United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/P-95/002Fa
August 1997
vvEPA
Exposure Factors
Handbook
Volume I
General Factors
-------
-------
EPA/600/P-95/002Fa
August 1997
VOLUME I - GENERAL FACTORS
EXPOSURE FACTORS HANDBOOK
Update to Exposure Factors Handbook
EPA/600/8-89/043 - May 1989
Office of Research and Development
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC 20460
Printed on Recycled Paper
-------
EFH
DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and
approved for publication. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
Page
ii
Exposure Factors Handbook
August 1997
-------
EFH.
CONTENTS
Page No.
VOLUME I
1. INTRODUCTION ,,
1.1. PURPOSE '.'.'.'.'.'.'. -
1.2. INTENDED AUDIENCE '.'.'.'.'.'.'. l"l
1.3. BACKGROUND '.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'. \~.\
1.3.1. Selection of Studies for the Handbook 1_1
1.3.2. Using the Handbook in an Exposure Assessment 1-3
1.3.3. Approach Used to Develop Recommendations for Exposure Factors 1-4
1.3.4. Characterizing Variability 15
1.4. GENERAL EQUATION FOR CALCULATING DOSE Ill
1.5. RESEARCH NEEDS j 14
1.6. ORGANIZATION j 15
1.7. REFERENCES FOR CHAPTER 1 , ,fi
APPENDIX i A I!!.'.'!!.'.'"!!!." i A-I
2. VARIABILITY AND UNCERTAINTY 21
2.1. VARIABILITY VERSUS UNCERTAINTY 2"l
2.2. TYPES OF VARIABILITY 22
2.3. CONFRONTING VARIABILITY 2~3
2.4. CONCERN ABOUT UNCERTAINTY " 23
2.5. TYPES OF UNCERTAINTY AND REDUCING UNCERTAINTY 2-4
2.6. ANALYZING VARIABILITY AND UNCERTAINTY 2-4
2.7. PRESENTING RESULTS OF VARIABILITY AND UNCERTAINTY ANALYSIS 2-6
2.8. REFERENCES FOR CHAPTER 2 " 2-1
3. DRINKING WATER INTAKE , ,
3.1. BACKGROUND '.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'. '3.1
3.2. KEY GENERAL POPULATION STUDIES ON DRINKING WATER INTAKE 3-1
3.3. RELEVANT GENERAL POPULATION STUDIES ON DRINKING WATER INTAKE 3-9
3.4. PREGNANT AND LACTATING WOMEN '"317
3.5. HIGH ACTIVITY LEVELS/HOT CLIMATES . 3 20
3.6. RECOMMENDATIONS 3 23
3.7. REFERENCES FOR CHAPTER 3 3-30
4. SOIL INGESTION AND PICA 4 ,
4.1. BACKGROUND 4"j
4.2. KEY STUDIES ON SOIL INTAKE AMONG CHILDREN ".'.'.'.'. 4]]
4.3. RELEVANT STUDIES ON SOIL INTAKE AMONG CHILDREN 4-11
4.4. SOIL INTAKE AMONG ADULTS .... 4 16
4.5. PREVALENCE OF PICA '.'.'.'.'.'.'.'. 4^7
4.6. DELIBERATE SOIL INGESTION AMONG CHILDREN 418
4.7. RECOMMENDATIONS 4 20
4.8. REFERENCES FOR CHAPTER 4 4"25
Exposure Factors Handbook
August 1997
Page
in
-------
EFH
CONTENTS (continued)
Page No.
5. INHALATION ROUTE
5.1. EXPOSURE EQUATION FOR INHALATION
5.2. INHALATION RATE
5.2.1. Background
5.2.2. Key Inhalation Rate Studies
5.2.3. Relevant Inhalation Rate Studies
5.2.4. Recommendations ...........
REFERENCES FOR CHAPTERS
5~\
5-
5~l
5-16
5.3.
APPENDIX5A
i A— 1
6. DERMAL ROUTE
6.1. EQUATION FOR DERMAL DOSE
6.2.
6.2.2.
6.2.3.
6.2.4.
6.2.5.
6.3.
6.4.
6.5.
SURFACE AREA
6.2.1. Background
Measurement Techniques
Key Body Surface Area Studies
Relevant Surface Area Studies
Application of Body Surface Area Data
SOIL ADHERENCE TO SKIN
6.3.1. Background
6.3.2. Key Soil Adherence to Skin Studies
6.3.3. Relevant Soil Adherence to Skin Studies
RECOMMENDATIONS
6.4.1. Body Surface Area
6.4.2. Soil Adherence to Skin
REFERENCES FOR CHAPTER 6
6"2
°-6
°'°
°~°
6~°
APPENDIX 6A
BODY WEIGHT STUDIES . . . .
7 1. KEY BODY WEIGHT STUDY
7.2. RELEVANT BODY WEIGHT STUDIES
7.3. RECOMMENDATIONS
7.4. REFERENCES FOR CHAPTER?
7~l
'~l
"-4
7-11
8. LIFETIME
8.1. KEY STUDY ON LIFETIME
8.2. RECOMMENDATIONS
8.3. REFERENCES FOR CHAPTER 8
Page
iv
Exposure Factors Handbook
August 1997
-------
CONTENTS (continued)
Page No.
VOLUME II
9. INTAKE OF FRUITS AND VEGETABLES 9-1
9.1. BACKGROUND 9-1
9.2. INTAKE STUDIES '9.2
9.2.1. U.S. Department of Agriculture Nationwide Food Consumption Survey and
Continuing Survey of Food Intake by Individuals 9-2
9.2.2. Key Fruits and Vegetables Intake Study Based on the USDA CSFII 9-2
9.2.3. Relevant Fruits and Vegetables Intake Studies '..... 9-4
9.2.4. Relevant Fruits and Vegetables Serving Size Study Based on the USDA NFCS 9-6
9.2.5. Conversion Between As Consumed and Dry Weight Intake Rates 9-7
9.3. RECOMMENDATIONS 9-7
9.4. REFERENCES FOR CHAPTER 9 "9.8
APPENDIX9A 9A_1
APPENDIX9B 9B-1
10. INTAKE OFFISH AND SHELLFISH .'.... 10-1
10.1. BACKGROUND iQ-l
10.2. KEY GENERAL POPULATION STUDIES '.'.[' 10-2
10.3. RELEVANT GENERAL POPULATION STUDIES 10-6
10.4. KEY RECREATIONAL (MARINE FISH STUDIES) 10-8
10.5. RELEVANT RECREATIONAL MARINE STUDIES 10-10
10.6. KEY FRESHWATER RECREATIONAL STUDIES ' 10-12
10.7. RELEVANT FRESHWATER RECREATIONAL STUDIES ..; 10-18
10.8. NATIVE AMERICAN FRESHWATER STUDIES :.... 10-20
10.9. OTHER FACTORS ' " ' iQ-24
10.10. RECOMMENDATIONS '.'..•'.'.'.'.'.'. 10-25
10.10.1. Recommendations - General Population 10-25
10.10.2. Recommendations - Recreational Marine Anglers 10-26
10.10.3. Recommendations - Recreational Freshwater Anglers 10-26
10.10.4. Recommendations - Native American Subsistence Populations 10-26
10.11. REFERENCES FOR CHAPTER 10 10-27
APPENDIX 10A 10A_1
APPENDIX 10B 10B_1
APPENDIX IOC .........../......... iqc-l
11. INTAKE OF MEAT AND DAIRY PRODUCTS ;. 11-1
11.1. INTAKE STUDIES ! " 11-1
11.1.1. U.S. Department of Agriculture Nationwide Food Consumption Survey and
Continuing Survey of Food Intake by Individuals ; 11-1
11.1.2. Key Meat and Dairy Products Intake Study Based on the CSFII 11-2
11.1.3. Relevant Meat and Dairy Products Intake Studies 11-3
Exposure Factors Handbook
August 1997
Page
v
-------
EFH
CONTENTS (continued)
Page No.
11.2. FAT CONTENT OF MEAT AND DAIRY PRODUCTS 11-6
11.3. CONVERSION BETWEEN AS CONSUMED AND DRY WEIGHT INTAKE RATES .... 11-7
11.4. RECOMMENDATIONS n-?
11.5. REFERENCES FOR CHAPTER 11 H'7
APPENDIX 11A 11A"1
12. INTAKE OF GRAIN PRODUCTS • • • • I2'1
12.1. INTAKE STUDIES • I2~l
12.1.1. U.S. Department of Agriculture Nationwide Food Consumption Survey and
Continuing Survey of Food Intake by Individuals 12-1
12.1.2. Key Grain Products Intake Studies Based on the CSFII 12-2
12.1.3. Relevant Grain Products Intake Studies 12-2
12.1.4. Key Grain Products Serving Size Study Based on the USDA NFCS 12-4
12.2. CONVERSION BETWEEN AS CONSUMED AND DRY WEIGHT INTAKE RATES .... 12-4
12.3. RECOMMENDATIONS 12-5
12.4. REFERENCES FOR CHAPTER 12 12-5
APPENDIX 12A 12A-]
13. INTAKE RATES FOR VARIOUS HOME PRODUCED FOOD ITEMS 13-1
13.1. BACKGROUND 13-J
13.2. METHODS 13'2
13 3 RESULTS 13-7
13A ADVANTAGES AND LIMITATIONS 13-9
13.5. RECOMMENDATIONS - 13-10
13.6. REFERENCES FOR CHAPTER 13 13-10
APPENDIX 13A 13A'1
14. BREAST MILK INTAKE 14-1
14.1. BACKGROUND 14'J
14.2. KEY STUDIES ON BREAST MILK INTAKE 14-1
14.3. RELEVANT STUDIES ON BREAST MILK INTAKE 14-4
14.4. KEY STUDIES ON LIPID CONTENT AND FAT INTAKE FROM BREAST MILK 14-5
14.5. OTHER FACTORS ...' 14'6
14.6. RECOMMENDATIONS 14-7
14.7. REFERENCES FOR CHAPTER 14 14-8
Page
vi
Exposure Factors Handbook
August 1997
-------
BFH
CONTENTS (continued)
Page No.
VOLUME III
15. ACTIVITY FACTORS 15.]
15.1. ACTIVITY PATTERNS ; 15.1
15.1.1. Key Activity Pattern Studies 15-1,
15.1.2. Relevant Activity Pattern Studies 15.7
15.2. OCCUPATIONAL MOBILITY 15-11
15.2.1. Background 15-11
15.2.2. Key Occupational Mobility Studies 15-11
15.3. POPULATION MOBILITY , . 15.12
15.3.1. Background 15-12
15.3.2. Key Population Mobility Studies ;... 15-13
15.3.3. Relevant Population Mobility Studies 15-15
15.4. RECOMMENDATIONS ; •.','.[ 15.15
15.4.1. Recommendations for Activity Patterns 15-15
15.4.2. Recommendations: Occupational Mobility 15-17
15.4.3. Recommendations: Population Mobility 15-17
15.4.4. Summary of Recommended Activity Factors 15-18
15.5. REFERENCES FOR CHAPTER 15 15-18
APPENDIX ISA 15A.!
APPENDIX 15B '.'.'.'.'.'. 15B-1
16. CONSUMER PRODUCTS 16-1
16.1. BACKGROUND •.......-.'.'.'.'.'.'.'.'.'.'.'.'.'. 16-1
16.2. KEY CONSUMER PRODUCTS USE STUDIES ....16-1
16.3. RELEVANT CONSUMER PRODUCTS USE STUDY 16-4
16.4. RECOMMENDATIONS 16-5
16.5. REFERENCES FOR CHAPTER 16 16-5
APPENDIX 16A 16A-1
17. RESIDENTIAL BUILDING CHARACTERISTICS 17-1
17.1. INTRODUCTION '.'.'.'.'.'. 17-1
17.2. BUILDING CHARACTERISTICS 17.2
17.2.1. Key Volumes of Residence Studies 17-2
17.2.2. Volumes and Surface Areas of Rooms 17-4
17.2.3. Mechanical System Configurations ; 17-6
17.2.4. Type of Foundation 17-7
Exposure Factors Handbook
August 1997
Page
vii
-------
EFH
CONTENTS (continued)
17.4.
17.5.
17.6
17.7.
GLOSSARY
Page No.
17.3. TRANSPORT RATES [[[ 17-8
17.3.1. Background [[[ 17'8
17.3.2. Air Exchange Rates .................................................. 17-10
17.3.3. Infiltration Models [[[ 17-12
17.3.4. Deposition and Filtration ............................................... 17-14
17.3.5. Interzonal Airflows .................................................. 17-15
17.3.6. Water Uses [[[ 17-15
17.3.7. House Dust and Soil ................................................ .17-19
SOURCES ......................................... ....................... 17-20
17.4.1. Source Descriptions for Airborne Contaminants .............. .............. 17-20
17.4.2. Source Descriptions for Waterborne Contaminants ......................... 17-22
17.4.3. Soil and House Dust Sources ........................................... 17-22
ADVANCED CONCEPTS ..................... . ........... .................. 17-23
17.5.1. Uniform Mixing Assumption ....................... .................... 17-23
17.5.2. Reversible Sinks ............... ..................................... 17-23
RECOMMENDATIONS ..................... .................... • ........... 17-23
-------
EFH
LIST OF TABLES
Page'No.
VOLUME I
Table 1-1. Considerations Used to Rate Confidence in Recommended Values 1-6
Table 1-2. Summary of Exposure Factor Recommendations and Confidence Ratings 1-7
Table 1-3. Characterization of Variability in Exposure Factors 1-10
Table 1A-1. Procedures for Modifying IRIS Risk Values for Non-standard Populations 1A-4
Table 2-1. Four Strategies for Confronting Variability 2-3
Table 2-2. Three Types of Uncertainty and Associated Sources and Examples 2-5
Table 2-3. Approaches to Quantitative Analysis of Uncertainty 2-6
Table 3-1. Daily Total Tapwater Intake Distribution for Canadians, by Age Group
(approx. 0.20 L increments, both sexes, combined seasons) 3-2
Table 3-2. Average Daily Tapwater Intake of Canadians (expressed as milliliters per kilogram
body weight) 3-3
Table 3-3. Average Daily Total Tapwater Intake of Canadians, by Age and Season (L/day) 3-3
Table 3-4. Average Daily Total Tapwater Intake of Canadians as a Function of Level of Physical
Activity at Work and in Spare Time (16 years and older, combined seasons, L/day) 3-3
Table 3-5. Average Daily Tapwater Intake by Canadians, Apportioned Among Various Beverages
(both sexes, by age, combined seasons, L/day) 3-4
Table 3-6. Total Tapwater Intake (mL/day) for Both Sexes Combined 3-5
Table 3-7. Total Tapwater Intake (mL/kg-day) for Both Sexes Combined 3-6
Table 3-8. Summary of Tapwater Intake by Age .' 3-7
Table 3-9. Total Tapwater Intake (as percent of total water intake) by Broad Age Category 3-7
Table 3-10. General Dietary Sources of Tapwater for Both Sexes 3-8
Table 3-11. Summary Statistics for Best-Fit Lognormal Distributions for Water Intake Rates 3-9
Table 3-12. Estimated Quantiles and Means for Total Tapwater Intake Rates (mL/day) 3-10
Table 3-13. Assumed Tapwater Content of Beverages 3-10
Table 3-14. Intake of Total Liquid, Total Tapwater, and Various Beverages (L/day) 3-11
Table 3-15. Summary of Total Liquid and Total Tapwater Intake for Males and Females (L/day) 3-12
Table 3-16. Measured Fluid Intakes (mL/day) 3-13
Table 3-17. Intake Rates of Total Fluids and Total Tapwater by Age Group 3-14
Table 3-18. Mean and Standard Error for the Daily Intake of Beverages and Tapwater by Age ....... 3-14
Table 3-19. Average Total Tapwater Intake Rate by Sex, Age, and Geographic Area 3-15
Table 3-20. Frequency Distribution of Total Tapwater Intake Rates 3-15
Table 3-21. Mean Per Capita Drinking Water Intake Based on USDA, CSFII Data From 1989-91
(mL/day) 3-16
Table 3-22. Number of Respondents that Consumed Tapwater at a Specified Daily Frequency 3-18
Table 3-23. Number of Respondents that Consumed Juice Reconstituted with Tapwater
at a Specified Daily Frequency 3-19
Table 3-24. Total Fluid Intake of Women 15-49 Years Old 3-20
Table 3-25. Total Tapwater Intake of Women 15-49 Years Old 3-21
Table 3-26. Total Fluid (mL/Day) Derived from Various Dietary Sources by
Women Aged 15-49 Years 3-21
Table 3-27. Water Intake at Various Activity Levels (L/hr) 3-22
-Exposure Factors Handbook
August 1997
Page
ix
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 3-28. Planning Factors for Individual Tapwater Consumption 3-23
Table 3-29. Drinking Water Intake Surveys 3-24
Table 3-30. Summary of Recommended Drinking Water Intake Rates 3-26
Table 3-31. Total Tapwater Consumption Rates From Key Studies 3-26
Table 3-32. Daily Tapwater Intake Rates From Relevant Studies 3-26
Table 3-33. Key Study Tapwater Intake Rates for Children 3-27
Table 3-34. Summary of Intake Rates for Children in Relevant Studies 3-27
Table 3-35. Confidence in Tapwater Intake Recommendations 3-29
Table 4-1. Estimated Daily Soil Ingestion Based on Aluminum, Silicon, and Titanium Concentrations . 4-2
Table 4-2. Calculated Soil Ingestion by Nursery School Children 4-3
Table 4-3. Calculated Soil Ingestion by Hospitalized, Bedridden Children 4-3
Table 4-4. Mean and Standard Deviation Percentage Recovery of Eight Tracer Elements 4-5
Table 4-5. Soil and Dust Ingestion Estimates for Children Aged 1-4 Years 4-5
Table 4-6. Average Daily Soil Ingestion Values Based on Aluminum, Silicon, and Titanium as
Tracer Elements 4-6
Table 4-7. Geometric Mean (GM) and Standard Deviation (GSD) LTM Values for Children at
Daycare Centers and Campgrounds 4-7
Table 4-8. Estimated Geometric Mean LTM Values of Children Attending Daycare Centers
According to Age, Weather Category, and Sampling Period 4-8
Table 4-9. Distribution of Average (Mean) Daily Soil Ingestion Estimates Per Child for 64
Children (mg/day) .4-9
Table 4-10. Estimated Distribution of Individual Mean Daily Soil Ingestion Based on Data for 64
Subjects Projected Over 365 Days 4-10
Table 4-11. Estimates of Soil Ingestion for Children 4-12
Table 4-12. Estimated Soil Ingestion Rate Summary Statistics and Parameters for Distributions
Using Binder et al. (1986) Data with Actual Fecal Weights 4-13
Table 4-13. Tukey's Multiple Comparison of Mean Log Tracer Recovery in Adults Ingesting Known
Quantities of Soil 4-14
Table 4-14. Positive/Negative Error (bias) in Soil Ingestion Estimates in the Calabrese et al. (1989)
Mass-balance Study: Effect on Mean Soil Ingestion Estimate (mg/day) 4-15
Table 4-15. Soil Ingestion Rates for Assessment Purposes 4-16
Table 4-16. Estimates of Soil Ingestion for Adults 4-17
Table 4-17. Adult Daily Soil Ingestion by Week and Tracer Element After Subtracting Food and
Capsule Ingestion, Based on Median Amherst Soil Concentrations: Means and
Medians Over Subjects (mg) 4-18
Table 4-18. Daily Soil Ingestion Estimation in a Soil-Pica Child by Tracer and by Week (mg/day) 4-19
Table 4-19. Ratios of Soil, Dust, and Residual Fecal Samples in the Pica Child 4-19
Table 4-20. Soil Intake Studies '. 4-22
Table 4-21. Confidence in Soil Intake Recommendation 4-24
Table 4-22. Summary of Estimates of Soil Ingestion By Children 4-25
Table 4-23. Summary of Recommended Values for Soil Ingestion 4-25
Page
x
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 5-1. Calibration and Field Protocols for Self-Monitoring of Activities Grouped by Subject
Panels .- .......'.... 5-4
Table 5-2. Subject Panel Inhalation Rates by Mean VR, Upper Percentiles, and Self-Estimated
Breathing Rates 5,5
Table«5-3. Distribution of Predicted IR by Location and Activity Levels for Elementary and
High School Students .'. 5-6
Table 5-4. Average Hours Spent Per Day in a Given Location and Activity Level for Elementary (EL)
and High School (HS) Students 5-6
Table 5-5. Distribution Patterns of Daily Inhalation Rates for Elementary (EL) and High School (HS)
Students Grouped by Activity Level ; 5.7
Table 5-6. Summary of Average Inhalation Rates (m3/hr) by Age Group and Activity Levels
for Laboratory Protocols 5.3
Table 5-7. Summary of Average Inhalation Rates (m3/hr) by Age Group and Activity Levels
in Field Protocols 5.9
Table 5-8. Distributions of Individual and Group Inhalation/Ventilation Rate for Outdoor Workers .. 5-10
Table 5-9. Individual Mean Inhalation Rate (m3/hr) by Self-Estimated Breathing Rate or Job Activity
Category for Outdoor Workers , 5-10
Table 5-10. Comparisons of Estimated Basal Metabolic Rates (BMR) with Average Food-Energy
Intakes for Individuals Sampled in the 1977-78 NFCS 5-12
Table 5-11. Daily Inhalation Rates Calculated from Food-Energy Intakes 5-13
Table 5-12. Daily Inhalation Rates Obtained from the Ratios of Total Energy Expenditure to
Basal Metabolic Rate (BMR) ; 5-14
Table 5-13. Daily Inhalation Rates Based on Time-Activity Survey 5-15
Table 5-14. Inhalation Rates for Short-Term Exposures 5-16
Table 5-15. Daily'Inhalation Rates Estimated From Daily Activities 5-17
Table 5-16. Summary of Human Inhalation Rates for Men, Women, and Children by
Activity Level (mVhour) 5-ig
Table 5-17. Activity Pattern Data Aggregated for Three Microenvironments by Activity Level
for all Age Groups 5-18
Table 5-18. Summary of Daily Inhalation Rates Grouped by Age and Activity Level 5-18
Table 5-19. Distribution Pattern of Predicted VR and EVR (equivalent ventilation rate)
for 20 Outdoor Workers 5-20
Table 5-20. Distribution Pattern of Inhalation Rate by Location and Activity Type for 20 Outdoor
Workers 5_2l
Table 5-21. Actual Inhalation Rates Measured at Four Ventilation Levels 5-22
Table 5-22. Confidence in Inhalation Rate Recommendations 5-23
Table 5-23. Summary of Recommended Values for Inhalation 5-24
Table 5-24. Summary of Inhalation Rate Studies 5-25
Table 5-25. Summary of Adult Inhalation Rates for Short-Term Exposure Studies 5-26
Table 5-26. Summary of Children's (18 years old or less) Inhalation Rates for Long-Term
Exposure Studies 5-26
Table 5-27. Summary of Children's Inhalation Rates for Short-Term Exposure Studies 5-26
Table 5A-1. Mean Minute Ventilation (VE, L/min) by Group and Activity for Laboratory Protocols .. 5A-3
Table 5A-2. Mean Minute Ventilation (VE> L/min) by Group and Activity for Field Protocols 5A-3
Exposure Factors Handbook
August 1997
Page
xi
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 5A-3. Characteristics of Individual Subjects: Anthropometric Data, Job Categories,
Calibration Results 5A'4
Table 5A-4. Statistics of the Age/Gender Cohorts Used to Develop Regression Equations for
Predicting Basal Metabolic Rates (BMR) , 5A-4
Table 5A-5. Selected Ventilation Values During Different Activity Levels Obtained From
Various Literature Sources 5A-5
Table 5A-6. Estimated Minute Ventilation Associated with Activity Level for Average Male Adult .. 5A-6
Table 5A-7. Minute Ventilation Ranges by Age, Sex, and Activity Level 5A-7
Table 6-1. Summary of Equation Parameters for Calculating Adult Body Surface Area 6-12
Table 6-2. Surface Area of Adult Males in Square Meters 6-13
Table 6-3. Surface Area of Adult Females in Square Meters 6-13
Table 6-4. Surface Area of Body Part for Adults (m2) 6-14
Table 6-5. Percentage of Total Body Surface Area by Part for Adults 6-14
Table 6-6. Total Body Surface Area of Male Children in Square Meters 6-15
Table 6-7. Total Body Surface Area of Female Children in Square Meters 6-15
Table 6-8. Percentage of Total Body Surface Area by Body Part for Children 6-16
Table 6-9. Descriptive Statistics for Surface Area/BodyWeight (SA/WB) Ratios (m2/kg) 6-17
Table 6-10. Statistical Results for Total Body Surface Area Distributions (m2) 6-17
Table 6-11. Summary of Field Studies 6-20
Table 6-12. Geometric Mean and Geometric Standard Deviations of Soil Adherence by Activity
and Body Region °-22
Table 6-13. Summary of Surface Area Studies 6-24
Table 6-14. Summary of Recommended Values for Skin Surface Area 6-25
Table 6-15. Confidence in Body Surface Area Measurement Recommendations 6-25
Table 6-16. Recommendations for Adult Body Surface Area 6-26
Table 6-17. Summary of Soil Adherence Studies 6-26
Table 6-18. Confidence in Soil Adherence to Skin Recommendations 6-27
Table 6-A1. Estimated Parameter Values for Different Age Intervals 6-A5
Table 6-A2. Summary of Surface Area Parameter Values for the DuBois and
DuBois Model 6-A6
Table 7-1. Smoothed Percentiles of Weight (in kg) by Sex and Age: Statistics from NCHS and Data
from Pels Research Institute, Birth to 36 Months 7-1
Table 7-2. Body Weights of Adults (kilograms) 7-4
Table 7-3. Body Weights of Children (kilograms) , 7-4
Table 7-4. , Weight in Kilograms for Males 18-74 Years of Age-Number Examined, Mean, Standard
Deviation, and Selected Percentiles, by Race and Age: United States, 1976-1980 .7-5
Table 7-5. Weight in Kilograms for Females 18-74 Years of Age-Number Examined, Mean, Standard
Deviation, and Selected Percentiles, by Race and Age: United States, 1976-1980 7-6
Table 7-6. Weight in Kilograms for Males 6 Months-19 Years of Age-Number Examined, Mean,
Standard Deviation, and Selected Percentiles, by Sex and Age: United States, 1976-1980 . . 7-7
Table 7-7. Weight in Kilograms for Females 6 Months-19 Years of Age-Number Examined, Mean,
Standard Deviation, and Selected Percentiles, by Sex and Age: United States, 1976-1980 .. 7-8
Page
xii
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 7-8. Statistics for Probability Plot Regression Analyses Female's Body Weights 6 Months to
20 Years of Age 7-9
Table 7-9. Statistics for Probability Plot Regression Analyses Male's Body Weights 6 Months to
20 Years of Age 7-10
Table 7-10. Summary of Body Weight Studies 7-11
Table 7-11. Summary of Recommended Values for Body Weight 7-11
Table 7-12. Confidence in Body Weight Recommendations 7-12
Table 8-1. Expectation of Life at Birth, 1970 to 1993, and Projections, 1995 to 2010 8-2
Table 8-2. Expectation of Life by Race, Sex, and Age: 1992 8-3
Table 8-3. Confidence in Lifetime Expectancy Recommendations ; 8-5
VOLUME II
Table 9-1. Sub-category Codes and Definitions Used in the CSFII 1989-91 Analysis 9-9
Table 9-2. Weighted and Unweighted Number of Observations for 1989-91 CSFII Data Used in
Analysis of Food Intake 9-10
Table 9-3. Per Capita Intake of Total Fruits (g/kg-day as consumed) .;.... ; 9-11
Table 9-4. Per Capita Intake of Total Vegetables (g/kg-day as consumed) 9-12
Table 9-5. Per Capita Intake of Individual Fruits and Vegetables (g/kg-day as consumed) 9-13
Table 9-6. Per Capita Intake of USDA Categories of Fruits and Vegetables (g/kg-day as consumed) .9-19
Table 9-7. Per Capita Intake of Exposed Fruits (g/kg-day as consumed) 9-20
Table 9-8. Per Capita Intake of Protected Fruits (g/kg-day as consumed) 9-21
Table 9-9. Per Capita Intake of Exposed Vegetables (g/kg-day as consumed) 9-22
Table 9-10. Per Capita Intake of Protected Vegetables (g/kg-day as consumed) 9-23
Table 9-11. Per Capita Intake of Root Vegetables (g/kg-day as consumed) . , 9-24
Table 9-12. Mean Daily Intake of Fruits and Vegetables Per Individual in a Day for USDA 1977-78,
87-88, 89-91, 94, and 95 Surveys •., 9-25
Table 9-13. Mean Per Capita Intake Rates (as consumed) for Fruits and Vegetables Based on All
Sex/Age/Demographic Subgroups 9-26
Table 9-14. Mean Total Fruit Intake (as consumed) in a Day by Sex and Age (1977-1978) 9-33
Table 9-15. Mean Total Fruit Intake (as consumed) in a Day by Sex an Age (1987-1988) 9-33
Table 9-16. Mean Total Vegetable Intake (as consumed) in a Day by Sex and Age (1977-1978) 9-34
Table 9-17. Mean Total Vegetable Intake (as consumed) in a Day by Sex and Age (1987-1988) .. 9-34
Table 9-18. Mean Total Fruit Intake (as consumed) in a Day by Sex and Age (1994 and 1995) 9-35
Table 9-19. Mean Total Vegetable Intake (as consumed) in a Day by Sex and Age (1994 and 1995) .. 9-35
Table 9-20. Mean Per Capita Intake of Fats and Oils (g/day as consumed) in a Day by Sex and
Age (1994 and 1995) 9-36
Table 9-21. Mean and Standard Error for the Per Capita Daily Intake of Food Class and Subclass by
Region (g/day as consumed) 9-36
Table 9-22. Mean and Standard Error for the Daily Intake of Food Subclasses Per Capita by Age
(g/day as consumed) , 9-37
Exposure Factors Handbook
August 1997
Page
xiii
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 9-23.
Table 9-24.
Table 9-25.
Table 9-26.
Table 9-27.
Table 9-28.
Table 9-29.
Table 9-30.
Table 9 A-1.
Table 9 B.
Table 10-1.
Table 10-2.
Table 10-3.
Table 10-4.
Table 10-5.
Table 10-6.
Table 10-7.
Table 10-8.
Table 10-9.
Table 10-10.
Table 10-11.
Table 10-12.
Table 10-13.
Table 10-14.
Table 10-15.
Table 10-16.
Table 10-17.
Consumption of Foods (g dry weight/day) for Different Age Groups and Estimated
Lifetime Average Daily Food Intakes for a US Citizen (averaged across sex) Calculated
from the FDA Diet Data 9-37
Mean Daily Intake of Foods (grams) Based on the Nutrition Canada Dietary Survey 9-38
Per Capita Consumption of Fresh Fruits and Vegetables in 1991 9-38
Quantity (as consumed) of Fruits and Vegetables Consumed Per Eating Occasion and the
Percentage of Individuals Using These Foods in Three Days 9-39
Mean Moisture Content of Selected Fruits and Vegetables Expressed as Percentages of
Edible Portions 9-40
Summary of Fruit and Vegetable Intake Studies 9-43
Summary of Recommended Values for Per Capita Intake of Fruits and Vegetables 9-44
Confidence in Fruit and Vegetable Intake Recommendations 9-45
Fraction of Grain and Meat Mixture Intake Represented by
Various Food Items/Groups 9A-3
Food Codes and Definitions Used in Analysis of the 1989-91 USDA CSFII Data .. . 9 B-3
Total Fish Consumption by Demographic Variables 10-30
Mean and 95th Percentile of Fish Consumption (g/day) by Sex and Age 10-31
Percent Distribution of Total Fish Consumption for Females by Age 10-32
Percent Distribution of Total Fish Consumption for Males by Age 10-32
Mean Total Fish Consumption by Species 10-33
Best Fits of Lognormal Distributions Using the NonLinear Optimization (NLO) Method . 10-33
Per Capita Distribution of Fish Intake (g/day) by Habitat and Fish Type for the
U.S. Population (Uncooked Fish Weight) , 10-34
Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) by Habitat
for Consumers Only (Uncooked Fish Weight) 10-35
Per Capita Distribution of Fish Intake (mg/kg-day) by Habitat and Fish Type for U.S.
Population (Uncooked Fish Weight) 10-36
Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) by Habitat for
Consumers Only (Uncooked Fish Weight) 10-37
Per Capita Distribution of Fish Intake (g/day) by Habitat and Fish Type for the U.S.
Population (Cooked Fish Weight - As Consumed)) 10-38
Per Capita Distribution of Fish Intake (g/day) by Habitat for Consumers Only
(Cooked Fish Weight - As Consumed)) 10-39
Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for the
U.S. Population by Age and Gender - As Consumed (Freshwater and Estuarine) 10-40
Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for the
U.S. Population by Age and Gender - As Consumed (Marine) 10-40
Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for the
U.S. Population by Age and Gender - As Consumed (All Fish) 10-41
Per Capita Distribution of Fish (Finfish and Shellfish) Intake (grams/day) for the
U.S. Population Aged 18 Years and Older by Habitat - As Consumed 10-41
Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for the
U.S. Population by Age and Gender - As Consumed (Freshwater and Estuarine) 10-42
Page
xiv
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 10-18. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for the
U.S. Population by Age and Gender - As Consumed (Marine) 10-42
Table 10-19. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for the
U.S. Population by Age and Gender - As Consumed (All Fish) 10-43
Table 10-20. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for the
U.S. Population Aged 18 Years and Older by Habitat - As Consumed 10-43
Table 10-21. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
Consumers Only by Age and Gender - As Consumed (Freshwater and Estuarine) 10-44
Table 10-22. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
Consumers Only by Age and Gender - As Consumed (Marine) • 10-44
Table 10-23. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
Consumers Only by Age and Gender - As Consumed (All Fish) 10-45
Table 10-24. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
Consumers Only Aged 18 Years and Older by Habitat - As Consumed 10-45
Table 10-25. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
Consumers Only by Age and Gender - As Consumed (Freshwater and Estuarine) 10-46
Table 10-26. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
Consumers Only by Age and Gender - As Consumed (Marine) 10-46
Table 10-27. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
Consumers Only by Age and Gender - As Consumed (All Fish) 10-47
Table 10-28. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
Consumers Only Aged 18 Years and Older by Habitat - As Consumed . 10-47
Table 10-29. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
the U.S. Population by Age and Gender - Uncooked Fish Weight
(Freshwater and Estuarine) 10-48
Table 10-30. . Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
the U.S. Population by Age and Gender - Uncooked Fish Weight (Marine) 10-48
Table 10-31. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
the U.S. Population by Age and Gender - Uncooked Fish Weight (All Fish) 10-49
Table 10-32. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
the U.S. Population Aged 18 Years and Older by Habitat - Uncooked Fish Weight 10-49
Table 10-33. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for the U.S.
Population by Age and Gender - Uncooked Fish Weight (Freshwater and Estuarine) 10-50
Table 10-34. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
the U.S. Population by Age and Gender - Uncooked Fish Weight (Marine) 10-50
Table 10-35. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
the U.S. Population by Age and Gender - Uncooked Fish Weight (All Fish) 10-51
Table 10-36. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for the U.S.
Population Aged 18 Years and Older by Habitat - Uncooked Fish Weight 10-51
Table 10-37. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for Consumers
Only by Age and Gender - Uncooked Fish Weight (Freshwater and Estuarine) 10-52
Table 10-38. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
Consumers Only by Age and Gender - Uncooked Fish Weight (Marine) 10-52
Exposure Factors Handbook
August 1997
Page
xv
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 10-39. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
Consumers Only by Age and Gender - Uncooked Fish Weight (All Fish) 10-53
Table 10-40. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (g/day) for
Consumers Only Aged 18 Years and Older by Habitat - Uncooked Fish Weight 10-53
Table 10-41. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for Consumers
Only by Age and Gender - Uncooked Fish Weight (Freshwater and Estuarine) 10-54
Table 10-42. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
Consumers Only by Age and Gender - Uncooked Fish Weight (Marine) 10-54
Table 10-43. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
Consumers Only by Age and Gender - Uncooked Fish Weight (All Fish) 10-55
Table 10-44. Per Capita Distribution of Fish (Finfish and Shellfish) Intake (mg/kg-day) for
Consumers Only Aged 18 Years and Older by Habitat - Uncooked Fish Weight 10-55
Table 10-45. Distribution of Quantity of Fish Consumed (in grams) Per Eating Occasion, by
Age and Sex 1Q-56
Table 10-46. Mean Fish Intake in a Day, by Sex and Age 10-56
Table 10-47. Percent of Respondents That Responded Yes, No, or Don't Know to Eating
Seafood in 1 Month (including shellfish, eels, or squid) 10-57
Table 10-48. Number of Respondents Reporting Consumption of a Specified Number of
Servings of Seafood in 1 Month 10-58
Table 10-49. ' Number of Respondents Reporting Monthly Consumption of Seafood That Was
Purchased or Caught by Someone They Knew 10-59
Table 10-50. Estimated Number of Participants in Marine Recreational Fishing by State
and Subregion 10-60 '
Table 10-51. Estimated Weight of Fish Caught (Catch Type A and B1) by Marine Recreational
Fishermen, by Wave and Subregion 10-61
Table 10-52. Average Daily Intake (g/day) of Marine Finfish, by Region and Coastal Status 10-62
Table 10-53. Estimated Weight of Fish Caught (Catch Type A and B1) by Marine
Recreational Fishermen by Species Group and Subregion, Atlantic and Gulf 10-62
Table 10-54. Estimated Weight of Fish Caught (Catch Type A and B1) by Marine
Recreational Fishermen by Species Group and Subregion, Pacific 10-63
Table 10-55. Median Intake Rates Based on Demographic Data of Sport Fishermen and
Their Family/Living Group 10-63
Table 10-56. Cumulative Distribution of Total Fish/Shellfish Consumption by Surveyed
Sport Fishermen in the Metropolitan Los Angeles Area 10-64
Table 10-57. Catch Information for Primary Fish Species Kept by Sport Fishermen (n=1059) 10-64
Table 10-58. Percent of Fishing Frequency During the Summer and Fall Seasons in Commencement
Bay, Washington 1Q-64
Table 10-59. Selected Percentile Consumption Estimates (g/day) for the Survey and Total Angler
Populations Based on the Reanalysis of the Puffer et al. (1981) and Pierce et al. (1981)
Data 10-65
Table 10-60. Means and Standard Deviations of Selected Characteristics by Subpopulation
Groups in Everglades, Florida 10-65
Table 10-61. Mean Fish Intake Among Individuals Who Eat Fish and Reside in Households
With Recreational Fish Consumption 10-66
Page
xvi
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 10-62. Comparison of Seven-Day Recall and Estimated Seasonal Frequency for Fish
Consumption 10-66
Table 10-63. Distribution of Usual Fish Intake Among Survey Main Respondents Who
Fished and Consumed Recreationally Caught Fish 10-66
Table 10-64. Estimates of Fish Intake Rates of Licensed Sport Anglers in Maine During the
1989-1990 Ice Fishing or 1990 Open-Water Seasons : 10-67
Table 10-65. Analysis of Fish Consumption by Ethnic Groups for "All Waters" (g/day) 10-67
Table 10-66. Total Consumption of Freshwater Fish Caught by All Survey Respondents
During the 1990 Season 10-68
Table 10-67. . Mean Sport-Fish Consumption by Demographic Variables, Michigan Sport
Anglers Fish Consumption Study, 1991-1992 10-68
Table 10-68. Distribution of Fish Intake Rates (from all sources and from sport-caught sources)
For 1992 Lake Ontario Anglers 10-69
Table 10-69. Mean Annual Fish Consumption (g/day) for Lake Ontario Anglers, 1992, by
Sociodemographic Characteristics 10-69
Table 10-70. Percentile and Mean Intake Rates for Wisconsin Sport Anglers 10-70
Table 10-71. Sociodemographic Characteristics of Respondents 10-70
Table 10-72. Number of Grams Per Day of Fish Consumed by All Adult Respondents
(Consumers and Non-consumers Combined) - Throughout the Year 10-71
Table 10-73. Fish Intake Throughout the Year by Sex, Age, and Location by All Adult Respondents . . 10-71
Table 10-74. Children's Fish Consumption Rates - Throughout Year 10-72
Table 10-75. Sociodemographic Factors and Recent Fish Consumption 10-72
Table 10-76. Number of Local Fish Meals Consumed Per Year by Time Period for All Respondents . . 10-74
Table 10-77. Mean Number of Local Fish Meals Consumed Per Year by Time Period for All
Respondents and Consumers Only 10-74
Table 10-78. Mean Number of Local Fish Meals Consumed Per Year by Time Period and
Selected Characteristics for All Respondents (Mohawk, N=97; Control, N=154) 10-75
Table 10-79. Percentage of Individuals Using Various Cooking Methods at Specified Frequencies .... 10-75
Table 10-80. Percent Moisture and Fat Content for Selected Species 10-76
Table 10-81. Recommendations - General Population 10-79
Table 10-82. Recommendations - General Population - Fish Serving Size 10-79
Table 10-83. Recommendations - Recreational Marine Anglers 10-79
Table 10-84. Recommendations - Freshwater Anglers 10-79
Table 10-85. Recommendations - Native American Subsistence Populations 10-80.
Table 10-86. Summary of Fish Intake Studies 10-81
Table 10-87. Confidence in Fish Intake Recommendations for General Population 10-85
Table 10-88. Confidence in Fish Intake Recommendations for Recreational Marine Anglers 10-86
Table 10-89. Confidence in Recommendations for Fish Consumption - Recreational Freshwater 10-87
Table 10-90. Confidence in Recommendations for Native American Subsistence Fish Consumption ... 10-88
Table 10B-1. Percent of Fish Meals Prepared Using Various Cooking Methods by Residence Size .. . 10B-3
Table 10B-2. Percent of Fish Meals Prepared Using Various Cooking Methods by Age 10B-3
Table 10B-3. Percent of Fish Meals Prepared Using Various Cooking Methods by Ethnicity 10B-4
Table 10B-4. Percent of Fish Meals Prepared Using Various Cooking Methods by Education 10B-4
Table 10B-5. Percent of Fish Meals Prepared Using Various Cooking Methods by Income 10B-5
Exposure Factors Handbook
August 1997
Page
xvii
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 10B-6. Percent of Fish Meals Where Fat was Trimmed or Skin was Removed, by Demographic
Variables 10B-6
Table 10B-7. Method of Cooking of Most Common Species Kept by Sportfishermeri 10B-7
Table 10B-8. Adult Consumption of Fish Parts 10B-7
Table 10C-1. Daily Average Per Capita Estimates of Fish Consumption U.S. Population - Mean
Consumption by Species Within Habitat - As Consumed Fish 10C-3
Table 10C-2. Daily Average Per Capita Estimates of Fish Consumption U.S. Population - Mean
Consumption by Species Within Habitat - Uncooked Fish 10C-4
Table 10C-3. Daily Average Per Capita Estimates of Fish Consumption As Consumed Fish - Mean
Consumption by Species Within Habitat - U.S. Population 10C-5
Table 10C-4. Daily Average Per Capita Estimates of Fish Consumption Uncooked Fish - Mean
Consumption by Species Within Habitat - U.S. Population 10C-6
Table 11-1. Per Capita Intake of Total Meats (g/kg-day as consumed)) 11-9
Table 11-2. Per Capita Intake of Total Dairy Products (g/kg-day as consumed)) 11-10
Table 11-3. Per Capita Intake of Beef (g/kg-day as consumed)) 11-11
Table 11-4. Per Capita Intake of Pork (g/kg-day as consumed) 11-12
Table 11-5. Per Capita Intake of Poultry (g/kg-day as consumed) 11-13
Table 11-6. Per Capita Intake of Game (g/kg-day as consumed)) 11-14
Table 11-7. Per Capita Intake of Eggs (g/kg-day as consumed) 11-15
Table 11-8. Main Daily Intake of Meat and Dairy Products Per Individual in a Day for USDA
1977-78, 87-88, 89-91, 94, and 95 Surveys 11-16
Table 11-9. Mean Per Capita Intake Rates for Meat, Poultry, and Dairy Products (g/kg-day as consumed)
Based on All Sex/Age/Demographic Subgroups 11-17
Table 11-10. Mean Meat Intakes Per Individual in a Day, by Sex and Age (g/day as consumed)
for 1977-1978 11-18
Table 11-11. Mean Meat Intakes Per Individual in a Day, by Sex and Age (g/day as consumed)
for 1987-1988 11-18
Table 11-12. Mean Dairy Product Intakes Per Individual in a Day, by Sex and Age (g/day as
consumed) for 1977-1978 11-19
Table 11-13. Mean Dairy Product Intakes Per Individual in a Day, by Sex and Age (g/day as consumed)
for 1987-1988 11-19
Table 11-14. Mean Meat Intakes Per Individual in a Day, by Sex and Age (g/day as consumed)
for 1994 and 1995 11-20
Table 11-15. Mean Dairy Product Intakes Per Individual in a Day, by Sex and Age
(g/day as consumed) for 1994 and 1995 11-20
Table 11-16. Mean and Standard Error for the Dietary Intake of Food Sub Classes Per Capita by Age
(g/day as consumed) 11-21
Table 11-17. Mean and Standard Error for the Per Capita Daily Intake of Food Class and
Sub Class by Region (g/day as consumed) 11-21
Table 11-18. Consumption of Meat, Poultry, and Dairy Products for Different Age Groups (averaged
across sex), and Estimated Lifetime Average Intakes for 70 Kg Adult Citizens
Calculated from the FDA Diet Data 11-22
Page
xvtii
Exposure Factors Handbook
August 1997
-------
LIST OF TABLES (continued)
Page No.
Table 11-19. Per Capita Consumption of Meat and Poultry in 1991 .• 11-22
Table 11-20. Per Capita Consumption of Dairy Products in 1991 11-23
Table 11-21. Adult Mean Daily Intake (as consumed) of Meat and Poultry Grouped by Region and
Gender 11-24
Table 11-22. Amount (as consumed) of Meat Consumed by Adults Grouped by Frequency of Eatings . 11-24
Table 11-23. Quantity (as consumed) of Meat, Poultry, and Dairy Products Consumed Per
Eating Occasion and the Percentage of Individuals Using These Foods in Three Days ... 11 -25
Table 11-24. Percentage Lipid Content (Expressed as Percentages of 100 Grams of Edible
Portions) of Selected Meat and Dairy Products 11-26
Table 11-25. Fat Content of Meat Products 11-27
Table 11 -26. Fat Intake, Contribution of Various Food Groups to Fat Intake, and Percentage of the
Population in Various Meat Eater Groups of the U.S. Population 11-28
Table 11-27. Mean Total Daily Dietary Fat Intake (g/day) Grouped by Age and Gender 11-28
Table 11-28. Percentage Mean Moisture Content (Expressed as Percentages of 100 Grams of
Edible Portions) 11-29
Table 11-29. Summary of Meat, Poultry, and Dairy Intake Studies 11-30
Table 11 -30. Summary of Recommended Values for Per Capita Intake of Meat and Dairy
Products and Serving Size 11-31
Table 11-31. Confidence in Meats and Dairy Products Intake Recommendations .. . 11-32
Table 12-1. Per Capita Intake of Total Grains Including Mixtures (g/kg-day as consumed) 12-6
Table 12-2. Per Capita Intake of Breads (g/kg-day as consumed)) 12-7
Table 12-3. Per Capita Intake of Sweets (g/kg-day as consumed) 12-8
Table 12-4. Per Capita Intake of Snacks Containing Grain (g/kg-day as consumed) 12-9
Table 12-5. Per Capita Intake of Breakfast Foods (g/kg-day as consumed) 12-10
Table 12-6. Per Capita Intake of Pasta (g/kg-day as consumed) 12-11
Table 12-7. Per Capita Intake of Cooked Cereals (g/kg-day as consumed) 12-12
Table 12-8. Per Capita Intake of Rice (g/kg-day as consumed) .-..-. :.,.-. 12-13
Table 12-9. Per Capita Intake of Ready-to-Eat Cereals (g/kg-day as consumed)) 12-14
Table 12-10. Per Capita Intake of Baby Cereals (g/kg-day as consumed) 12-15
Table 12-11. Mean Daily Intakes of Grains Per Individual in a Day for USDA 1977-78,
87-88, 89-91, 94, and 95 Surveys 12-16
Table 12-12. Mean Per Capita Intake Rates for Grains Based on All Sex/Age/Demographic
Subgroups 12-16
Table 12-13. Mean Grain Intake Per Individual in a Day by Sex and Age (g/day as consumed)
for 1977-1978 12-17
Table 12-14.. Mean Grain Intakes Per Individual in a Day by Sex and Age (g/day as consumed)
for 1987-1988 12-17
Table 12-15. Mean Grain Intakes Per Individual in a Day by Sex and Age (g/day as consumed)
for 1994 and 1995 12-18
Table 12-16. Mean and Standard Error for the Daily Per Capita Intake of Grains, by Age
(g/day as consumed) 12-18
Table 12-17. Mean and Standard Error for the Daily Intake of Grains, by Region (g/day as
consumed) , 12-19
Exposure Factors Handbook
August 1997
Page
xix
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 12-18. Consumption of Grains (g dry weight/day) for Different Age Groups and Estimated
Lifetime Average Daily Food Intakes for a U.S. Citizen (averaged across sex) Calculated
from the FDA Diet Data 12-19
Table 12-19. Per Capita Consumption of Flour and Cereal Products in 1991 12-20
Table 12-20. Quantity (as consumed) of Grain Products Consumed Per Eating Occasion and the
Percentage of Individuals Using These Foods in Three Days 12-20
Table 12-21. Mean Moisture Content of Selected Grains Expressed as Percentages of Edible Portions . 12-21
Table 12-22. Summary of Grain Intake Studies 12-22
Table 12-23. Summary of Recommended Values for Per Capita Intake of Grain Products 12-22
Table 12-24. Confidence in Grain Products Intake Recommendation 12-23
Table 12 A-1. Food Codes and Definitions Used in the Analysis of the 1989-91 USDA CSFH
Grains Data 12A-3
Table 13-1. 1986 Vegetable Gardening by Demographic Factors 13-1
Table 13-2. Percentage of Gardening Households Growing Different Vegetables in 1986 13-1
Table 13-3. Sub-category Codes and Definitions 13-4
Table 13-4. Weighted and Unweighted Number of Observations (Individuals) for NFCS Data
Used in Analysis of Food Intake 13-6
Table 13-5. Percent Weight Losses from Preparation of Various Meats 13-8
Table 13-6. Percent Weight Losses from Preparation of Various Fruits 13-8
Table 13-7. Percent Weight Losses from Preparation of Various Vegetables 13-9
Table 13-8. Consumer Only Intake of Homegrown Fruits (g/kg-day) - All Regions Combined 13-12
Table 13-9. Consumer Only Intake of Homegrown Fruits (g/kg-day) - Northeast 13-13
Table 13-10. Consumer Only Intake of Homegrown Fruits (g/kg-day) - Midwest 13-13
Table 13-11. Consumer Only Intake of Homegrown Fruits (g/kg-day) - South 13-14
Table 13-12. Consumer Only Intake of Homegrown Fruits (g/kg-day) - West 13-14
Table 13-13. Consumer Only Intake of Homegrown Vegetables (g/kg-day) - All Regions Combined .. 13-15
Table 13-14. Consumer Only Intake of Homegrown Vegetables (g/kg-day) - Northeast 13-16
Table 13-15. Consumer Only Intake of Homegrown Vegetables (g/kg-day) - Midwest .• 13-16
Table 13-16. Consumer Only Intake of Homegrown Vegetables (g/kg-day) - South 13-17
Table 13-17. Consumer Only Intake of Homegrown Vegetables (g/kg-day) - West 13-17
Table 13-18. Consumer Only Intake of Home Produced Meats (g/kg-day) - All Regions Combined ... 13-18
Table 13-19. Consumer Only Intake of Home Produced Meats (g/kg-day) - Northeast 13-19
Table 13-20. Consumer Only Intake of Home Produced Meats (g/kg-day) - Midwest 13-19
Table 13-21. Consumer Only Intake of Home Produced Meats (g/kg-day) - South 13-20
Table 13-22. Consumer Only Intake of Home Produced Meats (g/kg-day) - West 13-20
Table 13-23. Consumer Only Intake of Home Caught Fish (g/kg-day) - All Regions Combined 13-21
Table 13-24. Consumer Only Intake of Home Caught Fish (g/kg-day) - Northeast 13-22
Table 13-25. Consumer Only Intake of Home Caught Fish (g/kg-day) - Midwest 13-22
Table 13-26. Consumer Only Intake of Home Caught Fish (g/kg-day) - South 13-23
Table 13-27. Consumer Only Intake of Home Caught Fish (g/kg-day) - West 13-23
Table 13-28. Consumer Only Intake of Home Produced Dairy (g/kg-day) - All Regions 13-24
Table 13-29. Consumer Only Intake of Home Produced Dairy (g/kg-day) - Northeast 13-25
Table 13-30. Consumer Only Intake of Home Produced Dairy (g/kg-day) - Midwest 13-25
Page
xx
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 13-31. Consumer Only Intake of Home Produced Dairy (g/kg-day) - South 13-26
Table 13-32. Consumer Only Intake of Home Produced Dairy (g/kg-day) - West 13-26
Table 13-33. Seasonally Adjusted Consumer Only Homegrown Intake (g/kg-day) 13-27
Table 13-34. Consumer Only Intake of Homegrown Apples (g/kg-day) 13-28
Table 13-35. Consumer Only Intake of Homegrown Asparagus (g/kg-day) 13-29
Table 13-36. Consumer Only Intake of Home Produced Beef (g/kg-day) 13-30
Table 13-37. Consumer Only Intake of Homegrown Beets (g/kg-day) 13-31
Table 13-38. Consumer Only Intake of Homegrown Broccoli (g/kg-day) 13-32
Table 13-39. Consumer Only Intake of Homegrown Cabbage (g/kg-day) 13-33
Table 13-40. Consumer Only Intake of Homegrown Carrots (g/kg-day) ... '. 13-34
Table 13-41. Consumer Only Intake of Homegrown Corn (g/kg-day) 13-35
Table 13-42. Consumer Only Intake of Homegrown Cucumbers (g/kg-day) 13-36
Table 13-43. Consumer Only Intake of Home Produced Eggs (g/kg-day) 13-37
Table 13-44. Consumer Only Intake of Home Produced Game (g/kg-day) 13-38
Table 13-45. Consumer Only Intake of Home Produced Lettuce (g/kg-day) 13-39
Table 13-46. Consumer Only Intake of Home Produced Lima Beans (g/kg-day) 13-40
Table 13-47. Consumer Only Intake of Homegrown Okra (g/kg-day) 13-41
Table 13-48. Consumer Only Intake of Homegrown Onions (g/kg-day) 13-42
Table 13-49. Consumer Only Intake of Homegrown Other Berries (g/kg-day) 13-43
Table 13-50. Consumer Only Intake of Homegrown Peaches (g/kg-day) 13-44
Table 13-51. Consumer Only Intake of Homegrown Pears (g/kg-day) 13-45
Table 13-52. Consumer Only Intake of Homegrown Peas (g/kg-day) 13-46
Table 13-53. Consumer Only Intake of Homegrown Peppers (g/kg-day) : 13-47
Table 13-54. Consumer Only Intake of Home Produced Pork (g/kg-day) 13-48
Table 13-55. Consumer Only Intake of Home Produced Poultry (g/kg-day) 13-49
Table 13-56. Consumer Only Intake of Homegrown Pumpkins (g/kg-day) 13-50
Table 13-57. Consumer Only Intake of Homegrown Snap Beans (g/kg-day) 13-51
Table 13-58. Consumer Only Intake of Homegrown Strawberries (g/kg-day) 13-52
Table 13-59. Consumer Only Intake of Homegrown Tomatoes (g/kg-day) 13-53
Table 13-60. Consumer Only Intake of Homegrown White Potatoes (g/kg-day) .. 13-54
Table 13-61. Consumer Only Intake of Homegrown Exposed Fruit (g/kg-day) 13-55
Table 13-62. Consumer Only Intake of Homegrown Protected Fruits (g/kg-day) 13-56
Table 13-63. Consumer Only Intake of Homegrown Exposed Vegetables (g/kg-day) 13-57
Table 13-64. Consumer Only Intake of Homegrown Protected Vegetables (g/kg-day) 13-58
Table 13-65. Consumer Only Intake of Homegrown Root Vegetables (g/kg-day) ' 13-59
Table 13-66. Consumer Only Intake of Homegrown Dark Green Vegetables (g/kg-day) 13-60
Table 13-67. Consumer Only Intake of Homegrown Deep Yellow Vegetables (g/kg-day) 13-61
Table 13-68. Consumer Only Intake of Homegrown Other Vegetables (g/kg-day) 13-62
Table 13-69. Consumer Only Intake of Homegrown Citrus (g/kg-day) 13-63
Table 13-70. Consumer Only Intake of Homegrown Other Fruit (g/kg-day) 13-64
Table 13-71. Fraction of Food Intake that is Home Produced 13-65
Table 13-72. Confidence in Homegrown Food Consumption Recommendations 13-67
Table 13A-1. Food Codes and Definitions Used in Analysis of the 1987-88 USDA NFCS Data 13A-3
Exposure Factors Handbook
August 1997
Page
xxi
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 14-1. Daily Intakes of Breast Milk 14'2
Table 14-2. Breast Milk Intake for Infants Aged 1 to 6 Months 14-2
Table 14-3 Breast Milk Intake Among Exclusively Breast-fed Infants During the First 4 Months
of Life 14'3
Table 14-4. Breat Milk Intake During a 24-Hour Period 14-3
Table 14-5. Breast Milk Intake Estimated by the DARLING Study 14-4
Table 14-6. Milk Intake for Bottle- and Breast-fed Infants by Age Group 14-4
Table 14-7. Milk Intake for Boys and Girls 14-4
Table 14-8. Intake of Breast Milk and Formula 14-5
Table 14-9 Lipid Content of Human Milk and Estimated Lipid Intake Among Exclusively Breast-fed
Infants 14'6
Table 14-10. Predicted Lipid Intakes for Breast-fed Infants Under 12 Months of Age 14-6
Table 14-11. Number of Meals Per Day 14-7
Table 14-12. Percentage of Mothers Breast-feeding Newborn Infants in the Hospital and Infants
at 5 or 6 Months of Age in the United States in 1989, by Ethnic Background and
Selected Demographic Variables 14-10
Table 14-13. Breast Milk Intake Studies ; 14-J l
Table 14-14. Confidence in Breast Milk Intake Recommendations 14-13
Table 14-15. Breast Milk Intake Rates Derived From Key Studies 14-14
Table 14-16. Summary of Recommended Breast Milk and Lipid Intake Rates 14-15
VOLUME III
Table 15-1. Time Use Table Locator Guide 15-20
Table 15-2. Mean Time Spent (minutes) Performing Major Activities Grouped by Age, Sex and
Type of Day • 15'21
Table 15-3. Mean Time Spent (minutes) in Major Activities Grouped by Type of Day for Five
Different Age Groups 15-22
Table 15-4. Cumulative Frequency Distribution of Average Shower Duration for 2,550 Households . 15-23
Table 15-5. Mean Time Spent (minutes/day) in Ten Major Activity Categories Grouped by
Total Sample and Gender for the CARB and National Studies (age 18-64 years) 15-24
Table 15-6. Total Mean Time Spent at Three Major Locations Grouped by Total Sample and
Gender for the CARB and National Study (ages 18-64 years) 15-24
Table 15-7. Mean Time Spent at Three Locations for both CARB and National Studies
(ages 12 years and older) 15-25
Table 15-8. Mean Time Spent (minutes/day) in Various Microenvironments Grouped by Total
Population and Gender (12 years and over) in the National and CARB Data 15-26
Table 15-9. Mean Time Spent (minutes/day) in Various Microenvironments by Type of Day for
the California and National Surveys (sample population ages 12 years and older) 15-27
Table 15-10. Mean Time Spent (minutes/day) in Various Microenvironments by Age Groups
for the National and California Surveys 15-28
Table 15-11. Mean Time (minutes/day) Children Spent in Ten Major Activity Categories for All
Respondents 15-30
Page
xxii
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 15-12. Mean Time Children Spent in Ten Major Activity Categories Grouped by Age
and Gender •, 15-30
Table 15-13. Mean Time Children Spent in Ten Major Activity Categories Grouped by Seasons
and Regions . ...........'., 15-31
Table 15-14. Mean Time Children Spent in Six Major Location Categories for All Respondents
(minutes/day) 15.3 j
Table 15-15. Mean Time Children Spent in Six Location Categories Grouped by Age and Gender ... 15-32
Table 15-16. Mean Time Children Spent in Six Location Categories Grouped by Season and Region .. 15-32
Table 15-17.' Mean Time Children Spent in Proximity to Three Potential Exposures Grouped by
All Respondents, Age, and Gender 15-33
Table 15-18. Range of Recommended Defaults for Dermal Exposure Factors 15-33
Table 15-19. Number of Times Taking a Shower at Specified Daily Frequencies by the Number of
Respondents 15-34
Table 15-20. Times (minutes) Spent Taking Showers by the Number of Respondents 15-35
Table 15-21. Number of Minutes Spent Taking a Shower (minutes/shower) 15-36
Table 15-22. Time (minutes) Spent in the Shower Room Immediately After Showering by the
Number of Respondents 15-37
Table 15-23. Number of Minutes Spent in the Shower Room Immediately After Showering
(minutes/shower) 15-38
Table 15-24. Number of Baths Given or Taken in One Day by Number of Respondents 15-39
Table 15-25. Total Time Spent Taking or Giving a Bath by the Number of Respondents 15-40
Table 15-26. Number of Minutes Spent Giving and Taking the Bath(s) (minutes/bath) 15-41
Table 15-27. Time Spent in the Bathroom Immediately After the Bath(s) by the Number
of Respondents • j 5.42
Table 15-28. Number of Minutes Spent in the Bathroom Immediately After the Bath(s)
(minutes/bath) 15-43
Table 15-29. Total Time Spent Altogether in the Shower or Bathtub by the Number of Respondents . . 15-44
Table 15-30. Total Number of Minutes Spent Altogether in the Shower or Bathtub (minutes/bath) 15-45
Table 15-31. Time Spent in the Bathroom Immediately Following a Shower or Bath by the
Number of Respondents 15-46
Table 15-32. Numbejr.of Minutes Spent in the Bathroom Immediately Following a Shower or °
Bath (minutes/bath) 15-47
Table 15-33. Range of Number of Times Washing the Hands at Specified Daily Frequencies by
the Number of Respondents 15.48
Table 15-34. Number of Minutes Spent (at home) Working or Being Near Food While Fried,
Grilled, or Barbequed (minutes/day) 15-49
Table 15-35. Number of Minutes Spent (at home) Working or Being Near Open Flames
Including Barbeque Flames (minutes/day) 15-50
Table 15-36. Number of Minutes Spent Working or Being Near Excessive Dust in the Air
(minutes/day) 15.5 j
Table 15-37. Range of the Number of Times an Automobile or Motor Vehicle was Started in
a Garage or Carport at Specified Daily Frequencies by the Number of Respondents 15-52
Table 15-38. Range of the Number of Times Motor Vehicle Was Started with Garage Door
Closed at Specified Daily Frequencies by the Number of Respondents 15-53
Exposure Factors Handbook
August 1997
Page
xxiii
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 15-39. Number of Minutes Spent at a Gas Station or Auto Repair Shop (minutes/day) 15-54
Table 15-40. Number of Minutes Spent at Home While the Windows Were Left Open
(minutes/day) 15~55
Table 15-41. Number of Minutes the Outside Door Was Left Open While at Home (minutes/day) 15-56
Table 15-42. Number of Times an Outside Door Was Opened in the Home at Specified Daily
Frequencies by the Number of Respondents 15-57
Table 15-43. Number of Minutes Spent Running, Walking, or Standing Alongside a Road with
Heavy Traffic (minutes/day) •* 15"58
Table 15-44. Number of Minutes Spent in a Car, Van, Truck, or Bus in Heavy Traffic (minutes/day) .. 15-59
Table 15-45. Number of Minutes Spent in a Parking Garage or Indoor Parking Lot (minutes/day) 15-60
Table 15-46. Number of Minutes Spent Walking Outside to a Car in the Driveway or Outside
Parking Areas (minutes/day) 15-61
Table 15-47. Number of Minutes Spent Running or Walking Outside Other Than to the Car
(minutes/day) 15"62
Table 15-48. Number of Hours Spent Working for Pay (hours/week) 15-63
Table 15-49. Number of Hours Spent Working for Pay Between 6PM and 6AM (hours/week) 15-64
Table 15-50. Number of Hours Worked in a Week That Was Outdoors (hours/week) 15-65
Table 15-51. Number of Times Floors Were Swept or Vacuumed at Specified Frequencies by the
Number of Respondents 15~66
Table 15-52. Number of Days Since the Floor Area in the Home Was Swept or Vacuumed by the
Number of Respondents 15-67
Table 15-53. Number of Loads of Laundry Washed in a Washing Machine at Home by the
Number of Respondents • 15'68
Table 15-54. Number of Times Using a Dishwasher at Specified Frequencies by the Number of
Respondents 15-69
Table 15-55. Number of Times Washing Dishes by Hand at Specified Frequencies by the Number
of Respondents 15-70
Table 15-56. Number of Times for Washing Clothes in a Washing Machine at Specified Frequencies
by the Number of Respondents 15-71
Table 15-57. ' Number of Minutes Spent Playing on Sand or Gravel in a Day by the Number of
Respondents 15~72
Table 15-58. Number of Minutes Spent Playing in Sand or Gravel (minutes/day) 15-73
Table 15-59. Number of Minutes Spent Playing in Outdoors on Sand, Gravel, Dirt, or Grass When
Fill Dirt Was Present by the Number of Respondents 15-74
Table 15-60. Number of Minutes Spent Playing on Sand, Gravel, Dirt, or Grass When Fill Dirt
Was Present (minutes/day) 15-75
Table 15-61. Range of the Time Spent Working in a Garden or Other Circumstances in a Month
by the Number of Respondents 15-76
Table 15-62. Number of Hours Spent Working with Soil in a Garden or Other Circumstances
Working (hours/month) 15-77
Table 15-63. Range of Number of Minutes Spent Playing on Grass in a Day by the Number of
Respondents 15~78
Table 15-64. Number of Minutes Spent Playing on Grass (minutes/day) 15-79
Page
xxiv
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 15-65. Number of Times Swimming in a Month in Freshwater Swimming Pool by the
Number of Respondents 15-80
Table 15-66. Range of the Average Amount of Time Actually Spent in the Water by Swimmers by
the Number of Respondents 15-82
Table 15-67. Number of Minutes Spent Swimming in a Month in Freshwater Swimming Pool
(minutes/month) 15-83
Table 15-68. Statistics for 24-Hour Cumulative Number of Minutes Spent Working in a Main Job 15-84
Table 15-69. Statistics for 24-Hour Cumulative Number of Minutes Spent in Food Preparation ..'.'.'.'. 15-85
Table 15-70. Statistics for 24-Hour Cumulative Number of Minutes Spent in Food Cleanup '.'.'.'.'. 15-86
Table 15-71. Statistics for 24-Hour Cumulative Number of Minutes Spent Cleaning House 15-87
Table 15-72. Statistics for 24-Hour Cumulative Number of Minutes Spent in Outdoor Cleaning 15-88
Table 15-73. Statistics for 24-Hour Cumulative Number of Minutes Spent in Clothes Care 15-89
Table 15-74. Statistics for 24-Hour Cumulative Number of Minutes Spent in Car Repair/Maintenance '. 15-90
Table 15-75. Statistics for 24-Hour Cumulative Number of Minutes Spent in Other Repairs 15-91
Table 15-76. Statistics for 24-Hour Cumulative Number of Minutes Spent in Plant Care 15-92
Table 15-77. Statistics for 24-Hour Cumulative Number of Minutes Spent in Animal Care .......... 15-93
Table 15-78. Statistics for 24-Hour Cumulative Number of Minutes Spent in Other Household Work . 15-94
Table 15-79. Statistics for 24-Hour Cumulative Number of Minutes Spent in Indoor Playing 15-95
Table 15-80. Statistics for 24-Hour Cumulative Number of Minutes Spent in Outdoor Playing 15-96
Table 15-81. Statistics for 24-Hour Cumulative Number of Minutes Spent for Car Repair Services ..'. 15-97
Table 15-82. Statistics for 24-Hour Cumulative Number of Minutes Spent Washing, etc 15-98
Table 15-83. Statistics for 24-Hour Cumulative Number of Minutes Spent Sleeping/Napping 15-99
Table 15-84. Statistics for 24-Hour Cumulative Number of Minutes Spent Attending
Full Time School . 15-100
Table 15-85. Statistics for 24-Hour Cumulative Number of Minutes Spent in Active Sports '. 15-101
Table 15-86. Statistics for 24-Hour Cumulative Number of Minutes Spent in Outdoor Recreation .'.'. 15-102
Table 15-87. Statistics for 24-Hour Cumulative Number of Minutes Spent in Exercise 15-103
Table 15-88. Statistics for 24-Hour Cumulative Number of Minutes Spent in Food Preparation .... .15-104
Table 15-89. Statistics for 24-Hour Cumulative Number of Minutes Spent Doing Dishes/Laundry .'.'. 15-105
Table 15-90. Statistics for 24-Hour Cumulative Number of Minutes Spent in Housekeeping 15-106
Table 15-91. Statistics for 24-Hour Cumulative Number of Minutes Spent in Bathing 15-107
Table 15-92. Statistics for 24-Hour Cumulative Number of Minutes Spent in Yardwork/Maintenance 15-108
Table 15-93. Statistics for 24-Hour Cumulative Number of Minutes Spent in Sports/Exercise 15-109
Table 15-94. Statistics for 24-Hour Cumulative Number of Minutes Eating or Drinking 15-110
Table 15-95. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at an
Auto Repair Shop/Gas Station ]5.j j ]
Table 15-96. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at a
Gym/Health Club 15_j 12
Table 15-97. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at the
Laundromat j^_j jo
Table 15-98. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at Work
(non-specific) 15_j 14
Table 15-99. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at the
Dry Cleaners <_
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 15-100. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at a
Bar/Nightclub/Bowling Alley l5'1 ^
Table 15-101. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at a Restaurant ... 15-117
Table 15-102'. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at School 15-118
Table 15-103. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at a
Plant/Factory/Warehouse 15
Table 15-104. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors on a
Sidewalk, Street, or in the Neighborhood 15-120
Table 15-105. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors in a
Parking Lot ?. 15"121
Table 15-106. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors at a
Service Station or Gas Station 15~12
Table 15-107. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors at a
Construction Site 15-123
Table 15-108. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors on School
Grounds/Playground 15-124
Table 15-109. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors at a
Park/Golf Course 15-125
Table 15-110. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors at a
Pool/River/Lake 15~126
Table 15-111. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors at a
Restaurant/Picnic • ]^']^7
Table 15-112. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors at a Farm 15-128
Table 15-113. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home in the Kitchen . . . 15-129
Table 15-114 Statistics for 24-Hour Cumulative Number of Minutes Spent in the Bathroom 15-130
Table 15-115. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home in the Bedroom .. 15-131
Table 15-116. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home in the Garage ... 15-132
Table 15-117. Statistics for 24-Hour Cumulative Number of Minutes Spent in the Basement 15-133
Table 15-118. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home in the
Utility Room or Laundry Room ' 15-134
Table 15-119. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home in the Outdoor
Pool or Spa • • • • 15~135
Table 15-120. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home in the Yard or
Other Areas Outside the House 15-136
Table 15-121. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling in a Car 15-137
Table 15-122. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling in a Truck
(Pick-up/Van) 15'138
Table 15-123. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling on a Motorcycle,
Moped, or Scooter ^"l^n
Table 15-124. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling in Other Trucks 15-14U
Table 15-125. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling on a Bus 15-141
Table 15-126. Statistics for 24-Hour Cumulative Number of Minutes Spent Walking 15-142
Table 15-127. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling on a
Bicycle/Skateboard/Rollerskate 15-143
Page
xxvi
Exposure Factors Handbook
August 1997
-------
EFH
Table 15-128.
Table 15-129.
Table 15-130.
Table 15-131.
Table 15-132.
Table 15-133.
Table 15-135.
Table 15-136.
Table 15-137.
Table 15-138.
Table 15-139.
Table 15-140.
Table 15-141.
Table 15-142.
Table 15-143.
Table 15-146.
Table 15-147.
Table 15-148.
Table 15-149.
Table 15-150.
Table 15-151.
Table 15-152.
Table 15-153.
Table 15-154.
LIST OF TABLES (continued)
Statistics for 24-Hour Cumulative Number of Minutes Spent Waiting on a Bus Train
etc., Stop
Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling on a
Train/Subway/Rapid Transit ...
Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling on an Airplane
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors in a Residence
(all rooms)
Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors (outside the
residence)
Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling Inside
a Vehicle
Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors Near a Vehicle
Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors Other Than
Near a Residence or Vehicle Such as Parks, Golf Courses, or Farms
Statistics for 24-Hour Cumulative Number of Minutes Spent in an Office or Factory
Statistics for 24-Hour Cumulative Number of Minutes Spent in Malls, Grocery Stores
or Other Stores ,
Statistics for 24-Hour Cumulative Number of Minutes Spent in Schools, Churches
Hospitals, and Public Buildings .
Statistics for 24-Hour Cumulative Number of Minutes Spent in Bars/Nightclubs
Bowling Alleys, and Restaurants . .
Statistics for 24-Hour Cumulative Number of Minutes Spent in Other Outdoors
Such as Auto Repair Shops, Laundromats, Gyms, and at Work (non-specific)
Statistics for 24-Hour Cumulative Number of Minutes Spent with Smokers Present
Range of Time (minutes) Spent Smoking Based on the Number of Respondents
Number of Minutes Spent Smoking (minutes/day)
Range of Time Spent Smoking Cigars or Pipe Tobacco by the Numberof Respondents
Number of Minutes Spent Smoking Cigars or Pipe Tobacco (minutes/day)
Range of Numbers of Cigarettes Smoked Based on the Number of Respondents
Range of Numbers of Cigarettes Smoked by Other People Based on Number
of Respondents
Range of Numbers of Cigarettes Smoked While at Home Based on the
Numberof Respondents ...
Differences in Time Use (hours/week) Grouped by Sex, Employment Status,
and Marital Status for the Surveys Conducted in 1965 and 1975
Time Use (hours/week) Differences by Age for the Surveys Conducted in 1 965
and 1975
Time Use (hours/week) Differences by Education for the Surveys Conducted in 1965
and 1975
Time Use (hours/week) Differences by Race for the Surveys Conducted in 1965
and 1975
Mean Time Spent (hours/week) in Ten Major Activity Categories Grouped by Regions
Total Mean Time Spent (minutes/day) in Ten Major Activity Categories Grouped by
Exposure Factors Handbook
August 1997
Page No.
1^1 A A
1 ^ I/IS
. 15-1.46
1 C 1 A*J
. . 1O-14/
1 £ 1 A 0
. . 1J-145
. . ij-14y
. 15-150
15-151
, . 15-152
1 ^ 1 ^3
1< 1C/1
IS 1SS
15-156
. 15-157
, 15-158
15-160
. 15-161
15-162
. 15-163
1 < 1 f*A
. O-104
. 15-165
. 15-166
. 15-167
. 15-168
, 15-169
15-169
15-170
Page
xxvii
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 15-155. Mean Time Spent (minutes/day) in Ten Major Activity Categories During Four Waves ^^
Table 15-156. MeSmrSpent (hours/week) in Ten Major Activity Categories Grouped by Gender . 15-171
Table 15-157 Percent Responses of Children's "Play" (activities) Locations m Maryvale, Arizona ... 5-71
Table 15-158. Occupational Tenure of Employed Individuals by Age and Sex «-
Table 15-159 Occupational Tenure for Employed Individuals Grouped by Sex and Race 15-172
Table 15-160! Occupational Tenure for Employed Individuals Grouped by Sex and Employment ^^
Table 15-161. Occupational Tenure'of Employed Individuals Grouped by Major Occupational ^^
Groups and Age •'; ''''' , ^ 17,
Table 15-162 Voluntary Occupational Mobility Rates for Workers Age 16 Years and Older 15-173
Table 15-163. Values and Their Standard Errors for Average Total Residence Time, T, tor ^^
Each Group in Survey ''''''
Table 15-164. Total Residence Time, t (years), Corresponding to Selected Values of R(t) by ^^
Housing Category ]5 J75
Table 15-165. Residence Time of Owner/Renter Occupied Units
Table 15-166 Percent of Householders Living in Houses for Specified Ranges of Time 3-/3
Table 15-167. Descriptive Statistics for Residential Occupancy Period "
Table 15-168 Descriptive Statistics for Both Genders by Current Age ^
Table 15-169. Summary of Residence Time of Recent Home Buyers (1993) »-
Table 15-170. Tenure in Previous Home (Percentage Distribution)
Table 15-171. Number of Miles Moved (Percentage Distribution) [5 n*
Table 15-172. Confidence in Activity Patterns Recommendations '
Table 15-173. Confidence in Occupational Mobility Recommendations 15.1%%
Table 15-174 Recommendations for Population Mobility "
Table 15-175 Confidence in Population Mobility Recommendations ^
Table 15-176. Summary of Recommended Values for Activity Factors »•>-"
Table 15A-1 Activity Codes.and Descriptors Used for Adult Time Diaries
Table 15A-2 Differences in Average Time Spent in Different Activities Between California
and National Studies (minutes per day for age 18-64 years) 15A-19
Table 15A-3. Time Spent in Various Microenvironments 15A-21
Table 15A-4. Major Time Use Activity Categories •• • • • • • • • • • • • "
Table 15A-5. Mean Time Spent (minutes/day) for 87 Activities Grouped by Day of the Week 5A-22
Table 15A-6 Weighted Mean Hours Per Week by Gender: 87 Activities and 10 Subtotals 15A-24
Table 15A-7 Ranking of Occupations by Median Years of Occupational Tenure
Table 15B-l" Annual Geographical Mobility Rates, by Type of Movement for Selected
1-Year Periods: 1960-1992 (numbers in thousands)
Table 15B-2. Mobility of the Resident Population by State: 1980
Table 16-1 Consumer Products Found in the Typical U.S. Household
Table 16-2 Frequency of Use for Household Solvent Products (users-only)
Table 16-3 Exposure Time of Use for Household Solvent Products (users-only)
Table 16-4' Amount of Products Used for Household Solvent Products (users-only) 0- ^
Table 16-5 Time Exposed After Duration of Use for Household Solvent Products (users-only) 6-3
Table 16-6. Frequency of Use and Amount of Product Used for Adhesive Removers io-1«
Page
xxviii
Exposure Factors Handbook
August 1997
-------
EFH
LIST OF TABLES (continued)
Pagfe No.
Table 16-7. Adhesive Remover Usage by Gender 16_14
Table 16-8. Frequency of Use and Amount of Product Used for Spray Paint ! 16~15
Table 16-9. Spray Paint Usage by Gender , ]\ j/,-
Table 16-10. Frequency of Use and Amount of Product Used for Paint Removers/Strippers 16 16
Table 16-11. Paint Stripper Usage by Gender '" 16-16
Table 16-12. Total Exposure Time of Performing Task and Product Type Used by Task for
Household Cleaning Products 1617
Table 16-13. Percentile Rankings for Total Exposure Time in Performing Household Tasks 16-19
Table 16-14. Mean Percentile Rankings for Frequency of Performing Household Tasks 16-20
Table 16-15. Mean and Percentile Rankings for Exposure Time Per Event of Performing
Household Tasks 1621
Table 16-16. Total Exposure Time for Ten Product Groups Most Frequently Used for "
Household Cleaning \(>2\
Table 16-17. Total Exposure Time of Painting Activity of Interior Painters (hours) '.'." 16-22
Table 16-18. Exposure Time of Interior Painting Activity/Occasion (hours) and Frequency of '" '
Occasions Spent Painting Per Year 1622
Table 16-19. Amount of Paint Used by Interior Painters '.'.'.'.'.'.'.'.'.'.'.'. 16-22
Table 16-20. Number of Respondents Using Cologne, Perfume, Aftershave or Other
Fragrances at Specified Daily Frequencies 16-23
Table 16-21. Number of Respondents Using Any Aerosol Spray Product for Personal Care
Item Such as Deodorant or Hair Spray at Specified Daily Frequencies 16-24
Table 16-22. Number of Minutes Spent in Activities Working with or Being Near Freshly Applied
Paints (minutes/day) ]6 25
Table 16-23. Number of Minutes Spent in Activities Working with or Near Household
Cleaning Agents Such as Scouring Powders or Ammonia (minutes/day) 16-26
Table 16-24. Number of Minutes Spent in Activities (at home or elsewhere) Working with
or Near Floorwax, Furniture Wax or Shoe Polish (minutes/day) 16-27
Table,16-25. Number of Minutes Spent in Activities Working with or Being Near Glue 16 28
Table 16-26. Number of Minutes Spent in Activities Working with or Near Solvents, Fumes
or Strong Smelling Chemicals (minutes/day) 16_29
Table 16-27. Number of Minutes Spent in Activities Working with or Near Stain or Spot
Removers (minutes/day) ., 16 30
Table 16-28. Number of Minutes Spent in Activities Working with or Near Gasoline or
Diesel-powered Equipment, Besides Automobiles (minutes/day) 16-31
Table 16-29. Number of Minutes Spent Using Any Microwave Oven (minutes/day) 16 32
Table 16-30. Number of Respondents Using a Humidifier at Home ..." " " ' 16.33
Table 16-31. Number of Respondents Indicating that Pesticides Were Applied by the Professional at
Home to Eradicate Insects, Rodents, or Other Pests at Specified Frequencies . 16-34
1 able 16-32. Number of Respondents Reporting Pesticides Applied by the Consumer at Home to
Eradicate Insects, Rodents, or Other Pests at Specified Frequencies 16-35
Table 16-33. Number of Minutes Spent in Activities Working with or Near Pesticides, Including
Bug Sprays or Bug Strips (minutes/day) 16.36
Table 16-34. Amount and Frequency of Use of Various Cosmetic and Baby Products 16-37
Table 16-35. Summary of Consumer Products Use Studies 16 40
Table 16A-1. Volumes Included in 1992 Simmons Study ,*! ,
•' * 10/V- j
Exposure Factors Handbook
August 1997
Page
xxix
-------
EFH
LIST OF TABLES (continued)
Page No.
; 17-3
Table 17-1. Summary of Residential Volume Distributions • • • •
Tab e 17-2 Average Estimated Volumes of U.S. Residences, by Housing Type and Ownership 7-4
Table 17-3*. Residential Volumes in Relation to Household Size and Year of Construction 17-4
Table 17-4 Dimensional Quantities for Residential Rooms • • • • -
Table 17-S'. Examples of Products and Materials Associated with Floor and Wall Surfaces ^
in Residences • • •''''; 17 8
Table 17-6 Percent of Residences with Basement, by Census Region and EPA Region " ' -7~Q
Table 17-?' Percent of Residences with Certain Foundation Types by Census Region /-*
Table 17-8 States Associated with EPA Regions and Census Regions - -
Table 17-9. Summary of Major Projects Providing Air Exchange Measurements in the ^ ^
PFTDatabase ''•'''''' D" ' 17 ,9
Table 17-10 Summary Statistics for Air Exchange Rates (air changes per hour-ACH) by Region .... /- z
Table 17-11! Distributions of Residential Air Exchange Rates by Climate Region and Season 17-13
Table 17-12. Deposition Rates for Indoor Particles • 17 15
Table 17-13 Particle Deposition During Normal Activities "
Table 17-14 In-house Water Use Rates (gcd), by Study and Type of Use ''
Table 17-15. Summary of Selected HUD and Power Authority Water Use Studies J/- '
Table 17-16 Showering and Bathing Water Use Characteristics '
Table 17-17. Showering Characteristics for Various Types of Shower Heads • • ^^
Table 17-18. Toilet Water Use Characteristics • 17_lg
Table 17-19. Toilet Frequency Use Characteristics .'' J7"lg
Table 17-20. Dishwasher Frequency Use Characteristics ^ ^
Table 17-21. Dishwasher Water Use Characteristics
Table 17-22. Clothes Washer Frequency Use Characteristics "
Table 17-23. Clothes Washer Water Use Characteristics ]7_J9
Table 17-24. Range of Water Uses for Clothes Washers ^'^
Table 17-25. Total Dust Loading for Carpeted Areas • • "
Table 17-26 Particle Deposition and Resuspension During Normal Activities
Table 17-27. Dust Mass Loading After One Week Without Vacuum Cleaning '-^
Table 17-28. Simplified Source Descriptions for Airborne Contaminants ^'^
Table 17-29. Volume of Residence Surveys 17_29
Table 17-30. Air Exchange Rates Surveys 17]30
Table 17-31. Recommendations - Residential Parameters
Table 17-32. Confidence in House Volume Recommendations '
Table 17-33. Confidence in Air Exchange Rate Recommendations
^
Exposure Factors Handbook
August1997
-------
VOLUME I
EFH
LIST OF FIGURES
Page No.
Figure 1-1. Schematic of Dose and Exposure: Oral Route ! 13
Figure 1-2. Road Map to Exposure Factor Recommendations .... i"]7
Figure 6-1. Schematic of Dose and Exposure: Dermal Route " 6"I2
Figure 6-2. SA/BW Distributions for Infants, Adults, and All Ages Combined ".'.'. 6"l 8
Figure 6-3. Surface Area Frequency Distribution: Men and Women 6 19
Figure 7-1. Weight by Age Percentiles for Boys Aged Birth-36 Months " " '' " _
Figure 7-2. Weight by Age Percentiles for Girls Aged Birth-36 Months '.'.'.'.'.'.'.'.'.'.'. 7-3
VOLUME II
Figure 10-1. Seasonal Fish Consumption: Wisconsin Chippewa, 1990 1073
Figure 10-2. Peak Fish Consumption: Wisconsin Chippewa, 1990 '.'.'.'.'.'.'.'.'.'.'.'.'"'"' 10-73
VOLUME HI
Figure 15-1. Distribution of Individuals Moving by Type of Move-1991-92 IS 14
Figure 17-1. Elements of Residential Exposure '.'.'.'.'.'.'.]'. ' 17.1
Figure 17-2. Cumulative Frequency Distributions for Residential Volumes from the PFT Data
Base and the U.S. DOE's RECs 173
Figure 17-3. Configuration for Residential Forced-air Systems . 17~7
Figure 17-4. Idealized Patterns of Particle Deposition Indoors 17 ,"4
Figure 17-5. Air Flows for Multiple-zone Systems ""
Exposure Factors Handbook
August 1997
Page
xxxi
-------
EFH
PREFACE
The National Center for Environmental Assessment (NCEA) of EPA's Office of Research and
Development (ORD) has prepared this handbook to address factors commonly used in exposure assessments. This
handbook was first published in 1989 in response to requests from many EPA Program and Regional offices for
additional guidance on how to select values for exposure factors.
Several events sparked the efforts to revise the Exposure Factors Handbook. First, since its
publication in 1989, new data have become available. Second, the Risk Assessment Council issued a memorandum
titled, "Guidance on Risk Characterization for Risk Managers and Risk Assessors," dated February 26, 1992, which
emphasized the use of multiple descriptors of risk (i.e., measures of central tendency such as average or mean, or
high end), and characterization of individual risk, population risk, important subpopulations. A new document was
issued titled "Guidance for Risk Characterization," dated February 1995. This document is an update of the
guidance issued with the 1992 policy. Third, EPA published the revised Guidelines for Exposure Assessment in
1992.
As part of the efforts to revise the handbook, the EPA Risk Assessment Forum sponsored a
two-day peer involvement workshop which was conducted during the summer of 1993. The workshop was attended
by 57 scientists from academia, consulting firms, private industry, the States, and other Federal agencies. The
purpose of the workshop was to identify new data sources, to discuss adequacy of the data and the feasibility of
developing statistical distributions and to establish priorities.
As a result of the peer involvement workshop, three new chapters were added to the handbook.
These chapters are: Consumer Product Use, Residential Building Characteristics, and Intake of Grains. This
document also provides a summary of the available data on consumption of drinking water; consumption of fruits,
vegetables, beef, dairy products, grain products, and fish; breast milk intake; soil ingestion; inhalation rates; skin
surface area; soil adherence; lifetime; activity patterns; and body weight.
A new draft handbook that incorporated comments from the 1993 workshop was published for
peer review in June 1995. A peer review workshop was held in July 1995 to discuss comments on the draft
handbook. A new draft of the handbook that addressed comments from the 1995 peer review workshop was
submitted to the Science Advisory Board (SAB) for review in August 1996. An SAB workshop meeting was held
December 19-20, 1996, to discuss the comments of the SAB reviewers. Comments from the SAB review have been
incorporated into the current handbook.
Page
xxxii
Exposure Factors Handbook
August 1997
-------
EFH
FOREWORD
The National Center for Environmental Assessment (NCEA) of EPA's Office of Research and Development
(ORD) has five main functions: (1) providing risk assessment research, methods, and guidelines; (2) performing
health and ecological assessments; (3) developing, maintaining, and transferring risk assessment information and
training; (4) helping ORD set research priorities; and (5) developing and maintaining resource support systems for
NCEA. The activities under each of these functions are supported by and respond to the needs of the various
program offices. In relation to the first function, NCEA sponsors projects aimed at developing or refining techniques
used in exposure assessments.
This handbook was first published in 1989 to provide statistical data on the various factors used in assessing
exposure. This revised version of the handbook provides the up-to-date data on these exposure factors. The
recommended values are based solely on our interpretations of the available data. In many situations different values
may be appropriate to use in consideration of policy, precedent or other factors.
Michael A. Callahan
Director
National Center for Environmental Assessment
Washington Office
Exposure Factors Handbook
August 1997
Page
xxxiii
-------
EFH
AUTHORS, CONTRIBUTORS, AND REVIEWERS
The National Center for Environmental Assessment (NCEA), Office of Research and Development was
responsible for the preparation of this handbook. The original document was prepared by Versar Inc. under EPA
Contract No. 68-02-4254, Work Assignment No. 189. John Schaum, of NCEA-Washington 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 provided by Versar Inc.
under Contract Numbers 68-DO-0101, 68-D3-0013, and 68-D5-0051. Russell Kinerson and Greg Kew have served
as EPA Work Assignment Managers during previous efforts of the update process. Jackie Moya served as Work
Assignment Manager for the current updated version, providing overall direction, technical assistance, and serving as
contributing author.
AUTHORS
Patricia Wood
Linda Phillips
Aderonke Adenuga
Mike Koontz
Harry Rector
Charles Wilkes
Maggie Wilson
DESKTOP PUBLISHING
Susan Perry
WORD PROCESSING
Valerie Schwartz
GRAPHICS
Kathy Bowles
Jennifer Baker
Exposure Assessment Division
Versar Inc.
Springfield, VA
Page
xxxiv
Exposure Factors Handbook
August 1997
-------
EFH
CONTRIBUTORS AND REVIEWERS
The following EPA individuals have reviewed and/or have been contributing authors of this document.
Michael Dellarco
Robert McGaughy
Amy Mills
Jacqueline Moya
Susan Perl in
Paul Pinsky
John Schaum
Paul White
Amina Wilkins
Chieh Wu
The following individuals were Science Advisory Board Reviewers:
Members
Dr. Joan Daisey
Lawrence Berkley Laboratory
Berkley, California
Dr. Paul Bailey
Mobil Business Resources Corporation
Paulsboro, New Jersey
Dr. Robert Hazen
State of New Jersey Department of
Environmental Protection and Energy
Trenton, New Jersey
Dr. Timothy Larson
Department of Civil Engineering
University of Washington
Seattle, Washington
Dr. Kai-Shen Liu
California Department of Health Services
Berkeley, California
Dr. Paul Lioy
Environmental Occupational Health
Sciences Institute
Piscataway, New Jersey
Dr. Maria Morandi
University of Texas School of Public Health
Houston, Texas
Dr. Jonathan M. Samet
The Johns Hopkins University
Baltimore, Maryland
Mr. Ron White
American Lung Association
Washington, D.C.
Dr. Lauren Zeise
California Environmental Protection Agency
Berkeley, California
Federal Experts
Dr. Richard Ellis
U.S. Department of Agriculture
Washington, D.C.
Ms. Alanna J. Moshfegh
U.S. Department of Agriculture
Washington, D.C.
Exposure Factors Handbook
August 1997
Page
xxxv
-------
EFH
An earlier draft of this document was peer reviewed by a panel of experts at a peer-review workshop held in
1995. Members of the Peer Review Panel were as follows:
Edward Avol
Department of Preventive Medicine
School of Medicine
University of Southern California
James Axley
School of Architecture
Yale University
David Burmaster
Alceon Corporation
Steven Colome
Integrated Environmental Services
Michael DiNovi
Chemistry Review Branch
U.S. Food & Drug Administration
Dennis Druck
Environmental Scientist
Center of Health Promotion & Preventive
Medicine
U.S. Army
J. Mark Fly
Department of Forestry, Wildlife, &
Fisheries
University of Tennessee
Larry Gephart
Exxon Biomedical Sciences, Inc.
Patricia Guenther
Beltsville Human Nutrition Research Center
U.S. Department of Agriculture
P.J. (Bert) Hakkinen
Paper Product Development & Paper
Technology Divisions
The Proctor & Gamble Company
Mary Hama
Beltsville Human Nutrition Research Center
U.S. Department of Agriculture
Dennis Jones
Agency for Toxic Substances & Disease Registry
John Kissel
Department of Environmental Health
School of Public Health & Community Medicine
Neil Klepeis
Information Systems & Services, Inc.
Andrew Persily
National Institute of Standards & Technologies
Barbara Petersen
Technical Assessment Systems, Inc.
Thomas Phillips
Research Division
California Air Resources Board
Paul Price
ChemRisk
John Risher
Division of Toxicology
The Agency for Toxic Substances & Disease Registry
John Robinson
University of Maryland
Peter Robinson
The Proctor & Gamble Company
P. Barry Ryan
Department of Environmental & Occupational
Health
Rollins School of Public Health
Emory University
Val Schaeffer
U.S. Consumer Product Safety Commission
Brad Shurdut
DowElanco
John Talbott
Page
xxxvi
Exposure Factors Handbook
August 1997
-------
EFH
U.S. Department of Energy
Frances Vecchio
Beltsville Human Nutrition Research Center
U.S. Department of Agriculture
The following individuals within EPA have reviewed an earlier draft of this document and provided valuable
comments:
OFFICE
REVIEWERS/CONTRIBUTORS
Office of Research and Development
Office of Pollution, Pesticides and Toxic
Substances
Maurice Berry
Jerry Blancato
Elizabeth Bryan
Curtis Dary
Stan Durkee
Manuel Gomez
Wayne Marchant
Sue Perlin
James Quanckenboss
Glen Rice
Lance Wallace
Office of Emergency and Remedial Response Jim Konz
Pat Kennedy
Cathy Fehrenbacker
Office of Water
Office of Air Quality Planning and Standards
EPA Regions
Denis Borum
Helen Jacobs
Warren Peters
Steve Ehlers - Reg. VI
Maria Martinez - Reg. VI
Mike Morton - Reg. VI
Jeffrey Yurk - Reg. VI
Youngmoo Kim - Reg. VI
In addition, the National Exposure Research Laboratory (NERL) of the Office of Research and
Development of EPA made an important contribution to this handbook by conducting additional analyses of the
National Human Activity Pattern Survey (NHAPS) data. EPA input to the NHAPS data analysis came from Karen
A. Hammerstrom and Jacqueline Moya from NCEA-Washington Office; William C. Nelson from NERL-RTP, and
Stephen C. Hern, Joseph V. Behar (retired), and William H. Englemann from NERL-Las Vegas.
The EPA Office of Water made an important contribution by conducting an analysis of the USDA
Continuing Survey of Food Intakes by Individual (CSFII) data. They provided fish intake rates for the general
population. The analysis was conducted under the direction of Helen Jacobs from the Office of Water.
Exposure Factors Handbook
August 1997
Page
xxxvii
-------
-------
Volume I - General Factors
Chapter 1 - Introduction
EFH
1. INTRODUCTION
1.1. PURPOSE
The purpose of the Exposure Factors Handbook is
to: (1) summarize data on human behaviors and
characteristics which affect exposure to environmental
contaminants, and (2) recommend values to use for these
factors. These recommendations are not legally binding
on any EPA program and should be interpreted as
suggestions which program offices or individual
exposure assessors can consider and modify as needed.
Most of these factors are best quantified on a site or
situation-specific basis. The handbook has strived to
include full discussions of the issues which assessors
should consider hi deciding how to use these data and
recommendations. The handbook is intended to serve as
a support document to EPA's Guidelines for Exposure
Assessment (U.S. EPA, 1992a). The Guidelines were
developed to promote consistency among the various
exposure assessment activities that are-carried out by the
various EPA program offices. This handbook assists in
this goal by providing a consistent set of exposure factors
to calculate dose.
1.2. INTENDED
AUDIENCE
The Exposure Factors
Handbook is addressed to
exposure assessors inside the
Agency as well as outside, who
need to obtain data on standard
factors needed to calculate
human exposure to toxic
chemicals.
Purpose
• Summarize data on human
behaviors and characteristics
affecting exposure
• Recommend exposure factor
values
1.3. BACKGROUND
This handbook is the update of an earlier version
prepared in 1989. Revisions have been made in the
following areas:
• addition of drinking water rates for children;
• changes in soil ingestion rates for children;
• addition of soil ingestion rates for adults;
• addition of tapwater consumption for adults
and children;
• addition of mean daily intake of food class
and subclass by region, age and per capita
rates;
addition of mean moisture content of selected
fruits, vegetables, grains, fish, meat, and
dairy products;
addition of food intake by class in dry weight
per kg of body weight per day;
update of homegrown food intake;
expansion of data hi the dermal chapter;
update of fish intake data;
expansion of data for time spent at residence;
update of body weight data;
addition of body weight data for infants;
update of population mobility data;
addition of new data for average time spent in
different locations and various microenviron-
ments;
addition of data for occupational mobility;
addition of breast milk ingestion;
addition of consumer product use; and
addition of reference residence factors.
Variation Among Studies
This handbook is a compilation of available data
from a variety of different
sources. With very few
exceptions, the data presented
are the analyses of the
individual study authors. Since
the studies included in this
handbook varied in terms of
their objectives, design, scope,
presentation of results, etc., the
level of detail, statistics, and
terminology may vary from
study to study and from factor
to factor. For example, some
authors used geometric means to present their results,
while others used arithmetic means or distributions.
Authors have sometimes used different terms to describe
the same racial populations. Within the constraint of
presenting the original material as accurately as possible,
EPA has made an effort to present discussions and
results in a consistent manner. Further, the strengths and
limitations of each study are discussed to provide the
reader with a better understanding of the uncertainties
associated with the values derived from the study.
1.3.1. Selection of Studies for the Handbook
Information in this handbook has been summarized
from studies documented in the scientific literature and
Exposure Factors Handbook
August 1997
Page
1-1
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
other available sources. Studies were chosen that were
seen as useful and appropriate for estimating exposure
factors. The handbook contains summaries of selected
studies published through August 30, 1997.
General Considerations
Many scientific studies were reviewed for possible
inclusion in this handbook. Studies were selected based
on the following considerations:
• Level of peer review: Studies were selected
predominantly from the peer-reviewed
literature and final government reports.
Internal or interim reports were therefore
avoided.
• Accessibility: Studies were preferred that the
user could access in their entirety if needed.
• Reproducibility: Studies were sought that
contained sufficient information so that
methods could be reproduced, or at least so
the details of the author's work could be
accessed and evaluated.
• Focus on exposure factor of interest: Studies
were chosen that directly addressed the
exposure factor of interest, or addressed
related factors that have significance for the
factor under consideration. As an example of
the latter case, a selected study contained
useful ancillary information concerning fat
content in fish, although it did not directly
address fish consumption.
• Data pertinent to the U.S.: Studies were
selected that addressed the U.S. population.
Data from populations outside the U.S. were
sometimes included if behavioral patterns and
other characteristics of exposure were
similar.
Primary
data: Studies were deemed
preferable if based on primary data, but
studies based on secondary sources were also
included where they offered an original
analysis. For example, the handbook cites
studies of food consumption based on original
data collected by the USDA National Food
Consumption Survey.
• Current information: Studies were chosen
only if they were sufficiently recent to
represent current exposure conditions. This
is an important consideration for those factors
that change with time.
• Adequacy of data collection period: Because
most users of the handbook are primarily
addressing chronic exposures, studies were
sought that utilized the most appropriate
techniques for collecting data to characterize
long-term behavior.
• Validity of approach: Studies utilizing
experimental procedures or approaches that
more likely or closely capture the desired
measurement were selected. In general,
direct exposure data collection techniques,
such as direct observation, personal
monitoring devices, or other known methods
were preferred where available. If studies
utilizing direct measurement were not
available, studies were selected that rely on
validated indirect measurement methods such
as surrogate measures (such as heart rate for
inhalation rate), and use of questionnaires. If
questionnaires or surveys were used, proper
design and procedures include an adequate
sample size for the population under
consideration, a response rate large enough to
avoid biases, and avoidance of bias in the
design of the instrument and interpretation of
the results.
• Representativeness of the population: Studies
seeking to characterize the national
population, a particular region, or sub-
population were selected, if appropriately
representative of that population. In cases
where data were limited, studies with
limitations in this area were included and
limitations were noted in the handbook.
• Variability in the population: Studies were
sought that characterized any variability
within populations.
Page
1-2
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 1 - Introduction
EFH
• Minimal (or defined) bias in study design:
Studies were sought that were designed with
minimal bias, or at least if biases were
suspected to be present, the direction of the
bias (i.e., an over or under estimate of the
parameter) was either stated or apparent from
the study design.
• Minimal (or defined) uncertainty in the data:
Studies were sought with minimal uncertainty
in the data, which was judged by evaluating
all the considerations listed above. At least,
studies were preferred that identified
uncertainties, such as those due to inherent
variability in environmental and exposure-
related parameters or possible measurement
error. Studies that documented Quality
Assurance/Quality Control measures were
preferable.
Key versus relevant studies
Certain studies described
in this handbook are designated
as "key," that is, the most
useful for deriving exposure
factors. The recommended
values for most exposure factors
are based on the results of the
key studies. Other studies are
designated "relevant," meaning
applicable or pertinent, but not
necessarily the most important. This distinction was
made on the strength of the attributes listed in the
"General Considerations." For example, in Chapter 14
of Volume III, one set of studies is deemed to best
address the attributes listed and is designated as "key."
Other applicable studies, including foreign data, believed
to have value to handbook users, but having fewer
attributes, are designated "relevant."
1.3.2. Using the Handbook in an Exposure
Assessment
Some of the steps for performing an exposure
assessment are (1) determining the pathways of
exposure, (2) identifying the environmental media which
transports the contaminant, (3) determining the
contaminant concentration, (4) determining the exposure
time, frequency, and duration, and (5) identifying the
exposed population. Many of the issues related to
Key vs. Relevant Studies
• Key studies used to derive
recommendations
• Relevant studies included to provide
characterizing exposure from selected exposure pathways
have been addressed in a number of existing EPA
guidance documents. These include, but are not limited
to the following:
• Guidelines for Exposure Assessment (U.S.
EPA 1992a);
• Dermal Exposure Assessment: Principles
and Applications (U.S. EPA 1992b);
• Methodology for Assessing Health Risks
Associated with Indirect Exposure to
Combustor Emissions (U.S. EPA, 1990);
• Risk Assessment Guidance for Superfund
(U.S. EPA, 1989);
• Estimating Exposures to Dioxin-Like
Compounds (U.S. EPA, 1994);
• Superfund Exposure Assessment Manual
(U.S. EPA, 1988a);
• Selection Criteria for Mathematical Models
Used hi Exposure Assessments (U.S. EPA
1988b);
• Selection Criteria for
Mathematical Models
Used in Exposure
Assessments (U.S. EPA
1987);
• Standard Scenarios for
Estimating Exposure to
Chemical Substances
During Use of Consumer
Products (U.S. EPA
1986a);
• Pesticide Assessment Guidelines, Subdivisions K
and U (U.S. EPA, 1984, 1986b); and
• Methods for Assessing Exposure to Chemical
Substances, Volumes 1-13 (U.S. EPA, 1983-
1989).
These documents may serve as valuable information
resources to assist in the assessment of exposure. The
reader is encouraged to refer to them for more detailed
discussion.
In addition to the references listed above, this
handbook discusses the recommendations provided by the
American Industrial Health Council (AIHC) - Exposure
Factors Sourcebook (May 1994) for some of the major
exposure factors. The AIHC Sourcebook summarizes
and evaluates statistical data for various exposure factors
used hi risk assessments. Probability distributions for
Exposure Factors Handbook
August 1997
Page
1-3
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
specific exposure factors were derived from the available
scientific literature using ©Risk simulation software.
Each factor is described by a specific term, such as
lognormal, normal, cumulative type, or triangular.
Other distributions included Weibull, beta logistic, and
gamma. Unlike this handbook, however, the Sourcebook
does not provide a description and evaluation of every
study available on each exposure factor.
Most of the data presented in this handbook are
derived from studies that targeted (1) the general
population (e.g., USDA food consumptin surveys); and
(2) a sample population from a specific area or group
(e.g., Calabrese's et al. (1989) soil ingestion study using
children from the Amherst, Massachusetts, area). Due
to unique activity patterns, preferences, practices and
biological differences, various segments of the
population may experience exposures that are different
from those of the
general population,
which, in many cases,
may be greater. It is
necessary for risk or
exposure assessors
characterizing a diverse
population, to identify
and enumerate certain
groups within the
general population who
are at risk for greater
contaminant exposures
or exhibit a heightened
sensitivity to particular
chemicals. For further
guidance on addressing susceptible populations, it is
recommended to consult the EPA, National Center for
Environmental Assessment document Socio-demographic
Data Used for Identifying Potentially Highly Exposed
Subpopulations (to be released as a final document in the
Fall of 1997).
Most users of the handbook will be preparing
estimates of exposure which are to be combined with
dose-response factors to estimate risk. Some of the
exposure factors (e.g., life time, body weight) presented
in this document are also used hi generating dose-
response relationships. In order to develop risk estimates
properly, assessors must use dose-response relationships
in a manner consistent with exposure conditions.
Although, it is beyond the scope of this document to
explain in detail how assessors should address this issue,
a discussion (see Appendix A of this chapter) has been
included which describes how dose-response factors can
be modified to be consistent with the exposure factors for
a population of interest. This should serve as a guide for
when this issue is a concern.
1.3.3. Approach Used to Develop
Recommendations for Exposure Factors
As discussed above, EPA first reviewed all
literature pertaining to a factor and determined relevant
and key studies. The key studies were used to derive
recommendations for the values of each factor. The
recommended values were derived solely from EPA's
interpretation of the available data. Different values may
be appropriate for the user to select in consideration of
policy, precedent, strategy, or other factors such as site-
specific information. EPA's procedure for developing
recommendations was
as follows:
Recommendations and Confidence Ratings
• Recommendations based on data from single or
multiple key studies
• Variability and limitation of the data evaluated
• Recommendations rated as low, medium, and
high confidence
1. Key studies were
evaluated in terms
of both quality
and relevance to
specific popula-
tions (general U.
S. population, age
groups, gender,
etc.). The criteria
for assessing the
quality of studies
is described in
Section 1.3.1.
2. If only one study was classified as key for a
particular factor, the mean value from that study
was selected as the recommended central value for
that population. If there were multiple key studies,
all with reasonably equal quality, relevance, and
study design information were available, a weighted
mean (if appropriate, considering sample size and
other statistical factors) of the studies were chosen
as the recommended mean value. If the key studies
were judged to be unequal in quality, relevance, or
study design, the range of means were presented and
the user of this handbook must employ judgment in
selecting the most appropriate value for the
population of interest. In cases where the national
population was of interest, the mid-point of the
Page
1-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 1 - Introduction
EFH
range was usually judged to be the most appropriate
value.
3. The variability of the factor across the population
was discussed. If adequate data were available, the
variability was described as either a series of
percentiles or a distribution.
4. Limitations of the data were discussed hi terms of
data limitations, the range of circumstances over
which the estimates were (or were not) applicable,
possible biases in the values themselves, a statement
about parameter uncertainties (measurement error,
sampling error) and model or scenario uncertainties
if models or scenarios have been used hi the
derivation of the recommended value.
5. Finally, EPA assigned a confidence rating of low,
medium or high to each recommended value. This
rating is not intended to represent an uncertainty
analysis, rather it represents EPA's judgment on the
quality of the underlying data used to derive the
recommendation. This judgment was made using
the guidelines shown in Table 1-1. Table 1-1 is an
adaptation of the General Considerations discussed
earlier in Section 1.3.1. Clearly this is a continuum
from low to high and judgment was used to
determine these ratings. Recommendations given in
this handbook are accompanied by a discussion of
the rationale for their rating.
Table 1-2 summarizes EPA's recommendations and
confidence ratings for the various exposure factors.
It is important to note that the study elements
listed in Table 1-1 do not have the same weight when
arriving at the overall confidence rating for the various
exposure factors. The relative weight of each of these
elements depend on the exposure factor of interest.
Also, the relative weights given to the elements for the
various factors were subjective and based on the
professional judgement of the authors of this handbook.
In general, most studies would rank high with regard to
"level of peer review," "accessibility," "focus on the
factor of interest," and "data pertinent to the U.S."
These elements are important for the study to be included
in this handbook. However, a high score of these
elements does not necessarily translate into a high overall
score. Other elements in Table 1-1 were also examined
to determine the overall score. For example, the
adequacy of data collection period may be more
important when determining usual intake of foods in a
population. On the other hand, it is not as important for
factors where long-term variability may be small such as
tapwater intake. In the case of tapwater intake, the
currency of the data was a critical element in determining
the final rating. In addition, some exposure factors are
more easily measured than others. For example, soil
ingestion by children is estimated by measuring, in the
feces, the levels of certain elements found in soil. Body
weight, however, can be measured directly and it is,
therefore, a more reliable measurement. This is
reflected hi the confidence rating given to both of these
factors. In general, the better the methodology used to
measure the exposure factor, the higher the confidence
in the value.
1.3.4. Characterizing Variability
This document attempts to characterize variability
of each of the factors. Variability is characterized in one
or more of three ways: (1) as tables with various
percentiles or ranges of values; (2) as analytical
distributions with specified parameters; and/or (3) as a
qualitative discussion. Analyses to fit standard or
parametric distributions (e.g., normal, lognormal) to the
exposure data have not been performed by the authors of
this handbook, but have been reproduced in this
document wherever they were found in the literature.
Recommendations on the use of these distributions are
made where appropriate based on the adequacy of the
supporting data. The list of exposure factors and the
way that variability has been characterized (i.e.,
average, upper percentiles, multiple percentiles, fitted
distribution) are presented in Table 1-3. The term upper
percentile is used throughout this handbook and it is
intended to represent values in the upper tail (i.e.,
between 90th and 99.9th percentile) of the distribution of
values for a particular exposure factor.
An attempt was made to present percentile values
in the recommendations that are consistent with the
exposure estimators defined in the Exposure Guidelines
(i.e., mean, 50th, 90th, 95th, 98th, and 99.9th
percentile). This was not, however, always possible
because either the data available were limited for some
factors, or the authors of the study did not provide such
information. It is important to note, however, that these
percentiles were discussed hi the Exposure Guidelines
within the context of risk descriptors and not individual
exopusure factors. For example, the Guidelines stated
Exposure Factors Handbook
August 1997
Page
1-5
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
Table 1-1. Considerations Used to Rate Confidence in Recommended Values
CONSIDERATIONS
HIGH CONFIDENCE
LOW CONFIDENCE
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection period
Validity of approach
Study sizes
Representativeness of the population
Variability in the population
Lack of bias in study design
(a high rating is desirable)
Response rates
In-person interviews
Telephone interviews
Mail surveys
Measurement error
Other Elements
Number of studies
Agreement between researchers
The studies received limited peer review.
The studies received high level of peer
review (e.g., they appear in peer review
journals).
The studies are widely available to the public.
The results can be reproduced or
methodology can be followed and evaluated.
The studies focused on the exposure factor of
interest.
The studies focused on die U.S. population.
The studies analyzed primary data.
The data were published after 1990.
The study design captures the measurement
of interest (e.g., usual consumption patterns
of a population).
The studies used the best methodology
available to capture the measurement of
interest.
The sample size is greater than 100 samples. The sample size is less than 20 samples.
The sample size depends on how the target population is defined. As the size of a sample
relative to the total size of the target population increases, estimates are made with greater
statistical assurance that the sample results reflect actual characteristics of the target population.
The studies are difficult to obtain (e.g., draft
reports, unpublished data).
The results cannot be reproduced, the
methodology is hard to follow, and the
author(s) cannot be located.
The purpose of the studies was to characterize
a related factor.
The studies focused on populations outside the
U.S.
The studies are based on secondary sources.
The data were published before 1980.
The study design does not very accurately
capture the measurement of interest.
There are serious limitations with the approach
used.
The study population is the same as
population of interest.
The studies characterized variability in the
population studied.
Potential bias in the studies are stated or can
be determined from the study design.
The response rate is greater than 80 percent.
The response rate is greater dian 80 percent.
The respnose rate is greater than 70 percent.
The study design minimizes measurement
errors.
The number of studies is greater than 3.
The results of studies from different
researchers are in agreement.
The study population is very different from the
population of interest.3
The characterization of variability is limited.
The study design introduces biases in the
results.
The response rate is less than 40 percent.
The response rate is less than 40 percent.
The response rate is less than 40 percent.
Uncertainties with the data exist due to
measurement error.
The number of studies is 1.
The results of studies from different
researchers are in disagreement.
" Differences include age, sex, race, income, or other demographic parameters.
Page
1-6
Exposure Factors Handbook
August 1997
-------
Volume I- General Factors
Chapter 1 - Introduction
EFH
Table 1-2. Summary of Exposure Factor Recommendations and Confidence Ratings
EXPOSURE FACTOR
RECOMMENDATION
CONFIDENCE RATING
Drinking water intake rate
Total fruit intake rate
Total vegetable intake rate
Total meat intake rate
Total dairy intake rate
Grain intake
Breast milk intake rate
Fish intake rate
21 ml/kg-day/1.4 L/day (average)
34 ml/kg-day/2.3 L/day (90th percentile)
Percentiles and distribution also included
Means and percentiles also included for pregnant and
lactating women
3.4 g/kg-day ( per capita average)
12.4 g/kg-day (per capita 95th percentile)
Percentiles also included
Means presented for individual fruits
4.3 g/kg-day (per capita average)
10 g/kg-day (per capita 95th percentile)
Percentiles also included
Means presented for individual vegetables
2.1 g/kg-day (per capita average)
5.1 g/kg-day (per capita 95th percentile)
Percentiles also included
Percentiles also presented for individual meats
8.0 g/kg-day (per capita average)
29.7 g/kg-day (per capita 95th percentile)
Percentiles also included
Means presented for individual dairy products
4.1 g/kg-day (per capita average)
10.8 g/kg-day (per capita 95th percentile)
Percentiles also included
742 ml/day (average)
1,033 ml/day (upper percentile)
General Population
20.1 g/day (total fish) average
14.1 g/day (marine) average
6.0 g/day (freshwater/estuarine)average
63 g/day (total fish) 95th percentile long-term
Percentiles also included
Serving size
129 g (average)
326 g (95th percentile)
Recreational marine anglers
2-7 g/day (finfish only)
Recreational freshwater
8 g/day (average)
25 g/day (95th percentile)
Native American Subsistence Population
70 g/day (average)
170 g/dav (95th percentile)
Medium
Medium
Medium
Low
Medium
Low
Medium
Low
Medium
Low
High
Lowjn long-term upper percentiles
Medium
Medium
High
High
High
Medium
High
High
Medium
Medium
Medium
Medium
Low
Exposure Factors Handbook
August 1997
Page
1-7
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
Table 1-2. Summary of Exposure Factor Recommendations and Confidence Ratings (continued)
EXPOSURE FACTOR
RECOMMENDATION
CONFIDENCE RATING
Home produced food intake
Inhalation rate
Surface area
Soil adherence
Soil ingestion rate
Life expectancy
Body weight for adults
Body weights for children
Body weights for infants (birth to 6
Total Fruits
2.7 g/kg-day (consumer only average)
11.1 g/kg-day (consumer only 95th percentile)
Percentiles also included
Total vegetables
2.1 g/kg-day ( consumer only average)
7.5 g/kg-day (consumer only 95th percentile)
Percentiles also included
Total meats
2.2 g/kg-day (consumer only average)
6.8 g/kg-day (consumer only 95th percentile)
Percentiles also included
Total dairy products
14 g/kg-day (consumer only average)
44 g/kg-day (consumer only 95th percentile)
Percentiles also included
Children (<1 year)
4.5 nvVday (average)
Children (1-12 years)
8.7 nrVday (average)
Adult Females
11.3 m3/day (average)
Adult Males
15.2 m3/day (average)
Water contact (bathing and swimming)
Use total body surface area for children in Tables 6-6
through 6-8; for adults use Tables 6-2 through 6-4
(percentiles are included)
Soil contact (outdoor activities)
Use whole body part area based on Table 6-6 through 6-
8 for children and 6-2 through 6-4 for adults (percentiles
are included)
Use values presented in Table 6-16 depending on activity
and body part
(central estimates only)
Children
100 mg/day (average)
400 mg/day (upper percentile)
Adults
50 mg/day (average)
Pica child
10 g/day
75 years
71.8kg
Percentiles also presented in tables 7-4 and 7-5
Use values presented in Tables 7-6 and 7-7 (mean and
percentiles)
Use values presented in Table 7-1 (percentiles)
Medium (for means and short-term
distributions)
Low (for long-term distributions)
High
High
High
High
High
High
Low
Medium
Low
Low
High
High
High
High
Page
1-8
Exposure Factors Handbook
August 1997
-------
Volume I- General Factors
Chapter 1 - Introduction
EFH
Table 1-2.
EXPOSURE FACTOR
Showering/Bathing
Swimming
Time indoors
Time outdoors
Time spent inside vehicle
Occupational tenure
Population mobility
Residence volume
Residential air exchange
Summary of Exposure Factor Recommendations and
RECOMMENDATION
Showering time
10 min/day (average)
35 min/day (95th percentile)
(percentiles are also included)
Bathing rime
20 min/event (median)
45 min/event (90th percentile)
Bathing/showering frequency
1 shower event/day
Frequency
1 event/month
Duration
60 min/event (median)
180 min/event (90th percentile)
Children (ages 3-11)
19 hr/day (weekdays)
17 hr/day (weekends)
Adults (ases 12 and older)
21 hr/day
Residential
16.4 hrs/day
Children (ages 3-11)
5 hr/day (weekdays)
7 hr/day (weekends)
Adults
1.5 hr/day
Residential
2 hrs/day
Adults
1 hr 20 min/day
6.6 years (16 years old and older)
9 years (average)
30 years (95th percentile)
369 m3 (average)
217 m3 (conservative)
0.45 (median)
0.18 (conservative)
CONFIDENCE RATING
High
High
High
High
High
Medium
Medium
High
Medium
Medium
High
Medium
High
Medium
Medium
Medium
Medium
Low
Low
Exposure Factors Handbook
August 1997
Page
1-9
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
Table 1-3. Characterization of Variability in Exposure Factors
xposure Factors
Average
Upper percentile
Multiple Percentiles Fitted Distributions
Tinking water intake rate
Total fruits and total vegetables intake rate
ndividual fruits and individual vegetables
ntake rate
Total meats and dairy products intake rate
ndividual meats and dairy products intake
ate
Grains intake
Breast milk intake rate
Fish intake rate for general population,
ecreational marine, recreational
reshwater, and native american
Icrving size for fish
Homeproduced food intake rates
Soil intake rate
Inhalation rate
Surface area
Soil adherence
Life expectancy
Body weight
Time indoors
Time outdoors
Showering time
Occupational tenure
Population mobility
Residence volume
Residential air exchange
Qualitative discussion for long-
term
Qualitative discussion for long-
term
/
Qualitative discussion for long-
term
that the assessor may derive a high-end estimate of
exposure by using maximum or near maximum values
for one or more sensitive exposure factors, leaving
others at their mean value.
The use of Monte Carlo or other probabilistic
analysis require a selection of distributions or histograms
for the input parameters. Although this handbook is not
intended to provide a complete guidance on the use of
Monte Carlo and other probabilistic analyses, the
following should be considered when using such
techniques:
The exposure assessor should only consider
using probabilistic analysis when there are
credible distribution data (or ranges) for the
factor under consideration. Even if these
distributions are known, it may not be
necessary to apply this technique. For
example, if only average exposure values are
needed, these can often be computed
accurately by using average values for each
of the input parameters. Probabilistic
analysis is also not necessary when
conducting assessments for screening
Page
1-10
Exposure Factors Handbook
^ August 1997
-------
Volume I - General Factors
Chapter 1 - Introduction
EFH
purposes, i.e., to determine if
unimportant pathways can be
eliminated. In this case,
bounding estimates can be
calculated using maximum or
near maximum values for
each of the input parameters.
It is important to note that the selection of
distributions can be highly site specific and
will always involve some degree of judgment.
Distributions derived from national data may
not represent local conditions. To the extent
possible, an assessor should use distributions
or frequency histograms derived from local
surveys to assess risks locally. When
distributional data are drawn from 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 assump-
tion should be appropriately addressed as part
of a sensitivity analysis.
Distribution functions to be used in Monte
Carlo analysis may be derived by fitting an
appropriate function to empirical data. In
doing this, it should be recognized that in the
lower and upper tails of the distribution the
data are scarce, so that several functions,
with radically different shapes in the extreme
tails, may be consistent with the data. To
avoid introducing errors into the analysis by
the arbitrary choice of an inappropriate
function, several techniques can be used.
One way is to avoid the problem by using the
empirical data itself rather than an analytic
function. Another is to do separate analyses
with several functions which have adequate
fit but form upper and lower bounds to the
empirical data. A third way is to use
truncated analytical distributions. Judgment
must be used in choosing the appropriate
goodness of fit test. Information on the
theoretical basis for fitting distributions can
be found in a standard statistics text such as
Statistical Methods for Environmental
Pollution Monitoring, Gilbert, R.O., 1987,
Van Nostrand Reinhold; off-the-shelf
computer software such as Best-Fit by
Palisade Corporation can be used to
statistically determine the distributions that fit
the data.
• If only a range of values is known for an
exposure factor, the assessor has several
options.
- keep that variable constant at its central
value;
- assume several values within the range of
values for the exposure factor;
- calculate a point estimate(s) instead of
using probabilistic analysis; and
- assume a distribution (The rationale for the
selection of a distribution should be
discussed at length.) There are, however,
cases where assuming a distribution is not
recommended. These include:
— data are missing or very limited for a
key parameter - examples include: soil
ingestion by adults;
~ data were collected over a short time
period and may not represent long term
trends (the respondent usual behavior) -
examples include: food consumption
surveys; activity pattern data;
— data are not representative of the
population of interest because sample
size was small or the population studied
was selected from a local area and was
therefore not representative of the area
of interest - examples include: soil
ingestion by children; and
— ranges for a key variable are uncertain
due to experimental error or other
limitations in the study design or
methodology - examples include: soil
ingestion by children.
1.4. GENERAL EQUATION FOR
CALCULATING DOSE
The definition of exposure as used in the Exposure
Guidelines (U.S. EPA, 1992a) is "condition of a
Exposure Factors Handbook
August 1997
Page
1-11
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
chemical contacting the outer boundary of a human."
This means contact with the visible exterior of a person
such as the skin, and openings such as the mouth,
nostrils, and lesions. The process of a chemical entering
the body can be described in two steps: contact
(exposure), followed by entry (crossing the boundary).
The magnitude of exposure (dose) is the amount of agent
available at human exchange boundaries (skin, lungs,
gut) where absorption takes place during some specified
time. An example of exposure and dose for the oral
route as presented in the the EPA Exposure Guidelines
is shown in Figure 1-1. Starting with a general integral
equation for exposure (U.S. EPA 1992a), several dose
equations can be derived depending upon boundary
assumptions. One of the more useful of these derived
equations is the Average Daily Dose (ADD). The ADD,
which is used for many noncancer effects, averages
exposures or doses over the period of time over which
exposure occurred. The ADD can be calculated by
averaging the potential dose (D^) over body weight and
an averaging time.
ADD,
'pot
Total Potential Dose
Body Weight x Averaging Time
(Eqn. 1-1)
For cancer effects, where the biological response
is usually described in terms of lifetime probabilities,
even though exposure does not occur over the entire
lifetime, doses are often presented as lifetime average
daily doses (LADDs). The LADD takes the form of the
Equation 1-1 with lifetime replacing averaging time.
The LADD is a very common term used in carcinogen
risk assessment where linear non-threshold models are
employed.
The total exposure can be expressed as follows:
Total Potential Dose = C x IR x ED
(Eqn. 172)
Where:
C = Contaminant Concentration
IR = Intake Rate
ED = Exposure Duration
Contaminant concentration is the concentration of
the contaminant in the medium (air, food, soil, etc.)
contacting the body and has units of mass/volume or
mass/mass.
The intake rate refers to the rates of inhalation,
ingestion, and dermal contact depending on the route of
exposure. For ingestion, the intake rate is simply the
amount of food containing the contaminant of interest
that an individual ingests during some specific time
period (units of mass/tune). Much of this handbook is
devoted to rates of ingestion for some broad classes of
food. For inhalation, the intake rate is the rate at which
contaminated air is inhaled. Factors that affect dermal
exposure are the amount of material that comes into
contact with the skin, and the rate at which the
contaminant is absorbed.
The exposure duration is the length of time that
contaminant contact lasts. The tune a person lives hi an
area, frequency of bathing, time spent indoors versus
outdoors, etc. all affect the exposure duration. The
Activity Factors Chapter (Volume III, Chapter 15) gives
some examples of population behavior patterns, which
may be useful for estimating exposure durations to be
used in the exposure calculations.
When the above parameter values remain constant
over time, they are substituted directly into the exposure
equation. When they change with time, a summation
approach is needed to calculate exposure. In either case,
the exposure duration is the length of time exposure
occurs at the concentration and intake rate specified by
the other parameters in the equation.
Dose can be expressed as a total amount (with
units of mass, e.g., mg) or as a dose rate in terms of
mass/time (e.g., mg/day), or as a rate normalized to
body mass (e.g., with units of mg of chemical per kg of
body weight per day (mg/kg-day)). The LADD is
usually expressed in terms of mg/kg-day or other
mass/mass-time units.
In most cases (inhalation and ingestion exposure)
the dose-response parameters for carcinogen risks have
been adjusted for the difference hi absorption across
body barriers between humans and the experimental
annuals used to derive such parameters. Therefore, the
exposure assessment in these cases is based on the
potential dose with no explicit correction for the fraction
absorbed. However, the exposure assessor needs to
make such an adjustment when calculating dermal
exposure and in other specific cases when current
information indicates that the human absorption factor
Page
1-12
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 1 - Introduction
EFH
Biologically
Effective
Dose
Exposure
Chemical
Effect
Mouth
Intake
Source: U.S. EPA, 1992a
G.I. Tract
Uptake
Figure 1-1. Schematic of Dose and Exposure: Oral Route
used in the derivation of the dose-response factor is
inappropriate.
The lifetime value used in the LADD version of
Equation 1-1 is the period of time over which the dose is
averaged. For carcinogens, the derivation of the dose-
response parameters usually assumes no explicit number
of years as the duration of a lifetime, and the nominal
value of 75 years is considered a reasonable
approximation. For exposure estimates to be used for
assessments other than carcinogenic risk, various
averaging periods have been used. For acute exposures,
the administered doses are usually averaged over a day
or a single event. For nonchronic noncancer effects, the
time period used is the actual period of exposure. The
objective in selecting the exposure averaging time is to
express the exposure in a way which can be combined
with the dose-response relationship to calculate risk.
The body weight to be used in the exposure
Equation 1-1 depends on the units of the exposure data
presented in this handbook. For food ingcstion, the body
weights of the surveyed populations were known in the
USDA surveys and they were explicitly factored into the
food intake data in order to calculate the intake as grams
per day per kilogram body weight. In this case, the body
weight has already been included in the "intake rate"
term in Equation 1-2 and the exposure assessor does not
need to explicitly include body weight.
The units of intake in this handbook for.the
ingestion of fish, breast milk, and the inhalation of air
are not normalized to body weight. In this case, the
exposure assessor needs to use (hi Equation 1-1) the
average weight of the exposed population during the time
when the exposure actually occurs. If the exposure
occurs continuously throughout an individual's life or
only during the adult ages, using an adult weight of 71.8
kg should provide sufficient accuracy. If the body
weight of the individuals in the population whose risk is
being evaluated is non-standard in some way, such as for
children or for first-generation immigrants who may be
smaller than the national population, and if reasonable
values are not available in the literature, then a model of
intake as a function of body weight must be used. One
such model is discussed in Appendix 1A of this chapter.
Some of the parameters (primarily concentrations) used
in estimating exposure are exclusively site specific, and
therefore default recommendations could not be used.
The food ingestion rate values provided in this
handbook are generally expressed as "as consumed"
since this is the fashion in which data are reported by
Exposure Factors Handbook
August 1997
Page
1-13
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
survey respondents. This is of importance because
concentration data to be used in the dose equation are
generally measured in uncooked food samples. In most
situations, the only practical choice is to use the "as
consumed" ingestion rate and the uncooked
concentration. However, it should be recognized that
cooking generally results in some reductions in weight
(e.g., loss of moisture), and that if the mass of the
contaminant in the food remains constant, then the
concentration of the contaminant in the cooked food item
will increase. Therefore, if the "as consumed" ingestion
rate and the uncooked concentration are used in the dose
equation, dose may be underestimated. On the other
hand, cooking may cause a reduction in mass of
contaminant and other ingredients such that the overall
concentration of contaminant does not change
significantly. In this case, combining cooked ingestion
rates and uncooked concentration will provide an
appropriate estimate of dose. Ideally, food concentration
data should be adjusted to account for changes after
cooking, then the "as consumed" intake rates are
appropriate. In the absence of data, it is reasonable to
assume that no change in contaminant concentration
occurs after cooking. Except for general population fish
consumption and home produced foods, uncooked intake
rate data were not available for presention in this
handbook. Data on the general population fish
consumption have been presented in this handbook
(Section 10.2) in both "as consumed" and uncooked
basis. It is important for the assessor to be aware of
these issues and choose intake rate data that best matches
the concentration data that is being used.
The link between the intake rate value and the
exposure duration value is a common source of
confusion in defining exposure scenarios. It is important
to define the duration estimate so that it is consistent with
the intake rate:
• The intake rate can be based on an individual
event, such as 129 g of fish eaten per meal
(U.S. EPA, 1996). The duration should be
based on the number of events or, in this
case, meals.
• The intake rate also can be based on a long-
term average, such as 10 g/day. In this case
the duration should be based on the total time
interval over which the exposure occurs.
The objective is to define the terms so that when
multiplied, they give the appropriate estimate of mass of
contaminant contacted. This can be accomplished by
basing the intake rate on either a long-term average
(chronic exposure) or an event (acute exposure) basis, as
long as the duration value is selected appropriately.
Consider the case in which a person eats a 129-g fish
meal approximately five times per month (long-term
average is 21.5 g/day) for 30 years; or 21.5 g/day of fish
every day for 30 years.
(129 g/meal)(5 meals/mo)(mo/30 d)(365 d/yr)(30 yrs) = 235,425 g
(21.5 g/day)(365 d/yr)(30 yrs) = 235,425 g
Thus, a frequency of either 60 meals/year or a duration
of 365 days/year could be used as long as it is matched
with the appropriate intake rate.
1.5. RESEARCH NEEDS
In an earlier draft of this handbook, reviewers
were asked to identify factors or areas where further
research is needed. The following list is a compilation
of areas for future research identified by the peer
reviewers and authors of this document:
• The data and information available with
respect to occupational exposures are quite
limited. Efforts need to be directed to
identify data or references on occupational
exposure.
• Further research is necessary to refine
estimates of fish consumption, particularly by
subpopulations of subsistence fishermen.
• Research is needed to better estimate soil
intake rates, particularly how to extrapolate
short-term data to chronic exposures. Data
on soil intake rates by adults are very limited.
Research in this area is also recommended.
Research is also needed to refine methods to
calculate soil intake rate (i.e., inconsistencies
among tracers and input/output misalignment
errors indicate a fundamental problem with
the methods). Research is also needed to
obtain more data to better estimate soil
adherence.
Page
1-14
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 1 - Introduction
EFH
• In cases where several studies of equal
quality and data collection procedures are
available for an exposure factor, procedures
need to be developed to combine the data in
order to create a single distribution of likely
values for that factor.
• Reviewers recommended that the handbook
be made available in CD ROM and that the
data presented be made available in a format
that will allow the users to conduct their own
analysis. The intent is to provide a
comprehensive factors tool with interactive
menu to guide users to areas of interest, word
searching features, and data base files.
• Reviewers recommended that EPA derive
distribution functions using the empirical data
for the various exposure factors to be used in
Monte Carlo or other probabilistic analysis.
• Research is needed to derive a methodology
to extrapolate from short-term data to long-
term or chronic exposures.
• Reviewers recommended that the consumer
products chapter be expanded to include more
products. A comprehensive literature search
needs to be conducted to investigate other
sources of data.
• Breastmilk intake.
• More recent data on tapwater intake.
• SAB recommended analysis of 1994 and 1995
CSFII data.
1.6. ORGANIZATION
The handbook is organized into three volumes as
follows:
Volume I - General Factors
Chapter 1 Provides the overall introduction to
the handbook
Chapter 2 Presents an analysis of uncertainty
and discusses methods that can be
used to evaluate and present the
uncertainty associated with exposure
scenario estimates.
Chapter 3 Provides factors for estimating
human exposure through ingestion
of water.
Chapter 4 Provides factors for estimating
exposure through ingestion of soil.
Chapter 5 Provides factors for estimating
exposure as a result of inhalation of
vapors and particulates.
Chapter 6 Presents factors for estimating
dermal exposure to environmental
contaminants that come in contact
with the skin.
Chapter 7 Provides data on body weight.
Chapter 8 Provides data on life expectancy.
Volume n - Ingestion Factors
Chapter 9 Provides factors for estimating
exposure through ingestion of fruits
and vegetables.
Chapter 10 Provides factors for estimating
exposure through ingestion of fish.
Chapter 11 Provides factors for estimating
exposure through ingestion of meats
and dairy products.
Chapter 12 Presents data for estimating
exposure through ingestion of grain
products.
Chapter 13 Presents factors for estimating
exposure through ingestion of home
produced food.
Chapter 14 Presents data for estimating
exposure through ingestion of breast
milk.
Exposure Factors Handbook
August 1997
Page
1-15
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
Volume HI - Activity Factors
Chapter 15 Presents data on activity factors
(activity patterns, population
mobility, and occupational
mobility).
Chapter 16 Presents data on consumer product
use.
Chapter 17 Presents factors used in estimating
residential exposures.
Figure 1-2 provides a roadmap to assist users of
this handbook in locating recommended values and
confidence ratings for the various exposure factors
presented hi these chapters. A glossary is provided at
the end of Volume III.
1.7. REFERENCES FOR CHAPTER 1
A1HC. (1994) Exposure factors sourcebook.
Washington, DC: American Industrial Health
Council.
Calabrese, E.J.; Pastides, H.; Barnes, R.; Edwards,
C.; Kostecki, P.T.; et al. (1989) How much soil
do young children ingest: an epidemiologic study.
In: Petroleum Contaminated Soils, Lewis
Publishers, Chelsea, MI. pp. 363-397.
Gilbert, R.O. (1987) Statistical methods for
environmental pollution monitoring. New York:
Van Nostrand Reirihold.
U.S. EPA. (1983-1989) Methods for assessing
exposure to chemical substances. Volumes 1-13.
Washington, DC: Office of Toxic Substances,
Exposure Evaluation Division.
U.S. EPA. (1984) Pesticide assessment guidelines
subdivision K, exposure: reentry protection.
Office of Pesticide Programs, Washington, DC.
EPA/540/9-48/001. Available from NTIS,
Springfield, VA; PB-85-120962.
U.S. EPA. (1986a) Standard scenarios for estimating
exposure to chemical substances during use of
consumer products. Volumes I and II.
Washington, DC: Office of Toxic Substance,
Exposure Evaluation Division.
U.S. EPA. (1986b) Pesticide assessment guidelines
subdivision U, applicator exposure monitoring.
Office of Pesticide Programs, Washington, DC.
EPA/540/9-87/127. Available from NTIS,
Springfield, VA; PB-85-133286.
U.S. EPA. (1987) Selection criteria for mathematical
models used in exposure assessments: surface
water models. Exposure Assessment Group,
Office of Health and Environmental Assessment,
Washington, DC. WPA/600/8-87/042. Available
from NTIS, Springfield, VA; PB-88-139928/AS.
U.S. EPA. (1988a) Superfund exposure assessment
manual. Office of Emergency and Remedial
Response, Washington, DC. EPA/540/1-88/001.
Available from NTIS, Springfield, VA; PB-89-
135859.
U.S. EPA. (1988b) Selection criteria for mathematical
models used in exposure assessments: groundwater
models. Exposure Assessment Group, Office of
Health and Environmental Assessment,
Washington, DC. EPA/600/8-88/075. Available
from NTIS, Springfield, VA; PB-88-248752/AS.
U.S. EPA. (1989) Risk assessment guidance for
Superfund. Human health evaluation manual: part
A. Interim Final. Office of Solid Waste and
Emergency Response, Washington, DC. Available
from NTIS, Springfield, VA; PB-90-155581.
U.S. EPA. (1990) Methodology for assessing health
risks associated with indirect exposure to
combustor emissions. EPA 600/6-90/003.
Available from NTIS, Springfield, VA; PB-90-
187055/AS.
U.S. EPA. (1992a) Guidelines for exposure
assessment. Washington, DC: Office of Research
and Development, Office of Health and
Environmental Assessment. EPA/600/Z-92/001.
U.S. EPA. (1992b) Dermal exposure assessment:
principles and applications. Washington, DC:
Office of Health and Environmental Assessments.
EPA/600/8-9/01 IF.
U.S. EPA. (1994) Estimating exposures to dioxin-like
compounds. (Draft Report). Office of Research
and Development, Washington, DC. EPA/600/6-
88/005Cb.
U.S. EPA. (1996) Daily-average per capita fish
consumption estimates based on the combined
1989, 1990, and 1999 continuing survey of food
intakes by individuals (CSFII) 1989-91 data.
Volumes I and II. Preliminary Draft Report.
Washington, DC: Office of Water.
Page
1-16
Exposure Factors Handbook
August 1997
-------
EXPOSURE ROUTE
RECOMMENDATIONS/
RATINGS TABLE PAGE NOS.
L-Ofi7.^~'^'"*t>/T0.8fi
:£^-,*T?*Q^
S?~ *•»,-, -."i.. ^»«- •. l >? "" •
**5»K*"
'""^"^•"'"^r"
pAS4-,^ ^4, - '
Inhalation, -
fT^r-^T -',**, ~r~^ '
K, ... _^,,a^1* v
<-~-\ *- ^ 3:*.-.
igpgfL Dermal
i"%*>
«
K
„ "=; - -^ - ,i •*• r?-/ ^c ^ "~?3r ' ~< * ~^r^"r«»~ <
;i,Iir*s" .. ' - x ' J- 9 c*-^^-^^--^SKi- •*?>" *-" » --T % ;*,
inalation, ?—: j / «-xs« ;, -i&5j*'«.<-r^^^-^ —-" ~ *.
" -'^^ ' ^%"i ^>>2.^z.l^r^y&tf*.'* t-^.^-4
^;^r;ii,^^. fei , •• -;
^"rv—-\'--\ 1 f.rj
• - *-- - —^^-1 -7 ' ;
__ ; - f e^-* •«, —- ^_ , ~=.-.jr r-"/ **"* .„«, -
: ~S: 4—. * (• , »*.^ "• -"^ ,--" %-Vjix»- J; ,.'-y ^Msii^i? -..".rr;
1 "• * V ll^^^if-* ^ ^v-^-*!^," *£%£* ~"" 5i\' - ?
^ •"• !f" *| i^^f a* •* i," *-.-^^*c *-*•*>:-\a!t* "^ >
Loutes) ^ ~" J~ , ty\'*-*=''~*~8~1fo&
Characteristics ^' „/ ^^$y fe*;^p5lj >£ > ?--"/
^^^v4S?5^f"?«^
onsumer Product Use
r*<-(All'koutes) ^ * 'I
EsaP^"*-, C. ^S^j^ 1 ^* -"w^J
/
Characterist
-------
-------
Volume I - General Factors
Appendix 1A _^
EFH
APPENDIX 1A
RISK CALCULATIONS USING EXPOSURE FACTORS HANDBOOK DATA
AND DOSE-RESPONSE INFORMATION FROM THE
INTEGRATED RISK INFORMATION SYSTEM (IRIS)
Exposure Factors Handbook
August 1997
Page
1A-1
-------
-------
Volume I - General Factors
Appendix 1A
EFH
APPENDIX 1A
RISK CALCULATIONS USING EXPOSURE FACTORS HANDBOOK
DATA AND DOSE-RESPONSE INFORMATION FROM IRIS
1. INTRODUCTION
When calculating risk estimates for a specific population, whether the entire national population or some sub-
population, the exposure information (either from this handbook or from other data) must be combined with dose-
response information. The latter typically comes from the IRIS data base, which summarizes toxicity data for each
agent separately. Care must be taken that the assumptions about population parameters in the dose-response analysis
are consistent with the population parameters used in the exposure analysis. This Appendix discusses procedures for
insuring this consistency.
In the IRIS derivation of threshold based dose-response relationships (U.S. EPA, 1996), such as the RfD and the
RfCs based on adverse systemic effects, there has generally been no explicit use of human exposure factors. In these
cases the numerical value of the RfD and RfC comes directly from animal dosing experiments (and occasionally from
human studies) and from the application of uncertainty factors to reflect issues such as the duration of the experiment,
the fact that animals are being used to represent humans and the quality of the study. However in developing cancer
dose-response (D-R) assessments, a standard exposure scenario is assumed in calculating the slope factor (i.e., human
cancer risk per unit dose) on the basis of either animal bioassay data or human data. This standard scenario has
traditionally been assumed to be typical of the U.S. population: 1) body weight = 70 kg; 2) air intake rate = 20
mVday; 3) drinking water intake = 2 liters/day; 4) lifetime = 70 years. In RfC derivations for cases involving an
adverse effect on the respiratory tract, the air intake rate of 20 m3/day is assumed. The use of these specific values
has depended on whether the slope factor was derived' from animal or human epidemiologic data:
• Animal Data: For dose-resopnse (D-R) studies based on animal data, scale animal doses to human
equivalent doses using a human body weight assumption of 70 kg. No explicit lifetime adjustment is
necessary because the assumption is made that events occurring in the lifetime animal bioassay will occur
with equal probability in a human lifetime, whatever that might happen to be.
• Human Data - In the analysis of human studies (either occupational or general population), the Agency has
usually made no explicit assumption of body weight or human lifetime. For both of these parameters there
is an implicit assumption that the population usually of interest has the same descriptive parameters as the
population analyzed by the Agency. In the rare situation where this assumption is known to be wrong, the
Agency has made appropriate corrections so that the dose-response parameters represent the national
average population.
When the population of interest is different than the national average (standard) population, the dose-response
parameter needs to be adjusted. In addition, when the population of interest is different than the population from which
the exposure factors in this handbook were derived, the exposure factor needs to be adjusted. Two generic examples
of situations where these adjustments are needed are as follows:
A) Detailed study of recent data, such as are presented in this handbook, show that EPA's standard assumptions
(i.e., 70 kg body weight, 20 m3/day air inhaled, and 2 L/day water intake) are inaccurate for the national population
and may be inappropriate for sub-populations under consideration. The handbook addresses most of these situations
by providing gender- and age-specific values and by normalizing the intake values to body weight when the data are
available, but it may not have covered all possible situations. An example of a sub-population with a different mean
Exposure Factors Handbook
August 1997
Page
1A-3
-------
EFH
Volume I - General Factors
Appendix 1A
body weight would be females, with an average body weight of 60 kg or children with a body weight dependent on
age. Another example of a non-standard sub-population would be a sedentary hospital population with lower than 20
m3/day air intake rates.
B) The population variability of these parameters is of interest and it is desired to estimate percentile limits of the
population variation. Although the detailed methods for estimating percentile limits of exposure and risk in a
population are beyond the scope of this document, one would treat the body weight and the intake rates discussed in
Sections 2 to 4 of this appendix as distributions, rather than constants.
2. CORRECTIONS FOR DOSE-RESPONSE PARAMETERS
The correction factors for the dose-response values tabulated in the IRIS data base for carcinogens are summarized
in Table 1A-1. Use of these correction parameters is necessary to avoid introducing errors into the risk analysis. The
second column of Table 1A-1 shows the dependencies that have been assumed in the typical situation where the human
dose-response factors have been derived from the administered dose in animal studies. This table is applicable in most
cases that will be encountered, but it is not applicable when: a) the effective dose has been derived with a
pharmacokinetic model and b) the dose-response data has been derived from human data. In the former case, the
subpopulation parameters need to be incorporated into the model. In the latter case, the correction factor for the dose-
response parameter must be evaluated on a case-by case basis by examining the specific data and assumptions in the
derivation of the parameter.
Table 1A-1. Procedures for Modifying IRIS Risk Values for Non-standard Populations3'15
IRIS Risk Measure
[Units]
IRIS Risk Measure is Proportional to:b Correction Factor (CF) for modifying
IRIS Risk Measures:0
Slope Factor
[per mg/(kg/day)]
Water Unit Risk
[per /tg/1]
Air Unit Risk:
(Ws)1/3 = (70)1/3
IWS/[(WS)2/3] = 2/[(70)2/3]
IAS/[(WS)2/3] = 20/[(70)2/3]
(Wp/70)1/3
(Iwp)/2 x [70/(WP)]2/3
(IAP)/20 x [70/(WP)]2/3
A. Particles or aerosols
[per jtg/m3], air concentration by
weight
Air Unit Risk:
B. Gases
[per parts per million], air
concentration by volume,
No explicit proportionality to body
weight or air intake is assumed.
1.0
ppm by volume is assumed to be the
effective dose in both animals and
humans.
a W = Body weight (kg)
Iw « Drinking water intake (liters per day)
IA = Air intake (cubic meters per day)
b Ws, IwSl, IAS denote standard parameters assumed by IRIS
c Modified risk measure = (CF) x IRIS value
Wp, Iwp, IAP denote non-standard parameters of the actual population
Page
1A-4
Exposure Factors Handbook
August 1997
-------
Volume T - General Factors
Appendix 1A
EFH
As one example of the use of Table 1 A-l, the recommended value for the average consumption of tapwater for
adults in the U. S. population derived in this document (Chapter 3), is 1.4 liters per day. The drinking water unit risk
for dichlorvos, as given in the IRIS information data base is 8.3 x 10~6 per /tg/1, and was calculated from the slope
factor assuming the standard intake, Iws, of 2 liters per day. For the United States population drinking 1.4 liters of
tap water per day the corrected drinking water unit risk should be 8.3 x 10"6 x (1.4/2) = 5.8 x 10"6 per ,ug/l. The risk
to the average individual is then estimated by multiplying this by the average concentration in units of jug/1.
Another example is when the risk for women drinking water contaminated with dichlorvos is to be estimated.
If the women have an average body weight of 60 kg, the correction factor for the drinking water unit risk is
(disregarding the correction discussed in the above paragraph), from Table 1A-1, is (70/60)273 = 1.11. Here the ratio
of 70 to 60 is raised to the power of 2/3. The corrected water unit risk for dichlorvos is 8.3 x 10"6 x 1.11 = 9.2 x
10"6 per jUg/1. As before, the risk to the average individual is estimated by multiplying this by the water concentration.
When human data are used to derive the risk measure, there is a large variation in the different data sets
encountered in IRIS, so no generalizations can be made about global corrections. However, the typical default
exposure values used for the air intake of an air pollutant over an occupational lifetime are: air intake is 10 m3/day
for an 8-hour shift, 240 days per year with 40 years on the job. If there is continuous exposure to an ambient air
pollutant, the lifetime dose is usually calculated assuming a 70-year lifetime.
3. CORRECTIONS FOR INTAKE DATA
When the body weight, Wp, of the population of interest differs from the body weight, WE, of the population from
which the exposure values in this handbook were derived, the following model furnishes a reasonable basis for
estimating the intake of food and air (and probably water also) in the population of interest. Such a model is needed
in the absence of data on the dependency of intake on body size. This occurs for inhalation data, where the intake data
are not normalized to body weight, whereas the model is not needed for food and tap water intakes if they are given
hi units of intake per kg body weight.
The model is based on the dependency of metabolic oxygen consumption on body size. Oxygen consumption is
directly related to food (calorie) consumption and air intake and indirectly to water intake. For mammals of a wide
range of species sizes (Prosser and Brown, 1961), and also for individuals of various sizes within a species, the oxygen
consumption and calorie (food) intake varies as the body weight raised to a power between 0.65 and 0.75. A value
of 0.667 = 2/3 has been used in EPA as the default value for adjusting cross-species intakes, and the same factor has
been used for intra-species intake adjustments.
[NOTE: Following discussions by an interagency task force (Federal Register, 1992), the agreement was that a
more accurate and defensible default value would be to choose the power to 3/4 rather than 2/3. A recent article (West
et al., 1997) has provided a theoretical basis for the 3/4 power scaling. This will be the standard value to be used in
future assessments, and all equations in this Appendix will be modified in future risk assessments. However, because
risk assessors now use the current IRIS information, this discussion is presented with the previous default assumption
of 2/3].
With this model, the relation between the daily air intake in the population of interest, IAP = (m3/day)p, and the
intake in the population described hi this handbook, IAE = (m3/day)E is:
p _
:
\2/3
Exposure Factors Handbook
August 1997
Page
1A-5
-------
EFH
Volume I - General Factors
Appendix 1A
4. CALCULATION OF RISKS FOR AIR CONTAMINANTS
The risk is calculated by multiplying the IRIS air unit risk, corrected as described in Table 1A-1, by the air
concentration. But since the correction factor involves the intake in the population of interest (IAP), that quantity must
be included in the equation, as follows:
(Risk)p = (air unit risk)p x (air concentration)
= (air unit risk)8 x (IAP/20) x (70/WP)2/3 x (air concentration)
= (air unit risk)8 x [( L.E x (Wp/WE)2/3/20)] x (70/WP)2/3 x (air concentration)
= (air unit risk)8 x (IA/20) x (70/WE)2/3 x (air concentration)
In this equation the air unit risk from the IRIS data base (air unit risk)5, the air intake data hi the handbook for
the populations where it is available (IAE) and the body weight of that population (WE) are included along with the
standard IRIS values of the air intake (20 m3/day) and body weight (70 kg).
For food ingestion and tap water intake, if body weight-normalized intake values from this handbook are used,
the intake data do not have to be corrected as in Section 3 above. In these cases, corrections to the dose-response
parameters in Table 1A-1 are sufficient.
5. REFERENCES
Federal Register. (1992) Cross-species scaling factor for carcinogen risk assessments based on equivalence of (mg/kg-
day)3M. Draft report. Federal Register, 57(109): 24152-24173, June 5, 1992.
Prosser, C.L.; Brown, F.A. (1961) Comparative Animal physiology, 2nd edition. WB Saunders Co. p. 161.
U.S. EPA. (1996) Background Documentation. Integrated Risk Information System (IRIS). Online. National Center
for Environmental Assessment, Cincinnati, Ohio. Background Documentation available from: Risk Information
Hotline, National Center for Environmental Assessment, U.S. EPA, 26 W. Martin Luther King Dr. Cincinnati,
OH 45268. (513) 569-7254
West, G.B.; Brown, J.H.; Enquist, B.J. (1997) A general model of the origin of allometric scaling laws in biology.
Science 276:122-126.
Page
1A-6
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 2 - Variability and Uncertainty
2. VARIABILITY AND UNCERTAINTY
The chapters that follow will discuss exposure
factors and algorithms for estimating exposure. Exposure
factor values can be used to obtain a range of exposure
estimates such as average, high-end and bounding
estimates. It is instructive here to return to the general
equation for potential Average Daily Dose (ADDpot) that
was introduced in the opening chapter of this handbook:
....... _ Contaminant Concentration x Intake Rale x Exposure Duration
*" Body Weight x Averaging Time
(Eqn. 2-1)
With the exception of the contaminant
concentration, all parameters in the above equation are
considered exposure factors and, thus, are treated in fair
detail in other chapters of this handbook. Each of the
exposure factors involves humans, either in terms of their
characteristics (e.g., body weight) or behaviors (e.g.,
amount of time spent in a specific location, which affects
exposure duration). While the topics of variability and
uncertainty apply equally to contaminant concentrations
and the rest of the exposure factors in equation 2-1, the
focus of this chapter is on variability and uncertainty as
they relate to exposure factors. Consequently, examples
provided in this chapter relate primarily to exposure
factors, although contaminant concentrations may be used
when they better illustrate the point under discussion.
This chapter also is intended to acquaint the
exposure assessor with some of the fundamental concepts
and precepts related to variability and uncertainty,
together with methods and considerations for evaluating
and presenting the uncertainty associated with exposure
estimates. Subsequent sections in this chapter are devoted
to the following topics:
• Distinction between variability and
uncertainty;
• Types of variability;
• Methods of confronting variability;
• Types of uncertainty and reducing uncertainty;
• Analysis of variability and uncertainty; and
• Presenting results of variability/uncertainty
analysis.
Fairly extensive treatises on the topic of uncertainty
have been provided, for example, by Morgan and Henrion
(1990), the National Research Council (NRC, 1994) and,
to a lesser extent, the U.S. EPA (1992; 1995). The topic
commonly has been treated as it relates to the overall
process of conducting risk assessments; because exposure
assessment is a component of risk-assessment process, the
general concepts apply equally to the exposure-assessment
component.
2.1. VARIABILITY VERSUS UNCERTAINTY
While some authors have treated variability as a
specific type or component of uncertainty, the
U.S. EPA (1995) has advised the risk assessor
(and, by analogy, the exposure assessor) to
distinguish between variability and uncertainty.
Uncertainty represents a lack of knowledge about
factors affecting exposure or risk, whereas variability
arises from true heterogeneity across people, places or
time. In other words, uncertainty can lead to inaccurate or
biased estimates, whereas variability can affect the
precision of the estimates and the degree to which they
can be generalized. Most of the data presented in this
handbook concerns variability.
Variability and uncertainty can complement or
confound one another. An instructive analogy has been
drawn by the National Research Council (NRC, 1994:
Chapter 10), based on the objective of estimating the
distance between the earth and the moon. Prior to fairly
recent technology developments, it was difficult to make
accurate measurements of this distance, resulting in
measurement uncertainty. Because the moon's orbit is
elliptical, the distance is a variable quantity. If only a few
measurements were to be taken without knowledge of the
elliptical pattern, then either of the following incorrect
conclusions might be reached:
• That the measurements were faulty, thereby
ascribing to uncertainty what was actually
caused by variability; or
• That the moon's orbit was random, thereby not
allowing uncertainty to shed light on
seemingly unexplainable differences that are
in fact variable and predictable.
A more fundamental error in the above situation
would be to incorrectly estimate the true distance, by
assuming that a few observations were sufficient. This
latter pitfall - treating a highly variable quantity as if it
were invariant or only uncertain — is probably the most
relevant to the exposure or risk assessor.
Now consider a situation that relates to exposure,
such as estimating the average daily dose by one exposure
Exposure Factors Handbook
August 1997 , •
Page
2-1
-------
Volume I - General Factors
Chapter 2 - Variability and Uncertainty
route — ingestion of contaminated drinking water.
Suppose that it is possible to measure an individual's daily
water consumption (and concentration of the contaminant)
exactly, thereby eliminating uncertainty in the measured
daily dose. The daily dose still has an inherent day-to-day
variability, however, due to changes in the individual's
daily water intake or the contaminant concentration in
water.
It is impractical to measure the individual's dose
every day. For this reason, the exposure assessor may
estimate the average daily dose (ADD) based on a finite
number of measurements, in an attempt to "average out"
the day-to-day variability. The individual has a true (but
unknown) ADD, which has now been estimated based on
a sample of measurements. Because the individual's true
average is unknown, it is uncertain how close the estimate
is to the true value. Thus, the variability across daily
doses has been translated into uncertainty in the ADD.
Although the individual's true ADD has no variability, the
estimate of the ADD has some uncertainty.
The above discussion pertains to the ADD for one
person. Now consider a distribution of ADDs across
individuals in a defined population (e.g., the general U.S.
population). In this case, variability refers to the range
and distribution of ADDs across individuals in the
population. By comparison, uncertainty refers to the
exposure assessor's state of knowledge about that
distribution, or about parameters describing the
distribution (e.g., mean, standard deviation, general shape,
various percentiles).
As noted by the National Research Council (NRC,
1994), the realms of variability and uncertainty have
fundamentally different ramifications for science and
judgment. For example, uncertainty may force decision-
makers to judge how probable it is that exposures have
been overestimated or underestimated for every member
of the exposed population, whereas variability forces them
to cope with the certainty that different individuals are
subject to exposures both above and below any of the
exposure levels chosen as a reference point.
2.2. TYPES OF VARIABILITY
Variability in exposure is related to an individual's
location, activity, and behavior or preferences at a
particular point in time, as well as pollutant emission rates
and physical/chemical processes that affect concentrations
in various media (e.g., air, soil, food and water). The
variations in pollutant-specific emissions or processes,
and in individual locations, activities or behaviors, are not
necessarily independent of one another. For example,
both personal activities and pollutant concentrations at a
specific location might vary in response to weather
conditions, or between weekdays and weekends.
At a more fundamental level, three types of
variability can be distinguished:
• Variability across locations (Spatial
Variability);
• Variability over time (Temporal Variability);
and
• Variability among individuals (Inter-
individual Variability).
Spatial variability can occur both at regional
(macroscale) and local (microscale) levels. For example,
fish intake rates can vary depending on the region of the
country. Higher consumption may occur among
populations located near large bodies of water such as the
Great Lakes or coastal areas. As another example,
outdoor pollutant levels can be affected at the regional
level by industrial activities and at the local level by
activities of individuals. In general, higher exposures tend
to be associated with closer proximity to the pollutant
source, whether it be an industrial plant or related to a
personal activity such as showering or gardening. In the
context of exposure to airborne pollutants, the concept of
a "microenvironment" has been introduced (Duan, 1982)
to denote a specific locality (e.g., a residential lot or a
room in a specific building) where the airborne
concentration can be treated as homogeneous (i.e.,
invariant) at a particular point in time.
Temporal variability refers to variations over
time, whether long- or short-term. Seasonal fluctuations
in weather, pesticide applications, use of woodburning
appliances and fraction of time spent outdoors are
examples of longer-term variability. Examples of shorter-
term variability are differences in industrial or personal
activities on weekdays versus weekends or at different
times of the day.
Inter-individual variability can be either of two
types: (1) human characteristics such as age or body
weight, and (2) human behaviors such as location and
activity patterns. Each of these variabilities, in turn, may
be related to several underlying phenomena that vary. For
example, the natural variability in human weight is due to
a combination of genetic, nutritional, and other lifestyle or
environmental factors. Variability arising from
independent factors that combine multiplicatively
Page
2-2
Exposure Factors Handbook
• August 1997
-------
Volume I - General Factors
Chapter 2 - Variability and Uncertainty
A
generally will lead to an approximately lognormal
distribution across the population, or across
spatial/temporal dimensions.
2.3. CONFRONTING VARIABILITY
According to the National Research Council (NRC
1994), variability can be confronted in four basic ways
(Table 2-1) when dealing with science-policy questions
surrounding issues such as exposure or risk assessment.
The first is to ignore the variability and hope for the
best. This strategy tends to work best when the variability
is relatively small. For example, the assumption that all
adults weigh 70 kg is likely to be correct within ±25% for
most adults.
The second strategy involves disaggregating the
variability in some explicit way, in order to better
understand it or reduce it. Mathematical models are
appropriate in some cases, as in fitting a sine wave to the
annual outdoor concentration cycle for a particular
pollutant and location. In other cases, particularly those
involving human characteristics or behaviors, it is easier
to disaggregate the data by considering all the relevant
subgroups or subpopulations. For example, distributions
of body weight could be developed separately for adults,
adolescents and children, and even for males and females
within each of these subgroups. Temporal and spatial
analogies for this concept involve measurements on
appropriate time scales and choosing appropriate
subregions or microenvironments.
The third strategy is to use the average value of a
quantity that varies. Although this strategy might appear
as tantamount to ignoring variability, it needs to be based
on a decision that the average value can be estimated
reliably in light of the variability (e.g., when the
variability is known to be relatively small, as in the case
of adult body weight).
The fourth strategy involves using the maximum
or minimum value for an exposure factor. In this case,
the variability is characterized by the range between the
extreme values and a measure of central tendency. This
is perhaps the most common method of dealing with
variability in exposure or risk assessment — to focus on
one time period (e.g., the period of peak exposure), one
spatial region (e.g., in close proximity to the pollutant
source of concern), or one subpopulation (e.g., exercising
asthmatics). As noted by the U.S. EPA (1992), when an
exposure assessor develops estimates of high-end
individual exposure and dose, care must be taken not to
set all factors to values that maximize exposure or dose —
such an approach will- almost always lead to an
overestimate.
2.4. CONCERN ABOUT UNCERTAINTY
Why should the exposure assessor be concerned
with uncertainty? As noted by the U.S. EPA (1992),
exposure assessment can involve a broad array of
information sources and analysis techniques. Even in
situations where actual exposure-related measurements
exist, assumptions or inferences will still be required
because data are not likely to be available for all aspects
of the exposure assessment. Moreover, the data that are
available may be of questionable or unknown quality.
Thus, exposure assessors have a responsibility to present
not just numbers, but also a clear and explicit explanation
of the implications and limitations of their analyses.
Table 2-1. Four Strategies for Confronting Variability
Strategy
Ignore variability
Disaggregate the
variability
Use the average value
Use a maximum or
minimum value
Example
Assume that all adults weigh
70kg
Develop distributions of
body weight for age/gender
groups
Use average body weight for
adults
Use a lower-end value from
the weight distribution
Comment
Works best when variability is small
Variability will be smaller in each group
Can the average be estimated reliably given what is
known about the variability?
Conservative approach — can lead to unrealistically
high exposure estimate if taken for all factors
Exposure Factors Handbook
August 1997
Page
2-3
-------
Volume I - General Factors
Chapter 2 - Variability and Uncertainty
Morgan and Henrion (1990) provide an argument
by analogy. When scientists report quantities that they
have measured, they are expected to routinely report an
estimate of the probable error associated with such
measurements. Because uncertainties inherent in policy
analysis (of which exposure assessment is a part) tend to
be even greater than those in the natural sciences,
exposure assessors also should be expected to report or
comment on the uncertainties associated with their
estimates.
Additional reasons for addressing uncertainty in
exposure or risk assessments (U.S. EPA, 1992, Morgan
and Henrion, 1990) include the following:
• Uncertain information from different sources
of different quality often must be combined
for the assessment;
• Decisions need to be made about whether or
how to expend resources to acquire additional
information,;
• Biases may result in so-called "best estimates"
that in actuality are not very accurate; and
• Important factors and potential sources of
disagreement in a problem can be identified.
Addressing uncertainty will increase the likelihood
that results of an assessment or analysis will be used in an
appropriate manner. Problems rarely are solved to
everyone's satisfaction, and decisions rarely are reached
on the basis of a single piece of evidence. Results of prior
analyses can shed light on current assessments,
particularly if they are couched in the context of
prevailing uncertainty at the time of analysis. Exposure
assessment tends to be an iterative process, beginning
with a screening-level assessment that may identify the
need for more in-depth assessment. One of the primary
goals of the more detailed assessment is to reduce
uncertainty in estimated exposures. This objective can be
achieved more efficiently if guided by presentation and
discussion of factors thought to be primarily responsible
for uncertainty in prior estimates.
2.5. TYPES OF UNCERTAINTY AND
REDUCING UNCERTAINTY
The problem of uncertainty in exposure or risk
assessment is relatively large, and can quickly become too
complex for facile treatment unless it is divided into
smaller and more manageable topics. One method of
division (Bogen, 1990) involves classifying sources of
uncertainty according to the step in the risk assessment
process (hazard identification, dose-response assessment,
exposure assessment or risk characterization) at which
they can occur. A more abstract and generalized approach
preferred by some scientists is to partition all uncertainties
among the three categories of bias, rand9mness and true
variability. These ideas are discussed later in some
examples.
The U.S. EPA (1992) has classified uncertainty in
exposure assessment into three broad categories:
1. Uncertainty regarding missing or incomplete
information needed to fully define exposure
and dose (Scenario Uncertainty).
2. Uncertainty regarding some parameter
(Parameter Uncertainty).
3. Uncertainty regarding gaps in scientific theory
required to make predictions on the basis of
causal inferences (Model Uncertainty).
Identification of the sources of uncertainty in an exposure
assessment is the first step in determining how to reduce
that uncertainty. The types of uncertainty listed above can
be further defined by examining their principal causes.
Sources and examples for each type of uncertainty are
summarized in Table 2-2.
Because uncertainty in exposure assessments is
fundamentally tied to a lack of knowledge concerning
important exposure factors, strategies for reducing
uncertainty necessarily involve reduction or elimination of
knowledge gaps. Example strategies to reduce uncertainty
include (1) collection of new data using a larger sample
size, an unbiased sample design, a more direct
measurement method or a more appropriate target
population, and (2) use of more sophisticated modeling
and analysis tools.
2.6. ANALYZING VARIABILITY AND
UNCERTAINTY
Exposure assessments often are developed in a
phased approach. The initial phase usually screens out the
exposure scenarios or pathways that are not expected to
pose much risk, to eliminate them from more detailed,
resource-intensive review. Screening-level assessments
typically examine exposures that would fall on or beyond
the high end of the expected exposure distribution.
Because screening-level analyses usually are included in
the final exposure assessment, the final document may
contain scenarios that differ quite markedly in
Page
2-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 2 - Variability and Uncertainty
A
Table 2-2. Three Types of Uncertainty and Associated Sources and Examples
Type of Uncertainty
Scenario Uncertainty
Parameter Uncertainty
Model Uncertainty
Sources
Descriptive errors
Aggregation errors
Judgment errors
Incomplete analysis
Measurement errors
Sampling errors
Variability
Surrogate data
Relationship errors
Modeling errors
Examples
Incorrect or insufficient information
Spatial or temporal approximations
Selection of an incorrect model
Overlooking an important pathway
Imprecise or biased measurements
Small or unrepresentative samples
In time, space or activities
Structurally-related chemicals
Incorrect inference on the basis for correlations
Excluding relevant variables
sophistication, data quality, and amenability to
quantitative expressions of variability or uncertainty.
According to the U.S. EPA (1992), uncertainty
characterization and uncertainty assessment are two ways
of describing uncertainty at different degrees of
sophistication. Uncertainty characterization usually
involves a qualitative discussion of the thought processes
used to select or reject specific data, estimates, scenarios,
etc. Uncertainty assessment is a more quantitative process
that may range from simpler measures (e.g., ranges) and
simpler analytical techniques (e.g., sensitivity analysis) to
more complex measures and techniques. Its goal is to
provide decision makers with information concerning the
quality of an assessment, including the potential
variability in the estimated exposures, major data gaps,
and the effect that these data gaps have on the exposure
estimates developed.
A distinction between variability and uncertainty
was made in Section 2.1. Although the quantitative
process mentioned above applies more directly to
variability and the qualitative approach more so to
uncertainty, there is some degree of overlap. In general,
either method provides the assessor or decision-maker
with insights to better evaluate the assessment in the
context of available data and assumptions. The following
paragraphs describe some of the more common
procedures for analyzing variability and uncertainty in
exposure assessments. Principles that pertain to
presenting the results of variability/uncertainty analysis
are discussed in the next section.
Several approaches can be used to characterize
uncertainty in parameter values. When uncertainty is
high, the assessor may use order-of-magnitude bounding
estimates of parameter ranges (e.g., from 0.1 to 10 liters
for daily water intake). Another method describes the
range for each parameter including the lower and upper
bounds as well as a "best estimate" (e.g., 1.4 liters per
day) determined by available data or professional
judgement.
When sensitivity analysis indicates that a parameter
profoundly influences exposure estimates, the assessor
should develop a probabilistic description of its range. If
there are enough data to support their use, standard
statistical methods are preferred. If the data are
inadequate, expert judgment can be used to generate a
subjective probabilistic representation. Such judgments
should be developed in a consistent, well-documented
manner. Morgan and Henrion (1990) and Rish (1988)
describe techniques to solicit expert judgment.
Most approaches to quantitative analysis examine
how variability and uncertainty in values of specific
parameters translate into the overall uncertainty of the
assessment. Details may be found in reviews such as Cox
and Baybutt (1981), Whitmore (1985), Inman and Helton
(1988), Seller (1987), and Rish and Marnicio (1988).
These approaches can generally be described (in order of
increasing complexity and data needs) as: (1) sensitivity
analysis; (2) analytical uncertainty propagation;
(3) probabilistic uncertainty analysis; or (4) classical
Exposure Factors Handbook
August 1997
Page
2-5
-------
Volume I - General Factors
Chapter 2 - Variability and Uncertainty
statistical methods (U.S. EPA 1992). The four approaches
are summarized in Table 2-3.
medium, or low; and state whether they were based on
data, analogy, or professional judgment. Where
Table 2-3. Approaches to Quantitative Analysis of Uncertainty
Approach
Description
Example
Sensitivity Analysis
Analytical Uncertainty Propagation
Probabilistic Uncertainty Analysis
Classical Statistical Methods
Changing one input variable at a time while
leaving others constant, to examine effect on
output
Examining how uncertainty in individual
parameters affects the overall uncertainty of the
exposure assessment
Varying each of the input variables over various
values of their respective probability distributions
Estimating the population exposure distribution
directly, based on measured values from a
representative sample
Fix each input at lower (then upper) bound
while holding others at nominal values (e.g.,
medians)
Analytically or numerically obtain a partial
derivative of the exposure equation with
respect to each input parameter
Assign probability density function to each
parameter; randomly sample values from each
distribution and insert them in the exposure
equation (Monte Carlo)
Compute confidence interval estimates for
various percentiles of the exposure distribution
2.7. PRESENTING RESULTS OF VARIABILITY
AND UNCERTAINTY ANALYSIS
Comprehensive qualitative analysis and rigorous
quantitative analysis are of little value for use in the
decision-making process, if their results are not clearly
presented. In this chapter, variability (the receipt of
different levels of exposure by different individuals) has
been distinguished from uncertainty (the lack of
knowledge about the correct value for a specific exposure
measure or estimate). Most of the data that are presented
in this handbook deal with variability directly, through
inclusion of statistics that pertain to the distributions for
various exposure factors.
Not all approaches historically used to construct
measures or estimates of exposure have attempted to
distinguish between variability and uncertainty. The
assessor is advised to use a variety of exposure
descriptors, and where possible, the full population
distribution, when presenting the results. This
information will provide risk managers with a better
understanding of how exposures are distributed over the
population and how variability in population activities
influences this distribution.
Although incomplete analysis is essentially
unquantifiable as a source of uncertainty, it should not be
ignored. At a minimum, the assessor should describe the
rationale for excluding particular exposure scenarios;
characterize the uncertainty in these decisions as high,
uncertainty is high, a sensitivity analysis can be used to
credible upper limits on exposure by way of a series of
"what if questions.
Although assessors have always used descriptors to
communicate the kind of scenario being addressed, the
1992 Exposure Guidelines establish clear quantitative
definitions for these risk descriptors. These definitions
were established to ensure that consistent terminology is
used throughout the Agency. The risk descriptors defined
in the Guidelines include descriptors of individual risk
and population risk. Individual risk descriptors are
intended to address questions dealing with risks borne by
individuals within a population, including not only
measures of central tendency (e.g., average or median),
but also those risks at the high end of the distribution.
Population risk descriptors refer to an assessment of the
extent of harm to the population being addressed. It can
be either an estimate of the number of cases of a particular
effect that might occur in a population (or population
segment), or a description of what fraction of the
population receives exposures, doses, or risks greater than
a specified value. The data presented in the Exposure
Factors Handbook is one of the tools available to
exposure assessors to construct the various risk
descriptors.
However, it is not sufficient to merely present the
results using different exposure descriptors. Risk
managers should also be presented with an analysis of the
Page
2-6
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 2 - Variability and Uncertainty
A
uncertainties surrounding these descriptors. Uncertainty
may be presented using simple or very sophisticated
techniques, depending on the requirements of the
assessment and the amount of data available. It is beyond
the scope of this handbook to discuss the mechanics of
uncertainty analysis in detail. At a minimum, the assessor
should address uncertainty qualitatively by answering
questions such as:
• What is the basis or rationale for selecting
these assumptions/parameters, such as data,
modeling, scientific judgment, Agency policy,
"what if considerations, etc.?
• What is the range or variability of the key
parameters? How were the parameter values
selected for use in the assessment? Were
average, median, or upper-percentile values
chosen? If other choices had been made, how
would the results have differed?
• What is the assessor's confidence (including
qualitative confidence aspects) in the key
parameters and the overall assessment? What
are the quality and the extent of the data
base(s) supporting the selection of the chosen
values?
Any exposure estimate developed by an assessor
will have associated assumptions about the setting,
chemical, population characteristics, and how contact with
the chemical occurs through various exposure routes and
pathways. The exposure assessor will need to examine
many sources of information that bear either directly or
indirectly on these components of the exposure
assessment. In addition, the assessor will be required to
make many decisions regarding the use of existing
information in constructing scenarios and setting up the
exposure equations. In presenting the scenario results, the
assessor should strive for a balanced and impartial
treatment of the evidence bearing on the conclusions with
the key assumptions highlighted. For these key
assumptions, one should cite data sources and explain any
adjustments of the data.
The exposure assessor also should qualitatively
describe the rationale for selection of any conceptual or
mathematical models that may have been used. This
discussion should address their verification and validation
status, how well they represent the situation being
assessed (e.g., average versus high-end estimates), and
any plausible alternatives in terms of their acceptance by
the scientific community.
Table 2-2 summarizes the three types of
uncertainty, associated sources, and examples. Table 2-3
summarizes four approaches to analyze uncertainty
quantitatively. These are described further in the 1992
Exposure Guidelines.
2.8. REFERENCES FOR CHAPTER 2
Bogen, K.T. (1990) Uncertainty in environmental
health risk assessment. Garland Publishing, New
York, NY.
Cox, D.C.; Baybutt, P.C. (1981) Methods for
uncertainty analysis. A comparative survey. Risk
Anal. l(4):251-258. •
Duan, N. (1982) Microenvironment types: A model for
human exposure to air pollution. Environ. Intl.
8:305-309.
Inman, R.L.; Helton, J.C. (1988) An investigation of
uncertainty and sensitivity analysis techniques for
computer models. Risk Anal. 8(1):71-91.
Morgan, M.G.; Henrion, M. (1990) Uncertainty: A
guide to dealing with uncertainty in quantitative
risk and policy analysis. Cambridge University
Press, New York, NY.
National Research Council (NRC). (1994) Science and
judgment in risk assessment. National Academy
Press, Washington, DC.
Rish, W.R. (1988) Approach to uncertainty in risk
analysis. Oak Ridge National Laboratory.
ORNL/TM-10746.
Rish, W.R.; Marnicio, R.J. (1988) Review of studies
related to uncertainty in risk analysis. Oak Ridge
National Laboratory. ORNL/TM-10776.
Seller, F.A. (1987) Error propagation for large errors.
Risk Anal. 7(4):509-518.
U.S. EPA (1992) Guidelines for exposure assessment.
Washington, DC: Office of Research and
Development, Office of Health and Environmental
Assessment. EPA/600/2-92/001.
U.S. EPA (1995) Guidance for risk characterization.
Science Policy Council, Washington, DC.
Whitmore, R.W. (1985) Methodology for
characterization of uncertainty in exposure
assessments. EPA/600/8-86/009.
Exposure Factors Handbook
Ausust 1997
Page
2-7
-------
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
3. DRINKING WATER INTAKE
3.1. BACKGROUND
Drinking water is a potential source of human
exposure to toxic substances. Contamination of drinking
water may occur by, for example, percolation of toxics
through the soil to ground water that is used as a source
of drinking water; runoff or discharge to surface water
that is used as a source of drinking water; intentional or
unintentional addition of substances to treat water (e.g.,
chlorination); and leaching of materials from plumbing
systems (e.g., lead). Estimating the magnitude of the
potential dose of toxics from drinking water requires
information on the quantity of water consumed. The
purpose of this section is to describe key published
studies that provide information on drinking water
consumption (Section 3.2) and to provide
recommendations of consumption rate values that should
be used in exposure assessments (Section 3.6).
Currently, the U.S. EPA uses the quantity of 2 L
per day for adults and 1 L per day for infants (individuals
of 10 kg body mass or less) as default drinking water
intake rates (U.S. EPA, 1980; 1991). These rates
include drinking water consumed in the form of juices
and other beverages containing tapwater (e.g., coffee).
The National Academy of Sciences (NAS, 1977)
estimated that daily consumption of water may vary with
levels of physical activity and fluctuations in temperature
and humidity. It is reasonable to assume that some
individuals in physically-demanding occupations or living
in warmer regions may have high levels of water intake.
Numerous studies cited in this chapter have
generated data on drinking water intake rates. In
general, these sources support EPA's use of 2 L/day for
adults and 1 L/day for children as upper-percentile
tapwater intake rates. Many of the studies have reported
fluid intake rates for both total fluids and tapwater.
Total fluid intake is defined as consumption of all types
of fluids including tapwater, milk, soft drinks, alcoholic
beverages, and water intrinsic to purchased foods. Total
tapwater is defined as water consumed directly from the
tap as a beverage or used in the preparation of foods and
beverages (i.e., coffee, tea, frozen juices, soups, etc.).
Data for both consumption categories are presented in
the sections that follow. However, for the purposes of
exposure assessments involving source-specific
contaminated drinking water, intake rates based on total
tapwater are more representative of source-specific
tapwater intake. Given the assumption that purchased
foods and beverages are widely distributed and less likely
to contain source-specific water, the use of total fluid
intake rates may overestimate the potential exposure to
toxic substances present only in local water supplies;
therefore tapwater intake, rather than total fluid intake,
is emphasized in this section.
All studies on drinking water intake that are
currently available are based on short-term survey data.
Although short-term data may be suitable for obtaining
mean intake values that are representative of both short-
and long-term consumption patterns, upper-percentile
values may be different for short-term and long-term
data because more variability generally occurs in short-
term surveys. It should also be noted that most drinking
water surveys currently available are based on recall.
This may be a source of uncertainty hi the estimated
intake rates because of the subjective nature of this type
of survey technique.
The distribution of water intakes is usually, but
not always, lognormal. Instead of presenting only the
lognormal parameters, the actual percentile distributions
are presented in this handbook, usually with a comment
on whether or not it is lognormal. To facilitate
comparisons between studies, the mean and the 90th
percentiles are given for all studies where the distribution
data are available. With these two parameters, along
with information about which distribution is being
followed, one can calculate, using standard formulas, the
geometric mean and geometric standard deviation and
hence any desired percentile of the distribution. Before
doing such a calculation one must be sure that one of
these distributions adequately fits Hie data.
The available studies on drinking water
consumption are summarized in the following sections.
They have been classified as either key studies or
relevant studies based on the applicability of their survey
designs to exposure assessment of the entire United
States population. Recommended intake rates are based
on the results of key studies, but relevant studies are also
presented to provide the reader with added perspective
on the current state-of-knowledge pertaining to drinking
water intake.
3.2. KEY GENERAL POPULATION STUDIES ON
DRINKING WATER INTAKE
Canada Department of Health and Welfare (1981)
- Tapwater Consumption in Canada - In a study
conducted by the Canadian Department of Health and
Welfare, 970 individuals from 295 households were
surveyed to determine the per capita total tapwater intake
Exposure Factors Handbook
August 1997
Page
3-1
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
rates for various age/sex groups during winter and
summer seasons (Canadian Ministry of National Health
and Welfare, 1981). Intake rate was also evaluated as a
function of physical activity. The population that was
surveyed matched the Canadian 1976 census with respect
to the proportion in different age, regional, community
size and dwelling type groups. Participants monitored
tapwater consumed away from home. The survey also did
not attempt to estimate intake rates for fluids other than
tapwater. Consequently, no intake rates for total fluids
were reported.
Daily consumption distribution patterns for various
age groups are presented in Table 3-1. For adults (over
18 years of age) only, the average total tapwater intake
Table 3-1 . Daily Total Tapwater Intake Distribution for Canadians, by Age Group
(approx. 0.20 L increments, both sexes, combined seasons)
Amount Consumed3
L/day
0.00 - 0.21
0.22 - 0.43
0.44 - 0.65
0.66 - 0.86
0.87-1.07
1.08 - 1.29
1.30-1.50
1.51 - 1.71
1.72-1.93
1.94-2.14
2.15-2.36
2.37 - 2.57
2.58 - 2.79
2.80 - 3.00
3.01 - 3.21
3.22 - 3.43
3.44 - 3.64
3.65 - 3.86
>3.86
TOTAL
Age Group (years)
5 and under 6-17
%
11.1
17.3
24.8
9.9
11.1
11.1
4.9
6.2
1.2
1.2
1.2
-
-
-
-
-
-
-
-
100.0
Number
9
14
20
8
9
9
4
5
1
1
1
0
0
0
0
0
0
0
0
81
%
2.8
10.0
13.2
13.6
14.4
14.8
9.6
6.8
2.4
1.2
4.0
0.4
2.4
2.4
0.4
-
-
-
1.6
100.0
Number
7
25
33
34
36
37
24
17
6
3
10
1
6
6
1
0
0
0
4
250
18 and over
%
0.5
1.9
5.9
8.5
13.1
14.8
15.3
12.1
6.9
5.6
3.4
3.1
2.7
1.4
1.1
0.9
0.8
_
2.0
100.0
Number
3
12
38
54
84
94
98
77
44
36
22
20
17
9
7
6
5
0
13
639
a Includes tapwater and foods and beverages derived from tapwater.
Source: Canadian Ministry of National Health and Welfare, 1981.
water intake for a 2-day period (1 weekday, and 1
weekend day) in both late summer of 1977 and winter of
1978. All 970 individuals participated in both the summer
and winter surveys. The amount of tapwater consumed
was estimated based on the respondents' identification of
the type and size of beverage container used, compared to
standard sized vessels. The survey questionnaires
included a pictorial guide to help participants in
classifying the sizes of the vessels. For example, a small
glass of water was assumed to be equivalent to 4.0 ounces
of water, and a large glass was assumed to contain 9.0
ounces of water. The study also accounted for water
derived from ice cubes and popsicles, and water in soups,
infant formula, and juices. The survey did not attempt to
differentiate between tapwater consumed at home and
rate was 1.38 L/day, and the 90th percentile rate was 2.41
L/day as determined by graphical interpolation. These
data follow a lognormal distribution. The intake data for
males, females, and both sexes combined as a function of
age and expressed in the units of milliliters (grams) per
kilogram body weight are presented in Table 3-2. The
tapwater survey did not include body weights of the
participants, but the body weight information was taken
from a Canadian health survey dated 1981; it averaged
65.1 kg for males and 55.6 kg for females. Intake rates
for specific age groups and seasons are presented in Table
3-3. The average daily total tapwater intake rates for all
ages and seasons combined was 1.34 L/day, and the 90th
percentile rate was 2.36 L/day. The summer intake rates
are nearly the same as the winter intake rates. The authors
Page
3-2
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
speculate that the reason for the small seasonal variation
here is that in Canada, even in the summer, the ambient
temperature seldom exceeded 20 degrees C and marked
increase in water consumption with high activity levels
has been observed in other studies only when the ambient
temperature has been higher than 20 degrees. Average
daily total tapwater intake rates as a function of the level
of physical activity, as estimated subjectively, are
presented in Table 3-4. The amounts of tapwater
consumed that are derived from various foods and
beverages are presented in Table 3-5. Note that the
consumption of direct "raw" tapwater is almost constant
across all age groups from school-age children through
the oldest ages. The increase in total tapwater
consumption beyond school age is due to coffee and tea
consumption.
Table 3-2. Average Daily Tapwater Intake of Canadians
(expressed as milliliters per kilogram body weight)
Average Daily Intake (mL/kg)
Age Group (years)
<3
3-5
6-17
18-34
35-54
55+
Females
53
49
24
23
25
24
Males Both Sexes
35
48
27
19
19
21
Total Population 24 21
Source: Canadian Ministry of National Health and Welfare,
45
48
26
21
22
22
22
1981.
Table 3-3. Average Daily Total Tapwater Intake of Canadians, by Age and Season (IVday)a
Age (years)
Average
Summer
Winter
Summer/Winter
90th Percentile
Summer/Winter
<3
0.57
0.66
0.61
1.50
3-5
0.86
0.88
0.87
1.50
6-17
1.14
1.13
1.14
2.21
18-34
1.33
1.42
1.38
2.57
35-54
1.52
1.59
1.55
2.57
<55
1.53
1.62
1.57
2.29
All Ages
1.31
1.37
1.34
2.36
a Includes tapwater and foods and beverages derived from tapwater.
Source: Canadian Ministry of National Health and Welfare, 198 1 .
Table 3-4. Average Daily Total Tapwater Intake of Canadians as a Function of
Level of Physical Activity at Work and in Spare Time
(16 years and older, combined seasons, L/day)
Activity
Level"
Consumption11
L/day
Work
Number of Respondents
Spare Time
Consumption
L/day
Number of Respondents
Extremely Active
Very Active
Somewhat Active
Not Very Active
Not At All Active
Did Not State
TOTAL
1.72
1.47
1.47
1.27
1.30
1.30
99
244
217
67
16
45
688
1.57
1.51
1.44
1.52
1.35
1.31
52
151
302
131
26
26
688
* The levels of physical activity listed here were not defined any further by the survey report, and categorization of activity level by survey
participants is assumed to be subjective.
b Includes tapwater and foods and beverages derived from tapwater.
Source: Canadian Ministry of National Health and Welfare, 1981.
Exposure Factors Handbook
August 1997
Page
3-3
-------
Volume I- General Factors
Chapter 3 - Drinking Water Intake
Table 3-5. Average Daily Tapwater Intake by Canadians, Apportioned Among Various Beverages
(both sexes, by age, combined seasons, L/day)a
Age Group (years)
Total Number in Group 34
Water
Ice/Mix
Tea
Coffee
"Other Type of Drink"
Reconstituted Milk
Soup
Homemade Beer/Wine
Homemade Popsicles
Baby Formula, etc.
TOTAL
Under 3
47
0.14
0.01
*
0.01
0.21
0.10
0.04
*
0.01
0.09
0.61
3-5
250
0.31
0.01
0.01
*
0.34
0.08
0.08
*
0.03
*
0.86
6-17
232
0.42
0.02
0.05
0.06
0.34
0.12
0.07
0.02
0.03
*
1.14
18-34
254
0.39
0.04
0.21
0.37
0.20
0.05
0.06
0.04
0.01
*
1.38
35-54
153
0.38
0.03
0.31
0.50
0.14
0.04
0.08
0.07
*
*
1.55
55 and Over
0.38
0.02
0.42
0.42
0.11
0.08
0.11
0.03
*
*
1.57
* Includes tapwater and foods and beverages derived from tapwater.
* Less than 0.0 1 L/day
Source: Canadian Ministry of National Health and Welfare,
1981.
Data concerning the source of tapwater (municipal,
well, or lake) was presented in one table of the study.
This categorization is not appropriate for making
conclusions about consumption of ground versus surface
water.
This survey may be more representative. of total
tapwater consumption than some other less
comprehensive surveys because it included data for some
tapwater-containing items not covered by other studies
(i.e., ice cubes, popsicles, and infant formula). One
potential source of error in the study is that estimated
intake rates were based on identification of standard
vessel sizes; the accuracy of this type of survey data is not
known. The cooler climate of Canada may have reduced
the importance of large tapwater intakes resulting from
high activity levels, therefore making the study less
applicable to the United States. The authors were not able
to explain the surprisingly large -variations between
regional tapwater intakes; the largest regional difference
was between Ontario (1.18 liters/day) and Quebec (1.55
liters/day).
Ershow and Cantor (1989) - Total Water and
Tapwater Intake in the United States: Population-Based
Estimates of Quantities and Sources - Ershow and Cantor
(1989) estimated water intake rates based on data
collected by the USDA 1977-1978 Nationwide Food
Consumption Survey (NFCS). Daily intake rates for
tapwater and total water were calculated for various age
groups for males, females, and both sexes combined.
Tapwater was defined as "all water from the household
tap consumed directly as a beverage or used to prepare
foods and beverages." Total water was defined as
tapwater plus "water intrinsic to foods and beverages"
(i.e., water contained in purchased food and beverages).
The authors showed that the age, sex, and racial
distribution of the surveyed population closely matched
the estimated 1977 U. S. population.
Daily total tapwater intake rates, expressed as mL
(grams) per day by age group are presented in Table 3-6.
These data follow a lognormal distribution. The same
data, expressed as mL (grams) per kg body weight per day
are presented in Table 3-7. A summary of these tables,
showing the mean, the 10th and 90th percentile intakes,
expressed as both mL/day and mL/kg-day as a function of
age, is presented in Table 3-8. This shows that the mean
and 90th percentile intake rates for adults (ages 20 to 65+)
are approximately 1,410 mL/day and 2,280 mL/day and
for all ages the mean and 90th percentile intake rates are
1,190 mL/day and 2,090 mL/day. Note that older adults
have greater intakes than do adults between age 20 and
Page
3-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
8
00
•S
£
o
W
S £
3 JD
z o
VO *-< r-t
crt co
§ *0
•SI
Is
= £
-« 3
f S
O rt
H >
Exposure Factors Handbook
August 1997
Page
3-5
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
s
1
o.
2
o
ja
w
•*
la
o .o .« U
"I S
cocsooooooooo
csc-i'—iooco*oot^ooo;>n
incscscS'-^'-* ^H ^H
o r- M es >o
tsooooo
inosoooooo'*
5
11
4= fc-
*£
i-S
a o
s a
II
in O
.IW |_
a 3
ll
rt # CO
o\
2"
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Age Group
Infants (<1 year)
Children (1-10 years)
Teens (11-19 years)
Adults (20 -64 years)
Adults (65+ years)
All ages
Mean
302
736
965
1,366
1,459
1,193
Table 3-8. Summary of Tapwater Intake by
Intake (mL/day)
10th-90th Percentiles
0-649
286-1,294
353-1,701
559-2,268
751-2,287
423-2,092
Age
43.5
35.5
18.2
19.9
21.8
22.6
Intake (mL/kg-day)
10th-90th Percentiles
0 - 100
12.5 - 64.4
6.5 - 32.3
8.0 - 33.7
10.9-34.7
8.2 - 39.8
Source: Ershow and Cantor (1989)
65, an observation bearing on the interpretation of the
Cantor, et al. (1987) study which surveyed a population
that was older than the national average (see Section 3.3).
Ershow and Cantor (1989) also measured total
water intake for the same age groups and concluded that
it averaged 2,070 mL/day for all groups combined and
that tap water intake (1,190 mL/day) is 55 percent of the
total water intake. (The detailed intake data for various
age groups are presented in Table 3-9). Ershow and
Cantor (1989) also concluded that, for all age groups
combined, the proportion of tapwater consumed as
drinking water, foods, and beverages is 54 percent, 10
percent and 36 percent, respectively. (The detailed data
on proportion of tapwater consumed for various age
groups are presented in Table 3-10). Ershow and Cantor
(1989) also observed that males of all age groups had
higher total water and tapwater consumption rates than
females; the variation of each from the combined-sexes
mean was about 8 percent.
Ershow and Cantor (1989) also presented data on
total water intake and tapwater intake for children of
various ages. They found, for infants and children
between the ages of 6 months and 15 years, that the total
water intake per unit body weight increased smoothly and
sharply from 30 mL/kg-day above age 15 years to 190
mL/kg-day for ages less than 6 months. This probably
represents metabolic requirements for water as a dietary
constituent. However, they found that the intake of
tapwater alone went up only slightly with decreasing age
(from 20 to 45 mL/kg-day as age decreases from 11 years
to less than 6 months). Ershow and Cantor (1989)
attributed this small effect of age on tapwater intake to the
large number of alternative water sources (besides
tapwater) used for the younger age groups.
Table 3-9. Total Tapwater Intake (as percent of total water intake) by Broad Age Categorya'b
Age (years)
Mean
10
Percentile Distribution
25 50 75
90
95
99
1-10
11-19
20-64
65+
26
45
47
59
65
0
6
6
12
25
0
19
18
27
41
0
24
24
35
47
12
34
35
49
58
22
45
47
61
67
37
57
59
72
74
55
67
69
79
81
62
72
74
83
84
82
81
83
90
90
Does not include pregnant women, lactating women, or breast-fed children.
Total tapwater is defined as "all water from the household tap consumed directly as a beverage or used to prepare foods and beverages."
0 = Less than 0.5 percent.
Source: Ershow and Cantor, 1989.
Exposure Factors Handbook
August 1997
Page
3-7
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
a b
Table 3-10. General Dietary Sources of Tapwater for Both Sexes '
% of Tapwater
Age
(years)
1-10
11-19
20-64
65+
All
Source
Food0
Drinking Water
Other Beverages
All Sources
Food6
Drinking Water
Other Beverages
All Sources
Food0
Drinking Water
Other Beverages
All Sources
Food0
Drinking Water
Other Beverages
All Sources
Food0
Drinking Water
Other Beverages
All Sources
Food0
Drinking Water
Other Beverages
All Sources
* Does not include pregnant women,
11
69
20
100
15
65
20
100
13
65
22
100
8
47
45
100
8
50
42
100
10
54
36
100
Standard
24
37
33
16
25
21
15
25
23
10
26
26
9
23
23
13
27
27
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
25
0
39
0
5
52
0
3
52
0
2
29
25
2
36
27
2
36
14
50
0
87
0
10
70
15
8
70
16
5
48
44
5
52
40
6
56
34
75
10
100
22
19
84
32
17
85
34
11
67
63
11
66
57
13
75
55
95
70
100
100
44
96
63
38
98
68
25
91
91
23
87
85
31
95
87
99
100
100
100
100
100
93
100
100
96
49
100
100
38
99
100
64
100
100
lactating women, or breast-fed children.
k Individual values may not add to totals due to
rounding.
c Food category includes soups.
0 -
Source:
Less than 0.5 percent.
Ershow and Cantor, 1989.
With respect to region of the country, the northeast
states had slightly lower average tap water intake (1,200
mL/day) than the three other regions (which were
approximately equal at 1,400 mL/day).
This survey has an adequately large size (26,446
individuals) and it is a representative sample of the United
States population with respect to age distribution, sex,
racial composition, and residential location. It is therefore
suitable as a description of national tapwater
consumption. The chief limitation of the study is that the
data were collected in 1978 and do not reflect the
expected increase in the consumption of soft drinks and
bottled water or changes in the diet within the last two
decades. Since the data were collected for only a three-
day period, the extrapolation to chronic intake is
uncertain.
Roseberry and Burmaster (1992) - Lognormal
Distributions for Water Intake - Roseberry and Burmaster
(1992) fit lognormal distributions to the water intake data
reported by Ershow and Cantor (1989) and estimated
population-wide distributions for total fluid and total
tapwater intake based on proportions of the population in
each age group. Their publication shows the data and the
fitted log-normal distributions graphically. The mean
Page
3-8
Exposure Factors Handbook •
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
was estimated as the zero intercept, and the standard
deviation was estimated as the slope of the best fit line for
the natural logarithm of the intake rates plotted against
their corresponding z-scores (Roseberry and Burmaster,
1992). Least squares techniques were used to estimate the
best fit straight lines for the transformed data. Summary
statistics for the best-fit lognormal distribution are
presented in Table 3-11. In this table, the simulated
balanced population represents an adjustment to account
for the different age distribution of the United States
population in 1988 from the age distribution in 1978 when
Ershow and Cantor (1989) collected their data. Table 3-
12 summarizes the quantiles and means of tap water intake
as estimated from the best-fit distributions. The mean
total tapwater intake rates for the two adult populations
(age 20 to 65 years, and 65+ years) were estimated to be
1.27andl.34L/day.
Table 3-11. Summary Statistics for Best-Fit Lognormal
Distributions for Water Intake Ratesa
Group
(age in years)
In Total Fluid
Intake Rate
a R2
0 < age < 1
1 s age <11
11 s age <20
20 <; age <65
65 s age
All ages
6.979
7.182
7.490
7.563
7.583
7.487
Simulated balanced population 7.492
0.291 0.996
0.340 0.953
0.347 0.966
0.400 0.977
0.360 0.988
0.405 0.984
0.407 1.000
Group
(age in years)
In Total Tapwater
Intake
. a R2
0 < age < 1
1 <. age <11
11 <; age <20
20 <; age <65
65 <; age
All ages
5.587
6.429
6.667
7.023
7.088
6.870
Simulated balanced population 6.864
0.615 0.970
0.498 0.984
0.535 0.986
0.489 0.956
0.476 0.978
0.530 0.978
0.575 0.995
These values (mL/day) were used in the following equations
to estimate the quantiles and averages for total tapwater intake
shown in Tables 3-12.
97.5 percentile intake rate = exp Cu + (1.96 : a)]
75 percentile intake rate = exp \]j. + (0.6745 • o)l
50 percentile intake rate = exp J)u]
25 percentile intake rate = exp [> - (0.6745 • o)]
2.5 percentile intake rate = exp [^ - (1.96 • a)]
Mean intake rate - exp fr/ + 0.5 • o2)]
Source: Roseberry and Burmaster, 1992.
These intake rates were based on the data originally
presented by Ershow and Cantor (1989). Consequently,
the same advantages and disadvantages associated with
the Ershow and Cantor (1989) study apply to this data set.
3.3. RELEVANT GENERAL POPULATION
STUDIES ON DRINKING WATER INTAKE
National Academy of Sciences (1977) - Drinking
Water and Health - NAS (1977) calculated the average
per capita water (liquid) consumption per day to be 1.63
L. This figure was based on a survey of the following
literature, sources: Evans (1941); Bourne and
Kidder (1953); Walker etal. (1957); Wolf (1958); Guyton
(1968); McNall and Schlegel (1968); Randall (1973);
NAS (1974); and Pike and Brown (1975). Although the
calculated average intake rate was 1.63 L per day,
NAS (1977) adopted a larger rate (2 L per day) to
represent the intake of the majority of water consumers.
This value is relatively consistent with the total tapwater
intakes rate estimated from the key studies presented
previously. However, the use of the term "liquid" was not
clearly defined in this study, and it is not known whether
the populations surveyed are representative of the adult
U.S. population. Consequently, the results of this study
are of limited use in recommending total tapwater intake
rates and this study is not considered a key study.
Hopkins and Ellis (1980) - Drinking Water
Consumption in Great Britain - A study conducted in
Great Britain over a 6-week period during September and
October 1978, estimated the drinking water consumption
rates of 3,564 individuals from 1,320 households in
England, Scotland, and Wales (Hopkins and Ellis, 1980).
The participants were selected randomly and were asked
to complete a questionnaire and a diary indicating the type
and quantity of beverages consumed over a 1-week
period. Total liquid intake included total tapwater taken
at home and away from home; purchased alcoholic
beverages; and non-tapwater-based drinks. Total tapwater
included water content of tea, coffee, and other hot water
drinks; homemade alcoholic beverages; and tapwater
consumed directly as a beverage. The assumed tapwater
contents for these beverages are presented in Table 3-13.
Based on responses from 3,564 participants, the mean
intake rates and frequency distribution data for various
beverage categories were estimated by Hopkins and Ellis
(1980). These data are listed in Table 3-14. The mean
per capita total liquid intake rate for all individuals
surveyed was L59 L/day, and the mean per capita total
tapwater intake rate was 0.95 L/day, with a 90th percentile
Exposure Factors Handbook
August 1997
Page
3-9
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-12. Estimated Quantiles and Means for Total Tapwater Intake Rates (mL/day)d
Age Group
(years)
0
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
1?
1
s
2
U
«
0
1
•a
CJ
§
ra*
E-i
o
E-1
a"
.3
.2*
*3
0
. Intake of
^5
CO
>
o
<5
E
03
1
^2
"cd
3
">
1
|pc
lip
.§•0,3
g I 1
15!
< % °
1 1
S ^
°||
g1 1 2
111
o. H o
85 1
T= §
"* oJ
o SS
S 2
o "5
O<
wa
Ilil
|lis
. c
3 S
on 5
. ^*
* (4-.
o o
G. o
fg
< W
SJ3
w S
^ >s
S>
2
1
ON CO C^ "^ C1^ *O CO CO t** O CO ^3 O ^
C^ CO f"* OO OO *O VO CS ^f CS CO T^ CO CS
'-H'Y p OO O OOO OOOOO
*-»'—« O OO O OOO OOOOO
op p op p-poo SSSoo
OO O OO* O OOO OOOOO
CNg C\ OOC\ CO COCNO COCOOOCOTj-
•O »— < >O C\l^ CN O CO *— < CN "— ' M" r-< ^
^^ O OO O OOO OOO'OO
O O O\ OO Tj~ *O O\ O Ol O CS CO O >O
O O OO ^ GN t~* C\ "O *O ^i" ON *co rt- «t" ^ ooes-^j- cocNOsco^^
C4 CO ' — i OO r^ *-* O O «•-* *— « VO CO ^H tri
T*'? p* oo* o* ppo" pppp'p"
•^•t^- >o esco c\ >OC^-CD CN 10 i— < o co
I-H i-i O* OO O OOO OOOOO
COCO CM C\\O VO C^OsiO C7\C^COr-CO
OO O OO O OOO OOOOO
OO O OO O OOO OOOOO
coo co io»o o COO^H oiocs*-*o
10 I-H TJ* ON I**- Ol
o.
I
•s
.s
3
aj
M
2
s
J2
rS
1
3
c
8
._
1
o
—
3
- _-o
*>
T3
.s
o
•5
>t •
11
J.^r
m
•a "S
•s 2
tl
o o
S2 S3
3
B u
6I
n CO
Exposure Factors Handbook
August 1997
Page
3-11
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
f
a
s
1
1
.2
|
S
3
o
f-
•g
TO
1
13
s
i
'o
§
1
ui
m
•i
H
S3
S
g
0
§
1
03
0
S 1
O ^
ts "2
Approx 95 '
Interval
Error of
i
•a s
g
a
^
"a
^c
c
s
S
"I
3
z
fT,
B—
TO
JD
1
o cs o o
oo cs co t** ^^ cs
oo c> i-; »n vo Th
O O *H ^ ^ ^
en \O »-< Tt- CS O
IO OO O OO ^-» €*•
o o ~1 cs cs -5
•n o n »n in
f- cS ^^ en »n TJ-
oo ON O en cs 1o
oo TJ- co en »-< c\
cs »-t en >n en
^r s v ° ? +
- £ 2 2 s £
•o
ii
HS
os en o »n oo c^-
co Ox *-; in o in
o o ^H ^-i i— J i— <
S r^ - 6 c!) 4
1-* cs en »n o in
O C3 CD O O C3
in o in cs oo cs
co 5> en "O oo t^
O ^5 ^H 1—4 ^H ^-1
t** CS ON vS ^ CS
i-< CS CS Tt; O *O
CD O O O O O
1/1 ^H ,-H cS OX CS
in oo o\ m en oo
in m (-• p •-; p
p O C3 -H -4 ^
en >n ON o m cs
r*- oo in en •*£ r*-
en Ti* NO ON O O\
O CD O O ~* O
S^ ON S; ^ S S
in >n 55 o cs cs
=? 9 o -r -r -r
\o >n en ON ^
ON O Zr. en en "o
en >n p ON ^H p
O O Q O -^ ^
en ON oo -^ o m
»n en cs o TJ- r-
*g* cs en en cs CS
O O O CD O O
en en cS en ON r>
^ cs m en en en
O p p p p p
O O O O C5 O
•^t- en >n •— * — < t^
*o en cs ON ON eN
Tt «n r- ON o c
CD CD CD CD t-^ -^
r- o >n vo «-H en
r- »n o CD o en
rj- tn oo p cs «-;
CD O CD ^ *-l ^H
in o ^o »n in >n
r- cs — * en m rt-
oo ON O en cS NO
oo Th oo en i— « ON
cs *-* en
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-16. Measured Fluid Intakes (mL/day)
Subject
Adults ("normal" conditions)6
Adults (high environmental
temperature to 32 °C)
Adults (moderately active)
Children (5-14 yr)
a Includes tea, coffee, soft drinks, beer, cider, wine,
b "Normal" conditions refer to typical environmental
Source: ICRP, 1981.
Total Fluids Milk
1000-2400 120-450
2840-3410
3256 ± ...
SD = 900
3700
1000-1200 330-500
1310-1670 540-650
etc.
temperature and activity levels.
Water-Based
Tapwater Drinks2
45-730 320-1450
ca. 200 ca. 380
540-790
Gillies and Paulin (1983) - Variability of Mineral
Intakes from Drinking Water - Gillies and Paulin (1983)
conducted a study to evaluate variability of mineral intake
from drinking water. A study population of 109 adults
(75 females; 34 males) ranging in age from 16 to 80 years
(mean age = 44 years) in New Zealand was asked to
collect duplicate samples of water consumed directly from
the tap or used in beverage preparation during a 24-hour
period. Participants were asked to collect the samples on
a day when all of the water consumed would be from their
own home. Individuals were selected based on their
willingness to participate and their ability to comprehend
the collection procedures. The mean total tapwater intake
rate for this population was 1.25 (±0.39) L/day, and the
90th percentile rate was 1.90 L/day. The median total
tapwater intake rate (1.26 L/day) was very similar to the
mean intake rate (Gillies and Paulin, 1983). The reported
range was 0.26 to 2.80 L/day.
The advantage of these data are that they were
generated using duplicate sampling techniques. Because
this approach is more objective than recall methods, it
may result in more accurate response. However, these
data are based on a short-term survey that may not be
representative of long-term behavior, the population
surveyed is small and the procedures for selecting the
survey population were not designed to be representative
of the New Zealand population, and the results may not be
applicable to the United States. For these reasons the
study is not regarded as a key study in this document.
Pennington (1983) - Revision of the Total Diet
Study Food List and Diets - Based on data from the U.S.
Food and Drug Administration's (FDA's) Total Diet
Study, Pennington (1983) reported average intake rates
for various foods and beverages for five age groups of the
population. The Total Diet Study is conducted annually
to monitor the nutrient and contaminant content of the
U.S. food supply and to evaluate trends in consumption.
Representative diets were developed based on 24-hour
recall and 2-day diary data from the 1977-1978 U.S.
Department of Agriculture (USDA) Nationwide Food
Consumption Survey (NFCS) and 24-hour recall data
from the Second National Health and Nutrition
Examination Survey (NHANES II), The number of
participants in NFCS and NHANES II was approximately
30,000 and 20,000, respectively. The diets were
developed to "approximate 90 percent or more of the
weight of the foods usually consumed" (Pennington,
1983). The source of water (bottled water as
distinguished from tapwater) was not stated in the
Pennington study. For the purposes of this report, the
consumption rates for the food categories defined by
Pennington (1983) were used to calculate total fluid and
total water intake rates for five age groups. Total water
includes water, tea, coffee, soft drinks, and soups and
frozen juices that are reconstituted with water.
Reconstituted soups were assumed to be composed of 50
percent water, and juices were assumed to contain 75
percent water. Total fluids include total water in addition
to milk, ready-to-use infant formula, milk-based soups,
carbonated soft drinks, alcoholic beverages, and canned
fruit juices. These intake rates are presented in Table
3-17. Based on the average intake rates for total water for
the two adult age groups, 1.04 and 1.26 L/day, the average
adult intake rate is about 1.15 L/day. These rates should
be more representative of the amount of source-specific
water consumed than are total fluid intake rates. Because
this study was designed to measure food intake, and it
used both USDA 1978 data and NHANES II data, there
was not necessarily a systematic attempt to define
tapwater intake per se, as distinguished from bottled
Exposure Factors Handbook
August 1997
Page
3-13
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
water. For this reason, it is not considered a key tapwater
study in this document.
Table 3-17. Intake Rates of Total Fluids and Total Tapwater by
Age Group
Average Daily Consumption Rate (L/day)
Age Group Total Fluids3 Total Tapwaterb
6-11 months
2 years
14-16 years
25-30 years
60-65 years
0.80
0.99
1.47
1.76
1.63
0.20
0.50
0.72
1.04
1.26
a Includes milk, "ready-to-use" formula, milk-based soup,
carbonated soda, alcoholic beverages, canned juices, water,
coffee, tea, reconstituted juices, and reconstituted soups.
Does not include reconstituted infant formula.
b Includes water, coffee, tea, reconstituted juices, and
reconstituted soups.
Source: Derived from Pennington. 1983.
U.S. EPA (1984) - An Estimation of the Daily
Average Food Intake by Age and Sex for Use in Assessing
the Radionuclide Intake of the General Population -
Using data collected by USDA in the 1977-78 NFCS,
U.S. EPA (1984) determined daily food and beverage
intake levels by age to be used in assessing radionuclide
intake through food consumption. Tapwater, water-based
drinks, and soups were identified subcategories of the
total beverage category. Daily intake rates for tapwater,
water-based drinks, soup, and total beverage are presented
in Table 3-18. As seen in Table 3-18, mean tapwater
intake for different adult age groups (age 20 years and
older) ranged from 0.62 to 0.76 L/day, water-based drinks
intake ranged from 0.34 to 0.69 L/day, soup intake ranged
from 0.03 to 0.06 L/day, and mean total beverage intake
levels ranged from 1.48 to 1.73 L/day. Total tapwater
intake rates were estimated by combining the average
daily intakes of tapwater, water-based drinks, and soups
for each age group. For adults (ages 20 years and older),
mean total tapwater intake rates range from 1.04 to 1.47
L/day, and for children (ages <1 to 19 years), mean intake
rates range from 0.19 to 0.90 L/day. These intake rates do
not include reconstituted infant formula. The total
tapwater intake rates, derived by combining data on
tapwater, water-based drinks, and soup should be more
representative of source-specific drinking water intake
than the total beverage intake rates reported in this study.
These intake rates are based on the same USDA NFCS
data used in Ershow and Cantor (1989). Therefore, the
data limitations discussed previously also apply to this
study.
Cantor et al. (1987) - Bladder Cancer, Drinknig
Water Source, and Tapwater Consumption - The National
Cancer Institute (NCI), in a population-based, case control
study investigating the possible relationship between
bladder cancer and drinking water, interviewed
approximately 8,000 adult white individuals, 21 to 84
years of age (2,805 cases and 5,258 controls) in their
homes, using a standardized questionnaire (Cantor et al.,
1987). The cases and controls resided in one of five
metropolitan areas (Atlanta, Detroit, New Orleans, San
Table 3-18. Mean and Standard Error for the Daily Intake of Beverages and Tapwater by Age
Age (years)
Tapwater Intake
(mL)
Water-Based Drinks
(mL)a
Soups
(mL)
Total Beverage Intakeb
(mL)
All ages
Under 1
Ito4
5to9
10 to 14
15 to 19
20 to 24
25 to 29
30 to 39
40 to 59
60 and over
662.5 ±9.9
170.7 ±64.5
434.6 ±31.4
521.0 ±26.4
620.2 ±24.7
664.7 + 26.0
656.4 ± 33.9
619.8 ± 34.6
636.5 ± 27.2
735.3 ±21.1
762.5 ±23.7
457.1 ±6.7
8.3 ±43.7
97.9 ±21.5
116.5 ±18.0
140.0 ± 16.9
201.5 ±17.7
343.1 ±23.1
441.6 ±23.6
601.0 ±18.6
686.5 ± 14.4
561.1 ± 16.2
45.9 ± 1.2
10.1 ±7.9
43.8 ± 3.9
36.6 ± 3.2
35.4 ± 3.0
34.8 ± 3.2
38.9 ±4.2
41.3 ±4.2
40.6 ± 3.3
51.6 ±2.6
59.4 ± 2.9
1434.0 ± 13.7
307.0 ± 89.2
743.0 ± 43.5
861.0 ±36.5
1025.0 ± 34.2
1241.0 ±35.9
1484.0 ±46.9
1531.0 ±48.0
1642.0 ± 37.7
1732.0 ±29.3
1547.0 ± 32.8
* Includes water-based drinks such as coffee, etc. Reconstituted infant formula does not appear to be included in this group.
b Includes tapwater and water-based drinks such as coffee, tea, soups, and other drinks such as soft drinks, fruitades, and alcoholic drinks.
Source: U.S. EPA, 1984.
Page
3-14
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Francisco, and Seattle) and five States (Connecticut, Iowa,
New Jersey, New Mexico, and Utah). The individuals
interviewed were asked to recall the level of intake of
tapwater and other beverages in a typical week during the
winter prior to the interview. Total beverage intake was
divided into the following two components: 1) beverages
derived from tapwater; and 2) beverages from other
sources. Tapwater used in cooking foods and in ice cubes
was apparently not considered. Participants also supplied
information on the primary source of the water consumed
(i.e., private well, community supply, bottled water, etc.).
The control population was randomly selected from the
general population and frequency matched to the bladder
cancer case population in terms of age, sex, and
geographic location of residence. The case population
consisted of Whites only, had no people under the age of
21 years and 57 percent were over the age of 65 years.
The fluid intake rates for the bladder cancer cases were
not used because their participation in the study was based
on selection factors that could bias the intake estimates for
the general population. Based on responses from 5,258
White controls (3,892 males; 1,366 females), average
tapwater intake rates for a "typical" week were compiled
by sex, age group, and geographic region. These rates are
listed in Table 3-19. The average total fluid intake rate
was 2.01 L/day for men of which 70 percent (1.4 L/day)
was derived from tapwater, and 1.72 L/day for women of
which 79 percent (1.35 L/day) was derived from tapwater.
Frequency distribution data for the 5,081 controls, for
which the authors had information on both tapwater
consumption and cigarette smoking habits, are presented
in Table 3-20. These data follow a lognormal distribution
having an average value of 1.30 L/day and an upper 90th
percentile value of approximately 2.40 L/day. These
values were determined by graphically interpolating the
data of Table 3-20 after plotting it on log probability
graph paper. These values represent the usual level of
intake for this population of adults in the winter.
A limitation associated with this data set is that the
population surveyed was older than the general population
and consisted exclusively of Whites. Also, the intake data
are based on recall of behavior from the winter previous
to the interview. Extrapolation to other seasons and
intake durations is difficult.
The authors presented data on person-years of
residence with various types of water supply sources
(municipal versus private, chlorinated versus
nonchlorinated, and surface versus well water).
Unfortunately, these data can not be used to draw
conclusions about the National average apportionment of
surface versus groundwater since a large fraction (24
percent) of municipal water intake in this survey could not
be specifically attributed to either ground or surface
water.
Table 3-19. Average Total Tapwater Intake Rate by Sex
Age, and Geographic Area
Group/Subgroup
Number of
Respondents
Average Total
Tapwater Intake,a'b
L/day
Total group
Sex
Males
Females
Age, years
21-44
45-64
65-84
Geographic area
Atlanta
Connecticut
Detroit
Iowa
New Jersey
New Mexico
New Orleans
San Francisco
Seattle
Utah
5,258
3,892
1,366
291
1,991
2,976
207
844
429
743
1,542
165
112
621
316
279
1.39
1.40
1.35.
1.30
1.48
1.33
1.39
1.37
1.33
1.61
1.27
1.49
1.61
1.36
1.44
1.35
a Standard deviations not reported in Cantor et al. (1987).
b Total tapwater defined as all water and beverages derived
from tapwater.
Source: Cantor et al.. 1987.
Table 3-20. Frequency Distribution of Total
Tapwater Intake Rates3
Consumption
Rate (L/day)
Frequency15 (%)
Cumulative
Frequency1" (%)
<;0.80
0.81-1.12
1.13-1.44
1.45-1.95
a 1.96
20.6
21.3
20.5
19.5
18.1
20.6
'41.9
62.4
81.9
100.0
a Represents consumption of tapwater and beverages derived
from tapwater in a "typical" winter week.
b Extracted from Table 3 in Cantor etal. (1987).
Source: Cantor, etal., 1987.
AIHC (1994) - Exposure Factors Handbook- The
Exposure Factors Sourcebook (AIHC, 1994) presented
Exposure Factors Handbook
August 1997
Page
3-15
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
drinking water intake rate recommendations for adults.
Although AIHC (1994) provided little information on the
studies used to derive mean and upper percentile recom-
mendations, the references indicate that several of the
studies used were the same as ones categorized as relevant
studies in this handbook. The mean adult drinking water
recommendations in AIHC (1994) and this handbook are
in agreement. However, the upper percentile value
recommended by AIHC (1994) (2.0 L/day) is slightly
lower than that recommended by this handbook (2.4
L/day). Based on data provided by Ershow and Cantor
(1989), 2.0 L/day corresponds to only approximately the
84th percentile of the drinking water intake rate
distribution. Thus, a slightly higher value is appropriate
for representing the upper percentile (i.e., 90 to 95th
percentile) of the distribution. AIHC (1994) also presents
simulated distributions of drinking water intake based on
Roseberry and Burmaster (1992). These distributions are
also described in detail in Section 3.2 of this handbook.
AIHC (1994) has been classified as a relevant rather than
a key study because it is not the primary source for the
data used to make recommendations for this document.
USDA (1995) - Food and Nutrient Intakes by
Individuals in the United States, 1 Day, 1989-91. - USDA
(1995) collected data on the quantity of "plain drinking
water" and various other beverages consumed by
individuals in 1 day during 1989 through 1991. The data
were collected as part of USDA's Continuing Survey of
Food Intakes by Individuals (CSFII). The data used to
estimate mean per capita intake rates combined one-day
dietary recall data from 3 survey years: 1989, 1990, and
1991 during which 15,128 individuals supplied one-day
intake data. Individuals from all income levels in the 48
conterminous states and Washington D.C. were included
in the sample. A complex three-stage sampling design
was employed and the overall response rate for the study
was 58 percent. To minimize the biasing effects of the
low response rate and adjust for the seasonality, a series
of weighting factors was incorporated into the data
analysis. The intake rates based on this study are
presented in Table 3-21. Table 3-21 includes data for: a)
"plain drinking water", which might be assumed to mean
Table 3-21 Mean Per Capita Drinking Water Intake Based on USDA, CSFII Data From
Sex and Age
(years)
Males and Females:
Under 1
1-2
3-5
5 & Under
Males:
6-11
12-19
20-29
30-39
40-49
50-59
60-69
70-79
80 and over
20 and over
Females:
6-11
12-19
20-29
30-39
40-49
50-59
60-69
70-79
80 and over
20 and over
AH individuals
a Includes regular and
Plain Drinking
Water
194
333
409
359
537
725
842
793
745
755
946
824
747 .
809
476
604
739
732
781
819
829
772
856
774
711
low calorie fruit drinks,
1989-91
(mL/day)
Fruit Drinks
Coffee Tea and Adesa
0
<0.5
2
1
2
12
168
407
534
551
506
430
326
408
1
21
154
317
412
438
429
324
275
327
260
punches, and ades,
<0.5
9
26
17
44
95
136
136
149
168
115
115
165
139
40
87
120
136
174
137
124
161
149
141
114
including those made from powdered
17
85
100
86
114
104
101
50
53
51
34
45
57
60
86
87
61
59
36
37
36
34
28
46
65
mix and
Total
211.5
427.5
537
463
697
936
,247
,386
,481
,525
,601
,414
,295
,416
603
799
1,074
1,244
1,403
1,431
1,418
1,291
1,308
1,288
1,150
frozen concentrate.
Excludes fruit juices and carbonated drinks.
Source: USDA, 1995.
Page
3-16
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
tapwater directly consumed rather than bottled water; b)
coffee and tea, which might be assumed to be constituted
from tapwater; and 3) fruit drinks and ades, which might
be assumed to be reconstituted from tapwater rather than
canned products; and 4) the total of the three sources.
With these assumptions, the mean per capita total intake
of water is estimated to be 1,416 mL/day for adult males
(i.e., 20 years of age and older), 1,288 mL/day for adult
females (i.e., 20 years of age and older) and 1,150
mL/day for all ages and both sexes combined. Although
these assumptions appear reasonable, a close reading of
the definitions used by USDA (1995) reveals that the
word "tapwater" does not occur, and this uncertainty
prevents the use of this study as a key study of tapwater
intake.
The advantages of using these data are that; 1) the
survey had a large sample size; 2) the authors attempted
to represent the general United States population by
oversampling low-income groups and by weighting the
data to compensate for low response rates; and 3) it
reflects more recent intake data than the key studies. The
disadvantages are that: 1) the response rate was low; 2)
the word "tapwater" was not defined and the assumptions
that must be used in order to compare the data with the
other tapwater studies might not be valid; 3) the data
collection period reflects only a one-day intake period,
and may not reflect long-term drinking water intake
patterns; and 4) data on the percentiles of the distribution
of intakes were riot given.
Tsang and Klepeis (1996) - National Human
Activity Pattern Survey (NHAPS) - The U.S. EPA
collected information on the number of glasses of drinking
water and juice reconstituted with tapwater consumed by
the general population as part of the National Human
Activity Pattern Survey (Tsang and Klepeis, 1996).
NHAPS was conducted between October 1992 and
September 1994. Over 9,000 individuals in the 48
contiguous United States provided data on the duration
and frequency of selected activities and the time spent in
selected microenvironments via 24-hour diaries. Over
4,000 NHAPS respondents also provided information of
the number of 8-ounce glasses of water and the number of
8-ounce glasses of juice reconstituted with water than they
drank during the 24-hour survey period (Tables 3-22 and
3-23). The median number of glasses of tapwater
consumed was 1-2 and the median number of glasses of
juice with tapwater consumed was 1-2.
For both individuals who drank tapwater and
individuals who drank juices reconstituted with tapwater,
the number of glasses ranged from 1 to 20. The highest
percentage of the population (37.1 percent) who drank
tapwater consumed 3-5 glasses and the highest percentage
of the population (51.5 percent) who consumed juice
reconstituted with tapwater drank 1-2 glasses. Based on
the assumption that each glass contained 8 ounces of
water (226.4 mL), the total volume of tapwater and juice
with tapwater consumed would range from 0.23 L/day (1
glass) to 4.5 L/day (20 glasses) for respondents who drank
tapwater. Using the same assumption, the volume of
tapwater consumed for the population who consumed 3-5
glasses would be 0.68 L/day to 1.13 L/day and the volume
of juice with tapwater consumed for the population who
consumed 1-2 glasses would be 0.23 L/day to 0.46 L/day.
Assuming that the average individual consumes 3-5
glasses of tapwater plus 1-2 glasses of juice with tapwater,
the range of total tapwater intake for this individual would
range from 0.9 L/day to 1.64 L/day. These values are
consistent with the average intake rates observed in other
studies.
The advantages of NHAPS is that the data were
collected for a large number of individuals and that the
data are representative of the U.S. population. However,
evaluation of drinking water intake rates was not the
primary purpose of the study and the data do not reflect
the total volume of tapwater consumed. However, using
the assumptions described above, the estimated drinking
water intake rates from this study are within the same
ranges observed for other drinking water studies.
3.4. PREGNANT AND LACTATING WOMEN
Ershow et al. (1991) - Intake of Tapwater and
Total Water by Pregnant and Lactating Women - Ershow
et al. (1991) used data from the 1977-78 USDA MFCS to
estimate total fluid and total tapwater intake among
pregnant and lactating women (ages 15-49 years). Data
for 188 pregnant women, 77 lactating women, and 6,201
non-pregnant, non-lactating control women were
evaluated. The participants were interviewed based on 24
hour recall, and then asked to record a food diary for the
next 2 days. "Tapwater" included tapwater consumed
directly as a beverage and tapwater used to prepare food
and tapwater-based beverages. "Total water" was defined
as all water from tapwater and non-tapwater sources,
including water contained in food. Estimated total fluid
and total tapwater intake rates for the three groups are
Exposure Factors Handbook
August 1997
Page
3-17
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-22. Number of Respondents that Consumed Tapwater at a Specified Daily Frequency
Population Group
Overall
Gender
Male
Female
Refused
Age (years)
1-4
5-11
12-17
18-64
>64
Race
White
Black
Asian
Some Others
Hispanic
Refused
Hispanic
"NO" —
Yes
DK
Refused
Employment
Full-time
Part-time
Not Employed
Refused
< High School
High School Graduate
< College
College Graduate
Post Graduate
Census Rceion
Northeast
Midwest
South
West
Day of Week
Weekday
Weekend
Season
Winter
Spring
Summer
Fall
Asthma
No
Yes
DK
Angina
No
Yes
DK
Bronchitis/Emphysema
No
Yes
DK
NOTE: "•"» Missing Data
"DK" » Don't know
N » sample size
Number of Glasses in a Day
Total N
4,663
2,163
2,498
2
263
348
326
2,972
670
3,774
463
77
96
193
60
4,244
347
26
46
2,017
379
1,309
32
399
1,253
895
650
445
1,048
1,036
1,601
978
3,156
1,507
1,264
1,181
1,275
943
4,287
341
35
4,500
125
38
4,424
203
36
None
1,334
604
728
2
114
90
86
908
117
1,048
147
25
36
63
15
1,202
116
5
11
637
90
313
6
89
364
258
195
127
351
243
450
290
864
470
398
337
352
247
1,232
96
6
1,308
18
8
1,280
48
6
1-2
1,225
582
643
•
96
127
109
751
127
1,024
113
18
18
42
10
1,134
80
6
5
525
94
275
4
95
315
197
157
109
262
285
437
241
840
385
321
282
323
299
1,137
83
5
1,195
25
5
1,161
55
9
3-5
1,253
569
684
•
40
86
88
769
243
1,026
129
23
22
40
13
1,162
73
7
11
497
120
413
11
118
330
275
181
113
266
308
408
271
862
391
336
339
344
234
1,155
91
7
1,206
40
7
1,189
58
6
6-9
500
216
284
•
7
15
22
334
112
416
38
6
6
28
6
451
41
4
4
218
50
188
1
51
132
118
82
62
95
127
165
113
334
166
128
127
155
90
459
40
1
470
27
3
474
24
2
10-19
151
87
64
•
1
7
7-
115
20
123
9
1
7
10
1
129
18
3
1
72
13
49
2
14
52
31
19
16
32
26
62
31
96
55
45
33
41
32
134
16
1
143
6
2
142
9
•
20+
31
25
6
•
0
2
• •
26
2
25
1
•
2
2
1
26
4
•
1
18
7
3
1
2
13
5
4
3
7
9
11
4
27
4
5
10
9
7
29
1
1
29
1
1
29
1
1
DK
138
65
73
•
5
20
11
54
42
92
21
4
5
7
9
116
13
1
8
40
5
54
4
28
37
9
6
12
28
33
, 57
20
106
32
26
40
40
32
115
13
10
123
6
9
124
5
9
Refused = respondent refused to answer
Source: Tsang and Kleipeis,
1996
Page
3-18
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-23. Number of Respondents that Consumed Juice Reconstituted with Tapwater at a Specified Daily Frequency
Number of Glasses in a Day
Population Group
Overall
Gender
Male
Female
Refused
Age (years)
1-4
5-11
12-17
18-64
>64
Race
White
Black
Asian
Some Others
Hispanic
Refused
Hispanic
No
Yes
DK
Refused
Employment
Full-time
Part-time
Not Employed
Refused
Education
< High School
High School Graduate
< College
College Graduate
Post Graduate
Census Region
Northeast
Midwest
South
West
Day of Week
Weekday
Weekend
Season
Winter
Spring
Summer
Fall
Asthma
No
Yes
DK
Angina
No
Yes
DK
Bronchitis/Emphysema
No •
Yes
DK
NOTE: "•" = Missing Data
"DK" = Don't know
N = sample size
Total N
4,663
2,163
2,498
2
263
348
326
2,972
670
3,774
463
77
96
193
60
4,244
347
26
46
2,017
379
1,309
32
399
1,253
895
650
445
1,048
1,036
1,601
978
3,156
1,507
1,264
1,181
1,275
943
4,287
341
35
4,500
125
38
4,424
203
36
None
1,877
897
980
•
126
123
112
1,277
206
1,479
200
33
46
95
24
1,681
165
11
20
871
156
479
15
146
520
367
274
182
440
396
593
448
1,261
616
529
473
490
385
1,734
130
13
1,834
31
12
1,782
84
11
1-2 '"
1,418
590
• 826
2
71
140
118
817
252
1,168
142
27
19
51
11
1,318
87
6
7
559
102
426
4
131
355
253
, 201
130
297
337
516
268
969
449
382
382
389
265
1,313
102
3
1,362
53
3
1,361
53
4
3-5
933
451
482
«
48
58
63
614
133
774
83
15
24
30
7
863
61
5
4
412
88
265
4
82
254
192
125
92
220
200
332
181
616
307
245
215
263
210
853
74
6
900
25
8
882
44
7
6-9
241
124
117
*
11
12
18
155
43
216
15
1
2
5
2
226
14
•
1
103
19
75
2
25
68
47
31
26
51
63
84
43
162
79
66
54
68
53
216
25
•
231
7
3
230
10
1
10-19
73
35
38
•
4
2
7
46
12
57
9
•
1
5
1
64
7
1
1
32
7
20
1
7
21
18
7
5
13
17
26
17
51
22
23
19
18
13
69
3
1
67
5
1
65
6
2
20+
21
17
4
•
1
1
1
16
2
16
1
•
3
1
•
17
4
•
•
9
2
7
•
2
7
5
1
3
4
4
10
3
11
10
4
8
6
3
20
1
•
20
1
•
21
•
•
DK
66
33
33
•
2
11
4
30
14
44
7
0
1
5
9
49
7
3
7
20
5
21
3 .
4
17
11
5
4
15
14
28
9
46
20
10.
17
28
11
55
5
6
59
1
6
57
3
6
Refused = Respondent refused to answer
Source: Tsang and Klepeis,
1996
.Exposure Factors Handbook
August 1997
Page
3-19
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
presented in Tables 3-24 and 3-25, respectively.
Lactating women had the highest mean total fluid intake
rate (2.24 L/day) compared with both pregnant women
(2.08 L/day) and control women (1.94 L/day). Lactating
women also had a higher mean total tapwater intake rate
(1.31 L/day) than pregnant women (1.19 L/day) and
control women (1.16 L/day). The tapwater distributions
are neither normal nor lognormal, but lactating women
had a higher mean tapwater intake than controls and
pregnant women. Ershow et al. (1991) also reported that
rural women (n=l,885) consumed more total water (1.99
L/day) and tapwater (1.24 L/day) than urban/suburban
women (n=4,581, 1.93 and 1.13 L/day, respectively).
Total water and tapwater intake rates were lowest in the
northeastern region of the United States (1.82 and 1.03
L/day) and highest in the western region of the United
States (2.06 L/day and 1.21 L/day). Mean intake per unit
body weight was highest among lactating women for both
total fluid and total tapwater intake. Total tapwater intake
accounted for over 50 percent of mean total fluid in all
these data sets (Section 3.2). A further advantage of this
study is that it provides information on estimates of total
waterand tapwater intake rates for pregnant and lactating
women. This topic has rarely been addressed in the
literature.
3.5. HIGH ACTIVITY LEVELS/HOT CLIMATES
McNall and Schlegel (1968) - Practical Thermal
Environmental Limits for Young Adult Males Working in
Hot, Humid Environments - McNall and Schlegel (1968)
conducted a study that evaluated the physiological
tolerance of adult males working under varying degrees of
physical activity. Subjects were required to pedal pedal-
driven propeller fans for 8-hour work cycles under
varying environmental conditions. The activity pattern for
each individual was: cycled at 15 minute pedalling and 15
miute rest for each 8-hour period. Two groups of eight
subjects each were used. Work rates were divided into
three categories as follows: high activity level [0.15
horsepower (hp) per person], medium activity level (0.1
Table 3-24. Total Fluid Intake of Women 15-49 Years Old
Percentile Distribution
Reproductive
Status"
mLAlav
Control
Pregnant
Lactating
rnL/kg/dav
Control
Pregnant
Lactating
Mean
1940
2076
2242
32.3
32.1
37.0
Standard
Deviation
686
743
658
12.3
11.8
11.6
5
995
1085
1185
15.8
16.4
19.6
10
1172
1236
1434
18.5
17.8
21.8
a Number of observations: nonpregnant, nonlactating controls (n = 6,201);
Source: Ershow etal., 1991.
25
1467
1553
1833
23.8
17.8
21.8
pregnant (n =
50
1835
1928
2164
30.5
30.5
35.1
188); lactating (n
75
2305
2444
2658
38.7
40.4
45.0
= 77).
90
2831
3028
3169
48.4
48.9
53.7
95
3186
3475
3353
55.4
53.5
59.2
three groups of women (Table 3-25). Drinking water
accounted for the largest single proportion of the total
fluid intake for control (30 percent), pregnant (34
percent), and lactating women (30 percent) (Table 3-26).
All other beverages combined accounted for
approximately 46 percent, 43 percent, and 45 percent of
the total water intake for control, pregnant, and lactating
women, respectively. Food accounted for the remaining
portion of total water intake.
The same advantages and limitations associated
with the Ershow and Cantor (1989) data also apply to
hp per person), and low activity level (0.05 hp per
person). Evidence of physical stress (i.e., increased body
temperature, blood pressure, etc.) was recorded, and
individuals were eliminated from further testing if certain
stress criteria were met. The amount of water consumed
by the test subjects during the work cycles was also
recorded. Water was provided to the individuals on
request. The water intake rates obtained at the three
different activity levels and the various environmental
temperatures are presented in Table 3-27. The data
Page
3-20
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-25. Total Tapwater Intake of Women 15-49 Years Old
Reproductive
Status"
mL/day
Control
Pregnant
Lactating
mL/kg/day
Control
Pregnant
Lactating
Fraction of daily fluid
Control
Pregnant
Lactating
Percentile Distribution
Mean
1157
1189
1310
19.1
18.3
21.4
Standard
Deviation
635
699
591
10.8
10.4
9.8
5
310
274
430
5.2
4.9
7.4
10
453
419
612
7.5
5.9
9.8
25
709
713
855
11.7
10.7
14.8
50
1065
1063
1330
17.3
16.4
20.5
75
1503
1501
1693
24.4
23.8
26.8
90
1983
2191
1945
33.1
34.5
35.1
95
2310
2424
2191
39.1
39.6
37.4
intake that is tapwater (%)
57.2
54.1
57.0
18.0
18.2
15.8
24.6
21.2
27.4
32.2
27.9
38.0
a Number of observations: nonpregnant, nonlactating controls (n = 6,201);
Source: Ershowetal., 1991.
45.9
42.9
49.5
pregnant (n
59.0
54.8
58.1
= 188);
70.7
67.6
65.9
lactating (n
79.0
76.6
76.4
= 77).
83.2
83.2
80.5
Table 3-26. Total Fluid (mL/Day) Derived from Various Dietary Sources by Women Aged 15-49 Years3
Control Women
Sources
Drinking Water
Milk and Milk Drinks
Other Dairy Products
Meats, Poultry, Fish, Eggs
Legumes, Nuts, and Seeds
Grains and Grain Products
Citrus and Noncitrus Fruit Juices
Fruits, Potatoes, Vegetables, Tomatoes
Fats, Oils, Dressings, Sugars, Sweets
Tea
Coffee and Coffee Substitutes
Carbonated Soft Drinks0
Noncarbonated Soft Drinks0
Beer
Wine Spirits, Liqueurs, Mixed Drinks
All Sources
a Number of observations: nonpregnant,
Mean5
583
162
23
126
13
90
57
198
9
148
291
174
38
17
10
1940
Percentile
50
480
107
8
114
0
65
0
171
3
0
159
no
0
0
0
NA
nonlactating controls (n =
95
1440
523
93
263
77
257
234
459
41
630
1045
590
222
110
66
NA
Pregnant Women
Meanb
695
308
24
121
18
98
69
212
9
132
197
130
48
7
5
2076
6,201); pregnant (n = 188);
Percentile
50
640
273
9
104
0
69
0
185
3
0
0
73
0
0
0
NA
95
1760'
749,
93
252
88
246
280
486
40
617
955
464 '
257
0
25
NA.
Mean"
677
306
36
133
15
!l9
64
245
10
253
205
117
38
17
6
2242
Lactating Women
Percentile
50
560
285
27
117
0
82
0
197
6
77
80
57
0
0
0
NA
95
1600
820
113
256
72
387
219
582
50
848
955
440
222
147
59
NA
lactating (n = 77).
b Individual means may not add to all-sources total due to rounding.
c Includes regular, low-calorie, and noncalorie soft drinks.
NA: Not appropriate to sum the columns for the 50th and 95th percentiles of intake.
Source: Ershow et al. , 1991 .
fxposure Factors Handbook
August 1997
Page
3-21
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-27. Water Intake at Various Activity Levels (L/hr)a
Room
Temperature1* (°F)
Activity Level
High_(QJ.5 hp/man)c Medium (0.10 hp/man)c
No.d
100
95 18
90 7
85 7
80 16
Intake No. Intake
„
0.540 12 0.345
(0.31) (0.59)
0.286 7 0.385
(0.26) (0.26)
0.218 16 0.213
(0.36) (0.20)
0.222
(0.14)
Low (0.05 hp/man)c
No. Intake
15 0.653
(0.75)
6 0.50
(0.31)
16 0.23
(0.20)
—
-
* Data expressed as mean intake with standard deviation in parentheses.
b Humidity = 80 percent; air velocity = 60 ft/min.
c The symbol "hp" refers to horsepower.
d Number of subjects with continuous data.
Source: McNall and Schlegel, 1968.
presented are for test subjects with continuous data only
(i.e., those test subjects who were not eliminated at any
stage of the study as a result of stress conditions). Water
intake was the highest at all activity levels when
environmental temperatures were increased. The highest
intake rate was observed at the low activity level at 100° F
(0.65 L/hour) however, there were no data for higher
activity levels at 100°F. It should be noted that this study
estimated intake on an hourly basis during various levels
of physical activity. These hourly intake rates cannot be
converted to daily intake rates by multiplying by 24
hours/day because they are only representative of intake
during the specified activity levels and the intake rates for
the rest of the day are not known. Therefore, comparison
of intake rate values from this study cannot be made with
values from the previously described studies on drinking
water intake.
United States Army (1983) - Water Consumption
Planning Factors Study - The U.S. Army has developed
water consumption planning factors to enable them to
transport an adequate amount of water to soldiers in the
field under various conditions (U.S. Army, 1983). Both
climate and activity levels were used to determine the
appropriate water consumption needs. Consumption
factors have been established for the following uses:
1) drinking, 2) heat treatment, 3) personal hygiene,
4) centralized hygiene, 5) food preparation, 6) laundry,
7) medical treatment, 8) vehicle and aircraft maintenance,
9) graves registration, and 10) construction. Only
personal drinking water consumption factors are described
here.
Drinking water consumption planning factors are
based on the estimated amount of water needed to replace
fluids lost by urination, perspiration, and respiration. It
assumes that water lost to urinary output averages one
quart/day (0.9 L/day) and perspiration losses range from
almost nothing in a controlled environment to 1.5
quarts/day (1.4 L/day) in a very hot climate where
individuals are performing strenuous work. Water losses
to respiration are typically very low except in extreme
cold where water losses can range from 1 to 3 quarts/day
(0.9 to 2.8 L/day). This occurs when the humidity of
inhaled air is near zero, but expired air is 98 percent
saturated at body temperature (U.S. Army, 1983).
Page
3-22
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Drinking water is defined by the U.S. Army (1983) as "all
fluids consumed by individuals to satisfy body needs for
internal water." This includes soups, hot and cold drinks,
and tapwater. Planning factors have been established for
hot, temperate, and cold climates based on the following
mixture of activities among the work force: 15 percent of
the force performing light work, 65 percent of the force
performing medium work, and 20 percent of the force
performing heavy work. Hot climates are defined as
tropical and arid areas where the temperature is greater
than 80°F. Temperate climates are defined as areas where
the mean daily temperature ranges from 32°F to 80°F.
Cold regions are areas where the mean daily temperature
is less than 32 °F. Drinking water consumption factors for
these three climates are presented in Table 3-28. These
factors are based on research on individuals and small unit
training exercises. The estimates are assumed to be
conservative because they are rounded up to account for
the subjective nature of the activity mix and minor water
losses that are not considered (U.S. Army, 1983). The
advantage of using these data is that they provide a
conservative estimate of drinking water intake among
individuals performing at various levels of physical
activity in hot, temperate, and cold climates. However,
the planning factors described here are based on
assumptions about water loss from urination, perspiration,
and respiration, and are not based on survey data or actual
measurements.
3.6. RECOMMENDATIONS
The key studies described in this section were used
in selecting recommended drinking water (tapwater)
consumption rates for adults and children. The studies on
other subpopulations were not classified as key versus
relevant. Although different survey designs and
populations were utilized by key and relevant studies
described in this report, the mean and upper-percentile
estimates reported in these studies are reasonably similar.
The general design of both key and relevant studies and
their limitations are summarized in Table 3-29. It should
be noted that studies that surveyed large representative
samples of the population provide more reliable estimates
of intake rates for the general population. Most of the
surveys described here are based on short-term recall
which may be biased toward excess intake rates.
However, Cantor et al. (1987) noted that retrospective
dietary assessments generally produce moderate
correlations with "reference data from the past." A
summary of the recommended values for drinking water
intake rates is presented in Table 3-30.
Adults - The total tapwater consumption rates for
adults (older than 18 or 20 years) that have been reported
in the key surveys can be summarized in Table 3-31. For
comparison, values for daily tapwater intake for the
relevant studies are shown in Table 3-32.
Note that both Ershow and Cantor (1989) and
Pennington (1983) found that adults above 60 years of age
had larger intakes than younger adults. This is difficult to
reconcile with the Cantor et al. (1987) study because the
latter, older population had a smaller average intake.
Because of these results, combined with the fact that the
Cantor et al. (1987) study was not intended to be
representative of the U. S. population, it is not included
here in the determination of the recommended value. The
Table 3-28. Planning Factors for Individual Tapwater Consumption
Environmental Condition
Recommended Planning Factor (gal/day)a
Recommended Planning Factor (L/day)a'b
Hot
Temperate
Cold
3.0C
1.5"
2.0e
11.4
5.7
7.6
a Based on a mix of activities among the work force as follows: 15% light work; 65% medium work; 20% heavy work. These factors
apply to the conventional battlefield where no nuclear, biological, or chemical weapons are used.
b Converted from gal/day to L/day.
c This assumes 1 quart/12-hour rest period/man for perspiration losses and 1 quart/day/man for urination plus 6 quarts/12-hours light
work/man, 9 quarts/12-hours moderate work/man, and 12 quarts/12-hours heavy work/man.
This assumes 1 quart/12-hour rest period/man for perspiration losses and 1 quart/day/man for urination plus 1 quart/12-hours light
work/man, 3 quarts/12-hours moderate work/man, and 6 quarts/12-hours heavy work/man.
e This assumes 1 quart/12-hour rest period/man for perspiration losses, 1 quart/day/man for urination, and 2 quarts/day/man for respiration
losses plus 1 quart/12-hours light work/man, 3 quarts/12-hours moderate work/man, and 6 quarts/6-hours heavy work/man.
Source: U.S. Army, 1983.
Exposure Factors Handbook
August 1997
Page
3-23
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
ing
od
.
CO O Q. (/)
0 E
•" 'S "
s s
B
« o
« J5
'H S •-
•" w oo
Sa-2
lag
3 i
ll
H o
-I
s3
2 a
II
a s
ia
O 4)
£P >
•2 'i c
a S-S
III
1
J3
~
> ° '§
111
C C d,
.S •= ,x
•— o.
Ii!
t*, e oo
o g ^
•— u o
SI
g
is "2 3
|l|
< s a
•2 =
§
1
a
!
1
<
8 '
2.§ |g
l§ if if-
|_-g S | -g I
ll 11 ll
<
z
5
Page
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
?
J3
1
1
3
CO
j!£
1
a
|
i
•c
Q
ci
, ^ | ^ > 1 1 c..
1 111 SlfHg
i * ?. § i Pill:?
rt rt S *O 2 e* O if^ ° £
g -a -a ^"S C S-o°t'~'£
^ ^ ^ -oS Y *Jrt'H3|g>T
! i i 11 i ifiiii
§ i| || is § ftl ^il
i If II II i In ill
•s
llf 1| 1^1
£-5 « -§.03 -fL1" S3 ^"^J 1
2 ifi ii 12 ii itii
"fsi"! &5i E?I; §>^ § I ^ ">>
^^tuu0- w'c-S wH-2 ur'o K"S"^ '^
P^^3 — O.D- — Q.O. «S* "^b"-- ^)E
•y^W^fc- ^?S*^ ^S"O ^W ^OO^J MM
ZZ-s^o - ffi — « 0
o ... § .S « CL —.SuS*^
£8. E8-S ^ S 5 z^s f2 v 02* g, Sj.,
2ta-"QiS CO TO ^ Z^S"5 ^ Me3
__ 2 o .S* —.^"O X"O ^"Q, G
atC?-1- S ^ -a S^-^ liffi 2S5 M *^f C
1 CO °? "3 ? 2 CO
a. 3 3 . 3 S co O
1
1
3
rt
Exposure Factors Handbook
August 1997
Page
3-25
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-30. Summary of Recommended Drinking Water Intake Rates
'Percentiles
Age Group/
mlation
Mean
50th
,90th
95th
Multiple
Fitted
^Distributions
<3 years0
3-5 years'
I-10 years*
11-19 years"
Adults"
Pregnant Women'd
Lactating Women4
Adults in High
Activity/Hot Climate
Conditions*
Active Adultsf
0.30 L/day
44 mL/kg-day
0.61 L/day
0.87 L/day
0.74 L/day
35 mL/kg-day
0.97 L/day
18 mL/kg-day
1.4 L/day
21 mL/kg-day
1.2 L/day
18.3 mL/kg-day
1.3 L/day
21.4 mL/kg-day
0.21 to 0.65 L/hour,
0.24 L/day
35 mL/kg-day
0.66 L/day
31 mL/kg-day
0.87 L/day
16 mL/kg-day
1.3 L/day
19 mL/kg-day
1.1 L/day
16 mL/kg-day
1.3 L/day
21 mL/fcg-day
0.65 L/day
102 mL/kg-day
1.5 L/day
1.5 L/day
1.3 L/day
64niL/kg-day
1.7 L/day '
* 32 mL/kg-day
2.3 L/day
34 mL/kg-day
, 2.2 L/day
35 mL/kg-day
- 1.9 L/day
35 mL/kg-day
0.76 L/day
127 mL/kg-day
1.5 L/day
2.0 L/day -
40 mL/kg-day
2.4 L/day
40 mL/kg-day
'2.2 L/day
"37 mL/kg-day
depending on ambient temperature and activity level; see Table 3
Tables 3-6, '
3-7, and 3-8
Table3-3 ,
Table3-3 -
Tables 3-6,
3-7, and 3-8 '
Tables 3-6,
3-7, and 3-8
Tables 3-6,
3-7, and 3-8
Table 3-25 ,
Table 3-25 '
i-27.
Table 3-llb
Table 3-1 lh
Table 3-1 lb
Table 3-1 lb
6 L/day (temperate climate) to 11 L/day (hot climate); see Table 3-28.
a Source: Ershow and Cantor, 1989
b Source: Roseberry and Burmaster, 1992
c Source: Canadian Ministry of Health and Welfare, 1981
d Ershow et a!. (1991) presented data for pregnant women, lactating women, and control women.
c Source: McNall and Schlegal, 1968
. Armv. 1983
Table 3-31. Total Tapwater Consumption Rates From Key Studies
Mean
cL/dav)
1.38
1.41
90th
Percentile
(Udav)
2.41
2.28
Number in
Survey
639
11,731
Reference
Canadian Ministry of Health
and Welfare, 1981
Ershow and Cantor, 1989
Table 3-32. Daily Tapwater Intake Rates From Relevant Studies
Mean (LMay)
1.30s
1.63 (calculated)
1.25
1.04 (25 to 30 yrs)
1,26 (60(o 65 yrs)
1.04-1 .47 (ages 20+)
1.37 (20 to 64 yrs)
1.46 (65+ yrs)
1.15
107
" Age of the Cantor et al.
average.
90th
Percentile
2.40
1.90
2.27
2.29
1.87
Reference
Cantor etal., 1987
NAS, 1977
Gillies and Paulin, 1983
Pennington, 1983
Pennington, 1983
U.S. EPA, 1984
Ershow and Cantor, 1989
Ershow and Cantor, 1989
USDA, 1995
Hopkins and Ellis. 1980
(1987) population was higher than the U.S.
USDA (1995) data are not included because tapwater was
not defined in the survey and because the response rate
was low, although the results (showing lower intakes than
the studies based on older data) may be accurately
reflecting an expected lower use of tapwater (compared to
1978) because of increasing use of bottled water and soft
drinks in recent years.
A value of 1.41 L/day, which is the population-
weighted mean of the two national studies (Ershow and
Cantor, 1989 and Canadian Ministry of Health and
Welfare, 1981) is the recommended average tapwater
intake rate.
The average of the 90th percentile values from the
same two studies (2.35 L/day) is recommended as the
appropriate upper limit. (The commonly-used 2.0 L/day
intake rate corresponds to the 84th percentile of the intake
rate distribution among the adults in the Ershow and
Cantor (1989) study). In keeping with the desire to
incorporate body weight into exposure assessments
without introducing extraneous errors, the values from the
Page
3-26
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Ershow and Cantor (1989) study (Tables 3-7 and 3-8)
expressed as mL/kg-day are recommended in preference
to the liters/day units. For adults, the mean and 90th
percentile values are 21 mL/kg-day and 34.2 mL/kg/day,
respectively.
In the absence of actual data on chronic intake, the
values in the previous paragraph are recommended as
chronic values, although the chronic 90th upper percentile
may very well be larger than 2.35 L/day. If a
mathematical description of the intake distribution is
needed, the parameters of lognormal fit to the Ershow and
Cantor (1989) data (Tables 3-11 and 3-12) generated by
Roseberry and Burmaster (1992) may be used. The
simulated balanced population distribution of intakes
generated by Roseberry and Burmaster is not
recommended for use in the post-1997 time frame, since
it corrects the 1978 data only for the differences in the age
structure of the U. S. population between 1978 and 1988.
These recommended values are different than the 2
liters/day commonly assumed in EPA risk assessments.
Assessors are encouraged to use values which most
accurately reflect the exposed population. When using
values other than 2 liters/day, however, the assessors
should consider if the dose estimate will be used to
estimate risk by combining with a dose-response
relationship which was derived assuming a tap water
intake of 2 liters/day. If such an inconsistency exists, the
assessor should adjust the dose-response relationship as
described in Appendix 1 of Chapter 1. IRIS does not use
a tap water intake assumption in the derivation of RfCs
and RfDs, but does make the 2 liter/day assumption in the
derivation of cancer slope factors and unit risks.
Children - The tapwater intake rates for children
reported in the key studies are summarized in Table 3-33.
The intake rates, as expressed as liters per day,
generally increase with age, and the data are consistent
across ages for the two key studies except for the
Canadian Ministry of Health and Welfare (1981) data for
ages 6 to 17 years; it is recommended that any of the
liters/day values that match the age range of interest
except the Canada data for ages 6 to 17 years be used.
The mL/kg-day intake values show a consistent downward
trend with increasing ages; using the Ershow and Cantor
(1989) data in preference to the Canadian Ministry of
National Health and Welfare (1981) data is recommended
where the age ranges overlap.
The intakes for children as reported in the relevant
studies are shown in Table 3-34.
Table 3-33. Key Study Tapwater Intake Rates for Children
Age
(years)
<1
<3
3-5
1-10
6-17
11-19
Mean
(L/day)
0.30
0.61
0.87
0.74
1.14
0.97
90th
Percentile
(L/day)
0.65
1.50
1.50
1.29
2.21
1.70
f-
Reference
Ershow and Cantor, 1989
Canadian Ministry of
National Health and
Welfare, 1981
Canadian Ministry of
National Health and
Welfare, 1981
Ershow arid Cantor, 1989
Canadian Ministry of
National Health and
Welfare, 1981 .
Ershow and Cantor, 1989
Table 3-34. Summary of Intake Rates for
Children in Relevant Studies
Age
6-11 months
<1 yr
<1 yr
2yrs
1-4 yrs
5-9 yrs
1-10 yrs
10-14 yrs
14-16 yrs
15-19 yrs
11-19 yrs
Mean
(L/day)
0.20
0.19
0.32
0.50
0.58
0.67
0.70
0.80
0.72
0.90
0.91
Reference
Pennington, 1983
U.S. EPA, 1984
Roseberry and Burmaster, 1992
Pennington, 1983
U.S. EPA, 1984
U.S. EPA, 1984
Roseberry and Burmaster, 1992
U.S. EPA, 1984
Pennington, 1983
U.S. EPA, 1984
Roseberry and Burmaster, 1992
Disregarding the Roseberry and Burmaster study,
which is a recalculation of the Ershow and Cantor (1989)
study, the non-key studies generally have lower mean
intake values than the Ershow and Cantor (1899) study.
The reason is not known, but the results are not persuasive
enough to discount the recommendations based on the
latter study. Intake rates for specific percentiles of the
distribution may be selected using the lognormal
distribution data generated by Roseberry and Burmaster
(1992) (Tables 3-11 and 3-12).
Pregnant and Lactating Women -The data on
tapwater intakes for control, pregnant, and lactating
Exposure Factors Handbook
August 1997
Page
3-27
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
women are presented in Table 3-25. The recommended
intake values are presented in Table 3-30.
High Activity/Hot Climates - Data on intake rates for
individuals performing strenuous activities under various
environmental conditions are limited. None of these is
classed as a key study because the populations in these
studies are not representative of the general U.S.
population. However, the data presented by McNall and
Schlegel (1968) and U.S. Army (1983) provide bounding
intake values for these individuals. According to McNall
and Schlegel (1968), hourly intake can range from 0.21 to
0.65 L/hour depending on the temperature and activity
level. Intake among physically active individuals can
range from 6 L/day in temperate climates to 11 L/day in
hot climates (U.S. Army, 1983).
A characterization of the overall confidence in the
accuracy and appropriateness of the recommendations for
drinking water is presented in Table 3-35. Although the
study of Ershow and Cantor (1989) is of high quality and
consistent with the other surveys, the low currency of the
information (1978 data collection), in the presence of
anecdotal information (not presented here) that the
consumption of bottled water and beverages has increased
since 1980 was the main reason for lowering the
confidence score of the overall recommendations from
high to medium.
Page
3-28
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapters - Drinking Water Intake
Table 3-35. Confidence in Tapwater Intake Recommendations
Considerations
Study Elements
• Level of peer review
• Accessibility
• Reproducibility
• Focus on factor of interest
• Data pertinent to U.S.
• Primary data
• Currency
• Adequacy of data collection
period
• Validity of approach
• Study size
• Representativeness of the
population
• Characterization of
variability
• Lack of bias in study design
(high rating is desirable)
• Measurement error
Other Elements
• Number of studies
• Agreement between
researchers
Overall Rating
Rationale
The study of Ershow and Cantor (1989) had a thorough expert
panel review. Review procedures were not reported in the
Canadian study; it was a government report. Other reports
presented are published in scientific journals.
The two monographs are available from the sponsoring agencies;
the others are library-accessible.
Methods are well-described.
The studies are directly relevant to tapwater.
See "representativeness" below.
The two monographs used recent primary data (less than one week)
on recall of intake.
Data were all collected in the 1978 era. Tapwater use may have
changed since that time period.
These are one- to three-day intake data. However, long term
variability may be small. Their use as a chronic intake measure can
be assumed.
The approach was competently executed.
This study was the largest monograph that had data for 1 1,000
individuals.
The Ershow and Cantor (1989) and Canadian surveys were
validated as demographically representative.
The full distributions were given in the main studies.
Bias was not apparent.
No physical measurements were taken. The method relied on
, recent recall of standardized volumes of drinking water containers,
and was not validated.
There were two key studies for the adult and child
recommendations. There were six other studies for adults, one
study for pregnant and lactating women, and two studies for high
activity/hot climates.
This agreement was good.
The data are excellent, but are not current.
Rating
High
High
High
High
NA
High
Low
Medium
High
High
High
High
High
Medium
High for adult and
children.
Low for the other
recommended
subpopulation values.
High
Medium
Exposure Factors Handbook
August 1997 '
Page
3-29
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
3.7. REFERENCES FOR CHAPTER 3
American Industrial Health Council (AIHC). (1994)
Exposure factors sourcebook. AIHC, Washington,
DC.
Bourne, G.H.; Kidder, G.W., eds. (1953) Biochemistry
and physiology of nutrition. Vol.1. New York, NY:
Academic Press.
Canadian Ministry of National Health and Welfare
(1981) Tapwater consumption in Canada. Document
number 82-EHD-80. Public Affairs Directorate,
Department of National Health and Welfare, Ottawa,
Canada.
Cantor, K.P.; Hoover, R.; Hartge, P.; Mason, T.J.;
Silverman, D.T.; et al. (1987) Bladder cancer,
drinking water source, and tapwater consumption: A
case-control study. J. Natl. Cancer Inst.
79(6): 1269-1279.
Ershow, A.G.; Brown, L.M.; Cantor, K.P. (1991)
Intake of tapwater and total water by pregnant and
lactating women. American Journal of Public Health.
81:328-334.
Ershow, A.G.; Cantor, K.P. (1989) Total water and
tapwater intake in the United States: population-based
estimates of quantities and sources. Life Sciences
Research Office, Federation of American Societies
for Experimental Biology.
Evans, C.L., ed. (1941) Starling's principles of human
physiology, 8th ed. Philadelphia, PA: Lea and
Febiger.
Gillies, M.E.; Paulin, H.V. (1983) Variability of
mineral intakes from drinking water: A possible
explanation for the controversy over the relationship
of water quality to cardiovascular disease. Int. J.
Epid. 12(1):45-50.
Guyton, A.C. (1968) Textbook of medical physiology,
3rd ed. Philadelphia, PA: W.B. Saunders Co.
Hopkins, S.M.; Ellis, J.C. (1980) Drinking water
consumption in Great Britain: a survey of drinking
habits with special reference to tap-water-based
beverages. Technical Report 137, Water Research
Centre, Wiltshire Great Britain.
ICRP. (1981) International Commission on
Radiological Protection. Report of the task group on
reference man. New York: Pergammon Press.
McNall, P.E.; Schlegel, J.C. (1968) Practical thermal
environmental limits for young adult males working
in hot, humid environments. American Society of
Heating, Refrigerating and Air-Conditioning
Engineers (ASHRAE) Transactions 74:225-235.
National Academy of Sciences (NAS). (1974)
Recommended dietary allowances, 8th ed.
Washington, DC: National Academy of Sciences-
National Research Council.
National Academy of Sciences (NAS). (1977)
Drinking water and health. Vol. 1. Washington, DC:
National Academy of Sciences-National Research
Council.
Pennington, J.A.T. (1983) Revision of the total diet
study food list and diets. J. Am. Diet. Assoc.
82:166-173.
Pike, R.L.; Brown, M. (1975) Minerals and water in
nutrition~an integrated approach, 2nd ed. New York,
NY: John Wiley.
Randall, H.T., (1973) Water, electrolytes and acid base
balance. In: Goodhart RS, Shils ME, eds. Modern
nutrition in health and disease. Philadelphia, PA: Lea
and Febiger.
Roseberry, A.M.; Burmaster, D.E. (1992) Lognormal
distribution for water intake by children and adults.
Risk Analysis 12:99-104.
Tsang, A.M.; Klepeis, N.E. (1996) Results tables from
a detailed analysis of the National Human Activity
Pattern Survey (NHAPS) responses. Draft Report
prepared for the U.S. Environmental Protection
Agency by Lockheed Martin, Contract No. 68-W6-
001, Delivery Order No. 13.
U.S. Army. (1983) Water Consumption Planning
Factors Study. Directorate of Combat Developments,
United States Army Quartermaster School, Fort Lee,
Virginia.
USDA. (1995) Food and nutrient intakes by
individuals in the United States, 1 day, 1989-91.
United States Department of Agriculture, Agricultural
Research Service. NFS Report No. 91-2.
U.S. EPA. (1980) U.S. Environmental Protection
Agency. Water quality criteria documents;
availability. Federal Register, (November 28)
45(231):79318-79379.
U.S. EPA. (1984) An estimation of the daily average
food intake by age and sex for use in assessing the
radionuclide intake of individuals in the general
population. EPA-520/1-84-021.
U.S. EPA. (1991) U.S. Environmental Protection
Agency. National Primary Drinking Water
Regulation; Final Rule. Federal Register
56(20):3526-3597. January 30, 1991.
Page
3-30
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Walker, B.S.; Boyd, W.C.; Asimov, I. (1957)
Biochemistry and human metabolism, 2nd ed.
Baltimore, MD: Williams & Wilkins Co.
Wolf, A.V. (1958) Body water. Sci. Am. 99:125.
Exposure Factors Handbook
August 1997
Page
3-31
-------
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
4. SOIL INGESTION AND PICA
4.1. BACKGROUND
The ingestion of soil is a potential source of human
exposure to toxicants. The potential for exposure to
contaminants via this source is greater for children
because they are more likely to ingest more soil than
adults as a result of behavioral patterns present during
childhood. Inadvertent soil ingestion among children may
occur through the mouthing of objects or hands.
Mouthing behavior is considered to be a normal phase of
childhood development. Adults may also ingest soil or
dust particles that adhere to food, cigarettes, or their
hands. Deliberate soil ingestion is defined as pica and is
considered to be relatively uncommon. Because normal,
inadvertent soil ingestion is more prevalent and data for
individuals with pica behavior are limited, this section
focuses primarily on normal soil ingestion that occurs as
a result of mouthing or unintentional hand-to-mouth
activity.
Several studies have been conducted to estimate the
amount of soil ingested by children. Most of the early
studies attempted to estimate the amount of soil ingested
by measuring the amount of dirt present on children's
hands and making generalizations based on behavior.
More recently, soil intake studies have been conducted
using a methodology that measures trace elements in feces
and soil that are believed to be poorly absorbed in the gut.
These measurements are used to estimate the amount of
soil ingested over a specified time period. The available
studies on soil intake are summarized in the following
sections. Studies on soil intake among children have been
classified as either key studies or relevant studies based on
their applicability to exposure assessment needs.
Recommended intake rates are based on the results of key
studies, but relevant studies are also presented to provide
the reader with added perspective on the current state-of-
knowledge pertaining to soil intake. Information on soil
ingestion among adults is presented based on available
data from a limited number of studies. This is an area
where more data and more research are needed. Relevant
information on the prevalence of pica and intake among
individuals exhibiting pica behavior is also presented.
4.2. KEY STUDIES ON SOIL INTAKE AMONG
CHILDREN
Binder et al. (1986) - Estimating Soil Ingestion:
Use of Tracer Elements in Estimating the Amount of Soil
Ingested by Young Children - Binder et al. (1986) studied
the ingestion of soil among children 1 to 3 years of age
who wore diapers using a tracer technique modified from
a method previously used to measure soil ingestion among
grazing animals. The children were studied during the
summer of 1984 as part of a larger study of residents
living near a lead smelter in East Helena, Montana.
Soiled diapers were collected over a 3-day period from
65 children (42 males and 23 females), and composited
samples of soil were obtained from the children's yards.
Both excreta and soil samples were analyzed for
aluminum, silicon, and titanium. These elements were
found in soil, but were thought to be poorly absorbed in
the gut and to have been present in the diet only in limited
quantities. This made them useful tracers for estimating
soil intake. Excreta measurements were obtained for 59
of the children. Soil ingestion by each child was
estimated based on each of the three tracer elements using
a standard assumed fecal dry weight of 15 g/day, and the
following equation:
(Eqn. 4-1)
where:
Ti,e =
fi,e =
F! =
Si,e =
estimated soil ingestion for child i based on element
e (g/day);
concentration of element e in fecal sample of child
i(mg/g);
fecal dry weight (g/day); and
concentration of element e in child i's yard soil
(mg/g).
The analysis conducted by Binder et al. (1986)
assumed that: (1) the tracer elements were neither lost nor
introduced during sample processing; (2) the soil ingested
by children originates primarily from their own yards; and
(3) that absorption of the tracer elements by children
occurred in only small amounts. The study did not
distinguish between ingestion of soil and housedust nor
did it account for the presence of the tracer elements in
ingested foods or medicines.
The arithmetic mean quantity of soil ingested by
the children in the Binder et al. (1986) study was
estimated to be 181 mg/day (range 25 to 1,324) based on
the aluminum tracer; 184 mg/day (range 31 to 799) based
on the silicon tracer; and 1,834 mg/day (range 4 to
17,076) based on the titanium tracer (Table 4-1). The
Exposure Factors Handbook
August 1997
Page
4-1
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-1. Estimated Daily Soil Ingestion Based on Aluminum, Silicon, and Titanium Concentrations
Estimation
Method
Aluminum
Silicon
Titanium
Minimum
Source: Binder etal
Mean
(mg/day)
181
184
1,834
108
, 1986.
Median
(mg/day)
121
136
618
88
Standard
Deviation
(mg/day)
203
175
3,091
121
Range
(mg/day)
25-1,324
31-799
4-17,076
4-708
95th Percentile
(mg/day)
584
5,78
9,590
386
Geometric
Mean
(mg/day)
128
130
401
65
overall mean soil ingestion estimate based on the
minimum of the three individual tracer estimates for each
child was 108 mg/day (range 4 to 708). The 95th
pcrcentile values for aluminum, silicon, and titanium were
584 mg/day, 578 mg/day, and 9,590 mg/day, respectively.
The 95th percentile value based on the minimum of the
three individual tracer estimates for each child was 386
mg/day.
The authors were not able to explain the difference
between the results for titanium and for the other two
elements, but speculated that unrecognized sources of
titanium in the diet or in the laboratory processing of stool
samples may have accounted for the increased levels. The
frequency distribution graph of soil ingestion estimates
based on titanium shows that a group of 21 children had
particularly high titanium values (i.e., >1,000 mg/day).
The remainder of the children showed titanium ingestion
estimates at lower levels, with a distribution more
comparable to that of the other elements.
The advantages of this study are that a relatively
large number of children were studied and tracer elements
were used to estimate soil ingestion. However, the
children studied may not be representative of the U.S.
population and the study did not account for tracers
ingested via foods or medicines. Also, the use of an
assumed fecal weight instead of actual fecal weights may
have biased the results of this study. Finally, because of
the short-term nature of the survey, soil intake estimates
may not be entirely representative of long-term behavior,
especially at the upper-end of the distribution of intake.
Clausing et al. (1987) - A Method for Estimating
Soil Ingestion by Children - Clausing et al. (1987)
conducted a soil ingestion study with Dutch children using
a tracer element methodology similar to that of Binder et
al. (1986). Aluminum, titanium, and acid-insoluble
residue (AIR) contents were determined for fecal samples
from children, aged 2 to 4 years, attending a nursery
school, and for samples of playground dirt at that school.
Twenty-seven daily fecal samples were obtained over a
5-day period for the 18 children examined. Using the
average soil concentrations present at the school, and
assuming a standard fecal dry weight of 10 g/day,
Clausing et al. (1987) estimated soil ingestion for each
tracer. Clausing et al. (1987) also collected eight daily
fecal samples from six hospitalized, bedridden children.
These children served as a control group, representing
children who had very limited access to soil.
The average quantity of soil ingested by the school
children in this study was as follows: 230 mg/day (range
23 to 979 mg/day) for aluminum; 129 mg/day (range 48
to 362 mg/day) for AIR; and 1,430 mg/day (range 64 to
11,620 mg/day) for titanium (Table 4-2). As in the Binder
et al. (1986) study, a fraction of the children (6/19)
showed titanium values well above 1,000 mg/day, with
most of the remaining children showing substantially
lower values. Based on the Limiting Tracer Method
(LTM), mean soil intake was estimated to be 105 mg/day
with a population standard deviation of 67 mg/day (range
23 to 362 mg/day). Use of the LTM assumed that "the
maximum amount of soil ingested corresponded with the
lowest estimate from the three tracers" (Clausing et al.,
1987). Geometric mean soil intake was estimated to be
90 mg/day. This assumes that the maximum amount of
soil ingested cannot be higher than the lowest estimate for
the individual tracers.
Mean soil intake for the hospitalized children was
estimated to be 56 mg/day based on aluminum (Table 4-
3). For titanium, three of the children had estimates well
in excess of 1,000 mg/day, with the remaining three
children in the range of 28 to 58 mg/day. Using the LTM
Page
4-2
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-2, Calculated Soil Ingestion by Nursery School Children
Child
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Arithmetic Mean
Source: Adapted fron
Sample
Number
L3
L14
L25
L5
L13
L27
L2
L17
L4
Lll
L8
L21
L12
L16
LI 8
L22
LI
L6
L7
L9
L10
L15
L19
L20
L23
L24
L26
ft Clausing et al.
Soil Ingestion as
Calculated from Ti
(mg/dav)
103
154
130
131
184
142
124
670
246
2,990
293
313
1,110
176
11,620
11,320
3,060
624
600
133
354
2,400
124
269
1,130
64
184
1,431
1987.
Soil Ingestion as
Calculated from Al
(ma/day)
300
211
23
_
103
81
42
566
62
65
_
-
693
-
_
.77
82
979
200
_
195
-
71
212
51
566
56
232
Soil Ingestion as
Calculated from AIR
(mg/dav)
107
172
-
71 '
82
84
84
174
145
139
108
152
362
145-
120
-
96
111
124
95
106
48
93
274
84
-
-'
129
Limiting Tracer
(mg/day)
103
154
23
71
82
81
42
174
62
65
108
152
362
145
120
77
82
111
124
95
106
48
71
212
51
64
56
105
Child
1
2
3
4
5
6
Arithmetic Mean
Table 4-3.
Sample
G5
G6
Gl
G2
G8
G3
G4
G7
Calculated Soil Ingestion by Hospitalized, Bedridden Children
Soil Ingestion as Calculated
from Ti
(mg/dav)
3,290
4,790
28
6,570
2,480
28
1,100
58'
2,293
Soil Ingestion as Calculated
from Al
(mg/dav)
57
71
26
94
57
77
30
38
56
Limiting Tracer
(mg/dav)
57
71
26
84
57
28
30
38
49
Source: Adapted from Clausing et al. 1987.
method, the mean soil ingestion rate was estimated to be
49 mg/day with a population standard deviation of 22
mg/day (range 26 to 84 mg/day). The geometric mean
soil intake rate was 45 mg/day. The data on hospitalized
children suggest a major nonsoil source of titanium for
some children, and may suggest a background nonsoil
Exposure Factors Handbook
August 1997
Page
4-3
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
source of aluminum. However, conditions specific to
hospitalization (e.g., medications) were not considered.
AIR measurements were not reported for the hospitalized
children. Assuming that the tracer-based soil ingestion
rates observed in hospitalized children actually represent
background tracer intake from dietary and other nonsoil
sources, mean soil ingestion by nursery school children
was estimated to be 56 mg/day, based on the LTM (i.e.,
105 mg/day for nursery school children minus 49 mg/day
for hospitalized children) (Clausing et al. 1987).
The advantages of this study are that Clausing et al.
(1987) evaluated soil ingestion among two populations of
children that had differences in access to soil, and
corrected soil intake rates based on background estimates
derived from the hospitalized group. However, a smaller
number of children were used in this study than in the
Binder et al. (1986) study and these children may not be
representative of the U.S. population. Tracer elements in
foods or medicines were not evaluated. Also, intake rates
derived from this study may not be representative of soil
intake over the long-term because of the short-term nature
of the study. In addition, one of the factors that could
affect soil intake rates is hygiene (e.g., hand washing
frequency). Hygienic practices can vary across countries
and cultures and may be more stringently emphasized in
a more structured environment such as child care centers
in The Netherlands and other European countries than in
child care centers in the United States.
Calabrese et al. (1989) - How Much Soil do Young
Children Ingest: An Epidemiologic Study - Calabrese et
al. (1989) studied soil ingestion among children using the
basic tracer design developed by Binder et al. (1986).
However, in contrast to the Binder et al. (1986) study,
eight tracer elements (i.e., aluminum, barium, manganese,
silicon, titanium, vanadium, yttrium, and zirconium) were
analyzed instead of only three (i.e., aluminum, silicon, and
titanium). A total of 64 children between the ages of 1
and 4 years old were included in the study. These
children were all selected from the greater Amherst,
Massachusetts area and were predominantly from two-
parent households where the parents were highly
educated. The Calabrese et al. (1989) study was
conducted over eight days during a two week period and
included the use of a mass-balance methodology in which
duplicate samples of food, medicines, vitamins, and others
were collected and analyzed on a daily basis, in addition
to soil and dust samples collected from the child's home
and play area. Fecal and urine samples were also
collected and analyzed for tracer elements. Toothpaste,
low in tracer content, was provided to all participants.
In order to validate the mass-balance methodology
used to estimate soil ingestion rates among children and
to determine which tracer elements provided the most
reliable data on soil ingestion, known amounts of soil (i.e.,
300 mg over three days and 1,500 mg over three days)
containing eight tracers were administered to six adult
volunteers (i.e., three males and three females). Soil
samples and feces samples from these adults and duplicate
food samples were analyzed for tracer elements to
calculate recovery rates of tracer elements in soil. Based
on the adult validation study, Calabrese et al. (1989)
confirmed that the tracer methodology could adequately
detect tracer elements in feces at levels expected to
correspond with soil intake rates in children. Calabrese et
al. (1989) also found that aluminum, silicon, and yttrium
were the most reliable of the eight tracer elements
analyzed. The standard deviation of recovery of these
three tracers was the lowest and the percentage of
recovery was closest to 100 percent (Calabrese, et al.,
1989). The recovery of these three tracers ranged from
120 to 153 percent when 300 mg of soil had been ingested
over a three-day period and from 88 to 94 percent when
1,500 mg soil had been ingested over a three-day period
(Table 4-4).
Using the three most reliable tracer elements, the
mean soil intake rate for children, adjusted to account for
the amount of tracer found in food and medicines, was
estimated to be 153 mg/day based on aluminum, 154
mg/day based on silicon, and 85 mg/day based on yttrium
(Table 4-5). Median intake rates were somewhat lower
(29 mg/day for aluminum, 40 mg/day for silicon, and 9
mg/day for yttrium). Upper-percentile (i.e., 95th) values
were 223 mg/day for aluminum, 276 mg/day for silicon,
and 106 mg/day for yttrium. Similar results were
observed when soil and dust ingestion was combined
(Table 4-5). Intake of soil and dust was estimated using
a weighted average of tracer concentration in dust
composite samples and in soil composite samples based
on the timechildren spent at home and away from home,
and indoors and outdoors. Calabrese et al. (1989)
suggested that the use of titanium as a tracer in earlier
studies that lacked food ingestion data may have
significantly overestimated soil intake because of the high
levels of titanium in food. Using the median values of
aluminum and silicon, Calabrese et al. (1989) estimated
the quantity of soil ingested daily to be 29 mg/day and
40 mg/day, respectively. It should be noted that soil
Page
4-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-4. Mean and Standard Deviation Percentage Recovery of Eight Tracer Elements
Tracer Element
Al
Ba
Mn
Si
Ti
V
Y
Zr
300 mg
Mean
152.8
2304.3
1177.2
139.3
251.5
345.0
120.5
80.6
Soil Ingested
SD
107.5
4533.0
1341.0
149.6
316.0
247.0
42.4
43.7
1500
Mean
93.5
149.8
248.3
91.8
286.3
147.6
87.5
54.6
mg Soil Ingested
SD
15.5
69.5
183.6
16.6
380.0
66.8
12.6
33.4
Source: Adapted from Calabrese et al., 1989.
Table 4-5. Soil and Dust Ingestion Estimates for Children Aged 1-4 Years
Tracer Element
Aluminum
soil
dust
soil/dust combined
Silicon
soil
dust
soil/dust combined
Yttrium
soil
dust
soil/dust combined ,
Titanium
soil
dust
N
64
64
64
64
64
64
62
64
62
64
64
64
Mean
153
317
154
154
964
483
85
62
65
218
163
170
Median
29
31
30
40
49
49
9
15
11
55
28
30
Intake (mg/day)a
SD
852
1,272
629
693
6,848
3,105
890
687
717
1,150
659
691
95th Percentile
223
506
478
276
692
653
106
169
159
1,432 .
1,266
1.059
Maximum
6,837
8,462
4,929
5,549
54,870
24,900
•
6,736
5,096
5,269
6,707
3,354
3.597
a Corrected for Tracer Concentrations in Foods
Source: Adapted from Calabrese et al.
1989.
ingestion for one child in the study ranged from
approximately 10 to 14 grams/day during the second week
of observation. Average soil ingestion for this child was
5 to 7 mg/day, based on the entire study period.
The advantages of this study are that intake rates
were corrected for tracer concentrations in foods and
medicines and that the methodology was validated using
adults. Also, intake was observed over a longer time
period in this study than in earlier studies and the number
of tracers used was larger than for other studies. A
relatively large population was studied, but they may not
be entirely representative of the U.S. population because
they were selected from a single location.
Davis et al. (1990) - Quantitative Estimates of Soil
Ingestion in Normal Children Between the ages of 2 and
7 years: Population-Based Estimates Using Aluminum,
Exposure Factors Handbook
August 1997
Page
4-5
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Silicon, and Titanium as Soil Tracer Elements - Davis et
al. (1990) also used a mass-balance/tracer technique to
estimate soil ingestion among children. In this study, 104
children between the ages of 2 and 7 years were randomly
selected from a three-city area in southeastern Washington
State. The study was conducted over a seven day period,
primarily during the summer.. Daily soil ingestion was
evaluated by collecting and analyzing soil and house dust
samples, feces, urine, and duplicate food samples for
aluminum, silicon, and titanium. In addition, information
on dietary habits and demographics was collected in an
attempt to identify behavioral and demographic
characteristics that influence soil intake rates among
children. The amount of soil ingested on a daily basis was
estimated using the following equation:
(DWr * DWp
where:
sie
DWf
DWp
Ef
DWfd
Efd
Esoi!
soil ingested for child i based on tracer e (g);
feces dry weight (g);
feces dry weight on toilet paper (g);
tracer amount in feces (Mg/g);
tracer amount in urine (A
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
occupation of the parent, and city of residence. However,
none of these factors were predictive of soil intake rates
when tested using multiple linear regression.
The advantages of the Davis et al. (1990) study are
that soil intake rates were corrected based on the tracer
content of foods and medicines and that a relatively large
number of children were sampled. Also, demographic and
behavioral information was collected for the survey group.
However, although a relatively large sample population
was surveyed, these children were all from a single area of
the U.S. and may not be representative of the U.S.
population as a whole. The study was conducted over a
one-week period during the summer and may not be
representative of long-term (i.e., annual) patterns of
intake.
Van Wijnen et al. (1990) - Estimated Soil
Ingestion by Children - In a study by Van Wijnen et al.
(1990), soil ingestion among Dutch children ranging in
age from 1 to 5 years was evaluated using a tracer element
methodology similar to that used by Clausing et al.
(1987). Van Wijnen et al. (1990) measured three tracers
(i.e., titanium, aluminum, and AIR) in soil and feces and
estimated soil ingestion based on the LTM. An average
daily feces weight of 15 g dry weight was assumed. A
total of 292 children attending daycare centers were
sampled during the first of two sampling periods and 187
children were sampled in the second sampling period; 162
of these children were sampled during both periods (i.e.,
at the beginning and near the end of the summer of 1986).
A total of 78 children were sampled at campgrounds, and
15 hospitalized children were sampled. The mean values
for these groups were: 162 mg/day for children in daycare
centers, 213 mg/day for campers and 93 mg/day for
hospitalized children. Van Wijnen et al. (1990) also
reported geometric mean LTM values because soil intake
rates were found to be skewed and the log transformed
data were approximately normally distributed. Geometric
mean LTM values were estimated to be 111 mg/day for
children in daycare centers, 174 mg/day for children
vacationing at campgrounds (Table 4-7) and 74 mg/day
for hospitalized children (70-120 mg/day based on the 95
percent confidence limits of the mean). AIR was the
limiting tracer in about 80 percent of the samples. Among
children attending daycare centers, soil intake was also
found to be higher when the weather was good (i.e., <2
days/week precipitation) than when the weather was bad
(i.e., >4 days/week precipitation (Table 4-8). Van Wijnen
et al. (1990) suggest that the mean LTM value for
hospitalized infants represents background intake of
tracers and should be used to correct the soil intake rates
based on LTM values for other sampling groups. Using
mean values, corrected soil intake rates were 69 mg/day
(162 mg/day minus 93 mg/day) for daycare children and
120 mg/day (213 mg/day minus 93 mg/day) for campers.
Table 4-7. Geometric Mean (GM) and Standard Deviation (GSD) LTM Values
* for Children at Daycare Centers and Campgrounds
Daycare Centers
Age (yrs) Sex
<1
l-<2
2-<3
3-4
4-<5
All girls
All boys
Total
Girls
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
Boys
n
3
1
20
17
34
17
26
29
1
4
86
72
162a
GMLTM
(mg/day)
81
75
124
114
118
96
111
110
180
99
117
104
111
GSD LTM
(mg/day)
1.09
-
1.87
1.47
1.74
1.53
1.57
1.32
-
1.62
1.70
1.46
1.60
n
-
-
3
5
4
8
6
8
19
18
36
42
78b
Campgrounds
GM LTM.
(mg/day)
'
-
207
312
367
232
164
148
164
136
179
169
174
GSD LTM
(mg/day)
-
-
1.99
2.58
2.44
2.15
1-27 ,
1.42
1.48
1.30
1.67
1.79
1.73
a Age and/or sex not registered for eight children.
b Age not registered for seven children.
Source:
Adapted from Van Wijnen et al., 1990.
Exposure Factors Handbook
August 1997
Page
4-7
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-8. Estimated Geometric Mean LTM Values of Children Attending Daycare Centers
According to Age, Weather Category, and Sampling Period
First Sampling Period
Weather Category
Bad
(>4 days/week precipitation)
Reasonable
(2-3 days/week precipitation)
Good
(<2 days/week precipitation)
Age (years)
<1
l-<2
2-<3
4-<5
<1
l-<2
2-<3
3-<4
4-<5
<1
l-<2
2-<3
3-<4
4-<5
Second Sampling Period
Estimated Geometric Mean Estimated Geometric Mean
LTM Value LTM Value
n (me/day) n (mg/dav)
3
18
33
5
4
42
65
67
10
94
103
109
124
102
229
166
138
132
3
33
48
6
1
10
13
19
1
, 67
80
91
109
61
96
99
94
61
Source: Van Wiinen et al., 1990.
Corrected geometric mean soil intake was estimated to
range from 0 to 90 mg/day with a 90th percentile value of
190 mg/day for the various age categories within the
daycare group and 30 to 200 mg/day with a 90th
percentile value of 300 mg/day for the various age
categories within the camping group.
The advantage of this study is that soil intake was
estimated for three different populations of children; one
expected to have high intake, one expected to have
"typical" intake, and one expected to have low or
background-level intake. Van Wijnen et al. (1990) used
the background tracer measurements to correct soil intake
rates for the other two populations. Tracer concentrations
in food and medicine were not evaluated. Also, the
population of children studied was relatively large, but
may not be representative of the U.S. population. This
study was conducted over a relatively short time period.
Thus, estimated intake rates may not reflect long-term
patterns, especially at the high-end of the distribution.
Another limitation of this study is that values were not
reported element-by-element which would be the
preferred way of reporting. In addition, one of the factors
that could affect soil intake rates is hygiene (e.g., hand
washing frequency). Hygienic practices can vary across
countries and cultures and may be more stringently
emphasized in a more structured environment such as
child care centers in The Netherlands and other European
countries than in child care centers in the United States.
Stanek and Calabrese (1995a) - Daily Estimates of
Soil Ingestion in Children - Stanek and Calabrese (1995a)
presented a methodology which links the physical passage
of food and fecal samples to construct daily soil ingestion
estimates from daily food and fecal trace-element
concentrations. Soil ingestion data for children obtained
from the Amherst study (Calabrese et al., 1989) were
reanalyzed by Stanek and Calabrese (1995a). In the
Amherst study, soil ingestion measurements were made
over a period of 2 weeks for a non-random sample of
sixty-four children (ages of 1-4 years old) living adjacent
to an academic area in western Massachusetts. During
each week, duplicate food samples were collected for 3
consecutive days and fecal samples were collected for 4
consecutive days for each subject. The total amount of
each of eight trace elements present in the food and fecal
samples were measured. The eight trace elements are
aluminum, barium, manganese, silicon, titanium,
vanadium, yttrium, and zirconium. The authors
expressed the amount of trace element in food input or
Page
4-8
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
fecal output as a "soil equivalent," which was defined as
the amount of the element in average daily food intake (or
average daily fecal output) divided by the concentration
of the element in soil. A lag period of 28 hours between
food intake and fecal output was assumed for all
respondents. Day 1 for the food sample corresponded to
the 24 hour period from midnight on Sunday to midnight
on Monday of a study week; day 1 of the fecal sample
corresponded to the 24 hour period from noon on Monday
to noon on Tuesday (Stanek and Calabrese, 1995a).
Based on these definitions, the food soil equivalent was
subtracted from the fecal soil equivalent to obtain an
estimate of soil ingestion for a trace element. A daily
"overall" ingestion estimate was constructed for each
child as the median of trace element values remaining
after tracers falling outside of a defined range around the
overall median were excluded. Additionally, estimates of
the distribution of soil ingestion projected over a period
of 365 days were derived by fitting log-normal
distributions to the "overall" daily soil ingestion estimates.
Table 4-9 presents the estimates of mean daily soil
ingestion intake per child (mg/day) for the 64 study
participants. (The authors also presented estimates of the
median values of daily intake for each child. For most
risk assessment purposes the child mean values, which are
proportional to the cumulative soil intake by the child, are
needed instead of the median values.) The approach
adopted in this paper led to changes in ingestion estimates
from those presented in Calabrese et al. (1989).
Specifically, among elements that may be more useful for
estimation of ingestion, the mean estimates decreased for
Al (153 mg/d to 122 mg/d) and Si ( 154 mg/d to 139
mg/d), but increased for Ti (218 mg/d to 271 mg/d) and Y
(85 mg/d to 165 mg/d). The "overall" mean estimate from
this reanalysis was 179 mg/d. Table 4-9 presents the
empirical distribution of the the "overall" mean daily soil
ingestion estimates for the 8-day study period (not based
on lognormal modeling). The estimated intake based on
the "overall" estimates is 45 mg/day or less for 50 percent
of the children and 208 mg/day or less for 95 percent of
the children. The upper percentile values for most of the
individual trace elements are somewhat higher. Next,
estimates of the respondents soil intake averaged over a
period of 365 days were presented based upon the
lognormal models fit to the daily ingestion estimates
(Table 4-10). The estimated median value of the 64
respondents' daily soil ingestion averaged over a year is
75 mg/day, while the 95th percentile is 1,751 mg/day.
A strength of this study is that it attempts to make
full use of the collected data through estimation of daily
ingestion rates for children. The data are then screened to
remove less consistent tracer estimates and the remaining
values are aggregated. Individual daily estimates of
ingestion will be subject to larger errors than are weekly
average values, particularly since the assumption of a
constant lag time between food intake and fecal output
may be not be correct for many subject days. The
aggregation approach used to arrive at the "overall"
ingestion estimates rests on the assumption that the mean
Table 4-9. Distribution of Average (Mean) Daily Soil Ingestion Estimates Per Child for 64 Children (mg/day)
Type of Estimate
Number of Samples
Overall
(64)
Al
(64)
Ba
(33)
Mn
(19)
Si
(63)
Ti
(56)
V
(52)
Y
(61)
Zr
(62)
Mean
25th Percentile
50th Percentile
75th Percentile
90th Percentile
95th Percentile
Maximum
179
10
45
88
186
208
7,703
122
10
19
73
131
254
4,692
655
28
65
260
470
518
17,991
1,053
35
121
319
478
17,374
17,374
139
5
32
94
206
224
4,975
271
8.
31
93
'154
279
12,055
112
8
47
177
340
398
845
165
0
15
47
105
144
8,976
23
0
15
41
87
117
208
a For each child, estimates of soil ingestion were formed on days 4-8 and the mean of these estimates was then evaluated for each child. The
values in the column "overall" correspond to percentiles of the distribution of these means over the 64 children. When specific trace elements
were not excluded via the relative standard deviation criteria, estimates of soil ingestion based on the specific trace element were formed for
108 days for each subject. The mean soil ingestion estimate was again evaluated. The distribution of these means for specific trace elements is
shown.
Source: Stanek and Calabrese, 1995a.
Exposure Factors Handbook
August 1997
Page
4-9
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
ingestion estimates across acceptable tracers provides the
most reliable ingestion estimates. The validity of this
assumption depends on the particular set of tracers used
in the study, and is not fully assessed.
Table 4-10. Estimated Distribution of Individual Mean Daily Soil
Ingestion Based on Data for 64 Subjects
Projected Over 365 Days"
Range
50th Pcrcentile (median)
90th Percemile
95th Pereentile
1 - 2,268 mg/db
75mg/d
l,190mg/d
1.751 mg/d
" Based on fitting a log-normal distribution to model daily soil
ingestion values.
b Subject with pica excluded.
Source: Slanek and Calabrese. 1995a.
In developing the 365 day soil ingestion estimates,
data that were obtained over a short period of time (as is
the case with all available soil ingestion studies) were
extrapolated over a year. The 2-week study period may
not reflect variability in tracer element ingestion over a
year. While Stanek and Calabrese (1995a) attempt to
address this through lognormal modeling of the long term
intake, new uncertainties are introduced through the
parametric modeling of the limited subject day data.
Also, the sample population size of the original study was
small and site limited, and, therefore, is not representative
of the U.S. population. Study mean estimates of soil
ingestion, such as the study mean estimates presented in
Table 4-9, are substantially more reliable than any
available distributional estimates.
Stanek and Calabrese (1995b) - Soil Ingestion
Estimates for Use in Site Evaluations Based on the Best
Tracer Method - Stanek and Calabrese (1995b)
recalculated ingestion rates that were estimated in three
previous mass-balance studies (Calabrese et al., 1989 and
Davis et al., 1990 for children's soil ingestion, and
Calabrese et al., 1990 for adult soil ingestion) using the
Best Tracer Method (BTM). This method allows for the
selection of the most recoverable tracer for a particular
subject or group of subjects. The selection process
involves ordering trace elements for each subject based on
food/soil (F/S) ratios. These ratios are estimated by
dividing the total amount of the tracer in food by the
tracer concentration in soil. The F/S ratio is small when
the tracer concentration in food is almost zero when
compared to the tracer concentration in soil. A small F/S
ratio is desirable because it lessens the impact of transit
time error (the error that occurs when fecal output does
not reflect food ingestion, due to fluctuation in
gastrointestinal transit time) in the soil ingestion
calculation. Because the recoverability of tracers can vary
within any group of individuals, the BTM uses a ranking
scheme of F/S ratios to determine the best tracers for use
in the ingestion rate calculation. To reduce biases that
may occur as a result of sources of fecal tracers other than
food or soil, the median of soil ingestion estimates based
on the four lowest F/S ratios was used to represent soil
ingestion among individuals.
For adults, Stanek and Calabrese (1995b) used data
for 8 tracers from the Calabrese et al. (1990) study to
estimate soil ingestion by the BTM. The lowest F/S ratios
were Zr and Al and the element with the highest'F/S ratio
was Mn. For soil ingestion estimates based on the median
of the lowest four F/S ratios, the tracers contributing most
often to the soil ingestion estimates were Al, Si, Ti, Y, V,
and Zr. Using the median of the soil ingestion rates based
on the best four tracer elements, the average adult soil
ingestion rate was estimated to be 64 rng/day with a
median of 87 mg/day. The 90th percentile soil ingestion
estimate was 142 mg/day. These estimates are based on
18 subject weeks for the six adult volunteers described in
Calabrese et al. (1990).
For children, Stanek and Calabrese (1995b) used
data on 8 tracers from Calabrese et al., 1989 and data on
3 tracers from Davis et al. (1990) to estimate soil
ingestion rates. The median of the soil ingestion estimates
from the lowest four F/S ratios from the Calabrese et al.
(1989) study most often included Al, Si, Ti, Y, and Zr.
Based on the median of soil ingestion estimates from the
best four tracers, the mean soil ingestion rate was 132
mg/day and the median was 33 mg/day. The 95th
percentile value was 154 mg/day. These estimates are
based on data for 128 subject weeks for the 64 children in
the Calabrese et al. (1989) study. For the 101 children in
the Davis et al. (1990) study, the mean soil ingestion rate
was 69 mg/day and the median soil ingestion rate was 44
mg/day. The 95th percentile estimate was 246 mg/day.
These data are based on the three tracers (i.e., Al, Si, and
Ti) from the Davis et al. (1990) study. When the
Calabrese et al. (1989) and Davis et al. (1990) studies
were combined, soil ingestion was estimated to be 113
mg/day (mean); 37 mg/day (median); and 217 mg/day
(95th percentile), using the BTM.
This study provides a reevaluation of previous
studies. Its advantages are that it combines data from 2
studies for children, one from California and one from
Massachusetts, which increases the number of
Page
4-10
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
observations. It also corrects for biases associated with
the differences in tracer metabolism. The limitations
associated with the data used in this study are the same as
the limitations described in the summaries of the
Calabrese et al. (1989), Davis et al. (1990) and Calabrese
etal. (1990) studies.
4.3. RELEVANT STUDIES ON SOIL INTAKE
AMONG CHILDREN
Lepow et al. (1975) - Investigations Into Sources
of Lead in the Environment of Urban Children - Lepow et
al. (1975) used data from a previous study (Lepow et al.,
1974) to estimate daily soil ingestion rates of children.
Lepow et al.' (1974) estimated ingestion of airborne lead
fallout among urban children by: (1) analyzing surface
dirt and • dust samples from locations where children
played; (2) measuring hand dirt by applying preweighed
adhesive labels to the hands and weighing the amount of
dirt that was removed; and (3) observing "mouthing"
behavior over 3 to 6 hours of normal play. Twenty-two
children from an urban area of Connecticut were included
in the study. Lepow et al. (1975) used data from the 1974
study and found that the mean weight of soil/dust on the
hands was 11 mg. Assuming that a child would put
fingers or other "dirty" objects into his mouth about 10
times a day ingesting 11 mg of dirt each time, Lepow et
al. (1975) estimated that the daily soil ingestion rate
would be about 100 mg/day. According to Lepow et al.
(1975), the amount of hand dirt measured with this
technique is probably an underestimate because dirt
trapped in skin folds and creases was probably not
removed by the adhesive label. Consequently, mean soil
ingestion rates may be somewhat higher than the values
estimated in this study.
Day et al. (1975) - Lead in Urban Street Dust -
Day et al. (1975) evaluated the contribution of incidental
ingestion of lead-contaminated street dust and soil to
children's total daily intake of lead by measuring the
amount of lead in street dust and soil and estimating the
amount of dirt ingested by children. The amount of soil
that might be ingested was estimated by measuring the
amount of dirt that was transferred to a "sticky sweet"
during 30 minutes of play and assuming that a child might
eat from 2 to 20 such sweets per day. Based on "a small
number of direct measurements," Day et al. (1975) found
that 5 to 50 mg of dirt from a child's hands may be
transferred to a "sticky sweet" during 30 minutes of
"normal playground activity. Assuming that all of the dirt
is ingested with the 2 to 20 "sticky sweets," Day et al.
(1975) estimated that intake of soil among children could
range from 10 to 1000 mg/day.
Duggan and Williams (1977) - Lead in Dust in City
Streets - Duggan and Williams (1977) assessed the risks
associated with lead in street dust by analyzing street dust
from areas in and around London for lead, and estimating
the amount of hand dirt that a child might ingest. Duggan
and Williams (1977) estimated the amount of dust that
would be retained on the forefinger and thumb by
removing a small amount of dust from a weighed amount,
rubbing the forefinger and thumb together, and
reweighing to determine the amount retained on the finger
and thumb. The results of "a number of tests with several
different people" indicated that the mean amount of dust
retained on the finger and thumb was approximately 4 mg
with a range of 2 to 7 mg (Duggan and Williams, 1977).
Assuming that a child would suck his/her finger or thumb
10 times a day and that all of the dirt is removed each time
and replaced with new dirt prior to subsequent mouthing
behavior, Duggan and Williams (1977) estimated that 20
mg of dust would be ingested per day.
Hawley et al (1985) - Assessment of Health Risk
from Exposure to Contaminated Soil - Using existing
literature, Hawley (1985) developed scenarios for
estimating exposure of young children, older children, and
adults to contaminated soil. Annual soil ingestion rates
were estimated based on assumed intake rates of soil and
housedust for indoor and outdoor activities and
assumptions about the duration and frequency of the
activities. These soil ingestion rates were based on the
assumption that the contaminated area is in a region
having a winter season. Housedust was assumed to be
comprised of 80 percent soil.
Outdoor exposure to contaminated soil among
young children (i.e., 2.5 years old) was assumed to occur
5 days per week during only 6 months of the year (i.e.,
mid-April through mid-October). Children were assumed
to ingest 250 mg soil/day while playing outdoors based on
data presented in Lepow et al. (1974; 1975) and Roels et
al. (1980). Indoor exposures among this population were
based on the assumption that young children ingest 100
mg of housedust per day while spending all of their time
indoors during the winter months, and 50 mg of housedust
per day during the warmer months when only a portion of
their time is spent indoors. Based on these assumptions,
Hawley (1985) estimated that the annual average soil
intake rate for young children is 150 mg/day (Table 4-11).
Older children (i.e., 6 year olds) were assumed to ingest
50 mg of soil per day from an area equal to the area of the
Exposure Factors Handbook
August 1997
Page
4-11
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Scenarios
Youne Child (2.5 Years Old)
Outdoor Activities (Summer)
Indoor Activities (Summer)
Indoor Activities (Winter
TOTAL SOIL INTAKE
Older Child (6 Years Old)
Outdoor Activities (Summer)
Indoor Activities (Year-Round)
TOTAL SOIL INTAKE
Table 4-11.
Media
Soil
Dust
Dust
Soil
Dust
Estimates of Soil Ingestion for Children
Exposure
(mg/day)
250
50
100
50
3
Days/Year
Activity
130
182
182
152
365
Fraction Soil
Content
1
0.8
0.8
1
0.8
Annual Average Soil
Intake
(mg/day)
90
20
40
150
21
2.4
23.4
Source: Hawley, 1985.
fingers on one hand while playing outdoors. This
assumption was based on data from Lepow et al. (1975).
Outdoor activities were assumed to occur each day over
5 months of the year (i.e., during May through October).
These children were also assumed to ingest 3 mg/day of
housedust from the indoor surfaces of the hands during
indoor activities occurring over the entire year. Using
these data, Hawley (1985) estimated the annual average
soil intake rate for older children to be 23.4 mg/day
(Table 4-11).
Thompson and Burmaster (1991) - Parametric
Distributions for Soil Ingestion by Children - Thompson
and Burmaster (1991) developed parameterized
distributions of soil ingestion rates for children based on
areanalysis of the data collected by Binder et al. (1986).
In the original Binder et al. (1986) study, an assumed
fecal weight of 15 g/day was used. Thompson and
Burmaster reestimated the soil ingestion rates from the
Binder et al. (1986) study using the actual stool weights of
the study participants instead of the assumed stool
weights. Because the actual stool weights averaged only
7.5 g/day, the soil ingestion estimates presented by
Thompson and Burmaster (1991) are approximately one-
half of those reported by Binder et al. (1986). Table 4-12
presents the distribution of estimated soil ingestion rates
calculated by Thompson and Burmaster (1991) based on
the three tracers elements (i.e., aluminum, silicon, and
titanium), and on the arithmetic average of soil ingestion
based on aluminum and silicon. The mean soil intake
rates were 97 mg/day for aluminum, 85 mg/day for
silicon, and 1,004 mg/day for titanium. The 90th
percentile estimates were 197 mg/day for aluminum, 166
mg/day for silicon, and 2,105 mg/day for titanium. Based
on the arithmetic average of aluminum and silicon for
each child, mean soil intake was estimated to be 91
mg/day and 90th percentile intake was estimated to be 143
mg/day.
Thompson and Burmaster (1991) tested the
hypothesis that soil ingestion rates based on the adjusted
Binder et al. (1986) data for aluminum, silicon and the
average of these two tracers were lognormally distributed.
The distribution of soil intake based on titanium was not
tested for lognormality because titanium may be present
in food in high concentrations and the Binder et al. (1986)
study did not correct for food sources of titanium
(Thompson and Burmaster, 1991). Although visual
inspection of the distributions for aluminum, silicon, and
the average of these tracers all indicated that they may be
lognormally distributed, statistical tests indicated that only
silicon and the average of the silicon and aluminum
tracers were lognormally distributed. Soil intake rates
based on aluminum were not lognormally distributed.
Table 4-12 also presents the lognormal distribution
parameters and underlying normal distribution parameters
(i.e., the natural logarithms of the data) for aluminum,
silicon, and the average of these two tracers. According
to the authors, "the parameters estimated from the
underlying normal distribution are much more reliable and
robust" (Thompson and Burmaster, 1991).
The advantages of this study are that it provides
percentile data and defines the shape of soil intake
distributions. However, the number of data points used to
fit the distribution was limited. In addition, the study did
not generate "new" data. Instead, it provided a reanalysis
of previously-reported data using actual fecal weights. No
corrections were made for tracer intake from food or
Page
4-12
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-12. Estimated Soil Ingestion Rate Summary Statistics and Parameters for Distributions
Using Binder et al. (1986) Data with Actual Fecal Weights
Soil Intake (mg/day)
Trace Element Basis
Mean
Min
10th
20th
30th
40th
Med
60th
70th
80th
90th
Max
Al
97
11
21
33
39
43
45
55
73
104
197
1,201
Si
85
10
19
23
36
52
60
65
79
106
166
642
Ti
1,004
1
3
22
47
172
293
475
724
1,071
2,105
14,061
MEAN3
91
13
22
34
43
49
59
69
92
100
143
921
Lognormal Distribution Parameters
Median
Standard Deviation
Arithmetic Mean
45
169
97
60
95
85
__
—
-
. 59
126
91
Underlying Normal Distribution Parameters
Mean
Standard Deviation
4.06
0.88
4.07
0.85
„
4.13
0.80
a MEAN = arithmetic average of soil ingestion based on aluminum and silicon.
Source: Thompson and Burmaster, 1991.
me'dicine and the results may not be representative of
long-term intake rates b.ecause the data were derived from
a short-term study.
Sedman and Mahmood (1994) - Soil Ingestion by
Children and Adults Reconsidered Using the Results of
Recent Tracer Studies - Sedman and Mahmood (1994)
used the results of two recent children's (Calabrese et al.
1989; Davis et al. 1990) tracer studies to determine
estimates of average daily soil ingestion in young children
and for over a lifetime. In the two studies, the intake and
excretion of a variety of tracers were monitored, and
concentrations of tracers in soil adjacent to the children's
dwellings were determined (Sedman and Mahmood,
1994). From a mass balance approach, estimates of soil
ingestion in these children were determined by dividing
the excess tracer intake (i.e., quantity of tracer recovered
in the feces in excess of the measured intake) by the
average concentration of tracer in soil samples from each
child's dwelling. Sedman and Mahmood (1994) adjusted
the mean estimates of soil ingestion in children for each
tracer (Y) from both studies to reflect that of a 2-year old
child using the following equation:
Y. = x e("°-112*yr)
(Eqn. 4-3)
where:
YJ = adjusted mean soil ingestion (mg/day)
x = a constant
yr = average age (2 years)
In addition to the study in young children, a study
(Calabrese et al., 1989) in adults was conducted to
evaluate the tracer methodology. In the adult studies,
percent recoveries of tracers were determined in six adults
who ingested known quantities of tracers in 1.5 or 0.3
grams of soil. The distribution of tracer recoveries from
adults was evaluated using data analysis techniques
involving visualization and exploratory data analysis
(Sedman and Mahmood, 1994). From the results obtained
in these studies, the distribution of tracer recoveries from
adults were determined. In addition, an analysis of
variance (ANOVA) and Tukey's multiple comparison
Exposure Factors Handbook
August 1997
Page
4-13
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
methodologies were employed to identify differences in
the recoveries of the various tracers (Sedman and
Mahmood, 1994).
From the adult studies, the ANOVA of the natural
logarithm of the recoveries of tracers from 0.3 or 1.5 g of
ingested soil showed a significant difference (« =0.05)
among the estimates of recovery of the tracers regardless
of whether the recoveries were combined or analyzed
separately (Sedman and Mahmood, 1994). Sedman and
Mahmood (1994) also reported that barium, manganese,
and zirconium yielded significantly different estimates of
soil ingestion than the other tracers (aluminum, silicon,
yttrium, titanium, and vanadium). Table 4-13 presents the
Tukey's multiple comparison of mean log tracer recovery
in adults ingesting known quantities of soil.
The average ages of children in the two recent
studies were 2.4 years in Calabrese, et al. (1989) and 4.7
years in Davis et al. (1990). The mean of the adjusted
levels of soil ingestion for a two year old child was 220
mg/kg for the Calabrese et al. (1989) study and 170 mg/kg
for the Davis et al. (1990) study (Sedman and Mahmood,
1994). From the adjusted soil ingestion estimates, based
on a normal distribution of means, the mean estimate for
a 2-year old child was 195 mg/day and the overall mean
of soil ingestion and the standard error of the mean was 53
mg/day (Sedman and Mahmood, 1994). Based on
uncertainties associated with the method employed,
Sedman and Mahmood (1994) recommended a
conservative estimate of soil ingestion in young children
of 250 mg/day. Based on the 250 mg/day ingestion rate
in a 2-year old child, an average daily soil ingestion over
a lifetime was estimated to be 70 mg/day. The lifetime
estimates were derived using the equation presented above
that describes changes in soil ingestion with age (Sedman
and Mahmood, 1994).
AIHC Exposure Factors Sourcebook (1994) - The
Exposure Factors Sourcebook (AIHC, 1994) uses data
from the Calabrese et al. (1990) study to derive soil
ingestion rates using zirconium as the tracer. More recent
papers indicate that zirconium is not a good tracer.
Therefore, the values recommended in the AIHC
Sourcebook are not appropriate. Furthermore, because
individuals were only studied for a short period of time,
deriving a distribution of usual intake is not possible and
is inappropriate.
Calabrese and Stanek (1995) - Resolving
Intertracer Inconsistencies in Soil Ingestion Estimation -
Calabrese and Stanek (1995) explored sources and
magnitude of positive and negative errors in soil ingestion
estimates for children on a subject-week and trace element
basis. Calabrese and Stanek (1995) identified possible
sources of positive errors to be the following:
• Ingestion of high levels of tracers before the
study starts and low ingestion during study
period may result in over estimation of soil
ingestion; and
• Ingestion of element tracers from a non-food or
non-soil source during the study period.
Table 4-13. Tukey's Multiple Comparison of Mean Log Tracer Recovery in Adults Ingesting Known Quantities of Soil
Tracer
•
Aluminum
Silicon
Titanium
Vanadium
Yttrium
Aluminum
Silicon
Titanium
Reported Mean Age Adjusted Mean
(mg/day) (mg/day)
Calabrese et al., 1989 Study
153
154
218
459
85
Davis et al., 1990 Study
39
81
246
160
161
228
480
89
53
111
333
" Age adjusted mean estimates of soil ingestion in young children. Mean estimates of soil ingestion for each tracer in each study were
adjusted using the following equation:
Y = x e*""'1 12 * ^, where Y = adjusted mean soil ingestion (mg/day), x = a constant, and yr = age in years.
Source: Sedman and Mahmood, 1994.
Page
4-14
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Possible sources of negative bias identified by Calabrese
and Stanek (1995) are the following:
• Ingestion of tracers in food, but the tracers are
not captured in the fecal sample either due to
slow lag time or not having a fecal sample
available on the final study day; and
• Sample measurement errors which result in
diminished detection of fecal tracers, but not in
soil tracer levels.
The authors developed an approach which attempted to
reduce the magnitude of error in the individual trace
element ingestion estimates. Results from a previous
study conducted by Calabrese et al. (1989) were used to
quantify these errors based on the following criteria: (1)
a lag period of 28 hours was assumed for the passage of
tracers ingested in food to the feces (this value was
applied to all subject-day estimates); (2) daily soil
ingestion rate was estimated for each tracer for each 24-hr
day a fecal sample was obtained; (3) the median tracer-
based soil ingestion rate for each subject-day was
determined. Also, upper and lower bound estimates were
determined based on criteria formed using an assumption
of the magnitude of the relative standard deviation (RSD)
presented in another study conducted by Stanek and
Calabrese (1995a). Daily soil ingestion rates for tracers
that fell beyond the upper and lower ranges were excluded
from subsequent calculations, and the median soil
ingestion rates of the remaining tracer elements were
considered the best estimate for that particular day. The
magnitude of positive or negative error for a specific
tracer per day was derived by determining the difference
between the value for the tracer and the median value; (4)
negative errors due to missing fecal samples at the end of
the study period were also determined (Calabrese and
Stanek, 1995).
Table 4-14 presents the estimated magnitude of
positive and negative error for six tracer elements in the
children's study (i.e., conducted by Calabrese et al., 1989).
The original mean soil ingestion rates ranged from a low
of 21 mg/day based on zirconium to a high of 459 mg/day
based on titanium (Table 4-14). The adjusted mean soil
ingestion rate after correcting for negative and p&sitive
errors ranged from 97 mg/day based on yttrium to 208
mg/day based on titanium (Table 4-14). Calabrese and
Stanek (1995) concluded that correcting for errors at the
individual level for each tracer element provides more
reliable estimates of soil ingestion.
This report is valuable in providing additional
understanding of the nature of potential errors in trace
element specific estimates of soil ingestion. However, the
operational definition used for estimating the error in a
trace element estimate was the observed difference of that
tracer from a median tracer value. Specific identificatio"h
of sources of error, or direct evidence that individual
tracers were indeed in error was not developed.
Corrections to individual tracer means were then made
according to how different values for that tracer were
from the median values. This approach is based on the
hypothesis that the median tracer value is the most
Table 4-14. Positive/Negative Error (bias) in Soil Ingestion Estimates in the Calabrese et al. (1989) Mass-balance Study:
Effect on Mean Soil Ingestion Estimate (mg/day)a
Negative Error
Aluminum
Silicon
Titanium
Vanadium
Yttrium
Zirconium
Lack of Fecal
Sample on Final
Study Day
14
15
82
66
8
6
Other Causes'"
11
6
187
55
26
91
Total Negative
Error
25
21
269
121
34
97
Total Positive
Error
43
41
282
432
22
. 5
Net Error
+18
+20
+13
+311
-12
-92
Original
Mean
153
154
218
459
85
21
Adjusted
Mean
136
133
208
148
97
113
a How to read table: for example, aluminum as a soil tracer displayed both negative and positive error. The cumulative total negative error is
estimated to bias the mean estimate by 25 mg/day downward. However, aluminum has positive error biasing the original mean upward by
43 mg/day. The net bias in the original mean was 18 mg/day positive bias. Thus, the original 156 mg/day mean for aluminum should be
corrected downward to 136 mg/day.
b Values indicate impact on mean of 128-subject-weeks in milligrams of soil ingested per day.
Source: Calabrese and Stanek. 1995.
Exposure Factors Handbook
August 1997
Page
4-15
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
accurate estimate of soil ingestion, and the validity of this
assumption depends on the specific set of tracers used in
the study and need not be correct. The approach used for
the estimation of daily tracer intake is the same as in
Stanek and Calabrese (1995a), and some limitations of
that approach are mentioned in the review of that study.
Sheppard (1995) - Parameter Values to Model the
Soil Ingestion Pathway - Sheppard (1995) summarized
the available literature on soil ingestion to estimate the
amount of soil ingestion in humans for the purposes of
risk assessment. Sheppard (1995) categorized the
available soil ingestion studies into two general
approaches: (1) those that measured the soil intake rate
with the use of tracers in the soil, and (2) those that
estimated soil ingestion based on activity (e.g.,
hand-to-mouth) and exposure duration. Sheppard (1995)
provided estimates of soil intake based on previously
published tracer studies. The data from these studies were
assumed to be lognormally distributed due to the broad
range, the concept that soil ingestion is never zero, and the
possibility of very high values. In order to account for
skewness in the data, geometric means rather than
arithmetic means, were calculated by age, excluding pica
and geophagy values. The geometric mean for soil
ingestion rate for children under six was estimated to be
100 mg/day. For children over six and adults, the
geometric mean intake rate was estimated to be 20
mg/day. Sheppard (1995) also provided soil ingestion
estimates for indoor and outdoor activities based on data
from Hawley (1985) and assumptions regarding duration
of exposure (Table 4-15).
Sheppard's (1995) estimates, based on activity and
exposure duration, are quite similar to the mean values
from intake rate estimates described in previous sections.
The advantages of this study are that the model can be
used to calculate the ingestion rate from non-food sources
with variability in exposure ingestion rates and exposure
durations. The limitation of this study is that it does not
introduce new data; previous data are re-evaluated. In
addition, because the model is based on previous data, the
same advantages and limitations of those studies apply.
4.4. SOIL INTAKE AMONG ADULTS
Hawley 1985 - Assessment of Health Risk from
Exposure to Contaminated Soil - Information on soil
ingestion among adults is very limited. Hawley (1985)
estimated soil ingestion among adults based on
assumptions regarding activity patterns and corresponding
ingestion amounts. Hawley (1985) assumed that adults
ingest outdoor soil at a rate of 480 mg/day while engaged
in yardwork or other physical activity. These outdoor
exposures were assumed to occur 2 days/week during 5
months of the year (i.e., May through October). The
ingestion estimate was based on the assumption that a 50
/^m/thick layer of soil is ingested from the inside surfaces
of the thumb and fingers of one hand. Ingestion of indoor
housedust was assumed to occur from typical living space
activities such as eating and smoking, and work in attics
or other uncleaned areas of the house. Hawley (1985)
assumed that adults ingest an average of 0.56 mg
housedust/day during typical living space activities and
110 mg housedust/day while working in attics. Attic work
Table 4-15. Soil Ingestion Rates for Assessment Purposes
Receptor Age
Pica Child
2.5 yrs
6yrs
Adult
Setting
Outdoor
Indoor
Outdoor
Indoor
Gardening
Indoor
Soil Load on
Hands
(mg/cm2)
—
0.5
0.4
0.5
0.04
1.0
0.04
a Hawley (1985) assumed the child spent all the time at home,
Source: Sheppard, 1995
Soil Exposure Ingestion
Rate
(mg/hr)
1,000
20
3
10
0.15
20
0.03
Suggested Exposure
Durations
(hr/yr)
200
1,000
Remaining3
700
5,000
300
5,000
Average Daily Soil
Ingestion
(mg/day)
500
50
60
20
2
20
0.4
so that the indoor time was 8,760 hours/year minus the outdoor time.
Page
4-16
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
was assumed to occur 12 days/year. Hawley (1985) also
assumed that soil comprises 80 percent of household dust.
Based on these assumptions about soil intake and the
frequency of indoor and outdoor activities, Hawley (1985)
estimated the annual average soil intake rate for adults to
be 60.5 mg/day (Table 4-16).
of 500 mg of soil per day) during the three days.
Duplicate meal samples (food and beverage) were
collected from the six adults. The sample included all
foods ingested from breakfast Monday, through the
evening meal Wednesday during each of the 3 weeks. In
addition, all medications and vitamins ingested by the
Table 4-16. Estimates of Soil Ingestion for Adults
Scenarios
Adult
Work in attic (year-round)
Living Space (year-round)
Outdoor Work (summer)
TOTAL SOIL INTAKE
Exposure
Media (mg/day)
Dust 110
Dust 0.56
Soil 480
Days/Year
Activity
12
365
43
Annual Average Soil
Fraction Soil Intake
Content (mg/day)
0.8 3
0.8 0.5
1 57
60.5
Source: Hawley, 1985.
The soil intake value estimated by Hawley (1985)
is consistent with adult soil intake rates suggested by other
researchers. Calabrese et al. (1987) suggested that soil
intake among adults ranges from 1 to 100 mg/day.
According to Calabrese et al. (1987), these values "are
conjectural and based on fractional estimates" of earlier
Center for Disease Control (CDC) estimates. In an
evaluation of the scientific literature concerning soil
ingestion rates for children and adults (Krablin, 1989),,
Arco Coal Company suggested that 10 mg/day may be an
appropriate value for adult soil ingestion. This value is
based on "extrapolation from urine arsenic
epidemiological studies and information on mouthing
behavior and time activity patterns" (Krablin, 1989).
Calabrese et al. (1990) - Preliminary Adult Soil
Ingestion Estimates: Results of a Pilot Study- Calabrese
et al. (1990) studied six adults to evaluate the extent to
which they ingest soil. This adult study was originally part
of the children soil ingestion study conducted by
Calabrese and was used to validate part of the analytical
methodology used in the children study. The participants
were six healthy adults, three males and three females, 25-
41 years old. Each volunteer ingested one empty gelatin
capsule at breakfast and one at dinner Monday, Tuesday,
and Wednesday during the first week of the study. During
the second week, they ingested 50 mg of sterilized soil
within a gelatin capsule at breakfast and at dinner (a total
of 100 mg of sterilized soil per day) for 3 days. For the
third week, the participants ingested 250 mg of sterilized
soil in a gelatin capsule at breakfast and at dinner (a total
adults were collected. Total excretory output were
collected from Monday noon through Friday midnight
over 3 consecutive weeks. Table 4-17 provides the mean
and median values of soil ingestion for each element by
week. Data obtained from the first week, when empty
gelatin capsules were ingested, may be used to derive an
estimate of soil intake by adults. The mean intake rates
for the eight tracers are: Al, 110 mg; Ba, -232 mg; Mn,
330 mg; Si, 30 mg; Ti, 71 mg; V, 1,288 mg; Y, 63 mg;
andZr, 134mg.
The advantage of this study is that it provides
quantitative estimates of soil ingestion for adults. The
study also corrected for tracer concentrations in foods and
medicines. However, a limitation of this study is that a
limited number of subjects were studied. In addition, the
subjects were only studied for one week before soil
capsules were ingested.
4.5. PREVALENCE OF PICA
The scientific literature define pica as "the repeated
eating of non-nutritive substances" (Feldman, 1986). For
the purposes of this handbook, pica is defined as an
deliberately high soil ingestion rate. Numerous articles
have been published that report on the incidence of pica
among various populations. However, most of these
papers describe pica for substances other than soil
including sand, clay, paint, plaster, hair, string, cloth,
glass, matches, paper, feces, and various other items.
These papers indicate that the pica occurs in
approximately half of all children between the ages of 1
Exposure Factors Handbook
August 1997
Page
4-17
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-17. Adult Daily Soil Ingestion Estimates by Week and Tracer Element After Subtracting Food and Capsule Ingestion,
Based on Median Amherst Soil Concentrations: Means and Medians Over Subjects (mg)a
Week
Means
1
2
3
Medians
1
2
3
Al
110
98
28
60
85
66
Ba
-232
12,265
201
-71
597
386
a Data were converted to milligrams
Negative values occur because of correction for food
Source: Calabreseetal.. 1990
Mn
330
1,306
790
388
1,368
831
and capsule ingestion.
Si
30
14
-23
31
15
-27
Ti
71
25
896
102
112
156
V
1,288
43
532
1,192
150
047
Y
63
21
67
44
35
60
Zr
134
58
-74
124
65
-144
and 3 years (Sayetta, 1986). The incidence of deliberate
ingestion behavior in children has been shown to differ for
different subpopulations. The incidence rate appears to
be higher for black children than for white children.
Approximately 30 percent of black children aged 1 to 6
years are reported to have deliberate ingestion behavior,
compared with 10 to 18 percent of white children in the
same age group (Danford, 1982). There does not appear
to be any sex differences in the incidence rates for males
or females (Kaplan and Sadock, 1985). Lourie et al.
(1963) states that the incidence of pica is higher among
children in lower socioeconomic groups (i.e., 50 to 60
percent) than in higher income families (i.e., about 30
percent). Deliberate soil ingestion behavior appears to be
more common in rural areas (Vermeer and Frate, 1979).
A higher rate of pica has also been reported for pregnant
women and individuals with poor nutritional status
(Danford, 1982). In general, deliberate ingestion behavior
is more frequent and more severe in mentally retarded
children than in children in the general population
(Behrman and Vaughan 1983, Danford 1982, Forfar and
Arneil 1984, Illingworth 1983, Sayetta 1986).
It should be noted that the pica statistics cited
above apply to the incidence of general pica and not soil
pica. Information on the incidence of soil pica is limited,
but it appears that soil pica is less common. A study by
Vermeer and Frate (1979) showed that the incidence of
geophagia (i.e., earth-eating) was about 16 percent among
children from a rural black community in Mississippi.
However, geophagia was described as a cultural practice
among the community surveyed and may not be
representative of the general population. Average daily
consumption of soil was estimated to be 50 g/day. Bruhn
and Pangborn (1971) reported the incidence of pica for
"dirt" to be 19 percent in children, 14 percent in pregnant
women, and 3 percent in nonpregnant women. However,
"dirt" was not clearly defined. The Bruhn and Pangborn
(1971) study was conducted among 91 non-black, low
income families of migrant agricultural workers in
California. Based on the data from the five key tracer
studies (Binder et al., 1986; Clausing et al., 1987; Van
Wijnen et al., 1990; Davis et al., 1990; and Calabrese et
al., 1989) only one child out of the more than 600 children
involved in all of these studies ingested an amount of soil
significantly greater than the range for other children.
Although these studies did not include data for all
populations and were representative of short-term
ingestions only, it can be assumed that the incidence rate
of deliberate soil ingestion behavior in the general
population is low. However, it is incumbent upon the user
to use the appropriate value for their specific study
population.
4.6. DELIBERATE SOIL INGESTION AMONG
CHILDREN
Information on the amount of soil ingested by
children with abnormal soil ingestion behavior is limited.
However, some evidence suggests that a rate on the order
of 10 g/day may not be unreasonable.
Calabrese et al. (1991) - Evidence of Soil Pica
Behavior and Quantification of Soil Ingestion - Calabrese
et al. (1991) estimated that upper range soil ingestion
values may range from approximately 5-7 grams/day.
This estimate was based on observations of one pica child
among the 64 children who participated in the study. In
the study, a 3.5-year old female exhibited extremely high
soil ingestion behavior during one of the two weeks of
observation. Intake ranged from 74 mg/day to 2.2 g/day
Page
4-18
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
during the first week of observation and 10.1 to 13.6
g/day during the second week of observation
(Table 4-18). These results are based on mass-balance
analyses for seven (i.e., aluminum, barium, manganese,
silicon, titanium, vanadium, and yttrium) of the eight
tracer elements used. Intake rates based on zirconium was
significantly lower but Calabrese et al. (1991) indicated
that this may have "resulted from a limitation in the
analytical protocol."
Table 4-18. Daily Soil Ingestion Estimation in a Soil-Pica Child by
Tracer and by Week (mg/day)
Tracer
Al
Ba
Mn
Si
Ti
V
Y
Zr
Source:
Weekl
Estimated Soil Ingestion
74
458
2,221
142
1,543
1,269
147
86
Calabrese et al.. 1991
Week 2
Estimated Soil Ingestion
13,600
12,088
12,341
10,955
11,870
10,071
13,325
2695
Calabrese and Stanek (1992) - Distinguishing
Outdoor Soil Ingestion from Indoor Dust Ingestion in a
Soil Pica Child - Calabrese and Stanek (1992)
quantitatively distinguished the amount of outdoor soil
ingestion from indoor dust ingestion in a soil pica child.
This study was based on a previous mass-balance study
(conducted in 1991) in which a 3-1/2 year old child
ingested 10-13 grams of soil per day over the second
week of a 2-week soil ingestion study. Also, the previous
study utilized a soil tracer methodology with eight
different tracers (Al, Ba, Mn, Si, Ti, V, Y, Zr). The
reader is referred to Calabrese et al. (1989) for a detailed
description and results of the soil ingestion study.
Calabrese and Stanek (1992) distinguished indoor dust
from outdoor soil in ingested soil based on a methodology
which compared differential element ratios.
Table 4-19 presents tracer ratios of soil, dust, and
residual fecal samples in the soil pica child. Calabrese
and Stanek (1992) reported that there was a maximum
total of 28 pairs of tracer ratios based on eight tracers.
However, only 19 pairs of tracer ratios were available for
quantitative evaluation as shown in Table 4-19. Of these
19 pairs, 9 fecal tracer ratios fell within the boundaries for
soil and dust (Table 4-19). For these 9 tracer soils, an
interpolation was performed to estimate the relative
contribution of soil and dust to the residual fecal tracer
ratio. The other 10 fecal tracer ratios that fell outside the
soil and dust boundaries were concluded to be 100
percent of the fecal tracer ratios from soil origin
(Calabrese and Stanek, 1992). Also, the 9 residual fecal
samples within the boundaries revealed that a high
percentage (71-99 percent) of the residual fecal tracers
were estimated to be of soil origin. Therefore, Calabrese
and Stanek (1992) concluded that the predominant
proportion of the fecal tracers was from outdoor soil and
not from indoor dust origin.
Table 4-19. Ratios of Soil, Dust, and Residual Fecal Samples in the Soil Pica Child
Tracer Ratio Pairs
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
Source
Mn/Ti
Ba/Ti
Si/Ti
V/Ti
Ai/Ti
Y/Ti
Mn/Y
Ba/Y
Si/Y
V/Y
Al/Y
Mn/Al
Ba/Al
Si/Al
V/A1
Si/V
Mn/Si
Ba/Si
Mn/Ba
: Calabrese and Stanek,
Soil
208.368
187.448
148.117
14.603
18.410
8.577
24.293
21.854
17.268
1.702
2.146
11.318
10.182
8.045
0.793
10.143
1.407
1.266
1.112
1992.
Fecal
215.241
206.191
136.662
10.261
21.087
9.621
22.373
21.432
14.205
1.067
2.192
10.207
9.778
6.481
0.487
13.318
1.575
1.509
1.044
Estimated % of Residual Fecal Tracers of Soil
Dust Origin as Predicted by Specific Tracer Ratios
260.126
115.837 '
7.490
17.887
13.326
5.669
45.882
20.432
1.321
3.155 •
2.351
19.520
8.692
0.562
1.342
0.419
34.732
15.466
2.246
87
100
92
100
100
100
100
71
81
100
88
100
73
81 '
100
100
99
83
100
Exposure Factors Handbook
August 1997
Page
4-19
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
In conducting a risk assessment for TCDD, U.S.
EPA (1984) used 5 g/day to represent the soil intake rate
for pica children. The Centers for Disease Control (CDC)
also investigated the potential for exposure to TCDD
through the soil ingestion route. CDC used a value of 10
g/day to represent the amount of soil that a child with
deliberate soil ingestion behavior might ingest
(Kimbrough et al., 1984). These values are consistent
with those observed by Calabrese et al. (1991).
4.7. RECOMMENDATIONS
The key studies described in this section were used
to recommend values for soil intake among children. The
key and relevant studies used different survey designs and
study populations. These studies are summarized in Table
4-20. For example, some of the studies considered food
and nonfood sources of trace elements, while others did
not. In other studies, soil ingestion estimates were
adjusted to account for the contribution of house dust to
this estimate. Despite these differences, the mean and
upper-percentile estimates reported for these studies are
relatively consistent. The confidence rating for soil intake
recommendations is presented in Table 4-21.
It is important, however, to understand the various
uncertainties associated with these values. First,
individuals were not studied for sufficient periods of time
to get a good estimate of the usual intake. Therefore, the
values presented in this section may not be representative
of long term exposures. Second, the experimental error in
measuring soil ingestion values for individual children is
also a source of uncertainty. For example, incomplete
sample collection of both input (i.e., food and nonfood
sources) and output (i.e., urine and feces) is a limitation
for some of the studies conducted. In addition, an
individual's soil ingestion value may be artificially high or
low depending on the extent to which a mismatch between
input and output occurs due to individual variation in the
gastrointestinal transit time. Third, the degree to which
the tracer elements used in these studies are absorbed in
the human body is uncertain. Accuracy of the soil
ingestion estimates depends on how good this assumption
is. Fourth, there is uncertainty with regard to the
homogeneity of soil samples and the accuracy of parent's
knowledge about their child's playing areas. Fifth, all the
soil ingestion studies presented in this section with the
exception of Calabrese et al. (1989) were conducted
during the summer when soil contact is more likely.
Although the recommendations presented below are
derived from studies which were mostly conducted in the
summer, exposure during the winter months when the
ground is frozen or snow covered should not be
considered as zero. Exposure during these months,
although lower than in the summer months, would not be
zero because some portion of the house dust comes from
outdoor soil.
Soil Ingestion Among Children - Estimates of the
amount of soil ingested by children are summarized in
Table 4-22. The mean values ranged from 39 mg/day to
271 mg/day with an average of 146 mg/day for soil
ingestion and 191 mg/day for soil and dust ingestion.
Results obtained using titanium as a tracer in the Binder
et al. (1986) and Clausing et al. (1987) studies were not
considered in the derivation of this recommendation
because these studies did not take into consideration other
sources of the element in the diet which for titanium
seems to be significant. Therefore, these values may
overestimate the soil intake. One can note that this group
of mean values is consistent with the 200 mg/day value
that EPA programs have used as a conservative mean
estimate. Taking into consideration that the highest
values were seen with titanium, which may exhibit greater
variability than the other tracers, and the fact that the
Calabrese et al. (1989) study included a pica child, 100
mg/day is the best estimate of the mean for children under
6 years of age. However, since the children were studied
for short periods of time and the prevalence of pica
behavior is not known, excluding the pica child from the
calculations may underestimate soil intake rates. It is
plausible that many children may exhibit some pica
behavior if studied for longer periods of time. Over the
period of study, upper percentile values ranged from 106
mg/day to 1,432 mg/day with an average of 383 mg/day
for soil ingestion and 587 mg/day for soil and dust
ingestion. Rounding to one significant figure, the
recommended upper percentile soil ingestion rate for
children is 400 mg/day. However, since the period of
study was short, these values are not estimates of usual
intake. The recommended values for soil ingestion among
children and adults are summarized in Table 4-23.
Data on soil ingestion rates for children who
deliberately ingest soil are also limited. An ingestion rate
of 10 g/day is a reasonable value for use in acute exposure
assessments, based on the available information. It should
be noted, however, that this value is based on only one
pica child observed in the Calabrese et al. (1989) study.
Soil Ingestion Among Adults - Only three studies
have attempted to estimate adult soil ingestion. Hawley
(1985) suggested a value of 480 mg/day for adults
Page
4-20
Exposure Factors Handbook
August 1997
-------
Volume I -General Factors
Chapter 4 - Soil Ingestion and Pica
engaged in outdoor activities and a range of 0.56 to 110
mg/day of house dust during indoor activities. These
estimates were derived from assumptions about soil/dust
levels on hands and mouthing behavior; no supporting
measurements were made. Making further assumptions
about frequencies of indoor and outdoor activities,
Hawley (1985) derived an annual average of 60.5 mg/day.
Given the lack of supporting measurements, these
estimates must be considered conjectural. Krablin (1989)
used arsenic levels in urine (n=26) combined with
information on mouthing behavior and activity patterns to
suggest an estimate for adult soil ingestion of 10 mg/day.
The study protocols are not well described and has not
been formally published. Finally, Calabrese et al. (1990)
conducted a tracer study on 6 adults and found a range of
30 to 100 mg/day. This study is probably the most
reliable of the three, but still has two significant
uncertainties: (1)
representativeness of the general population is unknown
due to the small study size (n=6); and (2)
representativeness of long-term behavior is unknown
since the study was conducted over only 2 weeks. In the
past, many EPA risk assessments have assumed an adult
soil ingestion rate of 50 mg/day for industrial settings and
100 mg/day for residential and agricultural scenarios.
These values are within the range of estimates from the
studies discussed above. Thus, 50 mg/day still represents
a reasonable central estimate of adult soil ingestion and is
the recommended value in this handbook. This
recommendation is clearly highly uncertain; however, and
as indicated in Table 4-21, is given a low confidence
rating. Considering the uncertainties in the central
estimate, a recommendation for an upper percentile value
would be inappropriate. Table 4-23 summarizes soil
ingestion recommendations for adults.
Exposure Factors Handbook
August 1997
Page
4-21
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
J
&
1
1
T!
if.
Table 4-20.
C
1
's
w
S
1
1"
a
"
£•!
zl
i
•t
i
t
•o "a -a *2 .S •
!•§, 6 2 § o -51 1 ^ _: _r § u « |
ItJl!
§191 6Mllw)lliJl uslSl ea § iS £ o a E <2 ,1 a. y 1
.£
1 _ i, i « I
Ja eg « -^ •£? C ON ^
S«3 '•S V »>.>>>. >* >*•* ^
vlt ^t fJl f^ ^-r^.(Sri
o
x:
i 1 flti ! i il till!
R S 2l.ll 2 SS^S-SiS^'g
„ = u
1 -11 -3 "11
e" oSc 3 £ S
O •— .fa "" w e* *O
^ •§ J-f ™ '| -g « H
"1 « ^'1 "1 « -S S w '§
j ||| |J || | || fj
>» 2 ^ « >*-a eS'^ ^ "°^1S^'"2
"§ ce'> "Bni S"13 w ra^^Sw
11 ill If Ii ! ill II I
i iii
OT oo t*" ss M i— < m
8 i 2r ". 1 1 1 «' |
"(3 — ^" r^r^" *^
Z-- ™ - UCJa S
i - 8 E ^ lit s
riS"S 'i •? See »3
rjSu O Q &oco> O
Used data from Calabrese et al.
(1990) study to derive soil ingestion
rates using zirconium as a tracer;
recent studies indicate that zirconium
is not a good tracer
Based on Calabrese et al., 1989 data.
g
o 2
o _:
'C *p
| 3 *
1 fl
S w U
§
J I
2 t
-
0 4J
.2 *rt
^- «
ll 1
S
U
i
CO
s 1
ON 1)
K —
< 3
Exposure Factors Handbook
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
1
c
'i
to
0.
1
J
j
V.
3
1
1
i
f.
S
*o
H
CO
1
aj
e
•^
O c
11
1 1
c
•c
c
CO
1
CO
si • gll
\ I fill, L 111 ill
I 1 P!is j* ill ill
i| si I*
eoE fflSZSam-5.am2 fiSfig a! 3 .S s
§ gg 1
"1 I •- 2 2 § 1
0 i a - - 3S.
•o -o-a £ g ^ M c1--
i Is 13 2*H IS
8. s H 1 1 1 § 1 S ll
I 1 1 ll 1331 I 11
= . s. §
•S "2 -S-l ^ aS
S 5 TJ 2 ° 3 iM
l | |JS! H 11 I
M-i Si'G ^^
SB" "gt-rt-- ^ 2fe<^ ^^.S ~
II Hfi i Hi iii' I
• !i lill 11 !« fill 1
S"oB Se'o"0 S^ (Son "S ^ '§ «« °
IlltilJi if Ilii
H en
Q FT]
S g i i 1
^. - O\ -a" « M
§ 1 r. 1 i <
|gSasri s ^ j
g^l'§crjl5'g ^ w O
s « I i i i f 1 I
B* 8 1 1 1 I 1 i
H
No data on soil intake collected;
estimates based on assumptions
regarding data from previous studie
Data used to validate the analytical
methodology used in the children's
study (Calabrese, 1989).
1 IE
1 II
„
| |
3 a> « Tfr
02^2
> ^ S ?<
i
•O
OJ
§
I I i
01
_C
Child was observed as part of the
Calabrese et al., 1989 study.
Distinguished between outdoor soil
ingestion and indoor dust ingestion
soil oica child.
"5 'S
K H
£ J a S § a
2 f " 2 1 1
Isf Is!
2-11 *U
U9 M
f^ oS
•o *a
1 1
.1 .a
ex a.
S
Si
a
3
(3
n **-
— S
JD W
M 'o
? Is
S §2
S 2 r
E- o: 13
g
g -JJ
13 S
s S
to to
1 1
3 3
Exposure Factors Handbook
August 1997
Page
4-23
-------
Volume I - General Factors
Chapter 4 -Soil In^estion and Pica
Table 4-21. Confidence in Soil Intake Recommendation
Rationale
Rating
Study Elements
• Level of peer review
• Accessibility
• Reproducibility
• Focus on factor of interest
• Data pertinent to U.S.
• Primary data
• Currency
• Adequacy of data collection period
• Validity of approach
• Study size
• Representativeness of the
population
• Characterization of variability
• Lack of bias in study design (high
rating is desirable)
• Measurement error
Other Elements
• Number of studies
• Agreement between researchers
Overall Rating
All key studies are from peer review literature.
Papers are widely available from peer review journals.
Methodology used was presented, but results are difficult to
reproduce.
The focus of the studies was on estimating soil intake rate by
children; studies did not focus on intake rate by adults.
Two of the key studies focused on Dutch children; other studies
used children from specific areas of the U.S.
All the studies were based on primary data.
Studies were conducted after 1980.
Children were not studied long enough to fully characterize day to
day variability.
The basic approach is the only practical way to study soil intake,
but refinements are needed in tracer selection and matching input
with outputs. The more recent studies corrected the data for
sources of the tracers in food. There are, however, some concerns
about absorption of the tracers into the body and lag time between
input and output.
The sample sizes used in the key studies were adequate for
children. However, only few adults have been studied.
The study population may not be representative of the U.S. in terms
of race, socio-economics, and geographical location; Studies
focused on specific areas; two of the studies used Dutch children.
Day-to-day variability was not very well characterized.
The selection of the population studied may introduce some bias in
the results (i.e., children near a smelter site, volunteers in nursery
school, Dutch children).
Errors may result due to problems with absorption of the tracers in
the body and mismatching inputs and outputs.
There are 7 key studies.
Despite the variability, there is general agreement among
researchers on central estimates of daily intake for children.
Studies were well designed; results were fairly consistent; sample
size was adequate for children and very small for adults; accuracy
of methodology is uncertain; variability cannot be characterized due
to limitations in data collection period. Insufficient data to
recommend upper percentile estimates for both children and adults.
High
High
Medium
High (for children)
Low (for adults)
Medium
High
High
Medium
Medium
Medium (for children)
Low (for adults)
Low
Low
Medium
Medium
High
Medium
Medium (for children
long-term central
estimate)
Low (for adults)
Low (for upper
percentile)
Page
4-24
Exposure Factors Handbook
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-22. Summary of Estimates of Soil Ingestion by Children
Mean (mg/day)
Al Si AIRa Ti Y
181 184
230 129
39 . 82 245.5
64.5b 160b 268.4b
153 154 218 85
154b 483b 170b 65b
122 139 - 271 165
133C
69-120d
Average = 146 mg/day soil
191 mg/day soil and dust
combined
a AIR = Acid Insoluble Residue
b Soil and dust combined
c BTM
d LTM; corrected value
Al
584
223
478b
254
217C
Si Ti Y
578
276 1,432 106
653b l,059b 159b
224 279 144
Binder etal. 1986
Clausing et al. 1987
Davis etal. 1990
Calabrese et al. 1989
Stanek and Calabrese,
Stanek and Calabrese.
1995a
1995b
Van Wljnen et al. 1990
383 mg/day soil
587 mg/day soil and dust combined
Table 4-23. Summary of Recommended Values for Soil Ingestion
Population Mean
Children , IQQ mg/daya
Adults 50 mg/day
Pica child lOe/dav^
Upper Percentile
400 mg/dayb
r% '
, 200 mg/day may be used as a conservative estimate of the mean (see text).
Study period was short; therefore, these values are not estimates of usual intake.
To be used in acute exposure assessments. Based on only one pica child (Calabrese et al 1989)
4.8. REFERENCES FOR CHAPTER 4
American Industrial Health Council (AIHC). (1994)
Exposure factors sourcebook. AIHC, Washington,
DC.
Binder, S.; Sokal, D.; Maughan, D. (1986) Estimating
soil ingestion: the use of tracer elements in
estimating the amount of soil ingested by young
children. Arch. Environ. Health. 41(6):341-345.
'Behrman, L.E.; Vaughan, V.C., III. (1983) Textbook
of Pediatrics. Philadelphia, PA: W.B. Saunders
Company.
Bruhn, C.M.; Pangborn, R.M. (1971) Reported
incidence of pica among migrant families. J. of the
Am. Diet. Assoc. 58:417-420.
Calabrese, E.J.; Kostecki, P.T.; Gilbert, C.E. (1987)
How much soil do children eat? An emerging
consideration for environmental health risk
assessment. In press (Comments in Toxicology).
Calabrese, E.J.; Pastides, H.; Barnes, R.; Edwards, C.;
Kostecki, P.T.; et al. (1989) How much soil do
young children ingest: an epidemiologic study. In:
Petroleum Contaminated Soils, Lewis Publishers,
Chelsea, MI. pp. 363-397.
Calabrese, E.J.; Stanek, E.J.; Gilbert, C.E.; Barnes,
R.M. (1990) Preliminary adult soil ingestion
estimates; results of a pilot study. Regul. Toxicol.
Pharmacol. 12:88-95.
Calabrese, E.J.; Stanek, E.J.; Gilbert, C.E. (1991)
Evidence of soil-pica behavior and quantification
Exposure Factors Handbook
August 1997
Page
4-25
-------
Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
of soil ingested. Hum. Exp. Toxicol. 10:245-
249.
Calabrese, E.J.; Stanek, EJ. (1992) Distinguishing
outdoor soil ingestion from indoor dust ingestion in
a soil pica child. Regul. Toxicol. Pharmacol.
15:83-85.
Calabrese, E.J.; Stanek, EJ. (1995) Resolving
intertracer inconsistencies in soil ingestion
estimation. Environ. Health Perspect. 103(5):454-
456.
Clausing, P.; Brunekreef, B.; Van Wijnen, J.H. (1987)
A method for estimating soil ingestion by children.
Int. Arch. Occup. Environ. Health (W. Germany)
59(l):73-82.
Danford, D.C. (1982) Pica and nutrition. Annual
Review of Nutrition. 2:303-322.
Davis, S.; Waller, P.; Buschbon, R.; Ballou, J.; White,
P. (1990) Quantitative estimates of soil ingestion
in normal children between the ages of 2 and 7
years: population based estimates using aluminum,
silicon, and titanium as soil tracer elements. Arch.
Environ. Hlth. 45:112-122.
Day, J.P.; Hart, M.; Robinson, M.S. (1975) Lead in
urban street dust. Nature 253:343-345.
Duggan, M.J.; Williams, S. (1977) Lead in dust in city
streets. Sci. Total Environ. 7:91-97.
Feldman, M.D. (1986) Pica: current perspectives.
Psychosomatics (USA) 27(7):519-523.
Forfar, J.O.; Arneil, G.C., eds. (1984) Textbook of
Paediatrics. 3rd ed. London: Churchill
Livingstone.
Hawley, J.K. (1985) Assessment of health risk from
exposure to contaminated soil. Risk Anal. 5:289-
302.
Illingworth, R.S. (1983) The normal child. New York:
Churchill Livingstone.
Kaplan, H.I.; Sadock, B.J. (1985) Comprehensive
textbook of psychiatry/TV. Baltimore, MD:
Williams and Wilkins.
Kimbrough, R.; Falk, H.; Stemr, P.; Fries, G. (1984)
Health implications of 2,3,7,8-tetrachlorodibenzo-
p-dioxin (TCDD) contamination of residential soil.
J. Toxicol. Environ. Health 14:47-93.
Krablin, R. (1989) [Letter to Jonathan Z. Cannon
concerning soil ingestion rates.] Denver, CO:
Arco Coal Co.; October 13, 1989.
Lepow, M.L.; Bruckman, L.; Robino, R.A.; Markowitz,
S.; Gillette, M.; et al. (1974) Role of airborne lead
in increased body burden of lead in Hartford
children. Environ. Health Perspect. 6:99-101.
Lepow, M.L.; Buckman, L.; Gillette, M.; Markowitz,
S.; Robino, R.; et al. (1975) Investigations into
sources of lead in the environment of urban
children. Environ. Res. 10:415-426.
Lourie, R.S.; Layman, E.M.; Millican, F.K. (1963)
Why children eat things that are not food. Children
10:143-146.
Reels, H.; Buchet, J.P.; Lauwerys, R.R. (1980)
Exposure to lead by the oral and pulminary route of
children living in the vicinity of a primary lead
smelter. Environ. Res. 22:81-94.
Sayetta, R.B. (1986) Pica: An overview. American
Family Physician 33(5):181-185.
Sedman, R.; Mahmood, R.S. (1994) Soil ingestion by
children and adults reconsidered using the results
of recent tracer studies. Air and Waste, 44:141-
144.
Sheppard, S.C. (1995) Parameter values to model the
soil ingestion pathway. Environmental Monitoring
and Assessment 34:27-44.
Stanek, E.J.; Calabrese, EJ. (1995a) Daily estimates
of soil ingestion in children. Environ. Health
Perspect. 103(3):276-285.
Stanek, EJ.; Calabrese, EJ. (1995b) Soil ingestion
estimates for use in site evaluations based on the
best tracer method. Human and Ecological Risk
Assessment. 1:133-156.
Thompson, K.M.; Burmaster, D.E. (1991) Parametric
distributions for soil ingestion by children. Risk
Analysis. 11:339-342.
U.S. EPA. (1984) Risk analysis of TCDD
contaminated soil. Washington, DC: U.S.
Environmental Protection Agency, Office of Health
and Environmental Assessment. EPA
600/8-84-031.
Van Wijnen, J.H.; Clausing, P.; Brunekreff, B. (1990)
Estimated soil ingestion by children. Environ. Res.
51:147-162.
Vermeer, D.E.; Frate, D.A. (1979) Geophagia in rural
Mississippi: environmental and cultural contexts
and nutritional implications. Am. J. Clin. Nutr.
32:2129-2135.
Page
4-26
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
5. INHALATION ROUTE
This chapter presents data and recommendations
for inhalation rates that can be used to assess exposure to
contaminants in air. The studies discussed in this chapter
have been classified as key or relevant. Key studies are
used as the basis for deriving recommendations and the
relevant studies are included to provide additional
background and perspective. The recommended
inhalation rates are summarized in Section 5.2.4 and cover
adults, children, and outdoor workers/athletes.
Inclusion of this chapter in the Exposure Factors
Handbook does not imply that assessors will always need
to select and use inhalation rates when evaluating
exposure to air contaminants. In fact, it is unnecessary to
calculate inhaled dose when using dose-response factors
from Integrated Risk Information System (IRIS) (U.S.
EPA, 1994). This is due to the fact that IRIS
methodology accounts for inhalation rates in the
development of "dose-response" relationships. When
using IRIS for inhalation risk assessments, "dose-
response" relationships require only an average air
concentration to evaluate health concerns:
• For non-carcinogens, IRIS uses Reference
Concentrations (RfC) which are expressed in
concentration units. Hazard is evaluated by
comparing the inspired air concentration to the
RfC.
• For carcinogens, IRIS uses unit risk values
which are expressed in inverse concentration
units. Risk is evaluated by multiplying the unit
risk by the inspired air concentration.
Detailed descriptions of the IRIS methodology for
derivation of inhalation reference concentrations can be
found in two methods manuals produced by the Agency
(U.S. EPA, 1992; 1994).
IRIS employs a default inhalation rate of 20
m3/day. This is greater than the recommendated value in
this chapter. When using IRIS, adjustments of dose-
response relationships using inhalation rates other than the
default, 20 m3/day, are not currently recommended.
There are instances where the inhalation rate data
presented in this chapter may be used for estimating
average daily dose. For example, the inhalatino average
daily dose is often estimated in cases where a compative
pathway analysis is desired or to determine a total dose by
adding across pathways in cases where RfCs and unit risk
factors are not available.
5.1. EXPOSURE EQUATION FOR INHALATION
For those cases where the average daily dose
(ADD) needs to be estimated, the general equation is:
ADD = [[C x IR x ED] / [BW x AT]]
where:
(Eqn. 5-1)
ADD = average daily dose (mg/kg-day);
C = contaminant concentration in inhaled air (/ug/m3);
IR = inhalation rate (m3/day);
ED = exposure duration (days);
BW = body weight (kg); and
AT = averaging time (days), for non-carcinogenic effects
AT = ED, for carcinogenic or chronic effects AT =
70 years or 25,550 days (lifetime).
The average daily dose is the dose rate averaged
over a pathway-specific period of exposure expressed as
a daily dose on a per-unit-body-weight basis. The ADD
is used for exposure to chemicals with non-carcinogenic
non-chronic effects. For compounds with carcinogenic or
chronic effects, the lifetime average daily dose (LADD)
is used. The LADD is the dose rate averaged over a
lifetime. The contaminant concentration refers to the
concentration of the contaminant in inhaled air. Exposure
duration refers to the total time an individual is exposed
to an air pollutant.
5.2. INHALATION RATE
5.2.1. Background
The Agency defines exposure as the chemical
concentration at the boundary of the body (U.S. EPA,
1992). In the case of inhalation, the situation is
complicated by the fact that oxygen exchange with carbon
dioxide takes place in the distal portion of the lung. The
anatomy and physiology of the respiratory system
diminishes the pollutant concentration in inspired air
(potential dose) such that the amount of a pollutant that
actually enters the body through the lung (internal dose)
is less than that measured at the boundary of the body
(Figure 5-1). When constructing risk assessments that
concern the inhalation route of exposure, one must be
aware if any adjustments have been employed in the
estimation of the pollutant concentration to account for
this reduction in potential dose.
Exposure Factors Handbook
August 1997
Page
5-1
-------
Volume I - General Factors
Chapter 5 - Inhalation
Biologically
Effective
Dose
Exposure
Chemical
Effect
Mouth / Nose
Intake
Lung
Uptake
Figure 5-1. Schematic of Dose and Exposure: Respiratory Route
Source: U.S. EPA, 1992.
The respiratory system is comprised of three
regions: nasopharyngeal, tracheobronchial, and
pulmonary. The nasopharyngeal region extends from the
nose to the larynx. The tracheobronchial region forms the
conducting airways between nasopharynx and alveoli
where gas exchange occurs. It consists of the trachea,
bronchi, and bronchioles. The pulmonary regions consists
of the acinus which is the site where gas exchange occurs;
it is comprised of respiratory bronchioles, alveolar ducts
and sacs, and alveoli. A detailed discussion of pulmonary
anatomy and physiology can be found in: Benjamin
(1988) and U.S. EPA (1989 and 1994).
Each region in the respiratory system can be
involved with removing pollutants from inspired air. The
nasopharyngeal region filters out large inhaled particles,
moderates the temperature, and increases the humidity of
the air. The surface of the tracheobronchial region is
covered with ciliated mucous secreting cells which forms
a mucociliary escalator that moves particles from deep
regions of the lung to the oral cavity where they may be
swallowed and then excreted. The branching pattern and
physical dimensions of the these airways determine the
pattern of deposition of airborne particles and absorption
of gases by the respiratory tract. They decrease in
diameter as they divide into a bifurcated branching
network dilutes gases by axial diffusion of gases along the
streamline of airways and radial diffusion of gases due to
an increase in cross sectional area of the lungs. The
velocity of the airstream in this decreasing branching
network creates a turbulent force such that airborne
particles can be deposited along the walls of these airways
by impaction, interception, sedimentation, or diffusion
depending on their size. The pulmonary region contains
macrophages which engulf particles and pathogens that
enter this portion of the lung.
Notwithstanding these removal mechanisms, both
gaseous and particulate pollutants can deposit in various
regions of the lung. Both the physiology of the lung and
the chemistry of the pollutant influences where the
pollutant tends to deposit.
Gaseous pollutants are evenly dispersed in the air
stream. They come into contact with a large portion of
the lung. Generally, their solubility and reactivity
determines where they deposit in the lung. Water soluble
and chemically reactive gases tend to deposit in the upper
respiratory tract. Lipid soluble or non-reactive gases
usually are not removed in the upper airways and tend to
deposit in the distal portions of the lung.. Gases can be
Page
5-2
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
absorbed into the blood stream or react with lung tissue.
Gases can be removed from the lung by reaction with
tissues or by expiration. The amount of gas retained in
the lung or other parts of the body is mainly due to their
solubility in blood.
Chemically, particles are quite heterogenous. They
range from aqueous soluble particles to solid insoluble
particles. Their size, chemical composition, and the
physical forces of breathing dictate where they tend to
deposit in the lung. Large particles, those with a diameter
of greater than 0.5 micrometers (um), not filtered out in
the nasopharynx, tend to deposit in the upper respiratory
tract at airway branching points due to impaction. The
momentum of these particles in the air stream is such that
they tend to collide with the airway wall at branching
points in the tracheobronchial region of the lung. Those
particles not removed from the airstream by impaction
will likely be deposited in small bronchi and bronchioles
by sedimentation, a process where by particles settle out
of the airstream due to the decrease in airstream velocity
and the gravitational force on the particles. Small
particles, less than 0.2 um, acquire a random motion due
to bombardment by air molecules. This movement can
cause particles to be deposited on the wall of an air way
throughout the lungs.
A special case exists for fibers. Fibers can deposit
along the wall of an airway by a process known as
interception. This occurs when a fiber makes contact with
an airway wall. The likelihood of interception increases
as airway diminish in diameter. Fiber shape influences
deposition too. Long, thin, straight fibers tend to deposit
in the deep region of the lung compared to thick or curved
fibers.
The health risk associated with human exposure to
airborne toxics is a function of concentration of air
pollutants, chemical species, duration of exposure, and
inhalation rate. The dose delivered to target organs
(including the lungs) , the biologically effective dose, is
dependent on the potentail dose, the applied dose and the
internal dose (Figure 5-1) A detailed discussion of this
concept can be found in Guidelines for Exposure
Assessment (U.S. EPA, 1992).
The estimation of applied dose for a given air
pollutant is dependent on inhalation rate, commonly
described as ventilation rate (VR) or breathing rate. VR
is usually measured as minute volume, the volume in liters
of air exhaled per minute(VE). VE is the product of the
number of respiratory cycles in a minute and the volume
of air respired during each respiratory cycle, the tidal
volume( VT).
When interested in calculating internal dose,
assessors must consider the alveolar ventilation rate. This
is the amount of air available for exchange with alveoli
per unit time. It is equivalent to the tidal volume( VT)
minus the anatomic dead space of the lungs (the space
containing air that does not come into contact with the
alveoli). Alveolar ventilation is approximately 70 percent
of total ventilation; tidal volume is approximately 500
milliliters (ml) and the amount of anatomic dead space in
the lungs is approximately 150 ml, approximately 30% of
the amount of air inhaled (Menzel and Amdur, 1986).
Breathing rates are affected by numerous individual
characteristics, including age, gender, weight, health
status, and levels of activity (running, walking, jogging,
etc.). VRs are either measured directly using a spirometer
and a collection system or indirectly from heart rate (HR)
measurements. In many of the studies described in the
following sections, HR measurements are usually
correlated with VR in simple and multiple regression
analysis.
The available studies on inhalation rates are
summarized in the following sections. Inhalation rates are
reported for adults and children (including infants)
performing various activities and outdoor workers/
athletes. The activity levels have been categorized as
resting, sedentary, light, moderate, and heavy. In most
studies, the sample population kept diaries to record their
physical activities, locations, and breathing rates.
Ventilation rates were either measured, self-estimated or
predicted from equations derived using VR-HR
calibration relationships.
5.2.2. Key Inhalation Rate Studies
Linn et al. (1992) - Documentation of Activity
Patterns in "High-Risk" Groups Exposed to Ozone in the
Los Angeles Area - Linn et al. (1992) conducted a study
that estimated the inhalation rates for "high-risk"
subpopulation groups exposed to ozone (O3) in their daily
activities in the Los Angeles area. The population
surveyed consisted of seven subject panels: Panel 1: 20
healthy outdoor workers (15 males, 5 females, ages 19-50
years); Panel 2: 17 healthy elementary school students (5
males, 12 females, ages 10-12 years); Panel 3: 19 healthy
high school students (7 males, 12 females, ages 13-17
years); Panel 4: 49 asthmatic adults (clinically mild,
moderate, and severe, 15 males, 34 females, ages 18-50
years); Panel 5:24 asthmatic adults from 2 neighborhoods
Exposure Factors Handbook
August 1997
Page
5-3
-------
Volume I - General Factors
Chapter 5 - Inhalation
of contrasting O3 air quality (10 males, 14 females, ages
19-46 years); Panel 6: 13 young asthmatics (7 males, 6
females, ages 11-16 years); Panel 7: construction workers
(7 males, ages 26-34 years).
Initially, a calibration test was conducted, followed
by a training session. Finally, a field study was conducted
which involved subjects' collecting their own heart rate
and diary data. During the calibration tests, VR and HR
were measured simultaneously at each exercise level.
From the calibration data an equation was developed
using linear regression analysis to predict VR from
measured HR (Linn et al., 1992).
In the field study, each subject (except construction
workers) recorded in diaries: their daily activities, change
in locations (indoors, outdoors, or in a vehicle), self-
estimated breathing rates during each activity/location,
and time spent at each activity/location. Healthy subjects
recorded their HR once every 60 seconds, Asthmatic
subjects recorded their diary information once every hour
using a Heart Watch. Construction workers dictated their
diary information to a technician accompanying them on
the job. Subjective breathing rates were defined as slow
(walking at their normal pace); medium (faster than
normal walking); and fast (running or similarly strenuous
exercise). Table 5-1 presents the calibration and field
protocols for self-monitoring of activities for each subject
panel.
Table 5-2 presents the mean VR, the 99th
percentile VR, and the mean VR at each subjective
activity level (slow, medium, fast). The mean VR and
99th percentile VR were derived from all HR recordings
(that appeared to be valid) without considering the diary
data. Each of the three activity levels was determined
from both the concurrent diary data and HR recordings by
direct calculation or regression (Linn et al., 1992). The
mean VR for healthy adults was 0.78 m3/hr while the
mean VR for asthmatic adults was 1.02 m3/hr (Table 5-2).
The preliminary data for construction workers indicated
that during a 10-hr work shift, their mean VR (1.50 nrVhr)
exceeded the VRs of all other subject panels (Table 5-2).
Linn et al. (1992) reported that the diary data showed that
most individuals except construction workers spent most
of their time (in a typical day) indoors at slow activity
level. During slow activity, asthmatic subjects had higher
VRs than healthy subjects, except construction workers
(Table 5-2). Also, Linn et al. (1992) reported that in
every panel, the predicted VR correlated significantly with
the subjective estimates of activity levels.
Table 5-1. Calibration and Field Protocols for Self-Monitoring of Activities Grouped by Subject Panels
Panel
Calibration Protocol
Field Protocol
Panel 1 - Healthy Outdoor Workers -15
female, 5 male, age 19-50
Panel 2 -Healthy Elementary School
Students -5 male, 12 female, age 10-12
Panel 3 - Healthy High School Students
-7 male, 12 female, age 13-17
Panel 4 - Adult Asthmatics, clinically
mild, moderate, and severe - 15 male,
34 female, age 18-50
Panel 5 - Adult Asthmatics from 2
neighborhoods of contrasting O3 air
quality - 10 male, 14 female, age 19-46
Panel 6 - Young Asthmatics - 7 male, 6
female, age 11-16
Pane! 7 - Construction Workers - 7
male, age 26-34
Laboratory treadmill exercise tests, indoor
hallway walking tests at different self-chosen
speeds, 2 outdoor tests consisted of 1-hour
cycles each of rest» walking, and jogging.
Outdoor exercises each consisted of 20 minute
rest, slow walking, jogging and fast walking
Outdoor exercises each consisted of 20 minute
rest, slow walking, jogging and fast walking
Treadmill and hallway exercise tests
Treadmill and hallway exercise tests
Laboratory exercise tests on bicycles and
treadmills
Performed similar exercises as Panel 2 and 3,
and also performed job-related tests including
lifting and carrying a 9-kg pipe.
3 days in 1 typical summer week (included most
active workday and most active day off); HR
recordings and activity diary during waking
hours.
Saturday, Sunday and Monday (school day) in
early autumn; HR recordings and activity diary
during waking hours and during sleep.
Same as Panel 2, however, no HR recordings
during sleep for most subjects.
1 typical summer week, 1 typical winter week;
hourly activity/health diary during waking hours;
lung function tests 3 times daily; HR recordings
during waking hours on at least 3 days (including
most active work day and day off).
Similar to Panel 4, personal NO, and acid
exposure monitoring included. (Panels 4 and 5
were studied in different years, and had 10
subjects in common).
Similar to Panel 4, summer monitoring for 2
successive weeks, including 2 controlled
exposure studies with few or no observable
respiratory effects.
HR recordings and diary information during 1
typical summer work day.
Source: Linnet al.. 1992
Page
5-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
Table 5-2. Subject Panel Inhalation Rates by Mean VR, Upper Percentiles, and Self-Estimated Breathing Rates
Inhalation Rates (m /hr)
Panel
MeanVR
fm3/hr)
99th Percentile
VR
Mean VR at Activity Levels
On3/hrtb
Slow
Medium'
Fast0
Healthy
1-Adults 20 0,78
2 - Elementary School Students 17 0.90
3 - High School Students 19 0.84
7 - Construction Workers0 7 1.50
Asthmatics ;
4-Adults 49 1.02
5-Adultsd 24 1.20
6 - Elementary and High School Students 13 1.20
2.46
1.98
2.22
4.26
1.92
2.40
2.40
0.72
0.84
0.78
1.26
1.02
1.20
1.20
1.02
0.96
1.14
1.50
1.68
2.04
1.20
3.06
1.14
1.62
1.68
2.46
4.02
1.50
Number of individuals in each survey panel.
Some subjects did not report medium and/or fast activity. Group means were calculated from individual means (i.e., give equal weight
to each individual who recorded any time at the indicated activity level).
Construction workers recorded only on 1 day, mostly during work, while others recorded on a 1 work or school day and a 1 day off.
Excluding subjects also in Panel 4.
Source: Linn etal., 1992.
A limitation of this study is that calibration data
may overestimate the predictive power of HR during
actual field monitoring. The wide variety of exercises in
everyday activities may result in greater variation of the
VR-HR relationship than calibrated. Another limitation
of this study is the small sample size of each
subpopulation surveyed. An advantage of this study is
that diary data can provide rough estimates of ventilation
patterns which are useful in exposure assessments.
Another advantage is that inhalation rates were presented
for various subpopulations (i.e., healthy outdoor adult
workers, healthy children, asthmatics, and construction
workers).
Spier et al. (1992) - Activity Patterns in
Elementary and High School Students Exposed To
Oxidant Pollution - Spier et al. (1992) investigated
activity patterns of 17 elementary school students (10-12
years old) and 19 high school students (13-17 years old)
in suburban Los Angeles from late September to October
(oxidant pollution season). Calibration tests were
conducted in supervised outdoor exercise sessions. The
exercise sessions consisted of 5 minutes for each: rest,
slow walking, jogging, and fast walking. HR and VR
were measured during the last 2 minutes of each exercise.
Individual VR and HR relationships for each individual
were determined by fitting a regression line to HR values
and log VR values. Each subject recorded their daily
activities, change in location, and breathing rates in
diaries for 3 consecutive days. Self-estimated breathing
rates were recorded as slow (slow walking), medium
(walking faster than normal), and fast (running). HR was
recorded during the 3 days once per minute by wearing a
Heart Watch VR values for each self-estimated breathing
rate and activity type were estimated from the HR
recordings by employing the VR and HR equation
obtained from the calibration tests.
The data presented in Table .5-3 represent HR
distribution patterns and corresponding predicted VR for
each age group during hours spent awake. At the same
self-reported activity levels for both age. groups,
inhalation rates were higher for outdoor activities than for
indoor activities. The total hours spent indoors by high
school students (21.2 hours) were higher than for
elementary school students (19.6 hours). The converse
was true for outdoor activities; 2.7 hours for high school
students, and 4.4 hours for elementary school students
(Table 5-4). Based on the data presented in Tables 5-3
and 5-4, the average activity-specific inhalation rates for
elementary (10-12 years) and high school (13-17 years)
students were calculated in Table 5-5. For elementary
school students, the average daily inhalation rates (based
on indoor and outdoor locations) are 15.8 m3/day for
light activities, 4.62 m3/day for moderate activities, and
0.98 m3/day for heavy activities. For high school students
the daily inhalation rates for light, moderate, and heavy
activities are estimated to be 16.4 m3/day, 3.1 m3/day, and
0.54 m3/day, respectively (Table 5-5).
Exposure Factors Handbook
August 1997
Page
5-5
-------
1 Volume I - General Factors
PHUri Chapter 5 - Inhalation
Table 5-3. Distribution of Predicted IR by Location and Activity Levels for Elementary and High School Students
Inhalation Rates (nrVhr)
Age
(yrs)
Student
Location Activity Level
% Recorded
Time2
Percentile Rankings1"
Mean ± SD
10-12
13-17
ELC
(nd=17)
HSC
(nd=19)
Indoors
Outdoors
Indoors
Outdoors
slow
medium
. fast
slow
medium
fast
slow
medium
fast
slow
medium
fast
49.6
23.6
2.4
8.9
11.2
4.3
70.7
10.9
1.4
8.2
7.4
1.4
0.84
0.96
1.02
0.96
1.08
1.14
0.78
0.96
1.26
0.96
1.26
1.44
±
±
+
±
±
±
±
+
±
±
±
+
0.36
0.42
0.60
0.54
0.48
0.60
0.36
0.42
0.66
0.48
0.78
1.08
a Recorded time averaged about 23 hr per elementary school student and 33 hr. per high school
b Geometric means closely approximated 50th percentiles; geometric standard deviations were 1
e EL — elementary school student; HS = high school student.
d N = number of students that participated in survey.
e Highest single value.
1st
0.18
0.24
0.24
0.36
0.24
0.48
0.30
0.42
0.54
0.42
0.48
0.48
50th
0.78
0.84
0.84
0.78
0.96
0.96
0.72
0.84
1.08
0.90
1.08
1.02
99
.9th
2.34
2.58
3.42
4.32
3.36
3.60
3.24
4.02
6.84C
5.28
5.70
5.94
student, over 72-hr, periods.
.2-1.3 forHR, 1.5-1.8'for VR.
Source: Spier et al., 1992.
Table 5-4. Average Hours Spent Per Day in a Given Location and Activity Level for Elementary (EL) and High School (HS) Students
Student
(EL?, nc= 17; HSb, Nc= 19) Location
a
b
c
EL Indoor
EL Outdoor
HS Indoor
HS Outdoor
Elementary school (EL) students were between 10-12 years
High school (HS) students were between 13-17 years old.
N corresponds to number of school students.
Slow
16.3
2.2
19.5
1.2
old.
Activity Level
Medium
2.9
1.7
1.5
1.3
Fast
0.4
0.5
0.2
0.2
Total Time Spent
(hrs/dav)
19.6
4.4
21.2
2.7
Source: Spier et al., 1992.
Page
5-6
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
Students
Age
(yrs) Location
Activity type"
Mean IRb
(m3/day)
Percentile Rankings
1st 50th
99.9th
Table 5-5. Distribution Patterns of Daily Inhalation Rates for Elementary (EL) and High School (HS) Students Grouped by Activity Level
EL(nc=17) 10-12 Indoor
EL
Outdoor
HS (n=19) 13-17 Indoor
Light
Moderate
Heavy
Light
Moderate
Heavy
Light
Moderate
Heavy
13.7
2.8
0.4
2.1
1.84
0.57
15.2
1.4
0.25
2.93
0.70
0.096
0.79
0.41
0.24
5.85
0.63
0.11
12.71
2.44
0.34
1.72
1.63
0.48
14.04
1.26
0.22
38.14
7.48
1.37 .
9.50
5.71
1.80
63.18
6.03
1.37
HS Outdoor
Light
Moderate
Heavy
1.15
1.64
0.29
0.50
0.62
0.096
1.08
1.40
0.20
6.34
7.41
1.19
a For this report, activity type presented in Table 5-2 was redefined as light activity for slow, moderate activity for medium, and heavy
activity for fast.
b Daily inhalation rate was calculated by multiplying the hours spent at each activity level (Table 5-4) by the corresponding inhalation rate
(Table 5-3). .
0 Number of elementary (EL) and high school students (HS).
Source: Adapted from Spier et al., 1992 (Generated using data from Tables 5-3 and 5-4).
A limitation of this study is the small sample size.
The results may not be representative of all children in
these age groups. Another limitation is that the accuracy
of the self-estimated breathing rates reported by younger
age groups is uncertain. This may affect the validity of
the data set generated. An advantage of this study is that
inhalation rates were determined for children and
adolescents. These data are useful in estimating exposure
for the younger population.
Adams (1993) - Measurement of Breathing Rate
and Volume in Routinely Performed Daily Activities -
Adams (1993) conducted research to accomplish two
main objectives: (1) identification of mean and ranges of
inhalation rates for various age/gender cohorts and
specific activities; and (2) derivation of simple linear and
multiple regression equations used to predict inhalation
rates through other measured variables: heart rate (HR),
breathing frequency (fB), and oxygen consumption (VO2).
A total of 160 subjects participated in the primary study.
There were four age dependent groups: (1) children 6 to
12.9 years old, (2) adolescents between 13 and 18.9 years
old, (3) adults between 19 and 59.9 years old, and (4)
seniors >60 years old (Adams, 1993). An additional 40
children from 6 to 12 years old and 12 young children
from 3 to 5 years old were identified as subjects for pilot
testing purposes in this age group (Adams, 1993).
Resting protocols conducted in the laboratory for
all age groups consisted of three phases (25 minutes each)
of lying, sitting, and standing. They were categorized as
resting and sedentary activities. Two active protocols,
moderate (walking) and heavy (jogging/ running) phases,
were performed on a treadmill over a progressive
continuum of intensities made up of 6 minute intervals, at
3 speeds, ranging from slow to moderately fast. All
protocols involved measuring VR, HR, fB (breathing
frequency), and VO2 (oxygen consumption).
Measurements were taken in the last 5 minutes of each
phase of the resting protocol, and the last 3 minutes of the
6 minute intervals at each speed designated in the active
protocols.
In the field, all children completed spontaneous
play protocols, while the older adolescent population (16-
18 years) completed car driving and riding, car
maintenance (males), and housework (females) protocols.
All adult females (19-60 years) and most of the senior
(60-77 years) females completed housework, yardwork,
and car driving and riding protocols. Adult and senior
males completed car driving and riding, yardwork, and
Exposure Factors Handbook
August 1997
Page
5-7
-------
Volume I - General Factors
Chapter 5 - Inhalation
mowing protocols. HR, VR, and fB were measured during
each protocol. Most protocols were conducted for 30
minutes. All the active field protocols were conducted
twice.
During all activities in either the laboratory or field
protocols, IR for the children's group revealed no
significant gender differences, but those for the adult
groups demonstrated gender differences. Therefore, IR
data presented in Appendix Tables 5A-1 and 5A-2 were
categorized as young children, children (no gender),and
for adult female, and adult male by activity levels (resting,
sedentary, light, moderate, and heavy). These categorized
data from the Appendix tables are summarized as IR in
m3/hr in Tables 5-6 and 5-7. The laboratory protocols are
shown in Table 5-6. Table 5-7 presents the mean
inhalation rates by group and activity levels (light,
sedentary, and moderate) in field protocols. A
comparison of the data shown in Tables 5-6 and 5-7
suggest that during light and sedentary activities in
laboratory and field protocols, similar inhalation rates
were obtained for adult females and adult males.
Accurate predictions of IR across all population groups
and activity types were obtained by including body
surface area (BSA), HR, and fB in multiple regression
analysis (Adams, 1993). Adams (1993) calculated BSA
from measured height and weight using the equation:
BSA = Height<°-725> x Weight(a425> x 71.84.
(Eqn. 5-2)
A limitation associated with this study is that the
population does not represent the general U.S. population.
Also, the classification of activity types (i.e., laboratory
and field protocols) into activity levels may bias the
inhalation rates obtained for various age/gender cohorts.
The estimated rates were based on short-term data and
may not reflect long-term patterns. An advantage of this
study is that it provides inhalation data for all age groups.
Linn et al. (1993) - Activity patterns in Ozone
Exposed Construction Workers - Linn et al. (1993)
estimated the inhalation rates of 19 construction workers
who perform heavy outdoor labor before and during a
typical work shift. The workers (laborers, iron workers,
and carpenters) were employed at a site on a hospital
campus in suburban Los Angeles. The construction site
included a new hospital building and a separate medical
office complex. The study was conducted between mid-
July and early November, 1991. During this period, ozone
(O3) levels were typically high. Initially, each subject was
calibrated with a 25-minute exercise test that included
slow walking, fast walking, jogging, lifting, and carrying.
All calibration tests were conducted in the mornings. VR
Table 5-6. Summary of Average Inhalation Rates (m3/hr) by Age Group and Activity Levels for Laboratory Protocols
Age Group
Resting
Sedentaryb
Lightc
Moderate
Heavy6
Young Children*
Children11
Adult Females'
Adult Males*
0.37
0.45
0.43
0.54
0.40
0.47
0.48
0.60
0.65
0.95
1.33
1.45
DNPg
1.74
2.76
1.93
DNP
2.23
2.96*
3.63
f Resting defined as lying (see Appendix Table 5A-1 for original data).
Sedentary defined as sitting and standing (see Appendix Table 5A-1 for original data).
jj Light defined as walking at speed level 1.5 - 3.0 mph (see Appendix Table 5A-1 for original data).
Moderate defined as fast walking (3.3 - 4.0 mph) and slow running (3.5 - 4.0 mph) (see Appendix Table 5A-1 for original data).
Heavy defined as fast running (4.5 - 6.0 mph) (see Appendix Table 5A-1 for original data).
Young children (both genders) 3 - 5.9 yrs old.
• DNP. Group did not perform this protocol or N was too small for appropriate mean comparisons. All young children did not run.
I1 Children (both genders) 6 - 12.9 yrs old.
Adult females defined as adolescent, young to middle aged, and older adult females.
* Older adults not included in mean value since they did not perform running protocols at particular speeds.
Adult males defined as adolescent, young to middle aged, and older adult males.
Source: Adapted from Adams. 1993.
Page
5-8
Exposure Factors Handbook
August 1997
-------
Volume I -General Factors
Chanter 5 - Inhalation
Table 5-7. Summary of Average Inhalation Rates (m3/hr) by Age
Group and Activity Levels in Field Protocols
Age Group
Light"
Sedentary
Moderate
Young Children0
Childrenf
Adult Females8
Adult Males'
DNPe
DNP
1.10h
1.40
DNP
DNP
0.51
0.62
0.68
1.07
DNP
1.78j
a Light activity was defined as car maintenance (males),
housework (females), and yard work (females) (see Appendix
Table 5A-2 for original data).
b Sedentary activity was defined as car driving and riding (both
genders) (see Appendix Table 5A-2 for original data).
c Moderate activity was defined as mowing (males); wood
working (males); yard work (males); and play (children) (see
Appendix Table 5A-2 for original data).
d Young children (both genders) = 3-5.9 yrs old.
c DNP. Group did not perform this protocol or N was too small
for appropriate mean comparisons.
f Children (both genders) = 6 -12.9 yrs old.
8 Adult females defined as adolescent, young to middle aged, and
older adult females.
h Older adults not included in mean value since they did not
perform this activity.
' Adult males defined as adolescent, young to middle aged, and
older adult males.
•> Adolescents not included in mean value since they did not
perform this activity.
Source: Adams, 1993.
and HR were measured simultaneously during the test.
The data were analyzed using least squares regression to
derive an equation for predicting VR at a given HR.
Following the calibration tests, each subject recorded the
type of activities to be performed during their work shift
(i.e., sitting/standing, walking, lifting/carrying, and
"working at trade" - defined as tasks specific to the
individual's job classification). Location, and self-
estimated breathing rates ("slow" similar to slow walking,
"medium" similar to fast walking, and "fast" similar to
running) were also recorded in the diary. During work, an
investigator recorded the diary information dictated by the
subjects. HR was recorded minute by minute for each
subject before work and during the entire work shift.
Thus, VR ranges for each breathing rate and activity
category were estimated from the HR recordings by
employing the relationship between VR and HR obtained
from the calibration tests.
A total of 182 hours of HR recordings were
obtained during the survey from the 19 volunteers; 144
hours reflected actual working time according to the diary
records. The lowest actual working hours recorded was
6.6 hours and the highest recorded for a complete work
shift was 11.6 hours (Linn et al., 1993). Summary
statistics for predicted VR distributions for all subjects,
and for job or site defined subgroups are presented in
Table 5-8. The data reflect all recordings before and
during work, and at break times. For all subjects, the
mean IR was 1.68 m3/hr with a standard deviation of
±0.72 (Table 5-8). Also, for most subjects, the 1st and
99th percentiles of HR were outside of the calibration
range (calibration ranges are presented in Appendix Table
5A-3). Therefore, corresponding IR percentiles were
extrapolated using the calibration data (Linn et al., 1993).
The data presented in Table 5-9 represent
distribution patterns of IR for each subject, total subjects,
and job or site defined subgroups by self-estimated
breathing rates (slow, medium, fast) or by type of job
activity. All data include working and non-working hours.
The mean inhalation rates for most individuals showed
statistically significant increases with higher self-
estimated breathing rates or with increasingly strenuous
job activity (Linn et al., 1993). Inhalation rates were
higher in hospital site workers when compared with office
site workers (Table 5-9). In spite of their higher predicted
VR workers at the hospital site reported a higher
percentage of slow breathing time (31 percent) than
workers at the office site (20 percent), and a lower
percentage of fast breathing time, 3 percent and 5 percent,
respectively (Linn et al., 1993). Therefore, individuals
whose work was objectively heavier than average (from
VR predictions) tended to describe their work as lighter
than average (Linn et al., 1993). Linn et al. (1993) also
concluded that during an O3 pollution episode,
construction workers should experience similar
microenvironmental O3 exposure concentrations as other
healthy outdoor workers, but with approximately twice as
high a VR. Therefore, the inhaled dose of O3 should be
almost two times higher for typical heavy-construction
workers than for typical healthy adults performing less
strenuous outdoor jobs.
A limitation associated with this study is the small
sample size. Another limitation of this study is that
calibration data were not obtained at extreme conditions.
Exposure Factors Handbook
August 1997
Page
5-9
-------
Volume I - General Factors
Chapter 5 - Inhalation
Table 5-8. Distributions of Individual and Group Inhalation/Ventilation Rate for Outdoor Workers
Population Group and Subgroup3
All Subjects (nb = 19)
Job
GCWVLaborers (n=5)
Iron Workers (n=3)
Carpenters (n=l 1)
Site
Medical Office Site (n=7)
Hospital Site (n=12)
Mean ± SD
1.68 ±0.72
1.44 ±0.66
1.62 ±0.66
1.86 + 0.78
1.38 + 0.66
1.86 ±0.78
Ventilation Rate (VR) (nrVhr)
1
0.66
0.48
0.60
0.78
0.60 '
0.72
Percentile
50 99
1.62 3.90
1.32 3.66
1.56 3.24
1.74 4.14
1.20 3.72
1.80 3.96
" Each group or subgroup mean was calculated from individual means, not from pooled data.
n = number of individuals performing specific jobs or number of individuals at survey sites.
0 GCW - general construction worker.
Source: Linn et a!., 1993.
Table 5-9. Individual Mean Inhalation Rate (m3/hr) by Self-Estimated Breathing Rate or Job Activity Category for Outdoor Workers
Self-Estimated Job Activity Category (m3/hr)
Breathing Rate (m3/hr)
Population Group and Subgroup Slow Med Fast Sit/Std Walk Carry Tradeb
All Subjects (n=19) 1.44 1.86
Job
GCW'/Laborers (n=5) 1.20 1.56
Iron Workers (n=3) 1.38 1.86
Carpenters (n= 11) 1.62 2.04
Site
Office Site (n=7) 1 . 14 1 .44
Hospital Site (n= 12) 1.62 2.16
° GCW - general construction worker
b Trade - "Working at Trade" (i.e., tasks specific to the individual
Source: Linn etal., 1993
Therefore, it was necessary to predict IR values that were
outside the calibration range. This may introduce an
>
Page
5-10
2.04 1.56 1.80 2.10 1.92
1.68 1.26 1.44 1.74 1.56
2.10 1.62 1.74 1.98 1.92
2.28 1.62 1.92 2.28 2.04
1.62 1.14 1.38 1.68 1.44
2.40 1.80 2.04 2.34 2.16
s job classification)
unknown amount of uncertainty to the data set
Subjective self-estimated breathing rates may be anothei
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
source of uncertainty in the inhalation rates estimated. An
advantage is that this study provides empirical data useful
in exposure assessments for a subpopulation thought to be
the most highly exposed common occupational group
(outdoor workers).
Layton (1993) - Metabolically Consistent
Breathing Rates for Use in Dose Assessments - Layton
(1993) presented a new method for estimating
metabolically consistent inhalation rates for use in
quantitative dose assessments of airborne radionuclides.
Generally, the approach for estimating the breathing rate
for a specified time frame was to calculate a time-
weighted-average of ventilation rates associated with
physical activities of varying durations (Layton, 1993).
However, in this study, breathing rates, were calculated
based on oxygen consumption associated with energy
expenditures for short (hours) and long (weeks and
months) periods of time, using the following general
equation to calculate energy-dependent inhalation rates:
VE = E x H x VQ
(Eqn. 5-3)
where:
VE = ventilation rate (L/min or m3/hr);
E = energy expenditure rate; [kilojoules/minute
(KJ/min) or megajoules/hour (MJ/hr)];
H = volume of oxygen [at standard temperature and
pressure, dry air (STPD) consumed in the
production of 1 kilojoule (KJ) of energy expended
(L/KJorm3/MJ)];and
VQ = ventilatory equivalent (ratio of minute volume
(L/min) to oxygen uptake (L/min)) unitless.
Three alternative approaches were used to estimate
daily chronic (long term) inhalation rates for different
age/gender cohorts of the U.S. population using this
methodology.
First Approach
Inhalation rates were estimated by multiplying
average daily food energy intakes for different age/gender
cohorts, volume of oxygen (H), and ventilatory equivalent
(VQ), as shown in the equation above. The average food
energy intake data (Table 5-10) -are based on
approximately 30,000 individuals and were obtained from
the USD A 1977-78 Nationwide Food Consumption
Survey (USDA-NFCS). The food energy intakes were
adjusted upwards by a constant factor of 1.2 for all
individuals 9 years and older (Layton, 1993). This factor
compensated for a consistent bias in USDA-NFCS
attributed to under reporting of the foods consumed or the
methods used to ascertain dietary intakes. Layton (1993)
used a weighted average oxygen uptake of 0.05 L O2/KJ
which was determined from data reported in the 1977-78
USDA-NFCS and the second National Health and
Nutrition Examination Survey (NHANES II). The survey
sample for NHANES II was approximately 20,000
participants. The ventilatory equivalent (VQ) of 27 used
was calculated as the geometric mean of VQ data that
were obtained from several studies by Layton (1993).
The inhalation rate estimation techniques are
shown in footnote (a) of Table 5-11. Table 5-11 presents
the daily inhalation rate for each age/gender cohort. The
highest daily inhalation rates were reported for children
between the ages of 6-8 years (10 m3/day), for males
between 15-18 years (17 m3/day), and females between 9-
11 years (13 m3/day). Estimated average lifetime
inhalation rates for males and females are 14 m3/day and
10 m3/day, respectively (Table 5-11). Inhalation rates
were also calculated for active and inactive periods for the
various age/gender cohorts.
The inhalation rate for inactive periods was
estimated by multiplying the basal metabolic rate (BMR)
times the oxygen uptake (H) times the VQ. BMR was
defined as "the minimum amount of energy required to
support basic cellular respiration while at rest and not
actively digesting food"(Layton, 1993). The inhalation
rate for active periods was calculated by multiplying the
inactive inhalation rate by the ratio of the rate of energy
expenditure during active hours to the estimated BMR.
This ratio is presented as F in Table 5-11. These data for
active and inactive inhalation rates are also presented in
Table 5-11. For children, inactive and active inhalation
rates ranged between 2.35 and 5.95 mVday and 6.35 to
13.09 m3/day, respectively. For adult males (19-64 years
old), the average inactive and active inhalation rates were
approximately 10 and 19 m3/day, respectively. Also, the
average inactive and active inhalation rates for adult
females (19-64 years old) were approximately 8 and 12
mVday, respectively.
Second Approach
Inhalation rates were calculated by multiplying the
BMR of the population cohorts times A (ratio of total
daily energy expenditure to daily BMR) times H times
VQ. The BMR data obtained from literature were
statistically analyzed and regression equations were
developed to predict BMR from body weights of various
age/gender cohorts (Layton, 1993). The statistical data
used to develop the regression equations are presented in
Appendix Table 5A-4. The data obtained from the second
Exposure Factors Handbook
August 1997
Page
5-11
-------
Volume I - General Factors
Chapter 5 - Inhalation
Table 5-10. Comparisons of Estimated Basal Metabolic Rates (BMR) with Average Food-Energy Intakes for
Individuals Sampled in the 1977-78 NFCS
Cohort/Age Body Weight
(years)
Children
Under 1
Ito2
3to5
6 to 8
Males
9toll
12 to 14
IS to 18
19 to 22
23 to 34
35to50
51 to 64
65 to 74
75 +
Females
9toll
12 to 14
15 to 18
19 to 22
23 to 34
35 to 50
51 to 64
65 to 74
75 +
kg
7.6
13
18
26
36
50
66
74
79
82
80
76
71
36
49
56
59
62
66
67
66
62
BMRa
MJ d'lb
1.74
3.08
3.69
4.41
5.42
6.45
7.64
7.56
7.87
7.59
7.49
6.18
5.94
4.91
5.64
6.03
5.69
5.88
5.78
5.82
5.26
5.11
kcal d'lc
416
734
881
1053
1293
1540
1823
1804
1879
1811
1788
1476
1417
1173
1347
1440
1359
1403
1380
1388
1256
1220
Energy Intake (EFD)
MJd'1
3.32
5.07
6.14
7.43
8.55
9.54
10.8
10.0
10.1
9.51
9.04
8.02
7.82
7.75
7.72
7.32
6.71
6.72
6.34
6.40
5.99
5.94
kcal d"1
793
1209
1466
1774
2040
2276
2568
2395
2418
2270
2158
1913
1866
1849
1842
1748
1601
1603
1514
1528
1430
1417
Ratio
EFD/BMR
1.90
1.65
1.66
1.68
1.58
1.48
1.41
1.33
1.29
1.25
1.21
1.30
1.32
1.58
1.37
1.21
1.18
1.14
1.10
1.10
1.14
1.16
* Calculated from the appropriate age and gender-based BMR equations given in Appendix Table 5A-4.
MJd'1 -mega joules/day
c kcal d"' - kilo calories/day
Source: Layton, 1993.
Page
5-12
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chanter 5 - Inhalation
Table 5-11. Daily Inhalation Rates Calculated from Food-Energy Intakes
Daily Inhalation
Rate*
Sleep
MET" Value
Inhalation Rates
Inactive0 Active"
Cohort/Age (years)
(nvVdav)
Children
<1
I -2
3-5
6-8
Males
9-11
12-14
15-18
19-22
23-34
35-50
51-64
65-74
75+
Lifetime average &
Females
3
3
4
4
11
16
14
10
1
4.5
6.8
8.3
10
14
15
17
16
16
15
15
13
12
14
11
11
10
10
1.9
1.6
1.7
1.7
1.9
1.8
1.7
1.6
1.5
1.5
1.4
1.6
1.6
2.7
2.2
2.2
2.2
2.5
2.2
2.1
1.9
1.8
1.8
1.7
1.8
1.9
2.35 • 6.35
4.16 9.15
4.98 10.96
5.95 13.09
7.32
8.71
10.31
10.21
10.62
10.25
10.11
8.34
8.02
18.3
19.16
21.65
19.4
19.12
18.45
17.19
15.01
15.24
9-11
12-14
15-18
19-22
23-34
35-50
51 -64
65-74
75+
3
3
4
4
11
16
14
10
1
13
12
12
11
11
10
10
• 9.7
M
10
9 1.9
9 1.6
8 1.5
8 1.4
8 1.4
8 1.3
8 1.3
8 1.4
8 1.4
2.5
2.0
1.7
1.6
1.6
1.5
1.5
1.5
1.6
6.63
7.61
8.14
7.68
7.94
7.80
7.86
7.10
6.90
16.58
15.20
13.84
12.29
12.7
11.7
11.8
10.65
11.04
a Daily inhalation rate was calculated by multiplying the EFD values (see Table 5-10) by H x VQ x (m3 1,000 L'1) for subjects under 9 years of age and by
1.2xHxVQ x (m3 1,000 L'1) (for subjects 9 years of age and older (see text for explanation).
Where:
EFD = Food energy intake (Kcal/day) or (MJ/day)
H = Oxygen uptake = 0.05 LO2/KJ or 0.21 LO2/Kcal
VQ = Ventilation equivalent = 27 = geometric mean of VQs (unitless)
b MET = Metabolic equivalent
c Inhalation rate for inactive periods was calculated as BMR x H x VQ x (d 1,440 min'1) and for active periods by multiplying inactive inhalation rate by F
(See footnote f); BMR values are from Table 5-10.
Where:
BMR = Basal metabolic rate (MJ/day) or (kg/hr)
d L is the number of years for each age cohort.
c For individuals 9 years of age and older, A was calculated by multiplying the ratio for EFD/BMR (unitless) (Table 5-10) by the factor 1.2 (see text for
explanation). . •
f F = (24A - S)/(24 - S) (unitless), ratio of the rate of energy expenditure during active hours to the estimated BMR (unitless)
Where:
S = Number of hours spent sleeping each day (hrs)
g Lifetime average was calculated by multiplying individual inhalation rate by corresponding L values summing the products across cohorts and dividing the
result by 75, the total of the cohort age spans.
Source: Lavton. 1993. ; ,——
Exposure Factors Handbook
August 1997
Page
5-13
-------
Volume I - General Factors
Chapter 5 - Inhalation
approach are presented in Table 5-12. Inhalation rates for
children (6 months - 10 years) ranged from 7.3-9.3 m3/day
for male and 5.6 to 8.6 m3/day for female children and
(10-18 years) was 15 m3/day for males and 12 nvVday for
females. Adult females (18 years and older) ranged from
9.9-11 nvVday and adult males (18 years and older)
ranged from 13-17 m3/day. These rates are similar to the
daily inhalation rates obtained using the first approach.
Also, the inactive inhalation rates obtained from the first
approach are lower than the inhalation rates obtained
using the second approach. This may be attributed to the
BMR multiplier employed in the equation of the second
approach to calculate inhalation rates.
Third Approach
Inhalation rates were calculated by multiplying
estimated energy expenditures associated with different
levels of physical activity engaged in over the course of an
average day by VQ and H for each age/gender cohort.
The energy expenditure associated with each level of
activity was estimated by multiplying BMRs of each
activity level by the metabolic equivalent (MET) and by
the time spent per day performing each activity for each
age/gender population. The time-activity data used in this
approach were obtained from a survey conducted by Sallis
et al. (1985) (Layton, 1993). In that survey, the physical-
activity categories and associated MET values used were
sleep, MET=1; light-activity, MET=1.5; moderate
activity, MET=4; hard activity, MET=6; and very hard
activity, MET=10. The physical activities were based on
recall by the test subject (Layton, 1993). The survey
sample was 2,126 individuals (1,120 women and 1,006
men) ages 20-74 years that were randomly selected from
four communities in California. The BMRs were
estimated using the metabolic equations presented in
Appendix Table 5A-4. The body weights were obtained
from a study conducted by Najjar and Rowland (1987)
which randomly sampled individuals from the U.S.
population (Layton, 1993). Ta6le 5-13 presents the
inhalation rates (VE) in m3/day and m3/hr for adult males
and females aged 20-74 years at five physical activity
levels. The total daily inhalation rates ranged from 13-17
m3/day for adult males and 11-15 mVday for adult
females.
The rates for adult females were higher when
compared with the other two approaches. Layton (1993)
reported that the estimated inhalation rates obtained from
the third approach were particularly sensitive to the MET
value that represented the energy expenditures for light
activities. Layton (1993) stated further that in the original
time-activity survey (i.e., conducted by Sallis et al., 1985),
time spent performing light activities was not presented.
Therefore, the time spent at light activities was estimated
Male
0.5-<3
3-<10
10-<18
I8-<30
30-<60
60+
Female
Table 5-12. Daily Inhalation Rates Obtained from the Ratios
Gender/Age
(yrs)
Body Weight8
(kg)
BMRb
(MJ/day)
VQ
Ac
H
(m3O,/MJ)
Inhalation Rate, VE
(m3/day)d
14
23
53
76
80
75
3.4
4.3
6.7
7.7
7.5
6.1
27
27
27
27
27
27
1.6
1.6
1.7
1.59
1.59
1.59
0.05
0.05
0.05
0.05
0.05
0.05
7.3
9.3
15
17
16
13
0.5 - <3
3-<10
10-<18
I8-<30
30-<60
60+
11
23
50
62
68
67
2.6
4.0
5.7
5.9
5.8
5.3
27
27
27
27
27
27
1.6
1.6
1.5
1.38
1.38
1.38
0.05
0.05
0.05
0.05
0.05
0.05
5.6
8.6
12
11
11
9.9
Body weight was based on the average weights for age/gender cohorts in the U.S. population.
b The BMRs (basal metabolic rate) are calculated using the respective body weights and BMR equations (see Appendix Table 5A-4).
c The values of the BMR multiplier (EFD/BMR) for those 18 years and older were derived from the Basiotis et al. (1989) study: Male = 1.59,
Female= 1.38. For males and females under 10 years old, the mean BMR multiplier used was 1.6. For males and females aged 10to< 18
years, the mean values for A given in Table 5-11 for 12-14 years and 15-18 years, age brackets for males and females were used: male = 1.7
and female= 1.5.
Inhalation rate = BMR x A x H x VQ; VQ = ventilation equivalent and H = oxygen uptake.
Source: Lavton. 1993.
Page
5-14
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
>*
g
•I
c
i
E
H
e
, o
•^
1
(I
1
p
c
tc
J
c
- r|
JI
d
3
09
JH
c
«
ji
s
- S
-S-
10 u]*O
>^g
~
"i
5
^j.
||
|e
.0 -c
II
T3 DO 'S
«| -
^
""m*
>•*£
•-'
, — ,
i, S
^
--•
5
^ 1 -S
§.
°=-s
o j
11
Q -"
J2i *|
.1
*S -^ ,]
CQ ^ ^-^
S
s
?>•>
*!'
^. VQ i/-j (rj oo CO V^ CO O O5 f*^ ""} f^l O f^ ^* ^" CM OO O
Q f^i T— i CM CO . CJ ^3 •"* CN f^ O '•— •• *™^ tM ^^ ^ ^ '"' ""^ *^
OOCOOOmSSio COCMOOCM^ro -*-*-*o!§cO CMt^COOS^^,
cxiod-^ — o— csc-^^'-^c;— cst---i— IQ— CMVO- m rf ov <= ov oo n- - i- * «• o ov o t- - -n
CN ^O Q O O tJ
^^^ss^ ^5^^^s p2'22*s ?5^2^s
^^00 -HOO -< -*
0000000000 9^^?^ ^^^^^ cscscs c^ ^ "^ t~* vi c**) to *o ^ I~H *o
OOi-HCS1^* O O ^— i c4 ^ O O -— i C^ -^ oOT-»C^rO
OQ
CO^ - - o
• rt ,,. ^ 0
co o vi cs S, [^. c^ ^ oo <~H Q^ f<^ en o "^ Q^ ^- ^ o^ r^ ^ oo ^^ o<
oic^;T_;_;^'-^ ^ ^ ^ ^ ^ ^ csjc^-^oo1""1 ^m-HO^^
t^^T~*OOC>l f-i-i'"HOOC^ r^"i-i'~; C-
'i -a §
1 §11
.£: g >m
8 ^^e
g. ^ iim
I g~S 0
•S o u S
oo co w> _
c o >> x
'S3 — o 7
•a 2"- S
8 =- E ^
^2 ^ "^ ^t
j3 S"o S
S g g'g
u rt^§
P {2 ^-~m ™ §.
O\ O OO bO ^ X
1HHI
Ililtl
1*1 111
fg iJ M S
cd ^ "a •
U W V-< y <
m .a -g « a •« s
g _g ;> 3 2 g -
J E -g S.'l -C-
'53 *e3 c i> 2 *r- (-
S|.o gjg^E
•T3 4i *-• 4> tl3 U4
«J XI 0 -O U *t- ^
Exposure Factors Handbook
August 1997
Page
5-15
-------
Volume I - General Factors
Chapter 5 - Inhalation
by subtracting the total time spent at sleep, moderate,
heavy, and very heavy activities from 24 hours (Layton,
1993). The range of inhalation rates for adult females
were 9.6 to 11 m3/day, 9.9 to 11 m3/day, and 11 to 15
m3/day, for the first, second, and third approach,
respectively. The inhalation rates for adult males ranged
from 13 to 16 m3/day for the first approach, and 13 to 17
nrVday for the second and third approaches.
Inhalation rates were also obtained for short-term
exposures for various age/gender cohorts and five energy-
expenditure categories (rest, sedentary, light, moderate,
and heavy). BMRs were multiplied by the product of
MET, H, and VQ. The data obtained for short term
exposures are presented in Table 5-14.
The major strengths of the Layton (1993) study are
that it obtains similar results using three different
approaches to estimate inhalation rates in different age
groups and that the populations are large, consisting of
men, women, and children. Explanations for differences
in results due to metabolic measurements, reported diet,
or activity patterns are supported by observations reported
by other investigators in other studies. Major limitations
of this study are that activity pattern levels estimated in
this study are somewhat subjective, the explanation that
activity pattern differences is responsible for the lower
level obtained with the metabolic approach (25 percent)
compared to the activity pattern approach is not well
supported by the data, and different populations were used
in each approach which may introduce error.
5.2.3. Relevant Inhalation Rate Studies
International Commission on Radiological Protection
(ICRP) (1981) - Report of the Task Group on Reference
Man - The International Commission of Radiological
Protection (ICRP) estimated daily inhalation rates for
reference adult males, adult females, children (10 years
old), infant (1 year old), and newborn babies by using a
time-activity-ventilation approach. This approach for
estimating inhalation rate over a specified period of time
was based on calculating a time weighted average of
inhalation rates associated with physical activities of
varying durations. ICRP (1981) compiled reference
values (Appendix Table 5A-5) of minute
volume/inhalation rates from various literature sources.
ICRP (1981) assumed that the daily activities of a
reference man and woman, and child (10 yrs) consisted of
Table 5-14. Inhalation Rates for Short-Term Exposures
Gender/Age (yrs)
Weight
(kg)"
BMRb
(MJ/day)
Activity Type
Rest Sedentary Light Moderate
MET (BMR Multiplier)
1 1.2 2C 4d
Heavy
10C
Inhalation Rate (m'/hr/'8
Mate
0,5 -<3
3-<10
!0-<18
18-<30
30-<60
60+
Female
0.5 -<3
3-<10
10-<18
18-<30
30-<60
60+
14
23
53
76
80
75
11
23
50
62
68
67
3.40
4.30
6.70
7.70
7.50
6.10
2.60
4.00
5.70
5.90
5.80
5.30
0.19
0.24
0.38
0.43
0.42
0.34
0.14
0.23
0.32
0.33
0.32
0.30
0.23
0.29
0.45
0.52
0.50
0.41
0.17
0.27
0.38
0.40
0.39
0.36
0.38
0.49
0.78
0.84
0.84
0.66
0.29
0.45
0.66
0.66
0.66
0.59
0.78
0.96
1.50
1.74
1.68
1.38
0.60
0.90
1.26
1.32
1.32
1.20
1.92
2.40
3.78
4.32
4.20
3.42
1.44
2.28
3.18
3.30
3.24
3.00
f Body weights were based on average weights for age/gender cohorts of the U.S. population
The BMRs for the age/gender cohorts were calculated using the respective body weights and the BMR equations (Appendix Table 5A-4).
" Rangcofl .5 -2.5.
" Range of 3 -5.
" Range of >5 - 20.
The inhalation rate was calculated by multiplying BMR (MJ/day) x H (0.05 L/KJ) x MET x VQ (27) x (d/1 ,440 min)
* Original data were presented in L/min. Conversion to m3/hr was obtained as follows:
60 min m3 L
Source: Lavton. 1993.
hr ' 1000L
mm
Page
5-16
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
8 hours of rest and 16 hours of light activities. It was also
assumed that 16 hours were divided evenly between
occupational and nonoccupational activities. It was
assumed that a day consisted of 14 hours resting and 10
hours light activity for an infant (1 yr). A newborn's daily
activities consisted of 23 hours resting and 1 hour light
activity. Table 5-15 presents the daily inhalation rates
obtained for all ages/genders. The estimated inhalation
rates were 22.8 m3/day for adult males, 21.1 m3/day for
adult females, 14.8 m3/day for children (age 10 years),
3.76 mVday for infants (age 1 year), and 0.78 m3/day for
newborns.
Table 5-15. Daily Inhalation Rates Estimated From Daily Activities"
Inhalation Rate (IR)
Subject Resting Light Daily Inhalation
(m3/hr)
Activity
(m3/hr)
Rate (DIR)1
(nrVday)
Adult Man
Adult Woman
Child (10 yrs)
Infant(1 yr)
Newborn
0.45
0.36
0.29
0.09
0.03
1.2
1.14
0.78
0.25
0.09
22.8
21.1
14.8
3.76
0.78
a Assumptions made were based on 8 hours resting and 16 hours light
activity for adults and children (10 yrs); 14 hours resting and 10 hours
light activity for infants (1 yr); 23 hours resting and 1 hour light activity
for newborns.
b
DIR = 1 £ IRA
T 1,1
IR, = Corresponding inhalation rate at im activity
t! = Hours spent during the i"1 activity
k = Number of activity periods
T = Total time of the exposure period (i.e., a day)
Source: ICRP, 1981 __^_
A limitation associated with this study is that the
validity and accuracy of the inhalation rates data used in
the compilation were not specified. This may introduce
some degree of uncertainty in the results obtained. Also,
the approach used involved assuming hours spent by
various age/gender cohorts in specific activities. These
assumptions may over/under-estimate the inhalation rates
obtained.
U.S. EPA (1985) - Development of Statistical
Distributions or Ranges of Standard Factors Used in
Exposure Assessments - Due to a paucity of information
in the literature regarding equations used to develop
statistical distributions of minute ventilation/ventilation
rate at all activity levels for male and female children and
adults, the U.S. EPA (1985) compiled measured values of
minute ventilation for various age/gender cohorts from
early studies. In more recent investigations, minute
ventilations have been measured more as background
information than as research objective itself and the
available studies have been for specific subpopulations
such as obese, asthmatics, or marathon runners. The data
compiled by the U.S. EPA (1985) for each age/gender
cohorts were obtained at various activity levels. These
.levels were categorized as light, moderate, or heavy
according to the criteria developed by the EPA Office of
Environmental Criteria and Assessment for the Ozone
Criteria Document. These criteria were developed for a
reference male adult with a body weight of 70 kg (U.S.
EPA, 1985). The minute ventilation rates for adult males
based on these activity level categories are detailed in
Appendix Table 5A-6.
Table 5-16 presents a summary of inhalation rates
by age, gender, and activity level (detailed data are
presented in Appendix Table 5A-7). A description of
activities included in each activity level is also presented
in Table 5-16. Table 5-16 indicates that at rest, the
average adult inhalation rate is 0.5 m3/hr. The mean
inhalation rate for children at rest, ages 6 and 10 years, is
0.4 m3/hr. Table 5-17 presents activity pattern data
aggregated for three microenvironments by activity level
for all age groups. The total average hours spent indoors
was 20.4, outdoors was 1.77, and in transportation vehicle
was 1.77. Based on the data presented in Tables 5-16 and
5-17, a daily inhalation rate was calculated for adults and
children by using a time-activity-ventilation approach.
These data are presented in Table 5-18. The calculated
average daily inhalation rate is 16 m3/day for adults. The
average daily inhalation rate for children (6 and 10 yrs) is
18.9 nvVday ([16.74 + 21.02]/2).
A limitation associated with this study is that many
of the values used in the data compilation were from early
studies. The accuracy and/or validity of the values used
and data collection method were not presented in U.S.
EPA (1985). This introduces uncertainty in the results
obtained. An advantage of this study is that the data are
actual measurement data for a large number of subjects
and the data are presented for both adults and children.
Shamoo et al. (1990) - Improved Quantitation of
Air Pollution Dose Rates by Improved Estimation of
Ventilation Rate- Shamoo et al. (1990) conducted this
study to develop and validate new methods to accurately
estimate ventilation rates for typical individuals during
their normal activities. Two practical approaches were
tested for estimating ventilation rates indirectly: (1)
volunteers were trained to estimate their own VR at
Exposure Factors Handbook
August 1997
Page
5-17
-------
Volume I - General Factors
Chapter 5 - Inhalation
Adult male
Adult female
Average adult'
Child, age 6 years
Child, age 10 years
n"
454
595
8
10
Resting0
0.7
0.3
0.5
0.4
0.4
n
102
786
16
40
Light"
0.8
0.5
0.6
0.8
1.0
102
106
4
29
2.5
1.6
2.1
2.0
267
211
5
43
Heavy'
4.8
2.9
3.9
2.3
3.9
Values of inhalation rates for males, females, and children (male and female) presented in this table represent the mean of values reported for each
activity level in 1985. (See Appendix Table 5A-7 for a detailed listing of the data from U.S. EPA, 1985.)
n = number of observations at each activity level.
Includes watching television, reading, and sleeping.
Includes most domestic work, attending to personal needs and care, hobbies, and conducting minor indoor repairs and home improvements
Includes heavy indoor cleanup, performance of major indoor repairs and alterations, and climbing stairs.
Includes vigorous physical exercise and climbing stairs carrying a load.
Derived by taking the mean of the adult male and adult female values for each activity level.
Source: Adapted from U.S. EPA. 1985.
Table 5-17. Activity Pattern Data Aggregated for Three
Microenvironments by Activity Level for all Age Groups
Microenvironment
Indoors
Outdoors
In Transportation
Vehicle
Source: Adapted from
Average Hours Per Day in
Activity Each Microenvironment at
Level
Resting
Light
Moderate
Heavy
TOTAL
Resting
Light
Moderate
Heavy
TOTAL
Resting
Light
Moderate
Heavy
TOTAL
U.S. EPA. 1985.
Each Activity Level
9.82
9.82
0.71
0.098
20.4
0.505
0.505
0.65
0.12
1.77
0.86
0.86
0.05
0.0012
1.77
Table 5-18. Summary of Daily Inhalation Rates Grouped by
Age and Activity level
Daily Inhalation Rate (m3/day)a
Subject
Total
Daily IRb
Light Moderate Heavy (nvVday)
Adult Male 7.83 8.95 3.53
Adult 3.35 5.59 2.26
Female
Adult 5.60 6.71 2.96
Average0'
Child 4.47 8.95 2.82
(age 6)
Child 4.47 11.19 4.51
(age 10)
1.05
0.64
21.4
11.8
0.85 16
0.50 16.74
0.85 21.02
Daily inhalation rate was calculated using the following equation:
!R! = inhalation rate at i"1 activity (Table 5-18)
I, = hours spent per day during i™ activity (Table 5-19)
k = number of activity periods
T = total time of the exposure period (e.g., a day)
Total daily inhalation rate was calculated by summing the specific
activity (resting, light, moderate, heavy) daily inhalation rate.
Source:
Generated using the data from U.S. EPA (1985) as shown in
Tables 5-16 and 5-17. _
Page
5-18
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
various controlled levels of exercise; and (2) individual
VR and HR relationships were determined in another set
of volunteers during supervised exercise sessions
(Shamoo et al., 1990). In the first approach, the training
session involved 9 volunteers (3 females and 6 males)
from 21 to 37 years old. Initially the subjects were trained
on a treadmill with regularly increasing speeds. VR
measurements were recorded during the last minute of the
3-minute interval at each speed. VR was reported to the
subjects as low (1.4 m3/hr), medium (1.5-2.3 m3/hr),
heavy (2.4-3.8 m3/hr), and very heavy (3.8 m3/hr or
higher) (Shamoo et al., 1990).
Following the initial test, treadmill training sessions
were conducted on a different day in which 7 different
speeds were presented, each for 3 minutes in arbitrary
order. VR was measured and the subjects were given
feedback with the four ventilation ranges provided
previously. After resting, a treadmill testing session was
conducted in which seven speeds were presented in
different arbitrary order from the training session. VR
was measured and each subject estimated their own
ventilation level at each speed. The correct level was then
revealed to each subject after his/her own estimate.
Subsequently, two 3-hour outdoor supervised exercise
sessions were conducted in the summer on two
consecutive days. Each hour consisted of 15 minutes each
of rest, slow walking, jogging, and fast walking. The
subjects' ventilation level and VR were recorded;
however, no feedback was given to the subjects.
Electrocardiograms were recorded via direct connection
or telemetry and HR was measured concurrently with
ventilation measurement for all treadmill sessions.
The second approach consisted of two protocol
phases (indoor/outdoor exercise sessions and field
testing). Twenty outdoor adult workers between 19-50
years old were recruited. Indoor and outdoor supervised
exercises similar to the protocols in the first approach
were conducted; however, there were no feedbacks. Also,
in this approach, electrocardiograms were recorded and
HR was measured concurrently with VR. During the field
testing phase, subjects were trained to record their
activities during three different 24-hour periods during
one week. These periods included their most active
working and non-working days. HR was measured quasi-
continuously during the 24-hour periods that activities
were recorded. The subjects recorded in a diary all
changes in physical activity, location, and exercise levels
during waking hours. Self-estimated activities in
supervised exercises and field studies were categorized as
slow (resting, slow walking or equivalent), medium (fast
walking or equivalent), and fast (jogging or equivalent).
Inhalation rates were not presented in this study. In
the first approach, about 68 percent of all self-estimates
were correct for the 9 subjects sampled (Shamoo et al.,
1990). Inaccurate self-estimates occurred in the younger
male population who were highly physically fit and were
competitive aerobic trainers. This subset of sample
population tended to underestimate their own physical
activity levels at higher VR ranges. Shamoo et al. (1990)
attributed this to a "macho effect." In the second
approach, a regression analysis was conducted that related
the logarithm of VR to HR. The logarithm of VR
correlated better with HR than VR itself (Shamoo et al.,
1990).
A limitation associated with this study is that the
population sampled is not representative of the general
U.S. population. Also, ventilation rates were not
presented. Training individuals to estimate their VR may
contribute to uncertainty in the results because the
estimates are subjective. Another limitation is that
calibration data were not obtained at extreme conditions;
therefore, the VR/HR relationship obtained may be
biased. An additional limitation is that training subjects
may be too labor-intensive for widespread use in exposure
assessment studies. An advantage of this study is that HR
recordings are useful in predicting ventilation rates which
in turn are useful in estimating exposure.
Shamoo etal. (1991) - Activity Patterns in a Panel
of Outdoor Workers Exposed to Oxidant Pollution -
Shamoo et al. (1991) investigated summer activity
patterns in 20 adult volunteers with potentially high
exposure to ambient oxidant pollution. The selected
volunteer subjects were 15 men and 5 women ages 19-50
years from the Los Angeles area. All volunteers worked
outdoors at least 10 hours per week. The experimental
approach involved two stages: (1) indirect objective
estimation of VR from HR measurements; and (2) self
estimation of inhalation/ventilation rates recorded by
subjects in diaries during their normal activities.
The approach consisted of calibrating the
relationship between VR and HR for each test subject in
controlled exercise; monitoring by subjects of their own
normal activities with diaries and electronic HR recorders;
and then relating VR with the activities described in the
diaries (Shamoo et al., 1991). Calibration tests were
conducted for indoor and outdoor supervised exercises to
determine individual relationships between VR and HR.
Indoors, each subject was tested on a treadmill at rest and
sExposure Factors Handbook
August 1997
Page
5-19
-------
Volume I - General Factors
Chapter 5 - Inhalation
at increasing speeds. HR and VR were measured at the
third minute at each 3-minute interval speed. In addition,
subjects were tested while walking a 90-meter course in a
corridor at 3 self-selected speeds (normal, slower than
normal, and faster than normal) for 3 minutes.
Two outdoor testing sessions (one hour each) were
conducted for each subject, 7 days apart. Subjects
exercised on a 260-meter asphalt course. A session
involved 15 minutes each of rest, slow walking, jogging,
and fast walking during the first hour. The sequence was
also repeated during the second hour. HR and VR
measurements were recorded starting at the 8th minute of
each 15-minute segment. Following the calibration tests,
a field study was conducted in which subject's self-
monitored their activities by filling out activity diary
booklets, self-estimated their breathing rates, and their
HR. Breathing rates were defined as sleep, slow (slow or
normal walking); medium (fast walking); and fast
(running) (Shamoo et al., 1991). Changes in location,
activity, or breathing rates during three 24-hr periods
within a week were recorded. These periods included
their most active working and non-working days. Each
subject wore Heart Watches which recorded their HR
once per minute during the field study. Ventilation rates
were estimated for the following categories: sleep, slow,
medium, and fast.
Calibration data were fit to the equation log (VR)
= intercept + (slope x HR), each individual's intercept and
slope were determined separately to provide a specific
equation that predicts each subject's VR from measured
HR (Shamoo et al., 1991). The average measured VRs
were 0.48,0.9,1.68, and 4.02 m3/hr for rest, slow walking
or normal walking, fast walking and jogging, respectively
(Shamoo et al., 1991). Collectively, the diary recordings
showed that sleep occupied about 33 percent of the
subject's time; slow activity 59 percent; medium activity
7 percent; and fast activity 1 percent. The diary data
covered an average of 69 hours per subject (Shamoo et
al., 1991). Table 5-19 presents the distribution pattern of
predicted ventilation rates and equivalent ventilation rates
(EVR) obtained at the four activity levels. EVR was
defined as the VR per square meter of body surface area,
and also as a percentage of the subjects average VR over
the entire field monitoring period (Shamoo et al., 1991).
Table 5-19. Distribution Pattern of Predicted VR and EVR (equivalent ventilation rate) for 20 Outdoor Workers
VR (m3/hr)a
Self-Reported Arithmetic Geometric
Activity Level Nc Mean ± SD Mean ± SD
Sleep
Slow
Medium
Fast
18,597 0.42 ±0.16
41,745 0.71+0.4
3,898 0.84 ±0.47
572 2.63 ±2. 16
0.39 ± 0.08
0.65 ±0.09
0.76 ±0.09
1.87-1-0.14
EVRb (m3/hr/m2 body surface)
Arithmetic Geometric
Mean ± SD Mean + SD
0.23 ±0.08
0.38 ±0.20
0.48 ±0.24
1.42 + 1.20
0.22 ± 0.08
0.35 ± 0.09
0.44 ±0.09
1.00 + 0.14
Percentile Rankings, VR
Sleep
Slow
Medium
Fast
1 5
0.18 0.18
0.30 0.36
0.36 0.42
0.42 0.54
10
0.24
0.36
0.48
0.60
50
0.36
0.66
0.72
1.74
90
0.66
1.08
1.32
5.70
95
0.72
1.32
1.68
684
99
0.90
1.98
2.64
9 18
99.9
1.20
4.38
3.84
1026
Percentile Rankings, EVR
Sleep
Slow
Medium
Fast
1 5
0.12 0.12
0.18 0.18
0.18 0.24
0.24 0.30
10
0.12
0.24
0.30
0.36
50
0.24
0.36
0.42
0.90
90
0.36
0.54
0.72
3.24
95
0.36
0.66
0.90
3.72
99
0.48
1.08
1.38
4.86
99.9
0.60
2.40
2.28
5.52
" Data presented by Shamoo et al. (1991) in liters/minute were converted to m3/hr.
b EVR = VR per square meter of body surface area.
0 Number of minutes with valid .appearing heart rate records and corresponding daily records of breathing rate.
Source: Shamoo et al., 1991
Page
5-20
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
The overall mean predicted VR was 0.42 m3/hr for sleep;
0.71 mVhr for slow activity; 0.84 m3/hr for medium
activity; and 2.63 m3/hr for fast activity.
The mean predicted VR and standard deviation, and
the percentage of time spent in each combination of VR,
activity type (essential and non-essential), and location
(indoor and outdoor) are presented in Table 5-20.
Essential activities include income-related work,
household chores, child care, study and other school
activities, personal care and destination-oriented travel.
Non-essential activities include sports and active leisure,
passive leisure, some travel, and social or civic activities
(Shamoo et al., 1991). Table 5-20 shows that inhalation
rates were higher outdoors than indoors at slow, medium,
and fast activity levels. Also, inhalation rates were higher
for outdoor non-essential activities than for indoor non-
essential activity levels at slow, medium, and fast self-
reported breathing rates (Table 5-20).
An advantage of this study is that subjective activity
diary data can provide exposure modelers with useful
rough estimates of VR for groups of generally healthy
people. A limitation of this study is that the results
obtained show high within-person and between-person
variability in VR at each diary-recorded level, indicating
that VR estimates from diary reports could potentially be
substantially misleading in individual cases. Another
limitation of this study is that elevated HR data of slow
activity at the second hour of the exercise session reflect
persistent effects of exercise and/or heat stress.
Therefore, predictions of VR from the VR/HR
relationship may be biased.
Shamoo et al. (1992) - Effectiveness of Training
Subjects to Estimate Their Level of Ventilation - Shamoo
et al. (1992) conducted a study where nine non-sedentary
subjects in good health were trained on a treadmill to
estimate their own ventilation rates at four activity levels:
low, medium, heavy, and very heavy. The purpose of the
study was to train the subjects self-estimation of
ventilation in the field and assess the effectiveness of the
training (Shamoo et al., 1992). The subjects included 3
females and 6 males between 21 to 37 years of age. The
tests were conducted in four stages. First, an initial
treadmill pretest was conducted indoors at various speeds
until the four ventilation levels were experienced by each
subject; VR was measured and feedback was given to the
subjects. Second, two treadmill training sessions which
involved seven 3-minute segments of varying speeds
based on initial tests were conducted; VR was measured
and feedback was given to the subjects. Another similar
session was conducted; however, the subjects estimated
Table 5-20. Distribution Pattern of Inhalation Rate by Location and Activity Type for 20 Outdoor Workers
Location
Indoor
Outdoor
Activity Type
Self-reported
Activity Level
% of Time
Inhalation rate (m3/hr)b
±SD
Non-essential
. Essential
Slow .
Medium
Fast
Slow
Medium
Fast
20.4
0.9
0.2
11.3
1.8
0
0.66 ± 0.36
0.78 ± 0.30
1.86 ±0.96
0.78 ± 0.36
0.84 ±0.54
0
%of Avg.c
Indoor Essential
Sleep
Slow
Medium
Fast
28.7
29.5
2.4
0
0.42 ±0.12
0.72 ± 0.36
0.72 ± 0.30
0
69 ±15
106 ±43
129 ±38
0
98 ±36
120 ±50
278 ±124
117 ±42
130 ± 56
0
Outdoor
Non-essential
Slow
Medium
Fast
3.2
0.8
0.7
0.90 ± 0.66
1.26 ±0.60
2.82 ± 2.28
136 ± 90
213 ±91
362 ± 275
a Essential activities include income-related, work, household chores, child care, study and other school activities, personal care, and
destination-oriented travel; Non-essential activities include sports and active leisure, passive leisure, some travel, and social or civic
activities.
Data presented by Shamoo et al. (1991) in liters/mintue were converted to m3/hr.
0 Statistic was calculated by converting each VR for a given subject to a percentage of her/his overall average.
Source: Adapted from Shamoo et al.. (1991).
^Exposure Factors Handbook
August 1997
Page
5-21
-------
Volume I - General Factors
Chapter 5 - Inhalation
their own ventilation level during the last 20 seconds of
each segment and VR was measured during the last
minute of each segment. Immediate feedback was given
to the subject's estimate; and the third and fourth stages
involved 2 outdoor sessions of 3 hours each. Each hour
comprised 15 minutes each of rest, slow walking, jogging,
and fast walking. The subjects estimated their own
ventilation level at the middle of each segment. The
subject's estimate was verified by a respirometer which
measured VR in the middle of each 15-minute activity.
No feedback was given to the subject. The overall
percent correct score obtained for all ventilation levels
was 68 percent (Shamoo et ah, 1992). Therefore, Shamoo
et al. (1992) concluded that this training protocol was
effective in training subjects to correctly estimate their
minute ventilation levels.
For this handbook, inhalation rates were analyzed
from the raw data provided by Shamoo et al. (1992).
Table 5-21 presents the mean inhalation rates obtained
from this analysis at four ventilation levels in two
microenvironments (i.e., indoors and outdoors) for all
subjects. The mean inhalation rates for all subjects were
0.93, 1.92,3.01,4.80 m3/hr for low, medium, heavy, and
very heavy activities, respectively.
Table 5-21. Actual Inhalation Rates Measured at
Four Ventilation Levels
Mean Inhalation Rate0 (m3/hr)a
Subject Location
Low Medium
Very
Heavy Heavy
All
subjects
Indoor 1.23
(Treadmill
post)
Outdoor 0.88
Total 0.93
1.83
1.96
1.92
3.13 4.13
2.93 4.90
3.01 4.80
" Original data were presented in L/min. Conversion to m3/hr was
obtained as follows:
hr 1000L min
Source: Adapted from Shamoo et al., 1992
The population sample size used in this study was
small and was not selected to represent the general U.S.
population. The training approach employed may not be
cost effective because it was labor intensive; therefore,
this approach may not be viable in field studies especially
for field studies within large sample sizes.
AIHC(1994) - The Exposure Factors Sourcebook -
AIHC (1994) recommends an average adult inhalation
rate of 18 mVday and presents values for children of
various ages. These recommendations were derived from
data presented in U.S. EPA (1989). The newer study by
Layton (1993) was not considered. In addition, the
Sourcebook presents probability distributions derived by
Brorby and Finley (1993). For each distribution, the
@Risk formula is provided for direct use in the @Risk
simulation software (Palisade, 1992). The organization of
this document makes it very convenient to use in support
of Monte Carlo analysis. The reviews of the supporting
studies are very brief with little analysis of their strengths
and weaknesses. The Sourcebook has been classified as
a relevant rather than key study because it is not the
primary source for the data used to make
recommendations in this document. The Sourcebook is
very similar to this document in the sense that it
summarizes exposure factor data and recommends values.
As such, it is clearly relevant as an alternative information
source on inhalation rates as well as other exposure
factors.
5.2.4. Recommendations
In the Ozone Criteria Document prepared by the
U.S. EPA Office of Environmental Criteria and
Assessment, the EPA identified the collapsed range of
activities and its corresponding VR as follows: light
exercise (VE < 23 L/min or 1.4 m3/hr); moderate/ medium
exercise (VE= 24-43 L/min or 1.4-2.6 m3/hr); heavy
exercise (VE= 43-63 L/min or 2.6-3.8 m3/hr); and very
heavy exercise (VE> 64 L/min or 3.8 nrVhr), (Adams,
1993).
Recent peer reviewed scientific papers and an EPA
report comprise the studies that were evaluated in this
Chapter. These studies were conducted in the United
States among both men and women of different age
groups. All are widely available. The confidence ratings
in the inhalation rate recommendations are shown in Table
5-22.
Each study focused on ventilation rates and factors
that may affect them. Studies were conducted among
randomly selected volunteers. Efforts were made to
include men, women, different age groups, and different
kinds of activities. Measurement methods are indirect,
but reproducible. Methods are well described (except for
questionnaires) and experimental error is well
Page
5-22
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
Table 5-22. Confidence in Inhalation Rate Recommendations
Considerations
Rationale
Ratine
Study Elements
• Peer Review
• Accessibility
• Reproducibility
• Focus on factor of interest
• Data pertinent to U.S.
• Primary data
• Currency
• Adequacy of data collection period
• Validity of approach
• Representativeness of the population
• Characterization of variability
• Lack of bias in study design
• Measurement error
Other Elements
• Number of studies
• Agreement between researchers
Overall Rating
Studies are from peer reviewed journal articles and an EPA peer reviewed High
report.
Studies in journals have wide circulation. High
EPA reports are available from the National Technical Information Service.
Information on questionnaires and interviews were not provided. Medium
Studies focused on ventilation rates and factors influencing them. High
Studies conducted in the U.S. High
Both data collection and re-analysis of existing data occurred. Medium
Recent studies were evaluated. High
Effort was made to collect data over time. High
Measurements were made by indirect methods. Medium
An effort has been made to consider age and gender, but not systematically. Medium
An effort has been made to address age and gender, but not systematically. High
Subjects were selected randomly from volunteers and measured in the same High
way-
Measurement error is well documented by statistics, but procedures Medium
measure factor indirectly.
Five key studies and six relevant studies were evaluated.
There is general agreement among researchers using different experimental High
methods.
Several studies exist that attempt to estimate inhalation rates according to High
age, gender and activity.
documented. There is general agreement with these
estimates among researchers.
The recommended inhalation rates for adults,
children, and outdoor workers/athletes are based on the
key studies described in this chapter (Table 5-23).
Different survey designs and populations were utilized in
the studies described in this Chapter. A summary of these
designs, data generated, and their limitations/advantages
are presented in Table 5-24, Excluding the study by
Layton (1993), the population surveyed in all of the key
studies described in this report were limited to the Los
Angeles area. This regional population may not represent
the general U.S. population and may result in biases.
However, based on other aspects of the study design,
these studies were selected as the basis for recommended
inhalation rates.
The selection of inhalation rates to be used for
exposure assessments depends on the age of the exposed
population and the specific activity levels of this
population during various exposure scenarios. The
recommended values for adults, children (including
infants), and outdoor workers/athletes for use in various
exposure scenarios are discussed below. These rates were
calculated by averaging the inhalation rates for each
activity level from the various key studies (see Table 5-
25).
Adults (19-65+ yrs) - Adults in this
recommendation include young to middle age adults (19-
64 yrs), and older adults (65+ yrs). The daily average
inhalation rates for long term exposure for adults are: 11.3
m3/day for women and 15.2 m3/day for men. These
values are averages of the inhalation rates provided for
males and females in each of the three approaches of
Layton (1993) (Tables 5-11 through 5-14). An upper
percentile is not recommended. Additional research and
analysis of activity pattern data and dietary data in the
future is necessary to attempt to calculate upper
percentiles.
The recommended value for the general population
average inhalation rate, 11.3 m3/day for women and 15.2
m3/day for men, is different than the 20 m3/day which has
commonly been assumed in past EPA risk assessments.
In addition, recommendations are presented for various
ages and special populations (athletes, outdoor workers)
which also differ from 20 m3/day. Assessors are
encouraged to use values which most accurately reflect
the exposed population.
For exposure scenarios where the distribution of
activity patterns is known, the following results,
calculated from the studies referenced are shown in Table
5-25. Based on these key studies, the following
recommendations are made: for short term exposures in
Exposure Factors Handbook
August 1997
Page
5-23
-------
Volume I - General Factors
Chapter 5 - Inhalation
Table 5-23. Summary of Recommended Values for Inhalation
Population
Lonp-term Exposures
Infants
<1 year
Children
1-2 years
3-5 years
6-8 years
9-1 1 years
males
females
12-14 years
males
females
15-18 years
males
females
Adults (19-65+ yrs)
females
males
Short-term Exposures
Adults
Rest
Sedentary Activities
Light Activities
Moderate Activities
Heavy Activities
Children
Rest
Sedentary Activities
Light Activities
Moderate Activities
Heavy Activities
Outdoor Workers
Hourly Average
Slow Activities
Moderate Activities
Heavy Activities
Note: See Tables 5-25. 5-26.
Mean Upper Percentile
4.5mVday — -
6.8m3/day — ;_
8.3 m3/day —
lOmVday —
14m3/day —
13 nrVday
15m3/day — •?:
12m3/day
17m3/day
12m3/day — .
11.3m3/day
15.2m3/day ~-
0.4m3/hr —
0.5 m3/hr —
1.0 m3/hr — '•-'
1.6 m3/hr —
3.2 m3/hr
0.3m3/hr — 'i+
0.4m3/hr —
1.0m3/hr — .;',..,-.
1.2m3/hr —
1.9m3/hr — ;v: \\
1.3m3/hr 3.3m3/hr
1.1 m3/hr
1.5m3/hr
2.5m3/hr "V: '- ^
and 5-27 for reference studies.
which distribution of activity patterns are specified, the
recommended average rates are 0.4 m3/hr during rest; 0.5
m3/hr for sedentary activities; 1.0 m3/hr for light
activities; 1.6 irrVhr for moderate activities; and 3.2 m3/hr
for heavy activities.
Children (18 yrs old or less including infants) - For
the purpose of this recommendation, children are defined
as males and females between the ages of 1-18 years old,
while infants are individuals less than 1 year old. The
inhalation rates for children are presented below
according to different exposure scenarios. The daily
inhalation rates for long-term dose assessments, are based
on the first approach of Layton (1993) (Table 5-11) and
are summarized in Table 5-26.
Based on the key study results (i.e., Layton, 1993),
the recommended daily inhalation rate for infants
(children less than 1 yr), during long-term dose
assessments is 4.5 mVday. For children 1-2 years old, 3-
5 years old, and 6-8 years old, the recommended daily
inhalation rates are 6.8 m3/day, 8.3 mVday, and 10
mVday, respectively. Recommended values for children
aged 9-11 years are 14 m3/day for males and 13 m3/day
for females. For children aged 12-14 years and 15-18
years, the recommended values are shown in Table 5-23.
For short-term exposures for children aged 18 years
and under, in which activity patterns are known, the data
are summarized in Table 5-27. For short term exposures,
the recommended average hourly inhalation rates are
based on these key studies. They are averaged over each
activity held as follows: 0.3 m3/hr during rest; 0.4 m3/hr
for sedentary activities; 1.0 m3/hr for light activities; 1.2
m3/hr for moderate activities; and 1.9 m3/hr for heavy
activities. The recommended short-term exposure data
also include infants (less than 1 yr). These values
represent averages of the activity level data from key
studies (Table 5-27).
Outdoor Worker - Inhalation rate data for outdoor
workers/athlete are limited. However, based on the key
studies (Linn et al., 1992 and 1993), the recommended
average hourly inhalation rate for outdoor workers is
1.3 m3/hr and the upper-percentile rate is 3.3 m3/hr (see
Tables 5-5 and 5-8). This is calculated as the weighted
mean of the 99th percentile values reported for the
individuals on Panels 1 and 7 in Tables 5-5 and the 19
subjects in Table 5-8. The recommended average
inhalation rates for outdoor workers based on the activity
levels categorized as slow (light activities), medium
(moderate activities), and fast (heavy activities) are 1.1
m3/hr, 1.5 m3/hr, and 2.5 m3/nr, respectively. These
values are based on the data from Linn et al. (1992 and
1993) and are the weighted mean of the values for the
individuals on Panels 1 and 7 in Table 5-5 and the 19
outdoor workers in Table 5-9. Inhalation rates may be
higher among outdoor workers/athletes because levels of
activity outdoors may be higher. Therefore, this
subpopulation group may be more susceptible to air
pollutants and are considered a "high-risk" subgroup
(Shamoo et al., 1991; Linn et al., 1992).
Page
5-24
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 5 - Inhalation
£
J
4
ft
C
Table 5-24. Summary of Inhalatio
]
i
t
i
•3
1
c
C
S
C
Survey Time Period
Pooulation Sun
>
1
en
w
I
£
I
z
g
3
m
g
}5
2
£ 1:1 •- ^
i !} lit iii IP.
>» tfl.S /j e£ *o ™ ^ .a -^ .0 ;£ £3
1 If! |l| lf| || ll
1 lllfll fil 1|M
I til ill {fl-lt&i
x sal 111 1131 33E1
i! !i i- ! ' f.i
1* flsl I - IS.
22 '£ 5 rt JU ti_ t*_O
o 2 J3 e £ >, ° ° "•&
S-o s i «* ^ *i i — 1 Js
in sjtp fi it
c t£l -5 >» S -3 c n 'c "S ^ 'S
f-o -a :5'OesSa> Srt t«w
§« Q«inSS 5.S* 5^*-
•o
> ^ - " >
*,*£% "S S §
g* eO G. ^ • Jtf Cs * g
Jill ij ijl II
is^& fi jii o=
.ti^i ii tl1 if
EC- SO 50- ^S^ «•-
tflfe. cT«>- >iS° 3
- ?
1" s| >,^: S2 r-
Tl^ 5*
>^^jga. _§ "*3 'o ^ o >• *a" '
111 ii i i|ii|ii
•ogo^" — ' •£ u * 5" -a o c
111 2 1 1 1 1 1 1 1 1 ||
e. i l-a
g = 2 J »
o ^ •^*- o~ S ii •— ' >:
-gcg j| •§ ^ l-o 1
!| 3 J o. °-!E| S"^ =
H— rted :2"o.2*t5^ 2^J>
iSl£z - Q BJ J < §. Us
O V3
"='>•? S
.2 -g ^ - S ^
o ** -S *^ "c 3 ™
l!i ll iii
o 1
ii R S
1 ii I 1 i
C3 S t/T ~"
•" ' Si « >» * g 5-
! f1 ! I S
i ll i 1 S
o 3 S = B c
•g ^ | o J ^
CQ COC G CQ
g S g
O^ CT\ ox
i-j i-. ^H in
r r • §
-3- «s rt rt ""•
S B S 3 <;
'-' o O O CU
5 o o M
Sill ri
U S % % D
a
E
|
I
p
c
II
ta
i
e
t
X
II
a
i
E
'^
1
II
^
1
Exposure Factors Handbook
August 1997
5-25
-------
Volume I - General Factors
Chapter 5 - Inhalation
Table 5-25.
Summary of Adult Inhalation Rates for Short-Term Exposure Studies
Arithmetic Mean (m3/hr)
Activity Level
Rest
0.5
..
0.4
0.4
--
Sedentary
0.5
0.6
0.4
—
--
Light
1.4
1.2
0.7
0.6
1.0
Moderate
2.4
1.8
1.4
1.5
1.6
High
3.3
--
3.6
3.0
3.0
Reference
Adams, 1993 (Lab protocols)
Adams, 1993 (Field protocols)
Layton, 1993 (Short-term
exposure)
Layton, 1993 (3rd approach)
Linn et al., 1992
Table 5-26. Summary of Children's (18 years old or less) Inhalation Rates for Long-Term Exposure Studies"
Age Males
less than 1 yr
1-2 years
3-5 years
6-8 years
9-11 years 14
12-14 years 15
15-18 years 17
Arithmetic Mean (m3/day)
Males and
Females Females
4.5
6.8
8.3
10
13
12
12
Reference
Layton, 1993
Layton, 1993
Layton, 1993
Layton, 1993
Layton, 1993
Layton, 1993
Layton, 1993
* Layton, 1993 1st approach.
Table 5-27. Summary of Children's Inhalation Rates for Short-Term Exposure Studies
Arithmetic Mean
(nvVhr)
Activity Level
Rest Sedentary
0.4 0.4
~
0.2 0.3
..
„
Light
0.8
—
0.5
1.8
0.8
Moderate
-
0.9
1.0
2.0
1.0
High
-
-
2.5
2.2
11
Reference
Adams, 1993 (Lab protocols)
Adams, 1993 (Field protocols)
Layton, 1993 (Short-term data)
Spier etal., 1992 (10-12 yrs)
Linn etal., 1992 (10-12 yrs)
Page
5-26
Exposure Factors Handbook
August 1997
-------
Volume 1 - General Factors
Chapter 5 - Inhalation
5.3. REFERENCES FOR CHAPTER 5
Adams, W.C. (1993) Measurement of breathing rate
and volume in routinely performed daily activities,
Final Report. California Air Resources Board
(GARB) Contract No. A033-205. June 1993. 185
Pgs-
American Industrial Health Council (AIHC). (1994)
Exposure factors sourcebook. AIHC, Washington,
DC.
Basiotis, P.P.; Thomas, R.G.; Kelsay, J.L.; Mertz, W.
(1989) Sources of variation in energy intake by men
and women as determined from one year's daily
dietary records. Am. J. Clin. Nutr. 50:448-453.
Benjamin, G.S. (1988) "The lungs." In:
Fundamentals of Industrial Hygiene, Third Edition,
Plog, B.A., ed. Chicago, IL: National Safety
Council, p. 31-45.
Brorby, G.; Finley, B. (1993) Standard probability
density functions for routine use in environmental
health risk assessment. Presented at the Society of
Risk Analysis Meeting, December 1993, Savannah,
GA.
ICRP. (1981) International Commission on
Radiological Protection. Report of the task group
on reference man. New York: Pergammon Press.
Layton, D.W. (1993) Metabolically consistent
breathing rates for use in dose assessments. Health
Physics 64(l):23-36.
Linn, W.S.; Shamoo, D.A.; Hackney, J.D. (1992)
Documentation of activity patterns in "high-risk"
groups exposed to ozone in the Los Angeles area.
In: Proceedings of the Second EPA/AWMA
Conference on Tropospheric Ozone, Atlanta, Nov.
1991. pp. 701-712. Air and Waste Management
Assoc., Pittsburgh, PA.
Linn, W.S.; Spier, C.E.; Hackney, J.D. (1993) Activity
patterns in ozone-exposed construction workers. J.
Occ. Med. Tox. 2(1):1-14.
Menzel, D.B.; Amdur, M.O. (1986) Toxic responses of
the respiratory system. In: Klaassen, C.; Amdur,
M.O.; Doull, J., eds. Toxicology, The Basic Science
of Poisons. 3rd edition. New York: MacMillan
Publishing Company.
Najjar, M.F.; Rowland, M. (1987) Anthropometric
reference data and prevalence of overweight: United
States. 1976-80. Hyattsville, MD: National Center
for Health Statistics. U.S. Department of Health and
Human Services: DHHS Publication No. (PHS)87-1688.
Palisade. (1992) ©Risk User Guide. Newfield, NY:
Palisade Corporation.
Sallis, J.F.; Haskell, W.L.; Wood, P.O.; Fortmann, S.P.;
Rogers, T.; Blair, S.N.; Paffenbarger, Jr., R.S.
(1985) Physical activity assessment methodology in
the Five-City project. Am. J. Epidemiol. 121:91-
106.
Shamoo, D.A.; Trim, S.C.; Little, D.E.; Linn, W.S.;
Hackney, J.D. (1990) Improved quantitation of air
pollution dose rates by improved estimation of
ventilation rate. In: Total Exposure Assessment
Methodology: A New Horizon, pp. 553-564. Air
and Waste Management Assoc., Pittsburgh, PA.
Shamoo, D.A.; Johnson, T.R.; Trim, S.C.; Little, D.E.;
Linn, W.S.; Hackney, J.D. (1991) Activity patterns
in a panel of outdoor workers exposed to oxidant
pollution. J. Expos. Anal. Environ. Epidem.
l(4):423-438.
Shamoo, D.A.; Trim, S.C.; Little, D.E.; Whynot, J.D.;
Linn, W.S. (1992) Effectiveness of training subjects
to estimate their level of ventilation. J. Occ. Med.
Tox. l(l):55-62.
Spier, C.E.; Little, D.E.; Trim, S.C.; Johnson, T.R.;
Linn, W.S.; Hackney, J.D. (1992) Activity patterns
in elementary and high school students exposed to
oxidant pollution. J. Exp. Anal. Environ. Epid.
2(3):277-293.
U.S. EPA. (1985) Development of statistical
distributions or ranges of standard factors used in
exposure assessments. Washington, DC: Office of
Health and Environmental Assessment; EPA report
No. EPA 600/8-85-010. Available from: NTIS,
Springfield, VA; PB85-242667.
U.S. EPA. (1989) Exposure factors handbook.
Washington, DC: Office of Research and
Development, Office of Health and Environmental
Assessment. EPA/600/18-89/043.
U.S. EPA. (1992) Guidelines for exposure assessment.
Washington, DC: Office of Research and
Development, Office of Health and Environmental
Assessments. EPA/600/Z-92/001.
U.S. EPA. (1994) Methods for derivation of inhalation
reference concentrations and application of
inhalation dosimetry. Washington, DC: Office of
Health and Environmental Assessment. EPA/600/8-
90/066F.
Exposure Factors Handbook
August 1997
Page
5-27
-------
-------
Volume I- General Factors
Appendix 5A
APPENDIX 5A
VENTILATION DATA
Exposure Factors Handbook
August 1997
Page
5A-1
-------
-------
Volume I - General Factors
Appendix 5A
Table 5A-1. Mean Minute Ventilation (VF. L/min) by Group and Activity for Laboratory Protocols
Activity
Young Children3
Children
Adult Females
Adult Male
Lying
Sitting
Standing
Walking
Running
1.5 mph
1.875 mph
2.0 mph
2.25 mph
2.5 mph
3.0 mph
3.3 mph
4.0 mph
3.5 mph
4.0 mph
4.5 mph
5.0 mph
6.0 mph
6.19
6.48
6.76
10.25
10.53
DNP
11.68
DNP
DNP
DNP
DNP
DNP
DNP
DNP
DNP
DNP
7.51
7.28
8.49
DNP
DNP
14.13
DNP
15.58
17.79
DNP
DNP
26.77
31.35
37.22
DNP
DNP
7.12
7.72
8.36
DNP
DNP
DNP
DNP
20.32
24.20
DNP
DNP
DNP
46.03b
47.86b
50.78b
DNP
8.93
9.30
10.65
DNP
DNP
DNP
DNP
24.13
DNP
27.90
36.53
DNP
DNP
57.30
58.45
65.66b
Young Children, male and female 3-5.9 yr olds; Children, male and female 6-12.9 yr olds; Adult Females, adolescent,
young to middle-aged, and older adult females; Adult Males, adolescent, young to middle-aged, and older adult males';
b DNP, group did not perform this protocol or N was too small for appropriate mean comparisons
Older adults not included in the mean value since they did not perform running protocol at particular speeds
Source: Adams. 1993.
Table 5A-2. Mean Minute Ventilation (VK, L/min) by Group and Activity for Field Protocols
Activity
Young Children
Children
Play
Car Driving
Car Riding
Yardwork
Housework
Car Maintenance
Mowing
Woodworking
11.31
DNP
DNP
DNP
DNP
DNP
DNP
DNP
17.89
DNP
DNP
DNP
DNP
DNP
DNP
DNP
DNP
8.95
8.19
19.23e
17.38
DNP
DNP
DNP
DNP
10.79
9.83
26.07b/31.89c
DNP
23.21d
36.55e
24.42e
Young Children, male and female 3-5.9 yr olds; Children, male and female 6-12.9 yr olds; Adult Females, adolescent,
young to middle-aged, and older adult females; Adult Males, adolescent, young to middle-aged, and older adult males;
DNP, group did not perform this protocol or N was too small for appropriate mean comparisons;
Mean value for young to middle-aged adults only
Mean value for older adults only
Older adults not included in the mean value since they did not perform this activity.
Adolescents not included in mean value since they did not perform this activity
Source: Adams. 1993.
Exposure Factors Handbook
August 1997
Page
5A-3
-------
Volume I - General Factors
Appendix 5A
a
b
c
d
c
Subj. #
1761
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1778
1779
1780
1781
Mean
SD
Anthropometric Data, Job Categories, Calibration Results
Age (years) Ht. (in.) Wt. (Ib.) Ethnic Group3
26
29
32
30
31
34
32
32
26
39
32
39
23
42
29
35
40
37
38
33
71 180
63 135
71 165
73 145
67 170
74 220
69 155
77 230
70 180
66 150
71 260
69 170
68 150
67 150
70 180
76 220
70 175
75 242
65 165
70 181
4 36
Abbreviations are interpreted as follows. Ethnic Group: Asn =
White
Job: Car =
carpenter,
GCW = general construction worker, Irn
Site: Hosp = hospital buidling, Ofc = medical office complex.
Wht
Asn
Elk
Wht
His
Wht
Blk
Wht
Wht
Wht
Wht
Wht
His
Wht
His
Ind
Wht
His
His
Jobb
GCW
GCW
Car
GCW
Car
Car
GCW
Car
Car
Car
Car
Irn
Car
Irn
Car
Car
Car
Irn
Lab
Asian-Pacific, Blk = Black, His
= ironworker, Lab
Calibration data
= laborer
Sitec
Ofc
Ofc
Ofc
Ofc
Ofc
Ofc
Ofc
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Calibration
HR Ranged
69-108
80-112
56-87
66-126
75-112
59-114
62-152
69-132
63-106
88-118
83-130
77-128
68-139
76-118
68-152
70-129
72-140
68-120
66-121
70-123
8-16
r20
.91
.95
.95
.97
.89
.98
.95
.99
.89
.91
.97
.95
.98
.88
.99
.94
.99
.98
.89
.94
.04
= Hispanic, Ind = American Indian, Wht =
H R range = range of heart rates in calibration study
r2 = coefficient of determination (proportion of ventilation rate variability explainable by heart rate variability under calibration-study
conditions.
ource: Linnet
using quadratic prediction equation).
al.. 1993.
Table 5A-4
Gerider/Age
(y)
Males
Under 3
3 to < 10
10 to < 18
18 to < 30
30 to < 60
60 +
Female^
Under 3
3 to < 10
10to< 18
18 to < 30
30 to < 60
60+
Statistics of the Age/Gender Cohorts Used to Develop Regression Equations for Predicting Basal Metabolic Rates (BMR)
BMR
MJd'1
1.51
4.14
5.86
6.87
6.75
5.59
1.54
3.85
5.04
5.33
5.62
4.85
tSD
0.918
0.498
1.171
0.843
0.872
0.928
0.915
0.493
0.780
0.721
0.630
0.605
cva
0.61
0.12
0.20
' 0.12
0.13
0.17
0.59
0.13
0.15
0.14
0.11
0.12
Body Weight
(kg)
6.6
21
42
63
64
62
6.9
21
38
53
61
56
Nb
162
338
734
2879
646
50
137
413
575
829
372
38
BMR Equation0
0.249 bw- 0.127
0.095 bw + 2. 110
0.074 bw + 2.754
0.063 bw + 2.896
0.048 bw + 3.653
0.049 bw + 2.459
0.244 bw- 0.130
0.085 bw + 2.033
0.056 bw + 2.898
0.062 bw + 2.036
0.034 bw + 3.538
0.038 bw + 2.755
rd
0.95
0.83
0.93
0.65
0.6
0.71
0.96
0.81
0.8
0.73
0.68
0.68
a Coefficient of variation (SD/mean)
N = number of subjects
0 Body weight (bw) in kg
d coefficient of correlation
Source: Lavton. 1993. ___ — — '
Page
5A-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors I,
Appendix 5A -=:-BlE
I
CO
0
1
3
o
'E
>
1
s
lit
•s
c
a
.0
O
1
,3
•5
°y
^
c
Si
S
5
C?
Q
8
3
«
O
rt
•a
S
OJ
en
iri
In
2
"rt
°
*
m
cs
— .
S
M> £
c ^
Q
o "o H
^ *•* 7>
•al
s
1 -
.*•
| H
cd
S
a
14-
>
.£>
">
1 >
1
a
(4—
>
00
1 >
&
3
CO
a
^ o S^ oo i— «^^o^ >/">
^H °^ ^ °° r-o-^co ^
o o o o oo00 *L
R ?3 « 22 22§^ 5?
O O fO C4 OO ^^ O \O "QO
5 a
° s
s »
?) rn
c* CS — i — -*
1 | 11 1
t^ VO 2 0 gj
«
^K°J25SO2 ^5 2 ^ooc
^O§?O^ ^O § OOtrj^H^
C^tO'nm'^t-VO C^co m Tt-^-CSCN
CSOC^*OVO ^OVl O OTi-'nCN
CO
oo dSd o\lo opidco"^"'1^'"'
C<1
J j
1 E s
°i o ^
1-1 - ? S
tf *O^ . - *~* ^
-j ^ooS ^.^^^ ^^^^^i
c* • r^ >> >^ vo *O e ^
g ISf -^-"VV |^Z3««> -g
^o o-rg^JS-S Co66^^>>cv> M
- "e^^l-^i ^""••a'-a" •§ .-«'«'|| g^lll
j|^|s^o§llllll isiillillss
^^^^.u^vof^oo (5,0-^cs m^i->
S
f
II
(*-*
• *
g
J
o
g
11
g-,,
c
•2 •*
CO >
I BO
=3 ^
•S 2 >
2 S *
3 II II
<§ r^ > g
ti S *
'§•32 fc
s g> ^ob
IV S| j.
11 J e
& 1 „ ^ c?
Exposure Factors Handbook Page
August 1997 5A-5
-------
Volume I - General Factors
Appendix 5A
f%
Table 5A-6. Estimated Minute Ventilation Associated with Activity Level for Average Male Adult
Level of work L/min
Light 13
Light 19
Light 25
Moderate 30
Moderate 35
Moderate 40
Heavy 55
Heavy 63
Very heavy 72
Very heavy 85
Severe 100+
Representative activities
Level walking at 2 mph; washing clothes
Level walking at 3 mph; bowling; scrubbing floors
Dancing; pushing wheelbarrow with 15-kg load; simple construction;
stacking firewood
Easy cycling; pushing wheelbarrow with 75-kg load; using sledgehammer
Climbing stairs; playing tennis; digging with spade
Cycling at 13 mph; walking on snow; digging trenches
Cross-country skiing; rock climbing; stair climbing
with load; playing squash or handball; chopping
with axe
Level running at 10 mph; competitive cycling
Competitive long distance running; cross-country skiing
a Average adult assumed to weigh 70 kg.
Source: Adapted from U.S. EPA, 1985
Page
5A-6
Exposure Factors Handbook
August 1997
-------
Volume I- General Factors
Appendix 5A
S
>,
>£
a
g
S
. c/3
a
.0
<8
i
c
•S
=
£
a
i
'S
3
£
, !,_
2 1
§1
•= -s
1 S
ge
C
S
r £
S 3
c
c
a
53
*" 4>
1 I
C
S
S
S s
jj Qj
C
c
g> u
I 1
a
X
£
,_,
t I
m in f- cs
r^^rit^t^oCKoi^Qol^ooen
^^ ^ j^j ^ ,^ Q esoooN voa> oo voo
o o enenenM-i-'O en oc-lO
en oo \o^o\ooor^tn *o en -*oo
iiiiiiiiii^iiiliii^ii^^^^^,; ' oi ! ' ' i ' ' r^4
S 5 2 2 S2 2 S S R § S3
r-l o ^ ^ ^ „ „ m „ ^ ^. M
S °° 2 S!n'n2Rti""00S°2 SS
en TJ -*
Ul
Su.Su.Sti.Sii.Sii.SB.Su.Su.S(i.Su.Su.Stt.Sii.S u.Su.Su.Su-Su.S
a . 22
.S S
® ^ o
o S.
1 <
§ ii 1
11 S g
Exposure Factors Handbook
August 1997
Page
5A-7
-------
-------
Volume I - General Factors
Chapter 6 - Dermal
6. DERMAL ROUTE
Dermal exposure can occur during a variety of
activities in different environmental media and
microenvironments (U.S. EPA, 1992). These include:
• Water (e.g., bathing, washing, swimming);
• Soil (e.g., outdoor recreation, gardening,
construction);
• Sediment (e.g., wading, fishing);
• Liquids (e.g., use of commercial products);
• Vapors/fumes (e.g., use of commercial
products); and
• Indoors (e.g., carpets, floors, countertops).
The major factors that must be considered when
estimating dermal exposure are: the chemical
concentration in contact with the skin, the potential dose,
the extent of skin surface area exposed, the duration of
exposure, the absorption of the chemical through the skin,
the internal dose, and the amount of chemical that can be
delivered to a target organ (i.e., biologically effective
dose) (see Figure 6-1). A detailed discussion of these
factors can be found in Guidelines for Exposure
Assessment (U.S. EPA, 1992a).
This chapter focuses on measurements of body
surface areas and various factors needed to estimate
dermal exposure to chemicals in water and- soil.
Information concerning dermal exposure to pollutants in
indoor environments is limited. Useful information
concerning estimates of body surface area can be found in
"Development of Statistical Distributions or Ranges of
Standard Factors Used in Exposure Assessments" (U.S.
EPA, 1985). "Dermal Exposure Assessment: Principles
and Applications (U.S. EPA, 1992b), provides detailed
information concerning dermal exposure using a stepwise
guide in the exposure assessment process.
The available studies have been classified as either
key or relevant based on their applicability to exposure
assessment needs and are summarized in this chapter.
Recommended values are based on the results of the key
studies. Relevant studies are presented to provide an
added perspective on the state-of-knowledge pertaining to
dermal exposure factors. All tables and figures presenting
data from these studies are shown at the end of this
chapter.
6.1. EQUATION FOR DERMAL DOSE
The average daily dose (ADD) is the dose rate
averaged over a pathway-specific period of exposure
expressed as a daily dose on a per-unit-body-weight basis.
The ADD is used for exposure to chemicals with non-
carcinogenic non-chronic effects. For compounds with
carcinogenic or chronic effects, the lifetime average daily
dose (LADD) is used. The LADD is the dose rate
averaged over a lifetime.
For dermal contact with chemicals in soil or water,
dermally absorbed average daily dose can be estimated by
(U.S. EPA, 1992b):
ADD
BW x AT
(Eqn. 6-1)
where:
ADD
DAeve
EV
ED
EF
SA
BW
AT
average daily dose (mg/kg-day);
absorbed dose per event (mg/cm2-event);
event frequency (events/day);
exposure duration (years);
exposure frequency (days/year);
skin surface area available for contact (cm2);
body weight (kg);, and
averaging time (days) for noncarcinogenic
effects, AT = ED and for carcinogenic effects,
AT = 70 years or 25,550 days.
This method is to be used to calculate the absorbed dose
of a chemical. Total body surface area (SA) is assumed
to be exposed for a period of time (ED).
For dermal contact with water, the DAevent is
estimated with consideration for the permeability
coefficient from water, the chemical concentration in
water, and the event duration. The approach to estimate
DAevent is different for inorganic and organic compounds.
The nonsteady-state approach to estimate the dermally
absorbed dose from water is recommended as the
preferred approach for organics which exhibit octanol-
water partitioning (U.S. EPA, 1992b). First, this
approach 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 approach accounts for uptake
that can occur after the actual exposure event due to
absorption of residual chemical trapped in skin tissue.
Use of the nonsteady-state model for organics has
implications for selecting permeability coefficient (Kp)
values (U.S. EPA, 1992b). It is recommended that the
traditional steady-state approach be applied to inorganics
(U.S. EPA, 1992b). Detailed information concerning how
to estimate absorbed dose per event (DAevent) and Kp
Exposure Factors Handbook
August 1997
Page
6-1
-------
Volume I - General Factors
Chapter 6 - Dermal
values can be found in Section 5.3.1 of "Dermal Exposure
Assessment: Principles and Applications" (U.S. EPA,
I992b).
For dermal contact with contaminated soil,
estimation of the DAevem is different from the estimation
for dermal contact with chemicals in water. It is based on
the concentration of the chemical in soil, the adherence
factor of soil to skin, and the absorption fraction.
Information for DAevent estimation from soil contact can
be found in U.S. EPA (I992b), Section 6.4.
The apparent simplicity of the absorption fraction
(percent absorbed) makes this approach appealing.
However, it is not practical to apply it to water contact
scenarios, such as swimming, because of the difficulty in
estimating the total material contacted (U.S. EPA, I992b).
It is assumed that there is essentially an infinite amount of
material available, and that the chemical will be replaced
continuously, thereby increasing the amount of material
(containing the chemical) available by some large
unknown amount. Therefore, the permeability coefficient
-based approach is recommended over the absorption
fraction approach for determining the dermally absorbed
dose of chemicals in aqueous media.
Before the absorption fraction approach can be
used in soil contact scenarios, the contaminant
concentration in soil must be established. Not all of the
chemical in a layer of dirt applied to skin may be
bioavailable, nor is it assumed to be an internal dose.
Because of the lack of Kp data for compounds bound to
soil, and reduced uncertainty in defining an applied dose,
the absorption fraction-based approach is suggested for
determining the internal dose of chemicals in soil. More
detailed explanation of the equations, assumptions, and
approaches can be found in "Dermal Exposure
Assessment: Principles and Applications" (U.S. EPA.
I992b).
6.2. SURFACE AREA
6.2.1. Background
The total surface area of skin exposed to a
contaminant must be determined using measurement or
estimation techniques before conducting a dermal
exposure assessment. Depending on the exposure
scenario, estimation of the surface area for the total body
or a specific body part can be used to calculate the contact
rate for the pollutant. This section presents estimates for
total body surface area and for body parts and presents
information on the application of body surface area data.
6.2.2. Measurement Techniques
Coating, triangulation, and surface integration are
direct measurement techniques that have been used to
measure total body surface area and the surface area of
specific body parts. Consideration has been given for
differences due to age, gender, and race. The results of
the various techniques have been summarized in
"Development of Statistical Distributions or Ranges of
Standard Factors Used in Exposure Assessments" (U.S.
EPA, 1985). The coating method consists of coating
either the whole body or specific body regions with a
substance of known or measured area. Triangulation
consists of marking the area of the body into geometric
figures, then calculating the figure areas from their linear
dimensions. Surface integration is performed by using a
planimeter and adding the areas.
The triangulation measurement technique
developed by Boyd (1935) has been found to be highly
reliable. It estimates the surface area of the body using
geometric approximations that assume parts of the body
resemble geometric solids (Boyd, 1935). More recently,
Popendorf and Leffingwell (1976), and Haycock et al.
(1978) have developed similar geometric methods that
assume body parts correspond to geometric solids, such as
the sphere and cylinder. A linear method proposed by
DuBois and DuBois (1916) is based on the principle that
the surface areas of the parts of the body are proportional,
rather than equal to the surface area of the solids they
resemble.
In addition to direct measurement techniques,
several formulae have been proposed to estimate body
surface area from measurements of other major body
dimensions (i.e., height and weight) (U.S. EPA, 1985).
Generally, the formulae are based on the principles that
body density and shape are roughly the same and that the
relationship of surface area to any dimension may be
represented by the curve of central tendency of their
plotted values or by the algebraic expression for the curve.
A discussion and comparison of formulae to determine
total body surface area are presented in Appendix 6A.
6.2.3. Key Body Surface Area Studies
U.S. EPA (1985) - Development of Statistical
Distributions or Ranges of Standard Factors Used in
Exposure Assessments - U.S. EPA (1985) analyzed the
direct surface area measurement data of Gehan and
George (1970) using the Statistical Processing System
(SPS) software package of Buhyoff et al. (1982). Gehan
and George (1970) selected 401 measurements made by
Page
6-2
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
Boyd (1935) that were complete for surface area, height,
weight, and age for their analysis. Boyd (1935) had
reported surface area estimates for 1,114 individuals using
coating, triangulation, or surface integration methods
(U.S. EPA, 1985).
U.S. EPA (1985) used SPS to generate equations
to calculate surface area as a function of height and
weight. These equations were then used to calculate body
surface area distributions of the U.S. population using the
height and weight data obtained from the National Health
and Nutrition Examination Survey (NHANES) II and the
computer program QNTLS of Rochon and Kalsbeek
(1983).
The equation proposed by Gehan and George
(1970) was determined by U.S. EPA (1985) to be the best
choice for estimating total body surface area. However,
the paper by Gehan and George (1970) gave insufficient
information to estimate the standard error about the
regression. Therefore, U.S. EPA (1985) used the 401
direct measurements of children and adults and reanalyzed
the data using the formula of Dubois and Dubois (1916)
and SPS to obtain the standard error (U.S. EPA, 1985).
Regression equations were developed for specific
body parts using the Dubois and Dubois (1916) formula
and using the surface area of various body parts provided
by Boyd (1935) and Van Graan (1969) in conjunction
with SPS. Regression equations for adults were
developed for the head, trunk (including the neck),-upper
extremities (arms and hands, upper arms, and forearms)
and lower extremities (legs and feet, thighs, and lower
legs) (U.S. EPA, 1985). Table 6-1 presents a summary of
the equation parameters developed by U.S. EPA (1985)
for calculating surface area of adult body parts. Equations
to estimate the body part surface area of children were not
developed because of insufficient data.
Percentile estimates of total surface area and
surface area of body parts developed by U.S. EPA (1985)
using the regression equations and NHANES II height and
weight data are presented in Tables 6-2 artd 6-3 for adult
males and adult females, respectively. The calculated
mean surface areas of body parts for men and women are
presented in Table 6-4. The standard deviation, the
minimum value, and the maximum value for each body
part are included. The median total body surface area for
men and women and the corresponding standard errors
about the regressions are also given. It has been assumed
that errors associated with height and weight are
negligible (U.S. EPA, 1985). The data in Table 6-5
present the percentage of total body surface by body part
for men and women.
Percentile estimates for total surface area of male
and female children presented in Tables 6-6 and 6-7 were
calculated using the total surface area regression equation,
NHANES II height and weight data, and using QNTLS.
Estimates are not included for children younger than 2
years old because NHANES height data are not available
for this age group. For children, the error associated with
height and weight cannot be assumed to be zero because
of their relatively small sizes. Therefore, the standard
errors of the percentile estimates cannot be estimated,
since it cannot be assumed that the errors associated with
the exogenous variables (height and weight) are
independent of that associated with the model; there are
insufficient data to determine the relationship between
these errors.
Measurements of the surface area of children's
body parts are summarized as a percentage of total surface
area in Table 6-8. Because of the small sample size, the
data cannot be assumed to represent the average
percentage of surface area by body part for all children.
Note that the percent of total body surface area
contributed by the head decreases from childhood to
adult, while the percent contributed by the leg increases.
Phillips et al. (1993) - Distributions of Total Skin
Surface Area to Body Weight Ratios - Phillips et al.
(1993) observed a strong correlation (0.986) between
body surface area and body weight and studied the effect
of using these factors, as independent variables in the
LADD equation. Phillips et al. (1993) concluded that,
because of the correlation between these two variables,
the use of body surface area to body weight (SA/BW)
ratios in human exposure assessments is more appropriate
than treating these factors as independent variables.
Direct measurement (coating, triangulation, and surface
integration) data from the scientific literature were used
to calculate body surface area to body weight (SA/BW)
ratios for three age groups (infants aged 0 to 2 years,
children aged 2.1 to 17.9 years, and adults 18 years and
older). These ratios were calculated by dividing body
surface areas by corresponding body weights for the 401
individuals analyzed by Gehan and George (1970) and
summarized by U.S. EPA (1985). Distributions of
SA/BW ratios were developed and summary statistics
were calculated for each of the three age groups and the
combined data set. Summary statistics for these
populations are presented in Table 6-9. The shapes of
these SA/BW distributions were determined using
Exposure Factors Handbook
August 1997
Page
6-3
-------
Volume I - General Factors
Chapter 6 - Dermal
D'Agostino's test. The results indicate that the SA/BW
ratios for infants are lognormally distributed and the
SA/BW ratios for adults and all ages combined are
normally distributed (Figure 6-2). SA/BW ratios for
children were neither normally nor lognormally
distributed. According to Phillips et al. (1993), SA/BW
ratios should be used to calculate LADDs by replacing the
body surface area factor in the numerator of the LADD
equation with the SA/BW ratio and eliminating the body
weight factor in the denominator of the LADD equation.
The effect of gender and age on SA/BW
distribution was also analyzed by classifying the 401
observations by gender and age. Statistical analyses
indicated no significant differences between SA/BW
ratios for males and females. SA/BW ratios were found
to decrease with increasing age.
6.2.4. Relevant Surface Area Studies
Murray and Burmaster (1992) - Estimated
Distributions for Total Body Surface Area of Men and
Women in the United States - In this study, distributions
of total body surface area for men and women ages 18 to
74 years were estimated using Monte Carlo simulations
based on height and weight distribution data. Four
different formulae for estimating body surface area as a
function of height and weight were employed: Dubois
and Dubois (1916); Boyd (1935); U.S. EPA (1985); and
Costeff (1966). The formulae of Dubois and Dubois
(1916); Boyd (1935); and U.S. EPA (1985) are based on
height and weight. They are discussed in Appendix 6A.
The formula developed by Costeff (1966) is based on 220
observations that estimate body surface area based on
weight only. This formula is:
4W+7/W+90
(Eqn. 6-2)
where:
SA = Surface Area (m2); and
W = Weight (kg).
Formulae were compared and the effect of the correlation
between height and weight on the body surface area
distribution was analyzed.
Monte Carlo simulations were conducted to
estimate body surface area distributions. They were based
on the bivariate distributions estimated by Brainard and
Burmaster (1992) for height and natural logarithm of
weight and the formulae described above. A total of
5,000 random samples each for men and women were
selected from the two correlated bivariate distributions.
Body surface area calculations were made for each
sample, and for each formula, resulting in body surface
area distributions. Murray and Burmaster (1992), found
that the body surface area frequency distributions were
similar for the four models (Table 6-10). Using the U.S.
EPA (1985) formula, the median surface area values were
calculated to be 1.96 m2 for men and 1.69 m2 for women.
The median value for women is identical to that generated
by U.S. EPA (1985) but differs for men by approximately
1 percent. Body surface area was found to have
lognormal distributions for both men and women (Figure
6-3). It was also found that assuming correlation between
height and weight influences the final distribution by less
than 1 percent.
AIHC (1994) - Exposure Factors Sourcebook - The
Exposure Factors Sourcebook (AIHC, 1994) provides
similar body surface area data as presented here.
Consistent with this document, average and percentile
values are presented on the basis of age and gender. In
addition, the Sourcebook presents point estimates of
exposed skin surface areas for various scenarios on the
basis of several published studies. Finally, the Sourcebook
presents probability distributions based on U.S. EPA
(1989) and as derived by Thompson and Burmaster
(1991); Versar (1991); and Brorby and Finley (1993).
For each distribution, the ©Risk formula is provided for
direct use in the ©Risk simulation software (Palisade,
1992). The organization of this document, makes it very
convenient to use in support of Monte Carlo analysis.
The reviews of the supporting studies are very brief with
little analysis of their strengths and weaknesses. The
Sourcebook has been classified as a relevant rather than
key study because it is not the primary source for the data
used to make recommendations in this document. The
Sourcebook is very similar to this document in the sense
that it summarizes exposure factor data and recommends
values. As such, it is clearly relevant as an alternative
information source on body surface area as well as other
exposure factors.
6.2.5. Application of Body Surface Area Data
In many settings, it is likely that only certain parts
of the body are exposed. All body parts that come in
contact with a chemical must be considered to estimate
the total surface area of the body exposed. The data in
Table 6-4 may be used to estimate the total surface area of
Page
6-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
the particular body part(s). For example, to assess
exposure to a chemical in a cleaning product for which
only the hands are exposed, surface area values for hands
from Table 6-4 can be used. For exposure to both hands
and arms, mean surface areas for these parts from Table
6-4 may be summed to estimate the total surface area
exposed. The mean surface area of these body parts for
men and women is as follows:
Surface Area (m )
Men Women
Arms (includes upper arms and forearms) 0.228 0.210
Hands 0.084 0.075
Total area 0.312 0.285
Therefore, the total body part surface area that may be in
contact with the chemical in the cleaning product in this
example is 0.312 m2 for men and 0.285 m2 for women.
A common assumption is that clothing prevents
dermal contact and subsequent absorption of
contaminants. This assumption may be false in cases
where the chemical may be able to penetrate clothing,
such as in a fine dust or liquid suspension. Studies using
personal patch monitors placed beneath clothing of
pesticide workers exposed to fine mists and vapors show
that a significant proportion of dermal exposure may
occur at anatomical sites covered by clothing (U.S. EPA,
1992b). In addition, it has been demonstrated that a
"pumping" effect can occur which causes material to
move under loose clothing (U.S. EPA, 1992b).
Furthermore, studies have demonstrated that hands cannot
be considered to be protected from exposure even if
waterproof gloves are worn (U.S. EPA, 1992b). This may
be due to contamination to the interior surface of the
gloves when donning or removing them during work
activities (U.S. EPA, 1992b). Depending on the task,
pesticide workers have been shown to experience 12
percent to 43 percent of their total exposure through their
hands, approximately 20 percent to 23 percent through
their heads and necks, and 36 percent to 64 percent
through their torsos and arms, despite the use of protective
gloves and clothing (U.S. EPA, 1992b).
For swimming and bathing scenarios, past exposure
assessments have assumed that 75 percent to 100 percent
of the skin surface is exposed (U.S. EPA, 1992b). As
shown in Table 6-4, total adult body surface areas can
vary from about 17,000 cm2 to 23,000 cm2. The mean is
reported as approximately 20,000 cm2.
For default purposes, adult body surface areas of
20,000 cm2 (central estimate) to 23,000 cm2 (upper
percentile) are recommended in U.S. EPA (1992b).
Tables 6-2 and 6-3 can also be used when the default
values are not preferred. Central and upper-percentile
values for children should be derived from Table 6-6 or
6-7.
Unlike exposure to liquids, clothing may or may
not be effective in limiting the extent of exposure to soil.
The 1989 Exposure Factors Handbook presented two
adult clothing scenarios for outdoor activities (U.S. EPA,
1989):
Central tendency mid range: Individual wears
long sleeve shirt, pants, and shoes. The exposed
skin surface is limited to the head and hands (2,000
cm2).
Upper percentile: Individual wears a short sleeve
shirt, shorts, and shoes. The exposed skin surface
is limited to the head, hands, forearms, and lower
legs (5,300 cm2).
The clothing scenarios presented above, suggest that
roughly 10 percent to 25 percent of the skin area may be
exposed to soil. Since some studies have suggested that
exposure can occur under clothing, the upper end of this
range was selected in Dermal Exposure Assessment:
Principles and Applications (U.S. EPA, 1992b) for
deriving defaults. Thus, taking 25 percent of the total
body surface area results in defaults for adults of 5,000
cm2 to 5,800 cm2. These values were obtained from the
body surface areas in Table 6-2 after rounding to 20,000
cm2 and 23,000 cm2, respectively. The range of defaults
for children can be derived by multiplying the 50th and
95th percentiles by 0.25 for the ages of interest.
When addressing soil contact exposures, 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
percent of the skin is exposed during the winter, 10
percent during the spring and fall, and 25 percent during
the summer.
The previous discussion, has presented information
about the area of skin exposed to soil. These estimates of
exposed skin area should be useful to assessors using the
traditional approach of multiplying the soil adherence
factor by exposed skin area to estimate the total amount of
soil on skin. The next section presents soil adherence data
specific to activity and body part and is designed to be
Exposure Factors Handbook
August 1997
Page
6-5
-------
Volume I - General Factors
Chapter 6 - Dermal
combined with the total surface area of that body part. No
reduction of body part area is made for clothing coverage
using this approach. Thus, assessors who adopt this
approach, should not use the defaults presented above for
soil exposed skin area. Rather, they should use Table 6-4
to obtain total surface areas of specific body parts. See
detailed discussion below.
6.3. SOIL ADHERENCE TO SKIN
6.3.1. Background
Soil adherence to the surface of the skin is a
required parameter to calculate dermal dose when the
exposure scenario involves dermal contact with a
chemical in soil. A number of studies have attempted to
determine the magnitude of dermal soil adherence. These
studies are described in detail in U.S. EPA (1992b). This
section summarizes recent studies that estimate soil
adherence to skin for use as exposure factors.
6.3.2. Key Soil Adherence to Skin Studies
Kissel et al. (1996a) - Factors Affecting Soil
Adherence to Skin in Hand-Press Trials: Investigation of
Soil Contact and Skin Coverage - Kissel et al. (1996a)
conducted soil adherence experiments using five soil
types (descriptor) obtained locally in the Seattle,
Washington, area: sand (211), loamy sand (CP), loamy
sand (85), sandy loam (228), and silt loam (72). All soils
were analyzed by hydrometer (settling velocity) to
determine composition. Clay contents ranged from 0.5 to
7.0 percent. Organic carbon content, determined by
combustion, ranged from 0.7 to 4.6 percent. Soils were
dry sieved to obtain particle size ranges of <150,150-250,
and >250 yum. For each soil type, the amount of soil
adhering to an adult female hand, using both sieved and
unsieved soils, was determined by measuring the
difference in soil sample weight before and after the hand
was pressed into a pan containing the test soil. Loadings
were estimated by dividing the recovered soil mass by
total hand area, although loading occurred primarily on
only one side of the hand. Results showed that generally,
soil adherence to hands could be directly correlated with
moisture content, inversely correlated with particle size,
and independent of clay content or organic carbon
content.
Kissel et al. (1996b) - Field Measurement of
Dermal Soil Loading Attributable to Various Activities:
Implications for Exposure Assessment - Further
experiments were conducted by Kissel et al. (1996b) to
estimate soil adherence associated with various indoor and
outdoor activities: greenhouse gardening, tae kwon do
karate, soccer, rugby, reed gathering, irrigation
installation, truck farming, and playing in mud. A
summary of field studies by activity, gender, age, field
conditions, and clothing worn is presented in Table 6-11.
Subjects' body surfaces (forearms, hands, lower legs in all
cases, faces, and/or feet; pairs in some cases) were washed
before and after monitored activities. Paired samples
were pooled into single ones. Mass recovered was
converted to loading using allometric models of surface
area. These data are presented in Table 6-12. Results
presented are based on direct measurement of soil loading
on the surfaces of skin before and after occupational and
recreational activities that may be expected to have soil
contact (Kissel et al., 1996b).
6.3.3. Relevant Soil Adherence to Skin Studies
Lepow etal. (1975) - Investigations into Sources of
Lead in the Environment of Urban Children - This study
was conducted to identify the behavioral and
environmental factors contributing to elevated lead levels
in ten preschool children. The study was performed over
6 to 25 months. Samples of dirt from the hands of
subjects were collected during the course of play around
the areas where they lived. Preweighed self-adhesive
labels were used to sample a standard area on the palm of
the hands of 16 male and female children. The labels
were pressed on a single area, often pressed several times,
to obtain an adequate sample. In the laboratory, labels
were equilibrated in a desiccant cabinet for 24 hours
(comparable to the preweighed desiccation), then the total
weight was recorded. The mean weight of dirt from the 22
hand sample labels was 11 mg. This corresponds to 0.51
mg/cm2. Lepow et al. (1975) reported that this amount
(11 mg) represented only a small fraction (percent not
specified) of the total amount of surface dirt present on
the hands, because much of the dirt may be trapped in skin
folds and creases or there may be a patchy distribution of
dirt on hands.
Roels et al. (1980) - Exposure to Lead by the Oral
and the Pulmonary Routes of Children Living in the
Vicinity of a Primary Lead Smelter - Roels et al. (1980)
examined blood lead levels among 661 children, 9 to 14
years old, who lived in the vicinity of a large lead smelter
in Brussels, Belgium. During five different study periods,
lead levels were assessed by rinsing the childrens' hands
in 500 mL dilute nitric acid. The amount of lead on the
hands was divided by the concentration of lead in soil to
Page
6-6
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
estimate the amount of soil adhering to the hands. The
mean soil amount adhering to the hands was 0.159 grams.
Que Hee et al. (1985) - Evolution of Efficient
Methods to Sample Lead Sources, Such as House Dust
and Hand Dust, in the Homes of Children - Que Hee et al.
(1985) used soil haying particle sizes ranging from <. 44
to 833 urn diameters, fractionated into six size ranges, to
estimate the amount that adhered to the palm of the hand
that are assumed to be approximately 160 cm2 (test
subject with an average total body surface area of 16,000
cm2 and a total hand surface area of 400 cm2). The
amount of soil that adhered to skin was determined by
applying approximately 5 g of soil for each size fraction,
removing excess soil by shaking the hands, and then
measuring the difference in weight before and after
application. Several assumptions were made to apply
these results to other soil types and exposure scenarios:
(a) the soil is composed of particles of the indicated
diameters; (b) all soil types and particle sizes adhere to
the skin to the degree observed in this study; and an
equivalent weight of particles of any diameter adhere to
the same surface area of skin. On average, 31.2 mg of soil
adhered to the palm of the hand.
Driver et al. (1989) - Soil Adherence to Human
Skin - Driver et al. (1989) conducted soil adherence
experiments using various soil types collected from sites
in Virginia. A total of five soil types were collected:
Hyde, Chapanoke, Panorama, Jackland, and Montalto.
Both top soils and subsoils were collected for each soil
type. The soils were also characterized by cation
exchange capacity, organic content, clay mineralogy, and
particle size distribution. The soils were dry sieved to
obtain particle sizes of ^250 urn and < 150 [am. For each
soil type, the amount of soil adhering to adult male hands,
using both sieved and unsieved soils, was determined
gravimetrically (i.e., measuring the difference in soil
sample weight before and after soil application to the
hands).
An attempt was made to measure only the minimal
or "monolayer" of soil adhering to the hands. This was
done by mixing a pre-weighed amount of soil over the
entire surface area of the hands for a period of
approximately 30 seconds, followed by removal of excess
soil by gently rubbing the hands together after contact
with the soil. Excess soil that was removed from the
hands was collected, weighed, and compared to the
original soil sample weight. The authors measured
average adherence of 1.40 mg/cm2 for particle sizes less
than 150 um, 0.95 mg/cm2 for particle sizes less than 250
um, and 0.58 mg/cm2 for unsieved soils. Analysis of
variance statistics showed that the most important factor
affecting adherence variability was particle size (p <
0.001). The next most important factor is soil type and
subtype (p < 0.001). The interaction of soil type and
particle size was also significant, but at a lower
significance level (p < 0.01).
Driver et al. (1989) found statistically significant
increases in soil adherence with decreasing particle size;
whereas, Que Hee et al. (1985) found relatively small
changes with changes in particle size. The amount of soil
adherence found by Driver et al. (1989) was greater than
that reported by Que Hee et al. (1985).
Sedman (1989) - The Development of Applied
Action Levels for Soil Contact: A Scenario for the
Exposure of Humans to Soil in a Residential Setting -
Sedman (1989) used the estimate from Roels et al. (1980),
0.159 g, and the average surface area of the hand of an 11
year old, 307 cm2 to estimate the amount of soil adhering
per unit area of skin to be 0.9 mg/cm2. This assumed that
approximately 60 percent (185 cm2) of the lead on the
hands was recovered by the method employed by Roels et
al. (1980).
Sedman (1989) used estimates from Lepow et al.
(1975), Roels et al. (1980), and Que Hee et al. (1985) to
develop a maximum soil load that could occur on the skin.
A rounded arithmetic mean of 0.5 mg/cm2 was calculated
from these three studies. According to Sedman (1989),
this was near the maximum load of soil that could occur
on the skin but it is unlikely that most skin surfaces would
be covered with this amount of soil (Sedman, 1989).
Yang et al. (1989) - In vitro and In vivo
Percutaneous Absorption of Benzo[a]pyrene from
Petroleum Crude - Fortified Soil in the Rat - Yang et al.
(1989) evaluated the percutaneous absorption of
benzo[a]pyrene (BAP) in petroleum crude oil sorbed on
soil using a modified in vitro technique. This method was
used in preliminary experiments to determine the
minimum amount of soil adhering to the skin of rats.
Based on these results, percutaneous absorption
experiments with the crude-sorbed soil were conducted
with soil particles of <150 ^m only. This particle size
was intended to represent the composition of the soil
adhering to the skin surface. Approximately 9 mg/cm2 of
soil was found to be the minimum amount required for a
"monolayer" coverage of the skin surface in both in vitro
and in vivo experiments. This value is larger than reports
for human skin in the studies of Kissel et al., 1996a,b;
Lepow et al., 1975; Roels et al., 1980; and Que Hee et al.,
Exposure Factors Handbook
August 1997
Page
6-7
-------
Volume I - General Factors
Chapter 6 - Dermal
1985. Differences between the rat and human soil
adhesion findings may be the result of differences in rat
and human skin texture, the types of soils used, soil
moisture content or possibly the methods of measuring
soil adhesion (Yang et al., 1989).
6.4. RECOMMENDATIONS
6.4.1. Body Surface Area
Body surface area estimates are based on direct
measurements. Re-analysis of data collected by Boyd
(1935) by several investigators (Gehan and George, 1970;
U.S. EPA, 1985; Murray and Burmaster, 1992; Phillips et
al., 1993) constitutes much of this literature. Methods are
highly reproducible and the results are widely accepted.
The representativeness of these data to the general
population is somewhat limited since variability due to
race or gender have not been systematically addressed.
Individual body surface area studies are
summarized in Table 6-13 and the recommendations for
body surface area are summarized in Table 6-14. Table
6-15 presents the confidence ratings for various aspects of
the recommendations for body surface area. The U.S.
EPA (1985) study is based on generally accepted
measurements that enjoy widespread usage, summarizes
and compares previous reports in the literature, provides
statistical distributions for adults, and provides data for
total body surface area and body parts by gender for
adults and children. However, the results are based on
401 selected measurements from the original 1,114 made
by Boyd (1935). More than half of the measurements are
from children. Therefore, these estimates may be subject
to selection bias and may not be representative of the
general population nor specific ethnic groups. Phillips et
al. (1993) analyses are based on direct measurement data
that provide distributions of body surface area to calculate
LADD. The results are consistent with previous efforts to
estimate body surface area. Analyses are based on 401
measurements selected from the original 1,114
measurements made by Boyd (1935) and data were not
analyzed for specific body parts. The study by Murray
and Burmaster (1992) provides frequency distributions for
body surface area for men and women and produces
results that are similar to those obtained by the U.S. EPA
(1985), but do not provide data for body parts nor can
results be applied to children.
For most dermal exposure scenarios concerning
adults, it is recommended that the body surface areas
presented in Table 6-4 be used after determining which
body parts will be exposed. Table 6-4 was selected
because these data are straightforward determinations for
most scenarios. However, for others, additional
considerations may need to be addressed. For example,
(1) the type of clothing worn could have a significant
effect on the surface area exposed, and (2) climatic
conditions will also affect the type of clothing worn and,
thus, the skin surface area exposed.
Frequency, event, and exposure duration for water
activities and soil contact are presented in Activity
Patterns, Volume III, Chapter 15 of this report. For each
parameter, recommended values were derived for average
and upper percentile values. Each of these considerations
are also discussed in more detail in U.S. EPA (1992b).
Data in Tables 6-2 and 6-3 can be used when surface area
distributions are preferred. A range of recommended
values for estimates of the skin surface area of children
may be taken from Tables 6-6 and 6-7 using the 50th and
95th percentile values for age(s) of concern. The
recommended 50th and 95th percentile values for adult
skin surface area provided in U.S. EPA (1992b) are
presented in Table 6-16.
6.4.2. Soil Adherence to Skin
Table 6-17 summarizes the relevant and key studies
addressing soil adherence to skin. Both Lepow et al.
(1975) and Roels et al. (1980) monitored typical
exposures in children. They attempted to estimate typical
exposure by recovery of accumulated soil from hands at
specific time intervals. The efficiency of their sample
collection methods is not known and may be subject to
error. Only children were studied which may limit
generalizing these results to adults. Later studies (Que
Hee et al., 1985 and Driver et al., 1989) attempted to
characterize both soil properties and sample collection
efficiency to estimate adherence of soil to skin. However,
the experimental conditions used to expose skin to soil
may not reflect typical dermal exposure situations. This
provides useful information about the influence of soil
characteristics on skin adherence, but the intimate contact
of skin with soil required under the controlled
experimental conditions in the studies by Driver et al.
(1989) and Que Hee et al. (1985) may have exaggerated
the amount of adherence over what typically occurs.
More recently, Kissel et al. (1996a; 1996b) have
related dermal adherence to soil characteristics and to
specific activities. In all cases, experimental design and
measurement methods are straightforward and
reproducible, but application of results is limited. Both
controlled experiments and field studies are based on a
Page
6-8
Exposure Factors Handbook
Augustl997
-------
Volume I - General Factors
Chapter 6 - Dermal
limited number of measurements. Specific situations have
been selected to assess soil adherence to skin.
Consequently, variation due to individuals, protective
clothing, temporal, or seasonal factors remain to be
studied in more detail. Therefore, caution is required in
interpretation and application of these results for exposure
assessments.
These studies are based on limited data, but
suggest:
• Soil properties influence adherence. Adherence
increases with moisture content, decreases with
particle size, but is relatively unaffected by clay
or organic carbon content.
• Adherence levels vary considerably across
different parts of the body. The highest levels
were found on common contact points such as
hands, knees, and elbows; the least was detected
on the face.
• Adherence levels vary with activity. In general,
the highest levels of soil adherence were seen in
outdoor workers such as farmers and irrigation
system installers, followed by outdoor
recreation, and gardening activities. Very high
adherence levels were seen in individuals
contacting wet soils such as might occur during
wading or other shore area recreational
activities.
In consideration, of these general observations and
the recent data from Kissel et al. (1996a, 1996b), changes
are needed from past EPA recommendations which used
one adherence value to represent all soils, body parts, and
activities. One approach would be to select the activity
from Table 6-11 which best represents the exposure
scenario of concern and use the corresponding adherence
value from Table 6-12. Although this approach represents
an improvement, it still has shortcomings. For example,
it is difficult to decide which activity in Table 6-12 is
most representative of a typical residential setting
involving a variety of activities. It may be useful to
combine these activities into general classes of low,
moderate, and high contact. In the future, it may be
possible to combine activity-specific soil adherence
estimates with survey-specific soil adherence estimates
with survey-derived data on activity frequency and
duration to develop overall average soil contact rates.
EPA is sponsoring research to develop such an approach.
As this information becomes availble, updated
recommendations will be issued.
Table 6-12 provides the best estimates available on
activity-specific adherence values, but are based on
limited data. Therefore, they have a high degree of
uncertainty such that considerable judgment must be used
when selecting them for an assessment. The confidence
ratings for various aspects of this recommendation are
summarized in Table 6-18. Insufficient data are available
to develop a distribution or a probability function for soil
loadings.
Past EPA guidance has recommended assuming
that soil exposure occurs primarily to exposed body
surfaces and used typical clothing scenarios to derive
estimates of exposed skin area. The approach
recommended above for estimating soil adherence
addresses this issue in a different manner. This change
was motivated by two developments. First, increased
acceptance that soil and dust particles can get under
clothing and be deposited on skin. Second, recent studies
of soil adherence have measured soil on entire body parts
(whether or not they were covered by clothing) and
averaged the amount of soil adhering to skin over the area
of entire body part. The soil adherence levels resulting
from these new studies must be combined with the surface
area of the entire body part (not merely unclothed surface
area) to estimate the amount of contaminant on skin. An
important caveat, however, is that this approach assumes
that clothing in the exposure scenario of interest matches
the clothing in the studies used to derive these adherence
levels such that the same degree of protection provided by
clothing can be assumed in both cases. If clothing differs
significantly between the studies reported here and the
exposure scenarios under investigation, considerable
judgment is needed to adjust either the adherence level or
surface area assumption.
The dermal adherence value 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, these adherence Values can be
interpreted as daily contact rates (U.S. EPA, 1992b).
However, this is not recommended because the residence
time of soils on skin has not been studied. Instead, it is
recommended that these adherence values be interpreted
on an event basis (U.S. EPA, 1992b).
Exposure Factors Handbook
August 1997
Page
6-9
-------
Volume I - General Factors
Chapter 6 - Dermal
6.5. REFERENCES FOR CHAPTER 6
American Industrial Health Council (AIHC). (1994)
Exposure factors sourcebook. Washington, DC:
AIHC.
Boyd, E. (1935) The growth of the surface area of the
human body. Minneapolis, Minnesota:
University of Minnesota Press.
Brainard, J.B.; Burmaster, D.E. (1992) Bivariate
distributions for height and weight, men and
women in the United States. Risk Anal.
12(2):267-275.
Brorby, G.; Finley B. (1993) Standard probability
density functions for routine use in
environmental health risk assessment. Presented
at the Society of Risk Analysis Annual Meeting,
December 1993, Savannah, GA.
Buhyoff, G.J.; Rauscher, H.M.; Hull, R.B.; Killeen, K.;
Kirk, R.C. (1982) User's Manual for Statistical
Processing System (version 3C.1). Southeast
Technical Associates, Inc.
Costeff, H. (1966) A simple empirical formula for
calculating approximate surface area in children.
Arch. Dis. Childh. 41:681-683.
Driver, J.H.; Konz, J.J.; Whitmyre, O.K. (1989) Soil
adherence to human skin. Bull. Environ.
Contam. Toxicol. 43:814-820.
Dubois, D.; Dubois, E.F. (1916) A formula to estimate
the approximate surface area if height and weight
be known. Arch, of Intern. Med. 17:863-871.
Gehan, E.; George, G.L. (1970) Estimation of human
body surface area from height and weight.
Cancer Chemother. Rep. 54(4):225-235.
Geigy Scientific Tables (1981) Nomograms for
determination of body surface area from height
and mass. Lentner, C. (ed.). CIBA-Geigy
Corporation, West Caldwell, NJ. pp. 226-227.
George, S.L.; Gehan, E.A.; Haycock, G.B.; Schwartz,
G.J. (1979) Letters to the editor. J. Ped.
94(2):342.
Haycock, G.B.; Schwartz, G.J.; Wisotsky, D.H. (1978)
Geometric method for measuring body surface
area: A height-weight formula validated in
infants, children, and adults. J. Ped. 93(l):62-66.
Holmes, K.K.; Kissel, J.C.; Richter,K.Y. (1996)
Investigation of the influence of oil on soil
adherence to skin. J. Soil Contam. 5(4):301-
308.
Kissel, J.; Richter, K.; Duff, R.; Fenske, R. (1996a)
Factors Affecting Soil Adherence to Skin in Hand-
Press Trials. Bull. Environ. Contamin. Toxicol.
56:722-728.
Kissel, J.; Richter, K.; Fenske, R. (1996b) Field
measurements of dermal soil loading attributable to
various activities: Implications for exposure
assessment. Risk Anal. 16(1):116-125.
Lepow, M.L.; Bruckman, L.; Gillette, M.; Markowitz,
S.; Rubino, R.; Kapish, J. (1975) Investigations
into sources of lead in the environment of urban
children. Environ. Res. 10:415-426.
Murray, D.M.; Burmaster, D.E. (1992) Estimated
distributions for total surface area of men and ,
women in the United States. J. Expos. Anal.
Environ. Epidemiol. 3(4):451-462.
Palisade. (1992) @Risk users guide. Palisade
Corporation, Newfield, NY.
Phillips, L.J.; Fares, R.J.; Schweer, L.G. (1993)
Distributions of total skin surface area to body
weight ratios for use in dermal exposure
assessments. J. Expos. Anal. Environ. Epidemiol.
3(3):331-338.
Popendorf, W.J.; Leffingwell, J.T. (1976) Regulating
OP pesticide residues for farmworker protection.
In: Residue Review 82. New York, NY:
Springer-Verlag New York, Inc., 1982. pp. 125-
201.
Que Hee, S.S.; Peace, B.; Clark, C.S.; Boyle, J.R.;
Bornschein, R.L.; Hammond, P.B. (1985)
Evolution of efficient methods to sample lead
sources, such as house dust and hand dust, in the
homes of children. Environ. Res. 38: 77-95.
Rochon, J.; Kalsbeek, W.D. (1983) Variance
estimation from multi-stage sample survey data:
the jackknife repeated replicate approach.
Presented at 1983 SAS Users Group Conference,
New Orleans, Louisiania, January 1983.
Roels, H.A.; Buchet, J.P.; Lauwenys, R.R.; Branx, P.;
Claeys-Thoreau, F.; Lafontaine, A.; Verduyn, G.
(1980) Exposure to lead by oral and pulmonary
routes of children living in the vicinity of a primary
lead smelter. Environ. Res. 22:81-94.
Sedman, R.M. (1989) The development of applied
action levels for soil contact: a scenario for the
exposure of humans to soil in a residential setting.
Environ. Health Perspect. 79:291-313.
Page
6-10
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
Sendroy, J.; Cecchini, L.P. (1954) Determination of
human body surface area from height and weight.
J. Appl. Physiol. 7(1):3-12.
Thompson, K.M.; Burmaster, D.E. (1991) Parametric
distributions for soil ingestion by children. Risk .
Anal. ll(2):339-342.
U.S. EPA." (1985) Development of statistical
distributions or ranges of standard factors used in
exposure assessments. Washington, DC: Office
of Research and Development, Office of Health
and Environmental Assessment. EPA
600/8-85-010. Available from: NTIS,
Springfield, VA. PB85-242667.
U.S. EPA. (1989) Exposure factors handbook.
Washington, DC: Office of Research and
Development, Office of Health and
Environmental Assessment. EPA/600/18-
89/043.
U.S. EPA. (1992a) Guidelines for exposure
assessment. Federal Register. FR
57:104:22888-22938. May 29, 1992.
U.S. EPA. (1992b) Dermal exposure assessment:
principles and applications. Washington, DC:
Office of Research and Development, Office of
Health and Environmental Assessment/OHEA.
U.S. EPA/600/8-9-91.
Van Graan, C.H. (1969) The determination of body
surface area. Supplement to the South African J. of
Lab. and Clin. Med. 8-2-69.
Versar, Inc. (1991) Analysis of the impact of exposure
assumptions on risk assessment of chemicals in the
environment, phase II: uncertainty analyses of
existing exposure assessment methods. Draft
Report. Prepared for Exposure Assessment Task
Group, Chemical Manufacturers Association,
Washington, DC.
Yang, J.J.; Roy, T.A.; Krueger, A.J.; Neil, W.;
Mackerer, C.R. (1989) In vitro and in vivo
percutaneous absorption of benzo[a]pyrene from
petroleum crude-fortified soil in the rat. Bull.
Environ. Contam. Toxicol. 43: 207-214.
Exposure Factors Handbook
August 1997 '
Page
6-11
-------
Volume 1 - General Factors
Chapter 6 - Dermal
Exposure ^^
^***«^
**«i_ * * ^s"~
Potential
Dose
Applied »
^- Dose ^s.
^^
^-
Sk
Internal
Dose
^*
Metabolism
in
Biologically
Effective
Dose
\
Organ
^
P**" Effect
Uptake
Figure 6-1. Schematic of Dose and Exposure: Dermal Route
Source: U.S. EPA, 1992a.
Table 6-1. Summary of Equation Parameters for Calculating Adult Body Surface Area
Equation for surface areas (m2)
Body Part
Head
Female
Male
Trunk
Female
Male
Upper Extremities
Female
Male
Arms
Female
Male
Upper Arms
Mole
Forearms
Male
Hands
Female
Mole
Lower Extremities'1
Legs
Thighs
Lower legs
Feet
8 SA =a0 Wal Ha2
N
57
32
57
32
57
48
13
32
6
6
12b
32
105
45
45
45
45
BO
0.0256
0.0492
0.188
0.0240
0.0288
0.00329
0.00223
0.00111
8.70
0.326
0.0131
0.0257
0.00286
0.00240
0.00352
0.000276
0.000618 ,
Wal H112
0.124 0.189
0.339 -0.0950
0.647 -0.304
0.808 -0.0131
0.341 0.175
0.466 0.524
0.201 0.748
0.616 0.561
0.741 -1.40
0.858 -0.895
0.412 0.0274
0.573 -0.218
0.458 0.696
0.542 0.626
0.629 0.379
0.416 0.973
0.372 0.725
p
0.01
0.01
R2
0.302
0.222
0.001 0.877
0.001 0.894
0.001 0.526
0.001 0.821
0.01
0.731
0.001 0.892
0.25
0.05
0.1
0.576
0.897
0.447
0.001 0.575
0.001 0.802
0.001 0.780
0.001 0.739
0.001 0.727
0.001 0.651
S.E.
• 0.00678
0.0202
0.00567
. 0.0118
0.00833
0.0101
0.00996
0.0177
0.0387
0.0207
0.0172
0.0187
0.00633
' 0.0130
0.0149
0.0149
0.0147
W - Weight in kilograms; H = Height in centimeters; P = Level of significance; R2 = Coefficient of determination;
S A ~ Surface Area; S.E. = Standard error; N = Number of observations
b One observation for a female whose body weight exceeded the 95 percentile was not used.
c Although two separate regressions were marginally indicated by the F test, pooling was done for consistency with individual components
of lower extremities.
Source: U.S. EPA. 1985.
Page
6-12
Exposure Factors Handbook
August 199?
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-2. Surface Area of Adult Males in Square Meters
Percentile ;
Body Dart 5
Total 1.66
Head 0.119
Trunkb 6.591
Upper extremities 0.321
Arms 0.241
Forearms 0.106
Hands 0.085
Lower extremities 0.653
Legs 0.539
Thighs 0.318
Lower legs 0.218
Feet 0.114
10
1.72
0.121
0.622
0.332
0.252
0.111
0.088
0.676
0.561
0.331
0.226
0.118
15 25
1.76 1.82
0.123 0.124
0.643 0.674
0.340 0.350
0.259 0.270
0.115 0.121
0.090 0.093
0.692 0.715
0.576 0.597
0.341 0.354
0.232 0.240
0.120 0.124
50
1.94
0.130
0.739
0.372
0.291
0.131
0.099
0.761
0.640
0.382
0.256
0.131
75
2.07
0.135
0.807
0.395
0.314C
0.144C
0.105
0.810
0.686°
0.41 lc
0.272
0.138
85
2.14
0.138
0.851
0.408
0.328C
0.151C
0.109
0.838
0.714°
0.429°
0.282
0.142
90
- 2.20
0.140
0.883
0.418
0.339°
0.157°
0.112
0.858
0.734°
0.443°
0.288
0.145
95
2.28
0.143
0.935°
0.432°
0.354°
0.166°
0.117
0.888°
0.762°
0.463°
0.299
0.149
S.E.a
0.00374
0.0202
0.0118
0.00101
0.00387
0.0207
0.0187
0.00633
0.0130
0.0149
0.0149
0.0147
a Standard error for the 5-95 percentile of each body part.
b Trunk includes neck.
'
c Percentile estimates exceed the maximum measured values upon which the equations are based.
Source: U.S. EPA. 1985.
Table 6-3. Surface Area of Adult Females in Square Meters
Percentile
Body part
Total
Head
Trunk"
Upper extremities
Arms
Hands
Lower extremities
Legs
Thighs
Lower legs
Feet
5
1.45
0.106
0.490
0.260
0.210
0.0730
0.564
0.460
0.271
0.186
0.100
10
1.49
0.107
0.507
0.265
0.214
0.0746
0.582
0.477
0.281
0.192
0.103
15
1.53
0.108
0.518
0.269
0.217
0.0757
0.595
0.488
0.289
0,197
0.105
25
1.58
0.109
0.538
0.274
0.221
0.0777
0.615
0.507
0.300
0.204
0.108
50
1.69°
0.111
0.579
0.287
0.230
0.0817
0.657
0.546
0.326
0.218
0.114
75
1.82
0.113
0.636
0.301
0.238°
0.0868°
0.704
0.592
0.357
0.233
0.121
85
1.91
0.114
0.677
0.311
0.243°
0.0903°
0.736
0.623
0.379
0.243
0.126
90
1.9S
0.115
0.704
0.318
0.247°
0.0927°
0.757
0.645
0.394
0.249
0.129
95
2.09
0.117
0.752
0.329
0.253°
0.0966°
0.796
0.683°
0.421°
0.261
0.134
S.E.a
0.00374
0.00678
0.00567
0.00833
0.00996
0.0172
0.00633
0.0130
0.0149
0.0149
0.0147
a Standard error for the 5-95 percentile of each body part.
b Trunk includes neck. ;
° Percentile estimates exceed the maximum measured values upon which the equations are based.
Source: U.S. EPA,
1985.
Exposure Factors Handbook
August 1997
Page
6-13
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-4. Surface Area by Body Part for Adults (m2)
Men
Body part
Head
Trunk
(Incl. Neck)
Upper extremities
Arms
Upper arms
Forearms
Hands
Lower extremities
Legs
Thighs
Lower legs
Feet
TOTAL
N"
32
32
48
32
6
6
32
48
32
32
32
32
Mean
0.118
0.569
0.319
0.228
0.143
0.114
0.084
0.636
0.505
0.198
0.207
0.112
1.94C
(sd)b
(0.0160)
(0.104)
(0.0461)
(0.0374)
(0.0143)
(0.0127)
(0.0127)
(0.0994)
(0.0885)
(0.1470)
(0.0379)
(0.0177)
(0.00374)d
Min.
0.090
0.306
0.169
0.109
0.122
0.0945 -
0.0596 -
0.283
0.221
0.128
0.093
0.0611 -
1.66
Max.
0.161
0.893
0.429
0.292
0.156
0.136
0.113
0.868
0.656
0.403
0.296
0.156
2.28°
N
57
57
57
13
-
-
12
57
13
13
13
13
Mean
0.110
0.542
0.276
0.210
-
-
0.0746
0.626
0.488
0.258
0.194
0.0975
1.69C
Women
(sd)
(0.00625)
(0.0712)
(0.0241)
(0.0129)
-
-
(0.00510)
(0.0675)
(0.0515)
(0.0333)
(0.0240)
(0.00903)
(0.00374)d
Min.
0.0953
0.437
0.215
0.193
-
-
0.0639
0.492
0.423
0.258
0.165
0.0834
1.45
Max.
0.127
0.867
0.333
0.235
-
-
0.0824
0.809
0.585
0.360
0.229
0.115
2.09e
a number of observations.
b standard deviation.
c median (see Table 6-2).
d standard error.
c percentiles (5th -
95th).
Source: Adapted from U.S. EPA, 1985.
Table 6-5. Percentage of Total Body Surface Area by Part for Adults
Men
Body part
Head
Trunk
Upper extremities
Arms
Upper arms
Forearms
Hands
Lower extremities
Legs
Thighs
Lower legs
Feet
Na
32
32
48
32
6
6
32
48
32
32
32
32
Mean
7.8
35.9
18.8
14.1
7.4
5.9
5.2
37.5
31.2
18.4
12.8
7.0
(s.d.)b
(1.0)
(2.1)
(1.1)
(0.9)
(0.5)
(0.3)
(0.5)
(1.9)
(1.6)
(1.2)
(1.0)
(0.5)
Min.
6.1
30.5
16.4
12.5
6.7
5.4
4.6
33.3
26.1
15.2
11.0
6.0
Max.
10.6
41.4
21.0
15.5
8.1
6.3
7.0
41.2
33.4
20.2
15.8
7.9
N
57
57
57
13
- -
-
12
57
13
13
13
13
Mean
7.1
34.8
17.9
14.0
-
-
5.1
40.3
32.4
19.5
12.8
6.5
Women
(s.d.)
(0.6)
(1-9)
(0.9)
(0.6)
-
-
(0.3)
(1.6)
(1.6)
(1.1)
(1.0)
(0.3)
Min.
5.6
32.8
15.6
12.4
-
-
4.4
36.0
29.8
18.0
11.4
6.0
Max.
8.1
41.7
19.9
14.8
-
-
5.4
43.2
35.3
21.7
14.9
7.0
" Number of observations.
b Standard deviation.
Source: Adapted from U.S
EPA, 1985.
Page
6-14
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-6. Total Body Surface Area of Male Children in Square Metersa
1-
Age (yr)b
2<3
3<4
4<5
5<6
6<7
7<8
8<9
9<10
10<11
11<12
12<13
13<14
14<15
15<16
16<17
17<18
3<6
6<9
9<12
12<15
15<18
5
0.527
0.585
0.633
0.692
0.757
0.794
0.836
0.932
1.01
1.00
1.11
1.20
1.33
1.45
1.55 '
1.54
0.616
0.787
0.972
1.19
1.50
10
0.544
6.606
0.658
0.721
0.788
0.832
0.897
0.966
1.04
1.06
1.13
1.24
1.39
1.49
1.59
1.56
0.636
0.814
1.00
1.24
1.55
15
0.552
0.620
0.673
0.732
0.809
0.848
0.914
0.988
1.06
1.12
1.20
1.27
1.45
1.52
1.61
1.62
0.649
0.834
1.02
1.27
1.59 '
25
0.569
0.636
0.689
0.746
0.821
0.877
0.932
.00
.10
.16
.25
.30
.51
.60
.66
.69
0.673
0.866
1.07
1.32
1.65
Percentile
50
0.603
0.664
0.731
0.793
0.866
0.936
1.00
1.07
1.18
1.23
1.34
1.47
1.61
1.70
1.76
1.80
0.728
0.931
1.16
1.49
1.75
75
0.629
0.700
0.771
0.840
0.915
0.993
1.06
1.13
1.28
1.40
1.47
1.62
1.73
1.79
1.87
1.91
0.785
1.01
1.28
1.64
1.86
* Lack of height measurements for children <2 years in NHANES II precluded calculation of surface areas for this
85
0.643
0.719
0,796
0.864
0.957
.01
.12
.16
.35
.47
.52
.67
.78
.84
.98
.96
0817
.05
.36
.73
.94
age group.
90
0.661
0.729
0.809
0.895
1.01
1.06
1.17
1.25
1.40
1.53
1.62
1.75
1.84
1.90
2.03
2.03
0.842
1.09
1.42
1.77
•2.01 '
95
0.682
0.764
0.845
0.918
.06
.11
.24
.29
.48
.60
.76
1.81
1.91
2.02
2.16
2.09
0.876
1.14
1.52
1.85
2.11
Estimated values calculated using NHANES II data.
Source: U.S
EPA. 1985.
Table 6-7. Total Body Surface Area of Female Children in Square Meters"
Percentile
Age (yr)b
2<3
3<4
4<5
5<6
6<7
7<8
8<9
9<10
10<11
11 <12
12<13
13<14
14<15
15<16
16<17
17<18
3<6
6<9
9<12
12<15
15<18 '
5
0.516
0.555
0.627
0.675
0.723
0.792
0.863
0.897
0.981
1.06
1.13
1.21
1.31
1.38
1.40
1.42
0.585
0.754
0.957
1.21
1.40
10
0.532
0.570
0.639
0.700
0.748
0.808
0.888
0.948
1.01
1.09
1.19
1.28
1.34
1.49
1.46
1.49
0.610
0.790
0.990
1.27 ..
1.44
15
0.544
0.589
0.649
0.714
0.770
0.819
0.913
0.969
1.05
.12
.24
.32
.39
.43
.48
.51
0.630
0.804
1.03
1.30
1.47
25 50 75
0.557 0.579 0.610
0.607 0.649 0.688
0.666 0.706 0.758
0.735 0.779 0.830
0.791 0.843 0.914
0.854 0.917 0.977
0.932
1.01
1.10
1.16
1.27
1.38
1.45
1.47
1.53
1.56
.00 1.05
.06 .14
.17 .29
.30 .40
.40 .51
.48 .59
.55 .66
.57 .67
.60 .69
.63 .73
0.654 0.711 0.770
0.845 0.919 1.00
1.06
1.37
1.51
.16 1.31
.48 1.61
.60 1.70
85
0.623
0.707
0.777
b.870
0.961
' .02
.08
.22
.34
.50
.62
.67
.74
- .72
1.79
1.80
0.808
1.04
1.38
1.68
1.76 '
90
0.637
0.721
0.794
0.902
0.989
1.06
1.11
1.31
1.37
1.56
1.64
1.75
1.76
1.76
1.84
1.84
0.831
1.07
1.43
1.74
1.82
95
0.653
0.737
0.820
0.952
1.03
1.13
1.18
1.41
1.43
1.62
1.70
1.86
1.88
1.83
1.91
1.94
0.879
1.13
1.56
1.82
1.92
" Lack of height measurements for children <2 years in NHANES n precluded calculation of surface areas for this age group.
Estimated values calculated using NHANES II data.
Source: U.S. EPA.
1985.
Exposure Factors Handbook
^August 1997
Page
6-15
-------
Volume I - General Factors
Chapter 6 - Dermal
1
2
j5
O
£
•g
cu
t
09
J2
£i
«
1
=
t
03
73
£
o
a
s
1
i
£
-3
S
U-.
O
C3
CJ
a
£
1
a
.3
•3
i
a:
i
1
|
H
•o
S
2
S
5
c
'2
1
5
3
C
is
e
s
s
2
c
is
|
s
^
s
B
I
S
g
e
is
1
s
1
c
is
s
•s
a>!
ON O oo O C"-
in f— oo — • t";
vd vo t^« oo r^*
ON "'T O "^ OO
\d in* vo vo C"**
^ t- P- — • ON O oo encs enoq
inoJoeNeN ON m op °^^i
vdvdr^r*-r^- vd f- f-^ oo vot*-
ON p vq en oq
es ^d" od ON* oo
cs CN eN e^ c^
01 -i o p tn
22 S" S « cs
vo^c^oooo — t- mo vooo
o' en en* vd t-* (-^ oo" O eS en o
eseSr^eNR es cs men enen
CN OO C^ C^ 3"
en f- en vq ^T
in tn vd vd *n
— t— en in *n
CNJ in oq ~* — ;
oo o t— o — • o ON ~-- ooen
^m'tnvo'tn ^ »n m »n - m tn
_ __, t-- in oq
in en Tt in eM
•^- oq c^ p r-;
CM CN ^ 2 z:
t^-ooo^-p — i en f^1"! ^""
m en — ' '^J* ^* en eN eneS en r^
\o \o oo ^ o^
\d vd cs cs "*^"
en en en en en
oq tn ON in ^
•^ -^- ON o en
en en CN en en
r^tntnoN-
enencnenen en en men enen
en in o en »n
od vd ^ tn* e^l
CN in en -^ vq
od vd en CN — '
esme'-ivooo — o ""^riv ^v
odvd^t-enen en CN odoN r-*r-
o — i o in en o eN oo oo
^ _ -. o — - 0 -^ -H^,
"nvvvvvvvvvvvvvvvvv
8
1
<£
0
•a .
E 00
„ O
1 5
•°" S
S
•fe «
O i-
*i
13
Za
c
• • c
Z (/
Page
6-16
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
Age fvrs.1
0-2
2.1 - 17.9
s 18
All aees
Mean
0.0641
0.0423
0.0284
0.0489
Table 6-9.
Range
Min-Max
0.0421-0.1142
0.0268-0.0670
0.0200-0.0351
0.0200-0.1 142
a Standard deviation.
b Standard error of the mean.
Source: Phillies et al.. 1993.
Descriptive Statistics for Surface Area/Body Weight (SA/BW) Ratios (nvVkg)
SD"
0.0114
0.0076
0.0028
0.0187
SEb
7.84e-4
1.05e-3
7.68e-6
9.33e-4
5
0.0470
0.0291
0.0238
0.0253
10
0.0507
0.0328
0.0244
0.0272
25
0.0563
0.0376
0.0270
00299
Percentiles
50
0.0617
0.0422
0.0286
- 0.0495
75
0.0719
0.0454
0.0302
0.0631
90
0.0784
0.0501
0.0316
0.0740
95
0.0846
0.0594
0.0329
0.0788
Table 6-1 0. Statistical Results for Total Body Surface Area Distributions (m2)
Mean
Median
Mode
Standard Deviation
Skewness
Kurtosis
Mean
Median
Mode
Standard Deviation
Skewness
Kurtosis
Source: Murray and Burmaste
U.S. EPA
1.97
1.96
1.96
0.19
0.27
3.08
U.S. EPA
1.73
1.69
1.68
0.21
0.92
4.30
tr, 1992
Bovd
1.95
1.94
1.91
0.18
0.26
3.06
Bovd
1.71
1.68
1.62
0.20
0.88
4.21
Men
1.94
1.94
1.90
0.17
0.23
3.02
Women
1.69
1.67
1.60
0.18
0.77
4.01
1.89
1.89
1.90
0.16
0.04
292
Costeff
1.71
1.68
1.66
0.21
0.69
3.52
Exposure Factors Handbook
August 1997
Page
6-17
-------
Volume I - General Factors
Chapter 6 - Dermal
0.25
0.25 r
Infant SA/BW Ratios: Lognonm(O.OB41,0.0114)
Expected Value*
6.410E-02
13
is
All Ages SA/BW Ratios: Normal(0.0489.0.0187)
Expected Value«
4.890E-02
14
Adult SA/BW Ratios: NormaHO.0284,0.0028)
Expected Value •
2.840E-02
17
42
Figure 6-2. SA/BW Distributions for Infants, Adults, and All Ages Combined
Source: Phillips et al., 1993.
Page
6-18
Exposure Factors Handbook
August 1996
-------
Volume I - General Factors
Chapter 6 - Dermal
.08
.00
1.00
.00
1.00
Surface Area: Men
Frequency Distribution
1.50
2.00
2.50
Area in m2, n=5,000, LHS
Surface Area: Women
Frequency Distribution
1.50
2.00
2.50
424
318
(D
O
3.00
3.00
Area in m2. n=5.000. LHS
Figure 6-3. Frequency Distributions for the Surface Area of Men and Women
Source: Murray and Burmaster, 1992.
Exposure Factors Handbook
^August 1996
Page
6-19
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-11. Summary of Field Studies
Activity
ndoor
'ae Kwon Do
Greenhouse Workers
Indoor Kids No. 1
Indoor Kids No. 2
Month
Feb.
Mar.
Jan.
Feb.
Event"
(hrs)
1.5
5.25
2
2
Nb
7
2
4
6
Indoor Totals
Outdoor
Jaycare Kids No. la
Daycare Kids No. Ib
Daycare Kids No.2c
Daycare Kids No. 3
Soccer No. 1
Soccer No. 2
Soccer No. 3
Groundskeepers No. 1
Groundskeepers No. 2
Groundskeepers No. 3
Groundskeepers No. 4
Groundskeepers No. 5
Landscape/Rockery
Irrigattonlnstallers
Gardeners No. 1
Aug.
Aug.
Sept.
Nov.
Nov.
Mar.
Nov.
Mar.
Mar.
Mar.
Aug.
Aug.
June
Oct.
Aug.
3.5
4
8
8
0.67
1.5
1.5
1.5
4.25
8
4.25
8
9
3
4
6
6
5
4
8
8
7
2
5
7
7
8
4
6
8
M
6
1
3
4
19
5
5
4
3
8
0
0
1
3
5
4
6
3
6
1
F
1
1
1
2
Age Conditions Clothing
8-42 Carpeted floor All in longsleeve-long pants martial
arts uniform, sleeves rolled back,
barefoot
37-39 Plant watering.spraying, soil Long pants, elbow length short sleeve
blending, sterilization shirt, no gloves
6- 1 3 Playing on carpeted floor 3 of 4 short pants, 2 of 4 short sleeves,
socks, no shoes
3-13 Playing on carpeted floor 5of 6 long pants, 5 of 6 long sleeves,
socks, no shoes
14 5
1
1
1
1
0
8
7
1
2
2
3
2
1
0
7
1-6.5 Indoors: linoleum surface; 4 of 6 in long pants, 4 of 6 short
outdoors: grass, bare earth, barked sleeves, shoes
area
1-6.5 Indoors: linoleum surface; 4 of 6 in long pants, 4 of 6 short
outdoors: grass, bare earth, barked sleeves, no shoes
area
1 -4 Indoors, low napped carpeting, 4 of 5 long pants, 3of 5 long sleeves,
linoleum surfaces all barefoot for part of the day
1-4.5 Indoors: linoleum surface, outside: All long pants, 3 of 4 long sleeves,
grass, bare earth, barked area socks and shoes
13-15 Half grass-half bare earth 6 of 8 long sleeves, 4 of 8 long pants,
3 of 4 short pants and shin guards
24-34 All-weather field (sand-ground All in short sleeve shirts, shorts, knee
tires) socks, shin guards
24-34 All-weather field (sand-ground All in short sleeve shirts, shorts, knee
tires) socks, shin guards
29-52 Campus grounds, urban All in long pants, intermittent use of
horticulture center, arboretum gloves
22-37 Campus grounds.urban All in long pants, intermittent use of
horticulture center, arboretum gloves
30-62 Campus grounds,urban All in long pants, intermittent use of
horticulture center, arboretum gloves
22-38 Campus grounds.urban 5 of 7 in short sleeve shirts,
horticulture center, arboretum intermittent use of gloves
19-64 Campus grounds.urban 5 of 8 in short sleeve shirts,
horticulture center, arboretum intermittent use of gloves
27-43 Digging (manual andmechanical), All long pants, 2 long sleeves, all
rock moving socks and boots
23-41 Landscaping.surface restoration All in long pants, 3 of 6 short sleeve or
sleeveless shirts
16-35 Weeding, pruning.digging a trench 6 of 8 long pants, 7 of 8 short sleeves,
1 sleeveless, socks, shoes, intermittent
use of gloves
Page
6-20
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
Activity
Gardeners No. 2
Rugby No. 1
Rugby No. 2
Rugby No. 3
Archeologists
Construction Workers
Utility Workers No.l
Utility Workers No.2
Equip. Operators No.l
Equip. Operators No.2
'armers No. 1
Farmers No. 2
Reed Gatherers
Kids-in-mud No. 1
Kids-in-mud No. 2
Month
Aug.
Mar.
July
Sept.
July
Sept.
July
Aug.
Aug.
Aug.
May
July
Aug.
Sept.
Sept.
(hrs)
4
1.75
2
2.75
11.5
8
9.5
9.5
8
8
2
2
2 '
0.17
0.33
Nb
7
8
8
7
7
8
5
6
4
4
4
6
4
6
6
Outdoor Totals
i Number of subject
M
2
8
8
7
3
8
5
6
4
4
2
4
0
5
5
7S^
F
5
0
0
0
4
0
0
0
0
0
2
2
4
1
1
Age
26-52
20-22
23-33
24-30
16-35
21-30
24-45
23-44
21-54
21-54
39-44
18-43
42-67
9-14
9-14
Weeding, pruning, digging a
trench, picking fruit, cleaning
Mixed grass-barewet field
Grass field (80% oftime) and all-
weather field (mix of gravel, sand,
and clay) (20% oftime)
Compacted mixedgrass and bare
earth field
Digging withtrowel, screening dirt,
sorting
Mixed bare earth and concrete
surfaces, dust and debris
Cleaning, fixing mains, excavation
(backhoe and shovel)
Cleaning, fixing mains, excavation
(backhoe and shovel)
Earth scraping withheavy
machinery, dusty conditions
Earth scraping withheavy
machinery, dusty conditions
Manual weeding,mechanical
cultivation
Manual weeding.mechanical
cultivation
Tidal flats
,
ILake shoreline
J.-ake shoreline
Clothing
3 of 7 long pants, 5of 7 short sleeves,
1 sleeveless, socks, shoes, no gloves
All in short sleeve shirts, shorts,
variable sock lengths
All in shorts, 7 of 8 in short sleeve
shirts, 6 of 8 in low socks
All short pants, 7 of 8 short or rolled
up sleeves, socks, shoes
6 of 7 short pants.all short sleeves, 3
no shoes or socks, 2 sandals
5 of 8 pants,? of 8 short sleeves, all
socks and shoes
All long pants,short sleeves, socks,
boots, gloves sometimes
All long pants, 5 of 6 short sleeves,
socks, boots, gloves sometimes
All long pants, 3 of 4 short sleeves,
socks, boots, 2 of 4 gloves
All long pants, 3 of 4 short sleeves,
socks, boots, 1 gloves
All in long pants, heavy shoes, short
sleeve shirts, no gloves
2 of 6 short, 4 of 61ong pants, 1 of 6
long sleeve shirt, no gloves
2 of 4 shortsleeve shirts/knee length
pants, all wore shoes
All in short sleeve T-shirts, shorts,
barefoot
All in short sleeveT-shirts, shorts,
barefoot
725 5
-------
^T Chapter 6 - Dermal
Table 6-12. Geometric Mean and Geometric Standard Deviations of
Soil Adherence by Activity and Body Region :
Post-activity Dermal Soil Loadings (mg/cm2)
Activity Na
ndoor
Fae Kwon Do 7
3reenhouseWorkers 2
Indoor Kids No. 1 4
Indoor Kids No. 2 6
Daycare Kids No. la 6
Daycare Kids No. Ib 6
Daycare Kids No. 2 5
Daycare Kids No. 3 4
Outdoor
Soccer No. 1 8
Soccer No. 2 8
Soccer No. 3 7
Groundskeepers No. 1 , 2
Groundskeepers No. 2 5
Groundskeepers No. 3 7
Groundskeepers No. 4 7
Groundskeepers No. 5 8
Landscape/Rockery 4
Irrigation Installers 6
Gardeners No. 1 8
Hands
0.0063
1.9
0.043
0.0073
1.9
0.014
1.5
0.11
1.9
0.15
2.1
0.073
1.6
0.036
1.3
0.11
1.8
0.035
3.9
0.019
1.5
0.15
0.098
2.1
0.030
2.3
0.045
1.9
0.032
1.7
0.072
2.1
0.19
1.6
0.20
1.9
Arms
0.0019
- 4.1
0,0064
0.0042 .
, 1.9
0.0041
2.0
0.026
1.9
0.031 .
1.8
0.023V
1.4
0.012
0.011
2.0
0.0043
2.2
0.0029
2.2
0.005
0.0021
2.6
0.0022 '
1.9
0.014
1.8
0.022
2.8
0.030
2.1
0.018
3.2
0.050
2.1
Legs
0.0020
2.0
0.0015
0.0041
2.3
0.0031
1.5
0.030
1.7
0.023
1.2
0.011
1.4
0.014
3.0
0.031
3.8
0.014
5.3
0.0081
1.6
0.0010 ,
; i .5
0.0009
• 1.8
0.0008
1.9
0.0010
1.4
0.0054
1.8
0.072
Faces
0.0050
0.012
1.5
0.016
1.5
0.012
1.6
0.0021
0.010
2.0
0.0044
2.6
0.0026
1.6
0.0039
2.1
0.0057
1.9
0.0063
1.3
0.058
1.6
Feet
0.0022
2.1
0.012
1.4
0.0091
1.7
0.079
2.4
0.13
1.4
0.044
1.3
0.0053
5.1
0.018
0.0040
0.018
0.17
Page
6-22
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-12. Geometric Mean and Geometric Standard Deviations of
Soil Adherence by Activity and Body Region (continued) -
Activity
Gardeners No. 2
Rugby No. 1
Rugby No. 2
iugby No. 3
Archeologists
Construction Workers
Utility Workers No. 1
Utility Workers No. 2
Equip. Operators No. 1
Equip. Operators No. 2
Farmers No. 1
'armers No. 2
Reed Gatherers
Kids-in-mud No. 1
Kids-in-mud No. 2
a Number of subjects.
Sources: Kissel et al.. 1996b;
Na
7
8
8
7
7
8
5
6
4
4
4
6
4
6
6
Holmes et
Post-activity Dermal Soil Loadings fmp/cm2'>
0.18
3.4
0.40
1,7
0.14
1.4
0.049
1.7
0.14
1.3
0.24
1.5
0.32
1.7
0.27
2.1
0.26
2.5
0.32
1.6
0.41
1.6
0.47
1.4
0.66
1.8
35
2.3
58
2.3
al., 1996
Arms
0.054
2.9
0.27
1.6
0.11
1.6
0.031
1.3
0.041
1.9
0.098
1.5
0.20
2.7
0.30
1.8
0.089
1.6
0.27
1.4
0.059
'•'3.2
0.13
;...'• 2.2 ' ,
.0.036
2.1
....-." 11
r 6.1
11
3.8
(submitted for publication).
Legs
0.022
2.0
0.36
1.7
0.15
1.6
0.057
1.2
0.028
4.1
. 0.066
1.4
-
0.0058
2.7
0.037
3.9
0.16
9.2
36
2.0
9.5
2.3
0.047 0.26
1,6 ...:;\ - . ;
0.059 . '
2.7 •
0.046
1.4
0.020
1.5
0.050 0.24
1.8 1.4 :
0.029
1.6
0.10
1.5
0.10
1.5
0.10
1.4
0.23
1.7
0.018
1.4
0.041
3.0 ";
0.63
.7.1... .
24
3.6 ' , '
6.7 '
• 12.4
'
Exposure Factors Handbook
August 1997 '
Page
6-23
-------
Volume I - General Factors
Chapter 6 - Dermal
.8
*c
c/;
I
c
i
c
c
S
5
c*
VJ
,
J
c
a
t
a
U
U
11
II
•o i
•f £
G e
1 j
»
< g
II
00 i
o J
£"
•f
*
*
•§*
S
>.
E "g '§ £ ^ "SS gfflS
'sis. 1 < 1 1 s i. 2 1 -^ ® g l
•i i 11 'i s •§•§«! !§•=•=•§.!
1 1 a I 15 «£"§»•§• "l-s^^l
B "3 *^ "O Wryi PS2ra>>"25 cJi'^W (r^ +3
.S2onT3 .g ^ -03goo§^ «^^-Sg
llil 111 liliil IlliJJ
ii ll ll |i
C_) U ^ ^
o r:
a M =
1 | .. if | 1
|| $ | >
-n S ^
1 §
1 I
0 «
^^
§ §>i
S '3 """
i 1 g §S
Is- 1 1 |g
5J J-- 3 *° S *"*
c S- -1 "§ o *^
"S w? .« 3 H "^
z lo ® Sal
oo «i *> ^ . . — 1
= 1 1 § 1 § .a S "
^^ -| "flCS^ "aZ^dC2;
!l« i lisl lii^i
°--§ ^ °c.g;ip; S.s^"^
CO , g 2
S = S H ^ M
H 63 ^w* £* ON g
fe i3 oo S O a ON
a g D a < s c.
Page
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 6 - Dermal
Table'6-14. Summary of Recommended Values for Skin Surface Area
ildren
Whole body
Body gaits-
"&: > ^ *< ' **'*• <
'"<.„''•% ^Tables6-6~and%-7J •• *4'^'
Table 6-15. Confidence in Body Surface Area Measurement Recommendations
Considerations
Study Elements
Rationale
Rating
• Level of Peer Review
• Accessibility
• Reproducibility
• Focus on factor of interest
• Data pertinent to U.S.
• Primary data
• Currency
• Adequacy of data collection
period
• Validity of approach
• Representativeness of the
population
• Characterization of variability
• Lack of bias in study design
• Measurement error
Other Elements
Number of studies
Agreement among researchers
Overall Rating
Studies were from peer reviewed journal articles
EPA report was peer reviewed before distribution.
The journals used have wide circulation.
EPA report available from National Technical Information
Service.
Experimental methods are well-described.
Experiments measured skin area directly.
Experiments conducted in the U.S.
Re-analysis of primary data in more detail by two different
investigators.
Neither rapidly changing nor controversial area; estimates
made in 1935 deemed to be accurate and subsequently
used by others. J
Not relevant to exposure factor; parameter not time
dependent.
Approach used by other investigators; not challenged in
other studies.
Not statistically representative of U.S. population.
Individual variability due to age, race, or gender not
studied.
Objective subject selection and measurement methods
used; results reproduced by others with different methods.
Measurement variations are low; adequately described by
normal statistics. J
1 experiment; two independent re-analyses of this data set.
Consistent results obtained with different analyses; but
from a single set of measurements.
This factor can be directly measured. It is not subject to
dispute. Influence of age, race, or gender have not been
detailed adequately in these studies.
High
High
High
High
High
Low
Low
NA
High
Medium
Low
High
Low/Medium
Medium
Medium
High
Exposure Factors Handbook
August 1997
Page
6-25
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-16. Recommendations for Adult Body Surface Area
Bathing and Swimming
Water Contact
50th
20,000 cm2 '
Soil Contact
50th
5,000 cm2
95th - __
23,000 cm2
95th,, >-";
5,800 cm2
Source: U.S. EPA, 1992.
Study
KEY STUDIES
Kissel etal., 1995a
Kisselletal., 1996b
RELEVANT STUDIES
Driver etal., 1989
Lcpowct al., 1975
Que Hee et al., 1985
Roels etal., 1980
Sedman, 1989
Yang et al., 1989
Table 6- 17. Summary of Soil
Size Soil
Fraction Adherence
(^m) (mg/cm2)
<150, 150- Various
200, >250
Various
<150 1.40
<250 0.95
unsieved 0.58
0.5
1.5
0.9-1.5
*
0.9; 0.5
<150 9
Adherence Studies
Population
Surveyed
28 adults
24 children
12 children
89 adults
Adults
Adults
Adults
10 children
1 adult
661 children
Children
Rats
Comments
Data presented for soil loadings by
body part. See Table 6-11.
Data presented by activity and body
part.
Used 5 soil types and 2-3 soil horizons
(top soils and subsoils); placed soil
over entire hand of test subject, excess
removed by shaking the hands.
Dirt from hands collected during play.
Represents only fraction of total
present, some dirt may be trapped in
skin folds.
Assumed exposed area = 20 cm2. Test
subject was 14 years old.
Subjects lived near smelter in
Brussels, Belgium. Mean amount
adhering to soil was 0.159 g.
Used estimate of Roels et al. (1980)
and average surface of hand of an 11
year old; used estimates of Lepow et
al. (1975), Roels et al. (1980), and
Que Hee et al. (1985) to develop
mean of 0.5 mg/cm2.
Rat skin "monolayer" (i.e., minimal
amount of soil covering the skin); in
vitro and in vivo experiments.
Page
6-26
Exposure Factors Handbook
August
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-18. Confidence in Soil Adherence to Skin Recommendations
Considerations
Rationale
Rating
Study Elements
• Level of Peer Review
• Accessibility
• Reproducibility
• Focus on factor of interest
• Data pertinent to U.S.
• Primary data
• Currency
• Adequacy of data collection
period
• Validity of approach
• Representativeness of the
population
• Characterization of variability
• Lack of bias in study design
• Measurement error
Other Elements
Number of studies
Agreement among researchers
Overall Rating
Studies were from peer reviewed journal articles. High
Articles were published in widely circulated journals. High
Reports clearly describe experimental method. High
The goal of the studies was to determine soil adherence to High
skin.
Experiments were conducted in the U.S. High
Experiments were directly measure soil adherence to skin; High
exposure and dose of chemicals in soil were measured
indirectly or estimated from soil contact.
New studies were presented. High
Seasonal factors may be important, but have not been Medium
studied adequately. '
Skin rinsing technique is a widely employed procedure. High
Studies were limited to the State of Washington and may Low
not be representative of other locales.
Variability in soil adherence is affected by many factors , - Low
including soil properties, activity and individual behavior
patterns.
The studies attempted to measure soil adherence in High
selected activities and conditions to identify important
activities and groups.
The experimental error is low and well controlled, but Low/High
application of results to other similar activities may be
subject to variation.
The experiments were controlled as they were conducted Medium
by a few laboratories; activity patterns were studied by
only one laboratory.
Results from key study were consistent with earlier Medium
estimates from relevant studies and assumptions, but are
limited to hand data.
Data are limited, therefore it is difficult to extrapolate Low
from experiments and field observations to general
conditions.
Exposure Factors Handbook
August 1997
Page
6-27
-------
-------
Volume I - General Factors
Appendix 6A
APPENDIX 6A
FORMULAE FOR TOTAL BODY SURFACE AREA
Exposure Factors Handbook
August 1997
Page
6A-1
-------
-------
Volume I - General Factors
Appendix 6A
APPENDIX 6A
FORMULAE FOR TOTAL BODY SURFACE AREA
Most formulae for estimating surface area (SA), relate height to weight to surface area. The following formula
was proposed by Gehan and George (1970):
= KW2/3
(Eqn. 6A-1)
where:
SA = surface area in square meters;
W = weight in kg; and
K = constant.
While the above equation has been criticized because human bodies have different specific gravities and because
the surface area per unit volume differs for individuals with different body builds, it gives a reasonably good estimate
of surface area.
A formula published in 1916 that still finds wide acceptance and use is that of DuBois and DuBois. Their
model can be written:
SA = an H ' W
ai w "2
(Eqn. 6A-2)
where:
SA = surface area in square meters;
H = height in centimeters; and
W = weight in kg.
The values of ag (0.007182), al (0.725), and a2 (0.425) were estimated from a sample of only nine individuals
for whom surface area was directly measured. Boyd (1935) stated that the Dubois formula was considered a reasonably
adequate substitute for measuring surface area. Nomograms for determining surface area from height and mass presented
in Volume I of the Geigy Scientific Tables (1981) are based on the DuBois and DuBois formula. In addition, a
computerized literature search conducted for this report identified several articles written in the last 10 years in which
the DuBois and DuBois formula was used to estimate body surface area.
Boyd (1935) developed new constants for the DuBois and DuBois model based on 231 direct measurements
of body surface area found in the literature. These data were limited to measurements of surface area by coating methods
(122 cases), surface integration (93 cases), and triangulation (16 cases). The subjects were Caucasians of normal body
build for whom data on weight, height, and age (except for exact age of adults) were complete. Resulting values for the
constants in the DuBois and DuBois model were a$ = 0.01787, at = 0.500, and a2 = 0.4838. Boyd also developed a
formula based exclusively on weight, which was inferior to the DuBois and DuBois formula based on height and weight.
Exposure Factors Handbook
August 1997
Page
6A-3
-------
Volume I - General Factors
Appendix 6A
Gehan and George (1970) proposed another set of constants for the DuBois and DuBois model. The constants
were based on a total of 401 direct measurements of surface area, height, and weight of all postnatal subjects listed in
Boyd (1935). The methods used to measure these subjects were coating (163 cases), surface integration (222 cases),
and triangulation (16 cases).
Gehan and George (1970) used a least-squares method to identify the values of the constants. The values of
the constants chosen are those that minimize the sum of the squared percentage errors of the predicted values of surface
area. This approach was used because the importance of an error of 0.1 square meter depends on the surface area of the
individual. Gehan and George (1970) used the 401 observations summarized in Boyd (1935) in the least-squares method.
The following estimates of the constants were obtained: ag = 0.02350, a! = 0.42246, and ^ = 0.51456. Hence, their
equation for predicting surface area (SA) is:
SA = 0.02350 H0-42246 W°-51456
(Eqn. 6A-3)
or in logarithmic form:
In SA= -3.75080 + 0.42246 In H + 0.51456 In W
(Eqn. 6A-4)
where:
SA = surface area in square meters;
H = height in centimeters; and
W = weight in kg.
This prediction explains more than 99 percent of the variations in surface area among the 401 individuals measured
(Gehan and George, 1970).
The equation proposed by Gehan and George (1970) was determined by the U.S. EPA (1985) as the best choice
for estimating total body surface area. However, the paper by Gehan and George gave insufficient information to
estimate the standard error about the regression. Therefore, the 401 direct measurements of children and adults (i.e.,
Boyd, 1935) were reanalyzed in U.S. EPA (1985) using the formula of Dubois and Dubois (1916) and the Statistical
Processing System (SPS) software package to obtain the standard error.
The Dubois and Dubois (1916) formula uses weight and height as independent variables to predict total body
surface area (SA), and can be written as:
or in logarithmic form:
where:
Sai
Hi
Wi
30, alt and
In (SA); = In ao + a, In H; + ^ In Wt + In e;
surface area of the i-th individual (m2);
height of the i-th individual (cm);
weight of the i-th individual (kg);
parameters to be estimated; and
a random error term with mean zero and constant variance.
(Eqn. 6A-5)
(Eqn. 6A-6)
Page
6A-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Appendix 6A
Using the least squares procedure for the 401 observations, the following parameter estimates and their standard
errors were obtained:
The model is then:
or in logarithmic form:
=-3.73 (0.18), aj =0.417 (0.054), 83 = 0.517 (0.022)
SA = 0.0239 H°-417 W°-517
In SA = -3.73 + 0.417 In H + 0.517 In W
(Eqn. 6A-7)
(Eqn. 6A-8)
with a standard error about the regression of 0.00374. This model explains more than 99 percent of the total variation
in surface area among the observations, and is identical to two significant figures with the model developed by Gehan
and George (1970).
When natural logarithms of the measured surface areas are plotted against natural logarithms of the surface
predicted by the equation, the observed surface areas are symmetrically distributed around a line of perfect fit, with only
a few large percentage deviations. Only five subjects differed from the measured value by 25 percent or more. Because
each of the five subjects weighed less than 13 pounds, the amount of difference was small. Eighteen estimates differed
from measurements by 15 to 24 percent. Of these, 12 weighed less than 15 pounds each, 1 was overweight (5 feet 7
inches, 172 pounds), 1 was very thin (4 feet 11 inches, 78 pounds), and 4 were of average build. Since the same observer
measured surface area for these 4 subjects, the possibility of some bias in measured values cannot be discounted (Gehan
and George 1970).
Gehan and George (1970) also considered separate constants for different age groups: less than 5 years old, 5 years
old to less than 20 years old, and greater than 20 years old. The different values for the constants are presented below:
Table 6A-1. Estimated Parameter Values for Different Age Intervals
Age
group
All ages
<5 years old
;> 5 - <20 years old
^ 20 years oldl
Number
of persons
401
229
42
30
ao
0.02350
0.02667
0.03050
0.01545
ai
0.42246
0.38217
0.35129
0.54468
a2
0.51456
0.53937
0.54375
0.46336
The surface areas estimated using the parameter values for all ages were compared to surface areas estimated
by the values for each age group for subjects at the 3rd, 50th, and 97th percentiles of weight and height. Nearly all
differences in surface area estimates were less than 0.01 square meter, and the largest difference was 0.03 m2 for an
18-year-old at the 97th percentile. The authors concluded that there is no advantage in using separate values of a^ a^
and a2 by age interval.
Exposure Factors Handbook
August 1997
Page
6A-5
-------
Volume I - General Factors
Appendix 6A
Haycock et al. (1978) without knowledge of the work by Gehan and George (1970), developed values for the
parameters a^, aj, and a2 for the DuBois and DuBois model. Their interest in making the DuBois and DuBois model
more accurate resulted from their work in pediatrics and the fact that DuBois and DuBois (1916) included only one child
in their study group, a severely undernourished girl who weighed only 13.8 pounds at age 21 months. Haycock et al.
(1978) used their own geometric method for estimating surface area from 34 body measurements for 81 subjects. Their
study included newborn infants (10 cases), infants (12 cases), children (40 cases), and adult members of the medical and
secretarial staffs of 2 hospitals (19 cases). The subjects all had grossly normal body structure, but the sample included
subjects of widely varying physique ranging from thin to obese. Black, Hispanic, and white children were included in
their sample. The values of the model parameters were solved for the relationship between surface area and height and
weight by multiple regression analysis. The least squares best fit for this equation yielded the following values for the
three coefficients: ag = 0.024265, a! = 0.3964, and a2 = 0.5378. The result was the following equation for estimating
surface area:
expressed logarithmically as:
SA = 0.024265 H°-3964 Wa5378
In SA = In 0.024265 + 0.3964 In H + 0.5378 In W
(Eqn. 6A-9)
(Eqn. 6A-10)
The coefficients for this equation agree remarkably with those obtained by Gehan and George (1970) for 401
measurements.
George et al. (1979) agree that a model more complex than the model of DuBois and DuBois for estimating
surface area is unnecessary. Based on samples of direct measurements by Boyd (1935) and Gehan and George (1970),
and samples of geometric estimates by Haycock et al. (1978), these authors have obtained parameters for the DuBois
and DuBois model that are different than those originally postulated in 1916. The DuBois and DuBois model can be
written logarithmically as:
lnSA = lna0 + a1lnH + a2lnW (Eqn. 6A-11)
The values for ag, a,, and a% obtained by the various authors discussed in this section are presented to follow:
Table 6A-2. Summary of Surface Area Parameter Values for the DuBois and DuBois Model
Author
(year)
DuBois and DuBois (1916)
Boyd (1935)
Gehan and George (1970)
Haycock etal. (1978)
Number
of persons
9
231
401
81
ao
0.007184
0.01787
0.02350
0.024265
ai
0.725
0.500
0.42246
0.3964
a2
0.425
0.4838
0.51456
0.5378
The agreement between the model parameters estimated by Gehan and George (1970) and Haycock et al. (1978)
is remarkable in view of the fact that Haycock et al. (1978) were unaware of the previous work. Haycock et al. (1978)
used an entirely different set of subjects, and used geometric estimates of surface area rather than direct measurements.
Page
6A.-6
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Appendix 6A
It has been determined that the Gehan and George model is the formula of choice for estimating total surface area of the
body since it is based on the largest number of direct measurements.
Nomograms , '
Sendroy and Cecchini (1954) proposed a graphical method whereby surface area could be read from a diagram
relating height and weight to surface area. However, they do not give an explicit model for calculating surface area. The
graph was developed empirically based on 252 cases, 127 of which were from the 401 direct measurements reported by
Boyd (1935). In the other 125 cases the surface area was estimated using the linear method of DuBois and DuBois
(1916). Because the Sendroy and Cecchini method is graphical, it is inherently less precise and less accurate than the
formulae of other authors discussed above.
Exposure Factors Handbook
August 1997
Page
6A-7
-------
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
7. BODY WEIGHT STUDIES
There are several physiological factors needed to
calculate potential exposures. These include skin surface
area (see Volume I, Section 6), inhalation rate (see
Volume I, Section 5) life expectancy (see Volume I,
Section 8), and body weight. The average daily dose is
typically normalized to the average body weight of the
exposed population, If exposure occurs only during
childhood years, the average child body weight during the
exposure period should be used to estimate risk (U.S.
EPA, 1989). Conversely, if adult exposures are being
evaluated, an adult body weight value should be used.
The purpose of this section is to describe published
studies on body weight for the general U.S. population.
The studies have been classified as either key or relevant
studies, based on the criteria described in Volume I,
Section 1.3.1. Recommended values are based on the
results of key studies, but relevant studies are also
presented to provide the reader with added perspective on
the current state of knowledge pertaining to body weight.
7.1. KEY BODY WEIGHT STUDY
Hamill et al. (1979) - Physical Growth: National
Center for Health Statistics Percentiles - A National
Center for Health Statistics (NCHS) Task Force that
included academic investigators and representatives from
CDC Nutrition Surveillance Program selected, collated,
integrated, and defined appropriate data sets to generate
growth curves for the age interval: birth to 36 months
developed (Hamill et al., 1979). The percentile curves
were for assessing the physical growth of children in the
U.S. They are based on accurate measurements made on
large nationally representative samples of children
(Hamill et al., 1979). Smoothed percentile curves were
derived for body weight by age (Hamill et al., 1979).
Curves were developed for boys and for girls. The data
used to construct the curves were provided by the Pels
Research Institute, Yellow Springs, Ohio. These data
were from an ongoing longitudinal study where
anthromopetric data from direct measurements are
collected regularly from participants (-1,000) in various
areas of the U.S. The NCHS used advanced statistical
and computer technology to generate the growth curves.
Table 7-1 presents the percentiles of weight by sex and
age. Figures 7-1 and 7-2 present weight by age
percentiles for boys and for girls aged birth to 36 months,
respectively. Limitations of this study are that mean body
weight values were not reported and the data are more
Table 7-1. Smoothed Percentiles of Weight (in kg) by Sex and Age:
Statistics from NCHS and Data from Pels Research Institute, Birth to 36 Months
Smoothed" Percentile
Sex and Age
Male
Birth
1 Month
3 Months
6 Months
9 Months
12 Months
18 Months
24 Months
30 Months
36 Months
Female
Birth
1 Month
3 Months
6 Months
9 Months
12 Months
18 Months
24 Months
30 Months
36 Months
5th
2.54
3.16
4.43
6.20
7.52
8.43
9.59
10.54
11.44
12.26
2.36
2.97
4.18
5.79
7.00
7.84
8.92
9.87
10.78
11.60
10th
2.78
3.43
4.78
6.61
7.95
8.84
9.92
10.85
11.80
12.69
2.58
3.22
4.47
6.12
7.34
8.19
9.30
10.26
11.21
12.07
25th
3.00
3.82
5.32
7.20
8.56
9.49
10.67
11.65
12.63
13.58
2.93
3.59
4.88
6.60
7.89
8.81
10.04
11.10
12.11
12.99
50th
Weight in Kilograms
3.27
4.29
5.98
7.85
9.18
10.15
11.47
12.59
13.67
14.69
3.23
3.98
5.40
7.21
8.56
9.53
10.82
11.90 .
12.93
13.93
75th
3.64
4.75
6.56
8.49
9.88
10.91
12.31
13.44
14.51
15.59
3.52
4.36
5.90
7.83
9.24
10.23
11.55
12.74
13.93
15.03
90th
3.82
5.14
7.14
9.10
10.49
11.54
13.05
14.29
15.47
16.66
3.64
4.65
6.39
8.38
9.83
10.87
12.30
13.57
14.81
15.97
95th
4.15
5.38
7.37
9.46
10.93
11.99
13.44
14.70
15.97
17.28
3.81
4.92
6.74
8.73
10.17
11.24
12.76
14.08
15.35
16.54
a Smoothed by cubic-spline approximation.
Source: Hamill etal., 1979.
Exposure Factors Handbook
August 1997
Page
7-1
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
CO
CO
LO
CO
T—
CM
(5 £
c.
D)
1 •
CO
r-
CD
in
CO
CM
Source: Hz
L
//
tin
m
w
'
j
///
I//,
'///,
///
'/
//
y/,
7,
#
//
//
'/.
//
'//
/
Xx
X v
X
x
x
'
'/
x"
^
^x
X
^
^X
^
^
x
^x
^
^
x
x"
X
^
x.
x
x"
^^
^
x
x
^
^
x
X
x^
^x
^:
95th
90th
75th
50th
25th
10th
5th
03 6 9 12 15 18 21 24 27 30 33 36
Age in Months
Figure 7-1. Weight by Age Percentiles for Boys Aged Birth-36 Months
imill et al., 1979.
CO
CO
en
CO
en
CO
CO
CO
CO
p
co
en
s 1
CO
b
Ol
CO
ro
— L
CO
CD
0)
cn
Page Exposure Factors Handbook
7-2 . August 1996
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
12 15 18 21 24 27 30 33 36
Age in Months
Figure 7-2. Weight by Age Percentiles for Girls Aged Birth-36 Months
Source: Hamill etal., 1979
(D
3"
C
3
a.
w
Exposure Factors Handbook
August 1996
Page
7-3
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
that 15 years old. However, this study does provide body
weight data for infants less than 6 months old.
NCHS (1987) - Anthropometric Reference Data
and Prevalence of Overweight, United States, 1976-80-
Statistics on anthropometric measurements, including
body weight, for the U.S. population were collected by
NCHS through the second National Health and Nutrition
Examination Survey (NHANES II). NHANES II was
conducted on a nationwide probability sample of
approximately 28,000 persons, aged 6 months to 74 years,
from the civilian, non-institutionalized population of the
United States. Of the 28,000 persons, 20,322 were
interviewed and examined, resulting in a response rate of
73.1 percent. The survey began in February 1976 and was
completed in February 1980. The sample was selected so
that certain subgroups thought to be at high risk of
malnutrition (persons with low incomes, preschool
children, and the elderly) were oversampled. The
estimates were weighted to reflect national population
estimates. The weighting was accomplished by inflating
examination results for each subject by the reciprocal of
selection probabilities adjusted to account for those who
were not examined, and post stratifying by race, age, and
sex (NCHS, 1987).
The NHANES II collected standard body
measurements of sample subjects, including height and
weight, that were made at various times of the day and in
different seasons of the year. This technique was used
because one's weight may vary between winter and
summer and may fluctuate with recency of food and water
intake and other daily activities (NCHS, 1987). Mean
body weights of adults, by age, and their standard
deviations are presented in Table 7-2 for men, women,
and both sexes combined. Mean body weights and
standard deviations for children, ages 6 months to 19
years, are presented in Table 7-3 for boys, girls, and boys
and girls combined. Percentile distributions of the body
weights of adults by age and race for males are presented
in Table 7-4, and for females in Table 7-5. Data for
children by age are presented in Table 7-6 for males, and
for females in Table 7-7.
Results shown in Tables 7-4 and 7-5 indicate that
the mean weight for adult males is 78.1 kg and for adult
females, 65.4 kg. It also shows that the mean weight for
White males (78.5 kg) is greater than for Black males
(77.9 kg). Additionally, mean weights are greater for
Black females (71.2 kg) than for White females (64.8 kg).
From Table 7-3, the mean body weights for girls and boys
are approximately the same from ages 6 months to 14
years. Starting at years 15-19, the difference in mean
body weight ranges from 6 to 11 kg.
Table 7-2. Body Weights of Adults" (kilograms)
Men
Women
Men and
Women
Age (years)
Mean
(kg)
Std.
Dev.
Mean
(kg)
Std.
Dev.
Mean (kg)
18<25
25<35
35<45
4S<55
55<65
65<7S
18<75
73.8
78.7
80.9
80.9
78.8
74.8
78.1
12.7
13.7
13.4
13.6
12.8
12.8
13.5
60.6
64.2
67.1
68.0
67.9
66.6
65.4
11.9
15.0
15.2
15.3
14.7
13.8
14.6
67.2
71.5
74.0
74.5
73.4
70.7
71.8
Note: 1 kg = 2.2046 pounds.
a Includes clothing weight, estimated as ranging from 0.09 to 0.28 kilogram.
Source: Adapted from National Center for Health Statistics (NCHS), 1987.
Table 7-3. Body Weights of Children" (kilograms)
Boys
Girls
Boys and
Girls
Age
Mean
(kg)
Std.
Dev.
Mean
(kg)
Std.
Dev.
Mean
(kg)
6-11 months
1 year
2 years
3 years
4 years
5 years
6 years
7 years
8 years
9 years
10 years
11 years
12 years
13 years
14 years
15 years
16 years
17 years
18 years
19 years
9.4
11.8
13.6
15.7
17.8
19.8
23.0
25.1
28.2
31.1
36.4
40.3
44.2
49.9
57.1
61.0
67.1
66.7
71.1
71.7
1.3
1.9
1.7
2.0
2.5
3.0
4.0
3.9
6.2
6.3
7.7
10.1
10.1
12.3
11.0
11.0
12.4
11.5
12.7
11.6
8.8
10.8
13.0
14.9
17.0
19.6
22.1
24.7
27.9
31.9
36.1
41.8
46.4
50.9
54.8
55.1
58.1
59.6
59.0
60.2
1.2
1.4
1.5
2.1
2.4
3.3
4.0
5.0
5.7
8.4
8.0
10.9
10.1
11.8
11.1
9.8
10.1
11.4
11.1
11.0
9.1
11.3
13.3
15.3
17.4
19.7
22.6
24.9
28.1
31.5
36.3
41.1
45.3
50.4
56.0
58.1
62.6
63.2
65.1
66.0
Note: 1 kg = 2.2046 pounds.
a Includes clothing weight, estimated as ranging from 0.09 to 0.28 kilogram.
Source: Adapted from National Center for Health Statistics (NCHS). 1987.
7.2. RELEVANT BODY WEIGHT STUDIES
Brainard and Burmaster (1992) - Bivariate
Distributions for Height and Weight of Men and Women
in the United States - Brainard and Burmaster (1992)
examined data on the height and weight of adults
published by the U.S. Public Health Service and fit
bivariate distributions to the tabulated values for men and
women, separately.
Page
7-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
"8
CO
|1
- vo
"S ON
Is
"£
L. W
OJ T3
Hi
I*
0> 0)
?J
rt 1>
OJ O
?*
00 .
1&
•a '§
!•§ u
«§£
11
&3
2 «
a -a
.5 °*
•i §
•Sf!
;s
t-1-
.«
.0
£
1
•S
«•>
. 00
•5
£
•S
J9 in
£ S
•s
in
-S
o
.-H
-s
«•)
•S.I
•3 a
oo Q
ESS.
"jr.
o *o
u a c
IM
Race and Age
r- w> t~ en en en vo
sasssss
t-* rt- r-t oo tn t-- cs
»n o >n oo ON ^t- *— i
ON ON O\ ON C\ c\ o\
en I-H T-H oo m «n d\
i— 1 tO r-H Tj- -^ O t^-
c\ oo ON ON o\ ON oo
VO CO VO r~1 Tj- \O t-
«n o >o oo ON >n cs
CO CO OO OO OO OO OO
ON o »n ON o r* cs
\O (N r- ON ON r- TJ-
r- t^ r^ r* i> r^ r-
r*- oo co »-H r- cs »-H
oo TJ- ON n m en oo
\o \o vo vo vo vo »n
\o oo »n r-» oo ON ^f
oo \o ON ON o ON TJ-
in in tn >n «n »n »n
J CS
r-< OO f- ON ON OO OO
fspsssss
\o oo r-
oo oo in n in
*-• r-H cs en ^f in \o
en o r- I-H »n t- o
ssssss^
m o en r- o in CN
•n »— i >n oo ON ^f «-H
ON ON ON ON ON ON ON
^t m en vo CN ^* ON
ON oo ON ON ON ON oo
vo vo \o co ^ \o o
in o in oo ON in en
OO CO OO OO OO OO OO
en rf o ^ o e» t-
r- es oo o ON oo ^<*
r- i> i> oo t^- t"» r-
•^t O oo ON ON vo o
ON »n ON e>< ^- * O r-
\o vo ^o r- t*~ r- vo
m O ON oo en vo m
in ro «n oo r- *o eN
*o ^o vo ^o ^o *>o ^o
co in r- *o T-H »n »n
S8SS«S?!
en oo ON en o «n m
ON vo ON c^ eN o m
in in in vo \o vo in
^H OO i-i OO Tj* ^ ^f
en eN en eN en eN eN
m eN O "d- O ON rf
oo rf ON i— i i— i co *n
t— r- t-- oo oo r- r^-
oo "O '— i en t-» vo in
2 oo ON vo S O S
S2 S2 S2 2 E S2 £
§. SI £ §. §. S. I *
^^s^sss g
oo oo u-i »n »n in in
>-i •-" tS t^ -^- 1O VO
•* vo m r~ -H
sss* * ss
c<^ t- n vo m
oo m ts ^ ts oo t~
O\ OO ON O O O\ O\
1— 1 »— 1
o\ oo vo •* o oo oo
O\ 00 S O S O\ ON
•* i-l •* OO O "0 rt
in (~~ •* -* co vo ~H
OO f^ OO ON ON OO OO
en oo en T— t oo o cs
•n o »o en *— • t^- ^^
r- r— t— oo oo r- t—
CN ON TJ- t~ o) o o
t— -* oo ON en oo T-H
vo vo vo vo r^ vo vo
vo en ON CN o co o
vo vo vo vo vo vo w~l
I-H ON •* t— r- •<* r~-
T-H o en »— < -^ ^ vo
vo vo vo vo vo vo >o
O en t— oo to
oo oo oo y ,, vo CN
>n >n T> * * v> n CM \o vi -^- Tf >n
ON CN cs >r> •* vb en
r~- CN oo CN CM oo m
t- r- t~- oo oo r~ r-
ON ^ ON O CN ON OO
SCN en r- vo CN CM
T-H T-H T-H T-H
en co co OT co c/3 tn
s s s § § s s
>>>>>>>»>,>->>»
t|- CM en 5 u|> vo t~-
oo oo TI n
T-I --I CN cn •* m vo
c
1
_§>
2
CN
O
3
8 d
II ^
* 1 ' i
g1 «j 1
!»! • 1
28- -S
" M S
w S co
as •£
n §
i!-a *
§ pC 09 t-
*^ rt ^
^ * *" «j u
S c5 S -S "S
M 5 0 -jg , §
•3 •« S J
^•8 -i 1 ..
fli "o *O ca fc-.
I-3"*3 1
i«-i « A O fJ
Exposure Factors Handbook
August 1997
Page
7-5
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
1
2
w
B n
11
P
Ij
li
3. '£
I3
ea u
< M
<*. **.
o-o
>2 i
0
!
00 A
«-< .
8J3
•« •=
s*
1?
c W
2 "43
g>co
|1
•w g-
•si
*i
s
1
J3
in
o\
J=
f
£
_u
1 -s
frl t-
•n
4=
si
J3
•s
•s
o\ en >n >n tn co •* vo t- t~ t~-
in at
-------
Volume I - General Factors
Chapter? - Body Weight Studies
II
vo r- oo os
2
%
3
o
Q
O
S
SP
f
2
£5
o\
•s
00
a
if 1
in "h
cs ff
tN '.§
i!
-1
ll
/Zt CO
5
2
1
oo
Exposure Factors Handbook
'August 1997
Page
7-7
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
i
i
3ws
5 ^
P.
ii
fi
li
II
II
o. x
A<^
Sjf
\o -a
tl
|l
g CO
S-g
g"i
•a,
ft
JU
g
"P o
S3 -s
•s-S
2; S w
VO »A VO VO •—I OO OO
r- r- t-~ r- oo r- r-
;in cs oo oo vo o
vo vo \o \o vo r-
o4-3>/->-HTlcor-"*o\— o—t
co co co co
8
o
2
VI
a
a co
€ -s
I g
K
u
•§ "
|§
"• u
•
•§
i> g a
ir I
Exposure Factors Handbook
• August 1997
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
Height and weight of 5,916 men and 6,588 women
in the age range of 18 to 74 years were taken from the
NHANES II study and statistically adjusted to represent
the U.S. population aged 18 to 74 years with regard to age
structure, sex, and race. Estimation techniques were used
to fit normal distributions to the cumulative marginal data
and goodness-of-fit tests were used to test the hypothesis
that height and lognormal weight follow a normal
distribution for each sex. It was found that the marginal
distributions of height and lognormal weight for both men
and women are Gaussian (normal) in form. This
conclusion was reached by visual observation and the high
R2 values for best-fit lines obtained using linear
regression. The R2 values for men's height and lognormal
weight are reported to be 0.999. The R2 values for
women's height and lognormal weight are 0.999 and
0.985, respectively.
Brainard and Burmaster (1992) fit bivariate
distributions to estimated numbers of men and women
aged 18 to 74 years in cells representing 1 inch height
intervals and 1-0 pound weight intervals. Adjusted height
and lognormal weight data for men were fit to a single
bivariate normal distribution with an estimated mean
height of 1.75 meters (69.2 inches) and an estimated mean
weight of 78.6 kg (173.2 pounds). For women, height and
lognormal weight data were fit to a pair of superimposed
bivariate normal distributions (Brainard and Burmaster,
1992). The average height and weight for women were
estimated from the combined bivariate analyses. Mean
height for women was estimated to be 1.62 meters (63.8
inches) and mean weight was estimated to be 65.8 kg
(145.0 pounds). For women, a calculation using a single
bivarite normal distribution gave poor results (Brainard
and Burmaster, 1992). According to Brainard and
Burmaster, the distributions are suitable for use in Monte
Carlo simulation.
Burmaster et al. (1994) (Submitted 2/19/94 to Risk
Analysis for Publication) - Lognormal Distributions of
Body Weight as a Function of Age for Female and Male
Children in the United States - Burmaster et al. (1994),
performed data analysis to fit normal and lognormal
distributions to the body weights of female and male
children at age 6 months to 20 years (Burmaster et al.,
1994).
Data used in this analysis were from the second
survey of the National Center for Health Statistics,
NHANES II, which included responses from 4,079
females and 4,379 males 6 months to 20 years of age in
the U.S. (Burmaster et al., 1994). The NHANES II data
had been statistically adjusted for non-response and
probability of selection, and stratified by age, sex, and
race to reflect the entire U.S. population prior to reporting
(Burmaster et al., 1994). Burmaster et al. (1994)
conducted exploratory and quantitative data analyses, and
fit normal and lognormal distributions to percentiles of
body weight for children. Cumulative distribution
functions (CDFs) were plotted for female and male body
weights on both linear and logarithmic scales.
Two models were used to assess the probability
density functions (PDFs) of children's body weight.
Linear and quadratic regression lines were fitted to the
data. A number of goodness-of-fit measures were
conducted on data generated by the two models.
Burmaster et al. (1994) found that lognormal distributions
give strong fits to the body weights of children, ages 6
months to 20 years. Statistics for the lognormal
probability plots are presented in Tables 7-8 and 7-9.
These data can be used for further analyses of body
weight distribution (i.e., application of Monte Carlo
analysis).
Table 7-8. Statistics for Probability Plot Regression Analyses
Female's Body Weights 6 Months to 20 Years of Age
Age
Lognormal Probability Plots
Linear Curve
0V
6 months to 1 year
1 to 2 years
2 to 3 years
3 to 4 years
4 to 5 years
5 to 6 years
6 to 7 years
7 to 8 years
8 to 9 years
9 to 10 years
10 to 11 years
11 to 12 years
12 to 13 years
13 to 14 years
14 to 15 years
15 to 16 years
16 to 17 years
17 to 18 years
18 to 19 years
19 to 20 years
2.16
2.38
2.56
2.69
2.83
2.98
3.10
3.19
3.31
3.46
3.57
3.71
3.82
3.92
3.99
4.00
4.06
4.08
4.07
4.10
0.145
0.128
0.112
0.137
0.133
0.163
0.174
0.174
0.156
0.214
0.199
0.226
0.213
0.216
0.187
0.156
0.167
0.165
0.147
0.149
a ^2> °2 " correspond to the mean and standard deviation, respectively, of
the lognormal distribution of body weight (kg).
Source: Burmaster et al.. 1994.
Exposure Factors Handbook
August 1997
Page
7-9
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
Table 7-9. Statistics for Probability Plot Regression Analyses
Male's Body Weights 6 Months to 20 Years of Age
Age
Lognormal Probability Plots
Linear Curve
6 months to 1 year
1 to 2 years
2 to 3 years
3 to 4 years
4 to 5 years
5 to 6 years
6 to 7 years
7 to 8 years
8 to 9 years
9 to 10 years
10 to 11 years
11 to 12 years
12 to 13 years
13 to 14 years
14 to 15 years
IS to 16 years
16 to 17 years
17 to 18 years
18 to 19 years
19 to 20 years
2.23
2.46
2.60
2.75
2.87
2.99
3.13
3.21
3.33
3.43
3.59
3.69
3.78
3.88
4.02
4.09
4.20
4.19
4.25
4.26
0.132
0.119
0.120
0.114
0.133
0.138
0.145
0.151
0.181
0.165
0.195
0.252
0.224
0.215
0.181
0.159
0.168
0.167
0.159
0.154
* ju2, O2 - correspond to the mean and standard deviation,
respectively, of the lognormal distribution of body weight (kg).
Source; Burmasteret al... 1994.
AIHC - Exposure Factors Sourcebook - The
Exposure Factors Sourcebook (AIHC, 1994) provides
similar body weight data as presented here. Consistent
with this document, an average adult body weight of 72 kg
is recommended on the basis of the NHANES II data
(NCHS, 1987). These data are also used to derive
probability distributions for adults and children. In
addition, the Sourcebook presents probability
distributions derived by Brainard and Burmaster (1992),
Versar (1991) and Brorby and Finley (1993). For each
distribution, the ©Risk formula is provided for direct use
in the ©Risk simulation software (Palisade, 1992). The
organization of this document, makes it very convenient
to use in support of Monte Carlo analysis. The reviews of
the supporting studies are very brief with little analysis of
their strengths and weaknesses. The Sourcebook has been
classified as a relevant rather than key study because it is
not the primary source for the data used to make
recommendations in this document. The Sourcebook is
very similar to this document in the sense that it
summarizes exposure factor data and recommends values.
As such, it is clearly relevant as an alternative information
source on body weights as well as other exposure factors.
7.3. RECOMMENDATIONS
The key studies described in this section was used
in selecting recommended values for body weight. The
general description of both the key and relevant studies
are summarized in Table 7-10. The recommendations for
body weight are summarized in Table 7-11. Table 7-12
presents the confidence ratings for body weight
recommendations. The mean body weight for all .adults
(male and female, all age groups) combined is 71.8 kg as
shown in Table 7-2. The mean values for each age group
in Table 7-2 were derived by adding the body weights for
men and women and dividing by 2. If age and sex
distribution of the exposed population is known, the mean
body weight values in Table 7-2 can be used. If percentile
data are needed or if race is a factor, Tables 7-4 and 7-5
can be used to select the appropriate data for percentiles
or mean values.
For infants (birth to 6 months), appropriate values
for body weight may be selected from Table 7-1. These
data (percentile only) are presented for male and female
infants.
For children, appropriate mean values for weights
may be selected from Table 7-3. If percentile values are
needed, these data are presented in Table 7-6 for male
children and in Table 7-7 for female children.
Body weight is a function of age, gender, and race
and populations of many geographic regions may vary
from the general population across geographic regions.
Therefore, the user should make appropriate adjustments
when applying the percentiles to other geographic regions.
The mean recommended value for adults (71.8 kg)
is different than the 70 kg commonly assumed in EPA risk
assessments. Assessors are encouraged to use values
which most accurately reflect the exposed population.
When using values other than 70 kg, however, the
assessors should consider if the dose estimate will be used
to estimate risk by combining with a dose-response
relationship which was derived assuming a body weight of
70 kg. If such an inconsistency exists, the assessor should
adjust the dose-response relationship as described in the
appendix to Chapter 1. The Integrated Risk Information
System (IRIS) does not use a 70 kg body weight
assumption in the derivation of RfCs and RfDs, but does
make this assumption in the derivation of cancer slope
factors and unit risks.
Page
7-10
Exposure Factors Handbook
August 199:7
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
Table 7-10. Summary of Body Weight Studies
Study
KEY STUDIES
Hamilletal. (1979)
NCHS, 1987
(NHANES II)
RELEVANT STUDIES
Braihard and Burmaster, 1992
Burmaster et al., 1994
Number of Subjects
-1,000
20,322
12,501 (5,916 men and 6,588
women)
8,458 (4,079 females and
4,379 males)
Population
U.S. general
population
U.S. general
population
U.S. general
population
U.S. general
population
Comments
Authors noted that data are accurate measurements from a
large nationally representative sample of children.
Based on civilian non-institutionalized population aged 6
months to 74 years. Response rate was 73.1 percent.
Used data from NHANES II to fit bivarite distributions to
women and men age 18 to 74 years.
Used data from NHANES II to develop fitted distributions
for children aged 6 to 20 years old. Adjusted for non-
response by age, gender, and race.
._ xabte 7-41. >Sumraarrof Recommenaed Values for Body Weight
"Populaion
"Mea
s 'Uooerl'ercentile -"f
^v
Children ,/> •
\» ^ //>3'^
Infants * *
s 71.8 kg (Se4 fable 7-2)' ^ ** Se^I.able&7-4andi7-5 "*N- ' ;_;
\See^ile7-3 ' -^ '? ^ ,'' "SeeTabIes7-6and7^7 ^
Nor Available^ ? - '*" ** S
See, Tables 7-6 ''ta&'TZf
See Table 7-f
7.4. REFERENCES FOR CHAPTER 7
American Industrial Health Council (AIHC). (1994)
Exposure factors sourcebook. AIHC, Washington,
DC.
Brainard, J.; Burmaster, D. (1992) Bivariate
distributions for height and weight of men and
women in the United States. Risk Anal. 12(2):267-
275.
Brorby, G.; Finley, G. (1993) Standard probability
density functions for routine use in environmental
health risk assessment. Presented at the Society of
Risk Analysis Annual Meeting, December 1993,
Savannah, GA.
Burmaster, D.E.; Lloyd, K.J.; Crouch, E.A.C. (1994)
Lognormal distributions of body weight as a
function of age for female and male children in the
United States. Submitted 2/19/94 to Risk Analysis
for publication.
Hamill, P.V.V.; Drizd, T.A.; Johnson, C.L.; Reed, R.B.;
Roche, A.F.; Moore, W.M. (1979) Physical
growth: National Center for Health Statistics
Percentiles. American J. Clin. Nutr. 32:607-609.
National Center for Health Statistics (NCHS) (1987)
Anthropometric reference data and prevalence of
overweight, United States, 1976-80. Data from the
National Health and Nutrition Examination Survey,
Series 11, No. 238. Hyattsville, MD: U.S.
Department of Health and Human Services, Public
, Health Service, National Center for Health
Statistics. DHHS Publication No. (PHS) 87-1688.
Palisade. (1992) ©Risk Users Guide. Palisade
Corporation, Newfield, NY.
U.S. EPA (1989) Risk assessment guidance for
Superfund, Volume I: Human health evaluation
manual. Washington, DC: U.S. Environmental
Protection Agency, Office of Emergency and
Remedial Response. EPA/540/1-89/002.
Versar, Inc. (1991) Analysis of the impact of exposure
assumptions on risk assessment of chemicals in the
environment, phase II: uncertainty analyses of
existing exposure assessment methods. Draft
Report. Prepared for Exposure Assessment Task
Group, Chemical Manufacturers Association,
Washington, DC.
; Exposure Factors Handbook
^August 1997
Page
7-11
-------
Volume I - General Factors
Chapter 7 - Body Weight Studies
Table 7-12. Confidence in Body Weight Recommendations
Considerations
Rationale
Rating
Study Elements
• Level of peer review
• Accessibility
• Reproducibility
• Focus on factor of interest
• Data pertinent to US
• Primary data
• Currency
• Adequacy of data collection
period
• Validity of approach
> Study size
• Representativeness of the
population
1 Characterization of
variability
1 Lack of bias in study design
(high rating is desirable)
1 Measurement error
Other Elements
• Number of studies
• Agreement between researchers
Overall Rating
NHANES H was the major source of data for NCHS (1987). This is a
published study which received a high level of peer review. The Hamill et
al. (1979) is a peer reviewed journal publication.
Both studies are available to the public.
Results can be reproduced by analyzing NHANES II data and the Pels
Research Institute data.
The studies focused on body weight, the exposure factor of interest.
The data represent the U.S. population.
The primary data were generated from NHANES II data and Pels studies,
thus these data are secondary.
The data were collected between 1976-1980.
The NHANES II study included data collected over a period of 4 years.
Body weight measurements were taken at various times of the day and at
different seasons of the year.
Direct body weights were measured for both studies. For NHANES II,
subgroups at risk for malnutrition were over-sampled. Weighting was
accomplished by inflating examination results for those not examined and
were stratified by race, age, and sex. The Pels data are from an ongoing
longitudinal study where the data are collected regularly.
The sample size consisted of 28,000 persons for NHANES II. Author
noted in Hamill et al. (1979) that the data set was large.
Data collected focused on the U.S. population for both studies.
Both studies characterized variability regarding age and sex. Additionally
NHANES II characterized race (for Blacks, Whites and total populations)
and sampled persons with low income.
There are no apparent biases in the study designs for NHANES II. The
study design for collecting the Pels data was not provided.
For NHANES II, measurement error should be low since body weights
were performed in a mobile examination center using standardized
procedures and equipment. Also, measurements were taken at various
times of the day to account for weight fluctuations as a result of recent food
or water intake. The authors of Hamill etal. (1979) report that study data
are based on accurate direct measurements from an ongoing longitudinal
study.
There are two studies.
There is consistency among the two studies.
High
High
High
High
High
Medium
Low
High
High
High
High
High
Medium-
High
High
Low
High
High
Page
7-12
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 8 - Lifetime
.rf
8. LIFETIME
The length of an individual's life is an important
factor to consider when evaluating cancer risk because the
dose estimate is averaged over an individual's lifetime.
Since the averaging time is found in the denominator of
the dose equation, a shorter lifetime would result in a
higher potential risk estimate, and conversely, a longer life
expectancy would produce a lower potential risk estimate.
8.1. KEY STUDY ON LIFETIME
Statistical data on life expectancy are published
annually by the U.S. Department of Commerce in the
publication: "Statistical Abstract of the United States."
The latest year for which statistics are available is 1993.
Available data on life expectancies for various
subpopulations born in the years 1970 to. 1993 are
presented in Table 8-1. Data for 1993 show that the life
expectancy for an average person born in the United
States in 1993 is 75.5 years (U.S. Bureau of the Census,
1995). The table shows that the overall life expectancy
has averaged approximately 75 years since 1982. The
average life expectancy for males in 1993 was 72.1 years,
and 78.9 years for females. The data consistently show an
approximate 7 years difference in life expectancy for
males and females from 1970 to present. Table 8-1 also
indicates that life expectancy for white males (73.0 years)
is consistently longer than for Black males (64.7 years).
Additionally, it indicates that life expectancy for White
females (79.5 years) is longer than for Black females
(73.7), a difference of almost 6 years. Table 8-2 presents
data for expectation of life for persons who were at a
specific age in year 1990. These data are available by
age, gender, and race and may be useful for deriving
exposure estimates based on the age of a specific
subpopulation. The data show that expectation of life is
longer for females and for Whites.
8.2. RECOMMENDATIONS
Current data suggest that 75 years would be an
appropriate value to reflect the average life expectancy of
the general population and is the recommended value. If
gender is a factor considered in the assessment, note that
the average life expectancy value for females is higher
than for males. It is recommended that the assessor use
the appropriate value of 72.1 years for males or 78.9 years
for females. If race is a consideration in assessing
exposure for male individuals, note that the life
expectancy is about 8 years longer for Whites than for
Blacks. It is recommended that the assessor use the
values of 73 years and 64.7 years for White males and
Black males, respectively. Table 8-3 presents the
confidence rating for life expectancy recommendations.
This recommended value is different than the 70
years commonly assumed for the general population in
EPA risk assessments. Assessors are encouraged to use
values which most accurately reflect the exposed
population. When using values other than 70 years,
however, the assessors should consider if the dose
estimate will be used to estimate risk by combining with
a dose-response relationship which was derived assuming
a lifetime of 70 years. If such an inconsistency exists, the
assessor should adjust the dose-response relationship by
multiplying by (lifetime/70). The Integrated Risk
Information System (IRIS) does not use a 70 year lifetime
assumption in the derivation of RfCs and RfDs, but does
make this assumption in the derivation of some cancer
slope factors or unit risks.
8.3. REFERENCES FOR CHAPTER 8
U.S. Bureau of the Census. (1995) Statistical abstracts of
the United States.
Exposure Factors Handbook
August 1997
Page
8-1
-------
Volume I - General Factors
Chapter 8 - Lifetime
Table 8-1. Expectation of Life at Birth, 1970 to 1993, and Projections, 1995 to 2010 (years)a
TOTAL
YEAR
1970
1975
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
Total Male
70.8 67.1
72.6 68.8
73.7 70.0
74.1 70.4
74.5 70.8
74.6 71.0
74.7 71.1
74.7 71.1
74.7 71.2
74.9 71.4
74.9 71.4
75.1 71.7
75.4 71.8
75.5 71.0
75.8 72.3
75.5 72.1
Projections0 1995 76.3 72.8
a
b
c
Source:
2000 76.7 73.2
2005 77.3 73.8
2010 77.9 74.5
Female Total
74.7
76.6
77.4
77.8
78.1
78.1
78.2
78.2
78.2
78.3
78.3
78.5
78.8
78.9
79.1
78.9
79.7
80.2
80.7
81.3
71.7
73.4
74.4
74.8
75.1
75.2
75.3
75.3
75.4
75.6
75.6
75.9
76.1
76.3
76.5
76.3
77.0
77.6
78.2
78.8
WHITE
Male Female
68.0
69.5
70.7
71.1
71.5
71.6
71.8
71.8
71.9
72.1
72.2
72.5
72.7
72.9
73.2
73.0
73.7
74.3
74.9
75.6
Excludes deaths of nonresidents of the United States.
Racial descriptions were not provided in the data source.
Based on middle mortality assumptions; for details, see U.S.
Bureau of the Census, 1995.
75.6
77.3
78.1
78.4
78.7
78.7
78.7
78.7
78.8
78.9
78.9
79.2
79.4
79.6
79.8
79.5
80.3
'80.9
81.4
81.0
BLACK AND OTHERb
Total
65.3
68.0
69.5
70.3
70.9
70.9
71.1
71.0
70.9
71.0
70.8
70.9
71.2
71.5
71.8
71.5
72.5
•72.9
73.6
74.3
Bureau of the Census,
Male
61.3
63.7
65.3
66.2
66.8
67.0
67.2
67.0
66.8
66.9
66.7
66.7
67.0
67.3
67.7
67.4
68.2
68.3
69.1
69.9
Female
69.4
72.4
73.6
74.4
74.9
74.7
74.9
74.8
74.9
75.0
74.8
74.9
75.2
75.5
75.7
75.5
76.8
77.5
78.1
78.7
Total
64.1
66.8
68.1
68.9
69.4
69.4
69.5
69.3
69.1
69.1
68.9
68.8
69.1
69.3
69.6
69.3
70.3
70.2
70.7
71.3
BLACK
Male
60.0
62.4
63.8
64.5
65.1
65.2
65.3
65.0
64.8
64.7
64.4
64.3
64.5
64.6
65.0
64.7
65.8
65.3
65.9
66.5
Current Population Reports, Series P-25, No.
Female
68.3
71.3
72.5
73.2
73.6
73.5
73.6
73.4
73.4
73.4
73.2
73.3
73.6
73.8
73.9
73.7
74.8
75.1
75.5
76.0
1104.
Page
8-2
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 8 - Lifetime
Table 8-2. Expectation of Life by Race, Sex, and Age:
1992
Expectation of Life in Years
Age in 1990
(years)
At birth
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Total
75.8
75.4
74.5
73.5
72.5
71.6
70.6
69.6
68.6
67.6
66.6
65.6
64.6
63.7
62.7
61.7
60.7
59.8
58.8
57.9
56.9
56.0
55.1
54.1
53.2
52.2
51.3
50.4
49.4
48.5
47.5
46.6
45.7
44.7
43.8
42.9
42.0
41.0
40.1
39.2
38.3
37.4
36.5
35.6
34.7
33.8
32.9
32.0 '
31.1
30.2
Male
73.2
72.8
71.8
70.9
69.9
68.9
67.9
66.9
65.9
65.0
64.0
63.0
62.0
61.0
60.0
59.1
58.1
57.2
56.2
55.3
54.3
53.4
52.5
51.6
50.6
49.7
48.8
47.8
46.9
46.0
45.1
44.1
43.2
42.3
41.4
40.5
39.6
38.7
37.8
36.9
36.0
35.1
34.2
33.3
32.4
31.5
30.6
29.7
28.8
28.0
White
Female
79.8
79.3
78.3
77.3
76.3
75.4
74.4
73.4
72.4
71.4
70.4
69.4
68.4
67.4
66.5
65.5
64.5
63.5
62.5
61.6
60.6
59.6
58.7
57.7
56.7
55.7
54.8
53.8
52.8
51.8
50.9
49.9
48.9
48.0
47.0
46.0
45.1
44.1
43.2 .
42.2
41.2
40.3
39.3
38.4
37.5
36.5
35.6
34.7
33.7
32.8
Male
65.0
65.2
64.3
63.4
62.4
61.4
60.5
59.5
58.5
57.5
56.5
55.5
54.6
53.6
52.6
51.7
50.7
49.8
48.9
48.1
47.2
46.3
45.5
44.6
43.8
42.9
42.1
41.2
40.4
39.5
38.7
37.8
37.0
36.2
35.3
34.5
33.7
32.9
32.1
31.3
30.5
29.7
28.9
28.2
27.4
. 26.7
25.9
25.2
24.4
23.7
Black
Female
73.9
74.1
' - 73.1
72.2
71.2
70.3
69.3
68.3
67.3
66.3
65.4
64.4 •
63.4
62.4
61.4
60.4
59.5
58.5
57.5
56.6
55.6
54.6
53.7
52.7
• 51.8
50.8
49.9
48.9
48.0
47.1
46.1
45.2
44.3
43.4
42.4
41.5
40.6
39.7
38.8
37.9
37.1
36.2
35.3
. . 34.4
33.6
32.7
31.9
31.0
30.2
29.3
.Exposure Factors Handbook
August 1997 .
Page
8-3
-------
Volume I - General Factors
Chapter 8~ Lifetime
Table 8-2. Expectation of Life by Race, Sex, and Age: 1992 (continued)
Expectation of Life in Years
White
Age in 1990
(years)
SO
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
70
75
80
85 and over
Source: U.S. Bureau of Census,
Total
29.3
28.5
27.6
26.8
25.9
25.1
24.3
23.5
22.7
21.9
21.1
20.4
19.7
18.9
18.2
17.5
14.2
11.2
8.5
6.2
1995.
Male
27.1
26.3
25.4
24.6 •
23.7
22.9
22.1
21.3
20.6
19.8
19.1
18.3
17.6
16.9
16.2
15.5
12.4
9.6
7.2
5.3
Female
31.9
31.0
30.1
29.2
28.3
27.5
26.6
25.7
24.9
24.1
23.2
22.4
21.6
20.8
20.0
19.3
15.6
12.2
9.2
6.6
Black
Male
23.0
22.3
21.5
20.8
20.1
19.5
18.8
18.2
17.6
16.9
16.3
15.8
15.2
14.6
14.1
13.5
11.0
8.9
6.8
5.1
Female
28.5
27.7
26.8
26.0
25.3
24.5
23.7
23.0
22.2
21.5
20.8
20.1
19.4
18.7
18.0
17.4
14.3
11.4
8.6
6.3
Page
8-4
Exposure Factors Handbook
August 1997
-------
Volume I - General Factors
Chapter 8 - Lifetime
•rf
Table 8-3. Confidence in Lifetime Expectancy Recommendations
Considerations
Rationale
Rating
Study Elements
• Level of peer review
• Accessibility
• Reproducibility
• Focus on factor of interest
• Data pertinent to US
• Primary data
• Currency
• Adequacy of data collection period
• Validity of approach
• •• Study size
• Representativeness of the population
• Characterization of variability •
Lack of bias in study design (High rating
is desirable) ,
Measurement error
Other Elements
• Number of studies
• Agreement between researchers
Overall Rating
Data are published and have received extensive peer review.
The study was widely available to the public (Census data).
Results can be reproduced by analyzing Census data.
Statistical data on life expectancy were published in this study.
The study focused on the U.S. population.
Primary data were analyzed.
The study was published in 1995 and discusses life expectancy trends from
1970 to 1993. The study has also made projections for 1995 until the year
2010.
The data analyzed were collected over a period of years.
Census data is collected and analyzed over a period of years.
This study was based on U.S. Census data, thus the population study size
is expected to be greater than 100.
The data are representative of the U.S. population.
Data were averaged by gender and race but only for Blacks and Whites; no
other nationalities were represented within the section.
There are no apparent biases.
Measurement error may be attributed to portions of the population that
avoid or provide misleading information on census surveys.
Data presented in the section are from the U.S. Bureau of the Census
publication.
Recommendation was based on only one study, but it is widely accepted.
High
High
High
High
High
High
High
High
High
High
High
Medium
High
Medium
Low
High
HIGH
Exposure Factors Handbook
August 1997
Page
8-5
AU.S GOVERNMENT PRINTING OFFICE: 1998 650-001/80172
-------
------- |