October 31, 2006
                             External Review Draft
    METABOLICALLY-DERIVED HUMAN VENTILATION RATES: A REVISED
          APPROACH BASED UPON OXYGEN CONSUMPTION RATES
                      U.S. Environmental Protection Agency
                       Office of Research and Development
                  National Center for Environmental Assessment
                            Washington, DC 20460
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                                      Disclaimer

This document is an external review draft for review purposes only. It has not been subjected to
peer and administrative review and does not constitute U.S. Environmental Protection Agency
policy. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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                              TABLE OF CONTENTS
                                                                              Pag

PREFACE	vii

EXECUTIVE SUMMARY	viii

AUTHORS, CONTRIBUTORS, AND REVIEWERS	ix

1.0   BACKGROUND AND OBJECTIVES	1

2.0   DATA SOURCES	3
      2.1    Source of Body Weight Data: 1999-2002 NHANES	3
      2.2    Source of BMR Calculation: Schofield (1985)	4
      2.3    Source of Activity and METS Data: Consolidated Human
             Activity Database (CHAD)	5
             2.3.1  The National Human Activity Pattern Survey (NHAPS)	6

3.0   APPROACH 	8
      3.1    Step 1: Group NHANES and NHAPS Participants by Age and
             Gender Categories	8
      3.2    Step 2: Calculate BMR Estimates for NHANES Participants	9
      3.3    Step 3: Generate  a Simulated 24-hour Activity Pattern for Each
             NHANES Individual	9
      3.4    Step 4: Generate  a METS Value for Each Activity Within the
             Simulated 24-hour Activity Pattern for Each NHANES Participant	10
      3.5    Step 5: Calculate Energy Expenditure and VO2 for Each Activity
             Within an Individual's Simulated 24-hour Activity Pattern 	11
      3.6    Step 6: Calculate Ventilation Rate for Each Activity Within the
             Simulated 24-hour Activity Pattern for Each NHANES Participant	12
      3.7    Step 7: Calculate Average Ventilation Rate for Time Spent Performing
             Activities Within Specified METS Categories, as Well as 24-hour
             Average Ventilation Rate, for Each NHANES Participant	14
      3.8    Step 8: Calculate Summary Tables Across Individuals	15

4.0   RESULTS	16
      4.1    Strengths and Limitations	26

5.0   REFERENCES	28

APPENDIX A:  INTERNAL EPA RESEARCH REPORT BY S. GRAHAM AND
               T. MCCURDY:  Revised Ventilation Rate (VE) Equations for Use in
               Inhalation-Oriented Exposure Models	A-l
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APPENDIX B:   STATISTICAL DISTRIBUTIONS ASSIGNED TO ACTIVITY CODES FOR
                USE IN SIMULATING METS VALUES	B-l

APPENDIX C:   ADDITIONAL ANALYSIS TABLES 	C-l
                                 LIST OF TABLES
Table 2-1.    Numbers of Individuals from NHANES 1999-2002 With Available
             Age, Gender, and Body Weight Data, by Age and Gender Categories	4
Table 2-2.    Equations from Schofield (1985) That Predict BMR (MJ/day) as a
             Function of Body Weight (BW, kg)	5
Table 2-3.    Numbers of Individuals from the NHAPS Study by Age and Gender
             Categories	7
Table 3-1.    Maximum Possible METS Values Assigned to Children, by Age and
             Gender	12
Table 3-2.    Estimated Values, by Age Range, of the Parameters within the
             Multiple Linear Regression Model for Predicting Body-Weight
             Adjusted Ventilation Rate (VE/BW; L/min/kg)	13
Table 3-3.    Estimated Values, by Age Range, of the Parameters within the
             Mixed Effect Regression Model for Predicting Body-Weight
             Adjusted Ventilation Rate (VE/BW; L/min/kg)	14
Table 4-la.   Descriptive Statistics for Daily Average Ventilation Rate (L/min)
             in Males, by Age Category	18
Table 4-lb.   Descriptive Statistics for Daily Average Ventilation Rate (L/min)
             in Females, by Age Category	19
Table 4-2a.   Average Time Spent Per Day Performing Activities Within Specified
             Intensity Categories, and Average Ventilation Rates Associated With
             These Activity Categories, for Males According to Age Category	20
Table 4-2b.   Average Time Spent Per Day Performing Activities Within Specified
             Intensity Categories, and Average Ventilation Rates Associated With
             These Activity Categories, for Females According to Age Category	23

Appendix A:
Table 1.      Parameter estimates used to estimate activity-specific VO2 for males and
             females  of different age groups	A-5
Table 2.      Parameter and residuals distribution estimates derived from two different
             statistical techniques and reported from Johnson (2002) for use in predictive
             equation (1) or (2) 	A-7
Table 3.      Ventilation parameter estimates (bi), standard errors (se), and residual
             distributions standard  deviation estimates (e;) using Adams data and
             assuming equation (3) or (4)  	A-9
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Table 4.      Residual distributions standard deviation estimates (eb and ew) using
             data characterized by percentage of maximum VC>2 (VC^m) assuming
             equation (3) 	A-10
Table 5.      Residual distributions standard deviation estimates (eb and ew) using
             data categorized by percentage of maximum VC>2 (VC^m) assuming
             equation (4) 	A-10
Table 6.      Recommended inhalation rates (L/min) from USEPA (1997) Table 5-23 ... A-12
Table A-l.    Total subjects for each study, gender, and exercise ergometry used 	A-19

Appendix B:
Table B-l.    METS Distributions Assigned to Activity ID Codes Within CHAD	B-l
Table B-2.    Activity Codes Whose METS Distributions Were Assigned to Those
             Codes Encountered in the NHAPS Database But Having No
             METS Distribution Assigned by CHAD	B-7

Appendix C:
Table C-la.   Descriptive Statistics of Body Weight (kg) and BMR (kcal/min)
             Across Male NHANES Participants,  by Age Group	C-l
Table C-lb.   Descriptive Statistics of Body Weight (kg) and BMR (kcal/min)
             Across Female NHANES Participants, by Age Group	C-2
Table C-2a.   Descriptive Statistics for Daily Average Ventilation Rate (mVday),
             in Males, by Age Category	C-3
Table C-2b.   Descriptive Statistics for Daily Average Ventilation Rate (mVday),
             in Females, by Age Category	C-4
Table C-3.    Descriptive Statistics for Duration of Time (hr/day) Spent Performing
             Activities Within the Specified Activity  Category, by Age
             and Gender Categories	C-5
Table C-4.    Descriptive Statistics for Average Ventilation Rate (L/min),
             Unadjusted for Body Weight, While  Performing Activities Within
             the Specified Activity Category, by Age and Gender Categories	C-10
Table C-5.    Descriptive Statistics for Average Ventilation Rate (L/min/kg),
             Adjusted for Body Weight, While Performing Activities Within the
             Specified Activity Category, by Age and Gender Categories	C-l5
Table C-6.    Descriptive Statistics for Daily Ventilation Rate (L/day), Unadjusted
             for Body Weight, While Performing  Activities Within the Specified
             Activity Category, by Age and Gender Categories	C-20
Table C-7.    Descriptive Statistics for Daily Ventilation Rate (L/day/kg), Adjusted
             for Body Weight, While Performing  Activities Within the Specified
             Activity Category, by Age and Gender Categories	C-25

                                  LIST OF FIGURES
                                                                                   Page
Appendix A:
Figure 1.     Pathways for estimating various ventilation parameters and metrics 	A-l
Figure 2.     Ventilatory quotient (VQ) as a function of age during exercise 	A-8
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Figure 3.     Estimated ventilation rates (VE, L/min) for females (left) and males
             (right) while performing low-level (top), moderate (middle), and
             vigorous (bottom) activities 	A-13
Figure 4.     Estimated population ventilation rates (VE, L/min) for 20,000 persons
             using APEX and the mixed effects regression (MER) algorithm
             (Equation 4 and Table 3)	A-14
Figure B-l.   Relationship between total ventilation rate (VE) and oxygen
             consumption rate (VO2) during exercise	A-31
Figure B-2.   Relationship between body mass normalized total ventilation rate
             (VE/BM) to oxygen consumption rate (VO2/BM) during exercise	A-31
Figure B-3.   Relationship between the natural logarithm of total ventilation rate
             Ln(VE) and oxygen consumption rate Ln(VO2) during exercise 	A-32
Figure B-4.   Relationship between body mass normalized total ventilation rate
             (VE/BM) to oxygen consumption rate (VO2/BM) during exercise	A-32
Figure C-l.   Comparison of selected percentiles of estimated event-based
             ventilation rates from 20,000 person APEX model simulation using
             different ventilation algorithms	A-34
Figure C-2.   Comparison of estimated event-based ventilation rate percentiles from
             20,000 person APEX model simulation using mixed effects regression
             (MER-left) and Johnson (2002) (right) ventilation algorithms	A-35
Figure C-3.   Percent difference of estimated event-based ventilation  rate percentiles
             from 20,000 person APEX model simulation using mixed effects
             regression  (MER-left) and Johnson (2002) (right) ventilation algorithms ... A-36
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 1
 2                                         PREFACE
 3
 4          The Exposure Factors Program of the U.S. Environmental Protection Agency's (EPA's)
 5    Office of Research and Development (ORD) has three main goals: (1) provide updates to the
 6    Exposure Factors Handbook and the Child-Specific Exposure Factors Handbook; (2) identify
 7    exposure factors data gaps and needs in consultation with clients; and (3) develop companion
 8    documents to assist clients in the use of exposure factors data. The activities under each goal are
 9    supported by and respond to the needs of the various program offices.
10
11          ORD's National Center for Environmental Assessment (NCEA) published the Exposure
12    Factors Handbook in 1997.  This comprehensive document provides summaries of available
13    statistical data on various factors that can impact an individual's exposure to environmental
14    contaminants.  NCEA maintains the Exposure Factors Handbook and periodically updates the
15    document using current literature and other reliable data made available through research.  This
16    draft report, Metabolically-Derived Human Ventilation Rates: A Revised Approach Based Upon
17    Oxygen Consumption Rates, will be used to update the ventilation rate values in the next edition
18    of the Exposure Factors Handbook.
19
20
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 1
 2                                  EXECUTIVE SUMMARY
 3
 4          The Exposure Factors Handbook was published by the U. S. Environmental Protection
 5    Agency's (EPA's) National Center for Environmental Assessment (NCEA) to provide data on
 6    various factors that can impact an individual's exposure to environmental contaminants. The
 7    two primary purposes of the Exposure Factors Handbook are: (1) to summarize data on human
 8    behaviors and characteristics which can affect exposure to environmental contaminants, and
 9    (2) to recommend values for specific exposure factors when included within an exposure
10    assessment. NCEA maintains the Exposure Factors Handbook and periodically updates the
11    document using current literature and other reliable data made available through research.  Many
12    program offices within EPA rely on the data from this handbook to conduct their exposure and
13    risk assessments.
14
15          The Exposure Factors Handbook was first published in  1977, and the data presented have
16    been compiled from various sources, including government reports and information presented in
17    the scientific literature. Among the exposure factors addressed by the Exposure Factors
18    Handbook are drinking water consumption, soil ingestion, inhalation rates, dermal factors, food
19    consumption, breast milk intake, human activity factors, consumer product use, and residential
20    characteristics. These exposure factors represent the general population as well as specific target
21    populations that may have differing characteristics from those of the general population.
22
23          One important determinant of a person's exposure to contaminants in air is the ventilation
24    rate, or the volume of air that is inhaled by an individual in a specified time period.  Ventilation
25    rates, also known as breathing or inhalation rates, are given in Chapter 5 of the Exposure Factors
26    Handbook. Calculations of the currently recommended ventilation rates were limited by their
27    dependence on a "ventilatory equivalent" which relied on a person's fitness level.
28
29          This draft report, Metabolically-Derived Human Ventilation Rates: A Revised Approach
30    Based Upon Oxygen Consumption Rate?, presents a revised approach which calculates ventilation
31    rates directly from an individual's oxygen consumption rate, and applies this method to data
32    provided from more recent sources as the 1999-2002 National Health and Nutrition Examination
33    Survey (NHANES) and EPA's Consolidated Human Activity Database (CHAD). In the next
34    edition of the Exposure Factors Handbook,  EPA would like to update the metabolically-derived
35    ventilation rate values using this revised approach and the more recently released data.
36
37
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 1
 2                     AUTHORS, CONTRIBUTORS, AND REVIEWERS
 3
 4          The National Center for Environmental Assessment (NCEA), Office of Research and
 5   Development was responsible for the preparation of this document. The report was prepared by
 6   Battelle Memorial Institute in Columbus, Ohio, under EPA Contract No. EP-C-04-027. Laurie
 7   Schuda served as Work Assignment Manager, providing overall direction, technical assistance,
 8   and serving as contributing author.
 9
10   AUTHORS
11
12   Battelle
13   Bob Lordo
14   Jessica Sanford
15   Marci e Mohn son
16
17   U.S. EPA
18   Laurie Schuda
19   Jacqueline Moya
20   Tom McCurdy
21   Steven Graham
22
23          The following EPA individuals reviewed an earlier draft of this document and provided
24   valuable comments:
25
26          Bob Benson, U.S. EPA, Region 8
27          Brenda Foos, U. S. EPA, Office of Children's Health Protection
28          Gary Foureman, U.S. EPA, National Center for Environmental Assessment
29          Deirdre Murphy, U.S. EPA, Office of Air Quality Planning and Standards
30          Harvey Richmond, U.S. EPA, Office of Air Quality Planning and Standards
31          John Schaum, U.S. EPA, National Center for Environmental Assessment
32          Michel Stevens, U.S. EPA, National Center for Environmental Assessment
33          Paul White, U.S. EPA, National Center for Environmental Assessment
34
35          This document was reviewed by an external panel of experts. The panel was composed
36   of the following individuals:
37
38   [to be added upon review]
39
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 1          METABOLICALLY-DERIVED HUMAN VENTILATION RATES:  A REVISED
 2                 APPROACH BASED UPON OXYGEN CONSUMPTION RATES
 3
 4
 5      1.0    BACKGROUND AND OBJECTIVES
 6
 7            The U.S. Environmental Protection Agency (EPA) and its program offices conduct
 8      various types of exposure assessment activities to characterize human exposure to toxic
 9      chemicals. To assist in these efforts, EPA's National Center for Environmental Assessment
10      (NCEA) has developed the Exposure Factors Handbook (USEPA, 1997), a comprehensive
11      document that provides a summary of available statistical data on various factors that can impact
12      a person's exposure to environmental contaminants. The two primary purposes of the Exposure
13      Factors Handbook (the "Handbook") are
14
15            $     to summarize data on human behaviors and characteristics which can affect
16                  exposure to environmental contaminants, and
17            $     to recommend values for specific exposure factors when included within an
18                  exposure assessment.
19
20      The exposure factors addressed by the Handbook include drinking water consumption, soil
21      ingestion, inhalation rates, dermal factors including skin area and soil adherence factors, food
22      consumption, breast milk intake, human activity factors, consumer product use, and residential
23      characteristics.  Values documented in the Handbook for these exposure factors represent the
24      general population as well as specific target populations that may have differing characteristics
25      from those of the general population.  The Handbook is a compilation of information obtained
26      from a variety of different sources and studies, presented in a consistent manner while retaining
27      much of the original formats that the individual study authors used in their publications.
28      Exposure assessors are the primary intended audience for the Handbook, with a particular focus
29      placed on researchers requiring data on standard factors to calculate human exposure to toxic
30      chemicals.
31
32            EPA maintains the Exposure Factors Handbook and periodically updates the document
33      using current literature and data available through EPA's research and other reliable sources.
34      The current Handbook available on EPA's website (USEPA, 1997) presents information
35      published through August  1997. EPA is currently updating the Handbook, with an updated draft
36      expected to be submitted for peer review in 2007.
37
38            When characterizing the inhalation exposure route, one important determinant of a
39      person's exposure to contaminants in air is ventilation rate, or the volume of air that a person
40      inhales in a specified time  period  (e.g., liters per minute, hour, or day). In the scientific
41      literature, ventilation rate is abbreviated VE (with the dot above the Vindicating that the
42      abbreviation represents ventilation "rate"  rather than "volume")  and has occasionally been
43      referred to as "breathing rate" or "inhalation rate," among other terms.  Values of for both adults
44      and children are given within Chapter 5 (Inhalation) of the 1997 Handbook and originate from
45      several published studies, each having different approaches and target populations. One of these
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 1      studies was by Layton (1993), who calculated metabolically consistent ventilation rates for
 2      different age/gender cohorts as the product of energy expenditure (EE; energy units per unit time
 3      - typically expressed as daily EE), oxygen uptake (H; volume of oxygen consumed per energy
 4      unit) and ventilatory equivalent (VQ; a unitless ratio of inhaled air volume to oxygen uptake).
 5      Layton used a constant value for H (equal to 0.05 L O2/KJ or 0.21 L (Vkcal) and VQ (equal to
 6      27), while representing average daily EE by each of the following three approaches:
 7
 8            1.      EE = average daily intake of food energy as determined from dietary survey data,
 9                   adjusting for the under-reporting of foods.
10            2.      EE = basal metabolic rate (BMR; energy  expended per day, determined as a
11                   function of body weight) multiplied by the ratio of total daily energy expenditure
12                   to BMR that is reported in earlier publications.
13            3.      EE = average energy expenditure associated with different levels of physical
14                   activity experienced in an average day, as determined from time-activity survey
15                   data. Activity-specific energy expenditures were calculated as the product of a
16                   person's BMR, the activity's metabolic equivalents (METS) score, and the
17                   duration of time spent performing the  activity.
18
19      Among the data sources used by Layton (1993) in these  calculations were  the USDA 1977-78
20      Nationwide Food Consumption Survey, the Second National Health and Nutrition Examination
21      Survey (NHANES II), and various exposure and activity studies published primarily in the
22      1980s.
23
24            One limitation of Layton's approach to calculating VE is its dependence on ventilatory
25      equivalent (VQ) which relies on an individual's fitness level.  In addition,  the relationship
26      between oxygen consumption and ventilation rate has been documented to be non-linear
27      (Hebestreit et al., 1998, 2000), even among equally-fit individuals.  As a result, staff at EPA's
28      National Exposure Research Laboratory (NERL) have developed a revised approach,
29      documented in the internal EPA report within Appendix A, which calculates VE as a direct
30      function of a person's oxygen consumption rate (FCb).  In its next edition  of the Exposure
31      Factors Handbook, EPA wishes to update the metabolically-derived values of VE (originating
32      from the third approach of Layton (1993)) using this  revised approach and more recently
33      released data. This report presents the method for calculating VE that is documented in the
34      report within Appendix A and applies this method to data provided from such sources as the
3 5      1999-2002 NHANES and EPA's Consolidated Human Activity Database (CHAD).
36
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 1    2.0    DATA SOURCES
 2
 3          The approach presented in Section 3 of this report for calculating VE associated with
 4    specific age and gender subpopulations requires the following information on persons within
 5    these subpopulations:
 6
 7          $      Body weight
 8          $      Basal metabolic rate (BMR)
 9          $      Typical activity patterns (i.e., types of activities performed in a given day and the
10                 duration for which each activity was conducted)
11          $      METS  values associated with each activity type.
12
13    After carefully identifying and evaluating various sources for these different types of
14    information, EPA selected the following data sources for use in this effort. Each data source
15    provided a specific type of information for an individual.
16
17    2.1    SOURCE OF BODY WEIGHT DATA: 1999-2002 NHANES
18
19          The Centers for Disease Control and Prevention's (CDC) National Center for Health
20    Statistics (NCHS) operates the National Health and Nutrition Examination Survey (NHANES)
21    program of studies.  NHANES is designed to  assess the health and nutritional status of adults and
22    children in the United States.  Begun in the 1960s, the NHANES program has consisted of a
23    series of surveys focusing on different population groups or health topics. Data collected within
24    the NHANES originates from personal interviews and physical examinations.
25
26          Beginning in 1999, the NHANES became a continuous, annual survey rather than the
27    periodic survey that it had been in the past. The survey examines a nationally representative
28    sample of persons each year. CDC now releases public use data files every two years.  Data used
29    in this report originated from public use data files labeled as "NHANES 1999-2000" and
30    "NHANES 2001-2002," upon CDC's recommendation that NHANES data collected from 1999
31    to 2002 should be considered as originating from a single survey (CDC, 2005). A total of 21,004
32    individuals were represented in the combined data set, with this total divided as follows (CDC,
33    2004):
34
35          $      1999-2000: Interview sample size=9,965; examination sample size=9,282
36          $      2001-2002: Interview sample size=l 1,039; examination sample size=10,477
37
38          The NHANES  1999-2002 database was selected due to being a recent nationally-
39    representative source of body weight data for the U.S. population and for subcategories
40    determined by age and gender. Reported body weights were measured by trained health
41    professionals during an interview process using measuring equipment that was consistent from
42    one year to the next. Within this database, a total of 19,022 individuals had recorded data for
43    age, gender, and body weight. Table 2-1  presents a breakdown of the number of individuals
44    according to the age and gender categories considered in this report.
45
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 1
 2
Table 2-1.     Numbers of Individuals from NHANES 1999-2002 With Available Age, Gender, and
              Body Weight Data, by Age and Gender Categories
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Age Category1

Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
Total

Male
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
9,284
Gender Category
Female
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
9,738

Total
834
553
516
1,083
1,834
2,788
2,423
,724
,597
,516
,249
,378
966
561
19,022
1    An age category labeled as "x to 
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 2    Table 2-2.     Equations from Schofield (1985) That Predict BMR (MJ/day) as a Function of Body
 3                 Weight (BW, kg)
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Age Category1
Birth to < 3 years
3 to <10 years
10 to <18 years
18 to <30 years
30 to <60 years
60 years and older
Male
BMR = 0.249*BW- 0.127
BMR = 0.095*BW+2.110
BMR = 0.074*BW + 2.754
BMR = 0.063*BW + 2.896
BMR = 0.048*BW + 3.653
BMR = 0.049*BW + 2.459
Female
BMR = 0.244*BW- 0.130
BMR = 0.085*BW + 2.033
BMR = 0.056*BW + 2.898
BMR = 0.062*BW + 2.036
BMR = 0.034*BW + 3.538
BMR = 0.038*BW + 2.755
1 An age category labeled as "x to 
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 1    assigned a statistical distribution to each activity ID code (McCurdy et al, 2000) representing the
 2    distribution of possible METS values associated with that activity.  Whenever a specific activity
 3    ID code was encountered within a study respondent's data records, the CHAD generated a
 4    random value from the code's assigned distribution to serve as the METS value for that
 5    particular activity. The statistical distributions that the CHAD assigned to each activity ID code
 6    were specified in USEPA (2002) and are presented in Appendix B. The distributional forms
 7    included normal, lognormal, uniform, triangular, and exponential distributions, as well as point
 8    estimates (i.e., when the same METS value was to be assigned for all occurrences). Three
 9    distributions were occasionally assigned to a single activity ID code, each representing one of
10    three age categories (<25 years, 25-40 years, >40 years), in order to account for different ranges
11    of intensity levels that may occur among these age groups when performing the specified
12    activity. Appendix B also lists lower and upper bounds for certain  distributions, where the lower
13    bound was assigned in lieu of the randomly-generated METS value when the latter fell below the
14    bound, and the upper bound was assigned whenever the randomly-generated METS value fell
15    above the bound. More information on the specific approach used  in this report to assign METS
16    values to activities prior to calculating VE  are presented in Section  3.
17
18    2.3.1   The National Human Activity Pattern Survey
19
20           Many of the studies in CHAD focused their sample within a certain age range, such as
21    children or senior citizens, and/or a single region or city. Only one study did not focus on a
22    specific region or age range:  the EPA-sponsored National Human Activity Pattern Survey
23    (NHAPS). Conducted from 1992 to 1994 by the University of Maryland Survey Research
24    Center, the NHAPS was a probability-based national telephone interview survey of 9,386
25    respondents which collected retrospective diary information on activities performed over a 24-
26    hour day, along with personal and exposure-related data (Klepeis et al., 2001).  Participants were
27    selected using a stratified sampling approach, with stratification corresponding to the four major
28    U.S. census regions (Northeast, Midwest, South, West) within the 48 contiguous states (Klepeis
29    et al., 2001). EPA adopted the method used in the NHAPS study for assigning activity codes as
30    the common method for coding activities across all studies within the CHAD.
31
32           Based upon the NHAPS study's more general representation of the U.S. population
33    compared to the other studies within CHAD, activity data from the NHAPS study were selected
34    for use in characterizing activity patterns and obtaining METS values when calculating
35    ventilation rate estimates for this report. Within CHAD, NHAPS data records were labeled as
36    either "Study A" or "Study B," according to the type of questionnaire which the survey provided
37    to the study subjects.  Because this discernment was irrelevant to the recording of information
38    within activity diaries, both sets of data records were utilized  in this report. Table  2-3 presents a
39    breakdown of the number of NHAPS respondents with available activity data, according to the
40    age and gender categories considered in this report. A  total of 9,196 respondents had available
41    age and gender information, and therefore, contributed information to this analysis. (Each of
42    these respondents contributed 24 hours worth of activity pattern data.)
43
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 1
 2
Table 2-3.     Numbers of Individuals from the NHAPS Study by Age and Gender Categories
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Age Category1
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
Total
Gender Category
Male
53
67
63
184
261
234
234
755
737
588
453
354
199
59
4,241
Female
30
64
61
169
225
239
227
748
848
736
548
536
380
144
4,955
Total
83
131
124
353
486
473
461
1,503
1,585
1,324
1,001
890
579
203
9,196
1 An age category labeled as "x to 
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 1    3.0    APPROACH
 2
 3          The EPA report in Appendix A describes an approach for estimating VE from VO2
 4    (oxygen consumption rate) using a series of regression-based equations derived from 25 years of
 5    clinical studies conducted by Dr. William C. Adams of the University of California at Davis
 6    (Adams, 1993; Adams et al, 1995).  The multi-step approach presented in this section applies
 7    these equations to the data sources cited within Section 2 to estimate VE.  An overview of the
 8    steps involved in this approach is as follows:
 9
10          $      Categorize individuals in the NHANES 1999-2002 and NHAPS data sets by age
11                 and gender.
12          $      Calculate BMR for NHANES individuals as a function of body weight.
13          $      Obtain a simulated 24-hour activity pattern for each NHANES individual.
14          $      Assign a METS value to each activity represented in an NHANES individual's
15                 simulated 24-hour activity pattern.
16          $      Calculate energy expenditure and F02 for each activity within an NHANES
17                 individual's simulated 24-hour activity pattern.
18          $      Calculate activity-specific VE values for an NHANES individual using the

19                 equations derived in the EPA report (Appendix A) that express VE (adjusted for
20                 body weight) as a function of FCb (adjusted for body weight), age, and gender.
21          $      Calculate average daily VE,  as well as average VE for activities sharing a similar
22                 intensity level, for each NHANES individual.
23          $      Summarize average VE values across individuals for each age and gender
24                 category.
25
26    Each step is now discussed in detail.
27
28    3.1    STEP 1: GROUP NHANES AND NHAPS PARTICIPANTS BY AGE AND
29          GENDER CATEGORIES
30
31          Once the NHANES and NHAPS data were obtained for this analysis, the individuals
32    represented within both data sets were grouped into age and gender categories using information
33    stored within the data records.  The age categories were defined based upon discussion with
34    EPA.  Adults from 21 to 80 years were divided into six groups,  each of size ten years
35    (21-30 years, 31-40 years, etc.), while adults above 80 years were placed in a single group.
36    Children (< 21 years) were divided into seven age categories according to groupings given in
37    USEPA (2005) with the following exception: children less than one year old were placed into a
38    single group due to the fact that any further segregating of these children into age-related groups
39    would have resulted in small sample sizes within the groups.
40
41          Table 2-1 and Table 2-3 in Section 2 list the age and gender categories used in this
42    analysis, along with the numbers of individuals within the NHANES and NHAPS data sets,
43    respectively, that were grouped into each category. A total of 19,022 NHANES participants and
      DRAFT - Do not cite or quote                   8                             October 31, 2006

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 1    9,196 NHAPS participants were grouped into these categories, corresponding to those
 2    individuals having sufficient data to allow the grouping and to contribute to this analysis.
 O
 4    3.2    STEP 2: CALCULATE BMR ESTIMATES FOR NHANES PARTICIPANTS
 5
 6          As noted in Section 2, body weight data were available for individuals in the NHANES
 7    data set (originating from data collected during the survey's medical examinations) but not for
 8    NHAPS participants.  Therefore, BMR estimates could be obtained only for the 19,022
 9    NHANES individuals. The Schofield equations given in Table 2-2 of Section 2 were used to
10    calculate these estimates as a function of age, gender, and body weight. However, the approach
11    in the report in Appendix A assumed that BMR is expressed in kcal/min, while the Schofield
12    equations calculate BMR in MJ/day. Given that 1 MJ equals approximately 238.846 kcal, BMR
13    was converted from MJ/day to kcal/min as follows:
14
15                           BMR (kcal/min) = 0.16587*[BMR (MJ/day)]
16
17
18    3.3    STEP 3: GENERATE A SIMULATED 24-HOUR ACTIVITY PATTERN FOR
19          EACH NHANES INDIVIDUAL
20
21          Table 2-3 of Section 2 gives the number of NHAPS participants within each age/gender
22    category.  Each of these participants had activity pattern data available for a single 24-hour
23    monitoring period. For a given age/gender category, let N correspond to the number of NHAPS
24    participants in that category, as given in Table 2-3. Each participant in this category was then
25    assigned a unique group ID number from 1 to N.
26
27          For each of the 19,022 individuals in the NHANES data set, the following procedure was
28    performed to generate a simulated 24-hour activity pattern for that individual:
29
30          $      The individual's age/gender category was noted.
31          $      Twenty (20) random integers were generated, with replacement, from the set of
32                 integers ranging from 1  to N (i.e., N = number of NHAPS participants within the
33                 individual's age/gender category).
34          $      For each random integer that was generated, data on the recorded 24-hour activity
35                 pattern (activity ID codes and the duration of time spent performing each activity)
36                 were obtained for the NHAPS participant whose group ID number within the
37                 given age/gender category matched the random integer.  This resulted in assigning
38                 a "simulated" set of activity data to the NHANES individual that represented a
39                 total of 20*24=480 hours.  (Because an integer could occur multiple times within
40                 the  generated set of 20 random integers, a given set of 24-hour activity pattern
41                 data could likewise be represented  multiple times within the simulated set of
42                 activity data.)
43          $      The different activity ID codes were identified in this simulated  set, and for each
44                 code, the duration of time (in minutes) spent performing that activity was totaled
45                 across all records within this set. This total duration was then divided by 28,800
46                 (i.e., the number of minutes in 480 hours) to estimate the proportion of this total
      DRAFT - Do not cite or quote                   9                             October 31, 2006

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 1                 time that is represented by the given activity. The proportions associated with
 2                 each activity were then each multiplied by 24 to yield a simulated number of
 3                 hours that the given NHANES individual was deemed to perform the activity
 4                 within a 24-hour period.  This yielded a simulated 24-hour activity pattern for the
 5                 NHANES individual.
 6
 7   Note that activities could not be assigned to NHANES participants based on prior knowledge of
 8   their preferences and lifestyles, as this information was unavailable.
 9
10   3.4    STEP 4: GENERATE A METS VALUE FOR EACH ACTIVITY WITHIN THE
11          SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES
12          PARTICIPANT
13
14          Once a simulated 24-hour activity pattern was assigned to  a given NHANES individual, it
15   was necessary to assign a METS value to each activity ID code represented within that activity
16   pattern. METS values were assigned following the same approach used in the CHAD.  As first
17   noted in Section 2.3, the CHAD has assigned statistical distributions to each activity ID code.
18   These statistical distributions are listed in Appendix B. While most activity ID codes were
19   assigned a single distribution, a few codes were assigned different distributions for different age
20   ranges, apparently to account for different ranges of intensity levels that may occur among
21   different age groups performing the same type of activity.
22
23          As is done in the CHAD, for each activity ID code encountered within the simulated
24   24-hour activity pattern for an NHANES individual, a METS value was assigned to that activity
25   by randomly sampling from the statistical distribution that CHAD has assigned to that code (and,
26   when necessary, to the age range in which the individual falls).  The procedure developed to
27   generate random numbers from each of the distribution types represented within Appendix B
28   used random number generator functions available within the SASŪ System (SAS, 2005).  These
29   functions yield the following:
30
31          $      RANEXP, a random number from a standard exponential distribution (scale
32                 parameter=l)
33          $      RANNOR, a random number from a standard normal distribution (mean=0,
34                 standard deviation=l)
35          $      RANTRI, a random number from a triangular distribution on the interval  (0, 1)
36                 with parameter//, a number between 0 and 1 which represents the distribution's
37                 modal value
38          $      RANUNI, a random number from a uniform distribution on the interval (0,  1).
39
40   The random number generation procedure depended not only on the particular distributional
41   form (e.g., uniform, normal, lognormal,  exponential, triangular), but also on specific parameters
42   associated with the distribution, such as  the mean (mean), standard deviation (std), minimum
43   (min), and maximum (max), which are specified along with the distributions in Appendix B. If
44   exp denotes the exponentiation  function, log denotes the natural logarithmic function, and sqrt
45   denotes the square root function, then random numbers for the distributions in Appendix B were
46   generated as follows:
      DRAFT - Do not cite or quote                  10                            October 31, 2006

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 2          $     Exponential distribution: METS = min + std*RANEXP
 3          $     Lognormal distribution: METS = exp(log(mean2/sqrt(mean2+std2)) +
 4                                                 sqrt(log(l+(std/mean)2) *RANNOR
 5          $     Normal distribution: METS = mean + std*RANNOR
 6          $     Triangular distribution: The generated METS value depends on the value of the
 7                mode of the triangular distribution, which equals 3*mean - min - max .
 8                -   If mode = min, then METS = max - sqrt((l-RANUNI) *(max - min) *(max -
 9                    mode))
10                -   If mode = max, then METS = min + sqrt(RANUNI*(max - min) *(mode - min))
11                -   If min < mode < max, then METS = min + (max - min) *RANTRI, where the
12                    value ofH used to determine RANTRI equals (mode - min)/(max - min).
13          $     Uniform distribution: METS = min + (max - min)*RANUNI.
14
15          Whenever an activity ID code's distribution was specified as "point estimate," the
16   distribution consisted of a single value that occurred with  100% probability. Therefore, for such
17   an activity ID code, the METS value was always assigned to equal this single value.
18
19          The distributions for some activity ID codes were accompanied by a specified lower and
20   upper bound (Appendix B). In these situations, the lower bound was assigned in lieu of the
21   randomly-generated METS value when the latter fell below the bound, and the upper bound was
22   assigned whenever the randomly-generated METS value fell above the bound.
23
24          In November 2003, the CHAD incorporated a new feature which identified "maximum
25   possible METS values" that could be assigned to children aged 16 years and younger when
26   performing an activity that is five minutes or more in duration. This feature was implemented
27   due to EPA's finding that a child does not experience a METS value above a certain threshold
28   (USEPA, 2001).  Table 3-1 presents these maximum possible values, by age and gender.  When
29   METS values were generated from the statistical distributions specified in Appendix B, those
30   values exceeding the maximum specified in Table 3-1 were replaced by the maximum.
31
32   3.5    STEP 5: CALCULATE ENERGY EXPENDITURE AND VO? FOR EACH
33          ACTIVITY WITHIN AN INDIVIDUAL'S SIMULATED 24-HOUR ACTIVITY
34          PATTERN
35
36          Once the METS values were generated, energy expenditure (EE, expressed in kcal/min)
37   associated with a given activity was calculated by multiplying the activity's assigned METS
38   value by the BMR value assigned to the individual within Step 2:
39
40                                      EE = BMR*METS
41
     DRAFT - Do not cite or quote                  11                            October 31, 2006

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 1
 2
Table 3-1.    Maximum Possible METS Values Assigned to Children, by Age and Gender
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Age (years)
6 and younger
7
8
9
10
11
12
13
14
15
16
Gender
Males
7.2
7.7
8.2
8.7
9.2
9.8
10.5
11.1
11.8
12.6
13.4
Females
6.4
6.8
7.3
7.7
8.2
8.7
9.3
10.0
10.6
11.3
12.2
Source: http://oaspub.epa.gov/chad/recent additionsS.startup
       This calculation was done for each activity ID code encountered within an individual's
simulated 24-hour activity pattern.

       Once the set of activity-specific EE values were obtained for a given NHANES
individual, activity-specific values of the oxygen consumption rate (FCb, expressed in L (Vmin)
were calculated from these values according to the approach given in the report in Appendix A.
As was done by Layton (1993), F02 was calculated as the product ofEE (kcal/min) and H, the
volume of oxygen consumed per unit of energy (L (Vkcal):

                                     VO2 = EE*H,

       In each application of this equation, the value of His obtained by randomly sampling
from the uniform distribution over the  interval (0.20, 0.22) for males and (0.19, 0.21) for
females.  (These two distributions were obtained from Table 1 of the EPA report in Appendix A
and differ slightly from the distribution given in McCurdy, 2000.  For a given gender, the
specified uniform distribution did not differ according to age.) FCb values were expressed both
adjusted and unadjusted for the individual's body weight, where adjustment involved dividing
F02 by the individual's body weight (in kg).

3.6     STEP 6: CALCULATE VENTILATION RATE FOR EACH ACTIVITY WITHIN
       THE SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES
       PARTICIPANT

       Within this step, two of the regression-based equations presented in Section 2 of the EPA
report in Appendix A were considered for use in predicting an individual's ventilation rate
(VE, expressed in L/min), adjusted for body weight, as a function of F02 estimated within Step 5
(also after adjusting for body weight),  age, and gender. The first equation takes the form of a
multiple linear regression model with a single random error term:
      DRAFT - Do not cite or quote
                                          12
October 31, 2006

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 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
                   log(VE/BW) = b0 + b!*log(VO2/BW) + b2*log(age) + b3*gender + ,

      where "log" indicates the natural logarithmic transformation, BWcorresponds to the individual's
      body weight (kg), age denotes the individual's age (in years), and gender equals -1 for males and
      +1 for females.  The term , represents random deviation between the actual and predicted value
      of the left-hand side of the equation for individuals having the same age, gender, and (VO2/BW)
      value and is assumed to originate from a normal distribution with mean 0 and standard
      deviation O. Estimated values of the intercept and slope parameters (bo, bj, b2, and bs) and O
      were provided for specified age ranges and are given in Table 3-2.  These age ranges were
      determined based on prior usage (such as in Johnson, 2002) and on what would result in a best fit
      of the regression model, as noted in the report within Appendix A.

      Table 3-2.     Estimated Values, by Age Range, of the Parameters within the Multiple Linear
                   Regression Model for Predicting Body-Weight Adjusted Ventilation Rate (VE/BW;
                   L/min/kg)
Age
<20 years
20-33 years
34-60 years
> 60 years
bo
4.4329
3.5718
3.1876
2.4487
b1
1.0864
1.1702
1.1224
1.0437
b2
-0.2829
0.1138
0.1762
0.2681
b3
0.0513
0.0450
0.0415
-0.0298

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 1
 2
 3
 4
Table 3-3.    Estimated Values, by Age Range, of the Parameters within the Mixed Effects
             Regression Model for Predicting Body-Weight Adjusted Ventilation Rate (VE/BW;
             L/min/kg)
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
Age
<20 years
20-33 years
34-60 years
> 60 years
b0
4.3675
3.7603
3.2440
2.5828
b1
1.0751
1.2491
1.1464
1.0840
b2
-0.2714
0.1416
0.1856
0.2766
b3
0.0479
0.0533
0.0380
-0.0208
2, and bs, from Table 3-2 or 3-3) that are relevant to the
             individual's age.
       $     For each random error term in the model (i.e., for , in the multiple linear
             regression model, or for  ,\, and ,w in the mixed effect regression model), a random
             number was generated from a normal distribution with mean zero and standard
             deviation equal to the estimate given in Table 3-2 or 3-3 for that term (i.e., O for
             the term ,; Ob for the term ,b, and Ow for the term ,w). This random number was
             then substituted for the given error term in the regression equation.
       $     The equation was then calculated, and the result was exponentiated.

The predicted value of VE that is unadjusted for body weight was determined by multiplying this
result by the individual's body weight.

3.7    STEP 7: CALCULATE AVERAGE VENTILATION RATE FOR TIME SPENT
       PERFORMING ACTIVITIES WITHIN SPECIFIED METS CATEGORIES, AS
       WELL AS 24-HOUR AVERAGE VENTILATION RATE, FOR EACH NHANES
       PARTICIPANT

       Once values of VE and VE/BWwere predicted for each reported activity ID code within
an individual's simulated 24-hour activity pattern (Step 6), an average daily ventilation rate was
calculated for the individual, both across the entire 24-hour activity pattern, as well as within
specified activity categories that were determined by level of intensity (based on assigned METS
      DRAFT - Do not cite or quote
                                           14
October 31, 2006

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 1    values). Within the individual's simulated 24-hour activity pattern, each activity was placed into
 2    one of four activity categories:
 O
 4           $      Sedentary/Passive Activities: Activities with METS values no higher than 1.5.
 5           $      Light Intensity Activities: Activities with METS values exceeding 1.5, but no
 6                  higher than 3.0.
 7           $      Moderate Intensity Activities: Activities with METS values exceeding 3.0, but no
 8                  higher than 6.0.
 9           $      High Intensity Activities: Activities with METS values exceeding 6.0.
10
11    (These categories were defined based on general information in the scientific literature on how
12    researchers have grouped activities according to intensity level.) Within an activity category,  let
13    A represent the number of activities within the individual' s 24-hour activity pattern that fall
14    within the category, and let T equal the total duration of time (in minutes) that the individual
15    spent performing these A  activities. Let VE,I represent the individual's ventilation rate calculated
16    in Step 6 for the /th activity within this activity category, and let Tt correspond to the duration of
17    time spent by the individual performing this activity (/' = 1, ..., A). Then the individual's average
18    daily ventilation  rate for that METS activity group was calculated as a weighted average of the
19    activity-specific  VE values, with weights corresponding to time spent performing the activities:
20
                                          E~      T
21
22    For each NHANES individual, this average  VE statistic was calculated within each of the four
23    activity categories, as well as across all activities within the individual's simulated 24-hour
24    activity pattern. The latter average was calculated using the same formula as above, with A
25    equaling the total number of activities within the 24-hour activity pattern, and T equaling
26    1,440 minutes (i.e., the total number of minutes in a 24-hour period). These average daily VE
27    values were adjusted for body weight by dividing by the individual's body weight.
28
29    3.8    STEP 8: CALCULATE SUMMARY TABLES ACROSS INDIVIDUALS
30
31           For each age and gender category noted in Tables 2-1 and 2-3, individual-specific
32    average VE values from Step 7 were summarized across individuals for each of the four METS
33    activity categories, for a 24-hour period,  and for sleeping and napping activities only
34    (i.e., activity code 14500). These summaries corresponded to weighted descriptive statistics,
35    with the weights corresponding to the individuals' 4-year sampling weights stored within the
36    NHANES 1999-2002 database. The descriptive statistics, which were calculated using the
37    UNIVARIATE procedure within the SASŪ System, included the mean, maximum, and selected
38    percentiles of the observed distribution among the 19,022 NHANES participants.
39
      DRAFT - Do not cite or quote                   15                             October 31, 2006

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 1    4.0    RESULTS
 2
 3          This section presents tables containing the results of applying the multi-step statistical
 4    technique presented in Section 3 to predict ventilatory rate from simulated 24-hour activity data
 5    on individuals represented within the NHANES 1999-2002 data base (Section 2).  The results in
 6    this section were generated using Version 9 (Release 9.1.3) of the SASŪ System. (SAS, 2005).
 7    Appendix C provides supplemental tables that provide more detailed information that
 8    accompanies the results presented in this section.
 9
10          As noted in Section 3.6, two regression models were considered for predicting ventilatory
11    rate as a function of F02, age, and gender.  These two models, the multiple linear regression
12    model and the mixed effects model, differed in how the random component of the model was
13    specified (i.e., as a single random error term versus two additive terms that represented between-
14    individual  and within-individual variability). In this section, ventilatory rate predictions from the
15    multiple linear regression model are summarized. The extent to which predictions differed
16    between the two types of models was minimal; the median percentage change in the mixed effect
17    regression model prediction relative to the multiple linear regression model prediction  was a two
18    percentage point decline. The multiple linear regression model predicted higher ventilatory rate
19    estimates 53 percent of the time compared to the mixed  effect regression model, and this
20    percentage did not deviate much between the two genders or among different METS categories.
21    Because no model tended to consistently produce higher predictions compared to the other, the
22    choice of models was not expected to impact the types of summaries presented in this section.
23    (It should be noted, however, that if the prediction process did not incorporate a realization of the
24    random error term(s), then the multiple linear regression model led to higher ventilatory rate
25    predictions compared to the mixed effect regression model more frequently - about 62 percent of
26    the time.)
27
28          Descriptive statistics presented in tables within this section and Appendix C include the
29    observed mean and selected percentiles of the analyzed  data.  These statistics were selected to
30    characterize the central tendency and the general range of the observed data distribution.  While
31    no parametric distributional assumptions were placed on the observed data distributions before
32    these statistics were calculated,  the four-year sampling weights assigned to the individuals within
33    NHANES  1999-2002 were used to weight each individual's data values in the calculations of
34    these statistics.
35
36          Table C-l in Appendix C contains descriptive statistics on body weight and BMR for the
37    NHANES  individuals, by gender and age category. This table serves to summarize the reported
38    body weights of the individuals represented in these analyses, as well as the outcome of the
39    BMR calculations (using the Schofield equations and conversion to kcal/min), both of which
40    enter into calculation of EE, VO2, and VE.  Sample sizes within each age/gender category were
41    provided in Table  2-1 of Section 2.
42
      DRAFT - Do not cite or quote                   16                            October 31, 2006

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 1           Table 4-1 summarizes daily average ventilation rate, both adjusted and unadjusted for
 2    body weight, by gender and age category, which was calculated in Step 7 (Section 3.7).  These
 3    results, in L/min, represent an average rate taken over a 24-hour period (and, therefore, its typical
 4    activity pattern) among individuals in the specified category. Table C-2 in Appendix C presents
 5    the same information, but expressed in m3/day, as is currently done in the Exposure Factors
 6    Handbook.
 7
 8           As noted in Section 3.7, average daily ventilation rate was also calculated for each of four
 9    groups of activities defined according to specified ranges of METS values representing
10    sedentary/passive activity, light intensity, middle intensity, and high intensity activities.  In
11    addition, average ventilation rate was calculated for the period of time when an individual is
12    sleeping or napping.  This activity occurs more than any other and represents the lowest intensity
13    activity.  Thus, while sleeping and napping was included within the sedentary/passive  activity
14    category for this data analysis, it was also treated as a separate activity in the calculations. Table
15    4-2a (for males) and Table 4-2b (for females) summarize average ventilation rate, both adjusted
16    and unadjusted for body weight, within each activity category by gender and age category.
17    These results are initially presented in  L/min, representing an average rate while performing the
18    activity.  Then, the L/min result for each individual was multiplied by the number of minutes
19    spent performing the activities in the specified category, and the  resulting L/day measurements,
20    labeled as "daily ventilation rate" while performing the activity, are also presented in these
21    tables.
22
23           Table 4-2a and Table 4-2b also summarizes the number of NHANES participants whose
24    simulated 24-hour activity pattern included activities falling within the specified category, as
25    well as the average number of hours per day (across individuals) that individuals spent
26    performing these activities.
27
28           Additional descriptive statistics to accompany the results in Table 4-2a and  Table 4-2b
29    can be found in Table C-3 through Table C-7 in Appendix C. These five tables address the
30    following:
31
32           $     Duration of time spent performing activities (hr/day)
33           $     Average ventilation rate (L/min), unadjusted for body weight
34           $     Average ventilation rate (L/min/kg), adjusted for body weight
35           $     Daily ventilation rate (L/day), unadjusted for body weight
36           $     Daily ventilation rate (L/day/kg), adjusted for body weight
37
      DRAFT - Do not cite or quote                   17                             October 31, 2006

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Table 4-la.    Descriptive Statistics for Daily Average Ventilation Rate (L/min) in Males, by Age Category
Age Category
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body Weight
(VE; L/min)
Mean
6.08
9.37
9.19
8.78
9.32
10.64
11.95
13.07
14.09
14.54
14.52
12.46
11.35
10.52
Percentiles
5th
3.32
6.76
6.56
7.24
7.00
7.92
8.75
8.81
9.72
10.18
10.41
9.66
9.10
8.30
10th
3.96
7.23
7.09
7.55
7.42
8.41
9.31
9.42
10.39
10.79
11.16
10.07
9.45
8.73
25th
4.97
8.09
7.94
7.91
8.15
9.22
10.06
10.76
11.78
12.15
12.22
11.03
10.18
9.60
50th
6.04
9.11
9.16
8.74
9.09
10.27
11.55
12.62
13.77
14.30
14.17
12.22
11.27
10.35
75th
7.24
10.43
10.07
9.47
10.23
11.68
13.31
14.75
15.98
16.59
16.08
13.57
12.20
11.33
90th
8.28
11.82
11.30
10.16
11.50
13.57
15.23
17.06
18.59
18.55
18.76
15.12
13.49
12.51
95th
8.81
12.43
12.30
10.70
12.31
14.73
16.23
18.84
20.07
19.70
20.20
16.32
14.18
12.98
Maxi-
mum
11.84
16.83
19.56
13.56
17.34
19.82
27.23
30.15
28.28
31.93
26.51
19.51
17.03
15.72
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW:L/mm/kg)
Mean
0.759
0.823
0.658
0.488
0.307
0.198
0.159
0.160
0.166
0.168
0.167
0.144
0.140
0.141
Percentiles
5th
0.634
0.669
0.542
0.363
0.221
0.144
0.116
0.108
0.112
0.117
0.113
0.119
0.117
0.119
10th
0.655
0.706
0.567
0.386
0.238
0.153
0.126
0.117
0.122
0.124
0.123
0.123
0.122
0.123
25th
0.696
0.756
0.606
0.426
0.261
0.171
0.140
0.134
0.139
0.138
0.141
0.131
0.129
0.129
50th
0.754
0.813
0.655
0.481
0.302
0.192
0.158
0.155
0.161
0.161
0.166
0.142
0.137
0.140
75th
0.808
0.876
0.704
0.540
0.346
0.220
0.176
0.182
0.188
0.193
0.188
0.155
0.149
0.151
90th
0.872
0.949
0.757
0.606
0.381
0.252
0.194
0.208
0.216
0.220
0.211
0.168
0.160
0.161
95th
0.898
1.027
0.782
0.639
0.403
0.267
0.206
0.224
0.235
0.234
0.233
0.175
0.167
0.173
Maxi-
mum
1.025
1.201
0.944
0.753
0.559
0.351
0.274
0.356
0.319
0.324
0.298
0.224
0.217
0.192
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in
this table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
18
October 31, 2006

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Table 4-lb.    Descriptive Statistics for Daily Average Ventilation Rate (L/min) in Females, by Age Category
Age Category
Birth to <1
year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body Weight
(VE; L/min)
Mean
5.92
9.24
8.85
8.45
8.62
9.33
9.44
10.12
10.40
11.25
11.24
9.02
8.36
7.74
Percentiles
5th
3.36
6.31
6.19
6.86
6.94
7.27
6.85
7.05
7.69
8.41
8.56
7.22
6.87
6.38
10th
3.81
7.03
6.99
7.21
7.19
7.72
7.37
7.41
8.20
8.73
9.00
7.48
7.08
6.57
25th
4.75
7.81
7.90
7.78
7.65
8.36
8.18
8.29
9.04
9.83
9.77
8.18
7.56
7.04
50th
5.84
9.05
8.75
8.35
8.30
9.08
9.17
9.79
10.20
11.03
11.04
8.97
8.21
7.65
75th
6.79
10.17
9.69
9.04
9.32
10.10
10.43
11.54
11.33
12.47
12.36
9.66
9.00
8.24
90th
8.09
12.12
10.82
9.74
10.51
11.29
11.89
13.42
12.85
13.83
13.84
10.69
9.80
8.92
95th
8.79
12.93
11.36
10.37
11.35
12.09
12.70
14.68
14.20
14.82
14.73
11.21
10.55
9.68
Maxi-
mum
18.23
17.20
15.98
13.71
14.46
18.46
20.91
20.99
19.64
24.92
17.85
14.12
12.29
11.76
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW: L/min/kg)
Mean
0.793
0.831
0.663
0.480
0.297
0.174
0.148
0.143
0.145
0.153
0.151
0.123
0.122
0.124
Percentiles
5th
0.634
0.677
0.569
0.335
0.194
0.131
0.110
0.100
0.098
0.103
0.107
0.096
0.097
0.099
10th
0.673
0.703
0.583
0.372
0.214
0.138
0.117
0.110
0.107
0.114
0.114
0.101
0.101
0.103
25th
0.720
0.765
0.618
0.414
0.248
0.153
0.132
0.124
0.122
0.129
0.128
0.110
0.109
0.110
50th
0.782
0.818
0.664
0.475
0.296
0.170
0.144
0.140
0.141
0.149
0.147
0.120
0.120
0.123
75th
0.863
0.901
0.703
0.533
0.339
0.194
0.163
0.161
0.162
0.174
0.169
0.134
0.133
0.137
90th
0.922
0.976
0.740
0.614
0.381
0.217
0.186
0.179
0.187
0.197
0.194
0.148
0.146
0.146
95th
0.961
1.017
0.767
0.636
0.404
0.236
0.197
0.193
0.207
0.213
0.208
0.156
0.159
0.153
Maxi-
mum
1.112
1.200
0.857
0.775
0.519
0.327
0.248
0.279
0.301
0.288
0.276
0.189
0.235
0.196
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in
this table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
19
October 31, 2006

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Table 4-2a.    Average Time Spent Per Day Performing Activities Within Specified Intensity
              Categories, and Average Ventilation Rates Associated With These Activity
              Categories, for Males According to Age Category
Age Category
# NHANES
Partici-
pants
Reporting
Activity
Average
Duration
(hr/day)
Spent at
Activity
Ventilation Rate During
This Activity1
Unadjusted
for Body
Weight
(L/min)
Adjusted
for Body
Weight
(L/min/kg)
Daily Ventilation Rate
Associated With This
Activity2
Unadjusted
for Body
Weight
(L/day)
Adjusted for
Body Weight
(L/day/kg)
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
13.5
12.6
12.1
11.2
10.2
9.4
8.7
8.4
8.1
7.9
8.0
8.3
8.5
9.2
3.08
4.50
4.61
4.36
4.61
5.26
5.31
4.73
5.16
5.65
5.78
5.98
6.07
5.97
0.385
0.395
0.330
0.243
0.151
0.098
0.071
0.058
0.061
0.065
0.066
0.069
0.075
0.080
2,499
3,405
3,334
2,928
2,814
2,958
2,769
2,368
2,496
2,676
2,757
2,979
3,098
3,309
311.8
298.9
239.1
162.9
92.5
54.9
36.9
29.0
29.4
30.9
31.7
34.5
38.1
44.3
Sedentary & Passive Activities (METS. 1.5 ~ Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
15.0
14.3
14.6
14.1
13.5
13.8
13.2
12.4
12.3
12.3
13.1
14.5
15.9
16.6
3.18
4.62
4.79
4.58
4.87
5.64
5.76
5.11
5.57
6.11
6.27
6.54
6.65
6.44
0.397
0.406
0.343
0.255
0.160
0.105
0.077
0.062
0.066
0.071
0.072
0.076
0.082
0.086
2,858
3,958
4,206
3,886
3,949
4,692
4,575
3,807
4,117
4,522
4,918
5,693
6,345
6,411
355.9
347.5
301.7
216.0
130.2
87.1
61.1
46.6
48.6
52.2
56.5
66.1
78.1
85.9
DRAFT - Do not cite or quote
20
October 31, 2006

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                                  Table 4.2a   (cont.)
Age Category
# NHANES
Partici-
pants
Reporting
Activity
Average
Duration
(hr/day)
Spent at
Activity
Ventilation Rate During
This Activity1
Unadjusted
for Body
Weight
(L/min)
Adjusted
for Body
Weight
(L/min/kg)
Daily Ventilation Rate
Associated With This
Activity2
Unadjusted
for Body
Weight
(L/day)
Adjusted for
Body Weight
(L/day/kg)
Light Intensity Activities (1.5 < METS 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
5.3
5.5
5.5
6.6
7.6
7.5
7.1
6.1
5.7
6.1
5.6
5.5
5.0
4.9
7.94
11.56
11.67
11.36
11.64
13.22
13.41
12.97
13.64
14.38
14.56
14.12
13.87
13.76
0.988
1.019
0.837
0.633
0.384
0.246
0.179
0.158
0.161
0.166
0.167
0.164
0.171
0.185
2,603
3,959
3,917
4,561
5,345
5,943
5,745
4,821
4,714
5,271
5,005
4,669
4,131
4,014
322.7
350.7
281.9
255.2
177.5
110.9
76.9
58.5
55.5
60.8
57.0
54.0
50.8
53.9
Moderate Intensity Activities (3.0 < METS 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
419
308
261
540
940
1,337
1,241
701
728
753
627
678
496
255
3.7
4.0
3.8
3.2
2.7
2.3
3.3
5.2
5.7
5.4
5.0
3.7
2.9
2.3
14.49
21.35
21.54
21.03
22.28
26.40
29.02
29.19
30.30
31.58
32.71
29.76
29.29
28.53
1.804
1.878
1.546
1.173
0.736
0.491
0.387
0.357
0.357
0.366
0.376
0.344
0.360
0.383
3,157
5,141
4,958
3,890
3,567
3,733
5,904
9,369
10,560
10,438
9,953
6,705
5,058
4,036
396.5
451.0
353.4
214.5
115.1
68.8
78.3
115.2
124.1
121.3
115.1
77.4
62.0
54.1
DRAFT - Do not cite or quote
21
October 31, 2006

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                                      Table 4.2a    (cont.)
Age Category
# NHANES
Partici-
pants
Reporting
Activity
Average
Duration
(hr/day)
Spent at
Activity
Ventilation Rate During
This Activity1
Unadjusted
for Body
Weight
(L/min)
Adjusted
for Body
Weight
(L/min/kg)
Daily Ventilation Rate
Associated With This
Activity2
Unadjusted
for Body
Weight
(L/day)
Adjusted for
Body Weight
(L/day/kg)
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
183
164
162
263
637
1,111
968
546
567
487
452
490
343
168
0.2
0.3
0.1
0.3
0.3
0.4
0.4
0.3
0.4
0.3
0.4
0.4
0.4
0.3
27.47
40.25
40.45
39.04
43.62
50.82
53.17
53.91
54.27
57.31
58.42
54.13
52.46
53.31
3.477
3.523
2.889
2.167
1.410
0.950
0.711
0.660
0.644
0.655
0.675
0.624
0.646
0.716
325
799
242
639
851
1,154
1,275
1,041
1,183
1,124
1,441
1,158
1,181
1,052
41.2
68.3
17.4
34.3
28.2
21.9
16.9
12.8
14.1
12.7
16.5
13.3
14.6
13.9
1 An 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 the
multiple linear regression model in Section 3.6, with weights corresponding to the number of minutes
spent performing the activity. Numbers in these two columns represent averages, calculated across
individuals in the specified 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.

2 An individual's daily average ventilation rate equals the product of the individual's weighted average
ventilation rate for the given activity category (L/min), estimated using the multiple linear regression
model in Section 3.6, and the number of minutes per day that the individual performs an activity within
the category. Numbers in these two columns represent weighted averages across individuals in the
specified age category, with the weights corresponding to the 4-year sampling weights assigned within
NHANES 1999-2002.
DRAFT - Do not cite or quote
22
October 31, 2006

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Table 4-2b.    Average Time Spent Per Day Performing Activities Within Specified Intensity
              Categories, and Average Ventilation Rates Associated With These Activity
              Categories, for Females According to Age Category
Age Category
# NHANES
Partici-
pants
Reporting
Activity
Average
Duration
(hr/day)
Spent at
Activity
Ventilation Rate During
This Activity1
Unadjusted
for Body
Weight
(L/min)
Adjusted
for Body
Weight
(L/min/kg)
Daily Ventilation Rate
Associated With This
Activity2
Unadjusted
for Body
Weight
(L/day)
Adjusted for
Body Weight
(L/day/kg)
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
13.0
12.6
12.1
11.1
10.3
9.6
9.1
8.6
8.3
8.3
8.1
8.4
8.6
9.1
2.92
4.59
4.56
4.18
4.36
4.81
4.40
3.89
4.00
4.40
4.56
4.47
4.52
4.49
0.391
0.414
0.342
0.238
0.151
0.090
0.069
0.055
0.056
0.060
0.061
0.061
0.066
0.072
2,275
3,466
3,307
2,788
2,686
2,766
2,398
2,009
1,996
2,197
2,222
2,255
2,325
2,456
304.9
313.0
248.4
158.9
92.7
51.6
37.7
28.6
27.8
29.9
29.8
30.5
33.9
39.1
Sedentary & Passive Activities (METS # 1.5 ~ Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
14.1
14.3
14.9
14.3
14.0
14.2
13.6
12.6
12.3
12.2
12.7
14.3
15.4
16.5
3.00
4.71
4.73
4.40
4.64
5.21
4.76
4.19
4.33
4.75
4.96
4.89
4.95
4.89
0.402
0.425
0.355
0.251
0.160
0.097
0.075
0.060
0.060
0.065
0.067
0.066
0.072
0.078
2,538
4,046
4,215
3,773
3,898
4,442
3,876
3,164
3,197
3,489
3,771
4,183
4,569
4,841
339.4
365.9
316.4
214.8
134.3
83.1
61.0
45.0
44.7
47.5
50.7
56.6
66.6
77.3
DRAFT - Do not cite or quote
23
October 31, 2006

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                                  Table 4.2b.   (cont.)
Age Category
# NHANES
Partici-
pants
Reporting
Activity
Average
Duration
(hr/day)
Spent at
Activity
Ventilation Rate During
This Activity1
Unadjusted
for Body
Weight
(L/min)
Adjusted
for Body
Weight
(L/min/kg)
Daily Ventilation Rate
Associated With This
Activity2
Unadjusted
for Body
Weight
(L/day)
Adjusted for
Body Weight
(L/day/kg)
Light Intensity Activities (1.5 < METS # 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
6.0
5.6
5.8
6.3
7.3
7.6
7.0
6.4
6.5
6.6
6.5
6.2
6.0
5.3
7.32
11.62
11.99
10.92
11.07
12.02
11.08
10.55
11.07
11.78
12.02
10.82
10.83
10.40
0.978
1.050
0.897
0.619
0.382
0.225
0.174
0.149
0.154
0.161
0.161
0.147
0.158
0.167
2,727
4,019
4,255
4,148
4,845
5,454
4,660
4,075
4,338
4,656
4,714
4,046
3,873
3,308
362.7
366.8
318.5
235.6
167.0
101.9
73.2
57.7
60.5
63.8
63.2
55.1
56.6
52.9
Moderate Intensity Activities (3.0 < METS # 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
415
245
255
543
894
1,451
1,182
1,023
869
763
622
700
470
306
3.9
4.0
3.3
3.4
2.6
2.0
3.3
4.8
5.0
5.0
4.6
3.3
2.5
2.1
13.98
20.98
21.34
20.01
21.00
23.55
23.22
22.93
22.70
24.49
25.24
21.42
21.09
20.87
1.866
1.896
1.600
1.135
0.723
0.441
0.365
0.325
0.316
0.333
0.339
0.292
0.308
0.335
3,222
5,118
4,076
3,986
3,220
2,852
4,586
6,769
6,927
7,559
7,026
4,255
3,140
2,580
434.0
452.5
306.0
226.0
111.0
53.3
72.0
95.9
96.4
102.1
94.6
58.0
45.8
41.4
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24
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                                      Table 4.2b.    (cont.)
Age Category
# NHANES
Partici-
pants
Reporting
Activity
Average
Duration
(hr/day)
Spent at
Activity
Ventilation Rate During
This Activity1
Unadjusted
for Body
Weight
(L/min)
Adjusted
for Body
Weight
(L/min/kg)
Daily Ventilation Rate
Associated With This
Activity2
Unadjusted
for Body
Weight
(L/day)
Adjusted for
Body Weight
(L/day/kg)
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and older
79
55
130
347
707
1,170
887
796
687
515
424
465
304
188
0.2
0.2
0.2
0.2
0.2
0.3
0.2
0.3
0.2
0.3
0.3
0.3
0.3
0.3
24.19
36.48
37.58
34.53
39.39
46.56
44.09
45.68
44.44
46.98
47.35
40.02
40.64
41.88
3.263
3.376
2.800
1.979
1.331
0.879
0.696
0.650
0.613
0.653
0.634
0.544
0.594
0.666
244
471
355
407
568
840
621
725
646
725
965
111
718
654
32.3
44.3
25.6
23.4
18.7
15.8
9.8
10.2
8.9
10.1
13.0
10.5
10.5
10.7
1 An 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
the multiple linear regression model in Section 3.6, with weights corresponding to the number of minutes
spent performing the activity. Numbers in these two columns represent averages, calculated across
individuals in the specified 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.

2 An individual's daily average ventilation rate equals the product of the individual's weighted average
ventilation rate for the given activity category (L/min), estimated using the multiple linear regression
model in Section 3.6, and the number of minutes per day that the individual performs an activity within
the category. Numbers in these two columns represent weighted averages across individuals in the
specified age category, with the weights corresponding to the 4-year sampling weights assigned within
NHANES 1999-2002.
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25
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 1    4.1     STRENGTHS AND LIMITATIONS
 2
 3           The major strengths of the approach used in this report and Appendix A are that it
 4    accounts for differences in VE that occur due to activity level, the effect of age and gender, and

 5    natural variation both between and within individuals. The approach yields an estimate of VE
 6    that is a function of VO2 rather than an indirect measure of oxygen consumption such as VQ.
 1    (While other researchers have estimated VE given VQ, the appropriate value of VQ to use can
 8    depend on an individual's work rate, and thus, can introduce bias and additional variability.) The
 9    primary sources of input data to this approach, the NHANES and NHAPS data sets, are each
10    nationally-representative data sets with a large sample size, even within the age and gender
11    categories considered in this report, thereby allowing for improved characterization of body
12    weight and activity patterns that can represent everyone in an age/gender subpopulation.
13
14           By simulating an individual's 24-hour activity pattern based on information for a
15    subpopulation with the same age and gender range, this procedure attempted to address the
16    correlation that is present between  an individual's BMR measure and the METS values
17    associated with the activities that the individual  performs. However, because the NHAPS
18    database within  CHAD does not include body weight, information on both METS values and
19    BMR were not available for an individual that would allow a more rigorous characterization and
20    handling of their correlation. This  was one limitation of the analysis outcome. Other data
21    sources within CHAD which did include body weight were considered, but they were deemed to
22    have limited target populations that would likewise limit the ability to infer findings to larger
23    populations.
24
25           The approach does not specifically account for variability that is introduced by assigning
26    a random METS value to an activity that originates from a pre-specified statistical distribution.
27    In addition, a potential bias may be introduced if the distribution is not appropriate in reality for a
28    given activity, although the CHAD identified appropriate distributions based upon a review of
29    the exercise physiology and clinical nutrition literature. The METS randomization process
30    allows for different METS values to be assigned to the same activity being performed by the
31    same individual at a given moment in time. This variability associated with this randomization
32    process is currently confounded with variability in METS values that is present from one
33    individual to another.
34
35           By using the NHANES sampling weights in the calculation of the statistics in this report,
36    the goal of this effort was to generate statistics that could represent national estimates. In the
37    calculation, use  of the sample weights is considered to be superior to ignoring them. However,
38    because the 24-hour activity pattern assigned to each NHANES individual was simulated using
39    activity information from the NHAPS study, the observed distribution of VE values across
40    individuals can only approximate a national distribution. In addition, because the simulated
41    24-hour activity patterns are limited to the set of activities reported within the NHAPS database,
42    and because each simulated pattern represented  an average of multiple patterns observed within
43    the NHAPS database, an individual's true activity pattern in any given 24-hour period may be
44    more variable than that considered in this exercise.  Furthermore, because the simulated activity
      DRAFT - Do not cite or quote                   26                            October 31, 2006

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 1   profiles did not consider possible limits on the "maximum possible METS value" that would
 2   account for previous activities, ventilation rates may be overestimated as a result.
 O
 4          Data from the NHAPS were used to characterize activity levels for individuals in the U. S.
 5   population. Because the NHAPS was conducted over ten years ago, it may not accurately
 6   portray activity profiles in certain subpopulations, especially those seeing greater trends toward
 7   overweight incidence and obesity (e.g., children and adolescents). In addition, the growing
 8   sedentary nature of the population as a whole may be affecting the continued relevance of
 9   NHAPS activity data to the contemporary U.S. population. METS distributions also may not be
10   adequately characterized when activities are conducted by children, due to the more frequent and
11   sudden movement by children from one activity to another compared to other subpopulations.
12
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 1    5.0    REFERENCES
 2
 3    Adams WC, Shaffrath, JD, and Ollison, WM. (1995) The relation of pulmonary ventilation and
 4          heart rate in leg work alone, arm work alone, and in combined arm and leg work.  Paper
 5          WA-84A.04 presented at the Annual Meeting of the Air & Waste Management Assoc.
 6
 7    Adams WC. (1993) Measurement of Breathing Rate and Volume in Routinely Performed Daily
 8          Activities.  Davis CA: University of California.
 9
10    CDC (2005) NHANES Analytic and Reporting Guidelines. National  Center for Health Statistics,
11          Centers for Disease Control and Prevention. December 2005. Access:
12          http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_20
13          05.pdf.
14
15    CDC (2004) NHANES Analytic Guidelines. National Center for Health Statistics, Centers for
16          Disease Control and Prevention.  June 2004. Access:
17          http://www.cdc.gov/nchs/data/nhanes/nhanes_general_guidelinesjune_04.pdf.
18
19    Derumeaux-Burel, H, Meyer, M, Morin, L, and Boirie, Y. (2004) Prediction of resting energy
20          expenditure in  a large population of obese children. American Journal of Clinical
21          Nutrition.  80:1544-1550.
22
23    Hebestreit, H, Staschen, B, and Hebestreit, A. (2000) Ventilatory threshold: a useful method to
24          determine aerobic fitness in children? Medicine and Science  in Sports and Exercise.
25          32(11):1964-1969.
26
27    Hebestreit, H, Kriemler,  S, Hughson, RL, and Bar-Or, O.  (1998) Kinetics of oxygen uptake at
28          the onset of exercise in boys and men.  Journal of Applied Physiology. 85:1833-1841.
29
30    Klepeis, NE, Nelson, WC, Ott, WR, Robinson, JP, Tsang, AM, Switzer, P, Behar, JV, Hern, SC,
31          and Engelmann, WH. (2001) The National Human Activity Pattern Survey  (NHAPS):  a
32          resource for assessing exposure to environmental pollutants.  Journal of Exposure
33          Analysis and Environmental Epidemiology.  11(3):231-252.
34
35    Johnson, T. (2002)^4 Guide to Selected Algorithms, Distributions, and Databases Used in
3 6          Exposure Models Developed by the Office of Air Quality Planning and Standards.
37          Chapel Hill: TRJ Environmental.
38
39    Layton, DW. (1993) Metabolically consistent breathing rates for use in dose assessments.
40          Health Physics. 64(1) 23-36.
41
42    McCurdy, T.  (2000) Conceptual basis for multi-route intake dose modeling using an energy
43          expenditure approach. Journal of Exposure Analysis and Environmental Epidemiology.
44          10:86-97.
45
      DRAFT - Do not cite or quote                 28                           October 31, 2006

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 1   McCurdy, T, Glen, G, Smith, L, and Lakkadi, Y.  (2000) The National Exposure Research
 2          Laboratory' s Consolidated Human Activity Database.  Journal of Exposure Analysis and
 3          Environmental Epidemiology.  10:566-578.
 4
 5   SAS (2005) SAS OnlineDocŪ 9.1.3. Gary, NC: SAS Institute, Inc. Access:
 6          http://support.sas.com/onlinedoc/913/docMainpage.jsp.
 7
 8   Schofield, WN. (1985) Predicting basal metabolic rate, new standards and review of previous
 9          work. Human Nutrition: Clinical Nutrition. 39C(Suppl.):5-41.
10
11   USEPA (2005) Guidance on Selecting Age Groups for Monitoring and Assessing Childhood
12          Exposures to Environmental Contaminants. Risk Assessment Forum, U.S. Environmental
13          Protection Agency, Washington, DC. EPA/630/P-03/003F. November 2005.  Access:
14          http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid= 146583
15
16   USEPA (2002) CHAD User's Guide: Extracting Human Activity Information from CHAD on
17          the PC.  Prepared by ManTech Environmental Technologies and modified by Science
18          Applications International Corporation for the National Exposure Research Laboratory,
19          U.S. Environmental Protection Agency. 22 March 2002.
20
21   USEPA (2001) Research note: analyses to understand relationships among physiological
22          parameters in children and adolescents aged 6-16. Issued by T.  McCurdy, National
23          Exposure Research Laboratory, U.S. Environmental Protection Agency.
24
25   USEPA (1999) Exposure Factors Handbook. Office of Research and Development, U.S.
26          Environmental Protection Agency. EPA/600/C-99/001, February 1999. Access:
27          http://www.epa.gov/ncea/efh.
28
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                             APPENDIX A:
         INTERNAL EPA RESEARCH REPORT BY S. GRAHAM
                          AND T. McCURDY:

                 Revised Ventilation Rate (VE) Equations for Use
                    in Inhalation-Oriented Exposure Models
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 2                                         Abstract
 3          Using data compiled from 32 clinical exercise studies, algorithms were developed
 4          to estimate body mass-normalized ventilation rate (VE, L/min kg"1) for 4  age
 5          groups  (<20, 20-<34,  34-<61,  61+ years of age)  and both  genders.   The
 6          algorithms  account  for  differences in ventilation rate due to  activity  level,
 7          variability within age groups, and variation both between and within individuals.
 8          A multiple linear regression (MLR) model was first used to estimate significant
 9          explanatory parameters (p<0.01) following natural log  (Ln) transformation of
10          body mass (BM) normalized oxygen consumption rate (VCh). Log transformed
11          age (Ln(age)), gender (-1 for males, 1  for females), and Ln(VO2/BM) served as
12          independent variables and regressed on multiple VE  measurements that were
13          collected  during incremental exercise to obtain regression parameter estimates.
14          The  (MLR) model  showed  marginal statistical improvement  (R2 +5%)  in
15          comparison with a previous simple linear regression model for estimating  VE,
16          however  the MLR  can estimate  population  VE with one-half the equations
17          formerly used and can be used to address uncertainty in VE estimations. A mixed-
18          effects regression (MER) model was then  constructed utilizing the independent
19          variables  as fixed parameters and  retaining individuals  and study  of origin as
20          random effects variables.  The MER model was used to allocate the random error
21          (e) to between-person residuals distributions (inter-individual  variability)  and
22          within-person residuals  distributions (intra-individual variability).  Predictive
23          equations were executed for 5,000 iterations at a given age (e.g., 5 year olds) or
24          age group classification (e.g., 45-55 years old) and estimated ventilation rates for
25          each  model were compared at their respective  50th, 95th and  99th percentiles.
26          EPA's Air Pollution  Exposure (APEX) model was used to  estimate population
27          ventilation rates using a variety of ventilation algorithms for comparison with the
28          MLR and MER at individual years  in  age. VE estimations from the MLR  and
29          MER algorithms were similar across all ages and provided reasonable ventilation
30          rates  at all  percentiles  and ages,  suggesting  either approach is reasonable for
31          stochastic modeling  exercises where  simulation  of  activity-specific  person-
32          oriented ventilation rates is desired.
33
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1
2                                     Disclaimer
o
4                This document is intended for internal Agency use only. Mention of trade
5         names or commercial products does not constitute an endorsement or
6         recommendation for use.
7
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30



APEX
BMR
BM
BMI
BSA
CHAD
EE
EVR
F,
HR
HT
LBM
METS
NAAQS
NERL
OAQPS
Pac02
RQ
SHEDS
VA
vco
VD
VE
VT
V'o2
VQ

                           Keywords / Acronyms

             Air Pollution Exposure model (OAQPS)
             Basal metabolism rate
             Body mass
             Body mass index
             Body surface area
             Consolidated Human Activity Database
             Energy expenditure
             Equivalent ventilation rate [VE/BSA]
             Conversion Factors
             Heart rate
             Height
             Lean Body Mass (equivalent to fat-free mass)
             Metabolic equivalents of work
             National Ambient Air Quality Standard
             National Exposure Research Laboratory
             Office of Air Quality Planning and Standards
             partial pressure of arterial carbon dioxide
                                 •    •
             Respiratory quotient (Vco IV0 )
             Stochastic Human Exposure and Dose Simulation model (NERL)

             Alveolar ventilation rate (due to formatting issues, VA is used in report)

             Carbon dioxide expiration rate
             Dead space volume of the lung

             Total ventilation rate  (due to formatting issues, VE is primarily used here)
             Tidal volume of the lung

             Oxygen consumption rate (due to formatting issues, VO2 is primarily used here)
                                   •   •
             Ventilatory equivalent (VE IV0  )
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A-iv
October 31,2006

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 1
 2                                Acknowledgments
 3
 4         The authors are indebted to a number of people who invested time in improving this
 5   report.  Special thanks are due our OAQPS colleagues who shared their expertise in human
 6   exposure modeling and risk assessment which helped focus our efforts; they are, in particular:
 7   John Langstaff, Ted Palma, and Harvey Richmond.  Gratitude is also due to Ted Johnson of TOJ
 8   Environmental, who provided us with information on past practices regarding uptake dose
 9   modeling.  Finally, we thank our EPA colleague, Dr. James Starr who reviewed this report and
10   discussed ventilation issues with us.
11
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                               Table of Contents

                                                                                  Page
1.  Introduction	A-l
2.  Methods	A-3
    Data Set Description	A-3
    Statistical Analysis	A-3
    Algorithm Evaluation	A-5
3.  Results and Discussion	A-6
    Statistical Analysis	A-6
    Extrapolation Issues and Assumptions	A-9
    Performance Evaluation	A-11
4.  Recommendations	A-15
5.  Future Research	A-l5
6.  References	A-16
Appendix A	A-l8
    A-l. First memo from Dr. Adams describing major data set	A-18
    A-2.  Second memo from Dr. Adams describing additional data from study #30	A-23
Appendix B	A-29
    B-l. Data Set Manipulation	A-29
    B-2. Quality Assurance	A-30
    B-3. Data Transformation	A-31
Appendix C	A-33
    C-l. Comparison of selected percentiles of estimated event-based ventilation rates from
    20,000 person APEX model simulation using different ventilation algorithms	A-34
    C-2. Comparison of estimated event-based ventilation rate percentiles from 20,000 person
    APEX model simulation using mixed effects regression (MER-left) and Johnson (2002)
    (right) ventilation algorithms	A-3 5
    C-3. Percent Difference of estimated event-based ventilation rate percentiles from 20,000
    person APEX model simulation using mixed effects regression (MER-left) and Johnson
    (2002) (right) ventilation algorithms	A-36
                                  List of Tables

Table 1.      Parameter estimates used to estimate activity-specific VO2 for males and
             females of different age groups	A-5
Table 2.      Parameter and residuals distribution estimates derived from two different
             statistical techniques and reported from Johnson (2002) for use in predictive
             equation (1) or (2)  	A-7
Table 3.      Ventilation parameter estimates (bi), standard errors (se), and residual
             distributions standard deviation estimates (e;) using Adams data and
             assuming equation  (3) or (4) 	A-9
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Table 4.      Residual distributions standard deviation estimates (eb and ew) using
             data characterized by percentage of maximum VC>2 (VC^m) assuming
             equation (3) 	A-10
Table 5.      Residual distributions standard deviation estimates (eb and ew) using
             data categorized by percentage of maximum VC>2 (VC^m) assuming
             equation (4) 	A-10
Table 6.      Recommended inhalation rates (L/min) from USEPA (1997) Table 5-23 ... A-12
Table A-l.    Total subjects for each study, gender, and exercise ergometry used  	A-19
                                 List of Figures

Figure 1.     Pathways for estimating various ventilation parameters and metrics 	A-l
Figure 2.     Ventilatory quotient (VQ) as a function of age during exercise  	A-8
Figure 3.     Estimated ventilation rates (VE, L/min) for females (left) and males
             (right) while performing low-level (top), moderate (middle), and
             vigorous (bottom) activities 	A-13
Figure 4.     Estimated population ventilation rates (VE, L/min) for 20,000 persons
             using APEX and the mixed effects regression (MER) algorithm
             (Equation 4 and Table 3)	A-14
Figure B-l.   Relationship between total ventilation rate (VE) and oxygen
             consumption  rate (VO2) during exercise	A-31
Figure B-2.   Relationship between body mass normalized total ventilation rate
             (VE/BM) to oxygen consumption rate (VO2/BM) during exercise	A-31
Figure B-3.   Relationship between the natural logarithm of total ventilation rate
             Ln(VE) and oxygen consumption rate Ln(VO2) during exercise 	A-32
Figure B-4.   Relationship between body mass normalized total ventilation rate
             (VE/BM) to oxygen consumption rate (VO2/BM) during exercise	A-32
Figure C-l.   Comparison of selected percentiles of estimated event-based
             ventilation rates from 20,000 person APEX model simulation using
             different ventilation algorithms	A-34
Figure C-2.   Comparison of estimated event-based ventilation rate percentiles from
             20,000 person APEX model simulation using mixed effects regression
             (MER-left) and Johnson (2002) (right) ventilation algorithms	A-35
Figure C-3.   Percent difference of estimated event-based ventilation rate percentiles
             from 20,000 person APEX model simulation using mixed effects
             regression (MER-left) and Johnson (2002) (right) ventilation algorithms ... A-36
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 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
1.  Introduction

       The use of population-based probabilistic exposure models in risk assessments has
increased over the past few decades, largely due to their ability to simulate human activities more
realistically than previous models that used mostly static but conservative estimates of
physiologic parameters such as ventilation rate (VE, commonly in L min"1).  Some of the early,
more advanced human exposure models were developed by EPA's Office of Air Quality
Planning and Standards (OAQPS) in the  1980s, each containing an inhalation dose metric since
their inception (Johnson, 1995; McCurdy, 1994a,  1995). The first series of these models were
known as NAAQS Exposure Model NEM and probabilistic NEM (pNEM) models.  The
ventilation algorithm became more detailed over time, culminating with equivalent ventilation
rate (EVR; VE normalized to body surface area (BSA)) and alveolar ventilation rate (VA)
estimations used by a number of the pNEM models that are described in numerous
OAQPS-sponsored papers and reports (Johnson, 2002; Johnson and Adams, 1994; Johnson and
Capel, 2002; Johnson et al., 1995, 1996; Johnson and McCoy, 1995; McCurdy, 1994b; and
McCurdy, 1997a). More recently, the National Exposure Research Laboratory (NEJAL) has
developed the Stochastic Human Exposure and Dose Simulation (SJrtEDS) model, essentially
adopting the ventilation algorithm used in OAQPS's Air Pollution Exposure (APEX) model,
itself a variant of the pNEM models.  The impact of using advanced procedures for dose rate
metrics has been evaluated by McCurdy (1997b, c); however an integrated approach for
estimating multiple ventilation parameters has not yet been developed.

       To estimate inhalation exposure and dose in these fairly complex models, a standard but
flexible algorithm is required.  One that not only addresses variability in breathing rates but can
simulate differences  in the site of action of pollutants within the respiratory system (e.g., ozone,
particulate matter deposition) and variable chemical uptake characteristics (e.g., absorption
across the alveolar membrane versus total absorption).  Using current EPA exposure model
approaches for approximating ventilation rates and considering the need to address ventilation
for multiple classes of pollutants, a framework of activity-specific ventilation parameters  was
constructed and is depicted in Figure  1.
                           BMR     *    METS *     F1      =  V0o —,
                                                  (EE •* VO2)
                                 CHAD
                         i- BM
                                                 RQ/Pa
                                                   VD/VT
                                BSA-
                                               I
                                              EVR
                                                                   -VQ
                           HT
Figure 1.     Pathways for estimating various ventilation parameters and metrics.
      DRAFT - Do not cite or quote
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October 31,2006

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 1
 2          Central to the framework is the EPA's Consolidated Human Activity Database (CHAD),
 3    a database of nearly 23,000 person-days of time-location-activity data useful for exposure
 4    modeling purposes (McCurdy et al., 2000). Distributions of metabolic equivalents (METS) are
 5    assigned in CHAD to every activity that respondents participated in. These METS distributions
 6    have been developed from a review of the exercise physiology and clinical nutrition literatures
 7    (McCurdy, 2000) and represent the ratio of the energy needed for the activity performed to the
 8    energy needed to sustain life (basal metabolism).  The METS are fundamental to simulating an
 9    individual's breathing rate while the person is performing a variety of activities (e.g., running,
10    walking, sleeping).
11
12          To estimate activity-specific ventilation rates, first a prediction equation for basal
13    metabolic rate (BMR, in kilocalories min"1) is used to estimate the simulated individual's resting
14    metabolic rate from their body mass (BM), or from BM and height (HT) together.  Then activity -
15    specific METS (METSA) are sampled via Monte-Carlo techniques and multiplied by a person's
16    estimated BMR to obtain a single realization  of the energy expenditure rate (EE, kilocalories
17    min"1). This rate of energy expenditure is retained over the duration of the activity (termed here
18    as an "event"), which can be as short as 1 minute or as long as one hour (due to the structure of
19    CHAD).
20
21          Thus mathematically, event-specific EE for an individual (EEE;) is defined as:
22
23                                    EEE/=BMR/*  METSA
24
25          Estimated EEE;  can then be converted to an activity-specific oxygen consumption rate
26    (VO2E;) using a gender-specific relationship expressed as a uniform distribution (F^-,
27    L-CVkilocalorie) (McCurdy, 2000) as follows:
28
29                                          V02E/=  EEEi*Fn
30
31    VC>2, however, is not the final physiological process to be simulated since most air pollution
32    clinical studies do not use it as the end-point ventilation metric.  Most of these studies use VE or
33    EVR,  and some exposure models, particularly OAQPS's APEX model for carbon monoxide
34    (APEX-CO), need VA (commonly in L min"1) for their inhalation modeling approach.  By
35    definition, VA is a fraction of VE and is important in estimating respiratory uptake of gases
36    (e.g., O2, CO, CO2) and chemicals that likely act as gases (e.g., benzene, 1-3-butadiene
37    [Lin, et al., 2001]). Regardless, all three mentioned ventilation metrics (VE, EVR, VA) can be
38    obtained from VO2, either directly or indirectly, thus VO2 is fundamental to the development of
39    each of these ventilation algorithms.
40
41          The pathway from VO2 to VE can be direct or indirect, with the indirect approach itself
42    having a few options: from VO2 to VA and then to VE, or from VO2 to VE using the ventilatory
43    quotient (VQ or alternatively, the ventilatory  equivalent). VQ is simply the unitless ratio of VE
44    to VO2 when both metrics are in the same units.  This ratio is non-linear with work rate however,
45    varying between 20 and 32 in healthy people at low-to-moderate work rates while higher at more
46    extreme exercise levels (McArdle et al., 1991).  While there are nuances among the many ways
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 1    that VE and EVR have been estimated over the years, in general the approach taken has been the
 2    VQ pathway depicted in Figure 1.  VA has been estimated by Johnson (2002) using a direct
 3    relationship between VC>2 and VA originally described by Galletti (1959). For a more complete
 4    discussion of how ventilation rate has been modeled by OAQPS, see  Section 9 of Johnson
 5    (2002).
 6
 7          This NERL Research Report describes an approach to estimating VE directly from VC>2
 8    using a series of regression-based equations derived from 25 years  of clinical studies conducted
 9    by Dr. William C. Adams of the University of California at Davis.  Much of the work cited
10    above has been predicated upon past work and data provided by Dr. Adams, particularly Adams
11    (1993) and Adams et al. (1995). OAQPS and NERL at different times acquired independent
12    (non-overlapping) data sets from his laboratory at the University of California-Davis. These data
13    have been extensively analyzed by OAQPS contractors, particularly Ted Johnson of TRJ
14    Environmental (and previously with IT Technology). In addition to the citations noted  above
15    regarding analysis of Dr. Adams' data, see also Johnson et al. (1998).
16
17          OAQPS requested that staff in the Exposure Modeling Research Branch of NERL review
18    the literature on calculating VA since a previous review of the algorithms used in pNEM/CO
19    indicated that a constant in the equation possibly varied non-linearly with exercise  rate.  That
20    review has not been completed as of this date, but as an outgrowth  of this work NERL staff
21    decided to first investigate a VE algorithm for use in both the APEX and SHEDS inhalation
22    modules. It is this work that is described below.
23
24    2.  Methods
25
26    Data Set Description
27          The data set acquired is listed and briefly described in communication memos authored
28    by Dr. Adams and provided in Appendix A.  Data from 32 panel studies collected over  a 25-year
29    period by the same laboratory were obtained in electronic format. The number of subjects
30    included within these studies was nearly one-thousand, undoubtedly one of the largest datasets of
31    its kind. The data set used was a Microsoft Ū Excel (.xls) file obtained from a disk labeled
32    "Converted Adams Data". The file used in this analysis (adam2.xls) was  considered as the raw
33    data file, since also on this disc was included an ASCII text version of the file and the memo
34    from Dr. Adams describing the data set.
35
36          The raw data required physical manipulation and mathematical transformation to allow
37    for statistical analyses.  Details of the procedures used as part of this research are described
38    further in Appendix B.  Briefly, due to the format of the original  study data sets, a file was
39    created containing a single vector for each individual ventilation parameter. Data were then
40    screened for erroneous and potentially extreme values. Ventilation parameters (VE and VO2)
41    were normalized to body mass and followed with a natural logarithm (Ln) transformation.
42
43    Statistical Analysis
44          All statistical analyses were performed using SASŪ software, version 8.2.1 (SAS
45    Institute, Gary, NC).  Parameters considered useful in model simulations (i.e., those that could
46    capture a significant degree of variability and are consistent with current exposure modeling
      DRAFT - Do not cite or quote                 A-3                            October 31, 2006

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 1    structure) were first evaluated for statistical significance (p<0.01) using an analysis of variance
 2    (ANOVA). Then, a simple linear regression (SLR) model was developed of the form yt = b0 +
 3    bjXj + Sj to estimate parameter coefficients for use in predictive equations:
 4
 5                               Ln(VE/BM)i = b0 + (b., * Ln(VO2/BMi)) + 61                    Eq. (1)
 6
 1    where b0 = the regression intercept, b} = the regression slope coefficient, and et representing
 8    individual variability in ventilation rate. The  coefficient of determination (R2) was used in
 9    evaluating the regression model since it represents the proportion of total variance of the
10    dependent variable "explained" by the independent variables.
11
12           The approach was modified slightly for predictive purposes to reflect additional test
13    factors contributing to variance in the ventilation rate.  The model presented here was given as
14    Equation 9-6 in Johnson (2002) and interpreted as follows, where bo = the intercept and bi = the
15    slope regression coefficient:
16
17                               Ln(VE/BM)i = b0 Mbi * Ln(V02/BMi)) + ebi + ewi               Eq. (2)
18
19           It was assumed here that the predictive regression equation represents a mixed-effects
20    regression (MER) model containing both fixed and random effects variables.  VC>2 was
21    considered a fixed parameter and subject and  study were random effects variables used to
22    estimate the between-person (inter-individual variability) residuals distribution (et,) and within-
23    person (intra-individual variability) residuals distribution (ew) rather than simply random error
24    (e) alone. Each of the residuals are normally distributed, with a mean of 0 and an estimated
25    standard deviation of 
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             Modification of the age groupings originally developed by Johnson (2002) was
      performed to determine if the statistical performance of the predictive equations could be
      improved. Criteria for the model development included individual regression coefficient
      significance (p- or lvalue), total model explanatory power (R2), and stability of the regression
      coefficients. For this last criterion, it was desired that coefficients neither greatly increase nor
      decrease in the individual regression equations compared with previous coefficient estimates
      while expanding/compressing age classifications. Age groupings were varied by one-year
      increments until the evaluation criteria described above was optimized, that is, models containing
      the greatest R2,  with statistically significant coefficients that varied minimally were retained.

      Algorithm Evaluation
             Each of the algorithms for estimating ventilation were evaluated using one or both
      methods described below to determine the range possible outcomes for individuals and a
      population.  Selected evaluations for the MLR and MER (using equations 3 and 4, respectively)
      are presented in the main text, while additional evaluations are provided in Appendix C.

             Ventilation rates  were first estimated using Crystal Ball™ software (Decisioneering, Inc.,
      Denver Colorado). Age- and gender-specific body weights for simulated individuals were
      estimated by probabilistic sampling of distributions provided by Burmaster and Crouch (1997).
      Basal metabolic rate was estimated using age- and gender-specific equations presented in
      Schoefield (1985), with age itself being sampled from uniform distributions within the age
      groupings used  in our analyses.  Activity-specific VO2 was generated using METS distributions
      for low, moderate, and vigorous intensity activities combined with the unit conversions given in
      Table 1. Ventilation rates were estimated for 5,000 hypothetical  persons within each  age (or age
      grouping) and gender category using predictive equations (3)  and (4) and their respective
      parameters.  To estimate variability in ventilation rates, each of the residuals distributions were
      probabilistically sampled while the intercept and coefficients held as constants, thus each
      estimated ventilation rate is representative of one activity performed by one hypothetical
      individual.  Median (p50), 95th (p95), and 99th (p99) percentiles of the hypothetical population
      distribution of estimated ventilation rates were compiled by age.  The output represents the
      possible range of expected ventilation rates across the population at a moment in time.

      Table 1.      Parameter estimates used to estimate activity  specific VO2 for males and females
                   of different age groups.1
Age group
Child
(0-18yrs)
Adult
(>18yrs)
Gender
Male
Female
Male
Female
METS-Activity Level'1
Low
N{2.0,0.34}
N{1. 5,0.26}
N{2.5,0.43}
N{2.0,0.34}
Moderate
N{5.0,0.85}
N{4.5,0.77}
N{6.5,1.1}
N{5.0,0.85}
Vigorous
N{9.0,1.5}
N{8.0,1.4}
N{10,1.7}
N{9.0,1.5}
Conversion Factors
Energy to
Oxygen
(L-C-2/kcal)
11(0.20-0.22}
U{0.19-0.21}
11(0.20-0.22}
U{0.19-0.21}
Unit
(MJ/kcal)/
(m in/day)
239/1440
37
38
39
40
41
          Distribution type and parameters used: N=normal {arithmetic mean, standard deviation}; U=Uniform
          {min,max}.
          It was assumed that the relative standard deviation of the METS for each distribution was 17% (see
          McCurdy and Graham, 2004)
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 1
 2          A second method for evaluation was conducted using OAQPS's APEX model, version
 3    4.0 (see US EPA, 2005 for details on the model algorithms). Twenty thousand individuals were
 4    simulated for one day to allow for the comparison of selected ventilation algorithms developed
 5    as would be used in an actual exposure model. Activity-specific ventilation rates were generated
 6    by APEX using human activity diaries from CHAD and the general approach described above
 7    and outlined in Figure 1. Diaries in CHAD are at a minimum disaggregated to hourly
 8    components, that is, the maximum time step for an activity or location inhabited could be one
 9    hour, thus up to 24 events in a day.  However much of the data are further divided such that
10    within one hour there may be multiple activities performed or multiple locations inhabited,
11    upwards to 1  minute in duration.  Since every simulated individual had multiple estimations for
12    ventilation rate depending on their activities performed (generally ranging from 30-40 events in a
13    day), distributions were first calculated for each person followed by an estimate of the population
14    distribution at each age (generally between 1 and 400 persons were simulated for each year of
15    age). The median (p50), 95th (p95), and 99th (p99) percentiles and maximum ventilation rates
16    estimated with the APEX model represent the variability in the mid-upper range of ventilation
17    rates for individuals within a population. It should be noted that the maximum for all individuals
18    is the same as the 99th percentile unless there was more than 99 events (rare if occurs at all).
19
20    3.  Results and Discussion
21
22    Statistical Analysis
23          Both age and gender were used in the development of several  regression equations
24    derived from the Adams data set and summarized in Table 9-1 of Johnson (2002); however
25    significance of these variables was not reported there. An analysis of variance was performed
26    here on VE, utilizing the 4 age groups (i.e., <18, 18-44, 45-64, >65 years old) and two genders as
27    classification variables indicated by Johnson (2002). VO2 normalized to body mass was
28    included as an additional independent variable.  Age group, gender, their interaction term (age
29    group by gender), and VC>2 were each significant explanatory parameters (all p<0.003).
30
31          Results of the simple linear regression analysis, the simple mixed model addressing fixed
32    and random effects, and parameter coefficients reported by Johnson (2002) assuming equations
33    (1) or (2) are presented in Table 2. Regression model intercept and slope were statistically
34    significant parameters in each of the regression models.
35
36          There were marginal differences between the simple regression coefficients and the
37    simple mixed model coefficients developed in this work; both the intercepts and slopes were
38    systematically lower for the simple regression. The results from the simple mixed model and
39    Johnson (2002) were nearly identical with the most notable differences seen in the residuals
40    distributions, albeit at a minimal level.
41
42          Following this single variable model comparison, age and gender were investigated as
43    additional independent variables for use in a multiple linear regression model. Gender was
44    already deemed significant based on the ANOVA and, since its use as a parameter in a multiple
45    linear regression would halve the number of equations needed for ventilation simulations, was to
46    be included as a parameter in the  regression model. For age, it was hypothesized that it would
      DRAFT - Do not cite or quote                 A-6                            October 31, 2006

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      have a statistically significant effect on the relationship between VE and VC>2, not just among the
      different age groups but also within a given age group. Figure 2 shows the relationship between
      VQ and age, with the most notable variation of VQ for those under age 18. These data (age<18)
      were not analyzed by Johnson (2002) due to lack of availability. Age, when included in a
      preliminary multiple regression model, was determined to be a significant explanatory parameter
      for both genders where age<18 and for males only within the other age groups (data not shown
      here).  Estimated coefficients for the females, although not statistically significant, were
      generally consistent with those of the males.

            When VQ was plotted by age (Figure 2), it was observed that a few of the subjects
      contained excessive VQ values, such that further culling of the data set was warranted.
      Observations of VQ in excess of 50 were removed based on a review of the relevant literature
      undertaken as part of the work documented by McCurdy and Graham (2004). Based on this
      criterion,  13  data points were removed. No single  subject had more than one data point
      removed. The impact of the additional culling was negligible (not reported).

      Table 2.       Parameter and residuals distribution estimates derived from two different
                   statistical techniques and reported from Johnson (2002) for use in predictive
                   equation (1) or (2).
Age
group
<18
18-44
45-64
65+
n
315
288
1473
3145
60
641
45
317
Gender
F
M
F
M
F
M
F
M
Method3
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson
SLR
MER
Johnson

bo
3.214
3.263
seb0
0.089
0.050
Ln(VO,/BM)
bi
0.941
0.950
seb1
0.022
0.012
Residuals
eb
ew
0.1609
0.1427
0.0735
R2
0.8504

Not Performed
3.054
3.180
0.103
0.052
0.913
0.941
0.026
0.012
0.1715
0.1600
0.0722
0.8069

Not Performed
4.021
4.358
4.357
3.758
3.983
3.991
3.360
3.462
3.454
3.824
4.019
4.018
2.687
2.958
2.956
3.686
3.731
3.730
0.040
0.034

0.023
0.022

0.239
0.153

0.060
0.047

0.297
0.143

0.090
0.055

1.182
1.276
1.276
1.130
1.194
1.197
0.998
1.023
1.021
1.117
1.166
1.165
0.846
0.908
0.908
1.060
1.071
1.071
0.011
0.009

0.007
0.006

0.055
0.034

0.016
0.012

0.068
0.032

0.023
0.013

0.1736
0.1351
0.1351
0.1176
0.1182
0.1826
0.1219
0.1228
0.1382
0.1395
0.1401
0.1152
0.1106
0.0774
0.0769
0.1584
0.1172
0.1107
0.1073
0.1112
0.0960
0.0920
0.0886
0.0341
0.0338
0.1280
0.1092
0.1082
0.0632
0.0632
0.8790


0.8965


0.8498


0.8884


0.7820


0.8729


21
22
23
24
          SLR: simple linear regression model (PROC REG in SAS) when using equation (1); MER: mixed
          effects regression model (PROC MIXED in SAS) when using equation (2); Johnson: data reported in
          Johnson (2002) for use with equation (2)
      DRAFT - Do not cite or quote
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                       VQ
                       70 i

                       60

                       50:

                       40;

                       30-

                       20

                       10
Figure 2.
      0     10    20    30    40    50    60    70    80    90
                             Age


Ventilatory quotient (VQ) as a function of age during exercise.
       To determine optimum age groups for the final multiple linear regression model, the
boundary values of the age groups—i.e., the youngest and oldest age groups determined by
Johnson (2002) (<18 and 65+ years of age, respectively) were first evaluated. Based on the
criteria described above, the lower and upper age groups were redefined to be <20 years old and
>60 years old. Two "inner" age groupings (20 to <34; 34 to <61) were also optimized based on
their fit with each other and with the lower and upper boundaries. The group comprising ages
34 to <61 could have been further subdivided (e.g., 34 to <45, 45 to <61 groups provided a good
statistical fit based on the semi-quantitative criteria), however the regression coefficients for the
intercept and age variables were dramatically altered for the 34-<45 age group (decreased and
increased, respectively) in comparison with the other age groups. It is not apparent whether this
response is physiologically representative of this age group, or that it is a function of the data set
itself; therefore, the larger age grouping was retained.

       Final ventilation parameter estimates for use in equations (3) or (4) following age group
optimization are presented in Table 2.  Slightly improved explanatory power was achieved with
the new models (as measured by the  multiple linear regression model, about 90% of total
variance is  now explained) compared with the earlier analyses (on average 85%).  Each of the
regression models and all estimated coefficients were statistically significant (p<0.01)  except
where noted.
      DRAFT - Do not cite or quote
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                                                            October 31,2006

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1
2 Table 3. Ventilation parameter estimates (b,), standard errors (se), and residual
3 distributions standard deviation estimates (e,) using Adams data and assuming
4 equation (3) or (4).
5
Age
group
<20
20-<34
34-<61
61 +
6
7 a
Ln(VO2/BM)
n Method3 b0
1085 MLR 44329
MER 4.3675
MLR 3.5718
MER 3.7603
MLR 3.1876
MER 3.2440
457 MLR Z4487
MER 2.5826
se b0
0.0579
0.0650
0.0792
0.1564
0.1271
0.2578
0.3646
0.7013
MLR: multiple linear regression model
b-i
1 .0864
1.0751
1.1702
1 .2491
1.1224
1.1464
1 .0437
1 .0840
se b1
0.0097
0.0087
0.0067
0.0061
0.0120
0.0088
0.0195
0.0122
(PROC REG in SAS)
Ln(age)
b2
-0.2829
-0.2714
0.1138
0.1416
0.1762
0.1856
0.2681
0.2766
when using
se b2
0.0124
0.0190
0.0243
0.0493
0.0335
0.0674
0.0834
0.1652
equation
Gender
b3
0.0513
0.0479
0.0450
0.0533
0.0415
0.0380b
-0.0298
-0.02081°
(3); MER:
seb3
0.0045
0.0077
0.0031
0.0061
0.0095
0.0172
0.0100
0.0149
Residuals
eb
0.
0.0955
0.
0.1217
0.
0.1260
0.
0.1064
ew
1444
0.1117
1741
0.1296
1727
0.1152
1277
0.0676
R2
0.9250
0.8927
0.8925
0.8932
mixed-effects regression
 9
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15
16
17
18
19
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26
27
28
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31
32
33
34
35
          (PROC MIXED in SAS) when using equation (4); b p=0.0286; c p = 0.1656.
Extrapolation Issues and Assumptions
       Prior to algorithm evaluation, an analysis of the residuals distributions was first
undertaken in a manner that mimicked the way the equations would be applied in human
exposure modeling simulations.  Note that all of the data were collected while individuals were
performing exercise; however exposure modelers will commonly extrapolate the data to activity
situations outside of the sample collection range. For example, when estimating a typical
person's daily exposure, there is not a significant time spent exercising but more spent
performing less strenuous activities such as sleeping.  Since resting measurements were not
collected by Dr. Adams for most of his subjects, an evaluation of the data bracketed by percent
of maximum VC>2 (VC^m) was decidedly appropriate in determining whether the data could be
extrapolated downward to reasonably simulate low energy-expenditure activities.  Typically VC>2
reserve (VO2res) is used; however, this was not measured in the Adams' studies.  A tripartite
categorization of the measured VC>2 for a step relative to the VC^m of each subject was
undertaken using <33.3%, 33.3-66.6%, >66.6% of VC^m as the category boundary values.  This
categorization has been done previously based on intervals of low, moderate, and vigorous
exercise and recently summarized from the exercise physiology literature (McCurdy and
Graham, 2004). Residuals distributions were estimated using the multiple linear regression and
mixed models as was done above [equations (3) and (4)], but now accounting for the tripartite
categorization.

       Residuals for the MLR model using equation (3) and the tripartite categorization
(Table 4) were generally lower at the lower and moderate level exercise levels compared with
the estimated total residuals in Table 3. This indicates there is less variability in ventilation rate
at the low and moderate  exercise levels.
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Table 4.       Residual distributions standard deviation estimates (eb and ew) using data
              categorized by percentage of maximum VO2 (VO2m) assuming equation (3).
Age
Group
<20
20-<34
34-<61
61 +
<33.3% V02m
6|
0.1233
0.1486
0.1954
0.0974
X
123
127
74
9
n
2.0
1.9
1.8
1.9
33.3-66.6% V02m
6|
0.1007
0.1184
0.1568
0.1144
X
179
428
144
78
s
2.5
2.9
3.2
2.7
>66.6% V02m
6|
0.1523
0.1734
0.1592
0.1344
X
137
521
139
67
s
2.8
4.1
3.5
3.4
 5
 6
 1
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
x   is the number of subjects in given age group and tripartite categorization where measurements were
    collected.
n   is the average number of V02 samples subjects had within each age group and tripartite
    categorization.
       For the mixed model, between-person residuals (eb) were generally higher and the within-
person variability was lower for all age groups using the tripartite breakdown (Table 5)
compared to the residuals distributions estimated using all of the data combined (Table 3).  This
indicates that there is greater variability in ventilation between persons and less variability within
a person than would be simulated when an individual is performing low-level activities.  One
may expect this to occur intuitively since the tripartite breakdown basically restricts the total
number of measurements for an individual while the number of individuals for the most part has
remained the same.  There was a small difference in the total number of subjects in each  exercise
category because some of the individuals did not attain a level of exercise >66.6% VC^m;
however, this was not the principal reason for the observed residual differences since
consistently even fewer individuals were measured at exercise <33.3% VC^m (Tables 4 and 5).
In addition, more measurements were consistently obtained for exercise >66.6% VC^m on
average per person than at the low or moderate levels of exercise.

Table 5.       Residual distributions standard deviation estimates (eb and ew) using data
              categorized by percentage of maximum VO2 (VO2m) assuming equation (4).
Age Group
<20
20-<34
34-<61
61 +
<33.3% V02m
eb
0.1217
0.1291
0.1522
0.1244
ew
0.0506
0.0728
0.0938
0.0164
33.3-66.6% V02m
eb
0.0951
0.1088
0.1444
0.1112
ew
0.0456
0.0524
0.0581
0.0362
>66.6% V02m
eb
0.1637
0.2190
0.1936
0.1422
ew
0.0741
0.0740
0.0710
0.0563
Numbers of individuals and samples collected per individual are the same as indicated in Table 4.
29
30
31
32
33
34
       These results in Tables 4 and 5 imply that activity-level specific equations may be
warranted to better simulate an individual's ventilation rate over all ranges of exercise levels.
However, given the sample size of the data set analyzed, further subclassification of the data
would likely lead to greater instability of the regression coefficients and prevent reasonable
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 1    ventilation estimations for all exercise levels, age groups, or genders.  Using the data provided in
 2    Table 3 and implementation of either equation (3) or equation (4) should not have a large impact
 3    on a population-based exposure analyses.
 4
 5          It should be noted that in extrapolating lower than the age range of the original data
 6    (e.g., <3.6 years old), it is assumed that regression equations are suitable for these children and
 7    infants.  The trend for VQ illustrated in Figure 6 is likely to be continued upward for younger
 8    children and infants due to the anticipated reduction in efficiency (i.e., underdevelopment) of
 9    their respiratory systems.  However, since the natural log for age <1 is negative [i.e., ln(l)=0; for
10    x
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     depending primarily on age. Values at older ages are compressed, possibly biased by the small
     number of persons simulated (10-50 persons for each year in age 80 to 90; 1-10 for each year in
     age >90). Rarely did the upper percentile ventilation rate exceed 100 L/min, the majority of
     simulated persons performed activities requiring less than 50 L/min, with most breathing about
     10 L/min throughout the day.

            Results are also compared to those summarized by USEPA (1997), but much of the data
     presented here are in fact approximations to that report utilizing similar approaches. Table 5-6 in
     USEPA (1997) contains somewhat comparable data disaggregated by age and gender, adults
     only, for average inhalation rates. The origin of the USEPA (1997) data, however, is Adams
     (1993), which is used extensively in this report. Recommended inhalation rates from Table 5-23
     in USEPA (1997), based on measured and approximated data, are presented in Table 6 and are
     assumed to be reflective of "average" or likely inhalation rates and are generally comparable to
     the medians reported here in Figures 3 and 4.
     Table 6.
Recommended inhalation rates (L/min) from USEPA (1997) Table 5-23.

Children
Adults
Rest
5.0
6.7
Sedentary
6.7
8.3
Low
16.7
16.7
Medium
20
26.7
High
31.7
53.3
18
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                            A-12
                                                                             October 31, 2006

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 Percentlles of Estimated VE for Females Performing Low-Exertion Activities Using Selected
                    Parameter Distributions
              Percentlles of Estimated VE for Males Performing Low-Exertlon Activities Using Selected
                                Parameter Distributions
                                                                  I 20
  Percentiles of Estimated VE for Females Performing Moderate-Exertion Activities Using
                 Selected Parameter Distributions
                                                                        Percentiles of Estimated VE for Males Performing Moderate-Exertion Activities Using Selected
                                                                                           Parameter Distributions
                                                                                   •>cŧ
                                                                                 •„<&>•
                                                                             ^
                                                                                    *?*,**#***•-
   Percentiles of Estimated VE for Females Performing Vigorous-Exertion Activities Using
                  Selected Parameter Distributions
                                                  Ŧp50-MLR
                                                    )5-MLR
                                                    )9-MLR
                                                   )50-MER
                                                   )95-MER
                                                   )99-MER
            Percentiles of Estimated VE for Males Performing Vigorous -Exertion Activities Using Selected
                                Parameter Distributions
                                                                       ~*?-
Figure 3.        Estimated ventilation  rates (VE, Urn in) for females (left) and males (right) while
                   performing low-level (top), moderate (middle), and vigorous (bottom) activities.
                   Median (p50), 95th (p95) and 99th (p99) percentiles are given for a 5,000 person
                   simulation for each of the multiple parameter regression models.
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October 31,  2006

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                               p50 V, using 20,000 persons from APEX and using Mixed Model
                                p95 VE using 20,000 persons from APEX and using Mixed Model
                               p99 VE using 20,000 persons from APEX and using Mixed Model
Figure 4.      Estimated population ventilation rates (VE, L/min) for 20,000 persons using APEX
               and the mixed effects regression (MER) algorithm (Equation 4 and Table 3). The
               full distribution of the median (p50-top), 95th (p95-middle) and 99th (p99-bottom)
               percentiles are represented for each age.
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October 31, 2006

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 i    4.  Recommendations
 2          We recommend that for inhalation exposure modeling purposes, the regression equation
 3    coefficients listed in Table 3 be used with equation (3) or (4) to estimate activity-specific body
 4    mass-adjusted VE for simulated individuals in the age groups listed. Estimated regression
 5    coefficients and output from each of the algorithms were very similar, however gender within the
 6    MER algorithm was not considered statistically significant for the older age group compared
 7    with the MLR.
 8
 9          To obtain estimates of VE in units of L min"1,  the antilog of the predicted value multiplied
10    by the subject's body mass (BM in kg) would be taken. Ages less than one year old are not to be
11    approximated (i.e., persons with age2 to VA is considered as  a direct linear proportionality (i.e., a constant value of 19.63)
26    and estimated independently from VE. A preliminary literature review indicates that the
27    approximation is reasonable and  may be  linear for low to moderate  exercise levels, but at a
28    minimum, there is variability in VA at all exercise levels that is not accounted for by the point
29    estimate used to modify VC>2. Further investigation is needed to determine if the VC>2 to VA
30    relationship is maintained for vigorous activity levels. In addition, the lack of a direct
31    computational link with VE potentially can lead to simulated values of VA in excess of VE, a
32    physiological impossibility.
33
34          One potential method would be to estimate VA from VE by using another physiological
35    relationship: the ratio of dead space volume-to-tidal volume (Vo/Vr, see Figure 1).
36    Physiological dead space is the volume of the lung that does not take part in gas exchange and is
37    comprised of basic anatomic dead space  (e.g. volume of trachea and bronchioles) and areas of
38    lung with reduced functionality (e.g., damaged alveolar regions, increased dead space due to
39    bronchiole expansion during exercise). Tidal volume is the total amount of air breathed upon
40    inspiration, not all of which comes in contact with the alveolar region of the lung due to the
41    presence of physiologic dead space.  It has been found that VD/VT does not remain constant over
42    varying exercise levels, with VT increasing at a greater rate than VD during increasing exercise
43    level.  The effect of this non-linear relationship in simulating VA (does VA increase linearly with
44    increasing VO2 at all exercise levels?) has not yet been determined. The relationships of VE,


      DRAFT - Do not cite or quote                 A-15                            October 31, 2006

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           , and VC>2 with VA and other ventilation parameters (e.g., the respiratory quotient or RQ)
     will be explored in greater detail and integrated in a second report.
 4    6.  References
 5    Adams WC.  1993. Measurement of Breathing Rate and Volume in Routinely Performed Daily
 6       Activities.  Davis CA: University of California.
 7    Adams WC, Shaffrath JD and Ollison WM.  1995. The relation of pulmonary ventilation and
 8       heart rate in leg work alone, arm work alone, and in combined arm and leg work.  Paper WA-
 9       84A.04 presented at the Annual Meeting of the Air & Waste Management Assoc.
10    Baba R, Mori E, et al. 2002. Simple exponential regression model to describe the relation
11       between minute ventilation and oxygen uptake during incremental exercise.  Nagoya JMed
12       Sci. 65: 95-102.
13    Burmaster DE and Crouch EAC. 1997. Lognormal distributions for body weight as a fucntion of
14       age for males and females in the United States, 1976-1980. Risk Analysis. 17(4): 499-505.
15    Galletti PM.  1959.  Les echanges respiratoires pendant 1'exercice musculaire. Helv Physiol
16       Acta.  17:34-61.
17    Johnson T.  1995. Recent advances in the estimation of population exposure to mobile source
18       pollutants. JExp Anal Environ Epidemiol. 5: 551-571.
19    Johnson T. 2002. A Guide to Selected Algorithms, Distributions, and Databases Used in
20       Exposure Models Developed by the Office of Air Quality Planning and Standards.  Chapel
21       Hill: TRJ Environmental.
22    Johnson T and Adams WC.  1994.  An algorithm for determining maximum sustainable
23       ventilation rate according to gender, age, and exercise duration. Unpublished paper.
24    Johnson T and Capel J.  2002. User's Guide: Software for Estimating Ventilation (Respiration)
25       Rates for Use in Dosimetry Models. Chapel Hill NC: TRJ Environmental.
26    Johnson T, Capel J, McCoy M, and Warnasch J.  1996.  Estimation of Ozone Exposures
27       Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of
28       NEM. Durham NC: IT Corporation.
29    Johnson T and McCoy M. 1995. A Monte Carlo Approach to Generating Equivalent
30       Ventilation Rates in Population Exposure Assessments.  Washington DC: American
31       Petroleum Institute (API Publication # 4617).
32    Johnson T, McCoy Jr. M, and Ollison W.  1995.  A Monte Carlo approach to generating
33       equivalent ventilation rates in population exposure assessments."  Paper 95-TA42.05
34       presented at the annual meeting of the Air & Manage. Waste Assoc.; San Antonio TX.
35    Johnson T and Mihlan G. 1998.  Analysis of clinical data provided by Dr. William Adams and
36       revisions to proposed probabilistic algorithm for estimating ventilation rate in the 1998
37       version of pNEM/CO. Memo to A. Rosenbaum, Systems Applications International.
38    Lin Y-S, Smith TJ, et al. 2001.  Human physiologic factors in respiratory uptake of 1,3-
39       butadiene. Environ Health Perspect.  109(9):921-926.
40    McArdle WD, Katch FI, and Katch VL. 1991. Exercise Physiology.  3rd Ed. Philadelphia: Lea
41       &Febiger.
42    McCurdy T.  1994a. Human exposure to ambient ozone, pp. 85-127 in: D.J. McKee (ed.)
43       Tropospheric Ozone. Ann Arbor MI: Lewis Publishers.
44    McCurdy T.  1994b. Repackaging Adams (1993) breathing rate data. EPA  memo, Research
45       Triangle Park NC (April 20).
      DRAFT - Do not cite or quote                 A-16                           October 31, 2006

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 1   McCurdy T. 1995. Estimating human exposure to selected motor vehicle pollutants using the
 2       NEM series of models: Lessons to be learned. JExp Anal Environ Epidemiol. 5: 533-550.
 3   McCurdy T. 1997a. Comparison of cumulative inhaled ozone dose estimates using a
 4       disaggregated, sequential approach and alternative recommended approaches. Paper
 5       presented at the 7th Annual Meeting of the International Society of Exposure Analysis.
 6   McCurdy T. 1997b. Human activities that may lead to high inhaled intake doses in children
 7       aged 6-13.  Environ TaxP'harm. 4:251-260.
 8   McCurdy T. 1997c. Modeling the dose profile in human exposure assessments: ozone as an
 9       example. Rev Tox: In Vivo Tox Risk Assess. 1: 3-23.
10   McCurdy T. 2000. Conceptual basis for multi-route intake dose modeling using an energy
11       expenditure approach. J Expos Anal Environ Epidemiol. 10:86-97.
12   McCurdy T. 2001. Research Note: Analyses to under stand relationships among physiological
13       parameters in children and adolescents aged 6-16.  RTF: U.S. Environmental Protection
14       Agency (being reviewed).
15   McCurdy T, Glen G, Smith L, and Lakkadi Y. 2000. The National Exposure Research
16       Laboratory's  Consolidated Human Activity Database. J Exp Anal Environ Epidemiol. 10:
17       566-578.
18   McCurdy TR and Graham SE.  2004. Analyses to understand relationships among physiological
19       parameters in children and adolescents aged 6-16.  EPA/600/X-04/092.
20   Nieman DC. 1999. Exercise Testing and Prescription. A Health-Related Approach. 4th edition.
21       Mayfield Publishing Company, Mountain View, CA.
22   SchofieldWN. 1985. Predicting basal metabolic rate, new standards and review of previous
23       work. Human Nutrition: Clinical Nutrition.  39C(Suppl):5-41.
24   US EPA. 1997.  Exposure Factors Handbook. EPA/600/P-95/002Fa.
25   US EPA. 2005.  Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model
26       Documentation (TRIM.Expo / APEX,  Version 4) Volume I: User's Guide. Office of Air
27       Quality Planning and Standards, US EPA. November 2005.
28       http://www.epa.gov/ttn/fera/data/apex/APEX4UG120505.pdf
29
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 i    Appendix A
 2    A-1. First memo from Dr. Adams describing major data set
 3                                                                       21 August 1998
 4
 5    Dear Tom:
 6
 7    Enclosed is a diskette which includes the electronic data base containing data my graduate
 8    students and I have collected over the last 25 years on a large number of subjects of varying ages
 9    that includes VE, VO2, and other physiological data that should be very useful for estimating VE
10    and respiratory intake dose. It is in an Excel (5.0a) spread sheet format, as well as an ASCII
11    format, blank delimited file with headings.
12
13    A description of the subjects for which data were potentially available was detailed in a list of 37
14    studies (pages 5-8) in my proposal dated 28 April  1998. Table 1 details the 31 studies for which
15    valid physiologic data were available, together with the total number of subjects, their gender,
16    and whether they were tested on a cycle ergometer or on a motor driven treadmill. Missing study
17    numbers from the original proposal list denotes that no valid body composition and multi-stage
18    VO2max data were available. In Study 21,16 male subjects exercised on a cycle ergometer
19    (21.1), while 22 male subjects exercised on a treadmill (21.2).
20
21    The total number of subjects with multi-stage, steady-state corresponding VC>2 and VE values,
22    including those at VC^max, was 521 males and 224 females.  Most were obtained on a cycle
23    ergometer test (262 males and 158 females), with the remainder on a treadmill, utilizing a
24    walking and/or running protocol. In addition, steady-state VO2 and VE values at several
25    submaximal workloads on the treadmill were available on 211 other subjects as  described in
26    Study  30, above.  Time at each work level was usually two or three minutes, except at the
27    maximal work level, which sometimes was as short as 15 sec. (with the physiologic data
28    extrapolated to per minute values).  A variety of progressive increment protocols were used on
29    both the cycle ergometer and the treadmill. However, each (except for Study 30) was designed
30    to obtain at least near steady-state physiologic response at progressively intensified work rates
31    ranging from light, or moderate, through very heavy, ending with voluntary exhaustion.
32
33    In the  electronic data base, the array of data for each subject is arranged horizontally in the
34    following order:
35    1.      study ID number (l=Study 1, 2=Study 2, etc.)
36    2.      subject ID number
37    3.      subject gender (0=male, l=female)
38    4.      subject age (years)
39    5.      special characteristics of the subject (e.g., 1= trained athlete, 2= trained non-athlete, 3=
40           normally active, 4= sedentary, and 5= obese)
41    6.      subj ect height (cm)
42    7.      subj ect body  mass (kg)
43    8.      subject lean body mass (kg)
44    9.      machine used (1= cycle ergometer, 2= treadmill)
45    10.     total test time (min)
      DRAFT - Do not cite or quote                 A-18                           October 31, 2006

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1
2
3
4
5
6
7
8
9
10
11
11. obs
12. for
a.
b.

c.
d.
e.

Table A-l

            observed VO2max (1/min, STPD) for the test
            for each step of the test for each subject, the following sequence was used:
                   cumulative test time at end of step
                   machine setting (cycle ergometer in Watts, treadmill in speed (m/min) and percent
                   grade)
                   VE (1/min BTPS) measured during the last minute of each step
                   VC>2 (1/min, STPD) measured during the last minute of each step
                   HR (b/min) measured during the last minute of each step

                Total Subjects for Each Study, Gender, and Exercise Ergometry Used.
12
13
14
15

Study
1
2
5
6
7
8
9
10
12
13
14
16
18
19
20
21.1
21.2
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Total
Total Subject:
148
42
60
12
4
6
10
8
10
8
32
10
25
15
39
16
22
17
13
37
13
21
40
11
211
20
10
40
10
6
12
28
956
Males
148
42
30
6
0
6
10
8
10
8
0
10
25
0
18
16
22
9
13
37
13
11
20
0
105
0
10
20
6
3
6
14
626
Females
0
0
30
6
4
0
0
0
0
0
32
0
0
15
21
0
0
8
0
0
0
10
20
11
106
20
0
20
4
3
6
14
330
Cycle Ergometer Tests
Males
0
0
0
0
0
6
10
8
10
8
0
10
25
0
18
16
0
9
13
37
13
0
20
0
0
0
10
20
6
3
6
14
262
Females
0
0
0
0
4
0
0
0
0
0
32
0
0
15
21
0
0
8
0
0
0
0
20
11
0
0
0
20
4
3
6
14
158
Treadmill Tests
Males
148
42
30
6
0
0
0
0
0
0
0
0
0
0
0
0
22
0
0
0
0
11
0
0
105
0
0
0
0
0
0
0
364
Females
0
0
30
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
106
20
0
0
0
0
0
0
172
Consistent units of measurement for all entries were used throughout the file. For machine
setting, two columns were needed for treadmill tests, one each for speed and percent grade, while
     DRAFT - Do not cite or quote
                                         A-19
October 31, 2006

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 1    only one (work rate in Watts) was required for Quinton electronically braked cycle ergometer
 2    tests. A Monark cycle ergometer was used in Studies 9 and 33-37. Calibration of the Monark
 3    device displayed on the ergometer itself only accounts for braking force produced by the
 4    flywheel friction strap, and does not include internal friction produced in the drive train.
 5    Therefore, work rate values displayed on the ergometer were converted to Watts and then
 6    increased by 9%  in order to obtain corrected values (E. Harman, Medicine and Science in Sports
 7    and Exercise 21(4):487, 1989).
 8
 9    Quality assurance of the basic data,  including that from handwritten records and computer print
10    outs, was initiated by my review of each subject's data. Where apparent spurious data appeared,
11    or notably aberrant subject responses were identified, they were eliminated from transfer to the
12    electronic data base.  I also noted any missing data for any subject, so that it was clear to the
13    graduate student  transferring the data which were valid and what data were missing. The
14    graduate student  transferring data to the electronic data base was thoroughly trained as to what
15    data were to be entered and the format that they were to be entered in.  After data were entered
16    for a study, the graduate student read the data appearing on the original data record for each
17    subject's protocol, while another graduate student verified that what was being said was what
18    appeared on the spreadsheet. Errors identified by this procedure proved to be relatively small in
19    number, non-systematic, and easily  correctable.  I have great confidence that the data  furnished
20    you are a valid representation of what appears in our original handwritten or computer print-out
21    records.
22
23    A list of subjects who participated in more than one study is given below in ascending Study
24    Number (and subject  number) for the first study they participated in, and then the other
25    study(ies), with their  subject number(s), that they participated in.
26
27    Study 1
28    Subject #2 also subject #2 in study 2.
29    Subj ect #6 al so subj ect # 10 in study 18.
30    Subject #25 also  subject #7 in study 2, and #3 in study 5.
31    Subj ect #29 al so  subj ect # 18 in study  18.
3 2    Subj ect #3 0 al so  subj ect #23 in study  18.
3 3    Subj ect #43 al so  subj ect #3 in study 18.
34    Subject #52 also  subject #2 in study 18.
35    Subject #54 also  subject #17 in study  18.
3 6    Subj ect #5 5 al so  subj ect #20 in study 2.
37    Subject #56 also  subject #19 in study 2, and #5 in study 19.
3 8    Subj ect #60 al so  subj ect # 13 in study 2.
3 9    Subj ect #61 al so  subj ect # 19 in study  18.
40    Subject #63 also  subject #18 in study 2, and #5 in study 8.
41    Subj ect #69 al so  subj ect # 16 in study  18.
42    Subj ect #8 8 al so  subj ect #21 in study  18.
43    Subject #89 also  subject #14 in study  18.
44    Subj ect #91 al so  subj ect #22 in study  18.
45    Subject #97 also  subject #11 in study  18.
      DRAFT - Do not cite or quote                 A-20                            October 31, 2006

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 2    Study 2
 3    Subject #17 also subject #6 in study 18.
 4    Subj ect #3 2 al so subj ect #3 0 in study 5.
 5    Subject #33 also subject #26 in study 5.
 6    Subject #34 also subject #1 in study 8.
 7    Subject #35 also subject #3 in study 8.
 8
 9    Study 5
10    Subject #18 also subject #1 in study 6.
11    Subject #19 also subject #3 in study 6.
12    Subject #21 also subject #6 in study 6, #1 in study 9, #1 in study 12, #2 in study 20, #17 in
13    study 21.2, and #23 in study #25.
14    Subj ect #27 al so subj ect #2 in study 9.
15    Subject #43 also subject #10 in study 6.
16    Subject #48 also subject #11 in study 6.
17
18    Study 9
19    Subj ect #9 al so subj ect # 15 in study 21.2.
20
21    Study 10
22    Subject #1 also subject #7 in study 13, #3 in study 20, and #34 in study 25.
23    Subject #2 also subject #4 in study 13 and #1 in study 20.
24    Subject #7 also subject #8 in study 13.
25
26    Study 12
27    Subj ect # 10 al so subj ect #5 in study 20.
28
29    Study 13
30    Subject #2 also subject #5 in study 16.
31
32    Study 20
33    Subject #7 also subject #16 in study 21.1 and #8 in study 25.
34
35    Study 21.1
36    Subject #3 also subject #3 in study 24 and #33 in study 25.
37
38    Study 21.2
39    Subject #18 also subject #18 in study 25.
40
41    Study 23
42    Subject #1 also subject #10 in study 28.
43    Subject #5 also subject #12 in study 24.
44
45    Study 24
46    Subject #13 also subject #21 in study 25.
      DRAFT - Do not cite or quote                  A-21                            October 31, 2006

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 2    Study 28
 3    Subject #12 also subject #3 in study 32.
 4    Subject #28 also subject #20 in study 31.
 5
 6    Study 31
 7    Subj ect # 10 al so subj ect #40 in study 3 3.
 8    Subject #15 also subject #3 in study 34.
 9
10    Study 32
11    Subject #2 also subject #12 in study 33.
12
13    Study 33
14    Subject #3 also subject #7 in study 34.
15    Subject #7 also subject #4 in study 35.
16    Subject #9 also subject #10 in study 34, and #5 in study 35.
17    Subj ect #3 5 al so subj ect #4 in study 3 4.
18
19    Study 34
20    Subject #1 also subject #2 in study 35.
21
22    Study 35
23    Subject #3 also subject #3 in study 36.
24
25    Study 36
26    Subj ect # 12 al so subj ect #26 in study 3 7.
27
28    I believe that this final report letter contains additional information beyond the electronic data
29    base that you wanted and clarifies the format that was used.  If you have questions, however,
30    please do not hesitate to give me a call  or drop me a note by FAX. I look forward to hearing from
31    you and working with you and Ted on developing a publishable paper or two.
32
33    Best regards,
34
35
36
37    William C. Adams
38    Professor
39
      DRAFT - Do not cite or quote                 A-22                           October 31, 2006

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 1    A-2. Second memo from Dr. Adams describing additional data from study #30
 2                                                                              8 October 2001
 O
 4    Dear Tom:
 5
 6    Pursuant to the EPA Order for Supplies and Services, No. 1D-5590-NATX, approved for the
 7    period, 1 August - 1  November 2001, I believe that I have now completed  all professional
 8    services stipulated. Specifically, it was requested that I provide certain "raw" data on a group of
 9    children and adolescents who were  part of the subject pool utilized in a California State Air
10    Resources Board sponsored  study,  entitled:  Measurement  of Breathing Rate  and Volume in
11    Routinely  Performed  Daily  Activities (Adams,  1993). The professional services stipulated
12    included: 1) providing a complete listing of all  variables that were obtained during the  study in
13    accordance with the attached Statement of Work; 2) the development of an electronic data base
14    of selected physiological information for  children and  adolescents from  the  aforementioned
15    study, again in accordance with the attached Statement of Work; and 3) the submittance of a
16    transcribed data file for the aforementioned study in ASC II format, together with a description
17    of data quality objectives that were established in accordance with the attached Statement of
18    Work.
19
20    The subject pool of interest included 132 individuals, half female and half male, including 12
21    young children, age 3.6-5.8 yrs., 80 children, age 6.0-12.9 yrs., and 40 adolescents, age 13.2-18.9
22    yrs. All  subjects were apparently healthy. In  all cases, subject identification, including age and
23    gender, as well as body weight, height, and activity habitus, were obtained. Body composition, as
24    assessed by gender/age specific skinfold formulae, were used to  calculate lean body mass. All
25    subjects completed  a laboratory treadmill walk (usually three different speeds, i.e., steps) and
26    jog (ranging form  1  to  3 different  speeds)  protocol.  The treadmill grade  was horizontal
27    throughout. Each subject  completed a laboratory resting protocol (40 of the children did only
28    sitting and standing, while the others also rested in a lying position). The 12 young children each
29    did two spontaneous play protocols of 20 minutes duration, while 40 children also  did two
30    spontaneous play protocols,  but of 30 minutes duration. The other 40 children did  a single
31    spontaneous play protocol of 35 minutes duration.  The 40 adolescent subjects were not  asked to
32    perform a  spontaneous play protocol. In addition,  each  subject  (or their parent/guardian)
33    completed an 11-item health history questionnaire.
34
35    Enclosed is a 3.75 ZIP disk which includes the  electronic data base containing data described in
36    general above. It is in an Excel (5.0a) spread sheet format produced on a Macintosh Performa
37    6214CD hard drive, as well as an ASCII format, blank delimited  file with headings. Consistent
38    units of measurement for all  entries  were used  throughout this  file. In the electronic data base,
39    the array of data for each subject  is  separated into five  distinct files:  1)  active (treadmill)
40    protocol; 2) resting  protocol;  3) spontaneous play  protocol;  4) health history responses to
41    selected questions; and 5)  predicted VO2max values from measured submaximal HR and VO2
42    values contained in File #1. Details of what items, variables, time periods, etc., and their order,
43    which are arranged horizontally in each file, is as we agreed  on via my FAX of 22 August 2001,
44    with minor modifications we agreed on by phone the  next day. The order for each file is given
45    below:
      DRAFT - Do not cite or quote                  A-23                           October 31, 2006

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 1
 2   ACTIVE (File #1)
 3      1. File ID number (#1)
 4      2.   subject ID number (same number for each subject as identified for Study #30 in 1998
 5        data base)
 6      3.   subject gender (0=male, l=female)
 7      4.   subject age (years)
 8      5.   special characteristics of the subject (viz., 1= trained athlete, 2= trained non-athlete, 3=
 9        normally active, 4= sedentary, and 5= obese)
10      6.   subject height (cm)
11      7.   subject body mass (kg)
12      8.   subject lean body mass (kg)
13      9.   machine used  (1= cycle ergometer, 2= treadmill) - NOTE: TREADMILL  ONLY
14          USED IN THIS STUDY.
15      10.  total test time (min)
16      11.  observed VO2max (1/min,  STPD) for the test - NOTE: VO2max NOT MEASURED IN
17          THIS STUDY.
18      12.  for each step of the test for each subject, the following sequence was used:
19          a.      cumulative test time at end of step
20          b.      machine setting (two columns: one for treadmill in speed (m/min) and  one for
21            percent grade). The latter was always zero.
22          c.      VE (1/min BTPS) measured during the last two minutes of each step
23          d.      VO2 (1/min, STPD) measured during the last two minutes of each step
24          e.      HR (b/min) measured during the last two minutes of each step
25
26
27   RESTING (File #2)
28      1. File ID number (#2)
29      2.   subject ID number (same number for each subject as identified for Study #30 in 1998
30        database)
31      3.   subject's body surface area in square meters; from measured body height and body mass,
32        using the standard DuBois and DuBois formula
33      4. for each resting posture for each subject, the following sequence was used:
34        a. VE (1/min BTPS) measured during the 5 minutes of each test
35        b.       VO2 (1/min, STPD) measured for the 5 minute of the test
36        c. average of five HR (b/min) measurements taken each minute of the 5 minute test
37        d.       average  of five breathing  frequency (breaths/min) measurements  taken each
38           minute of the 5 minute test
39
40
41   SPONTANEOUS PLAY (File #3)
42      1. File ID number (#3)
43      2.   subject ID number (same number for each subject as identified for Study #30 in 1998
44        data base)
45      3. for each 5 minutes data collection period for each subject, the following sequence was used:
46        a. VE (1/min BTPS) measured during the 5 minutes
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 1         b.       average of five HR (b/min) measurements taken each minute of the 5 minute
 2           period
 3         c. average of five breathing frequency (breaths/min) measurements taken each minute of the
 4           5 minute period. NOTE: Because these data were obtained on a tape cassette that rather
 5           routinely malfunctioned, valid data were obtained in only -75% of the subject 5-minute
 6           time periods
 7         d.       activity intensity rating by the technician. NOTE:  There was some confusion
 8           among the technicians as to what they were to indicate  in the comments column; e.g.,
 9           any problems with the equipment, what the subject was playing, and/or an estimation of
10           the intensity of activity. The occasional noted problems with equipment were dealt with
11           as described on pp. 38-39 of the CARS Final Report (Adams,  1993). While the play
12           activity was occasionally recorded, it was not systematic (i.e., estimated at between 15-
13           20%). Intensity of play was recorded -55% of the time. The intensity scale devised and
14           used for the first time in the enclosed data base was:  1 = standing, or just "hanging out";
15           2 = moderate intensity, i.e., walking, swinging an implement, kicking or throwing a ball,
16           etc.;  and 3 = vigorous, or very active. Ratings of  1.5  and 2.5 were used to indicate
17           activity intensity somewhere in-between  the  absolute number categories.  The mean
18           value for each 5-minute period was near 2.0, moderate, which closely agrees with the
19           observed VE estimated intensity discussed on p. 110 of the CARB Final Report.
20
21
22   HEALTH HISTORY (File #4)
23      1. File ID number (#4)
24      2.   subject ID number (same number for each subject as identified for Study #30 in 1998
25         data base)
26      3. Re question #1, how often do you exercise? Numerals in column 3 correspond to which of
27         5 choices were circled.
28      4. Re question #2, describe the intensity of your exercise. Numerals in column 4 correspond to
29         which of 5 choices were circled. In  six cases, two adjoining numbers (e.g., 2 and 3) were
30         circled, and the mean entered (in this case, 2.5).
31      5. Re question #3, what types of exercise do you engage in? Numerals in column 5 correspond
32         to which of 9 choices were circled. No one circled No. 1 (none). Most subjects circled more
33         than  one choice, which is reflected by the numerals 2 through 8 in column 5  for each
34         subject. If the subject circled 9 (other), the following numerals were entered in column  5 to
35         indicate which other activities they engaged in (10, play; 11,  dance; 12, horseback riding;
36         13, gymnastics; 14, rollerblading; 15, karate;  16, ice skating; 17, aerobics (high impact);
37         18, aerobics (machines at fitness club); 19, hockey; and 20, boxing
38      6.  Re question #7, any medical complaints? 1 = yes; 2 = no. If yes,  1 was not entered, but
39         what "caused" the yes answer was  entered in column  6 as follows:  3, asthma; 4, ear, 5,
40         scoliosis; 6, cerebral palsy; 7, allergies
41      7. Re question #11, do you have, or have you ever had, any  of the following? Numerals from
42         1 through 12 in column 7 indicate that only one choice was circled. If more than one choice
43         was indicated, higher numbers were used as follows: 13,  choices 7, 9, and  10; 14, choices
44         9, 10, and 11; and 15, choices 10 and 11.
      DRAFT - Do not cite or quote                 A-25                           October 31, 2006

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 1
 2   PREDICTION OF VO2MAX FROM SUBMAXIMAL MEASURED HR AND VO2 VALUES
 3   OBTAINED FROM FILE #1 (File #5)
 4      1. File ID number (#5)
 5      2.   subject ID number (same number for each subject as identified for Study #30 in  1998
 6        data base)
 7      3.   subject body mass (kg)
 8      4.   subject age (years)
 9      5. estimated HRmax
10      6.   VO2max Y intercept
11      7.   VO2max b exponent
12      8.   predicted VO2max (1/min)
13      9.   predicted VO2max (1/min/kgBM)
14
15
16   The rationale for predicting percent VO2max at any given percent HRmax is developed in brief on
17   p. 403 of McArdle et al.'s exercise physiology text (4th ed., 1996) and in more detail in Astrand
18   and Rodahl's Textbook of Work Physiology (2nd ed., 1977), pp. 344-348. Using data from both
19   sources, I calculated very closely similar submaximal % VO2max values as a function of %
20   HRmax values (i.e., never more than 2%, and  usually the same or only 1% difference). To  get a
21   clear  visual  perspective  overview of the  estimated  VO2max  prediction from measured
22   submaximal HR and VO2, see Fig. 10-4 (line A), p. 346, in Astrand and Rodahl.  To use this
23   procedure, it  is first necessary to obtain a valid HRmax value which decreases an average of 1
24   b/min  each year of age  from 10  years on. The best data I'm aware of on young children and
25   adolescents that had HR and VO2 measured in both submaximal and maximal treadmill exercise
26   is that of Astrand (Experimental  Studies of Physical Working Capacity in Relation to Sex and
27   Age, 1952, Ejnar Munksgaard, Copenhagen).  Between the ages of 4 and 10 years, there was no
28   significant relationship between HRmax and age for either sex, averaging 205 b/min. Thereafter,
29   up to 33 years, there was the now widely accepted decrease of 1 b/min per year of age for both
30   males and females, with  10 year-old boys and girls averaging 210 b/min. Accordingly, in File #5,
31   the estimated HRmax in  column 5 is 205 b/min for subjects less than 10 years of age and 220
32   minus age in years for subjects 10 to 18.9 years of age.  The y intercept and b exponent values for
33   predicting VO2max were obtained by  calculating, via simple  regression analyses, individual
34   subject values from measured submaximal HR and VO2 values taken from  File #1. Predicted
35   VO2max (in 1/min), given in column  8 for  each subject, was obtained  by  multiplying the b
36   exponent value (column 7) times the estimated HRmax value (column 5) for each subject, and
37   then subtracting their y intercept value (column 6). Each  subject's VO2max value in ml/min/kg
38   (column 9) was calculated by dividing the column 8 value by body mass (column 3).
39
40   Accuracy of the data in the enclosed electronic files began with data management and quality
41   control procedures employed in the original CARB study, and which are  described in detail on
42   pages  38-39  of the Final  Report (Adams,  1993). In  summary,  very  few problems  were
43   encountered  in  the  acquisition  of active  and  resting  protocol  data.   Accuracy assurance
44   procedures for the transfer of the data from handwritten records  to master data  sheets, and
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 1    subsequently  to  electronic  spreadsheet  data bases, is  described  in the aforementioned Final
 2    Report. The retrospective quality control program for  all field protocol data bases, including
 3    spontaneous play, revealed  that 5 children needed to repeat a protocol. Elimination  of aberrant
 4    bits of data obtained during  the play protocols (due to the result of momentary saliva blockage in
 5    the Harvard respirometer, Heart Watch heart rate artifacts, etc.), which rarely included more than
 6    one or two 1-min "glitches" in any one protocol, were part of the aforementioned quality control
 7    program.  When this was done, the remaining data for the 5-min period was used to calculate an
 8    average for the full time period (i.e., 20, 30, or 35 min). A significant number of play protocols
 9    (-35%) were completed with incomplete,  or no, fB  data. This occurred because there was no
10    way to determine whether  the expiration electronic  pulse from the Harvard respirometer was
11    being  recorded on the tape cassette until after the protocol was completed. However,  since these
12    were random  occurrences, and fB was not of such prime concern as HR and VE, these protocols
13    were not repeated.
14
15    Per the Statement of Work for this project, to ensure that an accurate translation of the data was
16    accomplished, all data entries were checked by me. The data quality objectives described in
17    detail  below were developed before data were translated to the enclosed electronic data files.
18    These objectives were applied against  100% of the  entries  transcribed, including file  column
19    headings, for the first 500 datum points. In each of  the five files, this objective was met, and
20    double-checking procedures described  in detail  below were employed to achieve the highest
21    accuracy  possible. I have great confidence  that the data furnished you are a valid representation
22    of what appears in our original CARB study computer data files and the original handwritten
23    records used to transfer data to electronic files for the first time in this project.
24
25    The specific procedures used for each of the five files  differed somewhat and are described in
26    detail  here. For the  active file, a copy  of the 1998 Excel file was made and all data not from
27    Study  #30 for the 132 subjects of interest were deleted.  A search of the original  1998 Excel file
28    was done, and a print out of these data obtained (i.e., pp. 14-18,  36-40, 58-62, and  80-84). All
29    entries in the  2001 file were double-checked against the 1998 print-out for the first 12 subjects,
30    and for subjects 13,  45, 46,  58, 59, 66,  107, 131, 132, 150, 151, and 152. Finally, the values on
31    the last page of data for all subjects was verified. In no case was any difference seen.
32
33    Formulation of the  file for the  resting protocol (#2) was  initiated by transferring data from
34    summary CARB study electronic data files  (in a similar, but not exact format for each subject) to
35    the  present electronic data  file.  Individual data  values  for all variables in each posture were
36    double-checked against a print-out of the 1998  data  for the first 12 subjects,  and for every  10
37    subjects thereafter.  In no  case  was any  difference seen.  As a  further  cross-check,  I  then
38    calculated entire group (N = 132) means for each posture in the present file, and compared these
39    values to weighted tabular  mean values in the original  Final Report,  and found no difference
40    greater than 0.7%, i.e., within the range of rounding  error.
41
42    Formulation of the  file for the  play protocol (#3)  entailed entering VE, HR, and breathing
43    frequency  data  from handwritten  data summary  sheets.  All  values were  double-checked
44    immediately after entry for each time period (4 to 7) for each subject (N=92). In addition, I then
45    calculated an entire group mean for each time period, and compared these  values to weighted
46    tabular mean values in the original Final Report.  Again, I found very close agreement. Intensity
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 1    values  available for  each time period for  each subject were entered  from handwritten data
 2    acquisition sheets into the electronic data base (File #3). As I entered them, I double-checked
 3    these values against  that read from the data sheet, and that the adjacent  HR  and breathing
 4    frequency data were for the correct subject and time period.
 5
 6    Procedures used for establishing the health history data base from handwritten responses to a
 7    questionnaire, together with how data was entered in each column, are described above. The data
 8    were typed directly into the electronic  file (#4) for each subject from the handwritten responses
 9    on the questionnaire. The numerical values entered were double-checked for each question (#s 1,
10    2, 3, 7, and 11) for each subject immediately after each subject's data entry.
11
12    Procedures used for predicting each subject's VO2max from submaximal HR and VO2 data (the
13    latter obtained from File #1), together with how  data was entered in each column, are described
14    above.  The  data for  columns 1-4  were transferred directly from File #1  and  a mean, with
15    standard deviation, calculated for each column which matched those previously calculated in File
16    #1.  The  individual submaximal FIR  and  VO2 values entered  into  a  STATVIEW  simple
17    regression analysis were  each double-checked before each individual  analysis was done. The
18    resultant  y   intercept and b  exponent values  were written on  a  printout of  the  subjects'
19    submaximal  HR and VO2 values, with each set double-checked as  they were entered in the File
20    #5 Excel  spread sheet. In addition to recalculating all values for the first  10 subjects, any subject
21    who had a predicted VO2max value < 33 or > 66  ml/min/kg was double-checked. In no case was
22    an error found. Please note that 18 subjects only had 3 sets of submaximal values (i.e., all at three
23    walking speeds). In all but 4 cases (subjects # 3, 29, 108, and 142), the spread of observed HR
24    and VO2 values was sufficient (in my estimation) to obtain valid predicted VO2max values. Thus,
25    I recommend deleting the predicted VO2max values  for these four subjects.  If this is done, the
26    mean VO2max for the group is 47.63 ml/min/kg,  a value that I consider highly likely in a group
27    of healthy  children  and  adolescents  of probable  slightly  greater fitness  than  the  average
28    population.
29
30    I believe  that this final report letter contains additional  information beyond  the electronic data
31    base  that you wanted  and clarifies the format and procedure that were used. If you have
32    questions, however, please do not hesitate to give me a call or drop me a note by FAX. I look
33    forward to hearing from you and working again with you in the future.
34
35    Best regards,
36
37
38
39    William C. Adams, Ph.D.
40
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 i    Appendix B
 2    B-1. Data Set Manipulation
 O
 4    The data file needed significant manipulation to facilitate statistical analysis.  Principally, the
 5    row and column structure of the file had to be altered to put them into proper alignment. Row
 6    headings were scattered within rows of the data set due to two different test protocols (cycle and
 7    treadmill) that required different parameter measurements. In addition, within-person
 8    measurements for the same parameter (e.g., total ventilation or VE) over multiple stages of the
 9    test (VEI, VE2, VES, etc.) were carried across the dataset in multiple columns.  It was desired to
10    have the multiple measurements as a single vector for a given parameter. Therefore, the
11    following changes were made to the data set:
12
13       •   11 separate data sets were created in Excel by the 11 heading groupings within the raw
14           data set (more than one study could be combined under previous headers)
15       •   A master list of parameters was created such that the 11 data sets could be combined
16           under one heading having 102 unique designations. Specific changes made were:
17              o  Parameter heading for step 14 was removed since there were no parameters
18                 supplied for this step (e.g. VEM, VC>2 14, etc.).
19              o  Common data were receded into vectors having a common descriptor.  Originally
20                 identical names were not used to describe the same parameter at different steps
21                 (e-g-, the speed parameter for the cycle ergonometer used "spd" for steps under 10
22                 (e-g-, spdl) and "sp" for steps >9 (e.g., sp!3).  It was assumed that "sp"="spd",
23                 and for grade, "gr"="grd").
24              o  Removed inconsistent coding.  Spdl2 on one instance was mislabeled as Spdl 1 in
25                 Study #1. This was corrected.
26              o  Cleaned up variable name conventions. Both "Age" and "LBM" parameters
27                 contained a space after the label characters. This space was removed.
28       •   These 11 Excel data sets were combined in SAS to create a SAS data set
29           (adams.sas7dbat).
30       •   In SAS, multiple measurements for a parameter (e.g.,  VEI, VE2, VES, etc.) were combined
31           under a single vector (e.g.,  VE) to create a second SAS data file: adams2.sas7dbat. A new
32           variable was created to account for the multiple measurements for a given parameter
33           termed 'step' (e.g., step=l is for where VE  and VC>2 were first recorded; step=2 for the
34           second measurement of VE, etc.).
35       •   This data set contained a total of 19 variables:
36              o  Step    Step or stage measurement taken within an individual
37              o  Age    Subjects age in years (yrs)
38              o  BM   Body mass (kg)
39              o  Char  A characteristic of an individual acting as a surrogate for fitness level
40                    •   1= Trained athlete
41                    •  2= Trained non-athlete
42                    •  3= Active individual
43                    •  4= Sedentary individual
44                    •  5= Obese
45              o  ET    Cumulative test time at the end of each step (min)
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 1             o   Gend  Gender: $ =-\; ? = 1
 2             o   Grd   Grade on treadmill (in percent)
 3             o   HR   Heart rate (bpm or beats min"1)
 4             o   HT   Height (cm)
 5             o   LBM  Lean body mass (kg)
 6             o   Mach  Machine used: Cycle Ergometer = 1; Treadmill = 2
 7             o   VO2  VO2 (L min'1 BTPS)
 8             o   Spd   Speed of the Subject on Treadmill (m min"1)
 9             o   stud   Study number
10             o   Subj   Subject number
11             o   TT    Total test time (min)
12             o   VE   VE^min'1)
13             o   VO2m Observed or estimated VC>2 maximum for the test (L min"1 STPD)
14             o   Wk   Watts (power setting for the cycle ergometer)
15
16   Maximum VO2 (VO2m) was reported for all of the studies but one.  Study 30 contained estimates
17   of VO2m for some of the data (individuals < 18.9 years old) however the study also contained 79
18   individuals where VC^m was neither measured nor estimated.  The method reported by Adams
19   (see Appendix B) to estimate VC^m for the younger individuals was duplicated here for the
20   missing data. Briefly, maximum heart rate (HRm) was estimated using an equation provided in
21   Nieman (1999) (i.e., HRm=220-age). A simple linear regression analysis followed for each
22   individual (of the form y=mx+b) where HR measurements were regressed on concomitant VC>2.
23   The slope (m) and intercept (b) estimates were then used to approximate VC^m from the HRm
24   estimate and added to the final data set.
25
26   B-2. Quality Assurance
27
28   Data values—mostly VE, VC>2, and BM, since these were the principal analytical parameters—
29   were spot-checked by hand from the original Excel spreadsheet to both newly created SAS data
30   sets. No errors were found in either of the SAS data sets. The number of individuals in the
31   newly created data sets was each 956, equivalent to that reported by Dr. Adams upon transfer of
32   the data set (in Appendix A) and the total number  of measurements of VE and VO2 for
33   individuals >18 years old was equivalent (n=5,681) to that reported by Johnson (2002).
34
35   A simple plot of the body mass-normalized total ventilation versus the body mass-normalized
36   oxygen consumption revealed that two individuals (i.e., stud=l subj=25 step=8; stud=31 subj=9
37   step=8) had exceptionally large oxygen consumption levels during one sample collection.  These
38   data were considered to be questionable, and upon inspection seemed to be the result of a
39   misplaced decimal point (30.8 and 28.5 should be  3.08 and 2.85, respectively).
40
41   Data were replaced in the SAS data sets to reflect this assumption rather than delete the
42   datapoints altogether, even though there is no direct evidence that the decimal was misplaced.
43   Due to the number of samples for a given parameter in the data set (>5,000), the impact of this
44   change on the analyses presented here is negligible.  The new dataset was saved as
45   'adams3.sas7dbaf (from data set 'adams.sas7dbat') and 'adams4.sas7dbat'(from data set
46   'adams2.sas7dbat').
47
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1
2
      B-3. Data Transformation
 4
 5
 6
 7
 8
 9
10
    Figure B-l shows the relationship between total ventilation and oxygen consumption rates. In
    general, the relationship is non-linear and exhibits greater variability among individuals at higher
    oxygen consumption rates (i.e., the data are heteroscadistic),  similar to findings of other
    researchers (e.g., Baba et al. 2002). Normalization of VE and VO2 by body mass is commonly
    done to account for a portion of the variability inherent between the two physiological measures
    (Figure B-2).
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
     Figure B-1.  Relationship between total ventilation
     rate (VE) and oxygen consumption rate (VO2)
     during exercise.
                              Figure B-2. Relationship between body
                              mass normalized total ventilation rate
                              (VE/BM) to oxygen consumption rate
                              (VO2/BM) during exercise.
           Due to the non-linear relationship between VE and VO2, a number of the parameters were
    transformed by taking the natural logarithm (Ln) of the variable.  These include:
natural log of VE
natural log of VO2
natural log of body mass normalized VE
natural log of body mass normalized VO2
ventilatory equivalent or VE / VO2
natural log of age
           Ln(VO2)
           Ln(VE/BM)
           Ln(VO2/BM)
           VQ
           Ln(age)
    A logarithmic transformation directly applied to the parameters allows for a significant reduction
    in the dispersion (Figure B-3 compared to Figure B-l), and when used in combination with body
    mass normalization, yields a mostly linear relationship having a more balanced dispersion across
    the range of oxygen consumption rates (Figure B-4), that is, it better demonstrates a degree of
    homoscadisticity. It should be noted that this linearity and balanced dispersion was also
    demonstrated among different age groups investigated in the body of the report.
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4
5
6
7
Figure B-3. Relationship between the natural
logarithm of total ventilation rate Ln(VE) and
oxygen consumption rate Ln(VO2) during
exercise.
Figure B-4. Relationship between the
natural logarithm of body mass
normalized total ventilation rate
Ln(VE/BM) and oxygen consumption rate
Ln(VO2/BM) during exercise.
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 i    Appendix C
 2    Selected ventilation algorithms were evaluated using the APEX model by adjusting the
 3    ventilation.txt file (see US EPA, 2005).  20,000 persons were simulated for one day using the
 4    algorithms described in the main body text and parameters in Tables 2 and 3. Model output was
 5    nearly 800,000 event-based ventilation rates, typically around 40 events per individual simulated.
 6    Figure C-l presents the mid to upper range percentiles based on these 800,000 events to
 7    encompass the possible maximum ventilation rates generated by each simulation. Algorithms
 8    evaluated included the following:
 9    MLR: multiple linear regression algorithm using equation 3 and parameters from Table 3.
10    MER: mixed-effects regression model using equation 4 and parameters from Table 3.
11    MLR+MER: regression coefficients from MLR coupled with variance components estimated
12    from the MER model.
13    Johnson: Johnson (2002) regression model using equation 2 and parameters from Table 2.
14    SMER: a simplified mixed effects regression model using equation 2 and parameters derived for
15    all age groups from the Adams data set as follows:
16

Females
Males
bo
4.1017
3.9332
Ln(VO,/BM)
bi
1.1904
1.1638
Residuals
eb
0.1408
0.1445
ew
0.1186
0.1277
17
18    Results are very similar for each of the algorithms, not surprisingly since they were for the most
19    part derived from the same data set.  At any given percentile, ventilation rates increase rapidly
20    with age for those less than 20 years old, stabilize from ages 20 to about 60, then gradually
21    decline with further increases in age. Increased variability at ages greater than 75 is also evident,
22    a function of both the limited amount of data available for the development of the algorithm and
23    the limited number of persons simulated at these ages from the population of 20,000.  At each of
24    the percentiles, the Johnson (2002) algorithm generated lower ventilation  estimates for persons
25    under age 5, a function of the method of the algorithm derivation, whereas the intercept was
26    modified based on published literature  VE/VO2 relationships while the residuals were assumed
27    the same as those greater than 18 years of age.  When considering a simple mixed effects
28    regression (SMER)  algorithm, flattening out of the percentiles occurs across the ages, mostly due
29    to elevation of ventilation rates of young children that resulted from ignoring age as an
30    independent variable in development of the regression parameters.
31
32    Figure C-2 presents the full range of percentiles for the event-based ventilation  rates generated
33    from the APEX model  using the mixed effects regression (MER) model and the Johnson (2002)
34    model.  Results are very similar, however at young ages (<5 years old), the Johnson (2002)
35    model estimates lower ventilation rates at both the lower and upper percentiles.  The percent
36    difference between the two model estimates is  large, ranging from about 40-120% lower (Figure
37    C-3). The lower percentiles (min, pi, p5) for all ages >5 are moderately different, the Johnson
38    (2002) ventilation estimates are less than the MER by about 20-40% for ages 10-45, then 10-
39    20% greater than the MER estimates for ages above 45.  The MER algorithm estimates higher
40    ventilation rates for persons above age 60 by about 20% considering the upper percentiles (p95,
41    p99, max), with greater differences at age 90 and older (20-60%).
      DRAFT - Do not cite or quote                 A-33                           October 31, 2006

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Figure C-1. Comparison of selected percentiles of estimated event-based ventilation rates from 20,000 person APEX model simulation
using different ventilation algorithms.
                   max VE from events of 20,000 person APEX simulation
                                                                                          p99 VE from events of 20,000 person APEX simulation
 I  80
    20<^
              X.  DL
                     +
           X JyHf D X ^ .- •
     0     10    20     30    40     50     60    70     80    90
                               age (yeare)


                    p95 VE from events of 20,000 person APEX simulation
                                                                                          p50 VE from events of 20,000 person APEX simulation
                           40     50    60

                              age (years)
                                            70     80    90     1 00
                                                                                                                             90    100
DRAFT - Do not cite or quote
A-34
October 31,  2006

-------
Figure C-2.  Comparison of estimated event-based ventilation rate percentiles from 20,000 person APEX model simulation using mixed
effects regression (MER-left) and Johnson (2002) (right) ventilation algorithms.
          Mixed Model Estimates of VE from for 796,714 events from 20,000 person APEX simulation
                                   Lower Percentiles
                                           Johnson Model Estimates of VE from for 799,355 events from 20,000 person APEX simulation
                                                                     Lower Percentiles
          Mixed Model Estimates of VE from for 796,714 events from 20,000 person APEX simulation
                                   Upper Percentiles
                            *    *
                                    "  "
         -Ŧ.
*  **   *
   X  *   *
             ^+++AA^v^v /^+^++++^^^^
                                                                -H- +  x
        *• *^    __,_.               _14 ... ^^-.^^-.i „„.„ -.1,,              5<
             ''              '                              '
                                                             ^  ^S<   "'X
       ^^P^S^^S
            10     20     30      40     50     60     70     80      90     100
                                   age (years)
                                           Johnson Model Estimates of VE from for 799,355 events from 20,000 person APEX simulation
                                                                     Upper Percentiles

                                                                                        0     10
                                                    20     30     40      50     60     70     80     90
                                                                     age (years)
DRAFT - Do not cite or quote
                           A-35
October 31, 2006

-------
Figure C-3. Percent difference of estimated event-based ventilation rate percentiles from 20,000 person APEX model simulation using

mixed effects regression (MER-left) and Johnson (2002) (right) ventilation algorithms.
          80
      o
      o
      CM
      m
      o
      in
      c
      HI
      c
      O
      in
      c
      s
      i
 60




 40 --




 20




  0




 -20




 -40




 -60




 -80




-100




-120




-140
& o



• max
• p99
Ap95
Op50
Ap5
Dpi
omin




                     10
                              20
                                        30
                                                 40
                                                          50


                                                       Age (years)
                                                                   60
                                                                             70
                                                                                      80
                                                                                               90
                                                                                                        100
DRAFT - Do not cite or quote
                                                     A-36
October 31, 2006

-------
                        APPENDIX B:
  STATISTICAL DISTRIBUTIONS ASSIGNED TO ACTIVITY CODES
            FOR USE IN SIMULATING METS VALUES
DRAFT - Do not cite or quote                                 October 31, 2006

-------
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
            Appendix B: Statistical Distributions Assigned To Activity Codes
                          For Use In Simulating METS Values

       Table B-l documents the activity ID codes included in the CHAD, along with the
statistical distributions underlying the METS values that CHAD has assigned to each code.
These distributions were documented in Appendix 1 of the CHAD User's Guide (USEPA, 2002).

       The last two columns of Table B-l indicate when limits were placed on the METS values
generated by the specified distribution.  For a given activity ID code, the CHAD randomly
generates a METS value from the specified distribution.  If "Truncate Left Tail?" equals "Y",
then any METS value falling below the distribution's specified minimum was set to equal the
minimum.  Likewise, if "Truncate Right Tail?" equals "Y", then any METS value falling above
the distribution's specified maximum was set to equal the maximum. Truncation of the left and
right tails occurred with the normal and lognormal distributions, while truncation of the right tail
only occurred with the exponential distribution. In such  situations, more METS observations
tend to occur at the minimum and/or maximum values. Note that truncation did not affect the
initial random generation of METS values (i.e., randomization did not occur on truncated
distributions).

       Activity ID codes followed by "*" in Table B-l were encountered within the NHAPS
data set.

Table B-l.    METS Distributions Assigned to Activity ID Codes Within CHAD
Activity Description
Work, general
Work, general
Work, general
Work, general
Work, general
Work, general
Work, general
Work, general
Work, general
Work, general
Work, general
Work, general
breaks
General household
activities
'repare food
'repare and clean-up
food
ndoor chores
Activity
ID Code
10000
10000
10000
10000
10000
10000
10000
10000
10000
10000
10000
10000
10300*
11000
11100*
11110
11200
Age(a)

















Occu-
pation0^
ADMIN
ADMSUP
FARM
HSHLD
MACH
PREC
PROF
PROTECT
SALE
SERV
TECH
TRANS





Distribu-
tion Type
LogNormal
LogNormal
LogNormal
LogNormal
Uniform
Triangle
Triangle
Triangle
Triangle
Triangle
Triangle
LogNormal
Uniform
Triangle
LogNormal
Exponential
Exponential
Mean
1.7
1.7
7.5
3.6
5.3
3.3
2.9
2.9
2.9
5.2
3.3
3.3
1.8
4.7
2.6
2.8
3.4
Med-
ian
1.7
1.7
7.0
3.5
5.3
3.3
2.7
2.7
2.7
5.3
3.3
3.0
1.8
4.6
2.5
2.5
3.0
Std.
Dev.
0.3
0.3
3.0
0.8
0.7
0.4
1.0
1.0
1.0
1.4
0.4
1.5
0.4
1.3
0.5
0.9
1.4
Min-
imum
1.4
1.4
3.6
2.5
4.0
2.5
.2
.2
.2
.6
2.5
.3
.0
.5
2.0
1.9
2.0
Max-
imum
2.7
2.7
17.0
6.0
6.5
4.5
5.6
5.6
5.6
8.4
4.5
8.4
2.5
8.0
4.0
4.0
5.0
Trun
-cate
Left
Tail?
Y
Y
Y
Y

Y





Y


Y


Trun-
cate
Right
Tail?
Y
Y
Y
Y

Y





Y


Y
Y
Y
DRAFT - Do not cite or quote
                                               B-1
October 31,2006

-------
                                   Table B-1.  (cont.)
Activity Description
Clean-up food
Clean house
Outdoor chores
Clean outdoors
Care of clothes
Wash clothes
3uild a fire
lepair, general
lepair of boat
'aint home / room
lepair / maintain car
-lome repairs
Other repairs
Care of plants
Care for pets/animals
Other household
Child care, general
Care of baby
Care of child
Help / teach
Talk /read
Play indoors
Play outdoors
Medical care -child
Other child care
Obtain goods and
services, general
Dry clean
Shop / run errands
Shop for food
Shop for clothes or
lousehold goods
Run errands
Obtain personal care
service
Obtain medical service
Obtain govern't /
inancial services
Obtain car services
Other repairs
Other services
Personal needs and
care, general
Activity
ID Code
11210*
11220*
11300*
11310
11400*
11410
11500
11600
11610
11620
11630*
11640
11650*
11700*
11800*
11900*
12000
12100*
12200*
12300*
12400*
12500*
12600*
12700*
12800*
13000
13100*
13200
13210*
13220*
13230*
13300*
13400*
13500*
13600*
13700*
13800*
14000
Age(a)






































Occu-
pation0^






































Distribu-
tion Type
Uniform
Exponential
Normal
Exponential
Exponential
Point Est.
Point Est.
Normal
Point Est.
Exponential
Triangle
Exponential
Uniform
Uniform
Uniform
Exponential
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Triangle
Uniform
Triangle
Triangle
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Mean
2.5
4.1
5.0
5.3
2.2
2.0
2.0
4.5
4.5
4.9
3.5
4.7
4.5
3.5
3.3
6.6
3.1
3.3
3.3
2.8
2.8
2.8
4.5
3.2
3.0
3.8
3.3
3.7
3.9
3.4
3.5
3.5
3.5
3.5
3.5
3.5
3.5
2.0
Med-
ian
2.5
3.5
5.0
4.5
2.0
2.0
2.0
4.5
4.5
4.5
3.4
4.5
4.5
3.5
3.3
5.5
3.0
3.3
3.3
2.8
2.8
2.8
4.5
3.2
3.0
3.7
3.3
3.6
3.8
3.4
3.5
3.5
3.5
3.5
3.5
3.5
3.5
2.0
Std.
Dev.
0.1
1.9
1.0
2.7
0.7


1.5

1.4
0.4
0.7
1.4
0.9
0.1
3.6
0.7
0.
0.
0.
0.
0.
0.3
0.1
0.3
0.8
0.4
0.8
0.8
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
Min-
imum
2.3
2.2
2.0
2.6
1.5
2.0
2.0
2.0
4.5
3.5
3.0
4.0
2.0
2.0
3.0
3.0
2.5
3.0
3.0
2.5
2.5
2.5
4.0
3.0
2.5
2.0
2.5
2.0
2.2
2.3
2.5
2.5
2.5
2.5
2.5
2.5
2.5
1.0
Max-
imum
2.7
5.0
7.0
6.0
4.0
2.0
2.0
8.0
4.5
6.0
4.5
6.0
7.0
5.0
3.5
9.0
5.0
3.5
3.5
3.0
3.0
3.0
5.0
3.3
3.5
6.0
4.0
6.0
6.0
4.5
4.5
4.5
4.5
4.5
4.5
4.5
4.5
3.0
Trun
-cate
Left
Tail?


Y




Y








Y





















Trun-
cate
Right
Tail?

Y
Y
Y
Y


Y

Y

Y



Y
Y





















DRAFT - Do not cite or quote
B-2
October 31,2006

-------
                                   Table B-1.  (cont.)
Activity Description
Shower, bathe, pers.
lygiene
Shower, bathe
'ersonal hygiene
Medical care
Help and care
Eat
Sleep or nap
dress, groom
Other personal needs
General educ. and pro.
training
Attend full-time
school
Attend day-care
Attend K-12
Attend college or trade
school
Adult education and
special training
Attend other classes
)o homework
Jse library
Other education
General entertainment
/ social activities
Attend sports events
'articipate in social,
political, or religious
activities
Practice religion
Watch movie
Attend theater
Visit museums
Visit
Attend a party
Go to bar / lounge
Other entertainment /
social events
Leisure, general
xisure, general
xisure, general
Sports and active
eisure
Activity
ID Code
14100
14110*
14120*
14200*
14300*
14400*
14500*
14600*
14700*
15000
15100*
15110
15120
15130
15140
15200*
15300*
15400*
15500*
16000
16100*
16200
16210*
16300*
16400*
16500*
16600*
16700*
16800*
16900*
17000
17000
17000
17100
Age(a)






























20
30
40
20
Occu-
pation0^


































Distribu-
tion Type
Normal
Uniform
Uniform
Uniform
LogNormal
Uniform
LogNormal
Point Est.
Triangle
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Point Est.
Uniform
Uniform
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
LogNormal
LogNormal
Uniform
LogNormal
Normal
Normal
LogNormal
Mean
2.0
3.0
1.8
1.8
3.1
1.8
0.9
2.5
2.0
1.9
2.1
2.3
2.1
2.0
1.8
2.2
1.8
2.3
2.8
2.2
2.7
1.7
1.7
1.3
1.7
2.5
1.5
3.3
3.3
3.8
5.7
5.0
4.5
5.7
Med-
ian
2.0
3.0
1.8
1.8
3.0
1.8
0.9
2.5
2.0
1.8
2.1
2.3
2.1
2.0
1.8
2.2
1.8
2.3
2.8
2.0
2.7
1.7
1.7
1.3
1.7
2.5
1.5
3.0
3.0
3.8
5.0
5.0
4.5
5.0
Std.
Dev.
0.3
0.6
0.4
0.4
0.7
0.1
0.1

0.4
0.7
0.4
0.4
0.4
0.3
0.2
0.5

0.4
0.7
1.1
0.8
0.2
0.2
0.2
0.4
0.3
0.3
1.4
1.4
1.3
3.0
2.0
1.4
3.0
Min-
imum
1.0
2.0
1.0
1.0
2.5
1.5
0.8
2.5
1.0
1.4
1.4
1.5
1.4
1.4
1.4
1.4
1.8
1.5
1.5
1.0
1.4
1.4
.4
.0
.0
2.0
.0
.5
.5
.5
.4
.0
.7
.4
Max-
imum
4.0
4.0
2.5
2.5
5.0
2.0
1.1
2.5
2.9
4.0
2.8
3.0
2.8
2.5
2.2
3.0
1.8
3.0
4.0
6.0
4.0
2.0
2.0
1.6
2.3
2.9
1.9
8.0
8.0
6.0
16.0
9.0
7.3
16.0
Trun
-cate
Left
Tail?
Y



Y

Y


Y









Y







Y
Y

Y
Y
Y
Y
Trun-
cate
Right
Tail?
Y



Y

Y


Y









Y







Y
Y

Y
Y
Y
Y
DRAFT - Do not cite or quote
B-3
October 31,2006

-------
                                   Table B-1.  (cont.)
Activity Description
Sports and active
eisure
Sports and active
eisure
Participate in sports
'articipate in sports
'articipate in sports
-hinting, fishing,
liking
Hunting, fishing,
liking
Hunting, fishing,
liking
Golf
Golf
Golf
Bowling / pool / ping
pong / pinball
Yoga
Participate in outdoor
eisure
'articipate in outdoor
eisure
Participate in outdoor
eisure
Play, unspecified
Play, unspecified
Play, unspecified
Passive, sitting
exercise
exercise
exercise
Walk, bike, or jog (not
in transit)
Walk, bike, or jog (not
in transit)
Walk, bike, or jog (not
in transit)
Create art, music,
work on hobbies
Create art, music,
work on hobbies
Create art, music,
work on hobbies
Participate in hobbies
Activity
ID Code
17100
17100
17110*
17110*
17110*
17111
17111
17111
17112
17112
17112
17113
17114
17120
17120
17120
17121
17121
17121
17122*
17130*
17130*
17130*
17131
17131
17131
17140
17140
17140
17141*
Age(a)
30
40
20
30
40
20
30
40
20
30
40


20
30
40
20
30
40

20
30
40
20
30
40
20
30
40

Occu-
pation0^






























Distribu-
tion Type
Normal
Normal
LogNormal
LogNormal
LogNormal
Normal
Normal
Normal
Uniform
Uniform
Uniform
Uniform
Triangle
LogNormal
LogNormal
Point Est.
LogNormal
LogNormal
Point Est.
Uniform
LogNormal
Normal
Normal
LogNormal
Normal
Normal
Normal
Normal
Normal
Triangle
Mean
5.0
4.5
3.6
3.6
3.4
5.6
5.8
4.7
3.8
3.8
3.5
3.0
3.1
4.2
4.2
3.5
4.2
4.2
3.5
1.5
5.8
5.7
4.7
5.8
5.7
4.7
5.3
5.2
3.8
2.8
Med-
ian
5.0
4.5
3.2
3.2
3.0
5.6
5.8
4.7
3.8
3.8
3.5
3.0
3.2
3.9
3.9
3.5
3.9
3.9
3.5
1.5
5.5
5.7
4.7
5.5
5.7
4.7
5.3
5.2
3.8
2.7
Std.
Dev.
2.0
1.4
1.9
1.9
1.7
2.1
2.4
1.8
1.0
1.0
0.9
0.6
0.6
1.5
1.5

1.5
1.5

0.2
1.8
1.8
1.2
1.8
1.8
1.2
1.8
1.7
1.0
0.8
Min-
imum
1.0
1.7
1.4
1.4
1.4
1.4
1.0
1.1
2.0
2.0
2.0
2.0
1.4
2.0
2.0
0.0
2.0
2.0
0.0
1.2
1.8
2.1
2.3
1.8
2.1
2.3
1.7
1.7
1.8
1.5
Max-
imum
9.0
7.3
10.0
10.0
9.0
9.8
10.6
8.3
5.5
5.5
5.0
4.0
4.0
9.0
9.0
0.0
9.0
9.0
0.0
1.8
11.3
9.3
7.1
11.3
9.3
7.1
8.9
8.9
5.8
5.0
Trun
-cate
Left
Tail?
Y
Y
Y
Y
Y
Y
Y
Y





Y
Y

Y
Y


Y
Y
Y
Y
Y
Y
Y
Y
Y

Trun-
cate
Right
Tail?
Y
Y
Y
Y
Y
Y
Y
Y





Y
Y

Y
Y


Y
Y
Y
Y
Y
Y
Y
Y
Y

DRAFT - Do not cite or quote
B-4
October 31,2006

-------
                                   Table B-1.  (cont.)
Activity Description
Create domestic crafts
Create art
Perform music / drama
/ dance
'erform music / drama
/ dance
'erform music / drama
/ dance
Play games
Use of computers
lecess and physical
education
Other sports and active
eisure
Other sports and active
eisure
Other sports and active
eisure
'articipate in passive
eisure
Watch
Watch adult at work
Watch someone
provide childcare
Watch personal care
Watch education
Watch organizational
activities
Watch recreation
Listen to radio /
recorded music /
watch T.V.
^isten to radio
^isten to recorded
music
Watch TV
Read, general
lead books
lead magazines / not
ascertained
Read newspaper
Converse / write
Converse
Write for leisure /
pleasure / paperwork
Activity
ID Code
17142*
17143*
17144*
17144*
17144*
17150*
17160*
17170
17180
17180
17180
17200
17210
17211
17212
17213
17214
17215
17216
17220
17221*
17222*
17223*
17230
17231*
17232*
17233*
17240
17241*
17242*
Age(a)


20
30
40



20
30
40



















Occu-
pation0^






























Distribu-
tion Type
Triangle
Uniform
Normal
Normal
Normal
Triangle
Uniform
Uniform
LogNormal
Normal
Normal
LogNormal
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
LogNormal
Uniform
Uniform
Point Est.
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
Mean
2.0
2.5
5.3
5.2
3.8
3.3
1.6
5.0
6.6
6.0
4.8
1.3
1.5
0.0
0.0
0.0
0.0
0.0
2.7
1.2
1.2
1.9
1.0
1.3
1.3
1.3
1.3
1.4
1.4
1.4
Med-
ian
1.9
2.5
5.3
5.2
3.8
3.2
1.6
5.0
5.9
6.0
4.8
1.3
1.5
0.0
0.0
0.0
0.0
0.0
2.7
1.2
1.2
1.9
1.0
1.3
1.3
1.3
1.3
1.4
1.4
1.4
Std.
Dev.
0.4
0.3
1.8
1.7
1.0
0.6
0.2
1.7
3.2
2.0
1.4
0.3
0.2
0.0
0.0
0.0
0.0
0.0
0.8
0.4
0.1
0.2

0.2
0.2
0.2
0.2
0.2
0.2
0.2
Min-
imum
1.5
2.0
1.7
1.7
1.8
2.4
1.2
2.0
2.0
2.0
2.0
1.0
.2
.2
.2
.2
.2
.2
1.4
0.9
1.0
1.5
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Max-
imum
3.0
3.0
8.9
8.9
5.8
5.0
2.0
8.0
17.4
10.0
7.6
2.3
1.8
0.0
0.0
0.0
0.0
0.0
4.0
2.3
1.3
2.3
1.0
1.6
1.6
1.6
1.6
1.8
1.8
1.8
Trun
-cate
Left
Tail?


Y
Y
Y



Y
Y
Y
Y







Y










Trun-
cate
Right
Tail?


Y
Y
Y



Y
Y
Y
Y







Y










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B-5
October 31,2006

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                                          Table B-1. (cont.)
Activity Description
Fhink and relax
Other passive leisure
Other leisure
Travel, general
Travel during work
Travel to/from work
Travel for child care
Travel for goods and
services
Travel for personal
care
Travel for education
Travel for organ.
activity
Travel for event /
social act
Travel for leisure
Travel for active
eisure
Travel for passive
eisure
Activity
ID Code
17250*
17260
17300
18000
18100*
18200*
18300*
18400*
18500*
18600*
18700*
18800*
18900
18910*
18920*
Age(a)















Occu-
pation0^















Distribu-
tion Type
Uniform
Uniform
Uniform
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
LogNormal
Mean
1.2
1.9
1.5
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
Med-
ian
1.2
1.9
1.5
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
Std.
Dev.
0.1
0.2
0.2
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
Min-
imum
1.0
1.5
1.2
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.8
Max-
imum
1.3
2.3
1.8
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
Trun
-cate
Left
Tail?



Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Trun-
cate
Right
Tail?



Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
 2    (a)   Age Group ("20" = <25 years; "30" = 25-39 years; "40" = >40 years)
 3    (b)   Occupation (activity ID code=1000 only): ADMrN=executive/administrative/managerial;
 4        PROF=professional; TECH=technicians; SALE=sales; ADMSUP=administrative support;
 5        HSHLD=private household; PROTECT=protective services; SERV=service;
 6        FARM=farming/forestry/fishing; PREC=precision production/craft/repair; MACH=machine
 7        operators/assemblers/inspectors; TRANS=transportation and material moving;
 8        LABOR=handling/equipment cleaners/helpers/laborers
 9    *    Activity ID codes encountered within the NHAPS data set.
10
      DRAFT - Do not cite or quote
B-6
October 31,2006

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 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
            A total often activity ID codes were encountered in the NHAPS data set that did not have
     a METS distribution assigned to them within CHAD.  These codes, listed in Table B-2, were
     occupation-related activity codes that appeared to represent sub-codes to code 10000 (general
     work and other income-producing activities).  Such sub-codes may have required knowledge of
     the individual's occupation in order to assign the proper METS distribution to the activity.
     Because the occupation of the NHAPS participants was not specified in the activity data records
     within CHAD, the available information within CHAD was not sufficient to assign a METS
     distribution to these sub-codes as CHAD would have done. Therefore, for each of these codes, it
     was necessary to identify an activity that was "similar" in description to the code and assign that
     activity's METS distribution to the code. Table B-2 specifies the activity whose METS
     distribution was assigned to each of these ten codes.

     Table B-2.     Activity Codes Whose METS Distributions Were Assigned to Those Codes
                   Encountered in the NHAPS Database But Having No METS Distribution Assigned
                   by CHAD
Codes Encountered in the NHAPS Data with
No METS Distribution Assigned by CHAD
Activity
Code
10111
10112
10113
10114
10115
10116
10117
10118
10120
10200
Activity Description
Work for professional/union
organizations
Work for special interest identity
organizations
Work for political party and civic
participation
Work for volunteer/helping
organizations
Work of/for religious groups
Work for fraternal organizations
Work for child/youth/family
organizations
Work for other organizations
Work, income-related only
Unemployment
Activity Code Whose METS Distribution Was
Assigned to the Code in the First Column
Activity
Code
10000
(PROF)
16200
16200
14300
16200
16200
12800
10000
(ADMIN)
16900
13500
Activity Description
Work and other income producing
activities, general - professional
positions
Participate in social, political, or
religious activities
Participate in social, political, or
religious activities
Help and care
Participate in social, political, or
religious activities
Participate in social, political, or
religious activities
Other child care
Work and other income producing
activities, general - executive,
administrative, and managerial
positions
Other entertainment/social events
Obtain government/financial services
17
      DRAFT - Do not cite or quote
                                                B-7
October 31,2006

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                          APPENDIX C:
                 ADDITIONAL ANALYSIS TABLES
DRAFT - Do not cite or quote                                     October 31, 2006

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Table C-la.    Descriptive Statistics of Body Weight (kg) and BMR (kcal/min) Across NHANES Male Participants, by Age Group
Age Category
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Body Weight (kg)
Mean
8.0
11.4
13.9
18.5
31.8
56.4
76.5
83.8
87.1
88.4
89.0
87.6
82.4
75.4
Percentiles
5th
4.8
9.1
11.1
13.4
19.9
32.8
54.3
56.8
61.0
64.0
62.6
63.4
60.6
57.9
10th
5.5
9.8
11.7
14.5
21.9
35.2
57.6
60.9
65.6
67.7
67.4
66.7
64.4
61.8
25th
6.7
10.3
12.5
16.0
24.8
43.3
63.9
69.5
73.9
76.7
76.6
76.1
72.5
67.0
50th
8.1
11.3
13.8
17.8
29.6
53.8
72.2
80.8
83.4
85.4
86.6
85.7
81.0
74.6
75th
9.4
12.3
15.3
20.2
36.3
65.7
83.6
93.7
96.3
97.8
99.6
97.1
92.0
82.0
90th
10.4
13.2
16.2
23.3
45.4
79.9
102.8
108.7
112.6
111.8
110.5
111.2
101.1
91.6
95th
10.8
13.7
17.2
25.2
50.0
92.5
111.2
123.4
126.7
121.2
120.3
119.0
108.8
100.5
Maxi-
mum
13.4
16.1
23.3
42.0
86.9
143.6
176.0
196.8
193.3
188.3
179.0
162.8
132.7
111.8
BMR (kcal/min)
Mean
0.31
0.45
0.55
0.64
0.85
1.15
1.33
1.35
1.30
1.31
1.30
1.12
1.08
1.02
Percentiles
5th
0.18
0.35
0.44
0.56
0.66
0.86
1.08
1.07
1.09
1.12
1.07
0.92
0.90
0.88
10th
0.21
0.38
0.46
0.58
0.70
0.89
1.11
1.12
1.13
1.14
1.12
0.95
0.93
0.91
25th
0.26
0.40
0.50
0.60
0.74
0.99
1.18
1.21
1.19
1.22
1.20
1.03
1.00
0.95
50th
0.31
0.45
0.55
0.63
0.82
1.12
1.28
1.32
1.27
1.29
1.29
1.10
1.07
1.01
75th
0.37
0.49
0.61
0.67
0.91
1.26
1.42
1.45
1.37
1.38
1.39
1.20
1.16
1.07
90th
0.41
0.52
0.65
0.72
1.04
1.44
1.60
1.62
1.50
1.50
1.48
1.31
1.23
1.15
95th
0.42
0.54
0.69
0.75
1.11
1.59
1.73
1.74
1.61
1.57
1.55
1.38
1.29
1.22
Maxi-
mum
0.53
0.64
0.94
1.01
1.57
2.22
2.62
2.54
2.14
2.11
2.03
1.73
1.49
1.32
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this
table. The numbers of male NHANES participants with data entering into these statistics are given in Table 2-1.
DRAFT - Do not cite or quote
C-1
October 31, 2006

-------
Table C-lb.   Descriptive Statistics of Body Weight (kg) and BMR (kcal/min) Across NHANES Female Participants, by Age Group
Age Category
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Body Weight (kg)
Mean
7.4
11.1
13.3
18.2
30.9
55.6
65.2
72.4
74.7
76.6
77.0
75.5
70.3
63.9
Percentiles
5th
4.6
8.8
11.0
13.3
18.9
35.6
46.2
47.5
51.0
51.3
53.1
51.7
46.8
45.2
10th
4.9
9.1
11.2
14.2
20.6
38.1
47.8
51.4
54.6
54.2
56.2
55.9
52.0
47.4
25th
6.3
9.9
12.0
15.5
23.3
45.0
54.3
58.3
60.7
60.7
62.8
63.8
59.4
54.5
50th
7.5
10.9
13.1
17.4
28.1
53.1
61.3
69.0
69.7
72.7
73.6
73.1
68.5
62.6
75th
8.6
12.1
14.4
19.5
36.2
62.4
72.5
82.5
84.0
87.5
87.7
83.9
80.3
71.4
90th
9.6
13.1
15.6
23.0
44.7
75.3
89.9
98.3
103.8
102.8
104.6
99.9
91.8
79.4
95th
10.4
13.8
16.8
26.9
50.4
86.2
96.2
109.6
112.8
117.2
113.4
109.2
97.7
91.4
Maxi-
mum
20.2
18.9
22.7
38.6
87.0
134.4
156.4
159.1
191.1
182.8
150.1
138.7
127.6
120.0
BMR (kcal/min)
Mean
0.28
0.43
0.52
0.59
0.76
1.00
1.04
1.07
1.01
1.02
1.01
0.93
0.90
0.86
Percentiles
5th
0.16
0.33
0.42
0.52
0.60
0.81
0.83
0.83
0.87
0.88
0.86
0.78
0.75
0.74
10th
0.18
0.35
0.43
0.54
0.63
0.83
0.87
0.87
0.89
0.89
0.89
0.81
0.78
0.76
25th
0.23
0.38
0.46
0.56
0.67
0.90
0.93
0.93
0.93
0.93
0.93
0.86
0.83
0.80
50th
0.28
0.42
0.51
0.58
0.74
0.97
1.01
1.03
0.98
1.00
1.00
0.92
0.89
0.85
75th
0.33
0.47
0.56
0.61
0.84
1.06
1.11
1.17
1.06
1.08
1.07
0.99
0.96
0.91
90th
0.37
0.51
0.61
0.66
0.95
1.18
1.27
1.33
1.17
1.17
1.17
1.09
1.04
0.96
95th
0.40
0.54
0.66
0.72
1.02
1.28
1.35
1.45
1.22
1.25
1.22
1.15
1.07
1.03
Maxi-
mum
0.80
0.74
0.90
0.88
1.56
1.73
1.95
1.97
1.66
1.62
1.43
1.33
1.26
1.21
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this
table. The numbers of female NHANES participants with data entering into these statistics are given in Table 2-1.
DRAFT - Do not cite or quote
C-2
October 31, 2006

-------
Table C-2a.    Descriptive Statistics for Daily Average Ventilation Rate (m3/day) in Males, by Age Category
Age Category
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body Weight
(FB;m3/day)
Mean
8.76
13.49
13.23
12.65
13.42
15.32
17.22
18.82
20.29
20.93
20.91
17.94
16.35
15.15
Percentiles
5th
4.77
9.73
9.45
10.42
10.08
11.41
12.60
12.69
14.00
14.66
14.98
13.92
13.10
11.95
10th
5.70
10.41
10.20
10.87
10.69
12.11
13.41
13.57
14.97
15.54
16.07
14.50
13.61
12.57
25th
7.16
11.65
11.43
11.40
11.73
13.27
14.48
15.49
16.96
17.50
17.60
15.88
14.67
13.82
50th
8.70
13.11
13.19
12.58
13.09
14.79
16.63
18.18
19.83
20.60
20.41
17.60
16.23
14.90
75th
10.43
15.02
14.49
13.64
14.73
16.81
19.16
21.23
23.02
23.89
23.16
19.54
17.57
16.31
90th
11.93
17.03
16.27
14.63
16.56
19.54
21.94
24.57
26.77
26.71
27.01
21.78
19.43
18.02
95th
12.69
17.89
17.71
15.41
17.72
21.21
23.38
27.14
28.90
28.37
29.09
23.50
20.42
18.68
Maxi-
mum
17.05
24.24
28.17
19.52
24.97
28.54
39.21
43.42
40.72
45.98
38.17
28.09
24.53
22.63
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW:m3/day/kg)
Mean
1.093
1.186
0.948
0.703
0.441
0.285
0.229
0.230
0.239
0.242
0.240
0.207
0.201
0.203
Percentiles
5th
0.913
0.964
0.781
0.523
0.318
0.208
0.168
0.155
0.161
0.168
0.163
0.171
0.168
0.171
10th
0.943
1.017
0.816
0.555
0.343
0.221
0.181
0.168
0.176
0.179
0.177
0.178
0.176
0.177
25th
1.002
1.088
0.873
0.613
0.376
0.246
0.202
0.193
0.201
0.199
0.203
0.189
0.185
0.186
50th
1.085
1.171
0.943
0.693
0.434
0.276
0.228
0.224
0.232
0.232
0.239
0.205
0.197
0.202
75th
1.163
1.261
1.014
0.778
0.499
0.317
0.253
0.262
0.271
0.278
0.271
0.223
0.214
0.217
90th
1.256
1.367
1.090
0.873
0.549
0.362
0.279
0.300
0.311
0.317
0.304
0.241
0.231
0.233
95th
1.293
1.479
1.127
0.920
0.581
0.384
0.296
0.323
0.339
0.336
0.335
0.253
0.241
0.250
Maxi-
mum
1.476
1.730
1.360
1.084
0.805
0.505
0.395
0.513
0.459
0.466
0.430
0.323
0.312
0.277
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in
this table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
C-3
October 31, 2006

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Table C-2b.    Descriptive Statistics for Daily Average Ventilation Rate (m3/day) in Females, by Age Category
Age Category
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
Daily Average Ventilation Rate, Unadjusted for Body Weight
(FB;m3/day)
Mean
8.53
13.31
12.74
12.16
12.41
13.44
13.59
14.57
14.98
16.20
16.18
12.99
12.04
11.14
Percentiles
5th
4.84
9.08
8.91
9.87
9.99
10.47
9.86
10.15
11.07
12.10
12.33
10.40
9.90
9.19
10th
5.48
10.12
10.07
10.38
10.35
11.11
10.61
10.67
11.80
12.58
12.96
10.77
10.20
9.45
25th
6.83
11.24
11.38
11.20
11.01
12.04
11.78
11.93
13.02
14.16
14.08
11.78
10.89
10.13
50th
8.41
13.03
12.60
12.02
11.95
13.08
13.20
14.10
14.68
15.88
15.90
12.92
11.82
11.02
75th
9.78
14.64
13.96
13.01
13.42
14.54
15.02
16.62
16.32
17.95
17.81
13.90
12.96
11.87
90th
11.65
17.45
15.58
14.03
15.13
16.25
17.12
19.32
18.51
19.91
19.93
15.40
14.11
12.85
95th
12.66
18.62
16.37
14.93
16.34
17.41
18.29
21.14
20.45
21.35
21.22
16.15
15.20
13.94
Maxi-
mum
26.26
24.77
23.01
19.74
20.82
26.58
30.11
30.23
28.28
35.89
25.70
20.34
17.70
16.93
Daily Average Ventilation Rate, Adjusted for Body Weight
(VE/BW:m3/day/kg)
Mean
1.142
1.197
0.955
0.691
0.427
0.251
0.214
0.207
0.209
0.220
0.218
0.177
0.176
0.178
Percentiles
5th
0.913
0.975
0.820
0.482
0.279
0.189
0.158
0.144
0.141
0.148
0.154
0.138
0.140
0.143
10th
0.969
1.013
0.840
0.536
0.307
0.198
0.169
0.158
0.154
0.164
0.164
0.145
0.145
0.148
25th
1.037
1.102
0.890
0.596
0.357
0.220
0.190
0.178
0.176
0.186
0.184
0.158
0.156
0.159
50th
1.127
1.178
0.956
0.684
0.427
0.245
0.208
0.202
0.204
0.215
0.212
0.173
0.173
0.177
75th
1.243
1.297
1.012
0.768
0.489
0.279
0.235
0.232
0.233
0.250
0.244
0.193
0.192
0.197
90th
1.327
1.405
1.065
0.884
0.548
0.312
0.268
0.258
0.270
0.283
0.280
0.213
0.211
0.210
95th
1.384
1.465
1.105
0.916
0.582
0.340
0.284
0.277
0.298
0.306
0.299
0.225
0.229
0.220
Maxi-
mum
1.601
1.728
1.234
1.116
0.748
0.471
0.357
0.402
0.433
0.415
0.397
0.272
0.338
0.282
Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in
this table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
C-4
October 31, 2006

-------
Table C-3.     Descriptive Statistics for Duration of Time (hr/day) Spent Performing Activities Within the Specified Activity Category,
              by Age and Gender Categories
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
13.51
12.61
12.06
11.18
10.18
9.38
8.69
8.36
8.06
7.89
7.96
8.31
8.51
9.24
12.63
11.89
11.19
10.57
9.65
8.84
7.91
7.54
7.36
7.15
7.29
7.65
7.80
8.48
12.78
12.15
11.45
10.70
9.75
8.94
8.08
7.70
7.50
7.30
7.51
7.78
8.02
8.64
13.19
12.34
11.80
10.94
9.93
9.15
8.36
8.02
7.77
7.58
7.69
8.01
8.27
8.97
13.53
12.61
12.07
11.18
10.19
9.38
8.67
8.36
8.06
7.88
7.96
8.30
8.53
9.25
13.88
12.89
12.39
11.45
10.39
9.61
9.03
8.67
8.36
8.17
8.23
8.60
8.74
9.54
14.24
13.13
12.65
11.63
10.59
9.83
9.34
9.03
8.59
8.48
8.48
8.83
8.99
9.74
14.46
13.29
12.75
11.82
10.72
9.95
9.50
9.23
8.76
8.68
8.66
9.01
9.10
9.96
15.03
13.79
13.40
12.39
11.24
10.33
10.44
9.77
9.82
9.38
9.04
9.66
9.89
10.69
12.99
12.58
12.09
11.13
10.26
9.57
9.08
8.60
8.31
8.32
8.12
8.40
8.58
9.11
12.00
11.59
11.45
10.45
9.55
8.82
8.26
7.89
7.54
7.58
7.36
7.67
7.85
8.35
12.16
11.88
11.68
10.70
9.73
8.97
8.44
7.99
7.70
7.75
7.53
7.88
8.01
8.53
12.53
12.29
11.86
10.92
10.01
9.27
8.74
8.26
7.98
7.99
7.81
8.15
8.26
8.84
12.96
12.63
12.08
11.12
10.27
9.55
9.08
8.59
8.28
8.31
8.11
8.40
8.55
9.10
13.44
12.96
12.34
11.38
10.54
9.87
9.39
8.90
8.59
8.63
8.43
8.68
8.89
9.34
13.82
13.16
12.57
11.58
10.74
10.17
9.79
9.20
8.92
8.93
8.73
8.93
9.19
9.73
14.07
13.31
12.66
11.75
10.91
10.31
10.02
9.38
9.17
9.13
8.85
9.09
9.46
10.04
14.82
14.55
13.48
12.23
11.43
11.52
11.11
10.35
10.22
10.02
9.29
9.80
10.34
10.55
DRAFT - Do not cite or quote
C-5
October 31, 2006

-------
                                               Table C-3. (Continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sedentary & Passive Activities (METS # 1.5 ~ Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
14.95
14.27
14.62
14.12
13.51
13.85
13.21
12.41
12.31
12.32
13.06
14.49
15.90
16.58
13.82
13.22
13.52
13.01
12.19
12.39
11.39
10.69
10.73
10.56
11.47
12.96
14.22
15.13
14.03
13.33
13.67
13.18
12.45
12.65
11.72
11.06
10.98
11.00
11.86
13.24
14.67
15.45
14.49
13.76
14.11
13.54
12.86
13.06
12.32
11.74
11.61
11.67
12.36
13.76
15.25
15.92
14.88
14.25
14.54
14.03
13.30
13.61
13.08
12.39
12.24
12.30
13.03
14.48
15.94
16.64
15.44
14.74
15.11
14.53
13.85
14.30
13.97
13.09
12.98
12.95
13.72
15.16
16.65
17.21
15.90
15.08
15.60
15.26
14.82
15.41
14.83
13.75
13.63
13.67
14.38
15.72
17.11
17.70
16.12
15.38
15.77
15.62
15.94
16.76
15.44
14.16
14.05
13.98
14.76
16.24
17.46
18.06
17.48
16.45
17.28
17.29
19.21
18.79
18.70
15.35
15.58
15.48
15.95
17.50
18.47
18.76
14.07
14.32
14.86
14.27
13.97
14.19
13.58
12.59
12.29
12.22
12.66
14.25
15.38
16.48
12.86
13.02
13.81
12.88
12.49
12.38
11.80
10.97
10.91
10.78
11.08
12.89
13.66
14.87
13.05
13.25
13.95
13.15
12.74
12.76
12.17
11.29
11.14
11.08
11.40
13.16
14.20
15.09
13.53
13.73
14.44
13.56
13.22
13.34
12.79
11.88
11.61
11.56
12.08
13.68
14.76
15.80
14.08
14.31
14.81
14.23
13.82
14.05
13.52
12.60
12.24
12.18
12.64
14.22
15.41
16.59
14.54
14.88
15.32
14.82
14.50
14.82
14.29
13.21
12.91
12.82
13.30
14.86
16.05
17.15
15.08
15.36
15.78
15.43
15.34
15.87
15.08
13.75
13.50
13.40
13.89
15.38
16.62
17.71
15.49
15.80
16.03
15.85
16.36
16.81
15.67
14.19
13.90
13.79
14.12
15.69
16.94
18.07
16.14
16.40
16.91
17.96
18.68
19.27
16.96
16.24
15.18
15.17
15.80
17.14
17.90
19.13
DRAFT - Do not cite or quote
C-6
October 31, 2006

-------
                                               Table C-3. (Continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Light Intensity Activities (1.5 < METS # 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
5.30
5.52
5.48
6.60
7.62
7.50
7.13
6.09
5.72
6.07
5.64
5.49
4.96
4.86
2.97
2.68
3.06
3.86
5.07
4.48
4.37
3.15
2.80
2.97
3.21
3.50
3.45
3.54
3.25
2.89
3.26
4.25
5.57
5.59
4.97
3.50
3.12
3.41
3.44
3.82
3.75
3.71
3.71
3.37
3.85
5.16
6.63
6.75
6.00
4.20
3.70
3.92
4.03
4.58
4.29
4.17
4.52
4.31
4.58
6.20
7.63
7.67
7.02
5.08
4.64
4.82
4.79
5.29
4.81
4.74
7.29
8.23
7.58
8.26
8.72
8.51
8.29
8.49
8.34
8.56
7.59
6.41
5.59
5.39
8.08
9.04
8.83
9.31
9.78
9.19
9.43
9.96
9.87
10.19
8.94
7.40
6.26
6.33
8.50
9.73
9.04
9.70
10.12
9.63
10.03
10.47
10.49
10.79
9.75
7.95
6.59
6.59
9.91
10.90
9.92
10.74
11.59
10.91
11.50
12.25
12.10
12.68
12.09
10.23
9.90
7.56
6.00
5.61
5.78
6.25
7.27
7.55
6.98
6.42
6.51
6.56
6.52
6.23
5.96
5.30
3.49
2.83
3.20
3.78
4.63
4.89
4.60
3.66
4.06
3.99
4.09
4.40
4.22
3.67
3.70
2.94
3.54
4.10
5.46
5.62
5.08
4.09
4.33
4.30
4.42
4.74
4.51
3.96
4.26
3.46
4.29
4.79
6.33
6.75
5.91
4.84
5.06
4.97
5.19
5.47
5.24
4.63
5.01
4.39
5.33
5.84
7.17
7.67
6.85
5.82
5.98
5.90
6.05
6.23
5.92
5.16
8.43
8.28
7.48
7.86
8.34
8.55
7.96
8.18
8.14
8.40
7.95
6.96
6.63
6.00
9.31
9.03
8.46
8.84
9.42
9.27
9.16
9.56
9.46
9.75
9.12
7.67
7.46
6.70
9.77
9.39
8.74
9.38
9.79
9.57
9.57
10.14
9.93
10.18
9.43
8.17
7.91
7.01
10.53
10.57
9.93
10.32
11.06
10.85
12.29
12.11
13.12
11.83
11.58
11.13
9.43
8.78
DRAFT - Do not cite or quote
C-7
October 31, 2006

-------
                                               Table C-3. (Continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Moderate Intensity Activities (3.0 < METS # 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.67
4.04
3.83
3.15
2.66
2.35
3.35
5.24
5.69
5.40
5.00
3.73
2.87
2.35
0.63
0.45
0.59
0.55
0.65
0.88
1.13
1.15
1.26
1.21
1.29
1.62
1.56
1.32
0.97
0.59
0.76
0.75
0.92
1.09
1.42
1.58
1.65
1.55
1.63
1.97
1.83
1.45
1.74
1.14
1.23
1.30
1.65
1.66
2.19
2.52
2.84
2.39
2.72
2.81
2.28
1.79
4.20
5.29
4.74
3.80
2.68
2.30
3.45
6.01
6.67
6.46
5.68
3.70
2.86
2.29
5.20
6.06
5.37
4.52
3.57
3.02
4.37
7.15
7.75
7.57
6.75
4.67
3.45
2.85
5.80
6.61
5.82
5.11
4.36
3.62
5.24
7.95
8.45
8.40
7.60
5.45
3.95
3.28
6.21
6.94
6.15
5.32
4.79
3.89
5.59
8.39
8.90
8.85
8.01
6.01
4.31
3.61
7.52
7.68
7.40
6.30
5.95
5.90
6.83
9.94
9.87
10.52
9.94
7.45
5.44
4.37
3.91
4.02
3.27
3.35
2.57
2.01
3.26
4.80
5.00
5.05
4.58
3.31
2.48
2.06
0.53
0.52
0.50
0.70
0.65
0.89
1.27
1.62
1.71
1.75
1.71
1.65
1.19
1.01
0.74
0.73
0.78
0.89
0.95
1.08
1.48
1.94
2.06
2.00
2.13
1.97
1.36
1.25
1.10
1.08
1.22
1.61
1.82
1.45
2.21
2.78
3.09
2.97
3.10
2.56
1.82
1.55
4.87
5.14
4.01
3.88
2.66
1.96
3.39
5.37
5.41
5.48
4.79
3.34
2.48
1.99
5.77
6.10
4.88
4.71
3.41
2.51
4.24
6.42
6.60
6.66
5.98
4.01
2.99
2.51
6.27
7.00
5.35
5.29
3.95
3.03
4.74
7.19
7.31
7.50
6.89
4.61
3.64
3.07
6.54
7.37
5.57
5.65
4.32
3.28
5.07
7.52
7.58
7.97
7.14
5.01
4.01
3.44
7.68
8.07
6.93
7.58
6.10
4.96
6.68
9.21
9.59
10.16
8.97
6.90
5.63
4.68
DRAFT - Do not cite or quote
C-8
October 31, 2006

-------
                                                    Table C-3.  (Continued)
Age Category
Duration (hr/day) Spent at Activity - Males
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Duration (hr/day) Spent at Activity - Females
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.20
0.31
0.10
0.27
0.32
0.38
0.40
0.33
0.38
0.34
0.41
0.37
0.39
0.32
0.00
0.01
0.00
0.02
0.01
0.03
0.03
0.02
0.03
0.03
0.03
0.03
0.01
0.02
0.00
0.01
0.01
0.03
0.01
0.04
0.04
0.05
0.07
0.05
0.05
0.05
0.03
0.03
0.01
0.03
0.03
0.04
0.03
0.10
0.14
0.11
0.14
0.09
0.13
0.13
0.10
0.08
0.14
0.22
0.05
0.13
0.13
0.21
0.27
0.27
0.28
0.23
0.34
0.28
0.29
0.25
0.28
0.56
0.14
0.33
0.38
0.47
0.53
0.45
0.51
0.50
0.59
0.49
0.57
0.47
0.50
0.78
0.25
0.75
1.10
1.03
0.99
0.69
0.83
0.78
0.87
0.80
0.90
0.71
0.59
0.93
0.33
1.16
1.50
1.34
1.29
0.85
1.03
1.00
1.13
1.08
1.11
0.88
0.96
1.52
0.48
1.48
3.20
2.35
2.59
1.95
1.77
2.40
1.95
2.21
2.06
1.76
0.17
0.22
0.15
0.19
0.24
0.30
0.24
0.26
0.25
0.26
0.34
0.32
0.29
0.26
0.03
0.03
0.00
0.01
0.02
0.03
0.01
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.05
0.05
0.01
0.02
0.03
0.04
0.03
0.05
0.05
0.04
0.04
0.04
0.05
0.03
0.09
0.09
0.03
0.05
0.06
0.08
0.08
0.10
0.09
0.09
0.12
0.10
0.10
0.09
0.14
0.18
0.08
0.10
0.12
0.19
0.18
0.19
0.19
0.20
0.28
0.23
0.25
0.21
0.21
0.35
0.16
0.22
0.26
0.40
0.34
0.36
0.33
0.36
0.50
0.46
0.43
0.38
0.33
0.40
0.48
0.46
0.67
0.66
0.51
0.56
0.52
0.55
0.74
0.68
0.60
0.59
0.40
0.43
0.65
0.73
0.98
0.96
0.60
0.67
0.72
0.68
0.85
0.89
0.71
0.71
0.58
0.48
1.01
1.43
1.71
3.16
1.61
1.40
1.40
1.49
1.58
1.77
1.24
1.23
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this
table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
C-9
October 31, 2006

-------
Table C-4.     Descriptive Statistics for Average Ventilation Rate (L/min), Unadjusted for Body Weight, While Performing Activities
              Within the Specified Activity Category, by Age and Gender Categories
Age Category
Average Ventilation Rate (L/min) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.08
4.50
4.61
4.36
4.61
5.26
5.31
4.73
5.16
5.65
5.78
5.98
6.07
5.97
1.66
3.11
3.01
3.06
3.14
3.53
3.55
3.16
3.37
3.74
3.96
4.36
4.26
4.20
1.91
3.27
3.36
3.30
3.39
3.78
3.85
3.35
3.62
4.09
4.20
4.57
4.55
4.49
2.45
3.78
3.94
3.76
3.83
4.34
4.35
3.84
4.23
4.73
4.78
5.13
5.17
5.23
3.00
4.35
4.49
4.29
4.46
5.06
5.15
4.56
5.01
5.53
5.57
5.81
6.00
5.90
3.68
4.95
5.21
4.86
5.21
5.91
6.09
5.42
5.84
6.47
6.54
6.68
6.77
6.68
4.35
5.90
6.05
5.54
6.01
6.94
6.92
6.26
6.81
7.41
7.74
7.45
7.65
7.36
4.77
6.44
6.73
5.92
6.54
7.81
7.60
6.91
7.46
7.84
8.26
7.93
8.33
7.76
7.19
10.02
8.96
7.67
9.94
11.49
12.82
11.17
10.86
10.84
11.81
12.27
10.50
9.98
2.92
4.59
4.56
4.18
4.36
4.81
4.40
3.89
4.00
4.40
4.56
4.47
4.52
4.49
1.54
3.02
3.00
2.90
2.97
3.34
2.78
2.54
2.66
3.00
3.12
3.22
3.31
3.17
1.72
3.28
3.30
3.20
3.17
3.57
2.96
2.74
2.86
3.23
3.30
3.35
3.47
3.49
2.27
3.76
3.97
3.62
3.69
3.99
3.58
3.13
3.31
3.69
3.72
3.78
3.89
3.82
2.88
4.56
4.52
4.10
4.24
4.66
4.26
3.68
3.89
4.25
4.41
4.38
4.40
4.39
3.50
5.32
5.21
4.71
4.93
5.39
5.05
4.44
4.54
4.95
5.19
4.99
5.11
4.91
4.04
5.96
5.76
5.22
5.67
6.39
5.89
5.36
5.28
5.66
6.07
5.72
5.67
5.61
4.40
6.37
6.15
5.73
6.08
6.99
6.63
6.01
5.77
6.25
6.63
6.37
6.06
6.16
8.69
9.59
9.48
7.38
8.42
9.39
12.25
9.58
8.10
8.97
8.96
9.57
7.35
8.27
DRAFT - Do not cite or quote
C-10
October 31, 2006

-------
                                               Table C-4. (Continued)
Age Category
Average Ventilation Rate (L/min) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sedentary & Passive Activities (METS # 1.5 ~ Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.18
4.62
4.79
4.58
4.87
5.64
5.76
5.11
5.57
6.11
6.27
6.54
6.65
6.44
1.74
3.17
3.25
3.47
3.55
4.03
4.17
3.76
3.99
4.65
4.68
5.02
5.26
5.09
1.99
3.50
3.66
3.63
3.78
4.30
4.42
3.99
4.42
4.92
5.06
5.31
5.55
5.37
2.50
3.91
4.10
4.07
4.18
4.79
4.93
4.33
4.86
5.37
5.50
5.85
5.96
5.82
3.10
4.49
4.69
4.56
4.72
5.43
5.60
5.00
5.45
6.02
6.16
6.47
6.59
6.43
3.80
5.03
5.35
5.03
5.40
6.26
6.43
5.64
6.17
6.65
6.89
7.12
7.18
7.01
4.40
5.95
6.05
5.58
6.03
7.20
7.15
6.42
6.99
7.46
7.60
7.87
7.81
7.57
4.88
6.44
6.71
5.82
6.58
7.87
7.76
6.98
7.43
7.77
8.14
8.22
8.26
7.90
7.09
9.91
9.09
7.60
9.47
11.08
13.45
10.30
9.98
10.53
10.39
10.86
9.92
9.13
3.00
4.71
4.73
4.40
4.64
5.21
4.76
4.19
4.33
4.75
4.96
4.89
4.95
4.89
1.60
3.26
3.34
3.31
3.41
3.90
3.26
3.04
3.22
3.60
3.78
3.81
4.07
3.93
1.80
3.44
3.53
3.49
3.67
4.16
3.56
3.19
3.45
3.82
4.00
4.02
4.13
4.10
2.32
3.98
4.19
3.95
4.04
4.53
4.03
3.55
3.77
4.18
4.36
4.34
4.41
4.39
2.97
4.73
4.67
4.34
4.51
5.09
4.69
4.00
4.24
4.65
4.87
4.81
4.89
4.79
3.58
5.30
5.25
4.84
5.06
5.68
5.32
4.63
4.80
5.19
5.44
5.30
5.42
5.25
4.11
5.95
5.75
5.29
5.88
6.53
6.05
5.38
5.33
5.74
6.06
5.86
5.89
5.71
4.44
6.63
6.22
5.73
6.28
7.06
6.60
6.02
5.79
6.26
6.44
6.29
6.15
6.12
9.59
9.50
9.42
7.08
8.31
9.07
11.82
9.22
7.70
8.70
8.30
8.18
7.59
7.46
DRAFT - Do not cite or quote
C-11
October 31, 2006

-------
                                               Table C-4. (Continued)
Age Category
Average Ventilation Rate (L/min) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Light Intensity Activities (1.5 < METS # 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
7.94
11.56
11.67
11.36
11.64
13.22
13.41
12.97
13.64
14.38
14.56
14.12
13.87
13.76
4.15
8.66
8.52
9.20
8.95
9.78
10.01
9.68
10.63
11.16
11.08
11.07
11.17
11.02
5.06
8.99
9.14
9.55
9.33
10.26
10.54
10.18
11.05
11.81
11.58
11.74
11.68
11.71
6.16
9.89
9.96
10.23
10.20
11.34
11.53
11.25
11.99
12.95
12.97
12.69
12.73
12.56
7.95
11.42
11.37
11.12
11.26
12.84
12.95
12.42
13.33
14.11
14.35
13.87
13.69
13.75
9.57
12.91
13.02
12.28
12.79
14.65
14.95
14.04
14.83
15.61
15.90
15.37
14.96
14.70
10.76
14.39
14.66
13.40
14.60
16.42
16.95
16.46
16.46
17.39
17.96
16.91
16.23
16.03
11.90
15.76
15.31
14.00
15.60
18.65
18.00
17.74
18.10
18.25
19.37
17.97
16.89
16.72
15.50
21.12
18.98
19.65
21.83
26.86
29.07
27.22
25.50
23.01
25.48
20.54
20.02
20.71
7.32
11.62
11.99
10.92
11.07
12.02
11.08
10.55
11.07
11.78
12.02
10.82
10.83
10.40
3.79
8.59
8.74
8.83
8.51
9.40
8.31
7.75
8.84
9.64
9.76
8.87
8.84
8.69
4.63
8.80
9.40
9.04
9.02
9.73
8.73
8.24
9.30
10.00
10.17
9.28
9.23
8.84
5.73
10.03
10.27
9.87
9.79
10.63
9.64
9.05
9.96
10.67
10.87
9.85
9.94
9.36
7.19
11.20
11.69
10.69
10.79
11.76
10.76
10.24
10.94
11.61
11.79
10.64
10.74
10.29
8.73
12.94
13.17
11.74
11.98
13.09
12.27
11.67
11.93
12.66
12.97
11.67
11.69
11.37
9.82
15.17
15.63
12.85
13.47
14.66
13.80
13.40
13.11
13.85
14.23
12.62
12.52
12.06
10.80
15.80
16.34
13.81
14.67
15.82
14.92
14.26
13.87
14.54
14.87
13.21
13.01
12.63
16.97
20.22
23.61
16.43
22.22
22.10
21.40
21.46
17.40
17.67
17.94
17.40
17.59
16.05
DRAFT - Do not cite or quote
C-12
October 31, 2006

-------
                                               Table C-4. (Continued)
Age Category
Average Ventilation Rate (L/min) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Moderate Intensity Activities (3.0 < METS # 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
14.49
21.35
21.54
21.03
22.28
26.40
29.02
29.19
30.30
31.58
32.71
29.76
29.29
28.53
7.41
14.48
15.37
16.31
16.36
19.33
20.30
19.65
21.40
22.58
22.36
22.47
22.81
22.45
8.81
15.88
16.71
17.16
17.23
20.45
21.69
20.97
22.70
24.44
24.01
24.04
23.92
23.36
11.46
18.03
18.42
18.72
19.34
22.60
24.52
24.16
25.08
27.21
27.95
26.05
26.14
25.47
14.35
20.62
20.82
20.55
21.64
25.41
27.97
27.92
29.09
30.44
31.40
29.22
28.78
28.19
16.95
24.06
24.07
22.94
25.00
29.19
31.74
33.00
34.10
35.11
36.96
32.27
32.04
31.03
20.08
26.94
26.87
25.60
27.59
33.77
38.15
38.79
39.60
40.28
41.66
36.93
35.65
33.44
22.50
28.90
29.68
27.06
29.50
36.93
42.14
43.11
43.48
44.97
45.77
39.98
37.32
35.52
30.54
39.87
50.93
34.88
43.39
55.02
67.35
71.71
57.69
63.36
70.48
52.26
44.86
41.11
13.98
20.98
21.34
20.01
21.00
23.55
23.22
22.93
22.70
24.49
25.24
21.42
21.09
20.87
7.91
15.62
14.21
15.26
15.98
18.16
16.60
15.56
16.87
17.60
18.83
16.90
16.86
16.51
9.00
16.30
15.57
16.32
16.83
19.47
17.61
16.68
17.57
18.88
19.80
17.70
17.61
17.53
11.15
17.92
18.17
17.84
18.47
20.83
19.62
18.98
19.50
20.79
21.78
19.22
18.87
19.09
13.53
20.14
21.45
19.76
20.39
23.04
22.39
21.94
21.95
23.94
24.30
20.86
20.68
20.62
16.32
23.51
23.92
21.61
22.98
25.38
26.13
26.02
24.81
27.41
28.11
23.22
22.85
22.51
19.41
27.09
27.61
23.83
26.06
28.42
30.28
30.02
28.94
30.79
31.87
25.72
24.94
24.59
22.30
29.25
28.76
25.89
28.08
31.41
31.98
32.84
31.10
33.58
35.02
27.32
26.35
26.01
40.87
34.53
37.58
32.86
43.13
42.42
52.47
54.18
47.27
50.67
46.18
35.45
34.41
29.27
DRAFT - Do not cite or quote
C-13
October 31, 2006

-------
                                                    Table C-4.  (Continued)
Age Category
Average Ventilation Rate (L/min) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
27.47
40.25
40.45
39.04
43.62
50.82
53.17
53.91
54.27
57.31
58.42
54.13
52.46
53.31
15.07
28.33
28.15
29.46
30.66
34.31
35.96
33.55
37.79
38.31
38.95
36.28
36.99
35.35
17.26
31.68
29.74
31.35
32.76
36.84
38.33
37.95
40.36
42.47
41.57
39.51
39.50
39.17
20.63
34.66
34.45
34.01
35.77
41.53
43.51
44.83
45.43
48.29
48.65
45.17
44.12
45.51
27.79
39.80
40.57
37.80
41.94
49.12
50.51
51.51
52.05
55.20
55.90
52.41
49.95
50.93
32.47
44.34
46.17
43.23
49.52
57.40
59.33
61.63
61.21
64.45
65.95
60.81
58.95
61.18
38.41
51.62
51.90
48.93
56.58
66.25
71.45
72.38
71.42
75.61
78.57
71.96
67.56
69.55
42.24
55.92
55.06
52.22
62.40
72.92
83.03
82.07
77.35
84.39
86.46
75.23
76.45
77.05
57.90
60.66
92.01
66.17
89.86
122.9
129.9
111.9
103.9
110.3
140.7
102.2
97.34
96.76
24.19
36.48
37.58
34.53
39.39
46.56
44.09
45.68
44.44
46.98
47.35
40.02
40.64
41.88
12.36
25.94
28.99
27.00
28.59
31.06
28.69
28.84
30.27
31.04
31.54
27.56
28.49
28.48
13.26
26.24
30.51
28.21
30.13
33.76
30.61
31.18
32.93
34.02
34.82
30.63
30.08
30.09
17.15
30.42
32.33
29.98
33.66
38.76
36.51
36.65
37.02
38.35
39.38
34.59
34.25
34.35
22.45
36.11
36.43
33.33
38.02
45.34
42.71
43.10
42.23
45.61
45.69
38.71
39.56
41.38
29.27
41.97
40.81
37.63
44.08
52.90
50.23
52.22
50.45
54.06
54.07
45.30
46.98
47.57
35.59
47.28
48.07
43.22
50.48
60.81
58.15
61.93
59.54
61.52
62.30
50.81
51.96
55.58
40.67
48.64
51.36
44.72
54.60
66.32
63.44
68.91
65.26
67.40
68.75
56.42
54.07
58.33
74.55
76.97
73.01
56.62
82.88
102.4
108.8
107.9
89.51
88.72
84.40
71.34
75.25
72.12
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this
table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
C-14
October 31, 2006

-------
Table C-5.     Descriptive Statistics for Average Ventilation Rate (L/min/kg), Adjusted for Body Weight, While Performing Activities
              Within the Specified Activity Category, by Age and Gender Categories
Age Category
Average Ventilation Rate (L/min/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.385
0.395
0.330
0.243
0.151
0.098
0.071
0.058
0.061
0.065
0.066
0.069
0.075
0.080
0.281
0.295
0.248
0.160
0.102
0.067
0.047
0.038
0.038
0.044
0.045
0.051
0.055
0.061
0.301
0.313
0.260
0.174
0.109
0.072
0.052
0.042
0.043
0.047
0.049
0.054
0.058
0.064
0.337
0.345
0.289
0.198
0.125
0.081
0.061
0.048
0.050
0.054
0.055
0.060
0.064
0.071
0.380
0.384
0.326
0.237
0.148
0.094
0.069
0.056
0.060
0.064
0.064
0.068
0.073
0.078
0.427
0.441
0.362
0.279
0.174
0.110
0.080
0.066
0.070
0.074
0.076
0.076
0.083
0.088
0.465
0.491
0.405
0.314
0.200
0.129
0.090
0.076
0.080
0.086
0.086
0.086
0.093
0.097
0.503
0.524
0.442
0.350
0.215
0.141
0.098
0.083
0.086
0.092
0.093
0.093
0.099
0.111
0.666
0.626
0.538
0.484
0.302
0.208
0.147
0.132
0.127
0.137
0.141
0.117
0.125
0.122
0.391
0.414
0.342
0.238
0.151
0.090
0.069
0.055
0.056
0.060
0.061
0.061
0.066
0.072
0.280
0.315
0.258
0.145
0.089
0.059
0.044
0.035
0.034
0.039
0.039
0.043
0.047
0.051
0.301
0.329
0.271
0.163
0.097
0.065
0.047
0.038
0.037
0.041
0.042
0.046
0.051
0.056
0.335
0.361
0.293
0.195
0.120
0.075
0.057
0.045
0.045
0.048
0.050
0.052
0.056
0.063
0.386
0.405
0.333
0.233
0.146
0.087
0.067
0.054
0.054
0.057
0.059
0.059
0.064
0.070
0.434
0.464
0.391
0.275
0.176
0.102
0.080
0.065
0.065
0.070
0.071
0.067
0.074
0.079
0.479
0.521
0.425
0.320
0.211
0.118
0.093
0.074
0.076
0.084
0.083
0.076
0.084
0.091
0.517
0.536
0.453
0.353
0.229
0.130
0.102
0.082
0.082
0.090
0.088
0.081
0.090
0.096
0.739
0.661
0.494
0.519
0.297
0.176
0.152
0.098
0.115
0.114
0.135
0.101
0.125
0.115
DRAFT - Do not cite or quote
C-15
October 31, 2006

-------
                                               Table C-5. (Continued)
Age Category
Average Ventilation Rate (L/min/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sedentary & Passive Activities (METS # 1.5 ~ Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.397
0.406
0.343
0.255
0.160
0.105
0.077
0.062
0.066
0.071
0.072
0.076
0.082
0.086
0.303
0.321
0.274
0.178
0.113
0.077
0.055
0.047
0.046
0.054
0.055
0.061
0.067
0.071
0.317
0.331
0.286
0.193
0.118
0.080
0.060
0.049
0.050
0.057
0.058
0.064
0.070
0.075
0.351
0.363
0.309
0.215
0.135
0.088
0.068
0.055
0.057
0.062
0.063
0.069
0.075
0.080
0.391
0.397
0.340
0.250
0.157
0.101
0.076
0.061
0.065
0.070
0.071
0.075
0.081
0.086
0.437
0.448
0.369
0.288
0.180
0.118
0.085
0.069
0.074
0.078
0.079
0.081
0.088
0.092
0.470
0.488
0.405
0.327
0.209
0.135
0.095
0.077
0.082
0.086
0.088
0.089
0.094
0.099
0.498
0.525
0.446
0.346
0.218
0.142
0.102
0.082
0.086
0.091
0.092
0.094
0.098
0.106
0.657
0.619
0.510
0.454
0.289
0.195
0.132
0.118
0.119
0.129
0.135
0.111
0.115
0.115
0.402
0.425
0.355
0.251
0.160
0.097
0.075
0.060
0.060
0.065
0.067
0.066
0.072
0.078
0.297
0.335
0.285
0.164
0.099
0.071
0.053
0.043
0.040
0.044
0.046
0.052
0.055
0.063
0.316
0.348
0.296
0.179
0.110
0.075
0.057
0.045
0.042
0.048
0.051
0.054
0.060
0.065
0.352
0.376
0.320
0.211
0.131
0.083
0.063
0.051
0.051
0.055
0.057
0.059
0.065
0.070
0.396
0.418
0.348
0.248
0.157
0.095
0.074
0.059
0.059
0.063
0.065
0.066
0.071
0.077
0.446
0.469
0.391
0.284
0.185
0.109
0.085
0.067
0.069
0.073
0.076
0.072
0.078
0.086
0.482
0.512
0.420
0.328
0.212
0.123
0.096
0.075
0.078
0.083
0.083
0.078
0.088
0.093
0.519
0.543
0.442
0.358
0.234
0.133
0.104
0.080
0.083
0.091
0.090
0.084
0.092
0.096
0.719
0.642
0.485
0.489
0.293
0.174
0.141
0.099
0.105
0.114
0.118
0.104
0.148
0.112
DRAFT - Do not cite or quote
C-16
October 31, 2006

-------
                                               Table C-5. (Continued)
Age Category
Average Ventilation Rate (L/min/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Light Intensity Activities (1.5 < METS # 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
0.988
1.019
0.837
0.633
0.384
0.246
0.179
0.158
0.161
0.166
0.167
0.164
0.171
0.185
0.786
0.836
0.683
0.441
0.267
0.176
0.137
0.124
0.118
0.126
0.127
0.137
0.143
0.152
0.830
0.859
0.716
0.480
0.286
0.187
0.144
0.130
0.128
0.133
0.135
0.141
0.148
0.160
0.897
0.918
0.761
0.544
0.324
0.209
0.156
0.142
0.140
0.147
0.148
0.150
0.158
0.168
0.972
1.007
0.826
0.626
0.377
0.238
0.178
0.154
0.157
0.164
0.165
0.163
0.170
0.183
1.065
1.098
0.887
0.711
0.437
0.282
0.199
0.171
0.177
0.181
0.183
0.175
0.182
0.198
1.174
1.218
0.995
0.794
0.493
0.311
0.218
0.190
0.198
0.200
0.201
0.187
0.195
0.212
1.204
1.299
1.033
0.871
0.529
0.332
0.230
0.207
0.209
0.214
0.216
0.195
0.203
0.224
1.435
1.485
1.178
1.077
0.709
0.442
0.332
0.290
0.281
0.332
0.287
0.269
0.263
0.247
0.978
1.050
0.897
0.619
0.382
0.225
0.174
0.149
0.154
0.161
0.161
0.147
0.158
0.167
0.791
0.845
0.730
0.448
0.252
0.163
0.129
0.116
0.107
0.114
0.120
0.117
0.124
0.131
0.817
0.868
0.763
0.484
0.270
0.174
0.138
0.123
0.115
0.123
0.127
0.122
0.130
0.138
0.880
0.949
0.819
0.537
0.315
0.196
0.154
0.134
0.133
0.138
0.141
0.132
0.143
0.150
0.962
1.035
0.893
0.599
0.376
0.217
0.173
0.149
0.154
0.158
0.158
0.145
0.156
0.164
1.045
1.138
0.964
0.698
0.442
0.249
0.193
0.163
0.176
0.182
0.180
0.161
0.169
0.182
1.176
1.246
1.040
0.783
0.503
0.284
0.213
0.178
0.192
0.203
0.199
0.173
0.188
0.197
1.234
1.274
1.098
0.828
0.539
0.305
0.224
0.190
0.202
0.216
0.210
0.182
0.202
0.208
1.654
1.636
1.258
1.017
0.710
0.396
0.286
0.227
0.267
0.283
0.265
0.244
0.277
0.234
DRAFT - Do not cite or quote
C-17
October 31, 2006

-------
                                               Table C-5. (Continued)
Age Category
Average Ventilation Rate (L/min/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Moderate Intensity Activities (3.0 < METS # 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
1.804
1.878
1.546
1.173
0.736
0.491
0.387
0.357
0.357
0.366
0.376
0.344
0.360
0.383
1.395
1.406
1.210
0.805
0.503
0.359
0.281
0.243
0.242
0.255
0.259
0.272
0.291
0.312
1.486
1.503
1.284
0.883
0.545
0.375
0.296
0.264
0.265
0.272
0.278
0.284
0.306
0.323
1.616
1.654
1.396
0.999
0.618
0.418
0.334
0.296
0.300
0.310
0.313
0.313
0.328
0.347
1.776
1.821
1.536
1.124
0.714
0.473
0.380
0.345
0.344
0.353
0.366
0.342
0.359
0.377
1.944
2.015
1.660
1.312
0.834
0.552
0.431
0.404
0.400
0.408
0.431
0.371
0.388
0.416
2.177
2.335
1.842
1.562
0.958
0.635
0.486
0.468
0.471
0.469
0.482
0.399
0.418
0.447
2.279
2.531
2.016
1.684
1.035
0.681
0.518
0.509
0.521
0.518
0.549
0.424
0.436
0.470
3.007
3.233
2.294
2.103
1.427
1.056
0.711
0.824
0.762
0.716
0.764
0.573
0.549
0.529
1.866
1.896
1.600
1.135
0.723
0.441
0.365
0.325
0.316
0.333
0.339
0.292
0.308
0.335
1.472
1.519
1.270
0.792
0.462
0.317
0.267
0.235
0.213
0.221
0.235
0.224
0.240
0.247
1.518
1.617
1.308
0.853
0.512
0.338
0.282
0.245
0.231
0.236
0.254
0.238
0.250
0.266
1.674
1.734
1.438
0.964
0.598
0.380
0.310
0.281
0.268
0.276
0.283
0.259
0.270
0.298
1.853
1.870
1.576
1.107
0.715
0.431
0.351
0.316
0.304
0.325
0.326
0.285
0.299
0.333
2.009
2.016
1.749
1.305
0.838
0.492
0.407
0.360
0.350
0.376
0.383
0.320
0.340
0.372
2.254
2.244
1.918
1.453
0.942
0.551
0.463
0.416
0.410
0.441
0.438
0.351
0.375
0.402
2.398
2.369
2.018
1.564
1.006
0.611
0.494
0.452
0.460
0.488
0.486
0.371
0.407
0.420
2.831
3.243
2.587
1.929
1.366
0.986
0.650
0.657
0.708
0.620
0.639
0.511
0.677
0.520
DRAFT - Do not cite or quote
C-18
October 31, 2006

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                                                    Table C-5.  (Continued)
Age Category
Average Ventilation Rate (L/min/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Average Ventilation Rate (L/min/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3.477
3.523
2.889
2.167
1.410
0.950
0.711
0.660
0.644
0.655
0.675
0.624
0.646
0.716
2.697
2.522
2.165
1.545
0.936
0.635
0.475
0.449
0.442
0.438
0.446
0.441
0.466
0.505
2.931
2.891
2.338
1.661
1.033
0.696
0.527
0.474
0.470
0.485
0.481
0.470
0.502
0.544
3.095
3.217
2.583
1.811
1.186
0.790
0.599
0.543
0.533
0.548
0.547
0.531
0.553
0.602
3.456
3.571
2.870
2.107
1.380
0.909
0.691
0.644
0.625
0.625
0.643
0.612
0.626
0.700
3.805
3.913
3.197
2.496
1.587
1.089
0.802
0.749
0.731
0.741
0.767
0.703
0.716
0.805
4.139
4.109
3.432
2.725
1.832
1.267
0.917
0.855
0.853
0.856
0.913
0.788
0.849
0.942
4.324
4.338
3.537
2.978
1.933
1.362
0.997
0.973
0.930
0.944
1.023
0.855
0.910
0.991
5.081
4.859
4.299
3.617
2.678
1.978
1.938
1.271
1.228
1.768
1.315
1.084
1.043
1.351
3.263
3.376
2.800
1.979
1.331
0.879
0.696
0.650
0.613
0.653
0.634
0.544
0.594
0.666
2.530
2.568
2.200
1.359
0.885
0.589
0.452
0.417
0.384
0.379
0.393
0.364
0.395
0.454
2.621
2.748
2.314
1.506
0.967
0.625
0.496
0.462
0.420
0.444
0.431
0.404
0.445
0.480
2.886
2.971
2.478
1.694
1.122
0.712
0.567
0.546
0.496
0.517
0.507
0.449
0.498
0.543
3.227
3.242
2.809
1.903
1.331
0.853
0.686
0.627
0.590
0.641
0.612
0.529
0.580
0.626
3.633
3.714
3.125
2.193
1.519
1.010
0.793
0.730
0.708
0.765
0.755
0.610
0.675
0.768
3.962
4.157
3.355
2.500
1.718
1.184
0.916
0.884
0.835
0.879
0.851
0.718
0.776
0.932
4.082
4.874
3.482
2.989
1.806
1.306
1.000
0.939
0.905
0.950
0.928
0.803
0.829
0.972
5.021
4.875
3.876
3.244
2.217
2.049
1.498
1.298
1.549
1.610
1.369
1.113
1.262
1.219
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this
table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
C-19
October 31, 2006

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Table C-6.     Descriptive Statistics for Daily Ventilation Rate (L/day), Unadjusted for Body Weight, While Performing Activities Within
              the Specified Activity Category, by Age and Gender Categories
Age Category
Daily Ventilation Rate (L/day) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
2,499
3,405
3,334
2,928
2,814
2,958
2,769
2,368
2,496
2,676
2,757
2,979
3,098
3,309
1,389
2,261
2,203
2,063
1,888
1,969
1,772
1,534
1,619
1,754
1,865
2,144
2,133
2,294
1,551
2,485
2,419
2,229
2,079
2,155
1,969
1,663
1,756
1,908
2,025
2,264
2,335
2,521
1,975
2,845
2,859
2,498
2,342
2,420
2,264
1,911
2,039
2,229
2,263
2,551
2,641
2,875
2,416
3,289
3,203
2,890
2,712
2,838
2,651
2,283
2,428
2,586
2,680
2,916
3,037
3,280
2,966
3,785
3,790
3,278
3,185
3,349
3,149
2,678
2,843
3,050
3,119
3,309
3,464
3,732
3,499
4,617
4,459
3,706
3,660
3,912
3,680
3,172
3,304
3,512
3,664
3,754
3,955
4,131
3,926
4,984
4,877
3,968
3,926
4,413
4,009
3,504
3,644
3,766
3,923
4,143
4,397
4,361
5,744
7,734
6,399
5,396
6,365
6,479
6,622
5,363
5,470
5,802
5,526
6,124
5,072
5,502
2,275
3,466
3,307
2,788
2,686
2,766
2,398
2,009
1,996
2,197
2,222
2,255
2,325
2,456
1,186
2,279
2,247
1,959
1,820
1,894
1,502
1,276
1,290
1,418
1,491
1,597
1,659
1,746
1,372
2,402
2,404
2,136
1,942
2,039
1,654
1,376
1,429
1,585
1,582
1,661
1,779
1,902
1,761
2,894
2,863
2,388
2,266
2,277
1,944
1,620
1,642
1,824
1,806
1,896
1,980
2,064
2,199
3,397
3,267
2,713
2,618
2,661
2,297
1,898
1,946
2,123
2,138
2,204
2,281
2,394
2,723
4,020
3,730
3,072
3,037
3,079
2,784
2,280
2,264
2,485
2,533
2,516
2,629
2,767
3,140
4,489
4,158
3,612
3,494
3,716
3,222
2,839
2,645
2,841
3,025
2,887
2,912
3,030
3,420
4,763
4,453
3,848
3,746
4,058
3,701
3,139
2,980
3,123
3,315
3,280
3,134
3,319
6,641
7,585
6,846
5,110
5,516
5,595
6,357
5,163
3,972
4,447
4,352
4,347
3,771
4,394
DRAFT - Do not cite or quote
C-20
October 31, 2006

-------
                                               Table C-6. (Continued)
Age Category
Daily Ventilation Rate (L/day) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sedentary & Passive Activities (METS # 1.5 ~ Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
2,858
3,958
4,206
3,886
3,949
4,692
4,575
3,807
4,117
4,522
4,918
5,693
6,345
6,411
1,523
2,661
2,752
2,921
2,799
3,226
3,084
2,647
2,917
3,180
3,613
4,207
4,833
4,988
1,758
2,908
3,231
3,034
2,977
3,476
3,346
2,837
3,093
3,502
3,873
4,443
5,096
5,227
2,216
3,332
3,640
3,350
3,302
3,881
3,823
3,159
3,511
3,924
4,291
4,950
5,707
5,833
2,745
3,852
4,081
3,820
3,786
4,529
4,438
3,681
4,076
4,458
4,849
5,673
6,306
6,319
3,398
4,386
4,695
4,308
4,433
5,251
5,159
4,302
4,634
5,062
5,487
6,292
7,059
7,032
4,040
5,094
5,245
4,861
5,045
6,217
5,947
4,934
5,196
5,603
6,115
7,006
7,557
7,549
4,408
5,586
5,925
5,093
5,542
6,798
6,565
5,302
5,574
5,926
6,616
7,388
7,979
7,948
6,323
7,954
7,752
6,581
8,077
11,114
11,915
7,284
7,007
8,405
9,239
9,838
9,272
10,274
2,538
4,046
4,215
3,773
3,898
4,442
3,876
3,164
3,197
3,489
3,771
4,183
4,569
4,841
1,326
2,645
3,019
2,728
2,811
3,150
2,665
2,191
2,290
2,533
2,719
3,159
3,487
3,804
1,524
2,978
3,208
2,921
2,951
3,371
2,830
2,341
2,439
2,701
2,908
3,329
3,735
3,940
1,970
3,431
3,731
3,328
3,235
3,777
3,195
2,637
2,717
3,011
3,249
3,676
4,046
4,300
2,511
4,040
4,115
3,713
3,737
4,303
3,808
3,036
3,125
3,426
3,713
4,110
4,508
4,749
3,010
4,684
4,665
4,207
4,332
4,969
4,447
3,557
3,576
3,847
4,172
4,583
4,997
5,263
3,476
5,138
5,189
4,591
5,059
5,712
4,996
4,151
4,033
4,350
4,727
5,163
5,530
5,721
3,931
5,532
5,510
5,025
5,563
6,283
5,451
4,609
4,400
4,717
5,185
5,467
5,926
6,257
8,598
8,183
8,267
6,618
7,553
8,801
9,525
7,631
5,994
6,313
6,382
7,553
7,127
7,700
DRAFT - Do not cite or quote
C-21
October 31, 2006

-------
                                               Table C-6. (Continued)
Age Category
Daily Ventilation Rate (L/day) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Light Intensity Activities (1.5 < METS # 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
2,603
3,959
3,917
4,561
5,345
5,943
5,745
4,821
4,714
5,271
5,005
4,669
4,131
4,014
935
1,586
1,723
2,299
3,037
3,208
3,132
2,214
2,125
2,388
2,451
2,560
2,682
2,663
1,159
1,814
1,989
2,684
3,410
3,940
3,562
2,529
2,359
2,701
2,715
3,048
2,874
2,928
1,552
2,187
2,501
3,290
4,307
4,928
4,500
3,019
2,915
3,281
3,350
3,655
3,418
3,331
2,231
2,891
3,202
4,315
5,261
5,871
5,498
3,972
3,826
4,253
4,195
4,482
4,027
3,886
3,408
5,688
5,387
5,847
6,365
6,905
6,717
6,274
6,191
7,131
6,429
5,403
4,695
4,666
4,614
7,479
6,624
6,738
7,318
7,893
8,044
8,499
8,408
9,599
8,542
6,723
5,380
5,379
5,317
8,063
7,481
7,457
8,145
8,895
9,020
9,843
9,601
10,763
9,852
7,356
5,981
5,971
7,898
10,556
10,320
9,885
12,747
14,488
15,179
15,756
15,881
15,491
14,072
8,960
10,203
6,803
2,727
4,019
4,255
4,148
4,845
5,454
4,660
4,075
4,338
4,656
4,714
4,046
3,873
3,308
1,013
1,568
2,026
2,196
2,869
3,169
2,815
2,084
2,542
2,620
2,707
2,742
2,601
2,162
1,164
1,796
2,246
2,452
3,231
3,732
3,054
2,330
2,744
2,862
3,052
2,942
2,799
2,406
1,587
2,280
2,827
2,921
3,915
4,580
3,626
2,876
3,223
3,396
3,550
3,419
3,302
2,780
2,207
2,961
3,599
3,744
4,714
5,419
4,458
3,691
3,947
4,218
4,444
3,995
3,806
3,195
3,619
5,939
5,619
5,288
5,610
6,361
5,488
5,018
5,258
5,651
5,751
4,629
4,396
3,816
5,006
7,112
7,163
6,232
6,513
7,111
6,533
6,230
6,546
7,210
6,706
5,251
5,046
4,333
5,730
8,310
7,400
6,855
7,222
7,626
7,286
7,259
7,251
7,949
7,376
5,633
5,345
4,507
8,942
11,638
11,386
9,319
12,081
11,548
11,987
9,822
10,475
10,669
9,702
6,899
7,354
6,092
DRAFT - Do not cite or quote
C-22
October 31, 2006

-------
                                               Table C-6. (Continued)
Age Category
Daily Ventilation Rate (L/day) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Moderate Intensity Activities (3.0 < METS # 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
3,157
5,141
4,958
3,890
3,567
3,733
5,904
9,369
10,560
10,438
9,953
6,705
5,058
4,036
508
502
633
754
754
1,199
1,659
1,853
2,081
2,146
2,344
2,880
2,471
2,109
702
711
899
1,068
1,119
1,630
2,268
2,330
2,663
2,587
2,809
3,252
3,010
2,368
1,508
1,427
1,614
1,743
2,128
2,576
3,541
4,028
4,936
4,246
5,190
4,683
3,783
2,950
3,171
5,945
5,507
4,321
3,435
3,499
5,613
9,475
11,059
11,254
10,022
6,354
5,011
3,898
4,454
7,422
6,789
5,476
4,867
4,840
7,769
13,046
14,634
14,850
13,582
8,468
6,113
4,972
5,711
9,172
8,632
6,435
6,052
6,019
9,867
16,182
17,994
17,910
16,778
10,478
7,502
5,803
6,408
9,897
9,365
7,182
6,759
6,891
11,047
18,255
19,446
19,352
18,739
12,127
7,985
6,326
9,537
14,883
15,762
11,422
11,272
11,550
21,588
29,912
29,741
36,421
28,607
16,443
10,672
10,770
3,222
5,118
4,076
3,986
3,220
2,852
4,586
6,769
6,927
7,559
7,026
4,255
3,140
2,580
435
692
733
807
874
1,179
1,616
1,909
2,020
2,188
2,343
1,938
1,423
1,180
572
907
882
1,086
1,233
1,448
1,869
2,263
2,562
2,549
2,732
2,377
1,689
1,453
975
1,265
1,730
1,952
2,185
1,946
2,792
3,399
4,154
3,939
4,411
3,221
2,200
1,865
3,422
5,764
4,227
4,223
3,179
2,690
4,527
6,711
7,030
7,869
6,963
4,195
3,029
2,449
4,737
7,720
5,896
5,552
4,196
3,581
5,855
9,277
9,014
10,182
9,406
5,286
3,962
3,258
5,980
9,933
7,100
6,546
5,257
4,340
7,562
11,408
11,343
12,312
11,346
5,999
4,777
3,937
6,878
10,724
7,551
7,421
5,829
5,032
8,436
12,714
12,470
13,624
12,549
6,657
5,278
4,400
12,051
15,303
11,205
9,485
8,892
8,230
15,797
22,083
19,410
26,002
16,411
11,242
8,404
6,252
DRAFT - Do not cite or quote
C-23
October 31, 2006

-------
                                                    Table C-6.  (Continued)
Age Category
Daily Ventilation Rate (L/day) for Males,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day) for Females,
Unadjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
325
799
242
639
851
1,154
1,275
1,041
1,183
1,124
1,441
1,158
1,181
1,052
7
16
3
51
24
66
81
70
87
78
79
99
36
59
7
27
20
61
35
118
141
146
218
133
149
156
101
76
20
73
63
100
92
281
422
341
428
321
452
353
316
245
244
499
129
287
337
615
795
804
909
784
1,088
891
850
749
456
1,271
320
700
942
1,333
1,565
1,471
1,621
1,630
2,033
1,626
1,742
1,574
869
1,879
624
1,679
2,807
3,121
3,158
2,201
2,595
2,578
3,229
2,661
2,526
2,379
1,056
2,267
839
2,588
3,772
4,437
4,028
2,870
3,223
3,289
3,913
3,372
3,170
2,815
2,298
5,531
1,564
4,735
10,042
10,345
10,767
5,576
5,520
7,919
8,034
6,327
7,263
5,603
244
471
355
407
568
840
621
725
646
725
965
777
718
654
54
70
13
29
39
60
43
75
60
65
58
57
65
52
60
104
26
42
63
116
68
115
117
101
129
83
110
77
108
194
59
93
130
231
210
240
241
238
289
225
245
206
166
451
153
191
282
528
449
491
504
573
787
558
601
528
279
658
338
442
611
1,118
890
1,027
902
1,009
1,396
1,012
1,062
916
511
886
1,040
1,037
1,557
1,851
1,313
1,562
1,372
1,619
2,147
1,782
1,555
1,372
699
1,089
1,846
1,612
2,192
2,680
1,672
1,815
1,802
1,994
2,637
2,061
1,737
1,800
1,789
1,403
2,568
3,542
4,955
9,580
4,728
6,481
3,550
4,301
5,851
4,746
4,007
3,637
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this
table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
C-24
October 31, 2006

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Table C-7.    Descriptive Statistics for Daily Ventilation Rate (L/day/kg), Adjusted for Body Weight, While Performing Activities
              Within the Specified Activity Category, by Age and Gender Categories
Age Category
Daily Ventilation Rate (L/day/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sleep or nap (Activity ID = 14500)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
311.8
298.9
239.1
162.9
92.5
54.9
36.9
29.0
29.4
30.9
31.7
34.5
38.1
44.3
225.9
225.7
174.3
105.6
60.4
37.7
24.3
19.4
18.6
20.8
21.5
24.5
28.2
33.4
242.7
237.1
189.6
116.6
66.3
40.1
26.8
20.6
20.1
22.4
23.2
26.7
29.4
35.6
271.8
258.8
206.7
133.1
76.6
45.4
31.3
23.8
24.1
25.1
25.9
30.2
32.4
39.2
308.2
292.8
236.3
156.8
90.2
52.5
36.5
28.3
28.6
30.0
30.6
34.0
37.3
43.5
348.2
333.4
263.0
188.7
105.8
61.8
41.9
32.9
33.9
35.5
36.3
38.3
43.4
48.2
387.1
372.4
303.2
216.0
123.1
73.3
47.6
38.3
39.6
40.8
41.5
43.0
48.0
55.0
408.1
386.6
318.3
236.1
129.0
80.7
52.6
41.7
43.6
44.1
44.4
46.4
51.1
58.3
531.8
483.4
390.2
314.5
189.6
119.4
81.3
64.5
64.3
71.1
70.0
59.4
65.5
72.1
304.9
313.0
248.4
158.9
92.7
51.6
37.7
28.6
27.8
29.9
29.8
30.5
33.9
39.1
217.7
225.7
189.5
98.5
54.1
33.7
23.7
18.3
16.7
18.7
17.9
21.6
24.2
27.8
234.7
240.7
196.1
110.3
59.2
36.3
25.7
19.5
18.6
20.5
20.3
23.1
25.8
30.2
261.2
274.7
215.2
130.4
73.1
43.0
30.5
23.1
22.2
24.0
24.1
26.2
29.0
33.8
300.6
308.9
245.7
155.2
90.2
49.9
36.3
27.4
27.0
28.8
29.1
30.0
32.9
38.5
342.6
354.4
280.6
183.8
108.3
58.5
43.4
33.0
32.5
34.0
34.8
34.1
37.8
43.2
380.9
394.4
314.4
215.3
129.3
68.0
51.3
39.2
38.2
41.7
40.3
39.1
43.6
49.8
400.9
408.3
326.0
240.0
143.7
74.8
56.6
42.7
42.7
45.5
44.7
41.3
46.6
53.2
588.3
512.5
366.0
349.1
173.9
103.7
85.9
60.6
60.7
61.2
57.6
56.7
65.8
62.5
DRAFT - Do not cite or quote
C-25
October 31, 2006

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                                               Table C-7. (Continued)
Age Category
Daily Ventilation Rate (L/day/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Sedentary & Passive Activities (METS # 1.5 ~ Includes Sleep or Nap)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
355.9
347.5
301.7
216.0
130.2
87.1
61.1
46.6
48.6
52.2
56.5
66.1
78.1
85.9
267.6
270.3
235.7
145.6
85.9
61.6
41.8
33.6
32.4
38.2
41.6
49.7
61.3
69.2
279.0
279.9
243.7
157.4
92.6
65.0
45.1
35.7
35.9
40.3
43.0
53.8
64.4
72.3
315.2
310.1
269.9
180.8
107.1
71.2
52.7
39.5
40.5
44.9
48.9
59.0
69.7
79.0
353.3
341.4
297.1
211.6
126.9
83.4
60.6
45.3
47.9
51.3
55.4
65.1
77.9
85.6
396.8
380.1
323.6
248.7
148.6
98.8
68.6
52.3
55.5
58.1
62.8
71.9
85.1
93.2
424.4
416.3
364.8
280.4
170.8
115.2
77.8
59.1
61.9
64.5
70.6
81.1
92.5
98.9
458.1
442.7
396.7
300.9
185.3
123.2
83.4
63.9
66.1
69.9
76.0
85.7
96.2
105.6
585.5
529.4
446.8
414.7
264.5
206.0
117.3
90.7
90.4
106.4
105.9
106.1
118.3
119.7
339.4
365.9
316.4
214.8
134.3
83.1
61.0
45.0
44.7
47.5
50.7
56.6
66.6
77.3
246.9
269.6
256.1
137.1
81.3
56.3
41.7
31.0
29.2
31.3
34.0
43.4
50.8
60.3
266.8
290.8
263.5
149.1
89.1
60.7
44.3
33.8
31.3
34.3
37.5
46.1
54.5
62.5
294.2
323.0
278.8
178.7
108.2
69.4
50.4
38.0
36.3
39.5
42.6
50.6
58.9
69.0
334.2
363.0
313.2
209.0
132.8
80.5
59.7
44.0
43.7
46.3
49.6
56.1
65.2
76.3
375.1
411.0
346.6
242.7
154.1
94.2
70.0
51.4
51.5
53.8
58.1
61.6
72.7
84.4
421.9
457.3
372.2
285.6
180.5
108.5
79.0
57.3
59.2
61.5
65.8
68.4
81.8
94.3
444.9
466.9
396.1
307.7
201.8
118.1
86.2
62.4
64.0
67.1
70.4
72.2
88.0
99.4
626.6
565.0
425.1
411.1
277.6
180.8
122.9
79.4
85.5
88.7
87.7
88.2
136.1
113.3
DRAFT - Do not cite or quote
C-26
October 31, 2006

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                                               Table C-7. (Continued)
Age Category
Daily Ventilation Rate (L/day/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Light Intensity Activities (1.5 < METS # 3.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
322.7
350.7
281.9
255.2
177.5
110.9
76.9
58.5
55.5
60.8
57.0
54.0
50.8
53.9
161.2
143.4
137.7
123.6
94.5
60.1
41.5
29.3
24.6
26.3
28.4
32.7
35.0
36.0
171.8
156.2
154.6
138.5
107.1
71.6
49.2
32.2
27.9
30.9
31.8
35.8
37.2
39.2
196.7
201.0
190.7
172.8
132.4
88.6
59.8
38.7
33.8
38.9
38.9
42.5
42.2
44.9
271.2
249.3
221.0
241.3
171.6
109.4
74.9
47.9
44.9
48.8
47.9
52.1
49.6
53.4
461.4
531.3
407.4
323.6
217.7
132.5
92.5
74.9
73.3
81.4
69.9
63.2
57.8
60.9
533.8
637.5
475.3
396.6
256.3
150.6
107.2
100.1
101.4
108.1
96.6
74.7
64.5
71.6
595.1
682.8
546.1
440.5
278.6
165.8
119.1
114.7
114.4
123.6
111.2
82.6
70.8
76.4
766.2
812.9
633.1
541.2
363.4
251.3
170.3
189.9
159.9
167.8
150.3
108.5
103.9
96.6
362.7
366.8
318.5
235.6
167.0
101.9
73.2
57.7
60.5
63.8
63.2
55.1
56.6
52.9
176.7
154.0
155.6
116.0
90.5
60.3
43.2
31.2
31.9
33.4
35.9
35.5
36.1
32.1
198.4
168.2
168.8
136.9
104.4
67.2
49.6
34.7
36.2
37.1
39.4
38.9
40.6
37.8
226.6
200.1
214.6
162.1
126.6
82.4
58.4
41.5
43.4
45.6
47.9
45.4
47.4
44.3
277.8
261.3
266.5
214.3
161.6
101.1
70.3
52.6
56.4
57.5
59.0
54.0
55.3
51.3
521.0
561.2
428.7
303.2
198.9
117.1
86.0
70.0
72.1
77.8
74.2
63.2
64.2
61.1
628.0
653.8
498.4
368.6
240.2
139.1
101.0
90.2
93.1
99.3
94.5
72.5
75.1
69.1
686.6
682.6
545.0
406.4
268.6
151.3
111.0
99.9
103.3
119.3
104.7
78.0
81.4
74.7
898.4
916.4
670.6
546.5
371.9
197.3
186.0
149.6
149.9
171.4
142.9
117.8
107.4
92.1
DRAFT - Do not cite or quote
C-27
October 31, 2006

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                                               Table C-7. (Continued)
Age Category
Daily Ventilation Rate (L/day/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Moderate Intensity Activities (3.0 < METS # 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
396.5
451.0
353.4
214.5
115.1
68.8
78.3
115.2
124.1
121.3
115.1
77.4
62.0
54.1
65.8
42.1
51.9
45.7
27.4
24.4
24.6
20.6
24.7
23.1
24.5
32.2
31.5
26.5
98.8
69.2
71.3
55.0
39.3
32.3
31.0
29.5
31.8
30.4
33.3
38.9
36.3
30.9
191.1
138.0
116.0
101.9
69.3
46.4
47.4
49.9
57.2
51.4
56.6
55.3
49.0
39.0
422.9
542.9
407.8
224.1
109.9
65.8
76.7
117.4
131.9
125.9
119.4
75.5
61.9
52.0
566.3
653.2
498.7
305.3
151.6
89.6
103.1
159.4
170.4
176.3
158.8
96.5
75.2
66.8
656.2
753.6
579.8
362.3
197.2
108.1
129.2
198.2
213.0
210.6
192.5
116.1
86.6
78.8
726.2
850.8
636.8
394.0
222.2
120.7
144.7
221.7
238.2
235.2
210.8
129.7
93.5
85.0
1047.4
1278.0
915.0
555.2
308.4
214.9
245.8
432.7
328.9
358.0
312.0
167.0
133.4
114.4
434.0
452.5
306.0
226.0
111.0
53.3
72.0
95.9
96.4
102.1
94.6
58.0
45.8
41.4
61.5
61.1
49.5
44.0
28.9
22.1
23.6
28.0
27.8
28.9
30.3
26.0
19.7
19.7
79.7
81.4
73.9
59.6
38.6
27.5
29.5
34.3
37.9
35.4
37.2
31.3
24.2
22.2
125.6
118.8
124.3
114.6
69.8
36.4
44.1
49.5
53.6
58.5
60.7
43.7
32.2
30.5
509.3
528.8
338.7
232.9
109.3
49.3
72.3
98.8
96.1
103.1
88.1
56.7
44.4
37.6
643.0
694.2
448.6
312.5
145.7
67.2
94.0
132.0
127.6
136.9
124.3
71.0
56.5
51.1
759.1
809.7
508.8
376.5
176.7
85.2
115.1
161.3
153.4
170.7
160.3
86.3
68.2
63.0
813.8
828.8
568.3
439.6
208.0
96.3
126.9
174.6
182.4
186.4
179.0
95.0
77.8
69.5
976.6
1193.6
694.3
541.3
357.5
147.1
201.2
317.3
280.8
324.3
248.0
125.8
119.3
116.7
DRAFT - Do not cite or quote
C-28
October 31, 2006

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                                                    Table C-7.  (Continued)
Age Category
Daily Ventilation Rate (L/day/kg) for Males,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
Daily Ventilation Rate (L/day/kg) for Females,
Adjusted for Body Weight
Mean
Percentiles
5th
10th
25th
50th
75th
90th
95th
Maxi-
mum
High Intensity (METS > 6.0)
Birth to <1 year
1 year
2 years
3 to <6 years
6 to <11 years
11 to <16 years
16 to <21 years
21 to <31 years
31 to <41 years
41 to <51 years
51 to <61 years
61 to <71 years
71 to <81 years
81 years and
older
41.2
68.3
17.4
34.3
28.2
21.9
16.9
12.8
14.1
12.7
16.5
13.3
14.6
13.9
0.9
1.3
0.2
2.7
0.7
1.3
1.1
0.8
0.9
0.8
0.9
1.2
0.4
0.8
0.9
2.4
1.6
3.2
1.1
2.0
2.0
1.7
2.5
1.5
1.8
1.9
1.3
1.1
2.6
6.1
4.8
6.0
3.0
5.4
5.4
4.2
5.3
3.6
4.9
4.6
3.7
3.5
30.4
44.3
9.4
16.8
10.0
11.8
10.4
9.8
10.8
8.9
13.4
10.4
10.1
9.8
59.0
109.9
23.7
37.8
30.6
25.4
22.4
18.3
19.1
18.6
23.0
18.3
21.8
22.3
109.1
172.4
44.9
86.5
98.6
59.5
41.1
27.2
31.0
29.0
34.4
29.2
32.9
30.8
121.7
217.8
64.6
132.4
131.1
83.3
52.3
35.6
40.5
36.2
44.4
35.5
42.6
38.5
227.5
352.3
90.9
307.5
293.9
187.1
126.1
79.0
65.1
87.1
79.3
61.4
87.0
73.5
32.3
44.3
25.6
23.4
18.7
15.8
9.8
10.2
8.9
10.1
13.0
10.5
10.5
10.7
5.6
5.2
1.0
1.2
1.0
1.1
0.7
1.1
0.9
0.9
0.9
0.7
0.9
0.7
11.5
9.3
2.0
2.1
2.1
2.1
1.2
1.7
1.4
1.3
1.7
1.1
1.6
1.1
15.2
17.6
4.5
5.4
4.7
4.2
3.0
3.6
3.2
3.5
3.8
3.0
3.5
3.3
27.2
37.8
12.0
10.9
9.5
9.6
7.5
7.2
6.3
7.5
9.9
7.7
9.1
8.5
39.2
67.8
29.7
25.6
21.2
21.7
13.7
14.0
12.1
14.2
18.6
15.4
14.2
13.6
72.0
91.4
71.2
57.4
50.7
36.2
21.3
22.0
19.4
21.1
29.7
24.5
21.7
23.8
81.3
96.4
125.9
94.8
73.5
50.8
26.8
26.6
23.8
28.5
36.5
29.5
30.6
31.0
118.7
138.9
147.6
227.2
136.1
171.4
58.2
81.5
52.7
71.7
65.7
68.0
45.2
65.2
Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999-2002 when calculating the statistics in this
table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
DRAFT - Do not cite or quote
C-29
October 31, 2006

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