Office of Research and DevelopmentNational Center for Environmental AssessmentWashington, DC 20460Official BusinessPenalty for Private Use$300PRESORTED STANDARDPOSTAGE & FEES PAIDEPAPERMIT NO. G-35 EPA/600/R-06/096F | September 2008 | www.epa.gov/ ncea 

# Child-Specific Exposure Factors Handbook EPA/600/R-06/096FEPA/600/R-06/096F | September 2008 | www.epa.gov/nceaChild-SpecificExposure FactorsHandbookNational Center for Environmental AssessmentOffice of Research and Development, Washington, DC 20460

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DISCLAIMER

This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Mention of trade names or commercial products does not constitute endorsement or

recommendation for use.



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TABLE OF CONTENTS

1 INTRODUCTION

............................................................... . 1-1

1.1 PURPOSE

.............................................................. . 1-1

1.2 INTENDED AUDIENCE

................................................... . 1-1

1.3 BACKGROUND

......................................................... . 1-1

1.4 SELECTION OF STUDIES FOR THE HANDBOOK

............................. . 1-3

1.4.1 General Assessment Factors

.......................................... . 1-4

1.4.2 Selection Criteria

.................................................. . 1-4

1.5 APPROACH USED TO DEVELOP RECOMMENDATIONS FOR EXPOSURE

FACTORS

.............................................................. . 1-6

1.6 SUGGESTED REFERENCES FOR USE IN CONJUNCTION WITH THIS HANDBOOK

. . 1-8

1.7 THE USE OF AGE GROUPINGS WHEN ASSESSING EXPOSURE

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

1.8 CONSIDERING LIFESTAGE WHEN CALCULATING EXPOSURE AND RISK

...... . 1-11

1.9 FUNDAMENTAL PRINCIPLES OF EXPOSURE ASSESSMENT

.................. . 1-11

1.9.1 Dose Equations

................................................... . 1-12

1.9.2 Use of Exposure Factors Data in Probabilistic Analyses

.................... . 1-14

1.10 CUMULATIVE EXPOSURES

.............................................. . 1-15

1.11 ORGANIZATION

....................................................... . 1-15

1.12 REFERENCES FOR CHAPTER 1

........................................... . 1-16

2 VARIABILITY AND UNCERTAINTY

............................................... . 2-1

2.1 VARIABILITY VERSUS UNCERTAINTY

..................................... . 2- 1

2.2 TYPES OF VARIABILITY

................................................. . 2-2

2.3 ADDRESSING VARIABILITY

.............................................. . 2-3

2.4 TYPES OF UNCERTAINTY

................................................ . 2-4

2.5 REDUCING UNCERTAINTY

............................................... . 2-4

2.6 ANALYZING VARIABILITY AND UNCERTAINTY

............................ . 2-4

2.7 PRESENTING RESULTS OF VARIABILITY AND UNCERTAINTY ANALYSIS

...... . 2-5

2.8 REFERENCES FOR CHAPTER 2

............................................ . 2-6

3 INGESTION OF WATER AND OTHER SELECT LIQUIDS

.............................. . 3-1

3.1 INTRODUCTION

........................................................ . 3-1

3.2 RECOMMENDATIONS

................................................... . 3-2

3.2.1 Water Ingestion from Consumption of Water as a Beverage and from Food

and Drink

........................................................ . 3-2

3.2.2 Water Ingestion while Swimming

...................................... . 3-2

3.3 DRINKING WATER INGESTION STUDIES

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

3.3.1 Key Drinking Water Ingestion Study

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

3.3.1.1 Kahn and Stralka, 2008

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



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TABLE OF CONTENTS (Continued)

3.3.2 Relevant Drinking Water Ingestion Studies

.............................. . 3-7

3.3.2.1 Levy et al., 1995

............................................ . 3-7

3.3.2.2 Heller et al., 2000

........................................... . 3-8

3.3.2.3 Sichert-Hellert et al., 2001

.................................... . 3-8

3.3.2.4 Sohn et al., 2001

............................................ . 3-9

3.3.2.5 Hilbig et al., 2002

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

3.3.2.6 Marshall et al., 2003a

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

3.3.2.7 Marshall et al., 2003b

....................................... . 3-11

3.3.2.8 Skinner et al., 2004

......................................... . 3-11

3.4 WATER INGESTION WHILE SWIMMING

................................... . 3-11

3.4.1 Dufour et al., 2006

................................................ . 3-11

3.5 REFERENCES FOR CHAPTER 3

........................................... . 3-12

NON-DIETARY INGESTION FACTORS

............................................. . 4-1

4.1 INTRODUCTION

........................................................ . 4-1

4.2 RECOMMENDATIONS

................................................... . 4-2

4.3 NON-DIETARY INGESTION - MOUTHING FREQUENCY STUDIES

.............. . 4-5

4.3.1 Key Studies of Mouthing Frequency

.................................... . 4-5

4.3.1.1 Zartarian et al.,1997a/Zartarian et al., 1997b/Zartarian et al., 1998

...... . 4-5

4.3.1.2 Reed et al., 1999

........................................... . 4-5

4.3.1.3 Freeman et al., 2001

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

4.3.1.4 Tulve et al., 2002

........................................... . 4-7

4.3.1.5 Black et al., 2005

........................................... . 4-7

4.3.1.6 Xue et al., 2007

............................................. . 4-8

4.3.2 Relevant Studies of Mouthing Frequency

................................ . 4-8

4.3.2.1 Davis et al., 1995

........................................... . 4-8

4.3.2.2 Lew and Butterworth, 1997

.................................... . 4-9

4.3.2.3 Tudella et al., 2000

......................................... . 4-10

4.3.2.4 AuYeung et al., 2004

....................................... . 4-10

4.3.2.5 Ko et al., 2007

............................................ . 4-11

4.4 NON-DIETARY INGESTION - MOUTHING DURATION STUDIES

............... . 4-12

4.4.1 Key Mouthing Duration Studies

...................................... . 4-12

4.4.1.1 Juberg et al., 2001

.......................................... . 4-12

4.4.1.2 Greene, 2002

............................................. . 4-12

4.4.2 Relevant Mouthing Duration Studies

.................................. . 4-13

4.4.2.1 Barr et al., 1994

........................................... . 4-13

4.4.2.2 Zartarian et al., 1997a/Zartarian et al., 1997b/Zartarian et al., 1998

.... . 4-14

4.4.2.3 Groot et al., 1998

.......................................... . 4-14

4.4.2.4 Smith and Norris, 2003/Norris and Smith, 2002

................... . 4-15

4.4.2.5 Au Yeung et al, 2004

....................................... . 4-16



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TABLE OF CONTENTS (Continued)

4.5 MOUTHING PREVALENCE

.............................................. . 4-17

4.5.1 Stanek et al., 1998

................................................ . 4-17

4.5.2 Warren et al., 2000

................................................ . 4-18

4.6 REFERENCES FOR CHAPTER 4

........................................... . 4-18

SOIL AND DUST INGESTION

.................................................... . 5-1

5.1 INTRODUCTION

........................................................ . 5-1

5.2 RECOMMENDATIONS

................................................... . 5-2

5.3 KEY AND RELEVANT STUDIES

........................................... . 5-7

5.3.1 Methodologies Used in Key Studies

.................................... . 5-7

5.3.1.1 Tracer Element Methodology

.................................. . 5-7

5.3.1.2 Biokinetic Model Comparison Methodology

....................... . 5-8

5.3.1.3 Survey Response Methodology

................................. . 5-8

5.3.2 Key Studies of Primary Analysis

....................................... . 5-9

5.3.2.1 Vermeer and Frate, 1979

..................................... . 5-9

5.3.2.2 Calabrese et al., 1989/Barnes, 1990/Calabrese et al., 1991

............ . 5-9

5.3.2.3 Van Wïjnen et al., 1990

..................................... . 5-10

5.3.2.4 Davis et al., 1990

.......................................... . 5-10

5.3.2.5 Calabrese et al., 1997a

...................................... . 5-11

5.3.2.6 Stanek et al. 1998/Calabrese et al., 1997b

........................ . 5-11

5.3.2.7 Davis and Mirick, 2006

..................................... . 5-12

5.3.3 Key Studies of Secondary Analysis

.................................... . 5-12

5.3.3.1 Wong, 1988/Calabrese and Stanek, 1993

........................ . 5-12

5.3.3.2 Hogan et al., 1998

.......................................... . 5-13

5.3.4 Relevant Studies of Primary Analysis

.................................. . 5-13

5.3.4.1 Dickins and Ford, 1942

...................................... . 5-14

5.3.4.2 Cooper, 1957

............................................. . 5-14

5.3.4.3 Barltrop, 1966

............................................. . 5-14

5.3.4.4 Bruhn and Pangborn, 1971

................................... . 5-14

5.3.4.5 Robischon, 1971

........................................... . 5-14

5.3.4.6 Binder et al., 1986

......................................... . 5-15

5.3.4.7 Clausing, et al., 1987

....................................... . 5-15

5.3.4.8 Smulian et al., 1995

........................................ . 5-16

5.3.5 Relevant Studies of Secondary Analysis

................................ . 5-16

5.3.5.1 Stanek et al., 2001a

......................................... . 5-16

5.3.5.2 Calabrese and Stanek, 1995

.................................. . 5-17

5.3.5.3 Stanek and Calabrese, 1995a

.................................. . 5-17

5.3.5.4 Calabrese and Stanek, 1992b

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

5.3.5.5 Calabrese et al., 1996

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

5.3.5.6 Stanek et al., 1999

......................................... . 5-19



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5.3.5.7 Stanek and Calabrese, 1995b

................................. . 5-19

5.3.5.8 Stanek and Calabrese, 2000

.................................. . 5-19

5.3.5.9 Stanek et al., 2001b

......................................... . 5-19

5.3.5.10 von Lindern et al., 2003

..................................... . 5-20

5.4 LIMITATIONS OF KEY STUDY METHODOLOGIES

.......................... . 5-20

5.4.1 Tracer Element Methodology

........................................ . 5-20

5.4.2 Biokinetic Model Comparison Methodology

............................. . 5-23

5.4.3 Survey Response Methodology

....................................... . 5-23

5.4.4 Key Studies: Representativeness of U.S. Population

....................... . 5-24

5.5 SUMMARY OF SOIL AND DUST INGESTION ESTIMATES FROM KEY STUDIES

. . . 5-25

5.6 REFERENCES FOR CHAPTER 5

........................................... . 5-26

6 INHALATION RATES

........................................................... . 6-1

6.1 INTRODUCTION

........................................................ . 6-1

6.2 RECOMMENDATIONS

................................................... . 6-2

6.3 KEY INHALATION RATE STUDIES

......................................... . 6-6

6.3.1 Brochu et al., 2006

................................................. . 6-6

6.3.2 U.S. EPA, 2006

................................................... . 6-6

6.3.3 Arcus-Arth and Blaisdell, 2007

....................................... . 6-8

6.3.4 Stifelman, 2007

................................................... . 6-9

6.3.5 Key Studies Combined

.............................................. . 6-9

6.4 RELEVANT INHALATION RATE STUDIES

.................................. . 6-9

6.4.1 International Commission on Radiological Protection (ICRP), 1981

........... . 6-9

6.4.2 U.S. EPA, 1985

.................................................. . 6-10

6.4.3 Linn et al., 1992

.................................................. . 6-10

6.4.4 Spier et al., 1992

................................................. . 6-11

6.4.5 Adams, 1993

.................................................... . 6-11

6.4.6 Layton, 1993

.................................................... . 6-12

6.4.7 Rusconi et al., 1994

............................................... . 6-14

6.4.8 Price et al., 2003

.................................................. . 6-14

6.5 REFERENCES FOR CHAPTER 6

........................................... . 6-15

7 DERMAL EXPOSURE FACTORS

.................................................. . 7-1

7.1 INTRODUCTION

........................................................ . 7-1

7.2 RECOMMENDATIONS

................................................... . 7-2

7.2.1 Body Surface Area

................................................. . 7-2

7.2.2 Adherence of Solids to Skin

.......................................... . 7-2

7.3 SURFACE AREA

....................................................... . 7-10

7.3.1 Key Body Surface Area Studies

...................................... . 7-10

7.3.1.1 U.S. EPA, 1985

........................................... . 7-10

7.3.1.2 U.S. EPA Analysis of NHANES 1999-2006 Data

.................. . 7-10



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7.3.2 Relevant Body Surface Area Studies

................................... . 7-10

7.3.2.1 Phillips et al., 1993

......................................... . 7-10

7.3.2.2 Wong et al., 2000

.......................................... . 7-11

7.4 ADHERENCE OF SOLIDS TO SKIN

........................................ . 7-11

7.4.1 Key Adherence of Solids to Skin Studies

............................... . 7-11

7.4.1.1 Kissel et al., 1996a

......................................... . 7-11

7.4.1.2 Holmes et al., 1999

......................................... . 7-12

7.4.1.3 Shoaf et al., 2005

.......................................... . 7-12

7.4.2 Relevant Adherence of Solids to Skin Studies

............................ . 7-13

7.4.2.1 Kissel et al., 1996b

......................................... . 7-13

7.4.2.2 Kissel et al., 1998

.......................................... . 7-13

7.5 REFERENCES FOR CHAPTER 7

........................................... . 7-14

APPENDIX 7A

...................................................................... . 7A-1

8 BODY WEIGHT STUDIES

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

8.1 INTRODUCTION

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

8.2 RECOMMENDATIONS

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

8.3 KEY BODY WEIGHT STUDY

.............................................. . 8-4

8.3.1 U.S. EPA analysis of NHANES 1999-2006 data

.......................... . 8-4

8.4 RELEVANT BODY WEIGHT STUDIES

...................................... . 8-4

8.4.1 National Center for Health Statistics, 1987

.............................. . 8-4

8.4.2 Burmaster and Crouch, 1997

......................................... . 8-5

8.4.3 U.S. EPA, 2000

................................................... . 8-5

8.4.4 Kuczmarski et al., 2002

............................................. . 8-5

8.4.5 Ogden et al., 2004

................................................. . 8-6

8.4.6 Freedman et al., 2006

............................................... . 8-6

8.4.7 Martin et al., 2007

................................................. . 8-7

8.4.8 Portier et al., 2007

................................................. . 8-7

8.4.9 Kahn and Stralka, 2008

............................................. . 8-8

8.5 RELEVANT FETAL WEIGHT STUDIES

...................................... . 8-8

8.5.1 Brenner et al., 1976

................................................ . 8-8

8.5.2 Doubilet et al., 1997

................................................ . 8-8

8.6 REFERENCES FOR CHAPTER 8

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

9 INTAKE OF FRUITS AND VEGETABLES

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

9.1 INTRODUCTION

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

9.2 RECOMMENDATIONS

................................................... . 9-2



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9.3 INTAKE STUDIES

....................................................... . 9-6

9.3.1 Key Fruits and Vegetables Intake Study

................................. . 9-6

9.3.1.1 U.S. EPA Analysis of CSFII 1994-96, 1998

....................... . 9-6

9.3.2 Relevant Fruit and Vegetable Intake Studies

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

9.3.2.1 USDA, 1999

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

9.3.2.2 Smiciklas-Wright et al., 2003

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

9.3.2.3 Fox et al., 2004

............................................ . 9-9

9.3.2.4 Ponza et al., 2004

........................................... . 9-9

9.3.2.5 Menella et al., 2006

........................................ . 9-10

9.3.2.6 Fox et al., 2006

............................................ . 9-10

9.4 CONVERSION BETWEEN WET AND DRY WEIGHT INTAKE RATES

............ . 9-10

9.5 REFERENCES FOR CHAPTER 9

........................................... . 9-11

PENDIX 9A

..................................................................... . 9A-1

INTAKE OF FISH AND SHELLFISH

............................................... . 10-1

10.1 INTRODUCTION

....................................................... . 10-1

10.2 RECOMMENDATIONS

.................................................. . 10-2

10.3 GENERAL POPULATION STUDIES

....................................... . 10-6

10.3.1 Key General Population Study

....................................... . 10-6

10.3.1.1 U.S. EPA 2002

........................................... . 10-6

10.3.2 Relevant General Population Studies

.................................. . 10-6

10.3.2.1 U.S. EPA, 1996

........................................... . 10-6

10.3.2.2 Moya et al., 2008

.......................................... . 10-7

10.4 MARINE RECREATIONAL STUDIES

....................................... . 10-8

10.4.1 Relevant Marine Recreational Studies

.................................. . 10-8

10.4.1.1 KCA Research Division, 1994

................................ . 10-8

10.4.1.2 Alcoa, 1998

.............................................. . 10-8

10.5 FRESHWATER RECREATIONAL STUDIES

.................................. . 10-9

10.5.1 Relevant Freshwater Recreational Studies

............................... . 10-9

10.5.1.1 West et al., 1989

.......................................... . 10-9

10.5.1.2 Benson et al., 2001

....................................... . 10-10

10.6 NATIVE AMERICAN STUDIES

.......................................... . 10-10

10.6.1 Relevant Native American Studies

................................... . 10-10

10.6.1.1 Columbia River Inter-Tribal Fish Commission (CRITFC), 1994

..... . 10-10

10.6.1.2 Toy et al., 1996

.......................................... . 10-12

10.6.1.3 Duncan, 2000

........................................... . 10-13

10.6.1.4 Polissar et al., 2006

....................................... . 10-13

10.7 SERVING SIZE STUDY

................................................. . 10-14

10.7.1 Smiciklas-Wright et al., 2002

...................................... . 10-14 10 

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10.8 OTHER FACTORS TO CONSIDER FOR FISH CONSUMPTION

................. . 10-14

10.8.1 Conversion Between Wet and Dry Weight

............................. . 10-14

10.8.2 Conversion Between Wet Weight and Lipid Weight Intake Rates

............ . 10-15

10.9 REFERENCES FOR CHAPTER 10

......................................... . 10-15

11 INTAKE OF MEATS, DAIRY PRODUCTS AND FATS

................................ . 11-1

11.1 INTRODUCTION

....................................................... . 11-1

11.2 RECOMMENDATIONS

.................................................. . 11-2

11.3 INTAKE STUDIES

...................................................... . 11-6

11.3.1 Key Meat and Diary Intake Study

..................................... . 11-6

11.3.1.1 U.S. EPA Analysis of CSFII 1994-96, 1998

...................... . 11-6

11.3.2 Relevant Meat and Dairy Intake Studies

................................ . 11-7

11.3.2.1 USDA, 1999a

............................................. . 11-7

11.3.2.2 Smiciklas-Wright et al., 2002

................................. . 11-8

11.3.2.3 Fox et al., 2004

............................................ . 11-8

11.3.2.4 Ponza et al., 2004

.......................................... . 11-9

11.3.2.5 Mennella et al., 2006

....................................... . 11-9

11.3.2.6 Fox et al., 2006

........................................... . 11-10

11.4 FAT INTAKE

......................................................... . 11-10

11.4.1 Key Fat Intake Study

............................................. . 11-10

11.4.1.1 U.S. EPA, 2007

.......................................... . 11-10

11.4.2 Relevant Fat Intake Studies

........................................ . 11-11

11.4.2.1 Cresanta et al., 1988; Nicklas et al., 1993; and Frank et al., 1986

..... . 11-11

11.4.2.2 CDC, 1994

.............................................. . 11-11

11.5 CONVERSION BETWEEN WET AND DRY WEIGHT INTAKE RATES

........... . 11-12

11.6 CONVERSION BETWEEN WET WEIGHT AND LIPID WEIGHT INTAKE RATES

. . . 11-12

11.7 REFERENCES FOR CHAPTER 11

......................................... . 11-1 3

APPENDIX 11A

.................................................................... . 11A-1

APPENDIX 11B

.................................................................... . 11B-1

12 INTAKE OF GRAIN PRODUCTS

................................................. . 12-1

12.1 INTRODUCTION

....................................................... . 12-1

12.2 RECOMMENDATIONS

.................................................. . 12-2

12.3 INTAKE STUDIES

...................................................... . 12-6

12.3.1 Key Grain Intake Study

............................................ . 12-6

12.3.1.1 U.S. EPA Analysis of CSFII 1994-96, 1998

............... . 12-6



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12.3.2 Relevant Grain Intake Studies

....................................... . 12-7

12.3.2.1 USDA, 1999

....................................... . 12-7

12.3.2.2 Smiciklas-Wright et al., 2002

.......................... . 12-8

12.3.2.3 Fox et al., 2004

.................................... . 12-8

12.3.2.4 Ponza et al., 2004

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

12.3.2.5 Mennella et al., 2006

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

12.3.2.6 Fox et al., 2006

................................... . 12-10

12.4 CONVERSION BETWEEN WET AND DRY WEIGHT INTAKE RATES

........... . 12-10

12.5 REFERENCES FOR CHAPTER 12

......................................... . 12-10

APPENDIX 12A

................................................................... . 12A-1

13 INTAKE OF HOME-PRODUCED FOODS

........................................... . 13-1

13.1 INTRODUCTION

....................................................... . 13-1

13.2 RECOMMENDATIONS

.................................................. . 13-1

13.3 KEY STUDY FOR INTAKE OF HOME-PRODUCED FOODS

..................... . 13-6

13.3.1 U.S. EPA Analysis of NFCS 1987-1988

................................ . 13-6

13.4 REFERENCES FOR CHAPTER 13

.......................................... . 13-8

APPENDIX 13A

................................................................... . 13A-1

APPENDIX 13B

................................................................... . 13B-1

14 TOTAL DIETARY INTAKE

...................................................... . 14-1

14.1 INTRODUCTION

....................................................... . 14-1

14.2 RECOMMENDATIONS

.................................................. . 14-1

14.3 KEY STUDY OF TOTAL FOOD INTAKE

.................................... . 14-4

14.3.1 U.S. EPA, 2007

.................................................. . 14-4

14.4 REFERENCES FOR CHAPTER 14

.......................................... . 14-5

15 HUMAN MILK INTAKE

........................................................ . 15-1

15.1 INTRODUCTION

....................................................... . 15-1

15.2 RECOMMENDATIONS

.................................................. . 15-1

15.2.1 Human Milk Intake

............................................... . 15-2

15.2.2 Lipid Content and Lipid Intake

....................................... . 15-2

15.3 KEY STUDIES ON HUMAN MILK INTAKE

................................. . 15-10

15.3.1 Pao et al., 1980

.................................................. . 15-10

15.3.2 Dewey and Lönnerdal, 1983

........................................ . 15-10

15.3.3 Butte et al., 1984

................................................ . 15-10



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15.3.4 Neville et al., 1988

............................................... . 15-11

15.3.5 Dewey et al., 1991a, b

............................................. . 15-11

15.3.6 Butte, et al., 2000

............................................... . 15-12

15.3.7 Arcus-Arth et al., 2005

........................................... . 15-13

15.4 KEY STUDIES ON LIPID CONTENT AND LIPID INTAKE FROM HUMAN MILK

. . . 15-13

15.4.1 Butte et al., 1984

................................................ . 15-14

15.4.2 Mitoulas et al., 2002

............................................. . 15-14

15.4.3 Mitoulas et al., 2003

............................................. . 15-15

15.4.4 Arcus-Arth et al., 2005

........................................... . 15-15

15.4.5 Kent et al., 2006

................................................ . 15-15

15.5 RELEVANT STUDY ON LIPID INTAKE FROM HUMAN MILK

................ . 15-16

15.5.1 Maxwell and Burmaster, 1993

...................................... . 15-16

15.6 OTHER FACTORS

..................................................... . 15-16

15.6.1 Population of Nursing Infants

....................................... . 15-16

15.6.2 Intake Rates Based on Nutritional Status

.............................. . 15-19

15.6.3 Frequency and Duration of Feeding

.................................. . 15-19

15.7 REFERENCES FOR CHAPTER 15

......................................... . 15-20

ACTIVITY FACTORS

.......................................................... . 16-1

16.1 INTRODUCTION

....................................................... . 16-1

16.2 RECOMMENDATIONS

.................................................. . 16-1

16.3 ACTIVITY PATTERNS

.................................................. . 16-7

16.3.1 KEY STUDIES

.................................................. . 16-7

16.3.1.1 Wiley et al., 1991

.......................................... . 16-7

16.3.1.2 U.S. EPA, 1996

........................................... . 16-8

16.3.2 RELEVANT STUDIES

............................................ . 16-9

16.3.2.1 Timmer et al., 1985

........................................ . 16-9

16.3.2.2 Robinson and Thomas, 1991

................................ . 16-10

16.3.2.3 Funk et al., 1998

......................................... . 16-11

16.3.2.4 U.S. EPA, 2000

.......................................... . 16-11

16.3.2.5 Hubal et al., 2000

......................................... . 16-12

16.3.2.6 Wong et al., 2000

......................................... . 16-12

16.3.2.7 Graham and McCurdy, 2004

................................ . 16-13

16.3.2.8 Vandewater et al., 2004

.................................... . 16-14

16.3.2.9 Juster et al. (2004)

........................................ . 16-14

16.3.2.10 U.S. Department of Labor, 2007

....................... . 16-15

16.3.2.11 Nader et al. 2008

................................... . 16-15

16.4 REFERENCES FOR CHAPTER 16

......................................... . 16-15



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TABLE OF CONTENTS (Continued)

CONSUMER PRODUCTS

....................................................... . 17-1

17.1 INTRODUCTION

....................................................... . 17-1

17.2 RECOMMENDATIONS

.................................................. . 17-2

17.3 CONSUMER PRODUCTS USE STUDIES

.................................... . 17-2

17.3.1 CTFA, 1983

..................................................... . 17-2

17.3.2 U.S. EPA, 1996

.................................................. . 17-2

17.3.3 Bass et al., 2001

................................................. . 17-3

17.3.4 Loretz et al., 2005

................................................ . 17-4

17.3.5 Loretz et al., 2006

................................................ . 17-4

17.3.6 Loretz et al., 2008

................................................ . 17-5

17.3.7 Sathyanarayana et al., 2008

......................................... . 17-5

17.4 REFERENCES FOR CHAPTER 17

.......................................... . 17-5

GLOSSARY OF TERMS

......................................................... . G-1



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LIST OF TABLES

Table 1-1. Characterization of Variability in Exposure Factors

.............................. . 1-21

Table 1-2. Considerations Used to Rate Confidence in Recommended Values

................... . 1-22

Table 1-3. Integrating U.S. EPA ’ s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants with U.S. EPA ’ s Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens For Those Contaminants Which Act Via a Mutagenic Mode of Action

........................ . 1-24

Table 2-1. Four Strategies for Confronting Variability

..................................... . 2-9

Table 2-2. Three Types of Uncertainty and Associated Sources and Examples

................... . 2-9

Table 2-3. Approaches to Quantitative Analysis of Uncertainty

.............................. . 2-10

Table 3-1. Recommended Values for Drinking Water Ingestion Rates

.......................... . 3-3

Table 3-2. Confidence in Recommendations for Drinking Water Ingestion Rates

................. . 3-4

Table 3-3. Confidence in Recommendations for Water Ingestion while Swimming

................ . 3-5

Table 3-4. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: Community Water (mL/day)

............................................... . 3-14

Table 3-5. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: Bottled Water (mL/day)

................................................... . 3-14

Table 3-6. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: Other Sources (mL/day)

.................................................. . 3-15

Table 3-7. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: All Sources (mL/day)

..................................................... . 3-15

Table 3-8. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: All Sources (mL/day)

..................................................... . 3-16

Table 3-9. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: Community Water (mL/kg-day)

............................................. . 3-17

Table 3-10. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: Bottled Water (mL/kg-day)

................................................ . 3-17

Table 3-11. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: Other Sources (mL/kg-day)

................................................ . 3-18

Table 3-12. Per Capita Estimates of Combined Direct and Indirect Water Ingestion: All Sources (mL/kg-day)

.................................................. . 3-18

Table 3-13. Per Capita Estimates of Total Direct and Indirect Water Ingestion: All Sources (mL/kg-day)

.................................................. . 3-19

Table 3-14. Consumers Only Estimates of Combined Direct and Indirect Water Ingestion: Community Water (mL/day)

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

Table 3-15. Consumers Only Estimates of Combined Direct and Indirect Water Ingestion: Bottled Water (mL/day)

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

Table 3-16. Consumers Only Estimates of Combined Direct and Indirect Water Ingestion: Other Sources (mL/day)

................................................... . 3-21

Table 3-17. Consumers Only Estimates of Combined Direct and Indirect Water Ingestion: All Sources (mL/day)

..................................................... . 3-21



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LIST OF TABLES (Continued)

Table 3-18. Consumers Only Estimates of Combined Direct and Indirect Water Ingestion: All Sources (mL/day)

..................................................... . 3-22

Table 3-19. Consumers Only Estimates of Direct and Indirect Water Ingestion: Community Water (mL/kg-day)

............................................. . 3-23

Table 3-20. Consumers Only Estimates of Direct and Indirect Water Ingestion: Bottled Water (mL/kg-day)

................................................ . 3-23

Table 3-21. Consumers Only Estimates of Direct and Indirect Water Ingestion: Other Sources (mL/kg-day)

................................................ . 3-24

Table 3-22. Consumers Only Estimates of Direct and Indirect Water Ingestion: All Sources (mL/kg-day)

.................................................. . 3-24

Table 3-23. Consumer Only Estimates of Total Direct and Indirect Water Ingestion (mL/kg-day)

..... . 3-25

Table 3-24. Water Ingested (mL/day) from Water By Itself and Water Added to Other Beverages

and Foods

.............................................................. . 3-26

Table 3-25. Mean Water Consumption (mL/kg-day) by Race/Ethnicity

......................... . 3-27

Table 3-26 Plain Tap Water and Total Water Consumption by Age, Sex, Region, Urbanicity, and Poverty Category

........................................................ . 3-28

Table 3-27. Intake of Water from Various Sources in 2-13-y-old Participants of the DONALD Study 1985-1999

................................................ . 3-29

Table 3-28. Mean (± Standard Error) Fluid Intake (mL/kg/day) by Children Aged 1-10 years, NHANES III, 1988-94

.................................................... . 3-30

Table 3-29. Estimated Mean (± Standard Error) Amount of Total Fluid and Plain Water Intake among Children Aged 1-10 Years: (NHANES III, 1988-94)

........................ . 3-31

Table 3-30. Tap Water Intake in Breastfed and Formula-fed Infants and Mixed-fed Young Children at Different Age Points

...................................................... . 3-32

Table 3-31. Percentage of Subjects Consuming Beverages and Mean Daily Beverage Intakes (mL/day) for Children With Returned Questionnaires

.................................... . 3-33

Table 3-32. Mean (± Standard Deviation) Daily Beverage Intakes Reported on Beverage Frequency Questionnaire and 3-day Food and Beverage Dairies

............................. . 3-34

Table 3-33. Consumption of Beverages by Infants and Toddlers (Feeding Infants and Toddlers Study)

. . 3-35

Table 3-34. Pool Water Ingestion by Swimmers

.......................................... . 3-36

Table 4-1. Summary of Recommended Values for Mouthing Frequency and Duration

............. . 4-3

Table 4-2. Confidence in Recommendations for Mouthing Frequency and Duration

............... . 4-4

Table 4-3. New Jersey Children ’ s Mouthing Frequency (contacts/hour) from Video-transcription

.... . 4-20

Table 4-4. Survey-Reported Percent of 168 Minnesota Children Exhibiting Behavior, by Age

....... . 4-20

Table 4-5. Video-transcription Median (Mean) Observed Mouthing in 19 Minnesota Children (contacts/hour)

.......................................................... . 4-20

Table 4-6. Variability in Objects Mouthed by Washington State Children (contacts/hour)

.......... . 4-21

Table 4-7. Videotaped Mouthing Activity of Texas Children, Median Frequency (Mean ± SD)

...... . 4-22 Table 4-8. Indoor Hand-to-Mouth Frequency (contacts/hour) Distributions from Various

Studies

................................................................ . 4-22 Table 4-9. Outdoor Hand-to-Mouth Frequency (contacts/hour) Distributions from Various

Studies

................................................................ . 4-22



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LIST OF TABLES (Continued)

Table 4-10. Survey Reported Mouthing Behaviors for 92 Washington State Children

.............. . 4-23

Table 4-11. Indoor Mouthing Frequency (Contacts per hour), Video-transcription of 9 Children

with >15 minutes in View Indoors

........................................... . 4-23

Table 4-12. Outdoor Mouthing Frequency (Contacts per hour), Video-transcription of 38 Children

... . 4-24

Table 4-13. Estimated Daily Mean Mouthing Times of New York State Children, for Pacifiers and Other Objects

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

Table 4-14. Percent of Houston-area and Chicago-area Children Observed Mouthing, by Category

and Child ’ s Age

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

Table 4-15. Estimates of Mouthing Time for Various Objects (minutes/hour)

.................... . 4-26

Table 4-16. Mouthing Times of Dutch Children Extrapolated to Total Time While Awake, Without Pacifier, in Minutes per Day

................................................ . 4-28

Table 4-17. Estimated Mean Daily Mouthing Duration by Age Group for Pacifiers, Fingers, Toys, and Other Objects (hours:minutes:seconds)

.................................... . 4-29

Table 4-18. Outdoor Median Mouthing Duration (Seconds per contact), Video-transcription

of 38 Children

.......................................................... . 4-30

Table 4-19. Indoor Mouthing Duration (Minutes per hour), Video-transcription of 9 Children

with >15 minutes in View Indoors

........................................... . 4-30

Table 4-20. Outdoor Mouthing Duration (Minutes per hour), Video-transcription of 38 Children

..... . 4-31

Table 4-21. Reported Daily Prevalence of Massachusetts Children ’ s Non-Food Mouthing/Ingestion

Behaviors

.............................................................. . 4-32

Table 5-1. Recommended Values for Soil, Dust, and Soil + Dust Ingestion

..................... . 5-5

Table 5-2. Confidence in Recommendations for Ingestion of Soil and Dust

..................... . 5-6

Table 5-3. Soil, Dust and Soil + Dust Ingestion Estimates for Amherst, Massachusetts Study

Children

............................................................... . 5-31

Table 5-4. Amherst, Massachusetts Soil-Pica Child ’ s Daily Ingestion Estimates by Tracer and by Week (mg/day)

.......................................................... . 5-32

Table 5-5. Amherst, Massachusetts Soil-Pica Child ’ s Tracer Ratios

.......................... . 5-33

Table 5-6. Van Wïjnen et al., 1990 Limiting Tracer Method (LTM) Soil Ingestion Estimates for Sample of Dutch Children

................................................. . 5-34

Table 5-7. Estimated Geometric Mean Limiting Tracer Method (LTM) Values of Children Attending Daycare Centers According to Age, Weather Category, and Sampling

Period

................................................................ . 5-35

Table 5-8. Estimated Soil Ingestion for Sample of Washington State Children

.................. . 5-35

Table 5-9. Soil Ingestion Estimates for 64 Anaconda Children

.............................. . 5-36

Table 5-10. Soil Ingestion Estimates for Massachusetts Child Displaying Soil Pica Behavior (mg/day)

.............................................................. . 5-36

Table 5-11. Soil Ingestion Estimates for Sample of 12 Washington State Children

................ . 5-37

Table 5-12. Estimated Soil Ingestion for Six High Soil Ingesting Jamaican Children

.............. . 5-38

Table 5-13. Estimated Daily Soil Ingestion for East Helena, Montana Children

.................. . 5-39

Table 5-14. Estimated Soil Ingestion for Sample of Dutch Nursery School Children

............... . 5-39

Table 5-15. Estimated Soil Ingestion for Sample of Dutch Hospitalized, Bedridden Children

........ . 5-40

Table 5-16. Positive/negative Error (Bias) in Soil Ingestion Estimates in Calabrese et al. (1989)



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Study: Effect on Mean Soil Ingestion Estimate (mg/day)

.......................... . 5-40

Table 5-17. Distribution of Average (Mean) Daily Soil Ingestion Estimates per Child for 64 Children (mg/day)

....................................................... . 5-41

Table 5-18. Estimated Distribution of Individual Mean Daily Soil Ingestion Based on Data for 64 Subjects Projected over 365 Days

................................................... . 5-41

Table 5- 19. Summary of Estimates of Soil and Dust Ingestion by Children (0.5-14 years old) from Key Studies (mg/day)

..................................................... . 5-42

Table 6-1. Recommended Long-Term Exposure (More Than 30 Days) Values for Inhalation (Males and Females Combined)

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

Table 6-2. Recommended Short-Term Exposure (Less Than 30 Days) Values for Inhalation (Males and Females Combined)

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

Table 6-3. Confidence in Recommendations for Inhalation Rates

............................. . 6-5

Table 6-4. Physiological Daily Inhalation Rates for Newborns Aged 1 Month or Less

............. . 6-17

Table 6-5. Distribution Percentiles of Physiological Daily Inhalation Rates (m 3 

### /day) for Free-living Normal-weight Males and Females Aged 2.6 months to 23 years

................... . 6-18

Table 6-6. Mean and 95 th 

### Percentile Inhalation Rate Values (m 3 

### /day) for Free-living Normal-weight Males, Females, and Males and Females Combined.

............................. . 6-19

Table 6-7. Distribution Percentiles of Physiological Daily Inhalation Rates (m 3 

### /day) for Free-living Normal-weight and Overweight/obese Males and Females Aged 4 to 18 years

.......... . 6-20

Table 6-8. Distribution Percentiles of Physiological Daily Inhalation Rates per Unit of Body Weight (m 3 

### /kg-day) for Free-living Normal-weight Males and Females Aged 2.6 months to 23 years

............................................................. . 6-21

Table 6-9. Distribution Percentiles of Physiological Daily Inhalation Rates (m 3 

### /kg-day) for Free-living Normal-weight and Overweight/obese Males and Females Aged 4 to 18 years

.......... . 6-22

Table 6-10. Descriptive Statistics for Daily Average Inhalation Rate in Males, by Age Category

...... . 6-23

Table 6-11. Descriptive Statistics for Daily Average Inhalation Rate in Females, by Age Category

.... . 6-24

Table 6-12. Mean and 95 th 

### Percentile Inhalation Rate Values (m 3 

### /day) for Males, Females and

Males and Females Combined

.............................................. . 6-25

Table 6-13. Descriptive Statistics for Average Ventilation Rate While Performing Activities Within the Specified Activity Category, for Males by Age Category

.......................... . 6-26

Table 6-14. Descriptive Statistics for Average Ventilation Rate While Performing Activities Within the Specified Activity Category, for Females by Age Category

........................ . 6-28

Table 6-15. Descriptive Statistics for Duration of Time (hours/day) Spent Performing Activities Within the Specified Activity Category, by Age and Gender Categories

............... . 6-30

Table 6-16. Nonnormalized Daily Inhalation Rates (m 3 

### /day) Derived Using Layton ’ s (1993) Method and CSFII Energy Intake Data

........................................ . 6-32

Table 6-17. Mean and 95 th 

### Percentile Inhalation Rate Values (m 3 

### /day) for Males and Females

Combined

............................................................. . 6-33

Table 6-18. Summary of Institute of Medicine Energy Expenditure Recommendations for Active and Very Active People with Equivalent Inhalation Rates

................. . 6-34

Table 6-19. Mean Inhalation Rate Values (m 3 

### /day) for Males, Females, and Males and Females

Combined

............................................................. . 6-35



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LIST OF TABLES (Continued)

Table 6-20. Mean Inhalation Rate Values (m 3 

### /day) from Key Studies for Males and Females

Combined

............................................................. . 6-36

Table 6-21. 95 th 

### Percentile Inhalation Rate Values (m 3 

### /day) from Key Studies for Males and Females

Combined

............................................................. . 6-37

Table 6-22. Daily Inhalation Rates Estimated From Daily Activities

........................... . 6-38

Table 6-23. Selected Inhalation Rate Values During Different Activity Levels Obtained From Various

Literature Sources

....................................................... . 6-39

Table 6-24. Summary of Human Inhalation Rates for Children by Activity Level (m 3 

### /hour)

......... . 6-40

Table 6-25. Activity Pattern Data Aggregated for Three Microenvironments by Activity Level for All Age Groups

......................................................... . 6-40

Table 6-26. Summary of Daily Inhalation Rates Grouped by Age and Activity Level

.............. . 6-41

Table 6-27. Calibration and Field Protocols for Self-monitoring of Activities Grouped by Subject Panels

.......................................................... . 6-42

Table 6-28. Subject Panel Inhalation Rates by Mean VR, Upper Percentiles, and Self-estimated Breathing Rates

......................................................... . 6-42

Table 6-29. Distribution of Predicted Inhalation Rates by Location and Activity Levels for Elementary and High School Students

................................................. . 6-43

Table 6-30. Average Hours Spent Per Day in a Given Location and Activity Level for Elementary and High School Students

..................................................... . 6-44

Table 6-31. Summary of Average Inhalation Rates (m 3 

### /hour) by Age Group and Activity Levels for Laboratory Protocols

..................................................... . 6-45

Table 6-32. Summary of Average Inhalation Rates (m 3 

### /hour) by Age Group And Activity Levels in Field

Protocols

.............................................................. . 6-46

Table 6-33. Mean Minute Inhalation Rate (m 3 

### /minute) by Group and Activity for Laboratory Protocols

. 6-47

Table 6-34. Mean Minute Inhalation Rate (m 3 

### /minute) by Group and Activity for Field Protocols

..... . 6-47

Table 6-35. Comparisons of Estimated Basal Metabolic Rates (BMR) with Average Food-energy Intakes (EFD) for Individuals Sampled in the 1977-78 NFCS

...................... . 6-48

Table 6-36. Daily Inhalation Rates Calculated from Food-energy Intakes

....................... . 6-49

Table 6-37. Statistics of the Age/gender Cohorts Used to Develop Regression Equations for Predicting Basal Metabolic Rates (BMR)

.............................................. . 6-50

Table 6-38. Daily Inhalation Rates Obtained from the Ratios of Total Energy Expenditure to Basal Metabolic Rate (BMR)

.................................................... . 6-51

Table 6-39. Inhalation Rates for Short-term Exposures

..................................... . 6-52

Table 6-40. Mean, Median, and SD of Inhalation Rate According to Waking or Sleeping in 618 Infants and Children Grouped in Classes of Age

................................ . 6-53

Table 7-1. Recommended Values for Total Body Surface Area, Males and

Females Combined

........................................................ . 7-4

Table 7-2. Recommended Values for Surface Area of Body Parts

............................. . 7-5

Table 7-3. Confidence in Recommendations for Body Surface Area

........................... . 7-6

Table 7-4. Recommended Values for Mean Solids Adherence

................................ . 7-8

Table 7-5. Confidence in Recommendations for Solids Adherence to Skin

...................... . 7-9

Table 7-6. Percentage of Total Body Surface Area by Body Part For Children

................... . 7-16



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LIST OF TABLES (Continued)

Table 7-7. Mean and Percentile Skin Surface Area (m 2 

### ) Derived from U.S. EPA Analysis of NHANES

1999-2006 Males and Females Combined

..................................... . 7-17

Table 7-8. Mean and Percentile Skin Surface Area (m 2 

### ) Derived from U.S. EPA Analysis of NHANES

1999-2006 Males

........................................................ . 7-17

Table 7-9. Mean and Percentile Skin Surface Area (m 2 

### ) Derived from U.S. EPA Analysis of NHANES

1999-2006 Females

...................................................... . 7-18

Table 7-10. Descriptive Statistics For Surface Area/Body Weight (SA/BW) Ratios (m 2 

### /kg)

.......... . 7-19

Table 7-11. Estimated Skin Surface Exposed During Warm Weather Outdoor Activities

........... . 7-20

Table 7-12. Summary of Field Studies

................................................. . 7-21

Table 7-13. Geometric Mean and Geometric Standard Deviations of Solids Adherence by Activity and Body Region

.................................................. . 7-22

Table 7-14. Summary of Controlled Greenhouse Trials - Children Playing

...................... . 7-23

Table 7A-1. Estimated Parameter Values for Different Age Intervals

.......................... . 7A-5

Table 7A-2. Summary of Surface Area Parameter Values for the Dubois and Dubois Model

........ . 7A-5

Table 8-1. Recommended Values for Body Weight

........................................ . 8-2

Table 8-2. Confidence in Recommendations for Body Weight

................................ . 8-3

Table 8-3. Mean and Percentile Body Weights (kilograms) Derived from NHANES 1999-2006, Males and

Females Combined

...................................................... . 8-10

Table 8-4. Mean and Percentile Body Weights (kilograms) for Males Derived from NHANES

1999-2006

............................................................. . 8-11

Table 8-5. Mean and Percentile Body Weights (kilograms) for Females Derived from

NHANES 1999-2006

..................................................... . 8-11

Table 8-6. Weight in Kilograms for Males 2 Months-19 Years of Age– Number Examined, Mean, and Selected Percentiles, by Age Category: United States, 1976-1980

................... . 8-12

Table 8-7. Weight in Kilograms for Females 6 Months-19 Years of Age– Number Examined, Mean, and Selected Percentiles, by Age Category: United States, 1976-1980

................... . 8-13

Table 8-8. Statistics for Probability Plot Regression Analyses: Females Body Weights 6 Months to 20 Years of Age

............................. . 8-14

Table 8-9. Statistics for Probability Plot Regression Analyses: Males Body Weights 6 Months to 20 Years of Age

............................... . 8-15

Table 8-10. Body Weight Estimates (kilograms) by Age and Gender, U.S. Population Derived From NHANES III (1988-94

.................................................... . 8-16

Table 8-11. Body Weight Estimates (in kilograms) by Age, U.S. Population Derived From NHANES III (1988-94

................................................... . 8-17

Table 8-12. Observed Mean, Standard Deviation and Selected Percentiles for Weight (kilograms) by Gender and Age: Birth to 36 Months

............................................... . 8-18

Table 8-13. Mean Body Weight (kilograms) by Age and Gender Across Multiple Surveys

.......... . 8-25

Table 8-14. Mean Height (centimeters) by Age and Gender Across Multiple Surveys

.............. . 8-26

Table 8-15. Mean Body Mass Index (BMI) by Age and Gender Across Multiple Surveys

........... . 8-27

Table 8-16. Sample Sizes by Age, Sex, Race, and Examination

.............................. . 8-28

Table 8-17. Mean BMI (kg/m 2 

### ) Levels and Change in the Mean Z-Scores by Race-Ethnicity and Sex

.. . 8-29

Table 8-18. Prevalence of Overweight and Obesity a 

### Among Children

.......................... . 8-30



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LIST OF TABLES (Continued)

Table 8-19. Numbers of Live Births by Weight and Percentages of Live Births with Low and Very Low Birth Weights, by Race and Hispanic Origin of Mother: United States, 2005

............... . 8-31

Table 8-20. Estimated Mean Body Weights of Males and Females by Single-Year Age Groups Using

NHANES II Data

....................................................... . 8-32

Table 8-21. Estimated Mean Body Weights of Males and Females by Single-Year Age Groups Using

NHANES III Data

................................................. . 8-33

Table 8-22. Estimated Mean Body Weights of Males and Females by Single-Year Age Groups Using

NHANES IV Data

....................................................... . 8-34

Table 8-23. Estimated Body Weights of Typical Age Groups of Interest in U.S. EPA Risk Assessments

. 8-35

Table 8-24. Estimated Percentile Distribution of Body Weight by Fine Age Categories

............ . 8-35

Table 8-25. Estimated Percentile Distribution of Body Weight By Fine Age Categories With Confidence

Interval

............................................................... . 8-36

Table 8-26. Fetal Weight (grams) Percentiles Throughout Pregnancy

......................... . 8-37

Table 8-27. Neonatal Weight by Gestational Age for Males and Females Combined

.............. . 8-38

Table 9-1. Recommended Values for Intake of Fruits and Vegetables, As Consumed

.............. . 9-3

Table 9-2. Confidence in Recommendations for Intake of Fruits and Vegetables

.................. . 9-4

Table 9-3. Per Capita Intake of Fruits and Vegetables (g/kg-day as consumed)

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

Table 9-4. Consumer Only Intake of Fruits and Vegetables (g/kg-day as consumed)

.............. . 9-13

Table 9-5. Per Capita Intake of Individual Fruits and Vegetables (g/kg-day as consumed)

.......... . 9-14

Table 9-6. Consumer Only Intake of Individual Fruits and Vegetables (g/kg-day as consumed)

...... . 9-17

Table 9-7. Mean Quantities of Vegetables Consumed Daily by Sex and Age, Per Capita (g/day)

..... . 9-20

Table 9-8. Percentage of Individuals Consuming Vegetables, by Sex and Age (%)

............... . 9-21

Table 9-9. Mean Quantities of Fruits Consumed Daily by Sex and Age, Per Capita (g/day)

......... . 9-22

Table 9-10. Percentage of Individuals Consuming, Fruits by Sex and Age (%)

................... . 9-23

Table 9-11. Quantity (as consumed) of Fruits and Vegetables Consumed Per Eating Occasion and Percentage of Individuals Using These Foods in Two Days

........................ . 9-24

Table 9-12. Characteristics of the FITS Sample Population

.................................. . 9-25

Table 9-13. Percentage of Infants and Toddlers Consuming Different Types of Vegetables

.......... . 9-26

Table 9-14. Top Five Vegetables Consumed by Infants and Toddlers

.......................... . 9-27

Table 9-15. Percentage of Infants and Toddlers Consuming Different Types of Fruits

.............. . 9-28

Table 9-16. Top Five Fruits Consumed by Infants and Toddlers

.............................. . 9-29

Table 9-17. Characteristics of WIC Participants and Non-participants (Percentages)

.............. . 9-30

Table 9-18. Food Choices for Infants and Toddlers by WIC Participation Status

.................. . 9-32

Table 9-19. Percentage of Hispanic and Non-Hispanic Infants and Toddlers Consuming Different Types of Fruits and Vegetables on A Given Day

......................... . 9-33

Table 9-20. Top Five Fruits and Vegetables Consumed by Hispanic and Non-Hispanic Infants and Toddlers Per Age Group

............................................... . 9-34

Table 9-21. Average Portion Sizes per Eating Occasion of Fruits and Vegetables Commonly Consumed by Infants from the 2002 Feeding Infants and Toddlers Study

.............. . 9-35

Table 9-22. Average Portion Sizes per Eating Occasion of Fruits and Vegetables Commonly Consumed by Toddlers from the 2002 Feeding Infants and Toddlers Study

............ . 9-36



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LIST OF TABLES (Continued)

Table 9-23. Mean Moisture Content of Selected Food Groups Expressed as Percentages of Edible

Portions

............................................................... . 9-37

Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data

.. . 9A-2

Table 10-1. Recommended Values for General Population Fish Intake

......................... . 10-4

Table 10-2. Confidence in Recommendations for General Population Fish Intake

................. . 10-5

Table 10-3. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, As-Consumed

............. . 10-17

Table 10-4. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, As-Consumed

......... . 10-17

Table 10-5. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, As-Consumed

............. . 10-18

Table 10-6. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, As-Consumed

......... . 10-18

Table 10-7. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, Uncooked Fish Weight

....... . 10-19

Table 10-8. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, Uncooked Fish Weight

... . 10-19

Table 10-9. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, Uncooked Fish Weight

....... . 10-20

Table 10-10. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, Uncooked Fish Weight

... . 10-20

Table 10-11. Number of General Population Respondents Reporting Consumption of a Specified Number of Servings of Seafood in 1 Month, and Source of Seafood Eaten

..... . 10-21

Table 10-12. Fish Consumption Among General Population Children in Four States, Consumers Only, g/kg-day As-Consumed

.................................... . 10-22

Table 10-13. Fish Consumption Among General Population in Four States According to Caught or Bought Status, g/kg-day As-Consumed

............................................. . 10-23

Table 10-14. Fish Consumption Among General Population and Anglers in Three States, g/kg-day As-Consumed

.................................................. . 10-24

Table 10-15. Recreational Fish Consumption in Delaware Consumers Only

..................... . 10-24

Table 10-16. Consumption of Self-Caught Fish by Recreational Anglers Lavaca Bay, Texas, g/day

................................................. . 10-25

Table 10-17. Number of Meals and Portion Sizes of Self-Caught Fish Consumed by Recreational Anglers Lavaca Bay, Texas

............................................... . 10-25

Table 10-18. Mean Fish Intake Among Individuals Who Eat Fish and Reside in Households With Recreational Fish Consumption - Michigan

.............. . 10-26

Table 10-19. Consumption of Sports-caught and Purchased Fish by Minnesota and North Dakota Children, Ages 0 to 14 Years (g/day)

........................................ . 10-26

Table 10-20. Fish Consumption Rates among Native American Children (age 5 years and under)

.... . 10-27

Table 10-21. Number of Fish Meal Eaten per Month and Fish Intake Among Native American Children who Consume Particular Species

.................................... . 10-28

Table 10-22. Consumption Rates for Native American Children, Age Birth to Five Years (g/kg-day)

.. . 10-29



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LIST OF TABLES (Continued)

Table 10-23. Consumption Rates for Native American Children (g/kg-day), All Children (including non-consumers): Individual Finfish and Shellfish and Fish Groups

......... . 10-30

Table 10-24. Consumption Rates for Native American Children (g/kg-day), Consumers Only: Individual Finfish and Shellfish and Fish Groups

................. . 10-31

Table 10-25. Fish Consumption Rates for Tulalip and Squaxin Island Children Consumers Only (g/kg-day)

............................................... . 10-32

Table 10-26. Fish Consumption Rates by Gender for Tulalip and Squaxin Island Children Consumers Only (g/kg-day)

............................................... . 10-33

Table 10-27. Distribution of Quantity of Canned Tuna Consumed (grams) Per Eating Occasion, by Age and Sex

........................................................ . 10-34

Table 10-28. Distribution of Quantity of Other Finfish Consumed (grams) Per Eating Occasion, by Age and Sex

........................................................ . 10-34

Table 10-29. Mean Percent Moisture and Total Fat Content for Selected Species

................. . 10-35

Table 11-1. Recommended Values for Intake of Meats, Dairy Products, and Fats, As Consumed

..... . 11-3

Table 11-2. Confidence in Recommendations for Intake of Meats, Diary Products, and Fats

......... . 11-4

Table 11-3. Per Capita Intake of Total Meat and Dairy Products (g/kg-day as consumed)

.......... . 11-14

Table 11-4. Consumer Only Intake of Total Meat and Dairy Products (g/(kg-day as consumed)

..... . 11-15

Table 11-5. Per Capita Intake of Individual Meats and Dairy Products (g/kg-day as consumed)

..... . 11-16

Table 11-6. Consumer Only Intake of Individual Meats and Dairy Products (g/kg-day as consumed)

. . 11-16

Table 11-7. Mean Quantities of Meat and Eggs consumed Daily by Sex and Age, Per Capita (g/day)

. . 11-17

Table 11-8. Percentage of Individuals Consuming Meats and Eggs, by Sex and Age (%)

.......... . 11-18

Table 11-9. Mean Quantities of Dairy Products Consumed Daily by Sex and Age, Per Capita (g/day)

. . 11-19

Table 11-10. Percentage of Individuals Consuming Dairy Products, by Sex and Age (%)

........... . 11-20

Table 11-11. Quantity (as consumed) of Meat and Dairy Products Consumed Per Eating Occasion and Percentage of Individuals Using These Foods in Two Days

....................... . 11-21

Table 11-12. Characteristics of FITS Sample Population

.................................... . 11-22

Table 11-13. Percentage of Infants and Toddlers Consuming Meat or Other Protein Sources

........ . 11-23

Table 11-14. Characteristics of WIC Participants and Non-participants (Percentages)

............. . 11-24

Table 11-15. Food Choices for Infants and Toddlers by WIC Participation Status

................. . 11-25

Table 11-16. Percentage of Hispanic and Non-Hispanic Infants and Toddlers Consuming Different Types of Milk, Meats or Other Protein Sources on A Given Day

................... . 11-26

Table 11-17. Average Portion Sizes Per Eating Occasion of Meats and Dairy Products Commonly Consumed by Infants from the 2002 Feeding Infants and Toddlers Study

............. . 11-27

Table 11-18. Average Portion Sizes Per Eating Occasion of Meats and Dairy Products Commonly Consumed by Toddlers from the 2002 Feeding Infants and Toddlers Study

........... . 11-28

Table 11-19. Total Fat Intake (Per capita; g/day)

......................................... . 11-29

Table 11-20. Total Fat Intake (Per capita; g/kg-day)

....................................... . 11-30

Table 11-21. Total Fat Intake (Consumers Only; g/day)

.................................... . 11-31

Table 11-22. Total Fat Intake (Consumers Only; g/kg-day)

.................................. . 11-32

Table 11-23. Total Fat Intake - Top 10% of Animal Fat Consumers (Consumers Only; g/day)

....... . 11-33

Table 11-24. Total Fat Intake - Top 10% of Animal Fat Consumers (Consumers Only; g/kg-day)

..... . 11-34



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LIST OF TABLES (Continued)

Table 11-25. Fat Intake Among Children Based on Data from the Bogalusa Heart Study, 1973-1982 (g/day)

...................................................... . 11-35

Table 11-26. Fat Intake Among Children Based on Data from the Bogalusa Heart Study, 1973-1982 (g/kg-day)

.................................................... . 11-37

Table 11-27. Mean Total Daily Dietary Fat Intake (g/day) Grouped by Age and Gender

............ . 11-39

Table 11-28. Mean Percent Moisture and Total Fat Content of Selected Meat and Dairy Products

.... . 11-40

Table 11A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data

. . 11A-2

Table 12-1. Recommended Values for Intake of Grains, As Consumed

......................... . 12-3

Table 12-2. Confidence in Recommendations for Intake of Grain Products

...................... . 12-4

Table 12-3. Per Capita Intake of Total Grains (g/kg-day as consumed)

........................ . 12-12

Table 12-4. Consumer Only Intake of Total Grains (g/kg-day as consumed)

.................... . 12-12

Table 12-5. Per Capita Intake of Individual Grain Products (g/kg-day as consumed)

.............. . 12-13

Table 12-6. Consumer Only Intake of Individual Grain Products (g/kg-day as consumed)

.......... . 12-13

Table 12-7. Mean Quantities of Grain Products Consumed Daily by Sex and Age, Per Capita (g/day)

. . 12-14

Table 12-8. Percentage of Individuals Consuming Grain Products, by Sex and Age (%)

........... . 12-15

Table 12-9. Quantity (as consumed) of Grain Products Consumed Per Eating Occasion and Percentage of Individuals Using These Foods in Two Days

....................... . 12-16

Table 12-10. Characteristics of the FITS Sample Population

................................. . 12-17

Table 12-11. Percentage of Infants and Toddlers Consuming Different Types of Grain Products

...... . 12-18

Table 12-12. Characteristics of WIC Participants and Nonparticipants (Percentages)

.............. . 12-19

Table 12-13. Food Choices for Infants and Toddlers by WIC Participation Status

................. . 12-20

Table 12-14. Percentage of Hispanic and Non-Hispanic Infants and Toddlers Consuming Different Types of Grain Products on A Given Day

..................................... . 12-21

Table 12-15. Average Portion Sizes Per Eating Occasion of Grain Products Commonly Consumed by Infants from the 2002 Feeding Infants and Toddlers Study

..................... . 12-22

Table 12-16. Average Portion Sizes Per Eating Occasion of Grain Products Commonly Consumed by Toddlers from the 2002 Feeding Infants and Toddlers Study

.................... . 12-22

Table 12-17. Mean Moisture Content of Selected Grain Products Expressed as Percentages

of Edible Portions

....................................................... . 12-23

Table 12A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data

. . 12A-2

Table 13-1. Summary of Recommended Values for Intake of Home-produced Foods (Consumers Only)

. 13-3

Table 13-2. Confidence in Recommendations for Intake of Home-produced Foods

................ . 13-4

Table 13-3. Weighted and Unweighted Number of Observations (Individuals) for NFCS Data Used in Child-specific Analysis of Food Intake

....................................... . 13-10

Table 13-4. Consumer Only Intake of Home-produced Foods (g/kg-day)

....................... . 13-11

Table 13-5. Percent Weight Losses from Food Preparation

................................. . 13-12

Table 13-6. Fraction of Food Intake that is Home-produced

................................ . 13-13

Table 13A-1. Food Codes and Definitions Used in Child-specific Analysis of the 1987-1988 USDA NFCS Data to Estimate Intake of Home-produced Foods

.................... . 13A-2

Table 13B-1. Food Codes and Definitions Used in Analysis of the 1987-1988 USDA NFCS Household Data to Estimate Fraction of Food Intake that is Home-produced

.................. . 13B-2



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LIST OF TABLES (Continued)

Table 14-1. Recommended Values for Per Capita Total Intake of Foods, As Consumed

............ . 14-2

Table 14-2. Confidence in Recommendations for Total Food Intake

........................... . 14-3

Table 14-3. Per Capita Total Food Intake

............................................... . 14-6

Table 14-4. Per Capita Intake of Total Food and Intake of Major Food Groups (g/day, As Consumed)

. . 14-7

Table 14-5. Per Capita Intake of Total Food and Intake of Major Food Groups (g/kg-day, As Consumed) 14-11

Table 14-6. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake for Individuals with Low-end, Mid-range, and High-end Total Food Intake

........... . 14-15

Table 14-7. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake for Individuals with Low-end, Mid-range, and High-end Total Meat Intake

........... . 14-19

Table 14-8. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake for Individuals with Low-end, Mid-range, and High-end Total Meat and Dairy Intake

... . 14-23

Table 14-9. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake for Individuals with Low-end, Mid-range, and High-end Total Fish Intake

............ . 14-27

Table 14-10. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake for Individuals with Low-end, Mid-range, and High-end Total Fruit and Vegetable Intake

. 14-31

Table 14-11. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake for Individuals with Low-end, Mid-range, and High-end Total Dairy Intake

........... . 14-35

Table 15-1. Recommended Values for Human Milk And Lipid Intake Rates for Exclusively Breastfed

Infants

................................................................ . 15-3

Table 15-2. Confidence in Recommendations for Human Milk Intake

.......................... . 15-4

Table 15-3. Human Milk Intake Rates Derived from Key Studies for Exclusively Breast-fed Infants (mL/day)

.............................................................. . 15-6

Table 15-4. Human Milk Intake Rates Derived from Key Studies for Exclusively Breast-fed Infants (mL/kg/day)

............................................................ . 15-7

Table 15-5. Lipid Intake Rates Derived from Key Studies for Exclusively Breastfed Infants (mL/day)

.. . 15-8

Table 15-6. Lipid Intake Rates Derived from Key Studies for Exclusively Breast-fed Infants (mL/kg/day)

............................................................ . 15-9

Table 15-7. Daily Intakes of Human Milk

.............................................. . 15-23

Table 15-8. Human Milk Intakes for Infants Aged 1 to 6 Months

............................ . 15-23

Table 15-9. Human Milk Intake Among Exclusively Breast-fed Infants During the First 4 Months

of Life

............................................................... . 15-24

Table 15-10. Human Milk Intake During a 24-hour Period

.................................. . 15-25

Table 15-11. Human Milk Intake Estimated by the Darling Study

............................. . 15-26

Table 15-12. Mean Breastfed Infants Characteristics

...................................... . 15-26

Table 15-13. Mean Human Milk Intake of Breastfed Infants (mL/day)

......................... . 15-27

Table 15-14. Feeding Practices by Percent of Infants

...................................... . 15-27

Table 15-15. Body Weight of Breastfed Infants

........................................... . 15-28

Table 15-16. AAP Dataset Milk Intake Rates at Different Ages

.............................. . 15-29

Table 15-17. Average Daily Human Milk Intake (mL/kg day)

................................ . 15-30

Table 15-18. Lipid Content of Human Milk and Estimated Lipid Intake Among Exclusively Breast-fed

Infants

............................................................... . 15-30

Table 15-19. Human Milk Production and Composition Over the First 12 Months of Lactation

...... . 15-31



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LIST OF TABLES (Continued)

Table 15-20. Changes in Volume of Human Milk Produced and Milk Fat Content Over the First Year of

Lactation

............................................................. . 15-32

Table 15-21. Changes in Fatty Acid Composition of Human Milk Over the First Year of Lactation (g/100 g total fatty acids)

................................................. . 15-33

Table 15-22. Comparison of Lipid Content Assumptions (mL/kg-day)

......................... . 15-33

Table 15-23. Distribution of Average Daily Lipid Intake (mL/kg day) assuming 4% Milk Lipid Content 15-34

Table 15-24. Predicted Lipid Intakes for Breast-fed Infants Under 12 Months of Age

.............. . 15-34

Table 15-25. Socio-economic Characteristics of Exclusively Breast-fed Infants Born in 2004

........ . 15-34

Table15-26. Geographic-specific Breastfeeding Percent Rates Among Children Born in 2004

....... . 15-36

Table 15-27. Percentage of Mothers in Developing Countries by Feeding Practices for Infants 0-6

Months Old

........................................................... . 15-37

Table 15-28. Percentage of Mothers in Developing Countries by Feeding Practices for Infants 6-12

Months Old

........................................................... . 15-38

Table 15-29. Population Weighted Averages of Mothers Who Reported Selected Feeding Practices During the Previous 24-hours

.............................................. . 15-39

Table 15-30. Racial and Ethnic Differences in Proportion of Children Ever Breastfed, NHANES III (1988-1994)

........................................................... . 15-40

Table 15-31. Racial and Ethnic Differences in Proportion of Children Who Received Any Human Milk at 6 Months (NHANES III, 1988-1994)

...................................... . 15-41

Table 15-32. Racial and Ethnic Differences in Proportion of Children Exclusively Breastfed at 4 Months (NHANES III, 1991-1994)

......................................... . 15-42

Table 15-33. 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 and 1995, by Ethnic Background and Selected Demographic Variables

.................................................. . 15-43

Table 15-34. Percentage of Mothers Breast-feeding Newborn Infants in the Hospital and Infants at 6 and 12 Months of Age in the United States in 2003, by Ethnic Background and Selected Demographic Variables

.................................................. . 15-44

Table 15-35. Number of Meals Per Day

................................................ . 15-45

Table 15-36. Comparison of Breastfeeding Patterns Between Age and Groups (Mean ±SD)

......... . 15-45

Table 16-1. Recommended Values for Activity Factors

..................................... . 16-3

Table 16-2. Confidence in Recommendations for Activity Factors

............................. . 16-6

Table 16-3. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Ten Major Activity Categories, for All Respondents and Doers

.................................... . 16-17

Table 16-4. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Ten Major Activity Categories, by Age and Gender

............................................ . 16-18

Table 16-5. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Ten Major Activity Categories, Grouped by Seasons and Regions

................................. . 16-19

Table 16-6. Time (minutes/day) Children Under 12 Years of Age Spent in Six Major Location Categories, for All Respondents and Doers

.................................... . 16-20

Table 16-7. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Six Location Categories, Grouped by Age and Gender

..................................... . 16-21



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LIST OF TABLES (Continued)

Table 16-8. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Six Location Categories, Grouped by Season and Region

................................... . 16-22

Table 16-9. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Proximity to Two Potential Sources of Exposure, Grouped by All Respondents, Age, and Gender

....................................................... . 16-23

Table 16-10. Mean Time (minutes/day) Children Under 12 Years of Age Spent Indoors and Outdoors, Grouped by Age and Gender

.............................................. . 16-24

Table 16-11. Time Spent (minutes/day) in Various Rooms at Home and in All Rooms Combined Whole Population and Doers Only

.......................................... . 16-25

Table 16-12. Time Spent (minutes/day) at Selected Indoor Locations Whole Population and Doers Only

........................................................ . 16-29

Table 16-13. Time Spent (minutes/day) in Selected Outdoor Locations Whole Population and Doers Only

........................................................ . 16-31

Table 16-14. Mean Time Spent (minutes/day) Inside and Outside, by Age Category

............... . 16-33

Table 16-15. Time Spent (minutes/day) in Selected Vehicles and All Vehicles Combined Whole Population and Doers Only

.......................................... . 16-34

Table 16-16. Time Spent (minutes/day) in Selected Activities Whole Population and Doers Only

..... . 16-36

Table 16-17. Number of Showers Taken per Day, by Number of Respondents

.................... . 16-40

Table 16-18. Time Spent (minutes) Bathing, Showering, and in Bathroom Immediately after Bathing and Showering

........................................................ . 16-41

Table 16-19. Range of Number of Times Washing the Hands at Specified Daily Frequencies by the Number of Respondents

............................................... . 16-43

Table 16-20. Number of Times Swimming in a Month in Freshwater Swimming Pool by the Number of Respondents

................................................. . 16-44

Table 16-21. Time Spent (minutes/month) Swimming in Freshwater Swimming Pool

............. . 16-45

Table 16-22. Time Spent (minutes/day) Playing on Dirt, Sand/Gravel, or Grass Whole Population and Doers Only

........................................................ . 16-46

Table 16-23. Time Spent (minutes/day) Working or Being Near Excessive Dust in the Air

......... . 16-48

Table 16-24. Time Spent (minutes/day) with Smokers Present

............................... . 16-48

Table 16-25. Mean Time Spent (minutes/day) Performing Major Activities, by Age, Sex and Type of Day

....................................................... . 16-49

Table 16-26. Mean Time Spent (minutes/day) in Major Activities, by Type of Day for Five Different Age Groups

........................................................... . 16-50

Table 16-27. Mean Time Spent (hours/day) Indoors and Outdoors, by Age and Day of the Week

..... . 16-51

Table 16-28. Mean Time Spent (minutes/day) in Various Microenvironments, Children Ages 12 to 17 Years National and California Surveys

.................................... . 16-52

Table 16-29. Gender and Age Groups

................................................. . 16-53

Table 16-30. Assignment of At-Home Activities to Inhalation Rate Levels for Children

........... . 16-54

Table 16-31. Aggregate Time Spent (minutes/day) At-Home in Activity Groups, by Adolescents

and Children

.......................................................... . 16-55

Table 16-32. Comparison of Mean Time Spent (minutes/day) At-Home, by Gender (Adolescents)

.... . 16-55

Table 16-33. Comparison of Mean Time Spent (minutes/day) At-Home, by Gender and Age

for Children

........................................................... . 16-56



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LIST OF TABLES (Continued)

Table 16-34. Number of Person-Days/Individuals for Children in CHAD Database

............... . 16-57

Table 16-35. Time Spent (hours/day) in Various Microenvironments, by Age

................... . 16-58

Table 16-36. Mean Time Children Spent (hours/day) Doing Various Macroactivities

While Indoors at Home

.................................................. . 16-59

Table 16-37. Time Children Spent (hours/day) in Various Microenvironments, by Age Recast into New Standard Age Categories

.................................... . 16-60

Table 16-38. Time Children Spent (hours/day) in Various Macroactivities While Indoors at Home Recast Into New Standard Age Categories

.................................... . 16-61

Table 16-39. Number and Percentage of Respondents with Children and Those Reporting Outdoor Play Activities in both Warm and Cold Weather

........................ . 16-62

Table 16-40. Play Frequency and Duration for all Child Players (from SCS-II data)

.............. . 16-63

Table 16-41. Hand Washing and Bathing Frequency for all Child Players (from SCS-II data)

....... . 16-63

Table 16-42. NHAPS and SCS-II Play Duration Comparison

................................ . 16-64

Table 16-43. NHAPS and SCS-II Hand Wash Frequency Comparison

......................... . 16-64

Table 16-44. Time Spent (minutes/day) Outdoors Based on CHAD Data (Doers Only)

............. . 16-65

Table 16-45. Comparison of Daily Time Spent Outdoors (minutes/day), Considering Gender and Age Cohort (Doers Only)

.............................................. . 16-66

Table 16-46. Time Spent (minutes/day) Indoors Based on CHAD Data (Doers Only)

.............. . 16-67

Table 16-47. Time Spent (minutes/day) in Motor Vehicles Based on CHAD Data (Doers Only)

...... . 16-68

Table 16-48. Time Spent (minutes/two-day period) in Various Activities by Children Participating in the Panel Study of Income Dynamics (PSID), 1997 Child Development Supplement (CDS)

............................................................... . 16-69

Table 16-49. Mean Time Spent (minutes/day) in Various Activity Categories, by Age - Weekday

..... . 16-70

Table 16-50. Mean Time Spent (minutes/day) in Various Activity Categories, by Age - Weekend Day

. . 16-71

Table 16-51. Mean Time Spent (minutes/week) in Various Activity Categories for Children, Ages 6 to 17 16-72

Table 16-52. Mean Time Use (hours/day) by Children, Ages 15 to 19 Years

..................... . 16-73

Table 16-53. Mean Time Spent (minutes/day) in Moderate-to-Vigorous Physical Activity

.......... . 16-74

Table 17-1. Consumer Products Commonly Found in Some U.S. Households

.................... . 17-7

Table 17-2. Amount and Frequency of Use of Various Cosmetic and Baby Products

.............. . 17-10

Table 17-3. Number of Minutes Spent in Activities Working With or Near Freshly Applied Paints (minutes/day)

..................................................... . 17-13

Table 17-4. Number of Minutes Spent in Activities Working With or Near Household Cleaning Agents Such as Scouring Powders or Ammonia (minutes/day)

..................... . 17-13

Table 17-5. Number of Minutes Spent in Activities (at home or elsewhere) Working With or Near Floorwax, Furniture Wax or Shoe Polish (minutes/day)

.................... . 17-13

Table 17-6. Number of Minutes Spent in Activities Working With or Near Glue (minutes/day)

..... . 17-14

Table 17-7. Number of Minutes Spent in Activities Working With or Near Solvents, Fumes or Strong Smelling Chemicals (minutes/day)

.................................... . 17-14

Table 17-8. Number of Minutes Spent in Activities Working With or Near Stain or Spot Removers (minutes/day)

.......................................................... . 17-14

Table 17-9. Number of Minutes Spent in Activities Working With or Near Gasoline or Diesel-powered Equipment, Besides Automobiles (minutes/day

.................... . 17-15



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LIST OF TABLES (Continued)

Table 17-10. Number of Minutes Spent in Activities Working with or Near Pesticides, Including Bug Sprays or Bug Strips (minutes/day)

.............................. . 17-15

Table 17-11. Number of Respondents Using Cologne, Perfume, Aftershave or Other Fragrances at Specified Daily Frequencies

............................................... . 17-15

Table 17-12. Number of Respondents Using Any Aerosol Spray Product for Personal Care Item Such as Deodorant or Hair Spray at Specified Daily Frequencies

................... . 17-16

Table 17-13. Number of Respondents Using a Humidifier at Home

........................... . 17-16

Table 17-14. Number of Respondents Indicating that Pesticides Were Applied by the Professional at Home to Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

.......... . 17-16

Table 17-15. Number of Respondents Reporting Pesticides Applied by the Consumer at Home To Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

............... . 17-17

Table 17-16. Number of Respondents Indicating that Pesticides Were Applied by a Professional at Home to Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

........................................................... . 17-17

Table 17-17. Number of Respondents Reporting Pesticides Applied by the Consumer at Home to Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

................. . 17-17

Table 17-18. Household Demographics, and Pesticide Types, Characteristics, and Frequency of

Pesticide Use

.......................................................... . 17-18

Table 17-19. Frequency of Use of Cosmetic Products

...................................... . 17-19

Table 17-20. Amount of Test Product used (grams) for Lipstick, Body Lotion and Face Cream

....... . 17-20

Table 17-21. Frequency of Use of Personal Care Products

................................... . 17-22

Table 17-22. Average Amount of Product Applied per Application (grams)

..................... . 17-23

Table 17-23. Average Amount of Product Applied per Use Day (grams)

........................ . 17-24

Table 17-24. Average Number of Applications Per Use Day

................................. . 17-25

Table 17-25. Average Amount of Product Applied Per Use Day (grams)

........................ . 17-26

Table 17-26. Average Amount of Product Applied Per Application (grams)

..................... . 17-27

Table 17-27. Characteristics of the Study Population and the Percent Using Selected Baby Care

Products

.............................................................. . 17-28



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LIST OF FIGURES

Figure 1-1. The Exposure-Dose Effect Continuum

........................................ . 1-25

Figure 6-1. 5th, 10th, 25th, 50th, 75th, 90th, and 95th Smoothed Centiles by Age in Awake Subjects

. . 6-54 Figure 6-2. 5th, 10th, 25th, 50th, 75th, 90th, and 95th Smoothed Centiles by Age in Asleep Subjects

. . 6-54

Figure 7-1. Skin Coverage as Determined by Fluorescence vs. Body Part for Children Playing

in Wet Soils

............................................................ . 7-24 Figure 7-2. Gravimetric Loading vs. Body Part for Children Playing in Wet and Dry Soils

.......... . 7-24

Figure 8-1. Weight by Age Percentiles for Boys Aged Birth to 36 Months

...................... . 8-19 Figure 8-2. Weight by Age Percentiles for Girls Aged Birth to 36 Months

...................... . 8-20 Figure 8-3. Weight by Length Percentiles for Boys Aged Birth to 36 Months

.................... . 8-21 Figure 8-4. Weight by Length Percentiles for Girls Aged Birth to 36 Months

.................... . 8-22 Figure 8-5. Body Mass Index-for-Age Percentiles: Boys, 2 to 20 Years

......................... . 8-23 Figure 8-6. Body Mass Index-for-Age Percentiles: Girls, 2 to 20 Years

........................ . 8-24



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ACRONYMS AND ABBREVIATIONS

AAP = American Academy of Pediatrics

ADD = Average Daily Dose

AF = Adherence Factor

AIR = Acid Insoluble Residue

ANOVA = Analysis of Variance

ARS = Agricultural Research Service

ATSDR = Agency for Toxic Substances and Disease Registry

ATUS = American Time Use Study

BI = Bootstrap Interval

BMD = Benchmark Dose

BMI = Body Mass Index

BMR = Basal Metabolic Rate

BTM = Best Tracer Method

C = Contaminant Concentration

CARB = California Air Resources Board

CATI = Computer Assisted Telephone Interviewing

CDC = Centers for Disease Control and Prevention

CDS = Child Development Supplement

CHAD = Consolidated Human Activity Database

CI = Confidence Interval

cm 2 

### = Square Centimeter

cm 3 

### = Cubic Centimeter

CNRC = Children ’ s Nutrition Research Center

CRITFC = Columbia River Inter-Tribal Fish Commission

CSFII = Continuing Survey of Food Intake by Individuals

CTFA = Cosmetic, Toiletry, and Fragrance Association

CV = Coefficient of Variation

DARLING = Davis Area Research on Lactation, Infant Nutrition and Growth

DIY = Do-it-yourself

DLW = Doubly Labeled Water

DONALD = Dortmund Nutritional and Anthropometric Longitudinally Designed

E or EE = Energy Expenditure

EBF = Exclusively Breastfed

ECG = Energy Cost of Growth

ED = Exposure Duration

EI = Energy Intake

EPA = Environmental Protection Agency

f B 

### = Breathing Frequency

FCID = Food Commodity Intake Database

FITS = Feeding Infant and Toddler Study FQPA = Food Quality Protection Act

F/S = Food/Soil

g = Gram



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GAF = General Assessment Factor

GLM = General Linear Model

H = Oxygen Uptake Factor

HEC = Human Equivalent Exposure Concentrations

HPV = High Production Volume

HR = Heart Rate

I = Tabulated Intake Rate

I A 

### = Adjusted Intake Rate

ICRP = International Commission on Radiological Protection

IEUBK = Integrated Exposure and Uptake Biokinetic Model

IFS = Iowa Fluoride Study

IOM = Institute of Medicine

IPCS = International Programme on Chemical Safety

IR = Intake Rate

IR

p 

### = Intake Rate Percentile

IRIS = Integrated Risk Information System

KJ = Kilo Joules

KS = Kolmogorov-Smirnov kg = Kilogram

L 1 

### = Cooking or Preparation Loss

L 2 

### = Post-cooking Loss

LADD = Lifetime Average Daily Dose

LTM = Limiting Tracer Method

m 2 

### = Square Meter

m 3 

### = Cubic Meter

mg = Milligram

MJ = Mega Joules

mL = Milliliter

METS = Metabolic Equivalents of Work

MSA = Metropolitan Statistical Area

N = Number of Subjects or Respondents

N

c 

### = Weighted Number of Individuals Consuming Homegrown Food Item

N T 

### = Weighted Total Number of Individuals Surveyed

NAS = National Academy of Sciences

NCEA = National Center for Environmental Assessment

NCHS = National Center for Health Statistics

NERL = National Exposure Research Laboratory

NFCS = Nationwide Food Consumption Survey

NHANES = National Health and Nutrition Examination Survey

NHAPS = National Human Activity Pattern Survey

NHES = National Health Examination Survey

NHEXAS = National Human Exposure Assessment Survey

NIS = National Immunization Survey

NOAEL = No-observed-adverse-effect-level

NRC = National Research Council



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OPP = Office of Pesticide Programs

ORD = Office of Research and Development

PBPK = Physiologically-Based Pharmacokinetic

PDIR = Physiological Daily Inhalation Rate

PSID = Panel Study of Income Dynamics

RAGS = Risk Assessment Guidance for Superfund

RDD = Random Digit Dial

RfD = Reference Dose

RfC = Reference Concentration

RME = Reasonable Maximum Exposure RQ = Respiratory Quotient

RTF = Ready to Feed

SA = Surface Area

SA/BW = Surface Area to Body Weight Ratio

SCS = Soil Contact Survey

SD = Standard Deviation

SDA = Soaps and Detergent Association

SE = Standard Error

SEM = Standard Error of the Mean

SES = Socioeconomic Status

SPC = Science Policy Council

SPS = Statistical Processing System

SRD = Source Ranking Database

TDEE = Total Daily Energy Expenditure

TFEI = Total Food Energy Intake

USDA = United States Department of Agriculture

USDL = United States Department of Labor

USDHHS = United States Department of Health and Human Services

UV = Ultraviolet

VO 2 

### = Oxygen Consumption Rate VQ = Ventilatory Equivalent

VR = Ventilation Rate

W = Weight

w i 

### = Sample Weight Assigned to Observation x i 

### . WHO = World Health Organization

WIC = USDA ’ s Women, Infants, and Children Program

x i 

### = i th 

### observation



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PREFACE

The Exposure Factors Progr am of the U.S. Environmental Protection Agency ’ s (U.S. EPA ’ s) Office of

Research and Development (ORD) has three main goals: (1) provide updates to the Exposure Factors Handbook and

the Child-Specific Exposure Factors Handbook ; (2) identify exposure factors data gaps and needs in consultation with clients; and (3) develop companion documents to assist clients in the use of exposure factors data. The activities under each goal are supported by and respond to the needs of the various program offices.

The National Center for Environmental Assessment (NCEA) of th e U.S. EPA ’ s ORD has prepared this

handbook to provide information on various physiological and behavioral fa ctors commonly used in assessing

children ’ s exposure to environmental chemicals. Children have different exposure circumstances than do adults. Understanding these differences is key for evaluating potential for environmental hazards from polluta nts. They

consume more of certain foods and water and have higher inhalation rates per unit of body weight than adults. Young

children play close to the ground and come into contact with contaminated soil outdoors and with contaminated dust on surfaces and carpets indoors. Ingestion of breast milk is another potential pathway of exposure for infants and young children.

NCEA published the Exposure Factors Handbook in 1997. That document includes exposure factors and related data on children, as well as adults. However, the U.S. EPA Program Offices identified the need to prepare a document specifically for children ’ s exposure factors. The C hild-Specific Exposure Factors Handbook is intended to

fulfill this need.

This handbook was first offered to the public in 2002. Since that time, the U.S. EPA has incorporated updated

data and revised the recommendations for several exposure factors and developed a standardized set of age categories

to be used for children ’ s exposure assessment. Where possible, the U.S. EPA has used this standard set of age categories to permit easier comparison of data among multiple sources and to allow consistency between different types of exposure factors.



### Child-Specific Exposure Factors Handbook Page September 2008 xxxi

### • ingestion of water a nd other select liquids (Chapter 3);

• non-dietary ingestion factors (Chapter 4);

• ingestion of soil and dust (Chapter 5);

• inhalation rates (Chapter 6);

• dermal exposure factors such as surface area and adherence (Chapter 7);

• body weight (Chapter 8);

• intake of fruits and vegetables (Chapter 9);

• intake of fish and shellfish (Chapter 10);

• intake of meat, dairy products, and fats (Chapter 11);

• intake of grain products (Chapter 12);

• intake of home-produced foods (Chapter 13);

• total food intake (Chapter 14);

• human milk intake (Chapter 15);

• activity factors (Chapter 16); and

• consumer products (Chapter 17). • Less than 12 months old: birth to <1 month,

1 to <3 months, 3 to <6 months, and 6 to <12

months.

• Greater than 12 months old: 1 to <2 years, 2 to <3 years, 3 to <6 years, 6 to <11 years, 11 to <16 years, and 16 to <21 years. 

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EXECUTIVE SUMMARY

This Child-Specific Expo s ure Factors

Handbook has been prepared to focu s on various

fact ors used in assessing exposure, specifically for children ages 0 to <21 years old. This handbook provides nonchemical-specific data on exposure factors

for the U.S. EPA reco m mended set of childhood age groups in the following areas:

T h e Child-specific Exposure Factors

Handbook was first published in 2002. Subsequently, recognizing that exposures among infants, toddler s, adolescents, and teenagers can vary significantly, the U.S. EPA published its G uidance on Selecting Ag e Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA. 2005). To the extent possible, source data for the independent studies cited in the earlier version of this

handbook were obtained and re-analyzed to conform to
the standard age categories. This update and revision

of the 2002 interim final Child-specific Exposure

Factors Handbook is designed specifically to

comp lement the U.S. EPA ’ s recommended set of childhood age groups: The data present ed in this handbook have

been compiled from various sources, which include the

U.S. EPA ’ s Exposure Factor Handbook (U.S. EPA, 1997), government reports, and information presented

in the scientific literature. The data presented are the

r esult of analyses by the individual study auth ors. However, in some cases the U.S. EPA has conducted analysis of published primary data to present res ults

for the recommended age groups. Studies presented in

this handbook were chosen because they were seen as

useful and appropriate for estimating exposure factors

based on the following considerations: (1) s oundness (adequacy of approach and minimal or defined bias); (2) applicability and utility (foc u s on the exposure factor of interest, representativeness of the population,

currency of the information, and adequacy of the data

collection period); (3) c larity and completeness (accessibility, reproducibility, and quality assurance); (4) variability and uncertainty (v ariability in the population and uncertainty in the results); and (5)

evalua tion and review (level of peer review and

number and agreement of studies). Overall confidence ratings of high, medium, or low were derived for the

various exposure factors based on the evaluation of the

elements described above. Many scientific studies were reviewed for possible inclusion in this handbook. The handbook

contains summaries of selected studies publish e d through July 2008. Generally, studies were designated

as “ key ” or “ relevant ” studies. Key studies were considered the most useful for deriving recommendations; while relevant studies provided applicable or pertinent dat a, but not necessarily the

most important for a variety of reasons (e.g., data were outdated, limitations in study desig n). The

recommended values for exposure factors are based on the results of key studies. The U.S. EPA ’ s procedure for developing recommendations was as follows:



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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 are described in Section 1. 4

.

2. If only one study was clas sified as key for a particular factor, t he mean value from that study was selected as the recommended central tendency value for that population. If multiple key studies with reasonab ly equal quality, rel evance, and study design

information were available, a weighted mean (if appropriate, conside ring sample size and

other statistical factors) of the st udies was

chosen as the recommended mean va lue. If

the key studies were judged to be unequal in quality, relevance, or study design, the range of means is pr esented and the user of this

handbook should employ j u dgment in selecting the most appr opriate value for the

l ifestage or local population of inter est.
Recommendations for upper percentiles,

when multiple studies were available, were calculated as the mi d point of the range of

upper percentile values of the studies for each age group where data were available.

3. Aspects of exposure factors variability have

been discussed. This document attempts to

characterize the variability of each of the

fact ors. Variability refers to true heterogeneity or divers ity in a population. Differences among individuals in a population are referred to as inter-individual variability, differences for one individual over time is referred to as intra-individua l variability. Since most of the studies used to
derive exposure factors data are short term in

nature, they present the variability in short

term exposur es across a population sample

and often do not allo w analysis of either inter-temporal variability within individ uals

nor inter-individual variability of long term average exposures. Inter-individual variability in this handbo ok is characterized

in one or more of the following ways: (1) as a tab le with various percentiles or ranges of

va lues; (2) as analytical distributions with

sp e cified parameters; and/or (3) as a qualitative discussion.

4. Uncertainties were discussed in terms of data limitations, the range of circum s tances over which the estimates were (or were not) applicable, p ossible biases in the values themselves, a statement about par ameter
uncertai nties (measurement error, sampling error) and model or scenario uncertainties if

m odels or scenarios were used to derive the

recommended value.

5. The U.S. EPA assigned a confidence rating of low, medium or high to each reco mmended

value. This rating is not intended to

represent an uncertainty analysis; r a ther, it

represents the U.S. EPA ’ s judg ment on the quality of the underlying data used to derive

the recommendation.

6. Finally, the U.S. EPA developed a table for

each exposure factor to summa r ize the

re c ommended values for that factor. Table ES-1 summarizes key recommended values

for the exposure factors included in this

handbook. Additional recommendations and

detailed supporting information can be found

in the individuals chapters that address these

factors.

In providing recommendations for the

va r ious exposure factors, an attempt was made to present percenti le values that are consistent with the

exposure estimators d e fined in Guidelines for Exposure Assessment (U.S. EPA, 1992) (i.e., mean, 50th, 90th, 95th, 98th, and 99.9th percentile). However, this was not always possible, because the

data available were limited for some facto rs, or the

authors of the study did not provide such information.

It is important to note, however, that these percentiles



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were discussed in the guidelines within the context of

risk descriptors and not individual exposure factors.

For example, the guidelines state that the assessor may derive a hig h-end estimate of exposure by using

maximum or near maximum values for one or more sensitive exposure factors, leaving others at their mean value. The term “ upper percentile ” is used throughout

this handbook, and it is intended to represent values in

the upper tail (i.e., between 90th and 99.9th percentile)

of the distribution of val ues for a particular exposure

factor.

Most of the data presented in this handbook

are derived from studies that target (1) the general population (e.g., USDA food consumption surveys) or (2) a sample population from a specific area or group (e.g., soil in gestion study using children from the three-city area in southeastern Washington State). The

decision as to whether to use site-specif ic or national

values for an assessment may depend on the quality of

the competing data sets as well as on the purpose of the specific assessment.

It is important to note that the recommended

values were derive d solely from the U.S. EPA ’ s interpretat ion of the available data. Different values

may b e appropriate for the user in consideration of policy, precedent, strategy, or other factors (e.g., more up-to-date d a ta of better quality and more representative of the population of concern).

U.S. EPA. (1992) Guidelines for exposure assessment. Washington, DC: Office of Research and

Devel o pment, Office of Health and Environmental

Assessment. EPA/600/Z-92/001.

U.S. E PA. (1997) Exposure factors handbook. Washington, DC: National Center for Environmental
Assessment, Office of Research and Development. EPA/600/P-95/002Fa,b,c.

U .S. EPA. (2005) Guidance on selecting age groups

for monitoring and assessing childhood exposures to environmental contaminants (2005). U.S. Environmental Protection Agency, Washington, D.C.,

EPA/630/P-03/003F.



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# CSEFHTable ES-1. Summary of Recommended Exposure Factors for ChildrenAge Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Ingestion of Drinking Water (mL/day) - See Chapter 3Mean per capita95thpercentlile per capitaMean consumer only95thpercentile consumer only1848394708582278965521,0533621,0565561,1713601,0554671,1472718373088933178773569123801,0784171,0994471,2354801,2516061,7276521,7447311,9837922,0028262,5408952,565Ingestion of Drinking Water (mL/kg-day) - See Chapter 3Mean per capita95thpercentile per capitaMean consumer only95thpercentile consumer only52232137238482051192855215980173411265312923712775236026622261246516431745123413341131123212351335Ingestion of Water while Swimming (mL/hour) - See Chapter 3MeanUpper percentile--50100--2070Hand-to-mouth Frequency (contacts/hour) - See Chapter 4Indoor Mean95thpercentileOutdoor Mean95thpercentile----2865--195215472063144213375201554936721312----Object-to-mouth Frequency (contacts/hour) - See Chapter 4Mean95thpercentile--20-10-1---Object-to-mouth Duration (minutes/hour) - See Chapter 4Mean95thpercentile--11268221316--



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### Child-Specific Exposure Factors Handbook 

# CSEFHTable ES-1. Summary of Recommended Exposure Factors for Children (Continued)Age Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Soil/dust Ingestion (mg/day) - See Chapter 5Soil CentralDust CentralSoil + Dust CentralSoil pica Upper percentileGeophagy Upper percentile-----303060--50601001,00050,000Inhalation Rate - Long-term (m3/day)- See Chapter 6Mean95thpercentile3.67.1--4.16.15.48.18.012.89.515.910.916.212.418.715.123.516.527.6Inhalation Rate - Short-term (m3/minute) - See Chapter 6Sleep/nap Mean95thpercentileSedentary Mean95thpercentileLight Mean95thpercentileModerate Mean95thpercentileHeavy Mean95thpercentile3.0E-034.6E-033.1E-034.7E-037.6E-031.1E-021.4E-022.3E-022.6E-024.1E-024.5E-036.4E-034.7E-036.5E-031.2E-021.6E-022.1E-022.9E-023.8E-025.2E-024.6E-036.4E-034.8E-036.5E-031.2E-021.6E-022.1E-022.9E-023.9E-025.3E-024.3E-035.8E-034.5E-035.8E-031.1E-021.4E-022.1E-022.7E-023.7E-024.8E-024.5E-036.3E-034.8E-036.4E-031.1E-021.5E-022.2E-022.9E-024.2E-025.9E-025.0E-037.4E-035.4E-037.5E-031.3E-021.7E-022.5E-023.4E-024.9E-027.0E-024.9E-037.1E-035.3E-037.2E-031.2E-021.6E-022.6E-023.7E-024.9E-027.3E-02Skin Surface Area - Total (m2) - See Chapter 7Total Body Mean95th0.290.340.330.380.380.440.450.510.530.610.610.700.760.951.081.481.592.061.842.33



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xxxvi September 2008Table ES-1. Summary of Recommended Exposure Factors for Children (Continued)Age Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Skin Surface Area - Body Parts (m2) - See Chapter 7Head Mean95thpercentileTrunk Mean95thpercentileArms Mean95thpercentileHands Mean95thpercentileLegs Mean95thpercentileFeet Mean95thpercentile0.0530.0620.1040.1210.0400.0470.0150.0180.0600.0700.0190.0220.0600.0690.1180.1360.0450.0520.0170.0200.0680.0780.0210.0250.0690.0800.1360.1570.0520.0600.0200.0230.0780.0910.0250.0290.0820.0930.1610.1820.0620.0700.0240.0270.0930.1050.0290.0330.0870.1010.1880.2170.0690.0790.0300.0350.1220.1410.0330.0380.0870.0990.2350.2700.0720.0830.0320.0370.1420.1620.0430.0500.1040.1300.2410.3010.1080.1350.0450.0560.2070.2590.0550.0690.1360.1860.3750.5140.1370.1880.0540.0740.3010.4130.0780.1070.1490.1940.5360.6940.2050.2660.0840.1090.4980.6450.1190.1550.1440.1820.5920.7500.2820.3560.0990.1260.5920.7500.1310.165Adherence of Solids to Skin (means; mg/cm2) - See Chapter 7 for specific activities and age groups represented by these valuesResidential indoorDaycare (in & outdoors)Outdoor sportsIndoor sportsActivities with soilPlaying in mudPlaying in sediment0.0041 (arms); 0.011 (hands); 0.0035 (legs); 0.010 (feet)0.024 (arms); 0.099 (hands); 0.020 (legs); 0.071 (feet)0.012 (face); 0.011 (arms); 0.11 (hands); 0.031 (legs)0.0019 (arms); 0.0063 (hands); 0.0020 (legs); 0.0022 (feet)0.054 (face); 0.046 (arms); 0.17 (hands); 0.051 (legs); 0.20 (feet)11 (arms); 47 (hands); 23 (legs); 15 (feet)0.040 (face); 0.17 (arms); 0.49 (hands); 0.70 (legs); 21 (feet)Body Weight (kg) - See Chapter 8Mean4.85.67.49.211.413.818.631.856.871.6Total Fruit Intake (g/kg-day)a- See Chapter 9Mean per capita95thpercentile per capitaMean consumer only95thpercentile consumer5.72110266.2196.9194.6145.1152.48.82.79.30.83.51.13.8 

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Child-Specific Exposure Factors Handbook Page September 2008 xxxviiTable ES-1. Summary of Recommended Exposure Factors for Children (Continued)Age Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Total Vegetable Intake (g/kg-day)a- See Chapter 9Mean per capita95thpercentile per capitaMean consumer only95thpercentile consumer4.5156.2166.9176.9175.9155.9154.19.94.19.92.96.92.96.9Fish and Shellfish Intake (g/kg-day)a- See Chapter 10General PopulationTotal FishMean per capita95thpercentile per capitaMean consumer only95thconsumer----0.433.04.2100.281.93.28.70.231.52.26.20.161.32.16.6----MarineMean per capita95thpercentile per capitaMean consumer only95thpercentile consumer----0.312.33.79.30.201.52.88.00.151.32.05.20.100.462.06.5----FreshwaterMean per capita95thpercentile per capitaMean consumer only95thpercentile consumer----0.120.712.37.20.080.351.86.20.080.481.34.40.070.291.43.3----Recreational Marine - No age-specific recommendations; see Chapter 10.Recreational Freshwater - No age-specific recommendations; see Chapter 10.Native American - No age-specific recommendations; see Chapter 10. 

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xxxviii September 2008Table ES-1. Summary of Recommended Exposure Factors for Children (Continued)Age Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Total Meat Intake (g/kg-day)a- See Chapter 11Mean per capita95thpercentile per capitaMean consumer only95thpercentile consumer1.26.73.09.24.19.84.29.84.19.44.29.42.96.52.96.52.14.82.14.8Total Dairy Intake (g/kg-day)a- See Chapter 11Mean per capita95thpercentile per capitaMean consumer only95thpercentile consumer134916583788378823492349143214325.6165.616Total Fat Intake (g/kg-day)a- See Chapter 11Mean per capita95thpercentile per capitaMean consumer only95thpercentile consumer5.2167.8164.5116.0124.18.24.48.33.77.03.77.04.07.14.07.13.66.43.66.43.45.83.45.82.64.22.64.21.63.01.63.01.32.71.32.7Total Grain Intake (g/kg-day)a- See Chapter 12Mean per capita95thpercentile per capitaMean consumer only95thpercentile consumer2.58.63.69.26.4126.4126.3126.3124.38.24.38.22.55.12.55.1 

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Child-Specific Exposure Factors Handbook Page September 2008 xxxixTable ES-1. Summary of Recommended Exposure Factors for Children (Continued)Age Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Home-produced Food Intake (g/kg-day)b- See Chapter 13Fruits Mean95thpercentile--8.760.64.18.93.615.81.98.3Vegetables Mean95thpercentile--5.219.62.57.72.06.21.56.0Meats Mean95thpercentile--3.710.03.69.13.714.01.74.3Fish Mean95thpercentile--2.87.11.54.7Total Food Intake (g/kg-day) - See Chapter 14Mean per capita95thpercentile per capita20611640286556134901617412661102407024451835Human Milk Intake (mL/day) - See Chapter 15MeanUpper percentile5109506909807701,0006201,000NANAHuman Milk Intake (mL/kg-day) - See Chapter 15MeanUpper percentile15022014019011015083130NANALipid Intake from Human Milk (mL/day) - See Chapter 15MeanUpper percentile2038274030422542NANA 

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xl September 2008Table ES-1. Summary of Recommended Exposure Factors for Children (Continued)Age Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Lipid Intake from Human Milk (mL/kg-day) - See Chapter 15MeanUpper percentile6.08.75.58.04.26.03.35.2NANAActivity Factors - See Chapter 16Mean (minutes/day)Indoors, total MeanOutdoors, total Mean1,44001,43281,414261,3011391,353361,316761,2781071,2441321,2601001,248102Indoors, at residence Mean95thShoweringMean95thBathingMean95thPlaying on sand/gravel Mean95thPlaying on grass Mean95thPlaying on dirt Mean95th1,1081,44015-193018-52-33-1,0651,44020-23324312168121561219791,296224423455312162121471219571,355173424606012179121631218931,275184124466712173121631218891,315184025436712175121491208331,2882045336083-60-30-Mean (minutes/month)SwimmingMean95th96-105-116181137181151181139181145181 

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Child-Specific Exposure Factors Handbook Page September 2008 xliTable ES-1. Summary of Recommended Exposure Factors for Children (Continued)Age Group0 to <1mo.1 to <3mos.3 to <6mos.6 to <12mos.1 to <2yrs.2 to <3yrs.3 to <6yrs.6 to <11yrs.11 to<16 yrs.16 to<18yrs.18 to <21yrs.Consumer Products - See Chapter 17No age-specific recommendations; see Chapter 17.aAnalysis was conducted using slightly different age groups than those recommended inGuidance on Selecting Age Groups for Monitoring and Assessing ChildhoodExposures to Environmental Contaminants(U.S. EPA. 2005). Data were placed in the recommended age categories closest to those used in the analysis.bAnalysis was conducted prior to Agency’s issuance ofGuidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to EnvironmentalContaminants(U.S. EPA. 2005). Thus, age groups in the original study are slightly different than those presented here. See chapter for details.- = No data available and/or no recommendation made.NA = Not applicable. 

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AUTHORS, CONTRIBUTORS, AND

REVIEWERS

The National Center for Environmental

Assessment (NCEA), Office of R esearch and

D evelopment (ORD) was responsible for t h e preparation of this handbook. Adraft of this document

was prepared by the Exposure Assessment Division of

Versar Inc. in Springfield, Virginia, under U.S. EPA

Contract No. EP-W-04-035. Earlier versions (i.e.,

2002 and 2005) were prepared under Contract Nos.

68- W-99-0041 and EP-W-04-035. The U.S.

EPA/NCEA ’ s Jacqueline Moya served as Work Assignment Manager for both the original and the revision, providing overall direction, technical assistance, and serving as contrib uting author. The

draft was review ed by U.S. EPA staff who have an

interest in exposure factors as well as by an independent panel of outside experts.

AUTHORS WORD

PROCESSING

Versar, Inc. Versar, Inc.

Adria Diaz Susan Perry Marit Espevik Malikah Moore Christopher Greene Valerie Schwartz

Ron Lee

Clarkson Meredith Nica Mostaghim

Chuck Peck Todd Perryman Linda Phillips

Kathleen Saunders-Coon

Diane Sinkowski

Patricia Wood U.S. EPA Becky Cuthbertson Jacqueline Moya Linda Phillips

John Schaum

Laurie Schuda

The following U.S. EPA individuals reviewed

an earlier draft of this document and provided valuable

comments:

Marcia Bailey, U.S. EPA, Region X

Gary Bangs, U.S. EPA, Risk Assessment Forum, Office of Research and Development

Denis R. Borum, U.S. EPA, Office of Wate r, Health and Ecological Criteria Division

Dave Crawford, U.S. EPA, Office of Solid Waste

Becky Cuthbertson, U.S. EPA, Office of Solid Waste

Michael Dellarco, U.S. EPA, Nation al Center for

Environmental Assessment

Lyn n Delpire, U.S. EPA, Office of Prevention, Pesticides, and Toxic Substances

Jeff Evans, U.S. EPA, Office of Pesticide Programs

Cathy Fehrenbacher, U.S. EPA, Office of Prevention, Pesticides, and Toxic Substances

Michael Firestone, U.S. EPA, Office of Children ’ s Health Protection

Brenda Foos, U.S. EPA, Office of Children ’ s Health

Protection

Henry Kahn, U.S. EPA, National Center for

Environmental Assessment



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Youngmoo Kim, U.S. EPA, Region VI

Steve Kroner, U.S. EPA, Office of Solid Waste

Tom McCurdy, U.S . EPA, National Exposure Research Laboratory

David Miller, U.S. EPA Office of Pesticide Programs

Deirdre Murphy, U.S. EPA, Offi ce of Air Quality Planning and Standards

Steve Nako, U.S. EPA, Office of Pesticide Programs Marian Olsen, U.S. EPA, Region II

Glenn Rice, U.S. EPA, National Center for

Environmental Assessment

Harvey Richmond, U.S . EPA, Office of Air Quality Planning and Standards

David Riley, U.S. EPA Region VI

Marybeth Smuts, U.S. EPA, Region I

Marc Stifelman, U.S. EPA, Region X

Valerie Zartarian, U.S. EPA, National Exposure Research Laboratory

Acknowledgment

The authors wish to a cknowledge the important

contributions of the following U.S. EPA individ u als

who conducted additional analyses for the revisions of

this handbook:

David Hrdy, Office of Pesticide Programs

Henry Kahn, National Center for Environmental

Assessment

David Miller,Office of Pesticide Programs

James Nguyen, Office of Pesticide Programs Bernard Scheneider, Office of Pesticide Programs

Nicole Tulve, National Exposure Research Laboratory

Philip Villanueva, Office of Pesticide Programs

Jianping Xue, National Exposure Research Laboratory

Valeri e Zartarian, National Exposure Research Laboratory

This document was reviewed by an external panel of experts. The panel was composed of the following

individuals:

Ed Avol, Keck Sch o ol of Medicine, University of

Southern California

James Bruckner, College of Pharmacy, Universit y of Georgia

Anna Fan, California Envir onmental Protection Agency

Panos Georgopoulos, UMDNJ Robert Wood Joh nson Medical School

Lynn G o ldman, Johns Hopkins Bloomberg School of

Public Health

Annette Guiseppi-Elie, Dupont Engineering

Dale Hattis, Clark University

Diane Rohiman, Oregon Health and Science University

P. Barry Ryan, Emory University

Edward Stanek, University of Massachusetts at

Amherst

Alan Stern, Unive r sity of Medicine and Dentistry of New Jersey



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Nga Tran, Exponent

Bernard Weiss, University of Rochest er School of Medicine and Dentistry

Robin Wyatt, Columbia University

Rosemary Zaleski, ExxonMobil Biomedical Sciences,

Inc.

Finally, the following U.S. EPA/NCEA individuals

reviewed this document for final clearance:

David Bussard Jeffrey Frithsen Nicole Hagan

Charles Ris John Vandenberg



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1 INTRODUCTION

1.1 PURPOSE

The purpose of the Child-Specific Exposure

Factors Handbook is to provide exposure factors for

children. The handbook highlights the changes in risk assessment practices that were first presented in

the U.S. Environmental Protection Agency ’ s (EPA)

Cancer Guidelines, regarding the need to consider

children as lifestages rather than as subpo pulations (U.S. EPA, 2005b). It also emphasizes a major

recommendation in U.S. EPA ’ s Supplemental

Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens (U.S. EPA, 2005c) to sum

exposures and risks acros s lifestages rather than relying on the use of a lifetime average adult exposure

to calcula te risk. This handbook also uses updated information to incorporate any new exposure factors

data/research that have become available si nce the early 2 000's, and is consistent with the U.S. EPA's

new set of recommended childhood age groups (U.S. EPA 2005a), including a standardized way to define specific age groups.

As with the earlier version of the handbook,

this new version summar izes key data on human

behaviors a nd characteristics that affect children ’ s exposure to environmental contaminants, and provides recommended values to use for these factors.

These recommendations are not legally binding on

any U.S. EPA program and should be interpreted as

s uggestions that Program Offices or individual exposure/risk assessors can consider and mo dify as

needed. The decision as to whether to use sitespecific or national values for an assessmen t may depend on the quality of the competing data sets as
well as on the purpose of the specific asse ssment.
The handbook has strived to include discuss ions of

the issues that assessors may consider i n assessing

exposure among children of different ages, and may be used in conjunction with the U.S. EPA document

entitled Socio-demographic Data U sed for Identifying Potentially Highly Exposed Populations (U.S. EPA, 1999)

.

1.2 INTENDED AUDIENCE

The Child-Specific Exposure Factors

Handbook may be used by exposure and risk

assessors, economists, and other interested parties as a source for da ta and/or U.S. EPA recommendations

on numeric estimates for behavioral and physiological

characteristics needed to estimate childhood exposure to toxic contaminants.

1.3 BACKGROUND

Because of phy siological and behavioral differences, exposures among children are expected to

be different from exposures a mong adults. Children

may be more exposed to some environmental contaminants, because they consume more of certain

foods and water per unit of body weight and have a higher ratio of body surface area to volume than adults. Equally important, rapid changes in behavior and physiology may lead to differences in exposure as a

child grows up. Recognizing that exposures among infants, toddlers, adolescents, and teenagers can vary significantly, the U.S. EPA published its “ G uidance

on Selecting Age Groups for Monitoring and Assessing

Childhood Exposures to Environmental Contaminants (U.S. EPA. 2005a). ” This update and revision of the

2002 interim final Child- Specific Exposure Factors

Handbook (U.S. EPA, 2002a) is designed specifically

to complement U.S. EPA ’ s recommended set of childhood age groups:

? 

### Less than 12 months old: birth to <1 month,

1 to <3 months, 3 to <6 months, and 6 to <12

months.

? 

### Greater than 12 months old: 1 to <2 years, 2

to <3 years, 3 to <6 years, 6 to <11 years, 11 to <16 years, and 16 to <21 years. Many studies have shown that young children

can be exposed to various contaminants, including

pest icides, during normal oral exploration of their

environment (i.e., hand-to-mouth behavior) and by

to uching floors, surfaces, and objects such as toys (Eskenazi et al., 1999; Gurunathan et al., 1998; Lewis

et al., 1999; Nishioka et al., 1999; Garry, 2004). Dust

and tracked-in soil accumulate in carpets, where young children spend a significant amount of time (Lewis et al., 1999). Children living in agricultural areas may experience higher exposures to pesticides than do other

children (Curwin et al., 2007). Pesticides may be

tracked into their homes by family m embers. In addition, children living in agricultural areas may also play in nearby fields or be exposed via consumption of



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contaminated human milk from their farmwork e r mother (Eskenazi et al., 1999).

In terms of risk, children may also differ

from adults in their vulnerability to environmental pollutants because of toxicodynamic differences (e.g.,

when exposures occ ur during periods of enhanced susceptibility) and/or toxicokine tic differences (i.e.,

differences in absorption, metabolism, and excretion) (U.S. EPA, 2000a). The immaturity of metabolic

enzyme systems and clearance mechanisms in young children can result in l onger half-lives of

environmental contaminants (Ginsberg et al. , 2002,

Clewell et al ., 2004). The cellular immaturity of

children and the ongoing growth processes account

for elevated risk (AAP, 1997). Toxic chemicals in

the environment can c ause neurodevelopmental

disabilities

, and the developing brain can be particularly sensitive to environmental contaminants. For example, elevated blood lead levels and prenatal

exposu res to even relatively low levels of lead can

result in b ehavior disorders and reductions of

intellectual func tion in children (Landrigan et al., 2005). Exposure to high levels of methylmercury can

re s ult in developmental disabilities among children ( Myers et al., 2000). Other authors have described the importance of exposure timing (i.e., preconceptional, prenatal, and postnatal) and how it affects the outcomes observed (Selevan et al., 2000).

Bre ysee et al. (2005) suggests that higher levels of

exposure to indoor air pollution and allergens among inner -city children compared to non-inner-city children may explain the difference in asthma levels

between these two groups. With respect to

contaminants that are carcinogenic via a mu tagenic

mode of action, the U.S. EPA has found that

childhood is a particularly sensitive period of development, in which cancer potencies per year of exposure can be an order of magnitude higher than during adulthood (U.S. EPA, 2005c).

Executive Ord er 13045: Protection of

Children from Environmental Health Risks and Safety Risks

, signed in 1997, requires all federal agencie s to address health and safety risks to children, t o coordinate research priorities on

children ’ s healt h, and to ensure that their standards

take into account special risks to children (EO, 1997).

To implement the Order, the U.S. EPA established the Office of C h ildren ’ s Health Protection (OCHP) (renamed the Office of Children ’ s He alth Protection

and E n vironmental Education (OCHPEE) in 2005),

whose job it is to work with Program and regional

offices within the U.S. EPA to promote a safe and healthy environment for children by ensuring that all regulat ions, standards, policies, and risk assessments
take into account risks to children. Legislation, such as the Food Quality Protection Act and the Safe Drinking

Water Act amendments, has made coverage of

children ’ s health issues more explicit, and research on children ’ s health issues is continually expanding. As

a result of the emphasis on chi ldren ’ s risk, the U.S.

EPA Office of Research and De velopment (ORD) developed a Strategy for Research on Environmental

Risks to Children (U.S. EPA, 2000a). The goal of the Strategy is to improve the quality of risk assessments
for children. This Child-Specific Exposure Factors

Handbook is also intended to support the U.S.

EPA/ORD/NCEA ’ s efforts to im prove exposure and

risk assessments for children.

In 1997, the U.S. EPA/ORD /NCEA published

the Exposure Factors Handbook (U.S. EPA, 1997a).

The handbook includes exp osure factors and related

dat a on both adults and children. Subsequently, the

U.S. EPA Program Offices iden tified the need to

consolidate all children ’ s exposure data into a single

document and the Child-Specific Exposure Factors

H

andbook was published in 2002 to fulfill this need.

This handbook updates the 2002 edition of the ChildSpecific Exposure Factors Handbook (U.S. EPA 2002a). It provides non-chemical-specific data on

exposure factors that can be used to assess contributions from dietary and non-dietary ingestion

exposure, dermal exposure , and inhalation exposure

a mong children. Although the preconceptional and prenatal (fetal) life stages are important to consider they are not covered in this handbook.

Preco n ceptional exposures are included in the Exposure Factor s Handbook since they relate to
maternal and paternal exposures, and exposure factors

for pregnant and lactating women are being developed

a s part of a separate effort. This document does not include chemical-specific data or information on physiological parameters that may be needed for

exposure assessments involving physiologically-based pharmacokinetic (PBPK) modeling. The U.S. EPA



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has developed guidance on how to use PBPK

information in risk assessment. More information on

the application of PBPK models and supporting data is found in U.S. EPA (2006a, 2006b).

This handboo k provides updated exposure

factor information for children in the following areas:

• ingestion of water and other select liquids;

• non-dietary ingestion;

• soil and dust ingestion;

• inhalation rates;

• dermal exposure factors such as surface area and adherence;

• body weight;

• intake of fruits and vegetables;

• intake of fish and shellfish;

• intake of meat, dairy products, and fats;

• intake of grain products;

• intake of home-produced foods;

• total food intake;

• human milk intake;

• activity factors; and

• consumer products.

This handbook is a compilation of available data from a variety of sources. Most of these data

have been described in detail in t h e U.S. EPA ’ s Exposure Facto rs Handbook (1997a), but data published after the release of the Exposure Fact ors Handbook are also included here. This latest

handb ook updates the 2002 interim final ChildSpecific Exposure Factors Han d book (U.S. EPA 2002). With very few exceptions, the data presented

here derive from the analyses of the individual study

authors. Because the studies included in this

handbook vary in terms of their objective s, design,

scope, presentation of results, etc., the level of detail, statistics, and terminology may vary from s tudy to study and from factor to factor. For example, some

authors used geometric means to present their results,

while others used arithmetic means or distributions.

Authors have sometimes used different age ranges to
describe data for children. In most cases, the original data are unavailable, and the study results cannot be

reallocated into the standard age groups used in this

handbook. Every effort has been made to reallocate

sou rce data into the standard age groups recommended by the U.S. E P A in the report entitled

Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental

Contaminants (U.S. EPA, 2005a; see Section 1.7),

when sufficiently detailed data are available. Within the constraint of presenting the original material as accurately as possi ble, the U.S. EPA has made an

effort to present discussions and results in a consistent

manner. 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.

Most of the data presented in this han dbook

are derived from studies that target (1) the general population (e.g., USDA food consumption surveys) or (2) a sample population from a specific area or group (e.g., fish consumption among Native American

c hildren). If it is necessary to characterize a population th at is not directly covered by the data in

this handbook, the risk or exposure assessor may need

to evaluate whether these data may be used as suitable

subs titutes for the population of interest or whether

there is a need to seek additional population-speci f ic

data. If information is needed for identifying and enumerating populations who may be at risk for

great e r contaminant exposures or who exhibit a heightened sensitivity to particular chemicals, the

reader is referred to Socio-demographic Data Used for Identifying Potentially H i ghly Exposed Populations (U.S. EPA, 1999). Because of the large number of tables in this handbook, tables are presented at the end of each chapter, before the appendices, if any. In conjunction

with the Guidance on Sele cting Age Groups for

Monitorin g and Assessing Childhood Exposures to

Environmental Contaminants (U.S. EPA, 2005a), this

handbook is adopting the age group notation “X to <
Y ” (e.g., the age group 3 to < 6 years is meant to span

a 3-ye ar time interval from a child ’ s 3rd birthday up until the day before his or her 6th birthday).

1.4 SELECTION OF STUDIES F O R THE

HANDBOOK

Information in thi s handbook has been

summarized from studies documented in the scientific

literature and other available sources . Studies were

chosen that were seen as useful and approp riate for



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estimating exposure fac tors for children. The

handbo o k contains summaries of selected studies published through July 2008.

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 (See Section 1.5). Other studies are designated "relevant," meaning applicable or pertinent, but not necessarily the most imp ortant.
Thi s distinction was made on the strength of the

attributes listed in the "General Assessment Factors"

listed below.

1.4.1 General Assessment Factors Many scienti fic studies were reviewed for possible inclusion in this handbook. Generally,

studies identified in the Exposure Factors Handbook (U.S. EPA, 1997a) as key studies are also included in

this handbook as key studies. Also included are new

studies that became available after publication of the Exposure Fact ors Handbook and the 2002 ChildSpecific Exposure Factors Handbook (U.S. EPA, 2002a). Key studies from the E xposure Factors

Handbook were generally defined as the most useful

for deriving recommendations for exposure f actors.
The recommended values for most exp osure factors

are based on the results of these studies. The Agency recognizes the need to evaluate the qual ity and

relevance of scientific and technical information used

in support of Agency actions (U.S. EPA 2002b, 2003a, 20 06c). When evaluating scientific and

technical information, the U.S. EPA ’ s Science Policy

Council (SPC) recommends using five General

Assessment Factors (GAFs): (1) so undness, (2) applicability and utility, (3) clarity and completeness, (4) uncertainty and variability, and (5) evaluation and

review (U.S. EPA 2003 a). These GAFs were

ada pted and expanded to include specific

considerations deemed to be impor tant during

evaluation of exposure factors data, and were used to judge the quality of the underlying data used to derive

recommendations.

1.4.2 Selection Criteria

T he confidence ratings for the various

exposure factor recommendations, and selection of the key studies that form the basis for these recommendations, were based on specific criteria within each of the five GAFs, as follows:

(1) Soundness: Scientific and technical p rocedures,

measures, method s or models employed to generate

the information are reasonable for, and consistent with, the intended application. The soundness of the experimental procedures or approaches in the study designs of the available st udies were evaluated according to the following:

Adequacy of the Study Approach Used : I n

g eneral, more confidence was placed on experimenta l procedures or approaches that

more likely or closely captured the desired

m easurement. Direct exposure data

collection tech niques, such as direct observation, personal monitoring devices, or

other known methods were preferred where

avail a ble. If studies utilizing direct

measurement were not available, studies were selected that relied on validated indirect

measurement methods such as surrogate

measures (such as heart rate for inhalation rate), a n d use of questionnaires. If questionnaires or surveys we re used, proper design and procedures include a n 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. More confidence was placed in

exposures factors that relied on studies that

gave appropriate consideration to these study design issues. Studies were also deemed preferable if based on primary data, but

studies based on secondary sources were also

included where they offered an original analysis. In general, higher confidence was placed on exposure factors based on primary

data.

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 p r esent, the direction of the



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bias (i.e., an ove r or underestimate of the parameter) was either s tated or apparent

from the study design. More confidence was placed on exposure factors based on studies

that minimized bias.

The applicability and utility of the available studies were evaluated based on the following criteria:

F ocus on Exposure Factor of Interest : Studies were preferred 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 stud y contained useful ancillary information concerning fat content in fish, alth ough it did not directly address fish consumption.

Representativeness of the Populat i on : More confidence was placed in studies that

addressed the U.S. population. Data from populations outside the U.S. were sometimes

included if behavioral p atterns or other

characteristics of exposure were similar. Studies seeking to chara c terize a particular region or sub-po pulation were selected, if appropriately representative of that

popu lation. In cases where data were limited, studies with limitations in this area were included and limitations were noted in

the handbook. Higher confidence ratings

were given to exposure factors whe re the

available data were representative of the population of interest.

Currency of Information : More

confidence was placed in studies that were sufficiently re c ent to represent current

exposure conditions. This is an important

consideration for those factors that change

with t ime. Older data were evaluated and

considered in instances where the variability

of the exposure factor over time was determined to be insignificant or unimportant. In some cases, recent data were

very limited. Therefore, the data provided in

these instances were the only available data.

Limitations on the age of the data were noted.

Recent studies are more likely to use state-ofthe-art methodologies that reflect advances in

the exposure assessment field. Consequently,

exposure factor recommendations based on

current data were given higher confidence ratings than those based on older data, except
in cases where the age of the data would not
affect the recommended values.

A dequacy of data collection period :
Because most users of the handbook are primarily addressing chronic exposures, studies were sought that uti lized the most

appropr iate techniques for collecting data to
characterize long-term behavior. Higher

c o nfidence ratings were given to exposure factor rec ommendations that were based on an adequate data collection period.

(3) Clarity and completeness : The degree of clarity

and completeness with which the data, assumptions, methods, quality assurance, sponsoring organizations

and analyses employed to generate the information are documented

. Clarity and completeness was evaluated based on the following criteria.

Accessibility : Studies that the user could

access in their entirety, if needed, were preferred.

Reproducibility : S t udies that containe d

sufficient information so that methods could

be reproduced, o r could be evaluated, based

on the details of the author ’ s work, were preferred.

Quality Assu rance : S tudies with

documented quality assurance/quality control

measures were preferred. Higher confidence ratings were given to e xposure factors that

w ere based on studies where appropriate quality assu r ance/quality control measures were used.



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(4) Variability and uncertainty: T he variability and uncertainty (quantitative and qualitative) in the information or the procedures, measures, methods or models are evaluated and characterized. Variability

arises from true heterogeneity across people, places or time and can affect the precision of exposure

estimates and the degree to w hich they can be generalized. The types of variability include: spatial, temporal, and inter-individual. Uncertainty represen t s

a lack of knowledge about factors affecting exposure

or risk and can lead to inaccurate or biased estimates

of exposure. The types of u ncertainty include: scenario, parameter, and model. The uncertainty and variability associated with the studies was evaluated based on the following criteria.

Variability in the population : Studies

were sought that characterized any variability within p opulations. The variability associated with the studies presented in this handbook is characterized

as described in Section 1.5. Higher

confidence ratings were giv en to exposure
factors that were based on studies where variability was well characterized.

Uncertain ty : Studies were sought with minimal uncertainty in the data, which was judged by evaluating all the considerations

listed above. Studies were preferred that

identified uncertainties, such as those due to
inherent variability in env ironmental and

exposure-r e lated parameters or possible

measurement e rror. Higher confidence ratings were given to exposure factors based

on studies where uncertainty had been

minimized.

(5) Evaluation and review: The information or the procedures, measures , methods or models are independently verified, validated, and peer reviewed.

Relevant factors that were considered included:

Peer review : Studies selected were those

from the peer-reviewed literature and final

gove rnment reports. Unpublished and internal or interim reports were avoided. Number and agreement of studies : Higher

confidence was p laced on recommendations

where data were available from more than one key study and there was good agreement

between studies.

1.5 APPROACH USED TO DEVE LOP

RECOMMENDATIONS FO R

EXPOSURE FACTORS

As discussed above, the U.S. EPA first

reviewed the literature pe rtaining to a factor and

determined key studies. These key studies were used

to derive recommendations for th e values of each

factor. The recommended values were derived solely

from the U.S. EPA ’ s inte rpretation of the available

data. Different values may be appropriate for the user

in consideration of policy, precedent, strategy, or other

factors such as si te-specific information. The U.S.

EPA ’ s procedure for developing recommendations

was as follows:

(1) Study Review and Evaluation: Key studies were

evaluated in term s of both quality and relevance to specific populations (general U. S. population, age

groups, gender, etc.). The criteria for assessing the quality of studies are described in Section 1.4.

(2) Single versus Multiple Key Studies: If only one study was classified as key for a particular factor, the

mean value from that stu dy was selected as the

recommended central value for that population. If multiple key studies with reasonably equal quality, relevance, and study design information were

a vailable, a weighted mean (if appropriate, consideri n g 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 is presented and the user of this handbook must employ judgment in selecting the mo st appropriate value for

the population of interest. Recommendations for upper percentiles, when multiple studies were available, were calculated as the midpoint of the range of upper percentile values of the studies for each age gr oup where data were available.



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(3) Variability: The variability of the factor across the population is discussed. For recommended values, as well as for each of the studies on which the recommendations are base, variability is characterized in one or more of three ways: (1) as a

table 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 parame t ric distributions (e.g., normal, lognormal) to the exposure data have not been performed by the authors of this handbook, but have

been reproduced as they were found in the literature.

Recommendations on the us e of these distributions

are made where appropriate based on the adequacy of

the supporting data. The list of exposure factors and

the way in which variability has been characterized throughout this handbook (i.e., average, median,

upper pe rcentiles, multiple percentiles, fitted distribution) are presented in Table 1-1.

In the p roviding recommendations for the

various exposure factors , an attempt was made to present percentile values that are consistent with the

exposure estimators defined in G uidelines for Exposure Assessment (U.S. EPA, 1992a) (i.e., mean, 50th, 90th, 95th, 98th, and 9 9.9th percentile).
However, this was not always possible, becau se the

data ava ilable were limited for some factors, or the

authors of the study did not provide such information.

It is important to note, however, that these percentiles

were discussed in the guidelines within the context of

risk descriptors and not individual exposure factors.

For example, the guidelines state that the ass essor

may derive a high-end estimate of exposure by using

maximum or near maximum values for one or more sensitive exposure factors, leaving others at their

mean v alue. The term “ upper percentile ” is used throughout this ha ndbook, and it is intended to represent values in the upper tail (i.e., between 90th

and 99.9th percentile) of the distribution of values for a particular exposure factor.

(4) Uncertainty: U ncertainties are discussed in terms
of data limitations, the range of circumstances over

w hich the estimates were (or were not) appli cable, possible biases in the values themselves, a statement

about par ameter uncertainties (measurement error, sampling error) and model or scenario uncertainties if models or scenarios were used to derive the

recommended value. A d iscussion of variability and uncertainty for exposure factors is presented in Chapter 2 of this handbook.

(5) Confidence Ratings: F inally, the U.S. EPA assigned a confidence rating of low, medium or high

to each recommended value. This rating is not

intended to represent an uncertainty analysis; rather, it represents the U.S. EPA ’ s judgment on the quality of the underlying data used to derive the

recommendation. This judgment was made using the

G eneral Assessment Factors (GAFs) described in

Section 1.4. Table 1-2 provides an adaptation of the GAFs, as they pertain to the confidence ratings for the exposure factor recommendations. Clearly, there is a

continuum from low to high, and judgment that was

used to determine these ratings. Recommendations

g iven in this handbook are accompanied by a discussion of the rationale for their rating.

It is important to note that the study elements

listed in Table 1-2 do not have the same weight when arriving at the overall confidence rating for the various

exposure factors. The relative weight of each of these

elem ents for the various factors were subjective and based on the professional judgement of the authors of this handbook. Also, the relative weights depend on

the exposure factor of int e rest. For example, the adequacy of the data colle ction period may be more important when determining usual intake of foods in

a population, but it is not as important for factors

where long-term variability may be small, such as tapwater intake. In the case of tapwater intake, the

currency of the data was a critical el ement in determining the final rating. In general, most studies

ranked high with regard to "level of peer review," "accessibility," "focus on the facto r of interest," and

"dat a pertinent to the U.S." because the U.S. EPA specifi cally sought studies for the handbook that met

these criteria.

The elements in Table 1-2 were important

considerations for inclusion of a study in this

handbook. However, a high score for these elements did not necessarily translate into a high overall score. Other considerations went into determining the overall

score. One such consideration was the ease at which

the exposure factor of interest could be measured. For



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example, soil ingestion by children can be estimated by measuring, in the feces of children, the levels of

certain elements found in soil. Body weight, however, can be measured directly, and it is therefore

a more reliable m easurement. The fact that soil ingestion is more d ifficult to measure than body weight is reflected in th e overall confidence rating given to both of these factors. In general, the better

the methodology used to measure the exposure factor, the higher the confidence in the value.

( 6) Recommendation Tables: The U.S . EPA developed a table at the beginning of each chapter

that summarizes the recommended values for the

r elevant factor. Table ES-1 of the Executive Summary of this handbook summarizes the principal

exposure factors addressed in this handbook and provide s the confidence ratings for each exposure

factor.

1.6 SUGGESTED REFERENCES FOR USE

IN CONJUNCTION WITH THIS

HANDBOOK

Some o f the steps for performing an

exposure assessment are: (1) identifying source of the

environme ntal contamination and the media that

transports the contaminant; (2) determining the

contaminant conce ntration; (3) determining the

exposure scenarios , and pathways and routes of

exposure; (4) determining the exposure time, frequency, and durati on; 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 U.S. EPA documents. Some of these provide guidance while others demonstrate various aspects of

the exposure process. These include, but are not limited, to the following references listed in chronological order:

• Methods for Asses sing Exposure to

C

hemical Substances, Volumes 1-13 (U.S. EPA, 1983-1989);

• Standard Scen arios for Estimating Exposure

to Chemic a l Substances During Use of

Consumer Products (U.S. EPA, 1986a); • Selection Criteria fo r Mathematical Models

Used in Exposure Assessments: Surface

Water Models (U.S. EPA, 1987);

• Selection C riteria for Mathematical Models

Used in Exposure Assessments: Groundwater

Models (U.S. EPA, 1988);

• Risk Assessme n t Guidance for Superfund,

Volume I, Part A, Human Health Evaluation

Manual (U.S. EPA, 1989);

• Methodo logy for Assessing Health Risks

Associated with I ndirect Exposure to

Combustor Emissions (U.S. EPA, 1990);

• Risk Assessment Guidanc e for Superfund,

Volume I, Part B, Development of

Pre liminary Remediation Goals (U.S. EPA, 1991a);

• R isk Assessment Guidance for Superfund,

Volume I, Part C, Risk Evaluation of

Remedial Alternatives (U.S. EPA, 1991b);

• Guidelines for Exposure Assessment (U.S. EPA, 1992a);

• Dermal Exposure Assessment: Principles and Applications (U.S. EPA, 1992b);

• Est i mating Exposures to Dioxin-Like Compounds (U.S. EPA, 1994a);

• Soil Screening Guidance (U.S. EPA 1996a);

• Series 875 Occupational and Residential Exposure Test Guidelines - Final

Guidelines

- Group A

- Application Exposure Monitoring Test Guide lines (U.S. EPA 1996b);

• Series 875 Occupational and Residential Exposure Test Guidelines

- Gro up B - Post Application Exposur e Monitoring Test

Guidelines (U.S. EPA 1996c);



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• Policy for Use of Probabilistic Analysis in

Risk Assessment at the U.S. Environmental Protection Agency , (U.S. EPA, 1997b);

• G uiding Principles for Monte Carl o Analysis (U.S. EPA, 1997c);

• Sociodemographic Data for Identifying Potentially Highly Exposed Populations (U.S. EPA, 1999);

• Options for Develop ing Parametric

P robability Distributions for Exposur e
Factors (U.S. EPA 2000b);

• Risk Assessment Guidance for Supe rfund,

Volume I, Part D, Standardized Planning, Reporting, and Review of Superfund Risk

Assessments (U.S. EPA, 2001a);

• Risk Assessment Guida nce for Superfund

Volume III, Part A, Process for Conducting

P robabilistic Risk Assessments (U.S. EPA

, 2001b);

• Framework for Cumulative Risk Assessment (U.S. EPA, 2003b);

• Example Exposure Scenarios (U.S. EPA, 2003c);

• Risk Assessmen t Guidance for Superfund,

Volume I, Part E, S upplemental Guidance for Dermal Risk Asse ssment (U.S. EPA, 2004);

• Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposur e s to Environmental Contaminants (U.S. EPA, 2005a);

• Cancer Guidelines for Carcinogen Risk

Ass essment Supplemental Guidance for Assessing Susceptibility from Early-Life Exposur e to Carcinogens (U.S. EPA, 2005b); • Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens (U.S. EPA, 2005c);

• Protocol for Human Health Risk Assessment

Protocol for Hazardous Waste Combustion

Facilities (U.S. EPA, 2005d);

• A F ramework for Assessing Health Risk of

Environmental Exposures to Children (Final). (U.S. EPA 2006d); and

• Con cepts, methods, and data sources for

cumulative health risk assessment of multiple chemicals, exposures and effects: a resource document (Final) (U.S. EPA, 2008).

These docum e nts 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.

1.7 THE USE OF AGE GROUPINGS WHEN

ASSESSING EXPOSURE

When this handbook was first published in 2002, no specific g uidance existed with regard to

which age groupings should be used when assessing

children ’ s exposure. Age groupings varied from case

to case and among Prog ram Offices within the U.S.

EPA. They depended on availability of data and were

often based on professional judgement. More recently,

the U.S. EPA has endeavored to establish a consistent

set of age groupings and publish guidance on this topic (U.S. EPA 2005a). This revision of the handbook

attempts to present data in a manner consistent with

the U.S. EPA ’ s recommended set of age groupings.

The development of standardized age bins

was the subject of discussion in a 2000 workshop sponsored by the U.S. EPA Risk Assessment Forum.

The w orkshop was titled “ Issues Associated with Considering Developmental Changes in Behavior and

Anato my When Assessing Exposure to Children ” (U.S. EPA, 2001c). The purpose of this workshop was

to gain insight and input into factors that need to be

considered when developing standardized age bins and identify future research necessary to accomplish these goals. Panelists were divided into two groups. One



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group focused their discussi ons on defining and characterizing the important facets of b e havioral development during childhood, while the other group focused on defining and characterizing physiological development du ring childhood. During the workshop, it was recognized that the ultimate goal of

exposure assessment is to develop a day-to-day model

of human life that can predict the chemical exposures an individual is likely to face at any point in life. However, this is not likely to be accomplished in the

n ear future, and assessors often need to classify

individuals into age bins in order to simplify the

exposure model. The recommendations listed below are those of the panel members and were considered by the U.S. EPA in the development of age groupings:

• Panelists agreed that child development is a series of discrete events, but these events occur along a conti nu um.

• Age grouping/bins are a useful guide to

fulfill the Agency ’ s immediate need, but are only a crude approximation of an underlying

dis t ribution. Ultimately, sufficient data

should be gathered to develop a continuous multivariate model that can replace bins.

• Adequacy of existing exposure data is highly

variable.

• A considerable amount of additional

information already exists, but it is dispersed

in the liter ature. It was recommended that

the U.S. EPA consults with expert s in developmental biology, physiology, pharmacology, and toxicology and conducts an in-depth review of the literature.

• Long term research sho uld include the development of integrated data sets that

c ombines information about the exposure

factors with biomarkers of exposure and

effects.

• The definition of age grou ps/bins for childhood exposure assessment are inextricably linked to toxicokinetic and toxicodynamic issues.

• The two break out groups (i.e., behavioral

and physiological) offe red the following preliminary ideas for age groupings:

Age grouping based on behavioral

characteristics

0-2 months

2 - 6 months

6-12 months 1-2 years 2-6 years 6-11 years 11-16 years 16-21 years Age grouping based on physiological

characteristics

0-1 month

1-6 months

6-12 months 1- 3 years 3-9 years 9-21 years

One can observe tha t there was fairly good

agreement among the two groups with regard to the

age groupings that are important for infa nts and

toddlers. However, there was some disagreement with

reg ard to the older children. Appropriate age groupings depend not only on behavioral and physiological characteristics, bu t also on the specific scenario being studied and chemical of concern. Based upon consideration of the findings of

the technical workshop, as well as analysis of available data, U.S. EPA developed guidance that established a

set of reco mmended age groups for development of

expos u re factors for children entitled “ Guidance for Selecting Age Groups for Monitor ing and Assessing Childhood Exposures to Environmental

Con taminants ” (U.S. EPA, 2005a). This revision of

the handbook was developed specifically to present

exposure factors data in a manner consistent with U.S.

EPA ’ s recommended set of childhood age groupings. The recommended age groups (U.S. EPA, 2005a) are as follows:

Birth to <1 month



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1 to <3 months

3 to <6 months

6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years

1.8 CONSIDERING LIFEST A GE WHEN

CALCULATING EXPOSURE AND

RISK

A key component of U.S. EPA ’ s Guidance

on Selecting Age Groups f or Monitoring and Assessing Child hood Exposures to Environmental

Contaminants (U.S. EPA 2005a) involves the need to sum age-specific differences in exposure across time when assessing long-t erm exposure, as well as integrating these age-spec i fic exposures with age-specific differen ces in toxic potency in those

cases where information exists to describe s u ch differences: an example is carcinogens that act via a mutagenic mode of action ( Supplemental Guidance f

o r Assessing Susceptibility from Early-Life Exposure to Carcinogens

- U.S. EPA, 2005c). When assessing chronic risks (i.e., exposures greater than 10 percent of human lifespan), rather than assuming a constant

level of exposure for 70 years (usually consistent with

an adult level of exposur e), the Agency is now recommending that assessors calculate chronic

exposures by summing time-weighted exposures that

occur at each lifestage; this handbook provides data arrayed by childhood age in order to follow this new

g uidance (U.S. EPA 2005a). This approach is expected to increase the accuracy of risk assessments,
because it will take into account lifestage differences in exposure. Depending on whethe r body-weight-adjusted childhood exposures are either

smal ler or larger compared to those for adults,

calculated risks coul d either decrease or increase

w hen compared with the historical approach of assuming a lifetime of a constant adult level of

exposure. The Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to

Carci nogens also recommended that in those cases where age-related differences in toxicity were also found to occur, differences in bo th toxicity and exposure would need to be int egrated across all

relevant age intervals. T his guidance describes such a

case for carcinogens that act via a mutagenic mode of action, where age dependent potency adj ustments
factors (ADAFs) of 10x and 3x are recommended for children ages birth < 2 years, and 2 < 16 years, respectively when there is exposure during those years and available data are insufficient to derive chemicalspecific adjustment factors.

Table 1-3, along with Chapter 6 of the

“ Supplemental Guidance ” have been developed to help

the reader understand how to use the new sets of

exposure and potency age groupings when calculating

risk through the integration of lifestage specific changes in exposure and potency. Thus, Lifetime Cancer Risk (for a population

with average lif e expectancy of 70 years) = ? (Exposure x Duration/ 70 yrs x Potency x ADAF)

summed across all the age groups presented in Tabl e 1-3. This is a departure from the way cancer risks

have historically been calcula ted based upon the premise that risk is proportional to the daily average of the long term adult dose.

1.9 FUNDAME NTAL PRINCIPLES OF

EXPOSURE ASSESSMENT

The definition of exposure as used by the

International Programme on Chemical Safety (IPCS, 2001) is the “ contact of an organism with a chemical

or physical agent, quantified as the amount of chemical

available at the exchange boundaries of the organism

and available for absorption. ” This means contact

with the visible exterior of a person such as the skin,

and openings such as the mouth, nostrils, and lesions.

The pr ocess of a chemical entering the body can be

described in two steps: contact (exposure) followed by

entry (crossing the boundary). In the context of

environmental risk assessment, risk to an individual or population can be represented as a continuum from the source through exposure to dose to effect as shown in Figu re 1-1 (U.S. EPA, 2003d; IPCS, 2006). The

process begins with a chemical or agent released from

a source into the environment. Once in the environment, the chemical or agent can be transformed

and transported through the en vironment via air,

water, soil, dust, and diet. Individuals become in

contact with the chemical throu gh inhalation,



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### (Eqn 1-1) Body Weigh t x Averagi ng Time 

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ingestion, or skin/eye contact. The individual ’ s activity patterns as well as the concentration of the

chemical will determine the magnitude, fr equency, and duration of the exposure. The exposure becomes

an absorbed dose when the chemical crosse s an

a bsorption barrier. When the chemical or its

metabolites interact with a target tissue, it becomes a target tissue dose, which may lead to an adverse

health outcome. The text under the boxes in Figure

1-1 indicates the spe cific information that may be

needed to characterize each box.

1.9.1 Dose Equations 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 exposure o ccurred.

The AD D can be calculated by averaging the potential dose over body weight and an averaging

time.

The exposure can be expressed as follows:

External Dose = C × IR × E (Eqn 1­ 2)

Where:

C = Contaminant Concentration

IR = Intake Rate ED = Exposure Duration

Contaminant concentration is the

concentration of the contaminant in the medium (air, food, soil, etc.) contacting the body and has units of

mass/volume or mass/mass.

The intake rate refers to the rates of inhalation, ingestion, and dermal contact, depending

on the route of exposure. For ingestion, the intake

rate is si mply the amount of food containing the

contaminant of interest that a n individual ingests during some specific time period (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 a t which contaminated air is

inhaled. Factors presented in this handbook that affect

dermal exposure are skin surface area and estimates of

the amount of soil that adheres to the skin.
The exposure duration is the length of time of

contaminant contact. The length time a person lives in

a n area, frequency of bathing, time spent indoors versus outdoors, etc., all affect the exposure duration. Chapter 16, Activity Factors, gives some examples of

popu lation behavior/activity patterns that may be useful for estimating exposure durations.

When the above parameter values IR and ED

remain constant over time, they are substituted directly

int o the exposure equation. When they change with time, a s ummation approach is needed to calculate exposure. In either case, the exposure duration is the length of time exposure occurs at the concentrat ion

and the intake rate specified by the other parameters in the equation.

Note t hat the advent of childhood age groupings means that separate ADD ’ s should be

cal c ulated for each age group considered. Chronic

expo sures can then be calculated by summing across each lifestage-specific ADD.

Cancer risks have traditionally been

calculated in those cases where a linear non-threshold

model is assumed, in terms of lifetime probabilities by utilizing dose values p resented in terms of lifetime

ADDs (LADDs). The LADD takes the form of the Equation 1-1, with lifetime replacing averaging time.

While the u se of LADD may be appropriate when developing screening level estimates of cancer risk, as discussed in Section 1.8 above, the U.S. EPA is now recommending that risks should be cal culated by integrating exposures or risks throughout all lifestages ( U.S. E PA, 1992a).

For some types of analyses, dos e can be expressed as a total amount (with units of mass, e.g., mg) or as a dose rate in terms of mass/time (e.g., mg/day), or as a rate normalized to body mass (e.g.,

with units of mg of chemical per kg of body weight per day (mg/kg-day)). The LADD is usually expressed in terms of mg/kg-day or other mass/mass-time units.

In most cases (inhalation and ingestion exposures), the dose-response parameters for carcinogenic risks have been adjusted for the



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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.

H owever, the exposure assessor needs to make such an adjustment when calculating dermal exposure and

in other specific cases when current information

indicates that the human absorption factor used in the derivation of the dose-response factor is inappropriate.

For carci nogens, the duration of a lifetime has traditionally been assigned the nominal value of

70 years as a reasonable approx i mation. For

exposure estimates to be used for assess ments other

than carcinogenic risk, various averaging periods

have been used. For acute exposures, the doses are usually averaged over a day or a sing le event. For

nonchronic noncancer effects, the time period used is

the actual period of exposure (exposure duration). The object i ve 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 de pends on the units of the exposure

data presented in thi s handbook. For example, for

food ingestion, the body weights of the surveyed populations were k nown in the USDA surveys, and they were explicitly factored into the food intake data

in order to calculate the intake as g/kg body weightday. In this case, the body weight has already been

included in the “ intak e 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 incidental ingestion of soil and dust are not

normalized to body weight. In this case, the exposure

assessor will need to use (in Equation 1-1) the average weight of the exposed population during the

time when the exposure actually occurs. When making body weight assumptions, care must be taken

that the values used for the population parameters in

the dose-response ana lysis are consistent with the population parameters used in the exposure analysis. Intraspe c ies adjustments based on lifestage can be

made using a scaling factor of BW 3⁄4 

### (U.S. EPA 2006d, 2006e). Some of the parameters (primarily concentrations) used in es t imating exposure are exclusively site specific, and ther efore default

recommendations should not be used. It should be

noted that body weight is correlated with food consumption rates and inhalation rates.

The link between the intake rate value and the

exp osure duration value is a common source of

confusion in defining exposure scenarios. It is important to define the duration estimate s o that it is

consistent with the intake rate:

• The intake rate can be based on an individual

event (e.g., se rving size per event). The

durati on should be based on the number of events or, in this case, meals.

• The intake rate also can be based on a longterm average, such as 10 g/day. In this case

the duration should be based on the total time interval over which the exposure occurs.

The objective is to define the te r ms so that,

when multiplied, they give the appropriate estimate of

mass of contaminant contacted. This can be

acc omplished 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.

I nhalation dosimetry is employed to deriv e the human equivalent expos ure concentrations on which

inhalation unit risks, and reference concentrations, are based (U.S. EPA, 1994b). U.S. EPA has traditionally approximated children ’ s respiratory exposure by using

adult values, although a recent review (Ginsberg et al., 2005) concluded that there may be some cases where

young children ’ s greater inh a lation rate per body weight or pulmonary surface area as comp ared to

adults can result in greater exposures than adults. The implications of this difference for inhalation dosimetry

and children ’ s risk assessment were discussed at a peer involvement workshop hosted by the U.S.EPA in 2006 (Foos et al., 2008). Consideration of lifestage-particular physiological characteristics in the dosimetry analysis

may result in a refinement to the human equivalent

concentration to insure relevance in risk assessment

across lifestages, or might conceivably conclude with multiple human equivalent concentrations, and



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correspon d ing inhalation unit risk values (e.g.,
separate fo r childhood and adulthood) (U.S. EPA, 2005b). The RfC methodology, which is described in

Methods for D erivation of Inhalation Reference

Concentrations and Applications of Inhalation Dosimetry (U.S. EPA, 1994b), allows the use r to incorporate population-specific assumptions into the

models. The reader is referred to U.S. EPA guidance (U.S. EPA, 1994b) on how to m ake these adjustments.

There are no sp ecific exposure factor assumptions in the derivation o f Reference Doses (RfDs). The assessment of the potential for adverse

health effects in infants and chil dren is part of the

over all hazard and dose-response assessment for a

chemical. Available data pertinent to children ’ s healt h risks are evaluated along with data on adults

and the no-observed-adverse-effect-le vel (NOAEL) or

benchmark dose (BMD) for the most sensitive critical effect(s), based on consideration of all health effects. By doing this, protection of the health of children will

be considered along with that of other sensitive populations. In some cases, it is appropriate to

evaluate the potential hazard to children s eparately

from the asses sment for the general population or other population subgroups.

1.9.2 Use of Ex p osure Factors Data in Probabilistic Analyses Although this handbook is not inte n ded to provide complete guidance on the use of Monte Carlo

and other probabilistic analyses, some of the data in

this handbook may be approp riate for use in probabilistic assessments. The use of Monte Carlo or other probabilistic analysis requires characterization

of the variability of exposure factors and requires the

selection of distributions or histograms for the input

parameters of the dose equations presented in Section

1.9.1. The following suggestions are provided for consideration when using such techniques:

• The exposure assessor should only consider using probabilistic analysis whe n there are

credible distribution data (or ranges) for the

fac tor under consideration. Even if these

distributions a r e known, it may not be

necessary to appl y this technique. For example, if only average exposure values are nee ded, these can often be computed accurately by using average values for each of

the input parameters unless a non-linear model is used. Probabilistic analysis is also

not nec essary 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 para meters. Alternatively, the assessor m ay use the

maximum values for those parameters that have the greatest variance.

• It is important to note that the selection of

distributions can be highly site-specific and dependent on the purpose of the assessment.

In some cases the selection of distributions

are driven by specific legislation. It will always invo l ve some degree of judgment.

Distributions derived from national data may

not represent local conditions. The assessor

needs to evaluate the site-specific data, when available, to assess th eir quality and applicability. The assessor may decide to use

distributional data drawn from the national or other surrogate population. In this case, it is important that the assessor address the extent

to which local conditions may differ from the surrogate data.

• It is also important to consider the independence/dependence of variables and

data used in a simulation. For example, it

may be reasonable to as sume that ingestion

rate and contaminant concentration in foods are independent variables, but ingestion rate
and body weight may or may not be independent.

In addition to a qualitative statement of uncertainty, the representativeness assumption should

be appropriately address e d as part of a sensitivity analysis.

• Distribution functions to be used in

pro babilistic analysis may be derived by fitting an appropriate function to empirical



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data. In doing this, it should be recognized that

in the lower and upper tails of the di stribution

the data are sc arce, 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

arbitrar y choice of an inappropriate function,

several techniques can be used. One technique 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 that have adequate fit but form upper and lower

bounds to th e 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 ca n be found in a standard statistics text, (e.g., Gilbert,

1987 , among others). Off-the-shelf computer

software can be used to statistically determine the

distributions that fit the d ata. Other software

tools are available to identif y outliers and for conducting Monte Carlo simulations.

• If only a range of values is k nown 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.

- assume a distribution. (The rationale for the

selection of a distr ibution should be discussed at length.) There are, however,

cases w here assuming a distribution is not

recommended. These include:

-- d ata are missing or very limited fo r a key parameter;

-- data were collected ov er 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 becaus e sample

size was small or the population studied

was selected from a local area and was therefore not representative of the area of interest; for example, soil ingestion by children; and

-- r anges for a key variable are uncert ain

d ue to experimental error or othe r limitations in the study design or methodology; for example, soil ingestion by children.

1.10 CUMULATIVE EXPOSURES

The U.S. EPA recognizes that child ren may be exposed to mixtures of chemicals both indoors and

o utdoors through more than one pathway. New

dir ections in risk assessments in the U.S. EPA put

m ore emphasis on total exposures via multip l e pathways (U.S. EPA, 2003d, U.S. EPA, 2008). Over the last several years, the U.S. EPA has developed a methodology for assessing risk from multiple

chemicals (U.S. EPA, 1986b, 2000c). For more information, the reader is referred to th e U.S. EPA ’ s Framework for Cumulative Risk Assessment (U.S. EPA, 2003b).

1.11 ORGANIZATION The handbook is organized as follows:

Chapter 1 Introduction

Chapter 2 Variability and uncertainty

Chapter 3 Ingestion of water and other select liquids

Chapter 4 Non-dietary ingestion

Chapter 5 Soil and dust ingestion

Chapter 6 Inhalation rates

Chapter 7 Dermal exposure factors

Chapter 8 Body weight

Chapter 9 Intake of fruits and vegetables

Chapter 10 Intake of fish and shellfish



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Chapter 11 Intake of meats, dairy products,

and fats

. Chapter 12 Intake of grain products

Chapter 13 Intake of home-produced foods

Chapter 14 Total food intake

Chapter 15 Human milk intake

Chapter 16 Activity factors

Chapter 17 Consumer products

Recommended values for exposure factors are presented at the beginning of each chapter, followed by detailed discussions of the data on

which these recommendations are based. Because of the large number of tables in this handbook, tables are presented at the end of each chapter, before the appendices, if any.

1.12 REFERENCES FOR CHAPTER 1 AAP (1997) Child Health Issues for the Second Session of the 106th Congress. Environmental Health. American Academy of Pediatrics Department of Federal Affairs 601 13th Street, NW Suite 400 North Washington, DC 20005. AAP Washington

Office Child Health Issues for the Second Session of the 106th Congress:

Environmental Health. Available online at http://www.aap.org/advocacy/washing/chieh/

htm. Breysee, P.N.; Buckley, T.J.; Williams, D.; Beck, C.M.; Jo, S.; Merriman, B.; Kanchanaraksa, S.; Swartz, L.J.; Callahan, K.A.; Butz, A.M.; Rand, C.S.; Diette, G.B.; Krishman, J.A.; Moseley, A.M.; Curlin-Brosnan, J.; Durkin, N.B.; Eggleston, P.A. (2005) Indoor exposures to air pollutants and allergens in the homes of asthmatic children in inner-city

Baltimore. Environ Res. 98:167-176. Clewell, H.J.; Gentry, P.R.; Covington, T.R.; Sarangapani, R.; Teeguarden, J.G. (2004)Evaluation of the potential impact of ageand gender-specific pharmacokinetic differences on tissue dosimetry. Toxicol Sci.

79:381-393. Curwin, B.D.; Hein, M.J.; Sanderson, W.T.; Striley, C.; Heederik, D.; Kromhout, H.; Reynolds, J.j.; Alavanja, M.C. (2007) Pesticide dose

estimates for children of Iowa farmers and

non-farmers. Environ Res. 105: 307-315. EO (1997) Executive Order 13045. Protection of

Children for Environmental Health Risks and Safety Risks. Available on line at http://yosemite.epa.gov/ochp/ochpweb.nsf/con
tent/whatwe_executiv.htm

. Eskenazi, B.; Bradman, A.; Castriona, R. (1999) Exposure of children to organophosphate pesticides and their potential adverse health effects. Environ Health Perspect. 107(3):409­

419. Foos, B.; Marty, M.; Schwartz, J.; Bennet, W.; Moya, J.; Jarabek, A.M.; Salmon, A.G. (2008) Focusing on children's inhalation dosimetry and health effects for risk

assessment: an introduction. J Toxicol Environ Health, Part A, 71(3):149-165. Garry, V.F. (2004) Pesticides and children. Toxicol Appl Pharmaol. 198(2004):152-163. Gilbert, R.O. (1987) Statistical methods for environmental pollution monitoring. New

York: Van Nostrand Reinhold. Ginsberg, G.; Hattis, D.; Sonawame, B.; Russ, A.; Banati, P.; Kozlak, M.; Smolenski, S.; Goble, R. (2002) Evaluation of child/adult pharmacokinetic differences from a database derived from the therapeutic drug literature.

Toxicol Sci. 66:185-200. Gurunathan, S.; Robson, M.; Freeman N.; Buckley, B.; Roy, A.; Meyer, R.; Bukowski, J.; Lioy, P.J. (1998) Accumulation of chloropyrifos on residential surfaces and toys accessible to children. Environ Health Perspect. 106(1):9­

16. IPCS (2001) Glossary of exposure assessment-related terms: a compilation. IPCS Exposure Terminology Subcommittee, International Programme on Chemical Safety, World Health Organization. Available on line at http://www.who.int/ipcs/publications/methods /harmonization/en/compilation_nov2001.pdf



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IPCS (2006) Principles for Evaluating Health Risks in Children Associated with Exposure

to Chemicals. Environmental Health Criteria 237. International Programme on Chemical Safety, World Health Organization. Landrigan, P.J.; Sonawane, B.; Butler, R.N.; Transande, L.; Callan, R. (2005) Early environmental origins of neurodegenerative

disease in later life. Environ Health Perspect. 113(9):1230-1233. Lewis, R. G.; Fortune C.; Willis, R. D.; Camann, D. E.; Antley, J. T. (1999) Distribution of pesticides and polycyclic aromatic hydrocarbons in house dust as a function of particle size. Environ Health Perspect. 107(9):721-726. Myers, G.J.; Davidson, P.W. (2000) Does methylmercury have a role in causing developmental disabilities in children? Environ Health Perspect. 108(3):413-419. Nishioka, M.G.; Burkholder, H.M.; and Brinkman, M.C.; Lewis, R.G. (1999) Distribution of 2,4-dihlorophenoxyacetic acid in floor dust throughout homes following homeowner and commercial lawn application: quantitative effects of children, pets, and shoes. Environ

Sci Technol. 33:1359-1365. Selevan, S.G.; Kimmel, C.A.; Mendola, P. (2000) Identifying critical windows of exposure for children's health- monograph based on papers developed from the Workshop: Identifying Critical Windows of Exposure for Children's Health held September 14-15, 1999 in Richmond, VA. Environ Health Perspect. 108(3):451-455. U.S. EPA. (1983-1989) Methods for assessing exposure to chemical substances. Volumes 1 - 13. Office of Toxic Substances, Exposure Evaluation Division, Washington, DC. U.S. EPA. (1986a) Standard scenarios for estimating exposure to chemical substances during use of consumer products. Volumes I and II. Office of Toxic Substance, Exposure Evaluation Division, Washington, DC. U.S. EPA. (1986b) Guidance for the health risk

assessment of chemical mixtures. Risk Assessment Forum, Washington, DC. EPA/630/R-98/002. Available on line at http://cfpub.epa.gov/si/si_public_record_Repo rt.cfm?dirEntryID=20533 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. (1988) Selection criteria for mathematical models used in exposure assessments: groundwater models. Exposure Assessment Group, Office of Health and Environmental Assessment, Washington, DC. EPA/600/8 88/075. Available from NTIS, Springfield, VA; PB-88-248752/AS. U.S. EPA. (1989) Risk assessment guidance for Superfund. Volume I Human health

evaluation manual: Part A. Interim Final. Office of Solid Waste and Emergency Response, Washington, DC. EPA/540-1­

89/002. Available online at http://www.epa.gov/oswer/riskassessment/rags

a/index.htm

. 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. (1991a) Risk assessment guidance for Superfund. Volume I Human health evaluation manual: Part B, Development of preliminary remediation goals. Office of Solid Waste and Emergency Response, Washington, DC. EPA/540/R-92-003.

Available online at http://www.epa.gov/oswer/riskassessment/rags

b/index.htm. U.S. EPA. (1991b) Risk assessment guidance for Superfund. Volume I Human health evaluation manual: Part C, Risk evaluation of

remedial alternatives. Office of Solid Waste and Emergency Response, Washington, DC.

Publication 9285.7-01C. Available online at http://www.epa.gov/oswer/riskassessment/rags

c/index.htm

.



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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. Available at http://www.epa.gov/NCEA/raf/pdfs/exposure .pdf

. U.S. EPA. (1992b) Dermal exposure assessment: principles and applications. Washington,

DC: Office of Health and Environmental

Assessments. EPA/600/8-9/011F. Available

online at http://www.epa.gov/ncea/pdfs/efh/references/

DEREXP.PDF

. U.S. EPA. (1994a) Estimating exposures to dioxin-like compounds. Draft Report. Office of Research and Development, Washington, DC. EPA/600/6-88/005Cb.

U.S. EPA. (1994b) Methods for derivation of

inhalation reference concentrations and applications of inhalation dosimetry Washington, DC. EPA/600/8-90/066F. U.S.EPA (1996a) Soil screening guidance. Office of Solid Waste and Emergency Response, Washington, DC. EPA/540/F-95/041.

Available online at http://www.epa.gov/superfund/health/conme

dia/soil/index.htm U.S.EPA. (1996b) Series 875 Occupational and Residential Exposure Test Guidelines - Final Guidelines - Group A - Application Exposure Monitoring Test Guidelines.

EPA712-C96-261

. Available online at http://www.epa.gov/opptsfrs/publications/OP PTS_Harmonized/875_Occupational_and_R esidential_Exposure_Test_Guidelines/Series/ 875_000.pdf

U.S. EPA. (1996c) Series 875 Occupational and Residential Exposure Test Guidelines ­ Group B - Post Application Exposure Monitoring

Test Guidelines. EPA712-C96-266.

Available online at http://www.epa.gov/opptsfrs/publications/OP PTS_Harmonized/875_Occupational_and_R esidential_Exposure_Test_Guidelines/Series/ 875-2000.pdf U.S. EPA. (1997a) Exposure factors handbook.

National Center for Environmental Assessment, Office of Research and Development, Washington, DC. EPA/600/P-95/002Fa,b,c. Available online at http://www.epa.gov/ncea/efh/

. U.S. EPA. (1997b) Policy for use of probabilistic analysis in risk assessment at the U.S. Environmental Protection Agency. Science Policy Council. Available online at http://www.epa.gov/OSA/spc/pdfs/probpol.pdf U.S. EPA. (1997c) Guiding principles for Monte Carlo analysis. Office of Research and Development, Risk Assessment Forum, Washington, DC. EPA/600/R-97/001.

Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=29596

. U.S. EPA. (1999) Sociodemographic data used for identifying potentially highly exposed populations. U.S. Environmental ProtectionAgency, Washington, DC. EPA/600/R99/060. Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=22562

. U.S. EPA. (2000a) Strategy for research on risks to

children. Office of Research andDevelopment, Washington, DC. EPA/600/R00/068.

U.S. EPA. (2000b) Options for developing parametric probability distributions for exposure factors. National Center for Environmental Assessment, Office of Research and Development, Washington, DC.

EPA/600/R-00/058. Available on line at

http://www.epa.gov/ncea/paramprob4ef.htm

. U.S. EPA. (2000c) Supplementary guidance for conducting health risk assessment of chemical

mixutres. Risk Assessment Forum, Washington, DC. EPA/630/00/002F.

Available on line at http://www.epa.gov/NCEA/raf/pdfs/chem_mi x/chem_mix_08_2001.pdf U.S. EPA. (2001a) Risk assessment guidance for Superfund. Volume I Human health evaluation manual: Part D, Standardized planning, reporting and review of Superfund

risk assessments. Office of Solid Waste and



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Emergency Response, Washington, DC.

Publication 9285.7-47. Available online at

http://www.epa.gov/oswer/riskassessment/ra gsd/tara.htm. U.S. EPA. (2001b) Risk assessment guidance for Superfund. Volume III, Part A, Process for conducting probabilistic risk assessment. Office of Solid Waste and EmergencyResponse, Washington, DC. EPA/540/R02/002. Available online at http://www.epa.gov/oswer/riskassessment/ra gs3adt/ U.S. EPA (2001c) Summary report of the technical workshop on issues associated with considering developmental changes in behavior and anatomy when assessing exposure to children. Office of Research and Development, Risk Assessment Forum, Washington, DC. EPA/630/R-00/005.

Available online at http://cfpub.epa.gov/ncea/raf/recordisplay.cf

m?deid=20680 U.S. EPA. (2002a) Child-Specific exposure factors

handbook. Interim Final. National Center for Environmental Assessment, Office of Research and Development, Washington,

DC. EPA/600/P-00/002B. U.S. EPA (2002b). Overview of the EPA quality system for environmental data and technology. Office of Environmental Information, Washington DC. EPA/240­

R/02/003. Accessed online at http://www.epa.gov/quality/qs-docs/overview -final.pdf U.S. EPA (2003a). A summary of general assessment factors for evaluating the quality

of scientific and technical information. Science Policy Council, Washington DC.

EPA/100/B-03/001. Accessed online at

http://www.epa.gov/osa/spc/pdfs/assess2.pdf U. S. EPA. (2003b) Framework for cumulative risk assessment. Risk Assessment Forum, Washington, DC. EPA/630/P-02/001F.

Available online at http://cfpub.epa.gov/ncea/raf/recordisplay.cf

m?deid=54944

. U.S. EPA. (2003c) Example exposure scenarios. Office of Research and Development, Washington, DC; EPA/600/R03/036.

Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=85843

. U.S. EPA. (2003d) Human health research strategy. Office of Research and Development, Washington DC 20460. EPA/600/R-02/050.

Available online at http://www.epa.gov/ORD/htm/researchstrategi

es.htm#rs01 U.S. EPA. (2004) Risk assessment guidance for Superfund. Volume I Human health evaluation manual: Part E, Supplemental guidance for dermal risk assessment. Interim. Office of Solid Waste and Emergency Response, Washington, DC.

EPA/540/R/99/005. Available online at http://www.epa.gov/oswer/riskassessment/rags

e/index.htm

. U.S. EPA. (2005a) Guidance on selecting age groups for monitoring and assessing childhood exposures to environmental

contaminants. Office of Research andDevelopment, Washington, DC. EPA/630/P03/003F. Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=146583 U.S. EPA. (2005b) Guidelines for carcinogen risk

assessment. EPA/630/P-03/001F. Available

online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=116283

. U.S. EPA. (2005c) Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens. Risk AssessmentForum, Washington, DC. EPA/630/R03/003F. Available online at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=116283. U.S. EPA. (2005d) Protocol for human health risk assessment protocol for hazardous waste

combustion facilities. EPA530-R-05-006.

Available online at http://www.epa.gov/combustion/risk.htm

U.S. EPA. (2006a) Approaches for the application of physiologically based pharmacokinetic



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(PBPK) models and supporting data in risk

assessment. Office of Research andDevelopment, Washington DC. EPA/600/R05/043F. Available online at http://cfpub.epa.gov/ncea/CFM/recordisplay.

cfm?deid=157668 U.S. EPA. (2006b) Use of physiologically based pharmacokinetic (PBPK) models to quantify the impact of human age and interindividual differences in physiology and biochemistry pertinent to risk (Final Report). Office of Research and Development, Washington,

DC. EPA/600/R-06/014A. Available online

at http://cfpub.epa.gov/ncea/CFM/recordisplay.

cfm?deid=151384 U.S. EPA (2006c) Guidance on systematic planning using the data quality objectives process. Office of Environmental Information, Washington DC.

EPA/240B/06/001. Availabel online at

http://www.epa.gov/QUALITY/qs-docs/g4-fi nal.pdf U.S. EPA. (2006d) A framework for assessing health risk of environmental exposures to children (Final). Office of Research and Development, Washington, DC.

EPA/600/R-05/093F. Available online at

http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=158363 U.S. EPA (2006e) Harmonization in interspecies

3/4 

### extrapolation: use of BW as default

method in derivation of the oral RfD (External Review Draft). Risk AssessmentForum, Washington, DC. EPA/630/R06/001. Available online at http://cfpub.epa.gov/ncea/raf/recordisplay.cf

m?deid=148525 U.S. EPA (2008) Concepts, methods, and data

sources for cumulative health risk assessment of multiple chemicals, exposures and effects: a resource document (final). Office of Research and Development, Washington, DC. EPA/600/R-06/013F.

Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cf

m?deid=190187



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### Table 1-1. Characterization of Variability in Exposure Factors

Exposure Factors Average Median Upper percentile Multiple

Percentiles

Ingestion of water and other select liquids 

### T T T T

Non-dietary ingestion 

### T T T

Soil and dust ingestion 

### T T T a



### Inhalation rate 

### T T T T

Surface area

Soil adherence 

### T
T T T

Body weight 

### T T T T

Intake of fruits and vegetables 

### T T T T

Intake of fish and shellfish 

### T T T T

Intake of meats, dairy products,

and fats 

### T T T T

Intake of grain products 

### T T T T

Intake of home produced foods 

### T T T T

Total food intake 

### T T T T

Human milk intake 

### T T

Time indoors

Time outdoors Time showering Time bathing Time swimming Time playing on sand/gravel Time playing on grass Time playing on dirt 

### T
T
T
T
T
T
T
T T
T
T
T
T
T T
T
T
T
T
T T
T
T
T
T
T

a 

### Soil pica and geophagy.



### T = Data available Child-Specific Exposure Factors Handbook

Chapter 1 - Introduction 

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Child-Specific Exposure Factors Handbook Page September 2008 1-21Table 1-2. Considerations Used to Rate Confidence in Recommended Values

General Assessment Factors Increasing Confidence Decreasing Confidence

Soundness

Adequacy of Approach

Minimal (or defined) Bias The studies used the best available methodology and capture the measurement of

interest.

As the sample size relative to that of the target population increases, there is greater

assurance that the results are reflective of the target population.

The response rate is greater than 80 percent for in-person interviews and telephone surveys, or greater than 70 percent for mail

surveys.

The studies analyzed primary data.

The study design minimizes measurement

errors. There are serious limitations with the approach used; study design does not accurately capture the

measurement of interest.

Sample size too small to represent the population of

interest.

The response rate is less than 40 percent.

The studies are based on secondary sources.

Uncertainties with the data exist due to measurement

error. Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The studies focused on the exposure factor of

interest.

The studies focused on the U.S. population.

The studies represent current exposure

conditions.

The data collection period is sufficient to estimate long-term behaviors. The purpose of the studies was to characterize a

related factor.

Studies are not representative of the U.S. population.

Studies may not be representative of current exposure

conditions.

Shorter data collection periods may not represent long-term exposures.

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The study data could be accessed.

The results can be reproduced or methodology

can be followed and evaluated.

The studies applied and documented quality assurance/quality control measures Access to the primary data set was limited.

The results cannot be reproduced, the methodology is hard to follow, and the author(s) cannot be located.

Information on quality assurance/control was limited

or absent. 

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1-22 September 2008Table 1-2. Considerations Used to Rate Confidence in Recommended Values (continued)

General Assessment Factors Increasing Confidence Decreasing Confidence

Variability and Uncertainty

Variability in Population

Uncertainty The studies characterize variability in the population studied.

The uncertainties are minimal and can be

identified. Potential bias in the studies are stated or can be determined from the study design. The characterization of variability is limited.

Estimates are highly uncertain and cannot be characterized. The study design introduces biases in

the results.

Evaluation and Review

Peer Review

Number and Agreement of Studies The studies received high level of peer review (e.g., they are published in peer review journals).

The number of studies is greater than 3. The

results of studies from different researchers are in agreement. The studies received limited peer review.

The number of studies is 1. The results of studies from different researchers are in disagreement. 

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Table 1-3. Integrating U.S. EPA ’ s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005a) with U.S. EPA ’ s Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens (U.S. EPA, 2005c) For Those Contaminants Which Act Via a Mutagenic Mode of Action

Exposure Age Group a 

### Exposure Duration (yr) ADAF (Age-Dependent Potency Adjustment Factor)

Birth to < 1 month 0.083 10x

1 < 3 months 0.167 10x

3 < 6 months 0.25 10x

6 < 12 months 0.5 10x

1 to < 2 years 1 10x

2 to < 3 years 1 3x

3 to < 6 years 3 3x

6 to < 11 years 5 3x

11 to < 16 years 5 3x

16 to < 21 years 5 1x

> 21 years (21 to < 70 yr) 49 1x

a 

### EPA ’ s recommended childhood age groups (excluding ages >21 years).



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Source: U.S. EPA, 2003d; IPCS, 2006.



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### Child-Specific Exposure Factors Handbook

Chapter 2 - Variability and Uncertainty

VARIABILITY AND UNCERTAINTY Variability and uncertainty are inherent in the exposure assessment process. Addressing variability and uncertainty will increase the likelihood that results of an assessment or analysis will be used in an appropriate manner. Thus, careful consideration of the variabilities and uncertainties associated with the exposure factors information used in an exposure assessment is of utmost importance. The characterization of variability and uncertainty will also assist in communicating risks to the risk manager and the public. Exposure assessment can involve a broad array of information sources and analysis techniques (U.S. EPA, 1992). 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 for the need for variability and uncertainty analysis in exposure assessment. They state that when scientists report quantities that they have measured, they are expected to routinely report an estimate of the probable error associated with such measurements. They conclude

that because variabilities and 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 variabilities and uncertainties associated

with their estimates. Some additional reasons for addressing variability and uncertainty in exposure or risk assessments (U.S. EPA, 1992, Morgan and Henrion, 1990) include the following:

• Decisions may need to be made about whether or how to expend resources to acquire additional information;

• Biases may occur in providing a so-called "best estimate" that in actuality is not very accurate; and

• Important factors and potential sources of disagreement in a problem may be able to be

identified. This chapter is intended to acquaint the exposure assessor with some of the fundamental concepts and precepts of variability and uncertainty as they relate to exposure assessment and the exposure factors presented in this handbook. It also provides methods and considerations for evaluating and presenting the uncertainty associated with exposure estimates. Subsequent sections in this chapter are devoted to the following topics:

• Variability versus uncertainty;

• Types of variability;

C 

### Addressing variability;

• Types of uncertainty;

• Reducing uncertainty;

• Analysis of variability and uncertainty; and

• Presenting results of variability/uncertainty analysis.

Fairly extensive treatises on the topic of uncertainty have been provided, for example, by Morgan and Henrion (1990), the National Research Council (NRC, 1994) and, to a lesser extent, the U.S. EPA (1992; 1995). The topic commonly has been treated as it relates to the overall process of conducting risk assessments; because exposure assessment is a component of risk-assessment process, the general concepts apply equally to the exposure-assessment component. Since the publication of the National Research Council’s report entitled Science and Judgement

in Risk Assessment (NRC, 1994), the field of variability and uncertainty analysis has continued to evolve. The use of probabilistic techniques to address variability and uncertainty have continued to increase. There are numerous on going efforts in the Agency and elsewhere to further improve the characterization of variability and uncertainty. For example, an Agency task force is developing white papers on the use of expert elicitation for characterizing uncertainty in risk assessments. The

U.S. EPA’s Risk Assessment Forum has established a workgroup to promote the use of probabilistic techniques

to better assess and communicate risk. The International Programme on Chemical Safety (IPCS) is developing guidance on characterizing and communicating uncertainty in exposure assessment (WHO, 2006).

2.1 VARIABILITY VERSUS UNCERTAINTY While some authors have treated variability as a specific type or component of uncertainty, the U.S. EPA (1995) has advised the risk assessor (and, by analogy, the



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Chapter 2 - Variability and Uncertainty

exposure assessor) to distinguish between variability and uncertainty. Uncertainty represents a lack of knowledge about factors affecting exposure or risk, whereas variability arises from true heterogeneity across people, places or time. In other words, uncertainty can lead to inaccurate or biased estimates, whereas variability can affect the precision of the estimates and the degree to which they can be generalized. Most of the data presented in this handbook concerns variability. Variability and uncertainty can complement or confound one another, and it may not always be appropriate to give special Uncertainty - a lack of knowledge significance to about factors affecting exposure or distinguishing risk. between the Variability - arises from true two in every heterogeneity across people, places case.	 Consider or time. a situation that

r e l a t e s t o 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 or the contaminant concentration in

water. It is impractical to measure the individual's dose every day. For this reason, the exposure assessor may estimate the average daily dose (ADD) based on a finite number of measurements, in an attempt to "average out" the day-to-day variability. The individual has a true (but unknown) ADD, which has now been estimated based on a sample of measurements. Because the individual's true average is unknown, it is uncertain how close the estimate is to the true value. Thus, the variability across daily doses has been translated into uncertainty in the ADD. Although the individual's true ADD has no variability, the estimate of the ADD has some uncertainty. It should be noted, however, that a rigid delineation of variability and uncertainty may not be as useful as assessing the available information and attendant variation and properly accounting for it (e.g., sensitivity analysis). The above discussion pertains to the ADD for one person. Now consider a distribution of ADDs across individuals in a defined population (e.g., the general U.S. population). In this case, variability refers to the range

and distribution of ADDs across individuals in the population. By comparison, uncertainty refers to the exposure assessor's state of knowledge about that distribution, or about parameters describing the distribution (e.g., mean, standard deviation, general shape, various percentiles). As noted by the National Research Council (NRC, 1994), the realms of variability and uncertainty have fundamentally different ramifications for science andjudgment. For example, uncertainty may force decisionmakers to judge how probable it is that exposures have been overestimated or underestimated for every member of the exposed population, whereas variability forces them to cope with the certainty that different individuals are subject to exposures both above and below any of the exposure levels chosen as a reference point.

2.2 TYPES OF VARIABILITY Variability in exposure is a function of the variability in human exposure factors (i.e., those related to an individual's location, activity, behavior or preferences at a particular point in time, or physiological characteristics such as body weight), as well as variations in contaminants concentrations (i.e., those related to pollutant emission rates and physical/chemical processes that affect concentrations in various media; e.g., air, soil, food and water). The variations in human exposure factors and chemical concentrations 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, four types of variability can be distinguished:

• Variability across locations (Spatial Variability);

• Variability over time (Temporal Variability);

• Variability within an individual (Intra-individual Variability; and

• Variability among individuals (Inter-individual Variability).

Spatial variability can occur both at regional (macroscale) and local (microscale) levels. For example, fish intake rates can vary depending on the region of the country. Higher consumption may occur among populations located near large bodies of water such as the Great Lakes or coastal areas. As another example,



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Chapter 2 - Variability and Uncertainty

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 areexamples of longer-term variability. Examples of shorterterm variability are differences in industrial or personal activities on weekdays versus weekends or at different times of the day. Intra-individual variability is a function of fluctuations in an individual’s physiologic (e.g., body weight), or behavioral characteristics (e.g., ingestion rates or activity patterns). For example, patterns of food intake change from day to day, and may change significantly over a lifetime. Intra-individual variability may be associated with spatial or temporal variability. For example, because an individual’s dietary intake may reflect local food sources, intake patterns may change if place of residence changes. Also, physical activity may vary depending upon the season, lifestage, or other factors associated with temporal variability. 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, activity patterns, and ingestion rates. Each of these variabilities, in turn, may be related to several underlying phenomena that vary. For example, the natural variability in human weight is due to a combination of genetic, nutritional, and other lifestyle or environmental factors. Variability arising from independent factors that combine multiplicatively generally will lead to an approximately lognormal distribution across the population, or across spatial/temporal dimensions. Inter-individual variability may also be related to spatial and temporal factors.

2.3 ADDRESSING VARIABILITY As noted in Section 1.6 of this handbook, this document attempts to characterize variability of each of the exposure factors presented. Variability is addressed by presenting data on the exposure factors in one of the following three ways: (1) as tables with percentiles or ranges of values, (2) as analytical distributions with specified parameters, or (3) as a qualitative discussion. According to the National Research Council (NRC 1994), variability in exposure estimates can be addressed, especially with regard to point estimates such as central tendency (CT) or high end exposures (e.g., reasonable maximum exposure (RME) used in the Superfund program) in four basic ways (Table 2-1) when dealing with science-policy questions surrounding issues such as exposure or risk assessment. The first is to ignore the variability . This strategy is likely to be used in combination with one of the other strategies described below (e.g., use the average value), and tends to work best when the variability is relatively small, as in the case with adult body weights. For example, the U.S.EPA practice of assuming that all adults weigh 70 kg is likely to be

correct within ±25% for most adults and within a factor of 3 for virtually all adults (NRC,1994). However, it is cautioned that this approach may not be appropriate for children, where variability may be large. The second strategy involves disaggregating the variability in some explicit way, in order to better

understand it or reduce it. Mathematical models are appropriate in some cases, as in fitting a sine wave to the annual outdoor concentration cycle for a particular pollutant and location. In other cases, particularly those involving human characteristics or behaviors, it is easier to disaggregate the data by considering all the relevant subgroups or subpopulations. For example, distributions of body weight could be developed separately for adults, adolescents and children, and even for males and females within each of these subgroups. Temporal and spatial analogies for this concept involve measurements on appropriate time scales and choosing appropriate subregions or microenvironments. The third strategy is to use the average value of a quantity that varies. Although this strategy might appear as tantamount to ignoring variability, it needs to be based on a decision that the average value can be estimated reliably in light of the variability (e.g., when the variability is known to be relatively small, as in the case of adult body weight). The fourth strategy involves using the maximum

or minimum value for an exposure factor. In this case, the variability is characterized by the range between the extreme values and a measure of central tendency. This



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is perhaps the most common method of dealing with variability in exposure or risk assessment -- to focus on one time period (e.g., the period of peak exposure), one spatial region (e.g., in close proximity to the pollutant source of concern), or one subpopulation (e.g., exercising asthmatics). As noted by the U.S. EPA (1992), when an exposure assessor develops estimates of high-end individual exposure and dose, care must be taken not to set all factors to values that maximize exposure or dose -­ such an approach will almost always lead to an

overestimate. Probabilistic techniques (e.g., Monte Carlo or Latin Hypercube Simulation) are frequently used for characterizing the variability in risk estimates by repeatedly sampling the probability distributions of the risk equation inputs and using these inputs to calculate a distribution of risk. This approach is used less frequently in uncertainty analysis. Techniques for characterizing both uncertainty and variability are available, and generally require two-dimensional Monte Carlo analysis (U.S. EPA, 2001). In situations in which an analyst wishes to apply probabilistic techniques, and data lend themselves to such analysis, more robust techniques to describe data goodness-of-fit, identification and deposition of data outliers, and sensitivity analysis of the respective model should be used to address parameter variability. These techniques are described in Section

1.9.2 of this document.

2.4 TYPES OF UNCERTAINTY Uncertainty in exposure analysis is related to the lack of knowledge concerning one or more components of the assessment process. 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).

Sources and examples for each type of uncertainty are

summarized in Table 2-2. As described in Section 1.6 of this handbook, U.S. EPA has attempted to address the uncertainty associated with the various exposure factors presented in the handbook by applying confidence ratings to the recommended data. In general, these confidence rating are based on detailed discussions of any limitations of the data presented. This information may be useful in analyzing the uncertainty associated with an overall exposure/risk assessment.

2.5 REDUCING 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 in Table 2-2 can be further defined by examining their principal causes. 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 if data quality allows.

2.6 ANALYZING VARIABILITY AND

UNCERTAINTY Exposure assessments often are developed in a tiered approach. The initial tier usually screens out the exposure scenarios or pathways that are not expected to pose much risk, to eliminate them from more detailed, resource-intensive review. Screening-level assessments typically examine exposures that would fall on or beyond the high end of the expected exposure distribution. Because screening-level analyses usually are included in the final exposure assessment, the final document may contain scenarios that differ quite markedly in sophistication, data quality, and amenability to quantitative expressions of variability or uncertainty. According to the U.S. EPA (1992), uncertainty characterization and uncertainty assessment are two ways of describing uncertainty at different degrees of sophistication. Uncertainty characterization usually involves a qualitative discussion of the thought processes used to select or reject specific data, estimates, scenarios, etc. Uncertainty assessment is a more quantitative process that may range from simpler measures (e.g., ranges) and simpler analytical techniques (e.g., sensitivity analysis) to more complex measures and techniques. Its goal is to provide decision makers with information concerning the



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quality of an assessment, including the potential variability in the estimated exposures, major data gaps, and the effect that these data gaps have on the exposure estimates developed. A distinction between variability and uncertainty was made in Section 2.1. Although the quantitative process mentioned above applies more directly to variability and the qualitative approach more so to uncertainty, there is some degree of overlap. In general, either method provides the assessor or decision-maker with insights to better evaluate the assessment in the context of available data and assumptions. The following paragraphs describe some of the more common procedures for analyzing variability and uncertainty in exposure assessments. Principles that pertain to presenting the results of variability/uncertainty analysis

are discussed in the next section. Several approaches can be used to characterize uncertainty in parameter values. When uncertainty is high, the assessor may use order-of-magnitude bounding estimates of parameter ranges (e.g., from 0.1 to 10 liters for daily water intake). Another method describes the range for each parameter including the lower and upper bounds as well as a "best estimate" (e.g., 1.4 liters per day) determined by available data or professional judgement. When sensitivity analysis indicates that a parameter profoundly influences exposure estimates, the assessor should develop a probabilistic description of its range. If there are enough data to support their use, standard statistical methods are preferred. If the data are inadequate, expert judgment can be used to generate a subjective probabilistic representation. Such judgments should be developed in a consistent, well-documented manner. Morgan and Henrion (1990) and Rish (1988) describe techniques to solicit expert judgment. Most approaches to quantitative analysis examine how variability and uncertainty in values of specific parameters translate into the overall uncertainty of the assessment. Details may be found in various papers and reviews such as Bogen and Spear (1987), 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 2-3. Additional discussions describing approaches to address variability and uncertainty in human exposure assessments can be found in the following references: Burin and Saunders (1999), Burmaster (1998a, b, and c), Burmaster and Crouch (1997), Calaberse and Baldwin (1998), Cox (1999), Cullen and Frey (1999), Fayerweather et al. (1999), Finkel (1997), Frey (2002), Frey and Patil (2002), Greenland, (2001), Hattis (1997), Hattis and Anderson (1999), Hattis and Silver (1994), Illing (1999), Jayjock (1997), Kalberlah et al. (2003), Kelley and Campbell (2000), Meek (2001), Nayak and Kundu (2001), Nicas and Jayjock (2002), Peretz et al. ( 1997), Price et al. (1997, 1999), Rai and Krewski (1998), Renwick (1999), Renwick et al. (2001), Robinson and Hurst (1997), Saltelli (2002), Semple et al. (2003), Simon (1997), Shlyakhter (1994), Slob and Pieters (1998), Wallace et al. (1994), Wallace and Williams (2005), Weiss (2001), and Zheng and Frey (2005).

2.7 PRESENTING RESULTS OF VARIABILITY

AND UNCERTAINTY ANALYSIS Comprehensive qualitative analysis and rigorous quantitative analysis are of little value for use in the decision-making process, if their results are not clearly presented. In this chapter, variability (the receipt of different levels of exposure by different individuals) has been distinguished from uncertainty (the lack of knowledge about the correct value for a specific exposure measure or estimate). Most of the data that are presented in this handbook deal with variability directly, through inclusion of statistics that pertain to the distributions for various exposure factors. Not all approaches historically used to construct measures or estimates of exposure have attempted to distinguish between variability and uncertainty. The assessor is advised to use a variety of exposure descriptors, and where possible, the full population distribution, when presenting the results. This information will provide risk managers with a better understanding of how exposures are distributed over the population and how variability in population activities

influences this distribution. Although incomplete analysis is essentially unquantifiable as a source of uncertainty, it should not be ignored. At a minimum, the assessor should describe the rationale for excluding particular exposure scenarios; characterize the uncertainty in these decisions as high, medium, or low; and state whether they were based on data, analogy, or professional judgment. Where



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uncertainty is high, a sensitivity analysis can be used to estimate upper limits on exposure by way of a series of "what if" questions. Although assessors have always used descriptors to communicate the kind of scenario being addressed, the 1992 Exposure Guidelines establish clear quantitative definitions for these risk descriptors. These definitions were established to ensure that consistent terminology is used throughout the Agency. The risk descriptors defined in the Guidelines include descriptors of individual risk and population risk. Individual risk descriptors are intended to address questions dealing with risks borne by individuals within a population, including not only measures of central tendency (e.g., average or median), but also those risks at the high end of the distribution. Population risk descriptors refer to an assessment of the extent of harm to the population being addressed. It can be either an estimate of the number of cases of a particular effect that might occur in a population (or population segment), or a description of what fraction of the population receives exposures, doses, or risks greater than a specified value. The data presented in this handbook is one of the tools available to exposure assessors to construct the various risk descriptors. However, it is not sufficient to merely present the results using different exposure descriptors. Risk managers should also be presented with an analysis of the uncertainties surrounding these descriptors. Uncertainty may be presented using simple or very sophisticated techniques, depending on the requirements of the assessment and the amount of data available. It is beyond the scope of this handbook to discuss the mechanics of uncertainty analysis in detail. The assessor can address uncertainty qualitatively by answering questions such as:

• What is the basis or rationale for selecting these assumptions/parameters, such as data, modeling, scientific judgment, Agency policy, "what if" considerations, etc.?

• What is the range or variability of the key parameters? How were the parameter values selected for use in the assessment? Were average, median, or upper-percentile values chosen? If other choices had been made, how would the

results have differed?

• What is the assessor's confidence (including qualitative confidence aspects) in the key parameters and the overall assessment? What are the quality and the extent of the data base(s) supporting the selection of the chosen values?

Any exposure estimate developed by an assessor will have associated assumptions about the setting, chemical, population characteristics, and howcontact 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 may need to make many decisions regarding the use of existing information in constructing scenarios and setting up the exposure equations. In presenting the scenario results, the assessor should strive for a balanced and impartial treatment of the evidence bearing on the conclusions with the key assumptions highlighted. For these key assumptions, one should cite data sources and explain any adjustments of

the data. The exposure assessor also should qualitatively describe the rationale for selection of any conceptual or mathematical models that may have been used. This

discussion should address their verification and validation status, how well they represent the situation being assessed (e.g., average versus high-end estimates), and any plausible alternatives in terms of their acceptance by the scientific community. Table 2-2 summarizes the three types of uncertainty, associated sources, and examples. Table 2-3 summarizes four approaches to analyze uncertainty quantitatively. These are described further in the 1992 Exposure Guidelines (U.S. EPA, 1992). To the extent possible, this handbook provides information that can be used to characterize the variability and uncertainty of data for the various exposure factors. In general, variability is addressed by providing distribution of data, where available, or qualitative discussions of the data sets used. Uncertainty is addressed by applying confidence rating to the recommendations provided for the various factors, along with detailed discussions of any limitations of the data presented.

2.8 REFERENCES FOR CHAPTER 2 Bogen, K.T. (1990) Uncertainty in environmental health risk assessment. Garland Publishing, NewYork, NY.



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Bogen, K.T.; Spear, R.C. (1987). Integrating uncertainty and interindividual variability in environmental risk assessment. Risk Analysis. 7(4):427-436. Burin, G.J.; Saunders, D.R. (1999). Addressing human variability in risk assessment–the robustness of the intraspecies uncertainty factor. Reg. Tox. Pharm.

30: 209-216. Burmaster, D.E.; Crouch, E.A.C. (1997). Lognormal distributions for body weight as a function of age for males and females in the United States, 1976-1980. Risk Analysis. 17: 499-505. Burmaster, D.E. (1998a). A lognormal distribution for time spent showering. Risk Analysis. 18: 33-35. Burmaster, D.E. (1998b). Lognormal distributions for total water intake and tap water intake by pregnant and lactating women in the United States. Risk Analysis 18: 215-219. Burmaster, D.E. (1998c). Lognormal distributions for skin area as a function of body weight. Risk Analysis 18: 27-32. Calabrese, E.J.; Baldwin, L.A. (1998). Hormesis as a biological hypothesis. Environ. Health Persp. 106(Supp. 1): 357-362. Cox, D.C.; Baybutt, P.C. (1981) Methods for uncertainty analysis. A comparative survey. Risk Analysis 1(4):251-258. Cox Jr., L.A. (1999). Internal dose, uncertainty analysis, and complexity of risk models. Environ. Inter. 25:

841-852. Cullen, A.C.; Frey, H.C. (1999). Probabilistic Techniques in Exposure Assessment. New York:

Plenum Press. Duan, N. (1982) Microenvironment types: A model for human exposure to air pollution. Environ. Intl.

8:305-309. Fayerweather, W.E.; Collins, J.J.; Schnatter, A.R.; Hearne, F.T.; Menning, R.A.; Reyner, D.P. (1999). Quantifying uncertainty in a risk assessment using human data. Risk Analysis 19: 1077-1090. Finkel, A.M. (1997). Not to decide is to decide: ignoring susceptibility in not 'good science'. Environ. Tox.

Pharm. 4: 219-228. Frey, H.C. (2002). Guest Editorial: Introduction to special section on sensitivity analysis and summary of NCSU/USDA workshop on sensitivity analysis. Risk Analysis 22: 539-545. Frey, H.C.; Patil, S.R. (2002).	 Identification and review of sensitivity analysis methods. Risk Analysis 22:

553-578. Greenland, S. (2001). Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment. Risk Analysis 21: 579-583. Hattis, D. (1997). Human variability in susceptibility: how big, how often, for what responses to what agents? Environ. Tox. Pharm. 4: 195-208. Hattis, D. and Anderson, E.L. (1999). What should be the implications of uncertainty, variability, and inherent ‘biases’/’conservatism’ for risk management decision-making. Risk Analysis 19: 95-107. Hattis, D. and Silver, K. (1994). Human interindividual variability - A major source of uncertainty in assessing risks for noncancer health effects. Risk Analysis. 14(4):421-431. Illing, H.P.A. (1999). Are societal judgements being incorporated into the uncertainty factor used in toxicological risk assessment? Reg. Toxicol. Pharm.

29: 300-308. Inman, R.L.; Helton, J.C. (1988) An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk Analysis. 8(1):71-91. Jayjock,	 M.A. (1997). Uncertainty analysis in the estimation of exposure. Amer. Ind. Hyg. Assoc. J.

58: 380-382. Kalberlah, F.; Schneider, K.; et al. (2003). Uncertainty in toxicological risk assessment for non-carcinogenic health effects. Reg. Tox. Pharm. 37: 92-104. Kelly, E.J.; Campbell, K. (2000). Separating variability and uncertainty in environmental risk assessment–making choices. Human Ecol. Risk

Assess. 6: 1-13. Meek, M.E. (2001) Categorical default uncertainty factors–interspecies variation and adequacy of

database. Human Ecol. Risk Assess. 7: 157-163. 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. Nayak, T.K.; Kundu, S. (2001). Calculating and describing uncertainty in risk assessment: The Bayesian approach. Human Ecol. Risk Assess. 7:

307-328. Nicas, M.; Jayjock, M. (2002).	 Uncertainty in exposure estimates made by modeling versus monitoring.

AIHA J. 63: 275-283.



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Peretz, C.; Goldberg, P.; Kahan, E.; Grady, S.; Goren, A. (1997). The variability of exposure over time: a prospective longitudinal study. Ann. Occup. Hyg.

41: 485-500. Price, P.S.; Keenan, R.E.; Schwab, B. (1999). Defining the interindividual (intraspecies) uncertainty factor.

Human Ecol. Risk Assess. 5: 1023-1033. Price, P.S.; Keenan, R.E.; Swartout, J.C., Gillis, C.A., Carlson-Lynch, H., and Dourson, M.L. (1997). An approach for modeling noncancer dose responses with an emphasis on uncertainty. Risk Analysis 17:

427-437. Rai, S.N.; Krewski, D. (1998). Uncertainty and variability analysis in multiplicative risk models. Risk Analysis. 18: 37-45. Renwick, A.G. (1999). Subdivision of uncertainty factors to allow for toxicokinetics and toxicodynamics.

Human Ecol. Risk Assess. 5: 1035-1050. Renwick, A.G.; Dorne, J.-L.C.M.; Walton, K. (2001). Pathway-related factors: The potential for human data to improve the scientific basis of risk assessment.

Human Ecol. Risk Assess. 7: 165-180. 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. Robinson, R.B.; Hurst, B.T. (1997). Statistical quantification of the sources of variance in uncertainty analyses. Risk Analysis. 17: 447-453. Saltelli, A. (2002). Sensitivity Analysis for Importance Assessment. Risk Analysis. 22: 579-590. Seller, F.A. (1987) Error propagation for large errors. Risk Analysis 7(4):509-518. Semple, S.E.; Proud, L.A.; Cherrie, J.W. (2003).	 Use of Monte Carlo simulation to investigate uncertainty in exposure modeling. Scand. J. Work Environ. Health.

29: 347-353. Shlyakhter, A.I. (1994). An improved framework for uncertainty analysis: Accounting for unsupected errors. Risk Analysis. 14(4): 441-447. Simon, T.W. (1997). Combining physiologically based pharmacokinetic modeling with Monte Carlo simulation to derive an acute inhalation guidance value for trichlorethylene. Reg. Tox. Pharm. 26:

257-270. Slob, W.; Pieters, M.N. (1998). A probabilistic approach for deriving acceptable human intake limits and human health risks from toxicological studies: General framework. Risk Analysis. 18: 787-798.

U.S. EPA (1992)	 Guidelines for exposure assessment. Washington, DC: Office of Research and Development, Office of Health and Environmental

Assessment. EPA/600/2-92/001.

U.S. EPA (1995) Guidance for risk characterization. Science Policy Council, Washington, DC.

U.S. EPA (2001) Risk Assessment Guidance for Superfund: Volume III - Part A, Process for Conducting Probabilistic Risk Assessment. Washington, DC: Office of Solid Waste and Emergency Response. EPA/540-R-02-002. Wallace, L.A. et al. (1994). Can long-term exposure distributions be predicted from short-term measurements? Risk Analysis . 14(1):75-85. Wallace, L;. Williams, R. (2005). Validation of a methodfor estimating long-term exposures based on shortterm measurements. Risk Analysis. 25(3): 687-694. Weiss, B. (2001). A Web-based survey method for evaluating different components of uncertainty in relative health risk judgments. Neurotoxicology 22:

707-721. WHO (2006) Draft guidance on characterizing and communicating uncertainty in exposure assessment.

Accessed on line at: http://www.who.int/ipcs/methods/harmonization/ar eas/exposure_assessment/en/index.html Whitmore, R.W. (1985) Methodology for characterization of uncertainty in exposure

assessments. EPA/600/8-86/009. Zheng, J.; Frey, H.C. (2005).	 Quantitative analysis of variability and uncertainty with known measurement error: Methodology and case study. Risk Analysis.

25: 663-675.



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Table 2-1. Four Strategies for Confronting Variability

Strategy Example Comment

Ignore variability Assume that all adults weigh 70 kg Works best when variability is small

Disaggregate the variability Develop distributions of body weight for age/gender groups Variability will be smaller in each group; it depends on availability of data

Use the average value Use average body weight for

adults Can the average be estimated reliably given what is known about the variability of a specific population or group with potential exposures?

Use a maximum or

minimum value Use a lower-end value from the weight distribution Conservative approach -- can lead to unrealistically high exposure estimate if taken for all factors. It may be useful as a screening method for eliminating pathways of exposure that are not significant.

Source: NRC, 1994.

Table 2-2. Three Types of Uncertainty and Associated Sources and Examples

Type of Uncertainty Sources Examples

Scenario Uncertainty

Parameter Uncertainty

Model Uncertainty Descriptive errors Incorrect or insufficient information

Aggregation errors Spatial or temporal approximations

Judgment errors Selection of an incorrect model

Incomplete analysis Overlooking an important pathway

Measurement errors Imprecise or biased measurements

Sampling errors Small or unrepresentative samples

Variability In time, space or activities

Surrogate data Structurally-related chemicals

Relationship errors Incorrect inference on the basis for correlations

Modeling errors Excluding relevant variables

Source: U.S. EPA, 1992.



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Table 2-3. Approaches to Quantitative Analysis of Uncertainty

Approach Description Example

Sensitivity Analysis

Analytical Uncertainty Propagation

Probabilistic Uncertainty Analysis

Classical Statistical Methods Changing one input variable at a time while leaving others constant, to examine effect on
output Fix each input at lower (then upper) bound while holding others at nominal values (e.g., medians)

Examining how uncertainty in individual parameters affects the overall uncertainty of the
exposure assessment Analytically or numerically obtain a partial derivative of the exposure equation with respect to each input parameter

Varying each of the input variables over various values of their respective probability

distributions Assign probability density function to each parameter; randomly sample values from each distribution and insert them in the exposure equation (Monte Carlo)

Estimating the population exposure distribution directly, based on measured values from a representative sample Compute confidence interval estimates for various percentiles of the exposure

distribution

Source: U.S. EPA, 1992.



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3	 INGESTION OF WATER AND OTHER SELECT LIQUIDS

3.1 INTRODUCTION Water ingestion can be a pathway of exposure to environmental chemicals among children. Contamination of water may occur at the water supply source (ground water or surface water); during treatment (for example toxic by-products may be formed during chlorination); or post-treatment (such as leaching of lead or other materials from plumbing systems). Children may be exposed to contaminants in water when consuming water directly as a beverage, indirectly from foods and drinks made with water, or incidentally while swimming. Estimating the magnitude of the potential dose of toxics from water ingestion requires information on the quantityof water consumed. The purpose of this section is to describe key and relevant published studies that provide information on water ingestion among children and to provide recommended ingestion rate values for use in exposure assessments. The studies described in this section provide information on ingestion of water consumed as a beverage, ingestion of other select liquids, and ingestion of water while swimming. Currently, the U.S. EPA uses the quantity 1 L per day for infants (individuals of 10 kg body mass or less) and children as a default drinking water ingestion rate (U.S. EPA, 2000). This rate includes water consumed in the form of juices and other beverages containing tapwater. 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 children engaging in physically-demanding activities or living in warmer regions may have higher levels of water ingestion. However, there is limited information on the effects of activity level and climatic conditions on water ingestion.

Various studies cited in this section have generated data on water ingestion rates; in general, these sources support U.S. EPA's use of 1 L/day as an upper-percentile tapwater ingestion rate for children under 10 years of age. Based on the applicability of the survey design to exposure assessments of the entire US population, the study by Khan and Stralka (2008) was selected as a keystudyof drinking water ingestion. In this study, ingestion rates for direct and indirect ingestion of water are reported. Direct ingestion is defined as direct consumption of water as a beverage,

while indirect ingestion includes water added during food preparation, but not water intrinsic to purchased foods (i.e. water that is naturally contained in foods) (Kahn and Stralka, 2008). Data for consumption of water from various sources (i.e., the community water supply, bottled water, and other sources) are also presented. For the purposes of exposure assessments involving site-specific contaminated drinking water, ingestion rates based on the community supply are most appropriate. Given the assumption that bottled water, and purchased foods and beverages that contain water are widely distributed and less likely to contain source-specific water, the use of total water ingestion rates may overestimate the potential exposure to toxic substances present only in local water supplies; therefore, tapwater ingestion of community water, rather than total water ingestion, is emphasized in this

section. The studies on water ingestion that are currentlyavailable are based on short-term surveydata (two days). Although short-term data may be suitable for obtaining mean or median ingestion values that are representative of both short- and long-term ingestion distributions, upper and lower -percentile values may be different for short-term and long-term data. It should also be noted that most currently available water ingestion surveys are based on recall. This may be a source of uncertainty in the estimated ingestion rates because of the subjective nature of this type of survey technique. Percentile distributions for water ingestion are presented in this handbook, where

sufficient data are available. Data were not available to estimate drinking water ingestion rates for children during high activity levels or in extreme climates (i.e., hot weather). Also, data are not provided for the location of water consumption (i.e., home, school, day care center, etc.). Limited information was available regarding children’s incidental ingestion of water while swimming. This exposure pathway may be important since children are likely to ingest larger volumes of water while swimming compared to adults; and therefore, may have a greater exposure to pathogenic microorganisms and chemicals present in the water than adults. A recent pilot study (Dufour et al., 2006) has provided some quantitative experimental data on water ingestion for child and adult swimmers. These data are provided in this chapter. The recommendations for water ingestion rates are provided in the next section, along with a summary of the confidence ratings for these

recommendations. The recommended values are based on the key study identified by U.S. EPA for this factor. Following the recommendations, the key study on



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water ingestion is summarized. Relevant data on ingestion of water and other select liquids are also provided. These studies are presented to provide thereader with added perspective on the current state-ofknowledge pertaining to ingestion of water and select liquids.

3.2	 RECOMMENDATIONS

3. 2.1	 Water Ingestion from Consumption of

Water as a Beverage and from Food and

Drink

The recommended water ingestion rates for

children are based on Kahn and Stralka (2008 and supplementary data). This study presents estimates of

water ingestion by age range categories for the population of the United States using data collected in

the U.S. Department of Agriculture’s (USDA’s) 1994­ 96 and 1998 Continuing Survey of Food Intakes by

Individuals (CSFII) (USDA, 1998). A summary of the

recommended values for water ingestion rates is presented in Table 3-1. A characterization of the

overall confidence in the accuracy and appropriateness

of the recommendations for drinking water intake is presented in Table 3-2.

3.2.2	 Water Ingestion while Swimming

Based on the results of the Dufour et al. (2006) study, a mean water ingestion rate of 50

mL/hour for children ages 6 to 15 years is

recommended for exposure scenarios involving swimming activities. The recommended upper percentile value is 100 mL/hour. The recommended

values for children between 18 and 21 years of age are based on the results for adults from Dufour et al. (2006). The mean value is 20 mL/hour and the upper percentile value is 70 mL/hour. Although this estimate

was derived from swimming pool experiments, Dufour

et al. (2006) noted that swimming behavior of pool

swimmers may be similar to freshwater swimmers.

Estimates may be different for salt water swimmers.

The confidence ratings for these recommendations are presented in Table 3-3. Data on the amount of time

spent swimming can be found in chapter 16, Table 16­

21.



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Table 3-1. Recommended Values for Drinking Water Ingestion Rates a

Age Group Mean 95 th Percentile Multiple

Percentiles Source mL/day mL/kg-day mL/day mL/kg-day
Per Capita

Birth to <1 month 184 52 839 b 232 b

See Tables

3-4 and 3-9 Kahn and Stralka (2008) 1 to <3 months 227 48 896 b 205 b

3 to <6 months 362 52 1,056 159

6 to <12 months 360 41 1,055 126

1 to <2 years 271 23 837 71

2 to <3 years 317 23 877 60

3 to <6 years 380 22 1,078 61

6 to <11 years 447 16 1,235 43

11 to <16 years 606 12 1,727 34

16 to <18 years 731 11 1,983 b 31 b

18 to <21 years 826 12 2,540 b 35 b

Consumers Only

Birth to <1 month 470 b 137 b 858 b 238 b

See Tables

3-14 and 3­

19 Kahn and Stralka (2008) 1 to <3 months 552 119 1,053 b 285 b

3 to <6 months 556 80 1,171 b 173 b

6 to <12 months 467 53 1,147 129

1 to <2 years 308 27 893 75

2 to <3 years 356 26 912 62

3 to <6 years 417 24 1,099 65

6 to <11 years 480 17 1,251 45

11 to <16 years 652 13 1,744 34

16 to <18 years 792 12 2,002 b 32 b

18 to <21 years 895 13 2,565 b 35 b

a Ingestion rates for combined direct and indirect water from community water supply.

b The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).



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Table 3-2. Confidence in Recommendati ons for Drinking Water Ingestion Rates

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or defined) Bias The survey methodology and data analysis was adequate. The survey sampled approximately 10,000 individuals under the age of 21 years; sample size varied with age.

No physical measurements were taken. The method

relied on recent recall of standardized volumes of drinking water containers. Medium to High

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The key study was directly relevant to water ingestion.

The data were demographically representative (based on stratified random sample).

Data were collected between 1994 and 1998.

Data were collected for two non-consecutive days. However, long term variability may be small. Use of a short-term average as a chronic ingestion measure can

be assumed. Medium to High

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The CSFII data are publicly available. The Kahn and Stralka (2008) analysis of the CSFII 1994-96, 1998 data was published in a peer-reviewed journal.

The methodology was clearly presented; enough information was included to reproduce the results.

Quality assurance of the CSFII data was good; quality control of the secondary data analysis was not well

described. High

Variability and Uncertainty

Variability in Population

Uncertainty Full distributions were give n in a separate document (Khan and Stralka, 2008b).

Except for data collection based on recall, sources of uncertainty were minimal. High

Evaluation and Review

Peer Review

Number and Agreement of Studies The USDA CSFII survey received high level of peer review. The Kahn and Stralka (2008) study was published in a peer-reviewed journal.

There was 1 key study for drinking water ingestion. Medium

Overall Rating Medium to High



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Table 3-3. Confidence in Recommendati ons for Water Ingestion while Swimming

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or defined) Bias The approach appears to be appropriate given that cyanuric acid (a tracer used in treated pool water) is not metabolized, but the sample size was small (41 children). The Dufour et al. (2006) study analyzed primary data on water ingestion during swimming.

Data were collected over a period of 45 minutes; this may not accurately reflect the time spent by a recreational swimmer. Medium

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The key study was directly relevant to water ingestion while swimming.

The sample was not representa tive of the U.S. population. Data cannot be broken out by age categories

It appears that the study was conducted in 2005.

Data were collected over a period of 45 minutes. Low to Medium

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The Dufour et al. (2006) study was published in a peerreviewed journal.

The methodology was clearly presented; enough information was included to reproduce the results..

Quality assurance methods were not described in the study. Medium

Variability and Uncertainty

Variability in Population

Uncertainty Only mean values for water i ngestion were provided. Data were not broken out by age groups

There were multiple sources of uncertainty (e.g., sample population may not reflect swimming practices for all swimmers, rates based on swi mming duration of 45 minutes, differences by age group not defined). Low

Evaluation and Review

Peer Review

Number and Agreement of Studies Dufour et al. (2006) was published in a peer-reviewed journal.

There was 1 study for ingestion of water when swimming. Medium

Overall Rating Low



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3.3 DRINKING WATER INGESTION

STUDIES

3.3.1 Key Drinking Water Ingestion Study

3.3.1.1 Kahn and Stralka, 2008 - Estimated Daily Average Per Capita Water Ingestion by Child and Adult Age Categories Based on USDA’s 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals Kahn and Stralka (2008) analyzed the combined 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals (CSFII) data sets to examine water ingestion rates of adults and children. USDA surveyed households in the United States and District of Columbia and collected food and beverage recall data as part of the CSFII (USDA,1998). In the initial 1994-96 survey, over 15,000 respondents provided data on what they ate and drank over two non-consecutive days. A 1998 supplement, using the same methodology, added responses for approximately 5,000 children aged 9 years and younger to the database. Of the more than 20,000 individuals surveyed, approximately10,000 were under 21 years of age, and approximately 9,000 were under the age of

11. For both survey days, data were collected by an inhome interviewer. The day two interview was conducted 3 to 10 days later and on a different day of the week. The 1994-96 survey and 1998 supplement are referred to collectively as CSFII 1994-96, 1998. Each individual in the survey was assigned a sample weight based on his or her demographic data. These weights were taken into account when calculating mean and percentile water ingestion rates from various

sources. Khan and Stralka (2008) derived mean and percentile estimates of daily average water ingestion for children in eleven different age categories: <1 month, 1 to <3 months, 3 to <6 months, 6 to <12 months, 1 to <2 years of age, 2 to <3 years, 3 to <6 years, 6 to <11 years, 11 to <16 years, 16 to <18 years, and 18 to <21 years of age. The increased sample size for children younger than 11 years of age (from 4,339 in the initial 1994-96 survey to 9,643 children in the combined 1994-96, 1998 survey) enabled water ingestion estimates to be categorized into the finer age categories recommended by U.S. EPA (2005). Per capita and consumers only water ingestion estimates were reported in the Kahn and Stralka (2008) studyfor two water source categories: all sources and community water. “All sources” included water from all supply sources such as community water supply (i.e., tap water), bottled water, other sources, and missing sources. “Community water” included tap water from a community or municipal water supply. Other sources included wells, springs, and cisterns; missing sources represented water sources that the survey respondent was unable to identify. The water ingestion estimates included both water ingested directly as a beverage (direct water) and water added to foods and beverages during final preparation at home or by local food service establishments such as school cafeterias and restaurants (indirect water). Commercial water added bya manufacturer (i.e., water contained in soda or beer) and intrinsic water in foods and liquids (i.e., milk and natural undiluted juice)

were not included in the estimates. Kahn and Stralka (2008) only reported the mean, 90 th 

### and 95 th 

### percentile estimates of per capita and consumers only ingestion. The full distribution of ingestion estimates for various water source categories (all sources, communitywater, bottled water, and other sources) were provided by the author. Tables 3-4 to 3-7 provide mean and percentile per capita ingestion estimates of total water (combined direct and indirect water) in mL/day for the various water source categories (i.e., community, bottled, other, and all sources). The 90 percent confidence

intervals around the estimated means and the 90 percent bootstrap intervals around the 90 th 

### and 95 th 

### percentiles of total water ingestion from all water sources are presented in Table 3-8. Tables 3-9 to 3-13 present the same information as Tables 3-4 to 3-8 but in units of mL/kg-day. Consumers only combined direct and indirect water ingestion estimates in mL/day for the various source categories are provided in Tables 3-14 to 3-17. Table 3-18 presents confidence and bootstrap intervals for total water ingestion estimates by consumers only from all sources. Tables 3-19 to 3­ 23 present the same information as Tables 3-14 to 3-18 but in units of mL/kg-day. The data show that the total quantity of water ingested per unit mass of body weight is at a maximum in the first month of life and decreases with increasing age. The per capita ingestion rate of water from all sources combined for children under 1 month of age is approximately four times higher than that adults, and consumers younger than 1 month of age ingest approximately 8 times the amount of water (all sources combined) as adults (Kahn and Stralka, 2008). The pattern of decreasing water ingestion per unit of body weight is also observed in per capita and consumers only estimates of community water (Tables 3-9 and 3­



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19), bottled water (Table 3-10 and 3-20) and other sources (Tables 3-11 and 3-21). The CSFII 1994-96, 1998 data have both strengths and limitations with regard to estimating water ingestion. These are discussed in detail in U.S. EPA (2004) and Kahn and Stralka (2008). The principal advantages of this survey are (1) that the survey was designed to obtain a statistically valid sample of the entire United States population that included children and low income groups; (2) sample weights were provided that facilitated proper analysis of the data and accounted for non-response; and (3) that the sample size (approximately 10,000 children) is sufficient to allow categorization within narrowly defined age categories. Over sampling of children enhanced the precision and accuracy of the estimates for the child population subsets. One limitation of this survey is that data were collected for only 2 days and does not necessarily represent “usual intake.” “Usual dietary intake” refers to the long-term average of daily intakes by an individual. Thus, upper percentile water ingestion estimates based on short-term data maydiffer from long-term rates because short-term consumption data tend to be inherently more variable. However, Kahn and Stralka (2008) noted that variability due to short term duration of the survey does not result in bias of estimates of overall mean. In addition, the survey was conducted on non-consecutive days, which improves the variance over consecutive days of consumption. However, the two non-consecutive days of data collection, although an advantage over two consecutive days, provide limited information on individual respondents. The two-day mean for an individual can easily be skewed for numerous reasons. Estimation at the individual respondent level was not, however, an objective of the survey. The large sample provides useful information on the overall distribution of ingestion by the population, and should adequately reflect the range among respondent variability. Another limitation of these data is that the survey design, while being well-tailored for the overall population of the United States and conducted throughout the year to account for seasonal variation, is of limited utility for assessing small and potentially at-risk subpopulations based on ethnicity, medical status, geography/climate, or other factors such as activity level. 3.3.2 Relevant Drinking Water Ingestion Studies

3.3.2.1 Levy et al., 1995 ­ Infant Fluoride Intake From Drinking Water Added to Formula, Beverages, and Food Levy et al. (1995) conducted a study to determine fluoride intake by infants through drinking water and other beverages prepared with water and baby foods. The study was longitudinal and covered the ages from birth to 9 months old. A total of 192 mothers, recruited from the post partum wards of two hospitals in Iowa City, completed mail questionnaires and three-day beverage and food diaries for their infants at ages 6 weeks, and 3, 6, and 9 months of age (Levy et al., 1995). The questionnaire addressed feeding habits, water sources and ingestion, and the use of dietary fluoride supplements during the preceding week (Levy et al., 1995). Data on the quantity of water consumed by itself or as an additive to infant formula, other beverages, or foods were obtained. In addition, the questionnaire addressed theinfants’ ingestion of cow’s milk, breast-milk, ready-tofeed infant products (formula, juices, beverages, baby food), and table foods. Mothers were contacted for any clarifications of missing data and discrepancies (Levy et al., 1995). Levy et al. (1995) assessed non-response bias and found nosignificant differences in the reported number of adults or children in the family, water sources, or family income at 3, 6, or 9 months. Table 3-24 provides the range of water ingestion from water by

itself and from addition to selected foods and beverages. The percentage of infants ingesting water by itself increased from 28 percent at 6 weeks to 66 percent at 9 months, respectively, and the mean intake increased slightly over this time frame. During this time frame, the largest proportion of the infants’ water ingestion (i.e., 36 percent at 9 months to 48 percent at 6 months) came from the addition of water to formula. Levy et al. (1995) noted that 32 percent of the infants at age 6 weeks and 23 percent of the infants at age 3 months did not receive any water from any of the sources studied. Levy et al. (1995) also noted that the proportion of children ingesting some water from all sources gradually increased with age. The advantages of this study are that it provides information on water ingestion of infants starting at 6 weeks old and the data are for water only and for water added to beverages and foods. The limitations of the study are that the sample size was small for each age group, it captured information from a select geographical location, and data were collected through self reporting. The authors noted, however,



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that the three-day diary has been shown to be a valid assessment tool. Levy et al. (1995) also stated that (1) for each time period, the ages of the infants varied by a few days to a few weeks, and are, therefore, not exact and could, at early ages, have an effect on age-specific intake patterns, and (2) the same number of infants were not available at each of the four time periods.

3.3.2.2 Heller et al., 2000 - Water Consumption and Nursing Characteristics of Infants by Race and Ethnicity Heller et al. (2000) analyzed data from the

1994-96 CSFII to evaluate racial/ethnic differences in the ingestion rates of water in children younger than 2 years old. Using data from 946 children in this age group, the mean amounts of water consumed from eight sources were determined for various racial/ethnicgroups, including black non-Hispanic, white nonHispanic, Hispanic and “other” (Asian, Pacific Islander, American Indian, Alaskan Native, and other non-specified racial/ethnic groups). The sources analyzed included: (1) plain tap water, (2) milk and milk drinks, (3) reconstituted powdered or liquidinfant formula made from drinking water, (4) ready-tofeed and other infant formula, (5) baby food, (6) carbonated beverages, (7) fruit and vegetable juices and other noncarbonated drinks, and (8) other foods and beverages. In addition, Heller et al. (2000) calculated mean plain water and total water ingestion rates for children by age, sex, region, urbanicity, and poverty category. Ages were defined as less than 12 months and 12 to 24 months. Region was categorized as Northeast, Midwest, South, and West. The states represented by each of these regions was not reported in Heller et al. (2000). However, it is likely that these regions were defined in the same way as in Sohn et al. (2001). See Section 3.3.2.4 for a discussion on the Sohn et al. (2001) study. Urbanicity of the residence was defined as urban (i.e., being in a Metropolitan Statistical Area [MSA], suburban [outside ofan MSA], or rural [being in a non-MSA]). Poverty category was derived from the poverty income ratio. In this study, a poverty income ratio was calculated by dividing the family’s annual income by the federal poverty threshold for that size household. The poverty categories used were 0-1.30, 1.31-3.50, and greater than 3.50 times the federal poverty level (Heller et al., 2000). Table 3-25 provides water ingestion estimates for the eight water sources evaluated, for each of the race/ethnic groups. Heller et al. (2000) reported that black non-Hispanic children had the highest mean plain tap water intake (21.3 mL/kg-day), and white non-Hispanic children had the lowest mean plain tap water intake (12.7 mL/kg-day). The only statistically significant difference between the racial/ethnic groups was found to be in plain tap water consumption and total water consumption. Reconstituted baby formula made up the highest proportion of total water intake for all race/ethnic groups. Table 3-26 presents tap water and total water ingestion by age, sex, region, urbanicity, and povertycategory. On average, children younger than 12 months of age consumed less plain tap water (11.0 mL/kg-day) than children aged 12-24 months (17.7 mL/kg-day). There were no significant differences in plain tap water consumption by sex, region, or urbanicity. Heller et al. (2000) reported a significant association between higher income and lower plain tap water consumption. For total water consumption, ingestion per kg body weight was lower

for the 12-24-month-old children than for those younger than 12 months of age. Urban children consumed more plain tap water and total water than suburban and rural children. In addition, plain tap water and total water ingestion was found to decrease with increasing poverty category (i.e., higher wealth). A major strength of the Heller et al. (2000) study is that it provides information on tap water and total water consumption by race, age, sex, region, urbanicity, and family income. The weaknesses in the

CSFII data set have been discussed under Kahn and Stralka (2008) and U.S. EPA (2004) and include surveying participants for only two days.

3.3.2.3 Sichert-Hellert et al., 2001 - Fifteen Year

Trends in Water Intake in German Children and Adolescents: Results of the DONALD Study Water and beverage consumption was evaluated by Sichert-Hellert et al. (2001) using 3-day dietary records of 733 children, ages 2 to 13 years,

enrolled in the Dortmund Nutritional and Anthropometric Longitudinally Designed Study (DONALD study). The DONALD study is a cohort study, conducted in Germany, that collects data on diet, metabolism, growth and development from healthy subjects between infancy and adulthood (Sichert-Hellert et al., 2001). Beginning in 1985, approximately 40 to 50 infants were enrolled in the study annually. Mothers of the participants were



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recruited in hospital maternity wards. Older children and parents of younger children were asked to keep dietary records for three days by recording and weighing (to the nearest 1 gram) all foods and fluids, including water, consumed. Sichert-Hellert et al. (2001) evaluated 3,736 dietary records from 733 subjects (354 males and 379 females) collected between 1985 and 1999. Total water ingestion was defined as the sum of water content from food (intrinsic water), beverages and oxidation. Beverages included milk, mineral water, tap water, juice, soft drinks, and coffee and tea. Table 3-27 presents the mean water ingestion rates for these different sources, as well as mean total water ingestion rates for three age ranges of children (age 2 to 3 years, age 4 to 8 years, and age 9 to 13 years). According to Sichert-Hellert et al. (2001), mean total water ingestion increased with age from 1,114 mL/day in the 2 to 3 year old subjects to 1,891 and 1,676 mL/day in 9 to 13-year-old boys and girls, respectively. However, mean total water intake per body weight decreased with age. Sichert-Hellert et al. (2001) observed that the most important source of total water ingestion was mineral water for all children, except the 2 to 3 year olds. For these children, the most important source of total water ingestion was milk . One of the limitations of this study is that it evaluated water and beverage consumption in German children and, as such, it may not be representative of consumption patterns of U.S. children.

3.3.2.4 Sohn et al., 2001 - Fluid Consumption Related to Climate Among Children in the

United States Sohn et al. (2001) investigated the relationship between fluid consumption among children aged l to 10 years and local climate using data

from the third National Health and Nutrition Examination Survey (NHANES III, 1988-94). Children aged 1 to 10 years who completed the 24­ hour dietary interview (or proxy interview for the younger children) during the NHANES III surveywere selected for the analysis. Breast-fed children were excluded from the analysis. Among 8,613 children who were surveyed, 688 (18 percent) were excluded due to incomplete data. A total of 7,925 eligible

children remained. Since data for climatic conditions were not collected in the NHANES III survey, the mean daily maximum temperature from 1961 to 1990, averaged for the month during which the NHANES III survey was conducted, was obtained for each survey location from the U.S. Local Climate Historical Database. Of the 7,925 eligible children with complete dietary data, temperature information was derived for only 3,869 children (48.8 percent) since detailed information on survey location, in terms of county and state, was released only for counties with a population

of more than a half million Sohn et al. (2001) calculated the total amount of fluid intake for each child by adding the fluid intake from plain drinking water and the fluid intake from foods and beverages other than plain drinking water provided by NHANES III. Sohn et al. (2001) identified major fluid sources as milk (and milk drinks), juice (fruit and vegetable juices and other noncarbonated drinks), carbonated drinks, and plain

water. Fluid intake from sources other than these major sources were all grouped into other foods and beverages. Other foods and beverages included bottled water, coffee, tea, baby food, soup, water-based beverages, and water used for dilution of food. Mean fluid ingestion rates of selected fluids for the total sample population and for the subsets of the sample population with and without temperature information are presented in Table 3-28. The estimated mean total fluid and plain water ingestion rates for the 3,869 children for whom temperature information was obtained are presented in Table 3-29 according to age (years), sex, race/ethnicity, poverty/income ratio, region, and urban or rural. Poverty/income ratio was defined as the ratio of the reported family income to the federal poverty level. The following categories were assigned: low socioeconomic status (SES) = 0.000-1.300 times the poverty/income ratio; medium SES = 1.3.01-3.500 times the poverty/income level; and high SES = 3.501 or greater times the poverty/income level. Regions were as Northeast, Midwest, South, and West, as defined by the U.S. Census (see Table 3-29). Sohn et al. (2001) did not find significant association between mean daily maximum temperature and total fluid or plain water ingestion, either before or after controlling for sex, age, SES and race or ethnicity. However, significant associations between fluid ingestion and age, sex, socioeconomic status and race and ethnicity were reported. The main strength of the Sohn et al. (2001) study is the evaluation of water intake as it relates to weather data. The main limitations of this study were that northeast and western regions were over represented since temperature data was only available for counties with populations in excess of a half



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million. In addition, whites were under-represented compared to other racial or ethnic groups. Other

limitations include lack of data for children from extremely cold or hot weather conditions.

3.3.2.5 Hilbig et al., 2002 - Measured Consumption of Tap Water in German Infants and Young

Children as Background for Potential

Health Risk Assessment: Data of the

DONALD Study Hilbig et al. (2002) estimated tap water ingestion rates based on 3-day dietary records of 504

German children aged 3, 6, 9, 12, 18, 24 and 36

months. The data were collected between 1990 and

1998 as part of the DONALD study. Details of data

collection for the DONALD study have been provided previously under the Sichert-Hellert et al. (2001) study

in Section 3.3.2.3 of this handbook. Tap water ingestion rates were calculated for three subgroups of

children: (1) breast-fed infants # 

### 12 months of age (exclusive and partial breast-fed infants) (2) formulafed infants # 

### 12 months of age (no human milk, but including weaning food) and (3) mixed-fed young

children aged 18 to 36 months. Hilbig et al. (2002)

defined “total tap water from household” as water from

the tap consumed as a beverage or used in food preparation. “Tap water from food manufacturing”

was defined as water used in industrial production of foods, and “Total Tap Water” was defined as tap water
consumed from both the household and that used in manufacturing.

Table 3-30 summarizes total tap water ingestion (in mL/day and mL/kg-day) and tap water ingestion from household and manufacturing sources (in mL/kg-day) for breastfed, f ormula fed and mixedfed children. Mean total tap water intake was higher

in formula-fed infants (53 mL/kg-day) than in breastfed infants (17 g/kg-day) and mixed-fed young

children (19 g/kg-day). Tap water from household

sources constituted 66 to 97 percent of total tap water ingestion in the different age groups. The major limitation of this study is that the study sample consists of families from an upper social background in Germany (Hilbig et al., 2002). Because

the study was conducted in Germany, the data may not
be directly applicable to the U.S. population. 3.3.2.6 Marshall et al., 2003a - Patterns of Beverage Consumption During the

Transition Stage of Infant Nutrition

Marshall et al. (2003a) investigated beverage ingestion during the transition stage of infant

nutrition. Mean ingestion of infant formula, cow’s milk, combined juice and juice drinks, water, and other beverages were estimated using a frequency questionnaire. A total of 701 children, ages six

months through 24 months, participated in the Iowa

Fluoride Study (IFS). Mothers of newborns were recruited from 1992 through 1995. The parents were

sent questionnaires when the children were 6, 9, 12, 16, 20, and 24 months old. Of the 701 children, 470

returned all six questionnaires, 162 returned five, 58

returned four and 11 returned three, with the minimum

criteria being three questionnaires to be included in the

data set (Marshall et al., 2003a). The questionnaire

was designed to assess the type and quantity of the beverages consumed during the previous week. The validity of the questionnaire was assessed using a three-day food diary for reference (Marshall et al., 2003a). The percentage of subjects consuming beverages and mean daily beverage ingestion for

children with returned questionnaires are presented in

Table 3-31. Human milk ingestion was not quantified,

but the percent of children consuming human milk was provided at each age category (Table 3-31). Juice (100 percent) and juice drinks were not distinguished separately, but categorized as juice and juice drinks.

Water used to dilute beverages beyond normal dilution

and water consumed alone were combined. Based on

Table 3-31, 97 percent of the children consumed

human milk, formula, or cow’s milk throughout the study period, and the percentage of infants consuming

human milk decreased with age, while the percent consuming water increased (Marshall et al., 2003a).

Marshall et al. (2003a) observed that in general, lower family incomes were associated with less breastfeeding and increased ingestion of other beverages.

The advantage of this study is that it provides

mean ingestion data for various beverages.

Limitations of the study are that the it is based on samples gathered in one geographical area and may

not be reflective of the general population. The

authors also noted the following limitations: the

parents were not asked to differentiate between 100
percent juice and juice drinks; the data are parentreported and could reflect perceptions of appropriate ingestion instead of actual ingestion, and a substantial



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number of the infants from well educated, economically secure households dropped out during the initial phase.

3.3.2.7 Marshall et al., 2003b - Relative Validation of a Beverage Frequency Questionnaire in Children Ages 6 Months through 5 Years Using 3-day Food and Beverage Diaries This study was based on data taken from 700 children in the IFS. This study compared estimated beverage ingestion rates reported in questionnaires for the preceding week and dairies for the following week. Packets were sent periodically (every 4 to 6 months) to parents of children aged 6 weeks through 5 years of age. This study analyzed data from children, ages 6 and 12 months, and 2 and 5 years of age. Beverages were categorized as human milk, infant formula, cow’s milk, juice and juice drinks, carbonated and rehydration beverages, prepared drinks (from powder) and water. The beverage questionnaire was completed by parents and summarized the average amount of each beverage consumed per day by their children. The data collection for the diaries maintained by parents included 1 weekend day and 2 week days and included detailed information about beverages consumed. Table 3-32 presents the mean ingestion rates of all beverages for children aged 6 and 12 months and 3 and 5 years. Marshall et al. (2003b) concluded that estimates of beverage ingestion derived from quantitative questionnaires are similar to those derived from diaries. They found that it is particularly useful to estimate ingestion of beverages consumed frequently using quantitative questionnaires. The advantage of this study is that the survey was conducted in two different forms (questionnaire and diary) and that diaries for recording beverage ingestion were maintained by parents for three days.

The main limitation is the lack of information regarding whether the diaries were populated on consecutive or non-consecutive days. The IFS survey participants may not be representative of the general population of the U.S. since participants were primarily white, and from affluent and well-educated families in one geographic region of the country.

3.3.2.8 Skinner et al., 2004 - Transition in Infants’ and Toddlers’ Beverage Patterns Skinner et al. (2004) investigated the pattern of beverage consumption by infants and children participating in the Feeding Infant and Toddlers Study (FITS) sponsored by Gerber Products Company. The FITS is a cross-sectional study designed to collect and analyze data on feeding practices, food consumption,

and usual nutrient intake of U.S. infants and toddlers (Devaney et al., 2004). It included a stratified random sample of 3,022 infants and toddlers between 4 and 24 months of age. Parents or primary caregivers of sampled infants and toddlers completed a single 24­ hour dietary recall of all foods and beverages consumed by the child on the previous day by telephone interview. All recalls were completed between March and July 2002. Detailed information on data collection, coding and analyses related to FITS are provided in Devaney et al. (2004). Beverages consumed by FITS participants were identified as total milks (i.e., human milk, infant formulas, cows milk, soy milk, goat milk), 100 percent juices, fruit drinks, carbonated beverages, water and “other” drinks (i.e., tea, cocoa, dry milk mixtures, and electrolyte replacement beverages). There were six age groupings in the FITS study: 4 to 6, 7 to 8, 9 to 11, 12 to 14, 15 to 18, and 19 to 24 months. Skinner et al. (2004) calculated the percentage of children in each age group consuming any amount in a beverage categoryand the mean amounts consumed. Table 3-33 provides the mean beverage consumption rates in mL/day for the six age categories. Skinner et al. (2004) found that some form of milk beverage was consumed by almost all children at each age; however, total milk ingestion decreased with increasing age. Water consumption also doubled with age, from 163 mL/day in children aged 4 to 6 months old to 337 mL/day at 19 to 24 months old. The percentages of children consuming water increased from 34 percent at 4 to 6 months of age to 77 percent at 19 to 24 months of age. A major strength of the Skinner et al. (2004) study is the large sample size (3,022 children). However, beverage ingestion estimates are based on one day of dietary recall data and human milk quantity derived from studies that weighed infants before and after each feeding to determine the quantity of human milk consumed (Devaney et al., 2004); therefore, estimates of total milk ingestion may not be accurate.

3.4 WATER INGESTION WHILE

SWIMMING

3.4.1 Dufour et al., 2006 - Water Ingestion During Swimming Activities in a Pool: A Pilot Study Dufour et al. (2006) estimated the amount of water ingested while swimming, using cyanuric acid as



### Child-Specific Exposure Factors Handbook Page September 2008 3-11

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

an indicator of pool water ingestion exposure. Cyanuric acid is a breakdown product of chloroisocyanates which are commonly used as

disinfectant stabilizers in recreational water treatment. Because ingested cyanuric acid passes through the body unmetabolized, the volume of water ingested can be estimated based on the amount of cyanuric acid measured in the pool water and in the urine of swimmers, as follows:

V pool water ingested 

### = V urine 

### x CA urine 

### /CA pool 

### (Eqn. 3-1)

where:

V pool water ingested 

### = volume of pool water ingested (mL)

V urine 

### = volume of urine collected over a 24-hour period (mL)

CA urine 

### = concentration of cyanuric acid in urine (mg/L)

CA pool 

### = concentration of cyanuric acid in pool water (mg/L)

Dufour et al. (2006) estimated pool water intake among 53 swimmers that participated in a pilot study at an outdoor swimming pool treated with chloroisocyanate. This pilot studypopulation included 12 adults (4 males and 8 females) and 41 children between 6 and 15 years of age (20 males and 21 females). The study participants were asked not to swim for 24 hours before or after a 45 minute period of active swimming in the pool. Pool water samples were collected prior to the start of swimming activities and

swimmers’ urine was collected for 24 hours after the swimming event ended. The pool water and urine sample were analyzed for cyanuric acid. The results of this pilot study are presented in Table 3-34. The mean volume of water ingested by children over a 45-minute period was 37 mL. The maximum volume of water ingested by children was

154 mL/45 minutes and the 97 th 

### percentile was 90 mL. Individuals older than 18 years of age ingested an average of 16 mL over a 45-minute period; the maximum amount ingested by these individuals was 53mL over a 45-minute period. The mean ingestion rates for males tended to be higher than that of females, but these differences were not statistically significant. The advantages of this study is that it is one of the first attempts to measure water ingested while swimming. However, the number of study participants was low and data cannot be broken out by the recommended age categories. As noted by the Dufour et al. (2006), swimming behavior of pool swimmers may be similar to freshwater swimmers, but may differ from salt water swimmers.

Based on the results of the Dufour et al. (2006) study, the recommended mean water ingestion rate for exposure scenarios involving swimming activities is 50 mL/hour for children under 16 years of age (37 mL/0.75 hour, rounded to one significant figure) and the upper percentile value is 100 mL/hour (90 mL/0.75 hour, rounded to one significant figure). For children, ages 18 to <21 years, the recommended mean water ingestion rate for scenarios involving swimming activities is 20 mL/hour (16 mL/0.75 hour, rounded to one significant figure). Because the data set is limited, the upper percentile water ingestion rate for 18 to <21 year olds is based on the maximum value observed in adults in the Dufour et al. (2006) study: 70 mL/hour (53 mL/0.75 hour, rounded to one significant figure).

3.5 REFERENCES FOR CHAPTER 3

Devaney, B.; Kalb, L.; Briefel, R.; Zavitsky-Novak, T.; Clusen, N.; Ziegler, P. (2004) Feeding Infants and Toddlers Study: Overview of the study design. J Am Diet Assoc 104 (supplement 1): S8-S13. Dufour, A.P.; Evans, O.; Behymer, T.D., Cantu, R. (2006) Water ingestion during swimming activities in a pool: a pilot study. J Water Health 4(4):425-430. Heller, K.; Sohn, W.; Burt, B.; Feigal, R. (2000) Water consumption and nursing characteristics of infants by race and ethnicity. J Public Health Dent 60 (3) 140­

146. Hilbig, A.; Kersting, M.; Sichert-Hellert, W. (2002) ­ Measured consumption of tap water in German infants and young children as background for potential health risk assessment: Data of the DONALD study. Food Addit Contam 19 (9): 829-836. Kahn, H.; Stralka, K. (2008) Estimated daily average per capita water ingestion by child and adult age categories based on USDA’s 1994-96 and 1998 continuing survey of food intakes by individuals. J Expo Anal Environ Epidemiol (accepted for publication May 2008).



### Page Child-Specific Exposure Factors Handbook

3-12 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Levy, S.; Kohout, F.; Guha-Chowdhury, N.; Kiristy, J., Heilman, et al. (1995) Infants’ fluoride intake from drinking water alone, and from water added to formula, beverages, and food. J Dent Res (74):1399-1407. LSRO	 (1995) Life Sciences Research Office,

Federation of American Societies for Experimental Biology Prepared for the Interagency Board for Nutrition Monitoring and Related Research. Third Report on Nutrition Monitoring in the United States: Volume 1. U.S. Government Printing Office, Washington, D.C.Marshall, T.; Levy, S.; Broffitt, B.; EichenbergerGilmore, J.; Stumbo, P. (2003a) Patterns of beverage consumption during the transition stage of infant nutrition. J Amer Diet Assoc 103 (10):1350-1353. Marshall, T.; Eichenberger Gilmore, J; Broffitt, B.; Levy, S.; Stumbo, P. (2003b) Relative Validation of a beverage frequency questionnaire in children Ages 6 months through 5 years using 3-day food and beverage diaries. J Amer Diet Assoc 103 (6):714-720. National Academy of Sciences (NAS). (1977) Drinking water and health. Vol. 1. Washington, DC: National Academy of

Sciences-National Research Council. Sichert-Hellert, W.; Kersting, M.; Manz, F. (2001) Fifteen year trends in water intake in German

children and adolescents: results of the DONALD study. Acta Paediatr 90: 732-7. Skinner, J.; Ziegler, P.; Ponza, M. (2004) - Transition in Infants’ and Toddles’ Beverage Patterns. J Amer Diet Assoc Supplement 1 104 (1):

S45-S50. Sohn, W.; Heller, K. E.; Burt, B. A. (2001) Fluid consumption related to climate among

children in the United States. J Public Health Dent. 61(2): 99-106. USDA.	 (1998) Continuing survey of food intakes by individuals: 1994-96, 1998. U.S. Department ofAgriculture, Agricultural Research Service.

U.S. EPA.	 (2000) Methodology for deriving ambient water quality criteria for the protection of human health (2000). U.S. Environmental Protection Agency, Office of Water, Washington, D.C., EPA-822-00-004. October

2000. U.S. EPA. (2004) Estimated Per Capita Water Ingestion and Body Weight in the United States— an Update: Based on Data Collected by the United States Department of Agriculture’s 1994-96 and 1998 Continuing Survey of Food Intakes by Individuals. U.S. Environmental Protection Agency, Office of Water, Washington, D.C., EPA-822-R-00­

001. October 2004.

U.S. EPA. (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants.

U.S. Environmental Protection Agency, Washington, D.C., EPA/630/P-03/003F.

November 2005.



### Child-Specific Exposure Factors Handbook Page September 2008 3-13Table 3-4. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: Community Water (mL/day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 91

253

428

714

1,040

1,056

4,391

1,670

1,005

363

389 184

227

362

360

271

317

380

447

606

731

826 -

-

-

-

-

- 4

22

30

16

24 -

-

- 17

60

78

98

133

182

194

236 -

- 148

218

188

246

291

350

459

490

628 322

456

695

628

402

479

547

648

831

961

1,119 687*

804

928

885

624

683

834

980

1,387

1,562

1,770 839*

896*

1,056

1,055

837

877

1,078

1,235

1,727

1,983*

2,540* 860*

1,165*

1,424*

1,511*

1,215*

1,364*

1,654

1,870*

2,568*

3,720*

3,889*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data.

Table 3-5. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: Bottled Water (mL/day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 91

253

428

714

1,040

1,056

4,391

1,670

1,005

363

389 104 - -

106 - -

120 - -

120 - -

59 - -

76 - -

84 - -

84 - -

111 - -

109 - -

185 - - - 18

- -

- - - 53

- -

- -

- -

- -

- -

- -

- - 437*

541

572

506

212

280

325

330

382

426

514 556*

771*

774

761

350

494

531

532

709

680*

1,141* 1,007*

1,056*

1,443*

1,284*

801*

1,001*

1,031*

1,079*

1,431*

1,605*

2,364*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-14 September 2008Table 3-6. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: Other Sources (mL/day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 91

253

428

714

1,040

1,056

4,391

1,670

1,005

363

389 13 - -

35 - -

45 - -

45 - -

22 - -

39 - -

43 - -

61 - -

102 - -

97 - -

47 - - - - -

- - -

- - - - - 31

- - - - - 52

- - 58

- - 181

- - 344

- - 295

- - - - 367*

365

406

118

344

343

468

786

740*

246* 393*

687*

938*

963*

482*

718*

830

1,047*

1,698*

1,760*

1,047*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data.

Table 3-7. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: All Sources (mL/day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 91

253

428

714

1,040

1,056

4,391

1,670

1,005

363

389 301

368

528

530

358

437

514

600

834

964

1,075 -

-

- 37

68

104

126

169

224

236

189 -

- 89

181

147

211

251

304

401

387

406 135

267

549

505

287

372

438

503

663

742

803 542

694

812

771

477

588

681

803

1,099

1,273

1,394 846*

889

1,025

1,029

735

825

980

1,130

1,649

1,842

2,117 877*

1,020*

1,303

1,278

961

999

1,200

1,409

1,960

2,344*

2,985* 1,088*

1,265*

1,509*

1,690*

1,281*

1,662*

1,794

2,167*

3,179*

3,854*

4,955*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Child-Specific Exposure Factors Handbook Page September 2008 3-15Table 3-8. Per CapitaaEstimates of Combined Direct and IndirectbWater Ingestion: All Sources (mL/day)Mean90thpercentile95thpercentileSample90% C.I.90% B.I.90% B.I.AgesizeLower UpperLower UpperLower UpperEstimateEstimateEstimateBound BoundBound BoundBound BoundBirth to <1 month 91 301 215 387 846* 638* 859* 877* 798* 993*1 to <3 months 253 368 304 432 889 862 896 1,020* 918* 1,070*3 to <6 months 428 528 485 571 1,025 955 1,083 1,303 1,170 1,3516 to <12 months 714 530 495 564 1,029 973 1,100 1,278 1,142 1,3851 to <2 years 1,040 358 338 377 735 686 778 961 879 1,0012 to <3 years 1,056 437 418 455 825 784 857 999 952 1,0513 to <6 years 4,391 514 494 533 980 953 1,004 1,200 1,167 1,2406 to <11 years 1,670 600 571 629 1,130 1,065 1,162 1,409 1,314 1,46811 to <16 years 1,005 834 770 898 1,649 1,567 1,775 1,960 1,873 2,21816 to <18 years 363 964 870 1,057 1,842 1,743 1,988 2,344* 2,071* 2,599*18 to <21 years 389 1,075 980 1,171 2,117 1,952 2,299 2,985* 2,504* 3,785*aIncludes all participants whether or not they ingested any water from the source during survey period.bDirect water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food orbeverages.*The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the UnitedStates” (LSRO, 1995).CI= Confidence Interval.BI= Bootstrap Interval.Source:Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Child-Specific Exposure Factors Handbook

3-16 September 2008Table 3-9. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: Community Water (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 88

245

411

678

1,002

994

4,112

1,553

975

360

383 52

48

52

41

23

23

22

16

12

11

12 -

-

-

-

-

-

- 1

1

- 1 -

-

- 2

5

6

6

5

4

3

4 -

- 20

24

17

17

17

12

9

8

10 101

91

98

71

34

33

31

22

16

15

16 196*

151

135

102

53

50

48

34

25

23

17 232*

205*

159

126

71

60

61

43

34

31*

35* 253*

310*

216*

185*

106*

113*

93

71*

54*

55*

63*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data.

Table 3-10. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: Bottled Water (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 88

245

411

678

1,002

994

4,112

1,553

975

360

383 33 - -

22 - -

16 - -

13 - -

5 - -

5 - -

5 - -

3 - -

2 - -

2 - -

3 - - - 6

- -

- - - 4

- -

- -

- -

- -

- -

- -

- - 131*

97

74

52

18

19

18

10

8

6

8 243*

161*

117

87

28

35

30

18

14

10*

19* 324*

242*

193*

139*

67*

84*

59

41*

26*

27*

34*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Child-Specific Exposure Factors Handbook Page September 2008 3-17Table 3-11. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: Other Sources (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 88

245

411

678

1,002

994

4,112

1,553

975

360

383 4 - -

7 - -

7 - -

5 - -

2 - -

3 - -

2 - -

2 - -

2 - -

2 - -

1 - - - - -

- - -

- - - - - 3

- - - - - 4

- - 3

- - 7

- - 7

- - 5

- - - - 52*

55

35

11

23

19

16

14

11*

4* 122*

148*

155*

95*

45*

61*

48

36*

34*

27*

14*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data.

Table 3-12. Per Capita a Estimates of Combined Direct and Indirect b Water Ingestion: All Sources (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 88

245

411

678

1,002

994

4,112

1,553

975

360

383 89

77

75

59

31

31

29

21

16

15

16 -

-

- 4

6

7

7

6

4

4

3 -

- 9

20

13

15

14

10

8

6

6 21

46

73

53

24

26

25

18

13

12

12 168

134

118

86

39

41

38

27

20

18

21 235*

173

156

118

63

59

56

39

31

28

32 269*

246*

186

148

85

73

69

50

39

37*

41* 338*

336*

225*

194*

122*

130*

102

76*

60*

59*

73*

a

b

- *

Source: Includes all participants whether or not they ingested any water from the source during survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

= Zero. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-18 September 2008Table 3-13. Per CapitaaEstimates of Total Direct and IndirectbWater Ingestion: All Sources (mL/kg-day)Mean90thpercentile95thpercentileSample90% C.I.90% B.I.90% B.I.AgesizeLower UpperLower UpperLower UpperEstimateEstimateEstimateBound BoundBound BoundBound BoundBirth to <1 month 88 89 64 114 235* 198* 269* 269* 236* 332*1 to <3 months 245 77 62 91 173 164 217 246* 187* 295*3 to <6 months 411 75 68 82 156 145 162 186 176 1996 to <12 months 678 59 54 63 118 112 128 148 134 1661 to <2 years 1,002 31 29 32 63 59 68 85 73 952 to <3 years994 31 30 33 59 57 62 73 69 813 to <6 years 4,112 29 28 30 56 54 56 69 66 726 to <11 years1,553 21 20 22 39 36 41 50 47 5211 to <16 years 975 16 15 17 31 29 34 39 36 4116 to <18 years 360 15 13 16 28 27 32 37* 33* 44*18 to <21 years383 16 14 17 32 29 35 41* 36* 44*aIncludes all participants whether or not they ingested any water from the source during survey period.bDirect water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food orbeverages.*The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the UnitedStates” (LSRO, 1995).CI= Confidence Interval.BI= Bootstrap Interval.Source:Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Child-Specific Exposure Factors Handbook Page September 2008 3-19Table 3-14. Co

Age

Birth to <1 month nsumers Only a

Sample

size

40 Estimates

Mean

470* ofCombi

10

32* ned Direct (mL/day)

25

215* and Indire

50

482* ct b Water Ing

Percentiles

75

692* estion: C

90

849* ommunity

95

858* Water

99

919*

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years

a Excludes i

b Direct wat preparatio

* The sampl

Monitorin 114

281

562

916

934

3,960

1,555

937

341

364

ndividuals wh

er defined as n of food or beverages.

e size does no g in the Unite 552

556

467

308

356

417

480

652

792

895

o did not i water inges

t meet min d States” ( 67*

44

44

43

49

57

74

106

106

114

ngest wate
ted direct

imum req 339

180

105

107

126

146

177

236

266

295

r from the ly as a beve

uirements a LSRO, 1995). 533

561

426

229

281

336

373

487

591

674

source dur rage; indi

s describe 801

837

710

428

510

581

682

873

987

1,174
ing the surve

rect water de

d in the “Thi 943*

1,021

971

674

700

867

994

1,432

1,647

1,860
y period.

fined as w

rd Report 1,053*

1,171*

1,147

893

912

1,099

1,251

1,744

2,002*

2,565*

ater added 1,264*

1,440*

1,586*

1,248*

1,388*

1,684

2,024*

2,589*

3,804*

3,917*

in the

on Nutrition

Source: Kahn and Stralka, 2008 and supplementary data.

Table 3-15. Consumers Only a Estimates of Combined Direct and Indirect b Water Ingestion: Bottled Water (mL/day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 25

64

103

200

229

232

1,021

332

192

63

97 - 450*

507

425

262

352

380

430

570

615*

769 - 31*

48*

47

45

57

72

88

116*

85*

118* - 62*

88

114

88

116

149

168

229

198*

236 - 329*

493

353

188

241

291

350

414

446*

439 - 743*

747

630

324

471

502

557

719

779*

943 - 886*

1,041*

945*

600

736

796

850

1,162*

1,365*

1,788* - 1,045*

1,436*

1,103*

709*

977*

958

1,081*

1,447*

1,613*

2,343* ­ 1,562*

1,506*

1,413*

1,083*

1,665*

1,635*

1,823*

2,705*

2,639*

3,957*

a Excludes individuals who did not ingest water from the source during the survey period.

b Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

- Insufficient sample size to estimate mean and percentiles.

* The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Source: Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-20 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Table 3-16. Consumers Only a Estimates of Combined Direct and Indirect b Water Ingestion: Other Sources (mL/day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 3

19

38

73

98

129

533

219

151

53

33 -

- 562*

407*

262

354

396

448

687

657*

569* -

- 59*

31*

18*

56*

59

89

171*

152*

103* -

- 179*

121*

65

134

148

177

296

231*

142* -

- 412*

300*

143

318

314

347

482

398*

371* -

- 739*

563*

371

472

546

682

947

823*

806* -

- 983*

961*

602*

704*

796

931

1,356*

1,628*

1,160* -

- 1,205*

1,032*

899*

851*

1,019

1,090*

1,839*

1,887*

1,959* ­

­ 2,264*

1,144*

1,204*

1,334*

1,543*

1,596*

2,891*

2,635*

1,962*

a Excludes individuals who did not ingest water from the source during the survey period.

b Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

- Insufficient sample size to estimate means and percentiles.

* The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Source: Kahn and Stralka, 2008 and supplementary data.

Table 3-17. Consumers Only a Estimates of Combined Direct and Indirect b Water Ingestion: All Sources (mL/day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 58

178

363

667

1,017

1,051

4,350

1,659

1,000

357

383 511*

555

629

567

366

439

518

603

837

983

1,094 51*

68*

69

90

84

105

134

177

229

252

219 266*

275

384

250

159

213

255

310

404

395

424 520*

545

612

551

294

375

442

506

665

754

823 713*

801

851

784

481

589

682

805

1,105

1,276

1,397 858*

946*

1,064

1,050

735

825

980

1,131

1,649

1,865

2,144 986*

1,072*

1,330*

1,303

978

1,001

1,206

1,409

1,961

2,346*

3,002* 1,274*

1,470*

1,522*

1,692*

1,281*

1,663*

1,796

2,168*

3,184*

3,866*

4,967*

a Excludes individuals who did not ingest water from the source during the survey period.

b Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages.

* The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Source: Kahn and Stralka, 2008 and supplementary data.



### Child-Specific Exposure Factors Handbook Page September 2008 3-21Table 3-18. Consumers OnlyaEstimates of Combined Direct and IndirectbWater Ingestion: All Sources (mL/day)Mean90thpercentile95thpercentileSample90% C.I.90% B.I.90% B.I.AgesizeEstimateLower UpperEstimateLower UpperEstimateLower UpperBirth to <1 month 58 511* 417* 606* 858* 856* 993* 986* 974* 1,076*1 to <3 months 178 555 487 622 946* 891* 1,042* 1,072* 1,022* 1,183*3 to <6 months 363 629 587 672 1,064 1,011 1,177 1,330* 1,183* 1,431*6 to <12 months 667 567 534 600 1,050 1,001 1,141 1,303 1,181 1,3721 to <2 years 1,017 366 346 385 735 715 765 978 915 1,0012 to <3 years 1,051 439 420 457 825 784 857 1,001 944 1,0753 to <6 years 4,350 518 499 537 980 961 1,000 1,206 1,171 1,2536 to <11 years 1,659 603 574 632 1,131 1,075 1,162 1,409 1,336 1,46811 to <16 years 1,000 837 773 901 1,649 1,568 1,749 1,961 1,873 2,10416 to <18 years 357 983 896 1,071 1,865 1,774 1,982 2,346* 2,129* 2,599*18 to <21 years 383 1,094 999 1,189 2,144 1,951 2,299 3,002* 2,576* 3,785*aExcludes individuals who did not ingest water from the source during the survey period.bDirect water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food orbeverages.*The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the UnitedStates” (LSRO, 1995).CI= Confidence Interval.BI= Bootstrap Interval.Source:Kahn and Stralka, 2008 . 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-22 September 2008Table 3-19. Consumers Only a Estimates of Direct and Indirect b Water Ingestion: Community Water (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 37

108

269

534

880

879

3,703

1,439

911

339

361 137*

119

80

53

27

26

24

17

13

12

13 11*

12*

7

5

4

4

3

3

2

1

2 65*

71

27

12

9

9

8

6

5

4

5 138*

107

77

47

20

21

19

13

10

9

10 197*

151

118

81

36

36

33

23

17

16

17 235*

228*

148

112

56

52

49

35

26

24

29 238*

285*

173*

129

75

62

65

45

34

32*

35* 263*

345*

222*

186*

109*

121*

97

72*

54*

58*

63*

a

b

*

Source Excludes individuals who did not ingest water from the source during the survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data.

Table 3-20. Consumers Only a Estimates of Direct and Indirect b Water Ingestion: Bottled Water (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 25

64

95

185

216

211

946

295

180

63

93 - 92*

72

47

22

25

21

15

11

10*

11 - 7*

6*

5*

5

4

4

3

2*

1*

2* - 12*

15

11

8

8

8

5

4

3*

3 - 76*

69

34

16

17

16

11

8

7*

6 - 151*

100

73

27

35

29

19

14

11*

14 - 164*

149*

104*

49

54

45

30

24*

23*

27* - 220*

184*

120*

66*

81*

57

42*

27*

27*

30* ­ 411*

213*

166*

103*

91*

90*

69*

44*

37*

54*

a

b

- *

Source: Excludes individuals who did not ingest water from the source during the survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages. Insufficient sample size to estimate means and percentiles. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Child-Specific Exposure Factors Handbook Page September 2008 3-23Table 3-21. Consumers Only a Estimates of Direct and Indirect b Water Ingestion: Other Sources (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 3

19

38

68

95

124

505

208

148

52

33 -

- 80*

44*

23

26

22

16

13

10*

8* -

- 10*

4*

1*

4*

3

3

3*

2*

1* -

- 23*

10*

5

10

8

6

6

4*

2* -

- 59*

33*

13

21

17

12

9

7*

6* -

- 106*

65*

28

34

30

23

18

12*

10* -

- 170*

95*

46*

55*

46

32

27*

24*

16* -

- 200*

106*

84*

66*

56

39*

36*

29*

27* ­

­ 246*

147*

125*

114*

79*

62*

56*

43*

31*

a

b

- *

Source Excludes individuals who did not ingest water from the source during the survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages. Means insufficient sample size to estimate distribution percentiles. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data.

Table 3-22. Consumers Only a Estimates of Direct and Indirect b Water Ingestion: All Sources (mL/kg-day)

Age Sample

size Mean Percentiles

10 25 50 75 90 95 99

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <18 years
18 to <21 years 55

172

346

631

980

989

4,072

1,542

970

354

378 153*

116

90

63

31

31

29

21

16

15

16 13*

12*

9

10

7

7

7

6

4

4

3 83*

50

52

27

14

15

15

10

8

7

6 142*

107

86

58

25

27

25

18

13

12

12 208*

161

125

88

40

41

38

27

20

18

21 269*

216*

161

120

64

59

56

39

31

29

32 273*

291*

195*

152

86

73

70

50

39

37*

41* 400*

361*

233*

198*

122*

130*

102*

76*

60*

60*

73*

a

b

*

Source Excludes individuals who did not ingest water from the source during the survey period. Direct water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food or beverages. The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-24 September 2008Table 3-23. Consumer OnlyaEstimates of Total Direct and IndirectbWater Ingestion (mL/kg-day)Mean90thpercentile95thpercentileSample90% C.I.90% B.I.90% B.I.AgesizeLower UpperLower UpperLower UpperEstimateEstimateEstimateBound BoundBound BoundBound BoundBirth to <1 month 55 153* 125* 181* 269* 234* 273* 273* 263* 332*1 to <3 months 172 116 100 132 216* 176* 259* 291* 214* 316*3 to <6 months 346 90 83 97 161 145 178 195* 174* 212*6 to <12 months 631 63 59 67 120 117 127 152 137 1661 to <2 years980 31 30 33 64 57 67 86 70 892 to <3 years989 31 30 33 59 56 61 73 67 813 to <6 years 4,072 29 28 30 56 54 57 70 67 736 to <11 years1,542 21 20 22 39 37 41 50 46 5211 to <16 years 970 16 15 17 31 29 33 39 38 4216 to <18 years 354 15 14 16 29 27 32 37* 33v 44*18 to <21 years378 16 15 18 32 29 35 41* 36* 56*aExcludes individuals who did not ingest water from the source during the survey period.bDirect water defined as water ingested directly as a beverage; indirect water defined as water added in the preparation of food orbeverages.*The sample size does not meet minimum requirements as described in the “Third Report on Nutrition Monitoring in the UnitedStates”(LSRO, 1995).CI= Confidence Interval.BI= Bootstrap Interval.Source:Kahn and Stralka, 2008 and supplementary data. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Child-Specific Exposure Factors Handbook Page September 2008 3-25Table 3-24.Water Ingested (mL/day)afrom Water By Itself and Water Added to Other Beverages and FoodsCategory6 Weeks3 Months6 Months9 Months(N = 124)(N = 120)(N = 99)(N = 77)Water by ItselfRange0-3550-3550-2660-473Per capita meanb± SD30 ± 8930 ± 5930 ± 5989 ± 89cConsumer only mean8989118118dPercent consuming28244266Water Added to FormulaRange0-1,2420-1,2420-1,1240-1,064Powdered ConcentratePer capita mean ± SD177 ± 296266 ± 384266 ± 355207 ± 325Consumer only mean473621562562Percent consuming39424836Liquid ConcentrateRange0-6210-6800-7100-532Per capita mean ± SD89 ±148237 ± 207148 ± 20759 ± 148Consumer only mean355384414325Percent consuming23303521All Concentrated Formula Range0-1,2420-1,2420-1,1230-1,064Per capita mean ± SD266 ± 296384 ± 355414 ± 325266 ± 296Consumer only mean444562532503Percent consuming60688156Water Added to JuicesRange0-1180-7100-4730-887and Other BeveragesPer capita mean ± SD<30 ± 3030 ± 8930 ± 8959 ± 148Consumer only mean89207148207Percent consuming391832Water Added to Powdered Range0-300-1770-2660-177Baby Foods and CerealsPer capita mean ± SD<30 ± 30<30 ± 3059 ± 5930 ± 59Consumer only mean30598989Percent consuming2176443Water Added to Other Foods Range-0-1180-1180-355(Soups, Jell-o, Puddings)Per capita mean ± SD-30 ± 30<30 ± 3030 ± 59Consumer only mean-8959118Percent consuming02829ALL SOURCES OF WATER Range0-1,2420-1,4190-1,1230-1,745Per capita mean ± SD296 ± 325414 ± 414473 ± 325444 ± 355Consumer only mean414562503473Percent consuming68779497aConverted from ounces/day; 1 fluid ounce = 29.57 mL.bMean intake among entire sample.cMean intake for only those ingesting water from the particular category.dPercentage of infants receiving water from that individual source.N= Number of observations.SD= Standard Deviation.Source:Levy et al., 1995. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-26 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water IngestionTable 3-25. Mean Water Consumption (mL/kg-day) by Race/EthnicityRace/EthnicGroupNPlainTap WaterMilk andMilkDrinksReconstituted FormulaRTFFormulaBabyFoodJuices andCarbonatedDrinksNon­carbonatedDrinksOther TotalaBlack nonHispanic121 21(1.7)24(4.6)35(6.0)4(2.0)8(1.6)2(0.7)14(1.3)21(1.7)129(5.7)White nonHispanic620 13(0.8)23(1.2)29(2.7)8(1.5)10(1.2)1(0.2)11(0.7)18(0.8)113(2.6)Hispanic 146 15(1.2)23(2.4)38(7.3)12(4.0)10(1.4)1(0.3)10(1.6)16(1.4)123(5.2)Other59 21(2.4)19(3.7)31(9.1)19(11.2)7(4.0)1(0.5)8(2.0)19(3.2)124(10.6)aTotals may be slightly different from the sums of all categories due to rounding.N = Number of observations.RTF = Ready-to-Feed.Note: Standard Error shown in parentheses.Source: Heller et al., 2000.



### Child-Specific Exposure Factors Handbook Page September 2008 3-27Table 3-26. Plain Tap Water and Total Water Consumption by Age, Sex, Region, Urbanicity, and Poverty Category

Plain Tap Water

(mL/kg-day) Total Water (mL/kg-day)

Variable N Mean SE Mean SE

Age

<12 months

12-24 months

Sex

Male

Female

Region

Northeast

Midwest

South

West

Urbanicity

Urban

Suburban

Rural

Poverty category a

0-1.30

1.31-3.50

>3.50

Total 296

650

475

471

175

197

352

222

305

446

195

289

424

233

946 11

18

15

15

13

14

15

17

16

13

15

19

14

12

15 1.0

0.8

1.0

0.8

1.4

1.0

1.3

1.1

1.5

0.9

1.2

1.5

1.0

1.3

0.6 130

108

116

119

121

120

113

119

123

117

109

128

117

109

118 4.6

1.7

4.1

3.2

6.3

3.1

3.7

4.6

3.5

3.1

3.9

2.6

4.2

3.5

2.3

a

N

SE

Source: Poverty category represents family’s annual incomes of 0-1.30, 1.31-3.50, and greater than 3.50 times the federal poverty level.

= Number of observations.

= Standard Error.

Heller et al., 2000. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-28 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Table 3-27. Intake of Water from Various Sources in 2-13-y-old Participants of the DONALD Study 1985-1999

Water Intake from: Boys and girls 2-3 years

N = 858 b Boys and girls 4-8 years N = 1,795 b Boys 9-13 years

N = 541 b Girls 9-13 years

N = 542 b

Mean

Water in Food (mL/day) a 365 (33) c 487 (36) 673 (36)

Beverages (mL/day) a 614 (55) 693 (51) 969 (51)

Milk (mL/day) a 191 (17) 177 (13) 203 (11)

Mineral water (mL/day) a 130 (12) 179 (13) 282 (15)

Tap water (mL/day) a 45 (4) 36 (3) 62 (3)

Juice (mL/day) a 114 (10) 122 (0) 133 (7)

Soft drinks (mL/day) a 57 (5) 111 (8) 203 (11)

Coffee/tea (mL/day) a 77 (7) 69 (5) 87 (4) 634 (38)

823 (49)

144 (9)

242 (15)

56 (3)

138 (8)

155 (9)

87 (5)

Mean ± SD

Total water intake a,d (mL/day) 1,114 ± 289 1,363 ± 333 1,891 ± 428

Total water intake a,d (mL/kg-day) 78 ± 22 61 ± 13 49 ± 11

Total water intake a,d (mL/kcalday) 1.1 ± 0.3 0.9 ± 0.2 1.0 ± 0.2 1,676 ± 386

43 ± 10

1.0 ± 0.2

a Converted from g/day, g/kg-day, or g/kcal-day; 1 g = 1 mL.

b N = Number of records.

c Percent of total water shown in parentheses.

d Total water = water in food + beverages + oxidation.

SD = Standard deviation.

Source: Sichert-Hellert et al., 2001.



### Child-Specific Exposure Factors Handbook Page September 2008 3-29Table 3-28. Mean (± Standard Error) Fluid Intake (mL/kg/day) by Children Aged 1-10 years, NHANES III, 1988-94

Sample with Sample without Total Sample Temperature Information Temperature Information (N = 7,925) (N = 3,869) (N = 4,056)

Total fluid 84 ± 1.0 84 ± 1.0 85 ± 1.4

Plain water 27 ± 0.8 27 ± 1.0 26 ± 1.1

Milk 18 ± 0.3 18 ± 0.6 18 ± 0.4

Carbonated drinks 6 ± 0.2 5 ± 0.3 6 ± 0.3

Juice 12 ± 0.3 11 ± 0.6 12 ± 0.4

N = Number of observations.

Source: Sohn et al., 2001. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Page Ch ild-Specific Exposure Factors Handbook

3-30 September 2008Table 3-29. Estimated Mean (± Standard Error) Amount of Total Fluid and Plain Water Intake among Children a Aged 1-10 Years: (NHANES III, 1988-94)

N Total Fluid Plain Water

mL/day mL/kg-day mL/day mL/kg-day
Age (years)

1

2

3

4

5

6

7

8

9

10

Sex

Male

Female Race/ethnicity

White

African American

Mexican American

Other

b Poverty income ratio

Low

Medium High Region c,d

Northeast

Midwest

South

West

Urban/rural d

Urban

Rural

Total 578

579

502

511

465

255

235

247

254

243

1,974
1,895

736 1,122
1,728

283

1,868
1,204

379

679

699

869
1,622

3,358

511
3,869 1,393 ± 31 1,446 ± 31
1,548 ± 75 1,601 ± 41
1,670 ± 54 1,855 ± 125
1,808 ± 66 1,792 ± 37
2,113 ± 78 2,051 ± 97

1,802 ± 30
1,664 ± 24

1,653 ± 26 1,859 ± 42
1,817 ± 25 1,813 ± 47

1,828 ± 32
1,690 ± 31 1,668 ± 54

1,735 ± 31
1,734 ± 45 1,739 ± 31

737 ± 25

1,736 ± 18 1,737 ± 19
1,737 ± 15 124 ± 2.9

107 ± 2.3

100 ± 4.6

91 ± 2.8

84 ± 2.3

81 ± 4.9

71 ± 2.3

61 ± 1.8

65 ± 2.1

58 ± 2.4

86 ± 1.8

81 ± 1.5

79 ± 1.8

88 ± 1.8

89 ± 1.7

90 ± 4.2

93 ± 2.6

80 ± 1.6

76 ± 2.5

87 ± 2.3

84 ± 1.5

83 ± 2.2

81 ± 1.7

84 ± 1.0

84 ± 4.3

84 ± 1.1 298 ± 19

430 ± 26

482.± 27

517 ± 23

525 ± 36

718 ± 118

674 ± 46

626 ± 37

878 ± 59

867 ± 74

636 ± 32

579 ± 26

552 ± 34

795 ± 36

633 ± 23

565 ± 39

662 ± 27

604 ± 35

533 ± 41

568 ± 52

640 ± 54

613 ± 24

624 ± 44

609 ± 29

608 ± 20

609 ± 24 26 ± 1.8

32 ± 1.9

31 ± 1.8

29 ± 1.3

26 ± 1.7

31 ± 4.7

26 ± 1.9

21 ± 1.2

26 ± 1.4

24 ± 2.0

29 ± 1.3

26 ± 1.0

24 ± 1.3

36 ± 1.5

29 ± 1.1

26 ± 1.7

32 ± 1.3

26 ± 1.4

22 ± 1.7

26 ± 2.1

29 ± 1.8

28 ± 1.3

27 ± 1.9

27 ± 1.1

28 ± 1.2

27 ± 1.0

a

b

c

d

N

Source: Children for whom temperature data were obtained. Based on ratio of household income to federal poverty threshold. Low: <1.300; medium: 1.301-3.500; high >3.501. All variables except for Region and Urban/rural showed statistically significant differences for both total fluid and plain water intake by Bonferroni multiple comparison method. Northeast = Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont; Midwest = Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; South = Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia; West = Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming.

= Number of observations.

Sohn et al., 2001. 

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water Ingestion

Child-Specific Exposure Factors Handbook Page September 2008 3-31Table 3-30.Tap Water Intake in Breastfed and Formula-fed Infants and Mixed-fed Young Children at Different Age PointsTap water intakeb(mL/day)Tap water intakeb(mL/kg-day)Age NaTotalMean SD Median P95 MaxdTotalFrom HouseholdcFrom ManufacturingMean SD Median P95 Max %eMean SD %fMean SD %fBreastfed#1 year, total 30 130 180 50 525 1,1723 months11 67 167 0 493 7466 months12 136 150 68 479 6349 months47 254 218 207 656 1,17212 months18 144 170 85 649 64917 24** 6 65 150 17 15 23** 85 2.4 4.7** 1510 25** 0 74 125 10 10 25** 97 0.3 1.9** 318 20** 8 5`8 85 18 14 19** 79 3.8 6.3* 2130 27** 23 77 150 28 26 27** 87 3.7 3.4 1315 18** 9 66 66 19 13 18** 86 2.2 2.1 14Formula fed#1 year, total 75 441 244 440 828 1,6033 months78 662 154 673 874 9946 months14 500 178 519 757 8889 months24 434 236 406 839 1,57912 months29 360 256 335 789 1,60353 33 49 115 200 51 49 33 92 4.0 8.0 8107 23 107 147 159 93 103 28 97 3.4 17.9 363 23 65 99 109 64 59 25 92 4.8 8.0 849 27 45 94 200 50 44 27 91 4.5 6.3 937 26 32 83 175 39 33 25 91 3.3 3.7 9Mixed - Breast and Formula1-3 years, total 90 241 243 175 676 2,44118 months27 280 264 205 828 1,88124 months29 232 263 158 630 2,44136 months33 217 199 164 578 1,54419 20 14 56 203 24 15 20 78 3.9 5.5 2225 23 18 70 183 28 22 23 88 3.0 4.1 1218 21 12 49 203 23 15 21 80 3.7 5.0 2014 13 11 36 103 22 9 12 66 4.9 6.6 34aNumbers of 3-day diet records.bTotal tapwater = tap water from the household and tap water from food manufacturing.Converted from g/day and g/kg-day; 1 g = 1 mL.cTap water from household = tap water from the household tap consumed directly as a beverage or used to prepare foods and beverages.dTap water from food = manufacturing tap water from the industrial food production used for the preparation of foods (bread, butter/margarine, tinned fruit, vegetablesand legumes, ready to serve meals, commercial weaning food) and mixed beverages (lemonade, soft drinks).eMean as a percentage of total water.fMean as a percentage of total tap water.*Significantly different from formula-fed infants, p<0.05.**Significantly different from formula-fed infants, p<0.0001.SD =Standard Deviation.P95= 95thpercentile.Source: Hilbig et al., 2002. 

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3-32 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 3 - Water IngestionTable 3-31. Percentage of Subjects Consuming Beverages and Mean Daily Beverage Intakes (mL/day) for Children With Returned QuestionnairesAge at QuestionnaireActual Age (Months)Nb6 Months6.29 ± 0.356779 Months9.28 ± 0.3568112 Months12.36 ± 0.4665916 Months16.31 ± 0.4964120 Months20.46 ± 0.5763224 Months24.41 ± 0.536056-24 Monthsa-585cHuman Milkd301911530-Infant Formulae%dmL/dayf68798 ± 23469615 ± 32829160 ± 275412 ± 7729 ± 830-67g207 ± 112Cows’ Milke%dmL/dayf530 ± 14525136 ± 27879470 ± 31091467 ± 25193402 ± 23797358 ± 22567g355 ± 163Formula and Cows’ Milke%dmL/dayf70828 ± 18681751 ± 21388630 ± 24592479 ± 24894411 ± 23798358 ± 22867g562 ± 154Juice and Juice Drinks%dmL/dayf5565 ± 9573103 ± 11289169 ± 15194228 ± 16695269 ± 18993228 ± 17299h183 ± 103Water%dmL/dayf3627 ± 475953 ± 717592 ± 10987124 ± 11890142 ± 12794145 ± 14899h109 ± 74Other Beveragesi%dmL/dayf13 ± 1896 ± 272327 ± 714253 ± 1096283 ±1218689 ± 13380h44 ± 59Total Beverages mL/daye,f,j934 ± 219 917 ± 245 926 ± 293 887 ± 310 908 ± 310 819 ± 299 920 ± 207aCumulative number of children and percentage of children consuming beverage and beverage intakes for the 6 through 24 month period.bNumber of children with returned questionnaires at each time period.cNumber of children with cumulative intakes for six-through 24 month period.dPercentage of children consuming beverage.eChildren are not included when consuming human milk.fMean ± standard deviation of beverage intake. Converted from ounces/day; 1 fluid ounce = 29.57 mL.gPercentage of children consuming beverage during six-through 24 month period. Children who consumed human milk are not included.hPercentage of children consuming beverage during six-through 24 month period.iOther beverages include non juice beverages (e.g., carbonated beverages, Kool-Aid).jTotal beverages includes all beverages except human milk.Source: Marshall et al., 2003a.



### Child-Specific Exposure Factors Handbook Page September 2008 3-33Table 3-32. Mean (±Standard Deviation) Daily Beverage Intakes Reported on Beverage Frequency Questionnaire and 3-day Food and Beverage DairiesAge6 months (N = 240)12 months (N = 192)3 years (N = 129)5 years (N = 112)BeverageQuestionnaire DiaryQuestionnaire DiaryQuestionnaire DiaryQuestionnaire Diarya%ba%ba%ba%bmL/daymL/daymL/daymL/dayHuman Milk204 ± 373195 ± 358 28.09 ± 2156 ± 225 12.6 NAcNA - NA NA ­Infant formula609 ± 387603 ± 364 85.8180 ± 290139 ± 251 37.0 NA NA - NA NA ­Cow’s milk24 ± 12424 ±124 6.7429 ± 349408 ± 331 90.4316 ± 216358 ± 216 100319 ± 198325 ± 177 98.2Juice/juice drinks56 ± 12433 ± 59 57.5151 ± 136106 ± 101 92.2192 ± 169198 ± 169 96.9189 ± 169180 ± 163 95.5Liquid soft drinks6 ± 680 ± 0 1.39 ± 303 ± 15 20.962 ± 7174 ± 101 74.274 ± 95101 ± 121 82.1Powdered soft drinks0 ± 180 ± 0 0.412 ± 473 ± 18 10.562 ± 11547 ± 101 51.274 ± 12447 ± 95 52.7Water44 ± 8030 ± 53 61.7127 ± 13680 ± 109 84.9177 ± 204136 ± 177 95.3240 ± 242169 ± 183 99.1Total940 ± 319896 ± 195 100905 ± 387804 ± 284 100795 ± 355816 ± 299 100896 ± 399819 ± 302 100aMean ± standard deviation of all subjects.Converted from ounces/day; 1 fluid ounce = 29.57 mL.bPercent of subjects consuming beverage on either questionnaire or diary.cNA = not applicable.N= Number of observations.Source:Marshall et al., 2003b. 

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3-34 September 2008Table 3-33.Consumption of Beverages by Infants and Toddlers (Feeding Infants and Toddlers Study)Age (months)4-6 (N = 862)7-8 (N = 483)9-11 (N = 679)12-14 (N = 374)15-18 (N = 308)19-24 (N = 316)BeveragecategoryConsumersMean ± SD ConsumersMean ± SD ConsumersMean ± SD ConsumersMean ± SD ConsumersMean ± SD ConsumersMean ± SD%ab%ab%ab%ab%ab%abmL/daymL/daymL/daymL/daymL/daymL/daycTotal milks 100778 ± 257 100692 ± 257 99.7659 ± 284 98.2618 ± 293 94.2580 ± 305 93.4532 ± 281d100% juice 21.3121 ± 89 45.6145 ± 109 55.3160 ± 127 56.2186 ± 145 57.8275 ± 189 61.6281 ± 189eFruit drinks 1.6101 ± 77 7.198 ± 77 12.4157 ± 139 29.1231 ± 186 38.6260 ± 231 42.6305 ± 308Carbonated 0.186 ± 0 1.16 ± 9 1.789 ± 92 4.5115 ± 83 11.2157 ± 106 11.9163 ± 172Water 33.7163 ± 231 56.1174 ± 219 66.9210 ± 234 72.2302 ± 316 74.0313 ± 260 77.0337 ± 245fOther1.4201 ± 192 2.2201± 219 3.5169 ± 166 6.6251 ± 378 12.2198 ± 231 11.2166 ± 248Total100863 ± 254 100866 ± 310 100911 ± 361 1001,017 ± 399 1001,079 ± 399 1001,097 ± 482beveragesaWeighted percentages, adjusted for over sampling, nonresponse, and under representation of some racial and ethnic groups.bAmounts consumed only by those children who had a beverage from this beverage category.Converted from ounces/day; 1 fluid ounce = 29.57 mL.cIncludes human milk, infant formula, cow’s milk, soy milk, and goat’s milk.dFruit or vegetable juices with no added sweeteners.eIncludes beverages with less than 100% juice and often with added sweeteners; some were fortified with one or more nutrients.f“Other” beverages category included tea, cocoa and similar dry milk beverages, and electrolyte replacement beverages for infants.N= Number of observations.SD= Standard deviation.Source:Skinner et al., 2004. 

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Child-Specific Exposure Factors Handbook Page

S eptember 2008 3-35Table 3-34. Pool Water Ingestion by Swimmers

Study Group Number of Average Water Ingestion Rate Average Water Ingestion Rate Participants (mL/45-minute interval) (mL/hour) a

Children <16 years old 41 37 49

Males<16 years old 20 45 60

Females <16 years old 21 30 43

Adults (>18 Years) 12 16 21

Men 4 22 29

Women 8 12 16

a Converted from mL/45 minute interval.

Source: Dufour et al., 2006. 

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Chapter 4 - Non-dietary Ingestion Factors

4 NON-DIETARY INGESTION FACTORS

4.1 INTRODUCTION Young children have the potential for exposure to toxic substances through non-dietary ingestion pathways other than soil and dust ingestion (e.g., ingesting pesticide residues that have been transferred from treated surfaces to the hands or objects that are mouthed). Young children mouth objects or their fingers as they explore their environment. Mouthing behavior includes all activities in which objects, including fingers, are touched by the mouth or put into the mouth except for eating and drinking, and includes licking, sucking, chewing, and biting (Groot et al., 1998). Videotaped observations of children’s mouthing

behavior demonstrate the intermittent nature of hand to mouth and object to mouth behaviors in terms of the number of contacts recorded per unit of time (e.g., Ko et al., 2007). In a large non-random sample of children born in Iowa, non-nutritive sucking behaviors were reported by parents to be very common in infancy, and to continue for a substantial proportion of children up to the third and fourth birthdays (Warren et al., 2000).Hand to mouth behavior has been observed in both preterm and full term infants (Rochat et al., 1988, Blass et al., 1989, Takaya et al., 2003). Infants are born with a sucking reflex for breast feeding, and within a few months, they begin to use sucking or mouthing as a means to explore their surroundings. Sucking also

becomes a means of comfort when a child is tired or upset. In addition, teething normally causes substantial mouthing behavior (i.e., sucking or chewing) to alleviate discomfort in the gums (Groot et al., 1998). Children’s mouthing behavior can potentially result in ingestion of toxic substances (Lepow et al., 1975). There are three general approaches to gather data on children’s mouthing behavior: real-time hand recording, in which trained observers manually recordinformation (e.g., Davis et al., 1995); videotranscription, in which trained videographers tape a child’s activities and subsequently extract the pertinent data manually or with computer software (e.g., Black et al., 2005); and questionnaire, or survey response, techniques (e.g., Stanek et al., 1998). With real-time hand recording, observations made by trained professionals (rather than parents) may offer the advantage of consistency in interpreting visible behaviors and may be less subjective than observations made by someone who maintains a care giving relationship to the child. On the other hand, young children’s behavior may be influenced by the presence of unfamiliar people (e.g., Davis et al., 1995). Groot et al. (1998) indicated that parent observers perceived that deviating from their usual care giving behavior by observing and recording mouthing behavior appeared to

have influenced the children’s behavior. With videotranscription methodology, an assumption is made that the presence of the videographer or camera does not influence the child’s behavior. This assumption may result in minimal biases introduced when filming newborns, or when the camera and videographer are not visible to the child. However, if the children being

studied are older than newborns and can see the camera or videographer, biases may be introduced. Ferguson et al. (2006) described apprehension caused by videotaping and described situations where a child’s awareness of the videotaping crewcaused “play-acting” to occur, or parents indicated that the child was behaving differently during the taping session. Another possible source of measurement error may be introduced when children’s movements or positions cause their mouthing not to be captured by the camera. Data transcription errors can bias results in either the negative or positive direction. Finally, measurement error can occur if situations arise in which care givers are absent during videotaping and researchers must stop videotaping and intervene to prevent risky behaviors (Zartarian et al., 1995). Survey response studies rely on responses to questions about a child’s mouthing behavior posed to parents or care givers. Measurement

errors from these studies could occur for a number of different reasons, including language/dialect differences between interviewers and respondents, question wording problems and lack of definitions for terms used in questions, differences in respondents’ interpretation of questions, and recall/memory effects. Some researchers express mouthing behavior as the frequency of occurrence (e.g., contacts per hour or contacts per minute). Others describe the duration of specific mouthing events, expressed in units of seconds

or minutes. This handbook does not address issues related to contaminant transfer from thumbs, fingers, or objects or surfaces, into the mouth, and subsequent ingestion. The recommendations for mouthing



### Child-Specific Exposure Factors Handbook Page September 2008 4-1

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Chapter 4 - Non-dietary Ingestion Factors

frequency and duration are provided in Section 4.2, along with a summary of the confidence ratings for

these recommendations. The recommended values are based on key studies identified by U.S. EPA for this factor. Although some studies in sections 4.3.1 and

4.4.1 are classified as key, they were not directly used to provide the recommendations. They are included as key because they were used by Xue et al., 2007 in a meta analysis, which is the primary source of therecommendations provided in this chapter for hand-tomouth frequency. Following the recommendations, key and relevant studies on mouthing frequency (section 4.3) and duration (section 4.4) are summarized and the methodologies used in the key and relevant studies are described. Information on the prevalence of mouthing behavior is presented in Section 4.5.

4.2 RECOMMENDATIONS The key studies described in Section 4.3 and Section 4.4 were used to develop recommended values for mouthing frequency and duration, respectively, among children. In several cases, key studies pre-dated the recommendations on age groups in U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental

Contaminants (U.S. EPA, 2005), and were performed on groups of children of varying ages. For cases in which age groups of children in the key studies did not correspond exactly to U.S. EPA’s recommended age groups, the closest age group was used. Table 4-1 shows recommended mouthing frequencies, expressed in units of contacts per hour, between either any part of the hand (including fingers and thumbs) and the mouth, or between an object or

surface and the mouth. The recommended hand-tomouth frequencies are based on data from Xue et al. (2007). Xue et al. (2007) conducted a secondary analysis of data from several of the studies summarized in this chapter, as well as data from unpublished studies. Xue et al. 2007, provided data for the age groups of interest to U.S. EPA and categorized the data according to indoor and outdoor contacts. The recommendations for frequency of object-to-mouth contact are based on data from Reed et al., (1999), Freeman et al., (2001), Tulve et al., (2002), AuYeung et al., (2004), and Black et al., 2005. Recommendations for duration of object-to-mouth are based on data from Juberg et al., (2001) and Greene, (2002). Recommendations for hand-to-mouth duration are not provided since those estimates may not be relevant to environmental exposures. Table 4-2 presents the confidence ratings for the recommended values. The overall confidence rating is low for bothfrequency and duration of hand-to-mouth and object-tomouth.



### Page Child-Specific Exposure Factors Handbook September 2008 4-2

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Chapter 4 - Non-dietary Ingestion Factors

Table 4-1. Summary of Recommended Values for Mouthing Frequency and Duration

Age Group Hand-to-Mouth

Source Indoor Frequency (contacts/hour) Outdoor Frequency (contacts/hour)

Mean 95 th Percentiile Mean 95 th Percentile

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years -

- 28

19

20

13

15

7

-

- -

- 65

52

63

37

54

21

-

- -

-

- 15

14

5

9

3

-

- -

-

- 47

42

20

36

12

-

- Xue et al., 2007

Object-to-mouth

Mean Frequency (contacts/hour) 95 th Percentile Frequency (contacts/hour)

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years -

-

- 24 a

20 b

10 c

10 c

1 d

-

- -

-

-

-

-

-

-

-

-

- Reed et al., 1999; Freeman et al., 2001; Tulve et al., 2002; AuYeung et al., 2004;

and Black et al., 2005.

Mean Duration (minutes/hour) 95 th Percentile Duration (minutes/hour)

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years -

- 11 e

11 e

8

13 g

-

-

-

- -

- 26 f

26 f

22

16 h

-

-

-

- Juberg et al., 2001 and Greene, 2002.

a Mean calculated from Black et al., 2005 (7 to 12 months).

b Mean calculated from Tulve et al., 2002 ( # 24 months), AuYeung et al., 2004 ( # 24 months), and Black et al., 2005 (1 and 2 years).

c Mean calculated from Reed et al., 1999 (2 to 6 years), Freeman et al., 2001 (3 to 4 years and 5 to 6 years), Tulve et al., 2002 ( 

### > 24 months), AuYeung et al., 2004 (2 to 6 years), and Black et al., 2005 (37 to 53 months).

d Mean calculated from Freeman et al., 2001 (7 to 8 years and 10 to 12 years).

e Mean calculated from Juberg et al., 2001 (0 to 18 months) and Greene, 2002 (3 to 12 months).

f Calculated 95 th percentile from Greene, 2002 (3 to 12 months).

g Mean calculated from Juberg, et al., 2001 (19 to 36 months) and Greene, 2002 ( 24 to 36 months).

h Calculated 95 th percentile from Greene, 2002 ( 24 to 36 months).

S = No data.



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Chapter 4 - Non-dietary Ingestion Factors

Table 4-2. Confidence in Recommendations for Mouthing Frequency and Duration

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or defined) Bias The approaches for data collection and analysis used were adequate to provide estimates of children’s mouthing frequencies and durations. Sample sizes were very small relative to the population of interest. Almost all key studies published primary data; in cases where secondary data were used, U.S. EPA judged the secondary data to be of suitable utility for the purposes for developing recommendations.

Bias in either direction likely exists in both frequency and duration estimates; the magnitude of bias is unknown. Low

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period Key studies for older children focused on mouthing behavior while the infant studies were designed to research developmental issues.

Most key studies were of samples of U.S. children, but due to the small sample sizes and small number of locations under study, the study subjects may not be representative of the overall U.S. child population.

The studies were conducted over a wide range of dates. However, the currency of the data are not expected to affect mouthing behavior

recommendations.

Extremely short data collection periods may not represent behaviors over longer time periods. Low

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The journal articles are in the public domain, but in many cases, primary

data were unavailable.

Data collection methodologies were capable of providing results that were reproducible within a certain range, when compared with results obtained using alternate data collection techniques (e.g., Smith and Norris, 2003).

Several of the key studies applied and documented quality assurance/quality

control measures. Low

Variability and Uncertinty

Variability in Population

Description of Uncertainty The key studies characterized inter-individual variability to a limited extent, and did not characterize intra-individual variability over diurnal or longer

term time frames.

The study authors typically did not attempt to quantify uncertainties inherent in data collection methodology (such as the influence of observers on behavior), although some described these uncertainties qualitatively. The study authors typically did attempt to quantify uncertainties in data analysis methodoloogies (if video-transcription methods were used). Uncertainties arising from short data collection periods typically were unaddressed either qualitatively or quantitatively. Low

Evaluation and Review

Peer Review

Number and Agreement of

Studies All key studies appear in peer review journals.

Several key studies were available for both frequency and duration, but data were not available for all age groups. The results of studies from different researchers are generally in agreement. Medium

Overall rating Low



### Page Child-Specific Exposure Factors Handbook September 2008 4-4

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Chapter 4 - Non-dietary Ingestion Factors

4.3 NON-DIETARY INGESTION ­ MOUTHING FREQUENCY STUDIES

4.3.1 Key Studies of Mouthing Frequency

4.3.1.1 Zartarian et al.,1997a - Quantifying Videotaped Activity Patterns: Video Translation Software and Training Technologies/Zartarian et al., 1997b ­ Quantified Dermal Activity Data From a Four-Child Pilot Field Study/Zartarian et al., 1998 - Quantified Mouthing Activity Data From a Four-Child Pilot Field Study Zartarian et al. (1997a, 1997b, 1998) conducted a pilot study of the video-transcription methodology to investigate the applicability of using videotaping for gathering information related to children’s activities, dermal exposures and mouthing behaviors. The researchers had conducted studies using the real-time hand recording methodology, resulting in poor inter-observer reliability and observer fatigue when attempted for long periods of time, prompting the investigation into using videotaping with transcription of the children’s activities at a point in time after the observations (videotaping) occurred.

Four Mexican-American farm worker children in the Salinas Valley of California each were videotaped with a hand-held videocamera during their waking hours, excluding time spent in the bathroom, over one day in September 1993. The boys were 2 years 10 months old and 3 years, 9 months old; the girls were 2 years 5 months old and 4 years 2 months old. Time of videotaping was 6.0 hours for the younger girl,

6.6 hours for the older girl, 8.4 hours for the younger boy and 10.1 hours for the older boy. The videotaping gathered information on detailed micro-activitypatterns

of children to be used to evaluate software for videotaped activities and translation training methods.The researchers reported measures taken to assess interobserver reliability and several problems with the video-transcription process. The hourly data showed that non-dietary object mouthing occurred in 30 of the 31 hours of tape time, with one child eating during the hour in which no non-dietary object mouthing occurred. Average object to mouth contacts for the four children were reported to be 9 contacts per hour, with the average per child ranging from 1 to 19 contacts per hour (Zartarian et al., 1997a). Objects mouthed included bedding/towels, clothes, dirt, grass/vegetation, hard surfaces, hard toys, paper/card, plush toy, and skin (Zartarian et al., 1997a). Average hand to mouth contacts for the four children were reported to be 13 contacts per hour (averaging the sum of left hand and right hand to mouth contacts and averaging across children, from Zartarian et al., 1997b), with the average per child ranging from 9 to 19 contacts per hour. This study’s primary purpose was to develop and evaluate the video-transcription methodology; a secondary purpose was collection of mouthing behavior data. The sample of children studied was very small and not likely to be representative of the national population. As with other video-transcription studies, the presence of non-family-member videographers and a video camera may have influenced the children’s

behavior.

4.3.1.2 Reed et al., 1999 - Quantification of Children’s Hand and Mouthing Activities Through a Videotaping MethodologyIn this study, Reed et al. (1999) used a videotranscription methodology to quantify the frequency and type of children’s hand and mouth contacts, as well as a survey response methodology, and compared the videotaped behaviors with parents’ perceptions of those behaviors. Twenty children ages 3 to 6 years old selected randomly at a day care center in New Brunswick, New Jersey, and ten children ages 2 to 5 years old at residences in Newark and Jersey City, New Jersey who were not selected randomly, were studied (gender not specified). For the video-transcription methodology, inter-observer reliability tests were performed during observer training and at four points during the two years of the study. The researchers compared the results of videotaping the ten children in the residences with their parents’ reports of the children’s daily activities. Mouthing behaviors studied included hand to mouth and hand bringing object to

mouth. The video-transcription mouthing contact frequency results are presented in Table 4-3. The authors analyzed parents’ responses on frequencies of their children’s mouthing behaviors and compared those responses with the children’s videotaped behaviors, which revealed certain discrepancies. Parents’ reported hand to mouth contact of “almost never”



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Chapter 4 - Non-dietary Ingestion Factors

corresponded to overall somewhat lower videotaped hand to mouth frequencies than those of children whose parents reported “sometimes,” but there was little correspondence between parents’ reports of object to mouth frequency and videotaped behavior. The advantages of this study were that it compared the results of video-transcription with the survey response methodology results, and described quality assurance steps taken to assure reliability of transcribed videotape data. However, only a small number of children were studied, some were not selected for observation randomly, and the sample of children studied may not be representative of either the locations studied or the national population. Due to the children’s ages, the presence of unfamiliar persons following the children with a video camera may influence the video-transcription results. The parents’ survey responses may also be influenced by recall/memory effects and other limitations of survey methodologies.

4.3.1.3 Freeman et al., 2001 - Quantitative Analysis of Children’s Microactivity Patterns: The Minnesota Children’s Pesticide Exposure Study Freeman et al. (2001) conducted a survey response and video-transcription study of some of the respondents in a phased study of children’s pesticide exposures in the summer and early fall of 1997. A probability-based sample of 168 families with children ages 3 to <14 years old in urban (Minneapolis/St. Paul) and non-urban (Rice and Goodhue Counties) areas of Minnesota answered questions about children’s mouthing of paint chips, food-eating without utensils,eating of food dropped on the floor, mouthing of nonfood items, and mouthing of thumbs/fingers. For the survey response portion of the study, parents provided the responses for children ages 3 and 4 years, and

collaborated with or assisted older children with their responses. Of the 168 families responding to the survey, 102 were available, selected, and agreed to measurements of pesticide exposure. Of these 102 families, 19 agreed to videotaping of the study children’s activities for a period of four consecutive

hours. Based on the survey responses for 168 children, the 3 year olds had significantly more positive responses for all reported behavior compared to the other age groups. The authors stated that they did not know whether parent reporting of 3 year olds’ behavior influenced the responses given. Table 4-4 shows the percent of children, grouped by age, who were reported to exhibit non-food related mouthing behaviors. Table 4-5 presents the mean and median number of mouthing contacts by age for the 19 videotaped children. Among the four age categories of these children, object to mouth activities were significantly greater for the 3 and 4 year olds than any other age group, with a median of 3 and a mean of 6 contacts per hour ( P = 0.002, Kruskal Wallis test comparison across four age groups). Hand

to mouth contacts had a median of 3.5 and mean of 4 contacts per hour for the three 3 and 4 year olds observed, median of 2.5 and mean of 8 contacts per hour for the seven 5 and 6 year olds observed, median of 3 and mean of 5 contacts per hour for the four 7 and 8 year olds observed, and median of 2 and mean of 4 for the five 10, 11 and 12 year olds observed. Gender differences were observed for some of the activities, with boys spending significantly more time outdoors than girls. Hand to mouth and object to mouth activities were less frequent outdoors than indoors for both boys and girls. For the 19 children in the video-transcription portion of the study, inter-observer reliability checks and quality control checks were performed on randomly sampled tapes. For four children’s tapes, comparison of the manual video-transcription with a computerized transcription method (Zartarian et al., 1995) was also performed; no significant differences were found in the frequency of events recorded using the two techniques. The frequency of six behaviors (hand to mouth, hand to object, object to mouth, hand to smooth surface, hand to textured surface, and hand to clothing) was recorded. The amount of time each child spent indoors, outdoors, in contact with soil or grass, and whether the child was

barefoot was also recorded. For the four children whose tapes were analyzed with the computerized transcription method, which calculates event durations, the authors stated that most hand to mouth and object to mouth activities were observed during periods of lower physical activity, such as television viewing. An advantage to this study is that it included results from two separate methodologies, and included quality assurance steps taken to assure reliability of



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transcribed videotape data. However, the children in this study may not be representative of all children in the U.S. Variation in who provided the survey responses (sometimes parents only, sometimes children with parents) may have influenced the responses given. Children studied using the video-transcription methodology were not chosen randomly from the survey response group. The presence of unfamiliar persons following the children with a video camera may have influenced the video-transcription methodology

results.

4.3.1.4 Tulve et al., 2002 - Frequency of Mouthing Behavior in Young Children Tulve et al. (2002) coded the unpublished Davis et al. (1995) data for location (indoor and outdoor) and activity type (quiet or active) and analyzed the subset of the data that consisted of indoor mouthing behavior during quiet activity (72 children, ranging in age from 11 to 60 months). A total of 186 15-minute observation periods were included in the study, with the number of observation periods per child ranging from

1 to 6. Results of the data analyses indicated that there was no association between mouthing frequency and gender, but a clear association between mouthing frequency and age was observed. The analysis

indicated that children # 

### 24 months had the highest frequency of mouthing behavior (81 events/hour) and children >24 months had the lowest (42 events/hour) (Table 4-6). Both groups of children were observed to mouth toys and hands more frequently than household surfaces or body parts other than hands. An advantage of this study is that the randomized design may mean that the children studied were relatively representative of young children living in the study area, although they may not be representative of the U.S. population. Due to the ages of the children studied, the observers’ use of headphones and manual recording of mouthing behavior on observation sheets may have influenced the

children’s behavior. 4.3.1.5 AuYeung et al., 2004 - Young Children’s Mouthing Behavior: An Observational Study via Videotaping in a Primarily Outdoor Residential Setting AuYeung et al

. (2004) used a videotranscription methodology to study a group of 38 children (20 females and 18 males; ages 1 to 6 years), 37 of whom were selected randomly via a telephone screening survey of a 300 to 400 square mile portion of the San Francisco, California peninsula, along with one child selected by convenience due to time constraints.

Families who lived in a residence with a lawn and whose annual income was >$35,000 were asked to participate. Videotaping took place between August 1998 and May 1999 for approximately two hours per child. Videotaping by one researcher was supplemented with field notes taken by a second researcher who was also present during taping. Most of the videotaping took place during outdoor play, however, data were included for several children (one child <2 years old and 8 children >2 years old) who had more than 15 minutes of indoor play during their videotaping sessions. The videotapes were translated into ASCII computer files using VirtualTimingDevice TM 

### software described in Zartarian et al. (1997a). Both frequency and duration (see Section 4.4.2.5 of this Chapter) were analyzed. Between 5 and 10 percent of the data files translated were randomly chosen for quality control checks for inter-observer agreement. Ferguson et al. (2006) described quality control aspects of the study in

detail. For analysis, the mouthing contacts were divided into indoor and outdoor locations, and 16 object/surface categories. Mouthing frequency was analyzed by age and gender separately, and in combination. Mouthing contacts were defined as contact with the lips, inside of the mouth, and/or the tongue; dietary contacts were ignored. Mouthing frequencies for indoor locations are shown in Table 4-7.

For the one child observed that was # 

### 24 months of age, the total mouthing frequency was 84.8 contacts/hour; for children >24 months, the median indoor mouthing frequency was 19.5 contacts/hour. Outdoor median mouthing frequencies (Table 4-8) were very similar for

children # 

### 24 months of age (13.9 contacts/hour) and >24 months (14.6 contacts/hour).



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Nonparametric tests, such as the Wilcoxon rank sum test were used for the data analyses. Both age and gender were found to be associated with differences in mouthing behavior. Girls had significantly higher frequencies of mouthing contacts with the hands and non-dietary objects than boys ( p = 0.01 and p = 0.008, respectively). This study provides distributions of outdoor mouthing frequencies with a variety of objects and surfaces. Although indoor mouthing data were also included in this study, the results were based on a small number of children (N=9) and a limited amount of indoor play. The sample of children may be representative of certain socioeconomic strata in the study area, but is not likely to be representative of the national population. Due to the children’s ages, the presence of unfamiliar persons following the childrenwith a video camera may have influenced the videotranscription methodology results.

4.3.1.6 Black et al., 2005 - Children’s Mouthing and Food-Handling Behavior in an Agricultural Community on the U.S./Mexico Border Black et al. (2005) studied mouthing behavior of children in a Mexican-American community along the Rio Grande River in Texas, in the spring andsummer of 2000, using a survey response and a videotranscription methodology. A companion study of this community (Shalat et al., 2003) identified 870 occupied households during the April 2000 U.S. census and contacted 643 of these via in-person interview to determine presence of children under the age of 3 years. Of the 643 contacted, 91 had at least one child under the age of 3 years (Shalat et al., 2003). Of these 91 households, the mouthing and food-handling behavior of 52 children (26 boys and 26 girls) from 29 homes was videotaped, and the children’s parents answeredquestions about children’s hygiene, mouthing and foodhandling activities (Black et al., 2005). The study was of children ages 7 to 53 months, grouped into four age categories: infants (7 to 12 months), 1 year olds (13 to 24 months), 2 year olds (25 to 36 months), and preschoolers (37 to 53 months). The survey asked questions about children’s ages, genders, reported hand-washing, mouthing and food-handling behavior (N=52), and activities (N=49). Parental reports of thumb/finger placement in the mouth showed decreases with age. The researchers attempted to videotape each child for four hours. The children were followed by the videographers through the house and yard, except for times when they were napping or using the bathroom. Virtual Timing DeviceTM software was used to analyze the videotapes. Based on the results of videotaping, most of the children (49 of 52) spent the majority of their time indoors. Of the 39 children who spent time both indoors and outdoors, all three behaviors (hand to mouth, object to mouth and food handling) were more frequent and longer while the child was indoors. Hand to mouth activity was recorded during videotaping for all but one child, a 30 month old girl. For the four age groups, the mean hourly hand to mouth frequency ranged from 11.9 (2 year olds) to

22.1 (preschoolers), and the mean hourly object to mouth frequency ranged from 7.8 (2 year olds) to 24.4 (infants). No significant linear trends were seen with age or gender for hand to mouth hourly frequency. A significant linear trend was observed for hourly object to mouth frequency, which decreased as age increased (adjusted R 2 

### = 0.179; P = 0.003). Results of this study

are shown in Table 4-9. One advantage of this study is that it compared survey responses with videotaped information on mouthing behavior. A limitation is that the sample was fairly small and was from a limited area (mid-Rio Grande Valley) and is not likely to be representative of the national population. Due to the children’s ages, the presence of unfamiliar persons following the childrenwith a video camera may have influenced the videotranscription methodology results.

4.3.1.7 Xue et al., 2007 - A Meta-analysis of Children’s Hand-to-Mouth Frequency Data for Estimating Nondietary Ingestion Exposure Xue et al. (2007) gathered hand-to-mouth frequency data from 9 available studies representing 429 subjects and more than 2,000 hours of behavior observation. The studies used in this analysis included several of the studies summarized in this chapter (Zartarian et al. ,1998; Reed et al., 1999; Freeman et al., 2001; Greene, 2002; Tulve et al., 2002; and Black et al., 2005), as well as several other sets of unpublished data. These data were used to conduct a meta-analysis



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to study differences in hand-to-mouth behavior. The purpose of the analysis was to: 1)	 examine differences across studies by age (using the new U.S. EPA recommended age groupings (U.S. EPA, 2005)), gender, and indoor/outdoor location; 2) fit variability distributions to the available hand-to-mouth frequency data for use in one dimensional Monte Carlo exposure

assessments; 3) fit uncertainty distributions to the available hand-to-mouth frequency data for use in two dimensional Monte Carlo exposure assessments; and 4) assess hand-to-mouth frequency data needs using the new U.S. EPA recommended age groupings (U.S. EPA, 2005). The data were sorted into age groupings. Visual inspection of the data and statistical methods (method of moments and maximum likelihood estimation) were used, and goodness-of-fit tests were applied to verify the selection among lognormal, Weibull, and normal distributions (Xue et al., 2007). Analyses to study inter- and intra- individual variability of indoor and outdoor hand to mouth frequency were

conducted. There were 894 hours of behavior observation data for the 429 children, ages 0.3 to 12 years, across all available studies. It was found that age and location (indoor vs. outdoor) were important factors contributing to hand to mouth frequency, but study and gender were not (Xue et al., 2007). Distributions of hand to mouth frequencies were developed for both indoor and outdoor activities. Distributions are presented in Table 4-10 for indoor settings and Table 4-11 for outdoor settings. Hand to mouth frequencies decreased for both indoor and outdoor activity as age increased, and were higher indoors than outdoors for all age groups (Xue et al., 2007). A strength of this study is that it is the first effort to fit hand to mouth distributions using U.S. EPA’s recommended age groups using available data on mouthing behavior from studies using different methodologies, of children in different locations. Limitations of the studies used in this meta-analysis apply to the results from the meta-analysis as well; the uncertainty analysis in this study does not account for uncertainties arising out of differences in approaches used in the various studies used in the meta-analysis.

4.3.2 Relevant Studies of Mouthing Frequency

4.3.2.1 Davis et al., 1995 - Soil Ingestion in Children with Pica: Final Report In 1992, under a Cooperative Agreement with

U.S. EPA, the Fred Hutchinson Cancer Research Center conducted a survey response and real-time hand recording study of mouthing behavior data. The study included 92 children (46 males, 46 females) ranging in age from <12 months to 60 months, from Richland, Kennewick, and Pasco, Washington. The children were selected randomly based on date of birth through a

combination of birth certificate records and random digit dialing of residential telephone numbers. For each child, data were collected during a seven day period in January to April, 1992. Eligibility included residence within the city limits, residence duration >1 month, and at least one parent or guardian who spoke English. Most of the adults who responded to the survey reported their marital status as being married (90 percent), their race as Caucasian (89 percent), their household income in the >$30,000 range (56 percent) or their housing status as single-family home occupants (69 percent).The survey asked questions about thumbsucking and frequency questions about pacifier use, placing fingers, hands and feet in the mouth, and mouthing of furniture, railings, window sills, floor, dirt, sand, grass, rocks, mud, clothes, toys, crayons, pens, and other items. Table 4-12 shows the survey responses for the 92 study children. For most of the children in the study, the mouthing behavior real-time hand recording data were collected simultaneously by parents and by trained observers who described and quantified the mouthing behavior of the children in their

home environment. The observers recorded mouth and tongue contacts with hands, other body parts, natural objects, surfaces, and toys every 15 seconds during 15­ minute observation periods spread over 4 days. Parents and trained observers wore headphones that indicated elapsed time (Davis et al., 1995). If all attempted observation periods were successful, each child would have a total of 16 15-minute observation periods with 60 15-second intervals per 15-minute observation period, or 960 15-second intervals in all. The number



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of successful intervals of observation ranged from 0 to 840 per child. Comparisons of the inter-observer reliability between the trained observers and parents showed “a high degree of correlation between the overall degree of both mouth and tongue activity recorded by parents and observers. For total mouth activity, there was a significant correlation between the rankings obtained according to parents and observers, and parents were able to identify the same individuals as observers as being most and least oral in 60 percent

of the cases.” One advantage of this study is the simultaneous observations by both parents and trained observers that allows comparisons to be made regarding the consistency of the recorded observations. The random nature in which the population was selected may provide a representative population of the study area, within certain limitations, but not of the national population. Simultaneous collection of food, medication, fecal, and urine samples that occurred as part of the overall study (not described in this summary) may have contributed a degree of deviation from normal routines within the households during the 7 days of data collection and may have influenced children’s usual behaviors. Wearing of headphones by parents and trained observers during mouthing observations, presence of non-family-member observers, and parents’ roles as observers as well as care givers may also have influenced the results; the authors state “Having the child play naturally while being observed was challenging. Usually the first day of observation was the most difficult in this respect, and by the third or fourth day of observation the child generally paid little

attention to the observers.”

4.3.2.2 Lew and Butterworth, 1997 - The Development of Hand-Mouth Coordination in 2- to 5-Month-Old Infants: Similarities With Reaching and Grasping Lewand Butterworth (1997) studied 14 mostly first-born infants (10 males, 4 females) in Stirling, United Kingdom, in 1990 using a video-transcription methodology. Attempts were made to study each infant within a week of the infant’s 2-month, 3-month, 4-month and 5-month birthdays. After becoming accustomed to the testing laboratory, and with their mothers present, infants were placed in semi-reclining seats and filmed during an experimental protocol in which researchers placed various objects into the

infants’ hands. Infants were observed for two baseline periods of 2 minutes each. The researchers coded all contacts to the face and mouth that occurred during baseline periods (prior to and after the object handling period) as well as contacts occurring during the object handling period. Hand to mouth contacts included contacts that landed directly in or on the mouth as well

as those in which the hand landed on the face first and

then moved to the mouth. The researchers assessed inter-observer agreement using a rater not involved with the study, for a random proportion (approximately 10 percent) of the movements documented during the object handling period, and reported inter-observer agreement of 0.90 using Cohen’s kappa (a measure of the agreement between two raters) for the location of contacts. The frequency of contacts ranged between 0 and 1 contacts per minute. The advantages of this study were that use of video cameras could be expected to have minimal impact on infant behavior for infants of these ages, and the researchers performed tests of inter-observer reliability. A disadvantage is that the study included baseline observation periods of only 2 minutes’ duration, during which spontaneous hand to mouth

movements could be observed. The extent to which these infants’ behavior is representative of other infants of these ages is unknown.

4.3.2.3 Tudella et al., 2000 - The Effect of OralGustatory, Tactile-Bucal, and TactileManual Stimulation on the Behavior of the

Hands in Newborns Tudella et al. (2000) studied the frequency of hand to mouth contact, as well as other behaviors, in 24 full-term Brazilian newborns (10 to 14 days old) using a video-transcription methodology. Infants were in an alert state, in their homes in silent and previously heated rooms in a supine position and had been fed between 1 and 1 1/2 hours before testing. Infants were studied for a four minute baseline period without stimuli before experimental stimuli were administered. Results from the four minute baseline period, without stimuli, indicated that the mean frequency of hand to mouth contact (defined as right hand or left hand touching the lips or entering the buccal cavity, either with or without



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rhythmic jaw movements) was almost 3 right hand contacts and slightly more than 1.5 left hand contacts, for a total hand to mouth contact frequency of about 4 contacts in the four minute period. The researchers performed inter-observer reliability tests on the videotape data and reported an inter-coder Index of Concordance of 93 percent. The advantages of this study were that use of video cameras could be expected to have virtually no impact on newborns’ behavior, and inter-observer reliability tests were performed. However, the study data may not represent newborn hand to mouth contact during non-alert periods such as sleep. The extent to which these infants’ behavior is representative of other full-term 10 to 14 day old infants’ behavior is unknown.

4.3.2.4 Ko et al., 2007 - Relationships of Video Assessments of Touching and Mouthing Behaviors During Outdoor Play in Urban Residential Yards to Parental Perceptions of

Child Behaviors and Blood Lead Levels Ko et al. (2007) compared parent survey responses with results from a video-transcription study of children’s mouthing behavior in outdoor settings, as part of a study of relationships between children’s mouthing behavior and other variables with blood lead levels. A convenience sample of 37 children (51 percent males, 49 percent females) 14 to 69 months old

was recruited via an urban health center and direct contacts in the surrounding area, apparently in Chicago, Illinois. Participating children were primarily Hispanic (89 percent). The mouth area was defined as within 1 inch of the mouth, including the lips. Items passing beyond the lips were defined as in the mouth. Placement of an object or food item in the mouth along with part of the hand was counted as both hand and food or object in mouth. Mouthing behaviors includedhand-to-mouth area both with and not with food, handin-mouth with or without food, and object-in-mouth including food, drinks, toys or other objects. Survey responses for the 37 children who were also videotaped included parents reporting children’s inserting hand, toys or objects in mouth when playing outside, and inserting dirt, stones or sticks in mouth. Video-transcription results of outdoor play for these 37 children indicated 0 to 27 hand-in-mouth, and 3 to 69 object-in-mouth touches per hour for the 13 children reported to frequently insert hand, toys or objects in mouth when playing outside; 0 to 67 hand in mouth, and 7 to 40 object-in-mouth touches per hour for the 10 children reported to “sometimes” perform this behavior; 0 to 30 hand-in-mouth, and 0 to 125 object in mouth touches per hour for the 12 children reported to “hardly ever” perform this behavior, and 1 to 8 hand-in-mouth, and 3 to 6 object-in-mouth touches per hour for the 2 children reported to “never” perform this behavior. Videotaping was attempted for two hours per child over two or more play sessions, with videographers trying to avoid interacting with the children. Children played with their usual toys and partners, and no instructions were given to parents regarding their supervision of the children’s play. The authors stated that during some portion of the videotape time, children’s hands and mouths were out of camera view. Videotape transcription was performed manually, according to a modified version of the protocol used in the Reed et al. (1999) study. Inter-observer reliability

between three video-transcribers was checked with seven 30 minute video segments. One strength of this study is its comparison ofsurvey responses with results from the videotranscription methodology. A limitation is that the nonrandomly selected sample of children studied is unlikely to be representative of the national population. Comparing results from this study with results from other video-transcription studies may be problematic due to inclusion of food handling with hand to mouth and object to mouth frequency counts. Due to the children’s ages, their behavior may have differed from normal patterns due to the presence of strangers who videotaped them.

4.4 NON-DIETARY INGESTION

- MOUTHING DURATION STUDIES

4.4.1 Key Mouthing Duration Studies

4.4.1.1 Juberg et al., 2001 - An Observational Study of Object Mouthing Behavior by Young

Children Juberg et al. (2001) studied 385 children ages 0 to 36 months in western New York state, with parents collecting real-time hand-recording mouthing behavior data, primarily in children’s own home environments. The study consisted of an initial pilot study conducted in February 1998, a second phase conducted in April



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1998, and a third phase conducted at an unspecified later time. The study’s sample was drawn from families identified in a child play research center database or whose children attended a child care facility in the same general area; some geographic variation within the local area was obtained by selecting families with different zip codes in the different study phases. The pilot phase had 30 children who participated out of 150 surveys distributed; the second phase had 187 children out of approximately 300 surveys distributed, and the third phase had 168 participants out of 300 surveys

distributed.

Parents were asked to observe their child’s mouthing of objects only; hand to mouth behavior was not included. Data were collected on a single day (pilot and second phases) or five days (third phase); parents recorded the insertion of objects into the mouth by noting the “time in” and “time out” and the researchers summed the recorded data to tabulate total times spent mouthing the various objects during the day(s) of observation. Thus, the study data were presented as minutes per day of object mouthing time. Mouthed items were classified as pacifiers, teethers, plastic toys, or other objects. The results of the combined pilot and second phase II data are shown in Table 4-13. For both age groups, mouthing time for pacifiers greatly exceeded mouthing time for non-pacifiers, with the difference more acute for the older age group than for the younger age group. Histograms of the observed data show a peak in the low end of the distribution (0 to 100 minutes per day) and a rapid decline at longer durations. A third phase of the study focused on children between the ages of 3 and 18 months and included onlynon-pacifier objects. Subjects were observed for 5 nonconsecutive days over a 2 month period. A total of 168 participants returned surveys for at least one day, providing a total of 793 person-days of data. The data yielded a mean non-pacifier object mouthing duration of 36 minutes per day; the mean was the same when calculated on the basis of 793 person-days of data as on the basis of 168 daily average mouthing times. One advantage of this study is the large sample size (385 children); however, the children apparently were not selected randomly, although some effort was made to obtain local geographic variation among study participants. There is no description of the socioeconomic status or racial and ethnic identities of the study participants. The authors do not describe the methodology (such as stopwatches, analog or digital clocks, or guesses) parents used to record mouthing event durations. The authors stated that using mouthing event duration units of minutes, rather than seconds, may have yielded observations rounded to the nearest

minute.

4.4.1.2 Greene, 2002 - A Mouthing Observation Study of Children Under Six Years of Age The U.S. Consumer Product Safety Commission (CPSC) conducted a survey response and real-time hand recording studybetween December 1999 and February 2001 to quantify the cumulative time per day that young children spend awake, not eating, and mouthing objects. “Mouthing” was defined as sucking, chewing, or otherwise putting an object on his/her lips or into his/her mouth. Participants were recruited via a random digit dialing telephone survey in urban and nearby rural areas of Houston, Texas and Chicago, Illinois. Of the 115,289 households surveyed, 1,745 households had a child under the age of 6 years and were willing to participate. In the initial phase of the study, 491children ages 3 to 81 months participated.

Parents were instructed to use watches with second hands, or count seconds to estimate mouthing event durations. Parents also were to record mouthing frequency and types of objects mouthed. Parents collected data in four separate, non-consecutive 15­ minute observation periods. Initially, parents were called back by the researchers and asked to provide their data over the telephone. Of the 491 children, 43 children (8.8 percent) had at least one 15-minute observation period with mouthing event durations recorded as exceeding 15 minutes. Due to this data quality problem, the researchers excluded the parent observation data from further analysis. In a second phase, trained observers used stopwatches to record the mouthing behaviors and mouthing event durations of the subset of 109 of these children ages 3 to 36 months, and an additional 60 children (total in second phase, 169), on two hours of each of two days. The observations were done at different times of the day at the child’s home and/or child care facility. Table 4-14 shows the prevalence of observed mouthing among the 169 children in the



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second phase. All children were observed to mouth during the four hours of observation time; 99 percent mouthed the category defined as “anatomy.” Pacifiers were mouthed by 27 percent in an age-declining pattern ranging from 47 percent of children less than 12 months old to 10 percent of the 2 to <3 year olds. Table 4-15 provides the average mouthing time by object category and age in minutes per hour. The average mouthing time for all objects ranged from

5.3 to 10.5 minutes per hour, with the highest mouthing time corresponding to children <1 year of age and the lowest to the 2 to <3 years of age category. Among the objects mouthed, pacifiers represented about one third of the total mouthing time, with 3.4 minutes per hour for the youngest children, 2.6 minutes per hour for the children between 1 and 2 years and 1.8 minutes per hour for children 2 to <3 years old. The next largest single item category was anatomy. In this category, children under 1 year of age spent 2.4 minutes per hour mouthing fingers and thumbs; this behavior declined with age to 1.2 minutes per hour for children 2 to <3 years old. Of the 169 children in the second phase, there were usable data on the time awake and not eating (or “exposure time”) for only 109; data for the remaining 60 children were missing. Thus, in order to develop extrapolated estimates of daily mouthing time, from the 2 hours of observation per day for two days, for the 109 children, the researchers developed a statistical model that accounted for the children’s demographic characteristics, in order to estimate exposure times for the 60 children for whom exposure time data were missing, and then computed statistics for the extrapolated daily mouthing times for all 169 children, using a “bootstrap” procedure. Using this method, the estimated mean daily mouthing time of objects other than pacifiers ranged from 37 minutes/day to 70 minutes/day with the lowest number corresponding to the 2 to <3 year old children and the largest number corresponding to the 3 to <12 month old children. The 551 child participants were 55 percent males, 45 percent females. The study’s sample was drawn in an attempt to duplicate the overall U.S. demographic characteristics with respect to race, e t h n i c i t y , s o c i o e c o n o m i c s t a t u s a n d urban/suburban/rural settings. The sample families’ reported annual incomes were generally higher than those of the overall U.S. population. This study’s strength was that it consisted of a randomly selected sample of children from both urban and non-urban areas in two different geographic areas within the U.S. However, the observers’ presence and use of a stopwatch to time mouthing durations may

have affected the children’s behavior.

4.4.2 Relevant Mouthing Duration Studies

4.4.2.1 Barr et al., 1994 - Effects of Intra-OralSucrose on Crying, Mouthing and HandMouth Contact in Newborn and Six Week Old Infants Barr et al. (1994) studied hand to mouth contact, as well as other behaviors, in 15 newborn (8 males, 7 females) and 15 five to seven week old (8 males, 7 females) full-term Canadian infants using a video-transcription methodology. The newborns were 2 to 3 days old, in a quiet, temperature-controlled room at the hospital, in a supine position and had been fed between 2 1/2 and 3 1/2 hours before testing. Barr et al. (1994) analyzed a one minute baseline period, with no experimental stimuli, immediately before a sustained crying episode lasting 15 seconds. For the newborns, reported durations of hand to mouth contact during 10 second intervals of the one minute baseline period were in the range of 0 to 2 percent. The five to seven week old infants apparently were studied at primary care pediatric facilities when they were in bassinets inclined at an angle of 10 degrees. For these slightly older infants, the baseline periods analyzed were less than 20 seconds in length, but Barr et al. (1994) reported similarly low mean percentages of the 10 second intervals (approximately 1 percent of the time with hand to mouth contact). Hand to mouth contact was defined as “any part of the hand touching the lips and/or theinside of the mouth.” The researchers performed interobserver reliability tests on the videotape data and reported a mean inter-observer reliability of 0.78 by Cohen’s kappa (a measure of the agreement between two raters). The advantages of this study were that use of video cameras could be expected to have virtually no impact on newborns’ or five to seven week old infants’ behavior, and inter-observer reliability tests were performed. The study data did not represent newborn

or five to seven week old infant hand to mouth contact



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during periods in which infants of these ages were in a sleeping or other non-alert state, and may only represent behavior immediately prior to a state of distress (sustained crying episode). The extent to which these infants’ behavior is representative of other full-term infants of these ages is unknown.

4.4.2.2 Zartarian et al., 1997a - Quantifying Videotaped Activity Patterns: Video Translation Software and Training Technologies/Zartarian et al., 1997b ­ Quantified Dermal Activity Data From a Four-Child Pilot Field Study/Zartarian et al., 1998 - Quantified Mouthing Activity Data From a Four-Child Pilot Field Study As described in Section 4.3.1.1, Zartarian et al. (1997a, 1997b, 1998) conducted a pilot study of the video-transcription methodology to investigate the applicability of using videotaping for gathering information related to children’s activities, dermal exposures and mouthing behaviors. The researchers had conducted studies using the real-time hand recording methodology, resulting in poor inter-observer reliability and observer fatigue when attempted for long periods of time, prompting the investigation into using videotaping with transcription of the children’s activities at a point in time after the observations (videotaping) occurred.

Four Mexican-American farmworker children in the Salinas Valley of California each were videotaped with a hand-held videocamera during their waking hours, excluding time spent in the bathroom, over one day in September 1993. The boys were 2 years 10 months old and 3 years, 9 months old; the girls were 2 years 5 months old and 4 years 2 months old. Time of videotaping was 6.0 hours for the younger girl,

6.6 hours for the older girl, 8.4 hours for the younger boy and 10.1 hours for the older boy. The videotaping gathered information on detailed micro-activity patterns

of children to be used to evaluate software for videotaped activities and translation training methods. The four children mouthed non-dietaryobjects an average of 4.35 percent (range 1.41 to 7.67 percent) of the total observation time, excluding the time during

which the children were out of the camera’s view (Zartarian et al., 1997a). Objects mouthed included bedding/towels, clothes, dirt, grass/vegetation, hard surfaces, hard toys, paper/card, plush toy, and skin (Zartarian et al., 1997a). Frequency distributions for the four children’s non-dietary object contact durations were reported to be similar in shape. Reported hand to mouth contact presumably is a subset of the object to mouth contacts described in Zartarian et al., 1997a, and is described in Zartarian et al., 1997b. The four children mouthed their hands an average of 2.35 percent (range 1.0 to 4.4 percent) of observation time.The researchers reported measures taken to assess interobserver reliability and several problems with the video-transcription process. This study’s primary purpose was to develop and evaluate the video-transcription methodology; a secondary purpose was collection of mouthing behavior data. The sample of children studied was very small and not likely to be representative of the national population. Thus, U.S. EPA did not judge it to be suitable for consideration as a key study of children’s mouthing behavior. As with other video-transcription studies, the presence of non-family-member videographers and a video camera may have influenced

the children’s behavior.

4.4.2.3 Groot et al., 1998 - Mouthing Behavior of Young Children: An Observational Study In this study, Groot et al. (1998) examined the mouthing behavior of 42 Dutch children (21 boys and 21 girls) between the ages of 3 and 36 months in late July and August 1998. Parent observations were made

of children in 36 families. Parents were asked to observe their children ten times per day for 15 minute intervals (i.e., 150 minutes total per day) for two days and measure mouthing times with a stopwatch. In this study, mouthing was defined as “all activities in which objects are touched by mouth or put into the mouth except for eating and drinking. This term includes licking as well as sucking, chewing and biting.” For the study, a distinction was made between toys meant for mouthing (e.g., pacifiers, teething rings) and those not meant for mouthing. Inter-observer and intra-observer reliability was measured by trained observers who co-observed a portion of observation periods in three families, and who co-observed and repeatedly observed some video-transcriptions made of one child. Another quality assurance procedure performed for the extrapolated total mouthing time data



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was to select 12 times per hour randomly during the entire waking period of four children during one day, in

which the researchers recorded activities and total mouthing times. Although the sample size was relatively small, the results provided estimates of mouthing times, other than pacifier use, during a day. The results were extrapolated to the entire day based on the 150 minutes of observation per day, and the mean value for each child for the two days of observations was interpreted as the estimate for that child. Summary statistics are

shown in Table 4-16. The standard deviation in all four age categories except the 3 to 6 month old children

exceeded the estimated mean. The 3 to 6 monthchildren (N=5) were estimated to have mean nonpacifier mouthing durations of 36.9 minutes per day, with toys as the most frequently mouthed product category, and the 6 to 12 month children (N=14) 44 minutes per day (fingers most frequently mouthed).The 12 to 18 month olds’ (N=12) estimated mean nonpacifier mouthing time was 16.4 minutes per day, with fingers most frequently mouthed, and 18 to 36 month olds’ (N=11) estimated mean non-pacifier mouthing time was 9.3 minutes per day (fingers most frequently mouthed). One strength of this study is that the researchers recognized that observing children’s behavior might affect the behavior, and emphasized to the parents the importance of making observations under conditions that were as normal as possible. In spite of these efforts, many parents perceived that their children’s behavior was affected by being observed, and observation interfered with care giving responsibilities such as comforting children when they were upset. Other limitations included a small sample size that was not representative of the Dutch population and that also may not be representative of U.S. children. Technical problems with the stopwatches affected at least 14 of 36 parents’ data. 4.4.2.4 Smith and Norris, 2003 - Reducing the Risk of Choking Hazards: Mouthing Behavior of Children Aged 1 Month to 5 Years/Norris and Smith, 2002 - Research Into the Mouthing Behaviour of Children up to 5

Years Old Smith and Norris (2003) conducted a real-time hand recording study of mouthing behavior among 236 children (111 males, 125 females) in the United Kingdom (exact locations not specified) who were from 1 month to 5 years old. Children were observed at home by parents, who used stopwatches to record the time that mouthing began, the type of mouthing, the type of object being mouthed, and the time that mouthing

ceased. Children were observed for a total of 5 hours over a two week period; the observation time consisted of twenty 15 minute periods spread over different times and days during the child’s waking hours. Parents also recorded the times each child was awake and not eating meals so that the researchers could extrapolate estimates of total daily mouthing time from the shorter observation periods. Mouthing was defined as licking/lip touching, sucking/trying to bite, biting or chewing, with a description of each category, together with pictures, given to parents as guidance for what to

record. The results of the study are shown in Table 4­

17. While no overall pattern could be found in the different age groups tested, a Kruskal-Wallis test on the

data for all items mouthed indicated that there was a significant difference between the age groups. Across all age groups and types of items, licking and sucking accounted for 64 percent of all mouthing behavior. Pacifiers and fingers exhibited less variety on mouthing behavior (principally sucking), while other items had a higher frequency of licking, biting, or other mouthing.

The researchers selected 25of the 236 children randomly for a single 15 minute observation of each child (total observation time across all children: 375 minutes), in order to compare the mouthing frequency and duration data obtained according to the real-time hand recording and the video-transcription methodologies, as well as the reliability of parent observations versus those made by trained professionals. For this group of 25 children, the total number of mouthing behavior events recorded by video (160) exceeded those recorded by parents (114) and



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trained observers (110). Similarly, the total duration recorded by video (24 minutes and 15 seconds) exceeded that recorded by observers (parents and

trained observers both recorded identical totals of 19 minutes and 44 seconds). The mean and standard deviation of observed mouthing time were both lower when recorded by video versus real-time hand recording. The maximum observed mouthing time was also lower (6 minutes and 7 seconds by video versus 9 minutes and 43 seconds for both parents and trained observers). The strengths of this study were its comparison of three types of observation (parents, trained professional observers, and videotaping), and its detailed reporting of mouthing behaviors by type, object/item mouthed, and age group. However, the children studied may not be representative of the study population, and may not be representative of U.S.

children.

4.4.2.5 Au Yeung et al, 2004

- Young Children’s Mouthing Behavior: An Observational Study via Videotaping in a Primarily Outdoor Residential Setting As described in Section 4.3.1.5, AuYeung et

al

. (2004) used a video-transcription methodology to study a group of 38 children (20 females and 18 males; ages 1 to 6 years), 37 of whom were selected randomly via a telephone screening survey of a 300 to 400 square mile portion of the San Francisco, California peninsula, along with one child selected by convenience due to

time constraints. Families who lived in a residence with a lawn and whose annual income was >$35,000 were asked to participate. Videotaping took place between August 1998 and May 1999 for approximately two hours per child. Videotaping by one researcher was supplemented with field notes taken by a second researcher who was also present during taping. Most of the videotaping took place during outdoor play, however, data were included for several children (one child <2 years old and 8 children >2 years old) who had more than 15 minutes of indoor play during their videotaping sessions. The videotapes were translated into ASCII computer files using VirtualTimingDevice TM 

### software described in Zartarian et al. (1997a). Both frequency (see Section 4.3.1.5 of this Chapter) and duration were analyzed. Between 5 and 10 percent of the data files translated were randomly chosen for quality control checks for inter-observer agreement. Ferguson et al. (2006) described quality control aspects of the study in

detail. For analysis, the mouthing contacts were divided into indoor and outdoor locations, and 16 object/surface categories. Mouthing durations were analyzed by age and gender separately, and in combination. Mouthing contacts were defined as contact with the lips, inside of the mouth, and/or the tongue; dietary contacts were ignored. Mouthing durations are shown in Table 4-18 (outdoor locations). For the children in all age groups, the median duration of each mouthing contact was 1 to 2 seconds, confirming the observations of other researchers that children’s mouthing contacts are of very short duration.

For the one child observed that was # 

### 24 months, the total indoor mouthing duration was 11.1 minutes/hour; for children >24 months, the median indoor mouthing duration was 0.9 minutes/hour (Table 4-19). For outdoor environments, median contact durations for these age groups decreased to 0.8 and 0.6 minutes/hour, respectively (Table 4-20). Nonparametric tests, such as the Wilcoxon rank sum test were used for the data analyses. Both age and gender were found to be associated with differences in mouthing behavior. Girls’ hand to mouth contact durations were significantly shorter than for boys ( p = 0.04). This study provides distributions of outdoor mouthing durations with a variety of objects and surfaces. Although indoor mouthing data were also included in this study, the results were based on a small number of children (N=9) and a limited amount of indoor play. The sample of children may be representative of certain socioeconomic strata in the study area, but is not likely to be representative of the national population. Due to the children’s ages, the presence of unfamiliar persons following the childrenwith a video camera may have influenced the videotranscription methodology results.



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4.5 MOUTHING PREVALENCE

4.5.1 Stanek et al., 1998 - Prevalence of Soil Mouthing/Ingestion Among Healthy Children Aged 1 to 6 Stanek et al. (1998) characterized the prevalence of mouthing behavior among healthy children based on a survey response study of parents or guardians of 533 children (289 females, 244 males) ages 1 to 6 years old. Study participants were attendees

at scheduled well-child visits at three clinics in Western Massachusetts in August through October, 1992. Participants were questioned about the frequency of 28 mouthing behaviors of the children over the preceding month in addition to exposure time (e.g., time outdoors, play in sand or dirt) and children’s characteristics (e.g., teething). Table 4-21 presents the prevalence of reported non-food ingestion/mouthing behaviors by child’s age as the percent of children whose parents reported the behavior in the preceding month. The table includes a column of data for the 3 to <6 year age category; this column was calculated by U.S. EPA as a weighted mean value of the individual data for 3, 4, and 5 year olds in order to conform to the standardized age categories used in this handbook. Among all the age groups, 1 year olds had the highest reported daily sucking of fingers/thumb; the proportion dropped for two year olds, but rose slightly for three and four year olds and declined again after age 4. A similar pattern was reported for more than weekly finger/thumb sucking, while more than monthly finger/thumb sucking showed a very slight increase for 6 year olds. Reported pacifier use was highest for one year olds and declined with age for daily and more than weekly use; for more than monthly use of a pacifier several six year olds werereported to use pacifiers, which altered the agedeclining pattern for the daily and more than weekly reported pacifier use. A pattern similar to pacifier use existed with reported mouthing of teething toys, with highest reported use for one year olds, a decline with age until age 6 when reported use for daily, more than weekly, and more than monthly use of teething toys

increased. The authors developed an outdoor mouthing rate for each child as the sum of rates for responses to four questions on mouthing specific outdoor objects. Survey responses were converted to mouthing rates per week, using values of 0, 0.25, 1, and 7 for responses of never, monthly, weekly, and daily ingestion. Reported outdoor soil mouthing behavior prevalence was found to be higher than reported indoor dust mouthing prevalence, but both behaviors had the highest reported prevalence among 1 year old children and decreased for children 2 years and older. The investigators conducted principal component analyses on responses to four questions relating to ingestion/mouthing of outdoor objects in an attempt to characterize variability. Outdoor ingestion/mouthing rates constructed from the survey responses were that children 1 year of age were reported to mouth or ingest outdoor objects 4.73 times per week while 2 to 6 year olds were reported to mouth or ingest outdoor objects 0.44 times per week. The authors developed regression models to identify factors related to high outdoor mouthing rates. The authors found that children who were reported to play in sand or dirt had higher outdoor object ingestion/mouthing

rates. A strength of this study is that it was a large sample obtained in an area with urban and semi-urban residents within various socioeconomic categories and with varying racial/ethnic identities. However, difficulties with parents’ recall of past events may have

caused either over-estimates or under-estimates of the

behaviors studied.

4.5.2 Warren et al., 2000 - Non-nutritive Sucking

Behaviors in Preschool Children: A Longitudinal Study Warren et al. (2000) conducted a survey response study of a non-random cohort of children born in certain Iowa hospitals from early 1992 to early 1995, as part of a study of children’s fluoride exposure. For this longitudinal study of children’s non-nutritive sucking behaviors, 1,374 mothers were recruited at the time of their newborns’ birth, and over 600 were active in the study until the children were at least 3 years old. Survey questions on non-nutritive sucking behaviors

were administered to the mothers when the children were 6 weeks, 3, 6, 9, 12, 16 and 24 months old, and yearly after age 24 months. Questions were posed regarding the child’s sucking behavior over the previous 3 to 12 months. The authors reported that nearly all children sucked non-nutritive items, including pacifiers, thumbs



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or other fingers, and/or other objects, at some point in their early years. The parent-reported sucking behavior prevalence peaked at 91 percent for 3 month old children. At 2 years of age, a majority (53 percent) retained a sucking habit, while 29 percent retained the habit at age 3 years and 21 percent at age 4 years. Parent-reported pacifier use was 28% for 1 year olds, 25% for 2 year olds, and 10% for 3 year olds. The authors cautioned against generalizing the results to other children due to study design limitations. Strengths of this study were its longitudinal design and the large sample size. A limitation is that the non-random selection of original study participants and the self-selected nature of the cohort of survey respondents who participated over time means that the results may not be representative of other U.S. children of these ages.

4.6 REFERENCES FOR CHAPTER 4 AuYeung, W.; Canales, R.; Beamer, P.; Ferguson, A.C.; Leckie, J.O. (2004) Young children’s mouthing behavior: An observational study via videotaping in a primarily outdoor residential setting. J Children’s Health 2(3­ 4):271-295. Barr, R.G.; Quek, V.S.H.; Cousineau, D.; Oberlander, T.F.; Brian, J.A.; Young, S.N. (1994) Effects of Intra-oral sucrose on crying, mouthing and

hand-mouth contact in newborn and six-weekold infants. Dev Med Child Neurol 36:608­

618. Black, K.; Shalat, S.L.; Freeman, N.C.G.; Jimenez, M.; Donnelly, K.C.; Calvin, J.A. (2005) Children’s mouthing and food-handling behavior in an agricultural community on the US/Mexico border. J Expo Anal Environ Epidemiol 15:244-251. Blass, E.M.; Fillion, T.J.; Rochat, P.; Hoffmeyer, L.B.; Metzger, M.A. (1989) Sensorimotor and

Motivational Determinants of Hand-Mouth Coordination in 1-3-Day-Old Human Infants. Dev Psych 25(6):963-975. Davis, S.; Myers, P.A.; Kohler, E.; Wiggins, C. (1995). Soil Ingestion in Children with Pica: Final Report. U.S. EPA Cooperative Agreement CR 816334-01. Seattle, Washington: Fred

Hutchinson Cancer Research Center. Ferguson, A.C.; Canales, R.A.; Beamer, P.; AuYeung, W.; Key, M.; Munninghoff, A.; Lee, K. T.-W.; Robertson, A., Leckie, J.O. (2006) Video methods in the quantification of children’s exposures. J Expo Sci Environ Epidemiol

16:287-298. Freeman, C.G.; Jimenez, M.; Reed, K.J.; Gurunathan, S.; Edwards, R.D.; Roy, A.; Adgate, J.L.; Pellizzari, E.D.; Quackenboss, J.; Sexton, K.; Lioy, P.J. (2001) Quantitative analysis of children’s microactivity patterns: The Minnesota children’s pesticide exposure study. J Expo Anal Environ Epidemiol

11:501-509. Greene, M.A. (2002) Mouthing times for children from the observational study. U.S. Consumer Product Safety Commission, Bethesda, MD. Groot, M. E.; Lekkerkerk, M. C.; Steenbekkers, L. P.

A. (1998) Mouthing Behavior of Young Children: An observational Study. Wageningen Agricultural University, Wageningen, the Netherlands. Juberg, D.R.; Alfano, K.; Coughlin, R.J.; Thompson,

K.M. (2001) An Observational Study of Object Mouthing Behavior by Young Children. Pediatrics 107(1)135-142. Ko, S.; Schaefer, P.; Vicario, C.; Binns, H.J. (2007) Relationships of video assessments of touching and mouthing behaviors during outdoor play in urban residential yards to parental perceptions of child behaviors and blood lead levels. J Expo Sci Environ Epidemiol 17:47-47. Lepow, M.L.; Bruckman, L.; Gillette, M.; Markowitz, S.; Robino, R.; Kapish, J. (1975) Investigations into Sources of Lead in the

Environment of Urban Children. Environ Res

10:415-26. Lew, A.R.; Butterworth, G. (1997) The Development

of Hand-Mouth Coordination in 2- to 5­ Month-Old Infants: Similarities with Reaching and Grasping. Infant Behav Dev 20(1):59-69. Norris,	 B.; Smith, S. (2002) Research into the mouthing behaviour of children up to 5 years old. London: Consumer and Competition Policy Directorate, Department of Trade and Industry.



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Reed, K.; Jimenez, M.; Freeman, N.; Lioy, P. (1999) Quantification of children’s hand and mouthing activities through a videotaping methodology. J Expo Anal Environ Epidemiol 9:513-520. Rochat,	 P.; Blass, E.M.; Hoffmeyer, L.B. (1988) Oropharyngeal Control of Hand-Mouth Coordination in Newborn Infants. Dev Psych 24(4):459-63. Shalat, S.L.; Donnelly, K.C.; Freeman, N.C.G.; Calvin, J.A.; Ramesh, S.; Jimenez, M.; Black, K.; Coutinho, C.; Needham, L.L.; Barr, D.B.; Ramirez, J. (2003) Nondietary ingestion of pesticides by children in an agricultural community on the U.S./Mexico border: Preliminary Results. J Expos Anal Environ Epidemiol 13:42-50. Smith, S.A.; Norris, B. (2003). Reducing the risk of choking hazards: mouthing behavior of children aged 1 month to 5 years. Injury Control and Safety Promotion 10(3):145-154. Stanek, E.J.; Calabrese, E.J.; Mundt, K.; Pekow, P.; Yeatts, K.B. (1998) Prevalence of soil mouthing/ingestion among healthy children aged 1 to 6. J Soil Contam 7(2):227-242. Takaya,	 R.; Yukuo, K.; Bos, A.F.; Einspieler, C. (2003) Preterm to early postterm changes in the development of hand-mouth contact and other motor patterns. Early Hum Dev 75 Suppl. S193-S202. Tudella, E.; Oishi, J.; Puglia Bermasco, N.H. (2000) The Effect of Oral-Gustatory, Tactile-Bucal,

and Tactile-Manual Stimulation on the

Behavior of the Hands in Newborns. Dev Psychobiol 37:82-89. Tulve, N.S.; Suggs, J.C.; McCurdy, T.; Cohen Hubal, E.A.; Moya, J. (2002) Frequency of mouthing behavior in young children. J Expo Anal Environ Epidemiol 12:259-264.

U.S. EPA. (2005) Guidance on selecting age groups for monitoring and assessing childhood exposures to environmental contaminants. Washington, DC.: U.S. Environmental Protection Agency, Office of Research and Development.

EPA/630/P-03/003F.Warren, J.J.; Levy, S.M.; Nowak, A.J.; Tang. S.	 Nonnutritive sucking behaviors in preschool children: a longitudinal study. (2000) Pediatr Dent 22(3):187-91. Xue, J.; Zartarian, V.; Moya, J.; Freeman, N.; Beamer, P.; Black, K; Tulve, N.; Shalat, S. (2007) A Meta-Analysis of Children’s Hand-to-Mouth Frequency Data for Estimating Nondietary Ingestion Exposure. Risk Analysis 27(2):411­

420. Zartarian, V.G.; Streicker, J.; Rivera, A.; Cornejo, C.S.; Molina, S.; Valadez, O.F.; Leckie, J.O.(1995) A Pilot Study to Collect MicroActivity Data of Two- to Four-Year-Old Farm Labor Children in Salinas Valley, California. J Expo Anal Environ Epidemiol 5(1):21-34. Zartarian V.G.; Ferguson A.C.; Ong, C.G.; Leckie J. (1997a) Quantifying Videotaped Activity

Patterns: Video Translation Software and Training Methodologies. J Expo Anal Environ Epidemiol 7(4):535-542. Zartarian V.G.; Ferguson A.; Leckie J. (1997b)Quantified dermal activity data from a fourchild pilot field study. J Expo Anal Environ Epidemiol 7(4):543-553. Zartarian, V.G.; Ferguson, A.C.; Leckie, J.O. (1998)Quantified mouthing activity data from a fourchild pilot field study. J Expo Anal Environ Epidemiol 8(4):543-553.



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Table 4-3. New Jersey Children’s Mouthing Frequency (contacts/hour) from Video-transcription

Category Minimum Mean Median 90th Percentile Maximum

Hand to mouth

Object to mouth 0.4 9.5 8.5 20.1

0 16.3 3.6 77.1 25.7

86.2

Source: Reed et al., 1999.

Table 4-4. Survey-Reported Percent of 168 Minnesota Children Exhibiting Behavior, by Age

Age Group Thumbs/fingers in Mouth Toes in Mouth Non-food Items in Mouth

3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years 11 years 12 years 71

63

33

30

28

33

43

38

33

33 29

0

-

-

-

-

-

-

-

- 71

31

20

29

28

40

38

38

48

17

-

Source: = No data.

Freeman et al., 2001.

Table 4-5. Video-transcription Median (Mean) Observed Mouthing in 19 Minnesota Children (contacts/hour)

Age Group N Object-to-mouth a Hand-to-mouth

3 to 4 years 5 to 6 years 7 to 8 years 10 to 12 years 3

7

4

5 3 (6) 0 (1) 0 (1) 0 (1) 3.5 (4) 2.5 (8) 3 (5) 2 (4)

a Kruskal Wallis test comparison across four age groups, P =0.002.

N = Number of observations.

Source: Freeman et al., 2001.



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Chapter 4 - Non-dietary Ingestion FactorsTable 4-6. Variability in Objects Mouthed by Washington State Children (contacts/hour)All Subjects#24 Months>24 MonthsVariableNaMeanbMedian 95% CIcNaMeanbMedian 95% CIcNaMeanbMedian 95% CIcMouth-body 186 8 2 2-369 10 4 3-6117 7 1 0.8-1.3Mouth-hand 186 16 11 9-1469 18 12 9-16117 16 9 7-12Mouth-surface 186 4 1 0.8-1.269 7 5 3-8117 2 1 0.9-1.1Mouth-toy 186 27 18 14-2369 45 39 31-48117 17 9 7-12Total events 186 56 44 36-5269 81 73 60-88117 42 31 25-39aNumber of observations.bArithmetic mean.cThe 95% confidence intervals (CI) apply to median. Values were calculated in logs and converted to original units.Source: Tulve et al., 2002.



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Table 4-7. Indoor Mouthing Frequency (Contacts per hour), Video-transcription of 9 Children with >15 minutes in View Indoors

Age Group N Statistic Hands Total non-dietary a

13 to 84 months 9 Mean

Median Range 20.5

14.8

2.5 - 70.4 29.6

22.1

3.2 - 82.2

# 24 months 1

- 73.5 84.8

>24 months 8 Mean

Median Range 13.9

13.3

2.2 - 34.1 22.7

19.5

2.8 - 51.3

a Object/surface categories mouthed indoors included: Clothes/towels, hands, metal, paper/wrapper, plastic, skin, toys, and wood. N = Number of subjects.

Source: AuYeung et al., 2004.

Table 4-8. Outdoor Mouthing Frequency (Contacts per hour), Video-transcription of 38 Children

Age Group N Statistic Hands Total non-dietary a

13 to 84 months 38 Mean

5 th percentile

25 th percentile

50 th percentile

75 th percentile

95 th percentile

99 th percentile 11.7

0.4

4.4

8.4

14.8

31.5

47.6 18.3

0.8

9.2

14.5

22.4

51.7

56.6

# 24 months 8 Mean

Median Range 13.0

7.0

1.3 - 47.7 20.4

13.9

6.2 - 56.4

>24 months 30 Mean

5 th percentile

25 th percentile

50 th percentile

75 th percentile

95 th percentile

99 th percentile 11.3

0.2

4.7

8.6

14.8

27.7

39.5 17.7

0.6

7.6

14.6

22.4

43.8

53.0

a Object/surface categories mouthed outdoors included: animal, clothes/towels, fabric, hands, metal, non-dietary water, paper/wrapper, plastic, skin, toys, vegetation/grass, and wood. N = Number of subjects.

Source: AuYeung et al., 2004.



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Table 4-9. Videotaped Mouthing Activity of Texas Children, Median Frequency (Mean ± SD)

Hand to mouth Object to Mouth

Age N Frequency Frequency (contacts/hour) (contacts/hour)

Infant 13 14 (19.8 ± 14.5) 18.1 (24.4 ± 11.6) 1 year 12 13.3 (15.8 ± 8.7) 8.4 (9.8 ± 6.3) 2 years 18 9.9 (11.9 ± 9.3) 5.5 (7.8 ± 5.8)

Preschool 9 19.4 (22.1 ± 22.1) 8.4 (10.1 ± 12.4)

N = Number of subjects.

SD = Standard deviation.

Source: Black et al., 2005.

Table 4-10. Indoor Hand-to-Mouth Frequency (contacts/hour) Distributions from Various Studies

Age Group N Mean SD Percentiles

5 25 50 75 95

3 to <6 months

6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 23

119

245

161

169

14 28.0

18.9

19.6

12.7

14.7

6.7 21.7

17.4

19.6

14.2

18.4

5.5 3.0

1.0

0.1

0.1

0.1

1.7 8.0

6.6

6.0

2.9

3.7

2.4 23.0

14.0

14.0

9.0

9.0

5.7 48.0

26.4

27.0

17.0

20.0

10.2 65.0

52.0

63.0

37.0

54.0

20.6

N = Number of subjects.

SD = Standard deviation.

Source: Xue et al., 2007.

Table 4-11. Outdoor Hand-to-Mouth Frequency (contacts/hour) Distributions from Various Studies

Age Group N Mean SD Percentiles

5 25 50 75 95

6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 10

32

46

55

15 14.5

13.9

5.3

8.5

2.9 12.3

13.6

8.1

10.7

4.3 2.4

1.1

0.1

0.1

0.1 7.6

4.2

0.1

0.1

0.1 11.6

8.0

2.6

5.6

0.5 16.0

19.2

7.0

11.0

4.7 46.7

42.2

20.0

36.0

11.9

N = Number of subjects.

SD = Standard deviation.

Source: Xue et al., 2007.



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Table 4-12. Survey Reported Mouthing Behaviors for 92 Washington State Children

Behavior Never

N % Seldom

N % Occasionally

N % Frequently

N % Always

N % Unknown

N %

Hand/Foot in Mouth 4 4 27 30 23 25 31 34 4 4 3 3

Pacifier 74 81 6 7 2 2 9 10 1 1 0 0

Mouth on Object 14 15 30 33 25 27 19 21 1 1 3 3

Non-Food in Mouth 5 5 25 27 33 36 24 26 5 5 0 0

Eat Dirt/Sand 37 40 39 43 11 12 4 4 1 1 0 0

N = Number of subjects.

Source: Davis et al. 1995.

Table 4-13. Estimated Daily Mean Mouthing Times of New York State Children, for Pacifiers and Other Objects

Age 0 to 18 months Age 19 to 36 months

Object Type All Children Only Children Who Mouthed Object a All Children Only Children Who Mouthed Object a

Minutes Minutes Minutes Minutes

Pacifier

Teether Plastic Toy Other Objects 108 (N = 107) 6 (N=107) 17 (N=107) 9 (N=107) 221 (N=52) 20 (N=34) 28 (N=66) 22 (N=46) 126 (N=110) 0 (N=110) 2 (N=110) 2 (N=110) 462 (N=52) 30 (N=1) 11 (N=21) 15 (N=18)

a Refers to means calculated for the subset of the sample children who mouthed the object stated (zeroes are eliminated from the calculation of the mean).

N = Number of children.

Source: Juberg et al., 2001.

Table 4-14. Percent of Houston-area and Chicago-area Children Observed Mouthing, by Category and Child’s Age

Object Category All ages <1 year 1 to 2 years 2 to 3 years

All Objects

Pacifiers Non-pacifiers

Soft Plastic Food Content Items Anatomy Non-soft Plastic Toys, Teethers, and Rattles

Other Items 100

27

100

28

99

91

98 100

43

100

13

100

94

98 100

27

100

30

97

91

97 100

10

100

41

100

86

98

Source: Greene, 2002.



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Chapter 4 - Non-dietary Ingestion Factors

Table 4-15. Estimates of Mouthing Time for Various Objects (minutes/hour)

Age Group Mean (SD) Median 95 th Percentile 99 th Percentile

All Items a

3 to <12 months

12 to <24 months

24 to <36 months 10.5 (7.3) 7.3 (6.8) 5.3 (8.2) 9.6

5.5

2.4 26.2

22.0

15.6 39.8

28.8

47.8

Non Pacifiers b

3 to <12 months

12 to <24 months

24 to <36 months 7.1 (3.6) 4.7 (3.7) 3.5 (3.6) 6.9

3.6

2.3 13.1

12.8

12.8 14.4

18.9

15.6

All Soft Plastic Items

3 to <12 months

12 to <24 months

24 to <36 months 0.5 (0.6) 0.4 (0.4) 0.4 (0.6) 0.1

0.2

0.1 1.8

1.3

1.6 2.5

1.9

2.9

Soft Plastic Items Not Food Contact

3 to <12 months

12 to <24 months

24 to <36 months 0.4 (0.6) 0.3 (0.4) 0.2 (0.4) 0.1

0.1

0.0 1.8

1.1

1.3 2.0

1.5

1.8

Soft Plastic Toys, Teethers, and Rattles

3 to <12 months

12 to <24 months

24 to <36 months 0.3 (0.5) 0.2 (0.3) 0.1 (0.2) 0.1

0.0

0.0 1.8

0.9

0.2 2.0

1.3

1.6

Soft Plastic Toys

3 to <12 months

12 to <24 months

24 to <36 months 0.1 (0.3) 0.2 (0.3) 0.1 (0.2) 0.0

0.0

0.0 0.7

0.9

0.2 1.1

1.3

1.6

Soft Plastic Teethers and Rattles

3 to <12 months

12 to <24 months

24 to <36 months 0.2 (0.4) 0.0 (0.1) 0.0 (0.1) 0.0

0.0

0.0 1.0

0.1

0.0 2.0

0.6

1.0

Other Soft Plastic Items

3 to <12 months

12 to <24 months

24 to <36 months 0.1 (0.2) 0.1 (0.1) 0.1 (0.3) 0.0

0.0

0.0 0.8

0.4

0.5 1.0

0.6

1.4



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Chapter 4 - Non-dietary Ingestion Factors

Table 4-15. Estimates of Mouthing Time for Various Objects (minutes/hour) (continued)

Age Group Mean (SD) Median 95 th Percentile 99 th Percentile

Soft Plastic Food Contact Items

3 to <12 months

12 to <24 months

24 to <36 months 0.0 (0.2) 0.1 (0.2) 0.2 (0.4) 0.0

0.0

0.0 0.3

0.7

1.2 0.9

1.2

1.9

Anatomy

3 to <12 months

12 to <24 months

24 to <36 months 2.4 (2.8) 1.7 (2.7) 1.2 (2.3) 1.5

0.8

0.4 10.1

8.3

5.1 12.2

14.8

13.6

Non Soft Plastic Toys, Teethers, and Rattles

3 to <12 months

12 to <24 months

24 to <36 months 1.8 (1.8) 0.6 (0.8) 0.2 (0.4) 1.3

0.3

0.1 6.5

1.8

0.9 7.7

4.6

2.3

Other Items

3 to <12 months

12 to <24 months

24 to <36 months 2.5 (2.1) 2.1 (2.0) 1.7 (2.6) 2.1

1.4

0.7 7.8

6.6

7.1 8.1

9.0

14.3

Pacifiers

3 to <12 months

12 to <24 months

24 to <36 months 3.4 (6.9) 2.6 (6.5) 1.8 (7.9) 0.0

0.0

0.0 19.5

19.9

4.8 37.3

28.6

46.3

a Object category “all items” is subdivided into pacifiers and non-pacifiers.

b Object category “non-pacifiers” is subdivided into all soft plastic items, anatomy ( which includes hair, skin, fingers and hands), non-soft plastic toys/teethers/rattles, and other items.

c Object category “all soft plastic items” is subdivided into food contact items, nonfood contact items (toys, teethers and rattles) and other soft plastic.

SD = Standard deviation.

Source: Greeene, 2002.



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Chapter 4 - Non-dietary Ingestion Factors

Table 4-16. Mouthing Times of Dutch Children Extrapolated to Total Time While Awake, Without Pacifier, in Minutes per Day

Age Group N Mean SD Minimum Maximum

3 to 6 months

6 to 12 months

12 to 18 months

18 to 36 months 5

14

12

11 36.9

44

16.4

9.3 19.1

44.7

18.2

9.8 14.5

2.4

0

0 67

171.5

53.2

30.9

Note: The object most mouthed in all age groups was the fingers, except for the 6 to 12 month group which mostly mouthed toys.

N = Number of children.

SD = Standard deviation.

Source: Groot et al., 1998.



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Chapter 4 - Non-dietary Ingestion FactorsTable 4-17. Estimated Mean Daily Mouthing Duration by Age Group for Pacifiers, Fingers, Toys, and Other Objects (hours:minutes:seconds)Age GroupItem1 to 33 to 66 to 99 to 1212 to 1515 to 1818 to 2121 to 242345MouthedmonthsmonthsmonthsmonthsmonthsmonthsmonthsmonthsyearsyearsyearsyearsN =91415171614161239312924Dummy (Pacifier)0:47:130:27:450:14:360:41:391:00:150:25:221:09:020:25:120:32:550:48:420:16:400:00:20Fingers0:18:220:49:030:16:540:14:070:08:240:10:070:18:400:35:340:29:430:34:420:19:260:44:06Toys0:00:140:28:200:39:100:23:040:15:180:16:340:11:070:15:460:12:230:11:370:03:110:01:53Other Objects0:05:140:12:290:24:300:16:250:12:020:23:010:19:490:12:530:21:460:15:160:10:440:10:00Not Recorded0:00:450:00:240:00:000:00:010:00:020:00:080:00:110:14:130:02:400:00:010:00:050:02:58Total (all objects)1:11:481:57:411:35:111:35:161:36:010:15:131:58:491:43:391:39:271:50:190:50:050:59:17N = Number of children in sample.Source: Smith and Norris, 2003.



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Chapter 4 - Non-dietary Ingestion Factors

Table 4-18. Outdoor Median Mouthing Duration (seconds per contact), Video-transcription of 38 Children

Age Group N Statistic Hands Total non-dietary a

13 to 84 months 38 Mean

5 th percentile

25 th percentile

50 th percentile

75 th percentile

95 th percentile

99 th percentile 3.5

0

1

1

2

12

41.6 3.4

0

1

1

3

11

40

# 24 months 8 Mean

Median Range 9

3

0 to 136 2

1

0 to 40

>24 months 30 Mean

5 th percentile

25 th percentile

50 th percentile

75 th percentile

95 th percentile

99 th percentile 3.5

0

1

1

2

12

41.6 3.4

0

1

1

3

11

40

a Object/surface categories mouthed outdoors included: animal, clothes/towels, fabric, hands, metal, non-dietary water, paper/wrapper, plastic, skin, toys, vegetation/grass, and wood. N = Number of subjects.

Source: AuYeung et al., 2004.

Table 4-19. Indoor Mouthing Duration (minutes per hour), Video-transcription of 9 Children with >15 minutes in View Indoors

Age Group N Statistic Hands Total non-dietary a

13 to 84 months 9 Mean

Median Range 1.8

0.7

0-10.7 2.3

0.9

0-11.1

# 24 months 1 Observation 10.7 11.1

>24 months 8 Mean

Median Range 0.7

0.7

0-1.9 1.2

0.9

0-3.7

a Object/surface categories mouthed indoors included: Clothes/towels, hands, metal, paper/wrapper, plastic, skin, toys, and wood. N = Number of subjects.

Source: AuYeung et al., 2004.



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Chapter 4 - Non-dietary Ingestion Factors

Table 4-20. Outdoor Mouthing Duration (minutes per hour), Video-transcription of 38 Children

Age Group N Statistic Hands Total non-dietary a

13 to 84 months 38 Mean

5 th percentile

25 th percentile

50 th percentile

75 th percentile

95 th percentile

99 th percentile Range 0.9

0

0.1

0.2

0.6

2.6

11.2

0-15.5 1.2

0

0.2

0.6

1.2

2.9

11.5

0-15.8

# 24 months 8 Mean

5 th percentile

25 th percentile

50 th percentile

75 th percentile

95 th percentile

99 th percentile Range 2.7

0

0.2

0.4

1.5

11.5

14.7

0-15.5 3.1

0.2

0.2

0.8

3.1

11.7

15

0.2-15.8

>24 months 30 Mean

5 th percentile

25 th percentile

Median

75 th percentile

95 th percentile

99 th percentile Range 0.4

0

0.1

0.2

0.4

1.2

2.2

0-2.4 0.7

0

0.2

0.6

1

2.1

2.5

0-2.6

a Object/surface categories mouthed outdoors included: animal, clothes/towels, fabric, hands, metal, non-dietary water, paper/wrapper, plastic, skin, toys, vegetation/grass, and wood. N = Number of subjects.

Source: AuYeung et al., 2004.



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Chapter 4 - Non-dietary Ingestion Factors

Table 4-21. Reported Daily Prevalence of Massachusetts Children’s Non-Food Mouthing/Ingestion Behaviors

Percent of children reported to mouth/ingest daily Object or substance mouthed or ingested 1 year 2 years 3 to <6 years a 6 years All years

N=171 N=70 N=265 N=22 N=528

Grass, leaves, flowers 16 0 1 0 6 Twigs, sticks, woodchips 12 0 0 0 4 Teething toys 44 6 2 9 17 Other toys 63 27 12 5 30 Blankets, cloth 29 11 10 5 16 Shoes, Footwear 20 1 0 0 7 Clothing 25 7 9 14 14 Crib, chairs, furniture 13 3 1 0 5 Paper, cardboard, tissues 28 9 5 5 13 Crayons, pencils, erasers 19 17 5 18 12 Toothpaste 52 87 89 82 77 Soap, detergent, shampoo 15 14 2 0 8 Plastic, plastic wrap 7 4 1 0 3 Cigarette butts, tobacco 4 0 1 0 2 Suck fingers/thumb 44 21 24 14 30

Suck feet or toes 8 1 0 0 3

Bite nails 2 7 10 14 7 Use pacifier 20 6 2 0 9

a Weighted mean of 3, 4, and 5 year-olds’ data calculated by U.S. EPA to conform to standardized age categories used in this

Handbook.

Source:	 Stanek et al. (1998).



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Chapter 5

­ Ingestion of Soil and Dust

5 SOIL AND DUST INGESTION

5.1 INTRODUCTION
The ingestion of soil and dust is a potential

route of exposure to environmental chemicals. Children

may ingest significant quantities of soil, due to their tendency to play on the floor indoors and on the ground

outdoors and their tendency to mouth objects or their

hands. Children may also ingest soil and dust through

deliberate hand to mouth movements, or unintentionally by eating food that has dropped on the floor. Thus, understanding soil and dust ingestion patterns is an important part of estimating children's overall

exposures to environmental chemicals.

At this point in time, knowledge of soil and

dust ingestion patterns within the United States is

somewhat limited. Only a few researchers have attempted to quantify soil and dust ingestion patterns in

U.S. children. This chapter explains the concepts of

soil ingestion, soil pica, and geophagy, defines these

terms for the purpose of this handbook’s exposure factors, and presents available data from the literature

on the amount of soil and dust ingested.

The Centers for Disease Control and Prevention's Agency for Toxic Substances and Disease Registry (ATSDR) held a workshop in June 2000 in which a panel of soil ingestion experts developed definitions for soil ingestion, soil­pica, and geophagy, to distinguish aspects of soil ingestion patterns that are important from a research perspective (ATSDR, 2001). This chapter uses the definitions that are based on those developed by participants in that workshop:

Soil ingestion is the consumption of soil. This may result from various behaviors including, but not limited to, mouthing, contacting dirty hands, eating dropped food, or consuming soil directly. Soil­pica is the recurrent ingestion of unusually high amounts of soil (i.e., on the order of 1,000 ­ 5,000 mg/day or more). Geophagy is the intentional ingestion of earths and is usually associated with cultural practices.

Some studies are of a behavior known as “pica,” and the subset of “pica” that consists of ingesting soil. A general definition of the concept of pica is that of ingesting non­food substances, or ingesting large quantities of certain particular foods. Definitions of pica often include references to recurring or repeated ingestion of these substances. Soil­pica is pica that is specific to ingesting materials that are defined as soil, such as clays, yard soil, and flower­pot soil. Researchers in many different disciplines have hypothesized motivations for human soil­pica or geophagy behavior, including alleviating nutritional deficiencies, a desire to remove toxins or self­medicate, and other physiological or cultural influences (e.g., Danford, 1982). Bruhn and Pangborn (1971) and Harris and Harper (1997) suggest a religious context for certain geophagy or soil ingestion practices. Some researchers have investigated subpopulations of children who may be more likely than other children to exhibit soil­pica behavior on a recurring basis. These subpopulations might include children who practice geophagy (Vermeer and Frate, 1979), institutionalized children (Wong, 1988), and children with developmental delays (Danford, 1983), autism (Kinnell, 1985), or celiac disease (Korman, 1990). However, identifying specific soil­pica and geophagy subpopulations remains difficult due to limited research on this topic. In this handbook, soil, indoor settled and outdoor settled dust, and dust ingestion are defined generally as:

Soil . Particles of unconsolidated mineral and/or organic matter from the earth’s surface that are located outdoors, or are used indoors to support plant growth. It includes particles that have settled onto outdoor objects and surfaces (outdoor settled dust).

Indoor Settled Dust . Particles in building interiors that have settled onto objects, surfaces, floors, and carpeting. These particles may include soil particles that have

been tracked into the indoor environment from outdoors as well as organic matter.

Outdoor Settled Dust . Particles that have settled onto outdoor objects and surfaces due to either wet or dry deposition. Note that it is not possible to distinguish between soil and outdoor settled dust, since outdoor settled dust generally would be present on the uppermost surface layer of soil. For the purposes of this handbook, soil ingestion includes both soil and outdoor settled dust, and dust ingestion includes indoor settled dust only. There are several methodologies represented in the literature related to soil and dust ingestion by children. Three methodologies combine biomarker

measurements with measurements of the biomarker substance’s presence in environmental media. A fourth



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methodology offers indirect evidence of soil/dust ingestion behaviors from the responses of caregivers and/or children to survey questions. The first of the biomarker methodologies measures quantities of specific elements present in children’s feces, urine, food and medications, yard soil, house dust, and sometimes also community soil and dust, and combines this information using certain assumptions about the elements’ behavior in the gastrointestinal tract to produce estimates of soil and dust quantities ingested (e.g., Davis et al., 1990). In this chapter, this methodology is referred to as the “tracer element” methodology. The second biomarker methodology compares results from a biokinetic model of lead exposure and uptake that predict children’s blood lead levels, with biomarker measurements of lead in children’s blood (e.g., von Lindern et al., 2003). The model predictions are made using assumptions about ingested soil and dust quantities that are based, in part, on results from early versions of the first methodology. Therefore, the comparison with actual measured blood lead levels serves to confirm, to some extent, the assumptions about ingested soil and dust quantities used in the biokinetic model. In this chapter, this methodology is referred to as the “biokinetic model comparison” methodology. The third biomarker methodology, the “lead isotope ratio” methodology, involves measurements of different lead isotopes in children’s blood and/or urine, food, water, and house dust and compares the ratio of different lead isotopes to infer sources of lead exposure that may include dust or other environmental exposures (e.g., Manton et al., 2000). In the fourth, “survey response” methodology, responses to survey questions regarding soil and dust ingestion are analyzed. This methodology includes questions asked of children directly, or their caregivers, about soil and dust ingestion behaviors, frequency, and sometimes quantity (e.g., Barltrop, 1966). Although not directly evaluated in this chapter, a fifth methodology uses assumptions regarding ingested quantities of soil and dust that are based on general knowledge of children’s behavior, and potentially supplemented or informed by data from other methodologies (e.g., Hawley, 1985; Kissel et al., 1998; Wong et al., 2000). The recommendations for soil, dust, and soil

+ dust ingestion rates are provided in the next section, along with a summary of the confidence ratings for

these recommendations. The recommended values are based on key studies identified by U.S. EPA for this factor. Following the recommendations, key studies on soil and dust ingestion are summarized. Summaries of the relevant studies, methodology descriptions and methodological strengths and limitations are also provided.

5.2 RECOMMENDATIONS The key studies described in Section 5.3 were used to recommend values for soil and dust ingestion among children. The key studies pre­dated the age groups recommended by U.S. EPA (2005) and were performed on groups of children of varying ages. As a result, central tendency recommendations can be used for the life stage categories of 6 to <12 months, 1 to <2 years, 2 to <3 years, 3 to <6 years, and part of the 6 to <11 years categories. Upper percentile recommendations can be used for the life stage categories of 1 to <2 years, 2 to <3 years, 3 to <6 years, 6 to <11 years, and part or all of the 11 to <16 years category. Due to the current state of research on soil and dust ingestion, the upper percentile recommendations are called “soil­pica” or “geophagy” recommendations that are likely to represent high soil ingestion episodes or behaviors at an unknown point on the high end of the distribution of soil ingestion. The soil ingestion recommendations in Table 5­1 are intended to represent ingestion of a combination of soil and outdoor settled dust, without distinguishing

between these two sources. The source of the soil in these recommendations could be outdoor soil, indoor containerized soil used to support growth of indoor plants, or a combination of both outdoor soil and

containerized indoor soil. These recommendations are called “soil.” The dust ingestion recommendations in Table 5­1 include soil tracked into the indoor setting, indoor settled dust and air­suspended particulate matter that is inhaled and swallowed. Central tendency “dust” recommendations are provided, in the event that

assessors need recommendations for an indoor or inside a transportation vehicle scenario in which dust, but not outdoor soil, is the exposure medium of concern. The soil + dust recommendations would include soil, either from outdoor or containerized indoor sources, dust that is a combination of outdoor settled dust, indoor settled dust, and air­suspended particulate matter that is inhaled, subsequently trapped in mucous and moved from the respiratory system to the gastrointestinal tract, and a soil­origin material located on indoor floor



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surfaces that was tracked indoors by building occupants. Soil and dust recommendations exclude the soil or dust’s moisture content. In other words, recommended values represent mass of ingested soil or dust that is represented on a dry weight basis. Table 5­1 shows the central tendency recommendations for daily ingestion of soil, dust, or soil + dust, in mg/day. It also shows the soil­pica or geophagy recommendations for daily ingestion of soil, in mg/day. No data are available on which to base comparable upper percentile recommendations for

“dust” or “soil + dust.” Published estimates from the key studies have been rounded to one significant figure. The recommended central tendency soil + dust ingestion estimate for infants from 6 months up to their first birthday is 60 mg/day. If an estimate is needed for soil only, from outdoor or indoor sources, or both outdoor and indoor sources, the recommendation is 30 mg/day. If an estimate for indoor dust only is needed, that would include a certain quantity of tracked­in soil from outside, the recommendation is 30 mg/day. The confidence rating for this recommendation is low due to the small numbers of study subjects in the study on

which the recommendation is based and the inferences needed to develop a quantitative estimate. Examples of these inferences include: an assumption that the relative proportions of soil and dust ingested by 6 to 12 month old children is the same as the central tendency assumption for older children (45 percent soil, 55 percent dust, based on U.S. EPA (1994a)), and the assumption that pre­natal or non­soil, non­dust sources of lead exposure do not dominate these children’s blood

lead levels. When assessing risks for children who are not expected to exhibit soil­pica or geophagy behavior, the recommended central tendency soil + dust ingestion estimate is 100 mg/day for children ages 1 to <6 years. If an estimate for soil only is needed, for exposure to soil such as manufactured topsoil or potted­plant soil that could occur in either an indoor or outdoor setting, or when the risk assessment is not considering children's ingestion of indoor dust (in an indoor setting) as well, the recommendation is 50 mg/day. If an estimate for indoor dust only is needed, the recommendation is 60 mg/day. Although these quantities add up to 110 mg/day, the sum is rounded to one significant figure. Although there were no tracer element studies or biokinetic model comparison studies performed for children 6 to < 21 years, as a group, their mean or central tendency soil ingestion would not be zero. In the absence of data that can be used to develop specific central tendency soil and dust ingestion recommendations for children aged 6 to <11 years, 11 to <16 years and 16 to <21 years, U.S. EPA recommends using the same central tendency soil and dust ingestion rates that are recommended for children in the 1 to < 6 year old age range. When assessing risks for children who may exhibit soil­pica behavior, or a group of children that includes individual children who may exhibit soil­pica behavior, the soil­pica ingestion estimate for children up to age 14 ranges from 400 to 41,000 mg/day. Due to the definition of soil­pica used in this chapter, thatsets a lower bound on the quantity referred to as “soilpica” at 1,000 mg/day, and due to the significant

number of observations in the U.S. tracer element studies that are at or exceed that quantity, the recommended soil­pica ingestion rate is 1,000 mg/day. Currently, no data are available for upper percentile, soil­pica behavior for children ages 16 to <21 years. Because pica behavior may occur among some children ages ~1 to 21 years old (Hyman et al., 1990), it is prudent to assume that, for some children, soil­pica behavior may occur at any age up to <21 years. The recommended geophagy soil estimate is 50,000 mg/day (50 grams). Risk assessors should use this value for soil ingestion in areas where residents are known to exhibit geophagy behaviors. These recommendations are not robust enough for use in probabilistic risk assessments. Table 5­2 shows the confidence ratings for these recommendations. Section 5.4 gives a more detailed explanation of the basis for the confidence ratings. An important factor to consider when using these recommendations is that they are limited to estimates of soil and dust quantities ingested. The scope of this chapter is limited to quantities of soil and dust taken into the gastrointestinal tract, and does not extend to issues regarding bioavailability of environmental contaminants present in that soil and

dust. Information from other sources is needed to address bioavailability. In addition, as more information becomes available regarding gastrointestinal absorption of environmental contaminants, adjustments to the soil and dust ingestion exposure equations may need to be made, to better



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represent the direction of movement of those

contaminants within the gastrointestinal tract.

To place these recommendations into context,
it is useful to compare these soil ingestion rates to

common measurements. The bulk densities of surface

soils are often in the range of 1.3 to 1.7 g/cm 3



### . U.S.

EPA (1996) recommends using 1.5 g/cm 3 

### as a default

value for dry soil bulk density. The central tendency

recommendation of 50 mg/day, or 0.050 g/day, dry weight basis, with a 1.5 g/cm 3 

### bulk density would be equivalent to approximately 0.03 cm 3



### . A teaspoon is approximately 5 cm 3 

### in volume, so the 50 mg/day quantity would be roughly equivalent to seven thousandths of a teaspoon per day. The 50 g/day ingestion rate recommended to represent geophagy

behavior would be roughly equivalent to 5 to 7
teaspoons p er day in volume.

Indoor settled dust could be expected to have

a lower dry bulk density than the surface soil bulk density cited above (for example, bulk densities of five grain dusts are reported by Parnell et al. (1986) to be

0.15­0.31 g/cm 3



### , “specific density” of Danish office building dust is reported by Mølhave et al. (2000) to be

1.0 gm/cm 3 

### ). Thus, volumes of indoor settled dust

could be expected to weigh less than comparable

volumes of surface soil. The central tendency “dust”

recommendation for children of 60 mg/day, or 0.060 g/day, dry weight basis, with a 1.0 g/cm 3 

### bulk density

would be equivalent to approximately 0.06 cm 3



### , or roughly equivalent to twelve thousandths of a teaspoon

per day.



### Page Child­Specific Exposure Factors Handbook

5­4 September 2008Table 5­1. Recommended Values for Daily Soil, Dust, and Soil + Dust Ingestion

Age Group Soil a b Dust Soil + Dust

Central Tendency (mg/day) Upper Percentile Central Tendency (mg/day) Central Tendency (mg/day) Soil­Pica Geophagy (mg/day) (mg/day)

6 to <12months 30

­ ­ 30 60

1 to <6years 50 1,000 50,000 60 100 c

6 to <21 years 50 1,000 50,000 60 100 c

­

a

b

c No recommendation.

Includes soil and outdoor settled dust. Includes indoor settled dust only. Total soil and dust ingestion rate is 110 mg/day; rounded to one significant figure it is 100 mg/day. 

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Child­Specific Exposure Factors Handbook Page September 2008 5­5Table 5­2. Confidence in Recommendations for Ingestion of Soil and Dust

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach The methodologies have significant limitations. The studies did not capture all of the information needed (quantities ingested, frequency of high soil ingestion episodes, prevalence of high soil ingestion). Four of the 9 studies were of census or randomized design. Sample selection may have introduced some bias in the results (i.e., children near smelter or Superfund sites, volunteers in nursery schools). Thetotal number of children in key studies was 1,203 (859 U.S. children, 292 Dutch, and 52 Jamaican children), while the target population currently numbers more than 74 million (U.S. DOC, 2008). The response rates for in­person interviews and telephone surveys were often not stated in published articles. Primary data were collected for 381 U.S. children

and 292 Dutch children 

### ; secondary data for 478 U.S. children and 52 Jamaican

children. Low

Minimal (or defined) Bias Numerous sources of measurement error exist in the tracer element studies. Biokinetic model comparison study may contain less measurement error than tracer element studies. Survey response study may contain measurement error.

Applicability and Utility

Exposure Factor of Interest 8 of the 9 key studies focused on the soil exposure factor, with no or less focus on the dust exposure factor. Biokinetic model comparison study did not focus exclusively on soil and dust exposure factors. Low

Representativeness The study samples may not be representative of the U.S. in terms of race, ethnicity, socio­economics, and geographical location; studies focused on specific areas.

Currency Studies results are likely to represent current conditions.

Data Collection Period Tracer element studies’ data collection periods may not represent long­term behaviors. Biokinetic model comparison and survey response studies do represent longer term

behaviors.

Clarity and Completeness

Accessibility Observations for individual children are available for only 3 of the 9 key studies. Low

Reproducibility For the methodologies used by more than one research group, reproducible results were

obtained in some instances. Somemethodologies have been used by only one research group and have not been reproduced by others.

Quality Assurance For some studies, information on quality assurance/quality control was limited or absent.

Variability and Uncertainty

Variability in Population Tracer element studies characterized variability among study sample members; biokinetic model comparison and survey response studies did not. Day­to­day and seasonal variability was not very well characterized. Numerous factors that may influence variability have not been explored in detail. Low

Minimal Uncertainty Estimates are highly uncertain. Tracer element studies’ design appears to introduces

biases in the results.

Evaluation and Review

Peer Review All key studies appeared in peer review journals. Medium

Number and Agreement

Studies of 9 key studies. Researchers using similar methodologies obtained generally similar results; somewhat general agreement between researchers using different methodologies.

Overall Rating Low 

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5.3 KEY AND RELEVANT STUDIES
The key tracer element, biokinetic model comparison, and survey response studies are summarized in the following sections. Certain studies

were considered "key" and were used as a basis for developing the recommendations, using judgment about

the study’s design features, applicability, and utility of

the data to U.S. children’s soil and dust ingestion rates, clarity and completeness, and characterization of uncertainty and variability in ingestion estimates.

Because the studies often were performed for reasons unrelated to developing soil and dust ingestion recommendations, their attributes that were characterized as “limitations” in this chapter might not
be limitations when viewed in the context of the study’s original purpose. However, when studies are used for developing a soil or dust ingestion recommendation,

U.S. EPA has categorized some studies’ design or implementation as preferable to other studies’ design or implementation. In general, U.S. EPA chose studies designed either with a census, or randomized sample, approach, over studies that used a convenience sample

or other, non­randomized, approach, as well as studies

that more clearly explained various factors in the study’s implementation that affect interpretation of the

results. However, in some cases, studies that used a non­randomized design contain information that is

useful for developing exposure factor recommendations (for example, if they are the only studies of children in

a particular age category), and thus may have been designated as “key” studies. Other studies were considered “relevant” but not “key” because they provide useful information for evaluating the

reasonableness of the data in the key studies, but in U.S.

EPA’s judgment they did not meet the same level of soundness, applicability and utility, clarity and completeness, and characterization of uncertainty and variability that the key studies did. In addition, studies

that did not contain information that can be used to develop a specific recommendation for mg/day soil and

dust ingestion were classified as relevant rather than key. Some studies are re­analyses of data previously published. For this reason, the sections that follow are organized into key and relevant studies of primary analysis (that is, studies in which researchers have developed primary data pertaining to soil and dust ingestion) and key and relevant studies of secondary analysis (that is, studies in which researchers have interpreted pr eviously published results, or data that

were originally collected for a different purpose).

5.3.1 Methodologies Used in Key Studies

5.3.1.1 Tracer Element Methodology
The tracer element methodology attempts to quantify the amounts of soil ingested by analyzing samples of soil and dust from children’s residences

and/or play areas, and the children’s feces, and

sometimes also urine. The soil, dust, fecal, and urine samples are analyzed for the presence and quantity of

tracer elements

­ typically, aluminum, silicon, titanium,

and other elements. A key underlying assumption is

that these elements are not metabolized into other

substances in the body or absorbed from the gastrointestinal tract in significant quantities, and thus

their presence in feces and urine can be used to estimate

the quantity of soil ingested by mouth. Although they

are sometimes called mass balance studies, none of the

studies attempt to quantify amounts excreted in perspiration, tears, glandular secretions, or shed skin,

hair or finger­ and toe­nails, nor do they account for

tracer element exposure via the dermal or inhalation

into the lung routes, and thus they are not a complete

“mass b alance” methodology. Early studies using this methodology did not always account for the

contribution of tracer elements from non­soil substances (food, medications, and non­food sources such as toothpaste) that children might swallow. U.S. studies using this methodology in or after the mid to late 1980s

account for, or attempt to account for, tracer element

contributions from these non­soil sources. Some study

authors adjust their soil ingestion estimate results to

account for the potential contribution of tracer elements

found in household dust as well as soil.

The general algorithm that is used to calculate

the quantity of soil or dust estimated to have been ingested by each child is as follows: the quantity of a given tracer element, in milligrams, present in the

child’s feces and urine, minus the quantity of that tracer element, in milligrams, present in the child’s food and medicine, the result of which is divided by the tracer
element’s soil concentration, in milligrams of tracer per

gram of soil, to yield an estimate of ingested soil, in

grams. The U.S. tracer element researchers have all

assumed a certain offset, or lag time between ingestion

of food, medication and soil, and the resulting fecal and urinary output. The lag times used are typically 24 or 28 hours; thus, these researchers subtract the previous



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day’s food and medication tracer element quantity ingested from the current day’s fecal and urinary tracer element quantity that was excreted. When compositing food, medication, fecal and urine samples across the entire study period, daily estimates can be obtained by dividing the total estimated soil ingestion by the number of days in which fecal and/or urine samples were collected. A variation of the algorithm that provides slightly higher estimates of soil ingestion is to divide the total estimated soil ingestion by the number of days on which feces were produced, which by definition would be equal to or less than the total number of days of the study period’s fecal sample collection. Substituting tracer element dust concentrations for tracer element soil concentrations yields a dustingestion estimate. Because the actual non­food, nonmedication quantity ingested is a combination of soil and dust, the unknown true soil and dust ingestion is likely to be somewhere between the estimates that are

based on soil concentrations and estimates that are

based on dust concentrations. Tracer element researchers have described ingestion estimates for soil that actually represent a combination of soil and dust,

but were calculated based on tracer element concentrations in soil. Similarly, they have described ingestion estimates for dust that are actually for a

combination of soil and dust but were calculated based

on tracer element concentrations in dust. Other variations on these general soil and dust ingestion algorithms have been published, in attempts to account for time spent indoors, time spent away from the house, etc. that could be expected to influence the relative proportion of soil vs. dust. Each child’s soil and dust ingestion can be represented as an unknown constant in a set of simultaneous equations of soil or dust ingestion represented by different tracer elements. To date, only one of the U.S. research teams (Lásztity et al., 1989) has published estimates calculated for pairs of tracer elements using simultaneous equations.

The U.S. tracer element studies have been performed for only short­duration study periods, and only for 241 children (101 in Davis et al., 1990, 12 of whom were studied again in Davis and Mirick, 2006; 64 in Calabrese et al., 1989/Barnes 1990; 64 in Calabrese et al., 1997a; and 12 in Calabrese et al., 1997b). They provide information on quantities of soil and dust ingested for the studied groups of children for short time periods, but provide limited information on overall prevalence of soil ingestion by U.S. children, and limited information on the frequency of higher soil ingestion episodes. The tracer element studies appear to contain

numerous sources of error that influence the estimates upward and downward. Sometimes the error sources cause individual children’s soil or dust ingestion estimates to be negative, which is not physically possible. In some studies, for some of the tracers, so many individual children’s “mass balance” soil ingestion estimates were negative that median or mean estimates based on that tracer were negative. For soil and dust ingestion estimates based on each particular tracer, or averaged across tracers, the net impact of these competing upward and downward sources of error

is unclear.

5.3.1.2 Biokinetic M odel Comparison Methodology
The Biokinetic Model Comparison methodology compares direct measurements of a biomarker, such as blood or urine levels of a toxicant,

with predictions from a biokinetic model of oral, dermal

and inhalation exposure routes with air, food, water, soil, and dust toxicant sources. An example is to

compare children’s measured blood lead levels with predictions from the Integrated Exposure and Uptake

Biokinetic (IEUBK) model. Where environmental

contamination of lead in soil, dust, and drinking water
has been measured and those measurements can be used

as model inputs for the children in a specific community, the model’s assumed soil and dust ingestion values can be confirmed or refuted by comparing the model’s predictions of blood lead levels

with those children’s measured blood lead levels. It

should be noted, however, that such confirmation of the predicted blood lead levels would be confirmation of

the net impact of all model inputs, and not just soil and

dust ingestions. Under the assumption that the actual

measured blood lead levels of various groups of

children studied have minimal error, and those

measured blood lead levels roughly match the

biokinetic model predictions for those groups of children, then the model’s default assumptions may be roughly accurate for the central tendency, or typical,

children in an assessed group of children. The model’s

default assumptions likely are not as useful for predicting outcomes for highly exposed children.

5.3 .1.3 Survey Response Methodology The survey response methodology includes studies that survey children’s caretakers, or children



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themselves, via in­person or mailed surveys that askabout mouthing behavior and ingestion of various nonfood items. Sometimes, questions about amounts ingested are included in the survey instrument. There could be either false positive or false negative responses to these questions, for various reasons.

5.3.2 Key Studies of Primary Analysis

5.3.2.1 Vermeer and Frate, 1979 ­ Geophagia in

rural Mississippi: environmental and

cultural contexts and nutritional implications Vermeer and Frate (1979) performed a survey response study in Holmes County, Mississippi in the 1970s (date unspecified). Questions about geophagy (defined as regular consumption of clay over a period of weeks) were asked of household members (N=229 in 50 households; 140 were children or adolescents) of a subset of a random sample of nutrition survey respondents. Caregiver responses to questions about 115 children under 13 indicate that geophagy was likely to be practiced by a minimum of 18 (16 percent) of these children; however, 16 of these 18 children were 1 to 4 years old, and only 2 of the 18 were older than 4 years. There was no reported geophagy among 25 adolescent study subjects questioned. The average daily amount of clay consumed was reported to be about 50 grams, for the 32 adult and 18 under­age­13 years child respondents who acknowledged practicing geophagy. Quantities were usually described as either portions or multiples of the amount that could be held in a single, cupped hand. Clays for consumption were generally obtained from the B soil horizon, or subsoil rather than an uppermost layer, at a depth of 50 to 130

centimeters.

5.3.2.2 Calabrese et al., 1989 ­ How Much Soil Do Young Children Ingest: An Epidemiologic Study/Barnes, 1990 ­ Childhood Soil Ingestion: How Much Dirt Do Kids

Eat?/Calabrese et al., 1991 ­ Evidence of

Soil­Pica Behaviour and Quantification of

Soil Ingested Calabrese et al. (1989) and Barnes (1990) studied soil ingestion among children using eight tracer elements—aluminum, barium, manganese, silicon,titanium, vanadium, yttrium, and zirconium. A nonrandom sample of 30 male and 34 female 1, 2 and 3 year­olds from the greater Amherst, Massachusetts area were studied, presumably in 1987. The children were predominantly from two­parent households where the parents were highly educated. The study was conducted over a period of eight days spread over two weeks. During each week, duplicate samples of food, beverages, medicines, and vitamins were collected on Monday through Wednesday, while excreta were collected for four 24­hour cycles running from Monday/Tuesday through Thursday/Friday. Soil and dust samples were also collected from the child’s home and play area. Study participants were supplied with toothpaste, baby cornstarch, diaper rash cream, and soap with low levels of most of the tracer elements. Fecal and urine samples, excluding wipes and toilet paper, were also collected and analyzed for tracer

elements. Table 5­3 shows the published mean soil ingestion estimates ranging from ­294 mg/day based on manganese to 459 mg/day based on vanadium, median soil ingestion estimates ranging from ­261 mg/day based on manganese to 96 mg/day based on vanadium,

and 95 th 

### percentile estimates ranged from 106 mg/day based on yttrium to 1,903 mg/day based on vanadium. Maximum daily soil ingestion estimates ranged from 1,391 mg/day based on zirconium to 7,281 mg/day based on manganese. Dust ingestions calculated using tracer concentrations in dust were often, but not always, higher than soil ingestions calculated using tracer

concentrations in soil. Data for the uppermost 23 subject­weeks (the highest soil ingestion estimates, averaged over the four days of excreta collection during each of the two weeks) were published in Calabrese et al. (1991). One child’s soil­pica behavior was estimated in Barnes (1990) using both the subtraction/division algorithm and the simultaneous equations method. On two particular days during the second week of the study period, the child’s aluminum­based soil ingestion estimates were 19 g/day (18,700 mg/day) and 36 g/day (35,600 mg/day), silicon­based soil ingestion estimates were 20 g/day (20,000 mg/day) and 24 g/day (24,000), and simultaneous­equation soil ingestion estimates were 20 g/day (20,100 mg/day) and 23 g/day (23,100 mg/day) (Barnes 1990). By tracer, averaged across the entire week, this child’s estimates ranged from approximately 10 to 14 g/day during the second week of observation (Calabrese et al., 1991, shown in Table 5­4), and averaged 6 g/day across the entire study period. Additional information about this child’s apparent ingestion of soil vs. dust during the study



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period, shown in Table 5­5, was published in Calabrese

and Stanek (1992a).

5.3.2.3 Van Wïjnen et al., 1990

­ Estimated Soil Ingestion by Children

In a tracer element study by Van Wïjnen et al. (1990), soil ingestion among Dutch children ranging in

age from 1 to 5 years was evaluated using a tracer
element methodology. Van Wïjnen et al. (1990)

measured three tracers (titanium, aluminum, and acid

insoluble residue (AIR)) in soil and feces. The authors

estimated soil ingestion based on an assumption called

the Limiting Tracer Method (LTM), which assumed

that soil ingestion could not be higher than the lowest

value of the three tracers. LTM values represented soil ingestion estimates that were not corrected for dietary

intake.

An average daily feces dry weight of 15 g was assumed. A total of 292 children attending daycare

centers were studied during the first of two sampling periods and 187 children were studied in the second sampling period; 162 of these children were studied during both periods (i.e., at the beginning and near the

end of the summer of 1986). A total of 78 children

were studied at campgrounds. The authors reported geometric mean LTM values because soil ingestion

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 and 174 mg/day

for children vacationing at campgrounds (Table 5­6).

For the 162 daycare center children studied during both sampling periods the arithmetic mean LTM was 162 mg/day, and the median was 114 mg/day.

Fifteen hospitalized children were studied and

used as a control group. These children’s LTM soil ingestion estimates were 74 (geometric mean), 93 (mean), and 110 (median) mg/day. The authors

assumed the hospitalized children’s soil ingestion

estimates represented dietary intake of tracer elements,

and used rounded 95 percent confidence limits on the

arithmetic mean, 70 to 120 mg/day, to correct the daycare and campground children’s LTM estimates for dietary intake of tracers. Corrected soil ingestion 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 ingestion was estimated to range from 0 to 90 mg/day, with a 90th percentile value of up to 190 mg/day for the various age categories within the daycare group and 30 to 200 mg/day, with a 90th percentile value of up to 300 mg/day for the various age categories within the camping group. AIR was the limiting tracer in about 80 percent
of the samples. Among children attending daycare

centers, soil ingestion 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 5­7).

5.3.2.4 Davis et al., 1990

­ Quantitative Estimates of

Soil Ingestion in Normal Children between

the Ages of 2 and 7 Years: Population­based

Estimates Using Aluminum, Silicon, and

Titanium as Soil Tracer Elements
Davis et al. (1990) used a tracer element 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 three­city area in

southeastern Washington State. Soil and dust ingestion

was evaluated by analyzing soil and house dust, feces, urine, and duplicate food, dietary supplement,

medication and mouthwash samples for aluminum, silicon, and titanium. Data were collected for 101 of

the 104 children during July, August or September,

1987. In each family, data were collected over a seven day period, with four days of excreta sample collection. Participants were supplied with toothpaste with known

tracer element content. In addition, information on dietary habits and demographics was collected in an attempt to identify behavioral and demographic

characteristics that influence soil ingestion rates among children. The amount of soil ingested on a daily basis

was estimated using equation 5­1:

S

i.e =(((DWf + DW P ) x E f ) + 2E

u )

­ (DW fd x E fd

) (Eq. 5­1)

E

soil 

### where:

S i,e 

### = soil ingested f or child i based on

trac er e (g);

DW f 

### = feces dry weight (g);

DW p 

### = feces dry weight on toilet paper (g);

E f 

### = tracer concentration in feces (�g/g);

E

u 

### = tracer amount in urine (�g);

DW fd 

### = food dry weight (g);

E fd 

### = tracer concentration in food (�g/g);

and

E soil 

### = tracer concentration in soil (�g/g).



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The soil ingestion rates were corrected by adding the

amount of tracer in vitamins and medications to the amount of tracer in food, and adjusting the food, fecal and urine sample weights to account for missing samples. Food, fecal and urine samples were composited over a 4­day period, and estimates for daily soil ingestion were obtained by dividing the 4 day composited tracer quantities by 4. 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 5­8). Median values were 25 mg/day for aluminum, 59 mg/day for silicon, and 81 mg/day for titanium. The investigators 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 soil ingestion estimate 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, using an assumption that the likelihood of ingesting soil outdoors was the same as that of ingesting dust indoors. The adjusted mean soil/dust ingestion 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 ingestion rates were: 51.8 mg/day for aluminum, 112.4 mg/day for silicon, and 116.6 mg/day for titanium. The authors investigated whether nine behavioral and demographic factors could be used to predict soil ingestion, and found family income less than $15,000/year and swallowing toothpaste to be significant predictors with silicon­based estimates; residing in one of the three cities to be a significant predictor with aluminum­based estimates, and washing the face before eating significant

for titanium­based estimates.

5.3.2.5 Calabrese et al. 1997a ­ Soil Ingestion

Estimates for Children Residing on a Superfund Site Calabrese et al. (1997a) estimated soil ingestion rates for children residing on a Superfund site using a methodology in which eight tracer elements were analyzed. The methodology used in this study is similar to that employed in Calabrese et al. (1989), except that rather than using barium, manganese, and vanadium as three of the eight tracers, the researchers replaced them with cerium, lanthanum and neodymium. A total of 64 children ages 1­3 years (36 male, 28 female) were selected for this study of the Anaconda, Montana area. The study was conducted for seven consecutive days during September or September and October, apparently in 1992, shortly after soil was removed and replaced in some residential yards in the area. Duplicate samples of meals, beverages, and

over­the­counter medicines and vitamins were collected over the seven day period, along with fecal samples. In addition, soil and dust samples were collected from the children’s home and play areas. Toothpaste containing nondetectable levels of the tracer elements, with the exception of silica, was provided to all of the children. Infants were provided with baby cornstarch, diaper rash cream, and soap which were found to contain low levels

of tracer elements. Calabrese et al. (1997a) estimated soil ingestion by each tracer element, as shown in Table 5­9.

5.3.2.6 Stanek et al. 1998 ­ Prevalence of Soil Mouthing/Ingestion among Healthy

Children Aged 1 to 6/Calabrese et al. 1997b

­ Soil Ingestion Rates in Children Identified by

Parental Observation as Likely High Soil Ingesters Stanek et al. (1998) conducted a survey response study using in­person interviews of parents of children attending well visits at three western Massachusetts medical clinics in August, September and October of 1992. Of 528 children ages 1 to 7 with completed interviews, parents reported daily mouthing or ingestion of sand and stones in 6 percent, daily mouthing or ingestion of soil and dirt in 4 percent, and daily mouthing or ingestion of dust, lint and dustballs in 1 percent. Parents reported more than weekly mouthing or ingestion of sand and stones in 16 percent, more than weekly mouthing or ingestion of soil and dirt in 10 percent, and more than weekly mouthing or ingestion of dust, lint and dustballs in 3 percent. Parents reported more than monthly mouthing or ingestion of sand and stones in 27 percent, more than monthly mouthing or ingestion of soil and dirt in 18 percent, and more than monthly mouthing or ingestion of dust, lint and dustballs in 6 percent.Calabrese and colleagues performed a followup tracer element study (Calabrese et al. 1997b) for a subset (n=12) of the Stanek et al. (1998) children whose caregivers had reported daily sand/soil ingestion (n=17). The time frame of the follow­up tracer study relative to the original survey response study was not stated; the study duration was 7 days. Of the 12



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children in Calabrese et al. 1997b, one exhibited behavior that the authors believed was clearly soil pica; Table 5­10 shows estimated soil ingestion rates for this child during the study period. Estimated average daily soil ingestion estimates (calculated based on soil tracer element concentrations only) ranged from ­0.015 to +1.783 g/day based on aluminum, ­0.046 to +0.931 g/day based on silicon, and ­0.047 to +3.581 g/day based on titanium. Estimated average daily dust ingestion estimates (calculated based on dust tracer element concentrations only) ranged from ­0.039 to +2.652 g/day based on aluminum, ­0.028 to +3.145 g/day based on silicon, and ­0.098 to +3.632 g/day based on titanium. Calabrese et al. (1997b) question the validity of retrospective caregiver reports of soil pica on the basis of the tracer element results.

5.3.2.7 Davis and Mirick, 2006 ­ Soil ingestion in

children and adults in the same family Davis and Mirick (2006) calculated soil ingestion for children and adults in the same family using a tracer element approach. Data were collected in 1988, one year after the Davis et al. (1990) study was conducted. Samples were collected and prepared for laboratory analysis and then stored for a 12 year period prior to tracer element quantification with laboratory analysis. The 20 families in this study were a nonrandom subset of the 104 families who participated in the soil ingestion study by Davis et al. (1990), and were chosen based on high compliance with the previous study protocol and expressed willingness to participate in a future study. Data collection issues resulted in sufficiently complete data for only 19 of the 20 families consisting of a child participant from the Davis et al. (1990) study ages 3 to 7, inclusive, and a female and male parent or guardian living in the same house. Duplicate samples of all food and medication items consumed, and all feces excreted, were collected for 11 consecutive days. Urine samples were collected twice daily for 9 of the 11 days; for the remaining 2 days, attempts were made to collect full 24­hour urine specimens. Soil and house dust samples were also collected. Only 12 children had sufficiently complete data for use in the soil and dust ingestion estimates. Tracer elements for this study included aluminum, silicon and titanium. Toothpaste was supplied for use by study participants. In addition, parents completed a daily diary of activities for themselves and the participant child for 4 consecutive days during the study period. Children’s estimated soil ingestion rates are

shown in Table 5­11. The mean and median estimates for children for all three tracers ranged from 36.7 to

206.9 mg/day and 26.4 to 46.7 mg/day, respectively, calculated by setting negative estimates to zero. These estimates fall within the range of those reported by Davis et al., 1990. Similar to the previous Davis et al. study, the soil ingestion estimates were the highest for

titanium. Only two of a number of children’s behaviors examined for their relationship to soil ingestion were found to be associated with increased soil ingestion in this study:

• reported eating of dirt; and

• hand washing before meals (based on 2 of 12

c hild ren who were reported not to wash hands before eating). Several typical childhood behaviors, however, including thumb­sucking, furniture licking, and carrying around a blanket or toy were not associated with increased soil ingestion for the participating children. When investigating correlations within the same family, a child’s soil ingestion rate was not found to be associated with either parent’s soil ingestion rate.

5.3.3 Key Studies of Secondary Analysis

5.3.3.1 Wong, 1988 ­ The Role of Environmental

and Host Behavioural Factors in Determining Exposure to Infection with

Ascaris lumbricoides and Trichuris

Trichiura/Calabrese and Stanek, 1993 ­ Soil

Pica: Not a Rare Event Calabrese and Stanek (1993) reviewed a tracer element study that was conducted by Wong (1988) to estimate the amount of soil ingested by two groups of children. Wong (1988) studied a total of 52 children in two government institutions in Jamaica. The younger group included 24 children with an average age of 3.1 years (range of 0.3 to 7.5 years). The older group included 28 children with an average age of 7.2 years (range of 1.8 to 14 years). One fecal sample wascollected each month from each subject over the fourmonth study period. The amount of silicon in dry feces was measured to estimate soil ingestion. An unspecified number of daily fecal samples were collected from a hospital control group of 30 children with an average age of 4.8 years (range of 0.3 to 12 years). Dry feces were observed to contain 1.45 percent silicon, or 14.5 mg Si per gram of dry feces. This quantity was used to correct measured fecal silicon



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from dietary sources. Fecal silicon quantities greater than 1.45 percent in the 52 studied children were interpreted as originating from soil ingestion. For the 28 children in the older group, soil ingestion was estimated to be 58 mg/day, based on the mean minus one outlier, and 1,520 mg/day, based on

the mean of all the children. The outlier was a child with an estimated average soil ingestion rate of 41 g/day over the 4 months. Estimates of soil ingestion were higher in the younger group of 24 children. The mean soil ingestion of all the children was 470 ± 370 mg/day. Due to some sample losses, of the 24 children studied, only 15 had samples for each of the 4 months of the study. Over the entire 4­month study period, 9 of 84 samples (or 10.5 percent) yielded soil ingestion estimates in excess of 1 g/day. Of the 52 children studied, 6 had one­day estimates of more than 1,000 mg/day. The estimated soil ingestion for these six children is shown in Table 5­

12. The article describes 5 of 24 (or 20.8 percent) in the younger group of children as having a >1,000 mg/day estimate on at least one of the four study days; in the older group one child is described in this manner. A high degree of daily variability in soil ingestion wasobserved among these six children; three showed soilpica behavior on 2, 3, and 4 days, respectively, with the most consistent (4 out of 4 days) soil­pica child having the highest estimated soil ingestion, 3.8 to 60.7 g/day.

5.3.3.2 Hogan et al., 1998 ­ Integrated Exposure Uptake Biokinetic Model for Lead in

Children: Empirical Comparisons with Epidemiologic Data Hogan et al. (1998) used the biokinetic model comparison methodology to review the measured blood

lead levels of 478 children. These children were a subset of the entire population of children living in three historic lead smelting communities, whose environmental lead exposures (soil and dust lead levels) had been collected as part of public health evaluations

in these communities. The Integrated Exposure and Uptake Biokinetic (IEUBK) model is a biokinetic model for predicting children’s blood lead levels that uses measurements of lead content in house dust, soil, drinking water, food and air, and child­specific estimates of intake for each exposure medium (dust, soil, drinking water, food and air). Model users can also use default assumptions for the lead contents and intake rates for each exposure medium when they do not have specific information for each child. Hogan et al. (1998) compared children’s

measured blood lead levels with biokinetic model predictions (IEUBK version 0.99d) of blood lead levels, using the children’s measured drinking water, soil, and dust lead contamination levels together with default IEUBK model inputs for soil and dust ingestion, relative proportions of soil and dust ingestion, lead bioavailability from soil and dust, and other model parameters. Thus, the default soil and dust ingestion rates in the model, and other default assumptions in the model, were tested by comparing measured blood lead levels with the model’s predictions for those children’s

blood lead levels. For Palmerton, Pennsylvania (n=34), the community­wide geometric mean measured blood lead levels (6.8 ug/dl) were slightly over­predicted by the m o d e l ( 7 . 5 u g / d l ) ; f o r s o u t h e a s t e r n Kansas/southwestern Missouri (n=111), the blood lead levels (5.2 ug/dl) were slightly under­predicted (4.6 ug/dl), and for Madison County, Illinois (n=333), the geometric mean measured blood lead levels matched the model predictions (5.9 ug/dl measured and predicted), with very slight differences in the 95 percent confidence interval. These results suggest that the default soil and dust ingestion rates used in this version of the IEUBK model (approximately 50 mg/day soil and 60 mg/day dust for a total soil + dust ingestion of 110 mg/day, averaged over children ages 1 through 6) may be roughly accurate in representing the centraltendency soil and dust ingestion rates of residencedwelling children in the three locations studied.

5.3.4 Relevant Studies of Primary Analysis The following studies are classified as relevant rather than key. The tracer element studies described in this section are not designated as key because the methodology to account for non­soil tracer exposures was not as well­developed as the methodology in the five U.S. tracer element studies. However, Clausing et al. (1987) was used in developing the biokinetic model default soil and dust ingestion rates (U.S. EPA 1994a) used in the Hogan et al. (1998) study, which was designated as key. In the survey response studies, in

most cases the studies were of a non­randomized design, insufficient information was provided to determine important details regarding study design, or no data were provided to allow quantitative estimates of soil and/or dust ingestion rates.



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5.3.4.1 Dickins and Ford, 1942 ­ Geophagy (Dirt Eating) Among Mississippi Negro School

Children Dickens and Ford conducted a survey response study of rural black school children (4th grade and above) in Oktibbeha County, Mississippi in September 1941. A total of 52 of 207 children (18 of 69 boys and 34 of 138 girls) studied gave positive responses to questions administered in a test­taking format regarding having eaten dirt in the previous 10 to 16 days. The authors stated that the study sample likely was more representative of the higher socioeconomic levels in the community, because older children from lower

socioeconomic levels sometimes left school in order to work, and because children in the lower grades, who were more socioeconomically representative of the overall community, were excluded from the study. Clay was identified as the predominant type of soil eaten.

5.3.4.2 Cooper, 1957 ­ Present Study Cooper (1957) conducted a non­randomized survey response study in the 1950s of children age 7 months or older referred to a Baltimore, Maryland mental hygiene clinic. For 86 out of 784 children studied, parents or caretakers gave positive responses to the question “Does your child have a habit, or did he ever have a habit, of eating dirt, plaster, ashes, etc.?” and identified dirt, or dirt combined with other substances, as the substance ingested. Cooper (1957) described a pattern of pica behavior, including ingesting substances other than soil, being most common between ages 2 and 4 or 5 years, with one of the 86 children ingesting clay at age 10 years and 9 months.

5.3.4.3 Barltrop, 1966 ­ The Prevalence of Pica Barltrop (1966) conducted a randomized survey response study of children born in Boston, Massachusetts between 1958 and 1962, inclusive, whose parents resided in Boston and who were neither illegitimate nor adopted. A stratified random subsample of 500 of these children were contacted for in­person caregiver interviews, in which a total of 186 families (37 percent) participated. A separate stratified subsample of 1,000 children was selected for a mailed survey, in which 277 (28 percent) of the familiesparticipated. Interview­obtained data regarding caregiver reports of pica (in this study is defined as placing nonfood items in the mouth and swallowing them) behavior in all children ages 1 to 6 in the 186 families (n=439) indicated 19 had ingested dirt (defined as yard dirt, house dust, plant­pot soil, pebbles, ashes, cigarette ash, glass fragments, lint, and hair combings) in the preceding 14 days. It does not appear that these data were corrected for unequal selection probability in the stratified random sample, nor were they corrected for non­response bias. Interviews were conducted in theMarch/April time frame, presumably in 1964. Mailsurvey obtained data regarding caregiver reports of pica in the preceding 14 days indicated that 39 of 277 children had ingested dirt, presumably using the same definition as above. Barltrop (1966) mentions severalpossible limitations of the study, including nonparticipation bias and respondents’ memory, or recall,

effects.

5.3.4.4 Bruhn and Pangborn, 1971 ­ Reported

Incidence of Pica among Migrant Families Bruhn and Pangborn (1971) conducted a survey among 91 low income families of migrant agricultural workers in California in May through August 1969. Families were of Mexican descent in two labor camps (Madison camp, 10 miles west of Woodland, and Davis camp, 10 miles east of Davis) and were “Anglo” families at the Harney Lane camp 17 miles north of Stockton. Participation was 34 of 50 families at the Madison camp, 31 of 50 families at the Davis camp, and 26 of 26 families at the Harney Lane camp. Respondents for the studied families (primarily wives) gave positive responses to open­ended questions such as “Do you know of anyone who eats dirt or laundry starch?” Bruhn and Pangborn (1971) apparently asked a modified version of this question pertaining to the respondents’ own or relatives’ families. They reported 18 percent (12 of 65) of Mexican families’ respondents as giving positive responses for consumption of “dirt” among children within the Mexican respondents’ own or relatives’ families. They reported 42 percent (11 of 26) of “Anglo” families’ respondents as giving positive responses for consumption of “dirt” among children within the Anglo respondents’ own or relatives’

families.

5.3.4.5 Robischon, 1971 ­ Pica Practice and Other

Hand­Mouth Behavior and Children’s Developmental Level A survey response sample of 19­ to 24­month

old children examined at an urban well­child clinic in the late 1960s or 1970 in an unspecified location indicated that 48 of the 130 children whose caregivers



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were interviewed, exhibited pica behavior (defined as “ate nonedibles more than once a week”). The specific substances eaten were reported for 30 of the 48 children. All except 2 of the 30 children habitually ate

more than one nonedible substance. The soil and dustlike substances reported as eaten by these 30 children were: ashes (17), “earth” (5), dust (3), fuzz from rugs (2), clay (1), and pebbles/stones (1). Caregivers for some of the study subjects (between 0 and 52 of the 130 subjects, exact number not specified) reported that the

children “ate nonedibles less than once a week.”

5.3.4.6 Binder et al., 1986 ­ Estimating Soil Ingestion: The Use of Tracer Elements in Estimating the Amount of Soil Ingested by Young Children Binder et al. (1986) used a tracer technique modified from a method previously used to measure soil ingestion among grazing animals to study the ingestion of soil among children 1 to 3 years of age who wore diapers. 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 3­day 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. Excreta measurements were obtained for 59 of the children. Soil ingestion by each

child was estimated on the basis of each of the three tracer elements using a standard assumed fecal dry weight of 15 g/day, and the following equation (5­2):

T

i,e 

### = f i,e 

### x F i 

### (Eq. 5­2)

S

i,e 

### where:

estimated soil ingestion for child i
based on element e (g/day); T i,e

­ 

### =

concentration of element e in fecal sample of child i (mg/g);

F i 

### = fecal dry weight (g/day); and f i,e

­ 

### =

concentration of element e in child i 's yard soil (mg/g). S i,e

­ 

### =

The analysis 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 house dust, 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 5­13). 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 median values were 121 mg/day, 136 mg/day, and 618 mg/day for aluminum, silicon, and titanium, respectively. 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 they 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.

5.3.4.7 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. Clausing et al. (1987) measured aluminum, titanium, and acid­insoluble residue contents of fecal samples from children aged 2 to 4 years attending a nursery school, and for samples of playground dirt at that school. Over a 5­day period, 27 daily fecal samples were obtained for 18 children. Using the average soil concentrations present at the school, and assuming a standard fecal dry weight of 10 g/day, soil ingestion was estimated for each tracer. Six hospitalized, bedridden children served as a control



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group, representing children who had very limited access to soil; 8 daily fecal samples were collected from the hospitalized children. Without correcting for the tracer element contribution from background sources, represented by the hospitalized children’s soil ingestion estimates, the aluminum­based soil ingestion estimates for the school children in this study ranged from 23 to 979 mg/day, the AIR­based estimates ranged from 48 to 362 mg/day, and the titanium­based estimates ranged from 64 to 11,620 mg/day. As in the Binder et al. (1986) study, a fraction of the children (6/18) showed titanium values above 1,000 mg/day, with most of the remaining children showing substantially lower values. Calculating an arithmetic mean quantity of soil ingested based on each fecal sample yielded 230 mg/day for aluminum; 129 mg/day for AIR, and 1,430 mg/day for titanium (Table 5­14). Based on the Limiting Tracer Method (LTM) and averaging across each fecal sample, the arithmetic mean soil ingestion was estimated to be 105 mg/day with a population standard deviation of 67 mg/day (range 23 to 362 mg/day); geometric mean soil ingestion was estimated to be 90 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). The hospitalized children’s arithmetic mean aluminum­based soil ingestion estimate was 56 mg/day; titanium­based estimates included estimates for three of the six children that exceeded 1,000 mg/day, with the remaining three children in the range of 28 to 58 mg/day (Table 5­15). AIR measurements were not reported for the hospitalized children. 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 ingestion rate was 45 mg/day. The hospitalized children’s data suggested a major nonsoil source of titanium for some children and a background nonsoil source of aluminum. However, conditions specific to hospitalization (e.g., medications) were not

considered. Clausing et al. (1987) estimated that the average soil ingestion of the nursery school children was 56 mg/day, after subtracting the mean LTM soil ingestion for the hospitalized children (49 mg/day) from the nursery school children’s mean LTM soil ingestion (105 mg/day), to account for background tracer intake from dietary and other nonsoil sources. 5.3.4.8 Smulian et al., 1995 ­ Pica in a Rural

Obstetric Population In 1992, Smulian et al. (1995) conducted a survey response study of pica in a convenience sample of 125 pregnant women in Muscogee County, Georgia, who ranged in age from 12 to 37. Of the 18 women who acknowledged practicing pica, 4 acknowledged eating “white dirt” (common name for white clay) or “red dirt.” Of the 18 women, 9 stated the amount of substances that they ingested (which included several substances besides white or red dirt). Thus, of the 4 respondents who acknowledged ingesting white or red dirt, an unknown number of them acknowledged ingesting 0.5 to 1.0 pounds of dirt or clay per week (roughly 200­500 g/week). Of the 9 women who stated amounts of substances ingested, 6 stated that their ingestion occurred daily and 3 stated that it occurred three times per week. The authors found a prevalence for the overall pica, by race/ethnicity, of 17.8 percent of the black women, 10.6 percent of the white women, and 0 percent of the Asian and Hispanic women in the sample, with no significant differences between pica and nonpica groups with respect to age distribution or

race.

5.3.5 Relevant Studies of Secondary Analysis The secondary analysis literature on soil and dust ingestion rates gives important insights into methodological strengths and limitations. The tracer element studies described in this section are grouped to some extent according to methodological issues associated with the tracer element methodology. These methodological issues include attempting to determine the origins of apparent positive and negative bias in the methodologies, including: food input/fecal output misalignment; missed fecal samples; assumptions about children’s fecal weights; particle sizes of, and relative

contributions of soils and dusts to total soil and dust ingestion; and attempts to identify a “best” tracer

element or combination of tracer elements. Potential error from using short­term studies’ estimates for long term soil and dust ingestion behavior estimates is also

discussed.

5.3.5.1 Stanek et al., 2001a ­ Biasing Factors for Simple Soil Ingestion Estimates in Mass

Balance Studies of Soil Ingestion In order to identify and evaluate biasing factors for soil ingestion estimates, the authors developed a simulation model based on data from



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previous soil ingestion studies. The soil ingestion data

used in this model were taken from Calabrese et al. (1989) (the Amherst study); Davis et al. (1990) (southeastern Washington State); Calabrese et al. (1997a) (the Anaconda study) and Calabrese et al. (1997b) (soil­pica in Massachusetts), and relied only on

the aluminum and silicon trace element estimates provided in these studies. Of the biasing factors explored, the impact of study duration was the most striking, with a positive bias of more than 100 percent for percentile 95 th 

### estimates in a 4­day tracer element study. A smaller bias was observed for the impact of absorption of trace elements from food. Although the trace elements

selected for use in these studies are believed to have low absorption, whatever amount is not accounted for will result in an underestimation of the soil ingestion distribution. In these simulations, the absorption of trace elements from food of up to 30 percent was shown to negatively bias the estimated soil ingestion distribution by less than 20 mg/day. No biasing effect was found for misidentifying play areas for soil sampling (i.e., ingested soil from a yard other than the subject’s yard).

5.3.5.2 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 subject­week and trace element basis. Calabrese and Stanek (1995) identified possible sources of positive errors to be:

• Ingestion of high levels of tracers before the

s tar t of the study and low ingestion during the study period; and

• Ingestion of element tracers from a non­food

o r n on­soil source during the study period. Possible sources of negative bias were identified as:

• Ingestion of tracers in food that are not

c ap tured in the fecal sample either due to slow lag time or not having a fecal sample available on the final study day; and

• Sample measurement errors that result in

d imin ished detection of fecal tracers, but not

in soil tracer levels. The authors developed an approach that 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 subject­day estimates); (2) a daily soil ingestion rate was estimated for each tracer for each 24­hour day a fecal sample was obtained; (3) the median tracer­based soil ingestion rate for each subject­day was determined; and (4) negative errors due to missing fecal samples at the end of the study period were also determined. Also, upper­ and lower­bound

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. Table 5­16 presents the estimated magnitude of positive and negative error for six tracer elements in the children's study (conducted by Calabrese et al., 1989). The original non­negative mean soil ingestion rates (Table 5­3) ranged from a low of 21 mg/day based on zirconium to a high of 459 mg/day based on vanadium. 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. Calabrese and Stanek (1995) concluded that correcting for errors at the individual level for each tracer element provides more reliable estimates of soil ingestion.

5.3.5.3 Stanek and Calabrese, 1995a ­ Daily

Estimates of Soil Ingestion in Children Stanek and Calabrese (1995a) presented a methodology which links the physical passage of food and fecal samples to construct daily soil ingestion estimates from daily food and fecal trace­element concentrations. Soil ingestion data for children obtained from the Amherst study (Calabrese et al., 1989) were reanalyzed by Stanek and Calabrese (1995a). 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



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corresponded to the 24 hour period from noon on Monday to noon on Tuesday. 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. Table 5­17 presents adjusted estimates, modified according to the input/output misalignment correction, of mean daily soil ingestion per child (mg/day) for the 64 study participants. The approach adopted in this paper led to changes in ingestion estimates from those presented in Calabrese et al. (1989). Estimates of children’s soil ingestion projectedover a period of 365 days were derived by fitting lognormal distributions to the overall daily soil ingestion estimates using estimates modified according to the input/output misalignment correction (Table 5­18). The estimated median value of the 64 respondents' daily soil ingestion averaged over a year was 75 mg/day, while

the 95 th 

### percentile was 1,751 mg/day. In developing the 365­day 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 2­week study period may not reflect variability in tracer element ingestion over a year. While Stanek and Calabrese (1995a) attempted to address this through modeling of the long term ingestion, new uncertainties were introduced through the parametric modeling of the limited subject day data.

5.3.5.4 Calabrese and Stanek, 1992b ­ What Proportion of Household Dust is Derived from Outdoor Soil? Calabrese and Stanek (1992b) estimated the amount of outdoor soil in indoor dust using statistical modeling. The model used soil and dust data from the 60 households that participated in the Calabrese et al. (1989) study, by preparing scatter plots of each tracer’s concentration in soil versus dust. Correlation analysis of the scatter plots was performed. The scatter plots showed little evidence of a consistent relationship

between outdoor soil and indoor dust concentrations. The model estimated the proportion of outdoor soil in indoor dust using the simplifying assumption that the following variables were constants in all houses: the amount of dust produced every day from both indoor and outdoor sources; the proportion of indoor dust due to outdoor soil; and the concentration of the tracer element in dust produced from indoor sources. Using these assumptions, the model predicted that 31.3 percent by weight of indoor dust came from outdoor soil. This model was then used to adjust the soil ingestion estimates from Calabrese et al. (1989). Using an assumption that 50 percent of excess fecal tracers were from indoor origin and 50 percent were fromoutdoor origin, and multiplying the 50 percent indoororigin excess fecal tracer by the model prediction that

31.3 percent of indoor dust came from outdoor soil, results in an estimate that 15 percent of excess fecal tracers were from soil materials that were present in indoor dust. Adding this 15 percent to the 50 percent assumed outdoor (soil) origin excess fecal tracer quantity results in an estimate that approximately 65 percent of the total residual excess fecal tracer was of soil origin (Calabrese and Stanek, 1992b).

5.3.5.5 Calabrese et al., 1996 ­ Methodology to
Estimate the Amount and Particle Size of

Soil Ingested by Children: Implications for Exposure Assessment at Waste Sites Calabrese et al., 1996 examined the hypothesis

that one cause of the variation between tracers seen in soil ingestion studies could be related to differences in soil tracer concentrations by particle size. This study, published prior to the Calabrese et al. (1997a) primary analysis study results, used laboratory analytical results for the Anaconda, Montana soil’s tracer concentration after it had been sieved to a particle size of <250 �m in diameter (it was sieved to <2 mm soil particle size in Calabrese et al. (1997a)). The smaller particle size was examined based on the assumption that children principally ingest soil of small particle size adhering to fingertips and under fingernails. For five of the tracers used in the original study (aluminum, silicon, titanium, yttrium, and zirconium), soil concentration was not changed by particle size. However, the soil concentrations of three tracers (lanthanum, cerium, and neodymium) were increased two­ to fourfold at the smaller soil particle size. Soil ingestion estimates for these three tracers were decreased by approximately 60 percent at the 95th percentile compared to the Calabrese et al. (1997a) results.



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5.3.5.6 Stanek et al., 1999 ­ Soil Ingestion Estimates for Children in Anaconda Using Trace

Element Concentrations in Different Particle
Size Fractions Stanek et al. (1999) extends the findings from Calabrese et al. (1996) by quantifying trace element concentrations in soil based on sieving to particle sizes of 100 to 250 �m and to particle sizes of 53 to < 100

�m. This study used the data from soil concentrations from the Anaconda, Montana site reported by Calabrese et al. (1997a). Results of the study indicated that soil concentrations of aluminum, silicon and titanium do not increase at the two finer particle size ranges measured. However, soil concentrations of cerium, lanthanum and neodymium increased by a factor of 2.5 to 4.0 in the 100­250 �m particle size range when compared with the 0 to 2 �m particle size range. There was not a significant increase in concentration in the 53 to 100

�m particle size range.

5.3.5.7 Stanek and Calabrese, 1995b ­ Soil Ingestion

Estimates for Use in Site Evaluations Based

on the Best Tracer Method Stanek and Calabrese (1995b) recalculated children’s soil ingestion rates from two previous studies, using data for 8 tracers from Calabrese et al., 1989 and 3 tracers from Davis et al., 1990. Recalculations were performed using the Best Tracer Method (BTM). This method selected the “best”tracer(s), by dividing the total amount of tracer in a particular child’s duplicate food sample by tracer concentration in that child’s soil sample to yield a food/soil (F/S) ratio. The F/S ratio was small when the tracer concentration in food was low compared to the

tracer concentration in soil. Small F/S ratios were desirable because they lessened 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. The BTM used a ranking scheme of F/S ratios to determine the best tracers for use in the ingestion rate calculation. To reduce the impact of 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. Using the lowest four F/S ratios for each child, calculated on a per­week (“subject­week”) basis, the median of the soil ingestion estimates from the Calabrese et al. (1989) study most often included aluminum, silicon, titanium, yttrium, and zirconium. 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. 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., aluminum, silicon and titanium) from the Davis et al.(1990) study. When the results for the 128 subjectweeks in Calabrese et al. (1989) and 101 children in Davis et al. (1990) were combined, soil ingestion for children was estimated to be 104 mg/day (mean); 37 mg/day (median); and 217 mg/day (95th percentile), using the BTM.

5.3.5.8 Stanek and Calabrese, 2000 ­ Daily Soil Ingestion Estimates for Children at a Superfund Site Stanek and Calabrese (2000) reanalyzed the soil ingestion data from the Anaconda study. The authors assumed a lognormal distribution for the soil ingestion estimates in the Anaconda study to predict average soil ingestion for children over a longer time period. Using “best linear unbiased predictors,” the authors predicted 95 th 

### percentile soil ingestion values over time periods of 7 days, 30 days, 90 days, and 365 days. The 95 th 

### percentile soil ingestion values were predicted to be 133 mg/day over 7 days, 112 mg/day over 30 days, 108 mg/day over 90 days, and 106 mg/day over 365 days. Based on this analysis, estimates of the distribution of longer term average soil ingestion are expected to be narrower, with the 95 th 

### percentile estimates being as much as 25 percent lower (Stanek and Calabrese, 2000).

5.3.5.9 Stanek et al., 2001b ­ Soil Ingestion

Distributions for Monte Carlo Risk

Assessment in Children Stanek et al. (2001b) developed “best linear unbiased predictors” to reduce the biasing effect of short­term soil ingestion estimates. This study estimated the long­term average soil ingestion distribution using daily soil ingestion estimates from children who participated in the Anaconda, Montana study. In this long­term (annual) distribution, the soil ingestion estimates were: mean 31, median 24, 75 th



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percentile 42, 90 th 

### percentile 75, and 95 th 

### percentile 91 mg/day.

5.3.5.10 von Lindern et al., 2003 ­ Assessing remedial effectiveness through the blood lead:soil/dust

lead relationship at the Bunker Hill Superfund Site in the Silver Valley of Idaho Similar to Hogan et al. (1998), von Lindern et al. (2003) used the IEUBK model to predict blood lead levels in a non­random sample of several hundred children ages 0­9 years in an area of northern Idaho from 1989­1998 during community­wide soil remediation. Von Lindern et al. (2003) used the IEUBK default soil and dust ingestion rates together with observed house dust/soil lead levels (and imputed values based on community soil and dust lead levels, when observations were missing). The authors compared the predicted blood lead levels with observed

blood lead levels and found that the default IEUBK soil and dust ingestion rates and lead bioavailability value overpredicted blood lead levels, with the overprediction decreasing as the community soil remediation progressed. The authors stated that the overprediction may have been caused either by a default soil and dust ingestion that was too high, a default bioavailability value for lead that was too high, or some combination of the two. They also noted underpredictions for some children, for whom follow up interviews revealed exposures to lead sources not accounted for by the model, and noted that the study sample included many

children with a short residence time within the community. Von Lindern et al. (2003) developed a statistical model that apportioned the contributions of community soils, yard soils of the residence, and house dust to lead intake; the models’ results suggested that community soils contributed more (50 percent) than neighborhood soils (28 percent) or yard soils (22 percent) to soil found in house dust of the studied

children.

5.4 LIMITATIONS OF KEY STUDY

METHODOLOGIES The three types of information needed to provide recommendations to exposure assessors on soil and dust ingestion rates among U.S. children include quantities of soil and dust ingested, frequency of high soil and dust ingestion episodes, and prevalence of high soil and dust ingesters. The methodologies provide different types of information: the tracer element and biokinetic model comparison methodologies provide information on quantities of soil and dust ingested; the tracer element methodology provides limited evidence of the frequency of high soil ingestion episodes; the survey response methodology can shed light on prevalence of high soil ingesters and frequency of high soil ingestion episodes. The methodologies used to estimate soil and dust ingestion rates and prevalence of soil and dust ingestion behaviors have certain limitations, when used for the purpose of developing recommended soil and dust ingestion rates. This section describes some of the known limitations, presents an evaluation of the current state of the science for U.S. children’s soil and dust ingestion rates, and

describes how the limitations affect the confidence ratings given to the recommendations.

5.4.1 Tracer Element Methodology This section describes some previously identified limitations of the tracer element methodology as it has been implemented by U.S. researchers, as well as additional potential limitations that have not been explored. Some of these same limitations would also apply to the Dutch and Jamaican studies that used a control group of hospitalized children to account for dietary and pharmaceutical tracer intakes. Binder et al. (1986) described some of the major and obvious limitations of the early U.S. tracer element methodology as follows: [T]he algorithm assumes that children ingest predominantly soil from their own yards and that concentrations of elements in composite soil samples from front and back yards are representative of overall concentrations in the yards....children probably eat a combination of soil and dust; the algorithm used does not distinguish between soil and dust ingestion....fecal sample weights...were much lower than expected...the assumption that aluminum, silicon and titanium are not absorbed is not entirely true....dietary intake of aluminum, silicon and titanium is not negligible when compared with the potential

intake of these elements from soil....Before accepting these estimates as true values of soil ingestion in toddlers, we need a better understanding of the metabolisms of aluminum, silicon and titanium in children, and the validity of the assumptions we made in our calculations should be explored further.



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The subsequent U.S. tracer element studies (Calabrese et al. (1989)/Barnes (1990), Davis et al. (1990), Calabrese et al. (1997a), and Davis and Mirick (2006)) made some progress in addressing some of the Binder et al. (1986) study’s stated limitations.Regarding the issue of non­yard (communitywide) soil as a source of ingested soil, one study (Calabrese et al. 1989/Barnes 1990) addressed this issue to some extent, by including samples of children’s day care center soil in the analysis. Calabrese et al. (1997a) attempted to address the issue by excluding children in day care from the study sample frame. Homogeneity of community soils’ tracer element content would play a role in whether this issue is an important biasing factor for the tracer element studies’ estimates. Davis et al. (1990) evaluated community soils’ aluminum, silicon and titanium content and foundlittle variation among 101 yards throughout the threecity area. Stanek et al. (2001a) conclude that there is “minimal impact” on estimates of soil ingestion due to mis­specifying a child’s play area. Regarding the issue of soil and dust both contributing to measured tracer element quantities in excreta samples, the five key U.S. tracer element studies all attempt to address the issue by including samples of household dust in the analysis, and in some cases estimates are presented in the published articles that adjust soil ingestion estimates on the basis of the

measured tracer elements found in the household dust. The relationship between soil ingestion rates and indoor settled dust ingestion rates has been evaluated in some of the secondary studies (e.g., Calabrese and Stanek (1992b)). An issue similar to the community­wide soil exposures in the previous paragraph could also exist with community­wide indoor dust exposures (such as dust found in schools and community buildings occupied by study subjects during or prior to the study period). A portion of the community­wide indoor dust exposures (that due to occupying day care facilities) was addressed in the Calabrese et al. (1989)/Barnes (1990) study, but not in the other three key tracer element studies. In addition, if the key studies’ vacuum cleaner collection method for household and day care indoor settled dust samples influenced tracer element composition of indoor settled dust samples, the dust sample collection method would be another area of uncertainty with the key studies’ indoor dust related estimates. The survey response studies suggest that some young children may prefer ingesting dust to ingesting soil. The existing literature on soil versus dust sources of children’s lead exposure may provide useful information that has not yet been compiled for use in soil and dust ingestion recommendations. Regarding the issue of fecal sample weights and the related issue of missing fecal and urine samples, the four key tracer element studies have varying strengths and limitations. The Calabrese et al. (1989) article stated that wipes and toilet paper were not collected by the researchers, and thus underestimates of fecal quantities may have occurred. Calabrese et al. (1989) stated that cotton cloth diapers were supplied for use during the study; commodes apparently were used

to collect both feces and urine for those children who were not using diapers. Barnes (1990) described cellulose and polyester disposable diapers with significant variability in silicon and titanium content and suggested that children’s urine was not included in the analysis. Thus, it is unclear to what extent complete fecal and urine output was obtained, for each study subject. The Calabrese et al. (1997a) study did not describe missing fecal samples and did not state whether urinary tracer element quantities were used in the soil and dust ingestion estimates, but stated that wipes and toilet paper were not collected. Missing fecal samples may have resulted in negative bias in the

estimates from both of these studies. Davis et al. (1990) and Davis and Mirick (2006) were limited to children who no longer wore diapers. Missed fecal sample adjustments might affect those studies’ estimates in either a positive or negative direction, due to the assumptions the authors made regarding the quantities of feces and urine in missed samples. Adjustments for missing fecal and urine samples could introduce errors sufficient to cause negative estimates if missed samples were heavier than the collected samples used in the soil and dust ingestion estimate

calculations. Regarding the issue of dietary intake, the five key U.S. tracer element studies have all addressed dietary (and non­dietary, non­soil) intake by subtracting quantitated estimates of these sources of tracer elements from excreta tracer element quantities, or by providing study subjects with personal hygiene products that were low in tracer element content. Applying the food and non­dietary, non­soil corrections required subtracting

the tracer element contributions from these non­soil

sources from the measured fecal/urine tracer element quantities. To perform this correction required assumptions to be made regarding the gastrointestinaltransit time, or the time lag between inputs (food, non

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dietary non­soil, and soil) and outputs (fecal and urine). The gastrointestinal transit time assumption introduced a new potential source of bias that some authors (e.g., Stanek and Calabrese, 1995a) called input/output misalignment or transit time error. This lag time may also be a function of age. Davis et al. (1990) and Davis and Mirick (2006) assumed a 24 hour lag time in contrast to the 28 hour lag times used in Calabrese et al. (1989)/Barnes (1990) and Calabrese et al. (1997a). ICRP (2002) suggested a lag time of 37 hours for one year old children and 5 to 15 year old children. Stanek and Calabrese (1995a) describe a method designed to

reduce bias from this error source. Regarding gastrointestinal absorption, the authors of three of the studies appeared to agree that the presence of silicon in urine represented evidence that silicon was being absorbed from the gastrointestinal tract (Davis et al., 1990; Calabrese et al., 1989/Barnes (1990); Davis and Mirick, 2006). There was some evidence of aluminum absorption in Calabrese et al., 1989/Barnes (1990); Davis and Mirick (2006) stated that aluminum and titanium did not appear to have been absorbed, based on low urinary levels. Davis et al. (1990) stated that silicon appears to have been absorbed to a greater degree than aluminum and titanium, based

on urine concentrations. Aside from the gastrointestinal absorption, lag time and missed fecal sample issues, Davis and Mirick (2006) offer another other possible explanation for the negative soil and dust ingestion rates estimated for some study participants. Because the weights of dried food and liquid (input) samples were sufficiently great, relative to the urine and fecal (output) samples, overestimates in laboratory analytical values for the input samples would not be compensated for by a similar overestimate in the output samples. Another limitation on accuracy of tracer element­based estimates of soil and dust ingestion

relates to inaccuracies inherent in environmental sampling and laboratory analytical techniques. The “percent recovery” of different tracer elements varies (according to validation of the study methodology performed with adults who swallowed gelatin capsules with known quantities of sterilized soil, as part of the Calabrese et al., 1989 and 1997a studies). Estimates based on a particular tracer element with a lower or higher recovery than the expected 100 percent in any of the study samples would be influenced in either a positive or negative direction, depending on the recoveries in the various samples and their degree of deviation from 100 percent (e.g., Calabrese et al., 1989). Davis et al. (1990) offered an assessment of the impact of swallowed toothpaste on the tracer­based estimates by adjusting estimates for those children whose caregivers reported that they had swallowed toothpaste. Davis et al. (1990) had supplied study children with toothpaste that had been pre­analyzed for its tracer element content, but it is not known to what extent the children actually used the supplied toothpaste. Similarly, Calabrese et al., 1989 and 1997a supplied children in the Amherst, Massachusetts and Anaconda, Montana studies with toothpaste containing low levels of most tracers, but it is unclear to what extent those children used the supplied toothpaste. Other research suggests additional possible limitations that have not yet been explored. First, lymph tissue structures in the gastrointestinal tract might serve as reservoirs for titanium dioxide food additives and soil particles, which could bias estimates either upward or downward depending on tracers’ entrapment within, or release from, these reservoirs during the study period (ICRP (2002); Shepherd et al. (1987); Powell et al. (1996)). Second, gastrointestinal uptake of silicon may have occurred, which could bias

those estimates downward. Evidence of silicon’s role in bone formation (e.g., Carlisle (1980)) supported by newer research on dietary silicon uptake (Jugdaohsingh et al. (2002); Van Dyck et al. (2000)) suggests a possible negative bias in the silicon­based soil ingestion estimates, depending on the quantities of silicon absorbed by growing children. Third, regarding the potential for swallowed toothpaste to bias soil ingestion estimates upward, commercially available toothpaste may contain quantities of titanium and perhaps silicon and aluminum in the range that could be expected to affect the soil and dust ingestion estimates. Fourth, for those children who drank bottled or tap water during the study period, and did not include those drinking water samples in their duplicate food samples, slight upward bias may exist in some of the estimates for those children, since drinking water may contain small, but relevant, quantities of silicon and potentially other tracer elements. Fifth, the tracer element studies conducted to date have not explored the impact of soil properties’ influence on toxicant uptake or excretion within the gastrointestinal tract. Nutrition researchers investigating influence of clay geophagy behavior on human nutrition have begun using in vitro models of the human digestion (e.g., Dominy et al., 2003; Hooda et



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al., 2004). A recent review (Wilson, 2003) covers a wide range of geophagy research in humans and various hypotheses proposed to explain soil ingestion behaviors, with emphasis on the soil properties of geophagy materials.

5.4.2 Biokinetic M odel Comparison Methodology

It is possible that the IEUBK biokinetic model comparison methodology contained sources of both positive and negative bias, like the tracer element studies, and that the net impact of the competing biases

was in either the positive or negative direction. U.S.

EPA’s judgment about the major sources of bias in the

biokinetic model comparison studies is that there may be three significant sources of bias. The first source of potential bias was the possibility that the biokinetic

model failed to account for sources of lead exposure that are important for certain children. For these children, the model might either under­predict, or accurately predict, blood lead levels compared to actual

measured lead levels. However, this result may actually

mean that the default assumed lead intake rates via

either soil and dust ingestion, or another lead source that is accounted for by the model, are too high. The

second source of potential bias was use of the biokinetic

model for predicting blood lead levels in children who

have not spent a significant amount of time in the areas characterized as the main sources of environmental lead

exposure for those children, which could result in either upward or downward biases in those children’s predicted blood lead levels. Comparing upward­biased predictions with actual measured blood lead levels and finding a relatively good match could lead to inferences

that the model’s default soil and dust ingestion rates are accurate, when in fact the children’s soil and dust ingestion rates, or some other lead source, were actually higher than the default assumption. Comparing

downward­biased predictions with actual measured

blood lead levels and finding a relatively good match

could lead to inferences that the model’s default soil

and dust ingestion rates were accurate, when in fact the

children’s soil and dust ingestion rates, or some other

lead source, were actually lower than the default assumption. The third source of potential bias was the assumption within the model itself regarding the

biokinetics of absorbed lead, which could result in

either positively or negatively biased predictions and

the same kinds of incorrect inferences as the second

source of potential bias. 5.4 .3 Survey Response Methodology Each data collection methodology (in­person interview, mailed questionnaire, or questions administered in “test” format in a school setting) may have had specific limitations. In­person interviews could result in either positive or negative response bias due to distractions posed by young children, especially when interview respondents simultaneously care for young children and answer questions. Other limitations include positive or negative response bias due to respondents’ perceptions of a “correct” answer, question wording difficulties, lack of understanding of definitions of terms used, language and dialect differences between investigators and respondents, respondents’ desires to avoid negative emotions associated with giving a particular type of answer, and respondent memory problems (“recall” effects) concerning past events. Mailed questionnaires have many of the same limitations as in­person interviews, but may allow respondents to respond when they are not distracted by childcare duties. An in­school test format is more problematic than either interviews or mailed surveys, because respondent bias related to teacher expectations could influence responses. Unweighted survey responses from the National Health and Nutrition Examination Survey (NHANES) I and II regarding children’s clay and dirt ingestion are available (U.S. DHHS 1981a, U.S. DHHS 1981b, U.S. DHHS 1985a, U.S. DHHS 1985b) and appear generally to corroborate the results of the survey response studies summarized in this chapter, in that a small proportion of respondents acknowledge eating dirt or clay. U.S. EPA has undertaken an effort to weight the survey responses among adult caregiver respondents who acknowledged clay and dirt ingestion by children under age 12 years and among child respondents ages 12 up to 21 years who acknowledged clay and dirt ingestion, to develop an estimate of prevalence of the behavior among children. One approach to evaluating the degree of bias in survey response studies may be to make use of a surrogate biomarker indicator providing suggestive evidence of ingestion of significant quantities of soil (although quantitative estimates would not be possible). The biomarker technique measures the presence of

serum antibodies to Toxocara species, a parasitic roundworm from cat and dog feces. Two U.S. studies have found associations between reported soil ingestion and positive serum antibody tests for Toxocara infection (Marmor et al., 1987; Glickman et al., 1981);



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a third (Nelson et al., 1996) has not, but the authors state that reliability of survey responses regarding soil ingestion may have been an issue. Further refinement of survey response methodologies, together with recent NHANES data on U.S. prevalence of positive serum antibody status regarding infection with Toxocara species, may be useful.

5.4.4 Key Studies: Representativeness of U.S. Population The two key studies of Dutch and Jamaican children may represent different conditions and different study populations than those in the U.S.; thus,

it is unclear to what extent those children’s soil ingestion behaviors may differ from U.S. children’s soil ingestion behaviors. Limitations regarding the key studies performed in the U.S. for estimating soil and dust ingestion rates in the entire population of U.S. children ages 0 to < 21 years fall into the broad categories of geographic range and demographics (age, gender, race/ethnicity, socioeconomic status). Regarding geographic range, the two most obvious issues relate to soil types and climate. Soil properties might influence the soil ingestion estimates

that are based on excreted tracer elements. The Davis et al. (1990), Calabrese et al. (1989)/Barnes (1990), Davis and Mirick (2006) and Calabrese et al. (1997a)

tracer element studies were in locations with soils that had sand content ranging from 21­80 percent, silt content ranging from 16­71 percent, and clay content ranging from 3­20 percent by weight, based on data from USDA (2008). The location of children in the Calabrese et al. (1997b) study was not specified, but due to the original survey response study’s occurrence in western Massachusetts, the soil types in the vicinity of the Calabrese et al. (1997b) study are likely to be similar to those in the Calabrese et al. (1989)/Barnes (1990) study. The Hogan et al. (1998) study included locations in the central part of the U.S. (an area along the Kansas/Missouri border, and an area in western Illinois) and one in the eastern U.S. (Palmerton, Pennsylvania). The only key study conducted in the southern part of the U.S. was Vermeer and Frate (1979). Children might be outside and have access to soil in a very wide range of weather conditions (Wong et al., 2000). In the parts of the U.S. that experience moderate temperatures year­round, soil ingestion rates may be fairly evenly distributed throughout the year. During conditions of deep snow cover, extreme cold, or extreme heat, children could be expected to have minimal contact with outside soil. All children, regardless of location, could ingest soils located indoors in plant containers, or outdoor soil tracked inside buildings by human or animal building occupants. Davis et al. (1990) did not find a clear or consistent association between the number of hours spent indoors per day and soil ingestion, but reported a consistent association between spending a greater number of hours outdoors and high (defined as the uppermost tertile) soil ingestion levels across all three tracers used. The five key tracer element studies all took place in northern latitudes. The temperature and precipitation patterns that occurred during these four studies’ data collection periods was difficult to discern due to no mention of specific data collection dates in the published articles. The Calabrese et al. (1989)/Barnes (1990) study apparently took place in mid­ to late September 1987 in and near Amherst, Massachusetts; Calabrese et al. (1997a) apparently took place in late September and early October 1992, in Anaconda, Montana; Davis et al. (1990) took place in July, August and September 1987, in Richland, Kennewick and Pasco, Washington; and Davis and Mirick (2006) took place in the same Washington state location in late July, August and very early September 1988 (raw data). Inferring exact data collection dates, a wide range of temperatures may have occurred during the four studies’ data collection periods (daily lows

from 22­60 o 

### F and 25­48 o 

### F, and daily highs from 53­81

o 

### F o 

### F and 55­88 in Calabrese et al. (1989) and Calabrese et al. (1997a), respectively, and daily lows

from 51­72 o 

### F and 51 ­ 67 o 

### F, and daily highs from 69­

103 o 

### F and 80­102 o 

### F in Davis et al. (1990) and Davis and Mirick (2006), respectively) (National Climatic Data Center, 2008). Significant amounts of precipitation occurred during Calabrese et al. (1989) (more than 0.1 inches per 24 hour period) on several days; somewhat less precipitation was observed during Calabrese et al. (1997a); precipitation in Kennewick and Richland during the data collection periods of Davis et al. (1990) was almost nonexistent; there was no recorded precipitation in Kennewick or Richland during the data collection period for Davis and Mirick (2006) (National Climatic Data Center, 2008). The key biokinetic model comparison study (Hogan et al., 1998) targeted three locations in more southerly latitudes (Pennsylvania, southern Illinois, and



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southern Kansas/Missouri) than the five tracer element studies. The biokinetic model comparison methodology had an advantage over the tracer element studies in that the study represented long­term environmental exposures over periods up to several years, that would include a range of seasons and climate conditions. A brief review of the representativeness of the key studies’ samples with respect to gender and age suggested that males and females were represented roughly equally in those studies for which study subjects’ gender was stated. Children up to age 8 years were studied in seven of the nine studies, with an emphasis on younger children. Wong (1988)/Calabrese et al. (1993) and Vermeer and Frate (1979) are the only studies with children 8 years or older. A brief review of the representativeness of the key studies’ samples with respect to socioeconomic status and racial/ethnic identity suggested that there were some discrepancies between the study subjects and the current U.S. population of children age 0 to <21 years. The single survey response study (Vermeer and Frate (1979)) was specifically targeted toward a predominantly rural black population in a particular county in Mississippi. The tracer element studies are of predominantly white populations, apparently with limited representation from other racial and ethnic groups. The Amherst, Massachusetts study (Calabrese et al. 1989/Barnes 1990) did not publish the study participants’ socioeconomic status or racial and ethnic

identities. The socioeconomic level of the Davis et al. (1990) studied children was reported to be primarily of middle to high income. Self­reported race and ethnicity of relatives of the children studied (in most cases, they were the parents of the children studied) in Davis et al. (1990) were White (86.5 percent), Asian (6.7 percent), Hispanic (4.8 percent), Native American (1.0 percent),and Other (1.0 percent), and the 91 married or livingas­married respondents identified their spouses as White (86.8 percent), Hispanic (7.7 percent), Asian (4.4 percent), and Other (1.1 percent). Davis and Mirick(2006) did not state the race and ethnicity of the followup study participants, who were a subset of the original study participants from Davis et al. (1990). For the Calabrese et al. (1997a) study in Anaconda, Montana, population demographics were not presented in the published article. The study sample appeared to have

been drawn from a door­to­door census of Anaconda

residents that identified 642 toilet trained children who were less than 72 months of age. Of the 414 children participating in a companion study (out of the 642 eligible children identified), 271 had complete study data for that companion study, and of these 271, 97.4 percent were identified as white and the remaining 2.6 percent were identified as native American, black, Asian and Hispanic (Hwang et al., 1997). The 64 children in the Calabrese et al. (1997a) study apparently were a stratified random sample drawn from the 642

children identified in the door­to­door census. Presumably these children identified as similar races and ethnicities to the Hwang et al. (1997) study children. The Calabrese et al. (1997b) study indicated

that 11 of the 12 children studied were white.

5.5 SUMMARY OF SOIL AND DUST

INGESTION ESTIMATES FROM KEY

STUDIES
Table 5­19 summarizes the soil and dust ingestion estimates from the 9 key studies. For the U.S. tracer element studies, in order to compare estimates that were calculated in a similar manner, the summary is limited to estimates that use the same basic algorithm of ((fecal and urine tracer content) ­ (food and medication tracer content))/(soil or dust tracer concentration). Note that several of the published reanalyses suggested different variations on these algorithms, or suggest adjustments that should be made for various reasons. However, because individual

observations were not available from the studies with reanalyzed data, those reanalyzed estimates were not included in the summary table. Other reanalyses suggested that omitting some of the data according to

statistical criteria would be a worthwhile exercise. Due to the current state of the science regarding soil and dust ingestion estimates, U.S. EPA does not advise omitting an individual child’s soil or dust ingestion estimate, based on statistical criteria, at this point in

time. There is a wide range of estimated soil and dust ingestion across key studies. Note that some of the soil­pica ingestion estimates from the tracer element

studies were consistent with the estimated mean soil ingestion from the survey response study of geophagy

behavior. Also note that the biokinetic model comparison methodology’s confirmation of central tendency soil and dust ingestion default assumptions corresponded roughly with some of the central tendency tracer element study estimates.



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5.6 REFERENCES FOR CHAPTER 5 Agency for Toxic Substances and Disease Registry (ATSDR). (2001) Summary report for the

ATSDR soil­pica workshop. ATSDR, Atlanta, GA. March 20, 2001. Available

o n l i n e a t : http://www.atsdr.cdc.gov/NEWS/soilpica.html



### . Agency for Toxic Substances and Disease Registry (ATSDR) (2006) Toxicological profile for Aluminum ( Draft for Public Comment ). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Anonymous (1969). Nutritional Status ­ U.S.A. Nutr Rev 27(7): 196­200. Aufreiter, S.; Hancock, R.G.V.; Mahaney, W.C.; Stambolic­Robb, A.; Sanmugadas, K. (1997) Geochemistry and mineralogy of soils eaten by humans. Int J Food Sci Nutr 48(5) 293­

305. Barltrop, D. (1966) The Prevalence of Pica. Amer J

Dis Child 112:116­123. Barnes, R. (1990) Childhood Soil Ingestion: How

M uc h Dirt Do Kids Eat? Anal Chem

62:1024­1033. Binder, S.; Sokal, D.; Maughan, D. (1986) Estimating soil ingestion: the use of tracer elements in estimating the amount of soil ingested by young children. Arch Environ Health 41(6):341­345. Bissé, E.; Epting, T.; Beil, A.; Lindinger, G.; Lang, H.; Wieland, H. (2005) Reference values for

serum silicon in adults. Anal Biochem

337:130­135. Bruhn, C.M.; Pangborn, R.M. (1971) Reported incidence of pica among migrant families. J

Am Diet Assoc 58:417­420. Calabrese, E.J., and Stanek, E.J. (1991) A Guide to Interpreting Soil Ingestion Studies II. Quantitative and QualitativeEvidence of Soil Ingestion. Regul Toxicol Pharmacol 13:278­

292. Calabrese, E.J., and Stanek, E.J. (1992a) Distinguishing outdoor soil ingestion from indoor dust ingestion in a soil picachild. Regul Toxicol

Pharmacol 15:83­85. Calabrese, E.J., and Stanek, E.J. (1992b) What proportion of household dust is derived from outdoor soil? J Soil Contam 1(3):253­263. Calabrese, E.J., and Stanek, E.J. (1993) Soil pica: not

a rare event. J Environ Sci Health A28(2):373­384. Calabrese, E.J., and Stanek, E.J. (1995) Resolving intertracer inconsistencies in soil ingestion estimation. Environ Health Perspect 103(5):454­456. Calabrese, E.J.; Barnes, R.; Stanek, E.J. III; Pastides, H.; Gilbert, C.; Veneman, P.; Wang, X.; Lásztity, A.; Kostecki, P.T. (1989) How much soil do young children ingest: an epidemiologic study. In: Petroleum Contaminated Soils, Lewis Publishers, Chelsea, MI. pp. 363­397 and in: Regul

Toxicol Pharm 10:123­137. Calabrese, E.J.; Stanek, E.J.; Gilbert, C.E.; and Barnes,

R. (1990) Preliminary adult soil ingestion estimates: Results of a pilot study. Regul

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Evidence of Soil­Pica behavior and Quantification of Soil Ingested. Hum Exp

Toxicol 10:245­249. Calabrese, E.J.; Stanek, E.J.; Barnes, R.M. (1996) Methodology to estimate the amount and particle size of soil ingested by children: Implications for exposure assessment at waste sites. Regul Toxicol Pharmacol 24: 264­268. Calabrese, E.J.; Stanek, E.J.; Pekow, P.; Barnes, R.M. (1997a) Soil ingestion estimates for children residing on a Superfund site. Ecotoxicol

Environ Saf 36:258­268. Calabrese, E.J.; Stanek, E.J.; Barnes, R.M. (1997b) Soil ingestion rates in children identified by parental observation as likely high soil ingesters. J Soil Contam 6(3): 271­279. Carlisle, E. (1980) Biochemical and Morphological Changes Associated with Long Bone Abnormalities in Silicon Deficiency. J Nutr

110:1046­1055. Clausing, P.; Brunekreef, B.; Van Wïjnen, J.H. (1987) A method for estimating soil ingestion by children. Int Arch Occup Environ Health 59(1):73­82. Colquitt, P.J. (2002) Do powered toothbrushes contaminate toothpaste with metals? Sci Total

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and anthropology, the present study of pica in young children, and a discussion of its pediatric and psychological implications. Springfield, IL: Charles C. Thomas. Corbett, R.W.; Ryan, C.; Weinrich, S.P. (2003) Pica in pregnancy: does it affect pregnancy outcomes? MCM Am J Matern Child Nurs. 28(3):183­

189. Danford, D.E. (1982) Pica and nutrition. Annu Rev

Nutr 2:303­322. Danford, D.E. (1983) Pica and zinc. Zinc Deficiency in Human Subjects, pp. 185­195. New York:

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W.J. (1978) Relationship of metal metabolism to vascular disease mortality rates in Texas. Am J Clin Nutr 31(7):1188­1197. Dickens, D. and Ford, R.N. (1942) Geophagy (Dirt Eating) Among Mississippi Negro School

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Rev 7:59­65. Dominy, N.J.; Davoust, E.; Minekus, M. (2003) Adaptive function of soil consumption: an in vitro study modeling the human stomach and small intestine. J Exp Biol 207(Pt 2): 319­324. Geissler, P.W.; Mwaniki, D.L.; Thiong’o, F.; Michaelsen, K.F., Friis, H. (1998) Geophagy, Iron Status and Anaemia Among Primary School Children in Western Kenya. Trop Med Int Health 3(7):529­534. Glickman, L.T., Chaudry, I.U., Costantino, J., Clack, F.B., Cypess. R.H., Winslow, L. (1981) Pica Patterns,Toxocariasis, and Elevated Blood Lead in Children. Am J Trop Med Hyg 30(1):77­80. Gulson, B.L.; Mizon, K.J.; Palmer, J.M.; Patison, N.; Law, A.J.; Korsch, M.J.; Mahaffey, K.R.; Donnelly, J.B. (2001) Longitudinal Study of Daily Intake and Excretion of Lead in Newly

Born Infants. Environ Res Sec A 85:232­245. Halsted, J.A. (1968) Geophagia in Man: Its Nature and Nutritional Effects. Am J Clin Nutr 21(12):

1381­1393. Harris, S.G.; Harper, B.L. (1997) A Native American Exposure Scenario. Risk Anal 17(6):789­795. Hawley, J.K. (1985) Assessment of Health Risk from Exposure to Contaminated Soil. Risk Anal 5(4): 289­302. Hertz, H. (1946) Notes on Clay and Starch Eating Among Negroes in a Southern Urban Community. Soc F 25:343­344. Hogan, K.; Marcus, A.; Smith, R.; White, P. (1998) Integrated exposure uptake biokinetic model

for lead in children: empirical comparisons with epidemiologic data. Environ Health Perspect 106(Supp 6): 1557­1567. Hooda, P.; Henry, C.; Seyoum, T.; Armstrong, L.; and Fowler, M. (2004) The potential impact of soil ingestion on

human mineral nutrition. Sci Total Environ

333:74­87. Hwang, Y.­H.; Bornschein, R.L.; Grote, J.; Menrath, W.; and Roda, S. (1997) Environmental Arsenic Exposure of Children around a Former Copper Smelter

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values: ICRP Publication 89. Annals of the ICRP 32 (3­4):1­277. Johns, T.; Duquette, M. (1991) Detoxification and mineral supplementation as functions of geophagy. Am J Clin Nutr 53:448­456. Jugdaohsingh, R.; Anderson, S.; Tucker, K.; Elliott, H.; Kiel, D.; Thompson, R.; Powell, J. (2002) Dietary silicon intake and absorption. Am J Clin Nutr 75(5): 887­893. Kinnell, H.G. (1985) Pica as a Feature of Autism. Br J Psychiatry 147:80­82. Kissel, J.C., Shirai, J.H., Richter, K.Y., Fenske, R.A.(1998) Empirical Investigation of Hand­toMouth Transfer of Soil Bull Environ Contam

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Korman, S. (1990) Pica as a presenting symptom in

childhood celiac disease. Am J Clin Nutr

51:139­141. Lacey, E. (1990) Broadening the Perspective of Pica: Literature Review. Public Health Rep 105(1):29­35. Lourie, R.S.; Layman, E.M.; Millican, F.K. (1963) Why children eat things that are not food.

Children 10:143­146. Manton, W.I.; Angle, C.R.; Stanek, K.L.; Reese, Y.R.; Kuehnemann, T.J. (2000) Acquisition and Retention of Lead by Young Children.

Environ Res Sec A 82:60­80. Marmor, M., Glickman, L., Shofer, F., Faich, L.A., Rosenberg, C., Cornblatt, B., Friedman, S. (1987) Toxocara canis Infection of Children: Epidemiologic and Neuropsychologic Findings. Am J Public Health 77:554­559. Minnich, V.; Okçuo — 

### lu, A.; Tarcon, Y.; Arcasoy, A.; Cin, S.; Yörüko — 

### lu, O.; Renda, F.; Demira —



### , B. (1968) Pica in Turkey. Am J Clin Nutr 21(1): 78­86. Mølhave, L.; Schneider, T.; Kjærgaard, S.K.; Larsen, L.; Norn, S.; Jørgensen, O. (2000) House dust in seven Danish offices. Atmospher Environ

34:4767­4779. National Academy Press (2000) Dietary Reference Intakes for Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium, and Zinc. Washington, D.C.:

2000. National Climatic Data Center (2008). U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data and Information Service. Data Set TD3200: U.S. Cooperative Summary of the Day Data, Daily Surface

Data. Accessed online at: http://www.ncdc.noaa.gov/oa/climate/station

locator.html on March 7 ­ 13, 2008. Nelson, S.; Greene, T.; Ernhart, C.B. (1996) Toxocara

canis Infection in Preschool Age Children: Risk Factors and the Cognitive Development

of Preschool Children. Neurotoxicol Teratol 18(2):167­174. Parnell, C.B.; Jones, D.D.; Rutherford, R.D.; Goforth,

K.J. (1986) Physical Properties of Five Grain Dust Types. Environ Health Perspect 66:183­

188. Powell, J.J.; Ainley, C.C.; Harvey, R.S.; Mason, I.M.; Kendall, M.D.; Sankey, E.A.; Dhillon, A.P.; Thompson, R.P. (1996) Characterisation of inorganic microparticles in pigment cells of human gut associated lymphoid tissue. Gut (38):390­395. Rainville, A.J. (1998) Pica practices of pregnant

women are associated with lower maternal hemoglobin level at delivery. J Am Diet Assoc 98(3):293­296.Robischon, P. (1971) Pica Practice and Other HandMouth Behavio r and Children’s Developmental Level. Nurs Res 20(1):4­16. Shacklette, H.T.; Boerngen, J.G. (1984) Element

Concentrations in Soils and Other Surficial

Materials of the Conterminous United States. United States Geological Survey Professional Paper 1270. Washington, D.C.: U.S. Government Printing Office. Smulian, J.C.; Motiwala, S.; Sigman, R.K. (1995) Pica in a rural obstetric population. South Med J 88(12):1236­1240. Shepherd, N.A.; Crocker, P.R.; Smith, A.P.; Levison,

D.A. (1987) Exogenous pigment in Peyer’s patches. Hum Pathol 18(1):50­54. Stanek, E.J., and Calabrese, E.J. (1995a) Daily

es tima tes of soil ingestion in children. Environ Health Perspect 103(3):276­285. Stanek, E.J., and Calabrese, E.J. (1995b) Soil ingestion

estimates for use in site evaluations based on

the best tracer method. Hum Ecol Risk Assess

1:133­156. Stanek, E.J., and Calabrese, E.J. (2000) Daily soil ingestion estimates for children at a Superfund site. Risk Anal 20(5): 627­635. Stanek, E.J.; Calabrese, E.J.; Mundt, K.; Pekow, P.; Yeatts, K.B. (1998) Prevalence of Soil Mouthing/Ingestion among Healthy Children Aged 1 to 6. J Soil Contam 7(2):227­242. Stanek, E.J.; Calabrese, E.J.; Barnes, R. (1999) Soil ingestion estimates for children in Anaconda using trace element concentrations in different particle size fractions. Human Ecol Risk Assess 5(3): 547­558. Stanek, E.J.; Calabrese, E.J.; Zorn, M. (2001a) Biasing factors for simple soil ingestion estimates in mass balance studies of soil ingestion. Hum Ecol Risk Assess 7(2): 329­355. Stanek, E.J.; Calabrese, E.J.; Zorn, M. (2001b) Soil

in ge stion distributions for Monte Carlo risk



### Page Child­Specific Exposure Factors Handbook

5­28 September 2008

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­ Ingestion of Soil and Dust

assessment in children. Hum Ecol Risk Assess 7(2): 357­368. USDA. (2008) Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture, Web Soil Survey, accessed February 25, 2008.

U.S. DHHS (1981a) U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Health Statistics. Public Use Data Tape Do cumentation: Medical Histo r y Questionnaire, Ages 1­11, Tape Number

4067. National Health and Nutrition Examination Survey, 1971­1975. May 1981. Hyattsville, MD.

U.S. DHHS (1981b) U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Health Statistics. Public Use Data Tape Documentatio n: M ed ical History Questionnaire, Ages 12­74, Tape Number

4081. National Health and Nutrition Examination Survey, 1971­1975. May 1981. Hyattsville, MD.

U.S. DHHS (1985a) U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Health Statistics. Public Use Data Tape Documentation: Medical History 6 Months ­ 11 Years, Tape Number 5010. National Health and Nutrition Examination Survey, 1976­1980. May 1985. Hyattsville, MD.

U.S. DHHS (1985b) U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Health Statistics. Public Use Data Tape Documentation: Medical History Ages 12­74 Years, Tape Number 5020. National Health and Nutrition Examination Survey, 1976­ 1980. May 1985. Hyattsville, MD.

U.S. DOC (2008) U.S. Department of Commerce,

B ur eau of the Census, Population Division. Table 2: Annual Estimates of the Population by Sex and Selected Age Groups for the United States: April 1, 2000 to July 1, 2007 (NC­EST2007­02).

U.S. EPA (1994a) Guidance Manual for the IEUBK Model for Lead in Children. Washington, DC: U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response. EPA 540/R­93/081.

U.S. EPA (1994b) Technical Support Document:

P ar ameters and Equations Used in the Integrated Exposure Uptake Biokinetic (IEUBK) Model for Lead in Children (v 0.99d). Washington, D.C.: U.S. Environmental Protection Agency, Technical Review Workgroup for Lead with assistance

from the Environmental Criteria and Assessment Office, Research Triangle Park,

NC. EPA 9285.7­22.

U.S. EPA. (1996) Soil Screening Guidance: User’s

G uid e. Washington, DC: U.S. Environmental Protection Agency, Office of Solid Waste and EmergencyResponse. Publication 9355.4­23.

U.S. EPA. (1997) Exposure Factors Handbook

R ev ised. Washington, DC: U.S. Environmental Protection Agency, Office ofResearch and Development. EPA/600/P95/002F.

U.S. EPA. (2005) Guidance on selecting age groups for monitoring and assessing childhood exposures to environmental contaminants. Washington, DC.: U.S. Environmental Protection Agency, Office of Research and Development.

EPA/630/P­03/003F. Van Dyck, K.; Robberecht, H.; Van Cauwenburgh, R.; Van Vlaslaer, V.; Deelstra, H. (2000) Indication of silicon essentiality in humans: serum concentrations in Belgian children and adults, including pregnant women. Biol Trace Elem Res 77(1):25­32. Van Wïjnen, J.H.; Clausing, P.; Brunekreff, B. (1990) Estimated soil ingestion by children. Environ

Res 51:147­162. Vermeer, D.E.; Frate, D.A. (1979) Geophagia in rural Mississippi: environmental and cultural contexts and nutritional implications. Am J

Clin Nutr 32:2129­2135. Von Lindern, I.; Spalinger, S.; Petroysan, V.; von Braun, M. (2003) Assessing remedial effectiveness through the blood lead:soil/dust lead relationship at the Bunker Hill Superfund Site in the Silver Valley of Idaho. Sci Total Environ 303(1­2): 139­170. Wilson, M.J. Clay Mineralogical and Related

C ha racteristics of Geophagic Materials. (2003) J Chem Ecol 29(7):1525­1547.



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Wong, E.Y., Shirai, J.H., Garlock, T.J., and Kissel, J.C. (2000) Adult proxy responses to a survey of children’s dermal soil contact activities. J Exp Anal Environ Epidem 10:509­517. Wong, M.S. (1988) The Role of Environmental and Host Behavioural Factors in Determining Exposure to Infection with Ascaris

lumbricoldes and Trichuris trichiura. Ph.D. Thesis, Faculty of Natural Sciences, University of the West Indies. 1988.



### Page Child­Specific Exposure Factors Handbook

5­30 September 2008Table 5­3. Soil, Dust and Soil + Dust Ingestion Estimates for Amherst, Massachusetts Study Children

Tracer Element N Ingestion (mg/day)

Mean Median SD 95th Percentile Maximum

Aluminum

soil

dust

soil/dust

combined

Barium

soil

dust

soil/dust

combined

Manganese

soil

dust

soil/dust

combined

Silicon

soil

dust

soil/dust

combined

Vanadium

soil

dust

soil//dust

combined

Yttrium

soil

dust

soil/dust

combined

Zirconium

soil

dust

soil/dust

combined

Titanium

soil

dust

soil/dust

combined 64

64

64

64

64

64

64

64

64

64

64

64

62

64

62

62

64

62

62

64

62

64

64

64 153

317

154

32

31

29

­294 ­1,289

­496

154

964

483

459

453

456

85

62

65

21

27

23

218

163

170 29

31

30

­37

­18

­19

­261

­340

­340

40

49

49

96

127

123

9

15

11

16

12

11

55

28

30 852 1,272

629

1,002

860

868

1,266 9,087 1,974

693 6,848 3,105

1,037 1,005 1,013

890

687

717

209

133

138

1,150

659

691 223

506

478

283

337

331

788 2,916 3,174

276

692

653

1,903 1,918 1,783

106

169

159

110

160

159

1,432 1,266 1,059 6,837 8,462 4,929

6,773 5,480 5,626

7,281 20,575 4,189

5,549 54,870 24,900

5,676 6,782 6,736

6,736 5,096 5,269

1,391

789

838

6,707 3,354 3,597

SD

Source: = Standard deviation.

Calabrese et al., 1989. 

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Child­Specific Exposure Factors Handbook Page September 2008 5­31Table 5­4. Amherst, Massachusetts Soil­Pica Child’s Daily Ingestion Estimates by Tracer and by Week (mg/day)

Tracer Estimated Soil Ingestion (mg/day)

element Week 1 Week 2

Al 74 13,600

Ba 458 12,088

Mn 2,221 12,341

Si 142 10,955

Ti 1,543 11,870

V 1,269 10,071

Y 147 13,325

Zr 86 2,695

Source: Calabrese et al., 1991. 

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Table 5­5. Amherst, Massachusetts Soil­Pica Child’s Tracer Ratios

Tracer Pairs Ratio

Soil Fecal Dust Estimated Residual Fecal Tracers of Soil Origin as

Predicted by Specific

Tracer Ratios (%)

1. Mn/Ti

2. Ba/Ti

3. Si/Ti

4. V/Ti

5. Ai/Ti

6. Y/Ti

7. Mn/Y

8. Ba/Y

9. Si/Y

10. V/Y

11. Al/Y

12. Mn/Al

13. Ba/Al

14. Si/Al

15. V/Al

16. Si/V

17. Mn/Si

18. Ba/Si

19. Mn/Ba 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

1.112 215.241

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

1.044 260.126

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

Source: Calabrese and Stanek, 1992.



### Child­Specific Exposure Factors Handbook Page September 2008 5­33Table 5­6. Van Wïjnen et al., 1990 Limiting Tracer Method (LTM) Soil Ingestion Estimates for Sample of Dutch Children

Age (years) Sex Daycare Centers Campgrounds

N GM LTM (mg/day) GSD LTM (mg/day) N GM LTM (mg/day) GSD LTM (mg/day)

Birth to <1

1 to <2

2 to <3

3 to <4

4 to <5

All girls All boys

Total Girls Boys

Girls Boys

Girls Boys

Girls Boys

Girls Boys 3

1

20

17

34

17

26

29

1

4

86

72

162 a 81

75

124

114

118

96

111

110

180

99

117

104

111 1.09

­

1.87

1.47

1.74

1.53

1.57

1.32

­ 1.62

1.70

1.46

1.60 NA

NA

3

5

4

8

6

8

19

18

36

42

78 b NA

NA

207

312

367

232

164

148

164

136

179

169

174 NA

NA

1.99

2.58

2.44

2.15

1.27

1.42

1.48

1.30

1.67

1.79

1.73

a

b

N

GM

LTM

GSD

NA

Source: Age and/or sex not registered for 8 children; one untransformed value = 0. Age not registered for 7 children; geometric mean LTM value = 140. = Number of subjects.

= Geometric mean. = Limiting tracer method.

= Geometric standard deviation.

= Not available.

Adapted from Van Wïjnen et al., 1990. 

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Page Child­Specific Exposure Factors Handbook

5­34 September 2008Table 5­7. Estimated Geometric Mean Limiting Tracer Method (LTM) Values of Children Attending Daycare Centers According to Age, Weather Category, and Sampling Period

Weather Category Age (years) First Sampling Period Second Sampling Period

N Estimated

Geometric Mean

LTM Value

(mg/day) Estimated

Geometric Mean N LTM Value
(mg/day)

Bad
(>4 days/week
precipitation)

Reasonable
(2­3 days/week
precipitation)

Good
(<2 days/week
precipitation) <1

1 to <2

2 to <3

4 to <5

<1

1 to <2

2 to <3

3 to <4

4 to <5

<1

1 to <2

2 to <3

3 to <4

4 to <5 3

18

33

5

4

42

65

67

10 94

103

109

124

102

229

166

138

132 3

33

48

6

1

10

13

19

1 67

80

91

109

61

96

99

94

61

N

LTM

Source: = Number of subjects. = Limiting tracer method.

Van Wïjnen et al., 1990. 

### Child­Specific Exposure Factors Handbook

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Tab le 5­8. Estimated Soil Ingestion for Sample of Washington State Children a

Element Mean (mg/day) Median (mg/day) Standard Error of the

Mean (mg/day) Range (mg/day) b

Aluminum 38.9 25.3 14.4 ­279.0 to 904.5

Silicon 82.4 59.4 12.2 ­404.0 to 534.6

Titanium 245.5 81.3 119.7 ­5,820.8 to 6,182.2

Minimum 38.9 25.3 12.2 ­5,820.8

Maximum 245.5 81.3 119.7 6,182.2

a Excludes three children who did not provide any samples (N=101).

b Negative values occurred as a result of correction for non­soil sources of the tracer elements. For aluminum, lower end of range published as 279.0 mg/day in article appears to be a typographical error that omitted the negative sign.

Source: Adapted from Davis et al., 1990.



### Child­Specific Exposure Factors Handbook Page September 2008 5­35Table 5­9. Soil Ingestion Estimates for 64 Anaconda Children

Estimated Soil Ingestion (mg/day)

Tracer

P1 P50 P75 P90 P95 Max Mean SD

Al ­202.8 ­3.3 17.7 66.6 94.3 461.1 2.7 95.8

Ce ­219.8 44.9 164.6 424.7 455.8 862.2 116.9 186.1

La ­10,673 84.5 247.9 460.8 639.0 1,089.7 8.6 1,377.2

Nd ­387.2 220.1 410.5 812.6 875.2 993.5 269.6 304.8

Si ­128.8 ­18.2 1.4 36.9 68.9 262.3 ­16.5 57.3

Ti ­15,736 11.9 398.2 1,237.9 1,377.8 4,066.6 ­544.4 2,509.0

Y ­441.3 32.1 85.0 200.6 242.6 299.3 42.3 113.7

Zr ­298.3 ­30.8 17.7 94.6 122.8 376.1 ­19.6 92.5

P = Percentile.

SD = Standard deviation.

Note: Negative values are a result of limitations in the methodology.

Source: Calabrese et al., 1997a. 

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Tabl e 5­10. Soil Ingestion Estimates for Massachusetts Child Displaying Soil Pica Behavior (mg/day)

Study day Al­based estimate Si­based estimate Ti­based estimate

1 53 9 153

2 7,253 2,704 5,437

3 2,755 1,841 2,007

4 725 573 801

5 5 12 21

6 1,452 1,393 794

7 238 92 84

Source: Calabrese et al., 1997b.



### Page Child­Specific Exposure Factors Handbook

5­36 September 2008Table 5­11. Soil Ingestion Estimates for Sample of 12 Washington State Children a

TracerElement Estimated Soil Ingestion (mg/day) b

Mean Median SD Maximum

Aluminum 36.7 33.3 35.4 107.9

Silicon 38.1 26.4 31.4 95.0

Titanium 206.9 46.7 277.5 808.3

a

b

SD For some study participants, estimated soil ingestion resulted in a negative value. for tabulation and analysis. Results based on 12 children with complete food, excreta and soil data.

= Standard deviation. These estimates have been set to zero mg/day

Source: Davis and Mirick, 2006. 

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Child­Specific Exposure Factors Handbook Page September 2008 5­37Table 5­12. Estimated Soil Ingestion for Six High Soil Ingesting Jamaican Children

Child Month Estimated soil ingestion (mg/day)

11

12

14

18

22

27 1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4 55 1,447

22

40

0

0 7,924

192

1,016

464 2,690

898

30 10,343 4,222 1,404

0

­ 5,341

0

48,314 60,692 51,422 3,782

­

Source: = No data.

Calabrese and Stanek, 1993. 

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5­38 September 2008

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Table 5­13. Estimated Daily Soil Ingestion for East Helena, Montana Children

Estimation

Method Mean (mg/day) Median (mg/day) Standard

Deviation (mg/day) Range (mg/day) 95th Percentile (mg/day) Geometric Mean (mg/day)

Aluminum 181 121 203 25­1,324 584

Silicon 184 136 175 31­799 578

Titanium 1,834 618 3,091 4­17,076 9,590

Minimum 108 88 121 4­708 386 128

130

401

65

Source: Binder et al., 1986.

Table 5­14. Estimated Soil Ingestion for Sample of Dutch Nursery School Children

Child Sample

Number Soil Ingestion as

Calculated from Ti (mg/day) Soil Ingestion as

Calculated from Al (mg/day) Soil Ingestion as

Calculated from

AIR (mg/day) Limiting Tracer (mg/day)

1 L3

L14

L25 103

154

130 300

211

23 107

172

­

2 L5

L13

L27 131

184

142 ­ 103

81 71

82

84

3 L2

L17 124

670 42

566 84

174

4 L4

L11 246 2,990 62

65 145

139

5 L8

L21 293

313 ­

­ 108

152

6 L12

L16 1,110

176 693

­ 362

145

7 L18

L22 11,620 11,320 ­ 77 120

­

8

9

10

11

12

13

14

15

16

17

18 L1

L6

L7

L9

L10

L15

L19

L20

L23

L24

L26 3,060

624

600

133

354 2,400

124

269 1,130

64

184 82

979

200

­ 195

­ 71

212

51

566

56 96

111

124

95

106

48

93

274

84

­

­

Arithmetic Mean 1,431 232 129 103

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

­ = No data.

Source: Adapted from Clausing et al., 1987.



### Child­Specific Exposure Factors Handbook Page September 2008 5­39Table 5­15. Estimated Soil Ingestion for Sample of Dutch Hospitalized, Bedridden Children

Child Sample Soil Ingestion as

Calculated from Ti (mg/day) Soil Ingestion as

Calculated from Al (mg/day) Limiting Tracer (mg/day)

1

2

3

4

5

6

Arithmetic Mean G5

G6

G1

G2

G8

G3

G4

G7 3,290 4,790

28

6,570 2,480

28

1,100

58

2,293 57

71

26

94

57

77

30

38

56 57

71

26

84

57

28

30

38

49

Source: Adapted from Clausing et al., 1987.

Table 5­16. Positive/negative Error (Bias) in Soil Ingestion Estimates in Calabrese et al. (1989) Study:

­ Effect on Mean Soil Ingestion Estimate (mg/day) a ­

Tracer Negative Error

Lack of Fecal

Sample on Final Study Day b 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

6 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

a

b How to read table: forexample, 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. Values indicate impact on mean of 128­subject­weeks in milligrams of soil ingested per day.

Source: Calabrese and Stanek, 1995. 

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Table 5­17. Distribution of Average (Mean) Daily Soil Ingestion Estimates per Child for 64 Children a (mg/day)

Type of Estimate Overall A1 Ba Mn Si Ti V Y Zr

Number of Samples 64 64 33 19 63 56 52 61 62

Mean 179 122 655 1,053 139 271 112 165 23

25th Percentile 10 10 28 35 5 8 8 0 0

50th Percentile 45 19 65 121 32 31 47 15 15

75th Percentile 88 73 260 319 94 93 177 47 41

90th Percentile 186 131 470 478 206 154 340 105 87

95th Percentile 208 254 518 17,374 224 279 398 144 117

Maximum 7,703 4,692 17,991 17,374 4,975 12,055 845 8,976 208

a For each child, estimates of soil ingestion were formed on days 4­8 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.

Table 5­18. Estimated Distribution of Individual Mean Daily Soil Ingestion Based on Data for 64 Subjects Projected over 365 Days a

Range 1 ­ 2,268 mg/d b

50th Percentile (median) 75 mg/d

90th Percentile 1,190 mg/d

95th Percentile 1,751 mg/d

a Based on fitting a log­normal distribution to model daily soil ingestion values.

b Subject with pica excluded.

Source: Stanek and Calabrese, 1995a.



### Child­Specific Exposure Factors Handbook Page September 2008 5­41

### Child­Specific Exposure Factors Handbook

Chapter 5 ­ Ingestion of Soil and Dust

Table 5­ 19. Summary of Estimates of Soil and Dust Ingestion by Children (0.5­14 years old) from Key Studies (mg/day)

Sample

Size Age (years) Ingestion

medium Mean P25 P50 P75 P90 P95 Reference

292

101

64

12

64

478

140

52 0.1 ­ < 1 Soil 0 to 30 a NR NR NR NR NR Van Wijnen et al., 1990

1 ­ < 5 Soil 0 to 200 a NR NR NR # 300 NR

2­ < 8 Soil 39 to 246 NR 25 to 81 NR NR NR Davis et al.,

1990

Soil and Dust 65 to 268 NR 52 to 117 NR NR NR

1­ < 4 Soil ­294 to +459 NR ­261 to +96 NR 67 to 1,366 106 to 1,903 Calabrese et al.,

1989
Dust ­1,289 to +964 NR ­340 to +127 NR 91 to 1,700 160 to 2,916

Soil and Dust ­496 to +483 NR ­340 to +456 NR 89 to 1,701 159 to 3,174

3­<8 Soil 37 to 207 NR 26 to 47 NR NR NR Davis and Mirick, 2006

1­ < 4 Soil ­544 to +270 ­582 ­ +65 ­31 to +220 1 to 411 37 to 1,238 69 to 1,378 Calabrese et al.,

1997a

<1 ­ < 7 Soil and Dust 113 NR NR NR NR NR Hogan et al.,

1998

1 ­ 13+ Soil 50,000 b NR NR NR NR NR Vermeer and Frate, 1979

0.3 ­ 14 Soil NR NR NR NR ~1,267 ~4,000 Wong (1988)/Calabres

e and Stanek (1993)

a

b

NR Geometric mean. Average includes adults and children. = Not reported.



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Chapter 6

- Inhalation Rates

6 INHALATION RATES

6.1 INTRODUCTION Ambient and indoor air are potential sources of children’s exposure to toxic substances. Children can be exposed to contaminated air during a variety of activities in different environments. Children may be exposed due to sources that contribute pollution to ambient air. Children may also inhale chemicals from the indoor use of various consumer products. Due to their size, physiology, and activity level, the inhalation

rates of children differ from those of adults. Infants and children have a higher resting metabolic rate and oxygen consumption rate per unit of body weight than adults, because of their rapid growth and relatively larger lung surface area per unit of body weight that requires cooling. For example, the oxygen consumption rate for a resting infant between one week and one year of age is 7 milliliters per kilogram of body weight (mL/kg) per minute, while the rate for an adult under the same conditions is 3-5 mL/kg per minute (WHO, 1986). Thus, while greater amounts of air and pollutants are inhaled by adults than children over similar time periods on an absolute basis, the volume of air passing through the lungs of a resting infant is up to twice that of a resting adult on a body weight basis. The Agency defines exposure as the chemical concentration at the boundary of the body (U.S. EPA, 1992). In the case of inhalation, the situation is complicated by the fact that oxygen exchange with carbon dioxide takes place in the distal portion of the lung. The anatomy and physiology of the respiratory system as well as the characteristics of the inhaled agent diminishes the pollutant concentration in inspired air (potential dose) such that the amount of a pollutant that actually enters the body through the lung (internal dose) is less than that measured at the boundary of the body. A detailed discussion of this concept can be found in Guidelines for Exposure Assessment (U.S. EPA, 1992). When constructing risk assessments that concern the inhalation route of exposure, one must be aware of any adjustments that have been employed in the estimation of the pollutant concentration to account for this reduction in potential dose. Children’s inhalation dosimetry and health effects were topics of discussion at a U.S. EPA workshop held in June 2006 (Foos and Sonawane, 2008). Age related differences in lung structure and function, breathing patterns, and how these affect the inhaled dose and the deposition of particles in the lung are important factors in assessing risks from inhalation exposures (Foos et al., 2008). Children may have a lesser nasal contribution to breathing during rest and while performing various activities. The uptake of particles in the nasal airways is also less efficient in children. Thus, the deposition of particles in the lower respiratory tract may be greater (Foos et al., 2008). Inclusion of this chapter in the Child-Specific Exposure Factors Handbook does not imply that assessors will always need to select and use inhalation rates when evaluating exposure to air contaminants. For example, it is unnecessary to calculate inhaled dose when using dose-response factors from the Integrated Risk Information System (IRIS) (U.S. EPA, 1994), because the IRIS methodology accounts for inhalation rates in the development of “dose-response” relationships. Information in this chapter may be used by toxicologists in their derivation of human equivalent concentrations. When using IRIS for inhalation risk assessments, “dose-response” relationships require only an average air concentration to evaluate health

concerns:

• For non-carcinogens, IRIS uses Reference Concentrations (RfCs) which are expressed in concentration units. Hazard is evaluated by comparing the inspired air concentration to the

RfC.

• For carcinogens, IRIS uses unit risk values which are expressed in inverse concentration units. Risk is evaluated by multiplying the unit risk by the inspired air concentration.

Detailed descriptions of the IRIS methodology for

derivation of inhalation reference concentrations can be found in two methods manuals produced by the Agency (U.S. EPA, 1992; 1994). The Superfund Program has also updated its approach for determining inhalation risk, eliminating the use of inhalation rates when evaluating exposure to air contaminants (U.S. EPA, 2008). The current methodology recommends that risk assessors use the concentration of the chemical in air as the exposure



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Chapter 6

- Inhalation Rates

metric (e.g., mg/m 3 

### ), instead of the intake of a contaminant in air based on inhalation rate and body weight (e.g., mg/kg-day). Recommended inhalation rates (both long- and short-term) are provided in the next section, along with the confidence ratings for these recommendations. These recommendations are based on four key studies identified by U.S. EPA for this factor. Long-term exposure is repeated exposure for more than 30 days, up to approximately 10% of the life span in humans (more than 30 days). Long-term inhalation rates for children (including infants) are presented as daily rates (m 3 

### /day). Short-term exposure is repeated exposure for more than 24 hours, up to 30 days. Short-term inhalation rates are reported for children (including infants) performing various activities in m 3 

### /minute. Following the recommendations, the available studies (both key and relevant studies) on inhalation rates are

summarized.

6.2 RECOMMENDATIONS

The recommended in halation rates for children

are based on four recent studies: Brochu et al., 2006;

U.S. EPA, 2006; Arcus-Arth and Blaisdell, 2007; and Stifelman, 2007. These studies represent an improvement upon those previously used for

recommended inhalation rates in this handbook,

because they use a large data set that is representative

of the United States as a whole and consider the

correlation between body weight and inhalation rate.
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 long-term values for children (including infants) for use in various exposure scenarios are presented in Table 6-1 for the standard U.S. EPA

childhood age groups used in this handbook. As

shown in Table 6-1, the daily average inhalation rates
for long-term exposures for male and female children

combined (unadjusted for body weight) range from 3.6

m 3 

### /day for children from birth to <1 month to 16.5

m 3 

### /day for children aged 16 to <21 years. These values
represent averages of the inhalation rate data from the

four key studies. The 95 th 

### percentile values range from

7.1 m 3 

### /day to 27.6 m 3 

### /day for the same age categories.

The 95 th 

### percentile values represent averages of the inha la tion rate data from the three key studies for which

95 th 

### percentile values were available for selected agegroups (Brochu et al., 2006; U.S. EPA, 2006; ArcusArth and Blaisdell, 2007). It should be noted that there may be a high degree of uncertainty associated with the upper percentiles. These values equate to unusually high estimates of caloric intake per day, and are unlikely to be representative of the average child. For example, using Layton’s equation (Layton, 1993) for estimating metabolically consistent inhalation rates to calculate caloric equivalence (see Section 6.4.6), the

95 th 

### percentile value for 16 to <21 year old children is 4,840 kcal/day. All of the 95 th 

### percentile values listed in Table 6-1 may represent unusually high inhalation rates for long-term exposures, even for the upper end of the distribution, but were included in this handbook to provide exposure assessors a sense of the possible range

of inhalation rates for children. These values should be used with caution when estimating long-term exposures. For short-term exposures for children aged 21 years and under, for which activity patterns are known,

mean and 95 th 

### percentile data are provided in Table 6-2 for males and females combined, in m 3 

### /minute. These values represent averages of the activity level data from the one key study from which short-term inhalation rate data were available (U.S. EPA, 2006). The confidence ratings for the inhalation rate recommendations are shown in Table 6-3. Multiple percentiles for long- and short-term inhalation rates for both males and females are provided in Tables 6-5 through 6-11 and Table 6-16.



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Table 6-1. Recommended Long-Term Exposure (More Than 30 Days) Values for Inhalation (Males and Females Combined).

Age Group Mean

m 3 /day Sources Used

for Means 95 th Percentile

m 3 /day Sources Used

for 95 th Percentiles Multiple Percentiles

Birth to <1 month 3.6 a 7.1 a

See Tables 6-5 through 6-11 and 6-16 1 to <3 months - b

- - -
3 to <6 months 4.1 a,c 6.1 a,c

6 to <12 months 5.4 a,c 8.1 a,c
1 to <2 years 8.0 a,c,d,e 12.8 a,c,d

2 to <3 years 9.5 a,d,e 15.9 a,d

3 to <6 years 10.9 a,d,e 16.2 a,d

6 to <11 years 12.4 a,d,e 18.7 a,d

11 to <16 years 15.1 a,d,e 23.5 a,d

16 to <21 years 16.5 a,d,e 27.6 a,d

a Arcus-Arth and Blaisdell, 2007.

b No data for this age group.

c Brochu et al., 2006.

d U.S. EPA, 2006.

e Stifelman, 2007.

Note: Some 95 th percentile values may be unusually high, and may not be representative of the average child.



### Child-Specific Exposure Factors Handbook Page September 2008 6-3Table 6-2. Recommended Short-Term Exposure (Less Than 30 Days) Values for Inhalation (Males and Females Combined)

Activity Level Age Group

years Mean

m 3 /minute 95 th Percentile

m 3 /minute Multiple Percentiles

Sleep or Nap

Sedentary/

Passive

Light Intensity

Moderate Intensity

High Intensity Birth to <1 year
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
Birth to <1 year
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
Birth to <1 year
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
Birth to <1 year
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
Birth to <1 year
1 to <2 years
2 to <3 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years 3.0E-03

4.5E-03

4.6E-03

4.3E-03

4.5E-03

5.0E-03

4.9E-03

3.1E-03

4.7E-03

4.8E-03

4.5E-03

4.8E-03

5.4E-03

5.3E-03

7.6E-03

1.2E-02

1.2E-02

1.1E-02

1.1E-02

1.3E-02

1.2E-02

1.4E-02

2.1E-02

2.1E-02

2.1E-02

2.2E-02

2.5E-02

2.6E-02

2.6E-02

3.8E-02

3.9E-02

3.7E-02

4.2E-02

4.9E-02

4.9E-02 4.6E-03

6.4E-03

6.4E-03

5.8E-03

6.3E-03

7.4E-03

7.1E-03

4.7E-03

6.5E-03

6.5E-03

5.8E-03

6.4E-03

7.5E-03

7.2E-03

1.1E-02

1.6E-02

1.6E-02

1.4E-02

1.5E-02

1.7E-02

1.6E-02

2.2E-02

2.9E-02

2.9E-02

2.7E-02

2.9E-02

3.4E-02

3.7E-02

4.1E-02

5.2E-02

5.3E-02

4.8E-02

5.9E-02

7.0E-02

7.3E-02 See Tables 6-11 and 6-12

Source: U.S. EPA, 2006. 

### Child-Specific Exposure Factors Handbook

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Page Child-Specific Exposure Factors Handbook

6-4 September 2008Table 6-3. Confidence in Recommendations for Inhalation Rates

General Assessment Factors Rationale Rating

Soundness Medium

Adequacy of Approach The survey methodology and data analysis was adequate. Measurements were made by indirect methods. The studies analyzed existing primary data.

Minimal (or defined) Bias Potential bias within thestudies was fairly well

documented. Applicability and Utility

Exposure Factor of Interest The studies focused on inhalation rates and factors High

influencing them.

Representativeness The studies focused on the U.S. population. Awide range of age groups were included.

Currency The studies were published during 2006 and 2007 and represent current exposure conditions.

DataCollection Period The data collection period for the studies may not be representative of long-term exposures. Clarity and Completeness

Accessibility All key studies are available from the peer reviewed Medium

literature.

Reproducibility The methodologies were clearly presented; enough information was included to reproduce most results.

Quality Assurance Information on ensuring data quality in the key studies

was limited. Variability and Uncertainty

Variability in Population In general, the key studies addressed variability in Medium

inhalation rates based on age and activity level. However, other factors that may affect inhalation rates (e.g., weight, body mass index [BMI], ethnicity) are not

discussed.

Uncertainty Multiple sources of uncertainty exist for these studies. Assumptions associated with Energy Expenditure (EE) based estimation procedures are a source of uncertainty

in inhalation rate estimates.

Evaluation and Review High

Peer Review Three of the key studies appeared in peer reviewed journals, and one key study is a U.S. EPA peer reviewed
report.

Number and Agreement of Studies There are four key studies. Theresults of studies from different researchers are in general agreement.

Overall Rating Medium 

### Child-Specific Exposure Factors Handbook

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Chapter 6 - Inhalation Rates

6.3 KEY INHALATION RATE STUDIES

6.3.1 Brochu et al., 2006 - Physiological Daily

Inhalation Rates for Free-living Individuals Aged 1 Month to 96 Years, Using Data from Doubly Labeled Water Measurements: A proposal for Air Quality Criteria, Standard Calculations and Health

Risk Assessment Brochu et al. (2006) calculated physiological daily inhalation rates (PDIR) for 2,210 individuals aged 3 weeks to 96 years using the reported disappearance rates of oral doses of doubly labeled water (DLW) ( 2 

### H 2 

### O and H 2 18 

### O) in urine, monitored by gas-isotope

ratio mass spectrometry for an aggregate period of more than 30,000 days. DLW data were complemented with indirect calorimetry and nutritional balance

measurements. In the DLW method, the disappearance of the stable isotopes deuterium ( 2 

### H) and heavy oxygen-18 ( 18 

### O) are monitored in urine, saliva, or blood samples over a long period of time (from 7 to 21 days) after subjects receive oral doses of 2 

### H 2 

### O and H 2 18 

### O. The disappearance rate of 2 

### H reflects water output and that

of 18 

### O represents water output plus carbon dioxide (CO 2 

### ) production rates. The CO 2 

### production rate is then calculated by difference between the two disappearance rates. Total daily energy expenditures (TDEEs) are determined from CO 2 

### production rates using classic respirometry formulas, in which values for the respiratory quotient (RQ = CO 2 produced 

### /O 2 consumed 

### ) are derived from the composition of the diet during the period of time of each study. The DLW method also allows for measurement of the energy cost of growth (ECG). TDEE and ECG measurements can be converted into PDIR values using the following equation developed by Layton (1993):

PDIR = (TDEE + ECG) x H x VQ 10 -3 

### (Eqn. 6-1)

where:

PDIR = physiological daily inhalation rates (m 3 

### /day); TDEE = total daily energy expenditure (kcal/day); ECG = stored daily energy cost for growth (kcal/day); H = oxygen uptake factor, volume of

0.21 L of oxygen (at standard temperature and pressure, dry air) consumed to produce 1 kcal of energy expended; VQ = ventilatory equivalent ratio of the minute volume (V E 

### ) at body temperature pressure saturation) to the oxygen uptake rate (VO 2 

### at standard temperature and pressure, dry air) V E 

### /VO 2 

### = 27; and

10 -3


### = conversion factor (L/m 3 

### ).

Brochu et al. (2006) calculated daily inhalation rates (expressed in m 3 

### /day and m 3 

### /kg-day) for a variety of age groups and physiological conditions. Published data on BMI, body weight, basal metabolic rate (BMR), ECG, and TDEE measurements (based on DLW method and indirect calorimetry) for subjects aged 2.6 months to 96 years were used. Only the data for children are presented in this handbook. Data for underweight, healthy normal-weight, and overweight/obese individuals were gathered and defined according to BMI cutoffs. Data for newborns were included regardless of BMI values, because they were clinically evaluated as being healthy infants.

Mean inhalation rates for newborns are presented in Table 6-4. Due to the insufficient number of subjects, no distributions were derived for this group.The distribution of daily inhalation rates for normalweight and overweight/obese individuals by gender and age groups are presented in Tables 6-5 to 6-9. An advantage of this study is that data are provided for age groups of less than one year. A limitation of this study is that data for individuals with pre-existing medical conditions was lacking.

6.3.2 U.S. EPA, 2006

- Metabolically-derived

Human Ventilation Rates: A Revised Approach Based Upon Oxygen Consumption Rates

U.S. EPA (2006) conducted a study to

ascertain inhalation rates for children and adults. Specifically, U.S. EPA sought to improve upon the methodology used by Layton (1993) and other studies that relied upon the ventilatory equivalent (VQ) and a linear relationship between oxygen consumption and fitness rate. A revised approach, developed by U.S.



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fitness rate. A revised approach, developed by U.S. EPA’s National Exposure Research Laboratory (NERL), was used, in which an individual’s inhalation rate was derived from his or her assumed oxygen consumption rate. U.S. EPA applied this revised approach using body weight data from the 1999-2002 National Health and Nutrition Examination Survey (NHANES) and metabolic equivalents (METS) data from U.S. EPA’s Consolidated Human Activity Database (CHAD). In this database, metabolic cost is given in units of “METS” or “metabolic equivalents of work,” an energy expenditure metric used by exercise physiologists and clinical nutritionists to represent activity levels. An activity’s METS value represents a dimensionless ratio of its metabolic rate (energy expenditure) to a person’s resting, or basal metabolic rate (BMR). NHANES provided age, gender, and body weight data for 19,022 individuals from throughout the United States. From these data, basal metabolic rate (BMR) was estimated using an age-specific linear equation used in the Exposure Factors Handbook (U.S. EPA, 1997), and in several other studies and reference

works. The CHAD database is a compilation of several databases of human activity patterns. U.S. EPA used one of these studies, the National Human Activity Pattern Survey (NHAPS), as its source for METS values because it was more representative of the entire United States population than the other studies in the database. The NHAPS data set included activity data for 9,196 individuals, each of which provided 24 hours of activity pattern data using a diary-based questionnaire. While NHAPS was identified as the best available data source for activity patterns, there were some shortcomings in the quality of the data. Study respondents did not provide body weights; instead, body weights are simulated using statistical sampling. Also, the NHAPS data extracted from CHAD could not be corrected to account for non-random sampling of study participants and survey days. NHANES and NHAPS data were grouped into age categories using the standardized age categories presented elsewhere in this handbook, with the exception that children under the age of one year were placed into a single category to preserve an adequate sample size within the category. For each NHANES participant, a “simulated” 24-hour activity pattern was generated by randomly sampling activity patterns from the set of NHAPS participants with the same gender and age category as the NHANES participant. Twenty such patterns were selected at random for each NHANES participant, resulting in 480 hours of simulated activity data for each NHANES participant.

The data were then scaled down to a 24-hour time frame to yield an average 24-hour activity pattern for each of the 19,022 NHANES individuals. Each activity was assigned a METS value based on statistical sampling of the distribution assigned by CHAD to each activity code. For most codes, these distributions were not age-dependent, but age was a factor for some activities for which intensity level varies strongly with age. Using statistical software, equations for METS based on normal, lognormal, exponential, triangular, and uniform distributions were generated as needed for the various activity codes. The METS values were then translated into energy expenditure (EE) by multiplying the METS by the basal metabolic rate (BMR), which was calculated as a linear function of body weight. The oxygen consumption rate (VO 2 

### ) was calculated by multiplying EE by H, the volume of oxygen consumed per unit of energy. VO 2 

### was calculated both as volume per time and as volume per time per unit body weight. The inhalation rate for each activity within the 24-hour simulated activity pattern for each individual

was estimated as a function of VO 2 

### , body weight, age, and gender. Following this, the average inhalation rate

was calculated for each individual for the entire 24-hour period, as well as for four separate classes of activities based on METS value (sedentary/passive (METS less than or equal to 1.5), light intensity (METS greater than

1.5 and less than or equal to 3.0), moderate intensity (METS greater than 3.0 and less than or equal to 6.0), and high intensity (METS greater than 6.0). Data for individuals were then used to generate summary tables based on gender and age categories. Data from this study are presented in Tables 6­ 10 through 6-15. Tables 6-10 and 6-11 present, for male and female subjects, respectively, summary statistics for daily average inhalation rate by age category on a volumetric (m 3 

### /day) and body-weight adjusted (m 3 

### /day-kg) basis. Table 6-12 presents the

mean and 95 th 

### percentile values for males, females, and



### Child-Specific Exposure Factors Handbook Page September 2008 6-7

### VE = H × VQ × EE (Eqn. 6-2) 

### Child-Specific Exposure Factors Handbook

Chapter 6

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males and females combined. Tables 6-13 and 6-14 present, for male and female subjects, respectively, mean ventilation rates by age category on a volumetric (m 3 

### /min) and body-weight adjusted (m 3 

### /min-kg) basis for the five different activity level ranges described above. Table 6-15 presents the number of hours spent per day at each activity level by males and females. An advantage of this study is the large sample size. In addition, the datasets used, NHAPS and NHANES, are representative of the U.S. general population. Limitations are that the NHAPS data are 10 years old, there is variability in the 24-hour activity, and there is uncertainty in the METs randomization, all of which were noted by the authors.

6.3.3 Arcus-Arth and Blaisdell, 2007

- Statistical Distributions of Daily Breathing Rates for Narrow Age Groups of Infants and

Children Arcus-Arth and Blaisdell (2007) derived daily breathing rates for narrow age ranges of children using the metabolic conversion method of Layton (1993) and energy intake data adjusted to represent the U.S. population from the Continuing Survey of Food Intake for Individuals (CSFII) 1994-1996, 1998. Normalized (m 3 

### /kg-day) and nonnormalized (m 3 

### /day) breathing rates for children 0-18 years of age were derived using the general equation developed by Layton (1993) to calculate energy-dependent inhalation rates (see Equation 6-2).

where:

VE = volume of air breathed per day (m 3 

### /day);

H = volume of oxygen consumed to produce 1 kcal of energy (m3/kcal); VQ = ratio of the volume of air to the

volume of oxygen breathed per unit

time (unitless); and EE = energy (kcal) expended per day.

Arcus-Arth and Blaisdell (2007) calculated H

values of 0.22 and 0.21 for infants and noninfant children, respectively, using the 1977-1978 NFCS and

CSFII data sets. Ventilatory equivalent (VQ) data, including those for infants, were obtained from 13 studies that reported VQ data for children aged 4-8 ears. Separate preadolescent (4-8 years) and adolescent (9-18 years) VQ values were calculated in addition toseparate VQ values for adolescent boys and girls. Twoday-averaged daily energy intake (EI) values reported in the CSFII data set were used a surrogate for EE. CSFII records that did not report body weight and those

for children who consumed breast milk or were breast fed were excluded from their analyses. The EIs of children 9 years of age and older were multiplied by 1.2, the value calculated by Layton (1993) to adjust for potential bias related to underreporting of dietary intakes by older children. For infants, EI values were adjusted by subtracting the amount of energy put into storage by infants as estimated by Scrimshaw et al. (1996). Self-reported body weights for each individual

from the CSFII data set were used to calculate nonnormalized (m 3 

### /day) and normalized (m 3 

### /kg-day) breathing rates, which decreased the variability in the resulting breathing rate data. Daily breathing rates weregrouped into three-month age groups for infants, oneyear age groups for children 1-18 years of age, and the age groups recommended by U.S. EPA cancer guidelines supplement (U.S. EPA, 2005) to receive greater weighting for mutagenic carcinogens (0 to < 2 years of age, and 2 to < 16 years of age). Data were also presented for adolescent boys and girls, aged 9-18 years (Table 6-16). For each age and age-gender group, Arcus-Arth and Blaisdell (2007) calculated the arithmetic mean, standard error of the mean, percentiles (50 th 

### , 90 th 

### , and 95 th 

### ), geometric mean, standard deviation, and best-fit parametric models of the breathing rate distributions. Overall, the CSFII-derived nonnormalized breathing rates progressively increased with age from infancy through 18 years of age, while normalized breathing rates progressively decreased. The data are presented in Table 6-16 in units of m 3 

### /day. There were statistical differences between boys and girls 9-18 years of age, both for these years combined ( p < 0.00) and for each year of age separately ( p < 0.05).

The authors reasoned that since the fat-free mass (basically muscle mass) of boys typically increases during adolescence, and because fat-free mass is highly

correlated to basal metabolism which accounts for the majority of EE, nonnormalized breathing rates for adolescent boys may be expected to increase with



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increasing age. Table 6-17 presents the mean and 95 th 

### percentile values for males and females combined, averaged to fit within the standard EPA age groups. The CSFII-derived mean breathing rates derived by Arcus-Arth and Blaisdell (2007) were compared to the mean breathing rates estimated in studies that utilized doubly labeled water (DLW) technique EE data that had been coupled with the Layton (1993) method. The infants’ CSFII-derived breathing rates were 15 to 27 percent greater than the comparison DLW EE breathing rates while the children’s CSFII rates ranged from 23 percent less to 14 percent greater than comparison rates. Thus, the CSFII and comparison rates were quite similar across age

groups. An advantage of this study is that it provides breathing rates specific to narrow age ranges, which can be useful for assessing inhalation dose during periods of greatest susceptibility. However, the study is limited by the potential for misreporting, underestimating, or overestimating of food intake data in the CSFII. In addition to underreporting of food intake by adolescents, EI values for younger children may be under- or overestimated. Overweight children (or their parents) may also underreport food intakes. In addition, adolescents who misreport food intake may have also misreported body weights.

6.3.4 Stifelman, 2007 - Using Doubly-labeled Water Measurements of Human Energy Expenditure to Estimate Inhalation Rates Stifelman (2007) estimated inhalation rates using DLW energy data. The DLW method administers two forms of stable isotopically labeled water: deuterium-labeled ( 2 

### H 2 

### O) and 18 

### oxygen-labeled (H 2 18 

### O). The difference in disappearance rates between the two isotopes represents the energy expended over a period of 1–3 half-lives of the labeled water (Stifelman, 2007). The resulting duration of observation is typically 1–3 weeks, depending on the size and activity level. The DLW database contains subjects from areas around the world and represents diversity in ethnicity, age, activity, body type, and fitness level. DLW data have been compiled by the Institute of Medicine (IOM) Panel on Macronutrients and the Food and Agriculture Organization of the United Nations (FAO). Stifelman (2007) used the equation of Layton (1993) to convert the recommended energy levels of IOM for the active-very active people to their equivalent inhalation rates. The IOM reports recommend energy expenditure levels organized by gender, age and body size (Stifelman, 2007). The equivalent inhalation rates are shown in

Table 6-18. Shown in Table 6-19 are the mean and 95 th 

### percentile values for the IOM “active” energy level category, averaged to fit within the standard EPA age groups. Stifelman (2007) noted that the estimates based on the DLW are consistent with previous findings of Layton (1993) and the Exposure Factors Handbook (U.S. EPA, 1997) and that inhalation rates based on the

IOM active classification are consistent with the mean

inhalation rate in the handbook. The advantages of this study are that the inhalation rates were estimated using the DLW data from a large data set. Stifelman (2007) noted that DLW methods are advantageous; the data are robust,

measurements are direct and avoid errors associated with indirect measurements (heart rate), subjects are free-living, and the period of observation is longer than what is possible from staged activity measures. Observations over a longer period of time reduce the uncertainties associated with using short duration studies to infer long-term inhalation rates. A limitation with the study is that the inhalation rates that are presented are for active/very active persons only.

6.3.5 Key Studies CombinedIn order to provide the recommended longterm inhalation rates shown in Table 6-1
, data from the four key studies were combined. The data from each study were averaged by gender and grouped according to the standard U.S. EPA childhood age groups used in this handbook, when possible. Mean and 95 th 

### percentile inhalation rate values for the four key studies are shown in Tables 6-20 and 6-21, respectively.

6.4 RELEVANT INHALATION RATE

STUDIES

6.4.1 International Commission on Radiological Protection (ICRP), 1981 - Report of the Task Group on Reference Man The International Commission on Radiological Protection (ICRP) (1981) estimated daily inhalation rates for reference children (10 years old), infants (1



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year old), and newborn babies by using a time-activityventilation approach. This approach for estimating an inhalation rate over a specified period of time was based on calculating a time weighted average of inhalation rates associated with physical activities of varying durations (Table 6-22). ICRP (1981) compiled reference values (Table 6-23) of minute

volume/inhalation rates from various literature sources. ICRP (1981) assumed that the daily activities of a reference child (10 yrs) consisted of 8 hours of rest and 16 hours of light activities. It was assumed that a day consisted of 14 hours resting and 10 hours light activity for an infant (1 year). A newborn's daily activities consisted of 23 hours resting and 1 hour light activity.

The estimated inhalation rates were 14.8 m 3 

### /day for children (age 10 years), 3.76 m 3 

### /day for infants (age 1 year), and 0.78 m 3 

### /day for newborns (Table 6-22). A limitation associated with this study is that the validity and accuracy of the inhalation rate data used in the compilation of reference values were not specified. This introduces some degree of uncertainty in the results obtained. Also, the approach used required that assumptions be made regarding the hours spent by various age/gender cohorts in specific activities. These assumptions may over/under-estimate

the inhalation rates obtained.

6.4.2 U.S. EPA, 1985 - Development of Statistical Distributions or Ranges of Standard Factors Used in Exposure Assessments The U.S. EPA (1985) compiled measured values of minute ventilation for various age/gender cohorts from early studies. The data compiled by the

U.S. EPA (1985) for each age/gender cohorts were obtained at various activity levels (Table 6-24). These levels were categorized as light, moderate, or heavy according to the criteria developed by the U.S. 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). Table 6-24 presents a summary of inhalation rates by age and activity level. A description of activities included in each activity level is also presented in Table 6-24. Table 6-24 indicates that at rest, the mean inhalation rate for children, ages 6 and 10 years, is 0.4 m 3 

### /hr. Table 6-25 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 a transportation vehicle was 1.77. Based on the data presented in Tables 6-24 and 6-25, a daily inhalation rate was calculated for adults and children by using a time-activity-ventilation approach. These data are presented for children in Table 6-26. The average daily inhalation rate for 6 and 10 years old children is 16.74

and 21.02 m 3 

### /day, respectively. Limitations associated with this study are its age and 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 children.

6.4.3 Linn et al., 1992 - Documentation of Activity Patterns in "High-risk" Groups Exposed to Ozone in the Los Angeles Area Linn et al. (1992) conducted a study that estimated the inhalation rates for "high-risk" subpopulation groups exposed to ozone in their daily activities in the Los Angeles area. The population surveyed consisted of several panels of children. The panels included Panel 2 : 17 healthy elementary school students (5 males and 12 females, ages 10-12 years);

Panel 3 : 19 healthy high school students (7 males and 12 females, ages 13-17 years); Panel 6 : 13 young asthmatics (7 males and 6 females, ages 11-16 years). An initial calibration test was conducted, followed by a training session. Finally, a field study that involved the subjects collecting their own heart rates and diary data was conducted. During the calibration tests, ventilation rate (VR), breathing rate, and heart rate (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. In the field study, each subject 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 using a Heart



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Watch, an automated system consisting of a transmitter and receiver worn on the body. Asthmatic subjects recorded their diary information once every hour. 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 6-27 presents the calibration and field protocols for self-monitoring of activities for each subject panel. Table 6-28 presents the mean, 99 th 

### percentile, and mean VR at each subjective activity level (slow, medium, fast). The mean 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. The authors reported that the diary data showed that on a typical day, most individuals spent most of their time indoors at slow activity level. During slow activity, asthmatic subjects had higher VRs than healthy subjects (Table 6-28). The authors also reported that in every panel the predicted VR correlated significantly with the subjective estimates of activity levels. A limitation of this study is that calibration data may overestimate the predictive power of HR during actual field monitoring. The wide variety of exercises in everyday activities may result in greater variation of the VR-HR relationship than was calibrated. Another limitation 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 both healthy and asthmatic children.

6.4.4 Spier et al., 1992 - Activity Patterns in Elementary and High School Students Exposed to Oxidant Pollution Spier et al. (1992) investigated the 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 each of rest, slow walking, jogging, and fast walking. HR and VR were measured during the last 2 minutes of each exercise. Individual VR and HR relationships for each individual were determined by fitting a regression line to HR values and log VR values. Each subject recorded their daily activities, changes in location, and breathing rates in diaries for 3 consecutive days. Self-estimated breathing rates were recorded as slow (slow walking), medium (walking faster than normal), and fast (running). HR was recorded once per minute during the3 days using a Heart Watch. VR values for each selfestimated 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 6-29 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 number of hours spent indoors was higher for high school students (21.2 hours) 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 6-30). A limitation of this study is the small sample size. The results may not be representative of all children in these age groups. Another limitation is that the accuracy of the self-estimated breathing rates reported by younger age groups is uncertain. This may affect the validity of the data set generated. An advantage of this study is that inhalation rates were

determined for children and adolescents. These data are useful in estimating exposure for the younger population.

6.4.5 Adams, 1993

- Measurement of Breathing Rate and Volume in Routinely Performed Daily Activities, Final Report Adams (1993) conducted research to accomplish two main objectives: (1) identification of mean and ranges of inhalation rates for various age/gender cohorts and specific activities, and (2) derivation of simple linear and multiple regression equations that could be used to predict inhalation rates through other measured variables: breathing frequency and oxygen consumption. A total of 160 subjects participated in the primary study. For children, there



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### V E 

### =E x H x VQ (Eqn. 6-4) 

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were two age-dependent groups: children 6 to 12.9 years old and adolescents 13 to18.9 years old. An additional 40 children from 6 to 12.9 years old and 12 young children from 3 to 5.9 years old were identified as subjects for pilot testing purposes. Resting protocols conducted in the laboratory for all age groups consisted of three phases (25 minutes each) of lying, sitting, and standing. The phases were categorized as resting and sedentary activities. Two active protocols— moderate (walking) and heavy (jogging/ running) phases— were performed on a treadmill over a progressive continuum of intensity levels made up of 6-minute intervals at three speeds ranging from slow to moderately fast. All protocols involved measuring VR, HR, f B 

### (breathing frequency),

and VO 2 

### (oxygen consumption). Measurements were taken in the last 5 minutes of each phase of the resting protocol and the last 3 minutes of the 6-minute intervals at each speed designated in the active protocols. In the field, all children completed spontaneous play protocols; most protocols were conducted for 30 minutes. All the active field protocols

were conducted twice. Results are shown in Tables 6­

31 and 6-32. During all activities in either the laboratory or field protocols, VR for the children’s group revealed no significant gender differences. Therefore, VR data presented in Tables 6-33 and 6-34 were categorized by activity type (lying, sitting, standing, walking, and running) for young children and children without regard to gender. These categorized data from Tables 6-33

and 6-32 are summarized as inhalation rates in Tables 6-31 and 6-32. The laboratory protocols are shown in Table 6-31. Table 6-32 presents the mean inhalation rates by group and for moderate activity levels in field protocols. Data were not provided for the light and sedentary activities because the group did not perform for this protocol or the number of subjects was too small for appropriate comparisons. Accurate predictions of inhalation rates across all population groups and activity types were obtained by including body surface area (SA), HR, and breathing frequency in multiple regression analysis (Adams, 1993). Adams (1993) calculated SA from measured height and body weight using the equation: SA = Height (0.725) 

### x Weight (0.425) 

### x 71.84 (Eqn. 6-3)

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.

6.4.6 Layton, 1993 - Metabolically Consistent Breathing Rates for Use in Dose

Assessments Layton (1993) presented a 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. However, in this study, breathing rates were calculated on the basis of 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:

whe r e:

V E 

### =	 ventilation rate (m 3 

### /min or

m 3 

### /day); E =	 energy expenditure rate; [kilojoules/minute (KJ/min) or megajoules/hour (MJ/hr)]; H =	 volume of oxygen (at standard temperature and pressure, dry air consumed in the production of 1 kilojoule (KJ) of energy expended (L/KJ or m 3 

### /MJ)); and VQ =	 ventilatory equivalent (ratio of minute volume (m 3 

### /min) to oxygen uptake (m 3 

### /min)) unitless.



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Layton (1993) used two alternative approaches to estimate daily chronic (long term) inhalation rates for different age/gender cohorts of the U.S. population using this methodology.

First Approach Inhalation rates were estimated by multiplying average daily food energy intakes for different age/gender cohorts, H, and VQ, as shown in the equation above. The average food energy intake data (Table 6-35) are based on approximately 30,000

individuals and were obtained from the 1977-78 USDA-NFCS. The food energy intakes were adjusted upwards by a constant factor of 1.2 for all individuals 9 years and older. This factor compensated for a

consistent bias in USDA-NFCS that was attributed to under-reporting of the foods consumed or the methods used to ascertain dietary intakes. Layton (1993) used a weighted average oxygen uptake of 0.05 L O 2 

### /KJ which was determined from data reported in the 1977-78 USDA-NFCS and the second NHANES (NHANES II). The survey sample for NHANES II was approximately 20,000 participants. A VQ of 27 used in the calculations was calculated as the geometric mean of VQ data that were obtained from several studies. The inhalation rate estimation techniques are shown in footnote (a) of Table 6-36. Table 6-37 presents the daily inhalation rate for each age/gender cohort. The highest daily inhalation rates were 10

m 3 

### /day for children between the ages of 6 and 8 years,

17 m 3 

### /day for males between 15 and 18 years, and 13

m 3 

### /day for females between 9 and 11 years. 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 BMR times H times VQ. BMR was defined as "the minimum amount of energy required to support basic cellular respiration while at rest and not actively digesting food" (Layton, 1993). The inhalation rate for active periods was calculated by multiplying the inactive inhalation rate by the ratio of the rate of energy expenditure during active hours to the estimated BMR. This ratio is presented as F in Table 6­

36. These data for active and inactive inhalation rates are also presented in Table 6-36. For children, inactive and active inhalation rates ranged from 2.35 to 5.95

m 3 

### /day and from 6.35 to 13.09 m 3 

### /day, respectively.

Second Approach Inhalation rates were calculated as the product of the BMR of the population cohorts, the ratio of total daily energy expenditure to daily BMR, H, and VQ.

The BMR data obtained from the literature were statistically analyzed, and regression equations were developed to predict BMR from body weights of various age/gender cohorts. The statistical data used to develop the regression equations are presented in Table 6-37. The data obtained from the second approach are presented in Table 6-38. Inhalation rates for children (6 months - 10 years) ranged from 7.3 to 9.3 m 3 

### /day for

male and 5.6 to 8.6 m 3 

### /day for female children; for older children (10 to 18 years), inhalation rates were 15

m 3 

### /day for males and 12 m 3 

### /day for females. These rates are similar to the daily inhalation rates obtained using the first approach. Also, the inactive inhalation rates obtained from the first approach are lower than the inhalation rates obtained using the second approach. This may be attributed to the BMR multiplier employed in the equation of the second approach to calculate

inhalation rates.

Inhalation rates were also obtained for shortterm exposures for various age/gender cohorts and five energy-expenditure categories (rest, sedentary, light, moderate, and heavy). BMRs were multiplied by the product of the metabolic equivalent, H, and VQ. The data obtained for short-term exposures are presented in

Table 6-39. This study obtained similar results using two different approaches. The major strengths of this study are that it estimates inhalation rates in different age groups and that the populations are large. 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 (1) the estimated activity pattern levels are somewhat subjective; (2) the explanation that activity pattern differences are responsible for the lower level obtained with the metabolic approach (25 %) compared to the activity pattern approach is not well supported by the data; and (3) different populations were used in each approach, which may have introduced error.



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6.4.7 Rusconi et al., 1994

- Reference Values for Respiratory Rate in the First 3 Years of

Life Rusconi et al. (1994) examined a large number of infants and children in Milano, Italy in order to determine the reference values for respiratory rate in children aged 15 days to 3 years. A total of 618 infants and children (336 males and 282 females) who did not have respiratory infections or any severe disease were included in the study. Of the 618, a total of 309 were in good health and were observed in day care centers, while the remaining 309 were seen in hospitals or as outpatients. Respiratory rates were recorded twice, 30 to 60 minutes apart, listening to breath sounds for 60 seconds with a stethoscope, when the child was awake and calm and when the child was sleeping quietly (sleep not associated with any spontaneous movement, including eye movements or vocalizations) (Table 6­ 40). The children were assessed for one year in order to determine the repeatability of the recordings, to compare respiratoryrate counts obtained by stethoscope and by observation, and to construct reference percentile curves by age in a large number of subjects. The authors plotted the differences between respiratory rate counts determined by stethoscope at 30­ to 60-minute intervals against their mean count in waking and sleeping subjects. The standard deviation

of the differences between the two counts was 2.5 and

1.7 breaths/minute, respectively, for waking and sleeping children. This standard deviation yielded 95% repeatability coefficients of 4.9 breaths/minute when the

infants and children were awake and 3.3 breaths/minute when they were asleep. In both waking and sleeping states, the respiratory rate counts determined by stethoscope were found to be higher than those obtained by observation. The mean difference was 2.6 and 1.8 breaths per minute, respectively, in waking and sleeping states. The mean respiratory rate counts were significantly higher in infants and children at all ages when awake and calm than when asleep. A decrease in respiratory rate with increasing age was seen in waking and sleeping infants and children. A scatter diagram of respiratory rate counts by age in waking and sleeping subjects showed that the pattern of respiratory rate decline with age was similar in both states, but it was much faster in the first few months of life. The authors constructed centile curves by first log-transforming the data and then applying a second degree polynormal curve, which allowed excellent fitting to observed data. Figures 6-1 and 6-2 show smoothed percentiles by age in waking and sleeping subjects, respectively. The variability of respiratory rate among subjects was higher in the first few months of life, which may be attributable to biological events that occur during these months, such as maturation of the neurologic control of breathing and changes in lung and chest wall compliance and lung

volumes. An advantage of this study is that it provides distribution data for respiratory rate for children from infancy (less than 2 months) to 36 months old. These data are not U.S. data; U.S. distributions were not available. Although, there is no reason to believe that the respiratory rates for Italian children would be different from that of U.S. children, this study only provided data for a narrow range of activities.

6.4.8 Price et al., 2003

- Modeling Interindividual Variation in Physiological Factors Used in

PBPK Models of Humans Price et al. (2003) developed a database of values for physiological parameters often used in physiologically-based pharmacokinetic models (PBPK). The database consisted of approximately 31,000 records containing information on volumes and masses of selected organs and tissues, blood flows for the organ and tissues, and total resting cardiac output and average

inhalation rates. Records were created based on data from the NHANES III survey. The study authors note that the database provides a source of data for human physiological parameters were the parameter values for an individual are correlated with one another and capture interindividual variation in populations of a specific gender, race, and age range. A computer program, Physiological Parameters for PBPK Modeling (PPPM

or P 3 

### M), which is publicly available (The Lifeline Group, 2007), was also developed to randomly retrieve records from the database for groups of individuals of specified age ranges, gender, and ethnicities. Price et al. (2003) recommends that output sets be used as inputs to Monte Carlo-based PBPK models of

interindividual variation in dose.



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Child-Specific Exposure Factors Handbook Page September 2008 6-15 6.5 REFERENCES FOR CHAPTER 6

Adams, W.C. (1993) Measurement of breathing rate and volume in routinely performed daily activities, Final Report. California Air Resources Board (CARB) Contract No. A033-205.June 1993. 185 pgs. Arcus-Arth, A. and Blaisdell, R. J. (2007) Statistical distributions of daily breathing rates for narrow age groups of infants and children. Risk Anal 27(1):97-110 Brochu, P.; Ducré-Robitaille, J.; Brodeur, J. (2006)Physiological daily inhalation rates for freeliving individuals aged 1 month to 96 years, using data from doubly labeled water measurements: a proposal for air quality criteria, standard calculations and health risk

assessment. Hum Ecol Risk Assess 12:675-

701. FASEB/LSRO (Federation of American Societies for Experimental Biology, Life Sciences Research Office). (1995) Joint policy on variance

estimation and statistical standards on NHANES III and CSFII reports (Appendix III). In: Third Report on Nutrition Monitoringin the United States. Prepared for the Interagency Board for Nutrition Monitoring and Related Research. Washington, DC: U.S. Government Printing

Office. Foos, B., Sonwane, B. (2008) Overview: workshop on children’s inhalation dosimetry and health

effects for risk assessment. J Toxicol Environ Health Part A 71(3):147-148. Foos, B.; Marty, M.; Schwartz, J.; Bennett, W.; Moya, J.; Jarabek, A; Salmon, A. (2008) Focusing on children’s inhalation dosimetry

and health effects for risk assessment: an

introduction. J Toxicol Environ Health Part A 71(3):149-165. International Commission on Radiological Protection. (1981) 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 Phys 64(1):23-36. Linn, W.S.; Shamoo, D.A.; Hackney, J.D. (1992)Documentation of activity patterns in "highrisk" 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. Price, P.; Conolly, R.; Chaisson, C.; Gross, E.; Young, J.; Mathis, E.; Tedder, D. (2003) Modeling interindividual variation in physiological factors used in PBPK models of humans. Crit Rev Toxicol 33 (5):469-

503. Rusconi, F.; Castagneto, M.; Garliardi, L.; Leo, G.; Pellegatta, A.; Porta, N.; Razon, S.; Braga, M. (1994) Reference values for respiratory rate in the first 3 years of life. Pediatrics 94(3):350-

355. Scrimshaw, N. S.; Waterlow, J. C.; Schurch, B. (Eds.). (1996) Energy and Protein Requirements. Proceedings of an International Dietary and Energy Consultancy Group Workshop; 1994 Oct 31–Nov 4; London, UK: Stockton Press. 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 Epidemiol 2(3):277-293. Stifelman, M. (2007) Using doubly-labeled water measurements of human energy expenditure to

estimate inhalation rates. Sci Total Environ

373:585-590. The Lifeline Group. (2007) Physiological parameters for PBPK modelingTM version 1.3 (P3MTM). Accessed May 2007. Available at: http://www.thelifelinegroup.org/p3m/ U.S. EPA. (1985) Development of statistical distributions or ranges of standard factors used in exposure assessments. Washington, DC:

Office of Health and Environmental Assessment; EPA Report No. EPA 600/8-85-010. Available from: NTIS, Springfield, VA; PB85-242667. U.S. EPA. (1992) Guidelines for exposure assessment. Washington, DC: Office of

### Child-Specific Exposure Factors Handbook

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- Inhalation Rates

Research and Development, Office of Health

and Environmental Assessments.

U.S. EPA. (1994) Methods for derivation of inhalation reference concentrations and application of inhalation dosimetry. Washington, DC:

Office of Health and Environmental

Assessment. EPA/600/8-90/066F.

U.S. EPA. (1997) Exposure Factors Handbook. Washington, DC: Office of Research and Development, Office of Health and

Environmental Assessment.

U.S. EPA. (2005). Supplemental guidance for assessing susceptibility from early-life exposure to carcinogens. Washington, DC:

Risk Assessment Forum. EPA/630/R03/003F.

U.S. EPA. (2006) Metabolically-derived human ventilation rates: A revised approach based upon oxygen consumption rates. Washington,

DC: National Center for Environmental Assessment. External Review Draft. Prepared for USEPA/ORD, Contract No. EP-C-04-027.

U.S. EPA. (2008) Risk Assessment Guidance for Superfund: Volume I: Human Health Evaluation Manual (Part F, Supplemental Guidance for Inhalation Risk Assessment). Washington, DC: Office of Superfund Remediation and Technology Innovation. Peer Review Draft. Prepared for USEPA,

Contract No. 68-W-01-05. WHO.	 (1986) Principles for evaluating health risks from chemicals during infancy and early childhood: the need for a special approach. Environmental Health Criteria 59, World Health Organization, International Programme on Chemical Safety.



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6-16 September 2008Table 6-4. Physiological Daily Inhalation Rates for Newborns Aged 1 Month or Less

Age Group N Body Weight (kg)

Mean ± SD Physiological Daily Inhalation Rates e

Mean ± SD

(m 3 /day) (m 3 /kg-day)
21 days (3 weeks) 32 days (~ 1 month) 33 days (~ 1 month) 13 a,c

10 b,d

10 a,d 1.2 ± 0.2

4.7 ± 0.7

4.8 ± 0.3 0.85 ± 0.17 f

2.45 ± 0.59 g

2.99 ± 0.47 g 0.74 ± 0.09 f

0.53 ± 0.10 g

0.62 ±0.09 g

a

b

c

d

e

f

g

N

SD

Source: Formula-fed infants.

Breast-fed infants. Healthy infants with very low birth weight. Infants evaluated as being clinically healthy and neither underweight or overweight. Physiological daily inhalation rates were calculated using the following equation: (TDEE + ECG)*H*(V E /VO 2 )*10 -3 , where H = 0.21 L of O 2 /Kcal, V E /VO 2 = 27 (Layton, 1993), TDEE = total daily energy expenditure (kcal/day) and ECG = stored daily energy cost for growth (kcal/day). TDEEs based on nutritional balance measurements during 3-day periods.

TDEEs based on 2 H 2 O and H 2 18 O disappearance rates from urine

= Number of individuals.

= Standard deviation.

Brochu et al., 2006. 

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Child-Specific Exposure Factors Handbook Page September 2008 6-17Table 6-5. Distribution Percentiles of Physiological Daily Inhalation Rates (m 3 /day) for Free-living Normal-weight Males and Females Aged 2.6 months to 23 years

AgeGroup

(years) N Body Weight a (kg)

Mean

± SD b (m Physiological Daily Inhalation Rates 3 /day)

Mean ± SD Percentile c

5 th 10 th 25 th 50 th 75 th 90 th 95 th 99 th

Males

0.22 to <0.5 32 6.7 ± 1.0 3.38 ± 0.72 2.19 2.46 2.89 3.38 3.87 4.30 4.57 5.06

0.5 to <1 40 8.8 ± 1.1 4.22 ± 0.79 2.92 3.21 3.69 4.22 4.75 5.23 5.51 6.05

1 to <2 35 10.6 ± 1.1 5.12 ± 0.88 3.68 3.99 4.53 5.12 5.71 6.25 6.56 7.16

2 to <5 25 15.3 ± 3.4 7.60 ± 1.28 5.49 5.95 6.73 7.60 8.47 9.25 9.71 10.59

5 to <7 96 19.8 ± 2.1 8.64 ± 1.23 6.61 7.06 7.81 8.64 9.47 10.21 10.66 11.50

7 to <11 38 28.9 ± 5.6 10.59 ± 1.99 7.32 8.04 9.25 10.59 11.94 13.14 13.87 15.22

11 to <23 30 58.6 ± 13.9 17.23 ± 3.67 11.19 12.53 14.75 17.23 19.70 21.93 23.26 25.76

Females

0.22 to <0.5 53 6.5 ± 0.9 3.26 ± 0.66 2.17 2.41 2.81 3.26 3.71 4.11 4.36 4.81

0.5 to <1 63 8.5 ± 1.0 3.96 ± 0.72 2.78 3.05 3.48 3.96 4.45 4.88 5.14 5.63

1 to <2 66 10.6 ± 1.3 4.78 ± 0.96 3.20 3.55 4.13 4.78 5.43 6.01 6.36 7.02

2 to <5 36 14.4 ± 3.0 7.06 ± 1.16 5.15 5.57 6.28 7.06 7.84 8.54 8.97 9.76

5 to <7 102 19.7 ± 2.3 8.22 ± 1.31 6.06 6.54 7.34 8.22 9.11 9.90 10.38 11.27

7 to <11 161 28.3 ± 4.4 9.84 ± 1.69 7.07 7.68 8.70 9.84 10.98 12.00 12.61 13.76

11 to <23 87 50.0 ± 8.9 13.28 ± 2.60 9.00 9.94 11.52 13.28 15.03 16.61 17.56 19.33

a Measured body weight. Normal-weight individuals defined according to the body mass index (BMI) cut-offs.

b Physiological daily inhalation rates were calculated using the following equation: (TDEE + ECG)*H*(V E /VO 2 )*10 -3 , where H =

0.21 L of O 2 /Kcal, V E /VO 2 = 27 (Layton, 1993), TDEE = total daily energy expenditure (kcal/day) and ECG = stored daily energy cost for growth (kcal/day).

c Percentiles based on a normal distribution assumption for age groups.

N = Number of individuals.

SD = Standard deviation.

Source: Brochu et al., 2006. 

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Table 6-6. Mean and 95 th Percentile Inhalation Rate Values (m 3 /day) for Free-living Normal-weight Males, Females, and Males and Females Combined.

Age Group a N Mean 95 th

Males

3 to <6 months b 32 3.38 4.57

6 to <12 months 40 4.42 5.51 1 to <2 years 35 5.12 6.56

Females

3 to <6 months b 53 3.26 4.36

6 to <12 months 63 3.96 5.14 1 to <2 years 66 4.78 6.36

Males and Females Combined

3 to <6 months b 85 3.32 4.47

6 to <12 months 103 4.09 5.53 1 to <2 years 101 4.95 6.46

a No other age groups from Table 6-5 (Brochu et al., 2006) fit into the U.S. EPA age groupings.

b Age group from Brochu et al. (2006) was 2.6 to <6 months.

N = Number of individuals.

Source: Brochu et al., 2006.



### Child-Specific Exposure Factors Handbook Page September 2008 6-19Table 6-7. Distribution Percentiles of Physiological Daily Inhalation Rates (m 3 /day) for Free-living Normal-weight and Overweight/obese Males and Females Aged 4 to 18 years

Age Group

(years) N Body Weight a (kg)

Mean ± SD b (m Physiological Daily Inhalation Rates 3 /day)

Mean ± SD Percentile c

5 th 10 th 25 th 50 th 75 th 90 th 95 th 99 th Males - Normal-weight

4 to <5.1 77 19.0 ± 1.9 7.90 ± 0.97 6.31 6.66 7.25 7.90 8.56 9.15 9.50 10.16

5.1 to <9.1 52 22.6 ± 3.5 9.14 ± 1.44 6.77 7.29 8.17 9.14 10.11 10.99 11.51 12.49

9.1 to <18.1 36 41.4 ± 12.1 13.69 ± 3.95 7.19 8.63 11.02 13.69 16.35 18.75 20.19 22.88 Males - Overweight/obese

4 to <5.1 54 26.5 ± 4.9 9.59 ± 1.26 7.52 7.98 8.74 9.59 10.44 11.21 11.66 12.52

5.1 to <9.1 40 32.5 ± 9.2 10.88 ± 2.49 6.78 7.69 9.20 10.88 12.56 14.07 14.98 16.68

9.1 to <18.1 33 55.8 ± 10.8 14.52 ± 1.98 11.25 11.98 13.18 14.52 15.85 17.06 17.78 19.13 Females - Normal-weight

4 to <5.1 82 18.7 ± 2.0 7.41 ± 0.91 5.92 6.25 6.80 7.41 8.02 8.57 8.90 9.52

5.1 to <9.1 151 25.5 ± 4.1 9.39 ± 1.62 6.72 7.31 8.30 9.39 10.48 11.47 12.05 13.16

9.1 to <18.1 124 42.7 ± 11.1 12.04 ± 2.86 7.34 8.38 10.11 12.04 13.97 15.70 16.74 18.68 Females - Overweight/obese

4 to <5.1 56 26.1 ± 5.5 8.70 ± 1.13 6.84 7.26 7.94 8.70 9.47 10.15 10.56 11.33

5.1 to <9.1 68 34.6 ± 9.9 10.55 ± 2.23 6.88 7.69 9.05 10.55 12.06 13.41 14.22 15.75

9.1 to <18.1 68 59.2 ± 12.8 14.27 ± 2.70 9.83 10.81 12.45 14.27 16.09 17.73 18.71 20.55

a

b

c

N Measured body weight. Normal-weight and overweight/obese males defined according to the body mass index (BMI) cut-offs. Physiological daily inhalation rates were calculated using the following equation: (TDEE + ECG)*H*(V E /VO 2 )*10 -3 , where H =

0.21 L of O 2 /Kcal, V E /VO 2 = 27 (Layton, 1993), TDEE = total daily energy expenditure (kcal/day) and ECG = stored daily energy cost for growth (kcal/day). Percentiles based on a normal distribution assumption for age groups.

= Number of individuals.

SD = Standard deviation.

Source: Brochu et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 6

- Inhalation Rates

Page Child-Specific Exposure Factors Handbook

6-20 September 2008Table 6-8. Distribution Percentiles of Physiological Daily Inhalation Rates per Unit of Body Weight (m 3 /kg-day) for Free-living Normal-weight Males and Females Aged 2.6 months to 23 years

Age Group

(years) Physiological Daily Inhalation Rates a (m 3 /kg-day)

Mean ± SD b Percentile

5 th 10 th 25 th 50 th 75 th 90 th 95 th 99 th

Males

0.22 to <0.5 0.51 ± 0.09 0.36 0.39 0.45 0.51 0.57 0.63 0.66 0.73

0.5 to <1 0.48 ± 0.07 0.36 0.39 0.43 0.48 0.53 0.57 0.60 0.64

1 to <2 0.48 ± 0.06 0.38 0.41 0.44 0.48 0.52 0.56 0.58 0.62

2 to <5 0.44 ± 0.04 0.38 0.39 0.42 0.44 0.47 0.50 0.51 0.54

5 to <7 0.42 ± 0.05 0.34 0.35 0.38 0.42 0.45 0.48 0.49 0.52

7 to <11 0.37 ± 0.06 0.27 0.29 0.33 0.37 0.41 0.45 0.47 0.52

11 to <23 0.30 ± 0.05 0.22 0.24 0.27 0.30 0.33 0.36 0.38 0.41

Females

0.22 to <0.5 0.50 ± 0.09 0.35 0.39 0.44 0.50 0.57 0.62 0.66 0.72

0.5 to <1 0.46 ± 0.06 0.36 0.38 0.42 0.46 0.51 0.55 0.57 0.61

1 to <2 0.45 ± 0.08 0.33 0.35 0.40 0.45 0.50 0.55 0.58 0.63

2 to <5 0.44 ± 0.07 0.32 0.35 0.39 0.44 0.49 0.53 0.56 0.61

5 to <7 0.40 ± 0.05 0.32 0.33 0.36 0.40 0.43 0.46 0.47 0.51

7 to <11 0.35 ± 0.06 0.25 0.27 0.31 0.35 0.39 0.43 0.45 0.50

11 to <23 0.27 ± 0.05 0.19 0.21 0.24 0.27 0.30 0.33 0.35 0.38

a

b

SD Physiological daily inhalation rates were calculated using the following equation: (TDEE + ECG)*H*(V E /VO 2 )*10 -3 , where

H = 0.21 L of O 2 /Kcal, V E /VO 2 = 27 (Layton, 1993), TDEE = total daily energy expenditure (kcal/day) and ECG = stored daily energy cost for growth (kcal/day). Percentiles based on a normal distribution assumption for age groups.

= Standard deviation.

Source: Brochu et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 6

- Inhalation Rates

Child-Specific Exposure Factors Handbook Page September 2008 6-21Table 6-9. Distribution Percentiles of Physiological Daily Inhalation Rates (m 3 /kg-day) for Free-living Normal-weight and Overweight/obese Males and Females Aged 4 to 18 years

Age Group (years) Physiological Daily Inhalation Rates a (m 3 /kg-day)

Mean ± SD b Percentile

5 th 10 th 25 th 50 th 75 th 90 th 95 th 99 th

Males - Normal-weight

4 to <5.1 0.42 ± 0.04 0.35 0.36 0.39 0.42 0.45 0.47 0.49 0.52

5.1 to <9.1 0.41 ± 0.06 0.31 0.34 0.37 0.41 0.45 0.48 0.50 0.54

9.1 to <18.1 0.33 ± 0.05 0.26 0.27 0.30 0.33 0.37 0.40 0.41 0.45 Males - Overweight/obese

4 to <5.1 0.37 ± 0.04 0.30 0.31 0.34 0.37 0.40 0.42 0.44 0.47

5.1 to <9.1 0.35 ± 0.08 0.22 0.25 0.29 0.35 0.40 0.45 0.47 0.53

9.1 to <18.1 0.27 ± 0.04 0.20 0.22 0.24 0.27 0.29 0.32 0.33 0.36 Females - Normal-weight

4 to <5.1 0.40 ± 0.05 0.32 0.34 0.37 0.40 0.43 0.46 0.48 0.51

5.1 to <9.1 0.37 ± 0.06 0.27 0.29 0.33 0.37 0.41 0.45 0.47 0.52

9.1 to <18.1 0.29 ± 0.06 0.20 0.22 0.25 0.29 0.33 0.36 0.38 0.42 Females - Overweight/obese

4 to <5.1 0.34 ± 0.04 0.27 0.28 0.31 0.34 0.37 0.40 0.41 0.44

5.1 to <9.1 0.32 ± 0.07 0.21 0.23 0.27 0.32 0.36 0.40 0.43 0.47

9.1 to <18.1 0.25 ± 0.05 0.17 0.18 0.21 0.25 0.28 0.31 0.33 0.36

a Physiological daily inhalation rates were calculated using the following equation: (TDEE + ECG)*H*(V E /VO 2 )*10 -3

, where H = 0.21 L of O 2 /Kcal, V E /VO 2 = 27 (Layton, 1993), TDEE = total daily energy expenditure (kcal/day) and ECG = stored daily energy cost for growth (kcal/day).

b Percentiles based on a normal distribution assumption for age groups.

SD = Standard deviation.

Source: Brochu et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 6

- Inhalation Rates

Page Child-Specific Exposure Factors Handbook

6-22 September 2008Table 6-10. Descriptive Statistics for Daily Average Inhalation Rate in Males, by Age Category a

Age Group N Daily Average Inhalation Rate, Unadjusted for Body Weight (m 3 /day)

Mean Percentiles Maximum 5 th 10 th 25 th 50 th 75 th 90 th 95 th

Birth to <1 year 419 8.76 4.77 5.70 7.16 8.70 10.43 11.93 12.69 17.05

1 to < 2 years 308 13.49 9.73 10.41 11.65 13.11 15.02 17.03 17.89 24.24

2 to < 3 years 261 13.23 9.45 10.20 11.43 13.19 14.49 16.27 17.71 28.17

3 to <6 years 540 12.65 10.42 10.87 11.40 12.58 13.64 14.63 15.41 19.52

6 to <11 years 940 13.42 10.08 10.69 11.73 13.09 14.73 16.56 17.72 24.97

11 to <16 years 1337 15.32 11.41 12.11 13.27 14.79 16.81 19.54 21.21 28.54

16 to <21 years 1241 17.22 12.60 13.41 14.48 16.63 19.16 21.94 23.38 39.21

Age Group N Daily Average Inhalation Rate, Adjusted for Body Weight (m 3 /day-kg)

Mean Percentiles Maximum 5 th 10 th 25 th 50 th 75 th 90 th 95 th

Birth to <1 year 419 1.09 0.91 0.94 1.00 1.09 1.16 1.26 1.29 1.48

1 to < 2 years 308 1.19 0.96 1.02 1.09 1.17 1.26 1.37 1.48 1.73

2 to < 3 years 261 0.95 0.78 0.82 0.87 0.94 1.01 1.09 1.13 1.36

3 to <6 years 540 0.70 0.52 0.56 0.61 0.69 0.78 0.87 0.92 1.08

6 to <11 years 940 0.44 0.32 0.34 0.38 0.43 0.50 0.55 0.58 0.81

11 to <16 years 1337 0.29 0.21 0.22 0.25 0.28 0.32 0.36 0.38 0.51

16 to <21 years 1241 0.23 0.17 0.18 0.20 0.23 0.25 0.28 0.30 0.40

a

N

BW Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating

the statistics in this table. Inhalation rate was estimated using a multiple linear regression model.

= Number of individuals. = Body weight.

Source: U.S. EPA, 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Child-Specific Exposure Factors Handbook Page September 2008 6-23Table 6-11. Descriptive Statistics for Daily Average Inhalation Rate in Females, by Age Category a

Age Group N Daily Average Inhalation Rate, Unadjusted for Body Weight (m 3 /day)

Mean Percentiles Maximum 5 th 10 th 25 th 50 th 75 th 90 th 95 th

Birth to <1 year 415 8.53 4.84 5.48 6.83 8.41 9.78 11.65 12.66 26.26

1 year 245 13.31 9.08 10.12 11.24 13.03 14.64 17.45 18.62 24.77

2 years 255 12.74 8.91 10.07 11.38 12.60 13.96 15.58 16.37 23.01

3 to <6 years 543 12.16 9.87 10.38 11.20 12.02 13.01 14.03 14.93 19.74

6 to <11 years 894 12.41 9.99 10.35 11.01 11.95 13.42 15.13 16.34 20.82

11 to <16 years 1,451 13.44 10.47 11.11 12.04 13.08 14.54 16.25 17.41 26.58

16 to <21 years 1,182 13.59 9.86 10.61 11.78 13.20 15.02 17.12 18.29 30.11

Age Group N Daily Average Inhalation Rate, Adjusted for Body Weight (m 3 /day-kg)

Mean Percentiles Maximum 5 th 10 th 25 th 50 th 75 th 90 th 95 th

Birth to <1 year 415 1.14 0.91 0.97 1.04 1.13 1.24 1.33 1.38 1.60

1 year 245 1.20 0.98 1.01 1.10 1.18 1.30 1.41 1.47 1.73

2 years 255 0.96 0.82 0.84 0.89 0.96 1.01 1.07 1.11 1.23

3 to <6 years 543 0.69 0.48 0.54 0.60 0.68 0.77 0.88 0.92 1.12

6 to <11 years 894 0.43 0.28 0.31 0.36 0.43 0.49 0.55 0.58 0.75

11 to <16 years 1,451 0.25 0.19 0.20 0.22 0.25 0.28 0.31 0.34 0.47

16 to <21 years 1,182 0.21 0.16 0.17 0.19 0.21 0.24 0.27 0.28 0.36

a

N

Source: Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating

the statistics in this table. Inhalation rate was estimated using a multiple linear regression model.

= Number of individuals.

U.S. EPA, 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Page Chi ld-Specific Exposure Factors Handbook

6-24 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-12. Mean and 95 th Percentile Inhalation Rate Values (m 3 /day) for Males, Females and

Males and Females Combined

Age Group a N Mean 95 th

Males
Birth to <1 year 419 8.76 12.69
1 to <2 years 308 13.49 17.89
2 to <3 years 261 13.23 17.71
3 to <6 years 540 12.65 15.41
6 to <11 years 940 16.42 17.72
11 to <16 years 1,337 15.32 21.21
16 to <21 years 1,241 17.22 23.38

Females
Birth to <1 year 415 8.53 12.66
1 to <2 years 245 13.31 18.62
2 to <3 years 255 12.74 16.37
3 to <6 years 543 12.16 14.93
6 to <11 years 894 12.41 16.34
11 to <16 years 1,451 13.44 17.41
16 to <21 years 1,182 13.59 18.29

Males and Females Combined
Birth to <1 year 834 8.65 12.68
1 to <2 years 553 13.40 18.26
2 to <3 years 516 12.99 17.04
3 to <6 years 1,083 12.41 15.17
6 to <11 years 1,834 12.92 17.03
11 to <16 years 2,788 14.38 19.31
16 to <21 years 2423 15.41 20.84

a No other age groups from Tables 6-9 and 6-10 (U.S. EPA, 2006) fit into the EPA age groupings.

N = Number of individuals.

Source: U.S. EPA, 2006.



### Child-Specific Exposure Factors Handbook Page September 2008 6-25Table 6-13.Descriptive Statistics for Average Ventilation RateaWhile Performing Activities Within the Specified Activity Category, forMalesby Age CategoryAge GroupNAverage Ventilation Rate (m3/min),Unadjusted for Body WeightAverage Ventilation Rate (m3/min-kg),Adjusted for Body WeightMeanPercentilesMaximumMeanPercentilesMaximum5th10th25th50th75th90th95th5th10th25th50th75th90th95thSleep or nap (Activity ID = 14500)Birth to <1 year4193.08E-03 1.66E-03 1.91E-03 2.45E-03 3.00E-03 3.68E-03 4.35E-03 4.77E-037.19E-033.85E-042.81E-043.01E-043.37E-043.80E-044.27E-044.65E-045.03E-046.66E-041 year3084.50E-03 3.11E-03 3.27E-03 3.78E-03 4.35E-03 4.95E-03 5.90E-03 6.44E-031.00E-023.95E-042.95E-043.13E-043.45E-043.84E-044.41E-044.91E-045.24E-046.26E-042 years2614.61E-03 3.01E-03 3.36E-03 3.94E-03 4.49E-03 5.21E-03 6.05E-03 6.73E-038.96E-033.30E-042.48E-042.60E-042.89E-043.26E-043.62E-044.05E-044.42E-045.38E-043 to <6 years5404.36E-03 3.06E-03 3.30E-03 3.76E-03 4.29E-03 4.86E-03 5.54E-03 5.92E-037.67E-032.43E-041.60E-041.74E-041.98E-042.37E-042.79E-043.14E-043.50E-044.84E-046 to <11 years9404.61E-03 3.14E-03 3.39E-03 3.83E-03 4.46E-03 5.21E-03 6.01E-03 6.54E-039.94E-031.51E-041.02E-041.09E-041.25E-041.48E-041.74E-042.00E-042.15E-043.02E-0411 to <16 years1,3375.26E-03 3.53E-03 3.78E-03 4.34E-03 5.06E-03 5.91E-03 6.94E-03 7.81E-031.15E-029.80E-056.70E-057.20E-058.10E-059.40E-051.10E-041.29E-041.41E-042.08E-0416 to <21 years1,2415.31E-03 3.55E-03 3.85E-03 4.35E-03 5.15E-03 6.09E-03 6.92E-03 7.60E-031.28E-027.10E-054.70E-055.20E-056.10E-056.90E-058.00E-059.00E-059.80E-051.47E-04Sedentary & Passive Activities (METS#1.5--Includes Sleep or Nap)Birth to <1 year4193.18E-03 1.74E-03 1.99E-03 2.50E-03 3.10E-03 3.80E-03 4.40E-03 4.88E-037.09E-033.97E-043.03E-043.17E-043.51E-043.91E-044.37E-044.70E-044.98E-046.57E-041 year3084.62E-03 3.17E-03 3.50E-03 3.91E-03 4.49E-03 5.03E-03 5.95E-03 6.44E-039.91E-034.06E-043.21E-043.31E-043.63E-043.97E-044.48E-044.88E-045.25E-046.19E-042 years2614.79E-03 3.25E-03 3.66E-03 4.10E-03 4.69E-03 5.35E-03 6.05E-03 6.71E-039.09E-033.43E-042.74E-042.86E-043.09E-043.40E-043.69E-044.05E-044.46E-045.10E-043 to <6 years5404.58E-03 3.47E-03 3.63E-03 4.07E-03 4.56E-03 5.03E-03 5.58E-03 5.82E-037.60E-032.55E-041.78E-041.93E-042.15E-042.50E-042.88E-043.27E-043.46E-044.54E-046 to <11 years9404.87E-03 3.55E-03 3.78E-03 4.18E-03 4.72E-03 5.40E-03 6.03E-03 6.58E-039.47E-031.60E-041.13E-041.18E-041.35E-041.57E-041.80E-042.09E-042.18E-042.89E-0411 to <16 years1,3375.64E-03 4.03E-03 4.30E-03 4.79E-03 5.43E-03 6.26E-03 7.20E-03 7.87E-031.11E-021.05E-047.70E-058.00E-058.80E-051.01E-041.18E-041.35E-041.42E-041.95E-0416 to <21 years1,2415.76E-03 4.17E-03 4.42E-03 4.93E-03 5.60E-03 6.43E-03 7.15E-03 7.76E-031.35E-027.70E-055.50E-056.00E-056.80E-057.60E-058.50E-059.50E-051.02E-041.32E-04Light Intensity Activities (1.5 < METS#3.0)Birth to <1 year4197.94E-03 4.15E-03 5.06E-03 6.16E-03 7.95E-03 9.57E-03 1.08E-02 1.19E-021.55E-029.88E-047.86E-048.30E-048.97E-049.72E-041.07E-031.17E-031.20E-031.44E-031 year3081.16E-02 8.66E-03 8.99E-03 9.89E-03 1.14E-02 1.29E-02 1.44E-02 1.58E-022.11E-021.02E-038.36E-048.59E-049.18E-041.01E-031.10E-031.22E-031.30E-031.49E-032 years2611.17E-02 8.52E-03 9.14E-03 9.96E-03 1.14E-02 1.30E-02 1.47E-02 1.53E-021.90E-028.37E-046.83E-047.16E-047.61E-048.26E-048.87E-049.95E-041.03E-031.18E-033 to <6 years5401.14E-02 9.20E-03 9.55E-03 1.02E-02 1.11E-02 1.23E-02 1.34E-02 1.40E-021.97E-026.33E-044.41E-044.80E-045.44E-046.26E-047.11E-047.94E-048.71E-041.08E-036 to <11 years9401.16E-02 8.95E-03 9.33E-03 1.02E-02 1.13E-02 1.28E-02 1.46E-02 1.56E-022.18E-023.84E-042.67E-042.86E-043.24E-043.77E-044.37E-044.93E-045.29E-047.09E-0411 to <16 years1,3371.32E-02 9.78E-03 1.03E-02 1.13E-02 1.28E-02 1.47E-02 1.64E-02 1.87E-022.69E-022.46E-041.76E-041.87E-042.09E-042.38E-042.82E-043.11E-043.32E-044.42E-0416 to <21 years1,2411.34E-02 1.00E-02 1.05E-02 1.15E-02 1.30E-02 1.50E-02 1.70E-02 1.80E-022.91E-021.79E-041.37E-041.44E-041.56E-041.78E-041.99E-042.18E-042.30E-043.32E-04 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Page Chi ld-Specific Exposure Factors Handbook

6-26 September 2008Table 6-13.Descriptive Statistics for Average Ventilation RateaWhile Performing Activities Within the Specified Activity Category, forMales by Age Category (continued)Age GroupNAverage Ventilation Rate (m3/min),Unadjusted for Body WeightAverage Ventilation Rate (m3/min-kg),Adjusted for Body WeightMeanPercentilesMaximumMeanPercentilesMaximum5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS#6.0)Birth to <1 year4191.45E-027.41E-038.81E-031.15E-021.44E-021.70E-022.01E-022.25E-023.05E-021.80E-031.40E-031.49E-031.62E-031.78E-031.94E-032.18E-032.28E-033.01E-031 year3082.14E-021.45E-021.59E-021.80E-022.06E-022.41E-022.69E-022.89E-023.99E-021.88E-031.41E-031.50E-031.65E-031.82E-032.02E-032.34E-032.53E-033.23E-032 years2612.15E-021.54E-021.67E-021.84E-022.08E-022.41E-022.69E-022.97E-025.09E-021.55E-031.21E-031.28E-031.40E-031.54E-031.66E-031.84E-032.02E-032.29E-033 to <6 years5402.10E-021.63E-021.72E-021.87E-022.06E-022.29E-022.56E-022.71E-023.49E-021.17E-038.05E-048.83E-049.99E-041.12E-031.31E-031.56E-031.68E-032.10E-036 to <11 years9402.23E-021.64E-021.72E-021.93E-022.16E-022.50E-022.76E-022.95E-024.34E-027.36E-045.03E-045.45E-046.18E-047.14E-048.34E-049.58E-041.04E-031.43E-0311 to <16 years1,3372.64E-021.93E-022.05E-022.26E-022.54E-022.92E-023.38E-023.69E-025.50E-024.91E-043.59E-043.75E-044.18E-044.73E-045.52E-046.35E-046.81E-041.06E-0316 to <21 years1,2412.90E-022.03E-022.17E-022.45E-022.80E-023.17E-023.82E-024.21E-026.74E-023.87E-042.81E-042.96E-043.34E-043.80E-044.31E-044.86E-045.18E-047.11E-04High Intensity (METS > 6.0)Birth to <1 year1832.75E-021.51E-021.73E-022.06E-022.78E-023.25E-023.84E-024.22E-025.79E-023.48E-032.70E-032.93E-033.10E-033.46E-033.81E-034.14E-034.32E-035.08E-031 year1644.03E-022.83E-023.17E-023.47E-023.98E-024.43E-025.16E-025.59E-026.07E-023.52E-032.52E-032.89E-033.22E-033.57E-033.91E-034.11E-034.34E-034.86E-032 years1624.05E-022.82E-022.97E-023.45E-024.06E-024.62E-025.19E-025.51E-029.20E-022.89E-032.17E-032.34E-032.58E-032.87E-033.20E-033.43E-033.54E-034.30E-033 to <6 years2633.90E-022.95E-023.14E-023.40E-023.78E-024.32E-024.89E-025.22E-026.62E-022.17E-031.55E-031.66E-031.81E-032.11E-032.50E-032.73E-032.98E-033.62E-036 to <11 years6374.36E-023.07E-023.28E-023.58E-024.19E-024.95E-025.66E-026.24E-028.99E-021.41E-039.36E-041.03E-031.19E-031.38E-031.59E-031.83E-031.93E-032.68E-0311 to <16 years1,1115.08E-023.43E-023.68E-024.15E-024.91E-025.74E-026.63E-027.29E-021.23E-019.50E-046.35E-046.96E-047.90E-049.09E-041.09E-031.27E-031.36E-031.98E-0316 to <21 years9685.32E-023.60E-023.83E-024.35E-025.05E-025.93E-027.15E-028.30E-021.30E-017.11E-044.75E-045.27E-045.99E-046.91E-048.02E-049.17E-049.97E-041.94E-03aNMETAn individual’s ventilation rate for the given activity category equals the weighted average of the individual’s activity-specific ventilation rates for activities falling within the category, estimated using amultiple linear regression model, with weights corresponding to the number of minutes spent performing the activity.Numbers in these two columns represent averages, calculated across individuals in thespecified age category, of these weighted averages.These are weighted averages, with the weights corresponding to the 4-year sampling weights assigned within NHANES 1999-2002.=Number of individuals.= Metabolic equivalent.Source:U.S. EPA, 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Child-Specific Exposure Factors Handbook Page September 2008 6-27

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation RatesTable 6-14. Descriptive Statistics for Average Ventilation RateaWhile Performing Activities Within the Specified Activity Category, for Females by Age CategoryAge GroupNAverage Ventilation Rate (m3/min),Unadjusted for Body WeightAverage Ventilation Rate (m3/min-kg),Adjusted for Body WeightMeanPercentilesMaximum MeanPercentilesMaximum5th10th25th50th75th90th95th5th10th25th50th75th90th95thSleep or nap (Activity ID = 14500)Birth to <1 year 415 2.92E-03 1.54E-03 1.72E-03 2.27E-03 2.88E-03 3.50E-03 4.04E-03 4.40E-03 8.69E-033.91E-04 2.80E-04 3.01E-04 3.35E-04 3.86E-04 4.34E-04 4.79E-04 5.17E-04 7.39E-041 year245 4.59E-03 3.02E-03 3.28E-03 3.76E-03 4.56E-03 5.32E-03 5.96E-03 6.37E-03 9.59E-034.14E-04 3.15E-04 3.29E-04 3.61E-04 4.05E-04 4.64E-04 5.21E-04 5.36E-04 6.61E-042 years 255 4.56E-03 3.00E-03 3.30E-03 3.97E-03 4.52E-03 5.21E-03 5.76E-03 6.15E-03 9.48E-033.42E-04 2.58E-04 2.71E-04 2.93E-04 3.33E-04 3.91E-04 4.25E-04 4.53E-04 4.94E-043 to <6 years 543 4.18E-03 2.90E-03 3.20E-03 3.62E-03 4.10E-03 4.71E-03 5.22E-03 5.73E-03 7.38E-032.38E-04 1.45E-04 1.63E-04 1.95E-04 2.33E-04 2.75E-04 3.20E-04 3.53E-04 5.19E-046 to <11 years 894 4.36E-03 2.97E-03 3.17E-03 3.69E-03 4.24E-03 4.93E-03 5.67E-03 6.08E-03 8.42E-031.51E-04 8.90E-05 9.70E-05 1.20E-04 1.46E-04 1.76E-04 2.11E-04 2.29E-04 2.97E-0411 to <16 years 1,451 4.81E-03 3.34E-03 3.57E-03 3.99E-03 4.66E-03 5.39E-03 6.39E-03 6.99E-03 9.39E-039.00E-05 5.90E-05 6.50E-05 7.50E-05 8.70E-05 1.02E-04 1.18E-04 1.30E-04 1.76E-0416 to <21 years 1,182 4.40E-03 2.78E-03 2.96E-03 3.58E-03 4.26E-03 5.05E-03 5.89E-03 6.63E-03 1.23E-026.90E-05 4.40E-05 4.70E-05 5.70E-05 6.70E-05 8.00E-05 9.30E-05 1.02E-04 1.52E-04Sedentary & Passive Activities (METS#1.5 -­ Includes Sleep or Nap)Birth to <1 year 415 3.00E-03 1.60E-03 1.80E-03 2.32E-03 2.97E-03 3.58E-03 4.11E-03 4.44E-03 9.59E-034.02E-04 2.97E-04 3.16E-04 3.52E-04 3.96E-04 4.46E-04 4.82E-04 5.19E-04 7.19E-041 year245 4.71E-03 3.26E-03 3.44E-03 3.98E-03 4.73E-03 5.30E-03 5.95E-03 6.63E-03 9.50E-034.25E-04 3.35E-04 3.48E-04 3.76E-04 4.18E-04 4.69E-04 5.12E-04 5.43E-04 6.42E-042 years 255 4.73E-03 3.34E-03 3.53E-03 4.19E-03 4.67E-03 5.25E-03 5.75E-03 6.22E-03 9.42E-033.55E-04 2.85E-04 2.96E-04 3.20E-04 3.48E-04 3.91E-04 4.20E-04 4.42E-04 4.85E-043 to <6 years 543 4.40E-03 3.31E-03 3.49E-03 3.95E-03 4.34E-03 4.84E-03 5.29E-03 5.73E-03 7.08E-032.51E-04 1.64E-04 1.79E-04 2.11E-04 2.48E-04 2.84E-04 3.28E-04 3.58E-04 4.89E-046 to <11 years 894 4.64E-03 3.41E-03 3.67E-03 4.04E-03 4.51E-03 5.06E-03 5.88E-03 6.28E-03 8.31E-031.60E-04 9.90E-05 1.10E-04 1.31E-04 1.57E-04 1.85E-04 2.12E-04 2.34E-04 2.93E-0411 to <16 years 1,451 5.21E-03 3.90E-03 4.16E-03 4.53E-03 5.09E-03 5.68E-03 6.53E-03 7.06E-03 9.07E-039.70E-05 7.10E-05 7.50E-05 8.30E-05 9.50E-05 1.09E-04 1.23E-04 1.33E-04 1.74E-0416 to <21 years 1,182 4.76E-03 3.26E-03 3.56E-03 4.03E-03 4.69E-03 5.32E-03 6.05E-03 6.60E-03 1.18E-027.50E-05 5.30E-05 5.70E-05 6.30E-05 7.40E-05 8.50E-05 9.60E-05 1.04E-04 1.41E-04Light Intensity Activities (1.5 < METS#3.0)Birth to <1 year 415 7.32E-03 3.79E-03 4.63E-03 5.73E-03 7.19E-03 8.73E-03 9.82E-03 1.08E-02 1.70E-029.78E-04 7.91E-04 8.17E-04 8.80E-04 9.62E-04 1.05E-03 1.18E-03 1.23E-03 1.65E-031 year245 1.16E-02 8.59E-03 8.80E-03 1.00E-02 1.12E-02 1.29E-02 1.52E-02 1.58E-02 2.02E-021.05E-03 8.45E-04 8.68E-04 9.49E-04 1.04E-03 1.14E-03 1.25E-03 1.27E-03 1.64E-032 years 255 1.20E-02 8.74E-03 9.40E-03 1.03E-02 1.17E-02 1.32E-02 1.56E-02 1.63E-02 2.36E-028.97E-04 7.30E-04 7.63E-04 8.19E-04 8.93E-04 9.64E-04 1.04E-03 1.10E-03 1.26E-033 to <6 years 543 1.09E-02 8.83E-03 9.04E-03 9.87E-03 1.07E-02 1.17E-02 1.29E-02 1.38E-02 1.64E-026.19E-04 4.48E-04 4.84E-04 5.37E-04 5.99E-04 6.98E-04 7.83E-04 8.28E-04 1.02E-036 to <11 years 894 1.11E-02 8.51E-03 9.02E-03 9.79E-03 1.08E-02 1.20E-02 1.35E-02 1.47E-02 2.22E-023.82E-04 2.52E-04 2.70E-04 3.15E-04 3.76E-04 4.42E-04 5.03E-04 5.39E-04 7.10E-0411 to <16 years 1,451 1.20E-02 9.40E-03 9.73E-03 1.06E-02 1.18E-02 1.31E-02 1.47E-02 1.58E-02 2.21E-022.25E-04 1.63E-04 1.74E-04 1.96E-04 2.17E-04 2.49E-04 2.84E-04 3.05E-04 3.96E-0416 to <21 years 1,182 1.11E-02 8.31E-03 8.73E-03 9.64E-03 1.08E-02 1.23E-02 1.38E-02 1.49E-02 2.14E-021.74E-04 1.29E-04 1.38E-04 1.54E-04 1.73E-04 1.93E-04 2.13E-04 2.24E-04 2.86E-04



### Page Child-Specific Exposure Factors Handbook

6-28 September 2008Table 6-14.Descriptive Statistics for Average Ventilation RateaWhile Performing Activities Within the Specified Activity Category, forFemalesby Age Category (continued)AgeGroupNAverage Ventilation Rate (m3/min),Unadjusted for Body WeightAverage Ventilation Rate (m3/min-kg),Adjusted for Body WeightMeanPercentilesMaximumMeanPercentilesMaximum5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS#6.0)Birth to <1 year415 1.40E-02 7.91E-03 9.00E-03 1.12E-02 1.35E-02 1.63E-02 1.94E-02 2.23E-024.09E-021.87E-03 1.47E-03 1.52E-03 1.67E-03 1.85E-03 2.01E-03 2.25E-032.40E-032.83E-031 year245 2.10E-02 1.56E-02 1.63E-02 1.79E-02 2.01E-02 2.35E-02 2.71E-02 2.93E-023.45E-021.90E-03 1.52E-03 1.62E-03 1.73E-03 1.87E-03 2.02E-03 2.24E-032.37E-033.24E-032 years255 2.13E-02 1.42E-02 1.56E-02 1.82E-02 2.15E-02 2.39E-02 2.76E-02 2.88E-023.76E-021.60E-03 1.27E-03 1.31E-03 1.44E-03 1.58E-03 1.75E-03 1.92E-032.02E-032.59E-033 to <6 years543 2.00E-02 1.53E-02 1.63E-02 1.78E-02 1.98E-02 2.16E-02 2.38E-02 2.59E-023.29E-021.14E-03 7.92E-04 8.53E-04 9.64E-04 1.11E-03 1.31E-03 1.45E-031.56E-031.93E-036 to <11 years894 2.10E-02 1.60E-02 1.68E-02 1.85E-02 2.04E-02 2.30E-02 2.61E-02 2.81E-024.31E-027.23E-04 4.62E-04 5.12E-04 5.98E-04 7.15E-04 8.38E-04 9.42E-041.01E-031.37E-0311 to <16 years1,451 2.36E-02 1.82E-02 1.95E-02 2.08E-02 2.30E-02 2.54E-02 2.84E-02 3.14E-024.24E-024.41E-04 3.17E-04 3.38E-04 3.80E-04 4.31E-04 4.92E-04 5.51E-046.11E-049.86E-0416 to <21 years1,182 2.32E-02 1.66E-02 1.76E-02 1.96E-02 2.24E-02 2.61E-02 3.03E-02 3.20E-025.25E-023.65E-04 2.67E-04 2.82E-04 3.10E-04 3.51E-04 4.07E-04 4.63E-044.94E-046.50E-04High Intensity (METS > 6.0)Birth to <1 year79 2.42E-02 1.24E-02 1.33E-02 1.72E-02 2.25E-02 2.93E-02 3.56E-02 4.07E-027.46E-023.26E-03 2.53E-03 2.62E-03 2.89E-03 3.23E-03 3.63E-03 3.96E-034.08E-035.02E-031 year55 3.65E-02 2.59E-02 2.62E-02 3.04E-02 3.61E-02 4.20E-02 4.73E-02 4.86E-027.70E-023.38E-03 2.57E-03 2.75E-03 2.97E-03 3.24E-03 3.71E-03 4.16E-034.87E-034.88E-032 years130 3.76E-02 2.90E-02 3.05E-02 3.23E-02 3.64E-02 4.08E-02 4.81E-02 5.14E-027.30E-022.80E-03 2.20E-03 2.31E-03 2.48E-03 2.81E-03 3.13E-03 3.36E-033.48E-033.88E-033 to <6 years347 3.45E-02 2.70E-02 2.82E-02 3.00E-02 3.33E-02 3.76E-02 4.32E-02 4.47E-025.66E-021.98E-03 1.36E-03 1.51E-03 1.69E-03 1.90E-03 2.19E-03 2.50E-032.99E-033.24E-036 to <11 years707 3.94E-02 2.86E-02 3.01E-02 3.37E-02 3.80E-02 4.41E-02 5.05E-02 5.46E-028.29E-021.33E-03 8.85E-04 9.67E-04 1.12E-03 1.33E-03 1.52E-03 1.72E-031.81E-032.22E-0311 to <16 years1,170 4.66E-02 3.11E-02 3.38E-02 3.88E-02 4.53E-02 5.29E-02 6.08E-02 6.63E-021.02E-018.79E-04 5.89E-04 6.25E-04 7.12E-04 8.53E-04 1.01E-03 1.18E-031.31E-032.05E-0316 to <21 years887 4.41E-02 2.87E-02 3.06E-02 3.65E-02 4.27E-02 5.02E-02 5.82E-02 6.34E-021.09E-016.96E-04 4.52E-04 4.96E-04 5.67E-04 6.86E-04 7.93E-04 9.16E-041.00E-031.50E-03aAn individual’s ventilation rate for the given activity category equals the weighted average of the individual’s activity-specific ventilation rates for activities falling withinthe category, estimated using a multiple linear regression model, with weights corresponding to the number of minutes spent performing the activity.Numbers in these twocolumns represent averages, calculated across individuals in the specified age category, of these weighted averages.These are weighted averages, with the weightscorresponding to the 4-year sampling weights assigned within NHANES 1999-2002.N= Number of individuals.MET= Metabolic equivalent.Source: U.S. EPA, 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Child-Specific Exposure Factors Handbook Page September 2008 6-29

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation RatesTable 6-15. Descriptive Statistics for Duration of Time (hours/day) Spent Performing Activities Within the Specified Activity Category,by Age and Gender CategoriesaAgeGroupNDuration (hours/day) Spent at Activity – MalesNDuration (hours/day) Spent at Activity – FemalesMeanPercentilesMaximum MeanPercentiles5th10th25th50th75th90th95th5th10th25th50th75th90th95thMaximumSleep or nap (Activity ID = 14500)Birth to <1 year419 13.51 12.63 12.78 13.19 13.53 13.88 14.24 14.46 15.03415 12.99 12.00 12.16 12.53 12.96 13.44 13.82 14.0714.821 year308 12.61 11.89 12.15 12.34 12.61 12.89 13.13 13.29 13.79245 12.58 11.59 11.88 12.29 12.63 12.96 13.16 13.3114.552 years261 12.06 11.19 11.45 11.80 12.07 12.39 12.65 12.75 13.40255 12.09 11.45 11.68 11.86 12.08 12.34 12.57 12.6613.483 to <6 years540 11.18 10.57 10.70 10.94 11.18 11.45 11.63 11.82 12.39543 11.13 10.45 10.70 10.92 11.12 11.38 11.58 11.7512.236 to <11 years940 10.18 9.65 9.75 9.93 10.19 10.39 10.59 10.72 11.24894 10.26 9.55 9.73 10.01 10.27 10.54 10.74 10.9111.4311 to <16 years1337 9.38 8.84 8.94 9.15 9.38 9.61 9.83 9.95 10.331451 9.57 8.82 8.97 9.27 9.55 9.87 10.17 10.3111.5216 to <21 years1241 8.69 7.91 8.08 8.36 8.67 9.03 9.34 9.50 10.441182 9.08 8.26 8.44 8.74 9.08 9.39 9.79 10.0211.11Sedentary & Passive Activities (METS#1.5 -­ Includes Sleep or Nap)Birth to <1 year419 14.95 13.82 14.03 14.49 14.88 15.44 15.90 16.12 17.48415 14.07 12.86 13.05 13.53 14.08 14.54 15.08 15.4916.141 year308 14.27 13.22 13.33 13.76 14.25 14.74 15.08 15.38 16.45245 14.32 13.02 13.25 13.73 14.31 14.88 15.36 15.8016.402 years261 14.62 13.52 13.67 14.11 14.54 15.11 15.60 15.77 17.28255 14.86 13.81 13.95 14.44 14.81 15.32 15.78 16.0316.913 to <6 years540 14.12 13.01 13.18 13.54 14.03 14.53 15.26 15.62 17.29543 14.27 12.88 13.15 13.56 14.23 14.82 15.43 15.8517.966 to <11 years940 13.51 12.19 12.45 12.86 13.30 13.85 14.82 15.94 19.21894 13.97 12.49 12.74 13.22 13.82 14.50 15.34 16.3618.6811 to <16 years1337 13.85 12.39 12.65 13.06 13.61 14.30 15.41 16.76 18.791451 14.19 12.38 12.76 13.34 14.05 14.82 15.87 16.8119.2716 to <21 years1241 13.21 11.39 11.72 12.32 13.08 13.97 14.83 15.44 18.701182 13.58 11.80 12.17 12.79 13.52 14.29 15.08 15.6716.96Light Intensity Activities (1.5 < METS#3.0)Birth to <1 year419 5.30 2.97 3.25 3.71 4.52 7.29 8.08 8.50 9.91415 6.00 3.49 3.70 4.26 5.01 8.43 9.31 9.7710.531 year308 5.52 2.68 2.89 3.37 4.31 8.23 9.04 9.73 10.90245 5.61 2.83 2.94 3.46 4.39 8.28 9.03 9.3910.572 years261 5.48 3.06 3.26 3.85 4.58 7.58 8.83 9.04 9.92255 5.78 3.20 3.54 4.29 5.33 7.48 8.46 8.749.933 to <6 years540 6.60 3.86 4.25 5.16 6.20 8.26 9.31 9.70 10.74543 6.25 3.78 4.10 4.79 5.84 7.86 8.84 9.3810.326 to <11 years940 7.62 5.07 5.57 6.63 7.63 8.72 9.78 10.12 11.59894 7.27 4.63 5.46 6.33 7.17 8.34 9.42 9.7911.0611 to <16 years1337 7.50 4.48 5.59 6.75 7.67 8.51 9.19 9.63 10.911451 7.55 4.89 5.62 6.75 7.67 8.55 9.27 9.5710.8516 to <21 years1241 7.13 4.37 4.97 6.00 7.02 8.29 9.43 10.03 11.501182 6.98 4.60 5.08 5.91 6.85 7.96 9.16 9.5712.29



### Page Child-Specific Exposure Factors Handbook

6-30 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation RatesTable 6-15. Descriptive Statistics for Duration of Time (hours/day) Spent Performing Activities Within the Specified Activity Category,by Age and Gender Categoriesa(continued)Age Group NDuration (hours/day) Spent at Activity – MalesNDuration (hours/day) Spent at Activity – FemalesMeanPercentilesMaximum MeanPercentilesMaximum5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS#6.0)Birth to <1 year 419 3.67 0.63 0.97 1.74 4.20 5.20 5.80 6.21 7.52415 3.91 0.53 0.74 1.10 4.87 5.77 6.27 6.54 7.681 year308 4.04 0.45 0.59 1.14 5.29 6.06 6.61 6.94 7.68245 4.02 0.52 0.73 1.08 5.14 6.10 7.00 7.37 8.072 years261 3.83 0.59 0.76 1.23 4.74 5.37 5.82 6.15 7.40255 3.27 0.50 0.78 1.22 4.01 4.88 5.35 5.57 6.933 to <6 years 540 3.15 0.55 0.75 1.30 3.80 4.52 5.11 5.32 6.30543 3.35 0.70 0.89 1.61 3.88 4.71 5.29 5.65 7.586 to <11 years 940 2.66 0.65 0.92 1.65 2.68 3.57 4.36 4.79 5.95894 2.57 0.65 0.95 1.82 2.66 3.41 3.95 4.32 6.1011 to <16 years 1337 2.35 0.88 1.09 1.66 2.30 3.02 3.62 3.89 5.901451 2.01 0.89 1.08 1.45 1.96 2.51 3.03 3.28 4.9616 to <21 years 1241 3.35 1.13 1.42 2.19 3.45 4.37 5.24 5.59 6.831182 3.26 1.27 1.48 2.21 3.39 4.24 4.74 5.07 6.68High Intensity (METS > 6.0)Birth to <1 year 183 0.20 0.00 0.00 0.01 0.14 0.28 0.50 0.59 0.9679 0.17 0.03 0.05 0.09 0.14 0.21 0.33 0.40 0.581 year164 0.31 0.01 0.01 0.03 0.22 0.56 0.78 0.93 1.5255 0.22 0.03 0.05 0.09 0.18 0.35 0.40 0.43 0.482 years162 0.10 0.00 0.01 0.03 0.05 0.14 0.25 0.33 0.48130 0.15 0.00 0.01 0.03 0.08 0.16 0.48 0.65 1.013 to <6 years 263 0.27 0.02 0.03 0.04 0.13 0.33 0.75 1.16 1.48347 0.19 0.01 0.02 0.05 0.10 0.22 0.46 0.73 1.436 to <11 years 637 0.32 0.01 0.01 0.03 0.13 0.38 1.10 1.50 3.20707 0.24 0.02 0.03 0.06 0.12 0.26 0.67 0.98 1.7111 to <16 years 1111 0.38 0.03 0.04 0.10 0.21 0.47 1.03 1.34 2.351170 0.30 0.03 0.04 0.08 0.19 0.40 0.66 0.96 3.1616 to <21 years 968 0.40 0.03 0.04 0.14 0.27 0.53 0.99 1.29 2.59887 0.24 0.01 0.03 0.08 0.18 0.34 0.51 0.60 1.61aIndividual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this table. Ventilation rate wasestimated using a multiple linear regression model.N = Number of individuals.MET =Metabolic equivalent.Source: U.S. EPA, 2006.



### Child-Specific Exposure Factors Handbook Page September 2008 6-31

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-16. Nonnormalized Daily Inhalation Rates (m 3 /day) Derived Using Layton’s (1993) Method and CSFII Energy Intake Data

Age Sample Size (Nonweighted) Mean SEM Percentiles

SE of 95 th percentile 50 th 90 th 95 th

Infancy

0-2 months 182 3.63 0.14 3.30 5.44 7.10 0.64

3-5 months 294 4.92 0.14 4.56 6.86 7.72 0.48

6-8 months 261 6.09 0.15 5.67 8.38 9.76 0.86

9-11 months 283 7.41 0.20 6.96 10.21 11.77 -

0-11 months 1,020 5.70 0.10 5.32 8.74 9.95 0.55

Children
1 year 934 8.77 0.08 8.30 12.19 13.79 0.25 2 years 989 9.76 0.10 9.38 13.56 14.81 0.35 3 years 1,644 10.64 0.10 10.28 14.59 16.03 0.27 4 years 1,673 11.40 0.09 11.05 15.53 17.57 0.23 5 years 790 12.07 0.13 11.56 15.72 18.26 0.47 6 years 525 12.25 0.18 11.95 16.34 17.97 0.87 7 years 270 12.86 0.21 12.51 16.96 19.06 1.27 8 years 253 13.05 0.25 12.42 17.46 19.02 1.08 9 years 271 14.93 0.29 14.45 19.68 22.45 a 1.35 10 years 234 15.37 0.35 15.19 20.87 22.90 a 1.02 11 years 233 15.49 0.32 15.07 21.04 23.91 a 1.62 12 years 170 17.59 0.54 17.11 25.07 a 29.17 a 1.61 13 years 194 15.87 0.44 14.92 22.81 a 26.23 a 1.11 14 years 193 17.87 0.62 15.90 25.75 a 29.45 a 4.38 15 years 185 18.55 0.55 17.91 28.11 a 29.93 a 1.79 16 years 201 18.34 0.54 17.37 27.56 31.01 2.07 17 years 159 17.98 0.96 15.90 31.42 a 36.69 a

- 18 years 135 18.59 0.78 17.34 28.80 a 35.24 a 4.24

Adolescent Boys

9-18 years 983 19.27 0.28 17.96 28.78 32.82 1.39

Adolescent Girls

9-18 years 992 14.27 0.22 13.99 21.17 23.30 0.61

U.S. EPA Cancer Guidelines’ Age Groups with Greater Weighting

0 through 1 year 1,954 7.50 0.08 7.19 11.50 12.86 0.17 2 through 15 years 7,624 14.09 0.12 13.13 20.99 23.88 0.50

a FASEB/LSRO (1995) convention, adopted by CSFII, denotes a value that might be less statistically

reliable than other estimates due to small cell size.

- Denotes unable to calculate.

SEM = Standard error of the mean.

SE = Standard error.

Source: Arcus-Arth and Blaisdell, 2007.



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### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-17. Mean and 95 th Percentile Inhalation Rate Values (m 3 /day) for Males and Females Combined

Age Group a Sample Size Mean 95 th

Birth to <1 month b

3 to <6 months

6 to <12 months c 182

294

544 3.63

4.92

6.75 7.10

7.72

10.77
1 to <2 years 934 8.77 13.79
2 to <3 years 989 9.76 14.81
3 to <6 years d 4,107 11.37 17.29
6 to <11 years e 1,553 13.69 20.28
11 to <16 years f 975 17.07 27.74
16 to <21 years g 495 18.31 34.32

a No other age groups from Table 6-14 (Arcus-Arth and Blaisdell, 2007) fit into the U.S. EPA age groupings.

b Age group from Arcus-Arth and Blaisdell (2007) was 0-2 months.

c Age groups of 6-8 months and 9-11 months from Arcus-Arth and Blaisdell (2007) were averaged.

d Age groups of 3, 4 and 5 years from Arcus-Arth and Blaisdell (2007) were averaged.

e Age groups of 6, 7, 8, 9 and 10 years from Arcus-Arth and Blaisdell (2007) were averaged.

f Age groups of 11, 12, 13, 14 and 15 years from Arcus-Arth and Blaisdell (2007) were averaged.

g Age groups of 16, 17 and 18 years from Arcus-Arth and Blaisdell (2007) were averaged.

Source: Arcus-Arth and Blaisdell, 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 6-33

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-18. Summary of Institute of Medicine Energy Expenditure Recommendations for Active and Very Active People with Equivalent Inhalation Rates

Males Females Age Energy Expenditure Inhalation Rate Energy Expenditure Inhalation Rate Years (kcal/day) (m 3 /day) (kcal/day) (m 3 /day)

<1 607 3.4 607 3.4

1 869 4.9 869 4.9

2 1050 5.9 977 5.5

3 1,485––1,683 8.4––9.5 1,395––1,649 7.9––9.3 4 1,566––1,783 8.8––10.1 1,475––1,750 8.3––9.9 5 1,658––1,894 9.4––10.7 1,557––1,854 8.8––10.5 6 1,742––1,997 9.8––11.3 1,642––1,961 9.3––11.1 7 1,840––2,115 10.4––11.9 1,719––2,058 9.7––11.6 8 1,931––2,225 10.9––12.6 1,810––2,173 10.2––12.3 9 2,043––2,359 11.5––13.3 1,890––2,273 10.7––12.8 10 2,149––2,486 12.1––14.0 1,972––2,376 11.1––13.4 11 2,279––2,640 12.9––14.9 2,071––2,500 11.7––14.1 12 2,428––2,817 13.7––15.9 2,183––2,640 12.3––14.9 13 2,618––3,038 14.8––17.2 2,281––2,762 12.9––15.6 14 2,829––3,283 16.0––18.5 2,334––2,831 13.2––16.0 15 3,013––3,499 17.0––19.8 2,362––2,870 13.3––16.2 16 3,152––3,663 17.8––20.7 2,368––2,883 13.4––16.3 17 3,226––3,754 18.2––21.2 2,353––2,871 13.3––16.2 18 2,823––3,804 18.4––21.5 2,336––2,858 13.2––16.1 19––30 3,015––3,490 17.0––19.7 2,373––2,683 13.4––15.2 31––50 2,862––3,338 16.2––18.9 2,263––2,573 12.8––14.5 51––70 2,671––3,147 15.1––17.8 2,124––2,435 12.0––13.8

Source: Stifelman, 2007.



### Page Child-Specific Exposure Factors Handbook

6-34 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-19. Mean Inhalation Rate Values (m 3 /day) for Males, Females, and Males and Females Combined. a

Age Group b Males Females Combined

Birth to <1 year 3.4 3.4 3.4
1 to <2 years 4.9 4.9 4.9
2 to <3 years 5.9 5.5 5.7
3 to <6 years c 9.2 8.3 8.8
6 to <11 years d 10.9 10.2 10.6
11 to <16 years e 14.9 12.7 13.8
16 to <21 years f 18.2 13.3 15.8

a Inhalation rates are for IOM Physical Activity Level (PAL) category "active"; the total number of subjects for all PAL categories was 3007.

b No other age groups from Table 6-15 (Stifelman, 2007) fit into the EPA age groupings.

c Age groups of 3, 4, and 5 years from Stifelman, 2007 were averaged.

d Age groups of 6, 7, 8, 9 and 10 years from Stifelman, 2007 were averaged.

e Age groups of 11, 12, 13, 14 and 15 years from Stifelman, 2007 were averaged.

f Age groups of 16, 17 and 18 years from Stifelman, 2007 were averaged.

Source: Stifelman, 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 6-35Table 6-20. Mean Inhalation Rate Values (m 3 /day) from Key Studies for Males and Females Combined

Age Group U.S. EPA (2006) Brochu et al. (2006) Arcus-Arth and Blaisdell (2007) Stifelman (2007) Combined Key

Studies

N a Mean N Mean N Mean N Mean N Mean

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months

Birth to <1 year

1 to <2 years

2 to <3 years

3 to <6 years

6 to <11 years

11 to <16 years

16 to <21 years b

-

-

-

-

834

553

516

1,083

1,834

2,788

2,423 -

-

-

-

8.65

13.40

12.99

12.41

12.92

14.38

15.41 -

-

85

103

-

101

-

-

-

-

- -

-

3.32

4.09

-

4.95

-

-

-

-

- 182

-

294

544

--

934

989

4,107

1,553

975

495 3.63

-

4.92

6.75

--

8.77

9.76

11.37

13.69

17.07

18.31 - -

- -

- -

- -

- 3.40

- 4.90

- 5.70

- 8.77

- 10.57

- 13.78

- 15.75 182

-

379

647

834

1,588

1,505

5,190

3,387

3,763

2,918 3.63

­

4.12

5.42

6.03

8.01

9.48

10.85

12.39

15.08

16.48

a

b Number of individuals; the total number of subjects for Stifelman (2007) was 3,007. No data from this study for this age group. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Page Chi ld-Specific Exposure Factors Handbook

6-36 September 2008Table 6-21. 95 th Percentile Inhalation Rate Values (m 3 /day) from Key Studies for Males and Females Combined

Age Group U.S. EPA (2006) Brochu et al. (2006) Arcus-Arth and Blaisdell (2007) Stifelman (2007) Combined Key

Studies

N a 95 th 95 th N 95 th N 95 th N 95 th N

Birth to <1 month b

- - - - 182 7.10 - - 182 7.10

1 to <3 months

- - - - - - - - - ­

3 to <6 months

- - 85 4.47 294 7.72 - - 379 6.09

6 to <12 months

- - 103 5.33 544 10.77 - - 647 8.05

Birth to <1 year 834 12.68

- - - - - - 834 12.68

1 to <2 years 553 18.26 101 6.46 934 13.79 - - 1,588 12.84

2 to <3 years 516 17.04

- - 989 14.81 - - 1,505 15.93

3 to <6 years 1,083 15.17

- - 4,107 17.29 - - 5,190 16.23

6 to <11 years 1,834 17.03

- - 1,553 20.28 - - 3,387 18.66

11 to <16 years 2,788 19.31

- - 975 27.74 - - 3,763 23.53

16 to <21 years 2,423 20.84

- - 495 34.32 - - 2,918 27.58

a

b Number of individuals; the total number of subjects for Stifelman (2007) was 3,007. No data from this study for this age group. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Child-Specific Exposure Factors Handbook Page September 2008 6-37

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-22. Daily Inhalation Rates Estimated From Daily Activities a

Subject Inhalation Rate (m 3 /hour) Daily Inhalation Rate (DIR) b
(m 3 /day) Resting Light Activity
Child (10 years) 0.29 0.78 14.8
Infant (1 year) 0.09 0.25 3.76

Newborn 0.03 0.09 0.78

a Assumptions made were based on 8 hours resting and 16 hours light activity for adults and children (10 yrs); 14 hours resting and 10 hours light activity for infants (1 yr); 23 hours resting and 1 hour light activity for newborns.

b

DIR = Daily Inhalation Rate

IR i = Corresponding inhalation rate at i th activity

t i = Hours spent during the i th activity k = Number of activity periods T = Total time of the exposure period (i.e., a day)

Source: ICRP, 1981. DIR T IR t i i

i K

=

= 

## ∑ 1

1



### Page Child-Specific Exposure Factors Handbook

6-38 September 2008Table 6-23.Selected Inhalation Rate Values During Different Activity Levels Obtained From Various Literature SourcesSubjectW (kg)RestingLight ActivityHeavy WorkMaximal Work DuringExercisefVTV*fVTV*fVTV*f VT V*Adolescentmale, 14-16 ymale, 14-15 yfemale, 14-16 yfemale, 14-15 y; 164.9 cm LChildren10 y; 140 cm Lmales, 10-11 ymales, 10-11 y; 140.6 cm Lfemales, 4-6 yfemales, 4-6 y; 111.6 cm LInfant, 1 yNewborn20 hrs-13 wk9.6 hrs6.6 days59.45636.532.520.818.42.52.5-5.33.63.716151630342529330300300481521215.24.54.81.4a0.50.50.6246001453525861706668b252018701330105060052051a,b11388716140343.5bWabSource:= Body weights; f = frequency (breaths/min); VT = tidal volume (ml); V* = minute volume (l/min); cm L = length/height; y = years of age; wk = week.Calculated from V* = f x VT.Crying.ICRP, 1981. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Child-Specific Exposure Factors Handbook Page September 2008 6-39

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-24. Summary of Human Inhalation Rates for Children by Activity Level (m 3 /hour) a

N b Resting c N b Light d N b Moderate e N b Heavy f

Child, 6 years 8 0.4 16 0.8 4 2 5 2.3

Child, 10 years 10 0.4 40 1 29 3.2 43 3.9

a Values of inhalation rates for children (male and female) presented in this table represent the mean of values reported for each activity level in 1985.

b Number of observations at each activity level.

c Includes watching television, reading, and sleeping.

d includes most domestic work, attending to personal needs and care, hobbies, and conducting minor indoor repairs and home improvements.

e Includes heavy indoor cleanup, performance of major indoor repairs and alterations, and climbing stairs.

f Includes vigorous physical exercise and climbing stairs carrying a load.

Source: Adapted from U.S. EPA, 1985.

Table 6-25. Activity Pattern Data Aggregated for Three Microenvironments by Activity Level for All Age Groups

Microenvironment Activity Level Average Hours Per Day in Each Microenvironment at Each Activity Level

Indoors

Outdoors

In Transportation

Vehicle Resting Light

Moderate

Heavy

TOTAL

Resting Light

Moderate

Heavy

TOTAL

Resting Light

Moderate

Heavy

TOTAL 9.82

9.82

0.71

0.10

20.4

0.51

0.51

0.65

0.12

1.77

0.86

0.86

0.05

0.0012

1.77

Source: Adapted from U.S. EPA, 1985.



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-26. Summary of Daily Inhalation Rates Grouped by Age and Activity Level

Subject Daily Inhalation Rate (m 3 /day) a Total Daily IR b

(m 3 /day) Resting Light Moderate Heavy

Child, 6 years 4.47 8.95 2.82 0.50 16.74

Child, 10 years 4.47 11.19 4.51 0.85 21.02

a Daily inhalation rate was calculated using the following equation:

IR i = Inhalation rate at i th activity (Table 6-13 and 6-14)

t i = Hours spent per day during i th activity (Table 6-15) k = Number of activity periods T = Total time of the exposure period (e.g., a day)

b Total daily inhalation rate was calculated by summing the specific activity (resting, light, moderate, heavy) and dividing them by the total amount of time spent on all activities.

Source: Generated using the data from U.S. EPA (1985) as shown in Tables 6-24 and 6-25.



### Child-Specific Exposure Factors Handbook Page September 2008 6-41Table 6-28. Subject Panel Inhalation Rates by Mean VR, Upper Percentiles, and Self-estimated Breathing Rates

Panel Number and Description N a Inhalation Rates (m 3 /hour)

Mean VR b Mean VR at Activity Levels 99th Percentile

VR Slow Medium Fast

Healthy

2- Elementary School Students 3- High School Students 17

19 0.90

0.84 1.98

2.22 0.84

0.78 0.96

1.14 1.14

1.62

Asthmatics

6- Elementary and High School

Students 13 1.20 2.40 1.20 1.20 1.50

a Number of individuals in each survey panel.

b 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).

VR = Ventilation rate.

Source:	 Linn et al., 1992. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-27. Calibration and Field Protocols for Self-monitoring of Activities Grouped by Subject Panels

Panel Calibration Protocol Field Protocol

Panel 2 - Healthy Elementary School Students - 5 male, 12 female, ages 10-12 Outdoor exercises each consisted of 20 minute rest, slow walking, jogging and fast walking Saturday, Sunday and Monday (school day) in early autumn; heart rate recordings and activity diary during waking hours and during sleep.

Panel 3 - Healthy High School Students - 7 male, 12 female, ages

13-17 Outdoor exercises each consisted of 20 minute rest, slow walking, jogging and fast walking Same as Panel 2, however, no heart rate recordings during sleep for most subjects.

Panel 6 - Young Asthmatics - 7 male, 6 female, ages 11-16 Laboratory exercise tests on bicycles

and treadmills Summer monitoring for 2 successive weeks, including 2 controlled exposure

studies with few or no observable respiratory effects.

Source: Linn et al., 1992.



### Page Child-Specific Exposure Factors Handbook

6-42 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-29. Distribution of Predicted Inhalation Rates by Location and Activity Levels for Elementary and High School Students

Inhalation Rates (m 3 /hour)

Age (years) Student Location Activity % Recorded Percentile Rankings b

Level Time a Mean ± SD 1 st 50 th 99.9th

10-12 EL c Indoors slow 49.6 0.84 ± 0.36 0.18 0.78 2.34 (N d =17) medium 23.6 0.96 ± 0.36 0.24 0.84 2.58

fast 2.4 1.02 ± 0.60 0.24 0.84 3.42

Outdoors slow 8.9 0.96 ± 0.54 0.36 0.78 4.32

medium 11.2 1.08 ± 0.48 0.24 0.96 3.36

fast 4.3 1.14 ± 0.60 0.48 0.96 3.60

13-17 HS c Indoors slow 70.7 0.78 ± 0.36 0.30 0.72 3.24 (N d =19) medium 10.9 0.96 ± 0.42 0.42 0.84 4.02

fast 1.4 1.26 ± 0.66 0.54 1.08 6.84 e

Outdoors slow 8.2 0.96 ± 0.48 0.42 0.90 5.28

medium 7.4 1.26 ± 0.78 0.48 1.08 5.70

fast 1.4 1.44 ± 1.08 0.48 1.02 5.94

a Recorded time averaged about 23 hr per elementary school student and 33 hours per high school student over 72­ hour periods.

b Geometric means closely approximated 50th percentiles; geometric standard deviations were 1.2-1.3 for HR,1.5-1.8

for VR.

c Elementary school student or high school student.

d Number of students that participated in survey.

e Highest single value.

SD = Standard deviation.

Source: Spier et al., 1992.



### Child-Specific Exposure Factors Handbook Page September 2008 6-43

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-30. Average Hours Spent Per Day in a Given Location and Activity Level for Elementary and High School Students

Students Location Activity Level Total Time Spent (hours/day) Slow Medium Fast

Elementary school, ages 10-12 years (N=17) Indoors 16.3 2.9 0.4 19.6

Outdoors 2.2 1.7 0.5 4.4

High school, ages 13-17 years (N=19) Indoors 19.5 1.5 0.2 21.2

Outdoors 1.2 1.3 0.2 2.7

N = Number of students that participated in survey.

Source: Spier et al., 1992.



### Page Child-Specific Exposure Factors Handbook

6-44 September 2008Table 6-31. Summary of Average Inhalation Rates (m 3 /hour) by Age Group and Activity Levels for Laboratory Protocols

Age Group Activity Level

Resting a b Sedentary Light c d Moderate Heavy e

Young Children (3-5.9 years) Average inhalation rate (m 3 /hour)

(N=12, gender not specified)

Children (6-12.9 years) Average inhalation rate (m 3 /hour)

(N=40, 20 male and 20 female) 0.37

0.45 0.40

0.47 0.65

0.95 DNP f

1.74 DNP f

2.23

a

b

c

d

e

f

Source: Resting defined as lying (see Table 6-33 for original data). Sedentary defined as sitting and standing (see Table 6-33 for original data). Light defined as walking at speed level 1.5 - 3.0 mph (see Table 6-33 for original data). Moderate defined as fast walking (3.3 - 4.0 mph) and slow running (3.5 - 4.0 mph) (see Table 6-33 for original data). Heavy defined as fast running (4.5 - 6.0 mph) (see Table 6-33 for original data). Group did not perform (DNP) this protocol or N was too small for appropriate mean comparisons. Allyoung

children did not run.

Adapted from Adams, 1993. 

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Child-Specific Exposure Factors Handbook Page September 2008 6-45

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-32. Summary of Average Inhalation Rates (m 3 /hour) by Age Group And Activity Levels in Field Protocols

Age Group Moderate Activity a

Young Children (3-5.9 years) Average inhalation rate (m 3 /hour)

(N=12, gender not specified) 0.68

Children (6-12.9 years) Average inhalation rate (m 3 /hour)

(N=40, 20 male and 20 female) 1.07

a Moderate activity was defined as play.

N = Number of individuals.

Source: Adams, 1993.



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6-46 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-33. Mean Minute Inhalation Rate (m 3 /minute) by Group and Activity for Laboratory Protocols

Activity Young Children a Children a

Lying Sitting Standing 6.19E-03

6.48E-03

6.76E-03 7.51E-03

7.28E-03

8.49E-03

Walking

1.5 mph 1.875 mph 2.0 mph 2.25 mph 2.5 mph 3.0 mph 3.3 mph 4.0 mph 1.03E-02

1.05E-02

DNP

1.17E-02

DNP

DNP

DNP

DNP DNP b

DNP

1.41E-02

DNP

1.56E-02

1.78E-02

DNP

DNP

Running

3.5 mph 4.0 mph 4.5 mph 5.0 mph 6.0 mph DNP

DNP

DNP

DNP

DNP 2.68E-02

3.12E-02

3.72E-02

DNP

DNP

a Young Children, male and female 3-5.9 years old; Children, male and female 6-12.9 years old.

b Group did not perform (DNP) this protocol or N was too small for appropriate mean comparisons.

Source: Adams , 1993.

Table 6-34. Mean Minute Inhalation Rate (m 3 /minute) by Group and Activity for Field Protocols

Activity Young Children a Children a

Play 1.13E-02 1.89E-02

a

Source: Young children, male and female 3-5.9 years old; children, male and female 6-12.9 years old.

Adams, 1993.



### Child-Specific Exposure Factors Handbook Page September 2008 6-47

### Child-Specific Exposure Factors Handbook

Chapter 6 - Inhalation Rates

Table 6-35. Comparisons of Estimated Basal Metabolic Rates (BMR) with Average Food-energy Intakes (EFD) for Individuals Sampled in the 1977-78 NFCS

Cohort/Age

(years) Body Weight (kg) BMR a Energy Intake (EFD) Ratio

EFD d /BMR MJ/day b Kcal/day c MJ/day Kcal/day

Males and Females

< 1 7.6 1.74 416 3.32 793 1.90

1 to 2 13 3.08 734 5.07 1209 1.65

3 to 5 18 3.69 881 6.14 1466 1.66

6 to 8 26 4.41 1053 7.43 1774 1.68

Males

9 to 11 36 5.42 1293 8.55 2040 1.58

12 to 14 50 6.45 1540 9.54 2276 1.48

15 to 18 66 7.64 1823 10.80 2568 1.41

Females

9 to 11 36 4.91 1173 7.75 1849 1.58

12 to 14 49 5.64 1347 7.72 1842 1.37

15 to 18 56 6.03 1440 7.32 1748 1.21

a Calculated from the appropriate age and gender-based BMR equations given in Table 6-37.

b MJ/day - mega joules/day.

c Kcal/d - kilo calories/day.

d Food energy intake (Kcal/day) or (MJ/day).

Source: Layton, 1993.



### Page Child-Specific Exposure Factors Handbook

6-48 September 2008Table 6-36. Daily Inhalation Rates Calculated from Food-energy Intakes

Cohort/Age

(years) L b Daily Inhalation Rate c (m 3 /day) Sleep (hours) MET a Value Inhalation Rates

A d F e f f Inactive Active (m 3 /day) (m 3 /day)

Males and Females

<1

1 to 2

3 to 5

6 to 8 1

2

3

3 4.5

6.8

8.3

10 11

11

10

10 1.9

1.6

1.7

1.7 2.7

2.2

2.2

2.2 2.35

4.16

4.98

5.95 6.35

9.15

10.96

13.09

Males

9 to 11

12 to 14

15 to 18 3

3

4 14

15

17 9

9

8 1.9

1.8

1.7 2.5

2.2

2.1 7.32

8.71

10.31 18.3

19.16

21.65

Females

9 to 11

12 to 14

15 to 18 3

3

4 13

12

12 9

9

8 1.9

1.6

1.5 2.5

2.0

1.7 6.63

7.61

8.14 16.58

15.22

13.84

a MET = Metabolic equivalent.

b L = Number of years for each age cohort.

a Daily inhalation rate was calculated by multiplying the EFD values (see Table 6-35) by H × VQ for subjects under 9 years of age and by 1.2 × H × VQ (for subjects 9 years of age and older (see text for explanation), where EFD = Food energy intake (Kcal/day) or (MJ/day), H = Oxygen uptake = 0.05 LO 2 /KJ or 0.21 LO 2 /Kcal, and VQ = Ventilation equivalent = 27 = geometric mean of VQs (unitless).

d For individuals 9 years of age and older, A was calculated by multiplying the ratio for EFD/BMR (unitless) (Table 6-35) by the factor 1.2 (see text for explanation).

e F = (24 × A - 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.

f Inhalation rate for inactive periods was calculated as BMR × H × VQ, and for active periods by multiplying the inactive inhalation rate by F (see footnote c); BMR values are from Table 6-35, where BMR = basal metabolic rate (MJ/day) or (kg/hr).

Source:	 Layton, 1993. 

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Chapter 6 - Inhalation Rates

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Chapter 6 - Inhalation Rates

Table 6-37. Statistics of the Age/gender Cohorts Used to Develop Regression Equations for Predicting Basal Metabolic Rates (BMR)

Gender, Age (years) BMR

CV Body Weight

(kg) N BMR Equation a MJ d -1 SD r

Males

Under 3

3 to < 10

10 to < 18 1.51

4.14

5.86 0.92

0.50

1.17 0.61

0.12

0.20 6.6

21

42 162

338

734 0.249 bw - 0.127

0.095 bw + 2.110

0.074 bw + 2.754

Females

Under 3

3 to < 10

10 to < 18 1.54

3.85

5.04 0.92

0.49

0.78 0.59

0.13

0.15 6.9

21

38 137

413

575 0.244 bw - 0.130

0.085 bw + 2.033

0.056 bw + 2.898 0.95

0.83

0.93

0.96

0.81

0.8

a Body weight (bw) in kg.

SD = Standard deviation. CV = Coefficient of variation (SD/mean).

N = Number of observations.

r = Coefficient of correlation.

Source: Layton, 1993.



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Table 6-38. Daily Inhalation Rates Obtained from the Ratios of Total Energy Expenditure to Basal Metabolic Rate (BMR)

Gender/Age

(years) Body Weight a (kg) BMR b (MJ/day) VQ A c H (m 3 O 2 /MJ) Inhalation Rate, V E (m 3 /day) d

Males

0.5 to <3

3 to <10

10 to <18 14

23

53 3.4

4.3

6.7 27

27

27 1.6

1.6

1.7 0.05

0.05

0.05 7.3

9.3

15

Females

0.5 to <3

3 to <10

10 to <18 11

23

50 2.6

4.0

5.7 27

27

27 1.6

1.6

1.5 0.05

0.05

0.05 5.6

8.6

12

a Body weight was based on the average weights for age/gender cohorts in the U.S. population.

b The BMRs (basal metabolic rate) are calculated using the respective body weights and BMR equations (see Table 6­ 36).

c The values of the BMR multiplier (EFD/BMR) for those 18 years and older were derived from the Basiotis et al. (1989) study: Male = 1.59, Female = 1.38. For males and females under 10 years old, the mean BMR multiplier used was 1.6. For males and females aged 10 to < 18 years, the mean values for A given in Table 6-36 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: Layton, 1993.



### Child-Specific Exposure Factors Handbook Page September 2008 6-51Table 6-39. Inhalation Rates for Short-term Exposures

Gender/Age

(years) Body Weight

(kg) a BMR b (MJ/day) Activity Type

Rest Sedentary Light Moderate Heavy

MET (BMR Multiplier)

1 2 c 1.2 4 d 10 e

3 /minute) f,g Inhalation Rate (m

Males

0.5to<3

3to<10

10to<18

Females

0.5to<3

3to<10

10to<18 14

23

53

11

23

50 3.40

4.30

6.70

2.60

4.00

5.70 3.2E-03

4.0E-03

6.3E-03

2.4E-03

3.8E-03

5.3E-03 3.8E-03

4.8E-03

7.5E-03

2.9E-03

4.5E-03

6.4E-03 6.4E-03

8.1E-03

1.3E-02

4.9E-03

7.5E-03

1.1E-02 1.3E-02

1.6E-02

2.5E-02

1.0E-02

1.5E-02

2.1E-02 h

–
h

– 6.3E-02

h

–
h

– 5.3E-02

a

b

c

d

e

f

g

h

Source: Body weights were based on average weights for age/gender cohorts of the U.S. population The BMRs for the age/gender cohorts were calculated using the respective body weights and the BMR equations (Table 6-37). Range = 1.5 - 2.5. Range = 3 - 5. Range = >5 - 20. The inhalation rate was calculated as IR = BMR (MJ/day) × H (0.05 L/KJ) × MET × VQ (27) × (day/1440 min)

3 m L Original data were presented in L/min. Conversion to m 3 /min was obtained as follows: × 1000L min

The maximum possible MET sustainable for more than 5 minutes does not reach 10 for females and males until age 13 and 12, respectively. Therefore, a METs of 10 is not possible for this age category.

Layton, 1993. 

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Chapter 6 - Inhalation Rates

Table 6-40. Mean, Median, and SD of Inhalation Rate According to Waking or Sleeping in 618 Infants and Children Grouped in Classes of Age

Inhalation Rate (breaths/min)

Age (months) N Waking Sleeping

Mean ± SD Median Mean ± SD Median

<2 104 48.0 ± 9.1 47 39.8 ± 8.7 39

2 to <6 106 44.1 ± 9.9 42 33.4 ± 7.0 32

6 to <12 126 39.1 ± 8.5 38 29.6 ± 7.0 28

12 to <18 77 34.5 ± 5.8 34 27.2 ± 5.6 26

18 to <24 65 32.0 ± 4.8 32 25.3 ± 4.6 24

24 to <30 79 30.0 ± 6.2 30 23.1 ± 4.6 23

30 to 36 61 27.1 ± 4.1 28 21.5 ± 3.7 21

SD

N = Standard deviation.

= Number of individuals.

Source: Rusconi et al., 1994.



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Chapter 6 - Inhalation Rates

Figure 6-1. 5th, 10th, 25th, 50th, 75th, 90th, and 95th Smoothed Centiles by Age in Awake Subjects (RR = respiratory rate). Source: Rusconi et al., 1994.

Figure 6-2. 5th, 10th, 25th, 50th, 75th, 90th, and 95th Smoothed Centiles by Age in Asleep Subjects (RR = respiratory rate). Source: Rusconi et al., 1994.



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Chapter 7

- Dermal Exposure Factors

7 DERMAL EXPOSURE FACTORS

7.1 INTRODUCTION Dermal exposure can occur during a variety of

activities in different environmental media and microenvironments (U.S. EPA, 1992a; 1992b; 2004).

These include:

C 

### Water (e.g., bathing, washing, swimming);

C 

### Soil (e.g., outdoor recreation, gardening, construction);

C 

### Sediment (e.g., wading, fishing);

C 

### Liquids (e.g., use of commercial products);

C 

### Vapors/fumes (e.g., use of commercial products); and

C 

### Indoor dust (e.g., carpets, floors, counter tops). Children may be more highly exposed to environmental toxicants through dermal routes than adults. For instance, children may crawl, roll or sit on surfaces treated with chemicals (i.e., carpets and floors) and play with objects such as toys where residues may settle. Children also are more likely to wear less clothing than adults. As a result, children may have higher dermal contact with contaminated media. In addition, young children who wear diapers may be exposed for long periods of time to chemical components of lotions and other products used for diapering. Children also have a higher surface area relative to body weight compared to adults. The surface-area-to-body weight ratio for newborn infants is more than two times greater than that for adults (Cohen-Hubal et al., 1999). Therefore, the dose relative to body weight would be greater for a child than for an adult with an equal amount of skin exposure

to a chemical. This chapter focuses on measurements of body

surface area and dermal adherence of solids to the skin. These are only two of a several parameters that influence dermal absorption. Other factors include the concentration of chemical in contact with the skin, characteristics of the chemical (i.e., lipophilicity, polarity, volatility, solubility), the site of application (i.e., the thickness of the stratum corneum varies over parts of the body), absorption of chemical through the skin and factors that affect absorption (i.e, thickness, age, condition), and the amount of chemical delivered to the target organ. For guidance on how to use skin surface area and dermal adherence factors, as well as these other factors to assess dermal exposure, readers

are referred to Dermal Exposure Assessment: Principles and Applications (U.S. EPA, 1992b) and Risk Assessment Guidelines for Superfund (RAGs) Part

E (U.S. EPA, 2004). Frequency and duration of contact also affect dermal exposure. Information on activity factors is presented in Chapter 17 of this handbook.

Surface area of the skin can be determined using measurement or estimation 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. The coating method consists of coating either the whole body or specific body regions with a substance of known density and thickness. 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 results of studies conducted using these various techniques have been summarized in Development of Statistical Distributions or Ranges of Standard Factors Used in Exposure Assessments (U.S. EPA, 1985).

Because of the difficulties associated with direct measurements of body surface area, the existing direct measurement data are limited and dated. However, several researchers have developed methods for estimating body surface area from measurements of other body dimensions (DuBois and DuBois, 1916; Boyd, 1935; Gehan and George, 1970). Generally, these formulas are based on the observation that body weight and height are correlated with surface area and are derived using multiple regression techniques. U.S. EPA (1985) evaluated the various formulas for estimating total body surface area. A discussion and comparison of formulas are presented in Appendix 7A. The key studies on body surface area that are presented in Section 7.3 of this chapter are based on these formulas, and weight and height data from the National Health and Nutrition Examination Survey (NHANES).

Several field studies have been conducted to

estimate the adherence of solids to skin. These field studies consider factors such as activity, gender, age, field conditions, and clothing worn. These studies are presented in Section 7.4 of this chapter.

The recommendations for skin surface area and dermal adherence of solids to skin are provided in the next section, along with a summary of the confidence ratings for these recommendations. The



### Child-Specific Exposure Factors Handbook Page September 2008 7-1

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Chapter 7

- Dermal Exposure Factors

recommended values are based on key studies identified by U.S. EPA for these factors. Following the recommendations, the two key studies on skin surface area and the three key studies on dermal adherence of

solids to skin are summarized. Relevant data on these factors are also presented to provide added perspective on the state-of-knowledge pertaining to dermal exposure factors.

7.2 RECOMMENDATIONS

7.2.1 Body Surface Area

The recommended mean and 95 th 

### percentile total body surface area values for children are summarized in Table 7-1. If gender-specific data or data for percentiles other than the mean or 95 th 

### percentile are needed, the reader is referred to Tables 7­ 7 through 7-9 of this chapter. The recommendations for total body surface area are based on the U.S. EPA analysis of NHANES 1999-2006 data and are presented for the standard age groupings recommended by U.S. EPA (2005) for male and female children combined. The U.S. EPA analysis of NHANES 1999-2006 data uses correlations with body weight and height for deriving skin surface area (see Section 7.3.1.2 and Appendix 7A). NHANES 1999-2006 used a statistically-based survey design which should ensure that the data are reasonably representative of the general population. The recommendations for the percentage of total body surface area represented by individual body parts are based on data from U.S. EPA (1985), and are presented in Table 7-2 (See Section 7.3.1). Table 7-2 also provides age-specific body part surface areas (m 2 

### ) that were obtained by multiplying the mean body part percentages by the total body surface areas presented in Table 7-1. If gender-specific data or data for percentiles other than the mean and 95 th 

### percentile are needed, the body part percentages in Table 7-2 may be applied to the total skin surface area data in Tables 7-7 through 7-9. Table 7-3 presents the confidence ratings for the recommendations for body

surface area. For swimming and bathing scenarios, past exposure assessments have assumed that 75 to 100 percent of the skin surface is exposed (U.S. EPA, 1992b). More recent guidance recommends assuming 100 percent exposure for these scenarios (U.S. EPA, 2004). For other exposure scenarios, it is reasonable to assume that clothing reduces the contact area. However, while it is generally assumed that adherence of solids to skin occurs to only the areas of the body not covered by clothing, it is important to understand that soil and dust particles can get under clothing and be deposited on skin to varying degrees depending on the protective properties of the clothing. Likewise, liquids may soak through clothing and contact covered areas of the skin. Assessors should consider these possibilities

for the scenario of concern and select skin areas that are judged appropriate.

7.2.2 Adherence of Solids to Skin The adherence factor (AF) describes the amount of material that adheres to the skin per unit of surface area. Although most research in this area has focused on soils, a variety of other solid residues can accumulate on skin, including household dust, sediments and commercial powders. Studies on soil adherence have shown that: 1) soil properties influence adherence; 2) soil adherence varies considerably across different parts of the body; and 3) soil adherence varies with activity (U.S. EPA, 2004). It is recommended that exposure assessors use adherence data derived from testing that matches the exposure scenario of concern in terms of solid type, exposed body parts, and activities, as closely as possible. Assessors should refer to the

activities described in Table 7-12 to select those that best represent the exposure scenarios of concern and use the corresponding adherence values from Table 7­

13. Table 7-12 lists the age ranges covered by each study. This may be used as a general guide to the ages covered by these data. Recommended mean AF values are summarized in Table 7-4 according to common activities involving children. Insufficient data were available to develop distributions or probability functions for these values. Also, the small number of subjects in these studies prevented the development of recommendations for the specific age groups recommended by U.S. EPA (2005). RAGS Part E (U.S. EPA, 2004) recommends that scenario-specific adherence values be weighted according to the body parts exposed. Weighted adherence factors may be estimated according to the following equation:

AF wtd 

### = (AF 1 

### )(SA 1 

### ) + (AF 2 

### )(SA 2 

### ) + . . . . (AF i 

### )(SA i 

### )

SA 1 

### + SA 2 

### + . . . SA i 

### (Eqn. 7-1)



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Chapter 7

- Dermal Exposure Factors

where:

AF wtd 

### = weighted adherence factor; AF = adherence factor; and

SA = surface area.

For the purposes of this calculation, the surface area of the face may be assumed to be 1/3 that of the head, forearms may be assumed to represent 45 percent of the arms and lower legs may be assumed to represent 40 percent of the legs (U.S. EPA, 2004). The recommended dermal AFs represent the

amount of material on the skin at the time of measurement. U.S. EPA (1992b) recommends interpreting AFs as representative of contact events. Assuming that the amount of solids measured on the skin represents accumulation between washings, and that people wash at least once per day, these adherence values can be interpreted as daily contact rates (U.S. EPA, 1992b). The rate of solids accumulation on skin over time has not been well studied, but probably occurs fairly quickly. Therefore, pro-rating the adherence values for exposure time periods of less than one day is not recommended. The confidence ratings for these AF

recommendations are shown in Table 7-5. It should be

noted that while the recommendations are based on the best available estimates of activity-specific adherence, they are based on limited data from studies that have focused primarily on soil. Therefore, they have a high degree of uncertainty and considerable judgment must be used when selecting them for an assessment. It

should also be noted that the skin adherence studies

have not considered the influence of skin moisture on adherence. Skin moisture varies depending on a number of factors, including activity level and ambient temperature/humidity. It is uncertain how well this variability has been captured in the dermal adherence

studies.



### Child-Specific Exposure Factors Handbook Page September 2008 7-3Table 7-1. Recommended Values for Total Body Surface Area,

Males and Females Combined

Age Group Mean 95 th Percentile Multiple

Percentiles Source

2 m

Birth to <1 month 0.29 0.34

1 to <3 months 0.33 0.38

3 to <6 months 0.38 0.44

6 to <12 months 0.45 0.51

1 to <2 years 0.53 0.61 See Tables 7-7, U.S. EPA Analysis of
2 to <3 years 0.61 0.70 7-8, and 7-9 NHANES 1999-2006 data

3 to <6 years 0.76 0.95

6 to <11 years 1.08 1.48

11 to <16 years 1.59 2.06

16 to <21 years 1.84 2.33 

### Child-Specific Exposure Factors Handbook

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Page Child-Specific Exposure Factors Handbook

7-4 September 2008Table 7-2. Recommended Values for Surface Area of Body Parts

Age Group Head Trunk Arms Hands Legs Feet Source Mean Percent of Total Surface Area

Birth to <1 month 18.2 35.7 13.7 5.3 20.6 6.5

1 to <3 months 18.2 35.7 13.7 5.3 20.6 6.5

3 to <6 months 18.2 35.7 13.7 5.3 20.6 6.5

6 to <12 months 18.2 35.7 13.7 5.3 20.6 6.5
1 to <2 years 16.5 35.5 13.0 5.7 23.1 6.3 U.S. EPA, 1985 2 to <3 years 14.2 38.5 11.8 5.3 23.2 7.1 3 to <6 years 13.7 31.7 14.2 5.9 27.3 7.3 6 to <11 years 12.6 34.7 12.7 5.0 27.9 7.2 11 to <16 years 9.4 33.7 12.9 5.3 31.3 7.5 16 to <21 years 7.8 32.2 15.3 5.4 32.2 7.1 Mean Surface Area by Body Part a

2 m

Birth to <1 month 0.053 0.104 0.040 0.015 0.060 0.019

1 to <3 months 0.060 0.118 0.045 0.017 0.068 0.021

3 to <6 months 0.069 0.136 0.052 0.020 0.078 0.025

6 to <12 months
1 to <2 years 2 to <3 years 3 to <6 years 0.082

0.087

0.087

0.104 0.161

0.188

0.235

0.241 0.062

0.069

0.072

0.108 0.024

0.030

0.032

0.045 0.093

0.122

0.142

0.207 0.029

0.033

0.043

0.055 U.S. EPA Analysis

of NHANES 1999­

2006 data and U.S. EPA, 1985

6 to <11 years 0.136 0.375 0.137 0.054 0.301 0.078 11 to <16 years 0.149 0.536 0.205 0.084 0.498 0.119 16 to <21 years 0.144 0.592 0.282 0.099 0.592 0.131

95 th Percentile Surface Area by Body Part b

2 m

Birth to <1 month 0.062 0.121 0.047 0.018 0.070 0.022

1 to <3 months 0.069 0.136 0.052 0.020 0.078 0.025

3 to <6 months 0.080 0.157 0.060 0.023 0.091 0.029

6 to <12 months
1 to <2 years 2 to <3 years 3 to <6 years 0.093

0.101

0.099

0.130 0.182

0.217

0.270

0.301 0.070

0.079

0.083

0.135 0.027

0.035

0.037

0.056 0.105

0.141

0.162

0.259 0.033

0.038

0.050

0.069 U.S. EPA Analysis

of NHANES 1999­

2006 data and U.S. EPA, 1985

6 to <11 years 0.186 0.514 0.188 0.074 0.413 0.107 11 to <16 years 0.194 0.694 0.266 0.109 0.645 0.155 16 to <21 years 0.182 0.750 0.356 0.126 0.750 0.165

a

b

Note: Calculated as mean percentage of body part times mean total body surface area. Calculated as mean percentage of body part times 95 th percentile total body surface area.

2 2 Surface area values reported in m canbe converted to cm 2 by multiplying by 10,000 cm 2 /m . 

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Child-Specific Exposure Factors Handbook Page September 2008 7-5Table 7-3. Confidence in Recommendations for Body Surface Area

General Assessment Factors Rationale Rating

Soundness Medium Adequacy of Approach Total surface area estimates were based on algorithms developed using direct measurements and data from NHANES surveys. Themethods used for developing these algorithms were adequate. TheNHANES data and the secondary data analyses to estimate total surface areas were appropriate. NHANES included a large sample sizes; sample size varied with age. Body part percentages were based on direct measurements from a limited number of subjects.

Minimal (or Defined) Bias The data used to develop the algorithms for estimating surface area from height and weight data were limited. NHANEScollected physical measurements of weight and height. Bodypart data were based on direct measurements from a limited number of subjects.
Applicability and Utility

Exposure Factor of Interest The key studies were directly relevant to surface area estimates. Medium

Representativeness The direct measurement data used to develop the algorithms for estimating total body surface area from weight and height may not be representative of the U.S. population. However, NHANES height and weight data were collected using a complex, stratified, multi-stage probability cluster sampling design intended to be representative of the U.S. population. The sample used to derive body part percentages of total surface was not representative of U.S. population.

Currency The U.S. EPA analysis used the most current data at the time both

studies were conducted. The data on body part percentages were dated; however, the age of the data is not expected to affect its utility.

DataCollection Period The U.S. EPA analysis was based on four NHANES data sets covering

1999-2006.
Clarity and Completeness

Accessibility The U.S. EPA analysis of the NHANES data is unpublished, but available upon request. U.S. EPA (1985) is a U.S. EPA-published
report. Medium

Reproducibility The methodology was clearly presented; enough information was included to reproduce the results.

Quality Assurance Quality assurance of NHANES data was good; quality control of secondary data analysis was not well described. 

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Table 7-3. Confidence in Recommendations for Body Surface Area (continued)

General Assessment Factors Rationale Rating

Variability and Uncertainty

Variability in Population

Uncertainty The full distributions were given for total surface area.

A source of uncertainty in total surface areas resulted from the limitations in data used to develop the algorithms for estimating total surface from height and weight. Because of the small sample size, there is uncertainty in the body part percentage estimates. Medium

Evaluation and Review

Peer Review

Number and Agreement of Studies The NHANES surveys received a high level of peer review. The U.S. EPA analysis was not published in a peer-reviewed journal.

There is one key study for total surface area and one key study for the surface area of body parts. Medium

Overall Rating Medium for Total

Surface Area and

Low for Surface

Area of Individual Body Parts



### Child-Specific Exposure Factors Handbook Page September 2008 7-7Table 7-4. Recommended Values for Mean Solids Adherence to Skin

Face Arms Hands Legs Feet Source 2 mg/cm
Residential (indoors) a Daycare (indoors & outdoors) b
Outdoor sports c

d Indoor sports

Activities with soil e Playing in mud f
Playing in sediment g -

- 0.012

- 0.054

- 0.040 0.0041

0.024

0.011

0.0019

0.046

11

0.17 0.011

0.099

0.11

0.0063

0.17

47

0.49 0.0035

0.020

0.031

0.0020

0.051

23

0.70 0.010

0.071

- 0.0022

0.20

15

21 Holmes et al., 1999
Holmes et al., 1999
Kissel et al., 1996a
Kissel et al., 1996a
Holmes et al., 1999
Kissel et al., 1996a
Shoaf et al., 2005

a

b

c

d

e

e

f

- Based on weighted average of geometric mean soil loadings for 2 groups of children (ages 3 to13 years; N = 10) playing indoors. Based on weighted average of geometric mean soil loadings for 4 groups of daycare children (ages 1 to 6.5 years; N = 21) playing both indoors and outdoors. Based on geometric mean soil loadings of 6 children (ages >8 years) and 1 adult engaging in Tae Kwon Do. Based on geometric mean soil loadings of 8 children (ages 13 to 15 years) playing soccer. Based on weighted average of geometric mean soil loadings for gardeners and archeologists (ages 16 to 35 years). Based on weighted average of geometric mean solids loading of 2 groups of children (age 9 to 14 years; N= 12) playing in mud. Based on geometric mean solids loading of 9 children (ages 7 to 12 years) playing in tidal flats.

= No data. 

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Table 7-5. Confidence in Recommendations for Solids Adherence to Skin

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or Defined) Bias The approach was adequate; the skin rinsing technique is widely employed for purposes similar to this. Small sample sizes (4 to 9 children) were used in the studies; the key studies directly measured soil adherence to skin.

The studies attempted to measure soil adherence for

selected activities and conditions. The number of activities and study participants was limited. Medium

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The studies were relevant to the factor of interest; the goal

was to determine soil adherence to skin.

The soil/dust studies were limited to the State of Washington and the sediment study was limited to Rhode Island. The data may not be representative of other

locales.

The studies were published between 1996 and 2005

Short-term data were collected. Seasonal factors may be important, but have not been studied adequately. Low

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance Articles were published in widely circulated journals/reports.

The reports clearly describe the experimental methods, and enough information was provided to allow for the study to be reproduced.

Quality control was not well described. Medium

Variability and Uncertainty

Variability in Population

Uncertainty Variability in soil adherence is affected by many factors including soil properties, activity and individual behavior patterns. Not all age groups were represented in the sample.

The estimates are highly uncertain; the soil adherence

values were derived from a small number of observations

for a limited set of activities. Low

Evaluation and Review

Peer Review

Number and Agreement of Studies The studies were reported in peer reviewed journal articles.

There are three key studies that evaluated different

activities in children. Medium

Overall Rating Low



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7.3 SURFACE AREA

7.3.1 Key Body Surface Area Studies

7.3.1.1 U.S. EPA, 1985 - Development of Statistical Distributions or Ranges of Standard Factors Used in Exposure Assessments The U.S. EPA (1985) summarized the direct measurements of the surface area of children's body parts provided by Boyd (1935) and Van Graan (1969) as a percentage of total surface area. A total of 21 children less than 18 years of age were included. These percentages are presented in Table 7-6. Because of the small sample size, it is unclear how accurately these estimates represent averages for the age groups. Note that the proportion of total body surface area contributed by the head decreases from childhood to adulthood, whereas the proportion contributed by the leg increases.

7.3.1.2 U.S. EPA Analysis of NHANES 1999-2006

Data The U.S. EPA estimated total body surface areas for children in U.S. EPA’s standard age categories, using the empirical relationship shown in Appendix 7A and U.S. EPA (1985), and body weight and height data from the 1999-2006 NHANES. NHANES is conducted annually by the Center for Disease Control (CDC), National Center of Health Statistics (NCHS). The survey’s target population is the civilian, noninstitutionalized U.S. population. The NHANES 1999-2006 survey was conducted on a nationwide probabilitysample of approximately 40,000 persons for all ages, of which approximately 20,000 were children. The survey is designed to obtain nationally representative information on the health and nutritional status of the population of the United States through interviews and direct physical examinations. A number of anthropometrical measurements were taken for each participant in the study, including body weight and height. Unit nonresponse to the household interview was 19 percent, and an additional 4 percent did not participate in the physical examinations (including body weight measurements). The NHANES 1999-2006 survey includes over-sampling of low-income persons, adolescents 12-19 years, persons 60+ years of age, African Americans, and Mexican Americans. Sample data were assigned weights to account both for the disparity in sample sizes for these groups and for other inadequacies in sampling, such as the presence of non-respondents. Because the U.S. EPA utilized four NHANES data sets in its analysis (NHANES 1999­ 2000, 2001-2002, 2003-2004, and 2005-2006), sample weights were developed for the combined data set in accordance with CDC guidance from the NHANES’ web site (http://www.cdc.gov/nchs/about/major/ nhanes/nhanes2005-2006/faqs05_06.htm#question%2 012). Table 7-7 presents the mean and percentile estimates of body surface area by age category for male and female children, combined. Tables 7-8 and 7-9 present mean and percentiles of body surface area by age category for male and female children, respectively. An advantage of using the NHANES datasets to derive

surface area estimates is that data are available for infants from birth and older. In addition, the NHANES data are nationally representative and remain the principal source of body weight and height data collected nationwide from a large number of subjects. It should be noted that in the NHANES surveys height measurements for children under 2 years of age were based on recumbent length while standing height information was collected for children aged 2 years and older. Some studies have reported differences between recumbent length and standing height measurements for the same individual, ranging from 0.5 to 2 cm, with recumbent length being the larger of the two measurements (Buyken et al., 2005). The use of height data obtained from two different types of height measurements to estimate surface area of children may potentially introduce errors into the estimates.

7.3.2 Relevant Body Surface Area Studies

7.3.2.1 Phillips et al., 1993 - Distributions of Total Skin Surface Area to Body Weight Ratios Phillips et al. (1993) observed a strong correlation (0.986) between body surface area and body weight and studied the effect of using these factors as independent variables in the lifetime average daily dose (LADD) equation (See Chapter 1). The authors suggested 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 may be more appropriate than treating these factors as independent variables. Direct measurement data from

the scientific literature were used to calculate SA/BW ratios for two age groups of children (infants aged 0 to 2 years and children aged 2.1 to 17.9 years). These ratios were calculated by dividing body surface areas by



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corresponding body weights for the 401 individuals analyzed by Gehan and George (1970) and summarized by U.S. EPA (1985). Distributions of SA/BW ratios were developed, and summary statistics were calculated for the two age groups and the combined data set. Summary statistics for the two children’s age groups are presented in Table 7-10. The shapes of these SA/BW distributions were determined using D'Agostino's test, as described in D’Agostino et al. (1990). The results indicate that the SA/BW ratios for infants are lognormally distributed. SA/BW ratios for children were neither normally nor lognormally distributed. According to Phillips et al. (1993), SA/BW ratios may 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. The advantage of this study is that it studied correlations between surface area and body weight. However, data could not be broken out by finer age categories.

7.3.2.2 Wong et al., 2000 - Adult Proxy Responses to a Survey of Children’s Dermal Soil Contact

Activities Wong et al. (2000) reported on two surveys that gathered information on activity patterns related to

dermal contact with soil. The first of these national phone surveys (also reported on by Garlock et al., 1999) was conducted in 1996 using random digit dialing. Information about children was gathered from adults over the age of 18, and obtained information on 211 children. For older children (those between the ages of 5 and 17 years), information was gathered on their participation in “gardening and yardwork,” “outdoor sports,” and “outdoor play activities.” For children less than 5 years old, information was gathered on “outdoor play activities,” including whether the activity occurred on a playground or yard with “bare dirt or mixed grass and dirt” surfaces. Information on the types of clothing worn while participating in these play activities during warm weather months (April though October) was obtained. The results of this survey indicate that most children wore short pants, a dress or skirt, short sleeve shirts, no socks, and leather or canvas shoes during the outdoor play activities of interest. Using the survey data on clothing and total body surface area data from U.S. EPA (1985), estimates were made of the skin area exposed (expressed as percentages of total body surface area) associated with various age ranges and activities. These estimates are provided in Table 7-11.

7.4 ADHERENCE OF SOLIDS TO SKIN

7.4.1 Key Adherence of Solids to Skin Studies

7.4.1.1 Kissel et al., 1996a - Field Measurements of Dermal Soil Loading Attributable to Various Activities: Implications for Exposure

Assessment Kissel et al. (1996a) collected direct measurements of soil loading on the surface of the skin of volunteers, before and after activities expected to

result in soil contact. Soil adherence associated with the following indoor and outdoor activities were estimated: greenhouse gardening, tae kwon do karate, soccer, rugby, reed gathering, irrigation installation, truck farming, and playing in mud. Skin surface areas monitored included hands, forearms, lower legs, faces and/or feet (Kissel et al., 1996a). Several of the activities studied by Kissel et al. (1996a) involved children, as shown in Table 7-12. A group of young male soccer players (Soccer) was monitored before and after 40 minutes of practice on a field consisting of half grass and half bare earth. Six

children were monitored after 10 and 20 minutes of playing in the mud at a lake with an exposed shoreline (Kids-in-mud No. 1 and No. 2). For indoor activities, soil loadings were estimated from six children and one adult practicing tae kwon do (Tae Kwon Do); the activity lasted 90 minutes including a 30-min warm up. Information on activity duration, sample size and clothing worn by participants is provided in Table 7-12. The subjects’ body surfaces (forearms, hands, lower legs for all sample groups; faces and/or feet pairs in some sample groups) were washed before and after the monitored activities. Paired samples were pooled into single ones. The mass recovered was converted to soil loading using allometric models of surface area. Geometric means for post-activity soil adherence by activity and body region for the four groups of volunteers evaluated are presented in Table 7-13. Children playing in the mud had the highest soil



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loadings among the groups evaluated. The results also indicate that, in general, the amount of soil adherence to the hands is higher than for other parts of the body during the same activity. An advantage of this study is that it provides information on soil adherence to various body parts resulting from unscripted activities. However, the study authors noted that, because the activities were unstaged, “control of variables such as specific behaviors within each activity, clothing worn by participants, and duration of activity was limited.” In addition, soil

adherence values were estimated based on a small number of observations and very young children and indoor activities were under-represented in the study.

7.4.1.2 Holmes et al., 1999 - Field Measurements of Dermal Loadings in Occupational and

Recreational ActivitiesHolmes et al. (1999) collected pre- and postactivity soil loadings on various body parts of individuals within groups engaged in various occupational and recreational activities. These groups included: children at a daycare center (Daycare Kids), children playing indoors in a residential setting (Indoor Kids), individuals (aged 16 to 35) removing historical artifacts from a site (Archeologists), and individuals (aged 16 to 35) performing gardening work (Gardeners). This study was conducted as a follow up to previous field sampling of soil adherence on individuals participating in various activities (Kissel et al., 1996a). For this round of sampling, soil loading data were collected utilizing the same methods used and described in Kissel et al. (1996a). Information regarding the groups studied and their observed activities is presented in Table 7-12. The daycare children studied were all at one location, and measurements were taken on three different days. The children freely played both indoors in the house and outdoors in the backyard. The backyard was described as having a grass lawn, shed, sand box, and wood chip box. In this setting, the children engaged in typical activities including: playing with toys and each other, wrestling, sleeping, and eating. The number of children within each day’s group and the clothing worn is described in Table 7-12. The five children measured on the first day were washed first thing in the morning to establish a preactivity level. They were next washed at noon to determine the postactivity soil loading for the morning (Daycare Kids No. 1a). The same children were washed once again at the close of the day for measurement of soil adherence from the afternoon play activities (Daycare Kids No. 1b). For the second observation day (Daycare Kids No. 2), postactivity data were collected for five children. All the activities on this day occurred indoors. For the third daycare group (Daycare Kids No. 3), four children

were studied. On two separate days, children playing indoors in a home environment were monitored. The first group (Indoor Kids No. 1) had four children while the second group (Indoor Kids No. 2) had six children. The play area was described by the authors as being primarily carpeted. The clothing worn by the children within each day’s group is described in Table 7-12. Seven individuals (Archeologists), ages 16 to 35 years, were monitored while excavating, screening, sorting, and cataloging historical artifacts from an ancient Native American site during a single event. Eight volunteers (Gardeners), ages 16 to 35 years, were monitored while performing gardening activities (i.e., weeding, pruning, digging small irrigation trenches, picking and cleaning fruit). The clothing worn by these groups is described in Table 7-12. The geometric means and standard deviations of the postactivity soil adherence for each group of individuals and for each body part are summarized in Table 7-13. According to the authors, variations in the soil loading data from the daycare participants reflect

differences in the weather and access to the outdoors. An advantage of this study is that it provides a supplement to soil loading data collected in a previous round of studies (Kissel et al., 1996a). Also, the data support the assumption that hand loading can be used as a conservative estimate of soil loading on other body surfaces for the same activity. The activities studied represent normal child play both indoors and outdoors, as well as different combinations of clothing. The small number of participants is a disadvantage of this study. Also, the children studied and the activity setting may not be representative of the U.S. population.

7.4.1.3 Shoaf et al., 2005 - Child Dermal Sediment Loads Following Play in a Tide Flat The purpose of this study was to obtain sediment adherence data for children playing in a tidal flat (Shoreline Play). The study was conducted on one day in late September 2003 at a tidal flat in Jamestown, Rhode Island. Nine subjects (three females and six



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males) ages 7 to 12 years old participated in the study. Information on activity duration, sample size and clothing worn by participants is provided in Table 7-12. Participants’ parents completed questionnaires regarding their child’s typical activity patterns during tidal flat play, exposure frequency and duration, clothing choices, bathing practices and clothes laundering. This study reported direct measurements of sediment loadings on five body parts (face, forearms, hands, lower legs, and feet) after play in a tide flat. Each of nine subjects participated in two timed sessions and pre- and post-activity sediment loading data were collected. Geometric mean (geometric standard deviations) dermal loadings (mg/cm 2 

### ) on the face, forearm, hands, lower legs, and feet for the combined sessions, as shown in Table 7-13, were 0.04 (2.9), 0.17 (3.1), 0.49 (8.2), 0.70 (3.6) and 21 (1.9), respectively. The primary advantage of this study is that it provides adherence data specific to children and sediments which had previously been largely

unavailable. Results will be useful to risk assessors considering exposure scenarios involving child

activities at a coastal shoreline or tidal flat. The limited number of participants (9) and sampling during just one day and at one location, make extrapolation to other

situations uncertain.

7.4.2 Relevant Adherence of Solids to Skin

Studies

7.4.2.1 Kissel et al., 1996b - Factors Affecting Soil Adherence to Skin in Hand-press Trials: Investigation of Soil Contact and Skin Coverage Kissel et al. (1996b) conducted soil adherence experiments using five soil types obtained locally in the Seattle, WA, area: sand, 2 types of loamy sand, sandy loam, and silt loam. All soils were analyzed by hydrometer (settling velocity) to determine composition. Clay content 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 μm. 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 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 was directly correlated with moisture content, inversely correlated with particle size, and independent of clay content or organic carbon content. The advantage of this study is that it provides information on how soil type can affect adherence to the skin. However, the soil adherence data are for a single subject and the data are limited to five soil samples.

7.4.2.2 Kissel et al., 1998 - Investigation of Dermal

Contact with Soil in Controlled Trials Kissel et al. (1998) measured dermal exposure to soil from staged activities conducted in a greenhouse.

A fluorescent marker was mixed in soil so that soil contact for a particular skin surface area could be identified. The subjects, which included a group of children, were video-imaged under a long-wave ultraviolet (UV) light before and after soil contact. In this manner, soil contact on hands, forearms, lower legs, and faces was assessed by presence of fluorescence. In addition to fluorometric data, gravimetric measurements for preactivity and postactivity were obtained from the different body parts examined. The studied group of children played for 20 minutes in a soil bed of varying moisture content representing wet and dry soils. Three trials with children were conducted, each representing a different clothing/soil moisture scenario. For wet soils, both combinations of long sleeves and long pants, and short sleeves and short pants were tested. For dry soil, only short sleeves and short pants were worn during play. Clothing was laundered after each trial. The parameters describing each of these trials are summarized in Table 7-14. Before each trial, each child was washed in order to obtain a preactivity or background gravimetric

measurement. For wet soil, postactivity fluorescence results indicated that the hand had a much higher fractional coverage than other body surfaces (see Figure 7-1). No fluorescence was detected on the forearms or lower legs of children dressed in long sleeves and pants. As shown in Figure 7-2, postactivity gravimetric measurements showed higher soil loading on hands and much lower amounts on other body surfaces, as was observed with fluorescence data. According to Kissel et al. (1998), the relatively low loadings observed on non-hand body parts may be a result of a more limited



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area of contact for the body part rather than lower localized loadings. The highest soil loading observed was a geometric mean dermal loading of 0.7 mg/cm 2



### , found on the children’s hands following play in wet soil. Mean loadings were lower on hands in the dry soil trial and on lower legs, forearms, and faces in both the wet and dry soil trials. Higher loadings were observed for all body surfaces with the higher moisture content

soils. This report is valuable in showing soil loadings from soils of different moisture content and providing evidence that dermal exposure to soil is not uniform for various body surfaces. This study also provides some evidence of the protective effect of clothing. Disadvantages of the study include the small number of study participants and a short activity

duration.

7.5 REFERENCES FOR CHAPTER 7 Boyd, E. (1935) The growth of the surface area of the human body. Minneapolis, MN: University

of Minnesota Press. Buyken, A.E.; Hahn, S.; Kroke, A. (2005) Differences between recumbent length and stature measurement in groups of 2- and 3-y-old

children and its relevance for the use of European body mass index references. Int J

Obes 29:24-28. Cohen-Hubal, E.A.; Sheldon, L.S.; Burke, J.M.; McLundy, T.R.; Berry, M.R.; Rigas, M.L.; Zartarian, V.G.; Freeman, N.C.G. (1999) Children’s exposure assessment: A review of factors influencing children’s exposure, and

the data available to characterize and assess that exposure. Research Triangle Park, NC:

U.S. EPA, National Exposure Research Laboratory. D'Agostino, R.B.; Belanger, A.; D'Agostino, R.B. Jr. A suggestion for using powerful and informative tests of normality. The American Statistician 44(4):316-321. Dubois, D.; Dubois, E.F. (1916) A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med 17:863­

871. Garlock, T.J.; Shirai, J.H.; Kissel, J.C.	 (1999) Adult responses to a survey of soil contact related behaviors. J Expo Anal Environ Epidemiol

9:134-142. Gehan, E.; George, G.L. (1970) Estimation of human body surface area from height and weight. Cancer Chemother Rep 54(4):225-235. George, S.L.; Gehan, E.A.; Haycock, G.B.; Schwartz,

G.J. (1979) Letters to the editor. J Pediatr 94(2):342. Holmes, Jr., K.K.; Shirai, J.H.; Richter, K.Y.; Kissel,

J.C. (1999) Field measurement of dermal loadings in occupational and recreational

activities. Environ Res Section A 80:148-157. Kissel, J.C.; Richter, K.; Fenske, R. (1996a) Field measurements of dermal soil loading attributable to various activities: Implications for exposure assessment. Risk Anal 16(1):116-125. Kissel, J.C.; Richter, K.; Duff, R.; Fenske, R. (1996b) Factors affecting soil adherence to skin in hand-press trials. Bull Environ Contam

Toxicol 56:722-728. Kissel, J.C.; Shirai, J.H.; Richter, K.Y.; Fenske, R.A. (1998) Investigation of dermal contact with soil in controlled trials. J Soil Contam 7(6):

737-752. 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 Expo Anal Environ Epidemiol 3(3):331-338. Shoaf, M.B.;	 Shirai, J.H.; Kedan, G.; Schaum, J.; Kissel, J.C. (2005) Child dermal sediment loads following play in a tide flat. J Expo Anal Environ Epidemiol 15:407-412.

U.S. EPA (1985) Development of statistical distributions or ranges of standard factors used in exposure assessments. Washington, DC: Office of Research and Development, Office

of Health and Environmental Assessment. EPA 600/8-85-010. Available from: NTIS, Springfield, VA. PB85-242667.

U.S. EPA (1992a) Guidelines for exposure assessment. Federal Register. FR 57:104:22888-22938. May 29, 1992.

U.S. EPA (1992b) Dermal exposure assessment: principles and applications. Washington, DC: Office of Research and Development, Office

o f H e a l t h a n d E n v i r o n m e n t a l

Assessment/OHEA. U.S. EPA/600/8-9-91.

U.S. EPA (2004) Risk assessment guidance for Superfund (RAGS): Volume I, Human Health



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Evaluation Manual, Part E. Washington, DC.

EPA/540/R/99/005.

U.S. EPA (2005) Guidance on selecting age groups for monitoring and assessing childhood exposures

to environmental contaminants. U.S. Environmental Protection Agency, Washington, DC. EPA/630/P-03/003F.

November 2005. Van Graan, C.H. (1969) The determination of body surface area. S Afr Med J 43(31):952-959. Wong, E.Y.; Shirai, J.H.; Garlock, T.J.; Kissel, J.C. (2000) Adult proxy responses to a survey of

children’s dermal soil contact activities. J Expo Anal Environ Epidemiol 10:509-517.



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Chapter 7 - Dermal Exposure FactorsTable 7-6. Percentage of Total Body Surface Area by Body Part For ChildrenMales and Females CombinedPercent of TotalAge(years)NM:FHeadTrunkArmsHandsLegsFeetMeanMin-MaxMeanMin-MaxMeanMin-MaxMeanMin-MaxMeanMin-MaxMeanMin-Max< 12:018.218.2-18.335.734.8-36.613.712.4-15.15.35.2-5.420.618.2-22.96.56.5-6.61 < 21:116.516.5-16.535.534.5-36.613.012.8-13.15.75.6-5.823.122.1-24.06.35.8-6.72 < 31:014.238.511.85.323.27.13 < 40:513.613.3-14.031.929.9-32.814.414.2-14.76.15.8-6.326.826.0-28.67.26.8-7.94 < 51:313.812.1-15.331.530.5-32.414.013.0-15.55.75.2-6.627.826.0-29.37.36.9-8.15 < 66 < 71:013.135.113.14.727.16.97 < 88 < 99 < 100:212.011.6-12.534.233.4-34.912.311.7-12.85.35.2-5.428.728.5-28.87.67.4-7.810 < 1111 < 1212 < 131:08.734.713.75.430.57.013 <141:010.032.712.15.132.08.014 < 1515 < 1616 < 171:08.032.713.15.733.66.917 < 181:07.631.717.55.130.87.3N= Number of subjects, (M:F = males:females).Min.= Minimum percent.Max.= Maximum percent.Source:U.S. EPA, 1985.



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7-16 September 2008Table 7-7. Mean and Percentile Skin Surface Area (m 2 ) Derived from U.S. EPA Analysis of NHANES 1999-2006

Males and Females Combined

Age Group N Mean Percentiles

5 th 10 th 15 th 25 th 50 th 75 th 85 th 90 th 95 th

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months

1 to <2 years

2 to <3 years

3 to <6 years

6 to <11 years

11 to <16 years

16 to <21 years 154

281

488

923

1159

1122

2303

3590

5294

4843 0.29

0.33

0.38

0.45

0.53

0.61

0.76

1.08

1.59

1.84 0.24

0.27

0.33

0.38

0.45

0.52

0.61

0.81

1.19

1.47 0.25

0.29

0.34

0.39

0.46

0.54

0.64

0.85

1.25

1.53 0.26

0.29

0.35

0.40

0.47

0.55

0.66

0.88

1.31

1.58 0.27

0.31

0.36

0.42

0.49

0.57

0.68

0.93

1.4

1.65 0.29

0.33

0.38

0.45

0.53

0.61

0.74

1.05

1.57

1.80 0.31

0.35

0.40

0.48

0.56

0.64

0.81

1.21

1.75

1.99 0.31

0.37

0.42

0.49

0.58

0.67

0.85

1.31

1.86

2.10 0.33

0.37

0.43

0.50

0.59

0.68

0.89

1.36

1.94

2.21 0.34

0.38

0.44

0.51

0.61

0.70

0.95

1.48

2.06

2.33

N

Source: = Number of observations.

U.S. EPA Analysis of NHANES 1999-2006 data.

Table 7-8. Mean and Percentile Skin Surface Area (m 2 ) Derived from U.S. EPA Analysis of NHANES 1999-2006

Males

Age Group N Mean Percentiles

5 th 10 th 15 th 25 th 50 th 75 th 85 th 90 th 95 th

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months

1 to <2 years

2 to <3 years

3 to <6 years

6 to <11 years

11 to <16 years

16 to <21 years 85

151

255

471

620

548

1150

1794

2593

2457 0.29

0.33

0.39

0.45

0.53

0.62

0.76

1.09

1.61

1.94 0.24

0.28

0.34

0.39

0.46

0.54

0.61

0.82

1.17

1.61 0.25

0.29

0.35

0.41

0.47

0.56

0.64

0.86

1.23

1.66 0.26

0.30

0.36

0.42

0.48

0.56

0.66

0.89

1.28

1.7 0.27

0.31

0.37

0.43

0.50

0.58

0.69

0.94

1.39

1.76 0.29

0.34

0.39

0.46

0.53

0.62

0.75

1.06

1.60

1.91 0.31

0.36

0.41

0.48

0.57

0.65

0.82

1.21

1.79

2.08 0.33

0.37

0.42

0.49

0.58

0.67

0.86

1.29

1.90

2.22 0.34

0.37

0.43

0.50

0.59

0.68

0.89

1.34

1.99

2.30 0.36

0.38

0.44

0.51

0.62

0.70

0.95

1.46

2.12

2.42

N

Source: = Number of observations.

U.S. EPA Analysis of NHANES 1999-2006 data. 

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Child-Specific Exposure Factors Handbook Page September 2008 7-17Table 7-9. Mean and Percentile Skin Surface Area (m 2 ) Derived from U.S. EPA Analysis of NHANES 1999-2006

Females

Age Group N Mean Percentiles

5 th 10 th 15 th 25 th 50 th 75 th 85 th 90 th 95 th

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months

1 to <2 years

2 to <3 years

3 to <6 years

6 to <11 years

11 to <16 years

16 to <21 years 69

130

233

452

539

574

1153

1796

2701

2386 0.28

0.32

0.38

0.44

0.52

0.60

0.75

1.08

1.57

1.73 0.24

0.27

0.32

0.38

0.44

0.51

0.61

0.80

1.20

1.42 0.25

0.28

0.33

0.39

0.46

0.53

0.64

0.85

1.28

1.47 0.26

0.29

0.34

0.40

0.47

0.54

0.66

0.87

1.34

1.51 0.27

0.30

0.35

0.41

0.48

0.56

0.68

0.92

1.42

1.57 0.28

0.31

0.38

0.44

0.52

0.59

0.74

1.04

1.55

1.69 0.30

0.35

0.40

0.47

0.56

0.63

0.80

1.21

1.69

1.85 0.30

0.36

0.40

0.48

0.57

0.66

0.84

1.33

1.8

1.98 0.31

0.37

0.41

0.49

0.58

0.67

0.88

1.39

1.88

2.06 0.33

0.37

0.43

0.51

0.59

0.70

0.94

1.51

2.00

2.17

N

Source: = Number of observations.

U.S. EPA Analysis of NHANES 1999-2006 data. 

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Chapter 7 - Dermal Exposure FactorsTable 7-10. Descriptive Statistics For Surface Area/Body Weight (SA/BW) Ratios (m2/kg)Age(years)MeanRangeMin-MaxSDSE5th10th25thPercentiles50th75th90th95th0 to 20.0640.042-0.1140.0110.0010.0470.0510.0560.0620.0720.07840.08462.1 to 17.90.0420.027-0.0670.0080.0010.0290.0330.0380.0420.0450.05010.0594SDSE= Standard deviation.= Standard error of the mean.Source:Phillips et al., 1993.



### Child-Specific Exposure Factors Handbook Page September 2008 7-19Table 7-11. Estimated Skin Surface Exposed During Warm Weather Outdoor Activities

Age (years)

N Skin Area Exposed (% of total body surface area)

Play Gardening/yardwork Organized Team Sport

<5

41 5-17 5-17

437 65

Mean 38.0 33.8 29.0

Median 36.5 33.0 30.0

SD 6.0 8.3 10.5

N

SD = Number of observations.

= Standard deviation.

Source: Wong et al., 2000. 

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Chapter 7 - Dermal Exposure FactorsTable 7-12. Summary of Field StudiesActivity Month Eventa(hrs)NMF Age (years)ConditionsClothingStudyIndoorTae Kwon Do Feb. 1.5761 8-42 Carpeted floorAll in long sleeve-long pants martialarts uniform, sleeves rolled back,barefootKissel et al.,1996aIndoor Kids No. 1 Jan. 2Indoor Kids No. 2 Feb. 2Daycare Kids No. 1a Aug. 3.5Daycare Kids No. 1b Aug. 4Daycare Kids No. 2bSept. 8Daycare Kids No. 3 Nov. 84666543455431 6-13 Playing on carpeted floor2 3-13 Playing on carpeted floor1 1-6.5 Indoors: linoleum surface;Outdoors: grass, bare earth,barked area1 1-6.5 Indoors: linoleum surface;Outdoors: grass, bare earth,barked area1 1-4 Indoors: low nappedcarpeting, linoleum surfaces1 1-4.5 Indoors: linoleum surface,Outside: grass, bare earth,barked area3 of 4 short pants, 2 of 4 shortsleeves, socks, no shoesHolmes et al.,19995 of 6 long pants, 5 of 6 long sleeves,socks, no shoes4 of 6 in long pants, 5 of 6 shortsleeves, socks, shoes4 of 6 long pants, 5 of 6 shortsleeves, 3 of 6 barefoot all afternoon,others barefoot half the afternoon4 of 5 long pants, 3 of 5 long sleeves,all barefoot for part of the dayAll long pants, 3 of 4 long sleeves,socks and shoesOutdoorSoccerNov. 0.67Kids-in-mud No. 1 Sept. 0.17Kids-in-mud No. 2 Sept. 0.338668550 13-15 Half grass-half bare earth1 9-14 Lake shoreline1 9-14 Lake shoreline6 of 8 long sleeves, 4 of 8 long pants,3 of 4 short pants and shin guardsKissel et al.,1996aAll in short sleeve T-shirts, shorts,barefootAll in short sleeve T-shirts, shorts,barefootGardenersAug. 4Archeologists July 11.587137 16-35 Weeding, pruning, digging atrench4 16-35 Digging with trowel,screening dirt, sorting6 of 8 long pants, 7 of 8 shortsleeves, 1 sleeveless, socks, shoes,intermittent use of glovesHolmes et al.,19996 of 7 short pants, all short sleeves, 3no shoes or socks, 2 sandalsShoreline Play Sept. 0.33-1.0963 7-12 Tidal flatNo shirt or short sleeve T-shirts,shorts, barefootShoaf et al.,2005aEvent duration.bActivities were confined to the house.N = Number of subjects.M = Male.F = Female.



### Child-Specific Exposure Factors Handbook Page September 2008 7-21Table 7-13. Geometric Mean and Geometric Standard Deviations of Solids Adherence by Activity and Body Region a

Activity N Post-activity Dermal Solids Loadings (mg/cm 2 )

Hands Arms Legs Faces Feet

Indoor

Tae Kwon Do 7 0.0063 0.0019 0.0020 0.0022

1.9 4.1 2.0 2.1

Indoor Kids No. 1 4 0.0073 0.0042 0.0041 0.012

1.9 1.9 2.3 1.4

Indoor Kids No. 2 6 0.014 0.0041 0.0031 0.0091

1.5 2.0 1.5 1.7

Daycare Kids No. 1a 6 0.11

1.9 0.026

1.9 0.030

1.7 0.079

2.4

Daycare Kids No. 1b 6 0.15

2.1 0.031

1.8 0.023

1.2 0.13

1.4

Daycare Kids No. 2 5 0.073

1.6 0.023

1.4 0.011

1.4 0.044

1.3

Daycare Kids No. 3 4 0.036

1.3 0.012

1.2 0.014

3.0 0.0053

5.1

Outdoor

Soccer 8 0.11 0.011 0.031 0.012

1.8 2.0 3.8 1.5

Kids-in-mud No. 1 6 35 11 36 24

2.3 6.1 2.0 3.6

Kids-in-mud No. 2 6 58 11 9.5 6.7

2.3 3.8 2.3 12.4

Gardeners 8 0.20 0.050 0.072 0.058 0.17

1.9 2.1

-- 1.6

-­

Archeologists 7 0.14

1.3 0.041

1.9 0.028

4.1 0.050

1.8 0.24

1.4

Shoreline Play 9 0.49

8.2 0.17

3.1 0.70

3.6 0.04

2.9 21

1.9

a Means are presented above the standard deviations. The standard deviations generally exceed the means by large amounts indicating high variability in the data.

N = Number of subjects.

Sources: Kissel et al., 1996a; Holmes et al., 1999; Shoaf et al., 2005. 

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Table 7-14. Summary of Controlled Greenhouse Trials - Children Playing

Activity Ages (years) Duration (min) Soil Moisture (%) Clothing a N Male Female

Playing 8 to 12 20 17-18

16-18

3-4 L

S

S 4

9

5 3

5

3 1

4

2

a L, long sleeves and long pants; S, short sleeves and short pants. N = Number of subjects.

Source: Kissel et al., 1998.



### Child-Specific Exposure Factors Handbook Page September 2008 7-23

### Figure 7-1.	 Skin Coverage as Determined by Fluorescence vs. Body Part for Children Playing in Wet Soils (bars are arithmetic means and corresponding 95% confidence intervals)

Source: Kissel et al., 1998.

Figure 7-2. Gravimetric Loading vs. Body Part for Children Playing in Wet and Dry Soils (symbols are geomettric means and 95% confidence intervals)

Source: Kissel et al., 1998. 

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APPENDIX 7A

FORMULAS FOR TOTAL BODY SURFACE AREA



### Child-Specific Exposure Factors Handbook Page September 2008 7A-1

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APPENDIX 7A - FORMULAS FOR TOTAL

BODY SURFACE AREA

Most formulas for estimating surface area (SA) relate height to weight to surface area. The following formula was proposed by Gehan and George (1970):

KW 2/3 

### (Eqn. 7A-1) SA =



### where:

SA = surface area in square meters; W = weight in kg; and

K = constant.

While the above equation has been criticized because human bodies have different specific gravities and because the surface area per unit volume differs for individuals with different body builds, it gives a reasonably good estimate of surface area. A formula published in 1916 that still finds wide acceptance and use is that of DuBois and DuBois (1916). Their model can be written:

1 2 

### (Eqn. 7A-2) 

### SA = a 0 

### H a 

### W a



### where:

SA = surface area in square meters; H = height in centimeters; and W = weight in kg.

The values of a 0 

### (0.007182), a 1 

### (0.725), and a 2 

### (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 

### = 0.01787, a 1 

### = 0.500, and a 2 

### = 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. 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 

### = 0.02350, a 1 

### = 0.42246, and a 2 

### = 0.51456. Hence, their equation for predicting SA is:

SA = 0.02350 H 0.42246 

### W 0.51456 

### (Eqn. 7A-3)

or in logarithmic form:

lnSA = -3.75080 + 0.42246 lnH + 0.51456 lnW (Eqn. 7A-4)

where:

SA = surface area in square meters; H = height in centimeters; and W = weight in kg.

This prediction explains more than 99 percent of the variations in surface area among the 401 individuals measured (Gehan and George, 1970).



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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:



### a

1 

### a
2 

### SA i = a 0 

### H i 

### W i e i (Eqn. 7A-5)

or in logarithmic form:

ln(SA) i 

### = lna 0 

### + a 1 

### lnH i 

### + a 2 

### lnW i 

### + lne i 

### (Eqn. 7A-6)

where:

SA i 

### = surface area of the i-th individual (m 2 

### );

H i 

### = height of the i-th individual (cm);

W i 

### = weight of the i-th individual (kg);

a 0 

### , a 1 

### , and a 2 

### = parameters to be estimated;

and

e i 

### = 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:

a 0 

### = -3.73 (0.18), a 1 

### = 0.417 (0.054), a 2 

### = 0.517 (0.022)

The model is then:

SA = 0.0239 H 0.417 W 0.517 

### (Eqn. 7A-7)

or in logarithmic form:

ln SA = 3.73 + 0.417 lnH + 0.517 lnW (Eqn. 7A-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 in Table 7A-1. The surface areas estimated using the parameter values for all ages were compared to surface areas estimated by the values for each age group for subjects at the 3rd, 50th, and 97th percentiles of weight and height. Nearly

all differences in surface area estimates were less than

0.01 square meter, and the largest difference was 0.03

m 2 

### for an 18-year-old at the 97th percentile. The authors concluded that there is no advantage in using separate values of a 0 

### , a 1 

### , and a 2 

### by age interval. Haycock et al. (1978) without knowledge of the work by Gehan and George (1970), developed values for the
parameters a 0 

### , a 1 

### , and a 2 

### 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



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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 

### = 0.024265, a 1 

### = 0.3964, and a 2 

### = 0.5378. The result was the following equation for estimating surface area:



### SA = 0.024265H 

### 0.3964 W 

### 0.5378 (Eqn. 7A-9)

expressed logarithmically as:

ln SA = ln 0.024265 + 0.3964 ln H + 0.5378 ln W (Eqn. 7A-10)

The coefficients for this equation agree remarkably with those obtained by Gehan and George (1970) for 401 measurements. George et al. (1979) agree that a model more complex than the model of DuBois and DuBois for estimating surface area is unnecessary. Based on samples of direct measurements by Boyd (1935) and Gehan and George (1970), and samples of geometric estimates by Haycock et al. (1978), these authors have obtained parameters for the DuBois and DuBois model that are different than those originally postulated in

1916. The DuBois and DuBois model can be written logarithmically as:

lnSA = lna 0 

### + a 1 

### lnH + a 2 

### lnW (Eqn. 7A-11)

The values for a 0 

### , a 1 

### , and a 2 

### obtained by the various authors discussed in this section are presented

in Table 7A-2. 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. Sendroy and Cecchini (1954) proposed a method of creating a nomogram , a diagram relating height and weight to surface area. However, they do not give an explicit model for calculating surface area. The nomogram 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

formulas of other authors discussed above.

REFERENCES FOR APPENDIX 7A

Boyd, E. (1935) The growth of the surface area of the human body. Minneapolis, MN: University of

Minnesota Press.

Dubois, D.; Dubois, E.F. (1916) A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med 17:863-871.

Gehan, E.; George, G.L. (1970) Estimation of human body surface area from height and weight. Cancer Chemother Rep 54(4):225-235.

Geigy Scientific Tables (1981) Nomograms for determination of body surface area from height and mass. Lentner, C. (ed.). CIBA-Geigy Corporation, West Caldwell, NJ. pp. 226-227.

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 Pediatr 93(1):62-66.



### Child-Specific Exposure Factors Handbook Page September 2008 7A-4Table 7A-2. Summary of Surface Area Parameter Values for the Dubois and Dubois Model

Author Number (year) of persons a 0 a 1 a 2

DuBois and DuBois (1916) 9 0.007184 0.725 0.425

Boyd (1935) 231 0.01787 0.500 0.4838

Gehan and George (1970) 401 0.02350 0.42246 0.51456

Haycock et al. (1978) 81 0.024265 0.3964 0.5378 

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Sendroy, J.; Cecchini, L.P. (1954) Determination of human body surface area from height and weight. J Appl Physiol 7(1):3-12.

Table 7A-1. Estimated Parameter Values for Different Age Intervals

Age Group Number of persons a 0 a 1 a 2

All ages

<5 years old

$ 5 to <20 years old

$ 20 years old 401 0.02350 0.42246

229 0.02667 0.38217

42 0.03050 0.35129

30 0.01545 0.54468 0.51456

0.53937

0.54375

0.46336



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

8 BODY WEIGHT STUDIES

8.1 INTRODUCTION The average daily dose (ADD) is a dose that is typically normalized to the average body weight of the exposed population. If exposure occurs only during childhood years, the average child body weight during the exposure period should be used to estimate risk (U.S. EPA, 1989). The purpose of this section is to describe a key published study on body weight for children in the general U.S. population, as described in Section 1.5 of this handbook. The recommendations for body weight are provided in the next section, along with a summary of the confidence ratings for these recommendations. The recommended values are based on one key study identified by U.S. EPA for this factor. Following the recommendations, the key study on body weight is summarized. Relevant data on body weight are also provided. Since childhood obesity is a growing concern and may increase the risk of chronic diseases during adulthood, information on body mass index (BMI) and height are also provided.

8.2 RECOMMENDATIONS The recommended values for body weight are summarized in Table 8-1. Table 8-2 presents the confidence ratings for body weight recommendations. The recommended values represent mean body weights in kilograms for the age groups recommended by U.S.

EPA in Guidance for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). Use of upper percentile body weight values are not routinely recommended for calculating ADDs because inclusion of an upper percentile value inthe denominator of the ADD equation would be a nonconservative approach. However, distributions of body weight data are provided in section 8.3 of this chapter. These distributions may be useful if probabilisticmethods are used to assess exposure. Also, if genderspecific data are needed, or if data for finer age bins are needed, the reader should refer to the tables in Section

8.3.



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Chapter 8 - Body Weight

Table 8-1. Recommended Values for Body Weight

Age Group Mean Multiple

Percentiles Source kg

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <1 2 months

1 to <2 years

2 to <3 years

3 to <6 years

6 to <11 years

11 to <16 years

16 to <21 years 4.8

Tables 8-3 through 8-5 U.S. EPA analysis of NHANES, 1999-2006

data 5.6

7.4

9.2

11.4

13.8

18.6

31.8

56.8

71.6



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Chapter 8 - Body Weight

Table 8-2. Confidence in Recommendations for Body Weight

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or Defined) Bias The survey methodology and secondary data analysis analysis was adequate. NHANES consisted of a large sample size; sample size varied with age. Direct measurements were taken during a physical examination.

No significant biases were apparent. High

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The key study is directly relevant to body weight.

NHANES was a nationally representative sample of the U.S. population; participants are selected using a complex, stratified, multi-stage probability cluster sampling design.

The U.S. EPA analysis used the most current NHANES

data.

The U.S. EPA analysis was based on 4 data sets of NHANES data covering 1999-2006. High

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance NHANES data are available from NCHS; the U.S. EPA analysis of the NHANES data is available upon request.

The methods used were well-described; enough information was provided to allow for reproduction of results.

Quality assurance of NHANES data was good; quality control of secondary data analysis was not well described. High

Variability and Uncertainty

Variability in Population

Uncertainty The full distributions were given in the key study.

No significant uncertainties were apparent in the NHANES data, nor in the secondary analyses of the data. High

Evaluation and Review

Peer Review

Number and Agreement of Studies NHANES received a high level of peer review. The U.S. EPA analysis was not published in a peer-reviewed journal.

The number of studies is 1. Medium

Overall Rating High



### Child-Specific Exposure Factors Handbook Page September 2008 8-3

### ∑ w x ii

x = i 

### ( Eqn. 8-1) 

### ∑ w i

i 

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

8.3 KEY BODY WEIGHT STUDY

8.3.1 U.S. EPA analysis of NHANES 1999-2006

data The U.S. EPA analyzed data from the 1999­

2006 National Health and Nutrition Examination Survey (NHANES) to generate distributions of body weight for various age ranges of children. NHANES is conducted annually by the Center for Disease Control (CDC), National Center of Health Statistics (NCHS). The survey’s target population is the civilian, noninstitutionalized U.S. population. The NHANES 1999-2006 survey was conducted on a nationwide probability sample of approximately 40,000 persons for all ages, of which approximately 20,000 were children. The survey is designed to obtain nationally representative information on the health and nutritional status of the population of the United States through interviews and direct physical examinations. A number of anthropometric measurements, including body weight, were taken for each participant in the study. Unit non-response to the household interview was 19 percent, and an additional 4 percent did not participate in the physical examinations (including body weight measurements). The NHANES 1999-2006 survey includes over-sampling of low-income persons, adolescents 12-19 years, persons 60+ years of age, African Americans and Mexican Americans. Sample data were assigned weights to account both for the disparity in sample sizes for these groups and for other inadequacies in sampling, such as the presence of non-respondents. Because the U.S. EPA utilized four NHANES data sets in its analysis (NHANES 1999­ 2000, 2001-2002, 2003-2004, and 2005-2006) sample weights were developed for the combined data set in accordance with CDC guidance from the NHANES'

we b s i t e (http://www.cdc.gov/nchs/about/major/nhanes/nhane s2005-2006/faqs05_06.htm#question%2012). Using the data and the weighting factors from the four NHANES data sets, U.S. EPA calculated body weight statistics for the standard age categories. The mean value for a given group was calculated using the following formula: where:



## x = sample mean;

x i 

### = the i th 

### observation;

w i 

### = sample weight assigned to observation

x i 

### .

Percentile values were generated by first calculating the sum of the weights for all observations in a given group and multiplying this sum by the percentile of interest (e.g., multiplying by 0.25 to determine the 25 th 

### percentile). The observations were then ordered from least to greatest, and each observation was assigned a cumulative weight, equal to its own weight plus all weights listed before the observation. The first observation listed with a cumulative weight greater than the value calculated for the percentile of interest was

selected. Table 8-3 presents the body weight means and percentiles, by age category, for male and female children, combined. Tables 8-4 and 8-5 present the body weight means and percentiles for male and female children, respectively. The advantage of this study is that it provides body weight distributions for children at ages ranging from infancy to young adults. A limitation of the study is that the data in Tables 8-3 to 8-5 may underestimate current body weights due to an observed upward trend in body weights (Ogden et al., 2004). However, the NHANES data are nationally representative and remain the principal source of body weight data collected nationwide from a large number of subjects.

8.4 RELEVANT BODY WEIGHT STUDIES

8.4.1 National Center for Health Statistics, 1987 ­ Anthropometric reference data and prevalence of overweight, United States,

1976-80 This study used anthropometric measurement data for body weight for the U.S. population that were collected by NCHS as part of the second National Health and Nutrition Examination Survey (NHANES II). NHANES II began in February 1976 and was completed in February 1980. The survey was conducted on a nationwide probability sample of 27,801 persons aged 6 months to 74 years from the civilian, noninstitutionalized population of the United States. A total of 20,322 individuals in the sample were interviewed and



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Chapter 8 - Body Weight

examined, resulting in a response rate of 73.1 percent. The sample was selected so that certain subgroups thought to be at high risk of malnutrition (persons with lowincomes, preschool children, and the elderly) were over sampled. 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. NHANES II collected standard body measurements of sample subjects, including height and weight, that were made at various times of the day and in different seasons of the year. This technique was used because an individual’s weight may vary between winter and summer and may fluctuate with patterns of food and water intake and other daily activities (NCHS, 1987). NCHS (1987) provided descriptive statistics of the body weight data. Means and percentiles, by age category, are presented in Table 8­ 6 for males, and in Table 8-7 for females. The advantages of the study are that it is nationally representative and provides data for various age groups of children, beginning at 2 months of age. The limitation of the study is the age of the data.

8.4.2 Burmaster and Crouch, 1997 - Lognormal distributions for body weight as a function of age for males and females in the United States, 1976-1980 Burmaster and Crouch (1997) performed data analysis to fit normal and lognormal distributions to the body weights of females and males aged 9 months to 70 years. The data used in this analysis were from the second survey of the National Center for Health Statistics, NHANES II, which was based on a national probability sample of 27,801 persons 6 months to 74 years of age in the U.S. (Burmaster and Crouch 1997). The NHANES II data had been statistically adjusted for non-response and probability of selection, and stratified by age, sex, and race to reflect the entire U.S. population prior to reporting. Burmaster and Crouch (1997) conducted exploratory and quantitative data analyses and fit normal and lognormal distributions to percentiles of body weights of children and teens, as a function of age. Cumulative distribution functions were plotted for female and male body weights on both linear and logarithmic scales. Burmaster and Crouch (1997) used “maximum likelihood” estimation to fit lognormal distributions to the data. Linear and quadratic regression lines were fitted to the data. A number of goodness-of-fit measures were conducted on the data generated. The investigators found that lognormal distributions gave strong fits to the data for each gender across all age groups. The statistics for the lognormal probability plots for female and male children aged 9 months to 20 years are presented in Tables 8-8 and 8-9, respectively. These data can be used for further analyses of body weight distribution (i.e., application of Monte Carlo analysis). The advantage of this study is that NHANES data were used for the analysis and the data are representative nationally. It also provides statistics for probability plot regression analyses for females and males from 6 months to 20 years old. However, the analysis is based on an older set of NHANES data.

8.4.3 U.S. EPA, 2000 - Body weight estimates on

NHANES III Data

U.S. EPA’s Office of Water has estimated body weights for children by age and gender using data from NHANES III, which was conducted from 1988 to 1994. NHANES III collected body weight data for approximately 15,000 children between the ages of 2 months and 17 years. Table 8-10 presents the body weight estimates in kilograms by age and gender. Table 8-11 shows the body weight estimates for infants under the age of 3 months. The limitations of this analysis are that data were not available for infants under 2 months old, and that the data are roughly 14 to 20 years old. With the upward trends in body weight from NHANES II (1976­ 1980) to NHANES III, which may still be valid, the data in Tables 8-10 and 8-11 may underestimate current body weights. However, the data are national in scope and represent the general children’s population.

8.4.4 Kuczmarski et al., 2002 - 2000 CDC growth

charts for the United States: methods and development NCHS published growth charts for infants, birth to 36 months of age, and children and adolescents, 2 to 20 years of age (Kuczmarski et al., 2002). Growth charts were developed with data from five national health examination surveys: National Health Examination Survey (NHES) II (1963-65) for ages 6-11 years, NHES



### Child-Specific Exposure Factors Handbook Page September 2008 8-5

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Chapter 8 - Body Weight

III (1966-70) for ages 12-17 years, National Health and Nutrition Examination Survey (NHANES) I (1971-74) for ages 1-17 years, NHANES II (1976-80) beginning at 6 months of age, and NHANES III (1988­ 94) beginning at 2 months of age. Data from these national surveys were pooled because no single survey had enough observations to develop these charts. For the infant charts, a limited number of additional data points were obtained from other sources where

national data were either not available or insufficient. Birth weights <1,500 grams were excluded when generating the charts for weights and lengths. Also, the length-for-age charts exclude data from NHANES III for ages <3.5 months. Supplemental birth

certificate data from the U.S. vital statistics were used in the weight-for-age charts and supplemental birth

certificate data from Wisconsin and Missouri vital statistics, CDC Pediatric Nutrition Surveillance System data were used for ages 0.5, 1.5, 2.5, 3.5, and

4.5 months for the length-for-age charts. The Missouri

and Wisconsin birth certificate data were also used to supplement the surveys for the weight-for-length charts. Table 8-12 presents the percentiles of weight by gender and age. Figures 8-1 and 8-2 present weight by age percentiles for boys and girls, aged birth to 36 months, respectively. Figures 8-3 and 8-4 present weight by length percentiles for boys and girls, respectively. Figures 8-5 and 8-6 provide the Body Mass Index (BMI) for boys and girls aged 2 to 20 years old. A limitation of this analysis is that trends in the weight data cannot be assessed because data from various years were combined. The advantages of this analysis are that it is based on a nationally representative sample of the U.S. population and it provides body weight on a month-by-month basis up to 36 months of age, as well as BMI data for children through age 20 years.

8.4.5 Ogden et al., 2004 - Mean body weight, height, and body mass index, United States

1960-2002 Ogden et al. (2004) analyzed trends in body weight measured by the National Health Examination Surveys II and III (NHES II and III), the National Health and Nutrition Examination Surveys I, II, and III (NHANES I, II, and III), and NHANES 1999-2002. The surveys covered the period from 1960 to 2002. Table 8-13 presents the measured body weights for various age groups as measured in NHES and NHANES. Tables 8-14 and 8-15 present the mean height and BMI data for the same population, respectively. The BMI data were calculated as weight in kilograms divided by the square of height in meters. Population means were calculated using sample weights to account for variation in sampling for certain subsets of the U.S. population, non-response, and non-coverage (Ogden et al., 2004). The data indicate that mean body weight has increased over the period analyzed. There is some uncertainty inherent in such an analysis, however, because of changes in sampling methods during the 42 year time span covered by the studies. Because this study is based on an analysis of NHANES data, its limitations are the same as those for that study. However, it serves to illustrate the importance of the use of timely data when analyzing body weight.

8.4.6 Freedman et al., 2006 - Racial and ethnic

differences in secular trends for childhood BMI, weight, and height Freedman et al. (2006) examined sex and race/ethnicity differences in secular trends for childhood BMI, overweight, weight, and height in the United States using data from NHANES I (1971 to 1974), NHANES II (1976- 1980), NHANES III (1988 to 1994) and NHANES 1999-2002. The analyses included children 2 to 17 years olds. Persons with missing weight or height information were excluded from the analyses (Freedman et al., 2006). The authors categorized the data across thefour examinations and presented the data for nonHispanic White, non-Hispanic Black, or Mexican American. Freedman et al. (2006) excluded other categories of race/ethnicity such as other Hispanics, because the sample sizes were small. Height and weight data were obtained for each survey and BMI was calculated as weight in kilograms divided by height in meters square. Sex specific z-scores and percentiles of weight-for-age, height-for-age, and BMI-for-age werecalculated. Childhood overweight was defined as BMIfor-age $ 

### 95 th 

### percentile and childhood obesity was defined as children with a BMI-for-age $ 

### 99 th 

### percentile. In the analyses, sample weights were used to account for differential probabilities, non-selection, non



### Page Child-Specific Exposure Factors Handbook September 2008 8-6

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Chapter 8 - Body Weight

response, and non-coverage. The sample sizes used in the analyses by age, race and survey are presented in Table 8-16. Mean BMI levels are provided in Table 8-17 and the prevalence of overweight and obesity is

shown in Table 8-18. Table 8-17 shows that in 1971­ 1974 survey total population, Mexican American children had the highest mean BMI level (18.6 kg/m 2 

### ). However the greatest increase throughout the survey occurred among Black children increasing from 17.8 to 20 kg/m 2 

### (Freedman et al., 2006). Table 8-18 shows that 2 to 5 year old White children had slightly larger increases in overweight, but among the older children, the largest increases were among the Black and Mexican American children (Freedman et al., 2006). Overall, in most sex-age groups, Mexican Americans experienced the greater increase in BMI and overweight than what was experienced by Black and White Children (Freedman et al., 2006). Black children experienced larger secular increases in BMI, weight, and height than did White children (Freedman et al., 2006). According to Freedman et al. (2006) racial/ethnicity differences were less marked in the 2 to 5 years old children. The advantages of the study are that the sample size is large and the analysis was designed to represent the general population of the racial and ethnic groups studied. The disadvantage is that some ethnic population groups were excluded because of small sample sizes.

8.4.7 Martin et al., 2007 - Births: final data for

2005 Martin et al.(2007) provided statistics on the percentage of live births categorized as having low or very low birth weights in the U.S. Low birth weight was defined as <2,500 grams (<5 pounds 8 ounces) and very low birth weight was defined as <1,500 grams (<3 pounds 4 ounces). The data used in the analysis were from birth certificates registered in all states and the District of Columbia for births occurring in 2005. Data were presented for maternal demographic characteristics including race ethnicity: non-Hispanic White, non-Hispanic Black, and Hispanic.

The numbers of live births within various weight ranges, and the percentages of live births with low or very low birth weights are presented in Table 8­

19. The percentage of live births with low birth weights was 8.2, and the percentage of very low birth weights was 1.5 in 2005. Non-Hispanic Blacks had the highest percentage of low birth weights (14.0 percent) and very low birth weights (3.3 percent). Martin et al. (2007) also provided statistics on the numbers and percentages of pre-term live births in the U.S. Of the 4,138,349 live births in the U.S. in 2005, 522,913 were defined as pre-term (i.e., less than 37 weeks gestation). A total of 43.3 percent of these pre-term infants had low birth weights an 11.3 percent had very low birth weights. The advantage of this data set is that it is nationally representative and provides data for infants.

8.4.8 Portier et al., 2007 - Body weight

distributions for risk assessment Portier et al. (2007) provided age-specific distributions of body weight based on NHANES II, III,

and IV data. The number of observations in these surveys was 20,322, 33,311, and 9,965, respectively. Portier et al. (2007) computed the means and standard deviations of body weight as back transformations of the weighted means and standard deviations of natural log-transformed body weights. Body weight distributions were computed by gender and various age brackets (Portier et al., 2007). The estimated mean body weights are shown in Tables 8-20, 8-21, and 8-22 using NHANES II, III, and IV data, respectively. The sample size (N) shown in the tables is the observed number of individuals and not the expected population size (sum of the sample weights) in each age category (Portier et al., 2007). The authors noted that the age groups are defined as starting at the birth month and include the next eleven months (i.e., age group 2 includes children 24-35 months at the time of the health assessment). Table 8-23 provides estimates for age groups that are often considered in risk assessments (Portier et al., 2007). The authors concluded that the data show changes in the average body weight over time and that the changes are not constant for all ages. The reader is referred to Portier et al. (2007) for equations suggested by the authors to be used when performing risk assessments where shifts and changes in body weight distributions need factoring in. The advantages of this study are that it represents the U.S. general population, it provides distribution data, and can be used for trend analysis. In addition, the data are provided for both genders and for single-year age groups. The study results are also based on a large sample size.



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Chapter 8 - Body Weight

8.4.9 Kahn and Stralka, 2008 - Estimated daily average per capita water ingestion by child and adult age categories based on USDA’s 1994-96 and 1998 Continuing Survey of Food Intakes As part of an analysis of water ingestion, Kahn and Stralka (2008) provided body weight distributions for children. The analysis was based on self reported body weights from the 1994 - 1996, 1998 Continuing Survey of Food Intake Among Individuals (CSFII). The average body weight across all individuals was 65 kilograms. According to Kahn and Stralka (2008), 10 kilograms, which is often used as the default body weight for babies, is the 95 th 

### value of the distribution of body weight for children in the 3 to <6 months category. The median weight is 9 kilograms for the 6 to 12 month age category and 11 kilograms for the 1 to 2 year old category (Kahn and Stralka, 2008). The body weight distributions are presented in Table 8-24 and the intervals around the

mean and 90 th 

### and 95 th 

### percentiles are presented in

Table 8-25. The advantages of the study are its large sample size and that it is representative of the U.S. population for the age groups presented. A limitation of the study is that the data are based on self reporting from the participants.

8.5 RELEVANT FETAL WEIGHT STUDIES

8.5.1 Brenner et al., 1976 - A Standard of Fetal

Growth for the United States of America Brenner et al. (1976) determined fetal weights for 430 fetuses aborted at 8 to 20 weeks of gestation and for 30,772 liveborn infants delivered at 21 to 44 weeks of gestation. Gestational age for the aborted fetuses was determined through a combination of the physician’s estimate of uterine size and the patient’s stated last normal menstrual period. Data were not used when these two estimates differed by more than 2 weeks. To determine fetal growth, the fetuses were weighed and measured (crown-to-rump and crown-to heel lengths). All abortions were legally performed at Memorial Hospital, University of North Carolina at Chapel Hill from 1972 to 1975. For the liveborn infants, data were analyzed from single birth deliveries with the infant living at the onset of labor, among pregnancies not complicated by pre-eclampsia, diabetes or other disorders. Infants were weighed on a balance scale immediately after delivery. The liveborn infants were delivered at MacDonald House, University Hospitals of Cleveland, Ohio from 1962 to 1969. Percentiles for fetal weight were calculated from the data at each week of gestation and are shown in Table 8-26. The resulting percentile curves were smoothed with two-point weighted means. Variables associated with significant differences in fetal weight in the latter part of pregnancy (after 34-38 weeks of gestation) included maternal parity and race, and fetal gender. The advantage of this study is the large sample size. Limitations of the study are that the data were collected more than 30 years ago in only two U.S. states. In addition, a number of variables which may affect fetal weight (i.e., maternal smoking, disease, nutrition, and addictions) were not evaluated in this study.

8.5.2 Doubilet et al., 1997 - Improved Birth Weight Table for Neonates Developed from Gestations Dated by Early Ultrasonography Doubilet et al. (1997) matched a database of obstetrical ultrasonograms over a period of 5 years from 1988 to 1993 to birth records for 3,718 infants (1,857 males and 1,861 females). The study population included 1,514 Whites, 770 Blacks, 1,256 Hispanics, and 178 who were either unclassified, or classified as “other.” Birth weights were obtained from hospital records and a gestational age was assigned based on the earliest first trimester sonogram. The database was screened for possible outliers, defined as infants with birth weights that exceeded 5000 grams. Labor and delivery records and mother-infant medical records were retrieved to correct any errors in data entry for infants with birth weights exceeding 5000 grams. The mean gestational age at initial sonogram was 9.5 ± 2.3 weeks. Regression analysis techniques were used to derive weight tables for neonates at each gestational age for 25 weeks of gestation onward. Weights for each gestational age were found to conform to a natural logarithm distribution. Polynomial equations were derived from the regression analysis to estimate mean weight by gestational age for males, females, and males and females combined. Table 8-27 provides the distribution of neonatal weights by gestational age from 25 weeks of gestation onward.



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Chapter 8 - Body Weight

8.6 REFERENCES FOR CHAPTER 8

Brenner, W.E.; Edelman, D.A.; Hendricks, C.H. (1976) A standard of fetal growth for the

United States of America. Am J Obstet Gynecol 1:126(5):555-64. Burmaster, D.E.; Crouch, E.A.C. (1997) Lognormal distributions for body weight as a function of age for males and females in the United States, 1976-1980. Risk Anal 17(4):499­

505. Doubilet, P.M.; Benson, C.B.; Nadel, A.S.; Ringer,

S.A. (1997) Improved birth weight table for neonates developed from gestations dated by early ultrasonography. J Ultrasound Med

16:241-249. Freedman, D.; Kettel, K.; Serdula, M.; Ogden, C.; Dietz, W. (2006) Racial and ethnic

differences in secular trends for childhood BMI, weight, and height. Obesity 14(2):301:307. Kahn, H.; Stralka, K. (2008) Estimated daily average per capita water ingestion by child and adult age categories based on USDA’s 1994-96 and 1998 continuing survey of food intakes (CSFII). J Expo Sci Environ Epidemiol (2008) 1-9.Kuczmarski, R.J.; Ogden, C.L.; Guo, S.S.; GrummerStrawn, L.; Flegal, K., et al. (2000) CDC growth charts for the United States: methods and development. National Center for Health Statistics. Vital Health Stat. 11(246)2002. LSRO (1995) Third report on nutrition monitoring in the United States: Volume 1. Prepared by:

Federation of American Societies for Experimental Biology, Life Sciences Research Office for the Interagency Board for Nutrition Monitoring and Related Research. Washington, D.C.: U.S. Government Printing Office. Martin, J.; Hamilton, B.; Sutton, P.; Ventura, S.; Fay, M.; et al. (2007) Births: final data for 2005. CDC National Vital Statistics Report,

Volume 56. No. 6. 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. Ogden, C.L.; Fryar, C.D.; Carroll, M.D.; Flegal, K. M. (2004) Mean Body Weight, Height, and Body Mass Index, United States 1960-2002. Advance Data from Vital and Health Statistics, No. 347, October 27, 2004. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for

Health Statistics. Portier K.; Tolson, J.; Roberts, S. (2007) Body weight

distributions for risk assessment. Risk Anal 27(1)11-26.

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.

U.S. EPA (2000) Memorandum entitled:	 Body weight estimates on NHANES III data, revised, Contract 68-C-99-242, Work Assignment 0-1 from Bob Clickner, Westat Inc. to Helen Jacobs, U.S. EPA dated March 3, 2000.

U.S. EPA (2005) Guidance on selecting age groups for monitoring and assessing childhood exposures to environmental contaminants (2005). Washington, D.C.: U.S. Environmental Protection Agency, EPA/630/P-03/003F.



### Child-Specific Exposure Factors Handbook Page September 2008 8-9Table 8-3. Mean and Percentile Body Weights (kilograms) Derived from NHANES 1999-2006,

Males and Females Combined

Age Group N Mean Percentiles

5 th 10 th 15 th 25 th 50 th 75 th 85 th 90 th 95 th

Birth to <1 month

1 to <3 months

3 to <6 months

6 to <12 months

1 to <2 years

2 to <3 years

3 to <6 years

6 to <11 years

11 to <16 years

16 to <21 years 158

284

489

927

1176

1144

2318

3593

5297

4851 4.8

5.9

7.4

9.2

11.4

13.8

18.6

31.8

56.8

71.6 3.6

4.5

5.7

7.1

8.9

10.9

13.5

19.7

34.0

48.2 3.9

4.7

6.1

7.5

9.3

11.5

14.4

21.3

37.2

52.0 4.1

4.9

6.3

7.9

9.7

11.9

14.9

22.3

40.6

54.5 4.2

5.2

6.7

8.3

10.3

12.4

15.8

24.4

45.0

58.4 4.8

5.9

7.3

9.1

11.3

13.6

17.8

29.3

54.2

67.6 5.1

6.6

8.0

10.1

12.4

14.9

20.3

36.8

65.0

80.6 5.5

6.9

8.4

10.5

13.0

15.8

22.0

42.1

73.0

90.8 5.8

7.1

8.7

10.8

13.4

16.3

23.6

45.6

79.3

97.7 6.2

7.3

9.1

11.3

14.0

17.1

26.2

52.5

88.8

108.0

Source: U.S. EPA Analysis of NHANES 1999-2006 data. 

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Chapter 8 - Body Weight

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8-10 September 2008Table 8-4. Mean and Percentile Body Weights (kilograms) for Males Derived from NHANES 1999-2006

Percentiles Age Group N Mean
5 th 10 th 15 th 25 th 50 th 75 th 85 th 90 th 95 th

Birth to <1 month 88 4.9 3.6 3.6 4.0 4.4 4.8 5.5 5.8 6.2 6.8

1 to <3 months 153 6.0 4.6 5.0 5.1 5.4 6.1 6.8 7.0 7.2 7.3

3 to <6 months 255 7.6 5.9 6.4 6.6 6.9 7.5 8.2 8.6 8.8 9.1

6 to <12 months 472 9.4 7.3 7.9 8.2 8.5 9.4 10.3 10.6 10.8 11.5

1 to <2 years 632 11.6 9.0 9.7 10.0 10.5 11.5 12.6 13.2 13.5 14.3

2 to <3 years 558 14.1 11.4 12.0 12.2 12.8 14.0 15.2 15.9 16.4 17.0

3 to <6 years 1158 18.8 13.5 14.4 14.9 15.9 18.1 20.8 22.6 23.8 26.2

6 to <11 years 1795 31.9 20.0 21.8 22.9 24.8 29.6 36.4 41.2 45.2 51.4

11 to <16 years 2593 57.6 33.6 36.3 38.9 44.2 55.5 66.5 75.5 81.2 91.8

16 to <21 years 2462 77.3 54.5 57.6 60.0 63.9 73.1 86.0 96.8 104.0 113.0

Source: U.S. EPA Analysis of NHANES 1999-2006 data.

Table 8-5. Mean and Percentile Body Weights (kilograms) for Females Derived from NHANES 1999-2006

Percentiles Age Group N Mean
5 th 10 th 15 th 25 th 50 th 75 th 85 th 90 th 95 th

Birth to <1 month 70 4.6 3.6 4.0 4.1 4.2 4.6 4.9 5.0 5.2 5.9

1 to <3 months 131 5.7 4.3 4.6 4.74 5.1 5.5 6.4 6.6 6.9 7.3

3 to <6 months 234 7.2 5.5 5.9 6.2 6.4 7.2 7.9 8.2 8.4 9.0

6 to <12 months 455 9.0 7.1 7.3 7.6 8.0 8.9 9.8 10.3 10.6 11.2

1 to <2 years 544 11.1 8.7 9.1 9.4 10.0 11.1 12.2 12.9 13.2 13.7

2 to <3 years 586 13.5 10.5 11.0 11.5 12.1 13.2 14.6 15.5 16.2 17.1

3 to <6 years 1160 18.3 13.5 14.3 14.7 15.6 17.5 19.7 21.3 23.2 26.2

6 to <11 years 1798 31.7 19.3 20.9 22.0 23.9 29.0 37.3 43.1 46.7 53.4

11 to <16 years 2704 55.9 34.9 38.6 41.6 45.7 53.3 62.8 70.7 76.5 86.3

16 to <21 years 2389 65.9 46.2 48.6 51.1 54.5 61.5 73.3 83.4 89.9 99.7

Source: U.S. EPA Analysis of NHANES 1999-2006 data. 

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Chapter 8 - Body WeightTable 8-6. Weight in Kilograms for Males 2 Months-19 Years of Age– Number Examined, Mean, and Selected Percentiles,by Age Category: United States, 1976-1980aNumber ofPercentilesPersons MeanAge Group5th10th15th25th50th75th85th90th95thExamined (kg)Birth to <1 month--- - - - - - - - ­1 to <2 months--- - - - - - - - ­2 to <3 months103 6.6 5.3 5.5 5.7 5.9 6.8 7.2 7.6 7.8 8.43 to <6 months287 7.7 6.3 6.6 6.7 7.0 7.7 8.4 8.9 9.2 9.66 to <12 months589 9.4 7.5 7.9 8.1 8.6 9.4 10.2 10.6 10.9 11.41 to <2 years613 11.7 9.4 9.8 10.1 10.8 11.7 12.6 13.1 13.7 14.52 to <3 years627 13.7 11.4 11.8 12.2 12.6 13.6 14.6 15.2 15.8 16.53 to <6 years1556 18.0 13.7 14.6 14.9 15.7 17.5 19.7 21.0 22.0 24.06 to <11 years1373 30.7 19.5 21.1 22.1 24.0 28.5 35.2 40.5 43.5 48.711 to <16 years1037 55.2 34.0 36.5 38.7 42.8 53.0 63.0 69.4 74.8 84.316 to <21 years890 71.8 54.1 56.6 58.3 61.8 68.7 77.9 84.3 89.7 101.0aIncludes clothing weight, estimated as ranging from 0.09 to 0.28 kilogram.- No data available for infants less than two months old.Source: National Center for Health Statistics, 1987.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body WeightTable 8-7. Weight in Kilograms for Females 6 Months-19 Years of Age– Number Examined, Mean, and Selected Percentiles,by Age Category: United States, 1976-1980aNumber ofPercentilesAge GroupMeanPersons(kg)5th10th15th25th50th75th85th90th95thExaminedBirth to <1 month--- - - - - - - - ­1 to <2 months--- - - - - - - - ­2 to <3 months131 6.0 4.7 5.1 5.2 5.6 6.0 6.5 7.1 7.3 7.83 to <6 months269 7.1 5.8 5.9 6.1 6.4 7.1 7.7 7.9 8.4 8.76 to <12 months574 8.8 7.2 7.5 7.7 8.0 8.7 9.4 10.1 10.4 10.81 to <2 years617 11.0 9.1 9.4 9.6 9.9 10.9 11.9 12.6 12.9 13.42 to <3 years597 13.4 10.8 11.2 11.6 12.1 13.2 14.6 15.4 15.6 16.33 to <6 years1658 18.0 13.3 14.0 14.5 15.4 17.2 19.7 21.1 22.6 25.16 to <11 years1321 30.6 19.0 20.5 21.3 23.4 28.9 35.0 39.6 44.3 50.211 to <16 years1144 53.2 34.1 37.2 40.4 45.2 51.6 60.0 67.2 70.6 78.216 to <21 years1001 62.2 46.7 48.2 49.7 52.2 58.9 68.3 74.7 80.8 92.6aIncludes clothing weight, estimated as ranging from 0.09 to 0.28 kilogram.- No data available for infants less than two months old.Source: National Center for Health Statistics, 1987.



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Chapter 8 - Body Weight

Table 8-8. Statistics for Probability Plot Regression Analyses: Females Body Weights 6 Months to 20 Years of Age

Lognormal Probability Plots Age Midpoint (years) Linear Curve

μ 2 a F 2 a

0.75 2.16 0.145

1.5 2.38 0.129

2.5 2.56 0.112

3.5 2.69 0.136

4.5 2.83 0.134

5.5 2.98 0.164

6.5 3.10 0.174

7.5 3.19 0.174

8.5 3.31 0.156

9.5 3.46 0.214

10.5 3.57 0.199

11.5 3.71 0.226

12.5 3.82 0.213

13.5 3.92 0.215

14.5 3.99 0.187

15.5 4.00 0.156

16.5 4.05 0.167

17.5 4.08 0.165

18.5 4.07 0.147

19.5 4.10 0.149

a μ 2 , F 2 - correspond to the mean and standard deviation, respectively, of the lognormal distribution of body weight (kg).

Source: Burmaster and Crouch, 1997.



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Chapter 8 - Body Weight

Table 8-9. Statistics for Probability Plot Regression Analyses: Males Body Weights 6 Months to 20 Years of Age

Lognormal Probability Plots Age Midpoint (years) Linear Curve

μ 2 a F 2 a

0.75 2.23 0.132

1.5 2.46 0.119

2.5 2.60 0.120

3.5 2.75 0.114

4.5 2.87 0.133

5.5 2.98 0.138

6.5 3.13 0.145

7.5 3.21 0.151

8.5 3.33 0.181

9.5 3.43 0.165

10.5 3.59 0.195

11.5 3.69 0.252

12.5 3.78 0.224

13.5 3.88 0.215

14.5 4.02 0.181

15.5 4.09 0.159

16.5 4.20 0.168

17.5 4.19 0.167

18.5 4.25 0.159

19.5 4.26 0.154

a μ 2 , F 2 - correspond to the mean and standard deviation, respectively, of the lognormal distribution of body weight (kg).

Source: Burmaster and Crouch, 1997.



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Chapter 8 - Body Weight

Table 8-10. Body Weight Estimates (kilograms) by Age and Gender, U.S. Population Derived From NHANES III (1988-94)

Male and Female Male Female Age Group Sample Size Population Median Mean Median Mean Median Mean

2 to 6 months 1,020 1,732,702 7.4 7.4 7.6 7.7 7.0 7.0

7 to 12 months 1,072 1,925,573 9.4 9.4 9.7 9.7 9.1 9.1

1 year 1,258 3,935,114 11.3 11.4 11.7 11.7 10.9 11.0

2 years 1,513 4,459,167 13.2 12.9 13.5 13.1 13.0 12.5

3 years 1,309 4,317,234 15.3 15.1 15.5 15.2 15.1 14.9

4 years 1,284 4,008,079 17.2 17.1 17.2 17.0 17.3 17.2

5 years 1,234 4,298,097 19.6 19.4 19.7 19.3 19.6 19.4

6 years 750 3,942,457 21.3 21.7 21.5 22.1 20.9 21.3

7 years 736 4,064,397 25.0 25.5 25.4 25.5 24.1 25.6

8 years 711 3,863,515 27.4 28.1 27.2 28.4 27.9 27.9

9 years 770 4,385,199 31.8 32.7 32.0 32.3 31.1 33.0

10 years 751 3,991,345 35.2 35.6 35.9 36.0 34.3 35.2

11 years 754 4,270,211 40.6 41.5 38.8 40.0 43.4 42.8

12 years 431 3,497,661 47.2 46.9 48.1 49.1 45.7 48.6

13 years 428 3,567,181 53.0 55.1 52.6 54.5 53.7 55.9

14 years 415 4,054,117 56.9 61.1 61.3 64.5 53.7 57.9

15 years 378 3,269,777 59.6 62.8 62.6 66.9 57.1 59.2

16 years 427 3,652,041 63.2 65.8 66.6 69.4 56.3 61.6

17 years 410 3,719,690 65.1 67.5 70.0 72.4 60.7 62.2

1 and older 31,311 251,097,002 66.5 64.5 73.9 89.0 80.8 80.3

1 to 3 years 4,080 12,711,515 13.2 13.1 13.4 13.4 13.0 12.9

1 to 14 years 12,344 56,653,796 24.9 29.9 25.1 30.0 24.7 29.7

15 to 44 years 10,393 118,430,653 70.8 73.5 77.5 80.2 63.2 67.3

Source: U.S. EPA, 2000.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Table 8-11. Body Weight Estimates (in kilograms) by Age, U.S. Population Derived From NHANES III (1988-94)

Male and Female Age Group Sample Size Population

Median Mean 95% CI

2 Months 243 408,837 6.3 6.3 6.1-6.4

3 Months 190 332,823 7.0 6.9 6.7-7.1

3 Months and Younger 433 741,660 6.6 6.6 6.4-6.7

= Confidence Interval.

Source: U.S. EPA, 2000.



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Chapter 8 - Body Weight

Table 8-12. Observed Mean, Standard Deviation and Selected Percentiles for Weight (kilograms) by Gender and Age: Birth to 36 Months

Percentile Age Group Mean SD 10 th 25 th 50 th 75 th 90 th 95 th

Boys Birth 3.4 0.6 2.7 3.1 3.4 3.8 4.1 4.3

0 < 1 months

! ! ! ! ! ! ! !

1< 2 months

! ! ! ! ! ! ! !

2 < 3 months 6.5 0.8 5.6 5.8 6.7 6.9 7.4 7.5

3 < 4 months 7.0 0.9 5.9 6.5 7.0 7.5 8.2 8.5

4 < 5 months 7.2 0.8 6.3 6.7 7.2 7.7 8.0 8.4

5 < 6 months 7.9 0.9 6.7 7.5 7.8 8.6 9.4 9.6

6 < 7 months 8.4 1.1 7.3 7.6 8.4 9.0 10.2 10.7

7 < 8 months 8.6 1.1 7.1 7.8 8.6 9.5 10.1 10.4

8 < 9 months 9.3 1.1 7.9 8.6 9.2 10.1 10.5 11.0

9 < 10 months 9.3 0.9 8.2 8.6 9.3 10.0 10.8 10.9

10 < 11 months 9.5 1.1 8.3 8.7 9.3 10.1 11.3 11.5

11 < 12 months 10.0 1.0 8.7 9.5 10.0 10.6 11.1 11.6

12 < 15 months 10.6 1.2 9.2 9.8 10.6 11.3 12.1 12.4

15 < 18 months 11.4 1.9 9.9 10.5 11.3 12.0 12.8 13.5

18 < 21 months 12.1 1.5 10.4 11.0 11.9 12.7 13.9 15.5

21 < 24 months 12.4 1.3 10.9 11.6 12.4 13.1 14.4 14.7

24 < 30 months 13.1 1.7 11.3 12.1 12.9 14.1 15.1 15.9

30 < 36 months 14.0 1.5 12.0 13.0 13.8 14.7 16.0 16.6

Girls

Birth 3.3 0.5 2.6 3.0 3.3 3.6 3.9 4.1

0 < 1 months

! ! ! ! ! ! ! !

1< 2 months

! ! ! ! ! ! ! !

2 < 3 months 5.4 0.5 4.8 5.0 5.6 5.9 6.0

!

3 < 4 months 6.3 0.7 5.6 5.8 6.3 6.8 7.4 7.8

4 < 5 months 6.7 0.9 5.8 6.1 6.6 7.4 8.0 8.3

5 < 6 months 7.3 0.9 6.3 6.7 7.1 7.7 8.5 8.8

6 < 7 months 7.7 0.8 6.6 7.1 7.6 8.1 8.9 9.0

7 < 8 months 8.0 1.4 6.7 7.4 7.8 8.6 9.4 9.8

8 < 9 months 8.3 0.9 7.3 7.8 8.3 8.9 9.4 9.8

9 < 10 months 8.9 0.9 7.8 8.1 8.7 9.4 10.1 10.5

10 < 11 months 9.0 1.1 7.8 8.4 9.0 9.5 10.4 10.9

11 < 12 months 9.3 1.0 7.9 8.6 9.2 10.1 10.6 10.9

12 < 15 months 9.8 1.1 8.5 9.1 9.8 10.4 11.3 11.6

15 < 18 months 10.4 1.1 9.1 9.7 10.3 11.2 11.8 12.0

18 < 21 months 11.1 1.4 9.6 10.2 11.0 11.9 12.8 13.5

21 < 24 months 11.8 1.3 10.1 10.9 11.8 12.8 13.5 13.9

24 < 30 months 12.5 1.5 10.8 11.5 12.4 13.3 14.5 15.1

30 < 36 months 13.6 1.7 11.8 12.5 13.4 14.52 15.7 16.4

- No data available.

Source: Kuczmarski et al. 2002.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Figure 8-1. Weight by Age Percentiles for Boys Aged Birth to 36 Months

Source: Kuczmarski et al., 2002.



### Child-Specific Exposure Factors Handbook Page September 2008 8-19

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Figure 8-2. Weight by Age Percentiles for Girls Aged Birth to 36 Months

Source: Kuczmarski et al., 2002.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Figure 8-3. Weight by Length Percentiles for Boys Aged Birth to 36 Months

Source: Kuczmarski et al., 2002.



### Child-Specific Exposure Factors Handbook Page September 2008 8-21

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Figure 8-4. Weight by Length Percentiles for Girls Aged Birth to 36 Months

Source: Kuczmarski et al., 2002.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Figure 8-5. Body Mass Index-for-Age Percentiles: Boys, 2 to 20 Years

Source: Kuczmarski et al., 2002.



### Child-Specific Exposure Factors Handbook Page September 2008 8-23

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Figure 8-6. Body Mass Index-for-Age Percentiles: Girls, 2 to 20 Years

Source: Kuczmarski et al., 2002.



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8-24 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body WeightTable 8-13. Mean Body Weight (kilograms) by Age and Gender Across Multiple SurveysGenderNHES II, 1963-65NHES III, 1966-70NHANES I, 1971-74NHANES II, 1976-80NHANES III, 1988-94NHANES 1999-2002andAge(years)NMeanSENMeanSENMeanSENMeanSENMeanSENMeanSEMale2------29813.60.237013.40.164413.60.126213.70.13------30815.60.142115.50.151615.80.221615.90.24------30417.70.140517.60.154917.60.217918.50.25------27320.20.239319.70.149720.10.214721.30.5657522.00.1---17922.00.314622.80.428323.20.618223.50.4763224.70.2---16424.90.415024.90.426926.30.418527.20.4861827.80.2---15226.40.314528.00.626630.20.821432.71.0960331.20.4---16931.60.814130.70.628134.41.017436.00.71057633.70.3---18434.20.616536.20.729737.30.918738.60.81159538.20.3---17838.80.815339.70.928142.50.918243.71.112---64342.90.420044.00.814744.11.020349.11.129950.41.313---62650.00.517449.91.016549.51.218754.01.029853.91.914---61856.70.617456.30.918856.40.918864.13.626663.91.615---61361.60.417160.31.218061.21.018766.91.928368.31.116---55664.80.616966.91.318066.51.219468.71.630674.41.417---45868.10.417668.61.118366.70.819672.91.331375.61.418------12474.31.315671.11.217671.31.728475.61.119------13672.61.315071.80.816873.02.227078.21.3Female2------27213.00.133012.80.162413.20.124813.30.13------29215.00.236714.80.158715.40.117815.20.24------28116.80.238816.80.253717.90.319117.90.35------31419.70.336919.40.355420.20.218620.60.6653621.50.2---17621.60.315021.90.427222.60.617122.40.5760924.20.2---16924.30.415424.60.527426.40.819625.90.5861327.50.2---15227.50.512527.50.424829.90.618431.91.2958131.40.4---17132.00.615431.70.728034.41.218335.40.71058435.20.4---19733.80.612835.70.625837.91.216440.01.01152539.80.4---16641.20.814341.40.927544.11.119447.91.312---54746.60.417746.71.014646.10.923649.01.231652.01.113---58250.50.519851.81.015550.91.222055.81.632157.71.414---58654.20.418454.61.018154.31.021858.51.432459.91.015---50356.50.516756.60.914455.00.819158.11.126661.11.716---53658.10.717156.81.116757.70.920861.31.427363.01.217---44257.60.615059.51.613459.61.020162.41.225661.71.218------14158.21.115659.01.017561.21.924365.21.519------13059.51.415859.81.017763.21.922567.91.2-Data not available.N= Number of individuals.SE= Standard error.Source:Ogden et al., 2004.



### Child-Specific Exposure Factors Handbook Page September 2008 8-25

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body WeightTable 8-14. Mean Height (centimeters) by Age and Gender Across Multiple SurveysGenderand Age(years)NHES II, 1963-65N Mean SENHES III, 1966-70 NHANES I, 1971-74 NHANES II, 1976-80 NHANES III, 1988-94N Mean SE N Mean SE N Mean SE N Mean SENHANES 1999-2002N Mean SEMale2345678910111213141516171819----575632618603576595------------118.9124.5130.0135.5140.2145.5------------0.20.30.30.40.30.3------------------643626618613556458------------152.3159.8166.7171.4174.3175.6------------0.40.40.50.30.40.4--29830830427317916415216918417820017417417116917612413691.198.5106.0112.8118.1125.0129.0135.1140.0146.3152.8159.3166.7170.8175.0176.9176.6176.50.40.30.30.30.60.50.50.60.50.70.70.80.60.90.80.50.70.935042140539314615014514116515314716518818018018315615091.198.7105.5112.3119.1124.5129.6135.0141.3145.5152.5158.3166.8171.2173.4174.8177.3176.10.20.30.40.30.50.50.70.60.60.60.70.80.60.70.50.50.60.558951355149728327026928029728520719019118819719617616990.998.8105.2112.3118.9125.9131.3137.7142.0147.4155.5161.6169.0172.8175.0176.5177.3175.50.20.30.40.30.70.60.60.71.10.71.10.80.91.00.90.91.00.625422218315618818721717718818730129826728731031728927591.298.6106.5113.0119.2126.21325.138.1141.4148.7154.8160.1168.5173.8175.3175.3176.4176.70.30.30.40.50.50.60.70.40.60.90.70.80.90.60.60.60.70.6Female2345678910111213141516171819----536609613581584525------------117.8123.5129.4135.5140.9147.3------------0.30.20.30.30.30.3------------------547582586503536442------------46.650.554.256.558.157.6------------0.30.30.30.50.30.3--27229228131417616915217119716617719818416717115014113090.197.7104.2112.2118.2124.6129.2135.9140.1148.2154.6158.9160.8163.6161.7162.1164.7163.10.30.30.40.40.50.70.60.50.80.80.60.50.60.60.50.90.50.531436738836915015412515412814314615518114416713415615889.497.1104.2111.2117.9123.4129.5134.1141.7147.4143.8158.7160.7163.3162.8163.5162.8163.20.30.20.40.40.60.70.50.50.60.70.60.50.70.50.50.60.50.456459053555727427524728226227523922522419521420117517889.798.2105.1112.2117.9124.3131.1136.6142.7150.2155.5159.9161.2162.8163.0163.6163.2163.40.20.20.30.50.60.70.60.70.60.70.70.90.70.60.70.60.90.723318719519017220018418916419431832432627127525824923190.197.6105.9112.4117.1124.4130.9136.9143.3151.4156.0159.1161.8162.0161.9163.2163.0163.10.40.50.50.70.70.50.60.70.90.70.70.60.60.60.50.60.50.7-NSEData not available.= Number of individuals.= Standard error.Source:Ogden et al., 2004.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body WeightTable 8-15. Mean Body Mass Index (BMI) by Age and Gender Across Multiple SurveysGenderNHES II, 1963-65NHES III, 1966-70NHANES I, 1971-74NHANES II, 1976-80NHANES III, 1988-94NHANES 1999-2002and Age(years)NMeanSENMeanSENMeanSENMeanSENMeanSENMeanSEMale2------29816.30.135016.20.158816.50.122516.60.13------30816.00.142115.90.151216.10.220916.20.14------30415.70.140515.80.154715.90.117816.30.25------27315.60.139315.60.149515.90.114716.50.3657515.60.1---17915.70.214616.00.228216.30.318216.40.2763215.90.1---16415.80.215016.00.226916.50.218517.00.2861816.30.1---15215.80.214516.50.226617.30.421418.40.4960316.90.2---16917.10.314116.80.227918.00.717418.70.31057617.10.1---18417.30.216518.00.329718.40.318719.10.31159517.90.1---17818.00.315318.60.328019.40.318219.60.412---64318.40.120018.70.214718.80.320320.10.329920.70.413---62619.40.117419.60.316519.50.418720.50.329820.70.514---61820.20.217420.20.318820.20.218822.31.126622.30.415---61320.90.117120.50.318020.80.318722.30.528322.50.316---55621.30.116921.80.318022.00.319422.30.530624.10.417---45822.10.117621.90.318321.80.219623.40.431324.50.418------12423.70.315622.60.417622.60.528424.20.319------13623.30.515023.10.316823.70.626924.90.4Female2------27215.90.131416.10.156216.50.121416.40.13------29215.70.136715.60.158215.90.117316.00.14------28115.50.138815.50.153316.00.219015.90.25------31415.50.136915.60.155415.90.118616.10.36536115.40.1---17615.40.115015.60.227216.10.317016.20.2760915.80.1---16915.60.215416.10.227416.90.319616.60.2861316.40.1---15216.40.212516.30.224717.30.318418.30.5958117.00.1---17117.20.215417.50.328018.20.518318.70.31058417.60.2---19717.10.212817.70.325818.40.416319.30.31152518.20.2---16618.60.314318.90.327519.40.419420.70.412---54719.20.117719.50.414619.30.323620.20.531521.20.413---58219.90.119820.40.315520.10.422021.80.632122.60.414---58620.80.118421.10.318121.00.321822.40.532422.90.415---50321.40.216721.10.314420.60.319121.90.426623.20.516---53621.90.217121.70.316721.80.320823.00.527324.00.417---44221.70.215022.60.513422.30.420123.30.525523.10.418------14121.50.315622.30.417522.90.624324.40.519------13022.50.615822.40.317723.70.822525.50.4-Data not available.N= Number of individuals.SE= Standard error.Source:Ogden et al., 2004.



### Child-Specific Exposure Factors Handbook Page September 2008 8-27

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body WeightTable 8-16. Sample Sizes by Age, Sex, Race, and ExaminationNHANES ExaminationAge GroupSexRaceaI (1971-1974)II (1976-1980)III (1988-1994)1999-2002Overall6431 (10.3)b6395 (10.6)9610 (9.9)6710 (10.1)2 to 5 yearsBoysWhite829 (3.9)1082 (4.1)605 (4.0)226 (3.9)Black286 (3.9)273 (4.1)693 (3.9)234 (4.0)Mexican American51 (3.8)105 (4.2)732 (4.0)231 (3.9)GirlsWhite772 (4.0)1028 (4.0)639 (4.0)235 (4.1)Black297 (4.0)234 (4.0)684 (3.9)222 (4.0)Mexican American56 (4.1)102 (4.2)800 (3.9)238 (4.16 to 11 yearsBoysWhite711 (9.1)667 (9.0)446 (8.9)298 (8.9)Black249 (9.0)137 (9.0)584 (9.0)371 (9.0)Mexican American51 (9.0)60 (9.2)565 (9.0)384 (9.0)GirlsWhite722 (9.1)631 (9.1)428 (9.1)293 (8.9)Black268 (9.0)155 (9.0)538 (9.0)363 (9.1)Mexican American45 (8.9)40 (9.3)581 (8.9)361 (9.0)12 to 17 yearsBoysWhite764 (14.9)786 (15.1)282 (14.9)449 (14.9)Black252 (14.9)155 (15.1)412 (15.0)543 (14.9)Mexican American42 (15.0)49 (15.0)406 (15.0)648 (15.0)GirlsWhite749 (15.0)695 (15.1)344 (15.0)456 (14.9)Black251 (14.8)159 (15.0)450 (14.9)528 (14.8)Mexican American36 (14.9)37 (15.2)421 (14.8)631 (14.9)aRace was recoded in the first two examinations (using data concerning ancestry/national origin) to create comparable categories in allsurveys.bMean ages are shown in parentheses.Source:Freeman et al., 2006.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body WeightTable 8-17. Mean BMI (kg/m2) Levels and Change in the Mean Z-Scores by Race-Ethnicity and SexExamination YearaIncrease in Mean z-scoreFrom 1971-1974 to 1999-2002Race 1971-1974 1976-1980 1988-1994 1999-2002 BMI Weight HeightOverall White18.0b18.0 18.8 19.0 +0.33 +0.36 +0.20Black17.8 18.2 19.1 20.0 +0.61 +0.63 +0.31Mexican-American 18.6 18.8 19.5 20.1 +0.32 +0.52 +0.39SexBoys White17.9 18.0 18.8 19.0 +0.37 +0.42 +0.25Black17.7 17.8 18.8 19.6 +0.53 +0.58 +0.32Mexican-American 18.6 18.9 19.4 20.3 +0.38 +0.67 +0.57Girls White18.0 18.0 18.7 19.0 +0.30 +0.32 +0.16Black17.9 18.6 19.5 20.4 +0.71 +0.69 +0.30Mexican-American 18.5 18.6 19.6 19.9 +0.25 +0.35 +0.21Age (years)2 to 5 White15.8 15.7 16.0 16.2 +0.21 +0.22 +0.13Black15.8 15.7 15.9 16.2 +0.34 +0.32 +0.18Mexican-American 16.5 16.2 16.5 16.5 -0.02 +0.29 +0.436 to 11 White16.7 16.9 17.6 17.9 +0.42 +0.47 +0.30Black16.5 17.1 17.9 18.7 +0.67 +0.69 +0.36Mexican-American 16.9 17.7 18.5 18.8 +0.50 +0.65 +0.4112 to 17 White20.7 20.6 21.8 22.0 +0.32 +0.35 +0.15Black20.4 20.9 22.4 23.7 +0.72 +9,77 +0.33Mexican-American 21.6 21.5 22.6 24.0 +0.37 +0.55 +0.34aSecular trends for BMI, BMI-for-age, weight-for-age, and height-for-age were each statistically significant at the 0.001 level. Trends inBMI, BMI-for-age, and weight also differed (p <0.001) by race.bMean BMI levels have been adjusted for differences in age and sex across exams.Source: Freedman et al., 2006.



### Child-Specific Exposure Factors Handbook Page September 2008 8-29

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body WeightTable 8-18. Prevalence of Overweight and ObesityaAmong ChildrenExamination yearIncrease in Prevalence From 1971­1974 to 1999-2002Race1971-1974 1976-1980 1988-1994 1999-2002 Overweight ObesityOverallWhite5% (1)b5% (1) 9% (2) 12% (3) +8+2Black6% (1) 7% (2) 12% (3) 18% (5) +12+4Mexican-American 8% (1) 10% (1) 14% (4) 21% (5) +12+4SexBoys White5% (1) 5% (1) 10% (2) 13% (4) +8+3Black6% (2) 5% (1) 11% (3) 16% (5) +10+3Mexican-American 8% (1) 12% (1) 15% (4) 24% (4) +16+6Girls White5% (1) 5% (1) 9% (2) 12% (2) +7+1Black6% (1) 9% (2) 14% (3) 21% (6) +14+5Mexican-American 8% (2) 7% (0) 14% (3) 17% (4) +9+2Age (years)2 to 5 White4% (1) 3% (1) 5% (1) 9% (3)+5+2Black7% (3) 4% (0) 8% (3) 9% (4)+2+1Mexican-American 10% (5) 11% (3) 12% (5) 13% (5) +306 to 11 White4% (0) 6% (1) 11% (3) 13% (4) +10+3Black4% (0) 9% (3) 15% (3) 20% (5) +15+4Mexican-American 6% (0) 11% (0) 17% (4) 22% (5) +16+512 to 17 White6% (1) 4% (0) 11% (2) 13% (2) +7+1Black8% (1) 8% (1) 13% (3) 22% (6) +14+5Mexican-American 9% (0) 8% (1) 14% (2) 25% (5) +15+5aOverweight is defined as a BMI$95thpercentile or$30 kg/m2; obesity is defined as a BMI$99thpercentile or$40 kg/m2.bValues are percentage of overweight children (percentage of obese children).Source: Freedman et al., 2006.



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### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Table 8-19. Numbers of Live Births by Weight and Percentages of Live Births with Low and Very Low Birth Weights, by Race and Hispanic Origin of Mother: United States, 2005

All Races a Non-Hispanic

White b Non-Hispanic

Black b Hispanic c

Total Births 4,138,349 2,279,768 583,759 985,505

Weight (grams) Number of Live Births

< 500 6,599 2,497 2,477 1,212

500-999 23,864 10,015 8,014 4,586

1,000-1,499 31,325 14,967 8,573 5,988

1,500-1,999 66,453 33,687 15,764 12,710

2,000-2,499 210,324 104,935 46,846 43,300

2,500-2,999 748,042 364,726 144,803 176,438

3,000-3,499 1,596,944 857,136 221,819 399,295

3,500-3,999 1,114,887 672,270 108,698 266,338

4,000-4499 289,098 167,269 22,149 64,704

4,500-4999 42,119 27,541 3,203 9,167

>5,000 4,715 2,840 405 1,174

Not stated 3,979 1,885 1,008 593

Percent of Total

Low Birth Weight d 8.2 7.3 14.0 6.9

Very Low Birth Weight e 1.5 1.2 3.3 1.2

a All Races includes White, Black, and races other than White and Black and origin not stated.

b Race categories are consistent with the 1977 Office of Management and Budget standards.

c Hispanic includes all persons of Hispanic origin of any race.

d Low birth weight is birth weight less than 2,500 grams (5 lb 8 oz).

e Very low birth weight is birth weight less than 1,500grams (3 lb 4 oz).

Source: Martin et al., 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 8-31

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Table 8-20. Estimated Mean Body Weights of Males and Females by Single-Year Age Groups Using NHANES II Data

Males (kg) Females (kg) Overall (kg) Age Group a Mean SD N Mean SD N Mean SD N

0 to 1 year 9.4 1.3 179 8.8 1.3 177 9.1 1.2 356 1 to 2 years 11.8 1.6 370 10.8 1.4 336 11.3 1.5 706 2 to 3 years 13.6 1.8 375 13.0 1.5 336 13.3 1.6 711 3 to 4 years 15.6 1.9 418 14.9 2.1 366 15.2 1.8 784 4 to 5 years 17.8 2.4 404 17.0 2.3 396 17.4 2.4 800 5 to 6 years 19.8 2.8 397 19.6 3.2 364 19.7 2.8 761 6 to 7 years 23.0 3.7 133 22.1 3.9 135 22.5 3.6 268 7 to 8 years 25.1 3.8 148 24.7 4.6 157 24.8 3.8 305 8 to 9 years 28.2 5.6 147 27.8 4.8 123 28.1 5.6 270 9 to 10 years 31.1 5.8 145 31.8 7.3 149 31.4 5.9 294 10 to 11 years 36.4 7.2 157 36.1 7.7 136 36.2 7.1 293 11 to 12 years 40.2 9.8 155 41.8 10.1 140 41.0 9.9 295 12 to 13 years 44.2 9.8 145 46.4 10.1 147 45.4 10.0 292 13 to 14 years 49.8 11.4 173 50.9 11.2 162 50.4 11.5 335 14 to 15 years 57.1 10.7 186 54.7 10.7 178 55.9 10.5 364 15 to 16 years 61.0 10.4 184 55.1 9.0 145 58.0 9.9 329 16 to 17 years 67.1 11.7 178 58.1 9.6 170 62.4 10.9 348 17 to 18 years 66.7 11.3 173 59.6 10.4 134 63.3 10.7 307 18 to 19 years 71.0 12.0 164 59.0 10.2 170 64.6 10.9 334 19 to 20 years 71.7 11.3 148 60.1 10.1 158 65.3 10.3 306 20 to 21 years 71.6 12.0 114 60.5 10.7 162 65.2 10.9 276

a Data were converted from ages in months to ages in years. For instance, age 1–2 years represents ages from 12 to 23 months.

SD = Standard Deviation.

N = Number of individuals.

Source: Portier et al., 2007.



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Chapter 8 - Body Weight

Table 8-21. Estimated Mean Body Weights of Males and Females by Single-Year Age Groups Using NHANES III Data

Males (kg) Females (kg) Overall (kg) Age Group a
Mean SD N Mean SD N Mean SD N

0 to 1 years 8.5 1.5 902 7.8 1.6 910 8.17 1.7 1,812 1 to 2 years 11.6 1.5 660 10.9 1.4 647 11.2 1.5 1,307 2 to 3 years 13.6 1.5 644 13.2 1.8 624 13.4 1.8 1,268 3 to 4 years 15.8 2.3 516 15.4 2.2 587 15.6 2.2 1,103 4 to 5 years 17.6 2.4 549 17.9 3.2 537 17.8 3.2 1,086 5 to 6 years 20.1 3.0 497 20.2 3.5 554 20.2 3.5 1,051 6 to 7 years 23.2 5.0 283 22.6 4.7 272 22.9 4.8 555 7 to 8 years 26.3 5.0 269 26.3 6.2 274 26.4 6.2 543 8 to 9 years 30.1 6.9 266 29.8 6.7 248 30.0 6.7 514 9 to 10 years 34.4 7.9 281 34.3 9.0 280 34.4 9.0 561
10 to 11 years 37.3 8.6 297 37.9 9.5 258 37.7 9.4 555
11 to 12 years 42.5 10.5 281 44.2 10.5 275 43.4 10.3 556 12 to 13 years 49.1 11.1 203 49.1 11.6 236 49.1 11.7 439 13 to 14 years 54.0 12.9 187 55.7 13.2 220 54.8 13.0 407 14 to 15 years 63.7 17.1 188 58.3 11.8 220 60.6 12.2 408 15 to 16 years 66.8 14.9 187 58.3 10.1 197 61.7 10.7 384 16 to 17 years 68.6 14.9 194 61.5 12.8 215 65.2 13.6 409 17 to 18 years 72.7 13.3 196 62.4 11.9 217 67.6 12.9 413 18 to 19 years 71.2 14.3 176 61.5 14.2 193 66.4 15.3 369 19 to 20 years 73.0 12.8 168 63.6 14.5 193 68.3 15.6 361 20 to 21 years 72.5 13.4 149 61.7 12.9 180 66.1 13.8 329

a Data were converted from ages in months to ages in years. For instance, age 1–2 years represents ages from 12 to 23 months.

SD = Standard Deviation.

N = Number of individuals.

Source: Portier et al., 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 8-33

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Table 8-22. Estimated Mean Body Weights of Males and Females by Single-Year Age Groups Using NHANES IV Data

Males (kg) Females (kg) Overall (kg) Age Group a
Mean SD N Mean SD N Mean SD N

0 to 1 year 9.3 1.8 116 9.3 1.5 101 9.3 1.5 217 1 to 2 years 11.3 1.4 144 11.5 1.9 98 11.4 1.8 242 2 to 3 years 13.7 2.0 130 13.3 1.9 113 13.5 2.0 243 3 to 4 years 16.4 2.3 105 15.2 2.1 77 15.9 2.2 182 4 to 5 years 18.8 2.6 95 18.1 3.2 87 18.5 3.3 182 5 to 6 years 20.2 3.3 65 20.7 4.9 92 20.6 4.9 157 6 to 7 years 22.9 4.3 94 22.0 4.5 74 22.5 4.6 168 7to 8 years 28.1 5.6 100 26.0 6.2 82 27.4 6.5 182 8 to 9 years 31.9 8.6 100 30.8 7.2 89 31.3 7.3 189 9 to 10 years 36.1 7.5 76 36.0 8.4 84 36.2 8.5 160 10 to 11 years 39.5 9.0 92 39.4 10.2 84 39.5 10.2 176 11 to 12 years 42.0 10.2 84 47.2 12.2 97 44.6 11.6 181 12 to 13 years 49.4 12.7 158 51.6 12.3 160 50.3 11.9 318 13 to 14 years 54.9 16.2 161 59.8 15.3 156 56.9 14.6 317 14 to 15 years 65.1 19.9 137 59.9 13.3 158 61.5 13.7 295 15 to 16 years 68.2 15.7 142 63.4 13.9 126 65.9 14.4 268 16 to 17 years 72.5 18.6 153 63.4 16.0 142 68.0 17.1 295 17 to 18 years 75.4 17.9 146 59.9 11.9 128 66.6 13.2 274 18 to 19 years 74.8 15.9 131 65.0 15.2 139 70.2 16.4 270 19 to 20 years 80.1 17.2 129 68.7 17.4 132 74.6 19.0 261 20 to 21 years 80.0 15.5 37 66.3 15.5 44 74.3 17.4 81

a Data were converted from ages in months to ages in years. For instance, age 1–2 years represents ages from 12 to 23 months.

SD = Standard Deviation.

N = Number of individuals.

Source: Portier et al., 2007.



### Page Child-Specific Exposure Factors Handbook

8-34 September 2008Table 8-23. Estimated Body Weights of Typical Age Groups of Interest in U.S. EPA Risk Assessments a

Age Group NHANES Males (kg) Females (kg) Overall (kg)

Mean SD N Mean SD N Mean SD N

II 17.0 4.6 2,097 16.3 4.7 1,933 16.7 4.5 4,030

1 to 6 years III 16.9 4.7 3,149 16.5 4.9 3,221 16.8 5.0 6,370

IV 17.1 4.9 633 17.5 5.0 541 17.3 5.0 1,174

II 45.2 17.6 1,618 43.9 15.9 1,507 44.8 17.5 3,125

7 to 16 years III 49.3 20.9 2,549 46.8 18.0 2,640 47.8 18.4 5,189

IV 47.9 20.1 1,203 47.9 19.2 1,178 47.7 19.1 2,381

a

SD

N Estimates were weighted using the sample weights provided with each survey.

= Standard Deviation.

= Number of individuals.

Source: Portier et al., 2007.

Table 8-24. Estimated Percentile Distribution of Body Weight by Fine Age Categories Derived From 1994-96, 1998 CSFII

Weight (kilograms)

Age Group Sample

Size Mean Percentile

1 st 5 th 10 th 25 th 50 th 75 th 90 th 95 th 99 th

Birth to 1 month

1 to <3 months

3 to <6 months

6 to <12 months

1 to <2 years

2 to <3 years

3 to <6 years

6 to <11 years

11 to <16 years

16 to <18 years

18 to <21 years 88

245

411

678

1,002

994

4,112

1,553

975

360

383 4

5

7

9

12

14

18

30

54

67

69 1 a

2 a

4 a

6 a

8 a

10 a

11

16 a

29 a

41 a

45 a 2 a

3 a

5

7

9

10

13

18

33

46 a

48 a 3 a

4

5

7

9

13

20

36

50

51 3

4

6

8

10

12

16

23

44

56

58 3

5

7

9

11

14

18

27

52

63

66 4

6

8

10

13

16

20

35

61

73

77 4 a

6

9

11

14

18

23

41

72

86

89 5 a

7 a

10

12

15

19

25

45

82

100 a

100 a 5 a

8 a

12 a

13 a

19 a

22 a

32

57 a

95 a

114 a

117 a

a

Source: Sample size does meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

Kahn and Stralka, 2008. 

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Child-Specific Exposure Factors Handbook Page September 2008 8-35Table 8-25.Estimated Percentile Distribution of Body Weight By Fine Age Categories With Confidence IntervalWeight (Kilograms)Mean90thPercentile95thPercentileSample90% CI90% BI90% BIAge GroupSizeEstimateEstimateEstimateLower UpperLower UpperLower UpperBound BoundBound BoundBound Bound4a4a5a5a5a5aBirth to 1 month 884341 to <3 months245555 6 6 7 7a7 73 to <6 months411777 9 9 9 10 10 106 to <12 months 678999 11 11 11 12 12 121 to <2 years1,002 1212 12 14 14 15 15 15 162 to <3 years994 1414 14 18 17 18 19 18 193 to <6 years4,112 1818 18 23 23 23 25 25 256 to <11 years1,553 3029 30 41 41 43 45 44 4811 to <16 years975 5453 55 72 70 75 82 81 84100a95a109a16 to <18 years360 6766 68 86 84 95100a95a104a18 to <21 years383 6968 70 89 88 95aSample size does meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States”(Vol. I).Interval estimates may involve aggregation of variance estimation units when data are too sparse to support estimation of variance.CI= Confidence interval.BI= Percentile intervals estimated using percentile bootstrap method with 1,000 bootstrap replications.Source:Kahn and Stralka, 2008. 

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

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8-36 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 8 - Body Weight

Table 8-26. Fetal Weight (grams) Percentiles Throughout Pregnancy

Gestational Age (weeks) Number of Women 10th 25th 50th 75th 90th

8 6

– a

– 6.1 b

– – 9 7

– – 7.3 b

– –
10 15

– – 8.1 b

– –

11 13

– – 11.9 b

– –

12 18

– 11 21 34

–
13 43

– 23 35 55

–

14 61

– 3,405 51 77

–
15 63

– 51 77 108

–
16 59

– 80 117 151

–

17 36

– 125 166 212

–
18 58

– 172 220 298

–
19 31

– 217 283 394

–
20 21

– 255 325 460

–

21 43 280 330 410 570 860

22 69 320 410 480 630 920

23 71 370 460 550 690 990

24 74 420 530 640 780 1,080

25 48 490 630 740 890 1,180

26 86 570 730 860 1,020 1,320

27 76 660 840 990 1,160 1,470

28 91 770 980 1,150 1,350 1,660

29 88 890 1,100 1,310 1,530 1,890

30 128 1,030 1,260 1,460 1,710 2,100

31 113 1,180 1,410 1,630 1,880 2,290

32 210 1,310 1,570 1,810 2,090 2,500

33 242 1,480 1,720 2,010 2,280 2,690

34 373 1,670 1,910 2,220 2,510 2,880

35 492 1,870 2,130 2,430 2,730 3,090

36 1,085 2,190 2,470 2,650 2,950 3,290

37 1,798 2,310 2,580 2,870 3,160 3,470

38 3,908 2,510 2,770 3,030 3,320 3,610

39 5,413 2,680 2,910 3,170 3,470 3,750

40 10,586 2,750 3,010 3,280 3,590 3,870

41 3,399 2,800 3,070 3,360 3,680 3,980

42 1,725 2,830 3,110 3,410 3,740 4,060

43 507 2,840 3,110 3,420 3,780 4,100

44 147 2,790 3,050 3,390 3,770 4,110

a Data not available.

b Median fetal weights may be overestimated. They were derived from only a small proportion of the fetuses delivered at these weeks’ gestation.

Source: Brenner et al., 1976.



### Child-Specific Exposure Factors Handbook Page September 2008 8-37Table 8-27. Neonatal Weight by Gestational Age for Males and Females Combined

Gestational Age (weeks) Weight (g)

5 th 10 th 25 th 50 th 75 th 90 th 95 th

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43 450

523

609

707

820

947

1,090

1,249

1,422

1,608

1,804

2,006

2,210

2,409

2,595

2,762

2,900

3,002

3,061 490

568

660

765

884

1,020

1,171

1,338

1,519

1,714

1,919

2,129

2,340

2,544

2,735

2,904

3,042

3,142

3,195 564

652

754

870

1,003

1,151

1,317

1,499

1,696

1,906

2,125

2,349

2,572

2,786

2,984

3,155

3,293

3,388

3,432 660

760

875

1,005

1,153

1,319

1,502

1,702

1,918

2,146

2,383

2,622

2,859

3,083

3,288

3,462

3,597

3,685

3,717 772

885

1,015

1,162

1,327

1,511

1,713

1,933

2,169

2,416

2,671

2,927

3,177

3,412

3,622

3,798

3,930

4,008

4,026 889

1,016

1,160

1,322

1,504

1,706

1,928

2,167

2,421

2,687

2,959

3,230

3,493

3,736

3,952

4,127

4,254

4,322

4,324 968

1,103

1,257

1,430

1,623

1,836

2,070

2,321

2,587

2,865

3,148

3,428

3,698

3,947

4,164

4,340

4,462

4,523

4,515

Source: Doubilet et al., 1997. 

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Chapter 8 - Body Weight

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8-38 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

9 INTAKE OF FRUITS AND VEGETABLES

9.1 INTRODUCTION

The American food supply is generally

considered to be one of the safest in the world. Nevertheless, fruits and vegetables may become contaminated with toxic chemicals by several different pathways. Ambient pollutants from the air may be deposited on or absorbed by the plants, or dissolved in rainfall or irrigation waters that contact the plants. Pollutants may also be absorbed through plant roots from contaminated soil and ground water. The addition of pesticides, soil additives, and fertilizers may also result in contamination of fruits and vegetables. To assess exposure through this pathway, information on fruit and vegetable ingestion rates is needed. Children’s exposure from contaminated fruits and vegetables may differ from that of adults because of differences in the types and amounts of food eaten. Also, for many foods, the intake per unit body weight is greater for children than for adults. Common fruits and vegetables eaten by children include apple juice, fresh apples, orange juice, fresh pears, fresh peaches, carrots, fresh bananas, succulent garden peas, and succulent garden beans (Goldman, 1995). A variety of terms may be used to define intake of fruits and vegetables (e.g., consumer-only intake, per capita intake, total fruit intake, total vegetable intake, as-consumed intake, dry weight intake). These terms are defined below to assist the reader in interpreting and using the intake rates that are appropriate for the exposure scenario being assessed. Consumer-only intake is defined as the quantity of fruits and vegetables consumed by children during the survey period. These data are generated by averaging intake across only the children in the survey who consumed these food items. Per capita intake rates are generated by averaging consumer-only intakes over the entire population of children (including those children that reported no intake). In general, per capita intake rates are appropriate for use in exposure assessments for which average dose estimates for children are of interest because they represent both children who ate the foods during the survey period and children who may eat the food items at some time, but did not consume them during the survey period. Per capita intake, therefore, represents an average across the entire population of interest, but does so at the expense of underestimating consumption for the subset of the population that consumed the food in question.

Total fruit intake refers to the sum of all fruits consumed in a day including canned, dried, frozen, and fresh fruits. Likewise, total vegetable intake refers to the sum of all vegetables consumed in a day including canned, dried, frozen, and fresh vegetables. Intake rates may be expressed on the basis of the as-consumed weight (e.g., cooked or prepared) or on the uncooked or unprepared weight. As-consumed intake rates are based on the weight of the food in the

form that it is consumed and should be used in

assessments where the basis for the contaminant

concentrations in foods is also indexed to the asconsumed weight. The food ingestion values provided in this chapter are expressed as as-consumed intake

rates because this is the fashion in which data were reported by survey respondents. This is of importance

because concentration data to be used in the dose equation are often measured in uncooked food samples. It should be recognized that cooking can either increase or decrease food weight. Similarly, cooking can increase the mass of contaminant in food (due to formation reactions, or absorption from cooking oils or water) or decrease the mass of contaminant in food (due to vaporization, fat loss or leaching). The combined effects of changes in weight and changes in contaminant

mass can result in either an increase or decrease in contaminant concentration in cooked food. Therefore, if the as-consumed ingestion rate and the uncooked concentration are used in the dose equation, dose maybe under-estimated or over-estimated. Ideally, aftercooking food concentrations should be combined with the as-consumed intake rates. In the absence of data, it is reasonable to assume that no change in contaminant concentration occurs after cooking. It is important for

the assessor to be aware of these issues and choose

intake rate data that best match the concentration data that are being used. For more information on cooking losses and conversions necessary to account for such losses, the reader is referred to Chapter 13 of this

handbook.

Sometimes contaminant concentrations in food are reported on a dry weight basis. When these data are used in an exposure assessment, it is recommended that dry-weight intake rates also be used. Dry-weight food concentrations and intake rates are based on the weight



### Child-Specific Exposure Factors Handbook Page September 2008 9-1

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

of the food consumed after the moisture content has been removed. For information on converting the intake rates presented in this chapter to dry weight intake rates, the reader is referred to Section 9.4. The purpose of this chapter is to provide intake data for fruits and vegetables among children. The recommendations for fruit and vegetable ingestion rates are provided in the next section, along with a summary of the confidence ratings for these recommendations. The recommended values are based on the key study identified by U.S. EPA for this factor. Following the recommendations, the key study on fruit and vegetable ingestion is summarized. Relevant data on ingestion of fruits and vegetables are also provided. These data are presented to provide the reader with added perspective on the current state-of-knowledge pertaining to ingestion of fruits and vegetables.

9.2 RECOMMENDATIONS Table 9-1 presents a summary of the

recommended values for per capita and consumer-only intake of fruits and vegetables, on an as-consumed basis. Confidence ratings for the fruit and vegetable intake recommendations for general population children are provided in Table 9-2. The U.S. EPA analysis of data from the 1994-96

and 1998 C ontinuing Survey of Food Intake by

Ind ividuals (CSFII) was used in selecting recommended intake rates for general population children. The U.S. EPA analysis was conducted using age groups that differed slightly from U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). However, for the purposes of the recommendations presented here, data were placed in the standardized age categories closest to those used in the analysis. Also, the CSFII data on which the recommendations are based are short-term survey data and may not necessarily reflect the long-term distribution of average daily intake rates. However, for broad categories of food (i.e., total fruits and total vegetables), because they are eaten on a daily basis throughout the year with minimal seasonality, the short term distribution may be a reasonable approximation of the long-term distribution, although it will display somewhat increased variability. This implies that the upper percentiles shown here may tend to overestimate the corresponding percentiles of the true long-term

distribution. It should also be noted that because these

recommendations are based on 1994-96 and 1998 CSFII data, they may not reflect the most recent changes that may have occurred in consumption patterns. More current data from the National Health and Nutrition Survey (NHANES) will be incorporated as the data become available and are analyzed.



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9-2 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-1. Recommended Values for Intake of Fruits and Vegetables, As Consumed a

Age Group Per Capita Consumers Only
Multiple

Percentiles Source Mean 95 th Percentile Mean 95 th Percentile

g/kg-day g/kg-day g/kg-day g/kg-day

Total Fruits

Birth to 1 year 5.7 21 10 26

See Tables

9-3 and 9-4 U.S. EPA Analysis of

CSFII,

1994-96 and

1998. 1 to <2 years 6.2 19 6.9 19

2 to < 3 years 6.2 19 6.9 19

3 to <6 years 4.6 14 5.1 15

6 to <11 years 2.4 8.8 2.7 9.3

11 to <16 years 0.8 3.5 1.1 3.8

16 to <21 years 0.8 3.5 1.1 3.8

Total Vegetables

Birth to 1 year 4.5 15 6.2 16

See Tables

9-3 and 9-4 U.S. EPA Analysis of

CSFII,

1994-96 and

1998. 1 to <2 years 6.9 17 6.9 17

2 to <3 years 6.9 17 6.9 17

3 to <6 years 5.9 15 5.9 15

6 to <11 years 4.1 9.9 4.1 9.9

11 to <16 years 2.9 6.9 2.9 6.9

16 to <21 years 2.9 6.9 2.9 6.9

Individual Fruits and Vegetables - See Tables 9-5 and 9-6

a Analysis was conducted using slightly different age groups than those recommended in Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA. 2005). Data were placed in the standardized age categories closest to those used in the analysis.



### Child-Specific Exposure Factors Handbook Page September 2008 9-3

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-2. Confidence in Recommendations for Intake of Fruits and Vegetables

General Assessment Factors Rationale Rating

Soundness Adequacy of Approach

Minimal (or Defined) Bias The survey methodology and data analysis was adequate. The survey sampled more than 11,000 individuals up to age 18 years. However, samples size for some individual fruits and vegetables for some of the age groups are small. An analysis of primary data

was conducted.

No physical measurements were taken. The method relied on recent recall of fruits and vegetables eaten. High for total fruits and vegetables, low for some

individual fruits and vegetables with small sample size

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The key study was directly relevant to fruit and vegetable intake.

The data were demographically representative of the U.S. population (based on stratified random sample).

Data were collected between 1994 and 1998.

Data were collected for two non-consecutive days. Medium

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The CSFII data are publicly available.

The methodology used was clearly described; enough information was included to reproduce the results.

Quality assurance of the CSFII data was good; quality control of the secondary data analysis was not well

described. High

Variability and Uncertainty

Variability in Population

Uncertainty Full distributions were provided for total fruits and total vegetables. Means were provided for individuals fruits and vegetables.

Data collection was based on recall of consumption for a 2-day period; the accuracy of using these data to estimate long-term intake (especially at the upper percentiles) is uncertain. However, use of short-term data to estimate chronic ingestion can be assumed for broad categories of foods such as total fruits and total vegetables. Uncertainty is likely to be greater for individual fruits and vegetables. Medium



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Table 9-2. Confidence in Recommendations for Intake of Fruits and Vegetables (continued)

General Assessment Factors Rationale Rating

Evaluation and Review

Peer Review

Number and Agreement of Studies The USDA CSFII survey received a high level of peer review. The U.S. EPA analysis of these data has not been peer reviewed outside the Agency.

There was 1 key study. Medium

Overall Rating High confidence in the

averages; Low for some

individual fruits and vegetables with small sample size

Low confidence in the long-term upper

percentiles



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9.3 INTAKE STUDIES The primary source of recent information on consumption rates of fruits and vegetables among children is the U.S. Department of Agriculture’s (USDA) CSFII. Data from the 1994-96 CSFII and the 1998 Children’s supplement to the 1994-96 CSFII have been used in various studies to generate children’s consumer-only and per capita intake rates for both individual fruits and vegetables and total fruits and vegetables. The CSFII is a series of surveys designed to measure the kinds and amounts of foods eaten by

Americans. The CSFII 1994-96 was conducted between January 1994 and January 1997 with a target population of non-institutionalized individuals in all 50 states and Washington, D.C. In each of the 3 survey years, data were collected for a nationally representative sample of individuals of all ages. The

CSFII 1998 was conducted between December 1997 and December 1998 and surveyed children 9 years of age and younger. It used the same sample design as the CSFII 1994-96 and was intended to be merged with CSFII 1994-96 to increase the sample size for children. The merged surveys are designated as CSFII 1994-96, 1998. Additional information on these surveys can be

obtained at http://www.ars.usda.gov/Services/docs.htm?docid=14531

. 

### The CSFII 1994-96, 1998 collected dietaryintake data through in-person interviews on 2 nonconsecutive days. The data were based on 24-hour recall. A total of 21,662 individuals provided data for the first day; of those individuals, 20,607 provided data for a second day. Over 11,000 of the sample persons represented children up to 18 years of age. The 2-day response rate for the 1994-1996 CSFII was approximately 76 percent. The 2-day response rate for CSFII 1998 was 82 percent. The CSFII 1994-96, 98 surveys were based on a complex multistage area probability sample design. The sampling frame was organized using 1990 U.S. population census estimates, and the stratification plan took into account geographic location, degree of urbanization, and socioeconomic characteristics. Several sets of sampling weights are available for use with the intake data. By using appropriate weights, data for all fours years of the surveys can be combined. USDA recommends that all 4 years be combined in order to provide an adequate sample size for children.

9.3.1 Key Fruits and Vegetables Intake Study

9.3.1.1 U.S. EPA Analysis of CSFII 1994-96, 1998 For many years, the U.S. EPA’s Office of Pesticide Programs (OPP) has used food consumption data collected by the U.S. Department of Agriculture (USDA) for its dietary risk assessments. Most recently, OPP, in cooperation with USDA’s Agricultural Research Service (ARS), used data from the 1994-96, 1998 CSFII to develop the Food Commodity Intake Database (FCID). CSFII data on the foods people reported eating were converted to the quantities of agricultural commodities eaten. "Agricultural commodity" is a term used by U.S. EPA to mean plant (or animal) parts consumed by humans as food; when such items are raw or unprocessed, they are referred to as "raw agricultural commodities." For example, an apple pie may contain the commodities apples, flour, fat, sugar and spices. FCID contains approximately 553 unique commodity names and 8-digit codes. The FCID commodity names and codes were selected and defined by U.S. EPA and were based on the U.S. EPA Food Commodity Vocabulary ( http://www.epa.gov/pesticides/foodfeed/)

. The fruit and vegetable items/groups selected for the U.S. EPA analysis included total fruits and total vegetables, and individual fruits such as: apples, bananas, peaches, pears, strawberries, citrus fruits, pome fruit, stone fruit, and tropical fruits; and individual vegetables such as: asparagus, beets, broccoli, cabbage, carrots, corn, cucumbers, lettuce, okra, onions, peas, peppers, pumpkin, beans, tomatoes, white potatoes, bulb vegetables, fruiting vegetables, leafy vegetables, legumes, and small stalk stem vegetables. Appendix 9A presents the food codes and

definitions used to determine the various fruits and vegetables used in the analysis. Intake rates for these food items/groups represent intake of all forms of the product (e.g., both home produced and commercially produced). Children who provided data for two days of the survey were included in the intake estimates. Individuals who did not provide information on body weight or for whom identifying information was unavailable were excluded from the analysis. Two-day average intake rates were calculated for all individuals in the database for each of the food items/groups.



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These average daily intake rates were divided by each individual's reported body weight to generate intake rates in units of grams per kilogram of body weight per day (g/kg-day). The data were weighted according to the four-year, two-day sample weights provided in the 1994-96, 1998 CSFII to adjust the data for the sample population to reflect the national population. Summary statistics were generated on both a per capita and a consumer only basis. For per capita intake, both users and non-users of the food item were included in the analysis. Consumer only intake rates were calculated using data for only those individuals who ate the food item of interest during the surveyperiod. Intake data from the CSFII were based on asconsumed (i.e., cooked or prepared) forms of the food items/groups. Summary statistics, including: number of observations, percentage of the population consuming the fruits or vegetables being analyzed, mean intake rate, and standard error of the mean intake rate were calculated for total fruits, total vegetables, and selected individual fruits and vegetables. Percentiles of the intake rate distribution (i.e., 1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 99th, and 100th percentile ) were also provided for total fruits and total vegetables. Data were provided for the following age groups of children: birth to <1 year, 1 to <2 years, 3 to <5 years, 6 to <12 years, and 13 to <19 years. Because these data were developed for use in U.S. EPA’s pesticide registration program, the age groups used are slightly different than

those recommended in U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). Table 9-3 presents as-consumed per capita

in take data for total fruits and vegetables in g/kg-day; as-consumed consumer only intake data for total fruits and vegetables in g/kg-day are provided in Table 9-4. Table 9-5 provides per capita intake data for individual fruits and vegetables and Table 9-6 provides consumer only intake data for individual fruits and vegetables.

It should be noted that the distribution of average daily intake rates generated using short-termdata (e.g., 2-day) do not necessarily reflect the longterm distribution of average daily intake rates. The distributions generated from short-term and long-term

data will differ to the extent that each individual’s intake varies from day to day; the distributions will be similar to the extent that individuals’ intakes are constant from day to day. Day-to-day variation in intake among individuals will be high for fruits and vegetables that are highly seasonal and for fruits and vegetables that are eaten year-round, but that are not typically eaten every day. For these fruits andvegetables, the intake distribution generated from shortterm data will not be a good reflection of the long-term distribution. On the other hand, for broad categories of foods (e.g., total fruits and total vegetables) that areeaten on a daily basis throughout the year, the shortterm distribution may be a reasonable approximation of the true long-term distribution, although it will show somewhat more variability. In this chapter, distributions are provided only for broad categories of fruits and vegetables (i.e., total fruits and total vegetables). Because of the increased variability of the short-term distribution, the short-term upper percentiles shown here may overestimate the corresponding percentiles of the long-term distribution. For individual foods, only the mean, standard error, and percent consuming are provided. The strengths of U.S. EPA’s analysis are that it provides distributions of intake rates for various age groups of children, normalized by body weight. The analysis uses the 1994-96, 1998 CSFII data set which was designed to be representative of the U.S. population. The data set includes four years of intake data combined, and is based on a two-day survey period. As discussed above, short-term dietary data may not accurately reflect long-term eating patterns and may under-represent infrequent consumers of a given food. This is particularly true for the tails (extremes) of the distribution of food intake. Also, the analysis was conducted using slightly different age groups than

those recommended in U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). However, given the similarities in the age groups used, the data should provide suitable intake estimates for the age groups of interest.



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9.3.2 Relevant Fruit and Vegetable Intake

Studies

9.3.2.1 USDA, 1999 - Food and Nutrient Intakes by Children 1994-96, 1998, Table Set 17 USDA (1999) calculated national probability estimates of food and nutrient intake by children based on all 4 years of the CSFII (1994-96 and 1998) for children age 9 years and under, and on CSFII 1994-96 only for individuals age 10 years and over. Sample weights were used to adjust for non-response, to match the sample to the U.S. population in terms of demographic characteristics, and to equalize intakes over the 4 quarters of the year and the 7 days of the

week. A total of 503 breast-fed children were excludedfrom the estimates, but both consumers and nonconsumers were included in the analysis. USDA (1999) provided data on the mean per capita quantities (grams) of various food products/groups consumed per individual for one day, and the percent of individuals consuming those foods in one day of the survey. Tables 9-7 through 9-10 present data on the mean quantities (grams) of fruits and vegetables consumed per individual for one day, and the percentage of survey individuals consuming fruits and vegetables on that survey day. Data on mean intakes or mean percentages are based on respondents’ day-1 intakes. The advantage of the USDA (1999) study is that it uses the 1994-96, 98 CSFII data set, which includes four years of intake data, combined, and includes the supplemental data on children. These data are expected to be generally representative of the U.S. population and they include data on a wide variety of fruits and vegetables. The data set is one of a series of USDA data sets that are publicly available. One limitation of this data set is that it is based on a one-day, and short-term dietary data may not accurately reflect long-term eating patterns. Other limitations of this study are that it only provides mean values of food intake rates, consumption is not normalized by body weight, and presentation of results is not consistent with

U.S. EPA’s recommended age groups. 9.3.2.2 Smiciklas-Wright et al., 2002 - Foods Commonly Eaten in the United States: Quantities Consumed per Eating Occasion and in a Day, 1994-1996 Using data gathered in the 1994-96 USDA CSFII, Smiciklas-Wright et al. (2002) calculated distributions for the quantities of fruits and vegetables consumed per eating occasion by members of the U.S. population (i.e., serving sizes). The estimates of serving size were based on data obtained from 14,262 respondents, ages 2 years and above, who provided 2 days of dietary intake information. A total of 4,939 of these respondents were children, ages 2 to 19 years of age. Only dietary intake data from users of the specified food were used in the analysis (i.e., consumers only data). Table 9-1 present s serving size data for selected fruits and vegetables. These data are presented on an as-consumed basis (grams) and represent the quantity of fruits and vegetables consumed per eating occasion. These estimates may be useful for assessing acute exposures to contaminants in specific foods, or other assessments where the amount consumed per eating occasion is necessary. Only the mean and standard deviation serving size data and percent of the population consuming the food during the 2-day survey period are presented in this handbook. Percentiles of serving sizes of the foods consumed by these age groups of the U.S. population can be found in Smiciklas-Wright et al. (2002). The advantages of using these data are that they were derived from the USDA CSFII and are representative of the U.S. population. The analysis conducted by Smiciklas-Wright et al. (2002) accounted for individual foods consumed as ingredients of mixed foods. Mixed foods were disaggregated via recipe files so that the individual ingredients could be grouped together with similar foods that were reported separately. Thus, weights of foods consumed as ingredients were combined with weights of foods reported separately to provide a more thorough representation of consumption. However, it should be noted that since the recipes for the mixed foods consumed were not provided by the respondents, standard recipes were used. As a result, the estimates of quantity consumed for some food types are based on assumptions about the types and quantities of



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ingredients consumed as part of mixed foods. This study used data from the 1994 to 1996 CSFII; data from the 1998 children’s supplement were not included.

9.3.2.3 Fox et al., 2004 - Feeding Infants and Toddlers study: What Foods Are Infants and Toddlers Eating Fox et al. (2004) used data from the Feeding Infants and Toddlers study (FITS) to assess food consumption patterns in infants and toddlers. The FITS was sponsored by Gerber Products Company and was

conducted to obtain current information on food and nutrient intakes of children, ages 4 to 24 months old, in

the 50 states and the District of Columbia. The FITS is described in detail in Devaney et al. (2004). FITS was based on a random sample of 3,022 infants and toddlers for which dietary intake data were collected by telephone from their parents or caregivers between March and July 2002. An initial recruitment and household interview was conducted, followed by an

interview to obtain information on intake based on 24­ hour recall. The interview also addressed growth, development and feeding patterns. A second dietary

recall interview was conducted for a subset of 703randomly selected respondents. The study oversampled children in the 4 to 6 and 9 to 11 months age groups; sample weights were adjusted for non-response, over-sampling, and under-coverage of some subgroups. The response rate for the FITS was 73 percent for the recruitment interview. Of the recruited households, there was a response rate of 94 percent for the dietary recall interviews (Devaney et al., 2004). The characteristics of the FITS study population is shown in

Table 9-12. Fox et al. (2004) analyzed the first set of 24­ hour recall data collected from all study participants. For this analysis, children were grouped into six age categories: 4 to 6 months, 7 to 8 months, 9 to 11 months, 12 to 14 months, 15 to 18 months, and 19 to 24 months. Table 9-13 provides the percentage of infants and toddlers consuming different types of vegetables at least once in a day. The percentages of children eating any type of vegetable ranged from 39.9 percent for 4 to 6 month olds to 81.6 percent for 19 to 24 month olds. Table 9-14 provides the top five vegetables consumed by age group. Some of the highest percentages ranged from baby food carrots (9.6 percent) in the 4 to 6 month old group to french fries (25.5 percent) in the 19 to 24 month old group. Table 9-15 provides the percentage of children consuming different types of fruit at least once per day. The percentages of children eating any type of fruit ranged from 41.9 percent to 4 to 6 month olds to 77.2 percent for 12 to 14 month olds. Table 9­ 16 provides information on the top five fruits eaten by infants and toddlers at least once per day. The highest percentages were for bananas among infants 9 to 24 months, and baby food applesauce among infants 4 to

8 months old. The advantages of this study were that the study population represented the U.S. population and the sample size was large. One limitation of the analysis done by Fox et al. (2004) was that only frequency data were provided; no information on actual intake rates was included. In addition, Devaney et al. (2004) noted several limitations associated with the FITS data. For the FITS, a commercial list of infants and toddlers was used to obtain the sample used in the study. Since many of the households could not be located and did not have children in the target population, a lower response rate than would have occurred in a true national sample was obtained (Devaney et al., 2004). In addition, the sample was likely from a higher socioeconomic status when compared with all U.S. infants in this age group (4 to 24 months old) and the use of a telephone survey may have omitted lower-income households without telephones (Devaney et al., 2004).

9.3.2.4 Ponza et al., 2004 - Nutrient Food Intakes and Food Choices of Infants and Toddlers Participating in WIC Ponza et al. (2004) conducted a study using selected data from the FITS to assess feeding patterns,

food choices and nutrient intake of infants and toddlers participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Ponza et al. (2004) evaluated FITS data for the following age groups: 4 to 6 months (N = 862), 7 to 11 months (N = 1,159) and 12 to 24 months (N= 996). The total sample size described by WIC participants and non-participants is shown in Table 9-17. The foods consumed were analyzed by tabulating the percentage of infants who consumed specific foods/food groups per day (Ponza et al., 2004).



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Weighted data were used in all of the analyses used in the study (Ponza et al., 2004). Table 9-17 presents thedemographic data for WIC participants and nonparticipants. Table 9-18 provides information on the

food choices for the infants and toddlers studied. There was little difference in vegetable choices among WIC participants and non-participants (Table 9-18). However, there were some differences for fruits. An advantage of this study is that it had a relatively large sample size and was representative of the U.S. general population of infants and children. A limitation of the study is that intake values for foods were not provided. Other limitations are those associated with the FITS data, as described previously

in Section 9.3.2.3.

9.3.2.5 Menella et al., 2006 - Feeding Infants and Toddlers Study: The Types of Foods Fed to Hispanic Infants and Toddlers Menella et al. (2006) investigated the types of food and beverages consumed by Hispanic infants and toddlers in comparison to the non-Hispanic infants and

toddlers in the United States. The FITS 2002 data for children between 4 and 24 months of age were used for the study. The data represent a random sample of 371 Hispanic and 2,367 non-Hispanic infants and toddlers (Menella et al., 2006). Menella et al. (2006) grouped the infants as follows: 4 to 5 months (N = 84 Hispanic; 538 non-Hispanic), 6 to 11 months (N = 163 Hispanic and 1,228 non-Hispanic), and 12 to 24 months (N = 124 Hispanic and 871 non-Hispanic) of age. Table 9-19 provides the percentages of Hispanic and non-Hispanic infants and toddlers consuming fruits and vegetables. In most instances the percentages consuming the different types of fruits and vegetables were similar. However, 4 to 5 month oldHispanic infants were more likely to eat fruits than nonHispanic infants in this age group. Table 9-20 provides the top five fruits and vegetables consumed and the percentage of children consuming these foods at least once in a day. Apples and bananas were the foods with the highest percent consuming for both the Hispanic and non-Hispanic study groups. Potatoes and carrots were the vegetables with the highest percentage of infants and toddlers consuming in both study groups. The advantage of the study is that it providesinformation on food preferences for Hispanic and nonHispanic infants and toddlers. A limitation is that the study did not provide food intake data, but provided frequency of use data instead. Other limitations are those noted previously in Section 9.3.2.3 for the FITS

data.

9.3.2.6 Fox et al., 2006 - Average Portion of Foods Commonly Eaten by Infants and Toddlers in

the United States Fox et al. (2006) estimated average portion sizes consumed per eating occasion by children 4 to 24 months of age who participated in the Feeding Infant s and Toddlers Study (FITS)

. The FITS is a crossse ctio nal study designed to collect and analyze data on feeding practices, food consumption, and usual nutrient

intake of U.S. infants and toddlers and is described in Section 9.3.2.3 of this chapter. It included a stratified random sample of 3,022 children between 4 and 24 months of age. Using the 24-hour recall data, Fox et al. (2006) derived average portion sizes for major food groups, including fruits and vegetables. Average portion sizes for select individual foods within these major groups were also estimated. For this analysis, children were grouped into six age categories: 4 to 5 months, 6 to 8 months, 9 to 11 months, 12 to 14 months, 15 to 18 months, and 19 to 24 months. Tables 9-21 and 9-22 present the average portion sizes for fruits and vegetables for infants and toddlers, respectively.

9.4 CONVERSION BETWEEN WET AND

DRY WEIGHT INTAKE RATES The intake data presented in this chapter are reported in units of wet weight (i.e., as-consumed fruits and vegetables consumed per day or per eating occasion). However, data on the concentration of contaminants in fruits and vegetables may be reported in units of either wet or dry weight.(e.g., mg contaminant per gram-dry-weight of fruits and vegetables.) It is essential that exposure assessors be aware of this difference so that they may ensure consistency between the units used for intake rates and those used for concentration data (i.e., if the contaminant concentration is measured in dry weight of fruits and vegetables, then the dry weight units should be used for their intake values).



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### 100 −

W   IR IR (Eqn. 9-1) 

### =     dw ww 100

100 − W   C
ww = C dw     

### (Eqn. 9-2) 100 

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If necessary, wet weight (e.g., as

- consumed) r ecommendations for response. Environ

intak e rates may be converted to dry weight intake rates Health Perspect 103(6):13-17. using the moisture content percentages presented in Mennella, J.; Ziegler, P.; Briefel, R.; Novak, T. (2006) Table 9-23 and the following equation: Feeding Infants and Toddlers Study: the types of foods fed to Hispanic infants and toddlers. J Am Diet Assoc 106 (Suppl 1): S96-S106.

Pon z a, M.; Devaney, B.; Ziegler, P.; Reidy, K.;

where:

IR dw 

### = dry weight intake rate;

IR

ww 

### = wet weight intake rate; and W	 = percent water content

Alternatively, dry weight residue levels in fruits and vegetables may be converted to wet weight residue

levels for use with wet weight (e.g., as-consumed)

intake rates as follows: Squatrito, C. (2004) Nutrient intakes and food choices of infants and toddlers participating in WIC. J Am Diet Assoc 104 (Suppl): S71­

S79. Smiciklas-Wright, H.; Mitchell, D.C.; Mickle, S.J.; Cook, A.J.; Goldman, J.D. (2002) Foods commonly eaten in the United States: Quantities consumed per eating occasion and in a day, 1994-1996. U.S. Department ofAgriculture NFS Report No. 96-5, prepublication version, 252 pp. USDA.	 (1999) Food and nutrient intakes by children 1994-96, 1998: Table Set 17. Beltsville, MD:

Foo d Surveys Research Group, Beltsville

where:

C

ww 

### = wet weight intake rate;

C dw 

### = dry weight intake rate; and

W = percent water content.

The moisture data presented in Table 9-23 are for selected fruits and vegetables taken from USDA(2007).

9.5 REFERENCES FOR CHAPTER 9

Devaney, B.; Kalb, L.; Briefel, R.; Zavitsky-Novak, T.; Clusen, N.; Ziegler, P. (2004) Feeding infants and toddlers study: overview of the study design. J Am Diet Assoc 104(Suppl 1): S8­

S13. Fox, M.K.; Pac, S.; Devaney, B.; Jankowski, L. (2004) Feeding Infants and Toddlers Study: what foods are infants and toddlers eating. J Am Diet Assoc 104 (Suppl):S22-S30. Fox, M.K.; Reidy, K.; Karwe, V.; Ziegler, P. (2006) Average portions of foods commonly eaten by

infants and toddlers in the United States. J Am Diet Assoc 106 (Suppl 1):S66-S76. Goldman, L. (1995) Children - unique and vulnerable. Environmental risks facing children and Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture. USDA (2007) USDA National Nutrient Database for Standard Reference, Release 20. Agricultural Research Service Nutrient Data Laboratory Home Page, http://www.ars.usda.gov/ba/bhnrc/ndl

U.S. EPA.	 (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants.

U.S. Environmental Protection Agency, Washington, D.C., EPA/630/P-03/003F.



### Child-Specific Exposure Factors Handbook Page September 2008 9-11Table 9-3.Per Capita Intake of Fruits and Vegetables (g/kg-day as consumed)Age GroupNPercentConsumingMeanSEPercentiles1st5th10th25th50th75th90th95th99th100thFruitsBirth to 1 year1,48656.45.70.30.00.00.00.01.59.617.121.332.273.81 to 2 years2,09689.56.20.20.00.00.00.54.79.414.618.526.444.03 to 5 years4,39190.04.60.10.00.00.00.23.27.011.414.422.345.56 to 12 years2,08988.32.40.10.00.00.00.11.33.36.48.814.325.013 to 19 years1,22273.20.80.10.00.00.00.00.11.12.43.56.912.8VegetablesBirth to 1 year1,48672.14.50.20.00.00.00.02.77.412.214.825.356.81 to 2 years2,09699.76.90.20.00.71.53.25.69.313.917.126.558.23 to 5 years4,391100.05.90.10.00.81.42.84.77.711.714.723.450.96 to 12 years2,08999.94.10.10.10.61.01.83.25.37.89.917.453.713 to 19 years1,222100.02.90.10.00.40.71.42.43.85.56.911.429.5NSE= Sample size.= Standard error.Source:Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII. 

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kTable 9-4.Consumer Only Intake of Fruits and Vegetables (g/kg-day as consumed)Age GroupNMeanSEPercentiles1st5th10th25th50th75th90th95th99th100thFruitsBirth to 1 year83010.10.40.00.41.23.78.514.420.426.434.773.81 to 2 years1,8786.90.20.00.00.12.25.410.115.319.027.144.03 to 5 years3,9575.10.10.00.00.01.03.87.511.915.022.845.56 to 12 years1,8462.70.10.00.00.00.31.73.76.79.314.825.013 to 19 years8981.10.10.00.00.00.00.51.52.93.77.612.8VegetablesBirth to 1 year1,0626.20.30.00.10.12.04.99.413.416.126.456.81 to 2 years2,0906.90.20.00.71.53.25.69.313.917.126.558.23 to 5 years4,3895.90.10.00.81.42.84.77.711.714.723.450.96 to 12 years2,0874.10.10.10.61.01.83.25.37.89.917.453.713 to 19 years1,2222.90.10.00.40.71.42.43.85.56.911.429.5NSE= Sample size.= Standard error.Source:Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII. 

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Chapter 9 - Intake of Fruits and VegetablesTable 9-5. Per Capita Intake of Individual Fruits and Vegetables (g/kg-day as consumed)Age Group NPercentConsumingMean SEPercentConsumingMean SEPercentConsumingMean SEPercentConsumingMean SEApplesAsparagusBananasBeansBirth to 1 year 1,48634.6 2.32 0.130.21 0.01 0.0040.68 1.24 0.0621.6 0.43 0.041 to 2 years 2,09644.8 1.79 0.090.77 0.02 0.0162.76 1.77 0.0946.8 0.76 0.043 to 5 years 4,39144.6 1.64 0.050.54 0.01 0.0060.74 0.93 0.0443.0 0.52 0.026 to 12 years 2,08938.2 0.83 0.050.66 0.01 0.0057.69 0.38 0.0338.8 0.32 0.0213 to 19 years 1,22222.5 0.20 0.020.56 0.00 0.0042.09 0.13 0.0255.4 0.15 0.02BeetsBerries and Small FruitBroccoliBulb VegetablesBirth to 1 year 1,4860.4 0.01 0.0116.5 0.13 0.023.5 0.07 0.0233.4 0.07 0.011 to 2 years 2,0960.7 0.01 0.0066.2 0.91 0.0512.0 0.25 0.0393.3 0.30 0.013 to 5 years 4,3910.8 0.01 0.0072.7 0.72 0.0310.7 0.18 0.0195.8 0.27 0.016 to 12 years 2,0890.8 0.01 0.0073.4 0.40 0.0311.0 0.14 0.0297.3 0.21 0.0113 to 19 years 1,2220.7 0.00 0.0097.7 0.19 0.018.3 0.06 0.0112.3 0.11 0.02CabbageCarrotsCitrus FruitsCornBirth to 1 year 1,4861.0 0.01 0.0012.3 0.17 0.032.5 0.07 0.0246.0 0.48 0.031 to 2 years 2,0968.0 0.06 0.0146.8 0.41 0.0215.5 0.47 0.0596.5 1.13 0.053 to 5 years 4,3918.9 0.07 0.0146.2 0.34 0.0218.2 0.50 0.0398.7 1.24 0.036 to 12 years 2,0899.5 0.06 0.0144.4 0.22 0.0116.0 0.26 0.0298.9 0.87 0.0313 to 19 years 1,2229.0 0.04 0.0140.3 0.11 0.0112.3 0.11 0.0295.7 0.43 0.02



### Page Child-Specific Exposure Factors Handbook

9-14 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9 - Intake of Fruits and VegetablesTable 9-5. Per Capita Intake of Individual Fruits and Vegetables (g/kg-day as consumed) (continued)Age Group NPercentConsumingMean SEPercentConsumingMean SEPercentConsumingMean SEPercentConsumingMean SECucumbersCucurbitsFruiting VegetablesLeafy VegetablesBirth to 1 year 1,4861.7 0.00 0.0014.0 0.45 0.0425.50 0.32 0.0444.2 0.29 0.051 to 2 years 2,09620.5 0.11 0.0131.3 0.72 0.0692.14 1.56 0.0682.1 0.71 0.043 to 5 years 4,39129.3 0.16 0.0238.7 0.83 0.0795.38 1.46 0.0386.9 0.67 0.026 to 12 years 2,08932.6 0.14 0.0239.9 0.54 0.0695.87 1.05 0.0389.5 0.55 0.0313 to 19 years 1,22241.3 0.11 0.0346.7 0.32 0.0896.08 0.79 0.0390.3 0.43 0.02LegumesLettuceOkraOnionsBirth to 1 year 1,48651.7 1.21 0.061.1 0.00 0.000.2 0.00 0.0032.8 0.07 0.011 to 2 years 2,09696.9 1.30 0.0823.3 0.14 0.011.3 0.01 0.0093.0 0.29 0.013 to 5 years 4,39198.3 0.85 0.0633.4 0.21 0.010.8 0.01 0.0095.6 0.26 0.016 to 12 years 2,08998.1 0.48 0.0341.7 0.22 0.011.3 0.01 0.0096.8 0.20 0.0113 to 19 years 1,22294.9 0.27 0.0255.2 0.22 0.020.8 0.00 0.0097.3 0.18 0.01PeachesPearsPeasPeppersBirth to 1 year 1,48624.4 0.85 0.0815.9 0.73 0.0729.5 0.47 0.0415.6 0.01 0.001 to 2 years 2,09650.7 0.47 0.0417.2 0.40 0.0428.3 0.34 0.0377.5 0.05 0.013 to 5 years 4,39155.4 0.26 0.0216.6 0.26 0.0320.5 0.21 0.0284.6 0.05 0.006 to 12 years 2,08954.7 0.14 0.0217.5 0.14 0.0117.2 0.12 0.0185.1 0.05 0.0013 to 19 years 1,22239.1 0.06 0.015.9 0.03 0.0114.0 0.07 0.0184.8 0.04 0.00



### Child-Specific Exposure Factors Handbook Page September 2008 9-15

### Child-Specific Exposure Factors Handbook

Chapter 9 - Intake of Fruits and VegetablesTable 9-5. Per Capita Intake of Individual Fruits and Vegetables (g/kg-day as consumed) (continued)Age Group NPercentConsumingMean SEPercentConsumingMean SEPercentConsumingMean SEPercentConsumingMean SEPome FruitPumpkinsRoot Tuber VegetablesStalk, Stem VegetablesBirth to 1 year 1,48640.0 3.04 0.170.3 0.00 0.0061.7 2.60 0.151.9 0.01 0.001 to 2 years 2,09652.0 2.19 0.100.7 0.01 0.0099.6 3.38 0.0913.2 0.06 0.013 to 5 years 4,39151.7 1.90 0.060.9 0.01 0.00100.0 2.96 0.0710.9 0.04 0.006 to 12 years 2,08947.9 0.97 0.061.8 0.01 0.00100.0 2.09 0.0710.7 0.03 0.0113 to 19 years 1,22226.5 0.23 0.021.3 0.01 0.0099.9 1.36 0.0616.6 0.03 0.01StrawberriesStone FruitTomatoesTropical FruitsBirth to 1 year1,4866.8 0.02 0.0029.20 1.15 0.1021.5 0.30 0.0342.2 1.31 0.071 to 2 years 2,09633.5 0.19 0.0353.62 0.60 0.0480.7 1.50 0.0570.1 1.97 0.103 to 5 years 4,39137.1 0.14 0.0157.45 0.38 0.0285.7 1.40 0.0369.7 1.10 0.046 to 12 years 2,08937.3 0.10 0.0156.83 0.23 0.0286.9 1.00 0.0367.0 0.50 0.0413 to 19 years 1,22226.8 0.05 0.0141.08 0.09 0.0190.2 0.74 0.0354.5 0.19 0.02White PotatoesBirth to 1 year 1,48639.9 0.64 0.071 to 2 years 2,09691.2 1.95 0.083 to 5 years 4,39195.1 1.75 0.066 to 12 years 2,08993.9 1.21 0.0613 to 19 years 1,22292.6 0.93 0.05SE = Standard error.Note: Data for fruits and vegetables for which only small percentages of the population reported consumption may be less reliable than data for fruits andvegetables with higher percentages consuming.Source: Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII.



### Page Child-Specific Exposure Factors Handbook

9-16 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9 - Intake of Fruits and VegetablesTable 9-6. Consumer Only Intake of Individual Fruits and Vegetables (g/kg-day as consumed)Age GroupN Mean SEN Mean SEN Mean SEN Mean SEApplesAsparagusBananasBeansBirth to 1 year496 6.71 0.313 2.59 1.16605 3.04 0.12313 2.00 0.161 to 2 years947 4.00 0.1519 1.99 0.541,328 2.82 0.12996 1.63 0.083 to 5 years1,978 3.68 0.0823 1.37 0.322,746 1.54 0.061,909 1.22 0.046 to 12 years792 2.17 0.1213 1.77 0.431,214 0.66 0.05833 0.82 0.0513 to 19 years271 0.90 0.064 0.56 0.08511 0.30 0.04472 0.49 0.03BeetsBerries and Small FruitsBroccoliBulb VegetablesBirth to 1 year6 1.42 0.87229 0.81 0.0749 2.09 0.33489 0.22 0.021 to 2 years13 0.98 0.321,396 1.38 0.06242 2.11 0.161,957 0.32 0.013 to 5 years36 0.90 0.203,166 0.99 0.04475 1.67 0.094,207 0.28 0.016 to 12 years16 0.66 0.331,523 0.54 0.04213 1.29 0.162,040 0.22 0.0113 to 19 years9 0.20 0.12679 0.27 0.03102 0.69 0.071,194 0.20 0.01CabbageCarrotsCitrus FruitsCornBirth to 1 year15 0.61 0.41179 1.39 0.2037 2.79 0.53671 1.05 0.071 to 2 years160 0.73 0.11999 0.87 0.05336 3.06 0.202,027 1.17 0.053 to 5 years369 0.78 0.072,048 0.74 0.03751 2.75 0.154,334 1.26 0.036 to 12 years190 0.63 0.11904 0.50 0.03324 1.60 0.122,064 0.88 0.0313 to 19 years106 0.40 0.06482 0.27 0.02157 0.90 0.151,176 0.45 0.01



### Child-Specific Exposure Factors Handbook Page September 2008 9-17

### Child-Specific Exposure Factors Handbook

Chapter 9 - Intake of Fruits and VegetablesTable 9-6. Consumer Only Intake of Individual Fruits and Vegetables (g/kg-day as consumed) (continued)Age GroupN Mean SEN Mean SEN Mean SEN Mean SECucumbersCucurbitsFruiting VegetablesLeafy VegetablesBirth to 1 year25 0.28 0.11213 3.19 0.29371 1.24 0.11639 0.65 0.111 to 2 years439 0.52 0.05682 2.29 0.171,927 1.70 0.061,729 0.87 0.053 to 5 years1,266 0.56 0.051,694 2.15 0.174,180 1.53 0.033,815 0.77 0.036 to 12 years667 0.43 0.06833 1.34 0.152,014 1.10 0.031,860 0.62 0.0313 to 19 years500 0.26 0.06563 0.69 0.161,176 0.82 0.031,101 0.47 0.02LegumesLettuceOkraOnionsBirth to 1 year754 2.34 0.1115 0.17 0.024 1.50 0.54481 0.22 0.021 to 2 years2,037 1.34 0.08481 0.58 0.0429 0.64 0.191,948 0.31 0.013 to 5 years4,308 0.86 0.061,415 0.62 0.0334 1.16 0.324,200 0.27 0.016 to 12 years2,045 0.49 0.03858 0.53 0.0221 0.62 0.152,030 0.21 0.0113 to 19 years1,168 0.29 0.02669 0.40 0.0312 0.43 0.131,190 0.19 0.01PeachesPearsPeasPeppersBirth to 1 year344 3.47 0.28217 4.55 0.28417 1.60 0.09224 0.05 0.011 to 2 years1,067 0.93 0.08354 2.33 0.16609 1.21 0.061,627 0.06 0.013 to 5 years2,461 0.48 0.03711 1.59 0.12888 1.02 0.073,706 0.06 0.006 to 12 years1,150 0.26 0.03382 0.81 0.07346 0.68 0.061,784 0.05 0.0113 to 19 years480 0.15 0.0372 0.45 0.09168 0.48 0.061,041 0.05 0.00



### Page Child-Specific Exposure Factors Handbook

9-18 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9 - Intake of Fruits and VegetablesTable 9-6. Consumer Only Intake of Individual Fruits and Vegetables (g/kg-day as consumed) (continued)Age GroupN Mean SEN Mean SEN Mean SEN Mean SEPome FruitPumpkinsRoot Tuber VegetablesStalk, Stem VegetablesBirth to 1 year572 7.60 0.343 1.06 0.71916 4.21 0.1924 0.56 0.221 to 2 years1,097 4.21 0.1315 1.08 0.512,087 3.40 0.09272 0.48 0.053 to 5 years2,291 3.68 0.0836 0.56 0.104,388 2.96 0.07502 0.38 0.036 to 12 years.1,012 2.03 0.1037 0.52 0.112,089 2.09 0.07218 0.32 0.0413 to 19 years320 0.87 0.0614 0.42 0.161,221 1.36 0.06190 0.20 0.03StrawberriesStone FruitTomatoesTropical FruitsBirth to 1 year96 0.26 0.06418 3.95 0.25315 1.42 0.13630 3.09 0.121 to 2 years729 0.57 0.081,130 1.13 0.081,684 1.86 0.061,476 2.81 0.123 to 5 years1,710 0.38 0.032,556 0.66 0.033,764 1.63 0.033,106 1.57 0.056 to 12 years.783 0.28 0.021,194 0.41 0.031,832 1.15 0.031,407 0.75 0.0513 to 19 years326 0.18 0.03508 0.21 0.031,098 0.82 0.03652 0.35 0.04White PotatoesBirth to 1 year577 1.60 0.151 to 2 years1,918 2.14 0.093 to 5 years4,147 1.84 0.066 to 12 years.1,963 1.29 0.0613 to 19 years1,131 1.01 0.05SE = Standard error.Note: Data for fruits and vegetables for which only small percentages of the population reported consumption may be less reliable than data for fruits andvegetables with higher percentages consuming.Source: Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII.



### Child-Specific Exposure Factors Handbook Page September 2008 9-19

### Child-Specific Exposure Factors Handbook

Chapter 9 - Intake of Fruits and VegetablesTable 9-7. Mean Quantities of Vegetables Consumed Daily by Sex and Age, Per Capita (g/day)Age GroupSampleSizeTotalWhite PotatoesDark GreenVegetablesDeepYellowVegetablesTomatoesLettuce,lettucebasedsaladsGreenbeansTotal FriedCorn,greenpeas, limabeansOthervegetablesMales and FemalesUnder 1 year1 year2 years1 to 2 years3 years4 years5 years3 to 5 years5 years and under1,1261,0161,1022,1181,8311,8598844,5747,8185779878391971039788926322934374438311111714171922201625455645419957556571a71191311121210a,b12123332687755656591091112121110161617171618171717Males6 to 9 years6 to 11 yers12 to 19 years7871,0317371101151764750852627444565561616285512553a111110161825Females6 to 9 years6 to 11 years12 to 19 years704969732110116145424661222531559444141518671255413128212228Males and Females9 years and under19 years and under9,30911,287971253753192746661217376511101822aEstimate is not statistically reliable due to small samples size reporting intake.bValue less than 0.5, but greater than 0.Note: Consumption amounts shown are representative of the first day of each participant’s survey response.Source: USDA, 1999.



### Page Child-Specific Exposure Factors Handbook

9-20 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9 - Intake of Fruits and VegetablesTable 9-8. Percentage of Individuals Consuming Vegetables, by Sex and Age (%)Age GroupSampleSizeTotalWhite PotatoesDarkGreenVegetablesDeepYellowVegetablesTomatoesLettuce,lettuce-basedsaladsTotal FriedGreenbeansCorn,greenpeas, limabeansOthervegetablesMales and FemalesUnder 1 year1 year2 years1 to 2 years3 years4 years5 years3 to 5 years5 years and under1,1261,0161,1022,1181,8311,8598844,5747,81847.273.378.475.980.580.783.081.475.412.340.446.743.646.747.350.748.242.34.325.234.529.934.734.838.335.930.12.36.47.67.07.07.24.66.36.120.513.310.511.810.712.013.312.013.01.818.030.824.634.133.036.534.527.20.2a3.97.55.78.310.013.410.67.67.813.711.512.610.19.010.49.910.58.517.615.016.214.616.416.115.715.014.819.422.320.924.726.528.826.723.3Males6 to 9 years6 to 11 years12 to 19 years7871,03173778.879.378.247.948.749.538.038.438.66.36.13.612.512.48.038.238.743.013.113.923.87.86.73.515.013.87.429.730.833.2Females6 to 9 years6 to 11 years12 to 19 years70496973280.581.779.548.250.846.436.338.934.65.95.47.011.911.410.633.833.535.315.817.125.18.47.84.415.915.17.426.629.234.5Males and Females9 years and under19 years and under9,30911,28777.178.344.646.832.935.36.15.612.711.230.734.610.316.69.67.015.211.925.229.4aEstimate is not statistically reliable due to small samples size reporting intake.Note: Percentages shown are representative of the first day of each participant’s survey response.Source: USDA, 1999.



### Child-Specific Exposure Factors Handbook Page September 2008 9-21Table 9-9. Mean Quantities of Fruits Consumed Daily by Sex and Age, Per Capita (g/day)Citrus Fruits andJuicesOther fruits, mixtures, and juicesAge GroupSampleSizeTotalTotal JuicesDriedfruitsTotal ApplesBananas MelonsandberriesOtherfruits andmixtures(mainlyfruit)Noncitrusjuices andnectarsMales and FemalesUnder 1 year 1,126 131 4 4 -a,b126 14 10 1a39 611 year1,016 267 47 42 2 216 22 23 8 29 1342 years1,102 276 65 56 2 207 27 20 10 20 1301 to 2 years 2,118 271 56 49 2 212 24 22 9 24 1323 years1,831 256 61 51 1 191 27 18 13 24 1104 years1,859 243 62 52 1 177 31 17 14 22 925 years884 218 55 44 -a,b160 31 14 13 24 783 to 5 years 4,574 239 59 49 1 176 30 16 13 23 935 years and under 7,818 237 52 44 1 182 26 17 10 26 103Males6 to 9 years787 194 58 51 -a,b133 32 11 21 20 506 to 11 years 1,031 183 67 60 -a,b113 28 11 16 19 4012 to 19 years 737 174 102 94 1a70 13 8 11a10 29Females6 to 9 years704 180 63 54 1a113 23 10 10 25 466 to 11 years 969 169 64 54 -a,b103 21 8 8 23 4212 to 19 years 732 157 72 67 -a,b83 13 5 15 14 35Males and Females9 years and under 9,309 217 55 47 1 159 27 15 12 24 8119 years and under 11,287 191 70 62 1 118 21 11 12 19 56aEstimate is not statistically reliable due to small samples size reporting intake.bValue less than 0.5, but greater than 0.Note: Consumption amounts shown are representative of the first day of each participant’s survey responseSource: USDA, 1999.



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kTable 9-10. Percentage of Individuals Consuming, Fruits by Sex and Age (%)Citrus Fruits andOther fruits, mixtures, and juicesJuicesAge GroupSampleDriedOtherTotalNon-SizefruitsMelons fruits andcitrusTotal JuicesTotal Apples Bananas and mixturesjuices andberries (mainlynectarsfruit)Males and FemalesUnder 1 year 1,126 59.7 3.6 2.7 0.4a59.0 15.7 13.3 1.8 29.9 33.01 year1,016 81.0 23.6 19.0 5.9 73.0 23.4 25.1 6.9 26.5 43.22 years1,102 76.6 30.6 23.4 5.3 64.7 24.0 20.2 8.5 19.4 37.01 to 2 years 2,118 78.8 27.2 21.3 5.6 68.8 23.7 22.6 7.7 22.9 40.03 years1,831 74.5 27.9 21.4 4.1 64.2 22.4 17.5 7.8 20.1 33.34 years1,859 72.6 28.0 21.8 3.0 62.1 23.7 15.7 7.6 20.0 30.85 years884 67.6 26.9 19.5 1.3a56.9 21.9 12.6 7.4 19.0 24.53 to 5 years 4,574 71.6 27.6 20.9 2.8 61.0 22.7 15.3 7.6 19.7 29.55 years and under 7,818 72.6 24.6 18.8 3.5 63.5 22.2 17.6 6.9 22.0 33.5Males6 to 9 years787 59.0 24.8 20.5 0.8a49.1 20.3 8.7 7.3 16.8 15.56 to 11 years 1,031 56.5 25.2 21.6 1.1a44.2 18.2 8.0 6.6 15.4 12.712 to 19 years 737 44.5 24.7 21.7 1.0a27.1 8.2 6.0 4.1 7.1 8.2Females6 to 9 years704 64.9 27.9 22.3 1.5a50.4 17.3 8.8 7.4 20.4 17.36 to 11 years969 62.1 27.7 21.5 1.1a47.2 16.2 7.3 7.4 19.0 14.912 to 19 years 732 45.6 22.4 18.1 1.1a30.2 8.2 4.4 6.0 11.3 9.7Males and Females9 years and under 9,309 68.3 25.2 19.8 2.5 58.0 20.9 14.0 7.1 20.6 26.719 years and under 11,287 57.8 24.8 20.1 1.8 44.4 15.2 9.7 6.2 15.5 17.9aEstimate is not statistically reliable due to small samples size reporting intake.Note: Percentages shown are representative of the first day of each participant’s survey response.Source: USDA, 1999. 

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3 eTable 9-11. Quantity (as consumed) of Fruits and Vegetables Consumed Per Eating Occasion andPercentage of Individuals Using These Foods in Two DaysQuantity consumed per eating occasion (grams)2 to 5 years6 to 11 years12 to 19 yearsFood categoryMale and Female Male and FemaleMaleFemale(N = 2,109)(N = 1,432)(N = 696)(N = 702)PC Mean. SEM PC Mean SEM PC Mean SEM PC Mean SEMRaw VegetablesCarrots10.4 27 2 17.8 32 2 9.2 35 6 11.9 32 4Cucumbers6.4 32 4 6.6 39 6 6.1 71a22a6.8 48 11Lettuce34.0 17 1 40.8 26 1 56.0 32 3 52.3 34 2Onions3.9 9 2 4.5 17 2 11.1 28 4 7.9 23 4Tomatoes14.8 31 2 14.0 42 4 25.7 49 5 23.9 44 3Cooked VegetablesBeans (string)16.8 50 2 12.1 71 6 8.3 85 9 7.6 78 5Broccoli7.2 61 3 5.6 102 16 3.9 127a17a5.7 109a14aCarrots6.0 48 4 3.8 46 5 2.8 81a16a2.1 75a17aCorn18.9 68 3 22.2 79 4 12.8 125 9 12.3 100 6Peas8.4 48 3 6.8 72 9 3.6 115a15a2.4 93a17aPotatoes (French-fried)32.7 52 1 33.7 67 2 41.7 97 3 38.1 81 4Potatoes (home-fried and hash-browned) 9.3 85 5 10.1 93 6 10.1 145 13 6.1 138 13Potatoes (baked)7.6 70 4 8.2 95 6 8.6 152 15 8.8 115 10Potatoes (boiled)4.8 81 9 2.7 103a17a2.0 250a40a3.2 144a16aPotatoes (mashed)14.8 118 6 13.3 162 12 14.6 245 16 11.9 170 17FruitsApples (raw)26.8 106 2 21.9 123 3 11.7 149 9 12.4 129 5Apples (cooked and applesauce) 10.1 118 5 9.0 130 7 2.3 153a19a2.6 200a47aApple juice26.3 207 5 12.2 223 10 7.8 346 22 8.5 360 44Bananas (raw)25.0 95 2 16.5 105 3 10.3 122 6 8.4 119 5Oranges (raw)11.1 103 5 10.5 114 5 4.3 187a38a5.4 109a8aOrange juice34.4 190 4 30.9 224 6 30.8 354 16 29.5 305 11aIndicates a statistic that is potentially unreliable because of small sample size or large coefficient of variationPC = Percent consuming at least once in 2 days.SEM = Standard error of the mean.Source: Smiciklas-Wright et al., 2002 (based on 1994-1996 CSFII data). 

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### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-12. Characteristics of the FITS Sample Population

Sample Size Percentage of Sample

Gender

Male

Female 1,549 1,473

Age of Child

4 to 6 months

7 to 8 months

9 to 11 months

12 to 14 months

15 to 18 months

19 to 24 months 862

483

679

374

308

316

Child’s Ethnicity

Hispanic or Latino Non-Hispanic or Latino Missing 367 2,641

14

Child’s Race

White

Black

Other 2,417

225

380

Urbanicity

Urban

Suburban

Rural Missing 1,389 1,014

577

42

Household Income

Under $10,000 $10,000 to $14,999 $15,000 to $24,999 $25,000 to $34,999 $35,000 to $49,999 $50,000 to $74,999 $75,000 to $99,999 $100,000 and Over Missing 48

48

221

359

723

588

311

272

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Receives WIC

Yes

No Missing 821 2,196

5

Sample Size (Unweighted) 3,022 51.3

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100.0

WIC = Special Supplemental Nutrition Program for Women, Infants, and Children.

Source: Devaney et al., 2004.



### Child-Specific Exposure Factors Handbook Page September 2008 9-25

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-13. Percentage of Infants and Toddlers Consuming Different Types of Vegetables

Percentage of Infants and Toddlers Consuming at Least Once in a Day
Food Group/Food 4 to 6 7 to 8 9 to 11 12 to 14 15 to 18 19 to 24

months months months months months months
Any Vegetable 39.9 66.5 72.6 76.5 79.2 81.6

Baby Food Vegetables 35.7 54.5 34.4 12.7 3.0 1.6

Cooked Vegetables 5.2 17.4 45.9 66.3 72.9 75.6

Raw Vegetables 0.5 1.6 5.5 7.9 14.3 18.6

Types of Vegetables a

Dark Green Vegetables b 0.1 2.9 4.2 5.0 10.4 7.8

Deep Yellow Vegetables c 26.5 39.3 29.0 24.0 13.6 13.4

White Potatoes 3.6 12.4 24.1 33.2 42.0 40.6

French Fries and Other Fried Potatoes 0.7 2.9 8.6 12.9 19.8 25.5

Other Starchy Vegetables d 6.5 10.9 16.9 17.3 20.8 24.2

Other Vegetables 11.2 25.9 35.1 39.1 45.6 43.3

a Totals include commercial baby food, cooked vegetables, and raw vegetables.

b Reported dark green vegetables include broccoli, spinach and other greens, and romaine lettuce.

c Reported deep yellow vegetables include carrots, pumpkin, sweet potatoes, and winter squash.

d Reported starchy vegetables include corn, green peas, immature lima beans, black-eyed peas (not dried), cassava, and rutabaga.

Source: Fox et al., 2004.



### Page Child-Specific Exposure Factors Handbook

9-26 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-14. Top Five Vegetables Consumed by Infants and Toddlers

Top Vegetables by Age Group a Percentage Consuming at Least Once in a Day

4 to 6 months

Baby Food Carrots 9.6

Baby Food Sweet Potatoes 9.1

Baby Food Squash 8.1

Baby Food Green Beans 7.2

Baby Food Peas 5.0

7 to 8 months

Baby Food Carrots 14.2

Baby Food Sweet Potatoes 12.9

Baby Food Squash 12.9

Baby Food Green Beans 11.2

Baby Food Mixed/Garden Vegetables 10.1

9 to 11 months

Cooked Green Beans 9.7

Mashed/Whipped Potatoes 9.0

French Fries/Other Fried Potatoes 8.6

Baby Food Mixed/Garden Vegetables 8.4

Cooked Carrots 8.0

12 to 14 months

Cooked Green Beans 18.2

French Fries/Other Fried Potatoes 12.9

Cooked Carrots 11.5

Mashed/Whipped Potatoes 10.3

Cooked Peas 8.4

15 to 18 months

French Fries/Other Fried Potatoes 19.8

Cooked Green Beans 16.7

Cooked Peas 13.9

Cooked Tomatoes/Tomato Sauce 13.7

Mashed/Whipped Potatoes 12.4

19 to 24 months

French Fries/Other Fried Potatoes 25.5

Cooked Green Beans 16.8

Cooked Corn 15.2

Cooked Peas 11.4

Cooked Tomatoes/Tomato Sauce 9.4

a Baby food vegetables include single vegetables (majority of vegetables reported) as well as mixtures with the named vegetables the predominant vegetable, e.g., broccoli and cauliflower or broccoli and carrots.

Source: Fox et al., 2004.



### Child-Specific Exposure Factors Handbook Page September 2008 9-27

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-15. Percentage of Infants and Toddlers Consuming Different Types of Fruits

Percentage of Infants and Toddlers Consuming at Least Once in a Day

Food Group/Food 4 to 6 months 7 to 8 months 9 to 11 12 to 14 15 to 18 19 to 24

months months months months
Any Fruit 41.9 75.5 75.8 77.2 71.8 67.3

Baby Food Fruit 39.1 67.9 44.8 16.2 4.2 1.8

Non-baby Food Fruit 5.3 14.3 44.2 67.1 69.4 66.8

Types of Non-baby Food Fruit

Canned Fruit 1.4 5.8 21.6 31.9 25.1 20.2

Packed in Syrup 0.7 0.7 8.1 14.9 12.7 8.1

Packed in Juice or Water 0.7 4.5 13.5 18.5 11.3 11.4

Unknown Pack 0.0 0.7 1.5 1.2 3.1 1.2

Fresh Fruit 4.4 9.5 29.5 52.1 55.0 54.6

Dried Fruit 0.0 0.4 2.1 3.5 7.1 9.4

Types of Fruit a

Apples 18.6 33.1 31.6 27.5 19.8 22.4

Bananas 16.0 30.6 34.5 37.8 32.4 30.0

Berries 0.1 0.6 5.3 6.6 11.3 7.7

Citrus Fruits 0.2 0.4 1.6 4.9 7.3 5.1

Melons 0.6 1.0 4.4 7.3 7.2 9.6

a Totals include all baby food and non-baby food fruits.

Source: Fox et al., 2004.



### Page Child-Specific Exposure Factors Handbook

9-28 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-16. Top Five Fruits Consumed by Infants and Toddlers

Top Fruits by Age Group a Percentage Consuming at Least Once in a Day

4 to 6 months

Baby Food Applesauce 17.5

Baby Food Bananas 13.0

Baby Food Pears 7.5

Baby Food Peaches 7.4

Fresh Banana 0.3

7 to 8 months

Baby Food Applesauce 29.0

Baby Food Bananas 25.2

Baby Food Pears 18.2

Baby Food Peaches 13.1

Fresh Banana 6.6

9 to 11 months

Fresh Banana 19.0

Baby Food Applesauce 17.7

Baby Food Bananas 16.8

Baby Food Pears 12.4

Canned Applesauce 11.1

12 to 14 months

Fresh Banana 33.0

Canned Applesauce 15.2

Fresh Grapes 9.0

Fresh Apple 8.8

Canned Peaches 7.2

Canned Fruit Cocktail 7.2

15 to 18 Months

Fresh Banana 30.5

Fresh Grapes 13.2

Fresh Apple 11.2

Fresh Strawberries 10.6

Canned peaches 8.9

19 to 24 months

Fresh Banana 29.6

Fresh Apple 15.0

Fresh Grapes 11.2

Raisins 9.0

Fresh Strawberries 7.6

a Baby food fruits include single fruits (majority of fruits reported) as well as mixtures with the named fruit as the predominant fruit, e.g., pears and raspberries or prunes with pears. Baby food fruits with tapioca and other baby food dessert fruits were counted as desserts.

Source: Fox et al., 2004.



### Child-Specific Exposure Factors Handbook Page September 2008 9-29

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-17. Characteristics of WIC Participants and Non-participants a (Percentages)

Infants 4 to 6 months Infants 7 to 11 months Toddlers 12 to 24 months

WIC WIC WIC Participant Non-participant Participant Non-participant Participant Non-participant

Gender

Male 55 54 55 51 57 52

Female 45 46 45 49 43 48

Child’s Ethnicity **  

Hispanic or Latino 20 11 24 8 22 10 Non-Hispanic or 80 89 76 92 78 89

Latino

Child’s Race **  ** **

White 63 84 63 86 67 84

Black 15 4 17 5 13 5

Other 22 11 20 9 20 11

Child In Day Care ** *

Yes 39 38 34 46 43 53

No 61 62 66 54 57 47

Age of Mother **  

14 to 19 18 1 13 1 9 1

20 to 24 33 13 38 11 33 14

25 to 29 29 29 23 30 29 26
9 33 15 36 18 34 30 to 34 9 23 11 21 11 26 35 or Older 2 2 1 1 0 1 Missing

Mother’s Education **  ** **

11 th Grade or Less 23 2 15 2 17 3 Completed High School 35 19 42 20 42 19 Some Postsecondary 33 26 32 27 31 28
7 53 9 51 9 48 Completed College 2 1 2 0 1 2 Missing

Parent’s Marital Status   ** 

Married 49 93 57 93 58 88

Not Married 50 7 42 7 41 11 Missing 1 1 1 0 1 1



### Page Child-Specific Exposure Factors Handbook

9-30 September 2008Table 9-17. Characteristics of WIC Participants and Non-participants a (Percentages) (continued)

Infants 4 to 6 months Infants 7 to 11 months Toddlers 12 to 24 months

WIC Participant Non-participant WIC Participant Non-participant WIC Participant Non-participant

Mother or Female Guardian Works ** *

Yes 46
No 53 Missing 1 51
48

1 45
54

1 60
40

0 55
45

0 61
38

1

Urbanicity ** ** **

Urban 34
Suburban 36
Rural 28 Missing 2 Sample Size 265 (Unweighted) 55
31
13

1
597 37
31
30
2
351 50
34
15

1
808 35
35
28

2
205 48
35
16
2
791

a

WIC X 2 test were conducted to test for statistical significance in the differences between WIC participants and non-participants within each age group for each variable. The results of X 2 test are listed next to the variable under the column labeled non-participants for each of the three age groups. * P<0.05; **P>0.01; non-participants significantly different from WIC participants on the variable. = Special Supplemental Nutrition Program for Women, Infants, and Children.

Source: Ponza et al., 2004. 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Child-Specific Exposure Factors Handbook Page September 2008 9-31Table 9-18. Food Choices for Infants and Toddlers by WIC Participation Status

Infants 4 to 6 months Infants 7 to 11 months Toddlers 12 to 24 months

WIC NonParticipant participant WIC NonParticipant participant WIC NonParticipant participant

Vegetables

Any Vegetable Baby Food Vegetables Cooked Vegetables Raw Vegetables Dark Green Vegetables Deep Yellow Vegetables Other Starchy Vegetables

Potatoes 40.2

32.9

8.0

1.4

0.4

23.2

6.5

6.0 39.8

37.0

3.9*

0.1**

0.0

28.1

6.4

2.4* 68.2

38.2

33.8

3.6

2.9

30.1

12.9

20.7 70.7

45.0

33.8

4.1

4.0

34.8

15.2

18.2 77.5

4.8

73.1

11.8

6.3

12.5

21.1

43.1 80.2

4.7

72.3

15.4

8.4

16.9

21.5

38.3

Fruits

Any Fruit Baby Food Fruits Non-Baby Food Fruit

Fresh Fruit

Canned Fruit 47.8

43.8

8.1

5.4

3.4 39.2*

36.9

4.0

3.8

0.5** 64.7

48.4

22.9

14.3

10.3 81.0**

57.4*

35.9**

24.3**

17.3** 58.5

3.8

56.4

43.6

22.3 74.6**

6.5

70.9**

57.0**

25.3

Sample Size (unweighted) 265 597 351 808 205 791

*

**

WIC = P<0.05 non-participants significantly different from WIC participants. = P<0.01 non-participants significantly different from WIC participants. = Special Supplemental Nutrition Program for Women, Infants, and Children.

Source: Ponza et al. 2004. 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9-32 September 2008Table 9-19. Percentage of Hispanic and Non-Hispanic Infants and Toddlers Consuming Different Types of Fruits and Vegetables on A Given Day

Age 4 to 5 months Age 6 to 11 months Age 12 to 24 months

Hispanic Non-Hispanic (n=84) (n=538) Hispanic Non-Hispanic (n=163) (n=1,228) Hispanic Non-Hispanic (n=124) (n=871)

Fruits

Any Fruit or 100% Fruit Juice

AnyFruit a
100%Fruit Juice Fruit Preparation

BabyFood Fruit Non-Baby Food Fruit

CannedFruit

FreshFruit 45.0

39.4

19.3

32.6 9.1† 2.3† 9.1*† 35.9

28.8

15.3

28.4 1.3†

-

- 86.2

68.1

57.8

42.9*

35.8

8.8

30.0** 86.8

76.0

47.7

58.1

27.4

13.7

17.7 84.6

67.6

64.1

5.6†

64.2

12.1**

59.3 87.2

71.5

58.9

6.3

68.0

26.2

53.1

Vegetables

Any Vegetable or 100% Vegetable

b Juice Type of Preparation

BabyFood Vegetables CookedVegetables RawVegetables

b Types of Vegetables

DarkGreen Vegetables c

d DeepYellow Vegetables Starchy Vegetable:

WhitePotatoes

French Fries/Fried Potatoes

Baked/Mashed OtherStarchy Vegetables e

f OtherNon-starchy Vegetables 30.0

25.7 4.2† 2.3†

- 21.0

1.4†

-

- 5.0† 8.1† 27.3

25.4 2.4†

-

- 18.2

2.3†

-

- 4.0

8.0 66.2

34.4*

33.2 8.3†

3.3†

32.2

20.7 5.7† 14.4†

6.7**

28.5 70.3

47.6

29.4

2.6

3.1

25.9

17.4

5.3

10.7

15.1

29.0 76.0

4.1†

71.4

25.0

11.4†

20.0

43.5

23.4

19.8

16.6

42.0 80.5

4.9

72.9

13.1

7.5

15.4

39.0

20.3

17.7

22.2

43.4

a

b

c

d

e

f

- *

** † Total includes all baby food and non-baby food fruits and excludes 100% fruit juices and juice drinks. Total includes commercial baby food, cooked vegetables, raw vegetables, and 100% vegetable juices. Reported dark green vegetables include broccoli, spinach, romaine lettuce and other greens such as kale. Reported yellow vegetables include carrots, pumpkin, sweet potatoes, and winter squash. Reported starchy vegetables include corn, green peas, immature lima beans, black-eyed peas (not dried), cassava, and rutabaga. Corn is also shown as a subcategory of other starchy vegetables. Reported non-starchy vegetables include asparagus, cauliflower, cabbage, onions, green beans, mixed vegetables, peppers, and

tomatoes. = Less than 1 percent of the group consumed this food on a given day. = Significantly different from non-Hispanic at the P <0.05. = Significantly different from non-Hispanic at the P >0.01. = Statistic is potentially unreliable because of a high coefficient of variation.

Source: Mennella et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Child-Specific Exposure Factors Handbook Page September 2008 9-33Table 9-20. Top Five Fruits and Vegetables Consumed by Hispanic and Non-Hispanic Infants and Toddlers Per Age Group a

Ethnicity

Hispanic Non-Hispanic

Top Fruits By Age Group

4 to 5 months Bananas (16.3%) Apples (14.7%) Peaches (10.9%) Melons (3.5%) Pears (2.5%) Apples (12.5%) Bananas (10.0%) Pears (5.9%) Peaches (5.8%) Prunes (1.6%)

6 to 11 months Bananas (35.9%) Apples (29.7%) Pears (15.2%) Peaches (11.7%) Melons (4.7%) Apples (32.9%) Bananas (31.5%) Pears (17.5%) Peaches (13.9%) Apricots (3.7%)

12 to 24 months Bananas (41.5%) Apples (25.7%) Berries (8.5%) Melons (7.6%) Pears (7.3%) Bananas (30.9%) Apples (22.0%) Grapes (12.3%) Peaches (9.6%) Berries (8.7%)

Top Vegetables By Age Group

4 to 5 months Carrots (9.9%) Sweet Potatoes (6.8%) Green Beans (5.8%) Peas (5.0%) Squash (4.3%) Sweet Potatoes (7.5%) Carrots (6.6%) Green Beans (5.9%) Squash (5.4%) Peas (3.8%)

6 to 11 months Potatoes (20.7%) Carrots (19.0%) Mixed Vegetables (11.1%) Green Beans (11.0%) Sweet Potatoes (8.7%) Carrots (17.5%) Potatoes (16.4%) Green Beans (15.9%) Squash (11.8%) Sweet Potatoes (11.4%)

12 to 24 months Potatoes (43.5%) Tomatoes (23.1%) Carrots (18.6%) Onions (11.8%) Corn (10.2%) Potatoes (39.0%) Green Beans (19.6%) Peas (12.8%) Carrots (12.3%) Tomatoes (11.9%)

a Percentage consuming at least one in a day is in parentheses.

Source: Mennella, et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9-34 September 2008Table 9-21. Average Portion Sizes per Eating Occasion of Fruits and Vegetables Commonly Consumed by Infants from the 2002 Feeding Infants and Toddlers Study

4 to 5 months 6 to 8 months 9 to 11 months Reference Food group (N=624) (N=708) (N=687) unit Mean± SEM

Fruits and Juices

All fruits tablespoon 3.6±0.19 4.7±0.11 5.8±0.17

Babyfood fruit tablespoon 3.3±0.16 4.6±0.11 5.6±0.17 Babyfood peaches tablespoon 3.6±0.37 4.4±0.26 5.3±0.36 Babyfood pears tablespoon 3.5±0.46 4.5±0.21 6.0±0.40 Babyfood bananas tablespoon 3.4±0.23 5.0±0.21 5.9±0.35 Babyfood applesauce tablespoon 3.7±0.29 4.6±0.17 5.6±0.25

Cannedfruit tablespoon

- 4.5±0.59 4.8±0.25

Freshfruit tablespoon

- 5.3±0.52 6.4±0.37 100%juice fluid ounce 2.5±0.17 2.8±0.11 3.1±0.09

Apple/apple blends fluid ounce 2.7±0.22 2.9±0.13 3.2±0.11 Grape fluid ounce

- 2.6±0.19 3.1±0.21

Pear fluid ounce

- 2.6±0.29 3.1±0.28

Vegetables

All vegetables tablespoon 3.8±0.20 5.8±0.16 5.6±0.20

Babyfood vegetables tablespoon 4.0±0.20 5.9±0.16 6.6±0.21 Babyfood green beans tablespoon 3.5±0.33 5.1±0.28 6.1±0.50 Babyfood squash tablespoon 4.3±0.47 5.6±0.30 6.9±0.41 Babyfood sweet tablespoon 4.3±0.31 6.1±0.34 7.2±0.69 Babyfood carrots tablespoon 3.5±0.33 5.6±0.27 6.7±0.48 Cooked vegetables, excluding french fries tablespoon

- 4.2±0.47 3.8±0.31 Deepyellow vegetables tablespoon

- 3.2±0.59 3.2±0.39 Mashedpotatoes tablespoon

- 4.1±0.67 2.8±0.37

Greenbeans tablespoon

- 3.2±0.62 5.0±0.61

- = Cell size was too small to generate a reliable estimate.

N = Number of respondents.

SEM = Standard error.

Source: Fox et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Child-Specific Exposure Factors Handbook Page September 2008 9-35Table 9-22. Average Portion Sizes per Eating Occasion of Fruits and Vegetables Commonly Consumed by Toddlers from the 2002 Feeding Infants and Toddlers Study

Food group Reference

unit 12 to 14 months (N=371) 15 to 18 months (N=312) 19 to 24 months (N=320)

Mean± SEM

Fruits and Juices

All fruits

Cannedfruit

Freshfruit Freshapple cup

cup

cup cup, slice

1 medium 0.4±0.02

0.3±0.02

0.4±0.02

0.4±0.05

0.3±0.04 0.5±0.03

0.4±0.03

0.5±0.03

0.6±0.07

0.5±0.06 0.6±0.03

0.4±0.04

0.6±0.03

0.8+0.14

0.6±0.11

Freshbanana cup, slice

1 medium 0.4+0.02

0.6±0.03 0.5±0.03

0.7±0.03 0.5±0.03

0.7±0.04 Freshgrapes 100%juice

Orange/orange blends Apple/apple blends cup fluid ounce

fluid ounce

fluid ounce 0.2±0.01

3.7±0.15

3.3+0.38

3.6±0.21 0.3±0.03

5.0±0.20

4.5±0.33

4.5±0.29 0.3±0.02

5.1±0.18

5.2±0.35

4.9±0.27

Grape fluid ounce 3.6±0.38 5.6±0.43 4.7±0.31

Vegetables

All vegetables

Cookedvegetables,

excluding french fries Deepyellow vegetables

Corn

Peas

Greenbeans Mashedpotatoes Baked, boiled potatoes

Frenchfries cup

cup

cup

cup

cup

cup

cup

cup

cup 0.4±0.02

0.3±0.03

0.2±0.03

0.2±0.03

0.2±0.02

0.4±0.05

0.3±0.05

0.3±0.05

0.4±0.05 0.4±0.03

0.3±0.03

0.3±0.05

0.2±0.03

0.2±0.02

0.4±0.05

0.4±0.05

0.4±0.06

0.6±0.05 0.4±0.02

0.3±0.02

0.3±0.05

0.2±0.03

0.2±0.02

0.3±0.03

0.3±0.05

- 0.6±0.05

- N

SEM Cell size too small to generate reliable estimate. = Number of respondents.

= Standard error of the mean.

Source: Fox et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9-36 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-23. Mean Moisture Content of Selected Food Groups Expressed as Percentages of Edible Portions

Food Moisture Content

Comments Raw Cooked

Fruits

Apples - dried 31.76 84.13* sulfured; * without added sugar
Apples 85.56*

86.67** *with skin

**without skin
Apples - juice 87.93 canned or bottled
Applesauce 88.35* *unsweetened
Apricots 86.35 86.62* *canned juice pack with skin
Apricots - dried 30.09 75.56* sulfured; *without added sugar

Bananas 74.91

Blackberries 88.15

Blueberries 84.21 86.59* *frozen unsweetened
Boysenberries 85.90 frozen unsweetened
Cantaloupes 90.15

Casabas 91.85

Cherries - sweet 82.25 84.95* *canned, juice pack
Crabapples 78.94

Cranberries 87.13 Cranberries - juice cocktail 85.00 bottled
Currants (red and white) 83.95

Elderberries 79.80
Grapefruit (pink, red and white) 90.89 Grapefruit - juice 90.00 90.10* *canned unsweetened
Grapefruit - unspecified 90.89 pink, red, white
Grapes - fresh 81.30 American type (slip skin)
Grapes - juice 84.12 canned or bottled
Grapes - raisins 15.43 seedless
Honeydew melons 89.82

Kiwi fruit 83.07
Kumquats 80.85
Lemons - juice 90.73 92.46* *canned or bottled
Lemons - peel 81.60
Lemons - pulp 88.98

Limes 88.26 Limes - juice 90.79 92.52* *canned or bottled
Loganberries 84.61* *frozen

Mulberries 87.68

Nectarines 87.59
Oranges - unspecified 86.75 all varieties

Peaches 88.87 87.49* *canned juice pack

Pears - dried 26.69 64.44* sulfured; *without added sugar

Pears - fresh 83.71 86.47* *canned juice pack
Pineapple 86.00 83.51* *canned juice pack
Pineapple - juice 86.37 canned



### Child-Specific Exposure Factors Handbook Page September 2008 9-37Table 9-23. Mean Moisture Content of Selected Food Groups Expressed as Percentages of Edible Portions (continued)

Food Moisture Content

Raw Cooked Comments

Plums - dried (prunes)

Plums
Quinces
Raspberries

Strawberries
Tangerine - juice
Tangerines

Watermelon 30.92

87.23

83.80

85.75

90.95

88.90

85.17

91.45 84.02*

89.97*

87.00*

89.51* *canned juice pack

*frozen unsweetened

*canned sweetened
*canned juice pack

Vegetables

Alfalfa seeds - sprouted
Artichokes - globe & French

Artichokes - Jerusalem
Asparagus

Bamboo shoots
Beans - dry - blackeye peas (cowpeas)
Beans - dry - hyacinth (mature seeds)
Beans - dry - navy (mature seeds)
Beans - dry - pinto (mature seeds)

Beans - lima
Beans - snap - green - yellow

Beets
Beets - tops (greens)

Broccoli
Brussel sprouts
Cabbage - Chinese (pak-choi)
Cabbage - red
Cabbage - savoy

Carrots
Cassava (yucca blanca)

Cauliflower

Celeriac
Celery

Chives

Cole slaw

Collards

Corn - sweet
Cress - garden
Cucumbers - peeled
Dandelion - greens
Eggplant

Endive

Garlic

Kale

Kohlrabi 92.82

84.94

78.01

93.22

91.00

77.20

87.87

79.15

81.30

70.24

90.27

87.58

91.02

90.69

86.00

95.32

90.39

91.00

88.29

59.68

91.91

88.00

95.43

90.65

81.50

90.55

75.96

89.40

96.73

85.60

92.41

93.79

58.58

84.46

91.00 84.08

92.63

95.92

75.48

86.90

76.02

93.39

67.17

89.22

87.06

89.13

89.25

88.90

95.55

90.84

92.00

90.17

93.00

92.30

94.11

91.86

69.57

92.50

89.80

89.67

91.20

90.30 boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained

boiled, drained 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9-38 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9-23. Mean Moisture Content of Selected Food Groups Expressed as Percentages of Edible Portions (continued)

Food Moisture Content

Comments Raw Cooked

Lambsquarter 84.30 88.90 boiled, drained
Leeks - bulb and lower leaf-portion 83.00 90.80 boiled, drained
Lentils - sprouted 67.34 68.70 stir-fried
Lettuce - iceberg 95.64

Lettuce - cos or romaine 94.61
Mung beans - mature seeds (sprouted) 90.40 93.39 boiled, drained
Mushrooms - unspecified 91.08 boiled, drained
Mushrooms - oyster 88.80

Mushrooms - Maitake 90.53
Mushrooms - portabella 91.20
Mustard greens 90.80 94.46 boiled, drained

Okra 90.17 92.57 boiled, drained

Onions 89.11 87.86 boiled, drained
Onions - dehydrated or dried 3.93
Parsley 87.71
Parsnips 79.53 80.24 boiled, drained
Peas - edible-podded 88.89 88.91 boiled, drained
Peppers - sweet - green 93.89 91.87 boiled, drained
Peppers - hot chili-green 87.74 92.50* *canned solids & liquid
Potatoes (white) 81.58 75.43 baked
Pumpkin 91.60 93.69 boiled, drained

Radishes 95.27
Rutabagas - unspecified 89.66 88.88 boiled, drained
Salsify (vegetable oyster) 77.00 81.00 boiled, drained

Shallots 79.80
Soybeans - mature seeds - sprouted 69.05 79.45 steamed
Spinach 91.40 91.21 boiled, drained
Squash - summer 94.64 93.70 all varieties; boiled, drained
Squash - winter 89.76 89.02 all varieties; baked

Sweet Potatoes 77.28 75.78 baked in skin

Swiss chard 92.66 92.65 boiled, drained

Taro - leaves 85.66 92.15 steamed

Taro 70.64 63.80
Tomatoes - juice 93.90 canned
Tomatoes - paste 73.50 canned
Tomatoes - puree 87.88 canned

Tomatoes 93.95
Towelgourd 93.85 84.29 boiled, drained
Turnips 91.87 93.60 boiled, drained
Turnips - greens 89.67 93.20 boiled, drained

Water chestnuts - Chinese 73.46 86.42* *canned solids and liquids

Yambean - tuber 90.07 90.07 boiled, drained

Source: USDA, 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 9-39

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

APPENDIX 9A

CODES AND DEFINITIONS USED TO DETERMINE THE VARIOUS FRUITS AND

VEGETABLES USED IN THE U.S. EPA ANALYSIS OF CSFII DATA IN FCID

Child-Specific Ex posure Factors Handbook Page September 2008 9A-1Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data

Food Category EPA Food Commodity Codes

TOTAL FRUITS AND VEGETABLES

Total Fruits 95000010 Acerola 11000090 Apple, dried 11000091 Apple, dried-babyfood 11000070 Apple, fruit with peel 11000080 Apple, peeled fruit 11000081 Apple, peeled fruit-babyfood 11000110 Apple, sauce 11000111 Apple, sauce-babyfood 12000120 Apricot 12000130 Apricot, dried 12000121 Apricot-babyfood

95000200 Avocado

95000230 Banana 95000240 Banana, dried 95000241 Banana, dried-babyfood 95000231 Banana-babyfood 13010550 Blackberry 13020570 Blueberry 13020571 Blueberry-babyfood 13010580 Boysenberry

95000600 Breadfruit

95000740 Canistel 95000890 Cherimoya 12000900 Cherry 12000901 Cherry-babyfood

10001060 Citrus citron 10001070 Citrus hybrids 95001120 Coconut, dried 95001110 Coconut, meat 95001111 Coconut, meat-babyfood 95001130 Coconut, milk 11001290 Crabapple 95001300 Cranberry 95001310 Cranberry, dried 95001301 Cranberry-babyfood

13021360 Currant 13021370 Currant, dried

95001410 Date 13011420 Dewberry 08001480 Eggplant 13021490 Elderberry 95001510 Feijoa 95001530 Fig 95001540 Fig, dried 13021740 Gooseberry 95001750 Grape 95001780 Grape, raisin 10001800 Grapefruit

95001830 Guava 95001831 Guava-babyfood 13021910 Huckleberry

95001920 Jaboticaba 95001930 Jackfruit

95001950 Kiwifruit 10001970 Kumquat

10001990 Lemon 10002010 Lemon, peel

10002060 Lime 13012080 Loganberry 95002090 Longan 11002100 Loquat 95002110 Lychee 95002120 Lychee, dried 95002140 Mamey apple 95002150 Mango 95002160 Mango, dried 95002151 Mango-babyfood 95002270 Mulberry

12002300 Nectarine 10002400 Orange 10002420 Orange, peel 95002450 Papaya 95002460 Papaya, dried 95002451 Papaya-babyfood

95002520 Passionfruit 95002521 Passionfruit-babyfood 95002540 Pawpaw

12002600 Peach 12002610 Peach, dried 12002611 Peach, dried-babyfood 12002601 Peach-babyfood

11002660 Pear 11002670 Pear, dried 11002661 Pear-babyfood

95002770 Persimmon 95002790 Pineapple 95002800 Pineapple, dried 95002791 Pineapple-babyfood

95002830 Plantain 95002840 Plantain, dried

12002850 Plum 12002870 Plum, prune, dried 12002871 Plum, prune, dried-babyfood 12002860 Plum, prune, fresh 12002861 Plum, prune, fresh-babyfood 12002851 Plum-babyfood 95002890 Pomegranate

10003070 Pummelo 11003100 Quince 13013200 Raspberry 13013201 Raspberry-babyfood 95003330 Sapote, Mamey 95003460 Soursop 95003510 Spanish lime 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9A-2 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Total Fruits (continued) 95003580 Starfruit 95003590 Strawberry 95003591 Strawberry-babyfood 95003610 Sugar apple

95003680 Tamarind 10003690 Tangerine

Total Vegetables 18000020 Alfalfa, seed 04010050 Amaranth, leafy 01030150 Arrowroot, flour 01030151 Arrowroot, flour-babyfood 95000160 Artichoke, globe 01030170 Artichoke, Jerusalem 04010180 Arugula 95000190 Asparagus 09020210 Balsam pear 95000220 Bamboo, shoots 19010290 Basil, dried leaves 19010291 Basil, dried leaves-babyfood 19010280 Basil, fresh leaves 19010281 Basil, fresh leaves-babyfood 06020330 Bean, cowpea, succulent 06030360 Bean, kidney, seed 06030380 Bean, lima, seed 06020370 Bean, lima, succulent 06030390 Bean, mung, seed 06030400 Bean, navy, seed 06030410 Bean, pink, seed 06030420 Bean, pinto, seed 06010430 Bean, snap, succulent 06010431 Bean, snap, succulent-babyfood 01010500 Beet, garden, roots 01010501 Beet, garden, roots-babyfood 02000510 Beet, garden, tops 95000540 Belgium endive

05010610 Broccoli

05020630 Broccoli raab 05010620 Broccoli, Chinese 05010611 Broccoli-babyfood 05010640 Brussels sprouts 05010690 Cabbage 05020700 Cabbage, Chinese, bok choy 05010720 Cabbage, Chinese, mustard 05010710 Cabbage, Chinese, napa

95000730 Cactus 09010750 Cantaloupe

04020760 Cardoon

01010780 Carrot 01010781 Carrot-babyfood

09010800 Casaba

01030820 Cassava 01030821 Cassava-babyfood

05010830 Cauliflower

01010840 Celeriac 04020850 Celery 04020851 Celery-babyfood

04020870 Celtuce 09020880 Chayote, fruit 06030990 Chickpea, flour 06030980 Chickpea, seed 06030981 Chickpea, seed-babyfood 01011000 Chicory, roots 02001010 Chicory, tops 09021020 Chinese waxgourd

19011030 Chive 04011040 Chrysanthemum, garland

19021050 Cinnamon 19021051 Cinnamon-babyfood 19011180 Coriander, leaves 19011181 Coriander, leaves-babyfood 19021190 Coriander, seed 19021191 Coriander, seed-babyfood 04011380 Dandelion, leaves 01031390 Dasheen, corm 02001400 Dasheen, leaves

19011440 Dill 19021430 Dill, seed 04021520 Fennel, Florence

03001640 Garlic 03001650 Garlic, dried 03001651 Garlic, dried-babyfood 01031660 Ginger 01031670 Ginger, dried 01031661 Ginger-babyfood 01011680 Ginseng, dried 95001770 Grape, leaves 06031820 Guar, seed 06031821 Guar, seed-babyfood 19011840 Herbs, other 19011841 Herbs, other-babyfood

05021940 Kale

05011960 Kohlrabi

03001980 Leek 19012020 Lemongrass 04012040 Lettuce, head 04012050 Lettuce, leaf 19012200 Marjoram 19012201 Marjoram-babyfood

08002340 Okra 03002370 Onion, dry bulb 03002380 Onion, dry bulb, dried 03002381 Onion, dry bulb, dried-babyfood 03002371 Onion, dry bulb-babyfood 03002390 Onion, green 95002430 Palm heart, leaves 19012490 Parsley, dried leaves 19012491 Parsley, dried leaves-babyfood



### Child-Specific Exposure Factors Handbook Page September 2008 9A-3Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Total Vegetables (continued) 04012480

01012500

01012510

01012511

06032560

06032561

06012570

06032580

06022590

06022550

06022551

08002700

08002710

08002711

08002701

19022740

19022741

08002720

08002730

08002721

95002750

01032960

01032970

01032971

01032980

01032981

01033000

01033001

01032990

01032991

09023080

04013130

01013160

02003170

01013140

02003150

05023180

04023220 Parsley, leaves Parsley, turnip rooted Parsnip Parsnip-babyfood Pea, dry Pea, dry-babyfood Pea, edible podded, succulent Pea, pigeon, seed Pea, pigeon, succulent Pea, succulent Pea, succulent-babyfood Pepper, bell Pepper, bell, dried Pepper, bell, dried-babyfood Pepper, bell-babyfood Pepper, black and white Pepper, black and white-babyfood Pepper, nonbell Pepper, nonbell, dried Pepper, nonbell-babyfood Peppermint Potato, chips Potato, dry (granules/ flakes) Potato, dry (granules/ flakes)-babyfood Potato, flour Potato, flour-babyfood Potato, tuber, w/o peel Potato, tuber, w/o peel-babyfood Potato, tuber, w/peel Potato, tuber, w/peel-babyfood Pumpkin

Radicchio Radish, Oriental, roots Radish, Oriental, tops Radish, roots Radish, tops Rape greens

Rhubarb 01013270

01013310

02003320

19013340

95003351

03003380

06003480

06003481

06003470

19023540

19023541

09023560

09023561

09023570

09023571

01033660

01033661

04023670

01033710

08003740

08003750

08003780

08003781

08003760

08003761

08003770

08003771

95003800

08003751

01033870

05023890

01013880

95003970

95003980

09013990

01034070

01034060 Rutabaga Salsify, roots Salsify, tops Savory 95003350 Seaweed Seaweed-babyfood

Shallot Soybean, flour Soybean, flour-babyfood Soybean, seed Spices, other Spices, other-babyfood Squash, summer Squash, summer-babyfood Squash, winter Squash, winter-babyfood Sweet potato Sweet potato-babyfood

Swiss chard Tanier, corm

Tomatillo

Tomato Tomato, dried Tomato, dried-babyfood Tomato, paste Tomato, paste-babyfood Tomato, puree Tomato, puree-babyfood
Tomato, Tree Tomato-babyfood

Turmeric Turnip, greens Turnip, roots

Water chestnut

Watercress

Watermelon

Yam bean
Yam, true

INDIVIDUAL FRUIT CATEGORIES

Apples 11000090

11000091

11000070

11000100

11000101 Apple, dried Apple, dried-babyfood Apple, fruit with peel Apple, juice Apple, juice-babyfood 11000080

11000081

11000110

11000111 Apple, peeled fruit Apple, peeled fruit-babyfood Apple, sauce Apple, sauce-babyfood

Bananas 95000230

95000240

95000241

95000231 Banana Banana, dried Banana, dried-babyfood Banana-babyfood 95002830

95002840 Plantain Plantain, dried 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9A-4 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Berries and Small

Fruits 13010550 Blackberry 13010580 Boysenberry 13011420 Dewberry 13012080 Loganberry 13013200 Raspberry 13013201 Raspberry-babyfood 13020570 Blueberry 13020571 Blueberry-babyfood

13021360 Currant 13021370 Currant, dried 13021490 Elderberry 13021740 Gooseberry 13021910 Huckleberry 95001300 Cranberry 95001301 Cranberry-babyfood 95001310 Cranberry, dried 95001750 Grape 95001770 Grape, leaves 95001780 Grape, raisin

95001950 Kiwifruit 95002270 Mulberry 95003590 Strawberry 95003591 Strawberry-babyfood

Citrus Fruits 10001060 Citrus citron 10001070 Citrus hybrids 10001800 Grapefruit 10001970 Kumquat

10001990 Lemon 10002010 Lemon, peel 10002060 Lime 10002400 Orange 10002420 Orange, peel

10003070 Pummelo 10003690 Tangerine

Peaches 12002600 Peach 12002610 Peach, dried 12002611 Peach, dried-babyfood 12002601 Peach-babyfood

Pears 11002660 Pear 11002670 Pear, dried 11002680 Pear, juice 11002681 Pear, juice-babyfood 11002661 Pear-babyfood

Pome Fruits 11000070 Apple, fruit with peel 11000080 Apple, peeled fruit 11000081 Apple, peeled fruit-babyfood 11000090 Apple, dried 11000091 Apple, dried-babyfood 11000110 Apple, sauce 11000111 Apple, sauce-babyfood 11001290 Crabapple 11002100 Loquat

11002660 Pear 11002661 Pear-babyfood 11002670 Pear, dried 11003100 Quince

Strawberries 95003590 Strawberry 95003591 Strawberry-babyfood

Stone Fruits 12000120 Apricot 12000121 Apricot-babyfood 12000130 Apricot, dried 12000900 Cherry 12000901 Cherry-babyfood

12002300 Nectarine

12002600 Peach 12002601 Peach-babyfood 12002610 Peach, dried 12002611 Peach, dried-babyfood

12002850 Plum 12002851 Plum-babyfood 12002860 Plum, prune, fresh 12002861 Plum, prune, fresh-babyfood 12002870 Plum, prune, dried 12002871 Plum, prune, dried-babyfood



### Child-Specific Exposure Factors Handbook Page September 2008 9A-5Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Tropical Fruits 95000010

95000220

95000230

95000231

95000240

95000241

95000600

95000740

95000890

95001110

95001111

95001120

95001130

95001410

95001510

95001530

95001540

95001830

95001831

95001930

95002090

95002110

95002120 Acerola

Avocado

Banana Banana-babyfood Banana, dried Banana, dried-babyfood

Breadfruit

Canistel Cherimoya Coconut, meat Coconut, meat-babyfood Coconut, dried Coconut, milk

Date Feijoa Fig Fig, dried

Guava Guava-babyfood

Jackfruit Longan Lychee Lychee, dried 95002140

95002150

95002151

95002160

95002450

95002451

95002460

95002520

95002521

95002540

95002790

95002791

95002800

95002830

95002840

95002890

95003330

95003460

95003510

95003580

95003610

95003680 Mamey apple Mango Mango-babyfood Mango, dried Papaya Papaya-babyfood Papaya, dried

Passionfruit Passionfruit-babyfood Pawpaw Pineapple Pineapple-babyfood Pineapple, dried

Plantain Plantain, dried Pomegranate Sapote, Mamey Soursop Spanish lime

Starfruit Sugar apple

Tamarind

INDIVIDUAL VEGETABLE CATEGORIES

Asparagus 95000190 Asparagus

Beans 06030350

06030300

06030320

06020310

06030340

06020330

06030360

06030380 Bean, greatnorthern, seed Bean, black, seed Bean, broad, seed Bean, broad, succulent Bean, cowpea, seed Bean, cowpea, succulent Bean, kidney, seed Bean, lima, seed 06020370

06030390

06030400

06030410

06030420

06010430

06010431 Bean, lima, succulent Bean, mung, seed Bean, navy, seed Bean, pink, seed Bean, pinto, seed Bean, snap, succulent Bean, snap, succulent-babyfood

Beets 01010500

01010501

02000510 Beet, garden, roots Beet, garden, roots-babyfood Beet, garden, tops

Broccoli 05010610

05010611 Broccoli Broccoli-babyfood

Bulb Vegetables 03001640

03001650

03001651

03001980

03002370 Garlic Garlic, dried Garlic, dried-babyfood

Leek Onion, dry bulb 03002371

03002380

03002381

03002390

03003380 Onion, dry bulb-babyfood Onion, dry bulb, dried Onion, dry bulb, dried-babyfood Onion, green

Shallot

Cabbage 05010690

05010720

05010710 Cabbage Cabbage, Chinese, mustard Cabbage, Chinese, napa 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9A-6 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Carrots 01010780 Carrot

Corn 15001220 Corn, field, bran 15001200 Corn, field, flour 15001201 Corn, field, flour-babyfood 15001210 Corn, field, meal 15001211 Corn, field, meal-babyfood 15001230 Corn, field, starch 15001231 Corn, field, starch-babyfood 15001260 Corn, pop 15001270 Corn, sweet 15001271 Corn, sweet-babyfood

Cucumbers 09021350 Cucumber

Cucurbit 09010750 Cantaloupe 09021350 Cucumber Vegetables 09010800 Casaba 09011870 Honeydew melon

09013990 Watermelon 09020210 Balsam pear 09020880 Chayote, fruit 09021020 Chinese waxgourd 09023080 Pumpkin 09023090 Pumpkin, seed 09023560 Squash, summer 09023561 Squash, summer-babyfood 09023570 Squash, winter 09023571 Squash, winter-babyfood

Fruiting Vegetables 08001480 Eggplant

08002340 Okra 08002700 Pepper, bell 08002701 Pepper, bell-babyfood 08002710 Pepper, bell, dried 08002711 Pepper, bell, dried-babyfood 08002720 Pepper, nonbell 08002721 Pepper, nonbell-babyfood 08002730 Pepper, nonbell, dried

08003740 Tomatillo 08003750 Tomato 08003751 Tomato-babyfood 08003760 Tomato, paste 08003761 Tomato, paste-babyfood 08003770 Tomato, puree 08003771 Tomato, puree-babyfood 08003780 Tomato, dried 08003781 Tomato, dried-babyfood

Leafy Vegetables 02000510 Beet, garden, tops 04021520 Fennel, Florence (Brassica and 02001010 Chicory, tops 04023220 Rhubarb Nonbrassica) 02001400 Dasheen, leaves 02003150 Radish, tops 02003170 Radish, Oriental, tops 02003320 Salsify, tops 04010050 Amaranth, leafy 04010180 Arugula 04011040 Chrysanthemum, garland 04011330 Cress, garden 04011340 Cress, upland 04011380 Dandelion, leaves

04011500 Endive 04012040 Lettuce, head 04012050 Lettuce, leaf 04012480 Parsley, leaves

04013130 Radicchio 04013550 Spinach 04013551 Spinach-babyfood

04020760 Cardoon 04020850 Celery 04020851 Celery-babyfood

04020870 Celtuce 04023670 Swiss chard

05010610 Broccoli 05010611 Broccoli-babyfood 05010620 Broccoli, Chinese 05010640 Brussels sprouts 05010690 Cabbage 05010710 Cabbage, Chinese, napa 05010720 Cabbage, Chinese, mustard

05010830 Cauliflower

05011960 Kohlrabi

05020630 Broccoli raab 05020700 Cabbage, Chinese, bok choy

05021170 Collards

05021940 Kale 05022290 Mustard greens 05023180 Rape greens 05023890 Turnip, greens 95000540 Belgium endive

95003350 Seaweed 95003351 Seaweed - babyfood

95003980 Watercress



### Child-Specific Exposure Factors Handbook Page September 2008 9A-7Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Legume Vegetables 06003470

06003480

06003481

06003490

06003491

06010430

06010431

06012570

06020310

06020330

06020370

06022550

06022551

06022590

06030300

06030320 Soybean, seed Soybean, flour Soybean, flour-babyfood Soybean, soy milk Soybean, soy milk-babyfood or infant

formula Bean, snap, succulent Bean, snap, succulent-babyfood Pea, edible podded, succulent Bean, broad, succulent Bean, cowpea, succulent Bean, lima, succulent Pea, succulent Pea, succulent-babyfood Pea, pigeon, succulent Bean, black, seed Bean, broad, seed 06030340

06030350

06030360

06030380

06030390

06030400

06030410

06030420

06030980

06030981

06030990

06031820

06031821

06032030

06032560

06032561

06032580 Bean, cowpea, seed Bean, great northern, seed Bean, kidney, seed Bean, lima, seed Bean, mung, seed Bean, navy, seed Bean, pink, seed Bean, pinto, seed Chickpea, seed Chickpea, seed-babyfood Chickpea, flour Guar, seed Guar, seed-babyfood Lentil, seed Pea, dry Pea, dry-babyfood Pea, pigeon, seed

Lettuce 04012040

04012050 Lettuce, head Lettuce, leaf

Okra 08002340 Okra

Onions 03002370

03002380

03002381

03002371

03002390 Onion, dry bulb Onion, dry bulb, dried Onion, dry bulb, dried-babyfood Onion, dry bulb-babyfood Onion, green

Peas 06032560

06032561

06012570

06032580

06022590 Pea, dry Pea, dry-babyfood Pea, edible podded, succulent Pea, pigeon, seed Pea, pigeon, succulent 06022550

06022551 Pea, succulent Pea, succulent-babyfood

Peppers 08002700

08002710

08002711

08002701

08002720 Pepper, bell Pepper, bell, dried Pepper, bell, dried-babyfood Pepper, bell-babyfood Pepper, nonbell 08002730

08002721 Pepper, nonbell, dried Pepper, nonbell-babyfood

Pumpkin 09023080

09023090 Pumpkin Pumpkin, seed 

### Child-Specific Exposure Factors Handbook

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- Intake of Fruits and Vegetables

Page Child-Specific Exposure Factors Handbook

9A-8 September 2008Table 9A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Root and Tuber Vegetables 01030150

01030151

01030170

01010500

01010501

02000510

01010520

01010521

01010670

01010780

01010781

01030820

01030821

01010840

01011000

01031390

01031660

01031670

01031661

01011680

01011900

01012500 Arrowroot, flour Arrowroot, flour-babyfood Artichoke, Jerusalem Beet, garden, roots Beet, garden, roots-babyfood Beet, garden, tops Beet, sugar Beet, sugar-babyfood

Burdock

Carrot Carrot-babyfood

Cassava Cassava-babyfood

Celeriac Chicory, roots Dasheen, corm Ginger Ginger, dried Ginger-babyfood Ginseng, dried

Horseradish Parsley, turnip rooted 01012510

01012511

01032960

01032970

01032971

01032980

01032981

01033000

01033001

01032990

01032991

01013160

01013140

01013270

01033660

01033661

01033710

01033870

01013880

95003970

01034070

01034060 Parsnip Parsnip-babyfood Potato, chips Potato, dry (granules/ flakes) Potato, dry (granules/ flakes)-babyfood Potato, flour Potato, flour-babyfood Potato, tuber, w/o peel Potato, tuber, w/o peel-babyfood Potato, tuber, w/peel Potato, tuber, w/peel-babyfood Radish, Oriental, roots Radish, roots Rutabaga Sweet potato Sweet potato-babyfood Tanier, corm

Turmeric Turnip, roots

Water chestnut

Yam bean
Yam, true

Stalk and Stem Vegetable and Edible Fungi 95000160

95000190

95000220

95002280

95002430 Artichoke, globe Asparagus Bamboo, shoots

Mushroom Palm heart, leaves

Tomatoes 08003750

08003780

08003781

08003760

08003761 Tomato Tomato, dried Tomato, dried-babyfood Tomato, paste Tomato, paste-babyfood 08003770

08003771

08003751 Tomato, puree Tomato, puree-babyfood Tomato-babyfood

White Potatoes 01032960

01032970

01032971

01032980

01032981 Potato, chips Potato, dry (granules/ flakes) Potato, dry (granules/ flakes)-babyfood Potato, flour Potato, flour-babyfood 01033000

01033001

01032990

01032991 Potato, tuber, w/o peel Potato, tuber, w/o peel-babyfood Potato, tuber, w/peel Potato, tuber, w/peel-babyfood 

### Child-Specific Exposure Factors Handbook

Chapter 9

- Intake of Fruits and Vegetables

Child-Specific Exposure Factors Handbook Page September 2008 9A-9

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

10 INTAKE OF FISH AND SHELLFISH fishermen are interviewed while fishing), the target
10.1 INTRODUCTION population i s anyone who fishes at the locations being

Contaminated finfish and shellfish are potential studied; generally, in a creel study, the probab ility of sources of human exposure to toxic chemicals. Pollutants being sampled is not the same for all members of the

are carried in the surface waters, but also may be stored target population. For instance, if the survey is conducted

and accumulated in the sediments as a result of complex for one day at a site, then it wi ll include all persons who physical and chemical processes. Consequently, finfish fish there daily, but only about 1/7 of the people who fish

and shellf ish are exposed to these pollutants and may there weekly, 1/30th of the people who fish there

become sources of contaminated food. Exposure to some monthly, etc. In this example, the probability of being contaminants may be of concern for children because they sampled (or inverse weight) is seen to be proportional to

may be less able to metabolize, detoxify, and e x crete the frequency of fishing. However, if the survey involves these substances (Moya, 2004). interviewers revisiting the same site on multiple days, and

Accurate ly estimating exposure to a toxic persons are only interviewed once for the survey, then the

chemi cal among a population that consumes fish from a probability of being in the survey is not proportional to pollute d water body requires an estimation of intake rates frequency; in fact , it increases less than proportionally

of the caught fish by both fishermen and their fa milies. with frequency. At the extreme of surveying the same site Commercially caught fish are marketed widely , making every day over the survey period with no re-interviewing, the prediction of an individual's consum ption from a all members of the target population would have the same particular water body or contaminant source difficult. probabilit y of being sampled regardless of fishing

Since the catch of recreational and subsist ence fishermen frequenc y, implying that the survey weights should all

is not "diluted" by fish from other water bodies, these equal one. On the other hand, if the survey protocol calls

individuals and their families represent the population that for individuals to be interviewed each time an interviewer

is most vulnerable to exposure by intake of con taminated encounters them (i.e., without regard to whether they were

fish from a speci fic location. This chapter focuses on previously interviewed), then the inv e rse weights will

intake rates of fish. Note that in this section the term fish again be proportional to fishing freque n cy, no matter how

refers to both finfish and shellfis h. Intake rates for the many times interviewers revisit the same site. Note that general population, and recreational and Native American when individuals can be interviewed multiple times, the fishing populations are ad dressed, and data are presented results of each interview are included as separate records

for intake rates for both marine and freshwater fish, when in the data base and the survey weights should be

available. inversely proportional to the expected number of times Survey data on fish consumption have been that an individual ’ s interviews are inclu d ed in the data

c o llected using a number of different approaches which base.

need to be considered when interpreting the results. The U.S. EPA has prepared a review of and an Typical surveys seek to draw inferences ab out a larger evaluation of five different survey methods used for population from a smaller sample of that population. This obtaining fish consumption data. They are:

l arger population, from which the survey sample is t aken

and to which the results of the survey are generalized, is • Recall-Telephone Survey;

denoted the target population of the survey. In order to • Recall-Mail Survey; generalize from the sample to the target population, the • Recall-Personal Interview; probability of being sampled must be known for each • Diary; and

member of the target population. This probability is • Creel Census.

reflected in weights assigned to survey respondents, with weights be i ng inversely proportional to sampling The reader is referred to U.S. EPA (1998) G uidance for probability. When all members of the target population Conduc ting Fish and Wildlife Consumption Surveys for

have the same probability of being sampled, all weights more detail on these survey methods a nd their advantages

can be set to one and essentially ignored. For example, in and l imitations. The type of survey used, its design, and

a mail or phone study of licensed anglers, the target any weighting factors used in estimating cons u mption population is generally all licen sed anglers in a particular should be considered when interpreting survey dat a for

area, an d in the studies presented, the sampling exposure assessment pu rposes. For surveys used in this probability is essentially equal for all target population handbook, respondents are typically adults who have

members. In a creel study (i.e., a study in which



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Chapter 10 - Intake of Fish and Shellfish

reported on fish intake for children living in their

households.

Th e recommendations for fish and shellfish ingestion rates are provided in the next section, along with

a summary of the confidence rating s for these

recommendations. The recommended values are based on

the key study i d entified by U.S. EPA for this factor. Following the recommendations, the studies on fish ingestion among the general population (Section 10.3),

marine recreational angler households (Section 10.4),

freshwater recreational households (Section 10.5), and

Native American population s (Section 10.6) are

summarized. Information is provided on the key study

t hat forms the basis for the fish and shellfish inta ke rate

r ecommendations. Relevant data on ingestion of fish and

shellfish are also provided. These studies are presented to provide the reader with added perspectiv e on the current state-of-knowledge pertaining to i ngestion of fish and

shellfish among children. Information on serving size (Section 10.7), and other factors (Section 10.8) are also presented.

10.2 RECOMMENDATIONS

Considerable variation exists in the mean and upper percentile fish consumption rates obtained from the

studies pr esented in this chapter. This can be attributed largely to the type of w a ter body (i.e., marine, estuarine, freshwater) and the characteristics of the survey population (i.e., general population, recreational, Native American), but other factors such as study design, method

of data collection, and geographic location also play a role. Based on these study variations, fish consumption studies were classified into the following categories:

• General Population (total, marine, freshwater/estuarine);

? Recreational Marine Intake;

• Recreational Freshwater Intake; and

• Native American Populations

For exposure assessment purposes, the selection of intake rates for the appropriate category (or categories) will depend on the exposure scenario being evaluated. Fish consumption rates are r ecommended for

various ages of children in the general population, based

on the key study prese n ted in Section 10.3.1. The key study for estim ating mean fish intake among the general populati on is the U.S. EPA (2002) analysis of data from

the U.S. Dep a rtment of Agriculture (USDA) Continuing

S urvey of Food Intake by Individuals (CSFII) 1994­

1996 , 1998. Per capita and consumer-only values for ch ildren ages 3 to < 6, 6 to <11, 11 to < 16, and 16 to <

18 years, by habitat (i.e., marine, freshwater/e stuarine, or

total fish), are shown in Table 10-1. It should be noted, however, that the key general population study presented

in this cha pter pre-dated the age groups recommended by

U.S. EPA in Guidance on Selecting Age Groups for Monitoring and Asse ssing Childhood Exposures to

Environmental Contaminants (U.S. EPA, 2005). Thus,

recommended values were not available for children less

than 3 years old or 18 to < 21. The confidence ratings for

the fish intake recomm endations for the general population are presented in Table 10-2. Note that the fish

intake values presented in Table 10-1 are reported as uncooked fish weight s. The CSFII 1994-1996, 1998 recipe files were used to convert, for each fish-c ontaining food, the as-eaten fish weight consumed into an uncooked equivalent weight of fish. This is important because the

concentrations of the contaminants i n fish are generally

measured in the unco o ked samples. Assuming that cooking results in some reductions in weight (e.g., l oss of

m oisture), and the mass of the contaminant in the fish tissue remains constant, then the contaminant

concentration in th e cooked fish tissue will increase. In

terms of calculating the dose, actual consu m ption may be

overestimated when intake is expressed on an uncooked basis, but the actual concentr a tion may be underestimated

when it is based o n the uncooked sample. The net effect

on the dose would depend on the magnitude of the

o pposing effects on these two exposure factors. On the

other hand, if the "as-prepared" (i.e., as-consumed) intake

rate and the uncooked concentration are used in the dose equation, dose may be underestimated since the

concentration in the coo ked fish is likely to be higher, if

the mass of the contaminan t remains constant after cooking. Therefore, it is more conservative and appropriate to use unc o oked fish intake rates. If

concentration data can be adjusted to acco unt for changes

after cooking, then the "as-prepa red" (i.e., as-consumed)

intake rates are appropriate. However, data on the effects

of cooking on contamina nt concentrations are limited and assessors generally make the conservative assumption that cooking has no effect on the contaminant mass. Both "asprepar ed" (i.e., as-consumed) and uncooked general population fish intake values are presented in this

handbook so that the assessor can cho o se the intake data

that best matches the concentration data tha t are being

used. The CSFII data on which the general population

recommendat ions are based, are short-term survey data

and should not be used to estimate the distribution over the long term. Also, it is important to note that a limitation



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Chapter 10 - Intake of Fish and Shellfish

associated with these data is that the total amount of fish 10.4 of this chapter to determine if the values presented reported by respondents in cluded fish from all sources are applicable to their specific assessment. (e.g., fresh, frozen, canned, domest i c, international For recreational freshwa ter fish intake, the West et orig in). The CSFII surveys did not identify the source of al. (1989) study was conducted in Michigan to estimate

the fish c o nsumed. This type of information may be i ntake based on 7-day recall and the frequency of fish

relevant for some assessments. It should also be noted meals over each of the four seasons. Based on a U.S.

that because these recommendations are based on 1994­ EPA analysis of the data, mean recr e ational freshwater 1996, 1998 CSFII data, they may not reflect any recent fish intake rates were 5.6, 7 .9, and 7.3 grams/day for changes that may have occurred in consumption patterns. children ages 1 to 5 years (N = 121), 6 to 10 years (N =

Recommended values sho uld be selected that are 151), and 11 to 20 years (N = 349), respectively. Benson

relevant to the assessment, choosi ng the appropriate age et al. (20 0 1) reported median freshwater sports-caught groups and source of fish (i.e., freshwater/estuarine, fish intake rates of 1.2 and 1 .7 grams/day for children, marine, and total fish). In some cases a different study or ages 0 to 14 years, in Minnesota (N = 58 2 ) and North

studies may be particularly relevant to the needs of an Dakota (N = 3 43), respectively. Readers are referred to

assessm ent, in which case results from that specific study the studies provided in Section 10.5 of this chap ter to

or studies may be used instead of t he recommended determine if the values presented are applicable to their

values provided here. For example, it may be specific assessment. advantageous to use available regional or site-specific Fish consumption data for Nat ive American

estimates if the assessm ent targets a particular region or children are v ery limited, and fish consumption rates,

site. In addition, seasonal, gender, and fish species habits, and patterns can varyamong tribes and other subvariations should be considered w hen appropriate, if data populations. Therefore, fish intake data for a particular

are available. tribe may not be representativ e of other tribes. Available

Recommendations are not provided for recreational data on f i sh consumption among this population is marine fish intake, recreational freshwater fish intake, or presented in Section 10.6. These data should be used, as

intake among Native Ame rican children because the appropriate.

available data are limited to certain geographic areas and/or tribes and cannot be readily generalized to the U.S. population as a whole. However, data from two relevant

recreational marine studies (KCA, 1994 and Alcoa, 1998);

two relevant recreational f r eshwater studies (West et al.,

1989 and Benson et al., 2001); and four Native American

s tudies (CRITFC, 1994; Toy et al., 1996; Duncan, 2000;

and Polissar et al., 2006) are provided in this chapter.

Assessors may use these data, if appropriate to the

scenarios being assessed. These studies were performed

at various study locations usi ng various age groups of

children.

For recreational marine fish intake, the KCA (19 94) study was conducted in Delaware using the age groups 0 to 9 years and 10 to 19 years and the Alcoa (1998) study was conducted in Texas using the age groups <6 years and 6 to 19 years. Mean recreational marine fish

intake values in the KCA (1994) study we r e 6 grams/day

and 11.4 grams/day for the 0 to 9 years (N = 73) and 10 to 19 years (N = 102), respectively. The Alcoa (1998) study provided mean recreational marine intake values for

finfish at 11.4 grams/day f or the children <6 years old (N

= 320) an d 15.6 grams/day for children 6 to 19 years old (N = 749). Mean shellfish values were 0.4 grams/day and 0.7 gram s/day for the same age groups, respectively.

Readers are referred to the studies provi ded in Section



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Chapter 10 - Intake of Fish and ShellfishaTable 10-1. Recommended Values for General Population Fish IntakePer CapitaConsumer OnlyAge GroupMean95thPercentileMean95thPercentile MultipleSourcePercentilesg/day g/kg-day g/day g/kg-day g/day g/kg-day g/day g/kg-dayTotal Fish3 to < 6 years 7.7 0.43 51 3.0 74 4.2 184 106 to < 11 years 8.5 0.28 56 1.9 95 3.2 313* 8.7*11 to < 16 years 12 0.23 87 1.5 113 2.2 308* 6.2*16 to < 18 years 11 0.16 84 1.3 136* 2.1* 357* 6.6*U.S. EPAMarine FishAnalysis ofCSFII,3 to < 6 years 5.5 0.31 39 2.3 66 3.7 165 9.3*See Tables1994-96 and6 to < 11 years 5.6 0.20 38 1.5 78 2.8 202* 8.0*10-7 through1998.10-1011 to < 16 years 7.6 0.15 56 1.3 102 2.0 262* 5.2*(Rates are for16 to < 18 years 6.1 0.10 29 0.46 126* 2.0* 353* 6.5*uncookedweight)Freshwater/Estuarine Fish3 to < 6 years 2.2 0.12 12 0.71 40 2.3 129 7.2*6 to < 11 years 3.0 0.08 13 0.35 61 1.8 248* 6.2*11 to < 16 years 4.3 0.08 26 0.48 71 1.3 199* 4.4*16 to < 18 years 4.6 0.07 19 0.29 100* 1.4* 242* 3.3** The sample size does not meet the minimum reporting requirements, as described in the Third Report on Nutrition Monitoring in the UnitedStates (LSRO, 1995).aAnalysis was conducted prior to Agency’s issuance ofGuidance on Selecting Age groups for Monitoring and Assessing Childhood Exposuresto Environmental Contaminants(U.S. EPA 2005). Thus, data were not presented for children less than 3 years old or for 18to< 21 years.



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Chapter 10 - Intake of Fish and Shellfish

Table 10-2. Confidence in Recommendati ons for General Population Fish Intake

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (Or Defined) Bias The survey methodology and the analysis of the survey data were adequate. Primary data were collected and used in a secondary analysis of the data. The sample size was large.

The survey data were based on recent recall. Data were collected over a short-duration (i.e., 2 days). High

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The key study focused the exposure factor of interest.

The survey was conducted nationwide and was representative of the general U.S. population.

The most current CSFII 1994-96; 98 data were used.

Data were collected for two non-consecutive days. High

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The primary data are accessible through USDA.

The methodology was clearly presented; enough information was available to allow for reproduction of the

results.

Quality assurance of CSFII data was good; quality control of secondary analysis was good. High

Variability and Uncertainty

Variability in Population

Uncertainty Full distributions were provided by the key study.

The survey was not designed to capture long-term intake and was based on recall. Otherwise, the sources of uncertainty were minimal. Medium

Evaluation and Review

Peer Review

Number and Agreement of Studies The primary data were reviewed by USDA; U.S. EPA review conducted a review of the secondary data analysis

for fish intake.

The number of studies is 1. Medium

Overall Rating High (mean)
Medium (upper

percentile)



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Chapter 10 - Intake of Fish and Shellfish

10.3 GENERAL POPULATION STUDIES

10.3.1 Key General Population Study

10.3.1.1 U.S. EPA 2002

- Estimat ed Per Capita Fish Consumption in the United States

U.S. EPA ’ s Office of Water used data from the

1994-96 CSFII and i ts 1998 Children ’ s Supplement (referred to collectively as CSFII 1994-96, 1998) to
generate f ish intake estimates. Participants in the CSFII 1994-96, 98 provided two non-consecutive days

of dietary data. Respondents estimated the weight of

each food that they consumed. Information on the consumption of food was classified using 11,345

different food codes, and stored in a database in units of

grams consumed per day. A total of 831 of these food

c odes related to fish or shellfish; survey respondent s reported consumption across 665 of these codes. The

fish component (by wei ght) of the various foods was

calculated using data from the r ecipe file for release 7

of USDA ’ s Nutrie nt Data Base for Individual Food

Intake Surveys. The amount of fish consumed by each

individual was then calculated by summing, over all fish containing foods, the product of the weight of food

consumed and the fish component (i.e. , the percentage

fish by weight) of the food. The recipe file also contains cooking loss factors associated with each food.

These were used to convert, for each fish-containing food, the as-eaten fish weight consumed into an

uncooked eq u ivalent weight of fish. Analyses of fish

intak e were performed on both an “ as-prepared ” (i.e., as-consumed) and uncooked basis.

Each fish-related foo d code was assigned, by

U.S. EPA, to a habitat category. The habitat categories

included freshwater/estuarine, or mar ine. Food codes

were also designated as finfish or shellfi sh. Average daily individual con s umption (g/day) was calculated,

for a given fish type-by-habitat category (e.g., marine finfish) , by summing the amount of fish consumed by

the individual across the two reporting days for all fishrelated food codes in the given fish-by-habitat category

and then dividing by 2. Individual daily fish consumption (g/day) was calculated similarly e xcept
that total fish consumption was divided by the spec ific

numb er of survey days the individual reported consuming fish; this was calculated for fish consumers only (i.e., those consuming f ish on at least one of the

two survey days). The reported body weight of the

individual was used to convert consumption in g/day to consumption in g/kg-day.

There were a total of 20,607 respondents in the

combined data set who had two-day dietary intake data.

A total of 7,4 29 of these individuals were children be tween the ages of 3 and 17 years. Data for these

c hildren were used in estimating fish intake in g/ day. Slightly f ewer children were used in the fish intake

rates estimated in uni ts of g/kg-day because body weights were not reported for some individuals. Survey weights were assigned to this data set to make it

repres entative of the U.S. population with respect to
various demographic characteristics related to food

intake. These weights were used to project the

estimates for the 7,429 children in the data set to 58,923,560 children in the U.S. population.

U.S. EPA (2002) reported means and estimates

of the 90 th

, 95 th

, and 99 th percentiles of fis h intake.

Tables 10-3 through 10-10 present these statistics for daily average fi sh consumption. These data are presented for selected age groups: 3 to 5, 6 to 10, 11 to
15 , and 16 to 17 years of age. Tables 10-3 and 10-4
present per capita fish consumption, on an as-consumed

bas is, in g/day and in mg/kg-day, respectively. Tables

10-5 and 10-6 provide consumer-only fish in take data,

on an as-consumed basis, in units of g/day and mg/kgday, respectively. Tables 10-7 through 10-10 provide

per capita and consumer only fish intake data (g/day and mg/kg-day) on an uncooked equivalent basis.

The advantages of this study are that th e data

used were from the CSFI I survey, which had a large

s ample size and was representative of the U. S

. population. The CSFII survey was also designed to give unbiased estimates of food consumption (U.S. EPA, 2002). In addition, through use of the USDA recipe files, the anal ysis included all fish eaten (i.e., both fish eaten alone and in mixtures).

10.3.2 Relevant General Population Studies

10.3.2.1 U.S. EPA, 1996

- National Human Activity Pattern Survey (NHAPS)

The U.S. EPA (1996) collected information for the general population on the duration and frequency of

time spent in selected activitie s and time spent in

selected microenvironments via 24-hour diaries as part
of the Nation a l Human Activity Pattern Survey (NHAPS). Over 9,000 individuals from 48 contiguous states participated in NHAPS. Approximately 4,700 participants also pro vided information on seafood

con s umption, with 2,980 responding that they ate seafood (in c luding shellfish, eel, or squid) in the last

month. Over 900 of these participants were children

between the ages of 1 and 17 years. The survey was

conducted between October 1992 and September 1994.

Data were collecte d on the (1) number of people that

at e seafood in the last month, (2) the number of



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Chapter 10 - Intake of Fish and Shellfish

serv ings of seafood consumed, and (3) whether the

seafood consumed was caught or purchased. The participant response s were weighted according to

selected demog raphics such as age, gender, and race to

ensure tha t results were representative of the U.S. population. In order to conform to the standardized age categories used in this h andbook, U.S. EPA

s u bsequently accessed the source data from U.S. EPA (1996) and recalculated the re levant statistics using the age categories recommended in Guidance on Sel ecting Age Groups for Monitoring and Assessing C hildhood Exposures to Environmental Contaminants (U.S. EPA, 2005). The results of U.S. EPA ’ s analysis are shown in

Table 10-11.

Intak e data were not provided in the survey.
However, intake of fish can be estimated using the

information on the num ber of servings of fish eaten

from this study and serving size data for each age group

from other studi es (see Section 10.7.1). Using the

mean value for serving size and the number of servings

per month from Table 10-11, the age-specific amount of seafood eaten per month can be estimated. The advantages of NHAPS is that the data were collected for a large number of individuals and are

re presentative of the U.S. general population.
However, evaluation of seafood intake was not the primary purpose of the study and the data do not reflect

the actual amount of seafo od that was eaten. However, using the assumption described above, the estimated

seafood intakes from this study are comparable to those

observe d in the U.S. EPA CSFII analysis. It should be

noted that an all inclusive description for seafood was

not presente d in U.S. EPA (1996) or in the NHAPS

data. It is not known if processed or canned seafood

and seafood mixtures are included in the seafood
category.

10.3.2.2 Mo ya et al., 2008 - Estimates of Fish Consumption Rates for Consumers of Bought and Sel-caught fish in Connecticut, Florida, Minnesota, and North Dakota Moya et al. (2008) analyzed the raw data from

thre e fish consumption studies to derive fish consumption rates for various age , gender, and ethnic

groups, and according to the source of fish consumed (i.e., bought or caugh t) and habitat (i.e., freshwater, estuarine, or marine) . The studies represented data

from four st ates: Connecticut, Florida, Minnesota and

North Dakota.

The Connecti c ut data were collected in

1996 / 1997 by the University of Connecticut to obtain estimates of fish consumption for the general population, sports fishing households, commercial fishing households, minority and limited income

hou seholds, women of child-bearing years, and

children. Data were obtained from 810 households, representing 2,080 indivi d uals, using a combination of

a mail questionnair e that included a 10-day diary, and personal interviews. The response rate for this survey

was low (i.e., 6 per c ent for the general population and

10 percent for anglers), but was considered to be adequate by the study author s (Balcom et al., 1999).

Data from this survey were avail able for 54 children, ages 0 to 15 years.

The Florida data were collected by telephone and in-person interviews by the University of Florida,

and represented a random sample of 8,000 households (tele phone interviews), and 500 food stamp recipients (in-person interviews). Data from this survey were

ava ilable for 1,160 children, ages 0 to 15 years. The

purpose of the survey was to obtain informatio n on the quantity of fish and shellfish eaten, as well as the cooking method used. Additional information of the Florida survey can be found in Degner et al. (1994).

The Minnesota and North Dakota data were

collected by the Universit y of North Dakota in 2000

and repr esented 1,572 households and 4,273

in dividuals. Data from this survey were available for

273 children, ages 0 to 15 years (151 in Minnesota and

122 in North Dakota). Data on purchased and caught

fish were collected for the gen eral population, anglers,

new mothers, and Native American tribes. The survey

also collected information of the species of fish eaten.

Additional information on this study c an be found in Benson et al. (2001). Moya et al. (2008) utilized the data from these

three studies to generate intake rates for three age

groups of children (i.e., 1 t o <6 years, 6 to <11 years,

and 11 to <16 years). These data represented the

gen eral population of children in the four states.

Recreatio nal fish intake rates were not provided for children, and data were not provided for children according to the source of intake (i.e., bought or caught) or habitat (i.e., freshwater, estuarine, or marine). Table 10-12 presents the intake rates for the general population of children who cons umed fish and

she llfish in g/kg-day, as-consumed. Table 10-13 provides information on the fish intake among the

sa mple populations from the four states, based on the

source of the fish (i.e., caught or bought). Table 10-14 provides estimated f ish intake rates among the general populations an d angler populations from Connecticut,



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Minnesota, and North Dakota. While the data in Tables

10-13 and 10-14 do not pertain specifically to children, they provide an indication of the proportion of fish consumption that is either caug ht or bought among the sample population, and similarities and/or differences

between fish intake among the general p opulation and anglers.

10.4 MARINE RECREATIONAL STUDIES

10.4.1 Relevant Marine Recreational Studies

10.4.1.1 KCA Rese a rch Division, 1994

- Fish Consumption of Delaware Recreational
Fishermen and Their Households In support of the Delaware Estuar y Program,

the State of Dela ware ’ s Department of Natural

Resources and Environmental Control conducted a

survey of marine recreational fis hermen along the

coastal areas of Delaware between July 1 992 and June

1993 (KCA Research Division, 1994). There were two
components of the study. One was a field survey of

fishermen as they returned from their fishing trips and

the second part was a t elephone follow-up call. The

purpose of the first component was to obtain

information on their fishing trips and on their household composition. This information included the method

an d location of fishing, number of fish caught and kept by

species, and weight of each fish kept. Household

information included race, age, gender, and number of

persons in the household. Information was also

recorded as to the location of the angler intercept (i.e.,

where the angler was interviewed) and the locati on of

the household. The purpose of the second component

was to obtain information on the amount of fish caught

and kept from the fishin g trip and then eaten by the

household. The methods used for preparing and cooking the fish were also documented.

The field portion of th e study was designed to

intervi e w 2,000 anglers. Data were obtained from 1,901 anglers, representing 6,204 household members (KCA Research Division, 1994). A total of 1,717 of these were children between the ages of 0 and 19 years

of age. While the primary goal of the study was to

collect data on marine recreational fishing practices, the

survey included some freshwater fishing and crabbing

sites. Followup phone interviews typically occurred

two weeks after the field interview and were used to gather information about co n sumption. Interviewers

aided re spondents in their estimation of fish intake by describing the weight of ordinary products, for the

purpose of comparison to the quantity of fish eaten.

Information on the number of fishing trips a respondent had t aken during the month was used to estimate average annual consumption rates. Table 10-15 presents the results of the study

for children who consumed fish (i.e., consumers only). Children, age s 0 to 9 years old, had a mean fish consumption rate of 6.0 g/day (N = 73), while children, ages 10 to 19 years old, had a mean fish consumption

rate of 11.4 g/day (N = 102). More than half of the

s tudy respondents reported that they skinned th e fish

that they ate (i.e., 450 out of 807 who reported whether they skinned their catch); the major ity ate filleted fish (i.e., 617 out of 794 who reported the preparation method used), and over half fried their fish ( i.e., 506 out of 875 who reported the cooking method).

One limitation of this study is that information

on fish consumption by children is based on anglers ’

re call of amount of fish eaten. Also, the study was

limited to one geographic area and may n ot be representative of the U.S. population.

10.4.1.2 Alcoa , 1998 - Draft Report for the Finfish/Shellfish Consumption Study Alc oa (Point Comfort)/Lavaca Bay Superfund Site

The Texas Salt water Angler Survey was

conducted in 1996/97 to evaluate the quantit y and species of finfish and shellfish consumed by individuals who fish at Lavaca Bay. The target population for this

s tudy was residents of three Texas counties: Calhoun,

V ictoria, and Jackson (over 70 percent of the an glers

who fish La vaca Bay are from these three counties). The random sample design specified that the population
percentages for the counties shou ld be as follows: 50
percent from Calhoun, 30 percent from Victoria, and 20 percent from Jackson.

Each individual in the sample population was

sent an introductory note describing the stud y and then

was contacted by telephone. People who agreed to participate an d had taken fewer than six fishing trips to

Lav aca Bay were interviewed by telephone. Persons

who agreed to participate and had taken more than five fishing trips to Lavaca B ay were sent a mail survey

with the same questions. A total of 1,979 anglers participated in this surv ey, representing a response rate

grea ter than 68 percent. Data were collected from the

households for men, women, and children . There were 4,489 records with valid information and of those records, 320 were fo r small children (less than 6 years old) and 749 records for youths (6 to 19 years old).

The information collected as part of the survey

included recreational fishing trip information for

November 1996 (i.e., fishing site, site facilities,



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Chapter 10 - Intake of Fish and Shellfish

distance traveled, number and species caught), selfcaught fish consumption (by the respondent, spouse and child, if applicable), opinions on d ifferent types of fishing experiences, and socio-demographics. Portion

size for shellfish was determ ined by utilizing the number of shrimp, crabs, oysters, etc. that an individual

consumed during a meal and the assu m ed tissue weight

of the particular species of shellfish. Red drum was the most commonly consumed self-caught fish,

followed by speckled sea trout, flounder, all other

finfish and black drum. For shellfish, the order from highest to lowest amount consumed was o ysters, blue crab, and shrimp.

Table 10- 16 presents the mean and upperpercentile consumption rates of self -caught fish,

expre ssed as grams per day, for small children (<6

years of age) a nd youths (ages 6 to 19 years of age).

Small children cons umed an average of 11.4 grams of

finfish per day while youths consumed an average of

15.6 grams daily. Small children consumed an average

of 0.4 g/ day of shellfish, while youths consumed an average of 0.7 g/day. Note that these data represent the

amount of self-caught fish that is consumed from all

locations (i.e., not just from Lavaca B ay). Table 10-17

shows the average number of meals consumed by each

age group of children and the average portion size in

grams (converted from ounces) for these meals. Small

children an d youths consumed slightly less than three

meals per month of finfish and less than one meal per month of shellfish. For finfish, youths consumed an

average, per meal, portion size of 187 grams, and small

children cons umed less than 128 grams per meal.

Youths consumed an average s hellfish portion size of

71 grams per meal, while small children consumed 57 grams per meal.

The study authors noted that s ince the survey

relied o n the anglers ’ recall of meal frequency and portion, fish consumption may have been

over estimated. Also, the study was conducted at one geographic location and may not be representative of the U.S. population.

10.5 FRESHWATER RECREATIONAL

STUDIES

10.5.1 Relevant Freshwater Recreational Studies

10.5.1.1 West et al., 1989 - Michigan Sport Anglers Fish Consumption Survey The Michigan Sport Anglers Fish Consumption Survey (West et al., 1989) sur veyed a

stratified random sample of Michigan residents with fishing licences. The sample was divided into 18 cohorts, with one cohor t receiving a mail questionnaire each week between January and May 1989. The survey

included both a short -t erm recall component, and a

usual frequency compo nent. For the short-term recall component, responden ts were asked to identify all

household members and list all fish meals consumed by

each household mem ber during the past seven days.

Information on the source of the fish for each meal was

also requested ( s elf-caught, gift, market, or restaurant). Respondents were asked to catego rize serving size by comparison with pictures of 8 ounce fish portions; serving sizes could be designated as eithe r “ about the

same size ”

, “ less ”

, or “mo re ” than the size pictured.

Data on fish species, locations of self -caught fish and

methods of preparation and cooking were also obtained.

The usual frequenc y component of the survey

asked abou t the frequency of fish meals during each of

the four seasons and requested respondents to give the

overall percentage of household fish meals tha t came
f rom recreational sources. A sample of 2,600

individuals were selected from state record s to receive survey questionnaires . A total of 2,334 survey questionnaires were deliverable and 1

, 104 were completed and returne d, giving a response rate of 47.3
percent.. The responses represente d a total of 621 children between the ages of 1 and 20 years.

U.S. EPA obtained the raw data from the West

et al. (1989) survey and analyzed it to estimate mean

fish intake rates for children. Only respondents with

information on both short term and usual intake were

included in this analysis. For the analysis, U.S. EPA

modified the serving size weights used by West et al. (1989), which were 5, 8 and 10 ounces, respectively, for portions that were described as less, about the same,

and more than the 8 ounce picture. U.S. EPA examined

the percentiles of the distributions of fish meal sizes
report ed in Pao et al. (1982), derived from the 1977­

1978 USDA National Food Consumption Survey (NCFS), and observed that a lognormal distribution

p rovided a good visual fit to the percentile data. Using

this lognormal distribution, the mean values for serving sizes greater than 8 ounces and for serving sizes at least

10 percent greater than 8 ou nces were determined. In

both cases, a serving size of 12 ounces was consistent

with the Pao et a l. (1982) distribution. The weights

used in the U.S. EPA analys is then were therefore 5, 8,

and 12 ounces for fish meals described as less, about

the same, and more than the 8 ounce picture,

respe ctively. It should be noted that the mean serving

size from Pao et a l. (1982) was about 5 ounces, well



### Child-Specific Exposure Factors Handbook Page September 2008 10-9

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Chapter 10 - Intake of Fish and Shellfish

below the value of 8 ounces most commo nly reported by respondents in the West et al. (1989) survey. Table 10-18 displays the mean number of total

and recreational fish meals for each household member between age 1 and 20 years based on the seven day

recall data. Also shown are mean fish intake rates

derived by applying the w eights described above to

each fish meal. Intake was calculated in units of both grams/day and grams/kg body weight/day. This analysis was restricted to individuals who eat fish and

who reside in hous e holds reporting some recreational

fish consumpti on during the previous year. About 75
percent of the survey respondents (i.e ., licensed angle rs) and about 84 percent of the respondents who

fished in the prior year reported some household recreational fish consumption.

The advantag e s of this data set and analysis

are that the survey was relatively large and contained

both short-term and usual intake dat a. The response

rate of this survey, 47 percent, was relativel y low and

it was conducted in one geographic location

. This study was conducted in the winter and spring months of 1989. This period does not include the summer months

w hen peak fishing activity can be anticipated, lead ing

to the possibility that intake results based on the 7 day

recall data may under state individuals ’ usual (annual average) fish consumption.

10.5.1.2 Benson et al., 2001 - Fish Consumption Survey: Minnesota and North Dakota

Benson et al (2001) condu c ted a fish consumption survey among Minnesota and North

Dakota residents. The target population included the general population, lice nsed anglers, and members of

Native American trib es. The survey focused on obtaining the most rec ent year ’ s fish intake from all

sources, includi ng locally caught fish. Survey
quest ionnaires were mailed to potential respondent

househ o lds. For the entire population, approximately 1,570 surveys were returned completed (out of 7,835 that were mailed out). Information on fish consumption by children was collected if they were a part of a respondent household. Data were collected for a total

of 604 children (ages 0 to 14 years) in Minnesota and a total of 375 children (ages 0 to 14 years) in North

Dakota. Among these respondents, data on sportcaught fish intake were ava ilable for 582 Minnesota

children and 343 North Dakota children. Table 10-19

presen ts the recreational freshwater intake rates for

children (ages 0 to 14 years). Rates for both purchased

and sports-caught fish are provided. For Minnesota, the 50 th percentile sports- caught fish consumption rate was

1.2 grams/day and the 95 t h percentile rate was 14. 6 grams/day. For North Dakota, the 50 t h percentile sports-caught fish consumption rate was 1.7 grams/day,

and the 95 th percentile rate was 23.3 grams/day. Intake

rates of purchased fish w e re higher for both Minnesota (3.6 grams/day 50 th percentile; 30 .9 grams/day 95 t h

50 th percentile) a nd North Dakota (4.7 grams/day percentile; 42.8 grasm/day 95 th percentile).

An advantage of this study is its large overall sample size. A limitation of the study is the broad age

range of chi ldren used (i.e., 0 to 14 years). Also, the

stu dy was limited to two states. Therefore, the results

may not be representat ive of the U.S. population as a whole..

10.6 NATIVE AMERICAN STUDIES

10.6.1 Relevant Native American Studies

10.6.1.1 Columbia River Inter-Tribal Fish

Commis sion (CRITFC), 1994 - A Fish Consumption Survey of the Umatilla, Nez Perce, Yakama, and Warm Springs Tribes of

the Columbia River Basin

The Columbia River In ter-Tribal Fish

Commission (CRITF C) (1994) conducted a fish consumption survey among four Co lumbia River Basin

Native Ameri can tribes during the fall and winter of

1991-199 2. The target population included all adult

tribal members who lived o n or near the Yakama,

Warm Springs, Umatilla or Nez Perce reservations.

The survey was based on a stratified random sampling

desi gn where respondents were selected from patient registration files at the I ndian Health Service. The

overall resp o nse rate was 69 percent yielding a sample

size of 513 tribal members, 18 years old and above.

Interv iews were performed in person at a central

location on the member ’ s reserv ation. Each participating adult w a s asked if there were any children 5 years old or younger in his or he r household. Those responding affirmatively were asked a set of survey

ques tions about the fish consumption patterns of the
youngest child in the household (CRI TFC, 1994).

Informatio n for 204 children, 5 years old and younger,

was provided by participating adult respondents.

Con sumption data were available for 194 of these

children. Participants were asked to describe and quantify all food and drink consumed duri ng the previous day. They were then asked to identify the

months in which they ate the most and the le ast fish, and the number of fish meals consumed per week



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10-10 September 2008

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Chapter 10 - Intake of Fish and Shellfish

during each of those periods and an average va lue for

the whole year. The typical portion size (in ounces)

was determined wi th the aid of food models provided by the questioner. The next set of questions identified specific species of fish and addressed the number of times per month each was eaten, as well as what parts (e.g., fillet, skin, head, eggs , bones, other) were eaten. Respondent s were then asked to identify the frequency

with which they used various preparation me t hods, expressed as a percentage. Respondents sharing a household with a child, aged 5 years or less, were asked to repeat the serving size, eating frequency, and species questions for the child ’ s consumption behavior. All respondents were asked about th e geographic origin of

a ny fish they personally caught and consumed, and to identify the major sources of fish in their diet (e.g., selfcaught, groc ery store, tribe, etc.). Fish intake rates

were calculated by multiplying the annual frequency of fish meals by the average serving size per fish meal.

The population sizes of the four tribes were highly unequal, ranging from 818 to 3,872 indiv iduals (CRIT FC, 1994). In order to ensure an adequate

sam ple size from each tribe, the study was designed to give nearly equal sample sizes for each tribe. Weighting factors were app l ied to the pooled data (in proportion to tribal p opulation size) so that the survey

results would be representative of the overall population of the four tribes for adults on ly. Because

the sample size for children was co nsidered small, only

an unweighted analysis was performed for this population. Based on a desired sample size of approximately 500 and an expected response rate of 70
percent, 744 individuals were selected at random from

lists of eligible patients; the numbers from each trib e were approximately equal.

Intake rates were calculated f or children for

which both the number of fish meals per week and serving size information were available. A total of 49

percen t of respondents of the total survey population reported that they caught fish from the Columbia River

basin and its tributaries for personal use or for tribal

ceremonies and distributions to other tribe members

and 88 percent reported that they obta ined fish from

either self-harvesting, family or friends, at tribal

ceremonies or from tribal d istributions. Of all fish consumed, 41 percent came from self or family harvesting, 11 percent from the harvest of friends, 35
percent from tribal ceremonies or distribution, 9 percent
from stores and 4 percent from other sources (CRITFC, 1994). Of the 204 children, the total number of respondents used in the analysis varied from 167 to 202, de pending on the topic (amount and species consumed, fish meals consumed /week, age

c onsumption began, serving size, consumption of fish parts) of the analysis. The unweighted mean for the age

when children begin eating fish was 13.1 months of age (N = 167). The unweighted mean number of fish meals

consumed pe r week by children was 1.2 meals per

week (N = 195) and the unweighted mean serving size

of fish for children aged five years old and less was 95

grams (i.e., 3.36 ounces ) (N = 201). The unweighted
percent of fis h consumed by children by species was

82.7 percent for salmon, followed by 46.5 percent (N = 202) for trout. The analysis of seasonal intake showed

that May and June tended to be high-consumption

months and Decemb er and January low consumption

months. Table 10-20 presents the fish intake

distribution for children under 5 years of age (N = 194).

The mean intake rate was 19.6 g/day (N = 194) and the

95 th percentile was approximately 70 g/day. These

mean intake rates include both consumers and nonconsumers. These values are based on survey questions

i nvolving estimated behavior throughout the ye ar, which survey particip ants answered in terms of meals

per week or per month and typica l serving size per

meal. Table 10-21 presents consumption rates for

children who were reported to consume particular species of fish.

The authors noted that some non-response bias

may have occurred in the survey since respondents

were more likely to be female and live near the

reservation than non-respondents. In addition, they hypothesized t h at non-consumers may have been more likely to be non-respondents than fish consumers since

non-consumers may have tho ught their contribution to the survey would be meaningless; if such were the case, this study would overestimate the mean intake rate. It

was also noted that the timing of the survey, which was

conducted during low fish consumption months, may have led to underestimation of actual fish consumption;

the authors conjecture d that an individual may have reported higher annual consumption if interviewed during a relatively high consumption month a nd lower

annual consumptio n if interviewed during a relatively

low consumption month. Finally, with respect to

childr en ’ s intake, it was observed that some of the

res pondents provided the same information for their

child ren as for themselves; thereby, the reliability of

some of these data is questioned (CRITFC, 1994). The

combination of four different tribes ’ survey responses



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Chapter 10 - Intake of Fish and Shellfish

into a single pooled data set is somewhat problematic.

The data presented in Table 10-20 are unweighted and

ther efore contain a bias toward the smaller tribes, who were oversampled compared to the larger tribes.

The limitations of this study, particularly with regard to the estimates of children ’ s consumption, result

i n a high degree of uncertainty in the estimated rates of consumption. However, it is one of a relative few

studies aimed at the fish consumption patte rns of

Native Americans. It should be noted that the selection

process for chil dren may be biased because the 204

children included in the study were not selected independently, but were identified t hrough a parent ’ s patient r egistration file. This indicates that children

from larger households would be less likely to be

chosen to participate in the study than would be the

case if the children themselves, rather than the parents, were randomly selected.

10.6.1.2 Toy et al., 1996

- A Fish Consu mption Survey of the Tulalip and Squaxin Island Tribes of the Puget Sound Region Toy et al. (199 6) conducted a study to

determine fish and shellfish con sumption rates of the Tulalip and Squaxin Island tribes living in the Puget Sound region. These two Indian tribes were selected on

the basis of judgment that they would be represent ative

of the expe cted range of fishing and fish consumption

activities of the fourteen tribes in the region.

Commercial fishing is a major source of income for

members of bo th tribes; some members the Squaxin

Island tribe also participate in commercial sh e llfishing.

Both tribes part i cipate in subsistence fishing and shellfishing. Fish consumption patterns for the tw o tribes

were estimat ed using a survey in which sample sizes

were calculated separately for each tribe. This allowed separate analyses to be conducted for each tribe. The

ap p ropriate sample size was calculated based on the enrolled population of each tribe and a desired confidence interval of ±20 percent from the mean, with

an additional 25 percent added to the total to allow for

non-response or unusable data. The target population,

derived from lists of enrolled tri bal members provided by the tribes, consisted of enrolled tribal members aged 18 years and older and childre n aged five years and younger living in the same household as an enrolled member. Only members living on or within 50 miles of

the re servation were considered for the survey. Each eligible enrolled tribal member was assigned a number,

and computer-generated ran dom numbers were used to identify the surve y participants. Children were not sampled direct ly, but through adult members of their household; if one adult had more than one eligible child

in his or her household, one of the children was selected

at r andom. This indirect sampling method was

necessitat ed by the available tribal records, but may

have introduced sampling bias to the process of selecting children for the study. A total of 190 adult

tribal members (ages 18 yea rs old and older) and 69

children betw een ages birth and 5 years old (i.e., 0 to

<6 years) were surveyed about their c o nsumption of 52

fish species in six cate gories: anadromous, pelagic, bottom, shellfish, canned tuna, and miscellaneous. Respondents described their consumption

behavior for the past year in terms of freq uency of fish

meals eaten per week or per month, including seasonal

v ariations in consumption rates. Portion sizes (in ounces) were estimated with the aid of model portions provided by the questioner. Data were also collected

on fish parts consumed, preparation methods , patterns
of acquisition for all fish and shellfi sh consumption,

and children ’ s consumption rates. Interviews were

conducted between February and May 1994. The

response rate for adul ts was 77 percent for the Squaxin Island tribe and 76 percent for the Tulalip tribes. The mean and median consumption rates for

childr en 5 years and younger for both tribes combined

were 0.5 3 and 0.17 g/kg-day, respectively (Table 10­ 22) . Squaxin Island children tended to consume more

fish than Tulalip children (mean 0.83 g/kg-day vs. 0.24 g/kg-da y). The data were insufficient to allow reanalysis to fit the data to the s tandard U.S. EPA age categories used elsewhere in this handbook.

One limitation associated with this study is

that a lthough data from the Tulalip and Squaxin Island

tribes may be representative of consumption rates of

children in these specific tribes, fish consumption rates, habits, and patte rns can vary among tribes and other sub-populations; as a result, the c onsumption rates of

these two tribes may not be useful as a surr ogate for consumption rates of other Native American tribes. Furthermore, there were differences in consump tion
patterns between the two tribes included in this study; the study provided data for each tribe and for the pooled

data from both tribes, but the latter m ay not be a

statisticall y valid measure for tribes in the region.

There might also be a possible bias due to the time the

survey was conducted; many species in the survey are

seasonal. For example, because of the timing of the

survey, respo ndents may have overestimated annual consumption.



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Chapter 10 - Intake of Fish and Shellfish

10.6.1.3 Duncan, 2000

- Fish Consumption Survey of

the Squamish Indian Trib e of the Port

Madison Indian Reser vation, Puget Sound Region

The Squamish Tribal Council conducted a study of the Squamish triba l members living on and

near the Port Ma dison Indian Reservation in the Puget

Sound region (Duncan, 2 000). The study was funded by the Agency for Tox ic Substances and Disease Registry (ATSDR) t hrough a grant to the Washington

State Department of Health. The purpose of the study

was to determine seafood consumption rates , patterns,
and habits of the members of the Squ amish Tribe. The

second objective was to identify c u ltural practices and attributes that affect consumption rates, patterns and habits of members of the Squamish Tribe. A systematic random sample of adults, defined

as individuals age 16 years and older, w ere selected

from a sorted Tribal enrollm ent roster. The study had

a pa rticipation rate of 64.8 percent, which was

calcula ted on the basis of 92 respondents out of a total

of 142 potentially eligible adu lts on the list of those

select e d into the sample. Consumption data for

children under six years of age were gathered through

adult resp ondents who had children in this age group living in the househol d at the time of the survey. Data were collected for 31 children under six years old.

A survey questionnai re was administered by personal interview. The survey included four parts: (1)

24-hour dietary recall; (2) identification, portions, frequency of consumption, preparation, harvest location

of fish; (3) shellfish consum ption, preparation, harvest location; and (4) cha nges in consumption over time,

cultural infor mation, physical information, and

socioeconom i c information. A display booklet was

used to assi st respondents in providing consumption

data and identifying harvest locations of seafood

consu med. Physical models of finfish and shellfish

were c o nstructed to assist respondents in determining

t ypical food portions. Finfish and shellfish were grouped into categories based on similarities in life history as well as practices of Tribal members who fish

for subsistence, ceremonial, and comme r cial purposes. Inte r viewers collected data for 31 children

under six years of age. Table 10-23 provides the consumption rates for children in units of g/kg-day for

all respondents. Table 10-24 provides consumption

rates for consumers only. Because all of the children

involv ed in the study consumed some form of fish, the consumption distribution of all fish is the same in both

ta bles. The mean, median, and 95 t h percentile consumption rates of all fish were 1

. 5 g/kg-day, 0.72 g/kg-day, and 7.3 g/kg-day, respectively. These values

are significantly greater than those present ed for the Tulalip and Squaxin Is land tribes (Toy et al., 1996; see

Section 10.6.1.2). This disparity illustrates the high degree of variability fou nd between tribes even within

a sma ll geographic region (Puget Sound) and indicates that exposure and risk assessors should exercise care

when imputing fish consumption rates to a population of interest using data from tribal studies.

A limitation of this study is that the sample

size for children was fairly sma ll (31 children). An important attribute of this survey is that it provided consumption rates by individual type of fish and

shellfish.

10.6.1.4 P olissar et al., 2006

- A Fish Consumpti o n Survey of the Tulalip and Squaxin Island Tribes of the Puget Soun d Region–Consumption Rates fo r Fish-consumers Only

Usi ng fish consumption data from the Toy et

al. (1996) survey of the Tulalip and Squaxin Island

tribes of Puget Sound, Po lissar et al. (2006) calculated consumption rates for various fish species groups, considering only the consumers of fish within each

group. Weight-adjusted consumption rates were

c alculated by tribe, age, gender, and species groups. Species groups (anadromous, bottom, pelagic, and shellfish) were defined by life history and distribution

in the water column. Data were available for 69 children, birth to <6 years of age; 18 of these children

had no reported fish consumption and were excluded

from the analysis. Thus, estimated fish consumption

rates are based on data for 51 children; 15 from the Tulalip tribe and 36 from the Squaxin Isla nd tribe.

Both median and mean fish consum p tion rates for

children within each tribe were calculate d in terms of grams per kilogram of body weight per day (g/kg-day).

Anadromous fish and shellfish were the group s of fish

most frequently consumed by both tribes and genders. The consumption rates f or groups of fish differed

between the tribes. The distribution of consumption

rates was skewed toward large values. The estimated mean consumption rate of all fish was 0.45 g/kg-day for the Tulalip children and 2.9 g/kg-day for the Squaxin

Island children (Table 10-25). Table 10-26 presents consumption rates for children by species and gender.

Be cause this study used the data originally generated by Toy et al. (1996) the advantages and

limitations associated wit h the Toy et al. (1996) study,



### Child-Specific Exposure Factors Handbook Page September 2008 10-13

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

as described in Section 10.6.1.2, also apply to this study. However, an advantage of this study is that the consumption rates are based only on individuals who consumed fish within the selected categories.

10.7 SERVING SIZE STUDY

10.7.1 Smiciklas-Wright et al., 2002

- Foods Commonly Eaten in the United States: Quantities Consumed per Eating Occasion and in a Day,1994-1996 Using data gathered in the 1994-96 USDA CSFII, Smiciklas-Wright et al. (2002) calculated distributions for the quantities of canned tuna and other finfish consumed per eating occasion by members of the U.S. population (i.e., serving sizes), over a 2-day period. The estimates of serving size are based on data obtained from 14,262 respondents, ages 2 years and above, who provided 2 days of dietary intake information. A total of 4,939 of these respondents were children, ages 2 to 19 years of age. Only dietary intake data from users of the specified food were used in the analysis (i.e., consumers only data). Table 10-27 and Table 10-28 present serving size data for canned tuna and other finfish, respectively. These data are presented on an as-consumed basis (grams), and represent the quantity of fish consumed per eating occasion. These estimates may be useful for assessing acute exposures to contaminants in specific foods, or other assessments where the amount consumed per eating occasion is necessary. The advantages of using these data are that they were derived from the USDA CSFII and are representative of the U.S. population. The analysis conducted by Smiciklas-Wright et al. (2002) accounted for individual foods consumed as ingredients of mixed foods. Mixed foods were disaggregated via recipe files so that the individual ingredients could be grouped together with similar foods that were reported separately. Thus, weights of foods consumed as ingredients were combined with weights of foods reported separately to provide a more thorough representation of consumption. However, it should be noted that since the recipes for the mixed foods consumed by respondents were not provided by the respondents, standard recipes were used. As a result, 10.8 OTHER FACTORS TO CONSIDER FOR
FISH CONSUMPTION Other factors to consider when using the available survey data include location, climate, season, and ethnicity of the angler or consumer population, as well as the parts of fish consumed and the methods of preparation. Some contaminants (for example, persistent, bioaccumulative, aand toxic contaminants such as dioxins and polychlorinated biphenyls) have the affinity to accumulate more in certain tissues, such as the fatty tissue, as well as in certain internal organs. The effects of cooking methods for various food products on the levels of dioxin-like compounds have been addressed by evaluating a number of studies in

U.S. EPA (2003). These studies showed various results for contamination losses based on the methodology of the study and the method of food preparation. The reader is referred to U.S. EPA (2003) for a detailed review of these studies. Additionally, users of the data presented in this chapter should ensure that consistent

units are used for intake rate and concentration of contaminants in fish. The following sections provide information on converting between wet weight and dry weight, and between wet weight and lipid weight.

10.8.1 Conversion Between Wet and Dry Weight The intake data presented in this chapter are reported in units of wet weight (i.e., as-consumed or uncooked weight of fish consumed per day or per eating occasion). However, data on the concentration of contaminants in fish may be reported in units of either wet or dry weight (e.g., mg contaminant per gram-dry-weight of fish). It is essential that exposure assessors be aware of this difference so that they may ensure consistency between the units used for intake rates and those used for concentration data (i.e., if the contaminant concentration is measured in dry weight of fish, then the dry weight units should be used for fish intake values). If necessary, wet weight (e.g., as-consumed) intake rates may be converted to dry weight intake rates using the moisture content percentages presented in Table 10-29 and the following equation:



### 100

− W ⎡ ⎢ ⎣ ⎤ ⎥ ⎦ 

### IR dw IR ww (Eqn. 10-1) 

### = the estimates of the quantity of some food types are 

### 100 based on assumptions about the types and quantities of ingredients consumed as part of mixed foods.



### Page Child-Specific Exposure Factors Handbook

10-14 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

where:

IR dw 

### = dry weight intake

rate;
IR

ww 

### = wet weight intake rate; and W = percent water

content.

Alternately, dry weight residue levels in fish may be converted to wet weight residue levels for use with wet weight (e.g., as-consumed) intake rates, as follows:



### ⎡ 

### 100

− W ⎤ 

### C ww = C dw ⎣ ⎢ 

### 100 ⎦ ⎥ (Eqn. 10-2)

where:

C

ww 

### = wet weight intake

rate;
C dw 

### = dry weight intake rate; and

W = percent water

content.

The moisture content data presented in Table 10-29 are for selected fish taken from USDA, 2007.

10.8.2 Conversion Between Wet Weight and Lipid Weight Intake Rates In some cases, the residue levels of contaminants in fish are reported as the concentration of contaminant per gram of fat. This may be particularly true for lipophilic compounds. When using these residue levels, the assessor should ensure consistency in the exposure assessment calculations by using consumption rates that are based on the amount of fat consumed for the fish product of interest. If necessary, wet weight (e.g., as-consumed) intake rates may be converted to lipid weight intake rates using the fat content percentages presented in Table 10-29 and the following equation:



### ⎡ 

### L ⎤ 

### IR lw = IR ww ⎢ (Eqn. 10-3) 

### ⎣ 

### 100 ⎦ ⎥

where:

IR lw 

### = lipid weight intake

rate;
IR

ww 

### = wet weight intake rate; and L = percent lipid (fat)

content.

Alternately, wet weight residue levels in fish may be estimated by multiplying the levels based on fat by the fraction of fat per product as follows:



### C ww = C lw ⎡ ⎣ ⎢ ⎤ ⎦ ⎥ 

### L

100 (Eqn. 10-4)

where:

C

ww 

### = wet weight intake

rate;
C lw 

### = lipid weight intake rate; and

L = percent lipid (fat)

content.

The resulting residue levels may then be used in conjunction with wet weight (e.g., as-consumed) consumption rates. The total fat content data presented in Table 10-29 are for selected fish taken from USDA,

2007.

10.9 REFERENCES FOR CHAPTER 10

Alcoa (1998) Draft report for the finfish/shellfish consumption study Alcoa (Point Comfort)/Lavaca Bay Superfund Site, Volume B7b: Bay System Investigation Phase 2. Point Comfort, TX: Aluminum Company of

America. Balcom, N.; Capacchione, C.; Hirsch D.W. (1999) Quantification of seafood consumption rates for Connecticut. Report prepared for the Connecticut Department of Environmental Protection, Office of Long Island Sound Programs, Hartford, CT. Contract No. CWF

332-R. Benson, S.; Crocker, C.; Er javec, J.; Jensen, R.R.; Nyberg, C.M.; Wixo, C.Y.; Zola, J.M. (2001) Fish consumption survey: Minnesota and North Dakota. Report prepared for the U.S. Department of Energy by the Energy and Environmental Research Center, University of North Dakota, Grand Forks, ND. DOE Cooperative Agreement No, DE-FC26

- 98FT40321.



### Child-Specific Exposure Factors Handbook Page September 2008 10-15

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Columbia River Inter-Tribal Fish Commission (C RITFC) (1994) A fish consumption survey

of the U matilla, Nez Perce, Yakama and Warm Springs tri b es of the Columbia River

Basin. Technical Report 94-3. Portland, OR:

CRITFC. Degner, R.L.; Adams, C.M.; Moss, S.D.; Mack, S.K. (1994) Per capita fish and shellfish consumption in Florida. Gainesville, FL: University of Florida.
Duncan, M. (2000) Fish consumption survey of the Squamish Indian Tribe of the Port Madison

Indian Reservation, Puget Sound Region.

S quamish, WA: The Squamish Tribe, Port

Madison Indian Reservation.

KCA Research Divi sion (1994) Fish consumption of

Delaware recreational fishermen and their

hou seholds. Prepared for the State of Delaware, Department of Natural Res ources and Environmental Control in support of the Delaware Estuary Program, Dover, DE.

LSRO (1995) Life Sciences R esearch Office,

Federation of American Societies for

Ex perimental Biology Prepared for the Interagency Board for Nutr ition Monitoring

and Related Research. T hird Report on Nutrition Monitoring in the United Sta t es: Vo lume 1. U.S. Government Printing Office, Washington, DC. Moya, J. (2004) Overview of fish consumption rates in

the United States. Hum Eco Risk Assess

10:1195-1211. Moya, J.; Itk i n, C.; Selevan, S.G.; Rogers, J.W.; Clickner, R.P. (20 0 8) Estimates of fish consumption rates fo r consumers of bought and self-caught fish in Connecticut, Florida, Minnesota, and North Dakota. Sci Tot Environ (in press).
Pao, E.M.; Fleming, K.H.; Guenther, P.M.; Mickle, S.J. (1982) Foods commonly eaten by individuals:

amount per day and per eating occasion. U.S. Department of Agriculture. Home Economic Report No. 44. Polissar, N.L.; Neradilek, B.; Liao, S.; Toy, K.A.; Mittelstaedt, G.D. (2006) A fish consumption

survey of the Tulalip and Squaxin Island tribes

of the Puget So und region

– Consumption

rates for fish-consumers only

. Report prepared by Mountain-Whisper-Light Statistical Consulting, Seattle, WA. Smiciklas-Wright, H.; Mitchell, D.C.; Mickle, S.J.; Cook, A.J.; Goldman, J .D. (2002) Foods commonly eaten in the United State s 1994­

1996: Qu antities consumed per eating

occasion and in a day. U.S. Department of Agriculture. Agricultural Research Center NFS Report No. 96-5, 264 pp. Toy, K.A.; Polissar, N.L.; L iao, S.; Mittelstaedt, G.D. (1996) A fish consumption survey of the Tulalip and Squaxin Island Tribes of the Puget

Sound Reg ion. Marysville, WA: Tulalip Tribes, Department of Environment. USDA, Agric u ltural Research Service. (2007) USDA

National Nutrient Database for Standard Reference, Release 20. Nutrient Data

Labor atory Home Page: http://www.ars.usda.gov/ba/bhnrc/ndl

. U .S. EPA (1996) Descriptive statistics tables from a d etailed analysis of the National Human Activity P attern Survey (NHAPS) data. Washington, DC: O ffice of Research and Development. EPA/600/R-96/148.

U.S. EPA (1998) Guidance for conducting fish and

wildlife consumption surveys. Washington,

DC: Office of Water. EPA-823-B-98-007.

U.S. EPA (2002) Estimated Per Capita Fi s h Consumption in the United States. Washington, DC: Office of Water.

EPA/821/C-02/003.

U.S. EPA (2003) Exposure and human health

reassessment of 2,3,7,8-tetrachlorodibenzo-pdioxin (TCDD) and related compounds, Part

1 : Estimating exposure to dioxin-like compounds, Volume 2: Properties, environmental levels, and background

exposures. (National Academy of Sciences

Review Draft). Washington, DC: Office of

Research and Development, National Center

for Environmental Assessment

. www.epa.gov/NCEA/dioxin.

U.S. EPA (2005) Guidance on selecting age groups for monitoring and assessing childhood exposures

to environmental contaminants. Washington,

DC: U.S. Environmental P rotection Agency.

EPA/630/P-03/003F. West, P.C.; Fly, M.J.; Marans, R.; Larkin, F. (1989) Michigan spo rt anglers fish consumption

survey. A report to the Michigan Toxic

Substance Control Commission. Michigan Department of Managemen t and Budget

Contract No. 87-20141.



### Page Child-Specific Exposure Factors Handbook

10-16 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-3. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, As-Consumed

Age (years) Sample Size Mean (90% CI) 90 th % (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a
Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,391 1,670 1,005

363 1.5 (1.2-1.8) 2.1 (1.4-2.9) 3.0 (2.2-3.8) 3.4 (1.6-5.3) 0.1 (0.0-1.0) 0.0 (0.0-0.6) 1.4 (0.5-5.5) 0.0 (0.0-1.5) 5.1 (4.1-6.2) 5.9 (3.2-13) 18 (15-21) 13* (5.2-29) 39 (33-44) 61* (51-86) 70* (56-75) 81* (42-117)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,391 1,670 1,005

363 3.7 (3.2-4.3) 4.2 (3.5-4.9) 5.5 (4.2-6.7) 4.7 (2.9-6.4) 11 (10-13) 13 (9.7-17) 14 (9.8-21) 0.0 (0.0-6.9) 28 (24-29) 29 (28-34) 39 (31-50) 24* (7.8-71) 60 (52-71) 79* (49-84) 102* (84-114) 108* (68-119)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,391 1,670 1,005

363 5.2 (4.6-5.8) 6.3 (5.3-7.3) 8.5 (6.9-10) 8.1 (5.4-11) 19 (15-21) 24 (21-27) 28 (25-31) 19 (7.0-41) 35 (31-40) 40 (34-51) 60 (53-74) 74* (29-90) 72 (67-81) 108* (92-131) 122* (107-132) 142* (108-200)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.

Table 10-4. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, As-Consumed

Age (years) Sample Size Mean (90% CI) 90 th % (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a

Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,112 1,553

975

360 83 (67-99) 59 (39-79) 53 (42-64) 49 (23-76) 0 (0-55) 0 (0-5) 27 (0-78) 0 (0-33) 284 (240-353) 178 (88-402) 312 (253-390) 213* (106-390) 2,317 (1,736-2,463) 1,662* (1,433-2,335) 1,237* (950-1,521) 1,186* (600-2,096)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,112 1,553

975

360 209 (182-237) 150 (123-177) 109 (84-133) 75 (46-103) 614 (525-696) 416 (326-546) 338 (179-413)

0 (0-124) 1,537 (1,340-1,670) 1,055 (969-1,275) 821 (629-1,034) 381* (132-951) 3,447 (3,274-3,716) 2,800* (2,021-3,298) 1,902* (1,537-2,366) 1,785* (1,226-2,342)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,112 1,553

975

360 292 (259-326) 209 (176-242) 162 (133-191) 124 (83-165) 1,057 (931-1,232) 780 (644-842) 570 (476-664) 261 (110-600) 1,988 (1,813-2,147) 1,357 (1,173-1,452) 1,051 (991-1,313) 1,029* (390-1,239) 4,089 (3,733-4,508) 3,350* (2,725-4,408) 2,305* (1,908-2,767) 2,359* (2,096-2,676)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.



### Child-Specific Exposure Factors Handbook Page September 2008 10-17

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-5. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, As-Consumed

Age (years) Sample Size Mean (90% CI) 90 th % (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a

Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 442

147

107

28 27 (23-31) 43 (32-55) 49 (39-59) 76* (59-93) 73 (65-79) 122* (83-187) 127* (104-148) 159* (151-171) 96 (87-110) 187* (115-260) 150* (135-193) 168* (159-484) 159* (136-260) 260* (172-261) 307* (193-384) 372* (171-484)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 682

217

122

37 45 (41-48) 59 (53-66) 72 (60-85) 97* (65-129) 91 (84-105) 129 (112-158) 165* (158-203) 219* (180-238) 119 (102-143) 159* (135-219) 204* (169-227) 238* (180-293) 228* (169-293) 243* (219-292) 246* (214-269) 365* (230-428)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 834

270

172

52 50 (46-54) 71 (64-77) 80 (70-89) 104* (75-133) 103 (94.5-125) 155 (130-183) 167* (154-193) 201* (167-243) 134 (121-152) 218* (198-261) 209* (206-257) 242* (216-484) 260* (195-293) 281* (260-292) 285* (264-327) 451* (293-484)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.

Table 10-6. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, As-Consumed

Age (years) Sample Size Mean (90% CI) 90 th % (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a
Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 416

132

101

28 1,532 (1,320-1,743) 1,296 (1,004-1,588)

869 (725-1,013) 1,063* (781-1,346) 4,307 (3,472-4,624) 3,453* (2,626-4,671) 2,030* (1,628-2,104) 2,293* (2,096-2,577) 5,257 (4,926-5,746) 4,675* (3,459-8,816) 3,162* (2,104-3,601) 2,505* (2,096-6,466) 10,644* (9,083-12,735) 8,314* (4,684-9,172) 4,665* (3,597-7,361) 5,067* (2,295-6,466)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 640

203

120

37 2,492 (2,275-2,709) 2,120 (1,880-2,361) 1,427 (1,203-1,651) 1,534* (1,063-2,004) 5,303 (4,873-5,930) 4,950 (4,043-5,384) 2,971* (2,858-3,741) 3,602* (2,974-4,685) 6,762 (6,097-7,168) 5,817* (5,333-6,596) 4,278* (3,026-4,766) 4,475* (3,068-4,685) 11,457* (7,432-14,391) 8,092* (6,146-9,184) 5,214* (4,647-5,646) 4,982* (3,467-5,238)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 779

250

164

52 2,828 (2,608-3,049) 2,375 (2,199-2,551) 1,533 (1,384-1,682) 1,578* (1,187-1,969) 5,734 (5,268-6,706) 5,135 (4,684-5,816) 3,207* (2,945-3,485) 3,468* (2,676-4,752) 7,422 (6,907-8,393) 6,561* (5,404-8,816) 3,925* (3,485-4,764) 4,504* (3,709-6,466) 13,829* (11,349-14,391) 9,179* (8,130-10,485) 5,624* (4,764-6,929) 5,738* (4,752-6,466)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.



### Page Child-Specific Exposure Factors Handbook

10-18 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-7. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, Uncooked Fish Weight

Age (years) Sample Size Mean (90% CI) 90 th (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a

Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,391 1,670 1,005

363 2.2 (1.8-2.6) 3.0 (1.9-4.1) 4.3 (3.2-5.4) 4.6 (2.2-6.9) 0.1 (0.0-1.5) 0.0 (0.0-0.5) 2.3 (0.1-7.7) 0.0 (0.0-1.9) 12 (10-14) 13 (4.8-20) 26 (21-29) 19* (13-37) 52 (46-62) 78* (64-111) 95* (83-110) 109* (58-155)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,391 1,670 1,005

363 5.5 (4.8-6.2) 5.6 (4.6-6.5) 7.6 (5.9-9.4) 6.1 (3.7-8.4) 20 (17-23) 19 (14-24) 25 (16-35) 0.0 (0.0-9.3) 39 (38-41) 38 (38-42) 56 (45-67) 29* (12-91) 82 (73-95) 100* (63-111) 132* (110-149) 136* (92.0-177)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,391 1,670 1,005

363 7.7 (6.9-8.6) 8.5 (7.1-10) 12 (9.7-14) 11 (7.0-14) 33 (28-34) 33 (27-38) 43 (37-51) 29 (9.4-49) 51 (46-57) 56 (50-70) 87 (70-103) 84* (42-114) 101 (89.1-111) 144* (117-183) 171* (148-176.8) 193* (121-266)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.

Table 10-8. Per Capita Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, Uncooked Fish Weight

Age (years) Sample Size Mean (90% CI) 90 th % (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a

Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,112 1,553

975

360 124 (103-146) 84 (55-112) 77 (60-94) 65 (30-100) 0 (0-83) 0 (0-1) 20 (0-116)

0 (0-23) 712 (599-784) 354 (116-685) 477 (411-618) 285* (167-491) 3,091 (2,495-3,475) 2,322* (1,856-2,994) 1,610* (1,358-2,203) 1,542* (760-2,767)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,112 1,553

975

360 309 (270-348) 198 (161-235) 153 (117-189) 98 (58-137) 1,108 (984-1,332) 600 (474-733) 481 (361-609)

0 (0-177) 2,314 (2,096-2,481) 1,481 (1,310-1,549) 1,251 (808-1,390) 460* (197-1,079) 4,608 (4,301-5,354) 3,684* (2,458-4,353) 2,381* (2,162-3,207) 2,148* (1,648-3,901)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 4,112 1,553

975

360 433 (385-482) 282 (235-328) 231 (186-275) 163 (108-219) 1,841 (1,555-1,957) 1,045 (745-1,219)

824 (657-952) 406 (145-756) 2,964 (2,790-3,194) 1,854 (1,638-2,175) 1,531 (1,362-1,850) 1,272* (558-1,500) 5,604 (5,231-6,135) 4,371* (3,433-5,814) 3,651* (2,745-3,795) 3,544* (2,767-3,946)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.



### Child-Specific Exposure Factors Handbook Page September 2008 10-19

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-9. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - g/day, Uncooked Fish Weight

Age (years) Sample Size Mean (90% CI) 90 th % (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a

Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 442

147

107

28 40 (35-46) 61 (44-79) 71 (58-83) 100* (80-121) 95 (86-102) 157* (117-250) 173* (166-196) 203* (197-248) 129 (120-142) 248* (150-381) 199* (173-296) 242* (206-643) 205* (200-381) 386* (221-401) 392* (296-514) 501* (241-643)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 682

217

122

37 66 (60-71) 78 (67-89) 102 (86-118) 126* (80-171) 125 (114-150) 150 (129-201) 220* (205-265) 281* (241-354) 165 (139-190) 202* (165-317) 262. (227-307) 353* (241-390) 316* (227-390) 350* (223-392) 320* (277-379) 530* (291-650)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 834

270

172

52 74 (69-79) 95 (85-106) 113.(99-127) 136* (97-174) 149 (136-165) 200 (177-235) 227* (205-296) 242* (206-358) 184 (172-223) 313* (254-381) 308* (271-348) 357* (266-643) 363* (310-391) 387* (381-401) 380* (353-409) 645* (390-650)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.

Table 10-10. Consumer Only Distribution of Fish (Finfish and Shellfish) Intake General Population Children Ages 3 to 17 Years - mg/kg-day, Uncooked Fish Weight

Age (years) Sample

Size Mean (90% CI) 90 th % (90% BI) a 95 th % (90% BI) a 99 th % (90% BI) a

Freshwater/Estuarine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 416

132

101

28 2,292 (2,012-2,572) 1,830 (1,416-2,245) 1,273 (1,082-1,464) 1,401* (1,058-1,744) 5,852 (4,703-6,068) 4,688* (3,673-5,987) 2,777* (2,091-3,026) 2,971* (2,743-3,692) 7,160 (6,950-7,442) 6,207* (4,767-12,926) 4,419* (3,026-5,522) 3,279* (2,767-8,577) 15,600* (11,877-18,670) 12,365* (6,763-12,926) 5,717* (5,457-9,852) 6,819* (3,221-8,577)

Marine Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 640

203

120

37 3,689 (3,395-3,982) 2,787 (2,417-3,157) 2,020 (1,741-2,327) 2,007* (1,302-2,712) 7,253 (6,777-8,504) 5,910 (4,813-7,365) 4,224* (3,744-4,781) 4,468* (3,880-7,802) 9,270 (8,415-9,991) 8,001* (6,375-8,707) 5,195* (3,859-6,448) 6,537* (3,991-7,802) 16,100* (11,980-17,989) 10,754* (8,707-12,055) 6,839* (6,076-8,970) 7,886* (4,661-7,958)

All Fish Ages 3 to 5 Ages 6 to 10 Ages 11 to 15 Ages 16 to 17 779

250

164

52 4,198 (3,894-4,502) 3,188 (2,923-3,452) 2,199 (1,950-2,449) 2,066* (1,529-2,603) 8,061 (7,366-9,223) 6,544 (6,013-8,707) 4,387* (3,785-5,522) 3,902* (3,536-7,892) 10,444 (9,475-12,261) 8,654* (7,086-11,756) 6,234* (4,420-7,589) 6,594* (4,661-8,577) 17,874* (15,290-18,670) 12,785* (10,930-13,979)

8,345* (6,076-8,970) 8,210* (7,892-8,577)

a Percentile intervals were estimated using the percentile bootstrap method with 1,000 bootstrap replications.

* The sample size does not meet minimum reporting requirements as described in the “Third Report on Nutrition Monitoring in the United States” (LSRO, 1995).

CI = Confidence interval.

BI = Bootstrap interval.

Source: U.S. EPA, 2002.



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-11. Number of General Population Respondents Reporting Consumption of a

Specified Number of Servings of Seafood in 1 Month, and Source of Seafood Eaten

Number of Servings in a Month Source of Seafood Age Group N Mostly Mostly

(years) 0 1-2 3-5 6-10 11-19 20+ DK Purchased Caught DK

0 to <1 34 27 5 2 0 0 0 0 7 0 0

1 to <2 49 30 12 4 2 0 0 1 15 3 1

2 to <3 59 34 12 7 4 1 0 1 24 1 0

3 to <6 169 80 42 26 13 1 1 6 78 8 3

6 to <11 224 117 45 36 12 4 3 7 98 4 5

11 to <16 236 128 50 42 6 1 1 8 98 3 7

16 to <21 220 110 41 37 18 4 2 8 96 5 9

DK = Don ’ t know.

N = Sample size.

Source: U.S. EPA re-analysis of data in U.S. EPA, 1996.



### Child-Specific Exposure Factors Handbook Page September 2008 10-21

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and ShellfishTable 10-12. Fish Consumption Among General Population Children in Four States,Consumers Only, g/kg-day As-ConsumedPercentilesAge GroupN Mean CI10th25th50th75th90th95thMaximum(years)Connecticut1 to <614 0.61 0.42-0.81 0.16 0.26 0.55 0.83 1.4 1.6 1.66 to <1122 0.59 0.040-0.77 0.14 0.23 0.47 0.96 1.2 1.3 1.511 to <1618 0.32 0.17-0.46 0.07 0.14 0.19 0.38 0.52 0.84 1.3Florida1 to <6420 2.3 2.05-2.63 0.5 1.0 1.7 2.8 4.7 6.8 14.66 to <11375 1.1 0.98-1.22 0.28 0.52 0.81 1.4 2.2 3.0 9.411 to <16365 0.85 0.73-0.98 0.20 0.36 0.63 0.99 1.6 2.2 11.0Minnesota1 to <646 0.58 0.32-0.85 0.07 0.15 0.46 0.73 1.1 1.8 8.06 to <1142 0.38 0.21-0.54 0.05 0.07 0.25 0.47 1.0 1.4 5.311 to <1663 0.24 0.16-0.31 0.03 0.06 0.21 0.32 0.55 0.59 1.4North Dakota1 to <628 0.70 0.24-1.17 0.05 0.12 0.23 0.68 1.6 3.8 6.86 to <1141 0.56 0.31-0.81 0.11 0.21 0.30 0.66 1.2 1.5 4.311 to <1653 0.41 0.23-0.59 0.06 0.12 0.22 0.54 1.0 1.3 2.3N = Sample size.CI = Confidence interval.Source: Moya et al, 2008.



### Page Child-Specific Exposure Factors Handbook

10-22 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and ShellfishTable 10-13. Fish Consumption Among General Population in Four States According to Caught or Bought Status,g/kg-day As-ConsumedPercentilesCategoryN Mean CIMaximum10th25th50th75th90th95thConnecticutEats Caught Only1 0.01 -- - - - - - 0.01Eats Caught and Bought 70 0.49 0.36-0.61 0.10 0.17 0.34 0.75 1.1 1.3 2.2Eats Bought Only 291 0.48 0.40-0.57 0.06 0.16 0.32 0.61 1.1 1.4 7.0FloridaEats Caught Only 511 0.76 0.66-0.86 0.15 0.30 0.50 0.90 1.7 2.3 7.4Eats Caught and Bought 701 1.8 1.6-2.1 0.50 0.76 1.2 2.0 3.4 5.1 34Eats Bought Only 6545 0.85 0.81-0.89 0.18 0.30 0.54 0.98 1.8 2.5 24MinnesotaEats Caught Only38 0.16 0.05-0.26 0.02 0.03 0.08 0.25 0.37 0.51 0.57Eats Caught and Bought 555 0.40 0.27-0.52 0.08 0.11 0.23 0.49 0.70 1.3 9.2Eats Bought Only 200 0.23 0.18-0.28 0.02 0.05 0.14 0.26 0.56 0.91 8.0North DakotaEats Caught Only30 0.21 0.09-0.32 0.05 0.09 0.14 0.22 0.33 0.51 1.8Eats Caught and Bought 359 0.39 0.29-0.49 0.07 0.13 0.23 0.43 0.82 1.3 4.3Eats Bought Only 157 0.25 0.13-0.36 0.03 0.05 0.10 0.24 0.53 0.97 6.8N = Sample size.CI = Confidence interval.Source: Moya et al., 2008.



### Child-Specific Exposure Factors Handbook Page September 2008 10-23

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-14. Fish Consumption Among General Population and Anglers in Three States, g/kg-day As-Consumed

Percentiles Category N Mean 10 th 25 th 50 th 75 th 90 th 95 th 99 th

Connecticut

Anglers 244 0.66 0.10 0.20 0.40 0.80 1.6 2.1 3.5

General Population 362 0.48 0.07 0.16 0.32 0.63 1.1 1.4 2.4

Minnesota

Anglers 1,109 0.32 0.05 0.10 0.18 0.34 0.67 0.99 2.2

General Population 793 0.33 0.04 0.10 0.20 0.34 0.65 1.1 1.8

North Dakota

Anglers 808 0.34 0.05 0.10 0.20 0.39 0.81 1.2 2.0

General Population 546 0.34 0.05 0.09 0.19 0.35 0.74 1.2 2.2

N = Sample size.

Source: Moya et al., 2008.

Table 10-15. Recreational Fish Consumption in Delaware Consumers Only

Age Group N Mean consumption (g/day) a Standard Error (%)

0 to 9 years 10 to 19 years 73

102 6.0

11.4 13.4

16.8

a Converted from ounces/day; 1 ounce = 28.35 grams.

Source: KCA Research Division, 1994.



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-16. Consumption of Self-Caught Fish by Recreational Anglers Lavaca Bay, Texas, g/day

Age Group N Mean 95% Upper Confidence

Limit on Mean 90 th or 95 th Percentile of

Distribution a

Finfish

Small children (<6 years) 320 11.4 14.2 30.3

Youths (6 to 19 years) 749 15.6 17.8 45.4

Shellfish

Small children (<6 years) 320 0.4 0.6 2.0

Youths (6 to 19 years) 749 0.7 1.0 4.5

a The 90 th percentile values are presented for finfish. For shellfish, the 95 th percentile value is provided because less than 90 percent of the individuals consumed shellfish, resulting in a 90 th percentile of zero.

Source: Alcoa, 1998.

Table 10-17. Number of Meals and Portion Sizes of Self-Caught Fish Consumed by Recreational Anglers Lavaca Bay, Texas

Portion Size Number of Meals (grams) a
95% Upper 95% Upper Age Group Mean Confidence Mean Confidence Limit on

Limit on Mean Mean

Finfish

Small children (<6 years) 2.6 3.1 128 133

Youths (6 to 19 years) 2.4 2.7 187 196

Shellfish

Small children (<6 years) 0.3 0.5 57 68
Youths (6 to 19 years) 0.3 0.4 71 82

a Converted from ounces; 1 ounce = 28.35 grams.

Source: Alcoa, 1998.



### Child-Specific Exposure Factors Handbook Page September 2008 10-25

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-18. Mean Fish Intake Among Individuals Who Eat Fish and Reside in Households With Recreational Fish Consumption - Michigan

Intake
Meals/Week Age Group N g/day g/kg-day

All Fish Recreational Fish Total Fish Recreational Fish Total Fish Recreational Fish

1 to 5 years 121 0.46 0.22 11.4 5.6 0.74 0.37

6 to 10 years 151 0.49 0.28 13.6 7.9 0.48 0.28

11 to 20 years 349 0.41 0.23 12.3 7.3 0.22 0.12

N = Sample size.

Source: U.S. EPA analysis, using data from West et al., 1989.

Table 10-19. Consumption of Sports-caught and Purchased Fish by Minnesota and North Dakota Children, Ages 0 to 14 Years (g/day)

Percentile

N
50 th 75 th 90 th 95 th

Minnesota

Sports-caught 1.2 3.3 8.3 14.6 582 Purchased 3.6 8.7 19.2 30.9

North Dakota

Sports-caught 1.7 5.1 13.1 23.3 343 Purchased 4.7 11.6 26.3 42.8

Source: Benson et al., 2001.



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-20. Fish Consumption Rates among Native American Children (age 5 years and under) a

Grams/Day Unweighted Cumulative Percent

0.0

0.4

0.8

1.6

2.4

3.2

4.1

4.9

6.5

8.1

9.7

12.2

13.0

16.2

19.4

20.3

24.3

32.4

48.6

64.8

72.9

81.0

97.2

162.0 21.1

21.6

22.2

24.7

25.3

28.4

32.0

33.5

35.6

47.4

48.5

51.0

51.5

72.7

73.2

74.2

76.3

87.1

91.2

94.3

96.4

97.4

98.5

100

a

Note:

Source: Sample size = 194; unweighted mean = 19.6 grams/day; unweighted standard error = 1.94. Data are compiled from the Umatilla, Nez Perce, Yakama, and Warm Springs tribes of the Columbia River Basin.

CRITFC, 1994.



### Child-Specific Exposure Factors Handbook Page September 2008 10-27

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-21. Number of Fish Meal Eaten per Month and Fish Intake Among Native American Children who Consume Particular Species

Species N Unweighted Mean Fish Meals/Month

Unweighted SE Unweighted Mean Intake (g/day)

Unweighted SE

Salmon Lamprey

Trout

Smelt

Whitefish Sturgeon Walleye Squawfish

Sucker

Shad 164

37

89

39

21

21

5

2

4

3 2.3

0.89

0.96

0.40

3.5

0.43

0.22

0.00

0.35

0.10 0.16

0.27

0.12

0.09

2.83

0.12

0.20

- 0.22

0.06 19

8.1

8.8

3.8

21

4.0

2.0

0.0

2.6

1.1 1.5

2.8

1.4

0.99

16

1.3

1.5

- 1.7

0.57

SE = Standard error.

Source: CRITFC, 1994.



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-22. Consumption Rates for Native American Children, Age Birth to Five Years (g/kg-day)

Fish Category Mean (SE) 95% CI 50 th percentile 90 th percentile

Tulalip Tribes (N = 21)

Shellfish 0.13 (0.056) (0.014, 0.24) 0.0 0.60

Total finfish 0.11 (0.030) (0.056, 0.17) 0.060 0.29

Total, all fish 0.24 (0.077) (0.088, 0.39) 0.078 0.74

Squaxin Island Tribe (N = 48)

Shellfish 0.23 (0.053) (0.13, 0.37) 0.045 0.57

Total finfish 0.25 (0.063) (0.13, 0.37) 0.061 0.83

Total, all fish 0.83 (0.14) (0.55, 1.1) 0.51 2.1

Both Tribes Combined (weighted)

Shellfish 0.18 (0.039) (0.10, 0.25) 0.012 0.57

Total finfish 0.18 (0.035) (0.10, 0.25) 0.064 0.32

Total, all fish 0.53 (0.081) (0.37, 0.69) 0.17 1.4

SE = Standard error.

CI = Confidence interval.

N = Sample size.

Source: Toy et al., 1996.



### Child-Specific Exposure Factors Handbook Page September 2008 10-29

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and ShellfishTable 10-23. Consumption Rates for Native American Children (g/kg-day), All Children (including non-consumers): Individual Finfish and Shellfish and Fish GroupsGroup SpeciesN Mean SE 95% LCL 95% UCL P5 Median P75 P90 P95 MaximumGroup EManila/Littleneck clamsHorse clamsButter clamsGeoduckCocklesOystersMusselsMoon snailsShrimpDungeness crabRed rock crabScallopsSquidSea urchinSea cucumber3131313131313131313131313131310.0950.0220.0210.110.120.0190.0010.0000.0930.300.0070.0110.0020.00.00.0510.0130.0140.0410.0790.0120.001-0.0380.130.0030.0060.002--0.00.00.00.0330.00.00.0-0.0190.0530.0010.00.0--0.200.0480.0480.190.270.0430.002-0.170.550.0140.0220.005--0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0310.00.00.0270.00.00.00.00.0040.0470.00.00.00.00.00.0630.0060.00.120.0540.0560.00.00.0590.170.00.0050.00.00.00.180.0480.0410.250.240.0580.00.00.391.30.0460.0310.00.00.00.760.270.250.841.20.210.0110.00.712.70.0640.0890.00.00.01.60.350.421.12.40.360.0260.00.982.80.0820.170.410.00.0Group AaGroup BbGroup CcGroup DdGroup Fe31313131310.270.0040.130.030.240.120.0020.0400.0110.0750.0430.00.0520.0080.0940.500.0080.210.0530.390.00.00.00.00.00.0630.00.0360.0100.0920.220.00.210.0370.250.530.0150.340.0810.682.10.0380.840.191.63.60.0691.00.341.9All FinfishAll ShellfishAll Seafood3131310.680.801.50.170.270.350.350.260.801.01.32.20.0260.00.0420.310.290.720.740.802.02.12.33.33.55.07.34.17.99.1aGroup A is salmon, including king, sockeye, coho, chum, pink, and steelhead.bGroup B is finfish, including smelt and herring.cGroup C is finfish, including cod, perch, pollock, sturgeon, sablefish, spiny dogfish and greenling.dGroup D is finfish, including halibut, sole, flounder and rockfish.eGroup F includes tuna, other finfish, and all others not included in Groups A, B, C, and D.N = Sample size.SE = Standard errorLCL = Lower confidence limitUCL = Upper confidence limitP5...P95 = Percentile value.Note: The minimum consumption for all species and groups was zero, except for “all finfish” and “all seafood.” The minimum rate for “all finfish” was 0.023, and for “all seafood” was0.035.Source: Duncan, 2000.



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### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-24. Consumption Rates for Native American Children (g/kg-day), Consumers Only: Individual Finfish and Shellfish and Fish Groups

Percentiles Group Species N Mean SE Median
75 th 90 th

Group E Manila/Littleneck clams 23 0.13 0.068 0.043 0.066 0.20

Horse clams 12 0.058 0.032 0.009 0.046 0.31

Butter clams 6 0.11 0.066 0.032 0.20

- Geoduck 22 0.16 0.054 0.053 0.23 0.55

Cockles 10 0.36 0.23 0.078 0.29 2.2 Oysters 10 0.060 0.035 0.015 0.074 0.34

Mussels 1 0.026

- - - - Moon snails 0

- - - - - Shrimp 17 0.17 0.064 0.035 0.30 0.62 Dungeness crab 21 0.44 0.18 0.082 0.305 2.3

Red rock crab 5 0.046 0.011 0.051 0.067

- Scallops 8 0.042 0.019 0.027 0.032

- Squid 2 0.033 0.008 0.033

- - Sea urchin 0

- - - - - Sea cucumber 0

- - - - -

Group A a 28 0.300 0.13 0.11 0.25 0.60 Group B b 5 0.023 0.012 0.017 0.043

- Group C c 25 0.16 0.048 0.048 0.24 0.49 Group D d 17 0.055 0.019 0.033 0.064 0.14 Group F e (tuna/other finfish) 24 0.31 0.092 0.18 0.34 1.0

All finfish 31 0.68 0.17 0.31 0.74 2.1

All shellfish 28 0.89 0.30 0.36 0.85 2.5

All seafood 31 1.5 0.35 0.72 2.0 3.4

a Group A is salmon, including king, sockeye, coho, chum, pink, and steelhead.

b Group B is finfish, including smelt and herring.

c Group C is finfish, including cod, perch, pollock, sturgeon, sablefish, spiny dogfish and greenling.

d Group D is finfish, including halibut, sole, flounder and rockfish.

e Group F includes tuna, other finfish, and all others not included in Groups A, B, C, and D.

N = Sample size.

SE = Standard error.

- = No data.

Source: Duncan, 2000.



### Child-Specific Exposure Factors Handbook Page September 2008 10-31

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-25. Fish Consumption Rates for Tulalip and Squaxin Island Children Consumers Only (g/kg-day)

Percentiles b Species a N Mean SD 5 th 10 th 25 th 50 th 75 th 90 th 95 th

Squaxin Island Tribe

Anadromous fish 33 0.39 1.3 0.005 0.006 0.030 0.049 0.13 0.69 0.79 Pelagic fish 21 0.16 0.25 0.010 0.014 0.019 0.044 0.11 0.55 0.71

Bottom fish 18 0.17 0.36

- 0.006 0.014 0.026 0.050 0.48

- Shellfish 31 2.3 8.6 0.006 0.025 0.050 0.26 0.40 0.77 4.5

Other fish 30 0.58 0.58 0.012 0.051 0.11 0.40 0.57 1.6 1.6

All finfish 35 0.54 1.3 0.005 0.007 0.046 0.062 0.22 1.7 2.3

All fish 36 2.9 8.4 0.012 0.019 0.24 0.70 1.5 2.8 7.7

Tulalip Tribe

Anadromous fish 14 0.15 0.23

- 0.012 0.026 0.045 0.14 0.33

- Pelagic fish 7 0.15 0.18

- - 0.027 0.053 0.17

- - Bottom fish 2 0.044 0.005

- - - 0.041

- - - Shellfish 11 0.31 0.39

- 0.012 0.034 0.036 0.52 0.80

- Other fish 1 0.12 0.12

- - - - - - - All finfish 15 0.31 0.33

- 0.027 0.082 0.133 0.43 0.73

- All fish 15 0.45 0.53

- 0.066 0.088 0.22 0.60 0.88

-

a Anadromous included: salmon, steelhead,and smelt. Pelagic included: cod, pollock, sablefish, rockfish, greenling, herring, spiny dogfish, perch, mackarel, and shark. Bottom included: halibut, sole/flounder, sturgeon, skate, eel, and grunters. Shellfish included: clams, cockles, mussels, oysters, shrimp, crabs, snails, scallops, squid, sea urchins, geoduck, limpets, lobster, bullhead, manta ray, razor clam, chitons, octopus, abalone, barnacles, and crayfish. Other included canned tuna and trout,

b Due to the small sample size for some fish groups, some percentiles could not be computed. A percentile was only calculated if it was between 100%*1/(N+1) and 100%*N/(N+1), where N is the number of consumers of a species group.

N = Sample size.

SD = Standard deviation.

- = No data.

Source: Polissar et al., 2006.



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-26. Fish Consumption Rates by Gender for Tulalip and Squaxin Island Children Consumers Only (g/kg-day)

Percentiles b

Species a Gender N Mean SD 5 th 10 th 25 th 50 th 75 th 90 th 95 th

Squaxin Island Tribe

Anadromous fish Male 15 0.70 1.9

- 0.009 0.026 0.062 0.33 1.1

- Female 18 0.16 0.25

- 0.005 0.025 0.046 0.090 0.60

- Pelagic fish Male 8 0.10 0.14

- - 0.015 0.058 0.099

- - Female 13 0.18 0.28

- 0.015 0.020 0.040 0.11 0.68

- Bottom fish Male 6 0.038 0.057

- - 0.016 0.020 0.026

- - Female 12 0.24 0.44

- 0.005 0.010 0.028 0.11 0.74

- Shellfish Male 13 0.28 0.24

- 0.036 0.047 0.24 0.35 0.46

- Female 18 3.8 11.2

- 0.008 0.050 0.23 0.49 1.3

- Other fish Male 13 0.84 0.66

- 0.11 0.23 0.45 1.5 1.6

- Female 17 0.40 0.46

- 0.013 0.096 0.31 0.49 0.61

- All finfish Male 15 0.79 1.9

- 0.009 0.038 0.062 0.52 1.5

- Female 20 0.37 0.72 0.005 0.005 0.037 0.071 0.18 1.4 2.1

All fish Male 15 1.7 2.0

- 0.061 0.48 1.2 1.9 2.4

- Female 21 3.7 10.7 0.008 0.014 0.16 0.60 0.92 2.8 16.4
Tulalip Tribe

Anadromous fish Male 7 0.061 0.052

- - 0.023 0.034 0.067

- - Female 7 0.24 0.31

- - 0.032 0.080 0.20

- - Pelagic fish Male 5 0.11 0.081

- - 0.044 0.053 0.13

- - Female 2 0.27 0.35

- - - 0.017

- - - Bottom fish Male 0

- - - - - - - - - Female 2 0.044 0.005

- - - 0.041

- - - Shellfish Male 5 0.14 0.22

- - 0.012 0.027 0.11

- - Female 6 0.43 0.46

- - 0.034 0.22 0.65

- - Other fish Male 0

- - - - - - - - - Female 1 0.12 0.12

- - - - - - - All finfish Male 8 0.21 0.18

- - 0.087 0.13 0.32

- - Female 7 0.43 0.44

- - 0.045 0.17 0.65

- - All fish Male 8 0.20 0.17

- - 0.071 0.12 0.23

- - Female 7 0.75 0.67

- - 0.16 0.49 0.84

- -

a Anadromous included: salmon, steelhead,and smelt. Pelagic included: cod, pollock, sablefish, rockfish, greenling, herring, spiny dogfish, perch, mackarel, and shark. Bottom included: halibut, sole/flounder, sturgeon, skate, eel, and grunters. Shellfish included: clams, cockles, mussels, oysters, shrimp, crabs, snails, scallops, squid, sea urchins, geoduck, limpets, lobster, bullhead, manta ray, razor clam, chitons, octopus, abalone, barnacles, and crayfish. Other included canned tuna and trout,

b Due to the small sample size for some fish groups, some percentiles could not be computed. A percentile was only calculated if it was between 100%*1/(N+1) and 100%*N/(N+1), where N is the number of consumers of a species

group. N = Sample size.

SD = Standard deviation.

- = No data.

Source: Polissar et al., 2006.



### Child-Specific Exposure Factors Handbook Page September 2008 10-33

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-27. Distribution of Quantity of Canned Tuna Consumed (grams) Per Eating Occasion, by Age and Sex

Age (years)-Sex Group Mean SE

5 th 10 th 25 th Percentiles

50 th 75 th 90 th 95 th

2 to 5

Male-Female 38 3 7* 8 15 29 55 73 85*

6 to 11

Male-Female 57 8 14* 20* 26 49 59 99* 157*

12 to 19

Male

Female 84*

64 12*

6 - 14* 18*

18* 49*

28* 74

56 97*

77* 162*

105* - 156*

SE

*

- = Standard error. Indicates a statistic that is potentially unreliable because of small sample size or large coefficient of variation. Indicates a percentage that could not be estimated.

Source: Smiciklas-Wright et al., 2002 (based on 1994-1996 CSFII data).

Table 10-28. Distribution of Quantity of Other Finfish Consumed (grams) Per Eating Occasion, by Age and Sex

Percentiles
Age (years)-Sex Group Mean SE

5 th 10 th 25 th 50 th 75 th 90 th 95 th

2 to 5

Male-Female 64 4 8* 16 33 58 77 124 128*

6 to 11

Male-Female 93 8 17* 31* 50 77 119 171* 232*

12 to 19

Male 119* 11* 40* 50* 64* 89 170* 185* 249*

Female 89* 13* 20* 26* 47* 67 124* 164* 199*

SE = Standard error.

* Indicates a statistic that is potentially unreliable because of small sample size or large coefficient of variation.

Source: Smiciklas-Wright et al., 2002 (based on 1994-1996 CSFII data).



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-29. Mean Percent Moisture and Total Fat Content for Selected Species

Species Moisture Content (%) Total Fat Content (%) Comments

FINFISH

Anchovy, European

Bass, Freshwater

Bass, Striped

Bluefish

Burbot

Butterfish

Carp

Catfish, Channel, Farmed

Catfish, Channel, Wild

Cavier, Black and Red

Cisco

Cod, Atlantic

Cod, Pacific

Croaker, Atlantic

Cusk

Dolphinfish

Drum, Freshwater

Eel

Flatfish, Flounder, and Sole

Grouper

Haddock

Halibut, Atlantic and Pacific

Halibut, Greenland

Herring, Atlantic

Herring, Pacific

Ling 73.37

50.30 4.84

9.71 Raw Canned in oil, drained solids

75.66

68.79 3.69 4,73 Raw Cooked, dry heat

79.22

73.36 2.33

2.99 Raw Cooked, dry heat

70.86

62.64 4.24

5.44 Raw Cooked, dry heat

79.26

73.41 0.81

1.04 Raw Cooked, dry heat

74.13

66.83 8.02

10.28 Raw Cooked, dry heat

76.31

69.63 5.60

7.17 Raw Cooked, dry heat

75.38

71.58 7.59

8.02 Raw Cooked, dry heat

80.36

77.67 2.82

2.85 Raw Cooked, dry heat

47.50 17.90

-­ 78.93

1.91 69.80

11.90 Raw

Smoked

81.22

75.61

75.92

16.14 0.67

0.86

0.86

2.37 Raw Canned, solids and liquids Cooked, dry heat

Dried and salted

81.28

76.00 0.63

0.81 Raw Cooked, dry heat

78.03

59.76 3.17

12.67 Raw Cooked, breaded and fried

76.35 69,68 0.69

0.88 Raw Cooked, dry heat

77.55

71.22 0.70

0.90 Raw Cooked, dry heat

77.33

70.94 4.93

6.32 Raw Cooked, dry heat

69.26

59.31 11.66

14.95 Raw Cooked, dry heat

79.06

73.16 1.19

1.53 Raw Cooked, dry heat

79.22

73.36 1.02

1.30 Raw, mixed species Cooked, dry heat

79.92

74.25

71.48 0.72

0.93

0.96 Raw Cooked, dry heat

Smoked

77.92

71.69 2.29

2.94 Raw Cooked, dry heat

70.27

61.88 13.84

17.74 Raw Cooked, dry heat

72.05

64.16

59.70

55.22 9.04

11.59

12.37

18.00 Raw Cooked, dry heat Kippered

Pickled

71.52

63.49 13.88

17.79 Raw Cooked, dry heat

79.63 73,88 0.64

0.82 Raw Cooked, dry heat



### Child-Specific Exposure Factors Handbook Page September 2008 10-35

### Child-Specific Exposure Factors Handbook

Chapter 10 - Intake of Fish and Shellfish

Table 10-29. Mean Percent Moisture and Tota l Fat Content for Selected Species (continued)

Species Moisture Content (%) Total Fat Content (%) Comments

Lingcod

Mackerel, Atlantic

Mackerel, Jack Mackerel, King

Mackerel, Pacific and Jack

Mackerel, Spanish

Milkfish

Monkfish

Mullet, Striped

Ocean Perch, Atlantic

Perch

Pike, Northern

Pike, Walleye

Pollock, Atlantic

Pollock, Walleye

Pompano, Florida

Pout, Ocean

Rockfish, Pacific

Roe

Roughy, Orange

Sablefish

Salmon, Atlantic, Farmed

Salmon, Atlantic, WIld

Salmon, Chinook

Salmon, Chum

Salmon, Coho, Farmed

Salmon, Coho, Wild 81.03

75.68

63.55

53.27

69.17

75.85

69.04

70.15

61.73

71.67

68.46

70.85

62.63

83.24

78.51

77.01

70.52

78.70

72.69

79.13

73.25

78.92

72.97

79.31

73.47

78.18

72.03

81.56

74.06

71.12

62.97

81.36

76.10

79.26

73.41

67.73

58.63

75.67

66.97

71.02

62.85

60.14

68.90

64.75

68.50

59.62

71.64

65.60

72.00

75.38

68.44

70.77

70.47

67.00

72.66

71.50

65.39 1.06

1.36

13.89

17.81

6.30

2.00

2.56

7.89

10.12

6.30

6.32

6.73

8.63

1.52

1.95

3.79

4.86

1.63

2.09

0.92

1.18

0.69

0.88

1.22

1.56

0.98

1.26

0.80

1.12

9.47

12.14

0.91

1.17

1.57

2.01

6.42

8.23

0.70

0.90

15.30

19.62

20.14

10.85

12.35

6.34

8.13

10.43

13.38

4.32

3.77

4.83

5.50

7.67

8.23

5.93

4.30

7.50 Raw Cooked, dry heat

Raw Cooked, dry heat Canned, drained solids

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Smoked

Raw Cooked, dry heat

Raw Cooked, dry heat

Raw Cooked, dry heat

Smoked

Raw Cooked, dry heat

Drained solids with bone

Raw Cooked, dry heat

Raw Cooked, dry heat Cooked, moist heat



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Chapter 10 - Intake of Fish and Shellfish

Table 10-29. Mean Percent Moisture and Total Fat Content for Selected Species (Continued)

Species Moisture Content (%) Total Fat Content (%) Comments

Salmon, Pink

Salmon, Sockeye

Sardine, Atlantic Sardine, Pacific Scup

Sea Bass

Seatrout

Shad, American

Shark, mixed species

Sheepshead

Smelt, Rainbow

Snapper

Spot

Sturgeon

Sucker, white

Sunfish, Pumpkinseed

Surimi

Swordfish

Tilapia

Tilefish

Trout, Mixed Species

Trout, Rainbow, Farmed

Trout, Rainbow, Wild

Tuna, Fresh, Bluefin

Tuna, Fresh, Skipjack

Tuna, Fresh, Yellowfin

Tuna, Light

Tuna, White 76.35

69.68

68.81 3.45

4.42

6.05 Raw Cooked, dry heat Canned, solids with bone and liquid

70.24

61.84

67.51 8.56

10.97

7.31 Raw Cooked, dry heat Canned, drained solids with bone

59.61 11.45 Canned in oil, drained solids with bone

66.65 10.46 Canned in tomato sauce, drained solids with bone

75.37

68.42 2.73

3.50 Raw Cooked, dry heat

78.27

72.14 2.00

2.56 Raw Cooked, dry heat

78.09

71.91 3.61

4.63 Raw Cooked, dry heat

68.19

59.22 13.77

17.65 Raw Cooked, dry heat

73.58

60.09 4.51

13.82 Raw Cooked, batter-dipped and fried

77.97

69.04 2.41

1.63 Raw Cooked, dry heat

78.77

72.79 2.42

3.10 Raw Cooked, dry heat

76.87

70.35 1.34

1.72 Raw Cooked, dry heat

75.95

69.17 4.90

6.28 Raw Cooked, dry heat

76.55

69.94

62.50 4.04

5.18

4.40 Raw Cooked, dry heat

Smoked

79.71

73.99 2.32

2.97 Raw Cooked, dry heat

79.50

73.72 0.70

0.90 Raw Cooked, dry heat

76.34 0.90

- 75.62

68.75 4.01

5.14 Raw Cooked, dry heat

78.08

71.59 1.70

2.65 Raw Cooked, dry heat

78.90

70.24 2.31

4.69 Raw Cooked, dry heat

71.42

63.36 6.61

8.47 Raw Cooked, dry heat

72.73

67.53 5.40

7.20 Raw Cooked, dry heat

71.87

70.50 3.46

5.82 Raw Cooked, dry heat

68.09

59.09 4.90

6.28 Raw Cooked, dry heat

70.58

62.28 1.01

1.29 Raw Cooked, dry heat

70.99

62.81 0.95

1.22 Raw Cooked, dry heat

59.83

74.51 8.21

0.82 Canned in oil, drained solids Canned in water, drained solids

64.02

73.19 8.08

2.97 Canned in oil, drained solids Canned in water, drained solids



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Chapter 10 - Intake of Fish and Shellfish

Table 10-29. Mean Percent Moisture and Total Fat Content for Selected Species (Continued)

Species Moisture Content (%) Total Fat Content (%) Comments

Turbot, European

Whitefish, mixed species

Whiting, mixed species

Wolffish, Atlantic

Yellowtail, mixed species 76.95

70.45 2.95

3.78 Raw Cooked, dry heat

72.77

65.09

70.83 5.86

7.51

0.93 Raw Cooked, dry heat

Smoked

80.27

74.71 1.31

1.69 Raw Cooked, dry heat

79.90

74.23 2.39

3.06 Raw Cooked, dry heat

74.52

67.33 5.24

6.72 Raw Cooked, dry heat

SHELLFISH

Abalone

Clam

Crab, Alaska King

Crab, Blue

Crab, Dungeness

Crab, Queen

Crayfish, Farmed

Crayfish, Wild

Cuttlefish

Lobster, Northern

Lobster, Spiny

Mussel, Blue

Octopus

Oyster, Eastern 74.56

60.10 0.76

6.78 Raw Coofed, fried

81.82

63.64

97.70

61.55

63.64 0.97

1.95

0.02

11.15

1.95 Raw Canned, drained solids Canned, liquid Cooked, breaded and fried Cooked, moist heat

79.57

77.55

74.66 0.60

1.54

0.46 Raw Cooked, moist heat Imitation, made from surimi

79.02

79.16

77.43

71.00 1.08

1.23

1.77

7.52 Raw

Canned Cooked, moist heat

Crab cakes

79.18

73.31 0.97

1.24 Raw Cooked, moist heat

80.58

75.10 1.18

1.51 Raw Cooked, moist heat

84.05

80.80 0.97

1.30 Raw Cooked, moist heat

82.24

79.37 0.95

1.20 Raw Cooked, moist heat

80.56

61.12 0.70

1.40 Raw Cooked, moist heat

76.76

76.03 0.90

0.59 Raw Cooked, moist heat

74.07

66.76 1.51

1.94 Raw Cooked, moist heat

80.58

61.15 2.24

4.48 Raw Cooked, moist heat

80.25

60.50 1.04

2.08 Raw Cooked, moist heat

86.20

85.16

85.14

64.72

81.95

83.30

70.32 1.55

2.46

2.47

12.58

2.12

1.90

4.91 Raw, farmed Raw, wild

Canned Cooked, breaded and fried Cooked, farmed, dry heat Cooked, wild, dry heat Cooked, wild, moist heat



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Chapter 10 - Intake of Fish and Shellfish

Table 10-29. Mean Percent Moisture and Total Fat Content for Selected Species (Continued)

Species Moisture Content (%) Total Fat Content (%) Comments

Oyster, Pacific

Scallop, mixed species

Shrimp

Squid 82.06

64.12 2.30

4.60 Raw Cooked, moist heat

78.57

58.44

73.10 0.76

10.94

1.40 Raw Cooked, breaded and fried

Steamed

75.86

75.85

52.86

77.28 1.73

1.36

12.28

1.08 Raw

Canned Cooked, breaded and fried Cooked, moist heat

78.55

64.54 1.38

7.48 Raw Cooked, fried

Source: USDA, 2007.



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Chapter 11 - Intake of Meats, Dairy Products and Fats

11 INTAKE OF MEATS, DAIRY PRODUCTS

AND FATS

11.1 INTRODUCTION

The American food supply is generally

considered to be one of the safest in the world. Nevertheless, meats, dairy products, and fats may become contaminated with toxic chemicals by several pathways. These foods sources can become contaminated if animals are exposed to contaminated media (i.e., soil, water, or feed crops). To assess exposure through this pathway, information on meat, dairy, and fat ingestion rates are needed. Children’s exposure from contaminated meats, dairy products, and fats may differ from that of adults because of differences in the type and amounts of food eaten. Also, for many foods, the intake per unit body weight is greater for children than for adults. Common meats, dairy products, and fats eaten by children include non-fat milk solids, milk fat and solids, lean beef, and milk sugar (lactose) (Goldman, 1995). A variety of terms may be used to define intake of meats, dairy products, and fats (e.g., consumer-only intake, per capita intake, total meat, dairy product, or fat intake, as-consumed intake, dry weight intake). As described in Chapter 9, Intake of Fruits and Vegetables, consumer-only intake is defined as the quantity of meats, dairy products, or fats consumed by children during the survey period averaged across only the children who consumed these food items during the survey period. Per capita intake rates are generated by averaging consumer-only intakes over the entire population of children. In general, per capita intake rates are appropriate for use in exposure assessment for which average dose estimates for children are of interest because they represent both children who ate the foods during the survey period and children who may eat the food items at some time, but did not consume them during the survey period. Per capita intake, therefore, represents an average across the entire population of interest, but does so at the expense of underestimating consumption for the subset of the population that consume the food in question. Total intake refers to the sum of all meats, diary products, or fats consumed in a day. Intake rates may be expressed on the basis of the as-consumed weight (e.g., cooked or prepared) or on the uncooked or unprepared weight. As-consumed intake rates are based on the weight of the food in the

form that it is consumed and should be used in assessments where the basis for the contaminant

concentrations in foods is also indexed to the asconsumed weight. The food ingestion values provided in this chapter are expressed as as-consumed intake

rates because this is the fashion in which data were reported by survey respondents. This is of importance

because concentration data to be used in the dose equation are often measured in uncooked food samples. It should be recognized that cooking can either increase or decrease food weight. Similarly, cooking can increase the mass of contaminant in food (due to formation reactions, or absorption from cooking oils or water) or decrease the mass of contaminant in food (due to vaporization, fat loss or leaching). The combined effects of changes in weight and changes in contaminant

mass can result in either an increase or decrease in contaminant concentration in cooked food. Therefore, if the as-consumed ingestion rate and the uncooked concentration are used in the dose equation, dose maybe under-estimated or over-estimated. Ideally, aftercooking food concentrations should be combined with the as-consumed intake rates. In the absence of data, it is reasonable to assume that no change in contaminant concentration occurs after cooking. It is important for

the assessor to be aware of these issues and choose

intake rate data that best match the concentration data that are being used. For more information on cooking losses and conversions necessary to account for such losses, the reader is referred to Chapter 13 of this

handbook.

Sometimes contaminant concentrations in food are reported on a dry weight basis. When these data are used in an exposure assessment, it is recommended that dry-weight intake rates also be used. Dry-weight food concentrations and intake rates are based on the weight

of the food consumed after the moisture content has been removed. Similarly, when contaminant concentrations in food are reported on a lipid weight basis, lipid weight intake rates should be used. For information on converting the intake rates presented in this chapter to dry weight or lipid weight intake rates,

the reader is referred to Sections 11.5 and 11.6 of this chapter. The purpose of this chapter is to provide intake data for meats, diary products, and fats among children. The recommendations for ingestion rates of meats, dairy products, and fats are provided in the next section, along with a summary of the confidence ratings for

these recommendations. The recommended values are



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Chapter 11 - Intake of Meats, Dairy Products and Fats

based on the key studies identified by U.S. EPA for this factor. Following the recommendations, the key studies on ingestion of meats, dairy products, and fats are summarized. Relevant data on ingestion of meats, dairy products, and fats are also provided. These studies are presented to provide the reader with added perspective on the current state-of-knowledge pertaining to ingestion of meats, dairy products, and fats among

children.

11.2 RECOMMENDATIONS

Table 11-1 p resents a summary of the

rec ommended values for per capita and consumers-onlyintake of meats, diary products, and fats, on an asconsumed basis. Confidence ratings for the meats, dairy products, and fat intake recommendations for general population children are provided in Table 11-2.

U.S. EPA analyses of data from the 1994-96 and 1998 Continuing Survey o f Food Intake among Individuals (CSFII) were used in selecting recommended intake rates for general population children. The U.S. EPA analysis of meat and dairy products was conducted using age groups that differed slightly from U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). However, for the purposes of the recommendations presented here, data were placed in the standardized age categories closest to those used in the analysis. The U.S. EPA analysis of fat intake data from the CSFII used the age groups recommended by

U.S. EPA (2005). The CSFII data on which the recommendations for meats, dairy products, and fats are based are short-term survey data and may not necessarily reflect the long-term distribution of average daily intake rates. However, for these broad categories of food (i.e., total meats and diary products), because they are eaten on a daily basis throughout the year with minimal seasonality, the short term distribution may be a reasonable approximation of the long-term distribution, although it will display somewhat increased variability. This implies that the upper percentiles shown here will tend to overestimate the corresponding percentiles of the true long-term

distribution. It should be noted that because these

recommendations are based on 1994-96 and 1998 CSFII data, they may not reflect the most recent changes that may have occurred in consumption
patterns.



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Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-1. Recommended Values for Intake of Meats, Dairy Products, and Fats, As Consumed

Age Group Per Capita Consumers Only
Multiple

Percentiles Source Mean 95 th Percentile Mean 95 th Percentile

g/kg-day g/kg-day g/kg-day g/kg-day

Total Meats a

Birth to 1 year 1.2 6.7 3.0 9.2

See Tables 11-3

and 11-4 U.S. EPA Analysis of CSFII, 1994­

96 and 1998. 1 to <2 years 4.1 9.8 4.2 9.8

2 to <3 years 4.1 9.8 4.2 9.8

3 to <6 years 4.1 9.4 4.2 9.4

6 to <11 years 2.9 6.5 2.9 6.5

11 to <16 years 2.1 4.8 2.1 4.8

16 to <21 years 2.1 4.8 2.1 4.8

Total Dairy Products a

Birth to 1 year 13 49 16 58

See Tables 11-3

and 11-4 U.S. EPA Analysis of CSFII, 1994­

96 and 1998. 1 to <2 years 37 88 37 88

2 to <3 years 37 88 37 88

3 to <6 years 23 49 23 49

6 to <11 years 14 32 14 32

11 to <16 years 5.6 16 5.6 16

16 to <21 years 5.6 16 5.6 16

Individual Meat and Dairy Products - See Tables 11-5 and 11-6

Total Fats

Birth to <1 month 5.2 16 7.8 16

See Tables 11­

20 and 11-24 U.S. EPA Analysis of CSFII, 1994­

96 and 1998. 1 to <3 months 4.5 11 6.0 12

3 to <6 months 4.1 8.2 4.4 8.3

6 to <12 months 3.7 7.0 3.7 7.0

1 to <2 years 4.0 7.1 4.0 7.1

2 to <3 years 3.6 6.4 3.6 6.4

3 to <6 years 3.4 5.8 3.4 5.8

6 to <11 years 2.6 4.2 2.6 4.2

11 to <16 years 1.6 3.0 1.6 3.0

16 to <21 years 1.3 2.7 1.3 2.7

a Analysis was conducted using slightly different age groups than those recommended in Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA. 2005). Data were placed in the standardized age categories closest to those used in the analysis.



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Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-2. Confidence in Recommendations for Intake of Meats, Diary Products, and Fats

General Assessment Factors Rationale Rating

Soundness Adequacy of Approach

Minimal (or Defined) Bias The survey methodology and data analysis was adequate. The survey sampled approximately 11,000 children. An analysis of primary data was conducted.

No physical measurements were taken. The method relied on recent recall of meats and diary products eaten. High

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The key studies were directly relevant to meat, dairy, and

fat intake.

The data were demographically representative of the U.S. population (based on stratified random sample).

Data were collected between 1994 and 1998.

Data were collected for two non-consecutive days. Medium

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The CSFII data are publicly available.

The methodology used was clearly described; enough information was included to reproduce the results.

Quality assurance of the CSFII data was good; quality control of the secondary data analysis was not well

described. High

Variability and Uncertainty

Variability in Population

Uncertainty Full distributions were provided for total meats, total diary products, and total fats. Means were provided for individual meats and diary products.

Data collection was based on recall of consumption for a 2-day period; the accuracy of using these data to estimate long-term intake (especially at the upper percentiles) is uncertain. However, use of short-term data to estimate chronic ingestion can be assumed for broad categories of foods such as total meats, total diary products, and total fats. Uncertainty is likely to be greater for individual meats and diary products. Medium



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### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-2. Confidence in Recommendations for Intake of Meats, Diary Products, and Fats (continued)

General Assessment Factors Rationale Rating

Evaluation and Review

Peer Review

Number and Agreement of Studies The USDA CSFII survey received a high level of peer review. The U.S. EPA analysis of these data has not been peer reviewed outside the Agency.

There was 1 key study for intake of meat and diary products and 1 key study for fat intake. Both were based on the

1 99 4-96, 1998 CSFII. Medium

Overall Rating High confidence in the averages;

Low confidence in the long-term upper

percentiles



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Chapter 11 - Intake of Meats, Dairy Products and Fats

11.3 INTAKE STUDIES The primary source of recent information on consumption rates of meat and diary products among children is the U.S. Department of Agriculture’s (USDA) CSFII. Data from the 1994-96 CSFII and the 1998 Children’s supplement to the 1994-96 CSFII have been used in various studies to generate children’s consumer-only and per capita intake rates for both individual meats and diary products and total meats and diary products. The CSFII is a series of surveys designed to measure the kinds and amounts of foods eaten by Americans. The CSFII 1994-96 was conducted between January 1994 and January 1997 with a target population of non-institutionalized individuals in all 50 states and Washington, D.C. In each of the 3 survey years, data were collected for a nationally representative sample of individuals of all ages. The CSFII 1998 was conducted between December 1997 and December 1998 and surveyed children 9 years of age and younger. It used the same sample design as the CSFII 1994-96 and was intended to be merged with CSFII 1994-96 to increase the sample size for children. The merged surveys are designated as CSFII 1994-96, 1998. Additional information on these surveys can be obtained at http://www.ars.usda.gov/Services/docs.htm?docid=14

531

. The CSFII 1994-96, 1998 collected dietaryintake data through in-person interviews on 2 nonconsecutive days. The data were based on 24-hour recall. A total of 21,662 individuals provided data for the first day; of those individuals, 20,607 provided data for a second day. Over 11,000 of the sample persons represented children up to 18 years of age. The 2-day response rate for the 1994-1996 CSFII was approximately 76 percent. The 2-day response rate for CSFII 1998 was 82 percent. The CSFII 1994-96, 98 surveys were based on a complex multistage area probability sample design. The sampling frame was organized using 1990 U.S. population census estimates, and the stratification plan took into account geographic location, degree of urbanization, and socioeconomic characteristics. Several sets of sampling weights are available for use with the intake data. By using appropriate weights data for all fours years of the surveys can be combined. USDA recommends that all 4 years be combined in order to provide an adequate sample size for children. 11.3.1 Key Meat and Diary Intake Study

11.3.1.1 U.S. EPA Analysis of CSFII 1994-96,

1998 For many years, the U.S. EPA’ s Office of Pesticide Programs (OPP) has used food consumption data collected by the U.S. Department of Agriculture (USDA) for its dietary risk assessments. Most recently, OPP, in cooperation with USDA’s Agricultural Research Service (ARS), used data from the 1994-96, 1998 CSFII to develop the Food Commodity Intake Database (FCID). CSFII data on the foods people reported eating were converted to the quantities of agricultural commodities eaten. "Agricultural commodity" is a term used by U.S. EPA to mean animal (or plant) parts consumed by humans as food; when such items are raw or unprocessed, they are referred to as "raw agricultural commodities." For example, a beef stew may contain the commodities beef, carrots, and potatoes. FCID contains approximately 553 unique commodity names and 8-digit codes. The FCID commodity names and codes were selected and defined by U.S. EPA and were based on the U.S. EPA Food Commodity Vocabulary ( http://www.epa.gov/pesticides/foodfeed/)

. The meats and d a iry items/groups selected for the U.S. EPA analysis included total meats and total

d a iry products, and individual meats and d a i ry such as

bee f, pork, poultry, and eggs. Appendix 11A presents

the food codes and definitions used to determine the various meats and dairy products used in the analysis. Intake rates for these food items/groups represent intake of all forms of the product (e.g., both home produced and commercially produced). Children who provided data for two days of the survey were included in the intake estimates. Individuals who did not provide information on body weight or for whom identifying

information was unavailable were excluded from the analysis. Two-day average intake rates were calculated

for all individuals in the database for each of the food items/groups. These average daily intake rates were divided by each individual's reported body weight to generate intake rates in units of grams per kilogram of body weight per day (g/kg-day). The data were weighted according to the four-year, two-day sample weights provided in the 1994-96, 1998 CSFII to adjust the data for the sample population to reflect the national population. Summary statistics were generated on both a per capita and a consumer only basis. For per capita intake, both users and non-users of the food item were



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Chapter 11 - Intake of Meats, Dairy Products and Fats

included in the analysis. Consumer only intake rates were calculated using data for only those individuals who ate the food item of interest during the surveyperiod. Intake data from the CSFII are based on asconsumed (i.e., cooked or prepared) forms of the food items/groups. Summary statistics, including: number of observations, percentage of the population consuming the meat or dairy products being analyzed, mean intake rate, and standard error of the mean intake rate were calculated for total meats, total dairy products, and selected individual meats and dairy products. Percentiles of the intake rate distribution (i.e., 1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 99th, and 100th percentile were also provided for total meats and dairy products. Data were provided for the following age groups of children: birth to <1 year, 1 to <2 years, 3 to <5 years, 6 to <12 years, and 13 to <19 years. Because these data were developed for use in U.S. EPA’s pesticide registration program, the age groups used are slightly different than those recommended in U.S.

EPA’s Guida nce on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to

Environmental Contaminants (U.S. EPA, 2005).

Table 11-3 presents as-consumed per capitaintake data for total meats and dairy products in g/kgday; as-consumed consumer-only intake data for total meats and dairy products in g/kg-day are provided in Table 11-4. Table 11-5 provides per capita intake data for certain individual meats and dairy products and Table 11-6 provides consumer only intake data for these individual meats and dairy products.

It should be noted that the distribution of average daily intake rates generated using short-termdata (e.g., 2-day) do not necessarily reflect the longterm distribution of average daily intake rates. The distributions generated from short-term and long-term

data will differ to the extent that each individual’s intake varies from day to day; the distributions will be

similar to the extent that individuals’ intakes are constant from day to day. However, for broad categories of foods (e.g., total meats and dairy products) that are eaten on a daily basis throughout the year, the short-term distribution may be a reasonable approximation of the true long-term distribution, although it will show somewhat more variability. In this chapter, distributions are provided only for broad categories of meats and dairy products (i.e., total meats and dairy products). Because of the increased variability of the short-term distribution, the short-term upper percentiles shown here may overestimate the corresponding percentiles of the long-term distribution. For individual foods, only the mean, standard error, and percent consuming are provided. The strengths of U.S. EPA’s analysis are that it provides distributions of intake rates for various age groups of children, normalized by body weight. The analysis uses the 1994-96, 1998 CSFII data set which was designed to be representative of the U.S. population. The data set includes four years of intake data combined, and is based on a two-day survey period. As discussed above, short-term dietary data may not accurately reflect long-term eating patterns and may under-represent infrequent consumers of a given food. This is particularly true for the tails (extremes) of the distribution of food intake. Also, the analysis was conducted using slightly different age groups that

those recommended in U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). However, given the similarities in the age groups used, the data should provide suitable intake estimates for the age groups of interest

.

11.3.2 Relevant Meat and Dairy Intake Studies

11.3.2.1 USDA, 1999a - Food and Nutrient Intakes by Children 1994-96, 1998, Table Set 17

USDA (1999a) calculated national probability estimates of food and nutrient intake by children based on all 4 years of the CSFII (1994-96 and 1998) for children age 9 years and under and on CSFII 1994-96 only for individuals age 10 years and over. Sample weights were used to adjust for non-response, to match the sample to the U.S. population in terms of demographic characteristics, and to equalize intakes over the 4 quarters of the year and the 7 days of the

week. A total of 503 breast-fed children were excludedfrom the estimates, but both consumers and nonconsumers were included in the analysis. USDA (1999a) provided data on the mean per capita quantities (grams) of various food products/groups consumed per individual for one day, and the percent of individuals consuming those foods in one day of the survey. Tables 11-7 and 11-8 present data on the mean quantities (grams) of meat and eggs consumed per individual for one day, and the percentage of survey individuals consuming meats and eggs on that survey day. Tables 11-9 and 11-10 present similar data for dairy products. Data on mean intakes or mean percentages are based on respondents’ day-1

intakes.



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Chapter 11 - Intake of Meats, Dairy Products and Fats

The advantage of the USDA (1999a) study is that it uses the 1994-96, 98 CSFII data set, which includes four years of intake data, combined, and includes the supplemental data on children. These data are expected to be generally representative of the U.S. population and they include data on a wide variety of

meats and d a iry products. The data set is one of a

s er ies of USDA data sets that are publicly available.

One limitation of this data set is that it is based on one day, and short-term dietary data may not accurately reflect long-term eating patterns. Other limitations of this study are that it only provides mean values of food intake rates, consumption is not normalized by body weight, and presentation of results is not consistent with

U.S. EPA’s recommended age groups.

11.3.2.2 Smiciklas-Wright et al., 2002 - Foods Commonly Eaten in the United States: Quantities Consumed per Eating Occasion and in a Day, 1994-1996 Using data gathered in the 1994-96 USDA CSFII, Smiciklas-Wright et al. (2002) calculated distributions for the quantities of meat, poultry, and dairy products consumed per eating occasion by members of the U.S. population (i.e., serving sizes). The estimates of serving size are based on data obtained from 14,262 respondents, ages 2 and above, who provided 2 days of dietary intake information. A total of 4,939 of these respondents were children, ages 2 to 19 years of age. Only dietary intake data from users of the specified food were used in the analysis (i.e., consumers only data). Table 11-11 presents serving size data for meats and dairy products. These data are presented on an as-consumed basis (grams) and represent the quantity of meats and dairy products consumed per eating occasion. These estimates may be useful for assessing acute exposures to contaminants in specific foods, or other assessments where the amount consumed per eating occasion is necessary. Only the mean and standard deviation serving size data and percent of the population consuming the food during the 2-day survey period are presented in this handbook. Percentiles of serving sizes of the foods consumed by these age groups of the U.S. population can be found in Smiciklas-Wright et al. (2002). The advantages of using these data are that they were derived from the USDA CSFII and are representative of the U.S. population. The analysis conducted by Smiciklas-Wright et al. (2002) accounted for individual foods consumed as ingredients of mixed foods. Mixed foods were disaggregated via recipe files so that the individual ingredients could be grouped together with similar foods that were reported separately. Thus, weights of foods consumed as ingredients were combined with weights of foods reported separately to provide a more thorough representation of consumption. However, it should be noted that since the recipes for the mixed foods consumed were not provided by the respondents, standard recipes were used. As a result, the estimates of quantity consumed for some food types are based on assumptions about the types and quantities of ingredients consumed as part of mixed foods. This study used data from the 1994 to 1996 CSFII; data from the 1998 children’s supplement were not included.

11.3.2.3 Fox et al., 2004 - Feeding Infants and Toddlers Study: What Foods Are Infants and Toddlers Eating Fox et al. (2004) used data from the Feeding Infants and Toddlers study (FITS) to assess food consumption patterns in infants and toddlers. The FITS was sponsored by Gerber Products Company and was

conducted to obtain current information on food and nutrient intakes of children, ages 4 to 24 months old, in

the 50 states and the District of Columbia. The FITS is described in detail in Devaney et al. (2004). FITS was based on a random sample of 3,022 infants and toddlers for which dietary intake data were collected by telephone from their parents or caregivers between March and July 2002. An initial recruitment and household interview was conducted, followed by an

interview to obtain information on intake based on 24­ hour recall. The interview also addressed growth, development and feeding patterns. A second dietary

recall interview was conducted for a subset of 703randomly selected respondents. The study oversampled children in the 4 to 6 and 9 to 11 months age groups; sample weights were adjusted for non-response, over-sampling, and under-coverage of some subgroups. The response rate for the FITS was 73 percent for the recruitment interview. Of the recruited households, there was a response rate of 94 percent for the dietary recall interviews (Devaney et al., 2004). The characteristics of the FITS study population is shown in

Table 11-12. Fox et al. (2004) analyzed the first set of 24­ hour recall data collected from all study participants. For this analysis, children were grouped into six age



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categories: 4 to 6 months, 7 to 8 months, 9 to 11 months, 12 to 14 months, 15 to 18 months, and 19 to 24 months. Table 11-13 provides the percentage of infants and toddlers consuming milk, meats or other protein sources at least once in a day. The percentage of children consuming any type of meat or protein source ranged from 14.2 percent for 4 to 6 month olds to 97.2 percent for 19 to 24 month olds (Table 11-13). The advantages of this study were that the study population represented the U.S. population and the sample size was large. One limitation of the analysis done by Fox et al. (2004) was that only frequency data were provided; no information on actual intake rates was included. In addition, Devaney et al. (2004) noted several limitations associated with the FITS data. For the FITS, a commercial list of infants and toddlers was used to obtain the sample used in the study. Since many of the households could not be located and did not have children in the target population, a lower response rate than would have occurred in a true national sample was obtained (Devaney et al., 2004). In addition, the sample was likely from a higher socioeconomic status when compared with all U.S. infants in this age group (4 to 24 months old) and the use of a telephone survey may have omitted lower-income households without telephones (Devaney et al., 2004).

11.3.2.4 Ponza et al., 2004 - Nutrient Food Intakes and Food Choices of Infants and Toddlers Participating in WIC Ponza et al. (2004) conducted a study using selected data from FITS to assess feeding patterns, food

choices and nutrient intake of infants and toddlers participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Ponza et al. (2004) evaluated FITS data for the following age groups: 4 to 6 months (N = 862), 7 to 11 months( N = 1159) and 12 to 24 months (N= 996). Thetotal sample size described by WIC participant and nonparticipant is shown in Table 11-14. The foods consumed were analyzed by tabulating the percentage of infants who consumed specific foods/food groups per day (Ponza et al., 2004). Weighted data were used in all of the analyses used in the study (Ponza et al., 2004). Table 11-14 presents thedemographic data for WIC participants and nonparticipants. Table 11-15 provides the food choices for infants and toddlers. In general, there was little difference in food choices among WIC participants and non-participants, except for consumption of yogurt by infants 7 to 11 months of age and toddlers 12 to 24 months of age (Table 11-15). Non-participants, 7 to 24 months of age, were more likely to eat yogurt than WIC participants (Ponza et al., 2004). An advantage of this study is that it had a relatively the large sample size and was representative of the U.S. general population of infants and children. A limitation of the study is that intake values for foods were not provided. Other limitations are one

- associated

w ith th e FITS data and are described previously in

Section 11.3.2.3.

11.3.2.5 Mennella et al., 2006 - Feeding Infants and Toddlers Study: The Types of Foods Fed to Hispanic Infants and Toddlers Mennella et al. (2006) investigated the types of food and beverages consumed by Hispanic infants and toddlers in comparison to the non-Hispanic infants

and toddlers in the United States. The FITS 2002 data

for children between 4 and 24 months old were used for the study. The data represent a random sample of 371 Hispanic and 2,367 non-Hispanic infants and toddlers (Menella et al., 2006). Menella et al. (2006) grouped the infants as follows: 4 to 5 months (N = 84 Hispanic; 538 non-Hispanic), 6 to 11 months (N = 163 Hispanic and 1,228 non-Hispanic), and 12 to 24 months (N = 124 Hispanic and 871 non-Hispanic) of age. Table 11-16 provides the percentages of Hispanic and non-Hispanic infants and toddlers consuming milk, meats or other protein sources on a given day. In most instances the percentages consuming the different types of meats and protein sources were similar (Mennella et al., 2006). The advantage of the study is that it providesinformation on food preferences for Hispanic and nonHispanic infants and toddlers. A limitation is that the study did not provide food intake data, but provided frequency of use data instead. Other limitations are those noted previously in Section 11.3.2.3 for the FITS

data.



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11.3.2.6 Fox et al., 2006 - Average Portion of Foods Commonly Eaten by Infants and

Toddlers in the United States Fox et al. (2006) estimated average portion sizes consumed per eating occasion by children 4 to 24 months of age who participated in the FITS. The FITS is a cross-sectional study designed to collect and analyze data on feeding practices, food consumption,

and usual nutrient intake of U.S. infants and toddlers

and is described in Section 11.3.2.3 of this chapter. I t included a stratified random sample of 3,022 children between 4 and 24 months of age. Using the 24-hour recall data, Fox et al. (2006) derived average portion sizes for six major food groups, including meats and other protein sources. Average portion sizes for select individual foods within these major groups were also estimated. For this analysis, children were grouped into six age categories: 4 to 5 months, 6 to 8 months, 9 to 11 months, 12 to 14 months, 15 to 18 months, and 19 to 24 months. Tables 11-17 and 11-18 present the average portion sizes of meats and dairy products for infants and toddlers, respectively.

11.4 FAT INTAKE

11.4.1 Key Fat Intake Study

11.4.1.1 U.S. EPA, 2007 - Analysis of Fat Intake Based on the U.S. Department of Agriculture’s 1994–96, 1998 Continuing Survey of Food Intakes by Individuals (CSFII)

U.S. EPA conducted an analysis to evaluate the dietary intake of fats by individuals in the United States using data from the USDA’s 1994–1996, 1998 CSFII (USDA, 2000). Intakes of CSFII foods were converted to U.S. EPA food commodity codes using data provided in U.S. EPA’s FCID (U.S. EPA, 2000).

The FCID contains a “translation file” that was used to

break down the USDA CSFII food codes into 548 U.S. EPA commodity codes. The method used to translate USDA food codes into U.S. EPA commodity codes is discussed in detail in U.S. EPA (2000). Each of the 548 U.S. EPA commodity codes was assigned a value between 0 and 1 that indicated the mass fraction of fat in that food item. For many sources of fat, a commodity code existed solely for the nutrient fat portion of the food. For example, beef is represented in the FCID database by ten different commodity codes; several of these codes specifically exclude fat, and one code is described as “nutrient fat only.” In these cases, the fat fraction could be expressed as 0 or 1, as appropriate. Most animal food products and food oils were broken down in this way.

The fat contents of other foods in the U.S. EPA commodity code list were determined using the USDA Nutrient Database for Standard Reference, Release 13 (USDA, 1999b). For each food item in the U.S. EPA code list, the best available match in the USDA Nutrient database was used. If multiple values were

available for different varieties of the same food item (e.g., green, white and red grapes), a mean value was calculated. If multiple values were available for different cooking methods (i.e, fried vs. dry cooked), the method least likely to introduce other substances, such as oil or butter, was preferred. In some cases, not all of the items that fall under a given food commodity code could be assigned a fat content. For example, the food commodity code list identified “turkey, meat byproducts” as including gizzard, heart, neck and tail. Fat contents could be determined only for the gizzard

and heart. Because the relative amounts of the different items in the food commodity code was unknown, the

mean fat content of these two items was assumed to be the best approximation of the fat content for the food

code as a whole. The analysis was based on approximately 11,000 CSFII child respondents who had provided bodyweights and who had completed both days of the twoday survey process. These individuals were grouped according to various age categories. The mean, standard error, and a range of percentiles of fat intake were calculated for 12 food categories (i.e., all fats, animal fats, meat and meat products, beef, pork, poultry, organ meats, milk and dairy products, fish, oils, and nuts/seeds/beans/legumes/tubers) and 98 demographic cohorts. Fat intake was calculated as a two-day average consumption across both survey days in units of grams per day and grams per kilogram of body weight per day for the whole survey population and for consumers only. A secondary objective of the study was to evaluate fat consumption patterns of individuals who consume high levels of animal fats. The entire data analysis was repeated for a subset of individuals who were identified as high consumers of animal fats. The selection of the high-consumption group was done for each age category individually, rather than on the whole population, because fat intake on a per-body-weight basis is heavily skewed towards young children, and an analysis across the entire American population was desired. For infants, the “less



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than one year old” group was used instead of the smaller infant groups (<1 month, 1 to <3 months, etc.). Within each of the age categories, individuals that

ranked at or above the 90 th 

### percentile of consumption of all animal fats on a per-unit body weight basis were identified. Because of the sample weighting factors, the high consumer group was not necessarily 10 percent of each age group. The selected individuals made up a survey population of 1,175 children. Fat intake of individuals in this group was calculated in g/day and g/kg-day for the whole population (i.e., per capita) and for consumers only. The analysis presented in U.S. EPA (2007) was conducted before U.S. EPA published the guidance

entitled Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to

Environmental Contaminants (U.S. EPA, 2005). Therefore, the age groups used for children in U.S. EPA (2007) were not entirely consistent with the agegroups recommended in the 2005 guidance. A reanalysis of the some of the data was conducted for this chapter to conform with U.S. EPA’s recommended age groups for children. The results of this re-analysis are presented in Tables 11-19 through 11-26 for individuals less than 21 years of age. Only intake rates of all fats are provided in these tables; the reader is referred to

U.S. EPA (2007) for fat intake rates from individual food sources. Tables 11-19 and 11-20 present intake rates of all fats for the whole population (i.e., per capita) in g/day and g/kg-day, respectively. Table 11­ 21 and 11-22 present intake rates of all fats for consumers only in g/day and g/kg-day, respectively. Fat intake rates of all fats for the top decile of animal fat consumers from the consumers only group are presented in Table 11-23 in g/day and in Table 11-24 in g/kg-day (per capita total fat intake rates for the top decile of animal fat consumers are not provided because they are the same as those for consumers only).

11.4.2 Relevant Fat Intake Studies

11.4.2.1 Cresanta et al., 1988; Nicklas et al., 1993; and Frank et al., 1986 - Bogalusa Heart Study Cresanta et al. (1988), Nicklas et al. (1993), and Frank et al. (1986) analyzed dietary fat intake data as part of the Bogalusa heart study. The Bogalusa study, an epidemiologic investigation of cardiovascular risk-factor variables and environmental determinants, collected dietary data on subjects residing in Bogalusa, LA, beginning in 1973. Among other research, the study collected fat intake data for children, adolescents, and young adults. Researchers examined various cohorts of subjects, including (1) six cohorts of 10-year olds, (2) two cohorts of 13-year olds, (3) one cohort of subjects from 6 months to 4 years of age, and (4) one cohort of subjects from 10 to 17 years of age (Nicklas, 1995). To collect the data, interviewers used the 24­ hour dietary recall method. According to Nicklas (1995), “the diets of children in the Bogalusa study are similar to those reported in national studies of children.” Thus, these data are useful in evaluating the variability of fat intake among the general population. Data for 6-month old to 17-year old individuals collected during 1973 to 1982 are presented in Tables 11-25 and 11-26 (Frank et al., 1986). Data are presented for total fats, animal fats, vegetable fats, and fish fats in units of g/day (Table 11-25) and g/kg/day (Table 11-26).

11.4.2.2 CDC, 1994 - Dietary Fat and Total Foodenergy Intake: Third National Health and Nutrition Examination Survey, Phase 1,

1988-91

The Centers for Disease Control and Prevention (CDC, 1994) used data from NHANES III to calculate daily total food energy intake (TFEI), total dietary fat intake, and saturated fat intake for the U.S. population during 1988 to 1991. The sample population comprised 20,277 individuals ages 2 months and above, of which 14,801 respondents (73 percent response rate) provided dietary information based on a 24-hour recall. Of these, 6,870 were children between the ages of 2 months and 19 years. TFEI was defined as “all nutrients (i.e., protein, fat, carbohydrate, and alcohol) derived from consumption of foods and beverages (excluding plain drinking water) measured in kilocalories (kcal).” Total dietary fat intake was defined as “all fat (i.e., saturated and unsaturated) derived from consumption of foods and beverages measured in grams” (CDC, 1994). The authors estimated and provided data on the mean daily TFEI and the mean percentages of TFEI from total dietary fat grouped by age and gender. The overall mean daily TFEI for the total population was 2,095 kcal, of which 34 percent (712 kcal or 82 g) was from total dietary fat. Based on this information, the mean daily fat intake was calculated for the various age groups and genders (see Appendix 11B for detailed calculation). Table 11-27 presents the grams of fat per day obtained from the daily consumption of foods and



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Chapter 11 - Intake of Meats, Dairy Products and Fats

beverages grouped by age and gender for the U.S. population, based on this calculation.

11.5 CONVERSION BETWEEN WET AND

DRY WEIGHT INTAKE RATES The intake rates presented in this chapter are reported in units of wet weight (i.e., as-consumed or uncooked weight of meats and dairy products consumed per day or per eating occasion). However, data on the concentration of contaminants in meats and dairy products may be reported in units of either wet or dry weight (e.g., mg contaminant per gram-dry-weight of

m ea ts and dairy products.)

. It is essential that exposure assessors be aware of this difference so that they may ensure consistency between the units used for intake rates and those used for concentration data (i.e., if the contaminant concentration is measured in dry weight of 11.6 CONVERSION BETWEEN WET WEIGHT

AND LIPID WEIGHT INTAKE RATES In some cases, the residue levels of contaminants in meat and dairy products may be reported as the concentration of contaminant per gram of fat. This may be particularly true for lipophilic compounds. When using these residue levels, the assessor should ensure consistency in the exposure assessment calculations by using consumption rates that are based on the amount of lipids consumed for the meat or dairy product of interest. If necessary, wet weight (e.g., as-consumed) intake rates may be converted to lipid weight intake rates using the fat content percentages presented in Table 11-28 and the following equation:

 

### L  

### meats and dairy products, then the dry weight units 

### IR lw = IR ww   

### 100   

### (Eqn. 11-3) should be used for their intake values). If necessary, wet weight (e.g., as consumed) intake rates may be converted to dry weight intake rates where: using the moisture content percentages presented in IR lw 

### = lipid weight intake rate; Table 11-28 and the following equation: = wet weight intake rate; and Irww 

### L = percent lipid (fat) content. (Eqn. 11-1) 100 − W   IR dw IR 

### Alternately, wet weight residue levels in meat and dairy products may be estimated by multiplying the levels based on fat by the fraction of fat per product as =     ww 100



### where: follows:

IR dw 

### = dry weight intake rate; 

### L   

### IR

ww 

### = wet weight intake rate; and

W = percent water content 

### C ww = C lw (Eqn. 11-4)     

### 100

Alternatively, dry weight residue levels in meat and where:

d a iry products may be converted to wet weight residue C = w et weight intake rate;

ww 

### levels for use with wet weight (e.g., as-consumed) = lipid weight intake rate; and C lw 

### intake rates as follows: L = percent lipid (fat) content.

(Eqn. 11-2) The resulting residue levels may then be used in 100 − W   C
ww C dw 

### conjunction with wet weight (e.g., as-consumed) consumption rates. The total fat content data presented in Table 11-28 are for selected meat and dairy products =     100



### where: taken from USDA, 2007.

C

ww 

### = wet weight intake rate;

C dw 

### = dry weight intake rate; and

W = percent water content.

The moisture content data presented in Table 11-28 are for selected meats and dairy products taken from USDA (2007).



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11.7 REFERENCES FOR CHAPTER 11

CDC. (1994) Dietary fat and total food-energy intake:

Third National Health and Nutrition Examination Survey, Phase 1, 1988-91. Center for Disease Control. Morbidity and Mortality Weekly Report, February 25, 1994: 43(7)118­

125. Cresanta, J.L.; Farris, R.P.; Croft, J.B.; Frank, G.C.; Berenson, G.S. (1988) Trends in fatty acid intakes of 10-year-old children, 1973-1982. J

Am Diet Assoc 88: 178-184. Devaney, B.; Kalb, L.; Briefel, R.; Zavitsky-Novak, T.; Clusen, N.; Ziegler, P. (2004) Feeding Infants and Toddlers Study: overview of the study design. J Am Diet Assoc 104(Suppl 1): S8­

S13. Fox, M.K.; Pac, S.; Devaney, B.; Jankowski, L. (2004) Feeding Infants and Toddlers Study: what foods are infants and toddlers eating. J Am Diet Assoc 104 (Suppl): S22-S30. Fox, M.K.; Reidy, K.; Karwe, V.; Ziegler, P. (2006) Average portions of foods commonly eaten by

infants and toddlers in the United States. J Am Diet Assoc. 106 (Suppl 1): S66-S76. Frank, G.C.; Webber, L.S.; Farris, R.P.; Berenson, G.S. (1986) Dietary databook: quantifying dietary intakes of infants, children, and adolescents, the Bogalusa heart study, 1973-1983. National

Research and Demonstration Center ­ Arteriosclerosis, Louisiana State University Medical Center, New Orleans, Louisiana. Goldman, L. (1995) Children - unique and vulnerable. Environmental risks facing children and recommendations for response. Environ Health Perspect 103(6):13-17. Mennella, J.; Ziegler, P.; Briefel, R.; Novak, T. (2006) Feeding Infants and Toddlers Study: the types of foods fed to Hispanic infants and toddlers. J Am Diet Assoc 106 (Suppl 1): S96-S106. Nicklas, T.A. (1995) Dietary studies of children: The Bogalusa Heart Study experience. J Am Diet

Assoc 95:1127-1133. Nicklas, T.A.; Webber, L.S.; Srinivasan, S.R.; Berenson, G.S. (1993) Secular trends in dietary intakes and cardiovascular risk factors in 10-y-old children: the Bogalusa heart study (1973-1988). Am J Clin Nutr 57:930-937. Ponza,	 M.; Devaney, B.; Ziegler, P.; Reidy, K.; Squatrito, C. (2004) Nutrient intakes and food choices of infants and toddlers participating in WIC. J Am Diet Assoc 104 (Suppl): S71-S79. Smiciklas-Wright, H.; Mitchell, D.C.; Mickle, S.J.; Cook, A.J.; Goldman, J.D. (2002) Foods commonly eaten in the United States: quantities consumed per eating occasion and in a day, 1994-1996. U.S. Department of Agriculture NFS Report No. 96-5, pre-publication version, 252 pp. USDA. (1999a) Food and nutrient intakes by children 1994-96, 1998: Table Set 17. Beltsville, MD: Food Surveys Research Group, Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture. USDA. (1999b) USDA Nutrient Database for Standard Reference, Release 13. Agricultural Research Service, Nutrient Data Laboratory. http://www.nal.usda.gov/fnic/foodcomp USDA. (2000) 1994–96, 1998 Continuing Survey ofFood Intakes by Individuals (CSFII). CDROM. Agricultural Research Service, Beltsville Human Nutrition Research Center, Beltsville, MD. Available from the National Technical Information Service, Springfield, VA; PB-2000-500027. USDA. (2007) USDA National Nutrient Database for Standard Reference, Release 20. Agricultural Research Service, Nutrient Data Laboratory Home Page, http://www.ars.usda.gov/ba/bhnrc/ndl

U.S. EPA.	 (2000) Food commodity intake database [FCID raw data file]. Office of Pesticide Programs, Washington, DC. Available from the National Technical Information Service, Springfield, VA; PB2000-5000101.

U.S. EPA.	 (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants.

U.S. Environmental Protection Agency, Washington, D.C., EPA/630/P-03/003F.

U.S. EPA. (2007) Analysis of fat intake based on the

U.S. Department of Agriculture’s 1994–96, 1998 Continuing Survey of Food Intakes by Individuals (CSFII). National Center for Environmental Assessment, Washington, DC;

EPA/600/R-05/021F. Available from the National Technical Information Service, Sp r ingfield , VA, and online at http://www.epa.gov/ncea.



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Chapter 11 - Intake of Meats, Dairy Products and FatsTable 11-3.Per Capita Intake of Total Meat and Dairy Products (g/kg-day as consumed)Age GroupNPercentConsumingMeanSEPercentiles1st5th10th25th50th75th90th95th99th100thTotal MeatBirth to 1 year1,48640.01.20.10.00.00.00.00.01.64.26.710.729.61 to 2 years2,09697.34.10.10.00.20.81.93.65.78.09.814.120.63 to 5 years4,39198.84.10.050.00.61.22.23.65.47.79.412.723.46 to 12 years2,08998.72.90.050.00.40.81.52.53.85.46.59.618.013 to 19 years1,22298.82.10.050.00.20.51.01.92.73.84.87.130.3Total DairyBirth to 1 year1,48679.512.60.90.00.00.01.08.014.124.148.71271861 to 2 years2,09699.836.70.70.43.97.717.431.349.872.188.31262233 to 5 years4,391100.023.30.31.14.27.013.020.830.942.049.467.71986 to 12 years2,089100.013.60.40.31.83.56.711.718.526.031.542.780.613 to 19 years1,22299.85.60.20.010.20.51.54.28.112.515.525.432.7NSE= Sample size.= Standard error.Source:Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII.



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Chapter 11 - Intake of Meats, Dairy Products and FatsTable 11-4.Consumer Only Intake of Total Meat and Dairy Products (g/(kg-day as consumed)Age GroupNMeanSEPercentiles1st5th10th25th50th75th90th95th99th100thTotal MeatBirth to 1 year5753.00.20.010.10.31.02.24.27.49.212.929.61 to 2 years2,0444.20.10.040.61.02.13.65.78.19.814.120.63 to 5 years4,3344.20.10.040.81.22.23.65.57.79.412.723.46 to 12 years2,0652.90.10.10.50.91.52.53.95.46.59.618.013 to 19 years1,2082.10.10.020.30.61.11.92.83.84.87.130.3Total DairyBirth to 1 year1,19215.91.00.030.81.95.810.216.027.757.5141.8185.61 to 2 years2,09336.80.70.44.27.817.431.349.872.188.3126.2223.23 to 5 years4,39023.30.31.14.27.013.020.830.942.049.467.7198.46 to 12 years2,08913.60.40.31.83.56.711.718.526.031.542.780.613 to 19 years1,2215.60.20.010.30.51.54.28.112.515.525.432.7N= Sample size.SE= Standard error.Source: Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII.



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5 eTable 11-5. Per Capita Intake of Individual Meats and Dairy Products (g/kg-day as consumed)Age GroupNPercentConsumingMeanSEPercentConsumingMeanSEPercentConsumingMeanSEPercentConsumingMeanSEBeefPorkPoultryEggsBirth to 1 year1 to 2 years3 to 5 years6 to 12 years13 to 19 years1,4862,0964,3912,0891,22225.385.590.892.791.10.411.71.81.31.00.040.060.040.040.0517.769.779.882.481.50.150.720.840.590.400.020.030.020.030.0330.173.773.067.165.50.661.71.50.930.680.050.050.030.030.0327.992.395.195.895.40.301.30.910.510.330.040.040.030.020.02NSE= Sample size.= Standard error.Source:Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII.Table 11-6.Consumer Only Intake of Individual Meats and Dairy Products (g/kg-day as consumed)Age GroupNMeanSENMeanSENMeanSENMeanSEBeefPorkPoultryEggsBirth to 1 year1 to 2 years3 to 5 years6 to 12 years13 to 19 years3611,7953,9641,9321,1181.62.01.91.41.10.20.060.040.040.052481,4883,4911,7311,0020.831.01.10.720.500.080.040.030.030.034341,5523,2101,4218082.22.22.01.41.00.10.060.040.040.044021,9364,1712,0011,1671.11.40.960.530.340.10.040.030.020.02NSE= Sample size.= Standard error.Source:Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII. 

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### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and FatsTable 11-7. Mean Quantities of Meat and Eggs consumed Daily by Sex and Age, Per Capita (g/day)Age GroupSampleSizeTotal Beef PorkLamb,veal,gameOrganmeatsFrankfurters,sausages,luncheonmeatsPoultryTotal ChickenEggsMixtures,mainly meat/poultry/fishMales and FemalesUnder 1 year1 year2 years1 to 2 years3 years4 years5 years3 to 5 years5 years and under1,1261,0161,1022,1181,8311,8598844,5747,81824809487101115121112931a57681014118-a,b26466665-a,b-a,b-a,b-a,b-a,b-a,b-a,b-b-b-a,b-a,b-a,b-a,b-a,b-a,b-a,b-a,b-a,b21318151922222117312171519202221162121614181919191531318161313131313164341424349514742Males6 to 9 years6 to 11 years12 to 19 years7871,0317371511542501819307712-a,b-a,b1a-a,b-a,b02424282322312120261112227172134Females6 to 9 years6 to 11 years12 to 19 years704969732121130158171821455-a,b-a,b-a,b-a,b-a,b-a,b181915192021161719101113556085Males and Females9 years and under19 years and under9,30911,287110152121857-b-a,b-a,b-a,b19201822171912145076aEstimate is not statistically reliable due to small sample size reporting intake.bValue less than 0.5, but greater than 0.Note: Consumption amounts shown are representative of the first day of each participant’s survey response.Source: USDA, 1999a.



### Child-Specific Exposure Factors Handbook Page September 2008 11-17

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and FatsTable 11-8. Percentage of Individuals Consuming Meats and Eggs, by Sex and Age (%)Age GroupSampleSizeTotal Beef PorkLamb,veal,gameOrganmeatsFrankfurters,sausages,luncheonmeatsPoultryEggsMixtures,mainly meat/poultry/fishTotal ChickenMales and FemalesUnder 1 year1 year2 years1 to 2 years3 years4 years5 years3 to 5 years5 years and under1,1261,0161,1022,1181,8311,8598844,5747,81826.077.485.281.486.286.287.186.577.52.111.916.214.113.816.118.216.013.71.1a7.314.911.213.313.813.213.411.20.2a0.8a0.8a0.8a0.5a0.5a0.6a0.50.60.2a0.2a0.2a0.2aa,b0.2a0.2a0.2a0.2a6.126.333.229.936.437.035.136.130.46.324.027.625.828.327.427.727.824.55.023.125.624.426.025.124.825.322.66.722.827.325.119.816.916.417.718.913.732.231.431.829.230.530.830.228.8Males6 to 9 years6 to 11 years12 to 19 years7871,03173787.487.886.820.122.024.211.912.215.80.4a0.4a0.6a0.1a0.2a0.037.436.231.824.822.920.622.320.517.615.115.617.036.235.738.3Females6 to 9 years6 to 11 years12 to 19 years70496973284.686.580.119.420.222.09.210.011.20.4a0.4a0.1a0.2a0.1a0.1a33.533.124.623.122.921.620.219.818.913.413.315.032.432.834.0Males and Females9 years and under19 years and under9,30911,28780.982.816.119.610.912.10.50.40.2a0.1a24.322.724.322.722.020.117.116.431.033.3aEstimate is not statistically reliable dueto small sample size reporting intake.bValue less than 0.5, but greater than 0.Note: Percentages shown are representative of thefirst day of each participant’s survey response.Source: USDA, 1999a.



### Page Child-Specific Exposure Factors Handbook

11-18 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and FatsTable 11-9. Mean Quantities of Dairy Products Consumed Daily by Sex and Age, Per Capita (g/day)Age GroupSampleSizeTotal Milkand MilkProductsMilk, Milk Drinks, YogurtTotalFluid MilkYogurtTotal Whole Lowfat SkimMilkDessertsCheeseMales and FemalesUnder 1 year1 year2 years1 to 2 years3 years4 years5 years3 to 5 years5 years and under1,1261,0161,1022,1181,8311,8598844,5747,8187625464054744194074174144777575263774503843693763764476147534440834732833033532749347181262166147137150177111151411281501491591531275a171126272526184141012101091010311161422232523181911101214141311Males6 to 9 years6 to 11 years12 to 19 years7871,0317374504504094054023583433353031271219917617215829334066313529131219Females6 to 9 years6 to 11 years12 to 19 years704969732380382269337336220288283190105108661461369226293044293029131414Males and Females9 years and under19 years and under9,30911,28745340541736232329115312114113522298623271214aEstimate is not statistically reliable due to small sample size reporting intake.bValue less than 0.5, but greater than 0.Note: Consumption amounts shown are representative of the first day of each participant’s survey response.Source: USDA, 1999a.



### Child-Specific Exposure Factors Handbook Page September 2008 11-19

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and FatsTable 11-10. Percentage of Individuals Consuming Dairy Products, by Sex and Age (%)Age GroupSampleSizeTotalMilk andMilkProductsMilk, milk drinks, yogurtTotalFluid MilkYogurtTotal Whole Lowfat SkimMilkDessertsCheeseMales and FemalesUnder 1 year1 year2 years1 to 2 years3 years4 years5 years3 to 5 years5 years and under1,1261,0161,1022,1181,8311,8598844,5747,81885.495.391.693.494.393.293.193.592.584.692.787.390.088.387.886.487.588.011.187.784.386.084.685.081.283.675.78.361.744.853.042.541.338.140.641.02.426.536.331.539.540.441.740.632.90.2a1.5a5.23.46.87.76.57.04.93.110.06.88.47.35.85.56.26.64.513.917.515.821.421.721.421.517.56.029.732.631.237.036.934.936.330.9Males6 to 9 years6 to 11 years12 to 19 years7871,03173793.292.381.385.584.665.880.779.059.632.430.822.644.343.130.78.69.57.03.83.71.7a24.025.013.634.632.337.1Females6 to 9 years6 to 11 years12 to 19 years70496973290.290.275.482.581.554.077.576.049.731.533.217.540.837.823.98.18.49.52.93.02.2a24.122.417.130.931.936.1Males and Females9 years and under19 years and under9,30911,28792.286.786.475.677.168.137.430.136.833.16.37.55.33.820.118.631.733.5aEstimate is not statistically reliable due to small sample size reporting intake.Note: Percentages shown are representative of the first day of each participant’s survey response.Source: USDA, 1999a.



### Page Child-Specific Exposure Factors Handbook

11-20 September 2008Table 11-11.Quantity (as consumed) of Meat and Dairy Products Consumed Per Eating Occasion and Percentage of Individuals Using These Foods in Two DaysQuantity consumed per eating occasion (grams)Food category2 to 5 years old6 to 11 years old12 to 19 years oldMale and Female(N = 2,109)Male and Female(N = 1,432)Male(N = 696)Female(N = 702)PCMean SEMPCMean SEMPCMeanSEM PCMeanSEMMeatsBeef steaksBeef roastsGround beefHamPork chopsBaconPork breakfast sausageFrankfurters and luncheon meatsTotal chicken and turkeyChickenTurkey11.15.259.56.911.010.45.351.763.844.65.158493135481533494652634514312111711.34.863.78.510.19.76.050.953.836.05.78767414062193257627066971442322359.55.173.411.611.614.96.346.758.434.38.2168233a66681002540a7610011711714149a37824a345149.45.561.59.98.511.13.338.554.136.15.811297a5240721840a57718060a1016a35715a3239aDairy ProductsFluid milk (all)Fluid milk consumed with cerealWhole milkWhole milk consumed with cerealLowfat milkLowfat milk consumed with cerealSkim milkSkim milk consumed with cerealCheese, other than cream orcottageIce cream and ice milkBoiled, poached, and baked eggsFried eggsScrambled eggs92.568.150.033.847.531.57.84.953.218.48.017.310.419614920216118913617113124923648593435349111331489.264.739.526.252.832.711.17.550.421.18.214.07.12412022442122381982251882913534587245711449141432572.344.430.014.839.624.39.76.561.114.25.014.97.1337276333265326277375285a3822144a837281013188123823a2129a5564.442.722.414.132.421.113.58.353.915.27.713.58.92622222582352622272551812718745591038871313121413114739aPCSEMIndicates a statistic that is potentially unreliable because of small sample size or large coefficient of variation.= Percent consuming at least once in 2 days.= Standard error of the mean.Source:Smiciklas-Wright et al., 2002 (based on 1994-1996 CSFII data). 

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### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-12. Characteristics of the FITS Sample Population

Sample Size Percentage of Sample

Gender

Male

Female 1,549

1,473
Age of Child

4 to 6 months

7 to 8 months

9 to 11 months

12 to 14 months

15 to 18 months

19 to 24 months 862

483

679

374

308

316

Child’s Ethnicity

Hispanic or Latino
Non-Hispanic or Latino
Missing 367

2,641

14

Child’s Race

White

Black

Other 2,417

225

380

Urbanicity

Urban

Suburban

Rural
Missing 1,389

1,014

577

42

Household Income

Under $10,000

$10,000 to $14,999

$15,000 to $24,999

$25,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 to $99,999

$100,000 and Over

Missing 48

48

221

359

723

588

311

272

452

Receives WIC

Yes

No
Missing 821

2,196

5

Sample Size (Unweighted) 3,022 51.3

48.7

28.5

16.0

22.5

12.4

10.2

10.4

12.1

87.4

0.5

80.0

7.4

12.6

46.0

33.6

19.1

1.3

1.6

1.6

7.3

11.9

23.9

19.5

10.3

9.0

14.9

27.2

72.6

0.2

100.0

WIC = Special Supplemental Nutrition Program for Women, Infants, and Children.

Source: Devaney et al., 2004.



### Page Child-Specific Exposure Factors Handbook

11-22 September 2008Table 11-13. Percentage of Infants and Toddlers Consuming Meat or Other Protein Sources

Percentage of Infants and Toddlers Consuming at Least Once in a Day

Food Group/Food 4 to 6 7 to 8 9 to 11 12 to14 15 to18 19 to 24

months months months months months months

Cow’s Milk 0.8 2.9 20.3 84.8 88.3 87.7

Whole 0.5 2.4 15.1 68.8 71.1 58.8

Reduce-fat or non-fat 0.3 0.5 5.3 17.7 20.7 38.1

Unflavored 0.8 2.9 19.5 84.0 87.0 86.5

Flavored 0.0 0.0 0.9 1.8 4.4 5.6

Soy Milk 0.0 0.5 1.7 1.5 3.9 3.8

Any Meat or Protein Source 14.2 54.9 79.2 91.3 92.7 97.2

BabyFood Meat 1.7 4.0 3.1 1.1 0.0 0.0

Non-baby Food Meat 1.5 8.4 33.7 60.3 76.3 83.7

OtherProtein Soources 2.7 9.7 36.1 59.2 66.8 68.9

Dried Beans and Peas, Vegetarian Meat 0.6 1.3 3.3 7.0 6.6 9.9

Eggs 0.7 2.9 7.3 17.0 25.0 25.2

Peanut Butter, Nuts, and Seeds 0.0 0.5 1.9 8.8 11.6 10.4

Cheese 0.4 2.1 18.5 34.0 39.1 41.1

Yogurt 1.2 4.1 15.7 14.9 20.2 15.3

Protein Sources in Mixed Dishes 11.0 43.3 46.2 30.1 25.5 20.5

Baby Food Dinners 9.5 39.8 33.5 10.2 2.4 1.3

Beans and Rice, Chilli, Other Bean Mixtures 0.0 0.0 0.9 1.2 2.1 2.0

Mixtures with Vegetables and/or Rice/Pasta 0.9 1.2 4.7 8.2 9.0 7.8

Soup a 0.9 3.4 10.1 12.5 13.8 11.5

Types of Meat b

Beef 0.9 2.6 7.7 16.1 16.3 19.3

Chicken or Turkey 2.0 7.3 22.4 33.0 46.9 47.3

Fishand Shellfish 0.0 0.5 1.9 5.5 8.7 7.1

Hotdogs, Sausages, and Cold cuts 0.0 2.1 7.1 16.4 20.1 27.0

Pork/Ham 0.3 1.7 4.0 9.7 11.2 13.9

Other 0.3 0.6 2.5 2.8 2.1 3.9

a The amount of protein actually provided by soups varies. Soups could not be sorted reliably into different food groups because all
soups were assigned the same two-digit food code and many food descriptions lacked detail about major soup ingredients.

b Includes baby food and non-baby food sources.

Source: Fox et al., 2004. 

### Child-Specific Exposure Factors Handbook

Chapter 11

- Intake of Meats, Dairy Products and Fats

Child-Specific Exposure Factors Handbook Page September 2008 11-23Table 11-14. Characteristics of WIC Participants and Non-participants a (Percentages)

Infants 4 to 6 months Infants 7 to 11 months Toddlers 12 to 24 months

WIC NonParticipant participant WIC NonParticipant participant WIC NonParticipant participant

Gender

Male 55

Female 45

Child’s Ethnicity

Hispanic or Latino 20 Non-Hispanic or Latino 80

Child’s Race

White 69

Black 15

Other 22

Child In Day Care

Yes 39

No 61

Age of Mother

14to19years 18 20to24years 33 25to29years 29 30to34years 9 35years or Older 9 Missing 2

Mother’s Education

11 th Grade or Less 23 Completed High School 35 SomePostsecondary 33 Completed College 7 Missing 2

Parent’s Marital Status

Married 49

NotMarried 50 Missing 1

Mother or Female Guardian Works

Yes 46

No 53 Missing 1

Urbanicity

Urban 34

Suburban 36

Rural 28
Missing 2
Sample Size (Unweighted) 265 54

46

**

11

89

**

84

4

11

38

62

**

1

13

29

33

23

2

**

2

19

26

53

1

**

93

7

1

51

48

1

**

55

31

13

1

597 55

45

24

76

63

17

20

34

66

13

38

23

15

11

1

15

42

32

9

2

57

42

1

45

54

1

37

31

30

2

351 51

49

**

8

92

**

86

5

9

**

46

54

**

1

11

30

36

21

1

**

2

20

27

51

0

**

93

7

0

**

60

40

0

**

50

34

15

1

808 57

43

22

78

67

13

20

43

57

9

33

29

18

11

0

17

42

31

9

1

58

41

1

55

45

0

35

35

28

2

205 52

48

**

10

89

**

84

5

11

*

53

47

**

1

14

26

34

26

1

**

3

19

28

48

2

**

88

11

1

*

61

38

1

**

48

35

16

2

791

a

WIC

Source: X 2 test were conducted to test for statistical significance in the differences between WIC participants and non-participants within each age group for each variable. The results of X 2 test are listed next to the variable under the column labeled non-participants for each of the three age groups. *P<0.05; **P>0.01; non-participants significantly different from WIC participants on the variable. = Special Supplemental Nutrition Program for Women, Infants, and Children.

Ponza et al., 2004. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Page Chil d-Specific Exposure Factors Handbook

11-24 September 2008Table 11-15. Food Choices for Infants and Toddlers by WIC Participation Status

Infants 4 to 6 months

WIC NonParticipant participant Infants 7 to 11 months Toddlers 12 to 24 months

WIC NonParticipant participant WIC Non- Participant participant

Cow’s Milk 1.0 0.6 11.4 13.2 92.3 85.8*

Meat or Other Protein Sources

BabyFood Meat Non-Baby Meat Eggs Peanut Butter, Nuts, Seeds

Cheese Yogurt 0.9

3.7

0.9

0.0

0.0

0.8 2.0

0.5**

0.6

0.0

0.6

1.4 3.3

25.0

8.5

1.4

9.0

5.5 3.6

22.0

4.2**

1.3

12.5

13.3** 0.0

77.7

24.1

12.9

38.5

9.3 0.3

75.1

23.0

9.8

38.8

18.9**

Sample Size (unweighted) 265 597 351 808 205 791

*

**

WIC

Source: = P<0.05; non-participants significantly different from WIC participants.

= P<0.01; non-participants significantly different from WIC participants. = Special Supplemental Nutrition Program for Women, Infants, and Children.

Ponza et al., 2004. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Child-Specific Exposure Factors Handbook Page September 2008 11-25Table 11-16. Percentage of Hispanic and Non-Hispanic Infants and Toddlers Consuming Different Types of Milk, Meats or Other Protein Sources on A Given Day

Age 4 to 5 months

Hispanic Non-Hispanic (N=84) (N=538) Age 6 to 11months Age 12 to 24 months

Hispanic Non-Hispanic (N=163) (N=1,228) Hispanic Non-Hispanic (N=124) (N=871)

Milk Fed Any Cow’s or Goat Milk

FedCow’s Milk

Whole

Reduced Fat or Non-fat

Meat or Other Protein Sources

AnyMeat or Protein Source a Non-Baby Food Meat

OtherProtein Sources

Beansand Peas Eggs

Cheese Yogurt

Protein Sources in Mixed Dishes

BabyFood dinners

b Soup Types of Meat a

Beef Chicken and Turkey Hotdogs, Sausages, and Cold Cuts

Pork/Ham -

-

-

9.7†

- 1.4† 1.4†

-

-

- 7.5† 6.9†

-

-

-

-

- -

-

-

5.3

-

-

-

-

-

- 4.4

3.9

-

-

-

-

- 7.5†

5.6† 2.2†

71.6

22.5

26.5 5.8†

9.5

11.2

7.7

44.8

24.7*

16.3**

5.0†

11.2 7.2† 3.8† 11.3

8.3

3.0

62.0

19.2

21.2

1.8

4.2

9.4

9.8

41.6

35.3

5.1

4.6

11.9

3.4

1.7 85.6

61.7

29.0

90.3

72.3

70.1

19.1*

26.4

29.3

15.7

33.3 3.5†

23.4*

25.2

46.5

14.8

11.7 87.7

66.3

27.0

94.7

76.0

65.3

6.5

22.5

40.2

17.0

22.7

3.9

10.7

16.0

43.6

23.3

12.1

a

b

- *

** †

N

Source: Includes baby food and non-baby food sources. The amount of protein actually provided by soups varies. Soups could not be sorted reliably into different food groups because many food descriptions lacked detail about major soup ingredients. = Less than 1 percent of the group consumed this food on a given day. = Significantly different from non-Hispanic at the P <0.05. = Significantly different from non-Hispanic at the P >0.01. = Statistic is potentially unreliable because of a high coefficient of variation. = Sample size.

Mennella et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 11

- Intake of Meats, Dairy Products and Fats

Page Child-Specific Exposure Factors Handbook

11-26 September 2008Table 11-17. Average Portion Sizes Per Eating Occasion of Meats and Dairy Products Commonly Consumed by Infants from the 2002 Feeding Infants and Toddlers Study

Food group Reference Unit 4 to 5 months (N=624) 6 to 8 months (N=708) 9 to 11 months (N=687)

Non-baby food meats

Cheese Scrambled eggs
Yogurt
Baby food dinners ounce

ounce

cup

ounce

ounce Mean± SEM

-

-

-

-
2.9±0.24 0.9±0.16

-

-

-
3.3±0.09 0.8±0.05

0.7±0.05

0.2±0.02

3.1±0.20

3.8±0.11

- N

SEM = Cell size was too small to generate a reliable estimate.

= Number of respondents.

= Standard error of the mean.

Source: Fox et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 11

- Intake of Meats, Dairy Products and Fats

Child-Specific Exposure Factors Handbook Page September 2008 11-27

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-18. Average Portion Sizes Per Eating Occasion of Meats and Dairy Products Commonly Consumed by Toddlers from the 2002 Feeding Infants and Toddlers Study

Food Group 12 to 14 months 15 to 18 months 19 to 24 months Reference unit (N=371) (N=312) (N=320)

Mean ± SEM

Milk

Milk fluid ounce 5.6±0.14 5.9±0.14 6.2±0.17 Milk, as a beverage fluid ounce 5.7±0.14 6.1±0.14 6.4±0.17
Milk, on cereal fluid ounce 3.4±0.37 2.7±0.26 3.6±0.29

Meats and other protein sources

All meats ounce 1.2±0.06 1.3±0.08 1.3±0.07

Beef ounce 0.8±0.08 1.2±0.15 1.2±0.14 Chicken or turkey, plain ounce 1.3±0.10 1.3±0.16 1.3±0.10 Hot dogs, luncheon meats, sausages ounce 1.3±0.13 1.5±0.13 1.5±0.12
Chicken, breaded a ounce 1.5±0.14 1.5±0.13 1.8±0.12

nugget 2.4±0.22 2.4±0.21 2.8±0.19 Scrambled eggs cup 0.2±0.02 0.3±0.03 0.3±0.02

Peanut butter tablespoon 0.7±0.08 0.7±0.09 0.9±0.13 Yogurt ounce 3.4±0.19 3.8±0.26 3.8±0.28

Cheese ounce 0.8±0.05 0.8±0.05 0.7±0.04

a Not included in total for all meats because weight includes breading. N = Number of respondents. SEM = Standard error of the mean.

Source: Fox et al., 2006.



### Page Child-Specific Exposure Factors Handbook

11-28 September 2008Table 11-19. Total Fat Intake (Per capita; g/day)

Age Group a N Mean SE Percentiles

10 th 25 th 50 th 75 th 95 th 100 th

Birth to <1 year all

female

male

Birth to <1 month all

female

male

1 to <3 months all

female

male

3 to <6 months all

female

male

6 to <12 months all

female

male

1 to <2 years all

female

male

2 to <3 years all

female

male

3 to <6 years all

female

male

6 to <11 years all

female

male

11 to <16 years all

female

male

16 to <21 years all

female

male 1,422

728

694

88

50

38

245

110

135

411

223

188

678

345

333

1,002

499

503

994

494

500

4,112

2,018

2,094

1,553

742

811

975

493

482

743

372

371 29

28

30

17

19

15

22

20

23

28

27

30

33

32

34

46

45

46

51

49

52

59

56

61

68

64

72

80

69

91

85

79

92 18

17

18

16

15

18

18

16

19

17

17

18

17

17

16

19

18

20

21

20

21

22

21

23

24

22

25

38

29

42

47

39

53 0.03

0.03

0.04

0

0

0

0

0

0

0.10

0.02

0.15

8.5

5.1

11

24

25

23

27

24

29

34

33

35

41

38

43

42

37

50

37

35

41 19

18

20

0

0

0

0

0

0

20

16

22

25

24

25

33

33

32

37

35

39

44

43

45

50

48

55

56

49

64

54

49

57 31

30

32

19

18

19

27

24

28

31

29

31

34

33

34

43

43

44

48

46

50

56

54

59

66

61

70

74

65

84

76

75

77 40

39

40

32

29

31

34

33

34

39

38

39

43

43

44

55

54

56

60

59

61

70

68

72

81

77

86

97

82

111

108

96

114 59

57

61

52

39

43

47

45

55

52

51

50

62

62

62

79

77

80

87

83

89

99

96

103

111

101

115

145

123

163

168

154

186 107

92

107

64

52

64

75

50

75

107

74

107

100

92

100

159

116

159

197

127

197

218

194

218

179

156

179

342

259

342

463

317

463

a

N SE

Source: Age groups are based on U.S. EPA (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants

. = Sample size. = Standard error.

Based on U.S. EPA, 2007. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Child-Specific Exposure Factors Handbook Page September 2008 11-29Table 11-20. Total Fat Intake (Per capita; g/kg-day)

Age Group a N Mean SE Percentiles

10 th 25 th 50 th 75 th 95 th 100 th

Birth to <1 year all

female

male

Birth to <1 month all

female

male

1 to <3 months all

female

male

3 to <6 months all

female

male

6 to <12 months all

female

male

1 to <2 years all

female

male

2 to <3 years all

female

male

3 to <6 years all

female

male

6 to <11 years all

female

male

11 to <16 years all

female

male

16 to <21 years all

female

male 1,422

728

694

88

50

38

245

110

135

411

223

188

678

345

333

1,002

499

503

994

494

500

4,112

2,018

2,094

1,553

742

811

975

493

482

743

372

371 4.0

4.1

4.0

5.2

5.9

4.3

4.5

4.3

4.7

4.1

4.2

4.1

3.7

3.7

3.6

4.0

4.1

3.9

3.6

3.7

3.6

3.4

3.4

3.5

2.6

2.4

2.7

1.6

1.4

1.8

1.3

1.1

1.4 2.8

2.8

2.8

4.9

4.6

5.3

3.8

3.6

3.9

2.7

2.8

2.5

1.8

1.9

1.7

1.7

1.6

1.7

1.5

1.6

1.5

1.3

1.3

1.4

1.1

1.0

1.1

0.80

0.69

0.86

0.66

0.56

0.73 0.01

0.01

0.01

0

0

0

0

0

0

0.01

0.00

0.02

1.0

0.66

1.3

2.1

2.2

1.9

1.9

1.8

2.0

1.9

1.8

1.9

1.3

1.3

1.4

0.77

0.67

0.88

0.54

0.48

0.63 2.3

2.4

2.3

0

0

0

0

0

0

2.4

2.3

2.6

2.7

2.8

2.6

2.8

3.0

2.6

2.6

2.4

2.6

2.4

2.4

2.4

1.7

1.6

1.8

1.1

0.91

1.2

0.81

0.75

0.85 4.1

4.3

4.0

5.7

6.2

4.7

4.9

4.8

4.9

4.3

4.5

4.1

3.8

3.8

3.7

3.7

3.7

3.6

3.4

3.4

3.4

3.2

3.1

3.2

2.3

2.2

2.4

1.4

1.3

1.6

1.2

1.1

1.2 5.6

5.8

5.5

9.1

8.4

9.7

6.8

6.5

7.0

5.7

6.0

5.5

4.8

5.0

4.6

4.7

5.0

4.5

4.4

4.4

4.3

4.0

4.0

4.1

3.0

2.8

3.1

2.0

1.7

2.1

1.6

1.4

1.7 8.9

8.7

9.2

16

13

18

11

11

10

8.2

8.2

8.2

7.0

7.0

6.8

7.1

6.9

7.2

6.4

6.6

6.1

5.8

5.8

5.8

4.2

4.0

4.4

3.0

2.6

3.3

2.7

2.1

2.9 20

18

20

20

16

20

18

14

18

18

18

16

11

9.8

11

12

9.7

12

12

10

12

11

11

11

9.9

7.7

9.9

5.7

5.0

5.7

6.0

4.4

6.0

a

N SE

Source: Age groups are based on U.S. EPA (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants

. = Sample size. = Standard error.

Based on U.S. EPA, 2007. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Page Chil d-Specific Exposure Factors Handbook

11-30 September 2008Table 11-21. Total Fat Intake (Consumers Only; g/day)

Age Group a N Mean SE Percentiles

10 th 25 th 50 th 75 th 95 th 100 th

Birth to <1 year all

female

male

Birth to <1 month all

female

male

1 to <3 months all

female

male

3 to <6 months all

female

male

6 to <12 months all

female

male

1 to <2 year all

female

male

2 to <3 years all

female

male

3 to <6 years all

female

male

6 to <11 years all

female

male

11 to <16 years all

female

male

16 to <21 years all

female

male 1,301

664

637

59

37

22

182

79

103

384

205

179

676

343

333

1,002

499

503

994

494

500

4,112

2,018

2,094

1,553

742

811

975

493

482

743

372

371 31

30

32

26

26

25

29

28

31

30

29

31

33

32

34

46

45

46

51

49

52

59

56

61

68

64

72

80

69

91

85

79

92 16

16

16

13

11

17

14

12

16

16

16

17

16

17

16

19

18

20

21

20

21

22

21

23

24

22

25

38

29

42

47

39

53 7.0

5.1

9.0

6.7

7.8

-

5.8

4.3

8.5

2.5

1.2

4.6

8.9

6.2

11

24

25

23

27

24

29

34

33

35

41

38

43

42

37

50

37

35

41 24

24

25

17

17

-

24

21

27

24

24

25

25

24

25

33

33

32

37

35

39

44

43

45

50

48

55

56

49

64

54

49

57 32

32

33

27

25

-

31

30

31

32

31

33

34

34

34

43

43

44

48

46

50

56

54

59

66

61

70

74

65

84

76

75

77 41

40

41

32

32

-

35

35

38

40

39

39

43

43

44

55

54

56

60

59

61

70

68

72

81

77

86

97

82

111

108

96

114 61

58

62

52

39

-

53

46

59

54

52

53

62

62

62

79

77

80

87

83

89

99

96

103

111

101

115

145

123

163

168

154

186 107

92

107

64

52

64

75

50

75

107

72

107

100

92

100

159

116

159

197

127

197

218

194

218

179

156

179

342

259

342

463

317

463

a

- N SE

Source: Age groups are based on U.S. EPA (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants

. = Percentiles were not calculated for sample sizes less than 30. = Sample size. = Standard error.

Based on U.S. EPA, 2007. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Child-Specific Exposure Factors Handbook Page September 2008 11-31Table 11-22. Total Fat Intake (Consumers Only; g/kg-day)

Age Group a N Mean SE Percentiles

10 th 25 th 50 th 75 th 95 th 100 th

Birth to <1 year all

female

male

Birth to <1 month all

female

male

1 to <3 months all

female

male

3 to <6 months all

female

male

6 to <12 months all

female

male

1 to <2 years all

female

male

2 to <3 years all

female

male

3 to <6 years all

female

male

6 to <11 years all

female

male

11 to <16 years all

female

male

16 to <21 years all

female

male 1,301

664

637

59

37

22

182

79

103

384

205

179

676

343

333

1,002

499

503

994

494

500

4,112

2,018

2,094

1,553

742

811

975

493

482

743

372

371 4.4

4.5

4.3

7.8

8.0

7.4

6.0

5.9

6.1

4.4

4.5

4.3

3.7

3.7

3.6

4.0

4.1

3.9

3.6

3.7

3.6

3.4

3.4

3.5

2.6

2.4

2.7

1.6

1.4

1.8

1.3

1.1

1.4 2.6

2.6

2.6

4.1

3.5

4.9

3.1

2.9

3.3

2.5

2.6

2.4

1.8

1.9

1.7

1.7

1.6

1.7

1.5

1.6

1.5

1.3

1.3

1.4

1.1

1.0

1.1

0.80

0.69

0.86

0.66

0.56

0.73 0.94

0.67

1.2

1.4

2.0

-

1.0

0.80

1.8

0.35

0.14

0.57

1.0

0.75

1.3

2.1

2.2

1.9

1.9

1.8

2.0

1.9

1.8

1.9

1.3

1.3

1.4

0.77

0.67

0.88

0.54

0.48

0.63 2.9

3.1

2.8

5.4

5.3

-

4.1

4.3

4.1

3.1

3.1

3.1

2.7

2.8

2.6

2.8

3.0

2.6

2.6

2.4

2.6

2.4

2.4

2.4

1.7

1.6

1.8

1.1

0.91

1.2

0.81

0.75

0.85 4.3

4.5

4.1

8.0

7.7

-

6.0

6.0

6.0

4.5

4.7

4.2

3.8

3.8

3.7

3.7

3.7

3.6

3.4

3.4

3.4

3.2

3.1

3.2

2.3

2.2

2.4

1.4

1.3

1.6

1.2

1.1

1.2 5.8

6.0

5.6

9.7

9.1

-

7.8

7.7

7.8

5.8

6.1

5.6

4.8

5.0

4.6

4.7

5.0

4.5

4.4

4.4

4.3

4.0

4.0

4.1

3.0

2.8

3.1

2.0

1.7

2.1

1.6

1.4

1.7 9.2

8.9

9.3

16

13

-

12

12

12

8.3

8.2

8.8

7.0

7.0

6.8

7.1

6.9

7.2

6.4

6.6

6.1

5.8

5.8

5.8

4.2

4.0

4.4

3.0

2.6

3.3

2.7

2.1

2.9 20

18

20

20

16

20

18

14

18

18

18

16

11

9.8

11

12

9.7

12

12

10

12

11

11

11

9.9

7.7

9.9

5.7

5.0

5.7

6.0

4.4

6.0

a

- N SE

Source: Age groups are based on U.S. EPA (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants

. = Percentiles were not calculated for sample sizes less than 30. = Sample size. = Standard error.

Based on U.S. EPA, 2007. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Page Chil d-Specific Exposure Factors Handbook

11-32 September 2008Table 11-23. Total Fat Intake - Top 10% of Animal Fat Consumers (Consumers Only; g/day)

Age Group a N Mean SE Percentiles

10 th 25 th 50 th 75 th 95 th 100 th

Birth to <1 year all

female

male

1 to <2 years all

female

male

2 to <3 years all

female

male

3 to <6 years all

female

male

6 to <11 years all

female

male

11 to <16 years all

16 to <21 years all

11 to 20 years

a ll

f emale

male 140

70

70

109

54

55

103

58

45

461

217

244

198

71

127

96

68

165

53

112 45

45

45

75

68

81

79

77

81

88

84

92

94

88

97

133

167

146

117

160 16

15

17

20

16

22

20

16

24

25

24

25

25

21

27

53

64

60

30

65 28

26

28

52

52

54

55

55

52

62

59

66

66

58

69

85

98

90

81

94 35

35

34

61

57

67

64

65

61

72

68

76

77

70

78

95

122

105

92

117 45

45

44

74

70

78

74

74

73

84

80

90

88

86

91

121

154

139

111

151 54

54

53

85

78

90

85

79

90

102

95

103

105

100

112

154

189

168

140

191 77

69

79

108

89

125

116

109

121

135

130

136

140

123

168

223

278

254

162

276 100

92

100

159

114

159

133

116

133

218

194

218

178

156

178

342

463

463

195

463

a

N SE

Source: Age groups are based on U.S. EPA (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants

. = Sample size. = Standard error.

Based on U.S. EPA, 2007. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Child-Specific Exposure Factors Handbook Page September 2008 11-33Table 11-24. Total Fat Intake - Top 10% of Animal Fat Consumers (Consumers Only; g/kg-day )

Age Group a N Mean SE Percentiles

10 th 25 th 50 th 75 th 95 th 100 th

Birth to <1 year all

female

male

1 to <2 years all

female

male

2 to <3 years all

female

male

3 to <6 years all

female

male

6 to <11 years all

female

male

11 to <16 years all

16 to <21 years all

11 to 20 years

a ll

f emale

male 140

70

70

109

54

55

103

58

45

461

217

244

198

71

127

96

68

165

53

112 4.7

4.8

4.6

6.9

6.6

7.1

6.1

6.2

6.1

5.6

5.5

5.7

4.2

4.2

4.2

3.0

2.5

2.8

2.6

2.9 1.7

1.6

1.7

1.5

1.2

1.6

1.3

1.2

1.3

1.3

1.3

1.3

1.1

1.1

1.1

0.85

0.74

0.84

0.65

0.90 2.8

2.7

2.8

5.1

5.1

5.1

4.6

4.6

4.5

4.2

4.2

4.2

3.0

2.9

3.0

2.0

1.7

1.9

1.7

1.9 3.7

3.7

3.6

5.7

5.7

5.8

5.2

5.2

5.2

4.7

4.5

4.8

3.4

3.3

3.4

2.4

2.0

2.1

2.0

2.3 4.6

4.7

4.4

6.8

6.7

6.9

5.8

5.9

5.6

5.3

5.3

5.3

3.8

3.8

3.8

2.8

2.4

2.7

2.3

2.8 6.0

6.0

5.8

7.7

7.4

8.0

6.7

6.8

6.6

6.2

6.0

6.2

4.6

4.8

4.5

3.3

2.9

3.1

2.7

3.1 7.7

7.7

7.5

9.5

9.3

9.4

8.3

7.9

8.4

8.3

7.8

8.4

6.0

5.8

6.3

4.6

3.7

4.4

3.4

4.5 11

9.5

11

12

9.7

12

9.5

9.5

9.5

11

11

11

9.9

7.7

9.9

5.7

6.0

6.0

4.6

6.0

a

N SE

Source: Age groups are based on U.S. EPA (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants

. = Sample size. = Standard error.

Based on U.S. EPA, 2007. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Page Child -Specific Exposure Factors Handbook

11-34 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-25. Fat Intake Among Children Based on Data from the Bogalusa Heart Study, 1973-1982 (g/day)

Age N Mean SD Percentiles Minimum
10 th 25 th 50 th 75 th 90 th Maximum

Total Fat Intake

6 months

1 year

2 years

3 years

4 years

10 years

13 years

15 years

17 years 125 37.1 17.5 18.7 25.6 33.9 46.3 60.8 3.4

99 59.1 26.0 29.1 40.4 56.1 71.4 94.4 21.6

135 86.7 41.3 39.9 55.5 79.2 110.5 141.1 26.5

106 91.6 38.8 50.2 63.6 82.6 114.6 153.0 32.6

219 98.6 56.1 46.0 66.8 87.0 114.6 163.3 29.3

871 93.2 50.8 45.7 60.5 81.4 111.3 154.5 14.6

148 107.0 53.9 53.0 69.8 90.8 130.7 184.1 9.8

108 97.7 48.7 46.1 65.2 85.8 124.0 165.2 10.0

159 107.8 64.3 41.4 59.7 97.3 140.2 195.1 8.5 107.6

152.7

236.4

232.5

584.6

529.5

282.2

251.3

327.4

Total Animal Fat Intake

6 months

1year

2 years

3 years

4 years

10 years

13 years

15 years

17 years 125 18.4 16.0 0.7 4.2 13.9 28.4 42.5 0.0

99 36.5 20.0 15.2 23.1 33.0 45.9 65.3 0.0

135 49.5 28.3 20.1 28.9 42.1 66.0 81.4 10.0

106 50.1 29.4 21.3 29.1 42.9 64.4 88.9 14.1

219 50.8 31.7 21.4 28.1 42.6 66.4 92.6 5.9

871 54.1 39.6 20.3 30.6 45.0 64.6 97.5 0.0

148 56.2 39.8 19.8 28.5 44.8 72.8 109.4 4.7

108 53.8 35.1 15.9 28.3 44.7 67.9 105.8 0.6

159 64.4 48.5 15.2 30.7 51.6 86.6 128.8 2.6 61.1

127.1

153.4

182.6

242.2

412.3

209.6

182.1

230.3

Total Vegetable Fat Intake

6 months

1 year

2 years

3 years

4 years

10 years

13 years

15 years

17 years 125 9.2 12.8 0.6 1.2 2.8 11.6 29.4 0.0

99 15.4 14.3 3.7 6.1 11.3 18.1 38.0 0.2

135 19.3 16.3 3.8 7.9 14.8 26.6 42.9 0.7

106 21.1 15.5 3.9 8.6 18.7 26.6 45.2 1.0

219 24.5 18.6 5.7 10.4 21.8 33.3 48.5 0.9

871 23.7 21.6 4.3 9.5 18.3 30.6 49.0 0.6

148 34.3 27.4 8.4 17.9 31.2 44.6 57.5 0.0

108 27.3 22.8 5.1 11.9 22.6 38.1 54.4 0.7

159 25.7 21.3 4.2 11.7 20.8 32.9 47.6 0.0 53.2

70.2

96.6

70.4

109.0

203.7

238.3

132.2

141.5



### Child-Specific Exposure Factors Handbook Page September 2008 11-35

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-25. Fat Intake Among Children Based on Data from the Bogalusa Heart Study, 1973-1982 (g/day) (continued)

Age N Mean SD Percentiles Minimum
10 th 25 th 50 th 75 th 90 th Maximum

Total Fish Fat Intake

6 months

1 year

2 years

3 years

4 years

10 years

13 years

15 years

17 years 125 0.05 0.1 0.0 0.0 0.0 0.0 0.1 0.0

99 0.05 0.2 0.0 0.0 0.0 0.0 0.0 0.0

135 0.04 0.2 0.0 0.0 0.0 0.0 0.0 0.0

106 0.1 0.6 0.0 0.0 0.0 0.0 0.0 0.0

219 2.3 31.1 0.0 0.0 0.0 0.0 0.0 0.0

871 0.3 1.5 0.0 0.0 0.0 0.0 0.0 0.0

148 0.3 2.2 0.0 0.0 0.0 0.0 0.0 0.0

108 0.4 1.5 0.0 0.0 0.0 0.0 1.5 0.0

159 0.5 2.0 0.0 0.0 0.0 0.0 0.4 0.0 0.9

1.9

1.9

4.5

459.2

19.2

25.4

9.5

15.3

N SD

Source: = Sample size. = Standard deviation.

Frank et al., 1986.



### Page Child-Specific Exposure Factors Handbook

11-36 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-26. Fat Intake Among Children Based on Data from the Bogalusa Heart Study, 1973-1982 (g/kg-day)

Age N Mean SD Percentiles Minimum 10 th 25 th 50 th 75 th 90 th Maximum

Total Fat Intake

6 months

1 year

2 years

3 years

4 years

10 years

13 years

15 years

17 years 125 4.9 2.3 2.4 3.3 4.7 6.2 8.0 0.4

99 6.1 2.8 3.0 4.1 5.7 7.5 9.5 2.3

132 7.0 3.3 3.4 4.5 6.2 8.6 11.9 2.1

106 6.4 2.7 3.6 4.6 5.5 8.2 9.9 2.2

218 6.1 3.7 2.9 4.0 5.2 7.0 10.0 2.0

861 2.7 1.5 1.2 1.7 2.4 3.3 4.5 0.3

147 2.3 1.3 1.0 1.5 2.0 2.8 3.8 0.2

105 1.7 0.8 0.8 1.2 1.5 2.1 3.1 0.2

149 1.8 1.0 0.7 0.9 1.6 2.2 3.1 0.2 13.2

16.4

18.7

16.7

38.2

13.9

10.2

4.7

6.2

Total Animal Fat Intake

6 months

1 year

2 years

3 years

4 years

10 years

13 years

15 years

17 years 125 2.4 2.1 0.08 0.6 2.0 3.7 5.5 0.0

99 3.8 2.1 1.7 2.4 3.4 4.9 6.5 0.0

132 4.0 2.3 1.7 2.3 3.4 5.2 6.7 0.7

106 3.5 2.0 1.6 2.1 3.1 4.2 6.1 0.9

218 3.1 2.1 1.3 1.7 2.6 4.0 5.4 0.4

861 16 1.2 0.6 0.8 1.3 1.9 2.8 0.00

147 1.2 0.9 0.4 0.6 0.9 1.6 2.3 0.08

105 1.0 0.6 0.3 0.5 0.8 1.3 1.9 0.01

149 1.0 0.8 0.3 0.5 0.8 1.4 2.0 0.05 9.0

13.6

13.4

13.1

15.4

10.8

5.2

3.1

4.2

Total Vegetable Fat Intake

6 months

1year

2 years

3 years

4 years

10 years

13 years

15 years

17 years 125 1.2 1.8 0.08 0.2 0.4 1.6 4.1 0.0

99 1.6 1.6 0.4 0.6 1.2 1.9 3.8 0.02

132 1.6 1.4 0.3 0.7 1.1 2.0 3.5 0.06

106 1.5 1.1 0.3 0.6 1.4 2.0 3.0 0.08

218 1.5 1.2 0.4 0.6 1.2 2.1 2.8 0.06

861 0.7 0.6 0.1 0.3 0.5 0.9 1.4 0.02

147 0.8 0.8 0.2 0.4 0.6 0.9 1.3 0.0

105 0.5 0.4 0.09 0.2 0.4 0.7 0.9 0.01

149 0.4 0.4 0.07 0.2 0.4 0.6 0.9 0.0 8.2

7.6

8.5

5.1

7.3

4.2

8.6

2.2

2.1



### Child-Specific Exposure Factors Handbook Page September 2008 11-37

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-26. Fat Intake Among Children Based on Data from the Bogalusa Heart Study, 1973-1982 (g/kg-day) (continued)

Age N Mean SD Percentiles Minimum Maximum 10 th 25 th 50 th 75 th 90 th

Total Fish Fat Intake

6 months 125 0.01 0.02 0.0 0.0 0.0 0.0 0.02 0.0 0.1

1 year 99 0.01 0.03 0.0 0.0 0.0 0.0 0.0 0.0 0.2

2 years 132 0.003 0.02 0.0 0.0 0.0 0.0 0.0 0.0 0.2

3 years 106 0.01 0.04 0.0 0.0 0.0 0.0 0.0 0.0 0.3

4 years 218 0.2 2.0 0.0 0.0 0.0 0.0 0.0 0.0 30.0

10 years 861 0.01 0.05 0.0 0.0 0.0 0.0 0.0 0.0 0.6

13 years 147 0.01 0.04 0.0 0.0 0.0 0.0 0.0 0.0 0.4

15 years 105 0.01 0.03 0.0 0.0 0.0 0.0 0.04 0.0 0.2

17 years 149 0.01 0.03 0.0 0.0 0.0 0.0 0.008 0.0 0.2

N = Sample size. SD = Standard deviation.

Source: Frank et al., 1986.



### Page Child-Specific Exposure Factors Handbook

11-38 September 2008Table 11-27. Mean Total Daily Dietary Fat Intake (g/day) Grouped by Age and Gender a

Age Group Total Males Females

N Mean Fat Intake (g/day) N Mean Fat Intake (g/day) N Mean Fat Intake (g/day)

2 to 11months 1 to 2 years 3 to 5 year 6 to 11 years 12 to 16 years 16 to 19 years 871 1,231 1,647 1,745

711

785 37.5

50.0

60.4

74.2

85.2

100.5 439

601

744

868

338

308 38.3

51.6

62.3

79.4

98.1

123.2 432

630

803

877

373

397 36.8

48.4

57.7

69.0

71.3

77.5

a

N

Source: Total dietary fat intake includes all fat (i.e., saturated and unsaturated) derived from consumption of foods and beverages (excluding plain drinking water). = Sample size.

Adapted from CDC, 1994. 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Child-Specific Exposure Factors Handbook Page September 2008 11-39Table 11-28. Mean Percent Moisture and Total Fat Content of Selected Meat and Dairy Products a

Product Moisture Content

(%) Total Fat Content

(%) Comment

Meats
Beef (composite of trimmed retail cuts; all grades) 70.62
59.25

60.44

51.43 6.16

9.91

19.24

21.54 Raw; lean only Cooked; lean only Raw; lean and fat, 1/4 in. fat trim Cooked; lean and fat, 1/4 in. fat trim

Pork (composite of trimmed retail cuts)

Cured ham

Cured bacon 72.34

60.31

65.11

54.55

63.46
55.93 5.88

9.66
14.95

17.18

12.90

8.32

45.04

43.27

41.78

40.30

37.27 Raw; lean only Cooked; lean only Raw; lean and fat Cooked; lean and fat

Center slice, unheated; lean and fat Raw, center slice, country style; lean only

Raw Cooked, baked Cooked, broiled Cooked, pan-fried Cooked, microwaved

Lamb (composite of trimmed retail cuts) 73.42

61.96

60.70

53.72 5.25
9.52
21.59

20.94 Raw; lean only Cooked; lean only Raw; lean and fat, 1/4 in. fat trim Cooked; lean and fat, 1/4 in. fat trim

Veal (composite of trimmed retail cuts) 75.91

60.16

72.84

57.08 2.87

6.58

6.77
11.39 Raw; lean only Cooked; lean only Raw; lean and fat, 1/4 in. fat trim Cooked; lean and fat, 1/4 in. fat trim

Rabbit (domesticated) 72.82

60.61 5.55

8.05

8.41 Raw Cooked, roasted Cooked, stewed

Chicken (broilers or fryers)

Duck (domesticated)

Turkey (all classes) 75.46

63.79

65.99

59.45

73.77

64.22

48.50

51.84

74.16

64.88

70.40

61.70

71.97
59.42 3.08

6.71

7.41

9.12

15.06

12.56

13.60

14.92

5.95

11.20

39.34

28.35

2.86

4.97

8.02

9.73

8.26

13.15 Raw; meat only Cooked, stewed; meat only Cooked, roasted; meat only Cooked, fried; meat only Raw; meat and skin Cooked, stewed; meat and skin Cooked, roasted; meat and skin Cooked, fried, flour; meat and skin

Raw; meat only Cooked, roasted; meat only Raw; meat and skin Cooked, roasted; meat and skin

Raw; meat only Cooked, roasted; meat only Raw; meat and skin Cooked, roasted; meat and skin Raw; ground Cooked; ground



### Page Child-Specific Exposure Factors Handbook

11-40 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11-28. Mean Percent Moisture and Total Fat Content of Selected Meat and Dairy Products a (continued)

Product Moisture Content

(%) Total Fat Content

(%) Comment

Dairy

Milk

Cream

Butter
Cheese

Yogurt Eggs Whole

Human Lowfat (1%) Reduced fat (2%)

Skim or fat free

Half and half Light (coffee cream or table cream) Heavy-whipping

Sour Sour, reduced fat

American

Cheddar

Swiss

Cream

Parmesan Cottage, lowfat Colby

Blue

Provolone

Mozzarella 88.32

87.50

89.81

88.86

90.38

80.57

73.75

57.71
70.95

80.14

15.87

39.16

36.75

37.12

53.75 29.16; 20.84 82.48; 79.31

38.20

42.41
40.95 50.01; 53.78 85.07; 87.90

75.84 3.25

4.38

0.97

1.92

0.25

11.50

19.31

37.00

20.96

12.00

81.11

31.25

33.14

27.80

34.87 25.83; 28.61

1.02; 1.93

32.11

28.74

26.62 22.35; 15.92

1.55; 3.25

9.94 3.25% milkfat Whole, mature, fluid Fluid, with added non-fat milk solids and vitamin A Fluid, with added non-fat milk solids and vitamin A Fluid, with added non-fat milk solids and vitamin A

Fluid

Fluid

Fluid

Cultured

Cultured

Salted

Pasteurized

Hard; grated 1% fat; 2% fat

Whole milk; Skim milk Plain, lowfat; Plain, with fat Chicken, whole raw, fresh

a

Source: Based on the water and lipid content in 100 grams, edible portion. Total Fat Content = saturated, monosaturated and polyunsaturated. For additional information, consult the USDA nutrient database.

USDA, 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 11-41

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

APPENDIX 11A

CODES AND DEFINITIONS USED TO DETERMINE THE VARIOUS MEATS AND

DAIRY PRODUCTS USED IN THE U.S. EPA ANALYSIS OF CSFII DATA IN FCID

Child-Specific Exposure Factors Handbook Page September 2008 11A-1

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11A-1 Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data

Food Category EPA Food Commodity Codes

Total Meats 21000440 Beef, meat 21000441 Beef, meat-babyfood 21000450 Beef, meat, dried 21000460 Beef, meat byproducts 21000461 Beef, meat byproducts-babyfood 21000470 Beef, fat 21000471 Beef, fat-babyfood 23001730 Goat, liver 24001890 Horse, meat 25002900 Pork, meat 25002901 Pork, meat-babyfood 25002910 Pork, skin 25002920 Pork, meat byproducts 25002921 Pork, meat byproducts-babyfood 25002930 Pork, fat 25002931 Pork, fat-babyfood 25002940 Pork, kidney 25002950 Pork, liver 26003390 Sheep, meat 26003391 Sheep, meat-babyfood 26003400 Sheep, meat byproducts 26003410 Sheep, fat 26003411 Sheep, fat-babyfood 26003420 Sheep, kidney 26003430 Sheep, liver 28002210 Meat, game 29003120 Rabbit, meat 40000930 Chicken, meat 40000931 Chicken, meat-babyfood 40000940 Chicken, liver 21000480 Beef, kidney 21000490 Beef, liver 21000491 Beef, liver-babyfood 23001690 Goat, meat 23001700 Goat, meat byproducts 23001710 Goat, fat 23001720 Goat, kidney 40000950 Chicken, meat byproducts 40000951 Chicken, meat byproducts-babyfood 40000960 Chicken, fat 40000961 Chicken, fat-babyfood 40000970 Chicken, skin 40000971 Chicken, skin-babyfood 50003820 Turkey, meat 50003821 Turkey, meat-babyfood 50003830 Turkey, liver 50003831 Turkey, liver-babyfood 50003840 Turkey, meat byproducts 50003841 Turkey, meat byproducts-babyfood 50003850 Turkey, fat 50003851 Turkey, fat-babyfood 50003860 Turkey, skin 50003861 Turkey, skin-babyfood 60003010 Poultry, other, meat 60003020 Poultry, other, liver 60003030 Poultry, other, meat byproducts 60003040 Poultry, other, fat 60003050 Poultry, other, skin

Total Dairy 27002220 Milk, fat 27002221 Milk, fat - baby food/infant formula 27012230 Milk, non-fat solids 27012231 Milk, non-fat solids-baby food/infant

formula 27022240 Milk, water 27022241 Milk, water-babyfood/infant formula 27032251 Milk, sugar (lactose)-baby food/infant

formula



### Page Child-Specific Exposure Factors Handbook

11A-2 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Table 11A-1 Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data (continued)

Food Category EPA Food Commodity Codes

Beef 21000440 Beef, meat 21000441 Beef, meat-babyfood 21000450 Beef, meat, dried 21000460 Beef, meat byproducts 21000461 Beef, meat byproducts-babyfood 21000470 Beef, fat 21000471 Beef, fat-babyfood 21000480 Beef, kidney 21000490 Beef, liver 21000491 Beef, liver-babyfood

Eggs 70001450 Egg, whole 70001451 Egg, whole-babyfood 70001460 Egg, white 70001461 Egg, white (solids)-babyfood 70001470 Egg, yolk 70001471 Egg, yolk-babyfood

Pork 25002900 Pork, meat 25002901 Pork, meat-babyfood 25002910 Pork, skin 25002920 Pork, meat byproducts 25002921 Pork, meat byproducts-babyfood 25002930 Pork, fat 25002931 Pork, fat-babyfood 25002940 Pork, kidney 25002950 Pork, liver

Poultry 40000930 Chicken, meat 40000931 Chicken, meat-babyfood 40000940 Chicken, liver 40000950 Chicken, meat byproducts 40000951 Chicken, meat byproducts-babyfood 40000960 Chicken, fat 40000961 Chicken, fat-babyfood 40000970 Chicken, skin 40000971 Chicken, skin-babyfood 50003820 Turkey, meat 50003821 Turkey, meat-babyfood 50003830 Turkey, liver 50003831 Turkey, liver-babyfood 50003840 Turkey, meat byproducts 50003841 Turkey, meat byproducts-babyfood 50003850 Turkey, fat 50003851 Turkey, fat-babyfood 50003860 Turkey, skin 50003861 Turkey, skin-babyfood 60003010 Poultry, other, meat 60003020 Poultry, other, liver 60003030 Poultry, other, meat byproducts 60003040 Poultry, other, fat 60003050 Poultry, other, skin



### Child-Specific Exposure Factors Handbook Page September 2008 11A-3

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

APPENDIX 11B

SAMPLE CALCULATION OF MEAN DAILY FAT INTAKE BASED ON CDC (1994) DATA

Child-Specific Exposure Factors Handbook Page September 2008 11B-1kcal g − fat g − fat 0.34 × 2,095 × X = 82 day day day



### g − fat

∴ X = 0.12 kcal kcal g − fat g − fat 1,591 × 0.33 × 0.12 = 63 day kcal day 

### Child-Specific Exposure Factors Handbook

Chapter 11 - Intake of Meats, Dairy Products and Fats

Sample Calculation of Mean Daily Fat Intake Based on CDC ( 1994) Data

CDC (1994) provided data on the mean daily

total food energy intake (TFEI) and the mean percentages of TFEI from total dietary fat grouped by

age and gender. The overall mean daily TFEI was 2,095 kcal for the total population and 34 percent (or 82 g) of their TFEI was from total dietary fat (CDC, 1994).

Based on this information, the amount of fat per kcal

was calculated as shown in the following example. where 0.34 is the fraction of fat intake, 2,095 is the total

food intake, and X is the conversion factor from kcal/day to g-fat/day.

Using the conversion factor shown above (i.e., 0.12 g-fat/kcal) and the information on the mean daily TFEI and percentage of TFEI for the various age/gender groups, the daily fat intake was calculated

for these groups. An example of obtaining the grams of

fat from the daily TFEI (1,591 kcal/day) for children

ages 3-5 years and their percent TFEI from total dietary

fat (33 percent) is as follows:



### Page Child-Specific Exposure Factors Handbook

11B-2 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

12 INTAKE OF GRAIN PRODUCTS

12.1 INTRODUCTION

The American food supply is generally

considered to be one of the safest in the world. Nevertheless, grain products may become contaminated with toxic chemicals by several different pathways. Ambient air pollutants may be deposited on or absorbed by the plants, or dissolved in rainfall or irrigation waters that contact the plants. Pollutants may also be absorbed through plant roots from contaminated soil and ground water. The addition of pesticides, soil additives, and fertilizers may also result in contamination of grain products. To assess exposure through this pathway, information on ingestion rates of grain products are needed. Children’s exposure from contaminated foods may differ from that of adults because of differences in the type and amounts of food eaten. Also, for many foods, the intake per unit body weight is greater for children than for adults. Common grain products eaten by children include milled rice, oats, and wheat flour (Goldman, 1995). A variety of terms may be used to define intake of grain products (e.g., consumer-only intake, per capita intake, total grain intake, as-consumed intake, dry weight intake). As described in Chapter 9, Intake of Fruits and Vegetables, consumer-only intake is defined as the quantity of grain products consumed by children during the survey period. These data are generated by averaging intake across only the children in the survey who consumed these food items. Per capita intake rates are generated by averaging consumer-only intakes over the entire population of children (including those children that reported no intake). In general, per capita intake rates are appropriate for use in exposure assessment s for which average dose estimates for children are of interest because they represent both children who ate the foods during the survey period and children who may eat the food items at some time, but did not consume them during the survey period. Per capita intake, therefore, represents an average across the entire population of interest, but does so at the expense of underestimating consumption for the subset of the population that consumed the food in question. Total grain intake refers to the sum of all grain products consumed in a day. Intake rates may be expressed on the basis of the as-consumed weight (e.g., cooked or prepared) or on the uncooked or unprepared weight. As-consumed intake rates are based on the weight of the food in the

form that it is consumed and should be used in

assessments where the basis for the contaminant

concentrations in foods is also indexed to the asconsumed weight. The food ingestion values provided in this chapter are expressed as as-consumed intake

rates because this is the fashion in which data were reported by survey respondents. This is of importance

because concentration data to be used in the dose equation are often measured in uncooked food samples. It should be recognized that cooking can either increase or decrease food weight. Similarly, cooking can increase the mass of contaminant in food (due to formation reactions, or absorption from cooking oils or water) or decrease the mass of contaminant in food (due to vaporization, fat loss or leaching). The combined effects of changes in weight and changes in contaminant

mass can result in either an increase or decrease in contaminant concentration in cooked food. Therefore, if the as-consumed ingestion rate and the uncooked concentration are used in the dose equation, dose maybe under-estimated or over-estimated. Ideally, aftercooking food concentrations should be combined with the as-consumed intake rates. In the absence of data, it is reasonable to assume that no change in contaminant concentration occurs after cooking. It is important for

the assessor to be aware of these issues and choose

intake rate data that best match the concentration data that are being used. For more information on cooking losses and conversions necessary to account for such losses, the reader is referred to Chapter 13 of this

handbook.

Sometimes contaminant concentrations in food are reported on a dry weight basis. When these data are used in an exposure assessment, it is recommended that dry-weight intake rates also be used. Dry-weight food concentrations and intake rates are based on the weight

of the food consumed after the moisture content has been removed. For information on converting the intake rates presented in this chapter to dry weight intake rates, the reader is referred to Section 12.4. The purpose of this chapter is to provide intake data for grain products among children. The recommendations for ingestion rates of grain



### Child-Specific Exposure Factors Handbook Page September 2008 12-1

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

products are provided in the next section, along with a summary of the confidence ratings for these

recommendations. The recommended values are based on the key study identified by U.S. EPA for this factor. Following the recommendations, the key study on ingestion of grain products is summarized. Relevant data on ingestion of grain products are also provided. These data are presented to provide the reader with added perspective on the current state-of-knowledge pertaining to ingestion of grain products among

children.

12.2 RECOMMENDATIONS Table 12-1 presents a summary of the recommended values for per capita and consumer-only intake of grain products, on an as

- consumed basis. Confidence ratings for the grain intake recommendations for general population children are provided in Table 12-2. The U.S. EPA analysis of data from the 1994­ 96 and 1998 Continuing Survey of Food Intake among Individuals (CSFII) was used in selecting recommended intake rates for general population children. The U.S. EPA analysis was conducted using age groups that differed slightly from U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). However, for the purposes of the recommendations presented here, data were placed in the standardized age categories closest to those used in the analysis. Also, the CSFII data on which the recommendations are based are short-term survey data and may not necessarily reflect the long-term distribution of average daily intake rates. However, for broad categories of food (i.e., total grains), because they are eaten on a daily basis throughout the year with minimal seasonality, the short term distribution may be a reasonable approximation of the long-term distribution, although it will display somewhat increased variability. This implies that the upper percentiles shown here will tend to overestimate the corresponding percentiles of the true long-term

distribution. It should also be noted that because these

recommendations are based on 1994-96 and 1998 CSFII data, they may not reflect the most recent changes that may have occurred in consumption patterns. More current data from the National Health and Nutrition Survey (NHANES) will be incorporated as the data become available and are analyzed.



### Page Child-Specific Exposure Factors Handbook

12-2 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12-1. Recommended Values for Intake of Grains, As Consumed a

Per Capita Consumers Only Multiple Age Group Source Mean 95 th Percentile Mean 95 th Percentile Percentiles
g/kg-day g/kg-day g/kg-day g/kg-day

Total Grains

Birth to 1 year 2.5 8.6 3.6 9.2

1 to <2 years 6.4 12 6.4 12

U.S. EPA 2 to <3 years 6.4 12 6.4 12 Analysis of 3 to <6 years 6.3 12 6.3 12 See Tables CSFII,

1994-96 and 12-3 and 6 to <11 years 4.3 8.2 4.3 8.2 1998. 12-4 11 to <16 years 2.5 5.1 2.5 5.1

16 to <21 years 2.5 5.1 2.5 5.1

Individual Grain Products - See Tables 12-5 and 12-6

a Analysis was conducted using slightly different age groups than those recommended in Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA. 2005). Data were placed in the standardized age categories closest to those used in the analysis.



### Child-Specific Exposure Factors Handbook Page September 2008 12-3

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12-2. Confidence in Recommendations for Intake of Grain Products

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach The survey methodology and data analysis was adequate. The survey sampled more than 11,000 individuals up to age 18 years. An analysis of primary

data was conducted.

Minimal (or defined) Bias No physical measurements were taken. The method relied on recent recall of grain products eaten. High

Applicability and Utility

Exposure Factor of Interest The key study was directly relevant to grain intake.

Representativeness The data were demographically representative of the U.S. population (based on stratified random sample).

Currency

Data Collection Period Data were collected between 1994 and 1998.

Data were collected for two non-consecutive days. Medium

Clarity and Completeness

Accessibility The CSFII data are publicly available.

Reproducibility The methodology used was clearly described; enough information was included to reproduce the results.

Quality Assurance Quality assurance of the CSFII data was good; quality control of the secondary data analysis was not well

described. High

Variability and Uncertainty

Variability in Population Full distributions were provided for total grains. Means were provided for individual grain products.

Minimal Uncertainty Data collection was based on recall for a 2-day period; the accuracy of using these data to estimate long-term intake (especially at the upper percentiles) is uncertain. However, use of short-term data to estimate chronic ingestion can be assumed for broad categories of foods such as total grains. Uncertainty is likely to be greater for individual grain products. Medium



### Page Child-Specific Exposure Factors Handbook

12-4 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12-2. Confidence in Recommendations for Intake of Grain Products (continued)

General Assessment Factors Rationale Rating

Evaluation and Review

Peer Review

Number and Agreement of Studies The USDA CSFII survey received a high level of peer review. The U.S. EPA analysis of these data has not been peer reviewed outside the Agency.

There was 1 key study. Medium

Overall Rating High confidence in the

averages; Low confidence in the long-term upper

percentiles



### Child-Specific Exposure Factors Handbook Page September 2008 12-5

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

12.3 INTAKE STUDIES The primary source of recent information on consumption rates of grain products among children is the U.S. Department of Agriculture’s (USDA) CSFII.

Data from the 1994-96 CSFII and the 1998 c hildren’s supplement to the 1994-96 CSFII have been used in various studies to generate children’s consumer-only and per capita intake rates for both individual grain products and total grains. The CSFII is a series of surveys designed to measure the kinds and amounts of foods eaten by Americans. The CSFII 1994-96 was conducted between January 1994 and January 1997 with a target population of non-institutionalized individuals in all 50 states and Washington, D.C. In each of the 3 survey years, data were collected for a nationally representative sample of individuals of all ages. The CSFII 1998 was conducted between December 1997 and December 1998 and surveyed children 9 years of age and younger. It used the same sample design as the CSFII 1994-96 and was intended to be merged with CSFII 1994-96 to increase the sample size for children. The merged surveys are designated as CSFII 1994-96, 1998. Additional information on these surveys can be obtained at http://www.ars.usda.gov/Services/docs.htm?docid=14531. 

### The CSFII 1994-96, 1998 collected dietaryintake data through in-person interviews on 2 nonconsecutive days. The data were based on 24-hour recall. A total of 21,662 individuals provided data for the first day; of those individuals, 20,607 provided data for a second day. Over 11,000 of the sample persons represented children up to 18 years of age. The 2-day response rate for the 1994-1996 CSFII was approximately 76 percent. The 2-day response rate for CSFII 1998 was 82 percent. The CSFII 1994-96, 98 surveys were based on a complex multistage area probability sample design. The sampling frame was organized using 1990 U.S. population census estimates, and the stratification plan took into account geographic location, degree of urbanization, and socioeconomic characteristics. Several sets of sampling weights are available for use with the intake data. By using appropriate weights, data for all fours years of the surveys can be combined. USDA recommends that all 4 years be combined in order to provide an adequate sample size for children. 12.3.1 Key Grain Intake Study

12.3.1.1 U.S. EPA Analysis of CSFII 1994-96,

1998 For many years, the U.S. EPA’s Office of Pesticide Programs (OPP) has used food consumption data collected by the U.S. Department of Agriculture (USDA) for its dietary risk assessments. Most recently, OPP, in cooperation with USDA’s Agricultural Research Service (ARS), used data from the 1994-96, 1998 CSFII to develop the Food Commodity Intake Database (FCID). CSFII data on the foods people reported eating were converted to the quantities of agricultural commodities eaten. "Agricultural commodity" is a term used by U.S. EPA to mean plant (or animal) parts consumed by humans as food; when such items are raw or unprocessed, they are referred to as "raw agricultural commodities." For example, an apple pie may contain the commodities apples, flour, fat, sugar and spices. FCID contains approximately 553 unique commodity names and 8-digit codes. The FCID commodity names and codes were selected and defined by U.S. EPA and were based on the U.S. EPA Food Commodity Vocabulary ( http://www.epa.gov/pesticides/foodfeed/ ). The grain items/groups selected for the

U.S. EPA analysis included total grains, and individual grain products such as cereal and rice. Appendix 12A presents the food codes and definitions used to determine the various grain products used in the analysis. Intake rates for these food items/groups represent intake of all forms of the product (e.g., both home produced and commercially produced). Children who provided data for two days of the survey were

included in the intake estimates. Individuals who did not provide information on body weight or for whom identifying information was unavailable were excluded from the analysis. Two-day average intake rates were

calculated for all individuals in the database for each of the food items/groups. These average daily intake rates were divided by each individual's reported body weight to generate intake rates in units of grams per kilogram of body weight per day (g/kg-day). The data were weighted according to the four-year, two-day sample weights provided in the 1994-96, 1998 CSFII to adjust the data for the sample population to reflect the national population. Summary statistics were generated on both



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a per capita and a consumer only basis. For per capita intake, both users and non-users of the food item were included in the analysis. Consumer-only intake rates were calculated using data for only those individuals who ate the food item of interest during the surveyperiod. Intake data from the CSFII are based on asconsumed (i.e., cooked or prepared) forms of the food items/groups. Summary statistics, including: number of observations, percentage of the population consuming the grain product being analyzed, mean intake rate, and

standard error of the mean intake rate were calculated for total grains and selected individual grain products. Percentiles of the intake rate distribution (i.e., 1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 99th, and 100th percentile were also provided for total grains. Data were provided for the following age groups of children: birth to <1 year, 1 to <2 years, 3 to <5 years, 6 to <12 years, and 13 to <19 years. Because these data were developed for use in U.S. EPA’s pesticide registration program, the age groups used are slightly different than

those recommended in U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). Table 12-3 presents as-consumed percapita intake data for total grains in g/kg-day; asconsumed consumer only intake data for total grains in g/kg-day are provided in Table 12-4. Table 12-5 provides per capita intake data for individual grain products and Table 12-6 provides consumer only intake data for individual grain products.

It should be noted that the distribution of average daily intake rates generated using short-termdata (e.g., 2-day) do not necessarily reflect the longterm distribution of average daily intake rates. The distributions generated from short-term and long-term

data will differ to the extent that each individual’s intake varies from day to day; the distributions will be

similar to the extent that individuals’ intakes are constant from day to day. However, for broad categories of foods (e.g., total grains) that are eaten on a daily basis throughout the year, the short-term distribution may be a reasonable approximation of the true long-term distribution, although it will show somewhat more variability. In this chapter, distributions are provided only for total grains. Because of the increased variability of the short-term distribution, the short-term upper percentiles shown here may overestimate the corresponding percentiles of the long-term distribution. For individual grains, only the mean, standard error, and percent consuming are provided. The strengths of U.S. EPA’s analysis are that it provides distributions of intake rates for various age groups of children, normalized by body weight. The analysis uses the 1994-96, 1998 CSFII data set which was designed to be representative of the U.S. population. The data set includes four years of intake data combined, and is based on a two-day survey period. As discussed above, short-term dietary data may not accurately reflect long-term eating patterns and may under-represent infrequent consumers of a given food. This is particularly true for the tails (extremes) of the distribution of food intake. Also, the analysis was conducted using slightly different age groups that

those recommended in U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). However, given the similarities in the age groups used, the data should provide suitable intake estimates for the age groups of interest.

12.3.2 Relevant Grain Intake Studies

12.3.2.1 USDA, 1999 - Food and Nutrient Intakes by Children 1994-96, 1998, Table Set 17

USDA (1999) calculated national probability estimates of food and nutrient intake by children based on all 4 years of the CSFII (1994-96 and 1998) for children age 9 years and under, and on CSFII 1994-96 only for individuals age 10 years and over. Sample weights were used to adjust for non-response, to match the sample to the U.S. population in terms of demographic characteristics, and to equalize intakes over the 4 quarters of the year and the 7 days of the

week. A total of 503 breast-fed children were excludedfrom the estimates, but both consumers and nonconsumers were included in the analysis. USDA (1999) provided data on the mean per capita quantities (grams) of various food products/groups consumed per individual for one day, and the percent of individuals consuming those foods in one day of the survey. Tables 12-7 and 12-8 present data on the mean quantities (grams) of grain products consumed per individual for one day, and the



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percentage of survey individuals consuming grain products that survey day. Data on mean intakes or mean percentages are based on respondents’ day-1

intakes. The advantages of USDA (1999) study is that it uses the 1994-96, 98 CSFII data set, which includes four years of intake data, combined, and includes the supplemental data on children. These data are expected to be generally representative of the U.S. population and they include data on a wide variety of grain products. The data set is one of a series of USDA data sets that are publicly available. One limitation ofthis data set is that it is based on one-day, and shortterm dietary data may not accurately reflect long-term eating patterns. Other limitations of this study are that it only provides mean values of food intake rates, consumption is not normalized by body weight, and presentation of results is not consistent with U.S. EPA’s recommended age groups.

12.3.2.2 Smiciklas-Wright et al., 2002 - Foods Commonly Eaten in the United States: Quantities Consumed per Eating Occasion and in a Day, 1994-1996 Using data gathered in the 1994-96 USDA CSFII, Smiciklas-Wright et al. (2002) calculated distributions for the quantities of grain products consumed per eating occasion by members of the U.S. population (i.e., serving sizes). The estimates of serving size are based on data obtained from 14,262 respondents, ages 2 and above, who provided 2 days of dietary intake information. A total of 4,939 of these respondents were children, ages 2 to 19 years of age. Only dietary intake data from users of the specified food were used in the analysis (i.e., consumers only data ). Table 12-9 presents serving size data for selected grain products. These data are presented on an as-consumed basis (grams) and represent the quantity of grain products consumed per eating occasion. These estimates may be useful for assessing acute exposures to contaminants in specific foods, or other assessments where the amount consumed per eating occasion is necessary. Only the mean and standard deviation serving size data and percent of the population consuming the food during the 2-day survey period are presented in this handbook. Percentiles of serving sizes of the foods consumed by these age groups of the U.S. population can be found in Smiciklas-Wright et al. (2002). The advantages of using these data are that they were derived from the USDA CSFII and are representative of the U.S. population. The analysis conducted by Smiciklas-Wright et al. (2002) accounted for individual foods consumed as ingredients of mixed foods. Mixed foods were disaggregated via recipe files so that the individual ingredients could be grouped together with similar foods that were reported separately. Thus, weights of foods consumed as ingredients were combined with weights of foods reported separately to provide a more thorough representation of consumption. However, it should be noted that since the recipes for the mixed foods consumed were not provided by the respondents, standard recipes were used. As a result, the estimates of quantity consumed for some food types are based on assumptions about the types and quantities of ingredients consumed as part of mixed foods. This study used data from the 1994 to 1996 CSFII; data from the 1998 children’s supplement were not included.

12.3.2.3 Fox et al., 2004 - Feeding Infants and Toddlers study: What Foods Are Infants and Toddlers Eating Fox et al. (2004) used data from the Feeding Infants and Toddlers study (FITS) to assess food consumption patterns in infants and toddlers. The FITS was sponsored by Gerber Products Company and

was conducted to obtain current information on food and nutrient intakes of children, ages 4 to 24 months old, in the 50 states and the District of Columbia. The FITS is described in detail in Devaney et al. (2004). FITS was based on a random sample of 3,022 infants and toddlers for which dietary intake data were collected by telephone from their parents or caregivers between March and July 2002. An initial recruitment and household interviewwas conducted, followed by an

interview to obtain information on intake based on 24­ hour recall. The interview also addressed growth, development and feeding patterns. A second dietary

recall interview was conducted for a subset of 703randomly selected respondents. The study oversampled children in the 4 to 6 and 9 to11 months age groups; sample weights were adjusted for non-response,



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over sampling , and under coverage of some subgroups. The response rate for the FITS was 73 percent for the recruitment interview. Of the recruited households, there was a response rate of 94 percent for the dietary recall interviews (Devaney et al., 2004). The characteristics of the FITS study population is shown in

Table 12-10. Fox et al. (2004) analyzed the first set of 24-hour recall data collected from all study participants. For this analysis, children were grouped into six age categories: 4 to 6 months, 7 to 8 months, 9 to 11 months, 12 to 14 months, 15 to 18 months, and 19 to 24 months. Table 12-11 provides the percentage of infants and toddlers consuming different types of grains or grain products at least once in a day. The percentages of children eating any type of grain or grain product ranged from 65.8 percent for 4 to 6 month olds to 99.2 percent for 19 to 24 month olds. The advantages of this study were that the study population represents the U.S. population and the sample size was large. One limitation of the analysis done by Fox et al. (2004) is that only frequency data were provided; no information on actual intake rates was included. In addition, Devaney et al (2004) noted

several limitations associated with the FITS data. For the FITS, a commercial list of infants and toddlers was used to obtain the sample used in the study. Since many of the household s could not be located and did not have children in the target population, a lower response rate than would have occurred in a true national sample was obtained (Devaney et al., 2004). In addition, the sample was likely from a higher socioeconomic status when compared with all U.S. infants in this age group (4 to 24 months old) and the use of a telephone survey may have omitted lower-income households without telephones (Devaney et al., 2004).

12.3.2.4 Ponza et al., 2004 - Nutrient Food Intakes and Food Choices of Infants and Toddlers Participating in WIC Ponza et al. (2004) conducted a study using selected data from the FITS to assess feeding patterns,

food choices and nutrient intake of infants and toddlers participating in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Ponza et al. (2004) evaluated FITS data for the following age groups: 4 to 6 months (N = 862), 7 to 11 months (N = 1,159) and 12 to 24 months (N= 996). The total sample size described by WIC participants and non-participants is shown in Table 12-12. The foods consumed were analyzed by tabulating the percentage of infants who consumed specific foods/food groups per day (Ponza et al., 2004). Weighted data were used in all of the analyses used in the study (Ponza et al., 2004). Table 12-12 presents thedemographic data for WIC participants and nonparticipants. Table 12-13 provides information on the

food choices for the infants and toddlers studied. In general, there was little difference in grain product choices among WIC participants and non-participants, except for the 7 to 11 months age category (Table 12­ 13). Nonparticipants, ages 7 to 11 months, were more likely to eat non-infant cereals than WIC participants. An advantage of this study is that it had a relatively large sample size and was representative of the U.S. general population of infants and children. A limitation of the study is that intake values for foods were not provided. Other limitations are those associated with the FITS data, as described previously

in Section 12.3.2.3.

12.3.2.5 Mennella et al., 2006 - Feeding Infants and Toddlers Study: The Types of Foods Fed to Hispanic Infants and Toddlers Menella et al. (2006) investigated the types of food and beverages consumed by Hispanic infants and toddlers in comparison to the non-Hispanic infants

and toddlers in the United States. The FITS 2002 data for children between 4 and 24 months of age were used for the study. The data represent a random sample of 371 Hispanic and 2,367 non-Hispanic infants and toddlers (Menella et al., 2006). Menella et al. (2006) grouped the infants as follows: 4 to 5 months (N = 84 Hispanic; 538 non-Hispanic), 6 to 11 months (N = 163 Hispanic and 1,228 non-Hispanic), and 12 to 24 months (N = 124 Hispanic and 871 non-Hispanic) of age. Table 12-14 provides the percentage of Hispanic and non-Hispanic infants and toddlers consuming grain products. In most instances the percentages consuming the different types are similar. However, 6 to 11 month old Hispanic children were more likely to eat rice and pasta than non-Hispanic children in this age groups. The advantage of the study is that it



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provides information on food preferences for Hispanic and non-Hispanic infants and toddlers. A limitation is that the study did not provide food intake data, but provided frequency of use data instead. Other limitations are those noted previously in Section

12.3.2.3 for the FITS data.

12.3.2.6 Fox et al., 2006 - Average Portion of Foods Commonly Eaten by Infants and
Toddlers in the United States Fox et al. (2006) estimated average portion sizes consumed per eating occasion by children 4 to 24 months of age who participated in the FITS. The FITS is a cross-sectional study designed to collect and analyze data on feeding practices, food consumption,

and usual nutrient intake of U.S. infants and toddlers and is described in Section 12.3.2.3 of this chapter. It included a stratified random sample of 3,022 children between 4 and 24 months of age. Using the 24-hour recall data, Fox et al. (2006) derived average portion sizes for six major food groups, including breads and grains. Average portion sizes for select individual foods within these major groups were also estimated. For this analysis, children were grouped into six age categories: 4 to 5 months, 6 to 8 months, 9 to 11 months, 12 to 14 months, 15 to 18 months, and 19 to 24 months. Tables 12-15 and 12-16 present the average portion sizes for grain products for infants and toddlers, respectively.

12.4 CONVERSION BETWEEN WET AND

DRY WEIGHT INTAKE RATES The intake data presented in this chapter are reported in units of wet weight (i.e., as-consumed or uncooked weight of grain products consumed per day or per eating occasion). However, data on the concentration of contaminants in grain products may be reported in units of either wet or dry weight (e.g., mg contaminant per gram dry-weight of grain products.)

. It is essential that exposure assessors be aware of this difference so that they may ensure consistency between

the units used for intake rates and those used for concentration data (i.e., if the contaminant concentration is measured in dry weight of grain products, then the dry weight units should be used for their intake values). If necessary, wet weight (e.g., as consumed) intake rates may be converted to dry weight intake rates using the moisture content percentages presented in Table 12-17 and the following equation:



### ⎡ 100

− W ⎤ IR dw = IR
ww ⎣ ⎢ 100 ⎦ ⎥ (Eqn. 12-1)

where:

IR dw 

### = dry weight intake rate;

IR

ww 

### = wet weight intake rate; and

W = percent water content

Alternatively, dry weight residue levels in grain products may be converted to wet weight residue levels for use with wet weight (e.g., as-consumed) intake rates

as follows:



### ⎡ 100

− W ⎤

C
ww = C dw ⎢ ⎥ (Eqn. 12-2) 

### ⎣ 100 ⎦

where:

C

ww 

### = wet weight intake rate;

C dw 

### = dry weight intake rate; and W = percent water content.

The moisture data presented in Table 12-17 are for selected grain products taken from USDA (2007).

12.5 REFERENCES FOR CHAPTER 12

Devaney, B.; Kalb, L.; Briefe l, R.; Zavitsky-Novak, T.; Clusen, N.; Ziegler, P. (2004) Feeding infants and toddlers study: overview of the study design. J Am Diet Assoc 104(Suppl 1): S8

- S13.
Fox, M.K.; Pac, S.; Devane y, B.; Jankowski, L. (2004) Feeding i nfants and t oddlers s tudy: what foods are infants and toddlers eating. J Am Diet Assoc 104 (Suppl): S22-S30. Fox, M.K.; Reidy, K.; Karw e, V.; Ziegler, P. (2006) Average portions of foods commonly eaten by

infants and toddlers in the United States. J Am Diet Assoc 106 (Suppl 1): S66-S76. Goldman, L. (1995) Children - unique and vulnerable. Environmental risks facing children and



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recommendations for response. Environ

Health P er spect 103(6):13-17. Mennella, J.; Ziegler, P.; Briefel, R.; Novak, T. (2006) Feeding i nfants and t oddlers s tudy: the types of foods fed to Hispanic infants and toddlers. J Am Diet Assoc 106 (Suppl 1): S96. Ponza, M.; Devaney, B.; Ziegler, P.; Reidy, K.; Squatrito, C. (2004) Nutrient intakes and food choices of infants and toddlers participating in WIC. J Am Diet Assoc 104 (Suppl): S71­

S79. Smiciklas-Wright, H.; Mitchell, D.C.; Mickle, S.J.;
Cook, A.J.; Goldman, J.D. (2002) Foods commonly eaten in the United States: Quantities consumed per eating occasion and in a day, 1994-1996. U.S. Department ofAgriculture NFS Report No. 96-5, prepublication version, 252 pp. USDA.	 (1999) Food and nutrient intakes by children
1994-96, 1998: Table Set 17. Beltsville, MD: Food Surveys Research Group, Beltsville
Human Nutrition Research Center, Agricultural Research Service, U.S. Department of Agriculture.
USDA. (2007) USDA National Nutrient Database for Standard Reference, Release 20. Agricultural Research Service, Nutrient Data Laboratory
Home Page, http://www.ars.usda.gov/ba/bhnrc/ndl

U.S. EPA.	 (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants.

U.S. Environmental Protection Agency, Washington, D.C., EPA/630/P-03/003F.



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12-12 September 2008Table 12-3. Per Capita Intake of Total Grains (g/kg-day as consumed)Age GroupNPercentConsumingMeanSE1st5th10th25thPercentiles50th75th90th95th99th100thBirth to 1 year1,48670.52.50.10.00.00.00.01.63.86.28.612.726.31 to 2 years2,09699.86.40.11.12.12.84.25.97.910.412.116.831.63 to 5 years4,391100.06.30.11.82.63.24.35.97.89.911.515.627.06 to 12 years2,089100.04.30.10.91.72.02.84.05.47.08.211.117.213 to 19 years1,222100.02.50.050.40.81.11.52.33.14.45.17.912.4N= Sample size.SE= Standard error.Source:Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII.Table 12-4. Consumer Only Intake of Total Grains (g/kg-day as consumed)PercentilesAge GroupNMeanSE1st5th10th25th50th75th90th95th99th100thBirth to 1 year1,0483.60.10.10.30.61.42.84.87.49.213.426.31 to 2 years2,0926.40.11.22.12.84.25.97.910.412.116.831.63 to 5 years4,3896.30.11.82.63.24.35.97.89.911.515.627.06 to 12 years2,0894.30.10.91.72.02.84.05.47.08.211.117.213 to 19 years1,2222.50.050.40.81.11.52.33.14.45.17.912.4N= Sample size.SE= Standard error.Source:Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII.

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Table 12-5. Per Capita Intake of Individual Grain Products (g/kg-day as consumed)

Cereal Rice

Age Group N Percent Percent Mean SE Mean SE Consuming Consuming

Birth to 1 year 1,486 74.6 4.0 0.14 60.2 0.74 0.04

1 to 2 years 2,096 99.8 8.4 0.08 86.4 0.57 0.03

3 to 5 years 4,391 100.0 8.7 0.07 87.9 0.50 0.03

6 to 12 years 2,089 100.0 6.2 0.06 88.0 0.35 0.02

13 to 19 years 1,222 100.0 4.1 0.06 85.8 0.27 0.02

N = Sample size.

SE = Standard error.

Source: Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII

.

Table 12-6. Consumer Only Intake of Individual Grain Products (g/kg-day as consumed)

Cereal Rice Age Group N Mean SE N Mean SE

Birth to 1 year 1,116 5.4 0.16 900 1.23 0.07

1 to 2 years 2,092 8.4 0.08 1,819 0.67 0.04

3 to 5 years 4,389 8.7 0.07 3,869 0.57 0.03

6 to 12 years 2,089 6.2 0.06 1,847 0.40 0.02

13 to 19 years 1,222 4.1 0.06 1,038 0.31 0.03

N = Sample size.

SE = Standard error.

Source: Based on unpublished U.S. EPA analysis of 1994-96, 1998 CSFII

.



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Chapter 12 - Intake of Grain ProductsTable 12-7. Mean Quantities of Grain Products Consumed Daily by Sex and Age, Per Capita (g/day)QuickCakes,Cereals and Pastabreads,Yeast,cookies,pancakes,Samplebreads,pastries,Age GroupTotalReady-to-eatFrenchSizeand rollsTotalRice PastapiescerealstoastCrackers,popcorn,pretzels,corn chipsMixtures,mainlygrainMales and Females1aUnder 1year1,126 56 2 2121 31 year1,016 192 16 16 11 999 162 years1,102 219 26 26 16 1512 12 221 to 2 years 2,118 206 21 21 13 1211 11 193 years1,831 242 30 30 19 1312 16 234 years1,859 264 36 36 22 1511 17 305 years884 284 41 41 24 1711 15 333 to 5 years4,574 264 36 36 22 1511 16 295 years and under 7,818 219 27 27 16 1310 12 221798111313129208787879810210710287Males6 to 9 years787 310 45 77 28 18 15 23 396 to 11 years1,031 318 46 80 31 16 18 23 402 to 19 years737 406 54 82 29 27 17 26 49161519109115175Females6 to 9 years704 284 43 61 21 12 15 18 426 to 11 years969 280 43 62 20 14 15 19 4212 to 19 years732 306 40 67 17 19 22 15 37131415107101132Males and Females9 years and under 9,309 250 34 64 20 14 12 16 3019 years and under 11,287 298 40 69 22 17 15 18 36121496120aEstimate is not statistically reliable due to small sample size reporting intake.Note: Consumption amounts shown are representative of the first day of each participant’s survey response.Source: USDA, 1999.



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Chapter 12 - Intake of Grain ProductsTable 12-8. Percentage of Individuals Consuming Grain Products, by Sex and Age (%)Age GroupSampleSizeTotalYeast,breads,and rollsCereals and PastaQuickbreads,pancakes,FrenchtoastTotalReady-to­eatcerealsRice PastaCakes,cookies,pastries,piesCrackers,popcorn,pretzels,cornchipsMixtures,mainlygrainMales and FemalesUnder 1 year1 year2 years1 to 2 years3 years4 years5 years3 to 5 years5 years and under1,1261,0161,1022,1181,8311,8598844,5747,81870.698.2a99.0a98.799.4a99.5a99.9a99.6a95.810.948.458.753.764.167.069.266.855.562.870.671.170.969.769.170.469.769.39.145.351.948.753.354.854.954.346.93.411.314.412.911.111.411.411.310.92.19.49.49.48.67.16.87.57.54.423.027.525.328.828.625.227.524.016.547.046.646.846.152.352.450.345.010.339.037.938.438.539.432.136.734.115.047.845.346.549.046.247.447.543.3Males6 to 9 years6 to 11 years12 to 19 years7871,03173798.9a99.0a98.2a69.869.162.762.664.044.650.852.433.210.59.710.07.48.15.928.127.124.452.552.341.336.033.827.244.545.346.2Females6 to 9 years6 to 11 years12 to 19 years70496973299.7a99.3a97.6a71.571.060.961.259.345.947.645.630.39.09.48.67.97.19.326.327.119.857.155.040.638.337.130.948.045.746.1Males and Females9 years and under19 years and under9,30911,28797.297.661.662.466.457.647.941.710.59.97.67.625.324.248.946.135.332.544.445.1aEstimate is not statistically reliable due to small sample size reporting intake.Note: Percentages shown are representative of the first day of each participant’s survey response.Source: USDA, 1999.



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Chapter 12 - Intake of Grain ProductsTable 12-9. Quantity (as consumed) of Grain Products Consumed Per Eating Occasion andPercentage of Individuals Using These Foods in Two DaysQuantity consumed per eating occasion (grams)2 to 5 years6 to 11 years12 to 19 yearsMale and FemaleMale and FemaleMaleFemaleFood category(N = 2,109)(N = 1,432)(N = 696)(N = 702)PCMeanSEMPCMeanSEMPCMeanSEMPCMeanSEMWhite bread66.934a67.142161.356157.9471Whole grain and wheat bread24.337120.544114.560217.6532Rolls40.039153.548161.969248.8511Biscuits8.33829.748312.272410.3554Tortillas14.632216.447222.976520.1563Quickbreads and muffins9.65549.667511.01251211.07910Doughnuts and sweet rolls11.359213.469217.31021213.8785Crackers25.417117.226210.639514.2263Cookies51.028146.737229.053331.8422Cake14.670319.779415.199915.5858Pie2.97685.611686.6188154.8138b12bPancake and waffles19.149121.577313.59668.2745Cooked cereal16.8211109.0245145.2310b29b6.0256b31bOatmeal10.422195.7256192.4348b45b2.3321b40bReady-to-eat cereal72.933167.347145.672346.3522Corn Flakes11.233213.142210.46248.7494Toasted Oat Rings20.630112.54527.36258.1423Rice29.684324.6124624.22031028.815710Pasta49.490341.4130533.4203937.81559Macaroni and cheese17.8159813.2217137.54084610.726030Spaghetti with tomato sauce16.82421111.53221810.1583468.547951Pizza23.786332.8108639.62051330.51438Corn chips19.629225.633226.958525.1443Popcorn11.620112.73127.854510.5374aIndicates a SEM value that is greater than 0 but less than 0.5.bIndicates a statistic that is potentially unreliable because of small sample size or large coefficient of variation.PC = Percent consuming at least once in 2 days.SEM = Standard error of the mean.Source: Smiciklas-Wright et al., 2002 (based on 1994-1996 CSFII data).



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### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12-10. Characteristics of the FITS Sample Population

Sample Size Percentage of Sample

Gender

Male 1,549

Female 1,473
Age of Child

4 to 6 months 862

7 to 8 months 483

9 to 11 months 679

12 to 14 months 374

15 to 18 months 308

19 to 24 months 316

Child’s Ethnicity

Hispanic or Latino 367 Non-Hispanic or Latino 2,641 Missing 14

Child’s Race

White 2,417

Black 225

Other 380
Urbanicity

Urban 1,389

Suburban 1,014

Rural 577 Missing 42

Household Income

Under $10,000 48 $10,000 to $14,999 48 $15,000 to $24,999 221 $25,000 to $34,999 359 $35,000 to $49,999 723 $50,000 to $74,999 588 $75,000 to $99,999 311 $100,000 and Over 272 Missing 452

Receives WIC

Yes 821

No 2,196 Missing 5
Sample Size (Unweighted) 3,022 51.3

48.7

28.5

16.0

22.5

12.4

10.2

10.4

12.1

87.4

0.5

80.0

7.4

12.6

46.0

33.6

19.1

1.3

1.6

1.6

7.3

11.9

23.9

19.5

10.3

9.0

14.9

27.2

72.6

0.2

100.0

WIC = Special Supplemental Nutrition Program for Women, Infants, and Children.

Source: Devaney et al., 2004.



### Child-Specific Exposure Factors Handbook Page September 2008 12-17

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12-11. Percentage of Infants and Toddlers Consuming Different Types of Grain Products

Percentage of Infants and Toddlers Consuming at Least Once in a Day

Food Group/Food 4 to 6 7 to 8 9 to 11 12 to 14 15 to 18 19 to 24

months months months months months months

Any Grain or Grain Product 65.8 91.5 97.5 97.8 98.6 99.2

Infant Cereals 64.8 81.2 63.8 23.9 9.2 3.1

Noninfant Cereals a 0.6 18.3 44.3 58.9 60.5 51.9

Not Pre-sweetened 0.5 17.0 37.0 44.5 40.6 31.9

Pre-sweetened b 0.0 1.8 9.0 17.7 26.4 22.7

Breads and Rolls c 0.6 9.9 24.5 47.3 52.7 53.1

Crackers, Pretzels, Rice Cakes 3.0 16.2 33.4 45.2 46.4 44.7

Cereal or Granola Bars 0.0 1.1 3.4 9.8 10.0 9.7

Pancakes, Waffles, French Toast 0.1 0.8 7.5 15.1 16.1 15.4

Rice and Pasta d 2.3 4.5 18.2 26.2 39.0 35.9

Other 0.2 0.1 2.7 2.8 2.5 4.5

Grains in Mixed Dishes 0.4 5.3 24.1 48.3 52.0 55.1

Sandwiches 0.0 1.1 8.6 21.5 25.8 25.8

Burrito, Taco, Enchilada, Nachos 0.0 0.0 1.0 4.5 2.8 2.1

Macaroni and Cheese 0.2 1.6 4.9 14.6 15.0 15.0

Pizza 0.1 0.7 2.2 6.8 9.0 9.4

Pot Pie/Hot Pocket 0.0 0.9 0.5 2.0 1.0 1.8

Spaghetti, Ravioli, Lasagna 0.1 1.8 9.9 15.3 12.1 8.8

a Includes both ready-to-eat and cooked cereals.

b Defined as cereals with more than 21.1 g sugar per 100 g.

c Does not include bread in sandwiches. Sandwiches are included in mixed dishes.

d Does not include rice or pasta in mixed dishes.

Source: Fox et al., 2004.



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Chapter 12 - Intake of Grain Products

Table 12-12. Characteristics of WIC Participants and Nonparticipants a (Percentages)

(Unweighted) Infants 4 to 6 months Infants 7 to 11 months Toddlers 12 to 24 months

WIC WIC WIC Participant Non-participant Participant Non-participant Participant Non-participant

Gender

Male 55 54 55 51 57 52
Female 45 46 45 49 43 48

Child’s Ethnicity   ** 

Hispanic or Latino 20 11 24 8 22 10 Non-Hispanic or Latino 80 89 76 92 78 89

Child’s Race **  

White 69 84 63 86 67 84
Black 15 4 17 5 13 5
Other 22 11 20 9 20 11 Child In Day Care **  *

Yes 39 38 34 46 43 53
No 61 62 66 54 57 47 Age of Mother **  

14 to 19 18 1 13 1 9 1
20 to 24 33 13 38 11 33 14
25 to 29 29 29 23 30 29 26
30 to 34 9 33 15 36 18 34
35 or Older 9 23 11 21 11 26 Missing 2 2 1 1 0 1

Mother’s Education **  ** **

11 th Grade or Less 23 2 15 2 17 3 Completed High School 35 19 42 20 42 19 Some Postsecondary 33 26 32 27 31 28 Completed College 7 53 9 51 9 48 Missing 2 1 2 0 1 2

Parent’s Marital Status   ** 

Married 49 93 57 93 58 88
Not Married 50 7 42 7 41 11 Missing 1 1 1 0 1 1

Mother or Female Guardian Works ** *

Yes 46 51 45 60 55 61
No 53 48 54 40 45 38 Missing 1 1 1 0 0 1

Urbanicity ** ** **

Urban
Suburban
Rural Missing Sample Size 34
36
28

2
265 55
31
13

1
597 37
31
30
2
351 50
34
15

1
808 35
35
28

2
205 48
35
16
2
791

a X 2 test were conducted to test for statistical significance in the differences between WIC participants and non-participants within each age group for each variable. The results of X 2 test are listed next to the variable under the column labeled non-participants for each of the three age groups. *P<0.05; **P>0.01; non-participants significantly different from WIC participants on the

variable. WIC =Special Supplemental Nutrition Program for Women, Infants, and Children.

Source: Ponza et al., 2004.



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### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12-13. Food Choices for Infants and Toddlers by WIC Participation Status.

Infants 4 to 6 months Infants 7 to 11 months Toddlers 12 to 24 months

WIC Non- WIC Non- WIC NonParticipant participant Participant participant Participant participant

Infant Cereals 69.7 62.5 74.7 69.7 13.5 9.2

Noninfant Cereals, Total 0.9 0.5 21.7 38.5* 58.1 56.0

Not Pre-sweetened 0.5 0.5 18.7 32.9* 43.7 36.3

Pre-sweetened 0.0 0.0 4.0 6.9 17.7 24.1

Grains in Combination Foods 0.9 0.1 18.8 14.7 50.3 52.9

Sample Size (unweighted) 265 597 351 808 205 791

* = P<0.01 non-participants significantly different from WIC participants. WIC = Special Supplemental Nutrition Program for Women, Infants, and Children.

Source: Ponza et al., 2004.



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Chapter 12 - Intake of Grain Products

Table 12-14. Percentage of Hispanic and Non-Hispanic Infants and Toddlers Consuming Different Types of Grain Products on A Given Day

Age 4 to 5 months Age 6 to11 months Age 12 to 24 months

Hispanic Non-Hispanic Hispanic Non-Hispanic Hispanic Non-Hispanic (n=84) (n=538) (n=163) (n=1,228) (n=124) (n=871)

Any Grain or Grain Product 56.5 56.9 95.0 93.5 97.1 98.9

Infant Cereal 55.2 56.5 74.1 73.6 15.9 9.3

Noninfant Cereal

- - 18.5* 29.2 45.3 57.8

Breads a 1.4†

- 18.2 15.1 44.0 52.9

Tortillas 1.4†

- 4.0†

- 6.7†* 0.6†

Crackers, Pretzels, Rice Cakes 1.3†

- 27.8 22.5 35.6 46.9

Pancakes, Waffles, French Toast

- - 1.4† 4.3 13.0 16.0

Rice and Pasta b

- - 20.1* 10.3 44.3 32.9

Rice

- - 15.9** 4.7 26.9†* 13.0

Grains in Mixed Dishes

- - 15.9 13.0 38.8* 54.4

Sandwiches

- - 4.0† 4.6 24.2 24.9

Burrito, Taco, Enchilada, Nachos -

- 1.3†

- 2.1† 3.0

Macaroni and Cheese

- - 3.0† 3.1 10.1 15.5

Pizza

- - - 1.4 1.0**† 9.7
Spaghetti, Ravioli, Lasagna

- - 8.3† 4.6 9.3† 12.1

a Does not include bread in sandwiches. Sandwiches are included in mixed dishes. Includes tortillas, also shown separately.

b Does not include rice or pasta in mixed dishes. Includes rice (e.g. white, brown, wild, and Spanish rice without meat) and pasta (e.g. spaghetti, macaroni, and egg noodles). Rice is also shown separately. - = Less than 1 percent of the group consumed this food on a given day.

* = Significantly different from non-Hispanic at the P <0.05. ** = Significantly different from non-Hispanic at the P >0.01. † = Statistic is potentially unreliable because of a high coefficient of variation.

Source: Mennella et al., 2006.



### Child-Specific Exposure Factors Handbook Page September 2008 12-21

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12-15. Average Portion Sizes Per Eating Occasion of Grain Products Commonly Consumed by Infants from the 2002 Feeding Infants and Toddlers Study

Food group Reference

unit 4 to 5 months (N=624) 6 to 8 months (N=708) 9 to 11 months (N=687)

Mean± SEM

Infant cereal, dry tablespoon 3.1±0.14 4.5±0.14 5.2±0.18 Infant cereal, jarred tablespoon

- 5.6±0.26 7.4±0.34 Ready-to-eat cereal tablespoon

- 2.3±0.34 3.4±0.21

Crackers ounce

- 0.2±0.02 0.3±0.01

saltine

- 2.2±0.14 2.7±0.12

Bread slice

- 0.5±0.10 0.8±0.06
- = Cell size was too small to generate a reliable estimate. N = Number of respondents.

SEM = Standard error of the mean.

Source: Fox et al., 2006.

Table 12-16. Average Portion Sizes Per Eating Occasion of Grain Products Commonly Consumed by Toddlers from the 2002 Feeding Infants and Toddlers Study

12 to 14 months 15 to 18 months 19 to 24 months
(N=371) (N=312) (N=320) Food Group Reference Unit

Mean± SEM

Bread slice 0.8±0.04 0.9±0.05 0.9±0.05

Rolls ounce 0.9±0.11 1.0±0.10 0.9±0.15 Ready-to-eat cereal cup 0.3+0.02 0.5±0.03 0.6±0.04
Hot cereal, prepared cup 0.6±0.05 0.6±0.05 0.7±0.05

Crackers ounce 0.3+0.02 0.4±0.02 0.4±0.02

saltine 3.3+0.22 3.5±0.22 3.7±0.22

Pasta cup 0.4±0.04 0.4+0.04 0.5±0.05

Rice cup 0.3+0.04 0.4±0.05 0.4±0.05

Pancakes and waffles 1 (4-inch diameter) 1.0+0.08 1.4±0.21 1.4±0.17

N = Number of respondents.

SEM = Standard error of the mean.

Source: Fox et al., 2006.



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Chapter 12 - Intake of Grain Products

Table 12-17. Mean Moisture Content of Selected Grain Products Expressed as Percentages of Edible Portions

Food

Raw Moisture Content

Cooked Comments

Barley - pearled 10.09 68.80

Corn - grain - endosperm 10.37

Corn - grain - bran 4.71 crude

Millet 8.67 71.41

Oats 8.22

Rice - white - long-grained 11.62 68.44

Rye 10.95

Rye - flour - medium 9.85

Sorghum 9.20

Wheat - hard white 9.57

Wheat - germ 11.12 crude

Wheat - bran 9.89 crude

Wheat - flour - whole grain 10.27

Source: USDA, 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 12-23

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

APPENDIX 12A

CODES AND DEFINITIONS USED TO DETERMINE THE VARIOUS GRAIN

PRODUCTS USED IN THE U.S. EPA ANALYSIS OF CSFII DATA IN FCID

Child-Specific Exposure Factors Handbook Page September 2008 12A-1

### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Table 12A-1. Food Codes and Definitions Used in Analysis of the 1994-96, 1998 USDA CSFII Data

Total Grains 95000060

15000250

15000251

15000260

15000261

15000270

15000650

15000660

15001200

15001201

15001210

15001211

15001220

15001230

15001231

15001260

15001270

15001271

15002260

15002310

15002320

15002321

15002330 Amaranth, grain Barley, pearled barley Barley, pearled barley-babyfood Barley, flour Barley, flour-babyfood Barley, bran

Buckwheat
Buckwheat, flour
Corn, field, flour Corn, field, flour-babyfood
Corn, field, meal Corn, field, meal-babyfood
Corn, field, bran
Corn, field, starch Corn, field, starch-babyfood Corn, pop
Corn, sweet Corn, sweet-babyfood Millet, grain
Oat, bran
Oat, flour Oat, flour-babyfood Oat, groats/rolled oats 15002331

95003060

95003110

15003230

15003231

15003240

15003241

15003250

15003251

15003260

15003261

15003280

15003290

15003440

15003810

15003811

15004010

15004011

15004020

15004021

15004030

15004040

15004050 Oat, groats/rolled oats-babyfood Psyllium, seed Quinoa, grain
Rice, white Rice, white-babyfood
Rice, brown Rice, brown-babyfood
Rice, flour Rice, flour-babyfood
Rice, bran Rice, bran-babyfood Rye, grain Rye, flour Sorghum, grain
Triticale, flour Triticale, flour-babyfood Wheat, grain Wheat, grain-babyfood
Wheat, flour Wheat, flour-babyfood Wheat, germ
Wheat, bran

Wild rice

Cereal Grains 15000250

15000251

15000260

15000261

15000270

15000650

15000660

15001200

15001201

15001210

15001211

15001220

15001230

15001231

15001240

15001241

15001260

15001270

15001271

15002260

15002310

15002320

15002321

15002330

15002331 Barley, pearled barley Barley, pearled barley-babyfood Barley, flour Barley, flour-babyfood Barley, bran

Buckwheat
Buckwheat, flour
Corn, field, flour Corn, field, flour-babyfood
Corn, field, meal Corn, field, meal-babyfood
Corn, field, bran
Corn, field, starch Corn, field, starch-babyfood Corn, field, syrup Corn, field, syrup-babyfood Corn, pop
Corn, sweet Corn, sweet-babyfood Millet, grain
Oat, bran
Oat, flour Oat, flour-babyfood Oat, groats/rolled oats Oat, groats/rolled oats-babyfood 15003230

15003231

15003240

15003241

15003250

15003251

15003260

15003261

15003280

15003290

15003440

15003450

15003810

15003811

15004010

15004011

15004020

15004021

15004030

15004040

15004050

95000060

95003060

95003110 Rice, white Rice, white-babyfood
Rice, brown Rice, brown-babyfood
Rice, flour Rice, flour-babyfood
Rice, bran Rice, bran-babyfood Rye, grain Rye, flour Sorghum, grain Sorghum, syrup
Triticale, flour Triticale, flour-babyfood Wheat, grain Wheat, grain-babyfood
Wheat, flour Wheat, flour-babyfood Wheat, germ
Wheat, bran

Wild rice Amaranth, grain Psyllium, seed Quinoa, grain



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### Child-Specific Exposure Factors Handbook

Chapter 12 - Intake of Grain Products

Rice 15003260 Rice, bran 15003250 Rice, flour

15003261

15003240 Rice, bran-babyfood
Rice, brown 15003251

15003230 Rice, flour-babyfood
Rice, white

15003241 Rice, brown-babyfood 15003231 Rice, white-babyfood



### Child-Specific Exposure Factors Handbook Page September 2008 12 A -3

### Child­Specific Exposure Factors Handbook

Chapter 13

­ Intake of Home­Produced Foods

13 INTAKE OF HOME­PRODUCED

FOODS

13.1 INTRODUCTION Ingestion of home­produced foods can be a pathway for exposure to environmental contaminants. Home­produced foods can become contaminated in a variety of ways. Ambient pollutants in the air may be deposited on plants, adsorbed onto or absorbed by the plants, or dissolved in rainfall or irrigation waters that

contact the plants. Pollutants may also be adsorbed

onto plant roots from contaminated soil and water. Finally, the addition of pesticides, soil additives, and

fertilizers to crops or gardens may result in

contamination of food products. Meat and dairy products can become contaminated if animals consume contaminated soil, water, or feed crops. Farmers, as well as rural and urban residents who consume homeproduced foods, may be potentially exposed if these

foods become contaminated. Exposure via the consumption of home­produced foods may be a significant route of exposure for these populations (U.S. EPA, 1989; U.S. EPA, 1996). For example, consumption of home­produced fruits, vegetables,

game, and fish has been shown to have an impact on blood lead levels in areas where soil lead contamination

exists (U.S. EPA, 1994). At Superfund sites where soil

contamination is found, ingestion of home­produced

foods has been considered a potential route of exposure (U.S. EPA, 1991; U.S. EPA, 1993). Assessing

exposures to individuals who consume home­produced

foods requires knowledge of intake rates of such foods.

Data from the 1987­1988 Nationwide Food Consumption Survey (NFCS) were used to generate
intake rates for home­produced foods (U.S. EPA, 1997). Until 1988, USDA conducted the NFCS every 10 years to analyze the food consumption behavior and dietary status of Americans (USDA, 1992). While more

recent food consumption surveys have been conducted

to estimate food intake among the general population (e.g., USDA’s Continuing Survey of Food Intake

among Individuals [CSFII] and the National Health and

Nutrition Examination Survey [NHANES]), these

surveys have not collected data that can be used to
estimate consumption of home­produced foods. Thus,

the 1987­1988 NFCS data set is currently the best

available source of information for this factor. The 1987­1988 NFCS was conducted between April 1987 and August 1988. The survey used a statistical sampling technique designed to ensure that all

seasons, geographic regions of the 48 conterminous

states in the U.S., and socioeconomic and demographic

groups were represented (USDA, 1994). There were

two components of the NFCS. The household
component collected information over a seven­day period on the socioeconomic and demographic

characteristics of households, and the types, amount, value, and sources of foods consumed by the household (USDA, 1994). The individual intake component
collected information on food intakes of individuals

within each household over a three­day period (USDA, 1993). The sample size for the 1987­1988 survey was approximately 4,300 households (over 10,000 individuals; approximately 3,000 children). This was

a decrease over the previous survey conducted in 1977­ 1978, which sampled approximately 15,000 households (over 36,000 individuals) (USDA, 1994). The sample

size was lower in the 1987­1988 survey as a result of budgetary constraints and low response rate (38 percent
for the household survey and 31percent for the

individual survey) (USDA, 1993). The methods used

to analyze the 1987­1988 NFCS data and the results of

these analyses that pertain to children are presented in

Section 13.3.

13.2 RECOMMENDATIONS
The data presented in this section may be used

to assess exposure to contaminants in foods grown, raised, or caught at a specific site. The recommended

values fo mean r and upper percentile (i.e., 95 th 

### percentile) intake rates among consumers of the various home­produced food groups are presented in Table 13­ 1; these rates can be converted to per capita rates by multiplying by the fraction of the population consuming

these food groups during the survey period (See Section 13.3). Table 13­2 presents the confidence ratings for home­produced food intake. The data presented in this chapter for consumers of home­produced foods
represent average daily intake rates of food items/groups over the seven­day survey period and do

not account for variations in eating habits during the

rest of the year; thus the recommended upper percentile values, as well as the percentiles of the distributions



### Child­Specific Exposure Factors Handbook Page September 2008 13­1

### Child­Specific Exposure Factors Handbook

Chapter 13 ­ Intake of Home­Produced Foods

presented in Section 13.3 may not necessarily reflect

the long­term distribution of average daily intake of

home produced foods.

Because the home­produced food intake rates presented in this chapter are based on foods as brought

into the household and not in the form in which they are consumed, preparation loss factors should be applied,

as appropriate. These factors are necessary to convert

to intake rates to those that are representative of foods

“as consumed”. Additional conversions may be

necessary to ensure that the form of the food used to
estimate intake (e.g., wet or dry weight) is consistent

with the form used to measure contaminant

concentration (see Section 13.3).

The NFCS data used to generate intake rates
of home­produced foods are over 20 years old and may

not be reflective of current eating patterns among

consumers of home­produced foods. Although USDA

and others have conducted other food consumption

studies since the release of the 1987­1988 NFCS, these

studies do not include information on home­produced

foods.

Recommended home­produced food intake

rates are not provided for children under 1 year of age because the methodology used is based on apportionment of home­produced foods used by a household among the members of that household that

consume those foods. It was assumed that the diets of

children under 1 year of age differ markedly from that

of other household members; thus, they were not
assumed to consume any portion of the home­produced

food brought into the home. Also, recommended homeproduced food intake rates are not provided for

individual food items for children because, in general,

the sample size was too small to provide reliable data

for individual age groups. However, if intake rates are needed for age groups under 1 year of age or for food

items other than the major food groups presented here,

data in Section 13.3 on the fraction of household intake

that is home­produced may be used in conjunction with age­specific intake rates presented elsewhere in this

handbook to estimate intake of home produced foods (U.S. EPA, 1997).



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### Child­Specific Exposure Factors Handbook

Chapter 13

­ Intake of Home­Produced Foods

Table 13­1. Summary of Recommended Values for Intake of Home­produced Foods (Consumers Only)

Age Group a Mean 95 th Percentile Multiple

Percentiles Source g/kg­day
Home­produced Fruits

1 to 2 years 3 to 5 years 6 to 11 years 12 to 19 years 8.7

4.1

3.6

1.9 60.6

8.9

15.8

8.3 See Table 13­4 U.S. EPA Analysis of

1987­1988 NFCS

Home­produced Vegetables

1 to 2 years 3 to 5 years 6 to 11 years 12 to 19 years 5.2

2.5

2.0

1.5 19.6

7.7

6.2

6.0 See Table 13­4 U.S. EPA Analysis of

1987­1988 NFCS

Home­produced Meats

1 to 2 years 3 to 5 years 6 to 11 years 12 to 19 years 3.7

3.6

3.7

1.7 10.0

9.1

14.0

4.3 See Table 13­4 U.S. EPA Analysis of

1987­1988 NFCS

Home Caught Fish

1 to 2 years 3 to 5 years 6 to 11 years 12 to 19 years ­ b

­ 2.8

1.5 ­

­ 7.1

4.7 See Table 13­4 U.S. EPA Analysis of

1987­1988 NFCS

a

b Analysis was conducted prior to Agency’s issuance of Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005).

Data not presented for age groups/food groups where less than 20 observations were available.



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### Child­Specific Exposure Factors Handbook

Chapter 13 ­ Intake of Home­Produced Foods

Table 13­2. Confidence in Recommendations for Intake of Home­produced Foods

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or Defined) Bias The survey methodology and the approach to data analysis were adequate,

but individual intakes were inferred from

household consumption data. The sample size was large (approximately 3,000 children).

Non­response bias can not be ruled out
due to low response rate. Also, some biases may have occurred from using

household data to estimate individual

intake. Medium (Means)

Low (Distributions)

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The analysis specifically addressed home­produced intake.

Data from a nationwide survey, representative of the general U.S. population was used.

The data were collected in 1987­1988.

Household data were collected over 1

week. Low (Means & Short­term distributions)

Low (Long­term distributions)

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The methods used described to analyze

the data are described in detail in this handbook; the primary data are accessible through USDA.

Sufficient detail on the methods used to analyze the data are presented to allow

for the results to be reproduced.

Quality assurance of NFCS data was good; quality control of the secondary

data was sufficient. High



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### Child­Specific Exposure Factors Handbook

Chapter 13

­ Intake of Home­Produced Foods

Table 13­2. Confidence in Recommendations for Intake of Home­produced Food (continued)

General Assessment Factors Rationale Rating

Variability and Uncertainty

Variability in Population

Uncertainty Full distributions of home­produced

intake rates were provided.

Sources of uncertainty include:

individuals’ estimates of food weights, allocation of household food

to family members, and potential changes in eating patterns since these

data were collected, Low to Medium

Evaluation and Review

Peer Review

Number and Agreement of Studies The study was reviewed by USDA

and U.S. EPA.

The number of studies is 1. Medium

Overall Rating Low­Medium (means and short­term distributions)

Low (long­term distributions)



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Chapter 13 ­ Intake of Home­Produced Foods

13.3 KEY STUDY FOR INTAKE OF HOMEPRODUCED FOODS

13.3.1 U.S. EPA Analysis of NFCS 1987­1988

U.S. EPA’s National Center for Environmental

Assessment (NCEA) analyzed USDA's 1987­1988

NFCS data to generate intake rates for home­produced

foods (U.S. EPA, 1997). For the purposes of this study, home­produced foods were defined as homegrown

fruits and vegetables, meat and dairy products derived

from consumer­raised livestock or game meat, and

home caught fish. The food groups selected for analysis of children’s home­produced food intake

included major food groups such as total fruits, total vegetables, total meats, total dairy, total fish and

shellfish. These food groups were identified in the

NFCS data base according to NFCS­defined food

codes. Appendix 13A presents the codes and

definitions used to determine these major food groups. Foods with these codes, for which the source was identified as home­produced, were included in the analysis. This chapter presents the intake rate data for

these major food groups, except total dairy, for various

age ranges of children. An insufficient number of

observations (i.e., less than 30 households) were available to allowfor estimates of home­produced dairy products. Also, child­specific intake rates for

individual food items (e.g., carrots, citrus fruit) were

not estimated because, in general, the sample size was

too small to provide reliable data for the individual age

groups of interest.

The USDA data were adjusted by applying the sample weights calculated by USDA to the data set prior to analysis. The USDA sample weights were designed to “adjust for survey non­response and other vagaries of the sample selection process” (USDA, 1987­1988). Also, the USDA weights are calculated

“so that the weighted sample total equals the known population total, in thousands, for several

characteristics thought to be correlated with eating

behavior” (USDA, 1987­1988). The unweighted it could not be used directly to measure consumption of home­produced food because the individual component
does not identify the source of the food item (i.e., as home­produced or not). Therefore, an analytical

method which incorporated data from both the

household and individual survey components was developed to estimate individual home­produced food

intake. The USDA household data were used to
determine (1) the amount of each home­produced food

item used during a week by household members and (2)

the number of meals eaten in the household by each

household member during a week. As measured by the NFCS, the amount of food “consumed” by the

household is a measure of consumption in an economic

sense, i.e., a measure of the weight of food brought into

the household that has been consumed (used up) in

some manner. In addition to food being consumed by

persons, food may be used up by spoiling, by being

discarded (e.g., inedible parts), through cooking

processes, etc. Note that the household survey reports
the total amount of each food item used in the

household (whether by guests or household members);

the amount used by household members was derived by multiplying the total amount used in the household by

the proportion of all meals served in the household (during the survey week) that were consumed by

household members.

The individual survey data were used to generate average sex­ and age­specific serving sizes for

each food item. These serving sizes were used during subsequent analyses to generate home­produced food

intake rates for individual household members. Assuming that the proportion of the household quantity

of each home­produced food item/group was a function

of the number of meals and the mean sex­ and agespecific serving size for each family member, individual

intakes of home­produced food were calculated for all

members o f the survey population using the following general equation:



###    m
i 

### q
i 

### 

w = w 

n 

###  (Eqn. 13­1)

i f 

###  

# ∑ m
i 

### q
i 

###  

i = 1 

### 

where: sample included approximately 3,000 children (ages <1

to 19 years), which was weighted to reflect nearly 54

mi llion c hildren.

Al though th e individual intake component of

the N FCS gives the best measure of the amount of each

food group eaten by each individual in the household,



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Chapter 13

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w i 

### = Home­produced amount of food item/group attributed to member i during the week (g/week);

w f 

### = Total quantity of home­produced

food item/group used by the family

members (g/week);

m i 

### = Number of meals of household food

consumed by member i during the

week (meals/week); and

q i 

### = Serving size for an individual within

the age and sex category of the

member (g/meal).

Daily intake of a home­produced food group

was determined by dividing the weekly value (w i 

### ) by

seven. Intake rates were indexed to the self­reported body weight of the survey respondent and reported in

units of g/kg­day. For the major food groups (fruits, vegetables, meats, and fish), distributions of homeproduced intake among consumers were generated by

age group. Consumers were defined as members of

survey households who reported consumption of the

food group of interest during the one week survey period.

The age categories used in the analysis were as follows: 1 to 2 years; 3 to 5 years; 6 to 11 years; and 12

to 19 years Because this analysis was conducted prior

to issuance of U.S. EPA’s Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005), the age groups used are not entirely consistent

with recent guidelines. Intake rates were not calculated

for children under 1 year because their diet differs markedly from that of other household members, and thus, the assumption that all household members share

all foods would be invalid for this age group. The intake data presented here for consumers of home­produced foods and the total number of

individuals surveyed may be used to calculate the mean and the percentiles of the distribution of homeproduced food consumption in the overall population (consumers and non­consumers) as follows: Assuming that IR p 

### is the home­produced intake

rate of the food group at the p th 

### percentile and N

c 

### is the weighted number of individuals consuming the homeproduced food item, and N T 

### is the weighted total number of individuals surveyed, then N T 

### ­ N

c 

### is the weighted number of individuals who reported zero consumption of the food item. In addition, there are (p/100 x N

c 

### ) individuals below the p th 

### percentile. Therefore, the percentile that corresponds to a particular intake rate (IR
p 

### ) for the overall distribution of home­produced food consumption (including

consumers and non­consumers) can be obtained by:

 P   x N c + 

# ( N T − N c 

# ) 
th  100  

### (Eqn. 13­2) P = 100 x

overall N T



### Table 13­3 displays the weighted numbers N T 

### ,

as well as the unweighted total survey sample sizes, for

each age category. Table 13­4 presents homeproduced intake rates for fruits, vegetables, meats, and

fish. These intake rates are based on the amount of

household food consumption as well as age­specific serving size data.

USDA estimated preparation losses for various

foods (USDA, 1975). For meats, a net cooking loss,

which includes dripping and volatile losses, and a net post­cooking loss, which involves losses from cutting, bones, excess fat, scraps and juices, were derived for a variety of cuts and cooking methods. For total meats,
U.S. EPA has averaged these losses across all meat types, cuts and cooking methods to obtain a mean net cooking loss and a mean net post­cooking loss. Mean
percentage values for all meats and fish are provided in

Table 13­5. For individual fruits and vegetables,

USDA (1975) also gave cooking and post­cooking

losses. These data, averaged across all types of fruits

and vegetables to give mean net cooking and post cooking losses, are also provided in Table 13­5.

The following formula can be used to convert
the home­produced intake rates tabulated here to rates reflecting actual consumption:



### I = I × 

# ( 1­ L 

# )

× 

# ( 1

− L 

# ) (Eqn. 13­3)



### A 1 2

where:

I A 

### = the adjusted intake rate;

I = the tabulated intake rate;

L 1 

### = the cooking or preparation loss; and



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Chapter 13 ­ Intake of Home­Produced Foods

L 2 

### = the post­cooking loss.

For fruits, corrections based on post­cooking

losses only apply to fruits that are eaten in cooked

forms. For raw forms of the fruits, paring or preparation loss data should be used to correct for

losses from removal of skin, peel, core, caps, pits,

stems, and defects, or draining of liquids from canned

or frozen forms.

In calculating ingestion exposure, assessors should use consistent forms (e.g., “as­consumed” or dry weight) in combining intake rates with contaminant concentrations, as discussed in Chapter 9 of this

handbook.

The USDA 1987­1988 NFCS household data

were also used to estimate the fraction of household

intake that can be attributed to home­produced foods (Table 13­6). The analysis was conducted for the major

food groups (i.e., total meat, dairy, fruits, vegetables,

and fish), as well as for a variety of individual food

items (e.g., apples, tomatoes, beef, etc.). The fraction

of intake that was home­produced was calculated as the

ratio of total intake of the home­produced food item/group by the survey population to the total intake

of all forms of the food by the survey population. The

food codes used in this analysis are presented in Appendix 13­B.

The USDA NFCS data set is the largest publicly available source of information on homeproduced food consumption habits in the United States.

The advantages of using this data set are that it is expected to be representative of the U.S. population and

that it provides information on a wide variety of food

groups. However, the data collected by the USDA

NFCS are based on short­term dietary recall and the

intake distributions generated from this data set may not accurately reflect long­term intake patterns, particularly

with respect to the tails (extremes) of the distributions. Also, the two survey components (i.e., household and individual) do not define food items/groups in a consistent manner; as a result, some errors may be

introduced into these analyses because the two survey components are linked. The results presented here may also be biased by assumptions that are inherent in the analytical method utilized. The analytical method may

not capture all high­end consumers within households because average serving sizes are used in calculating

the proportion of home­produced food consumed by

each household member. Thus, for instance, in a twoperson household where one member had high intake

and one had low intake, the method used here would

assume that both members had an equal and moderate

level of intake. In addition, the analyses assume that all family members consume a portion of the homeproduced food used within the household. However,

not all family members may consume each homeproduced food item and serving sizes allocated here

may not be entirely representative of the portion of

household foods consumed by each family member. As

was mentioned earlier, no analyses were performed for

children under 1 year age. The preparation loss factors discussed above

are intended to convert intake rates based on “household consumption” to rates reflective of what

individuals actually consume. However, these factors

do not include losses to spoilage, feeding to pets, food

thrown away, etc. It should also be noted that because

this analysis is based on the 1987­1988 NFCS, it may

not reflect recent changes in food consumption patterns.
The low response rate associated with the 1987­1988

NFCS also contributes to the uncertainty of the homeproduced intake rates generated using these data.

13.4 REFERENCES FOR CHAPTER 13

USDA (1975) Food yields summarized by different
stages of preparation. Agricultural Handbook

No. 102. Washington, DC. U.S. Department

of Agriculture, Agriculture Research Service.

USDA (1987­1988) Dataset: Nationwide Food Consumption Survey 1987/88 Household

Food Use. Washington, DC. U.S. Department of Agriculture. 1987/88 NFCS

Database.

USDA (1992) Changes in food consumption and expenditures in American households during

the 1980's. Washington, DC. U.S. Department of Agriculture. Statistical

Bulletin No. 849.

USDA (1993) Food and nutrient intakes by individuals

in the United States, 1 Day, 1987­1988.

Nationwide Food Consumption Survey 1987­ 1988, NFCS Report No. 87­I­1.



### Page Child­Specific Exposure Factors Handbook

13­8 September 2008

### Child­Specific Exposure Factors Handbook

Chapter 13

­ Intake of Home­Produced Foods

USDA (1994) Food consumption and dietary levels of

households in the United States, 1987­1988.

U.S. Department of Agriculture, Agricultural

Research Service. Report No. 87­H­1.

U.S. EPA (1989) Risk Assessment Guidance for Superfund (RAGS): Volume I, Human Health

Evaluation Manual, Part A. Office of Solid

Waste and Emergency Response, Washington,

DC. EPA/540/1­89/002. Available online at http://www.epa.gov/oswer/riskassessment/ra gsa/index.htm

U.S. EPA (1991) Record of Decision. ROD ID
EPA/ROD/R10­91­029.

U.S. EPA (1993) Record of Decision. ROD ID
EPA/ROD/R04­93­166.

U.S. EPA (1994) Validation strategy for the Integrated Exposure Uptake Biokinetic Model for Lead

in Children. Office of Solid Waste and Emergency Response, Washington DC.

EPA/540/R­94­039. Available online at http://www.epa.gov/superfund/lead/products /valstrat.pdf

U.S. EPA (1996) Soil Screening Fact Sheet Guidance.

EPA/540/F­95/041. Available online at http://www.epa.gov/superfund/health/conme

dia/soil/index.htm

U.S. EPA (1997) Exposure Factors Handbook. Office

of Research and Development, Washington,

DC. EPA/600/P­95/002F. Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.c

fm?deid=12464



### Child­Specific Exposure Factors Handbook Page September 2008 13­9Table 13­3. Weighted and Unweighted Number of Observations (Individuals) Child­specific Analysis of Food Intake for NFCS Data Used in

Age Group Number of Observations

weighted unweighted

<1 year 2,814,000 156

1 to 2 years 5,699,000 321

3 to 5 years 8,103,000 461

6 to 11 years 16,711,000 937

12 to 19 years 20,488,000 1,084

Total 53,815,000 2,959

weighted unweighted =

= Weighted number of

Unweighted number observations.

of observations. 

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13­10 September 2008Table13­4.ConsumerOnlyIntakeofHome­producedFoods (g/kg­day)aNc Nc %Age wgtd unwgtd Consuming Mean SE P1 P5 P10 P25 P50 P75 P90 P95 P99 P100(years)Home­produced Fruits1to 2 360,000 23 6.3 8.7 3.1 1.0 1.1 1.3 1.6 3.5 8.0 19.3 60.660.6 60.63to 5 550,000 34 6.8 4.1 1.5 0.0 0.0 0.4 1.0 1.9 2.7 6.0 8.91 48.3 48.36to 11 1,044,000 75 6.3 3.6 0.7 0.0 0.2 0.4 0.7 1.3 3.1 11.8 15.8 32.2 32.212to 19 1,189,000 67 5.8 1.9 0.4 0.1 0.1 0.3 0.4 0.7 2.4 6.8 8.3 18.5 18.5Home­produced Vegetables1to 2 951,000 53 16.7 5.2 0.9 0.0 0.2 0.4 1.2 3.3 5.8 13.1 19.6 27.0 27.03to 5 1,235,000 76 15.2 2.5 0.3 0.0 0.1 0.4 0.7 1.3 3.9 6.4 7.7 10.6 12.86to 11 3,024,000 171 18.1 2.0 0.3 0.0 0.1 0.2 0.4 0.9 2.2 4.6 6.2 17.6 23.612to 19 3,293,000 183 16.1 1.5 0.1 0.0 0.1 0.1 0.3 0.81.8 3.7 6.0 7.7 9.0Home­produced Meats1to 2 276,000 22 4.8 3.7 0.6 0.4 1.0 1.0 1.2 2.7 4.7 8.7 10.0 11.5 11.53to 5 396,000 26 4.9 3.6 0.5 0.8 0.8 1.5 2.2 2.8 3.7 7.8 9.1 13.0 13.06to 11 1,064,000 65 6.4 3.7 0.5 0.4 0.7 0.7 1.3 2.1 4.7 8.0 14.0 15.3 15.312to 19 1,272,000 78 6.2 1.7 0.2 0.2 0.3 0.50.6 1.2 2.4 3.7 4.3 6.8 7.5Home­caught Fish1to 2 82,000 6 1.4 * * * * * * * * * * * *3to 5 142,000 11 1.8 * * * * * * * * * * * *6to 11 382,000 29 2.3 2.8 0.8 0.2 0.2 0.2 0.2 0.6 1.0 3.7 7.1 7.9 25.312to 19 346,000 21 1.7 1.5 0.4 0.2 0.2 0.2 0.2 0.3 1.0 1.8 4.7 6.7 8.4aAnalysis wasconductedpriortoAgency’sissuanceofGuidanceonSelectingAgeGroupsforMonitoringand AssessingChildhoodExposures toEnvironmentalContaminants(U.S.EPA, 2005).SE=Standard error.P= Percentileof thedistribution.Ncwgtd=Weightednumberof consumers.Ncunwgtd=Unweightednumberofconsumersin survey.*=Lessthan20 observations.Source:BasedonU.S.EPA'sanalysesofthe1987­1988 NFCS. 

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Chapter 13

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Child­Specific Exposure Factors Handbook Page September 2008 13­11Table 13­5. Percent Weight Losses from Food Preparation

Food Group Mean Net Preparation/Cooking Loss (%) Mean Net Post Cooking (%)

a Meats

Fish and shellfish d

Fruits

g Vegetables 29.7 b

31.5 b

25.4 e

12.4 h 29.7 c

10.5 c

30.5 f

22 i

a

b

c

d

e

f

g

h

i

Source: Averaged over various cuts and preparation methods for various meats including beef, pork, chicken, turkey, lamb, and veal.

Includes dripping and volatile losses during cooking.

Includes losses from cutting, shrinkage, excess fat, bones, scraps, and juices. Averaged over a variety of fish and shellfish, to include: bass, bluefish, butterfish, cod, flounder, haddock, halibut, lake trout, mackerel, perch, porgy, red snapper, rockfish, salmon, sea trout, shad, smelt, sole, spot, squid, swordfish steak, trout, whitefish, clams, crab, crayfish, lobster, oysters, and shrimp and shrimp

dishes.

Based on preparation losses. Averaged over apples, pears, peaches, strawberries, and oranges. Includes

losses from removal of skin or peel, core or pit, stems or caps, seeds, and defects. Also, includes losses from

removal of drained liquids from canned or frozen forms. Averaged over apples and peaches. Include losses from draining cooked forms. Averaged over various vegetables, to include: asparagus, beets, broccoli, cabbage, carrots, corn, cucumbers, lettuce, lima beans, okra, onions, green peas, peppers, pumpkins, snap beams, tomatoes, and potatoes.
Includes losses due to paring, trimming, flowering the stalk, thawing, draining, scraping, shelling, slicing, husking, chopping, and dicing and gains from the addition of water, fat, or other ingredients. Averaged over various preparation methods.

Includes losses from draining or removal of skin. Based on potatoes only.

U.S. EPA, 1997 (Derived from USDA, 1975). 

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Table 13­6. Fraction of Food Intake that is Home­produced

All Households Households who garden Households who farm

Total Fruits

Apples
Peaches
Pears Strawberries
Other Berries
Citrus
Other 0.04
0.030
0.147
0.067
0.111
0.217
0.038
0.042 0.101
0.070
0.316
0.169
0.232
0.306
0.087
0.107 0.161
0.292
0.461
0.606
0.057
0.548
0.005
0.227

All Households Households who garden Households who farm

Total Vegetables

Asparagus
Beets Broccoli Cabbage
Carrots
Corn Cucumbers
Lettuce Lima Beans Okra
Onions
Peas Peppers Pumpkin Snap Beans
Tomatoes White Potatoes 0.068
0.063
0.203
0.015
0.038
0.043
0.078
0.148
0.010
0.121
0.270
0.056
0.069
0.107
0.155
0.155
0.184
0.038 0.173
0.125
0.420
0.043
0.099
0.103
0.220
0.349
0.031
0.258
0.618
0.148
0.193
0.246
0.230
0.384
0.398
0.090 0.308
0.432
0.316
0.159
0.219
0.185
0.524
0.524
0.063
0.103
0.821
0.361
0.308
0.564
0.824
0.623
0.616
0.134

All Households Households who
raise animals/hunt Households who farm

Total Meats

Beef
Game Pork Poultry 0.024
0.038
0.276
0.013
0.011 0.306
0.485
0.729
0.242
0.156 0.319
0.478

­ 0.239
0.151

All Households Households who
raise animals Households who farm

Total Dairy

Eggs 0.012
0.014 0.207
0.146 0.254
0.214

All Households Households who
fish ­­

Total fish 0.094 0.325

­­

­ = No data.

Source: U.S. EPA Analysis of 1987­1988 NFCS.



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Chapter 13

­ Intake of Home­Produced Foods

APPENDIX 13A

FOOD CODES AND DEFINITIONS USED IN CHILD­SPECIFIC ANALYSIS

OF THE 1987­1988 USDA NFCS DATA TO ESTIMATE HOME­PRODUCED INTAKE RATES



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Chapter 13 ­ Intake of Home­Produced Foods

Table 13A­1. Food Codes and Definitions Used in Child­specific Analysis of the 1987­1988 USDA NFCS Data

to Estimate Intake of Home­produced Foods

Food Product Household Code/Definition a Individual Code

MAJOR FOOD GROUPS

Total Fruits 50­ Fresh Fruits

citrus

other vitamin­C rich

other fruits 512­ Commercially Canned Fruits 522­ Commercially Frozen Fruits

533­ Canned Fruit Juice

534­ Frozen Fruit Juice 535­ Aseptically Packed Fruit Juice

536­ Fresh Fruit Juice

542­ Dried Fruits (includes baby foods) 6­ Fruits

citrus fruits and juices

dried fruits

other fruits fruits/juices & nectar fruit/juices baby food (includes baby foods)

Total Vegetables 48­ Potatoes, Sweet potatoes
49­ Fresh Vegetables

dark green deep yellow

tomatoes light green other 511­ Commercially Canned Vegetables 521­ Commercially Frozen Vegetables

531­ Canned Vegetable Juice

532­ Frozen Vegetable Juice

537­ Fresh Vegetable Juice 538­ Aseptically Packed Vegetable Juice

541­ Dried Vegetables (does not include soups, sauces, gravies, mixtures, and readyto­eat dinners; includes baby foods except mixtures/dinners) 7­ Vegetables (all forms)

white potatoes & PR starchy

dark green vegetables deep yellow vegetables

tomatoes and tom. mixtures

other vegetables

veg. and mixtures/baby food

veg. with meat mixtures (includes baby foods; mixtures, mostly vegetables)

Total Meats 44­ Meat

beef pork

veal

lamb

mutton goat

game lunch meat
mixtures 451­ Poultry (does not include soups, sauces, gravies, mixtures, and readyto­eat dinners; includes baby foods except mixtures) 20­ Meat, type not specified

21­ Beef

22­ Pork 23­ Lamb, veal, game, carcass meat 24­ Poultry 25­ Organ meats, sausages, lunchmeats, meat spreads (excludes meat, poultry, and fish with non­meat items; frozen plate meals; soups and gravies with

meat, poultry and fish base; and gelatin­based drinks;

includes baby foods)



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Chapter 13 ­ Intake of Home­Produced Foods

Table 13A­1. Food Codes and Definitions Used in Child­specific Analysis of the 1987­1988 USDA NFCS Data

to Estimate Intake of Home­produced Foods 

### (continued)

Food Product Household Code/Definition 1 Individual Code

MAJOR FOOD GROUPS

Total Dairy 40­ Milk Equivalent

fresh fluid milk processed milk

cream and cream substitutes

frozen desserts with milk

cheese dairy­based dips (does not include soups, sauces, gravies, mixtures, and readyto­eat dinners) 1­ Milk and Milk Products

milk and milk drinks

cream and cream substitutes

milk desserts, sauces, and gravies

cheeses (includes regular fluid milk, human milk, imitation

milk products, yogurt, milk­based meal replacements,

and infant formulas)

Total Fish 452­ Fish, Shellfish

various species fresh, frozen, commercial, dried (does not include soups, sauces, gravies, mixtures, and readyto­eat dinners) 26­ Fish, Shellfish

various species and forms

(excludes meat, poultry, and fish with non­meat items; frozen plate meals; soups and gravies with

meat, poultry and fish base; and gelatin­based drinks)

a Food items within these categories that were identified by the household as being home­produced or home­caught (i.e., source code pertaining to home produced foods) were included in the analysis.



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Chapter 13 ­ Intake of Home­Produced Foods

APPENDIX 13B

1987­1988 NFCS FOOD CODES AND DEFINITIONS USED IN ESTIMATI NG FRACTION OF

HOUSEHOLD FOOD INTAKE THAT IS HOME­PRODUCED



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Chapter 13 ­ Intake of Home­Produced Foods

Table 13B­1. Food Codes and Definitions Used in Analysis of the 1987­1988 USDA NFCS Household Data to
Estimate Fraction of Food Intake that is Home­produced

Food Product

Household Code/Definition

INDIVIDUAL FOODS

White Potatoes 4811­ White Potatoes, fresh

4821­ White Potatoes, commercially canned

4831­ White Potatoes, commercially frozen

4841­ White Potatoes, dehydrated

4851­ White Potatoes, chips, sticks, salad (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners)

Peppers 4913­ Green/Red Peppers, fresh

5111201 Sweet Green Peppers, commercially canned

5111202 Hot Chili Peppers, commercially canned

5211301 Sweet Green Peppers, commercially frozen

5211302 Green Chili Peppers, commercially frozen

5211303 Red Chili Peppers, commercially frozen

5413112 Sweet Green Peppers, dry

5413113 Red Chili Peppers, dry (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners)

Onions 4953­ Onions, Garlic, fresh

onions

chives garlic

leeks

5114908 Garlic Pulp, raw 5114915 Onions, commercially canned

5213722 Onions, commercially frozen

5213723 Onions with Sauce, commercially frozen

5413103 Chives, dried

5413105 Garlic Flakes, dried

5413110 Onion Flakes, dried (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners)

Corn 4956­ Corn, fresh

5114601 Yellow Corn, commercially canned

5114602 White Corn, commercially canned

5114603 Yellow Creamed Corn, commercially canned

5114604 White Creamed Corn, commercially canned

5114605 Corn on Cob, commercially canned

5114607 Hominy, canned

5115306 Low Sodium Corn, commercially canned

5115307 Low Sodium Cr. Corn, commercially canned

5213501 Yellow Corn on Cob, commercially frozen

5213502 Yellow Corn off Cob, commercially frozen

5213503 Yell. Corn with Sauce, commercially frozen

5213504 Corn with other Veg., commercially frozen

5213505 White Corn on Cob, commercially frozen

5213506 White Corn off Cob, commercially frozen

5213507 Wh. Corn with Sauce, commercially frozen

5413104 Corn, dried

5413106 Hominy, dry

5413603 Corn, instant baby food (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby food)e



### Page Child­Specific Exposure Factors Handbook

13B­2 September 2008

### Child­Specific Exposure Factors Handbook

Chapter 13 ­ Intake of Home­Produced Foods

Table 13B­1. Food Codes and Definitions Used in Analysis of the 1987­1988 USDA NFCS Household Data to
Estimate Fraction of Food Intake that is Home­produced (continued)

Food Product

Household Code/Definition

Apples 5031­ Apples, fresh

5122101 Applesauce with sugar, commercially canned

5122102 Applesauce without sugar, comm. canned

5122103 Apple Pie Filling, commercially canned

5122104 Apples, Applesauce, baby/jr., comm. canned

5122106 Apple Pie Filling, Low Cal., comm. canned

5223101 Apple Slices, commercially frozen

5332101 Apple Juice, canned

5332102 Apple Juice, baby, Comm. canned

5342201 Apple Juice, comm. frozen

5342202 Apple Juice, home frozen

5352101 Apple Juice, aseptically packed

5362101 Apple Juice, fresh

5423101 Apples, dried (includes baby food; except mixtures)

Tomatoes 4931­ Tomatoes, fresh 5113­ Tomatoes, commercially canned

5115201 Tomatoes, low sodium, commercially canned

5115202 Tomato Sauce, low sodium, comm. canned

5115203 Tomato Paste, low sodium, comm. canned

5115204 Tomato Puree, low sodium, comm. canned

5311­ Canned Tomato Juice and Tomato Mixtures

5321­ Frozen Tomato Juice

5371­ Fresh Tomato Juice

5381102 Tomato Juice, aseptically packed

5413115 Tomatoes, dry

5614­ Tomato Soup

5624­ Condensed Tomato Soup 5654­ Dry Tomato Soup (does not include mixtures, and ready­to­eat dinners)

Snap Beans 4943­ Snap or Wax Beans, fresh

5114401 Green or Snap Beans, commercially canned

5114402 Wax or Yellow Beans, commercially canned

5114403 Beans, baby/jr., commercially canned

5115302 Green Beans, low sodium, comm. canned

5115303 Yell. or Wax Beans, low sod., comm. canned

5213301 Snap or Green Beans, comm. frozen

5213302 Snap or Green w/sauce, comm. frozen

5213303 Snap or Green Beans w/other veg., comm. fr.

5213304 Sp. or Gr. Beans w/other veg./sc., comm. fr.

5213305 Wax or Yell. Beans, comm. frozen (does not include soups, mixtures, and ready­to­eat dinners; includes baby foods)

Beef 441­ Beef (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Pork 442­ Pork (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)



### Child­Specific Exposure Factors Handbook Page September 2008 13B­3

### Child­Specific Exposure Factors Handbook

Chapter 13 ­ Intake of Home­Produced Foods

Table 13B­1. Food Codes and Definitions Used in Analysis of the 1987­1988 USDA NFCS Household Data to Estimate Fraction

of Food Intake that is Home­produced (continued)

Food Product Household Code/Definition

Game 445­ Variety Meat, Game (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Poultry 451­ Poultry (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Eggs 46­ Eggs (fresh equivalent)

fresh processed eggs, substitutes (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Broccoli 4912­ Fresh Broccoli (and home canned/froz.)

5111203 Broccoli, comm. canned

52112­ Comm. Frozen Broccoli (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Carrots 4921­ Fresh Carrots (and home canned/froz.)

51121­ Comm. Canned Carrots

5115101 Carrots, Low Sodium, Comm. Canned

52121­ Comm. Frozen Carrots

5312103 Comm. Canned Carrot Juice

5372102 Carrot Juice Fresh

5413502 Carrots, Dried Baby Food (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Pumpkin 4922­ Fresh Pumpkin, Winter Squash (and home canned/froz.) 51122­ Pumpkin/Squash, Baby or Junior, Comm. Canned

52122­ Winter Squash, Comm. Frozen

5413504 Squash, Dried Baby Food (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Asparagus 4941­ Fresh Asparagus (and home canned/froz.)

5114101 Comm. Canned Asparagus

5115301 Asparagus, Low Sodium, Comm. Canned

52131­ Comm. Frozen Asparagus (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Lima Beans 4942­ Fresh Lima and Fava Beans (and home canned/froz.)

5114204 Comm. Canned Mature Lima Beans

5114301 Comm. Canned Green Lima Beans

5115304 Comm. Canned Low Sodium Lima Beans

52132­ Comm. Frozen Lima Beans
54111­ Dried Lima Beans

5411306 Dried Fava Beans (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures; does not include succotash)



### Page Child­Specific Exposure Factors Handbook

13B­4 September 2008

### Child­Specific Exposure Factors Handbook

Chapter 13 ­ Intake of Home­Produced Foods

Table 13B­1. Food Codes and Definitions Used in Analysis of the 1987­1988 USDA NFCS Household Data to
Estimate Fraction of Food Intake that is Home­produced (continued)

Food Product

Household Code/Definition

Cabbage 4944­ Fresh Cabbage (and home canned/froz.)

4958601 Sauerkraut, home canned or pkgd

5114801 Sauerkraut, comm. canned

5114904 Comm. Canned Cabbage

5114905 Comm. Canned Cabbage (no sauce; incl. baby)

5115501 Sauerkraut, low sodium., comm. canned

5312102 Sauerkraut Juice, comm. canned (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Lettuce 4945­ Fresh Lettuce, French Endive (and home canned/froz.) (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Okra 4946­ Fresh Okra (and home canned/froz.)

5114914 Comm. Canned Okra

5213720 Comm. Frozen Okra

5213721 Comm. Frozen Okra with Oth. Veg. & Sauce (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Peas 4947­ Fresh Peas (and home canned/froz.)

51147­ Comm Canned Peas (incl. baby)

5115310 Low Sodium Green or English Peas (canned)

5115314 Low Sod. Blackeye, Gr. or Imm. Peas (canned)

5114205 Blackeyed Peas, comm. canned

52134­ Comm. Frozen Peas
5412­ Dried Peas and Lentils (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Cucumbers 4952­ Fresh Cucumbers (and home canned/froz.) (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Beets 4954­ Fresh Beets (and home canned/froz.)

51145­ Comm. Canned Beets (incl. baby)

5115305 Low Sodium Beets (canned)

5213714 Comm. Frozen Beets

5312104 Beet Juice (does not include soups, sauces, gravies, mixtures, and ready­to­eat dinners; includes baby

foods except mixtures)

Strawberries 5022­ Fresh Strawberries

5122801 Comm. Canned Strawberries with sugar 5122802 Comm. Canned Strawberries without sugar 5122803 Canned Strawberry Pie Filling

5222­ Comm. Frozen Strawberries (does not include ready­to­eat dinners; includes baby foods except mixtures)



### Child­Specific Exposure Factors Handbook Page September 2008 13B­5Table 13B­1. Food Codes and

Estimate Definitions

Fraction of Used

Food in Analysis of the 1987­1988

Intake that is Home­produced USDA NFCS (continued) Household Data to

Food Product

Household Code/Definition

Other Berries 5033­ Fresh Berries Other than Strawberries

5122804 Comm. Canned Blackberries with sugar 5122805 Comm. Canned Blackberries without sugar 5122806 Comm. Canned Blueberries with sugar 5122807 Comm. Canned Blueberries without sugar 5122808 Canned Blueberry Pie Filling

5122809 Comm. Canned Gooseberries with sugar 5122810 Comm. Canned Gooseberries without sugar 5122811 Comm. Canned Raspberries with sugar 5122812 Comm. Canned Raspberries without sugar 5122813 Comm. Canned Cranberry Sauce

5122815 Comm. Canned Cranberry­Orange Relish

52233­ Comm. Frozen Berries (not strawberries)

5332404 Blackberry Juice (home and comm. canned)

5423114 Dried Berries (not strawberries) (does not include ready­to­eat dinners; includes baby foods except mixtures)

Peaches 5036­ Fresh Peaches

51224­ Comm. Canned Peaches (incl. baby)

5223601 Comm. Frozen Peaches

5332405 Home Canned Peach Juice

5423105 Dried Peaches (baby)

5423106 Dried Peaches (does not include ready­to­eat dinners; includes baby foods except mixtures)

Pears 5037­ Fresh Pears

51225­ Comm. Canned Pears (incl. baby)

5332403 Comm. Canned Pear Juice, baby

5362204 Fresh Pear Juice

5423107 Dried Pears (does not include ready­to­eat dinners; includes baby foods except mixtures)

Citrus Fruits 501­ Fresh Citrus Fruits

5121 Comm. Canned Citrus Fruits

5331 Canned Citrus and Citrus Blend Juice

5341 Frozen Citrus and Citrus Blend Juice

5351 Aseptically Packed Citrus and Citr. Blend

5361 Fresh Citrus and Citrus Blend Juice (includes baby foods; excludes dried fruits) Juice

Other

Fruits 502­ Fresh Other Vitamin C­Rich Fruits

503­ Fresh Other Fruits

5122­ Comm. Canned Fruits Other than Citrus

5222­ Frozen Strawberries

5332­ Frozen Other than Citr. or Vitamin C­Rich Fr.
5333­ Canned Fruit Juice Other than Citrus

5352­ Frozen Juices Other than Citrus

5362­ Aseptically Packed Fruit Juice Other than Citr.

542­ Fresh Fruit Juice Other than Citrus Dry Fruits (includes baby foods; excludes dried fruits) 

### Child­Specific Exposure Factors Handbook

Chapter 13 ­ Intake of Home­Produced Foods

Page C hild­Specific Exposure Factors Handbook

13B­6 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 14 - Total Food Intake

14 TOTAL FOOD INTAKE

14.1 INTRODUCTION The U.S. food supply is generally considered to be one of the safest in the world. Nevertheless, contamination of foods may occur as a result of environmental pollution of the air, water, or soil, or the intentional use of chemicals such as pesticides or other agrochemicals. Ingestion of contaminated foods is a potential pathway of exposure to such contaminants among children. To assess chemical exposure through this pathway, information on food ingestion rates is needed. Per capita and consumers only data on food consumption rates for various food items and food categories are reported in Chapters 9 through 13 of this handbook. These intake rates were estimated by U.S. EPA using databases developed by the U.S. Department of Agriculture (USDA). U.S. EPA (2007) expanded the analysis of food intake in order to examine individuals’ food consumption habits in greater detail. Using data from the USDA’s Continuing Survey of Food Intake by Individuals (CSFII) conducted in 1994-1996, 1998,

U.S. EPA (2007) derived distributions to characterize (1) total food intake among various groups in the U.S. population, subdivided by age, race, geographic region, and urbanization; (2) the contribution of various food categories (e.g., meats, grains, vegetables, etc.) to total food intake among these populations; and (3) the contribution of various food categories to total food intake among individuals exhibiting low- or high-end consumption patterns of a specific food category (e.g.,

individuals below the 10 th 

### percentile or above the 90 th 

### percentile for fish consumption). These data may be useful for assessing exposure among populations exhibiting lower or higher than usual intake of certain types of foods (e.g., people who eat little or no meat, or people who eat large quantities of fish).

The recommendations for total food intake rates are provided in the next section, along with a summary of the confidence ratings for these recommendations. Following the recommendations, the key study on total

food intake is summarized.

14.2 RECOMMENDATIONS A summary of recommended values for total food intake, on an as-consumed basis, is presented in Table 14-1. The confidence ratings for these recommendations are presented in Table 14-2. The

recommended intake rates for children are based on data from the U.S. EPA (2007) analysis of CSFII data. However, the analysis presented in U.S. EPA (2007) was conducted before U.S. EPA published the guidance

entitled Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental

Contaminants (U.S. EPA, 2005). As a result, the age groups used for children in U.S. EPA (2007) were not entirely consistent with the age groups recommended in the 2005 guidance. Therefore, a re-analysis of the data

was conducted to conform with U.S. EPA’s recommended age groups for children.

Because these recommendations are based on 1994-96 and 1998 CSFII data, they may not reflect recent changes that may have occurred in consumption patterns. In addition, these distributions are based on data collected over a 2-day period and may not necessarily reflect the long-term distribution of average daily intake rates. However, for the broad categories of foods used in this analysis (e.g., total foods, total fruits, total vegetables, etc.), because they are typically eaten on a daily basis throughout the year with minimal seasonality, the short-term distribution may be a reasonable approximation of the long-term distribution, although it will display somewhat increased variability. This implies that the upper percentiles shown here will tend to overestimate the corresponding percentiles of the true long-term distribution.



### Child-Specific Exposure Factors Handbook Page September 2008 14-1

### Child-Specific Exposure Factors Handbook

Chapter 14 - Total Food Intake

Table 14-1. Recommended Values for Per Capita Total Food Intake, As Consumed

Age Group Mean 95 th Percentile Multiple

Percentiles Source g/kg-day

Birth to <1 month 20 61

See Table 14-3 U.S. EPA re-analysis of CSFII 1994-96, 98 data

(Based on U.S. EPA, 2007) 1 to <3 months 16 40

3 to <6 months 28 65

6 to <12 months 56 134

1 to <2 years 90 161

2 to <3 years 74 126

3 to <6 years 61 102

6 to <11 years 40 70

11 to <16 years 24 45

16 to <21 years 18 35



### Note: Total food intake was defined as intake of the sum of all foods in the following major food categories: dairy, meats, fish, eggs, grains, vegetables, fruits, and fats. Beverages, sugar, candy, and sweets, and nuts and nut products were not included because they could not be categorized into the major food groups. Also, human milk intake was not included.



### Page Child-Specific Exposure Factors Handbook

14-2 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 14 - Total Food Intake

Table 14-2. Confidence in Recommendations for Total Food Intake

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or Defined) Bias The survey methodology was adequate and the analytical approach was competently executed. The study size was very large; sample size varied with age. The response rate was good. The key study analyzed primary data on recall of ingestion.

No direct measurements were taken. The study relied on survey data. High

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The analysis was specifically designed to address food intake.

The population studied was representative of the U.S. population.

The data used were the most current data publicly available at the time the analysis was conducted for the handbook.

Ingestion rates were estimated based on short-term data collected in the CSFII 1994-96, 1998. Medium

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The CSFII data are publicly available. The U.S. EPA (2007) report is available online.

The methodology was clearly presented; enough information was included to reproduce results.

Quality assurance methods were not described in the study
report. Medium

Variability and Uncertainty

Variability in Population

Uncertainty Short term distributions were provided. The survey was not designed to capture long term day-to-day variability.

The survey data were based on recall over a 2-day period. Other sources of uncertainty were minimal. Medium

Evaluation and Review

Peer Review

Number and Agreement of Studies The USDA CSFII survey received a high level of peer review. U.S. EPA (2007) analysis was also peer-reviewed; however, the re-analysis of these data using the new age categories was not peer reviewed outside the Agency.

Only one key study was available for this factor Medium

Overall Rating Medium



### Child-Specific Exposure Factors Handbook Page September 2008 14-3

### Child-Specific Exposure Factors Handbook

Chapter 14 - Total Food Intake

14.3 KEY STUDY OF TOTAL FOOD INTAKE

14.3.1 U.S. EPA Re-analysis of 1994-96, 1998 CSFII, Based on U.S. EPA (2007) - Analysis of Total Food Intake and Composition of Individual’s Diet Based on USDA’s 1994–96, 1998 Continuing Survey of Food Intakes by Individuals (CSFII)

U.S. EPA’s National Center for Environmental Assessment (NCEA) conducted an analysis to evaluate

the total food intake of individuals in the United States using data from the USDA’s 1994–1996, 1998 CSFII (USDA, 2000) and U.S. EPA’s Food Commodity

Intake Database (FCID) (U.S. EPA, 2000). The 1994–96 CSFII and its 1998 Supplemental Children’s Survey were designed to obtain data from a statistically representative sample of noninstitutionalized persons living in the United States. Survey participants were selected using a multistage process. The respondents

were interviewed twice to collect information on food consumption during two non-consecutive days. For both survey days, data were collected by an in-home interviewer. The day two interview was conducted 3 to10 days later and on a different day of the week. Of the more than 20,000 individuals surveyed, approximately 10,000 were under 21 years of age, and approximately 9,000 were under the age of 11. The 1994-96 survey and 1998 supplement are referred to collectively as CSFII 1994-96, 1998. Each individual in the survey was assigned a sample weight based on his or her demographic data; these weights were taken into account when calculating mean and percentile values of food consumption for the various demographic categories that were analyzed in the study. The sample weighting process used in the CSFII 1994-96, 1998 are discussed in detail in USDA (2000). For the analysis of total food intake, food commodity codes provided in U.S. EPA’s Food Commodity Intake Database (FCID) (U.S. EPA, 2000) were used to translate as-eaten foods (e.g., beef stew) identified by USDA food codes in the CSFII data set into food commodities (e.g., beef, potatoes, carrots, etc.). The method used to translate USDA food codes into U.S. EPA commodity codes is discussed in detail in USDA (2000). The U.S. EPA commodity codes were assigned to broad food categories (e.g., total meats, total vegetables, etc.) for use in the analysis.

Total food intake was defined as intake of the sum of all foods in the following major food categories: dairy, meats, fish, eggs, grains, vegetables, fruits, and fats. Beverages, sugar, candy, and sweets, and nuts and nut products were not included because they could not be categorized into the major food groups. Also, human

milk intake was not included. Total food intake was calculated for various age groups of children. Percent consuming, mean, standard error, and a range of percentile values were calculated on the basis of grams of food per kilogram of body weight per day (g/kg-day) and on the basis of grams per day (g/day). In addition to total food intake, intake of the various major food groups for the various age groups in units of g/day and g/kg-day were also estimated for comparison to total

intake. To evaluate variability in the contributions of the major food groups to total food intake, individuals were ranked from lowest to highest, based on total food intake. Three subsets of individuals were defined, as follows: a group at the low end of the distribution of total intake (i.e., below the 10 th 

### percentile of total intake), a central group (i.e., the 45 th 

### to 55 th 

### percentile of total intake), and a group at the high end of the distribution of total intake (i.e., above the 90 th 

### percentile of total intake). Mean total food intake (in g/day and g/kg-day), mean intake of each of the major food groups (in g/day and g/kg-day), and the percent of total food intake that each of these food groups represents was calculated for each of the three populations (i.e., individuals with low-end, central, and high-end total food intake). A similar analysis was conducted to estimate the contribution of the major food groups to total food intake for individuals at the low-end, central, and high-end of the distribution of total meat intake, total dairy intake, total meat and dairy intake, total fish intake, and total fruit and vegetable intake. For example, to evaluate the variability in the diets of individuals at the low-end, central range, and high-end of the distribution of total meat intake, survey individuals were ranked according to their reported

total meat intake. Three subsets of individuals were formed as described above. Mean total food intake, intake of the major food groups, and the percent of total food intake represented by each of the major food groups were tabulated. U.S. EPA (2007) presented the results of the analysis for the following age groups: <1 year, 1 to 2 years, 3 to 5 years, 6 to 11 years, and 12 to 19 years. The data were tabulated in units of g/kg-day and g/day. In order to conform to the standard age categories recommended in Guidance on Selecting Age



### Page Child-Specific Exposure Factors Handbook

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### Child-Specific Exposure Factors Handbook

Chapter 14 - Total Food Intake

Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005) and used in this handbook, each of the tables from U.S. EPA (2007) was modified by re-analyzing the source data and applying the new age categories (i.e., <1 month, 1 to <3 months, 3 to <6 months, 6 to <12 months, 1 to <2 years, 2 to < 3 years, 3 to < 6 years, 6 to < 11 years, 11 to < 16 years, and 16 to < 21 years). The results of this re-analysis are presented in Tables 14-3 through 14-11. Distributions of total food intake are presented in Table 14-3 in units of g/day and g/kg-day. Tables 14-4 and 14-5 compare total food intake to intake of the various major food groups for the various age groups in units of g/day and g/kg-day, respectively. It should be noted that some U.S. EPA commodity codes are listed under more than one food category. For this reason, in the tables, the intake rates for the individual food categories do not necessarily add up to the figure given for total food intake (U.S. EPA, 2007). Also, data are not reported for food groups for which there were less than 20 consumers in a particular age group. Tables 14-6 through 14-11 present the contributions of the major food groups to total food intake for individuals (in the various age groups) at the low-end, central, and high-end of the distribution of total food intake (Table 14-6), total meat intake (Table 14-7), total meat and dairy intake (Table 14-8), total fish intake (Table 14-9), total fruit and vegetable intake (Table 14-10), and total dairy intake (Table 14-11) in units of g/day and g/kg-day. For each of the three classes of consumers, consumption of nine different food categories is presented (i.e., total foods, dairy, meats, fish, eggs, grains, vegetables, fruits, and fats). For example, in Table 14-9 one will find the mean consumption of meats, eggs, vegetables, etc. for individuals with an unusually high (or low or average) consumption of fish. As discussed in previous chapters, the 1994-96, 98 CSFII data set have both advantages and limitations with regard to estimating food intake rates. The large sample size (more than 20,000 persons; approximately 10,000 children) is sufficient to allow categorization within narrowly defined age categories. In addition, the survey was designed to obtain a statistically valid sample of the entire United States population that included children and low income groups. However, the survey design is of limited utility for assessing small and potentially at-risk subpopulations based on ethnicity, medical status, geography, or other factors such as activity level. Another limitation is that data are based on a two-day survey period and, as such, may not accurately reflect long-term eating patterns. This is particularly true for the tails (extremes) of the

distribution of food intake.

14.4 REFERENCES FOR CHAPTER 14 USDA (2000) 1994–96, 1998 Continuing survey of food intakes by individuals (CSFII). CD-ROM. Agricultural Research Service, Beltsville Human Nutrition Research Center, Beltsville, MD.

Available from the National Technical Information Service, Springfield, VA; PB-2000­

500027.

U.S. EPA	 (2000) Food commodity intake database [FCID raw data file]. Office of Pesticide Programs, Washington, DC. Available from the National Technical Information Service, Springfield, VA; PB2000-5000101.

U.S. EPA (2005) Guidance on selecting age groups for monitoring and assessing childhood exposures to

envir o nme n t a l c o n t aminants. U.S. Environmental Protection Agency, Washington, D.C., EPA/630/P-03/003F. Available from the National Technical Information Service, S p r i n g f i e l d , V A, a n d o nl i n e a t www.epa.gov/ncea.

U.S. EPA	 (2007) Analysis of total food intake and composition of individual’s diet based on USDA’s 1994–96, 1998 continuing survey of food intakes by individuals (CSFII). National Center for Environmental Assessment, Washington, DC; EPA/600/R-05/062F.

Available from the National Technical Information Service, Springfield, VA, and online at www.epa.gov/ncea.



### Child-Specific Exposure Factors Handbook Page September 2008 14-5Table 14-3.Per Capita Total Food IntakeAge GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Total Food Intake (g/day, as consumed)Birth to <1 month598867.0%67590000671081422212222221 to <3 month18324574.7%80700000941201681882734043 to <6 month6 to <12 month1 to <2 years2 to <3 years3 to <6 years6 to <11 years11 to <16 years16 to <21 years3856761,0029944,1121,5539757434116781,0029944,1121,55397574393.7%99.7%100%100%100%100%100%100%1975071,0391,0241,0661,1181,2091,1841503444073773803724996340342163124164383433080141414491548586536467121915705756296806575561002837707528058468517501674139989941,0201,0521,1241,0612866001,2441,2571,2761,3441,4911,4473859251,5561,5171,5481,6421,8601,8834761,2201,7561,6491,7461,8252,1792,2837051,8232,2152,0712,1682,2182,6683,2811,1512,4653,6052,7374,8863,6024,5488,840Total Food Intake (g/kg-day, as consumed)Birth to <1 month598867.0%201800001933436169691 to <3 month18324574.7%161400001825364055763 to <6 month38541193.7%282100215243853651071696 to <12 month67667899.7%563631722334766991342112331 to <2 years2 to <3 years3 to <6 years6 to <11 years11 to <16 years16 to <21 years1,0029944,1121,5539757431,0029944,1121,553975743100%100%100%100%100%100%90746140241837292417119172321105538343017964839342111865524428161285725738221610992734930221371139161383016112610270453520714613288554726519423912282115abPCSENumber of consumers.The number of consumers of total food may be less than the number of individuals in the study sample for the youngest age groups,because human milk was not included in the total food intake estimates presented here.Sample size.= Percent consuming.= Standard error.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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14-6 September 2008Table 14-4.Per Capita Intake of Total Food and Intake of Major Food Groups (g/day, As Consumed)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age Birth to <1monthTotal Food Intake598867.0%6759000067108142221222222Total Dairy IntakeTotal Meat Intake510888858.0%0.0%41-38-0-0-0-0-40-72-81-156-156-156-Total Egg IntakeTotal Fish Intake0088880.0%0.0%------------------------Total Grain Intake5885.7%------------Total Vegetable IntakeTotal Fruit Intake272888830.7%2.3%5-23-0-0-0-0-0-0.29-16-32-108-125-Total Fat Intake588865.9%19160000203238646464Age 1 to <3 monthsTotal Food Intake18324574.7%8070000094120168188273404Total Dairy IntakeTotal Meat Intake147124524560.0%0.4%37-40-0-0-0-0-19-72-89-103-129-155-Total Egg IntakeTotal Fish Intake002452450.0%0.0%------------------------Total Grain Intake4424518.0%15000000392045Total Vegetable IntakeTotal Fruit Intake882324524535.9%9.4%154332100000000000.9207409431119114211171Total Fat Intake17624571.8%21170000273442496572Age 3 to <6 monthsTotal Food Intake38541193.7%19715000121001672863854767051,151Total Dairy IntakeTotal Meat Intake3084441141174.9%10.7%562567000000006008501091124132602949692Total Egg IntakeTotal Fish Intake2814114116.8%0.2%0.23-3-0-0-0-0-0-0-0-0.49-4-50-Total Grain Intake28441169.1%811000041121274468Total Vegetable IntakeTotal Fruit Intake26321841141164.0%53.0%3468461020000000013155899102196120282184522226750Total Fat Intake35741186.9%2817000203038455381106 

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eTable 14-4.Per Capita Intake of Total Food and Intake of Major Food Groups (g/day, As Consumed) (continued)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age 6 to <12 monthsTotal Food IntakeTotal Dairy IntakeTotal Meat Intake67662850067867867899.7%92.6%73.7%50715122344246273400141001910.520283260.013413711460012432925401591,220722781,8231,2971172,4651,873269Total Egg IntakeTotal Fish Intake3523467867851.9%5.0%60.62133000000000.019020220420732110342Total Grain Intake65367896.3%332800.8361428456684125260Total Vegetable IntakeTotal Fruit Intake66263967867897.6%94.2%9116967142002014174170811471272321803352314252856704521,254Total Fat Intake66167897.5%311602723314051588190Age 1 to <2 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake1,0029999659061889971,0009861,0021,0021,0021,0021,0021,0021,0021,0021,0021,002100%99.7%96.3%90.4%18.8%99.5%99.8%98.4%100%1,03948947143661202543940733237211034752041721610008908414380.270019254155709460.0014027373020770241201042689928998451394060107209371,2446816623083155349481,5569171004511111220532621,7561,0901205721126255664692,2151,4741818645172402828873,6052,9352212121352097391,762146Age 2 to <3 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat Intake994994981994994994100%100%98.7%1,0243836037724341312604915485751041475220131994346511,257510801,5177091151,6498381392,0711,0791992,7371,378280Total Egg IntakeTotal Fish Intake94319099499494.9%19.1%184241200000.07001070270501360269353169127Total Grain Intake99399499.9%8135163241587899126147195263Total Vegetable IntakeTotal Fruit Intake994970994994100%97.6%145279892301804525725861171282311783822495943027504319928462,042Total Fat Intake994994100%42181117223040516573101129 

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14-8 September 2008Table 14-4.Per Capita Intake of Total Food and Intake of Major Food Groups (g/day, As Consumed) (continued)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age 3 to <6 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake4,1124,1124,0623,9108014,1114,1114,0214,1124,1124,1124,1124,1124,1124,1124,1124,1124,112100%100%98.8%95.1%19.5%100%100%97.8%100%1066392731651011702435038024949231641892201941614000293001454868110.0003204456223629121200.06505475162780522438107210985361,020356656095156196471,27652297240122213344601,5487061334719155280516741,7468051635936175329642852,1681,15123099712304541,0001134,8863,9784332901924109152,252167Age 6 to <11 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake1,5531,5531,5331,4902581,5531,5531,5151,5531,5531,5531,5531,5531,5531,5531,5531,5531,553100%100%98.7%95.9%16.6%100%100%97.6%100%11184088716611921019358372243562217481031842243810000314201658663120.001905476127680126240.130679683384622948208713660421,0523717960114193141561,344557116220143264280701,6427411564623179342440861,8258371955838201410545952,2181,1302681071022625608801213,6022,6804351631695138961,406168Age 11 to <16 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake975975970930167975975923975975975975975975975975975975100%100%99.5%95.4%17.1%100%100%94.7%100%120936811419913628019569499 343 536291 1 2575 1 1827 0 0.0008724 0 063 33 56146 65 105202 0 0.00007333 18 2865743320.120701240.683485115263209317631471,12430710170127246135641,491507154250168352273831,86074020853302124724831102,17994824472622495526351312,6681,4013551231253337139301764,5481,9725782442276451,3331,535321 

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Child-Specific Exposure Factors Handbook Page September 2008 14-9Table 14-4.Per Capita Intake of Total Food and Intake of Major Food Groups (g/day, As Consumed) (continued)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age 16 to <21 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake743742730703143743743671743743743743743743743743743743100%99.9%98.3%94.6%19.2%100%100%90.3%100%1184283139211015032516874634279127303393204237423080.24000134301346781200488602255619280.0780581280.002230750636410881943461,0611961167013228074671,447410185290190400242941,88364926659342565624321292,28393431089763076836651483,2811,2354581261465431,1601,0232138,8401,8662,3432233997302,4952,270391abPCSE-Number of consumers.The number of consumers of total food may be less than the number of individuals in the study sample for the youngest age groups,because human milk was not included in the total food intake estimates presented here.Sample size.= Percent consuming.= Standard error.= Data not reported where the number of consumers was less than 20.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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14-10 September 2008Table 14-5.Per Capita Intake of Total Food and Intake of Major Food Groups (g/kg-day, As Consumed)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age Birth to <1monthTotal Food Intake598867.0%20180000193343616969Total Dairy IntakeTotal Meat Intake510888858.0%0.0%12-12-0-0-0-0-13-21-25-43-49-49-Total Egg IntakeTotal Fish Intake0088880.0%0.0%------------------------Total Grain Intake5885.7%------------Total Vegetable IntakeTotal Fruit Intake272888830.7%2.3%2-6-0-0-0-0-0-0.11-4-12-30-35-Total Fat Intake588865.9%6500006911182020Age 1 to <3 monthsTotal Food Intake18324574.7%16140000182536405576Total Dairy IntakeTotal Meat Intake147124524560.0%0.4%8-9-0-0-0-0-4-15-20-26-34-43-Total Egg IntakeTotal Fish Intake002452450.0%0.0%------------------------Total Grain Intake4424518.0%0.2310000001239Total Vegetable IntakeTotal Fruit Intake882324524535.9%9.4%316500000000000.18013017726193443Total Fat Intake17624571.8%440000579111418Age 3 to <6 monthsTotal Food Intake38541193.7%28210021524385365107169Total Dairy IntakeTotal Meat Intake3084441141174.9%10.7%80.2180.970000000080120160.122013847313Total Egg IntakeTotal Fish Intake2814114116.8%0.2%0.024-0-0-0-0-0-0-0-0-0.055-1-4-Total Grain Intake28441169.1%1200001134610Total Vegetable Intake26341164.0%5700002814182552Total Fruit Intake21841153.0%9150000213293772110Total Fat Intake35741186.9%43000246781217 

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1 eTable 14-5.Per Capita Intake of Total Food and Intake of Major Food Groups (g/kg-day, As Consumed) (continued)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age 6 to <12 monthsTotal Food Intake67667899.7%56363172233476699134211233Total Dairy IntakeTotal Meat Intake62850067867892.6%73.7%16226300000.068030.0017811443867281651218030Total Egg IntakeTotal Fish Intake3523467867851.9%5.0%0.580.06410.35000000000.002300.210204072114Total Grain Intake65367896.3%4300.0970.67235791426Total Vegetable IntakeTotal Fruit Intake66263967867897.6%94.2%1019816000.2602258916142620362546348467138Total Fat Intake66167897.5%3200.200.7723467810Age 1 to <2 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake1,0029999659061889971,0009861,0021,0021,0021,0021,0021,0021,0021,0021,0021,002100%99.7%96.3%90.4%18.8%99.5%99.8%98.4%100%9043410.276102233730320.8837182170.100000.870.6500.733830.02300220.4114880.590.00009802332652020.08504692853830.3705918310959620714314137838419194451611001052112258620713714731533818265216211512196114411Age 2 to <3 yearsTotal Food Intake994994100%7429233439527292113126146194Total Dairy IntakeTotal Meat Intake994981994994100%98.7%2841830.42040.5571142244376528639841410820Total Egg IntakeTotal Fish Intake94319099499494.9%19.1%10.2720.8900000.005100.09800.4902040.9142641311Total Grain Intake99399499.9%631234579101428Total Vegetable IntakeTotal Fruit Intake994970994994100%97.6%10206171030.144268916132718442256347164114Total Fat Intake994994100%310.72112345579 

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14-12 September 2008Table 14-5.Per Capita Intake of Total Food and Intake of Major Food Groups (g/kg-day, As Consumed) (continued)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age 3 to <6 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake4,1124,1124,0623,9108014,1114,1114,0214,1124,1124,1124,1124,1124,1124,1124,1124,1124,112100%100%98.8%95.1%19.5%100%100%97.8%100%612240.890.296101432415310.8835131210.830002200.8530 344 70.61 10.000022 0.00350 02 33 40.13 0.941 2441220.08104652572040.32059113733051071220391418319163041024893210193951326613541426576239195231312276012410Age 6 to <11 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake1,5531,5531,5331,4902581,5531,5531,5151,5531,5531,5531,5531,5531,5531,5531,5531,5531,553100%100%98.7%95.9%16.6%100%100%97.6%100%401530.550.2147721710210.662471100.350000.92100.6017 212 40.44 0.820.000084 0.00340 02 22 30.049 0.240.91 128720.05403521381330.2104752492040.720591036127620.7971216370337218152148842104311203251227918871650559Age 11 to <16 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish IntakeTotal Grain IntakeTotal Vegetable IntakeTotal Fruit Intake975975970930167975975923975975975975975975975975100%100%99.5%95.4%17.1%100%100%94.7%24720.360.16354116110.4813450.0210.022000.62109 110.38 0.820.35 0.630.000015 0.00210 00.94 12 20.000001 0.1316310.0330230.6422620.140253301030.4503763815410.5759104520511511145529732714188238107793132Total Fat Intake975975100%110.350.48 0.611122345 

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Child-Specific Exposure Factors Handbook Page September 2008 14-13Table 14-5.Per Capita Intake of Total Food and Intake of Major Food Groups (g/kg-day, As Consumed) (continued)Food GroupNacons.NtotalbPCMeanSEPercentile15102550 75909599100Age 16 to <21 yearsTotal Food IntakeTotal Dairy IntakeTotal Meat IntakeTotal Egg IntakeTotal Fish Intake743742730703143743743743743743100%99.9%98.3%94.6%19.2%18420.310.159420.430.5150.005800060.130.180080.280.490.00120120.880.950.018016320.11022630.440301040.960.5135125114719722115253037Total Grain IntakeTotal Vegetable IntakeTotal Fruit IntakeTotal Fat Intake743743671743743743743743100%100%90.3%100%253113410.170.6100.210.65100.330.8520.0000300.47130.04912411364148725101027151631232295abPCSE-Number of consumers.The number of consumers of total food may be less than the number of individuals in the study sample for the youngest age groups,because human milk was not included in the total food intake estimates presented here.Sample size.= Percent consuming.= Standard error.= Data not reported where the number of consumers was less than 20.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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s HandbookTable 14-6. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Food IntakeFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge Birth to <1month (g/day, as consumed)Age Birth to <1month (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa0 0.0% 64 100.0% 196 100.0%0 0.0% 39 61.2% 109 55.4%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 4 2.1%0 0.0% 5 7.4% 24 12.1%0 0.0% 0 0.0% 8 4.1%0 0.0% 19 29.4% 52 26.2%Total Foods 0 0.0% 20 100.0% 58 100.0%Total Dairy 0 0.0% 14 70.5% 35 60.1%Total Meats 0 0.0% 0 0.0% 0 0.0%Total Fish0 0.0% 0 0.0% 0 0.0%Total Eggs 0 0.0% 0 0.0% 0 0.0%Total Grains 0 0.0% 0 0.0% 1 2.1%Total Vegetables 0 0.0% 0.012 0.1% 6 10.0%Total Fruits 0 0.0% 0 0.0% 0 0.0%Total Fatsa0 0.0% 6 29.4% 16 27.8%Age 1 to <3 months (g/day, as consumed)Age 1 to <3 months (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa0 0.0% 94 100.0% 206 100.0%0 0.0% 53 56.9% 63 30.8%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 1 1.1% 3 1.3%0 0.0% 11 12.0% 58 28.4%0 0.0% 0.033 0.0% 27 13.0%0 0.0% 27 28.4% 49 23.6%Total Foods 0 0.0% 18 100.0% 44 100.0%Total Dairy 0 0.0% 9 51.9% 20 45.4%Total Meats 0 0.0% 0 0.0% 0.012 0.0%Total Fish0 0.0% 0 0.0% 0 0.0%Total Eggs 0 0.0% 0 0.0% 0 0.0%Total Grains 0 0.0% 0.19 1.1% 0.23 0.5%Total Vegetables 0 0.0% 3 18.9% 7 16.4%Total Fruits 0 0.0% 0 0.0% 5 12.3%Total Fatsa0 0.0% 5 27.7% 11 24.4%Age 3 to <6 months (g/day, as consumed)Age 3 to <6 months (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa1 100.0% 166 100.0% 507 100.0%0.038 3.0% 69 41.9% 90 17.8%0 0.0% 0.38 0.2% 4 0.8%0 0.0% 0 0.0% 0.42 0.1%0 0.0% 0.50 0.3% 0.60 0.1%0.93 74.5% 8 4.9% 14 2.8%0.14 10.9% 27 16.3% 73 14.4%0.12 9.9% 24 14.6% 284 56.0%0.017 1.3% 34 20.4% 36 7.2%Total Foods 0.26 100.0% 24 100.0% 73 100.0%Total Dairy 0 0.5% 9 37.3% 13 17.9%Total Meats 0 0.0% 0.11 0.5% 0.62 0.8%Total Fish0 0.0% 0 0.0% 0.056 0.1%Total Eggs 0 0.0% 0 0.0% 0.031 0.0%Total Grains 0.22 85.0% 0.95 4.0% 2 3.4%Total Vegetables 0.019 7.4% 5 20.8% 11 14.5%Total Fruits 0.017 6.7% 4 15.0% 40 55.0%Total Fatsa0.00059 0.2% 5 21.3% 5 7.5%



### Child-Specific Exposure Factors Handbook Page September 2008 14-15Table 14-6.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Food Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 6 to <12 months (g/day, as consumed)Age 6 to <12 months (g/kg-day, as consumed)Total Foods124100.0%414100.0%1,358100.0%Total Foods15100.0%47100.0%144100.0%Total DairyTotal Meats33326.4%2.4%721917.5%4.6%7704756.7%3.5%Total DairyTotal Meats40.3425.4%2.3%6213.8%4.9%77553.1%3.4%Total Fish0.250.2%10.3%0.280.0%Total Fish0.0330.2%0.0900.2%0.0290.0%Total EggsTotal Grains0.62110.5%9.1%7371.6%8.9%8500.6%3.7%Total EggsTotal Grains0.1320.9%10.7%0.6941.5%9.1%150.8%3.6%Total VegetablesTotal Fruits303024.2%24.4%9015121.9%36.5%1213148.9%23.1%Total VegetablesTotal Fruits3421.9%25.9%101922.4%40.0%14379.8%25.8%Total Fatsa1411.6%358.4%443.2%Total Fatsa211.4%47.5%53.2%Age 1 to <2 years (g/day, as consumed)Age 1 to <2 years (g/kg-day, as consumed)Total Foods407100.0%998100.0%1,859100.0%Total Foods35100.0%85100.0%167100.0%Total DairyTotal Meats1132827.8%6.9%4874648.8%4.6%1,0086654.2%3.5%Total DairyTotal Meats10329.5%7.5%41448.1%4.7%94556.1%3.2%Total Fish10.3%30.3%40.2%Total Fish0.140.4%0.460.5%0.250.2%Total EggsTotal Grains9442.2%10.8%16631.6%6.3%22811.2%4.3%Total EggsTotal Grains0.7442.1%10.9%151.4%6.0%270.9%4.3%Total VegetablesTotal Fruits8210020.1%24.6%10123810.2%23.8%1654468.9%24.0%Total VegetablesTotal Fruits7818.6%23.0%101911.9%22.8%13407.8%24.0%Total Fatsa245.8%383.8%613.3%Total Fatsa26.4%33.8%53.2%Age 2 to <3 years (g/day, as consumed)Age 2 to <3 years(g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats44811850100.0%26.3%11.1%98937060100.0%37.4%6.1%1,76069872100.0%39.7%4.1%Total FoodsTotal DairyTotal Meats3284100.0%24.8%11.2%72264100.0%36.3%5.3%129545100.0%42.2%3.8%Total Fish10.3%40.4%70.4%Total Fish0.110.4%0.180.2%0.360.3%Total EggsTotal Grains12622.7%13.7%14861.4%8.7%24981.4%5.6%Total EggsTotal Grains143.6%13.8%161.7%8.0%271.3%5.6%Total VegetablesTotal Fruits987021.9%15.6%14525514.6%25.8%18560910.5%34.6%Total VegetablesTotal Fruits7522.0%16.2%102113.3%29.8%134210.0%32.9%Total Fatsa316.8%444.4%563.2%Total Fatsa27.1%33.9%43.2% 

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Page C hild-Specific Exposure Factors Handbook

14-16 September 2008

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s HandbookTable 14-6. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Food Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge 3 to <6 years (g/day, as consumed)Age 3 to <6 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa527 100.0% 1,020 100.0% 1,817 100.0%144 27.3% 378 37.0% 728 40.1%53 10.0% 72 7.0% 94 5.2%3 0.6% 5 0.5% 9 0.5%11 2.0% 15 1.5% 24 1.3%76 14.4% 103 10.1% 132 7.3%117 22.3% 163 16.0% 233 12.8%76 14.4% 216 21.2% 509 28.0%34 6.5% 50 4.9% 68 3.7%Total Foods 28 100.0% 57 100.0% 108 100.0%Total Dairy 8 27.3% 21 36.6% 43 40.3%Total Meats 3 10.4% 4 7.1% 5 4.8%Total Fish 0.14 0.5% 0.27 0.5% 0.43 0.4%Total Eggs 0.59 2.1% 0.92 1.6% 1 1.1%Total Grains 4 14.0% 6 9.9% 8 7.1%Total Vegetables 6 22.0% 9 16.0% 14 12.5%Total Fruits 4 15.2% 13 22.1% 31 29.0%Total Fatsa2 6.4% 3 4.8% 4 3.7%Age 6 to <11 years (g/day, as consumed)Age 6 to <11 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa565 100.0% 1,060 100.0% 1,886 100.0%147 26.1% 370 34.9% 766 40.6%65 11.4% 95 9.0% 104 5.5%2 0.3% 6 0.6% 10 0.5%10 1.7% 16 1.5% 22 1.2%89 15.8% 116 10.9% 157 8.3%136 24.1% 203 19.2% 294 15.6%66 11.6% 178 16.8% 426 22.6%39 6.8% 58 5.5% 76 4.0%Total Foods 16 100.0% 38 100.0% 73 100.0%Total Dairy 4 26.2% 15 38.6% 30 40.8%Total Meats 2 11.9% 3 8.1% 4 5.9%Total Fish 0.075 0.5% 0.20 0.5% 0.28 0.4%Total Eggs 0.28 1.8% 0.62 1.6% 0.95 1.3%Total Grains 2 14.7% 4 10.8% 7 9.0%Total Vegetables 4 24.7% 7 18.0% 11 15.5%Total Fruits 2 11.2% 6 14.9% 15 21.2%Total Fatsa1 7.3% 2 5.3% 3 4.3%Age 11 to <16 years (g/day, as consumed)Age 11 to <16 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa513 100.0% 1,127 100.0% 2,256 100.0%92 17.9% 308 27.3% 808 35.8%71 13.9% 116 10.3% 172 7.6%4 0.8% 7 0.6% 16 0.7%10 1.9% 20 1.8% 28 1.2%84 16.3% 133 11.8% 207 9.2%162 31.6% 258 22.9% 459 20.3%42 8.2% 203 18.0% 420 18.6%40 7.8% 64 5.7% 114 5.0%Total Foods 8 100.0% 22 100.0% 46 100.0%Total Dairy 1 17.3% 6 26.9% 18 38.4%Total Meats 1 14.7% 2 10.3% 3 7.0%Total Fish 0.072 0.9% 0.19 0.8% 0.38 0.8%Total Eggs 0.15 1.8% 0.49 2.2% 0.61 1.3%Total Grains 1 16.6% 3 11.7% 4 9.3%Total Vegetables 3 31.7% 5 23.4% 9 18.4%Total Fruits 0.60 7.2% 4 17.4% 8 18.2%Total Fatsa0.70 8.3% 1 5.9% 2 4.8%



### Child-Specific Exposure Factors Handbook Page September 2008 14-17Table 14-6.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Food Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 16 to <21 years (g/day, as consumed)Age 16 to <21 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats4385661100.0%12.8%14.0%1,060219141100.0%20.7%13.3%2,590759272100.0%29.3%10.5%Total FoodsTotal DairyTotal Meats60.760.97100.0%12.2%15.6%1642100.0%23.8%11.5%38104100.0%27.4%10.0%Total Fish71.5%111.1%140.5%Total Fish0.101.7%0.151.0%0.190.5%Total EggsTotal Grains8671.9%15.2%171381.6%13.0%292411.1%9.3%Total EggsTotal Grains0.110.921.8%14.8%0.2421.6%13.1%0.4141.1%9.9%Total VegetablesTotal Fruits1484833.8%11.0%31213829.4%13.1%62048723.9%18.8%Total VegetablesTotal Fruits20.6434.0%10.2%5230.0%10.9%10825.3%19.7%Total Fatsa337.6%726.8%1365.3%Total Fatsa0.508.1%17.1%25.0%aIncludes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such asmeats.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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Page C hild-Specific Exposure Factors Handbook

14-18 September 2008

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s HandbookTable 14-7. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Meat IntakeFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-end ConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge Birth to <1month (g/day, as consumed)bAge Birth to <1month (g/kg-day, as consumed)bTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa67 100.0% - - - -41 61.5% - - - -0 0.0% - - - -0 0.0% - - - -0 0.0% - - - -0.44 0.7% - - - -5 7.7% - - - -0.88 1.3% - - - -19 28.3% - - - -Total Foods 20 100.0% - - - -Total Dairy 12 61.6% - - - -Total Meats 0 0.0% - - - -Total Fish0 0.0% - - - -Total Eggs 0 0.0% - - - -Total Grains 0.14 0.7% - - - -Total Vegetables 2 7.7% - - - -Total Fruits 0.21 1.1% - - - -Total Fatsa6 28.4% - - - -Age 1 to <3 months (g/day, as consumed)cAge 1 to <3 months (g/kg-day, as consumed)cTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa79 100.0% - - 149 100.0%37 46.4% - - 103 68.9%0 0.0% - - 1 0.7%0 0.0% - - 0 0.0%0 0.0% - - 0 0.0%1 1.5% - - 0.18 0.1%15 18.6% - - 3 2.1%4 5.2% - - 0 0.0%21 26.4% - - 42 28.2%Total Foods 16 100.0% - - 47 100.0%Total Dairy 8 47.9% - - 32 68.9%Total Meats 0 0.0% - - 0.33 0.7%Total Fish0 0.0% - - 0 0.0%Total Eggs 0 0.0% - - 0 0.0%Total Grains 0.23 1.4% - - 0.06 0.1%Total Vegetables 3 16.8% - - 0.97 2.1%Total Fruits 0.91 5.6% - - 0 0.0%Total Fatsa4 26.5% - - 13 28.2%Age 3 to <6 months (g/day, as consumed)dAge 3 to <6 months (g/kg-day, as consumed)dTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa181 100.0% - - 316 100.0%55 30.1% - - 62 19.7%0 0.0% - - 16 4.9%0 0.0% - - 0.44 0.1%0.092 0.1% - - 1 0.5%7 3.7% - - 16 5.0%31 17.0% - - 56 17.9%59 32.9% - - 133 42.3%28 15.3% - - 28 8.9%Total Foods 26 100.0% - - 41 100.0%Total Dairy 8 30.6% - - 8 20.5%Total Meats 0 0.0% - - 2 4.9%Total Fish0 0.0% - - 0.055 0.1%Total Eggs 0.012 0.0% - - 0.13 0.3%Total Grains 0.97 3.7% - - 2 4.8%Total Vegetables 4 16.9% - - 7 17.6%Total Fruits 8 32.2% - - 17 41.7%Total Fatsa4 15.6% - - 4 9.2%



### Child-Specific Exposure Factors Handbook Page September 2008 14-19Table 14-7.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Meat Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 6 to <12 months (g/day, as consumed)Age 6 to <12 months (g/kg-day, as consumed)Total Foods347100.0%466100.0%922100.0%Total Foods40100.0%48100.0%99100.0%Total DairyTotal Meats80023.0%0.0%1081423.2%2.9%3848541.6%9.3%Total DairyTotal Meats9022.6%0.0%11123.9%3.0%41941.1%9.3%Total Fish0.130.0%0.340.1%0.190.0%Total Fish0.0160.0%0.0530.1%00.0%Total EggsTotal Grains2240.5%6.8%3290.6%6.2%11511.2%5.6%Total EggsTotal Grains0.1930.5%6.6%0.4531.0%6.0%0.9160.9%5.8%Total VegetablesTotal Fruits6914319.8%41.3%11616224.8%34.8%13521614.7%23.4%Total VegetablesTotal Fruits81719.7%41.9%101721.9%36.5%152315.4%23.1%Total Fatsa277.7%316.7%434.6%Total Fatsa37.8%37.1%54.6%Age 1 to <2 years (g/day, as consumed)Age 1 to <2 years (g/kg-day, as consumed)Total Foods921100.0%992100.0%1,229100.0%Total Foods82100.0%90100.0%108100.0%Total DairyTotal Meats464250.4%0.2%4833948.7%4.0%46012837.4%10.4%Total DairyTotal Meats410.1549.9%0.2%46350.5%3.8%431140.1%10.0%Total Fish30.3%20.2%60.5%Total Fish0.240.3%0.250.3%0.490.5%Total EggsTotal Grains8560.9%6.1%14641.5%6.5%24781.9%6.4%Total EggsTotal Grains0.6550.8%6.1%161.4%6.1%271.9%6.9%Total VegetablesTotal Fruits9725010.5%27.2%11322811.3%23.0%18929015.4%23.6%Total VegetablesTotal Fruits92211.1%27.3%102110.8%22.7%162215.1%20.8%Total Fatsa303.3%383.8%574.6%Total Fatsa33.3%33.8%54.7%Age 2 to <3 years (g/day, as consumed)Age 2 to <3 years(g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats9504267100.0%44.9%0.7%94737352100.0%39.3%5.4%1,131374148100.0%33.0%13.1%Total FoodsTotal DairyTotal Meats71310.51100.0%44.2%0.7%68264100.0%37.7%5.5%832710100.0%32.3%12.4%Total Fish40.5%40.5%20.2%Total Fish0.340.5%0.180.3%0.200.2%Total EggsTotal Grains12731.3%7.7%18761.9%8.1%21901.9%8.0%Total EggsTotal Grains0.9461.3%7.8%0.9261.3%8.3%271.8%8.1%Total VegetablesTotal Fruits10427910.9%29.4%14622615.4%23.8%20223217.9%20.5%Total VegetablesTotal Fruits82111.1%29.6%101815.1%26.7%141916.8%23.1%Total Fatsa293.0%404.2%625.5%Total Fatsa23.1%34.0%45.2% 

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Page C hild-Specific Exposure Factors Handbook

14-20 September 2008

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### Child-Specific Exposure Factors Handbook Page September 2008 14-21Table 14-7.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Meat Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 16 to <21 years (g/day, as consumed)Age 16 to <21 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats92230712100.0%33.3%1.3%1,084280115100.0%25.8%10.6%1,957403385100.0%20.6%19.7%Total FoodsTotal DairyTotal Meats1540.19100.0%30.3%1.3%1842100.0%24.0%9.6%2855100.0%18.1%19.8%Total Fish202.1%90.9%120.6%Total Fish0.322.2%0.181.0%0.120.4%Total EggsTotal Grains141311.5%14.2%151471.4%13.6%312311.6%11.8%Total EggsTotal Grains0.2121.4%14.5%0.3521.9%12.8%0.4631.6%12.3%Total VegetablesTotal Fruits21515123.3%16.4%28714726.5%13.5%53222627.2%11.6%Total VegetablesTotal Fruits4324.6%17.8%5327.5%15.7%8328.9%12.4%Total Fatsa424.5%736.7%1397.1%Total Fatsa0.674.6%16.2%26.5%abcdIncludes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such asmeats.All individuals in this sample group consumed 0 grams/day of meat.Therefore, results are reported in the low-end decile.Only one individual in this sample group consumed more than 0 grams/day of meat. This result is reported in the high-end decile.All other samples arereported in the low-end decile.All individuals in this sample group below the 89thpercentile consumed 0 grams/day of meat. Therefore, only high-end and low-end consumer groups arereported.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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s HandbookTable 14-8. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Meat and Dairy IntakeFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge Birth to <1month (g/day, as consumed)Age Birth to <1month (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa12 100.0% 60 100.0% 185 100.0%0 0.0% 40 67.3% 127 69.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0.031 0.3% 0 0.0% 4 2.2%8 66.1% 2 3.4% 0.78 0.4%0 0.0% 0 0.0% 0 0.0%3 27.1% 18 29.2% 52 28.4%Total Foods 4 100.0% 18 100.0% 56 100.0%Total Dairy 0 0.0% 12 67.1% 39 69.0%Total Meats 0 0.0% 0 0.0% 0 0.0%Total Fish0 0.0% 0 0.0% 0 0.0%Total Eggs 0 0.0% 0 0.0% 0 0.0%Total Grains 0.0086 0.2% 0 0.0% 1 2.1%Total Vegetables 2 64.4% 0.65 3.7% 0.26 0.5%Total Fruits 0 0.0% 0 0.0% 0 0.0%Total Fatsa1 27.5% 5 29.2% 16 28.4%Age 1 to <3 months (g/day, as consumed)Age 1 to <3 months (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa36 100.0% 84 100.0% 166 100.0%0 0.0% 19 22.4% 109 65.6%0 0.0% 0 0.0% 0.037 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0.32 0.9% 1 1.2% 1 0.8%21 58.8% 42 50.7% 4 2.7%2 4.3% 0.034 0.0% 6 3.7%10 26.7% 21 25.4% 45 27.2%Total Foods 7 100.0% 14 100.0% 41 100.0%Total Dairy 0 0.0% 3 24.0% 26 64.1%Total Meats 0 0.0% 0 0.0% 0.012 0.0%Total Fish0 0.0% 0 0.0% 0 0.0%Total Eggs 0 0.0% 0 0.0% 0 0.0%Total Grains 0.054 0.8% 0.29 2.0% 0.26 0.6%Total Vegetables 4 57.8% 7 48.7% 0.43 1.1%Total Fruits 0.37 5.4% 0.0067 0.0% 3 7.7%Total Fatsa2 26.4% 4 25.0% 11 26.5%Age 3 to <6 months (g/day, as consumed)Age 3 to <6 months (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa121 100.0% 204 100.0% 334 100.0%0 0.0% 60 29.7% 159 47.7%0 0.0% 0.55 0.3% 5 1.4%0 0.0% 0 0.0% 0.43 0.1%0 0.0% 0.30 0.1% 0.64 0.2%5 4.5% 7 3.2% 12 3.7%44 36.4% 29 14.5% 27 8.0%52 42.9% 80 39.0% 74 22.3%15 12.3% 27 13.2% 54 16.3%Total Foods 17 100.0% 30 100.0% 45 100.0%Total Dairy 0 0.0% 8 26.5% 24 53.4%Total Meats 0 0.0% 0.19 0.6% 0.57 1.3%Total Fish0 0.0% 0 0.0% 0.056 0.1%Total Eggs 0 0.0% 0.10 0.3% 0.057 0.1%Total Grains 0.78 4.5% 1 3.7% 2 3.6%Total Vegetables 6 37.1% 3 11.2% 2 5.3%Total Fruits 7 41.7% 14 46.0% 8 17.3%Total Fatsa2 12.6% 3 11.4% 8 18.7%



### Child-Specific Exposure Factors Handbook Page September 2008 14-23Table 14-8.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Meat and Dairy Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 6 to <12 months (g/day, as consumed)Age 6 to <12 months (g/kg-day, as consumed)Total Foods253100.0%403100.0%1,284100.0%Total Foods29100.0%43100.0%135100.0%Total DairyTotal Meats10.680.5%0.3%711717.6%4.1%8274564.5%3.5%Total DairyTotal Meats0.120.0830.4%0.3%8218.0%4.7%87564.2%3.3%Total Fish00.0%10.4%0.280.0%Total Fish00.0%0.140.3%0.0290.0%Total EggsTotal Grains3221.0%8.5%3320.7%8.0%7450.5%3.5%Total EggsTotal Grains0.3221.1%8.0%0.3930.9%7.1%0.6650.5%3.5%Total VegetablesTotal Fruits9511037.7%43.4%8216620.3%41.1%1082098.4%16.3%Total VegetablesTotal Fruits111338.2%43.4%91720.0%40.4%12228.6%16.6%Total Fatsa176.7%328.0%413.2%Total Fatsa26.7%48.3%43.2%Age 1 to <2 years (g/day, as consumed)Age 1 to <2 years (g/kg-day, as consumed)Total Foods569100.0%1,014100.0%1,687100.0%Total Foods51100.0%82100.0%155100.0%Total DairyTotal Meats46308.0%5.2%4564345.0%4.2%1,1655269.0%3.1%Total DairyTotal Meats437.7%5.5%38445.6%5.3%106468.2%2.8%Total Fish20.4%20.2%30.2%Total Fish0.130.2%0.220.3%0.200.1%Total EggsTotal Grains12542.0%9.5%13641.3%6.3%19651.1%3.8%Total EggsTotal Grains152.1%9.5%161.6%7.2%160.9%3.7%Total VegetablesTotal Fruits12826422.5%46.4%11427811.3%27.4%1112096.6%12.4%Total VegetablesTotal Fruits112422.2%46.6%111913.0%22.7%11216.9%13.7%Total Fatsa254.5%363.6%593.5%Total Fatsa24.5%33.8%53.4%Age 2 to <3 years (g/day, as consumed)Age 2 to <3 years(g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats6415745100.0%9.0%6.9%98134859100.0%35.5%6.0%1,54688360100.0%57.1%3.9%Total FoodsTotal DairyTotal Meats4643100.0%8.2%7.4%73245100.0%32.6%6.5%114674100.0%58.3%3.8%Total Fish40.6%30.3%40.3%Total Fish0.190.4%0.250.3%0.280.2%Total EggsTotal Grains21753.2%11.8%18861.9%8.7%20861.3%5.6%Total EggsTotal Grains153.2%11.6%161.6%8.7%271.3%5.7%Total VegetablesTotal Fruits15524024.1%37.5%14826415.1%26.9%1432869.2%18.5%Total VegetablesTotal Fruits111823.6%38.7%112214.9%29.9%11199.5%16.6%Total Fatsa325.0%424.3%553.6%Total Fatsa25.2%34.3%43.7% 

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### Child-Specific Exposure Factors Handbook Page September 2008 14-25Table 14-8.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Meat and Dairy Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 16 to <21 years (g/day, as consumed)Age 16 to <21 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats6102242100.0%3.5%6.8%1,017204128100.0%20.1%12.6%2,379923256100.0%38.8%10.8%Total FoodsTotal DairyTotal Meats90.350.63100.0%3.8%6.8%1532100.0%19.1%13.4%34134100.0%39.1%10.8%Total Fish121.9%121.2%80.3%Total Fish0.171.8%0.140.9%0.100.3%Total EggsTotal Grains13872.2%14.3%191401.8%13.8%282331.2%9.8%Total EggsTotal Grains0.1912.0%14.6%0.2821.8%14.3%0.3831.1%10.1%Total VegetablesTotal Fruits20217733.1%29.1%30513329.9%13.1%49228220.7%11.9%Total VegetablesTotal Fruits3334.0%28.1%5230.4%12.2%7420.8%11.2%Total Fatsa345.6%686.6%1275.3%Total Fatsa0.515.5%16.8%25.4%aIncludes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such asmeats.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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s HandbookTable 14-9. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Fish IntakeFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge Birth to <1month (g/day, as consumed)bAge Birth to <1month (g/kg-day, as consumed)bTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa67 100.0% - - - -41 61.5% - - - -0 0.0% - - - -0 0.0% - - - -0 0.0% - - - -0.44 0.7% - - - -5 7.7% - - - -0.88 1.3% - - - -19 28.3% - - - -Total Foods 20 100.0% - - - -Total Dairy 12 61.6% - - - -Total Meats 0 0.0% - - - -Total Fish0 0.0% - - - -Total Eggs 0 0.0% - - - -Total Grains 0.14 0.7% - - - -Total Vegetables 2 7.7% - - - -Total Fruits 0.21 1.1% - - - -Total Fatsa6 28.4% - - - -Age 1 to <3 months (g/day, as consumed)bAge 1 to <3 months (g/kg-day, as consumed)bTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa80 100.0% - - - -37 46.5% - - - -0 0.0% - - - -0 0.0% - - - -0 0.0% - - - -1 1.5% - - - -15 18.5% - - - -4 5.2% - - - -21 26.4% - - - -Total Foods 16 100.0% - - - -Total Dairy 8 48.2% - - - -Total Meats 0 0.0% - - - -Total Fish0 0.0% - - - -Total Eggs 0 0.0% - - - -Total Grains 0.23 1.4% - - - -Total Vegetables 3 16.6% - - - -Total Fruits 0.90 5.5% - - - -Total Fatsa4 26.5% - - - -Age 3 to <6 months (g/day, as consumed)cAge 3 to <6 months (g/kg-day, as consumed)cTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa196 100.0% - - 410 100.0%55 28.3% - - 159 38.8%2 0.8% - - 28 6.8%0 0.0% - - 17 4.1%0.22 0.1% - - 4 1.0%8 3.9% - - 47 11.5%34 17.2% - - 34 8.3%68 34.7% - - 30 7.2%28 14.1% - - 81 19.8%Total Foods 28 100.0% - - 53 100.0%Total Dairy 8 28.9% - - 21 38.8%Total Meats 0.20 0.7% - - 4 6.8%Total Fish0 0.0% - - 2 4.1%Total Eggs 0.022 0.1% - - 0.52 1.0%Total Grains 1 3.8% - - 6 11.5%Total Vegetables 5 17.1% - - 4 8.3%Total Fruits 9 33.9% - - 4 7.2%Total Fatsa4 14.5% - - 11 19.8%



### Child-Specific Exposure Factors Handbook Page September 2008 14-27Table 14-9.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Fish Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 6 to <12 months (g/day, as consumed)dAge 6 to <12 months (g/kg-day, as consumed)dTotal Foods799100.0% -- 770100.0%Total Foods81100.0% -- 74100.0%Total DairyTotal Meats3343841.8% -4.7% -- 287- 4637.3%6.0%Total DairyTotal Meats34441.8% -4.7% -- 27- 437.1%6.0%Total Fish00.0% -- 70.9%Total Fish00.0% -- 0.630.9%Total EggsTotal Grains11471.4% -5.9% -- 14- 661.9%8.6%Total EggsTotal Grains151.4% -5.9% -- 1- 62.0%8.4%Total VegetablesTotal Fruits10122712.6% -28.4% -- 117- 19415.3%25.2%Total VegetablesTotal Fruits102312.6% -28.4% -- 12- 1915.6%25.2%Total Fatsa374.7% -- 364.7%Total Fatsa44.7% -- 34.7%Age 1 to <2 years (g/day, as consumed)dAge 1 to <2 years (g/kg-day, as consumed)dTotal Foods1032100.0% -- 1,139100.0%Total Foods90100.0% -- 98100.0%Total DairyTotal Meats4964648.1% -4.5% -- 461- 5640.5%4.9%Total DairyTotal Meats43448.2% -4.4% -- 41- 542.4%4.8%Total Fish00.0% -- 262.3%Total Fish00.0% -- 22.2%Total EggsTotal Grains14651.4% -6.3% -- 19- 761.7%6.7%Total EggsTotal Grains161.3% -6.2% -- 2- 71.6%6.7%Total VegetablesTotal Fruits11824711.4% -24.0% -- 151- 30013.2%26.3%Total VegetablesTotal Fruits102211.4% -24.0% -- 12- 2512.3%25.5%Total Fatsa393.8% -- 433.8%Total Fatsa33.8% -- 43.8%Age 2 to <3 years (g/day, as consumed)dAge 2 to <3 years(g/kg-day, as consumed)dTotal FoodsTotal DairyTotal Meats101538162100.0% -37.6% -6.1% -- 1,107- 424- 53100.0%38.3%4.8%Total FoodsTotal DairyTotal Meats73284100.0% -37.9% -6.0% -- 82- 31- 4100.0%37.6%4.6%Total Fish00.0% -- 312.8%Total Fish00.0% -- 22.9%Total EggsTotal Grains18811.8% -7.9% -- 17- 841.6%7.6%Total EggsTotal Grains161.7% -7.9% -- 1- 61.5%7.5%Total VegetablesTotal Fruits14427614.2% -27.2% -- 142- 30412.8%27.4%Total VegetablesTotal Fruits102014.1% -27.0% -- 10- 2312.7%28.5%Total Fatsa424.2% -- 433.9%Total Fatsa34.2% -- 33.9% 

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s HandbookTable 14-9. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Fish Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge 3 to <6 years (g/day, as consumed)dAge 3 to <6 years (g/kg-day, as consumed)dTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa1,053 100.0% - - 1,156 100.0%390 37.1% - - 399 34.5%76 7.2% - - 62 5.3%0 0.0% - - 43 3.7%16 1.5% - - 17 1.4%101 9.6% - - 103 8.9%168 15.9% - - 193 16.7%237 22.5% - - 273 23.6%50 4.8% - - 50 4.3%Total Foods 60 100.0% - - 66 100.0%Total Dairy 22 37.1% - - 22 33.9%Total Meats 4 7.1% - - 3 5.3%Total Fish0 0.0% - - 2 3.7%Total Eggs 0.88 1.5% - - 1 1.6%Total Grains 6 9.5% - - 6 9.0%Total Vegetables 9 15.8% - - 11 16.9%Total Fruits 14 22.7% - - 16 23.8%Total Fatsa3 4.7% - - 3 4.3%Age 6 to <11 years (g/day, as consumed)dAge 6 to <11 years (g/kg-day, as consumed)dTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa1,109 100.0% - - 1,234 100.0%408 36.8% - - 430 34.8%89 8.0% - - 76 6.2%0 0.0% - - 51 4.1%15 1.3% - - 22 1.8%119 10.7% - - 126 10.2%208 18.8% - - 233 18.9%190 17.1% - - 218 17.7%58 5.2% - - 61 4.9%Total Foods 40 100.0% - - 44 100.0%Total Dairy 15 37.0% - - 16 35.6%Total Meats 3 7.9% - - 3 6.1%Total Fish0 0.0% - - 2 4.1%Total Eggs 0.53 1.3% - - 0.73 1.6%Total Grains 4 10.7% - - 4 10.1%Total Vegetables 7 18.5% - - 8 18.4%Total Fruits 7 17.3% - - 8 17.5%Total Fatsa2 5.2% - - 2 4.9%Age 11 to <16 years (g/day, as consumed)dAge 11 to <16 years (g/kg-day, as consumed)dTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa1,197 100.0% - - 1,378 100.0%372 31.1% - - 397 28.8%117 9.8% - - 104 7.5%0 0.0% - - 72 5.2%17 1.4% - - 28 2.0%135 11.3% - - 146 10.6%277 23.1% - - 310 22.5%190 15.8% - - 226 16.4%69 5.8% - - 76 5.5%Total Foods 24 100.0% - - 28 100.0%Total Dairy 7 31.1% - - 9 30.9%Total Meats 2 9.7% - - 2 6.9%Total Fish0 0.0% - - 1 4.9%Total Eggs 0.34 1.4% - - 0.52 1.9%Total Grains 3 11.3% - - 3 10.5%Total Vegetables 5 22.9% - - 6 21.1%Total Fruits 4 16.2% - - 5 17.1%Total Fatsa1 5.7% - - 1 5.2%



### Child-Specific Exposure Factors Handbook Page September 2008 14-29Table 14-9.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Fish Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 16 to <21 years (g/day, as consumed)dAge 16 to <21 years (g/kg-day, as consumed)dTotal Foods1,171100.0% -- 1,339100.0%Total Foods18100.0% -- 19100.0%Total DairyTotal Meats28814324.6% -12.2% -- 261- 13919.5%10.4%Total DairyTotal Meats4224.5% -11.9% -- 4- 220.3%9.4%Total Fish00.0% -- 866.5%Total Fish00.0% -- 16.7%Total EggsTotal Grains201461.7% -12.5% -- 21- 1621.6%12.1%Total EggsTotal Grains0.3021.7% -12.5% -- 0.30- 21.6%12.0%Total VegetablesTotal Fruits32516027.8% -13.7% -- 357- 21926.6%16.3%Total VegetablesTotal Fruits5227.9% -13.9% -- 5- 326.0%16.9%Total Fatsa756.4% -- 806.0%Total Fatsa16.4% -- 15.9%abcdIncludes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such asmeats.All individuals in this sample group consumed 0 grams/day of fish.Therefore, only low-end consumers are reported.Only one individual in this sample group consumed more than 0 grams/day of fish. Therefore, this sample is reported in the high-end consumer group and allother samples are placed in the low-end consumer group.All individuals in this sample group below the 80thpercentile consumed 0 grams/day of fish.Therefore, only high-end and low-end consumer groups arereported.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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s HandbookTable 14-10. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Fruit and Vegetable IntakeFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge Birth to <1month (g/day, as consumed)bAge Birth to <1month (g/kg-day, as consumed)bTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa49 100.0% - - 101 100.0%34 69.7% - - 21 21.1%0 0.0% - - 0 0.0%0 0.0% - - 0 0.0%0 0.0% - - 0 0.0%0.58 1.2% - - 0.21 0.2%0 0.0% - - 44 43.3%0 0.0% - - 8 7.6%14 29.1% - - 25 24.8%Total Foods 14 100.0% - - 29 100.0%Total Dairy 10 69.6% - - 6 19.4%Total Meats 0 0.0% - - 0 0.0%Total Fish0 0.0% - - 0 0.0%Total Eggs 0 0.0% - - 0 0.0%Total Grains 0.18 1.3% - - 0.057 0.2%Total Vegetables 0 0.0% - - 13 44.8%Total Fruits 0 0.0% - - 2 6.4%Total Fatsa4 29.1% - - 7 25.4%Age 1 to <3 months (g/day, as consumed)bAge 1 to <3 months (g/kg-day, as consumed)bTotal FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa49 100.0% - - 171 100.0%34 69.2% - - 16 9.5%0 0.0% - - 0 0.0%0 0.0% - - 0 0.0%0 0.0% - - 0 0.0%0.91 1.9% - - 2 1.0%0 0.0% - - 89 52.0%0 0.0% - - 18 10.2%14 28.9% - - 40 23.4%Total Foods 11 100.0% - - 35 100.0%Total Dairy 7 69.4% - - 4 11.5%Total Meats 0 0.0% - - 0 0.0%Total Fish0 0.0% - - 0 0.0%Total Eggs 0 0.0% - - 0 0.0%Total Grains 0.17 1.7% - - 0.38 1.1%Total Vegetables 0 0.0% - - 16 46.8%Total Fruits 0 0.0% - - 5 13.9%Total Fatsa3 29.0% - - 8 22.7%Age 3 to <6 months (g/day, as consumed)Age 3 to <6 months (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa69 100.0% 144 100.0% 495 100.0%47 68.0% 51 35.6% 49 9.9%0 0.0% 2 1.3% 4 0.8%0 0.0% 0.43 0.3% 0 0.0%0 0.0% 0.58 0.4% 0.094 0.0%2 3.3% 10 6.7% 12 2.4%0 0.0% 24 16.6% 88 17.7%0 0.0% 29 19.9% 311 62.8%20 28.4% 25 17.7% 27 5.4%Total Foods 11 100.0% 21 100.0% 70 100.0%Total Dairy 7 68.1% 8 37.2% 7 10.1%Total Meats 0 0.0% 0.32 1.5% 0.52 0.7%Total Fish0 0.0% 0.057 0.3% 0 0.0%Total Eggs 0 0.0% 0.10 0.5% 0.021 0.0%Total Grains 0.35 3.2% 1 6.6% 2 2.6%Total Vegetables 0 0.0% 3 15.1% 12 17.7%Total Fruits 0 0.0% 4 20.8% 44 62.4%Total Fatsa3 28.5% 4 16.9% 4 5.5%



### Child-Specific Exposure Factors Handbook Page September 2008 14-31Table 14-10.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Fruit and Vegetable Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 6 to <12 months (g/day, as consumed)Age 6 to <12 months (g/kg-day, as consumed)Total Foods189100.0%461100.0%951100.0%Total Foods21100.0%57100.0%100100.0%Total DairyTotal Meats91848.3%4.0%1291728.0%3.6%2073721.8%3.9%Total DairyTotal Meats100.7348.1%3.6%19233.2%4.3%18417.9%3.8%Total Fish0.800.4%0.800.2%0.160.0%Total Fish0.0880.4%0.0630.1%0.0180.0%Total EggsTotal Grains4231.9%12.1%9311.9%6.8%8410.8%4.3%Total EggsTotal Grains0.3421.7%11.4%0.5941.0%6.5%0.7350.7%4.6%Total VegetablesTotal Fruits18159.4%7.7%8315818.1%34.3%16045916.8%48.2%Total VegetablesTotal Fruits229.3%8.4%101816.9%30.8%195019.0%49.5%Total Fatsa3116.3%316.8%353.6%Total Fatsa316.8%46.6%43.9%Age 1 to <2 years (g/day, as consumed)Age 1 to <2 years (g/kg-day, as consumed)Total Foods796100.0%1,048100.0%1,499100.0%Total Foods68100.0%88100.0%133100.0%Total DairyTotal Meats5783572.7%4.5%5354651.0%4.4%4256228.4%4.2%Total DairyTotal Meats49371.8%4.7%44449.6%4.5%39529.5%3.6%Total Fish0.930.1%30.3%50.4%Total Fish0.160.2%0.240.3%0.310.2%Total EggsTotal Grains8491.0%6.2%16651.5%6.2%17771.1%5.1%Total EggsTotal Grains0.7741.1%6.2%161.2%6.9%271.2%5.2%Total VegetablesTotal Fruits56267.1%3.2%12321011.7%20.1%17968711.9%45.8%Total VegetablesTotal Fruits527.1%3.4%111812.6%20.5%156011.6%45.4%Total Fatsa364.6%413.9%392.6%Total Fatsa34.7%33.7%42.7%Age 2 to <3 years (g/day, as consumed)Age 2 to <3 years(g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats60130853100.0%51.2%8.8%94235259100.0%37.4%6.3%1,58938464100.0%24.1%4.0%Total FoodsTotal DairyTotal Meats43224100.0%51.3%8.8%69274100.0%39.3%6.0%114274100.0%23.6%3.8%Total Fish20.3%40.5%50.3%Total Fish0.140.3%0.250.4%0.400.4%Total EggsTotal Grains14722.3%12.0%18802.0%8.5%20911.3%5.7%Total EggsTotal Grains0.9952.3%12.0%161.9%8.6%271.4%5.7%Total VegetablesTotal Fruits812413.4%4.0%14123715.0%25.1%20276512.7%48.1%Total VegetablesTotal Fruits6213.8%3.7%101714.0%24.6%145612.4%49.1%Total Fatsa386.3%404.2%462.9%Total Fatsa36.3%34.1%32.9% 

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### Child-Specific Exposure Factors Handbook Page September 2008 14-33Table 14-10.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Fruit and Vegetable Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 16 to <21 years (g/day, as consumed)Age 16 to <21 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats62423876100.0%38.1%12.2%970203112100.0%21.0%11.5%2,353449245100.0%19.1%10.4%Total FoodsTotal DairyTotal Meats941100.0%39.0%11.7%1632100.0%21.0%12.7%3463100.0%17.8%9.6%Total Fish81.2%151.6%170.7%Total Fish0.131.4%0.130.8%0.210.6%Total EggsTotal Grains211003.3%16.1%161381.6%14.2%302111.3%9.0%Total EggsTotal Grains0.3113.4%16.2%0.4122.5%14.6%0.3331.0%10.0%Total VegetablesTotal Fruits1091817.5%2.9%28312129.2%12.5%61564426.1%27.4%Total VegetablesTotal Fruits20.1717.9%1.8%5130.7%9.1%91025.8%30.0%Total Fatsa467.3%666.8%1164.9%Total Fatsa0.667.2%17.5%24.4%abIncludes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such asmeats.All individuals in this sample group below the 75thpercentile consumed 0 grams/day of fruits and vegetables.Therefore, only high-end and low-endconsumer groups are reported.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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s HandbookTable 14-11. Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Dairy IntakeFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake Percent Intake Percent Intake PercentAge Birth to <1month (g/day, as consumed)Age Birth to <1month (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa12 100.0% 60 100.0% 185 100.0%0 0.0% 40 67.3% 127 69.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0.031 0.3% 0 0.0% 4 2.2%8 66.1% 2 3.4% 0.78 0.4%0 0.0% 0 0.0% 0 0.0%3 27.1% 18 29.2% 52 28.4%Total Foods 4 100.0% 18 100.0% 56 100.0%Total Dairy 0 0.0% 12 67.1% 39 69.0%Total Meats 0 0.0% 0 0.0% 0 0.0%Total Fish0 0.0% 0 0.0% 0 0.0%Total Eggs 0 0.0% 0 0.0% 0 0.0%Total Grains 0.0086 0.2% 0 0.0% 1 2.1%Total Vegetables 2 64.4% 0.65 3.7% 0.26 0.5%Total Fruits 0 0.0% 0 0.0% 0 0.0%Total Fatsa1 27.5% 5 29.2% 16 28.4%Age 1 to <3 months (g/day, as consumed)Age 1 to <3 months (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa36 100.0% 84 100.0% 166 100.0%0 0.0% 19 22.4% 109 65.6%0 0.0% 0 0.0% 0.037 0.0%0 0.0% 0 0.0% 0 0.0%0 0.0% 0 0.0% 0 0.0%0.32 0.9% 1 1.2% 0 0.8%21 58.8% 42 50.7% 4 2.7%2 4.3% 0.034 0.0% 6 3.7%10 26.7% 21 25.4% 45 27.2%Total Foods 7 100.0% 14 100.0% 41 100.0%Total Dairy 0 0.0% 3 24.0% 26 64.1%Total Meats 0 0.0% 0 0.0% 0.012 0.0%Total Fish0 0.0% 0 0.0% 0 0.0%Total Eggs 0 0.0% 0 0.0% 0 0.0%Total Grains 0.054 0.8% 0.29 2.0% 0.26 0.6%Total Vegetables 4 57.8% 7 48.7% 0.43 1.1%Total Fruits 0.37 5.4% 0.0067 0.0% 3 7.7%Total Fatsa2 26.4% 4 25.0% 11 26.5%Age 3 to <6 months (g/day, as consumed)Age 3 to <6 months (g/kg-day, as consumed)Total FoodsTotal DairyTotal MeatsTotal FishTotal EggsTotal GrainsTotal VegetablesTotal FruitsTotal Fatsa132 100.0% 217 100.0% 346 100.0%0 0.0% 59 27.0% 160 46.3%0.59 0.4% 2 1.0% 4 1.1%0 0.0% 0 0.0% 0.44 0.1%0 0.0% 0.38 0.2% 0.64 0.2%6 4.5% 8 3.8% 12 3.4%46 34.9% 37 17.0% 26 7.6%58 44.1% 84 38.8% 87 25.1%16 11.9% 26 12.1% 55 15.8%Total Foods 19 100.0% 32 100.0% 44 100.0%Total Dairy 0 0.0% 8 24.8% 24 54.9%Total Meats 0.10 0.5% 0.22 0.7% 0.45 1.0%Total Fish0 0.0% 0 0.0% 0.056 0.1%Total Eggs 0 0.0% 0.11 0.3% 0.057 0.1%Total Grains 0.84 4.5% 1 3.8% 2 3.4%Total Vegetables 7 35.6% 4 13.7% 2 5.0%Total Fruits 8 43.0% 14 45.8% 7 15.9%Total Fatsa2 12.2% 3 10.7% 8 19.2%



### Child-Specific Exposure Factors Handbook Page September 2008 14-35Table 14-11.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Dairy Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 6 to <12 months (g/day, as consumed)Age 6 to <12 months (g/kg-day, as consumed)Total Foods317100.0%368100.0%1,285100.0%Total Foods36100.0%43100.0%135100.0%Total DairyTotal Meats0.045110.0%3.4%711619.2%4.4%8334164.8%3.2%Total DairyTotal Meats0.006210.0%3.5%8218.2%4.8%87464.8%3.0%Total Fish0.00860.0%10.3%0.280.0%Total Fish00.0%0.150.3%0.0290.0%Total EggsTotal Grains3270.9%8.6%5231.4%6.3%6460.5%3.6%Total EggsTotal Grains0.3531.0%7.9%0.9232.1%7.7%0.6650.5%3.5%Total VegetablesTotal Fruits11413735.9%43.3%7514720.4%39.9%1062118.2%16.4%Total VegetablesTotal Fruits131635.3%44.6%81817.9%40.7%11228.2%16.6%Total Fatsa206.4%308.2%403.1%Total Fatsa26.3%48.1%43.1%Age 1 to <2 years (g/day, as consumed)Age 1 to <2 years (g/kg-day, as consumed)Total Foods601100.0%989100.0%1,700100.0%Total Foods55100.0%86100.0%154100.0%Total DairyTotal Meats40436.7%7.1%4515145.6%5.2%1,1704568.8%2.6%Total DairyTotal Meats346.1%7.2%38444.0%4.8%106468.5%2.6%Total Fish30.5%40.4%30.2%Total Fish0.280.5%0.500.6%0.180.1%Total EggsTotal Grains14572.3%9.5%15651.5%6.5%18631.1%3.7%Total EggsTotal Grains152.3%9.5%261.8%6.9%160.8%3.7%Total VegetablesTotal Fruits13926823.1%44.7%12024012.1%24.3%1122266.6%13.3%Total VegetablesTotal Fruits122521.8%46.3%112113.0%24.5%10216.7%13.8%Total Fatsa294.8%383.8%583.4%Total Fatsa34.7%33.7%53.4%Age 2 to <3 years (g/day, as consumed)Age 2 to <3 years(g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats6614861100.0%7.3%9.3%99634863100.0%34.9%6.3%1,52888555100.0%57.9%3.6%Total FoodsTotal DairyTotal Meats4734100.0%7.2%9.4%72244100.0%33.7%6.2%114674100.0%58.4%3.6%Total Fish20.3%60.6%50.3%Total Fish0.160.3%0.270.4%0.280.2%Total EggsTotal Grains25783.8%11.9%20822.1%8.2%19861.3%5.6%Total EggsTotal Grains253.7%11.6%161.5%8.5%161.3%5.7%Total VegetablesTotal Fruits16323724.7%35.8%14427914.5%28.0%1372779.0%18.1%Total VegetablesTotal Fruits121724.6%36.4%102214.0%30.2%11209.3%17.3%Total Fatsa375.5%414.1%553.6%Total Fatsa35.5%34.2%43.6% 

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### Child-Specific Exposure Factors Handbook Page September 2008 14-37Table 14-11.Per Capita Intake of Total Foods and Major Food Groups, and Percent of Total Food Intake forIndividuals with Low-end, Mid-range, and High-end Total Dairy Intake (continued)FoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersFoodGroupLow-endConsumersMid-rangeConsumersHigh-endConsumersIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentIntake PercentAge 16 to <21 years (g/day, as consumed)Age 16 to <21 years (g/kg-day, as consumed)Total FoodsTotal DairyTotal Meats6478101100.0%1.2%15.7%1,095197125100.0%18.0%11.4%2,233950197100.0%42.5%8.8%Total FoodsTotal DairyTotal Meats100.122100.0%1.2%15.1%1732100.0%16.6%13.6%33143100.0%42.8%8.9%Total Fish81.2%161.5%80.4%Total Fish0.111.1%0.160.9%0.110.3%Total EggsTotal Grains12901.8%13.9%281622.5%14.8%272171.2%9.7%Total EggsTotal Grains0.1711.7%14.1%0.3922.2%14.0%0.4031.2%9.6%Total VegetablesTotal Fruits22815235.2%23.5%32415429.6%14.1%43824919.6%11.2%Total VegetablesTotal Fruits4235.8%23.9%5328.6%16.1%7320.0%10.6%Total Fatsa375.8%736.7%1145.1%Total Fatsa0.585.6%16.5%25.1%aIncludes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such asmeats.Source:Based on U.S. EPA analysis of 1994-96, 1998 CSFII. 

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### Child­Specific Exposure Factors Handbook

Chapter 15 ­ Human Milk Intake

15 HUMAN MILK INTAKE

15.1 INTRODUCTION

Human lactation is known to impart a wide

range of benefits to nursing infants, including protection against infection, increases in cognitive development,

and avoidance of allergies due to intolerance to cow’s

milk (AAP, 2005). Ingestion of human milk has also

been associated with a reduction in risk of postneonatal

death in the U.S. (Chen and Rogan, 2004). The

American Academy of Pediatrics recommends

exclusive breastfeeding for approximately the first six

months and supports the continuation of breastfeeding

for the first year and beyond if desired by the mother

and child (AAP, 2005). However, contaminants may find their way into human milk of lactating mothers

because mothers are themselves exposed. Thus, making human milk a potential source of exposure to
toxic substances for nursing infants. Lipid soluble

chemical compounds accumulate in body fat and may be transferred to breast­fed infants in the lipid portion

of human milk. Water soluble chemicals may also partition into the aqueous phase and be excreted via

human milk. Because nursing infants obtain most (if

not all) of their dietary intake from human milk, they

are especially vulnerable to exposures to these compounds. Estimating the magnitude of the potential

dose to infants from human milk requires information

on the milk intake rate (quantity of human milk

consumed per day) and the duration (months) over which breast­feeding occurs. Information on the fat

content of human milk is also needed for estimating

dose from human milk residue concentrations that have

been indexed to lipid content.
Several studies have generated data on human

milk intake. Typically, human milk intake has been

measured over a 24­hour period by weighing the infant

before and after each feeding without changing its clothing (test weighing). The sum of the difference

between the measured weights over the 24­hour period

is assumed to be equivalent to the amount of human

milk consumed daily. Intakes measured using this procedure are often corrected for evaporative water
losses (insensible water losses) between infant weighings (NAS, 1991). Neville et al. (1988) evaluated

the validity of the test weight approach among bottlefed infants by comparing the weights of milk taken from

bottles with the differences between the infants' weights

before and after feeding. When test weight data were corrected for insensible weight loss, they were not significantlydifferent from bottle weights. Conversions

between weight and volume of human milk consumed

are made using the density of human milk (approximately 1.03 g/mL) (NAS, 1991). Techniques

for measuring human milk intake using stable isotopes

such as deuterium have been developed. The advantages of these techniques over test weighing procedures are that they are less burdensome for the

mother and do not interfere with normal behavior (Albernaz et al., 2002). However, few data based on this technique were found in the literature. Among infants born in 2004, 73.8% were breastfed postpartum, 41.5.% at 6 months, and 20.9%

at 12 months. Studies among nursing mothers in

industrialized countries have shown that average intakes

among infants ranged from approximately 500 to 800 mL/day, with the highest intake reported for infants 3 to

< 6 months old (see Table 15­1).

The recommendations for human milk intake

rates and lipid intake rates are provided in the next
section along with a summary of the confidence ratings

for these recommendations. The recommended values

are based on key studies identified by EPA for this

factor. Following the recommendations, key studies on human milk intake are summarized. Relevant data on lipid content and fat intake, breast­feeding duration, and

the estimated percentage of the U.S. population that

breast­feeds are also presented.

A number of other studies exist in the literature, but they focus on other aspects of lactation

such as growth patterns of nursing infants, supplementary food and energy intake, and nutrition of lactating mothers (Dewey et al., 1992; Drewett et al.,1993; Gonzalez­Cossio et al., 1998). These studies

are not included in this chapter because they do no focus on the exposure factor of interest. Other studies in

the literature focus on formula intake. Since some baby

formula are prepared by adding water, these data are presented in chapter 3 ­ Water Intake.

15.2 RECOMMENDATIONS
The studies described in Section 15.3 were used in selecting recommended values for human milk

intake and lipid intake. Although different survey designs, testing periods, and populations were utilized by the studies to estimate intake, the mean and standard

deviation estimates reported in these studies are relatively consistent. There are, however, limitations

with the data. With the exception of Butte et al. (1984)



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### Child­Specific Exposure Factors Handbook

Chapter 15 ­ Human Milk Intake

and Arcus­Arth et al. (2005), data were not presented

on a body weight basis. This is particularly important

since intake rates may be higher on a body weight basis

for younger infants. Also, the data used to derive the

recommendations are over 15 years old and the sample

size of the studies was small. Other populations of

concern such as mothers highly committed to breastfeeding, sometimes for periods longer than 1 year,

may not be captured by the studies presented in this chapter.

15.2.1 Human Milk Intake

A summary of recommended values for human

milk and lipid intake rates is presented in Table 15­1

and the confidence ratings for these recommendations

are presented in Table 15­2. The human milk intake

rates for nursing infants that have been reported in the

studies described in this section are summarized in

Table 15­3 in units of mL/day and in Table 15­4 in

units of mL/kg­day (i.e., indexed to body weight). It

should be noted that the decrease in human milk with

age is likely a result of complementary foods being

introduced as the child grows and not necessarily a decrease in total energy intake. In order to conform to
the new standardized age groupings used in this

handbook (see Chapter 1), data from Pao et al. (1980), Dewey and Lönnerdal (1983), Butte et al. (1984),

Neville et al. (1988), Dewey et al. (1991a), Dewey et
al. (1991b), Butte et al. (2000) and Arcus­Arth et al. (2005) were compiled for each month of the first year of life. Recommendations were converted to mL/day using a density of human milk of 1.03 g/mL rounded up

to two significant figures. Only two studies (i.e., Butte

et al., 1984 and Arcus­Arth et al., 2005) provided data

on a body weight basis. For some months multiple

studies were available; for others only one study was available. Weighted means were calculated for each

age in months. When upper percentiles were not
available from a study, these were estimated by adding

two standard deviations to the mean value.

Recommendations for upper percentiles, when multiple

studies were available, were calculated as the midpoint

of the range of upper percentile values of the studies

available for each age in months. These month­bymonth intakes were composited to yield intake rates for

the standardized age groups by calculating a weighted

average. Recommendations are provided for the population of exclusively breastfed infants since this population may have higher exposures than partially

breastfed infants. Exclusively breastfed in this chapter refers to infants whose sole source of milk

comes from human milk, with no other milk

substitutes. Partially breastfed refers to infants

whose source of milk comes from both human milk

and other milk substitutes (i.e., formula). Note that

some studies define partially breastfed as infants whose dietary intake comes from not only human milk and formula, but also from other solid foods (e.g., strained fruits, vegetables, meats).

15.2.2 Lipid Content and Lipid Intake

Recommended lipid intake rates are presented

in Table 15­5. The table parallels the human milk

intake tables (Table 15­ 3). With the exception of the

data from Butte et al. (1984), the rates were calculated assuming a lipid content of 4% (Butte et al.,1984; NAS, 1991; Maxwell and Burmaster, 1993). In the case of

the Butte et al. (1984) study, lipid intake rates were provided, and were used in place of the estimated lipid

intakes. Lipid intake rates on a body weight basis are presented in Table 15­6. These were calculated from

the values presented in Table 15­4 multiplied by 4% lipid content.



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Chapter 15 ­ Human Milk Intake

Table 15­1. Recommended Values for Human Milk And Lipid Intake Rates for Exclusively Breastfed Infants

Age Group Mean Upper Percentile a

Source mL/day mL/kg­day mL/day mL/kg­day

Human Milk Intake

Birth to <1 month 510 150 950 220 b

1 to <3 months 690 140 980 190 b, c, d, e,f

3 to <6 months 770 110 1,000 150 b, c, d, e, f, g

6 to <12 months 620 83 1,000 130 b, c, e, g

Lipid Intake h

Birth to <1 month 20 6.0 38 8.7 i

1 to <3 months 27 5.5 40 8.0 d, i

3 to <6 months 30 4.2 42 6.0 d, i

6 to <12 months 25 3.3 42 5.2 i

a Upper percentile is reported as mean plus 2 standard deviations.

b Neville et al., 1988.

c Pao et al., 1980.

d Butte et al., 1984.

e Dewey and Lönnerdal, 1983.

f Butte et al., 2000.

g Dewey et al., 1991b.

h The recommended value for the lipid content of human milk is 4.0 percent. See Section 15.5.

i Arcus­ Arth et al., 2005.



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Chapter 15 ­ Human Milk Intake

Table 15­2. Confidence in Recommendations for Human Milk Intake

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or defined) Bias Methodology uses changes in body weight as a surrogate for total ingestion. More sophisticated techniques measuring stable isotopes have been developed, but data with this technique were

not available. Sample sizes were relatively small (7­108).

Mothers selected for the studies were volunteers. The studies analyzed primary data.

Mothers were instructed in the use of infant scales to minimize

measurement errors. Three out of the 8 studies indicated correcting data for insensible water loss. Some biases may be

introduced by including partially­breastfed infants. Medium

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The studies focused on estimating human milk intake.

Most studies focused on the U.S. population, but were not
national samples. Population studied were mainly from high

socioeconomic status. One study included populations from

Sweden and Finland. However, this may not affect the amount of intake, but rather the prevalence and initiation of lactation.

Studies were conducted between 1980­2000. However, this may

not affect the amount of intake, but rather the prevalence and

initiation of lactation.

Infants were not studied long enough to fully characterize day to day variability. Medium

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance All key studies are available from the peer reviewed literature.

The methodology was clearly presented, but some studies did not
discuss adjustments due to insensible weight loss.

Some steps were taken to ensure data quality. For example,

mothers were trained to use the scales. However, this element

could not be fully evaluated from the information presented in the published studies. Medium

Variability and Uncertainty

Variability in Population

Uncertainty Not very well characterized. Mothers committed to breastfeeding

over 1 year were not captured.

Not correcting for insensible water loss may underestimate intake. Low



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Chapter 15 ­ Human Milk Intake

Table 15­2. Confidence in Recommendations for Human Milk Intake (continued)

General Assessment Factors Rationale Rating

Evaluation and Review

Peer Review

Number and Agreement of Studies The studies appeared in peer review journals.

There are 8 key studies. The results of studies from

different researchers are in agreement. High

Overall Rating Medium



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Chapter 15 ­ Human Milk Intake

Table 15­3. Human Milk Intake Rates Derived from Key Studies for Exclusively Breast­fed Infants (mL/day)

Age (months) Number of

Children Mean
Intake (mL/day) Upper
Percentile Consumption

(mL/day) a Source Weighted Mean Intake

and Upper Percentile Consumption (across all Key Studies) (mL/day)

Individual Age Composite Age Groups

Mean b Upper c Mean Upper

0 <1 6 to 13 511 951 Neville et al., 1988 511 951 511 951

1 11

37

12

16 600

729

679 d

673 918

981

889 1,057 Pao et al., 1980

Butte et al., 1984

Neville et al., 1988 Dewey and Lönnerdal, 1983 670 961

692 977

2 10 to 12

19

40 679 d

756

704 889 1,096

958 Neville et al., 1988 Dewey and Lönnerdal, 1983

Butte et al., 1984 713 992

3 2

37

10

16

73

40 833

702

713

782

788

728 ­ e

924

935 1,126 1,047

988 Pao et al., 1980

Butte et al., 1984

Neville et al., 1988 Dewey and Lönnerdal, 1983 Dewey et al., 1991b

Butte et al., 2000 758 1,025

769 1,024

4 12

13

41 690

810

740 888 1,094

996 Neville et al., 1988 Dewey and Lönnerdal, 1983

Butte et al., 1984 739 991

5 12

11 814

805 1,074 1,039 Neville et al., 1988 Dewey and Lönnerdal, 1983 810 1,057

6 1

13

11

60

30 682

744

896

747

637 ­ed

978 1,140 1,079 1,050 Pao et al., 1980

Neville et al., 1988 Dewey and Lönnerdal, 1983 Dewey et al., 1991b

Butte et al., 2000 741 1,000

622 1,024 7 12 700 1,000 Neville et al., 1988 700 1,006

8 9 604 1,012 Neville et al., 1988 604 1,012

9 12

50 600

627 1,028 1,049 Neville et al., 1988 Dewey et al., 1991b 614 1,039

10 11 535 989 Neville et al., 1988 535 989

11 8 538 1,004 Neville et al., 1988 538 1,004

12 8

42

13 391

435

403 877

922

931 Neville et al., 1988 Dewey et al., 1991a; 1991b

Butte et al., 2000 410 904 410 904

a Calculated as the mean of the means.

b Upper percentile is reported as mean plus 2 standard deviations.

c Middle of the range of upper percentiles.

d Calculated for infants 1 to < 2 months old.

e Standard deviations and upper percentiles not calculated for small sample sizes.



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Chapter 15 ­ Human Milk Intake

Table 15­4. Human Milk Intake Rates Derived from Key Studies for Exclusively Breast­fed Infants (mL/kg/day)

Age (months) Number

of

Children Mean
Intake(mL/kgday) Upper
Percentile Consumption (mL/kg­day) a Source Weighted Mean Intake

and Upper Percentile Consumption (across all Key Studies) (mL/kg­day)

Individual Age Composite Age Groups

Mean b Upper c Mean Upper c

0 <1 9 to 25 150 217 Arcus­Arth et al, 2005 150 217 150 217

1 37

25 154

150 200

198 Butte et al., 1984

Arcus­Arth et al, 2005 152 199

144 187

2 40

25 125

144 161

188 Butte et al., 1984

Arcus­Arth et al, 2005 135 175

3 37

108 114

127 152

163 Butte et al., 1984

Arcus­Arth et al, 2005 121 158

111 149 4 41

57 108

112 142

148 Butte et al., 1984

Arcus­Arth et al, 2005 110 145

5 26 100 140 Arcus­Arth et al, 2005 100 140

6 39 101 141 Arcus­Arth et al, 2005 101 141

83 130 7 8 75 125 Arcus­Arth et al, 2005 75 125

9 57 72 118 Arcus­Arth et al, 2005 72 118

12 42 47 101 Arcus­Arth et al, 2005 47 101 47 101

a Calculated as the mean of the means.

b Upper percentile is reported as mean plus 2 standard deviations.

c Middle of the range of upper percentiles.



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Chapter 15 ­ Human Milk Intake

Table 15­5. Lipid Intake Rates Derived from Key Studies for Exclusively Breastfed Infants (mL/day) a

Age (months) Number of

Children Mean

Intake (mL/day) Upper Percentile Consumption

(mL/day) b Source Weighted Mean Intake

and Upper Percentile Consumption (across all Key Studies) (mL/day)

Individual Age Composite Age Groups

Mean c Upper d Mean c Upper d

0 <1 6 to 13 20 38 Neville et al., 1988 20 38 20 38

1 11

37

10 to 12

16 24

27

27

27 37

43

36

42 Pao et al., 1980

Butte et al., 1984

Neville et al., 1988 Dewey and Lönnerdal, 1983 26 39

27 40

2 10 to 12

19

40 27

30

24 36

44

38 Neville et al., 1988 Dewey and Lönnerdal, 1983

Butte et al., 1984 27 40

3 2

37

10

16

73

40 33

23

29

31

32

29 ­ e

37

37

45

42

40 Pao et al., 1980

Butte et al., 1984

Neville et al., 1988 Dewey and Lönnerdal, 1983 Dewey et al., 1991b

Butte et al. 2000 30 41

30 42

4 12

13

41 28

32

25 36

44

41 Neville et al., 1988 Dewey and Lönnerdal, 1983

Butte et al., 1984 28 40

5 12

11 33

32 43

42 Neville et al., 1988 Dewey and Lönnerdal, 1983 33 43

6 1

13

11

60

30 27

30

36

30

25 ­ e

39

46

43

42 Pao et al., 1980

Neville et al., 1988 Dewey and Lönnerdal, 1983 Dewey et al., 1991b

Butte et al., 2000 30 40

25 42 7 12 28 40 Neville et al., 1988 28 40

8 9 24 40 Neville et al., 1988 24 41

9 12

50 24

25 41

42 Neville et al., 1988 Dewey et al., 1991b 24 41

10 11 21 40 Neville et al., 1988 21 40

11 9 22 40 Neville et al., 1988 22 40

12 9

42

13 17

17

16 35

37

37 Neville et al., 1988 Dewey et al., 1991a; 1991b

Butte et al., 2000 16 36 16 36

a Except for Butte et al. 1984, values were calculated from table 15­3 using 4% lipid content.

b Upper percentile is reported as mean plus 2 standard deviations.

c Calculated as the mean of the means.

d Middle of the range of upper percentiles

.

e Standard deviations and upper percentiles not calculated for small sample sizes.



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Chapter 15 ­ Human Milk Intake

Table 15­6. Lipid Intake Rates Derived from Key Studies for Exclusively Breast­fed Infants (mL/kg/day) a

Age (months) Number

of

Children Mean Intake (mL/kg­day) Upper
Percentile Consumption (mL/kg­day) b Source Weighted Mean Intake

and Upper Percentile Consumption b (across all Key Studies) (mL/kg­day)

Individual Age Composite Ages Groups

Mean c Upper d Mean e Upper d

0 <1 9 to 25 6.0 8.7 Arcus­Arth et al, 2005 6.2 8.7 6.0 8.7

37 5.7 9.1 Butte et al., 1984 1 5.9 8.9 25 6.0 8.7 Arcus­Arth et al, 2005

5.5 8.0

40 4.3 6.7 Butte et al., 1984 2 5.1 7.1 25 5.8 7.5 Arcus­Arth et al, 2005

37 3.7 6.1 Butte et al., 1984 3 4.4 6.3 108 5.1 6.5 Arcus­Arth et al, 2005

41 3.7 6.3 Butte et al., 1984 4.2 6.0 4 4.1 6.1 57 4.5 5.9 Arcus­Arth et al, 2005

5 26 4.0 5.6 Arcus­Arth et al, 2005 4.0 5.8

6 39 4.0 5.6 Arcus­Arth et al, 2005 4.0 5.6

7 8 3.0 5.0 Arcus­Arth et al, 2005 3.0 5.0

9 57 2.9 4.7 Arcus­Arth et al, 2005 2.9 4.7 3.3 5.2

12 42 1.9 4.0 Arcus­Arth et al, 2005 1.9 4.0 1.9 4.1

a Except for Butte et al. 1984, values were calculated from table 15­4 using 4% lipid

content.

b Upper percentile is reported as mean plus 2 standard deviations.

c Calculated as the mean of the means.

d Middle of the range of upper percentiles.



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Chapter 15 ­ Human Milk Intake

15.3 KEY STUDIES ON HUMAN MILK

INTAKE

15.3.1 Pao et al., 1980

­ Milk Intakes and Feeding

Patterns of Breast­fed Infants

Pao et al. (1980) conducted a study of 22 healthy nursing infants to estimate human milk intake

rates. Infants were categorized as completely breast­fed

or partially breast­fed. Breastfeeding mothers were recruited through LaLeche League groups. Except for

one black infant, all other infants were from white

middle­class families in southwestern Ohio. The goal

of the study was to enroll infants as close to one month

of age as possible and to obtain records near one, three, six, and nine months of age (Pao et al., 1980). However, not all mother/infant pairs participated at
each time interval. Data were collected for these 22

infants using the test weighing method. Records were collected for three consecutive 24­hour periods at each

test interval. The weight of human milk was converted

to volume by assuming a density of 1.03 g/mL. Daily

intake rates were calculated for each infant based on the

mean of the three 24­hour periods. Mean daily human

milk intake rates for the infants surveyed at each time

interval are presented in Table 15­7. These data (Table 15­7) are presented as they are reported in Pao et al. (1980). For completely breast­fed infants, the mean intake rates were 600 mL/day at 1 month of age, 833 mL/day at 3 months of age, and 682 mL/day at 6

months of age. Partially breast­fed infants had mean intake rates of 485 mL/day, 467 mL/day, 395 mL/day,

and <554 mL/day at 1, 3, 6, and 9 months of age, respectively. Pao et al. (1980) also noted that intake

rates for boys in both groups were slightly higher than

for girls.

The advantage of this study is that data for both exclusively and partially breast­fed infants were collected for multiple time periods. Also, data for

individual infants were collected over 3 consecutive days which would account for some individual variability. However, the number of infants in the study

was relatively small. In addition, this study did not

account for insensible weight loss which may underestimate the amount of human milk ingested. 15.3.2 Dewey and Lönnerdal, 1983

­ Milk and

Nutrient Intake of Breast­fed Infants from

1 to 6 Months: Relation to Growth and

Fatness Dewey and Lönnerdal (1983) monitored the dietary intake of 20 nursing infants between the ages of

1 and 6 months. The number of study participants dropped to 13 by the end of the sixth month. Most of

the infants in the study were exclusively breast­fed.

One infant’s intake was supplemented by formula during the first and second month of life. During the third, fourth, and fifth months, three, four, and five infants, respectively, were given some formula to supplement their intake. Two infants were given only

formula (no human milk) during the sixth month. According to Dewey and Lönnerdal (1983), the mothers

were all well educated and recruited through Lamaze

childbirth classes in the Davis area of California.

Human milk intake volume was estimated based on two
24­hour test weighings per month. Human milk intake

rates for the various age groups are presented in Table

15­8. Human milk intake averaged 673, 782, and 896 mL/day at 1, 3, and 6 months of age, respectively.

The advantage of this study is that it evaluated nursing infants for a period of 6 months based on two
24­hour observations per infant per month. However,

corrections for insensible weight loss apparently were

not made. Also, the number of infants in the study was relatively small and the study participants were not representative of the general population. Some infants during the study period were given some formula (i.e.,

up to 5 infants during the fifth month). Without the raw data, these subjects could not be excluded from the study results. Thus, these subjects may affect the

results when deriving recommendations for exclusively

breastfed infants.

15.3.3 Butte et al., 1984

­ Human Milk Intake and

Growth in Exclusively Breast­fed Infants

Human milk intake was studied in exclusively

breast­fed infants during the first 4 months of life (Butte

et al., 1984). Nursing mothers were recruited through

the Baylor Milk Bank Program in Texas. Forty­five

mother/infant pairs participated in the study. However,

data for some time periods (i.e., 1, 2, 3, or 4 months)

were missing for some mothers as a result of illness or other factors. The mothers were from the middle­ to upper­socioeconomic stratum and had a mean age of

28.0 ± 3.1 years. A total of 41 mothers were white, 2



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Chapter 15 ­ Human Milk Intake

were Hispanic, 1 was Asian, and 1 was West Indian.

Infant growth progressed satisfactorily over the course of the study.

The amount of milk ingested over a 24­hour period was determined by weighing the infant before

and after feeding. The study did not indicate whether

the data were corrected for insensible water or weight

loss. The mean and standard deviation milk intake

difference based on weighing the bottle before and after

nine successive feedings, was estimated to be 3.2 ± 3.1

g. Test weighing occurred over a 24­hour period for

most study participants, but intake among several

infants was studied over longer periods (48 to 96 hours)

to assess individual variation in intake. It was reported

that eight of the infants received some food supplementation during the study period. Six of them

received less than 60 kcal/day of formula, oatmeal, glucose water, or rice water for 1 or 2 days. One infant

received an additional 90 kcal/day of infant formula and

rice water for 6 days during the fourth month because of inadequate milk production. Converting values reported as g/day to mL/day, using a conversion factor

of 1.03 g/mL, mean human milk intake ranged from 702 mL/day at 3 months to 729 mL/day at 1 month, with an overall mean of 712 mL/day for the entire study period (Table 15­9). Intakes were also calculated on the basis

of body weight (Table 15­9). Based on the results of

test weighings conducted over 48 to 96 hours, the

overall mean variation in individual daily intake was estimated to be 7.9 ± 3.6 percent.
The advantage of this study is that data for a larger number of exclusively breast­fed infants were collected than in previous studies. However, data were collected for infants up to 4 months and day­to­day variability was not characterized for all infants. It was reported that eighteen percent (i.e., 8 out of 45) of the

infants received some formula supplementation during

the study period. Without the raw data, these subjects

could not be excluded from the study results. Therefore,

values derived from this study for exclusively breastfed

infants may be somewhat underestimated.

15.3.4 Neville et al., 1988

­ Studies in Human

Lactation: Milk Volumes in Lactating

Women During the Onset of Lactation and

Full Lactation
Neville et al. (1988) studied human milk

intake among 13 infants during the first year of life.

The mothers were all multiparous, nonsmoking, Caucasian women of middle­ to upper­socioeconomic

status living in Denver, CO. All women in the study practiced exclusive breast­feeding for at least 5 months.

Solid foods were introduced at mean age of 7 months. Daily milk intake was estimated by the test weighing

method with corrections for insensible weight loss.

Data were collected daily from birth to 14 days, weekly

from weeks 3 through 8, and monthly until the study period ended at 1 year after inception. One infant was weaned at 8 months, while all others were weaned on or
after the 12 months. Formula was used occasionally ( 

### #
240 mL/wk) after 4 months in three infants. The

estimated human milk intakes for this study are listed in

Table 15­10. Converting values reported as g/day to mL/day, using a conversion factor of 1.03 g/mL, mean human milk intakes were 748 mL/day, 713 mL/day, 744 mL/day, and 391 mL/day at 1, 3, 6, and 12 months of

age, respectively.

In comparison to the previously described studies, Neville et al. (1988) collected data on

numerous days over a relatively long time period (12 months) and they were corrected for insensible weight

loss. However, the intake rates presented in Table 15­

10 are estimated based on intake during only a 24­hour period. Consequently, these intake rates are based on short­term data that do not account for day­to­day variability among individual infants. Also, a smaller

number of subjects was included than in the previous

studies. Three infants were given some formula after 4

months. Without the raw data, these subjects could not
be excluded from the study results. Thus, data presented for infants between 5 and 12 months may be

an underestimate for the intake of exclusively breastfed

infants.

15.3.5 Dewey et al., 1991a, b

­ (a) Maternal

Versus Infant Factors Related to Human

Milk Intake and Residual Volume: The

DARLING Study; (b) Adequacy of Energy

Intake Among Breast­fed Infants in the

DARLING Study: Relationships to Growth, Velocity, Morbidity, and Activity

Levels
The Davis Area Research on Lactation, Infant

Nutrition and Growth (DARLING) study was conducted in 1986 to evaluate growth patterns, nutrient intake, morbidity, and activity levels in infants who

were breast­fed for at least the first 12 months of life (Dewey et al., 1991a, b). Subjects were non­randomly



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Chapter 15 ­ Human Milk Intake

selected through letters to new parents using birth listing. One of the criteria used for selection was that

mothers did not plan to feed their infants more than 120 mL/day of other milk or formula for the first 12 months

of life. Seventy­three infants aged 3 months were included in the study. At subsequent time intervals, the

number of infants included in the study was somewhat

lower as a result of attrition. All infants in the study

were healthy and of normal gestational age and weight

at birth, and did not consume solid foods until after the

first 4 months of age. The mothers were highly

educated and of “relatively high socioeconomic status.”

Human milk intake was estimated by weighing

the infants before and after each feeding and correcting

for insensible water loss. Test weighings were conducted over a 4­day period every 3 months. The

results of the study indicate that human milk intake

declines over the first 12 months of life. This decline is

associated with the intake of solid food. Converting

values reported as g/day to mL/day, using a conversion

factor of 1.03 g/mL, mean human milk intake was estimated to be 788 mL/day at 3 months and

435 mL/day at 12 months (Table 15­11). Based on the

estimated intakes at 3 months of age, variability

between individuals (coefficient of variation ([CV] = 16.3%) was higher than the average day­to­day variability ([CV] = 8.9 ± 5.4%) for the infants in the study (Dewey et al., 1991a).

The advantages of this study are that data were collected over a relatively long­time (4 days) period at
each test interval, which would account for some dayto­day infant variability, and corrections for insensible

water loss were made. Data from this study are assumed to represent exclusively breastfed infants,

since mothers were specifically recruited for that

purpose. It is, however, unclear from the Dewey et al.,

1991a if this criterion was met throughout the length of

the study period.

15.3.6 Butte, et al., 2000 ­ Infant Feeding Mode

Affects Early Growth and Body Composition

Butte et al. (2000) conducted a study to assess the impact of infant feeding mode on growth and body composition during the first two years of life. The study was conducted in the Houston, Texas area, recruited through the Children’s Nutrition Research

Center (CNRC) referral system. The study was approved by the Baylor Affiliates Review Boards for

Human Subject Research. The overall sample was 76 healthy term infants at 0.5, 3, 6, 9, 12, 18, and 24

months of age. The sample size varied between 71 to
76 infants for each age group. Repeated measurements
for body composition and anthropometric were performed. The mothers agreed to either exclusively

breast feed or formula feed the infants for the first 4

months of life.

At 3­month or 6­month study intervals, the feeding history was taken. The mothers or caretakers

were questioned about breastfeeding frequency, and the

use of formula, milk, juice, solids, water and vitamin or mineral supplements. Also, infant food intake was quantified at 3, 6, 12, and 24 months with a 3­day weighted intake record completed by the mother or caretaker (Butte et al., 2000). The intake of human

milk was assessed by test weighing; the infant weights

were measured before and after each feeding. Using a pre­weighing and post­weighing method, the intake of

formula and other foods and beverages was determined

for 3 days by the mothers using a digital scale and

recorded on predetermined forms.

The average duration of breastfeeding was 11.4 months (SD = 5.8). Butte et al.(2000) reported

that infants were exclusively breastfed for at least the

first four months except for the following: one was weaned at 109 days, another received formula at 102 days and another given cereal at 106 days. The infant feeding characteristics are shown in Table 15­12. The

intake of human milk for the infants are shown in Table

15­13. Converting values reported as g/day to mL/day, using a conversion factor of 1.03 g/mL, mean human

milk intake was estimated to be 728 mL/day at 3

months (weighted average of boys and girls),

637 mL/day at 6 months (weighted average of boys and girls), and 403 mL/day at 12 months (weighted average of boys and girls) (Table 15­13). Feeding practices by
percent for infants are shown in Table 15­14. The

mean weights are provided in Table 15­15. Advantages of this study are that it provides

intake data for breastfed infants for the first four months

of life. The study also provides the mean weights for

the infants by feeding type and by gender. The

limitations of the study are that the sample size is small

and it is limited to one geographical location. The

authors did not indicate if results were corrected for

insensible weight loss. Since mothers could introduce

formula after 4 months, only the data for the 3­month

old infants can be considered exclusively breastfed.



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Chapter 15 ­ Human Milk Intake

15.3.7 Arcus­Arth et al., 2005 ­ Human Milk and Lipid Intake Distributions for Assessing

Cumulative Exposure and Risk
Arcus­Arth et al. (2005) derived population

distributions for average daily milk and lipid intakes in g/kg day for infants 0­6 months and 0­12 months of age for infants fed according to the American Academy of

Pediatrics (AAP) recommendations. The AAP

recommends exclusively breastfeeding for the first 6

months of life, human milk as the only source of milk

age 1 year, with the introduction of solid foods after 6

months. The distributions were derived based on data

in the peer reviewed literature and datasets supplied by

the publication authors for infants 7 days and older (Arcus­Arth et al., 2005). As cited in Arcus­Arth et al. (2005), data sources included Dewey et al. (1991a, 199b), Hofvander et al. (1982), Neubauer et al. (1993),

Ferris et al. (1993), Salmenpera et al. (1985), and Stuff

and Nichols (1989). The authors also evaluated intake

rates for infants breastfed exclusively over the first year and provides a regression line of intake versus age for estimating short­term exposures. Arcus­Arth derived

human milk intake rates for the entire infant population (nursing and non­nursing) from U.S. data on consumption, prevalence and duration. Arcus­Arth et
al. (2005) defined exclusive breastfeeding (EBF) as “breast milk is the sole source of calories, with no or insignificant calories form other liquid or solid food

sources.” Predominant breastfeeding was described by

Arcus­Arth et al. (2005) as “breast milk is the sole milk

source with significant calories from other foods.” The

data that were consistent with AAP advice were used to

construct the AAP dataset (Arcus­Arth et al., 2005).

The 0­12 months EBF dataset was created using 0­6

month AAP data and data from the EBF infants older

than 6 months of age. Because there are no data in the

AAP dataset for any individual infant followed at regular, frequent intervals over the 12 month period, population distributions were derived with assumptions regarding individual intake variability over time (ArcusArth et al., 2005). Two methods were used. In Method 1, the average population daily intake at each age is

described by a regression line, assuming normality.

Arcus­Arth et al. (2005) noted that age specific intake

data were consistent with the assumption of normality.

In Method 2, intake over time is simulated for 2500 hypothetical infants and the distribution intakes derived

from 2500 individual intakes (Arcus­Arth et al., 2005).

The population intake distribution was derived following Method 1. Table 15­16 presents the means, and standard deviations for intake data at different

ages; the variability was greatest for the 2 youngest and

3 oldest age groups. The values in Table 15­6 using

Method 1 were used to derive recommendations presented in Table 15­4 since it provides data for the

fine age categories. Converting values reported as g/day to mL/day, using a conversion factor of 1.03 g/mL, mean human milk intake was estimated to be 150 mL/kg­day at 1 month, 127 mL/kg­day at 3 months, 101 mL/kg­day at 6 months, and 47 mL/kg­day at 12

months (Table 15­16). Time weighted average intakes

for larger age groups (i.e., 0 ­ 6 months, 0 ­ 12 months)

are presented in Table 15­17.

An advantage of this study is that it was designed to represent the infant  population whose

mothers follow the AAP recommendations. Intake was calculated on a body weight basis. In addition, the data

used to derive the distributions were from peer reviewed literature and datasets supplied by the publication authors. The distributions were derived

from data for infants fed in accordance to AAP recommendations, and they most likely represent daily

average milk intake for a significant portion of

breastfed infants today (Arcus­Arth et al., 2005). The

limitations of the study are that the data used were from

mothers that were predominantly white, well nourished

and from mid or high socioeconomic status. ArcusArth et al. (2005) also included data from Sweden and

Finland. However human milk volume in mL/day is

similar among all women except for severely

malnourished women (Arcus­Arth et al., 2005). According to Arcus­Arth et al. (2005), “Although few

infants are exclusively breastfed for 12 months, the

EBF distributions may represent a more highly exposed subpopulation of infants exclusively breastfed in excess of 6 months.”

15.4 KEY STUDIES ON LIPID CONTENT

AND LIPID INTAKE FROM HUMAN

MILK

Human milk contains over 200 constituents including lipids, various proteins, carbohydrates, vitamins, minerals, and trace elements as well as

enzymes and hormones. The lipid content of human

milk varies according to the length of time that an infant

nurses, and increases from the beginning to the end of

a single nursing session (NAS, 1991). The lipid portion

accounts for approximately 4% of human milk (3.9%



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± 0.4%) (NAS, 1991). This value is supported by

various studies that evaluated lipid content from human

milk. Several studies also estimated the quantity of lipid consumed by breast­feeding infants. These values

are appropriate for performing exposure assessments
for nursing infants when the contaminant(s) have

residue concentrations that are indexed to the fat portion of human milk.

15.4.1 Butte et al., 1984

­ Human Milk Intake and

Growth in Exclusively Breast­fed Infants

Butte et al. (1984) analyzed the lipid content
of human milk samples taken from women who participated in a study of human milk intake among exclusively breast­fed infants. The study was conducted with over 40 women during a 4­month period. The mean lipid content of human milk at
various infants’ ages is presented in Table 15­18. The

overall lipid content for the 4­month study period was 3.43 ± 0.69 %  (3.4%). Butte et al. (1984) also

calculated lipid intakes from 24­hour human milk

intakes and the lipid content of the human milk samples. Lipid intake was estimated to range from 22.9 mL/day (3.7 mL/kg­day) to 27.2 mL/day (5.7 mL/kgday).

The number of women included in this study

was small, and these women were selected primarily

from middle to upper socioeconomic classes. Thus,

data on human milk lipid content from this study may

not be entirely representative of human milk lipid

content among the U.S. population. Also, these

estimates are based on short­term data, and day­to­day variability was not characterized.

15.4.2 Mitoulas et al., 2002

­ Variation in Fat, Lactose, and Protein in Human Milk Over

24 h and Throughout the First Year of

Lactation
Mitoulas et al. (2002) conducted a study of healthy nursing women to determine the volume and composition of human milk during the first year of

lactation. Nursing mothers were recruited through the Nursing Mothers’ Association of Australia. All infants

were completely breastfed on demand for at least 4

months. Complementary solid food was introduced

between 4­6 months of age. Mothers consumed their

own ad libitum diets throughout the study. Seventeen

mothers initially provided data for milk production and

fat content, whereas lactose, protein, and energy were initially obtained from nine mothers. The number of mothers participating in the study decreased at 6 months

due to the cessation of sample collection from 11 mothers, the maximum period of exclusive breastfeeding.

Milk samples were collected before and after

each feed from each breast over a 24­28 hour period.

Milk yield was determined by weighing the mother

before and after each feed from each breast. Insensible

water loss was accounted for by weighing the mother 20

minutes after the end of each feeding. The rate of water
loss during this 20 minutes was used to calculate

insensible water loss during the feeding. Samples of

milk produced at the beginning of the feeding (foremilk) and at the end of the feeding (hindmilk) were averaged to provide the fat, protein, lactose, and energy

content for each feed. In all cases the left and right

breasts were treated separately, therefore, n, represents
the number of individual breasts sampled. Mean human milk production and composition

at each age interval are presented in Table 15­19. The

mean 24 hour milk production from both breasts was 798 (SD= 232) mL. The mean fat, lactose, and protein

contents (g/L) were 37.4 (SE= 0.6), 61.4 (SE =0.6), and

9.16 (SE= 0.19)

, respectively. Composition did not

vary between left and right breasts or preferred and non­preferred breasts. Milk production was constant
for the first 6 months and thereafter steadily declined.

The fat content of milk decreased between 1 and 4 months, before increasing to 12 months of lactation.

The concentration of protein decreased to 6 months and

then remained steady. Lactose remained constant throughout the 12 months of lactation. The decrease of

energy at 2 months and subsequent increase by 9

months can be attributed to the changes in fat content.
Milk production, as well as concentrations of fat, lactose, protein, and energy, differed significantly

between women. The focus of this study was on human milk composition and production, not on infant’s human

milk intake. The advantage of this study is that it

evaluated nursing mothers for a period of 12 months. However, the number of mother­infant pairs in the study was small (17 mothers with infants) and may not
be entirely representative of the U.S. population. This study accounted for insensible water loss which

increases the accuracy of the amount of human milk produced.



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15.4.3 Mitoulas et al., 2003 ­ Infant Intake of Fatty Acids from Human Milk Over the

First Year of Lactation
Mitoulas et al. (2003) conducted a study of 5 healthy nursing women to determine the content of fat

in human milk and fat intake by infants during the first

year of lactation. Nursing mothers were recruited through the Australian Breastfeeding Association or from private healthcare facilities. All infants were completely breastfed on demand for at least 4 months. Complementary solid food was introduced between 4­6

months of age. Mothers consumed their own ad libitum

diets throughout the study.

Milk samples were collected before and after

each feed from each breast over a 24­28 hour period.

Fore­ and hind­milk samples were averaged to provide

the fat content for each feed. Milk yield was determined by weighing the mother before and after

each feed from each breast. Insensible water loss was accounted for by weighing the mother 20 minutes after

the end of each feeding. The rate of water loss during

this 20 minutes was used to calculate insensible water
loss during the feeding. Changes in volume of human milk produced

and milk fat content over the first year of lactation is presented in Table 15­20. The mean volumes of milk produced for both breasts combined were 812.13, 790.34, 911.38, 810.20, 677.35, and 505.10 mL/day at 1, 2, 4, 6, 9,and 12 months, respectively. The average daily intake over the 12 months was 751.09 mL/day

with a mean fat content of 35.52 g/L. Their was a significant difference in the proportional composition of fatty acids over the course of lactation. Table 15­21 provides average fatty acid composition over the first

12 months of lactation. Additionally, fatty acid composition varied over the course of the day.

The focus of this study was on human milk composition and production, not on infant’s human

milk intake. The advantage of this study is that it

evaluated the human milk composition for a period of

12 months. However, the number of mother­infant pairs in the study was small (5 mothers with infants)

and may not be entirely representative of the entire U.S. population. This study accounted for insensible water
loss which increases the accuracy of the amount of

human milk produced

. 15.4.4 Arcus­Arth et al., 2005

­ Human Milk and Lipid Intake Distributions for Assessing

Cumulative Exposure and Risk
Arcus­Arth et al. (2005) derived population

distributions for average daily milk and lipid intakes in g/kg day for infants 0­6 months and 0­12 months of age for infants fed according to the American Academy of

Pediatrics (AAP) recommendations. Lipid intakes were calculated from lipid content and milk intakes were measured on the same infant (Arcus­Arth et al., 2005).

Table 15­22 provides lipid intakes based on data from Dewey et al. 1991a and Table 15­23 provides lipid

intakes calculated assuming 4% lipid content and milk

intake in the AAP dataset. Arcus­Arth et al. (2005)

noted that the distributions presented are intended to represent the U.S. infant population.

An advantage of this study is that it was designed to represent the population of infants who are breastfed according to the AAP recommendations. In addition, the data used to derive the distributions were from peer review literature and datasets supplied by the publication authors. The limitation of the study are that

the data used were from mothers that were predominantly white, well nourished and from mid­ or upper­socioeconomic status, however human milk

volume in mL/day is similar among all women except
for severely malnourished women (Arcus­Arth et al., 2005). The authors noted that “although few infants are exclusively breastfed for 12 months, the exclusively

breastfed distributions may represent a more highly exposed subpopulation of infants exclusively breastfed

in excess of 6 months.” The distributions were derived

from data for infants fed in accordance to AAP recommendations, and they most likely represent daily

average milk intake for a significant portion of

breastfed infants today (Arcus­Arth et al., 2005).

15.4.5 Kent et al., 2006

­ Volume and Frequency

of Breastfeeding and Fat Content of Breast

Milk Throughout the Day

Kent et al. (2006) collected data from 71

Australian mothers who were exclusively nursing their

1 to 6 months old infants. The study focused on examining the variation of milk consumed from each breast, the degree of fullness of each breast before and

after feeding, and the fat content of milk consumed

from each breast during daytime and nighttime feedings. The volume of milk was measured using testweighing procedures with no correction for infant



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insensible water loss. On average, infants had 11 ± 3 breastfeedings per day (range= 6 to18). The intervals

between feedings was 2 hours and 18 minutes ± 43

minutes (range = 4 minutes to 10 hours and 58 minutes). The 24­hour average human milk intake was 765 ± 164 mL/day (range = 464 to 1,317 mL/day). The

fat content of milk ranged from 22.3 g/L to 61.6 g/L (2.2% ­ 6.0 %) with an average of 41.1 g/L (4.0%).

This study examined breastfeeding practices of

volunteer mothers in Australia. Although amounts of

milk consumed by Australian infants may be similar to
infants in the U.S. population, results could not be

broken out by smaller age groups to examine variability

with age. The study provides estimates of fat content
from a large number of samples.

15.5 RELEVANT STUDY ON LIPID INTAKE

FROM HUMAN MILK

15.5.1 Maxwell and Burmaster, 1993

­ A

Simulation Model to Estimate a Distribution of Lipid Intake from Human

Milk During the First Year of Life
Maxwell and Burmaster (1993) used a hypothetical population of 5000 infants between birth

and 1 year of age to simulate a distribution of daily lipid

intake from human milk. The hypothetical population represented both bottle­fed and breast­fed infants aged

1 to 365 days. A distribution of daily lipid intake was developed, based on data in Dewey et al. (1991b) on human milk intake for infants at 3, 6, 9, and 12 months

and human milk lipid content, and survey data in Ryan

et al. (1991) on the percentage of breast­fed infants

under the age of 12 months (i.e., approximately 22%).

A model was used to simulate intake among 1113 of the

5000 infants that were expected to be breast­fed. The

results of the model indicated that lipid intake among nursing infants under 12 months of age can be

characterized by a normal distribution with a mean of

26.0 mL/day and a standard deviation of 7.2 mL/day (Table 15­24). The model assumes that nursing infants

are completely breast­fed and does not account for

infants who are breast­fed longer than 1 year. Based on data collected by Dewey et al. (1991b), Maxwell and

Burmaster (1993) estimated the lipid content of human

milk to be 36.7 g/L at 3 months (35.6 mg/g or 3.6%),

39.2 g/L at 6 months (38.1 mg/g or 3.8%), 41.6 g/L at
9 months (40.4 mg/g or 4.0%), and 40.2 g/L at

12 months (39.0 mg/g or 3.9%).

The limitation of this study is that it provides

a “snapshot” of daily lipid intake from human milk for breast­fed infants. These results are also based on a simulation model and there are uncertainties associated

with the assumptions made. Another limitation is that lipid intake was not derived for the EPA recommended

age categories. The estimated mean lipid intake rate represents the average daily intake for nursing infants

under 12 months of age. The study did not generate
“new” data. A reanalysis of previously reported data on human milk intake and human milk lipid intake were provided.

15.6 OTHER FACTORS
There are many factors that influence the initiation, continuation, and amount of human milk

intake. These factors are complex and may include

considerations such as: maternal nutritional status, parity, parental involvement, support from lactation consultants, mother’s working status, infant’s age, weight, gender, food supplementation, the frequency of breast­feeding sessions per day, the duration of breastfeeding per event, the duration of breast­feeding during childhood, ethnicity, geographic area, and other

socioeconomic factors. For example, a study conducted

in the United Kingdom found that social and

educational factors most influenced the initiation and

continuation of lactation (Wright et al. 2006). Prenatal

and postnatal lactation consultant intervention was found to be effective in increasing lactation duration

and intensity (Bonuck et al. 2005).

15.6.1 Population of Nursing Infants

To monitor progress towards achieving the

CDC Healthy People 2010 breastfeeding objectives (initiation and duration), Scanlon et al. (2007) analyzed

data from the National Immunization Survey (NIS).

NIS uses random­digit dialing to survey households to

survey age eligible children, followed by a mail survey

to eligible children’s vaccination providers to validate

the vaccination information. NIS is conduced annually by the CDC to obtain national, state, and selected urban

area estimation on vaccinations rates among U.S. children age 19­35 months. The interviewresponse rate

for years 2001­2006 ranged between 64.5% and 76.1%. Questions regarding breastfeeding were added to the

NIS survey in 2001. The sample population was infants

born during 2000­2004. Scanlon et al. (2007), noted

that because data in their analysis are for children aged

19­35 months at the time of the NIS interview, each

cross­sectional survey includes children from birth

cohorts that span 3 calendar years; the breastfeeding



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data were analyzed by year of birth during 2000­2004 (birth year cohort instead if survey year). Among infants born in 2000, breastfeeding

rates were 70.9% (CI= 69.0­72.8) for the postpartum period (in hospital before discharge), 34.2% (CI= 32.2­ 36.2) at 6 months, and 15.7 (CI= 14.2­17.2) at 12

months. For infants born in 2004, these rates had

increased to 73.8% (CI= 72.8­74.8) for the postpartum period, 41.5% (CI= 40.4­42.6) at 6 months, and 20.9 (CI= 20.0­21.8) at 12 months. Rates of breastfeeding through 3 months were lowest among black infants (19.8%), infants whose mothers were <20 years of age (16.8%), those whose mothers had a high school

education or less (22.9% and 23.9%), those whose

mothers were unmarried (18.8%), those who resided in

rural areas (23.9%), and those whose families had an income­to­poverty ratio of <100% (23.9%). Table 15­

25 provides data for exclusive breastfeeding through 3

and 6 months by socioeconomic characteristics for

infants born in 2004.

Scanlon et al. (2007) noted the following

limitations that could affect the utility of these data: (1) breastfeeding behavior was based on retrospective selfreport by mothers or other caregivers, whose responses might be subject to recall bias, (2) the NIS question that

defines early postpartum breastfeeding or initiation,

"Was [child's name] ever breastfed or fed breast milk?"

collects information that might differ from the HP2010 objective for initiation, and (3) although survey data

were weighted to make them representative of all U.S.

children aged 19­35 months, some bias might remain.

The advantage of the study is that is representative of

the U.S. infant population.

The rate of breastfeeding initiation in the

United States is near the national goal of 75%

established in Healthy People 2010 (Ruowei et al. 2005). Using the data obtained from the NIS survey conducted throughout 2002 for children who were 19 to
35 months old, Ruowi et al. (2005) shows that overall,

71.4% of children surveyed had ever been breastfed.

The percentage of children who are breastfed drops to
35.1% at 6 months and to 16.1% at 12 months (Rouwei

et al. 2005). These data also revealed significant

differences in breastfeeding participation related to race/ethnicity, day care and WIC participation, maternal

age, socioeconomic status, and geographical area. Overall, 51.5% of mothers of non­Hispanic black

children reported to ever breastfed their infants compared to 72.1% of mothers of non­Hispanic white children. Non­Hispanic black infants were exclusively

breastfed at 6 months at a rate of 5.4% compared to
14.6% of non­Hispanic white infants and 13.8% of Hispanic infants. Infants who attended day care and

infants whose mothers received WIC benefits were less likely to have ever been breastfed. Mothers with higher

socioeconomic status and older mothers were more likely to have ever breastfed their infants.

CDC (2007) developed the breastfeeding
report card. The CDC National Immunization Program

in partnership with the CDC National Center for Health Statistics, conducts the NIS within all 50 states, District

of Columbia, and selected geographic areas within the

states. Five breastfeeding goals are in the Healthy People 2010 report. The Breastfeeding Report Card
presents data for each state for the following categories

of infants: ever breastfed, breastfed at 6 months,

breastfed at 12 months, exclusive breastfeeding through

3 month, and exclusive breastfeeding through 6 months.

These indicators are used to measure a state’s ability to promote, protect, and support breastfeeding. These

data for the estimated percentage of infants born in

2004 are presented in Table 15­26. The weighted sample number is 17,654 for the U.S. population. The advantage of this report is that it provides data for each

state and is representative of the U.S. infant population. Analysis of breastfeeding practices in other developing countries was also found in the literature.

Marriott et al. (2007) researched feeding practices in developing countries in the first year of life, based on 24­hour recall data. Marriott et al. (2007), used secondary data from the Demographic and Health Surveys (DHS) for more than 35,000 infants in twenty
countries. This survey has conducted since 1986 and

was expanded to provide a standardized survey instrument that can be used by developing countries to
collect data on maternal/infant health, intake and

household variables and to build national health

statistics (Marriott et al., 2007). The analysis was based on the responses of the survey mothers for questions on whether they were currently breasfeeding

and had fed other liquids and solid foods to their infants

in the previous 24 hours. The data incorporated were from between 1999 and 2003. Marriott et al. (2007)

selected the youngest child less than 1 year old in each

of the families; multiples were included such as twins or triplets. Separate analyses were conducted for infants

less than 6 months old and infants 6 months and older,

but less than 12 months old. Food and liquid variables



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other than water and infant formulas were collapsed

into broader food categories for cross­country comparisons (Marriott et al., 2007). Tinned, powdered,

and any other specified animal milks were collapsed. In addition, all other liquids such as herbal teas, fruit juices, and sugar water (excluding unique countryspecific liquids) were collapsed into other liquids and

the 10 types of solid food groups into an any­solidfoods category (Marriott et al., 2007). Data were pooled from the 20 countries to provide a large sample

size and increase statistical power. Tables 15­27 and

15­28 present the percentage of mothers that were currently breastfeeding and separately had fed their

infants other liquids or solid food by age groups. Table

15­29 presents the pooled data summary for the study period. The current breastfeeding was consistent across countries for both age groups; the countries that reported the highest percentages of current breastfeeding for the 0 to 6 months old infants also reported the highest percentages in the 6 to12 month

old infants. Pooled data show that 96.6% of the 0 to 6

months old infants and 87.9% of the 6 to 12 month old

infants were breastfeeding. Feeding of other fluids was lowest in the 0 to 6 months infants, with the percentage feeding water the highest of this category. The
percentage of mothers feeding commercial infant

formulas was the lowest in most countries.

There are other older studies that analyze

ethnic and racial differences in breastfeeding practices.

Li and Grummer­Strawn (2002) investigated ethnic and

racial disparities in lactation in the United States using

data from the Third National Health and Nutrition

Examinations Survey (NHANES III) that was conducted between 1988­1994. NHANES II participants were ages 2 months and older. The data

were collected during a home interview from a parent

or a proxy respondent for the child (Li and GrummerStrawn, 2002). The sample population consisted of

children 12 to 71 months of age at time of interview.

The NHANES III response rate for children participating was approximately 94 percent (Li and Grummer­Strawn, 2002). Data for a total of 2,863 exclusively breastfed, 6,140 ever breastfed, and 6,123

continued breastfed children were included in the analysis (Li and Grummer­Strawn, 2002). The proportion of children ever­breastfed was 60% among non­Hispanic whites, 26% among non­Hispanic blacks,

and 54% among Mexican Americans. This number

decreased to 27, 9, and 23 respectively by 6 months.

Children fed exclusively human milk at 4 months was also significantly lower for blacks at 8.5%, compared to
22.6% for whites and 14.1% for Mexican­Americans.

The racial and ethnic differences in proportion of

children ever breastfed is presented in Table 15­30, the proportion of children who received any breast milk at
6 months are presented in Table 15­31, and the proportion of children exclusively breastfed at 4 months

is presented in Table 15­32.

Li and Grummer­Strawn (2002) noted that

there may have been some lag time between birth and

the time of the interview. This may have caused

misclassification if the predicator variables changed considerably between birth and the time of interview. Also, NHANES III did not collect information on maternal education. Instead, the educational level of

household head was used as a proxy. The advantages

of this study is that it is representative of the U.S.

children’s population.

Data from some older studies provide

historical information on breastfeeding practices in the

U.S. These data are provided here to show trends in the

U.S. population. In 1991, the National Academy of

Sciences (NAS) reported that the percentage of breast­feeding women has changed dramatically over the years (NAS, 1991). The Ross Products Division of

Abbott Laboratories conducted a large national mail

survey in 1995 to determine patterns of breastfeeding during the first 6 months of life. The Ross Laboratory

Mothers’s Survey was first developed in 1955 and has

been expanded to include many more infants. Before 1991, the survey was conducted on a quarterly basis,

and approximately 40,000 to 50,000 questionnaires

were mailed each quarter (Ryan, 1997). Beginning in 1991, the survey was conducted monthly; 35,000 questionnaires were mailed each month. Over time, the

response rate has been consistently in the range of 50 ±
5%. In 1989 and 1995, 196,000 and 720,000 questionnaires were mailed, respectively. Ryan (1997) reported rates of breast­feeding through 1995 and compared them with those in 1989.

The survey demonstrates increases in both the initiation of breast­feeding and continued breast­feeding

at 6 months of age between 1989 and 1991. Table 15­

33 presents the percent of breast­feeding in hospitals

and at 6 months of age by selected demographic

characteristics. In 1995, the incidence of breast­feeding

at birth and at 6 months for all infants was approximately 59.7% and 21.6 %, respectively. The largest increases in the initiation of breast­feeding

between 1989 and 1995 occurred among women who



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were Black, were less than 20 years of age, earned less

than $10,000 per year, had no more than a grade school education, were living in the South Atlantic region of

the U.S., had infants of low birth weight, were employed full time outside the home at the time they

received the survey, and participated in the Women, Infants, and Children program (WIC). In 1995, as in 1989, the initiation of breast­feeding was highest among

women who were greater than 35 years of age, earned

more than $25,000 per year, and were college educated,

did not participate in the WIC program, and were living

in the Mountain and Pacific regions of the U.S.

Data on the actual length of time that infants

continue to breast­feed beyond 5 or 6 months were limited (NAS, 1991). However, Maxwell and

Burmaster (1993) estimated that approximately 22
percent of infants under 1 year of age are breast­fed.

This estimate was based on a reanalysis of survey data

in Ryan et al. (1991) collected by Ross Laboratories (Maxwell and Burmaster, 1993). Studies have also

indicated that breast­feeding practices may differ

among ethnic and socioeconomic groups and among regions of the United States. More recently, the Ross

Products Division of Abbott Laboratories reported the

results of their ongoing "Ross Mothers Survey" in 2003 (Abbott 2003). The percentages of mothers who breast feed, based on ethnic background and demographic variables, are presented in Table 15­34. These data update the values presented in the NAS 1991 report.

15.6.2 Intake Rates Based on Nutritional Status
Information on differences in the quality and quantity of human milk on the basis of ethnic or socioeconomic characteristics of the population is

limited. Lönnerdal et al. (1976) studied human milk

volume and composition (nitrogen, lactose, proteins)

among underprivileged and privileged Ethiopian

mothers. No significant differences were observed

between the data for these two groups. Similar data

were observed for well­nourished Swedish mothers.

Lönnerdal et al. (1976) stated that these results indicate

that human milk quality and quantity are not affected by

maternal malnutrition. However, Brown et al. (1986a, b) noted that the lactational capacity and energy concentration of marginally­nourished women in Bangladesh were “modestly less than in better

nourished mothers.” Human milk intake rates for

infants of marginally­nourished women in this study

were 690 ± 122 g/day at 3 months, 722 ± 105 g/day at
6 months, and 719 ± 119 g/day at 9 months of age (Brown et al., 1986a). Brown et al. (1986a) observed

that human milk from women with larger measurements
of arm circumference and triceps skinfold thickness had higher concentrations of fat and energy than mothers

with less body fat. Positive correlations between

maternal weight and milk fat concentrations were also

observed. These results suggest that milk composition

may be affected by maternal nutritional status.

15.6.3 Frequency and Duration of Feeding
Hofvander et al. (1982) reported on the frequency of feeding among 25 bottle­fed and 25

breast­fed infants at ages 1, 2, and 3 months. The mean number of meals for these age groups was approximately 5 meals/day (Table 15­35). Neville et al. (1988) reported slightly higher mean feeding frequencies. The mean number of meals per day for exclusively breast­fed infants was 7.3 at ages 2 to 5

months and 8.2 at ages 2 weeks to 1 month. Neville et
al. (1988) reported that, for infants between the ages of

1 week and 5 months, the average duration of a breastfeeding session is 16­18 minutes. Buckley (2001) studied the breastfeeding
patterns, dietary intake, and growth measurement of

children who continued to breastfeed beyond 1 year of

age. The sample was 38 mother­child pairs living in the Washington, DC area. The criteria for inclusion in the study were that infants or their mothers had no hospitalization of either subject 3 months prior to the study and that the mother was currently breastfeeding a 1­year old or older child (Buckley, 2001). The participants were recruited through local medical

consultants and the La Leche League members. The

children selected as the final study subjects consisted of

22 boys and 16 girls with ages ranging from 12 to 43

month old. The data were collected using a 7­day breastfeeding diary. The frequency and length of breastfeeding varied with the age of the child (Buckley, 2001). The author noted a statistically significant

difference in the mean number of breastfeeding episodes per day and the average total minutes of breastfeeding between the 1, 2, and 3 year old groups. Table 15­36 provides the comparison of breastfeeding
patterns between age groups. An advantage of this study is that the frequency and duration data are based primarily on a 7­day diary and some dietary recall.

Limitations of the study are the small sample size and

that it is limited to one geographical area.



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15.7 REFERENCES FOR CHAPTER 15

AAP (2005) Breast feeding and the use of human milk. Policy Statement. Pediatrics. 115(2): 496­

5 0 6 . ava i l a b l e o n l i n e a t http://aappolicy.aappublications.org/cgi/cont ent/full/pediatrics;115/2/496

Abbott Laboratories (2003) Breastfeeding Trends

­ 2003. Ross Mothers Survey, Ross Products Division,Columbus, OH. Albernaz, E.;Victora, C.G.; Haisma, H.; Wright, A.; Coward, W.A. (2003) Lactation counseling

increases breast­feeding duration but not
breastmilk intake as measured by isotopic

methods. J Nutr. 133: 205–210. Arcus­Arth, A.; Krowech, G.; Zeise, L. (2005) Human

milk and Lipid Intake Distributions for assessing Cumulative Exposure and Risk. J Expos Anal Environ Epidemiol. 15: 357­365. Bonuck, K.A.; Trombley, M.; Freeman, K.; Mckee, D. (2005) Randomized, controlled trial of a prenatal and postnatal lactation consultant

intervention on duration and intensity of breastfeeding up to 12 months. Pediatrics.

116: 1413­1426. Brown, K.H.; Akhtar, N.A.; Robertson, A.D.; Ahmed,

M.G. (1986a) Lactational capacity of marginally nourished mothers: relationships

between maternal nutritional status and quantity and proximate composition of milk.

Pediatrics. 78: 909­919. Brown, K.H.; Robertson, A.D.; Akhtar, N.A. (1986b)

Lactational capacity of marginally nourished

mothers: infants' milk nutrient consumption

and patterns of growth. Pediatrics. 78: 920­

927. Butte, N.F.; Garza, C.; Smith, E.O.; Nichols, B.L. (1984) Human milk intake and growth in exclusively breast­fed infants. J Pediatr.

104:187­195. Butte, N.; Wong, W.; Hopkinson, J.; Smith E.; Ellis, J. (2000) Infant feeding mode affects early growth and body composition. Pediatrics. 106; 1355­1366. Buckley, K. (2001) Long­term breastfeeding:

nourishment or nurtance

. J Hum Lactat. 17(4):304­311.

CDC. (2007) Breastfeeding report card 2007. Breastfeeding practices­results from the

National Immunization Survey. Available on line at http://www.cdc.gov/breastfeeding/data Chen, A.; Rogan, W.J. (2004) Breastfeeding and the

risk of postneonatal death in the United States.

Pediatrics. 113: 435­439. Dewey, K.G.; Lönnerdal, B. (1983) Milk and nutrient

intake of breast­fed infants from 1 to 6

months:relation to growth and fatness. J

Pediatr Gastroenterol Nutr. 2:497­506. Dewey, K.G.; Heinig, J.; Nommsen, L.A.; Lönnerdal,

B. (1991a) Maternal versus infant factors

related to human milk intake and residual

volume: the DARLING study. Pediatrics.

87:829­837. Dewey, K.G.; Heinig, J.; Nommsen, L.; Lönnerdal, B. (1991b) Adequacy of energy intake among breast­fed infants in the DARLING study: relationships to growth, velocity, morbidity,

and activity levels. J Pediatr. 119:538­547. Dewey, K.G.; Peerson, J.M.; Heinig, M.J.; Nommsen, L.A.; Lonnerdal, B. (1992) Growth patterns
of breast­fed infants in affluent (United States)

and poor (Peru) communities: implications for timing of complementary feeding. Am J Clin

Nutr. 56: 1012­1018. Drewett, R.; Amatayakul, K.; Wongsawasdii, L.; Mangklabruks, A.; Ruckpaopunt, S.; Ruangyuttikarn, C.; Baum, D., Imong, S.; Jackson, D.; Woolridge, M. (1993) Nursing frequency and the energy intake from breast

milk and supplementary food in a rural Thai population: a longitudinal study. Eur J Clin

Nutr. 47: 880­891. Ferris, A.M.; Neubauer, S.H.; Bendel, R.B.; Green, K.W.; Ingardia, C.J.; Reece, E.A. (1993)

Perinatal lactation protocol and outcome in

mothers with and without insulin­dependent

diabetes mellitus. Am J Clin Nutr. 58: 43–48. Gonzalez­Cossio,T.; Habicht, J.P.; Rasmussen, K.M.; Delgado, H.L. (1998) Impact of food supplementation during lactation on infant

breast­milk intake and on the proportion of

infants exclusively breast­fed. J Nutr.

128:1692­1702. Hofvander, Y.; Hagman, U.; Hillervik, C.; Siolin, S. (1982) The amount of milk consumed by 1­3

months old breast or bottled­fed infants. Acta

Paediatrica Scand. 71: 953­958. Kent, J.C.; Mitoulas, L.R.; Cregan, M.D.; Ramsay, D.T.; Doherty, D.A.; Hartmann, P.E. (2006)

Volume and frequency of breastfeeding and



### Page Child­Specific Exposure Factors Handbook

15­20 September 2008

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

fat content of breast milk throughout the day.

Pediatrics. 117:387­395. Li, R.; Grummer­Strawn, L. (2002) Racial and ethnic disparities in breastfeeding among Unites

States infants: third national health and

nutrition examination survey, 1988­1994.

Birth. 29(4):251­257. Lönnerdal, B.; Forsum, E.; Gebre­Medhim, M.; Hombraes, L. (1976) Human milk composition in Ethiopian and Swedish

mothers: lactose, nitrogen, and protein

contents. Am J Clin Nutr. 29:1134­1141. Marriott, M.; Campbell, L.; Hirsch, E.; and Wilson, D. (2007) Preliminary data from demographic

and health surveys on infant feeding in 20 developing countries. J. Nutr. Vol 137: 518S523S. Maxwell, N.I.; Burmaster, D.E. (1993) A simulation

model to estimate a distribution of lipid intake

from human milk during the first year of life.

J Expo Anal Environ Epidemiol. 3:383­406. Mitoulas, L.; Kent, J.; Cox, D.; Owens, R.; Sherriff, J.; Hartman, P. (2002) Variation in fat, lactose,

and protein in human milk over 24 h and throughout the first year of lactation. Br J

Nutr. 88:29­37. Mitoulas, L.; Gurrin, L.; Doherty, D.; Sherriff, J.; Hartman, P. (2003) Infant intake of fatty

acids from human milk over the first year of

lactation. Br J Nutr. 90:979­986.

National Academy of Sciences (NAS). (1991)

Nutrition during lactation. Washington, DC.

National Academy Press. Neubauer, S. H.; Ferris, A.M.; Chase, C.G.; Fanelli J., Thompson, C.A.; Lammi­Keefe, C.J.; Clark,

R. M.; Jensen, R. G.; Bendel, R. B.; Green,

K. W. (1993) Delayed lactogenesis in women with insulin­dependent diabetes mellitus. Am

J Clin Nutr. 1993: 58:54–60. Neville, M.C.; Keller, R.; Seacat, J.; Lutes, V.; Neifert, M.; et al. (1988) Studies in human lactation:

milk volumes in lactating women during the

onset of lactation and full lactation. Am J Clin

Nutr. 48:1375­1386. Pao, E.M.; Hines, J.M.; Roche, A.F. (1980) Milk

intakes and feeding patterns of breast­fed

infants. J Am Diet Assoc. 77:540­545. Ryan, A.S.; Rush, D.; Krieger, F.W.; Lewandowski,

G.E. (1991) Recent declines in breastfeeding in the United States, 1984­1989. Pediatrics.

88:719­727. Ryan, A.S. (1997) The resurgence of breastfeeding in

the United States. Pediatrics. 99(4):e12. http://www.pediatrics.org/cqi/content/full/99

/4/e12. Salmenpera, L.; Perheentupa, J.,; Siimes, M.A. (1985) Exclusively breast­fed healthy infants grow slower than reference infants. Pediatr Res.

19: 307–312. Scanlon, K.S.; Grummer­Strawn, L.; Shealy, K.R.; Jefferds, M.E.; Chen, J.; Singleton, J.A.. (2007) Breastfeeding Trends and Updated

National Health Objectives for Exclusive Breastfeeding ­ United States, Birth Years

2000­2004. MMWR 56(30):760­763.

Stuff J.E.; Nichols B.L. (1989) Nutrient intake and growth performance of older infants fed

human milk. J Pediatr. 1989: 115:959–968. Wright, C.M.; Parkinson, K.; Scott, J. (2006) Breastfeeding in a UK urban context: who breastfeeds, for how long and does it matter? Public

Health Nutr. 9(6):686­691.



### Child­Specific Exposure Factors Handbook Page September 2008 15­21Table 15­7. Daily Intakes of Human Milk

Age Number of Infants Intake

Mean ± SD

a (mL/day) Intake Range (mL/day)

Completely

1 month

3 months

6 months Breast­fed

11

2

1 600 ± 159

833

682 426 ­ 989

645 ­ 1,000

616 ­ 786

Partially Breast­fed

1 month

3 months

6 months

9 months 4

11

6

3 485 ± 79

467 ± 100

395 ± 175

<554 398

242

147

451 ­ 655

­ 698

­ 684

­ 732

a Data expressed as mean ± standard deviation.

Source: Pao et al., 1980.

Table 15­8. Human Milk Intakes for Infants Aged 1 to 6 Months

Age Number of Infants Intake

Mean ± SD Intake Range (mL/day) (mL/day)

1 month 16 673 ± 192 341­1,003

2 months 19 756 ± 170 449­1,055

3 months 16 782 ± 172 492­1,053

4 months 13 810 ± 142 593­1,045

5 months 11 805 ± 117 554­1,045

6 months 11 896 ± 122 675­1,096

Source: Dewey and Lönnerdal, 1983. 

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Table 15­9. Human Milk Intake Among Exclusively Breast­fed Infants During the First 4 Months of Life

Age Number of

Infants Intake (mL/day) a

Mean ± SD Intake (mL/kg­day) a

Mean ± SD Feedings/Day Body Weight b

(kg)

1 month 37 729 ± 126 154 ± 23 8.3 ± 1.9 4.7

2 months 40 704 ± 127 125 ± 18 7.2 ±1.9 5.6

3 months 37 702 ± 111 114 ± 19 6.8 ± 1.9 6.2

4 months 41 718 ± 124 108 ± 17 6.7 ± 1.8 6.7

a Values reported by the author in units of g/day and g/kg­day were converted to units of mL/day and mL/kg­day by dividing by 1.03 g/mL (density of human milk).

b Calculated by dividing human milk intake (g/day) by human milk intake (g/kg­day).

SD = Standard deviation.

Source: Butte et al., 1984.



### Child­Specific Exposure Factors Handbook Page September 2008 15­23Table 15­10. Human Milk Intake During a 24­hour Period

Age (days) Number of Infants Intake ( mL/day) a Intake by Age Category (mL/day) a,c Mean ± SD Range

1 6 43 ± 68 b ­30­145

2 9 177 ± 83 43­345

3 10 360 ± 149 203­668

4 10 438 ± 171 159­674

5 11 483 ± 125 314­715

6 9 493 ± 162 306­836

7

8 7

8 556 ± 162

564 ± 154 394­817

398­896 511 ± 220

9 9 563 ± 74 456­699

10 9 569 ± 128 355­841

11 8 597 ± 163 386­907

14 9 634 ± 150 404­895

21 10 632 ± 82 538­763

28 13 748 ± 174 481­1,111

35 12 649 ± 114 451­903

42

49 12

10 690 ± 108

688 ± 112 538­870

543­895 679 ± 105

56 12 674 ± 95 540­834

90 10 713 ± 111 595­915 713 ± 111

120 12 690 ± 97 553­822 690 ± 97

150 12 814 ± 130 668­1,139 814 ± 130

180 13 744 ± 117 493­909 744 ± 117

210 12 700 ± 150 472­935 700 ± 150

240 9 604 ± 204 280­973 604 ± 204

270 12 600 ± 214 217­846 600 ± 214

300 11 535 ± 227 125­868 535 ± 227

330 8 538 ± 233 117­835 538 ± 233

360 8 391 ± 243 63­748 391 ± 243

a Values reported by the author in units of g/day were converted to units of mL/day by dividing by 1.03 g/mL

b (density of human milk). Negative value due to insensible weight loss correction.

c Multiple data sets were combined by producing simulated data sets fitting the known mean and SD for each

age, compositing the data sets to correspond to age groups of 0 to <1 month and 1 to <2 months, and calculating new means and SD’s on the composited data.

SD = Standard deviation.

Source: Neville et al., 1988. 

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Table 15­11. Human Milk Intake Estimated by the Darling Study

Age Number of Infants Intake (mL/day)

Mean ± SD

3 months

6 months

9 months

12 months 73

60

50

42 788 ± 129

747 ± 166

627 ± 211

435 ± 244

a Values reported by the author in units of g/day were converted to units of mL/day by dividing by

1.03 g/mL (density of human milk).

SD = Standard deviation.

Source: Dewey et al., 1991b.

Table 15­12. Mean Breastfed Infants Characteristics a

Boys (N=14) Girls (N=26)

Ethnicity (White, Black, Hispanic, Asian) (N) 10/1/2/1

Duration of Breastfeeding (days) 315 ± 152

Duration of Formula Feeding (days) 184 ± 153

Age at Introduction of Formula (months) 6.2 ± 2.9

Age at Introduction of Solids (months) 5.0 ± 1.5

Age at Introduction of Cow’s Milk (months) 13.1 ± 3.1 21/1/3/1

362 ± 190

105 ± 121

5.2 ± 2.3

5.0 ± 0.09

12.5 ± 3.8

a Mean ± standard deviation.

N  = Number of infants.

Source: Butte et al., 2000.



### Child­Specific Exposure Factors Handbook Page September 2008 15­25Table 15­13. Mean Human Milk Intake of Breastfed Infants (mL/day) a

Age Group Boys Girls

3 months 790 ± 172 (N=14) 694 ± 108 (N=26)

6 months 576 ± 266 (N=12) 678 ±250 (N=18)

12 months 586 ±286 (N=2) 370± 260 (N=11)

24 months

­ ­

a

N 3­day average; values

1.03 g/mL (density of

= Number of infants. reported by the author in units

human milk); mean ± standard of g/day were deviation. converted to units of mL/day by dividing by

Source: Butte et al., 2000.

Table 15­14. Feeding Practices by Percent of Infants

Age

Infants 3

months 6

months 9

months 12

months 18

months 24

months

Percentage

Infants Still Breastfed 100 80 58 38 25 5

Breastfed Infants Given Formula 0 40 48 30 10 2

Formula­fed Infants Given Breast Milk 100 100 94 47 6 0

Use of Cow’s Milk for Breastfed Infants

­ ­ 8 65 82 88

Use of Cow’s Milk for Formula­fed Infants

­ ­ 28 67 89 92

Source: Butte et al., 2000. 

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15­26 September 2008Table 15­15. Body Weight of Breastfed a Infants

Age Weight (kg)

Boys Girls

0.5 months 3.9 ± 0.4 (n=14) 3.7 ± 0.5 (n=19)

3 months 6.4 ± 0.6 (n=14) 6.0 ± 0.6 (n=19)

6 months 8.1 ± 0.8 (n=14) 7.5 ± 0.6 (n=18)

9 months 9.3 ± 1.0 (n=14) 8.4 ± 0.6 (n=19)

12 months 10.1 ± 1.1 (n=14) 9.2 ± 0.7 (n=19)

18 months 11.6 ± 1.2 (n=14) 10.7 ± 1.0 (n=19)

24 months 12.7 ± 1.3 (n=12) 11.8 ± 1.1 (n=19)

a

N Mean ± standard deviation.

= Number of infants.

Source: Butte et al., 2000. 

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Child­Specific Exposure Factors Handbook Page September 2008 15­27Table 15­16. AAP Dataset Milk Intake Rates at Different Ages

Age Mean (mL/kg day) a SD (mL/kg day) a CV Skewness

b Statistic N

7 days

14 days

30 days

60 days

90 days

120 days

150 days

180 days

210 days

270 days

360 days 143

156

150

144

127

112

100

101

75

72

47 37

40

24

22

18

18

21

20

25

23

27 0.26

0.26

0.16

0.15

0.14

0.16

0.21

0.20

0.33

0.32

0.57 0.598

­1.39

0.905

0.433

­0.168

0.696

­1.077

­1.860

­0.844

­0.184

0.874 10

9

25

25

108

57

26

39

8

57

42

a

b

SD

CV

N

Source: Values reported by the author in units of g/kg­day were converted dividing by 1.03 g/mL (density of human milk).

Statistic/SE: ­2 < Statistic/SE < +2 suggests a normal distribution

= Standard deviation.

= Coefficient of variation.

= Number of infants.

Arcus­Arth et al., 2005. to units of mL/kg­day by 

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15­28 September 2008Table 15­17. Average Daily Human Milk Intake (mL/kg day) a

Averaging Period Mean (SD) Population Percentile

AAP 0 to 6 months

Method 1

Method 2

AAP 0 to 12 months

Method 1

Method 2

EBF 0 to 12 months

General Pop.

0 to 6 months

0 to 12 months 126 (21)

123 (7)

98 (22)

99 (5)

110 (21)

79

51 5 10 25 50 75 90 95 99

92

112

61

90

75

0

0 99

114

69

92

83

0

0 112

118

83

95

95

24

12 126

123

98

99

110

92

49 140

127

113

102

124

123

85 152

131

127

105

137

141

108 160

133

135

107

144

152

119 174

138

150

110

159

170

138

a

Source: Values reported by the author in units

1.03 g/mL (density of human milk).

Arcus­Arth et al., 2005. of g/kg­day were converted to units of mL/kg­day by dividing by 

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Ta

ble 15­18. Lipid Content of Human Milk and Estimated Lipid Intake Among Exclusively Breast­fed Infants

Age (months) Number

of

Observations Lipid Content (mg/g)

Mean ± SD Lipid

Content % a Lipid

Intake (mL/day) b

Mean ± SD Lipid

Intake (mL/kg­day) b

Mean ± SD

1

2

3

4 37

40

37

41 36.2 ± 7.5

34.4 ± 6.8

32.2 ± 7.8

34.8 ± 10.8 3.6

3.4

3.2

3.5 27 ± 8

24 ± 7

23 ± 7

25 ± 8 5.7 ± 1.7

4.3 ± 1.2

3.7 ± 1.2

3.7 ± 1.3

a Percents calculated from lipid content reported in mg/g.

b Values reported by the author in units of g/day and g/kg­day were converted to units of mL/day and mL/kg­day by dividing by 1.03 g/mL (density of human milk).

Source: Butte et al., 1984.



### Child­Specific Exposure Factors Handbook Page September 2008 15­29Table 15­19. Human Milk Production and Composition Over the First 12 Months of Lactation a

Volume, per Breast Fat Lactose Protein Energy Age Group (mL/24h) (g/L) (g/L) (g/L) (kJ/mL)

(months)
Mean SE N Mean SE N Mean SE N Mean SE N Mean SE N

1 416 24 34 39.9 1.4 34 59.7 0.8 18 10.5 0.4 18 2.7 0.06 18

2 408 23 34 35.2 1.4 34 60.4 1.1 18 9.6 0.4 18 2.5 0.06 18

4 421 20 34 35.4 1.4 32 62.6 1.3 16 9.3 0.4 18 2.6 0.09 16

6 413 25 30 37.3 1.4 28 62.5 1.7 16 8.0 0.4 16 2.6 0.09 16

9 354 47 12 40.7 1.7 12 62.8 1.5 12 8.3 0.5 12 2.8 0.09 12

12 252 51 10 40.9 3.3 10 61.4 2.9 10 8.3 0.6 10 2.8 0.14 10

1 to 12 399 11 154 37.4 0.6 150 61.4 0.6 90 9.2 0.2 92 2.7 0.04 90

a Infants were completely breast­fed to 4 months and complementary solid food was introduced between 4­6

months.

SE = Standard error. N = Number of infants.

Source: Mitoulas et al., 2002. 

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15­30 September 2008Table 15­20. Changes in Volume of Human
First Year Milk Produced

a of Lactation and Milk Fat Content Over the

Age Group

(months) Volume, Left Breast (mL/day) Volume, Right Breast (mL/day) Fat, Left Breast (g/L) Fat, Right Breast (g/L)

N Mean SE Mean SE Mean SE Mean SE

1 5 338 52 475 69 38 1.5 38 2.6

2 5 364 52 427 42 31 2.2 30 2.9

4 5 430 51 482 58 32 3.3 29 2.6

6 5 373 75 437 56 33 2.5 33 2.5

9 5 312 65 365 94 43 2.2 38 3.3

12 5 203 69 302 85 40 4.8 42 5.0

1 to 12 30 337 26 414 28 36 1.4 35 1.5

Statistical significance: P NS NS 0.004 0.008

a

SE

NS

P Infants were completely breast­fed

months.

= Standard error.

= No statistical difference.

= Probability. to 4 months, and complementary solid food was introduced between 4­6

Source: Mitoulas et al., 2003. 

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Child­Specific Exposure Factors Handbook Page September 2008 15­31Table 15­21. Changes in Fatty Acid Composition of Human Milk Over the First Year of Lactation (g/100 g total fatty acids)

Fatty Acid

Medium­chain

Saturated

Odd­chain

Saturated

Long­chain

Saturated

Monounsaturated

TransPolyunsaturated 1 month

Mean SE

14.2 0.4

0.9 0.01

34.1 0.3

37.5 0.2

2.0 0.08

12.7 0.2 2 months

Mean SE

13.9 0.6

0.9 0.02

33.7 0.3

33.7 0.4

2.2 0.1

9.5 0.2 4 months

Mean SE

12.0 0.5

0.8 0.02

32.8 0.3

38.6 0.5

2.2 0.09

11.8 0.4 6 months

Mean SE

11.5 0.2

0.8 0.03

31.8 0.6

37.5 0.5

4.6 0.02

13.4 0.6 9 months

Mean SE

14.1 0.3

0.8 0.02

31.4 0.6

37.3 0.5

1.7 0.2

8.0 0.1 12 months

Mean SE

17.0 0.4

0.8 0.02

33.9 0.6

33.0 0.5

1.8 0.09

6.7 0.03

SE

Source: = Standard

Mitoulas et error.

al., 2003.

Table 15­22. Comparison of Lipid Content Assumptions (mL/kg­day) a

Lipid Content

Calculation Used in Mean Population Percentile

Measured b Lipid Content 3.6 5 10 25 50 75 90 95 99

2.0 2.3 2.9 3.6 4.3 4.9 5.2 5.9

4% Lipid Content c 3.9 2.5 2.8 3.3 3.8 4.4 4.9 5.2 5.8

a

b

c Values reported by the author in units of g/kg­day were converted to units g/mL (density of human milk). Lipid intake derived from lipid content and milk intake measurements. Lipid intake derived using 4% lipid content value and milk intake. of mL/kg­day by dividing by 1.03

Source: Arcus­Arth et al., 2005. 

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15­32 September 2008Table 15­23. Distribution of Average Daily Lipid Intake (mL/kg day) assuming 4% Milk Lipid Content

Mean Population Percentile

AAP Infants 0 to 12 months 3.9 5 10 25 50 75 90 95 99

2.4 2.8 3.3 3.9 4.5 5.1 5.4 6.0

a Values reported by the author in g/mL (density of human milk). units of g/kg­day were converted to units of mL/kg­day by dividing by 1.03

Source: Arcus­Arth et al., 2005.

Table 15­24. Predicted Lipid Intakes for Breast­fed Infants Under 12 Months of Age

Statistic Value

Number of Observations in Simulation

Minimum Lipid Intake

Maximum Lipid Intake

Arithmetic Mean Lipid Intake

Standard Deviation Lipid Intake 1,113

a 1.0 mL/day

a 51.0 mL/day

a 26.0 mL/day

a 7.2 mL/day

a Values reported by the author in (density of human milk). units of g/day were converted to units of mL/day by dividing by 1.03 g/mL

Source: Maxwell and Burmaster, 1993. 

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Child­Specific Exposure Factors Handbook Page September 2008 15­33Table 15­25

. Socio­economic Characteristics of Exclusively Breastfed Infants Born in 2004

Percent of Exclusive Breastfeeding Infants Through 3 and 6 Months

3 months 6 months

Characteristic % 95% CI % 95% CI

U.S. Overall (N=17,654) 30.5 29.4­31.6 11.3 10.5­12.1

Infant Sex

Male 30.7 29.1­32.3 10.8 9.8­11.8

a Female 30.3 28.7­31.9 11.7 10.5­12.9

Race/Ethnicity (child)

Hispanic 30.8 28.3­33.3 11.5 9.7­13.3

a White, non­Hispanic 33.0 31.6­34.4 11.8 10.9­12.7

Black, non­Hispanic b 19.8 17.0­22.6 b 7.3 5.5­9.1

Asian, non­Hispanic 30.6 25.0­36.2 14.5 10.0­19.0

Other 29.3 24.9­33.7 12.2 9.2­15.2

Maternal Age (years)

<20 b 16.8 10.3­23.3 b 6.1 1.5­10.7

20 to 29 b 26.2 24.4­28.0 b 8.4 7.3­9.5

$ 30 a 34.6 33.2­36.0 13.8 12.7­14.9

Household Head Education

<High school b 23.9 21.0­26.8 b 9.1 7.1­11.1

High school b 22.9 20.9­24.9 b 8.2 7.0­9.4

Some college b 32.8 30.3­35.3 b 12.3 10.2­14.4

a College graduate 41.5 39.7­43.3 15.4 14.1­16.7

Marital Status

Married a 35.4 34.0­36.8 13.4 12.4­14.4

Unmarried b 18.8 16.9­20.7 b 6.1 5.0­7.2

Residence

a MSA, center city 30.7 29.0­32.4 11.7 10.5­12.9

MSA, non­center city 32.8 30.9­34.7 12.1 10.8­13.4

Non­MSA b 23.9 21.8­26.0 b 8.2 6.9­9.5

Poverty income ratio (%)

<100 b 23.9 21.6­26.2 b 8.3 6.9­9.7

100 to <184 b 26.6 23.8­29.4 b 8.9 7.2­10.6

185 to <349 b 33.2 30.9­35.5 b 11.8 10.3­13.3

$ 350 a 37.7 35.7­39.7 14.0 12.6­15.4

a

b

N Referent group. p<0.05 by chi­square test, compared

= Number of infants. with referent group.

MSA = Metropolitan statistical area.

Source: Scanlon et al., 2007. 

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

Page Child­Specific Exposure Factors Handbook

15­34 September 2008

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

Table15­26. Geographic­specific Breastfeeding Percent Rates Among Children Born in 2004 a

State N Ever
Breastfed Breastfed at 6

Months Breastfed at 12

Months Exclusive Breastfeeding Through 3 Months Exclusive Breastfeeding Through 6 Months

U.S. National 17,654 73.8 41.5 20.9 30.5 11.3

Alabama 310 52.1 25.4 11.5 19.3 4.9

Alaska 217 84.8 60.9 31.8 47.2 24.3

Arizona 543 83.5 46.5 23.4 38.8 14.3

Arkansas 200 59.2 23.2 8.5 15.8 6.2

California 1,702 83.8 52.9 30.4 38.7 17.4

Colorado 249 85.9 42 23.6 36.2 10.8

Connecticut 249 79.5 44.6 23.7 35.6 10.1

Delaware 213 63.6 35.7 14.6 26.3 11.4

Dist of Columbia 292 68.0 40.0 21.4 27.8 9.8

Florida 955 77.9 37.5 15.6 27.8 9.1 Georgia 582 68.2 38.0 16.8 25.6 11

Hawaii 221 81 50.5 35.5 37.8 15.8

Idaho 183 85.9 49.0 22.6 38.7 10.3

Illinois 561 72.5 40.9 17.6 31.6 10

Indiana 472 64.7 34.6 18.0 28.3 10.4

Iowa 193 74.2 44.9 20.0 37.6 11.6

Kansas 480 74.4 42.2 16.9 30.0 9.2 Kentucky 245 59.1 26.4 14.4 25.3 7.5
Louisiana 429 50.7 19.2 8.3 15.2 2.8

Maine 203 76.3 46.6 27.6 42.1 15.9 Maryland 512 71.0 40.2 21.2 32.1 8.6

Massachusetts 469 72.4 42.1 19.0 32.7 11.9 Michigan 604 63.4 36.4 18.6 27.4 8.3

Minnesota 202 80.9 46.5 23.8 33.9 16.1 Mississippi 287 50.2 23.3 8.2 19.0 8

Missouri 327 67.3 32.5 15.8 26.6 7.4

Montana 232 87.7 53.8 28.8 50.9 18.3

Nebraska 228 79.3 47.6 21.8 31.7 9.8

Nevada 281 79.7 45.6 21.9 31.9 10.3

New Hampshire 228 73.7 48.7 27.5 34.3 13.6

New Jersey 631 69.8 45.1 19.4 27.0 11.8

New Mexico 420 80.7 41.2 21.1 32.9 14.3

New York 533 73.8 50.0 26.9 26.0 11.4

North Carolina 220 72.0 34.2 18.3 23.0 6.9

North Dakota 285 73.1 45.1 19.5 39.4 15.4

Ohio 617 59.6 33.3 12.9 27.2 9.8

Oklahoma 280 67.1 29.6 12.7 23.0 10.6 Oregon 191 88.3 56.4 33.5 41.5 19.9 Pennsylvania 757 66.6 35.2 16.8 27.1 8

Rhode Island 291 69.1 31.2 14.0 31.2 9.5

South Carolina 314 67.4 30.0 11.1 26.6 5.4

South Dakota 315 71.1 40.5 23.4 32.2 12.2

Tennessee 671 71.2 32.6 16.6 26.7 11.9

Texas 1,439 75.4 37.3 18.7 25.2 7.1

Utah 190 84.5 55.6 28.1 39.8 10.2

Vermont 190 85.2 55.3 34.1 47.3 15.9 Virginia 259 79.1 49.8 25.6 32.6 13.4 Washington 615 88.4 56.6 32.3 49.6 22.5

West Virginia 224 59.3 26.8 14.0 21.3 5.2

Wisconsin 478 72.1 39.6 19.0 32.5 13.4 Wyoming 246 80.5 42.9 18.5 36.2 11.4

a Exclusive breastfeeding information is from the 2006 NIS survey data only and is defined as ONLY breast milk­ No solids, no water, no other liquids. Sample sizes appearing in the NIS breastfeeding tables are slightly smaller than the

numbers published in other NIS publications due to the fact that in the DNPA breastfeeding analyses, the sample was limited to records with valid responses to the breastfeeding questions.

N = Number of infants.

Source: CDC, 2007.



### Child­Specific Exposure Factors Handbook Page September 2008 15­35Table 15­27. Percentage of Mothers in Developing Countries by Feeding Practices for Infants 0 to 6 Months Old a

Country Breastfeeding Water Milk Formula Other Liquids Solid Foods

Ethiopia

Ghana

Kenya

Malarwi

Nambia

Nigeria

Uganda

Zamibia

Zimbabwe

Armenia

Egypt

Jordan

Bangladesh

Cambodia

India

Indonesia

Nepal

Philippines

Vietnam

Kazakhstan

Pooled 98.8

99.6

99.7

100

95.3

99.1

98.7

99.6

100

86.1

95.5

92.4

99.6

98.9

98.1

92.8

100

80.5

98.7

94.4

96.6 26.3

41.9

60

46

65.4

78.2

15.1

52.6

63.9

62.7

22.9

58.5

30.2

87.9

40.2

37

23.3

53.4

45.9

53.7

45.9 19

6.7

35.1

1.4

0

9.2

20.3

2.1

1.6

22.9

11.1

3

13.6

2.1

21.2

0.7

12.3

4.4

16.9

21.4

11.9 0

3.5

4.8

1.7

0

12.7

1.5

2.7

3.2

13.1

4.3

25.1

5.3

3.3

0

24.2

0

30

0.8

8.2

9 10.8

4.3

35.9

5.2

17.9

17.9

10.3

6.7

9

48.1

27.6

13.8

19.7

6.7

7.1

8.7

2.8

12.4

8.9

37.4

15.1 5.3

15.6

46.3

42.3

33.4

18.5

11.4

31.2

43.7

23.9

13.2

20.2

20.3

16.6

6.5

43

9.3

16.8

18.7

15.4

21.9

a

Source: Percentage of mothers liquid or solid food in

Marriott et al., 2007. who stated

the past 24 that they

hours by currently breast­feed and separately had fed
country for infants age 0 to 6 months old. their infants 4 categories of 

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

Page Child­Specific Exposure Factors Handbook

15­36 September 2008

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

Table 15­28. Percentage of Mothers in Developing Countries by Feeding Practices for Infants 6 to 12 Months Old a

Country Breastfeeding Water Milk Formula Other Liquids Solid Foods

Ethiopia 99.4 69.2 37.6 0 23.9 54.7

Ghana 99.3 88.8 14.6 9.6 23.9 71.1

Kenya 96.5 77.7 58.7 6 56.4 89.6

Malarwi 99.4 93.5 5.9 3.2 31.2 94.9

Nambia 78.7 91.9 0 0 42.7 79.5

Nigeria 97.8 91.6 14.4 13.4 27.4 70.4

Uganda 97.4 65.9 32.1 1.6 56.2 82.1

Zamibia 99.5 91.7 8.2 5 25.9 90.2

Zimbabwe 96.7 92.5 8.7 2.4 49.9 94.8

Armenia 53.4 91.1 56.9 11.6 85.3 88.1

Egypt 89.1 85.9 36.8 16.7 48.5 75.7

Jordan 65.7 99.3 24.3 28.8 57.7 94.9

Bangladesh 96.2 87.7 29.8 10.1 21.9 65.2

Cambodia 94.4 97.5 3.7 6.7 29 81

India 94.9 81.4 45 0 25.2 44.1

Indonesia 84.8 85.4 4.9 38.8 35.4 87.9

Nepal 98.8 84.3 32 0 15.8 71.5

Philippines 64.4 95.1 12.2 47.1 31 88

Vietnam 93.2 95 36.1 5.3 37.9 85.8

Kazakhstan 81.2 74.3 85.4 11.4 91.8 85.9

Pooled 87.9 87.4 29.6 15.1 41.6 80.1

a Percentage of mothers who stated that they currently breast­feed and separately had fed their infants 4 categories of liquid or solid food in the past 24 hours by country for infants age 6 to 12 months old.

Source: Marriott et al., 2007.



### Child­Specific Exposure Factors Handbook Page September 2008 15­37Table 15­29. Population

Selected Feeding Weighted Averages of Mothers Who Reported

Practices During the Previous 24­hours

Feeding Practices Infant Age

0 to 6 months 6 to 12 months

Percentage (weighted N)

Current Breast­feeding 96.6 (22,781) 87.9 (18,944)

Gave Infant:

Water 45.9 (10,767) 87.4 (18,6663)

Tinned, Powdered, or Other Milk 11.9 (2,769) 29.6 (6,283)

Commercial Formula 9.0 (1,261) 15.1 (1,911)

Other Liquids 15.1 (3,531) 41.6 (8,902)

Any Solid Food 21.9 (5,131) 80.1 (17,119)

N = Number of infants.

Source: Marriott et al., 2007. 

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

Page Child­Specific Exposure Factors Handbook

15­38 September 2008Table 15­30. Racial and Ethnic Differences in Proportion of Children Ever Breastfed, NHANES III (1988­1994)

Absolute Difference (%,SE) a

Non­Hispanic White Non­Hispanic Black Mexican American White vs Black White vs Mexican

American

Characteristic N % (SE) N % (SE) N % (SE) % (SE) % (SE)

All infants 1,869 60.3 2.0 1,845 25.5 1.4 2,118 54.4 1.9 34.8 (2.0) b 6.0 (2.3) a

Infant sex

Male 901 60.4 2.6 913 24.4 1.6 1,033 53.8 1.8 35.9 (2.9) b 6.6 (2.8) a

Female 968 60.3 2.3 932 26.7 1.9 1,085 54.9 2.9 33.7 (2.6) b 5.4 (3.4) c

Infant birth weight (g)

<2,500 118 40.1 5.3 221 14.9 2.6 165 34.1 3.9 25.1 (5.8) b 5.9 (6.4) c

1,738 62.1 2.1 1,584 26.8 1.6 1,838 55.7 2.0 35.3 (2.1) b 6.4 (2.5) a $ 2,500

Maternal age (years)

<20 175 33.7 4.4 380 13.1 2.1 381 43.7 3.0 20.6 (4.8) b ­10 (5.1) c

20 to 24 464 48.3 3.0 559 22.0 2.0 649 54.8 2.6 26.4 (3.7) b ­6.4 (4.2) c

25 to 29 651 65.4 2.2 504 30.6 2.5 624 56.9 3.3 34.8 (3.1) b 8.6 (4.0) a

575 71.9 2.7 391 36.1 2.3 454 59.6 2.8 35.8 (3.4) b 12.3 (3.4) b $ 30

Household head education

<High school 313 32.3 4.0 583 14.7 2.5 1,262 51.0 2.6 17.6 (5.0) b ­18.8 (4.8) b

High school 623 52.6 2.8 773 21.9 2.0 479 51.4 3.4 30.7 (3.2) b 1.2 (4.1) c

Some college 397 63.8 2.3 317 37.2 3.5 226 68.0 5.2 26.6 (3.7) b ­4.1 (5.6) c

College graduate 505 83.0 2.4 139 54.4 4.9 74 78.3 7.4 28.6 (5.3) b 4.6 (7.6) c

Smoking during pregnancy

Yes 526 39.8 3.0 403 18.0 2.1 198 31.2 3.9 21.8 (3.7) b 8.6 (4.7) c

No 1,334 68.2 2.0 1,429 27.8 1.7 1,917 56.7 1.9 40.4 (2.1) b 11.5 (2.5) b

Maternal body mass index

<25.0 1,331 64.9 2.0 872 26.8 2.0 961 54.1 2.5 38.0 (2.5) b 10.8 (2.7) b

25.0 to 29.9 283 50.9 3.4 484 24.1 3.2 534 57.8 2.1 26.8 (4.5) b ­6.8 (4.1) c

204 48.6 4.8 415 24.3 2.7 359 47.1 4.4 24.3 (5.3) b 1.5 (6.1) c $ 30

Residence

Metropolitan 762 67.2 3.0 943 32.0 1.9 1,384 56.1 2.0 35.3 (2.6) b 11.2 (2.9) b

Rural 1,107 54.9 3.1 902 18.3 1.9 734 51.3 3.1 36.6 (2.7) b 3.6 (4.0) c
Region

Northeast 317 51.6 4.6 258 34.2 4.4 12 74.1 10.4 17.3 (3.6) b ­22.5 (14.5) c

Midwest 556 61.7 2.3 346 26.5 2.4 170 51.5 3.7 35.2 (3.3) b 10.2 (5.0) a

South 748 52.7 2.7 1,074 19.4 2.0 694 42.7 3.5 33.3 (2.7) b 10 (4.6) a

West 248 82.4 3.9 167 45.1 5.1 1,242 59.1 2.2 37.3 (7.1) b 23.4 (3.3) b

Poverty income ratio (%)

<100 257 38.5 4.2 905 18.2 1.9 986 48.2 2.8 20.3 (4.4) b ­9.6 (4.7) a

100 to <185 388 55.7 2.6 391 26.8 2.1 490 54.1 3.4 28.9 (3.5) b 1.5 (4.2) c

185 to <350 672 61.9 2.5 294 32.0 3.0 288 64.7 4.7 30.0 (3.7) b 2.8 (5.3) c

444 77.0 2.5 105 58.1 5.1 74 71.9 9.0 19.0 (5.6) b 5.2 (9.0) c $ 350

Unknown 108 44.7 7.1 150 25.5 3.9 280 59.5 2.8 19.2 (7.9) a ­14.8 (7.9) c

a p <0.05.

b p <0.01.

c No statistical difference.

N = Number of infants.

SE = Standard error. Source: Li and Grummer­Strawn, 2002. 

### Child­Specific Exposure Factors Handbook

Chapter 15 ­ Human Milk Intake

Child­Specific Exposure Factors Handbook Page September 2008 15­39Table 15­31.

Received Racial and Ethnic Any Human Milk Differences

at 6 Months in Proportion of Children Who (NHANES III, 1988­1994)

Non­Hispanic White Non­Hispanic Black Mexican American Absolute Difference (%,SE)

White vs Black White vs Mexican

American

Characteristic N % (SE) No. % (SE) N % (SE) % (SE) % (SE)

All infants 1863 26.8 1.6 1,842 8.5 0.9 2,112 23.1 1.4 18.3 (1.7) b 3.7 (2.1) c

Infant sex

Male 900 27.6 2.3 912 8.5 1.1 1,029 22.3 1.6 19.1 (2.6) b 5.2 (2.6) a

Female 963 26.1 1.8 930 8.6 1.1 1,083 24.0 2.0 17.5 (2.1) a 2.1 (2.7) c

Infant birth weight (g)

<2,500 118 10.9 3.1 221 4.2 1.8 165 15.2 4.7 6.7 (3.3) a ­4.3 (5.7) c

$ 2,500 1,733 28.3 1.8 1,581 9.0 0.9 1,832 23.1 1.7 19.3 (1.8) b 5.2 (2.3) a

Maternal age (years)

<20 174 10.2 2.9 380 4.7 1.4 380 11.6 1.7 5.5 (3.0) c ­1.3 (3.8) c

20 to 24 461 13.4 2.4 559 7.5 1.1 646 23.8 2.4 5.9 (2.5) a ­10.4 (3.3) b

25 to 29 651 29.3 2.6 503 10.9 2.0 624 24.6 2.6 18.4 (3.5) b 4.8 (3.6) c

$ 30 573 39.0 2.6 389 10.7 1.7 452 30.0 2.8 28.4 (3.3) b 9.0 (3.6) a

Household head education

<High school 312 14.6 3.8 582 4.4 1.2 1,258 20.7 1.4 10.2 (4.5) a ­6.2 (4.1) c

High school 622 19.9 1.7 771 5.0 1.0 478 22.4 2.5 14.9 (2.0) b 2.5 (3.1) c

Some college 396 26.8 2.4 317 16.6 2.5 225 28.4 5.3 10.2 (3.5) b ­1.6 (6.1) c

College graduate 502 42.2 2.9 139 21.1 3.2 74 45.5 7.3 21.1 (5.2) b 3.4 (7.6) c

Smoking during pregnancy

Yes 524 11.3 1.5 402 4.3 1.1 198 9.3 2.2 7.0 (1.9) b 2.1 (2.7) c

No 1,331 32.7 2.1 1,427 9.8 1.1 1,911 24.5 1.5 22.9 (2.3) b 8.1 (2.6) b

Maternal body mass index

<25.0 1,326 29.6 1.8 871 8.9 1.2 959 21.9 2.1 20.7 (2.1) b 7.8 (2.7) b

25.0 to 29.9 282 19.0 2.4 482 8.2 1.9 534 26.4 1.9 10.8 (3.2) b 7.4 (3.0) a

$ 30 204 20.4 4.1 415 7.3 1.6 357 17.2 3.0 13.1 (4.4) b 3.3 (5.2) c

Residence

Metropolitan 760 29.7 2.5 941 11.8 1.3 1,378 23.5 1.7 17.9 (2.4) b 6.1 (3.1) c

Rural 1,103 24.6 2.4 901 4.9 0.9 734 22.5 2.8 19.7 (2.2) b 2.2 (3.4) c
Region

Northeast 316 21.0 2.2 258 9.7 1.8 12 43.6 16.0 11.3 (1.8) b ­22.6 (16.5) c

Midwest 553 28.8 2.1 344 9.8 2.4 170 18.2 4.7 19.0 (3.7) b 10.6 (6.2) c

South 746 20.1 2.8 1,073 5.9 1.0 693 17.2 2.8 14.3 (2.8) b 2.9 (4.2) c

West 248 42.7 4.7 167 19.3 3.3 1,237 25.9 1.4 23.4 (5.3) b 16.8 (5.1) b

Poverty income ratio (%)

100 to <185 387 23.5 2.9 390 9.9 1.8 486 23.4 2.7 13.6 (3.9) b 0 (4.1) c

185 to <350 670 30.4 2.7 293 10.0 2.4 287 27.6 4.4 20.4 (4.0) b 2.9 (4.8) c

$ 350 443 33.0 3.0 105 15.2 2.8 74 32.3 9.0 17.8 (4.2) b 0.7 (9.5) c

Unknown 108 13.3 3.8 149 6.4 2.9 280 26.7 4.5 7.0 (5.3) c ­13.4 (6.6) a

a

b

c p <0.05.

p <0.01.

No statistical difference.

N = Number of individuals.

SE = Standard error. Source: Li and Grummer­Strawn, 2002. 

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

Page Child­Specific Exposure Factors Handbook

15­40 September 2008Table 15­32. Racial and Ethnic Differences in Proportion of Children Exclusively

Non­Hispanic Bre

White astfed at 4 Months

Non­Hispanic (NHAN

Black ES III, 1991­1994)

Mexican American Absolute Difference (%,SE)

White vs Black White vs Mexican

Characteristic

All infants

Infant sex

Male N

824

394 %

22.6

22.3 (SE)

1.7

1.9 N

906

454 %

8.5

7.0 (SE)

1.5

1.6 N

957

498 %

20.4

20.7 (SE)

1.4

1.5 %

14.1

15.3 (SE) (2.2) b

(2.6) b Am

%

2.3

1.5 erican

(SE) (1.6) c

(1.8) c

Female

Infant birth

<2500 weight 430

(g)

50 23.0

15.2 2.2

7.1 452

118 10.0

7.0 2.2

2.3 459

66 20.0

5.6 1.8

1.8 12.9

8.2 (3.0) b

(8.1) c 3.0

9.5 (2.1) c

(6.9) c

$ 2500

Maternal age

<20 774

(years)

76 23.1

6.6 1.8

3.2 786

172 8.8

6.4 1.6

2.1 880

170 21.6

12.1 1.4

2.5 14.4

0.2 (2.2) b

(3.7) c 1.5

­5.6 (1.6) c

(3.8) c

20 to 24 205 11.4 2.2 273 7.4 2.4 319 21.0 2.3 4.0 (2.7) c ­9.6 (3.2) b

25 to 29 271 21.6 2.3 254 8.6 2.5 256 22.1 2.5 13.0 (3.2) b ­0.5 (3.2) c

270 $ 30

Household head education

<High school 146 34.8

9.5 2.7

3.5 201

256 11.9

2.0 2.6

0.7 210

563 23.6

19.7 3.1

1.8 22.9

7.5 (4.2) b

(3.6) a 11.1

­10.2 (3.7) b

(4.0) a

High school 277 14.5 2.7 406 7.1 2.1 222 18.8 3.6 7.4 (3.2) a ­4.3 (4.7) c

Some college 175 30.8 3.8 141 17.4 3.0 120 21.0 3.9 13.4 (4.7) b 9.8 (6.1) c

College

Smoking

Yes graduate

during 219

pregnancy

224 34.1

10.0 3.9

2.8 92

168 17.4

5.4 4.7

2.2 37

64 31.5

3.2 4.5

1.8 16.7

4.6 (6.9) a

(3.7) c 2.6

6.8 (6.3) c

(3.4) c

No

Maternal

<25.0 body mass 596

index

597 27.2

24.8 2.1

2.1 730

407 9.4

8.0 1.9

1.9 892

417 21.7

19.4 1.5

1.9 17.8

16.8 (2.8) b

(3.0) b 5.6

5.4 (2.0) a

(2.3) a

25.0 to 29.9 117 19.7 4.3 230 8.6 1.9 261 23.1 3.4 11.1 (4.6) a ­3.4 (4.9) c

$ 30

Residence

Metropolitan 91

312 15.4

24.4 3.8

3 230

535 9.0

11.0 2.9

2.0 184

608 15.9

19.6 2.3

1.6 6.4

13.4 (5.2) c

(3.5) b ­0.5

4.8 (4.6) c

(2.8) c

Rural
Region

Northeast 512

138 21.3

20.0 1.8

1.4 371

131 4.2

11.1 1.3

2.9 349

10 22.3

9.4 3.3

9.5 17.1

8.8 (1.8) b

(2.2) b ­1.1

10.6 (3.0) c

(8.7) c

Midwest 231 26.5 3.2 143 12.6 5.6 98 19.2 4.1 13.9 (7.6) c 7.4 (3.7) c

South 378 14.1 2.8 574 5.9 1.4 383 15.9 3.1 8.2 (1.9) b ­1.8 (3.7) c

West

Poverty income

<100 ratio 77
(%)

116 34.7

13.1 2.7

3.3 58

448 12.5

5.7 5.0

1.6 466

471 23.0

18.4 1.3

1.8 22.2

7.4 (5.4) b

(3.5) a 11.7

­5.3 (2.5)

(3.1) c

100 to <185 166 18.9 3.2 197 10.6 2.8 234 21.9 4.1 8.3 (3.3) a ­3 (6.1) c

185 to <350 274 25.1 3.2 145 12.9 4.3 132 26.4 4.2 12.2 (5.0) a ­1.3 (4.1) c

$ 350 235 27.4 4.1 57 12.8 3.5 37 17.0 5.0 14.6 (5.0) b 10.4 (5.2) c

Unkno

a

b wn 33 16.5

p <0.05.

p <0.01. 7.6 59 7.3 3.7 83 16.1 5.1 9.2 (8.6) c 0.4 (9.5) c

c No statistical difference.

N = Number of individuals.

SE = Standard error. Source: Li and Grummer­Strawn, 2002. 

### Child­Specific Exposure Factors Handbook

Chapter 15 ­ Human Milk Intake

Child­Specific Exposure Factors Handbook Page September 2008 15­41Table 15­33. Percentage of Mothers Breast­feeding Newborn Infants in the Hospital and Infants at 5
United States in 1989 and 1995, by Ethnic Background and Selected Demographic or 6 Months

Variables of Age in the

Characteristic Percentage of Mothers Breast­Feeding

In Hospital At 6 Months

1989 1995 a Change 1989 1995 a Change

All Infants

White

Black Hispanic

Maternal Age (years)

<20

20 to 24

25 to 29

30 to 34

35+

Total Family Income <$10,000 $10,000 to $14,999 $15,000 to $24,999

$ 25,000

Maternal Education

Grade School High School College

Maternal Employment Employed Full Time Employed Part Time

Not Employed

Birth Weight

Low ( # 2,500 g)

Normal

Parity Primiparous Multiparous

c WIC Participation Participant Nonparticipant

U.S. Census Region

New England

Middle Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific 52.2

58.5

23.0

48.4

30.2

45.2

58.8

65.5

66.5

31.8

47.1

54.7

66.3

31.7

42.5

70.7

50.8

59.4

51.0

36.2

53.5

52.6

51.7

34.2

62.9

52.2

47.4

47.6

55.9

43.8

37.9

46.0

70.2

70.3 59.7

64.3

37.0

61.0

42.8

52.6

63.1

68.1

70.0

41.8

51.7

58.8

70.7

43.8

49.7

74.4

60.7

63.5

58.0

47.7

60.5

61.6

57.8

46.6

71.0

61.2

53.8

54.6

61.9

54.8

44.1

54.4

75.1

75.1 14.4

9.9

60.9

26.0

41.7

16.4

7.3

4.0

5.3

31.4

9.8

7.5

6.6

38.2

16.9

5.2

19.5

6.9

13.7

31.8

13.1

17.1

11.8

36.3

12.9

17.2

13.5

14.7

10.7

25.1

16.4

18.3

7.0

6.8 18.1

21.0

6.4

13.9

5.6

11.5

21.1

29.3

34.0

8.2

13.9

18.9

25.5

11.5

12.4

28.8

8.9

21.1

21.6

9.8

18.8

15.1

21.1

8.4

23.8

18.6

16.8

16.7

18.4

13.7

11.5

13.6

28.3

26.6 21.6

24.1

11.2

19.6

9.1

14.6

22.9

29.0

33.8

11.4

15.4

19.8

28.5

17.1

15.0

31.2

14.3

23.4

25.0

12.6

22.3

19.5

23.6

12.7

29.2

22.2

19.6

18.9

21.4

18.6

13.0

17.0

30.3

30.9 19.3

14.8

75.0

41.0

62.5

27.0

8.5 (1.0) b (0.6) b

39.0

10.8

4.8

11.8

48.7

21.0

8.3

60.7

10.9

15.7

28.6

18.6

29.1

11.8

51.2

22.7

19.4

16.7

13.2

16.3

35.8

13.0

25.0

7.1

16.2

a

b

c

Source: The percent change was calculated using the following formula: % breastfed in 1984 ­ % breastfed Figures in parentheses indicate a decrease in the rate of breastfeeding from 1989 to 1995.

WIC indicates Women, Infants, and Children supplemental food program.

Ryan, 1997. in 1989 / % breastfed in 1984. 

### Child­Specific Exposure Factors Handbook

Chapter 15 ­ Human Milk Intake

Page C h ild­Specific Exposure Factors Handbook

15­42 September 2008Table 15­34. Percentage of Mothers Breast­feeding Newborn Infants

Months of Age in the United States in 2003, by Ethnic Background in the Hospital and Infants

and Selected Demographic at 6 and 12

Variables

Characteristic Percentage of Mothers Breast­Feeding

In Hospital At 6 Months At 12 Months

All Infants

White

Black Hispanic

Asian

Maternal Age (years)

<20

20 to 24

25 to 29

30 to 34

35+

Maternal Education Any Grade School Any High School

No College College

Maternal Employment Employed Full Time Employed Part Time

Total Employed

Not Employed

Low Birth Weight <5 lbs

Parity Primiparous Multiparous

a WIC Participation Participant Nonparticipant

U.S. Census Region

New England

Middle Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific 9oz 44

53

26

33

39

28

40

48

50

47

26

35

35

55

44

49

45

43

27

48

43

32

55

52

36

44

55

42

37

37

53

50 18

20

10

15

23

9

13

20

23

23

13

12

12

24

11

19

14

21

10

17

19

11

25

22

17

17

18

16

11

15

23

24 10

12

5

12

12

4

8

10

14

14

17

8

8

14

6

11

8

13

6

10

11

7

14

11

9

9

9

10

7

8

16

15

a

Source: WIC indicates

Abbott, 2003. Women, Infants, and Children supplemental food program. 

### Child­Specific Exposure Factors Handbook

Chapter 15

­ Human Milk Intake

Child­Specific Exposure Factors Handbook Page September 2008 15­43Table 15­35. Number of Meals Per Day

Age (months) Bottle­fed Infants

a (meals/day) Breast­fed (meals/day) a

1 5.4 (4­7) 5.8 (5­7)

2 4.8 (4­6) 5.3 (5­7)

3 4.7 (3­6) 5.1 (4­8)

a Data expressed as mean with range in parentheses.

Source: Hofvander et al., 1982.

Table 15­36. Comparison of Breastfeeding Patterns Between Age and Groups (Mean ±SD)

Breastfeeding Episodes per Day 5.8 ± 2.6 6.8 ± 2.4 2.5 ± 2.0

Total Time Breastfeeding (min/day) 65.2 ± 44.0 102.2 ± 51.4 31.2 ± 24.6

Length of Breastfeeding (min/episode) 10.8 ± 6.1 14.2 ± 6.1 11.6 ± 5.6

SD = Standard deviation

Source: Buckley, 2001. 

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­ Human Milk Intake

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15­44 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

16 ACTIVITY FACTORS

16.1 INTRODUCTION As a consequence of a child’s immaturity and small stature, certain activities and behaviors specific to children place them at higher risk to certain environmental agents (Chance and Harmsen, 1998). Individual or group activities are important determinants of potential exposure, because toxic chemicals introduced into the environment may not cause harm to a child until an activity is performed that subjects the child to contact with those contaminants. An activity or time spent in a given activity will vary among children on the basis of, for example, culture, ethnicity, hobbies, location, gender, age, socioeconomic characteristics, and personal preferences. However, limited information is available regarding ethnic,

cultural and socioeconomic differences in children’s choice of activities or time spent in a given activity. It is difficult to accurately collect/record data for a child’s activity patterns (Hubal et al., 2000). Children engage in more contact activities than adults; therefore,

a much wider distribution of activities need to be considered when assessing children’s exposure (Hubal et al., 2000). Behavioral patterns, preferred activities, and developmental stages result in different exposures for children than for adults (Chance and Harmsen, 1998). Other factors that may affect children’s activity patterns include: social status, economics, and the cultural practices of their families. This chapter summarizes data on how much time children spend participating in various activities in

various microenvironments. Information on the frequency of performing various activities is also provided. The data in this chapter cover a wide range of activities and populations, arranged by age group when such data are available. One of the objectives of this handbook is to provide recommended exposure factor values using a consistent set of age groups. In this chapter, several studies are used as sources for activity pattern data. In some cases, the source data could be retrieved and analyzed using the standard age groupings recommended in Guidance for Monitoring and Assessing Childhood Exposures to Environmental

Contaminants (U.S. EPA, 2005). In other cases, the original source data were not available, and the study results are presented here using the same age groups as the original study, whether or not they conform to the standard age groupings. The recommendations for activity factors are provided in the next section, along with a summary of the confidence ratings for these recommendations. The recommended values are based on key studies identified by U.S. EPA for this factor. Following the recommendations, key studies on activity patterns are summarized. Relevant data on activity patterns are also presented to provide the reader with added perspective on the current state-of-knowledge pertaining to activity patterns in children.

16.2 RECOMMENDATIONS Assessors are commonly interested in quantitative information describing several types of time use data for children including: time spent indoors and outdoors; time spent bathing, showering, and swimming; and time spent playing on various types of

surfaces. The recommended values for these factors are summarized in Table 16-1. Note that, except for swimming, all activity factors are reported in units of minutes/day. Time spent swimming is reported in units of minutes/month. These data are based on two key studies presented in this chapter: a study of children’s activity patterns in California (Wiley et al., 1991) and the National Human Activity Pattern Survey (NHAPS) (U.S. EPA, 1996). Both mean and 95 th 

### percentile recommended values are provided. However, because these recommendations are based on short-term survey data, 95 th 

### percentile values may be misleading for estimating chronic (i.e., long term) exposures and should be used with caution. Also, the upper percentile

values for some activities are truncated as a result of the maximum response included in the survey (e.g., durations of more than 120 minutes/day were reported as 121 minutes/day), and could not be further refined). The confidence ratings for the recommendations are presented in Table 16-2. The recommendations for total time spent indoors and the total time spent outdoors are based on

U.S. EPA re-analysis of the source data from Wiley et al. (1991) for children < 1 year of age and U.S. EPA (1996) for age groups > 1 year of age. Although Wiley et al. (1991) is a study of California children and the sample size was very small for infants, it provides data for children’s activities for the younger age groups.



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Chapter 16 - Activity Factors

Data from U.S. EPA (1996) are representative of the

U.S. general population. In some cases, however, the time spent indoors or outdoors would be better addressed on a site-specific basis since the times are likely to vary depending on the climate, residential setting (i.e., rural versus urban), personal traits (e.g., health status) and personal habits. The recommended values for time spent indoors at a residence, duration of showering and bathing, and time spent swimming are based on a U.S. EPA re-analysis of the source data from

U.S. EPA (1996). Likewise, the recommended values for time spent playing on sand, gravel, grass or dirt are based on a U.S. EPA re-analysis of the source data from

U.S. EPA (1996).



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Chapter 16 - Activity Factors

Table 16-1. Recommended Values for Activity Factors

Age Group Mean 95 th Percentile Source

Time Indoors (total) minutes/day

Birth to <1 month
1 to <3 months
3 to <6 months
6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 1,440 1,432 1,414 1,301 1,353 1,316 1,278 1,244 1,260 1,248 -

-

-

-

-

-

-

-

-

- U.S. EPA analysis of source data from Wiley et al., 1991 for age groups from birth to < 12 months. Average for boys and girls. See Table 16-10. U.S. EPA re-analysis of source data from U.S. EPA, 1996 for age groups from 1 to < 21 years. See Table 16-14.

Time Outdoors (total) minutes/day

Birth to <1 month
1 to <3 months
3 to <6 months
6 to <12 months 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 0
8
26
139
36

76
107
132
100
102 -

-

-

-

-

-

-

-

-

- U.S. EPA analysis of source data from Wiley et al., 1991 for age groups from birth to < 12 months. Average for boys and girls. See Table 16-10. U.S. EPA re-analysis of source data from U.S. EPA, 1996 for age groups from 1 to < 21 years. See Table 16-14.

Time Indoors (at residence) minutes/day

Birth to <1year 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 1,108 1,065
979
957
893
889
833 1,440 1,440 1,296 1,355 1,275 1,315 1,288 U.S. EPA re-analysis of source data from U.S. EPA, 1996. Doers only. See Table 16-11.

Showering minutes/day

Birth to <1year 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 15
20
22
17
18
18
20 -

- 44
34
41
40
45 U.S. EPA re-analysis of source data from U.S. EPA, 1996. Doers only. See Table 16-18.



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### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-1. Recommended Values for Activity Factors (continued)

Age Group Mean 95 th Percentile Source

Bathing minutes/day

Birth to <1 year 1 to <2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 19
23
23
24
24
25
33 30
32
45
60
46
43
60 U.S. EPA re-analysis of source data from U.S. EPA, 1996. Doers only. See Table 16-18.

Swimming
minutes/month

Birth to <1 year 1 to < 2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 96
105
116
137
151
139
145 -

- 181
181
181
181
181 U.S. EPA re-analysis of source data from U.S. EPA, 1996. Doers only. See Table 16-21.

Playing on Sand/Gravel minutes/day

Birth to <1 year 1 to < 2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 18
43
53
60

67
67
83 - 121
121
121
121
121

- U.S. EPA re-analysis of source data from U.S. EPA, 1996. Doers only. See Table 16-22.

Playing on Grass

minutes/day

Birth to <1 year 1 to < 2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 52
68
62
79
73
75
60 - 121
121
121
121
121

- U.S. EPA re-analysis of source data from U.S. EPA, 1996. Doers only. See Table 16-22.



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Chapter 16 - Activity Factors

Table 16-1. Recommended Values for Activity Factors (continued)

Age Group Mean 95 th Percentile Source

Playing on Dirt

minutes/day

Birth to <1 year 1 to < 2 years 2 to <3 years 3 to <6 years 6 to <11 years 11 to <16 years 16 to <21 years 33
56
47
63
63
49
30 - 121
121
121
121
120

- U.S. EPA re-analysis of source data from U.S. EPA, 1996. Doers only. See Table 16-22.

- Percentiles were not calculated for sample sizes less than 10. Note: All activities are reported in units of minutes/day, except swimming, which is reported in units of minutes/month. There are 1,440 minutes in a day. Time indoors and outdoors may not add up to 1,440 minutes due to activities that
could not be classified as either indoors or outdoors.



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### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-2. Confidence in Recommendations for Activity Factors

General Assessment Factors Rationale Rating

Soundness

Adequacy of Approach

Minimal (or Defined) Bias The survey methodologies and data analyses were adequate. In the U.S. EPA (1996) study, responses were weighted according to this demographic data. The California children's activity pattern survey design (Wiley et al., 1991) and NHAPS (U.S. EPA, 1996) consisted of large overall sample sizes that varied with age. Data were collected via questionnaires and interviews.

Measurement or recording error may have occurred since the diaries were based on 24 hour recall. The sample sizes for some age groups were small for some activity factors. The upper ends of the distributions were truncated for some
factors. The data were based on short-term data. High

Applicability and Utility

Exposure Factor of Interest

Representativeness

Currency

Data Collection Period The key studies focused on activities of children.

U.S. EPA (1996) was a nationally representative survey of the U.S. population; the Wiley et al. (1991) survey was conducted in California and it was not representative of the U.S. population.

The Wiley et al. (1991) study was conducted between April 1989 and February 1990; the U.S. EPA (1996) study was conducted between October 1992 and September 1994.

Data were collected for a 24-hour period. Medium

Clarity and Completeness

Accessibility

Reproducibility

Quality Assurance The original studies are widely available to the public; U.S. EPA analysis of the original raw data from U.S. EPA (1996) is available upon request.

The methodologies were clearly presented; enough information was included to reproduce the results.

Quality assurance methods were not well described in study
reports. Medium

Variability and Uncertainty

Variability in Population

Uncertainty Variability was characterized across various age categories
of children.

The studies were based on short term recall data, and the upper ends of the distributions were truncated. Medium

Evaluation and Review

Peer Review

Number and Agreement of Studies The original studies received a high level of peer review. The re-analysis of the U.S. EPA (1996) data to conform to the standardized age categories was not peer-reviewed.

There were 2 key studies. Medium

Overall Rating Medium for the mean; low for upper percentile



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### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

16.3 ACTIVITY PATTERNS This section briefly describes published time-use studies that provide information on time-activity patterns of children in the U.S. For a detailed description of the studies, the reader is referred to the Exposure Factors Handbook (U.S. EPA, 1997).

16.3.1 KEY STUDIES

16.3.1.1 Wiley et al., 1991

- Study of Children’s Activity Patterns The California Study of Children's Activity Patterns survey (Wiley et al., 1991) provided estimates of the time children spent in various activities and locations (microenvironments) on a typical day. The sample population consisted of 1,200 children, under 12 years of age, selected from English-speaking households using Random Digit Dial (RDD) methods. This represented a survey response rate of 77.9 percent.

One child was selected from each household. If the selected child was 8 years old or less, the adult in the household who spent the most time with the child responded. However, if the selected child was between 9 and 11 years old, that child responded. The population was also stratified to provide representative estimates for major regions of the state. The survey questionnaire included a time diary which provided information on the children's activity and location patterns based on a 24-hour recall period. In addition, the survey questionnaire included questions about potential exposure to sources of indoor air pollution (e.g., presence of smokers) on the diary day, and the socio-demographic characteristics of children and adult respondents. The questionnaires and the time diaries were administered via a computer-assisted telephone interviewing (CATI) technology (Wiley et al., 1991). The telephone interviews were conducted during April 1989 to February 1990 over four seasons: spring (April to June 1989), summer (July to September 1989), fall (October to December 1989), and winter (January to February 1990). The data obtained from the survey interviews resulted in ten major activity categories, 113 detailed activity codes, 6 major categories of locations, and 63 detailed location codes. The time respondents under 12 years of age spent in the 10 activity categories (plus a “don’t know” or non-coded activity category) are presented in Table 6-3. For each of the 10 activity categories, this table presents the mean duration for all survey participants, the percentage of respondents who reported participating in the activity (i.e., percent doers), and the mean, median, and maximum duration for only those survey respondents who engaged in the activity (i.e., doers). It also includes the detailed activity with the highest mean duration of time for each activity category. The activity category with the highest time expenditure was personal needs and care, with a mean of 794 minutes/day (13.2 hours/day). Night sleep was the detailed activity that had the highest mean duration in that activity category. The activity category

“don't know” had a mean duration of about 2 minutes/day and only 4 percent of the respondents reported missing activity time. Table 16-4 presents the mean time spent in the 10 activity categories by age and gender. Becausethe original source data were available, U.S. EPA reanalyzed the data according to the standardized age categories used in this handbook. Differences between activity patterns in boys and girls tended to be small. Table 16-5 presents the mean time spent in the 10 activity categories grouped by season and geographic region in the state of California. There were seasonal differences for 5 activity categories: personal needs and care, education, entertainment/social, recreation, and communication/passive leisure. Time expenditure differences in various regions of the state were minimal for childcare, work-related, goods/services, personal needs and care, education, entertainment/social, and

recreation. Table 16-6 presents the distribution of time across six location categories. The mean duration for all survey participants, the percent of respondents engaging in the activity (i.e., percent doers); the mean, median, and maximum duration for doers only; and the detailed locations with the highest average time expenditure are shown. For all survey respondents, the largest mean amount of time spent was at home (1,078 minutes/day); 99 percent of respondents spent time at home (mean of 1,086 minutes/day for these individuals only). Tables 16-7 and 16-8 show the average time spent in the six locations grouped by age and gender, and season and region, respectively. Again, because the original source data were available, the age categories used by Wiley et al. (1991) have been replaced in Table 16-7 by the standardized age categories used in this



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### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

handbook. There were relatively large differences among the age groups in time expenditure for educational settings (Table 16-7). There were small differences in time expenditure at the six locations by region, but time spent in school decreased in the summer months compared to other seasons (Table 16­ 8). Table 16-9 shows the average time children spent in proximity to gasoline fumes and gas oven fumes. In general, the sampled children spent more time closer to gasoline fumes than to gas oven fumes. The age categories in Table 16-9 have been modified to conform to the standardized categories used in this

handbook.

The U.S. EPA estimated the total time indoors and outdoors using the data from the Wiley et al. (1991) study. Activities performed indoors were assumed to include household work, child care, personal needs and care, education, and communication/passive leisure. The average times spent in these indoor activities and half the time spent in each activity which could have occurred either indoors or outdoors (i.e., work-related, goods/services, organizational activities, entertainment/social, don’t know/not coded) were summed. Table 16-10 summarizes the results of this analysis using the standard age groups. A limitation of this study is that the sampling population was restricted to only English-speaking households; therefore, the data obtained do not represent the diverse population group present in

California. Another limitation is that time use values obtained from this survey were based on short-term recall (24-hr) data; therefore, the data set obtained may be biased. Other limitations are: the survey was conducted in California and is not representative of the national population, and the significance of the observed differences in the data obtained (i.e., gender, age, seasons, and regions) were not tested statistically. An advantage of this study is that time expenditure in various activities and locations were presented for children grouped by age, gender, and season. Also, potential exposures of respondents to pollutants were explored in the survey. Another advantage is the use of the CATI program in obtaining time diaries, which allows automatic coding of activities and locations onto a computer tape, and allows activities forgotten by respondents to be inserted into their appropriate position during interviewing.

16.3.1.2 U.S. EPA, 1996 - National Human Activity Pattern Survey (NHAPS)

U.S. EPA (1996) analyzed data collected by the National Human Activity Pattern Survey (NHAPS). This survey was conducted by U.S. EPA and is the largest and most current human activity pattern survey available (U.S. EPA, 1996). Data for 9,386 respondents in the 48 contiguous United States were collected via minute-by-minute 24-hour diaries. NHAPS was conducted from October 1992 through September 1994 by the University of Maryland’s Survey Research Center using CATI technology to collect 24-hour retrospective diaries and answers to a number of personal and exposure related questions from each respondent. Detailed data were collected for a maximum of 82 different possible locations, and a maximum of 91 different activities. Participants were selected using a RDD method. The response rate was 63 percent, overall. If the chosen respondent was a child too young to interview, an adult in the household gave a proxy interview. Each participant was asked to recount their entire daily routine from midnight to midnight immediately previous to the day that they were interviewed. The survey collected information on duration and frequency of selected activities and of the time spent in selected microenvironments. In addition, demographic information was collected for each respondent to allow for statistical summaries to be generated according to specific subgroups of the U.S. population (i.e., by gender, age, race, employment status, census region, season, etc.). The participants’ responses were weighted according to geographic, socioeconomic, time/season, and other demographic factors to ensure that results were representative of the

U.S. population. The weighted sample matched the 1990 U.S. census population for each gender, age group, census region, and the day-of-week and seasonal responses were equally distributed. Saturdays and Sundays were over sampled to ensure an adequate weekend sample. Tables 16-11 through 16-24 provide data from the NHAPS study. In most cases, the source data from U.S. EPA have been reviewed and re-analyzed by

U.S. EPA to conform to the age categories



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### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

recommended in Guidance for Monitoring and Assessing Childhood Exposures to Environmental

Contaminants (U.S. EPA, 2005) and used in this handbook. Because no data were available on subjects’ age in months, age groups less than 1 year old were consolidated into a single group. These tables provide statistics for 24-hour cumulative time spent (mean, minimum, percentiles, and maximum) in selected locations or engaging in selected activities. For each location or activity, statistics were calculated for the entire survey population (i.e., whole population) and for the subset of the survey population that reported being in the location or doing the activity in question (i.e., doers only). When the sample size was 10 persons or fewer, percentile values were not calculated. Also note that some of these activities were not necessarily mutually exclusive (e.g. time spent in active sports likely overlaps with exercise time). Table 16-11 presents data for the time children spent in various rooms of the house (i.e., kitchen, living room, dining room, bathroom, bedroom, and garage), and all rooms combined. Table 16-12 presents data for time spent in other indoor locations (i.e., restaurants, indoors at school, and grocery/convenience stores). Table 16-13 presents data for the time children spent outdoors on school grounds/playgrounds, parks or golf courses, or pool rivers, or lakes. Table 16-14 provides data on time spent in indoor and outdoor environments. The U.S. EPA estimated the time spent indoors by adding the average times spent indoors at the respondents’ home (kitchen, living room, bathroom, etc.), at other houses, and inside other locations such as school, restaurants, etc. Time outdoors was estimated by adding the average time spent outdoors at the respondents’ pool and yard, others’ pool and yard, and outside other locations such as sidewalk, street, neighborhood, parking lot, service station/gas station, school grounds, park/golf course, pool, river, lake, farm, etc. Table 16­ 15 presents data for the time spent in various types of vehicles (i.e., car, truck/van, bus), and in all vehicles combined. Table 16-16 presents data for the time children spent in various major activity categories (i.e., sleeping, napping, eating, attending school, outdoor recreation, active sports, exercise, and walking). Table 16-17 through 16-19 provide data related to showering, bathing, and handwashing activities. Tables 16-20 and 16-21 provide data on monthly swimming (in a freshwater pool) frequency by the number of respondents and swimming duration, respectively. Table 16-22 provides data on the time children spent playing on dirt, sand/gravel, or grass, and Table 16-23 provides data on the number of minutes spent near excessive dust. Table 16-24 provides information on time spent in the presence of smokers. For this data set, the authors’ original age categories were used because the methodology used to generate the data could not be reproduced. The advantages of the NHAPS data set are that it is representative of the U.S. population and it has been adjusted to be balanced geographically, seasonally, and for day/time. Also, it is inclusive of all ages, genders, and races. A disadvantage of the study is that for the standard age categories, the number of respondents is small for the “doers” of many activities. In addition, the durations exceeding 60, 120, and 181

minutes were not collected for some activities. Therefore, the actual time spent at the high end of the distribution for these activities could not be accurately

estimated.

16.3.2 RELEVANT STUDIES

16.3.2.1 Timmer et al., 1985 - How Children Use

Time Timmer et al. (1985) conducted a study using

the data obtained on children's time use from a 1981­ 1982 panel study. Data were obtained for 389 children between 3 and 17 years of age. Data were collected using a time diary and a standardized interview. The time diary involved children reporting their activities beginning at 12:00 a.m. the previous night, the duration and location of each activity, the presence of another individual, and whether they were performing other

activities at the same time. The standardized interview was administered to the children to gather information about their psychological, intellectual (using reading comprehension tests), and emotional well-being; their hopes and goals; their family environment; and their

attitudes and beliefs. For preschool children, parents provided information about the child's previous day's activities. Children in first through third grades completed the time diary with their parents assistance and, in addition, completed reading tests. Children in fourth grade and



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above provided their own diary information and participated in the interview. Parents were asked to

assess their children's socioemotional and intellectual development, and a survey form was sent to a teacher of each school-age child to evaluate their socioemotional and intellectual development. The activity descriptor codes used in this study were developed by Juster et al. (1983). The mean time spent performing major activities on weekdays and weekends by age, sex, and type of day is presented in Table 16-25. On weekdays, children spend about 40 percent of their time sleeping, 20 percent in school, and 10 percent eating, and performing personal care activities (Timmer et al., 1985). The data in Table 16-25 indicate that girls spent more time than boys performing household work and personal care activities and less time playing sports. Also, the children spent most of their free time watching television. Table 16-26 presents the mean time children spent during weekdays and weekends performing major activities by five different age groups. The significant effects of each variable (i.e., age and sex) are also shown. Older children spent more time performing household and market work, studying, and watching television and less time eating, sleeping, and playing. The authors estimated that, on average, boys spent 19.4 hours a week and girls spent 17.8 hours per week watching television.

U.S. EPA estimated the total time indoors and outdoors using the Timmer et al. (1985) data. Activities performed indoors were assumed to include household work, personal care, eating, sleeping, attending school, studying, attending church, watching television, and engaging in household conversations. The average times spent in these indoor activities and half the time spent in each activity which could have occurred indoors or outdoors (e.g., market work, sports, hobbies, art activities, playing, reading, and other passive leisure) were summed. Table 16-27 summarizes the results of this analysis by age group and

time of the week. A limitation associated with this study is that it was conducted in 1981. It is likely that activity patterns of children have changed from 1981 to the present. Thus, the application of these data to current exposure assessments may bias their results. Another limitation is that the data do not provide overall annual

estimates of children’s time use since data were collected only during the time of the year when children attended school and not during school vacations. An advantage of this survey is that diary recordings of activity patterns were kept and the data obtained were not based entirely on recall. Another advantage is that because parents assisted younger children with keeping their diaries and with interviews, any bias that may have been created by having younger children record their

data should have been minimized.

16.3.2.2 Robinson and Thomas, 1991 - Time Spent i n A c t i v i t i e s , L o c a t i o n s , a n d Microenvironments: A California-National Comparison Robinson and Thomas (1991) reviewed and compared data from the 1987-88 California Air Resources Board (CARB) time-activity study for

California residents and from a similar 1985 national study, Americans’ Use of Time , conducted at the University of Maryland. Both studies used the diary approach to collect data. Time- use patterns were collected for individuals aged 12 years and older. Telephone interviews based on the RDD procedure were conducted for 1,762 and 2,762 respondents for the CARB study and the national study, respectively. Of these respondents, 183 were children, ages 12 to17 years in the CARB study and 340 were children, ages 12 to 17 years, in the national study. Robinson and Thomas (1991) defined a set of 16 microenvironments based on the activity and location codes employed in the two studies. The mean durations of time spent in the 16 microenvironments by children, ages 12 to 17 years, are presented in Table 16-28. In both studies, children spent the majority of their time sleeping, and engaging in leisure and work/study-related activities.

The limitations associated with the Robinson and Thomas (1991) study are that the CARB survey was performed in California only and may not be representative of the U.S. population as a whole. In addition, the studies were conducted in the 1980s and activity patterns may have changed over time. Another

limitation is that the data are based on short-termstudies. Finally, the available data could not be reanalyzed to conform to the standardized age categories

used in this handbook.



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16.3.2.3 Funk et al., 1998

- Quantifying the Distribution of Inhalation Exposure in Human Populations: Distribution of Time Spent by Adults, Adolescents, and Children at Home, at Work, and at School Funk et al. (1998) used the data from the CARB study to determine distributions of exposuretime by tracking the time spent participating in daily athome and at-school activities for male and female children and adolescents. CARB performed two studies from 1987 to 1990; the first was focused on adults (18 years and older) and adolescents (12-17 years old), and the second focused on children (6-11 years old). The targeted groups were noninstitutionalized English speaking Californians with telephones in their residences. Individuals were contacted by telephone and asked to account for every minute within the previous 24 hours, including the amount of time spent on an activity and the location of the activity. The surveys were conducted on different days of the week as well as different seasons of the year. Using the location descriptors provided in the CARB study, Funk et al. (1998) categorized the activities into two groups, “at home” (any activity at principal residence) and “away.” Each activity was assigned to one of three inhalation rate levels (low, moderate, or high) based on the level of exertion expected from the activity. Ambiguous activities were assigned to moderate inhalation rate levels. Among the adolescents and children studied, means were determined for the aggregate age groups, as shown in

Table 16-29. Funk et al. (1998) used several statistical methods, such as Chi-square, Kolmogorov-Smirnov, and Anderson-Darling, to determine whether the time spent in an activity group had a known distribution. Most of the activities performed by children were assigned a low or moderate inhalation rate rate (Table 16-30). The aggregate time periods spent at home in each activity are shown in Table 16-31. Aggregate time spent at home performing different activities was compared between genders. There were no significant

differences between adolescent males and females in any of the activity groups (Table 16-32). In children, ages 6-11 years, differences between gender and age

were observed at the low inhalation rate levels. There were significant differences between two age groups (6­ 8 years, and 9-11 years) and gender at the moderate inhalation rate level (Table 16-33). A limitation of this study was that large proportions of the respondents in the study did not participate in high-inhalation rate-level activities. The Funk et al. (1998) study was based on data from one geographic location, collected more that a decade ago. Thus, it may not be representative of current activities among the general population of the U.S.

16.3.2.4 U.S. EPA, 2000 - Consolidated Human Activity Database (CHAD) The Consolidated Human Activity Database (CHAD), available online at http://www.epa.gov/chadnet1/ , was developed by the

U.S. EPA's National Exposure Research Laboratory (NERL) to provide access to existing human activity data for use in exposure and risk assessment efforts. Data from twelve activity pattern studies conducted at the city, state, and national levels are included in CHAD. CHAD contains both the original raw data from each study and data modified based on predefined format requirements. Modifications made to data included: recoding of variables to fit into them a common activity/location code system, and

standardization of time diaries to an exact 24-hour length. Detailed information on the coding system and

the studies included in CHAD is available in the CHAD User Manual, available at http://oaspub.epa.gov/chad/CHAD_Datafiles$.startup

#Manual , and in McCurdy et al. (2000). A total of 144 activity codes and 115 location codes were used in CHAD (McCurdy et al., 2000). Although some participants in a study conducted multiple activities, many activities were only conducted within a few studies. The same is true for activity locations. The selection of exposure estimates for a particular activity or particular location should be based on study parameters that closely relate to the exposure scenario being assessed. The maximum amount of time, on average, within a majority of the studies was sleeping or taking a nap, while the maximum amount of time spent at a particular location was at home or at work, depending on the study. Many of the limitations of CHAD data arise from the incorporation of multiple studies into the time



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diary functions specified in CHAD. Activities and locations were coded similarly to the NHAPS study; studies with differing coding systems were modified to

fit the NHAPS codes. In some cases start times and end times from a study had to be adjusted to fit a 24-hour period. Respondents were not randomly distributed in CHAD. For example, some cities or states were over sampled because entire studies were carried out in those places. Other studies excluded large groups of people such as smokers, or non-English speakers, or people without telephones. Many surveys were age-restricted, or they preferentially sampled certain target groups. As a result, users are cautioned against using random individuals in CHAD to represent the U.S. population as a whole (Glenn et al., 2000).

16.3.2.5 Hubal et al., 2000 - Children’s Exposure Assessment: A Review of Factors Influencing Children’s Exposure and the
Date Available to Characterize and Assess that Exposure Hubal et al. (2000) reviewed available data from CHAD, including activity pattern data, to characterize and assess environmental exposures to children. CHAD contains 3,009 person-days of macroactivity data for 2,640 children less than 12 years of age (Hubal et al., 2000) (Table 16-34). The number of hours these children spent in various

microenvironments are shown in Table 16-35 and the time they spent in various activities indoors at home is

shown in Table 16-36. Hubal et al. (2000) noted that CHAD contains approximately “140 activity codes and 110 location codes, but the data generally are not available for all activity locations for any single respondent. In fact, not all of the codes were used for most of the studies. Even though many codes are used in macroactivity studies, many of the activity codes do not adequately capture the richness of what children actually do. They are much too broadly defined and ignore many child-oriented behaviors. Thus, there is a

need for more and better-focused research into

children’s activities.”

U.S. EPA updated the analysis performed by Hubal et al. (2000) using CHAD data downloaded in 2000, sorted according to the age groups recommended

in Guidance for Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 2005). The results are shown in Tables 16-37 and 16­

38. In this analysis, individual study participants within CHAD whose behavior patterns were measured over multiple days were treated as multiple one-day activity patterns. This is a potential source of error or bias in the results because a single individual may contribute multiple data sets to the aggregate population being

studied.

16.3.2.6 Wong et al., 2000 - Adult Proxy Responses to a Survey of Children’s Dermal Soil

Contact Activities Wong et al. (2000) conducted telephone surveys to gather information on children’s activity patterns as related to dermal contact with soil during outdoor play on bare dirt or mixed grass and dirt surfaces. This study, the second Soil Contact Survey (SCS-II), was a follow-up to the initial Soil Contact Survey (SCS-I), conducted in 1996, that primarily focused on assessing adult behavior related to dermal contact with soil and dust (Garlock et al., 1999). As part of SCS-I, information was gathered on the behavior of children under the age of 18 years, however, the questions were limited to clothing choices and the length of time between soil contact and hand washing. Questions were posed for SCS-II to further define children’s outdoor activities and hand washing and bathing frequency. For both soil contact surveys households were randomly phoned in order to obtain nationally representative results. The adult respondents were questioned as surrogates for one randomly chosen child under the age of 18 residing within the household. In the SCS-II, of 680 total adult respondents with a child in their household, 500 (73.5 percent) reported that their child played outdoors on bare dirt or mixed grass and dirt surfaces (identified as “players”). Those children that reportedly did not play outdoors (“non-players”) were typically very young ( # 

### 1 year) or relatively older ( $ 

### 14 years). Of the 500 children that played outdoors, 497 played outdoors in warm weather months (April through October) and 390 were reported to play outdoors during cold weather months (November through March). These results are presented in Table 16-39. The frequency (days/week), duration (hours/day), and total hours per week spent playing outdoors was determined for those children



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identified as “players” (Table 16-40). The responses indicated that children spent a relatively high percentage of time outdoors during the warmer months,

and a lesser amount of time outdoors in cold weather. The median play frequency reported was 7 days/week in warm weather and 3 days/week in cold weather. Median play duration was 3 hours/day in warm weather and 1 hour/day during cold weather months. Adult respondents were then questioned as to how many times per day their child washed his/her hands and how many times the child bathed or showered per week, during both warm and cold weather months. This information provided an estimate of the

time between skin contact with soil and removal of soil by washing (i.e., exposure time). Hand washing and bathing frequencies for child players are reported in Table 16-41. Based on these results, hand washing occurred a median of 4 times per day during both warm and cold weather months. The median frequency for baths and showers was estimated to be 7 times per week

for both warm and cold weather. Based on reported household incomes, the respondents sampled in SCS-II tended to have higher incomes than that of the general population. This may be explained by the fact that phone surveys cannot sample households without telephones. Additional uncertainty or error in the study results may have occurred as a result of the use of surrogate respondents. Adult respondents were questioned regarding child activities that may have occurred in prior seasons, introducing the chance of recall error. In some instances, a respondent did not know the answer to a question or refused to answer. Table 16-42 compares mean play duration data from SCS-II to similar activities identified in NHAPS (U.S. EPA, 1996). Table 16-43 compares the number of times per day achild washed his or her hands, based on data from SCSII and NHAPS. As indicated in Tables 16-42 and 16­ 43, where comparison is possible, NHAPS and SCS-II

results showed similarities in observed behaviors.

16.3.2.7 Graham and McCurdy, 2004 - Developing Meaningful Cohorts for Human Exposure

Models Graham and McCurdy (2004) used a statistical model [general linear model and analysis of variance (GLM/ANOVA)] to assess the significance of various factors in explaining variation in time spent outdoors, indoors and in motor vehicles. These factors, which are commonly used in developing cohorts for exposure modeling, included age, gender, weather, ethnicity, day type, and precipitation. Activity pattern data from CHAD, containing 30 or more records per day, were used in the analysis (Graham and McCurdy, 2004). Data on time spent outdoors for people who spent >0 time outdoors (i.e., doers) are presented in Table 16-44. Graham and McCurdy (2004) found that all the factors evaluated were significant (p<0.001) in explaining differences in time spent outdoors (Graham and McCurdy, 2004). An evaluation of gender differences in time spent outdoors by age cohorts was also conducted. Table 16-45 presents descriptive statistics and the results of the two-sample Kolmogorov-Smirnov (KS) test for this evaluation. As shown in Table 16-45, there were statistically significant gender differences in time spent outdoors starting with the 6 to 10 year old age category. In addition, Graham and McCurdy (2004) evaluated the effect of physical activity and concluded that this was the most important factor in explaining time spent outdoors. For time spent indoors (Table 16-46), there were statistically significant effects for all the factors evaluated, with gender, weather, and day type being the most important variables. Regarding time spent in motor vehicles (Table 16-47), precipitation was the only factor found to have no significant effects (Graham and McCurdy, 2004). Based on the results of these analysis, Graham and McCurdy (2004) noted that “besides age and gender, other important attributes for defining cohorts are the physical activity level of individuals, weather factors such as daily maximum temperature in combination with months of the year, and combined weekday/weekend with employment status.” The authors also noted that even though the factors evaluated were found to be statistically significant in explaining differences in time spent outdoors, indoors, and in motor vehicles, “parameters such as lifestyle and life stages that are absent from CHAD might have reduced the amount of unexplained variance.” The authors recommended that, in defining cohorts for exposure modeling, age and gender should be used as ‘‘first-order’’ attributes, followed by physical activity



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level, daily maximum temperature, and day type (weekend/weekday or day-of-the-week/working status) (Graham and McCurdy, 2004).

16.3.2.8 Vandewater et al., 2004 - Linking Obesity and Activity Level with Children’s

Television and Video Game Use Vandewater et al. (2004) evaluated children’s media use and participation in active and sedentary activities using 24-hour time-use diaries collected in 1997, as part of the Child Development Supplement (CDS) to the Panel Study of Income Dynamics (PSID). The PSID is a ongoing, longitudinal study of U.S. individuals and their families conducted by the Survey Research Center of the University of Michigan. In 1997, PSID families with children younger than 12 years of age completed the CDS and reported all activities performed by the children on one randomly selected weekday and one randomly selected weekend day. Since minorities, low income families, and less educated individuals were oversampled in the PSID, sample weights were applied to the data (Vandewater et al., 2004). More information on the CDS can be found

on-line at http://psidonline.isr.umich.edu/CDS/. Using time diary data from 2,831 children participating in the CDS, Vandewater et al., (2004) estimated the time in minutes over the two-day study period (i.e., sum of time spent on one weekday and one weekend day) that children spent watching television, playing games on video games consoles or computers, reading, and using computers for other purposes besides playing games. In addition, the time spent participating in highly active (i.e., playing sports), moderately active (i.e., fishing, boating, camping, taking music lessons, and singing), and sedentary (i.e., using the phone, doing puzzles, playing board games, and relaxing) activities was determined. Table 16-48 presents the means and standard deviations for the time spent in the selected activities by age and gender. A limitation of this study is that the survey was not designed for exposure assessment purposes. Therefore, the time use data set may be biased. However, the survey provides a database of current

information on various human activities. This information can be used to assess various exposure pathways and scenarios associated with these activities. 16.3.2.9 Juster et al. (2004) - Changing Times of

American Youth: 1983-2003 Juster et al. (2004) evaluated changes in time use patterns of children by comparing data collected in a 1981-1982 pilot study of children ages 6 to 17 to data from the 2002-2003 Child Development Supplement (CDS) to the Panel Study of Income Dynamics (PSID). The 1981-1982 pilot study is the same study described in Timmer et al. (1985). The 2002-2003 CDS gathered 24-hour time diary data on 2,908 children ages 6 to 17; as was done in the 1997 CDS, information was collected on one randomly selected weekday and one randomly selected weekend day (Juster et al., 2004). Tables 16-49 and 16-50 present the mean time children spent (in minutes/day) performing major activities on weekdays and weekend days, respectively, for the years 1981-82 and 2002-2003. Table 16-51 shows the weekly time spent in these activities for the years 1981-82 and 2002-2003. Juster et al. (2004) noted that the time spent in school and studying increased while time spent in active sports and outdoors activities decreased during the period studied.

16.3.2.10 U.S. Department of Labor, 2007 - American Time Use Survey, 2006 Results The American Time Use Study (ATUS) has been conducted annually since 2003 by the U.S. Department of Labor’s Bureau of Labor Statistics (U.S. DL, 2007). The purpose of the study is to collect “data on what activities people do during the day and how much time they spend doing them.” In 2006, the survey focused on “the time Americans worked, did household activities, cared for household children, participated in educational activities, and engaged in leisure and sports activities.” Approximately 13,000 individuals, 15 years of age and older, were interviewed during 2006. Participants were randomly selected and interviewed using the CATI method and were asked to recall their activities on the day before the interview. Data were collected for all days of the week, including weekends (i.e., 10 percent of the individuals were interviewed about their activities on one of the five weekdays, and 25 percent of the individuals were interviewed about their activities on one of the two weekend days). Demographic information, including age, gender, race/ethnicity, marital status, and educational level were also collected, and sample weights were applied to



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records to “reduce bias in the estimates due to differences in sampling and response rates across subpopulations and days of the week.” Data were collected for 17 major activities, that were subsequently composited into 12 categories for publication of the results. Estimates of time use in these 12 major categories are presented in Table 16-52. These data represent the average hours per day spent by male, female, and all children ages 15 to 19 years in the various categories. Table 16-52 also provides a more detailed breakdown of the Leisure and Sports category for all children, ages 15 to 19 years old.

16.3.2.11 Nader et al. 2008 - Moderate-to-Vigorous Physical Activity from Ages 9 to 15 years Nader et al. (2008) conducted a longitudinal study of 1,032 children from ages 9 to 15 years. The purpose of the study was to determine the amount oftime children 9 to 15 years of age engaged in moderateto-vigorous activities (MVPA) and compare results with the recommendations issued by the U.S. Department of Health and Human Services and the U.S. Department of Agriculture of a minimum of 60 minutes per day. Children’s activity levels were recorded for four to seven days using an accelerometer. The study participants included 517 boys and 515 girls. The study found that at age 9 children engaged in 3 hours of MVPA per day. By age 15, the amount of time engaged in MVPA was dropped to 49 minutes/day on weekdays and 35 minutes per day on weekends. Boys spent 18 more minutes/day of MVPA than girls on weekdays and 13 more minutes/day on weekends.Estimates of the mean time spent in moderate-tovigorous activities by various age groups are presented in Table 16-53. The study did not provide information about the amount of time spent at specific activities.

16.4 REFERENCES FOR CHAPTER 16 Chance,	 W.G.; Harmsen, E. (1998) Children are

different: environmental contaminants and

children’s health. Can J Public Health 89(Supplement):59-513. Funk, L.; Sedman, R.; Beals, J.A.J.; Fountain, R. (1998) Quantifying the distribution of inhalation exposure in human populations: distributions of time spent by adults, adolescents, and children at home, at work, and at school. Risk Anal 18(1):47-56. Garlock, T.J.; Shirai, J.H.; Kissel, J.C. (1999) Adult responses to a survey of soil contact related behaviors. J Expo Anal Environ Epidemiol

9:134-142. Glenn, G.; Stallings, C.; Tippett, J.; Smith, L. (2000) CHAD’s user guide: Extracting human activity information from CHAD on the PC. Prepared for the U.S. EPA National Exposure Research Laboratory by ManTech Environmental Technology, Inc. Graham,	 S.E.; McCurdy, T. (2004) Developing meaningful cohorts for human exposure models. J Expo Anal Environ Epidemiol

14:23-43. Hubal, E.A.; Sheldon, L.S.; Burke, J.M.; McCurdy, T.R.; Berry, M.R.; Rigas, M.L.; Zartarian, V.G.; Freeman, N.G. (2000) Children’s exposure assessment: a review of factors influencing children’s exposure and the data

available to characterize and assess that exposure. Environ Health Persp 108:475­

485. Johnson, T. (1989) Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric

Power Research Institute. Juster, F.T.; Hill, M.S.; Stafford, F.P.; Parsons, J.E. (1983) Study description. 1975-1981 time use longitudinal panel study. Ann Arbor, MI: The University of Michigan, Survey Research Center, Institute for Social

Research. Juster, T.; Ono, H.; Stafford, F. (2004) Changing times of American youth: 1981-2003. Institute for Social Research, University of Michigan, Ann Arbor, Michigan. Available on-line at http://www.umich.edu/news/Releases/2004/ Nov04/teen_time_report.pdf McCurdy, T.; Glen, G.; Smith, L.; Lakkadi, Y. (2000) The National Exposure Research Laboratory’s Consolidated Human Database. J Expo Anal Environ Epidemiol 10:566-578.

Nader , P.R.; Bradley, R.H.; Houts, R.M.; McRitchie,S.L.; O’Brien, M. (2008) Moderate-tovigorous physical activity from ages 9 to 15 years. JAMA, 300(3):295-305.



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Robinson, J.P.; Thomas, J. (1991) Time spent in activities, locations, and microenvironments: a California-National Comparison Project report. Las Vegas, NV: U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory. Timmer, S.G.; Eccles, J.; O'Brien, K. (1985) How children use time. In: Juster, F.T.; Stafford, F.P.; eds. Time, goods, and well-being. Ann Arbor, MI: University of Michigan, Survey Research Center, Institute for Social Research, pp. 353-380.

U.S. Department of Health and Human Services and

U.S. Department of Agriculture. (2005) Dietary Guidelines for Americans, 2005. 6 th 

### edition, Washington, DC, Government Printing Office. Available online at http://www.health.gov/dietaryguidelines/dg a2005/document/pdf/DGA2005.pdf U.S. Department	 of Labor (U.S. DL), Bureau of Labor Statistics. (2007) American Time Use Survey

- 2006 Results. News release issued at http://www.bls.gov/tus on June 28, 2007.

U.S. EPA (1996) Descriptive statistics tables from a detailed analysis of the National Human Activity Pattern Survey (NHAPS) data. Washington, DC: Office of Research and Development. EPA/600/R-96/148.

U.S. EPA (1997) Exposure Factors Handbook. Washington, DC: National Center for Environmental Assessment, Office ofResearch and Development. EPA/600/P95/002Fa,b,c. U.S. EPA (2000) Consolidated Human Activity Database (CHAD). U.S. EPA/NERL.

Available online at http://www.epa.gov/chadnet1/

U.S. EPA. (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental Contaminants.

U.S. Environmental Protection Agency, Washington, D.C., EPA/630/P-03/003F. Vandewater, E.A.; Shim, M.; Caplovitz, A.G. (2004) Linking obesity and activity level with children’s television and video game use. J

Adolesc 27:71-85. Wiley, J.A.; Robinson, J.P.; Cheng, Y.; Piazza, T.; Stork, L.; Plasden, K. (1991) Study of children’s activity patterns. California Environmental Protection Agency, Air

Resources Board Research Division. Sacramento, CA. Wong, E.Y.; Shirai, J.H; Garlock, T.J.; Kissel, J.C. (2000) Adult proxy responses to a survey of

children’s dermal soil contact activities. J Expo Anal Environ Epidemiol 10:509-517.



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Table 16-3. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Ten Major Activity Categories, for All Respondents and Doers

Activity Category Mean

Duration

(All) %

Doers a Mean

Duration (Doers) a Median

Duration (Doers) a Maximum

Duration (Doers) a Detailed Activity with Highest Average Minutes

Work-related b 10 25 39 30 405 Eating at Work/School/Daycare

Household c 53 86 61 40 602 Travel to Household

Childcare d <1 <1 83 30 290 Other Child Care

Goods/Services e 21 26 81 60 450 Errands

Personal Needs and Care f 794 100 794 770 1,440 Night Sleep

Education g 110 35 316 335 790 School Classes

Organizational Activities h 4 4 111 105 435 Attend Meetings

Entertain/Social i 15 17 87 60 490 Visiting with Others

Recreation j 239 92 260 240 835 Games

Communication/Passive

Leisure k 192 93 205 180 898 TV Use

Don't know/Not coded 2 4 41 15 600 -

All Activities 1,440 -

- - - -

a Doers indicate the respondents who reported participating in each activity category.

b Includes: travel to and during work/school; children’s paid work; eating at work/school/daycare; and accompanying or watching

adult at work.

c Includes: food preparation; meal cleanup; cleaning; clothes care; car and home repair/painting; building a fire; plant and pet care; and traveling to household.

d Includes: baby and child care; helping/teaching children; talking and reading; playing while caring for children; medical care;
travel related to child care; and other care.

e Includes: shopping; medical appointments; obtaining personal care services (e.g., haircuts), government and financial services, and repairs; travel related to goods an services; and errands.

f Includes: bathing, showering, and going to bathroom; medical care; help and care; meals; night sleep and daytime naps, dressing and grooming; and travel for personal care.

g Includes: student and other classes; daycare; homework; library; and travel for education.

h Includes: attending meetings and associated travel.

i Includes: sports events; eating and amusements; movies and theater; visiting museums, zoos, art galleries, etc.; visiting others; parties and other social events; and travel to social activities.

j Includes: active sports; leisure; hobbies; crafts; art; music/drama/dance; games; playing; and travel to leisure activities.

k Includes: radio and television use; reading; conversation; paperwork; other passive leisure; and travel to passive leisure

activities.

Source: Wiley et al., 1991.



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Table 16-4. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Ten Major Activity Categories, by Age and Gender

Boys

(Unweighted) Activity Category a Birth to

1 Month 1 to <3

Months 3 to <6

Months 6 to <12

Months 1 to <2

Years 2 to <3

Years 3 to <6

Years 6 to <11

Years 11

Years b Birth to 11

Years

Work-related 0 0 0 1 8 9 10 12 13 11

Household 12 30 49 28 35 44 44 61 63 58

Childcare 0 0 0 0 0 0 0 0 3 2

Goods/Services 0 16 14 28 27 14 28 22 24 26

Personal Needs and Care 910 1,143 937 919 903 889 802 726 707 802

Education 180 c 0 75 70 33 69 67 120 120 100

Organizational Activities 0 0 0 0 7 0 5 11 16 6

Entertainment/Social 0 0 0 0 8 6 15 15 43 18

Recreation 0 0 26 104 314 304 294 265 227 228

Communication/Passive
Leisure 338 250 339 292 106 103 175 208 226 226

Sample Sizes (Unweighted) 3 7 15 31 54 62 151 239 62 624

Girls Activity Category a Birth to

1 Month 1 to <3

Months 3 to <6

Months 6 to <12

Months 1 to <2

Years 2 to <3

Years 3 to <6

Years 6 to <11

Years 11

Years b Birth to 11

Years

Work-related 0 0 5 1 3 22 9 10 19 11

Household 28 29 23 25 45 65 49 67 78 58

Childcare 0 0 0 0 0 0 0 2 9 2

Goods/Services 0 18 14 24 24 34 31 26 15 26

Personal Needs and Care 1,123 1,115 971 922 894 858 820 747 703 802

Education 0 0 110 94 25 40 81 134 151 100

Organizational Activities 0 0 0 0 0 2 3 8 13 6

Entertainment/Social 0 0 0 1 13 6 16 17 52 18

Recreation 0 0 10 147 256 305 270 224 175 228

Communication/Passive
Leisure 290 278 308 226 179 107 161 203 225 189

Sample Sizes 4 10 11 23 43 50 151 225 59 576

a See Table 16-3 for a description of what is included in each activity category.

b The source data end at 11 years of age, so the 11 to <16 year category is truncated and the 16 to <21 year category is not included. The data for this age group and category are two values of zero and one of 540. Note:	 Column totals may not sum to 1,440 due to rounding.

Source:	 U.S. EPA analysis of source data used by Wiley et al., 1991.



### Page Child-Specific Exposure Factors Handbook

16-18 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-5. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Ten Major Activity Categories, Grouped by Seasons and Regions

Season Region of California
Activity Category a Winter Spring Summer Fall All Southern Bay Rest of All (Jan-Mar) (Apr-June) (July-Sept) (Oct-Dec) Seasons Coast Area State Regions

Work-related 10 10 6 13 10 10 10 8 10

Household 47 58 53 52 53 45 62 55 53

Childcare <1 1 <1 <1 <1 <1 <1 1 <1

Goods/Services 19 17 26 23 21 20 21 23 21

Personal Needs and 799 774 815 789 794 799 785 794 794 Care

Education 124 137 49 131 110 109 115 109 110

Organizational 3 5 5 3 4 2 6 6 4 Activities

Entertainment/Social 14 12 12 22 15 17 10 16 15

Recreation 221 243 282 211 239 230 241 249 239

Communication/ 203 180 189 195 192 206 190 175 192 Passive Leisure

Don't know/Not coded <1 2 3 <1 2 1 1 3 2

All Activities b 1,442 1,439 1,441 1,441 1,441 1,440 1,442 1,439 1,441

Sample Sizes 318 204 407 271 1,200 224 263 713 1,200 (Unweighted)

a See Table 16-3 for a description of what is included in each activity category.

b The column totals may not be equal to 1,440 due to rounding.

Source: Wiley et al., 1991.



### Child-Specific Exposure Factors Handbook Page September 2008 16-19

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-6. Time (minutes/day) Children Under 12 Years of Age Spent in Six Major Location Categories, for All Respondents and Doers

Location Category Mean

Duration

(All) % Doers a Mean

Duration (Doers) a Median

Duration (Doers) a Maximum

Duration (Doers) a Detailed Location with Highest Average Time

Home 1,078 99 1,086 1,110 1,440 Home - Bedroom

School/Childcare 109 33 330 325 1,260 School or Daycare Facility

Friend's/Other's House 80 32 251 144 1,440 Friend's/Other's House - Bedroom

Stores, Restaurants, Shopping Places 24 35 69 50 475 Shopping Mall

In-transit 69 83 83 60 1,111 Traveling in Car

Other Locations 79 57 139 105 1,440 Park, Playground

Don't Know/Not Coded <1 1 37 30 90 -

All Locations 1,440 -

- - - -

a Doers indicate the respondents who reported participating in each activity category.

Source: Wiley et al., 1991.



### Page Child-Specific Exposure Factors Handbook

16-20 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-7. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Six Location Categories, Grouped by Age and Gender

(Unweighted) Boys

Location Category Birth to

1 Month 1 to <3

Months 3 to <6

Months 6 to <12

Months 1 to <2

Years 2 to <3

Years 3 to <6

Years 6 to <11

Years 11 Years a Birth to 11

Years

Home 938 1,295 1,164 1,189 1,177 1,161 1,102 1,016 1,010 1,079

School/Childcare 0 1 26 53 73 86 79 110 99 89

Friend's/Other's House 418 40 127 63 54 69 89 110 111 95

Stores, Restaurants, Shopping Places 0 14 21 36 29 22 24 23 20 24

In-transit 77 51 69 63 56 61 67 64 72 65

Other Locations 7 40 33 36 52 41 78 116 127 88

Don't Know/Not Coded 0 0 0 0 0 0 0 0 0 0

Sample Sizes (Unweighted) 3 7 15 31 54 62 151 239 62 624

Girls
Location Category Birth to

1 Month 1 to <3

Months 3 to <6

Months 6 to <12

Months 1 to <2

Years 2 to <3

Years 3 to <6

Years 6 to <11

Years 11 Years a Birth to 11

Years

Home 1,285 1,341 1,151 1,192 1,162 1,065 1,118 1,012 862 1,058

School/Childcare 0 0 109 99 56 61 78 116 128 95

Friend's/Other's House 0 12 44 32 109 103 66 119 193 103

Stores, Restaurants, Shopping Places 0 13 20 15 21 40 32 25 24 27

In-transit 73 56 42 58 55 86 78 70 95 74

Other Locations 83 19 73 43 38 86 67 97 137 84

Don't Know/Not Coded 0 0 0 0 0 0 1 0 0 0

Sample Sizes 4 10 11 23 43 50 151 225 59 576

a The source data end at 11 years of age, so the 11 to <16 year category is truncated and the 16 to <21 year category is

not included. Note:	 Column totals may not sum to 1,440 due to rounding.

Source:	 U.S. EPA analysis of source data used by Wiley et al., 1991.



### Child-Specific Exposure Factors Handbook Page September 2008 16-21

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-8. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Six Location Categories, Grouped by Season and Region

Season Region of California
Location Category Winter Spring Summer Fall All Southern Bay Rest of All (Jan-Mar) (Apr-June) (July-Sept) (Oct-Dec) Seasons Coast Area State Regions

Home 1,091 1,042 1,097 1,081 1,078 1,078 1,078 1,078 1,078

School/Childcare 119 141 52 124 109 113 103 108 109

Friend's/Other's

House 69 75 108 69 80 73 86 86 80

Stores, Restaurants, Shopping Places 22 21 30 24 24 26 23 23 24

In transit 75 75 60 65 69 71 73 63 69

Other Locations 63 85 93 76 79 79 76 81 79

Don't Know/Not

Coded <1 <1 <1 <1 <1 <1 <1 <1 <1

All Locations a 1,439 1,439 1,440 1,439 1,439 1,439 1,440 1,440 1,439

Sample Sizes (Unweighted N's) 318 204 407 271 1,200 224 263 713 1,200

a The column totals may not sum to 1,440 due to rounding.

Source: Wiley et al., 1991.



### Page Child-Specific Exposure Factors Handbook

16-22 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-9. Mean Time (minutes/day) Children Under 12 Years of Age Spent in Proximity to Two Potential Sources of Exposure, Grouped by All Respondents, Age, and Gender

Boys Potential Birth to	 1 to <3 3 to <6 6 to <12 1 to <2 2 to <3 3 to <6 6 to <11 11 Birth to 11 Exposures 1Month Months Months Months Years Years Years Years Years a Years

Gasoline Fumes 3 9 0 2 1 4 2 2 7 3

Gas Oven Fumes 0 0 2 2 1 3 0 1 0 1

Sample Sizes 3	 7 15 31 54 62 151 239 62 624 (Unweighted N's)

Girls Potential Birth to	 1 to <3 3 to <6 6 to <12 1 to <2 2 to <3 3 to <6 6 to <11 11 Birth to 11 Exposures 1Month Months Months Months Years Years Years Years Years a Years

Gasoline Fumes 0 3 0 3 1 2 1 2 1 2

Gas Oven Fumes 0 0 0 0 0 3 2 1 0 1

Sample Sizes 4 10 11 23 43 50 151 225 59 576 (Unweighted N's)

a The source data end at 11 years of age, so the 11 to <16 year category is truncated and the 16 to <21 year category is not

included.

Source:	 U.S. EPA analysis of source data used by Wiley et al., 1991.



### Child-Specific Exposure Factors Handbook Page September 2008 16-23c 

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-10. Mean Time (minutes/day) Children Under 12 Years of Age Spent Indoors and Outdoors, Grouped by Age and Gender

Age Group N Boys

Indoors a Outdoors b N Girls

Indoors a Outdoors b

Birth to <1 Month 3 1,440 0 4 1,440 0

1 to <3 Months 7 1,432 8 10 1,431 9

3 to <6 Months 15 1,407 33 11 1,421 19

6 to <12 Months 31 1,322 118 23 1,280 160

1 to <2 Years 54 1,101 339 43 1,164 276

2 to <3 Years 62 1,121 319 50 1,102 338

3 to <6 Years 151 1,117 323 151 1,140 300

6 to <11 Years 239 1,145 295 225 1,183 255

11 Years c 62 1,166 274 59 1,215 225

All Ages 624 1,181 258 576 1,181 258

a Time indoors was estimating by adding the average times spent performing indoor activities (household work, child care, personal needs and care, education, and communication/passive leisure) and half the time spent in each activity which could have occurred either indoors or outdoors (i.e., work-related, goods/services, organizational activities, entertainment/social, don’t know/not coded).

b Time outdoors was estimated by adding the average time spent in recreation activities and half the time spent in each activity which could have occurred either indoors or outdoors (i.e., work-related, goods/services, organizational activities, entertainment/social, don’t know/not coded). The source data end at 11 years of age, so the 11 to <16 year category is truncated and the 16 to <21 year category is not

included. N	 = Sample size. Note:	 Indoor and outdoor minutes/day may not sum to 1,440 minutes/day due to rounding.

Source:	 U.S. EPA analysis of source data used by Wiley et al., 1991.



### Page Child-Specific Exposure Factors Handbook

16-24 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-11. Time Spent (minutes/day) in Various Rooms at Home and in All Rooms Combined Whole Population and Doers Only

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Kitchen - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 36

56

48

47

42

37

34 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 10

40

30

30

30

24

15 70

90

75

75

60

55

50 109

132

120

105

105

90

90 125

195

146

150

135

130

130 134

232

173

180

150

180

170 158

242

188

222

196

249

195 195

392

215

362

690

450

545

Kitchen - DOERS ONLY

Birth to <1

1 to <4

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 33

76

80

252

342

323

305 69

87

70

67

61

54

54 10

10

10

2

1

1

1 10

10

10

5

2

2

2 10

13

11

10

5

4

3 13

19

15

15

10

5

5 15

30

15

15

15

10

10 30

45

30

30

30

20

20 70

70

60

60

50

40

35 90

110

105

90

79

65

65 124

173

136

133

120

114

120 133

214

155

165

145

150

159 157

240

184

210

172

218

194 176

281

195

232

229

281

209 195

392

215

362

690

450

545

Living Room/Family Room/Den - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 279

172

173

164

137

170

157 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 90

25

56

45

30

36

0 210

120

138

122

95

120

120 420

279

239

240

210

240

240 666

410

346

376

322

395

370 724

533

499

476

420

570

501 788

616

599

680

547

687

690 938

652

680

742

612

774

819 1,180

810 1,125

900

695 1,305 1,080

Living Room/Family Room/Den - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 54

93

105

290

403

380

352 326

219

195

202

169

209

214 25

10

1

5

5

2

5 28

15

5

8

10

10

10 31

19

10

19

10

16

15 57

25

22

30

20

30

24 90

60

34

50

30

45

40 136

90

90

90

60

85

85 268

180

150

153

130

165

165 450

310

255

270

240

275

285 686

444

377

415

349

436

440 744

540

527

498

449

594

547 789

642

603

705

579

705

720 973

667

691

778

655

776

909 1,180

810 1,125

900

695 1,305 1,080



### Child-Specific Exposure Factors Handbook Page September 2008 16-25

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-11. Time Spent (minutes/day) in Various Rooms at Home and in All Rooms Combined Whole Population and Doers Only (continued)

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Dining Room - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 9

19

19

17

13

11

7 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

17

30

10

5

0

0 30

60

80

60

57

33

30 70

90

105

96

70

65

45 86

176

118

133

120

119

90 96

260

146

150

135

164

112 105

315

150

300

225

390

330

Dining Room - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 9

32

34

93

126

90

67 60

72

65

65

53

59

50 15

10

15

10

5

5

5 - 12

15

10

5

5

5 - 13

15

10

5

5

7 - 16

18

15

6

10

15 - 30

29

16

15

15

15 - 34

30

30

30

30

20 - 53

60

55

45

38

35 - 66

90

85

60

69

60 - 110

105

120

98

122

90 - 237

134

150

135

166

124 - 287

150

209

150

202

135 - 301

150

286

196

283

201 105

315

150

300

225

390

330

Bathroom - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 16

26

29

22

22

20

26 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

1

0

0

0

10 0

15

20

15

15

15

20 30

30

30

30

30

30

32 40

45

60

49

45

45

59 59

60

62

65

60

60

65 81

80

138

90

81

86

105 87

239

290

120

118

97

123 90

600

345

270

535

220

547

Bathroom - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 31

77

88

240

356

335

392 32

39

38

33

31

29

31 5

6

2

1

1

1

1 7

6

3

1

2

2

2 8

8

5

2

3

2

5 10

10

12

5

5

5

5 15

15

15

11

9

6

10 18

15

15

15

15

12

15 30

30

30

30

25

20

25 40

30

45

38

35

35

40 60

57

60

60

50

50

60 78

60

70

75

60

64

72 87

176

208

112

90

90

111 89

349

319

123

180

100

135 90

600

345

270

535

220

547



### Page Child-Specific Exposure Factors Handbook

16-26 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-11. Time Spent (minutes/day) in Various Rooms at Home and in All Rooms Combined Whole Population and Doers Only (continued)

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Bedroom - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 749

771

701

696

653

626

588 0

0

0

0

0

0

0 0

56

5

92

0

0

0 104

340

91

210

0

20

0 468

443

419

432

304

134

60 566

559

517

540

480

403

335 653

645

618

630

585

543

475 750

808

718

695

660

645

595 863

884

835

790

735

745

720 972

975

894

875

840

860

855 1,092 1,029

931

945

906

950

960 1,119 1,190

979 1,033 1,005 1,027 1,082 1,179 1,325

990 1,135 1,096 1,118 1,146 1,275 1,440 1,040 1,440 1,440 1,277 1,375

Bedroom - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 61

116

116

353

486

457

463 774

785

713

704

667

638

611 435

330

30

165

120

15

15 453

362

215

210

183

55

34 470

384

266

268

261

115

100 495

450

484

464

439

179

273 590

570

520

540

513

430

395 660

656

620

630

599

550

480 750

810

720

695

660

646

600 865

885

836

790

735

750

725 975

975

896

875

843

860

859 1,095 1,030

931

945

912

951

974 1,119 1,191

981 1,034 1,005 1,029 1,090 1,182 1,328

990 1,137 1,100 1,122 1,147 1,275 1,440 1,040 1,440 1,440 1,277 1,375

Garage - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 1

0

0

1

0

2

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

19

0 34

0

0

7

0

51

0 89

0

0

165

120

240

60

Garage - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 1

0

0

4

3

12

4 -

-

-

-

- 79

- 89

-

- 15

30

10

10 -

-

-

-

- 11

- -

-

-

-

- 11

- -

-

-

-

- 13

- -

-

-

-

- 16

- -

-

-

-

- 20

- -

-

-

-

- 40

- -

-

-

-

- 139

- -

-

-

-

- 183

- -

-

-

-

- 210

- -

-

-

-

- 228

- -

-

-

-

- 234

- 89

-

- 165

120

240

60



### Child-Specific Exposure Factors Handbook Page September 2008 16-27

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-11. Time Spent (minutes/day) in Various Rooms at Home and in All Rooms Combined Whole Population and Doers Only (continued)

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

All Rooms Combined - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 1,091 1,047

971

951

873

876

819 0

0

0

0

0

0

0 391

63

66

284

0

0

0 631

377

342

402

0

117

165 742

651

640

621

420

370

375 786

705

727

716

631

575

510 943

915

852

810

758

751

645 1,105 1,050

995

930

880

871

810 1,258 1,239 1,120 1,110 1,005 1,043

995 1,440 1,440 1,232 1,245 1,175 1,215 1,170 1,440 1,440 1,295 1,354 1,275 1,314 1,287 1,440 1,440 1,354 1,440 1,374 1,440 1,419 1,440 1,440 1,369 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440

All Rooms Combined- DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 62

116

117

355

486

459

473 1,108 1,065

979

957

893

889

833 630

370

30

150

190

40

85 633

399

288

352

335

141

206 658

495

551

451

389

300

321 751

674

650

634

541

441

433 821

715

746

720

655

590

525 956

923

857

810

765

758

660 1,108 1,050 1,005

930

885

875

815 1,259 1,243 1,120 1,110 1,009 1,046 1,000 1,440 1,440 1,232 1,245 1,177 1,218 1,170 1,440 1,440 1,296 1,355 1,275 1,315 1,288 1,440 1,440 1,355 1,440 1,385 1,440 1,420 1,440 1,440 1,369 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440 1,440

N = Sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes less than 10.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Page Child-Specific Exposure Factors Handbook

16-28 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-12. Time Spent (minutes/day) at Selected Indoor Locations Whole Population and Doers Only

Percentiles Age (years) N Mean Min Max
1 2 5 10 25 50 75 90 95 98 99

Restaurants - Whole Population

Birth to <1 63 13 0 0 0 0 0 0 0 0 45 69 105 194 330

1 to <2 118 7 0 0 0 0 0 0 0 0 30 62 88 102 120

2 to <3 118 9 0 0 0 0 0 0 0 0 45 62 92 111 120

3 to <6 357 7 0 0 0 0 0 0 0 0 21 52 90 120 130

6 to <11 497 6 0 0 0 0 0 0 0 0 15 45 85 110 180

11 to <16 466 10 0 0 0 0 0 0 0 0 35 60 90 137 315

16 to <21 481 35 0 0 0 0 0 0 0 20 105 240 380 466 645

Restaurants - DOERS ONLY

Birth to <1 10 85 10 - - - - - - - -

- - - 330

1 to <2 15 58 5 6 8 12 21 33 55 83 99 110 116 118 120

2 to <3 17 63 20 21 22 24 28 45 60 80 102 116 118 119 120

3 to <6 43 57 4 7 9 10 16 30 45 90 120 120 122 126 130

6 to <11 57 54 5 5 6 10 15 30 45 60 107 124 140 158 180

11 to <16 78 59 2 3 7 10 18 30 45 65 102 141 223 283 315

16 to <21 135 126 1 4 5 10 17 30 60 170 334 437 537 546 645

School - Whole Population

Birth to <1 63 4 0 0 0 0 0 0 0 0 0 0 46 100 165

1 to <2 118 13 0 0 0 0 0 0 0 0 0 22 156 453 665

2 to <3 118 23 0 0 0 0 0 0 0 0 0 193 414 503 545

3 to <6 357 75 0 0 0 0 0 0 0 0 416 540 569 589 630

6 to <11 497 187 0 0 0 0 0 0 0 397 444 480 552 601 665

11 to <16 466 201 0 0 0 0 0 0 0 420 459 495 578 630 855

16 to <21 481 131 0 0 0 0 0 0 0 308 430 495 566 629 855

School - DOERS ONLY

Birth to <1 2

- 60 - - - - - - - -

- - - 165

1 to <2 8

- 5 - - - - - - - -

- - - 665

2 to <3 11 251 10 10 10 10 10 83 269 388 510 528 538 542 545

3 to <6 71 379 5 23 34 110 160 228 418 540 570 590 615 627 630

6 to <11 235 396 5 64 129 195 305 370 400 435 480 540 612 643 665

11 to <16 229 409 15 38 96 132 290 395 420 450 495 559 631 696 855

16 to <21 171 367 15 22 31 90 185 270 388 440 525 576 726 801 855



### Child-Specific Exposure Factors Handbook Page September 2008 16-29

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-12. Time Spent (minutes/day) at Selected Indoor Locations Whole Population and Doers Only (continued)

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Grocery/Convenience Stores, Other Stores, and Malls - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 39

16

18

17

14

18

36 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 30

0

0

0

0

0

15 98

62

60

62

49

54

120 178

87

86

111

101

122

230 224

146

133

189

167

204

402 241

202

250

223

225

300

484 250

255

360

420

320

413

960

Grocery/Convenience Stores, Other Stores, and Malls - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 21

23

27

64

91

104

146 88

81

80

96

76

82

120 5

5

10

5

3

1

2 5

7

11

5

3

2

4 5

9

13

5

5

5

5 5

17

20

16

5

10

5 24

30

33

23

14

10

10 30

55

45

50

20

20

22 55

65

60

73

60

45

60 130

93

82

116

110

120

149 190

152

120

204

170

199

330 235

205

234

236

230

300

456 244

235

313

339

255

359

517 247

245

337

382

262

383

562 250

255

360

420

320

413

960

N = Sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes less than 10.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Page Child-Specific Exposure Factors Handbook

16-30 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-13. Time Spent (minutes/day) in Selected Outdoor Locations Whole Population and Doers Only

Percentiles Age (years) N Mean Min Max 1 2 5 10 25 50 75 90 95 98 99

School Grounds/Playground - Whole Population

Birth to <1 63 2 0 0 0 0 0 0 0 0 0 0 0 53 140

1 to <2 118 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 to <3 118 4 0 0 0 0 0 0 0 0 0 0 50 131 175

3 to <6 357 5 0 0 0 0 0 0 0 0 0 0 64 127 625

6 to <11 497 8 0 0 0 0 0 0 0 0 10 60 121 170 315

11 to <16 466 10 0 0 0 0 0 0 0 0 20 80 120 160 570

16 to <21 481 8 0 0 0 0 0 0 0 0 0 50 135 180 510

School Grounds/Playground - DOERS ONLY

Birth to <1 1

- 140

- - - - - - - - - - - 140

1 to <2 0

- - - - - - - - - - - - - - 2 to <3 5

- 10

- - - - - - - - - - - 175

3 to <6 12 138 20 22 24 31 42 59 118 138 150 364 521 573 625

6 to <11 52 80 10 10 10 10 15 30 59 106 169 217 280 298 315

11 to <16 62 72 3 4 5 5 5 21 53 95 149 178 217 360 570

16 to <21 34 116 10 10 10 13 18 46 95 161 201 305 418 464 510

Parks or Golf Courses - Whole Population

Birth to <1 63 3 0 0 0 0 0 0 0 0 0 0 45 63 85

1 to <2 118 3 0 0 0 0 0 0 0 0 0 0 0 25 360

2 to <3 118 12 0 0 0 0 0 0 0 0 0 24 126 246 755

3 to <6 357 10 0 0 0 0 0 0 0 0 0 71 163 220 585

6 to <11 497 16 0 0 0 0 0 0 0 0 0 72 328 483 665

11 to <16 466 19 0 0 0 0 0 0 0 0 0 114 265 452 1,065

16 to <21 481 22 0 0 0 0 0 0 0 0 0 150 381 546 870

Parks or Golf Courses - DOERS ONLY

Birth to <1 3

- 30

- - - - - - - - - - - 85

1 to <2 2

- 30

- - - - - - - - - - - 360

2 to <3 7

- 21

- - - - - - - - - - - 755

3 to <6 26 144 25 26 28 31 44 63 113 165 273 388 505 545 585

6 to <11 34 236 25 30 35 43 52 73 123 394 568 644 662 663 665

11 to <16 38 237 15 15 15 15 27 86 164 266 470 851 954 1,010 1,065

16 to <21 47 225 1 7 14 15 24 60 160 308 557 633 677 773 870



### Child-Specific Exposure Factors Handbook Page September 2008 16-31

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-13. Time Spent (minutes/day) in Selected Outdoor Locations Whole Population and Doers Only (continued)

Percentiles Age (years) N Mean Min Max 1 2 5 10 25 50 75 90 95 98 99

Pool, River, or Lake - Whole Population

Birth to <1 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 to <2 118 1 0 0 0 0 0 0 0 0 0 0 0 0 118

2 to <3 118 12 0 0 0 0 0 0 0 0 0 14 228 352 435

3 to <6 357 5 0 0 0 0 0 0 0 0 0 0 85 163 630

6 to <11 497 9 0 0 0 0 0 0 0 0 0 0 220 295 375

11 to <16 466 4 0 0 0 0 0 0 0 0 0 0 60 160 235

16 to <21 481 8 0 0 0 0 0 0 0 0 0 0 145 240 570

Pool, River, or Lake - DOERS ONLY

Birth to <1 0 - - - - - - - - - -

- - - - 1 to <2 1 - 118 - - - - - - - -

- - - 118

2 to <3 6 - 95 - - - - - - - -

- - - 435

3 to <6 9 - 45 - - - - - - - -

- - - 630

6 to <11 24 178 25 26 27 32 46 75 155 294 319 359 370 373 375

11 to <16 16 121 58 58 59 59 60 60 85 206 225 228 232 234 235

16 to <21 22 179 20 22 24 31 40 55 125 238 415 548 564 567 570

N = Sample size.

Min = Minimum.

Max = Maximum.

- = Percentiles were not calculated for sample sizes less than 10.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Page Child-Specific Exposure Factors Handbook

16-32 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-14. Mean Time Spent (minutes/day) Inside and Outside, by Age Category

Age (years) N Average Indoor Minutes a Average Outdoor Minutes b Average Unclassified Minutes c

Birth to <1

1 to < 2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 25

90

131

360

511

449

493 1,353 1,353 1,316 1,278 1,244 1,260 1,248 44

36

76

107

132

100

102 43

51

48

54

64

80

90

a Time indoors was estimating by adding the average times spent indoors at the respondents’ home (kitchen, living room, bathroom, etc.), at other houses, and inside other locations such as school, restaurants, etc.

b Time outdoors was estimated by adding the average time spent outdoors at the respondents’ pool and yard, others’ pool and yard, and outside other locations such as sidewalk, street, neighborhood, parking lot, service station/gas station, school grounds, park/golf course, pool, river, lake, farm, etc.

c Includes time spent in vehicles or in activities that could not be assigned an indoor or outdoor location. N = Sample size.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Child-Specific Exposure Factors Handbook Page September 2008 16-33

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-15. Time Spent (minutes/day) in Selected Vehicles and All Vehicles Combined Whole Population and Doers Only

Percentiles Age (years) N Mean Min Max
1 2 5 10 25 50 75 90 95 98 99

Car - Whole Population

Birth to <1 63 36 0 0 0 0 0 0 10 49 107 171 208 220 235

1 to <2 118 41 0 0 0 0 0 0 20 60 98 151 246 336 390

2 to <3 118 33 0 0 0 0 0 0 20 50 90 126 163 187 215

3 to <6 357 43 0 0 0 0 0 0 20 60 117 155 221 272 620

6 to <11 497 37 0 0 0 0 0 0 15 55 102 146 185 212 630

11 to <16 466 39 0 0 0 0 0 0 15 55 99 150 254 302 900

16 to <21 481 61 0 0 0 0 0 8 40 90 155 195 249 321 380

Car - DOERS ONLY

Birth to <1 35 65 2 5 7 10 14 20 40 73 159 203 218 227 235

1 to <2 68 72 5 8 10 10 15 30 58 85 147 186 323 363 390

2 to <3 73 54 4 4 4 8 10 24 42 65 118 141 181 197 215

3 to <6 227 67 4 4 5 7 10 25 45 88 150 180 267 327 620

6 to <11 317 58 1 2 2 5 10 20 40 82 127 163 202 300 630

11 to <16 286 64 1 3 5 5 10 20 40 75 122 193 279 338 900

16 to <21 364 81 2 9 10 10 17 30 60 105 180 210 275 334 380

Truck (Pickup or Van) - Whole Population

Birth to <1 63 2 0 0 0 0 0 0 0 0 0 0 0 42 110

1 to <2 118 2 0 0 0 0 0 0 0 0 0 0 52 81 90

2 to <3 118 14 0 0 0 0 0 0 0 0 14 31 124 201 955

3 to <6 357 5 0 0 0 0 0 0 0 0 0 30 60 114 245

6 to <11 497 7 0 0 0 0 0 0 0 0 15 45 95 110 240

11 to <16 466 9 0 0 0 0 0 0 0 0 15 59 153 181 352

16 to <21 481 11 0 0 0 0 0 0 0 0 25 90 150 190 445

Truck (Pickup or Van) - DOERS ONLY

Birth to <1 1 - 110 - - - - - - - - -

- - 110

1 to <2 5 - 20 - - - - - - - - -

- - 90

2 to <3 15 109 10 10 10 10 11 15 30 53 188 434 746 851 955

3 to <6 34 53 1 2 4 8 10 16 30 59 117 207 222 233 245

6 to <11 69 48 1 4 6 10 10 15 30 65 110 124 151 186 240

11 to <16 62 67 5 5 5 5 7 15 35 89 180 185 258 299 352

16 to <21 70 78 5 5 5 10 11 22 54 115 170 213 238 304 445



### Page Child-Specific Exposure Factors Handbook

16-34 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-15. Time Spent (minutes/day) in Selected Vehicles and All Vehicles Combined Whole Population and Doers Only (continued)

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Bus - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 0

0

1

2

11

16

6 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

15

0 0

0

0

0

50

60

0 0

0

0

0

70

89

45 0

0

0

30

90

119

108 0

0

25

47

110

148

135 0

0

120

80

140

370

225

Bus - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 0

0

2

14

115

130

41 -

-

- 40

49

58

75 -

- 30

15

5

7

10 -

-

- 16

5

10

12 -

-

- 16

6

10

14 -

-

- 18

14

10

20 -

-

- 21

17

15

25 -

-

- 30

25

30

30 -

-

- 33

43

54

60 -

-

- 49

67

71

100 -

-

- 67

90

101

135 -

-

- 74

107

131

175 -

-

- 77

120

159

193 -

-

- 79

122

175

209 -

- 120

80

140

370

225

All Vehicles - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 63

118

118

357

497

466

481 39

44

50

50

57

67

84 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

15

15

25 20

28

30

30

40

45

62 60

60

60

65

85

85

120 113

98

120

122

124

155

180 171

151

151

167

155

206

239 208

246

203

238

212

291

328 220

336

214

272

289

383

382 235

390

955

620

630

900

675

All Vehicles - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 37

72

86

261

417

383

428 66

72

69

68

68

82

94 2

5

4

1

1

1

5 5

9

4

4

2

5

8 8

10

5

6

4

5

10 10

10

10

10

10

10

15 16

20

10

13

14

16

20 20

30

26

30

25

30

40 46

60

45

46

55

60

75 75

85

83

85

90

99

120 151

143

128

150

130

177

190 202

178

166

190

161

235

240 217

316

212

261

240

314

345 226

362

326

309

306

392

386 235

390

955

620

630

900

675

N = Sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes less than 10.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Child-Specific Exposure Factors Handbook Page September 2008 16-35

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-16. Time Spent (minutes/day) in Selected Activities Whole Population and Doers Only

Percentiles Age (years) N Mean Min Max 1 2 5 10 25 50 75 90 95 98 99

Sleeping/Napping - Whole Population

Birth to <1 63 782 485 519 546 579 613 668 762 873 1,011 1,080 1,121 1,144 1,175

1 to <2 118 779 360 483 510 579 627 700 780 855 925 962 987 1098 1,320

2 to <3 118 716 270 365 470 523 594 635 708 805 870 917 937 944 990

3 to <6 357 681 0 480 510 539 573 630 675 735 795 840 893 916 1,110

6 to <11 497 613 120 295 390 458 510 570 625 660 720 750 831 868 945

11 to <16 466 569 0 320 376 415 450 510 558 630 705 762 809 907 1,015

16 to <21 481 537 0 239 295 360 390 450 525 615 690 750 840 906 1,317

Sleeping/Napping - DOERS ONLY

Birth to <1 63 782 485 519 546 579 613 668 762 873 1,011 1,080 1,121 1,144 1,175

1 to <2 118 779 360 483 510 579 627 700 780 855 925 962 987 1,098 1,320

2 to <3 118 716 270 365 470 523 594 635 708 805 870 917 937 944 990

3 to <6 356 683 420 491 510 540 578 630 675 738 795 840 893 916 1,110

6 to <11 497 613 120 295 390 458 510 570 625 660 720 750 831 868 945

11 to <16 465 571 150 341 379 415 450 510 560 630 705 762 809 907 1,015

16 to <21 480 538 85 252 299 360 390 450 525 615 690 751 840 906 1,317

Eating - Whole Population

Birth to <1 63 117 0 6 12 36 45 73 110 145 194 224 334 345 345

1 to <2 118 98 0 10 10 29 40 60 90 120 167 206 233 244 270

2 to <3 118 92 15 15 15 20 30 60 89 120 157 176 198 208 270

3 to <6 357 78 0 0 0 15 28 45 75 105 135 150 180 217 265

6 to <11 497 65 0 0 0 10 20 35 60 88 115 139 155 176 255

11 to <16 466 52 0 0 0 0 10 30 45 74 100 120 146 162 205

16 to <21 481 52 0 0 0 0 0 20 40 65 105 135 192 210 630

Eating - DOERS ONLY

Birth to <1 62 118 10 16 23 40 46 77 110 148 195 224 335 345 345

1 to <2 117 99 10 10 12 30 40 60 90 120 167 206 234 244 270

2 to <3 118 92 15 15 15 20 30 60 89 120 157 176 198 208 270

3 to <6 349 80 2 10 15 20 30 45 75 105 135 150 180 218 265

6 to <11 480 67 5 10 10 15 20 40 60 90 115 140 157 179 255

11 to <16 432 56 2 5 7 10 20 30 50 75 100 125 148 163 205

16 to <21 426 59 2 5 9 10 15 30 45 75 105 144 197 210 630



### Page Child-Specific Exposure Factors Handbook

16-36 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-16. Time Spent (minutes/day) in Selected Activities Whole Population and Doers Only (continued)

Percentiles Age (years) N Mean Min Max 1 2 5 10 25 50 75 90 95 98 99

Attending School Full-Time - Whole Population

Birth to <1 63 11 0 0 0 0 0 0 0 0 0 0 83 265 550

1 to <2 118 28 0 0 0 0 0 0 0 0 0 204 546 594 665

2 to <3 118 65 0 0 0 0 0 0 0 0 334 502 564 618 710

3 to <6 357 73 0 0 0 0 0 0 0 0 392 510 558 581 630

6 to <11 497 183 0 0 0 0 0 0 0 390 435 460 525 570 645

11 to <16 466 187 0 0 0 0 0 0 0 409 445 464 487 500 595

16 to <21 481 117 0 0 0 0 0 0 0 270 408 445 489 551 825

Attending School Full-Time - DOERS ONLY

Birth to <1 3

- 60

- - - - - - - - - - - 550

1 to <2 9

- 20

- - - - - - - - - - - 665

2 to <3 20 385 20 37 53 103 119 226 458 520 576 632 679 694 710

3 to <6 71 366 30 37 66 128 165 203 395 510 558 583 615 627 630

6 to <11 234 389 60 125 164 211 311 370 390 425 460 497 570 600 645

11 to <16 217 401 10 86 108 270 343 385 415 440 467 485 505 548 595

16 to <21 162 347 20 46 78 126 195 270 370 420 459 519 567 609 825

Outdoor Recreation -Whole Population

Birth to <1 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 to <2 118 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 to <3 118 4 0 0 0 0 0 0 0 0 0 0 15 28 370

3 to <6 357 6 0 0 0 0 0 0 0 0 0 0 60 172 630

6 to <11 497 7 0 0 0 0 0 0 0 0 0 0 142 226 574

11 to <16 466 6 0 0 0 0 0 0 0 0 0 0 142 191 465

16 to <21 481 6 0 0 0 0 0 0 0 0 0 0 103 189 570

Outdoor Recreation - DOERS ONLY

Birth to <1 0

- - - - - - - - - - - - - - 1 to <2 0

- - - - - - - - - - - - - - 2 to <3 4

- 15

- - - - - - - - - - - 370

3 to <6 11 207 30 30 30 30 30 60 150 240 585 608 621 626 630

6 to <11 17 204 60 60 60 60 66 120 165 245 351 403 506 540 574

11 to <16 22 138 5 5 5 5 11 60 126 180 234 411 446 456 465

16 to <21 13 228 30 35 41 57 77 130 180 300 420 480 534 552 570



### Child-Specific Exposure Factors Handbook Page September 2008 16-37

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-16. Time Spent (minutes/day) in Selected Activities Whole Population and Doers Only (continued)

Percentiles Age (years) N Mean Min Max 1 2 5 10 25 50 75 90 95 98 99

Active Sports - Whole Population

Birth to <1 63 15 0 0 0 0 0 0 0 0 60 90 131 143 155

1 to <2 118 20 0 0 0 0 0 0 0 0 68 131 180 201 270

2 to <3 118 27 0 0 0 0 0 0 0 0 110 180 257 319 390

3 to <6 357 40 0 0 0 0 0 0 0 30 135 242 330 408 630

6 to <11 497 51 0 0 0 0 0 0 0 60 172 272 371 435 975

11 to <16 466 53 0 0 0 0 0 0 0 74 168 245 309 425 1,065

16 to <21 481 35 0 0 0 0 0 0 0 0 145 180 285 386 565

Active Sports - DOERS ONLY

Birth to <1 13 75 25 26 26 28 31 40 60 90 132 143 150 153 155

1 to <2 24 96 10 15 19 30 33 60 73 131 180 201 240 255 270

2 to <3 26 124 15 18 20 26 30 41 98 179 253 314 360 375 390

3 to <6 97 149 15 20 29 30 30 60 120 180 315 354 559 625 630

6 to <11 175 146 2 12 15 20 30 60 110 193 312 393 450 522 975

11 to <16 179 137 5 5 15 15 30 60 115 180 261 314 442 533 1,065

16 to <21 117 143 5 15 15 20 30 60 120 180 272 371 501 519 565

Exercise - Whole Population

Birth to <1 63 13 0 0 0 0 0 0 0 0 0 0 122 354 670

1 to <2 118 2 0 0 0 0 0 0 0 0 0 0 25 30 150

2 to <3 118 1 0 0 0 0 0 0 0 0 0 0 0 0 60

3 to <6 357 3 0 0 0 0 0 0 0 0 0 0 0 54 525

6 to <11 497 5 0 0 0 0 0 0 0 0 0 0 100 137 450

11 to <16 466 5 0 0 0 0 0 0 0 0 0 30 70 114 245

16 to <21 481 8 0 0 0 0 0 0 0 0 0 60 151 176 300

Exercise - DOERS ONLY

Birth to <1 2

- - - - - - - - - - - - - - 1 to <2 4

- - - - - - - - - - - - - - 2 to <3 1

- - - - - - - - - - - - - - 3 to <6 7

- - - - - - - - - - - - - - 6 to <11 20 124 15 17 19 25 30 60 100 146 226 284 384 417 450

11 to <16 28 75 20 21 23 27 30 42 60 101 128 148 194 219 245

16 to <21 41 99 15 15 15 25 30 40 90 145 180 240 260 280 300



### Page Child-Specific Exposure Factors Handbook

16-38 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-16. Time Spent (minutes/day) in Selected Activities Whole Population and Doers Only (continued)

Percentiles Age (years) N Mean Min Max 1 2 5 10 25 50 75 90 95 98 99

Walking - Whole Population

Birth to <1 63 6 0 0 0 0 0 0 0 0 9.2 29 64 104 160

1 to <2 118 2 0 0 0 0 0 0 0 0 0 10 40 58 60

2 to <3 118 3 0 0 0 0 0 0 0 0 10 17 45 54 60

3 to <6 357 3 0 0 0 0 0 0 0 0 4 20 35 60 60

6 to <11 497 4 0 0 0 0 0 0 0 0 14 30 40 55 170

11 to <16 466 10 0 0 0 0 0 0 0 0 30 55 79 130 190

16 to <21 481 8 0 0 0 0 0 0 0 0 20 45 90 127 410

Walking - DOERS ONLY

Birth to <1 9 - 4 - - - - - - -

- - - - 160

1 to <2 9 - 4 - - - - - - -

- - - - 60

2 to <3 19 19 1 1 1 2 2 7 10 28 51 56 58 59 60

3 to <6 44 20 1 1 1 1 2 5 15 30 56 60 60 60 60

6 to <11 118 18 1 1 1 2 2 5 10 25 40 51 65 94 170

11 to <16 190 25 1 1 1 2 3 5 14 30 60 78 134 154 190

16 to <21 128 30 1 1 2 2 3 5 18 32 62 120 148 175 410

N = Sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes less than 10.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Child-Specific Exposure Factors Handbook Page September 2008 16-39

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-17. Number of Showers Taken per Day, by Number of Respondents

Showers per Day Age (years) N

0 1 2 3 Don’t Know

Birth to <1 37 36 1 0 0 0

1 to <2 53 48 5 0 0 0

2 to <3 67 54 10 2 0 1

3 to <6 187 153 25 7 1 1

6 to <11 245 122 95 25 1 2

11 to <16 258 51 150 53 3 1

16 to <21 232 23 147 57 5 0

N = Total number.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Page Child-Specific Exposure Factors Handbook

16-40 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-18. Time Spent (minutes) Bathing, Showering, and in Bathroom Immediately after Bathing and Showering

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Duration of Bath (minutes)

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 26

37

48

125

89

38

17 19

23

23

24

24

25

33 5

10

1

5

5

5

10 5

10

2.9

5

5

6

11 5

10

5

5

5

6

12 6

10

7

6

10

10

14 8

10

10

10

10

10

18 10

15

15

15

15

16

20 18

20

20

25

20

20

30 28

30

30

30

30

30

45 30

30

30

35

31

40

60 30

32

45

60

46

43

60 45

41

60

60

60

60

61 53

43

60

61

60

61

61 60

45

60

61

61

61

61

Duration in Bathroom Immediately Following a Bath (minutes)

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 26

37

48

125

89

38

17 2

3

4

4

4

9

11 0

0

0

0

0

0

0 0

0

0

0

0

0

0 0

0

0

0

0

0

1 0

0

0

0

0

1

2 0

0

0

0

0

1

3 0

1

0

1

1

2

5 1

2

1.5

2

3

5

10 3

5

5

5

5

14

10 9

5

10

10

10

20

19 10

6

15

15

10

26

29 10

10

15

15

16

33

39 10

10

18

19

21

36

42 10

10

20

30

30

40

45

Sum of Duration in Bath and in Bathroom Immediately Following Bath (minutes)

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 26

37

48

125

89

38

17 22

26

26

28

28

33

45 6

10

6

5

6

7

15 7

10

7

6

6

8

15 8

11

8

7

9

10

16 9

12

10

10

10

12

17 10

16

14

12

13

16

21 12

17

16

18

20

23

30 19

30

23

30

25

31

40 29

32

34

32

33

41

60 32

35

45

48

41

52

73 38

41

50

60

60

64

77 55

46

60

66

63

70

82 63

48

61

69

71

70

83 70

50

61

76

80

70

85



### Child-Specific Exposure Factors Handbook Page September 2008 16-41

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-18. Time Spent (minutes) Bathing, Showering, and in Bathroom Immediately after Bathing and Showering (continued)

Age (years) N Mean Min Percentiles

Max
1 2 5 10 25 50 75 90 95 98 99

Duration of Shower (minutes)

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 1

5

12

33

119

204

207 15

20

22

17

18

18

20 15

5

5

3

4

3

3 -

- 5

4

5

4

5 -

- 5

4

5

5

5 -

- 5

5

5

5

5 -

- 6

5

7

6

8 -

- 14

10

10

10

10 -

- 20

15

15

15

15 -

- 30

20

20

20

30 -

- 30

30

30

30

40 -

- 44

34

41

40

45 -

- 53

47

57

50

60 -

- 57

54

60

60

60 15

30

60

60

60

60

61

Duration in Shower Room Immediately Following a Shower (minutes)

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 1

5

12

33

119

204

207 1

10

5

7

6

8

8 1

0

0

0

0

0

0 -

- 0

0

0

0

0 -

- 0

1

0

0

0 -

- 1

2

0

0

0 -

- 1

2

1

1

1 -

- 1

3

2

3

3 -

- 4

5

5

5

5 -

- 6

10

10

10

10 -

- 10

15

13

19

15 -

- 12

20

16

30

20 -

- 14

22

26

40

30 -

- 14

23

30

45

39 1

45

15

25

30

60

61

Sum of Shower Duration and Time Spent in Shower Room Immediately Following Shower (minutes)

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 1

5

12

33

119

204

207 16

30

27

24

24

26

28 16

6

6

8

5

4

4 -

- 6

8

6

5

5 -

- 7

8

6

7

7 -

- 8

8

8

10

10 -

- 11

8

10

11

10 -

- 19

13

15

15

15 -

- 21

25

20

22

25 -

- 33

30

30

35

35 -

- 44

40

43

50

50 -

- 56

45

50

60

60 -

- 65

57

61

65

74 -

- 67

64

68

70

89 16

60

70

70

90

70

121

N = Doer sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes less than 10. Note: A value of “61" was used for any shower, bath, or bathroom stay longer than 60 minutes. A value of "121" for the sum of shower duration and time spent in bathroom following shower (or the sum of bath duration and time spent in bathroom following bath) signifies that more than 120 minutes were spent.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Page Child-Specific Exposure Factors Handbook

16-42 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-19. Range of Number of Times Washing the Hands at Specified Daily Frequencies by the Number of Respondents

Number of Times/Day Age (years) N
0 1-2 3-5 6-9 10-19 20-29 30+ DK

Birth to <1 37 2 15 12 2 1 1 0 4

1 to <2 53 7 8 23 8 4 0 2 1

2 to <3 67 0 15 39 10 0 1 0 2

3 to <6 187 2 37 101 27 10 1 2 7

6 to <11 245 2 47 131 34 16 3 1 11

11 to <16 258 8 37 128 49 22 5 2 7

16 to <21 232 0 23 115 47 38 4 3 2

N = Total number. DK = Respondents answered "don't know".

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Child-Specific Exposure Factors Handbook Page September 2008 16-43

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

0

0

0

0

2

0

0

0

0

0

0

0

1

0 0

0

1

5

7

2

0

0

0

0

0

0

1

0 0

0

0

1

0

0

0

0

0

0

0

1

0

0 Table 16-20. Number of Times Swimming in a Month in Freshwater Swimming Pool by the Number of Respondents

0

0

0

1

0

0

0

0

0

0

0

0

0

0 Times/Month Age (years)

0

0

2

0

5

1

1

0

0

0

0

0

0

0 10 11 12 13 14 15 16

Birth to <1 10

(years) 18 20 23 24 25 26 28 29 30 32 40 42 45 50 60 DK

0

0

0

0

0

0

0 0

0

0

0

0

0

1 1to < 2

2 to <3 18 1

0

2

2

6

4

5

0

0

0

1

0

0

0 3 to <6 45

6 to <11 76 15 10 9

0

0

1

0

0

1

0

1

0

0

3

3

2

3 11 to <16 66 19 10

16 to <21 50 8

0

1

1

2

3

3

1

0

0

1

0

0

0

0 Times/Month Age 7

0

0

0

1

1

1

2

0

0

0

0

0

0

0 Birth to <1 10 6

2

0

1

1

3

4

2

0

0

0

0

0

0

2 1to < 2

2 to <3 18 5

0

1

1

2

5

5

6

0

0

0

3 to <6 45 1

1

0

1 6 to <11 76

11 to <16 66 4

0

0

0

5

5

3

6

0

0

0

0

1

0

0 16 to <21 50 3

1

1

1

6

5

6

2

0

0

0

0

0

0

0

= Doer sample size. DK = Respondents answered “don't know”. 2

4

3

4

7

6

0

0

0

2

3

2

6

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS). 1

1

2

3

5

6

0

0

0

0

0

1

0 8

8 N

N

N



### Page Child-Specific Exposure Factors Handbook

16-44 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-21. Time Spent (minutes/month) Swimming in Freshwater Swimming Pool

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 10

7

18

42

72

65

50 96

105

116

137

151

139

145 6

45

15

6

8

4

2 -

- 16

8

13

8

3 -

- 17

9

17

11

5 -

- 19

12

30

20

25 -

- 27

40

60

30

39 -

- 60

83

150

90

124 -

- 120

181

181

181

181 -

- 181

181

181

181

181 -

- 181

181

181

181

181 -

- 181

181

181

181

181 -

- 181

181

181

181

181 -

- 181

181

181

181

181 181

181

181

181

181

181

181

N = Doer sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes of 10 or fewer. Note: A value of 181 for number of minutes signifies that more than 180 minutes were spent.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Child-Specific Exposure Factors Handbook Page September 2008 16-45

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-22. Time Spent (minutes/day) Playing on Dirt, Sand/Gravel, or Grass Whole Population and Doers only

Percentiles Age (years) N Mean Min Max
1 2 5 10 25 50 75 90 95 98 99

Playing on Dirt - Whole Population

Birth to <1 11 15 0 0 0 0 0 0 0 10 20 71

1 to <2 37 20 0 0 0 0 0 0 0 10 84 121

2 to <3 61 18 0 0 0 0 0 0 0 20 60 120

3 to <6 179 29 0 0 0 0 0 0 0 59 120 121

6 to <11 98 28 0 0 0 0 0 0 0 60 120 121

11 to <16 35 25 0 0 0 0 0 0 1 30 77 120

16 to <21 7 9 0 - - - - - - - -

- Playing on Dirt - DOERS ONLY

Birth to <1 5 33 2 - - - - - - - -

- - - 121

1 to <2 13 56 5 5 5 5 6 10 45 120 121 121 121 121 121

2 to <3 24 47 5 5 5 5 7 15 30 60 121 121 121 121 121

3 to <6 82 63 1 1 1 1 6 30 60 120 121 121 121 121 121

6 to <11 44 63 2 3 5 10 15 30 60 120 121 121 121 121 121

11 to <16 18 49 1 2 2 4 9 19 30 60 120 120 121 121 121

16 to <21 2 30 30 - - - - - - - -

- - - 30

Playing on Sand/Gravel - Whole Population

Birth to <1 10 4 0 - - - - - - - -

- - - 20

1 to <2 37 17 0 0 0 0 0 0 0 30 60 84 121 121 121

2 to <3 58 24 0 0 0 0 0 0 0 30 120 121 121 121 121

3 to <6 186 30 0 0 0 0 0 0 2 60 120 121 121 121 121

6 to <11 101 30 0 0 0 0 0 0 0 60 120 121 121 121 121

11 to <16 36 30 0 0 0 0 0 0 0 38 120 121 121 121 121

16 to <21 8 42 0 - - - - - - - -

- - - 121

Playing on Sand/Gravel - DOERS ONLY

Birth to <1 2 18 15 - - - - - - - -

- - - 20

1 to <2 15 43 5 5 5 5 7 15 30 60 103 121 121 121 121

2 to <3 26 53 1 1 1 1 3 10 30 120 121 121 121 121 121

3 to <6 93 60 3 3 3 5 8 25 60 90 121 121 121 121 121

6 to <11 46 67 5 7 10 11 15 30 60 120 121 121 121 121 121

11 to <16 16 67 1 3 5 12 15 26 60 120 121 121 121 121 121

16 to <21 4 83 30 - - - - - - - -

- - - 121



### Page Child-Specific Exposure Factors Handbook

16-46 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-22. Time Spent (minutes/day) Playing on Dirt, Sand/Gravel, or Grass Whole Population and Doers Only (continued)

Age (years) N Mean Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Playing on Grass - Whole Population

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 11

38

59

180

99

36

8 43

62

55

69

62

67

45 0

0

0

0

0

0

0 0

0

0

0

0

0

- 0

0

0

0

0

0

- 0

0

0

0

0

0

- 0

9

1

0

0

1

- 2

16

15

28

20

30

- 30

60

30

60

60

60

- 73

120

120

121

120

120

- Playing on Grass - DOERS ONLY

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 9

35

53

157

85

32

6 52

68

62

79

73

75

60 1

5

1

1

1

1

15 - 7

2

2

5

5

- - 8

3

2

9

10

- - 10

3

10

11

23

- - 15

5

15

17

30

- - 25

20

60

30

30

- - 60

60

70

60

60

- - 120

120

121

120

120

- - 121

121

121

121

121

- - 121

121

121

121

121

- - 121

121

121

121

121

- - 121

121

121

121

121

- 121

121

121

121

121

121

120

N = Sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes of 10 or fewer. Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).



### Child-Specific Exposure Factors Handbook Page September 2008 16-47

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-23. Time Spent (minutes/day) Working or Being Near Excessive Dust in the Air

Age (years) N Min Percentiles

1 2 5 10 25 50 75 90 95 98 99 Max

Birth to <1

1 to <2

2 to <3

3 to <6

6 to <11

11 to <16

16 to <21 2

5

1

15

12

14

14 63

44

121

63

60

53

65 5

0

121

0

0

0

2 -

-

- 0

0

0

2 -

-

- 1

0

0

3 -

-

- 1

1

1

4 -

-

- 2

2

2

7 -

-

- 8

5

6

16 -

-

- 60

45

38

53 -

-

-

N = Doer sample size.

Min = Minimum.

Max = Maximum. - = Percentiles were not calculated for sample sizes of 10 or fewer. Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent.

Source: U.S. EPA re-analysis of source data from U.S. EPA, 1996 (NHAPS).

Table 16-24. Time Spent (minutes/day) with Smokers Present

Age (years) N Mean SD SE Min Percentiles

5 25 50 75 90 95 98 99 Max

1 to 4

5 to 11

12 to 17 155 367 325 26 5 30 90 273 570 825 1,010

224 318 314 21 1 25 105 190 475 775 1,050

256 246 244 15 1 10 60 165 360 595 774 1,140

1,210

864 1,305

1,250

1,020 1,440

1,440

1,260

N

Min

Max

Source: = Doer sample size.

= Minimum.

= Maximum.

U.S. EPA, 1996 (NHAPS).



### Page Child-Specific Exposure Factors Handbook

16-48 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-25. Mean Time Spent (minutes/day) Performing Major Activities, by Age, Sex and Type of Day

Age (3 to 11 years) Age (12 to 17 years)

Activity Weekdays Weekends Weekdays Weekends

Boys Girls Boys Girls Boys Girls Boys Girls (N=118) (N=111) (N=118) (N=111) (N=77) (N=83) (N=77) (N=83)

Market Work 16 0 7 4 23 21 58 25

Household Work 17 21 32 43 16 40 46 89

Personal Care 43 44 42 50 48 71 35 76

Eating 81 78 78 84 73 65 58 75

Sleeping 584 590 625 619 504 478 550 612

School 252 259

- - 314 342

- -
Studying 14 19 4 9 29 37 25 25

Church 7 4 53 61 3 7 40 36

Visiting 16 9 23 37 17 25 46 53

Sports 25 12 33 23 52 37 65 26

Outdoors 10 7 30 23 10 10 36 19

Hobbies 3 1 3 4 7 4 4 7

Art Activities 4 4 4 4 12 6 11 9

Playing 137 115 177 166 37 13 35 24

TV 117 128 181 122 143 108 187 140

Reading 9 7 12 10 10 13 12 19

Household Conversations 10 11 14 9 21 30 24 30

Other Passive Leisure 9 14 16 17 21 14 43 33

NA 22 25 20 29 14 17 10 4

Percent of Time Accounted for 94 92 93 89 93 92 88 89 by Activities Above

N = Sample size.

NA = Unknown.

- = No data.

Source: Timmer et al., 1985.



### Child-Specific Exposure Factors Handbook Page September 2008 16-49

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-26. Mean Time Spent (minutes/day) in Major Activities, by Type of Day for Five Different Age Groups

Activity Weekday Weekend Significant

Effects a Age (years) Age (years)

3-5 6-8 9-11 12-14 15-17 3-5 6-8 9-11 12-14 15-17

Market Work - 14 8 14 28 - 4 10 29 48

Personal Care 41 49 40 56 60 47 45 44 60 51 A,S,AxS (F>M)

Household Work 14 15 18 27 34 17 27 51 72 60 A,S, AxS (F>M)

Eating 82 81 73 69 67 81 80 78 68 65 A

Sleeping 630 595 548 473 499 634 641 596 604 562 A

School 137 292 315 344 314 - - - - -

Studying 2 8 29 33 33 1 2 12 15 30 A

Church 4 9 9 9 3 55 56 53 32 37 A

Visiting 14 15 10 21 20 10 8 13 22 56 A (Weekend Only)

Sports 5 24 21 40 46 3 30 42 51 37 A,S (M>F)

Outdoor Activities 4 9 8 7 11 8 23 39 25 26

Hobbies 0 2 2 4 6 1 5 3 8 3

Art Activities 5 4 3 3 12 4 4 4 7 10

Other Passive Leisure 9 1 2 6 4 6 10 7 10 18 A

Playing 218 111 65 31 14 267 180 92 35 21 A,S (M>F)

TV 111 99 146 142 108 122 136 185 169 157 A,S, AxS (M>F)

Reading 5 5 9 10 12 4 9 10 10 18 A

Being Read to 2 2 0 0 0 3 2 0 0 0 A

NA 30 14 23 25 7 52 7 14 4 9 A

a Effects are significant for weekdays and weekends, unless otherwise specified. A = age effect, P<0.05, for both weekdays and weekend activities; S = sex effect P<0.05, F>M, M>F = females spend more time than males, or vice versa; and AxS = age by sex interaction, P<0.05.

NA = Unknown.

- = No data.

Source: Timmer et al., 1985.



### Page Child-Specific Exposure Factors Handbook

16-50 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-27. Mean Time Spent (hours/day) Indoors and Outdoors, by Age and Day of the Week

Indoors a Outdoors b Age Group Weekday Weekend Weekday Weekend

3 to 5 years 19.4 18.9 2.5 3.1

6 to 8 years 20.7 18.6 1.8 2.5

9 to 11 years 20.8 18.6 1.3 2.3

12 to 14 years 20.7 18.5 1.6 1.9

15 to 17 years 19.9 17.9 1.4 2.3

a Time indoors was estimated by adding the average times spent performing indoor activities (household work, personal care, eating, sleeping, attending school, studying, attending church, watching television, and engaging in conversation) and half the time spent in each activity which could have occurred either indoors or outdoors (i.e., market work, sports, hobbies, art activities, playing, reading, and other passive leisure).

b Time outdoors was estimated by adding the average time spent in outdoor activities and half the time spent in each activity which could have occurred either indoors or outdoors (i.e., market work, sports, hobbies, art activities, playing, reading, and other passive leisure).

Source:	 Adapted from Timmer et al., 1985.



### Child-Specific Exposure Factors Handbook Page September 2008 16-51

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-28. Mean Time Spent (minutes/day) in Various Microenvironments, Children Ages 12 to 17 Years National and California Surveys

National Data Mean (Standard Error) a Microenvironment All Doers Only b N = 340 Autoplaces 2 (1) 73

Restaurant/Bar 9 (2) 60

In-vehicle/Internal Combustion 79 (7) 88

In-Vehicle/Other 0 (0) 12 Physical/Outdoors 32 (8) 130 Physical/Indoors 15 (3) 87 Work/Study-Residence 22 (4) 82 Work/Study-Other 159 (14) 354 Cooking 11 (3) 40

Other Activities/Kitchen 53 (4) 64

Chores/Child 91 (7) 92 Shop/Errands 26 (4) 68

Other/Outdoors 70 (13) 129

Social/Cultural 87 (10) 120

Leisure-Eat/Indoors 237 (16) 242 Sleep/Indoors 548 (31) 551

CARB Data Mean (Standard Error) a Microenvironment All Doer Only b N = 183 Autoplaces 16 (8) 124

Restaurant/Bar 16 (4) 44

In-Vehicle/Internal Combustion 78 (11) 89

In-Vehicle/Other 1 (0) 19 Physical/Outdoors 32 (7) 110 Physical/Indoors 20 (4) 65 Work/Study-Residence 25 (5) 76 Work/Study-Other 196 (30) 339 Cooking 3 (1) 19

Other Activities/Kitchen 31 (4) 51

Chores/Child 72 (11) 77 Shop/Errands 14 (3) 50

Other/Outdoors 58 (8) 78

Social/Cultural 63 (14) 109

Leisure-Eat/Indoors 260 (27) 270 Sleep/Indoors 557 (44) 560

a Weighted values.

b Doers only = respondents who reported participating in each activity/microenvironment.

Source: Robinson and Thomas, 1991.



### Page Child-Specific Exposure Factors Handbook

16-52 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-29. Gender and Age Groups

Age Group a N

6 to 8 years (males) 145

6 to 8 years (females) 124

9 to 11 years (males) 156

9 to 11 years(females) 160

12 to 17 years (males) 98

12 to 17 years (females) 85

a Children under the age of 6 were excluded because there were too few responses in the CARB study. N = Sample size.

Source: Funk et al., 1998.



### Child-Specific Exposure Factors Handbook Page September 2008 16-53

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-30. Assignment of At-Home Activities to Inhalation Rate Levels for Children

Low Moderate

Watching child care Night sleep Watch personal care

Homework

Radio use

TV use Records/tapes Reading books Reading magazines Reading newspapers Letters/writing

Other leisure

Homework/watch TV Reading/TV Reading/listen music Paperwork Outdoor cleaning Food Preparation Metal clean-up Cleaning house

Clothes care Car/boat repair Home repair

Plant care

Other household

Pet care Baby care

Child care Helping/teaching Talking/reading Indoor playing Outdoor playing

Medical child care Washing, hygiene

Medical care Help and care

Meals at home Dressing Visiting at home

Hobbies

Domestic crafts

Art

Music/dance/drama

Indoor dance

Conservations Painting room/home Building fire Washing/dressing Outdoor play Playing/eating Playing/talking Playing/watch TV TV/eating TV/something else Reading book/eating Read magazine/eat Read newspaper/eat

Source: Funk et al., 1998.



### Page Child-Specific Exposure Factors Handbook

16-54 September 2008c 

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-31. Aggregate Time Spent (minutes/day) At-Home in Activity Groups, by Adolescents and Children a

Activity Group Mean Adolescents

SD Mean Children

SD

Low 789 230 823 153

Moderate 197 131 241 b 136

High 1 11 3 17

High participants c 43 72 58 47

a Time spent engaging in all activities embodied by inhalation rate category (minutes/day).

b Significantly different from adolescents (p <0.05). Represents time spent at-home by individuals participating in high inhalation rate level activities (i.e., doers).

SD = Standard deviation.

Source: Funk et al., 1998.

Table 16-32. Comparison of Mean Time Spent (minutes/day) At-Home, by Gender (Adolescents)

Activity Group Mean Male

SD Mean Female

SD

Low 775 206 804 253

Moderate 181 126 241 134

High 2 16 0 0

SD = Standard deviation.

Source: Funk et al., 1998.



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Chapter 16 - Activity Factors

Table 16-33. Comparison of Mean Time Spent (minutes/day) At-Home, by Gender and Age for Children a

Males Females Activity Group 6-8 Years 9-11 Years 6-8 Years 9-11 Years

Mean SD Mean SD Mean SD Mean SD

Low 806 134 860 157 828 155 803 162

Moderate 259 135 198 111 256 141 247 146

High 3 17 7 27 1 9 2 10

High participant b 77 59 70 54 68 11 30 23

a Time spent engaging in all activities embodied by inhalation rate category (minutes/day).

b Participants in high inhalation rate activities (i.e., doers).

SD = Standard deviation.

Source: Funk et al., 1998.



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Table 16-34. Number of Person-Days/Individuals a for Children in CHAD Database

Age Group All Studies California b Cincinnati c NHAPS-Air NHAPS-Water

0 Year 223/199 104 36/12 39 44

0 to 6 Months

- 50 15/5

- -

6 to 12 Months

- 54 21/7

- -

1 Year 259/238 97 31/11 64 67

12 to 18 Months - 57

- - -

18 to 24 Months - 40

- - -

2 Years 317/264 112 81/28 57 67

3 Years 278/242 113 54/18 51 60

4 Years 259/232 91 41/14 64 63

5 Years 254/227 98 40/14 52 64

6 Years 237/199 81 57/19 59 40

7 Years 243/213 85 45/15 57 56

8 Years 259/226 103 49/17 51 55

9 Years 229/195 90 51/17 42 46

10 Years 224/199 105 38/13 39 42

11 Years 227/206 121 32/11 44 30

Total 3,009/2,640 1,200 556/187 619 634

a The number of person-days of data are the same as the number of individuals for all studies except for the Cincinnati study. Since up to three days of activity pattern data were obtained from each participant in this study, the number of person-days of data is approximately three times the number of individuals.

b The California study referred to in this table is the Wiley et al. (1991) study.

c The Cincinnati study referred to in this table is the Johnson (1989) study.

- = No data.

Source: Hubal et al., 2000.



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Chapter 16 - Activity Factors

Table 16-35. Time Spent (hours/day) in Various Microenvironments, by Age

Average Time ± Standard Deviation (Percent >0 Hours) Age (years) Indoors at Home Outdoors at Home Indoors at School Outdoors at Park In Vehicle

0 19.6 ± 4.3 (99) 1.4 ± 1.5 (20) 3.5 ± 3.7 (2) 1.6 ± 1.5 (9) 1.2 ± 1.0 (65)

1 19.5 ± 4.1 (99) 1.6 ± 1.3 (35) 3.4 ± 3.8 (5) 1.9 ± 2.7 (10) 1.1 ± 0.9 (66)

2 17.8 ± 4.3 (100) 2.0 ± 1.7 (46) 6.2 ± 3.3 (9) 2.0 ± 1.7 (17) 1.2 ± 1.5 (76)

3 18.0 ± 4.2 (100) 2.1 ± 1.8 (48) 5.7 ± 2.8 (14) 1.5 ± 0.9 (17) 1.4 ± 1.9 (73)

4 17.3 ± 4.3 (100) 2.4 ± 1.8 (42) 4.9 ± 3.2 (16) 2.3 ± 1.9 (20) 1.1 ± 0.8 (78)

5 16.3 ± 4.0 (99) 2.5 ± 2.1 (52) 5.4 ± 2.5 (39) 1.6 ± 1.5 (28) 1.3 ± 1.8 (80)

6 16.0 ± 4.2 (98) 2.6 ± 2.2 (48) 5.8 ± 2.2 (34) 2.1 ± 2.4 (32) 1.1 ± 0.8 (79)

7 15.5 ± 3.9 (99) 2.6 ± 2.0 (48) 6.3 ± 1.3 (40) 1.5 ± 1.0 (28) 1.1 ± 1.1 (77)

8 15.6 ± 4.1 (99) 2.1 ± 2.5 (44) 6.2 ± 1.1 (41) 2.2 ± 2.4 (37) 1.3 ± 2.1 (82)

9 15.2 ± 4.3 (99) 2.3 ± 2.8 (49) 6.0 ± 1.5 (39) 1.7 ± 1.5 (34) 1.2 ± 1.2 (76)

10 16.0 ± 4.4 (96) 1.7 ± 1.9 (40) 5.9 ± 1.5 (39) 2.2 ± 2.3 (40) 1.1 ± 1.1 (82)

11 14.9 ± 4.6 (98) 1.9 ± 2.3 (45) 5.9 ± 1.5 (41) 2.0 ± 1.7 (44) 1.6 ± 1.9 (74)

Source: Hubal et al., 2000.



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Chapter 16 - Activity Factors

Table 16-36. Mean Time Children Spent (hours/day) Doing Various

Macroactivities While Indoors at Home

Mean Time (Percent >0 Hours) Age Sleep or Shower or Watch TV or Read, Write, Think, Relax, (years) Eat Play Games Nap Bathe Listen to Radio Homework Passive

0 1.9 (96) 12.6 (99) 0.4 (44) 4.3 (29) 1.1 (9) 0.4 (4) 3.3 (62)

1 1.5 (97) 12.1 (99) 0.5 (56) 3.9 (68) 1.8 (41) 0.6 (19) 2.3 (20)

2 1.3 (92) 11.5 (100) 0.5 (53) 2.5 (59) 2.1 (69) 0.6 (27) 1.4 (18)

3 1.2 (95) 11.3 (99) 0.4 (53) 2.6 (59) 2.6 (81) 0.8 (27) 1.0 (19)

4 1.1 (93) 10.9 (100) 0.5 (52) 2.6 (54) 2.5 (82) 0.7 (31) 1.1 (17)

5 1.1 (95) 10.5 (98) 0.5 (54) 2.0 (49) 2.3 (85) 0.8 (31) 1.2 (19)

6 1.1 (94) 10.4 (98) 0.4 (49) 1.9 (35) 2.3 (82) 0.9 (38) 1.1 (14)

7 1.0 (93) 9.9 (99) 0.4 (56) 2.1 (38) 2.5 (84) 0.9 (40) 0.6 (10)

8 0.9 (91) 10.0 (96) 0.4 (51) 2.0 (35) 2.7 (83) 1.0 (45) 0.7 (7)

9 0.9 (90) 9.7 (96) 0.5 (43) 1.7 (28) 3.1 (83) 1.0 (44) 0.9 (17)

10 1.0 (86) 9.6 (94) 0.4 (43) 1.7 (38) 3.5 (79) 1.5 (47) 0.6 (10)

11 0.9 (89) 9.3 (94) 0.4 (45) 1.9 (27) 3.1 (85) 1.1 (47) 0.6 (10)

Source: Hubal et al., 2000.



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Chapter 16 - Activity Factors

Table 16-37. Time Children Spent (hours/day) in Various Microenvironments, by Age Recast into New Standard Age Categories

Indoors at Home Outdoors at Home Indoors at School Outdoors at Park In Vehicle

Age Group N Mean % Mean % Mean % Mean % Mean %

Time Doing TIme Doing Time Doing Time Doing TIme Doing

Birth to <1 month 123 19.6 98 1.7 21 4.3 3 1.3 3 1.3 63

1 to <3 months 33 20.9 100 1.8 9 0.2 3 1.6 9 1.3 27

3 to <6 months 120 19.6 100 0.8 8 7.8 7 1.3 6 1.1 14

6 to <12 months 287 19.1 99 1.1 15 7.6 8 1.8 5 1.3 14

1 to <2 years 728 19.2 99 1.4 34 6.4 9 1.5 5 1.1 27

2 to <3 years 765 18.2 99 1.8 38 6.8 12 2.1 7 1.3 28

3 to <6 years 2,110 17.3 100 1.9 43 5.9 26 1.6 10 1.3 29

6 to <11 years 3,283 15.7 99 1.9 40 6.5 44 2.1 17 1.1 29

11 to <16 years 2,031 15.5 97 1.7 30 6.6 45 2.6 15 1.3 42

16 to <21 years 1,005 14.6 98 1.4 20 5.7 33 3.1 10 1.7 90

N = Sample size.

Source: Based on data source used by Hubal et al., 2000 (CHAD).



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Chapter 16 - Activity Factors

Table 16-38. Time Children Spent (hours/day) in Various Macroactivities While Indoors at Home Recast Into New Standard Age Categories

Shower or Watch TV/ Read, Write, Think, Relax, Eat Sleep or Nap Play Games Bathe Listen to Radio Homework Passive Age Group N Mean % Mean % Mean % Mean % Mean % Mean % Mean % Time Doing Time Doing Time Doing Time Doing Time Doing Time Doing TIme Doing

Birth to <1 month 123 2.2 98 13.0 100 0.5 41 5.0 53 1.3 8 0.7 2 2.7 48

1 to <3 months 33 2.4 100 14.8 100 0.4 24 0.7 6 1.6 15 0.0 0 3.5 79

3 to <6 months 120 2.0 100 13.5 100 0.5 9 1.3 31 1.0 21 1.1 3 2.5 59

6 to <12 months 287 1.8 100 12.9 100 0.4 11 1.1 30 1.3 25 0.5 4 2.5 35

1 to <2 years 728 1.7 99 12.5 100 0.5 21 3.2 45 1.8 52 0.6 13 1.4 26

2 to <3 years 765 1.5 98 12.0 100 0.5 22 2.6 45 2.0 77 0.6 18 0.8 30

3 to <6 years 2,110 1.4 99 11.2 100 0.5 38 2.5 38 2.3 86 0.7 25 0.8 28

6 to <11 years 3,283 1.2 98 10.2 100 0.4 54 2.0 28 2.6 84 1.0 43 0.8 20

11 to <16 years 2,031 1.1 94 9.7 98 0.4 50 1.8 18 3.0 85 1.4 45 0.8 20

16 to <21 years 1,005 1.0 84 8.9 98 0.4 45 1.9 5 3.2 73 2.2 37 1.3 24

N = Sample size.

Source: Based on data source used by Hubal et al., 2000 (CHAD).



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Chapter 16 - Activity Factors

Table 16-39. Number and Percentage of Respondents with Children and Those Reporting Outdoor Play a Activities in both Warm and Cold Weather

Warm Cold Respondents Child non- Child Players a Weather Weather Players in Both Seasons with Children Players Playersa Players Source

N N % N % N N %

SCS-II base 197 128 65.0 69 35.0 127 100 50.8

SCS-II over 483 372 77.0 111 23.0 370 290 60.0 sample

Total 680 500 73.5 180 26.5 497 390 57.4

a “Play” and “player” refer specifically to participation in outdoor play on bare dirt or mixed grass and dirt.

b Does not include three “Don’t know/refused” responses regarding warm weather play. N = Sample size.

Source: Wong et al., 2000.



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Chapter 16 - Activity Factors

Table 16-40. Play Frequency and Duration for all Child Players (from SCS-II data)

Cold Weather Warm Weather

Statistic Frequency Duration Total Frequency Duration Total (days/week) (hours/day) (hours/week) (days/week) (hours/day) (hours/week)

N 372 374 373 488 479 480

5 th Percentile 1 1 1 2 1 4

50 th Percentile 3 1 5 7 3 20

95 th Percentile 7 4 20 7 8 50

N = Sample size.

Source: Wong et al., 2000.

Table 16-41. Hand Washing and Bathing Frequency for all Child Players (from SCS-II data)

Cold Weather Warm Weather

Statistic Hand washing Bathing Hand washing Bathing (times/day) (times/week) (times/day) (times/week)

N 329 388 433 494

5 th Percentile 2 2 2 3

50 th Percentile 4 7 4 7

95 th Percentile 10 10 12 14

N = Sample size.

Source: Wong et al., 2000.



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Chapter 16 - Activity Factors

Table 16-42. NHAPS and SCS-II Play Duration a Comparison

Mean Play Duration O 2 test b Data Source (minutes/day)

Cold Weather Warm Weather Total

NHAPS 114 109 223 p<0.0001

SCS-II 102 206 308

a Selected previous day activities in NHAPS; average day outdoor play on bare dirt or mixed grass and dirt in SCS-II.

b 2x2 Chi-square test for contingency between NHAPS and SCS-II.

Source: Wong et al., 2000.

Table 16-43. NHAPS and SCS-II Hand Wash Frequency a Comparison

Data
Source Season Percent b Reporting Frequency (times/day) of:

O 2 test c 0 1-2 3-5 6-9 10-19 20-29 30+ “Don’t

Know”

NHAPS

SCS-II

NHAPS

SCS-II Cold 3 18 51 17 7 1 1 3

p = 0.06 Cold 1 16 50 11 7 1 0 15

Warm 3 18 51 15 7 2 1 4

p = 0.001 Warm 0 12 46 16 10 1 0 13

a

b

c

Source: Selected previous day activities in NHAPS; average day outdoor play on bare dirt or mixed grass and dirt in SCS-II. Results are reported as percentage of total for clarity. Incidence data were used in statistical tests. 2x2 Chi-square test for contingency between NHAPS and SCS-II.

Wong et al., 2000.



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Chapter 16 - Activity Factors

Table 16-44. Time Spent (minutes/day) Outdoors Based on CHAD Data (Doers Only) a

Age Group N Time Spent Outdoors COV(%) Participation b (%) Minimum Median Maximum Mean SD

<1 month 57 2 60 700 99 124 125 47

1 to 2 months 5 4 60 225 102 90 89 36

3 to 5 months 27 10 90 510 114 98 86 23

6 to 11 months 91 5 60 450 91 76 84 33

1 year 389 1 75 1,035 102 99 97 58

2 years 448 1 100 550 134 108 80 64

3 to 5 years 1,336 1 120 972 146 117 80 68

6 to 10 years 2,216 1 120 1,440 162 144 89 71

11 to 15 years 1,423 1 110 1,440 154 163 106 73

16 to 17 years 356 1 85 1,083 129 145 112 81

18 to 20 years 351 1 70 788 132 155 118 72

a Only data for individuals that spent >0 time outdoors and had 30 or more records are included in the analysis.

b Participation rates or percent of sample days in the study spending some time (>0 minutes per day) outdoors. The mean time spent outdoors for the age group may be obtained by multiplying the participation rate by the mean time shown above.

SD = Standard deviation. COV = Coefficient of variation (SD/mean x 100).

Source: Graham and McCurdy, 2004.



### Child-Specific Exposure Factors Handbook Page September 2008 16-65

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity FactorsTable 16-45. Comparison of Daily Time Spent Outdoors (minutes/day),Considering Gender and Age Cohort (Doers Only)aTime Spent Outdoors in MinutesK-S TestbAge GroupGenderNMinimumMedianMaximumMeanSDCOV (%)DnP2 DRejectH0< 1 monthMale357687001161441250.240.900.3964NoFemale222583337378106----1 to 2 monthsMale4458165716895Cannot TestFemale1225225225225-03 to 5 monthsMale2010862108956630.420.960.3158NoFemale750140510187153816 to 11Male5310604509583870.071.000.3200NomonthsFemale385682708667771 yearMale1841801,0351101141040.070.710.6896NoFemale2054705119582862 yearsMale2321105550136105770.091.000.2705NoFemale216290525131111843 to 5 yearsMale7231120972146119810.040.740.6465NoFemale6122120701144113786 to 10 yearsMale12211321,440173148860.092.050.0004Yes8Female98721151,3801481389311 to 15 yearsMale77911251,440171169990.173.12<Yes0.0001Female6401901,37113415311416 to 17 yearsMale1682113810151147970.191.800.0030YesFemale1881681,08310914112918 to 20 yearsMale1842957881621761090.201.840.0023YesFemale16715060699119120aOnly data for individuals that spent >0 time outdoors and had 30 or more records are included in the analysis.bThe two-sample Kolmogorov-Smirnov (K-S) test H0is that the distribution of variable 1 is the same as variable 2, using aP2 test statistic at%=0.050.SD = Standard deviation.COV = Coefficient of variation (SD/mean x 100).Source: Graham and McCurdy, 2004.



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Chapter 16 - Activity Factors

Table 16-46. Time Spent (minutes/day) Indoors Based on CHAD Data (Doers Only) a

Age Group N Time Spent Indoors COV(%) Participation b (%) Minimum Median Maximum Mean SD

<1 month 121 490 1,380 1,440 1,336 137 10 100.0

1 to 2 months 14 1,125 1,380 1,440 1,348 105 8 100.0

3 to 5 months 115 840 1,385 1,440 1,359 93 7 100.0

6 to 11 months 278 840 1,370 1,440 1,353 81 6 100.0

1 year 668 315 1,350 1,440 1,324 107 8 100.0

2 years 700 290 1,319 1,440 1,286 138 11 100.0

3 to 5 years 1,977 23 1,307 1,440 1,276 136 11 100.0

6 to 10 years 3,118 7 1,292 1,440 1,256 153 12 100.0

11 to 15 years 1,939 69 1,300 1,440 1,255 160 13 99.8

16 to 17 years 438 161 1,296 1,440 1,251 171 14 100.0

18 to 20 years 485 512 1,310 1,440 1,242 180 15 100.0

a Only data for individuals that spent >0 time indoors and had 30 or more records are included in the analysis.

b Participation rates or percent of sample days in the study spending some time (>0 minutes per day) indoors. The mean time spent indoors for the age group may be obtained by multiplying the participation rate (as a decimal) by the mean time shown

above. N = Sample size.

SD = Standard deviation. COV = Coefficient of variation (SD/mean x 100).

Source: Graham and McCurdy, 2004.



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Chapter 16 - Activity Factors

Table 16-47. Time Spent (minutes/day) in Motor Vehicles Based on CHAD Data (Doers Only) a

Age Group N Time Spent in Motor Vehiicles COV(%) Participation b (%) Minimum Median Maximum Mean SD

<1 month 80 2 68 350 86 68 79 66

1 to 2 months 9 20 83 105 67 32 48 64

3 to 5 months 75 13 60 335 71 49 69 65

6 to 11 months 226 4 51 425 62 47 76 81

1 year 515 1 52 300 67 50 76 77

2 years 581 2 54 955 73 76 104 83

3 to 5 years 1,702 1 55 1,389 70 70 99 86

6 to 10 years 2,766 1 58 1,214 71 68 95 89

11 to 15 years 1,685 1 60 825 76 74 97 87

16 to 17 years 400 4 73 1,007 92 90 98 91

18 to 20 years 449 4 76 852 109 106 98 93

a Only data for individuals that spent >0 time in motor vehicles and had 30 or more records are included in the analysis.

b Participation rates or percent of sample days in the study spending some time (>0 minutes per day) in motor vehicles. The mean time spent in motor vehicles for the age group may be obtained by multiplying the participation rate (as a decimal) by the mean

time shown above. N = Sample size.

SD = Standard deviation. COV = Coefficient of variation (SD/mean x 100).

Source: Graham and McCurdy, 2004.



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Chapter 16 - Activity Factors

Table 16-48. Time Spent (minutes/two-day period) a in Various Activities by Children Participating in the Panel Study of Income Dynamics (PSID), 1997 Child Development Supplement (CDS)

Boys (N = 1,444) Girls (N = 1,387)
Age Group Mean a Standard

Deviation Mean a Standard

Deviation

Television Use

1 to 5 years 197 168 184 163
6 to 8 years 263 165 239 159
9 to 12 years 251 185 266 194

Electronic Game Use

1 to 5 years 8 38 5 40
6 to 8 years 44 113 14 39
9 to 12 years 57 102 18 47
Computer Use

1 to 5 years 7 28 7 35
6 to 8 years 13 43 8 28
9 to 12 years 27 71 15 43

Print Use b

1 to 5 years 21 32 23 34
6 to 8 years 20 37 20 32
9 to 12 years 19 47 29 56
Highly Active Activities c

1 to 5 years 42 74 34 78
6 to 8 years 107 123 62 92
9 to 12 years 137 149 63 88
Moderately Active Activities d

1 to 5 years 55 81 59 92
6 to 8 years 31 65 37 69
9 to 12 years 40 73 46 89

Sedentary Activities e

1 to 5 years 55 71 54 71
6 to 8 years 75 77 80 84

9 to 12 years 110 109 122 111

a Means represent minutes spent in each activity over a 2-day period (one weekday and one weekend day).

b Print use represents time spent using print media including reading and being read to.

c Includes all sport activities such as basketball, soccer, swimming, running or bicycling.

d Includes activities such as singing, camping, taking music lessons, fishing, and boating.

e Includes activities such as playing board games, doing puzzles, talking on the phone, and relaxing. N = Sample size.

Source: Vanderwater et al., 2004.



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Table 16-49. Mean Time Spent (minutes/day) in Various Activity Categories, by Age - Weekday

2002-2003 1981-1982

Activity Category 6 to 8 9 to 11 12 to 14 15 to 17 6 to 8 9 to 11 12 to 14 15 to 17

years years years years years years years years

Market work 0 0 1 22

- - - 28

Household work 25 32 38 39 15 18 27 34

Personal care 68 66 68 73 49 40 56 60

Eating 60 57 54 49 81 73 69 67

Sleeping, naps 607 583 542 515 595 548 473 499

School 406 398 395 352 292 315 344 314

Studying 29 39 49 50 8 29 33 33

Church 4 5 5 3 9 9 9 3

Visiting, socializing 16 25 25 53

- - - - Sports 10 17 33 33 24 21 40 46

Outdoor Activities 6 6 4 6 9 8 7 11

Hobbies 1 1 1 2 2 2 4 6

Art Activities 8 7 7 4 4 3 3 12

Television 94 106 111 115 99 146 142 108

Other passive leisure 9 10 24 39

- - - - Playing 74 56 45 35 111 65 31 14

Reading 11 12 11 7 5 9 10 12

Being read to 2 1 0 0

- - - - Computer activities 6 10 25 38

- - - - Missing data 4 8 4 6

- - - -

- = Data not provided.

Source: Juster et al., 2004.



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Table 16-50. Mean Time Spent (minutes/day) in Various Activity Categories, by Age - Weekend Day

2002-2003 1981-1982

Activity Category 6 to 8 9 to 11 12 to 14 15 to 17 6 to 8 9 to 11 12 to 14 15 to 17

years years years years years years years years

Market work 0 0 9 39

- - - 48

Household work 81 91 100 79 27 51 72 60

Personal care 78 72 73 77 45 44 60 51

Eating 89 80 69 64 80 78 68 65

Sleeping, naps 666 644 633 629 641 596 604 562

School 3 6 7 7

- - - - Studying 5 9 20 24 2 12 15 30

Church 41 37 36 30 56 53 32 37

Visiting, socializing 61 66 58 91

- - - - Sports 23 40 40 27 30 42 51 37

Outdoor Activities 12 12 12 11 23 39 25 26

Hobbies 2 1 4 5 5 3 8 3

Art Activities 11 7 9 6 4 4 7 10

Television 155 184 181 162 136 185 169 157

Other passive leisure 14 15 40 54

- - - - Playing 163 134 148 59 180 92 35 21

Reading 14 15 13 7 9 10 10 18

Being read to 1 1 0 0

- - - - Computer activities 12 19 39 58

- - - - Missing data 9 8 9 11

- - - -

- = Data not provided.

Source: Juster et al., 2004.



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Chapter 16 - Activity Factors

Table 16-51. Mean Time Spent (minutes/week) in Various Activity Categories for Children, Ages 6 to 17 Years

Activity Category 2002-2003 1981-1982

Market work 53

Household work 343

Personal care 493

Eating 426

Sleeping, naps 4,092

School 1,947

Studying 238

Church 94

Visiting, socializing 287

Sports 179

Outdoor Activities 50

Hobbies 12

Art Activities 48

Television 876

Other passive leisure 166

Playing 485

Reading 77

Being read to 5

Computer activities 165

Missing data 45 126

223

356

508

3,758

1,581

158

125

132

244

100

27

40

944

39

440

69

3

0

1,206

Source: Juster et al., 2004.



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Chapter 16 - Activity Factors

Table 16-52. Mean Time Use (hours/day) by Children, Ages 15 to 19 Years

hours/day Activity Male Female All

Personal Care a 10.26 10.34 10.30

Eating and Drinking b 1.02 1.11 1.07

Household Activities c 0.61 0.92 0.76

Purchasing Goods and Services d 0.38 0.74 0.56

Caring for and Helping Household Members e 0.10 0.19 0.15

Caring for and Helping Non-Household Members f 0.20 0.23 0.21

Working on Work-related Activities g 1.53 1.24 1.39

Educational Activities h 3.08 3.51 3.29

Organizational Civic and Religious Activities i 0.34 0.33 0.34

Leisure and Sports j 6.02 4.75 5.40

total leisure and sports - weekdays

- - 4.85

total leisure and sports - weekends

- - 6.68

sports, exercise, recreation - weekdays

- - 0.58

sports, exercise, recreation - weekends/holidays

- - 0.69

socializing and communicating - weekdays

- - 0.76

socializing and communicating, - weekends/holidays

- - 1.32

watching TV - weekdays

- - 1.96

watching TV - weekends/holidays

- - 2.45

reading - weekdays

- - 0.11

reading - weekends/holidays

- - 0.11

relaxing, thinking - weekdays

- - 0.15

relaxing, thinking - weekends/holidays

- - 0.13

playing games, computer use for leisure - weekdays

- - 0.69

playing games, computer use for leisure - weekends/holidays

- - 1.00

other sports/leisure including travel - weekdays

- - 0.61

other sports/leisure including travel - weekends/holidays

- - 0.98

Telephone Calls, Mail, and E-mail k 0.24 0.42 0.33

Other Activities not Elsewhere Classified l 0.23 0.21 0.22

a Includes sleeping, bathing, dressing, health-related self care, and personal and private activities.

b Includes time spent eating or drinking (except when identified as part of work or volunteer activity); does not include time spent purchasing meals, snacks, or beverages.

c Includes housework, cooking, yard care, pet care, vehicle maintenance and repair, home maintenance, repair, decoration, and renovation.

d Includes purchase of consumer goods, professional (e.g., banking, legal, medical, real estate) and personal care services (e.g., hair salons, barbershops, day
spas, tanning salons), household services (e.g., housecleaning, lawn care and landscaping, pet care, dry cleaning, vehicle maintenance, construction), and government services (e.g., applying for food stamps, government required licenses or paying fines).

e Includes time spent caring or helping to care for child or adult household member (e.g., physical care, playing with children, reading to child or adult, attending to health care needs, dropping off, picking up or waiting for children).

f Includes time spent caring or helping to care for child or adult who is not a household member (e.g., physical care, playing with children, reading to child or adult, attending to health care needs, dropping off, picking up or waiting for children). Does not include activities done through a volunteer
organization.

g Includes time spent as part of the job, income-generating activities, or job search activities. Also includes travel time for work-related activities.

h Includes taking classes, doing research and homework, registering for classes, and before and after school extra-curricular activities, except sports.

i Includes time spent volunteering for or through civic obligations (e.g., jury duty, voting, attending town hall meetings), or through participating in religious
or spiritual activities (e.g., church choir, youth groups, praying).

j Includes sports, exercise, and recreation. This category is broken down into subcategories for the 15 to 19 years old age category.

k Includes telephone use, mail and e-mail. Does not include communications related to purchase of goods and services or those related to work or volunteering.

l Includes residual activities that could not be coded or where information was missing.
Source:	 U.S. DL, 2007.



### Child-Specific Exposure Factors Handbook Page September 2008 16-73

### Child-Specific Exposure Factors Handbook

Chapter 16 - Activity Factors

Table 16-53. Mean Time Spent (minutes/day) in Moderate-to-Vigorous Physical Activity

Weekday Weekend

Age (years) Mean (SD) Mean (SD)

Boys Girls Both Boys Girls Both

9 190.8(53.2) 173.3(46.4) 181.8(50.6) 184.3(68.6) 173.3(64.3) 178.6(66.6)

11 133.0(42.9) 115.6(36.3) 124.1(40.6) 127.1(59.5) 112.6(53.2) 119.7(56.8)

12 105.3(40.2) 86.0(32.5) 95.6(37.8) 93.4(55.3) 73.9(45.8) 83.6(51.7)

15 58.2(31.8) 38.7(23.6) 49.2(29.9) 43.2(38.0) 25.5(23.3) 35.1(33.3)

SD = Standard deviation.

Source: Nader et al. 2008.



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Chapter 17 - Consumer Products

17 CONSUMER PRODUCTS

17.1 INTRODUCTION

Consumer products may contai n toxic or potentially toxic chemical constituents to which children may be exposed as a result of their use. For example, household clean ers can contain ammonia, alcohols, acids, and/or orga nic solvents which may

pose health concerns. Potential routes of exposure to

co nsumer products or chemicals released from

consumer pro d ucts during use include ingestion,

i nhalation, and dermal contact. Children can be in

environments where adults use househ old consumer products such as clean ers, solvents, and paints. As such, children can b e passively exposed to chemicals

i n these products. Since children spend a large amount of time indoors, the use of household

chemicals in the indoor environment can be a principal source of exposure (Franklin, 2008). Very little inf ormation is available on the

exact way the different ki nds of products are used by

consumers, including the many ways in which these products are handled, the frequency and d uration of

cont act, and the measures consumers may take to

minimize expo sure/risk (Steenbekkers, 2001). In addition, the factors that influence these behaviors are not well studied, but some studies have shown there is

a large variation in behavior between persons (Steenbekkers, 2001). This chapter presents available

information on the amounts, frequency, and duration

of use for various consumer products found in typical

households. The studies presented in the following

sections represent readily available surveys from which

data were collected on the frequency and duration of

use and amount of use of cleaning products, household

solvent products, cosmetic a nd other personal care products, and pesticides. For a more detailed presentation of data on the use of consumer products

among the general population, the reader is referred to
the Exposure Factors Handbook (U.S. EPA, 1997).

The National Library of Medicine Household

Products Database is a consumer guide that provides

information on the potential health effects of chemicals

contained in more than 7,000 common household products used inside and around the home. Although, this database does not provide exposure factor information, it contains information on chemical ingredients and their percentages in consumer products, which products con tain specific chemical ingredients, acute and chron ic effects of chemical ingredients, and ma n ufacturer information. These

data could be useful when conducting an exposure

assessme nt for a specific chemical/active ingredient.

The p roduct categories are: auto products, inside the home, pesticides, landscape/yard, personal care, home maintenance, arts and crafts, pet care, and home office.

The database can be searched by p r oduct name, product type, manufacturer, and ingredient. This

database can be found at htt p ://hpd.nlm.nih.gov

. Table 17-1 provides a list of household consumer products found in some U.S. households (U.S. EPA, 1987). It should be noted, however, that this list was compiled by U.S. EPA in 1987 and con sumer use of

some products listed may have changed (e.g., aerosol product use has declined ). Therefore, the reader is referred to the National Library of Medicine database

as a source of more current information. The U.S. EPA Source Ranking Database (SRD) is another source of i nformation on consumer products, but does not provide exposure factor data.

SRD can be used to perform systematic screening-level

reviews of more than 12,000 potential indoor pollution sources to identify high-priority product and material categories for further evaluation. It also can be used to

iden t ify products that contain a specific chemical.

Information on the SRD can be found at: http://www.epa.gov/oppt/exposure/pubs/srd.htm

. The Soaps and Detergents Association (SDA) developed a peer-rev iewed document that presents methodologies and specific exposure information that

can be used for screening-level risk assessments from

exposures to high production volume chemicals. The

document addresses the use o f consumer products, including laundry, cleaning, and personal care

pr o ducts. It includes data for daily frequency of use, and amount of prod uct used. The data used were compiled from a number of sources in cluding, the Exposure Factors Handbook (U.S. EPA, 1997), cosmetic associations, and d ata from the SDA. The

document entitled “ Exposure and Risk Screening

Methods for Consumer Product Ingredients ” can be

found on the SDA website und er: http://www.cleaning101.com/files/Exposure_and_ Risk_Screening_Methods_for_Consumer_Product_ Ingredients.pdf

.



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Chapter 17 - Consumer Products

17.2 RECOMMENDATIONS

Due to the large range and variation among

consumer products and their exposure pathways, it is

not feasible to recommend specific exposure values as has been done in other chapters of this handbook. The

user i s referred to the contents/references of this chapter and Chapter 17 of th e E xposure Factors

Handbook (U.S. EPA, 1997) to derive appropriate

expo sure factors. The following sections of this chapter provide summaries of data from s urveys involving the use of consumer products.

17.3 CONSUMER PRODUCTS USE STUDIES

17.3.1 CTFA, 1983 - Cosmetic, Toiletry, and Fragrance Association, Inc. - Summary of

Results of Surv eys of the Amount and Frequency of Use of Cosmetic Products by

Women

Th e Cosmetic, Toiletry, and Fragrance

Association Inc. (CTFA, 1983), a major manufacturer

and a market research bureau , conducted surveys to

obtain informatio n on frequency of use of various

cosmetic products. Three surveys were conducted to

collect data on the frequency of use of various cosmetic products an d selected baby products. In the first of

these three surv eys CTFA (1983) conducted a oneweek prospective survey of 4 7 female employees and

relatives of employees between the ages of 13 and 61 years. In the second survey, a cosmetic manufacturer

conducted a retrospectiv e survey of 1,129 of its

customers. The third survey was conducted by a

market research bureau which sampled 19,035 female

consumers nationwide over a 9-1/2 month period. Of

the 19,035 females interviewe d

, responses from only 9,684 females were tabulated (CTFA, 1983). The third

survey was designed to refl ect the sociodemographic (i.e., age, income, etc) characteristics of the entire U.S. population. The respondents in all three surveys were

asked to record the number of times they used the

various products in a given time period (i.e., a week, a day, a month, or a year).

To obtain the average frequenc y of use for

each cosmetic product, responses were averaged for

each product in each survey. Thus, the averages were

calculated by adding the reported number of uses per given time period for each product, dividing by the

total number of respondents in the survey, and then dividing again by the number of days in the given time period (CTFA, 1983). The average frequency of use of

cosmetic products was determined for both "users" and

"non-users." The frequency of use of baby products was determined among "users" only. The upper 90th percentile frequency of use values were determined by eliminating the top te n percent most extreme frequencies of use. Therefore, the highest remaining frequency of use was r ecorded as the upper 90th percentile value. Table 17-2 presents the amount of product used per application (grams) and the average

and 90th percentile frequency of use per day for baby products and various cosmeti c products for all the

surveys. An advantage of the frequency data obtained

from the third survey (market research bureau) is that

the sample p opulation was more likely to be

re p resentative of the U.S. population. Another advantage of the third dataset is that the survey was conducted over a longer period of time when compared

with the other two frequency datasets. Also, the study provided empir ical data which will be useful in generating more accurate estimates of consumer

exposure to cosmetic products. In contrast to the large

market research bureau survey, the CTFA employee

survey is very small and both that survey and the

cosmetic company survey are likely to be biased toward high end users. Therefore, data from these two surveys

should be used with caution. While the data in this study were not tabulated by age of the population, the study included some individuals in the age groups of

interest for this handbook.

17.3.2 U.S. EPA, 1996

- National Human Activity Pattern Survey (NHAPS)

U. S. EPA (1996) collected data on the duration and frequenc y of selected activities and the

time spent in selected microenvironments via 24-hour

diaries as part of the National Human Activity Pattern Survey (NHAPS). More than 9,000 individuals from

various age groups in 48 contiguous states participated

in NHAPS. Children represented approximately 2,000

of the respondents (499 re spondents under 5 years of age; 703 respondents between 5 and 11 years; 589 respondents between 12 and 17 years; and 799 respondents between 18 and 24 years). The survey was conducted between October 1992 and September 1994.

Individuals were interviewed to categorize their 24­

hour routines (diaries) and/ or to answer follow-up



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Chapter 17 - Consumer Products

questions that were related to exposure events

. For

children under 10 years of age, adult member s of the

households gave proxy interviews. Demographic,

in cluding socioeconomic (gender, age, race, education, etc.), geographic (census region, state, etc.), and temporal (day of week, month, season) data were

in cluded in the study. Data were collected for a

maximu m of 82 possible microenvironments and 91

different activities.

As part of the survey, data were also collected

on duration and frequency of use of selected consumer products. Tables 17-3 through 17-10 present data on

the number of minutes that survey respondents spent in

activities working with or being near certain consumer products, including: freshly applied paints; household cleaning agents such as scouring powders or ammonia; floor wax, furniture wax, or shoe polish; glue; solvents, fumes, or strong smelling ch emicals; stain or spot

re m overs; gasoline, diesel-powered equipment, or automobiles; and pesticides, bug sprays, or bug strips.

These data are presented accordin g to the age categories used in NHAPS (1 to 4 years, 5 to11 years, 12 t o17 years, and 18 to 64 years). Table 17-11 through 17-15 present data on the number of responde nts in these age categories that used fragrances, aerosol sprays, pesticides (professionallyapplied and consumer-applied), and humidifiers.

Because the age categories used by the study authors

did not coincide with the standardized age categories

recommended in U.S. EPA (2005) and used elsewhere

in this handbook, the source data from NHAPS on pesticide use (professionally applied and consumerapplied) were re-analyzed by U.S. EPA to generate
data for the standardized age categories. These data

are presented in Tables 17-16 and 17-17 for age groups less than 1 year, 1 to <2 years, 2 to <3 ye ars, 3 to <6

years, 6 to <11 years, 11 to <16 years, and 16 to <21 years. Data for subsets of the first year of life (e.g., 1 to 2 months, 3 to 5 months, etc.) were not available.

As discussed in previous chapters of this

handbook that used NHAPS a s a data source, the primary advantage of NHAPS is that the data were

collected for a larg e number of individuals and the

survey was d e signed to be representative of the U.S. general population . However, due to the wording of questions in the survey, precise data were not available

for consumers who spent more than 60 or 120 minutes (depending on the activity) usin g some consumer products. This prevents accurate characterization of

the high end of the distribution and may also introduce

error into the calculation of the mean.

17.3.3 Bass et a l., 2001 - What ’ s Being Used at Home: A Household Pesticide Survey

Bass et al. (2001) conducted a survey to assess the use of pesticide products in homes with children in

March 1999. The study obtained information on what pesticides were used, where they were used, and how frequently they were used. A total of 107 households

in Arizona that had a least on e child less than ten

years of age in the household, and had used a pesticide

within the last six months, were surveyed (Bass et al., 2001). The survey population was predominantly female Hispanic and represented a survey response rate of approximately 74 percent. Study participants were selected by systematic random sampling. Among the households sampled, 3 percent had one child less than

10 years old, 42 percent had two children less than 10 years old, and 23 percent had three to five children in

this age bracket. Pesticide use was assessed by a oneon-one interview in the home. Survey questions pertained to household pesticides used inside the house

for insect control and outside the house for the control

of weeds in the garden and to repel animals from the garden. As part of the interview, information was gathered on the frequency of use.

Table 17-18 presents information on the type, characteristics, and frequency of pesticide use, as well

as information on th e demographics of the survey population. A total of 148 pesticide products were

used in the 107 households surveyed. Respondents had

u sed pesticides in the kitchen, bathroom, floors, baseboards, and cabinets with dishes or cookware. The frequency of use data showed t he following: 13.5

perce nt of the households used pesticides more than

once per week; 18.2 percent used the products once per week; 28.4 percent used the products once per month;

15.5 percent used the products once in three months;

10.8 percent used the products once in six months; and

8.8 percent used the products once per year (Bass et al., 2001). Although this study was limited to a selected

area in Arizona, it provides useful information on the frequency of use of pesticides among households with

children. This may be useful for populations in similar geographical locations where site-specific data are not



### Child-Specific Exposure Factors Handbook Page September 2008 17-3

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Chapter 17 - Consumer Products

available. However, these data are the result of a community-based survey and are not representative of the U.S. general population.

17.3.4 Loretz et al., 2005 - Expos ure Data for

Cosmetic Products: Lipstick, Body Lotion,

and Face Cream

Loretz et al. (2005) cond ucted a nationwide

survey to estimate the usage (i

. e., frequency of application a nd amount used per application) of

l ipstick, body lotion, and face cream. The study was

conducte d from April to June 2000. Three hundred and sixty study subjects were recruited in ten U.S.

cities (Atlanta, Georgia; Boston, Massachusetts; Chicago, Illinois; Denver, Colorado; Houston, Texas; Minneapolis, Minnesota; St. Louis, Missouri; San

B ernadino, California; Tampa, Florida; and Seat tle, Washington). The survey participants were women,

ages 19-65 years, who regularly used the products of

interest. Typical cosmetic formulations of the three product types were weighed and provided to the

wo men for use over a two-week period. Subjects recorded information on product usage (e.g., whether

the product was used, number of applications, time of applications ) on a daily basis in a diary provided to

them. At the end of the two-week period, unused portions of product were returned and weighed. The

amount of product used was estimated as the difference

between the weight of product at the beginning and

end of the survey period. Of the 360 subjects recruited,

86.4 percent, 83.3 percent, and 85.6 percent completed

the study and returned the diaries for lipst ick, body lotion, and face cream, respectivel y (Loretz et al., 2005).

The survey data are presented in Table 17-19

and 17-20. Table 17-19 provides the mean, median,

and standard deviations for the f requency of use.

Table 17-20 provides distribution data for the total

amount applied, the average a mount applied per use day, and the average amount applied per application. An advantage of this study is that the survey population covered a diverse geographical area of the

U.S. and was not based on recall data. A limitation of

the study is that the short duration (two weeks) may

not acc u rately reflect long-term usage patterns.
Another limitation is that th e study only included

women who already used the products; therefore, the

usage patterns a re not representative of the entire female population. Also, the data are not presented by

age group, but the study does provide information on a population that includes the ages of interest for this

document. Data for children could not be separated from that of the rest of the survey population.

17.3.5 Loretz et al., 2006 - Exposure Data for

Personal Care Products: Hairspray, Spray Perfume, Liquid Foundation, Shampoo, Body Wash, and Solid Antiperspirant

Loretz et al. (2006) conducted a nationwide

survey to determine the usage (i.e., fre q uency of use and amount used) of hairspray, spray perfume, liquid foundation, shampoo, body wash, and solid antiperspirant. The survey was similar to that

described by Loretz et al. ( 2005). This study was

conducted between October 2001 and Octob e r 2002.

A total of 360 women were recruite d from ten U.S.

cities (Atlanta, Georgia; Boston, Massachusetts; Chicago, Illinois; Denver, Colorado; Houston, Texas; Minneapolis, Minnesota; St. Louis, Missouri; San Bernadino, Californi a; Tampa, Florida; and Seattle, Washington). The survey participants were women,

ages 19-65 years old, who re gularly used the test products. Subjects kept daily records on product usage (whether the product was used, number of applications,

time of applications) in a diary. For spray perfume, liquid foundat i on, and body wash, subjects recorded

the body area ( s) where these products were applied.

For shampoo, subjects recorded information on their

hair type (length, thickness, oiliness, straight or curly,

and color treated or not). At the end of the two week period, unused portions of products were returned and weighed. Of the 360 subjects recruited per product,

the study was completed by 329 participants for hairspray, 327 for spray perfu me, 326 for liquid

found ation, and 340 participants for shampoo, body wash, and solid antiperspirant.

The survey data are presented in Tables 17-21 through 17-23. Table 17-21 provides the minimum, maximum, mean, and standard deviations for the frequency of use. Table 17-22 provides percentile

values for the amount of product applied per application. Table 17-23 provides distribution data for the amount applied per use day. An advantage of this study is that the survey population covered a diverse geographical range of the

U.S. and did not rely on recall data. A limitation of



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### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

the study is that the short duration (two weeks) may

not accurately r eflect long-term usage patterns.
Another limitation is that t he study only included

women who already used these products; therefore, the

usage patterns are not entirely representative of the

e ntire female population. Also, the data are not presented by age group, but the study does provide

information on a population that includes the ages of

interest for this document. Data for children could not be separated from that of the re s t of the survey population.

17.3.6 Loretz et al., 2008 - Exposure Data for Cosmetic Products: Facial Cleanser, Hair Conditioner, and Eye Shadow

Loretz et al. (2008) used the data from a study

conducted in January 2005 to estimate frequency of use and usage amount for facial cleanser, hair conditioner,

and eye shadow. The study was conducted in a similar

manner as Loretz et al. (2 005; 2006). A total of 360

women, ages 18 to 69 years of age, were recruited by telephone to provide diary records of product use over a two-week period. The study subjects were representative of four U.S. Census regions (Northeast, Midwest, South, and West). A total of 295, 297, and

299 completed the stu dy for facial cleanser, hair conditioner, and eye shadow, respectively.

Th e participants recorded daily in a diary

whether the product was used that day, the number of applications, and the time of application(s) over a twoweek period. Products were weighed at the start and completion of the study to determine the amount used.

A statistical analysis of the data was conducted to

p rovide summary distributions of use pattern s, including number of applicatio ns, amount used per day, and amount of product used per application for

each product. Data on the number of applications per day are provided in Table 17-24. The average

amounts of product appli e d per use day are shown in

Table 17 -25, and the average amounts of product applied per application are shown in Table 17-26.

The advantages of this study are that it is representative of the U.S. female population for users of the products studied, it provides data for frequency

of u se and amount used, and it provides distribution

d ata. The limitations of the study are that the data

were not provided by age group, but included ages in

the study group that are relevant for this handbook. In addi tion, the participants were regular users of the product, so the amount applied and the f requency of

use may be higher than for other individuals who may

use the products. According to Loretz et al. (2008)

“ variability in amount used by the different subjects is high, but consistent with the data from other cosmetic

and personal care studies. ” The authors also noted

that it was not clear if the high-end users of products represented true usage.

17.3.7 Sathyanaray ana et al., 2008 - Baby Care Products; Possible Sources of Infant Phthalate Exposure Sathyanarayana et al. (2008) investig ated

dermal exposure to phthalates via the dermal application of personal care products. The study was conducted on 163 infants born between the year 2000

and 2005. The products studied were baby lotion, baby powder, baby shampoo, diaper cream, and baby wipes.

Infants were recruited through Future Families, a

multicenter pregnancy cohort study, at prenatal clinics

in Los Angeles, Calif o rnia; Minneapolis, Minnesota;

and Columbia, Missour i. Although the study was designed to assess exposure to phthalates, the authors

collected information on the percentage of the total partici pants that used the baby products. Data were

collected from questionnaire responses of the mothers

and at study visits. The characteristics and the percent

of the population using the studied baby products are shown in Table 17-27. Of the 163 infants studied, 94
percent of the participants u sed baby wipes and 54 percent used infant shampoo.

The advan tages of this study are that it specifi cally targeted consumer products used by

children. The percent of the study population using

these products was captured and the data were

collected from a diverse ethnic p opulation. The

limit a tions are that these data may not be entirely representative of the U.S. population because the study population was from only three states and the sample

size was small.

17.4 REFERENCES FOR CHAPTER 17
Bass, J.; Ortega, L.; Rosales, C.; Petersen, N., Philen,

R. (2001) What ’ s being used at home: a household pesticide survey. Pub Hea lth 9(3):138-144.



### Child-Specific Exposure Factors Handbook Page September 2008 17-5

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Cosmetic, Toiletry and Fragrance Associati o n (CTFA). (198 3) Summary of the results of

surveys of the amount and frequency of use of

cosmetic products by women. Prepared by Environ Corporation, Washington, DC for CTFA Inc., Washington, DC. Franklin, P. (2008) Household chemicals: good housekeeping or oc cupational hazard. Eur Respir J 31:489-491.
Loretz, L.; Api, A.; Barraj, L.; Burdick, J.; Dressler,
W.; Gettings, S.; Hsu, H.; Pan, Y.; Re, T.; Renskers, K.; Rothenstein, A.; Scrafford, C.; Sewall, C. (2005) Exposure data for cosmetic products: lipstick, body lotion, and face

cream. Food Chem Toxicol 43:279-291.
Loretz, L.; Api, A.; Barraj, L.; Burdick, J.; Davis, D.; Dressler, W.; Gilberti, E.; Jarrett, G.; Mann, S.; Pan, Y.; Re, T.; Renskers, K. ; Scrafford, C.; Vater, S. (20 06) Exposure data for personal care prod ucts: Hairspray, spray perfume, liquid fou ndation, shampoo, body wash, and solid antiperspira nt. Food Chem

Toxicol 44:2008-2018.
Loretz, L.; Ap i, A.; Babcock, L; Barraj, L.; Burdick,
J.; Cater, K.; Jarre tt, G.; Mann, s.; Pan, Y.;
Re, T.; Re n skers, K.; Scrafford, C. (2008) Exposu re data for cosmetic products: Facial cleanser, hair conditioner, and eye shadow.

Food Chem Toxicol 46:1516-1524. Sathyanarayana, S.; Karr, C.; Lozano, P., Brown, E.; Calafat, M. (2008) Baby care products; possible sources of infant phthalate exposure. Pedriatrics 121:260-268. Steenbekkers, L.P. (2001) Methods to study everyday

use of p roducts un households: The Wageni ngen mouthing study. Am Occup Hyg 45(1001):125-129.

U.S. EPA (1987) Methods for asses sing exposure to

chemical substances

- Volume 7 - Methods

for assessing consumer exposure to chemical substances. Washington, DC: Office of Toxic

Substances. EPA/560/5-85/007.

U.S. EPA (1996 ) Descriptive statistics tables from a

detailed analysis o f the National Human Activity Pattern Survey (NHAPS) data. Washington , DC: Office of Research and Development. EPA/600/R-96/148. U.S. EPA (1997) Exposure Factors Handb o ok.

Wash ington, DC: National Center for

Environm ental Assessment, Office of

Research and Development. EPA/60 0 /P95/002FC.

U.S. EPA. (2005) Guidance on Selecting Age Groups

for Monitoring and Ass essing Childhood Exposures to Environmental C o ntaminants.

U.S. Environmental Protection Agency

, Washington, DC: EPA/630/P-03/003F.



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Chapter 17 - Consumer Products

Table 17-1. Consumer Products Commonly Found in Some U.S. Households a

Consumer Product Category Consumer Product

Cosmetics Hygiene Products Adhesive bandages Bath additives (liquid) Bath additives (powder) Cologne/perfume/aftershave

Contact lens solutions Deodorant/antiperspirant (aerosol) Deodorant/antiperspirant (wax and liquid) Depilatories Facial makeup Fingernail cosmetics Hair coloring/tinting products Hair conditioning products Hairsprays (aerosol) Lip products

Mouthwash/breath freshener Sanitary napkins and pads Shampoo Shaving creams (aerosols) Skin creams (non-drug) Skin oils (non-drug) Soap (toilet bar) Sunscreen/suntan products Talc/body powder (non-drug) Toothpaste

Waterless skin cleaners

Household Furnishings Carpeting Draperies/curtains Rugs (area)

Shower curtains Vinyl upholstery, furniture

Garment Conditioning Products Anti-static spray (aerosol) Leather treatment (liquid and wax) Shoe polish Spray starch (aerosol) Suede cleaner/polish (liquid and aerosol) Textile water-proofing (aerosol)



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Chapter 17 - Consumer Products

Table 17-1. Consumer Products Commonly Found in Some U.S. Households a (continued)

Consumer Product Category Consumer Product

Household Maintenance Products Adhesive (general) (liquid) Bleach (household) (liquid) Bleach (see laundry)

Candles

Cat box litter Charcoal briquets Charcoal lighter fluid Drain cleaner (liquid and powder) Dishwasher detergent (powder) Dishwashing liquid Fabric dye (DIY) b Fabric rinse/softener (liquid) Fabric rinse/softener (powder) Fertilizer (garden) (liquid) Fertilizer (garden) (powder) Fire extinguishers (aerosol) Floor polish/wax (liquid) Food packaging and packaged food Furniture polish (liquid) Furniture polish (aerosol) General cleaner/disinfectant (liquid) General cleaner (powder) General cleaner/disinfectant (aerosol and pump) General spot/stain remover (liquid) General spot/stain remover (aerosol and pump) Herbicide (garden-patio) (liquid and aerosol) Insecticide (home and garden) (powder) Insecticide (home and garden) (aerosol and pump) Insect repellent (liquid and aerosol) Laundry detergent/bleach (liquid) Laundry detergent (powder) Laundry pre-wash/soak (powder) Laundry pre-wash/soak (liquid) Laundry pre-wash/soak (aerosol and pump) Lubricant oil (liquid) Lubricant (aerosol)

Matches Metal polish Oven cleaner (aerosol) Pesticide (home) (solid) Pesticide (pet dip) (liquid) Pesticide (pet) (powder) Pesticide (pet) (aerosol) Pesticide (pet) (collar) Petroleum fuels (home (liquid and aerosol) Rug cleaner/shampoo (liquid and aerosol) Rug deodorizer/freshener (powder) Room deodorizer (solid) Room deodorizer (aerosol) Scouring pad

Toilet bowl cleaner Toiler bowl deodorant (solid) Water-treating chemicals (swimming pools)



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Chapter 17 - Consumer Products

Table 17-1. Consumer Products Commonly Found in Some U.S. Households a (continued)

Consumer Product Category Consumer Product

Home Building/Improvement Products (DIY) b Adhesives, specialty (liquid) Ceiling tile

Caulks/sealers/fillers Dry wall/wall board Flooring (vinyl) House Paint (interior) (liquid) House Paint and Stain (exterior) (liquid) Insulation (solid) Insulation (foam)

Paint/varnish removers

Paint thinner/brush cleaners Patching/ceiling plaster Roofing Refinishing products (polyurethane, varnishes, etc.) Spray paints (home) (aerosol) Wall paneling Wall paper Wall paper glue

Automobile-related Products Antifreeze Car polish/wax

Fuel/lubricant additives

Gasoline/diesel fuel Interior upholstery/components, synthetic

Motor oil

Radiator flush/cleaner Automotive touch-up paint (aerosol)

Windshield washer solvents

Personal Materials Clothes/shoes Diapers/vinyl pants Jewelry Printed material (colorprint, newsprint, photographs)

Sheets/towels Toys (intended to be placed in mouths)

a A subjective listing based on consumer use profiles.

b DIY = Do It Yourself.

Source: U.S. EPA, 1987.



### Child-Specific Exposure Factors Handbook Page September 2008 17-9

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-2. Amount and Frequency of Use of Various Cosmetic and Baby Products

Product Type Amount of

Product Per Application a

(grams) Average Frequency of Use (per day) Upper 90th Percentile Frequency of Use (per day)

Survey Type Survey Type

Market b Cosmetic CTFA Research Co. Bureau Market Cosmetic CTFA Research Co. Bureau

Baby Lotion - baby use c

Baby Lotion - adult use

Baby Oil - baby use c

Baby Oil - adult use

Baby Powder - baby use c

Baby Powder - adult use

Baby Cream - baby use c

Baby Cream - adult use

Baby Shampoo - baby use c

Baby Shampoo - adult use

Bath Oils

Bath Tablets

Bath Salts

Bubble Baths

Bath Capsules

Bath Crystals

Eyebrow Pencil

Eyeliner

Eye Shadow

Eye Lotion

Eye Makeup Remover

Mascara

Under Eye Cover

Blusher & Rouge

Face Powders

Foundations

Leg and Body Paints

Lipstick & Lip Gloss

Makeup Bases

Makeup Fixatives

Sunscreen

Colognes & Toilet Water

Perfumes 1.4

1.0

1.3

5.0

0.8

0.8

–

–

0.5

5.0

14.7

–

18.9

11.8

–

–

–

–

–

–

–

–

–

0.011

0.085

0.265

–

–

0.13

–

3.18

0.65

0.23 0.38

0.22

0.14

0.06

5.36

0.13

0.43

0.07

0.14

0.02

0.08

0.003

0.006

0.088

0.018

0.006

0.27

0.42

0.69

0.094

0.29

0.79

0.79

1.18

0.35

0.46

0.003

1.73

0.24

0.052

0.003

0.68

0.29 1.0

0.19

1.2

0.13

1.5

0.22

1.3

0.10

–

–

0.19

0.008

0.013

0.13

0.019

–

0.49

0.68

0.78

0.34

0.45

0.87

–

1.24

0.67

0.78

0.011

1.23

0.64

0.12

–

0.85

0.26 –

0.24 d

–

–

0.35 d

–

–

–

0.11 f

–

0.22 g

–

–

–

–

–

–

0.27

0.40

–

–

0.46

–

0.55

0.33

0.47

–

2.62

–

–

0.002

0.56

0.38 0.57

0.86

0.14

0.29

8.43

0.57

0.43

0.14

0.14

0.86 e

0.29

0.14 e

0.14 e

0.43

0.29 e

0.29 e

1.0

1.43

1.43

0.43

1.0

1.29

0.29

2.0

1.29

1.0

0.14 e

4.0

0.86

0.14

0.14 e

1.71

0.86 2.0

1.0

3.0

0.57

3.0

1.0

3.0

0.14 e

--

--

0.86

0.14 e

0.14 e

0.57

0.14 e

0.14 e

1.0

1.0

1.0

1.0

1.0

1.0

--

1.43

1.0

1.0

0.14 e

2.86

1.0

1.0

–

1.43

1.0 -­

1.0 d

-­

-­

1.0 d

-­

-­

-­

0.43 f

-­

1.0 g

-­

-­

-­

-­

-­

-­

1.0

1.0

-­

-­

1.5

-­

1.5

1.0

1.5

-­

6.0

-­

-­

0.005

1.5

1.5



### Page Child-Specific Exposure Factors Handbook

17-10 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-2. Amount and Frequency of Use of Various Cosmetic and Baby Products (ontinued)

Average Frequency of Use Upper 90th Percentile Frequency of Use (per day) (per day) Amount of

Product Per Survey Type Survey Type Product Type Application a Market b Market (grams) Cosmetic Cosmetic CTFA Research CTFA Research Co. Co. Bureau Bureau

Powders 2.01 0.18 0.39

– 1.0 1.0

-­

Sachets 0.2 0.0061 0.034

– 0.14 e 0.14 e

-­ Fragrance Lotion

– 0.0061

-- -- 0.29 e

– -­

Hair Conditioners 12.4 0.4 0.40 0.27 1.0 1.0 0.86

Hair Sprays

– 0.25 0.55 0.32 1.0 1.0 1.0

Hair Rinses 12.7 0.064 0.18

– 0.29 1.0

-­ Shampoos 16.4 0.82 0.59 0.48 1.0 1.0 1.0

Tonics and Dressings 2.85 0.073 0.021

– 0.29 0.14 e

-­

Wave Sets 2.6 0.003 h 0.040

-- -- h 0.14

-­

Dentifrices

– 1.62 0.67 2.12 2.6 2.0 4.0

Mouthwashes

– 0.42 0.62 0.58 1.86 1.14 1.5

Breath Fresheners

– 0.052 0.43 0.46 0.14 1.0 0.57

Nail Basecoats 0.23 0.052 0.13

– 0.29 0.29

-­

Cuticle Softeners 0.66 0.040 0.10

– 0.14 0.29

-­

Nail Creams & Lotions 0.56 0.070 0.14

– 0.29 0.43

-­

Nail Extenders

– 0.003 0.013

-- 0.14 e 0.14 e

-­

Nail Polish & Enamel 0.28 0.16 0.20 0.07 0.71 0.43 1.0

Nail Polish & Enamel 3.06 0.088 0.19

– 0.29 0.43

-­

Remover

Nail Undercoats

– 0.049 0.12

– 0.14 0.29

-­ Bath Soaps 2.6 1.53 0.95

– 3.0 1.43

-­

Underarm Deodorants 0.52 1.01 0.80 1.10 1.29 1.29 2.0

Douches

– 0.013 0.089 0.085 0.14 e 0.29 0.29

Feminine Hygiene

– 0.021 0.084 0.05 1.0 e 0.29 0.14

Deodorants

Cleansing Products (cold 1.7 0.63 0.80 0.54 1.71 2.0 1.5

creams, cleansing lotions liquids & pads)

Depilatories

– 0.0061 0.051 0.009 0.016 0.14 0.033

Face, Body & Hand Preps 3.5 0.65

– 1.12 2.0

– 2.14

(excluding shaving preps)

Foot Powder & Sprays

– 0.061 0.079

-- 0.57 e 0.29

-­

Hormones

– 0.012 0.028

-- 0.57 e 0.14 e

-­

Moisturizers 0.53 0.98 0.88 0.63 2.0 1.71 1.5

Night Skin Care Products 1.33 0.18 0.50

– 1.0 1.0

–



### Child-Specific Exposure Factors Handbook Page September 2008 17-11Table 17-2. Amount and Frequency of Use of Various Cosmetic and Baby Products (continued)

Product Type Amount of

Product Per Application a (g) Average Frequency of Use (per day) Upper 90th Percentile Frequency of Use (per day)

Survey Type Survey Type

Market b Cosmetic CTFA Research Co. Bureau Market Cosmetic CTFA Research Co. Bureau

Paste Masks (mud packs)

Skin Lighteners

Skin Fresheners & Astringents

Wrinkle Smoothers (removers)

Facial Cream

Permanent Wave

Hair Straighteners

Hair Dye

Hair Lighteners

Hair Bleaches

Hair Tints

Hair Rinse (coloring)

Shampoo (coloring)

Hair Color Spray

Shave Cream 3.7

--

2.0

0.38

0.55

101

0.156

--

--

--

–

–

–

--

1.73 0.027

–

0.33

0.021

0.0061

0.003

0.0007

0.001

0.0003

0.0005

0.0001

0.0004

0.0005

–

– 0.20

0.024

0.56

0.15

–

–

–

–

–

–

–

–

–

–

– –

–

–

–

–

0.001

--

0.005

–

–

–

–

–

–

0.082 0.14

– d

1.0

1.0 d

0.0061

0.0082

0.005 d

0.004 d

0.005 d

0.02 d

0.005 d

0.02 d

0.02 d

– d

-- 0.43

0.14 d

1.43

1.0

–

–

–

–

–

–

–

–

–

–

– -­

-­

-­

-­

-­

0.005

-­

0.014

-­

-­

-­

-­

-­

-­

0.36

a

b

c

d

e

f

g

h

Source: Values reported are the averages of the responses reported by the twenty companies interviewed. (--'s) indicate no data available. The averages shown for the Market Research Bureau are not true averages - this is due to the fact that in many cases the class of most frequent users were indicated by "1 or more" also ranges were used in many cases, i.e., "10-12." The average, therefore, is underestimated slightly. The "1 or more" designation also skew the 90th percentile figures in many instances. The 90th percentile values may, in actuality, be somewhat higher for many products. Average usage among users only for baby products. Usage data reflected "entire household" use for both baby lotion and baby oil. Fewer than 10% of individuals surveyed used these products. Value listed is lowest frequency among individuals reporting usage. In the case of wave sets, skin lighteners, and hair color spray, none of the individuals surveyed by the CTFA used this product during the period of the study. Usage data reflected "entire household" use. Usage data reflected total bath product usage. None of the individuals surveyed reported using this product.

CTFA, 1983. 

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Page Child-Specific Exposure Factors Handbook

17-12 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-3. Number of Minutes Spent in Activities Working With or Near Freshly Applied Paints (minutes/day)

Age Group Percentiles

N 1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 7 3 3 3 3 5 15 121 121 121 121 121 121

5 to 11 years 12 5 5 5 15 20 45 120 120 121 121 121 121

12 to 17 years 20 0 0 0.5 3 8 45 75 121 121 121 121 121

18 to 64 years 212 0 0 1 2 11 60 121 121 121 121 121 121

Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.

Table 17-4. Number of Minutes Spent in Activities Working With or Near Household Cleaning Agents Such as Scouring Powders or Ammonia (minutes/day)

Age Group Percentiles

N 1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 21 0 0 0 0 5 10 15 20 30 121 121 121

5 to 11 years 26 1 1 2 2 3 5 15 30 30 30 30 30

12 to 17 years 41 0 0 0 0 2 5 10 40 60 60 60 60

18 to 64 years 672 0 0 1 2 5 10 20 60 121 121 121 121

Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.

Table 17-5. Number of Minutes Spent in Activities (at home or elsewhere) Working With or Near Floorwax, Furniture Wax or Shoe Polish (minutes/day)

Age Group Percentiles

N 1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 13 0 0 0 5 10 15 20 60 121 121 121 121

5 to 11 years 21 0 0 2 2 3 5 10 35 60 120 120 120

12 to 17 years 15 0 0 0 1 2 10 25 45 121 121 121 121

18 to 64 years 238 0 0 2 3 5 15 30 120 121 121 121 121

Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.



### Child-Specific Exposure Factors Handbook Page September 2008 17-13

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-6. Number of Minutes Spent in Activities Working With or Near Glue (minutes/day)

N Percentiles

1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 6 0 0 0 0 30 30 30 50 50 50 50 50

5 to 11 years 36 2 2 3 5 5 12.5 25 30 60 120 120 120

12 to 17 years 34 0 0 1 2 5 10 30 30 60 120 120 120

18 to 64 years 207 0 0 0 1 5 20 90 121 121 121 121 121

Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.

Table 17-7. Number of Minutes Spent in Activities Working With or Near Solvents, Fumes or Strong Smelling Chemicals (minutes/day)

Age Group N Percentiles

1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 7 0 0 0 0 1 5 60 121 121 121 121 121

5 to 11 years 16 0 0 0 2 5 5 17.5 45 70 70 70 70

12 to 17 years 38 0 0 0 0 5 10 60 121 121 121 121 121

18 to 64 years 407 0 0 1 2 5 30 121 121 121 121 121 121

Note: A Value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.

Table 17-8. Number of Minutes Spent in Activities Working With or Near Stain or Spot Removers (minutes/day)

Age Group Percentiles

N 1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 3 0 0 0 0 0 0 3 3 3 3 3 3

5 to 11 years 3 3 3 3 3 3 5 5 5 5 5 5 5

12 to 17 years 7 0 0 0 0 5 15 35 60 60 60 60 60

18 to 64 years 87 0 0 0 0 2 5 15 60 121 121 121 121

Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.



### Page Child-Specific Exposure Factors Handbook

17-14 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-9. Number of Minutes Spent in Activities Working With or Near Gasoline or Diesel-powered Equipment, Besides Automobiles (minutes/day)

Age Group N Percentiles

1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 14 0 0 0 1 5 22.5 120 121 121 121 121 121

5 to 11 years 12 1 1 1 3 7.5 25 50 60 60 60 60 60

12 to 17 years 25 2 2 5 5 13 35 120 121 121 121 121 121

18 to 64 years 312 0 0 1 3 15 60 121 121 121 121 121 121

Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.

Table 17-10. Number of Minutes Spent in Activities Working With or Near Pesticides, Including Bug Sprays or Bug Strips (minutes/day)

Age Group N Percentiles

1 2 5 10 25 50 75 90 95 98 99 100

1 to 4 years 6 1 1 1 1 3 10 15 20 20 20 20 20

5 to 11 years 16 0 0 0 0 1.5 7.5 30 121 121 121 121 121

12 to 17 years 10 0 0 0 0 2 2.5 40 121 121 121 121 121

18 to 64 years 190 0 0 0 1 2 10 88 121 121 121 121 121

Note: A value of "121" for number of minutes signifies that more than 120 minutes were spent; N = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.

Source: U.S. EPA, 1996.

Table 17-11. Number of Respondents Using Cologne, Perfume, Aftershave or Other Fragrances at Specified Daily Frequencies

Age Group Total N Number of Times Used in a Day

1-2 3-5 6-9 10+ Don ’ t Know 5 to 11 years 26 24 2 * * * 12 to 17 years 144 133 9 * 1 1 18 to 64 years 1,735 1,635 93 3 1 3

* = Missing Data.

N = Number of respondents.

Source: U.S. EPA, 1996.



### Child-Specific Exposure Factors Handbook Page September 2008 17-15

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-12. Number of Respondents Using Any Aerosol Spray Product for Personal Care Item Such as Deodorant or Hair Spray at Specified Daily Frequencies

Age Group Total N Number of Times Used in a Day

1 2 3 4 5 6 7 10 10+ Don ’ t Know 1 to 4 years 40 30 9 0 0 1 0 0 0 0 0 5 to 11 years 75 57 14 1 1 1 1 0 0 0 0 12 to 17 years 103 53 31 12 4 1 0 0 1 1 0 18 to 64 years 1,071 724 263 39 15 13 1 1 2 8 5

N = Number of respondents..

Source: U.S. EPA, 1996.

Table 17-13. Number of Respondents Using a Humidifier at Home

Age Group Total N Frequency

Almost Every 3-5 Times a 1-2 Times a 1-2 Times a Don ’ t Day Week Week Month Know
1 to 4 years 111 33 16 7 53 2 5 to 11 years 88 18 10 12 46 2 12 to 17 years 83 21 7 5 49 1 18 to 64 years 629 183 77 70 287 12

N = Number of respondents.

Source: U.S. EPA, 1996.

Table 17-14. Number of Respondents Indicating that Pesticides Were Applied by the Professional at Home to Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

Age Group Total N Number of Times Over a 6-month Period Pesticides Were Applied by Professionals

None 1-2 3-5 6-9 10+ Don ’ t Know 1 to 4 years 113 60 35 11 6 1 * 5 to 11 years 150 84 37 10 18 1 * 12 to 17 years 143 90 40 5 6 * 2 18 to 64 years 1,264 660 387 89 97 15 16

* = Missing data.

N = Number of respondents.

Source: U.S. EPA, 1996.



### Page Child-Specific Exposure Factors Handbook

17-16 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-15. Number of Respondents Reporting Pesticides Applied by the Consumer at Home To Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

Age Group Total N Number of Times Over a 6-month Period Pesticides Applied by Resident

None 1-2 3-5 6-9 10+ Don ’ t Know 1 to 4 years 113 46 46 15 3 3 * 5 to 11 years 150 50 70 24 1 4 1 12 to 17 years 143 45 64 21 5 8 * 18 to 64 years 1,264 473 477 192 48 55 19

Note: * = Missing Data
N = Number of respondents.

Source: U.S. EPA, 1996.

Table 17-16. Number of Respondents Indicating that Pesticides Were Applied by a Professional at Home to Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

Frequency Age Group Total N (number of times over a six-month period that pesticides were applied by a professional)

None 1 to 2 3 to 5 6 to 9 10+ Don ’ t Know 0 to <1 years 15 9 4 1 1 0 0 1to <2 years 23 13 5 3 1 1 0 2 to <3 years 32 9 15 5 3 0 0 3 to <6 years 80 51 22 5 2 0 0 6 to <11 years 106 59 22 7 17 1 0 11 to <16 years 115 68 35 4 6 0 2 16 to <21 years 87 40 36 2 5 1 3

N = Number of respondents.

Source: U.S. EPA re-analysis of NHAPS (U.S. EPA, 1996) data.

Table 17-17. Number of Respondents Reporting Pesticides Applied by the Consumer at Home to Eradicate Insects, Rodents, or Other Pests at Specified Frequencies

Frequency Age Group Total N (number of times over a six-month period that pesticides were applied by a resident)

None 1 to 2 3 to 5 6 to 9 10+ Don ’ t Know 0 to <1 year 15 4 8 2 0 1 0 1to <2 years 23 11 10 1 0 1 0 2 to <3 years 32 18 9 2 2 1 0 3 to <6 years 80 26 35 18 1 0 0 6 to <11 years 106 37 49 14 1 4 1 11 to <16 years 115 37 50 18 4 6 0 16 to <21 years 87 36 33 9 4 4 1

N = Number of respondents.

Source: U.S. EPA re-analysis of NHAPS (U.S. EPA, 1996) data.



### Child-Specific Exposure Factors Handbook Page September 2008 17-17

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-18. Household Demographics, and Pesticide Types, Characteristics, and Frequency of Pesticide Use

Survey Population Demographics

Gender female male Language of Interview Spanish English Reading Skills able to read English able to read Spanish Number in household 2-3 people 4-5 people 6-8 people Children under 10 years 1 child 2 children 3 to 5 children Type of home single family detached multi-family trailer/mobile home single-family attached apartment/other
Pets pets kept in household pesticides used on pets Number a Percent a

90 17

72 35

71 95

25 59 23

37 45 25

75
9 9 8 4

55 22 84.1 15.9

67.3 32.7

66.4 88.8

23.3 55.1 21.4

34.6 42.1 23.3

70.1 8.4 8.4 7.5 3.7

51.4 40.0

Pesticide Use Type of pesticide insecticide rodenticide herbicide Storage of pesticide kitchen garage/shed laundry/washroom other, inside home other, outside home bathroom basement closet Storage precautions child-resistant container pesticide locked away Storage risks < 4 feet from ground kept near food kept near dishes/cookware Disposal throw it away wrap in separate container, throw

away other Frequency of use more than once/week once/week once/month once every 3 months once every 6 months once/year Time stored in home < 6 months 6 to 12 months 12 to 24 months > 24 months 135 10 3

67 30 14 11
7 7 4 4

83 55

72 5 5

132 10 5

20 27 42 23 16 13

75
24 17 16 91.2 6.8 2.0

45.3 20.3 9.4 7.4 4.7 4.7 2.7 2.7

56.1 37.2

48.6 3.4 3.4

89.2 6.8 3.4

13.5 18.2 28.4 15.5 10.8 8.8

50.7 15.2 11.5 10.8

a

Source: Totals may not add to 107 participants or 148 products, and percentages may not add to 100 due to some non-responses to survey questions.

Bass et al., 2001.



### Page Child-Specific Exposure Factors Handbook

17-18 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-19. Frequency of Use of Cosmetic Products

Product Type N Number of Applications per Day

Mean Median SD
Lipstick 311 2.35 2 1.80
Body lotion, hands 308 2.12 2 1.59
Body lotion, arms 308 1.52 1 1.30
Body lotion, feet 308 0.95 1 1.01
Body lotion, legs 308 1.11 1 0.98
Body lotion, neck & throat 308 0.43 0 0.82
Body lotion, back 308 0.26 0 0.63
Body lotion, other 308 0.40 0 0.76

Face cream 300 1.77 2 1.16

N = Number of subjects (women, ages 19 to 65 years). SD = Standard deviation.

Source: Loretz et al., 2005.



### Child-Specific Exposure Factors Handbook Page September 2008 17-19

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-20. Amount of Test Product used (grams) for Lipstick, Body Lotion and Face Cream

Summary Statistics Total Amount Applied Average a Amount Applied per Use Day Average b Amount Applied per Application
Lipstick

Minimum 0.001 0.000 0.000

Maximum 2.666 0.214 0.214

Mean 0.272 0.024 0.010

SD 0.408 0.034 0.018

Percentiles

10th 0.026 0.003 0.001

20th 0.063 0.005 0.003

30th 0.082 0.008 0.004

40th 0.110 0.010 0.004

50th 0.147 0.013 0.005

60th 0.186 0.016 0.006

70th 0.242 0.021 0.009

80th 0.326 0.029 0.011

90th 0.655 0.055 0.024

95th 0.986 0.087 0.037

99th 2.427 0.191 0.089

Best Fit Distributions &

Parameters c Lognormal Distribution
GM = 0.14
GSD = 3.56 P-value (Gof) = 0.01 Lognormal Distribution
GM = 0.01
GSD = 3.45 P-value (Gof) <0.01 Lognormal Distribution
GM = 0.01
GSD = 3.29 P-value (Gof) <0.01

Body Lotion

Minimum 0.67 0.05 0.05

Maximum 217.66 36.31 36.31

Mean 103.21 8.69 4.42

SD 53.40 5.09 4.19

Percentiles

10th 36.74 3.33 1.30

20th 51.99 4.68 1.73

30th 68.43 5.71 2.32

40th 82.75 6.74 2.76

50th 96.41 7.63 3.45

60th 110.85 9.25 4.22

70th 134.20 10.90 4.93

80th 160.26 12.36 6.14



### Page Child-Specific Exposure Factors Handbook

17-20 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-20. Amount of Test Product Used (grams) for Lipstick, Body Lotion and Face Cream (continued)

Summary Statistics Total Amount Applied Average a Amount Applied per Use Day Average b Amount Applied per Application

90th 182.67 14.39 8.05

95th 190.13 16.83 10.22

99th 208.50 27.91 21.71

Best Fit Distributions &

Parameters c Beta Distribution c Alpha = 1.53

Beta = 1.77

Scale = 222.01 P-value (GoF) = 0.06 Gamma Distribution

Location = -0.86

Scale = 2.53 Shape = 3.77 P-value (GoF) = 0.37 Lognormal Distribution

GM = 3.26

GSD = 2.25 P-value (GoF) = 0.63

Face Cream

Minimum 0.04 0.00 0.00

Maximum 55.85 42.01 21.01

Mean 22.36 2.05 1.22

SD 14.01 2.90 1.76

Percentiles

10th 5.75 0.47 0.28

20th 9.35 0.70 0.40

30th 12.83 1.03 0.53

40th 16.15 1.26 0.67

50th 19.86 1.53 0.84

60th 23.79 1.88 1.04

70th 29.31 2.23 1.22

80th 36.12 2.90 1.55

90th 44.58 3.50 2.11

95th 48.89 3.99 2.97

99th 51.29 12.54 10.44

Best Fit Distributions &

Parameters c Triangle Distribution
Minimum = -1.09
Maximum = 58.71
Likeliest = 7.53 P-value (GoF) = 0.27 Lognormal Distribution c
GM = 1.39
GSD = 2.58 P-value (GoF) <0.01 Lognormal Distribution c
GM = 0.80
GSD = 2.55 P-value (GoF) = 0.02

a

b

c
GM
GSD
Gof

Note: Derived as the ratio of the total amount used to the number of use days. Derived as the ratio of the total amount used to the total number of applications during the survey. None of the tested distributions provided a good fit.
= Geometric mean.
= Geometric standard deviation.
= Goodness of fit. Data are for women, ages 19 to 65 years.

Source: Loretz et al., 2005.



### Child-Specific Exposure Factors Handbook Page September 2008 17-21

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-21. Frequency of Use of Personal Care Products

Average Number of Applications per Use Day a

Product Type N Mean SD Min Max

Hairspray (aerosol) 165 b 1.49 0.63 1.00 5.36

Hairspray (pump) 162 1.51 0.64 1.00 4.22

Liquid Foundation 326 1.24 0.32 1.00 2.00

Spray Perfume 326 1.67 1.10 1.00 11.64

Body wash 340 1.37 0.58 1.00 6.36

Shampoo 

### 340 1.11 0.24 1.00 2.14

Solid antiperspirant 

### 340 1.30 0.40 1.00 4.00

a Derived as the ratio of the number of applications to the number of use days. b Subjects who completed the study but did not repor t their number of applications were excluded. N = Number of subjects (women, ages 18 to 65 years).
SD = Standard deviation.

Source: Loretz et al., 2006.



### Page Child-Specific Exposure Factors Handbook

17-22 September 2008

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer ProductsTable 17-22. Average Amount of Product Applied per Applicationa(grams)HairsprayHairspraySolidSummary Statistics (aerosol)(pump) Spray Perfume Liquid Foundation Shampoo Body Wash AntiperspirantN163b161b310b321b340340340Mean2.583.640.330.5411.7611.30.61SD2.263.500.410.528.776.90.56Minimum0.050.000.000.000.391.10.00Maximum14.0821.445.082.6567.8958.25.55Percentiles10th0.660.700.060.083.904.60.1420th0.941.010.100.145.505.80.2230th1.261.590.130.196.787.10.3040th1.562.140.180.268.278.50.3750th1.832.660.230.369.569.50.4560th2.383.430.280.4811.3211.40.5570th2.873.840.360.6313.2913.40.6980th3.555.160.490.8616.0716.00.8990th5.337.810.681.2322.5921.11.2595th7.4210.950.941.7027.9524.31.6797.5th8.7714.681.252.0735.6528.42.1599thc11.3015.521.732.3651.1235.12.52Best fit distributions LognormalLognormal Lognormal LognormalLognormalLognormal Gammaand parameters DistributionDistribution Distribution DistributionDistributionGM = 1.84GM = 2.44 GM =0.21 GM = 0.33 GM = 9.32 Location = 0.51 GM = 0.43GSD = 2.40GSD = 2.67 GSD = 3.01 GSD. = 2.99 GSD = 2.02 Scale = 3.92 GSD = 2.37Shape = 2.76P-value(Kolmogorov- 0.060.070.0770.0410.13280.4860.339Smirnov)aDerived as the ratio of the total amount used to the total number of applications.bSubjects who completed the study, but did not report their number of applications, or who did not return the unused portion of the product, were excluded.cEstimate does not meet the minimum sample size criteria (N=800) as set by the National Center for Health Statistics. For upper percentile (>0.75), the minimum sample size (N)satisfies the following rule: n [8/(1-p)].http://www/cdc.gov/nchs/about/major/nhanes/nhanes3/nh3gui.pdf.N = Number of subjects (women, ages 19 to 65 years).GM = Geometric mean.GSD = Geometric standard Deviation.Source: Loretz et al., 2006.



### Child-Specific Exposure Factors Handbook Page September 2008 17-23Table 17-23. Average Amount of Product Applied per Use Daya(grams)HairsprayHairspraySolidSummary Statistics (aerosol)(pump) Spray Perfume Liquid Foundation Shampoo Body Wash AntiperspirantN163b161b310b321b340340340Mean3.575.180.530.6712.8014.50.79SD3.094.830.570.659.118.50.78Minimum0.050.000.000.000.551.30.00Maximum18.2524.125.083.0067.8963.45.55Percentiles10th0.840.910.080.104.125.70.1720th1.351.480.120.165.807.60.2930th1.652.330.190.237.329.30.3840th2.232.660.260.309.0910.90.4650th2.713.740.340.4510.7512.90.5960th3.304.710.450.5812.7214.80.7070th3.895.670.610.7614.7317.40.8680th4.867.380.811.0417.6120.71.0890th7.7312.221.451.7623.6325.51.7095th9.8915.621.772.1829.0829.12.3297.5th13.3419.411.862.4036.4635.63.3399thc15.0523.982.012.7051.1243.54.42Best fit distributions LognormalLognormal Lognormal LognormalLognormalLognormal Gammaand parameters DistributionDistribution Distribution DistributionDistributionGM= 2.57GM = 3.45 GM= 0.30 GM = 0.40 Location = 0.38 Location = 0.67 GM = 0.56GSD = 2.37GSD = 2.70 GSD = 3.36 GSD. = 3.10 Scale= 5.79 Scale = 4.89 GSD = 2.41Shape = 2.15 Shape = 2.84P-value(Kolmogorov- 0.050.050.0750.0470.82080.7600.293Smirnov)aDerived as the ratio of the total amount used to the total number of applications.bSubjects who completed the study, but did not report their number of applications , or who did not return the unused portion of the product, were excluded.cEstimate does not meet the minimum sample size criteria (n=800) as set by the National Center for Health Statistics. For upper percentile (>0.75), the minimum sample size (n) satisfiesthe followingrule: n [8/(1-p)].http://www/cdc.gov/nchs/about/major/nhanes/nhanes3/nh3gui.pdf.N = Number of subjects (women, ages 19 to 65 years).GM = Geometric mean.GSD = Geometric standard deviation.Source: Loretz et al., 2006. 

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Page Child-Specific Exposure Factors Handbook

17-24 September 2008Table 17-24. Average Number of Applications Per Use Day a

Summary Statistics Facial Cleanser (Lathering and Non-Lathering) Hair Conditioner Eye Shadow

N

Mean

SD

Minimum

Maximum

Percentiles

10th

20th

30th

40th

50th

60th

70th

80th

90th

95th

97.5th

99th b 295

1.6

0.52

1.0

3.2

1.0

1.0

1.2

1.4

1.7

1.9

2.0

2.0

2.2

2.4

2.9 b

3.1 b 297

1.1

0.19

1.0

2.4

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.1

1.2

1.4

1.8 b

2.1 b 299

1.2

0.33

1.0

2.7

1.0

1.0

1.0

1.1

1.1

1.1

1.2

1.4

1.7

2.0

2.2 b

2.5 b

a

b

N

SD

Source: Derived as the ratio of the number of applications to the number of use days. Estimate does not meet the minimum sample size criteria (n=800) as set by the National Center for Health Statistics. For upper percentile (>0.75), the minimum sample size (n) satisfies the following rule: n [8/(1-p.] http://www/cdc/gov/nchs/about/major/nhanes/nhanes3/nh3gui.pdf..

= Number of subjects (women, ages 18 to 69 years).

= Standard deviation.

Loretz et al., 2008. 

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Child-Specific Exposure Factors Handbook Page September 2008 17-25

### Child-Specific Exposure Factors Handbook

Chapter 17 - Consumer Products

Table 17-25. Average Amount of Product Applied Per Use Day (grams) a

Facial Cleanser Facial Cleanser Facial Cleanser (Non- Summary Statistics (Lathering and Non- Hair Conditioner Eye shadow (Lathering) Lathering) Lathering)

N 295 174 121 297 299

Mean 4.06 4.07 4.05 13.77 0.04

SD 2.78 2.87 2.67 11.50 0.11

Minimum 0.33 0.33 0.83 0.84 0.001

Maximum 16.70 15.32 16.70 87.86 0.74

Percentiles

10th 1.41 1.23 1.50 3.71 0.003

20th 1.79 1.72 1.94 5.54 0.005

30th 2.18 2.15 2.22 6.95 0.007

40th 2.66 2.64 2.80 8.73 0.009

50th 3.25 3.19 3.33 10.62 0.010

60th 3.86 3.84 3.88 12.61 0.013

70 th 4.62 4.71 4.59 15.54 0.017

80 th 6.24 6.33 5.92 20.63 0.025

90 th 8.28 8.24 8.40 28.20 0.052

95th 9.93 10.50 9.37 b 33.19 0.096

97.5th 10.71 b 11.47 b 10.26 b 45.68 b 0.525 b

99th b 12.44 b 13.07 b 15.29 b 60.20 b 0.673 b

Best fit distributions Lognormal Lognormal Lognormal Lognormal Lognormal and parameters distribution distribution distribution distribution distribution

GM = 3.26 GM = 3.21 GM = 3.35 GM = 10.28 GM = 0.01

GSD = 1.12 GSD = 2.03 GSD = 1.86 GSD - 2.20 GSD = 3.61

P-value 0.1251 0.4429 0.4064 0.8595 <0.0001 (Chi-square test)

a Derived as the ratio of the total amount used to the number of use days.

b Estimate does not meet the minimum sample size criteria (n=800) as set by the National Center for Health Statistics. For upper percentile (>0.75), the minimum sample size (n) satisfies the following rule: n [8/(1-p)]. http://www/cdc.gov/nchs/about/major/nhanes/nhanes3/nh3gui.pdf

. N = Number of subjects (women, ages 18 to 69 years).

GM = Geometric mean.

GSD = Geometric standard deviation.

Source: Loretz et al., 2008.



### Page Child-Specific Exposure Factors Handbook

17-26 September 2008

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Chapter 17 - Consumer Products

Table 17-26. Average Amount of Product Applied Per Application (grams) a

Summary Statistics Facial Cleanser Facial Facial Cleanser (Lathering and Non- Cleanser (Non- Hair Conditioner Eye Shadow Lathering) (Lathering) Lathering)

N 295 174 121 297 299

Mean 2.57 2.56 2.58 13.13 0.03

SD 1.78 1.78 1.77 11.22 0.10

Minimum 0.33 0.33 0.57 0.84 0.0004

Maximum 14.61 10.67 14.61 87.86 0.69

Percentiles

10th 0.92 0.83 1.10 3.48 0.003

20th 1.32 1.26 1.35 5.34 0.004

30th 1.57 1.55 1.59 6.71 0.006

40th 1.85 1.84 1.89 8.26 0.007

50th 2.11 2.11 2.15 10.21 0.009

60th 2.50 2.50 2.51 12.24 0.011

70th 2.94 2.96 2.96 14.54 0.015

80th 3.47 3.56 3.40 18.88 0.022

90th 4.81 5.10 4.52 27.32 0.041

95th 5.89 6.37 5.11 b 32.43 0.096

97.5th 7.16 b 7.77 b 6.29 b 45.68 b 0.488 b

99th b 9.44 b 9.61 b 15.46 b 60.20 b 0.562 b

Best fit distributions and Lognormal Lognormal Extreme value Gamma Extreme value parameters distribution distribution

Mode = 1.86 Loc = 0.28 Mode = 1.92 GM = 9.78 GM = 0.01

Scale = 1.12 Scale = 1.29 Scale = 1.03 GSD = 2.20 GSD = 3.59
P-value (Chi-square test) 0.0464 0.6123 0.5219 0.9501 <0.0001

a Derived as the ratio of the total amount used to the total number of applications.

b Estimate does not meet the minimum sample size criteria (n=800) as set by the National Center for Health Statistics. For upper percentile (>0.75), the minimum sample size (n) satisfies the following rule: n [8/(1-p)]. Http://www/cdc.gov/nchs/about/major/nhanes/nhanes3/nh3gui.pdf.

N = Number of subjects (women, ages 18 to 69 years).

GM = Geometric mean.

SD = Geometric standard deviation.

Source: Loretz et al., 2008.



### Child-Specific Exposure Factors Handbook Page September 2008 17-27Table 17-27. Characteristics of the Study Population and the Percent Using Selected Baby Care Products

Characteristic

Number of Participants

Los Angeles, California

Minneapolis, Minnesota

Columbia, Missouri

Gender

Male

Female

Age (months)

2-8

9-16

17-24

24-28

Infant Weight (kg)

? 10

> 10

Race

White

Hispanic/Latino

Native American

Asian

Black Sample Number (percent)

43 (26)

77 (47)

43 (26)

84 (52)

79 (48)

42 (26)

82 (50)

30 (18)

9 (6)

84 (52)

79 (48)

131 (80)

17 (10)

3 (2)

8 (5)

4 (3)

Product Use

Baby Lotion

Baby Shampoo

Baby Powder

Diaper Cream

Baby Wipes Percent Using

36

54

14

33

94

Source: Sathyanarayana et al., 2008. 

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17-28 September 2008

### Child-Specific Exposure Factors Handbook

Glossary

GLOSSARY OF TERMS



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Glossary

Activity pattern data - Information on human activities used in exposure assessments. These may include a description of the activity, frequency of activity, duration spent performing the activity, and the microenvironment in which the activity occurs.

Adherence factor - The amount of a material (e.g., soil) that adheres to the skin per unit of surface area.

Activity pattern (time use) data - Information on activities in which various individuals engage, length of time spent performing various activities, locations in which individuals spend time and length of time spent by individuals within those various environments.

Agricultural commodity - Used by U.S. EPA to mean plant (or animal) parts consumed by humans as food. When such items are raw or unprocessed, they are referred to as "raw agricultural commodities."

All water sources - Includes water from all supply sources such as community water supply (i.e., tap
water), bottled water, etc.

Analytical uncertainty propagation - Examining how uncertainty in individual parameters affects the overall uncertainty of the exposure assessment.

Anthropometric - The study of human body measurements for use in anthropological classification and comparison.

As-consumed intake - Intake rate based on the weight of the food in the form that it is consumed (e.g., cooked or prepared).

Assessment - A determination or appraisal of possible consequences resulting from an analysis of data.

Average Daily Dose (ADD) - 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. The ADD is usually expressed in terms of mg/kg-day or other

mass/mass-time units. Benchmark Dose (BMD) or Concentration (BMC) ­ A dose or concentration that produces a predetermined change in response rate of an adverse effect (called the benchmark response or BMR) compared to background.

Best Tracer Method (BTM) - Method for estimating soil ingestion that allows for the selection of the most recoverable tracer for a particular subject or group of subjects. Selection of the best tracer is made on the
basis of the food/soil (F/S) ratio.

Bias - A systematic error inherent in a method or caused by some feature of the measurement system.

Bioavailability - The rate and extent to which an agent can be absorbed by an organism and is available for metabolism or interaction with biologically significant receptors. Bioavailability involves both release from a medium (if present) and absorption by an organism.

Biomarker model comparison - A methodology that compares results from a biokinetic exposure model to

biomarker measurements children blood. The method is used to confirm assumptions about ingested soil and dust quantities in this handbook.

Basal Metabolic Rate (BMR) - Minimum level of energy required to maintain normal body functions.

Body Mass Index (BMI) - The ratio of weight and height squared.

Bootstrap - A statistical method of resampling data

use to estimate variance and bias of an estimator and provide confidence intervals for parameters.

Bounding estimate - An estimate of exposure, dose, or risk that is higher or lower than that incurred by the person with the highest or lowest exposure, dose, or risk in the population being assessed. Bounding estimates are useful in developing statements that exposures, doses, or risks are "not greater than" or “less than” the estimated value, because assumptions are used which define the likely bounding conditions.



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Glossary

Central tendency exposure - A measure of the middle or the center of an exposure distribution. The mean is the most commonly used measure of central tendency.

Chronic exposure - Repeated exposure by the oral, dermal, or inhalation route for more than approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal species).

Chronic intake - The long term period over which a substance crosses the outer boundary of an organism without passing an absorption barrier.

Classical statistical methods - Estimating the population exposure distribution directly, based on measured values from a representative sample

.

Coating - Method used to measure skin surface area, in which either the whole body or specific body regions are coated with a substance of known density and

thickness.

Community water - Includes tap water ingested from community or municipal water supply.

Comparability - The ability to describe likenesses and differences in the quality and relevance of two or more

data sets.

Concentration - Amount of a material or agent dissolved or contained in unit quantity in a given medium or system.

Confidence intervals - An estimated range of values with a given probability of including the population parameter of interest. The range of values is usually based on the results of a sample that estimated the mean and the sampling error or standard error.

Consumer-only intake rate - The average quantity of food consumed per person in a population composed only of individuals who ate the food item of interest during a specified period. 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.

Creel study - A study in which fishermen are interviewed while fishing.

Cumulative exposure - Exposure via mixtures of contaminants both indoors and outdoors. Exposure may also occur through more than one pathway. New directions in risk assessments in U.S. EPA put more emphasis on total exposures via multiple pathways.

Deposition - The removal of airborne substances to available surfaces that occurs as a result of gravitational settling and diffusion, as well as electrophoresis and thermophoresis.

Dermal absorption - A route of exposure by which substances can enter the body through the skin.

Dermal adherence - The loading of a substance onto

the outer surface of the skin.

Diary study - Survey in which individuals are asked to record food intake, activities, or other factors in a diary which is later used to evaluate exposure factors associated with specific populations.

Direct water ingestion - Consumption of plain water as a beverage. It does not include water used for preparing beverages such as coffee or tea.

Distribution - A set of values derived from a specific population or set of measurements that represents the range and array of data for the factor being studied.

Doers - Survey respondents who report participating in a specified activity.

Dose - The amount of a substance available for interaction with metabolic processes or biologically significant receptors after crossing the outer boundary of an organism. The potential dose is the amount ingested, inhaled, or applied to the skin. The applied



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Glossary

dose is the amount of a substance presented to an absorption barrier and available for absorption (although not necessarily having yet crossed the outer boundary of the organism). The absorbed dose is the amount crossing a specific absorption barrier (e.g., the exchange boundaries of skin, lung, and digestive tract) through uptake processes. Internal dose is a more general term denoting the amount absorbed without respect to specific absorption barriers or exchange

boundaries. The amount of a chemical available for interaction by any particular organ or cell is termed the delivered dose for that organ or cell.

Dose rate - Dose per unit time.

Dose-response assessment - Analysis of the relationship between the total amount of an agent administered to, taken up by, or absorbed by an organism, system, or (sub)population and the changes developed in that organism, system, or (sub)population in reaction to that agent, and inferences derived from such an analysis with respect to the entire population. Dose-response assessment is the second of four steps in risk assessment.

Dose-response curve - Graphical presentation of a dose-response relationship.

Dose-response relationship - The resulting biological responses in an organ or organism expressed as a

function of a series of doses.

Dressed weight - The portion of the harvest brought into kitchens for use, including bones for particular species.

Dry weight intake rates - Intake rates that are based on the weight of the food consumed after the moisture

content has been removed.

Dust Ingestion - Consumption of dust that results from various behaviors including, but not limited to, mouthing objects or hands, eating dropped food, consuming dust directly, or inhaling dust that passes from the respiratory system into the gastrointestinal

tract. Effect - Change in the state or dynamics of an organism, system, or (sub) population caused by

exposure to an agent.

Energy expenditures - The amount of energy expended by an individual during activities.

Exposure - Contact of a chemical, physical, or biological agent with the outer boundary of an organism. Exposure is quantified as the concentration of the agent in the medium in contact integrated over

the time duration of the contact.

Exposure assessment - The determination or estimation (qualitative or quantitative) of the magnitude, frequency, or duration, and route or

exposure.

Exposure concentration - The concentration of a chemical in its transport or carrier medium at the point

of contact.

Exposure duration - Length of time over which

contact with the contaminant lasts.

Exposure event - The occurrence of continuous contact between an agent and a target.

Exposure frequency - The number of exposure events in an exposure duration.

Exposure loading - The exposure mass divided by the exposure surface area. For example, a dermal exposure measurement based on a skin wipe sample, expressed as a mass of residue per skin surface area, is an exposure loading.

Exposure pathway - The physical course a chemical takes from the source to the organism exposed.

Exposure route - The way a chemical pollutant enters an organism after contact, e.g., by ingestion, inhalation, or dermal absorption.

Exposure scenario - A set of facts, assumptions, and interferences about how exposure takes place that aids



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Glossary

the exposure assessor in evaluating estimating, or quantifying exposures.

Fate - Pattern of distribution of an agent, its derivatives, or metabolites in an organism, system, compartment, or (sub)population of concern as a result of transport, partitioning, transformation, or degradation.

General population - The total of individuals inhabiting an area or making up a whole group.

Geometric mean - The n th 

### root of the product of n

values.

Geophagy - A form of soil ingestion involving the intentional ingestion of earths, usually associated with cultural practices.

Hazard - Inherent property of an agent or situation having the potential to cause adverse effects when an organism, system, or (sub)population is exposed to that
agent.

Hazard assessment - A process designed to determine the possible adverse effects of an agent or situation to which an organism, system, or (sub)population could be exposed. The process typically includes hazard identification, dose-response evaluation and hazard characterization. The process focuses on the hazard, in contrast to risk assessment, where exposure assessment is a distinct additional step.

High end exposure - An estimate of individual exposure or dose for those persons at the upper end of an exposure or dose distribution, conceptually above

the 90 th 

### percentile, but not higher than the individual in the population who has the highest exposure or dose.

Homegrown/home produced foods - Fruits and vegetables produced by home gardeners, meat and dairy products derived form consumer-raised livestock, game meat, and home caught fish.

Human Equivalent Concentration (HEC) or Dose (HED) - The human concentration (for inhalation exposure) or dose (for other routes of exposure) of an agent that is believed to induce the same magnitude of toxic effect as the experimental animal species concentration or dose. This adjustment may incorporate toxicokinetic information on the particular agent, if available, or use a default procedure, such as assuming that daily oral doses experienced for a lifetime are proportional to body weight raised to the 0.75 power.

Indirect water ingestion - Includes water added during food preparation, but not water intrinsic to purchased foods. Indirect water includes for example, water used to prepare baby formulas, cake mix, and concentrated orange juice.

Indoor settled dust - Particles in building interiors that have settled onto objects, surfaces, floors, and carpeting. These particles may include soil particles

that have been tracked into the indoor environment

from outdoors.

Inhalation dosimetry - Process of measuring or estimating inhaled dose.

Inhalation unit risk - The upper-bound excess lifetime

cancer risk estimated to result from continuous exposure to an agent at a concentration of 1 : 

### g/m 3 

### in air

for a lifetime.

Inhaled dose - The amount of an inhaled substance that is available for interaction with metabolic processes or biologically significant receptors after crossing the outer boundary of an organism.

Insensible water loss - Evaporative water losses that occur during breastfeeding. Corrections are made to account for insensible water loss when estimating breast milk intake using the test weighing method.

Intake - The process by which a substance crosses the outer boundary of an organism without passing an absorption barrier (e.g., through ingestion or inhalation).

Intake rate - Rate of inhalation, ingestion, and dermal contact depending on the route of exposure. For



### Child-Specific Exposure Factors Handbook Page September 2008 G-5

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Glossary

ingestion, the intake rate is simply the amount of food containing the contaminant of interest that an individual ingests during some specific time period (units of
mass/time). 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.

Inter-individual variability - Variations between

individuals in terms of human characteristics such as age or body weight, or behaviors such as location, activity patterns, and ingestion rates.

Internal dose - The amount of a substance penetrating across absorption barriers (the exchange boundaries) of an organism, via either physical or biological processes (synonymous with absorbed dose).

Intra-individual variability - Fluctuations in an individual’s physiologic (e.g., body weight), or behavioral characteristics (e.g., ingestion rates or activity patterns).

Key study - A study that is useful for deriving exposure

factors.

Lead isotope ratio methodology - A method that measures different lead isotopes in children’s blood and/or urine, food, water, and house dust and compares the ratio of these isotopes to infer sources of lead exposure that may include dust or other environmental

exposures.

Lifestage - A distinguishable time frame in an individual’s life characterized by unique and relatively stable behavioral and/or physiological characteristics that are associated with development and growth.

Lifetime Average Daily Dose (LADD) - Dose rate averaged over a lifetime. The LADD is used for compounds with carcinogenic or chronic effects. The LADD is usually expressed in terms of mg/kg-day or

other mass/mass-time units. Limiting Tracer Method (LTM) - Method for evaluating soil ingestion that assumes that the maximum amount of soil ingested corresponds with the

lowest estimate from various tracer elements.

Long-term exposure - Repeated exposure for more than 30 days, up to approximately 10% of the life span in humans (more than 30 days).

Lowest-Observed-Adverse-Effect Level (LOAEL): The lowest exposure level at which there are biologically significant increases in frequency or severity of adverse effects between the exposed population and its appropriate control group.

Margin of safety - For some experts, margin of safety has the same meaning as margin of exposure, while for others, margin of safety means the margin between the reference dose and the actual exposure.

Mass-balance/tracer techniques - Method for evaluating soil intake that accounts for both inputs and outputs of tracer elements. Tracers in soil, food, medicine and other ingested items as well as in feces

and urine are accounted for.

Mean value - Simple or arithmetic average of a range of values, computed by dividing the total of all values by the number of values.

Measurement error - A systematic error arising from inaccurate measurement (or classification) of subjects on the study variables.

Measurement end-point - Measurable (ecological)

characteristic that is related to the valued characteristic chosen as an assessment point.

Median value - The value in a measurement data set such that half the measured values are greater and half

are less.

Metabolic Equivalent of Work (MET) - A dimensionless energy expenditure metric used to represent an activity level.



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Glossary

Microenvironment - Surroundings that can be treated as homogeneous or well characterized in the concentrations of an agent (e.g., home, office,
automobile, kitchen, store).

Model uncertainty - Uncertainty regarding gaps in scientific theory required to make predictions on the

basis of causal inferences.

Moisture content - The portion of foods made up by water. The percent water is needed for converting food

intake rates and residue concentrations between whole weight and dry weight values.

Monte Carlo technique - A repeated random sampling

from the distribution of values for each of the parameters in a generic (exposure or dose) equation to derive an estimate of the distribution of (exposures or doses in) the population.

Mouthing behavior - Activities in which objects, including fingers, are touched by the mouth or put into the mouth except for eating and drinking, and includes licking, sucking, chewing, and biting.

Non-dietary ingestion - Ingestion of non-food substances, typically resulting from the mouthing of hands and objects.

No-Observed-Adverse-Effect-Level (NOAEL) - The highest exposure level at which there are no biologically significant increases in the frequency or severity of adverse effect between the exposed population and its appropriate control; some effects may be produced at this level, but they are not considered adverse or precursors of adverse effects.

Outdoor settled dust - Particles that have settled onto outdoor objects and surfaces due to either wet or dry deposition.

Oxygen consumption (VO ) - The rate at which oxygen is used by tissues. 2

Parameter uncertainty - Uncertainty regarding some

parameter. Pathway - The physical course a chemical or pollutant takes from the source to the organism exposed.

Per capita intake rate - The average quantity of food consumed per person in a population composed of both individuals who ate the food during a specified time period and those that did not.

Pica - Pica behavior is the repeated eating of non-nutritive substances, whereas soil-pica is a form of soil ingestion that is characterized by the recurrent ingestion of unusually high amounts of soil (i.e., on the order of 1,000 - 5,000 milligrams per day or more).

Plain tap water - Excludes tap water consumed in the form of juices and other beverages containing tap

water.

Population mobility - An indicator of the frequency at

which individuals move from one residential location to

another.

Population risk descriptor - An assessment of the extent of harm to the population being addressed. It

can be either an estimate of the number of cases of a particular effect that might occur in a population (or population segment), or a description of what fraction of the population receives exposures, doses, or risks greater than a specified value.

Potential dose - The amount of a chemical contained in material ingested, air breathed, or bulk material applied

to the skin.

Poverty/income ratio - Ratio of reported family income to federal poverty level.

Precision - A measure of the reproducibility of a measured value under a given set of circumstances.

Preparation losses - Net cooking losses, which include dripping and volatile losses, post cooking losses, which involve losses from cutting, bones, excess fat, scraps and juices, and other preparation losses which include losses from paring or coring.



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Primary data/analysis - Information gathered from observations or measurements of a phenomena or the surveying of respondents.

Probabilistic uncertainty analysis - Technique that 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. Monte Carlo is a common type of probabilistic Uncertainty analysis.

Questionnaire/survey response - A “question andanswer” data collection methodology conducted via inperson interview, mailed questionnaire, or questions administered in a test format in a school setting.

Random samples - Samples selected from a statistical population such that each sample has an equal probability of being selected.

Range - The difference between the largest and

smallest values in a measurement data set.

Ready-to-feed - Infant and baby products (formula, juices, beverages, baby food), and table foods that do not need to have water added to them prior to feeding.

Reasonable maximum exposure (or worst case) - A semiquantitative term referring to the lower portion of the high end of the exposure, dose, or risk distribution. As a semiquantitative term, it should refer to a range that can conceptually be described as above the 90 th

percentile in the distribution, but below the 98 th

percentile.

Recreational/sport fishermen - Individuals who catch fish as part of a sporting or recreational activity and not for the purpose of providing a primary source of food

for themselves or for their families.

Reference Concentration (RfC) - An estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. It can be derived from a NOAEL, LOAEL, or benchmark concentration, with uncertainty factors generally applied to reflect limitations of the data used. Generally used in EPA's noncancer health
assessments. Durations include acute, short-term,
subchronic, and chronic.

Reference Dose (RfD) - An estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. It can be
derived from a NOAEL, LOAEL, or benchmark dose, with uncertainty factors generally applied to reflect limitations of the data used. Generally used in EPA's
noncancer health assessments. Durations include acute,
short-term, subchronic, and chronic.

Relevant study - Studies that are applicable or pertinent, but not necessarily the most important to derive exposure factors.

Representativeness - The degree to which a sample is, or samples are, characteristic of the whole medium, exposure, or dose for which the samples are being used

to make inferences.

Risk - The probability of an adverse effect in an organism, system, or (sub)population caused under specified circumstances by exposure to an agent.

Risk assessment - A process intended to calculate or estimate the risk to a given target organism, system, or (sub)population, including the identification of attendant uncertainties, following exposure to a particular agent, taking into account the inherent characteristics of the agent of concern as well as the characteristics of the specific target system. The risk assessment process includes four steps: hazard
identification, hazard characterization (related term: Dose-response assessment), exposure assessment, and risk characterization. It is the first component in a risk analysis process.

Risk characterization - The qualitative and, wherever possible, quantitative determination, including attendant



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uncertainties, of the probability of occurrence of known and potential adverse effects of an agent in a given organism, system, or (sub)population, under defined exposure conditions. Risk characterization is the fourth step in the risk assessment process.

Risk communication - Interactive exchange of
information about (health or environmental) risks among risk assessors, managers, news media, interested groups, and the general public.

Route - The way a chemical or pollutant enters an organism after contact, e.g., by ingestion, inhalation, or dermal absorption.

Sample - A small part of something designed to show the nature or quality of the whole. Exposure-related measurements are usually samples of environmental or ambient media, exposures of a small subset of a population for a short time, or biological samples, all for the purpose of inferring the nature and quality of parameters important to evaluating exposure.

Scenario uncertainty - Uncertainty regarding missing or incomplete information needed to fully define exposure and dose.

Screening-level assessment - An exposure assessment that examines exposures that would fall on or beyond the high end of the expected exposure distribution.

Secondary data/analysis - The reanalysis of data collected by other individuals or group; an analysis of data for purposes other than those for which the data were originally collected.

Sensitivity analysis - 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. Serving sizes - The quantities of individual foods consumed per eating occasion. These estimates may be useful for assessing acute exposures.

Short-term exposure - Repeated exposure for more than 24 hours, up to 30 days.

Soil - Particles of unconsolidated mineral and/or organic matter from the earth’s surface that are located outdoors, or are used indoors to support plant growth.

Soil adherence - The quantity of soil that adheres to the

skin and from which chemical contaminants are available for uptake at the skin surface.

Soil ingestion - The intentional or unintentional consumption of soil, resulting from various behaviors including, but not limited to, mouthing, contacting dirty hands, eating dropped food, or consuming soil directly. Soil-pica is a form of soil ingestion that is characterized by the recurrent ingestion of unusually high amounts of soil (i.e., on the order of 1,000 - 5,000 milligrams per day or more). Geophagy is also a form of soil ingestion defined as the intentional ingestion of earths and is usually associated with cultural practices.

Spatial variability - Variability across location, whether long- or short-term.

Subsistence fishermen - Individuals who consume fresh caught fish as a major source of food.

Surface area - 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. Surface integration is performed by using a planimeter and adding the areas.

Surface integration - Method used to measure skin surface area in which a planimeter is used to measure
areas of the skin, and the areas of various surfaces are

summed.



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Survey response methodology - Responses to survey questions are analyzed. This methodology includes questions asked of children directly, or their care givers, about behaviors affecting exposures.

Tap water from food manufacturing - Water used in industrial production of foods.

Temporal variability - Variability over time, whether long- or short-term.

Threshold - Dose or exposure concentration of an agent below which a stated effect is not observed or expected to occur.

Time-averaged exposure - The time-integrated exposure divided by the exposure duration. An example is the daily average exposure of an individual to carbon monoxide. (Also called timeweighted average exposure.)

Total tap water - 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.).

Total fluid intake - Consumption of all types of fluids including tapwater, milk, soft drinks, alcoholic beverages, and water intrinsic to purchased foods.

Tracer-element studies - Soil ingestion studies that use trace elements found in soil and poorly metabolized in the human gut as indicators of soil intake.

Triangulation - Method used to measure skin surface area in which areas of the body are marked into geometric figures, then their linear dimensions are

calculated.

Uncertainty - Uncertainty represents a lack of knowledge about factors affecting exposure or risk and can lead to inaccurate or biased estimates of exposure. The types of uncertainty include: scenario, parameter,

and model. Upper percentile - Values in the upper tail (i.e., between 90th and 99.9th percentile) of the distribution of values for a particular exposure factor. Values at the upper end of the distribution of values for a particular

set of data.

Uptake - The process by which a substance crosses an absorption barrier and is absorbed into the body.

Variability - Variability arises from true heterogeneity across people, places or time and can affect the precision of exposure estimates and the degree to which they can be generalized. The types of variability include: spatial, temporal, and inter-individual.

Ventilation Rate (VR) - Alternative term for inhalation rate or breathing rate. Usually measured as minute volume, i.e. volume (liters) of air exhaled per minute.

Wet-weight intake rates - Intake rates that are based on the wet (or whole) weight of the food consumed. This in contrast to dry-weight intake rates.

Glossary entries adapted from:

International Programme on Chemical Safety (2004). IPCS Risk Assessment Terminology.

Available on-line at: http://www.who.int/ipcs/methods/harmoniza tion/areas/ipcsterminologyparts1and2.pdf

U.S. EPA (1992) 	Guidelines for exposure assessment. Washington, DC: Office of Research and Development, Office of Health and

Environmental Assessment. EPA/600/2­

92/001.

U.S. EPA.	 (1997) Exposure Factors Handbook Revised. Washington, DC: U.S. Environmental Protection Agency, Office ofResearch and Development. EPA/600/P95/002F.



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