DRAFT
DO NOT QUOTE OR CITE
EPA/600/P-95/002Ba
August 1996
SAB Review Draft
EXPOSURE FACTORS HANDBOOK
VOLUME I of III - GENERAL FACTORS
Update to Exposure Factors Handbook
EPA/600/8-89/043 - May 1989
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally
released by the U.S. Environmental Protection Agency and should not at this
stage be construed to represent Agency policy. It is being circulated for
comments on its technical accuracy and policy implications.
Office of Research and Development
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC 20460
Printed on Recycled Paper
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DISCLAIMER
This document is a draft report subject to review by the Science Advisory Board. Mention of trade names
or commercial products does not constitute endorsement or recommendation for use.
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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 consideratioriof policy, precedent or other factors.
Michael A. Callahan
Director
National Center for Environmental
Assessment, Washington Office
Exposure Factors Handbook
Aueust 1996
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PREFACE
The National Center for Environmental Assessment 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., a measure of tendency such as average or mean central
tendency, high end of individual risk, population risk, important subpopulations). Third, EPA published the revised
Guidelines for Exposure Assessment.
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 workshop, two new chapters have been added to the handbook. These chapters are:
Consumer Product Use and the Reference Residence. This document also provides a summary of the available data
on consumption of drinking water; consumption of fruits, vegetables, beef, dairy products, and fish; soil ingestion;
inhalation rates; skin surface area; soil adherence; lifetime; activity patterns; and body weight.
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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 die
EPA Work Assignment Manager, providing overall direction and coordination of die 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 DESKTOP PUBLISHING GRAPHICS
Patricia Wood
Linda Phillips
Aderonke Adenuga
Mike Koontz
Harry Rector
Charles Wilkes
Margaret Wilson
Susan Perry
WORD PROCESSING
Valerie Schwartz
Kathy Bowles .
Jennifer Baker
Exposure Assessment Division
Versar Inc.
Springfield, VA
Exposure Factors Handbook
Aueust 1996
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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 Perlin
Paul Pinsky
JohnSchaum
Paul White
Amina Wilkins
Chieh Wu
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
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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
ValSchaeffer
U.S. Consumer Product Safety Commission
Brad Shurdut
DowElanco
JohnTalbott
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 Emergency and Remedial Response
Office of Pollution, Pesticides and Toxic
Substances
Maurice Berry
Jerry Blancato
Elizabeth Bryan
Curtis Dary
StanDurkee
Manuel Gomez
Wayne Marchant
SuePerlin
James Quanckenboss
Glen Rice
Lance Wallace
JimKonz
Pat Kennedy
Cathy Fehrenbacker
Office of Water
Office of Air Quality Planning and Standards
EPA Regions
Denis Borum
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 analysis 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.
Exposure Factors Handbook
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TABLE OF CONTENTS
Page No.
1.
INTRODUCTION 1-1
1.1. 'PURPOSE 1-1
1.2. INTENDED AUDIENCE 1-1
1.3. BACKGROUND 1-1
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 1-5
1.4. GENERAL EQUATION FOR CALCULATING DOSE 1-10
1.5. RESEARCHNEEDS 1-12
1.6. ORGANIZATION 1-12
1.7. REFERENCES FOR CHAPTER 1 1-13
APPENDIX 1A 1A-1
2. ANALYSIS OF UNCERTAINTY 2-1
2.1. CONCERN ABOUT UNCERTAINTY 2-1
2.2. UNCERTAINTY VERSUS VARIABILITY 2-2
2.3. TYPES OFUNCERTAINTY 2-2
2.4. TYPES OFVARIABUJTY 2-4
2.5. METHODS OF ANALYZING UNCERTAINTY AND VARIABILITY 2-5
2.6. PRESENTING RESULTS OF UNCERTAINTY ANALYSIS 2-8
2.7. REFERENCESFORCHAPTER2 2-9
3. DRINKING WATER INTAKE 3-1
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 . . 3-10
3.4. " PREGNANT AND LACTATING WOMEN 3-21
3.5. HIGH ACTIVITY LEVELS/HOT CLIMATES 3-23
3.6. RECOMMENDATIONS ! 3-25
3.7. REFERENCES FOR CHAPTERS 3-31
4. SOILINGESTION AND PICA : 4-1
4.1 BACKGROUND .,. 4-1
4.2. KEY STUDIES ON SOIL INTAKE AMONG CHILDREN 4-1
4.3. RELEVANT STUDIES ON SOIL INTAKE AMONG CHILDREN 4-11
4.4. SOIL INTAKE AMONG ADULTS 4-17
4.5. PREVALENCE OF PICA . 4-18
4.6. DELIBERATE SOILINGESTION AMONG CHILDREN 4-19
,4.7. RECOMMENDATIONS 4-19
4.8. REFERENCES FORCHAPTER4 4-24
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TABLE OF CONTENTS (continued)
Page No.
5. INHALATION ROUTE 5-1
5.1. EXPOSURE EQUATION FOR INHALATION 5-1
5.2. INHALATION RATE . . . : 5-1
5.3. REFERENCES FOR CHAPTER 5 5-25
APPENDIX 5A 5A-1
6. DERMALROUTE 6-1
6.1. EQUATION FOR DERMAL DOSE 6-1
6.2. SURFACE AREA 6-2
6.3. DERMAL ADHERENCE TO SOIL 6-6
6.4. RECOMMENDATIONS . 6-8
6.5. REFERENCES FORCHAPTER6 6-9
APPENDIX 6A 6A-1
7. BODY WEIGHT STUDIES - 7-1
7.1. KEY BODY WEIGHT STUDY 7-1
7.2. RELEVANT BODY 'WEIGHT STUDIES -, 7-6
7.3. RECOMMENDATIONS 7-7
7.4. REFERENCES FORCHAPTER7 7-7
8. LIFETIME 8-1
8.1. KEY STUDY ON LIFETIME , 8-1
8.2. RECOMMENDATIONS 8-1
8.3. REFERENCES FOR CHAPTER 8 8-1
9. INTAKE OF FRUITS AND VEGETABLES 9-1
9.1. BACKGROUND 9-1
9.2. INTAKE STUDIES , 9-2
9.3. RECOMMENDATIONS - 9-8
9.4. REFERENCES FOR CHAPTER 9 9-9
APPENDIX 9A 9A-1
APPENDIX 9B 9B-1
10. INTAKE OF FISH AND SHELLFISH 10-1
10.1. BACKGROUND 10-1
10.2. KEY GENERAL POPULATION STUDIES 10-2
10.3. RELEVANT GENERAL POPULATION STUDIES 10-12
10.4. KEY RECREATIONAL (MARINE FISH STUDIES) 10-17
10.5 RELEVANT RECREATIONAL MARINE STUDIES 10-22
10.6. KEY FRESHWATER RECREATIONAL STUDIES 10-26
10.7. RELEVANT FRESHWATER RECREATIONAL STUDIES 10-34
10.8. NATIVE AMERICAN FRESHWATER STUDIES *. 10-36
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TABLE OF CONTENTS (continued)
Page No.
10.9 OTHERFACTORS 1(M4
10.10. RECOMMENDATIONS 10'49
10.11 REFERENCES FOR CHAPTER 10 10-52
APPENDIX 10A 10A'1
APPENDIX 10B 10B'1
APPENDIX IOC 10CM
11. INTAKE OF MEAT AND DAIRY PRODUCTS - - - -: 1 1-1
11.1. INTAKE STUDIES H'1
11.2. FAT CONTENT OF MEAT AND DAIRY PRODUCTS 11-5
11.3. CONVERSION BETWEEN AS CONSUMED AND DRY WEIGHT INTAKE RATES 11-6
11.4. RECOMMENDATIONS n'6
11.5. REFERENCES FOR CHAPTER 11 ;" H"7
APPENDIX 11A 11A'1
12. INTAKE RATES FOR VARIOUS HOME PRODUCED FOOD ITEMS 12-1
12.1. BACKGROUND 12-1
12.2. METHODS 12~2
12.3. RESULTS 12'8
12.4. ADVANTAGES AND LIMITATIONS ; I2'9
12.5. RECOMMENDATIONS 12'9
12.6. REFERENCES FOR CHAPTER 12 - 12-10
APPENDIX 12A - 12A-J
13. BREAST MILK INTAKE - I3~l
13 1 BACKGROUND '. l^'l
13.2. KEY STUDIES ON BREAST MILK INTAKE 13-1
13.3. OTHER RELEVANT STUDIES ON BREAST MILK INTAKE ,. 13-4
13.4. KEY STUDIES ON LIPID CONTENT AND FAT INTAKE FROM BREAST MILK 13-5
13.5. OTHERFACTORS : 13'6
13.6. RECOMMENDATIONS 13-8
13.7 REFERENCES FOR CHAPTER 13 13-10
14. ACTIVITY FACTORS ' I4~l
14.1. ACTIVITY PATTERNS I4~l
14.2. OCCUPATIONAL MOBILITY 14-10
14.3. POPULATION MOBILITY 14-11
14.4. RECOMMENDATIONS 14'14
14.5. REFERENCES FOR CHAPTER 1 I4'16
APPENDIX 14A UA~l
APPENDIX 14B 14B-i
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TABLE OF CONTENTS (continued)
Page No.
15. CONSUMER PRODUCTS 15-1
15.1. BACKGROUND 15-1
15.2. KEY CONSUMER.FRODUCTS USE STUDIES 15-1
15.3. RELEVANT CONSUMER PRODUCTS USE STUDY 15-4
15.4. RECOMMENDATIONS 15-5
15.5. REFERENCES FOR CHAPTER 15 15-5
APPENDIX 15A 15A-1
16.
REFERENCE RESIDENCE 16-1
16.1.
16.2.
16.3.
16.4.
16.5.
16.6
16.7.
GLOSSARY .
INTRODUCTION 16-1
BUILDING CHARACTERISTICS 16-2
TRANSPORT RATES , 16-8
SOURCES -...-. - 16-22
ADVANCED CONCEPTS 16-24
RECOMMENDATIONS 16-25
REFERENCES FOR CHAPTER 16 16-25
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LIST OF TABLES
Page No.
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 1-9
Table 1A-1. Procedures for Modifying IRIS Risk Values for Non-standard
Populations 1A'4
Table 2-1. Three Types of Uncertainty and Associated Sources and Examples 2-3
Table 2-2. 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) 3-4
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) 3-4
Table 3-5. Average Daily Tapwater Intake Apportioned Among Various
Beverages (Both Sexes, by Age, Combined Seasons, L/day) 3-5
Table 3-6. Total Tapwater Intake (mL/day) for Both. Sexes Combined 3-6
Table 3-7. Total Tapwater Intake (mL/kg-day) for Both Sexes Combined 3-7
Table 3-8. Summary of Tapwater Intake by Age 3-8
Table 3-9. Total Tapwater Intake (as Percent of Total Water Intake) by Broad
Age Category 3-8
Table 3-10. General Dietary Sources of Tapwater for Both Sexes 3-9
Table 3-11. Summary Statistics for Best-Fit Lognormal Distributions for Water
Intake Rates 3-10
Table 3-12. Estimated Quantiles and Means for Total Tapwater Intake
Rates (mL/day) 3-11
Table 3-13. Average Total Tapwater Intake Rate by Sex, Age, and Geographic
Area 3-11
Table 3-14. Frequency Distribution of Total Tapwater Intake Rates 3-12
Table 3-15. Intake Rates of Total Fluids and Total Tapwater by Age Group 3-12
Table 3-16. Mean Per Capita Drinking Water Intake Based on USDA, CSFII Data
From 1989-91 (mL/day) 3-13
Table 3-17. Assumed Tapwater Content of Beverages 3-14
Table 3-18. Intake of Total Liquid, Total Tapwater, and Various Beverages
(L/day) 3-16
Table 3-19. Summary of Total Liquid and Total Tapwater Intake for Males
and Females (L/day) . /. 3-17
Table 3-20. Mean and Standard Error for the Daily Intake of
Beverages and Tapwater by Age 3-18
Table 3-21. Measured Fluidlntakes (mL/day) 3-18
Table 3-22. Number of Glases of Tapwater Consumed in 24-Hour Period 3-19
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LIST OF TABLES (continued)
Page No.
Table 3-23. Number of Glasses of Juice Reconstituted with Tapwater Consumed
in 24-Hour Period 3-20
Table 3-24. Total Fluid Intake of Women 15-49 Years Old 3-22
Table 3-25. Total Tapwater Intake of Women 15-49 Years Old 3-22
Table 3-26. Total Fluid (mL/Day) Derived from Various Dietary Sources by
Women Aged 15-49 Years . 3-23
Table 3-27. Water Intake at Various Activity Levels (L/hr) 3-24
Table 3-28. Planning Factors for Individual Tapwater Consumption 3-25
Table 3-29. Drinking Water Intake Surveys 3-26
Table 3-30. Summary of Recommended Drinking Water Intake Rates . 3-29
Table 3-31. Confidence in Tapwater Intake Recommendations . . . ; 3-30
Table 4-1. Distribution of Average (Mean) Daily Soil Ingestion Estimates
per Child for 64 Children 4-2
Table 4-2. Estimated Distribution of Individual Mean Daily Soil Ingestion Based
on Data for 64 Subjects 4-2
Table 4-3. Estimated Daily Soil Ingestion Based on Aluminum, Silicon, and
Titanium Concentrations 4-4
Table 4-4. Calculated Soil Ingestion by Nursery School Children 4-5
Table 4-5. Calculated Soil Ingestion by Hospitalized, Bedridden Children 4-5
Table 4-6. Geometric Mean (GM) and Standard Deviation (GSD) LTM Values
for Children at Daycare Centers and Campgrounds 4-6
Table 4-7. Estimated Geometric Mean LTM Values of Children Attending
Day-Care Centers According to Age, Weather Category, and
Sampling Period .- 4-7
Table 4-8. Average Daily Soil Ingestion Values Based on Aluminum, Silicon,
and Titanium as Tracer Elements 4-8
Table 4-9. Mean and Standard Deviation Percentage Recovery of Eight
Tracer Elements 4-9
Table 4-10. Soil and Dust Ingestion Estimates for Children Aged 1-4 Years . 4-10
Table 4-11. Estimated Soil Ingestion Rate Summary Statistics and
Parameters for Distributions Using Binder et al. (1986) Data
with Actual Fecal Weights 4-12
Table 4-12. Estimates of Soil Ingestion for Children 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 - 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 4-20
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LIST OF TABLES (continued)
Page No.
Table 4-20.
Table 4-21.
Table 4-22.
Table 5-1.
Table 5-2.
Table 5-3.
Table 5-4.
Table 5-5.
Table 5-6.
Table 5-7.
Table 5-8.
Table 5-9.
Table 5-10.
Table 5-11.
Table 5-12.
Table 5-13.
Table 5-14.
Table 5-15.
Table 5-16.
Table 5-17.
Table 5-18.
Table 5-19.
Table 5-20.
Table 5-21.
Table 5-22.
Table 5-23.
Table 5-24.
Soil Intake Studies 4-21
Confidence in Soil Intake Recommendation 4-23
Summary of Recommended Values for Soil Ingestion 4-24
Comparisons of Estimated Basal Metabolic Rates (BMR) with
Average Food-energy Intakes for Individuals Sampled in
the 1977-78 MFCS 5-3
Daily Inhalation Rates Calculated from Food-Energy Intakes 5-4
Daily Inhalation Rates Obtained from the Ratios Of Total
Energy Expenditure to Basal Metabolic Rate (BMR) 5-5
Daily Inhalation Rates Based on Time-Activity Survey 5-7
Inhalation Rates for Short-Term Exposures . . 5-6
Calibration and Field Protocols for Self-Monitoring of Activities
Grouped by Subject Panels : 5-9
Subject Panel Inhalation Rates (IR) by Mean IR, Upper
Percentiles, and Self-Estimated Breathing Rates ,. . 5-9
Distributions of Individual and Group Inhalation/ Ventilation Rate for
Outdoor Workers 5-10
Individual Mean Inhalation Rate (mVhr) by Self-Estimated
Breathing Rate or Job Activity Category for Outdoor Workers 5-11
Distribution of HR and Predicted IR, by Location and Activity Levels
for Elementary (EL) and High School (HS) Students 5-12
Average Hours Spent per Day in a Given Location and Activity
Level for Elementary (EL) and High School (HS) Students 5-13
Distribution Patterns of Daily Inhalation Rates for Elementary (EL)
and High School Students (HS) Grouped by Activity Level 5-13
Summary of Average Inhalation Rates (mVhr) by Age Group and
Activity Levels for Laboratory Protocols 5-14
Summary of Average Inhalation Rates (m3/hr) by Age Group and
Activity Levels in Field Protocols 5-15
Distribution Pattern of Predicted VR and EVR (Equivalent
Ventilation Rate) for Outdoor Workers . 5-17
Distribution Pattern of Inhalation Rate by Location and
Activity Type for Outdoor Workers 5-18
Actual Inhalation Rates Measured at Four Ventilation Levels 5-18
Summary of Human Inhalation Rates for Men, Women, and Children
by Activity Level (m'/hour) 5-19
Activity Pattern Data Aggregated for Three Microenvironments
by Activity Level for all Age Groups 5-20
Summary of Daily Inhalation Rates Grouped by Age and Activity
Level ' .'. . . . 5-20
Daily Inhalation Rates Estimated From Daily Activities 5-20
Confidence in Inhalation Rate Recommendations 5-21
Summary of Recommended Values for Inhalation 5-22
Summary of Inhalation Rate Studies 5-23
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LIST OF TABLES (continued)
Page No.
APPENDIX 5A
Table 5A-1.
Table 5A-2.
Table 5A-3.
Table 5A-4.
Table 5A-5.
Table 5A-6.
Table 5A-7.
Table 6-1.
Table 6-2.
Table 6-3.
Table 6-4.
Table 6-5.
Table 6-6.
Table 6-7.
Table 6-8.
Table 6-9.
Table 6-10.
Table 6-11.
Table 6-12.
Table 6-13.
Table 6-14.
Table 6-15.
Table 6-16.
Table 6-17.
Table 6-18.
Table 6-A1.
Table 6-A2.
Table 7-1.
Table 7-2.
Table 7-3.
Table 7-4.
Statistics of the Age/Gender Cohorts Used to Develop Regression
Equations for Predicting Basal Metabolic Rates (BMR)
(from Schofield, 1985) 5A-3
Characteristics of Individual Subjects: Anthropometric Data,
Job Categories, Calibration Results .-.'. 5A-3
Mean Minute Ventilation (VE, L/min) by Group and Activity for
Laboratory Protocols 5A-4
Mean Minute Ventilation (VE, L/min) by Group and Activity for
Field Protocols 5A-4
Estimated Minute Ventilation Associated with Activity Level for
Average Male Adult 5A-5
Minute Ventilation Ranges by Age, Sex, and Activity Level . . 5A-6
Reference Values Obtained From Various Literature Sources . . 5A-7
Summary of Equation Parameters for Calculating Adult Body
Surface Area . . . 6-12
Surface Area of Adult. Males in Square Meters 6-13
Surface Area of Adult Females in Square Meters 6-13
Surface Area of Body part for Adults (m2) 6-14
Percentage of Total Body Surface Area by Part for Adults 6-14
Total Body Surface Area of Male Children in Square Meters 6-15
Total Body Surface Area of Female Children in Square Meters 6-15
Percentage of Total Body Surface Area by Body Part for Children 6-16
Descriptive Statistics for Surface Area/BodyWeight Ratios (m2/kg) 6-17
Statistical Results for Total Body Surface Area Distributions (m2) 6-17
Skin Coverage with Soil by Body Part and Activity 6-17
Summary of Field Studies 6-20
Mean Soil Adherence by Activity and Body Region 6-21
Surface Area Studies 6-22
Summary of Recommended Values for Skin Surface Area 6-23
Confidence in Body Surface Area Measurement Recommendation 6-23
Confidence in Dermal Adherence Recommendations 6-24
Summary of Soil Adherence Studies 6-25
Estimated Parameter Values for Different Age Intervals 6-A5
Summary of Surface Area Parameter Values for the DuBois and
DuBois Model 6-A6
Body Weights of Adults (kilograms) 7-1
Body Weights of Children (kilograms) . . . 7-1
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 7-2
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 . . . 7-3
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LIST OF TABLES (continued)
Page No.
Table 7-5. 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 7-4
Table 7-6. 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 . 7-5
Table 7-7. Statistics for Probability Plot Regression Analyses
Female's Body Weights 6 Months to 20 Years of Age 7-6
Table 7-8. Statistics for Probability Plot Regression Analyses
Male's Body Weights 6 Months to 20 Years of Age 7-6
Table 7-9. Summary of Body Weight Studies 7-8
Table 7-10. Summary of Recommended Values for Body Weight 7-8
Table 7-11. Confidence in Body Weight Recommendations 7-14
Table 8-1. Expectation of Life at Birth, 1970 to 1993, and Projections,
1995 to 2010 : - 8-2
Table 8-2. Confidence in Lifetime Expectancy Recommendations 8-3
Table 9-1. Sub-category Codes and Definitions Used in the CSFII
1989-91 Analysis 9-4
Table 9-2. Weighted and Unweighted Number of Observations for CSFII Data
Used in Analysis of Food Intake 9-5
Table 9-3. Intake of Total Fruits (g/kg-day) 9-11
Table 9-4. Intake of Total Vegetables (g/kg-day) 9-12
Table 9-5. Intake of Individual Fruits and Vegetables (g/kg-day) 9-13
Table 9-6. Intake of USDA Categories of Fruits and Vegetables (g/kg-day) 9-19
Table 9-7. Intake of Exposed, Protected, and Root Fruits and
Vegetables (g/kg-day) 9-20
Table 9-8. Quantity ("as consumed") of Fruits and Vegetables Consumed
Per Eating Occasion and the Percentage of Individuals Using
These Foods in 3 Days 9-21
Table 9-9. Mean Per Capita Intake Rates (as consumed) for Fruits and
Vegetables Based on All Sex/Age/Demographic Subgroups 9-22
Table 9-10. Mean Total Fruit Intake in a Day by Sex and Age (1977-1978) 9-29
Table 9-11. Mean Total Fruit Intake in a Day by Sex and Age (1987-1988) 9-29
Table 9-12. Mean Total Vegetable Intake in a Day by Sex and Age (1977-1978) 9-30
Table 9-13. Mean Total Vegetable Intake in a Day by Sex and Age (1987-1988) 9-30
Table 9-14. Mean and Standard Error for the Per Capita Daily Intake of
Food Class and Subclass by Region (g/day "as consumed") 9-31
Table 9-15. Mean and Standard Error for the Daily Intake of Food
Subclasses Per Capita by Age (g/day "as consumed") 9-32
Table 9-16. 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 9-33
Table 9-17. Mean Daily Intake of Foods (grams) Based on the Nutrition
Canada Dietary Survey 9-33
Page
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Exposure Factors Handbook
_^ August 1996
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LIST OF TABLES (continued)
Page No.
Table 9-18. Per Capita Consumption of Fresh Fruits and Vegetables in 1991 9-34
Table 9T19. Mean Moisture Content of Selected Fruits, Vegetables, and Grains
Expressed as Percentages of Edible Portions 9-35
Table 9-20. Summary of Fruit and Vegetable Intake Studies 9-38
Table 9-21. Summary of Recommended Values for Per Capita Intake
of Fruits and Vegetables and Serving Size 9-39
Table 9-22. Confidence in Fruit and Vegetable Intake Recommendations 9-40
Table 9-23. Confidence in Fruits and Vegetable Serving Size Recommendations 9-41
Table 9A-1. Fraction of Grain and Meat Mixture Intake Represented by
Various Food Items/Groups 9A-3
Table 10-1. Total Fish Consumption by Demographic Variables 10-3
Table 10-2. Mean and 95th Percentile of Fish Consumption (g/day) by Sex and Age 10-4
Table 10-3. Percent Distribution of Total Fish Consumption for Females by Age 10-5
Table 10-4. Percent Distribution of Total Fish Consumption for Males by Age 10-5
Table 10-5. Mean Total Fish Consumption by Species : : 10-6
Table 10-6. Best Fits of Lognormal Distributions Using the NonLiner
Optimization (NLO) Method 10-7
Table 10-7. Per Capita Fish Consumption Rates (g/day) By Habitat and Fish Type (Uncooked Fish Weights
Table 10-8. Distribution of Fish Intake (grams) Per Day Consuming Fish,
By Habitat (Uncooked Fish Weight) 10-9
Table 10-9. Per Capita Fish Consumption Rates (milligrams/kg-day) By Habitat
and Fish Type (Uncooked Fish Weight) 10-9
Table 10-10. Distribution of Fish Intake (milligrams/kg) Per Day Consuming
Fish, By Habitat (Uncooked Fish Weight) 10-10
Table 10-11. Per Capita Fish Consumption rates (g/day) By Habitat and Fish
Type (Cooked Fish Weight) . 10-10
Table 10-12. Distribution of Fish Intake (grams) Per Day Consuming Fish,
By Habitat (Cooked Fish Weight) . . .- 10-11
Table 10-13. Distribution of Quantity of Fish Consumed (in grams) Per Eating
Occasion, By Age and Sex ." - 10-11
Table 10-14. Percent of Population That Ate Seafood (Including Shellfish, Eels,
or Squid) 10-13
Table 10-15. Number of Servings of Seafood Consumed . . 10-14
Table 10-16. Frequency of Seafood That Was Consumed Being Purchased or
Caught By Someone They Knew 10-15
Table 10-17. Mean Fish Intake in a Day, by Sex and Age 10-16
Table 10-18. Estimated Number of Participants in Marine Recreational Fishing by
State and Subregion 10-19
Table 10-19. Estimated Weight of Fish Caught (Catch Type A and Bl) by
Marine Recreational Fishermen by Wave and Subregion . .- 10-20
Table 10-20. Average Daily Intake (g/day) of Marine Finfish, by Region and
Coastal Status 10-20
Table 10-21. Estimated Weight of Fish Caught (Catch Type A and B1) by
Marine Recreational Fishermen by Species Group and Subregion,
Atlantic and Gulf 10-21
Exposure Factors Handbook
Aueust 1996
Page
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LIST OF TABLES (continued)
Page No.
Table 10-22. Estimated Weight of Fish Caught (Catch Type A and B1) by
Marine Recreational Fishermen by Species Group and Subregion,
Pacific , 10-21
Table 10-23. Median Intake Rates Based on Demographic Data of Sport Fishermen
and Their Family/Living Group .. 10-22
Table 10-24. Cumulative Distribution of Total Fish/Shellfish Consumption by
Surveyed Sport Fishermen in the Metropolitan Los Angeles Area 10-23
Table 10-25. Catch Information for Primary Fish Species Kept by Sport Fishermen 10-23
Table 10-26. Percent of Fishing Frequency During the Summer and Fall Seasons
in Commencement Bay, Washington 10-24
Table 10-27. Selected Percentile Consumption Estimates (g/d) for the Survey and
Total Angler Populations Based on the Reanalysis of-the Puffer and
Pierce Data 10-24
Table 10-28. Means and Standard Deviations of Selected Characteristics by
Subpopulation Groups in Everglades, Florida 10-25
Table 10-29. Estimates of Fish Intake Rates of Licensed Sport Anglers in Maine
During the 1989-1990 Ice Fishing or 1990 Open-Water Seasons 10-27
Table 10-30. Analysis of Fish Consumption by Ethnic Groups for "All Waters"
(g/day) 10-27
Table 10-31. Total Consumption of Freshwater Fish Caught by All Survey
,. Respondents During the 1990 Season 10-28
Table 10-32. Mean Fish Intake Among Individuals Who Eat Fish and Reside in Households With
Recreatoinal Fish Consumption 10-30
Table 10-33. Comparison of Seven-Day Recall and Estimated Seasonal Frequency
for Fish Consumption 10-30
Table 10-34. Distribution of Usual Fish Intake Among Survey Main Respondents
Who Fished and Consumed Recreationally Caught Fish 10-31
Table 10-35. Mean Sport-Fish Consumption by Demographic Variables, Michigan
Sport Anglers Fish Consumption Study, 1991 - 1992 10-32
Table 10-36. Distribution of Fish Intake Rates (from all sources and from
sport-caught sources) for 1992 Lake Ontario Anglers 10-34
Table 10-37. Mean Annual Fish Consumption (g/day) for Lake Ontario Anglers,
1992, by Socio-demographic Characteristics 10-34
Table 10-38. Percentile and Mean Intake Rates for Wisconsin Sport Anglers . 10-35
Table 10-39. Socio-Demographic Characteristics of Respondents . 10-36
Table 10-40. Number of Grams per Day of Fish Consumed by All Adult Respondents (Consumers and Non-
consumers Combined) - Throughout the Year 10-37
Table 10-41. Fish Intake Throughout the Year by Sex, Age, and Location by All
Adult Respondents 10-38
Table 10-42. Children's Fish Consumption Rates - Throughout Year 10-38
Table 10-43. Number of Local Fish Meals Consumed Per Year by Time Period
for all Respondents 10-40
Table 10-44. Mean Number of Local Fish Meals Consumed Per Year by Time
Period for all Respondents and Consumers Only 10-41
Table 10-45. Mean Number of Local Fish Meals Consumed Per Year by Time
Period and Selected Characteristics for all Respondents 10-41
Page
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Exposure Factors Handbook
August 1996
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LIST OF TABLES (continued)
Page No.
Table 10-46. Sociodemographic Factors and Recent Fish Consumption 10-42
Table 10-47. Percentage of Individuals using Various Cooking Methods at
Specified Frequencies 10-45
Table 10-48. Percent Moisture and Fat Content for Selected Species 10-46
Table 10-49. Summary of Fish Intake Studies : . . 10-54
Table 10-50. Confidence in Fish Intake Recommendations for General Population 10-57
Table 10-51. Confidence in Fish Intake Recommendations for Recreational Marine Anglers 10-58
Table 10-52. Confidence in Recommendations for Fish Consumption - Recreational Freshwater 10-59
Table 10-53. Confidence in Recommendations for Native American Subsistence Fish Consumption . . 10-60
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 Variuos Cooking Methods
by Income 10B-5
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 Sportfishermen 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 10-C3
Table 11-1. Intake of Total Meats (g/kg-day) 11-8
Table 11-2. Intake of Total Dairy Products (g/kg-day) 11-9
Table 11-3. Intake of Individual Meat and Dairy Products and Mixtures (g/kg-day) 11-10
Table 11-4. Quantity ("as consumed") of Meat, Poultry, and Dairy Products
Consumed per Eating Occasion and the Percentage of Individuals
Using These Foods in 3 Days . 11-12
Table 11-5. Mean per Capita Intake Rates for Meat, Poultry, and Dairy Products
(g/kg-day as condumed) Based on All Sex/Age/Demographic
Subgroups 11-13
Table 11-6. Mean Meat Intakes per Individual in a Day by Sex and Age (g/day as consumed) for
1977-1978 '. . . 11-14
Table 11-7. Mean Meat Intakes per Individual in a Day by Sex and Age (g/day as consumed) for
1987-1988 11-14
Table 11-8. Mean Dairy Product Intakes per Individual in a Day, by Sex and Age
(g/day as consumed) for 1977-1978 11-15
Table 11-9. Mean Dairy Product Intakes per Individual in a Day, by Sex and Age
(g/day as consumed) for 1987-1988 11-15
Table 11-10. Mean and Standard Error for the Dietary Intake of Food Sub Classes
per Capita by Age (grams/day "as consumed") 11-16
Table 11-11. Mean and Standard Error for the Daily Intake of Food Class and
Sub Class by Region (grams/day "as consumed") 11-16
Exposure Factors Handbook
August 1996
Page
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LIST OF TABLES (continued)
Page No.
Table 11-12. 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 11-17
Table 11-13. Per Capita Consumption of Meat and Poultry in 1991 . . 11-17
Table 11-14. Per Capita Consumption for Dairy Products in 1991 11-18
Table 11-15. Adult Mean Daily Intake (as consumed) of Meat and Poultry Grouped
by Region and Gender 11-19
Table 11-16. Amount (as consumed) of Meat Consumed by Adults Grouped by
Frequency of Eatings 11-19
Table 11-17. Percentage Lipid Content (Expressed as Percentages of
100 Grams of Edible Portions) of Selected Meat and Dairy Products 11-20
Table 11-18. Fat Content of Meat Products H-21
Table 11-19. 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 11-22
Table 11-20. Mean Total Daily Dietary Fat Intake (g/day) Grouped by Age and
Gender 11-22
Table 11-21. Percentage Mean Moisture Content (Expressed as Percentages of
100 Grams of Edible Portions) 11-23
Table 11-22. Summary of Meat, Poultry, and Dairy Intake Studies 11-24
Table 11-23. Summary of Recommended Values for Intake of Meat and
Dairy Products and Serving Size 11-25
Table 11-24. Confidence in Meats and Dairy Products Intake Recommendation 11-26
Table 11-25. Confidence in Meat and Dairy Serving Size Recommendations 11-27
Table 12-1. 1986 Vegetable Gardening by Demographic Factors 12-1
Table 12-2. Percentage of Gardening Households Growing Different
Vegetables in 1986 12-1
Table 12-3. Sub-category Codes and Definitions 12-4
Table 12-4. Weighted and Unweighted Number of Observations for MFCS Data
Used inAnalysis of Food Intake : , 12-6
Table 12-5. Percent Weight Losses from Preparation of Various Meats 12-7
Table 12-6. Percent Weight Losses from Preparation of Various Fruits 12-7
Table 12-7. Percent Weight Losses from Preparation of Various Vegetables 12-8
Table 12-8. Intake of Homegrown Fruits (g/kg-day) - All Regions Combined 12-11
Table 12-9. Intake of Homegrown Fruits (g/kg-day) - Northeast 12-12
Table 12-10. Intake of Homegrown Fruits (g/kg-day) - Midwest 12-12
Table 12-11. Intake of Homegrown Fruits (g/kg-day) - South 12-13
Table 12-12. Intake of Homegrown Fruits (g/kg-day) - West 12-14
Table 12-13. Intake of Homegrown Vegetables (g/kg-day) - All Regions Combined 12-15
Table 12-14. Intake of Homegrown Vegetables (g/kg-day) - Northeast 12-16
Table 12-15. Intake of Homegrown Vegetables (g/kg-day) - Midwest 12-17
Table 12-16. Intake of Homegrown Vegetables (g/kg-day) - South . 12-18
Table 12-17. Intake of Homegrown Vegetables (g/kg-day) - West 12-19
Table 12-18. Intake of Home Produced Meats (g/kg-day) - All Regions Combined ,. . 12-20
Table 12-19. Intake of Home Produced Meats (g/kg-day) - Northeast 12-21
Page
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August 1996
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LIST OF TABLES (continued)
Page No.
Table 12-20. Intake of Home Produced Meats (g/kg-day) - Midwest 12-22
Table 12-21. Intake of Home Produced Meats (g/kg-day) - South 12-23
Table 12-22. Intake of Home Produced Meats (g/kg-day) - West 12-24
Table 12-23. Intake of Home Caught Fish (g/kg-day) - All Regions Combined 12-25
Table 12-24. Intake of Home Caught Fish (g/kg-day) - Northeast 12-26
Table 12-25. Intake of Home Caught Fish (g/kg-day) - Midwest 12-27
Table 12-26. Intake of Home Caught Fish (g/kg-day) - South 12-28
Table 12-27. Intake of Home Caught Fish (g/kg-day) - West 12-29
Table 12-28. Intake of Home Produced Dairy (g/kg-day) - All Regions Combined 12-30
Table 12-29. Intake of Home Produced Dairy (g/kg-day) - Northeast 12-31
Table 12-30. Intake of Home Produced Dairy (g/kg-day) - Midwest 12-32
Table 12-31. Intake of Home Produced Dairy (g/kg-day) - South 12-33
Table 12-32. Intake of Home Produced Dairy (g/kg-day) - West 12-34
Table 12-33. Seasonally Adjusted Homegrown Intake (g/kg-day) 12-35
Table 12-34. Intake of Homegrown Apples (g/kg-day) 12-36
Table 12-35. Intake of Homegrown Asparagus (g/kg-day) ....... 12-37
Table 12-36. Intake of Home Produced Beef (g/kg-day) 12-38
Table 12-37. Intake of Homegrown Beets (g/kg-day) 12-39
Table 12-38. Intake of Homegrown Broccoli (g/kg-day) 12-40
Table 12-39. Intake of Homegrown Cabbage (g/kg-day) 12-41
Table 12-40. Intake of Homegrown Carrots (g/kg-day) . 12-42
Table 12-41. Intake of Homegrown Corn (g/kg-day) 12-43
Table 12-42. Intake of Homegrown Cucumber (g/kg-day) 12-44
Table 12-43. Intake of Home Produced Eggs (g/kg-day) 12-45
Table 12-44. Intake of Home Produced Game (g/kg-day) ; 12-46
Table 12-45. Intake of Homegrown Lettuce (g/kg-day) 12-47
Table 12-46. Intake of Homegrown Lima Beans (g/kg-day) 12-48
Table 12-47. Intake of Homegrown Okra (g/kg-day) . 12-49
Table 12-48. Intake of Homegrown Onions (g/kg-day) ; 12-50
Table 12-49. Intake of Homegrown Other Berries (g/kg-day) 12-51
Table 12-50. Intake of Homegrown Peaches (g/kg-day) 12-52
Table 12-51. Intake of Homegrown Pears (g/kg-day) 12-53
Table 12-52. Intake of Homegrown Peas (g/kg-day) 12-54
Table 12-53. Intake of Homegrown Peppers (g/kg-day) 12-55
Table 12-54. Intake of Home Produced Pork (g/kg-day) . . 12-56
Table 12-55. Intake of Home Produced Poultry (g/kg-day) . 12-57
Table 12-56. Intake of Homegrown Pumpkin (g/kgrday) 12-58
Table 12-57. Intake of Homegrown Snap Beans (g/kg-day) 12-59
Table 12-58. Intake of Homegrown Strawberries (g/kg-day) 12-60
Table 12-59. Intake of Homegrown Tomatoes (g/kg-day) 12-61
Table 12-60. * Intake of Homegrown White Potatoes (g/kg-day) 12-62
Table 12-61. Intake of Homegrown Exposed Fruit (g/kg-day) 12-63
Table 12-62. Intake of Homegrown Protected Fruits (g/kg-day) 12-64
Table 12-63. Intake of Homegrown Exposed Vegetables (g/kg-day) 12-65
Table 12-64. Intake of Homegrown Protected Vegetables (g/kg-day) 12-66
Table 12-65. Intake of Homegrown Root Vegetables (g/Kg-day) 12-67
Exposure Factors Handbook
August 1996
Page
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LIST OF TABLES (continued)
Page No.
Table 12-66. Intake of Homegrown Dark Green Vegetables (g/kg-day) 12-68
Table 12-67. Intake of Homegrown Deep Yellow Vegetables (g/kg-day) 12-69
Table 12-68. Intake of Homegrown Other Vegetables (g/kg-day) 12-70
Table 12-69. Intake of Homegrown Citrus (g/kg-day) 12-71
Table 12-70. Intake of Homegrown Other Fruit (g/kg-day) 12-72
Table 12-71. Fraction of Food Intake that is Home Produced 12-73
Table 12-72. Confidence in Homegrown Food Consumption Recommendations 12-77
Table 13-1. Daily Intakes of Breast Milk 13-2
Table 13-2. Breast Milk Intake Among Exclusively Breast-fed Infants
During the First 4 Months of Life 13-2
Table 13-3. Breast Milk Intake During a 24-Hour Period 13-3
Table 13-4. Breast Milk Intake for Infants Aged 1 to 6 Months 13-3
Table 13-5. Breast Milk Intake Estimated by the DARLING Study 13-4
Table 13-6. Milk Intake for Bottle- and Breast-fed Infants by Age Group 13-4
Table 13-7. Milklntake for Boys and Girls 13-4
Table 13-8. Intake of Breast Milk and Formula 13-5
Table 13-9. Lipid Content of Human Milk and Estimated Lipid Intake
Among Exclusively Breast-fed Infants 13-6
Table 13-10. Predicted Lipid Intakes for Breast-fed Infants Under 12 Months of Age 13-6
Table 13-11. Total Energy Intake 13-7
Table 13-12. Energy Intake from Human Milk 13-7
Table 13-13. Number of Meals Per Day 13-8
Table 13-14. 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 13-9
Table 13-15. Breast Milk Intake Studies 13-12
Table 13-16. Confidence in Breast Milklntake Recommendations 13-14
Table 13-17. Breast Milk Intake Rates Derived From Key Studies 13-15
Table 13-18. Summary of Recommended Breast Milk and Lipid Intake Rates 13-16
Table 14-1. Time Use Table Locator Guide 14-18
Table 14-2. Mean Time Spent (Minutes) Performing Major Activities Grouped
by Age, Sex and Type of Day 14-19
Table 14-3. Mean Time Spent in Major Activities Grouped by Type of Day
for Five Different Age Groups . . 14-20
Table 14-4. Mean Time Spent in 10 Major Activity Cateogries Grouped
by Total Sample and Gender for the CARB and National Studies
(Age 18-64) . 14-21
Table 14-5. Total Mean Time Spent at 3 Major Locations Grouped by Total
Sample and Gender for the CARB and National Study (Ages 18-64) *. 14-21
Table 14-6. Mean Time Spent at Three Locations for both CARB and National
Studies (Ages 12 and Older) 14-22
Table 14-7. Mean Time Spent (mins/day) in Various Microenvironments
Grouped by Total Populationand Gender (12 years and over)
in the National and CARB Data 14-22
Page
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Exposure Factors Handbook
August 1996
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LIST OF TABLES (continued)
Page No.
Table 14-8. Mean Time Spent (mins/day) in Various Microenvironments by
Type of Day (Sample Population Ages 12 and Older) 14-23
Table 14-9. Mean Time Spent (mins/day) in Various Microenvironments
by Age Groups 14-24
Table 14-10. Mean Time Children Spent in 10 Major Activity Categories
for all Respondents , I4'25
Table 14-11. Mean Time Children Spent in 10 Major Activity Categories Grouped
by Age and Gender 14-25
Table 14-12. Mean Time Children Spent in 10 Major Activity Categories Grouped
by Seasons and Regions 14-26
Table 14-13. Mean Time Children Spent in Six Major Location Categories for
All Respondents , 14-26
Table 14-14. Mean Time Children Spent in Six Location Categories
Grouped by Age and Gender .--. 14-27
Table 14-15. Mean Time Children Spent in Six Location Categories Grouped by
Season and Region 14-27
Table 14-16. Mean Time Children Spent in Proximity to Three Potential Exposures
Grouped by All Respondents, Age, and Gender 14-28
Table 14-17. Range of Recommended Defaults for Dermal Exposure Factors 14-28
Table 14-18. Cumulative Frequency Distribution of Average Shower Duration
for 2,500 Households 14-29
Table 14-19. Frequency of Taking a Shower in One Day 14-30
Table 14-20. Range of the Number of Minutes Spent in the Shower After Showering 14-31
Table 14-21. Distribution for the Number of Minutes Spent in the Shower After Showering , 14-32
Table 14-22. Frequency of Taking or Giving a Bath in a Day 14-33
Table 14-23. Range of the Minutes Spent Taking or Giving a Bath 14-34
Table 14-24. Distribution for die Number of Minutes Spent Giving and Taking a Bath 14-35
Table 14-25. Range of the Number of Minutes Spent in the Bathroom Immediately
AfteraBath . . , 14-36
Table 14-26. Distribution for the Number of Minutes Spent in the Bathroom
Immediately After a Bath 14-37
Table 14-27. Range of the Total Number of Minutes Altogether Spent in the
Shower or Bathtub 14-38
Table 14-28. Distribution for the Total Number of Minutes Spent in the Shower
orBathtub 14-39
Table 14-29. Range of Number of Minutes Spent in the Bathroom Immediately
Following a Shower or Bath 14-40
Table 14-30. Distribution for the Number of Minutes Spent in the Bathroom
Immediately Following a Shower or Bath 14-41
Table 14-31. Frequency of Washing the Hands in a Day 14-42
Table 14-32. Distribution for Number of Minutes Working or Being Near Food
While Fired, Grilled, orBarbequed . 14-43
Table 14-33. Distribution for the Number of Minutes Working or Being Near
Open Flames Including Barbeque Flames , . . 14-44
Table 14-34. Distribution for the Number of Times Working or Being Near
Excessive Dust in the Air 14-45
Exposure Factors Handbook
August 1996
Page
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LIST OF TABLES (continued)
Page No.
Table 14-35.
Table 14-36.
Table 14-37.
Table 14-38.
Table 14-39.
Table 14-40.
Table 14-41.
Table 14-42.
Table 14-43.
Table 14-44.
Table 14-45.
Table 14-46.
Table 14-47.
Table 14-48.
Table 14-49.
Table 14-50.
Table 14-51.
Table 14-52.
Table 14-53.
Table 14-54.
Table 14-55.
Table 14-56.
Table 14-57.
Table 14-58.
Table 14-59.
Table 14-60.
Table 14-61.
Table 14-62.
Table 14-63.
Table 14-64.
Table 14-65.
Table 14-66.
Table 14-67.
Page
xxiv
Range of the Number of Times An Automobile or Motor Vehicle Was Started
Range of the Number of Times a Motor Vehicle Was Started with the Garage Door
Closed
Distribution for the Number of Minutes Spent at a Gas Station or
Auto Repair Shop .
Distribution for the Number of Minutes Spent While Windows Were Left Open While
at Home
Distribution for the Number of Minutes the Outside Door was Left
Open While at Home .
Frequency of Opening an Outside Door in the Home in a Day
Distribution for the Number of Minutes Spent Running, Walking, or Standing Alongside
a Road with Heavy Traffic
Distribution for the Number of Minutes Spent in a Car, Van, Truck,
or Bus inHeavy Traffic
Distribution for the Number of Minutes Spent in a Parking Garage or
Indoor Parking Lot
Distribution for the Number of Minutes Spent Walking Outside to a Car
in the Driveway or Outside Parking Areas
Distribution for the Number of Minutes Spent running or Walking
Outside
Distribution for the Number of Minutes Spent Working for Pay
Distribution for the Number of Minutes Spent Working for Pay
Between 6PM and 6AM
Distribution for Number of Minutes Worked Outdoors
Frequency of Sweeping or Vacuuming Floors
The Number of Days Since the Floor Area Was Swept or Vacuumed
Number of Separate Loads of Laundry Washed at Home
Frequency of Using a Dishwasher
Frequency of Washing Dishes by Hand
Frequency of Washing Clothes in a Washing Machine
Range of Number of Minutes Spent Playing on Sand or Gravel :
Distribution for the Number of Minutes Spent Playing in Sand or Gravel
Range of Number of Minutes Spent Playing in Outdoors
Distribution for the Number of Minutes Spent Playing in Dirt
Range of the Minutes Spent Working in a Garden or Other
Circumstances Working with Soil
Distribution for the Number of Minutes Spent Working in a Garden
or Other Circumstances Working with Soil
Range of Number of Minutes Spent Playing on Grass
Distribution for the Number of Minutes Spent Playing on Grass
The Number of Times Swimming in a Month in Freshwater Swimming Pool
Average Amount of Time Actually Spent in the Water by Swimmers
The Number of Times Swimming in a Month in Freshwater Swimming Pool
Statistics for 24-Hour. Cumulative Number of Minutes in a Main Job
Statistics for 24-Hour Cumulative Number of Minutes Spent in
Food Preparation
14-46
14-47
14-48
14-49
14-50
14-51
14-52
14-53
14-54
14-55
14-56
14-57
14-58
14-59
14-60
14-61
14-62
14-63
14-64
14-65
14-66
14-67
14-68
14-69
14-70
14-71
14-72
14-73
14-74
14-76
14-77
Exposure Factors Handbook
August 1996
-------
EFH
LIST OF TABLES (continued)
Page No.
Table 14-68.
Table 14-69.
Table 14-70.
Table 14-71.
Table 14-72.
Table 14-73.
Table 14-74.
Table 14-75.
Table 14-76.
Table 14-77. '
Table 14-78.
Table 14-79.
Table 14-80.
'
Table 14-81.
Table 14-82.
Table 14-83.
Table 14-84.
Table 14-85.
Table 14-86.
Table 14-87.
Table 14-88.
Table 14-89.
Table 14-90.
Table 14-91.
Table 14-92.
Table 14-93.
Table 14-94.
Table 14-95.
Table 14-96.
Table 14-97.
Table 14-98.
Table 14-99.
Table 14-100.
Statistics for 24-Hour Cumulative Number of Minutes Spent in
Food Cleanup : 14-78
Statistics for 24-Hour Cumulative Number of Minutes Spent Cleaning House
Statistics for 24-Hour Cumulative Number of Minutes Spent in Outdoor Cleaning 14-79
Statistics for 24-Hour Cumulative Number of Minutes Spent in
Clothes Care 14-80
Statistics for 24-Hour Cumulative Number of Minutes Spent in Car Repair/Maintenance . 14-81
Statistics for 24-Hour Cumulative Number of Minutes Spent in Other Repairs 14-82
Statistics for 24-Hour Cumulative Number of Minutes Spent in Plant Care 14-83
Statistics for 24-Hour Cumulative Number of Minutes Spent in
Animal Care 14-84
Statistics for 24-Hour Cumulative Number of Minutes Spent in Other Household Work . 14-85
Statistics for 24-Hour Cumulative Number of Minutes Spent in Indoor Playing 14-86
Statistics for 24-Hour Cumulative Number of Minutes Spent in Outdoor Playing ....... 14-87
Statistics for 24-Hour Cumulative Number of Minutes Spent for Car Repair Services . . . 14-88
Statistics for 24-Hour Cumulative Number of Minutes Spent
Washing, Etc 14-89
Statistics for 24-Hour Cumulative Number of Minutes Spent Sleeping/Napping 14-90
Statistics for 24-Hour Cumulative Number of Minutes Spent Attending Full time School . 14-91
Statistics for 24-Hour Cumulative Number of Minutes Spent in Active Sports 14-92
Statistics for 24-Hour Cumulative Number of Minutes Spent in Outdoor Recreation .... 14-93
Statistics for 24-Hour Cumulative Number of Minutes Spent in Exercise
Statistics for 24-Hour Cumulative Number of Minutes Spent in Food Preparation 14-94
Statistics for 24-Hour Cumulative Number of Minutes Spent Doing Dishes/Laundry . . . 14-95
Statistics for 24-Hour Cumulative Number of Minutes Spent in Housekeeping 14-96
Statistics for 24-Hour Cumulative Number of Minutes Spent Bathing 14-97
Statistics for 24-Hour Cumulative Number of Minutes Spent in Yardwork/Maintenance . 14-98
Statistics for 24-Hour Cumulative Number of Minutes Spent in Sports/Exercise
Statistics for 24-Hour Cumulative Number of Minutes Eating or Drinking .'.... 14-99
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at Auto Repair
Shop/Gas Station 14-100
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at a gym/Health
Club 14-101
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at the Laundromat 14-102
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors at Work
(Non-Specific) 14-103
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors
at the Dry Cleaners , 14-104
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors
at a Bar/Nightclub/Bowling Alley 14-105
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors
at a Restaurant 14-106
Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors
at School 14-107
Exposure Factors Handbook Page
August 1996 xxv
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EFH
LIST OF TABLES (continued)
Page No.
Table 14-101. Statistics for 24-Hour Cumulative Number of Minutes Spent Indoors
at a Plant/Factory/Warehouse 14-108
Table 14-102. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
on a Sidewalk, Street, or in the Neighborhood 14-109
Table 14-103. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
in a Parking Lot 14-110
Table 14-104. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
at a Service Station or Gas Station 14-111
Table 14-105. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
at a Construction Site 14-112
Table 14-106. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
on School Grounds/Playground 14-113
Table 14-107. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
at a Park/Golf Course . 14-114
Table 14-108. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
at a Pool/River/Lake 14-115
Table 14-109. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
at a Restaurant/Picnic 14-116
Table 14-110. Statistics for 24-Hour Cumulative Number of Minutes Spent Outdoors
at a Farm 14-117
Table 14-111. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home
in the Kitchen 14-118
Table 14-112. Statistics for 24-Hour Cumulative Number of Minutes Spent in
the Bathroom
Table 14-113. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home
in the Bedroom 14-119
Table 14-114. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home
in the Garage 14-120
Table 14-115. Statistics for 24-Hour Cumulative Number of Minutes Spent in the
Basement 14-121
Table 14-116. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home
in the Utility Room or Laundry Room 14-122
Table 14-117. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home
in the Outdoor Pool or Spa 14-123
Table 14-118. Statistics for 24-Hour Cumulative Number of Minutes Spent at Home
in the Yard or Other Areas Outside the House 14-124
Table 14-119. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling
in a Car 14-125
Table 14-120. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling
in a Truck (Pick-up/Van) 14-126
Table 14-121. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling
on a Motorcycle, Moped, or Scooter 14-127
Table 14-122. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling
in Other Trucks 14-128
Table 14-123. Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling
on a Bus 14-129
Page
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Exposure Factors Handbook
August 1996
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EFH
Table 14-124.
Table 14-125.
Table 14-126.
'
Table 14-127.
Table 14-128.
Table 14-129.
Table 14-130.
Table 14-131.
Table 14-132.
Table 14-133.
Table 14-134.
Table 14-135.
Table 14-136.
Table 14-137.
Table 14-138.
Table 14-139.
Table 14-140.
Table 14-141.
Table 14-142.
Table 14-143.
Table 14-144.
Table 14-145.
LIST OF TABLES (continued)
Statistics for 24-Hour Cumulative Number of Minutes Spent Walking
Statistics for 24-Hour Cumulative Number of Minutes Spent Traveling
on a Bicycle/Skate Board/Roller Skate
Statistics for 24-Hour Cumulative Number of Minutes Spent Waiting
at 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
Page No.
. 14-130
. 14-131
. 14-132
. 14-133
. 14-134
. 14-135
. 14-136
. 14-137
. 14-138
. 14-139
. 14-140
. 14-141
. 14-142
. 14-143
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
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 1965 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 10 Major Activity Categories
Grouped by Regions
Total Mean Time Spent (mins/day) in Ten Major Activity
Categories Grouped by Type of Day
Exposure Factors Handbook
August 1996
. 14-144
. 14-145
. 14-152
. 14-153
. 14-154
. 14-155
. 14-155
14-156
Page
xxvii
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EFH
LIST OF TABLES (continued)
Page No.
Table 14-146. Mean Time Spent (mins/day) in 10 Major Activity Categories
During Four Waves of Interviews 14-156
Table 14-147. Mean Time Spent (hours/week) in 10 Major Activity Categories
Grouped by Gender 14-157
Table 14-148. Percent Responses of Children's "Play" (activities) Locations
in Maryvale, Arizona 14-157
Table 14-149. Occupational Tenure of Employed Individuals by Age and Sex 14-158
Table 14-150. Occupational Tenure for Employed Individuals Grouped
by Sex and Race 14-158
Table 14-151. Occupational Tenure for Employed Individuals Grouped by Sex
and Employment Status 14-159
Table 14-152. Occupational Tenure of Employed Individuals Grouped by
Major Occupational Groups and Age 14-159
Table 14-153. Voluntary Occupational Mobility Rates for Workers Age 16
and Older 14-160
Table 14-154. Values and Their Standard Errors for Average Total Residence
Time, T, for Each Group in Survey 14-160
Table 14-155. Total Residence Time, t (years), Corresponding to Selected Values
of R(t) by Housing Category 14-161
Table 14-156. Residence Time of Owner/Renter Occupied Units 14-161
Table 14-157. Percent of Householders Living in Houses for Specified
Ranges of Time 14-162
Table 14-158. Descriptive Statistics for Residential Occupancy Period 14-162
Table 14-159. Descriptive Statistics for Both Genders by Current Age 14-163
Table 14-160. Summary of Residence Time of Recent Home Buyers 14-163
Table 14-161. Tenure in Previous Home (Percentage Distribution) 14-164
Table 14-162. Number of Miles Moved (Percentage Distribution) 14-164
Table 14-163. Confidence in Activity Patterns Recommendations 14-165
Table 14-164. Confidence in Occupational Mobility Recommendations 14-172
Table 14-165. Confidence in Population Mobility Recommendations 14-173
Table 14-166. Summary of Recommended Values for Activity Factors 14-174
Table 14A-1. Differences in Average Time Spent in Different Activities Between
California and National Studies (Minutes Per Day for Age 18-64) 14A-1
Table 14A-2. Time Spent in Various Micro-environments 14A-3
Table 14A-3. Activity Codes and Descriptors Used For Adult Time Diaries 14A-5
Table 14A-4. Major Time Use Activity Categories 14A-19
Table 14A-5. Mean Time Spent (mins/day) for 87 Activities Grouped by Day
of the Week 14A-20
Table 14A-6. Weighted Mean Hours Per Week by Gender: 87 Activities and
10 Subtotals 14A-23
Table 14A-7. Ranking of Occupations by Median Years of Occupational
Tenure 14A-26
Table 14B-1. Annual Geographical Mobility Rates, by Type of Movement
for Selected 1-Year Periods: 1960-1992
(Numbers in Thousands) 14B-1
Page
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Exposure Factors Handbook
August 1996
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Table 14B-2.
EFH
LIST OF TABLES (continued)
Page No.
Mobility of the Resident Population by State: 1980 - 14B-2
Table 15-1. Consumer Products Found in the Typical U.S. Household 15-7
Table 15-2. Frequency of Use For Household Solvent Products ". 15-10
Table 15-3. Exposure Time of Use For Household Solvent Products 15-11
Table 15-4. Amount of Products Used For Household Solvent Products 15-12
Table 15-5. Time Exposed After Duration of Use For Household
Solvent Products .-....'... 15-13
Table 15-6. Frequency of Use and Amount of Product Used for
Adhesive Removers 15-14
Table 15-7. Adhesive Remover Usage by Gender 15-14
Table 15-8. Frequency of Use and Amount of Product Used for
Spray Paint 15-15
Table 15-9. Spray Paint Usage by Gender 15-15
Table 15-10. Frequency of Use and Amount of Product Used for
Paint Removers/Strippers 15-16
Table 15-11. Paint Stripper Usage by Gender 15-16
Table 15-12. Total Exposure Time of Performing Task and Product
Type Used by Task For Household Cleaning Products 15-17
Table 15-13. Percentile Rankings for Total Exposure Time in
Performing Household Tasks . . 15-19
Table 15-14. Mean Percentile Rankings for Frequency of Performing
Household Tasks . 15-20
Table 15-15. Mean and Percentile Rankings for Exposure Time Per
Event of Performing Household Tasks 15-21
Table 15-16. Total Exposure Time for Ten Product Groups Most
Frequently Used For Household Cleaning 15-21
Table 15-17. Total Exposure Time of Painting Activity of
Interior Painters (hrs) 15-22
Table 15-18. Exposure Time of Interior Painting Activity/Occasion (hrs)
and Frequency of Occasions Spent Painting Per Year 15-22
Table 15-19. Amount of Paint Used by Interior Painters 15-22
Table 15-20. Number of Cans or Bottles of Carbonate Soft Drink Consumed
by the Respondent 15-23
Table 15-21. Frequency of Cologne, Perfume, Aftershave or Other Fragrances
Used in One Day 15-24
Table 15-22. Frequency of Use of Any Aerosol Spray Product for Personal Care
Such as Deodorant or Hair Spray 15-25
Table 15-23. Number of Minutes Spent in Activities Working With or Being Near
Freshly Applied Paints / 15-26
Table 15-24. Number of Minutes Spent in Activities Working With or Near Household Cleaning
Agents Such as Scouring Powders or Ammonia 15-27
Table 15-25. Number of Minutes Spent in Activities (At Home or Elsewhere)
Working With or Near Floorwax, Furniture Wax or Shoe Polish 15-28
Exposure Factors Handbook
August 1996
Page
xxix
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EFH
LIST OF TABLES (continued)
Page No.
Table 15-26. Number of Minutes Spent in Activities Working With or Being
Near Glue 15-29
Table 15-27. Number of Minutes Spent in Activitees Working with or Near Solvents, Fumes or Strong
Smelling Chemicals 15-30
Table 15-28. Number of Minutes Spent in Activities Working With or Near Spot Removers 15-31
Table 15-29. Number of Minutes Spent in Activites Working With or Near Gasoline
or Diesel-Powered Equipment, Besides Automobiles . . . 15-32
Table 15-30. Number of Minutes Spent Using Any Microwave Oven 15-33
Table 15-31. Frequency of Use of Humidifier at Home 15-34
Table 15-32. Number of Times Pesticides Were Applied by the Professional at Home
to Eradicate Insects, Rodents, or Other Pests 15-35,
Table 15-33. Number of Times Pesticides Were Applied by the Consumer at Home
to Eradicate Insects, Rodents, or Other Pests 15-36
Table 15-34. Number of Minutes Spent in Activities Working With or Near
Pesticides, Including Bug Sprays or Bug Strips . 15-37
Table 15-35. Range of Number of Minutes Spent Smoking Cigars or Pipe Tobacco By the Number of
Respondents '. 15-38
Table 15-36. Number of Minutes Spent Smoking Cigars or Pipe Tobacco 15-39
Table 15-37. Range of Numbers of Cigarettes Smoked Based on the Number of Respondents 15-40
Table 15-38. Range of the Number of Cigarettes Smoked While at Home 15-41
Table 15-39. Number of Cigarettes Smoked by Other People 15-42
Table 15-40. Number of Minutes Spent Smoking : 15-43
Table 15-41. Range of Time (Minutes) Spent Smoking 15-44
Table 15-42. Amount and Frequency of Use of Various Cosmetic and
Baby Products 15-46
Table 15-43. Summary of Consumer Products Use Studies 15-49
Table 15A-1. Volumes Included in 1992 Simmons Study 15A-3
Table 16-1. Summary of Residential Volume Distributions in Cubic Meters 16-2
.Table 16-2. Average Estimated Volumes of U.S. Residences, by Housing Type and Ownership 16-3
Table 16-3. Residential Volumes in Relation to Household Size and Year of
Construction 16-3
Table 16-4. Dimensional Quantities for Residential Rooms 16-5
Table 16-5. Examples of Products and Materials Associated with Floor and
Wall Surfaces in Residences 16-6
Table 16-6. Percent of Residences with Certain Foundation Types 16-8
Table 16-7. Percent of Residences with Basement, by EPA Region 16-8
Table 16-8. Summary of Major Projects Providing Air Exchange Measurements
in the PFT Database 16-11
Table 16-9. Summary of Statistics for Air Exchange Rates (Air Changes Per
Hour-ACH), by Region 16-12
Table 16-10. Regional and Seasonal Distributions for Residential Air Exchange
Rates 16-12
Table 16-11. Deposition Rates for Indoor Particles 16-14
Table 16-12. Particle Deposition During Indoor Activities 16-15
Table 16-13. In-house Water Use Rates (gcd), by Study and Type of Use 16-18
Page
XXX
Exposure Factors Handbook
August 1996
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EFH
LIST OF TABLES (continued)
Page No.
Table 16-14. Summary of Water Use - - - 16-19
Table 16-15. Showering and Bathing Water Use Characteristics 16-19
Table 16-16. Showering Characteristics for Various Types of Shower Heads 16-19
Table 16-17. Toilet Water Use Characteristics 16-19
Table 16-18. ^ Toilet Frequency Use Characteristics 16-20
Table 16-19. Dishwasher Frequency Use Characteristics 16-20
Table 16-20. Dishwasher Water Use Characteristics 16-20
Table 16-21. Clothes Washer Frequency Use Characteristics 16-20
Table 16-22. Clothes Washer Water Use Characteristics 16-20
Table 16-23. Range of Water Uses for Clothes Washers 16-20
Table 16-24. Particle Deposition and Resuspension During Normal Activities . 16-21
Table 16-25. Dust Mass Loading After One Week Without Vacuum Cleaning . 16-21
Table 16-26. Totalized Dust Loading for Carpeted Areas . 16-21
Table 16-27. Simplified Source Descriptions for Airborne Contaminants 16-22
Table 16-28. Volume of Residence Surveys . 16-30
Table 16-29. Air Exchange Rates Surveys 16-31
Table 16 30. Confidence in House Volume Recommendation 16-32
Table 16-31. Confidence in Air Exchange Rates Recommendation 16-33
Exposure Factors Handbook
August 1996
Page
xxxi
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EFH
LIST OF FIGURES
Page No.
Figure 1-1. Road Map to Exposure Factor Recommendations 1-15
Figure 6-1. SA/BW Distributions for Infants, Adults, and All Ages Combined 6-18
Figure 6-2. Surface Area Frequency Distribution: Men and Women 6-19
Figure 10-1. Seasonal Fish Consumption: Wisconsin Chippewa, 1990 10-43
Figure 10-2. Peak Fish Consumption: Wisconsin Chippewa, 1990 10-43
Figure 14-1. Distribution of Individuals Moving by Type of Move: 1991-92 ' 14-12
Figure 16-1. Elements of Residential Exposure 16-1
Figure 16-2. Cumulative Frequency Distributions for Residential Volumes 16-4
Figure 16-3. Configurations for Residential Forced-air Systems 16-6
Figure 16-4. EPA Regions and Census Regions 16-9
Figure 16-5. Idealized Patterns of Particle Deposition Indoors 16-14
Figure 16-6. Air Flows for Multiple-zone Systems 16-15
Figure 16-7. Characteristic Volumes and Airflow Rates for Two-zone Situations 16-17
Page
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Exposure Factors Handbook
August 1996
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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
in 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, 1992). 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.
1.3.
Purpose
Summarize data on human
behaviors and characteristics
affecting exposure
Recommend exposure factor
values
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 in the dermal chapter;
update of fish intake data;
expansion of data for time spent at residence;
update of body weight data;
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 arith-
metic 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
other available sources. Studies were chosen that were
seen as useful and appropriate for estimating exposure
factors.
General Considerations
Many scientific studies were reviewed for possible
inclusion in this handbook. Studies were selected based
on the following considerations:
Exposure Factors Handbook
August 1996
Page
1-1
-------
EFH
Volume I - General Factors
Chapter 1 - Introduction
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
populatioa 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.
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
Page
1-2
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 1 - Introduction
EFH
uncertainties, such as those due to inherent
variability in environmental and exposure-
related parameters or possible measurement
error. Studies that documented Quality
i 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.
Key vs. Relevant Studies
Key studies used to derive
recommendations
Using the
Handbook in an
Exposure
Assessment
Some of the .steps for
performing an exposure assess-
ment 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
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
Combustion 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 Models Used in
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 in risk
assessments. Probability distributions for 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.
Due to unique activity patterns, preferences,
practices and biological differences, various segments of
the population may experience exposures 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
Relevant studies included to provide
additional perspective
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Volume I - General Factors
Chapter 1 - Introduction
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 December 1996).
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 in 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:
1. Key studies were evaluated in terms of both quality
and relevance to specific populations (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 has been classified as key for a
particular factor, the mean value from that study is
selected as the recommended central value for that
populatioa If there are multiple key studies, all with
reasonably equal quality, relevance and study design
are available, a weighted mean (if appropriate,
considering sample size and other statistical factors)
of the studies was 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 are
presented and the
user of this
handbook must
employ judgment
in selecting the
most appropriate
value for the
population of
interest. Incases
where the
national
population is of
interest, the mid-
point of the range
would usually be
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 is described as either a series of percentiles
or a distribution.
4. The uncertainty in each recommended value was
discussed in terms of data limitations, the range of
circumstances over which the estimates are (or are
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 in the derivation of the recommended
value.
Recommendations and Confidence Ratings
Recommendations based on data from single or
multiple key studies
Variability and uncertainty of recommended values
evaluated
Factors rated as low, medium, and high confidence
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5. Finally, EPA assigned a confidence rating of low,
medium or high to each recommended value. This
rating is based on judgment 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.
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 nave 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 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 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 assumption
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
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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
Studies received limited peer review
Studies are difficult to obtain (e.g., draft
reports, unpublished data)
Results cannot be reproduced, methodology is
hard to follow, and author(s) cannot be located
Purpose of the studies were to characterize a
related factor
Studies focused on populations outside the
U.S.
Studies are based on secondary sources
Before 1980
Study design does not very accurately
captures the measurement of interest
There are serious limitations with the approach
used
n>100 n<20
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.
Studies received high level of peer review
(e.g., they appear in peer review journals)
Studies are widely available to the public
Results can be reproduced or methodology
can be followed and evaluated
Studies focused on the exposure factor of
interest
Studies focused on the U.S. population
Studies analyzed primary data
After 1990
Study design captures the measurement of
interest (e.g., usual consumption patterns of a
population)
Studies used the best methodology available
to capture the measurement of interest
Study population same as population of
interest
Studies characterized variability in the
population studied
Potential bias in the studies are stated or can
be determined from study design
>80%
>80%
>70%
Study design minimizes measurement errors
>3
Results of studies from different researchers
are in agreement
Study population very different from the
population of interest
Characterization of variability is limited
Study design introduces biases in the results
<40%
<40%
<40%
Uncertainties with the data exists due to
measurement error
Results of studies from different researchers
are in disagreement
1 Differences include age, sex, race, income, or other demographic parameters.
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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
Breast milk intake rate
Fish intake rate
Home produced food intake
21 ml/kg-day (average)
34 ml/kg-day (90th percentile)
Percentiles and distribution also included
3.4 g/kg-day (average)
12.4 g/kg-day (95th percentile)
Percentiles also included
Means presented for individual fruits
4.3 g/kg-day (average)
10 g/kg-day (95th percentile)
Percentiles also included
Means presented for individual vegetables
2.1 g/kg-day (average)
5.1 g/kg-day (95th percentile)
Percentiles also included
Means presented for individual meats
8.0 g/kg-day (average)
29.7 g/kg-day (95th percentile)
Percentiles also included
Means presented for individual dairy products
742 ml/day (average)
1,033 ml/day (upper percentile)
General Population
20.1 g/day (total fish) average
13.5 g/day (marine) average
6.6 g/day (freshwater/estuarine)average
63 g/day (total fish)95th percentile long-term
Serving size
123 g (average)
305 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/day (95th percentile)
Total Fruits
2.7 g/kg-day (average)
11.1 g/kg-day (95th percentile)"
Total vegetables
2.1 g/kg-day (average)
7.5 g/kg-day (95th percentile)
Total meats
2.2 g/kg-day (average)
6.8 g/kg-day (95th percentile)
Total dairy products
14 g/kg-day (average)
44 g/kg-day (95th percentile)
Percentiles also included
Means presented for individual food items
Medium
Medium
Low
Medium
Low
Medium
Low
Medium
Low
Medium
Medium
Medium
Medium
Medium
Medium
High
High
Medium
Medium
Medium
Medium
Low
Medium (for means and short-term
distributions)
Low (for long-term distributions)
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Table 1-2.
EXPOSURE FACTOR
Inhalation rate
Surface area
Soil adherence
Soil ingcstkm rate
Life expectancy
Body weight
Showering/Bathing
Swimming
Time indoors
Time outdoors
Summary of Exposure Factor Recommendations and Confidence Ratings
RECOMMENDATION
Children (<1 year)
4.5 mVday (average)
Children (1-12 years)
8.7 mVday (average)
Adult Females
11.3 mVday (average)
Adult Males
15.2 mVday (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 activkies)
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
71.8 kg
Showering time
8 min/day (average)
12 min/day (95th percentile)
(percentiles are also included)
Bathing time
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 (ages 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
(continued)
CONFIDENCE RATING
High
High
High
High
High
Low
Medium
Low
Low
High
High
Medium
High
High
High
High
Medium
Medium
High
Medium
Medium
High
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Table 1-2.
EXPOSURE FACTOR
Time spent inside vehicle
Occupational tenure
Population mobility
Residence volume
Residential air exchange
Summary of Exposure Factor Recommendations and Confidence
RECOMMENDATION
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 m! (conservative)
0.45 (median)
0.18 (conservative)
Ratings (continued)
CONFIDENCE RATING
Medium
High
Medium
Medium
Medium
Table 1-3. Characterization of Variability in Exposure Factors
Exposure Factors
Average
Upper percentile
Multiple Percentiles Fitted Distributions
Drinking water intake rate
Total fruits and total vegetables intake rate
Individual fruits and individual vegetables
intake rate
Total meats and dairy products intake rate
Individual meats and dairy products intake
rate
Serving size for various food items
Breast milk intake rate
Fish intake rate for general population,
recreational marine, recreational
freshwater, and native american
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
S
Qualitative discussion for long-
term
Qualitative discussion for long-
term
Only provided for the total
groups (i.e., total fruits, total
vegetables and total meats and
dairy)
Qualitative discussion for long-
term
/
Long-term values only for
the total groups (i.e., total
fruits, total vegetables and
total meats and dairy)
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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
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 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. 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^J over body weight and
an averaging time.
ADD..
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 = CC x IR x ED (Eqn. 1-2)
Where:
CC = Contaminant Concentration
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
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(units of mass/time). 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 time a person lives in an
area, frequency of bathing, time spent indoors versus
outdoors, etc. all affect the exposure duration. The
Activity Factors Chapter (Volume III, Chapter 2) 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.
Exposure can be expressed as a total amount (with
units of mass, e.g., mg) or as an exposure 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 in absorption across body
barriers between humans and the experimental animals
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, die 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 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 ingestion, 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 (in 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 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 123 g of fish eaten per meal
(Pao et al., 1982; CSFII, 1989-91). 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.
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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 123-g fish meal
approximately five times per month (long-term average is
20 g/day) for 30 years; or 20 g/day of fish every day for
30 years.
(123 g/mcalK5 meals/mo)(mo/30 d)(365 d/yr)(30 yrs) = 219,000 g
(20 g/day)(365 d/yr)(30 yrs) = 219,000 g
Thus, a frequency of either 36.5 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 offish 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).
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.
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.
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1-12
Exposure Factors Handbook
' August 1996
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Volume I - General Factors
Chanter 1 - Introduction
EFH
Chapters 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 bodyweight.
Chapter 8 Provides data on life expectancy.
Volume II - 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 factors for estimating
exposure through ingestion of home
produced food.
Chapter 13 Presents data for estimating exposure
through ingestion of breast milk.
Volume III - Activity Factors
Chapter 14 Presents data on activity factors
(activity patterns, population
mobility, and occupational mobility).
Chapter 15 Presents data on consumer product
use.
Chapter 16 Presents factors used in estimating
residential exposures.
Figure 1-1 provides a roadmap to assist users of
this handbook in locating recommended values and
confidence ratings for the various exposure factors
presented in these chapters. A glossary is provided at the
end of Volume III.
1.7. REFERENCES FOR CHAPTER 1
AIHC. (1994) Exposure factors sourcebook.
Washington, DC: American Industrial Health
Council.
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. (1985) Development of statistical
distributions or ranges of standard factors used in
exposure assessments. Washington, DC: Office
of Health and Environmental Assessment.
EPA No. 600/8-85-010. Available from: NTIS,
Springfield, VA. PB85-242667.
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.
Exposure Factors Handbook
August1996
Page
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Volume I - General Factors
Chapter 1 - Introduction
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 Health
and Environmental Assessment, Washington,
DC. EPA/600/6-88/005Cb.
Page
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Appendix LA
EFH
APPENDIX 1A
RISK CALCULATIONS USING EXPOSURE HANDBOOK DATA AND
DOSE-RESPONSE INFORMATION FROM THE
INTEGRATED RISK INFORMATION SYSTEM (IRIS)
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Appendix 1A
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APPENDIX A
RISK CALCULATIONS USING EXPOSURE 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 m.3/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 die 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 m'/day air inhaled, and 2 L/day water intake) are inaccurate for die 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 widi different mean body
weight would be females, with an average body weight of 60 kg or children with a body weight dependent on age.
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Volume I - General Factors
Appendix 1A
Another example of a non-standard sub-population would be a sedentary hospital population with lower than 20 mVday
air intake rates.
B) The population variability of these parameters is of interest and it is desired to estimate percentile limits of the
population variatioa 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 1 A-l 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 Populations"-11
IRIS Risk Measure
[Units]
IRIS Risk Measure is Proportional to:b
Correction Factor (CF) for modifying
IRIS Risk Measures:0
Slope Factor
[permg/(kg/day)]
Water Unit Risk
[per/ig/1]
Air Unit Risk:
A. Particles or aerosols
[per pg/m3], air concentration by
weight
Air Unit Risk:
B. Gases
[per parts per million], air
concentration by volume,
= (70)"3
= 20/K70)2*]
No explicit proportionality to body
weight or air intake is assumed.
(Wp/70)"3
(IwP)/2x[70/(Wp)f3
(U^O x [70/(Wp)f3
1.0
Ppm by volume is assumed to be the
effective dose in both animals and
humans.
" W = Body weight (kg)
Iw = Drinking water intake (liters per day)
IA = Air intake (cubic meters per day)
fc Ws, IwSl, IAS denote standard parameters assumed by IRIS
8 Modified risk measure = (CF) x IRIS value
W?, Iwp, IAP denote non-standard parameters of the actual population
As one example of the use of Table 1A-1, the recommended value for the average consumption of tap water for
adults in the U. S. population derived in this document (Chapter 3), is 1.4 liters per day. The drinking water unit risk
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for dichlorvos, as given in the IRIS information data base is 8.3 x 10'6 per ^g/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/1. The risk to the
average individual is then estimated by multiplying this by the average concentration in units of ,ug/l.
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)2/3 = 1.11. Here the ratio of 70 to 60 is
raised to the power of 2/3. Thecorrected water unit risk for dichlorvos is 8.3 x 10'6x 1.11 = 9.2x 10'6 per ,wg/l. 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 mVday 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 is not normalized
to body weight, whereas the model is not needed for food and tap water intakes if they are given in 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.
1
[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. 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 in this handbook, IAE = (m'/day)E is:
. (WP/W^2«_
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 (I/), that quantity must
be included in the equation, as follows:
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Volume 1 - General Factors
Appendix 1A
(Risfc)p
= (air unit risk)p x (air concentration)
= (air unit risk)5 x (I//20) x (70/WP)2/3 x (air concentration)
= (air unit risk)5 x [( IAE x (Wp/WE)2/3/20)] x (70/WP)2/3 x (air concentration)
= (air unit risk)3 x (IAE/20) x (70/WE)2/3 x (air concentration) '
In this equation the air unit risk from the IRIS data base (air unit risk)8, the air intake data in the Handbook for
the populations where it is available (1^ and the body weight of that population (WE) are included along with die standard
IRIS values of the air intake (20 mVday) and body weight (70kg) .
For food ingesdonand tap water intake, the intake values are empirically normalized to body weight and therefore
the intake data do not have to be corrected as hi section 3 above. In these cases corrections to the dose-response
parameters in Table 1A-1 are sufficient.
S. REFERENCES
Federal Register. (1992) Cross-species scaling factor for carcinogen risk assessments based on equivalence of (mg/kg-
day)3'4. 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
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Chapter 2 -Analysis of Uncertainty
2. ANALYSIS OF 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 (ADDpm) that
was introduced in the opening chapter of this handbook:
_ Contaminant Concentration \ Intake Rale x Exposure Dura
' 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 topic of uncertainty applies equally to
contaminant concentrations and exposure factors, the focus
of this chapter is on uncertainty as it relates 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 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:
Reasons for concern about uncertainty
Distinction between uncertainty and variability
Types and sources of uncertainty
Types and sources of variability
Methods of analyzing uncertainty and variability
Presenting results of 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. CONCERN ABOUT UNCERTAINTY
Why should the exposure assessor be concerned with
uncertainty? As noted by the U.S. EPA (1992), exposure
assessment utilizes 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.
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
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
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Chapter 2 - Analysis of Uncertainty
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.
2.2. UNCERTAINTY VERSUS VARIABILITY
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 uncertainty and
variability. 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.
Uncertainty and variability can complement or
confound one another. An instructive analogy has been
drawn by 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
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
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.
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 genera! 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, the
realms of uncertainty and variability 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.
2.3. TYPES OF 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
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Chapter 2 - Analysis of Uncertainty
three categories of bias, randomness 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 2-1 and discussed in further detail
below.
The sources of scenario uncertainty include
descriptive errors, aggregation errors, errors in professional
judgment, and incomplete analysis. Descriptive errors
include information errors such as the current producers of
the chemical and its industrial, commercial, and consumer
uses. Information of this type is the foundation for fate-and-
transport analysis and the eventual development of exposure
pathways, scenarios, exposed populations, and exposure
estimates.
Aggregation errors arise as a result of lumping
approximations. Included among these are assumptions of
homogeneous populations, temporal approximations such
as assuming steady-state conditions for a dynamic process,
and spatial approximations such as using a 2-dimensional
mathematical model to represent a 3-dimensional aquifer.
Errors in professional judgment can come into play
in virtually every aspect of the exposure assessment
process, including defining appropriate exposure scenarios,
selecting environmental fate models, determining
representative environmental conditions, etc. Judgment
errors can be the result of limited experience, or can arise
when the assessor has difficulty separating opinion from
fact.
Table 2-1 . 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
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Chapter 2 - Analysis of Uncertainty
A potentially serious source of uncertainty in
exposure assessments arises from incomplete analysis. For
example, the exposure assessor may overlook an important
exposure pathway due to lack of information regarding the
use of a chemical in a consumer product, or may fail to
include an important population subgroup that has
increased susceptibility to adverse health effects of
exposure.
Sources of parameter uncertainty include
measurement errors, sampling errors, variability, and use of
generic or surrogate data. Measurement errors may be
random or systematic. Random errors result from imprecise
measurements. For example, two observers who time an
individual's activity may record different durations.
Similarly, the second analysis of a split sample will not
necessarily yield the same result as the first analysis.
Systematic errors reflect a bias or tendency to measure
something other than what was intended, as could occur if
an ambient monitoring design inadvertently over-
represented heavily industrialized areas. Similarly, body
weight would be systematically overestimated if all
measurements were made using fully clothed individuals.
Sampling errors tend to reduce sample
representativeness. The general purpose of sampling is to
collect information on some fraction of a population in order
to make an inference about the entire group. If the sample
size for a given data collection effort is relatively small, then
the random sampling error associated with that effort will
tend to be correspondingly large. If the exposure
assessment uses data that were generated for another
purpose, then uncertainty will arise if the data do not
represent the exposure scenario being analyzed. For
example, use of product sales information to infer
residential usage patterns may be misleading if residential
and commercial sales cannot be reliably distinguished.
The inherent variability in environmental and
exposure-related parameters is a major source of
uncertainty. For example, meteorological and hydrological
conditions change seasonally at a given location, soil
characteristics exhibit large spatial variability, and human
activity patterns depend on the age, sex, and geographic
location of specific individuals in the population. Although
uncertainty and variability are treated in this chapter as
different entities, it is noteworthy that variation in one
quantity can contribute to uncertainty in another (NRC,
1994). The most relevant example involves the influence
of the variability in a quantity on the uncertainty of its mean
~ when the quantity varies by orders of magnitude, even
relatively large data sets may be insufficient to pin dawn the
mean with the desired degree of precision.
Generic data are commonly used when site-specific
data are not available. Examples include standard emission
factors for industrial processes and generalized descriptions
of environmental settings. Surrogate data are commonly
used when chemical-specific data are not available. One
example is the use of structurally-related chemicals as
surrogates for the chemical of interest. An example of
surrogate data not pertaining to chemicals is the use of an
individual's heart rate to infer his/her breathing rate. Since
surrogate data introduce additional uncertainty, they should
be avoided if actual data can be obtained.
Relationship and modeling errors are the primary
sources of model uncertainty. Relationship errors include
flaws in environmental fate models and poor correlations
between chemical properties or between structure and
reactivity. Modeling errors arise because models tend to be
simplified representations of physical and chemical
processes. Even after the exposure assessor has selected
the most appropriate model, he or she still faces the
question of how well the model represents actual
conditions. This question is compounded by the overlap
between modeling uncertainties and other uncertainties
(e.g., natural variability in environmental inputs, model,.
representativeness, aggregation errors). The dilemma
facing exposure assessors is that many existing models
(particularly the very complex ones) and the hypotheses
contained within them cannot be fully tested (Beck, 1987),
although certain components of the model may be testable.
Even if a model has been validated under a particular set of
conditions, its application in cases beyond the test
conditions will introduce uncertainty.
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.
2.4. 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
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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)
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. According to the central limit
theorem, variability arising from independent factors that
combine multiplicatively generally will lead to an
approximately lognormal distribution across the population,
or across spatial/temporal dimensions.
According to the National Research Council (NRC
1994), variability can be confronted in four basic ways
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. 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).
2.5. METHODS OF ANALYZING UNCERTAINTY
AND VARIABILITY
Exposure assessments often are developed in a
phased approach. The initial phase usually screens out the
scenarios 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
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analyses are usually included in the final exposure
assessment, the final document may contain scenarios that
differ quite markedly in sophistication, data quality, and
amenability to quantitative expressions of 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 uncertainty and variability was
made in Section 2.2. Although the qualitative approach
mentioned above applies more directly to uncertainty and
the quantitative process more so to variability, 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 briefly describe
some of the more common procedures for analyzing
uncertainty and variability in exposure assessments.
Principles that pertain to presenting the results of
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 (discussed below) 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 uncertainties in values of specific parameters translate
into the oyerall 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 statistical methods (U.S. EPA
1992). The four approaches are summarized in Table 2-2
and described in greater detail below.
Table 2-2. 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
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Sensitivity analysis is the process of changing one
variable while leaving the others constant to determine its
effect on the output. This procedure fixes each uncertain
quantity at its credible lower and upper bounds (holding all
others at their nominal values, such as medians) and
computes the results of each combination of values. The
results help to identify the variables that have the greatest
effect on exposure estimates and help focus further
information-gathering efforts. However, the results
themselves can be sensitive to the choices of nominal values
and lower/upper bounds, and do not indicate the probability
of a variable being at any point within its range; therefore,
this approach is most useful at the screening level, to
determine the need for and direction of further analyses.
Analytical uncertainty propagation examines how
uncertainty in individual parameters affects the overall
uncertainty of the exposure assessment. The uncertainties
associated with various parameters may propagate through
a model very differently, even if they have approximately
the same uncertainty. Since uncertainty propagation is a
function of both the data and the model structure, this
procedure evaluates both input variances and model
sensitivity. Application of this approach to exposure
assessment requires explicit mathematical expressions of
exposure, estimates of variance for each variable of interest,
and the ability to obtain a mathematical (analytical or
numerical) derivative of the exposure equation.
Although uncertainty propagation is a powerful tool,
it should be applied with caution: It is difficult to generate
and solve the equations for the sensitivity coefficients. The
technique is most accurate for linear equations, so any
departure from linearity must be carefully evaluated. In
addition, assumptions such as variable independence and
error normality must be verified. Finally, the information to
support required parameter variance estimates may not be
readily available. In some cases, analytical uncertainty
propagation may be more difficult than probabilistic
uncertainty analyses, discussed below.
The most common example of probabilistic
uncertainty analysis is the Monte Carlo method. This
simulation technique assigns a probability density function
to each input parameter, then randomly selects values from
each of the distributions and inserts them into the exposure
equation. Repeated calculations produce a distribution of
predicted values, reflecting the combined impact of
variability in each input to the calculation.
The principal advantage of Monte Carlo simulation
is its very general applicability. There is no restriction on
the form of the input distributions or the relationship
between input and output. Correlations among input
parameters can be expressed and taken into account, and
computations are straightforward. However, Monte Carlo
analysis does have its disadvantages the exposure
assessor should only consider using it when there are
credible distribution data (or ranges) for most key variables.
Even if these distributions are known, it may not be
necessary to apply this technique. For example, one could
use central-tendency values (e.g., means, medians) for each
input parameter to develop a preliminary estimate of
"typical exposure," recognizing that this combination of
parameters will not necessarily yield the average obtained
through Monte Carlo simulation. In addition, it is not
necessary to use this technique if a bounding exposure
estimate indicates that the particular pathway or chemical
being assessed does not present a significant risk.
As noted by Morgan and Henrion (1990), analysis of
Monte Carlo inputs and outputs also can shed light on the
attribution of uncertainty to specific input parameters. For
example, the correlation between any input and the output
provides an indication of the linear contribution of each
input to output uncertainty, and is therefore a global
measure of uncertainty importance. In a similar vein,
multiple regression analysis indicates the relative linear
contribution of each input to output uncertainty, after
statistically removing the effects attributable to other inputs,
provided that standardized regression coefficients are
examined. Rank-order correlations and scatterplots of each
input against the output offer the means to investigate
nonlinear relationships that may be important.
Classical statistical methods can be used to analyze
variability and uncertainty in measured exposures. Given
a data set of measured exposure values for a series of
individuals, the population distribution may be estimated
directly, provided that the sample design captures a
representative sample. Measured exposure values can also
be used to directly compute confidence intervals for
percentiles of the exposure distribution (ACS, 1989).
When the exposure distribution is estimated from measured
exposures for a probability sample of population members,
confidence interval estimates for percentiles of the exposure
distribution are the primary uncertainty characterization.
Data collection, survey design, and the accuracy and
precision of measurement techniques should also be
discussed.
Often the observed exposure distribution is skewed
because many points within the sample distribution fall at
or below the detection limit, in the case of concentrations,
or because few points fall at the upper end of the
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distribution. Fitting the data to a distribution type can be
problematic in these situations because (1) there is no way
to determine the distribution of values below the detection
limit and (2) data are usually scant in low-probability areas
(such as upper-end tails) where numerical values may vary
widely. Thus, for many data sets, means and standard
deviations maybe good approximations, but the tails of the
distribution will be much less well-characterized. For data
sets where sampling is still practical, the sample may be
stratified in order to over sample the tail, thereby increasing
the precision with which that portion of the distribution can
be estimated.
A variety of approaches can be used to quantitatively
characterize the uncertainty associated with model
constructs. One approach uses different modeling
formulations (including the preferred and plausible
alternatives) and assumes that the range of outputs
represents the range of uncertainty. This strategy is most
useful when available data do not support any "best"
approach, or when a model must be used to extrapolate
beyond the conditions for which it was designed.
The issues of verifying computer code and verifying
the model are not the same, and should be performed in
separate steps. Often there may be simplifications in the
programming that lead to errors, even though the model
formulation is correct. Once the computer code is verified,
the model ou^ut can be compared with real data to evaluate
the model itself.
Where the data base is sufficient, the exposure
assessor should characterize the uncertainty in the selected
model by describing the validation and verification efforts.
The validation process compares the performance of the
model to actual observations under situations representative
of those being assessed. Burns (1985) discusses
approaches for model validation. The verification process
confirms that the model computer code produces the correct
numerical output In most situations, only partial validation
is possible due to data deficiencies or model complexity.
2.6. PRESENTING RESULTS OF 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. The uncertainty surrounding data for the
exposure factors has been discussed qualitatively, by
describing the limitations and assumptions of each study or
data set.
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.
It is not sufficient to merely present the results of
these many decisions using different exposure descriptors.
A discussion also must be included that describes key
assumptions and indicates the parameters that are believed
to have the greatest impact on the exposure estimate(s).
The exposure assessor should strive to address 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
supporting the selection of the chosen values?
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The exposure assessor also should qualitatively
describe the rationale for selection of conceptual and
mathematical models. This discussion should address their
verification and validation status, how well they represent
the situation being assessed (e.g., average or high-end
estimates), and any plausible alternatives in terms of their '
acceptance by the scientific community.
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,
medium, or low; and state whether they were based on data,
analogy, or professional judgment. Where uncertainty is
high, a sensitivity analysis can be used to establish 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 mat 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.
2.7. REFERENCES FOR CHAPTER 2
American Chemical Society (ACS). (1989) Principles of
environmental sampling. ACS Professional
Reference Book, Laurence H. Keith, ed.
Washington, DC.
Beck, M.B. (1987) Water quality modeling: A review of
the analysis of uncertainty. Water Resour. Res.
23(8):1393-1442.
Bogen, K.T. (1990) Uncertainty in environmental health
risk assessment. Garland Publishing, New York,
NY.
Burns, L.A. (1985) Validation methods for chemical
exposure and hazard assessment models.
EPA/600/D-85/297.
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
notice. 57FR11888, May 29,1992.
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.
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Chapters - 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). 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 in 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 the 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 -
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 rates for
various age/sex groups during winter and summer seasons
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(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 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
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
rate was 1.38 L/day, and the 90th percentile rate was 2.41
L/day as determined by graphical interpolation. These
Table 3-1. Daily Total Tapwater Intake Distribution for Canadians, by Age Group
(Approx. 0.20 L Increments, Both Sexes, Combined Seasons)
Age Group (years)
Amount Consumed*
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
5 and under
%
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
6-17
%
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
* Includes tapwater and foods and beverages derived from tapwater.
Source: Canadian Ministry of National Health and Welfare, 1981.
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Exposure Factors Handbook
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Volume I - General Factors
Chapters -Drinking Water Intake
*»
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 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)
Fema
les
Males
Both Sexes
<3
3-5
6-17
18-34
35-54
55+
Total Population
53
49
24
23
25
24
24
35
48
27
19
19
21
21
45
48
26
21
22
22
22
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 - Total Water and Tapwater
Intake - 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 for adults (ages
20 to 65+) is approximately 1,410 mL/day and 2,280
mL/day and for all ages the mean and 90th percentile is
1,190 mL/day and 2,090 mL/day. Note that older adults
have greater intakes than do adults between age 20 and 65,
an observation bearing on the interpretation of the Cantor,
et al. (1987)
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August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-3. Average Daily Total Tapwater Intake of Canadians, by Age and Season (L/day)a
Age (years)
<3
Average
Summer 0.57
Winter 0.66
Summer/Winter 0.61
90th Percentile
Summer/Winter 1.50
3-5 6-17 18-34
0.86 1.14 1.33
0.88 1.13 1.42
0.87 1.14 1.38
1.50 2.21 2.57
35-54 <55 All Ages
1.52 1.53 1.31
1.59 1.62 1.37
1.55 1.57 1.34
2.57 2.29 2.36
* 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'
Extremely Active
Very Active
Somewhat Active
Not Very Active
Not At All Active
Did Not State
TOTAL
Work
Consumption'
L/day
1.72
1.47
1.47
1.27
1.30
1.30
Number of
Respondents
99
244
217
67
16
45
688
* The levels of physical activity listed here were not defined any further by the survey
survey participants is assumed to be subjective.
k Includes tapwater and foods and beverages derived from tapwater.
Source: Canadian Ministry of National Health and Welfare, 1981.
Consumption11
L/day
.57
.51
.44
.52
.35
.31
Spare Time
Number of
Respondents
52
151
302
131
26
26
688
report, and categorization of activity level by
Page
3-4
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-5. Average Daily Tapwater Intake Apportioned Among Various Beverages
(Both Sexes, by Age, Combined Seasons, L/day)a
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
Age Group (years)
6-17 18-34 35-54
232 254 153
0.42 0.39 0.38
0.02 0.04 0.03
0.05 0.21 0.31
0.06 0.37 0.50
0.34 0.20 0.14
0.12 0.05 0.04
0.07 0.06 0.08
0.02 0.04 0.07
0.03 0.01 *
* * *
1.14 1.38 1.55
55 and Over
0.38
0.02
0.42
0.42
0.11
0.08
0.11
0.03
*
*
1.57
1 Includes tapwater and foods and beverages derived from tapwater.
* Less than 0.01 L/day
Source: Canadian Ministry of National Health and Welfare,
1981.
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 tapwater 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). They 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). They found 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.
Exposure Factors Handbook
August 1996
Page
3-5
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
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Chapter 3 - Drinking Water Intake
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Exposure Factors Handbook
August 1996
Page
3-7
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Age Group
Infants (<1 year)
Children (1-10)
Teens (11-19)
Adults (20 -64)
Adults (65+)
Source: Ershow and Cantor (1989)
302
736
965
1,366
1,459
1,193
Table 3-8. Summary of Tapwater Intake by
Intake (mL/day)
0-649
286-1,294
353-1,701
559-2,268
751-2,287
423-2,092
Age
Mean
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
Age (yr)
Table 3-9. Total Tapwater Intake (as Percent of Total Water Intake) by Broad Age Category*
Mean
Percentile Distribution
10 25 50
75
90
95
99
MO
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
1 Docs not include pregnant women, lactating women, or breast-fed children.
* Total tapwatcr 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.
Page
3-8
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3- 10. General Dietary Sources of Tapwater for Both Sexes1*
% of Tapwater
Age (yr) Source
<1
1-10
Food'
Drinking Water
Other Beverages
All Sources
Food0
Drinking Water
Other Beverages
All Sources
11-19 Food0
Drinking Water
Other Beverages
All Sources
20-64 Food0
65 +
All
Drinking Water
Other Beverages
All Sources
Foodc
Drinking Water
Other Beverages
All Sources
Food0
Drinking Water
Other Beverages
All Sources
Mean
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
Deviation
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
a Does not include pregnant women, lactating women, or breast-fed children.
b
c
0
Individual values may not add to totals due to rounding.
Food category includes soups.
= Less than 0.5 percent.
Source: Ershow and Cantor, 1989.
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). They 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.
With respect to region of the country, the northeast
states had slightly lower average tapwater 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.
Exposure Factors Handbook
Ausust 1996
Page
3-9
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
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 18 years. Since the data
were collected for only a three-day period, the
extrapolation to chronic intake is uncertain.
Roseberry and Burmaster - Lognormal
Distributions far 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
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 collected their data. Table 3-12
summarizes the quantiles and means of tapwater intake as
estimated from the best-fit distributions. The mean total
tapwater irtake rates for the two adult populations (age 20
to 65 years, and 65+ years) were estimated to be 1.27
and 1.34 L/day.
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) apply to this data set.
3.3. RELEVANT GENERAL POPULATION
STUDIES ON DRINKING WATER INTAKE
Cantor et al. - National Cancer Institute Study -
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 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
Table 3-11. Summary Statistics for Best-Fit Lognormal
Distributions for Water Intake Rates'
Group
In Total Fluid
Intake Rate
a
R2
0 < age < 1
lsage
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-12. Estimated Quantiles and Means for Total Tapwater Intake Rates (mL/day)a
Age Group
0
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-14. Frequency Distribution of Total
Tapwater Intake Rates1
Consumption
Rate (L/day)
Frequency* (%)
Cumulative
Frequency' (%)
sO.80
0.81-1.12
1.13-1.44
1.45-1.95
il.96
20.6
21.3
20.5
19.5
18.1
20.6
41.9
62.4
81.9
100.0
Represents consumption of tapwater and beverages
derived from tapwater in a "typical" winter week.
Extracted from Table 3 in Cantor et al. (1987).
Source: Cantor, et al., 1987.
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.
National Academy of Sciences-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 et al. (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.
Pennington - Total Diet Study - 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 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
3-15. Based on the average intake rates for total water for
Table 3-15. Intake Rates of Total Fluids and Total Tapwater by
Age Group
Average Daily Consumption Rate (L/day)
Age Group Total Fluids' Total TapwateH*
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
1 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.
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
4k
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
tap water intake per se, as distinguished from bottled
water. For this reason, it is not considered a key tapwater
study in this document.
USDA - 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 B.C. were included in the sample.
A complex three-stage sampling design was employed and
the overall response rate of for the study was 58 percent.
To minimize the biasing effects of the low response rate
and adjust for the seasonally a series of weighting factors
was incorporated into the data analysis. The intake rates
based on this study are presented in Table 3-16. Table 3-
16 includes data for: a) "plain drinking water", which
might be assumed to mean tapwater directly consumed
rather than bottled water; b) coffee "and tea, which might
be assumed to be constituted from tapwater; and 3) fruit
Table 3-16 Mean Per Capita Drinking Water Intake Based on USDA, CSFII
Sex and Age
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
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
1 Includes regular and low calorie fruit drinks,
concentrate.
Source: USDA. 1995.
Coffee
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,
Tea
<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
Data From 1989-91 (mL/day)
Fruit Drinks
and Ades1
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
including those made from powdered mix and
Total
211.5
427.5
537
463
697
936
1,247
1,386
1,481
1,525
1,601
1,414
1,295
.1,416 '
603
799
1,074
1,244
1,403
1,431
1,418
1,291
1,308
1,288
1.150
frozen
Excludes fruit juices and carbonated drinks.
Exposure Factors Handbook
Ausust 1996
Page
3-13
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
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 not given.
Gillies and Pmtlin - New Zealand Study - 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.
Hopkins and Ellis -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 3-17. Based on
Table 3-17. Assumed Tapwater Content of Beverages
Beverage
% Tapwater
Cold Water
Home-made Beer/Cider/Lager
Home-made Wine
Other Hot Water Drinks
Ground/Instant Coffee:"
Black
White
Half Milk
All Milk
Tea
Hot Milk
Cocoa/Other Hot Milk Drinks
Water-based Fruit Drink
Fizzy Drinks
Fruit Juice lk
Fruit Juice 2'
Milk
Mineral Water0
Bought cider/beer/lager
Bought Wine
100
100
100
100
100
SO
50
0
80
0
0
75
0
0
75
0
0
0
0
" Black - coffee with all water, milk not added; White - coffee with
80% water, 20% milk;
Half Milk- coffee with 50% water, 50% milk; All Milk - coffee
with all milk, water not added;
* Fruit juice: individuals were asked in the questionnaire if they
consumed ready-made fruit juice (type 1 above), or the variety that
is diluted (type 2);
° Information on volume of mineral water consumed was obtained
only as "number of bottles per week." A bottle was estimated at 500
mL, and the volume was split so that 2/7 was assumed to be
consumed on weekends, and 5/7 during the week.
Source: Hopkins and Ellis. 1980.
Page
3-14
Exposure Factors Handbook
August 1996
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
*s
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 3-18. The mean per.capita
total liquid intake rate for all individuals surveyed was
1.59 L/day, and the mean per capita total tapwater intake
rate was 0.95 L/day, with a 90th percentile value of about
1.3 L/day (which is the value of the percentile for the
home tapwater alone in Table 3-18). Liquid intake rates
were also estimated for males and females in various age
groups. Table 3-19 summarizes the total liquid and total
tapwater intake rates for 1,758 males and 1,800 females
grouped into six age categories (Hopkins and Ellis, 1980).
The mean and 90th percentile intake values for adults over
age 18 years are, respectively, 1.07 L/day and 1.87
L/day, as determined by pooling data for males and
females for the three adult age ranges in Table 3-19. This
calculation assumes, as does Table 3-18 and 3-19, that the
underlying distribution is normal and not lognormal.
The advantage of using these data is that the
responses were not generated on a recall basis, but by
recording daily intake in diaries. The latter approach may
result in more accurate responses being generated. Also,
the use of total liquid and total tapwater was well defined
in this study. However, the relatively short-term nature
of the survey make extrapolation to long-term
consumption patterns difficult. Also, these data were
based on the population of Great Britain and not the
United States. Drinking patterns may differ among these
populations as a result of varying weather conditions and
socio-economic factors. For these reasons this study is
not considered a key study in this document.
U.S. EPA - Office of Radiation Programs - Using
data collected by USDA in the 1977-78 MFCS, 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 3-20. As seen in Table 3-20, 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.
International Commission on Radiological
Protection - Reference Man - Data on fluid intake levels
have also been summarized by the International
Commission on Radiological Protection (ICRP) in the
Report of the Task Group on Reference Man (ICRP,
1981). These intake levels for adults and children are
summarized in Table 3-21. The amount of drinking water
(tapwater and water-based drinks) consumed by adults
ranged from about 0.37 L/day to about 2.18 L/day under
"normal" conditions. The levels for children ranged
from 0.54 to 0.79 L/day. Because the populations, survey
design, and intake categories are not clearly defined, this
study has limited usefulness in developing recommended
intake rates for use in exposure assessment. It is reported
here as a relevant study because the findings, although
poorly defined, are consistent with the results of other
studies.
National Hitman 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.
Exposure Factors Handbook
August 1996
Page
3-15
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
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Exposure Factors Handbook
August 1996
-------
Volume I - General Factors
Chapters - Drinking Water Intake
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-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-20. Mean and Standard Error for the Daily
All ages
Under 1
Ito4
5to9
10 to 14
IS to 19
20 to 24
25 to 29
30 to 39
40 to 59
Tapwater Intake
(mL)
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
Water-Based
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
Intake of Beverages and Tapwater by Age
Soups
(mL)
45.9 ±
10.1 ±
43.8 ±
36.6 ±
35.4 ±
34.8 ±
38.9 ±
41.3 ±
40.6 ±
51.6 ±
59.4 ±
(mU
1.2
7.9
3.9
3.2
3.0
3.2
4.2
4.2
3.3
2.6
2.9
1434.0
307.0
743.0
861.0
1025.0
1241.0
1484.0
1531.0
1642.0
1732.0
1547.0
±
±
±
±
±
±
±
±
±
±
±
13.7
89.2
43.5
36.5
34.2
35.9
46.9
48.0
37.7
29.3
32.8
1 Includes water-based drinks such as coffee, etc. Reconstituted infant formula does not appear to be included in this group.
* Includes tapwater and water-based drinks such as coffee, tea, soups, and other drinks such as soft drinks, ftuitades, and alcoholic
drinks.
Source: U.S. EPA, 1984.
Table 3-21. Measured Fluid Intakes (mL/day)
Adults ("normal" conditions)' 1000-2400 120-450
Adults (high environmental 2840-3410
temperature to 32°C) 3256 ±
SD = 900
Adults (moderately active) 3700
Children (5-14 yr) 1000-1200 330-500
1310-1670 540-650
Water-Based
Tapwater Drinks"
45-730 320-1450
ca. 200 ca. 380
540-790
Includes tea, coffee, soft drinks, beer, cider, wine, etc.
* "Normal" conditions refer to typical environmental temperature and activity levels.
Source: ICRP, 1981. -
Page
3-18
Exposure Factors Handbook
August 1996
-------
Volume I - General Factors
Chapters - Drinking Water Intake
Table 3-22. Number of Glasses of Tapwater Consumed in 24-Hour Period
Overall
Gender
Male
Female
Ref
1-4
5-11
12-17
18-64
>64
Race
White
Black
Asian
Some Others
Hispanic
Ref
Hispanic
No
Yes
DK
Ref
Employment
Fulltime
Part-time
Not Employed
Ref
Education
< High School
High School Grad
< College
College Grad
Post Grad
Census Region
Northeast
Midwest
South
West
DayoTWeek
Weekday
Weekend
Season
Winter
Spring
Summer
Fall
Asthma
No
Yes
DK
Angina
No
Yes
DK
Bronchitis/Emphyszema
No
Yes
_DK
NOTE: "" = Missing Data
"DK" = Don't know
N = sample size
Ref = refused
Source: Tsang and Kleipeis, 1996
All
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
1464
1.181
1,275
943
4487
341
35
4,500
125
38
4,424
203
36
None
1,334
634
728
2
114
90
86
908
117
1,048
147
25
36
63
15
1^02
116
5
11
637
90
313
6
89
364
258
195
127
351
243
450
290
864
470
398
337
352
247
1432
96
6
1,308
18
8
1480
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
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
I
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
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
Exposure Factors Handbook
August 1996
Page
3-19
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-23. Number of Glasses of Juice Reconstituted with Tapwater Consumed in 24-Hour Period
Number of Respondents
Ovcnil
Gtndcr
Mile
FcmCc
Ref
AC
14
5-11
12-17
1561
Rare
Wane
Bbdc
AtUa
Some Others
Ifcpiok
Ref
IJiipunlc
No
Ye*
DK
Ref
FuUltoe
Purtkoe
Not Employed
Ref
Cdtiallon
< HiBh School
Hitb School Gnd
CdteteGnd
Pox Gnd
Nonheut
Midwest
South
Wat
DiyrfWcric
Wccfcdiy
Weekend
Wnttr
SP**
Fill
No
Yes
DK
No
Yes
DK
BrancMtlt/Emphytztirra
N&
Yes
DK
NOTE; - - MiuingDaU
DK- - Don't know
N - umple size
Ref "refused
Source* Tung tod Klepeii, 1996
All
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
1.27S
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
r.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
1Z
18
155
4S
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
I
S
1
64
7
1
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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
L
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
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1
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1
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DK
66
33
33
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11
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14
44
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0
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7
3
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3
4
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11
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28.
9
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20
10
17
28
11
55
5
6
59
1
6
57
J
6
Page
3-20
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
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.
AIHC Exposure Factors Handbook - The Exposure
Factors Sourcebook (AIHC, 1994) presented drinking
water intake rate recommendations for adults. Although
AIHC (1994) provided little information on the studies
used to derive mean and upper percentile
recommendations, 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.
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 NFCS to
estimate total fluid and total tapwater intake among
pregnant and lactating women (ages 15 to 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
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= 1,885) 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).
Totalwater and tapwater intake rates were lowest in the
northeastern region of the United States (1.82 and 1.03
L/day) andhighest 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
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
Exposure Factors Handbook
August 1996
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3-21
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-24. Total Fluid Intake of Women 15-49 Years Old
Reproductive
Status* Mean
mL/day
Control
Pregnant
mlJkg/day
Control
Pregnant
Lactating
1940
2076
2242
32.3
32.1
37.0
Standard
Division
686
743
658
12.3
11.8
11.6
* Number of observations: nonpregnant,
Source: Ershow ct al., 1991.
5
995
1085
1185
15.8
16.4
19.6
10
1172
1236
1434
18.5
17.8
21.8
nonlactating controls (n =
Percentile Distribution
25 50 75
1467
1553
1833
23.8
22.8
28.4
1835
1928
2164
30.5
30.5
35.1
6,201); pregnant (n =
2305
2444
2658
38.7
40.4
45.0
188); lactating (n
90
2831
3028
3169
48.4
48.9
53.7
= 77).
95
3186
3475
3353
55.4
53.5
59.2
Table 3-25. Total Tapwater Intake of Women 15-49 Years Old
Percentile Distribution
Reproductive Status*
mL/day
Control
Pregnant
Lactating
mL/kg/day
Control
Pregnant
Lactating
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
Fraction of daily fluid intake that is tapwater (%)
Control
Pregnant
57.2
54.1
57.0
18.0
18.2
15.8
24.6
21.2
27.4
* Number of observations: nonpregnant, nonlactating controls (n
Source: Ershow etal., 1991.
32.2
27.9
38.0
= 6,201);
45.9
42.9
49.5
pregnant (n
59.0
54.8
58.1
= 188);
70.7
67.6
65.9
lactating (n = 77)
79.0
76.6
76.4
83.2
83.2
80.5
Page
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Table 3-26. Total Fluid (mL/Day) Derived from Various Dietary Sources by Women Aged 15-49 Years'
Control Women
Pregnant Women
Percentile
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 Drinks'
Noncarbonated Soft Drinks"
Beer
Wine Spirits, Liqueurs, Mixed Drinks
All Sources
1 Number of observations: nonpregnant,
Mean*
583
162
23
126
13
90
57
198
9
148
291
174
38
17
10
1940
50
480
107
8
114
0
65
0
171
3
0
159
110
0
0
0
NA
nojilactating controls (n =
95
1440
523
93
263
77
257
234
459
41
630
1045
590
222
110
66
NA
Mean
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
119
64
IAS
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).
' Individual means may not add to all-sources total due to rounding.
° 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.
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 these
data sets (Section 3.2). A further advantage of this study
is drat it provides information on estimates of total water
and 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 die 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
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
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
Exposure Factors Handbook
August 1996
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Volume I ~ General Factors
Chapter 3 - Drinking Water Intake
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.
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
Table 3-27. Water Intake at Various Activity Levels (L/hr)1
Room
Activity
Level
High (0. 15 hp/man)e Medium (0.10 hp/mari)c
No.d
100
95 18
90 7
85 7
80 16
Intake No.
0.540 12
(0.31)
0.286 7
(0.26)
0.218 16
(0.36)
0.222
(0.14)
Intake
0.345
(0.59)
0.385
(0.26)
0.213
(0.20)
-
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.
* Humidity = 80 percent; air velocity = 60 ft/min.
« The symbol "hp" refers to horsepower.
* Number of subjects with continuous data.
Source: McNall and Schlegel, 1968.
United States Amy - 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,
(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).
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
Page
3-24
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
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.
Table 3-28. Planning Factors for Individual Tapwater
Consumption
Environmental Recommended Recommended Planning
Condition Planning Factor Factor (L/day)'-b
(gal/day)'
Hot
Temperate
Cold
3.0=
1.5"
2.0*
11.4
5.7
7.6
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.
Converted from gal/day to L/day.
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.
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.
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."
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 as follows:
Mean
(L/day)
1.38
1.41
90th
Percenlile
(L/day)
2.41
2.28
Number in
Survey
639
11,731
Reference
Canadian Minstry of Health
and Welfare, 1981
Ershow and Cantor, 1989
For comparison, the relevant studies had the
following values for daily tapwater intake:
Mean (L/day)
1.63 (calculated)
1.25
1.04 (25 to 30 yrs)
1.26 (60 to 65 yrs)
1.04-1.47 (ages 20+)
1.37 (20 to 64 yrs)
1.46 (65+ yrs)
1.15
1.07
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
Age of the Cantor et al. (1987) population was higher than the U.S.
average.
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.
Exposure Factors Handbook
August 1996
Page
3-25
-------
Volume I - General Factors
Chapter 3 - Drinking Water Intake
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
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Exposure Factors Handbook
August 1996
Page
3-27
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Volume I - General Factors
Chapter 3 - Drinking Water 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
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
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 90di 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
ssessor should adjust die 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 die key studies are summarized below.
Mean
Age (L/day)
< I 0.30
<3 0.61
3-5
0.87
1-10 0.74
6-17 1.14
11-19 0.97
90th
Percentile
(L/day) Reference
0.65 Ershow and Cantor,
1989
1.50 Canadian Ministry of
National Health and
Welfare, 1981
1.50 Canadian Ministry of
National Health and
Welfare, 1981
1.29 Ershow and Cantor,
1989
2.21 Canadian Ministry of
National Health and
Welfare, 1981
1.70 Ershow and Cantor,
1989
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 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 die 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 as follows:
Page
3-28
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
Age
6-11 months
-------
Volume I - General Factors
Chapter 3 - Printing Water Intake
A characterization of the overall confidence in the
accuracy and appropriateness of these recommendations is
presented in Table 3-31. 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 die
overall recommendations from high to medium.
Table 3-31. Confidence in Tapwater Intake 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
* 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 Ratine
Ershow and Cantor: Thorough expert panel review.
Canada: Review procedures not stated; government report.
Other reports: 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 tap water.
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. Tap water use may have
changed since then.
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.
Competently executed study.
Largest monograph had data for 11,000 individuals.
The Ershow and Cantor and Canada surveys were validated as
demographically representative.
The full distributions were given in the main studies
None apparent.
No physical measurements were taken. The method relied on
recent recall of standardized volumes of drinking water
containers, and was not validated.
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.
Good
The excellent data are not current.
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
Page
3-30
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 3 - Drinking Water Intake
3.7. REFERENCES FOR CHAPTER 3
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.; etal. (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
nutritionan 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.
Walker, B.S.; Boyd, W.C.; Asimov, I. (1957)
Biochemistry and human metabolish, 2nd ed!
Baltimore, MD: Williams & Wilkins Co.
Wolf, A. V. (1958) Body water. Sci. Am. 99:125.
Exposure Factors Handbook
August 1996
Page
3-31
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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 dieir
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-moudi
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. 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
Stanekand 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
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 midnight 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 4-1 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 4-1 presents
the empirical distribution of the the "overall" mean daily
Exposure Factors Handbook
August 1996
Page
4-1
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-1. Distribution of Average (Mean) Daily Soil Ingestion Estimates per Child for 64 Children8 (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
SOlh Percentile
75th Percentile
90ih 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
" 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.
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 4-2). 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.
Table 4-2. Estimated Distribution of Individual Mean Daily Soil
Ingestion Based on Data for 64 Subjects
Projected Over 365 Days'
Range
50th Percentile (median)
90th Percentile
95lh Pereentile
1 - 2,268 mg/db
75 mg/d
1,190 mg/d
1.751 mg/d
1 Based on fitting a log-normal distribution to model daily soil
ingestion values.
k Subject with pica excluded.
Source; Stanek and Calabrese. 1995a.
A strength of this study is that it attempts to make
full use of the collected data through estimation of daily
ingestion rates for childrea 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
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.
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 presentedln
Table 4-1, are substantially more reliable than any
available distributional estimates.
Page
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
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:
where:
T=,
Si.c =
(Eqn. 4-1)
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 4-3). The 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 percentile 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, 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.
Exposure Factors Handbook
August 1996
Page
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-3. Estimated Daily Soil Ingestion Based on Aluminum, Silicon, and Titanium Concentrations
Estimation
Method
Aluminum
Silicon
Titanium
Mean
181
184
1,834
108
, 1986.
Median
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
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 4-4). 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-
5). 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
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
source of aluminum. However, conditions specific to
hospitalization (e.g., medications) was 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.
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
Page
4-4
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-4. 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
Sample
Number
L3
L14
L25
L5
L13
L27
L2
L17
L4
Lll
L8
L21
L12
L16
L18
L22
LI
L6
L7
L9
L10
L15
L19
L20
L23
L24
L26
from Clausing
Soil Ingestion as
Calculated from Ti
(me/day)
103
154
130
131
134
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
(A
184
1 431
et al. 1987.
Soil Ingestion as
Calculated from Al
(me/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
(me/da v>
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
(me/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
Table 4-5.Calculated Soil Ingestion by Hospitalized, Bedridden Children
Child
1
2
3
4
5
6
Arithmetic Mean
Source' Adapted from
Sample
G5
G6
Gl
G2
G8
G3
G4
G7
Clausing et al. 1987.
Soil Ingestion as Calculated
fromTi
(me/day)
3,290
4,790
28
6,570
2,480
28
1,100
58
2,293
Soil Ingestion as
Calculated from Al
(me/day)
57
71
26
94
'57
77
30
38
56
Limiting Tracer
(me/day)
57
71
26
84
57
28
30
38
49
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Chapter 4 - Soil Ingestion and Pica
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 4-6) 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 4-7). 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.
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.
Table 4-6. Geometric Mean (GM) and Standard Deviation (GSD) LTM Values
for Children at Daycare Centers and Campgrounds
Daycare Centers
Age (yrs) Sex
<1 Girls
Boys
l-<2 Girls
Boys
2-<3 Girls
Boys
3-4 Girls
Boys
4-<5 Girls
Boys
All girls
All boys
Tolal
n
3
1
20
17
34
17
26
29
1
4
86
72
162'
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
78"
Campgrounds
GMLTM
(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
* Age and/or sex not registered for eight children.
* Age not registered for seven children.
Source: Adapted from Van Wijnen et al., 1990.
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Chapter 4 - Soil Ingestion and Pica
Table 4-7. Estimated Geometric Mean LTM Values of Children Attending Day-Care 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
n
3
18
33
5
4
42
65
67
10
Estimated Geometric
Mean
LTM Value
(ms/dav)
94
103
109
124
102
229
166
138
132
Second Sampling Period
n
3
33
48
6
1
10
13
19
1
Estimated Geometric
Mean
LTM Value
(me/day)
67
80
91
109
61
96
99
94
61
Source: VanWiinenetal., 1990.
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,
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
childrea The amount of soil ingested on a daily basis was
estimated using the following equation:
where:
soil ingested for child i based on tracer e (g);
DWf = feces dry weight (g);
DWp = feces dry weight on toilet paper (g);
Ej = tracer amount in feces O^g/g);
EU = tracer amount in urine (^g/g);
DWW = food dry weight (g);
Efd = tracer amount in food C"g/g); and
E^ = tracer concentration in soil G«g/g).
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
The soil intake rates were corrected by adding the amount
of tracer in vitamins and medications to the amount of
tracer in food, and adjusting the food quantities, feces dry
weights, and tracer concentrations in urine to account for
missing samples.
Soil ingestion rates were highly variable, especially
those based on titanium. Mean daily soil ingestion
estimates were 38.9 mg/day for aluminum, 82.4 mg/day
for silicon and 245.5 mg/day for titanium (Table 4-8).
Median values were 25 mg/day for aluminum, 50 mg/day
for silicon, and 81 mg/day for titanium. Davis et al.
(1990) also evaluated the extent to which differences in
tracer concentrations in house dust and yard soil impacted
estimated soil ingestion rates. The value used in the
denominator of the mass balance equation was recalculated
to represent a weighted average of the tracer concentration
in yard soil and house dust based on the proportion of time
the child spent indoors and outdoors. The adjusted mean
soil/dust intake rates were 64.5 mg/day for aluminum,
160.0 mg/day for silicon, and 268.4 mg/day for titanium.
Adjusted median soil/dust intake rates were: 51.8 mg/day
for aluminum, 112.4 mg/day for silicon, and 116.6
mg/day for titanium. Davis et al. (1990) also observed
that the following demographic characteristics were
associated with high soil intake rates: male sex, non-white
racial group, low income, operator/laborer as the principal
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.
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. (1987) 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 die 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
Table 4-8. Average Daily Soil Ingestion Values Based on Aluminum, Silicon, and Titanium as Tracer Elements0
Standard Error of the
Element Mean Median Mean Range
(mg/d) (mg/d) (mg/d) (mg/d)b
Aluminum 38.9
Silicon 82.4
Titanium 245.5
Minimum 38.9
Maximum 245.5
25.3
59.4
81.3
25.3
81.3
14.4 279.0 to 904.5
12.2 -404.0 to 534.6
119.7 -5,820.8 to 6,182.2
, 12.2 -5,820.8
119.7 6,182.2
* Excludes three children who did not provide any samples (N= 101).
* Negative values occurred as a result of correction for nonsoil sources of the tracer elements.
Source: Adapted from Davis etal., 1990.
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Chapter 4 - Soil Ingestion and Pica
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 4-9).
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 4-10). 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 4-10). 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
Table 4-9.
Tracer Element
Source:
Al
Ba
;Mn
Si
Ti
V
Y
Zr
Adapted from
Mean and Standard Deviation Percentage Recovery of Eight Tracer Elements
300 mg Soil
Mean
152,8
2304.3
1177,2
139.3
251.5
345.0
120.5
80.6
Calabrese etal., 1989.
Ingested
SD
107,5
4533.0
1341,0
149,6
316,0
247.0
42.4
43.7
1500 mg Soil
Mean
93.5
149.8
248,3
91.8
286.3
147.6
87.5
54.6
Ingested
SD
15,5
69,5
183,6
16,6
380,0
66,8
12.6
33.4
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Chapter 4 - Soil Ingestion and Pica
Table 4-10. 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
soit/dust combined
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
* Corrected for Tracer Concentrations in Foods
Source* Adapted from Calabrese et al
1989
of soil ingested daily to be 29 mg/day and 40 mg/day,
respectively. It should be noted that soil 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.
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 Ma 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 mg/day with a
median of 87 mg/day. The 90th percentile soil ingestion
estimate was 142 mg/day. These estimates are based on
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Chapter 4 - Soil Ingestion and Pica
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
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 etal. (1989), Davis et al. (1990) and Calabrese
et al. (1990) studies.
4.3. RELEVANT STUDIES ON SOIL INTAKE
AMONG CHILDREN
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
a reanalysis 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 etal. (1986). Table 4-11
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 9.1
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 lognormaliry 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 4-11
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
medicine and the results may not be representative of
long-term intake rates because the data were derived from
a short-term study.
Lepow et al. (1974) - Role of Airborne Lead in
Increased Body Burden of Lead in Hartford 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
Exposure Factors Handbook
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Chapter 4 - Soil Ingestion and Pica
Table 4-1 1.
Trace Element Basis
Mean
Min
10th
20th
30th
40th
Med
60th
70th
80th
90th
Max
Estimated Soil Ingestion Rate Summary Statistics and Parameters for Distributions
Using Binder et al.
Al
97
11
21
33
39
43
45
55
73
104
197
1,201
(1986) Data with Actual Fecal Weights
Soil Intake (mg/day)
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
MEAN*
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
4.05
0.88
4.07
0.85
..
4.13
0.80
" MEAN » arithmetic average of soil ingestion based on aluminum and silicon.
Source: Thompson and Burmaster,
1991.
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. (1974) 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. (1974) estimated that the daily
soil ingestion rate would be about 100 mg/day. According
to Lepow et al. (1974), 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.
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.
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
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Chapter 4 - Soil Ingestion and Pica
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.
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 4-12).
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
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 4-12).
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 childrens' (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 childrens'
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
Scenarios
Young 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 4-12.
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.
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
tracer (Y) from both studies to reflect that of a 2-year old
child using the following equation:
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
(Sedmanand 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
method-ologies 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
Table 4-13. Tukey's Multiple
Tracer
Aluminum
Silicon
Titanium
Vanadium
Yttrium
Aluminum
Silicon
* Age adjusted mean estimates of soil
Comparison of Mean LOB Tracer Recovery in Adults Ingesting Known Quantities of Soil
Reported Mean
{me/dav)
Calabrese et al., 1989 Study
153
154
218
459
85
Davis et al., 1990 Study
39
81
246
ingestion in young children. Mean estimates of soil
Age Adjusted Mean
fms/dav)
160
161
228
480
89
. 53
111
333
ingestion for each tracer in each study were
adjusted using the following equation:
Y = Xec*m'>t), where Y = adjusted mean soil ingestion (mg/day), x = a constant, and yr = age in years.
Page
4-14
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August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
estimates were derived using the equation presented above
that describes changes in soil ingestion with age (Sedman
andMahmood, 1994).
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
negative and positive errors to be the following: negative
bias may result from tracers being ingested in food but not
being captured either in the fecal sample due to slow lag
time or not having a fecal sample available on the final
study day; ingestion of high levels of tracers before the
study starts and low ingestion during study period may
result in over estimation of soil ingestion (positive bias);
positive bias may occur if a subject ingests element tracers
from a non-food or non-soil source during the study
period; sample measurement errors which result in
diminished detection of fecal tracers but not soil tracer
levels may result in negative bias. 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
positive 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
Table 4-14. Positive/Negative Error (bias) in Soil Ingestion Estimates in the Calabrese et al. (1989) Mass-balance Study (5):
Effect on Mean Soil Ingestion Estimate (mg/day)'
Negative Error
Lack of Fecal
Sample on Final
Study Day
Other Causes"
Total Negative
Error
Total Positive
Error
Net Error
Original
Mean
Adjusted
Mean
Aluminum
Silicon
Titanium
Vanadium
Yttrium
Zirconium
14
15
82
66
8
11
6
187
55
26
91
25
21
269
121
34
97
43
41
282
432
22
5
+ 18
+20
+ 13
+311
-12
-92
153
154
218
459
85
21
136
133
208
148
97
113
1 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 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
trace element estimate was the observed difference of that
tracer from a median tracer value. Specific identification
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
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.
Sfieppard (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 duratioa 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.
A1HC 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.
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
(rag/cm2)
0.5
0.4
0.5
0.04"
1.0
0.04
* 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
so that the indoor time was i
Suggested Exposure
Durations
(hr/yr)
200
1,000
Remaining11
700
5,000
300
5,000
Average Daily Soil
Ingestion
(mg/day)
500
50
60
20
2
20
0.4
$,760 hours/year minus the outdoor time.
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
4.4. SOIL INTAKE AMONG ADULTS
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 jwm/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 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).
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. - 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 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
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
SoM 480
Days/Year
Activity
12
365
43
Fraction Soil
Content
0.8
0.8
1
Annual Average Soil
Intake
(mg/day)
3
0.5
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
during each of the 3 weeks. In addition, all medications
and vitamins ingested by the 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
Exposure Factors Handbook
August 1996
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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
Mn
330
1,306
790
388
1,368
831
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
* Data were converted to milligrams
Source1 Calabrese et al 1990
mg; Ba, -232 mg; Mn, 330 mg; Si, 30 mg; Ti, 71 mg; V,
1,288 mg; Y, 63 mg; and Zr, 134 mg.
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
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
Wljnen 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 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.
Page
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
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. (1994) - 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
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
2.695
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 mere 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.
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 other 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,
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-19. Ratios of Soil, Dust, and Residual Fecal Samples in the Soil Pica Child
Estimated % of Residual Fecal Tracers of Soil
Tracer Ratio Pairs
1.
2.
3.
4.
5,
6.
7.
8.
9.
10.
11.
12.
13.
14.
IS.
16.
17.
18.
19.
Source
Mn/Ti
Ba/Ti
Si/Ti
V/Ti
Ai/Ti
Y/Ti
Mn/Y
B*/Y
Si/Y
V/Y
Al/Y
Mn/Al
Ba/Al
Si/Al
V/A1
Si/V
Mn/Si
Ba/Si
Mn/Bt
: 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
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
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.
So/7 Ingestion Among Children - Estimates of the
amount of soil ingested by children are summarized
below. 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 appears to represent, based on judgment, the best
estimate of the mean for children under 6 years of age.
However, since the children were studied for short periods
Page
4-20
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Table 4-21. Confidence in Soil Intake Recommendation
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
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.
A| 1 the studies were based on primary data
Studies were conducted after 1980
Children were not studied long enough to ftilly 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 abosorption 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.
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 5 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
Dercentilel
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 ingestioa 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 4-22.
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
Al
181
230
39
64.5*
153
154'
122
133*
69-120"
Si
184
82
160*
154
483*
139
AIR1 Ti
129
245.5
268.4k
218
170*
271
Y
85
65»
165
Uooer Percentile (me/day)
Al
584
223
478'
254
217"
Si
578
276
653"
224
Ti
1,432
1,059*
279
Y
106
159*
144
References
Binder et al. 1986
Clausing et al. 1987
Davis et al. 1990
Calabrese et al. 1989
Stanek and Calabrese, 1995a
Stanek and Calabrese, 1995b
VanWijnenetal. 1990
Average = 146 mg/day soil
191 mg/day soil and dust
combined
383 mg/day soil
587 mg/day soil and dust combined
AIR = Acid Insoluble Residue
Soil and dust combined
LTM; corrected value
BTM
Table 4-22, Summary of Recommended Values for Soil Ingestion
Population
Mean
Upper Percentile
Children
Adults
Pica child
100 mg/day
50 mg/day
10 e/d*v
400 mg/day
- mg/day (outdoor activities)
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
osual iitfake.
c To be used in acute exposure assessments. Based on only one pica
child (Calabresc et al.. 198?) ___
Data on soil ingestion rates for children who
deliberately ingest soil are also limited. However, an
ingestion rate of 10 g/day may not be an unreasonable
assumption 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 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 mourning behavior, no supporting measurements wre
made. Making further assumptions about frequencies of
indoor and outdoor activities Hawley 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 (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 hve
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 sbove. Thus, 50
mg/day still represents a reasonable central estimate of
adult soil ingestion and is recommended here. 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, any speculation about an upper percentile would
be inappropriate. Table 4-22 summarizes soil ingestion
recommendations for adults.
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
Page
4-24
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
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, CM.; 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 of
soil ingested. Hum. Exp. Toxicol. 10:24-5-249.
Calabrese, E.J.; Stanek, E.J. (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
expos.ure 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/IV. 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 ratesf. Denver, CO:
Arco Coal Co.; October 13, 1989.
Lepow, M.L.; Bmckman, 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.; etal. (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.
Roels, 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, E.J. (1995a) Daily estimates
of soil ingestion in children. Environ. Health
Perspect. 103(3):276-285.
Stanek, E.J.; Calabrese, E.J. (1995b) Soil ingestion
estimates for use in site evaluations based on the
best tracer method. Human and Ecological Risk
Assessment. 1:133-156.
Exposure Factors Handbook
August 1996 _____
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4-25
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Volume I - General Factors
Chapter 4 - Soil Ingestion and Pica
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) Geophagiain
rural Mississippi: environmental and cultural
contexts and nutritional implications. Am. J. Clin.
Nutr. 32:2129-2135.
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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 is not meant to 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). This is
due to the fact that the "dose-response" relationships
recommended in IRIS for air contaminants are not really
based on dose, but rather concentration. Such "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.
5.1. EXPOSURE EQUATION FOR INHALATION
The general equation for calculating average daily
dose (ADD) for inhalation exposure 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 Cug/m3) for
gaseous measurements expressed in ppm (1 ppm =
IR
ED
BW
AT
inhalation rate (m'/day);
exposure duration (days);
body weight (kg); and
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. Factors affecting inhalation rates
(expressed as cubic meters per hour) are age, gender,
weight, health status and activity patterns (i.e.,
frequencies and durations of physical activities) (Layton,
1993).
5.2. INHALATION RATE
5.2.1. Background
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 estimation for inhaled dose for a
given air pollutant is dependent on inhalation rate,
commonly described as ventilation rate (VR) or breathing
rate, which is usually measured as minute volume, the
volume in liters of air exhaled per minute (Ve). The
volume of air exhaled (V^) 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 absorbed 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 percent of the
amount of air inhaled (Menzel and Admur, 1986). This
adjustment is not needed for those assessments using dose-
response factors that are based on administered dose.
Breathing rates are affected by numerous individual
characteristics, including age, gender, weight, health
status, and levels of activity (running, walking, jogging,
etc.). Ventilation rates (VR) 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
Exposure Factors Handbook
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Volume I - General Factors
Chapter 5 - Inhalation
measurements are usually correlated with VR in simple
and multiple regression analysis.
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 nrYhr); moderate/
medium exercise (VE= 24-43 L/min or 1.4-2.6 mVhr);
heavy exercise (VE= 43-63 L/min or 2.6-3.8 mVhr); and
very heavy exercise (VE> 64 L/min or 3.8 mVhr),
(GARB, 1993). Also, 20 mVday has been adopted as a
standard inhalation rate for humans (Federal Register,
1980). This value is widely used to determine the inhaled
dose for a given air pollutant for adults.
The available studies on inhalation rates are
summarized in the following sections. Inhalation rates are
reported for outdoor workers/athletes, adults, and
children, including infants performing various activities.
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). 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
Layton - Metabolicalfy 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. 5-2)
where:
VE = ventilation rate (L/min or m'/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/KJ or mVMJ); 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.
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 (see footnote (a) on
Table 5-2). The average food energy intake data (Table
5-1) were obtained from the USDA 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 atrributed 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 Oj/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
ventilatory equivalent (VQ) of 27 used was calculated as
the geometric mean of VQ data that were obtained from
several studies by Layton (1993).
Table 5-2 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
mVday), for males between 15-18 years (17 mVday), and
females between 9-11 years (13 mVday). Estimated
average lifetime inhalation rates for males and females are
14 mVday and 10 m ?day, respectively (Table 5-2).
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 ventilatory
equivalent(VQ). BMR was defined as "the minimum
Page
5-2
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
Table 5-1. Comparisons of Estimated Basal Metabolic Rates (BMR) with Average Food-energy Intakes
Individuals Sampled in the 1977-78 NFCS
Cohort/ Age
(y)
Children
Under 1
Ito2
3 to 5
6 to 8
Males
9 to 11
12 to 14
15 to 18
19 to 22
23 to 34
35 to 50
51 to 64
65 to 74
75 +
Females
9 to 11
12 to 14
15 to 18
19 to 22
23 to 34
35 to 50
51 to 64
65 to 74
75 +
Body Weight
kg
7.6
13
18
26
36
50
66
74
79
82
80
76
71
36
49
56
59
62
66
67
66
62
MJ d-'b
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
BMR"
kcal dlc"
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
for
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-1.
b MJ d'1 - mega joules/day
c kcal d'1 - kilo calories/day
Source: Layton, 1993.
Exposure Factors Handbook
August 1996
Page
5-3
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Volume I - General Factors
Chapter 5 - Inhalation
Table 5-2. Daily Inhalation Rates Calculated from Food-Energy Intakes
Daily1 Inhalation
eep_
' Value
Inhalation Rates
Inactive0 Active0
ftn'/davl
JhL
A'
l-mVdavl
ta'/dav)
ChUdrtn
4.5
11
1.9
2.7
2.35
6.35
1-2
3-5
6-8
Males
9- 11
12- 14
15-18
19-22
23-34
35-50
51-64
65-74
75+
lifetime* average
Ferrules
9-11
12- 14
15-18
19-22
23-34
35-50
51-64
65-74
75+
Lifetime* average
2
3
3
3
3
4
4
11
16
14
10
1
3
3
4
4
11
16
14
10
1
6.8
8.3
10
14
15
17
16
16
15
15
13
13
14
13
12
12
11
11
10
10
9.7
9.6
11
10
10
9
9
8
8
8
8
8
8
8
9
9
8
8
8
8
8
8
8
1.6
1.7
1.7
1.9
1.8
1.7
1.6
1.5
1.5
1.4
1.6
1.6
1.9
1.6
1.5
1.4
1.4
1.3
1.3
1.4
1.4
2.2
2.2
2.2
2.5
2.2
2.1
1.9
1.8
1.8
1.7
1.8
1.9
2.5
2.0
1.7
1.6
1.6
1.5
1.5
1.5
1.6
4.16
4.98
5.95
7.32
8.71
10.31
10.21
10.62
10.25
10.11
8.34
8.02
6.63
7.61
8.14
7.68
7.94
7.80
7.86
7.10
. 6.90
9.15
10.96
13.09
18.3
19.16
21.65
19.4
19.12
18.45
17.19
15.01
15.24
16.58
15.20
13.84
12.29
12.7
11.7
11.8
10.65
11.04
Daily inhalation rate was calculated by multiplying the EFD values (see Table 5-1) by H x VQ for subjects under 9 years of age and by 1.2 x H x VQ for
subjects 9 years of age and older (see text for explanation).
Where:
EFD » Food energy intake (MJ/day) or (KCal/sec)
H « Oxygen uptake - 0.05 UOJKJ or M'CyMJ
VQ = Ventilation equivalent = 27 = geometric mean of VQs (unitless)
* MET = Metabolic equivalent
c Inhalation rate for inactive periods was calculated as BMR x H x VQ and for active periods by multiplying inactive inhalation rate by F (See footnote f);
BMR values are from Table 5-1.
Where:
BMR = Basal metabolic rate (MJ/day) or (kg/hr)
* Lis the number of years for each age cohort.
' For individuals 9 years of age and older, A was calculated by multiplying the ratio for EFD/BMR (unitless) (Table 5-1) 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)
* 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! Ijivton. 1993.
Page
5-4
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
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 5-2. These data for
active and inactive inhalation rates are also presented in
Table 5-2. For children, inactive and active inhalation
rates ranged between 2.35 and 5.95 mVday and 6.35 to
13.09 mVday, respectively. For adult males (19-64 years
old), the average inactive and active inhalation rates were
approximately 10 and 19 mVday, respectively. Also, the
average inactive and active inhalation rates for adult
females (19-64 years old) were approximately 8 and 12
m3/day, 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 (oxygen
uptake) times VQ (ventilation equivalent). The BMR data
obtained from literature had been 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-1. The
data obtained from the second approach are presented in
Table 5-3. Inhalation rates for children (6 months - 10
years) ranged from 7.3-9.3 mVday and ages 10-18 years
was 15 mVday, while adult females (18 years and older)
ranged from 9.9-11 nrVday and adult males (18 years and
older) ranged from 13-17 mVday: These rates are similar
to the daily inhalation rates obtained using the first
approach. Also, the inactive inhalation rates obtained
from (he 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 multiprying
estimated energy expenditures associated with different
levels of physical activity engaged in over the course of an
average day by VQ (ventilation equivalent) and H (oxygen
uptake) 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/gerder
population. The time-activity data used in this approach
were obtained from a survey conducted by Sallis et al.
Table 5-3. Daily Inhalation Rates Obtained from the Ratios
of Total Energy Expenditure to Basal Metabolic Rate (BMR)
Gender/Age
(yrs)
Body Weight'
(kg)
BMR"
(MJ/day)
VQ
H
(m3O2/MJ)
Inhalation Rate, VE
(itf/day)"
Male
0.5 - <3
3- <10
10 - < 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
3.4
4.3
6.7
7.7
7.5
6.1
2.6
4.0
5.7
5.9
5.8
5.3
27
27
27
27
27
27
27
27
27
27
27
27
1.6
1.6
1.7
1.59
1.59
1.59
1.6
1.6
1.5
1.38
1.38
1.38
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
7.3
9.3
15
17
16
13
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.
' The BMRs (basal metabolic rate) are calculated using the respective body weights and BMR equations (see Appendix Table 5A-1).
e 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
10 to < 18 years, the mean values for A given in Table 5-2 for 12-14 years and 15-18 years, age brackets for males and females were
used: male = 1.7 and female = 1.5.
d Inhalation rate = BMR x A x H x VQ; VQ = ventilation equivalent and H = oxygen uptake.
Source: Lavton. 1993. ; .
Exposure Factors Handbook
August1996
Page
5-5
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Volume I - General Factors
Chapter 5 - Inhalation
(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 (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-1.
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).
Table 5-4 presents the inhalation rates (Vg) in mVday and
m'/hr for adult males and females aged 20-74 years at five
physical activity levels. The total daily inhalation rates
ranged from 13-17 mVday 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 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 mVday,
9.9 to 11 mVday, and 11 to 15 mVday, for the first,
second, and third approach, respectively. The inhalation
rates for adult males ranged from 13 to 16 mVday for the
first approach, and 13 to 17 mVday 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 5-5.
Gender/Age (yrs)
Table 5-5.
Weight BMR"
(kg*) (kJ/day)
Inhalation Rates for Short-Term Exposures
Rest
1
Activity Type
Sedentary Light Moderate
MET (BMR Multiplier)
1.2 2" 4C
Heavy
10"
Inhalation Rate (mVhr/'8
Mile
0.5 -<3
3- <10
10- <18
18- <30
30- <60
60+
Femate
0.5- <3
3- <10
10- <18
18- <30
30- <60
60+
14 3.40
23 4.30
53 6.70
76 7.70
80 7.50
75 6.10
11 2.60
23 4.00
50 5.70
62 5.90
68 5.80
67 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
' The BMRs for the age/gender cohorts were calculated using the respective body weights and the BMR equations (Appendix Table 5A-1).
* Range of 1.5 -2.5.
Range of3-5.
Range of >5- 20.
Body weights were based on average weights for age/gender cohorts of the U.S. population
The inhalation rate was calculated by multiplying BMR (KJ/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 mVhr was obtained as follows:
60 min _ m' , _ L
Sowcci Lavton 1993,
hr 1000
Page
5-6
Exposure Factors Handbook
August 1996
-------
Volume I - General Factors
Chapter 5 - Inhalation
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Exposure Factors Handbook
August 1996
Page
5-7
-------
Volume I - General Factors
Chapter 5 - Inhalation
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.
Linn et al. - 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 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 sessioa Finally, a field study was conducted
which involved subjects' collecting their own heart rate
(HR) and diary data. During the calibration tests,
ventilation rates (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
(Linnetal., 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 and 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 5-6 presents the calibration and field
protocols for self-monitoring of activities for each subject
panel.
Table 5-7 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 according to Table 5-7 was
0.8 mVhr. while the mean VR for asthmatic adults was
1.02 mrVhr (Table 5-7). The preliminary data for
construction workers indicated that during a 10-hr work
shift, their mean VR (1.5 m'/hr) exceeded the VRs of
other subject panels (Table 5-7). 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 level, asthmatic subjects had higher VRs than
healthy subjects (Table 5-7). Also, Linn et al. (1992)
reported that in every panel, the predicted VR correlated
significantly with the subjective estimates of activity
levels.
A limitation of this stddy is that calibration data
may overestimate the predictive power of HR during
actual field monitoring, because the wider variety of
exercise in everyday activities may result in wider
variation of the VR-HR relationship. 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 workers,
asthmatics, healthy adults, and healthy children).
" Linn et al. - 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 were employed at a hospital
construction site in suburban Los Angeles. The study was
conducted between mid-July and early November, 1991.
Page
5-8
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
Table 5-6. Calibration and Field Protocols for Self-Monitoring of Activities Grouped by Subject Panels
Panel
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
Panel 7 - Construction Workers - 7
male, age 26-34
Calibration Protocol
Laboratory treadmill exercise tests, indoor
hallway walking tests at different self-chosen
speeds, 2 outdoor tests consisting of 1-hour
cycles each of rest, walking, and jogging.
Outdoor exercises consisted each of 20
minute rest, slow walking, jogging and fast
walking
Outdoor exercises consisted each of 20
minute rest, slow walking, jogging and fast
walking
Treadmill and hallway tests
Treadmill and hallway tests
Laboratory 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.
Field Protocol
3 days in 1 typical summer week (includes 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 NO2 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 I
typical summer work day.
Source: Linnet al.. 1992
Table 5-7. Subject Panel Inhalation Rates (IR) by Mean JR. Upper Percentiles, and Self-Estimated Breathing Rates
Panel
Inhalation Rates (mVhr)
N*
Mean IR
(mVhr)
99th Percentile
Mean Self-Estimated Breathing Rates
(mVhr)1
Slow
Medium'
Fast0
Healthy
1 - Adults
2 - Elementary School Students
3 - High School Students
7 - Construction Workers9
20
17
19
7
0.78
0.90
0.84
1.50
2.46
1.98
2.22
4.26
0.72
0.84
0.78
1.26
1.02
0.96
1.14
1.50
3.06
1.14
1.62
1.68
Asthmatics
4 - Adults
5 - Adults"
6 - Elementary and High School Students
49
25
13
1.02
1.20
1.20
1.92
2.40
2.40
1.02
1.20
1.20
1.68
2.04
1.20
2.46
4.02
1.50
* 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).
* Number of individuals in each survey panel.
c Construction workers recorded only on 1 day, mostly during work, while others recorded on 2 1 work or school day and i 1 day off.
d Excluding subjects also in Panel 4.
Source: Linnetal., 1992.
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
During this period, ozone (O3) levels were typically high.
Initially, each subject was calibrated with a 25-minutes
exercise test that included slow walking, fast walking,
jogging, lifting, and carrying. All calibration tests were
conducted in the mornings. Ventilation rates (VR) and
heart rates (HR) were measured simultaneously during the
test. The data were analyzed using the least squares
regression to derive an equation for predicting VR at a
given HR. Following the calibration tests and before
beginning work, each subject recorded their change in
activity (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 inhalation rate (IR) was 1.68 mVhr 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-2). Therefore, corresponding IR
percentiles were extrapolated using the calibration data
(Linnetal., 1993).
Table 5-8. Distributions of Individual and Group Inhalation/Ventilation Rate for Outdoor Workers
Population Group and Subgroup'
All Subjects (if = 19)
Job
GCWVLaborers (n=5)
Iron Workers (n=3)
Carpenters (n= 11)
Site
Office Site (n=7)
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) (m'/hr)
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.
e GCW - general construction worker.
Source: Linn et al., 1993.
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Volume I - General Factors
Chapter 5 - Inhalation
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, hospital site workers reported a higher percentage of
slow breathing time (31 percent) than the office site
workers (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 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.
Therefore, it was necessary to predict IR values outside
the calibration range which may introduce an unknown
uncertainty to the data set. Also, subjective self-estimated
breathing rates (i.e., "macho effect") may be another
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).
Spier et al. - Activity Patterns in Elementary and
High School Students Exposed To Qxidant 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. Heart rate (HR) and ventilation rate (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
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 (raVhr)
Breathing Rate (m3/hr)
Population Group and Subgroup
All Subjects (n= 19)
Job
GCW/Laborers (n=5)
Iron Workers (n=3)
Carpenters (n=ll)
Site
Office Site (n=7)
Hospital Site (n= 12)
Slow
1.44
1.20
1.38
1.62
1.14
1.62
1 GCW - general construction worker
* Trade - "Working at Trade" (i.e., tasks specific to the
Source: Linnet al., 1993
Med
1.86
1.56
1.86
2.04
1.44
2.16
individual's
Fast Sit/Std Walk
2.04 1.56 1.80
1.68 1.26 1.44
2.10 1.62 1.74
2.28 1.62 1.92
1.62 1.14 1.38
2.40 1.80 2.04
job classification)
Carry Trade6
2.10 1.92
1.74 1.56
1.98 1.92
2.28 2.04
1.68 1.44
2.34 2.16
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Volume I - General Factors
Chapter 5 - Inhalation
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 5-10 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 5-11).
Based on the data presented in Tables 5-10 and 5-11, the
average inhalation specific-activity rates for elementary
(10-12 years) and high school (13-17 years) students were
calculated in Table 5-12. For elementary school students,
the average daily inhalation rates are 15.8 mVday for light
activities, 4.62 mVday for moderate activities, and 0.98
mVday for heavy activities. Also, for high school
students the daily inhalation rate during light, moderate,
and heavy activities is estimated at 16.4 mVday, 3.1
mVday, and 0.54 rrrVday, respectively (Table 5-12).
A limitation of this study is the small sample size.
Also, it may not be representative of all children in these
age groups. Another limitation is that associated with the
accuracy of the self-estimated breathing rates reported by
younger age groups. 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.
California Air Resources Board (CARB) -
Measurement of Breathing Rate and Volume in Routinely
Performed Daily Activities - The California Air
Resources Board, CARB (1993) conducted research to
accomplish two main objectives: (1) identification of mean
and ranges of inhalation rates for various age/gender
cohorts; and (2) derivation of simple linear and multiple
Table 5-10. Distribution of Predicted IR by Location and Activity Levels for Elementary and High School Students
Age %
(yrs) Student Location Activity Level
10-12 ELe Indoors slow
(tf=l7) medium
fast
Outdoors slow
medium
fast
13-17 HS° Indoors slow
(n?=19) medium
fast
Outdoors slow
medium
Inhalation Rates (nvVhr)
Recorded Percentile Rankings1'
Time'
Mean ± SD 1st 50th 99.9th
49.6 0.84 ± 0.36
23.6 0.96 ± 0.42
2.4 1.02 ± 0.60
8.9 0.96 ± 0.54
11.2 1.08 ± 0.48
4.3 1.14 ± 0.60
70.7 0.78 ± 0.36
10.9 0.96 ± 0.42
1.4 1.26 ± 0.66
8.2 0.96 ± 0.48
7.4 1.26 ± 0.78
1.4 1.44 ± 1.08
0.18
0.24
0.24
0.36
0.24
0.48
0.30
0.42
0.54
0.42
0.48
0.48
0.78
0.84
0.84
0.78
0.96
0.96
0.72
0.84
1.08
0.90
1.08
1.02
2.34
2.58
3.42
4.32
3.36
3.60
3.24
4.02
6.84°
5.28
5.70
5.94
Recorded time averaged about 23 hr per elementary school student and 33 hr. per high school student, over 72-hr, periods.
Geometric means closely approximated 50th percentiles; geometric standard deviations were 1.2-1.3 for HR, 1.5-1.8 for VR.
EL = elementary school student; HS = high school student.
N * number of students that participated in survey.
Highest single value.
Source: Spier et al.. 1992. __________
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Chapter 5 - Inhalation
Table 5-11. Average Hours Spent per Day in a Given Location and Activity Level for Elementary (EL)
and High School (HS) Students
Student
(EL1. nc=17: HS". Nc=
b
c
EL
EL
HS
HS
= 19) Location
Indoor
Outdoor
Indoor
Outdoor
Slow
16.3
2.2
19.5
1.2
Activity Level
Medium
2.9
1.7
1.5
1.3
Total Time Spent
Fast (hrs/day)
0.4 19.6
0.5 4.4
0.2 21.2
0.2 2.7
Elementary school (EL) students were between 10-12 years old.
High school (HS) students were between 13-17 years old.
N corresponds to number of school students.
Source: Spier et al.
, 1992.
Table 5-12. Distribution Patterns of Daily Inhalation Rates for Elementary (EL) and High School (HS) Students Grouped by Activity Level
Students
Age
(yrs)
Location
Activity type"
MeanlR"
(mVday)
1st
Percentile Rankings
50th
99.9th
EL(nc=17) 10-12 Indoor
EL
HS
Outdoor
HS(n=19) 13-17 Indoor
Outdoor
Light
Moderate
Heavy
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
1.15
1.64
0.29
2.93
0.70
0.096
0.79
0.41
0.24
5.85
0.63
0.11
0.50
0.62
0.096
12.71
2.44
0.34
1.72
1.63
0.48
14.04
1.26
0.22
1.08
1.40
0.20
38.14
7.48
1.37
9.50
5.71
1.80
63.18
6.03
1.37
6.34
7.41
1.19
1 For this report, activity type presented in Table 5-7 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 5-11) by the corresponding inhalation
rate (Table 5-10).
e Number of elementary (EL) and high school students (HS).
Source: Adapted from Spier et al.. 1992 (Generated using data from Tables 5-10 and 5-11V
regression equations used to predict inhalation rates
through other measured variables: heart rate (HR),
breathing frequency (fe), and oxygen consumption (Vc^).
The survey population consisted of 160 individuals (both
genders) from California of various ages (6-77 years) and
ethnicity (CARB, 1993). CARB validated empirically
derived equations for children engaged in selected field
and laboratory studies. The test subjects were 40 children
from 6 to 12 years old and twelve young children (3-5
years) were identified as subjects for pilot testing purposes
(CARB, 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
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Chapter 5 - Inhalation
resting and sedentary activities. Two active protocols
including 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, and V^
Measurements were taken in the last 5 minutes of each
phase of the resting protocol (25 minutes), and the last 3
minutes of the 6 minutes 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 only completed car driving and riding, yardwork,
and mowing protocols. HR, VR, and fB were measured
during each protocol and 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, inhalation rate (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-3
and 5A-4 were categorized as young children, children,
adult female, and adult male by activity levels (resting,
sedentary, light, moderate, and heavy). These categorized
data for the laboratory protocols are shown in Table 5-13.
Table 5-14 presents die mean inhalation rates by group
and activity levels (light, sedentary, and moderate) in field
protocols. A comparison of the data shown in Tables 5-13
and 5-14 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
(CARS, 1993). GARB (1993) calculated BSA from
measured height and weight using the equation:
BSA = Height"-725' x Weight*0-423' x 71.84.
(Eqn. 5-3)
Table S-13. Summary of Average Inhalatioa Rates (mVhr) by Age Group and Activity Levels for Laboratory Protocols
Young Children'
Children*
Adult Females'
Adult Males*
Resting*
0.37
0.45
0.43-
0.54
Sedentary''
0.40
0.47
0.48
0.60
Light0
0.65
0.95
1.33
1.45
Moderate"
DNP8
1.74
2.76
1.93
Heavy'
DNP
2.23
2.961
3.63
Resting defined as lying (see Appendix Table 5A-3 for original data).
Sedentary defined as sitting and standing (see Appendix Table 5A-3 for original data).
Light defined as walking at speed level 1.5-3.0 mph (see Appendix Table 5A-3 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-3 for original data).
Heavy defined as fast running (4.5 - 6.0 mph) (see Appendix Table 5A-3 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.
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 CARS, 1993.
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Chapter 5 - Inhalation
Young Children"
Children1
Adult Females8
Adult Males'
DNP*
DNP
l.ltf1
1.40*
DNP
DNP
0.51
0.62
0.68
1.07
DNP
1.78j
Table 5-14. Summary of Average Inhalation Pates (rrrVhr) by
Age Group and Activity Levels in Field Protocols
Age
Light3
Sedentary
Moderate0
Light activity was defined as car maintenance (males),
housework (females), and yard work (females) (see Appendix
Table 5A-4 for original data).
b Sedentary activity was defined as car driving and riding (both
genders) (see Appendix Table 5A-4 for original data).
0 Moderate activity was defined as mowing (males); wood
working (males); yard work (males); and play (children), (see
Appendix Table 5A-4 for original data).
d 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.
' Children (both genders) = 6 - 12.9 yrs old.
* 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.
1 Adult males defined as adolescent, young to middle aged, and
older adult males.
j Adolescents not included in mean value since they did not
perform this activity.
Source: CARS, 1993.
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.
3.2.3. Relevant Inhalation Rate Studies
Shamoo et al. - 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 ventilation
rate (VR) at various controlled levels of exercise; and (2)
individual VR and heart rate (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
m'/hr), medium (1.5-2.3 m3/hr), heavy (2.4-3.8 mVhr),
and very heavy (3.8 mVhr 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 within 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).
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Chapter 5 - Inhalation
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 et al. - 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 ventilation rate (VR)
from heart rate (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
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 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 mVhr 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 5-15 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). 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 mVhr
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 arid outdoor) are presented
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Chapter 5 - Inhalation
Table 5-15. Distribution Pattern of Predicted VR and EVR (Equivalent Ventilation Rate) for Outdoor Workers
VR (raVhr)1
Self-Reported Arithmetic Geometric
Activity Level Nc Mean ± S.D. Mean ± S.D.
Sleep
Slow
Medium
Fast
18,597 0.42
41,745 0.71
3,898 0.84
572 2.63
±0.16
±0.4
± 0.47
±2.16
0.39
0.65
0.76
1.87
±0.08
± 0.09
±0.09
± 0.14
^ Percentile Rankings,
Sleep
Slow
Medium
Fast
1
0.18
0.30
0.36
0.42
5
0.18
0.36
0.42
0.54
10
0.24
0.36
0.48
0.60
50
0.36
0.66
0.72
1.74
EVR" (m3/hr/m2 body surface)
Arithmetic Geometric
Mean ± S.D. Mean ± S.D.
0.23 ±
0.38 ±
0.48 ±
1.42 ±
VR
90
0.66
1.08
1.32
5.70
0.08
0.20
0.24
1.20
95
0.72
1.32
1.68
6.84
0.22
0.35
0.44
1.00
99
0.90
1.98
2.64
9.18
±0.08
±0.09
±0.09
± 0.14
99.9
1.20
4.38
3.84
10.26
Percentile Rankings, EVR
Sleep
Slow
Medium
Fast
1
0.12
0.18
0.18
0.24
5
0.12
0.18
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 in liters/minute were converted to nrVhr.
b EVR = VR per square meter of body surface area.
° Number of minutes with valid appearing heart rate records and corresponding daily records of breathing rate.
Source: Shamoo et al.. 1991
in Table 5-16. 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 5-16 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 5-16).
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 etal. - Effectiveness of Training Subjects
to Estimate Their Level of Ventilation - Shamoo el: 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
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Volume I - General Factors
Chapter 5 - Inhalation
Table 5-16. Distribution Pattern of Inhalation Rate by Location and Activity Type for Outdoor Workers
Location
Indoor
Activity Type*
Essential
Self-reported
Activity Level
Sleep
Slow
Medium
Fast
% of Time
28.7
29.5
2.4
0
Inhalation rate (raVhr)
±S.D.
0.42 ± 0.12
0.72 ± 0.36
0.72 ± 0.30
0
% of Avg."
69 ± 15
106 ± 43
129 ± 38
0
Indoor Non-essential
Outdoor 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
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
* 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.
b Statistic was calculated by converting each VR for a given subject to a percentage of her/his overall average.
Source: Sharhooetal.. (1991).
on initial tests were conducted; VR was measured and
feedback was given to the subjects. Another similar
session was conducted; however, the subjects estimated
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.
For purposes of this study, inhalation rates were
analyzed from the raw data that were provided to the
authors by Shamoo et al. (1992). Table 5-17 presents the
mean inhalation rates obtained at four ventilation levels
and 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 '"Vhr for low, medium,
heavy, and very heavy activities, respectively. The
overall percent correct score obtained for all ventilation
levels was 68 percent (Shamoo et al., 1992). Therefore,
Shamoo et al. (1992) concluded that this training protocol
was effective in training subjects to correctly estimate
their minute ventilation levels.
Table 5-17. Actual Inhalation Rates Measured at Four
Ventilation Levels
Mean Inhalation Rate' (m'/hr)1
Subject Location
Low Medium
Very
Heavy Heavy
All Indoor (Tm 1.23 1.83 3.13 4.13
subjects post)
Outdoor 0.88 1.96 2.93 4.90
Total 0.93 .1.92 3.01 4.80
Original data were presented in L/min. Conversion to mVhr
was obtained as follows:
60 x -. x _.
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,
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Volume I - General Factors
Chapter 5 - Inhalation
this approach may not be viable in field studies especially
for field studies within large sample sizes.
U.S. EPA - Development of Statistical Distributions
or Ranges of Standard Factors Used in Exposure
Assessments - Due to a paucity of information in
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. Inmore 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-5.
Table 5-18 presents a summary of inhalation rates
by age, gender, and activity level (detailed data are
presented in Appendix Table 5A-6). A description of
activities included in each activity level is also presented
in Table 5-18. Table 5-18 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 each, respectively. Table 5-19 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 5-18 and 5-19, a daily inhalation rate
was calculated for adults and children by using a time-.
activity-ventilation approach. These data are presented in
Table 5-20. The calculated average daily inhalation rates
are 16 mVday for adults. The average daily inhalation
rate for children (6 and 10 yrs) is 18.9 mVday ([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.
International Commission on Radiological
Protection - 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 toe
Table 5-18. Summary of Human Inhalation Rates for Men, Women, and Children by Activity Level (m'/hour)*
n' Resting5 n Light* n Moderate' n
Adult male 454 0.7 102 0.8 102 2.5 267
Adult female 595 0.3 786 0.5 106 1.6 211
Average adult8 0.5 0.6 2.1
Child, age 6 8 0.4 16 0.8 4 2.0 5
Child, age 10 10 0.4 40 1.0 29 3.2 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 3A-6 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.
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August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
Table 5-19. Activity Pattern Data Aggregated for Three
Microenviroranents by Activity Level for all Age Groups
Microcnvironmcnt
Indoors
Outdoors
In Transportation
Vehicle
Source:
Average Hours Per Day
Activity in Each
Level Microenvironment at
Each Activity Level
Resting
Light
Moderate
Heavy
TOTAL
Resting
Light
Moderate
Heavy
TOTAL
Resting
Light
Moderate
Heavy
TOTAL
Adapted
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
from U.S. EPA. 1985.
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-7) 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
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 5-21 presents the daily inhalation
rates obtained for all ages/genders. The estimated
inhalation rates were 23 mVday for adult males, 21
mVday for adult females, 15 mVday for children (age 10
years), 3.8 mVday for infants (age 1 year), and 0.8
mVday for newboms.
TOTAL 1.77
Source; Adanted from U.S. EPA. 1985.
T»blc5-20. Summary of Daily Inhalation Rates Grouped by
Age and Activity level
Daily Inhalation Rate (nrVday)' Total
Daily IR"
SaHcct Resting Light Moderate Heavy fm'/davl
AdullMale 7.83 8.95 3.53 1.05 21.4
Adult 3.35 5.59 2.26 0.64 11.8
Female
Adult 5.60 6.71 2.96 0.85 16
Avenge*
Child 4.47 8.95 2.82 0.50 16.74
(age 6)
Child 4.47 11.19 4.51 0.85 21.02
* In this report, inhalation rate was calculated by using the following
equation:
iR-ilf., KA
IR, = inhalation rate at i* activity (Table 5-18)
t, = hours spent per day during i" activity (Table 5-19)
k x number of activity periods
T = total time of the exposure period (e.g., a day)
* In this report, total daily inhalation rate was calculated by summing
the specific activity daily inhalation rate.
Source: Generated wine data from Tables 5-18 and 5-19.
Table 5-21. Daily Inhalation Rates Estimated From Daily Activities'
Inhalation Rate (IR)
Subject Resting Light Daily Inhalation
(mVhr) Activity Rate (DIR)"
(m'/hr) (m'/day)
Adult Man 0.45 1.2 22.8
Adult Woman 0.36 1.14 21.1
Child (10 yrs) 0.29 0.78 14.8
Infant (1 yr) 0.09 0.25 3.76
Newborn 0.03 0.09 0.78
* 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 LIRA
T 1=1
t, = Hours spent during the i* activity
k = Number of activity periods
T = Total time of the exposure period (i.e. a day)
Source: ICRP, 1981
A limitatioa 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
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Volume I - General Factors
Chanter 5 - Inhalation
assumptions may over/under-estimate the inhalation rates
obtained.
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 EPA (1989). The newer study by Layron
(1993) is not considered. In addtion, 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
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.
Table 5-22. Confidence in Inhalation Rate Recommendations
Considerations
Rating
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
Peer reviewed journal articles High
EPA peer reviewed report
Journals-wide circulation High
EPA report available from the National Technical Information Service
Information on questionnaires and interviews not provided. Medium
Studies focus on ventilation rates and factors influencing them. High
Sstudies 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 made by indirect methods Medium
An effort has been made to consider age and gender but not Medium
systematically.
An effort has been made to address age and gender, but not High
systematically.
Subjects 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 five relevant studies were evaluated
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.
Exposure Factors Handbook
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Volume I - General Factors
Chapter 5 - Inhalation
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
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 hi 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.
Adults (19-65+ yrs) - For purposes of this
recommendation, adults 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 mVday for women and 15.2 m3/day for men.
An upper percentile is not recommended. Additional
research and analysis of activity pattern data and dietary
data in the future is necessarry to attempt to calculate
upper percentiles.
The recommended value for the general population
average inhalation rate, 11.3 mVday for women and 15.2
m'/day for men, is different than the 20 mVday which has
commonly been assumed in past EPA risk assessments.
Table 5-23. Summary of Recommended Values for Inhalation
Population
Long-term Exposures
Children
< 1 year
Children
1-12 years
Adult
females
males
Short-term Exposures
Adults and Children
Rest
Sedentary Activities
Light Activities
Moderate Activities
Heavy Activites
Outdoor Workers
Hourly Average
Slow Activities
Moderate Activities
Heavy Activities
Mean
4.5 mVday
8.7 mVday
11.3 irf/day
15.2 m3/day
0.3 m3/hr
0.4 m3/hr
1.0 m3/hr
1.2m3/hr
1.9m3/hr
1.3 nrVhr
l.lnrVhr
'1.5 m3/hr
2.3 m3/hr
Upper
Percentile
_
-
....
3.5 rnVhr
In addition, recommendations are presented for various
ages and special populations (athletes, outdoor workers)
which also differ from 20 mVday. Assessors are
encouraged to use values which most accurately reflect the
exposed population. If a risk assessment is being
'conducted where an inhalation rate other than 20 mVday
applies to the population of concern, the assessors should
consider if a dose-response relationship will be used which
was derived assuming ah inhalation rate of 20 mVday. If
such an inconsistency exists, the assessor should adjust the
dose-response relationship as described in the appendix to
Chapter 1. IRIS does not use a 20 mVday assumption in
the derivation of RfCs and RfDs, but does make this
assumption in the derivation of some cancer slope factors
or unit risks.
For exposure scenarios where the distribution of
activity patterns is known, the following results, calculated
from the studies referenced can be applied:
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Chapter 5 - Inhalation
73
S
a
s
a
8
a
s
a
ts
en
D
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Chapter 5 - Inhalation
Summary of Inhalation Rates for Short-Term Exposure
Arithmetic Mean (m'/hr)
Reference
Rest
0.5
_
0.4
0.4
_
-
Sedentary
0.5
0.6
0.4
_
_
-
Activity level
Light
1.4
1.2
0.7
0.6
1.7
0.8
Moderate
2.4
1.8
1.4
1.5
2.2
1.1
Heavy
3.3
-
3.6
3.0
2.7
1.6
GARB, 1993 (Lab protocols)
CARD, 1993 (Field protocols)
Layton, 1993 (Short-term exposure)
Layton, 1993 (3rd approach)
Spier etal., 1992
Linn etal., 1992
Based on these key studies, the following
recommendations are made: for short term exposures in
which distribution of activity patterns are specified, the
recommended average rates are 0.4 m3/hr during rest; 0.5
mVhr for sedentary activities; 1.1 rnVhr for light
activities; 1.7 mVhr for moderate activities; and 2.8 mVhr
for heavy activities.
Children (18 yrs old or less including infants) - For
purposes 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.
For long-term dose assessments, the daily
inhalation rates are summarized as follows:
4.5 mVday. The mean daily inhalation rate obtained from
the Spier et al. (1992) study is. much higher than the
values from the Layton (1993) study. This discrepancy
can be attributed to the survey methodologies used by
Spier et al. (1992), in which diary information and heart
rate (HR) recordings were obtained only when the children
were awake (i.e., during active hours). In contrast,
inhalation rates in the Layton (1993) study were calculated
either based on basal metabolic rate. (BMR) which
includes resting, or on food energy intake. Also, the two
studies represent different age groups. Therefore, based
on the Layton (1993) study, the recommended average
daily inhalation rate for children between the ages of 1 and
12 years is 8.7 mVday. The same shortcomings as those
discussed above can be used to reject the upper percentile
estimate (64 irf/day) obtained from the Spier et al. (1992)
study.
Summary of Long Term Exposure Data
M
Arithmetic Mean (mVday)
F M&F
Reference
less than 1 yr (1st approach)
1-11 yrs (1st approach)
0.5-10 yrs (2nd approach)
10-12 yrs (calculated)
12-18 yrs (1st approach)
10-18 yrs (2nd approach)
4.5
9.8
8.3
-
16.0
15.0
4.5
9.5
7.1
21.4
12.0
12.0
Layton, 1993
Layton, 1993
Layton, 1993
Spier etal., 1992
Layton, 1993
Layton, 1993
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
For short-term exposures in which activity patterns
are known, the data summarized below can be used:
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Chapters - Inhalation
Summary of Short-Term Exposure Data
Arithmetic mean (mVhr)
Activity level
Rest
0.4
-
0.2
-
-
Sedentary
0.4
.
0.3
-
-
Light
0.8
-
0.5
1.8
0.8
Moderate
.
0.9
1.0
2.0
1.0
Heavy
.
-
2.5
2.2
1.1
Reference
CARS, 1993 (lab. protocols)
CARB, 1993 (field protocols)
Layton, 1993 (Short-term data)
Spier et al. , 1992 (10-12 yrs)
Linn et al. , 1992 (10-12 yrs)
For short term exposures, the recommended average
hourly inhalation rates are based on these key studies.
They are as follows: 0.3 m3/hr during rest; 0.4 mVhr for
sedentary activities; 1.0 nrVhr for light activities; 1.2
m3/hr for moderate activities; and 1.9 mVhr for heavy
activities. The recommended short-term exposure data
also includes infants (less than 1 yr).
Outdoor Worker/Athlete - 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 mVhr and the upper-percentile rate is 3.5
mVhr (see Tables 5-7 and 5-8). The recommended
average inhalation rates for outdoor workers based on
their activity levels categorized as slow (light activities),
medium (moderate activities), and fast (heavy activities)
are 1.1 mVhr, 1.5 mVhr, and 2.3 mVhr, respectively.
These values are based on the data from Linn et al. (1992
and 1993) (see Tables 5-7 and 5-9). .
5.3. REFERENCES FOR CHAPTER 5
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.
CARB. (1993) California Air Resources Board.
Measurement of breathing rate and volume in
routinely performed daily activities. Human
Performance Lab. Contract No. A033-205. June
1993. 185 pgs.
Federal Register Notices (1980). November 28.
45(231): 79318-79379.
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, CE.; Hackney, J.D. (1993)
Activity patterns in ozone-exposed construction
workers. J. Occ. Med. Tox. 2(1): 1-14.
Menzel, D.B.; Admur, M.O. (1986) Toxic responses
of the respiratory system. In: Klaassen, C.;
Admur, 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.
Sallis, J.F.; Haskell, W.L.; Wood, P.D.; 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.
Exposure Factors Handbook
August 1996
Page
5-25
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Volume I - General Factors
Chapter 5 - Inhalation
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.
Page
5-26
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
APPENDIX 5-A
Ventilation Data
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August 1996
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Chapter 5 - Inhalation
Table 5A-1. Statistics of the Age/Gender Cohorts Used to Develop Regression Equations for Predicting Basal Metabolic Rates (BMR)
(from Schofield. 1985}
Gender/Age BMR
(v) MJ d'1 ±SD
Males
Under 3 1.51
3 to < 10 4.14
10 to < 18 5.86
18 to < 30 6.87
30 to < 60 6.75
60 + 5.59
Females
Under 3 1.54
3 to < 10 3.85
10 to < 18 5.04
18 to < 30 5.33
30 to < 60 5.62
60 + 4.85
* Coefficient of variation (SD/mean)
N = number of subjects
0 Body weight (bw) in kg
coefficient of correlation
Source: Lavton. 1993.
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
Table 5A-2. Characteristics of Individual Subjects: Anthropometric Data, Job Categories. Calibration Results
Subj. #
.Age.
Calibration
Ht. (in.)
Wt. (Ib.) Ethnic Group'
Job*
Site*
HR Range"
1761
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1778
1779
1780
1781
26
29
32
30
31
34
32
32
26
39
32
39
23
42
29
35
40
37
38
71
63
71
73
67
74
69
77
70
66
71
69
68
67
70
76
70
75
65
180
135
165
145
170
220
155
230
180
150
260
170
150
150
180
220
175
242
165
Wht
Asn
Blk
Wht
His
Wht
Blk
Wht
Wht
Wht
Wht
Wht
His
Wht
His
Ind
Wht
His
His
GCW
GCW
Car
GCW
Car
Car
GCW
Car
Car
Car
Car
Irn
Car
Irn
Car
Car
Car
Irn
Lab
Ofc
Ofc
Ofc
Ofc
Ofc
Ofc
Ofc
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
Hosp
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
.91
.95
.95
.97
.89
.98
.95
.99
.89
.91
.97
.95
.98
.88
.99
.94
.99
.98
.89
Mean
33
5
70
4
181
36
70-123
8-16
.94
.04
Abbreviations are interpreted as follows. Ethnic Group: Asn = Asian-Pacific, Blk = Black, His = Hispanic Ind = American
Indian, Wht = White
Job: Car = carpenter, GCW = general construction worker, Irn = ironworker, Lab = laborer
e Site: Hosp = hospital buidling, Ofc = medical office complex. Calibration data
* Hr 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, using quadratic prediction equation).
Source: Linnetal.. 1993.
Exposure Factors Handbook
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'Volume I - General Factors
Chapter 5 - Inhalation
Table 5A-3. Mean Minute Ventilation (VE, L/min) by Group and Activity for Laboratory Protocols
Activity
Lying
Sitting
Standing
Walking
Running
Young Children3 Children Adult Females
6.19 7.51 7.12
6.48 7.28 7.72 '
6.76 8.49 8.36
1.5 mph 10.25 DNP DNP
1.875 mph 10.53 DNP DNP
2.0 mph DNP 14.13 DNP
2.25 mph 11.68 DNP DNP
2.5 mph DNP 15.58 20.32
3.0 mph DNP 17.79 24.20
3.3 mph DNP DNP DNP
4.0 mph DNP DNP DNP
3.5 mph DNP 26.77 DNP
4.0 mph DNP 31.35 46.03*
4.5 mph DNP 37.22 47.86*
5.0 mph DNP DNP 50.78b
6,0 nwh DNP DNP DNP
Adult Males
8.93
9.30
10.65
DNP
DNP
DNP
DNP
24.13
DNP
27.Sk)
36.53
DNP
DNP
57.30
58.45
65.66*
* 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
b Older adults not included in the mean value -since they did not perform running protocol at particular speeds.
Source; CARS. 1993.
Table 5A-4.Mean Minute Ventilation (VE, L/min) by Group and Activity for Field Protocols
Activity
Young Children'
Children
Adult Females
Adult Males
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
19.23=
17.38
DNP
DNP
DNP
DNP
10.79
9.83
26.07b/31.89°
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: CARS. 1993.
Page
5A-4
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
Table 5A-5. Estimated Minute Ventilation Associated with Activity Level for Average Male Adulta
Level of work
Light
Light
Light
Moderate
Moderate '
Moderate
Heavy
Heavy
Very heavy
Very heavy
Severe
Average adult
Source: Adapted
L/min
13
19
25
30
35
40
55
63
72
85
100+
assumed to
from U.S.
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
weigh 70 kg.
EPA, 1985
Exposure Factors Handbook
August 1996
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5A-5
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Volume I - General Factors
Chapter 5 - Inhalation
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August 1996
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Volume I - General Factors
Chapter 5 - Inhalation
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Exposure Factors Handbook Page
August 1996 5A-7
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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 include: the chemical
concentration in contact with the skin, the extent of skin
surface area exposed, the duration of exposure, and the
rate of absorption of the chemical.
This chapter focuses on measurements of body
surface areas and various factors needed to estimate
dermal exposure to chemicals in water and soil. 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,
1992) provides detailed information concerning dermal
exposure using a stepwise guide in the exposure
assessment process. Information concerning dermal
exposure to pollutants in indoor environments is limited.
The available studies have been classified as either
key or relevant based on their applicability to exposure
assessment needs and 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 each study 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-bod.y-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 water,
dermally absorbed average daily dose can be estimated, by
(U.S. EPA, 1992):
ADD
BW x AT
(Eqn. 6-1)
where:
ADD
DA,
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 in water. Total body surface area (SA) is
assumed to be exposed to water for a period of time (ED).
The DA^,.,,, is estimated with consideration for the
permeability coefficient from water, the chemical
concentration in water, and the event duration.
The approach to estimate DA,.VHlt 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, 1992). 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, 1992).
It is recommended that the traditional steady-state
approach be applied to inorganics (U.S. EPA, 1992).
Detailed information concerning how to estimate absorbed
dose per event (DA^^,,) can be found in "Dermal
Exposure Assessment: Principles and Applications" (U.S.
EPA, 1992).
For dermal contact with contaminated soil, a
variation of Equation 6-1 is used. Dermally absorbed
dose is calculated using the equation below:
Exposure Factors Handbook
August 1996
Page
6-1
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Volume I - General Factors
Chapter 6 - Dermal
BW x AT
(Eqn. 6-2)
where:
ADD
SA
EF
ED
BW
AT
average daily dose (mg/kg-day);
absorbed dose per event (mg/cm2-evenl);
skin surface area available for contact (cm2);
exposure frequency (events/year);
exposure duration (years);
body weight (kg); and
averaging time (days), for non-carcinogenic
effects, AT ED, and for carcinogenic
effects, AT - 70 years or 25,550 days.
Estimation of the DACVC3lt is based on the concentration of
the chemical in soil, the adherence factor of soil to skin,
and the absorption fraction.
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, 1992).
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 become an absorbed 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
dermally absorbed 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.
1992).
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 pollntat 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.
Page
6-2
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August 1996
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Volume I - General Factors
Chapter 6 - Dermal
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 selected 401 measurements made by 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 Rochori 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 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 the 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 and 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. They studied the effect of
using these factors as independent variables in the LADD
equatioa They 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
Exposure Factors Handbook
August 1996
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6-3
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Volume I - General Factors
Chapter 6 - Dermal
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 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 6-1). 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 distributions. 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 (1989); and Costeff
(1966). The formulae of Dubois and Dubois (1916);
Boyd (1935); and U.S. EPA (1989) 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. 6-3)
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 distribution for both men and women (Figure
6-2). It was also found that assuming correlation between
height and weight influences the final distribution by less
than 1 percent.
AICH (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 Brainard and Burmaster et al.
(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
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Chapter 6 - Dermal
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 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 (m2)
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, 1992). In addition, it has been
demonstrated'that a "pumping" effect can occur which
causes material to move under loose clothing (U.S. EPA,
1992). Furthermore, studies have demonstrated that
hands cannot be considered to be protected from exposure
even if waterproof gloves are worn (U.S. EPA, 1992).
This may be due to contamination to the interior surface
of the gloves when donning or removing them during
work activities (U.S. EPA, 1992). 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, 1992).
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, 1992). 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 cm (upper
percentile) are recommended in U.S. EPA (1992). 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, 1992) 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 recommends a new form of
the soil adherence factor which is specific to activity and
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body pan and is designed to be 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. DERMAL ADHERENCE TO SOIL
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 (1992). This section
summarizes recent studies that estimate soil adherence to
skin for use as exposure factors.
6.3.2. Key Dermal Adherence to Soil Study
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 /um. 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. (1996a) used a fluorescent marking
technique and video imaging to assess the percentage of
skin coverage in several soil contact trials in a greenhouse
setting, and an irrigation pipe laying trial (Table 6-11).
The investigators concluded that adjusted loadings,
averaged over fluorescing areas only, may be two to three
orders of magnitude larger than average loadings, if
average loadings are small.
Further experiments by Kissel, et al. (1996a)
estimated 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 6-12.
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 6-13.
6.3.3. Relevant Dermal Adherence to Soil 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
6-25 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, 9-14
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
estimate the amount of soil adhering to the hands. The
mean soil amount adhering to the hands was 0.159 grams.
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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 Reels 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).
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 having particle sizes ranging from <. 44
to 833 ion diameters, fractionated into six size ranges, to
estimate the amount that adhered to the palm of the hand
assumed to be approximately 160 cm2 (test subject
approximately 14 years old 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. From this
experiment it was assumed that 0.2 mg of soil adhered to
1 cm2 of skin.
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, lackland, 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 /an and £ 150 /on. 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 /im, 0.95
mg/cm2 for particle sizes less than 250 fan, 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).
Yang et al. (1989) - In vitro and In vivo
Percutaneous Absorption of Benzofajpyrene 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 yum 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 the < 1
mg/cm2 of soil (dust) reported for human skin in the
studies of Lepow et al., 1975; Roels et al., 1980; and Que
Hee et al., 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
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moisture content or possibly the methods of measuring soil
adhesion (Yang et al., 1985).
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 6-14 and the recommendations for
body surface area are summarized in Table 6-15. Table
6-16 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. 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 m, Chapter 14 of this report. For each
parameter, recommended values were derived for average
and upper percenrile values. Each of these considerations
are also discussed in more detail in U.S. EPA (1992).
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 90th percentile values for adult
skin surface area provided in U.S. EPA (1992) are:
Water Contact
50th 95th
Bathing and Swimming 20,000 cm2 23,000 cm2
Soil Contact
Outdoor Activities
50th
5,000 cm2
95th
5,800 cm2
6.4.2. Dermal Ao"herence to Soil
Table 6-18 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 over long periods of time. 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.
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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
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.
The studies all have uncertainties, but suggest the
following generalizations about soil adherence:
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), this
document recommends a new approach for estimating soil
adherence to skin. First use Table 6-12 to select the
activity which best approximates the exposure scenario of
concern. Next, use Table 6-13 to select soil loadings on
exposed skin surfaces which correspond to the activity of
interest. This table contains soil loading estimates for
various body parts. The estimates were derived from soil
adherence measurements of body parts of individuals
engaged in specific activities described in Table 6-12.
These results provide the best estimate of central loadings,
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 6-17.
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, 1992).
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, 1992).
6.5. REFERENCES FOR CHAPTER 6
American Industrial Health Council (AIHC). (1994)
Exposure factors sourcebook. AIHC, Washington,
DC.
Boyd, E. (1935) The growth of the surface area of the
human body. Minneapolis, Minnesota: University
of Minnesota Press.
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Brainard, J.B.; Bunnaster, D.E. (1992) Bivariate
distributions for height and weight, men and
women in the United States. Risk Analysis
!2(2):267-275.
Brorby, G.; FinleyB. (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 Associats, 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, G.K. (1989) Soil
adherence to human skin. Bull. Environ.
Contain. 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.). CEBA-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. The Journal of
Pediactrics. 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.
Kissel, J.; Richter, K.; Duff, R.; Fenske, R. (1996a)
Factors Affecting Soil Adherence to Skin in
Hand-Press Trials. In press: Bull. Environ.
Contamin. Toxicol.
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.; Leffmgwell, 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.;
Bomschein, 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.;Kalsbeck, 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.
"Sendroy, J.; Cecchini, L.P. (1954) Determination of
human body surface area from height and weight.
Journal of Applied Physiology. 7(1):3-12.
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. U.S. EPA No.
600/8-85-010. Available from: NTIS, Springfield,
VA. PB85-242667.
U.S. EPA. (1989) Risk assessment guidance for
superfund. Human health evaluation manual: Part
A. Interim Final. Washington, DC: Office of
Page
6-10
Exposure Factors Handbook
August 1996
-------
Volume I - General Factors
Chapter 6 - Dermal
Solid Waste and Emergency Response. NTIS:
PB-90-155581.
U.S. EPA. (1992) 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.
VanGraan, 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 1996
Page
6-11
-------
Volume I - General Factors
Chapter 6 - Dermal
TS.WP fi-1 Summary of Equation Parameters for Calculating Adult Body Surface Area
,
Body Part
Head
Female
Male
Trunk
Female
Male
Upper Extremities
Fcnislc
Male
Amu
Female
Male
Upper Arms
Male
Male
Hands
Fcroslc
Male
Lower Extremities'
Leas
L*cga
Thighs
Lower legs
Feel
* SA = a. W*1 H**
N
57
32
57
32
57
48
13
32
6
6
12*
32
105
45
45
45
45
W « Weight in kilograms; H =
SA i Surface Area
Equation for surface areas (m2)
a.
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
Height in centimeters;
W"
0.124
0.339
0.647
0.808
0.341
0.466
0.201
0.616
0.741
0.858
0.412
0.573
0.458
0.542
0.629
0.416
0.372
H12
0.189
-0.0950
-0.304
-0.0131
0.175
0.524
0.748
0.561
-1.40
-0.895
0.0274
-0.218
0.696
0.626
0.379
0.973
0.725
P = Level of significancej R2 =
P
0.01
0.01
0.001
0.001
0.001
0.001
0.01
0.001
0.25
0.05
0.1
0.001
0.001
0.001
0.001
0.001
0.001
R2
0.302
0.222
0.877
0.894
0.526
0.821
0.731
0.892
0.576
0.897
0.447
0.575
0.802
0.780
0.739
0.727
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
Coefficient of determination;
: S.E. = Standard error; N = Number of observations .
fc One observation for a female whose body weight exceeded the 95 percentile was not used.
0 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 1996
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-2. Surface Area of Adult Males in Square Meters
Percentile
Total
Head
Trunk*
Upper extremities
Arms
Forearms
Hands
Lower extremities
Legs
Thighs
Lower legs
Feet
5
1.66
0.119
0.591
0.321
0.241
0.106
0.085
0.653
0.539
0.318
0.218
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
1.76
0.123
0.643
0.340
0.259
0.115
0.090
0.692
0.576
0.341
0.232
0.120
25
1.82
0.124
0.674
0.350
0.270
0.121
0.093
0.715
0.597
0.354
0.240
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
0.3 14C
0.144°
0.105
0.810
0.686°
0.41 lc
0.272
0.138
85
2.14
0.138
0.851
0.408
0.328°
0.151'
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°
0.354C
0.166°
0.117
0.888°
0.762°
0.463'
0.299
0.149
S.E.'
0.00374
0.0202
0.0118
0.00101
0.00387
0.0207
0.0187
0.00633
0.0130
0.0149
0.0149
0.0147
* Standard error for the 5-95 percentile of each body part.
-.* Trunk includes neck.
° Percentile estimates exceed the maximum measured values upon which the equations are based.
Source- US 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
0.07T7
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
1.98
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
I
S.E.1
0.00374
0.00678
0.00567
0.00833
0.009%
0.0172
0.00633
0.0130
0.0149
0.0149
0.0147
1 Standard error for the 5-95 percentile of each body part.
* 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 1996
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
Anns
Upper arms
Forearms
Hands
Lower extremities
Legs
Thighs
Lower legs
Feet
TOTAL
' standard deviation.
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
1.94
(sd)1
(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)'
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
1.69
Women
(sd)
(0.00625)
(0.0712)
(0.0241)
(0.0129)
-
-
(0.00510)
(0.0675)
(0.0515)
(0.0333)
(0.0240)
(0.00903)
(0.00374)°
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
2.09"
'number of observations.
* median (standard error).
* 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
N"
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.)*
(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.
v Standard deviation.
Source: Adapted from U.S.
EPA, 1985.
Page
6-14
Exposure Factors Handbook
August 1996
-------
Volume I - General Factors
Chapter 6 - Dermal
Table 6-6. Total Body Surface Area of Male Children in Square Meters'
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 < IS
IS < 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
0.606
0.658
0.721
0.788
0.832
0.897
0.966
1.04
1.06
.13
.24
.39
.49
.59
.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
1.66
1.69
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
.23
.34
.47
.61
.70
.76
.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
85
0.643
0.719
0,796
0.864
0.957
1.01
1.12
1.16
1.35
1.47
1.52
1.67
1.78
1.84
1.98
1.96
0.817
1.05
1.36
1.73
1.94
90
0.661
0.729
0.809
0.895
1.01
1.06
1.17
1.25
1.40
.53
.62
.75
.84
.90
2.03
2.03
0.842
1.09
1.42
1.77
2.01
95
0.682
0.764
0.845
0.918
1.06
1.11
1.24
1.29
1.48
1.60
1.76
1.81
1.91
2.02
2.16
2.09
0.876
1.14
1.52
1.85
2 11
* 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 n data.
Source: U.S.
EPA. 1985.
Table 6-7. Total Body Surface Area of Female Children in Square Meters'
Percentile
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
3
6
9
12
15
a
b
Age (yr)b
< 3
<4
< 5
< 6
< 7
< 8
< 9
< 10
< 11
< 12
< 13
< 14
< 15
< 16
< 17
< 18
< 6
< 9
< 12
< 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
.01
.09
.19
.28
.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
1.12
1.24
1.32
1.39
1.43
1.48 '
1.51
0.630
0.804
1.03
1.30
1.47
25
0.557
0.607
0.666
0.735
0.791
0.854
0.932
1.01
1.10
1.16
1.27
1.38
1.45
1.47
1.53
1.56
0.654
0.845
1.06
1.37
1.51
50
0.579
0.649
0.706
0.779
0.843
0.917
.00
.06
.17
.30
.40
.48
.55
.57
1.60
1.63
0.711
0.919
1.16
1.48
1.60
75
0.610
0.638
0.758
0.830
0.914
0.977
1.05
.14
.29
.40
.51
.59
.66
.67
1.69
1.73
0.770
1.00
1.31
1.61
1.70
85
0.623
0.707
0.777
0.870
0.961
.02
.08
.22
.34
.50
.62
.67
1.74
1.72
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 H 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 1996
Page
6-15
-------
r
Volume I - General Factors
Chapter 6 - Dermal
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co in o co in
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-------
Volume I - General Factors
Chapter 6 - Dermal
Aee (vrs.)
0-2
2.1 - 17.9
i 18
All ages
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
0.0200-0.1142
Descriptive Statistics for Surface Area/Body Weight (SA/BW) Ratios (mVkg)
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
0.0299
Percentiles
50
0.0617
0.0422
0.0286
00495
75
0.0719
0.0454
0.0302
0.0631
90
0.0784
0.0501
0.0316
00740
95
0.0846
0.0594
0.0329
0.0788
* Standard deviation.
" Standard error of the mean.
Source: Philliosetal.. 1993.
Table6-10. Statistical Results for Total Body Surface Area Distributions (of)
Mean
Median
Mode
Standard Deviation
Skewness
Kurtosis
Mean
Median
Mode
Standard Deviation
Skewness
Kurtosis
Source: Murray and Burmaster.
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
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
DuBois and DuBois
1.94
1.94
1.90
0.17
0.23
3.02
Women
DuBois and DuBois
1.69
1.67
1.60
0.18
0.77
4.01
Costeff
1.89
1.89
1.90
0.16
0.04
2.92
Costeff
1.71
1.68
1.66
0.21
0.69
3.52
Table 6- 1 1 . Skin Coverage with Soil by Body Part and Activity
Exposure Trial
Children playing in wet soil
Adults transplanting plants in wet soil
Pipe laying trials
dry soil, 15-30 min. duration
Pipe laying trials
wet soil. 15-30 min. duration
N"
24
28
3
3
4
3
Hands
80
70
36-52 (M)b
54-62 (W)b
75-82 (M)
56-86 (W)
Percent Skin Coverage by Body Part
Na Lower lees N" Forearms N" Face
18
24
3
3
4
3
20
10
6-12 (M)
15-33 (W)
12-25 (M)
4-14 fW)
18 10 13 0
26 0 15 0
_ _ _ Q
0
- - - 0
_ __ _ Q
" N = number of subjects
b M = men; W = women
Source: Kissel etal.. 1995.
Exposure Factors Handbook
August 1996
Page
6-17
-------
Volume I - General Factors
Chapter 6 - Dermal
0.25
0.2 j
E«.!
; 0.1
0.05
0
12
Infant SA/BW Ratios: Lognorm(0.0641,0.0114)
Expacted Value c
6.410E-02
13
All Ages SA/BW Ratios: Normal{0.0489,0.0187)
Expactad Value *
4.890E-02
14
Adult SA/BW Ratios: NormaKO.0284,0.0028)
Expected Value »
2.840E-02
17
17
22
37
42
27 32
Values in 10* -3
Figure 6-1. SA/BW Distributions for Infants, Adults, and All Ages Combined
Source: Phillips et al., 1993.
Page
6-18
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 6 - Dermal
.00
1.00
1.00
Surface Area: Men
Frequency Distribution
1.50
2.00
2.50
Area in m2, nsS.OOO, LHS
Surface Area: Women
Frequency Distribution '
1.50
2.00
2.50
424
318
Tl
"1
(D
to
3
n
uz
3.00
465
3.00
Area in m2, n=5,000, LHS
Figure 6-2. Frequency Distributions for the Surface Area of Men and Women
Source: Murray and Burmaster, 1992.
Exposure Factors Handbook
August 1996
Page
6-19
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Volume I - General Factors
Chapter 6 - Dermal
I
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 6 - Dermal
Table 6-13. Mean Soil Adherence by Activity and Body Region
Activity
Outdoor
Soccer No. 1
Soccer No. 2
Soccer No. 3
Grounds Keeper No. I
Grounds Keeper No. 2
Grounds Keeper No. 3
Grounds Keeper No. 4
Grounds Keeper No. 5
Irrigation Installers
Rugby Players
Farmers No. 1
Farmers No. 2
Reed Gatherers
Kids-in-mud No. I
Kids-in-mud No. 2
Indoor
Tae Kwon Do
Greenhouse Workers
N"
8
&
7
2
5
7
7
8
6
8
4
6
4
6
6
7
2
Hands
0.11
0.066-0.18
0.035
0.011-0.11
0.019
0.013-0.028
0.15
0.098
0.040-0.24
0.030
0.014-0.065
0.046
0.025-0.082
0.032
0.021-0.049
0.19
0.12-0.31
0.40
0.2fr0.62
0.41
0.20-0.84
. 0.47
0.33-0.69
0.66
0.25-1.7
35
15-84'
58
24-140
0.0062
0.0036-0.011
0.043
Arms
0.011
0.0058-0.019
0.0043
0.0022-0.0083
0.0029
0.0014-0.0060
0.0050
0.0021
0.00065-0.0067
0.0023
0.0012-0.0043
' 0.014
0.0079-0.023
0.023
0.0098-0.052
0.018
0.0053-0.062
0.27
0.18-0.40
0.059
0.0094-0.37
0.13
0.056-0.29
0.036
0.011-0.12
11
1.7-73
11
2.6-44
0.0019
0.0006-0.0062
0.0064
Body Part (mg/cm2)
Legs
0.031
0.010-0.093
0.014
0.0034-0.055
0.0081
0.0052-0.013
-
0.0012
0.00063-0.0021
0.0009
0.00044-0.0019
0.0008
0.00035-0.0018
0.0010
0.0008-0.0014
0.0054
0.0029-0.010
0.36
0.23-0.55
0.0059
0.0012-0.028
0.037
0.0088-0.16
0.16
0.0047-5.4
36
18-75
9.5
4.0-23
0.0020
0.0011-0.0034
0.0015
Face
0.012
0.0083-0.016
0.016
0.011-0.022
0.012
0.0078-0.018
0.0021
0.010
0.0045-0.023
0.0047
0.0021-0.010
0.0029
0.0018-0.0044
0.0037
0.0019-0.0073
0.0063
0.0047-0.0086
0.059
0.026-0.13
0.018
0.011-0.030
0.041
0.013-0.13
-
-
-
-
0.0051
Feet
-
-
-
0.018
-
0.0041
0.018
-
-
-
-
-
0.63
0.028-14
24
6.2-9.3
6.7
0.47-94
0.0024
0.0012-0.0049
-
" N = number of subjects
Source: Kissel etal., 1995
Exposure Factors Handbook
August 1996
Page
6-21
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Volume I - General Factors
Chapter 6 - Dermal
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Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 6 - Dermal
Table 6-15. Summary of Recommended Values for Skin Surface Area
Surface Area
Adults
Whole body and body parts
Bathing/swimming
Outdoor soil contact
Children
Whole body
Body nans
Central Tendency
see Table 6-4
20,000 cm2
5,000 as?
Uooer Percentile
see Tables 6-2 and 6-3
23,000cm2
5,800cm2
see Tables 6-6 and 6-7
see Table 6-8
Multiple Percentiles
see Tables 6-2 and 6-3
see Tables 6-6 and 6-7
see Table 6-8
Table 6-16. Confidence in Body Surface Area Measurement 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
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
Rationale
Peer reviewed journal articles
EPA report was peer reviewed before distribution
Journals - wide circulation
EPA report - available from National Technical Information Service
Experimental methods 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
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
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
Rating
High
High
High
High
High
Low
Low
NA
High
Medium
Low
High
Low/Medium
Medium
Medium
High
Exposure Factors Handbook
August 1996
Page
6-23
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Volume I - General Factors
Chapter 6 - Dermal
Table 6-17. Confidence in Dermal Adherence Recommendations
Considerations
Study Elements
Level of Peer Review
Accessibility
Reprodueibility
Focus on factor of interest
Dtu 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
Othtr Elements
Number of studies
* Agreement among researchers
Overall Hating
Rationale
Peer reviewed journal articles
Articles published in widely circulated journals
Reports clearly describe experimental method
Studies have goal to determine soil adherence to skin
Experiments conducted in the U.S.
Experiments directly measure soil adherence to skin; exposure and dose of
chemicals in soil measured indirectly or estimated from soil contact
New studies in rapidly changing area
Seasonal factors may be important but have not been studied adequately
Skin rinsing technique is a widely employed procedure
Studies, limited to Seattle, WA, may not be representative of other locales
Variability in soil adherence is affected by many factors including soil
properties, activity and individual behavior patterns
Studies attempt to measure soil adherence in selected activities and
conditions to identify important activities and groups
Experimental error is low and well controlled but application of results to
other similar activities may be subject to variation
Controlled experiments being conducted by a few laboratories; activity
patterns being studied by only one laboratory
Results from key study consistent with earlier estimates from relevant
studies and assumptions, but limited to hand data
Limited data is difficult to extrapolate from experiments and field
observations to general conditions
Rating
High
High
High
High
High
High
High
Medium
High
Low
Low
High
Low/High
Medium
Medium
Low
Page
6-24
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 6 - Dermal
KEY STUDIES
Kissel etal., 1995
REDEVANT STUDIES
Driver etal., 1989
Lepowetal., 1975
Que Hee et at, 1985
Roelsetal., 1980
Sedman, 1989
Yang etal., 1989
Table 6-18. Summary of Soil
Size Fraction Soil Adherence
(Mm) (mg/cm2)
Varions
< ISO 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
Adults
Adults
Adults
10 children
1 adult
661 children
Children
Rats
Comments
Data presented for soil loadings by body pan.
See Table 6-13.
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 (1980) and average
surface of hand of an 11 year old; used estimates
of Lepow, Roels, and Que Hee to develop mean
of 0.5 rag/cm2.
Rat skin "monolayer" (i.e., minimal amount of
soil covering the skin); in vitro and in vivo
experiments.
Exposure Factors Handbook
August 1996
Page
6-25
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Volume I - General Factors
Chapter 6 - Dermal
APPENDIX 6A
Formulae for Total Body Surface Area
Exposure Factors Handbook
August 1996
Page
6A-1
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Volume I - General Factors
Chapter 6 - Dermal
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):
SA = KW273
(Eqn. 6A-1)
where:
SA = surface area in square meters;
W = weight in kg; and
K = constant.
While (he 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 = a,, Ha' W"2
(Eqa 6A-2)
where:
SA = surface area in square meters;
H = height in centimeters; and
W = weight in kg.
The values of a,, (0.007182), a, (0.725), and ^ (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, a! = 0.500, and % =
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 1996
Page
6A-3
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Volume I - General Factors
Chapter 6 - Dermal
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: a,, = 0.02350, ^ = 0.42246, and a2 =
0.51456. Hence, their equation for predicting surface area (SA) is:
SA = 0.02350 H042246 W0-51456
(Eqn. 6A-3)
or in logarithmic form:
lnSA= -3.75080 + 0.42246 InH + 0.51456 In W
(Eqn. 6A-4)
where:
SA
H
W
= surface area in square meters;
= height in centimeters; and
= weight in kg.
This prediction explains more than 99 percent of the variations in surface area among die 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:
SA = an H" W,*2 e, (Eqn. 6A-5)
or in logarithmic form:
where:
Sai
Hi
Wi
ln(SA)i =
+ a. In H +
+
(Eqn. 6A-6)
surface area of the i-th individual (m2);
height of the i-th individual (cm);
weight of the i-th individual (kg);
Page
6A-4
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 6 - Dermal
BQ, a,, anda2 = parameters to be estimated; and
e; = a random error term with mean zero and constant variance.
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), a, = 0.417 (0.054), ^ = 0.517 (0.022)
SA = 0.0239 H0-417 W0-517
InSA = -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
z 5- <20 years old
z 20 years oldl
Number
of persons
401
229
42
30
a,,
0.02350
0.02667
0.03050
0.01545
a,
0.42246
0.38217
0.35129 J
0.54468
22
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 die values for each age group for subjects at the 3rd, 50tii, and 97th percentiles of weight and height.
Nearly all differences in surface area estimates were less than 0.01 square meter, and die largest difference was 0.03
Exposure Factors Handbook
August 1996
Page
6A-5
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Volume I - General Factors
Chapter 6 - Dermal
m2 for an 18-year-old at the 97th percentile. The authors concluded that there is no advantage in using separate
values of BO, a,, and s^ by age interval.
Haycock et al. (1978) without knowledge of the work by Gehan and George (1970), developed values for
the parameters EO, a,, 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: a,, = 0.024265, ^ = 0.3964, and z^. =
0.5378. The result was the following equation for estimating surface area:
expressed logarithmically as:
SA = 0.024265 H03964 W05378
InSA = In 0.024265 + 0.3964 InH + 0.5378 In W
(Eqa 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:
follow:
InSA = Inao + a, InH + a2 In W (Eqn. 6A-11)
The values for aa, 3j, and &z obtained by the various authors discussed in this section are presented to
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 et al. (1978)
Number
of persons
9
231
401
81
Bo
0.007184
0.01787
0.02350
0.024265
a,
0.725
0.500
0.42246
0.3964
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6A.-6
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 6 - Dermal
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. 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 1996
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6A-7
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Volume I - General Factors
Chapter 7 - Body Weight Studies
7. BODY WEIGHT STUDIES
There are several physiological factors needed to
calculate potential exposures which include skin surface
(see Volume I, Chapter 6), life expectancy (see Volume I,
Chapter 8), and body weight. The average daily dose is
based on the average body weight over the exposure period. i
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).
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.
The classifications of these studies are 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
NCHS - 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 the
National Center for Health Statistics (NCHS) through the
second National Health and Nutrition Examination Survey
(NHANES II). NHANES H 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 n 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
idults, by age, and their standard deviations are presented
n Table 7- 1 for men, women, and both sexes combined.
Mean body weights and standard deviations for children,
iges 6 months to 19 years, are presented in Table 7-2 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 7-3, and for females in
rable 7-4. Data for children by age are presented in Table
7-5 for males, and for females in Table 7-6.
Table 7- 1 . Body Weights of Adults' (kilograms)
Men and
Men Women Women
Mean Std. Mean Std. Mean (kg)
(kg) Dev. (kg) Dev.
I8<25 73.8 12.7 60.6 11.9 67.2
25<35 78.7 13.7 64.2 15.0 71.5
35<45 80.9 13.4 67.1 15.2 74.0
45<55 80.9 13.6 68.0 15.3 74.5
55<65 78.8 12.8 67.9 14.7 73.4
65<75 74.8 12.8 66.6 13.8 70.7
I8<75 78.1 13.5 65.4 14.6 71.8
Note: 1kg = 2.2046 pounds.
' Includes clothing weight, estimated as ranging from 0.09 to 0.28
kilogram.
Source: Adapted from National Center for Health Statistics (NCHS), 1987.
Table 7-2. Body Weights of Children" (kilograms)
Boys and
Boys Girls Girls
Age Mean Std. Mean Std. ' Main
(kg) Dev. (kg) Dev. (kg)
6-11 months 9.4 1.3 8.8 1.2 9.1
lyear 11.8 1.9 10.8 1.4 11.3
2 years 13.6 1.7 13.0 1.5 13.3
3 years 15.7 2.0 14.9 2.1 15.3
4 years 17.8 2.5 17.0 2.4 17.4
5 years 19.8 3.0 19.6 3.3 19.7
6 years 23.0 4.0 22.1 4.0 22.6
7 years 25.1 3.9 24.7 5.0 24.9
8 years 28.2 6.2 27.9 5.7 28.1
9 years 31.1 6.3 31.9 8.4 31.5
10 years 36.4 7.7 36.1 8.0 36.3
11 years 40.3 10.1 41.8 10.9 41.1
12 years 44.2 10.1 46.4 10.1 45.3
13 years 49.9 12.3 50.9 11.8 50.4
14 years 57.1 11.0 54.8 11.1 56.0
15 years 61.0 11.0 55.1 9.8 58.1
16 years 67.1 12.4 58.1 10.1 62.6
17 years 66.7 11.5 59.6 11.4 63.2
18 years 71.1 12.7 59.0 11.1 65.1
19 years 71.7 11.6 60.2 11.0 66.0
Note: 1 kg = 2.2046 pounds.
" Includes clothing weight, estimated as ranging from 0.09 to 0.28
kilogram.
Source: Adapted from National Center for Health Statistics (NCHS),
1987.
Exposure Factors Handbook Page
August 1996 7-1
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Page
7-5
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Volume I - General Factors
Results shown in Tables 7-3 and 7-4 indicate that the
mean welgnt lor aouit maies is /o.i Kg «inu iui auuu
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 7-2, 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 1 1 kg.
7.2. RELEVANT BODY WEIGHT STUDIES
Burmaster et al. (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 n,
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 H 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
7-7 and 7-8. These data can be used for further analyses of
body weight distribution (i.e., application of Monte Carlo
analysis).
Brainard and Burmaster - 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
Table 7-7. Statistics for Probability Plot Regression Analyses
Female's Body Weights 6 Months to 20 Years of Age
Lognormal Probability Plots
Aee Linear Curve
^' °i'
6 months to 1 year 2.16 0.145
1 to 2 years 2.38 0.128
2 to 3 years 2.56 0.112
3 to 4 years 2.69 0.137
4 to 5 years 2.83 0.133
5 to 6 years 2.98 0.163
6 to 7 years 3.10 0.174
7 to 8 years 3.19 0.174
8 to 9 years 3.31 0.156
9 to 10 years 3.46 0.214
10 to 11 years 3.57 0.199
11 to 12 years 3.71 0.226
12 to 13 years 3.82 0.213
13 to 14 years 3.92 0.216
14 to 15 years 3.99 0.187
15 to 16 years 4.00 0.156
16 to 17 years 4.06 0.167
17 to 18 years 4.08 0.165
18 to 19 years 4.07 0.147
19 to 20 years 4.10 0.149
^2, o2 - correspond to the mean and standard deviation, respectively, of
the lognormal distribution of body weight (kg).
Table 7-8. Statistics for Probability Plot Regression Analyses
Male's Body Weights 6 Months to 20 Years of Age
Lognormal Probability Plots
Aae Linear Curve
U,' °2m
6 months to 1 year 2.23 0.132
1 to 2 years 2.46 0.119
2 to 3 years 2.60 0.120
3 to 4 years 2.75 0.114
4 to 5 years 2.87 0.133
5 to 6 years 2.99 0.138
6 to 7 years 3.13 0.145
7 to 8 years 3.21 0.151
8 to 9 years 3.33 0.181
9 to 10 years 3.43 0.165
10 to 11 years 3.59 0.195
11 to 12 years 3.69 0.252
12 to 13 years' 3.78 0.224
13 to 14 years 3.88 0.215
14 to 15 years 4.02 0.181
15 to 16 years 4.09 0.159
16 to 17 years 4.20 0.168
17 to 18 years 4.19 0.167
18 to 19 years 4.25 0.159
19 to 20 years 4.26 0.154
1*2, a2 - correspond to the mean and standard deviation,
respectively, of the lognormal distribution of body weight (kg).
page
Exposure Factors Handbook
August 1996
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Volume I- General Factors
Chapter 7 - Body Weight Studies
Health Service and fit bivariate distributions to the tabulated
values for men and women, separately.
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 n 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 10 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.
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 study 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 7-9. The recommendations for body
weight are summarized in Table 7-10. Table 7-11 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 7-1.
The mean values for each age group in Table 7-1 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
7-1 can be used. If percentile data are needed or if race is a
factor, Tables 7-3 and 7-4 can be used to select the
appropriate data for percentiles or mean values. For
children, appropriate mean values for weights may be
selected from Table 7-2. If percentile values are needed,
these data are presented in Table 7-5 for male children and
in Table 7-6 for female children. This recommended value
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.
7.4. REFERENCES FOR CHAPTER 7
American Industrial Health Council (AIHC). (1994)
Exposure factors sourcebook. AIHC, Washington,
DC.
Exposure Factors Handbook
August 1996
Page
7-7
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Volume I - General Factors
Chapter 7 - Body Weight Studies
Brainard, J.; Burmaster, D. (1992) Bivariate distributions
for height and weight of men and women in the
United States. Risk Analysis 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.
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.
Table 7-9. Summary of Body Weight Studies
Study
Number of Subjects
Population
Comments
KEYSTUDY
NCHS, 1987
(NHANESII)
RELEVANT STUDIES
20,322
U.S. general
population
Brainard and Burmaster, 1992 12301 (5,916 men and 6,588 U.S. general
women) population
Burmaster etal., 1994
8,458 (4,079 females and U.S. general
4,379 males) population
Based on civilian non-institutionalized population aged 6
months to 74 years. Response 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.
Adults
,,cMte
71.8 kg (See Table 7-1)
See Table 7-2
See Tables 7-3 and 7-4
See Tables 7-5 and 7-6
Multiple Percentiles
See Tables 7-3 and 7-4
See Tables 7-5 and 7-6
Page
7-8
Exposure Factors Handbook
August 1996
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Volume I - General Factors
Chapter 7 - Body Weight Studies
Table 7-11. Confidence in Body Weight Recommendations
Considerations
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 Ratine
Rationale
NHANES II was the source of data for this study. This is a published study
which received a high level of peer review.
The study is widely available to the public.
Results can be reproduced by analyzing NHANES II data.
The study focused on body weight, the exposure factor of interest.
Data represents the U.S. population.
The primary data was generated from the NHANES II study, thus this data
is secondary.
The study was published in 1987.
The study included data collected from 1976 to 1980. Body weight
measurements were taken at various times of the day and at different seasons
of the year.
Direct body weight were measured. 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 sample size consisted of 28,000 persons.
Data collected focused on the U.S. population.
The study characterized variability regarding age, sex and race (for Blacks,
Whites and total populations). The study also sampled persons with low
income.
There are no apparent biases in the study design.
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.
One
The agreement is 100% since the recommendation is based on one key
study.
High
High
High
High
High
Medium
Medium
High
High
High
High
High
High
High
Low
High
High
Exposure Factors Handbook
August 1996
Page
7-9
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Volume I - General Factors
Chapter 8 - Lifetime
8. LIFETIME
The length of an individual's life is an important
factor to consider when evaluating cancer risk because the
dose estimte 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.
8.2. RECOMMENDATIONS
Current data suggest that 75 years would be an
appropriate value to reflect the average life expectancy 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 8-2 presents the
confidence rating for life expectancy recommendations.
This recommended value is different than the 70
years 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 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).
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 1996
Page
8-1
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Volume I - General Factors
Chapter 8 - Lifetime
Table 8-1.
Exnectation of Life at Birth, 1970 to 1993,
TOTAL
YEAR
1970
1975
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
70.8 67.1
72.6 68.8
73.7 70.0
74.1 70.4
743 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
755 72.1
Projections? 1995 763 72.8
a
h
e
Source:
2000 76.7 73.2
2005 773 73.8
2010 77.9 74.5
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
Total
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
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
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
and Projections. 1995 to 2010a
BLACK AND OTHER"
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
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
Excludes deaths of nonresidents of the United States
Racial denotations were not described in the data source.
Based on middle mortality assumptions; for details, see U.S. Bureau of the Census, Current Population Reports, Series P-25, No.
Bureau of the Census, 1995.
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 1996
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Volume I - General Factors
Chapter 8 - Lifetime
J&M
Table 8-2. 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 is published and has received extensive peer review. High
The study was widely available to the public (Census data). High
Results can be reproduced by analyzing Census data. High
Statistical data on life expectancy were published in this study. High
The study focused on the U.S. population. High
Primary data was analyzed. High
The study was published in 1995 and discusses life expectancy trends from High
1970 to 1993. The study has also made projections for 1995 until the year
2010.
The data analyzed was collected over a period of years. High
Census data is collected and analyzed over a period of years. High
This study was based on U.S. Census data thus the population study size is High
expected to be greater than 100.
The data is representative of the U.S. population. High
Data was averaged by gender and race but only for Blacks and Whites; no Medium
other nationalities were represented within the section.
There are no apparent biases. . High
Measurement error may be attributed to portions of the population that Medium
avoid or provide misleading information on census surveys.
One Low-
Recommendation was based on only one study, but it is widely accepted. High
HIGH
Exposure Factors Handbook
August 1996 ._
Page
8-3
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