Dry Deposition Velocities for Gases ............................... 6-29

6-4 Comparison of Predicted and Observed Biotransfer Factors for

Dioxin-like Compounds in Beef and Milk ............................ 6-43

6-5 Percent Weight Losses from Preparation of Various Meats ............. 6-52

6-6 Percent Home Consumption and Consumers Only Consumption 

Rates for Home Production of Meats, Dairy Products, and Eggs ......... 6-59

7-1 Tapwater Consumption Rates Recommended by U.S. EPA (1995a) ...... 7-50

8-1 Food Chain Multipliers for Trophic Levels 3 and 4 .................... 8-11

8-2 Bioaccumulation Equivalency Factors for Dioxin-like Compounds ........ 8-16

9-1 Ranges of Water Solubilities and Octanol:Wateer Partition Coefficients

for Representative Chemical Emissions Which May Occur in Breast Milk ... 9-4

9-2 Major PCB Congeners in Human Milk from Upstate New York and Ratio

to Maternal Blood ............................................... 9-7

9-3 Body Weights of Children ....................................... 9-30

9-4 Relative Predicted Contaminant Concentration in Breast Milk for

Steady-State Model versus Kinetic Modela.......................... 9-32

9-5 Relative Predicted Average Contaminant Concentration in Breast Milk

for Kinetic Model, alone, versus Kinetic Model with Losses from

Breast Feedinga............................................... 9-32

9-6 Combined Relative Predicted Average Contaminant Concentration in

Breast Milk for Steady-State Model versus Kinetic Model with Losses

from Breast Feedinga........................................... 9-34

10-1 U.S. EPA Risk Assessment Guidelines ............................. 10-3

A-1 Results of the Air-to-soil Model Testing for Dioxins and Furans ......... A-11

A-2 Observed Data for the Air-to-Plant Model Validation Exercise .......... A-15

A-3 Fate and Transport Parameters for the Dioxin-like Congeners .......... A-16

A-4 Model Results Comparing the Vapor Transfer Model and the Vapor

Deposition Model with the Field Data for 2,33,7,8-TCDD .............. A-22
Front Matter

List of Tables

MPE xv December 1998

LIST OF TABLES cont.

Page

A-5 Summary of the Results from Eight Sites of the Connecticut Department of

Environmental Protection Sampling, Including Soil, Sediment, and Fish

Concentrations, and the Key Concentration Ratios of Sediment to Soil and

the Biota Sediment Accumulation Factor (BSAF) Ratio ................ A-24

A-6 Data and Results of the Soil to Below-ground Vegetable Validation

Exercise .................................................... A-29

A-7 Comparison of Predicted Leafy Vegetation Samples of the Current,

Revised Validation Exercise with the Previous Predictions of Leafy

Vegetation and Several Observations in the Literature

(Units are pg/g dry weight) ...................................... A-34

A-8 Results of the 1994 Air-to-beef Model Validation Exercise Compared

Against Results from the Current Air-to-beef Model Validation Exercises

(all beef concentrations in terms of pg/g lipid; values in parentheses are

observations calculated assuming nondetects equal 0.0; values not in

parentheses assume nondetects equal 1⁄2 detection limit) .............. A-36

A-9 Particle Fractions, M, in Four Airsheds at 20EC for the Dioxin-like

Congeners .................................................. A-38

A-10 Regression Parameters Slope M and Intercept B for Equation A.2, log

Kp = M log PEL + B, Based on Field Measurements of Particle/gas

Distributions for Dioxins and Furans .............................. A-40

A-11 Parameters for the Empirical Relationship Relating the Subcooled Liquid

Vapor Pressure, PEL, to the Particle/Gas Partition Coefficient, Kp, of

Semivolatile Organic Compounds (SOC) .......................... A-45

A-12 Physical Chemical Properties of the Polycyclic Aromatic Hydrocarbons

Assigned for this Exercise ...................................... A-51

A-13 Observed Air Concentrations in the Particle and Vapor Phases, and

Comparison of Predicted and Observed Vapor Fractions .............. A-54

A-14 Observed and Predicted Pine Needle, Maple Leaf, and Maple Seed

Concentrations ............................................... A-55
Front Matter

List of Figures

MPE xvi December 1998

LIST OF FIGURES

Page

2-1 Conceptual Overview of Modeling the Environmental Fate of and

Exposure to Pollutants from an Emission Source ...................... 2-3

2-2 Simple Representation Outer Isopleth Ring Defining Study Area and 

Isopleth Rings Within Study Area .................................. 2-7

2-3 Approximate Risk Isopleth Derived from Area Blocks .................. 2-28

2-4 Comparison of Approximate Risk Isopleth Derived from Area Blocks

to a Potential True Risk Isopleth .................................. 2-30

3-1 Observed Deposition Velocities as a Function of Particles Size for

1.5 g/cm Density Particles ....................................... 3-29

3-2 Wet Scavenging Rate Coefficient as a Function of Particle Size ......... 3-38

3-3 Illustration of Plume Behavior in Elevated Terrain Assumed by 

the ISC3 Model ............................................... 3-47

3-4 Hypothetical Wind Rose ........................................ 3-56

4-1 Overview of Soil Concentration and Exposure Pathway ................. 4-2

6-1 Overview of Terrestrial Food Chain Modeling Compartments and

Processes .................................................... 6-4

7-1 Equilibrium Condition of Water Columns and Bed Sediments ............. 7-6

7-2 Steady-State Representation for Sediments in Water Bodies ............ 7-12

10-1 Risk Asssessment Paradigm ..................................... 10-2

A-1 Comparison of Observed Grass of Dioxin/Furans and Concentrations

Predicted by the Particle and Vapor Phase Impacts Models Described in

Chapter 6. The Perfect Match of Observed and Predicted is Shown in the

Dashed Observed = Predicted Line ............................... A-19

A-2 Comparison of Observed Dioxin/Furans Deposition and Deposition

Predicted by the EPA Model. The Perfect Match of Observed and

Predicted is Shown in the Dashed Observed = Predicted Line .......... A-20

A-3 Overview of Model to Predict Beef Concentrations from Air

Concentrations ............................................... A-31

A-4 Comparison of Measured Particulate Percentages of Dioxins and Furans

with Predictions of the Junge-Pankow Model as a Function of the

Subcooled Liquid Vapor Pressure, PEL, of the Dioxins and Furans ....... A-42

A-5 Comparison of Measured and Predicted Particulate Percentages of

PAHs in Urban and Rural Air .................................... A-46

A-6 Comparison of Measured and Predicted Particulate Percentages of

PCBs and Organochlorine Pesticides in Urban and Rural Air ........... A-47
Front Matter

List of Text Boxes

MPE xvii December 1998

LIST OF TEXT BOXES

Page

4-1 The Next Generation of Dermal-Contact Soil Exposure Models .......... 4-34

9-1 Breast-Feeding: Benefits vs. Risk .................................. 9-2

9-2 Choice of Model: Linear vs. PBPK ................................. 9-15
Front Matter

Abbreviations and Acronyms

MPE xviii December 1998

ABBREVIATIONS AND ACRONYMS

ADT Average Daily Traffic

APCD Air Pollution Control Devices

AQUIER Aquatic Toxicity Information Retrieval

BAF Bioaccumulation Factor

B(a)P Benzo(a)pyrene

BCF Bioconcentration Factor

BEF Bioaccumulation Equivalency Factor

BSAF Biota Sediment Accumulation Factor

BW Body Weight (kg)

CDEP Connecticut Department of Environmental Protection

DBAPE Data Base Analysis and Parameter Estimation

DDT 1,1'-(2,2,2-Trichloroethylidene)bis[4-chlorobenzene]

DI Daily Intake (mg/kg-day)

DOC Dissolved Organic Carbon

DOI United States Department of Interior

DW Dry Weight

EPA United States Environmental Protection Agency

ER Extra risk (unitless)

FCMs Food Chain Multipliers

FDA United States Food and Drug Administration

FGETS Food and Gill Exchange of Toxic Substances
Front Matter

Abbreviations and Acronyms

MPE xix December 1998

ABBREVIATIONS AND ACRONYMS cont.

FW Fresh Weight

GEMS Graphical Exposure Modeling System

GI Gastrointestinal Tract

H Henry’s Law constant (atm-m3/mol)

ha Hectacre

HEM Human Exposure Model

HI Hazard Index

HSDB Hazardous Substances Data Bank

IRIS Integrated Risk Information System

ISC Industrial Source Complex

ISCEV Industrial Source Complex - Short Term EVENT Dispersion Model.

ISCST Industrial Source Complex - Short Term Dispersion Model.

ISCLT Industrial Source Complex - Long Term Dispersion Model.

IUR Inhalation Unit Risk (per [mg/m3])

LOAEL Lowest-Observed-Adverse-Effect Level

LST Local Standard Time

MF Modifying Factor

MSW Municipal Solid Waste

MWC Municipal Waste Combustor

NATSGO National Soil Geographic Data Base

NCEA National Center for Environmental Assessment
Front Matter

Abbreviations and Acronyms

MPE xx December 1998

ABBREVIATIONS AND ACRONYMS cont.

NFCS National Food Consumption Survey

NOAA National Oceanic and Atmospheric Administration

NOAEL No-Observed-Adverse-Effect Level

NRMRL National Risk Management Research Laboratory

NSSAD National Soil Survey Area Data Base

NWS National Weather Service

OPTS Office of Pesticides and Toxic Substances

OSW Office of Solid Waste

PAH Polyaromatic Hydrocarbons

PCB Polychlorinated Biphenyls

PCDD/F Polychlorinated Dibenzo-p-dioxins/Polychlorinated Dibenzofurans

pH - log [H+]

POC Particulate Organic Carbon

RCF Root Concentration Factor

RCRA Resource Conservation Recovery Act

RfC Reference Concentration for Chronic Inhalation Exposure (mg/m3)

RfD Reference Dose for Chronic Oral Exposure (mg/kg-day)

SCS Soil Conservation Service

SEAM Superfund Exposure Assessment Manual

SIRS Soil Interpretations Data Base

SOC Semivolatile Organic Compounds
Front Matter

Abbreviations and Acronyms

MPE xxi December 1998

ABBREVIATIONS AND ACRONYMS cont.

SOILSF GEMS Soil Data Base

SSURGO Soil Survey Geographic Data Base

STATSGO State Soil Geographic Data Base

t1/2 Environmental half-time (days)

T Temperature (K)

TBI Total Background Intake

TCB 3,3'4,4'-Tetrachlorobiphenyl

TCDD 2,3,7,8-Tetrachlorodibenzo-p-dioxin

TCE Trichloroethylene

TDI Total Daily Intake

TEF Toxic Equivalent Factor

TEQ Toxic Equivalent Concentration

TSP Total Suspendable Particulate

TSS Total Suspended Solids

UF Uncertainty Factor

USDA United States Department of Agriculture

USGS United States Geological Survey

USLE Universal Soil Loss Equation

VKT Vehicle Kilometer Traveled

VOC Volatile Organic Compound

WATSTORE Water Data Storage and Retrieval Data Base
Front Matter

EPA Authors and Contributors

MPE xxii December 1998

EPA AUTHORS and CONTRIBUTORS

This methodology was prepared by the National Center for Environmental

Assessment (NCEA). The original document was prepared by Syracuse Research

Corporation under EPA Contract No. 69-C8-0004, and Oak Ridge National

Laboratories’ Office of Risk Analysis, under Interagency Agreement DW 89932633-01. 

Randall Bruins, NCEA-Cincinnati Office (formerly Environmental Criteria and

Assessment Office), served as the EPA Work Assignment Manager, providing overall

direction and coordination of the production effort as well as technical assistance and

guidance. Revisions, updates, and additional preparation were in part provided by

Syracuse Research Corporation (SRC) under Contract Number 68-C3-0369, Work

Assignments 1-16 and 2-4. Primary representatives from SRC include Anthony Gray,

Patricia McGinnis and Marc Odin. Eletha Brady-Roberts served as Work Assignment

Manager and Project Manager for the current updated version.

U.S. EPA’s OFFICE OF RESEARCH AND DEVELOPMENT

AUTHORS

National Center for Environmental Assessment

Eletha Brady-Roberts, Cincinnati Office

David Cleverly, Washington, DC

Matthew Lorber, Washington, DC

Debdas Mukerjee, Cincinnati Office

Glenn Rice, Cincinnati, Office

John Schaum, Washington, DC

Jeffrey Swartout, Cincinnati Office

National Risk Management Research Laboratory

Susan Schock, Cincinnati, OH (formerly - NCEA-CIN)

David Reisman, Cincinnati, OH (formerly - NCEA-CIN)

National Exposure Research Laboratory

Robert Ambrose, Athens, GA
Front Matter

EPA Authors and Contributors

MPE xxiii December 1998

U.S. EPA

CONTRIBUTORS

Femi Adeshina, U.S. EPA, National Center for Environmental Assessment,

Office of Research and Development, Cincinnati, OH

Randal Bruins, U.S. EPA, National Center for Environmental Assessment,

Office of Research and Development, Cincinnati, OH

Terrence Harvey, U.S. EPA, National Center for Environmental Assessment,

Office of Research and Development, Cincinnati, OH

Karen Pollard, U.S. EPA, Office of Solid Waste, Office of Research and

Development, Washington, DC

James Ryan, U.S. EPA, National Risk Management Research Laboratory,

Office of Research and Development, Cincinnati, OH

Carolyn Smallwood, National Center for Environmental Assessment

Office of research and Development, Cincinnati, OH

Glenn Suter, U.S. EPA, National Center for Environmental Assessment,

Office of Research and Development, Cincinnati, OH

Linda Teuschler, U.S. EPA, National Center for Environmental Assessment,

Office of Research and Development, Cincinnati, OH

EXTERNAL

CONTRIBUTORS

Terry Bidleman, Atmospheric Environmental Services, Canada (Chapter 6)

Larry Hansen, University of Illinois (Chapter 9)

Jon Reid, University of Cincinnati (Executive Summary)
Front Matter

Internal Reviewers

MPE xxiv December 1998

INTERNAL REVIEWERS

The following individuals at the U.S. Environmental Protection Agency have

reviewed an earlier draft of this methodology document and provided helpful

comments:

Beth Antley U.S. EPA Region 4, Georgia

Linda Birnbaum National Health and Environmental Effects Research

Laboratory-RTP

John Cicmanec National Risk Management Research Laboratory -Cin

Stanley B. Durkee Office of Science Policy -D.C.

Rob Elias National Center for Environmental Assessment -RTP

Gary L. Foureman National Center for Environmental Assessment -RTP

J.H.B. Garner National Center for Environmental Assessment -RTP

Dale Gillette National Effects Research Laboratory -RTP

Les Grant National Center for Environmental Assessment -RTP

Dave Guinnup Office of Air Quality Planning and Standards -RTP

Jim Kilgroe National Risk Management Research Laboratory -RTP

Roseanne Lorenzano U.S. EPA Region 10, Washington

Daniel J. Mazur U.S. EPA Region 5, Illinois

John Nichols National Health and Environmental Effects Research

Laboratory - MN

Joseph Pinto National Center for Environmental Assessment -RTP

James Ryan National Risk Management Research Laboratory -Cin 

Anne Sergeant National Center for Environmental Assessment -DC
Front Matter

Internal Reviewers

MPE xxv December 1998

INTERNAL REVIEWERS cont.

Donna Schwede National Exposure Research Laboratory -RTP

John Smith U.S. EPA Region 7, Kansas 

Judy Strickland National Center for Environmental Assessment -RTP

David Weeks U.S. EPA Region 6, Texas
Front Matter

External Peer Reviewers

MPE xxvi December 1998

EXTERNAL PEER REVIEWERS

Michael Barcelona, Ph.D.

Research Professor

University of Michigan

Ann Arbor, MI

Joel Brown, Ph.D.

Professor of Biology

University of Illinois at Chicago

Chicago, IL

Donald Cherry, Ph.D.

Professor of Ecotoxicology

Biology Department

Virginia Polytechnic Institute

2006 Derring Hall

Blacksburg, VA

Walter Dabberdt, Ph.D.

Associate Director

National Center for Atmospheric

Research

Boulder, CO

Kenneth Dixon, Ph.D.

The Institute of Environmental and

Human Health

Department of Biological Sciences

Texas Tech University

Reese Park, TX

Alan Eschenroeder, Ph.D.

Principal

Alanova, Inc.

Lincoln, MA

James Gillett, Ph.D.

Professor of Ecotoxicology

Department of Natural Resources

Cornell University

Ithaca, NY

Arthur Gregory, Ph.D.

President and Prinicipal Scientist

Techto Enterprises

One Gregory Lane

Luray, VA

Dennis Pautenbach, Ph.D.

Consulting Toxicologist

McLaren/Hart, Inc.

Irvine, CA

Gary Pierzynski, Ph.D.

Associate Professor of Soil

Chemistry/Fertility

Department of Agronomy

Kansas State University

Manhattan, KS

Richard Richter, Ph.D.

Principal Engineer

ChemRisk

McLaren/Hart, Inc.

16755 Von Karman Avenue

Irvine, CA

Lee Shull, Ph.D.

Toxicology Risk Assessment

Consultant

39622 Lupine Court

Davis, CA
Front Matter

Acknowledgement

MPE xxvii December 1998

ACKNOWLEDGEMENT

The authors of this methodology would like to thank the following individuals for

their support and wisdom: Nancy Bauer, Patricia Daunt, Terri Konoza, Karen Proffitt,

Donna Tucker, Patricia Wilder and Bette Zwayer.
Front Matter

Executive Summary

MPE ES-1 December 1998

EXECUTIVE SUMMARY

INTRODUCTION

The “Methodology for Assessing Health Risks associated with Multiple Pathways

of Exposure to Combustor” (MPE) document was developed by the Office of Research

and Development’s National Center for Environmental Assessment (NCEA) - Cincinnati

and Washington offices with the assistance of National Exposure Research Laboratory

(NERL) -Athens, Georgia.

Purpose

The intent of this methodology is to 1) provide a set of algorithms for assessing

human exposures to atmospheric pollutants emitted from stationary combustors, and 2)

provide information on how to apply the methodology.

Historical Perspective

The inception of multiple pathway assessment occurred in the late 1980's and

was first applied to rule-making in the evaluation of multiple pathways of exposure to air

pollutants emitted from municipal waste incinerators in 1987. At the time, the U.S.

Environmental Protection Agency (EPA) was undertaking risk assessments specific to

exposures to a single chemical through a single pathway. For example, the Office of

Air and Radiation would evaluate risks from inhalation exposures to single airborne

toxicants; Office of Water would evaluate only ingestion of individual contaminants in

drinking water.

The EPA published a document in 1990 entitled, “ Methodology for Assessing

Health Risks Associated with Indirect Exposure to Combustor Emissions: Interim Final”

(IEM), which charted new territory, and was a catalyst for change within the Agency. In

May of 1993, the Administrator released the “Hazardous Waste Minimization and

Combustion Strategy.” Among other recommendations regarding the regulation of this

industrial sector, the strategy urged that the EPA and/or the regulated community
Front Matter

Executive Summary

MPE ES-2 December 1998

conduct indirect exposure assessments to evaluate the impact of stack emissions. To

respond to the Administrator’s request, a workgroup comprising several EPA offices

was convened. In November of 1993, the Addendum to the IEM document was

released by the cross-Agency workgroup. The IEM: Addendum was reviewed by the

EPA’s Science Advisory Board and the public. Subsequently, the peer review

comments have been incorporated into this document. In addition, this MPE document

contains information from the 1990 IEM and the IEM: Addendum as well as, the Dioxin

Reassessment Document (1994), the Mercury Report to Congress (1997) and the

Utility Boiler Report to Congress (1997). Therefore, this document supersedes the IEM

and the IEM: Addendum.

Scope

The MPE methodology represents the state-of-the-science with respect to

providing reliable guidance in the proper conduct of assessments of human health risks

that may result from multimedia and multiple pathways of exposure. It includes

analytical procedures and models for estimating exposures through direct and indirect

pathways and risks resulting from atmospheric pollutants (excluding incinerator ash)

that are emitted from a stationary combustion source. Direct exposures normally result

from inhalation of combustor products. Indirect exposures result when the products of

the combustor are transferred through the atmosphere and deposited downwind to

environmental media (soil and water) and biota. Humans are secondarily exposed to

multiple media (multimedia) by dermal contact, direct ingestion and/or ingestion of

foods grown in the contaminated soil or water.

Intended Audience

This methodology has broad application in the field of environmental science. It

can be used by risk assessors, permit writers, scientists and engineers to estimate

human exposures for various types of waste sites. The audience is anyone who is

conducting a site assessment and needs to calculate pollutant concentration in
Front Matter

Executive Summary

MPE ES-3 December 1998

environmental media. It should be noted that this methodology is descriptive in nature,

and other appropriate documents should be consulted when performing tasks that

require step-by-step guidance. For example, this methodology does not provide default

values for parameters, instead it describes the parameters and provides the algorithms

for calculating media concentrations. A companion document, “Parameter Guidance” 

will provide a range of parameter values and direct the user to the source of

information.

METHODOLOGY

This document is organized into chapters that reflect the components of the MPE

process, as presented in Figure 1. The methodology begins with designing an

exposure scenario and ends with a presentation of risk characterization. Aspects of

this process significantly involve:

! Delineating the details of human exposure (Chapter 2);

! Determining the concentrations of pollutants emitted from the source

utilizing air dispersion modeling (Chapter 3);

! Estimating media concentrations and exposure dose (Chapters 4 through

9);

T Estimating cumulative soil concentration and exposure dose

(Chapter 4)

T Estimating dust resuspension (Chapter 5)

T Evaluating exposure through terrestrial systems (Chapter 6)

T Estimating water concentration and exposure dose (Chapter7)

T Evaluating exposure through aquatic systems (Chapter 8)

T Estimating infant exposure to contaminants in breast milk (Chapter 9)

! Discussion of toxicity assessment and risk characterization as they relate to

MPE (Chapter 10).
Front Matter

Executive Summary

MPE ES-4 December 1998

Source

Human 

Receptor

Atmospheric 

Dispersion 

Modeling

[C hapter 3]

Wet/Dry 

Deposition

-vapor and 

particulate

[Chapter3] 

inhalation

Water Concentration 

Model

[C hapter 7]

Soil Concentration

Model

[Chapter 4]

Terrestrial Food 

Chain

[C hapter 6]

Dust Resuspension

[Chapter 5]

Aquatic Food Chain

[Chapter 8]

Human 

Receptors 

[Chapter 2 & 

10]

Routes

- Dermal

- ingestion

- inhalation

Maternal Breast 

Milk

[Chapter 9]

Direct Exposure 

Pathway

Indirect Exposure Pathways 

inhalation

Figure 1. Conceptual overview of modeling the environmental 

fate of and exposure to pollutants from an emission source.

Chapter 2: Human Exposure

The main purpose of this chapter is to present a brief overview of exposure

assessment as it relates to combustor emissions. This chapter presents the key

elements and general approaches to developing an exposure scenario. In addition, a

brief discussion on the different descriptors of exposure is provided.

Human exposure (contact of the chemical with the outer boundary of a human

receptor) can occur by various pathways including dermal contact with pollutants in soil

or water, inhalation of contaminated air, ingestion of contaminated soil, water, or biota;

or consumption of breast milk contaminated by one or more of the other pathways. 

Development of exposure scenarios involves specification of: the time course of

exposure; the exposure pathways (physical source or medium (e.g., soil), route,

magnitude, duration); and the exposed individuals or populations.
Front Matter

Executive Summary

MPE ES-5 December 1998

Human exposure scenarios are developed using the following steps:

! identify emission source

! define source characteristics

! define the study area

! identify potential exposure pathways (environmental media and the

possible routes of exposure)

! identify factors related to human exposures

! identify study populations, and/or individuals that could be

exposed

Numerous exposure scenarios are therefore possible. The assessor has the

responsibility to determine which reasonable scenario poses the greatest threat to

human health.

In determining the exposure scenarios, land use, meteorological conditions, and 

topographic features are considered. After defining the scope of the assessment ,

other sources of exposure (background), defined as exposures to specific contaminants

not the result of the combustor source in the particular assessment may also be

considered.

The study area is characterized generally at a circular maximum distance of 50

km from the source (when the ISC3 air dispersion model is utilized.) Isopleths of

concentrations, including air concentrations, wet deposition and dry deposition values

within this region) are estimated. Each isopleth area can be examined individually

and/or in conjunction with areas that pose the greatest health risk. If the outer isopleth

is of concern, the study area must be enlarged. Assessment of the latter would require

use of long-range transport models and would add uncertainty in the assessment.

Following delineation of the study area, the pathways of exposure are defined.

They may include food ingestion, soil ingestion, dermal exposure to soil, inhalation of

resuspended, contaminated dust, groundwater exposure pathways (bathing or
Front Matter

Executive Summary

MPE ES-6 December 1998

inhalation of volatilize contaminants, crop irrigation, livestock watering, etc.), surface

water exposure pathways from deposition of emissions and runoff, consumption of fish

and other aquatic organisms, accidental ingestion during recreational activities,

collected precipitation (used as a water source), and maternal milk. The pathways may

be selected based on knowledge of pollutant chemistry or factors associated with the

exposed population.

Exposure factors which vary among exposed individuals and populations include

biological characteristics such as age, and personal preferences (e.g., diet and cultural

practices). Age can be a significant factor, especially when considering pollutants for

which children are highly susceptible. Normal default assumptions include: the life

span of adults, approximate average of 70 years, and adult body weight is 70kg. 

Residence time is a key factor and is best determined by site-specific information rather

than national averages. Other factors include: recreational activities (fishing, hunting,

outdoor exercise, sports), hobbies (home gardening), cultural practices (subsistence

hunting and fishing, rituals), occupational activities (especially if the individual works for

the combustor facility), lifestyle and habits (smoking, alcohol and drug use).

The approach to defining human exposure can be an in-depth analysis or a

screening framework. With the latter, a maximally exposed individual is defined (99th +

percentile involving all pollutants, pathways and exposure routes). If this individual

does not have a significant risk, other analysis may be unnecessary. Also, it may be

possible to eliminate some pollutants or pathways from a more detailed analysis.

Risks can be computed as point estimates (usually central tendencies and high

end estimates (90th percentile)) or risk distributions. They may, for example, be

presented as the potential number of cancer cases in an affected population or for

noncancer effects, the number of persons whose exposure exceeds a specific risk level

or reference dose (RfD). Risks can be defined for the population or specific subgroups.

Chapter 3: Air Dispersion and Deposition Modeling
Front Matter

Executive Summary

MPE ES-7 December 1998

Estimation of health risks associated with exposure to air releases of

contaminants from stationary industrial and combustion sources requires estimates of

the atmospheric concentrations and annual wet and dry deposition rates of the emitted

contaminants in the areas around those facilities. When atmospheric concentrations

and deposition rates cannot be measured in the vicinity of operating sources, the U.S.

EPA customarily uses air dispersion/deposition models to estimate the atmospheric

transport, the surface deposition flux, and the ambient air concentrations of specific

compounds attributable to smokestack emissions from an industrial combustion source. 

Air dispersion models are mathematical constructs that approximate the physical and

chemical processes occurring in the atmosphere that directly influence the dispersion

of gaseous and particulate emissions from the stack of a combustion source. These

models are computer programs encompassing a series of partial differential and

algebraic equations that calculate the dispersion and deposition of the emissions. 

Concentration and deposition estimates of the contaminants discharged from the stack

are computed at specified distances from the source. These quantities are used to

estimate the magnitude of potential exposures to receptors, and to estimate the

potential health risks in populations living and working in the vicinity of the stationary

combustion source.

 The Industrial Source Complex 3 (ISC3) model is recommended, recognizing

that improvements in this type of model are continuous and future recommendations

may vary. Using this model involves: selection of air pollutants that are measured in

stack emissions; estimation of the stack emission rate; estimation of increases in

emissions during malfunctions, startup and shutdown; modeling of the fate of stack

emissions, including wet and dry deposition; estimation of the size distribution of

particulate matter, and estimation of exposure through inhalation of dispersed

pollutants.

The assessor must define the contaminants that may be emitted from the source

combustor. This information can be obtained from feed material, stack emission rate

and operational circumstances and time factors of operation. A time frame of reference
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Executive Summary

MPE ES-8 December 1998

must be determined, such as over the lifetime of the facility. Additionally, start-up,

upset conditions, transitory meteorologic situations (inversions, etc.) need to be

considered although for indirect exposure considerations, these may not be important.

The ISC3 model predicts ground-level concentrations directly proportional to the

emissions rate. For operating facilities, stack emission analytical measurements are

recommended; for planned facilities, stack tests may be used. Before modeling, the

physical geography within 50 km of the combustor location must be systematically

examined to include surrounding terrain elevation and dimensions of structures that

may impact dispersion of the plume.

The Critical Inputs of the ISC3 are as follows: control, source, meteorological

data (hourly stability class, wind direction, wind speed, and receptor.

The Key Outputs from the ISC3 include program control parameters, source

data, receptor data, tables of hourly meteorological data for each specified day,

average concentrations or total deposition calculated at each receptor for any desired

source combination. Model outputs include both pollutant concentration and wet and

dry atmospheric deposition over a given receptor area.

Sources of uncertainty and variability are always present and must be

considered in all analyses and are inherent in transport variables (wind speed, mixed

layer height, surface heat flux, emission characteristics, etc.) Even with a perfect

model (which doesn’t exist) there will be deviations (residuals) from the observed

concentrations due to unknown conditions. Available evidence suggests that this

source of uncertainty alone may be responsible for a typical range of variation in

concentrations as much as 50%. Reducible uncertainty is caused by: uncertainties in

the input values, errors in measured concentrations, and inadequate model physics

and formulation. Regarding the latter, model verification is based on comparison of the

model predictions and actual air concentration measurements. However, model

limitations and database limitations are often confounded.
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Executive Summary

MPE ES-9 December 1998

Limitations related to the ISC3 model are include: 1) model predictions are better

for long-term averages than short-term and 2) beyond 50km the model utility

diminishes.

After concentrations in air are estimated for receptor sites, the human exposure

and dose are determined. Exposure is measured by the intensity and duration of

contact at the external boundary of the organism and can be represented by a timedependent profile of the environmental concentration at the point of contact. The

biologically significant measure is the amount of contaminant that crosses the external

boundary. This occurs by inhalation, ingestion or absorption through the skin.

Chapter 4: Soil Concentration and Exposure Pathways

Contaminants emitted from combustors either deposit onto surfaces in the

vicinity of the facility or travel downwind. Deposition to soils through the processes of 

wet or dry deposition occurs in the particle or the vapor phase. Following deposition,

contaminants may be incorporated into the upper layers of soil where crops or other

vegetation are grown. This chapter focuses on estimating the concentration of a given

contaminant in the soil following deposition from a combustor. The cumulative soil

concentrations (Cs) are used to estimate the risk to humans who may have dermal

contact with contaminated soil, ingest soil or household dust directly, or consume

vegetation or animals that have been exposed to contaminated soil.

The Key Inputs include soil loss constant, bulk density, and wet and dry

deposition rates of the contaminant to soil. When available, an overall environmental

dissipation rate can be used as the soil loss constant. Otherwise, the soil loss constant

can be determined by summing the applicable contaminant losses due to the individual

environmental processes of leaching, degradation, volatilization, soil erosion, and

surface water runoff. Losses due to degradation are chemical-specific and must be

obtained from published field studies; availability of such data is a fundamental

limitation on the application of the soil incorporation model. The other losses can be

modeled from data on the underlying environmental processes if specific loss-rate
Front Matter

Executive Summary

MPE ES-10 December 1998

constants are not available. Such modeling, however, introduces considerable

uncertainty in the overall dissipation rate estimate, as several of the inputs to the

individual loss-rate models are difficult to estimate and model uncertainty may be high.

Another key input parameter that appears in several equations, on which the

entire soil incorporation model is dependent, is the soil-water partition coefficient (Kds). 

Kds reflects the equilibrium sorption/desorption of chemicals to soil particles and is

chemical specific. Some values for Kds can be found in the literature but are central

tendency estimates. Variations in soil properties greatly affect Kds, which for inorganic

chemicals can vary up to 3 orders of magnitude depending on soil pH. Kds for

organics may be estimated as a function of organic carbon in soil; model and

parameter uncertainty is high in such cases. Therefore, it is advisable to use

site-specific data for Kds when available.

Key assumptions for soil concentration calculations are:

! Trace metal contaminants are assumed to be conserved indefinitely in the

upper layer of soil unless loss constants are available;

! Degradation of organic contaminants is first-order; and

! If soil incorporation of contaminant is assumed, incorporation depth is 20

cm.

Contaminants on soil can be ingested incidentally by hand-to-mouth transfer or

intentionally by eating soil. Human exposure to contaminants from soil ingestion is a

simple function of soil-contaminant concentrations and estimated ingestion rates. A

Key Assumption is as follows:

! Deposited contaminants are assumed to be distributed within the

uppermost 1 cm of soil (which is assumed to be the ingested layer). The 

Incorporation depth is likely to be somewhat variable depending on local

conditions; field studies are needed to define the variability in this

parameter.

A primary limitation to this model is the general lack of data on the bioavailability

of contaminants in soil, which is presumed to be much lower than bioavailability in food. 
Front Matter

Executive Summary

MPE ES-11 December 1998

The daily intake for this pathway is compared with the RfD (reference dose for chronic

oral exposure, mg/kg-day) for systemic toxicants to determine if the contaminant

adversely affects human health. If the chemical is a carcinogen, the daily intake is

used with the human cancer potency to determine excess risk.

A model is presented that estimates human exposure from skin contact with

contaminants in the soil using a simple linear absorption fraction approach. The model

relates absorbed dose to contaminant concentration in soil and depends on soil

adherence properties, the frequency and duration of soil contact, the area of exposed

skin, and the chemical-specific absorption fraction. The limited bioavailability of soil

contaminants from combustor emissions, however, may significantly reduce the overall

importance of this exposure pathway. In addition, the general lack of adequate data for

estimation of at least one key parameter further limit the applicability of this model. 

Nevertheless, there is still the potential for human exposure from this pathway. The

key parameters (inputs) of the model are absorption fraction, adherence factor, and

exposure frequency. Variability in all of these key inputs can be an order of magnitude

or more.

The absorption fraction (ABS) is particularly difficult to estimate. ABS is a

chemical-specific quantity that depends on the physicochemical properties of the

contaminant, but may also be affected by soil characteristics, skin characteristics,

physical environmental factors, and extent of exposure. Most of these factors are not

incorporated into the model, but may have significant impact on exposure. Predictive

procedures for ABS are generally very limited, with most providing upper bound, rather

than central tendency, estimates for ABS. The more robust approach based on

permeability coefficients has not been tested adequately.

Chapter 5: Dust Resuspension 

Contaminated particles deposited on soil may become aggregated with soil

particles, which can be resuspended into the air by wind erosion and mechanical

disturbances such as tilling or vehicular traffic. The risk assessor must identify
Front Matter

Executive Summary

MPE ES-12 December 1998

potential sources of dust generation within the study area and assess the potential for

exposure. The contaminant resuspension flux can be calculated for agricultural soils

and unpaved roads by multiplying the dust emission rate and the concentration of

contaminant in the soil. Therefore the Key Inputs are dust emission rate,

concentration of contaminant in the soil (Cs) and the enrichment ratio.

Key assumptions are as follows:

! Resuspended dust can add to exposure by the direct inhalation and by

indirect routes;

! Transport is important in determining whether the resuspension changes

the impact of the dust.

Factors which influence resuspension are: moisture content, presence of

nonerodible surfaces, wind velocity, particle size, surface crust stability, and soil

roughness height. Aspects of soil movement by wind are suspension (small particles

that are kept airborne by wind forces), saltation (particles bounced over surfaces by

wind, surface creep, and resuspension (re-entry of previously deposited contaminants

back to the atmosphere). Wind erosion of open, plowed fields is considered to be a

potentially significant source. Factors affecting this are soil erodibility, surface

roughness, climate, unsheltered field width, and vegetative cover.

Chapter 6: Terrestrial Food Chain

The terrestrial food chain includes plants that animals consume, plants that

humans consume, and animals that humans consume. Plants take up contaminants

directly from the soil or from the atmosphere. Animals are exposed to contaminants by

ingestion of plants and soils. The algorithms for estimating terrestrial food chain

impacts are relatively simple screening-level models.
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Key Input parameters in these fate algorithms fall into three categories. The

first category includes media concentrations, specifically the soil and air concentrations

and the atmospheric deposition fluxes presented in Chapters 3 and 4. A second

category of parameters includes those that are specific to the contaminant. Included

here, for example, are the bioconcentration/biotransfer factors used to translate an

animal diet of the contaminant into an animal tissue concentration. The final category

of parameters are the general physical parameters not related to the contaminants,

such as plant yields and animal diet descriptors.

The analysis procedures for plants and animals are similar. First, the fate

pathways are analyzed (transfer of contaminants from air to plant, or the

bioaccumulation of ingested contaminants in animal tissue). Following this, human

daily intake is determined, given the food concentrations.

The Key Assumptions are:

! The source strength remains constant throughout the period of exposure,

! Plant concentrations are modeled as a function of the deposition of

contaminants that have been emitted from the combustion source and

traversed to where the plants are growing (also considered is a component

due to root uptake and translocation),

! Exposure of terrestrial animals to bioaccumulating contaminants is primarily

through ingestion of food and soil.

Human daily contaminant intake from consumption of contaminated plants is

calculated by multiplying the concentration of contaminant in each plant group by the

amount of contaminated plant group consumed daily. Similarly, daily contaminant

intake from the consumption of meat, dairy products, or eggs is determined from the

concentration of contaminant in the animal tissue and the amount of each contaminated

animal tissue that is consumed daily.

Chapter 7: Water Concentration and Exposure Pathways
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To determine exposures to combustor-emitted contaminants in water, a model is

presented for estimating surface water concentrations resulting from contaminant

deposition. To proceed from concentrations to exposures, water consumption rates are

presented, and then a model for estimating dermal absorption is given. Modeling the

concentrations in surface waters requires determining the flows of water and sediments

into a water body (such as a lake or river near the combustor), within the water body

(especially between the water column and the bottom sediments) and out of the water

body. These are normally determined only on a long-term basis, but for some

contaminants it may be important to determine these flows during storm events, and a

procedure for doing this is also presented. Superimposed on these flows are the

partitioning characteristics of the contaminant, which determine how it will be

distributed between the respective soil, water, sediment and air compartments of the

watershed and water body, and environmental dissipation characteristics.

Besides the contaminant deposition rates and air and soil concentrations, which

are calculated earlier, the Key Inputs to this model fall into the categories of

watershed, hydrologic, partitioning and degradation. The key watershed and

hydrologic parameters are those used to estimate sediment runoff yields in the

Universal Soil Loss Equation (USLE) and its storm-event companion, the Modified

USLE. Problems may be encountered in determining these yields over large watershed

areas or for large water bodies; the complex hydrodynamics of estuaries makes them

particularly difficult to model. The needed partitioning and degradation parameters are

chemical-specific and sometimes are unavailable or entail large uncertainties.

Deposited contaminants may directly enter precipitation collection systems, but

attention must be given to the synchrony of stack emissions and precipitation events,

and to the behavior of contaminants within the collection system itself. Contamination

of potable ground water is ruled out unless unusual conditions of contaminant emission

or aquifer vulnerability exist.

Dermal absorption of contaminants, such as during bathing, depends on

knowing a contaminant’s permeability coefficient and other parameters related to the
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dynamics of uptake. For fat-soluble (lipophilic) contaminants, uptake through the skin

into the body occurs slowly and in stages, and may not reach steady state during brief

exposure episodes. Modeling procedures for this nonsteady-state process are recently

developed and are still under review within the scientific community.

Key assumptions are as follows:

! Soil concentrations within a depositional area are assumed to be uniform;

! Distribution of contaminants between the soil and water can be described

by partition coefficients;

! Routes of entry are direct deposition, dissolution in annual surface runoff,

soil erosion;

! For the steady-state surface water solution algorithm the assumption of

equilibrium is maintained between contaminants within the water column

and contaminants in bed sediments;

! The rate of contaminant burial in bed sediments is estimated as a function

of the rate at which sediments deposit from the water column onto the

surficial sediment layer;

! Separate water column and benthic decay rate constants allow for

consideration of decay mechanisms that remove contaminants from the

water body.

Chapter 8: Determining Exposure from Intake of Aquatic Organisms

The goal of chapter 8 is to present mathematical models that estimate human

exposure to chemicals that occur in the tissues of aquatic organisms. Aquatic tissue

concentrations of these chemicals are estimated from concentrations in either the water

column or the sediment. In the pathway model described in this MPE, concentrations of

chemicals in these two abiotic media are the result of the atmospheric and terrestrial

transport of combustor emissions (Models described in Chapters 3 and 6, respectively).

Chemicals that are known or suspected to bioaccumulate in aquatic food chains should

be evaluated with these models. These may include organic compounds that could
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MPE ES-16 December 1998

accumulate in adipose tissues or metallic compounds that bioaccumulate in other fish

tissues.

The key inputs to this model are chemical-specific medium concentration (in the

water column or aquatic sediments), a bioaccumulation factor, and a fish consumption

rate divided by human body weight.

A key assumption is this model assumes a steady-state concentration of

pollutant in the abiotic medium.

The models presented evaluate bioaccumulation of chemicals through the

aquatic food chain; several sources of such data as well as several alternative models

which estimate the bioaccumulation are presented. The bioaccumulation factor (BAF) is

defined as the equilibrium concentration of a chemical in a biological medium (as a

result of all environmental sources of exposure ) divided by the equilibrium

concentration of a chemical in an environmental medium. Many environmental factors

may affect bioaccumulation in an aquatic system. The uncertainty in this model

parameter (BAF) is the typically the greatest source of uncertainty when the model is

applied. The variability in individual human fish consumption rates also contributes to

uncertainty in the modeled exposure estimate, although less substantially than the

uncertainty in the bioaccumulation factor. Daily human fish consumption rates are

known to range from 0 to well over 100 grams per day; it is important to know or

estimate the distribution of local fish consumption rates in the study population. Body

weight is also known to vary in the population; the variability in this factor generally

affects model results less substantially than either bioaccumulation of consumption

rate. Several large fish consumption rate surveys express the quantity of fish

consumed on a body weight basis. These two parameters may be related, correlated,

survey of this type account for such correlations. Other factors such as dilution of

pollutants through growth in children are not modeled in this chapter.

Chapter 9: Infant Exposure to Contaminants in Breast Milk 
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Residues of organohalogen, organometallic and inorganic contaminants, many

of which are found in emissions from combustion sources, have been detected in

human breast milk. The contaminant concentrations in the milk, which in turn can be

estimated on the basis of the contaminant intake by the mother, is used to estimate the

average daily dose to the infant ingesting breast milk. Linear pharmacokinetic models

are used to predict levels of contaminants in both the fat and aqueous phases of breast

milk from maternal intake. Both phases are considered in order to estimate exposures

from lipophilic compounds (dioxins, dibenzofurans, PCBs, etc.) and nonlipophilic

compounds (metals, organo-metallics, etc.) alike.

The infant exposure model predicts contaminant concentrations in breast milk

based on values for absorption, distribution to fat or within blood compartments,

biological half lives, and plasma to milk partition coefficients. The Key Inputs to the

breast milk model are the biological elimination constants for lactating (kelac) or

nonlactating (kelim) women, and the red blood cell to plasma partition coefficient (PcRBC)

for aqueous-phase compounds. There are no default values for the elimination

constants, so existing data must be sufficient for estimation of these parameters in

order to use the breast milk model. Also, lacking data for lactating women,

breast-feeding losses must be modeled directly and combined with kelim for nonlactating

women. This approach may potentially overestimate kelac, as the kinetic component for

breast-feeding losses is much larger than kelim itself. PcRBC is a key parameter because

it can dramatically influence the concentration of contaminant in the plasma, which is

presumed to be the sole mode of transfer of contaminant to the aqueous phase of

breast milk. Although the default for PcRBC is set at 1.0, values higher than 20 have

been reported for a least one compound (MeHg).

The breast milk model is structured to allow for consideration of both steady

state and nonequilibrium conditions, the latter being of particular importance for

shorter-term maternal exposure to highly lipophilic compounds. The model also allows

for consideration of breast-feeding dynamics, in which the contaminant concentration in

breast milk declines over the duration of breast feeding.
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The Key Assumptions related to the breast milk model are as follows:

! The incorporation of time dynamics in the model is designed to the avoid

over prediction of infant exposure for some exposure scenarios if steady

state were assumed;

! In order for the dynamic model to be accurate. First, linear (first-order)

kinetics must be a reasonable approximation of the true kinetic behavior of

these compounds. The simple linear models are unlikely to be accurate

across a wide range of chemical properties and physiological processes,

but the accuracy of this assumption remains to be tested.

! Maternal fat content and metabolism for both body fat and milk fat must

remain in equilibrium; this assumption is very likely to be inaccurate, but a

demonstrable bias in any given direction has not been established.

Available data are insufficient for adequate assessment of model performance. 

If the dioxin data are consistent with all models, then they are just another example of

insufficient data, as described in the last bullet.

The incorporation of time dynamics is necessary in order to overcome a

demonstrable (mathematical) bias towards over prediction of infant exposure, but errors

in estimates of breast-feeding loss rate could over compensate for that bias resulting in

under prediction of infant exposure. The utility of the infant exposure model is limited

by the general lack of dose-response information for nursing infants. The model can

still be used, however, to estimate contribution to lifetime cancer risk and for comparing

exposure with the RfD for screening purposes. Potential risk to the infant from

exposure to contaminants in breast milk must be balanced against the well-documented

benefits of breast feeding.

Chapter 10: Risk Assessment

Chapter 10 provides an overview of human health risk assessment as practiced

at the U.S. EPA and discusses specific risk characterization issues that may arise while

conducting an assessment of risks posed by combustor emissions. This chapter shows

how to use information from the previous 9 chapters that discuss various aspects of
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Executive Summary

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exposure assessment in the larger context of human health risk assessment. Human

health risk assessment has been defined as a four-step process, viz., hazard

identification, dose-response assessment, exposure assessment, and risk

characterization.

Hazard Identification The problem and the potential toxic effects are defined

and the data needs are determined.

Exposure The exposure scenarios modeled may include exposure by multiple

pathways and routes. Models designed to estimate chemical concentrations in

environmental and biological media and, ultimately, human exposure are presented. In

the MPE, daily pollutant intake has been calculated independently for the following

routes and pathways: oral via produce, livestock, fish, breast milk, soil, and water;

dermal via soil and water; and inhalation. A given individual or population may

experience exposure by one or more of these pathways or routes.

To calculate total exposure to an individual, exposure estimates must be

combined across pathways and routes. Ideally, internal measures of exposure to

chemicals in question will be available. In the absence of measuring exposures or in

cases where the facility has not yet been constructed, chemical-specific

pharmacokinetic models should be used to estimate the internal doses that may result

from exposures through different routes. In the absence of data to the contrary and if

the risk assessor judges it to be appropriate, this methodology describes models

capable of summing chemical exposures across both pathways and routes. The total

daily intake (TDI) of a chemical attributable to indirect exposure to emissions from a

combustor for a particular exposure scenario is estimated by summing the oral

equivalent daily doses.

The MPE describes sources of exposure to chemicals other than from

incinerator emissions. Background levels are defined as the concentration of a

substance in an environmental media that (1) occurs naturally or (2) the concentration

of a substance in a defined area from the period of time before the introduction of a

specific anthropogenic source. Models are presented that describe how an assessor
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could include background levels into a human health risk assessment. Models are

presented that illustrate other approaches to assessing issues such as exposure

duration.

Dose-Response The dose-response assessment defines the relationship

between the exposure to a chemical and the associated health effects. The most

common approaches used by the Agency, the reference dose (RfD-oral, dermal

exposure) and reference concentration (RfC-inhalation) for noncarcinogens and the

slope factor and unit risk for carcinogens, are presented.

Risk Characterization The risk characterization step provides both a quantitative

and a qualitative evaluation and integration of the three previous steps; this evaluation

and integration is translated into an overall conclusion about the likelihood of one or

more adverse health effects occurring due to chemical exposure.

A component of the risk characterization is an assessment of uncertainty in the

risk assessment. The uncertainty may arise from a lack of knowledge (i.e., uncertainty)

or actual variability of a particular parameter. Sources of uncertainty include

uncertainties related to the model predictions (physical construction of the model and

uncertainties in inputs). Variability may include factors such as specific consumption

rates. Approaches for evaluating uncertainty in the assessment, such as Monte Carlo

analysis, are presented.

FUTURE RESEARCH NEEDS

Further development in the area is expected as new information becomes

available and is interpreted and integrated. A few topic areas related to MPE

development which were identified by external peer reviewers are as follows:

! Evaluation of “background” conditions and contribution to risk.

! Evaluation of degradation/transformation of chemicals (e.g., mercury,

chromium, arsenic, dioxins, PCBs). 

! Evaluation of multi-pollutant stressors.
Chapter 1

Introduction

MPE 1-1 December 1998

! Evaluation of cumulative risk.

! Evaluation of fetal and infant exposure; evaluation of child sensitivity.

INTRODUCTION

1.1. BACKGROUND

In 1990, the National Center for Environmental Assessment (formerly known as

the Office of Health and Environmental Assessment) released Methodology for

Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions

(IEM) (U.S. EPA, 1990). The document provided risk assessors with a methodology to

assess health risks that result from exposures to toxic pollutants in combustor

emissions by pathways other than inhalation. Procedures for assessing human health

risks through inhalation of combustor emissions were well established; the 1990 IEM

provided procedures for estimating the indirect human exposures and health risks that

can result from the transfer of emitted pollutants to soil, vegetation, and water bodies.

In May of 1993, the Administrator of the Environmental Protection Agency (EPA)

released the “Hazardous Waste Minimization and Combustion Strategy.” Among other

recommendations regarding the regulation of this industrial sector, the strategy urged

that EPA and/or the regulated community conduct indirect exposure assessments to

evaluate the impact of stack emissions. Although this strategy was specific to

hazardous waste incinerators, the importance of all exposure pathways, including

indirect pathways, is logically extended to all combustors. The available

methodologies were the 1990 IEM and the 1992 “Estimating Exposure to Dioxin-Like

Compounds.” To develop a response to the Administrator’s strategy, a workgroup

comprising several EPA offices was convened. The result was an “Addendum”

document to the 1990 IEM, released in November 1993. The EPA’s Science Advisory

Board (SAB) and the public reviewed the Addendum. The SAB and public comments

have been incorporated into this document. Therefore, this document supersedes the

IEM and the Addendum (U.S. EPA, 1990, 1993). 
Chapter 1

Introduction

MPE 1-2 December 1998

1.2. PURPOSE

The intent of this methodology is to provide a set of algorithms for assessing

human exposures to toxic atmospheric pollutants emitted from stationary combustors. 

The document describes procedures and models that can be used to estimate

exposure and risk using a variety of environmental pathways. In addition, it serves as a

preliminary source of data for carrying out the risk estimations. The degree of

scientific support, or uncertainty attendant to each calculation, varies widely. 

Therefore, the appropriate use of these procedures and the discussion of uncertainties

surrounding the results remain an important responsibility of the risk assessor, leading

to effective risk characterizations.

Although the focal point of this effort is exposure assessment, Chapter 10 

describes the other three steps of risk assessment (hazard identification, doseresponse assessment, and risk characterization) initially described by the National

Research Council (NRC, 1983). Several EPA documents are available that provide

details on other components of the risk assessment paradigm. In this document,

however, exposure assessment is presented before the other steps, as we believe the

four-step process is not necessarily sequential (Harvey et al., 1995). Exposure

assessment encompasses a determination of the pathways leading to exposure, the

contact media, estimates or measures of contaminant concentration in these media,

and the routes by which exposure occurs, including the time, frequency, and duration. 

The U.S. EPA (1992) describes exposure as “the condition of a chemical contacting

the outer boundary of a human”. For humans and other animals, the outer boundary

includes openings into the body such as the mouth, the nostrils, and the skin, including

skin damage by punctures and lesions. Most of the time, the chemical occurs in air,

water, soil, or biota. The exposure concentration is the chemical concentration at the

point of contact.

1.3. STATIONARY COMBUSTORS

Since this document deals primarily with the accumulation of pollutants in the

environment, this methodology is limited to stationary combustors. Stationary
Chapter 1

Introduction

MPE 1-3 December 1998

combustors are combustors built on a site and expected to operate for several years. 

This methodology does not address exposure and health effects from incinerator ash. 

A document from the National Center for Environmental Assessment (U.S. EPA, 1991)

entitled Methodology for Assessing Environmental Releases and Exposure to Municipal

Solid Waste Combustor Residuals considers incinerator ash exposure.

The most common types of stationary combustors are those used to treat

municipal solid waste, hazardous waste, medical waste, and sewage sludge. Other

anthropogenic sources of atmospheric emissions identified in the Mercury Report to

Congress are utility boilers, commercial/industrial boilers, residential boilers and wood

combustors. In addition, the draft Dioxin Reassessment Report (U.S. EPA, 1994)

describes various types of combustors and other high temperature sources, including

coal combustion and metal smelting/refining.

1.4. SCOPE

The scope of this methodology has been expanded to include a multiple

pathway exposure assessment that includes both direct and indirect routes of exposure

to combustor emissions. Since this methodology is primarily focused on combustors,

the direct route of exposure is inhalation. On the other hand, indirect exposures results

from atmospheric pollutants which are transferred and deposited onto environmental

media.

Pollutants from stack emissions can be incorporated into soil or water and, from

there, can accumulate in the terrestrial or aquatic food chains. Humans are exposed to

these pollutants in various media (multimedia) by dermal contact, direct ingestion,

and/or ingestion of foods grown in the contaminated soil or water.

This methodology guides risk assessors through the four steps of risk

assessment to characterize the exposure and risk posed by pollutants in various

pathways. The document starts by presenting guidelines on human exposure

scenarios (Chapter 2). After discussing air dispersion models appropriate for analyzing

combustor emissions (Chapter 3), the document describes assessment of exposure

through terrestrial and aquatic systems.
Chapter 1

Introduction

MPE 1-4 December 1998

For terrestrial systems, Chapter 4 presents methods for calculating pollutant

concentration in soil, ingestion of soil, and dermal absorption from soil. Exposure

through inhalation of resuspended dust is evaluated (Chapter 5). A method for

calculating daily intakes through ingestion of food can be found in Chapter 6.

For aquatic systems, Chapter 7 presents methods for calculating pollutant

concentration in surface water, rainwater, and groundwater. A method for calculating

daily intakes through ingestion of water and dermal absorption from water is also

provided. A method for calculating daily intake through ingestion of fish is presented in

Chapter 8.

Methods for calculating daily intake by infants through ingestion of breast milk

are discussed in Chapter 9. Chapter 10 discusses toxicity assessment and risk

characterization. A validation exercise is shown in Appendix A. Appendix B contains

an equation for estimating breast feeding losses. A list of definitions is provided in

Appendix C.

The U.S. EPA recognizes that there are limitations inherent in the methods

presented. For example, the methodology can be applied only to the finite number of

chemicals that have actually been identified in combustor emissions. There are

potentially hundreds of pollutants that may be emitted but have not been measured or

identified.

This document uses emission rates that were, for the most part, measured

during good operations of the incinerator. Methods for assessing emissions resulting

from perturbations, mechanical failures, and shutdown of the plant are presented in

Chapter 3.

There are other data limitations that ultimately constrain the methodology. This

document frequently relies on default input values based on current national monitoring

data. Readers should consider that many of the factors affecting exposure vary

according to region, climate, or local habits. Risk assessors should use local data to

conduct a community or site-specific assessment whenever possible to reduce the

uncertainty associated with applying national data to a local community.
Chapter 1

Introduction

MPE 1-5 December 1998

The methodology, therefore, is not a comprehensive environmental tool, but is

best regarded as an evolving and emerging process. However, these methods move

the U.S. EPA beyond the analysis of potential effects on only one medium (air) and

exposure pathway (inhalation) to the consideration of other media and exposure

pathways.

1.5. USE

This methodology is not intended to be prescriptive; that is, it provides a set of

procedures that the risk assessor can draw upon, where applicable, for an assessment.

It does not comprise a set of guidelines or recommended approaches that the U.S. EPA

believes should be applied in all circumstances. For example, the methodology does

not give a set default value for parameters; instead a range of default values will be 

provided in the

companion “Parameter

Guidance” document. 

Because this

methodology is

descriptive, it is

suggested that the user

consult other EPA

documents for additional

information and/or

prescriptive guidance. 

Guidance information

related to combustion

risk assessment can be

obtain in the “Human

Health Risk Assessment

Protocol for Hazardous

Waste Combustion

EPA documents related to MPE

! Mercury Study Report to Congress 

 EPA/452/R-97-0003

! Estimating Exposure to Dioxin-Like Compounds

 EPA/600/6-88/005 (External Review Draft)

! Human Health Risk Assessment Protocol for Hazardous

Waste Combustion Facilities 

EPA/530-D-98-001A (External Review Draft)

! Air Quality Criteria for Particulate Matter 

EPA/600/P-95/001

! Exposure Factors Handbook

EPA/600/P-95/002

! Dermal Exposure Assessment: Principles and Application :

EPA/600/8-91/001

! Region 6 Risk Management Addendum - Human Health Risk

Assessment Protocol for Hazardous Waste Combustion

Facilities. 

EPA-R6-98-002

! Guidance on Collection of Emissions Data to Support

Site-Specific Risk Assessments at Hazardous Waste

Combustion Facilities (Review Draft)

EPA/530-D-98-002
Chapter 1

Introduction

MPE 1-6 December 1998

Facilities” document and its addendum document. An accompanying document which

focuses on ecological assessment is under development.

Although this methodology focuses on providing appropriate site-specific

procedures for conducting exposure assessments of emissions from combustion

sources, it can be used to develop assessment on issues other than combustors. The

material also includes some information on individual parameter assumptions and other

specific factors to consider. However, several of the assumptions needed to complete

an exposure assessment may vary, depending on the specific application or program

involved. Therefore, decisions are still required in applying this methodology to

different situations.

This document provides some generalized information on limiting the scope of

an assessment (Chapter 2). It is very important that the exposure assessment include

a detailed description of all of the uncertainties involved and how they may affect the

final results. 
Chapter 1

Introduction

MPE 1-7 December 1998

REFERENCES

Harvey T., K.R. Mahaffey, S. Velazquez, M. Dourson. 1995. Holistic risk assessment:

An emerging process for environmental decisions. Reg. Toxicol. Pharmacol.

22:110-117.

NRC (National Research Council). 1983. Risk Assessment in the Federal

Government: Managing the Process. NAS Press, Washington, DC. 

U.S. EPA. 1990. Methodology for Assessing Health Risks Associated with Indirect

Exposure to Combustor Emissions. Office of Health and Environmental Assessment,

Washington, DC. EPA/600/6-90/003.

U.S. EPA. 1991. Methodology for Assessing Environmental Releases and Exposure to

Municipal Solid Waste Combustor Residuals. Office of Health and Environmental

Assessment, Washington, DC. EPA/600/8-91/031.

U.S. EPA. 1992. Federal Register Notice. Guidelines for Exposure Assessment. May

29, 1992. 57 FR 22888. EPA/600-2-92-001.

U.S. EPA. 1993. Addendum to Methodology for Assessing Health Risks Associated

with Indirect Exposure to Combustor Emissions: External review draft. EPA/600/AP93/003.

U.S. EPA. 1994. Estimating Exposure to Dioxin-Like Compounds. Review Draft. 

Office of Research and Development, Washington, DC. EPA/600/6-88/005C.
Chapter 2

Human Exposure Assessment

MPE 2-1 December 1998

 HUMAN EXPOSURE ASSESSMENT

2.1. INTRODUCTION

Pollutants emitted to the atmosphere from stationary combustion facilities are

transported through the atmosphere and may subsequently impact environmental

media (i.e., soil, water, and vegetation) near the combustor. Human exposure to the

emitted pollutants may occur by various pathways such as dermal contact with the

pollutants in soil or water; inhalation of contaminated air; ingestion of contaminated

soil, water, or biota; or consumption of breast milk contaminated via one or more of the

previously mentioned pathways. 

The main purpose of this chapter is to present a brief overview of exposure

assessment as it relates to combustor emissions. This chapter presents the key

elements and general approaches to develop an exposure scenario. In addition, a brief

discussion on the different descriptors of exposure is also provided.

2.2. OVERVIEW OF EXPOSURE ASSESSMENT

Exposure is defined as the condition of a chemical contacting the outer

boundary of a human (U.S. EPA, 1992a). Exposure assessment involves

characterizing the magnitude, duration, and frequency of human contact with the

contaminant of concern. The exposure assessment process consists of identifying

potential exposure pathways, estimating chemical intakes, and identifying exposed

individuals or populations. This chapter deals with all three components of the

exposure assessment process. An exposure pathway usually includes the physical

source or medium (e.g., soil) of the pollutant, the route of exposure (e.g., ingestion),

and the receptor (e.g., humans). For example, a typical exposure pathway statement

might be: ingestion of contaminated soil by humans. Exposure scenarios are real

(provable) or hypothetical situations that define the source (e.g., hazardous waste

combustor), characteristics of the human receptor (e.g., age, gender), pathway(s) of

exposure, exposure duration, frequency, and exposure dose. The significant difference

between an exposure pathway and exposure scenario is the inclusion of time, source
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Human Exposure Assessment

MPE 2-2 December 1998

information, and exposure dose. U.S. EPA (1988) states, “In exposure scenario

evaluation, the assessor attempts to determine the concentration of chemicals in a

medium or location and link this information with the time that individuals of populations

contact the chemical.” 

 Figure 2-1 presents a schematic of possible emission sources, contact media,

and routes of exposures which lead to the exposure pathways being defined or the

development of exposure scenarios.

2.3. CREATING EXPOSURE SCENARIOS. Successful application of this method

depends on the judgment of the risk assessor. The exposed individual(s) must be

defined in terms of age, location relative to the combustion source, and length of time

spent at that location. Although the age of the individual will affect exposure routes and

pathways, defining age in general terms such as child or adult generally suffices for

exposure assessment. In most cases, location will refer to the residence of the

individual. The length of time (i.e., number of years) that the individual spends at the

defined location influences potential pathways of both exposure and duration levels. 

Choices of appropriate exposure scenarios and study area are essential elements of a

multiple pathway exposure assessment. Human exposure scenarios are developed

from the following: 

! identify emission source (section 2.3.1.)

! define source characteristics (section 2.3.2.)

! define the study area (section 2.3.3.)

! identify potential exposure pathways (environmental media and the

possible routes of exposure) (section 2.3.4.)

! identify factors related to human exposures (section 2.3.5.)

! identify study populations, and/or individuals that could be

exposed (section 2.4.1.)
Chapter 2

Human Exposure Assessment

MPE 2-4 December 1998

FIGURE 2-1. Conceptual Overview of Modeling the Environmental Fate of and Exposure to Pollutants from 

an Emission Source.

Source

Atmospheric 

Dispersion 

Modeling

[Chapter 3]

Wet/Dry 

Deposition

-vapor and 

particulate

[Chapter3] 

Water 

Concentration 

Model

[Chapter 7]

Soil Concentration

Model

[Chapter 4]

Dust Resuspension

[Chapter 5]

Human Receptors 

[Chapter 2 & 10]

Routes

-Dermal

-ingestion

-inhalation

Maternal Breast 

Milk

Direct Exposure Pathway [Chapter 9] Indirect Exposure Pathways 

Aquatic Food Chain

[Chapter 8]

Terrestrial Food 

Chain

[Chapter 6]

Inhalation
Chapter 2

Human Exposure Assessment

MPE 2-5 December 1998

! determine exposure point and media concentrations (chapters

4,5,6,7,8, and 9)

! estimate exposure dose (chapter 10)

 The assessor has the responsibility to determine which exposure scenarios

pose the greatest threat to human health.

The environmental media likely to be impacted by combustor emissions should

be defined because the area surrounding the source is used to evaluate exposure

pathways. The land use, meteorological conditions, and topographic features in the

area of concern can help identify the possible pathways of exposure (see discussion

above). Sources of these data may include USGS maps and county land-use maps.

2.3.1. Identify Emission Source. This methodology is specifically written for

combustor emissions which are discussed in Chapter 3. If other waste management

options such as waste piles, landfills, recycling or composting are identified as the

emission source, more suitable source and transport models should be selected. 

Regardless of the emission source, the same fundamental approach would be used to

develop an exposure scenario.

2.3.1.1. Identification Other Sources of Exposures — Other sources of

exposures to humans exist that may not be definitively associated with combustor or

other waste management options. This would be the case if the chemical existed

naturally in the local environment, or if the chemical were ubiquitous in the environment

although it may not be natural to the environment. In this document, the term

“background exposure” will be used to describe the exposures to specific chemicals

that were not the result of combustor source emissions in a site-specific assessment. A

more detailed description of approaches to estimate background exposures is provided

in Chapter 10.

2.3.2. Defining Source Characteristics. For combustor exposure assessment, the

first step in the analysis is the identification and selection of contaminants that may be

emitted to the air during the combustion process. Several factors influence what
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chemicals are being released to the environment and their emission rates. Chapter 3

provides a description of important characteristics related to combustor facilities. 

These characteristics include feed material being combusted, stack emission rate of

toxic air pollutants, and operational specifications. For multiple pathway exposure

(specifically, indirect exposure assessment to combustor emissions), the length of time

of operation is an important issue.

2.3.2.1. Defining the Length of Time of Emissions — Estimates of annual

average air concentration and average deposition rate over defined areas is a

requirement. These concentrations may depend on a particular time frame of interest. 

For example, one may want to consider the impact of emissions over the lifetime of a

facility. Some facilities are designed to last 30 years or more. Facilities may be

replaced or upgraded periodically extending the period of emissions even longer.

This methodology primarily addresses long-term exposure to contaminants in

environmental media resulting from long-term combustor emissions. However, upset

conditions and intermittent emissions (e.g., combustor startup or shutdown conditions)

are considered in Chapter 3. Such occasional short-term releases are generally not an

issue for the indirect exposure analyses component of multiple pathway exposures

because long-term buildup in the food chain is the principal concern for indirect

exposure. The accumulation of short-term releases can be averaged and added to the

continuous emissions if data are available to do so. It is suggested that studies using

acute exposures be used in the assessment of potential acute effects as part of the

direct exposure assessment. The highest inhalation exposures are associated with

periods of highest air concentrations. Other meteorologic conditions (i.e., inversions)

cause stable air and may minimize dispersion and lead to unusually high air

concentrations, as can increases in emission rates. As discussed in Chapter 3, the air

model can estimate the highest daily air concentration expected to occur during a year. 

The model output is normalized by emission rate. Combining the highest expected

emission rate with daily high model output can estimate the highest short-term

exposure level, which in turn can be compared with acute exposure guidelines.
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2.3.3. Defining the Study Area. In defining the study area, the main objective is to

determine where the study area begins and ends (U.S. EPA, 1992a). For this

methodology, the spatial concern is the area around the combustor that is impacted by

the emissions. As discussed in Chapter 3, the recommended air dispersion/deposition

model (ISC3) is designed to provide air concentration and deposition estimates to a

maximum distance of 50 km from the source. Accordingly, the recommended approach

for establishing the study area is to initially assume that the study area is described by

a 50 km radius around the facility. Figure 2-2 presents a simple representation of

defining the study area using isopleth rings. This procedure can result in the inclusion

of minimally exposed persons in the study population or may exclude relevant

population. When detailed land-use and population-activity data are collected, it is

important to avoid collecting information for wide geographical areas where little or no

exposure occurs. The dispersion/deposition models provide air concentration, wet

deposition and dry deposition values over a polar grid (see Chapter 3 for a more

detailed description of the air modeling output). It is assumed that each polar grid

junction point represents a block of land surrounding the point, so that every point

within 50 km has associated with it an air concentration, wet deposition and dry

deposition value. Next, is the evaluation of the exposures at each area block, using

the site-based exposure scenario (contaminant, pathway and activity patterns) that

poses the highest potential for risk. 

It is possible that the risks at the outer edge of the study area may be

considered insignificant and a smaller study area would be appropriate. Alternatively,

the risks at the outer edge may still be of concern, suggesting that the study area

should be expanded. In this case, use of long-range atmospheric transport models

would be 

needed, adding considerably to the uncertainties inherent in the methodology.
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FIGURE 2-2

Simple Representation Outer Isopleth Ring Defining Study Area

and Isopleth Rings Within Study Area
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2.3.4. Potential Pathways of Human Exposure. Combustor emissions can impact

air, soil, water, and food products creating the potential for multiple pathway exposures. 

Figure 2-1 can serve as a guide to choosing which pathways are relevant for a

particular site-specific assessment. 

Plants and animals in the vicinity of a combustor may be impacted by its

emissions and become part of the human food chain. The principal exposure pathways

associated with combustor emissions are described below:

! Food Ingestion — Indirect exposure assessments should identify food

production and processing activities (e.g., home gardens, farms, etc.) within

the affected area. The assessment should evaluate exposures resulting

from consumption of food produced in the area by people who live inside

and outside the affected area. Consumption rate data for specific foods on

a national or regional basis can be found in the Exposure Factors

Handbook (U.S. EPA, 1997). Consumption rates of locally produced food

by residents in the area is best estimated on the basis of site-specific

surveys. Lacking such data, the Exposure Factors Handbook also contains

consumption rate data on typical homegrown foods. These issues are

discussed in greater detail in Chapter 6.

Soil in the vicinity of a combustor can be impacted by emissions, creating the

potential for several pathways of exposure, including the following:

! Soil Ingestion — The importance of this pathway will depend on

characteristics of the population of concern such as age and occupation, as

well as soil characteristics. Soil ingestion rates in children and adults have

been estimated from studies which measure the quantities of

nonabsorbable tracer minerals in the feces. These issues are discussed in

greater detail in Chapter 10.

! Dermal Exposure to Soil — The importance of this pathway depends on

exposed surface area, adherence of soil to skin, frequency of exposure,

and fraction of contaminants absorbed through skin. The amount of
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contaminant that can be absorbed through the skin (i.e., absorption

fraction) depends on the chemical properties of the contaminant, properties

of the soil matrix, length of exposure before removal by washing, and

dermal pharmacokinetics. If a compound cannot readily be absorbed

through the skin, the daily intake of the compound may be small even if

other exposure characteristics, such as adherence factor, are favorable. 

The other factors (exposed surface area, adherence and frequency of

exposure) are determined largely by personal behavioral or cultural

patterns. Exposure to dust may also be important in some circumstances

(e.g., children playing on the floor inside the home). Specific values for the

factors that affect dermal exposure are discussed in Chapter 4.

! Inhalation of Resuspended Contaminated Dust — Pollutants in

contaminated soil can be resuspended as particulates in the air by wind

erosion, agricultural tilling, or vehicular traffic on paved and unpaved roads. 

As a consequence to resuspension, persons may inhale the pollutant

particulates. The amount of resuspended dust is dependent on the moisture

content of the soil, the fraction of vegetation cover, the wind velocity, soil

particle size, pollutant concentration in the soil, and size of the

contaminated area. Methodologies have been developed to assess

exposure to pollutants resuspended by wind erosion at landfills (U.S. EPA,

1985) and CERCLA sites (U.S. EPA, 1988). Application of these

methodologies to combustor emissions suggests that dust resuspension by

wind erosion will not be a significant pathway in most cases. This is further

discussed in Chapter 5.

Combustor emissions can impact groundwater, surface water bodies, or

collected precipitation, such as in a cistern. Since the predicted annual ground-level

concentration of contaminants decreases with distance from the source, the location of

the precipitation collection apparatus, the surface water body on which emitted

pollutants are deposited, and the soil concentration (which affects leachate and runoff
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concentration) are important determinants of the water concentration. As discussed

below, human exposure to contaminants in these waters can occur through fishing,

bathing/showering, swimming, and drinking.

! Groundwater Exposure Pathways — Contaminants emitted from the

combustor can deposit on soils and then either move over the ground into a

surface water body or infiltrate into the ground. Groundwater used as a

domestic water supply can contribute to human exposure by direct

consumption, dermal contact during bathing or inhalation of volatilized

contaminants. Groundwater used for crop irrigation or livestock watering

purposes can cause human exposure via the food chain. This pathway is

discussed further in Section 7.4.

! Surface Water Exposure Pathways — Combustor emissions can enter

surface waters by direct deposition into the water or by deposition onto the

watershed and runoff to the water body. The location, topography, and size

of the watershed can affect the concentration of contaminants in runoff. 

The chemical concentration in the water, surface area of the exposed skin,

permeability of the skin to the chemical, and frequency and duration of

exposure events are the variables that contribute to dermal exposure. 

Incidental swallowing of water during recreational activities may also

contribute to exposure.

The manner in which the water is used will determine possible

exposure routes. Use of the surface water body for recreation or as a

drinking water source will make dermal contact and ingestion possible

exposure pathways. 

Another issue related to surface water is the consumption of fish.

Commercial and/or recreational fishing with subsequent use of the fish and

shellfish as a food source make the food web an important route of

exposure for communities with a surface water body in the vicinity of a

combustion source. Chapter 7 further discusses evaluation of exposure
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from water ingestion. Factors that affect human exposure by ingestion of

fish from a contaminated water body include sediment and water

contaminant concentration, types and amounts of locally caught

fish/shellfish consumed, and the bioaccumulation factor for these groups. 

The types of fish consumed will affect exposure because different types of

fish and shellfish take up contaminants at varying rates. For example, fatty

fish will tend to accumulate organic contaminants more readily than lean

fish. The populations most likely to be affected are recreational and

subsistence freshwater anglers. The types and amounts of fish consumed

by these subpopulations are discussed in Chapter 8. If the surface water

body is used for recreational purposes such as swimming and boating,

dermal absorption of contaminated water becomes another possible route. 

! Pathways of Exposure to Collected Precipitation — In several regions

of the U.S., rain and/or snowfall are collected and stored as a source of

drinking water. Individuals in these areas may be exposed to pollutants

deposited on the surface area from which water is collected. The input

variables for this exposure pathway include total annual deposition (both

wet and dry), annual precipitation, and the daily amount of water

consumed. Section 7.3. further discusses this exposure pathway.

A nursing mother exposed to contaminants by any of the pathways described

above over her lifetime can pass the contaminants on to her infant children through

breast milk.

! Human Breast Milk Pathway — This may be an important route of

exposure for lipophilic and nonlipophillic chemicals that may be emitted

from combustors. Models to estimate the daily exposure of a breastfeeding infant are presented in Chapter 9.

2.3.5. Factors Related to Human Exposures. Several exposure factors must be

considered when defining the exposed individuals or subpopulations within the study
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population. Human variability and personal preferences play a major role in

determining actual risk. 

As mentioned earlier, this section provides information on identifying potentially

exposed populations, estimating potential chemical doses, and identification of

potential exposure pathways. Unfortunately, it is beyond the purview of this document

to provide a thorough review of the available literature in the area of enumerating 

individuals and their dietary preference, cultural practices, geographic setting and

location which may contribute to increase susceptibility to environmental contaminants. 

In addition this document does not provide statistical data on human characteristics

and behaviors used in estimating exposures. This type of information will be available

in other U.S. EPA documents, such as the Exposure Factors Handbook (U.S. EPA,

1997). 

 Human factors related to exposure are as follows: demographics, residential

setting, recreational activities, cultural practices, lifestyle and habits, occupation, and

dietary consumption.

2.3.5.1. Demographics Factors — Demographic factors include the age, race,

gender, and ethnicity of the population in a particular area. In this section, the

demographic factors that are common among all individuals will be discussed such as

age and body weight. Specific information pertaining to gender, race and ethnicity can

be obtained from the Census Bureau.

! Age. The age of the individual influences exposure to the emitted

pollutants since pathways, duration and quantities of exposure may

vary with age. The daily activities of the individual, amounts of food

and water consumed, types of food consumed, exposed skin surface

area and body weight differ for children and adults. Health-based

criteria are also different for children and adults. Therefore, for some

exposure pathways such as soil ingestion, children may have a

greater exposure and be at greater risk than adults. Human life span

for risk assessment purposes is generally calculated to be 70 years. 
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The duration of childhood may vary somewhat in the different

exposure pathways, depending on age-relatedness of the key

scenario assumptions and the constraints of the available data. For

example, increased soil ingestion among children is generally

considered to be associated with young children; older children are

thought to be more similar to adults. The current data available are

limited to studies of children under 7 years of age and do not permit

further subdivision of this group.

In some exposure scenarios, individuals may be exposed only

during childhood or adulthood. In other cases, exposure may overlap

these periods, such as in the case where a child grows into adulthood

and remains in the same geographical area. Chapter 10 discusses

methods for combining estimates of childhood and adult exposure to

obtain an estimate of lifetime exposure.

! Body Weight. The daily intake for each pathway is defined as the

dose rate on a body-weight basis. Ingestion exposures on a bodyweight basis are substantially higher for infants and toddlers than for

teenagers or adults. Certain behaviors, such as mouthing of dirty

objects or direct ingestion of soil, which could also contribute to

exposure, are also much more prevalent in children than adults. 

Therefore, infants and toddlers may be at greater risk when exposure

is by food or soil ingestion. The effects, however, may have a long

latency period, in some instances approaching the human life span. In

these cases it may be reasonable to estimate daily intake using the

adult values of body-weight. In cases where effects have a shorter

latency (<10 years) and where children are known to be at special

risk, it is appropriate to use a body weight for children under 7. For

chemicals that are classified as carcinogens, adult body weight is

usually assumed to derive unit risk estimates for air or water. If
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exposure is lifelong, values of body weight are usually chosen so as to

be representative of adults. In this document, 70 kg is used as the

body weight for hypothetical adults. For children, the choice of body

weight for each pathway is made to coincide with the definition of

childhood employed and the available data, so that the daily intake

(mg/kg-day) is accurately determined. The form of the input data and

body weight value used for determination of daily intake (DI) are

delineated in the chapter for each pathway. The Exposure Factors

Handbook provides specific values for different age groups. 

2.3.5.2. Residential Factor — 

! Length of Time Individual Spends at Location. A person may

possibly have a lifetime exposure to emissions from a combustion

source; however, data on population mobility suggest that many

Americans do not remain in the same area for their assumed 70-year

lifetime (U.S. EPA, 1997). Therefore, a shorter period of time may

also be considered to estimate shorter duration exposure to combustor

emissions.

Data collected by the U.S. Bureau of the Census (1993) show that

the mobility rate in 1991-1992 was 17% of the total population (i.e.,

17% of the U.S. population moved to a different residence during the

year). This high mobility rate suggests that people are not likely to

remain in the same residence for their assumed 70-year lifetime. In

fact, it was estimated from these data that the median residence time

for all householders (owners and renters) is 9 years and that the upper

90th percentile residence time is 33 years (U.S. EPA, 1995a). Based

on analysis of census data from 1985 and 1987, Israeli and Nelson

(1992) estimated average residence time to be only 4.6 years. These

researchers also presented results showing that average residence
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time was higher in rural areas (7.8 years) than urban areas (4.2

years), and highest for farm families (17.3 years).

Change of residence may not necessarily indicate change in

exposure status, however. The U.S. Bureau of the Census (1993)

data show that local moves (those within the same county) accounted

for 62% of all moves in 1991-1992. It could be inferred from this

information that the majority of people who move remain in the vicinity

of the combustion source. However, the likelihood that these short

distance moves will influence exposure based on factors such as

atmospheric transport of pollutants can not be predicted accurately.

Local census data may be used to determine appropriate values

for the duration of exposure of the local population and population

subgroups of concern. Otherwise, the national data can be used to

derive estimates. The risk assessor may want to take into account the

expected life of the facility or duration of the burn (in the case of a

mobile incinerator) when estimating this parameter. However, the

assessor should consider that indirect exposures may continue after

emissions stop due to accumulation of contaminants in soil,

sediments, and other media.

In addition to the number of years at a particular location or

residence, the amount of time spent at that location each day directly

affects exposure. For example, children that attend day care or adults

that work in a different location for part of the day may be exposed to

higher or lower contaminant levels. This issue is discussed further

under the relevant pathways in Chapters 4 through 9 and 10.

! Location of the Individual Relative to the Source. The ground-level

concentration and deposition rate of pollutants emitted from the

combustion source are affected by air dispersion of pollutants. 

Numerous factors such as source characteristics (e.g., stack height),
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topography (terrain-flat or complex), meteorological data (e.g., wind

speed and direction) and distance from the source determine the air

dispersion and resultant deposition of the emitted pollutants. The

annual ground-level concentration of emitted pollutants decreases and

the population size increases with distance from the source (U.S. EPA,

1990). Maximal ground-level concentrations are usually measured at

distances in very close proximity to the source, although local

meteorological conditions and source characteristics play a role. In

generalizing this information to other combustors, the location of the

individual relative to the combustor becomes an important determinant

of exposure. Generally, individuals located closer to the source could

be expected to have higher direct exposure to emitted pollutants than

those at further distances from the source. In most cases, location will

refer to the residence of the individual. A range of exposures for

individuals representing the general public could be estimated by

choosing several distances from the combustor.

2.3.5.3. Other Exposure Related Factors — In addition to demographic and

residential factors, other exposure related occurrences must be considered which are

as follows:

! Recreational Activities: Activities such as sport fishing, hunting,

outdoor exercise, and sports may cause increased exposure to

environmental pollutants. Also certain personal hobbies may increase

exposure to contaminated media (e.g., home gardening). 

! Cultural Practices: Identification of certain cultural practices such as

subsistence hunting or fishing can significantly influence exposure. 

The risk assessor should be aware of cultural rituals involve

consuming soils for either spiritual or medicinal purposes. 

! Occupational: Although this methodology focuses on

population/individual exposures in the general study area, it may be
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prudent to evaluate if an individual included in the study population is

also a member of the work force at the combustor facility. This type of

scenario could yield a “high-end” exposed individual. 

! Lifestyle and Habits: Smoking, alcohol and drug abuse may not be

directly related to combustion emission; however, personal habits can

add to the susceptibility of the individual to environmental pollutants. 

2.4. DEFINING HUMAN EXPOSURE

 This section will be divided into three parts: 1) a discussion of the different

descriptors of exposure, 2) identifying study populations, and 3) identifying

subpopulations or individuals who could be highly susceptible to facility emissions. 

2.4.1. Approaches used to Describe Exposure/Risk Descriptors. The risk

assessment can be designed as a screening or as an in-depth assessment tool

depending on the needs of the risk manager. Initially, for many risk assessments, a

screening framework is typically designed and employed; a “maximally exposed

individual(s)” or reasonable maximally exposed individual is defined. This individual

ideally represents the 99th+ percentile of the distribution of exposure to the population

for all pollutants, pathways, and exposure routes. If this individual is deemed to not be

at risk, an additional assessment may not be needed. If certain pollutants, pathways,

or routes are predicted to contribute insignificantly to exposure, they may be eliminated

from further consideration. The remaining pollutants, pathways, and routes can then be

assessed in more detail.

2.4.1.1. Descriptors of Exposure and Risk — An exposure assessment may

be used to provide several different descriptors of risk. Individual risks can be

presented in two ways:

! Point estimates - Exposure scenarios can be constructed to represent

specific subpopulations that utilize the resources of a potentially affected

area around the combustion source. Ideally, point estimates should be

made to cover a range of scenarios representing both central tendencies

and high end estimates (generally above the 90th percentile) for the
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population (or subpopulation) affected by the combustor (U.S. EPA, 1992b

and 1995b). 

! Distributions - Where sufficient data are available, a complete distribution

of individual exposures/risks should be developed. Typically this involves

the use of probabilistic techniques such as Monte Carlo methods.

Population risks should also be expressed in several ways: 

! For nonthreshold-acting pollutants such as many carcinogens, risks may

be presented as the potential number of cancer cases in the affected

population (either yearly or over a longer period, such as the projected life

of the combustor). 

! For threshold-acting pollutants such as noncarcinogens, long-term

population risk can be presented as the number of persons whose

exposure exceeds a specific dose level or the RfD, or number of persons

whose exposure might exceed an acute effect threshold (if known). 

 For both carcinogens and noncarcinogens, specific subgroups of the population

should be highlighted, either because they are of interest (e.g., children), or because of

potentially high exposure/risk (e.g., subsistence farmers), or because they represent a

sensitive subpopulation.

2.4.2. Study Population. Individuals who live within the study area will form the basic

segment of the population to be evaluated, and should be evaluated for all appropriate

and relevant pathways. These individuals include, but are not limited to:

! persons living, working, or otherwise spending significant amounts of time

within the "study area boundary" defined through dispersion/deposition

modeling;

" persons potentially exposed to contaminated media such as air, soil, and/or

water -- near the emission source;

! persons potentially exposed to foodstuffs contaminated by the combustor

emissions.
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Certain individuals who live outside the study area boundary should be included

as well. For example, individuals who work, attend school, or otherwise spend

significant amounts of time within the study area boundary, even though they do not

live within the boundary, should also be evaluated and included in the studied

population.

The population must be defined in such a way that it is clear whether any given

individual is "in" or "out" of the population being considered. Geographical boundaries

and activity related criteria can serve a useful purpose here. If the population is

defined as "all those who live or work within [some specific] boundaries," these are

relatively straightforward criteria that can be used to define the size of the population

being evaluated. 

Another factor that varies with distance from the combustion facility is the size of

the population. The cumulative population increases as distance from the combustion

facility increases; increasing radial distances from the source encompass more of the

population. The total population in the study area then represents all persons who

could be exposed to the emissions, excluding those persons that temporarily travel into

the area (e.g., for work). After defining the study population, the next step in this

process is to define the exposed individuals.

2.4.3. Defining the Exposed Individual(s) and Developing Exposure Estimates.

The goal of this step is to assign an exposure to the individuals within the study

population. This is done by considering media concentrations and activities. By the

combination of concentration and activities, individual exposure estimates can be

made. Therefore, the assessor should attempt to reduce the scope of this effort in

terms of chemicals and pathways. One way to do this is to make bounding type

assessments using maximum exposure parameter values applied at points of maximum

concentrations or depositions. If these bounding assessments demonstrate exposure

below a concern level, then the associated chemicals and pathways can be safely

eliminated from further study.
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The risk assessor can identify the types of activities (e.g., vegetable gardening,

dairy farming, schooling, recreational angling, etc.) and numbers of people within a

study area by using land-use maps, aerial photographs, local surveys, "patch"

population information from the Human Exposure Model (HEM), and other sources. 

Because each activity is associated with certain pathways of exposure, the risk

assessor can use this information to identify which media levels to calculate for each

area (e.g., soil and vegetation in one block, and surface water and fish in another). 

Media levels of concern can then be calculated from the air modeling data, using the

methodology presented in this document.

Based on the land-use activities identified, the risk assessor can construct

exposure scenarios for various subgroups of the study population (e.g., city resident,

suburban resident with garden, non-resident sport angler, subsistence farmer, etc). 

Each scenario includes exposure by one or more pathways. In order to construct the

scenario, the assessor must characterize the physical and temporal parameters for

each pathway (e.g., soil ingestion rate, exposure duration, etc). The level of detail

used in characterizing behavior is dictated by the amount of data which is available or

could be collected. Theoretically, each person in the study area could be surveyed and

behavior identified, but this is clearly impractical in most situations due to the size of

the population and available resources. However, even a limited (statistically based)

local survey can be quite informative and is highly recommended. Some options, in

order of increasing detail on how to approach this problem include:

! Select the exposure parameter values that represent best

judgement of central tendency estimates for current conditions

of the groups identified.

! Divide each group into a low-, medium- or high-exposure

concentration and select the exposure parameter values for

each. The number of people in each subgroup should also be

identified.

! Determine statistical distributions for the exposure parameter

values in each group.
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Data available regarding activity parameters for receptors in each exposure

pathway are discussed in the accompanying “Parameter Guidance Document” and in

subsequent parts of this document.

Each of the scenarios employed by the risk assessor should be representative of

some portion of the population at the site. Local and national survey data and

population information from the HEM may be used to estimate the size of the

subpopulation represented by each of the scenarios employed. The process of

assigning risk estimates can be achieved by using isopleths of constant

concentration/deposition, and differentiating among activities (e.g., starting with one

isopleth and grouping the individuals by activity, such as farmers, residents with home

gardens, residents without home gardens, non-resident workers, etc.).

U.S. EPA (1993) suggests the following list of steps in assigning risk estimates. 

These are based on the assumption of constant concentration/deposition isopleths. If

emission rates and resulting concentration/deposition isopleths are likely to be

changing over time, then these steps can be applied to time periods during which the

emissions are relatively constant and summed to get the total exposure.

1. First, using the methodology presented in this document, compute

contaminant levels in media of concern (e.g., air, water, soil, groundwater,

beef, fruit/vegetables, milk) for each ring. This information can be

conveniently summarized on a table such as Table 2-1, or kept in a

spreadsheet. The model inputs should be based on local information as

much as possible. Considerable uncertainty is likely to be associated with

these estimates. See Chapter 10 for further information on how to assess

this uncertainty.

2. Next, overlay isopleths onto aerial photographs, population maps, land use

maps, etc. to identify the types of activities and numbers of people in each

ring. Several PC-based models are available for generating population

density maps based on census data. These include the Human Exposure

Model (HEM) from the EPA’s Office of Air Quality Planning and Standards
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and the Geographic Exposure Modeling System (GEMS) from the EPA’s

Office of Prevention, Pesticides and Toxic Substances. Geographic

Information Systems (GIS) could be used to identify land-use, complex

terrain, and population centers. Some of these systems use old census,

data and it would be more accurate to use the TIGER files from the most

recent census. Satellite photographs may provide the most recent

information. Local surveys may also help locate farms and home

gardeners.
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TABLE 2-1

Example Table for Media Levels

Ring 1 Ring 2 Ring 3 Ring 4 Ring 5

 Air (μg/m 3)

 Soil (μg/g)

 Surface water 

 (mg/L)

 Beef (mg/kg)

 Milk (mg/L)

 Fish (mg/kg)

 Vegetables 

 (mg/kg)

 Others

3. For each isopleth ring, estimate the number of farmers, residents with home

gardens, other residents, and other groups defined in the study population

(such as intermittently exposed non-resident workers). Also, for each ring

locate schools, fishing areas and other areas where unusual exposure may

occur. In addition, estimate the number of school children (school

location), and the number of people in any other special interest group like

subsistence fishermen, developments for retired people, etc. This

information could also be kept in a spreadsheet.

4. For each isopleth ring and population group, determine which exposure

pathways apply. Characterize exposure parameters for each pathway,

e.g., ingestion rate, exposure duration, etc. This information can be

summarized in a table such as Table 2-2, or stored in a spreadsheet. The
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level of detail used in characterizing behavior is dictated by the amount of

data which is available or could be collected. Theoretically, each person in

the ring could be surveyed and behavior identified, but this is clearly

impractical in most situations due to the size of the population and

resources available. However, even a limited (statistically based) local

survey can be quite informative and is highly recommended. Some options

in order of increasing detail are offered below for ways to approach this

problem:

a. Select the parameter values that represent best judgement of

central values for current conditions of the groups identified

above.

b. Divide each group into a low-, medium- or high-exposure level

and select the parameter values for each. The number of people

in each subgroup should also be identified.

c. Estimate statistical distributions for the parameter values in each

group.

5. The next step is to compute exposures and compile results. Once

exposure scenarios have been fully described and media levels have

been calculated, exposure and risks for the various scenarios can be

calculated. The results can be displayed in a variety of ways. Cumulative

distributions (exposure or risk vs number of people) are useful for

identifying central and high-end exposure levels. A frequency distribution

showing the number of people exposed at various exposure or risk levels

is a particularly effective way to communicate the results of the analysis. 

These distributions can be based on the entire study population or

individual special interest groups such as farmers, gardeners, school

children, etc. A variety of options are available for creating these
Chapter 2

Human Exposure Assessment

MPE 2-26 December 1998

TABLE 2-2

Example Exposure Parameters for an Isopleth Ring Within the Study Area

Gardeners Other

Residents

Farmers School

Children

 Population size

 Residence time (yr)

 Soil ingestion: 

 RATE (mg/d)

 FREQ (d/yr)

 Inhalation:

 RATE (m 3/d)

 FREQ (hr/d)

 Water ingestion:

 RATE (L/d)

 FREQ (d/yr)

 Beef Ingestion:

 RATE (g/d)

 FREQ (d/yr)

 percent local

 Milk Ingestion:

 RATE (g/d)

 FREQ (d/yr)

 percent local

 Fish Ingestion:

 RATE (g/d)

 FREQ (d/yr)

 percent local

 Vegetable ingestion:

 RATE (g/d)

 FREQ (d/yr)

 percent local
Chapter 2

Human Exposure Assessment

MPE 2-27 December 1998

distributions. If exposure parameters can be expressed as distributions,

then a Monte Carlo simulation is currently considered as the best

approach. This approach would involve using a frequency histogram of

number of people in each ring (based on information generated in Steps 2

and 3 presented above) to select a location and associated media

concentrations for each iteration. Further guidance on these issues and

other aspects about how to conduct Monte Carlo analysis is provided in

Chapter 10 of this document. For example, if it is not feasible to conduct

a Monte Carlo analysis, a series of point estimates can be used to

approximate the distribution. As discussed in Step 4, local surveys could

be used to characterize consumption rates of local grown foods,

frequency and duration. For example, if the surveys support generation

of a low, medium and high consumption rate for farmers and the isopleth

construction generates 5 rings, then 15 point estimates of exposure could

be made and plotted vs number of farmers. This yields an approximation

of the distribution. Since three consumption rates do not represent the

full range of variability, upper percentiles will be underestimated and

lower percentiles will be overestimated to some degree. Judgment will

need to be decide if this uncertainty is acceptable.

6. In addition to presenting the distributions of exposure, the assessor

should highlight individual exposure/risk estimates for several locations

(i.e., maximum deposition, place where most people are exposed,

schools, etc.) and subpopulations of interest (i.e., subsistence farmers,

recreational and subsistence anglers, nursing infants, ethnic groups or

any other population segment with unusually high exposures). For more

specific guidance on recommended exposure scenarios, the user should

refer to U. S. EPA (1998). These scenarios could be characterized using

a central or range of exposure values combined with an estimate of the

number of people in the group.
Chapter 2

Human Exposure Assessment

MPE 2-28 December 1998

This effort should first focus on characterizing behavior describing exposure that

occurs at a person's residence. A second step is to consider exposure that occurs

outside the residence. Certain activities may involve exposure from a ring different

than the residence. For example, a child may attend school in a different ring or an

adult may eat food grown in a different ring or fish in a different ring. In later

refinements, evaluation of how these parameter values and group sizes might change

in the future may be desired, especially if the area has seasonal residents.

The above discussion focuses on developing distributions of individual risk. It is

also possible to calculate overall risk to the exposed population. Issues associated

with characterization of population risk are discussed in Chapter 10.

Area blocks for which the calculated risk for the chosen scenario exceeds a

specified level of concern will be identified. The outer boundary of the larger area

composed of all area blocks with risks in excess of the concern level will identify an

approximate risk isopleth corresponding to the specified level of concern for the chosen

scenario (Figure 2-3).

The assessor can directly compare the approximate risk isopleth to population

corresponding exactly with the "patch" reporting units in the latter. The assessor can

constrain the size of the study area by eliminating areas with little or no relevant use by

humans. These concentration/deposition isopleth drawings can be printed on

transparencies and overlaid on a scaled land use map to provide the risk assessor with

another tool to help define the study area.

It should be noted that a risk isopleth derived in the manner described above is

an approximation; this is because concentration/deposition levels (and consequently,

the risk estimate) at a polar grid junction point are assumed to be representative of the
Chapter 2

Human Exposure Assessment

MPE 2-29 December 1998

FIGURE 2-3

Approximate Risk Isopleth Derived from Area Blocks 
Chapter 2

Human Exposure Assessment

MPE 2-30 December 1998

entire area block surrounding it. As shown in Figure 2-4, the true risk isopleth (if it

could be identified) probably would not correspond exactly to the block isopleth, and

could potentially encompass areas not included within the block risk isopleth. There

are several ways to correct this problem. One solution would be to use a specific level

of concern for the site based on information from the U.S. EPA Office of Air Quality

Planning and Standards' Human Exposure Model (HEM). An alternative would be to

model the true level of concern, and add one row of blocks around the perimeter of the

critical area as a buffer zone. Another solution would be to acknowledge that the

isopleth is approximate and allow the risk assessor to exercise professional judgment

regarding whether to include border regions in the critical study area based on land use

and the population within these areas. In case of a question about whether to include

some specific border area or not, the assesor can re-run the air model, placing a tight

grid around the area in question. The ensuing block risk isopleth would have a

corresponding increased resolution. It is possible that the study area definition

methodology described above will produce a study area that reaches the 50 km radius

in one or more locations. In this case, it would be desirable to extend the air modeling

radius beyond 50 km. However, doing so would require the application of long-range 

atmospheric transport models, the use of which would add considerably to the

uncertainties inherent in the methodology. Therefore, until models of long-range

atmospheric transport are sufficiently developed, it would be reasonable to make 50 km

the outer border for the study area.

Successful application of this method depends on the judgment of the risk

assessor. Choice of an appropriate exposure scenario to define the study area is

especially important. If risks along the outer boundary are estimated to be above the

concern level during the subsequent detailed analysis (considering all chemicals and

pathways), the study area will need to be expanded.

2.4.4. Commodity Shed Approach. Another approach for examining exposure to

combustor emissions examines the distribution of commodities produced locally which
Chapter 2

Human Exposure Assessment

MPE 2-31 December 1998

FIGURE 2-4

Comparison of Approximate Risk Isopleth Derived from

Area Blocks to a Potential True Risk Isopleth
Chapter 2

Human Exposure Assessment

MPE 2-32 December 1998

are predicted to exhibit increased pollutant concentrations. This is known as the

“commodity shed” approach, which is a population based assessment. The method can

be utilized for any medium to which a significant fraction of the population is exposed,

but is most applicable to foods. It can also be utilized in conjunction with the isopleth

approach. The isopleth approach could be used to estimate exposure for specific

subgroups and this approach for the general population.

The first step of this approach requires an analysis of the land uses within 50 km

of the source. Again, the 50 km distance selection is for the ISC3 Model. The land

uses of concern may include commercial farms or large water bodies. Subpopulations

who reside in the area and do not exhibit a normal pattern of dietary diversity should be

identified. These subpopulations may derive a large fraction of their nutritional

requirements from either a single commodity (e.g., fish consumption may fulfill a daily

protein or a daily energy requirement) or from a group of commodities collected from a

small geographical area (e.g., a large fraction of subsistence farmers’ daily intake may

be derived from a single location). These subpopulations should be assessed on a sitespecific basis in relation to the combustor. Estimates of the sizes of these

subpopulations and the fraction of the entire population they comprise should be

included in the risk characterization. As stated previously in this chapter, annual

average air concentrations and deposition rates should be utilized for the farm fields or

the watershed.

Exposure for the rest of the population that does not derive its food in these

manners could be assessed through the commodity shed approach. If one of the goals

of the assessment is to predict population exposure and risk, then the initial analysis of 

the site (at the beginning of the assessment), should include a determination of the

areas where foods are produced and an estimate of the average annual production

rates for each area. When modeling, determine the average concentration of pollutant

in the foods produced at each area. The sites could include farms which produce plants

or meats for the population or lakes which are fished for recreational or commercial

purposes.
Chapter 2

Human Exposure Assessment

MPE 2-33 December 1998

Ck'

3(Ck

i

× Qk

i

)

3Qk

i

(2-1)

Use the following procedure to estimate population exposure within the modeled

region:

1. Identify each site (i) in the area around the combustor which produces a

specific food item for public consumption. These sites could include

commercial farms or water bodies large enough to be fished

recreationally or commercially. Consider imported commodity (from

outside the modeled region) as a separate site.

2. Predict the contaminant concentration for each commodity (k) produced at

each site i (Ck)i. Include an estimate of the contaminant concentration in

the imported commodity. 

3. Determine the quantity of each commodity at each site (Qk)i that is

produced for public consumption (e.g., 2000 Kg of corn and 200 Kg of

cucumbers from a farm). Include an estimate of the amount of imported

commodity.

4. Construct a discrete distribution of contaminant concentration for each

commodity (Ck) assuming that the probability of consumption of

commodity produced at any given site is proportional to the amount

produced at that site {Pr[Ck=(Ck)i] = (Qk)i /3(Qk)i }. If only the mean

population exposure is desired, the production-weighted mean

contaminant concentration for each commodity can be calculated as in

Equation 2-1.

where:

CK = production-weighted mean contaminant concentration for

commodity k (mg/Kg)

CKi =contaminant concentration for commodity k at site i (mg/Kg)

QKi =production quantity of commodity k at site i (Kg)
Chapter 2

Human Exposure Assessment

MPE 2-34 December 1998

DI ' 3(CRk× Ck)

(2-2)

5. Determine the distribution of the population consumption rates (CR) from

consumption survey data. Local or regional consumption data are best

but national studies are acceptable. Commodity consumption rates are

often given as g/day in the literature. In these cases, the consumption

rates must be converted to an intake on a per body weight basis. This

may be accomplished by assuming a specific body weight for the entire

population (e.g., 70 Kg). Alternatively, some consumption rate data bases

may contain enough information to estimate a relationship between

consumption rate and body weight. This relationship may be factored into

the analysis. Some consumption analyses may measure the body weight

of respondents directly; these data are the most appropriate for this

approach.

6. Calculate the estimated mean contaminant intake for the local human

population by substituting the average values for consumption rates and

contaminant concentrations in Equation 2-2 or perform a Monte Carlo

analysis as described in Chapter 10 to obtain a population distribution of

contaminant intake. 

where:

DI = human daily intake of the contaminant (mg/kg-day)

CRk = the daily consumption rate of the kth commodity as a fraction

of body weight (Kg commodity/Kg Bw per day)

Ck = the predicted pollutant concentration for the k

th commodity

source (mg contaminant/Kg commodity)
Chapter 2

Human Exposure Assessment

MPE 2-35 December 1998

2.5. REFERENCES

Israeli, M. and C.B. Nelson. 1992. Distribution and expected time of residence for U.S.

households. Risk Analysis. 12: 65-72.

U.S. Bureau of the Census. 1993. Geographical mobility: March 1991 to March 1992. 

Current Population Reports. p.20-473.

U.S. EPA. 1985. Rapid Assessment of Exposure to Particulate Emissions from

Surface Contamination Sites. Prepared by Midwest Research Institute under contract

68-0303116. Office of Health and Environmental Assessment, Washington, DC. 

EPA/600/8-85/002. NTIS PB85-192219.

U.S. EPA. 1988. Superfund Exposure Assessment Manual. Office of Emergency and

Remedial Response, Washington, DC. EPA/540/1-88/001. NTIS PB-89-135859.

U.S. EPA. 1992a. Federal Register Notice. Guidelines for Exposure Assessment. 

May 29, 1992. 57 FR 22888. EPA/600-Z-92-001.

U.S. EPA. 1992b. Guidance on Risk Characterization for Risk Managers and Risk

Assessors. Internal EPA Memorandum for F. Henry Habicht, Deputy Administrator.

February 26, 1992

U.S. EPA. 1993. Addendum to the Methodology for Assessing Health Risks

Associated with Indirect Exposure to Combustor Emissions. National Center for

Environmental Assessment, Washington, DC. (SAB Review Draft)

U.S. EPA. 1995a. Exposure Factors Handbook. Review Draft. Office of Research and

Development, Washington, DC. EPA/600/P-95/002A.

U.S. EPA. 1995b. Guidance For Risk Characterization. Science Policy Council 

Washington, DC. February.

U.S. EPA. 1997. Exposure Factors Handbook. Office of Research and Development,

Washington, DC. EPA/600/P-95/002Fa.

U.S. EPA. 1998. Human Health Risk Assessment Protocol for Hazardous Waste

Combustion Facilities: Volume 1. Peer Review Draft. Office of Solid Waste and

Emergency Response. Washington, DC. EPA/530-D-98-001A.
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-1 December 1998

AIR DISPERSION AND DEPOSITION MODELING

OF POLLUTANT STACK EMISSIONS

3.1. INTRODUCTION

Estimation of health risks associated with exposure to air releases of

contaminants from stationary industrial and combustion sources requires estimates of

the atmospheric concentrations and annual wet and dry deposition rates of the emitted

contaminants in the areas around these facilities. When atmospheric concentrations

and deposition rates cannot be measured in the vicinity of operating sources, U.S. EPA

customarily uses air dispersion/deposition models to estimate the atmospheric

transport, the surface deposition flux, and the ambient air concentrations of specific

compounds attributable to smokestack emissions from an industrial combustion source. 

Air dispersion models are mathematical constructs that approximate the physical and

chemical processes occurring in the atmosphere that directly influence the dispersion

of gaseous and particulate emissions from the stack of a combustion source. These

models are computer programs encompassing a series of partial differential and

algebraic equations that calculate the dispersion and deposition of the emissions. 

Concentration and deposition estimates of the contaminants discharged from the stack

are computed at specified distances from the source. These quantities are used to

estimate the magnitude of potential exposures to receptors, and to estimate the

potential health risks in populations living and working in the vicinity of the stationary

combustion source.

The Guideline on Air Quality Models (U.S. EPA, 1997) has recommended the

use of numerous air dispersion and deposition models that may be applied to the

regulatory analysis of source stack emissions. The guideline is intended for use by

EPA Regional Offices in judging the adequacy of modeling analyses performed by

EPA, State and local agencies and by industry. A primary purpose of the guidelines is

to promote consistency in the application of air quality models, and to identify those
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-2 December 1998

techniques and data bases EPA considers acceptable. This chapter recommends the

application of the Industrial Source Complex 3 (ISC3) model for analysis of potential

exposures to combustor emissions. ISC3 is listed as a recommended model in

Appendix A to the Air Modeling Guidelines. The reader is reminded that air quality

models are continuing to be changed in response to regulatory requirements and the

expanded requirements for models to cover even more complex problems. Therefore

this current recommendation for the use of ISC3 may be changed as the state-of-the art

in model development changes.

The 1990 Methodology for Assessing Health Risks Associated With Indirect

Exposure to Combustor Emissions (U.S. EPA, 1990) and Addendum to Methodology for

Assessing Health Risks Associated With Indirect Exposure to Combustor Emissions

(U.S. EPA, 1993) discussed the need for modeling techniques that predict the effects of

contaminant dispersion and deposition in the presence of complex terrain (i.e., receptor

location above the source of contamination).

The purpose of this chapter is to provide general guidance on the use of the

ISC3 model in the context of the overall exposure assessment of emissions released

from the stacks of stationary combustion processes. Accordingly, the chapter is

subdivided by topic area, which are :

(a) Selection of toxic air pollutants that are commonly measured in stack

emissions from the incineration and combustion of anthropogenic waste materials;

(b) Estimation of the stack emission rate of the toxic air pollutants for air

modeling purposes; 

(c) Increases in emissions during equipment malfunctions, start-up and

shutdown; 

 (d) Air dispersion modeling of the stack gas emissions including descriptions the

ISC3 model applications and associated wet and dry deposition algorithms;
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-3 December 1998

(e) Procedures for applying the ISC3 model using vapor phase and particulate

phase partitioning of the emitted pollutants, and a specific particle size distribution of

particulate matter; and 

(f) Method for estimating exposure through inhalation of dispersed pollutants.

3.2. SELECTING TOXIC AIR POLLUTANTS

The first step in the analysis of multiple pathways of exposure is the

identification and selection of contaminants that may be emitted to the air during the

combustion process. The selection process should focus on chemicals that are

potentially toxic, and that have a definite propensity for bioaccumulating or

bioconcentrating in the human and ecological food chains. As the initial step in the

selection process, a select list of compounds that have been detected in combustor

emissions is shown in Tables 3-1, 3-2, and 3-3. These tables list potentially toxic

organic and inorganic contaminants that should be considered when assessing risks

associated with direct and indirect exposures to the combustor emissions. This list

includes chemicals identified as most frequently detected and measured in stack

emissions from incinerators, boilers and industrial furnaces combusting hazardous

waste, sewage sludge, municipal solid waste, biomass, automobile tires, and medical

and hospital waste (Dempsey and Oppelt, 1993; CARB, 1990a,b, 1991; CAPCOA,

1991; U.S. EPA, 1986, 1988). In addition the constituents listed have toxicity values

available on IRIS. To the extent possible, chemical speciation of the various forms of

these compounds should be identified. Using these tables as a starting point, the

assessor can narrow the number of compounds of concern for a particular combustor

using the following three steps, identified in Sections 3.2.1., 3.2.2. and 3.2.3.

3.2.1. Consider the Feed Material Being Combusted. The selection of the

chemicals for site-specific analysis will depend largely on the waste materials that will

routinely be accepted and incinerated at the combustion facility. If chlorine is present

in the feed, then it is imperative that chlorinated dioxins and dibenzofurans be

evaluated in the exposure assessment. Even if chlorine is not specifically identified in
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-4 December 1998

the waste feed, it is recommended that this class of compounds be addressed in the

assessment.

TABLE 3-1

Initial List of Dioxin-Like Compounds Identified in Emissions from the

Incineration and Combustion of Anthropogenic Wastes

Chlorinated Dibenzo-p-dioxins (CDDs) Chlorinated Dibenzofurans

2,3,7,8-TCDD 2,3,7,8-TCDF

1,2,3,7,8-PentaCDD 1,2,3,7,8-PentaCDF

1,2,3,4,7,8-HexaCDD 2,3,4,7,8-PentaCDF

1,2,3,6,7,8-HexaCDD 1,2,3,4,7,8-HexaCDF

1,2,3,7,8,9-HexaCDD 1,2,3,6,7,8-HexaCDF

1,2,3,4,6,7,8-HeptaCDD 1,2,3,7,8,9-HexaCDF

1,2,3,6,7,8,9-OctaCDD 2,3,4,6,7,8-HexaCDF

Other TetraCDDs 1,2,3,4,6,7,8-HeptaCDF

Other PentaCDDs 1,2,3,4,7,8,9-HeptaCDF

Other HexaCDDs 1,2,3,4,6,7,8,9-OctaCDF

Other HeptaCDDs Other TCDFs

Other PentaCDFs

Other HexaCDFs

Other HeptaCDFs
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-5 December 1998

TABLE 3-2

Initial List of Organic Compounds Identified in Emissions from the

Incineration and Combustion of Anthropogenic Wastes

Organic Compounds RfDa RfCb Carcc Reference

Acrylonitrile IRIS IRIS B1, IRIS U.S. EPA, 1989,

1998

Anthracene None None D, IRIS U.S. EPA, 1989,

1998

Azobenzene IRIS IRIS B2, IRIS U.S. EPA, 1989,

1998

Benzaldehyde IRIS None None U.S. EPA, 1989,

1998

Benzene None None A, IRIS U.S. EPA, 1989,

1998

Benz(a)anthracene None None B2, IRIS U.S. EPA, 1998

Benzo(a)pyrene None None B2, IRIS U.S. EPA, 1998

Benzo(b)fluoranthene None None B2, IRIS U.S. EPA, 1998

Benzo(e)pyrene None None C, IRIS U.S. EPA, 1998

Benzo(g,h,i)perylene None None D, IRIS U.S. EPA, 1998

Benzo(j)fluoranthene None None B2, IRIS U.S. EPA, 1998

Benzo(k)fluoranthene None None B2, IRIS U.S. EPA, 1998

Benzyl chloride None None B2, IRIS U.S. EPA, 1989,

1998

1,1-Biphenyl IRIS None D, IRIS U.S. EPA, 1989,

1998

Bis(2-chloroethoxy) methane None None D, IRIS U.S. EPA, 1998

Bis(2-chloroethyl) ether None None B2, IRIS U.S. EPA, 1998

Bis(2-ethylhexyl) phthalate None None * U.S. EPA, 1989

Bis(chloromethyl) ether None None A, IRIS U.S. EPA, 1989,

1998

Bromochloromethane None None D, IRIS U.S. EPA, 1998

Bromodichloromethane IRIS None B2, IRIS U.S. EPA, 1998

Bromomethane IRIS IRIS D, IRIS U.S. EPA, 1989,

1998

1,3-Butadiene None None B2, IRIS U.S. EPA, 1998
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-6 December 1998

TABLE 3-2 (cont.)

Organic Compounds RfDa RfCb Carcc Reference

Butyl benzyl phthalate IRIS None C, IRIS U.S. EPA, 1989,

1998

Captan IRIS None B2, IRIS U.S. EPA, 1998

Carbon disulfide IRIS IRIS None U.S. EPA, 1998

Carbon tetrachloride IRIS None B2, IRIS U.S. EPA, 1989,

1998

Chlorobenzene IRIS None D, IRIS U.S. EPA, 1998

Chlorocyclopentadiene None None D, IRIS U.S. EPA, 1998

Chloroform IRIS None B2, IRIS U.S. EPA, 1989,

1998

Chloromethane None None None U.S. EPA, 1998

Chrysene None None B2, IRIS U.S. EPA, 1998

Cumene IRIS None None U.S. EPA, 1989,

1998

Di(2-ethylhexyl)phthalate IRIS None B2, IRIS U.S. EPA, 1998

Dibenzofuran None None D, IRIS U.S. EPA, 1989,

1998

Dibenzo(a,e)fluoranthene None None B2, IRIS U.S. EPA, 1998

Dibenzo(a,h)anthracene None None B2, IRIS U.S. EPA, 1998

1,2-Dibromo-3-chloropropane None IRIS B2, IRIS U.S. EPA, 1998

1,2-Dichlorobenzene IRIS None D, IRIS U.S. EPA, 1989,

1998

1,3-Dichlorobenzene None None D, IRIS U.S. EPA, 1989,

1998

1,4-Dichlorobenzene None IRIS None U.S. EPA, 1989,

1998

1,1-Dichloroethane None None C, IRIS U.S. EPA, 1989,

1998

1,2-Dichloroethane None None B2, IRIS U.S. EPA, 1998

1,1-Dichloroethylene IRIS None C, IRIS U.S. EPA, 1989,

1998

Dichloromethane IRIS None B2, IRIS U.S. EPA, 1998

2,4-Dichlorophenol IRIS None None U.S. EPA, 1989,

1998
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

TABLE 3-2 (cont.)

Organic Compounds RfDa RfCb Carcc Reference

MPE 3-7 December 1998

Dimethyl phthalate None None D, IRIS U.S. EPA, 1998

1,4-Dioxane None None B2, IRIS U.S. EPA, 1998

Ethyl benzene IRIS IRIS D, IRIS U.S. EPA, 1989,

1998

Fluoranthene IRIS None D, IRIS U.S. EPA, 1998

Formaldehyde IRIS None B1, IRIS U.S. EPA, 1998

Hexachlorobenzene IRIS None B2, IRIS U.S. EPA, 1989,

1998

Hexachlorobutadiene IRIS None C, IRIS U.S. EPA, 1989,

1998

Hexachloroethane IRIS None C, IRIS U.S. EPA, 1989,

1998

Mercuric chloride IRIS None C, IRIS U.S. EPA, 1998

Mercury, elemental None IRIS D, IRIS U.S. EPA, 1998

Methylene bromide + - - U.S. EPA, 1989*

Methyl ethyl ketone IRIS IRIS D, IRIS U.S. EPA, 1989,

1998

Naphthalene None None D, IRIS U.S. EPA, 1989,

1998

Pentachlorobenzene IRIS None D, IRIS U.S. EPA, 1989,

1998

Pentachlorophenol IRIS None B2, IRIS U.S. EPA, 1989,

1998

Phenol IRIS None D, IRIS U.S. EPA, 1989,

1998

Polychlorinated biphenyls None None B2, IRIS U.S. EPA, 1998

Polycyclic organic matter None None B1, IRIS U.S. EPA, 1998

Pyrene IRIS None D, IRIS U.S. EPA, 1989,

1998

Quinones

Styrene IRIS IRIS None U.S. EPA, 1989,

1998

1,2,4,5-Tetrachlorobenzene IRIS None None U.S. EPA, 1989,

1998
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

TABLE 3-2 (cont.)

Organic Compounds RfDa RfCb Carcc Reference

MPE 3-8 December 1998

1,1,1,2-Tetrachloroethane IRIS None C, IRIS U.S. EPA, 1998

1,1,2,2-Tetrachloroethane None None C, IRIS U.S. EPA, 1998

Tetrachloroethylene IRIS None B2, IRIS U.S. EPA, 1989,

1998

Dibutyl phthalate IRIS None D, IRIS U.S. EPA, 1998

Toluene IRIS IRIS D, IRIS U.S. EPA, 1989,

1998

1,2,4-Trichlorobenzene IRIS IRIS D, IRIS U.S. EPA, 1989,

1998

1,1,1-Trichloroethane None None D, IRIS U.S. EPA, 1989,

1998

1,1,2-Trichloroethane IRIS None C, IRIS U.S. EPA, 1989,

1998

Trichloroethylene None None IRIS U.S. EPA, 1998

2,4,5-Trichlorophenol IRIS None None U.S. EPA, 1989,

1998

2,4,6-Trichlorophenol None None B2, IRIS U.S. EPA, 1989,

1998

Vinyl chloride None None A, IRIS U.S. EPA, 1989,

1998

m-Xylene None None None U.S. EPA, 1989,

1998

o-Xylene None None None U.S. EPA, 1989,

1998

p-Xylene None None None U.S. EPA, 1989,

1998

Xylenes IRIS None D, IRIS U.S. EPA, 1989,

1998

aRfD is the reference dose-defined as an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to

 the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a

 lifetime.

bRfC is the reference concentration-defined as an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily

 exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious

 effects during a lifetime.

cGroup A carcinogen: Human carcinogen (sufficient evidence of carcinogenicity in humans). Group B carcinogen: Probable human

 carcinogen (B1 -limited evidence of carcinogenicity in humans; B2 -sufficient evidence of carcinogenicity in animals with
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-9 December 1998

 inadequate or lack of evidence in humans). Group C carcinogen: Possible human carcinogen (limited evidence of carcinogenicity

 in animals and inadequate or lack of human data). Group D carcinogen: Not classifiable as to human carcinogenicity

 (inadequate or no evidence).

TABLE 3-3

Initial List of Inorganic Contaminants Identified in Emissions from the Incineration

and Combustion of Anthropogenic Wastes

Inorganic Contaminants RfDa RfCb Carcc Reference

Antimony IRIS IRIS

(AsO3)

— U.S. EPA, 1989, 1998

Arsenic, inorganic IRIS — IRIS; A U.S. EPA, 1989, 1998

Barium IRIS — — U.S. EPA, 1989, 1998

Cadmium IRIS — IRIS; B1 U.S. EPA, 1989, 1998

Chromium (III) IRIS — — U.S. EPA, 1989, 1998

Chromium (VI) — — IRIS; A U.S. EPA, 1998

Lead + compounds — — IRIS; B2 U.S. EPA, 1998

Manganese IRIS IRIS IRIS; D U.S. EPA, 1998

Mercuric chloride IRIS — IRIS

Mercury, elemental IRIS IRIS; D U.S. EPA, 1989, 1998

Nickel, soluble salts IRIS — — U.S. EPA, 1989, 1998

Selenium + compounds IRIS — IRIS; D U.S. EPA, 1989, 1998

Silver IRIS — IRIS; D U.S. EPA, 1998

Zinc + compounds IRIS — IRIS; D U.S. EPA, 1989, 1998

aRfD is the reference dose-defined as an estimate (with uncertainty spanning perhaps an order of magnitude) of a

 daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable

 risk of deleterious effects during a lifetime.

bRfC is the reference concentration-defined as an estimate (with uncertainty spanning perhaps an order of

 magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without an

 appreciable risk of deleterious effects during a lifetime.

cGroup A carcinogen: Human carcinogen (sufficient evidence of carcinogenicity in humans); Group B carcinogen:

 Probable human carcinogen (B1 -limited evidence of carcinogenicity in humans; B2 -sufficient evidence of

 carcinogenicity in animals with inadequate or lack of evidence in humans); Group C carcinogen: Possible human

 carcinogen (limited evidence of carcinogenicity in animals and inadequate or lack of human data); Group D

 carcinogen: Not classifiable as to human carcinogenicity (inadequate or no evidence); I = Carcinogenic by the

 inhalation route.
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MPE 3-10 December 1998

It may be that only an extremely small concentration of chlorine in the waste is

sufficient to participate in the formation of CDDs/CDFs. In addition, combustion air may

contain precursor compounds and halides that may cause dioxins to be thermalytically

formed. Therefore, it is generally suggested that assessments of waste incineration

sources include dioxin-like compounds in the analysis of emissions. Products of

incomplete combustion (PICs) will form in the stack gas emissions as a consequence of

the incomplete destruction of all organic species leaving the combustion chamber. 

Therefore, the polycyclic aromatic hydrocarbons (PAHs), i.e. benzo(a)pyrene, will need

to be routinely considered. Finally, to the extent a combustion facility accepts and

incinerates metal-bearing materials and wastes, the heavy metals having significant

toxicologic or bioaccumulative properties e.g., arsenic, cadmium, chromium, lead,

mercury and nickel, will need to be addressed in the exposure analysis.

3.2.2. Consider the Availability of Appropriate Toxicological Information. Risk

assessments cannot be completed for compounds that lack verified dose-response

relationships for specific health endpoints. Accordingly, such compounds can be

eliminated from consideration in the exposure assessment. Although they may need to

be considered only in a qualitative manner, the potential risks posed by emissions of

these compounds do need to be considered in the risk characterization. Tables 3-2

and 3-3 contain information that can assist in a toxicological evaluation, including

availability of reference doses (RfD), reference concentrations (RfC), and the current

carcinogenicity assessment that appears on U.S. EPA’s IRIS database. The risk

assessor should review the latest version of IRIS to confirm if new chemicals have

been added, if additional RfDs or RfCs have been verified, and if the carcinogenicity

assessment has been updated.

3.2.3. Narrow the List with a Quantitative Ranking Scheme. After following the first

two steps, the list in Tables 3-1 through 3-3 may be substantially narrowed. However,

it may not be necessary to conduct full assessments on the remaining chemicals. Say,

for example, a credible ranking scheme could be developed that would rank a list of 50
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MPE 3-11 December 1998

chemicals (and there are more than 90 chemicals listed in Tables 3-1 through 3-3) from

most to least concern. If the detailed site-specific assessment on the 20 chemicals of

most concern indicated that chemicals 15 through 20 resulted in negligible indirect

health risk, then there would be credible grounds to conclude that chemicals 21

through 50 do not require further indirect assessment. Likewise, if the assessment

indicated a health risk of potential consequence for chemicals 15 through 20, then

evaluation of more chemicals on the list of 50 would be required.

The Risk Assessment Guidance for Superfund (RAGS; U.S. EPA, 1989)

suggests a simple linear ranking scheme for focusing on the “most significant”

chemicals. This scheme multiplies a quantitative toxicity value (e.g., slope factor) by a

quantitative concentration of the contaminant in the exposure medium (e.g., soil). For

combustor emissions, the “concentration” term should be replaced with the emission

rate of the chemical. Procedures to estimate emission rates are presented in Section

3.3. below. A third factor that should be considered is the potential for the chemical to

bioaccumulate. Factors indicating bioaccumulation potential include persistence in the

environment (more persistence translates to more bioaccumulation) and partitioning

tendencies (more partitioning onto soils and sediments translates to more

bioaccumulation). A surrogate factor that can be used for bioaccumulation potential is

the logarithm of the octanol-water partition coefficient, denoted as log Kow. The higher

the value of the log Kow the greater the possibility for partitioning onto soils and

sediments. Longer persistence is correlated to tighter sorption to soils/sediments,

which is why the log Kow can capture both persistence and partitioning tendencies

appropriately.

3.3. ESTIMATING THE STACK EMISSION RATE OF THE TOXIC AIR

POLLUTANTS

For a Gaussian plume model, such as ISC3, the predicted ground-level

concentrations are directly proportional to the rate at which pollutants are emitted from

a source in units of mass per unit time. The emission rate is a variable source

characteristic. This rate may vary over time because of changing conditions (e.g.,
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MPE 3-12 December 1998

increases in volatile emissions with increases in temperature or a malfunction within the

stack processes). This section will focus on emission rates from stacks, both

intentional and accidental, as presented in the Addendum to Methodology for

Assessing Health Risks Associated With Indirect Exposure to Combustor Emissions

(U.S. EPA, 1993).

For the purposes of chemical selection as described above, and for air modeling

and exposure assessment, the release rate of pollutants from the combustor stack is

expressed in metric units of mass of the pollutant emitted per unit time (e.g., grams/s). 

The release rate used in indirect exposure assessments should generally be reflective

of the expected average emissions from the facility over the long term (e.g., over the

engineered life of the combustion facility). However, if acute inhalation exposures are

of concern, the release rate should be reflective of maximum short-term emissions

(e.g., as a result of equipment malfunction, startup and shutdown, or possible

accidental releases). Additionally, given the typically short duration of these events, a

wide range of meteorologic conditions (e.g., inversions) should be modeled to

encompass a range of potential impacts. Although mass emission rates can be

estimated in any number of ways, the following hierarchical order of preference is

recommended.

3.3.1. Operational Facilities. For facilities that are built and operational, it is

preferred that direct stack measurements be used, using U.S. EPA-recommended

chemical-specific (and, wherever possible, species- or congener-specific) stack

sampling, analytical, quality control, and quality assurance protocols and procedures. 

Stack monitoring should provide information on the concentrations of the pollutants

(e.g., ng/dscm), the actual velocity of stack gas release (m/s), and stack gas 

temperature (K). In addition, based on the characteristics and heat value of the waste

feed, the temperature of combustion, and stoichiometric conditions, the volume of flue

gases evolved per unit of time should be estimated (in metric units of dry standard

cubic meters per minute [dscm] at 12% CO2 or 7% O2). To calculate a mass emission

rate of the pollutant, it is necessary to multiply the estimated dry flue gas volume (m3)
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MPE 3-13 December 1998

per minute associated with combustion by the measured in-stack concentration of the

contaminant in units of g/dscm. (It should be noted that concentrations reported on an

equivalent oxygen or carbon dioxide basis or on a dry volume basis must be adjusted

to the actual stack gas conditions in order to determine the actual, in-stack

concentrations.) The product of this calculation (g/min) is then divided by 60 s/min to

give the grams per second emission rate needed as an input into the ISC3 model. In

cases where there are multiple stack tests from the same facility, care should be taken

to ensure that the emissions characterization reflects a wide range of operating

conditions and also accounts for any expected deterioration in facility performance that

would affect the facility’s emissions over its useful life. In arriving at the final estimate

of emissions of the contaminant, the average value is considered representative of

long-term emissions, taking into consideration the average yearly operating hours of

the combustion facility.

3.3.2. Planned Facilities. For facilities that have been constructed but are not yet

operational, or are in the planning stages of development, the following approach is

recommended. Reports of stack tests measuring the emissions of the pollutants of

interest should be collected from facilities that are most similar in technology, design,

operation, capacity, auxiliary fuels used, waste feed types and composition, and air

pollution controls to the facility under consideration. The stack test reports should be

evaluated to determine if U.S. EPA-recommended protocols, or equivalent, that are

appropriate for the specific pollutants being measured, were used for stack sampling

and analysis. When data from test results for a number of facilities are combined, care

should be taken to convert concentrations and stack gas parameters to a common

basis (and consistent units of measurement) appropriate for the facility under

consideration, considering process feed rates and other facility-specific operating

conditions. For purposes of exposure assessment and risk characterization, ranges

and average values should be developed in order to address uncertainties inherent in

the emissions estimates. However, the average emission rate of the contaminant is the
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-14 December 1998

value used to represent long-term and expected emissions from the combustor under

study.

3.3.3. Planned Facility With No Comparable Existing Facility. If no data exist

relevant to a specific facility, then the Office of Air Quality Planning and Standards’

AP-42, Compilation of Air Pollution Emission Factors (U.S. EPA, 1985) can be used. 

Care should be taken to select emission factors that were developed for technologies

that best match the facility under consideration. The basic limitation of these data is

the fact that emission factors are not usually reflective of specific emission control

equipment. Also, information is available for only a limited number of pollutants.

Specifically, the AP-42 document provides guidance on emission factors for

municipal waste combustors, sewage sludge incinerators, and medical waste

incinerators. At this time, emissions from hazardous waste incinerators are not

addressed in AP-42. Emission factors presented in AP-42 are designed for estimating

emissions from a large number of sources over a wide area. The use of AP-42

emission factors to estimate emissions from any one facility should be done with great

care. Whenever possible, emissions measured from the facility in question should be

used in preference to the emission factor. In the absence of site-specific emission test

data, emissions measured at an identical or similar facility combusting the same or

similar material should be used. AP-42 emission factors should be the last alternative

because they are averages of values determined at one or more individual facilities. 

The individual values that are used to develop the average may vary considerably (for

example, the extreme values used for the emission factor for mercury from municipal

mass burn combustors vary by more than a factor of 5 from the value presented in

AP-42). Emission factors for the following compounds will be available for the following

types of incineration systems:

! Municipal waste combustors: arsenic, cadmium, chromium, mercury,

nickel, lead, total tetra through octa chlorinated dibenzo-p-dioxin and

furans.
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! Sewage sludge incinerators: antimony, arsenic, cobalt, beryllium,

cadmium, chromium, lead, manganese, mercury, nickel, selenium, various

chlorinated dibenzo-p-dioxin and furans.

! Medical waste incinerators: antimony, arsenic, beryllium, cadmium,

chromium, manganese, mercury, nickel, chlorobenzene, chlorophenol,

total tetra through octa chlorinated dibenzo-p-dioxin and furans, total

PCB.

In the absence of suitable AP-42 emission factors, clearly documented

engineering evaluations may be used. Documentation should include copies of

emission test reports used to derive the emission estimates, any assumptions made,

and the rationale for the conclusions that are drawn.

3.4. INCREASES IN EMISSIONS DURING EQUIPMENT MALFUNCTIONS;

STARTUP, AND SHUTDOWN

One of the more difficult aspects in deriving pollutant emission rates from

incineration sources is accounting for temporary increases in emissions that may occur

as a result of startup and shutdown in operations, malfunctions or perturbations in the

combustion process, or changes in the removal efficiency of the air pollution control

equipment. In deriving a quantitative measure of the magnitude and duration of

increased emissions associated with these events, it is recommended that a procedure

similar to the one used to derive estimates of routine emissions be applied. In this

context the following sources of information, in order of preference, should be

reviewed:

! The modeler should review stack emission testing and engineering

reports on the performance and operations of combustion technologies,

furnace designs, and air pollution control devices that are most similar in

every respect to the facility under review. Often the emissions testing is

accompanied by records that may give a gross indication of whether the

system is functioning according to design specifications. Most facilities

routinely monitor carbon monoxide (CO) in the stack to provide a general

indication of combustion efficiency. Carbon monoxide inversely

correlates with combustion efficiency, and a sudden increase in

concentration could imply that an upset in the quality of the burn may

have occurred. The engineering analysis in the test report may

summarize these data, and qualitatively discuss upset conditions and the
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-16 December 1998

length of time they were maintained. Most facilities also routinely monitor

furnace temperature. Furnace temperature is directly related to

combustion efficiency and gives a qualitative measure of whether

incineration of the wastes is occurring according to the design of the

combustion chamber. Temperatures outside the prescribed envelope of

routine operations may signal a temporary malfunction or upset in the

quality of the burn. This may be due to the delivery of a highly

combustible and/or wet material into the combustion chamber. During

these conditions, temporary excursions in emissions of pollutants from the

stack may occur, and this may have been recorded in the engineering test

report.

! A second area of information regarding temporary increases in emissions

during equipment malfunctions is tests designed to determine the removal

efficiency of the air pollution control devices (APCD). More specifically,

some facilities may have been required by state air pollution control

agencies to provide information on the longer-term performance of the

APCD. In most cases such tests involve determining the variability in the

efficiency of removal of particulate matter from the combustion gases,

and/or the removal of acid gases such as sulfur dioxide and hydrogen

chloride. In some rare instances the engineering evaluation may have

been undertaken to determine the extent mercury was being reduced, or

to what extent dioxins were controlled with the APCD. These engineering

analyses may be public documents, and it may be possible to obtain

copies from the appropriate state agency or the EPA regional office. In

any case, these data may be helpful in establishing a quantitative basis

for estimating the magnitude, duration, and frequency of occurrence of

higher-than-expected emissions.

In the event these sources of information do not provide a quantitative (e.g.,

numerical) basis for estimating the magnitude and duration of increase of the pollutant

during upset conditions, then the procedure required by the State of California Air

Resources Board (CARB, 1990) can be applied. In this procedure, defaults for

estimating emissions representative of emergency operating conditions and poor

operating conditions at the facility are provided, as follows:

! Emergency operating conditions. Estimation of stack emissions of the

contaminants representative of emergency operating conditions should be

based on uncontrolled emissions over a 1-hour period. For example, if

dioxin is assumed to be controlled by the APCD at a rate of 99% relative

to uncontrolled concentration, then under the emergency operating
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MPE 3-17 December 1998

conditions scenario the stack concentration would be assumed to be 100

times higher during a 1-hour period. The CARB has defined this

emergency condition as a sudden upset in which the air pollution control

device (APCD) is bypassed to a waste stack, and uncontrolled emissions

occur until the facility is completely shutdown. These operating

conditions include such events as a turbine trip, blown boiler tubes, failure

in the APCD, feed system failure, fire, etc. However if the source could

demonstrate that the APCD would never be bypassed during an

emergency event, then use of uncontrolled emissions would not

realistically reflect the potential emissions from the facility. CARB gives

no guidance on an alternative approach, but does indicate that the

evaluation of potential health impacts resulting from acute exposures

should represent a range of operating conditions. These should reflect

poor operation of the equipment and total equipment failure. For these

operating conditions the increase in emissions resulting from reduced

control efficiency should be estimated and the duration of the event

should be noted.

! Poor operating conditions. To represent stack emissions during operating

conditions that are less than normal, CARB recommends, as a default,

multiplying the average emissions (found from the source test database)

by 10. This factor is incorporated into the annual emission rate of organic

and metal emissions. CARB further recommends using a default value for

the fraction of the time a facility operates under poor conditions on an

annual basis. For organic contaminants, it is assumed that the facility

operates normally 80% of the time and operates poorly for the other 20%. 

For metal emission estimates, CARB recommends the default assumption

that the facility operates normally 95% of the time and under poor

conditions for the other 5% of the time during the year.

 The appropriate regulatory authority may give consideration to exempting a source

from an analysis of potentially increased emissions during upset conditions. However,

it is recommended that such an exemption be based on a careful review of present and

past performance of the subject combustor. The ‘burden-of-proof’ should rest with the

applicant of an air permit to demonstrate, with factual information, that there exists a

low likelihood that upsets and process malfunctions will occur at the facility. If the

combustor is a planned facility, then it is recommended that the applicant provide

technical supporting information that has been derived from a study of a facility most

similar in design, size and operation as the subject facility.
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-18 December 1998

3.5. AIR DISPERSION/DEPOSITION MODELING OF THE STACK GAS EMISSIONS

It has been customary for EPA to use air dispersion and surface deposition

models to estimate the atmospheric transport, deposition flux, and ambient air

concentrations of specific pollutants attributable to smokestack emissions from an

industrial combustion source. Air dispersion models are mathematical constructs that

approximate the physical and chemical processes occurring in the atmosphere that

directly influence the dispersion of gaseous and particulate emissions from

smokestacks of stationary combustion sources. These models are computer programs

encompassing a series of partial differential and algebraic equations to calculate the

dispersion and deposition of the emissions. Concentration and deposition isopleths of

the toxic air contaminants discharged from the stack are computed at specified

distances from the smokestack. These quantities are used to estimate the magnitude

of potential exposures to the human receptor.

Numerous dispersion/deposition models have been developed. This document

focuses on the Industrial Source Complex (ISC3) dispersion model recently developed

by EPA to provide modeling outputs useful in the analysis of wet/dry deposition and

ambient air concentrations of stack emitted contaminants in all terrain settings (U.S.

EPA, 1995a,b). The ISC3 was developed as a general replacement to the COMPDEP

model first described in U.S. EPA (1990b). Modeling enhancements of ISC3 included

more refined small-particle dry and wet deposition algorithm than used by the

COMPDEP model. The ISC3 was used to generate the results for the hypothetical

incinerator of this assessment. However, the use of ISC3 in this assessment is not

intended to imply that ISC3 is the only acceptable model.

Section 3.5.1. below presents an overview of the dispersion and deposition

algorithms in the ISC3 model. Section 3.5.2. discusses dry deposition fluxes, including

pertinent assumptions made in the application of the ISC3 model. Section 3.5.3.

discusses particle size distributions for emitted particles. Wet deposition flux is

reviewed in Section 3.5.4. Estimating the phase distribution of semivolatile organic
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-19 December 1998

contaminants is reviewed in Section 3.5.5. Section 3.5.6. highlights the current

recommendations for the regulatory use of ISC3. The guidance indicates that the ISC3

model should be run with two simultaneous modes of operation: one providing

estimates of particulate concentrations in air and wet/dry particle deposition flux; the

other providing for the estimation of vapor-phase concentrations of dioxin-like

compounds in ambient air.

3.5.1. Basic Physical Principles Used to Estimate Atmospheric Dispersion and

Deposition of Stack Emissions. Air dispersion/deposition models mathematically

simulate the basic physical processes in the atmosphere to estimate the ground-level

air concentrations and deposition flux of contaminants known to be released from the

stacks/vents of stationary combustion sources. These processes include advection,

turbulent diffusion, and removal of atmospheric particles. Advection describes the

physical movement of the air contaminants by the horizontal movement of wind. 

Turbulent diffusion is the “spreading” of the emissions plume with distance from the

stack through mulidirectional fluctuations in air movement. Removal refers to

mechanisms that remove emissions from the atmosphere. This can be caused by the

force of gravity exerted on the particle mass, Brownian movement of aerosol particles,

and scavenging of particles. Scavenging is the removal of particles or vapors by

precipitation.

ISC3 contains modifications of the Industrial Source Complex model (short-term

version) and COMPLEX I to incorporate algorithms to estimate dispersion and resulting

ambient air concentrations and wet and dry deposition flux. COMPLEX I is a secondlevel screening model applicable to stationary combustion sources located in complex

and rolling topography (U.S. EPA, 1986). The ISC3 model was developed by EPA to

provide estimates of air concentrations and deposition rates of the stack emissions of

contaminants from industrial sources located in varied terrain (e.g., from simple to

complex terrain). Simple and complex terrain are defined as topography that is either

below or above the effective stack height of the source (Turner, 1986). To account for

pollutant deposition, the concentration algorithms in COMPLEX 1 were replaced with
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-20 December 1998

P '

QKVD

2BμsFyFz

exp &0.5

y

Fy

2

(3-1)

those from the Multiple Point Source Algorithm with Terrain Adjustments Including

Deposition and Sedimentation (MPTER-DS) model (Rao and Sutterfield, 1982). The

MPTER-DS algorithms incorporate the gradient transfer theory described by Rao and

Sutterfield (1982) and are extensions of the traditional Gaussian plume algorithms. 

The dispersion algorithms contained in the Industrial Source Complex, short-term

version (ISCST), have been incorporated in ISC3 to analyze ground-level receptors

located below the height of the emission plume (U.S. EPA, 1986). For a steady-state

Gaussian plume, the hourly concentrations at downwind distance x (meters) and

crosswind distance y (meters) are given by:

where:

P = ambient air concentration of contaminant, μg/m3

Q = contaminant emissions rate, g/s

K = units conversion factor

V = vertical term - accounts for vertical distribution of the Gaussian

plume, dimensionless

D = plume depletion term relating removal by physical or chemical 

processes, dimensionless

Fy, Fz = standard deviation of lateral and vertical concentration distribution,

m

μs = mean wind speed at release height, m/s

y = crosswind distance from source to receptor, m

ISC3 uses the generalized Briggs (1975, 1979) equation to estimate plume-rise and

downwind dispersion as a function of wind speed and atmospheric stability. A wind-
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-21 December 1998

Fy' 465.11628(x)tan(TH)

(3-2)

TH ' 0.017453293[c & d ln(x)]

(3-3)

Fz' ax b

(3-4)

profile exponent law is used to adjust the observed mean wind speed from the

measurement height to the emission height for the plume rise and pollutant

concentration calculations. The Pasquill-Gifford curves are used to calculate lateral

and vertical plume spread (U.S. EPA, 1986). These curves are based on Pasquill’s

definitions of atmospheric stability classes, e.g., extremely unstable, moderately

unstable, slightly unstable, neutral, slightly stable, and moderately stable, that

correspond to various intensities of solar radiation and wind speeds (Seinfeld, 1986). 

Equations that approximately fit the Pasquill-Gifford curves (Turner, 1970) are used to

calculate Fy and Fz (in meters) for the rural mode. The equations used to calculate Fy

are of the form:

where:

In Equations 3-2 and 3-3 the downwind distance x is in kilometers, and the

coefficients c and d are listed in Table 3-4. The equation used to calculate Fz is of the

form:

where the downwind distance x is in kilometers and Fz is in meters.

Tables 3-4 and 3-5 show the equations used to determine Fy and Fz for the

urban option. These expressions were determined by Briggs as reported by Gifford

(1976) and represent a best fit to urban vertical diffusion data reported by McElroy and

Pooler (1968). While the Briggs functions are assumed to be valid for downwind

distances less than 100 m, the user is cautioned that predicted concentrations at
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-22 December 1998

receptors less than 100 m from a source may be suspect. The incorporation of these

two basic models into ISC3 permits analysis of a source located in all types of terrain.

TABLE 3-4

Parameters Used to Calculate Pasquill-Gifford Fy

Pasquill Stability Category

Fy = 465.11628 (x)tan(TH)

TH = 0.017453293 [c - d ln(x)]

c d

A 24.167 2.5334

B 18.333 1.8096

C 12.5 1.0857

D 8.333 0.72382

E 6.25 0.54287

F 4.1667 0.36191

3.5.2. Estimation of Dry Surface Deposition Flux. The dry deposition of particlebound contaminants is a physical atmospheric removal process that is simulated by the

ISC3 model. Dry deposition refers to the transfer of airborne particulate matter to the

Earth's surface (including water, soil, and vegetation) whereby it is removed from the

atmosphere. The deposition of vapor-phase organic contaminants (e.g., gaseous

deposition) is not currently considered in the ISC3 model. The general processes

controlling the transfer of particulate from some height above the surface through the

surface layer down to the immediate vicinity of the surface are the forces of gravity and

turbulent diffusion, followed by diffusion through the laminar sub-layer (defined as a

thickness of 10-1 to 10-2 cm) to the surface (Seinfeld, 1986). The term “deposition flux”

is mass concentration of a contaminant sorbed to atmospheric particulates that is

delivered to the surface per unit of time by the physical forces of gravity, atmospheric
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MPE 3-23 December 1998

turbulence, and diffusion (Kapahi, 1991). Deposition flux is represented

mathematically by Fd. Note that the parameter Fd in equation 3-5 is synonymous with

Dyd in equation

TABLE 3-5

Parameters Used to Calculate Pasquill-Gifford Fz

Pasquill Stability

Category x (km)

Fz(meters) = axb (x in km)

a b

A

a <0.10

0.10 - 0.15

0.16 - 0.20

0.21 - 0.25

0.26 - 0.30

0.31 - 0.40

0.41 - 0.50

0.51 - 3.11

>3.11

122.800

158.080

170.220

179.520

217.410

258.890

346.750

453.850

b/

0.94470

1.05420

1.09320

1.12620

1.26440

1.40940

1.72830

2.11660

b/

B

a <0.20

0.21 - 0.40

>0.40

90.673

98.483

109.300

0.93198

0.98332

1.09710

C

a All 61.141 0.91465

D <0.30

0.31 - 1.00

1.01 - 3.00

 3.01 - 10.00

10.01 - 30.00

>30.00

34.459

32.093

32.093

33.504

36.650

44.053

0.86974

0.81066

0.64403

0.60486

0.56589

0.51179
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MPE 3-24 December 1998

E <0.10

0.10 - 0.30

0.31 - 1.00

1.01 - 2.00

2.01 - 4.00

 4.01 - 10.00

10.01 - 20.00

20.01 - 40.00

>40.00

24.260

23.331

21.628

21.628

22.534

24.703

26.970

35.420

47.618

0.83660

0.81956

0.75660

0.63077

0.57154

0.50527

0.46713

0.37615

0.29592
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-25 December 1998

Fd' 0.31536Vd Cpa

(3-5)

Vd' &

Fd

Cpa

(3-6)

4-2 used in calculating deposition rates. The annual dry deposition flux, Fd, is defined

as the product of the ambient air concentration of the chemical contaminant, Cpa,

sorbed to atmospheric particles times a deposition velocity (ms-1) of the contaminated

particles, and adjusted per year as in Equation 3-5.

where:

Fd = dry deposition flux of contaminants sorbed to particles,

g/m2 -yr

0.31536 = unit conversion factor [3.1536E+07 seconds/year]/[100

cm/m] [106 μg/g]

Vd = dry deposition velocity, m/sec

Cpa = particle-bound concentration of contaminant on settling

particles, μg/m3

In general, Chamberlain and Chadwick (1953) first defined the deposition velocity, Vd,

as the quotient of the deposition flux, Fd, divided by the airborne concentration, Cpa:

The value for Fd in Equation 3-6 has a minus sign because the downward flux is

negative, whereas the deposition velocity is positive (Sehmel, 1980). By this

relationship, Chamberlain and Chadwick (1953) first introduced the concept of plume

depletion, i.e., as the emission plume is dispersed with downwind distance from the

stack, the deposition flux decreases with distance from the source.
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-26 December 1998

μ ' (

μ(

8

) ln (

z % zo

zo

)

(3-7)

The basic dynamics of the physics of modeling dry deposition have not changed

significantly since Sehmel’s (1980) comprehensive scientific review. The factors that

most influence the predicted deposition flux can be divided as meteorological

influences or the influences of the properties of the pollutant under analysis. 

Meteorological influences include the friction velocity, represented as μø, and the

aerodynamic surface roughness, represented as zo. These terms are used to describe

the wind speed profile above the Earth’s surface. In most cases, the analyst uses a

graphical procedure to determine values for μø and zo. If the logarithm of wind speed is

plotted for near neutral atmospheric stability as a function of height from the surface,

then the values for the constant zo are fitted to a straight line on a semi-logarithmic

scale. This can be described mathematically by Equation 3-7. In most cases, the

friction velocity is a percentage of the wind speed.

where:

μ = measured wind speed, cm/s

μ* = the friction velocity, cm/s

z = the measured height above the surface, cm

zo = surface roughness length, cm

8 = von Karman’s constant, approx. = 0.4

As a general rule, particles greater than 30 micrometers (μm) in diameter will be

removed from the atmosphere primarily by the force of gravity, whereas particles less

than 30 μm will be removed primarily by atmospheric turbulence. The deposition flux

for the smaller particles is influenced by many factors, including the distribution of

particles by diameter and density, the atmospheric turbulence, the friction of the ground
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-27 December 1998

vd'

1

r

a

% r

d

% I

a

I

d

I

s

%Vg

(3-8)

surface, and the height of the stack release of emissions. Deposition flux is also

affected by the partitioning properties of the pollutant. These properties will determine

how much of the pollutant is sorbed to the particle and how much is in the vapor phase. 

A detailed list of the many factors that can affect dry deposition is shown in Table 3-6.

The ISC3 estimates dry deposition flux on the basis of empirical associations

developed by Sehmel (1980) and Sehmel and Hodgson (1978) relating the deposition

flux to the deposition velocity of particles. The downward motion represented by

deposition velocity is controlled by the gravitational settling velocity, atmospheric

resistance, surface resistance, and the atmospheric surface friction layer. This model

assumes that a fraction of the particulate comes into contact with the ground surface by

the combined processes of gravitational settling, atmospheric turbulence, and Brownian

diffusion. The ISC3 model contains enhancements to calculate dry deposition flux

using a dry deposition model developed by Pleim et al. (1984). The Pleim et al. (1984)

algorithms represent Sehmel’s (1980) empirical relationships for transfer resistances as

a function of particle size, density, surface roughness, and friction velocity. In the

Pleim et al. (1984) model, integrated resistances to mass transfer are computed within

two layers. In the first layer, which extends from 1 cm to 1m above the surface,

atmospheric turbulence dominates mass transfer. This is a fully turbulent region where

vertical fluxes are nearly constant, and is referred to as the aerodynamic resistance. In

the second layer, which lies within 1 cm of the surface, the resistance to mass transfer

is derived from particle deposition measurements that were taken in a wind tunnel over

various surfaces using monodispersed particles (Sehmel, 1980; Sehmel and Hodgson,

1978). The general approach used by ISC3 in the resistance methods for estimating

the dry deposition velocity of particles is given by Equation 3-8:
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-28 December 1998

where:

Vd = dry deposition velocity, cm/sec

Vg = gravitational settling velocity, cm/sec

TABLE 3-6

Factors That Influence the Dry Deposition Removal Rate in the Atmosphere*

Micro-meteorological

Variables

Characteristics of

Particles

Characteristics

of Gases

Surface Variables

Aerodynamic roughess

Mass transfer of

Particles

Gases

Heat transfer

Momentum transfer

Atmospheric stability

Diffusion

Friction velocity

Inversion layer

Pollutant concentration

Relative humidity

Seasonal variation

Solar radiation

Surface heating

Temperature

Terrain effects

Turbulence

Wind velocity

Zero plane 

displacement effect

Agglomeration

Diameter

Diffusion effects

Brownian

Eddy

Electrostatic

effects

 Attraction

 Repulsion

Gravity settling

Hygroscopicity

Impaction

Interception

Momentum

Physical

properties

Resuspension

Solubility

Thermophoresis

Chemical

activity

Diffusion effects

Brownian

Eddy

Partial pressure

in equilibrium

with the surface

Solubility

Accommodation

Exudates

Trichomes

Pubescence

Wax

Biotic surface

Canopy growth

 Dormant

 Expanding

Senescent canopy

structure

 Areal density

 Bark

 Bole

 Leaves

 Porosity

 Soils

 Stem

 Type

Electrostatic

properties

Penetration of

canopy

*Source: Adapted from Sehmel (1980)
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-29 December 1998

ra = aerodynamic resistance, sec/cm

rd = deposition layer resistance, sec/cm

Despite what is currently known about the physical and chemical processes that

influence the final deposition flux of particles released from a stationary combustion 

source, a more thorough understanding of the influence of particle size on deposition

velocity is needed. In Sehmel’s (1980) review of settling velocities corresponding to

particle diameter it was noted that the range of values spanned several orders of

magnitude. This complicates efforts to make generalizations of Vd by particle diameter

for air modeling purposes. Although dry particle deposition velocities have been

estimated from both field studies and laboratory experiments, derived velocities are

limited and highly uncertain. This is due largely to the complex and variable array of

factors that can influence the rate of deposition (as summarized in Table 3-6). Thus

Slinn et al. (1978) has noted the experimentally observed differences in Vd as shown in

Figure 3-1.

There are generally two modes In the general definitions of particle size

fractions of stack emitted particles: the coarse mode and the fine mode. The coarse

mode is defined as the distrubition of particles with diameters mostly greater than the

minimum in the particle mass distribution, which generally occurs between 1 and 3 μm

(U.S. EPA, 1996b; Whitby, 1978). These particles are usually mechanically generated. 

The fine mode is the distribution of particles with diameters mostly smaller than the

minimum in the particle mass distribution, which generally occurs between 1 μm and 3

μm. These particles are usually formed from gases. Over the years the definition

proposed by Whitby (1978) has been relaxed, and currently there is no standard

definition in the scientific literature (U.S. EPA, 1996b). A more commonly applied

definition of the mode distributions is the one used Sehmel (1980). Sehmel (1980)

defines the fine particle mode as consisting of particles <2.5 micrometers (μm) in

diameter, and those >2.5 μm are “coarse particles.” Sehmel (1980) offers the most

current review of dry deposition
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-30 December 1998
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-31 December 1998

settling velocities for a variety of depositing materials having a broad range of particle

diameters. This summary appears in Table 3-7.

TABLE 3-7

Summary of Dry Deposition Velocities for Particles*

Depositing Material Particle D

Miameter (μm)

Deposition Surface Deposition Velocity

(cm/s)

Particles 0.03-30 Grassland 10-3-40

Pollen 20

32-35

90-100

Grassland 4.5

9.9

20

Natural aerosol 1-10 Grass shard 0.8

Pb auto exhaust

*Source: Sehmel (1980)

3.5.3. The Particle Size Distribution in the Stack Emissions and Estimation of

Contaminant Particle-Bound Emission Rate. A distribution of particle size and the

diameter of the particulate stack emissions must be known before the ISC3 program

can predict deposition flux of the contaminant. The diameters of small particles

comprising particulate matter in stack emissions are usually measured in units of one

millionth of a meter (micrometer, commonly called micron, abbreviated by μm). 

However, few studies have been conducted that describe the distribution of particulate

matter entrained in the emissions from various combustion technologies broken down

and fractionated by particle diameter. The characterization of particulate matter by

particle diameter will differ from one combustion process to another, and is greatly

dependent on such factors as (1) the efficiency of various air pollution control devices

in removing particles over a broad range of diameters from the gas stream, (2) the
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-32 December 1998

composition of the feed/fuel, (3) the design of the combustion chamber, (4) the amount

of air used to sustain combustion, and (5) the temperature of combustion. Table 3-8

gives an example of a particle diameter distribution as measured at a stack of an

incinerator and, when data are lacking, the assessor may resort to this as a default

particle size distribution. Also, generalized particle size distributions for specific

combustor types and air pollution control systems may be found in Appendix B-1 and

B-2 to the Compilation of Air Pollution Emission Factors (U.S. EPA, 1995c). These

generic distributions have been developed from about 200 stationary source emissions.

TABLE 3-8

Generalized Particle Size Distribution (μm), and Proportion of Available Surface

Area, in Particulate Emissions from Incineration

(Recommended as Default Distribution)

Particle

Diameter

(am)a

Particle

Radius

(am)

Surface

Area/

Volume

Fraction of

Total

Weight

Proportion

Available

Surface

Area

Fraction 

of Total

Surface

Area

>15.0 7.50 0.400 0.128 0.0512 0.0149

12.5 6.25 0.480 0.105 0.0504 0.0146

8.1 4.05 0.741 0.104 0.0771 0.0224

5.5 2.75 1.091 0.073 0.0796 0.0231

3.6 1.80 1.667 0.103 0.1717 0.0499

2.0 1.00 3.000 0.105 0.3150 0.0915

1.1 0.55 5.455 0.082 0.4473 0.1290

0.7 0.40 7.500 0.076 0.5700 0.1656

<0.7 0.40 7.500 0.224 1.6800 0.4880

Total surface area: 3.4423 μm2.

Geometric mean diameter in a distribution. Distribution from U.S. EPA (1980).
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-33 December 1998

Although the ISC3 model can simulate up to 10 particle size categories, the use

of generalized distributions warrants using only three particle size categories. For

example, the three sizes categories below were generalized from the data in Table

3-8:

! Category 1: <2 μm

! Category 2: >2 to <10 μm

! Category 3: >10 μm

After selecting the particle size distribution, it is necessary to calculate the emission

rate of the particulate-bound contaminant by particle size category. This may be

accomplished in one of two ways: 1) by a surface area weighting approach, and 2) by a

mass weighting approach. 

3.5.3.1. Surface area weighting approach — The surface area weighting

approach assumes that the contaminant adsorbs to the surface of the particles during

emissions from the stack. First it is necessary to calculate the proportion of surface

area (available for adsorption of the contaminant) for a given particle diameter. With

the assumption of an aerodynamic shape to the particle, surface area available to

adsorption is determined as the ratio of the surface area to volume in proportion to the

ratio of the surface area to weight for a particle with a given radius. Multiplying this

proportionality by the weight fraction of particles of a specific diameter (μm) in a particle

size distribution gives a value that approximates the amount of surface area available

for chemical adsorption. The surface area to volume ratio can be described as follows:

(a) Assume aerodynamic spherical particles.

(b) Specific surface area of a spherical particle with radius r:

S = 4 Br

2

(c) Volume of spherical particle with radius r:

V = 4/3 Br

3

(d) The ratio of surface area to volume is:

S/V = 4 Br

2

 / (4/3 Br

3

)

S/V = 3/r
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-34 December 1998

Dividing the surface area for each particle category by the total available surface area

for all particles gives an estimation of the fraction of total area on any size particle. 

Multiplication of the emission rate of the dioxin-like congener by the fraction of

available surface area will estimate the emission rate of the pollutant per particle size. 

The fraction of total surface area was computed for the three particle size categories. 

The fractions of total surface areas for the ranges of particle diameters are summed

with each particle size category to represent a single fraction of total surface area for

the given particle size category. For example, if the three particle size categories from

Table 3-8 is assumed, then fraction of total surface is calculated to be as follows:

! Particulate size category 1: fraction of total surface area = 0.875

! Particulate size category 2: fraction of total surface area = 0.095 

! Particulate size category 3: fraction of total surface area = 0.030

The interpretation of this result is that 87.5% of the emission rate of the

contaminant adsorbed to particles is associated with particles <2 μm in diameter, 9.5%

of the emission rate is associated particles size of >2 to <10 μm, and only 3% of the

emission rate is associated with particles >10 μm. To assist in deposition modeling of

the particle-bound emissions from a combustor, it is recommended that the particle size

distribution be further simplified by assuming a median particle diameter to represent

each broad particle size category, as follows:

! Particulate category 1 = 1 μm particle diameter

! Particulate category 2 = 6.78 μm particle diameter

! Particulate category 3 = 20 μm particle diameter

3.5.3.2. Mass weighting approach — The mass weighting approach assumes

that the emission rate of the contaminant that is particle bound is proportional to the

mass distribution of particles emitted from the stack. This assumes that the

contaminant enriches the particles as the particles are formed withing the combustion
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-35 December 1998

V ' exp &0.5

zr& he

Fz

2

% exp &0.5

zr% he

Fz

2

% j

4

i ' 1

exp &0.5

H1

Fz

2

% exp &0.5

H2

Fz

2

% exp &0.5

H3

Fz

2

% exp &0.5

H4

Fz

2

(3-9)

system. Under the column heading “Fraction of the Total Weight” to Table 3-8 shows

an example of the type of information that is needed to compute the particle bound

emission rate of the contaminant by the mass weighting approach. To establish the

fraction by mass by particle size requires the measurement of the particle size

distribution of total mass emissions of particulate matter from the stack of a particular

combustor. This is accomplished with a measurement device attached to the stack

such as a cascade impactor. These devices collect and trap particles within

segmented compartments that allow for only specific particle diameters to be trapped

and retained (U.S. EPA, 1996b). If these specific measurements are not available for

the source of interest, then secondary sources of information must be used to estimate

the fractional distribution of particles by weight. It is recommended that the distribution

in Table 3-8 be used as a default, or the the AP-42 guidance for specific distributions

from specific combustors be used (U.S. EPA, 1995c). 

In general, the effects on ambient concentrations of gravitational settling and dry

deposition can be neglected for gaseous pollutants and small particulates (less than

about 0.1 μm in diameter). The vertical term (V) without deposition effects is then given

by:

where:

he = hs + )h
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-36 December 1998

V '

2BFz

zi

(3-10)

H1 = zr - (2izi - he)

H2 = zr + (2izi - he)

H3 = zr - (2izi + he)

H4 = zr + (2izi + he)

zr = receptor height above ground (flagpole) (m)

zi = mixing height (m)

The infinite series term in Equation 3-9 accounts for the effects of the restriction on

vertical plume growth at the top of the mixing layer. The method of image sources

accounts for multiple reflections of the plume from the ground surface and at the top of

the mixed layer. It should be noted that if the effective stack height, he, exceeds the

mixing height, zi, the plume is assumed to fully penetrate the elevated inversion and the

ground-level concentration is set equal to zero.

Equation 3-9 assumes that the mixing height in rural and urban areas is known

for all stability categories. As explained below, the meteorological preprocessor

program uses mixing heights derived from twice-daily mixing heights calculated using

the Holzworth (1972) procedures. The ISC models currently assume unlimited vertical

mixing under stable conditions, and therefore delete the infinite series term in Equation

3-9 for the E and F stability categories.

The vertical term defined in Equation 3-9 changes the form of the vertical

concentration distribution from Gaussian to rectangular (i.e., a uniform concentration

within the surface mixing layer) at long downwind distances. Consequently, in order to

reduce computational time without a loss of accuracy, Equation 3-9 is changed to the

form:

at downwind distances where the Fz/zi ratio is $1.6 (U.S. EPA, 1995b).
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-37 December 1998

Fw' 3.1536 7 P

(3-11)

3.5.4. Estimation of Wet Deposition Flux. Wet deposition occurs by precipitation

(rain, hail, snow) physically washing out the chemically contaminated particulate and

vapors from the atmosphere. Vapor scavenging is not yet well understood and is not

addressed in the ISC3 model. The remainder of this discussion refers only to the wet

deposition of particles.

Wet deposition flux depends primarily on the fraction of the time precipitation

occurs and the fraction of material removed by precipitation per unit of time by particle

size. Based on these relationships, scavenging coefficients were developed by Jindal

and Heinold (1991) for varying types and intensities of precipitation relative to different

particle diameters by incorporating the observations of Radke et al. (1980) in a study of

scavenging of aerosol particles by precipitation. The principal assumptions made in

computing wet deposition flux are: (1) the intensity of precipitation is constant over the

entire path between the source and the receptor; (2) the precipitation originates at a

level above the top of the emission plume so that the precipitation passes vertically

through the entire plume and (3) the flux is computed on the basis of the hour

precipitation occurs, as determined by hourly precipitation measurements compiled by

the National Weather Service. The remaining fraction is subject only to dry deposition

processes. Thus, no dry deposition occurs during hours of steady precipitation, and

dry deposition occurs between the periods of precipitation.

Wet deposition flux is estimated by ISC3 using a scavenging ratio approach in

which the flux of contaminant to the surface (Fw) is the product of the scavenging ratio

times the contaminant concentration, as in the following equation.

where:

Fw = wet deposition flux, g/m2-year

v = scavenging ratio, sec-1
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-38 December 1998

Q(x) ' Qoe

&7x/u 'Qo

e

&7t

(3-13)

3.1536 = unit conversion factor [3.1536E+07 sec/year]/[10-6 μg/g]

P = contaminant air concentration value calculated from

Gaussian plume equation, μg/m3

The scavenging ratio (7) is calculated as the product of the scavenging coefficient and

precipitation rate (Scire et al., 1990), as follows:

7 = 8 * R (3-12)

where:

7 = scavenging ratio, s-1

8 = scavenging coefficient, (s/mm/h)-1

R = precipitation rate, mm/h

The scavenging coefficient depends on the size distribution for particles and the nature

or form of precipitation, i.e., liquid or frozen.

Across the plume, the total flux to the surface must be approximately equal to

the mass lost from the plume. ISC3 contains a plume wet deposition depletion

equation as follows:

where:

Q(x) = wet plume depletion factor, dimensionless

7 = precipitation scavenging ratio, s-1

t = x/u = plume travel time, s

The relationship between the scavenging coefficient, 7, and the particle size and

precipitation intensity was derived from the review of wet deposition studies of aerosol

particles by Jindal and Heinold (1991), and is shown in Figure 3-2. Table 3-9 displays

the scavenging coefficients assigned to the generalized particle size categories and
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-39 December 1998

FIGURE 3-2

Wet Scavenging Rate Coefficient as a Function of Particle Size

Source: Jindal and Heinold, 1991
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-40 December 1998

N '

c 1

pEL% c 1

(3-14)

TABLE 3-9

Unit Wet Deposition Scavenging Coefficients Per Particle Diameter

Category (micrometers) Used In the Example ISC3 Analysis,

Expressed as 1/(s/mm/r)

Form of Precipitation 1 6.78 20

Liquid (rain) 0.43 x 10-4 0.46 x 10-3 0.66 x 10-3

Frozen (snow) 0.14 x 10-4 0.16 x 10-3 0.22 x 10-3

forms of precipitation (liquid rainfall, frozen) used as a default for computing estimates

of wet deposition in the application of the ISC3.

3.5.5. Estimation of the Phase Distribution of Semivolatile Organic Emissions. At

the point of stack emission and during the atmospheric transport, partitioning of the

contaminants occurs between two physical phases: vapor and particle. A vapor-toparticle ratio (V/P) needs to be established for each pollutant of interest. The V/P ratio

represents the ratio of the concentration of the pollutant in the vapor phase to the

concentration of that pollutant in the particulate phase, usually expressed in terms of

percentage with V plus P adding to 100%, e.g., 60% vapor and 40% particle-bound.

A relationship first proposed by Junge (1977) in Equation 3-14, and later reviewed and

critically evaluated by Pankow (1987) is the most widely used model for estimating the

adsorption of semivolatile compounds (e.g., SOCs) to atmospheric aerosol particles:
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-41 December 1998

ln

pEL

pES

'

) Sf( Tm& T )

RT

(3-15)

where:

N = fraction of compound adsorbed to aerosol particles

pEL = saturation liquid-phase vapor pressure of pure compound at

ambient temperature, Pa

1 = particle surface area per unit volume of air, cm2 aerosol/cm3 air

c = constant related to difference between heat of desorption from

particle surface, Qd, and heat of vaporization of the compound, Qv.

The value of c is often estimated at 17.2 Pa/cm

Pankow (1987) argued that different values of Qd - Qv (and therefore c) may be

appropriate for different classes of compounds.

Although Junge (1977) did not specify the physical state of the sorbing

compound in Equation 3-14, field and laboratory studies of the particle/gas distribution

of PAHs and organochlorine compounds indicate that the process is better described

by using the vapor pressure of the subcooled liquid rather than the solid-phase vapor

pressure (po

S

) (Bidleman et al., 1986; Cotham and Bidleman, 1992; Foreman and

Bidleman, 1987). In Equation 3-15, the two vapor pressures are related by:

where:

pEL = liquid subcooled vapor pressure of pure compund, Pa

pES = crystalline solid-phase vapor pressure of pure compound, Pa

)Sf = entropy of fusion, J/mol/K

Tm = melting point of the compound, K

T = ambient temperature, K

R = ideal gas constant, 8.314 J/mol/K
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-42 December 1998

Values of )Sf have been summarized for PCDD/Fs (Rordorf 1989) and other

semivolatile compounds (Hinckley et al., 1990). In the absence of an experimental

value, an average )Sf/R = 6.79 is often used (Hinckley et al., 1990), and is used in this

assessment. Liquid-phase vapor pressures have also been estimated for PCBs, PAHs,

PCDD/Fs, and organochlorine pesticides by capillary gas chromatography (Falconer

and Bidleman, 1994; Eitzer and Hites, 1988; Eitzer and Hites, 1989; Hinckley et al.,

1990; Yamasaki et al., 1984). The hypothesis that po

L

 controls sorption to aerosols is

especially significant for PCDD/Fs, as many of these compounds have high melting

points and thus large differences between po

L

 and po

S

.

Estimates of 2 are given by Bidleman (1988), based on a study by Whitby (1978)

of the size distribution of accumulation-mode aerosols. Whitby also estimated the

average total volume of particles per unit volume of air (VT = cm3 aerosol/cm3 air). 

Values of 2 and VT are given in Table 3-10. From VT and an assumed particle density

of 1.4 g/cm3, the average TSP concentrations in urban and average background air are

98 and 42 μg/m3. These can be compared to average monitored TSP concentrations of

79 and 30 μg/m3 in 46 U.S. cities and 20 rural locations in 1975 (Shah et al., 1986). 

Similar calculations using values of 2 yield estimates of the aerosol specific surface

area (Atsp) of 11 m2/g in urban air and 3.6 m2/g in average background air (Bidleman,

1988). Measurements of Atsp from particles collected on glass fiber filters were 1.9-3.1

m

2

/g in Pittsburgh (Corn et al., 1971) and 2.3-8.7 m2/g in Portland, OR (Sheffield and

Pankow, 1994).

The latter authors noted that particles tended to agglomerate to a greater extent

on teflon membrane filters than on glass fiber filters, and that experimentally

determined specific surface areas were higher on the glass fiber filters. This suggests

that values of Atsp measured on filtered particles may be biased on the low side,

although more data are needed in this regard.
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-43 December 1998

N '

Cp( TSP )

Cg% Cp( TSP )

(3-16)

TABLE 3-10

Values of 2, VT, and TSP in Different Air Regimes

Airshed type

2

cm2 aresol/cm3 air

VT

cm3 aerosol/cm3 air

TSP

μg/m3

Clean continental

background

4.2*10-7 6.5*10-12 9

Average background 1.5*10-6 3.0*10-11 42

Background plus local

sources

3.5*10-6 4.3*10-11 60

Urban 1.1*10-5 7.0*10-11 98

Sources: Bidleman (1988) and Whitby (1978)

The particulate fraction can also be expressed by Equation 3-16:

where:

N = fraction of compound adsorbed to aerosol particles

Cp = concentration of semivolatile compounds associated with aerosols,

ng/μg particles

Cg = gas-phase concentration, ng/m3

TSP = total suspended particle concentration, μg/m3

Equation 3-16 is a general relationship that applies to any experimental or model

estimate of Cg and Cp. Combining Equations 3-14 and 3-16 yields:
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-44 December 1998

Log

Cp

Cg

' Log Kp' Log c 2

TSP

& Log pEL

(3-17)

Log Kp' m Log pEL% b

(3-18)

Here and in other work (Falconer et al., 1995; Kamens et al., 1995; Hart and Pankow,

1994; Pankow and Bidleman, 1992) Cp/Cg is referred to as the particle/gas partition

coefficient, Kp (m3/μg). Its inverse, Cg/Cp = 1/Kp, has also been used for these

correlations (Cotham and Bidleman, 1992). According to Equation 3-17, the expected

slope of log Kp vs. log po

L

 is -1 and the intercept is related to the specific surface area

of the aerosol Atsp (m2/g) = 1082/TSP. Plots of log Kp vs. log po

L

 for partitioning data

obtained with filtration air samplers are usually well correlated and follow the general

relationship as expressed in Equation 3-18:

It is often the case that m Ö -1 because of kinetic limitations and/or sampling artifacts. 

In these situations the intercept b is partially dependent on the slope and cannot be

used to estimate 2 (Pankow and Bidleman, 1992).

3.5.6. Recommendations for Regulatory Use. ISC3 is considered appropriate for

the following applications:

! Industrial source complexes;

! Rural or urban areas;

! Flat or rolling terrain;

! Transport distances less than 50 km;

! 1-hour to annual averaging times; and

! Continuous toxic air emissions.
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-45 December 1998

ISC3 contains a regulatory default feature that controls the selection of modeling

options. When this feature is selected, ISC3 will implement the following regulatory

options:

! Use stack-tip downwash (except for Schulman-Scire downwash);

! Use buoyancy-induced dispersion (except for Schulman-Scire

downwash);

! Do not use gradual plume rise (except for building downwash);

! Use calm processing routines;

! Use upper-bound concentration estimates for sources influenced by

building downwash from super-squat buildings;

! Use default wind profile exponents; and

! Use default vertical potential temperature gradients.

3.5.7. Dispersion Parameters. The ISCST3 model uses the steady-state Gaussian

plume equation for a continuous elevated source. For each source and each hour, the

origin of the source’s coordinate system is placed at the ground surface at the base of

the stack. The x axis is positive in the downwind direction, the y axis is crosswind

(normal) to the x axis, and the z axis extends vertically. The fixed receptor locations

are converted to each source's coordinate system for each hourly concentration

calculation. The hourly concentrations calculated for each source at each receptor are

summed to obtain the total concentration produced at each receptor by the combined

source emissions.

3.5.8. Treatment of Terrain. Before modeling, the physical geography within 50 km of

the combustor location needs to be systematically examined. This examination should

include surrounding terrain elevation and the dimensions of structures that may impact

dispersion of the plume. Sources of these data may include detailed maps (such as

those provided by the U.S. Geologic Survey), aerial photographs, other survey

information, and architectural plans of nearby buildings.
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-46 December 1998

ISCST3 incorporates the COMPLEX1 screening model dispersion algorithms for

complex terrain, i.e., where the terrain elevation is above the top of the source’s lowest

stack. Terrain below the top of the stack is termed simple terrain. The user has the

option of specifying only simple terrain (i.e., ISCST) calculations, only complex terrain

(i.e., COMPLEX1) calculations, or of using both simple and complex terrain algorithms. 

In the latter case, the model will select the higher estimate of the simple and complex

terrain calculations on an hour-by-hour, source-by-source and receptor-by-receptor

basis for receptors in intermediate terrain, i.e., terrain between the lowest stack tip and

the base of the stack.

If no terrain is specified, the default is simple terrain. If the user wishes to model

receptors on elevated terrain, then the TERRHGTS keyword must be included on the

CO pathway. This keyword, which is described in more detail in Section 3.2.3. of the

ISC3 User’s Manual, accepts one of two possible secondary keywords, either FLAT or

ELEV. ISCLT3 does not include any complex terrain algorithms.

The user can input elevated receptor heights in order to model the effects of

terrain above (or below) stack base, and may also specify receptor elevations above

ground level to model flagpole receptors. For simple terrain calculations, any terrain

heights input above the release height for a particular source are “chopped off” at the

release height for that source’s calculations.

In the ISC short-term model, intermediate terrain is defined as terrain that

exceeds the height of the release but is below the plume centerline height. The plume

centerline height that defines whether a given receptor is on intermediate terrain is the

distance-dependent plume height calculated for the complex terrain algorithm, before

the terrain adjustment (Section 1.5.6.2. of the User’s Manual) is applied. If the plume

height is equal to or exceeds the terrain height, then that receptor is defined as

complex terrain for that hour and that source, and the concentration is based on the

complex terrain screening algorithm only. If the terrain height is below the plume height

but exceeds the physical release height, then that receptor is defined as intermediate

terrain for that hour and source. For intermediate terrain receptors, concentrations
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-47 December 1998

hs ́ ' hs%2ds

vs

us

&1.5 for vs<1.5us

or

hs ́ ' hsfor vs$1.5us

(3-19)

from both the simple terrain algorithm and the complex terrain algorithm are obtained

and the higher of the two concentrations is used for that hour and that source. If the

terrain height is less than or equal to the physical release height, then that receptor is

defined as simple terrain, and the concentration is based on the simple terrain

algorithm only. For deposition calculations, the intermediate terrain analysis is first

applied to the concentrations at a given receptor, and the algorithm (simple or complex)

that gives the highest concentration at that receptor is used to calculate the deposition

value. Figure 3-3 illustrates the typical behavior of combustor emission plumes in

elevated terrain.

3.5.9. Stack-Tip Downwash. Stack tip downwash generally occurs with poorly

constructed stacks or when the ratio of the stack exit velocity to wind speed is small. 

An algorithm developed by Briggs (Hanna et al., 1982) is recommended for this

situation and is found in the point source preferred models. In order to consider

stack-tip downwash, modification of the physical stack height is performed following

Briggs (1975). The modified physical stack height hs ́ is found from:

where hs is physical stack height (m), vs is stack gas exit velocity (m/s), and ds is inside

stack top diameter (m). This hs ́ is used throughout the remainder of the plume height

computation. If stack tip downwash is not considered, hs ́ = hs.

3.5.10. Transitional/Final Rise. The plume rise methods of Briggs (1975) and Hanna

et al. (1982) are incorporated in the preferred models and are recommended for use in

all modeling applications. No provisions are made in these models for fumigation,
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-48 December 1998

FIGURE 3-3. Illustration of Plume Behavior in Elevated Terrain Assumed by the ISC3 Model.
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-49 December 1998

Fb' gvsd

2

s

)T

4Ts

(3-20)

multistack plume rise enhancement, or the handling of such special plumes as flares;

these problems should be considered on a case-by-case basis.

Because there is insufficient information to identify and quantify dispersion

during the transitional plume rise period, gradual plume rise is not generally

recommended for use. There are two exceptions: in complex terrain screening

procedures to determine close-in impacts, or when calculating the effects of building

wakes. The building wake algorithm in the ISC model incorporates and

automatically(i.e., internally) exercises the gradual plume rise calculations. If the

building wake is calculated to affect the plume for any hour, gradual plume rise is also

used in downwind dispersion calculations to the distance of final plume rise, after which

final plume rise is used. In general, the wind speed used in determining plume rise is

defined as the wind speed at stack top.

3.5.11. Buoyancy Induced Dispersion. Buoyancy-induced dispersion (BID), as

identified by Pasquill, is included in the preferred models and should be used where

buoyant sources, e.g., those involving fuel combustion, are involved. For most plume

rise situations, the value of the Briggs buoyancy flux parameter, Fb (m4/s3), is needed. 

The following equation is equivalent to Equation 12, (Briggs, 1975):

where:

)T = Ts - Ta

Ts = stack gas temperature (K)

Ta = ambient air temperature (K)

For determining plume rise due to the momentum of the plume, the momentum

flux parameter, Fm (m4/s2), is calculated based on Equation 3-21:
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-50 December 1998

Fm' v

2

s

d

2

s

Ta

4Ts

(3-21)

3.5.12. Calms Processing. ISC uses CALMPRO (U.S. EPA, 1984), a post-processor

computer program coded to follow U.S. EPA recommendations. This program

disregards hourly concentrations calculated under calm conditions (<1 m/s wind

speed). Critical concentrations for 3-, 8- and 24-hour averages are calculated by

dividing the sum of the hourly concentration for the period by the number of valid hours. 

If the total number of valid hours is less than 18 for 24-hour averages, less than 6 for

8-hour averages or less than 3 for 3-hour averages, the total concentration is divided

by 18 for the 24-hour average, 6 for the 8-hour average, and 3 for the 3-hour average. 

For annual averages, the sum of all valid hourly concentrations is divided by the

number of non-calm hours during the year.

3.5.13. Building Wake Effects. The procedures used by the ISC models to account

for the effects of the aerodynamic wakes and eddies produced by plant buildings and

structures on plume dispersion originally followed the suggestions of Huber and Snyder

(1976). The data used by Huber and Snyder reflect only a specific stability, building

shape, and building orientation with respect to the mean wind direction. To account for

this, ISC models also provide for the revised treatment of building wake effects for

certain sources, which use modified plume rise algorithms following the suggestions of

Schulman and Hanna (1986). When the stack height is less than the building height

plus half the lesser of the building height or width, the methods of Schulman and Scire

are followed. Otherwise, the methods of Huber and Snyder are followed. In the ISC

models, direction-specific building dimensions may be used with either the

Huber-Snyder or Schulman-Scire downwash algorithms.

3.6. MODEL PARAMETER REQUIREMENTS

ISC3 requires four different kinds of inputs: control, source, meteorological, and

receptor. Most input requirements are identical for both the short-term and long-term
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-51 December 1998

models. In some cases the short-term model requires additional inputs, and this is

noted in the text.

3.6.1. Control Inputs. Control inputs are required to specify which options the model

is going to use to evaluate the specific case. Dispersion options, averaging time

options, pollutant type, exponential decay options, terrain options, and elevated

receptor options all require specification. The regulatory default option is also a control

input, and when it is selected the model uses all of the standard regulatory defaults.

3.6.1.1. Land Use for Dispersion Coefficients — The selection of either rural

or urban dispersion coefficients in a specific application should follow one of the

procedures briefly described below. These include a land-use classification procedure

or a population-based procedure to determine whether the character of an area is

primarily urban or rural.

Of the two methods, the land-use procedure is considered more definitive. 

Population density should be used with caution and should not be applied to highly

industrialized areas where the population density may be low and thus a rural

classification would be indicated, but the area is sufficiently built up so that the urban

land-use criteria would be satisfied. In this case, the classification should already be

“urban” and urban dispersion parameters should be used.

The land-use method of Auer (1978) is described in the Guideline on Air Quality

Models (U.S. EPA, 1997). This method can be used to specify appropriate dispersion

coefficients for use with ISCST3. Land use is categorized as “rural” or “urban.” Areas

typically defined as rural include residences and estates with grass lawns and trees,

metropolitan parks and golf courses, agricultural areas, undeveloped land, and water

surfaces. If an area has less than 35% vegetative cover or can be described as

predominantly industrial/ commercial or dense single or multifamily homes, then for air

modeling purposes it is considered urban. Although geographic information systems

and other land-use databases may be used, visual inspection of the area around the

combustor is also acceptable. 
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-52 December 1998

The determination of the rural or urban classification (i.e., the Auer method)

involves the following five steps:

Step 1 Locate the facility on a 7.5 minute topographical relief map. Site map.

Draw a circumference with a radius of 3 km around the center the stack.

Step 2Determine from the surface features and land uses identified on the map

whether the area within the radius is rural or urban. This is according to Aurer’s

definitions that less than 35% of vegetative cover is urban and greater than 35%

vegetative cover is rural.

Step 3Classify smaller areas within the 3 km radius as either rural or urban based on

Auer’s definition of rural/urban based on vegetative cover of the surface area. 

For this exercise, a grided overlay may be used within the radius, for example, a

square grid 100 m by 100 m. Each grid square should be identified as either

rural or urban.

Step 4Count the number of squares that have been identified as rural. If more than

50% of the total squares are rural, the area is rural, and if less than 50% of the

total number of squares are rural, then the area is urban.

Step 5If the area within the 3 km radius of the stack(s) has been determined to be

urban, then further subclassification of urban land use types can be made using

the definitions in Table 3-11.

TABLE 3-11

Determination of the Types of Urban Land Use*

Type Code Use and Structures Vegetative Cover 

11 Heavy industrial <5%

12 Light/moderate industrial <5%

C1 Commercial <15%

R2 Dense single/multi-family residential <30%

R3 Multi-family, two-story residential <35%

*Source: U.S. EPA, 1997
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MPE 3-53 December 1998

3.6.1.2. Terrain — There are three general classifications of terrain: simple,

intermediate, and complex. Simple terrain is an area in which terrain features are all

lower in elevation than the top of the source(s). Terrain between release height and

plume height is intermediate terrain. Complex terrain is an environment wherein the

receptor elevation is above the release height of the source.

Placement of receptors requires very careful attention when complex terrain is

modeled. Often the highest concentrations are predicted to occur under very stable

conditions, when the plume is near, or impinges on, the terrain. The plume under such

conditions may be quite narrow in the vertical, so that even relatively small changes in

a receptor's location may substantially affect the predicted concentration (U.S. EPA,

1996a). Receptors within about a kilometer of the source may be even more sensitive

to location. Thus, a dense array of receptors may be required in some cases. In order

to avoid excessively large computer runs due to such a large array of receptors, it is

often desirable to model the area twice. The first model run would use a moderate

number of receptors carefully located over the area of interest. The second run would

use a more dense array of receptors in areas showing potential for high concentrations,

as indicated by the results of the first run.

The ISC complex terrain algorithm makes the following assumptions about

plume behavior in complex terrain (U.S. EPA, 1995b):

! The plume axis remains at the plume stabilization height above

mean sea level as it passes over complex terrain for stable

conditions (categories E and F), and uses a “half-height”

correction factor for unstable and neutral conditions (categories

A-D).

! The plume centerline height is never less than 10 m above the

ground level in complex terrain.

! The mixing height is terrain following, i.e, the mixing height above

ground at the receptor location is assumed to be the same as the

height above ground at the source location.
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-54 December 1998

! The lateral (crosswind) distribution of concentrations is uniform

across a 22.5-degree sector.

! The attenuation correction factor has a value of unity for receptors

located at or below the elevation of the plume centerline in free air

prior to encountering terrain effects, and decreases linearly with

increasing height of the receptor above plume level to a value of

zero for receptors located at least 400 m above the undisturbed

plume centerline height.

3.6.2. Source Inputs. ISC3 requires the completion of two different cards with source

input data. The first, the location card, requires a source identification, the source type

(point, area, volume, or open pit), and the Cartesian coordinates of the source. The

second card specifies the release source parameters and is dependent on the source

type that was selected in the location card. Each source type requires certain

parameters to define the source. For example, if a point source is selected as the

source type, the required inputs are emission rate, release height, exit temperature, exit

velocity, and stack diameter.

In addition to these required cards, several other parameters about the source

can be specified. The models allow specification of parameters for building downwash,

variable emission rates, and settling-removal-deposition. The short-term model also

allows parameter specification for precipitation scavenging and wet deposition.

Sources with multiple smokestacks require additional analysis. If the critical

stack parameters such as exit gas velocity, stack height, and emitted pollutant

concentrations are the same for each stack, the results of the ISC3 modeling for the

emissions from an individual stack can be modified to account for the other stacks. If a

source has multiple stacks, each with different stack parameter values and pollutant

emission rates, then each stack should be modeled separately with ISCST3. The

results of the individual stacks can then be added at each receptor to estimate the total

predicted concentrations and deposition from the source.

3.6.3. Meteorological Inputs. The short-term ISC3 model requires hourly

meteorological data as one of its basic inputs. The program has several options to
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-55 December 1998

specify the file format. Parameters required include hourly stability class, wind

direction, wind speed, temperature, and mixing height. The long-term model uses a

standard stability array meteorological data file in place of the sequential hourly

meteorological data file. From this file stability wind rose, average afternoon mixing

height, average morning mixing height, and average air temperature are required.

Meteorological data should be selected on the basis of spatial and climatological

(temporal) representativeness as well as the ability of the individual parameters

selected to characterize the transport and dispersion conditions in the area of concern. 

The representativeness of the data is dependent on (1) the proximity of the

meteorological monitoring site to the area under consideration, (2) the complexity of the

topography, (3) the exposure of the meteorological monitoring site, and (4) the period

of time during which data are collected. The spatial representativeness of the data can

be adversely affected by large distances between the source and receptors of interest

and the complex topographic characteristics of the area. Temporal representativeness

is a function of the year-to-year variations in weather conditions.

Five years of representative meteorological data should be used when

estimating concentrations with an air quality model. Consecutive years from the most

recent, readily available 5-year period are preferred. The meteorological data may be

data collected either on-site or at the nearest National Weather Service (NWS) station. 

If the source is large, e.g., a 500 MW power plant, the use of 5 years of NWS

meteorological data or at least 1 year of site-specific data is required. Local

universities, Federal Aviation Administration (FAA), military stations, industry and

pollution control agencies may also be sources of such data.

Meteorological data and building dimension data to be used by ISCST3 must be

in the proper format for this model to run. Manipulation of these large data files is

typically accomplished through preprocessing programs. Two programs are commonly

used: PCRAMMET and BPIP. PCRAMMET (Personal Computer Version of the

Meteorological Preprocessor for the old RAM program) prepares meteorological data

for use with the ISCST3 model by organizing the data so that it can be input to the
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-56 December 1998

model. See the PCRAMMET User’s Guide (U.S. EPA, 1995b). The Building Profile

Input Program (BPIP) calculates the maximum crosswind widths of buildings, which the

air model uses to predict effects on dispersion. This is typically termed building

downwash or wake effects (U.S. EPA, 1995b).

3.6.3.1. Wind Direction and Speed — Wind carries pollutants to receptors

around the source. The direction and speed of the wind determines where pollutants

will be transported, as well as the concentration being transported. Wind direction and

speed vary with time and are often given in the form of a wind rose as shown in Figure

3-4. The type of wind direction and speed information (e.g., hourly vs. average)

required depends of the level of accuracy needed in the results.

3.6.3.2. Stability — In general, higher turbulence results in lower air

concentrations occurring over a greater distance. The amount of turbulence is

quantified in terms of the atmospheric stability. A stable atmosphere suppresses

vertical motions and increases air concentration. Atmospheric turbulence is difficult

and expensive to measure, and it is usually estimated from other, more easily

measurable quantities. In particular, the stability of the atmosphere is typically

characterized by the atmosphere’s vertical temperature profile or lapse rate.

For an isolated parcel of air in which no heat is transferred in or out (adiabatic),

one can show using the first law of thermodynamics and the hydrostatic equation that

there is a 1EC decrease for every 100-m increase in altitude, and a 1EC increase for

every 100-m decrease. This is called the adiabatic lapse rate.

In most cases, the actual environmental lapse rate is not the same as the

adiabatic lapse rate because it is both heated and cooled. The three broad classes of

stability and their associated lapse rates are summarized in Table 3-12.

It should be noted that any time the temperature increases with altitude, the

atmospheric condition is termed an inversion. Under these conditions, vertical motion

is minor and pollutant concentrations may be more dense.
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-57 December 1998

The most widely used scheme of atmospheric stability classification was

developed by Pasquill (1961) and modified by Gifford (1976). In this scheme, there are

six stability classes, denoted with the letters A through F. In general, classes A through
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-59 December 1998

FIGURE 3-4

Hypothetical Wind Rose

C indicate unstable conditions, D is roughly neutral, and classes E

and F represent stable conditions. The meteorological conditions that are used to
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-60 December 1998

determine the stability class are shown in Table 3-13.

From this table, it can be seen that extremely unstable conditions (class A) occur

during the day, with light winds and moderate to strong solar radiation. Conversely,

extremely stable conditions can occur only at night with clear skies and light winds.

Other stability classification schemes exist. For example, Smith (1951)

proposed a classification scheme that is based on wind direction, and Cramer (1957)

advocated a method based on observed wind fluctuations at a height of 10 m (often

referred to as the Brookhaven National Laboratory, or BNL, stability classes). In

addition, Irwin (1979) proposed a method of allowing for a continuum of stability, as

opposed to a discrete approach such as the Pasquill method. Use of the Pasquill letter

classes is common because of their ease of use, and because they have produced

satisfactory results.

3.6.3.3. Atmospheric Temperature — The greater the difference between the

atmospheric temperature and the stack gas temperature, the higher the plume rise. 

The atmospheric temperature input depends upon the model used and the

situation the model is to represent. For example, certain models require the input of

hourly temperature. Others require the input of an average temperature. This

temperature is used to model contaminant movement over the lifetime of the source

and should therefore represent the average temperature over this lifetime.

3.6.3.4. Mixing Height — Mixing height is the depth through which atmospheric

pollutants are typically mixed by dispersion processes. It is affected by the surface

characteristics around the source as well as by meteorological parameters. The

greater the mixing height, the larger the volume of atmosphere available to dilute the

pollutant concentration. It should be noted that if the effective stack height exceeds the

mixing height the plume is assumed to fully penetrate the elevated inversion and the

ground-level concentration is set equal to zero.
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TABLE 3-12

Classes of Atmospheric Stability and Associated

 Vertical Temperature Distribution

Environmental Lapse Rate General Result Class of Stability

Increases with height, or

decreases less rapidly than

adiabatic lapse rate

Vertical motions inhibited Stable

Nearly identical with adiabatic

rate

No significant buoyant forces Neutral

Decreases with height faster

than adiabatic lapse rate

Vertical motions enhanced Unstable

TABLE 3-13

Meteorological Conditions Defining Pasquill Turbulence Typesa

Daytime Solar Raditionb Nighttime Conditions

Surface wind

speed

(m/sec)

Strong Moderate Slight Mostly

Overcast Mostly Clear

<2 A A-B B

2-3 A-B B C E F

3-4 B B-C C D E

4-6 C C-D D D D

>6 C D D D D

aSource: Gifford, 1976

bA, extremely unstable; B, moderately unstable; C, slightly unstable; D, neutral

 (applicable to heavy overcast day or night); E, slightly stable; F, moderately stable;

 (for A-B take average of values for A and B, etc.)
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MPE 3-62 December 1998

3.6.4. Receptor Inputs. Receptor coordinates are required for each receptor, and

these may be input in either a Cartesian or polar format. In addition to the coordinates,

the receptor can have a height specified if there is elevated terrain or if the receptor is

modeled as a flagpole receptor.

3.6.4.1. Receptor Locations — Receptors are located at some fixed distance

from the source. Locations may be sectors within a grid surrounding the source or at

some specified location from the source in a given direction. If complex terrain is

involved, receptors may be at a given distance above stack height as well.

Typical receptor locations are:

! The boundary of the installation

! The closest actual dwelling downwind of the source

! Areas of agricultural development

! Sensitive locations such as schools, playgrounds, and hospitals

! Watershed area and water body surface area

! Wildlife refuges and nature preserves

In addition, estimates of the concentration and deposition rates and the resulting

concentrations in other media and biota at the location of maximum concentration

should be included.

3.6.4.2. Surface Roughness Height — The surface roughness height (also

referred to as the roughness length) is the height above the ground (meters) at which

the wind speed theoretically goes to zero. Urban and rural land uses as well as types

of vegetation covering the surface will cause the momentum of the wind to change as a

result of friction. This loss of wind speed induced by the friction along the features on

the surface primarily effects the deposition velocity of settling atmospheric particles. 

On the basis of the surface roughness height, the ISC3 model allows a particle to

impact the terrain at a point near the surface of the terrain. The difference (typically in

meters) between the point of particle impact and the surface of the terrain is defined as
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MPE 3-63 December 1998

the surface roughness height. It marks the point of departure of the particle from the

ambient air patterns to the “captured” area of deposition.

Surface roughness height is determined by individual components of the

landscape and may vary with the season of the year. Default surface roughness

heights are presented in Table 3-14.

TABLE 3-14

Surface Roughness Heights for Land Use Types and Seasons (meters)*

Land Use Type Spring Summer Autumn Winter

Water surface 0.0001 0.0001 0.0001 0.0001

Deciduous forest 1.00 1.30 0.80 0.50

Coniferous forest 1.30 1.30 1.30 1.30

Swamp 0.20 0.20 0.20 0.05

Cultivated land 0.03 0.20 0.05 0.01

Grassland 0.05 0.10 0.01 0.001

Urban 1.00 1.00 1.00 1.00

Desert shrub land 0.30 0.30 0.30 0.15

*Source: Sheih et al., 1979

Typically, professional judgment is combined with aerial survey or physical site

inspection to determine land use and the corresponding surface roughness. For some

sites an average or “composite” surface roughness is utilized, based on a combination

of two or more land use types present in the modeling domain.
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3.7. OUTPUT

Printed output options include:

! Program control parameters, source data, and receptor data;

! Tables of hourly meteorological data for each specified day;

! “N”-day average concentration or total deposition calculated at each

receptor for any desired source combinations;

! Concentration or deposition values calculated for any desired source

combinations at all receptors for any specified day or time period within

the day; and

! Tables of highest and second highest concentration or deposition values

calculated at each receptor for each specified time period during a(n)

“N”-day period for any desired source combinations, and tables of the

maximum 50 concentration or deposition values calculated for any

desired source combinations for each specified time period.

Model outputs include both pollutant concentration and wet and dry atmospheric

deposition over a given receptor area. These include:

! Highest 1-, 3-, and 24-hour average concentrations at each receptor,

! Deposition values calculated for any desired source combinations at all

receptors for any specified day or time period within the day, and

! Limited individual source contribution list

3.8. ISC3 MODEL APPLICATIONS

In short-term ISC3 model, the user may select any or all of the ouput types, e.g.,

air concentration, wet deposition, dry deposition, and combined wet and dry deposition,

to be generated in a single model run. Instructions for this appear in the User's Guide

for ISC3 (U.S. EPA, 1995a). To facilitate the modeling exercise, the modeler should

assume a “unit emissions release rate” of the contaminant from the stack, e.g., 1 gram

contaminant per second (1 g/s). Results from these unit runs can easily be

transformed to final outputs given assumptions on contaminant emissions in vapor and

particle forms. Two assumptions are required. One assumption is the total emission
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MPE 3-65 December 1998

rate of the compound, in units of mass/time (g/s), and the second is the vapor/particle

partitioning of this total emission. The two modes are:

Mode 1. To estimate vapor-phase concentration of the contaminant in

ambient air. The first mode assumes that the emissions of the contaminant of interest

are gaseous, e.g., with the wet/dry deposition switches turned to the “off” position. This

is to isolate the ambient air concentration of the contaminant in vapor phase from the

calculation of wet and dry particle deposition flux. This inactivates a plume depletion

equation that subtracts out losses in ambient air concentration due to particle

deposition. What remains is the Gaussian dispersion algorithm to calculate ambient air

concentrations.

With the “unitized” emission rate, one can reconstruct the actual predicted

ambient air concentration (μg/m3) of vapors by multiplying the “actual” vapor-phase

emission rate (g/s) by the “unitized” modeling result. For example, let the actual stack

gas emission rate of total (vapor plus particle components) contaminant be 1x10-5 g/s,

and the V/P ratio (expected under ambient conditions) be 60%V/40%P. Then the

“actual” emission rate of the vapor-phase portion of the contaminant is calculated to be

6x10-6 g/s (1x10-5 g/s * 0.6). If the “unitized” ambient air concentration at the groundlevel receptor is estimated by the ISC3 model to be 1x10-8 μg/m3

 (i.e., this

concentration is predicted with a unit emission rate of 1 g/s), then the “actual” predicted

air concentration at that receptor can be estimated as:

(6x10-6 g/s ÷ 1 g/s) * 1x10-8 μg/m3 = 6x10-14μg/m3(3-22)

Mode 2. To estimate wet and dry particle deposition flux, and the ambient

air concentration of the contaminant that is particle-bound. ISC3 should be run

with the wet/dry particle deposition switches turned to the “on” position, and using a

“unit emission rate” of 1 g/s. This second run is considered a simulation of particle-
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-66 December 1998

bound contaminant only. Outputs of this run include unitized deposition rate and

unitized ambient air concentrations of particles.

Like the vapor-phase run, the “actual” deposition flux (g/m-2/yr) and “actual”

particle-phase airborne concentrations can then be determined by multiplying the

“actual” emission rate (g/s) of the particle-bound portion of the total contaminant

emissions by the “unitized” modeling result at the ground receptor. For example, let the

“actual” emission rate of the particle-bound portion of the contaminant be 4x10-6 g/s,

and the “unitized” dry deposition flux at the ground receptor be 1x10-5 g/m2-yr. Then

the “actual” predicted dry deposition flux is 4x10-11 g/m2 (4x10-6 g/s ÷ 1 g/s * 1x10-5

g/m2/yr). Using this same procedure, this second run provides the airborne

concentration of contaminants bound to particles (μg/m3).

There are two meteorological preprocessors used by the ISC3 model program 

to access local conditions necessary to compute model concentrations in both modes

of operation: PCRAMMET (Catalano et al., 1987) and MPRM (Irwin and Paumier,

1990). These files contain hourly data for the wind speed, wind direction, stability

class, mixing height, ambient air temperature, precipitation, and surface friction

velocity.

Inhalation exposures are estimated as the sum of vapor- and particle-phase

concentrations. Air-to-plant transfers require the vapor-phase concentrations for vapor

transfers and the particle-phase depositions. The air-to-soil algorithm requires particlephase depositions.

3.8.1. Calculation of Amount Deposited. For some assessments involving

carcinogens and for estimation of mass balance, estimates of the total amount of an

emitted pollutant deposited within a 50-km radius may be needed. The total amount of

a pollutant deposited within a given annular region surrounding a facility is calculated

as the average of two methods, the difference between the two methods being the

interpolation scheme used to estimate deposition at unmodeled locations. For a given

number of radii r1,...,rN, exactly M receptors are placed at equal angles at each radius. 

In this example, the values used were N=30 (radii of 0.2, 0.5, 0.75, 1.00, 1.25, 1.50,
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-67 December 1998

Amount deposited between r

1

and r

2

km '

m

r2

r1

m

2B

0

f(r,2) rdrd2

(3-23)

j

29

i'1

j

16

j'1 m

r

i%1

r

i

m

2j%B/16

2j&B/16

f(r,2) rdrd2 . j

29

i'1

j

16

j'1

f(r

i

,2j)

m

r

i%1

r

i

m

2j%B/16

2j&B/16

rdrd2

' B/8 j

29

i'1

j

16

j'1

f(r

i

,2j)

r

2

i%1&r

2

i

2

' B/16 j

29

i'1

j

16

j'1

f(r

i

,2j) (r

2

i%1&r

2

i

)

(3-24)

1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4, 4.25, 4.5, 4.75, 6, 7, 8, 9, 10, 15,

20, 25, 30, 35, and 50 km) and M=16, for a total of 480 receptors.

Let f(ri,2j) denote the predicted deposition rate (total, dry or wet) at the radius ri

and angle 2j(=2Bj/M). The amount deposited over the annular region defined by the

radii r1 and r30 is given by

This integral is approximated in two methods.

With one method, the value predicted at a given radius ri and angle 2j is

assumed representative of the wedge-shaped region defined by ri < r <ri+1,2j-2B/M <2

<2j+2B/M. With this method, the values predicted at the largest radius considered are

not used in the calculation. The approximation to the integral is then

With the other method, the value predicted at a given radius ri and angle 2j is

assumed representative of the wedge-shaped region defined by ri-1 < r <ri,2j-2B/M <2

<2j+2B/M. With this method, the values predicted at the smallest radius considered are

not used in the calculation. In particular,
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-68 December 1998

j

29

i'1

j

16

j'1 m

r

i%1

r

i

m

2j%B/16

2j&B/16

f(r,2) rdrd2 . j

29

i'1

j

16

j'1

f(r

i%1

,2j)

m

r

i%1

r

i

m

2j%B/16

2j&B/16

rdrd2

' B/8 j

29

i'1

j

16

j'1

f(r

i%1

,2j)

r

2

i%1&r

2

i

2

' B/16 j

29

i'1

j

16

j'1

f(r

i%1

,2j) (r

2

i%1&r

2

i

)

(3-25)

Amount deposited between r

1

and rN. B/16 j

29

i'1

j

16

j'1

f(r

i%1

,2j)%f(r

i

,2j)

2

(r

2

i%1&r

2

i

)

(3-26)

The average of these two methods is then given by

The area-averaged value is simply the total amount deposited divided by the area of

the region.

It should be noted that this method is only an approximation; the grid size was

chosen as a balance between computation time required to run a year’s worth of data

(which consists of 8,760 hours) and the accuracy of the approximation. In the analyses

for a fixed hour performed in the sensitivity analyses, a much finer grid is used.

3.9. FUGITIVE DUST/EMISSIONS

Fugitive dust usually refers to the dust put into the atmosphere by the wind

blowing over plowed fields, dirt roads, or desert or sandy areas with little or no

vegetation. Reentrained dust is that which is put into the air by reason of vehicles

driving over dirt roads (or dirty roads) and dusty areas. Such sources can be

characterized as line, area, or volume sources. Emission rates may be based on sitespecific data or values from the general literature.
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-69 December 1998

Fugitive emissions are usually defined as emissions that come from an industrial

source complex. They include the emissions resulting from the industrial process that

are not captured and vented through a stack, but may be released from various

locations within the complex. Where such fugitive emissions can be properly specified,

the ISC model, with consideration of gravitational settling and dry deposition, is the

recommended model. In some unique cases a model developed specifically for the

situation may be needed. Because of the difficult nature of characterizing and

modeling fugitive dust and fugitive emissions, it is recommended that the proposed

procedure be cleared by the appropriate Regional Office for each specific situation

before the modeling exercise is begun. (Please see Chapter 5.)

3.10. MODELING INHALATION EXPOSURE

Exposure scenarios have several functions in risk assessment: they are a

means to quantify exposure and dose; they are a way to develop descriptors; and they

are a tool for evaluating remediation options (ORNL, 1996). The procedure for

estimation of inhalation exposure and dose and characterizing the risk is presented in

the following sections.

3.10.1. Estimating Exposure and Dose from Contaminant Concentrations in the

Air. Exposure results from the contact of an organism with environmental media (air,

water, food, soil) containing the contaminant. Exposure, as measured by the intensity

and duration of contact at the external boundary of the organism, can be represented

by a time-dependent profile of the environmental concentration at the point of contact. 

The area under the curve of the profile is the magnitude of the exposure in

concentration-time units. The biologically significant measure of exposure, the intake

or dose, is the amount of the contaminant that crosses the external boundary of the

organism. Substances cross the external boundaries of an organism primarily through

inhalation, ingestion, or absorption through the skin. A brief summary of the concepts

of direct exposure and dose, as described in the U.S. EPA (1992) Exposure

Assessment Guidelines is presented in the following sections.
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Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-70 December 1998

DI ' C @ IR @ ED

(3-27)

C

IR

ADD '

C @ IR @ ED

BW @ AT

(3-28)

3.10.2. Inhalation Doses. For the inhalation pathway, the assumption is made that

the substance is homogeneously distributed through the environmental medium (e.g.,

air, water) and that the intake of the chemical into the body is proportional to the intake

of the medium. Therefore, the dose is the integration of the chemical intake rate (i.e.,

the exposure concentration times the intake rate of the medium) over time. In cases

where the actual contact or exposure time is short and intermittent, such as in the

ingestion of food or drinking water, the intake rate can be expressed in terms of the

frequency of events times the intake per event, and the total dose can be expressed as

the sum of the doses received during each event. If the assumption is made that the

exposure concentration and the intake of the medium are nearly constant during each

event, the resulting dose can be expressed as:

where:

DI = dose (mg)

ED = exposure duration or frequency (days)

= concentration (mg/m3 of air)

= intake rate (m3 of air)

Doses are usually expressed in terms of the rate of intake of the substance per unit

time (e.g., mg/day) per unit body weight (e.g., mg/kg body weight/day). Therefore, for

the above equation, the average daily dose (ADD) is:
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-71 December 1998

DIinternal ' C @ IR @ ED @ AF

(3-29)

ADDinternal '

C @ IR @ ED @ AF

BW @ AT

(3-30)

where:

ADD = average daily dose [(mg/(kg × day)]

BW = body weight (kg)

AT = time period over which dose is averaged (days)

The average daily dose presented above is a measure of the potential or

administered dose (ADDadm) at the point of contact with the exposed individual. It may

or may not be equivalent to the “applied dose” (ADDapp) or the dose available for

absorption at the tissue interface (i.e., at the surface of the respiratory or

gastrointestinal tract), and it may or may not be equivalent to the “internal dose”

(ADDinternal), the dose that is absorbed and is available for interaction with biological

significant receptors. Theoretically, the internal dose can be estimated from the

concentration of the chemical in the gastrointestinal or respiratory tract, the internal

surface area, and the permeability coefficient for the specific tissue; however, this

information is not usually available. Therefore, U.S. EPA recommends that it be

assumed that the applied dose is equal to the administered (potential) dose (i.e., that

all the chemical ingested in food or drinking water or all that is inhaled touches an

absorption barrier inside a person). If this assumption is made, then the following

equation can be used to estimate the internal dose:

where:

AF = absorption fraction (units of mass absorbed per units of mass applied)

and the average daily internal dose (ADDinternal) becomes:
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-72 December 1998

Rates of absorption may differ among individuals, populations, and species,

resulting in different internal doses for the same administered dose. Information on

absorption factors is limited. Reference toxicity values for inhalation exposures are

expressed in terms of reference concentrations (RfC).

3.11. TREATMENT OF UNCERTAINTY

Increasing reliance has been placed on concentration estimates from models as

the primary basis for regulatory decisions concerning source permits and emission

control requirements. In many situations, such as review of a proposed source, no

practical alternative exists. Therefore, there is an obvious need to know how accurate

models really are and how any uncertainty in the estimates affects regulatory decisions. 

U.S. EPA recognizes the need for incorporating such information and has sponsored

workshops (Fox, 1981; Burton, 1981) on model accuracy, the possible ways to quantify

accuracy, and considerations in the incorporation of model accuracy and uncertainty in

the regulatory process. The Second (U.S. EPA) Conference on Air Quality Modeling,

August 1982 (U.S. EPA, 1981a) was devoted to that subject.

3.11.1. Overview of Model Uncertainty. Dispersion models estimate pollutant

transport through the air to specific receptor locations. Transport is characterized by

measured or known conditions that are input to the models, e.g., wind speed, mixed

layer height, surface heat flux, emission characteristics, etc. However, in addition to

the known conditions, there are unmeasured or unknown variations in the transport,

e.g., unresolved details of the atmospheric flow such as the turbulent velocity field. 

These unknown conditions may vary among repetitions of the event. As a result,

deviations in observed concentrations from those estimated by the model are likely to

occur even though the known conditions are fixed. Even with a perfect model, there

are likely to be deviations from the observed concentrations due to variations in the

unknown conditions. The statistics of these concentration residuals are termed

“inherent” uncertainty. Available evidence suggests that this source of uncertainty
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-73 December 1998

alone may be responsible for a typical range of variation in concentrations of as much

as 50% (Hanna, 1982).

Moreover, because neither models nor databases are perfect, there is reducible

uncertainty associated with the model and its input conditions. Reducible uncertainties

are caused by three factors:

! Uncertainties in the input values of the known conditions—source

characteristics, meteorological data, and receptor parameters;

! Errors in the measured concentrations used to compute the

concentration residuals; and

! Inadequate model physics and formulation.

The “reducible” uncertainties can be minimized through better (more accurate and more

representative) measurements and better model physics.

To use the terminology correctly, reference to model accuracy should be limited

to the portion of reducible uncertainty that deals with the physics and the formulation of

the model. The accuracy of the model is normally determined by an evaluation

procedure that involves the comparison of model concentration estimates with

measured air quality data (Bowne, 1981). The statement of accuracy is based on

statistical tests or performance measures such as bias, noise, correlation, etc. (Fox,

1981). However, information that allows a distinction among contributions of the

various elements of inherent and reducible uncertainty is only now beginning to

emerge. As a result, most discussions of the accuracy of models make no quantitative

distinction between limitations of the model and limitations of the database or of

knowledge concerning atmospheric variability. Note that statements on model

accuracy and uncertainty may imply the need for improvements in model performance

that even the “perfect” model could not satisfy.

3.11.2. Studies of Model Accuracy. A number of studies (Bowne and Londergan,

1983; Moore et al., 1982) have been conducted to examine model accuracy,

particularly with respect to the reliability of short-term concentrations required for
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-74 December 1998

ambient standard and increment evaluations. These studies show that models are

more reliable for estimating longer time-averaged concentrations than for estimating

short-term concentrations at specific locations and that the models are reasonably

reliable in estimating the magnitude of highest concentrations occurring sometime,

somewhere within an area.

3.11.3. Use of Uncertainty in Decision-Making. The accuracy of model estimates

varies with the model used, the type of application, and site-specific characteristics. 

Thus, it is desirable to quantify the accuracy or uncertainty associated with

concentration estimates used in decision making. Communications between modelers

and decision makers must be fostered and further developed. Communications

concerning concentration estimates currently exist in most cases, but communications

dealing with the accuracy of models and their meaning to the decision maker are

limited by the lack of a technical basis for quantifying and directly including uncertainty

in decisions. Although procedures for quantifying and interpreting uncertainty are

beginning to evolve (Burton, 1981; U.S. EPA, 1981b; Fox, 1983) much study is still

required.

In all applications of models, an effort is encouraged to identify the reliability of

the model estimates for that particular area and to determine the magnitude and

sources of error associated with the use of the model. The analyst is responsible for

recognizing and quantifying limitations in the accuracy, precision, and sensitivity of the

procedure. Information that might be useful to the decision maker in recognizing the

seriousness of potential air quality violations includes such model accuracy estimates

as accuracy of peak predictions, bias, noise, correlation, frequency distribution, spatial

extent of high concentration, etc. Both space/time pairing of estimates and

measurements and unpaired comparisons are recommended. Emphasis should be on

the highest concentrations and the averaging times of the standards or increments of

concern. Where possible, confidence intervals about the statistical values should be

provided. However, although such information can be provided by the modeler to the

decision maker, it is unclear how this information should be used to make an air
Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-75 December 1998

pollution control decision. Given a range of possible outcomes, it is easiest and tends

to ensure consistency if the decision maker confines his judgment to use of the “best

estimate” provided by the modeler (i.e., the design concentration estimated by a model

recommended in the Guideline or an alternative model of known accuracy). This is an

indication of the practical limitations imposed by current abilities of the technical

community.
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Pleim, J., A. Venkatram and R. Yamartino. 1984. ADOM/TADAP model development

program. Volume 4. The dry deposition module. Ontario Ministry of the Environment,

Rexdale, Ontario.

Radke, L.F., P.V. Hobbs and M.W. Eltgroth. 1980. Scavenging of aerosol particles by

precipitation. J. Appl. Meteorol. 19:715-722.

Rao, K.S. and L. Sutterfield. 1982. MPTER-DS. The MPTER model including

deposition and sedimentation. U.S. EPA, Research Triangle Park, NC. EPA

600/8-82/024.

Rordorf, B.F. 1989. Prediction of vapor pressures, boiling points and enthalpies of

fusion for twenty-nine halogenated dibenzo-p-dioxins and fifty-five dibenzofurans by a

vapor pressure correlation method. Chemosphere. 18:783-788.

Schulman, L.L. and S.R. Hanna. 1986. Evaluation of downwash modifications to the

industrial source complex model. J. Air Pollut. Control Assoc. 36(3):258-264.

Scire, J.S., D.G. Strimaitis and R.J. Yamartino. 1990. Model formulation and user’s

guide for the CALPUFF dispersion model. Sigma Research Corp., Concord, MA.

Sehmel, G.A. 1980. Particle and gas dry deposition: A review. Atmos. Environ.

14:983-1011.

Sehmel, G.A. and W.H. Hodgson. 1978. A model for predicting dry deposition of

particles and gases to environmental surfaces. Battelle, Pacific Northwest Laboratory,

Richland, WA. PNL-SA-6721.

Sehmel, G.A. and S.L. Sutter. 1974. Particle deposition rates on a water surface as a

function of particle diameter and air velocity. J. Rechs Atmos. III: 911-918.

Seinfeld, J.H. 1986. Atmospheric Chemistry and Physics of Air Pollution. John Wiley

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Shah, J.J., R.L. Johnson, E.K. Heyerdahl and J.J. Huntzicker. 1986. Carbonaceous

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Sheffield, A.E. and J.F. Pankow. 1994. Specific surface area of urban atmospheric

particulate matter in Portland, Oregon. Environ. Sci. Technol. 28:1759-1766.

Sheih, C.M., M.L. Wesley and B.B. Hicks. 1979. Estimated dry deposition velocities of

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Chapter 3

Air Dispersion and Deposition Modeling of Pollutant Stack Emissions

MPE 3-81 December 1998

Slinn, W.G.N., L. Hasse, B.B. Hicks et al. 1978. Some aspects of the transfer of

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Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-1 December 1998

SOIL CONCENTRATION AND EXPOSURE PATHWAYS

4.1. INTRODUCTION

Contaminants emitted from combustors either deposit onto land surfaces in the

vicinity of the facility or travel and deposit downwind from the facility. Atmospheric

pollutants contact the soil layer through processes of wet (i.e, precipitation) or dry (i.e.,

gravitational force) deposition. Following deposition, contaminants may be

incorporated into the upper layers of soil where crops or other vegetation are grown. 

This chapter focuses on estimating the cumulative concentration of a given

contaminant in the soil following deposition from a combustor. The concentrations will

be used to estimate the risk to humans who may have dermal contact with

contaminated soil, ingest soil or household dust directly, or consume vegetation and

animals that have been exposed to contaminated soil.

Contaminant levels in soil are the result of both wet and dry particle deposition

onto soil, vapor diffusion into soil, and the loss of contaminants from soil through

leaching, abiotic and biotic degradation, volatilization, soil erosion, and surface water

runoff. Figure 4-1 illustrates the factors required to determine the cumulative soil

concentration. Soil conditions such as pH, soil structure and characteristics, organic

matter content, and water content affect the distribution and mobility of contaminants

after deposition onto soil. Loss of contaminants from soil is modeled using rates that

depend on site-specific data about the physical and chemical characteristics of the soil. 

Note that the cumulative soil concentration (Cs) represents the concentration increment

due to accumulation of contaminant deposited onto soil from the combustor. 

The cumulative soil concentration does not represent the total concentration, because

it does not take into account background concentrations of the contaminant that may

already be present, whether natural or from other pollution sources.

4.2. OVERVIEW OF SOIL CONCENTRATION CALCULATIONS
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-2 December 1998

Soil Loss/Dissipation

- leaching

- erosion

- runoff

- volatization

- degradation

Dermal

Contact

Ingestion

Vapor, Particle-Phase

Dry and Wet Deposition

Deposition

modeling

Soil mixing

FIGURE 4-1

Overview of Soil Concentration and Exposure Pathway

Inhalation

The cumulative soil concentration of a pollutant is derived from the deposition

and diffusion rates over the lifetime of the combustor and the contaminant loss rate

from the soil. Contaminants may be lost from soils as a result of numerous factors,
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-3 December 1998

including leaching, abiotic and biotic degradation, volatilization, soil erosion and

surface water runoff. The equations used to estimate contaminant concentrations and

losses in soil are adapted from Travis et al. (1983), Miller and Gardiner (1998), Hwang

and Falco (1986), and Mills et al. (1985).

Key assumptions and limitations for soil concentration calculations are:

! Background soil concentration and the natural recycling of contaminated

detritus are not considered. The calculated soil concentration is only the

soil increment due to combustion fallout, and could under predict the

actual soil concentration.

! Trace metal contaminants are assumed to be conserved indefinitely in

the upper layer of soil unless loss constants are available. Although

most heavy metals are tightly bound to the soil, measurements show that

this assumption often over-predicts concentrations (i.e., at low pH metals

become increasingly mobile and may be leached out of the upper soil

layers).

! Loss processes occurring simultaneously are not taken into account.

This could lead to an over-predict losses because contaminant

concentration available for each loss process would be over-predicted.

! Degradation of organic contaminants is first-order. This assumption

could over- or under-predict degradation rate, which is complex and not

necessarily first-order.

! If soil incorporation of contaminant is assumed, incorporation depth is 20

cm and the upper 20-cm soil layer has a dry mass of 2.7x103 Mg/ha. A

soil concentration of 1 μg/g corresponds to a contaminant application of

2.7 kg/ha. If actual depth (and mass) was less, contaminant

concentration could be under-predicted (and vice versa).

4.2.1. Calculating Cumulative Soil Concentration. The cumulative soil

concentration of a pollutant (Cs) is calculated from the wet and dry particle deposition

rates, diffusive flux into soil, and the soil loss constant, as described by Travis et al.

(1983):
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-4 December 1998

Cs'

100 (Dyd % Dyw % LDIF) [1.0 & e

&ks tD]

Zs BD ks

(4-1)

where:

Cs = concentration of contaminant in soil after total time period of

deposition (μg/g)

100 = units conversion factor ([106 μg/g]/[104 cm2/m2])

Dyd = yearly dry deposition rate of contaminant to soil (g/m2-yr)

Dyw = yearly wet deposition rate of contaminant to soil (g/m2-yr)

LDIF = atmospheric diffusion flux of contaminant to soil (g/m2-yr)

ks = soil loss constant (yr-1)

tD = total time period over which deposition occurs (yrs)

Zs = soil mixing depth (cm)

BD = bulk density of soil (g/cm3)

Remember that the cumulative soil concentration (Cs) represents the

concentration due to accumulation of contaminant deposited onto soil from the

combustor, not the total concentration, because background concentrations are not

included. For the steady-state soil concentration, the bracketed exponential term that

contains a time-weighting factor is eliminated. 

Another approach for calculating soil concentration (Cs) based on time period of

deposition, exposure duration, and the toxicity characteristics (i.e., carcinogen or

noncarcinogen) of the chemical are provided in U.S. EPA 1998.

4.2.2. Calculating Deposition Rates. Determination of annual deposition was

discussed in Chapter 3. In the event that only ambient air concentrations are available,

rather than stack emission rates, the dry deposition rate (Dyd) can be calculated from

the dry deposition velocity and particle-phase air concentration:
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-5 December 1998

Dyd ' 0.31536 Vd Cpa

(4-2)

Dyw 'X May Wp 10&

8

(4-3)

where:

Dyd = dry deposition flux of contaminant sorbed to particles

(g/m2-yr)

0.31536 = units conversion factor ([3.1536E+07 sec/yr]/[100 cm/m][106

μg/g])

Vd = dry deposition velocity (cm/sec)

Cpa = particle-bound concentration of contaminant on setting

particles (μg/m3)

Particles settle to the ground surface and plant surfaces through the forces of

gravity. Gravitational settling velocity is a function of particle size, with more rapid

settling occurring with larger particles. Seinfeld (1986) listed a gravitational deposition

velocity of 1 cm/s for 10 -μm size particles. An assignment of Vd of 1.0 cm/s would be

conservative because most particles depositing to ground surfaces (including particles

originally emitted from the stack as well as particles of other origins in ambient air) are

less than10-μm diameter. In lieu of more site-specific data, this value should suffice for

most assessments.

This equation must be modified in order to calculate a wet deposition rate (Dyw)

using the volumetric washout ratio, and average annual moisture. Wet deposition is

calculated as:

where:

Dyw = yearly wet deposition rate of contaminant (g/m2-yr)

10-8 = units conversion factor [10-6 g/μg] [10-2 m/cm]
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-6 December 1998

May = average annual moisture (precipitation: rainfall, snowfall) (cm/yr)

 Wp = volumetric washout ratio for particulates (unitless)

X = contaminant air concentration (μg/m3)

Geraghty et al. (1973) provide a map showing isolines of average annual rainfall

throughout the United States. This map shows low rates of 10 to 50 cm/yr in the

desert Southwest, moderate rates of 60 to100 cm/yr in the Midwest cornbelt, 100 to150

cm/yr in the South, and so on. If the deposition is onto irrigated cropland, it may also

be appropriate to add irrigation amounts to rainfall amounts. Irrigation values range

from 0 to 100 cm/yr (data from 69 selected cities) (Baes et al., 1984).

Washout ratios are generally defined as the concentration of contaminant in rain

to the concentration of contaminant in air. Concentrations of contaminants in air and

rainwater can be derived as a mass of contaminant divided by a mass or a volume of

air/water. Mackay et al. (1986) show that volume-based washout ratios (mass of

contaminant mixing in m3 air or water) exceed mass-based washout ratios (mass of

contaminant mixing in kg of air or water) by a factor 815, which is the ratio for water and

air densities. The washout ratio used in this assessment is volumetric ratio which

allows for direct use of contaminant concentrations estimated in this methodology, as

they are already on a μg/m3 volume basis.

Bidleman (1988) defines the overall washout ratio as: (mass contaminant/volume

rain) / (mass contaminant/volume air). Bidleman (1988) also discusses that overall

washout includes both wet deposition of particulates and scouring of contaminants in

the vapor phase. A method is included for estimating the vapor particulate ratios for

semi-volatile organic compounds (abbreviated SVOCs) and also for estimating the

washout ratios for vapors. The study indicates that if Henry’s law constant (H) is

sufficiently high, vapor dissolution in droplets is negligible and only the particulate

fraction is removed by wet deposition. On the basis of this work, it can be assumed

that vapor scouring of contaminants emitted from combustors is small in comparison to
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-7 December 1998

LDIF ' 0.31536 Kt Cva

(4-4)

wet deposition, and that the washout ratio can be applied to the airborne particulate

concentration of the contaminants.

Bidleman (1988) does not provide a chemical or site-specific equation to

estimate the particle-phase washout ratio (as he does for the vapor-phase washout

ratio). Rather, he summarizes available data and concludes that there is a wide range

of particle-phase washout ratio, Wp for SVOC: between 2x103 and 1x106. He says that

a typical range is 105 to 106, and uses a Wp of 2x105 in his exercises to estimate the

overall washout ratio for several SVOCs. Koester and Hites (1992) list vapor and

particle scavenging ratios for congener groups of dioxin-like compounds. To derive

these ratios, they used air concentrations for congener groups that were taken at one

time period in Bloomington and Indianapolis, IN, and rainfall depositions of these

compounds at these sites measured at another. Using the Bidleman vapor/particle

partitioning model in this assessment, they estimated the vapor/particle split for the air

concentrations. With these observations and models, they concluded that the overall

washout ratio (sum of vapor and particle ratios) ranges from 104 to105, contrasted with

the typical range of 105 to106 noted above in Bidleman (1988). Also, their calculations

indicate that vapor scavenging of dioxin-like compounds is comparable to particle

scavenging, also in contrast to the Bidleman analysis summarized above. However,

they did not state whether their washout ratios were volume- or mass-based. If they

were mass-based, then a conversion to volume based would put them in the 107 to108

range, which seems improbable given the Bidleman summary presented above.

It is suggested that the user assign values of Wp based on data in Bidleman

(1988). If ratios for specific contaminants of interest are not listed, a value of 105

should suffice for most contaminants.

The diffusive loading (LDIF) refers to vapor-phase deposition or diffusion, and is

governed by gas-phase mass transfer:
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-8 December 1998

Kt'

Da2v

Zs

(4-5)

2v ' 1 & (

BD

Ds

) & 2SW

(4-6)

where:

LDIF = vapor-phase diffusion flux of contaminant to soil (g/m2-yr)

0.31536 = units conversion factor ([3.1536E+07 s/yr]/[100 cm/m][106

μg/g])

Kt = gas phase mass transfer coefficient (cm/s)

Cva = vapor phase air concentration of contaminant (μg/m3)

The particle- and vapor-phase air concentrations that are appropriate for the above

formulations are predicted by the air dispersion model. Chapter 3 details application of

the air dispersion model to estimate vapor-phase concentrations.

The gas-phase mass transfer coefficient (Kt) is based on general soil properties

(Hillel, 1980; Miller and Gardiner, 1998) which can be used to calculate the following

equation:

where:

Kt = gas phase mass transfer coefficient (cm/s)

Zs = soil mixing depth (cm)

Da = diffusion coefficient of contaminant in air (cm2/s) 

2v = soil void fraction (cm3/cm3)

The soil void fraction( 2v) is the volumetric fraction of a soil that does not contain

solids or water. 
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-9 December 1998

where:

2v = soil void fraction (cm3/cm3)

2sw = volumetric soil water content (mL (=cm3 )/cm3)

BD = soil bulk density (g/cm3)

Ds = solids particle density (g/cm3)

The expression containing bulk density (BD) divided by solids particle density

(Ds) gives the volume of soil occupied by pore space or voids (Miller and Gardiner,

1998). Blake and Hartge (1996) and Hillel (1980) both suggests that the mean density

of solid particles is about 2.6 to 2.7 gm/cm3. Since soil water content is unique for each

soil type, site-specific information is highly recommended. If site-specific information is

not available, the volumetric water content of soil content can be obtained by

multiplying the mass water content by BD (Miller and Gardiner, 1998). A more detailed

discussion of 2sw is presented in Section 4.3.7.1.

4.2.3. Calculating the Soil Loss Constant (ks). When available, an overall

environmental dissipation rate can be used as the soil loss constant (Equation 4-1). 

This rate would be calculated from field data and would include all routes of dissipation

and degradation. For example, the Dioxin Reassessment (U.S. EPA, 1994) reviewed

the literature to arrive at a dissipation rate constant of 0.0693 yr-1 for dioxin residues

depositing to a depth of 1 cm. Fries and Paustenbach (1990) suggested the use of a

half-life of at least 10 years, and used a 15-year half-life in their example scenarios on

the impact of airborne deposition of 2,3,7,8-TCDD originating from stack emissions. In

a later publication, Paustenbach et al. (1992) reviewed the literature on the

environmental half-life in soil. For surface soils, they cited the evidence from Eglin Air

Force Base (Young, 1983) suggesting a half-life of 10 to12 years for 2,3,7,8-TCDD,

and the work of Cerlesi et al. (1989), who estimated a soil half-life of 9.1 years for

2,3,7,8-TCDD in Seveso soil. Paustenbach et al. (1992) also discussed the fact that

the loss of TCDD from soil is predominantly through volatilization and photo

degradation of residues at the soil surface, and that loss mechanisms are minimal
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-10 December 1998

ks ' ksl % ksg % ksv % kse % ksr

(4-7)

below the soil surface. They suggested that ultraviolet radiation penetrates the surface

only to about 0.1 cm, which implies that photo degradation could be limited below that

depth. They concluded that their review supported the concept that TCDD probably has

a half-life of 9 to15 years in surface soil and 25 to100 years in subsurface soils.

If pertinent dissipation information is known for other compounds of interest, a ks

value can be calculated. Otherwise, the soil loss constant can be determined by

summing the applicable contaminant losses due to the individual environmental

processes of leaching, degradation, volatilization, soil erosion, and surface water

runoff:

where:

ks = soil loss constant due to all environmental processes (yr-1)

ksl = loss constant due to leaching (yr-1)

ksg = loss constant due to abiotic and biotic degradation (yr-1)

ksv = loss constant due to volatilization (yr-1)

kse = loss constant due to soil erosion (yr-1)

ksr = loss constant due to surface water runoff (yr-1)

Equations for calculating soil losses due to leaching, erosion, and runoff have

been adapted from Mills et al. (1985). The soil loss constant of contaminants due to

leaching (ksl) is obtained by calculating the water availability in the soil and the

influence of soil properties on water and contaminant movement by the following

equation:
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-11 December 1998

ksl '

My % I & Ev & RO

2sw Zl[1.0 %

BD Kds

2sw

]

(4-8)

ksv ' Ke Kt

(4-9)

where:

ksl = soil loss constant due to leaching (yr-1)

My = average annual precipitation (cm/yr)

I = average annual irrigation (cm/yr)

Ev = average annual evapotranspiration (cm/yr)

RO = average annual surface runoff (cm/yr)

2sw = volumetric soil water content (mL/cm3)

Zl = depth of soil where contaminant leaching occurs (cm)

BD = bulk density of soil (g/cm3)

Kds = soil-water partitioning coefficient (mL/g)

Losses due to degradation (ksg) are chemical-specific and have been

empirically determined from field studies. Values may be found in the literature. 

Degradation losses are discussed in more detail in Section 4.3.8.

The soil loss constant due to volatilization (ksv) is based on gas equilibrium

coefficients and gas phase mass transfer. The first order decay constant, Ksv, is

obtained by adapting the Hwang and Falco equation for soil vapor phase diffusion

(Hwang and Falco, 1986). 

where:

ksv = soil loss constant due to volatilization (yr-1)
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-12 December 1998

Ke '

3.1536E%10 H

Zv Koc f

oc R T BD

(4-10)

kse '

0.1 Xe SD ER

BD Ze

Kds BD

2sw % Kds BD

(4-11)

Ke = equilibrium coefficient (s/yr-cm)

Kt = gas phase mass transfer coefficient (cm/s) (refer to eq. 4-5)

The equilibrium coefficient (Ke) is calculated using the following equation:

where:

Ke = equilibrium coefficient (s/yr-cm)

3.1536E+10 = units conversion factor ([3.1536E+7 sec/yr][1000 L/m3])

H = Henry’s Law constant (atm-m3/mol)

Zv = depth of soil from which contaminants can volatilize (cm)

Koc = organic carbon partition coefficient (mL/g)

foc = fraction of organic carbon in soil (unitless)

 R = ideal gas constant (L-atm/mol-K)

T = temperature (EK)

BD = bulk density of soil (g/cm3)

The soil loss constant due to erosion of soil (kse) is calculated using the

following equation:

where:

kse = soil loss constant due to erosion (yr-1)

0.1 = units conversion factor ([1000 g/kg][104 cm2/m2])
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-13 December 1998

Xe' 0.224 RF K LS C P

(4-12)

ksr '

RO

2sw Zr

1

1 % (Kds BD/2sw )

(4-13)

Xe = unit soil loss (kg/m2-yr)

SD = sediment delivery ratio

ER = contaminant enrichment ratio

Kds = soil-water partition coefficient (cm3/g)

BD = bulk density of soil (g/cm3)

Ze = soil depth from which erosion occurs (cm)

2sw = volumetric soil water content (mL/cm3)

The unit soil loss (Xe) is dependent on terrain and land management features

and is calculated using the following equation:

where:

Xe = unit soil loss (kg/m2-yr)

0.224 = units conversion factor ([247.1 acres/km2][1 km2/1E+06 m2][907.18

kg/ton])

RF = erosivity factor (yr-1)

K = erodibility factor (tons/acre)

LS = topographic or slope length factor (dimension less)

C = cover management factor (dimension less)

P = supporting practice factor (dimension less)

The soil loss constant due to surface runoff (ksr) can be calculated by:
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-14 December 1998

where:

ksr = soil loss constant due to surface runoff (yr-1)

RO = average annual surface runoff (cm/yr)

2sw = volumetric soil water content (mL/cm3)

Zr = depth of soil affected by surface water runoff (cm)

Kds = soil-water partition coefficient (cm3/g)

BD = bulk density of soil (g/cm3)

4.3. DESCRIPTION OF INPUT VARIABLES

Calculating cumulative soil concentration requires the input of site-specific data

which must be calculated or derived from the available literature. This section defines

the input variables required for the calculations, discusses the factors that affect the

value of each variable, and outlines the typical range of values and sources of each

variable.

4.3.1. Dry and Wet Deposition Rates (Dyd and Dyw). Site-specific dry and wet

deposition rates are determined by air dispersion modeling. Several methods for

determining deposition rates are described in Chapter 3. In cases where ambient air

concentrations are used rather than stack emissions, deposition rates can be

calculated from the respective deposition velocity and air concentration, described in

Section 4.2.2. If the chemical is absorbed on the surface of the particle, the deposition

velocity would be a function of particle size and several other methodological

parameters. Deposition velocities can be found in the literature for specific chemicals

or classes of chemicals.

4.3.2. Diffusive Loading (LDIF). Vapor-phase deposition or diffusion also adds to the

concentration of contaminants in soil. It is assumed that diffusive loading is driven by

the same gas-phase mass transfer coefficient (Kt) as volatilization. It should be noted

that this formulation pertains to diffusive flux into the soil and does not address entry

into soil of vapor-phase contaminants via wet deposition. It is also not clear to what
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-15 December 1998

extent this treatment of diffusive entry addresses the issue of dry deposition of vapors

onto soils. Both areas are in need of further investigation.

4.3.3. Time Period of Deposition (tD). In this methodology, individuals are assumed

to be exposed to contaminants in the soil following a time period in which there have

been continuous combustor emissions. The lifetime of a combustion facility, such as a

MWC, could be considered to be >30 years. 

4.3.4. Soil Depth (Zs). When modeling exposures to contaminants found in soils are

modeled, the depth of assumed contamination is important in calculating the

appropriate soil concentration. Contaminants deposited onto soil surfaces may be

incorporated into lower soil profiles by tilling, whether done manually in a garden or

mechanically in a large field. In general, if the area under consideration is likely to be

tilled (e.g., agricultural soils), the soil mixing depth may be assumed to be 10 to 20 cm,

depending on local conditions and the equipment used. 

If soil incorporation does not occur, as in pastures or lawns, contaminants are

assumed to be retained in a shallower, uppermost, untilled soil layer. Although the

actual depth of this uppermost layer of mixing is unknown, a value of 1-cm is assumed

as the untilled surface depth in which contaminants evenly mix. For soluble

compounds, leaching might lead to movement below a 1-cm depth and perhaps justify

a greater mixing depth. In any event, if more specific information is available on soil

mixing depth for a particular site, a different value can be substituted.

Using the untilled soil concentration for soil-related exposures is a conservative

assumption. If instead, it is felt that dermal exposures or soil ingestion exposures occur

in conjunction with gardening or agricultural field crop production, then the tilled soil

concentration should be used.

Volatilization and photodegradation are assumed to occur in the uppermost soil

layer. Any volatile contaminants are likely to be desorbed from particles soon after

emission from the combustor, prior to deposition onto the soil. However, semivolatile

compounds (SVOCs) and volatile organic compounds(VOCs)emitted in sufficiently high

concentrations may be deposited in particulate form and may undergo volatilization
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-16 December 1998

losses from soils. Contaminants subject to volatilization losses may be incorporated to

20 cm by tilling and may not readily volatilize from this depth. The volatilization rate will

reflect the contaminant concentration near the soil surface. For erosion and surface

runoff, the affected soil layer will generally be the first few centimeters, while for

leaching the entire tilled depth becomes involved. Care must be taken in assigning

appropriate soil depths to each scenario.

For direct ingestion of soil and house dust by humans and herbivores, a depth of

1 cm is used. Surface soil is tracked into houses and incorporated into house dust. 

Strong correlations between soil concentrations and indoor dust levels have been

found (Brunekeef, 1984; Berny et al., 1994; Fergusson et al., 1986; Ferguson and Kim,

1991); therefore, indoor dust concentrations are estimated from the surface soil

concentration at 1 cm. Children can either intentionally or incidentally eat soil and are

most likely to be exposed to contaminated soil from gardens, lawns, landscaped areas,

parks, and recreational areas. Herbivorous animals are most likely to be exposed to

contaminated soils while grazing pastures. Deposition onto hard surfaces, such as

sidewalks, slides, swings, and other play areas may result in dust residues that have

negligible dilution with other residues. Exposure may result via mouthing of these

objects or dermal contact. If an exposure assessment is to include contact with dust

that is on hard surfaces, it is recommended that the concentration of contaminants in

that dust be assumed to be equal to soil concentrations estimated for the untilled depth

of 1 to 2 cm.

For dermal exposure, the conditions and areas of likely exposure are similar to

those of the soil ingestion pathway. Therefore, as in the soil ingestion pathway, the

dermal exposure model usually assumes a depth of 1 cm in estimating risk due to

contact with contaminated soil. This assumption overestimates soil concentrations

where incorporation occurs to any depth greater than 1 cm, and need not be used if

site-specific information is available.

For exposure through surface water, contaminants are assumed to travel from a

watershed into a surface water body dissolved in runoff water and adsorbed to eroded
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-17 December 1998

soil particles. The assumptions for soil depth for the watershed area are site-specific. 

If the area under consideration is an agricultural area that is likely to be tilled, soil

depth is assumed to be 20 cm. In all other cases, soil depth is assumed to be 1 cm in

order to be conservative.

4.3.5. Bulk Density of the Soil (BD). The bulk density of soil (BD) is the ratio of the

mass of the soil to its total volume. Bulk density is affected by the soil structure (e.g.,

the looseness or compaction of the soil, depending on the water and clay content of the

soil) (Hillel, 1980). Values range from 0.93 to 1.84 g/cm3, depending on soil type

(Hoffman and Baes, 1979). It is recommended that bulk density of the soil at the study

site be used, if available.

4.3.6. Soil Loss Constant (ks). Organic and some inorganic contaminants may be

lost from the soil by many processes, which may occur simultaneously and/or at

different rates. The rate at which a contaminant is lost from the soil is known as the soil

loss constant and is determined by the physical, chemical, and biological

characteristics of the soil. The processes that remove contaminants from the soil

include leaching, biotic and abiotic degradation, volatilization, erosion, and surface

water runoff.

Organics are subject to loss by leaching, degradation, volatilization, erosion, and

runoff. Inorganics such as metals may be lost by leaching and, in some cases, by

volatilization. This section discusses the kinetics of contaminant loss, including firstorder processes, which this methodology generally assumes, and other types of

kinetics that may govern loss of contaminants from soil.

First-order reaction rates depend on the concentration of one reactant (Bohn et

al., 1985). The loss of a contaminant by a first-order process depends only on the

concentration of the contaminant in the soil, and a constant fraction of the compound is

removed from the soil over time.

Those processes that apparently exhibit first-order kinetics without implying a

mechanistic dependence on a first-order loss rate are termed “apparent first-order” loss

rates (Sparks, 1989). Some higher order kinetic processes appear to be first-order
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-18 December 1998

because of the relative abundance of some reactants, such as water and oxygen, in

comparison with one reactant. This type of reaction is limited by the concentration of

only one reactant under the conditions at which the apparent rate law is applicable

(Sparks, 1989).

The assumption that contaminant loss follows first-order kinetics may be an

oversimplification because, at various concentrations or environmental conditions, the

loss rates from soil systems will follow different kinetic expressions. However, at low

concentrations, a first-order loss constant may be adequate to describe the loss of the

chemical from the soil.

In addition to first-order kinetics, contaminant loss in soil could follow zero-order

or second-order kinetics. Zero-order loss rates are independent of reactant

concentrations (Bohn et al., 1985). Zero-order rates describe some processes in which

the reactants are in very high concentration. Under zero-order kinetics, a constant

amount of a contaminant is lost from the soil over time, independent of its

concentration.

Processes that follow second-order kinetics are dependent on the

concentrations of two reactants or on the concentration of one reactant squared (Bohn

et al., 1985). The loss constant of contaminant disappearance following a secondorder process can be contingent on its own concentration, or on both its concentration

and the concentration of another reactant, such as an enzyme or catalyst.

Because contaminant loss from soil is dependent on many complex factors, it

may be difficult to model the overall rate of loss. Combined soil loss rates by these

processes can be derived experimentally, and values for certain compounds are

presented in U.S. EPA (1986). If field data where soil concentrations have been

followed over time are available, these data should be used in the calculation of soil

concentration. Loss of the contaminant can be measured as the total loss by multiple

processes. In this case it would not be necessary to calculate the constant as

described in Section 4.2.2. If the loss constant must be calculated, the input variables

described below should be used.
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-19 December 1998

4.3.7. Contaminant Losses By Physical Processes. Leaching, erosion, and runoff

represent the physical transport processes that remove contaminants from the area of

soil contamination. Leaching refers to the downward vertical movement of entrained or

water-soluble contaminants through the soil column. Erosion is the physical transport

of soil, with adsorbed contaminants, by wind, runoff, or gravity to another location. 

Runoff refers to the lateral transport of contaminants from the soil surface (as particles

or in solution) with flowing rain or flood waters. Often these losses from the soil

compartment are inputs to the surface or groundwater compartments. This section is

divided into three parts. The first part describes the soil properties upon which each of

these three processes is dependent. The second part discusses how available water is

used to determine leaching and runoff losses. Third is a discussion of how terrain and

land management factor into the calculation of soil erosion losses. Last is a discussion

on physical transfer processes associated with volatization. The appropriate soil

depths to use in the different calculations are provided in Section 4.3.4.

4.3.7.1. Soil Properties — Two variables take into account the soil properties

that affect leachate formation, erosion and surface water runoff: bulk density (BD) and

soil water content (1sw). Bulk density is described in Section 4.3.5. The soil water

content depends on the available water and on the soil structure. Values for 1sw range

from 0.03 to 0.40 mL/cm3 depending on soil type (Hoffman and Baes, 1979). The lower

values are typical of sandy soils, which cannot retain much water, and the higher

values are typical of soils such as clays or loamy soils which can retain water.

For hydrophobic organic chemicals, the relevant soil property is the fraction

organic carbon. For metals, relevant soil properties include pH and iron content.

4.3.7.2. Loss Due to Volatilization (ksv) — Equation 4-8 in Section 4.2.2.

shows the calculation of ksv. Semivolatile and volatile compounds emitted in high

concentrations may become adsorbed to soil particles and exhibit volatilization losses

from the soil. The loss of a chemical from the soil by volatilization is dependent on the

rate of movement of the chemical to the soil surface, the chemical vapor concentration
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-20 December 1998

at the soil surface (due to soil conditions), and the rate at which the boundary layer

between the soil and atmosphere is cleared at the contaminant (Jury, 1986).

Polar molecules are less volatile than slightly polar or nonpolar molecules

because of their greater absorption to dry soil constituents and greater water solubility

(Poe, 1988). Volatilization loss rates for specific chemicals may be found in the

literature. Laboratory-measured volatilization rates for soil-incorporated chemicals

adequately estimate the field-measured rates, because the rates are primarily

dependent on soil factors that can be mimicked in the laboratory (Spencer et al., 1982). 

Such experimental values may be used if site-specific data are unavailable.

4.3.7.3. Soil-Water Partitioning Coefficient (Kds) — The soil-water partitioning

coefficient (Kds) is used to calculate the infiltration rate, reflects the equilibrium

sorption/desorption of chemicals to soil particles. Mean values for Kds for many

chemicals can be found in Baes et al. (1984). Variations in soil properties, however, 

greatly affect Kds, which for inorganic chemicals can vary up to 3 orders of magnitude

in soils ranging from pH 4.5 to 9.0. Partitioning coefficients for inorganic pollutants

used in modeling may be derived from soil-to-plant transfer coefficients (Baes et al.,

1984). For organic compounds, the soil organic carbon content is an important soil

property. Partitioning coefficients for organics may be estimated as the product of Koc

and foc, where Koc is the organic carbon-normalized partition coefficient and foc is the

fraction of organic carbon in soil. Therefore, it is advisable to use site-specific data

for Kds when available.

4.3.7.4. Available Water — Losses of soil contaminants through leaching and

surface water runoff are dependent on the amount of water available and on the soil

properties (e.g., bulk density, soil porosity and sorptive properties) that affect how

water passes through the soil and how contaminants bind to it. The average annual

volume of water available to generate leachate is the sum of the average annual

precipitation (My) and the average annual irrigation (I) minus the average surface

runoff (RO) and minus the average annual evapotranspiration (Ev). However, the
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-21 December 1998

average annual volume of water available to create surface runoff is a function of just

the annual surface runoff (RO) and average annual irrigation (I).

Precipitation values range from 18 to164 cm/year (data for 69 selected cities;

U.S. Bureau of the Census, 1987). Irrigation values range from 0 to100 cm/year (data

for 69 selected cities; Baes et al., 1984). The average annual runoff can be estimated

given isopleths of surface runoff available in the Water Atlas of the United States

(Geraghty et al., 1973). The isopleths give annual surface water contributions, which

include interflow and ground water recharge, and would need to be adjusted downward

to reflect surface runoff only. Another approach for estimating surface runoff is to use

the curve number equation developed by the Soil Conservation Service (USDA, 1975). 

Isopleths of mean annual cropland runoff corresponding to various curve numbers have

been developed by Stewart et al. (1975), as reported by Mills et al. (1985). Curve

numbers are assigned to an area depending on the soil type, land use or cover, and

the hydrologic condition of the soil. Evapotranspiration values range from 35 to100

cm/year (Baes et al., 1984). The available water as calculated by (My + I - RO - Ev) is

assumed to be a mass balance of all water inputs and outputs from the area under

consideration. Other mechanisms of removal or retention in the soil are not considered

in this methodology.

4.3.7.5. Terrain and Land Management — While leaching and runoff losses

depend on inputs of water, erosion loss is determined by the terrain and land

management factors that set up erosion conditions. Seven factors describe the erosion

of soil and contaminants: sediment delivery ratio, contaminant enrichment ratio,

erosivity factor, erodibility factor, topographic or slope length factor, cover management

factor, and support practices factor. The sediment delivery ratio (SD) reflects the

sediment yield derived from erosion. In Chapter 7, SD is calculated based on the area

of the watershed, although alternative methods are being investigated. A default value

of 0.18 was chosen for this methodology. The contaminant enrichment ratio (ER)

accounts for the increased tendency for erosion of soils that have higher organic matter
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-22 December 1998

content and greater surface area-to-volume ratios. Sandy soils have a higher ER than

silty or loamy soils. Because ER is dependent on soil characteristics it is site-specific.

The remaining five factors comprise the unit soil loss constant (Xe). The

erosivity factor (RF) is the measure of the erosion potential due to the average rainfall

at the site. Annual values range from <50 for the arid western U.S. to >300 for the

southeast, as compiled by Wischmeier and Smith (1978). The erodibility factor (K) is

experimentally determined based on the predominant soil type at the site. Values for K

range from 0.02 for sand to 0.60 for silt. The topographic or slope length factor (LS)

takes into account the steepness and slope of the site. Values for LS range from 0.1 to

about 40 for slopes with inclines of 0.5 to 60% and slope lengths ranging between 20

and 800 m. The cover management factor (C) accounts for the vegetative cover, crop

sequence, crop rotation, and tilling practices at a site. Values of C range from 0.001 for

managed woodlands with 100% vegetative cover to 1.0 for bare soil with no coverage. 

The support practice factor (P) reflects the use of techniques such as surface

conditioning, diking, contouring, and terracing to control runoff/erosion. Values for P

range from 0-1.0 and should only be less than 1 when specific practices are in place to

minimize erosion. The Parameters Guidance Document will provide ranges of input

values for these soil parameters.

4.3.8. Losses by Chemical and Biological Processes. Chemical and biological

processes of contaminant loss include biodegradation (e.g., microbial), chemical

decomposition (e.g., hydrolysis, redox reactions, photolysis). Degradation loss is

chemical-specific and is not applicable to most metals, which are transformed, but not

degraded, by such mechanisms. The soil losses due to degradation and volatilization

processes are based on field observations or empirical data as discussed below.

4.3.8.1. Loss Due to Biotic Degradation — The rate of biological degradation

in soils depends upon the concentration and activity of the microbial populations in the

soil, the soil conditions, and the contaminant concentration (Jury and Valentine, 1986). 

First-order loss rates often do not account for the high variability of these parameters in

a single soil system. However, the use of simple rate expressions may be appropriate
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-23 December 1998

at low chemical concentrations (ng/kg range), where a first-order dependence on

chemical concentration may be reasonable.

The rate of biological degradation of contaminants is chemical-specific,

depending on the complexity of the chemical and the usefulness of the chemical to the

microorganisms. Some substrates are co-metabolized, that is not used by the

organisms as a nutrient or energy source, but simply transformed along with other

similar chemicals, which can be further utilized. Environmental and chemical factors

that may limit the biodegradation of chemicals in the soil environment include

availability of the chemical, nutrient limitations, toxicity of the compound, and

inactivation or nonexistence of the enzyme capable of degrading the compound

(Valentine and Schnoor, 1986).

4.3.8.2. Losses by Abiotic Degradation — Chemical and photodegradation of

organic pollutants can be a significant mechanism of removal of these compounds from

soil systems. Hydrolysis and oxidation-reduction (redox) reactions are the primary

chemical transformation processes occurring in the upper layers of soils (Valentine,

1986). General rate expressions describing transformation of some contaminants by all

nonbiological processes are available, and are helpful when division into component

reactions is not possible.

Hydrolysis in aqueous systems is characterized by three reaction processes:

acid-catalyzed, base-catalyzed, and neutral reactions (i.e., H+, OH- , and H2O-catalyzed

hydrolysis). The overall rate of hydrolysis is the sum of the first-order rates of these

processes (Valentine, 1986). In soil systems, sorption of the chemical can increase,

decrease or not affect its rate of hydrolysis, as numerous studies cited in Valentine

(1986) have shown. The total rate of hydrolysis in soil can be predicted by adding the

rates in the soil and water phases, which are assumed first-order at fixed pH

(Valentine, 1986). Methods for estimating these hydrolysis constants are described by

Lyman et al. (1982). Organic and inorganic compounds also undergo redox reactions

in the soil (Valentine, 1986). Organic redox reactions involve exchange of oxygen and

hydrogen atoms by the reacting molecules. For example, glucose is oxidized to CO2
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-24 December 1998

and H2O by oxygen and the oxygen is reduced to H2O. Inorganic redox reactions may

involve the exchange of atoms or electrons by the reactants.

In soil systems where the identities of oxidant and reductant species are not

specified, a first-order rate constant can be obtained for describing loss by redox

reactions (Valentine, 1986). Redox reactions involving metals do not constitute a

degradative process, but may promote losses by making metals more mobile or more

reactive. However, the effect of each redox reactions depends on the metal and on the

soil conditions.

Photodegradation or photolysis can be significant for organic compounds found

at the soil-atmosphere interface. Photolysis is generally restricted to the first millimeter

of soil (Hebert, 1987), though this depth receives a large amount of pollutants through

deposition. The presence of sunlight, atmospheric oxygen, and photosensitizing

agents at the interface offer a strongly oxidizing environment. Sunlight directly affects

the temperature and moisture content of soil, which can indirectly influence the soil’s

organic matter content, microbial activity, and physicochemical properties (Miller et al.,

1989a). Drying of soil (caused by increased soil temperature and decreased moisture

content) increases the sorption and retards movement of chemicals in soil. As with

other biotic and abiotic degradation processes, photodegradation can result in products

that are more toxicologically significant. In a study by Miller et al. (1989b), OCDD was

photo-reduced to a variety of lower chlorinated, more toxic dioxins (e.g., 2,3,7,8-

TCDD).

4.4. SOIL EXPOSURE PATHWAYS

The three soil exposure pathways described in this document include soil

ingestion, dermal contact, and dust resuspension (Chapter 5).

Contaminants from combustor emissions deposited on soil can be ingested

incidentally by hand-to-mouth transfer or intentionally by eating soil. Both adults and

children may incidentally ingest small amounts of soil; young children (i.e., preschool,

1 to 6 years of age) will incidentally consume more soil than adults and older children. 

Some children with pica, the intentional ingestion or mouthing of nonfood items, may
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-25 December 1998

consume larger quantities of soil. Both the rate of soil ingestion (i.e., amount/day) and

the soil concentration of the contaminant affect exposure by this pathway. Dermal

contact with contaminated soil represents a potential route of exposure to combustor

emissions for both children and adults. Children can be exposed to contaminated soil

when playing in the dirt, whereas adults might be exposed while gardening. The model

presented in this chapter estimates human exposure resulting from skin contact with

contaminants in the soil. The limited bioavailability of contaminants, both in the

particulate fraction of combustor emissions (van den Berg et al., 1983), which are

presumed to comprise a large fraction of the contaminant mass in soil, and in the soil,

itself (Shu et al., 1988) may significantly reduce the overall importance of this exposure

pathway. In addition, the general lack of adequate data for estimation of absorption

fractions further limit the applicability of this model.

4.4.1. Soil Contaminant Concentration (Cs). People could possibly be exposed to

combustor-emitted contaminants soon after deposition on soil and before contaminants

are incorporated into soil layers. Therefore, for this pathway 100% of the deposited

contaminant is assumed to be incorporated in the uppermost 1-cm of soil, and ingested

soil is assumed to originate from the same 1-cm layer. Another depth of soil

incorporation may be chosen if it is more appropriate for a site-specific assessment,

such as agricultural activities. The soil concentration as determined in Equation 4-1

(μg/g) should be used as input into Equations 4-14 and 4-16.

4.4.2. Soil Ingestion — Human Daily Intake (DIs). Because this methodology

assesses only the risk associated with the increase in exposure due to combustor

emissions (incremental risk), the background concentration of contaminants in soil is

not included in the quantitation of daily intake.

Human daily intake (DIs, mg/kg-day) resulting from ingestion of contaminated soil

is a function of soil concentration and soil ingestion rate:
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-26 December 1998

DIs'

Cs CRs ED EF

AT BW 1000

(4-14)

where:

DIs = daily intake of contaminant from soil ingestion (mg/kg-day)

Cs = concentration of contaminant in soil (μg/g soil)

CRs = soil ingestion rate (g soil/day)

BW = body weight (kg)

ED = exposure duration (days/year)

EF = exposure frequency (days/year)

AT = averaging time (period over which exposure is averaged 

((noncarcinogenic effects = ED x 365 days/year) and carcinogenic

= 70 year lifetime x 365 days/year) (U.S. EPA, 1989))

1000 = unit adjustment factor (μg contaminant/mg contaminant)

The DIs for this pathway is compared with the RfD (reference dose for chronic

oral exposure, mg/kg-day) for systemic toxicants to determine if the contaminant

adversely affects human health. If the chemical is a carcinogen, the DIs is used with

the human cancer potency to determine excess risk. This is further discussed in

Chapter 10.

Key assumptions and limitations for the soil ingestion exposure pathway are:

! Deposited emissions are not necessarily incorporated throughout the soil

and may be concentrated in the uppermost soil layer. Deposited

contaminant is assumed to be distributed within the uppermost 1 cm of

soil, and ingested soil is assumed to originate from the same 1 cm layer. 

This technique over estimates exposure in situations where soil

incorporation occurs to any depth >1 cm. If incorporation is to a lesser

depth, exposure is underestimated. For example, if exposure is to fallout

dust directly, ingested soil could approach 100% deposited particulate.
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-27 December 1998

! Deposition-contaminated soil may be ingested intentionally by children at

the rates observed in studies. This will over-estimate exposure to the

extent the child frequents areas where deposition has not occurred.

4.4.2.1. Soil Ingestion Rate (Crs) — Studies of human soil ingestion were

reviewed by U.S. EPA (1995). Table 4-1 contains brief summaries of the key studies

conducted in children. The results of these studies are presented in Table 4-2. There

are many uncertainties associated with these values. For one thing, the studies were

all conducted over two weeks or less, which is too short to get a good estimate of usual

intake and may not, therefore, be representative of long-term behavior. For another,

experimental error in measuring soil ingestion values for individual children appears to

have been considerable, such that the range of values seen in different children

appears to represent uncertainty more than true variability in behavior; this is especially

true of those studies that failed to sample all input and output tracer sources. Other

uncertainties include extent of absorption of tracer elements in the body, degree of

homogeneity of soil samples, and accuracy of parents’ knowledge about their child’s

play areas.

The results of these studies need to be evaluated cautiously. There are several

issues pertaining to study methodology that influence interpretation of the data. One
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-28 December 1998

TABLE 4-1

Studies of Soil Intake in Children*

Study Type Number of

Observations

Age Population Studied Comments Study

Tracer study using aluminum,

silicon, and titanium

59 children 1-3 years Children living near lead

smelter in Montana

Did not account for tracer in food and

medicine; used assumed fecal weight

of 15 g/day; short-term study

conducted over 3 days

Binder et al., 1986

Tracer - mass balance study

using aluminum, barium,

manganese, silicon, titanium,

vanadium, yttrium, and zirconium

64 children 1-4 years Children from greater

Amherst area of

Massachusetts; highlyeducated parents

Corrected for tracer in food and

medicine; study conducted over twoweek period; used adults to validate

methods; one pica child in study

group.

Calabrese et al., 1989

Tracer- mass balance 1 pica child 3.5 years 1 pica child from greater

Amherst area of

Massachusetts

Child was observed as part of the

Calabrese et al., 1989 study.

Calabrese et al., 1991

Tracer study using aluminum,

acid insoluble residue, and

titanium

18 nursery

school children;

6 hospitalized

children

2-4 years Dutch children Did not account for tracer in food and

medicines; used tracer-based intake

rates for hospitalized children as

background values; short-term study

conducted over 5 days

Clausing et al., 1987

Tracer - mass balance study

using aluminum silicon and

titanium

104 children 2-7 years Children from 3-city area in

Washington State

Corrected for tracer in food and

medicine; short-term study conducted

over seven-day period; collected

information on demographic

characteristics affecting soil intake.

Davis et al., 1990
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-29 December 1998

Re-evaluation of Binder et al.,

1986 data

59 children 1-3 years Children living near lead

smelter in Montana

Re-calculated soil intake rates from

Binder et al., 1986 data using actual

fecal weights instead of assumed

weights.

Thompson and

Burmaster, 1991

Tracer study using aluminum,

acid insoluble residue, and

titanium

292 daycare

children; 78

campers; 15

hospitalized

children

1-5 years Dutch children Did not account for tracer in food and

medicines; used tracer-based intake

for hospitalized children as

background values; evaluated

population (campers) with greater

access to soil; evaluated differences

in soil intake due to weather

conditions.

VanWijnen et al. 1990

 *Source: U.S. EPA (1995)
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-30 December 1998

TABLE 4-2

Soil Consumption by Children as Measured by Various Tracer Elements in Key Studies (mg/day)a

Mean Median 95th Percentile

Reference

Al Si AIRb Ti Y Al Si AIRb Ti Y Al Si Ti Y

181 184 -- 1834 -- 121 136 -- 618 -- 584 578 9590 -- Binder et al., 1986

97 85 -- 1004 -- 45 60 -- 293 -- 197c 166c 2105c-- Thompson and

Burmaster, 1991

232 -- 129 1431 -- 71 -- 107 259 -- -- -- -- -- Clausing et al., 1987

-- -- 162-

213d,e

-- -- -- -- -- -- -- -- -- -- -- Van Wijnen et al.,

1990

39 82 -- 246 -- 25 59 -- 81 -- -- -- -- -- Davis et al., 1990

153 154 -- 218 85 29 40 -- 55 9 223 276 1432 106 Calabrese et al., 1989

 

a

 Source: Adapted from U.S. EPA (1995)

 

b

 AIR = Acid Insoluble Residue

 

c

 90th percentile

 d Based on limiting tracer (AIR was limiting tracer in about 80% of samples, but Al and Ti were also used)

 e Range according to activity (162 mg/day for children at daycare center, 213 mg/day for children camping)
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-31 December 1998

issue relates to the tracer elements used. It is evident from Table 4-2 that estimates of

soil ingestion based on Ti are consistently higher than estimates based on the other

tracers, especially for the results reported by Binder et al. (1986), Thompson and

Burmaster (1991), and Clausing et al. (1987). The especially high estimates based on

Ti in these three studies occurred because the studies did not take into account dietary

sources of the trace elements other than soil (e.g., food and medicine). This appears

to have been especially significant for Ti, which may occur at relatively high levels in

food. Even in the studies that did monitor intake of Ti through food and medicine,

estimates of soil ingestion based on Ti were larger, and much more variable, than

those for Al and Si (Davis et al., 1990; Calabrese et al., 1989). Calabrese et al. (1989)

conducted a validation study in which known amounts of soil containing trace elements

were administered to adult volunteers, and found that Ti was much less reliable as a

tracer for soil ingestion than Al, Si, or Y.

Another issue relating to the study by Binder et al. (1986) is that fecal weight used to

estimate soil ingestion was assumed to be 15 g/day, even though actual fecal weights

were roughly half that value. The data reported by Thompson and Burmaster (1991)

are those collected by Binder et al. (1986) reanalyzed using the measured fecal

weights. The effect this adjustment has on the soil ingestion estimates is evident by

comparing the results of these studies in Table 4-2.

The most reliable data, therefore, appear to be soil intake estimates based on Al and

Si tracers in the studies by Davis et al. (1990) and Calabrese et al. (1989). Estimates

of mean soil ingestion in the study by Calabrese et al. (1989) were considerably higher

than those in the study by Davis et al. (1990) (153-154 mg/day versus 39-84 mg/day). 

Calabrese et al. (1989, 1991) noted that one child in their study exhibited soil pica

during the second week of the 2-week study, consuming 10-14 g/day of soil during this

time. It appears that mean values reported in this study were skewed by inclusion of a

data point that does not represent normal behavior.

This conclusion is confirmed by comparing the median soil ingestion estimates for

these two studies, which are very similar (29-40 mg/day for Calabrese et al., 1989,
Chapter 4

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MPE 4-32 December 1998

versus 25-59 mg/day for Davis et al., 1990). The median is affected much less than the

mean by inclusion of outlier data points. It is noteworthy that median soil ingestion

estimates based on Al and Si tracers from Thompson and Burmaster (1991) and

Clausing et al. (1987) are consistent with these values as well (45-71 mg/day), even

though dietary intake from sources other than soil was not considered for these data

(this may suggest that dietary intake of Al and Si from sources other than soil was not

significant for the children in these studies).

Considering the high uncertainty associated with these experimental values, 100

mg/day (0.1 g/day) appears to be a reasonable estimate of central tendency for soil

ingestion among non-pica children under 7 years of age. Based on 95th percentile soil

ingestion estimates of 223 to 276 mg/day using Al and Si tracers (Calabrese et al.,

1989), 300 mg/day (0.3 g/day) appears to be a reasonable estimate of upper percentile

soil ingestion among non-pica children of this age group.

These recommendations apply to children with normal mouthing tendencies. 

Children who exhibit pica (abnormal mouthing behavior), and especially soil pica

(deliberate mouthing or ingestion of soil) would have much higher ingestion rates. The

child in the Calabrese et al. (1989, 1991) study who showed soil pica behavior

consumed 10 to 14 g/day of soil during 1 week of the study. Although 10 to 14 g/day

might be a reasonable estimate of short-term soil ingestion for a child displaying soil

pica, insufficient information is available to produce a reasonable long-term estimate. 

For example, the child in the Calabrese et al. (1989, 1991) study displayed soil pica

only during the second week of the study; the overall average soil ingestion for this

child over the 2-week study period was 5 to 7 g/day. Few data are available regarding

the frequency and duration of soil pica behavior in children (U.S. EPA, 1995). Thus, no

quantitative basis can be offered to characterize long-term average or upper percentile

soil ingestion among children displaying soil pica.

Data regarding soil consumption among adults are scarce. Only one preliminary

experimental tracer study has been conducted in adults (Calabrese et al., 1990). This

study calculated mean soil ingestion estimates of 110 mg/day based on Al, 30 mg/day
Chapter 4

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MPE 4-33 December 1998

based on Si, 71 mg/day based on Ti, and 63 mg/day based on Y. Median soil ingestion

estimates were 60, 31, 102, and 44 mg/day, respectively, based on the same tracers. 

This study included only six subjects, lasted only 1 week, and was not designed

specifically to investigate soil ingestion by adults.

The only other source of an estimate for adult soil ingestion is a study by Hawley

(1985), who estimated adult soil ingestion based on assumptions regarding activity

patterns and related factors. Hawley estimated annual average adult soil ingestion to

be 60.5 mg/day (57 mg/day due to outdoor work in the summer, 0.5 mg/day due to

typical living space activities, and 3 mg/day due to periodic exposure to dust in the

attic).

An average adult soil ingestion estimate of 50 mg/day (0.05 g/day) would be

consistent with the estimate by Hawley (1985) and the preliminary data reported by

Calabrese et al. (1990), and would satisfy the reasonable expectation that soil

ingestion by adults should be less than that by young children, who were estimated

above to have an average soil ingestion rate of 100 mg/day. Because the average soil

ingestion recommended for adults is one-half that recommended for children, it may be

reasonable to use one-half of the 95th percentile soil consumption for young children

(300 mg/day) as an estimate of the 95th percentile soil consumption for adults (150

mg/day or 0.15 g/day).

Consult U.S. EPA (1997) for more detailed discussion of soil ingestion rates among

adults and children, including issues related to consumption of indoor dust.

4.4.2.2. Body Weight (BW) — A body weight of 70 kg has traditionally been used by

U.S. EPA to represent average adult body weight. Data reviewed by U.S. EPA (1995)

support continued use of this value for adults. For children, age-specific body weights

(means and upper 90th percentiles for males and females, and overall means for males

and females together) are presented in Table 4-3. Consult U.S. EPA (1995) for more

detailed information regarding human body weight.
Chapter 4

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MPE 4-34 December 1998

4.4.3. Daily Dermal Intake From Soil. The daily dermal intake (DDIs, mg/kg-day)

represents the increase above background in daily human dermal intake of the

contaminant due to combustor emissions. The DDIs is determined as follows:
Chapter 4

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MPE 4-35 December 1998

TABLE 4-3

Body Weights of Adults and Children (kg)a,b

Age

Males Females Males +

Females

Mean 90th

Percentile Mean 90th

Percentile Mean

<1 9.4 10.9 8.8 10.4 9.1

1 11.8 13.6 10.8 12.7 11.3

2 13.6 15.8 13.0 14.9 13.3

3 15.7 17.9 14.9 17.4 15.3

4 17.8 20.9 17.0 20.2 17.4

5 19.8 23.7 19.6 24.7 19.7

6 23.0 28.3 22.1 28.9 22.6

7 25.1 29.6 24.7 30.3 24.9

8 28.2 35.5 27.9 33.2 28.1

9 31.1 38.6 31.9 43.3 31.5

10 36.4 46.3 36.1 45.8 36.3

11 40.3 57.0 41.8 56.6 41.1

12 44.2 58.9 46.4 60.5 45.3

13 49.9 64.2 50.9 66.4 50.4

14 57.1 68.9 54.8 67.6 56.0

15 61.0 72.8 55.1 65.5 58.1

16 67.1 82.2 58.1 73.3 62.6

17 66.7 82.3 59.6 71.6 63.2

18 71.1 83.5 59.0 70.1 65.1

19 71.7 86.8 60.2 74.8 66.0

18-74 78.1 95.7 65.4 84.4 71.8

a

 Source: Adapted from U.S. EPA (1995) and NCHS (1987)

b

 includes clothing weight, estimated as ranging from 0.09-0.28 kg
Chapter 4

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MPE 4-36 December 1998

DDIs'

DAevent EF A

BW 365

(4-15)

DAevent ' Cs AF ABS 103

(4-16)

where:

DDIs = daily dermal intake of contaminant from soil (mg/kg-day)

DAevent = absorbed dose per event (mg/cm2-event)

A = skin surface area available for contact (cm2)

EF = exposure frequency (events/year)

BW = body weight (kg)

365 = adjustment to calculate average daily dose (days/year)

The assumptions and uncertainties associated with each of these input parameters are

discussed in the following sections.

To assess whether the contaminant poses a risk to human health by this pathway, the

DDIs may be compared with a dermal RfD (if available) or may be transformed to an

equivalent oral DIs (U.S. EPA, 1992; see also Chapter 10, Section 10.2.1.1.1.) and

compared with the oral RfD for chronic exposure for systemic toxicants. In addition, the

risk assessor is strongly encouraged to refer to the decision matrix found in Section 9

of the “Dermal Exposure Assessment” document (U.S. EPA, 1992). If the chemical is a

carcinogen, the DDIs may be used with the dermal human cancer potency (if available)

or may be transformed to an equivalent oral DIs and used with the human oral cancer

potency to determine excess risk (ER) Assessment of risk to humans is discussed in

Chapter 10.

4.4.3.1. Estimating the Dermally Absorbed Dose per Event (Daevent) — The

absorbed dose per event (DAevent) is estimated as follows:
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-37 December 1998

where:

DAevent = absorbed dose per event (mg/cm2-event)

Cs = concentration of contaminant in soil (μg/g soil)

AF = adherence factor of soil to skin (mg soil/cm2-event)

ABS = absorption fraction

103 = unit adjustment factor ((μg soil/g soil)

U.S. EPA (1992) notes that theoretical models that estimate DAevent on the basis of a

soil/skin permeability coefficient rather than ABS are being investigated. The

permeability coefficient approach offers some advantages in that the coefficient should

remain constant over a wider range of conditions, such as the amount of soil on the

skin and the concentration of the chemical in the soil. This approach is also better

suited to considering nonsteady-state conditions than ABS. However, since these

procedures are not as well developed, it is currently recommended that the users first

consider the ABS procedures for estimating dose.

A more complex two-layer dermal uptake model for soil exposure has been published

(McKone, 1990). The model is a two-layer model which incorporates competing loss

processes in addition to dermal absorption (see Text Box 4-1). As the dermal route of

exposure is estimated to comprise only a small fraction of the total exposure, and the

two-layer model requires significantly more data for the additional parameters, the

simple absorption fraction model presented in Equation 4-16 is judged to be adequate

in this application.

4.4.3.2. Absorption Fraction (ABS) — The absorption fraction (ABS) is the relative

amount of a substance on the skin that penetrates through the epidermis into the body;

it is generally reported as the unitless fraction of the applied dose or as the percent

absorbed. The ABS is a chemical-specific quantity that depends on the

physicochemical properties of the contaminant (e.g., lipid solubility, molecular size), but
Chapter 4

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MPE 4-38 December 1998

may also be affected by soil characteristics (e.g., particle size, organic carbon content),

skin characteristics (e.g., age, anatomical location, damage), physical factors (e.g.,
Chapter 4

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MPE 4-39 December 1998

McKone (1990) presents a two-layer model that shows the dependency of dermal

absorption on chemical properties (4 parameters), soil properties (7 parameters),

skin properties (4 parameters), and exposure conditions (4 parameters). The twolayer model was based on a previous model that showed that the efficiency of

dermal uptake can decrease with increasing thickness of applied soil (Kissel and

McAvoy, 1989; Kissel and Robarge, 1988). McKone (1990) presented results of the

two-layer model and two less-complex linear models (Hawley, 1985; Sedman, 1989)

applied to four compounds of widely varying Kow and Kh: 2,3,7,8-TCDD,

hexachlorobenzene (HCB), benzene, and trinitrotoluene (TNT). The two-layer model

gave significantly different predictions than the linear models, with the linear models

yielding both overestimates and underestimates (compared to the two-layer model)

across the range of applied soil thickness of 0.5 to 2 orders of magnitude on the

average. There was no specific range of applied soil thickness that yielded

consistently better agreement among the models. McKone (1990) concluded that

the efficiency of contaminant uptake depended strongly on the amount of soil on the

skin surface. In addition, McKone (1990) suggested a few generalizations arising

from the model results:

1) for compounds with a Kow <106

 (e.g., TCDD), 100% absorption in 12 hours is

a reasonable assumption;

2) for compounds with a Kh >0.01, absorption is unlikely to exceed 40% in 12

hours and will be much less when Kow >10;

3) for compounds with a Kh >0.1, absorption is unlikely to exceed 3% in 12

hours.

McKone (1990) identified a few remaining issues that need resolution with properly

designed experiments, such as the assumption that soil on skin behaves as a matrix

of soil particles mixed with air and water (as opposed to attaching to skin as

individual particles) and the relationship between Kow and skin permeability (the

model predicted increasing permeability with decreasing Kow, resulting from the

assumption that transport through the skin occurs only in the aqueous phase). 

Burmaster and Maxwell (1991) ran the McKone (1990) model for a series of

polyaromatic hydrocarbons with widely varying Kh and Kow . For the most lipophilic

(log Kow > 4.5) and least volatile of these compounds (Kh < 10-3), the fraction

absorbed was highly sensitive to the mass of the soil applied to the skin, varying

from almost 100% to less than 1% for soil loadings of 0.1 mg/cm2 and 100 mg/cm2,

respectively. 

Text Box 4-1. The Next Generation of Dermal-Contact Soil Exposure Models:
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MPE 4-40 December 1998

temperature, humidity), and extent of exposure (e.g., amount of soil on the skin,

duration of soil contact with the skin). Therefore, there is a great deal of uncertainty

associated with absorption fraction estimates. Some of this uncertainty is reflected in

the available estimates being presented as ranges rather than single values. It should

be noted that standard methods for adjusting experimental ABS do not exist and

attempts to make adjustments should be done with caution. 

When assigning an absorption fraction to a compound, first priority is given to values

determined experimentally. However, experimentally derived values of percutaneous

absorption from soil, expressed as the percentage of the applied dose absorbed, have

been reported for only 16 chemicals (14 organic chemicals and 2 inorganic chemicals):

2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 3,3',4,4'-tetrachlorobiphenyl (PCB 77),

benzo[a]pyrene (B[a]P), DDT, benzene, toluene, xylene, 2,4-D, PCBs(Aroclor 1242 and

1254), arsenic, phenol, pentachlorophenol, and cadmium (Bunge and Parks, in press). 

Even for these chemicals, experimental values generally cannot be used directly in

exposure assessments because the experimental conditions are usually not completely

consistent with conditions in the environment. Consequently, the results of each study

must be scrutinized in light of the assumptions of the exposure assessment, and the

experimental results must be adjusted to reflect likely percutaneous absorption under

the conditions of the exposure assessment. U.S. EPA (1992) discusses the specific

issues involved (e.g., use of in vitro data, use of animal data, quantity of soil in contact

with skin, duration of soil contact with skin) and how these issues were addressed for

the existing data set. After analyzing the available data, U.S. EPA (1992) was able to

offer recommended absorption fractions from soil for only three chemicals based on

experimental data. These recommendations are presented in Table 4-4.

Predictive procedures for estimation of the absorption fraction from soil include use of

structural analogy, data on neat absorption, oral bioavailability data, and permeability

coefficients. Most of these approaches are very limited, with some providing upper

bound, rather than central tendency, estimates for ABS. For this reason, DI estimates

should not be summed with exposures from other routes (as discussed in Chapter 10)
Chapter 4

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MPE 4-41 December 1998

without a determination of representativeness of the central tendency. The more robust

approach based on permeability coefficients has not been tested adequately. 

Otherwise, because the absorption fraction is heavily dependent

TABLE 4-4

Recommended Percentages of Applied Dose Absorbed for Soil Applications of

0.2-1.0 mg/cm2 and Exposure Less than or Equal to 24 Hoursa

Chemical Percent Absorbed

TCDDb0.1 - 3%

TCBc0.6 - 6%

Cadmium 0.1 - 1.0%

aSource: U.S. EPA (1992)

bUpper bound of 3% considered appropriate for use with other PCDD/Fs

cUpper bound of 6% considered appropriate for use with other PCBs and Aroclors

upon compound-specific properties, it may be possible to make qualitative judgments

regarding likely significance of this route of exposure for some chemicals on the basis

of properties. Relevant chemical-specific properties include lipophilicity, water

solubility, polarity, volatility, and chemical structure. Discussion of how these

properties might influence dermal absorption of a chemical is found in U.S. EPA (1992).

Structural Analogy. After analyzing the available data, U.S. EPA (1992) concluded

that use of the upper bound absorption fraction for TCDD (3%) would be sufficiently

conservative for use in assessments of other polychlorinated dibenzo-p-dioxins and

polychlorinated furans (PCDD/Fs), and that use of the upper bound absorption fraction

for PCB 77 (6%) would be appropriate for assessments of other PCBs and Aroclors.

Data on Absorption from Deposited Neat (undiluted or mixed) Chemicals. In some

cases, percutaneous absorption of the neat compound will have been studied when

data on absorption of the chemical applied in soil are not available. In these types of
Chapter 4

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MPE 4-42 December 1998

studies, the chemical is usually applied in a solvent, such as acetone, which

evaporates, leaving behind the pure compound. In such a study, the compound can

usually be considered to be completely bioavailable in the sense that it is unbound to a

vehicle and is in contact with the absorption barrier (i.e., the skin). Thus, the fraction of

the neat compound that is absorbed will likely represent an upper bound on the fraction

absorbed from soil, where the chemical may be bound to some extent to combustion

particles and soil particles, and where not all of the chemical may be immediately in

contact with the skin surface. In some cases, particularly for volatile organics, neat

absorption data may actually underestimate absorption from soil, as thicker layers of

soil may inhibit volatilization (see the modeling analysis of benzene and naphthalene in

Burmaster and Maxwell, 1991). Also, experimentally-determined neat absorption

fractions may be dependent on the thickness of the applied layer (U.S. EPA, 1992).

Data on Oral Bioavailability. The Agency is investigating the use of absorption

fractions for chemicals in the gastrointestinal tract as upper bounding estimates for

absorption via the dermal route. Such absorption fractions, sometimes referred to as

oral bioavailability, are often available for chemicals administered in a variety of

vehicles, including feed, water, and occasionally soil. When chemicals are

administered in solid vehicles such as feed and soil, oral absorption fractions provide a

combined estimate of the fraction of an administered dose that is extracted from the

vehicle and subsequently absorbed. Absorption is generally believed to be greater via

the oral route than via the dermal route.

Some issues need to be resolved in order to confidently use such data as upper

estimates of bioavailability. If the chemical is administered in a vehicle other than soil,

the affinity of the chemical for the vehicle may reduce absorption in the GI tract. Some

studies report the fraction of the chemical excreted in feces as a measure of material

that is not absorbed. However, because some chemicals are excreted in feces after

absorption, the assumption that the fraction of the dose that is not in the feces was

absorbed produces a minimum estimate rather than an upper bounding estimate of oral

absorption.
Chapter 4

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MPE 4-43 December 1998

Estimation of ABS from Permeability Coefficients and Soil loss Constants. For some

chemicals, it may not be possible to derive a reasonable estimate of absorption fraction

by any of these means. Theoretical models of dermal absorption from soil have been

proposed, but generally rely upon permeability coefficients rather than absorption

fractions, and in any case, are not well developed (see U.S. EPA, 1992). However, the

model presented in Section 6.3.3. or U.S. EPA (1992) can be used to estimate ABS for

a rough approximation of DAevent (Eq. 4-16), but the accuracy of such an estimate

cannot be assessed at this time.

Because the absorption fraction is heavily dependent upon compound-specific

properties, it may be possible to make qualitative judgments regarding likely

significance of this route of exposure for some chemicals on the basis of properties. 

Relevant chemical-specific properties include lipophilicity, water solubility, polarity,

volatility, and chemical structure. Discussion of how these properties might influence

dermal absorption of a chemical is found in U.S. EPA (1992).

4.4.3.3. Adherence Factor (AF) — The adherence factor (AF) is defined as the

amount of soil accumulating on the skin upon dermal contact with soil (mg/cm2). 

Adherence of soil to skin can vary considerably, depending on the particular

circumstances of exposure. For one thing, soil properties have been found to influence

adherence. Adherence increases with increasing moisture content and decreases with

increasing particle size, although it is only slightly affected by clay and organic carbon

content (Kissel et al., 1996; Driver et al., 1989). Adherence is also affected by the type

of activity in which the exposed individual is engaged and the region of the body

contacted (Kissel et al., 1996). In general, adherence is highest in outdoor workers,

followed by outdoor recreation (rugby played on a wet field) and gardening activities. 

The key factors are clothing, conditions (wet/dry) and level of activity. The hands,

knees, and elbows tend to have the highest adherence values, while the face tends to

have the lowest. Table 4-5 is a summary of the results obtained by Kissel et al. (1996)

regarding influence of activity and body contact region on soil adherence. The very

highest adherence values, 2 to 3 orders of magnitude higher than for other conditions
Chapter 4

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MPE 4-44 December 1998

tested, were for children playing in mud. This suggests that individuals contacting wet

soils, for example by wading or engaging in other shore area recreational activities,

may have adherence values considerably higher than individuals engaged in other

activities.

The results of other studies of dermal adherence reviewed by U.S. EPA (1992, 1995)

are summarized in Table 4-6. Based on the data contained in Table 4-4, U.S.
Chapter 4

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MPE 4-45 December 1998

TABLE 4-5

Mean Soil Adherence by Activity and Body Region (mg/cm2)*

Activity

Body Part

Hands Arms Legs Face Feet

Tae Kwon Do 0.0062 0.0019 0.0020 -- 0.0024

Greenhouse Workers 0.043 0.0064 0.0015 0.0051 --

Soccer Players 0.035-0.11 0.0011-0.0043 0.0081-0.031 0.012-0.016 --

Grounds Keepers 0.030-0.15 0.0021-0.023 0.0008-0.0012 0.0021-0.01 0.0041-

0.018

Irrigation Installers 0.19 0.18 0.0054 0.0063 --

Rugby Players 0.4 0.27 0.36 0.059 --

Farmers 0.41-0.47 0.059-0.13 0.0059-0.037 0.018-0.041 --

Reed Gatherers 0.66 0.036 0.16 -- 0.63

Kids-in-Mud 35-58 11 9.5-36 -- 6.7-24

 *Source: Adapted from Kissel et al. (1995) and U.S. EPA (1995)
Chapter 4

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MPE 4-46 December 1998

EPA (1992) recommended using an average value of 0.2 mg/cm2, and an upper

percentile value of 1.0 mg/cm2 to represent dermal adherence of soil. A site-specific

approach, which takes into account the type of activity and the region of the body

contacted, might be more appropriate. U.S. EPA (1995) recommends using the data

presented in Table 4-6 (numerical values are geometric means and the numerical

ranges reflect multiple group means) to select adherence values for activities that best

match those of the population being assessed. The adherence factor in all the studies

described above represents the amount of soil on the skin at the time of measurement. 

Assuming that the amount measured on the skin represents its accumulation between

washings and that people wash at least once per day, then these adherence values

could be interpreted as daily contact rates. However, because the residence time of

soils on skin has not been studied, and because the adherence studies are

independent of time, this is not recommended. Instead, U.S. EPA (1992, 1995)

recommends that these adherence values be interpreted on an event basis (i.e.,

mg/cm2/event). Consult U.S. EPA (1992, 1995) for more detailed discussion of soil

adherence values.

4.4.3.4. Exposure Frequency (EF) — Exposure frequency refers to how often the

contact event occurs (i.e., events/year). A range of values for the frequency of soil

contact has been developed in past exposure assessments. These estimates, shown

in Table 4-7, were derived from consideration of seasonal factors that may influence

human activity patterns and soil conditions. The upper end of the range presented in

Table 4-7 is based on the rationale that in warmer climates, people who actively garden

or play outdoors could have contact with soil almost every day. However, in cooler

climates, outdoor activities involving soil contact may occur only during about half of

the year. Where the contaminated soil is located outside the residential property,

contact may only occur a few days/year. In summary, this is a highly site-specific value

that could range from a few days/year to every day. For default purposes, a typical

value for an adult who gardens or works outside 1 to 2 days/week during the warmer
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-47 December 1998

months may correspond roughly to Hawley's estimate (after rounding) of 40 days/year. 

For
Chapter 4

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MPE 4-48 December 1998

TABLE 4-6

Soil Adherence Valuesa

Reference Size Fraction

(μm)

Soil Adherence

(mg/cm2)

Body Part

Lepow et al., 1975 -- 0.5 hands

Roels et al., 1980;

Sedman, 1989

-- 0.9 hands

Que Hee et al.,

1985b

-- 1.5 hands

Driver et al., 1989c <150

<250

unsieved

1.40

0.95

0.58

hands

hands

hands

aSource: Adapted from U.S. EPA (1995)

bAssume exposed area = 20 cm2

cFive different soil types and 2-3 soil horizons (top soils and subsoils)

TABLE 4-7

Assumptions of Frequency of Exposure to Soil*

Range Reference

350 days/year U.S. EPA, 1989

247-365 days/year U.S. EPA, 1984

180 days/year Paustenbach et al., 1986

130 days/year (<2-5 years) Hawley, 1985

130 days/year (older children) Hawley, 1985

43 days/year (adults) Hawley, 1985

*Source: U.S. EPA (1992)
Chapter 4

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MPE 4-49 December 1998

consistency with the default value for the frequency of soil ingestion recommended by

U.S. EPA (1989a), an upper estimate of 350 days/year was selected.

If the contaminated soil is carried into the house from the surrounding area,

household dust may be contaminated at levels approaching those found outdoors. In

certain instances, therefore, the risk assessor may need to consider the frequency of

dermal contact with contaminated indoor dust in addition to the frequency of dermal

contact with soil.

4.4.3.5. Skin Surface Area (SA) — Total dermal intake of a contaminant is

approximately proportional to the exposed surface area. Data regarding total body

surface area of human adults and children were reviewed by U.S. EPA (1992, 1995),

and median and upper 95th percentile values are presented in Table 4-8. For dermal

exposure to soil, it is not expected that the entire body will be exposed. Instead, the

exposed surface area will be some percentage of the total surface area. U.S. EPA

(1992, 1995) reported the exposed surface area associated with different soil 

exposure scenarios. Exposed surface area for these scenarios ranged from roughly

10% of total surface area when exposure was limited to the head and hands to roughly

25% of total surface area when the head, hands, forearms, and lower legs were

considered to be exposed. Because some studies have suggested that exposure can

occur under clothing (see U.S. EPA, 1992), and this type of exposure was not

considered in the scenarios described above, U.S. EPA (1992, 1995) suggested using

the upper end of this range (25%) to derive defaults for body surface area exposed to

soil.

U.S. EPA (1992, 1995) recommended using values of 20,000 and 23,000 cm2 to

represent central tendency and 95th percentile adult total body surface areas,

respectively. Applying 25% to these values for total body surface area produces

default soil-exposed surface areas for adults of 5000 cm2 (central tendency) and 5800

cm2 (95th percentile). For children, default soil-exposed surface areas (50th and 95th

percentile) can be derived by multiplying the 50th and 95th percentile total surface area

values for the appropriate age group from Table 4-8 by 0.25.
Chapter 4

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MPE 4-50 December 1998

TABLE 4-8

Total Surface Area of Adults and Children of Various Ages (m2)*

Age

Males Females

Median 95th Percentile Median 95th Percentile

2 0.603 0.682 0.579 0.653

3 0.664 0.764 0.649 0.737

4 0.731 0.845 0.706 0.820

5 0.793 0.918 0.779 0.952

6 0.866 1.06 0.843 1.03

7 0.936 1.11 0.917 1.13

8 1.00 1.24 1.00 1.18

9 1.07 1.29 1.06 1.41

10 1.18 1.48 1.17 1.43

11 1.23 1.60 1.30 1.62

12 1.34 1.76 1.40 1.70

13 1.47 1.81 1.48 1.86

14 1.61 1.91 1.55 1.88

15 1.70 2.02 1.57 1.83

16 1.76 2.16 1.60 1.91

17 1.80 2.09 1.63 1.94

3-5 0.728 0.876 0.711 0.879

6-8 0.931 1.14 0.919 1.13

9-11 1.16 1.52 1.16 1.56

12-14 1.49 1.85 1.48 1.82

15-17 1.75 2.11 1.60 1.92

Adult 1.94 2.28 1.69 2.09

*Source: Adapted from U.S. EPA (1995)
Chapter 4

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MPE 4-51 December 1998

Assessors may want to refine estimates of surface area exposed on the basis of

seasonal conditions. For example, in moderate climates, it may be reasonable to

assume that 5% of the skin is exposed during the winter, 10% during the spring and

fall, and 25% during the summer. Consult U.S. EPA (1992, 1995) for more detailed

discussion of dermal surface area of adults and children, including surface area data

for specific parts of the body that may be exposed to soil under various conditions.

4.4.3.6. Uncertainty in the Soil Dermal Exposure Model — Some idea of the

uncertainty can be obtained from the range of percent absorbed values reported. For

example, the range of experimentally-determined absorption fractions for TCDD, PCB

77, and cadmium is an order of magnitude or larger. Absorption fractions will likely

vary even more with varied soil loadings and exposure durations. An analysis of the

McKone two-layer dermal absorption model (McKone, 1990) shows up to two orders of

magnitude variation in uptake fractions across varied soil loadings and exposure

durations for several (modeled) organic compounds (Burmaster and Maxwell, 1991). 

The potential for introducing a conservative bias to the exposure assessment

depending on the approach used to estimate ABS has already been mentioned

(Section 4.4.3.2.). 

Additionally, both the amount of soil adhering to skin and exposure frequency are

highly uncertain. Finley et al. (1994) propose soil-to-skin adherence distributions

(based on published studies) that show about an order of magnitude variation between

the median and 95th percentile estimates. Exposure frequency could vary by almost an

order of magnitude also (Table 4-7). 

Model uncertainty is probably high, as well. The two-layer model gave significantly

different predictions than the linear models, with the linear models yielding both

overestimates and underestimates (compared to the two-layer model) across the range

of applied soil thickness of 0.5 to 2 orders of magnitude on the average (McKone,

1990).

Accordingly, final dose and risk estimates must be considered highly uncertain. The

overall magnitude of the range of uncertainty in the output of the dermal exposure
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-52 December 1998

model can be appreciated by a Monte Carlo analysis as described in Chapter 10 of this

document. Specification of the distributions assigned to each of the inputs, however,

remains as a research exercise. Some idea of the range of possible values can be

obtained by using central tendency (average or typical) values for each parameter (use

the center of the percent absorbed range where available) to get a central tendency

dose estimate. Then, by setting two or three of the most variable parameters to their

upper values and the others to their central-tendency values, it is possible to get an

approximation of an “upper-bound” estimate, but of unspecified probability. Otherwise,

by varying each input parameter one at a time, the contribution of each input to the

variability of the output can be investigated as a simple sensitivity analysis (see

Chapter 10).
Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-53 December 1998

4.5. REFERENCES

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Brunekreef, B. 1984. The relationship between air lead and blood lead in children: A

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soil ingestion estimates: Results of a pilot study. Reg. Tox. Pharmacol. 12:88-95.
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Calabrese, E.J., E.J. Stanek and C.E. Gilbert. 1991. Evidence of soil-pica behavior

and quantification of soil ingested. Unpublished. (Cited in U.S. EPA, 1995)

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(TCDD) persistence in the Seveso (Milan, Italy) soil. Environ. Saf. 18:149-164.

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ingestion by children. Int. Arch. Occup. Environ. Health. 59:73-82.

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estimates of soil ingestion in normal children between the ages of 2 and 7 years:

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elements. Arch. Environ. Health. 45:112-122.

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Environ. Contam. Toxicol. 43:814-820.

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composition and sources of house dust and street dust. Sci. Total Environ.

50:217-221.

Fries, G.F. and D.J. Paustenbach. 1990. Evaluation of potential transmission of

2,3,7,8-tetrachlorodibenzo-p-dioxin-contaminated incinerator emissions to humans via

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Geraghty, J.J., D.W. Miller, F. Vander Leeden and F.L. Troise. 1973. Water Atlas of

the U.S. Water Information Center, Port Washington, NY.

Hawley, J.K. 1985. Assessment of health risk from exposure to contaminated soil. 

Risk Anal. 5(4):289-302.

Hebert, V.R. 1987. Depth dependence of direct and indirect photolysis soil surfaces. 

M.S. Thesis, University of Nevada, Reno. (Cited in Miller et al., 1989a)

Hillel, D. 1980. Fundamentals of Soil Physics. Academic Press, Inc., New York.

Hoffman, F.O. and C.F. Baes. 1979. A statistical analysis of selected parameters for

predicting food chain transport and internal dose of radionuclides. ORNL/NUREG/TM882.
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Hwang S. T. and Falco, J. W. 1986. Estimation of multimedia exposures related to

hazardous waste facilities. In: Pollutants in a Multimedia Environment, Yoram Cohen,

Ed. Plenum Publishing Corp., New York. 

Jury, W.A. 1986. Volatilization from the soil. In: Vadose Zone Modeling of Organic

Pollutants, S.C. Hern and S.M. Melancorn, Ed. Lewis Publishers, Inc., Chelsea, MI.

Jury, W.A. and R.L. Valentine. 1986. Transport mechanisms and loss pathways for

chemicals in soils. In: Vadose Zone Modeling of Organic Pollutants, S.C. Hern and

S.M. Melancorn, Ed. Lewis Publishers, Inc., Chelsea, MI.

Kissel, J., K. Richter, R. Dunn, J. Shirai, J. Johnson and R. Fenske. 1995. Dermal soil

exposure: investigation of soil contact and skin coverage. Cooperative Agreement

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Kissel J.C. and D.R. McAvoy. 1989. Reevaluation of the dermal bioavailability of

2,3,7,8-TCDD in soil. Haz. Waste Haz. Matls. 6:231-240.

Kissel J.C. and G.M. Robarge. 1988. Assessing the elimination of 2,3,7,8-TCDD from

humans with a physiologically based pharmacokinetic model. Chemosphere.

17:2107-2027.

Kissel, J., K. Richter and R. Fenske. 1995. Field measurement of dermal soil loading

attributable to various activities: Implications for exposure assessment. Risk Anal.

16(1):115-125.

Koester, C.J. and R. A. Hites. 1992. West and dry deposition of chlorinated dioxins

and furans. Environ. Sci. Technol. 26:1375-1382.

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Investigations into sources of lead in the environment of urban children. Environ. Res. 

10:415-426.

Lyman, W.J., W.F. Reehl and D.H. Rosenblatt. 1982. Handbook of Chemical Property

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on land and water. Can. J. Chem. Eng. 51:434-439.

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transfer processes of organic chemicals between atmosphere and water. Environ. Sci.

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Chapter 4

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10: 407-419.

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Paustenbach, D.J., R.J. Wenning, V. Lau, N.W. Harrington, D.K. Rennix and A.H.

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tetrachlorodibenzo-p-dioxin in soil: Implications for setting risk-based cleanup levels at

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Chapter 4

Soil Concentration and Exposure Pathways

MPE 4-57 December 1998

Sedman, R.M. 1989. The development of applied action levels for soil contact: a

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EPA/600/2-75/026a.

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Chapter 4

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Office of Research and Development, Washington, DC. EPA/600/6-88/005C.

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Washington, DC. EPA/600/P-95/002FC.

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Valentine, R.L. and J. L. Schnoor. 1986. Biotransformation. In: Vadose Zone

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Chapter 5

Dust Resuspension

MPE 5-1 December 1998

DUST RESUSPENSION

5.1. INTRODUCTION

Dust resuspension may be considered an important exposure pathway under certain

conditions because of the potential for deposited contaminants to be recycled into the

air and subsequently inhaled by humans (Sehmel, 1980). Moosmüller et al. (1998)

reported resuspended dust is largely responsible for high PM10 (particles with

diameters less than 10 micrometers) concentrations during warm months in urban and

non-urban areas. This chapter evaluates the resuspension of dust particles from soils

that contain contaminants emitted from combustion sources. Resuspension involves

the deposition of substances from the air and their subsequent resuspension or reentrainment into the atmosphere. As mentioned in Chapter 4, contaminant particles

emitted from combustors are released in the atmosphere and subsequently deposited

on soil, vegetation, and surface water downwind from the combustor. Contaminant

particles deposited on soil may become aggregated with soil particles, which can be

resuspended into the air by natural (i.e., wind erosion) and mechanical disturbances

such as tilling of agricultural fields or vehicular traffic on paved or unpaved roads

(Sehmel, 1980 and Scott et al., 1997) and construction and demolition of buildings and

roads, and storage piles (Moosmüller et al., 1998)

Since the contribution of dust resuspension to human health risk may not always be

important (U.S. EPA, 1990), only dust sources that are considered significant

contributors are presented in this chapter: wind erosion, agricultural tilling, and

vehicular traffic on paved and unpaved roads. Procedures for estimating dust

emissions from these sources are based on AP-42 (U.S. EPA, 1985, 1996a), Control of

Open Fugitive Dust Sources (U.S. EPA, 1988), and Air Quality Criteria for Particulate

Matter (U.S. EPA, 1996b) are described below in Sections 5.3., 5.4., and 5.5. Emission

factors for other dust sources that might be considered important for a particular

assessment may be found in AP-42. The algorithms that follow should be used to

screen for the significance of this pathway in the site assessment. This is
Chapter 5

Dust Resuspension

MPE 5-2 December 1998

accomplished in a two step process. First, an emission rate for each process is

estimated. Second, the emission rate is used to calculate the contaminant

resuspension flux. 

5.2. OVERVIEW OF DUST RESUSPENSION

The risk assessor, in evaluating this pathway, must identify potential sources of dust

generation within the study area (e.g., agricultural fields, roads) and assess the

potential for exposure from these sources on the basis of environmental conditions

(e.g., climate, soil, topography), location of the sources relative to receptors, and 

characteristics of the sources (e.g., size, traffic volume). Since the contribution of the

dust resuspension pathway to human health may not always be significant (U.S. EPA,

1990), the algorithms presented in this methodology should be used for the initial

screening of this exposure pathway. If dust emissions from identified sources seem

likely to contribute to exposure, then these sources can be treated as area source

inputs to the air dispersion model. In this way, the resuspended dust emissions can be

added with the direct combustor emissions to determine air concentrations and

deposition rates at downwind receptor locations. However, if the predicted air

concentrations are less than 1% of the air concentration predicted to occur from stack

emissions, then fugitive dust emissions may be neglected.

5.2.1. Sources of Dust. Atmospheric dust arises from natural or mechanical

disturbances of eroded granular material. Resuspended dusts arise from many

sources and a distinction is made between fugitive dusts, and reentrained dusts (U.S.

EPA, 1995). Fugitive dust refers to dust put into the atmosphere by wind blowing over

plowed fields, dirt roads, deserts, and areas with little or no vegetation. Reentrained

dust is put into the air by agricultural operations (e.g., tilling) and by vehicles driving

over dirt roads, dirty roads, and dusty areas (i.e., mechanical forces). This

methodology considers the impact of stack emissions from an industrial source

(Chapter 3) as well as dust sources (outside the industrial source being modeled) that

may affect adjacent soils, surface waters, and vegetation. The key assumptions related

to the dust resuspension pathway are:
Chapter 5

Dust Resuspension

MPE 5-3 December 1998

! Resuspended dust (containing aggregated contaminant) can add to exposure

by the direct inhalation and by indirect routes when the resuspended dust is

deposited on soil, vegetation, or surface water.

! Transport is important in determining whether the resuspension changes the

impact of the dust. Dust that is resuspended and deposited back onto the

original area source will not impact the soil concentration at the source; there

is no net effect. It is only when the dust is transported to physically distinct

areas beyond the source (e.g., dust from roadways depositing onto adjacent

soils or vegetation, dust from agricultural tillage depositing onto nearby

residences) that it becomes important to evaluate the impact, since it

increases the exposure at this point.

! Since the contribution of dust resuspension to human health risk may not

always be important, only the most significant dust sources should be

considered on a site-specific basis.

5.2.2. Environmental Conditions of Dust Generation. Environmental conditions are

important in assessing resuspension of solid particles. In order for wind to cause

erosion, the land surface must be sufficiently dry and have areas with little or no

vegetative cover. The factors affecting dust resuspension due to wind disturbances

include moisture content, presence of nonerodible surfaces (i.e., rocks and vegetation),

wind velocity, particle size, surface crust stability, and soil roughness height.

Moisture hinders the resuspension of dust by forming a cohesive matrix among the

dust or soil particles. When the soil is saturated, erosion will not occur (Sehmel, 1980). 

As the surface layer moisture is removed by evaporation, resuspension may occur. 

Evaporation is influenced by wind speed, temperature, and soil characteristics.

The presence of nonerodible surfaces reduces the amount of dust resuspension by

decreasing the soil surface area exposed to the wind. Nonerodible surfaces include

clumps of grass, and stones >1 cm in diameter.

Particle size influences the wind speed required to initiate dust resuspension by wind

erosion. Particles ranging in diameter from 100 to 150 μm have the lowest threshold

wind speeds (wind speed required to initiate soil erosion). As the particle size

decreases below 100 μm the threshold speed increases. For particles larger than 150
Chapter 5

Dust Resuspension

MPE 5-4 December 1998

μm, the threshold speed also increases. If particle sizes are mixed, the threshold wind

speed is less than the speed required to erode the largest particle (Sehmel, 1980).

The surface crust, which is composed of small particles, hinders wind erosion and

evaporation. Thus, crusted surfaces have greater threshold friction velocities. Soils

without crusts, such as sandy or disturbed soils, are more erodible (U.S. EPA, 1985).

Soil roughness height is related to height, width, and spacing of clods and furrows. 

This parameter is required to convert the threshold friction velocity to a wind speed

comparable with the wind speeds at 7 m above the surface (U.S. EPA, 1985). Cowherd

and Guenther (1976) developed the roughness height scale for various conditions of

ground cover, such as natural snow and plowed fields (U.S. EPA, 1976).

Various methodologies have been developed to determine the soil erosion caused by

wind. However in applying these methods to deposited combustor emissions, the

analyst should review the approaches discussed in U.S. EPA (1985), and Sehmel

(1980) to select the most appropriate methodology for the exposure scenario.

5.2.3. Location of Dust Sources Relative to Receptor. When determining exposure

to pollutants deposited onto the study area by resuspension processes, one must

examine the proximity of the area source to the receptor. The resuspended dust

pathway is considered only when particles are deposited onto adjacent, but physically

distinct, water, land, or vegetation (e.g., dust from roadways depositing onto adjacent

soils or vegetation, dust from agricultural tillage depositing onto nearby residences). In

other words, dust that is resuspended and deposited back onto the original area source

will not impact the soil concentration at the source; this process has no net effect. Only

when the dust is transported beyond the source does it become important to evaluate

the impact. Resuspension of dust can make an important contribution under certain

circumstances. For example, an intervening area of agricultural fields between a

combustor and a downwind residential community might lead to substantial exposure,

particularly areas such as the west and southwest United States, which have

characteristically high dust emissions.

5.3. GENERATION OF AGRICULTURAL DUST
Chapter 5

Dust Resuspension

MPE 5-5 December 1998

5.3.1. Agricultural Wind Erosion. The terms used to describe soil movement by wind

are suspension, saltation, surface creep, and resuspension. Suspension applies to

small particles (ranging from <50 μm (Smith et al., 1982) to a maximum of 100 μm

(Sehmel, 1980) that are kept airborne by wind forces. Saltation involves particles

being bounces over the surface by the wind. These particles, ranging in size from 80 to

1000 μm, are too heavy to be aloft and quickly fall back to the surface (Smith et al.,

1982). When heavy particles (generally over 1000 μm) are rolled or slide across the

surface, this process is called surface creep. Resuspension describes the re-entry of

previously deposited contaminants back into the atmosphere. Particle size distribution

is an important parameter in the assessment of dust resuspension as a potential

exposure pathway.

Although this section refers to an agricultural setting, the same concepts apply to dust 

generated by wind eroding exposed areas on public property or within an industrial

setting (U.S. EPA, 1988). Nonuniform land surfaces (e.g., land surfaces interspersed

with nonerodible elements such as stones or clumps of vegetation) are considered to

have a finite or “limited” reservoir of erodible material. The presence of nonerodible

surfaces reduces the amount of dust available for resuspension by decreasing the soil

surface area exposed to the wind. On the other hand, bare surfaces of finely divided

material, such as sandy or dry agricultural soil, are considered to provide an “unlimited”

reservoir of erodible material. These surfaces require lower threshold wind speeds for

erosion to take place. The cutoff threshold friction velocity separating “limited” and

“unlimited” particle reservoirs has been defined at 50 cm/s (U.S. EPA, 1988) based on

observations of highly erodible surfaces (e.g., sandy surface soils). This corresponds

to an ambient wind speed of about 7 m/s (15 mph) measured at a height of about 7 m

(U.S. EPA, 1988). Surfaces with friction velocities larger than 50 cm/s tend to be

composed of aggregates too large to be eroded, mixed in with a small amount of

erodible material or crusts that are resistant to erosion. For example, an area covered

with a continuous layer of grass has minimal soil erosion by wind compared with a

fallow agricultural field consisting of erodible particles.
Chapter 5

Dust Resuspension

MPE 5-6 December 1998

Ep' 8E&6 @ k @ asus @ K @ KSR @ cf @ L

)

@ V

)

(5-1)

For this methodology, wind erosion of open, plowed fields (agricultural wind erosion)

is considered to present a potentially significant impact. Factors that affect whether

wind will resuspend dust in agricultural fields include soil erodibility, surface roughness,

climate (e.g., precipitation and evaporation), unsheltered field width, and vegetative

cover (after harvest). The average emission rate per unit area of contaminated

agricultural field with unlimited erosion potential (Ep) is calculated by U.S. EPA (1988):

where:

Ep = emission of resuspended dust due to wind erosion (g/m2-sec)

8E-6 = conversion factor [(1.016E+6 g/ton) / (4.047E+3

m

2

/acre)(3.1536E+7 sec/yr)]

k = particle size multiplier for total suspended particulate matter

(unitless); 1.0 (U.S. EPA, 1985)

asus = portion of total wind erosion losses expected to be measured as

suspended particulate (unitless); 0.025 (U.S. EPA, 1988)

K = soil erodibility (ton/acre-yr)

KSR = surface roughness factor (unitless)

cf = climatic factor (unitless)

LN = unsheltered field width factor (unitless)

VN = vegetative cover factor (unitless)

Soil erodibility (K) is a function of the amount of erodible fines in the soil, with

aggregates <0.84 mm in diameter being considered erodible (U.S. EPA, 1988). This

term is related to a rough size distribution of the soil and is usually associated with the

soil texture. If the percentage of erodible aggregates <0.84 mm in diameter has been

measured, then soil erodibility can be determined from the experimental plot provided

in Figure 7-1 of the EPA document (U.S. EPA, 1988). When particle size distributions
Chapter 5

Dust Resuspension

MPE 5-7 December 1998

are unknown, a value representative of the predominant soil type(s) from Table 5-1 can

be used. It is important to note that seasonal variations are not considered in the

values presented in Table 5-1. Gillette (1986) found that the value of K varies

depending on the climatic conditions.

TABLE 5-1

Soil Erodibility for Various Soil Textural Classesa

Predominant Soil

Textural Class

Soil Erodibility

(tons/acre/yr)

sandb220

loamy sandb134

sandy loamb86

clay 86

silty clay 86

loam 56

sandy clay loamb56

sandy clayb56

silt loam 47

clay loam 47

silty clay loam 38

silt 38

a

 Source: U.S. EPA, 1988

b

 Very fine, fine, or medium sand
Chapter 5

Dust Resuspension

MPE 5-8 December 1998

cf ' 0.345 W 3

(PE)

2

(5-2)

The K particle size multiplier for total suspended particles has been assigned a

default value of 1. However, U.S. EPA (1988) provides a range of multipliers as a

function of particle size.

The parameters KSR, cf, LN, and VN act to reduce the erodibility (K) of a land surface. 

First, the surface roughness factor (KSR) accounts for the wind resistance caused by

ridges, furrows, and large soil clods in an agricultural field. The resistance caused by

furrows is related to the establishment of an aerodynamic roughness height and by

sand being trapped in the furrows (Gillette, 1986). Its value ranges from 1.0 (no

resistance) for a smooth-surface field to a minimum of 0.5 for a field with the optimum

ratio of ridge height to ridge spacing. If the ridge dimensions are known, then a more 

precise estimate of KSR can be determined from Figure 7-3 provided in U.S. EPA

(1988). Generally, it will be sufficient to use a value of 0.5 for a ridged surface and 1.0 

for an unridged surface.

The climatic factor (cf) is a function of the wind velocity and the soil surface moisture. 

Climatic factors have been computed from available soil and precipitation data for

many locations throughout the United States; maps showing annual climatic factors are

provided in Figure 7-4 of U.S. EPA (1988) and, in more detail, the National Agronomy

Manual (USDA, 1987). The climatic factor has been calibrated using the conditions in

Garden City, KS (cf=1.00) and can be calculated at other geographic locations using

the following equation (U.S. EPA, 1988):

where:

cf = climatic factor (unitless)

0.345 = empirical constant (1/mph3)

W = mean annual wind velocity corrected to a standard height of

30 ft (mph)

PE = Thornthwaite’s precipitation-evaporation index (unitless)
Chapter 5

Dust Resuspension

MPE 5-9 December 1998

LN '

w

cos Ang

(5-3)

Surface moisture is approximated using Thornthwaite’s precipitation-evaporation index

(PE); this can be calculated by multiplying the sum of 12 monthly ratios of precipitation

to actual evapotranspiration by 0.83 (U.S. EPA, 1988). Mean wind speeds are

available through local Weather Service offices or the National Climatic Data Center. 

Soil erodes across a field in the direction of the prevailing wind. The rate of erosion is

zero at the windward edge of the field and increases approximately proportionately with

distance downwind until, if the field is large enough, a maximum rate of soil movement

is reached (U.S. EPA, 1988). Physical structures, such as trees and buildings, shelter

portions of a field from wind and decrease its capacity for erosion. Therefore, the

unsheltered field width (LN) is the distance across a field (w, in the direction of the

prevailing wind) less the distance from the windward edge of the field that is protected

from wind erosion by a physical barrier. The distance protected by a barrier is equal to

10 times the height of the barrier (H), or 10 x H. For example, a row of 30-foot high

trees along the windward side of a field reduces the effective width of the field by 10 x

30 or 300 feet (U.S. EPA, 1988).

The method presumes that the wind direction is perpendicular to the edge of the

field. If the prevailing wind direction differs significantly (>25 degrees) from

perpendicularity with the field, LN should be increased to account for this additional

distance of exposure to the wind. The unsheltered distance across the field (LN) is

equal to the field width (w) divided by the cosine of the angle between the prevailing

wind direction and the perpendicularity to the field (Ang):

where:

LN = unsheltered distance across field (ft)

w = width of field (ft)
Chapter 5

Dust Resuspension

MPE 5-10 December 1998

Ang = angle between prevailing wind direction and perpendicularity to

field (unitless)

If wind barriers are present and the prevailing wind angle is >25 degrees, the

increased distance due to angle is first calculated by Equation 5-3, then the resulting

distance is decreased based on the configuration of the wind barrier. The unsheltered

distance factor (LN) can be determined if the soil erodibility (K) and surface roughness

factor (KSR) are known. The product K@KSR and the unsheltered field width (LN) are

used to quantitatively estimate the unsheltered field width factor (LN) from Figure 7-5 of

U.S. EPA (1988).

The wind erosion equation (Equation 5-1) is configured so that individual crop

types can be taken into account (by altering the value of KSR for specific crops). Cropspecific LN values have also been developed. These values are representative of field

sizes in relatively flat terrain devoid of tall natural vegetation (U.S. EPA, 1988). The LN

values should be divided by 2 in areas of moderately uneven terrain and by 3 in hilly

areas before determining LN from Figure 7-5 in U.S. EPA (1988).

Vegetative cover on agricultural fields during periods other than the primary crop

season greatly reduces wind erosion of the otherwise bare soil (U.S. EPA, 1988). This

cover consists of residual matter from previous crops (e.g., standing stubble), which is

eventually mulched into the soil. The effects of various amounts of residue in reducing

erosion can be quantitatively estimated when the potential annual soil loss from a bare

field (K@KSR@cf@LN in tons/acre-yr) is known. The product K@KSR@cf@LN, along with an

estimate of the amount of crop-specific residual vegetative cover (V, in lb air-dried

residue/acre), can be used to quantitatively determine the resulting fractional amount of

this potential loss (VN) that results when the field has residual vegetative cover (Figure

7-6 of U.S. EPA, 1988).

5.3.2. Agricultural Tilling. Reentrained dusts can be generated by numerous

agricultural operations, including soil preparation (e.g., tilling, fertilizing), soil

maintenance (e.g., bedding, cultivating), and crop harvesting (U.S. EPA, 1988). This

section focuses on dusts emitted from tilling, the operation with the most impact on the
Chapter 5

Dust Resuspension

MPE 5-11 December 1998

Ef' (3.2E&9) (k) (5.38) s

0.6

c

(5-4)

adjacent terrain. Agricultural fields are mechanically plowed to create the desired soil

structure for planting crops and to eradicate weeds (U.S. EPA, 1985). Most tilling is

performed under dry soil conditions, causing dust particles to be resuspended into the

atmosphere as the soil is loosened, pulverized, inverted, and dropped back to the

surface (U.S. EPA, 1985, 1988). The quantity of dust generated from tilling is

proportional to the area of land being tilled and depends on the surface soil texture

(e.g., amount of silt, clay, sand, and loam) and the conditions of a particular field being

tilled (U.S. EPA, 1985).

The quantity of dust emissions from agricultural tilling may be estimated using

the following empirical expression (U.S. EPA, 1985, 1988):

where:

Ef = emission of resuspended dust due to agricultural tillage (g/m2- sec)

3.2E-9 = conversion factor [(1E+3 g/kg) / (1E+4 m2/ha)(3.1536E+7 sec/yr)]

k = particle size multiplier for total suspended particulate matter

(unitless); 1.0 (U.S. EPA, 1985)

5.38 = empirical constant (kg/ha)

sc = silt content of surface soil (%)

Dust emissions from agricultural tilling have been found to vary directly with the

silt content (defined as particles <75 μm in diameter) of the surface soil (U.S. EPA,

1985). Soil silt content generally ranges from 1.7 to 88%, with a mean value of 18%;

however, site-specific silt values should be used when possible (U.S. EPA, 1985).

5.3.3. Contaminant Emissions in Agricultural Areas. The emission rates (g/m2- sec)

presented in Sections 5.3.1. and 5.3.2. are used to estimate the dust resuspension flux. 

For this methodology, we are interested in emissions of contaminant aggregated with
Chapter 5

Dust Resuspension

MPE 5-12 December 1998

EC' Ed@ Cs@ 10&6@ ER

(5-5)

the dust, not the dust itself. It is reasonable to assume that the concentration of

contaminant in dust is the same as the concentration in the soil that is being

resuspended (i.e., aggregation with contaminant does not influence whether or not a

given particle will be resuspended). Therefore, the contaminant resuspension flux can

be calculated for agricultural soils by multiplying the dust emission rate, Ed (Equations

5-1 [Ep] and 5-4 [Ef]), by the concentration of contaminant in the soil at the site of the

dust source, and the enrichment ratio:

where:

EC = dust emission flux of contaminant in soil (g/m2-sec)

Ed = dust emission rate (g/m2-sec)

ER = enrichment ratio

Cs = concentration of contaminant in soil (μg/g)

10-6 = conversion factor (μg to g)

The enrichment ratio refers to the fact that erosion favors the lighter soil

particles. More information is provided in Section 7.2.10. of this methodology

document.

The concentration of contaminant in soil at the dust source (Cs) is calculated

using Equation 4-1, based on air dispersion modeling of the combustor emissions

alone. For the agricultural soils, soil concentration is calculated assuming a mixing

depth of 20 cm. A discussion of choice of mixing depth for various situations is

presented in Section 4.3.4.
Chapter 5

Dust Resuspension

MPE 5-13 December 1998

Euv ' k (1.7)

s

12

S

48

W

2.7

0.7 w

4

0.5

365&p

365

(5-6)

5.4. VEHICULAR TRAFFIC

Another source of reentrained dust is vehicular traffic. When a vehicle travels

on an unpaved road, the force of the wheels on the surface causes the pulverization of

surface material. Particles are lifted and dropped from the rolling wheels, and the road

surface is exposed to strong air currents in turbulent shear with the surface. The

turbulent wake behind the vehicle continues to act on the road surface after the vehicle

has passed (U.S. EPA, 1996a).

5.4.1. Unpaved Roads. The quantity of dust emissions generated from an unpaved

road depends on the volume of traffic, the silt content (particles <75 μm in diameter) in

the road surface materials, and several correction factors including average vehicle

speed, average vehicle weight, average number of wheels per vehicle, and road

surface moisture (U.S. EPA, 1996b).

The following empirical expression may be used to quantify particulate

emissions from an unpaved road (public or industrial), per vehicle kilometer traveled

(VKT) ( U.S. EPA, 1988, 1996b):

where:

Euv = annual emission of dust due to travel on unpaved roads

(kg/VKT-yr)

k = particle size multiplier for total suspended particulate matter

(unitless); 1.0 (U.S. EPA, 1996a)

s = silt content of road surface material (%)

S = mean vehicle speed, kilometers per hour (km/hr)

W = mean vehicle weight, megagrams (Mg)
Chapter 5

Dust Resuspension

MPE 5-14 December 1998

w = mean number of wheels

p = number of days with at least 0.254 mm of precipitation per year

Table 5-2 summarizes measured silt values for industrial and rural unpaved

roads (U.S. EPA, 1996a); mean values range from 4.3 to 24%. Since the silt content of

a rural dirt road will vary with location, site-specific measurements are most

appropriate; however, the silt content of the parent soil in the area can be used as a

conservative approximation. Tests show, however that road silt content is normally

lower than in the surrounding parent soil, because the fines are continually removed

by the vehicle traffic, leaving a higher percentage of coarse particles (U.S. EPA,

1996a).

Equation 5-6 requires input of the average vehicle speed and weight, and

average number of wheels of all vehicles traveling the road. For example, if 98% of
Chapter 5

Dust Resuspension

MPE 5-15 December 1998

TABLE 5-2

Typical Silt Content Values of Surface Material on

Industrial and Rural Unpaved Roads*

Industry

Road Use or

Surface

Material

Plant

Sites

No. of

Samples

Silt Content (%)

Range Mean

Copper smelting Plant road 1 3 16 - 19 17

Iron and steel production Plant road 19 135 0.2 - 19 6.0

Sand and gravel

processing

Plant road 1 3 4.1 - 6.0 4.8

Stone quarrying and

processing

Plant road

Haul road

2

1

10

10

2.4 - 16

5.0 - 15

10

9.6

Taconite mining and

processing

Service road

Haul road

1

1

8

12

2.4 - 7.1

3.9 - 9.7

4.3

5.8

Western surface coal

mining

Haul road

Access road

Scraper route

Haul road

(freshly graded)

3

2

3

2

21

2

10

5

2.8 - 18

4.9 - 5.3

7.2 - 25

18 - 29

8.4

5.1

17

24

Rural roads Gravel/crushed

 limestone

Dirt

3

7

9

32

5.0 - 13

1.6 - 68

8.9

12

Municipal roads Unspecified 3 26 0.4 - 13 5.7

Municipal solid waste

landfills

Disposal routes 4 20 2.2 - 21 6.4

* Source: U.S. EPA, 1996a
Chapter 5

Dust Resuspension

MPE 5-16 December 1998

vehicles on the road are 4-wheeled cars and trucks while the remaining 2% consists of

18-wheeled trucks, then the mean number of wheels (w) is 4.3 [(0.98 x 4) + (0.02 x 18)]. 

Equation 5-6 is not intended to be used to calculate a separate emission factor for each

vehicle class; instead, only one emission factor should be calculated that represents

the "fleet" average of all vehicles traveling the road (U.S. EPA, 1996a). Table 5-3

summarizes the range of conditions for sources used to derive Equation 5-6. For the

most reliable application of this equation, inputs should fall within the displayed ranges.

TABLE 5-3

Ranges of Source Conditions for Unpaved Roads*

Road Silt Content 

(wt %)

Mean Vehicle

Weight

(Mg)

Mean Vehicle

Speed

(km/hr)

Mean No.

of Wheels

4.3 - 20 2.7 - 142 21 - 64 4 - 13

 * Source: U.S. EPA, 1996a

The number of wet days per year (p) is defined as the number of days during

which there is at least 0.254 mm (0.01 in.) of precipitation; (p) can be determined from

local climatic data for the geographical area of interest. U.S. EPA (1996a) provides an

illustration of the geographical distribution of the mean annual number of wet days per

year in the United States. As noted in U.S. EPA (1996a), persons in dry, arid portions

of the country may wish to base (p) on a greater amount of precipitation than 0.254 mm

to account for rapid evaporation of moisture (and reduced dust mitigation) under these

conditions. A worst-case condition of no wet days can be simulated by setting p=0.
Chapter 5

Dust Resuspension

MPE 5-17 December 1998

Ep' k

sL

2

0.65 W

3

1.5

(5-7)

The dust emission rate in Equation 5-6 is in units of kg/VKT-yr. In order to add

these emissions to the air dispersion model, the emission rate must be converted to

units of g/sec. 

5.4.2. Paved Roads. Particles are deposited onto paved roadways from several

sources, including pavement wear and decomposition, vehicle-related deposition,

atmospheric dustfall, litter, mud and dirt carry out, erosion from adjacent areas, spills,

biological debris, and ice control compounds (U.S. EPA, 1988, 1996a). Particles are

resuspended whenever vehicles travel over paved surfaces and removed by wind

erosion, displacement, surface runoff, and street sweeping (U.S. EPA, 1988, 1996a). 

Surface loading, as it is redistributed or removed, is continuously replenished by other

sources. Several studies have shown that public and industrial streets and highways

can be major sources of resuspended atmospheric particulate matter in an area

(citations in U.S. EPA, 1988). Dust emissions from paved roads have been found to

vary with the silt loading present on the road surface and the average weight of

vehicles traveling the road. The quantity of dust emissions from vehicle traffic on a

paved road may be estimated using the following empirical expression (U.S. EPA,

1996a):

where:

Ep = annual emission of dust resuspended from vehicular traffic on

paved roads (g/VKT-yr)

k = particle size multiplier for total suspended particulate matter

(g/VKT);24 (U.S. EPA, 1996a)

sL = road surface silt loading (g/m2

)

W = average weight (tons) of the vehicles traveling the road
Chapter 5

Dust Resuspension

MPE 5-18 December 1998

ECv '

EuV @ Cs@ TC @ AR @ 1 m 2@ 10&

6

(3.1536E%7 sec/yr)

(5-8)

This equation is based on a regression analysis of numerous emission tests of freeflowing (not stop and go) traffic (MRI, 1993). Silt loading (sL) refers to the mass of siltsize material (particles <75 μm in diameter) per unit area of the travel surface (U.S.

EPA, 1996a) and is calculated as the product of the silt fraction and the total loading. 

In the event that site-specific values cannot be obtained, an appropriate value for an

industrial road may be selected from the mean values given in Table 5-4. In the

absence of site-specific sL data to serve as input to a public paved road inventory,

conservatively high emission estimates can be obtained by using the figures in U.S.

EPA (1996a). For annual conditions, median sL values of 0.4 g/m2 can be used for

high average daily traffic (ADT) roads (excluding limited-access roads) and 2.5 g/m2 for

low-ADT roads. Few sL data are available for limited-access roadways. For annual

conditions, a default value of 0.02 g/m2 is recommended for limited-access roadways

(U.S. EPA, 1996b). Equation 5-7 also calls for the average weight of all vehicles

traveling the road. For example, if 99% of vehicles on the road are 2 Mg cars/trucks

while the remaining 1% consists of 20 Mg trucks, then the mean vehicle weight (W) is

2.2 Mg [(0.99 x 2) + (0.01 x 20)]. Equation 5-7 is an accurate estimate of emissions

given that the input values fall within the ranges listed in Table 5-5 (U.S. EPA, 1996a).

5.4.3. Contaminant Emissions due to Vehicular Traffic. As mentioned earlier, this

methodology is interested in emissions of contaminant aggregated with the dust, not

the dust itself. The contaminant resuspension flux can be calculated unpaved roads by

multiplying the dust emission rate, Euv, (Equation 5-6) by the concentration of

contaminant in the soil. Equation 5-8 is adapted from Scott et al., (1998):

where:

Ecv = vehicle related dust emission flux of contaminant in soil (g/sec)
Chapter 5

Dust Resuspension

MPE 5-19 December 1998

Euv = annual emission of dust due to travel on unpaved roads (g/VKT)

AR = area of the road (m2)

TC = daily traffic count for the unpaved area (vehicles/day)

Cs = concentration of contaminant in soil (μg/g)

10-6 = conversion factor (μg to g)
Chapter 5

Dust Resuspension

MPE 5-20 December 1998 TABLE 5-4 Typical Silt Content and Loading Values for Paved Roads at Industrial Facilities a, b Industry No. of Sites No. of Sampl es Silt Content (%) No. of Trave

l

Lanes

Total Loading x 10

!3

(kg/km)

Silt Loading

(g/m

2

)

Range Mean Range Mean Range

Mea

n

Coppersmelting

1 3 15.4-

21.7

19.0

2 12.9-19.5 15.9 188-400 292

Iron andsteel production

9 48 1.1-35.7 12.5

2 0.006-4.77 0.495 0.09-79 9.7

Asphaltbatching

1 3 2.6-4.6 3.3 1 12.1-18.0 14.9 76-193 120

Concretebatching

1 3 5.2-6.0 5.5

2 1.4-1.8 1.7 11-12 12

Sand andgravelprocessing

1 3 6.4-7.9 7.1 1 2.8-5.5 3.8 53-95 70
Chapter 5

Dust Resuspension

MPE 5-21 December 1998

TABLE 5-5

Ranges of Source Conditions for Paved Roads*

Silt Loading 

(g/m2)

Mean Vehicle

Weight

(Mg)

Mean Vehicle

Speed

(km/hr)

0.02 - 400 1.8 - 38 16 - 88

 * Source: U.S. EPA, 1996a

The concentration of contaminant in soil at the dust source (Cs) is calculated

using Equation 4-1, based on air dispersion modeling of the combustor emissions

alone. For the unpaved road, a mixing depth of 1 cm may be appropriate. A

discussion of choice of mixing depth for various situations is presented in Section 4.3.4.

Calculation of contaminant emissions from paved roads is problematic. 

Equation 4-1 is not suitable for calculating the concentration of contaminant in dust on

paved roads. It may be necessary to obtain measured values of contaminant

concentrations in paved-road dust at the specific sites in question in order to include

this pathway. A bounding estimate could be obtained by assuming that resuspended

dust from the paved road consist entirely of contaminant particles, so that EC = Ep.

5.5. ESTIMATING EXPOSURES TO RESUSPENDED DUST EMISSIONS

If dust emissions from identified sources seem likely to contribute to exposure,

these sources can be treated as area-source inputs to the air dispersion model. In this

way, the resuspended dust emissions can be added to the direct combustor emissions

to determine air concentrations and deposition rates at downwind receptor locations. 

An alternative approach would be to convert the dust emissions to a screening-level

estimate of air concentration rather than add the resuspended dust emissions to the air

dispersion model. Both approaches would use the same value for dust emissions in

contaminated soil (Ec), g/m2-sec and (Ecv), g/sec.
Chapter 5

Dust Resuspension

MPE 5-22 December 1998

5.5.1. Air Dispersion Modeling of Resuspended Dust Emissions. The ISC3 air

model should be used (U.S. EPA, 1995). It is further assumed that all inputs needed to

model dispersion based on the combustor itself have already been collected. In this

case, just three additional types of information are needed to add a dust-generating site

to the air dispersion model as an area source of contaminant emissions: the emission

rate of contaminated dust from the site, the size distribution of dust particles emitted

from the site, and the size and location of the site. Each of these variables is

discussed below.

As mentioned above, there are many potential sources of resuspended dust. It

is important to note that the emission rates calculated using the equations from AP-42

are for dust, not contaminant. Calculation of contaminant emissions from resuspended

dust is provided below.

5.5.1.1. Particle Size Distribution of Resuspended Dust — Size is a key

determinant of the properties, effects, and fate of atmospheric particles (U.S. EPA,

1996b). The deposition rate of particles, and therefore the residence time in the

atmosphere, is a function of particle size. Minimum deposition rates occur at particle

sizes of 0.1-1.0 μm (diameter). Deposition rates increase with size for particles larger

than 1.0 μm. Therefore, larger particles, which settle more rapidly, will have more of an

effect on indirect routes of exposure via deposition to soil, vegetation, and surface

water than smaller particles. Conversely, the smaller particles, because they remain

suspended in the atmosphere for longer periods of time, have greater potential to

influence direct inhalation exposure. (Smaller particles will settle, but at greater

distances and after greater dilution in the atmosphere.) The ability of small particles to

influence inhalation exposure also reflects the deposition of particles in the respiratory

tract; smaller particles can penetrate deeper into the tract. Particles less than 10 μm in

diameter are generally considered to be inhalable (i.e., able to penetrate beyond the

nasopharyngeal region of the respiratory tract) in humans (U.S. EPA, 1996b).

Particles generated in uncontrolled conditions, in combustors and dust

resuspension events, are not uniform in size. These uncontrolled sources produce a
Chapter 5

Dust Resuspension

MPE 5-23 December 1998

range of sizes that can be described by a particle size distribution. Although the exact

distribution will vary with the specific source and may be influenced by many factors,

different types of sources generally produce characteristic distributions. A generalized

particle size distribution for combustor emissions is shown in Table 5-6. The particle

size distribution for resuspended dust differs markedly from that of combustors, with a

much larger contribution by large particles. Data on resuspended dust suggest a lognormal bimodal distribution with one mode at 2-5 μm and the other at 30-60 μm (U.S.

EPA, 1996b). The deposited pollutant particles may or may not become fully

aggregated with their host soil particles (Sehmel, 1980). For the sake of simplicity, it

will be assumed that the distribution of particle size found in resuspended dust applies

to resuspended contaminant.

TABLE 5-6

Particle Size Distribution for Resuspended Soils and Road Dusts*

Particle Diameter (μm)

Mass Fraction

Range Midpoint of Range

<1.0 0.5 0.035

1.0 - 2.5 1.75 0.065

2.5 - 10 6.25 0.40

10 - 30 20 0.50

* Source: Chow et al., 1994; Houck et al., 1989

Because most studies of dust emissions have used total suspended particulate

matter (TSP) or smaller size classes (e.g., PM10) as the basis for their measurements,

the emission factors presented in Section 5.2.1. represent emissions of TSP. Although
Chapter 5

Dust Resuspension

MPE 5-24 December 1998

TSP, as typically collected with a high-volume sampler, can capture a small percentage

of particles as large as 100 μm (the largest particles that can be resuspended by wind),

it is not very efficient for particles larger than 30 μm (U.S. EPA, 1996a). Therefore, the

emission rates for resuspended dust used in this methodology underestimate total dust

emissions and effectively exclude particles larger than 30 μm. This is a limitation of the

existing data, although it may be argued that particles larger than 30 μm are not likely

to be suspended long enough to be transported off-site anyway. Only dusts that are

resuspended and transported to physically distinct areas beyond the source will have

any net effect on exposure.

Laboratory and field studies of resuspended soil and road dust have been

conducted to estimate the size distribution as a percent of TSP mass (Chow et al.,

1994; Houck et al., 1989, 1990). It was found that roughly 50% of TSP was contained

in the PM10 fraction, 10% in the PM2.5 fraction, and 3.5% in the PM10 fraction for soils

and road dusts in both studies. In the absence of site-specific measured data, the

generalized particle size distribution can be used to represent resuspended solid and

road dusts in the ISC3 air dispersion model as shown in Table 5-6. For the alternate

simpler screening level approaches suggested in Section 5.6.2., particle size

distribution is not required as input.

5.5.1.2. Size and Location of Area Source — The size and location of the

dust-generating area source must be entered into the model as described in U.S. EPA

(1995). The ISC3 allows rectangular shaped areas with aspect ratios up to 10:1, which

may be placed in any orientation. An irregularly shaped area source can be

accommodated by subdividing it into smaller areas of varying shapes, sizes, and

orientations. Line sources (e.g., roads) are accommodated by dividing the source into

multiple rectangular (10:1 aspect ratio) segments. The PC version of the model is

preset to include up to 300 sources. This number can be increased if adequate

hardware resources are available.

5.5.2. Alternative to Air Dispersion Modeling of Resuspended Dust Emissions.

Once a dust-emission rate of contaminant due to dust resuspension has been
Chapter 5

Dust Resuspension

MPE 5-25 December 1998

calculated, it can easily be converted into a screening level estimate of air

concentration (screening-level in comparison to modeling with ISC3, at least). That

estimate of particulate phase air concentration can be compared to the particulate

phase air concentration at the exposure site predicted by the ISC3. In most cases, the

concentration due to dust resuspension will be much lower than the concentration

predicted to arrive at the exposure site from the stack emission. In situations where this

may not be the case, such as in heavy traffic over dirt roads, or wind erosion over bare

dry soils, the risk assessor should consider rerunning the ISC3 using the multiple

source option. Since the MPE is limited to single source evaluations, no guidance on

the use of ISC3 in modeling multiple sources will be offered here. The risk assessor

should obtain the ISC3 code and User’s Guide (U.S. EPA, 1995) to determine the

procedures to model the transport and deposition of an area source emission.

The risk assessor should be cautious when comparing a screening-level

estimate of air concentration due to dust resuspension and an ISC3 prediction of

concentration resulting from the stack emission. Recall that the ISC3 prediction is an

average over the course of a year. Depending on the process that caused the dust

resuspension, an estimate of air concentration due to dust emissions may be shortlived in comparison to a year. Therefore, the screening-level air concentration

predictions that can be made using the algorithms below are very specific to the time

embedded in the calculation of the dust emission rate (the predicted flux of contaminant

from the soil) used in the equations. A risk assessor should convert the predicted air

concentration to an annual basis for a proper comparison with the ISC3 air

concentration prediction.

Short- term impacts (e.g., from inhalation) from a dust resuspension event can

also be examined through a screening approach. One such approach to estimating air

concentrations from an area source emission is with a box model. This can be

described mathematically as:
Chapter 5

Dust Resuspension

MPE 5-26 December 1998

Cp'

Ec A 106

w Vwh

(5-9)

where:

Cp = particle phase air concentration due to dust resuspension (μg/m3)

Ec = dust emission rate of contaminant from soil (g/m2-sec)

A = area over which resuspension occurs (m2)

w = width of area perpendicular to wind direction (m)

Vw = velocity of wind (m/sec)

h = mixing height (m)

106 = converts g to μg

The assessor should be cognizant of what this rate term represents (e.g., a

single event, an average over multiple events covering a long period of time), and also

that the units should be properly converted to g/sec basis. The area term, A, refers to

the land area over which dust resuspension occurs. This could be the area of a plowed

field, the area of the road over which dust becomes resuspended, and so on. Since

wind direction would not be known, the risk assessor can use judgment to assign a

value to the width term, W. Perhaps this could be narrowest width associated with the

area-source emission, so that the air concentration estimation can be maximized. The

mean annual wind speed varies from 2.8 to 6.3 m/sec (U.S. EPA, 1985). On an event

basis, the wind velocity could be higher. A first-estimate assumption for this screening

model of 4.0 m/sec is probably reasonable. The mixing height, h, has often been

assumed to be 2 m, which approximates the breathing zone. This would be appropriate

in this application.

If the dust resuspension occurs at a distance from where the exposure occurs,

an risk assessor can use a simple virtual point source model (Turner, 1970). This
Chapter 5

Dust Resuspension

MPE 5-27 December 1998

Cp'

2.03 Ec A FR 106

VD Dva Vw

(5-10)

Dva ' 0.222 AD 0.724 & 1.7 , AD < 1000 m

(5-11)

Dva ' 1.26 AD 0.516 & 13.0 , AD > 1000 m

(5-12)

approach treats the area source as though it were a point source. The model

approximates the dispersion that occurs from an area source by using an imaginary

point source. This point is located upwind of the actual source at a distance calculated

to create a lateral dispersion at the site equal to its width:

where:

Cp = particle phase air concentration due to dust resuspension (μg/m3)

Ec = dust emission rate of contaminant from soil (g/m2-sec)

A = area over which resuspension occurs (m2)

FR = frequency wind blows from area source to receptor, fraction

VD = virtual distance, source center to receptor (m)

Dva = vertical dispersion coefficient (m)

Vw = velocity of wind (m/sec)

106 = converts g to μg

Dva is estimated as an empirical function of the distance from the source center

to receptor:
Chapter 5

Dust Resuspension

MPE 5-28 December 1998

VD ' 2.514 w % AD

(5-13)

where:

Dva = vertical dispersion coefficient (m)

AD = actual distance from source center to receptor (m)

Virtual distance, VD, is an empirical function of the width of the contaminated

area and the actual distance from source center to receptor:

where:

VD = virtual distance, source center to receptor (m)

w = width of area perpendicular to wind direction (m)

AD = actual distance from source center to receptor (m)

In these equations, the Ec, A, W, and Vw are parameterized in a manner similar

to the box model above. The frequency wind blows from the area source to the

receptor, FR, can be assigned a value of 1.00 (100% of the time) as an absolute worst

case, although it may be appropriate in evaluating the impacts of a single event. If the

dust resuspension events occur frequently and it is desirable to obtain an annual

average air concentration at the exposure site, a value of 0.50 or less is probably more

reasonable, yet still conservative. The actual distance term, AD, is, as defined, the

distance from the center of the area source to the receptor.

If a risk assessor has made a calculation of a particle-phase air concentration

predicted to occur at a site because of dust resuspension from a distant site, and has

judged it to be of importance, a few options are available. One is to rerun the ISC3,

model as noted above. Otherwise, the air concentration prediction can be used in the

assessment. First, it can be added to the other air concentration terms from the ISC3

model (the vapor- and particle-phase concentrations) to arrive at a total concentration
Chapter 5

Dust Resuspension

MPE 5-29 December 1998

for inhalation exposures. Also, the assessor can assume that the particles redeposit

back onto soils and vegetation at the exposure site. This particle deposition flux would

be added to the particle deposition rate from the stack emission already predicted by

ISC3. A particle deposition rate is easily calculated as the product of the air

concentration times the deposition concentration, Cp (μg/m3) * Vd (m/sec) (the units

need to be converted to be compatible with output from ISC3).

The deposition velocity, Vd, can be assigned a value of 0.01 m/sec (1 cm/sec)

as a reasonable screening-level estimate. Seinfeld (1986) lists a gravitational settling

velocity of 1 cm/sec for 10 μm particles. These are the “inhalable” particles; the

emission factor equations presented earlier were mostly developed to predict the dust

resuspension rate for particles this size and smaller. Therefore, the 1 cm/sec

assumption is reasonably conservative for this estimate.

As a final note, the risk assessor should not calculate resuspended

contaminants that are redeposited at the site where exposure occurs. This will amount

to double accounting. First, particles from a contaminated soil that fall back on the

contaminated soil are not going to increase the soil concentration. Second, impacts to

plants from soils are better handled with the use of a soil-to-plant algorithm, as

described in Chapter 6.
Chapter 5

Dust Resuspension

MPE 5-30 December 1998

5.6. REFERENCES

Chow, J.C., J.G. Watson, J.E. Houck et al. 1994. A laboratory resuspension chamber

to measure fugitive dust size distributions and chemical compositions. Atmos. Environ. 

28:3463-3481.

Gillette, D. 1986. Wind Erosion. Soil Conservation: Assessing the National Resource

Inventory, Vol 2. National Academy Press, Washington, DC.

Houck, J.E., J.C. Chow, J.G. Watson et al. 1989. Determination of Particle Size

Distribution and Chemical Composition of Particulate Matter from Selected Sources in

California. Volume I (Final Report). California Air Resources Board, Sacramento, CA.

Houck, J.E., J.M. Goulet, J.C. Chow et al. 1990. Chemical characterization of

emission sources contributing to light extinction. In: Visibility and Fine Particles: An

AWMA/EPA International Specialty Conference, October, 1989, Estes Park, CO, C.V.

Mathai, Ed. AWMA Transaction Series. 17:437-446.

Moosmüller H, J.A. Gillies, C.F. Rogers, D.W. DuBois, J.C. Chow, J.G. Watson, and R.

Langston. 1998 Particulate emission rates for unpaved shoulders along a paved road.

J. Air & Waste Manage. Assoc. 48:398-407.

MRI (Midwest Research Institute). 1993. Emission Factor Documentation for AP-42,

Sections 11.2.5 and 11.2.6 — Paved Roads. Prepared by Midwest Research Institute,

Kansas City, MO. EPA Contract No. 68-D0-0123. (Cited in U.S. EPA, 1996a)

Scott, P.K., B.L. Finley, H. Sung, R.H. Schulze, and D.B. Turner. 1997. Identification of

an accurate soil suspension/dispersion modeling method for use in estimating healthbased soil cleanup levels of hexavalent chromium in chromite ore processing residues.

J. Air & Waste manage. Assoc. 47:753-765.

Sehmel, G.A. 1980. Particulate resuspension: A review. Environ. Intern. 4:107-127.

Seinfeld, J.H. 1986. Atmospheric Chemistry and Physics of Air Pollution. John Wiley

and Sons, New York.

Smith, W.J. II, F.W. Whicker and H.R. Meyer. 1982. Review and categorization of

saltation, suspension and resuspension models. Nuclear Safety. 23(6):685-699.

Turner, D.B. 1970. Workbook of atmospheric dispersion estimates. U.S.

Environmental Protection Agency, Research Triangle Park, NC. PHS Publ. No. 999-

AP-26. Available from: NTIS, Springfield, VA; PB 191482.

USDA (U.S. Department of Agriculture). 1987. National Agronomy Manual.
Chapter 5

Dust Resuspension

MPE 5-31 December 1998

U.S. EPA. 1976. Development of a Methodology and Emission Inventory for Fugitive

Dust for the Regional Air Pollution Study. Office of Air Quality Planning and Standards,

Research Triangle Park, NC. EPA-450/3-766-003.

U.S. EPA. 1985. Compilation of Air Pollution Emission Factors. AP-42, 4th ed. Office

of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA. 1988. Control of Open Fugitive Dust Sources. Office of Air Quality

Planning and Standards, Research Triangle Park, NC. EPA-450/3-88-008.

U.S. EPA. 1990. Methodology for Assessing Health Risks Associated with Indirect

Exposure to Combustor Emissions. Environmental Criteria and Assessment Office,

Cincinnati, OH. EPA/600/6-90/003

U.S. EPA. 1995. User's Guide for the Industrial Source Complex (ISC3) Dispersion

Models. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

EPA-454/B-95-003a.

U.S. EPA. 1996a. Compilation of Air Pollution Emission Factors. AP-42, 5th ed.

(Supplement A). Office of Air Quality Planning and Standards, Research Triangle Park,

NC.

U.S. EPA. 1996b. Air Quality Criteria for Particulate Matter. Volume I. National

Center for Environmental Assessment, Research Triangle Park, NC. EPA-600/P95/001aF.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-1 December 1998

DETERMINING EXPOSURE THROUGH THE TERRESTRIAL FOOD CHAIN

6.1. INTRODUCTION

The previous chapter describes how pollutants associated with combustor

emissions are incorporated into soil. This chapter describes how contaminants enter

the terrestrial food chain. The terrestrial food chain includes plants that animals

consume, plants that humans consume, and animals that humans consume. Plants

take up contaminants directly from the soil or from the atmosphere. Animals are

exposed to contaminants by ingestion of plants and soils. In addition to contaminant

fate modeling, this chapter shows how daily exposure to food chain contaminants is

calculated and used to estimate human risk.

6.2. OVERVIEW OF TERRESTRIAL FOOD CHAIN

There are three general types of parameters in these fate algorithms. The first

category includes media concentrations, or quantities, that have been calculated using

other models of this assessment. Specifically, the soil and air concentrations and the

atmospheric deposition fluxes were modeled using the approaches described in

Chapters 3 and 4. A second category of parameters includes those that are specific to

the contaminant. Included here, for example, are the bioconcentration/biotransfer

factors used to translate an animal diet of the contaminant into an animal tissue

concentration. The third and final category of parameters are the general physical

parameters not related to the contaminants. These include, for example, plant yields

and animal diet descriptors. The guidance presented for these parameters is general

in nature and attempts to make important distinctions, such as guidance for inorganic

versus organic contaminants, where pertinent.

Sections 6.3 and 6.4 describe the algorithms for estimating contaminant

concentrations in plants and animals, respectively. Both sections follow a similar

structure. First, the overall equations describing the fate pathway (e.g., the transfer of

contaminants from the air to the plant, or the bioaccumulation of ingested contaminants

in animal tissue) are given. These are generic equations pertinent to organic and
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-2 December 1998

inorganic contaminants, soluble and insoluble contaminants, and so on. Then, general

guidance is offered for assignment of the equations’ parameters.

Section 6.5 presents algorithms for estimating human daily intake, given the food

concentrations predicted using the algorithms in Sections 6.3 and 6.4. Section 6.6

provides an alternative approach for terrestrial food chain modeling.

It is important to understand that the algorithms for estimating terrestrial food

chain impacts are relatively simple screening-level models. Simplifying assumptions

often are made in order to arrive at the desired results, which are long-term average

concentrations in the terrestrial foods. In summary, some of the key assumptions are

as follows:

! The source strength remains constant throughout the period of exposure. 

The “sources” for terrestrial impacts include the deposition fluxes and air

concentrations predicted by the air dispersion model. An assumption of a

constant source strength also was used in air dispersion modeling, which

predicted long-term average deposition fluxes and air concentrations

assuming that the emissions of the contaminants from the combustion source

were constant throughout the exposure period.

! Plant concentrations are modeled as a function of the deposition of

contaminants that have been emitted from the combustion source and

traversed to where the plants are growing; also considered is a component

due to root uptake and translocation. Plant impacts do not consider the

resuspension and redeposition of contaminants that have deposited onto

soils (although the use of the “plant biotransfer factor,” Br, for lipophilic

compounds may inherently model this process—see discussion in Section

6.3 below on this parameter). These resuspension/redeposition phenomena

could occur where processes such as tillage, wind erosion, vehicular

resuspension, and/or rainsplash might be important. Models are available to

estimate plant impacts from these processes, but they are not discussed in

this chapter.

! Previous analysis has suggested that the exposure of terrestrial animals to

bioaccumulating contaminants is primarily via ingestion of food and soil. 

Therefore, other possible routes of exposure, such as inhalation or water

ingestion, are not considered. There may be special circumstances where

an animal’s exposure is significantly influenced by pathways not considered

in this assessment (other than water ingestion or inhalation). One example

could be the impact of worms or other biota consumed by free-ranging

chickens. Another example would be the exposure of farm animals to wood
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-3 December 1998

that has been treated with pentachlorophenol or other preservatives. Fries et

al. (1996) found very high levels of dioxins in cattle in an experimental setting

that could not be explained by their diet but that correlated very well with

concentrations of dioxins in the pentachlorophenol-treated wood in their

environment.

Figure 6-1 shows the general structure of the modeling algorithms of this

chapter. The box shows the air reservoir and the quantities used to model the

terrestrial food chain impacts. For the terrestrial impact model, this box represents the

“source” of the contaminants. The circles show the media compartments that are

impacted by the source and to which humans are exposed (vegetables/fruits, animal

food products) or to which animals are exposed (forages, soil). The arrows show the

pathways between media compartments, and the terms near the arrows describe the

processes of importance in the terrestrial food chain.

6.3. CALCULATING CONTAMINANT CONCENTRATION IN PLANTS

This section examines three mechanisms by which contaminants can

bioaccumulate in plants: root uptake and translocation, deposition of particle-bound

contaminants to exposed plant tissues, and deposition of vapor-phase contaminants to

exposed plant tissues (deposition of vapor-phase contaminants also has been called

“air-to-plant transfer” of vapor-phase contaminants; one algorithm described below

does not contain a “deposition velocity” term, but rather is based on an empirical vapor 

“transfer” factor). The relative importance of each mechanism depends on both the

characteristics of the plant and the characteristics of the contaminant. Plant

characteristics of importance include the surface-area-to-plant-volume relationship (i.e.,

leafy vegetations are likely to accumulate more than bulky vegetations) and whether

the edible portion (edible by humans or by animals as part of the food chain) is

exposed, such as the skin of an apple or grass leaves, or protected, such as peas or

grain for animal feeds. Contaminant characteristics include the degree of partitioning

between a particle and a vapor phase and the persistence of the contaminant in the

environment.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-4 December 1998

Figure 6-1

Overview of Terrestrial Food Chain Modeling Compartments and Processes
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-5 December 1998

Pi' Pri% Pdi% Pvi

(6-1)

The food chain model calculates the contaminant concentration in each plant

group due to the appropriate mechanisms and sums them to obtain the total

contaminant concentration:

where:

Pi = total concentration of contaminant in ith plant group (μg/g DW)

Pri = concentration of contaminant in above-ground parts of ith plant group

due to translocation from roots (μg/g DW)

Pdi = concentration of contaminant in ith plant group due to particle-phase

deposition (μg/g DW)

Pvi = concentration of contaminant in ith plant group due to vapor-phase

deposition (μg/g DW).

Vegetations consumed by humans can be grouped into five categories: exposed

fruits, protected fruits, vegetables, protected vegetables, and root vegetables. The

terms “exposed” and “protected” refer to whether or not the edible portion is exposed to

the atmosphere. For example, apples are an exposed fruit, whereas peas are

considered a protected vegetable. Vapor-phase transfers and particle-phase

depositions are calculated for exposed fruits and vegetables but not for protected fruits

or vegetables or root vegetables, since these vegetations are not in direct contact with

the air. For protected vegetables and fruit grown above ground, accumulation could

occur by root uptake followed by translocation. Root vegetables could be impacted by

passive uptake of contaminants through transpiration water or by absorption onto the

outer parts of the root vegetables. Table 6-1 summarizes the mechanisms of impact

that are assumed for each of the categories of vegetation. Table 6-2 lists some of the

more common items from each category of produce consumed by humans.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-6 December 1998

TABLE 6-1

Uptake Mechanisms Based on Plant Morphology Assumed in this Methodology

Edible portion is:

Edible portion is:

Exposed Protected

Uptake mechanisms

Above ground Direct deposition

Vapor deposition

Root uptake and

translocation

Root uptake and

translocation

Below ground Root uptake Not applicable

TABLE 6-2

Common Items from Each Category of Produce Consumed by Humans

Category Common fruits and vegetables

Exposed fruits apples

pears

apricots

strawberries

cherries

nectarines

plums

grapes

rhubarb

peaches

berries

dates

Protected fruits citrus 

kiwi

passion fruit

melons

avocados

pomegranates

bananas

mangoes

figs

pineapples

papayas

guava

Exposed

vegetables

tomatoes

celery

pea pods

peppers

string beans

summer

squash

cabbage

mushrooms

cucumbers

asparagus

cabbage

brussels

sprouts

eggplant

cauliflower

lettuce

Protected

vegetables

peas

pumpkin 

artichoke

hearts

corn

winter squash

lima beans

kidney beans

soybeans

cactus
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-7 December 1998

Root vegetables potatoes

garlic

horseradish

beets

sweet

potatoes

rutabagas

carrots

turnips

parsnips

onions

radishes

water

chestnuts

Vegetations consumed by animals can be grouped into three categories: grains,

forage, and silage. Animal grains are categorized as protected vegetations, while

forage (e.g., pasture plants and grasses) is considered to be exposed vegetation. In

this document silage is used in a generic sense to refer to plants harvested for animal

consumption that could be partly protected and partly exposed. An example here is

corn silage fed to the animal as whole plants. Here, the corn grain could be considered

protected, the leaves of the plant could be considered unprotected, and the stalks are

in part protected and unprotected. Particle depositions and vapor transfers are

calculated for silage. As described below, the VGag empirical parameter for vapor

transfer is given a unique value for silage, based on the fact that silage is part

protected and part exposed.

Some uptake mechanisms can be excluded based on what is known about the

contaminant. For example, dioxin-like compounds will sorb to outer portions of belowground vegetation, but inner translocation has been shown to be negligible in EPA’s

Exposure Reassessment Document for Dioxin-Like Compounds. This reassessment,

when completed, will replace EPA’s current draft exposure document on dioxin entitled

Estimating Exposures to Dioxin-Like Compounds (EPA/600/6-88/005 Ca-Cc, June

1994). As it is still under development, it will be referred to throughout this exposure

pathways document as the “dioxin exposure reassessment.” Therefore, risk assessors

may choose not to consider an algorithm to estimate above-ground plant impacts as a

function of soil concentrations for risk assessments involving dioxins. This was the

approach taken for the site-specific methodologies developed for the dioxin-like

compounds in EPA’s dioxin exposure reassessment. Risk assessors may want to

consider site-specific data when developing appropriate plant groups. Note that
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-8 December 1998

Pri' Cs Br

(6-2)

separate Pi values must be calculated for each contaminant to be considered in the risk

assessment.

6.3.1. Plant Contaminant Concentration Due to Root Uptake (Pri). Contaminants in

soil may enter a plant via uptake through the roots, where they may accumulate in the

below-ground mass (e.g., tuber, bulb) and/or be translocated to the above-ground

shoots. The concentration of contaminant in plant tissue due to root uptake is

determined from the concentration of contaminants in soil and a chemical-specific

biotransfer/ bioconcentration factor. 

For above-ground crops (e.g., spinach), translocation from roots may contribute

to the contaminant concentration in the plant. A general model relating soil

concentrations to above-ground plant concentrations using a simple soil-to-plant

biotransfer factor (Br) was developed by Travis and Arms (1988):

where:

Pri = concentration of contaminant in above-ground parts of ith plant group

due to translocation from roots (μg/g DW)

Cs = concentration of contaminant in soil (μg/g) [see Equation 4-1]

Br = soil-to-plant bioconcentration factor for contaminant ([μg

contaminant/g DW plant]/[μg contaminant/g soil]).

In this equation, it would be inappropriate to assume that the Br is modeling

translocation from root to shoot in all cases, however. Some of the experiments

gathered by Travis and Arms (1988) to develop the Br term involved soluble pesticides

while others involved compounds of very low solubility whose transport is dictated more

by soil rather than by water movement, such as DDT. For the former class of soluble

compounds, translocation would explain the above-ground concentrations found in the

experiments, while in the latter case of sorbed contaminants, other mechanisms such

as resuspension followed by redeposition would be cause for above-ground plant
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-9 December 1998

Pr

C

Kd TSCF F

100 M

100

i

s

ts

ag

=

−

(6-3)

impacts. Therefore, it may be assumed that in the modeling of above-ground plant

impacts from soluble compounds in soil, the Br represents root uptake and

translocation, while in the modeling of above-ground plant impacts from tightly sorbed

contaminants, the Br represents resuspension followed by redeposition onto the plants.

A second approach to modeling above-ground plant impacts for soil reservoirs of

contaminants was suggested by the work of Briggs et al. (1982), who grew barley

plants in a growth solution in a laboratory setting and added O-methylcarbamoyloximes

and substituted phenylureas to the growth solution. From their data, they calculated

the Transpiration Stream Concentration Factor (TSCF), which they defined as the

concentration in the transpiration stream divided by the concentration in the solution in

which the barley plants were growing. For highly soluble contaminants, the TSCF

would approach 1.0. Otherwise, the TSCF would be less than 1.0. If one assumes that

the experimental solution concentration is analogous to soil pore water concentration

and that the concentration in the transpiration stream is equivalent to the concentration

in the moisture fraction of the actively growing plant, then the Briggs et al. (1982) TSCF

can be used for estimating above-ground plant concentrations:

where:

Pri = concentration of contaminant in above-ground parts of ith plant group

due to translocation from roots (μg/g DW)

Cs = concentration of contaminant in soil (μg/g)

TSCF = transpiration stream concentration factor ([μg contaminant/mL

transpiration stream]/[μg contaminant/mL pore water])

Mag = percentage moisture content of above-ground vegetation (% water)

Kd = soil-water partition coefficient (mL/g).

Fts = fraction of whole weight which is transpiration stream water, unitless
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-10 December 1998

Prrv '

Cs RCF VGbg

(100&Mbg)

100

Kd

(6-4)

In this equation, Cs/Kd solves for the concentration in soil pore water. Therefore,

multiplication of the soil pore water concentration by TSCF yields the concentration of

the contaminant in the transpiration stream water. The concentration of the

contaminant in transpiration stream water will, by definition, be higher than the

concentration in the whole plant, assuming transpiration is what brought the

contaminant from the soil to the above ground parts of the plant; hence the need to

include the Fts, which converts the concentration of the contaminant in transpiration

stream water to a concentration in the whole plant on a fresh weight basis. An

assumption of 50%, or a Fts of 0.50, would be reasonable. Finally, the term in the

denominator, (100- Mag)/100, converts the whole-plant fresh-weight concentration into a

whole-plant dry-weight concentration, which are the appropriate units for Prj.

Either of the two approaches for estimating above-ground impacts from the

contaminants in the soil is acceptable for any class of contaminants considered in this

assessment methodology. A risk assessor also can choose not to consider aboveground impacts from tightly sorbed contaminants depositing onto the soil in which the

plant is growing. In a modeling assessment for dioxin-like compounds, EPA’s dioxin

exposure reassessment did not consider this pathway when modeling the impacts of

dioxins emitted from combustion sources. The assumption was that the direct

deposition of dioxins onto plants led to a much greater impact as compared with the

impacts from the resuspension/redeposition of dioxins already deposited onto the soil.

Briggs et al. (1982) also suggested a framework for calculating the concentration

in roots as a function of concentration in their experimental solution. They developed a

Root Concentration Factor (RCF), which they defined as the concentration in the roots

divided by the concentration in soil pore water. Using the RCF, the algorithm used to

estimate the concentration in root vegetables is:
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-11 December 1998

where:

Prrv = concentration of contaminant in root vegetables due to root uptake

(μg/g DW)

Cs = concentration of contaminant in soil (μg/g)

RCF = root concentration factor ([μg contaminant/g plant FW]/[μg

contaminant/mL pore water])

VGbg = empirical below-ground vegetation correction factor (dimensionless)

Mbg = percentage moisture content of below-ground vegetation (% water)

Kd = soil-water partition coefficient (mL/g)

As in Equation 6-3, Cs/Kd solves for the concentration in soil pore water, and the

division by [100- Mbg]/100 converts the whole-plant fresh-weight concentration into a

whole-plant dry-weight concentration.

All these algorithms for soil impacts require, of course, the soil concentration of

the contaminant, Cs. Methods for calculating soil concentration are presented in

Chapter 4. When considering root uptake for root vegetables, above-ground fruits and

vegetables, or crops grown for animal feed, soil concentration should be calculated

assuming a tilled depth since the ground is usually tilled or disturbed in some manner

to plant food and grain crops. For vegetations that are not tilled, such as forage for the

terrestrial animal food chain, the soil concentration should equal that calculated for the

untilled condition. As described in Chapter 4, tilled depths are typically 15 to 20 cm

and untilled depths are typically 1 to 2 cm, depending on site-specific conditions and

the risk assessor’s judgment.

The VGbg is introduced on the basis of data on the transfer of dioxin-like

compounds into below-ground vegetables, including carrots and potatoes (Muller et al.,

1994; Hulster and Marschner, 1991). These data suggest that the whole barley root

concentrations would be similar to concentrations near the skin of a bulky belowground vegetable, but not the whole vegetable. Further, these experiments suggest

that the concentrations found on the outer parts of the vegetable are due more to

adsorption than to passive uptake via transpiration water. To adjust for the volume
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-12 December 1998

differences between barley roots (used in Briggs et al. [1982] experiment) and bulky

below-ground root crops, the empirical below-ground correction factor, VGbg, is used. 

EPA’s dioxin exposure reassessment derived a value of 0.25 for VGbg for all the dioxinlike compounds for all root vegetables from these data. Unless better data are

available, it would be appropriate to similarly assign a value of 0.25 for compounds

similar to the dioxins—lipophilic (near log Kow 6.0 and higher) and persistent. Other

factors that reduce vegetation concentrations, such as peeling, cooking, or cleaning,

can be considered in this empirical correction factor for site-specific assessments. 

Soluble compounds would be taken into the plant via transpiration water and would

tend to distribute more uniformly throughout the entire plant. Therefore, this adjustment

would not be appropriate, that is, the VGbg would be set equal to 1.0. For purposes of

VGbg assignments, a value of 1.0 can be assumed for organic contaminants with log Kow

less than 2.0.

The plant-soil bioconcentration factor, Br, is a measure of a contaminant's

tendency to accumulate in plant tissue and is defined as the contaminant concentration

in the plant divided by the contaminant concentration in the soil. Bioconcentration

factors may be derived differently for inorganic and organic pollutants, but they

generally are related to the bioavailability of the pollutant in the soil.

Data on uptake of inorganics by plants may be available in the form of

bioconcentration factors, which are discussed in Baes et al. (1984). The Parameters

Guidance Document (under development by EPA) will contain values for Bri for

numerous inorganics.

However, in some cases, accumulation of inorganics is measured by “uptake

response slopes” rather than by bioconcentration factors. The term “uptake response”

is used to denote the difference between pre- and postexposure plant tissue

concentrations. As defined in the literature, “exposure” here refers to the exposure of

the plants to specific applications of the inorganic to the land, such as in fertilization or

sludge application. In this case, the soil instantaneously achieves the long-term

average soil concentration. In the case of combustion depositions, the long-term
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-13 December 1998

average soil concentration builds up over time. Uptake response slopes have not

typically been derived for these long-term buildup situations, and it is not known if the

inorganic contaminants translocate into plants differently depending on whether the soil

is amended with one swift application or the concentration builds up over time. 

However, there does not appear to be any immediate justification for not using uptake

response curves for evaluating soil-to-plant impacts from depositions, as long as the

risk assessor makes the appropriate unit conversions. Uptake response slopes have

been derived from data sets where changes in plant concentration and contaminant

application rate have been determined; the units often are expressed as (μg

contaminant/g DW plant)/(kg contaminant applied/ha). Uptake response slopes can be

converted to bioconcentration factors by converting the term in the denominator, kg

contaminant applied/ha, to a concentration in similar units to the numerator, μg

contaminant applied/g soil. If the contaminant has been tilled into the ground, then the

conversion considers the mass of a 15- to 20-cm slice of a hectare. Assuming a 1.35

g/cm3 soil bulk density, the mass of 20 cm/ha is 2.7@109 g. Therefore, the denominator

is converted to appropriate units by the multiplication of [kg/ha] @ [109 μg/kg] @

[ha/2.7@109 g].

Uptake response slopes of metals by plants, reviewed by the Council for

Agricultural Science and Technology (CAST) (1980), Ryan et al. (1982), and Logan

and Chaney (1983), are influenced by application rate, soil pH, and soil organic matter

content. Ryan et al. (1982) used linear regression of plant tissue cadmium

concentration against cadmium application rate to derive uptake response slopes for

various crops. The authors stated that, given constant soil conditions, cadmium plant

tissue concentration had a highly significant positive linear correlation with the amount

of cadmium added to the soil.

More recent work, as reviewed by Page et al. (1986), has shown that plant

response to metals from sludge-amended soil is curvilinear, approaching a plateau

concentration in tissue as sludge application rate increases. However, metal-
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-14 December 1998

adsorptive materials present in the sludge matrix are thought to be responsible for this

effect.

Soil pH is probably the most important characteristic influencing metal retention,

and thus availability, by soils (LaBauve et al., 1988). The pH of soils varies greatly,

from 2.0 or less in acid soils to 9..0 or more in alkaline soils (Hillel, 1980). Agricultural

soils usually are limed to maintain the pH at approximately 6.5, the pH at which plant

nutrients are most readily available for plant uptake.

Metal cations exhibit a pH-dependent adsorption behavior, probably due to

several different adsorption mechanisms (Elliott et al., 1986). At pH less than 6.0,

some metal cations tend to be more mobile. For example, cadmium mobility tends to

increase as soil pH decreases; lead is less pH dependent (Harter, 1983). As soil pH

increases, generally above 6.0, metal cations are adsorbed more strongly and become

less available for uptake by plants. Several studies have demonstrated that, generally,

plant uptake of metals decreases with increasing pH (Lagerwerff, 1971; Ryan et al.,

1982; Giordano et al., 1983). However, the effects of liming acid soils to increase pH

are unclear. While Giordano et al. (1983) and Ryan et al. (1982) report decreased

cadmium concentration in plants after soil has been limed to pH 6.5, several studies

cited in CAST (1980) indicate that using lime to raise soil pH from 5.0 to 6.6 had little or

no effect on metal uptake by plants.

Ideally, site-specific data on soil pH and organic matter content should be used

to assist in the assignment of a bioconcentration factor of inorganic contaminants, since

these factors greatly influence the uptake of inorganic contaminants into plants. If

literature data are used to derive bioconcentration factors, the data should be from

studies in which the pH and organic matter content of the experimental soils are similar

to those of soils at the site. Where evidence suggests that the response may be other

than linear, a slope derived from soil concentrations similar to those predicted for the

site in question is preferred. In general, it is probably reasonable and, at least,

conservative to assume that there is no uptake-limiting effect for deposited metals, as

was shown for sludge-amended soils (Page et al., 1986). Data derived from studies of
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-15 December 1998

log Bri' 1.588 & 0.578 log Kow

(6-5)

deposited emissions are preferable, if available. If data on depositions are unavailable

(which is the case most of the time), bioconcentration factors (or uptake response

slopes converted to bioconcentration factors) derived from other types of chemical

application (such as sludge or pesticide additions) can be assumed to apply to

deposited contaminants.

A linear uptake response also is assumed for organic chemicals, although as

with inorganics, there may be situations where uptake could be nonlinear and a

function of the soil concentration or characteristics of the plant. Given this assumption

of linearity, a bioconcentration factor for organics can be derived from any data set that

includes both tissue concentration (μg/g DW) and soil concentration (μg/g).

As for inorganics, the most accurate factor would be derived from interpolation of

values for soil concentration similar to those predicted for the site of concern. Ideally,

factors determined using local soil data should be used. However, if literature data are

used, the experimental soils should be of similar type, pH, and organic matter as soils

on the site.

For organics, uptake from soil and transport to above-ground plant parts is

dependent on the solubility of a chemical in water, which is inversely proportional to the

octanol-water partition coefficient, Kow. Travis and Arms (1988) developed the

following empirical relationship from field experiments on 29 organic chemicals, mostly

pesticides, to estimate the bioconcentration factor (r = 0.73):

where:

Bri = soil-to-plant bioconcentration factor for ith plant group ([μg

contaminant/g DW]/[μg contaminant/g])

Kow = octanol-water partition coefficient (dimensionless).
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-16 December 1998

TSCF ' 0.784 e

& [log Kow & 1.78]

2

/ 2.44

(6-6)

log (RCF & 0.82) ' 0.77 log (Kow ) & 1.52

(6-7)

The TSCF is the other parameter that is used to model concentrations in above-ground

vegetations as a function of soil concentration. Like the Bri, it is a function of the

contaminant octanol-water partition coefficient (Briggs et al., 1982; n=17, r not given):

where:

TSCF = transpiration stream concentration factor ([μg contaminant/mL

transpiration stream]/[μg contaminant/mL pore water])

Kow = octanol-water partition coefficient (unitless).

The range of log Kow for which this relationship was derived was -0.57 to 4.6. Root-toshoot translocation will be minimal for strongly sorbed compounds, that is, those with

higher log Kow, perhaps above 4.5. The TSCF at log Kow = 4.5 is 0.038, meaning that

the concentration in the transpiration stream water is less than 4% of what it is in soil

water. Similarly, the Br at log Kow = 4.5 is 0.097, meaning that the above-ground plant

concentration in fresh weight is less than 10% of the soil concentration. EPA’s dioxin

exposure reassessment did not consider root-to-shoot translocation for the dioxins,

whose log Kow exceeds 6.00. In general, it is recommended that root-to-shoot

translocation need not be considered for organic contaminants with log Kows near to or

greater than 6.0.

The Parameters Guidance Document will contain values for Bri for numerous

organics.

Briggs et al. (1982) developed the following relationship for RCF, considering

compounds with a range of log Kows from -0.57 to 4.6 (n=18; r not given):

where:

RCF = root concentration factor ([μg contaminant/g plant FW]/[μg
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-17 December 1998

log (RCF) ' 0.77 log (Kow ) & 1.52

(6-8)

contaminant /mL pore water])

Kow = contaminant octanol-water partition coefficient (unitless).

When evaluating only the lipophilic compounds (which the authors defined as

contaminants having log Kow greater than 2.0), they derived the following variation (n=7,

r=0.981):

Equation 6-7 should be used for organic compounds with log Kow < 2.0, whereas

Equation 6-8 should be used for organic compounds with log Kow >2.0.

The highest log Kow of the seven for which the relationship in Equation 6-8 was

derived is 4.6. This could mean that the relationship may not hold for compounds with

log Kows greater than 4.6. However, a validation test done for dioxin-like compounds,

which have log Kows ranging from about 6.0 to greater than 8.0, showed this

relationship to perform fairly well. This exercise is summarized in Chapter 14 on Model

Validation.

The Parameters Guidance Document will contain values for RCF for numerous

organics. RCFs for inorganics have been devised from field studies and will be found

in the Parameters Guidance Document.

The soil-water partition coefficient, Kds, reflects the sorption tendencies of the

contaminant, in combination with the sorption capabilities of the soil. Kds is a function

of the contaminant organic-carbon partition coefficient, Koc, and the soil organic carbon

fraction, OCsl. Further information on this parameter can be found in Section 4.3.6.1.1.

6.3.2. Plant Concentration Due to Deposition of Particle-Bound Contaminants

(Pdi). Contaminants are deposited on exposed plant surfaces by both dry and wet

deposition of particle-bound contaminants. Contaminant concentration due to

deposition is calculated for exposed produce but not for protected produce, grains, or
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-18 December 1998

Pdi'

1000 [Dydp% (Fw Dywp)] Rpi(1.0 & e

&kp Tpi

)

Ypikp

(6-9)

root vegetables, which are not directly exposed to deposition. The concentration of

contaminant in plant tissue due to deposition onto plant surfaces is calculated as:

where:

Pdi = concentration of contaminant in ith plant group due to deposition of

particle-bound contaminants (μg/g DW)

1000 = conversion factor ([106 μg/g]/[103 g/kg])

Dydp = yearly dry deposition rate of particle-based contaminants (g

contaminant/m2/ yr)

Fw = fraction of wet deposition that adheres to plant surfaces

(dimensionless)

Dywp = yearly wet deposition rate of particle-based contaminants (g

contaminant/m2/ yr)

Rpi = interception fraction of edible portion of plant tissue for ith plant group

(dimensionless)

kp = plant surface loss coefficient (yr-1)

Tpi = time of plant's exposure to deposition per harvest of edible portion of

ith plant group (yr)

Ypi = yield or standing crop biomass of edible portion of ith plant group (kg

DW/m2).

The air dispersion model provides values for wet and dry deposition, Dyw and Dyd. 

Information on these terms can be found in Chapter 3 on Air Modeling and Chapter 4

on Soil Concentration.

All of the contaminants that come in contact with plant surfaces as a result of dry

deposition are assumed to remain on the surfaces until removed by weathering. 

However, only a fraction of contaminant material that intercepts plant surfaces as a

result of wet deposition adheres; the remainder is assumed to wash off almost
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-19 December 1998

Rpg ' 1 & e

&2.88 Yppg

(6-10)

immediately. Fw is a measure of the amount of wet deposition that adheres to the plant

surface.

In a simulated precipitation study by Hoffman et al. (1992), retention on leaf

surfaces was measured for radiolabeled particles of varying sizes (3, 9, and 25 μm). 

The authors found the “interception fractions” (equivalent to Rpi@ Fw in this document)

for the three particle sizes ranged from 0.24 to 0.37 (mean=0.3). Assuming an Rpi of

0.47 for forage (Baes et al., 1984), the Fw for organic contaminants sorbing to particles

is assumed to be 0.6. Upon completion other values for Fw will be found in the

Parameters Guidance Document.

Baes et al. (1984) define the interception fraction as a “factor which accounts for

the fact that not all of the airborne material depositing within a unit area will initially

deposit on edible vegetation surfaces.” Models developed for the Nuclear Regulatory

Commission assumed the interception fraction was a constant of 0.2 for dry and wet

deposition of particles (Boone et al., 1981). However, Shor et al. (1982) suggest that

the diversity of plant growth indicates the need for vegetation-specific interception

fractions. Baes et al. (1984) present equations for interception fractions for pasture

grasses (forage), leafy vegetables, and exposed produce. Based on an empirically

derived relationship to standing crop biomass, interception fractions for pasture

grasses (forage) can be calculated as:

where:

Rppg = interception fraction for pasture grasses (dimensionless)

Yppg = standing crop biomass for pasture grasses (kg DW/m2).

Baes et al. (1984) estimated the average interception fraction for forage at 0.47. 

Interception fractions for mature leafy vegetables can be calculated as:
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-20 December 1998

Rplv '

nrr

n

Br

2

f

[(nr& 1)dp% 2r

f

] [(r

n

& 1)dr% 2r

f

]

(6-11)

Rprf '

n B r

2

f

l w

(6-12)

where:

Rplv = interception fraction for mature leafy vegetables (dimensionless)

nr = number of plants per row

rn = number of rows of plants

rf = radius of individual fruit or plant (mm)

dp = distance between plants in a row (mm)

dr = distance between rows of plants (mm).

The average interception fraction for mature leafy vegetables was calculated to be 0.3. 

Interception fractions for exposed produce are based on the five most commercially

important noncitrus fruit and field crops (apples, snap beans, tomatoes, peaches,

cherries) (Baes et al., 1984) and are calculated by determining the total fruit crosssectional area per unit area:

where:

Rprf = interception fraction of round fruit (dimensionless)

n = number of fruit per unit area 

rf = radius of individual fruit or plant (mm)

l = length of unit area (mm)

w = width of unit area (mm).
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-21 December 1998

Rplf '

2 n r

f

l

f

l w

(6-13)

Rplv ' 1 & e

&0.0846 Yplv

(6-14)

Rpe' 1 & e

&0.0324 Ype

(6-15)

For long rather than round exposed produce (e.g., snap beans), the above equation is

modified to:

where:

Rplf = interception fraction of long fruit (dimensionless)

n = number of fruit per unit area

rf = radius of individual fruit or plant (mm)

l

f = length of long fruit (mm)

l = length of unit area (mm)

w = width of unit area (mm).

Interception fractions for leafy vegetables and exposed produce also can be calculated

based on an empirical relationship to standing crop biomass (Ypi) as for pasture

grasses:

where:

Rplv = interception fraction for leafy vegetables (dimensionless)

Yplv = standing crop biomass for leafy vegetables (kg DW/m2)

Rpe = interception fraction for exposed produce (dimensionless)
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-22 December 1998

Ype = standing crop biomass for exposed produce (kg DW/m2).

Risk assessors may wish to choose the appropriate set of equations for

interception fractions depending on the availability of site-specific data. In addition,

values for number of plants per row, number of fruit per square meter, etc., should be

selected from site-specific data, if possible.

Once contaminants are deposited onto plant surfaces via particle deposition

processes, several environmental processes, including wind removal, water removal,

and growth dilution, work to reduce the amount of contamination on the plant surface

(Miller and Hoffman, 1983). The plant-surface loss coefficient is a measure of the

amount of contaminant lost to these processes over time.

Miller and Hoffman (1983) derived a half-life of 14 days for particles depositing

onto vegetation. This value represented the mid-range of experimental half-lives

ranging from 2.8 to 34 days. These half-lives can be converted to the surface loss

coefficient value of Equation 6-9, kp, by first converting the half-lives to an equivalent

annual basis (i.e., 2.8 days = 0.00767 yr), and then dividing them into 0.693 (i.e.,

0.693/0.00767). Half-life values of 34 and 2.8 days correspond to kp values ranging

from 7.44 to 90.36 yrs-1.

U.S. EPA’s dioxin exposure reassessment used a kp of 18.07 yr-1, corresponding

to a half-life of 14 days, to model the surface loss of particle phase of all dioxin-like

compounds depositing onto all plant types, following the analysis of Miller and Hoffman

(1983). However, this parameter is perhaps the most uncertain of the parameters for

modeling the impacts of particle-phase contaminants depositing onto vegetation. This

half-life corresponds to the half-life of the particles themselves on the plants. It is

certainly possible that contaminants sorbed to particles would desorb and then resorb

onto the plants. In this case, the particles could be weathering off the plants but the

contaminants could be staying behind. Umlauf and McLachlan (1994) discuss the

phenomena of the transfer of semivolatile organic compounds (SOCs) from depositing

particles onto the leaf. The particles themselves may weather off the leaves with a
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-23 December 1998

half-life of 14 days, but the SOCs would transfer from the particle and not weather off

the leaves. Umlauf and McLachlan (1994) could not find information on the transfer of

SOCs from particles to leaves, but they speculated that the lipid covering of the cuticle

would facilitate a rapid particle-to-leaf transfer. In their modeling of several SOCs to

spruce leaves, they assumed that the SOCs completely transferred from the particles to

the leaves in both wet and dry particle-bound deposition with no subsequent loss of

contaminant via weathering or degradation. This is equivalent to assuming an infinite

half-life rather than a 14-day half-life. Umlauf and McLachlan (1994) also modeled the

wet and dry deposition of vapor-phase SOCs. They could not find reasonable model

agreement between modeled and measured leaf concentration. Still, their assumptions

demonstrate the uncertainty of the weathering dissipation constant, kp. For some

compounds where evidence suggests a rapid photodegradative loss on plant surfaces,

a surface loss coefficient corresponding to a half-life of less than 14 days may be

appropriate.

Few data could be found in the literature to derive chemical-specific or plantspecific surface loss coefficients, kp. For this reason, it is recommended that a value of

18.07 yr-1 be used unless experimental data can be found to support different values.

The time of a plant's exposure to deposition per harvest of the edible portion of

the plant, TPi, is treated as a constant and differs by crop type. For animal forage, a

time period of 45 days is recommended based on an average of typical periods

between successive hay harvests (60 days) and successive grazing by cattle (30 days)

(Belcher and Travis, 1989). Since the units of Tp are years, this 45-day period is

divided by 365 days to give a Tp of 0.123 years. For the remaining animal foods, time

between successive hay harvests is divided by 365 days to give a Tp of 0.164 (Belcher

and Travis, 1989). A Tp of 0.164 also is used in this document for foods consumed by

humans. This value can be easily refined based on site-specific information on local

food crops and growing seasons.

The standing crop biomass, Ypi, also has been termed “productivity” (Baes et al.,

1984; Shor et al., 1982). Belcher and Travis (1989) have calculated average
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-24 December 1998

Dywv' 10&8 May Wv Cva

(6-16)

Dydv' 0.31536 Vd Cva

(6-17)

productivity values for several animal and human foods. From their data compilations,

productivities for forage and silage are estimated at 0.31 kg DW/m 2. In addition, Shor

et al. (1982) present average U.S. productivity values for several exposed produce

varieties averaging 0.24 kg DW/m2.

6.3.3. Plant Concentration Due to Deposition of Vapor-Phase Contaminants (Pvi).

In addition to the root uptake and deposition routes, plants can accumulate vaporphase contaminants by the processes of wet and dry deposition. Plant impacts from

vapor-phase contaminants, therefore, also can be modeled using Equation 6-9, which

was the particle impact deposition algorithm. However, the current version of ISC3, the

air dispersion model recommended for use in Chapter 3, does not have fully developed

dry or wet vapor-phase deposition algorithms. A test-version dry deposition algorithm

available in ISC3 is most appropriate for use on inorganic contaminants (metals). The

dry and wet deposition of mercury, for example, has been modeled recently for an EPA

assessment of the impacts of mercury emissions from combustors. It is not appropriate

for use with organic vapor-phase deposition. The wet deposition algorithm is only for

wet deposition of particle-bound contaminants. 

In the absence of wet and dry deposition fluxes modeled by the ISC3 or another

dispersion/deposition model, these fluxes can be estimated from the ambient air

concentrations of the contaminant in the vapor phase:

where:

Dywv = yearly wet deposition rate of vapor-phase contaminants (g

contaminant/m2/yr)
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-25 December 1998

Dydv = yearly dry deposition rate of vapor-phase contaminants (g

contaminant/m2/yr)

10-8 = units conversion factor (g/μg) (m/cm)

Cva = vapor-phase concentration of contaminant in air due to stack

emissions (μg/m3)

May = annual average moisture (m/yr)

Wv = washout ratio for vapor-phase contaminants (unitless)

0.31536 = units conversion factor ([m/yr]/[cm/sec] and [g/μg])

Vd = vapor-phase dry deposition velocity (cm/sec).

In these equations, the vapor-phase air concentration, Cva, is predicted by the air

dispersion models.

Geraghty et al. (1973) provide a map showing isolines of annual average

moisture, the May in term in Equation 6-16, throughout the United States. This map

shows low rates of 5 to 20 in/yr in the desert Southwest, moderate rates of 25 to 40

in/yr in the Midwest cornbelt, 40 to 60 in/yr in the South, and so on.

Washout ratios, Wv of Equation 6-16, are generally defined as the concentration

of contaminant in rain to the concentration of contaminant in air. Concentrations of

contaminants in air and rainwater can be derived as a mass of contaminant divided by

a mass of air/water or a volume of air/water. Mackay et al. (1986) show that volumebased washout ratios (e.g., mass of contaminant mixing in m3

 air or water) exceed

mass-based washout ratios (mass of contaminant mixing in kg of air or water) by a

factor of 815, which is the ratio of water and air densities.

The washout ratio in Equation 6-16 is a volume-based ratio. Bidleman (1988)

defines the overall washout ratio as: [mass contaminant/volume rain] ÷ [mass

contaminant]/[volume air]. Bidleman (1988) also discusses the fact that overall

washout includes both wet deposition of particulates and scouring of contaminants in

the vapor phase. However, he claims that if H (Henry’s Law Constant) is sufficiently

high, vapor dissolution in droplets is negligible and only the particulate fraction is

removed by wet deposition. He claims this to be the situation for n-alkanes, PCBs,
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-26 December 1998

Wv '

R T

H

(6-18)

Vd '

1

Ra% Rb% Rc

(6-19)

chlordane, DDT, and 2,3,7,8-TCDD. Developing overall washout ratios for these and

several other SOCs, he estimates that vapor scouring accounts for 1% of the overall

washout ratio for 2,3,7,8-TCDD. For PCBs, he estimates similar percentages of 2%,

4%, and 28% for Aroclors 1260, 1254, and 1248, respectively. Based on this work, it

may be assumed that the washout of highly sorbed contaminants in the vapor phase

will be negligible in comparison with the wet deposition of particle-bound contaminants,

and it can be neglected. As given in Bidleman (1988), the washout ratio for soluble

contaminants can be estimated as:

where:

Wv = washout ratio for vapor-phase contaminants (unitless)

R = ideal gas constant (8.205@10-5 atm-m3/mol/EK)

T = ambient temperature (EK)

H = Henry’s Law Constant (atm m3/mole).

In Equation 6-18, the ambient temperature can be assumed to be equal to 298EK

(25EC) in the absence of site-specific temperatures. The Henry’s Law Constant is a

commonly available parameter.

The dry deposition velocity, Vd, for vapor-phase contaminants can be solved as

the inverse of the sum of the resistances to transfer to the plant surface as:

where:
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-27 December 1998

Vd = vapor-phase dry deposition velocity (cm/sec)

Ra = atmospheric resistance, a function of vertical turbulent transport

(sec/cm)

Rb = surface boundary layer resistance, a function of molecular diffusivity

(sec/cm)

Rc = plant canopy/leaf resistance, a function of vegetative density,

stomatal uptake, surface effects, humidity, and so on (sec/cm).

Assignment of values for Vd, or for the resistance terms of Equation 6-19, is very

difficult, and data are limited. Smith et al. (1995) assigned values of Ra = 0.4 sec/cm,

Rb = 0.38 sec/cm, and Rc = 0.5 sec/cm, leading to an overall Vd of 0.78 cm/sec. Their

modeling exercise was for the deposition of vapor-phase dioxins onto grass leaves. 

Trapp and Matthies (1995) modeled the dry deposition of dioxins onto a meadow and

developed a value of Vd of 0.5 cm/sec based on a leaf area index of 5 and a

“conductance” term of 0.1 cm/sec. The leaf area index is in units of m2 leaf area/m2

ground area and would vary as a function of the canopy onto which the vapor-phase

contaminant was depositing. The conductance term is in units similar to deposition

velocity of cm/sec. Trapp and Matthies (1995) stated that it ranged from 0.01 cm/sec to

0.5 m/sec, with the upper boundary term appropriate for lipophilic compounds (high

Kow) and plants with relatively permeable cuticles, and the lower boundary conductance

is where uptake is mainly via stomata and the compounds are less lipophilic. 

Deposition of vapor-phase mercury to soils used either a velocity deposition of 0.3

cm/sec or 1.0 cm/sec, depending on wind turbulence. Deposition velocities for several

vapor-phase inorganics are displayed in Table 6-3. Sehmel (1980) recommends the

use of 1 cm/sec for Vd for both organics and inorganic vapor-phase contaminants, in

the absence of data, and that is the recommendation made here as well.

The final term to consider if using the deposition framework for modeling the

vapor impacts to plants (Equation 6-9) is the rate of degradation of the contaminant on

the plant, kp. Smith et al. (1995) and Trapp and Matthies (1995) included a leaf surface

loss term for the dioxins, kp. Smith et al. (1995) derived a value of 0.495 day-1 (or on
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-28 December 1998

an annual basis, 180.7 yr-1), and Trapp and Matthies (1995) derived a value of 0.159

day-1 (58.0 yr-1). This is a very uncertain term for the modeling of the deposition of

vapor-phase contaminants to plants.

It may be appropriate to assume that certain vapor-phase contaminants, such as

metals or other inorganics, do not degrade once depositing onto plants. In that case,

Equation 6-9 is rewritten as:

TABLE 6-3

Dry Deposition Velocities for Gases (compiled in Bodek et al., 1988)

Depositing

Material Deposition Surface Deposition Velocity,

Vd (cm/sec) Reference

Cl2 Alfalfa 1.8, 2.1 Sehmel (1971)

CO2 Alfalfa 0.3 Sehmel (1971)

Fluorides Forage 1.9 + 0.5 Sehmel (1971)

HF Alfalfa 3.5 Sehmel (1971)

H2S Sandy loam soil 0.015 H2S Sehmel (1971)

Iodine Grass 7.2 + 0.9 Sehmel (1971)

NOx Pine needles 0.4 - 0.8 (average) Voldner et al. (1986)

NOx Crops and grassland

(soybeans, summer day,

unstable conditions)

0.6 Voldner et al. (1986)

HNO3 Crops and grassland (summer

day, 1 m reference height)

2.5, 0.9 Voldner et al. (1986)

O3 Deciduous forest (leafless,

during winter day)

(during winter night)

0.19 - 0.37

0.05

Wesely (1983)

O3 Soybean canopy (midday,

appr. 25% of soil exposed)

(windy conditions)

0.85

1.2

Wesely (1983)

SO2 Water (neutral stability, 0.2 m

reference height)

0.5 + 0.1 Voldner et al. (1986)

SO2 Snow (unstable conditions) 0.17 Voldner et al. (1986)

SO2 Forest (dry)

 (wet)

0.8

1.5

Voldner et al. (1986)
Chapter 6

Terrestrial Food Chain Modeling

TABLE 6-3

Dry Deposition Velocities for Gases (compiled in Bodek et al., 1988)

Depositing

Material Deposition Surface Deposition Velocity,Vd (cm/sec) Reference

MPE 6-29 December 1998

Pvi'

1000 [Dydv% (Fw Dywv)] RpiTpi

Ypi

(6-20)

SO2 Grass and crops (unstable

conditions; u = 0.11 - 0.26

m/sec, 2 m reference height)

0.33 + 0.30 Voldner et al. (1986)

where

Pvi = concentration of contaminant in ith plant group due to deposition of

vapor-bound contaminants (μg/g DW)

1000 = conversion factor ([106 μg/g]/[103 g/kg])

Dydv = yearly dry deposition rate of vapor-phase contaminants (g

contaminant/m2/yr)

Fw = fraction of wet deposition that adheres to plant surfaces

(dimensionless)

Dywv = yearly wet deposition rate of vapor-phase contaminants (g

contaminant/m2/yr)

Rpi = interception fraction of edible portion of plant tissue for ith plant

group (dimensionless)

Tpi = time of plant's exposure to deposition per harvest of edible portion of

ith plant group (yr)

Ypi = yield or standing crop biomass of edible portion of ith plant group (Kg

DW/m2).

The term “vapor deposition” can be misleading for some bioaccumulating

contaminants such as the highly lipophilic dioxin compounds. Evidence has shown that

these compounds can be essentially stripped from the air simply by coming in contact
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-30 December 1998

Pvi'

Cva Bvi VGag

Da

(6-21)

with vegetation. In other words, the visual image of deposition can be misleading. An

alternative model for the dry deposition of these vapor-phase lipophilic compounds is

termed the “transfer” approach. As noted above, wet deposition of vapor-phase

lipophilic compounds can be considered negligible. Therefore, this transfer approach

can be used to model the overall vapor-phase impacts to plants.

The air-to-plant transfer algorithm solves for plant concentrations as a function

of vapor-phase air concentrations, a “transfer factor,” and an empirical factor:

where:

Pvi = concentration of contaminant in ith plant group due to air-to-plant

transfer of vapor-phase contaminants (μg/g DW)

Cva = vapor-phase concentration of contaminant in air due to stack

emissions (μg/m3 air)

Bvi = air-to-plant biotransfer factor for ith plant group ([μg contaminant/g

DW]/[μg contaminant/g air])

VGag = empirical above-ground vegetation correction factor (dimensionless)

Da = density of air (1190 g/m3).

Chapter 3 describes the air dispersion modeling and the vapor/particle partitioning

algorithm used to model the vapor-phase air concentration term, Cva. The density of air

term, Da, converts this air concentration from mass/volume units to mass/mass units

appropriate for use with the air-to-leaf transfer factor, Bvi.

The air-to-plant biotransfer factor is defined as the ratio of contaminant

concentration in aerial plant parts (μg/g DW) to the vapor-phase concentration of

contaminant in air (μg/g). Little is known about the mechanisms of vapor uptake by

leaves, and few experimentally derived air-to-leaf biotransfer factors are available in

the literature. Bacci et al. (1990, 1992) conducted chamber studies on the transfer of
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-31 December 1998

log (Bvol) ' 1.065 log Kow & log H

R T

& 1.654

(6-22)

Bv'

Da Bvol

(100&Ml)

100

Dp

(6-23)

vapor-phase concentrations of 14 organic contaminants to azalea leaves. He

correlated the results to the octanol-water partition coefficient (Kow), Henry's Law

Constant (H), ideal gas constant (R), and ambient temperature (T) in the following

manner (Bacci et al., 1992):

where:

Bvol = volumetric air-to-leaf biotransfer factor ([μg contaminant/L leaf

FW]/[μg contaminant/L air])

1.065 = empirical constant (dimensionless)

Kow = octanol-water partition coefficient (dimensionless)

H = Henry's Law Constant for contaminant (atm/m3/mol)

R = ideal gas constant (8.205@10-5 atm/m3/mol/EK)

T = temperature (298.1 EK)

-1.654= empirical constant (dimensionless).

An earlier study by Bacci et al. (1990) showed that the volumetric transfer factor can be

transformed to a mass-based transfer factor considering leaf density, density of air, and

percent of wet leaf that is dry matter:

where:

Bv = mass-based air-to-leaf biotransfer factor ([μg contaminant/g DW]/[μg

contaminant/g air])

Da = density of air (1.19 g/L)
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-32 December 1998

Bvol = volumetric air-to-leaf biotransfer factor ([μg contaminant/L leaf FW]/[μg

contaminant/L air])

Ml = moisture content of wet leaf (% water)

Dp = leaf density (g/L FW).

In his application of this equation to azalea leaves, Bacci et al. (1990) assumed a

moisture content of 70% and a leaf density of 890 g/L. A moisture content of 85% and

plant density of 770 g/L was recommended by McCrady and Maggard (1993) for grass. 

McCrady and Maggard (1993) also empirically determined a Bvol for 2,3,7,8-TCDD in

their chamber experiments of the transfer of this compound to grass leaves and found

that their Bvol was 40 times lower than a Bvol calculated for 2,3,7,8-TCDD from Equation

6-22. They attributed the difference mainly to differences in the plants used (grass

leaves vs. azalea leaves) and to the fact that the Bacci experiments did not consider

photodegradation whereas their experiments did include a phase where the grass was

kept in sunlight, allowing for photodegradative loss of 2,3,7,8-TCDD from the leaves.

Based on this evidence, EPA developed Bvol for the dioxin-like compounds in the

following manner (U.S. EPA, 1994). The Bacci algorithm in Equation 6-22 was used for

all the compounds, but then each value calculated was divided by a factor of 40 based

on the McCrady and Maggard (1993) experiments. The mass-based transfer factor, Bv,

assumed the McCrady and Maggard (1993) moisture content of 85% and the grass

density of 770 g/L, on the basis that grass was more pertinent to vegetations of the

terrestrial food chain than azalea leaves.

Lorber (1995) developed Bv for dioxins on the basis of field data that included air

concentrations and grass concentrations. He found that the Bv calculated empirically

from these data was still lower than the Bv calculated using the Bacci algorithm and a

division by 40. Specifically, the field-derived Bvs were about 2 to 5 times lower than the

Bacci amended Bvs for the dioxin congeners.

For determination of Bv for organic contaminants other than dioxins (which

should be assigned the values derived in Lorber [1995]), the following is recommended. 

Values should be determined using the original Bacci algorithm as in Equations 6-22
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-33 December 1998

and 6-23. Bv values determined this way should be reduced by a factor of 100. This

empirical reduction factor is based on the dual evidence described above for dioxins: 

that a reduction is necessary, and that the reduction is very likely going to be more than

the factor of 40 derived from the McCrady and Maggard (1993) chamber experiments

for 2,3,7,8-TCDD. It is recommended that a leaf density of 770 g/L and a leaf moisture

of 85% be used for all vegetations, following the justification used by U.S. EPA (1994).

The Bv developed this way will allow for the prediction of impacts of vapor-phase

organic contaminants to leafy vegetation. For bulky vegetation, however, this term is

not fully appropriate because most organic contaminants that bioaccumulate (i.e., the

semivolatile, lipophilic, persistent compounds such as PAHs, dioxins, PCBs, and so on)

will not translocate from outer surfaces to inner plant parts. In other words, the Bv is

appropriate for calculating the concentration in the outer surfaces of an apple but not

the inner pulp.

The VGag was introduced by U.S. EPA (1994) to consider the difference between

outer-surface and whole-plant concentrations for the dioxins when calculating plant

impacts to vapor-phase dioxins using the Bv as developed above. The experiments by

McCrady (1994) demonstrate the concept behind the VGag parameter. He measured

the uptake rate of vapor-phase 2,3,7,8-TCDD into several vegetations. He found that

the uptake rate for an apple divided by the uptake for the grass leaf was 0.02 (where

uptake rates were from air to whole vegetation on a dry-weight basis). The ratio of the

uptake rates of tomato and pepper to that of grass was similarly 0.03 and 0.08.

U.S. EPA’s dioxin exposure reassessment used a VGag of 0.01 for the general

category of unprotected fruits and vegetables, rather than a value in the range of 0.03

to 0.08, on the assumption that washing or peeling would further reduce outer surface

residues of dioxins. The Agency also used a VGag of 0.50 for the animal food category

of “silage” on the assumption that silage (as described earlier) is part protected and

part unprotected. No correction for leafy vegetation (or a VGag of 1.00) is required

since the Bv was developed from data on leafy vegetation. For site-specific

applications, more refined values for VGag can be derived for specific vegetations for
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-34 December 1998

human or animal consumption. Otherwise, these values for fruits/vegetations, silage,

and grass are recommended for general use.

It is important to understand that the VGag is required only when the Bv used was

based on the transfer of vapor-phase contaminants to leafy vegetation. This was the

case in the Bacci experiments (Bacci et al., 1990, 1992), the McCrady experiments

(McCrady and Maggard, 1993; McCrady, 1994), and the field experiments by WelschPausch et al. (1995) that Lorber (1995) used to refine the Bv

 for the dioxins. If the Bv is

developed from data on the transfer of vapor-phase contaminant to whole plant, then a

VGag is not required.

The same Bv approach can be used for inorganic contaminants, if appropriate

data are available to empirically determine Bv.

The deposition velocity approach for estimating dry deposition of vapors

recognizes fundamental processes while the Bacci algorithm is a simple empirical

treatment of a complex process. For both models, assessors face the practical concern

of assigning parameters when selecting the appropriate model for use at a particular

site. The vapor deposition algorithm has been parameterized in two separate research

efforts for 2,3,7,8-TCDD vapor deposition onto grass plants (Smith et al., 1995; Trapp

and Matthies, 1995). However, in a model validation exercise described in Chapter 13,

it was shown that the parameterization by these two research teams led to a significant

underprediction in a field site where the appropriate data for model testing had been

gathered. The Bacci algorithm was amended by adding an empirical reduction factor of

100. This was based on field experiments which showed that the Bacci Bv, when

applied to 2,3,7,8-TCDD, would overpredict measured grass concentrations by about

100. This overprediction was attributed to the fact that the idealized laboratory

conditions in which the transfer of several organic contaminants to azalea leaves was

measured in Bacci’s experiments did not mimic outdoor conditions, particularly in that

sunlight could degrade contaminants in the air or after they transferred to leaves. 

However, when this general framework was applied to PAHs in a model validation

exercise described in Chapter 13, it was seen that plant concentrations were
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-35 December 1998

underpredicted, sometimes up to a factor of 10. In other words, while the empirical

reduction of factor of 100 may be appropriate for 2,3,7,8-TCDD, it may not be

appropriate for all organic contaminants. 

Therefore, both models for predicting the impact of vapor phase contaminants

onto plants may have flaws. Chapter 13 provides a tabular summary of the Bv as

assigned to the 17 congeners of dioxin and furan which have dioxin-like toxicity. These

values were shown to successfully reproduce measured grass concentrations in the

validation exercise in that chapter. It is recommended that the Bv framework has more

validity for use with the dioxins and furans based on this model validation exercise. For

other contaminants, the choice may not be as straightforward. If possible, assessors

should attempt to evaluate the validity of their predicted vegetation concentrations by

comparing predicted concentrations of their contaminants with measurements in the

literature. This could be done in the context of a simple “reality check” by comparing

predicted concentrations with those that can be found in the literature, or more

vigorously as a model validation exercise where air and grass concentrations are

concurrently available for such an exercise (see the air-to-leaf validation exercise in

Chapter 13). Certainly, the prediction of vegetation concentrations should be identified

as a key uncertainty in site assessments, since vegetation comprises the principal diet

component for terrestrial animals, and therefore drives the potential human health risk

posed by emissions of bioaccumulating contaminants from stacks.

6.4. CALCULATING CONTAMINANT CONCENTRATION IN ANIMAL TISSUES

The animals that humans usually consume as food take up contaminants from

the food chain by ingesting plants and soil while grazing. The food chain model

calculates the concentration of contaminant in animal tissues as a function of the

concentration of contaminant in plants and soil, the diet of the animal in plants and soil,

and the chemical- and animal tissue-specific bioconcentration or biotransfer factor. 

Two quite analogous approaches have been used, one termed the “biotransfer”

approach and the other a “bioconcentration” approach. The biotransfer approach is

expressed as:
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-36 December 1998

Aj' ['

n

i'1

(Pij QpijFij) % (Cs Qsj Bs)] Baj

(6-24)

AFj' ['

n

i'1

(Pij DFijFij) % (Cs DFsj Bs)] BCFj

(6-25)

where:

Aj = concentration of contaminant in jth animal tissue group (mg

contaminant/kg animal tissue whole weight)

Pij = concentration of contaminant in ith plant group eaten by jth animal

(mg/kg plant tissue DW)

Qpij = quantity of ith plant group eaten by jth animal each day (kg plant tissue

DW/day)

Fij = fraction of ith plant group grown on contaminated soil and eaten by jth

animal (dimensionless)

Cs = concentration of contaminant in soil (mg/kg)

Qsj = quantity of soil eaten by jth animal each day (kg soil/day)

Bs = bioavailability of contaminant in soil vehicle relative to vegetation

vehicle (dimensionless)

Baj = contaminant biotransfer factor for jth animal tissue group [(mg

contaminant/kg animal tissue whole weight)/(mg contaminant

ingested/day)].

The units of the biotransfer factor, Baj, often are expressed as day/kg, which is the

simplification of the units as expressed above. The bioconcentration algorithm is

expressed as:

where:

AFj = concentration of contaminant in lipid (or fat) portion of jth animal group

(mg contaminant/kg animal lipid)

Pij = concentration of contaminant in ith plant group eaten by jth animal
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-37 December 1998

(mg/kg plant tissue DW)

DFij = diet fraction of ith plant group eaten by jth animal each day (fraction)

Fij = fraction of ith plant group grown on contaminated soil and eaten by jth

animal (dimensionless)

Cs = concentration of contaminant in soil (mg/kg)

DFsj = diet fraction of soil eaten by jth animal each day (fraction) (note: 

GDFij+ DFsj = 1.00)

Bs = bioavailability of contaminant in soil vehicle relative to vegetation

vehicle (dimensionless)

BCFj = contaminant bioconcentration factor for jth animal tissue group ([mg

contaminant/kg lipid ]/[mg contaminant/kg DW diet]).

Risk assessors need to be very careful in their use of Equations 6-24 and 6-25

to consider the units for the parameters and the derivation of the biotransfer/

bioconcentration factors. The Aj of Equation 6-24 calculates animal tissue on a wholeweight basis, not a dry-weight basis or a lipid basis. The Baj

 of Equation 6-24

transforms a contaminant input in a mass ingested/day basis to this whole-weight

tissue concentration. Risk assessors should verify that their Baj will do this as well. 

Some literature values of the biotransfer factors for specific contaminants could be

derived to predict animal tissue concentration on a dry-weight or lipid basis. Risk

assessors should also verify that their plant mass ingested and their plant

concentrations are on the same basis. In Equation 6-24, both are on a dry weight

basis. Similarly, the AFj of Equation 6-25 is on a lipid basis. The BCFj of Equation 6-

25 transforms an average concentration in the animal diet to a lipid-based

concentration. Some BCFjs in the literature transform the concentration in the diet of

an animal to a whole weight rather than lipid concentration. Finally, the average

concentration required in Equation 6-25 is calculated on a dry-weight rather than a

fresh-weight basis.

General information on animal dietary habits and bioconcentration/biotransfer

factors will be presented principally in the Parameters Guidance Document for beef,

pork, poultry, eggs, and dairy products. In site-specific applications, risk assessors
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-38 December 1998

should ascertain whether these foods are locally produced or consumed. Other animal

food products may be included if they are consumed in significant quantities at a

particular site. However, information will not be provided on

biotransfer/bioconcentration factors or animal dietary habits for other animals.

The plant concentrations, Pij, are calculated using the equations in Section 6.3

for each relevant plant group. Animals raised for food are fed a variety of plants,

including grain (corn, oats, wheat, etc.), forage (pasture grass, hay), and silage. In

addition, grazing animals ingest soil that clings to plants or incidentally while they are

grazing. Plant ingestion generally is expressed as a rate, that is, the amount of plant

tissue (kg dry weight) consumed per day. This is the Qpij term in Equation 6-24. These

ingestion rates, plus the soil ingestion rate, Qsj, can be directly used to calculate

animal tissue concentrations if a “biotransfer” factor approach is used. If a

“bioconcentration” factor approach is used, all animal dietary components, including

soil, need to be summed to obtain a total daily intake in units of kg/day. Then each

component can be converted to a fraction for the Dfij (plant dietary fraction) and DFsj

(soil dietary fraction) of Equation 6-25. Note that the sum of the plant dietary fractions

and the soil dietary fraction should equal 1.00. The Parameters Guidance Document

will provide detail on the assignment of QPij and DFij for cattle (for beef or dairy), poultry

(for meat or eggs), and swine. The Parameters Guidance Document will also provide

guidance on the assignment of the animal soil ingestion parameters, Qsj and DFsj.

Default values of 1.0 should be assumed for the assignment of Fij, or the fraction

of the ith plant group grown on contaminated soil and eaten by the jth animal. Values

less than 1.0 should be based on information regarding the portion of grain, silage, and

other feeds that is imported from sites not impacted by the stack emission source.

The contaminant soil concentration, Cs, should be the concentration in the

untilled soil layer. This is typically 1 to 2 cm, as described in Chapter 4. The relevant

assumption is that the animals come in contact with soils during grazing or other

activities on untilled soils (e.g., swine rooting). If, instead, it is assumed that the

animals’ principal contact with soil is soil residue on plants that have been harvested
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-39 December 1998

(i.e., grains or silages) or other contact with tilled soils, then it would be appropriate to

assume a tilled soil concentration. Unless otherwise stated and known, the

conservative assumption of contact with untilled soils should be assumed.

Soil bioavailability, Bs, can be thought of as the ratio between bioconcentration

(or biotransfer) factors for soil and vegetation for a given contaminant. The efficiency

of transfer from soil may differ from efficiency of transfer from plant material for some

chemicals. If the transfer efficiency is lower for soils, then this ratio would be less than

1.0. If it were equal or greater than that of vegetation, the Bs would be equal to or

greater than 1.0. U.S. EPA’s dioxin exposure reassessment reviewed the evidence that

2,3,7,8-TCDD was less available to rats when rats were fed soil as compared with feed. 

Similarly, the Agency showed that the transfer of 2,3,7,8-TCDD from corn oil was more

efficient than that of feed or soil. The evidence was in terms of absorption into the rat’s

gut rather than bioconcentration. The ratio of absorbed dose to administered dose was

0.70 to 0.83 when corn oil was the carrier, 0.50 to 0.60 when feed was the carrier, and

0.30 to 0.40 when soil was the carrier. Therefore, U.S. EPA assigned a value of 0.65 to

Bs, based on the ratio of soil absorption to feed absorption, or [0.3-0.4]/[0.5-0.6].

It is also important to note that the bioconcentration factors for dioxin-like

compounds were developed from data on animal feeds in U.S. EPA’s dioxin exposure

reassessment. Stephens et al. (1995) determined bioconcentration factors for dioxins

into chickens based on soil concentrations. Their dosing experiments verified that the

chicken feed was free of dioxins (i.e., less than the detection limits). They dosed the

animals with 10% soil and 90% chicken feed, and after analyzing various matrices

(eggs, thigh meat, liver, and adipose tissue) they developed bioconcentration factors

based on lipid concentrations and soil concentrations. Therefore, their

bioconcentration factors were soil based rather than vegetation based. Since U.S. EPA

defined the dioxin bioconcentration factors in terms of animal vegetation instead of soil,

all the Stephens et al. (1995) soil concentration factors had to be converted to

vegetation-based bioconcentration factors. To do so, U.S. EPA assumed a Bs of 0.65

and divided all the Stephens et al. (1995) bioconcentration factors by 0.65. In this way,
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-40 December 1998

the bioconcentration factors of Stephens et al. (1995) for dioxins into poultry became

vegetation based and a Bs of 0.65 was assigned.

This discussion implies the following regarding the proper assignment of the soil

bioavailability term, Bs, in relation to bioconcentration factors. The risk assessor

should first ascertain the basis of the bioconcentration/biotransfer factors. If they are

based on vegetation (i.e., grains, forage, or silage), then a risk assessor has to decide

whether the contaminant is less bioavailable (i.e., Bs<1.0), equally bioavailable

(Bs=1.0), or more bioavailable (Bs>1.0) on soil. The assessor should determine the

value for Bs based on this decision. If the bioconcentration/biotransfer factors are

based on soil, then the risk assessor has two choices. One is to convert the soil-based

factor to a vegetation-based factor, as was done in U.S. EPA’s dioxin exposure

reassessment, and then assign a Bs value in keeping with this conversion. The other

is to retain the soil-based bioconcentration factor, make the same decision as above

with regard to whether the contaminant is less, equal, or more bioavailable on

vegetation as compared with soil, and then define a “Bp,” or a plant bioavailability term,

to use in Equations 6-24 and 6-25 in place of the “Bs.”

In most cases, bioconcentration factors developed from empirical data are based

on vegetation rather than soil. If this is the case, it is recommended that a Bs be

assigned a value of 0.65, in keeping with the data on 2,3,7,8-TCDD, unless other

chemical-specific data are available. It seems reasonable to assume that vegetation is

a more efficient vehicle of transfer of organic and inorganic contaminants than is soil,

since vegetation is digested and soil passes through the system.

In general, risk assessors need to be very careful in the use of the bioavailability

factor and the biotransfer/bioconcentration factors derived in the literature. The precise

derivation of these factors, with particular attention to the definition of the units of the

animal tissue and feed concentrations, need to be ascertained and converted to

appropriate units, if necessary. The Parameter Guidance Document will contain

bioconcentration and biotransfer parameters for several organics and inorganics.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-41 December 1998

log Babeef ' &7.6 % log Kow

(6-26)

log Bamilk ' &8.1 % log Kow

(6-27)

For organic compounds, the octanol-water partition coefficient, Kow, can be used

to estimate biotransfer factors for beef and milk if experimental data are not available. 

Travis and Arms (1988) derived the following empirical relationships between Kow and

biotransfer factor for beef and milk (for beef, n=36, r=0.81; for milk, n=28, r=0.74):

These equations produce estimates of Ba that predict whole-beef and wholemilk concentrations on a fresh-weight basis given dietary input of organic contaminant

in terms of mass ingested/day, as appropriate for use in Equation 6-24. 

Equations 6-26 and 6-27 were derived from experimental data for milk (28

chemicals, primarily pesticides, with log Kow values ranging from 2.8 to 6.9) and beef

(36 chemicals, primarily pesticides, with log Kow values ranging from 1.3 to 6.9). Their

applicability to chemicals with log Kow values outside this range is not recommended.

For example, these empirical relationships are not appropriate for dioxins. Data

in a mass balance study of dioxins in lactating cows by McLachlan et al. (1990) suggest

that these equations will greatly overestimate the transfer of dioxins into milk. This is

shown in Table 6-4, which displays the Bamilk determined by Equation 6-27 using log

Kow for the dioxin compounds as given in U.S. EPA’s dioxin exposure reassessment and

the Bamilk determined directly from the data of McLachlan et al. (1990). Specifically, the

data show that biotransfer of dioxin-like compounds decreases as the log Kow

increases, which is the opposite of the trend embodied in Equation 6-27. Failure of the

Travis and Arms (1988) equations to accurately predict dioxin biotransfer is due, in

part, to the fact that most of the organic compounds in their derivation had log Kow less

than that of the dioxins. The range of log Kow for the Travis and Arms data set was 2.8
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-42 December 1998

to 6.9, with only 6 of 28 data points from compounds with log Kow greater than 6.0. The

dioxin-like compounds have log Kow that range from 6.5 to 8.0. For these compounds, it

is recommended that the biotransfer factors calculated from McLachlan et al.’s (1990)

data be used to predict milk concentrations. Note that these biotransfer factors in

Table 6-4 are in the appropriate units for use in Equation 6-24.

The Bamilk based on the data of McLachlan et al. (1990) can be transformed to

Babeef. McLachlan (1994) argues that it is reasonable to assume that dioxins

bioconcentrate almost equally in the fat of commercial beef and milk. This is because

excretion of dioxins in the milk of the lactating animals is expected to be matched

(approximately) by dilution of the contaminant reservoir due to rapid growth in the

commercially raised beef cattle during their short lifespan (approximately 1.5 years).
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-43 December 1998

TABLE 6-4

Comparison of Predicted and Observed Biotransfer Factors for Dioxin-like

Compounds in Beef and Milk

Congener Predicted Bamilk

a Observed Bamilkb

2,3,7,8-TCDD 0.05 0.01

1,2,3,7,8-PCDD 0.03 0.01

1,2,3,4,7,8-HxCDD 0.50 0.006

1,2,3,6,7,8-HxCDD 0.16 0.005

1,2,3,7,8,9-HxCDD 0.16 0.005

1,2,3,4,6,7,8-HpCDD 0.79 0.001

OCDD 1.26 0.001

2,3,7,8-TCDF 0.01 0.003

2,3,4,7,8-PCDF 0.05 0.009

1,2,3,7,8-PCDF 0.03 0.002

1,2,3,4,7,8-HxCDF 0.08 0.007

1,2,3,6,7,8-HxCDF 0.08 0.006

12,3,7,8,9-HxCDF 0.08 0.006

2,3,4,6,7,8-HxCDF 0.08 0.005

1,2,3,4,6,7,8-HpCDF 0.20 0.001

1,2,3,4,6,7,8-HpCDF 0.79 0.003

OCDF 5.01 0.001

a

 These predictions use the Travis and Arms (1988) empirical relationship for milk

 displayed above in Equation 6-27 in conjunction with log Kow for the dioxins as given

 in EPA’s dioxin exposure reassessment.

b

 These observations were derived from data in McLachlan et al. (1990).
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-44 December 1998

Assuming this to be generally true, the milk biotransfer factors for dioxins listed in Table

6-4 can be converted to beef biotransfer factors by making assumptions regarding the

fat contents of beef and milk. U.S. EPA’s dioxin exposure reassessment assumed that

milk is 3.5% fat and that beef is 19% fat for its modeled scenarios. Biotransfer factors

for beef would be 5.4 times higher (19/3.5) than for milk with these fat content

assumptions. Therefore, the Babeef can be calculated as the McLachlan (1994) Bamilk

multiplied by 5.4.

Risk assessors should be aware, however, that beef biotransfer factors

estimated this way will generally tend to underestimate beef concentrations in cattle

that have not lactated and have lived longer than typical commercial beef cattle. This

is expected to be only a small fraction of the total beef supply in America. However,

this could be critical for certain site-specific circumstances. For example, bulls raised

for breeding generally live longer lives and, of course, have not lactated. If subsistence

cattle raising occurs in the vicinity of a stack emission source, assessors should

determine the prevalence of home slaughter of bulls.

Equations similar to 6-26 and 6-27 are not available for animal tissues/products

other than beef and milk, and the applicability of these equations to other types of

animal tissues is unknown. However, in the complete absence of data, assessors may

choose to apply the Babeef algorithm for organic contaminants with log Kow less than 6.0

(or thereabouts) with a fat content adjustment, as was recommended above when using

the McLachlan Bamilk to calculate a Babeef. This presumes, of course, that other

terrestrial animals such as poultry or swine tend to bioconcentrate organic

contaminants in the same way cattle do.

6.5. CALCULATING HUMAN DAILY INTAKE

Human daily contaminant intake from consumption of contaminated plants is

calculated by multiplying the concentration of contaminant in each plant group by the

amount of contaminated plant group consumed daily. Similarly, daily contaminant

intake from the consumption of meat, dairy products, or eggs is determined from the
Chapter 6

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MPE 6-45 December 1998

concentration of contaminant in the animal tissue and the amount of each

contaminated animal tissue that is consumed daily.

The sections below describe the equations used to calculate human daily intake. 

Before that, an overview is given of the critical exposure parameters, the consumption

rates of vegetables, fruits, and terrestrial animal food products. However, this

information is not provided as site-specific guidance. The risk assessor should

ascertain local food production and consumption patterns, if possible. If that is not

possible, then the appropriate reference for obtaining information on exposure factors

is the Exposure Factors Handbook (U.S. EPA, 1996). Overview information provided is

from that reference.

6.5.1. An Overview of Terrestrial Food Consumption Rates. Of most importance in

site-specific assessments is the proximity of food production to the stack emission

source. Existence of home gardening can be ascertained by visual inspection and with

the help of local agricultural extension agents, the local health department, or other

organizations. Similarly, prevalence of agricultural operations for larger scale

production of vegetable/fruit crops or terrestrial animals should be ascertained for sitespecific assessments. Once this information is at hand, the next decision is on the

fraction of food consumed by individuals that is impacted by the stack emissions

sources. Options for consideration range from subsistence behaviors (all of one or

more food products is produced at the exposure site) to consuming a small portion of

one or more food products that are locally produced. Finally, the risk assessor needs

to determine rates of consumption of specific food items. These issues are explored in

more depth in Chapter 2 on Human Exposure.

Of the issues identified, the rates of consumption are perhaps the most difficult

to ascertain for a site-specific assessment. The other information just outlined —

identification of terrestrial food production, prevalence of home gardening or

agricultural operations, and even judgments on the fraction of consumed food that is

home- or locally produced—can be ascertained with a degree of reasonableness with

some effort and assistance from local authorities. Only a site-specific food
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consumption survey can ascertain site-specific consumption rates, and this effort is

usually beyond the scope of a site-specific assessment. In the absence of this

information, risk assessors need to rely on data from existing food consumption

surveys.

The Exposure Factors Handbook (U.S. EPA, 1996) summarizes food

consumption surveys, most importantly, the U.S. Department of Agriculture’s (USDA,

1975) Nationwide Food Consumption Survey (NFCS) and the USDA Continuing Survey

of Food Intakes by Individuals (CSFII). The subset of data from these surveys

described below includes an NFCS household survey conducted between 1987 and

1988 and a CSFII 3-day individual consumption survey conducted between 1989 and

1991. For simplicity, these will be abbreviated as the 1987-88 NFCS and the 1989-91

CSFII. A substantial amount of analysis of these data was conducted by U.S. EPA in

order to interpret the information and display consumption rates for use in assessments

such as the indirect exposure assessments of this methodology. Following are some of

the more important issues and interpretative analyses done for the Exposure Factors

Handbook, which should be considered by the assessor in conducting site-specific

assessments.

6.5.1.1. Consumption Rates in Units of Mass Consumed/Body Weight/

Time — The earlier version of the Exposure Factors Handbook (U.S. EPA, 1989) and,

in fact, most exposure assessments through the latter 1980s and early 1990s,

considered consumption rates in terms of mass consumed per day and then would

assign body weight with a standard factor such as 70 kg for an adult and 16 kg for a

child. Since there is a relationship between rates of consumption and body weight, it is

more technically accurate to link the two by using a consumption rate expressed in

terms of mass consumed/body weight/day and then not include the body weight as a

separate factor in the calculation of average daily dose (ADD). The NFCS and the

CSFII data included individual body weights with the consumption data. With this

information, the consumption rates were derived in the Exposure Factors Handbook

(U.S. EPA, 1996) on a more appropriate g consumed/kg body weight/day.
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P

FW

i ' P

DW

i

100 & Mi

100

(6-28)

Cp DW

i ' Cp FW

i

100 & Mi

100

(6-29)

6.5.1.2. Proper Consideration of Fresh Versus Dry Weight and Lipid Versus

Whole Weight Concentrations — Most food consumption surveys express

consumption in fresh-weight terms, whereas the plant concentration algorithm typically

solves for concentrations on a dry-weight basis. The rate of consumption needs to be

on the same basis as the vegetation concentration, so that the mass of contaminant

ingested can be properly calculated. A risk assessor can convert the concentration

from a dry-weight to a fresh-weight basis, or alternately, the consumption rate from a

fresh- weight to a dry-weight basis. These two options are expressed mathematically

as: 

where:

Pi

FW = plant concentration of ith plant group in terms of fresh weight (μg/g

fresh weight)

Pi

DW = plant concentration of ith plant group in terms of dry weight (μg/g

dry weight)

Cpi

DW = consumption rate of ith plant group in terms of dry weight (g

DW/kg/ day)

Cpi

FW = consumption rate of ith plant group in terms of fresh weight (g

FW/kg/ day)

Mi = percent moisture for ith plant group (%).

Animal food products can be modeled in terms of lipid concentrations, as described

above, whereas food consumption is most often in terms of whole weight. The
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MPE 6-48 December 1998

Aj' AFjFj

(6-30)

following equation converts an animal food product concentration in lipid terms to

whole-weight terms:

where:

Aj = concentration of contaminant in jth animal tissue group (mg

contaminant/kg animal tissue whole weight)

AFj = concentration of contaminant in lipid (or fat) portion of jth animal

group (mg contaminant/kg animal lipid)

Fj = fat content of animal tissue.

The Exposure Factors Handbook provides information on food-specific moisture

contents and lipid fractions. Vegetables and fruits are typically between 80% and 90%

moisture. Animal tissue lipid concentrations range from 1% to 2% fat (low-fat milk or

cheese) to greater than 20% (ground beef).

6.5.1.3. Consumers Only Versus Per Capita Consumption — If an individual

reported consumption of a food product in the USDA surveys (or in general), then

he/she was considered for the calculation of a “consumers-only” consumption rate. 

This is the rate of consumption calculated only for those who consumed the food

product during the survey period. The “per capita” consumption rate includes

consideration of all individuals in the survey, whether they reported consumption or not. 

Intuitively, a consumers-only rate will be higher than a per capita rate. It is not

immediately obvious which rate to use in a site-specific assessment. For estimating

average exposures, a per capita consumption rate is most appropriate since it

considers individuals consuming and not consuming the food product (although

nonconsumers may consume the product at some time), and it is therefore the best

measure of “average” behaviors. A consumers-only rate may be more appropriate

when assessing individual impacts in site-specific exposure assessments. After all, if a

beef consumption exposure pathway is appropriate for a given site, it makes sense to
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MPE 6-49 December 1998

use a rate of consumption derived from individuals known to consume beef. When

using consumers-only rates, other considerations include seasonal variations (e.g.,

during what season were the data obtained from survey participants, and does their

consumption during particular seasons reflect availability of the food product) and the

proper consideration of annual averages, realizing that all “consumers” in a survey may

not consume the food product 52 weeks a year.

6.5.1.4. USDA Individual Surveys Versus Household Surveys — The

1989-91 CSFII surveys included an “individual” survey where individuals kept 3-day

records of all food consumed. The Exposure Factors Handbook (U.S. EPA, 1996) used

this survey to derive per capita and consumers-only consumption rates for a wide

variety of food products, including vegetable/fruit and terrestrial animal food products,

that could be considered for indirect exposure assessments. The 1987-88 NFCS

surveys included a “household” component that was used by U.S. EPA to generate per

capita and consumers-only consumption rates that were different from the individual

consumption rates and potentially more useful for site-specific assessments. 

Specifically, the head of the household completed a survey reporting all food brought

into and used within a household for a week. The term “used” does not necessarily

mean consumed. The reported values do not account for food that is spoiled or

discarded, food that is fed to pets, or inedible portions that get discarded during

preparation or cooking. Included in this survey was information as to whether the food

product brought into the house was “home produced” or store bought. The 1989-91

CSFII individual survey did not include the question of whether the consumed food was

home produced or not. The rate of consumption of home-produced food products can

be very important if scenarios considered for site-specific assessment include

subsistence behaviors, which they often do. Below are a few other considerations for

each type of survey.

6.5.1.4.1. Individual Surveys. The consumption rates are defined in this

survey as “as eaten,” which means that losses due to cooking, peeling,

nonconsumption of bones, and other preparations have been considered. This
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MPE 6-50 December 1998

contrasts the categorization of food brought into the house and “used,” where

adjustments are necessary (as noted above and described in more detail below). 

Furthermore, components in food mixtures (e.g., the tuna in tuna casserole) are

separated so that full consumption of the various components was estimated in the

Exposure Factors Handbook (U.S. EPA, 1996). The data from the individual surveys of

the NFCS are considered among the most robust for general assessment purposes and

are quite often used. Unfortunately, consumption of home or locally produced foods is

often of most importance in site-specific assessment, and the individual surveys do not

provide information on the sources of the foods eaten.

6.5.1.4.2. Household Surveys. The identification of foods brought into the

home as “home produced,” and the generation of consumption rates based on this

information, is the principal benefit from this survey. A second advantage of these data

over the individual survey data is that they cover behaviors over the course of a week,

rather than only 3 days as in the individual consumption surveys. The identification of

home-produced foods allows for the calculation of two quantities that are informative

and may be used in site-specific assessments. One is the fraction (or percent) of all

people who consume home-produced foods, and the second is the fraction of total food

consumed in households that is home produced. Although these quantities appear to

be similar, in fact they are developed differently and have different meanings. The

fraction of individuals who consume home-produced foods is calculated as the fraction

of all people in the 1987-88 NFCS who reported consumption of a specific homeproduced food item divided by all people in the survey, whether or not they consumed

the food product. The derivation of the fraction of food consumed that is home

produced is a little more complicated. The total amount of a specific food item

consumed is calculated as the total number of individuals in the survey multiplied by

the surveywide per capita consumption rate. The total amount of home consumption of

a specific food item is calculated as the total number of individuals reporting

consumption of that food item multiplied by their consumer-only consumption rate for

that food item. The ratio of this latter quantity (total amount of consumer-only
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MPE 6-51 December 1998

consumption of a specific home-produced food item) over the former quantity (total

amount of per capita consumption of a specific food item whether home-produced or

not) defines the fraction of food consumed that is home produced. Examples of these

two slightly different fractions is given below in the sections on daily intake from

contaminated plants and terrestrial animal food products.

Even though the 1987-88 NFCS home consumption data appear very pertinent

to site-specific assessments, the raw data as supplied by the head of the household

need interpretative analyses before arriving at individual consumption rates for homeproduced foods. First, the total food brought into the house needs to be portioned to

the various members of the household according to age (and hence portion size, etc.)

and sex. Next, the data are grouped in terms of total amounts of raw commodity

brought into the house and “used” (not “as eaten”). Consideration includes what the

Exposure Factors Handbook (U.S. EPA, 1996) has termed “preparation loss,” “net

cooking loss,” and “net postcooking loss,” which are defined as the percentages of

weight loss (1) before cooking due to preparations such as coring, peeling, and

stemming; (2) during cooking, such as water loss; and (3) after cooking, such as

drainage of liquids from canned or frozen foods or removal of skin for fruits and

vegetables. Table 6-5 presents meat weight losses due to cooking. This includes

dripping and volatile losses, and postcooking losses include cutting, shrinkage, excess

fat, bones, and juices. Preparation loss, cooking loss, and postcooking loss are not

considered for dairy and eggs. Procedures for consideration of these factors and the

delineation of consumption rates for home-produced foods are included in the

Exposure Factors Handbook (U.S. EPA, 1996).
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MPE 6-52 December 1998

TABLE 6-5

Percent Weight Losses from Preparation of Various Meats

Meat Type Mean Net Cooking Loss

(%)a

Mean Net Post Cooking Loss

(%)b

Beef 27 24

Chicken 30 30

Lamb 29 34

Pork 28 36

Turkey 31 28

Veal 30 25

a

 Includes dripping and volatile losses during cooking. Averaged over various cuts and

 preparation methods.

b

 Includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices.

 Averaged over various cuts and preparation methods.

Source: USDA (1975).

The following sections discuss the consumption of vegetables/fruits and

terrestrial animal food products. Some limited information on consumption rates is

provided here, but only as overview information. More detailed information, such as

the consumption of specific homegrown vegetables, the rates of consumption of beef

as a function of geography or demographics, or even specific percentiles of food

consumption, should be obtained from the Exposure Factors Handbook (U.S. EPA,

1996).

6.5.2. The Impact of Cooking on Food Concentrations. As indicated in the previous

section, an assessor may need to introduce food preparation factors if the consumption

rate that is used does not already account for losses in weight due to food preparation. 
Chapter 6

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MPE 6-53 December 1998

Another issue is the impact of cooking on the concentration of the contaminant in the

food. This becomes important because essentially all the algorithms in this chapter to

predict vegetative and terrestrial animal food concentrations were developed by using

raw, and not cooked, food product. Therefore, predictions of contaminants in foods are

valid for raw foods, and by not adjusting the concentration of the contaminant based on

cooking practice, an assessor is assuming that the concentration of the contaminant is

not effected by cooking. While weight loss of the food product from cooking is clear

(due mainly to loss of water when cooking), it is not clear that the concentration of the

contaminant should change as well. 

In most cases, it should be assumed that cooking does not change the

concentration because, as will be shown, there is very little data in the literature

regarding the impact of cooking on the concentration of bioaccumulating contaminants

in food products, and it is not all uniform. If an assessor makes an adjustment to the

predicted concentration in food based on the impact of cooking, there should be a good

reason for this adjustment and the assessor should be clear in the rationalization for

this adjustment factor. An example might be, “Outdoor barbequing is an important

aspect to the behaviors in this scenario, and several experiments have shown a

reduction in lipid concentrations of contaminant X when barbequing.” 

If considering a change in concentration due to food preparation, the assessor

first needs to understand the basis of the food concentration prediction: whether the

concentration prediction was on a whole weight or on a lipid basis. This is important for

bioaccumulating contaminants which are associated with the lipid portion of the food

product, as are most contaminants that will be considered in this assessment

methodology. If the modeled concentration was on a whole weight basis, than the

assessor needs to consider the impact of cooking and other preparations on the lipid

content of the fully prepared product. Say, for example, that predictions of the

contaminant in beef on a whole weight basis were made, and it was assumed that the

raw beef was 20% lipid. After broiling, evidence suggests that a substantial amount of

the lipid is cooked out of the beef, or the fat is trimmed off the beef, such that the final
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MPE 6-54 December 1998

eaten beef product is 10% lipid. In this case, one would have reason to reduce

predicted concentrations, that were made on a whole weight basis, by one-half. If, on

the other hand, raw and cooked product have approximately the same percent lipid

content (proportionally as much or more water was lost from cooking as was fat, e.g),

than there would be no basis for a reduction in a predicted whole weight concentration. 

If the concentration prediction was on a lipid basis, than it may not be important

what cooking does to the lipid content. What becomes important is simply the

assumption used for lipid content of the final prepared product (so that lipid-based

concentrations can be converted to whole weight concentrations to be consistent with

consumption rates expressed in whole weight terms; see Section 6.5.1.2.). 

On the other hand, there is some evidence that cooking can reduce the

concentration of some contaminants due to volatile loss of the contaminant from the

lipid reservoir. Armbruster et al. (1989) concluded that losses of PCBs by vaporization

best explained a 7.5% loss in concentration when cooking bluefish. Schecter et al.

(1996, 1997) conducted studies on the impact of cooking on final concentrations of

dioxins and coplanar PCB in hamburger, bacon, and catfish. Although they noted

about a 40-50% loss in the amount of the dioxins and PCBs in these food products, this

was found to be roughly equal to the decrease in overall weight of these products

during cooking, so that the final concentrations were roughly the same. Like these

studies, other studies have not been consistent or conclusive with regard to the impact

of cooking on volatile loss of contaminants. 

In summary, then, the assessor should assume that cooking and other

preparations do not affect the lipid-based concentration of contaminants in animal food

products, unless they have good reason to assume an increase or a reduction in

concentration. Assessors should be cognizant of the basis of the food concentration

prediction to be sure that the predicted concentrations are in units consistent with the

food consumption rates used.

6.5.3. Calculating Daily Intake from Contaminated Plants. The human daily intake

of contaminants due to consumption of a specific plant group is calculated by:
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-55 December 1998

DIpi' Pi CPi

(6-31)

DIp ' j

n

i'1

DIpi

(6-32)

where:

DIpi = daily intake of contaminant from consumption of ith plant group

(μg/kg/day)

Pi = concentration of contaminant in ith plant group (μg/g DW)

Cpi = daily human consumption of locally grown items of ith plant group (g

DW/kg/day).

Total daily intake from consumption of all plant groups is calculated by summing

the contributions of each individual plant group:

where:

DIp = total daily intake of contaminant from consumption of plant tissue

(μg/kg/day)

DIpi = daily intake of contaminant from consumption of ith plant group

(μg/kg/day).

The total plant concentration, Pi, is the total concentration of the contaminant in

the ith plant group, calculated according to the equations in Section 6.3. for each plant

group. For use in this section, plant contaminant concentration is reported in units of

μg contaminant/g plant tissue DW, as calculated in Section 6.3.

As discussed in Section 6.3., plants consumed by humans may be grouped into

five categories: exposed vegetables, protected vegetables, root vegetables, exposed

fruits, and protected fruits. Some consumption rates of vegetable/fruit consumption that
Chapter 6

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MPE 6-56 December 1998

were developed in the Exposure Factors Handbook (U.S. EPA, 1996) include the

following:

! The recommended per capita fruit intake rate for the general population is

3.4 g fresh weight/kg/day, and the recommended per capita vegetable

intake rate for the general population is 4.3 g fresh weight/kg/day. The

95th percentiles for these two categories are 12 g/kg/day (fruit) and 10

g/kg/day (vegetables). These data are from the individual surveys of the

1989-91 CSFII and cover all categories of fruits and vegetables.

! The mean per capita consumption rates for exposed fruits, exposed

vegetables, and root vegetables is 1.435 g fresh/kg/day, 1.490 g

fresh/kg/day, and 1.245 g/kg/day, respectively. These data are also from

the 1989-91 CSFII.

! Of all respondents in the household component of the 1987-88 NFCS,

7.8% reported consumption of homegrown fruits with an average

consumers-only consumption rate of 2.68 g fresh/kg/day. The percentage

of total fruits used in the household that were home produced was 4%. 

The distinction in the percentage of individuals consuming a homeproduced product (in this case, 7.8% consuming home-produced fruits)

and the percentage of a total product consumed that is home produced

(in this case 4% of all fruits consumed were home produced) is described

above. Of all respondents from this survey, 18.3% reported some

consumption of homegrown vegetables with an average consumers-only

rate of 2.08 g fresh/kg/day. Seven percent of all vegetables used in the

household are home produced.

! These NFCS data are in raw commodity terms and would need to be

converted to an as- eaten basis for use in exposure assessments. Mean

net cooking losses averaged over several fruits equals about -90%,

meaning that there was a net gain in the weight of the raw commodity due

to addition of water or sugar in the cooking of fruits. Since the

consumption of the food itself is of concern and not the food product plus

added sugar or water, these gains from cooking should probably not be

used in exposure assessments. Net preparation losses for fruits equaled

about 25%, and cooking losses for fruits equaled about 30%. The net

cooking loss for vegetables equaled about 12%.

! In general, a per capita consumption rate for a specific food product can

be calculated by multiplying a consumers-only consumption rate by the

fraction of consumers who consume that food product. For example, the

per capita consumption rate for homegrown fruits can be estimated as the

product of the consumers-only homegrown consumption rate of 2.68
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MPE 6-57 December 1998

DIaj' Aj Caj

(6-33)

g/kg/day, derived from the 1987-88 NFCS, and the fraction of household

individuals consuming homegrown fruit, 0.078, to arrive at the per capita

consumption rate for homegrown fruits of 0.21 g/kg/day. This

consumption rate refers to fruits “used” in households. Therefore, it

needs to be converted to an “as-eaten” basis. The information above

suggests a preparation loss of 25% and a cooking loss of 30%, so that

the final as- eaten rate is estimated as 0.11 g/kg/day (0.21 g/kg/day @ [1-

0.25] @

[1-0.30]). A second per capita consumption rate for homegrown fruits can

be arrived at by a different pathway: by multiplying the per capita

consumption rate for fruits, 3.4 g/kg/day from the 1989-91 CSFII, by the

fraction of fruit consumed that is homegrown, 0.04, to arrive at a per

capita consumption rate of homegrown fruit of 0.14 g/kg/day. The

analogous rates of per capita consumption of homegrown vegetables are

calculated as 0.33 g/kg/day and 0.30 g/kg/day. These per capita

consumption rates for homegrown fruits and vegetables are not relevant

when estimating individual exposures when the individual is assumed to

be a home producer. In that case, a risk assessor should apply the 2.68

and 2.08 g/kg/day consumption rates for homegrown fruit and vegetables,

respectively. However, if an assessor wishes to calculate population

impacts and does not have specific information on home-growing

practices (i.e., fraction of individuals in an exposed population who home

grow and their consumption rates), it would be appropriate to use the per

capita consumption rates for homegrown products.

6.5.4. Calculating Daily Intake from Animal Tissue. Human daily intake due to

ingestion of a single contaminated animal tissue group is calculated by:

where:

DIaj = daily intake of contaminant from ingestion of jth animal tissue group

(μg/kg/day)

Aj = concentration of contaminant in jth animal tissue group (μg

contaminant/g whole weight)

Caj = daily human consumption of locally produced items of jth animal

tissue group (g whole weight/kg body weight/day).
Chapter 6

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MPE 6-58 December 1998

DIa ' j

n

j'1

DIaj

(6-34)

Total human daily intake from consumption of all animal products is calculated by

summing the contributions of each specific animal tissue group:

where:

DIa = total daily intake of contaminant from consumption of animal tissue

(μg/kg/day)

DIaj = daily intake of contaminant from consumption of jth animal tissue

group (μg/kg/day).

Aj is the total concentration of the contaminant in the jth animal tissue, calculated

according to the equations in Section 6.4. for each animal tissue group.

The Exposure Factors Handbook (U.S. EPA, 1996) provides considerable detail

on the consumption rates for individual meat and dairy products. Following are some

consumption rates and other key information from that reference:

! The mean per capita consumption rate from the individual surveys of the

1989-91 CSFII for total meat intake was 2.1 g/kg/dayand for total dairy

intake was 8.0 g/kg/day. The 95% per capita consumption rates for these

two broad groups were 5.1 and 29.7 g/kg/day for meats and dairy,

respectively. The Exposure Factors Handbook also gives information on

the fractions of foods “used” that are home produced (in contrast to

fraction of individuals who home produce). The fraction of total meat that

is homegrown is 0.024, and the fraction of total dairy is 0.012. Therefore,

an estimate of a per capita consumption rate for homegrown meats is

0.05 g/kg/day, and an estimate of a per capita consumption rate for

homegrown dairy is 0.10 g/kg/day.

! The household component of the 1987-88 NFCS allowed for the

generation of consumption rates of home-produced meats, dairy products,

and eggs. As noted above, these rates can be directly used for dairy

products and eggs, since no preparation or cooking loss is assumed. For

meats, mean net cooking losses range from about 25% to 35%, while

postcooking losses range from 10% to 35%.
Chapter 6

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MPE 6-59 December 1998

! Table 6-6 summarizes information from the 1987-88 NFCS household

survey for home-produced meats, dairy products, and eggs. This 

information includes percentages of individuals in households reporting

home production/consumption of the product, the mean consumption rate,

the 50th percentile consumption rate, and the 95th percentile

consumption rate. Risk assessors may use these values in site-specific

assessments for subsistence behaviors, but it is recommended that

assessors rely on the Exposure Factors Handbook to understand these

rates better and to ascertain if there is more appropriate information

available for their site.

TABLE 6-6

Percent Home Consumption and Consumers-only Consumption Rates for Home

Production of Meats, Dairy Products, and Eggs (rates in g whole product/kg body

weight/day; rates in terms of raw commodity)

Food

Product

Percent

Home

Production

Mean

Consumers-only

Consumption

Rate

50th Percentile

Consumers-only

Consumption

Rate 

95th Percentile

Consumers-only

Consumption

Rate

All meats 4.92 2.21 1.39 6.78

Beef 2.64 2.45 1.61 7.24

Pork 0.92 1.23 0.90 3.37

Poultry 0.97 1.57 1.23 3.83

Dairy 0.75 14.0 10.2 44.0

Eggs 1.10 0.73 0.47 1.69

Source: U.S. EPA, 1996

6.6. ALTERNATIVE MODELING APPROACHES FOR TERRESTRIAL FOOD CHAIN

The food chain algorithms presented in this chapter may not be the best ones for

use in all assessments. However, they are, at least, reasonable starting points for sitespecific or general assessments, and in fact, most food chain exposure modeling has

included screening level approaches similar to those used in this assessment. The
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MPE 6-60 December 1998

assumptions behind models are described carefully throughout this chapter. If these

assumptions do not apply to a particular situation, or where assessors require more

spatial or temporal resolution, more-complex models should be considered. Finally, it

cannot be overemphasized that measured concentrations are generally more reliable

than modeled ones. Assessors should use measured concentrations if available and if

such measurements can be considered spatially and temporally representative of the

exposed populations.

Relatively simple, screening level models are used to model fate, transport, and

transfer of bioaccumulating contaminants in this methodology. Simple assumptions are

often made in order to arrive at the desired results, which are long-term average

exposure media concentrations. The most important assumption is that the stack

emissions remain constant throughout the period of emissions. While the air

dispersion models are run using temporally varying meteorological data, in fact, the

outputs used from these models are the average air concentrations and depositions

which drive all other terrestrial and aquatic models. 

The first examples of similar multimedia compartment modeling were probably

the "fugacity" models proposed by Mackay (1979) and Mackay and Paterson (1981,

1982). Fugacity modeling is perhaps the single most technically sophisticated

approach to multi-media modeling. Fugacity in this context is defined as the tendency

for a chemical to escape from one environmental media compartment into another. It is

based on the fact that at equilibrium, equal fugacities are established in all

compartments of a system. The fugacity of a chemical present in an environmental

media compartment is modeled using common fate and transport parameters such as

octanol water partition coefficients, Henry's Constants, water solubilities, and so on. 

Modeling of the transfer of contaminants between compartments using fugacity can

also involve more theoretical and less commonly available parameters such as

conductances, properties of the environmental medium where the contaminant resides,

boundary layer thicknesses, and so on. Examples of fugacity modeling include the

transfer of nonionic organic chemicals between the atmosphere and surface water
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-61 December 1998

(Mackay et al., 1986), between the atmosphere and plants (Riederer, 1990; McLachlan

et al., 1995), and for food chain modeling (Travis and Hattemer-Frey, 1987). A regional

fugacity model being used in regulatory risk assessment forums is the CALTOX (DTSC,

1993) model. Mackay (1991) provides a definitive text on multimedia compartment

modeling using the fugacity approach.

A key difference between fugacity modeling and the modeling in this assessment

is that for fugacity modeling, movement between compartments is considered for both

directions — from compartment A to compartment B and also compartment B to

compartment A. The modeling in this assessment is essentially one way — air to leaf,

air to soil, vegetation to terrestrial animal, and so forth. By definition, fugacity modeling

maintains a mass balance; concentrations of contaminants in adjoining compartments

are dependent on each other. The models in this chapter do not explicitly maintain a

mass balance as do fugacity models. However, correctly parameterized, the food chain

models of this chapter should not violate mass balance principles. Explicit

consideration of mass balance is the principal reason fugacity modeling can be

considered more technically valid than the models of this chapter. 

However, an important drawback for the fugacity approach applied to stack

emission impacts is it does not consider spatial variability of concentrations within a

compartment. For example, air concentrations vary depending on the distance from the

source of air emissions. The fugacity approach would typically treat air as a single

compartment with a uniform concentration. This would be a concern for a regional

model such as the CALTOX model, which is recommended for use on a regional scale

assuming a single regional air concentration. One could apply fugacity models to the

air:plant:animal food chain starting with an air concentration predicted to occur at an

exposure site using the ISC3 or other air dispersion model.

In addition to fugacity approaches, more sophisticated air:soil:plant modeling

systems have been described by Trapp and colleagues in Trapp and McFarlane

(1995), Trapp and Matthies (1995), and Trapp et al. (1990). For assessors wishing a

more rigorous approach to modeling the concentration of the contaminant within the
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-62 December 1998

animal instead of the simple bioconcentration/biotransfer approach, McLachlan (1994)

presents a fugacity model for predicting the concentration of hydrophobic contaminants

in the various compartments (milk, blood, different fat compartments) of a lactating cow.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-63 December 1998

6.7. REFERENCES

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Belcher, G.D. and C.C. Travis. 1989. Modeling support for the RURA and municipal

waste combustion projects: final report on sensitivity and uncertainty analysis for the

terrestrial food chain model. Prepared by ORNL for the Office of Health and

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Environmental Protection Agency, Cincinnati, OH, under contract no. IAG-1824-A020-

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Bidleman, T.F. 1988. Atmospheric process. Wet and dry deposition of organic

compounds are controlled by their vapor-particle positioning. Environ. Sci. Technol.

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Properties, Processes, and Estimation Methods. Pergamon Press.

Boone, F.W., Y.C. Ng and J.M. Palms. 1981. Terrestrial pathways of radionuclide

particulates. Health Phys. 41:735-747.

Briggs, G.G., R.H. Bromilow and A.A. Evans. 1982. Relationships between lipophilicity

and root uptake and translocation of non-ionized chemicals by barley. Pestic. Sci.

13:495-504.

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sludge on the cadmium and zinc content of crops. Report no. 83. ISSN 0194-4088.
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DTSC (Department of Toxic Substances Control). 1993. CALTOX, A Multimedia TotalExposure Model for Hazardous-Waste Sites. Part I: Executive Summary, and Part II:

The Dynamic Multimedia Transport and Transformation Model, and Part III: The

Multiple-Pathway Exposure Model. Prepared for the state of California, Department of

Toxic Substances Control, Lawrence Livermore National Laboratory, Livermore, CA

URCRL-CR-111456.

Elliott, H.A., M.R. Liberati and C.P. Huang. 1986. Competitive adsorption of heavy

metals by soils. J. Environ. Qual. 15(3): 214-219.

Fries, G.F., V.J. Feil and K.L. Davison. 1996. The significance of pentachlorophenoltreated wood as a source of dioxin residues in United States beef. Presented at the

16th International Symposium on Chlorinated Dioxins and Related Compounds, August

12-16, 1996, Amsterdam, Netherlands. Abstract in Organohalogen Compounds

28:156-159.

Geraghty, J.J., D.W. Miller, F. Vander Leeden et al. 1973. Water Atlas of the United

States. Water Information Center, Port Washington, NY.

Giordano, P.M., A.D. Behel, J.E. Lawrence et al. 1983. Mobility in soil and plant

availability of metals derived from incinerated municipal refuse. Environ. Sci. Technol.

17:193-198.

Harter, R.D. 1983. Effect of soil pH on adsorption of lead, copper, zinc, and nickel.

Soil Sci. Soc. Am. J. 47:47-51.

Hillel, D. 1980. Fundamentals of Soil Physics. Academic Press, Inc., New York.

Hoffman, F.O., K.M. Theissen, M.L. Frank et al. 1992. Quantification of the

interception and initial retention of radioactive contaminants deposited on pasture

grass by simulated rain. Atmos. Environ. 26A:3313-3321.

Hulster, A. and H. Marschner. 1991. Transfer of PCDD/PCDF from contaminated soils

into two vegetable crops. Poster presentation at Dioxin ‘91, held at Research Triangle

Park, NC, on September 23-27, 1991.

LaBauve, J.M., J. Kotuby-Amacher and R.P. Gambrell. 1988. The effect of soil

properties and a synthetic municipal landfill leachate on the retention of Cd, Ni, Pb, and

Zn in soil and sediment materials. J. Water Pollut. Control Fed. 60(3):379-385.

Lagerwerff, J.V. 1971. Uptake of Cd, Pb, and Zn by radish from soil and air. Soil Sci.

111:129-133.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-65 December 1998

Logan, T.J. and R.L. Chaney. 1983. Utilization of municipal wastewater and sludge on

land—Metals. In: Proceedings of the 1983 Workshop on Utilization of Municipal

Wastewater and Sludge on Land, Riverside, CA. p. 235-323.

Lorber, M. 1995. Development of an air-to-leaf vapor phase transfer factor for dioxins

and furans. Presented at the 15th International Symposium on Chlorinated Dioxins and

Related Compounds, August 21-25, 1995, Edmonton, Canada. Abstract in

Organohalogen Compounds 24:179-186.

Mackay, D. 1979. Finding fugacity feasible. Env. Sci. Tech. 13:1218-1223.

Mackay, D. 1991. Multimedia Environmental Models: The Fugacity Approach. Lewis

Publishers, Chelsea, MI.

Mackay, D. and S. Paterson. 1981. Calculating fugacity. Env. Sci. Tech. 15:1006-

1014.

Mackay, D. and S. Paterson. 1982. Fugacity revisited. Env. Sci. and Tech. 16:654-

660.

Mackay, D., S. Paterson and W.H. Schroeder. 1986. Model describing the rates of

transfer processes of organic chemicals between atmosphere and water. Environ. Sci.

Technol. 20(8):810-816.

McCrady, J.K. 1994. Vapor-phase 2,3,7,8-TCDD sorption to plant foliage—A species

comparison. Chemosphere. 28:207-216. 

McCrady, J.K. and S.P. Maggard. 1993. Uptake and photodegradation of 2,3,7,8-

tetrachlorodibenzo-p-dioxin sorbed to grass foliage. Environ. Sci. Technol. 27:343-350.

McLachlan, M.S. 1994. Model of the fate of hydrophobic contaminants in cows.

Environ. Sci. Technol. 28:2407-2414.

McLachlan, M.S., H. Thoma, M. Reissinger et al. 1990. PCDD/F in an agricultural food

chain. Part 1: PCDD/F mass balance of a lactating cow. Chemosphere. 20:1013-1020.

McLachlan, M.S., K. Welsch-Pausch and J. Tolls. 1995. Field validation of a model of

the uptake of gaseous SOC in Lolium multiflorum (Rye Grass). Env. Sci. Tech. 

29:1998-2004.

Miller, C.W. and F.O. Hoffman. 1983. An examination of the environmental half-time

for radionuclides deposited on vegetation. Health Phys. 45(3):731-744.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-66 December 1998

Muller, J.F., A. Hulster, O. Papke et al. 1994. Transfer of PCDD/PCDF from

contaminated soils into carrots, lettuce and peas. Chemosphere. 29:2175-2181.

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of Sewage Sludge Quality and Soil Properties on Plant Uptake of Sludge-Applied Trace

Contaminants, November 1985, Las Vegas, NV. Draft.

Riederer, M. 1990. Estimating Partitioning and Transport of Organic Chemicals in the

Foliage/Atmosphere System: Discussion of a Fugacity-Based Model. Env. Sci. Tech. 

24:829-837. 

Ryan, J.A., H.R. Pahren and J.B. Lucas. 1982. Controlling cadmium in the human

food chain: A review and rationale based on health effects. Environ. Res. 28:251-302.

Schecter, A., O. Papke, M. Dellarco and J. Olson. 1996. A comparison of dioxins and

dibenzofurans in cooked and uncooked food. Organohalogen Compounds.

28:166-170.

Schecter, A., O. Papke and M. Dellarco. 1997. Dioxin, dibenzofuran, and PCB

congeners in cooked and uncooked foods. Organohalogen Compounds. 33:462-466.

Sehmel, G.A. 1971. Particle diffusivities and deposition velocities over a horizontal

smooth surface. J. Colloid Interface Sci. 37:391-396.

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14:983-1011.

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States by county: A compilation of information from the 1974 census of agriculture for

use in terrestrial food-chain transport and assessment models. ORNL-5786.

Smith, D.G., I.S. Chaudhuri, D. Heinold et al. 1995. An alternative approach for

estimating plant uptake of dioxin vapors. Presented at the 15th International

Symposium on Chlorinated Dioxins and Related Compounds, August 21-25, 1995,

Edmonton, Canada. Abstract in Organohalogen Compounds. 24:187-193.

Stephens, R.D., M.X. Petreas and D.G. Haward. 1995. Biotransfer and

bioaccumulation of dioxins and furans from soil: Chickens as a model for grazing

animals. Sci. Total Environ. 175:253-273.

Trapp, S. and M. Matthies. 1995. Generic one-compartment model for uptake of

organic chemicals by foliar vegetation. Environ. Sci. Technol. 29:2333-2338.
Chapter 6

Terrestrial Food Chain Modeling

MPE 6-67 December 1998

Trapp, S. and J.C. McFarland, Ed. 1995. Plant Contamination Modeling and

Simulation of Organic Chemical Processes. Lewis Publishers.

Trapp, S., M. Matthies, I. Scheunert and E.M. Topp. 1990. Modeling the

bioconcentration of organic chemicals in plants. Env. Sci. Tech. 24:1246-1252.

Travis, C.C. and A.D. Arms. 1988. Bioconcentration of organics in beef, milk, and

vegetation. Environ. Sci. Technol. 22(3):271-274.

Travis, C.C. and H.A. Hattemer-Frey. 1987. Human exposure to 2,3,7,8-TCDD. 

Chemosphere. 16:2331-2342.

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spruce leaves. Environ. Sci. Pollut. Res. 1(3):146-150.

U.S. Department of Agriculture. (1975) Food yields: Summarized by different stages of

preparation. U.S. Department of Agriculture, Agricultural Research Service. 

Agricultural Handbook No. 102. 

U.S. EPA. 1989. Exposure Factors Handbook. Prepared by the Exposure Assessment

Group, Office of Health and Environmental Assessment, Office of Research and

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U.S. EPA. 1994. Estimating Exposure to Dioxin-like Compounds. Prepared by the

National Center for Environmental Assessment, Office of Research and Development,

Washington, DC. EPA/600/6-88/005Ca. External Review Draft. 

U.S. EPA. 1996. Exposure Factors Handbook. Prepared by the National Center for

Environmental Assessment, Office of Research and Development, Washington, DC.

EPA/600/P-95/002Ba. SAB Review Draft.

Voldner, E.C., L.A. Barrie and A. Sirois. 1986. A literature review of dry deposition of

oxides of sulfur and nitrogen with emphasis on long-range transport modelling in North

America. Atmos. Environ. p. 2101-2123.

Welsch-Pausch, K., M. McLachlan and G. Umlauf. 1995. Determination of the

principal pathways of polychlorinated dibenzo-p-dioxins and dibenzofurans to Lolium

multiflorum (Welsh ray grass). Environ. Sci. Technol. 29:1090-1098.

Wesely, M. 1983. Turbulent transport of ozone to surfaces common in the eastern half

of the United States. In: Trace Atmospheric Constituents: Properties, Transformations,

and Fates, S.E. Schwartz, Ed. John Wiley & Sons, New York.
Chapter 7

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MPE 7-1 September 1999

WATER CONCENTRATION AND EXPOSURE PATHWAYS

7.1. INTRODUCTION

Contaminants associated with particles emitted by combustors are deposited on

land and water downwind from the combustor at rates determined by meteorology, terrain,

and particle physics. Following deposition, these contaminants may be dissolved,

repartitioned, or degraded. They may be transported over the ground in runoff to a surface

water body or infiltrate the ground and recharge the ground water.

Contaminants from combustor emissions that have been transported into water can

be directly ingested when humans or animals drink water. The total intake concentration of

contaminant will be influenced by the source from which that drinking water is taken. Water

treatment processes can sometimes remove or alter the contaminants available for

ingestion. This alteration is specific to the chemistry of the system of water treatment and

to the source water quality.

Individuals frequently are exposed to several sources of drinking water in any given

day. On average, however, the drinking water ingested by an individual can be assumed

to be either from the residence of that individual or from the drinking water source at the

place at which he or she regularly spends time, such as the workplace or school. For the

purposes of estimation, under the circumstances that the normal work schedule or school

day occupies approximately one-half of the waking time of an individual, the intake of

drinking water can probably be assumed to be approximately one-half from the residence

and one-half from the workplace or school. In cases where an individual is not known to

drink water or beverages made with drinking water from the local source, the assumption

that the total intake is from his or her residence can be made, although the true total

amount ingested may be smaller.

Contaminant intake from drinking water is proportional to the water concentration of

the contaminant and the water ingestion rate from the source under examination. For

many adults, this will be from one or two primary sources. The estimation of the
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-2 September 1999

concentration of contaminants that are available for ingestion by an individual is

significantly determined by the sampling protocol used to collect such information. 

Samples that are collected from residences can be either first draw or flushed samples. 

First draw samples are those from the very first water to leave the tap in the morning or

after a long period of inactivity in the water line. These samples represent the amount of

contaminant that accumulates as the drinking water is (1) in contact with the faucet or as

the water lies stagnant allowing growth or regrowth of microbiological material,

(2) in the pipe leading to that specific faucet and the solder joints along the way, and

(3) in contact with the service line. Such samples have consistently shown higher

concentrations of metals and other contaminants that can be derived from extended

periods of time with the water in contact with the plumbing closest to the tap.

Another common sample used to estimate drinking water contamination is the

flushed sample. This means the sample that is drawn from water that has not stood in the

pipes or faucet but rather has passed through the system for only a short time and

therefore has had no contact time to leach contaminants from the system. Such samples

contain the contaminants that were in the water from the system or from the system

materials, which in most cases will not be many because the contact time usually is not

long.

Clearly, these two “typical” samples represent different levels of contaminants and

are representative of different patterns of water use and, therefore, different types of

exposure. It is valuable to know the type of samples from which the estimates of

contaminant concentrations in the exposure are taken.

This chapter focuses on the contaminant concentration found in surface water

bodies, collected precipitation and ground water, and the ingestion of contaminants

through drinking water.

7.2. WATER CONCENTRATIONS OVERVIEW

7.2.1. The Framework. The following framework for estimating impacts to water bodies

from combustor stack emissions estimates water column as well as bed sediment
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-3 September 1999

concentrations. Water column concentrations include dissolved, sorbed to suspended

sediments, and total (sorbed plus dissolved, or total contaminant divided by total water

volume). This framework also provides three concentrations for the bed sediments: 

dissolved in pore water, sorbed to bed sediments, and total. The model accounts for four

routes of contaminant entry into the water body: (1) sorbed to soils eroding into the water

body, (2) dissolved in runoff water, (3) direct deposition of particle-bound contaminants,

and (4) direct diffusion of vapor phase contaminants into the water body. The model also

accounts for four dissipation processes that remove contaminants from the water column

and/or bed sediment reservoirs: (1) decay of total contaminants (sorbed + dissolved)

within the water column, (2) decay of total contaminants (sorbed + dissolved) within the

bed sediment, (3) volatilization of dissolved phase out of the water column, and (4) removal

of total contaminant via "burial" from the surficial bed sediment layer.

This burial rate constant is a function of the deposition of sediments from the water

column to the upper bed; it accounts for the fact that much of the soil eroding into a water

body annually becomes bottom sediment rather than suspended sediment. The effect on

the water body usually is assumed to be uniform. This tends to be more realistic for

smaller water bodies than for large water bodies and river systems. Uncertainties having

to do with the size and spatial distribution of the water body should be considered by the

risk assessor. If a body of water is large, uniform deposition to that water body may not be

reasonable. In this case, the water body can be divided into segments for purposes of

analysis.

The values of the parameters used for each segment should be obtained or

calculated for that segment. This may be difficult to do. In cases where there are no

measured values for the segments, the risk assessor will have to determine if using an

average value for the entire water body is reasonable. If not, considerations such as

distance from source, water movement (direction and speed), water quality, and season

can enter into the decision of the portion of the total or variation from average that is

reasonable for the segment in question. In addition, there may be other sources of
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-4 September 1999

deposition-contaminated sediments to the various segments that need to be considered,

as well as dilution by noncontaminated inflows. This model is therefore not well-suited for

use with large water bodies that cannot be treated as a unit. It is also oversimplified for the

estuarine environment, and is not easily adapted for use in those systems.

Key assumptions in the surface water impact algorithm are as follows:

! Soil concentrations within a depositional area (which is distinct from a

watershed) are assumed to be uniform within the area and can be estimated

by the following key parameters representing the area as a whole: dry and

wet contaminant deposition rates, a soil dissipation rate, a soil bulk density,

and a soil mixing depth. The depositional area is the surface area where

contaminants are deposited directly whereas the watershed is often a much

larger drainage basin that can carry contaminants from other depositional

areas into this one.

! The partitioning of the contaminant within the soil/water matrices — surface

soils, suspended solids in the water body, and bed sediments of the water

body — can be described by partition coefficients.

! One route of entry into the surface water body is direct deposition. A second

route of entry is contaminant dissolved in annual surface runoff. This is

estimated as a function of the contaminant dissolved in soil water and annual

water runoff. A third route of entry is via soil erosion. A calculated sorbed

concentration of contaminant in soil, together with an annual soil erosion

estimate, a sediment delivery ratio, and an enrichment ratio, can be used to

describe the delivery of contaminant to the water body via soil erosion. A

sediment delivery ratio serves to reduce the total potential amount of soil

erosion (i.e., the total potential equals a unit erosion rate as in kg/m2

 times a

watershed area, in m2

) reaching the water body recognizing that most of the

erosion from a watershed during a year is deposited before reaching the

water body. The enrichment ratio recognizes the fact that soils that erode

tend to be lighter in texture, to be more abundant in surface area, and to

have higher organic carbon. All these characteristics lead to concentrations

in eroded soils that tend to be higher in concentration than in situ soils. A

fourth and final route of entry is via diffusion in the gaseous phase. The

dissolved concentration of contaminant in a water body is driven toward

equilibrium with the vapor phase concentration above the water body. At

equilibrium, gaseous diffusion into the water body is matched by

volatilization out of the water body. Gaseous diffusion is estimated with a

transfer rate (determined internally given user inputs) and a vapor phase air
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Water Concentration and Exposure Pathways

MPE 7-5 September 1999

concentration. This air concentration is specified by the user and is an

output of the atmospheric transport model.

! For the steady-state surface water solution algorithm, it is assumed that

equilibrium is maintained between contaminants within the water column and

contaminants in surficial bed sediments. Equilibrium is established when

the dissolved phase concentration in the water column is equal to the

dissolved phase concentration within the bed sediments. This condition is

imposed by the water body equations. For very large bodies of water, there

may be a question about the existence of equilibrium conditions across the

entire mass, since such things as currents, inversions, temperature, and

local land use may prevent equilibrium conditions from being established

across that mass. Here again, the risk assessor should use segments for

which the relevant information can be obtained, if indeed they can be

obtained or confidently estimated. The key factors in the decision to

segment or not to segment the water body are (1) whether the size and

shape of the water body and the location of the contaminant source with

respect to the water body indicate that variation in deposition onto the

surface can be expected to affect the contaminant load within the water

body, and (2) whether the conditions affecting equilibrium (mentioned above)

will overcome those variations. 

! A rate of contaminant burial in bed sediments is estimated as a function of

the rate at which sediments from the water column deposit onto the surficial

sediment layer. This burial represents a permanent sink; it is recognized

that a portion of the soil, and contaminant sorbed to it, erodes into a water

body to become bottom sediment whereas the remainder becomes

suspended sediment. This solution assumes that there will be a net

depositional loss, even though resuspension and redeposition of sediments

is ongoing, particularly with moving water bodies. For cases where the net

deposition rate is zero, no burial loss is calculated.

! Separate water column and benthic decay rate constants allow for the

consideration of decay mechanisms that remove contaminants from the

water body. If a transformation yield coefficient is specified, the decay of

one chemical is linked to the internal loading of a second chemical.

Figure 7-1 displays the framework for this analysis, with a listing of the 11

concentrations that are part of the solution algorithm. The state equations that link the

concentrations follow Figure 7-1.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-6 September 1999

Definitions

Cstot total concentration of contaminant on soil mg/L

Cds concentration dissolved in soil water mg/L

Cps concentration sorbed to soil mg/kg

Dyds yearly dry deposition to surface water mg/yr

Dyws yearly wet deposition to surface water mg/yr

Ca concentration in air above water :g/m3

Cwc concentration in water column mg/L

Cwtot total concentration, water column and benthic sediment mg/L

Cdw concentration dissolved in water phase mg/L

Csw concentration sorbed in water phase mg/kg

Cbs total concentration in benthic sediment mg/L

Cdbs concentration dissolved in bed sediment pore water mg/L

Csb concentration sorbed to bottom sediments mg/kg

Figure 7-1

Equilibrium Condition of Water Columns and Bed Sediments
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-7 September 1999

State Equations Corresponding to Figure 7-1

I. Soil

C C BD (7-1) stot S =

C C BDC (7-2) stot sw ds ps = + θ

C C Kd (7-3) ps ds =

C (7-4) C Kd BD

Kd BD ps

S

sw

= θ +

C (7-5) C BD

Kd BD ds

S

sw

= θ +

II. Surface Water System

C C d /d C d /d (7-6) wtot wc wc bs bs = ⋅ +⋅

 f (7-7) ( Kd TSS ) d /d

(( Kd TSS ) d /d) ( Kd BS) d /d wc

sw wc

sw wc bs bs bs

= + ⋅⋅ ⋅

+ ⋅ ⋅ ⋅ + + ⋅⋅

−

−

1 10

1 10

6

6 θ

 f (7-8) ( Kd BS) d /d)

( Kd TSS ) d /d ( Kd BS) d /d bs

bs bs bs

sw wc bs bs bs

= + ⋅⋅

+ ⋅ ⋅ ⋅ + + ⋅⋅ −

θ

1 10 θ 6
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Water Concentration and Exposure Pathways

MPE 7-8 September 1999

III. Water Column

C f C d/d (7-9) wc wc wtot wc =⋅ ⋅

C Cf C (7-10) Kd TSS dw wc dwc wc

sw

=⋅ = + ⋅⋅





 

 −  1

1 10 6

C Kd C (7-11) sw sw dw = ⋅

IV. Bed Sediment

C f C d/d (7-12) bs bs wtot bs =⋅ ⋅

C C f/ C (7-13) K BS dbs bs db bs bs

bs dbs

=⋅ = + ⋅





 

 θ  θ

1

C Kd C (7-14) sb bs dbs = ⋅

Note that by substituting the relationship Cwc and Cwtot into the expression for Cbt, we can

obtain benthic concentrations as a function of water column concentrations:

C C (7-15) Kd BS

Kd TSS bs wc

bs bs

sw

= ⋅ + ⋅

+ ⋅⋅ −

θ

1 10 6

C C (7-16) dbs dw =
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Water Concentration and Exposure Pathways

MPE 7-9 September 1999

C C Kd /Kd (7-17) sb ps bs sw = ⋅

where:

2sw = volumetric soil water content (L/L)

2bs = bed sediments porosity (L/L)

Kd = soil/water partition coefficient (mL/g)

Kdsw = suspended sediment/surface water partition coefficient (mL/g)

Kdbs = bottom sediment/sediment pore water partition coefficient (mL/g)

BD = bulk density (g/cm3)(soil)

BS = bulk density (g/cm3)(sediments)

Cstot = total concentration of contaminant in soil (mg/L)

TSS = total suspended solids (mg/L)

Cdbs = concentration of dissolved contaminant in benthic sediment (mg/L)

Cds = concentration of contaminant that is dissolved in soil water (mg/L)

Cps = concentration of contaminant that is sorbed to soil (mg/kg)

Cwtot = total concentration of contaminant in water body (mg/L)

Cdw = concentration of contaminant that is dissolved in water (mg/L)

Csw = concentration of sorbed phase contaminant in water (mg/kg)

Ca = concentration in air above water

Csb = concentration sorbed to bottom sediments

Cbs = total concentration in benthic sediment (mg/L)

dwc = depth of water column (m)

dbs = depth of upper benthic sediment layer (m)

d = depth of water body, dw+db (m)

fwc = fraction of total water body contaminant concentration in water

column

fbs = fraction of total water body contaminant concentration in bed

sediment
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-10 September 1999

fdwc = fraction of water column contaminant concentration that is

dissolved

fdb = fraction of benthic sediment contaminant concentration that is

dissolved

7.2.2. Chemical Mass Balance Equations. If Figure 7-1 is taken as a control volume for

the water body, a steady-state mass balance equation can be written that balances chemical

loadings with outflow and loss:

C (7-18)

L

Vf f k V wtot

T

x wc wtot tot

= +

where:

Cwtot = total water body concentration, water column and benthic sediment

(mg/L)

LT = total contaminant load to water body (g/yr)

Vfx = annual average flow rate through water body (m3/yr)

fwc = fraction of total water body contaminant concentration in water column

kwtot = total first-order dissipation rate constant, water column and benthic

degradation, volatilization, and burial (yr-1)

Vtot = total volume of water body or water body segment, including water

column and benthic sediment (m3

)

A mass balance equation can be written for the watershed soils, balancing areal

deposition fluxes with chemical loss processes:

C (7-19)

L

ks Z BD s ( . ( kstd)) C yW

+ −− + 10 100 ps exp

where:

Cs = soil concentration (:g/g)(average for watershed after deposition)

LyW = annual average load to watershed — areal basis (g pollutant/m2-yr)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-11 September 1999

ks = soil loss rate constant resulting from soil leaching (yr-1)

tD = total time period over which deposition has occurred (yr)

Z = watershed mixing depth (to which deposited pollutant incorporates

[cm])

BD = bulk density (g/cm3

) (representative watershed soil)

100 = units conversion factor (:g-m2/g-cm2)

Cps = concentration sorbed to soils (naturally) (mg/kg)

7.2.3. Sediment Mass Balance Equations. Before calculating chemical fate, a mass

balance equation for sediments in the water body must be solved, first, for the water column. 

Referring to Figure 7-2, the soil eroding into the water body, Xw, equals the sum of the

amount depositing into the upper bed, XD, and the advective loss from the water column, Xa. 

Solving for the suspended solids concentration in the water column:

TSS (7-20)

X A SD

Vf D A

e L

x ss w

= +

103

where:

TSS = total suspended solids (mg/L)

Xe = unit soil loss (kg/m2

-yr)

AL = total watershed surface area (m2

)

SD = sediment delivery ratio

Vfx = average volumetric flow rate through water body (m3

/yr)

Dss = suspended solids deposition rate (m/yr)

Aw = water body surface area (m2)

103 = units conversion factor

Often, the user will have data on average suspended solids but not on the deposition rate. In

this case, the deposition rate can be calculated as:
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-12 September 1999

D (7-21) X A SD Vf TSS

A TSS ss

eL x

w

= 10 − 3

where the terms are as defined above.

In the second part of the solids balance, the mass deposited to the bed, XD, is set

equal to the mass buried, Xb. Solving for the burial rate gives:
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-13 September 1999

Definitions

Xw soil erosion input from depositional area g/yr

Xa advective loss from water body g/yr

Dbs deposition onto bottom sediment g/yr

Xb burial below bottom sediment layer g/yr

TSS total suspended solids mg/L

BS bed (bottom) sediments bulk density g/cm3

Dss rate of deposition and suspended solids onto 

bed sediment m/yr

Wb rate of burial m/yr

XD amount depositing into upper bed g/yr

Figure 7-2

STEADY-STATE Representation for Sediments in Water Bodies
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-14 September 1999

In the second part of the solids balance, the mass deposited to the bed, XD, is set

equal to the mass buried, Xb. Solving for the burial rate gives:

W D (7-22) TSS

BS b W =

− 10 6

where:

Wb = rate of burial (m/yr)

DW = deposition rate (m/yr)

TSS = total suspended solids (mg/L)

BS = bed sediments bulk density (g/cm3)

10-6 = units conversion factor (kg/mg)

Finally, the benthic porosity, the volume of water per volume of benthic space, is

calculated from the benthic solids bulk (i.e., wetted) density concentration and sediment

solids density:

θ ρ (7-23) bs s = − 1 BS/

where:

2bs = benthic porosity (L/L)

BS = bed sediments bulk density (g/cm3

)

Ds = solids density, 2.65 kg/L.

For benthic solids bulk densities from 0.5 to 1.5 g/cm3

, benthic porosity will range

between 0.8 and 0.4.

7.2.4. Loads to the Water Body. Total chemical loading to the water body is composed

of six inputs:

LLL L LL L T D ROI RO E dif I =+ + ++ + (7-24)

where:

LT = total contaminant load to water body (g/yr)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-15 September 1999

LD = direct deposition load (mg/yr)

LROI = runoff load from impervious surfaces (g/yr)

LRO = runoff load from pervious surfaces (g/yr)

LE = soil erosion load (g/yr)

Ldif = diffusion of vapor phase contaminant (g/yr)

LI = internal transformation load (g/yr)

The runoff and erosion loads require estimating of average contaminant

concentration in watershed soils that comprise the depositional area. This is discussed first.

7.2.4.1. Load Owning to Direct Deposition — The load to surface waters via

direct deposition is solved as:

L (D D )A (7-25) D yds yws w = +

where:

LD = direct deposition load (mg/yr)

Dyds = yearly average dry deposition rate (g/m2-yr) (surface water)

Dyws = yearly average wet deposition rate (g/m2-yr) (surface water)

Aw = water body surface area (m2)

7.2.4.2. Load Owning to Impervious Surface Runoff — A fraction of the chemical

wet and dry deposition in the watershed will be to impervious surfaces. Dry deposition may

accumulate and be washed off during rain events. If the impervious surface includes gutters,

the pollutant load will be transported to surface waters, bypassing the watershed soils. The

average load from such impervious surfaces is given by:

L D DA (7-26) ROI yww ydw I = + ( )

where:

LROI = impervious surface runoff load (g/yr)

AI = impervious watershed area receiving pollutant deposition (m2)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-16 September 1999

Dyww = yearly average wet deposition (g/m2-yr) (watershed)

Dydw = yearly average dry deposition (g/m2-yr) (watershed)

7.2.4.3. Load Owning to Pervious Surface Runoff — Most of the chemical

deposition to a watershed will be to pervious soil surfaces. These loads are accounted for

in the soil mass balance equation. During periodic runoff events, dissolved chemical

concentrations in the soil are transported to surface waters as given by:

L RO A A (7-27)

C BD

Kd BD RO L I

S

sw s

= − +





 



 − ( ) θ 10 2

where:

LRO = pervious surface runoff load (g/yr)

RO = average annual runoff (cm/yr)

Cs = soil concentration (:g/g)

BD = bulk density (g/cm3) (soil)

2sw = volumetric soil water content (cm3/cm3)

Kds = soil/water partition coefficient (mL/mg3) [mL = cm2]

AL = total watershed area receiving pollutant deposition (m2)

AI = impervious watershed area receiving pollutant deposition

(m2)

10-2 = units conversion factor (g2

/kg-:g)

7.2.4.4. Load Due to Soil Erosion — During periodic erosion events, particulate

chemical concentrations in the soil are transported to surface waters as given by:

L X A A SD ER (7-28)

C Kd BD

Kd BD E eL I

s s

sw s

= − +





 



 − ( ) θ 10 3

where:

LE = soil erosion load (g/yr)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-17 September 1999

Xe = unit soil loss (kg/m2-yr)

Cs = soil concentration (:g/g)

BD = bulk density (g/cm3) (soil)

2sw = volumetric soil water content (cm3/cm3)

Kds = soil/water partition coefficient (mL/mg)

AL = total watershed area receiving pollutant deposition (m2)

AI = impervious watershed area receiving pollutant deposition (m2)

SD = sediment delivery ratio

ER = contaminant enrichment ratio

10-3 = units conversion factor (g-cm2

/:g-m2)

7.2.4.5. Load Owning to Gaseous Diffusion — The change in the total water

concentration over time because of volatilization is given by:

(7-29) ∂

∂

C

t

K

d ffC

C

H RT

wtot

volat

v

wc dwc wtot

a

wk

/ /( ) = − 



 





− 10 6

where:

Cwtot = total water body contaminant concentration (mg/L)

Kv = overall transfer rate (m/yr)

d = depth of water body (m)

fwc = fraction of total water body contaminant concentration in water

column

fdwc = fraction of water column contaminant concentration that is

dissolved

Ca = vapor phase air concentration over water body (:g/m3)

H = contaminant Henry's Constant (atm-m3/mole)

R = universal gas constant (8.206×10-5 atm-m3/mole-oK)

Twk = water body temperature (°K)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-18 September 1999

10-6 = units conversion factor

The right-hand side of Equation 7-29 contains two terms. The first term constitutes a

first-order loss rate of aqueous contaminant. The second term in Equation 7-29 describes

diffusion of gas-phase contaminant from the atmosphere into the water body. Because this

term is independent of water body contaminant concentration, it can be treated as an

external load. As formulated above, this second term has units of mg/L-yr. It must be

converted to loading units by multiplying by the water column volume, V. Noting that V/d is

equal to the surface water area AW, the atmospheric diffusion load is given as:

L (7-30)

KC A

H/RT dif

vaw

wk

=

− 10 6

where:

Ldif = diffusion of vapor phase contaminant (g/yr)

K v = overall transfer rate (m/yr)

Aw = water body surface area (m2)

Ca = vapor phase air concentration over water body (:g/m3)

H = contaminant Henry's Constant (atm-m3/mole)

R = universal gas constant (8.206×10-5 atm-m 3/mole-°K)

T wk = water body temperature (°K)

10-6 = units conversion factor

7.2.4.6. Load Due to Internal Transformation — Chemicals may be transformed

chemically or biologically into daughter products, which may be of interest in risk

assessment. A transformation yield can be specified for the sequential reaction:

B C (7-31) k B

Y BC

→( )

( )

where:
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-19 September 1999

B = parent compound

C = daughter compound (or reaction product)

k(B) = first-order transformation rate constant (yr-1)

Y(BC) = reaction yield coefficient (g of C/g of B)

The internal loading generated by this transformation reaction is given by:

L k B C B V k B C B V Y BC (7-32) l gwc wctot wc gb bt bs = + [ () () () () ]( )

where:

LI = internal transformation load to C (g/yr)

kgwc(B) = water column transformation rate constant—from B to C

(yr-1)

kgb(B) = benthic transformation rate constant — from B to C (yr-1)

Cwctot(B) = total water column concentration of B (mg/L)

Cbt(B) = total benthic concentration of B (mg/L)

Vwc = water column volume (m3

)

Vbs = benthic sediment volume (m3)

Y(BC) = reaction yield coefficient (g of C/g of B)

This loading is added to chemical C. If the yield coefficient is set to zero, then the

compounds are treated as independent.

7.2.5. Advective Flow from the Water Body. The first term in the denominator of

Equation 7-18 accounts for advective flow from the water body. It is the product of the annual

average volumetric flow rate, Vfx, and the fraction of the chemical in the water body that is

present in the water column, fwc. An affected water body derives its annual flow from its

watershed or effective drainage area. Flow and watershed area, then, are related, and

compatible values should be specified by the user. Given the area of drainage, one way to

estimate annual flow volume is to multiply total drainage area by a unit surface water runoff. 

The Water Atlas of the United States (Geraghty et al., 1973) provides maps with isolines of
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-20 September 1999

annual average surface water runoff, which is defined as all flow contributions to surface

water bodies, including direct runoff, shallow interflow, and ground water recharge. The

values ranged from 5 to 40 in/yr in various parts of the United States.

7.2.6. Chemical Dissipation Within the Water Body. The second term in the

denominator of Equation 7-18 accounts for dissipation within the water body. It is the

product of the water body volume, Vtot, and the total first order dissipation rate constant, kwtot. 

The water body volume, in units of m3, together with the annual flow rate, in m3/yr, determines

the average residence time of a pollutant traveling through the water body. The residence

time for Lake Erie is about 10 years, for example, whereas for the larger Lake Superior, it is

estimated to be 200 years. For a swiftly moving river, on the other hand, the residence time

can be on the order of hours (1 hour = 0.00011 yr). Larger volumes and residence times

allow the internal dissipation processes to have a greater effect on pollutant concentration;

smaller volumes and residence times lessen the effect. The user should take care to specify

reasonable volumes for the type of surface water body being represented. In addition, the

user should specify compatible values for related water body parameters, such as surface

area, Aw. The water body volume divided by the surface area gives the average depth,

which can vary from a fraction of a meter for small streams to a few meters for shallow

reservoirs to tens of meters for deep lakes.

The total dissipation rate constant, kwtot, applies to the total water body concentration,

Cwtot, and includes processes affecting any of the chemical phases— dissolved or sorbed in

the water column or benthic sediments. Volatilization, water column and benthic

degradation, and burial are considered:

k f k fk f k fk (7-33) wtot dwc gwc bs gb dwc vwc bs b = ++ +

where:

kwtot = overall total water body dissipation rate constant (yr-1)

kgwc = water column degradation or transformation rate constant (yr-1)

kgb = benthic degradation or transformation rate constant (yr-1)

kvwc = water column volatilization loss rate constant (yr-1)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-21 September 1999

kb = benthic burial rate constant (yr-1)

fdwc = fraction of total water body contaminant concentration in water

column

fbs = fraction of total water body contaminant concentration in benthic

sediment.

7.2.6.1. Chemical/Biological Degradation — Contaminants can be degraded by a

number of processes in the water column or in the benthic sediment. The major chemical

and biological processes include hydrolysis, photolysis, oxidation, and biodegradation. 

Each of these processes has been studied and is incorporated in a mechanistic way in

advanced water quality models. In this methodology, the overall first-order water column and

benthic degradation rate constants are user input variables and should be based on

knowledge about the contaminant behavior in similar surface water systems.

7.2.6.2. Volatilization — Volatile chemicals can move between the water column

and the overlying air. Diffusion into the water from the atmosphere is treated as an external

load. The quantity fdwc fdw Cwtot, which is equal to the water column dissolved phase

concentration, Cdw, is subject to volatilization loss. The rate constant for volatilization from

the water column, kvwc, is given as:

k (7-34) K f

d vwc

v dwc =

where:

kvwc = water column volatilization loss rate constant (yr-1)

Kv = overall transfer rate (m/yr)

fdwc = fraction of contaminant in the water column that is dissolved

d = depth of water body (m)

The overall transfer rate, Kv or conductivity, is determined by the two-layer resistance

model. The two-resistance method assumes that two “stagnant films” are bounded on either
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-22 September 1999

side by well-mixed compartments. Concentration differences serve as the driving force for

the water layer diffusion. Pressure differences drive the diffusion for the air layer. From

balance considerations, it is obvious that the same mass must pass through both films, thus

the two resistances combine in series, so that the conductivity is the reciprocal of the total

resistance:

K RR K K (7-35)

H

RT v LG L G

wK

=+ = + − − − − ( ) ( ( )) 1 1 1 1

where:

Kv = overall transfer rate (m/yr)

RL = liquid phase resistance (yr/m)

RG = gas phase resistance (yr/m)

KL = liquid phase transfer coefficient (m/yr)

KG = gas phase transfer coefficient (m/yr)

H = Henry's law constant for the pollutant (atm-m3/mole)

R = universal gas constant (8.206× 10-5 atm-m3/mole-°K)

Twk = water body temperature (°K)

The value of Kv, the conductivity, depends on the intensity of turbulence in a water

body and in the overlying atmosphere. As the Henry’s Law coefficient increases, the

conductivity tends to be increasingly influenced by the intensity of turbulence in water. As the

Henry’s Law coefficient decreases, the value of the conductivity tends to be increasingly

influenced by the intensity of atmospheric turbulence.

Because Henry’s Law coefficient generally increases with increasing vapor pressure

of a compound and generally decreases with increasing solubility of a compound, highly

volatile low-solubility compounds are most likely to exhibit mass transfer limitations in water,

and relatively nonvolatile high-solubility compounds are more likely to exhibit mass transfer
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-23 September 1999

limitations in the air. Volatilization is usually of relatively lower magnitude in lakes and

reservoirs than in rivers and streams.

The estimated volatilization rate constant is for a nominal temperature of 20°C. It is

adjusted for the actual water temperature using the equation:

K K (7-36) v T

T

,

( ) = −

20

20 θ

where:

2 = temperature correction factor, set to 1.026

T = water body temperature (note that because the temperature in the

model is in o

K, the water body temperature will have to be converted to

Kelvin—conversion factor 0oK = -273.16 oC.

A variety of methods have been proposed to compute the liquid phase (KL) and gas phase

(KG) transfer coefficients. The particular method that is recommended here is the O’Connor

method, as described below in Section 7.2.6.4.

The liquid and gas film transfer coefficients computed under this option vary with the

type of water body. The type of water body is specified as one of the surface water

constants and can either be a flowing stream, river, or estuary, or a stagnant pond or a lake. 

The primary difference is that in a flowing water body, the turbulence is primarily a function of

the stream velocity, whereas for stagnant water bodies, wind shear may dominate. The

formulations used to compute the transfer coefficients vary with the water body type, as

shown below.

7.2.6.3. Flowing Stream or River — For a flowing system (type 0), the transfer

coefficients are controlled by flow-induced turbulence. For these systems, the liquid film

transfer coefficient (KL) is computed using the O’Connor and Dobbins (1956) formula:

K (7-37)

D u

d x L

w = 



 





− 10

315 10

4 1 2

7

/

(. )

where:

KL = liquid phase transfer coefficient (m/yr)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-24 September 1999

u = current velocity (m/s)

Dw = diffusivity of the chemical in water (cm2/sec)

d = depth of water body (m)

10-4 = units conversion factor

3.15×107 = units conversion factor

The gas phase transfer coefficient (KG) is assumed constant at 36,500 m/yr for flowing

systems.

7.2.6.4. Quiescent Lake or Pond — For a stagnant system (type 1), the transfer

coefficients are controlled by wind-induced turbulence, and the liquid film transfer coefficient

(KL) is computed using the O’Connor et al. (1983) equations where:

K u (7-38) kS x L

a

w

Cw = 



 



 



 



 − * (. )

. .

. ρ

ρ λ

0 5 0 33

2

0 67 7 315 10

K u (7-39) kSc x G a = 



 



 − * (. )

.

.

0 33

2

0 67 7 315 10 λ

u CW (7-40) d* . = 0 5

10

Sc (7-41) D

V

D a

a

a a

a

a

= = μ

ρ

D (7-42) MW a = 19

2 3

. /

V Tx (7-43) a = + (. . ) 132 0 009 105

and where :

KL = liquid phase transfer coefficient (m/yr)

Sc (7-44) D w

w

w w

= μ

ρ
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-25 September 1999

D (7-45) x

MW w =

− 22 10 5

2 3/

ρ (7-46) w wc = − x T − 1 88 10 5 .

log( ) (7-47) . . ( ). ( ) μ . w T T wc wc= + −+ −





 



 − 1301

998 333 81855 20 0 00585 20 2 3 0233

KG = gas phase transfer coefficient (m/yr)

u* = shear velocity (m/s)

Cd = drag coefficient ( = 0.0011)

W10 = wind velocity, 10 m above water surface (m/s)

Da = density of air corresponding to water temperature (g/cm3)

Dw = density of water corresponding to water temperature (g/cm3)

k = von Karman's constant ( = 0.4)

82 = dimensionless viscous sublayer thickness ( = 4)

Sca = air Schmidt number (dimensionless)

Scw = water Schmidt number (dimensionless)

Da = diffusivity of chemical in air (cm2/sec)

Dw = diffusivity of chemical in water (cm2/sec)

:a = viscosity of air corresponding to air temperature (g/cm-s)

:w = viscosity of water corresponding to water temperature

(g/cm-s)

Va = dynamic viscosity of air (cm2/sec)

MW = molecular weight

T = air temperature (note that because the temperature in the model

is in o K; the result of this will have to be converted to

Kelvin—conversion factor 0o

K = -273.16°C)

Twc = water temperature (note that because the temperature in the

model is in °K; the result of this will have to be converted to

Kelvin—conversion factor 0o K = -273.16°C)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-26 September 1999

3.15×10 7 = units conversion factor

7.2.6.5. Deposition and Burial — The rate of burial, Wb, in the benthic situation is

determined as a function of user input variables as part of the sediment balance (see

Section 7.2.3.). This burial rate is used to determine the mass loss of contaminant from

below the surficial benthic sediment layer. As seen in Figure 7-1, the burial loss rate is

applied to the total benthic contaminant concentration, Cbt. The water body contaminant

burial loss rate is solved by equating the mass loss rate of total water body chemical with

mass loss rate of benthic chemical:

C V k CV (7-48) W

d wtot tot b bt bs

b

bs

=

where:

Cwtot = total water body contaminant concentration, including water column and

benthic sediment (mg/L)

Vtot = total volume of water body or water body segment, including water

column and benthic sediment (m3)

kb = first-order burial rate constant for total chemical (yr-1)

Cbt = total benthic contaminant concentration (mg/L)

Vbs = volume of upper benthic sediment layer (m3

)

dbs = depth of upper benthic sediment layer (m)

Wb = rate of burial (m/yr)

The state equations given after Figure 7-1 indicate that the total benthic contaminant

concentration, Cbt, can be expressed as a function of the total water body concentration,

Cwtot. Solving for the total chemical burial rate gives:

k f (7-49) W

d b bs

b

bs

=
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-27 September 1999

where:

fbs = fraction of total water body contaminant concentration in bed

sediment

Wb = rate of burial (m/yr)

dbs = depth of upper benthic sediment layer (m)

kb = benthic burial rate (yr-1)

7.2.6.6. Peak Storm Event Concentrations — The previous equations together

describe the long-term average water body concentrations of sediment and chemical. 

Short-term peak concentrations also may be of interest in assessing risk from some

chemicals. The basic mass balance equations for determining peak storm event

concentrations of sediment and chemical are:

TSS (7-50)

X

V storm

storm

storm

= 103

C (7-51)

L

V storm

storm

storm

=

where:

TSSstorm = total suspended solids during peak storm event (mg/L)

Cstorm = peak chemical concentration during storm event (mg/L)

Vstorm = peak water body volume during storm event (m3

) 

Xstorm = total load of sediment from watershed during storm (kg)

Lstorm = total load of chemical from watershed during storm, runoff

and erosion (g)

103 = g/kg

The water body volume during the storm is first calculated; the assumption is that the

storm runoff volume is added to the average streamflow volume:
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-28 September 1999

V (7-52) Vf t

x storm Q x storm

ROstorm = +

365 24

where:

Vstorm = peak water body volume during storm event (m3

)

Vfx = annual average stream flow (m3/yr)

tstorm = duration of storm event (hr)

QROstorm = total storm runoff volume (m3)

365 = days/yr

24 = hr/day

The total storm runoff volume, QROstorm, is the sum of runoff from pervious and impervious

areas of the watershed:

Q A A d AM (7-53) ROstorm L I RO l tot =− + − − ( ) 10 10 2 2

where:

QROstorm = total storm runoff volume (m3)

AL = total watershed surface area (m2)

AI = impervious surface area on watershed (m2)

Mtot = depth of total rainfall for storm event (cm)

dRO = depth of runoff from pervious areas of watershed (cm)

10-2 = m/cm

The runoff depth, dRO, is estimated by the following equation:

d (7-54) RO M S

RO M S RO

tot

storm

= + −

+ +

( .)

. )

0 2

0 8

2

where:

dRO = depth of runoff from pervious areas of watershed (cm)

RO = runoff (cm/yr)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-29 September 1999

Mtot = depth of total rainfall for storm event (cm)

Mstorm = depth of snowmelt during storm event (cm)

S = watershed retention parameter (cm)

The watershed retention parameter, S, is calculated from the Soil Conservation Service

runoff curve (number input by the user) according to the following equation:

S CN = − 2 54 1000 10 . [( / ) ] (7-55)

where:

S = watershed retention parameter (cm)

CN = runoff curve number

Next, the mass of sediment eroded from the watershed to the receiving water during

a storm event can be estimated with the Modified Universal Soil Loss Equation (MUSCLE):

X x Q q K LSC PSD (7-56) storm ps p = 2 04 106 0 56 . () .

where:

Xstorm = total load of sediment from watershed during storm (kg)

Qps = volume of runoff from pervious area (km2-cm)

qp = peak runoff from pervious area (m3

/sec)

K = erodibility factor (tons/acre)

LS = topographic or slope length factor (unitless)

C = cover management factor (unitless)

P = supporting practice factor (unitless)

SD = sediment delivery ratio

This equation is an empirical relationship, and the units must be consistent with those shown

above. Selection of K, LS, C, P, and SD is discussed in Wischmeier and Smith (1978) and

U.S. EPA (1985).
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-30 September 1999

The storm runoff volume from pervious areas is calculated from the runoff depth

according to:

Q A Ad (7-57) ps L l RO = − − ( ) 10 6

where:

Qps = volume of runoff from pervious area (km2-cm)

AL = total watershed surface area (m2)

AI = impervious surface area on watershed (m2)

dRO = depth of runoff from pervious areas of watershed (cm)

10-6 = km2

/m2

A trapezoidal hydrograph is assumed so that the peak runoff rate can be calculated as:

q (7-58)

A Ad

t

RO M

RO M S p

L I RO

storm

tot

storm

= − +

+ −





 





− ( )

0 2.

10

3600

2

where:

qp = peak runoff from pervious area (m3

/sec)

AL = total watershed surface area (m2)

AI = impervious surface area on watershed (m2)

dRO = depth of runoff from pervious areas of watershed (cm)

Mtot = depth of total rainfall for storm event (cm)

Mstorm = depth of snowmelt during storm event (cm)

RO = runoff (cm/yr)

S = watershed retention parameter (cm)

tstorm = duration of storm event (hr)

10-2 = m/cm

3600 = sec/hr
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-31 September 1999

Finally, the mass of chemical load to the receiving water during the storm event is

calculated:

LL L L (7-59) storm ROIstorm ROstorm sE = ++

where:

Lstorm = total load of chemical from watershed during storm, runoff and

erosion (g)

LROIstorm = storm runoff load from impervious surfaces (g)

LROstorm = storm runoff load from pervious surfaces (g)

LsE = storm erosion load from pervious surfaces (g)

Runoff from impervious surfaces includes dryfall accumulated since the previous rainfall

event and wetfall during the present rainfall event:

L (7-60)

Dt Dt

x ROIstorm A ydw acc yww event

I = +





 



 365 24 365

where:

LROIstorm = storm runoff load from impervious surfaces (g)

Dydw = yearly average dry deposition (g/m2 -yr)

Dyww = yearly average wet deposition (g/m2 -yr)

AI = impervious surface area on watershed (m2)

tacc = time since previous storm event (days)

tevent = duration of rainfall event (hr)

24 = hrs/day

365 = days/yr

Runoff from pervious soil surfaces transports dissolved chemical concentrations in the soil to

the water body:

L A Ad C ROstorm L I RO ds = − (7-61) − ( ) 10 2
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-32 September 1999

where:

LROstorm = storm runoff load from pervious surfaces (g)

Cds = dissolved chemical concentration in soil (mg/L)

AL = total watershed surface area (m2)

AI = impervious surface area on watershed (m2)

dRO = depth of runoff from pervious areas of watershed (cm)

10-2 = units conversion factor (m/cm)

The mass of chemical eroded from the watershed to the receiving water during a storm

event can be calculated from the sediment loss and the particulate soil concentration:

L X SD ERC (7-62) sEstorm storm ps = − 10 3

where:

LsEstorm = erosion load of chemical from watershed during storm (g)

Xstorm = total load of sediment from watershed during storm (kg)

Cps = chemical concentration sorbed to soil (mg/kg)

SD = sediment delivery rate

ER = soil enrichment ratio

10-3 = g/mg

For risk assessment purposes, the dissolved chemical concentration can be of

interest. It may be assumed, for example, that the sorbed chemical fraction is removed in a

water treatment plant, and that the dissolved fraction is passed through to the user. The

dissolved concentration in the river during the storm event is calculated by:

C C (7-63) Kd TSS dstorm storm

sw storm

= +





 

 −  1

1 10 6

where:

Cdstorm = dissolved concentration of chemical during storm event (mg/L)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-33 September 1999

Cstorm = peak chemical concentration during storm event (mg/L)

TSSstorm = total suspended solids (during peak storm event) (mg/L)

Kdsw = suspended sediment/surface water partition coefficient (L/kg)

10-6 = kg/mg

7.2.7. Parameter Guidance. The following text is offered as abbreviated guidance for all

user input and internally solved terms for the water equations.

! Dydw, Dyww, Ca: Based on output from the atmospheric transport model, the user

must specify the long-term watershed average values for three loading

parameters: dry and wet deposition of contaminants sorbed to particulates and

the vapor phase air concentration. In addition, the user must specify the

duration of the atmospheric loading. Together, these parameters drive the

watershed soil and water body contamination. The deposition fluxes are in units

of g/m2

-yr, the air concentration is in units of :g/m3, and the time of concentration

is in yr.

! ksg, Ypi, Csb: The overall chemical degradation/transformation rate constant,

yield coefficient, and background concentration must be specified for the soil. 

The soil degradation rate constant, ksg, is specific to the contaminant and to

environmental conditions, such as climate, soil type, and soil moisture. In EPA’s

indirect exposure document (U.S. EPA, 1990a), cadmium was assumed not to

undergo soil degradation, and ksg was given a value of 0. The degradation rate

constant for benzo(a)pyrene was set at 0.803 yr-1. If chemical C is created by a

transformation reaction of chemical B in soils, the yield coefficient, Ypi, must be

specified. One example is the methylation of inorganic mercury, represented as

chemical B, into methyl mercury, represented as chemical C, with a yield

coefficient of 1.0. If the yield coefficient is left at 0, no transformation will be

calculated. The background concentration is the concentration of the
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-34 September 1999

contaminant sorbed to the soil or the natural concentration in the soil prior to

contaminant deposition. This value will be 0.0 for most organic contaminants. 

Nonzero values for the naturally occurring metals, however, generally should be

specified.

! kgwc, kgb, Y: Chemical degradation/transformation rate constants and a yield

coefficient must be specified for the water column and benthic sediment. The

degradation rate constants are specific to the contaminant and to environmental

conditions, such as pH, bacterial levels, or light. If chemical C is created by a

transformation reaction of chemical B in the water column or benthic sediment,

the yield coefficient Y must be specified. If the yield coefficient is left at 0, no

transformation will be calculated.

! H, Tw, Ta, u, W: Henry’s Constant, H, water temperature, Tw, air temperature, Ta,

water velocity, u, and wind speed, W, are required for estimating volatile losses

and diffusive loads of contaminant into soils and water bodies. Henry's

constants are known for many contaminants of interest. They are expressed

here in units of atm-m3/mole. Average water and air temperatures should be

specified by the user. Values generally range between 10 and 20°C and are

converted internally to Kelvin units. Water velocity can vary from essentially 0 for

stagnant ponds or lakes to 1.5 m/sec for fast-moving streams. Sustained

average wind speeds may vary from close to 1 to 10 m/sec. Data may be

obtained from local weather stations.

! M, I, RO, EV: These are yearly water balance quantities. Precipitation, M, is

available from common meteorologic references. Irrigation, I, is a site-specific

parameter and should not be zero if the watershed is composed of irrigated

farm lands. Runoff, RO, can be estimated using the Water Atlas of the United
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-35 September 1999

States (Geraghty et al., 1973). This reference provides maps with isolines of

annual average surface-water runoff, which is defined as all flow contributions to

surface-water bodies, including direct runoff, shallow interflow, and ground-water

recharge. The range of values shown includes 5-15 in/yr throughout the Midwest

corn belt, 15-30 in/yr in the South and Northeast, 1-5 in/yr in the desert

Southwest, and a wide range of 10-40 in/yr in the far West. Since these values

are total contributions and not surface runoff, they need to be reduced to

estimate surface runoff. A reduction by 50% should suffice if using the Water

Atlas for the R term. Evapotranspiration, EV, can be estimated from pan

evaporation data. A “potential evapotranspiration,” PEV, can be estimated by

an annual pan evaporation times 0.70 (or thereabouts) and is defined as the

evapotranspiration that occurs when soil water is not limiting. "Actual"

evapotranspiration, or EV, is about one-half of PEV. Site-specific values for

these terms should be obtained if possible.

! Z: The user must specify the mixing depth into which contaminant is

incorporated for average watershed soils. If the watershed is dominated by

soils that are not tilled, a value of 1 cm is recommended. This is a value

commonly used for nontilled situations, such as undeveloped land, pasture land,

or residential properties. If the watershed is dominated by tilled agricultural

land, then a value of 20 cm is recommended. This is a value commonly used for

tilled soils, including tilled agricultural fields and home gardens.

! BD, 2s: These soil properties include the bulk density, BD, and the surface soil

water content, 2s. BD has a relatively narrow range of about

0.93-1.84 g/cm3 depending on the soil type (Hoffman and Baes, 1979). A value

of 1.50 g/cm 3 should suffice for most uses unless site-specific information is

available. Volumetric water content, 2, can be estimated as the midpoint
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-36 September 1999

between a soil's field capacity and wilting point, if a representative watershed

soil can be ascertained for the combustor being evaluated. A reasonable range

for 2 is 0.10 (for very sandy soils) to 0.30 (for heavy loam/clay soils).

! BS, TSS, Dw: These water body properties are analogous to the bulk density for

soils noted above. The benthic sediment bulk density, BS, should range

between 0.5 and 1.5 kg/L. A value of 1.0 kg/L should be reasonable for most

applications. DW is the depth of the water column.

As described in the beginning of Section 7.2.3., either the TSS or Dw must be

known; one can be estimated from the other (and other parameters).

Total suspended solids (TSS) is one of the most variable of all water quality

parameters. Annual average TSS may range among rivers from <1 to >104

mg/L, and may vary by 3 orders of magnitude within a given river (Thomas and

Meybeck, 1996). Given similar soil erosion loads, values will be lower for

standing water bodies such as ponds or lakes will be lower than that for rivers

and streams. The more turbulent flow in rivers will reduce the effective

deposition rate and suspend sediments to a greater degree than will the flow in

a relatively calm lake. The accurate specification of average annual TSS in the

model is important because the mass of soil entering the water body in excess

of the amount that can remain suspended (as determined by the TSS value) is

assumed by the model to be deposited as bed sediment and subsequently

buried. Therefore, site specific data should be used for TSS wherever possible. 

If measurements are not immediately available, users should refer to available

Federal or local governmental water quality monitoring databases to find nearby

monitoring values for the same system. Alternatively, values should be used for

nearby systems that are similar in terms of soils, land use and discharge. 
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-37 September 1999

Federal water quality databases include the STOrage and RETrieval System for

Water and Biological Monitoring Data (STORET,

http://www.epa.gov/OWOW/STORET/) maintained by the U.S. EPA Office of

Water, and the U.S. Geological Survey National Stream Water-Quality

Monitoring Networks (WQN, http://wwwrvares.er.usgs.gov/wqn96/).

Net solids deposition is a complex process and depends on the size of the

particle and the shear stress near the bed. Deposition will be a fraction of the

Stokes settling velocity, which varies from 117 m/yr for fine silt (2 micron

diameter) to 3000 m/yr for medium silt (10 microns) to 73,000 m/yr for fine sand

(50 microns).

! AL, AI: These are the watershed surface areas for determining loading to the

water body. AL is the total watershed surface area affected by deposition that

drains to the body of water. AI is the impervious watershed area affected by

deposition that is guttered and that drains to the water body.

Properly assigning values to AL and AI is nontrivial. The areas can be quite

extensive, as the dispersion and deposition model may predict only gradual

declines in deposition as a function of distance from the stack. What is also

important to consider is the watershed hydrology in the absence of any

deposition considerations. Total sediments in a water body may have

originated from watershed soils that have been (or have the potential to be)

affected by combustor deposition as well as by soils unaffected by such

depositions. If the combustor is depositing principally on a land area that feeds

a tributary of a larger river system, then what might be termed an “effective

drainage area” should be considered. An effective drainage area will almost

always be less than the total area of a watershed. A watershed includes all the
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-38 September 1999

land area that contributes water to a river system. For large river systems, this

area is in the order of thousands of square miles and includes several tributaries

and smaller streams feeding into the main branch of the river. Each stream and

tributary has its own drainage area. If the deposition area can be ascertained to

lie within that drainage area, then it would be appropriate to assign WAL and

WALN based on the drainage area size.

Another consideration for determining AL and AI is the location of the area

affected by deposition fallout with respect to the point where water is extracted

for drinking and fish are caught for consumption. If these points are significantly

upstream in the river system in relation to the deposition area, there is no

reason to conclude that sediments or water near where the water is extracted

are affected by the combustor. If these withdrawal points are downgradient from

the deposition area, then there is reason to believe that sediments and water

are affected. However, if they are downgradient from the deposition area but

not at the bottom of the watershed, then sediment and water quality further

downgradient from the withdrawal points is not of concern, and land draining into

these downgradient portions would not be part of the effective drainage area. 

When determining the size of the effective drainage area, another possible

consideration is how far upgradient in the watershed to go. Although sediments

introduced at the farthest points may eventually work their way down to the

mouth of the watershed, this may take geologic time and not recent historic

time. Therefore, sediment quality near a deposition area need not consider

these far reaches.

For a standing water body such as a lake or a pond substantially fed by groundwater recharge, an assumption is made using the simple framework of this

assessment, that all sediments within the lake/pond are completely mixed. 
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-39 September 1999

Therefore, AL should equal all the area around the lake/pond contributing

sediment and (as in the above discussion on river systems) a part of the land

area contributing sediments to streams or rivers that may feed the standing

water body.

! Aw, Vtot, dbs, Vfx: This set of parameters defines and characterizes the water

body or reach. Aw is the water body surface area in m2. The volume of the water

column, Vtot in m3, and the average flow volume, Vfx in m3/yr, determine the

average residence time of a pollutant in the water body. The time can vary from

hours in streams to years in lakes. Average flows can be obtained from gaging

stations or from calculations with the use of watershed area and average runoff

coefficients, as summarized elsewhere in this section. Flows may vary from 105

m3/yr in small streams or ponds draining less than a square kilometer to 109

m3/yr in large rivers the size of the Potomac. Volumes for a lake or pond can be

calculated as the product of surface area and average depth. Volumes for a

stream reach can be calculated as the product of reach length, average width,

and average depth. The upper benthic sediment depth, dbs, representing the

portion of the bed in equilibrium with the water column, cannot be precisely

specified. Values from 0.01 to 0.05 m would be appropriate.

! R, K, LS, My, C, SD, ER: These factors describe the erosion of soil and

contaminant from the watershed to the water body. The first five terms are

multiplied together to give the long-term average soil loss, Xe, in kg/m2

-yr. These

are the terms for the Universal Soil Loss Equation (USLE) and are also

described in Section 7.2.3. of the indirect exposure document (U.S. EPA,

1990a). Following are brief notes on the five terms needed for the USLE.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-40 September 1999

RO: Runoff expressed here as rainfall/erosivity index. The RO term represents

the influence of precipitation on erosion and is derived from data on the

frequency and intensity of storms. This value is typically derived on a

storm-by-storm basis, but it has been compiled regionally for the development of

average annual values (Wischmeier and Smith, 1978). Annual values range

from <50 for the arid western United States to >300 for the Southeast. The

value used in the indirect exposure document (U.S. EPA, 1990a) was 400.

K: Soil erodibility. The soil erodibility factor reflects the influence of soil

properties on erosion, with values ranging from <0.05 for nonerodible sandy

soils to >0.50 for highly erodible silty soils. The value used in the indirect

exposure document (U.S. EPA, 1990a) is 0.21.

LS: Length-slope factor. The topographic factor reflects the influence of slope

steepness and length of the field in the direction of the erosion. Steeper slopes

and longer lengths lead to higher LS values, with a range of 0.1 for slopes <1.0%

and lengths <100 ft to >2.0 for slopes generally >10%. The two key

considerations for its assignment, therefore, are the size of the field for which

erosion estimates are being made and the slope of that field. The indirect

exposure document (U.S. EPA, 1990a) uses the USLE to estimate erosion

losses off the edge of a unit area within a watershed, which might be an acre or

a hectare, for example. The LS assigned in the indirect exposure document is

0.179, implying flat terrain and a small unit field. For example, a combination of

slope length of 200 m and slope of 2%, gives an LS value of 0.20.

P: Support practice factor. The support practice factor reflects the use of

surface conditioning, dikes, or other methods to control runoff/erosion. P can be

no greater than 1.0. However, values less than 1.0 should be assigned only
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-41 September 1999

when specific practices are employed that are designed to reduce erosion. P is

assigned a value of 1.0 in the indirect exposure document (U.S. EPA, 1990a).

C: Management practice factor. The final term in the USLE is the cover and

management practice factor, C, which primarily reflects how vegetative cover

and cropping practices, such as planting across slope rather than up and down

slope, influences erosion. C values can be no greater than 1.0, with this value

appropriate for bare soils. A C value of 1.0 is an appropriate choice for active

landfills or sites of high soil contamination (like Superfund sites) mostly devoid

of vegetation. For an inactive landfill with grass cover or any area with dense

vegetative cover such as grass, a value of 0.1 or less is appropriate. Values

greater than 0.1 but less than 0.7 are appropriate for agricultural row crops,

which offer more protection than bare soil but not as much protection as dense

vegetation. The indirect exposure document (U.S. EPA, 1990a) assigns a value

of 0.5 for C.

SD: Sediment delivery ratio. The sediment delivery ratio for a large land area (a

watershed or part of a watershed) can be calculated based on the area of the

watershed using an approach proposed by Vanoni (1978) and shown here:

SD a A (7-64) L

b = − ( )

where:

SD = sediment delivery ratiDecember 4, 2001o

AL = watershed area receiving pollutant deposition (m2)

b = empirical slope coefficient ( = -0.125)

a = empirical intercept coefficient (see below)

Based on various studies of sediment yields from watersheds, it was concluded

that the sediment delivery ratios vary approximately with the -(1/8) power of the
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-42 September 1999

drainage area. Inspection of the data suggests that the “a” parameter in the

above equation varies with the size of the watershed. Although the EPA is

currently evaluating methods for estimating sediment yields, the equation along

with the following values for “a” are recommended in the interim:

Watershed area (sq mi) “a” coefficient(-)

< 0.1 2.1

 1 1.9

 10 1.4

 100 1.2

 1000 0.6

Note: 1 square mile is equivalent to 2.59×106

 m20.

For example, with AL given in the indirect exposure document (U.S. EPA,

1990a) as 1.5x107 m2, SD is estimated as 0.18.

ER: Enrichment ratio. Enrichment refers to the fact that erosion favors the lighter

soil particles, which have higher surface area to volume ratios and are higher in

organic matter content. Therefore, concentrations of organic contaminants,

which are a function of organic carbon content of sorbing media, would be

expected to be higher in eroded soil as compared with in situ soil. Although

enrichment is best ascertained with sampling or site-specific expertise,

generally ER has been assigned values in the range of 1 to 5 for organic matter,

phosphorous, and other soil-bound constituents of concern. The enrichment

ratio would be expected to be higher in sandy soils than in silty or loamy soils

because the finer silt particles that erode from a soil generally characterized as

sandy are more a deviation from the norm than are the silt particles that erode

from a soil generally characterized as silty or loamy. A value of 3 for organic

contaminants would be a reasonable first estimate.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-43 September 1999

! Kds, Kdsw, Kdbs: These three adsorption partition coefficients describe the

partitioning of a contaminant between sorbing material, in this case, soil,

surface water suspended solids and bed sediments, and water in a sorbing

material/water mixture. For organic contaminants, this partition coefficient has

been estimated as a function of the organic carbon partition coefficient and the

fraction of organic carbon in the partitioning media:

Kd Koc fOC (7-65) i i =

where:

Kdi = partition coefficient for pollutant j associated with

sorbing material characterized by organic carbon

content, OCi (L/kg or cm3/g)

Koc = organic carbon partition coefficient for contaminant j

(L/kg)

fOCi = fraction organic carbon content of sorbing material

(dimensionless)

In the absence of measured values, the Koc can be estimated from a chemical's

octanol water partition coefficient, Kow. Empirical equations relating Kow to

Koc are listed in Lyman et al. (1982). Of six different equations listed in that

reference, the following derived by Karickhoff et al. (1979) is recommended for

use for chemicals with high Kow (highly sorbed, hydrophobic contaminants such

as PCBs, dioxins):

log log . Koc Kow = − 0 21 (7-66)

where:

Koc = organic carbon partition coefficient (L/kg)

Kow = octanol-water partition coefficient (dimensionless)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-44 September 1999

This equation was empirically developed from a limited number of hydrophobic

contaminants (n=10, R2

= 1.00). It implies that Koc is very similar to Kow for

strongly sorbed compounds such as the dioxin-like compounds.

To assign Kds to organic contaminants, the organic carbon contents of solids

and sediments of water bodies are needed. Solids of water bodies are

generally higher than organic carbon contents of the surrounding lands. 

Furthermore, organic carbon contents of suspended organic materials and

solids are typically greater than those of bottom sediments. A significant sink

for strongly hydrophobic contaminants such as dioxin-like compounds is thought

to be suspended, or nonsettling, organic material. In modeling 2,3,7,8-TCDD in

Lake Ontario (U.S. EPA, 1990b) with the use of the WASP4 model, a

compartment separate from suspended solids, termed "nonsettling organic

matter," served as a permanent sink. For the framework offered above, a single

reservoir of suspended materials is modeled onto which incoming contaminants

sorb.

Foth (1978) lists the organic nitrogen content of several soil types ranging from

sand and sandy loam to clay. The range from that list is 0.0002 to 0.0024 on a

fractional basis. When a carbon to nitrogen ratio of 10 (Brady and Selle, 1984,

who notes that C:N ratios vary from 8 to 15, with the typical range of 10 to 12) is

assumed, organic carbon contents of soil generally might range from 0.002 to

0.024. The organic carbon content of bottom sediments will be higher given

arguments that erosion favors lighter textured soils with higher organic carbon

contents and also that bottom sediments are partially composed of detritus

materials. A range of 0.03 to 0.05, given surface soil content at 0.01, might be

reasonable. The organic carbon content of suspended materials can approach
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-45 September 1999

0.20, but a range between 0.05 and 0.10 might be more reasonable given that

previous assignments were 0.01 and 0.03 to 0.05.

The Kds supplied in the indirect exposure document (U.S. EPA, 1990a) for

benzo(a)pyrene and cadmium are 1.2×105 L/kg and 500 L/kg, respectively.

7.3. PRECIPITATION

The procedure to estimate contaminant concentration in water of precipitation

collection systems may lead to overestimates. The procedure divides the total deposition

rate by the precipitation rate. Some of the contaminant in dry deposition may have low

solubilities and settle out of suspension. For the purposes of estimating concentrations of

contaminants from stack emissions in precipitation, it is suggested that the frequency of

precipitation events be compared with the frequency of stack emission episodes for the

period of time of interest. When it can be reasonably assumed that stack emissions

occurred regularly during precipitation events, it is also reasonable to assume that the total

contaminant load was deposited on the surface of the water bodies, plants, and soil near

the emission point in the same manner as described for wet deposition. When it is not

reasonable to assume that the precipitation and emission events occurred simultaneously,

the emissions from the source are already accounted for in previous sections of this chapter.

If the collection system causes turbulent mixing of the water, particulates could be

suspended and the current approach would be appropriate. If such mixing does not occur,

the current procedure is acceptable for bounding estimates. If, however, more realistic

estimates are desired, the risk assessor should limit the concentrations of all contaminants

to their solubility limit. For organics, further refinement may be possible through the use of

equilibrium partitioning.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-46 September 1999

7.4. GROUNDWATER

Combustors may have different conditions that would exacerbate the effects of

groundwater and invalidate the conclusion that groundwaters are not affected by

combustor’s emissions. Combustors that deposit contaminants from their emissions onto

surfaces where groundwaters are considered to be under the influence of surface waters

should consider that the soluble portion of the contaminants that reach those surface waters

can reach the groundwater. This portion of the contaminant should be handled in the same

way the soluble portion of the contaminant was handled in previous sections of this chapter.

Site-specific conditions may be encountered where the effects of groundwater may

be greater. In contrast to the original examples, conditions that might enhance the effects of

groundwater include emission deposition rates that are several times greater than the

average, the existence of more soluble compounds in the emissions, and higher recharge

rates that would occur in areas with very permeable soil and bedrock near the surface. In

these circumstances, site-specific groundwater modeling may be required before this

pathway can be eliminated from consideration.

7.5. DAILY INTAKE FROM WATER (DI)

This methodology assesses only the risk associated with increased exposure from

combustor emissions; the background concentration of contaminants is not included in the

quantitation of DI. This may be a difficult distinction to make unless the maximum amount of

possible contaminants has been calculated. Then the maximum possible total is the upper

limit of the value used for DI. DI is calculated as follows:

DI (7-67) C CR

BW

w w =

where:

DI = daily intake of contaminant from water ingestion (mg/kg-day)

Cw = concentration of contaminant in water (mg/L)

CRw = consumption rate of water (L/day)

BW = body weight (kg)
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-47 September 1999

To determine if the contaminant adversely affects human health, the DI is compared with the

reference dose for chronic oral exposure to systemic toxicants or the dose used to

determine excess risk for carcinogens. This is discussed in Chapter 10.

7.5.1. Water Contaminant Concentration (Cw). The model in this chapter calculates the

dissolved phase concentration (along with the total concentration and sorbed phase

concentration) of contaminants in affected surface water bodies (mg/L). For drinking water

taken from systems that use filtration, use of the dissolved phase concentration, termed Cdw,

is most appropriate as the input for determining human exposure from water ingestion. The

basis for this recommendation is that water treatment systems filter out particulates; what

remains is the dissolved phase of the contaminant.

For systems that do not use filtration as part of their treatment process, it is

necessary to include the particulate component of the contaminant as well. The assumption

can be made that contaminants remain in the system if filtration is not used. Alterations in

the contaminants as a result of the type of treatment used are system specific, and the

analysis of finished water that is delivered to the drinking water system is probably the best

source of such information. This may be an oversimplification of the chemistry of the system,

especially in complex drinking water systems where a water may remain in the system for

some extended period, but it would be impossible to factor all of these changes into the

evaluation.

In rural areas individuals primarily, but not exclusively, use groundwater as their water

supply. The exposure to contaminants from an emission source are less likely in

groundwater systems. Those groundwaters that are under the influence of surface water

may, however, have contaminants from that surface water dissolved in them or, to a lesser

extent, carried in particulate form in them. In some rural areas, large-diameter shallow wells

are still the main water source for humans, animals, and crops. These large-diameter

shallow wells are frequently not as protected by covers and are more likely to receive

chemicals from contaminated emissions than are the standard small-diameter wells or wells

finished in material deeper than 50 feet, which are frequently closed at the ground surface.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-48 September 1999

Some individuals use cisterns and catchment basins to collect water for drinking and

home use. These cisterns and catchments are often metal or lined with metal. Some of

these are completely open to the air and should be considered in the same way as are

surface waters. The water that is collected in these containers is from precipitation and

runoff from roofs and gutters. The concentration of contaminants in such water can

reasonably be considered equal to that in the precipitation. There are some circumstances

where this concentration could change. If the pH of the water is low, contaminants could be

leached from the container materials or from residues that build up in such containers,

increasing the total concentration. If the water in the container is turbid or if the container is

not covered or screened in some way, particulate matter could be deposited directly into the

water. This matter could remove some contaminant from the dissolved phase of the water,

precipitating it into bottom sludge or leaving it in suspended matter, yet unavailable for

ingestion in dissolved form. The mechanisms described in this chapter for dealing with

concentrations in water are applicable to this kind of water.

7.5.2. Water Consumption Rate (Crw). The U.S. EPA has traditionally used a value of 2

L/day to represent the average daily water consumption of adults and 1 L/day to represent

the average daily water consumption of children with body weights of <10 kg. However, the

Exposure Factors Handbook (U.S. EPA, 1995) presents data that suggest these values may

overestimate actual water consumption rates. These data, generated in several

consumption surveys, show that a value of 1.4 L/day may be a more accurate average of

water consumption for adults. A value of 2 L/day most likely represents a 90th percentile

value. The data also show that average tapwater consumption by young children is lower

than suggested by the 1 L/day value typically applied. U.S. EPA (1995) recommendations

regarding average- and upper-percentile tapwater consumption rates for adults and children

of various ages, based on analysis of the results of available surveys, are listed in Table 7-1. 

Consult the Exposure Factors Handbook (U.S. EPA, 1997) for more detailed information

regarding human water consumption rates.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-49 September 1999

TABLE 7-1

Tapwater Consumption Rates Recommended by U.S. EPA (1995)

Age Group

 (yr)

Average

(L/day)

90th percentile

(L/day)

<1 0.3* 0.7*

1-10 0.7 1.3

11-19 1.0 1.7

20+ 1.4 2.0

*These values are for water-based beverages but may not include water used to prepare

powdered infant formula.

7.6. DERMAL EXPOSURE FROM WATER

Once contaminants in combustor emissions reach water sources, humans may be

exposed by absorbing them through the skin while swimming or bathing. Dermal absorption

can be a significant source of exposure from contaminated water when compared with

ingestion. For example, Brown et al. (1984) showed that the dermal route may be a

significant contributor to total exposure to volatile organic contaminants in tap water. They

noted that under some circumstances, the tap water could be, but not always is, a major

source of contaminants. The model presented in this chapter estimates human exposure

resulting from skin contact with contaminants in water.

7.6.1. Daily Dermal Intake from Water. The daily dermal intake from water represents the

increase above background in daily human dermal intake due to combustor emissions. 

Exposure resulting from dermal absorption from water is influenced by the contaminant

concentration in water, the nature of the contaminant, and the extent of contact. The nature of

the contaminant determines how well it permeates the skin. The extent of contact is affected

by duration and frequency of exposure, as well as the condition of the skin and surface area

available for contact.
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Water Concentration and Exposure Pathways

MPE 7-50 September 1999

The daily dermal intake of a compound in an aqueous medium is estimated using the

following equation:

DDI (7-68)

D EV EF A

BW w

event s = 365

where:

DDIw = daily dermal intake of contaminant from water (mg/kg-day)

Devent = dermally absorbed dose per event (mg/cm2

-event)

As = skin surface area available for contact (cm2)

EV = event frequency (events/day)

EF = exposure frequency (day/yr)

BW = body weight (kg)

365 = adjustment to calculate average daily dose (day/yr)

A decision matrix that appears in Section 9 of the dermal exposure assessment document

(U.S. EPA, 1992) can be used to determine whether absorption is significant enough to

require further assessment.

7.6.2. Estimating the Dermally Absorbed Dose per Event (Devent). Current agency

methodology includes a nonsteady-state approach for estimating the dermally absorbed

dose from water for organic chemicals (U.S. EPA, 1992). This approach appears to offer

significant advantages over the traditional steady-state approach for risk assessment

application. First, the nonsteady-state method more accurately reflects normal human

exposure conditions since the short contact times associated with bathing and swimming

generally mean that steady state will not occur. Second, the method accounts for the dose

that can occur after the actual exposure event as the result of absorbing contaminants stored

in skin lipids. For these reasons, U.S. EPA recommends the non-steady state approach.

The following procedures are recommended for estimating Devent.

 This term, as used

in this context, represents the absorbed dose per cm2 of exposed skin occurring during a

single water contact event such as bathing or swimming.
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Water Concentration and Exposure Pathways

MPE 7-51 September 1999

Un-ionized Organic Chemicals

Calculate Devent (mg/cm2

-event) using the nonsteady-state approach, as follows:

If t t then D (7-69)

KpC

T t

event event

w

event

< = *, :

2

6

1000

π

If t t then D (7-70)

KpC

t

B

T

B

B

event event

w

event

> =

+

+

+

+





 



 



 





*, :

1

2

1 3

1

1000

where:

Devent = dose absorbed per unit area per event (mg/cm2

-event)

Kp = permeability coefficient from water for contaminant (cm/hr)

Cw = concentration of contaminant in water (mg/L)

tevent = duration of event (hr/event)

T = lag time (hr)

t* = time to reach steady-state (hr)

B = parameter to describe relative contribution of permeability

coefficients in stratum corneum and viable epidermis

(dimensionless)

1000 = unit adjustment factor (cm3/L)

Values for Kp, t*, T, and B for many common organic pollutants are presented in

Table 5-8 of the dermal absorbtion assessment document (U.S. EPA, 1992). For other

chemicals, these quantities can be estimated as described below.

Inorganic Chemicals and Ionized Organic Chemicals

Most inorganic chemicals and ionized organic chemicals do not have measurable

octanol-water partition coefficients (or if measurable, the coefficients are very small). 

Consistent with this observation, the skin usually has a low storage capacity for these
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-52 September 1999

chemicals, thus reaching steady-state conditions soon after the initial exposure. Therefore,

it is appropriate to assume that t*, lag time and B are all nearly zero.

Consequently, Equations 7-69 and 7-70 simplify the traditional steady-state

approach. Calculate Devent for inorganic chemicals or ionized organic chemicals by using the

steady-state approach:

D (7-71) KpC t event

w event = 2

1000

where:

Devent = dose absorbed per unit area per event (mg/cm2-event)

Kp = permeability coefficient from water for contaminant (cm/hr)

Cw = concentration of contaminant in water (mg/L)

tevent = duration of event (hr/event)

1000 = unit adjustment factor (cm3/L)

Partially Ionized Organic Chemicals

For organic chemicals that are present as both ionized and un-ionized species at the

conditions of the aqueous solution, calculate Devent as the sum of the Devent for the un-ionized

species (by using Equations 7-69 and 7-70 and the concentration of the un-ionized species)

and the Devent for the ionized species (by using Equation 7-71 and the concentration of the

ionized form of the chemical).

7.6.2.1. Water Concentration (Cw) Ionized or Un-ionized — This chapter

provides a model that calculates both dissolved phase and total water column

concentrations of contaminants in impacted surface water bodies (mg/L). Dermal exposure

to contaminants in water can occur while bathing or swimming. For exposure while bathing

or swimming in a pool, employing the dissolved phase concentration (Cwd) would be most

appropriate because water treatment systems filter out particulates and what remain are the

dissolved phase residues of the contaminant. For exposure while swimming in a lake or

stream, particulate-bound contaminant may be present, but use of the dissolved phase
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Water Concentration and Exposure Pathways

MPE 7-53 September 1999

concentration (Cwd) is still recommended because this represents the concentration that is

freely available to enter the body (the total water column concentration includes particulatebound contaminant that is not readily bioavailable).

The concentration of ionized forms of organic acids and bases are estimated as

follow. For organic acids with one dominant acid-base reaction.

C (7-72) C

WI

wd

pH pka = + − 1 10( )

For organic bases with one dominant acid-base reaction,

C (7-73) C

WI

wd

pk pH a = + − 1 10( )

7.6.2.2. Event Duration (tevent) — Event duration (hr/event) is the time over which a

single contact event occurs. The two events associated with dermal contact to water are

swimming and bathing. Showers were reported to be the primary means of bathing for 75%

of American men and 50% of American women (Tarshis, 1981). U.S. EPA (1995a)

presented a cumulative frequency distribution of shower times based on a study conducted

in Australia (James and Knuiman, 1987). Examination of records from 2500 Australian

households showed that shower times ranged from 1 to 20 minutes with a median of 7

minutes, a 75th percentile of 9 minutes, and a 90th percentile of 12 minutes. Bath duration

is likely to be somewhat longer than shower duration; Brown and Hattis (1989) assumed a

20-minute bath time to estimate the dermal absorption from hypothetical bathtub scenarios. 

Adding a few minutes to the observed shower times for water residues to dry and to account

for presumably longer bath times, a default range of 10 (median) to 15 (90th percentile)

minutes is recommended.

Much less information is available on the time of swimming. The SEAM (U.S. EPA,

1988) suggested that a national average value based on the U.S. Department of the Interior

(DOI) Bureau of Outdoor Recreation survey (U.S. DOI, 1973) be applied. According to this

survey, the average duration of a swimming event is 2.6 hours. However, further

investigation of this survey revealed that the exposure time value represented more than
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Water Concentration and Exposure Pathways

MPE 7-54 September 1999

actual time in water. Accordingly, no reliable data on swimming time could be found, and it

is recommended that risk assessors make judgments of their own on a site-specific basis. 

Consideration should be given to geographic factors, such as proximity or availability of

surface waters for recreation, and seasonal factors. Furthermore, certain subpopulations

(e.g., competitive swimmers) will encounter a greater mean exposure time. Based on

judgment, a reasonable average value for a recreational swimmer may be 0.5 hr/event and a

reasonable upper percentile value for a person who swims regularly for exercise or

competition may be 1 hr/event.

7.6.2.3. Permeability Coefficient from Water (Kp) — The permeability coefficient

from water, Kp, for a contaminant is a flux value, normalized for concentration, that

represents the rate at which the contaminant permeates the skin from an aqueous solution

(cm/hr). Permeability coefficients are chemical and vehicle dependent. Permeability

coefficients from aqueous solution have been experimentally determined for many

chemicals. These permeability coefficients were derived from experiments that used

different protocols and skins from animals as well as from humans, and these coefficients

presented some significant uncertainty regarding their use for representing in vivo

permeability of human skin. Experimentally derived Kp values for potential environmental

contaminants are presented in U.S. EPA (1992). Kp values can also be estimated by a

number of methods, as described in U.S. EPA (1992). Recommended means of obtaining

Kp values for inorganic and organic chemicals are described below.

Inorganic Chemicals and Ionized Chemicals

For inorganic chemicals, the experimentally derived Kp values presented in U.S. EPA

(1992) are recommended. It is indicated that most inorganic chemicals have Kp values near

10-3 cm/hr, and a few are lower. Accordingly, a default assumption of 10-3 cm/hr is

recommended for inorganic chemicals not listed. Based on a recent review of the literature,

Kp values of metals are generally lower than 10-3 cm/hr (Hostynek et al., 1997).

Organic Chemicals
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-55 September 1999

For organic chemicals, Kp values estimated according to the following equation

derived by Potts and Guy (1992) are recommended:

log . . log . Kp Kow MW (7-74) i =− + − 2 72 0 71 0 0061

where:

Kp = permeability coefficient from water for contaminant (cm/hr)

Kowi = octanol-water partition coefficient for contaminant

MW = molecular weight of contaminant

The uncertainty in the predicted Kp values is judged to be within plus or minus one order of

magnitude from the best-fit value.

The Potts and Guy (1992) equation was derived empirically from a database

assembled by Flynn (1990) containing permeability coefficients for more than 90 structurally

diverse chemicals with molecular weights ranging from 18 to >750 and log Kowi values

ranging from -3 to +6. The equation is, therefore, most appropriate for use with chemicals

that meet these criteria. However, based on physical reasons and experimental data, in the

absence of skin metabolism, Kp should not exceed a value of about 0.3 cm/hr (Kasting and

Robinson, 1993). In the absence of any suitable alternative, the use of Equation 7-81 with

superlipophilic chemicals (e.g., dioxins) is not unreasonable provided the calculated value

for Kp is not allowed to exceed a limiting maximum. As a conservative estimate, Kp of 1

cm/hr should be used if the value calculated from Equation 7-81 is larger than 1 cm/hr.

The recommendation to use estimated Kp values for organic chemicals follows as a

consequence of the use of the nonsteady-state model to calculate the daily dermal event

(DDevent) for these chemicals. The nonsteady-state model requires input parameters that are

difficult to measure, such as the stratum corneum diffusion coefficient (Dsc, used to calculate

T as shown below). Thus, these chemical-specific factors must generally be estimated. 

Cleek and Bunge (1993) derived a procedure to estimate Dsc by using the Potts and Guy

(1992) correlation for estimating Kp values. Therefore, for purposes of internal consistency,

the Kp value used in the nonsteady-state model should also be the Kp derived from the Potts
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-56 September 1999

and Guy (1992) correlation. This leads to the somewhat unexpected recommendation that

for organics, Kp values predicted by the correlation should be used instead of the measured

values derived from experiments. This being the case, it is notable that the Kp values

predicted from the correlation agree reasonably well with the experimentally derived Kp

values for most chemicals.

7.6.2.4. Relative Contribution of Permeability Coefficients (B) — This

dimensionless parameter is used in the nonsteady-state method of DDevent calculation to

describe the relative contribution of permeability coefficients in the stratum corneum and the

viable epidermis. It is employed only in the nonsteady-state methodology and 

applies only to organic chemicals. It is calculated from the Kow as follows:

For organics B (7-75)

Kowi

: = 104

7.6.2.5. Lag Time (T) — In the nonsteady-state methodology recommended here to

calculate DDevent for organic contaminants, the nonsteady-state period is a function of the lag

time, T (hr). The following equation can be used to calculate T:

T (7-76) l

D

sc

sc

=

2

6

It is assumed that lsc, the apparent thickness of the stratum corneum, is equal to the

thickness of the stratum corneum, which is estimated to be roughly 10-20 :m over most of

the body (Kalia et al., 1996).

Dsc (cm2

/hr), the diffusion coefficient of the chemical in the stratum corneum, is

calculated from the following equation, assuming that lsc = 10 :m = 10-3 cm:

Log (7-77)

D

l MW sc

sc

=− − 2 72 0 0061 . .

Values of T are calculated by also assuming that lsc = 10 :m = 10-3 cm.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-57 September 1999

7.6.2.6. Time to Reach Steady-State (t*) — The t*, the time to reach steady- state

(hr) in the nonsteady-state methodology recommended for use with organic contaminants,

can be calculated from B and T as follows:

If B thent T ≤ = 01 24 ., * . (7-78)

If B thent B T 01 117 8 4 6 . . , * ( . log ) ≤≤ = + (7-79)

If B thent b b c T ≥ =− − 117 6 (7-80) 2 2 ., * ( )

b Bc = +− (7-81) 2

1 2

π ( )

c (7-82) B = 1 3 +

3

7.6.2.7. Event (EV) and Exposure Frequency (EF) — Event and exposure

frequency are two of the variables necessary for calculation of daily dermal intake (DDI) by

Equation 7-77. Event frequency (i.e., events/day) and exposure frequency (i.e., days/yr) refer

to how often the contact event occurs. The following discussion summarizes the values for

these parameters for dermal exposure to contaminants in water by bathing and swimming.

Approximately 90% of the American population bathes every day, and 5% average

more than one bath a day (Tarshis, 1981). Since the range of likely values is so narrow, a

single default value of 1 event/day, 350 days/yr is recommended for bathing frequency. The

upper default of 350 days/yr was selected over 365 days/yr to reflect the probability that

most people spend some time away from their residence, using water from a different

source.

Much less information is available on the frequency of swimming. The SEAM (U.S.

EPA, 1988) suggests that a national average value based on the U.S. DOI Bureau of

Outdoor Recreation survey (U.S. DOI, 1973) be applied. This survey reported an average

swimming frequency of 7 days/yr. The reliability of this frequency estimate appears

questionable, however. Accordingly, it is recommended that risk assessors make

judgments of their own on a site-specific basis. Consideration should be given to
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-58 September 1999

geographic factors, such as proximity or availability of surface waters for recreation, and

seasonal factors. Furthermore, certain subpopulations (e.g., competitive swimmers) will

encounter a greater mean exposure frequency. Based on judgment, a reasonable average

value for a recreational swimmer may be 5 days/yr, 1 event/day, and a reasonable upper

value for a person who swims regularly for exercise or competition may be 150 days/yr, 1

event/day.

7.6.3. Skin Surface Area (A). Dermal intake of a contaminant is proportional to the

exposed surface area (cm2

). For exposure via swimming and bathing, the exposed surface

area is assumed to be the total body surface area. Data regarding total body surface area

of human adults and children was reviewed by U.S. EPA (1992, 1995), and median and

upper 95th percentile values are presented in Table 4-5 (Chapter 4). U.S. EPA (1992,

1995) recommended using values of 20,000 cm2

 and 23,000 cm2 to represent central

tendency and upper percentile adult total body surface areas, respectively. For children,

U.S. EPA (1992, 1995) recommended using the median and 95th percentile values from

Table 4-5 for the appropriate age group. Consult U.S. EPA (1992, 1995) for more detailed

discussion of dermal surface area of adults and children.

7.7. UNCERTAINTY IN THE AQUEOUS DERMAL EXPOSURE MODEL

Exposure risk assessors are cautioned that the procedure recommended for deriving

DDevent for organic chemicals is based on a recently developed model that currently is being

reviewed by the scientific community. On a conceptual basis, it has advantages over the

traditional approach involving direct application of Fick’s first law. One, it accounts for the

unsteady-state conditions that characterize the relatively short exposure periods associated

with bathing. Two, it accounts for the dose that can occur after the actual exposure incident

because of absorption of contaminants stored in the skin lipids. Actual calculations

performed by EPA have shown that this approach provides a more conservative total

absorbed dose over the traditional steady-state equation for organic compounds. 

Preliminary testing showed that this new approach indicates that the dermal dose resulting

from 10-minute showers exceeds the dose associated with drinking 2 L/day for a number of
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-59 September 1999

the pollutants. For the fastest penetrating chemicals, the dermal dose was predicted to

exceed the ingested dose by about two orders of magnitude. This seems counterintuitive

and raises concerns that the model may be overly conservative. Lack of data makes

validation of the model very difficult.

One “reality check” that risk assessors should make for bathing scenarios is to

compare the total amount of contaminant in the bathing water with the dose. The amount of

contaminant in the water is easily computed by multiplying the contaminant concentration by

the volume of water used (modern water-saving showers typically use 2.5 gal/min, while

older showers use 5-15 gal/min). Obviously, the dose cannot exceed the amount of

contaminant in the water. In fact, it seems unlikely that a high percentage of the contaminant

in the water could be dermally absorbed. As a preliminary guide, if the dermal dose

estimate exceeds 50% of the contaminant in the water, the risk assessor should question

the validity of the dose estimate.

Volatile compounds have been shown to volatilize significantly during showering. 

Andelman (1988) found that about 90% of TCE volatilized during showering. This would

suggest that the effective concentration of the contaminant in water and resulting dermal

dose may be reduced. So, for volatile compounds, risk assessors may want to assume a

reduced contaminant concentration in water contacting the skin.

The dermal permeability estimates are probably the most uncertain of the parameters

in the dermal dose equation, with an uncertainty of as much as plus or minus two orders of

magnitude, although most chemicals are probably within an order of magnitude. The

measured values probably have an uncertainty of plus or minus a half order of magnitude. 

Systematic deviations may occur for some classes of chemicals, such as the halogenated

organic chemicals, for which the Kp is probably consistently underestimated by Equation

7-74. Accordingly, the final dose and risk estimates must be considered highly uncertain. 

Some idea of the range of possible values can be obtained by first using average or typical

values for each parameter to get a typical dose estimate, and then setting two or three of the
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-60 September 1999

most variable parameters to their upper values and the others to their average values to get

some idea of the possible upper-dose estimate.
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Water Concentration and Exposure Pathways

MPE 7-61 September 1999

7.8. REFERENCES

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Brown, H.A. and D. Hattis. 1989. The role of skin absorption as a route of exposure to

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Brown, H.S., D.R. Bishop and C.A. Rowan. 1984. The role of skin absorption as a route of

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Cleek, R.L. and A.L. Bunge. 1993. A new method for estimating dermal absorption from

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Chapter 7

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Potts, R.O. and R.H. Guy. 1992. Predicting skin permeability. Pharmacol. Res.

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Assessments, 2nd ed., D. Chapman, Ed. Chapter 4, p. 127-174. E & FN Spon, London.

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Air Quality Planning and Standards, Research Triangle Park, NC.

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Exposure to Combuster Emissions, Interim Final. Office of Health and Environmental

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U.S. EPA. 1990b. Lake Ontario TCDD Bioaccumulation Study Final Report. Cooperative

study including U.S. EPA, New York State Department of Environmental Conservation, New

York State Department of Health, and Occidental Chemical Corporation. May, 1990.
Chapter 7

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U.S. EPA. 1992. Dermal Exposure Assessment: Principles and Applications. Office of

Health and Environmental Assessment, Washington, DC. EPA/600/891/011B.

U.S. EPA. 1995. Exposure Factors Handbook. Review Draft. Office of Research and

Development, Washington, DC. EPA/600/P-95/002A.

U.S. EPA. 1997. Exposure Factors Handbook: Volume I. Office of Research and

Development, Washington, DC. EPA/600/P-95/002Fa.

Vanoni, V.A. 1975. Predicting sediment discharge in alluvial channels. California Institute

of Technology. Water Supply Manag. 1(4):399.

Wischmeier, W.H. and D.D. Smith. 1978. Predicting Rainfall Erosion Losses — A Guide to

Conservation Planning, Agricultural Handbook No. 537. USDA, Washington, DC.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-64 December 1998

7.8. REFERENCES

Andelman, J.B. 1988. Total exposure to volatile organic compounds in potable water. 

In: Significance and Treatment of Volatile Organic Compounds in Water Supplies, N.

Ram, R. Christman and K. Cantor, Ed. Lewis Publishers, Inc, Chelsea, MI.

Brady, G.L. and J.C. Selle. 1984. Acid rain: A survey of the issues. J. Resour. Manag.

Technol. 13(3):133.

Brown, H.A. and D. Hattis. 1989. The role of skin absorption as a route of exposure to

volatile organic compounds in household tap water: A simulated kinetic approach. J.

Am. Coll. Toxicol. 8(5):839-851.

Brown, H.S., D.R. Bishop and C.A. Rowan. 1984. The role of skin absorption as a

route of exposure for volatile organic compounds (VOCs) in drinking water. Am. J.

Publ. Health. 74:479-484.

Cleek, R.L. and A.L. Bunge. 1993. A new method for estimating dermal absorption

from chemical exposure. 1. General approach. Pharm. Res. 10:497-506.

Flynn, G.L. 1990. Physiochemical determinants of skin absorption. In: Principles of

Route-to-Route Extrapolation for Risk Assessment, T.R. Gerity and C.J. Henry, Ed.

Elsevier Science Publishing Co. Inc., Amsterdam. p. 93-127.

Foth, H.D. 1978. Fundamentals of Soil Science. John Wiley, New York.

Geraghty, J.J., D.W. Miller, F. Vander Leeden and F.L. Troise. 1973. Water Atlas of

the U.S. Water Information Center, Port Washington, NY.

Hostynek, J., R. Hinz, C. Lorence and R. Guy. 1997. Metals and the Skin: Topical 

Effects and Systemic Absorption. Marcell Dekker, New York. (In press)

James, I.R. and M.W. Knuiman. 1987. An application of Bayes methodology to the

analysis of diary records from a water use study. J. Am. Stat. Assoc. 82(399):705-711.

Kalia, Y.N., F. Pirot and R.H. Guy. 1996. Homogeneous transport in a heterogeneous

membrane: Water diffusion across human stratum comeum in vivo. Biophysical J. 

71:2692-2700.

Karickhoff, S.W., D.S. Brown and T. Scott. 1979. Sorption of hydrophobic pollutants

on natural sediments. Water Res. 13(3):241.

Kasting, G.B. and P.J. Robinson. 1993. Can we assign an upper limit to skin

permeability? Pharm. Res. 10:930-931.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-65 December 1998

Lyman, W.J., W.F. Reehl and D.H. Rosenblatt. 1982. Handbook of Chemical Property

estimation Methods. McGraw-Hill Book Co., New York.

O’Connor, D.J. and W.E. Dobbins. 1956. The mechanism of reaeration in natural

systems. Proc. Am. Soc. Civil Engrs. 82(SA-6): 1115-1-1115-30.

O’Connor, D.J., J.A. Mueller and K.J. Farley. 1983. Distribution of Kepone in the

James River Estuary. J. Environ. Eng-ASCE. 109(2):396.

Tarshis, B. 1981. The "Average American" Book. New American Library, New York.

p. 191.

Thomas, R. and M. Meybeck. 1996. The use of particulate material. In: Water Quality

Assessments, 2nd ed., D. Chapman, Ed. Chapter 4, p. 127-174. E & FN Spon, London.

U.S. DOI (U.S. Department of Interior). 1973. Outdoor Recreation: a Legacy for

America. U.S. Department of Interior, Washington, DC. Available from DOI. (Cited in

U.S. EPA, 1995)

U.S. EPA. 1985. Compilation of Air Pollution Emission Factors. AP-42, 4th ed. Office

of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. EPA. 1988. Superfund Exposure Assessment Manual. Office of Emergency and

Remedial Response, Washington, DC. EPA/540/1-88/001. NTIS PB-89-135859.

U.S. EPA. 1990a. Methodology for Assessing Health Risks Associated with Indirect

Exposure to Combuster Emissions, Interim Final. Office of Health and Environmental

Assessment, Washington, DC. EPA/600/6-90/003.

U.S. EPA. 1990b. Lake Ontario TCDD Bioaccumulation Study Final Report. 

Cooperative study including U.S. EPA, New York State Department of Environmental

Conservation, New York State Department of Health, and Occidental Chemical

Corporation. May, 1990.

U.S. EPA. 1992. Dermal Exposure Assessment: Principles and Applications. Office of

Health and Environmental Assessment, Washington, DC. EPA/600/891/011B.

U.S. EPA. 1995. Exposure Factors Handbook. Review Draft. Office of Research and

Development, Washington, DC. EPA/600/P-95/002A.

Vanoni, V.A. 1978. Predicting sediment discharge in alluvial channels. California

Institute of Technology. Water Supply Manag. 1(4):399.
Chapter 7

Water Concentration and Exposure Pathways

MPE 7-66 December 1998

Wischmeier, W.H. and D.D. Smith. 1978. Predicting Rainfall Erosion Losses — A

Guide to Conservation Planning, Agricultural Handbook No. 537. USDA, Washington,

DC.
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-1 December 1998

DETERMINING EXPOSURE FROM INTAKE OF AQUATIC ORGANISMS

8.1. INTRODUCTION

Once contaminants from combustor emissions are transported to surface water

bodies, they may be incorporated into the aquatic food chain. Humans and fish-eating

wildlife are then exposed to contaminants when they eat fish collected from

contaminated bodies of water. The model presented in this chapter estimates exposure

resulting from consumption of fish taken from water sources in the vicinity of a

combustor.

In the 1990 U.S. EPA document Methodology for Assessing Health Risks

Associated with Indirect Exposure to Combustor Emissions, a bioconcentration factor

was used to predict the concentrations of pollutants in fish attributable to receiving

water bodies. Bioconcentration is defined as the net accumulation of a substance by an

aquatic organism as a result of uptake directly from the ambient water; this occurs

through gill membranes or other external body surfaces. The term bioconcentration

factor (BCF) is defined as the equilibrium concentration of a chemical in a biological

medium divided by the equilibrium concentration of a chemical in the environmental

medium through which the exposure directly occurs. In this document a

bioaccumulation factor (BAF) is used. Bioaccumulation is the accumulation of a

substance by an organism as a result of all environmental sources. The BAF is roughly

defined as the equilibrium concentration of a chemical in a biological medium divided

by the equilibrium concentration of a chemical in an environmental medium (in this

case ambient water) where both the organism and its food were exposed. Both BCFs

and BAFs are proportionality constants that relate concentrations of a substance in

aquatic organisms to concentrations of the substance in water. Although similar to a

BCF, a BAF not only predicts the uptake and retention of chemicals that result from

direct contact with an environmental medium, but also accounts for uptake and

retention from the food chain that may connect the environmental medium to the

biological medium of interest.
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-2 December 1998

DIf' j

Wc BAFi DIfi

BW

(8-1a)

8.2. CALCULATING DAILY HUMAN INTAKE FROM FISH

Human daily intake of contaminant from fish (DIf) is proportional to the

concentration of contaminant in the water, the contaminant's bioaccumulation factor,

and the rate of ingestion of contaminated fish. DIf is calculated as follows:

where:

DIf = human daily intake of contaminant from fish (mg/kg-day)

Wc = contaminant concentration in water (mg/L)

BAFi = bioaccumulation factor for fish of trophic level i (L/kg)

DIfi = daily human consumption of locally-caught fish of trophic level i

(kg/day)

BW = body weight (kg)

Aquatic organisms may be categorized by their role in the flow of energy through

the biological community. Energy is transferred as autotrophic species are consumed

by heterotrophic species; autotroph-consuming heterotrophs may in turn be consumed

by other heterotrophs, and the transfers continue to the apex predators. This series of

transfers is conceptualized as a food chain or food web. The categories are defined by

the diets of the species that comprise the web; each category is called a trophic level.

Aquatic communities typically have three or more trophic levels: (1) primary producers,

such as phytoplankton; (2) primary consumers, such as zooplankton; and (3)

secondary consumers, such as small fish. Other trophic levels that may occur include

(4) tertiary consumers such as larger fish and quaternary consumers such as higher

chordates (e.g., fish-eating reptiles, birds, and mammals, including humans). Benthic

invertebrates (5) may accumulate and retain pollutants via uptake from interstitial water

of sediment, uptake from water column, ingestion of chemical bound to particulate and

consumption of contaminated detritus and phytoplankton.
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-3 December 1998

BSAFi DIfi

BW

) (8-1b)

These models assume chemical equilibrium between organisms of all trophic

levels and the environment in which they reside. This simplification does not examine

the dynamic and temporal nature of pollutant accumulation. For example, if pollutant

load to a water body is dramatically increased or decreased, the system will not be in

equilibrium. Since the models presented here assume chemical equilibrium, it is best to

not apply them to systems in disequilibrium.

For this methodology, it is recommended that the BAF and DIfi be determined

separately for aquatic species of different trophic levels. The total daily intake of

contaminant from fish, then, is the sum of the intake attributed to fish from each trophic

level. This accounts for different levels of accumulation for members of different trophic

levels.

The biota sediment accumulation factor (BSAF) for fish of trophic level i can be

substituted for the bioaccumulation factor (BAFi) in Equation 8-1a. In this equation the

contaminant concentration in water (Cw) is replaced by the concentration of the

pollutant in the benthic sediment (Cbs). Please see Equation 8-6 and the discussion

that follows.

where:

DIf = human daily intake of contaminant from fish (mg/kg-day)

Cbs = contaminant concentration in benthic sediment (mg/g)

BSAFi = biota-sediment accumulation factor for trophic level i

DIfi = daily human consumption of locally-caught fish of trophic level i

(g/day)

BW = body weight (kg)

8.2.1. Total Water Column Contaminant Concentration (Cwctot) and Benthic

Sediment Concentration (Cbs). To be compatible with the BAF as derived above, the

total water column concentration of contaminant (Cwctot, discussed in Chapter 7) should
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-4 December 1998

be used to calculate human daily intake from consumption of contaminated fish in

Equation 8-1a. To be compatible with the BSAF in Equation 8-1b, the benthic sediment

concentration should be used.

8.2.2. Bioaccumulation Factor (BAF). Bioaccumulation refers to the net

accumulation of a substance by an organism as a result of uptake and retention from all

environmental sources. The degree to which a chemical accumulates above ambient

concentrations in an aquatic organism is indicated by the magnitude of the BAF, which

is the result of pollutant uptake and retention. Specifically, the BAF is defined as the

ratio of the contaminant concentration in all or a specific part (e.g., muscle tissue) of an

aquatic organism (μg/g fresh weight) to the contaminant concentration in the water

body where the organism was exposed (μg/L), in situations where both the organism

and its food were exposed. The BAF, therefore, has units of (μg contaminant/g fresh

tissue)/(μg contaminant/L water) or L/g. Defined in this way, the BAF considers the

absorption of a pollutant by an organism from the ambient water, as well as

consumption of contaminated food and, in theory, is directly applicable to situations

found in the environment. The BAF also accounts for retention of a contaminant in the

body of an organism and thereby accounts for metabolism of the pollutant.

Direct uptake of a contaminant in the water by the organism occurs by simple

diffusion across external surfaces, including those used for respiration. Uptake rates

are often related to respiration rates, which in turn are a function of daily kilocalorie

expenditure and body size (Schmidt-Nielsen, 1970). A dietary contaminant enters

through the gut of the aquatic organism and must pass through the intestinal lining to

accumulate. Once the chemical is in the bloodstream, subsequent accumulation

depends upon the metabolic capabilities of the organism as well as factors such as

lipid content that determine chemical affinity for the organism relative to that of the

water. With continuous exposure to a compound, the rate of excretion eventually

equals the rate of uptake. At this point, a steady-state equilibrium is attained between

the concentration of contaminant in the tissues of the organism, in its prey, and in the

ambient water. (For extremely hydrophobic compounds that are not expected to
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-5 December 1998

achieve steady-state equilibrium, the use of a BSAF is recommended [see discussion

of BSAF].) Measured BAFs also account for metabolic transformations that may occur

in organic contaminants. Metabolism may have a significant impact on the ultimate

contaminant concentration in an organism and as a result will affect the BAF.

Bioaccumulation factors are chemical specific and, to some degree, specific to

the existing aquatic food web and the body of water in which they are measured. The

compounds with the greatest tendency to bioaccumulate are those that are lipophilic,

resistant to biological degradation, or readily transferred from the gut of aquatic

organisms and subsequently accumulated in the affected tissues. Because

bioaccumulation is influenced by the metabolic capabilities of the organism and, for

many pollutants, the lipid content of the organism's tissues, a measured BAF is specific

to the species and tissue in which it is evaluated. Bioaccumulation is also influenced

by the bioavailability of the chemical of concern in the water. Bioavailability is affected

by properties of the chemical, but also by properties of the water being tested (e.g.,

dissolved organic carbon content, particulate organic carbon content). The differences

between the water body in which the BAF was measured and the water body of study

and the anticipated impacts of these differences should be described in the risk

characterization.

For inorganic chemicals, in many cases the BCF and BAF are assumed to be

equal. Chemical concentrations in water in laboratory tests should be greater than

normal background concentrations in water bodies. If the chemical is an essential

micronutrient, the concentrations should be greater than the concentrations required for

normal nutrition. Additionally, the concentrations used should be below those that

adversely affect the study organisms.

For lipophilic nonpolar organic chemicals, several adjustments can be made in

order to make a BAF measured in a particular tissue of a particular species in a

particular water body more generally applicable across tissues/species and test waters

(U.S. EPA, 1995b). One adjustment is to calculate the lipid-normalized concentration
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-6 December 1998

Cl'

CB

f

l

(8-2)

of the chemical in the tissue. The lipid-normalized tissue concentration, Cl, is defined

according to the following equation:

where:

Cl = lipid-normalized tissue concentration of organic contaminant (μg/g

lipid)

CB = concentration of organic contaminant in tissue of aquatic biota (either

whole organism or specified tissue) (μg/g fresh tissue)

f

l = fraction of the tissue that is lipid

This adjustment is predicated on the assumption that lipophilic nonpolar organic

contaminants will partition into the tissue lipids. It implies that BAFs for organic

contaminants can be extrapolated from one tissue to another and from one aquatic

species to another on the basis of percent lipid within a trophic level. The lipidnormalized concentration (Cl

) is defined as the total concentration of a contaminant in a

tissue or a whole organism divided by the lipid fraction of the tissue or the whole

organism (Mackay, 1982).

While lipid normalization is thought to be a reasonable basis for extrapolating

across species for organisms that are generally similar to one another (e.g., in terms of

metabolism), it is not meant to imply here that lipid normalization will account for all of

the variability in bioaccumulation potential across species; there are limits as to the

extent of cross-species extrapolation that can reasonably be made based on lipid

content alone. For example, invertebrates are sufficiently different from vertebrates in

terms of metabolism, growth rate, and other considerations that it is generally not

recommended to extrapolate results between invertebrate and vertebrate species. It is

also not generally recommended to extrapolate results across trophic levels because of

food chain biomagnification effects.
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-7 December 1998

C

fd

w ' (f

fd)(C

t

w

)

(8-3)

The percent lipid fraction used when calculating the BAF for the trophic levels of

fish consumed should be weighted by the human consumption rate of the species for

the population being considered (e.g., recreational anglers). To derive a consumptionweighted average fish lipid concentration, the risk assessor needs to determine the

types and quantities of aquatic organisms consumed by humans as well as the percent

lipid content of these organisms. These data sets should ideally be collected from the

site of interest.

A second adjustment is to express the BAF in terms of the bioavailable

concentration (BC) of the contaminant in the water. It is assumed that the BC

corresponds to the concentration that is freely dissolved in the water. Contaminant

sorbed to dissolved organic carbon (DOC) or particulate organic carbon (POC) in the

water is not considered to be bioavailable. The freely dissolved concentration of the

organic chemical in the water, Cfd

w

, is defined by the following equation:

where:

C

fd

w = freely dissolved concentration of the organic contaminant in the

ambient water (mg/L)

C

t

w = total concentration of the organic contaminant in the ambient water

(mg/L)

f

fd = fraction of total organic contaminant in the ambient water that is freely

dissolved

The fraction of total organic chemical freely dissolved in the study water, ffd, can

be estimated from DOC and POC of the water body and Kow of the organic contaminant

as follows:
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-8 December 1998

f

fd '

1

1 %

(DOC)(Kow)

10

% (POC)(Kow)

(8-4)

where:

f

fd = fraction of organic contaminant freely dissolved in ambient water

DOC = concentration of dissolved organic carbon in test water (kg/L)
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-9 December 1998

POC = concentration of particulate organic carbon in test water (kg/L)

Kow = octanol-water partition coefficient for the organic contaminant

Using these adjustments a measured BAF (designated BAFt

T

, because it is

based on total contaminant concentration in tissue and water) for an organic chemical

can be used to derive a normalized BAF, designated BAFfd

l

 because it is based on the

freely dissolved concentration of contaminant in water and lipid-normalized tissue

concentration of the contaminant. The BAFfd

l

 is a general baseline BAF for an organic

contaminant that can subsequently be used to predict the BAFt

T

 applicable to a specific

exposure scenario by taking into account the bioavailability of the contaminant in the

specific water body of concern and the lipid content of the specific fish species and

tissues consumed. It is this predicted BAFt

T

 that is used to calculate human intake of

the contaminant due to fish consumption in Equation 8-1.

8.2.2.1. Derivation of Baseline BAF Values — It was noted previously that

normalization based on lipid content is a reasonable basis for species-to-species

extrapolation for organic chemicals, but only for species that are fundamentally similar

to one another (in terms of metabolism, growth rate, trophic level, etc.). For inorganic

chemicals, as well, it seems reasonable to limit extrapolation to species that are

fundamentally similar to one another. It is, therefore, recommended that baseline BAF

values be determined separately for fish, and shellfish if they are included in the

assessment. Fish should always be included, because the vast majority of aquatic

species locally caught and consumed in freshwater and estuarine environments (the

aquatic environments most likely to be affected by a combustor) are fish. Shellfish may

be included if there is significant human consumption of shellfish from water bodies in

the vicinity of the specific combustor being modeled. It is further recommended that

separate baseline BAF values be determined for fish that eat zooplankton (trophic level

3) and piscivorus fish (trophic level 4) because position in the food chain affects

bioaccumulation and because humans may consume fish from either trophic level

(although piscivorus fish predominate in the human diet).
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-10 December 1998

BAF fd

l '

Measured BAF t

T

f

fd

& 1

1

f

l

(8-5)

U.S. EPA (1995b) discusses four methods that can be used to derive baseline

BAF values for a chemical. In order of decreasing preference these are: (1) adapt BAF

values from field studies of acceptable quality, (2) predict baseline BAF values from

field-measured BSAF values of acceptable quality, (3) predict baseline BAF values

from BCF values measured in laboratory studies of acceptable quality, and (4) predict

baseline BAF values from Kow values of acceptable quality. Each of these options is

described in more detail below. The selection of approach may be driven by the

available data.

Adapt BAF values from field studies. An adequately conducted field study is the

most desirable source of a BAF value for either an inorganic or organic chemical. An

adequate study would need to have shown that contaminant concentrations over the

range of territory inhabited by the study organism (fish, or if applicable to the combustor

site, shellfish) remained relatively constant for a sufficiently long period of time for

steady state to have been reached, and that conditions at the site of the study were not

so unique that the results cannot be extrapolated to other sites. In addition, for organic

chemicals, the study should have included data regarding percent lipid of the tissue

used to determine the BAF, and concentrations of DOC and POC in the study water.

For an inorganic chemical, an adequate field-measured BAF based on wet

weight of an edible tissue (e.g., muscle) can simply be adopted as a baseline BAF. For

an organic chemical, a field-measured BAF from an adequate study can be converted

to a baseline BAF using the following equation (BAFfd

l

 is adopted as the baseline BAF):

where:

BAFfd

l = BAF for organic contaminant normalized for lipid content of

tissue/organism tested and bioavailability in test water (L/g lipid)

BAFt

T = field-measured BAF based on total concentration of contaminant

in fish tissue and test water (L/g fresh tissue)
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-11 December 1998

f

l = fraction of the tested tissue composed of lipid

f

fd = fraction of total organic contaminant freely dissolved in the test

water

Adequate field-measured BAF values are not widely available in the literature. 

EPA Ambient Water Quality Criteria Documents and the Aquatic Toxicity Information

Retrieval (AQUIRE) database may be useful sources for field-measured BAF values.

If more than one baseline BAF derived from a field study is available for a given

chemical in a particular species, then the geometric mean should be calculated to

produce the species mean baseline BAF. If a baseline BAF derived from a field study

is available for more than one species in a trophic level, then the geometric mean of the

species mean baseline BAF values should be calculated to produce the baseline BAF

for that trophic level. If a baseline BAF based on a field study can be calculated

directly only for fish from one trophic level (trophic level 3 or 4), then a baseline BAF for

fish from the other trophic level can be estimated using the ratio of the FCMs (food

chain multipliers) for the chemical (see Table 8-1).

As is the case with many other measures of media transfer coefficients, BAF

values are generally developed as a measure of bioaccumulation rather than as a

predictor of bioaccumulation. The application of this method results in their use as

predictors. 

Calculate BAF values based on BSAF values. For organic chemicals, BAF

values may be estimated using field-measured BSAF values from adequate studies

(U.S. EPA, 1994a). This method generally does not apply to inorganic chemicals. The

BSAF is the ratio of a substance's lipid-normalized concentration in tissue of an aquatic

organism to its organic carbon-normalized concentration in surface sediment. This

assumes that the ratio does not change substantially over time, both the organism and

its food are exposed, and the surface sediment is representative of average surface

sediment in the vicinity of the organism. Use of the BSAF to estimate bioaccumulation

is most suitable for highly hydrophobic chemicals such as dioxin and biphenyls, which

are detectable in fish tissues and bottom sediments but difficult to measure in the water
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-12 December 1998

TABLE 8-1

Food Chain Multipliers for Trophic Levels 3 and 4a

Log Kow Trophic Level 3b Trophic Level 4c

2.0 1.005 1.000

2.5 1.010 1.002

3.0 1.028 1.007

3.1 1.034 1.007

3.2 1.042 1.009

3.3 1.053 1.012

3.4 1.067 1.014

3.5 1.083 1.019

3.6 1.103 1.023

3.7 1.128 1.033

3.8 1.161 1.042

3.9 1.202 1.054

4.0 1.253 1.072

4.1 1.315 1.096

4.2 1.380 1.130

4.3 1.491 1.178

4.4 1.614 1.242

4.5 1.766 1.334

4.6 1.950 1.459

4.7 2.175 1.633

4.8 2.452 1.871

4.9 2.780 2.193

5.0 3.181 2.612

5.1 3.643 3.162

5.2 4.188 3.873

5.3 4.803 4.742

5.4 5.502 5.821

5.5 6.266 7.079

5.6 7.096 8.551

5.7 7.962 10.209

5.8 8.841 12.050

5.9 9.716 13.964

6.0 10.556 15.996
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-13 December 1998

TABLE 8-1 cont.

Log Kow Trophic Level 3b Trophic Level 4c

6.1 11.337 17.783

6.2 12.064 19.907

6.3 12.691 21.677

6.4 13.228 23.281

6.5 13.662 24.604

6.6 13.980 25.645

6.7 14.223 26.363

6.8 14.355 26.669

6.9 14.388 26.669

7.0 14.305 26.242

7.1 14.142 25.468

7.2 13.852 24.322

7.3 13.474 22.856

7.4 12.987 21.038

7.5 12.517 18.967

7.6 11.708 16.749

7.7 10.914 14.388

7.8 10.069 12.050

7.9 9.162 9.840

8.0 8.222 7.798

8.1 7.278 6.012

8.2 6.361 4.519

8.3 5.489 3.311

8.4 4.683 2.371

8.5 3.949 1.663

8.6 3.296 1.146

8.7 2.732 0.778

8.8 2.246 0.521

8.9 1.837 0.345

9.0 1.493 0.226

a

 Source: U.S. EPA (1995b)

b

 Fish that feed on zooplankton. FCM values for trophic level 3 are the geometric mean of FCM values for sculpin and alewife.

C

 Piscivorus fish
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-14 December 1998

BSAF '

Cl

CSOC

(8-6)

column. It should be noted that the BSAF, like the BAF, is a simplification of the

complex processes by which pollutants are transferred from aquatic ecosystems to

aquatic organisms. Since BSAF are measured values and include effects of metabolism

and bioaccumulation among others, they are appropriate predictors of accumulation. It

should also be noted that the values are generally developed as a measure of

bioaccumulation rather than as a predictor of bioaccumulation, which is the manner in

which they are used here.

Criteria for an adequate study are: use of fish or (if appropriate to the combustor

site) shellfish; determination of percent lipid in the tissue used to derive the BSAF; use

of surface-bottom sediment samples (0-1 cm) from a location where there is net

deposition of fine sediments that is representative of average surface sediment in the

vicinity of the organism and is not so unique that the results cannot be extrapolated to

other areas; and determination of the organic carbon content of the sediment. For

organisms that are highly mobile, average bottom sediment concentrations from a

larger area (e.g., lakewide sediment concentration data) may be needed. Special

consideration may need to be given if a larger body of water is included in the

assessment or if the species of concern feed in a specific area of the water body. In

these cases as well as in other related cases, the source of the environmental pollutant

concentration should be justified. An assessment of the chemical concentration in the

aquatic system is also important. If chemical inputs are decreasing or increasing, the

system may not be in equilibrium. This may result in an increase or a decrease in the

exposure of fish through the water column or bottom sediments. Like BAF, the BSAF

should be measured in systems at equilibrium.

The BSAF from an adequate study is calculated as follows:

where:
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-15 December 1998

CSOC '

Cbs

fOC

(8-7)

BSAF = biota-sediment accumulation factor

Cl = lipid-normalized concentration of the organic contaminant in tissue

(μg/g lipid)

CSOC = organic carbon-normalized concentration of the organic contaminant in

sediment (μg/g organic carbon)

The lipid-normalized contaminant concentration, Cl, is calculated from the tissue

fresh weight concentration and the lipid fraction of the tissue, as shown in Equation 8-2.

The organic carbon-normalized concentration of the contaminant in sediment,

CSOC, is calculated using the following equation:

where:

CSOC = organic carbon-normalized concentration of organic contaminant in

sediment (μg/g organic carbon)

Cbs = concentration of organic contaminant in sediment (μg/g sediment)

fOC = fraction of the sediment that is organic carbon

BSAF values of acceptable quality can be used to estimate BAF values based

on the expected proportionality between the two quantities. The method depends on

having not only an adequate BSAF for the chemical in question, but also adequate BAF

and BSAF values for a reference chemical that can be used as a basis of comparison. 

That is, predicting a BAF from a BSAF requires data from a steady-state (or near

steady-state) condition between sediment and ambient water for both a reference

organic chemical r with a BAFfd

l

 (baseline BAF) derived from a field study and the

organic chemical i for which the BAF is to be determined. The method is shown in the

following equation:
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-16 December 1998

(BAFbaseline)

i

' (BAFbaseline)

r

(BSAF)

i

(Kow)

i

(BSAF)

r

(Kow)

r

(8-8)

where:

(BAFbaseline)i = BAF for organic contaminant i

(BAFbaseline)r = BAF for reference organic contaminant r

(BSAF)i = BSAF for organic contaminant i

(BSAF)r = BSAF for reference organic contaminant r

(Kow)i = octanol-water partition coefficient for organic contaminant i

(Kow)r = octanol-water partition coefficient for reference organic

contaminant r

Adequate field-measured BSAF and BAF values are not widely available in the

literature. EPA Ambient Water Quality Criteria Documents and the Aquatic Toxicity

Information Retrieval (AQUIRE) database may be useful sources for BSAF and BAF

values. U.S. EPA (1993b, 1994a) discusses BSAF values for dioxin-like compounds

and PCBs. Based on the approach delineated above and the aforementioned BSAF

values, U.S. EPA (1995b) determined bioaccumulation equivalency factors (BEFs) for

the dioxin-like congeners. These BEFs show the propensity for each congener to

bioaccumulate, relative to TCDD; they are presented in Table 8-2.

If more than one baseline BAF is predicted from BSAFs in a particular species,

then the geometric mean should be calculated to produce the species mean baseline

BAF. If a baseline BAF predicted from the BSAF is available for more than one species

in a trophic level, then the geometric mean of the species mean baseline BAF values

should be calculated to produce the baseline BAF for that trophic level. If a baseline

BAF based on BSAF can be calculated directly only for fish from one trophic level

(trophic level 3 or 4), then a baseline BAF for fish from the other trophic level can be

estimated using the ratio of the FCMs for the chemical (see Table 8-1).
Chapter 8

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MPE 8-17 December 1998

If these data are not available, the BSAF term can replace the BAF term used to

predict fish concentrations. Note that sediment concentrations should be employed.

Estimated BAF values based on BCF values. The BCF (bioconcentration factor)

is defined as the ratio (in L/g) of a substance's concentration in the tissue of an aquatic

organism to its concentration in the ambient water, in situations where the organism is
Chapter 8

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MPE 8-18 December 1998

TABLE 8-2

Bioaccumulation Equivalency Factors for Dioxin-like Compounds

Congener BEF

2,3,7,8-TCDD 1.0

1,2,3,7,8-PeCDD 0.9

1,2,3,4,7,8-HxCDD 0.3

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

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

1,2,3,4,6,7,8-HpCDD 0.05

OCDD 0.01

2,3,7,8-TCDF 0.8

1,2,3,7,8-PeCDF 0.2

2,3,4,7,8-PeCDF 1.6

1,2,3,4,7,8-HxCDF 0.08

1,2,3,6,7,8-HxCDF 0.2

2,3,4,6,7,8-HxCDF 0.7

1,2,3,7,8,9-HxCDF 0.6

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

1,2,3,4,7,8,9-HpCDF 0.4

OCDF 0.02

Source: U.S. EPA (1995b)
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MPE 8-19 December 1998

BCF fd

l '

Measured BCF t

T

f

fd

& 1

1

f

l

(8-9)

exposed through the water only and the ratio does not change substantially with time. 

The BCF is, therefore, similar to the BAF but does not take into account accumulation

via the food chain (i.e., the organism’s prey are not exposed). BCF values are

generally derived from laboratory studies. An adequate study is generally a flowthrough or renewal test conducted long enough for steady state to be achieved, using a

measured water concentration that does not adversely affect the test organisms (fish,

or if applicable to the combustor site, shellfish). Results should be expressed in terms

of wet weight, and for organic chemicals, the tissue lipid content and POC and DOC of

the test solution should be reported.

An acceptable laboratory-derived BCF, based on total water and tissue

contaminant concentrations (designated by BCFt

T

), can be used to estimate a baseline

BCF. For an inorganic chemical, the baseline BCF can be estimated by adopting an

acceptable BCFt

T

 calculated on the basis of wet weight of an edible tissue. For an

organic chemical, the baseline BCF can be estimated as the lipid and bioavailabilitynormalized BCFfd

l

 derived from an acceptable BCFt

T

 using the following equation:

where:

BCFfd

l = BCF for organic contaminant normalized for lipid content of

tissue/organism tested and bioavailability in test water (L/g lipid)

BCFt

T = BCF based on total concentration of organic contaminant in

tissue and water (L/g)

f

l = fraction of the tissue composed of lipid

f

fd = fraction of the total organic contaminant freely dissolved in the

test water
Chapter 8

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MPE 8-20 December 1998

BAFbaseline ' (FCM)(BCFbaseline)

(8-10)

Laboratory-measured BCF values for many chemicals are available in the

published literature and in EPA reports. EPA Ambient Water Quality Criteria

Documents and the AQUIRE database are useful sources of measured BCF values.

For both inorganic and organic contaminants, the baseline BCF can be used to

estimate the baseline BAF. The relationship is shown in the following equation:

where:

BAFbaseline = baseline BAF for an organic or inorganic contaminant

BCFbaseline = baseline BCF for an organic or inorganic contaminant

FCM = food chain multiplier

The FCM is a function of the trophic level of the organism tested and the

lipophilicity of the test compound. For inorganic chemicals, the FCM is assumed to be

1 unless chemical-specific data suggest that a FCM other than 1 be used (e.g., some

metals, such as mercury, have organometallic forms that may biomagnify). For organic

chemicals, the FCM appropriate for use with fish of trophic levels 3 and 4 can be

obtained or estimated by linear interpolation from Table 8-1. If shellfish, which feed

primarily on detritus and plant material, are included in the assessment, it would be

appropriate to use a FCM of 1 for organic chemicals.

If more than one baseline BAF predicted from a BCF is available for a particular

species, then the geometric mean should be calculated to produce the species mean

baseline BAF. If a baseline BAF predicted from a BCF is available for more than one

species in a trophic level, then the geometric mean of the species mean baseline BAF

values should be calculated to produce the baseline BAF for that trophic level. If a

baseline BAF predicted from a BCF can be calculated directly only for fish from one

trophic level (level 3 or 4), then a baseline BAF for fish from the other trophic level can
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-21 December 1998

BAFbaseline ' (FCM)(predicted BCFbaseline) . (FCM)(Kow)

(8-11)

be estimated by applying the FCM from the other trophic level to the available baseline BCF.

Estimated BAF values based on Kow. The octanol-water partition coefficient, Kow,

can be used to estimate BAF values for an organic chemical. n-Octanol/water partition

coefficient (Kow) is defined as the ratio of the concentration of a substance in the

octanol phase to its concentration in the aqueous phase in an equilibrated two-phase

octanol-water system. The log of the octanol-water partition coefficient is a base 10

logarithm. Theory predicts that the baseline BCF, i.e., BCFfd

l

, for a slowly metabolized,

lipophilic nonpolar organic chemical with log Kow > 3 will be approximately equal to the

Kow (U.S. EPA, 1995b, 1994b). Therefore, for such chemicals, a baseline BAF can be

calculated according to the following relationship:

where:

BAFbaseline = baseline BAF for an organic contaminant

FCM = food chain multiplier

Kow = octanol-water partition coefficient

FCM values to calculate baseline BAF values for fish of trophic levels 3 and 4

can be obtained from Table 8-1. If shellfish are included in the assessment, a FCM of

1 should be assumed for these species.

8.2.2.2. Application of Baseline BAF Values — The methodology described

above leads to derivation of two baseline BAF values for each contaminant: one

applicable to fish that eat zooplankton (trophic level 3) and one applicable to piscivorus

fish (trophic level 4). In some cases, a third baseline BAF applicable to shellfish may

have been calculated as well. In order to use bioaccumulation information to calculate

human daily intake due to fish consumption at the combustor site, it is necessary to
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-22 December 1998

Predicted BAF t

T ' [(BAFbaseline)(Fl

)% 1](Ffd)

(8-12)

convert these baseline BAF values to predicted BAF values applicable to the specific

site.

For inorganic contaminants, the baseline BAF for a trophic level is adopted as

the predicted BAF for that trophic level. For organic contaminants, baseline BAF

values are tissue lipid- and bioavailability-normalized values (BAFfd

l

) that must be

converted, using data that describe conditions at the specific site in question, to values

based on total contaminant concentration in water and tissue in order to derive

predicted BAF values (BAFt

T

) for the site. The following equation shows how a

baseline BAF (BAFfd

l

) for an organic contaminant may be converted to the

corresponding BAFt

T

 predicted to apply to the site in question:

where:

BAFt

T = BAF for an organic contaminant predicted to apply to 

combustor site (will convert total water concentration to total

tissue concentration in Equation 8-1) — applies to a specific

trophic level

BAFbaseline = baseline BAF (BAFfd

l

) for an organic contaminant — applies to a

specific trophic level

Fl = lipid content of species caught at combustor site — applies to a

specific trophic level

Ffd = freely dissolved concentration of organic contaminant at

combustor site

The freely dissolved water fraction at the local site of interest, Ffd, can be

calculated as the ratio of dissolved water column concentration (Cdw from Chapter 7) to

total water column concentration (Cwt from Chapter 7).

Tissue lipid content, Fl, should be representative of species caught and

consumed locally in the vicinity of the combustor. A separate Fl would apply to each

trophic level included in the assessment. Ideally, Fl for each trophic level would be

calculated as the average of lipid content of meat from species of that trophic level
Chapter 8

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MPE 8-23 December 1998

caught in the water body of interest, weighted according to consumption. U.S. EPA

(1995a) and USDA (1979-1984) present data regarding lipid content for many species

of fish and shellfish. However, in the absence of data regarding the specific species

taken locally and/or the lipid content of the meat consumed from these species, default

values may be applied. U.S. EPA (1980) reported that the average lipid fraction of

freshwater and estuarine species, the species potentially affected by combustor

emissions, weighted by average daily consumption, was 0.03 (3%). U.S. EPA (1995b)

calculated average lipid fractions of 0.0182 (1.82%) and 0.0310 (3.10%) for freshwater

fish of trophic levels 3 and 4, respectively, in the Great Lakes water system. These

values may be applicable to other freshwater systems as well.

8.2.2.3. Benthic Invertebrates — If BAFi or BSAFi measures are not available

for a benthic species of concern or if the assessor is concerned about the applicability

of a reference BAF or BSAF, pollutant concentrations in benthic invertebrates can be

estimated through alternative models such as a model presented in Thomann et al.

(1992). In this model benthic organisms may accumulate pollutant in sediment,

sediment interstitial waters, and the water column as well as contaminated

phytoplankton. The Thomann et al. model also accounts for growth of the organism and

excretion.

The value or the distribution of the bioaccumulation factor utilized in an

assessment should be justified in the parameter description of the assessment. The

strengths as well as weaknesses of the data from which the BAF or the BAF distribution

was derived should be characterized. The description of the BAF should describe the

nature of the variability in the data and the uncertainty in the data.

If pollutant concentrations are simulated for larger water bodies and there is

predicted to be spatial variability in pollutant concentration, the dissolved water

concentration or sediment concentration utilized in the estimation of pollutant levels in

aquatic species should be carefully selected and justified, particularly for species that

are non-migratory or that are known to feed in a specific area.
Chapter 8

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MPE 8-24 December 1998

8.2.3. Fish Ingestion (DIf). Consumption of fish is highly variable across the U.S.

population, unlike consumption of other dietary components, which may be more

widespread. Inclusion of this source of protein in the diet varies with geographic

location, season of the year, and ethnicity, as well as personal food preferences. 

Analysis of dietary survey data (Crochetti and Guthrie, 1982) showed fish to be among

the most infrequently consumed food groups.

For the purposes of estimating human intake of fish potentially contaminated by

emissions from a nearby combustor, the most pertinent subpopulations are those that

consume fish from these waters; studies of consumption rates of local fish typically

identify a distribution of consumption rates across the subpopulation. This distribution

of consumption rates may include two classes of anglers, the recreational anglers and

subsistence anglers who fish these waters. Other members of the local population that

receive and consume local fish, such as family members of fishers, friends, etc., should

also be considered as part of the exposed population. Subsistence fish consumers are

generally defined as obtaining their daily protein requirements or their daily energy

requirements from fish. Commercially caught fish are usually distributed widely so that

predictions of exposure from a single combustor are difficult. 

Generally freshwater exposure scenarios are conducted for combustors’ risk

assessments; however, combustor emissions could also affect estuarine species if the

combustor is located in a coastal area. Except possibly for anadromous species such

as salmon, marine life that has been contaminated from a single combustion source is

not expected to pose a significant health risk to humans. Ingestion of marine fish and

shellfish is not generally considered in indirect exposure estimates of single emission

sources (although marine species could be included if the risk assessor deems an

assessment warranted).

Aquatic species consumed by humans vary by locale and are based, in part, on

local availability of an organism as well as the cultural background of the consumer.

Finfish, shellfish, and other benthic-dwelling organisms may be included in a local diet.

Consumption rates of these species may or may not include a survey of fish
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-25 December 1998

consumption rates. The risk assessor should identify the types of local aquatic

organisms consumed by the subpopulation of concern and compare this with the

information collected in the survey of fish consumption rates.

Data on fish consumption rates are typically calculated as either “per capita” or

“per user.” The former term is obtained by dividing the supply of fish across an entire

population to establish a per capita consumption rate. The latter term divides the

supply of fish across only the portion of the population that consumes fish, providing

per user rates of consumption. Because many people in a population may not eat any

fish, and even fewer may eat locally-caught fish, per capita consumption estimates

might significantly underestimate the actual consumption rates for fish consumers, such

as recreational fishers and subsistence fish consumers. Therefore, per user estimates

based on consumption in these groups provide more relevant estimates of central

tendency and upper percentile exposure than per capita estimates.

Recent fish consumption surveys of anglers who fish at the potentially affected

water bodies in the region around the combustor are the best source of fish

consumption data and estimates of the size of the local fish-consuming population. 

However, in the absence of adequate local data, the results of surveys from other

regions can be used as surrogates to estimate both the size of the local fish-consuming

population and the distribution of fish consumption rates within that population. When

surrogate fish-consuming populations are used, significant differences between the

surrogate and the site being evaluated should be described. For example, the fish

consumption rates or the size of the fish-consuming population may vary between two

sites because of the weather, the aesthetic quality of the site, access to the site, the

existence of posted health advisories, or the types of fish typically caught. The fish

consumption rates or the size of the population may also vary between sites because

of differences in ethnic composition or the distribution of income levels. Finally,

humans typically do not consume the entire fish; however, the body parts of fish that

are consumed may vary among some populations. (For example, Toy et al. 1995 noted

that members of the Tulalip and Squaxin Island tribes consume some fish with skin-on
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-26 December 1998

and that some members consumed head, bones, and eggs.) These differences should

be noted in the assessment.

The available data on human consumption of freshwater fish has been reviewed

and evaluated in U.S. EPA’s 1997 Exposure Factors Handbook (Volume II-Food

Ingestion Factors, Chapter 10). As a surrogate for a local survey of freshwater fish

consumption rates, U.S. EPA (1997) identified three relevant freshwater recreational

angler studies as follows: Fiore et al. (1989), Connelly et al. (1990), and Hudson River

Sloop Clearwater, Inc. (1993). U.S. EPA (1997) used the data in these studies of

recreational anglers to identify mean and 95th percentile intake consumption rates of 8

and 25 grams of fish per day, respectively.

U.S. EPA (1997) also evaluated freshwater fish consumption rates among Native

American Tribesmen that consume fish at subsistence quantities (i.e., fish is either the

primary source of dietary protein or the primary source of energy in the diet). As a

surrogate for subsistence consumers, U.S. EPA (1997) identifies four Native-American

subsistence freshwater subsistence angler studies as follows: Wolfe and Walker

(1987), AIHC (1994), Columbia River Inter-Tribal Fish Commission (CRITFC, 1994),

and Peterson et al. (1994). U.S. EPA (1997) used the data in these studies of Native

American subsistence anglers to identify mean and 95th percentile intake consumption

rates of 70 and 170 grams of fish per day, respectively. If subsistence fishers are

evaluated in an assessment, the parts of the fish consumed should be identified. For

example, Toy et al. (1995) examined fish consumption among members of the Tulalip

and Squaxin Island tribes of Puget Sound (Washington, USA). Toy and collaborators

reported that approximately 10% of the population surveyed indicated that they

consumed parts of the fish other than the muscle (e.g., head, bones, eggs).

Specific Native American Tribes are not the only subpopulation within the United

States that consume fish and shellfish at subsistence levels. Other ethnic groups such

as Asians (for example, individuals of Vietnamese, Laotian, Cambodian, and Hmong

descent) and Pacific-Islanders may consume large quantities of fish. Other individuals
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-27 December 1998

in the population may also consume subsistence levels of locally caught fish as a

dietary preference.

8.2.4. Body Weight. Dose estimates are normalized over body weight to express

them in a manner that is consistent with dose-response relationships. A weight of 70

kg has traditionally been used by U.S. EPA to represent average adult body weight. 

Data reviewed by U.S. EPA (1997) support continued use of this value for hypothetical

adults (see Table 7-11; Chapter 7 of U.S. EPA, 1997). In fact, U.S. EPA (1997) reports

that the mean body weight for all adults both sexes is 71.8 kg. For children, average

body weight for the entire population by age are presented for both genders in Table

7-3 of U.S. EPA (1997). Consult U.S. EPA (1997) for more detailed information

regarding human body weight.

8.3. OTHER AQUATIC MODELS

For some situations more complicated models are required that may require

more site and chemical data. More expertise may also be needed to run these models

and accurately interpret the predicted results. These other models may also be used to

corroborate estimates from the simpler models presented in this methodology.

For example, EPA has developed models that estimate the concentrations of

pollutants in fish. FGETS (Food and Gill Exchange of Toxic Substances) is a

FORTRAN simulation model that predicts temporal dynamics of whole-fish body

concentration (μg chemical/[g live weight fish]) of nonionic, nonmetabolized, organic

chemicals that are bioaccumulated from either: (a) water only, which is the

predominant route of exchange during acute exposures, or (b) water and food jointly,

which is more characteristic of chronic exposures. By assuming that a chemical elicits

its pharmacological response through a narcotic mode of action, FGETS estimates the

time needed to reach the chemical's lethal activity.

FGETS considers both the biological attributes of the fish and the

physicochemical properties of the chemical that determine diffusive exchange across

gill membranes and intestinal mucosa. Important biological characteristics addressed

by the model are the fish's gill and intestinal morphometry, the body weight of the fish,
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-28 December 1998

and the fractional aqueous, lipid, and structural organic composition. Relevant

physicochemical properties are the chemical's aqueous diffusivity, molar volume, and

the n-octanol/water partition coefficient that is used as a surrogate to quantify chemical

partitioning to the fish's lipid and structural organic fractions. The chemical's log Kow is

used in calculating the fish's bioconcentration factor. Its molecular volume is used to

estimate the chemical's aqueous diffusivity, and its melting point is used in conjunction

with log P to calculate the chemical's activity within the fish.

FGETS is parameterized for a particular fish species by means of two

morphological/physiological databases that delineate the fish's gill morphometry,

feeding and metabolic demands, and body composition. Presently, joint water and food

exposure is parameterized for salmonids, centrarchids, cyprinids, percids, and

ictalurids. 

 FGETS is a model developed by Center for Exposure Assessment Modeling

(CEAM). For information about obtaining this or one of the other CEAM models,

contact:

U.S. Environmental Protection Agency (U.S. EPA)

Office of Research and Development (ORD)

National Exposure Research Laboratory - Ecosystems Research Division

960 College Station Road

Athens, Georgia (GA) 30605-2700 USA

e-mail: ceam@epamail.epa.gov

Thomann and his collaborators have developed a series of steady-state and

dynamic models of pollutant accumulation in the aquatic food web. These

multicompartment models have been developed to predict concentrations of (primarily)

organic molecules in the tissues of aquatic species based on concentrations in

environmental compartments (e.g., dissolved chemical concentrations in the water

column). The modeled biologic compartments include benthic invertebrates,

phytoplankton, zooplankton, forage fish, and piscivorus fish. Some of the presented

models (e.g., Thomann, 1992; Thomann et al., 1992) base the predicted pollutant

concentration in fish tissues on food web transfers and, in part, on other factors such as

fish age (thereby accounting for changes in the fish diet as the size of the fish
Chapter 8

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MPE 8-29 December 1998

increases). Organism/pollutant interactions included in Thomann’s simulations include

diffusive exchange across membranes, consumption of contaminated prey, growth and

depuration. Uptake may result from bioconcentration and bioaccumulation. Growth

results in a dilution of accumulated pollutant. Depuration rates reduce predicted

pollutant concentrations in fish.

Examples of the aquatic organisms considered in the modeling efforts include

the following:

! The marine bivalves Crassostrea virginica and Mytilus edulis (Thomann et

al., 1995)

! Striped Bass and White Perch (Thomann et al., 1991)

! Alewife and Lake Trout (Thomann, 1992)

! Rainbow Trout (Thomann et al., 1997)

8.4. SPECIAL CONSIDERATIONS FOR FARM-RAISED FISH

In the United States an increasing number of individuals obtain freshwater fish

from fish farms; on these farms the fish are fed a special diet and there is little

exposure through the food chain. If there is a fish farm in the area around the

combustion source of concern, then the application of a BAF may be inappropriate, as

it may overestimate the amount of pollutant that accumulates in the bodies of the fish.

This overestimate is the result of including a contribution to the fish body burden from

the aquatic food chain. Since these fish are typically fed premixed feeds from other

sources, there would be no multiplication through the aquatic food chain. As a result, it

may be more appropriate to apply a water-column-to-fish bioconcentration factor

approach to these stocks. This assumes, of course, that the feed is not more

contaminated than the normal prey. The risk assessor should also consider benthic

sediments in the ponds, which may contain pollutant concentrations from runoff and

erosion.
Chapter 8

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MPE 8-30 December 1998

8.5. FISH PREPARATION FACTOR

The effect of preparation and cooking on chemical levels in fish tissue is a

complex issue. Various preparation and cooking procedures may decrease,

concentrate, or degrade pollutants in edible fish tissues. Several studies have

indicated that the trimming and removal of fish adipose tissue reduces the

concentrations of fat- soluble contaminants in the consumed portion of the fish (Sander

and Haynes, 1988; Armbruster et al., 1989; Skea et al., 1979; Voiland et al., 1990);

various cooking procedures have also been shown to reduce the concentrations of fatsoluble contaminants in the consumed portions of some fish (Skea et al., 1979;

Armbruster et al., 1989). Contaminants that are not fat soluble appear to act differently.

For example, Morgan et al. (1997) reported that, while the total amount of mercury in

fish remained constant, mercury concentrations in fillets were higher after preparation,

presumably because of water loss.

The use of fish preparation factors may be appropriate for some exposure

assessments; for example, for exposures in commercially prepared fish. Given the wide

range of preparation techniques, types of pollutants, and types of fish in the United

States, and also in consideration of the uncertainties associated with such factors,

preparation factors are not employed in this methodology.
Chapter 8

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MPE 8-31 December 1998

8.6. REFERENCES

AIHC (American Industrial Hygiene Council). 1994. Exposure Factors Sourcebook. 

AIHC, Washington, DC.

Armbruster, G., K.L. Gall, W. Gutenmann, C. Littman and D. Lisk. 1989. Effects of

trimming and cooking by several methods on polychlorinated biphenyls (PCB) levels in

bluefish. J. Food Safety. 8:235-243.

ChemRisk. 1991. Consumption of freshwater fish by Maine anglers. ChemRisk,

Portland, ME.

Connelly, N.A., T.L. Brown and B.A. Knuth. 1990. New York Statewide Angler Survey

1988. New York State Department of Environmental Conservation, Bureau of

Fisheries.

Cox, C., A. Vaillancourt and A.F. Johnson. 1990. The Results of the 1989 "Guide to

Eating Ontario Sport Fish" Questionnaire. Ontario Ministry of the Environment,

Toronto, Ontario.

CRITFC (Columbia River Inter-Tribal Fish Commission). 1994. A Fish Consumption

Survey of the Umatilla, Nez Perce, Yakama and Warm Springs Tribes of the Columbia

River Basin. Tech. Rpt. 94-3. Portland, OR.

Crochetti A.F. and H.A. Guthrie. 1982. Eating Behavior and Associated Nutrient

Quality of Diets. Final Report for U.S. Department of Agriculture Contract 53-22U4-9-

192, The Human Nutrition Center, Science and Education Administration, U.S.

Department of Agriculture. Anarem Systems Research Corporation, New York.

Fiore, B.J., H.A. Anderson, L.P. Hanrahan, L.J. Olsen and W.C. Sonzogni. 1989. 

Sport fish consumption and body burden levels of chlorinated hydrocarbons: A study of

Wisconsin anglers. Arch. Environ. Health. 44: 82-88.

Hudson River Sloop Clearwater, Inc. 1993. Hudson River angler survey. Hudson

River Sloop Clearwater, Inc., Poughkeepsie, NY.

Mackay, D. 1982. Correlation of bioconcentration factors. Environ. Sci. Technol.

16:274-278.

Morgan, J., M. Berry and R. Graves. 1997. Effects of commonly used cooking

practices on total mercury concentration in fish and their impact on exposure

assessments. J. Expo. Anal. Environ. Epidemiol. 7:119-133.
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-32 December 1998

Peterson, D., M. Kanarek, M. Kuykendall et al. 1994. Fish consumption patterns and

blood mercury levels in Wisconsin Chippewa Indians. Arch. Environ. Health.

49:53-58.

Sander, M. and B.L. Haynes. 1988. Distribution pattern and reduction of

polychlorinated biphenyls (PCB) in bluefish Pomatomas saltatrix (Linneatus) fillets

through adipose tissue removal. Bull. Environ. Contam. Toxicol. 41:670-677.

Schmidt-Nielsen, K. 1970. Energy metabolism, body size, and problems of scaling.

Federation Proceedings of the American Physiological Society. 20(4):1524-1532.

Skea, J., H . Simmons, E. Harris et al. 1979. Reducing levels of mirex, Aroclor 1254

and DDE by trimming and cooking Lake Ontario brown trout (Salmotrutta linneaus) and

smallmouth bass (Micropterus dolomieu lacepede). J. Great Lakes Res. 5:153-159.

Smith, B.F. and E.D. Enger. 1988. A survey of attitudes and fish consumption of

anglers on the Lower Tittabawassee River, Michigan. Michigan Department of Public

Health, Center for Environmental Health Science, Lansing, MI.

Thomann, R. 1992. Modeling accumulation of organic chemicals in aquatic food webs.

In: Chemical Dynamics in Freshwater Ecosystems, F. Gobas and J. McCorquodale, Ed.

Lewis Publishers.

Thomann, R. and J. Connolly. 1984. Model of PCB in the Lake Michigan lake trout

food chain. Environ. Sci. Technol. 18(2):65-71.

Thomann, R., J. Mueller, R. Winfield and C. Huang. 1991. Model of fate and

accumulation of PCB homologues in Hudson Estuary. J. Env. Eng. 117(2):161-178.

Thomann, R., J. Connolly and T. Parkerton. 1992. An equilibrium model of organic

chemical accumulation in aquatic food webs with sediment interaction. Environ.

Toxicol. Chem. (11):615-629.

Thomann, R., J. Mahony and R. Mueller. 1995. Steady-state model of biota sediment

accumulation factor for metals in two marine bivalves. Environ. Toxicol. Chem.

14(11):1989-1998.

Thomann, R., F. Shkreli and S. Harrison. 1997. A pharmacokinetic model of cadmium

in rainbow trout. Environ. Toxicol Chem. 16(11):2268-2274.

Toy, K.A., G.D. Gawne-Mittelstaedt, N.L. Polissar and S. Liao. 1995. A fish

consumption survey of the Tulalip and Squaxin Island tribes of Puget Sound, Seattle,

Washington.
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-33 December 1998

USDA (U.S. Department of Agriculture). 1979-1984. Agricultural Handbook No. 8.

U.S. EPA. 1980. Federal Register Notice. Water Quality Criteria Documents;

Availability. November 28. 45(231):79318-79379.

U.S. EPA. 1990. Methodology for Assessing Health Risks Associated with Indirect

Exposure to Combustor Emissions. Interim Final. EPA/600/6-90/003. January 1990.

U.S. EPA. 1993a. Wildlife Exposure Factors Handbook. EPA/600/R-93/187. Office of

Health and Environmental Assessment, Washington, DC. December.

U.S. EPA. 1993b. Interim Report on Data and Methods for Assessment of

2,3,7,8-Tetrachloro-p-dioxin Risks to Aquatic Life and Associated Wildlife. Office of

Research and Development, Duluth, MN. EPA/600/R-93/055.

U.S. EPA. 1993c. Fish Intake Study. Review Draft. Office of Health and

Environmental Assessment, Washington, DC. (Cited in U.S. EPA, 1995a)

U.S. EPA. 1994a. Great Lakes Water Quality Initiative Criteria Documents for the

Protection of Wildlife. DDT, Mercury; 2,3,7,8-TCDD, PCBs. Office of Water, Office of

Science and Technology, Washington, DC.

U.S. EPA. 1994b. Federal Register Notice. Proposed Water Quality Guidance for the

Great Lakes System. August 30. 59(167):44678-44684.

U.S. EPA. 1995a. Exposure Factors Handbook. Review Draft. ORD, Washington,

DC. EPA/600/P-95/002A.

U.S. EPA. 1995b. Federal Register Notice. Final Water Quality Guidance for the

Great Lakes System. March 23. 60(56):15366-15425.

U.S. EPA. 1996a. Mercury Report to Congress Volume III. SAB Draft. An Assessment

of Exposure from Anthropogenic Mercury Emissions in the United States. NTIS PB96-

184643. EPA-452/R-96-001c.

U.S. EPA. 1996b. Mercury Report to Congress Volume V. SAB Draft. An Ecological 

Assessment of Anthropogenic Mercury Emissions in the United States. NTIS PB96-

184668. EPA-452/R-96-001e.

U.S. EPA. 1997. Exposure Factors Handbook Volume II. Food Ingestion Factors,

August. Office of Research and Development, Washington, DC. EPA/600/P-95/002Fb.
Chapter 8

Determining Exposure From Intake Of Aquatic Organisms

MPE 8-34 December 1998

Voiland, M., K. Gall, D. Lisk and D. MacNiell. 1990. The effectiveness of

recommended fat-trimming procedures on the reduction of PCB and mirex levels in

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geography and developmental impacts. Arctic Anthropol. 24(2):56-81.
Chapter 9

Breast Milk Pathway

MPE 9-1 December 1998

BREAST MILK PATHWAY

9.1. INTRODUCTION

Numerous chemicals, both environmental and contaminants, have been

identified in human breast milk (Abadin et al., 1997; Adamovic et al., 1978; Atkinson,

1979; Byczkowski et al., 1994; Cerutti et al., 1976; Jensen, 1983, 1991; Jensen and

Slorach, 1991; U.S. EPA, 1980, 1983, 1994; WHO, 1985, 1989). Residues of

organohalogen, organometallic and inorganic contaminants, many of which are found

in emissions from combustion sources, have been detected in human milk. The major

route of human exposure to these occurs through ingestion of fresh-water fish, seafood,

plants, and other food contaminated by many sources including the fallout of air

pollutants (Smith, 1987; Siebert et al., 1991; Stevens, 1991; Sullivan et al., 1991). This

document focuses on contaminants that are likely to be emitted from stationary

combustion sources, but these must be considered within the context of existing

burdens from all sources.

Exposures predicted by the infant exposure model presented in this Chapter

should always be considered in the context of the benefits of breast feeding (see Text

Box 9-1). In addition, the application of the model is somewhat limited because of the

current lack of infant dose-response data. Predicted infant exposures, however, can

still be used to estimate incremental lifetime cancer risk and for comparison to the

Reference Dose (RfD) for noncancer effects for screening purposes.

9.2. CHEMICAL CONTAMINANTS IN BREAST MILK ARISING FROM COMBUSTION

The chemical contaminants in breast milk can be generally considered in three

major categories:

1) Persistent highly-lipophilic organic contaminants, such as polychlorinated

biphenyls (PCBs), polychlorinated dibenzofurans PCDFs, and

polychlorinated dibenzo-p-dioxins (PCDDs), are by far the most

documented group. These, by virtue of their lipophilicity, are found

almost
Chapter 9

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MPE 9-2 December 1998

entirely in the milk fat. Lipophilic metabolites such as PCB methyl

sulfones and hexachlorobenzene (HCB) methyl sulfones have also been

found in the lipid phase of breast milk. Polyaromatic hydrocarbons (PAHs)

occur in breast milk, but they are not as persistent as the halogenated

aromatics and metabolites are more laborious to analyze.

2) Combustion-source compounds with relatively low octanol:water partition

coefficients such as phenol, benzene, halobenzenes, halophenols, some

aldehydes and the more polar metabolites of PCBs, PAHs and pesticides

may occur in both the aqueous and lipid phases of breast milk. However,

Text Box 9-1 Breast-Feeding: Benefits vs. Risk

Potential risk to the infant from exposure to contaminants in breast milk must be balanced

against the well-documented benefits of breast feeding. In particular, infant exposure models

should be relatively free of bias, particularly towards over prediction of risk. Breast feeding

has a number of universally accepted benefits for the infant as well as for the mother. 

A discussion on physical and psychological benefits obtained by the nursing mother,

as well as by the developing infant from breast feeding can be found in Mukerjee

(1998). Several studies show the beneficial effects of breast feeding despite the

presence of contaminants associated with developmental (pre- and post-natal)

neurological effects. PCB and PCDD/PCDF exposures through breast milk resulted

in subtle neurological and developmental deficits, but many effects were more than

compensated for by the developmental benefits of breast feeding vs bottle feeding

(Huisman et al., 1995; Koopman-Esseboom et al., 1996). Neurological effects in

children were associated with (presumably) high levels of methylmercury in breast

milk (Amin-Zaki et al., 1981). In another study, however, infants exposed to lower

levels of MeHg in breast milk reached developmental milestones more quickly than

infants not breast fed (Grandjean, et al., 1995b). As toxicity is dose related, at some

level of the contaminant in breast milk, the benefits of breast feeding would be

neutralized. One study of PCB exposure to breast-fed human infants suggests

adverse effects associated with milk concentrations greater than 780 ng/ml, but not

below (Jacobson and Jacobson, 1990). Data addressing infant dose response from

contaminants in breast milk are generally lacking, so interpretation of exposure levels

with respect to toxicity to the infant is problematic. 
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MPE 9-3 December 1998

there is sparse information available, as the aqueous fraction is often

ignored.

3) Inorganic compounds, metals, and some organo-metallics, including the

heavy metals arsenic, lead, cadmium, and mercury, have been found in

breast milk. Trace nutrients such as copper, iron and zinc may be

present at elevated levels if maternal contamination is high. These

inorganics are generally found in the aqueous phase and most are bound

to proteins, small polypeptides, and free amino acids. Some organometallics and metalloids (such as As and Se) may also be found in the

lipid phase to some degree. Table 9-1 presents some examples of

organic contaminants in human milk, with their respective water

solubilities and Log Kow (log10 octanol-water partition coefficient) values to

help assess relative solubility in the aqueous or fat phase of breast milk.

9.2.1. Residues of Highly Lipophilic Organic Contaminants in Human Milk.

9.2.1.1. PCDDs and PCDFs — Several reports have been published on the

presence of PCDDs, PCDFs, PCBs, and polybrominated biphenyls (PBBs) in human

milk (Jensen and Slorach, 1991; Jensen, 1987; Dewailly et al., 1991; Rogan and

Ragan, 1994: Hong et al., 1994; Dahl et al., 1995). 2,3,7,8-Tetra-CDD (TCDD), the

most toxic PCDD, is found at a very low level compared to other PCDDs. More than

50% of the total PCDD content consists of Octa-CDD, perhaps the least toxic PCDD

congener. Average TCDD levels in human milk fat samples from West Germany

(Rappe, 1980), northern Sweden (Rappe et al., 1985), Netherlands (Fürst et al., 1986),

and Yugoslavia (Fürst et al., 1986) were reported to be 1-3 ppt, 0.6 ppt, 9.7 ppt, and

<0.1 ppt respectively. The average level of TCDD in human milk fat in industrial

countries is around or below 2 ppt (ng/kg) (Jensen, 1987).

One of the PCDDs which is not found in any commercial products as a

contaminant but always found in the emissions from the waste incinerators is 1,2,3,7,8-

Penta-CDD (PeCDD). Detection of residues of 1,2,3,7,8-PeCDD with other
Chapter 9

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MPE 9-4 December 1998

TABLE 9-1

Ranges of water solubilities and octanol:water partition coefficients for representative chemical

emissions which may occur in breast milk (Adapted from ATSDR, 1994a,b, 1995, 1997a,b,c and

Mackay et al., 1992)

Chemical Chlorine 

 Substitution Mol. Weight

 Water 

Solubility

(mg/L)

Log Kow

Phenol 94.1 86600 1.5 

Chlorophenol 2,4 163 6194 3.1 

Chlorophenol 2,3,4,5,6 266.4 14 5 

Benzene 78.1 1780 2.1 

Toluene 92.1 515 2.7 

Xylene para-CH3 (1,4) 106.2 215 3.2 

Chlorobenzene 1 112.6 484 2.8 

Chlorobenzene 1,2,3 181.4 21 4.1 

Chlorobenzene 1,2,4 181.4 40 4.1 

Chlorobenzene 1,2,3,4,5 250.3 0.65 5 

Chlorobenzene 1,2,3,4,5,6 284.8 0.005 5.5 

Benzo(a)pyrene 252.3 0.0038 6.1 

Biphenyl 154.2 7 3.9 

PCB 4 2,2' 223.1 1 4.9 

PCB 15 4,4' 223.1 0.008 5.3 

PCB 47 2,2',4,4' 292 0.09 5.9 

PCB 52 2,2',5,5' 292 0.03 6.1 

PCB 77 3,3',4,4' 292 0.001 6.5 

PCB 95 2,2',3,5',6 326.4 0.034 6.1 

PCB 99 2,2',4,4',5 326.4 0.018 6.5 

PCB 101 2,2',4,5,5' 326.4 0.01 6.4 

PCB 128 2,2',3,3',4,4' 360.9 0.0006 7 

PCB 138 2,2',3,4,4',5' 360.9 0.0017 6.8 

PCB 153 2,2',4,4',5,5' 360.9 0.001 6.9 

PCB 209 23456-2'3'4'5'6' 498.7 0.000001 8.3 

PCDF 83 2,3,7,8 306 0.00042 5.7 

PCDF 114 2,3,4,7,8 340.4 0.00024 6.9 

PCDF 131 1,2,3,4,6,7,8 409.3 0.000014 7.9 

PCDD 48 2,3,7,8 322 0.000019 6.7 
Chapter 9

Breast Milk Pathway

1

 TEFs, in this case, can be considered to be TCDD equivalency factors.

MPE 9-5 December 1998

PCDDs/PCDFs in human milk indicates that the major source of exposure to this

congener of PCDD is occurring from waste incinerator emissions (Rappe et al., 1984;

Buser and Rappe, 1984; Mukerjee and Cleverly, 1987). 

Around 70% of the total Hexa-CDDs (HxCDDs) is 1,2,3,6,7,8-HxCDD, and the

remainder is mainly 1,2,3,4,7,8-HxCDD and 1,2,3,7,8,9-HxCDD. Of the HeptaCDDs(HpCDDs) only 1,2,3,4,6,7,8-HpCDD residues have been reported in human milk.

Residue levels of PCDFs in human milk tend to be lower than those of PCDDs

(Dewailley et al., 1991), even though PCDFs dominate in combustion residues and are

more dominant in the atmospheres over these residues (Hansen and O'Keefe, 1996). 

HxCDDs/HxCDFs and HpCDDs/HpCDFs are prevalent in pentachlophenol. 

Incineration of wood and other products impregnated with pentachlorophenol results in

the further formation and emission of hexa- and hepta-CDDs/CDFs. Both 1,2,3,7,8-

and 2,3,4,7,8-PeCDFs have been detected in human milk, but 90% of the PeCDFs is

generally 2,3,4,7,8-PeCDF (Fürst et al., 1989). 1,2,3,4,7,8- , 1,2,3,6,7,8- , and

2,3,4,6,7,8-HxCDFs; and 1,2,3,4,6,7,8-HpCDF are also prevalent.

PCDDs, PCDFs, coplanar PCBs and other coplanar compounds with lateral

chlorines have one common mechanism of action through the aryl hydrocarbon

receptor (AhR) (Poland and Knutson, 1982; Safe, 1986; Blankenship et al., 1994). 

Similar predictable responses to these compounds make it possible to compare

potencies by measuring select endpoints. TCDD is the most potent of the AhR

agonists and is assigned a "Toxic Equivalency Factor" (TEF)1 of 1 for AhR mediated

effects only. If the composition of a mixture is known, the TCDD Equivalency (TEQ)

can be derived by summing TEF normalized concentrations. 

Women from industrial countries have more PCDDs/PCDFs in their milk than

developing countries. Human milk fat dioxin TEQs of PCDDs and PCDFs from United

States, Japan, Canada and Germany have been reported to be 20, 27, 26, and 27 ppt
Chapter 9

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MPE 9-6 December 1998

respectively, while those from Thailand, Cambodia, and Siberia were found to be 3, 3,

and 12 ppt respectively (Schecter, 1991).

9.2.1.2. PCBs and Related Compounds — Although a downward trend of PCB

levels has been observed in industrial countries from 1960 to 1990 (Jensen, 1991;

Johansen et al., 1994; Mes et al., 1993), exposures directly or indirectly related to

combustion sources still occur (Hansen, 1998; Siebert et al., 1991; Stevens, 1991). 

The levels of PCBs in human milk are still much higher than the total concentrations of

PCDDs and PCDFs (Jensen, 1987; Koopman-Esseboom et al., 1994). Orthosubstituted PCBs are are found to a greater extent in human milk than coplanar PCBs

(Norén et al., 1990; Norén and Lundén 1990; Brevik and Bjerk, 1978; Hansen, 1998). 

Coplanar PCBs 77, 126 and 169 are found at much lower concentrations, but monoortho congeners with TCDD-like activity may contribute TEQs equal to or greater than

those from PCDDs and PCDFs (Dewailley et al., 1991; Grandjean et al., 1995a;

Johansen et al., 1994; Koopman-Esseboom et al., 1994; Jensen, 1987; Hansen, 1998). 

Fish intake has long been considered to be the major source of PCBs. Human milk

samples in the United States contain high levels of PCBs (Bush et al., 1985; Safe et al.,

1985). The average level of total PCBs in human milk fat from industrial countries has

been estimated to be between 0.5 and 4 ppm (Jensen, 1987). PCBs have been

detected in the sera (Schantz et al., 1994), adipose tissues (Niessen et al., 1992;

Teufel et al., 1990) and bone marrow (Scheele et al., 1995) of breast-fed children.

As with the PCDDs and PCDFs, the PCB congeners reported in breast milk are

more closely linked to the analytes selected rather than the actual compounds present

(Hansen, 1998). Earlier studies did not report the coplanar PCBs because of analytical

limitations and low levels; more recent studies focus on coplanar ("non-ortho") and

mono-ortho congeners because of their similarity to TCDD. Due to technical difficulties

and the lack of many standards, early chromatographic studies may have mis-assigned

peaks, which may carry over to more recent documents.

 The ortho-substituted nonplanar PCB congeners 138, 153 and 180 have been

reported as the most frequent congeners found in human milk, followed by PCBs 70,
Chapter 9

Breast Milk Pathway

MPE 9-7 December 1998

99, 118 and 170 (Fürst et al., 1994; Koopman-Esseboom et al., 1994; Schecter et al.,

1994). The three most frequently found congeners (138, 153 and 180) were reported

at breast milk concentrations of 2.1, 3.2 and 1.2 ng/g, respectively (Bush et al., 1985),

with PCB-153 comprising 30% of the total PCBs measured (Abraham et al., 1995). The

prevalence of ortho-substituted PCBs is more likely a result of greater amounts of

ortho-congeners than coplanar congeners in the environment (Lake et al., 1995;

McFarland and Clarke, 1989). Furthermore, congener distribution patterns in

contaminated fish and human milk are very similar suggesting that one of the primary

sources of human exposure to PCBs is ingestion of contaminated fish (Jensen, 1987).

Identification of many PCB congeners is complicated by co-elution in the

fractionation process. As an example, PCB-70 has been reported at concentrations of

1.7 ng/g, but it is equally likely to be PCB-74, which nearly co-elutes with PCB-70 and

has been found to be prevalent in human samples. Considering co-elution phenonena,

whole milk concentrations and milk:blood ratios for the major PCB congeners (52% of

total PCBs, Bush et al., 1985) are probably best represented by Table 9-2.

TABLE 9-2

Major PCB congeners in human milk from upstate New York and concentration

ratio to maternal blood (adapted from Bush et al., 1985).

PCB Congener Mean + SE (ng/g Whole milk) Whole Milk:Maternal Blood

153 3.2 + 0.4 10

118 (not 179) 2.5 + 0.3 9.2

138 2.1 + 0.4 3.5

70 or 74 1.7 + 0.2 >500

180 1.2 + 0.1 9.4

 99 1.1 + 0.1 8.9

156 1.0 + 0.1 125

105 1.0 + 0.2 3.8
Chapter 9

Breast Milk Pathway

MPE 9-8 December 1998

The largest milk:blood concentration ratios in Table 9-2 (>100) are probably a result of

anomalously low maternal serum levels, so it is reasonable to suggest that whole

milk/blood ratios for PCBs will be in the range of 4 to 10. Serum PCB levels, however,

are not useful as reliable predictors of breast milk PCB concentrations. Mes et al.

(1984) found no statistically significant correlation between total PCB in maternal blood

and milk, mainly due to the accumulation of lower chlorinated congeners in milk fat but

clearance of these congeners from blood. There was some correlation between PCBs

118, 138, 153 and 180 in maternal plasma and total PCBs in human milk, but the

variability in the relationship was too great to be of predictive use (Koopman-Esseboom

et al., 1994).

9.2.2. Compounds Partitioning into both Lipid and Aqueous Phases. Human

breast milk is generally 2-4% fat and 0.8-1% protein. As most proteins are solubilized

in the aqueous phase, compounds binding to proteins will be considered together with

water-soluble compounds in the aqueous phase. Although nonpolar compounds

partition to some degree into the aqueous compartment, the most commonly analyzed

organic contaminants are highly concentrated in the lipid phase; the concentrations of

most halogenated aromatics in breast milk are not significantly underestimated by

ignoring the aqueous phase. On the other hand, compounds with greater water

solubilities and lower Kow may be present at significant levels in the aqueous phase. In

practice, these more polar compounds are often extracted by the solvents used to

extract the lipids and are discarded if not specifically targeted as analytes (e.g. Hong et

al., 1992; Noren et al., 1990, 1996). In some methods, the aqueous fraction is

discarded without extraction (Davies and Mes, 1987). Thus, the most common

methods for milk analysis tend to underestimate whole milk concentrations of “semipolar” organics, such as the less chlorinated benzenes and biphenyls (see Table 9-1). 

In addition to chloro-naphthalenes and chlorobenzenes, large amounts of

chlorofulvenes and chlorofulvalenes are produced by the incomplete incineration of

plastic and trichloroethylene containing wastes (Blankenship et al., 1994; Villalobos et

al., 1996). These structural isomers of benzene and naphthalene would be expected to
Chapter 9

Breast Milk Pathway

MPE 9-9 December 1998

have similar physical and chemical properties. Over 250 compounds were indicated in

these extracts with only pentachlorobenzene and HCB being common breast milk

analytes. Whether these would be in the aqueous or lipid phase would depend on

specific properties and metabolism, but it is unlikely that many would be target

analytes.

Some metabolites have been target analytes. Methyl sulfonyl metabolites of

chlorinated aromatics are produced through a series of hepatic/gut flora/hepatic

reactions and retain many of the lipophilic and enzyme induction properties of the

parent compounds. Methyl sulfonyl PCBs were found at 5% total PCBs in the breast

milk of an occupationally exposed Japanese mother (Yoshida and Nakamura, 1979). In

less contaminated subjects, breast milk methyl sulfones averaged 0.6% total PCB in

Canadian women (Newsome and Davies, 1996) and 0.4-0.9% in Swedish women

(Noren et al., 1996). In both studies, the 4,4'-DDE-3-methylsulfone was a consistent

0.2% total DDE. The presence of PCBs and DDE in human milk could be factored up

by about 1% to account for these metabolites, but this would still be well within the

range of analytical and individual uncertainty; therefore, it would make little difference

in the risk assessment unless particularly potent metabolites are described.

The more polar hydroxylated metabolites of lower chlorinated PCBs are more

potent estrogens than are the parent compounds (reviewed in Hansen, 1998); however,

these are generally conjugated or otherwise bound in the mother. The higher

chlorinated hydroxy PCBs may be antiestrogens and are more persistent. A highly

chlorinated 4-hydroxy PCB metabolite has been identified in milk from Canadian

donors at about 2 ppt (pg/g) or 0.02 % of the total PCB (Newsome and Davies, 1996). 

The hydroxy metabolites would be assumed to be at higher levels (relative to parent) in

the aqueous phase of milk, but the small contribution to total intake should have little

impact on the infant.

There are very limited data on the relative distribution of more water soluble

chemicals between phases in human milk. The presence of dichloro- and trichlorobenzenes (80-400 and 5-64 ppb, respectively) in the lipid fraction (Davies and Mes,
Chapter 9

Breast Milk Pathway

MPE 9-10 December 1998

1987) indicates that these less lipophilic chemicals contribute to the total milk burden,

but the total contribution cannot be known without a determination of the aqueous

phase concentrations.

There is a need for additional data on aqueous phase contaminants of human

milk, many of which may have been overlooked because:

1) They were not target analytes;

2) The aqueous phase may have been discarded; or

3) The total milk extract was partitioned to concentrate the more lipophilic

contaminants.

The complex nature of emissions from combustion sources makes it desirable to

characterize the transfer and milk-phase distribution of model compounds such as

benzene, chlorobenzenes, naphthalene, naphthols, methyl benzenes, phenols or

cresols to make further modeling possible.

9.2.3. Residues of Inorganic and Organometallic Contaminants in Human Milk.

Residues of various nonnutrient elements including aluminum, arsenic, lead, cadmium,

mercury and silicon are common in human milk. Inorganic and organic compounds of

metals are found in human breast milk, but are not associated with milk fat (Jensen,

1991). Trace nutrients including copper, fluorine, iodine, iron, manganese, selenium

and zinc may be deficient or reach toxic levels in human milk (Casey et al., 1995;

Grandjean et al., 1992; Sternowsky and Wessolowski, 1985; Pavlovic and Jokic, 1994). 

Great care must be taken when analyzing the trace elements; sample contamination is

common and the risk assessor must be certain appropriate blanks and quality control

procedures were used before applying the data.

Mercury, particularly in its methylated form, is a compound of concern because

of its bioaccumulation in fish and potential for developmental toxicity (see U.S. EPA,

1997a for a comprehensive review). Elevated levels of methylmercury have been

reported in breast milk for human populations consuming large amounts of pilot whale

meat and fish (Grandjean et al., 1995a), or fish alone (Oskarsson et al., 1996). The
Chapter 9

Breast Milk Pathway

MPE 9-11 December 1998

hair mercury concentrations in children nursed by mothers whose milk contained

methylmercury are also increased (Grandjean et al., 1995a).

Lead is also of concern because of developmental toxicity. The mean lead

concentration in milk collected during week 1 to 4 of lactation from 12 women living in

urban districts was 49 μg/L (range 25-95 μg/L). Three samples of colostrum contained

136-151 μg/L of lead. One of these three women was subsequently followed. The lead

concentration was 137 μg/L in colostrum, and 43, 57 and 47 μg/L in milk samples

during week 1, 3 and 4 post partum (Amore et al., 1983). Average levels of lead,

cadmium and zinc in milk samples from women living in a very polluted region of

Yugoslavia were found to be 43, 11 and 295 μg/L, respectively. Ninety percent of

these milk samples were positive for lead, while 60 and 100% of the samples were

positive for cadmium and zinc respectively (Pavlovic and Jokic, 1994). Although all

human milk contains zinc as a nutrient, this level was nearly 100 times normal values.

Ingestion of certain fish and crustacea which have arsenic, mainly

arsenobetaine, can result in the presence of arsenic in human milk (Dang et al., 1983;

Grimanis et al., 1979; Byrne et al., 1983). In spite of food chain accumulation of

cadmium from combustion and other sources (ATSDR, 1993; Hattemer-Frey and

Travis, 1991; Hansen and Schaeffer, 1995), cadmium levels are generally low in breast

milk and have been shown to increase the most with maternal cigarette smoking

(Casey et al., 1995). Concentrations of lead, cadmium, mercury and arsenic in human

milk from Taiwan ranged up to 56, 6, 5 and 3,002 μg/L, respectively (Hwia-Cheng et al.,

1993). In human milk, collected 2-6 days post partum, the levels of lead and cadmium

respectively were found to range from <4 to 421.5 μg/L and 0.024 to 20 μg/L, with

corresponding means of 29.9 and 18.0 μg/L (Riolfatti and Modenese, 1990).

Concentrations of iron, zinc and copper are generally maintained within narrow

ranges in human milk, being highest in the colostrum. Minor variations may occur with

geographical area and maternal nutritional status. Zinc deficiency is more likely than

zinc excess (Casey et al., 1995). Cadmium interacts with the status of these nutrients

as well as calcium (see Hansen and Schaeffer, 1995; Kross et al., 1995), and may
Chapter 9

Breast Milk Pathway

MPE 9-12 December 1998

influence nutrient levels in milk through maternal exposure. Manganese, like iron, is

mostly bound to lactoferrin in human milk, which makes the low levels (relative to

manganese in milk of other species) more bioavailable to the human infant (Casey et

al., 1995). Selenium, iodine and fluorine concentrations in human milk are more

affected by intake than the other trace nutrients, with large geographical variations

(Casey et al., 1995). 

9.2.4. Factors Influencing Transfer of Contaminants into Human Milk. Lifestyle

factors, such as cigarette smoking, may increase contaminant concentrations. For

example, increased cadmium (Radisch et al., 1987; Hallen et al., 1995) and PCB (Hong

et al., 1994) levels have been found in the breast milk of smokers. Alcohol consumption

has also been found to increase the levels of PCBs in human milk (Dewailly et al.,

1996; Kreiss et al., 1981; Wolff et al., 1982). Alcohol may increase the absorption

through the gastrointestinal tract of the organohalogens by enhancing their solubility

(Dewailly et al., 1996). More likely is the direct effect of ethanol on oxytocin release;

this decreases milk production and increases the lipid content which would favor higher

concentrations of lipophilic compounds (Neville and Walsh, 1995).

Metal concentrations have been reported to be lower in breast milk than in

plasma (Hallen et al., 1995), apparently due to binding of the metals to metallothionein

and protein in the mammary gland (Radisch et al., 1987). On the other hand, during

pregnancy and lactation, lead in bone is mobilized, increasing plasma lead which may

then be available for transport into breast milk. 

Maternal age, parity, length of lactation, and body weight influence the

concentration of PCB, PBB and PCDD/PCDF in breast milk (Drijver et al., 1988; Pluim

et al., 1993; Hong et al., 1994; Beck et al., 1994; Jacobsen et al., 1989; Jensen, 1987;

Jensen, 1995; Fürst et al., 1987, 1992). The extent to which these variables influence

the transfer of metals to milk is not well-characterized.

9.3. AVERAGE DAILY DOSE OF CONTAMINANT TO THE NURSING INFANT

The average daily dose to the infant ingesting breast milk can be estimated from

the levels of contaminant concentrations in the milk, which in turn can be estimated on
Chapter 9

Breast Milk Pathway

MPE 9-13 December 1998

DIINF '

[Cmilkfat fmbm % Caqueous (1 & fmbm)] CRmilk f

ai ED

BWINF f

am AT

(9-1)

the basis of the contaminant intake by the mother. Linear pharmacokinetic models

have been developed by Smith (1987), Sullivan et al. (1991) and Travis et al. (1988),

and are also presented in Estimating Exposure to Dioxin-Like Compounds (U.S. EPA,

1994). These models are theoretical constructs., based on first order kinetics (linear),

that were developed for persistent lipophilic organic chemicals, such as chlorinated

pesticides, PCBs, and dioxins/dibenzofurans, for which fat serves as a significant

reservoir. With slight modifications these approaches can, however, be extended to

include nonlipophilic compounds. Equation 9-1 is a modification of the model of Smith

(1987) to include a term for nonlipophilic compounds. The average daily dose (intake)

for the infant can be estimated as follows:

where:

DIINF = average daily contaminant intake for the infant (mg/kg-d)

Cmilkfat = concentration of contaminant in maternal milk fat (mg/kg)

Caqueous = concentration of contaminant in aqueous phase of maternal

milk(mg/kg)

fmbm = fraction of fat in breast milk (dimensionless)

CRmilk = ingestion rate of breast milk (kg/day)

fai = fraction of ingested contaminant that is absorbed by the infant

(dimensionless)

ED = exposure duration (yr)

BWINF = body weight of infant (kg)

fam = fraction of ingested contaminant that is absorbed by the mother

(dimensionless)

AT = averaging time (yr)
Chapter 9

Breast Milk Pathway

MPE 9-14 December 1998

DIINF is an estimate of the ingested intake (external dose) for the infant adjusted

for the difference in oral absorption between infants and adults; this allows for

incorporation of data on differential absorption when the difference is significant. For

most compounds, either Cmilkfat or Caqueous will be negligible. So, for example, as in the

practical application of Equation 9-1, the aqueous pathway can be ignored for highly

lipophilic compounds, such as PCDD/PCDFs, and the milk fat pathway can be ignored

for water soluble compounds, such as salts of metals or other polar compounds.

9.3.1. Contaminant Concentration in Breast Milk Fat (Cmilkfat): First Order Kinetics

Approach. Two approaches are presented here for determining contaminant

concentration in breast milk fat. Both approaches predict levels of contaminants on the

basis of the estimated contaminant intake by the mother. One approach uses

estimates of the contaminant concentration (or body burden) in the fat compartment of

the mother and assumes either steady-state (Smith, 1987) or changing (Sullivan et al.,

1991) contaminant concentrations with time. Both variations assume first order kinetics

for contaminant elimination from the body. The kinetic models are the preferred

methods for determining contaminant concentrations in breast milk. The other

approach, by Travis et al. (1988), uses a biotransfer factor (BTF) based on the octanolwater partition coefficient in conjunction with maternal intake to estimate contaminant

concentration. The BTF approach requires less data than the kinetic models, but

should be considered more of a screening model than a predictive one. The BTF

approach is presented in Section 9.3.2.

The approaches utilized in this methodology are linear, in which the predicted

contaminant concentrations in breast milk are directly proportional to the inputs, or, in

the case where steady state is not assumed, linear in the kinetic parameters. Another

approach, multi-compartment physiologically-based pharmacokinetic (PBPK) modeling,

explicitly accounts for the distribution and transfer of compounds among many body

compartments. The output of PBPK models is dynamic over time and nonlinear by

nature. PBPK modeling is designed to be a more realistic representation of the

behavior of xenobiotic compounds in living systems, but is usually compound specific.
Chapter 9

Breast Milk Pathway

MPE 9-15 December 1998

Text Box 9-2 discusses some of the issues involved in the choice of models for breast

milk exposure.

The steady-state model presented by Smith (1987) assumes that the

concentration in the maternal fat compartment has reached steady state and that the

concentration in breast milk fat is the same as in maternal body fat. The contaminant

concentration in breast milk fat can then be estimated as:

Cmilkfat '

DAIMAT f

f

kelim f

fm

(9-2)
Chapter 9

Breast Milk Pathway

MPE 9-16 December 1998

where:

C milkfat = concentration in maternal milk fat (mg/kg [ppm] of milk fat)

DAIMAT = daily maternal absorbed intake of contaminant (mg/kg of body

weight/day)

f

f = fraction of contaminant that is stored in maternal fat (dimensionless)

kelim = biological elimination constant for contaminant in adult women (days-1)

Text Box 9-2 Choice of Model: Linear vs. PBPK

There are a number of true physiologically based pharmacokinetic (PBPK) models in the open

peer-reviewed literature for describing the whole process of exposure and disposition

(absorption, distribution, metabolism and excretion), as well as lactational transfer of both

lipophilic and nonlipophilic compounds. These models can handle both maternal and infant's

pharmacokinetics (Shelley et al., 1988), multiple routes of administration and different

exposure scenarios, and can accommodate changes in physiology and metabolism over time

(Andersen and Greenlee, 1991). PBPK models are non-linear and have a power of predicting

(and extrapolating) chemical disposition beyond the experimental range, in contrast to the

classical one- or two-compartment linear models (Medinsky and Klaassen, 1997). PBPK

models were developed, for example, for several dioxin and PCB derivatives (Andersen et al.,

1993; Kedderis et al., 1993; Leung et al., 1990a,b; Santostefano et al., 1998). These models,

however, are not directly applicable to estimating infant exposure through consumption of

breast milk from mothers indirectly exposed to combustion emissions. Either the models lack a

breast milk compartment or they are designed specifically for inhalation exposure. Also, these

models require data for a large number of parameters, much of which is unavailable. The

adequacy of linear algebraic models for predicting human breast milk concentrations of highly

lipophilic compounds is unknown. Although, in laboratory animal studies, the

pharmacokinetics of TCDD and related compounds is highly non-linear and dose-dependent (at

acutely toxic doses), linear behavior was observed at lower, environmentally relevant doses

(Roth et al., 1994). A better approximation with linear models may be obtained, however, for

pharmacokinetics of nonlipophilic compounds, although bone-seeking compounds may be an

exception. The kinetics of compartmentalization of lead in bone is complex and nonlinear and

may not be well-represented by 1st order kinetics (Dalley et al., 1990; O’Flaherty, 1991). 

On the linear side, a two-compartment model for lipophilic compounds has also been published

(Hoover et al., 1991). While a multi-compartment model would be somewhat more

physiologically realistic than the one-compartment model used for the MPE breast milk model

(Equations 2, 3 and 4), the model parameters in Hoover et al. (1991) are essentially identical to

those in Equation 2. It is unlikely that predictions from the two models would differ

significantly.
Chapter 9

Breast Milk Pathway

MPE 9-17 December 1998

f

fm = fraction of mother's weight that is fat (kg maternal fat/kg total body

weight)

The assumption that contaminant concentration in milk fat will be the same as in

general body fat appears reasonable for highly lipophilic compounds (e.g., PCDDs,

PCDFs, PCBs, HCBs). Beck et al. (1994) showed similar breast milk and adipose

tissue concentrations for PCDDs/PCDFs. Ignoring the time required to reach nearsteady-state concentrations, however, results in an overprediction of C milkfat. 

Overpredictions of 10-fold, or greater, can occur when the half-life is large and the

exposure duration is small (see Section 9.3.). Equation 9-2 is a reasonable

simplification for scenarios in which the biological half-life of the compound is fairly

small relative to the duration of exposure. Otherwise, the time required for the fat

concentration to reach a relatively constant level should be considered, as in Equation

9-3. 

Cmilkfat '

DAIMAT f

f

kelim f

fm

(1 & e

&kelim tpn)

(9-3)

where:

C milkfat = concentration in maternal milk fat (mg/kg [ppm] of milk fat)

DAIMAT = daily maternal absorbed intake of contaminant (mg/kg of body

weight/day)

f

f = fraction of contaminant that is stored in maternal fat (dimensionless)

kelim = biological elimination constant for contaminant in adult women (day-1)

f

fm = fraction of mother's weight that is fat (kg maternal fat/kg total body

weight)

tpn = duration of mother's exposure prior to nursing (days)
Chapter 9

Breast Milk Pathway

MPE 9-18 December 1998

In both equations, maternal body burden of the chemical is estimated assuming

a constant intake and first-order elimination. The kinetic term (1 - e-kelim tpn) in Equation

9-3 accounts for the time required to reach a (pseudo) steady-state fat concentration. 

Over predictions of breast milk concentrations will also result if maternal

contaminant losses from breast feeding are not considered. As an example, PCDDs

and PCDFs tend to be excreted in breast milk at the highest levels within the first few

weeks after delivery; after 1 year of breast feeding, milk concentrations appear to

approach steady state and are about 30-50% of the initial levels (Fürst et al., 1989). 

The over prediction of milk concentration from ignoring breast-feeding losses, in

contrast to the steady-state over prediction, is greater for longer maternal exposure

durations (and for longer nursing periods), but of lower magnitude (10-90% over

prediction; see Section 9.3.). Both Smith (1987) and Sullivan et al. (1991) present

models that incorporate breast feeding losses. The Smith (1987) model retains the

steady-state assumption for pre-nursing exposures, which will still overestimate breast

milk concentrations. The Sullivan et al. (1991) model includes a term for initial

concentration of contaminant in infant fat, which complicates estimation of infant dose

from breast feeding alone. Equation 9-4 is a modification of the Smith (1987) model

that includes pre-nursing kinetics and gives the average contaminant concentration in

breast milk over the entire period of breast feeding. This model is also essentially the

same as Sullivan et al. (1991) without the initial infant body burden term. A detailed

derivation of Equation 9-4 is presented in Appendix C.

Cmilkfat '

DAIMAT f

f

kelim f

fm

[

kelim

kelac

%

1

kelac t

bf

(1 & e

&kelimtpn &

kelim

kelac

) (1 &

(9-4)

where:

C milkfat = average concentration of contaminant in maternal milk fat over

duration of breast feeding (mg/kg [ppm] of milk fat)
Chapter 9

Breast Milk Pathway

MPE 9-19 December 1998

kelim '

ln2

t1⁄2 b

(9-5)

DAIMAT = daily maternal absorbed intake of contaminant (mg/kg of body

weight/day)

f

f = fraction of contaminant that is stored in maternal fat (dimensionless)

f

fm = fraction of mother's weight that is fat (kg maternal fat/kg total body

weight)

kelim = biological elimination constant for contaminant in adult women (days-1)

kelac = biological elimination constant for contaminant in lactating women

(days-1) 

tpn = duration of mother's exposure prior to nursing (days)

tbf = duration of breast feeding (days) 

Each of these inputs is described in more detail in Sections 9.3.1.1-9.3.1.7. 

Note that Equation 9-4 relates the average whole-body concentration over the breastfeeding period to the maximum theoretical steady-state concentration predicted by

Equation 9-2 by a fraction that is dependent on the two rate constants, duration of

exposure, and duration of breast feeding. Although not immediately evident by

inspection, this fraction is always less than one.

9.3.1.1. Biological Elimination Constant for Contaminant in Nonlactating

Women (kelim) — The biological elimination constant is a first-order rate constant for

elimination of the contaminant from the body and is related to the half-life (in days) of

any given substance in the human body by the formula given in Equation 9-5.

where:

t1⁄2 b = biological half-life of contaminant in lactating women (days)

ln2 = natural logarithm of 2 (0.693)
Chapter 9

Breast Milk Pathway

MPE 9-20 December 1998

The half-life in the human body is chemical specific and is empirically derived. 

Lipophilic chemicals partition into body fat compartments and, in general, tend to have

relatively long half-lives (i.e., slow rate of elimination). These chemicals accumulate

with age, and the chemical concentration in fat increases over time. Lipophilic

chemicals with half-lives of several years (e.g., PCBs, dioxins, chlorinated pesticides)

may take more than 20 years to reach steady state. Thus, the half-life is an important

consideration in estimating chemical concentration in maternal fat. For dioxins, the

half-life is 7-10 years (U.S. EPA, 1994). The mean elimination half-lives of PCBs in

humans for several congeners ranged from 6.7 to 9.8 months (ATSDR, 1992). The

median half-lives for Arochlor 1242 and Arochlor 1254 were 2.6 and 4.8 years,

respectively, in workers occupationally exposed to PCBs during the manufacture of

electric equipment (Phillips et al., 1989). Another study on PCB elimination in humans

exposed to PCBs in contaminated cooking oil showed elimination half-lives in the range

of 4.1 to 6.1 years in the case of Yucheng patients, and 13.7 to 34.2 years for Yusho

patients (Masuda et al., 1995). The great variability in PCB half-lives most likely

reflects the variability in the half-lives of specific congeners. The measured half-lives

for mixtures of congeners will depend on the relative proportions of the congeners in

the mixture and on when the measurements were taken. Early measurements will tend

to yield shorter half-lives, reflecting the more rapidly eliminated congeners, than later

measurements when the slowly-eliminated congeners dominate the mixture. 

Furthermore, although the half-life of such a mixture will appear to stable when

exposure is constant, congeners with longer half-lives will accumulate to a greater

degree over time. In this case, the relative toxicities of the congeners must be known

to interpret the output of the model. Therefore, assigning a specific half life to mixtures,

such as dioxins or PCBs, is meaningless unless all the congeners are known to have

similar half-lives. Risk assessors should consult the open literature for the half-lives of

chemicals of importance at their site.

9.3.1.2. Biological Elimination Constant for Contaminant in Lactating

Women (kelac) — This elimination constant is the sum of kelim and the daily fraction of
Chapter 9

Breast Milk Pathway

MPE 9-21 December 1998

contaminant loss from breast feeding. Equation 9-6 gives the formula for kelac and is

from Smith (1987).

kelac ' kelim %

CRmilk f

f

fmbm

f

fm BWMAT

(9-6)

where:

kelac = biological elimination constant for contaminant in lactating women

(days-1)

kelim = biological elimination constant for contaminant in adult women (days-1)

CRmilk = ingestion rate of breast milk (kg/day)

f

f = fraction of contaminant that is stored in maternal fat (dimensionless)

fmbm = fraction of fat in breast milk (dimensionless)

f

fm = fraction of mother's weight that is fat (kg maternal fat/kg total body

weight)

BWMAT = maternal body weight (kg) 

The second term in Equation 9-6 is, essentialy, a first-order elimination constant for

breast-feeding losses, while kelim represents losses by all other pathways. For highly

lipophilic compounds, breast feeding losses can be larger than those by all other

pathways. As an example, the breast-feeding loss term in Equation 9-6 is 16 times

larger than Kelim for dioxin when all the defaults are used and an ff of 0.9 is assumed for

dioxin. Therefore, for highly lipophilic compounds with a long biological half life,

assuming that a linear model (first order kinetics) is appropriate, breast milk will

constitute the primary route of ellimination. Model uncertainty is discussed in

Section 9.3.

9.3.1.3. Average Daily Maternal Absorbed Intake of Contaminant (DAIMAT) —

The absorbed maternal intake of the contaminant (mg/kg-day) represents internal

intake from all pathways of exposure. Methodology limitations currently exclude
Chapter 9

Breast Milk Pathway

MPE 9-22 December 1998

DAIMAT ' DIMAT f

am

(9-7)

estimation of internal intake from inhalation exposure (see Chapter 10). If dermal

exposure is significant, the dermally-absorbed intake (see Chapters 4 and 7) can be

added to the orally-absorbed maternal intake (Eq. 9-7), or an equivalent oral intake

from dermal exposure can be estimated using the algorithm presented in Chapter 10 of

this document and summed with oral intake to derive an aggregate oral intake. The

orally-absorbed maternal intake is estimated by multiplying the oral intake (or

aggregate oral intake) by the fraction of contaminant absorbed from the gut, as in

Equation 9-7.

where:

DAIMAT = daily maternal absorbed intake of contaminant (mg/kg of body

weight/day)

DIMAT = average daily intake of contaminant ingested by the mother

(mg/kg-day)

fam = fraction of ingested contaminant that is absorbed by the mother

(dimensionless)

Values for fam can be found in the open literature. The default value for fam is 1,

which represents a conservative assumption.

9.3.1.4. Fraction of Contaminant Stored in Maternal Fat (ff) — This

parameter represents the proportion of the contaminant that is stored in the fat of the

mother. The amount of the chemical that partitions into the fat compartment is

generally empirically derived from studies of absorption and distribution. For most

lipophilic organics, absorption is high and most of the absorbed contaminant is stored

in deep compartments, such as the adipose tissue. For these compounds, the fraction

of ingested contaminant stored in fat may be >90% (ATSDR, 1992; Sullivan et al.,

1991; U.S. EPA, 1994). Information for this parameter can be located in the open

literature.
Chapter 9

Breast Milk Pathway

MPE 9-23 December 1998

9.3.1.5. Fraction of Mother's Weight That Is Fat (ffm) — The fraction of the

mother's weight that is fat is important for estimating the concentration of lipophilic

compounds in breast milk. Slim women will generally have smaller fat deposits,

suggesting a lower dilution of contaminants (greater concentrations) in fat stores. 

Changes in the mother's weight, before and during lactation, may influence the

concentration of chemicals in fat, in some cases leading to mobilization of chemicals

from fat stores (Jensen, 1983; Sim and McNeil, 1992). Both the Smith (1987) and

Sullivan et al. (1991) models assume that the fraction of maternal body fat remains

constant at approximately 30%.

9.3.1.6. Duration of Mother's Exposure Prior to Nursing (tpn) and Duration

of Breast Feeding (tbf) — Maternal exposure duration is partitioned into pre-nursing

and nursing phases for the kinetic model that includes breast-feeding contaminant

losses (Eq. 9-4). The default for tpn is the exposure duration for the scenario (ED)

minus the duration of breast feeding (tbf). This is a conservative default, which

assumes that the nursing period occurs at the end of the scenario duration when breast

milk concentrations will be highest. The default for tbf is 365 days, which is also

somewhat conservative for the U.S. population (NAS, 1991). If a central-tendency

estimate is desired, tbf can be set to 180 days. A site-specific duration should be used

if local breast feeding practices are known.

9.3.1.7. Maternal Body Weight (BWMAT) — Maternal body weight is the

average body weight for the entire duration of exposure. This model does not take into

account the changes in body weight during pregnancy and nursing. The default for

BWMAT is 60 kg (U.S. EPA, 1997b).

9.3.1.8. Model Performance — Available data are insufficient for adequate

assessment of model performance. The dioxin data, however, provide a rough

comparison of relative model performance for background conditions. As described

previously in this chapter (Section 9.1.1.11997.), dioxin TEQs of 20-27 ppt have been

found in human milk fat in highly industrialized countries. The steady-state model (Eq.

9-2) predicts the background dioxin concentrations found in breast milk fat to be 13-38
Chapter 9

Breast Milk Pathway

MPE 9-24 December 1998

Cmilkfat ' TDIMAT BTFm

(9-8)

ppt TEQ, assuming an 8-year half-life, values of 0.9 for ff and 0.3 for ffm, respectively,

and background exposure levels of 1-3 pg TEQ/kg-day for maternal intake (U.S. EPA,

1994) With the same inputs and assuming a 30-year maternal exposure and 6-month

nursing period, the kinetic model predicts 12-35 ppt and 8.5-26 ppt with (Eq. 9-4) and

without (Eq. 9-3) breast-feeding losses, respectively. All the models generate

predictions in the same range as the data, with the kinetic breast-feeding-loss model

(Eq. 9-4) at the lower end of the range. The model inputs are too uncertain to

distinguish among the model results. The assessor should note, however, that the

kinetic breast-feeding-loss model (Eq. 9-4) will predict lower milk fat concentrations

with longer breast-feeding durations.

9.3.2. Contaminant Concentration in Breast Milk Fat (Cmilkfat): Biotransfer Factor

Approach. Travis et al. (1988) presented an alternative approach to estimating breast

milk contaminant levels using a biotransfer factor. This model assumes an equilibrium

tissue concentration of the pollutant, which is proportional to the average daily intake of

the substance. The formula for estimating milk fat concentrations of lipophilic

compounds is given in Equation 9-8.

where:

Cmilkfat = contaminant concentration in breast milk fat (mg/kg)

TDIMAT = total daily maternal intake of contaminant (mg/day)

BTFm = biotransfer factor for breast milk fat (kg milk fat/day)-1

Maternal intake is expressed as total intake per day (independent of body

weight). 
Chapter 9

Breast Milk Pathway

2Octanol-water partition coefficient values are generally expressed as the logarithm to the

base 10 of the Kow.

MPE 9-25 December 1998

BTFm' 0.00062 Kow

(9-9)

The biotransfer factor is primarily a function of the octanol-water partition

coefficient (Kow) and was developed by Travis et al. (1988) from the following mean

regression:

where:

BTFm = biotransfer factor for breast milk fat (kg/day)-1

0.00062 = empirical constant (kg/day)-1

Kow = octanol-water partition coefficient (unitless)

The regression constant was estimated from data on six lipophilic compounds,

including PCBs and several organochlorine pesticides (log Kow

2

 range: 5.16 to 6.5), but

did not include any dioxins or furans. Hawker and Connell (1988) report log Kow values

of 13 PCB congeners (5.37-7.67) and calculated log Kow for all the remaining PCB

congeners; PCB lipophilicity generally increases with the degree of chlorination. Log

Kow values for PCDD, PCDF and PCBs can be found in U.S. EPA (1994). 

 The BTF approach does not appear to work well for dioxin. Assuming a log Kow

of 6.6 for 2,3,7,8-TCDD, a BTFm of 2468 kg/day is predicted. Multiplying this value by

an estimated background maternal intake of 10 pg/day (or 1 × 10-8 mg/day) yields (from

Eq. 9-5) a breast milk-fat TCDD concentration of about 2.5 × 10-5 mg/kg (25 ppt). This

prediction is about 10 times higher than what has been measured in the United States

(Jensen, 1987). Thus, this approach may not be as appropriate for PCDDs and PCDFs

as the first order kinetics models. The recommended approaches for any liphophilic

compound are those based on first order kinetics. Do not use the BTF approach for

any compound if kelim and ff can be found or estimated for use in the equations
Chapter 9

Breast Milk Pathway

MPE 9-26 December 1998

presented in Section 9.2.1. Otherwise, use the BTF approach only for compounds in

the log Kow range in which it was calibrated (5.2 to 6.5). The BTF approach may be

useful for PCB congeners for which elimination constants are not available. Also, as

the BTF approach assumes steady-state, it will tend to overestimate breast milk

concentrations for shorter exposure durations and should be used only for long-term

maternal exposures (> 20 years). Given the potential for overestimation of pollutant

concentrations in breast milk, the BTF should be considered as a screening model.

9.3.3. Fraction of Fat in Breast Milk (fmbm). The fat content in breast milk varies

throughout the lactation period, with circadian rhythms, during a feeding, and with

parity (reviewed in Sim and McNeil, 1992). Daly et al. (1993) suggest that individual

feeding patterns (i.e, length of time between feedings) and the degree of breast

emptying contribute to the variance of breast milk fat content within women. To

normalize for these variations, the concentration of contaminants that partition primarily

to the lipid fraction of the breast milk (e.g., PCDDs, PCDFs, PCBs), should be

expressed on a milk fat basis (i.e., g/g milk fat).

Jensen (1987) tabulated the average fat content from approximately 47 studies

in 19 countries conducted during 1971-1986 and reported average fat content of breast

milk to be 1.1-5.0%. More recently, the fraction of lipid in breast milk has been found

generally to vary between 2 and 4% (Schecter et al., 1994; Hong et al., 1994;

McLachlan, 1993; Bates et al., 1994; NAS, 1991; Butte et al. 1984; Maxwell and

Burmaster, 1993; U.S. EPA, 1997b). In development of their models to estimate

contaminant concentration in breast milk, Smith (1987) assumed 4% and Sullivan et al.

(1991) assumed 3% lipid content. The U.S. EPA (1997b) recommends using

approximately 4% as the percentage of fat in breast milk.

9.3.4. Contaminant Concentration in the Aqueous Phase of Breast Milk (Caqueous).

To assess the aqueous-phase contaminant contribution to the overall intake from

breast milk, Equation 9-2 was adapted for estimating the concentration of contaminants

circulating in the aqueous phase of breast milk (Eq. 9-10). Aqueous phase

contaminants would include chemicals with appreciable water solubility, such as salts
Chapter 9

Breast Milk Pathway

3 Because of the complexity of the kinetics of bone-seeking compounds, a linear

model may be inadequate for predicting breast milk concentrations of lead and

cadmium. 

MPE 9-27 December 1998

Caqueous '

DAIMAT f

pl Pcbm

kelim f

pm

(9-10)

of metals3, and protein-bound chemicals, such as methylmercury. The concentration of

contaminant in the aqueous phase of breast milk is assumed to be proportional to the

contaminant concentration in the blood plasma. The contaminant taken up by

erythrocytes is assumed to be unavailable for direct transfer to breast milk. 

where:

Caqueous = concentration of contaminant in aqueous phase of breast milk (mg/kg

[ppm])

DAIMAT = daily maternal intake of contaminant (mg/kg of body weight per day

[mg/kg-day])

kelim = biological elimination constant for contaminant in lactating women

(days-1)

fpl = fraction of contaminant (based on total absorbed intake) that is in the

blood plasma compartment (dimensionless)

Pcbm = partition coefficient for contaminant concentration between plasma

and breast milk aqueous phase (dimensionless)

fpm = fraction of mother's weight that is plasma (kg maternal plasma/kg total

body weight)

DIMAT and kelim are the same as in Equation 9-2. The applicability of Equation

9-10 is dependent on establishing the value of fpl, for which there is no appropriate

general default value. Therefore, if data from the general literature for establishing fpl

are not adequate, Caqueous cannot be estimated. The extent to which these data are

available in the open literature is unknown.
Chapter 9

Breast Milk Pathway

MPE 9-28 December 1998

f

pl '

f

bl fbp

f

bp % PcRBC (1 & fbp)

(9-11)

9.3.4.1. Fraction of Contaminant That Is in the Blood Plasma Compartment

(fpl) — This parameter represents the steady-state fraction of the total intake of

contaminant that is circulating in the plasma. The amount of contaminant that partitions

into the blood plasma compartment is generally empirically derived from chemicalspecific studies of absorption and distribution. There are no reliable independent

predictors of fpl. Information for this parameter must be located in the open literature in

order for the model to be used. Concentrations, however, are often given for the whole

blood compartment only (plasma plus red blood cells [RBCs]), and concentrations

between plasma and RBCs may differ. In such cases, the fraction of contaminant in

the plasma subcompartment can be estimated by Equation 9-11, which is simply the

weighted fraction of contaminant in the plasma dependent upon the RBC:plasma

concentration ratio for that contaminant.

where:

fpl = fraction of contaminant (based on total absorbed intake) that is in the

blood plasma compartment (dimensionless)

fbl = fraction of contaminant (based on total absorbed intake) in whole blood

compartment (dimensionless)

fbp = fraction of whole blood that is plasma (kg plasma/kg blood)

PcRBC = partition coefficient for contaminant concentration between red blood

cells and plasma (dimensionless)

Values for fbl must be found in the open literature; there is no appropriate default. 

Plasma and blood volumes can be used to calculate a default value fbp, which should

be generally adequate. Plasma volume is approximately 60% of total blood volume in

normal human females (Steinbeck, 1953) and about 63% to 70% in postpartum women

(Harrison, 1967; Ueland, 1976); the default value for fbp is 0.65. Specific values for
Chapter 9

Breast Milk Pathway

MPE 9-29 December 1998

PcRBC must be found in the open literature. Assuming equal distribution of the

contaminant in plasma and RBCs, the default value for PcRBC is 1; this is likely to be

the case for most contaminants.

9.3.4.2. Concentration Proportionality Constant Between Plasma and

Breast Milk Aqueous Phase (Pcbm) — This parameter relates the concentration of

contaminant in the aqueous phase of breast milk to the concentration of the

contaminant in plasma, with the assumption that the concentrations are directly

proportional. Specific information for this parameter must be located in the open

literature. The default value for Pcbm is 1, which should be a reasonable assumption in

most cases.

9.3.4.3. Fraction of Mother’s Weight that is Blood Plasma (fpm) — Plasma

volumes for adult women have been estimated in the range of 37 to 60 mL/kg body

weight, averaging about 45 mL/kg (summarized in Altmann and Dittmer, 1964). The

average plasma volume of 28 postpartum (6 weeks) women was 45 mL/kg (Ueland,

1976). Given the specific gravity of plasma of 1.026 (Conley, 1974), the default value

for fpm is 0.046.

9.3.5. Fraction of Ingested Contaminant that is Absorbed by the Infant (fai). This

parameter represents the absorption of the contaminant in the breast milk by the infant. 

It is reasonable to assume that the absorption fraction of most ingested lipophilic

organic compounds will be high. Smith (1987) assumed 90% absorption of ingested

PCDD. Dahl et al. (1995) reported oral absorption of PCBs, PCDDs and PCDFs in

breast milk to be >95% for most congeners in infants 1-6 months of age. McLachlan

(1993) measured gastrointestinal absorption of PCDDs, PCDFs and PCBs by

contaminant mass balance in a nursing infant over the course of 12 days. Absorption

was >96% for the 4 penta- and hexa-PCB congeners and >90% for the 12 PCDD/PCDF

congeners, although absorption appeared to be more variable for the hexa- and higher

chlorinated PCDD/PCDF congeners, perhaps because of contamination of diaper

blanks. Absorption estimates for chemicals are empirically derived and may be

available in the open literature. Empirical absorption estimates should be adjusted for
Chapter 9

Breast Milk Pathway

MPE 9-30 December 1998

vehicle-specific differential absorption if data suggest that this may be the case (for

experimental vehicles other than milk). In the absence of specific data for estimating fai,

fam can be used as a surrogate. The default value for fai is 1, which represents a

conservative assumption. This default may not be appropriate for salts of metals and

other highly polar compounds, as the absorption fraction for these compounds could be

very low (ca. <10% in adults). 

9.3.6. Ingestion Rate of Breast Milk (IRmilk). The daily amount of breast milk

consumed by an infant (i.e., the ingestion rate of breast milk, kg/day) varies with

numerous factors, predominantly with infant weight and nursing frequency, being

higher for larger infants and those that nurse more frequently. NAS (1991) estimated

average intake rates of 718-777 mL/day for infants up to 5 months of age. Whitehead

and Paul (1981) in a study of 48 British infants aged 1-8 months found that the breast

milk ingestion rate ranged from 677 to 922 mL/day; Smith (1987) used 0.8 kg/day in

application of the model. Prentice et al. (1994) compiled milk volume measurements

from 41 studies published during 1980-1992 from numerous countries for use in a

meta-analysis of body mass and lactation performance. Average breast milk volume

ranged from 307 to 961 g/day for infants from 1 to 6 months of age, with most studies

showing averages of 600-900 g/day. Using 1.03 g/mL as the density of human milk,

conversions can be made between the weight and volume of breast milk consumed. 

U.S. EPA (1997b) recommends using an average breast milk intake for infants 1 to 6

months of 730 mL/day, with an upper percentile intake of 1,029 mL/day. Breast milk

intake remains relatively constant until approximately 6-9 months of age, after which it

decreases (apparently due to dietary supplementation) (Whitehead and Paul, 1981;

U.S. EPA, 1997b). Therefore, U.S. EPA (1997b) recommends using 678 mL/day

(12-month time-weighted average) as the mean intake for infants up to 1 year with

1,022 mL/day as the upper percentile rate. The default value for a 6-month breast

feeding period is 730 mL/day (1,029 mL/day as the upper percentile rate), which is the

6-month time-weighted average as recommended by the U.S. EPA (1997b).
Chapter 9

Breast Milk Pathway

MPE 9-31 December 1998

9.3.7. Body weight of infant (BWINF). The default for BWINF is 8 kg, which is the

average for 6-month-old infants (National Center for Health Statistics, 1987; U.S. EPA,

1997b). If a breast feeding duration longer than 6 months is used, the assessor should

use the appropriate value from Table 9-3 or consult U.S. EPA (1997b).

9.3.8. Exposure Duration (ED). This approach assumes that the contaminant

concentration in milk represents the average over the breast feeding time period (i.e.,

exposure duration, years). Demographic factors such as maternal marital status, age,

education, employment, income, ethnic background and region of the country influence

TABLE 9-3

Body Weights of Childrena,b

Age

Mean Body Weight (Kg)

Boys Girls Both Sexes

6-11

months

9.4 8.8 9.1

1 year 11.8 10.8 11.3

2 years 13.6 13.0 13.3

a

 Source: U.S. EPA (1997b)

b

 Includes clothing weight

breast feeding practices within the United States (U.S. EPA, 1997b). NAS (1991)

reported that, in 1989, breast feeding was initiated in approximately 50% of newborn

infants, but was continued until 5-6 months of age in only 20% of the infants. Maxwell

and Burmaster (1993) reported that approximately 22% of infants under 1 year are

breast fed. In a study of 48 British infants that examined nutritional requirements for

growth, Whitehead and Paul (1981) showed that by 4-6 months of age most breast fed
Chapter 9

Breast Milk Pathway

MPE 9-32 December 1998

infants transition from being fully breast fed to being partly breast fed because of

dietary supplementation in order to maintain a positive growth rate. The National

Academy of Sciences report that only 37% of mothers who breast feed are still breast

feeding by 5-6 months (NAS, 1991). The risk assessor should consult U.S. EPA

(1997b) for further information. Local breast-feeding practices should be considered if

adequate data exist. The default value for a central estimate of ED is 6 months. An

upper percentile estimate of ED is 1 year.

9.3.9. Averaging Time (AT). Averaging time (years) is the timeframe or portion of the

lifetime over which the daily intake is to be averaged. To determine the average dose

to the infant, AT should equal ED (Eq. 9-1). If, however, the contribution to total

lifetime exposure is desired, such as for estimation of lifetime cancer risk, 70 years is

used as the averaging time (U.S. EPA, 1997b).

9.4. UNCERTAINTY IN THE BREAST MILK PATHWAY EXPOSURE TO THE

INFANT

Linear 1st order kinetics may yield better predictions for aqueous-phase

compounds than for liphophilic compounds, as toxicokinetic processes are generally

less complex. Bone-seeking compounds, however, may be an exception. The kinetics

of compartmentalization of lead in bone is complex and nonlinear and may not be wellrepresented by 1st order kinetics (Dalley et al., 1990; O’Flaherty, 1991). An

assessment of the potential magnitude or direction of the error has not yet been

performed. A better approach for lead would be to add a breast milk compartment to

the PBPK model of O’Flaherty (1991); this remains as a research need.

The preferred approach for estimating breast milk concentrations of liphophilic

chemicals is the dynamic model presented in Equation 9-4. The BTF approach may be

a useful alternative in certain circumstances when elimination constants are not

available. However, as the BTF approach can not account for kinetics and will tend to

overestimate breast milk pollutant concentrations, it should be considered as a

screening model.
Chapter 9

Breast Milk Pathway

MPE 9-33 December 1998

Bias in the simplified kinetic models for highly liphophilic compounds when time

to steady state and breast-feeding losses are ignored has already been mentioned

(Section 9.2.1.). Table 9-4 illustrates the potential over prediction of C milkfat for several

different exposure scenarios. Table 9-4 gives the ratio of the steady-state

concentration (Eq. 9-2) to the kinetic model prediction (Eq. 9-3) for several different

combinations of exposure duration and contaminant half-life. Significant over

predictions are evident for the steady-state assumption when the half-life is large and

the exposure duration is small.

Table 9-5 gives the ratio of the kinetic model concentration predictions (Eq. 9-3)

to the kinetic model prediction that includes breast-feeding losses (Eq. 9-4) for several

different combinations of exposure duration, contaminant half-life, and nursing periods. 

TABLE 9-4

Relative Predicted Contaminant Concentration in Breast Milk for 

Steady-State Model versus Kinetic Modela

Biological

Half-Lifeb

(years)

Total Exposure Durationc(ED in years)

30 20 10 5 1 0.5

0.5 1.00 1.00 1.00 1.00 1.33 2.00

1 1.00 1.00 1.03 1.00 2.00 3.41

4 1.01 1.03 1.22 1.73 6.29 12

8 1.08 1.22 1.73 2.84 12 23.6

16 1.38 1.73 2.84 5.14 23.6 46.7

a

 C milkfat (steady-state, Eq. 9-2) / C milkfat (kinetic, Eq. 9-3); hDIMAT, ff, and fm held constant

b

 0.693/Kelim

c

 tpn + tbf; tbf = 0.5 yrs
Chapter 9

Breast Milk Pathway

MPE 9-34 December 1998

TABLE 9-5

Relative Predicted Average Contaminant Concentration in Breast Milk for Kinetic Model, alone,

versus Kinetic Model with Losses from Breast Feedinga

Biological

Half-Lifeb

(years)

tbf

(years)

Total Exposure Durationc(ED in years)

30 10 5 1 0.5

0.5 0.5 1.11 1.11 1.11 1.29 1.94

1.0 1.18 1.18 1.18 1.87 -

1 0.5 1.12 1.12 1.13 1.38 2.05

1.0 1.23 1.23 1.24 2.09 -

4 0.5 1.14 1.15 1.17 1.47 2.15

1.0 1.27 1.30 1.35 2.29 -

8 0.5 1.14 1.16 1.18 1.48 2.17

1.0 1.29 1.32 1.39 2.33 -

16 0.5 1.14 1.16 1.19 1.49 2.17

1.0 1.30 1.34 1.40 2.35 -

a C milkfat (Eq. 9-3) / C milkfat (Eq. 9-4); hDIMAT, ff, and fm held constant

b

 0.693/Kelim

c

 tpn + tbf
Chapter 9

Breast Milk Pathway

MPE 9-35 December 1998

The consequences of ignoring breast-feeding losses are not as severe as those

resulting from the steady-state assumption, but can still be significant.

The combined effect of assuming steady-state and ignoring breast feeding

losses is illustrated in Table 9-6, in which the output of Equation 9-2 (steady state

assumption) is compared to the output of Equation 9-4 (tpn and tbf considered). The

overestimation of Cmilkfat exceeds 30% for all scenarios and is particularly large for

longer durations and half-lives. As an example, for a dioxin (half-life = 8 years)

exposure scenario that begins at the start of breast feeding, Cmilkfat could be

overestimated by a factor of 16 or greater by ignoring the kinetic elements.

Given the well-documented benefits of breast feeding, any estimation of risk

to the infant from contaminants in maternal milk needs to be free of bias. If a linear

model adequately describes the kinetic behavior of the substance in the body, both

time to steady state and breast feeding losses should be considered, given the

overestimation of infant dose otherwise. The simple linear models presented here,

however, are unlikely to be accurate across a wide range of chemical properties and

physiological processes. Model uncertainty may be substantial. The adequacy of

linear algebraic models for predicting human breast milk concentrations of highly

lipophilic compounds is unknown. Although, in laboratory animal studies, the

pharmacokinetics of TCDD and related compounds appears to be highly non-linear and

dose-dependent, linear behavior was observed at lower doses and some of the

nonlinear behavior may have been a result of the acutely toxic effects (wasting

syndrome) at the higher doses (Roth et al., 1994). Whether or not the linear models

are biased in any given direction remains as a topic for further research.

An implicit assumption in the breast milk model is that body weight and fat

content remain constant over the period of breast feeding. Postpartum, lactating

mothers are unlikely to have constant body weight or constant body fat. Many

consciously attempt to lose both fat and weight and some gain weight and fat. 

Differences in diet and in activity, compared to the pre-partum period, may significantly
Chapter 9

Breast Milk Pathway

MPE 9-36 December 1998

TABLE 9-6

Combined Relative Predicted Average Contaminant Concentration in Breast Milk for Steady-State

Model versus Kinetic Model with Losses from Breast Feedinga

Biological

Half-Lifeb

(years)

tbf

(years)

Total Exposure Durationc (ED in years)

30 10 5 1 0.5

0.5 0.5 1.11 1.11 1.11 1.73 3.88

1.0 1.18 1.18 1.19 2.49 -

1 0.5 1.12 1.13 1.17 2.77 7.01

1.0 1.23 1.23 1.28 4.18 -

4 0.5 1.14 1.39 2.02 9.21 25.9

1.0 1.28 1.57 2.34 14.4 -

8 0.5 1.23 1.99 3.36 17.8 51.1

1.0 1.39 2.28 3.94 28.1 -

16 0.5 1.57 3.30 6.11 35.1 101

1.0 1.78 3.80 7.20 55.4 -

a

 C milkfat (Eq. 9-2) / C milkfat (Eq. 9-4); hDIMAT, ff, and fm held constant

b

 0.693/Kelim

c

 tpn + tbf

alter the distribution of contaminants to breast milk. As an example, significant body fat

loss from dieting and exercise may tend to “dump” fat-stored compounds with

consequences that are not fully known; some may be stored again, some may be

eliminated more readily, and some may go into the breast milk. This issue contributes

to the overall uncertainty of the model, but does not necessarily indicate a bias in one

direction or the other.

A potential source of bias, however, is the assumption that breast milk fat and

maternal body fat are in equilibrium and equal in contaminant concentration. Milk fat

appears to be synthesized in the mammary glands (cite...?), which may result in lower

contaminant concentrations in milk fat than are present in the general body fat stores. 
Chapter 9

Breast Milk Pathway

MPE 9-37 December 1998

Unless equilibration of de novo milk fat with body fat is rapid, the model would tend to

over estimate infant exposure. 

Physiological factors such as maternal age, parity, length of lactation, and life

style factors such as cigarette smoking and alcohol consumption, which influence

transfer of compounds to breast milk (see §9.2.4.) are not explicitly included in the

model. The overall impact of these factors on the concentration of contaminants in

breast milk is difficult to assess. 

Uncertainties in model input variables also can contribute substantially to

uncertainty in the output. In particular, partitioning factors and chemical half lives can

vary among measurements by factors of 2 or 3. Given these uncertainties and the

large uncertainties in toxicity estimation, estimations of risk based on these models are

probably precise only to within an order of magnitude, at best. Risk assessors are

referred to Chapter 10 for quantitative approaches for the uncertainty analysis and risk

characterization.
Chapter 9

Breast Milk Pathway

MPE 9-38 December 1998

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Chapter 9

Breast Milk Pathway

MPE 9-43 December 1998

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Chapter 9

Breast Milk Pathway

MPE 9-44 December 1998

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Chapter 9

Breast Milk Pathway

MPE 9-45 December 1998

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Kinetics of lead disposition in rats. Toxicol. Appl. Pharmacol. 111:313-331.
Chapter 9

Breast Milk Pathway

MPE 9-46 December 1998

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Toxicokinetics of eight 13C-labelled polychlorinated dibenzo-p-dioxins and -furans in

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fish consumption and amalgam fillings in lactating women. Arch. Environ. Health.

51:234-241.

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in blood and milk of lactating women. Hrana i Ishrana. 35:1-4.

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1989. Half-life of polychlorinated biphenyls in occupationally exposed workers. Arch.

Environ. Health. 44(6):351-354.

Pluim, H.J., I. Kramer, J.W. van der Slikke, J.G. Koppe and K. Olie. 1993. Levels of

PCDDs and PCDFs in human milk: dependence on several parameters and dietary

habits. Chemosphere. 26(10):1889-1895.

Poland, A. and J.C. Knutson. 1982. 2,3,7,8-Tetrachlorodibenzo-p-dioxin and related

halogenated aromatic hydrocarbons: Examination of the mechanism of toxicity. Ann.

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performance. Eur. J. Clin. Nutr. 48(Suppl.3):S78-S89.

Radisch, B., W. Luck and H. Nau. 1987. Cadmium concentrations in milk and blood of

smoking mothers. Toxicol. Lett. 36:147-152.

Rappe, C. 1980. Analysis of polychlorinated dioxins and furans: All 75 PCDDs and

135 PCDFs can be identified by isomer-specific techniques. Environ. Sci. Technol.

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polychlorinated dioxins and dibenzofurans in biological samples. In: Biological

Mechanisms of Dioxin Action, Banbury Report 18, A. Poland and R.D. Kimbrough, Ed. 

Cold Spring Harbor Laboratory. p. 17-25.

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polychlorinated dibenzofurans and dioxins. Environ. Health Perspect. 60:303-304.

Riolfatti, M. and E. Modenese. 1990. Heavy metals in human milk. I. Lead and

cadmium. Igiene Moderna. 93:1082-1089.
Chapter 9

Breast Milk Pathway

MPE 9-47 December 1998

Rogan, W.J. and N.B. Ragan. 1994. Chemical contaminants, pharmacokinetics and

the lactating mother. Environ. Health. Perspect. 102:89-95. 

Roth, W.L., S. Ernst, L.W.D. Weber, L. Kerecsen and K.K. Rozman. 1994. A

pharmacodynamically responsive model of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)

transfer between liver and fat at low and high doses. Toxicol. Appl. Pharmacol. 

127:151-162.

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dibenzo-p-dioxins and dibenzofurans. Ann. Rev. Pharmacol. Toxicol. 26:371-399.

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33:24-29.

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gene expression in multiple tissues: Dose- and time-dependent effects. Toxicol. Appl.

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mothers and children from Michigan farms with PCB-contaminated silos. Arch. Environ.

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certain chlorinated hydrocarbons and polychlorinated biphenyls in bone marrow and fat

tissue of children and their concentrations in breast milk. J. Environ. Pathol. Toxicol.

Oncol. 14:11-14.

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the lactating mother and nursing child. Toxicol Lett. 43:23-29.

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resource recovery emissions and the implications for risk assessment. In: Health

Effects of Municipal Waste Incineration, H.A. Hattemer-Frey and C. Travis, Ed. CRC

Press, Boca Raton. p. 1-46.
Chapter 9

Breast Milk Pathway

MPE 9-48 December 1998

Sim, M.R. and J.J. McNeil. 1992. Monitoring chemical exposure using breast milk: A

methodological review. Am. J. Epidemiol. 136:1-11.

Smith, A.H. 1987. Infant exposure assessment for breast milk dioxins and furans

derived from waste incineration emissions. Risk Anal. 7:347-353.

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Austral. J. Exp. Biol. 32:95-100.

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Higher levels in urban vs. rural mothers during the first 3 months of lactation. Arch.

Toxicol. 57:41-45.

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Potential impact of MSW incineration on human health. In: Health Effects of Municipal

Waste Incineration, H.A. Hattemer-Frey and C. Travis, Ed. CRC Press, Boca Raton.

p. 104-113.

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mother's milk resulting from maternal ingestion of contaminated fish. Chemosphere. 

23:1387-1396. 

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tissue: Analyses of residues in healthy children, tumor patients and malformed children. 

Arch. Environ. Contam. Toxicol. 19:646-652.

Travis, C.C., H.A. Hattemer-Frey and A.A. Arms. 1988. Relationship between dietary

intake of organic chemicals and their concentrations in human adipose tissue and

breast milk. Arch. Environ. Contam. Toxicol. 17:473-478.

Ueland, K. 1976. Maternal cardiovascular dynamics, VII. Intrapartum blood volume

changes. Am. J. Obstet. Gynecol. 126:671-677.

U.S. EPA. 1980. Acquisition and chemical analysis of mother’s milk for selected toxic

substances. EPA-560/13-80-029, December 1980, US EPA, Washington, DC.

U.S. EPA. 1983. Chemicals identified in human breast milk, a literature search. EPA560/5-83-009, October 1983, US EPA, Washington, DC.

U.S. EPA. 1994. Estimating Exposure to Dioxin-Like Compounds. External Review

Draft. Office of Research and Development, Washington, DC. EPA/600/6-88/005Cb.
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Breast Milk Pathway

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U.S. EPA. 1997a. Mercury Study Report to Congress Volume V: Health Effects of

Mercury and Mercury Compounds. Office of Air Quality Planning and Standards and

Office of Research and Development. EPA-452/R-97-007. December.

U.S. EPA. 1997b. Exposure Factors Handbook Volume I. Office of Research and

Development, Washington, DC. EPA/600/P-95/002Fa. August.

Villalobos, S.A., M.J. Anderson, M.S. Denison et al. 1996. Dioxinlike properties of a

trichloroethylene combustion-generated aerosol. Environ. Health Perspect.

104:734-743.

Whitehead, R.G. and A.A. Paul. 1981. Infant growth and human milk requirements. 

Lancet. ii:161-163.

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Report on the WHO Collaborative Study on Breast-feeding. World Health

Organization, Geneva.

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Report of a Joint WHO/IAEA Collaborative Study. World Health Organization, Geneva.

Wolff, M.S., J. Thornton, A. Fishbein, R. Lilis and I.J. Selikoff. 1982. Disposition of

polychlorinated bipheny congeners in occupationally exposed persons. Toxicol. Appl.

Pharmacol. 62:294-306.

Yoshida, S. and A. Nakamura. 1979. Residual status after parturition of methylsulfone

metabolites of polychlorinated biphenyls in the breast milk of a former employee in a

capacitor factory. Bull. Environ. Contam. Toxicol. 21:11-115.
Chapter 10

Risk Assessment

MPE 10-1 December 1998

RISK ASSESSMENT

10.1. INTRODUCTION

Risk assessment is an approach utilized by U.S. EPA to evaluate environmental

concerns. Risk assessment has been defined by NAS (1983) as a four-step process

(see Figure 10-1) consisting of hazard identification, dose-response assessment, 

exposure assessment, and risk characterization. In the hazard identification step the

problem and the potential toxic effects are defined and the data needs are determined. 

The next step, dose-response assessment, consists of establishing and quantifying the

relationship between the adverse health effect and exposure to or dose of a toxic

agent, and characterizing the overall scientific confidence in the dose or exposure

concentration of the toxic agent to the study group and the health effects data. As

discussed in Chapter 2, the exposure assessment step consists of delineating the

pathways and routes of exposure that relate a source to an exposure event, identifying

exposed individuals, quantifying their exposures, and characterizing the overall

scientific confidence in these data, e.g., identifying sources of uncertainty in the step. 

In risk characterization, the results from the three previous steps are evaluated and

integrated. This is translated into an overall conclusion about the likelihood of an

adverse health effect occurring due to an exposure (NAS, 1983; NRC,1994). The risk

characterization is then utilized by a risk manager to make a decision. It should be

noted that the risk characterization, which attempts to provide a synthesis of the

science to the risk decision-maker, is but one factor that enters into a the final decision.

Other factors could include economic, political, and technologic considerations.

The U.S. EPA has developed a number of guidelines for human health risk

assessment that are applicable to assessing human health risks from exposures arising

from combustor emissions. Table 10-1 presents a brief summary of the guidelines.
MPE

10-2 December 1998
Chapter 10

Risk Assessment

MPE 10-3 December 1998

10.2. EXPOSURE ASSESSMENT

The U.S. EPA’s Guidelines for Exposure Assessment (U.S. EPA,1992a)

established a broad framework for conducting exposure assessments. The exposure

assessment step of combustion risk assessment consists of evaluating the

Carcinogenicity

The Guidelines for Carcinogen Risk Assessment (U.S. EPA, 1986a) and the proposed revisions

(U.S. EPA, 1996a) are intended to serve as a guide for Agency evaluation of suspect

carcinogenic agents in assessing the likelihood that the agent is a human carcinogen and in

quantitative estimation of potency. 

Mutagenicity

The Guidelines for Mutagenicity Risk Assessment (U.S. EPA, 1986b) provide guidance for the

assessment of the weight of evidence that an agent is a potential human mutagen capable of

inducing heritable genetic lesions and assessment of the resulting impact on public health.

Chemical Mixtures

The Guidelines for the Health Risk Assessment of Chemical Mixtures (U.S. EPA, 1986c)

provide approaches for assessing both carcinogenic and noncarcinogenic human health

outcomes from exposures to mixtures of chemicals.

Developmental Toxicity

The Guidelines for the Health Risk Assessment of Suspect Developmental Toxicants (U.S.

EPA, 1986d) are designed to guide EPA assessors in the evaluation of potential developmental

toxicity associated with human exposure to environmental pollutants. The Guidelines discuss

all embryonic and fetal effects from in utero exposure and deal with maternal effects, as well. 

Reproductive Toxicity

The Guidelines for Reproductive Toxicity Risk Assessment (U.S. EPA, 1996b) discuss the

scientific basis for concern about exposure to agents that cause reproductive toxicity and

describe the principles and procedures to be followed in conducting risk assessments for

reproductive toxicity. They include the female (non-pregnant and pregnant) and male

reproductive systems. 

Exposure

The Guidelines for Assessment of Exposures (U.S. EPA, 1992a) provide the Agency with a

general approach and framework for carrying out human or nonhuman exposure assessments for

environmental pollutants.

TABLE 10-1

U.S. EPA Risk Assessment Guidelines
Chapter 10

Risk Assessment

MPE 10-4 December 1998

environmental fate of emitted pollutants, identifying exposure routes of concern,

determining the bioavailable concentrations of emitted pollutants in exposure media,

evaluating exposure rates (e.g., consumption rates, inhalation rates) and finally

estimating exposures to pollutants.

Exposure scenarios developed for a site-specific assessment may include

exposure via multiple routes and pathways. This document has been concerned with

the assessment of exposures to combustor emissions. Chapter 2 discussed the

creation of exposure scenarios and developed a framework for application of the

methodology. Chapters 3-9 presented models designed to estimate contaminant

concentrations in environmental and biological media and, ultimately, human intake of

contaminants via relevant pathways of exposure. In this methodology, daily pollutant

intake has been calculated independently for the following routes and pathways: oral

via produce, livestock, fish, breast milk, soil and water, dermal via soil and water, and

inhalation. A given individual or population may experience exposure by one or more

of these pathways.

10.2.1. Estimation of Individual Total Exposure. To calculate total exposure to an

individual exposed by multiple pathways, estimates must be combined across pathways

and routes. In the absence of data to the contrary and if the risk assessor judges it to

be appropriate, this methodology recommends that chemical exposures be summed

across pathways and routes. This is consistent with Agency guidance for adding risks

across pathways, contained in Chapter 8 of the Risk Assessment Guidance for

Superfund: Volume I—Human Health Evaluation Manual (Part A) (U.S. EPA, 1989) and

the Guidelines for Health Assessment of Chemical Mixtures (U.S. EPA, 1986c).

The focus of this section is on how to combine intake across exposure pathways. 

It does not address the question of when to combine intakes across pathways. 

Decisions regarding whether it is appropriate to combine exposures by different

pathways are made by the risk assessor and risk manager during the development of

the exposure scenarios, a topic that is discussed in Chapter 2. (In general, risks
Chapter 10

Risk Assessment

MPE 10-5 December 1998

should be added across pathways whenever it is reasonable to expect an individual to

experience exposure by a given set of pathways.)

The risk assessor should be aware that predicting the risks posed to a specific

individual is highly uncertain. Both uncertainty and variability associated with the

individual could involve different activity patterns resulting in variable exposure rates,

toxicodynamic uncertainty, and variable nature of an individual’s general health. Many

of these factors cannot be modeled at present and, as a result, predicting risk to a

specific individual is uncertain.

10.2.1.1. Combining Exposures from Multiple Routes — Route-to-route

conversion should only be attempted for dermal exposures at this time. Adequate

inhalation-to-oral conversion methods, particularly for steady-state conditions, have not

yet been developed. A general outline of the oral-to-inhalation extrapolation process

and a discussion of route-to-route extrapolation issues can be found in Gerrity and

Henry (1990) and in the EPA’s Reference Concentration methodology document (U.S.

EPA, 1994b). Until such methodology is developed, inhalation and oral risk

characterization should be carried out separately. The assessor should note, however,

that total risk will appear to be smaller by not combining all routes of exposure.

Multiple-route exposures can be combined in two different ways: summing the

absorbed daily doses or summing the (external) oral equivalent daily doses. Both

approaches require an estimate for the oral absorption fraction but the latter is adopted

here as it is simpler for comparison with standard toxicity benchmarks based on

ingestion (external dose). 

In order to determine the health risk associated with daily intake of contaminant

by ingestion or dermal contact, the daily intake is either multiplied by the contaminant’s

cancer slope factor (for carcinogens) or compared with the contaminant’s RfD for 

noncarcinogenic effects. Cancer slope factors and RfDs are available for oral

exposure, but not dermal exposure. Therefore, dermal intake must be converted to the

equivalent oral intake (U.S. EPA, 1989).
Chapter 10

Risk Assessment

MPE 10-6 December 1998

DI '

DDI

AFo

(10-1)

A number of factors might contribute to differences in toxicological effectiveness

between oral and dermal exposures at equal dosages. The most obvious relates to

differences in absorption rates between the two routes. Other potential contributing

factors include differing sensitivity of absorption sites to damage and differences in

toxicokinetics (i.e., distribution, metabolism, elimination) between exposure routes. 

Ideally, the conversion from dermal to equivalent oral dose would be based on

experimentally derived values relating the doses that produced a particular toxicity by

the different routes. In practice, however, the conversion will usually be based on

absorption factors because of a general absence of appropriate data.

In this methodology, daily dermal intake (DDI) is calculated as an absorbed dose

(see Chapter 4). Oral intake and the RfD, by contrast, are based on ingested (external)

dose. The contaminant’s oral absorption factor can be used to convert the absorbed

dermal dose to an equivalent external oral dose, assuming equivalent toxicological

effectiveness of absorbed doses. The DDI can be transformed to an equivalent oral

daily intake (DI) by dividing the DDI by the oral absorption factor, as shown in Equation

10-1:

where:

DI = daily intake (oral equivalent) (mg/kg-day)

DDI = daily dermal intake (mg/kg-day)

AFo = oral absorption factor for contaminant (unitless)

Contaminants lacking data on oral absorption are assumed to be 100%

absorbed from the gastrointestinal tract (AFo = 1), so the equivalent oral dose would be

equal to the DDI. Note that this is a conservative assumption with respect to oral

ingestion but not for dermal-to-oral conversion. Equivalent oral DI values from dermal
Chapter 10

Risk Assessment

MPE 10-7 December 1998

DIW ' DI(child)

EDc

EDs

% DI(adult)

EDa

EDs

(10-2)

pathways can be added to DI values from oral pathways to determine total intake of

contaminant by these routes. The assumption of equivalent effectiveness of absorbed

doses between exposure routes, however, does not hold in some cases, such as when

effects occur at the portal of entry or when removal, inactivation, or activation of the

compound before reaching the target organ varies with the exposure route.

When this assumption is used, therefore, the data to support it should be

characterized in the final step of the assessment.

10.2.1.2. Exposure Duration Adjustments — The models presented in earlier

chapters of this methodology derived daily contaminant intake rates by various

pathways on the basis of estimated contaminant concentrations in environmental media

and human activity patterns. Both activity patterns and media levels are subject to

change over time. Activity patterns are defined in exposure scenarios. The most

common activity changes are associated with exposure as both a child and an adult. 

For example, the potential dermal exposure during childhood may be quite different

from that experienced by an adult. Scenarios, however, may also include activity

changes within one or both of these broad time frames. In order to arrive at an intake

representative of the entire duration of a scenario, a time-weighted average is

calculated. This is illustrated in the following equation for a simple scenario that

includes both childhood and adult exposure:

where:

DIW = time-weighted daily intake for pathway during exposure (mg/kg-day)

DI = daily intake for pathway (mg/kg-day)

EDc = duration of childhood exposure (years)

EDs = duration of scenario (years)

EDa = duration of adult exposure (years)
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MPE 10-8 December 1998

This equation can be easily modified to encompass alternative partitions of the

total exposure duration. If data were available regarding the change in contaminant

emission rates over time, averaging over the entire duration of the scenario would

produce a more realistic estimate of intake than an estimate based on emission rates at

any one point in time. For example, a risk assessor may decide to subdivide childhood

into specific phases such as, breast-fed infant up to 1 year of age, 2-6 years of age,

7-12 years of age, and 13-18 years of age.

When risk is characterized, the classification of exposure duration for a given

pollutant ultimately should consider the data set used to generate the dose-response

estimate. If there are a number of toxicologic evaluations, the risk assessor should

choose the dose-response curve from a study that best applies to the exposure

duration being evaluated. Typically, the Agency-verified data are presented on EPA’s

IRIS database. For noncarcinogens, no further duration adjustments of DIW are

necessary. However, the DIW does need to be categorized as chronic (typically

considered 7 years to lifetime; U.S. EPA, 1989) or subchronic (typically thought to be 2

weeks to 7 years; U.S. EPA, 1989), depending on the duration of the exposure

scenario (Ds). Chronic DIW values are compared with RfD values, while subchronic

DIW values, if calculated, are compared with subchronic RfD values. For all of these

subchronic RfDs, see the Health Effects Assessment Summary Tables (HEAST) (U.S.

EPA, 1998b). Acute exposures should be compared with estimates of short-term health

effects; for assessments of combustor emissions, acute exposures, if considered, are

generally evaluated for the inhalation route only (see Chapter 3).

Significant acute exposures resulting from combustor emissions are thought to

potentially occur only through the inhalation route. As described in Chapter 3, during

periods of operational perturbation, the magnitude of the emissions may increase and

compounds emitted from the source may change. Given the state-of-the-art of

commercial combustion sources in the United States, these are generally infrequent

events of an extremely short duration. Atmospheric conditions may also change over

short periods of time resulting in a low dispersion of emitted pollutants. While the types
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MPE 10-9 December 1998

DILW ' DIW

EDs

DL

(10-3)

of pollutants emitted would be unchanged, the predicted concentrations in the local

atmosphere may be elevated for these short time periods. The nature and duration of

these time periods depends on the site of the facility, local meteorology and

topography.

Predicted or measured chemical concentrations in the surrounding atmosphere

should be compared with appropriately derived short-term, dose-response estimates.

These short-term exposures in general should not be averaged over a lifetime.

Obviously, a mixture of chemicals could be emitted. Dose-response data for acute

exposures to chemical mixtures, if available, should also be considered.

For carcinogens, exposure is usually averaged over the entire lifetime of the

individual (assumed to be 70 years), even if the actual exposure duration is shorter

(U.S. EPA, 1995a). This is because cancer risk is generally described in terms of

lifetime probabilities of contracting the disease. To make this adjustment, the following

equation may be used:

where:

DILW = lifetime-weighted daily intake for pathway (mg/kg-day)

DIW = time-weighted daily intake for pathway during exposure (mg/kg-day)

EDs = duration of scenario (years)

DL = duration of lifetime (default = 70 years)

Although appropriate in most circumstances, there are times when adjustment to

lifetime average exposure is not recommended for a carcinogen. For example,

childhood may be the critical exposure period for some carcinogens, in which case the

risk would be underestimated by averaging over the entire lifetime. These exceptional

instances should be carefully evaluated on a case-by-case basis.
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MPE 10-10 December 1998

TDI ' j

n

i'1

DI(W, LW) i

(10-4)

10.2.1.3. Total Daily Intake Due To Indirect Exposure To Combustor

Emissions — Total daily intake (TDI) of a contaminant attributable to indirect exposure

to emissions from the combustor for a particular exposure scenariois estimated by

summing the oral equivalent daily doses. Equation 10-4 presents the formula for

summing doses (appropriately weighted for time as in Eq. 10-2) for all pathways

relevant to the exposure scenario.

where:

TDI = total daily intake for an exposure scenario including n exposure

pathways (mg/kg-day)

DI(W, LW) i = time-weighted daily intake by ith pathway (mg/kg-day)

[DIW for noncarcinogens, DILW for carcinogens]

The results of this equation can be used in the development of individual risk

assessments described in Chapter 2 (e.g., Maximum Exposed Individual).

Although oral absorption of a particular contaminant may differ for different

vehicles (water, food, or soil), vehicle-related adjustments are not generally

recommended (U.S. EPA, 1989), as RfDs and oral cancer slope factors are generally

derived to apply to all oral routes of exposure rather than to specific media. When

vehicle-related toxicity differences are adequately supported by the data, separate

toxicity values have been derived for different vehicles (e.g., cadmium has both foodand water-based RfD values; U.S. EPA, 1998a). In these cases, the pathways should

be sorted according to vehicle (e.g., water vs. food or soil for cadmium) and the DI

values within each group summed to obtain vehicle-related TDI values.

10.2.1.4. Cumulative Exposures — It is important to recognize that there may

be sources of exposures to chemicals other than from emissions of these chemicals

from the modeled combustion sources. Cumulative exposures occur when the
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MPE 10-11 December 1998

chemical exists naturally in the local environment, or when the chemical is ubiquitous in

the environment although it may not be natural to the environment. Examples of the

former are mercury or other metals, and examples of the latter are dioxins and PCBs. 

This concern, in general, has been expressed in terms such as “exposures from

multiple sources”, “cumulative exposures”, or “background exposures”. These terms

have been used in different contexts and may have different meanings in these

contexts. Cumulative exposures may be defined as the total chemical exposure that

results from all sources an individual comes into contact with, through multiple

exposure routes over a period of time. The quantification of cumulative exposure

includes consideration of all routes and sources of exposure as well as contact rates

and bioavailability of a pollutant within different media and over a specified temporal

period. Cumulative Risk may be narrowly defined as the likelihood of an adverse effect

that results from cumulative exposure to a single chemical. Alternatively, cumulative

risk may encompass an evaluation of the effects that result from exposure to multiple

stressors (chemical pollutants or otherwise) to a given individual over time.

The term “Background Level” may be defined alternatively as the concentration

of a chemical in an environmental medium (air, water, or soil) that occurs naturally (i.e.,

is not the result of human activities) or the concentration of a chemical in a defined

area over a period of time before the introduction of a specific anthropogenic source

only. Background exposure will be defined here in terms of the second definition

offered; it is the cumulative exposure experienced prior to the introduction of a specific

anthropogenic source. (TDI defines the exposure that results from the source only).

If and how the risk assessor considers background or cumulative exposures

depends on the nature of the question posed by the risk manager. It may be useful in

communicating the risks of a site-specific assessment to compare the TDI with an

estimate of background or cumulative exposures. Assessors may be interested in

background exposures in at least two ways: addressing how incremental impacts of a

single source compare with background impacts in the same geographic region, or
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MPE 10-12 December 1998

addressing how incremental impacts of a single source compare to the general

population background exposure distribution.

The first of these two assessment options is site-specific. In this case,

background exposures can be thought of as the exposures an individual would face if

the source in question were not in existence. Individuals living in the vicinity of the

source would not behave differently; they would just be exposed to environmental

media concentrations that would be lower because the source in question would not be

in operation. For example, if there is a subsistence fishing scenario pertinent to a

particular source, the subsistence fishers would be consuming fish at the same rate

whether or not the source were present. The fish concentrations to which the

individuals were exposed would be a function of the concentrations of the contaminant

in the water body where the fish were caught, which in turn resulted from the

cumulative impacts of all other sources in the geographic region (or, in a different

instance, could have resulted from natural levels of the contaminant in the

environment).

For the second of these assessment needs, background exposure may be

thought of in a more generic sense and not necessarily specific to a site or source. For

example, a risk assessor could devise a scenario for a specific source, estimate the

incremental daily dose from that source, and then compare that dose to a generic

background exposure distribution, which could be calculated considering the

distribution of medium specific contact rates across the nation and the distribution of

chemical concentration ratios in the medium. In the example in the previous

paragraph, an exposure resulting from subsistence fishing could be calculated as a

function of subsistence consumption and fish concentrations that result only from the

source in question. This exposure could be compared with an average national

exposure from fish consumption, calculated assuming national average fish

concentrations and average, rather than subsistence, fish consumption rates. 

Alternatively, the site- and source-specific exposure could be compared with an

exposure calculated by assuming the same subsistence behavior and a national
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MPE 10-13 December 1998

average fish concentration. There are numerous approaches to consider background

impacts in a site-specific assessment. This section will not give prescriptive guidance,

but rather will provide the risk assessor with a background in different options.

As seen in these examples, a background exposure estimate requires two

quantities: contact rates and exposure media concentrations. Contact rates could be

contact rates derived for a specific site, or generic values determined from different

data sets. A site-specific contact rate could be important if unique behaviors, such as

subsistence fishing, are associated with the source in question. Four options are

presented for estimating background exposure media concentrations:

1. Generic Exposure Media Concentrations: A risk assessor could construct

a generic background exposure media concentration. For example, the

Dioxin Exposure Reassessment Document (U.S. EPA, 1994a) derives

typical concentrations of the dioxin-like compounds in air, soil, and

various food products for the United States, and the Mercury Study

Report to Congress (U.S. EPA,1997) derives typical concentrations of

mercury in air, soil, and food products such as fish. The mercury report

also predicts exposures based on these data. Use of generic contact

behaviors and generic background concentrations based on measured

media concentrations allows one to derive a general background

exposure for any combination of pathways. This could be accomplished

through estimation of the distribution of concentrations that occur in a

specific area (e.g., the United States). The distribution of background

exposures can then be estimated by multiplying the distribution by the

distribution of contact rates.

2. Measurement of Exposure Media Concentrations: Measurement of

concentrations of the contaminant in question in all relevant exposure

media associated with a site would be optimal, but is costly and may not

be feasible if the source in question has been in existence for several

years. For example, a measurement of dioxins or mercury in air and soil

in the vicinity of an operating incinerator would reflect the impacts of the

incinerator, as well as any other sources that are present. Measurements

in environmental/exposure media in the vicinity of a closed incinerator

could still be influenced by past emissions from the incinerator. This

would be the case if measuring soil around a closed incinerator. On the

other hand, measurements of air concentrations around a closed

incinerator would be an appropriate reflection of background air

concentrations for that site. The measurement of exposure media

concentrations is usually feasible (but still costly) if the assessment is
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MPE 10-14 December 1998

prospective in nature, i.e., that it evaluates a new source not yet emitting

the contaminant. Here a careful monitoring program could give estimates

of environmental media prior to emissions from a new combustion source.

3. Modeling of Exposure Media Concentrations: There are at least two ways

in which background exposure media concentrations could be estimated

by models alone. One is to model the impacts from all major sources in

the area except for the source in question and then adds impacts of

points of interest. This assumes that the impacts from several sources

are independent. The model ISC3, recommended for use in this

document, can, in fact, model multiple sources. This document does not

discuss the procedures for using the ISC3 model in this mode. (This

option and the following RELMAP option raise additional questions such

as: Did the assessor identify all sources? Are the source emission

estimates correct? and so on.) Another possibility is use of the Regional

Legrangian Model of Air Pollution (RELMAP) (Eder et al., 1986; Clark et

al., 1992). EPA’s Office of Research and Development has developed

this national model. RELMAP takes, as input, emissions into the air from

specifically defined sources in the United States, and predicts, for any

location in the United States, amounts of depositions and air

concentrations for modeled contaminants. Air concentrations and

depositions could then serve as the input for modeling impacts to the

terrestrial and aquatic environment, using the steady-state models

discussed in this assessment. The RELMAP was used to predict national

fate of mercury emissions in U.S. EPA (1997), and is currently being used

in a similar manner for sources of dioxin release. Drawbacks of this

approach include the real possibility that not all sources have been

identified or properly quantified, as well as its low precision. The smallest

scale that such a regional modeling exercise can use is about 40 km2. 

Exposure media concentrations modeled by RELMAP or ISC3 would be

the “background” concentrations, on top of which the source in question

will be adding impacts.

4. A Combination of Modeling and Limited Monitoring: This may be the most

reasonable way to estimate background exposure media concentrations

for a specific site, if site-specific data are available. For example, if a

historical combustor source were no longer operating, than air

concentrations measured in the vicinity of the source could give an

estimate of background air concentrations. These could then be routed

through the terrestrial and aquatic environments to predict soil and food

concentrations for background exposure estimation, using the models in

this document. Careful monitoring of soil and air concentrations in the

vicinity of the source while it is still operating is another possibility. 

Specifically, if several air monitors in all directions from a combustor
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MPE 10-15 December 1998

CE 'TDI % j

n

i'1

BIi

(10-5)

source were sampled on several days, during which time wind-rose data

were also taken, one could probably identify air concentrations most and

least likely to be impacted by the source in question. Soil concentration

data several miles away may be an appropriate measure of impacts to

soils unaffected by the source in question. Again, these intermediate

exposure concentrations could be used with the steady-state models of

this document to model the full range of terrestrial and aquatic impacts.

Once contact rates and exposure media concentrations are decided, the risk

assessor can generate a background exposure estimate to suit the particular

assessment. Then, the assessor faces critical decisions on proper use and

interpretation of these estimates. One option that has been alluded to in previous

paragraphs is to do a simple comparison. Risk managers can compare the background

dose to the site-specific, predicted incremental dose and determine the most useful and

appropriate method of using the data.

The other option is to add the incremental exposure that results from the source

(TDI) to the background exposure resulting in a new cumulative exposure estimate.

This may be an appropriate option in at least two circumstances: 1) the health

endpoint is triggered by a threshold dose—there may arise a circumstance in which the

background exposure alone or the incremental exposure (TDI) alone would not trigger

the threshold, but the addition of the two may lead to the excedence of the threshold;

2) a risk management decision hinges on a non-threshold health impact, such as a

cancer endpoint, in which the addition of background plus incremental exposure may

lead to a level of concern (from a risk management standpoint) for a specific set of

exposure assumptions. These exposures may be summed through Equation 10-5 to

estimate cumulative exposure:

where:

CE = Cumulative exposure intake for exposure scenario including n
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MPE 10-16 December 1998

background exposure pathways (mg/kg-day)

BIi = daily background intake by ith pathway (mg/kg-day)

The only prescriptive guidance regarding background in this methodology is that

the risk assessor should consider background impacts in an indirect exposure

assessment, although there may be circumstances in which this is not necessary. 

There have been many Superfund site-specific assessments in which only incremental

impacts were considered in the establishment of cleanup levels. On the other hand,

heightened awareness of the ubiquitous nature of contaminants commonly emitted from

combustors, such as mercury or dioxins, coupled with an awareness that even the

background levels of these contaminants approach levels of concern, has required sitespecific combustor risk assessments to take background impacts into consideration.

10.2.2. Population Exposure. The population exposure is the aggregate exposure to

the affected individuals. U.S. EPA’s (1992a) Exposure Assessment Guidelines

recommend assessing the exposure and risk experienced by the aggregate population. 

Several potential approaches to describing population exposure are presented in

Chapter 2 of this methodology. One way is to simply present the distribution of the

individual exposures predicted for all individuals within the exposed population. For

example, for inhalation exposures U.S. EPA’s Human Exposure Model (HEM) can

predict the distribution of inhalation exposures experienced by the population around a

facility. (The HEM only accounts for inhalation exposures.) For combinations of large

populations and large numbers of pollutants that are transferred through multiple

environmental compartments, estimating individual exposures for the entire population

may be very resource intensive. The isopleth approach also presented in Chapter 2 of

this document can effectively be used to estimate and present population exposures

and the resulting risks. However, the resources needed to complete the approach for

large numbers of pollutants may not be available to the risk assessor. A third approach

assumes that the exposure predicted for the “average” individual in the study area

represents the population mean and the distribution of the exposure is not estimated.
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MPE 10-17 December 1998

A fourth procedure combines knowledge of the “commodity shed” with the

distribution of daily consumption rates of the commodity. This method could be used

for any medium to which a significant fraction of the population is exposed. Although

most applicable for foods, it could apply to other sources of exposure such as drinking

water or soil.

10.3. TOXICITY ASSESSMENT

Toxicity assessment consists of the evaluation of a particular contaminant‘s

ability to cause adverse effects in exposed individuals—hazard identification—and the

estimation of the relationship between the actual exposure to a contaminant and the

increased likelihood of adverse effects—dose-response assessment (U.S. EPA,1989). 

The purpose of the following sections is to summarize these two steps in the risk

assessment process as they relate to both cancer and noncancer toxicity. The

concepts put forward in these sections are not unique to combustor emissions but can

be applied in any toxicity assessment scenario. Toxicity assessments for specific

substances can be found in the U.S. EPA's Integrated Risk Information System (IRIS;

U.S. EPA, 1998a).

The first step in the toxicity assessment process, hazard identification,

determines the type of effects produced by a contaminant and whether those effects

are adverse. Hazard identification is a qualitative evaluation of all the available human

and experimental animal data to determine the range of adverse effects associated with

exposure to a given contaminant. Unique to the cancer assessment is the qualitative

classification of the potential of a contaminant to cause cancer in humans; a parallel

concept does not exist (formally) for noncancer assessment.

The second step in toxicity assessment—dose-response assessment—defines

the relationship between the exposure level of the contaminant and the associated

biological effects. The result of this assessment is a quantitative measure in the form

of a media concentration or dose of the contaminant associated with a given level of

risk. The formal quantitative measures are the Reference Dose (RfD) and Reference

Concentration (RfC) for oral and inhalation noncancer assessment, respectively, and
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the slope factor and unit risk for cancer assessment. Detailed explanations of the

underlying concepts, methodologies and assumptions for these quantitative estimates

is beyond the scope of this document and can be found elsewhere [RfD (U.S. EPA,

1988a; Barnes and Dourson, 1988), RfC (U.S. EPA, 1994b), slope factor/unit risk (U.S.

EPA, 1986a, 1988b, 1996a)]

The U.S. EPA's approach to assessing the risks associated with noncancer

toxicity has traditionally differed from its approach to assessing carcinogenic risks

because of different assumptions about the mechanisms of action involved. For most

carcinogens, the assumption is that even a single molecular event can evoke changes

in a single cell that can lead to uncontrolled cellular proliferation. This mechanism is

referred to as nonthreshold, as there is theoretically no level of exposure for such a

chemical that does not pose a small, but finite, probability of generating a carcinogenic

response. Noncancer effects, however, are presumed to arise from threshold

mechanisms. That is, homeostatic, compensating, and adaptive mechanisms must be

overcome before a recognizable toxic endpoint is manifested. The threshold concept is

that some finite level of exposure to a substance will not result in an adverse effect for

any individual. The key word in the interpretation of this concept is adverse, such that,

even if it can be shown that one molecule of a substance destroys a protein molecule,

such an effect would be considered biologically insignificant and would not be judged to

be adverse.

Common to both types of assessment is a statement of the confidence held in

the quantitative estimates. The confidence judgment is based on the quality and

completeness of the entire body of data supporting the quantitative estimate. The

statements of confidence should be interpreted in the context of the likelihood that the

quantitative estimate will not change when new data become available. The

confidence statement is not a commentary on the quality of the assessment or on the

relative protectiveness of the quantitative estimate; additional data could result in lower

or higher numbers. As an example, additional data could identify a more sensitive
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endpoint (lowering the NOAEL) or could fill in a data gap (lowering the uncertainty

factor).

A significant practical aspect of the cancer/noncancer mechanistic dichotomy

when assessing exposures is that quantitative potency estimates for carcinogenicity

allow for estimation of incremental risk in the human population given a specific

exposure level while noncancer potency estimates do not. Currently, noncarcinogenic

quantitative estimates only identify the exposure level below which adverse effects are

unlikely; an RfD or RfC does not say anything about incremental risk for higher

exposures. The Benchmark Dose or other dose-response models, combined with a

probabilistic treatment of uncertainty (Price et al., 1997; Swartout et al., 1998), may

provide a basis for estimating risk above the RfD or RfC in the future. However the risk

is expressed, as long as a threshold is assumed, the total exposure (including

background) for any given contaminant must be known for assessment of

noncarcinogenic risks.

10.3.1. Hazard Identification for Carcinogens. The U.S. EPA has recently published

(U.S. EPA, 1996a) proposed revisions to the 1986 Guidelines for Carcinogen Risk

Assessment (U.S. EPA, 1986a). Hazard identification in the proposed revisions to the

cancer guidelines (EPA Proposed Cancer Guidelines, U.S. EPA, 1996a) is highlighted

by an expanded weight-of-evidence narrative, which culminates in a classification of

the substance as to the likelihood that it is a human carcinogen. Proposed changes

include:

a greater focus on biological information other than tumor findings,

a revised classification system, 

a consideration of the agent’s mode of action,

use of biologically-based extrapolation models,

mathematical models other than the linearized multistage model, and

a two-step dose-response assessment process.

The revised classification system focuses on the likelihood that the agent is a

human carcinogen, which is reflected in the language. Determination of this

classification is dependent on an expanded and more inclusive weight-of-evidence

assessment. Also, the existence of a hazard is no longer strictly a yes/no decision, but
Chapter 10

Risk Assessment

4

 The lack of a formal WOE is now being addressed, somewhat, with the recent inclusion

on IRIS (U.S. EPA, 1998a) of text devoted to the synthesis and evaluation of major noncancer

effects. 

MPE 10-20 December 1998

can involve a consideration of dose, depending on the mode of action of the

carcinogen. That is, some carcinogens may be determined to act by indirect

(threshold) mechanisms rather than by direct genotoxic action. For dose-response

assessment, selection of mathematical model is liberalized and low-dose extrapolation

has been decoupled from high-dose modeling (see Section 10.3.3., following). The

reader is referred to U.S. EPA (1996a) for a detailed discussion of these topics.

10.3.2. Hazard Identification for Noncarcinogens. Unlike carcinogen risk

assessment, no formal guidelines exist for noncarcinogen risk assessment and so, the

latter will be presented here in somewhat greater detail than the former. Methodology

documents for both the RfD and RfC, however, are available (U.S. EPA, 1988a,

1994b), as well as a few related publications (Barnes and Dourson, 1988; Dourson,

1994; Swartout et al., 1998).

In the vast majority of cases, hazard identification for noncancer effects is not a

yes/no process, as for carcinogenic effects, but is dependent on dose. Virtually all

substances will eventually produce adverse effects as the dose is increased. Thus,

noncancer hazard identification is indistinguishable from dose-response analysis; the

existence of a hazard cannot be determined independently from the dose-response

analysis. A qualitative aspect of noncancer hazard identification, however, exists

conceptually, as a weight-of-evidence (WOE) approach for determining the likelihood

of occurrence of specific effects in humans. Guidance on how to conduct a WOE is

limited (U.S. EPA, 1988a; Barnes and Dourson, 1988) and a formal noncancer WOE is

not included in noncancer assessments4 (U.S. EPA, 1998a). An assessment of the

strength of the data, however, is discussed throughout an RfC or RfD document on

IRIS (U.S. EPA, 1998a) culminating in the confidence statement described previously. 

The identification of a critical effect, on which an RfD or RfC is based, is largely a

procedural issue rather than a WOE issue. The critical effect is always the adverse
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MPE 10-21 December 1998

effect at the LOAEL of the study defining the RfD—the principal study. The principal

study is almost always the one yielding the lowest RfD when the appropriate

uncertainty factor scenario is considered. Biological considerations may override the

procedural approach when specific mechanisms or effects are judged to be more

appropriate for application to human risk assessment.

10.3.3. Dose-Response Assessment for Carcinogens. The dose-response

assessment step in carcinogen risk assessment is to define the relationship between

the dose of an agent and the likelihood of a carcinogenic effect. Dose-response

assessment usually entails an extrapolation from the generally high doses administered

to experimental animals or exposures noted in epidemiologic studies to the exposure

levels expected from human contact with the agent in the environment. It also includes

considerations of the validity of these extrapolations. Extrapolation is ordinarily carried

out first by fitting a mathematical model to the observed data and then by extending

the model (or a bound on the risks it predicts) from the observed range down toward

risks expected at low exposure. The models are generally, but not always,

nonthreshold. That is, risk diminishes with dose, but does not become zero until dose

becomes zero. The proposed revised carcinogen risk assessment guidelines allow for

different approaches in different situations (U.S. EPA, 1996a).

Dose-response assessment includes (1) selection of the appropriate data sets to

use; (2) derivation of estimates at low doses from experimental data at high doses

using an extrapolation model; and (3) choice of an equivalent human dose when animal

data sets are used. A more detailed description of the dose-response assessment for

carcinogens can be found in other U.S. EPA documents (U.S. EPA, 1986a, 1988b,

1996a).

In the EPA Proposed Cancer Guidelines (U.S. EPA, 1996a), dose-response

assessment is a two-step process. The default assumption remains low-dose linear,

but the upper anchor for the extrapolation has changed. Dose response in the range of

the observations is first modeled to obtain a point of departure, from which low-dose

extrapolation begins. For oral exposure, the cancer potency factor (CPF) is estimated
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MPE 10-22 December 1998

CPF '

Rpd

Dpd

(10-6)

RSD '

TIR

CPF

(10-7)

as the slope of the line from the point of departure to the origin (zero risk at zero dose)

as in Equation 10-6.

where:

CPF = cancer potency factor

Rpd = response at point of departure (unitless)

Dpd = dose at point of departure (mg/kg-day)

The default for Rpd is the lower 95% confidence bound on the dose associated

with a10% risk derived from modeling the observations (in terms of human equivalent

dose), but could be different depending on the situation. The carcinogen dose

associated with a specific predicted predicted human risk can then be calculated from

Equation 10-7.

where:

RSD = risk-specific dose (mg/kg-day)

TIR = target incremental risk

CPF = cancer potency factor

Equations 10-6 and 10-7 are included as an example of the procedure for

calculating carcinogen risk and apply to the oral route of exposure. The equations for

inhalation and dermal exposure will be similar but with different units. Also, these

equations will be different for nonlinear models of low-dose risk and will depend on the
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MPE 10-23 December 1998

particular model used. The reader is referred to U.S. EPA (1996a) for a more thorough

discussion of these topics.

10.3.4. Dose-Response Assessment for Noncarcinogens. Noncancer doseresponse assessment centers on the no-observed-adverse-effect level (NOAEL)

associated with the LOAEL for the critical effect. The NOAEL is defined as the highest

experimental dose of a chemical at which there is no statistically or biologically

significant increase in frequency or severity of an adverse effect in individuals in an

exposed group when compared with individuals in an appropriate control group. The

NOAEL is then divided by a series of uncertainty factors that reflect specific major

areas of uncertainty in the RfD and RfC. If a NOAEL has not been identified, the

LOAEL is used, with application of an additional uncertainty factor. The formulas for

calculation of RfCs and RfDs are given in Equations 10-10 and 10-11, respectively.

More recently a benchmark dose (BMD) or concentration (BMC) has been used

instead of a NOAEL to derive RfDs or RfCs. The BMD/C is defined as the statistical

lower confidence limit on the dose estimated to produce a predetermined level of

change in response relative to controls (Crump, 1984). The BMD/C incorporates

information on the slope of the dose-response curve and sample-size uncertainty into

the quantitative estimate. The BMD approach is particularly useful when a NOAEL is

not defined. Current usage of the BMD/C is as a replacement for the NOAEL terms in

Equations 10-10 and 10-11. A discussion of the BMD methodology and its strengths

and weaknesses can be found in the draft guidance document for the use of the BMD

in risk assessment (U.S. EPA, 1996c).

Uncertainty factors are reductions in the dose rate (RfD) or concentration (RfC)

used to account for several areas of scientific uncertainty inherent in most toxicity data

bases. Uncertainty factors of up to 10 each are applied to extrapolate from test animals

to humans, to provide protection for sensitive human subpopulations, to account for the

effects of exposure duration, to estimate a NOAEL from a lowest-observed-adverseeffect level (LOAEL) and to account for the lack of a complete data base. There is no

standard description for the modifying factor, as it is situation specific. A detailed
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MPE 10-24 December 1998

RfD '

NOAEL

JUFi× MF

(10-8)

RfC '

NOAELHEC

JUFi× MF

(10-9)

description of uncertainty factors is beyond the scope of this document and can be

found elsewhere (U.S. EPA, 1988a; Barnes and Dourson, 1988).

10.3.4.1. Reference Dose (RfD) Calculation —The U.S. EPA's formal

definition of the RfD is:

an estimate (with uncertainty spanning perhaps an order of magnitude) of

the daily exposure to the human population (including sensitive

subgroups) that is likely to be without appreciable risk of deleterious

effect during a lifetime.

The RfD is derived from the NOAEL by application of uncertainty and modifying factors

that reflect various types of data sets used to estimate RfDs (U.S. EPA, 1988a; Barnes

and Dourson, 1988). The RfD is calculated as follows:

where:

RfD = Reference Dose (mg/kg-day)

NOAEL = no-observed-adverse-effect level (mg/kg-day)

JUFi = product of individual (i) uncertainty factors (unitless)

MF = modifying factor (unitless)

10.3.4.2. Reference Concentration (RfC) Calculation — The RfC value for

inhalation exposure to a chemical is expressed as a concentration in air as milligrams

per cubic meter (mg/m3). The RfC is calculated as follows:
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MPE 10-25 December 1998

where:

RfC = Reference Concentration (mg/m3)

NOAELHEC = no-observed-adverse-effect level, human equivalent

concentration (mg/m3)

JUFi = product of individual (i) uncertainty factors (unitless)

MF = modifying factor (unitless)

The RfD and RfC methodology differ primarily in the manner of expressing the

NOAEL. The RfC methodology explicitly provides for dosimetry adjustments among

species and for different exposure scenarios, such that the experimental NOAEL is

scaled specifically for humans (U.S. EPA, 1994b). Case-specific data applicable to

this scaling can be used to reduce the overall uncertainty factor. Such adjustments can

also be made for RfDs but are not explicit in the methodology.

10.3.4.3. Interpretation of Noncancer Assessments — Although the

noncancer risk assessment approach assumes thresholds—even for the most sensitive

individual in the population—the RfD is not an estimate of the human population

threshold. Although not stated explicitly in the RfD methodology, the RfD is an implicit

estimate of a NOAEL in a study of a sensitive human subpopulation (NOAELHS;

Swartout et al., 1998). This definition arises by considering the requirement for

reducing the overall uncertainty factor to unity, which is a NOAEL in a human study

comprising the presumed sensitive subpopulation (U.S. EPA, 1988a; Barnes and

Dourson, 1988). Thus, the actual human risk at the RfD is the residual risk at the

NOAELHS, which can be zero or nonzero; the upper limit depends on the power of the

study to detect an effect. Even if the latter were determinable, the residual risk applies

only to the sensitive subpopulation studied. A determination of the human population

risk would require an estimate of the proportion of the human population comprising the

sensitive subpopulation.

In addition, the RfD should be interpreted as a lower-bound estimate of the

NOAELHS (Swartout et al., 1998). This conclusion follows from the conceptual nature
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MPE 10-26 December 1998

of uncertainty factors, each of which are considered to be loose upper bounds on the

factor required to lower the dose producing an effect to account for missing information. 

As an example, an uncertainty factor of 10 is used to account for the effects of

prolonged exposure on toxicity when lifetime studies are not available (subchronic-tochronic UF). The expectation, however, is that the true reduction in the NOAEL when

the chronic study is performed will be less than 10 most of the time; the value of 10 is

thus meant to be protective. It follows, then, that the (multiplicative) application of each

additional factor of 10 will increase the degree of protection of the RfD with respect to

anticipating the NOAELHS. That is, the assessment of the likelihood that the RfD is less

than the NOAELHS depends on the number of uncertainty factors used in the RfD

derivation. A rough idea of the relative likelihood of any RfD exceeding the NOAELHS

can be appreciated by assuming that each uncertainty factor is independent and

identically lognormally distributed with a 95th percentile equal to 10. That is, the

probability that the reduction in the NOAEL will exceed 10-fold when the data is

acquired satisfying one area of uncertainty is 0.05. The value of two and three

multiplicative 10-fold uncertainty factors (100 and 1000) has a probability of 0.01 and

0.005, in their respective joint distributions (Swartout et al., 1998). Recognizing this

issue, the U.S. EPA has modified the standard values for combined uncertainty factors

when four or five areas of uncertainty apply to 3,000 and 10,000, respectively

(Dourson, 1994). The result is that, for more recent assessments, RfDs have moved

closer to becoming probabilistically equivalent in estimating the NOAELHS. When

evaluating specific RfDs, the assessor must pay close attention to the description of the

uncertainty factors to determine whether or not adjustments have been made to

account for compounded conservatism in the uncertainty factors.

Finally, the risk of noncancer effects above the RfD cannot be estimated with

current methodology, although an approach has been published that may be useful

(Price et al., 1997). Exposure-response relationships over a range of doses or

exposure durations are only appreciated qualitatively. As an example, the steepness of

the dose-response curve has no influence on the value of the resulting RfD. A NOAEL
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MPE 10-27 December 1998

associated with a steep dose-response slope is more likely to be subthreshold than a

NOAEL associated with a gradual increase in response. The latter, however, has a

little more room for error than the former with respect to other sources of error and

uncertainty.

10.4. RISK CHARACTERIZATION

10.4.1. Introduction. Risk characterization is the summarizing and essential final step

of the risk assessment process. In risk characterization, the major scientific evidence

and bottom-line results from hazard identification, dose-response assessment, and

exposure assessment are evaluated and integrated into an overall conclusion about

risk (NRC, 1983, 1994).

This section will show how the exposure information can be integrated in a

quantitative manner with hazard and dose-response information to estimate risk for

individuals and populations. It also will discuss how quantitative risk estimates can be

integrated with qualitative and quantitative information regarding uncertainty and

variability to produce the risk characterization. The general approach is based on

Agency guidelines for exposure assessment and risk characterization (U.S. EPA, 1989,

1992a,b, 1995b,c,d).

Various individuals and groups may be significantly impacted by the results of

the risk characterization. These potential stakeholders include industry scientists and

decision-makers, local government representatives, local residents, citizen action

groups, national environmental advocacy groups, and national industry advocacy

groups. Some issues with which they may be concerned include the number of people

exposed, the range of uncertainty around the exposure estimates, the critical variables

driving the assessment, the existence of data gaps, the bottom-line conclusion, whether

the risk characterization supports a regulatory decision, and whether the risk

assessment has been peer reviewed as well as the quality and thoroughness of the

peer review. The economic and social ramifications of the risk assessment require that

the risk characterization communicate the results clearly, highlighting the important

issues and uncertainties, and exploring their implications for different audiences. The
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MPE 10-28 December 1998

emphasis in the risk characterization process should be on clarity of communication,

transparency in decision making, consistency of policy, and reasonableness of the

assumptions incorporated into the assessment (U.S. EPA, 1995c).

10.4.2. Risk Estimation. This section discusses how exposure estimates developed

with this document can be used to estimate risk. Risk is the result of a combination of

hazard and exposure. For chemical pollutants emitted from combustors, the hazard is

a property of the pollutant and exposure results from contact with the pollutant

(Presidential/Congressional Commission on Risk Assessment and Risk Management,

1997). Risk can be ascribed to individuals, populations, or defined subgroups of a

population (U.S. EPA, 1992a, 1995b). Individual risk, on the other hand, is the risk

accruing to an individual represented by a defined exposure scenario. Section

10.4.2.1. describes how to calculate the individual risk associated with a given scenario

for both carcinogens and noncarcinogens. This section also discusses how to

incorporate results from a direct inhalation exposure analysis into the calculation of

individual risks.

Different exposure scenarios will lead to different levels of estimated risk. In

addition, stochastic variation inherent in most input parameters of the methodology will

contribute further variation. As a result, individual risks will be distributed within any

exposed population. Section 10.4.2.2. discusses ways to represent the variability

inherent in individual risk estimates.

Population risk is the aggregate risk experienced by the exposed population; it

may include estimates of the number of individuals expected to be affected during a

specified period of time or estimates of the distribution of risks to specific subgroups. It

takes into account not only the risk associated with the various scenarios, but also the

number of people represented by each scenario. Estimation of population risk is

discussed in Section 10.4.2.3.

Risk can also be determined for specified subgroups within the exposed

population. The subgroup of interest may be highly exposed, highly susceptible, or

both. Once identified, the subgroup can be treated as a population in itself, and
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MPE 10-29 December 1998

individual and aggregate risks (i.e., risk incorporating consideration of subgroup size)

for the subgroup can be determined. Issues associated with estimation of risk to

specific subgroups are discussed in Section 10.4.2.3.1.

10.4.2.1. Calculation of Individual Risk — Individual risk is the risk accruing to

an individual in a defined exposure scenario. Individual risk is calculated differently for

noncarcinogenic toxicants than for carcinogens. The different treatment follows from

assumptions made regarding the mechanisms by which these effects are exerted. 

Noncarcinogens are assumed to operate by threshold-dependent mechanisms. 

Carcinogens, by contrast, are generally assumed to operate by a linear (nonthreshold)

mechanism, although EPA methodology is flexible enough to accommodate nonlinear

mechanisms of carcinogenicity, and even those with apparent thresholds.

For estimation of risk to noncarcinogens, TDI (or the CE, if the latter were

calculated) is compared with the RfD to characterize the risk to human health. For

carcinogens, TDI is used to estimate excess carcinogenic risk. The excess risk (ER) is

derived from the daily incremental dose of the contaminant above the background dose

and the human cancer risk factor as established by U.S. EPA methods . It is defined as

the incremental upper bound lifetime cancer risk above background to a hypothetical

population in which all individuals are continuously exposed to a concentration equal to

the daily intake of the contaminant. For example, an ER of 10-6 means that lifetime

exposure to that specific concentration (or administered dose) would produce an upperbound excess cancer risk of one in a million.

Exposure to members of a chemical class or a chemical congener may be

estimated from emission data using the proportion of the total mass of the chemical

class that the congener comprises. Unless congener-specific data are available, all of

the congeners of the chemical class (e.g., PAHs or PCBs) are typically assumed to

have the same environmental transport characteristics. Furthermore, for the purposes

of risk assessment, the congener is assumed to comprise the same proportion of the

total chemical class in deposition, soils, plants, and animals as it does in emissions. 

This allows estimation of exposure to the congener as well as to the total chemical
Chapter 10

Risk Assessment

MPE 10-30 December 1998

HQ '

TDI

RfD

(10-10)

HQ ' j

m

k'1

TDIk

RfDk

(10-11)

class. Potential health effects resulting from exposure to the congener or the chemical

class can be evaluated using the RfD or cancer risk slope for the congener.

Although the focus of this methodology is to quantify exposure to combustor

emissions by indirect routes and characterize the resulting risk, the reality is that

individuals in the exposed population will also receive direct inhalation exposures. 

Results from direct inhalation exposure analysis can be incorporated into the

calculation of individual risk for both carcinogens and noncarcinogens.

10.4.2.1.1. Noncarcinogenic Toxicants. For noncarcinogenic toxicants, risk is

assessed by comparison of TDI (or the CE) with the RfD. This is done by calculating a

hazard quotient (HQ), as follows:

where:

HQ = hazard quotient (unitless)

TDI = total daily intake (mg/kg-day)

RfD = reference dose for oral exposure (mg/kg-day)

For a chemical like cadmium that has separate RfDs for food and water vehicles,

a hazard quotient can be determined according to the following equation (if the vehiclespecific RfDs are not based on portal-of-entry effects):

where:

HQ = hazard quotient for m exposure vehicles, k (unitless)

TDIk = total daily intake by kth vehicle (mg/kg-day)
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MPE 10-31 December 1998

RfDk = reference dose for oral exposure by kth vehicle (mg/kg-day)

The RfD (or subchronic RfD) used to calculate the HQ should correspond to the

duration of exposure in the scenario being considered. If exposure to the contaminant

from sources other than the combustor is important, then the CE should be used to

calculate the HQ for risk estimation.

The HQ merely expresses the exposure relative to the RfD and, by itself, says

nothing about risk. Expectations of risk are dependent on the nature of the RfD,

particularly on the magnitude of the total uncertainty factor (UF). That is, the RfD does

not represent the same level of risk in every case. The RfD could be several-fold lower

than the population threshold or it could be associated with a finite risk, perhaps as

high as 10%. The primary indicators of the plausible risk at the RfD are the size of the

population studied and the magnitude of the UF. Small studies imply greater residual

risk at the NOAEL, simply as a result of sample size uncertainty. A smaller UF implies

a smaller margin of protection. That is, as each individual uncertainty factor is

assumed to be protective, multiplicative combinations of uncertainty factors provide a

greater relative level of protection. As an example, a risk assessor may be more

concerned with an HQ of 1 for nitrate with a UF of 1 (and a LOAEL less than 2-fold

higher than the NOAEL) than an HQ of 10 for dichloropropene with a UF of 10,000. 

Furthermore, the risk embodied in the HQ refers only to the potential that some

individuals may be affected and cannot address the absolute level of risk simply

because the RfD is not expressed in terms of risk levels.

The HQ reflects the level of concern associated with exposure to a single

contaminant. However, combustor emissions will generally contain a mixture of many

contaminants. It will usually be desired to assess risk associated with exposure to the

emissions as a whole. In practice this means that the risks associated with exposure to

the individual contaminants will be summed to produce an estimate of risk posed by the

mixture (U.S. EPA, 1989). The simplifying assumption of additivity among emission

constituents fails to take into account potential synergistic and antagonistic interactions
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MPE 10-32 December 1998

HI ' j

n

i'1

HQi

(10-12)

among constituents, but is neutral with respect to conservativeness (U.S. EPA, 1986c). 

The risk associated with a mixture of contaminants is calculated as the hazard index

(HI):

where:

HI = hazard index for mixture of n chemicals (unitless)

HQi = hazard quotient for ith chemical (unitless)

The guidelines for adding risks across chemicals are included in the Guidelines

for the Health Assessment of Chemical Mixtures (U.S. EPA, 1986c) and RAGS (U.S.

EPA, 1989). Strictly speaking, it is appropriate to sum risks across noncarcinogenic

chemicals only when those chemicals act on the same target organ by the same

mechanism of action. In practice, however, as an initial screening method it is

acceptable to calculate an initial HI based on all chemicals of concern in the combustor

emissions (U.S. EPA, 1989). If this initial HI is less than 1, then intake of the mixture by

indirect routes is not expected to present a health hazard. If the initial HI is 1 or more,

then HQs for the chemicals of concern should be segregated according to target organ

of the chemical and (if sufficient data are available) mechanism of action, and targetbased HI values calculated. A target-based HI of greater than 1 may or may not

indicate a potential health risk due to indirect exposure to the mixture. Interpretation of

the HI is, again, dependent on the nature of the RfD for each component of the mixture.

10.4.2.1.2. Carcinogens. For chemicals classified as carcinogens and

assumed not to exhibit a threshold, the ER due to indirect exposure to combustor

emissions may be determined using Equation 10-13 (if risks are estimated to be below

0.01) or 10-15 (any risk level) (U.S. EPA, 1989):
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MPE 10-33 December 1998

ER ' TDI @ CRF

(10-13)

ER ' 1 & e

&(TDI @ CRF)

(10-14)

where:

ER = excess risk due to indirect exposure to a contaminant (unitless)

TDI = total daily intake of contaminant by indirect routes (mg/kg-day)

CRF = human cancer risk factor for contaminant (mg/kg-day)-1

ER is the unitless probability of an individual developing cancer from indirect

exposure to combustor emissions. Because cancer risk factors are typically derived as

upper 95th percentile values, ER as calculated above will generally be an upper bound

estimate of risk (i.e., the true risk is not likely to exceed ER and will probably be lower).

Background exposures are not taken into account when calculating ER because

ER is an expression of incremental risk due to the facility and not an expression of total

carcinogenic risk. It may be useful, however, to compare background exposure risks

with TDI to determine the fraction of exposure to a given carcinogen that is contributed

by the facility.

Equations 10-13 and 10-14 calculate ER for a single contaminant. However,

combustor emissions are mixtures of many contaminants. In order to calculate ER for a

mixture of contaminants, the estimates of ER for the individual mixture constituents are

summed as follows (U.S. EPA, 1989):
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Risk Assessment

MPE 10-34 December 1998

ERMix ' j

n

i'1

ERi

(10-15)

where:

ERMix = excess risk due to indirect exposure to combustor emissions for a

mixture of n chemicals (unitless)

ERi= excess risk for ith chemical (unitless)

This calculation is an approximation because it does not take into account the

joint probabilities of the same individual developing cancer from two or more

carcinogens. It is appropriate for use in cases where ERMix is less than 0.1 (expected to

be the vast majority of cases). See U.S. EPA (1989) for guidance when ERMix exceeds

0.1. This approach assumes independence of action by the carcinogens in a mixture

(i.e., it is assumed that no synergistic or antagonistic interactions occur between

mixture constituents). Other limitations are discussed in the Guidelines for the Health

Assessment of Chemical Mixtures (U.S. EPA, 1986c) and RAGS (U.S. EPA, 1989).

The fate and transport of individual pollutants is modeled individually. Elevated

concentrations of individual pollutants may impact the fate and transport of other

emitted pollutants. For example, two metals may compete for the same binding site of

a given cell; one metal may be preferentially taken up at the receptor to the exclusion of

the second, or the mixture of both metals may result in a reduced uptake of each. This

is considered an uncertainty in this model.

10.4.2.1.3. Incorporating Results from a Direct Exposure Risk Analysis.

Although the focus of this methodology is to quantify exposure to combustor emissions

by indirect routes and characterize the resulting risk, the reality is that individuals in the

exposed population will likely also receive direct inhalation exposures. Following is a

brief presentation of how risk from direct inhalation exposure to carcinogenic and

noncarcinogenic combustor emissions can be quantified and subsequently added to
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Risk Assessment

MPE 10-35 December 1998

ER ' IUR @ C

(10-16)

HQ '

C

RfC

(10-17)

the risks accruing from indirect exposure. This discussion is included for illustrative

purposes only; it is not meant to be a complete discussion of this issue.

The air models discussed in Chapter 3 calculate long-term average ambient air

concentrations. For the purposes of estimating direct inhalation risk, it is important that

the ambient air concentrations of the pollutant represent the sum of both the vapor

phase and the fine particulate phase (i.e., the sorbed phases). This was discussed in

Chapter 3. Except as noted below, the combined vapor and particulate phase

concentrations should generally be used directly for estimating risk.

For carcinogens, the inhalation risk is best computed by multiplying the

compound specific inhalation unit risk (IUR) by the average (over the exposure period)

concentration of the contaminant in air at the point of exposure. This procedure is

illustrated in Equation 10-16:

where:

ER = excess risk due to direct inhalation exposure to a contaminant

(unitless) 

IUR = inhalation unit risk for contaminant (mg/m3)

-1

C = average ambient air concentration of contaminant (mg/m3)

For noncarcinogens, risk is estimated using the hazard quotient (HQ), as

follows:

where:

HQ = hazard quotient (unitless)
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MPE 10-36 December 1998

C = average ambient air concentration of contaminant (mg/m3)

RfC = reference concentration for contaminant (mg/m3)

Interpretation of the HQ was discussed in Section 10.4.2.1.1.

The ER (carcinogens) or HQ (noncarcinogens) from direct inhalation of a

contaminant can be added to those accruing from indirect routes to obtain an estimate

of overall risk associated with exposure to the contaminant from combustor emissions.

In general, the ambient air concentration (which should include both particulates

and vapors, as stated before) should not be adjusted in order to calculate the inhalation

risk, unless the particle sizes are greater than approximately 10 microns. Below this

size, particles are considered inhalable and represent an inhalation exposure. Above

this size the particles are considered non-respirable (U.S. EPA, 1994b Appendix G;

Kennedy and Trochimowicz, 1984). In general, only fugitive dust emissions or

resuspended dust (e.g., from mechanical disturbance of soil) would be expected to

have a significant particle component in the size range larger than 10 microns. If

significant exposures to particles much larger than 10 microns are projected to occur,

then the portion of the ambient air concentration represented by particles above

10 microns should be considered along with other oral exposures for the purpose of

estimating risk.

10.4.2.2. Distributions of Individual Risk — Individual risk is the risk accruing

to an individual in a defined exposure scenario. A given population may include

individuals described by any number of different exposure scenarios. Each of these

scenarios will be associated with a different level of risk. In addition, the stochastic

variation inherent in most input parameters of the methodology will lead to further

variation in risk estimates. There will, therefore, be a distribution of individual risks

within any exposed population.

In order to capture the variability of individual risk within the exposed population,

individual risk is determined for both central-tendency and high-end portions of the

distribution (U.S. EPA, 1995b). Central-tendency estimates are estimates of risk for
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MPE 10-37 December 1998

persons in the middle of the risk distribution, and as such generally reflect central

estimates of exposure. Central tendency is best estimated by the median (50th

percentile). For normally distributed data, the arithmetic mean is also an appropriate

estimator of central tendency, because it corresponds to the median in this distribution. 

Similarly, for log-normally distributed data, the geometric mean is an appropriate

estimator of central tendency because it corresponds to the median of the distribution.

The central-tendency estimate may be derived in a number of different ways

(U.S. EPA, 1995b). If complete information on the population distribution of exposures

is available, then the median value can be taken directly from the distribution and used

to calculate risk. If this information is not available, but sufficient data are available

regarding variability in the exposure parameters, then the distribution of exposures can

be estimated using probabilistic modeling techniques, such as Monte Carlo analysis

(see Section 10.5.2.). Alternatively, the central-tendency exposure can be satisfactorily

estimated by using central-tendency estimates (preferably medians) for all input

parameters in the exposure model (even though this may not correspond to any actual

exposure scenario in the exposed population).

High-end estimates are plausible estimates of the individual risks for persons at

the upper end of the risk spectrum (U.S. EPA, 1992a). They are used to convey

estimates of risk in the upper range of the distribution while avoiding estimates that are

beyond the true distribution. The upper 90th or 95th percentile of the risk distribution is

generally used. The high-end estimate may be derived in different ways, depending on

the amount of data available (U.S. EPA, 1995b). If complete information on the

population distribution of exposures is available, then the upper 90th or 95th percentile

value can be taken directly from the distribution and used to calculate risk. If this

information is not available, but sufficient data are available regarding variability in the

exposure parameters, then the distribution of exposures can be estimated using

probabilistic modeling techniques, such as Monte Carlo analysis (see Section 10.5.2.). 

If data are not adequate to perform Monte Carlo analysis, the high-end exposure can

be estimated by using high-end exposure estimates for a few of the most sensitive input
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MPE 10-38 December 1998

parameters and central-tendency estimates for the remainder. Using high-end

estimates for all parameters would be expected to push the estimate beyond the high

end of the true distribution (U.S. EPA, 1995b). The choice of high-end exposure

estimates should be internally consistent; for example, the same individual should not

be assigned high-end food intake and low body weight.

Exposure estimates representative of central tendency and high end in the

exposed population, derived as described above, should be entered into the equations

of Section 10.4.2. to estimate the central-tendency and high-end risks of individuals in

the exposed population. Because the cancer risk factor (or unit risk) used to calculate

risk will be an upper bound for most chemicals, these are estimates of the upper-bound

risk for the given exposure levels.

10.4.2.3. Population Risk — Population risk refers to the aggregate risk to the

exposed population. It takes into account not only the risk associated with the various

exposure scenarios, but also the number of people represented by each of the

scenarios.

Population risk is estimated differently for carcinogens and noncarcinogens

(U.S. EPA, 1992a, 1995b). For carcinogens, there are two general approaches that

can be used. One is to simply sum the individual risks for all individuals within the

exposed population. If individual risk has not been determined for all members, then

this quantity can be approximated by multiplying the average individual risk by the

population size. This produces an estimate of the upper bound on the probabilistic

number of cancer cases in the population of interest over a specified time. Note that

this is not the same as an actuarial prediction of cases in the population, which is a

statistical prediction based on a great deal of empirical data.

An alternative way to estimate population risk for carcinogens is to base the

estimate on the amount of contaminant entering the food supply per year. This

approach is probably most practical for pathways involving ingestion of locally

produced food. This would involve estimating the annual amount of food produced in

various growing regions within the study area, computing the contaminant level in the
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MPE 10-39 December 1998

Population Risk '

CRF @ Ds

BW @ Ld

j

n

i'1

(FPi@ Cfi)

(10-18)

food from each growing area, and adding up the products of contaminant concentration

and the amount of food produced for each growing region. The population risk is

computed as follows:

where:

CRF = cancer risk factor (mg/kg-day)-1

Ds = duration of exposure scenario (years)

BW = body weight (kg)

Ld = lifetime (years)

n = number of growing regions (unitless)

FP i = average annual food production in growing region i/365 days per year

(kg/day)

Cf i = contaminant concentration in food from growing region i (mg/kg)

This approach assumes that all food produced in the study area is consumed (at

any location inside or outside the study area) and that all resulting individual risks are

in the linear range of the dose-response curve (i.e., directly proportional to the amount

consumed). A similar approach could also be followed for the dairy products ingestion

and fish ingestion pathways, if dairy farming and fishing (recreational, subsistence,

commercial) are important activities in the study area.

Either approach to estimating population risk yields the number of incremental

cancer cases expected to occur in the population over a 70-year period. The second

approach has the advantage of accounting for exposure that may occur as a result of

exporting food outside of the study area. However, neither approach accounts for the

exposure/population risk that may occur from contamination of food produced outside

of the study area. Although the individual risks outside the study area may be below
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MPE 10-40 December 1998

levels of concern, the population risks could be important if significant long-range

transport occurs. This issue can be evaluated by examining how much of the emitted

contaminant is deposited in the study area. If 90% of the contaminant is deposited in

the study area, then for carcinogens approximately 90% of the population risk is

accounted for. However, long-range transport and impacts of incinerator emissions are

not well understood; the local vs. long-range impacts may not be linearly proportional to

the relative amounts deposited.

As stated previously, neither approach accounts for exposure and risk from

outside the area. To account for this, the second approach may be slightly modified

using the commodity-shed approach described previously. This approach may provide

a better estimate of exposure for areas that are net local importers of select

commodities because it provides an estimate of exposure from outside the area. For

example, within a given 50-km radius around a combustion source, assume there is

only a single commercial beef farm and the quantity of beef produced on the farm is

much lower than the total amount of beef consumed by the local population. For

pollutants that do not exhibit a threshold, the population risk can be estimated by

summing individual risks for all members of the population. The product of the slope

factor, arithmetic mean of the exposure, and population size will also serve as an

estimate of risk.

Characterization of the population risks posed by threshold-acting pollutants

may be accomplished through comparison of the population exposure range with the

threshold level. The number of individuals in the population exposed above the

specified level of risk (e.g., RfD or LOAEL) is sufficient to estimate population risk. For

example, population risk can be characterized in terms of the number of individuals

estimated to have an HQ (or HI for a mixture of chemicals) greater than 1 based on

chronic (or subchronic, if desired) exposure (U.S. EPA, 1992a, 1995b). This range of

predicted individual exposures occurring within the population can be determined using

the commodity-shed approach.
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10.4.2.3.1. Subpopulations of Special Interest. Risk can be determined for

specified subgroups within the exposed population. Particular subgroups may be of

interest because they are highly exposed, highly susceptible, or both (U.S. EPA,

1995b). A highly exposed subgroup is one that receives significantly higher exposure

than the larger population. For example, subsistence fishermen would represent a

highly exposed subgroup for a contaminant that accumulates in fish. A highly

susceptible subgroup is one whose members are more sensitive to the toxic effects of

the contaminant than are other members of the population. For example, pregnant

women may be a highly susceptible subgroup for many contaminants. An example of a

subgroup that is both highly exposed and highly susceptible might be young children

exposed to environmental lead.

A particular subgroup may be selected for further analysis based on a priori

interest or as a result of discovery during the assessment process. Once identified, the

subgroup can be considered as a population in itself. Individual and aggregate risks

for the subgroup can be determined using the methods presented in Chapter 2,

treating the subgroup as the entire population.

10.5. UNCERTAINTY AND VARIABILITY IN THE RISK CHARACTERIZATION

The previous section described how to calculate numerical estimates of risk

associated with the modeled indirect exposure to combustor emissions. This section

will discuss how to integrate these estimates with qualitative information derived from

the modeling exercise in order to produce the risk characterization. The purpose of the

qualitative component of the risk characterization is to put the quantitative component

in its proper context. Discussion of confidence in the risk estimate, including major

uncertainties, limitations, and assumptions and their influence on the assessment is

essential to ensure appropriate interpretation of the numerical risk estimate (U.S. EPA,

1992b, 1995b,c).

The qualitative component, therefore, consists of a discussion of the strengths

and weaknesses of the quantitative risk estimate, based primarily on evaluation of

uncertainty. It is important to distinguish here between variability and uncertainty. 
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Variability arises from true heterogeneity in characteristics within a population (U.S.

EPA, 1995b). Variability is an inherent trait of the population being studied and cannot

be reduced by improvements in risk assessment methodology. The risk assessor

should make sure that the existing variability is adequately represented in the risk

characterization. Use of multiple descriptors of risk and, in particular, both centraltendency and upper-percentile estimates of individual risk (discussed in Section

10.4.2.), is intended to capture the variability present in the population being studied. 

Distributional analyses using tools such as Monte Carlo techniques may be useful in

assessing the impacts of population variability, if scientifically justified.

By contrast, uncertainty represents lack of knowledge about the true value of a

risk estimate (U.S. EPA, 1995b). Imprecise measurement techniques, sampling error,

use of models, and use of assumptions to bridge data gaps all contribute to uncertainty. 

Uncertainty can be reduced by additional study. Monte Carlo analysis and comparable

probabilistic modeling procedures can be used to analyze the uncertainty associated

with a particular risk estimate. More limited information regarding uncertainty can also

be obtained by other methods, such as incorporating mass-balance checks in fate and

transport models.

By incorporating critical information regarding uncertainty, the risk

characterization synthesizes an overall conclusion about risk that is complete,

informative, and useful for risk managers and other decision makers (U.S. EPA, 1992b,

1995c). It also presents a clear and transparent basis for communication of the results

of the risk assessment with the public and concerned stakeholders (U.S. EPA, 1995c).

10.5.1. Sources of Uncertainty. There are numerous sources of uncertainty in the

risk assessment process (U.S. EPA, 1992a, 1995a,b). One source that is especially

relevant to the Indirect Exposure Methodology presented is the use of models. This

methodology makes extensive use of models to predict the fate and transport of

chemicals in the environment. Uncertainty in a model is an inverse function of the

demonstrated accuracy of its predictions. Evaluations that assess the accuracy of

model predictions through comparison with real-world data are the primary means of
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addressing this type of uncertainty. Validation exercises have been conducted for

portions of the Indirect Exposure Methodology, and the results were presented in

Chapter 11. An alternative way to gauge model uncertainty is to compare the model in

question to other models that have been developed. Owing primarily to the paucity of

suitable data with which to test model validity, there remains substantial uncertainty

associated with use of the models of this document.

Another important source of uncertainty is the use of assumptions to bridge data

gaps. This methodology is data intensive and relies heavily upon use of site-specific

data. It is anticipated that appropriate data will frequently be unavailable and that, in

many cases, resources will not be adequate to allow for the collection of additional

data. Therefore, default values have been supplied for most of the input parameters. 

Rationale for these values, including all assumptions made and sources of relevant

data used as the bases for these values, are presented in the appropriate chapters of

this document. Although the use of these defaults enables a risk assessor to estimate

indirect exposure using minimal data, it also adds uncertainty to the estimate. To the

extent that the risk assessor can replace the defaults with more site-related

assumptions or, ideally, site-related data, uncertainty will be reduced accordingly. Any

assumptions used in place of defaults should be explained in full, including sources

and general logic used in the derivation.

Even in situations where data are available, there is uncertainty associated with

the results. The quantity, quality, and representativeness of the data all influence the

uncertainty associated with use of the data. Even the highest quality data will be

accompanied by some degree of error because scientific measurements are inevitably

made on a sample taken from a study population rather than on a complete population. 

Sampling error of this type can be addressed by standard statistical techniques, but all

uncertainties associated with data used in the models should be fully described.

The models described in this methodology can be divided into groups; the

assumptions, variability, and uncertainty in each group can then be evaluated

separately. The groups include the following: Emissions or Source Term, Atmospheric
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Dispersion and Deposition, Terrestrial Fate, Aquatic Fate, Exposure Estimate, Estimate

of Size of Exposed Population, Dose-Response, Modeling, and finally, Identification of

Sensitive Subpopulations.

10.5.2. Quantitative Evaluation of Uncertainty. Probabilistic modeling techniques

provide powerful tools for estimating overall ranges of uncertainty associated with

calculated risk estimates, and for examining the influence of particular assumptions,

input values, or pathways on the overall results. Occasionally, for simple multiplicative

models, an analytic approach can be taken by assuming that all inputs are lognormal

(Slob, 1994). The analytic approach is convenient in that determination of output

distribution percentiles does not require the use of random sampling techniques, such

as Monte Carlo. Another analytic approach that may be useful for estimating the

central tendency and one or two “upper bounds” of the output is discrete probability

calculus, which can be used to estimate joint variability and uncertainty (Bogen, 1995);

this method, however, does not give distribution percentiles, but averages of selected

portions of the distribution. An intermediate approach is stochastic response-surface

analysis, which combines aspects of the analytic approach and random-sampling

techniques (Isukapalli et al., 1998); this approach may be of use for very large and

complex models where computing resources become limiting. In most cases, however,

a complete Monte Carlo analysis will be required to estimate the distribution of model

outputs.

Furthermore, an attempt should be made to characterize uncertainties in a

probabilistic fashion even when data are minimal. This almost always involves

judgment, which means that there will be a subjective component in most uncertainty

distributions. Subjective probability can be dealt with by using Bayesian techniques or

fuzzy sets (Zadeh, 1984); a discussion of these approaches, however, is beyond the

scope of this document. Subjective probability can also be characterized in a

frequentist manner, much the same as for natural variability, as long as the subjective

nature of the distribution and its impact on the overall result are clearly presented. 

Often, uncertainty in the value of a specific characteristic for an individual is
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represented by the variability in the population. That is, the individual is treated as a

random selection from the general population when there is otherwise no information to

place the individual in the population distribution.

In the assignment of any uncertainty distribution one should strive to follow the

principle of maximum entropy (Shannon, 1949; Jaynes, 1986). This principle, which is

based on information theory (Shannon, 1949), in effect says that ignorance ought not to

be underestimated and that distributions assigned to any input should not require more

information than is available (Boyd and Regulinski, 1979). As an example, the uniform

distribution reflects maximum entropy when only minimum and maximum values can be

estimated and nothing is known about the relative likelihood of values between the

extremes (Englehardt and Lund, 1992). Also, if only a mean value is available, an

exponential distribution is the maximum-entropy choice (Tribus, 1969; Englehardt and

Lund, 1992). The entropy inherent to any distribution is dependent on the information

content of the functional form of the distribution (Rényi, 1955). Formal representation

of the information content for most distributions has not yet been developed. The

principle of maximum entropy, nevertheless, can be followed in spirit. As an example, 

it is not hard to see that the information requirement for a log-normal distribution is

greater than that for a triangular.

There are at least two alternatives to a full probabilistic characterization of

inputs. One option is a nominal range sensitivity analysis. This process consists of

varying each input from its low to high value while keeping all other inputs at their

nominal values and assessing the impact on the output of the model. The reader is

referred to Morgan and Henrion (1990) for a full description of this technique. Another

option is the use of partially characterized distributions, in which the assessor need

only estimate the expected value and a specific upper (or lower) bound or percentile

(Bogen, 1995). This approach simplifies input characterization and provides an

analytic solution, eliminating the need for a Monte Carlo simulation yet still providing a

probabilistic output. A presentation of the details of this approach is beyond the scope

of this document. The reader is referred to Bogen (1995), who presents two different
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methods for performing this kind of analysis with specific attention to the consideration

of joint uncertainty and variability.

An alternative to probabilistic modeling for quantitative evaluation of uncertainty

is to conduct mass balance checks. Although mass balance analyses will not provide

detailed information regarding uncertainty, they will allow the risk assessor to gauge

the reasonableness of model predictions and thereby address some of their inherent

uncertainties.

10.5.2.1. Probabilistic Modeling Techniques — Probabilistic modeling

techniques, such as Monte Carlo and Latin Hypercube assessments, are generic

statistical methods that generate a distribution for an output of a mathematical model

using the distributions of the input variables. Computer simulations are used to

repeatedly generate outputs based on parameter inputs, where values for parameters

are selected from their distributions. The outputs are compiled and expressed as a

frequency distribution. In the context of exposure assessment, for example, a Monte

Carlo application could involve developing distributions for each of the parameters in

the exposure equation and generating a distribution showing how the exposure levels

vary in the exposed population. The final distribution can be interpreted as the

probabilities of one individual (randomly selected from the exposed population)

experiencing various exposures. Since exposure levels are a function not only of the

exposure parameters but also of the concentration in exposure media, another

application of the Monte Carlo method would be to estimate the distribution of exposure

media concentrations using mathematical models for fate and transport.

Monte Carlo and related methods are used for analysis of both uncertainty and

variability. As noted previously, uncertainty results from lack of knowledge about the

true value of a parameter, whereas variability is the change in a parameter value

across a population or situation. The same parameters can have elements of both

uncertainty and variability. For example, the contaminant concentration in soil can be

uncertain because of measurement or modeling error and spatially variable because of
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differences in deposition at various locations. Distinction between these elements can

be made in a variety of ways.

The probabilistic assessment of joint uncertainty and variability in the exposure

assessment can be dealt with by at least two different techniques. The simplified

2-point distributional characterization of inputs (Bogen, 1995) has been mentioned

previously. Another approach is a two-dimensional Monte Carlo analysis (e.g., Keenan

et al., 1995). In the framework of Monte Carlo simulation, the impacts of uncertainty

and variability can be systematically addressed using a two-dimensional or nested loop

simulation approach. As in any Monte Carlo assessment, analytic (closed-form)

distributions, with user-specified parameters, are applied to the input variables. In the

two-dimensional approach, the parameters of these distributions are themselves

assigned distributions, which represent the uncertainty in the parameters. The outer

loop addresses the uncertain parameters and applies the user-specified probability

models to make random selections for these terms. For a given selection of the

uncertain parameters, the inner loop allows all of the population exposure variables to

vary, and a distribution of population exposure or risk is obtained. When this process

is repeated for other selections of the uncertain variables, a family of curves

representing potential distributions of risk in the population is obtained. These results

can then be applied, for example, to estimate the fraction of the population with

exposure above a specified level and to establish error bounds on this fraction. This

approach requires developing distributions of uncertainties, in addition to population

variability, and can be quite difficult. In cases where statistically based survey or

measurement data are available, statistical likelihood functions may be used to allow a

non-subjective quantification of uncertainties. However, such data are frequently

lacking. Otherwise, it may be possible to select distributions via professional judgment,

but the assessment should clearly state that the outcome is largely dependent on

judgment used to develop the distributions.

An alternative to the double-looping approach is to use sensitivity analysis to

demonstrate uncertainty and approximate error bounds. The distinction between
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uncertainty and variability should be clearly shown in the final display of a distribution

generated from a Monte Carlo simulation. For example, uncertainty bands can be

drawn around the distribution of variability.

Special care must be taken when variables are dependent on one another or

when the same variable appears more than once in the formula. In the former case,

some variables may be correlated. That is, one variable may co-vary with another,

such that high values for one variable are associated with high values of another

(positive correlation) or with low values for the other (negative correlation). In the

absence of evidence to the contrary, variables are usually assumed to be independent. 

Strong correlations between sensitive variables (those that contribute more to the

variance of the output), however, will have a fairly large effect on the output

distribution, so particular attention should be given to identifying theoretical or empirical

correlations. Where dependencies can be quantitatively described, a useful approach

is to determine a distribution for one variable and use values from this distribution to

compute the second variable (using a conditional distribution for the second variable

given knowledge of the first variable). When the same variable appears more than

once in the formula, the assessor must ensure that the same value is used for each

instance. If multiple assignments are inadvertently made, they will inappropriately alter

the shape of the resulting probability distribution.

It is often difficult to find appropriate distributions for Monte Carlo assessments. 

One problem is that distributions derived from national data may not represent local

conditions. To the extent possible, distributions or frequency histograms derived from

local surveys should be used. When distributional data are drawn from a national or

other surrogate population, it is important that the assessor address the extent to which

local conditions may differ from the surrogate data. In addition to a qualitative

statement of uncertainty, the representativeness assumption is appropriately

addressed as part of a sensitivity analysis.

Another problem is that many surveys (both local and national) addressing

issues such as food consumption were conducted over a short time period for each
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respondent and, therefore, may not represent long-term trends (the respondents’ usual

behavior). Generally, environmental risk assessments for chronic health effects

address risk as a function of the long-term average exposures of individuals, and thus

are most appropriately conducted using data on long-term average, “usual” behavior. 

Similar problems arise when distributions of concentration data arise from short-term

monitoring periods or when factors such as soil ingestion are measured using protocols

of only several days.

Finally, values near the tail of a distribution may be especially sensitive to the

form of distribution selected. Tails of many commonly used distributions extend to

infinity. The assessor should consider using empirical distributions when substantial

data are available, or infinite-tail distributions truncated at plausible bounds.

Probabilistic techniques can be a powerful tool for evaluating variability and

uncertainty in exposure assessments. The results, however, can be misleading unless

careful attention is paid to the distinction of variability and uncertainty, to the

qualification of the data and theoretical concepts used to define both types of

distributions, and to the interrelationships among the input variables. The risk assessor

must provide an adequate description and justification for the form and

parameterization of each distribution used in the analysis. Confidence in the Monte

Carlo analysis is directly related to confidence in the distributions used.

Examples of the use of probabilistic modeling techniques in the analysis of

uncertainty and variability in exposure assessment include studies by Belcher and

Travis (1989), McKone and Ryan (1989), McKone and Bogen (1991), Burmaster and

Stackelberg (1991), Anderson et al. (1992), Paustenbach et al. (1991, 1992a,b), Cullen

(1995), and Keenan et al. (1995). The study by Keenan et al. (1995) includes use of a

nested-loop approach to characterize uncertainty and variability separately.

10.5.3. Mass Balance Checks. Mass balance checks can be used as a means of

evaluating some aspects of uncertainty in the modeling. A mass balance would be

violated, for example, if the mass of contaminant estimated to enter a medium (e.g.,

soil) exceeds the mass being emitted from the stack. The ISC3 air emissions model is
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designed on mass balance principles, and therefore should not lead to impossible air

concentrations or deposition rates that exceed emission rates. Depositions are

subtracted out of the airborne reservoir of particle-bound contaminants as the

depositions occur. However, some of the media transfer models, which use the output

from the air models, are based on simple transfer/partitioning approaches, which do not

have built-in mass balances. If these models are properly parameterized, then the

possibility of meaningful mass balance violations is minimized. This is discussed

below. However, it is generally recommended that risk assessors make mass balance

checks to ensure that reasonable predictions are being made. These checks can be

made in various ways. One way is to compare estimates of the amounts of

contaminant in the various media (i.e. soil, vegetation, and biota) within the study area

(i.e., the area within the outer isopleth boundary, as described in Chapter 2) to the

amount emitted from the stack. Comparable time frames must be used. For example,

the amount of contaminant in the beef annually produced from the study area should

not exceed the amount of contaminant emitted during the year (except in an unusual

circumstance where a large cattle ranch begins operation within the study area several

years after incineration begins, and accumulation in the environment to that point could

lead to large withdrawals by the cattle initially). In addition to fate and transport mass

balance transfer, one should also be aware of mass balance violations when estimating

exposures from given media concentrations. For example, the amount of impacted

beef ingested by populations in the study area should not exceed the amount of beef

produced in the study area.

Because the fate and transport and the food chain algorithms do not contain

built-in mass balance checks, mass balance violations could occur. Contaminants

estimated to be deposited onto soils partition between a sorbed phase and a dissolved

phase. Transfers occur into below-ground and above-ground vegetation with a

biotransfer factor. Bioconcentration factors take concentrations in one medium (e.g.,

water concentrations), and translate them to a concentration in another medium (e.g.,

fish). In this simple framework, mass balances are considered but are not rigorously
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maintained. For example, soil-to-plant transfers are not explicitly modeled as a loss for

soil concentration estimation. However, there is an overall loss constant for the soil

concentration algorithm, which explicitly considers leaching, erosion, runoff,

degradation, and volatilization. The loss via plant uptake could be estimated and

added to the degradation rate constant; otherwise, plant uptake loss is not explicitly

considered.

There are other examples where exchanges between media and sinks are not

rigorously modeled. However, this particular type of theoretical mass balance issue is

not normally expected to be a fatal flaw for any contaminant evaluated. First,

dissipation from key media is modeled with loss rate constants; these key media

include soils, vegetation, and surface waters (water column and benthic sediments). 

Guidance has been presented for proper parameterization of these dissipation factors. 

Second, all transfer and bioconcentration parameters have been empirically developed

from appropriate data sets; these key transfer factors include soil and air to plants,

plants to animals, and aquatic media concentrations (water or sediment) to fish. These

empirical factors are, by design and definition, approximations of observed

phenomena.

Another type of mass balance violation could occur with improper

parameterization. For example, the aquatic impact requires the methodology user to

estimate the land area draining into an impacted water body as well as a contaminant

deposition rate that represents depositions over that entire area. If one selects a

deposition rate that would occur at the point of maximum deposition (e.g., within a few

hundred meters of the stack) as representing deposition over a drainage basin that is

millions of square meters in area, or one estimates a drainage basin size much too

large for an impacted water body (which is defined in this methodology in terms of flow

rates and volumes), one could very easily obtain results that obviously violate mass

balance considerations — that an impacted surface water body receives more

contaminant over a multi-year period in runoff, erosion, and direct deposition than is

emitted from the stack.
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Proper parameterization of the model is the key to not violating principles of

mass balance. The following are offered as points to consider for mass balance

concerns:

1) No parameter is trivial: All parameters used in the modeling should be

presented for others to scrutinize. Careful attention should be paid to the

stack emission rate assumed for air transport modeling, as every subsequent

media concentration is proportionally related to it. All fate, transport, and

transfer parameters specific to the contaminant are important. Ensure that all

parameters are in the proper units. If an air concentration is presented to the

risk assessor in units of μg/m3, and the food chain models require units to be

in pg/m3, then an oversight in converting will result in all media

concentrations (i.e., plants, animal food products, and water and fish of the

surface water body) being overestimated by a factor of a million.

2) Do reality checks with estimated media concentrations: This is, of course,

easier said than done. It means that risk assessors should evaluate their

predicted concentrations with observed concentrations in the literature. If

predicted concentrations exceed observations by an order of magnitude, for

example, one should very carefully evaluate model input parameters for

mistakes. Particularly, if the observed data are from contaminated sites.

10.6. THE COMPLETED RISK CHARACTERIZATION

The completed risk characterization incorporates noteworthy conclusions

regarding uncertainty with numerical risk estimates. It is a synthesis of the risk

estimation and uncertainty analysis phases of the risk assessment, ultimately yielding

an overall conclusion about the resulting risk.

The risk characterization is the mechanism by which the risk assessor will

convey his judgment to the risk manager, who will integrate the risk characterization

with other considerations (e.g., economic, legal, social) in order to make and justify

regulatory decisions (U.S. EPA, 1995b). The risk characterization also forms the basis

for communication of risk to the public (U.S. EPA, 1995b). By providing a clear and

transparent record of the reasoning that produced the risk assessment, the risk

characterization equips stakeholders in environmental issues with sufficient information

to evaluate the significance of environmental risks independently. It is critical to both

these applications that the numerical risk estimate not be separated from the statement
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of confidence, and that the latter include full disclosure regarding the uncertainties

embedded in the risk estimate.

The risk characterization should address the “big picture” questions that relate to

the analysis. Some important issues that might be addressed include: how well

exposures are characterized (e.g., identification and characterization of critical

modeling assumptions, strengths and weakness of supporting arguments, availability of

analytical measurements of pollutants in environmental media of concern, and

comparison of predicted media levels to measured values); effects of location and

exposure factors on exposure estimates; the range of exposures predicted for different

groups; identification of the most important pathways and routes of exposure for the

general, highly exposed, and highly susceptible human populations; identification of the

emission constituents that pose the greatest threat to human health; the existence of

other local sources of exposure to the pollutants posing the greatest threat; and the

relative risk to general, highly exposed, and highly susceptible human populations of

combustor emissions in relation to other sources. These are the types of issues with

which risk managers and stakeholders are concerned, and the risk characterization

should provide them with as much guidance as possible regarding these issues.

The final scientific step of the process involves an evaluation of the risk

assessment by scientific peers and a response to their comments. Peer review is an

important part of gaining widespread acceptance of the conclusions reached through

the risk assessment process. Reviewers can examine and comment on the scientific

reasoning as well as the scientific judgments made in the document. It is important that

the reviewers are independent and have the appropriate scientific experience. For

example, U.S. EPA employs several different levels of external review depending on

the importance of the product. Some scientific reports are reviewed by large groups of

individuals whose expertise includes health effects assessment, exposure assessment,

fate and transport characteristics, and emissions assessment.

10.7. REFERENCES
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Anderson, P., B. Ruffle and W. Gillespie. 1992. A Monte Carlo analysis of dioxin

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Barnes, D.G. and M. Dourson. 1988. Reference dose (RfD): Description and use in

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Eder, B.K., D.H. Coventry, T.L. Clark and C.E. Bollinger. 1986. RELMAP: A regional

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Englehardt, J.D. and J.R. Lund. 1992. Information theory in risk analysis. J. Environ.

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Isukapalli, S.S., A. Roy and P.G. Georgopoulos. 1998. Stochastic response surface

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Jaynes, E.T. 1986. Bayesian methods: General background. In: Maximum Entropy

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Calgary, 1984, J. Justice, Ed. Cambridge Univ. Press, New York, NY.

Keenan, R.E., P.S. Price, E.S. Ebert, S. Su and J.R. Harrington. 1995. Uncertainty

and variation in indirect exposure assessments: An analysis of exposure to TCDD from

a beef consumption pathway. Presented at the 15th International Symposium on

Chlorinated Dioxins and Related Compounds. August 21-25, 1995, Edmonton,

Canada. Abstract in Organohalogen Compounds. 26:45-50.

Kennedy, G.L. and H.J. Trochimowicz. 1984. Inhalation toxicology in Principals and

Methods in Toxicology (A.W. Hayes, Ed.), p. 185-207, Raven Press, NY.

McKone, T.E. and K. Bogen. 1991. Predicting the uncertainties in risk assessment. 

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APPENDICES

A. Model Validation

B. Derivation of Kinetic Breast Milk Exposure Model 

Incorporating Breast Feeding Losses

C. Glossary
Appendix A

Model Validation

MPE A-2 December 1998

MODEL VALIDATION

A.1. INTRODUCTION

The lack of model validation is an issue for essentially all fate and transport

models of chemicals in the environment, including those used for multiple-pathway

exposures. Therefore, the main purpose of this chapter is to describe efforts to validate

the multiple-pathway models, specifically indirect pathways. This effort is limited to the

fate and transport models, rather than the exposure models. Further, the exercises

presented here are focused on how well the fate models predict observed

concentrations. Other ways to evaluate the validity of models include sensitivity

analysis, issues of generic predictability, and the qualitative reliability of the observed

data sets referenced (it will be assumed that the published data described in this

chapter are valid). Finally, it should be understood that model validation is an ongoing

process and the exercises in this chapter are by no means expected to establish model

validity beyond any doubt. Users of this methodology are encouraged to subject

models to any number of tests, validation or otherwise, as they use the models

described in this document to conduct indirect exposure assessments.

A.2. OVERVIEW OF MODEL VALIDATION

In this document, the word validation refers to an exercise in which the following

holds true:

1. An impacted media concentration is known. A concentration of a

contaminant in a medium that can be predicted by indirect fate models, such

as a concentration in soil, vegetation, water, sediment, biota, or ambient air,

is known through a site-specific monitoring program. Model predictions of

these concentrations have often been termed the dependent model results

in a validation exercise.

2. The source causing this impact is known. Source-term information has often

been called the independent model input in a validation exercise. For

example, if the concentration of a contaminant in ambient air represents the

known dependent quantity, then the independent, or source, term could be

the emissions from one or more tall stacks. Specifically, source-term

information would translate to the level of emissions (in units of mass/time

such as g/sec) of the particular contaminant from the identified stacks. The
Appendix A

Model Validation

MPE A-3 December 1998

model validation exercise would take these known emissions, put them into

an air dispersion model along with other parameters (e.g., stack heights, exit

velocities, site-specific meteorological data, etc.), and predict ambient air

concentrations. In other model validation exercises, however, the source is

likely not going to be incinerator emissions. For example, if underground

vegetables were the impacted medium, then the independent source term

could be the concentration in the soil in which the vegetables were growing. 

One could also start with ambient air concentrations. In that case, the air

concentrations would be used to predict the soil concentrations, which in

turn would be used to predict the vegetable concentrations. In any case, the

source term needs to be known and the model user needs to be reasonably

certain, or at the very least needs to assume, that the source directly

impacts the affected medium.

3. All other model parameters are assigned values using the best available

information. Site-specific information is most appropriate, if it is available. 

Chemical-specific parameters, such as bioconcentration/biotransfer

parameters, should be assigned values using information in this document,

the companion Parameters Guidance Document, or other literature sources.

4. Model predictions of the impacted media are compared with observations. 

Once all the parameters are assigned values, the model is run and its

predictions of the dependent medium concentration are compared with realworld observations. This is the final step in a model validation exercise.

Model validation needs to be distinguished from model calibration. Calibration

exercises, like validation exercises, require real-world measurements of independent

source terms and dependent media impacts. What distinguishes the two is that a

calibration exercise is conducted in order to determine values for one or more key

model input parameters. The model is forced to duplicate the observed data by

adjusting the value of key input parameters until this duplication is achieved. As

described in step 3 above, in a validation exercise all model parameters are assigned

values; none are adjusted to fit the observed data.

An example of a calibration exercise in the realm of indirect exposure modeling

is the calibration of the air-to-leaf transfer factor for the vapor phase, Bv, for dioxins and

furans reported in Lorber (1995). Air concentrations of these compounds above

experimental grass pots were measured, as were the final concentrations in grass

harvested after three weeks of growth. All other model parameters necessary for the
Appendix A

Model Validation

MPE A-4 December 1998

prediction of the concentration of dioxins and furans in the grass, including the physical

parameters (mass of grass harvested, fresh to dry weight conversion, etc.),

vapor/particle partitioning in the air, and particle deposition algorithms, were assigned

values based on site-specific measurements or the literature. The Bv was then

adjusted until the model predictions of grass concentrations matched the observed

concentrations.

This chapter reviews several model validation exercises that have been

conducted and published and presents one exercise not otherwise published. The

previously conducted validations are for dioxins and furans and were reported in EPA’s

Exposure Reassessment Document for Dioxin-Like Compounds. This reassessment,

when completed, will replace EPA’s current draft exposure document on dioxin entitled

Estimating Exposures to Dioxin-Like Compounds (EPA/600/6-88/005Ca-Cc, June

1994). As it is still under development, it will be referred to throughout this exposure

pathways document as the “dioxin exposure reassessment.” The fate models

described in that document are substantially similar to the fate models of this

document; differences in the two approaches will be noted below. Rather than fully

duplicate the text from the dioxin exposure reassessment, summaries including final

results are presented below. The dioxin document also presents a validation exercise

involving nondioxins. This exercise tests the vapor/particle partitioning model on

PCBs, PAHs, and organochlorine pesticides, and is duplicated in this chapter as well. 

The one exercise conducted uniquely for this document is an air-to-plant model

validation exercise for PAHs.

A.3. ISSUES IN VALIDITY OF THE AIR DISPERSION MODEL

Air dispersion and deposition analysis is performed using the ISC3 model. The

model is intended to give approximate estimations of atmospheric dispersion and wet

and dry deposition flux, rather than absolute values. The model predicts future states

based on known characteristics of geographical location, local meteorological

conditions, temporal rates of emissions, and the physical description of the facility.
Appendix A

Model Validation

MPE A-5 December 1998

Atmospheric dispersion in ISC3 is modeled with the common Gaussian plume

method. Downwind concentrations of the contaminants are calculated as a function of

stack height, the mass emission rate, the wind speed, and general atmospheric

conditions. The Gaussian model assumes that the emission concentrations predicted

by the model will fit a normal distribution. The principal assumptions in the Gaussian

model are (Kapahi, 1991):

! The air concentration of the chemical at a fixed distance from the source is

directly proportional to the emission rate from the source;

! The air concentration of a given chemical is inversely proportional to the

wind speed corresponding to the effective height of release of the chemical

into the air;

! The predicted ground-level concentration of the chemical approaches zero

at large distances from the initial point of release;

! The model is steady state;

! The model assumes constant wind speed, wind direction, and atmospheric

stability over time and space for a given period.

In general, the stochastic features of the Gaussian plume model predict annual

average ambient air concentrations of chemical emissions from industrial sources to

within one order of magnitude of measured values and, in some cases, within a factor

of three to fourfold of field measurements (Cohrssen and Covello, 1989). This

modeling error spans both sides of the predicted concentration; that is, the actual

concentration may be plus or minus this amount of predicted value. In 1978, the

American Meteorological Society published a position paper stating that Guassian

plume air dispersion models should predict average concentrations to within a factor of

two in the absence of complicating factors (complex terrain, building effects, etc.)

(AMS, 1978). This factor-of-two accuracy is often cited when describing the

capabilities of the Gaussian plume model.

EPA undertook an extensive effort to validate the ISC model (the forerunner of

the ISC3 model) in 1981 (U.S. EPA, 1981), with particular attention to the validation of
Appendix A

Model Validation

MPE A-6 December 1998

the gravitational settling/dry deposition option and the building wake effects options. 

Three sets of deposition experiments were used to test the dry deposition algorithm,

and three sets of diffusion experiments were used to test the building wake effects

option of the model. The details of these validation tests are too extensive to provide

here; they can be found in U.S. EPA (1981). Some principal findings and conclusions

of this work are:

1) The accuracy of the gravitational dry deposition algorithms generally conform

to the factor-of-two accuracy attributed to air dispersion modeling. On average,

the ISC model predicted the maximum deposition to within 30 percent for all

three data sets. These very positive results were explained in part, however, by

the relatively large gravitational settling velocities of the glass microspheres

used in one experiments and the larger droplets used in another of the

experiments. For these large diameter particles, the effects of atmospheric

turbulence were small in comparison to the effects of gravitational settling. The

match between predictions and observations was best within 800 meters of the

source of release for this reason. Measurements further downwind at beyond 4

kilometers, where the droplets had smaller diameters and their deposition was

now dominated by atmospheric turbulence, were not as well matched by the

model, although the calculated deposition still showed to be within a factor of 2

of measurements.

2) In testing of the building wake effects, it was found that the building wake

effects option significantly improved the performance of the ISC model over that

of the corresponding models at the time time, CRSTER and MPTER, that did not

have the building wake effects option. The model performance, as measured by

ratios of calculated to observed concentrations (RCOs), and root mean square

errors, suggest that the fit of predictions and observations was not as good as

for the deposition experiments, with a general trend of underpredicting the
Appendix A

Model Validation

MPE A-7 December 1998

concentrations. Still, the RCOs showed that predictions still were mostly within

a factor of two, with most RCOs between 0.5 and 2.0. The complexities at each

of the three tested sites and the permutations of options tested by U.S. EPA

(1981) makes it difficult to generalize further on results.

The most sensitive aspects to variability in modeled predictions of ambient air

impacts, if emissions are held constant, are stack height (height of the release), and

terrain (flat versus complex topography). To investigate modeling variability, U.S. EPA

placed a prototype hazardous waste incinerator in both flat and elevated terrain in

geographical areas around the United States (U.S. EPA, 1991; analysis conducted with

the Industrial Source Complex, or ISC, model, which was a forerunner of the ISC3

model). The stack height was varied at these locations. Numerous runs were made at

12 specific sites to compare and contrast the influence of stack height and terrain on

predicted ambient air concentrations of various emission rates of specific inorganic

pollutants. A series of tables were developed from this sensitivity analysis, from which

estimates of variability as a function of stack height and terrain can be inferred. When

the hypothetical incinerator was modeled in flat terrain (i.e., no topography within a

distance of 5 km is above the height of the stack) and the stack height was varied from

4 meters to 120 meters, the variability in the predicted ambient air concentration

spanned two orders of magnitude (100). The lower stack height resulted in a predicted

ambient air concentration that was 100 times greater than the concentration predicted

using the tallest stack height. When the incinerator was located in complex terrain over

the same range of physical stack heights, the variability in estimated ground level

concentration of the subject pollutant spanned two orders of magnitude (100-fold). In

the latter case the stack height was adjusted for terrain by subtracting from the physical

stack height the influence of terrain on plume rise. From this limited analysis, it can be

assumed that the predictions of spatial ground-level ambient air concentrations of

contaminants in general could span two orders of magnitude, depending on changes in

stack height or terrain.
Appendix A

Model Validation

MPE A-8 December 1998

The most uncertain aspect of the modeling is the estimation of dry- and wetdeposition flux of contaminants. Settling velocities and scavenging coefficients

estimated for specific particle size diameters provide the most uncertainty (Cohrssen

and Covello, 1989; Doran and Horst, 1985). Seinfeld (1986) found that particles more

than 20 microns in diameter settle primarily by gravity, whereas smaller particles

deposit primarily by atmospheric turbulence and molecular diffusion. Considerable, but

nonquantifiable, uncertainty exists with respect to deposition velocities of particles 0.1

to 1.0 micron in diameter (Seinfeld, 1986). The uncertainty is difficult to define. The

wide variation of predicted deposition velocities as a function of particle size,

atmospheric turbulence, and terrain adds to this uncertainty (Sehmel, 1980). However,

Gaussian plume dispersion models have been field validated for their ability to spatially

predict dry deposition flux over some specified distance (Doran and Horst, 1985). In a

series of field experiments conducted by Pacific Northwest Laboratory (Doran and

Horst, 1985), zinc sulfide was used as a depositing tracer gas and sulfur hexafluoride

was used as a nondepositing tracer gas to compare and contrast modeling results with

field measurements of dry deposition and atmospheric diffusion of the gases. The

tracer was released from a height of 2 meters, and all releases were made under

relatively stable atmospheric conditions. Five sampling stations were located from 100

to 3,200 meters downwind of the release. The results of these experiments showed

good agreement with the predicted versus the measured deposition of the tracer ZnS. 

The overall correlation coefficient between predicted and measured deposition

concentration was found to be 0.82 (Doran and Horst, 1985), but the models marginally

overpredicted deposition flux near the source of release and underpredicted deposition

flux at 3,200 meters.

Travis and Yambert (1991) have evaluated the uncertainty in modeling the dry

deposition flux of particulates using four standard Gaussian plume dispersion models. 

Since deposition flux is dependent on deposition velocity for a given particle mass and

diameter, comparisons were made between model-generated deposition velocities and

measured values in the literature for particles ranging from 0.01 to 30 microns in
Appendix A

Model Validation

MPE A-9 December 1998

diameter. It was found that measured deposition velocities exhibit variability spanning

roughly two orders of magnitude. Comparison of predicted deposition velocities to

measured values showed that most measured data exceeded the predicted data for all

four models. Moreover, the models underestimated the mean deposition velocities for

particles between 0.05 and 1.0 microns in diameter.

Similar uncertainty probably exists with regard to rainfall scavenging of particles. 

Seinfeld (1986) has calculated scavenging coefficients in terms of the removal

efficiency of particles of a given size by rain droplets having a given momentum. 

Seinfeld (1986) found that the scavenging coefficient of a given particle diameter

corresponding to a given rainfall intensity can be calculated based on physical laws,

but there is a complete absence of research data to verify these calculations. 

A.4. MODEL VALIDATION EXERCISES FOR DIOXINS AND FURANS

The dioxin exposure document described the fate models and demonstrated

them in a series of scenarios. The scenarios were crafted to account for the magnitude

of the source strength, the assignment of all fate parameters, and the exposure

behaviors of the individuals who may have been impacted by their proximity to the

sources. Some of the model validations below have taken results from these scenarios

and compared them in a broad sense to similar quantities in the literature. This falls

somewhat outside our description of model validation in that not all the independent

source term data and dependent model results were from one site.

Another consideration is that the fate models in the dioxin exposure document

are not identical to the models described in this methodology. The models in both

documents are substantially similar, however, particularly in the use of empirical,

simple models (such as the use of bioconcentration factors) and the assumption of

steady state. One could consider the models of the dioxin document a specific

application of indirect exposure methods. Relevant deviations in the modeling

procedures of the dioxin document from those in this document will be described as

necessary.
Appendix A

Model Validation

MPE A-10 December 1998

A.4.1. Air-to-Soil Modeling. One of the scenarios demonstrated in the dioxin

document was termed the background scenario. Air concentrations and soil

concentrations were input as source terms, and the models predicted the impacts to the

aquatic environment (water, sediment, fish) and to terrestrial biota (vegetation and

animals). The air and soil concentrations were taken from an actual rural site in Ohio. 

If one can assume that the soil concentrations resulted from depositions of airborne

dioxins, then one can use the same data to test the air-to-soil modeling algorithms. 

The air data alone become the source-term independent data and the soil data become

the dependent model result.

Data were available on the 17 dioxin-like dioxin and furan congeners. A total

toxic equivalent concentration, abbreviated as total TEQ or simply TEQ, is often

calculated from individual congener concentrations. The total TEQ is calculated as the

sum of the TEQ concentrations of each of the 17 dioxin-like congeners. The TEQ

concentration of an individual congener is calculated as the congener’s actual

concentration times its toxic equivalent factor, or TEF. These TEFs relate the toxicity of

the individual congener to that of 2,3,7,8-TCDD. For example, the TEF for 2,3,7,8-

TCDF is 0.1, which means that this congener is judged to be 1/10 as toxic as 2,3,7,8-

TCDD. Individual congener and TEQ concentrations are displayed for this exercise.

The TEQ air and soil concentrations from this site were 0.019 pg/m3 and 1.37

pg/g, respectively. By modeling the deposition of the 17 congeners, the goal will be to

see how well the model predicts the soil concentration of each congener, as well as the

TEQ soil concentration. The reservoir of soil for this exercise will be a 7.5-cm depth,

which was the depth of the soil sampling at the rural site in Ohio, and the assumed soil

bulk density will be 1.50 g/cm3 (actual bulk density information was not available). The

soil dissipation rate assigned to all dioxin congeners in the dioxin document is 0.0277

yr-1, which corresponds to a half-life of 25 years. Since deposition was not monitored at

the rural site, it will be estimated as the particle-bound airborne reservoir times a

velocity of deposition. Koester and Hites (1992) measured a dry deposition rate of

0.002 m/sec for dioxins in Indiana. They also measured wet deposition and found it
Appendix A

Model Validation

MPE A-11 December 1998

roughly comparable for two sites. On this basis, dry deposition in this exercise will be

modeled using the 0.002 m/sec velocity and wet deposition will be assumed equal to

dry deposition.

The results of this validation exercise are shown in Table A-1. The soil

concentrations appear to be predicted reasonably well, with modeled concentrations

somewhat lower but within the realm of field observations. Specifically, 11 of the 17

observed concentrations were higher than model predictions. All but three congeners
Appendix A

Model Validation

MPE A-12 December 1998

TABLE A-1

Results of the Air-to-soil Model Testing for Dioxins and Furans

Congener Observed Air

(pg/m3)

Observed Soil

(pg/g)

Predicted Soil

(pg/g)

2,3,7,8-TCDD 1.4@10-3 0.39 0.03

1,2,3,7,8-PCDD 5.2@10-3 0.14 0.19

1,2,3,4,7,8-HxCDD 7.9@10-3 0.35 0.31

1,2,3,6,7,8-HxCDD 9.3@10-3 0.82 0.36

1,2,3,7,8,9-HxCDD 1.4@10-2 1.23 0.54

1,2,3,4,6,7,8-HpCDD 2.3@10-1 17.7 9.1

OCDD 9.0@10-1 161.0 36.2

2,3,7,8-TCDF 2.8@10-3 0.64 0.06

1,2,3,7,8-PCDF 6.5@10-3 0.17 0.20

2,3,4,7,8-PCDF 7.4@10-3 0.21 0.26

1,2,3,4,7,8-HxCDF 1.3@10-3 0.16 0.50

1,2,3,6,7,8-HxCDF 1.6@10-2 0.11 0.59

1,2,3,7,8,9-HxCDF 2.8@10-3 0.15 0.11

2,3,4,6,7,8-HxCDF 9.2@10-3 0.67 0.36

1,2,3,4,6,7,8-HpCDF 6.9@10-2 4.06 2.74

1,2,3,4,7,8,9-HpCDF 1.4@10-3 0.27 0.58

OCDF 6.7@10-3 10.7 2.70

TEQ 1.9@10-2 1.37 0.70
Appendix A

Model Validation

MPE A-13 December 1998

were modeled to within a factor of 5 of observations, and the three other congeners

were about a factor of 10 from observations (i.e., either observations were 5 to 10 times

higher than predictions, or predictions were 5 to 10 times higher than observations). 

The four highest observations are matched with the four highest predictions as follows:

17.7 ppt observed 1,2,3,4,6,7,8-HpCDD versus 9.1 ppt modeled, 161.0 versus 36.2 ppt

for OCDD, 4.06 versus 2.74 ppt 1,2,3,4,6,7,8-HpCDF, and 10.7 versus 2.70 ppt OCDF. 

The observed TEQ soil concentration of 1.37 ppt matches well with the predicted TEQ

concentration of 0.7 ppt.

Although the soil model appears to work reasonably well considering only impact

of particle depositions, vapor impacts are not considered, which would increase

predictions of soil concentrations. As noted above, soil concentrations may generally

be underpredicted, although for six congeners the model predictions were higher than

observations. Direct vapor deposition could impact soils, but at the rural site in Ohio all

samples were taken in grassy areas, and with a vegetative cover it is speculated that

there would be little direct vapor deposition. Therefore, detritus production would be a

mechanism for vapor impacts to soils. Barbour et al. (1980) list a detritus production

rate for a setting described as tallgrass prairie as 520 g/m2/yr. Given the

concentrations predicted to occur in grass because of vapor transfers, one can

estimate the loadings of dioxin corresponding to detritus production of this magnitude. 

The predicted TEQ concentration in leafy vegetation due to vapor transfers was 0.27

pg/g dry weight given the rural air concentrations at the site in Ohio. This

concentration times the detritus rate leads to a loading of 112 pg TEQ/m2/yr. In

contrast, the dry and wet deposition loading estimated by dry and wet deposition of

particle-bound dioxins is 2,130 pg TEQ/yr. Therefore, on a TEQ basis, vapor impacts

via detritus production would be only about 5% of loadings by dry and wet deposition of

particle-bound dioxins. Particle-bound dioxins also impact vegetation, and detritus

production might be considered as an additional loading. However, since 100% of

atmospheric depositions are loaded into the soil model, detritus production is inherently
Appendix A

Model Validation

MPE A-14 December 1998

handled, and considering it separately would double-count the impact of particle-bound

dioxins.

Another factor to consider is the representativeness of the air concentrations. 

The air concentration profile was the average of three samples, one taken in March

1994, one in April 1994, and one in June 1995. It has been observed that air

concentrations are higher during winter months in one site (Reed et al., 1990), and if

that were also the case at this site in Ohio, perhaps the inclusion of additional winter

samples would lead to a higher air concentration profile and higher predicted soil

impacts.

In summary, this section has shown that the air-to-soil model based on

deposition of particle-bound dioxins appears to work reasonably well. Vapor impacts

would occur primarily through dieback of vegetative materials, but the additional

increment to soil concentrations is estimated to be less than 10% by this route. 

Inclusion of wintertime air concentrations in the average air profile could lead to higher

predictions of soil concentrations and a superior match of predicted and observed soil

concentrations.

A.4.2. Air-to-Plant Modeling. Jones and Duarte-Davidson (1997) present the results

of an extensive monitoring study of dioxin concentrations in air and grass, as well as

deposition fluxes, from three sites over three time periods between 1992 and 1993 in

the United Kingdom. The three sites include a rural background site, an urban site,

and an industrial site. Two subsets of this data, which are appropriate for air-to-plant

model validation purposes, were presented by Jones and Duarte-Davidson (1997). 

Specifically, this includes concurrently measured concentrations of dioxin and furan

congeners, and homologue groups, for air and grass sampled for two of the sites, the

industrial and rural background site, for one of the sampling periods, September 14 -

October 30, 1993. They also presented the results of their deposition collection for

those two sites/sampling periods. Results for the rural site are used for model

evaluations in this section. In an expanded version of the exercise, Lorber and Pinsky

(1998) evaluate three air-to-life models for dioxins on both sites.
Appendix A

Model Validation

MPE A-15 December 1998

Air samples were taken using standard General Works high-volume samplers

which took weekly samples of 300-500 m3 air for 4-6 week sampling periods. The glass

fiber filters and the polyurethane foam plugs of these samplers (measuring, in theory,

the vapor and particle phases, respectively) were collected weekly and composited for

each full sampling period to allow for a single measurement of total air concentration of

the dioxins, representing around 2000-3000 m3 (depending on the time of the individual

sampling periods) of air volume. A bulk deposition sample for each site/sampling

period was also collected using “upturned frisbee” collectors. Grass was mowed at the

beginning and end of each sampling period; the second cutting represented the grass

yields over the sampling period. Data site used here are shown in Table A-2.

The models that will be evaluated here were described in Chapter 6. First, total

dioxin concentrations are partitioned into a vapor and a particle phase, using the

vapor/particle partitioning model provided in Chapter 3, based on aerosol particle

densities and the saturated liquid vapor pressure of the contaminant. This model is

evaluated for dioxins in Section A.4.6., and then for other organic contaminants in

Section A.5. Total plant concentrations are the sum of vapor phase and particle phase

impacts. The particle-phase impact model is a simple reservoir mixing model: particlebound dioxins deposit using a deposition velocity (0.2 cm/sec) to mix in a reservoir of

plant matter (the grass yield), and then dissipate (weather) from the plant with a fixed

dissipation rate, set equal to a half-life of 14 days in this application. Vapor phase

impacts are modeled in either of two ways, both described in Chapter 6. The first is,

quite simply, a bioconcentration approach: plant concentration equals the vapor phase

air concentration times an air-to-leaf bioconcentration factor, Bv. This factor was

calibrated (Lorber, 1995) for the dioxin-like compounds based on experimental data on

the transfer of dioxins to Welsh Ray Grass (Welsch-Pausch et al., 1995). The

partitioning of dioxins between a particle and a vapor phase for various airsheds

(where the airsheds are defined by particle densities), and the air-to-leaf

bioconcentration factor Bv, are shown in Table A-3. The background airshed

vapor/particle partitioning scheme was used in this exercise. The second model is the
Appendix A

Model Validation

MPE A-16 December 1998

vapor deposition model. It is, in fact, analogous to the model for particle phase impacts

to plants; plant

TABLE A-2

Observed Data for the Air-to-Plant Model Validation Exercise

Compound Air Deposition Grass Compound Air Deposition Grass

2378-TCDD 0.01 <0.46 0.72 TCDD 0.72 73 66

12378-PCDD 0.03 2.3 1.3 PCDD PCDD 0.56 54

123478-HxCDD 0.04 2.3 0.93 HxCDD 0.65 38 26

123678-HxCDD 0.08 4.8 2.3 HpCDD 0.71 41 22

123789-HxCDD 0.1 3.8 1.8 OCDD 2.5 166 94

1234678-

HpCDD

0.82 41 22 TCDF 1.6 24 93

2378-TCDF 0.33 12 14 PCDF 0.45 18 13

12378-PCDF 0.06 2.5 1.8 HxCDF 0.45 18 13

23478-PCDF 0.1 4.1 2.2 HpCDF 0.22 <1.8 <0.22

123478-HxCDF 0.3 11 5.6 OCDF 0.42 28 13

123678-HxCDF 0.1 4.5 2.2 Units: 

air - pg/m3

deposition - pg/m2-day

grass - pg/g. 

Grass yield = 89 g/m2 dry weight

123789-HxCDF 0.02 1.8 0.61

234678-HxCDF 0.14 4.8 2.6

1234678-

HpCDF

0.53 19 12

1234789-

HpCDF

0.11 2.9 1.1
Appendix A

Model Validation

MPE A-17 December 1998

TABLE A-3

Fate and Transport Parameters for the Dioxin-like Congeners

Congeners Particle fractions, N* Bv*

C C B B+LS U

2378-TCDD 0.1 0.29 0.49 0.75 6.55*104

12378-PCDD 0.44 0.74 0.87 0.95 2.39*105

123478-HxCDD 0.78 0.93 0.97 0.99 5.20*105

123678-HxCDD 0.78 0.93 0.97 0.99 5.20*105

123789-HxCDD 0.78 0.93 0.97 0.99 5.20*105

1234678-HpCDD 0.93 0.98 0.99 0.997 9.10*105

OCDD 0.98 0.995 0.998 0.999 2.36*106

2378-TCDF 0.09 0.27 0.47 0.73 4.57*104

12378-PCDF 0.27 0.57 0.75 0.91 9.75*104

23478-PCDF 0.38 0.69 0.84 0.94 9.75*104

123478-HxCDF 0.63 0.86 0.93 0.98 1.62*105

123678-HxCDF 0.63 0.86 0.93 0.98 1.62*105

123789-HxCDF 0.74 0.91 0.96 0.99 1.62*105

234678-HxCDF 0.74 0.91 0.96 0.99 1.62*105

1234678-HpCDF 0.86 0.96 0.98 0.99 8.30*105

1234789-HpCDF 0.92 0.98 0.99 0.997 8.30*105

OCDF 0.98 0.995 0.998 0.999 2.28*106

* C C: Clean continenetal; B = Background; B+LS: Background plus local sources; U = urban

Bv: Air-to-leaf biotransfer factor, (pg PCDD/g leaf dry)/(pg PCDD/g air)
Appendix A

Model Validation

MPE A-18 December 1998

concentrations are modeled by assuming a vapor-phase reservoir of dioxins depositing

onto, mixing in, and dissipating from a plant reservoir. This approach has been

described and parameterized for vapor phase 2,3,7,8-TCDD impacts to grassy plants

by Trapp and Matthies (1995) and Smith et al. (1995a). Their critical deposition

velocity terms were 0.50 and 0.76 cm/sec, respectively, and their dissipation rates

were 0.374 and 0.159 day-1 (half-lives of 1.8 and 4.4 days). This model is applied to

2,3,7,8-TCDD only, using the parameterization of these two authors. 

Of 25 air:grass observed data points, 17 dioxin-like congener concentrations

and 8 homologue group concentrations, only 22 could be used for this exercise. In

order to obtain independent data points, the congener-specific data were subtracted

from their respective homologue groups. For example, 2,3,7,8-TCDD concentrations in

air and plants were subtracted from the TCDD homologue group concentrations in air

and plants. In doing so for all congeners and their respective homologue groups, it

was found that, in three instances, HpCDF, HxCDF, and HpCDF, subtraction of the

congener concentrations from the homologue group concentrations resulted in negative

air concentrations. There was obviously some measurement error in this data set. The

air:grass pairs for these three homologue groups were, therefore, not considered

further. The remaining 22 data pairs were found to be highly correlated, with a

correlation coefficient of 0.92. Generation of dioxin toxic equivalent (TEQ)

concentrations were developed using the International Toxicity Equivalency Factors

(U.S. EPA, 1989). Model goodness-of-fit was evaluated using the absolute and signed

difference between the natural logs of the measured and modeled grass

concentrations. The signed error, or bias, measures the systematic tendency of the

model to under or overpredict; a bias near 0 suggests that the model underpredicts and

overpredicts by about the same amount. The absolute error calculation describes

model variation; how close the model predictions come to the observations, regardless

of whether the model over or underpredicted.

Figure A-1 shows the comparison between predicted and observed grass

concentrations of the 22 congener/homologue pairs of this exercise. Grass
Appendix A

Model Validation

MPE A-19 December 1998

concentrations were predicted as the sum of vapor plus particle impacts, where the

particle phase impacts were modeled using the deposition approach as previously

described, and the vapor phase impacts were modeled using the air-to-leaf

bioconcentration factor approach. The testing of the vapor deposition approach will be

described shortly. The predictions and observations were converted to a log basis

because of the wide spread of concentrations, with grass concentrations ranging from

less 1 ppt to nearly 100 ppt. Observed grass concentrations are on the x-axis and

predicted grass concentrations on the y-axis. The dashed line shows where the

predicted equals observed would occur. 

As seen in this figure, the model provided a very good fit to the data. The

predicted TEQ concentration by the model was 6.6 pg/g dry, compared with the

observed concentration of 6.0 pg TEQ/g dry. The bias was 0.012, giving a bias factor

of e0.012 = 1.01 and suggesting that there appeared to be no particular bias in either

under or overpredicting grass concentrations. The absolute error was 0.396, meaning

that the predicted grass concentrations tended to be within a factor of about 1.5 (e0.396)

of measured concentrations.

Figure A-2 shows the comparison of predicted and observed deposition of

dioxin; again a dashed line shows the perfect fit between observed and predicted. It is

clear that the modeled rates of deposition were consistently higher than the measured

rates. There was a high degree of correlation between measured and modeled rates,

however, with a correlation coefficient of 0.99. The absolute error and bias were 1.12

and 1.06, respectively (for all but one congener/homologue, the model overpredicted

deposition), suggesting that the model predictions were, on average, approximately

three times higher than observed (e1.12 equals about 3). This would indicate a

systematic bias, either that the model tended to overpredict depositions or that the

measurements tended to under-represent depositions to plants (or a combination of
Appendix A

Model Validation

MPE A-20 December 1998

-2

-1

0

1

2

3

4

5

-1 0 1 2 3 4 5

Ln Observed, pg/g dry

Ln Predicted, pg/g dry

FIGURE A-1

Comparison of Observed Grass Concentrations of Dioxins/furans and Concentrations

Predicted by the Particle and Vapor Phase Impacts Models Described in Chapter 6. 

The Perfect Match of Observed and Predicted Is Shown in the Dashed Observed =

Predicted Line.
Appendix A

Model Validation

MPE A-21 December 1998

-2

-1

0

1

2

3

4

5

6

7

-2 -1 0 1 2 3 4 5 6

Ln Observed, pg/m2-day

Ln Predicted, pg/m2-day

FIGURE A-2

Comparison of Observed Dioxin/furan Deposition and Deposition Predicted by the EPA

Model. The Perfect Match of Observed and Predicted Is Shown in the Dashed

Observed = Predicted Line.
Appendix A

Model Validation

MPE A-22 December 1998

both). If the model tended to overpredict deposition, this may have been due to the

assumption of a deposition velocity that was too high. Measured deposition velocities

can be calculated from the data of Jones and Duarte-Davidson (!997) simply as the

deposition flux divided by the air concentration (with proper conversions). Average

velocities calculated this way were 0.06 cm/sec for the rural site data shown in Figure

A-2; lower than the 0.20 cm/sec applied in the modeling exercise. Actually, the

discrepancy is larger: 0.20 cm/sec was applied to the particle phase concentration,

while 0.06 cm/sec was calculated from total air concentrations. Another key modeling

parameter that may have caused overestimation in deposition fluxes was the modeled

particle phase fraction. As shown in Section A.4.5., measured particle phase fractions

tend to be significantly lower than modeled fractions. If the smaller measured particle

phase fractions were used perhaps with 0.06 cm/sec deposition velocity, then modeled

deposition amounts would be similar to measured amounts.

Table A-4 shows the comparison of the measured grass concentration of

2,3,7,8-TCDD with the modeled vapor transfer concentration using the vapor

bioconcentration model and the vapor deposition model parameterized by Trapp and

Mattheis (1995) and Smith et al. (1995a). It is clear from this table that the vapor

deposition algorithm, as parameterized by these two research groups, predicts

concentrations that are over an order of magnitude lower than predictions made by the

vapor bioconcentration algorithm, and even lower still than observed grass

concentrations. Critical parameters include the deposition velocity and the rate of

degradation on the plant. The degradation term is more likely inappropriately assigned

by these researchers, at least for this field data set. That rate was based on a

controlled chamber study, that included an uptake and a degradation phase (McCrady

and Maggard, 1993). The degradation phase was conducted in outdoor bright sunlight

(personal communication with J. McCrady, and U.S. EPA, 1994). Degradation rates

derived under these sunlight conditions may not be appropriate for most conditions. A

longer half-life would improve model predictions. However, no data other than

McCrady’s could be found for the degradation value.
Appendix A

Model Validation

MPE A-23 December 1998

TABLE A-4

Model Results Comparing the Vapor Transfer Model and the Vapor Deposition

Model with the Field Data for 2,3,7,8-TCDD (Concentrations in pg/g Dry Weight)

Description 2,3,7,8-TCDD grass concentrations, pg/g

dry

Observed data 0.76

vapor bioconcentration model3,5 0.35

Smith et al. (1995a) model70.01

Trapp and Mattheis (1995) model80.02

A.4.3. Soil Concentrations and Concurrent Concentrations in Bottom Sediments

and Fish. The Connecticut Department of Environmental Protection (CDEP, 1992)

established a program in 1986 for monitoring TCDD, TCDF, and other dioxin-like

congeners of comparable toxicity in several environmental matrices near resource

recovery facilities (RRFs). Matrices monitored include ambient air, residues and

leachate from the ash disposal sites, surficial soils, surface water surficial bottom

sediments, and whole fish. The purpose of the program is to evaluate the impact of

RRFs. Eight locations were evaluated through 1990, with one location serving as a

baseline or reference site. This section will examine the soil, sediment, and fish data

from that program.

Two key quantities examined are the ratio of soil concentration to bottom

sediment concentration and the ratio of fish lipid concentration to organic carbon

normalized bottom sediment concentration. The first ratio is compared to an analogous

ratio in the dioxin document’s background scenario. In this scenario, soil

concentrations within a watershed and air concentrations are input as source terms,

and the fate models predict concentrations in the water body (water, sediment, fish)
Appendix A

Model Validation

MPE A-24 December 1998

and the terrestrial biota (vegetation, animal fats). The aquatic impacts in this scenario

are modeled as a function of both soil erosion from the watershed and deposition of

airborne dioxins directly into the water body. The second ratio is the definition of the

biota sediment accumulation factor, or BSAF, which is described as one option for fish

tissue concentration modeling in the indirect models of this document. One can derive

the BSAF from the Connecticut data because the important site-specific quantities, the

organic carbon content of the bottom sediments and the fish lipid fractions, were

measured by the CDEP.

In general, the dioxin document models that predict impacts to water bodies are

quite similar to the aquatic impact models of this assessment. The most important

similarities include the use of the Universal Soil Loss Equation for unit area soil losses

coupled with a sediment delivery ratio and an enrichment ratio to predict the delivery of

contaminants to a water body; the modeling of the deposition of airborne contaminants

to the water body as a second source term; the assumption of equilibrium between the

bottom sediments and the water column; the use of partitioning parameters (i.e., Kow or

Koc) to model the partitioning among bottom sediment, suspended sediment, and water;

and the overall assumption that the system is in steady state. The aquatic models of

this assessment are more generalized in that certain quantities, such as the

volatilization loss of contaminants from the water body to the atmosphere, are modeled,

whereas volatilization loss (and other quantities) are not modeled in the dioxin

document’s aquatic modeling framework. This and other differences are not expected

to be significant for the dioxin-like compounds. For example, dioxins are tightly bound

to sediments such that escape from the water via volatilization is insignificant.

Table A-5 summarizes the key results from the CDEP (1992) data. The Csed:Csoil

ratio is the ratio of sediment concentration to soil concentration, and the second ratio is

the BSAF. The contaminants examined include 2,3,7,8-TCDD, 2,3,7,8-TCDF,

2,3,4,7,8-PCDF, and the total toxic equivalents, or total TEQ. The CDEP (1992)

calculated the concentrations of all 17 dioxin and furan dioxin-like congeners, which

allowed for the calculation of the TEQ ratios in Table A-5. The background
Appendix A

Model Validation

MPE A-25 December 1998

TABLE A-5

Summary of the Results from Eight Sites of the Connecticut Department of

Environmental Protection Sampling, Including Soil, Sediment, and Fish

Concentrations, and the Key Concentration Ratios of Sediment to Soil and the Biota

Sediment Accumulation Factor (BSAF) Ratio

Dioxin congener Soil Sediment Fish

2,3,7,8-TCDD

Number of samples

Mean concentration, ppt

Csed:Csoil Ratio: 3.86

BSAF: 0.86

770.56 3462.16 5211.06

2,3,7,8-TCDF

Number of samples

Mean concentration, ppt

Csed:Csoil Ratio: 2.59

BSAF: 0.25

776.77 34617.52 5212.53

2,3,4,7,8-PCDF

Number of samples

Mean concentration, ppt

Csed:Csoil Ratio: 1.58

BSAF: 0.47

773.56 3465.62 5211.49

TEQ

Number of samples

Mean concentration, ppt

Csed:Csoil Ratio: 2.69

BSAF: 0.24

778.42 34622.69 5213.1

Source: CDEP, 1992
Appendix A

Model Validation

MPE A-26 December 1998

demonstration scenario modeled all 17 congeners, allowing for a prediction of the TEQ

ratios.

Key observations from the background demonstration scenario in the dioxin

document and the results of the CDEP (1992) program are:

(1) The predicted sediment-to-soil ratios of 2,3,7,8-TCDD, 2,3,7,8-TCDF, and

2,3,4,7,8-PCDF for the background demonstration scenario of the dioxin exposure

document were 2.55, 1.82, and 2.43. The differences in the sediment:soil ratios of

these congeners are due to slightly different organic carbon partition coefficients

assumed for the three congeners. These ratios compare with the 3.86, 2.59, and 1.58

calculated from the Connecticut data set for 2,3,7,8-TCDD, 2,3,7,8-TCDF, and

2,3,4,7,8-PCDF, respectively. The ratio for total TEQ was calculated at 2.69, compared

with the predicted 2.64. The predicted ratios appear to be in the same ballpark as the

observed ratios, and it is judged that this result tends to support the model’s approach.

One of the key uncertain model parameters in the dioxin document, as well as in

this document, in the soil-to-sediment models is the soil enrichment ratio. It was

assigned a value of 3.0 in the dioxin document, which means that concentrations in soil

eroding from the watershed are three times higher than concentrations on the field. If

the soil enrichment ratio is set to 1.0, then sediment:soil ratios would be predicted to be

less than 1.0. The closer match of observed and predicted sediment:soil ratios with an

enrichment ratio of 3.0 does not necessarily validate the models approach to predicting

sediment impacts based on soil erosion, however. The background demonstration of

the dioxin document suggests that nearly all surface water impacts were from the

erosion of basin soils; sensitivity analysis showed that less than 5% of the water body

impact was from deposition of airborne dioxins. However, sediment concentrations in

water bodies in actuality are also a function of other direct, industrial-related

discharges. The impact of industrial sources on the sediments in the CDEP (1992)

data is unclear, although CDEP (1992) did observe that the resource recovery facilities

do not appear to be influencing soils, sediments, or fish in their monitoring program

(CDEP, 1992).
Appendix A

Model Validation

MPE A-27 December 1998

In summary, the CDEP (1992) data appear to indicate that bottom sediment

concentrations exceed surface soil concentrations by more than a factor of 2.0 in

environmental settings that do not appear to be impacted by industrial activities. The

models also predict a similar enrichment of sediment concentrations, due mainly to the

use of a soil enrichment ratio, by a factor of 3.0.

(2) The overall BSAF ratios for the three dioxin compounds and the TEQ ratio,

ranging from 0.24 to 0.86, are higher than the BSAF used in the dioxin document,

which is 0.09 for 2,3,7,8-TCDD, 0.072 for 2,3,7,8-TCDF, and 0.144 for 2,3,4,7,8-PCDF. 

Higher BSAFs in the CDEP (1992) data are expected because the fish species

sampled were bottom feeders, except for the yellow perch. The selected BSAF of 0.09

for 2,3,7,8-TCDD is mainly supported by Lake Ontario data (U.S. EPA, 1990a), which

were on brown and lake trout, smallmouth bass, and white and yellow perch, all column

feeders. [The BSAF for other dioxin congeners was based on the bioequivalency

factor, or BEF, approach suggested by U.S. EPA (1995). This is a ratio approach like

the TEF used to assign BSAF or other aquatic bioconcentration/biotransfer factors for

other dioxin congeners given a value for 2,3,7,8-TCDD.] Bottom feeders are expected

to have more exposure to contaminants because of their direct contact with sediments. 

This implies that use of the BSAF for site-specific assessments should consider

whether fish are bottom feeders or column feeders. If they are bottom feeders, then a

higher BSAF would be in order.

One other study considered the relationship between watershed soils and

bottom sediments. Smith et al. (1995b) evaluated sediment core data from the Hudson

River National Estuary Research Reserve system located on the lower (southern)

Hudson River. They also took surface soil cores near the estuaries studied. Using

principal component analysis, as well as a mass balance approach, they concluded

that the CDD/CDF concentrations in the river sediments were dominated by soil erosion

(76% of total influx) and sewage-containing effluents (19%). In doing their mass

balance exercise, they used an organic enrichment factor of 1.6 for calculating the

contribution of watershed soil dioxins to the river sediment load. This is analogous to
Appendix A

Model Validation

MPE A-28 December 1998

the enrichment ratio described above and supports a value greater than 1.0. However,

it does suggest that the value may not be as high as 3.0 if other sources of bottom

sediments, such as sewage effluents, are known to impact the sediment.

A.4.4. Transfer of Dioxins from Soils to Below-Ground Vegetables. This section

describes a validation of the transfer algorithm from soils to below-ground vegetables. 

The dioxin exposure document’s model for predicting the concentration of dioxins in

below-ground vegetation is the same as the general model in this methodology: a soil

concentration times an empirical root concentration factor (RCF), equals the belowground vegetation concentration. The dioxin document introduces an empirical

adjustment factor, VGbg, which considers that the empirical algorithm used to develop

values of RCF for the dioxin congeners was developed from laboratory data on the

transfer of organic contaminants into root hairs, whereas below-ground vegetation is

bulky. The RCF would model transfers into the outer layers of bulky below-ground

vegetation (such as carrots or potatoes), but not their inner parts. On the basis of data,

the dioxin document assigned a value of 0.25 to the VGbg parameter for underground

bulky vegetation. The VGbg parameter is also described in Chapter 6 of this document.

Data from Muller et al. (1994) were used to validate this model. Specifically,

carrots were grown in pots with soil of two concentrations, a control soil and a

contaminated soil. Muller et al. (1994) provided graphs showing the congener group

concentrations for soil and for three parts of the carrot: peel, cortex, and stele. The

precise concentrations from these graphs were unavailable. However, the graphs were

digitized by Cambridge Environmental (58 Charles Street, Cambridge, MA;

concentration values contained in discussions in a public comment provided to U.S.

EPA), and those concentrations were used in this exercise. Data in this article also

included the soil organic carbon content, 8.1%.

Part of this data set was used in conjunction with the model described above. 

The VGbg was taken out of the equation and the soil concentration data were input into

the model to predict the peel concentrations. The cortex and stele concentrations were

not used in the validation exercise. The predicted peel concentrations were compared
Appendix A

Model Validation

MPE A-29 December 1998

with the observed concentrations. Since the data were available by congener groups

and not individual congeners, the values for the parameters RCF and Koc were

estimated as the congener-specific value if only one dioxin-like congener was in the

congener group (e.g., 2,3,7,8-TCDD), or as the average of the congener-specific

values of the multiple dioxin-like congeners in the congener group (e.g., the three

HxCDDs). Finally, the data in Muller et al. (1994) were given only in dry-weight terms

without discussion of the dry-weight fraction of their experimental carrots, and the

model predicts a fresh weight. For this exercise, it was assumed that carrots are 15%

dry weight; the fresh weight was divided by 0.15 to estimate the dry-weight

concentration.

Table A-6 shows the data that went into this validation exercise and the results. 

It would appear that the model works reasonably well. The difference in peel

concentrations due to soil concentration is apparent in both the data and the model

predictions, and the magnitude of the difference appears to be captured. With one

exception, the predictions and observations are within a factor of 4 of each other, with

the exception being a factor of 5. Thirteen of 20 predictions/observations are within a

factor of 2 of each other. The biggest discrepancies are the HpCDF and OCDF

congener groups, with predictions exceeding observations by a factor of about 4 for

both the control and contaminated soils. Generally, it is concluded that these data

support the use of the Briggs et al. (1982) RCF empirical formulation to predict the peel

concentration of dioxin-like compounds in underground bulky vegetation.

A.4.5. An Update of the Air-to-Beef Model Validation Exercise. An original version

of the air-to-beef model validation exercise can be found in a journal article (Lorber et

al., 1994) as well as in an earlier version of the dioxin document (U.S. EPA, 1994). The

current dioxin exposure document updates this exercise. This section provides an

overview of the exercise, the changes made, and the final results.

The air-to-beef model of the dioxin exposure document represents one of the

two approaches presented in this document to model animal fat concentrations. One

approach is termed the “biotransfer” approach. Animal fat concentrations or whole-
Appendix A

Model Validation

MPE A-30 December 1998

animal concentrations are predicted as a function of the mass intake of the

contaminant—i.e., the mass intake (in g/day or some other mass/time units) of the

contaminant times the biotransfer factor equals the animal tissue (whole or lipid-based)

concentration. The other option, which is used in the dioxin exposure document, is the

bioconcentration approach. Animal fat concentrations are predicted as a function of

the concentration of the contaminant in the animal’s diet (i.e., the weighted average
Appendix A

Model Validation

MPE A-31 December 1998

TABLE A-6

Data and Results of the Soil to Below-ground Vegetable Validation Exercise

Congener

Group

Koc

(L/kg)

RCF

Control Soil Contaminated Soil

Soil

(ppt)

Observed

Peel

(ppt dry)

Predicted

Peel

(ppt dry)

Soil

(ppt)

Observed

Peel

(ppt dry)

Predicted

Peel

(ppt dry)

TCDD 3.98@1065200 11 1.7 1.2 24.3 2.9 2.6

PCDD 2.69@1063900 6.8 1.2 0.8 80.5 5.6 9.6

HxCDD 2.12@10718600 23.5 1.6 1.7 176.7 7.3 12.7

HpCDD 6.17@10743700 45.6 1.6 2.7 238.6 5.4 13.9

OCDD 9.77@10762200 85.4 3 4.5 297.4 6.3 15.6

TCDF 7.76@1051500 21.8 11.9 3.5 270.7 36.7 43

PCDF 2.88@1064080 34.8 5.9 4.1 361.9 24.8 42.1

HxCDF 6.17@1067410 38.5 3.2 3.8 418.4 22 41

HpCDF 3.86@10729400 49 1.2 3.1 667.2 12.2 41.8

OCDF 3.89@108

180000 46.6 0.4 1.8 687.3 5 26.2
Appendix A

Model Validation

MPE A-32 December 1998

concentration in pg/g or a similar mass/mass concentration unit multiplied by the

bioconcentration factor).

The premise of this exercise to test the beef food chain model is that airborne

reservoirs of these compounds in rural environments are the “source term” explaining

concentrations found in beef. The principal assumption in the validation exercise is

that one can define an “average” rural air profile of dioxins, route this profile through

the food chain model, and predict an “average” beef concentration. This exercise does

not qualify as a validation exercise in the traditional sense, as described in the

introduction to this chapter. However, this exercise was undertaken for these reasons: 

(1) the ubiquitous nature of dioxins in the environment leads to the possibility of true

average air and beef fat dioxin concentrations; (2) a comprehensive site-specific data

set (i.e., air concentrations, vegetation concentrations, cattle fat concentrations, and

other data) was not available; (3) beef and air concentration data that are judged to be

reasonably representative for U.S. conditions were available; and (4) dioxins in animal

fat were judged by U.S. EPA (1994) and by researchers around the world to be the

most important matrix of exposure to these compounds; hence it was desired to

demonstrate that the modeling approach for this route of exposure would lead to

appropriate concentrations.

Figure A-3 presents an overview of the air-to-beef food chain model. This

approach is analogous to the terrestrial food chain models of this methodology. For

this exercise, air dispersion models are not employed; the ambient air concentrations

are the source terms. Besides using the bioconcentration option for animal fat

concentrations, the only other noteworthy deviation from the methodologies of this

document is that the soil-to-above-ground plant algorithms are not considered. Dioxin

impacts to plants are only from air-to-plant transfers (vapor and particle phases). The

dioxins are tightly bound to soils and soil-to-above-ground plant transfers are not

expected to occur. The key components of the current model validation exercise were:

1. Source Term: Air concentrations. These are the source terms for this

exercise. They are routed into cattle vegetation and the cattle fat
Appendix A

Model Validation

MPE A-33 December 1998

 TOTAL CONCENTRATION

 VAPOR PARTICLE

 PHASE PHASE

PASTURE

 GRASS

HAY, SILAGE,

 GRAIN

SOIL

BEEF CONCENTRATION

 VAPOR

TRANSFERS

 PARTICLE

DEPOSITIONS

BIOCONCENTRATION

CATTLE

 DIET

FIGURE A-3

Overview of Model to Predict Beef Concentrations from Air Concentrations

concentrations are predicted from the vegetation (and the soil

concentrations; see below). The air profile used was the same rural air
Appendix A

Model Validation

MPE A-34 December 1998

profile used in the background scenario of the dioxin document referred to

above. The original air-to-beef model validation exercise crafted a rural

profile based on a compilation of data for U.S. urban settings. The dioxin

exposure document describes why the rural profile is superior to this

crafted air profile, and why it can be considered reasonably representative

of rural air concentrations in general. The overall TEQ concentration in this

air profile is 0.019 pg TEQ/m3.

 2. Source Term: Soil concentrations. Although soil was demonstrated in

sensitivity analyses exercises in the exposure document to be much less

important than cattle vegetative feeds, it is the second primary source of

dioxins in cattle. The measured soil concentrations used in the background

scenario in the exposure document were used in these model validation

exercises, and soil concentrations were modeled from deposition of the

airborne reservoir of dioxins. The overall TEQ concentration in the soil is

1.37 pg TEQ/g.

3. Predicted Media Concentration: Beef concentrations. The original

validation exercise used an average profile of dioxins in whole beef

generated from 14 samples of beef from three literature reports of grab

samples. A far superior data base on this dependent model result was

generated in a collaborative national survey of beef fat from

slaughterhouses conducted by USDA and U.S. EPA (Winters et al., 1996). 

The overall TEQ concentration in this profile is 0.18 pg TEQ/g whole beef. 

This was calculated from the USDA/EPA survey, assuming nondetects

were equal to 1⁄2 the detection limit and that whole beef is 19% fat.

4. Key model parameters. Model sensitivity analyses as well as actual field

experimentation showed that vegetation concentrations of the lower

chlorinated dioxins (the tetra through hexa congeners) tend to be

dominated by vapor transfers rather than particle depositions. Therefore,

final beef concentrations of the lower chlorinated congeners will be similarly

sensitive to assignment of the air-to-leaf transfer factor, Bv. The original

values for Bv for the dioxins were derived using an empirical algorithm

relating this parameter to an organic contaminant’s octanol-water partition

coefficient, Kow, and its Henry’s Law Constant (U.S. EPA, 1992). McCrady

and Maggard (1993) showed that a value of Bv for 2,3,7,8-TCDD calculated

this way was 40 times higher than the value they obtained experimentally. 

Therefore, in the first version of the air-to-beef model validation exercise

(Lorber et al., 1994; U.S. EPA, 1994), the Bv values for the dioxins were

calculated by first using the empirical algorithm and then dividing each

value by a factor of 40. The calibration exercises described in Lorber

(1995) suggested that the Bv for dioxins should be even lower, by about a

factor of between 2 and 4. This was, in fact, the basis for the guidance
Appendix A

Model Validation

MPE A-35 December 1998

given in Chapter 5 of this methodology that assignment of Bv for organic

contaminants can be estimated by using the empirical algorithm and then

dividing the final result by a factor of 100 (i.e., the factor of 40 and then the

additional “real world” factor of between 2 and 4). The final Bv values for

the dioxins were very similar to those described in Lorber (1995). There

was an additional slight adjustment when the vapor/particle partitioning was

slightly refined, which led to a recalibration of the Bv. Other key parameters

that were varied from their original versions included the vapor/particle

partitioning of the ambient air concentrations of dioxins and the beef

bioconcentration factors. As with model validation, all parameters were

assigned values and none were calibrated for this exercise.

Further details on the construct of the modeling exercise can be found in the

dioxin exposure document. Table A-7 shows the original and current model predictions

for leafy vegetation and compares them against four available data sets. The data on

hay in the United States (Reed et al., 1990) seem to conflict with the other available

data on the table; they have significantly higher concentrations of the congeners (of the

ones that were quantified—most were nondetects), particularly OCDD. These data

could have been influenced by a local source, or the particular type of hay may have

had much higher particulate dioxin contributions than other grass data sets. The two

data sets from the U.K. are reasonably similar, with the more recent (Kjeller et al.,

1996) showing lower concentrations. In fact, these data were used by the authors

(among other data) to suggest that emissions of dioxins are being reduced in the U.K. 

The data set on alfalfa is consistent with these data on grass in the U.K.

It is easily seen that the current set of model predictions of dioxins in grass is

much more in line with these two U.K. observations and the U.S. alfalfa observation

than are the predictions in the 1994 data set. There was a general reduction in the

concentrations predicted between 1994 and the current predictions, due primarily to

reductions in the air-to-leaf vapor transfer factor, Bv. There were noticeable

improvements in some of the congeners, particularly the predictions for 2,3,7,8-TCDF

and 1,2,3,7,8-PCDF, and to a lesser extent but still noticeable, improvements in

1,2,3,7,8-PCDD, 1,2,3,4,7,8-HxCDD, 2,3,4,7,8-PCDF, 1,2,3,4,7,8-HxCDF, 1,2,3,6,7,8-
Appendix A

Model Validation

MPE A-36 December 1998

HxCDF, 2,3,4,6,7,8-HxCDF, and OCDF. There was also an improvement in OCDD. It

was noted that the vapor fraction assumed for OCDD was 0.0 in the 1994 exercise, but
Appendix A

Model Validation

MPE A-37 December 1998

TABLE A-7

Comparison of Predicted Leafy Vegetation Samples of the Current, Revised Validation Exercise with the

Previous Predictions of Leafy Vegetation and Several Observations in the Literature (Units are pg/g dry weight)

Congener

Predicted U.S.

Alfalfaa,

1994

U.K. Grassb,

1979-1988

U.K.

Grassc,

1996

U.S. Hayd,

1989 1994 1996

2,3,7,8-TCDD 0.1 0.05 0.11 0.03 0.12 ND

1,2,3,7,8-PCDD 0.9 0.18 0.16 0.14 0.07 ND

1,2,3,4,7,8-HxCDD 0.7 0.18 0.29 0.14 0.1 ND

1,2,3,6,7,8-HxCDD 0.2 0.22 0.25 3 0.17 1.2

1,2,3,7,8,9-HxCDD 0.2 0.32 0.23 1.4 0.08 ND

1,2,3,4,6,7,8-HpCDD 21 4.12 0.85 7.1 2.8 30

OCDD 6 13.2 6.21 24 15.6 285

2,3,7,8-TCDF 7.2 0.07 0.06 0.46 1.28 ND

1,2,3,7,8-PCDF 1.4 0.19 0.21 0.18 0.29 ND

2,3,4,7,8-PCDF 0.8 0.16 0.08 0.2 0.28 ND

1,2,3,4,7,8-HxCDF 0.5 0.26 0.19 0.32 0.21 ND

1,2,3,6,7,8-HxCDF 0.9 0.31 0.3 0.16 0.09 ND

1,2,3,7,8,9-HxCDF 0.3 0.04 0.24 0.02 0.02 ND

2,3,4,6,7,8-HxCDF 0.5 0.14 0.2 0.15 0.09 ND

1,2,3,4,6,7,8-HpCDF 1.4 1.68 0.31 1.9 1.02 5.4

1,2,3,4,7,8,9-HpCDF 0.1 0.25 0.45 0.14 0.13 ND

OCDF 0.4 0.96 0.96 2 0.98 7.5

TOTAL 42.6 22.3 11.1 41.34 32.2 ---

TEQ 3.2 0.46 0.44 0.89 0.57 ---

a

 From an unpublished data set for alfalfa supplied by V. Fiel, United States Department of Agriculture, for a beef

 feeding study that is currently underway. For these results, all but the hepta dioxin and the two octa congeners

 were not detected; results above are 1⁄2 detection for the two alfalfa samples taken. 

b

 Kjeller et al. (1991) 

c

 Kjeller et al. (1996)

d

 Reed et al. (1990) - detection limits not supplied for nondetects, but described as between 0.31 and 6.5 ppt 

 ND = Not detected
Appendix A

Model Validation

MPE A-38 December 1998

in a detailed examination in Lorber et al. (1994), use of a vapor fraction of 0.0002 for

OCDD, which is what the V/P model calculated, actually resulted in grass concentration

approaching the hay concentration found in Reed et al. (1990). Given the other leafy

concentrations, it would appear that an OCDD grass concentration in the 10-30 ppt 

range is more appropriate than one greater than 100 ppt. As a final and simple test of

the current model predictions for leafy vegetation, the two data sets from the U.K. were

averaged with the one set from alfalfa in the United States and correlated against the

two sets of model predictions. The best-fit regression line for the 1994 model

predictions and this average of three observations had a slope of 0.26, an intercept of

0.84, and, importantly, a rather poor r2 of 0.14. In contrast, the best fit regression line

for the 1996 model predictions had a slope of 1.13, an intercept of 0.007, and a much

improved r2 of 0.98.

Table A-8 compares the final predicted and observed beef data sets from the

1994 and the current validation exercise. There are reductions in both the observed

and predicted beef concentrations from the 1994 results to the current results. 

However, given the fact that the USDA/EPA beef data set is superior to the grab

sample relied on previously for this validation exercise, it is most appropriate to

compare the predictions made in 1994 and those made in the current exercise with the

USDA/EPA beef data set. Just by visual inspection, it is seen that notable

improvements in model predictions are seen for the following congeners: 1,2,3,7,8-

PCDD, OCDD, and 2,3,7,8-TCDF. There are some congener predictions for which the

1994 validation exercise appears to result in a superior match, including 1,2,3,4,7,8-

HxCDD, 1,2,3,7,8-PCDF, 1,2,3,7,8,9-HxCDF, and 2,3,4,6,7,8-HxCDF. Overall, the

TEQ concentration in the current validation is closer to the observed TEQ

concentration, although the total concentration appears more favorable for the 1994

validation exercise.

A simple correlation test suggests that the current model validation is superior to

the 1994 model predictions. The following shows the results of linear regressions of
Appendix A

Model Validation

MPE A-39 December 1998

the 1994 and 1996 model predictions with the EPA/USDA monitoring results, assuming

ND = 1⁄2 detection limit and ND = 0.0:

TABLE A-8

Results of the 1994 Air-to-beef Model Validation Exercise Compared Against Results from

the Current Air-to-beef Model Validation Exercises (all beef concentrations in terms of pg/g

lipid; values in parentheses are observations calculated assuming nondetects equal 0.0;

values not in parentheses assume nondetects equal 1⁄2 detection limit)

Congener

1994 Validation Results Current Validation Results

Observed

Beef Predicted Beef Observed Beef Predicted Beef

2,3,7,8-TCDD 0.13 (0.06) 0.16 0.05 (0.03) 0.13

1,2,3,7,8-PCDD 1.17 (0.13) 1.42 0.35 (0.04) 0.37

1,2,3,4,7,8-HxCDD 1.38 (0.74) 0.53 0.64 (0.18) 0.19

1,2,3,6,7,8-HxCDD 4.40 (4.40) 0.16 1.42 (1.21) 0.2

1,2,3,7,8,9-HxCDD 1.08 (0.34) 0.21 0.53 (0.26) 0.38

1,2,3,4,6,7,8-HpCDD 10.13 (9.99) 1.53 4.48 (4.39) 0.79

OCDD 15.32 (14.84) 1.53 4.78 (3.21) 4.54

2,3,7,8-TCDF 0.30 (0.25) 2.42 0.03 (0) 0.04

1,2,3,7,8-PCDF 0.23 (0.005) 0.37 0.31 (0) 0.07

2,3,4,7,8-PCDF 1.11 (0.90) 0.89 0.36 (0.06) 0.25

1,2,3,4,7,8-HxCDF 2.68 (2.44) 0.42 0.55 (0.27) 0.29

1,2,3,6,7,8-HxCDF 0.33 (0.10) 0.68 0.40 (0.12) 0.28

1,2,3,7,8,9-HxCDF 0.30 (0) 0.21 0.31 (0) 0.05

2,3,4,6,7,8-HxCDF 0.38 (0.11) 0.37 0.39 (0.10) 0.14

1,2,3,4,6,7,8-HpCDF 2.08 (1.74) 0.21 1.00 (0.75) 0.35

1,2,3,4,7,8,9-HpCDF 0.68 (0.07) 0.05 0.31 (0) 0.11

OCDF 1.18 (0.55) 0.05 1.88 (0) 0.13

TOTAL 42.9 (36.7) 11.21 17.8 (10.7) 8.29

TEQ 2.51 (1.55) 1.85 0.93 (0.35) 0.61
Appendix A

Model Validation

MPE A-40 December 1998

N '

c 1

PEL% c 1

(A-1)

Slope Intercept r

2

1994 model/ND = 1⁄2 detection 0.70 0.58 0.11

1994 model/ND = 0 0.74 0.13 0.16

1996 model/ND = 1⁄2 detection 1.02 0.55 0.57

1996 model/ND = 0 0.76 0.25 0.41

As seen, there is a better regression with the current modeling exercise as compared

with the 1994 model exercise, with current r2 of 0.57 and 0.41 compared against r2 of

0.11 and 0.16.

A.4.6. Modeling the Vapor/Particle (V/P) Partitioning of Dioxins and Furans.

Chapter 3 describes how the Junge-Pankow model is used to estimate particulate

percentages for airsheds characterized as clean continental background, average

background, background plus local sources, and urban. Briefly, this model is described

by this equation:

where:

N = fraction of compound adsorbed to aerosol particles

PEL = saturation liquid-phase vapor pressure of pure compound at ambient

temperature, Pa

1 = particle surface area per unit volume of air, cm2 aerosol/cm3 air

c = constant related to the difference between heat of desorption from the

particle surface, Qd, and heat of vaporization of compound, Qv,

estimated at 17.2 Pa/cm

The dioxin exposure document describes the derivation of the PEL for the dioxin

and furan congeners. The particle surface areas, 1, for the four watersheds identified

above are: 4.2@10-7 for clean continental, 1.5 10-6 for average background, 3.5 10-6 for
Appendix A

Model Validation

MPE A-41 December 1998

background plus local sources, and 1.1 10-5 for urban. Table A-9 shows the particle

fraction, N, for all 17 dioxin and furan congeners for the four watershed conditions and

TABLE A-9

Particle Fractions, M, in Four Airsheds at 20EC for the Dioxin-like Congeners

Congener Clean

Continental

Average

Background

Background

plus Local

Sources

Urban

2,3,7,8-TCDD 0.1 0.29 0.49 0.75

1,2,3,7,8-PCDD 0.44 0.74 0.87 0.95

1,2,3,4,7,8-HxCDD 0.78 0.93 0.97 0.99

1,2,3,6,7,8-HxCDD 0.78 0.93 0.97 0.99

1,2,3,7,8,9-HxCDD 0.78 0.93 0.97 0.99

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

HpCDD

0.93 0.98 0.99 0.997

OCDD 0.98 0.995 0.998 0.999

2,3,7,8-TCDF 0.09 0.27 0.47 0.73

1,2,3,7,8-PCDF 0.27 0.57 0.75 0.91

2,3,4,7,8-PCDF 0.38 0.69 0.84 0.94

1,2,3,4,7,8-HxCDF 0.63 0.86 0.93 0.98

1,2,3,6,7,8-HxCDF 0.63 0.86 0.93 0.98

1,2,3,7,8,9-HxCDF 0.74 0.91 0.96 0.99

2,3,4,6,7,8-HxCDF 0.74 0.91 0.96 0.99

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

HpCDF

0.86 0.96 0.98 0.99

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

HpCDF

0.92 0.98 0.99 0.997

OCDF 0.98 0.995 0.998 0.999
Appendix A

Model Validation

MPE A-42 December 1998

Log Kp' m Log PEL% b

(A-2)

at an ambient temperature of 20EC. The dioxin exposure document describes how the

PEL varies as a function of temperature, and how different particle fractions, N, for the

dioxins can be calculated at different temperatures.

Data on measured vapor/particle partitioning for dioxins (and other compounds)

comes from hi-volume two-stage samplers consisting of a glass or quartz fiber filter

followed by a sorbent trap such as polyurethane foam (PUF), XAD resins, or a

combination of the two. The filter is assumed to capture particle-bound contaminants

and the PUF (or XAD) is assumed to capture vapor-phase contaminants. 

Measurements on the partitioning of dioxins were available from Eitzer and Hites

(1989), Hunt and Maisel (1992), Hites (1991), and Welsch-Pausch et al. (1995). The

airsheds in these four studies were generally assumed to be semirural and the airshed

category of “background plus local sources” was assumed to apply. These

measurements were taken at different ambient air temperatures, and therefore needed

to be normalized to a common temperature. In order to normalize the data from the

four references, the following procedure was followed:

Step 1. The gas phase concentrations, Cg

, and the particle phase

concentrations, Cp, were calculated from the original references. The

ratio of Cp/Cg was defined to be “Kp.” The log Kp values of the various

dioxin congeners were examined for outliers (only one point was

omitted, from the Hites [1991] data set), then regressed against log

PEL of the various congeners at the ambient sampling temperature

listed for each site. The regression equation is:

where:

Kp = particle/gas partition coefficient, m3/μg, defined as: Cp (ng/μg)/Cg

(ng/m3), where Cp is concentration associated with aerosols, and Cg is

gas-phase concentration

PEL = liquid subcooled vapor pressure, Pa

m, b = empirically derived slope and intercept from data set
Appendix A

Model Validation

MPE A-43 December 1998

N '

Cp( TSP )

Cg% Cp( TSP )

(A-3)

Regression parameters of Equation A-2 are given in Table A-10 for the various field

studies.

TABLE A-10

Regression Parameters Slope M and Intercept B for Equation A.2, log

Kp = M log PEL + B, Based on Field Measurements of Particle/gas

Distributions for Dioxins and Furans

Reference Slope, m Intercept, b r

2

Eitzer and Hites (1989) -0.775 -5.72 0.96

Hites (1991) -0.988 -6.87 0.87

Hunt and Maisel (1992) -0.62 -5 0.61

Welsch-Pausch et al. (1995) -0.707 -5.73 0.99

Step 2. For each study, values of Kp

 for each dioxin and furan congener at

20EC were calculated from the liquid vapor pressures of each

congener at 20EC, using Equation A-2 and the regression parameters

in Table A-7.

Step 3. Although Equation A-1 describes the Junge-Pankow model that solves

for the particulate fraction, N, this quantity can also be expressed by:

where TSP is the total suspended particulate (in μg/m3). Manipulation

of this equation yields:
Appendix A

Model Validation

MPE A-44 December 1998

N '

Kp( TSP )

1 % Kp( TSP )

(A-4)

The TSP can be solved for as a function of the total volume of

particles per volume of air (cm3 aerosol/cm3 air) and a particle density

(g/cm3). Assuming a density of 1.4 g/cm3,, the TSPs for the four

airsheds are: clean continental background 9 μg/m3, average

background 42 μg/m3, background plus local sources 60 μg/m3, and

urban 98 μg/m3.

Step 4: As the final step, the particulate fractions at 20oC were calculated from

Kp by Equation A-4, assuming TSP = 60 μg/m3, which corresponds to

the background plus local sources assumption made for the four data

sources of V/P partitioning for the dioxins.

Thus, although the actual field measurements were done under a variety of

temperatures, they were normalized to 20oC for comparison. Figure A-4 shows the

particulate percentages from four field studies in comparison with those predicted by

Junge-Pankow model for the background plus local sources air regime.

 All of the field

data fall substantially below the model curve. For example, the particle-bound

percentages of tetrachlorodioxins and furans, which are predicted to be 43-65%,

averaged only 11-18% with the filter-sorbent sampler.

Reasons for these discrepancies may be related to both sampling artifacts and

model uncertainties. Blow-off losses during collection are likely to reduce the filterretained fraction (which is assumed to be equal to the particle-bound fraction). This

artifact is expected to be the most serious for long sampling times, especially if the dayto-night temperature changes are large. However, the Welsch-Pausch samples, which

were collected over 2-week integration times, show only slightly lower particulate

values than those of other workers, which were obtained over 24- to 36-hour sampling

periods. Aside from the Eitzer-Hites (1989) data, the data were from Whitby (1978). It

is likely that Whitby's values of 1 do not reflect the true surface area distribution, since
Appendix A

Model Validation

MPE A-45 December 1998

they were based on the average size spectrum of aerosols and assumed spherical

particles. Other limitations of the model are the inability to consider the kinetics of

Key: E-H = Eitzer and Hites (1989) HITES = Hites (1991)

H-M = Hunt and Maisel (1992) W-P = Welsch-Pausch et al. (1995)

FIGURE A-4
Appendix A

Model Validation

MPE A-46 December 1998

Comparison of Measured Particulate Percentages of Dioxins and Furans with

Predictions of the Junge-Pankow Model as a Function of the Subcooled Liquid Vapor

Pressure, PEL, of the Dioxins and Furans.

adsorption/desorption (Kamens et al., 1995; Rounds et al., 1993) and humidity effects

(Pankow et al., 1993).

In conclusion, neither the filter-sorbent sampler nor the Junge-Pankow model

necessarily give the correct vapor/particle distributions. Evidence based on limited

field data suggests that the model overestimates the particulate fraction of PCDD/Fs

relative to the filtration sampler, but it can just as well be stated that the sampler

underestimates the particulate fraction relative to the model. Further work needed to

improve the state of knowledge of PCDD/F partitioning between the aerosol and gas

phases includes:

! Comparative monitoring with filter-sorbent samplers and other speciating

devices such as denuders (Coutant et al., 1992; Eatough et al., 1993;

Krieger and Hites, 1994; Lane et al., 1988; Lewis et al., 1991; Gundel et

al., 1995; Subramanyam et al., 1994; Tang et al., 1994) and diffusion

separators (Turpin et al., 1993).

! Laboratory experiments to investigate the kinetics and thermodynamics of

the sorption of PCDD/Fs and other semivolatile compounds and sorption

aerosols.

! Improvements in modeling particle/gas distributions in ambient air.

Despite the differences in the monitored and modeled results, the JungePankow model is the recommended approach for estimating the particle/gas

distribution of PCDD/Fs at the present time. In addition to reproducing the general

trend in partitioning with vapor pressure, the Junge-Pankow equation was used in the

air-to-beef model validation described above. That exercise used the Junge-Pankow

model for partitioning dioxins in the air. A key finding of that work was that the transfer

of vapor-phase dioxins to plants and subsequently to animals dominated the terrestrial

food chain. Also, and as described below, vapor/particle partition data on other

semivolatile organic compounds are compared with model predictions, and the match
Appendix A

Model Validation

MPE A-47 December 1998

for these compounds is, in most cases (especially for PAHs), superior to that of the PCDD/Fs.

A.5. COMPARISON OF MEASURED AND MODELED VAPOR/PARTICLE

DISTRIBUTIONS FOR SEMIVOLATILE COMPOUNDS OTHER THAN

DIOXIN

The previous section described the use of the Junge-Pankow model to predict

the vapor/particle partitioning of dioxin congeners in ambient air. It was shown that the

model tended to predict more partitioning in the particle phase than had been

measured. While the general trend of an increasing particle component as the

chlorination increased was seen in measurements and in the modeled values, this

overprediction of modeled versus measured was also consistent as the degree of

chlorination increased. As described above, there is no general agreement as to which

is the better measure of reality—the measured or the modeled vapor/particle

partitioning for the dioxins.

This section describes the application of the Junge-Pankow model to other

semivolatile organic compounds. These include PAHs, PCBs, and organochlorine

pesticides. The procedure for normalizing data from different literature sources was

described in the previous section for dioxins and furans. The first step was the

development of empirical relationships from the literature data. Table A-11 presents a

summary of the m and b curve-fit parameters for this empirical relationship, which was

shown in the previous section as Equation A-2. The subcooled liquid vapor pressures

for the SOCs were taken from reports in the literature, including values for PAHs

(MacKay et al., 1992; Yamasaki et al., 1984), PCBs (Falconer and Bidleman, 1994),

and OC pesticides (Hinckley et al., 1990). The next step was to solve for the particle

fraction, N, as a function of Kp at a common temperature, 20oC, and the TSP (total

suspended particulate) parameter. If TSP was not reported in the original paper (as in

most cases), it was assumed that TSP = 98 μg/m3 for urban air and 10-42 μg/m3 for

nonurban air of varying cleanliness.

This procedure was used to develop the dotted-line “observed” particle

percentages shown in Figures A-5 and A-6. The modeled particle fractions were
Appendix A

Model Validation

MPE A-48 December 1998

determined for these sites using the Junge-Pankow model detailed in Chapter 3 and

summarized in the section above on vapor/particle partitioning of dioxin.

TABLE A-11

Parameters for the Empirical Relationship Relating the Subcooled Liquid Vapor Pressure,

PEL, to the Particle/Gas Partition Coefficient, Kp, of Semivolatile Organic Compounds (SOC)

Setting/SOC Location m b Reference

I. Urban

PAHs Portland, Oregon -0.882 -5.38 1

PAHs Portland, Oregon -0.89 -4.75 2

PAHs Denver, Colorado -0.76 -5.1 3

PCBs Denver, Colorado -0.946 -5.86 3

PAHs Chicago, Illinois -0.694 -4.61 4

PCBs Chicago, Illinois -0.726 -5.18 4

PAHs London, U.K. -0.631 -4.61 5

PAHs Osaka, Japan -1.04 -5.95 6

PAHs Brazzaville, Congo -0.81 -5.31 7

OC pesticides Brazzaville, Congo -0.74 -5.76 7

II. Rural

PAHs Coastal Oregon -0.724 -4.94 1

PAHs Lake Superior -0.586 -3.83 8

PAHs Lake Superior -0.614 -4.25 9

PAHs Green Bay -1 -5.47 4

PCBs, OC pesticides Bayreuth, Germany -0.61 -4.74 10

References: 1. Ligocki and Pankow (1989) 2. Hart (1989)

3. Foreman and Bidleman (1990) 4. Cotham and Bidleman (1995)

5. Baek et al. (1991) 6. Yamasaki et al. (1982)

7. Ngabe and Bidleman (1992) 8. McVeety and Hites (1988)

9. Baker and Eisenreich (1990) 10. Kaupp and Umlauf (1992)
Appendix A

Model Validation

MPE A-49 December 1998

PAHs, Urban PAHs, Rural

-7 -6 -5 -4 -3 -2

0

20

40

60

80

100

-7 -6 -5 -4 -3 -2

0

20

40

60

80

100

o

L

J-P 

POR2

OSK

POR1

CHI

DEN,BRZ

LON

GB

OR

SUP2

SUP1

J-P

J-P 

(a) (b)

Key: J-P: Junge-Pankow model; the two solid lines in the rural setting represent clean

continental background and background + local sources regimes

DEN = study in Denver (Foreman and Bidleman, 1990)

POR1 and POR2 = two studies in Portland (Ligocki and Pankow, 1989; Hart, 1989)

CHI = study in Chicago (Cotham and Bidleman, 1995)

SUP1 and SUP2 = two studies in Lake Superior (McVeety and Hites, 1988; Baker and

Eisenreich, 1990)

BRZ = study in Brazzaville, Congo (Ngabe and Bidleman, 1992)

LON = study in London (Baek et al., 1991)

OSK = study in Osaka, Japan (Yamasaki et al., 1982)

GB = study in Green Bay (Cotham and Bidleman, 1995)

FIGURE A-5

Comparison of Measured and Predicted Particulate Percentages of PAHs in

Urban and Rural Air.
Appendix A

Model Validation

MPE A-50 December 1998

PCBs and OC Pesticides

 Urban Rural

-7 -6 -5 -4 -3 -2

0

20

40

60

80

100

-7 -6 -5 -4 -3 -2

0

20

40

60

80

100

Log P (Pa) o

L

J-P J-P

DEN

CHI BAY

BRZ

(a) (b)

Key: J-P: Junge-Pankow model; the two solid lines in the rural setting represent clean

continental background and background + local sources regimes

DEN = study in Denver (Foreman and Bidleman, 1990)

CHI = study in Chicago (Cotham and Bidleman, 1995)

BRZ = study in Brazzaville, Congo (Ngabe and Bidleman, 1992)

BAY = study in Bayreuth, Germany (Kaupp and Umlauf, 1992)

FIGURE A-6

Comparison of Measured and Predicted Particulate Percentages of PCBs and

Organochlorine Pesticides in Urban and Rural Air
Appendix A

Model Validation

MPE A-51 December 1998

The agreement between the measured and predicted aerosol-bound fractions of

PAHs is remarkably good, as seen in Figure A-5. Two model curves are shown for

rural air in Figure A-5b, representing clean continental background and background

plus local sources regimes. Experimental PAH distributions fall reasonably close to the

range of particulate values predicted by the model, although the fraction of aerosolbound PAHs at rural sites (e.g., Lake Superior) is greater than expected for the more

volatile compounds. This may be due to a portion of these PAHs being

nonexchangeable (Pankow, 1988).

Fewer data are available for nondioxin organochlorine compounds. Figure A-6

shows the measured particulate percentages of PCB congeners and chlorinated

pesticides in comparison to predictions. Again, the two depictions of model predictions

for rural conditions (Figure A-6b) show a range from clean continental to background

plus local sources. The monitored data fall below the model values, but the

discrepancy is generally not as great as for the dioxins and furans.

Overall, it can be concluded the Junge-Pankow model appears to predict

vapor/particle partitioning very similar to measurements for PAHs, but not as similar for

PCBs and chlorinated pesticides, although the match there is still superior to that of

dioxins and furans. This exercise does lend additional credibility, in general, to the

Junge-Pankow model. However, the model and the measurements for PCDD/Fs do

diverge, as described in the previous section, and the debate remains as to which is

correct for the PCDD/Fs, the model or the measurements.

A.6. A VALIDATION EXERCISE FOR THE AIR-TO-PLANT ALGORITHMS FOR

POLYCYCLIC AROMATIC HYDROCARBONS

Validation of the air-to-plant pathway was performed by utilizing PAH data

collected in 1991-1992 in Bloomington, Indiana (Simonich and Hites, 1994). In this

study, researchers collected air and vegetation samples during the growing season and

analyzed them for 10 PAHs to suggest a link between plant lipids and resulting plant

concentrations. The site-specific data were obtained from the researchers and used as

input for this model validation exercise. Air concentrations of several PAHs became

the source term input data, and the observed vegetation concentrations became the
Appendix A

Model Validation

MPE A-52 December 1998

dependent model result. Both sets of data (i.e., air and plant) were available

temporally, and there was variation in both air and plant concentrations. For example,

the pine needle concentration of anthracene varied from 26 to 10 to 21 ng/g dry weight

from spring to summer to fall. Since the models of this assessment are steady state,

both the air and plant data will be averaged over the growing season to give one

independent air concentration and one dependent vegetation concentration for each

contaminant/vegetation pair. This data set also provided another test for the JungePankow model as the researchers provided measured vapor and particle fractions from

the ambient air monitoring results.

Air concentrations were measured each sampling day and reported as total,

particle-phase, and vapor-phase concentrations in ng/m3. Vegetation was sampled

from two species of trees: Acer saccharum (sugar maple) and Pinus strobus (white

pine). This exercise utilized the observed sugar maple leaf, sugar maple seed, and

white pine needle data for comparisons with the predicted concentrations. Bark

concentrations were also measured, but it was judged that this vegetation was not the

type for which the air-to-plant models were developed. The pine needle samples were

divided by the researchers into current-year growth and previous-year growth. An

evaluation of the concentrations showed that the previous year’s pine needles were

consistently slightly higher than the current year’s pine needles. This suggests that the

previous year’s pine needles, although not actively growing during the current season,

still serve as passive collectors for airborne contaminants. Since the models were

developed for actively growing vegetation, these previous-year pine needle

concentrations will not be utilized in this exercise. Data were available for 10 PAHs. 

However, the appropriate fate parameters were not available for one of the PAHs,

indeno[1,2,3-cd]pyrene, and results were not generated for it. In summary, then, there

were three vegetations (sugar maple leaf, sugar maple seed, and white pine needle)

and nine PAHs. Therefore, there were 9 independent source term concentrations and

27 dependent plant concentrations (9 PAHs @ 3 vegetation).
Appendix A

Model Validation

MPE A-53 December 1998

The following describes the development of the model parameters for this

exercise:

Particle fraction. These will be taken directly from Figure A-5, which shows the

modeled particle fraction of PAHs as a function of airshed particle density and log of

the subcooled liquid vapor pressure, PEL (Pa), at 20EC. This is likely not a

representative concentration for the spring/summer/fall of the full data set from

Simonich and Hites (1994), but it is judged here to be sufficient for current purposes. 

The PAH data were taken from Bloomington, Illinois, which will be assumed to qualify

as rural, specifically the background + local sources category of the two rural options

(the other being continental background). The PEL, taken from MacKay et al. (1992), is

displayed in Table A-12 along with other fate parameters necessary for modeling PAHs

in this exercise.

Other fate parameters. The other fate parameters that were used for calculating

plant concentrations are listed in Table A-12 and include the Henry’s Law Constant (H)

and octanol-water partition coefficient (Kow). The H and Kow were used to calculate the

volumetric air-to-leaf transfer factor, Bvol, using the Bacci empirical algorithm described

in Chapter 6. It is noted that the recommendation in Chapter 6 is to calculate the Bv

(which is calculated from the Bvol) and then divide the result by 100, based on evidence

that the Bacci algorithm overestimates the transfer of dioxins from air to grass leaves

by about a factor of 100.

Volume adjustment factor for vapor transfers. This adjustment factor, VGag (the

subscript ag is for “above ground”), was incorporated into the vapor-to-plant algorithms

to account for the differences in the plants for which the air-to-plant transfer factor (Bv)

was developed (azalea and grass leaves) and bulky above-ground vegetation to which

it might be applied, such as tomatoes or apples. A VGag of 1.0 was used for pine

needles and maple leaves, which assumes they are reasonably analogous to the thin

azalea and grass leaves, whereas a VGag of 0.1 was used for maple seeds. In this

case, the VGag would account for the dilution of vapor-phase contaminants dispersing

through the seed covering and into the seed.
Appendix A

Model Validation

MPE A-54 December 1998

TABLE A-12

Physical Chemical Properties of the Polycyclic Aromatic Hydrocarbons

Assigned for this Exercise

PAH Henry’s Law Constant

atm-m3-mole@25EC

Low Kow

 PEL,

pascals

Anthracene 3.83@10-5 4.54 7.78@10-2

Phenanthrene 3.13@10-5 4.57 1.13@10-1

Benz[a]anthracene 5.62@10-6 5.91 6.06@10-4

Chrysene/triphenylene 6.29@10-7 5.86 1.07@10-4

Fluoranthene 1.00@10-5 5.22 8.72@10-3

Pyrene 8.91@10-6 5.18 1.19@10-2

Benzo[a]pyrene 4.45@10-7 6.04 2.13@10-5

Benzo[e]pyrene 1.93@10-7 6.04 2.41@10-5

Benzo[ghi]perylene 7.26@10-7 6.5 2.25@10-5

Data from: MacKay et al. (1992)

Surface loss/weathering constant. The 14-day weathering half-life of particlebound contaminants depositing onto vegetation that was discussed in Chapter 6 for

general use was used in this exercise; this equates to a loss constant (kp) of

18.02/year.

Deposition velocity and rates. The wet and dry deposition velocities are used to

calculate respective particle-bound contaminant deposition rates: an ambient air

concentration of particle-bound contaminant times the deposition velocity gives the rate

of deposition in appropriate units of mass/area/time. A dry deposition velocity (Vd) of

0.37 cm/s for PAHs (McVeety and Hites, 1988) was used in this exercise. The McVeety

and Hites (1988) study also derived a ratio of 6:1 for dry to wet fluxes; therefore, the

wet deposition rate (Dyw) was calculated by dividing the dry deposition rate (Dyd) by 6. 

This ratio of 6:1 contrasts with the ratio of about 1:1 that Koester and Hites (1992)
Appendix A

Model Validation

MPE A-55 December 1998

found for dioxins and furans, and which was used in the air-to-beef model validation

exercise above.

Dry matter yield. Belcher and Travis (1989) specified crop yields (Ypi) for many

crops, including animal forage, animal grain, potatoes, leafy vegetables, root

vegetables, garden fruits, and legumes. The most reasonable match for maple leaves

and pine needles appeared to be the dry matter yield for leafy vegetables, 0.18 kg/m2;

for maple seeds, the dry matter yield for legumes, 0.10 kg/m2, was chosen.

Plant density, moisture content, surface retention, and interception. These are

plant properties required for various aspects of the air-to-plant model. The plant

density and plant moisture content are required to convert the parameter volume-based

air-to-leaf transfer factor, Bvol , to the mass-based transfer factor, Bv (see Chapter 6). 

This information was not provided in Simonich and Hites (1994), but these data were

presented for other vegetation in McCrady (1994). Specifically, McCrady lists densities

of 800 to 1000 g/L for various vegetation, including grass, pepper, azalea, and kale

leaves; spruce needle; and the fruits of tomato, pepper, and apple. For this exercise, it

will be simplistically assumed that the plant density for the leaves, needles, and seeds

will be 900 g/L. Simonich and Hites (1994) list moisture fractions of between 0.25 and

0.6 for all vegetation, without providing any more detail. It will be assumed that all the

vegetations have a moisture fraction of 0.5. A value of 0.6 is given to the surface

retention parameter, Fw , based on the recommendation made in Chapter 6. This

fraction is defined as the fraction of the wet deposition that is retained on the surfaces

of the vegetation after the rainfall is over; since the value of Fw is 0.6, this means 0.4 (or

40%) of the particle-bound contaminants depositing during rainfall wash off the plant. 

An interception fraction reflects the amount of depositing contaminant that is

intercepted before it reaches the ground. Since these studies are for trees, which will

be assumed to be dense vegetation, in general an interception fraction of 1.0 is

appropriate. This will be assumed for the pine needles. However, the maple leaves

will intercept some of the depositions that might have reached maple seeds, and vice
Appendix A

Model Validation

MPE A-56 December 1998

versa. For this reason, the interception fraction of 1.0 for the maple tree in general will

be split between the leaves, at 0.7, and seeds, at 0.3.

As a final note, the plant impact pathway of soil-to-plant, discussed in Chapter 6,

is not considered in this exercise. Because of the physical structure of the tree canopy

that restricts deposition to soil, the depth of tree root systems, and the relatively short

half-lives of PAHs in soil (Howard et al., 1991), the contribution of root uptake and

translocation was assumed to be negligible. Therefore, this exercise assumes that the

primary source of contamination to the vegetation is from the air. This is similar to the

assumption made above in the air-to-beef validation exercise for dioxins.

The results for this exercise are shown in Tables A-13 and A-14, which compare

observed and predicted vapor fractions and vegetation concentrations, respectively.

The model is generally consistent and accurate in predicting vapor fractions: the

observed was very near the predicted except for benz[a]anthracene and

benzo[e]pyrene, where the predicted vapor fraction was about 3 times as high as the

observed vapor fraction. These results are consistent with the tests of the

vapor/particle partitioning model for PAHs discussed in the section above. However,

the model consistently underpredicts the final concentration in the vegetation. This

underprediction was greater than a factor of 10 nearly half the time. The model and the

data do show reduced concentrations in seeds. This result lends some credibility to

the assignment of 0.1 for the VGag for seeds, in contrast to values of 1.0 for leaves and

needles.

The question to ask, obviously, is why the model did so poorly. One possibility,

of course, is that the basic assumptions and structure of the model are flawed. An

evaluation of whether this is the case is beyond the scope of this document, but there is

no immediate reason to negate the simple steady-state construct of this model for

PAHs. There is evidence of a varying PAH concentration in the pine and maple

vegetation in these experiments, which may question the use of a steady-state model. 

However, these variations in plant concentrations are not by orders of magnitude. 
Appendix A

Model Validation

MPE A-57 December 1998

Another possibility is that the input air concentrations data or the observed vegetation

concentrations are flawed, but again there is no reason to believe this to be the case.

TABLE A-13

Observed Air Concentrations in the Particle and Vapor Phases, and Comparison of

Predicted and Observed Vapor Fractions

PAH

Observed Air

Concentrations, ng/m3 Observed

Vapor

Fraction

Predicte

d Vapor

Fraction

Ratio

Pred/Obs Particle

Phase

Vapor

Phase

Anthracene 0.03 2.257 0.99 0.99 1

Phenanthrene 0.172 28.577 0.99 0.99 1

Benz[a]anthracene 0.15 0.069 0.32 0.9 2.8

Chrysene/triphenylene 0.254 0.28 0.53 0.58 1.1

Fluoranthene 0.295 5.357 0.95 0.99 1

Pyrene 0.221 3.194 0.94 0.99 1

Benzo[a]pyrene 0.18 0.047 0.21 0.25 1.2

Benzo[e]pyrene 0.275 0.055 0.17 0.25 1.5

Benzo[ghi]perylene 0.413 0.04 0.09 0.25 2.8
Appendix A

Model Validation

MPE A-58 December 1998

TABLE A-14

Observed and Predicted Pine Needle, Maple Leaf, and Maple Seed Concentrations

Vegetation Type PAH Observed conc.,

ng/g dry weight

Predicted 

ng/g dw Pred/Obs

Pine needles Anthracene 18.1 0.9 0.05

Phenanthrene 345.85 12 0.03

Benz[a]anthracene 16.7 1.7 0.1

Chrysene/triphenylene 61.41 19.1 0.31

Fluoranthene 136.11 4.7 0.03

Pyrene 78.31 2.8 0.04

Benzo[a]pyrene 16.81 7.1 0.42

Benzo[e]pyrene 24.93 11 0.44

Benzo[ghi]perylene 21.12 14.8 0.7

Maple leaves Anthracene 10.88 1 0.09

Phenanthrene 142.2 12.2 0.09

Benz[a]anthracene 18.51 1.7 0.09

Chrysene/triphenylene 56.36 19.2 0.34

Fluoranthene 179.92 4.7 0.03

Pyrene 104.53 2.8 0.03

Benzo[a]pyrene 18.62 7.2 0.39

Benzo[e]pyrene 24.79 11.2 0.45

Benzo[ghi]perylene 19.15 15 0.78

Maple seeds Anthracene 4.15 0.9 0.22

Phenanthrene 55.09 11.4 0.21

Benz[a]anthracene 6.53 1 0.15

Chrysene/triphenylene 18.03 9.9 0.55

Fluoranthene 51.09 2.5 0.05

Pyrene 37.53 1.5 0.04

Benzo[a]pyrene 10.69 6.8 0.64

Benzo[e]pyrene 11.88 9.9 0.83

Benzo[ghi]perylene 4.98 13.6 2.73
Appendix A

Model Validation

MPE A-59 December 1998

Assuming then that the model and the air/plant concentrations are appropriate for

this exercise, the logical next step is to examine the input data. The particle impact

parameters are uncertain. Changing the assumption that wet deposition was one-sixth

dry deposition to the assumption that wet and dry depositions were equal was tested

and found not to impact the results. Decreasing the rate of blowoff/washoff of particlebound contaminants, even by a great amount (assuming that particle-bound 

contaminants do not dissipate from the plants) would still not bring predicted

concentrations in line with observations (and assuming no washoff or blowoff appears

unreasonable anyway).

This leaves the vapor impact algorithm, which is a logical place to consider

variations in the assignment of model parameters. Simonich and Hites (1994)

concluded that the concentrations in tree vegetation (leaves, bark, needles, and seeds)

were primarily a function of the gas-phase concentrations of the PAHs (not the particle

phase), the temperature, and the plant lipid fractions. All these factors underscore the

importance of the vapor phase. The vapor phase dioxins were also found to be of

principal importance in explaining grass concentrations of dioxins both in experiments

(Welsch-Pausch et al., 1995) and in modeling (Lorber et al., 1994; Lorber, 1995).

Changing the vapor/particle modeling does not seem the best place to start, since both

the modeling and the monitoring showed reasonably similar vapor fractions. This

leaves the important air-to-leaf transfer factor, Bv, as a parameter to consider varying.

The first assignment of the Bv for the indirect methodologies of this document

(U.S. EPA, 1990b) was based on the empirical algorithm developed by Bacci and

colleagues (Bacci et al., 1992). They conducted chamber experiments with several

organic contaminants transferring to azalea leaves.

 However, this algorithm was found

to greatly overestimate the transfer of vapor-phase 2,3,7,8-TCDD to grass in similarly

designed chamber experiments by McCrady and Maggard (1993). These researchers

showed that 2,3,7,8-TCDD would volatilize out of grass at a rate about 20 times greater

than out of azalea leaves. Also, by exposing the grass to natural sunlight, they

demonstrated an additional twofold increase in the overall rate of disappearance of
Appendix A

Model Validation

MPE A-60 December 1998

dioxins from grass as compared to azalea leaves (which had not been exposed to

sunlight in the Bacci experiments). Subsequently, U.S. EPA (1994) assigned values of

Bv for each of the dioxin congeners by first deriving a Bacci Bv and dividing it by a factor

of 40. Even this assignment was found to overestimate grass concentrations for the

dioxins, and a later calibration by Lorber (1995) reduced the dioxin Bv by an additional

factor of around 2 to 4 for all the dioxin congeners. This led to the approach

recommended in Chapter 5 of this document, which was to assign Bv for organic

contaminants by first using the Bacci algorithm and then dividing the final results by a

factor of 100. However, the evidence presented in this section suggests that this leads

to a Bv that is much too low for the PAH, at least for the tree vegetation in this exercise.

As just described, McCrady and Maggard (1993) found much less net transfer of

2,3,7,8-TCDD into grass than would be predicted using the Bacci algorithm. They

attributed that to differences in plant species between the azalea leaves of the Bacci

experiments and their grass leaves, and the fact that the Bacci experiments did not

consider photodegradation. McCrady and Maggard (1993) did not identify or speculate

on what they meant about plant species differences.

 McCrady (1994) later provided

data that might lend some insight into this species difference. First, the lipid fractions

listed in McCrady (1994) for azalea and grass leaves were quite comparable, so the

amount of plant lipid may not explain the difference between the net transfer rates of

2,3,7,8-TCDD into azalea and grass leaves. However, the wax contents of grass and

azalea leaves were very different. McCrady (1994) lists the wax content of the grass

leaf as 0.046 g/cm2, whereas for azalea leaves the wax content is an order of

magnitude higher at 0.48 g/cm2. This plant characteristic may have been critical in

slowing the volatile release of organic contaminants (one of which was a dioxin) in the

Bacci experiments, as compared to the McCrady experiments with grass and 2,3,7,8-

TCDD.

(Interestingly, McCrady (1994) found that the uptake of 2,3,7,8-TCDD into various

vegetations was not a function of the wax content; the uptake was very similar for the

eight vegetations he tested, which ranged in wax content from 0.046 g/cm2 for grass to
Appendix A

Model Validation

MPE A-61 December 1998

4.9 g/cm2 for apple. However, he did not conduct a follow-up phase of his experiments,

which is the release phase, where he might have seen a difference. The air-to-leaf

transfer factors are experimentally determined as the ratio of the rate of uptake divided

by the rate of release.)

In any case, if a plant species difference is important in the uptake and (maybe

more importantly) release of vapor-phase organic contaminants transferring from the air

to plants, then it seems possible that the guidance for assignment of Bv in Chapter 6

might be reasonable for grass and similar vegetation of low wax content, but maybe not

for vegetation of higher wax content. Simonich and Hites (1994) do not provide wax

content data for their vegetation, but they do provide lipid fractions, which are very

similar to the lipid fractions for azalea and grass leaves listed in McCrady (1994). 

However, McCrady (1994) does list the wax content of a spruce needle as 2.5 g/cm2,

which perhaps is similar to the pine needles of these PAH experiments, and also a kale

leaf wax content of 0.50 g/cm2 which, along with the azalea leaf, may be closer in wax

content to the maple leaves of the PAH experiments.

The Simonich and Hites (1994) data and the analysis in this section imply that

there is an important difference in plant species when values are assigned for the airto-plant vapor transfer factor, Bv

. This can be stated only as a hypothesis and not a

conclusion.

 

It may also be true that the Simonich and Hites (1994) data suggest

something else, such as an important difference in organic contaminants and the

transfer of their vapor-phase fractions into vegetation. Regardless of which of these

two hypotheses (or both to some extent) is true, this exercise suggests that the

Simonich and Hites (1994) data may more appropriate as a data set for calibration

rather than validation. The calibrated parameter could be the air-to-leaf transfer factor,

Bv, for the PAHs. Recall that the general recommendation for assigning Bv is to first

calculate it using the Bacci algorithm and then divide the final result by 100. Instead of

dividing by 100, this exercise suggests that the calibrated Bv for the PAHs and the

(waxy) vegetation can be calculated as the Bacci Bv for each PAH with the following

adjustments: anthracene (Bacci) Bv times a factor of 3, phenanthrene Bv times a factor
Appendix A

Model Validation

MPE A-62 December 1998

of 4, benz[a]anthracene Bv divided by 5, chrysene/triphenylene divided by 20,

fluoranthene Bv divided by 2, pyrene Bv divided by 2, benzo[a]pyrene divided by 5,

benzo[e]pyrene Bv divided by 10 , and benzo[e]pyrene Bv divided by 20.

In summary, this exercise has demonstrated the importance of applying sitespecific data to test this methodology. The available data were sufficient to validate the

model; that is, appropriate source-term as well as dependent data were available. 

Gaps in site-specific data were filled by using judgment and information provided in this

document and other references. When the model was fully parameterized, it was found

to consistently underpredict the vegetation concentration. This suggested, therefore,

that one or more of the model parameters were improperly assigned (assuming, of

course, that the source term and observed vegetation concentrations, and the model in

general, were appropriate). An examination of the possibilities further suggested that it

was the air-to-leaf transfer factor for vapor-phase PAHs, the Bv, that was the most likely

candidate for reassignment. An examination of the literature on vapor transfers of

semivolatile organic contaminants such as PAHs and dioxins suggested that plant

characteristics may be important in the uptake and release of these contaminants from

air. Further, the data suggested that the high wax content of vegetation such as azalea

leaves or pine needles may lead to greater net uptake as compared to less waxy

vegetation such as grass. If this is the case, it may be most appropriate to use the

calibrated Bv for PAHs in this section when modeling the transfer of these compounds

to vegetation of high wax content, but to still use the recommended approach (i.e., the

Bacci-derived Bv divided by 100) when developing Bv for terrestrial animal food chain

vegetation such as grasses or fodder, which have lower wax content.

In any case, users of this methodology are encouraged to use any data available

to test its models. While the exercises in this chapter may have demonstrated that the

fate and transport models generally bring predictions in line with observations,

exercises such as the one in this last section should alert users that uncertainties

continue to exist and that model validation continues to be an issue to address.
Appendix A

Model Validation

MPE A-63 December 1998

A.7. REFERENCES

AMS (American Meteorological Society). 1978. Accuracy of dispersion models: A

position paper of the AMS Committee on Atmospheric Turbulence and Diffusion. Bull.

Am. Meteor. Soc, 59:1025. 

Bacci, E., M.J. Cerejeira, G.G. Gaggi et al. 1992. Chlorinated dioxins: Volatilization

from soils and bioconcentration in plant leaves. Bull. Environ. Contam. Toxicol.

48(3):401-408.

Baek, S.O., M.E. Goldstone, P.W.W. Kirk et al. 1991. Phase distribution and particle

size dependency of polycyclic aromatic hydrocarbons in the urban atmosphere.

Chemosphere. 22:503-520.

Baker, J.E. and S.J. Eisenreich. 1990. Concentrations and fluxes of polycyclic

aromatic hydrocarbons and polychlorinated biphenyls across the air-water interface of

Lake Superior. Environ. Sci. Technol. 24:342-352.

Barbour, M.G., J.H. Burk and W.D. Pitts. 1980. Terrestrial Plant Ecology. The

Benjamin/Cummings Publishing Company, Menlo Park, CA.

Belcher, G.D. and C.C. Travis. 1989. Modeling support for the RURA and municipal

waste combustion projects: Final report on sensitivity and uncertainty analysis for the

terrestrial food chain model. Prepared under IAG-1824-A020-A1 by ORNL for U.S.

EPA, Office of Health and Environmental Assessment, Environmental Criteria and

Assessment Office, Cincinnati, OH.

Briggs, G.G., R.H. Bromilow and A.A. Evans. 1982. Relationships between lipophilicity

and root uptake and translocation of non-ionised chemicals by barley. Pest. Sci.

13:495-504.

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polychlorinated biphenyls in the atmosphere at an urban and rural site near Lake

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Appendix A

Model Validation

MPE A-64 December 1998

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23:1389-1395.

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ambient air of Denver, Colorado. Atmos. Environ. 24A:2405-2416.

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Hites, R.A. 1991. Atmospheric transport and deposition of polychlorinated dibenzo-pdioxins and dibenzofurans. Prepared for the U.S. Environmental Protection Agency,

Methods Research Branch, Atmospheric Research and Exposure Assessment

Laboratory, Office of Research and Development, Research Triangle Park, NC.

EPA/600/3-91/002.

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Hunt, G.T. and B.E. Maisel. 1992. Atmospheric concentrations of PCDDs and PCDFs

in southern California. J. Air. Waste Manage. Assoc. 42:672-680.
Appendix A

Model Validation

MPE A-65 December 1998

Jones, K.C. and R. Duarte-Davidson. 1997. Transfer of airborne PCDD/Fs to bulk

deposition collectors and herbage. Environ. Sci. Technol. 31:2937-2943.

Kamens, R., J. Odum and Z.H. Fan. 1995. Some observations on times to equilibrium

for semivolatile polycyclic aromatic hydrocarbons. Environ. Sci. Technol. 29:43-50.

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solid waste incinerators. In: Health Effects of Municipal Waste Incineration, H.A.

Hattermer-Frey and C. Travis, Ed. CRC Press, Boca Raton, FL. p. 67-82.

Kaupp, H. and G. Umlauf. 1992. Atmospheric gas-particle partitioning of organic

compounds: a comparison of sampling methods. Atmos. Environ. 26A:2259-2267.

Kjeller, L.O., K.C. Jones, A.E. Johnston et al. 1991. Increases in the polychlorinated

dibenzo-p-dioxin and -furan content of soils and vegetation since the 1840s. Environ.

Sci. Technol. 25:1619-1627.

Kjeller, L.-O., K.C. Jones, A.E. Johnston et al. 1996. Evidence for a decline in

atmospheric emissions of PCDD/Fs in the UK. Environ. Sci. Technol. 30:1398-1403.

Koester, C.J. and R.A. Hites. 1992. Wet and dry deposition of chlorinated dioxins and

furans. Environ. Sci. Technol. 26:1375-1382.

Krieger, M.S. and R.A. Hites. 1994. Measurement of polychlorinated biphenyls and

polycyclic aromatic hydrocarbons in air with a diffusion denuder. Environ. Sci. Technol.

28:1129-1133.

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novel gas and particle sampler for semivolatile organic compounds in ambient air.

Environ. Sci. Technol. 22:941-947.

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Congress and Exhibition, Montréal, Canada, Aug. 30 -Sept. 4.

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atmospheric organic compounds. Environ. Sci. Technol. 23:75-83.

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and furans. Presented at the 15th International Symposium on Chlorinated Dioxins and

Related Compounds, Aug. 21-25, 1995, in Edmonton, Canada. Abstract in

Organohalogen Compounds, 24:179-186.
Appendix A

Model Validation

MPE A-66 December 1998

Lorber, M. and P. Pinsky. 1998. An evaluation of three empirical air-to-leaf models for

dioxin-like compounds. Environ. Sci. Technol. (Submitted for publication.)

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properties and environmental fate for organic chemicals. Volume II. Polynuclear

aromatic hydrocarbons, polychlorinated dioxins, and dibenzofurans. Lewis Publishers,

Chelsea, MI. 597 p.

McCrady, J.K. 1994. Vapor-phase 2,3,7,8-TCDD sorption to plant foliage — A species

comparison. Chemosphere. 28:207-216.

McCrady, J.K. and S.P. Maggard. 1993. The uptake and photodegradation of 2,3,7,8-

TCDD sorbed to grass foliage. Environ. Sci. Technol. 27:343-350.

McVeety, B.D. and R.A. Hites. 1988. Atmospheric deposition of polycyclic aromatic

hydrocarbons to water surfaces: A mass balance approach. Atmos. Environ.

22:511-536. 

Muller, J.F., A. Hulster, O. Papke et al. 1994. Transfer of PCDD/PCDF from

contaminated soils into carrots, lettuce, and peas. Chemosphere. 29:2175-2181.

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gas/particle partitioning of semivolatile organic compounds to urban particulate matter.

Environ. Sci. Technol. 27:2220-2226.

Reed, L.W., G.T. Hunt, B.E. Maisel et al. 1990. Baseline assessment of PCDDs/

PCDFs in the vicinity of the Elk River, Minnesota, generating station. Chemosphere. 

21:159-171.

Rounds, S.A., B.A. Tiffany and J.F. Pankow. 1993. Description of gas/particle sorption

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Appendix A

Model Validation

MPE A-67 December 1998

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14:983-1012.

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Environ.

28:3083-3091.

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Plenum Press, New York. p. 53-63.

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diffusion separator for measuring gas/particle distributions of semivolatile organic

compounds. Environ. Sci. Technol. 27:2441-2449.

U.S. EPA. 1981. An evaluation study for the Industrial Source Complex (ISC)

Dispersion Model. EPA-450/4-81-002. January, 1981.
Appendix A

Model Validation

MPE A-68 December 1998

U.S. EPA. 1989. Interim Procedures for Estimating Risks Associated with Exposures

to Mixtures of Chlorinated Dibenzo-p-dioxins and -dibenzofurans (CDDS and CDFs)

and the 1989 Update. Risk Assessment Forum, Washington, DC. EPA/625/3-89/016.

U.S. EPA. 1990a. Lake Ontario TCDD Bioaccumulation Study, Final Report.

Cooperative study including U.S. EPA, New York State Department of Environmental

Conservation, New York State Department of Health, and Occidental Chemical

Corporation, May,1990.

U.S. EPA. 1990b. Methodology for Assessing Health Risks Associated with Indirect

Exposure to Combustor Emissions. Interim Final. Office of Health and Environmental

Assessment, Washington, DC. EPA/600/6-90/003.

U.S. EPA. 1991. Burning of Hazardous Waste in Boilers and Industrial Furnaces,

Final Rule. (Feb 21) Federal Register. 56(13):7134-7240.

U.S. EPA. 1992. Estimating Exposure to Dioxin-like Compounds. Office of Research

and Development, Washington, DC. Review draft, August 1992. EPA/600/6-88/005B.

U.S. EPA. 1994. Estimating Exposure to Dioxin-like Compounds. Volume I. Executive

Summary. Volume II. Properties, Sources, Occurrence and Background Exposures.

Volume III. Site-specific Assessment Procedures. External review draft. EPA/600/6-

88/005Ca-Cc.

U.S. EPA. 1995. Great Lakes Water Quality Initiative Technical Support Document for

the Procedure to Determine Bioaccumulation Factors. Office of Water, March 1995.

EPA-820-B-95-005.

Welsch-Pausch, K., M. McLachlan and G. Umlauf. 1995. Determination of the

principal pathways of polychlorinated dibenzo-p-dioxins and dibenzofurans to Lolium

multiflorom (Welsh Ray Grass). Environ. Sci. Technol. 29:1090-1098.

Whitby, K.T. 1978. The physical properties of sulfur aerosols. Atmos. Environ.

12:135-159.

Winters, D., D. Cleverly, K. Meier et al. 1996. A statistical survey of dioxin-like

compounds in United States beef: A progress report. Chemosphere. 32:469-478.

Yamasaki, H., K. Kuwata and H. Miyamoto. 1982. Effects of ambient temperature on

aspects of atmospheric polycyclic aromatic hydrocarbons. Environ. Sci. Technol.

16:189-194.
Appendix A

Model Validation

MPE A-69 December 1998

Yamasaki, H., K. Kuwata and Y. Kuge. 1984. Determination of vapor pressures of

polycyclic aromatic hydrocarbons (PAH) in the supercooled liquid phase and their

adsorption on airborne particulate matter. Nippon Kagaku Kaishi. 8: 1324-1329.

(Japanese)
Appendix B

Derivation of Kinetic Breast Milk Exposure Model Incorporating

Breast Feeding Losses

MPE B-1 December 1998

DERIVATION OF KINETIC BREAST MILK EXPOSURE MODEL

INCORPORATING BREAST FEEDING LOSSES

B.1. Maternal Whole-Body Concentration at time t after start of Exposure

Scenario. The general formula for the rate of change of the maternal whole-body

concentration of a pollutant is given in Equation B-1:

dMt

dt

' Im & kMt

(B-1)

where:

Mt = maternal whole-body concentration of pollutant after t days of exposure

(mg/kg)

Im = maternal intake of pollutant (mg/kg-day)

k = whole-body elimination constant (day-1)

Dividing Equation B-1 through by k and rearranging yields Equation B-2.

1

Im

k

& Mt

dMt' kdt

(B-2)

Integrating both sides of Equation B-2 with respect to time (t) gives Equation B-3.

&ln (

Im

k

& Mt) ' kt % Ca

(B-3)

where Ca is the aggregate constant of integration.

Exponentiating both sides of Equation B-3 gives Equation B-4. 
Appendix B

Derivation of Kinetic Breast Milk Exposure Model Incorporating

Breast Feeding Losses

MPE B-2 December 1998

Im

k

& Mt ' e

& kt & Ca

(B-4)

Substituting C for -Ca in Equation B-4 and solving for Mt yields Equation B-5.

Mt '

Im

k

& e

C e&kt

(B-5)

At t = 0, Equation B-5 becomes:

M0'

Im

k

& e

C

(B-6)

where M0 is the maternal whole-body concentration at the start of the exposure period.

Solving for eC gives Equation B-7.

e

C '

Im

k

& M0

(B-7)

Substituting for eC in Equation B-5 yields Equation B-8. 

Mt'

Im

k

(1& e

&kt) % M0

e

&kt

(B-8)

Equation B-8 is the general formula for calculating non-steady-state whole-body

concentrations at time t following the start of the exposure, given a constant intake. For

M0 = 0, the equation simplifies to Equation B-9:
Appendix B

Derivation of Kinetic Breast Milk Exposure Model Incorporating

Breast Feeding Losses

MPE B-3 December 1998

Mt '

Im

k

(1& e

&kt)

(B-9)

where Im/k represents the theoretical steady-state whole-body concentration. A more

intuititive description of Equation B-8 is that it represents the sum of the fraction of the

steady-state level reached at time t with constant intake and constant elimination, and

the fraction of the initial concentration remaining after time t, given constant elimination.

B.2. Average Whole-body Concentration over Duration of Exposure Scenario. 

The average body-burden concentration over time period T (T > 0) is given in Equation

B-10:

Mavg '

1

T m

T

0

Mtdt

(B-10)

Substituting for Mt from Equation B-8 yields Equation B-11a.

Mavg '

1

T m

T

0

[

Im

k

(1& e

&kt) % M0

e

&kt] dt

(B-11a)

The integral is simplified in Equations B-11b through B-11c.

Mavg '

1

T m

T

0

(

Im

k

&

Im

k

e

&kt % M0

e

&kt ) dt

(B-11b)

Mavg '

1

T

[

m

T

0

Im

k

dt % (M0&

Im

k

)

m

T

0

e

&kt dt ]

(B-11c)
Appendix B

Derivation of Kinetic Breast Milk Exposure Model Incorporating

Breast Feeding Losses

MPE B-4 December 1998

Solving the integral yields Equation B-12, which is algebraically expanded to

give Equation B-13. 

Mavg '

1

T

[

Im

k

T %

1

k

(M0 &

Im

k

) (1 & e

&kT) ]

(B-12)

Mavg '

Im

k

%

1

kT

(M0 &

Im

k

) (1 & e

&kT)

(B-13)

From Equation B-9, the expression for maternal whole-body concentration prior

to breast feeding is:

M0'

Im

km

(1 & e

&km tm

)

(B-14)

where Im, km, and tm are equivalent to hDIMAT (Eq. 9-2), kelim (Eq. 9-5), and tpn (Eq. 9-3),

respectively.

Substituting for M0 in Equation B-13, the average maternal whole-body

concentration over the period of breast feeding is given by Equation B-15:

Mavg '

Im

k

%

1

kT

(

Im

km

(1 & e

&km tm

) &

Im

k

) (1 & e

&kT)

(B-15)

where k is now equivalent to kelac (Eq. 9-6) and T is equivalent to tbf (Eq. 9-4).

Equation B-15 can be algebraically manipulated to give Equation B-16:

Mavg '

Im

km

[

km

k

%

1

kT

(1 & e

&km tm &

km

k

) (1 & e

&kT) ]

(B-16)
Appendix B

Derivation of Kinetic Breast Milk Exposure Model Incorporating

Breast Feeding Losses

MPE B-5 December 1998

B.3. Average Breast Milk Concentration over Duration of Breast Feeding. As the

concentration of the pollutant in milkfat is assumed to be the same as in body fat and

the fraction of ingested pollutant in body fat is assumed to be a constant, the average

concentration in milkfat can be related to Mavg as in Equation B-17.

Cmilkfat ' Mavg

f

f

f

fm

(B-17)

where:

C milkfat = contaminant concentration in maternal milk fat (mg/kg)

f

f = fraction of contaminant that is stored in maternal fat (dimensionless)

f

fm = fraction of mother's weight that is fat (kg maternal fat/kg total body

weight)

Substituting from Equation B-16 and converting to the notation used in the body

of the Chapter yields Equation B-18, which is identical to Equation 9-4.

Cmilkfat '

hDIMAT f

f

kelim f

fm

[

kelim

kelac

%

1

kelac t

bf

(1 & e

&kelimt

pn &

kelim

kelac

) (1 & e

&kelact

bf) ]

(B-18)
Appendix C

Glossary

MPE C-1 December 1998

GLOSSARY

Absorption: The process of a chemical crossing the boundary and entering the body of

the organism.

Aerometric Information Retrieval System (AIRS): A computer-based repository of US

air pollution information administered by the EPA Office of Air Quality Planning and

Standards AIRWeb Air Resources Web, a US National Park-focused air quality

information retrieval system developed by the Air Resources Division of the National

Park Service.

Aerosol: A suspension of microscopic solid or liquid particles in air. See also haze,

particulate matter.

Air Pollutant: An unwanted chemical or other material found in the air. See also air

pollution.

Air Pollution: Degradation of air quality resulting from unwanted chemicals or other

materials occurring in the air. See also air pollutant.

Air Quality: (in context of the national parks) The properties and degree of purity of air

to which people and natural and heritage resources are exposed.

Air Pollution Control Permitting Process: Process by which facilities are permitted to

emit specified types and quantities of air pollutants.

Air Quality Related Values (Aqrvs): Values including visibility, flora, fauna, cultural and

historical resources, odor, soil, water, and virtually all resources that are dependent

upon and affected by air quality. "These values include visibility and those scenic,

cultural, biological, and recreation resources of an area that are affected by air quality"

(43 Fed. Reg. 15016).

Air Quality: Ambient pollutant concentrations and their temporal and spatial

distribution.

Algorithm: A specific mathematical calculation procedure. A model may contain

several algorithms.

Ambient Air: Air that is accessible to the public.

Attainment Area: A geographic area in which levels of a criteria air pollutant meet the

health-based National Ambient Air Quality Standard for that specific pollutant.
Appendix C

Glossary

MPE C-2 December 1998

Background: Ambient pollutant concentrations due to:

(1) natural sources;

(2) nearby sources other than the one(s) currently under consideration; and

(3) unidentified sources.

Best Available Control Technology (BACT): An emission limitation based on the

maximum degree of reduction for each pollutant, that must be applied by sources

subject to the Prevention of Significant Deterioration program.

Bioaccumulation: The accumulation of a substance by an organism as a result of all

environmental sources.

Bioconcentration Factor (BCF): The equilibrium concentration of a chemical in a

biological medium divided by the equilibrium concentration of a chemical in the

environmental medium through which the exposure directly occurs. 

Bioaccumulation Factor (BAF): The equilibrium concentration of a chemical in a

biological medium divided by the equilibrium concentration of a chemical in the

environmental medium where both the organism and its food were exposed.

Biological Effects: Ecological studies to determine the nature or extent of air pollution

injury to biological systems. See also biological effects pages.

Bulk Density: A common soil parameter, which is the mass of a unit of dry soil

(includes pore and solids)

Calibrate: An objective adjustment using measured air quality data (e.g., an adjustment

based on least-squares linear regression).

Calm: For purposes of air quality modeling, calm is used to define the situation when

the wind is indeterminate with regard to speed or direction.

Carbon Monoxide: A criteria air pollutant that is a colorless, odorless, poisonous gas

produced by incomplete combustion; particularly, incomplete burning of carbon-based

fuels e.g. gasoline, oil, and wood.

CD-144 Format: Card Deck-144 data format available from NCDC for National

Weather Service surface observations commonly used for dispersion models. Each

record represents an 80-column "card image".

Clean Air Act: Originally passed in 1963, our current national air pollution control

program is based on the 1970 version of the law. Substantial revisions were made by

the 1990 Clean Air Act Amendments. See also Clean Air Act pages.
Appendix C

Glossary

MPE C-3 December 1998

Complex Terrain: Terrain exceeding the height of the stack being modeled.

Contact Medium: The contaminated material that comes into contact with the outer

boundary of the body through a specific route of exposure. For example, with

methylmercury-contaminated fish, fish is the contact medium; with benzene

contaminated air, the air is the contact medium when the exposure occurs.

Criteria: (in the context of criteria pollutants) Information on health and/or

environmental effects of pollution.

Dispersion Model: A group of related mathematical algorithms used to estimate

(model) the dispersion of pollutants in the atmosphere due to transport by the mean

(average) wind and small scale turbulence.

Dose: The amount of a substance available for interaction with metabolic processes or

biologically significant receptors after crossing the outer boundary of an organism.

Dose-response: The relationship between the dose of a pollutant and its effect on a

biological system.

Emissions: Release of pollutants into the air from a source.

Erosivity: Represents the influence of precipitation on soil erosion.

Exposure: The condition of a chemical contacting the outer boundary of an organism.

Exposure Duration: The amount of time individuals are exposed.

Exposure Route: The way a chemical enters an organism after contact. Routes of

human exposure include dermal, oral and respiratory.

Fine Particle: Particulate matter less than 2.5 microns in diameter.

Flow Vector: The direction towards which the wind is blowing.

Fluid Modeling: Modeling conducted in a wind tunnel or water channel to quantitatively

evaluate the influence of buildings and/or terrain on pollutant concentrations.

Fugitive Dust: Dust discharged to the atmosphere in an unconfined flow stream such

as that from unpaved roads, storage piles and heavy construction operations.

Hazardous Air Pollutants (HAP): Airborne chemicals that cause serious health and

environmental effects.
Appendix C

Glossary

MPE C-4 December 1998

Henry’s Law Constant: The ratio at equilibrium of the gas phase concentration to the

liquid phase concentration of the gas.

Meteorological Data File: Any file containing meteorological data, whether it be mixing

heights, surface observations or on-site data.

Mixing Height: The depth through which atmospheric pollutants are typically mixed by

dispersive processes.

Mobile Sources: Moving objects that release regulated air pollutants, e.g. cars, trucks,

buses, planes, trains, motorcycles, and gas-powered lawn mowers. See also source;

stationary source.

Model: A quantitative or mathematical representation or simulation which attempts to

describe the characteristics or relationships of physical events.

Monin Obukhov Length: A constant, characteristic length scale for anu particular

example of flow. It is negative in unstable conditions (upward heat flux), positive for

stable conditions, and approach infinity as the actual lapse rate for the ambient air

reaches the dry adiabatic lapse rate.

Monitoring: Measurement of air pollution. See also continuous sampling device,

integrated sampling device.

MPRM: Meteorological Processor for Regulatory Models, a program designed for the

purpose of processing on-site meteorological data to prepare them for input to the

regulatory models, such as ISC. Produces a file comparable to the RAMMET

pre-processor output, and also capable of producing STAR summaries.

National Ambient Air Quality Standards (NAAQS): Permissible levels of criteria air

pollutants established to protect public health and welfare. See also EPA's NAAQS

page.

NCDC: National Climatic Data Center, the federal agency responsible for distribution

of the National Weather Service upper air, mixing height and surface observation data.

Nitrogen Oxides: A criteria air pollutant, compounds NO, NO2, NO3, N2O5, alkyl

nitrates, etc. See also NOx and Noy.

NOx: The sum of NO + NO2. See also nitrogen oxides, Noy.

Octanol-water Partition Coefficient (Kow): The ratio of the concentration of a substance

in the octanol phase to its concentration in the aqueous phase in a an equilibrated twophase octanol-water system. 
Appendix C

Glossary

MPE C-5 December 1998

On-Site Data: Data collected from a meteorological measurement program operated in

the vicinity of the site to be modeled in the dispersion analysis.

Organic Compounds: Chemicals that contain the element carbon.

Ozone: A gas similar to oxygen that is a criteria air pollutant and a major constituent of

smog. See also reactive organic compounds; volatile organic compounds.

Particle Bulk Density: The mass of a volume of soil solids. (See bulk density)

Particle Sampler: An instrument to measure particulate matter in ambient air.

Particulate Matter: Dust, soot, other tiny bits of solid materials that are released into

and move around in the air. See also fine particle, PM10, Visibility Research Program

pages.

Pasquill Stability Categories: A classification of the dispersive capacity of the

atmosphere, originally defined using surface wind speed, solar insolation (daytime) and

cloudiness (nighttime). They have since been reinterpreted using various other

meteorological variables.

PM10: A criteria air pollutant that is particulate matter in ambient air exceeding 10

microns in diameter.

Prevention of Significant Deterioration (PSD): A program established by the Clean Air

Act that limits the amount of additional air pollution that is allowed in Class I and Class

II areas.

Primary Standard. A pollution standard based on human health effects. Primary

standards are set for criteria air pollutants. See also secondary standard.

Quality Assessment Check: Determining if the reported value of a variable is

reasonable (see also Range Check).

Range Check: Determining if a variable falls within predefined upper and lower

bounds.

Reactive Organic Compounds: (in the context of photochemically produced air

pollution) Organic compounds that produce ozone in the presence of nitrogen oxides

and sunlight. See also Volatile Organic Compounds.

Receptor: A location at which ambient air quality is measured or estimated.
Appendix C

Glossary

MPE C-6 December 1998

Receptor Models: Procedures that examine an ambient monitor sample of particulate

matter and the conditions of its collection to infer the types or relative mix of sources

impacting on it during collection.

Refined Model: An analytical technique that provides a detailed treatment of physical

and chemical atmospheric processes and requires detailed and precise input data. 

Specialized estimates are calculated that are useful for evaluating source impact

relative to air quality standards and allowable increments. The estimates are more

accurate than those obtained from conservative screening techniques.

Reformulated Gasoline: Specially-refined gasoline with low levels of smog-forming

volatile organic compounds and low levels of hazardous air pollutants.

Regulatory Applications: Dispersion modeling involving regulatory decision-making as

described in the Guideline on Air Quality Models (Revised), (U.S. EPA, 1987b).

Regulatory Model: A dispersion model that has been approved for use by the

regulatory offices of the U.S. EPA, specifically one that is included in Appendix A of the

Guideline on Air Quality Models (Revised), (U.S. EPA, 1987b), such as the ISC model.

Screening Technique: A relatively simple analysis technique to determine if a given

source is likely to pose a threat to air quality. Concentration estimates from screening

techniques are conservative.

Secondary Standard: An air pollution limit based on environmental effects, e.g.

damage to property, plants, visibility, etc. Secondary standards are set for criteria air

pollutants. See also primary standard.

Simple Terrain: An area where terrain features are all lower in elevation than the top of

the stack of the source.

Smog: A mixture of air pollutants, principally ground-level ozone, produced by

chemical reactions involving smog-forming chemicals. See also haze.

Soil Erodibility Factor: This factor reflects the influence of soil properties on erosion.

Source: Any place or object from which air pollutants are released. Sources that are

fixed in space are stationary sources; sources that move are mobile sources. See also

major source.

Sulfur Dioxide (SO2): A criteria air pollutant that is a gas produced by burning coal and

some industrial processes. See also acid deposition, sulfur dioxide park topics.
Appendix C

Glossary

MPE C-7 December 1998

Surface Weather Observations: A collection of atmospheric data on the state of the

atmosphere as observed from the earth's surface. In the U.S. the National Weather

Service collect these data on a regular basis at selected locations.

Surface Roughness Length: Height at which the wind speed extrapolated from a

near-surface wind speed profile becomes zero.

Temperature Inversion: Weather condition in which warm air sits atop cooler air,

promoting stagnation and increased concentrations of air pollutants.

Toxic Air Pollutants: See hazardous air pollutants.

Total Suspended Particulates (TSP): Total particulate matter in a sample of ambient

air.

Upper Air Data (or soundings): Meteorological data obtained from balloon- borne

instrumentation that provides information on pressure, temperature, humidity, and wind

away from the surface of the earth.

Vertical Potential Temperature Gradient: The change of potential temperature with

height, used in modeling the plume rise through a stable layer, and indicates the

strength of the stable temperature inversion. A positive value means that potential

temperature increases with height above ground and indicates a stable atmosphere.

Visual Air Quality: Air quality evaluated in terms of pollutant particles and gases that

affect how well one can see through the atmosphere.

Volatile Organic Compounds (VOC): Organic compounds that vaporize readily and

contribute to the development of ozone. Many VOCs are also hazardous air pollutants. 

See also reactive organic compounds.

Watershed: The land area that contributes water to a river system.