EPA/600/R-06/129F 

 May 2009

METABOLICALLY DERIVED HUMAN VENTILATION RATES: A 

REVISED APPROACH BASED UPON OXYGEN CONSUMPTION RATES 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Washington, DC 20460 
DISCLAIMER

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

ABSTRACT

The U.S. Environmental Protection Agency’s (EPA’s) National Center for Environmental 

Assessment (NCEA) publishes the Exposure Factors Handbook and the Child-Specific Exposure 

Factors Handbook to provide risk assessors with data on various factors that can impact an 

individual’s exposure to environmental contaminants. Both of these handbooks included 

estimates of ventilation rate (V&

E )—the volume of air that is inhaled by an individual in a 

specified time period. Previous approach to calculate V&

E is limited by its dependence on a 

“ventilatory equivalent” which, in turn, relies on a person’s fitness level. In this document, U.S. 

EPA presents a revised approach in which V&

E is calculated directly from an individual’s oxygen 

consumption rate. U.S. EPA then applies this method to data provided from more recent 

sources: the 1999−2002 National Health and Nutrition Examination Survey (NHANES) and 

U.S. EPA’s Consolidated Human Activity Database (CHAD). 

Preferred Citation:

U.S. Environmental Protection Agency (EPA). (2009) Metabolically derived human ventilation rates: a revised 

approach based upon oxygen consumption rates. National Center for Environmental Assessment, Washington, DC; 

EPA/600/R-06/129F. 

 ii
CONTENTS

LIST OF TABLES ...........................................................................................................................v 

LIST OF ABBREVIATIONS AND ACRONYMS ...................................................................... vi 

PREFACE ..................................................................................................................................... vii 

AUTHORS, CONTRIBUTORS, AND REVIEWERS ............................................................... viii 

EXECUTIVE SUMMARY ........................................................................................................... ix 

1. BACKGROUND AND OBJECTIVES ................................................................................. 1-1 

2. DATA SOURCES ................................................................................................................. 2-1 

2.1. SOURCE OF BODY WEIGHT DATA: 1999−2002 NHANES ................................. 2-1 

2.2. SOURCE OF BMR CALCULATION: SCHOFIELD (1985)..................................... 2-3 

2.3. SOURCE OF ACTIVITY AND METS DATA: CONSOLIDATED HUMAN 

ACTIVITY DATABASE (CHAD) .............................................................................. 2-4 

2.3.1. The National Human Activity Pattern Survey .................................................. 2-5 

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

3.1. STEP 1: GROUP NHANES AND NHAPS PARTICIPANTS BY AGE AND 

GENDER CATEGORIES ............................................................................................ 3-2 

3.2. STEP 2: CALCULATE BMR ESTIMATES FOR NHANES PARTICIPANTS ....... 3-2 

3.3. STEP 3: GENERATE A SIMULATED 24-HOUR ACTIVITY PATTERN 

FOR EACH NHANES PARTICIPANT ....................................................................... 3-2 

3.4. STEP 4: GENERATE A METS VALUE FOR EACH ACTIVITY WITHIN 

THE SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES 

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

3.5. STEP 5: CALCULATE ENERGY EXPENDITURE AND VO2 FOR EACH 

ACTIVITY WITHIN AN INDIVIDUAL’S SIMULATED 24-HOUR 

ACTIVITY PATTERN ................................................................................................. 3-6 

3.6. STEP 6: CALCULATE VENTILATION RATE FOR EACH ACTIVITY 

WITHIN THE SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH 

NHANES PARTICIPANT ........................................................................................... 3-7 

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

3.8. STEP 8: CALCULATE SUMMARY TABLES ACROSS INDIVIDUALS ............ 3-10 

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

4.1. STRENGTHS AND LIMITATIONS ......................................................................... 4-11 

REFERENCES ............................................................................................................................R-1 

 iii
CONTENTS (continued)

APPENDIX A: EVISED VENTILATION RATE (VE) EQUATIONS FOR USE IN 

INHALATION-ORIENTED EXPOSURE MODELS ............................................................... A-1 

APPENDIX B: STATISTICAL DISTRIBUTIONS ASSIGNED TO ACTIVITY 

CODES FOR USE IN SIMULATING METS VALUES ............................................................B-1 

APPENDIX C: ADDITIONAL ANALYSIS TABLES .............................................................C-1 

APPENDIX D: RESPONSE PREPARED BY S. GRAHAM (U.S. EPA) TO PEER 

REVIEWER COMMENTS ON APPENDIX A ......................................................................... D-1 

 iv
LIST OF TABLES 

2-1. Numbers of individuals from NHANES 1999−2002 with available age, gender, 

and body weight data, by age and gender categories ....................................................... 2-2 

2-2. Equations from Schofield (1985) that predict BMR (MJ/day) as a function of 

body weight (BW, kg) ...................................................................................................... 2-3 

2-3. Numbers of individuals from the NHAPS study by age and gender categories .............. 2-7 

3-1. Maximum possible METS values assigned to children, by age and gender .................... 3-6 

3-2. Estimated values, by age range, of the parameters within the multiple linear 

regression model for predicting body-weight adjusted ventilation rate 

(V&

E /BW; L/min-kg) .......................................................................................................... 3-7 

3-3. Estimated values, by age range, of the parameters within the mixed effects 

regression model for predicting body-weight adjusted ventilation rate 

(V&

E /BW; L/min-kg) .......................................................................................................... 3-9 

4-1a. Descriptive statistics for daily average ventilation rate (L/min) in males, by age 

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

4-1b. Descriptive statistics for daily average ventilation rate (L/min) in females, by age 

category ............................................................................................................................ 4-4 

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

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

 v
LIST OF ABBREVIATIONS AND ACRONYMS 

APEX Air Pollution Exposure 

BMR basal metabolic rate 

CDC Centers for Disease Control and Prevention 

CHAD Consolidated Human Activity Database 

EE energy expenditure 

EPA U.S. Environmental Protection Agency 

H oxygen uptake 

METS metabolic equivalent 

NERL National Exposure Research Laboratory 

NHANES National Health and Nutrition Examination Survey 

NHAPS National Human Activity Pattern Survey 

SHEDS Stochastic Human Exposure and Dose Simulation 

USDA U.S. Department of Agriculture 

V&

E ventilation rate 

VO2 oxygen consumption rate 

VQ ventilatory equivalents 

 vi
PREFACE 

 The Exposure Factors Program of the U.S. Environmental Protection Agency’s (EPA’s) 

Office of Research and Development (ORD) has three main goals: (1) provide updates to the 

Exposure Factors Handbook and the Child-Specific Exposure Factors Handbook; (2) identify 

data gaps in exposure factors and needs in consultation with clients; and (3) develop companion 

documents to assist clients in the use of exposure factors’ data. The activities under each goal 

are supported by and respond to the needs of the various program offices. 

ORD’s National Center for Environmental Assessment (NCEA) published the Exposure 

Factors Handbook in 1997 and the Child-Specific Exposure Factors Handbook in 2008. These 

documents provide summaries of available statistical data on various factors impacting an 

individual’s exposure to environmental contaminants. NCEA maintains these handbooks and 

periodically updates them using current literature and other reliable data made available through 

research. This document, Metabolically Derived Human Ventilation Rates: A Revised Approach 

Based Upon Oxygen Consumption Rates, provides information that can be used to update the 

ventilation rate values in the next editions of the Exposure Factors Handbook and the 

Child-Specific Exposure Factors Handbook.

 vii
AUTHORS, CONTRIBUTORS, AND REVIEWERS 

 The National Center for Environmental Assessment (NCEA), Office of Research and 

Development was responsible for the preparation of this document. The document was prepared 

by Battelle under U.S. EPA Contract No. EP-C-04-027. Laurie Schuda served as Work 

Assignment Manager, providing overall direction, technical assistance, and serving as 

contributing author. 

AUTHORS

Battelle

Bob Lordo 

Jessica Sanford 

Marcie Mohnson 

U.S. EPA

Laurie Schuda 

Jacqueline Moya 

Tom McCurdy (Appendix A) 

Steven Graham (Appendices A and D) 

 The following U.S. EPA individuals reviewed an earlier draft of this document and 

provided valuable comments: 

Bob Benson, U.S. EPA, Region 8 

Brenda Foos, U.S. EPA, Office of Children’s Health Protection 

Gary Foureman, U.S. EPA, National Center for Environmental Assessment 

Deirdre Murphy, U.S. EPA, Office of Air Quality Planning and Standards 

Harvey Richmond, U.S. EPA, Office of Air Quality Planning and Standards 

John Schaum, U.S. EPA, National Center for Environmental Assessment 

Michel Stevens, U.S. EPA, National Center for Environmental Assessment 

Paul White, U.S. EPA, National Center for Environmental Assessment 

 An external panel of experts serving as peer reviewers, along with experts representing 

the general public, reviewed this document (i.e., the main document and Appendices A through 

C). The peer-review panel was composed of the following individuals: 

Dr. William C. Adams (Chair), Professor Emeritus, University of California, Davis 

Dr. Amy Arcus-Arth, Office of Environmental Health Hazard Assessment, Cal/EPA 

Dr. David W. Layton (Chair), University of California, Retired. 

 viii
EXECUTIVE SUMMARY 

The U.S. Environmental Protection Agency’s (EPA’s) National Center for Environmental 

Assessment (NCEA) published the Exposure Factors Handbook and the Child-Specific Exposure 

Factors Handbook to provide data on various factors impacting an individual’s exposure to 

environmental contaminants. The two primary purposes of these handbooks are (1) to 

summarize data on human behaviors and characteristics that can affect exposure to 

environmental contaminants, and (2) to recommend values for specific exposure factors when 

included within an exposure assessment. NCEA maintains the Exposure Factors Handbook and 

the Child-Specific Exposure Factors Handbook and periodically updates them using current 

literature and other reliable data made available through research. Many program offices (e.g., 

Office of Solid Waste and Emergency Response, Office of Pesticides and Toxic Substances, 

Office of Water) within U.S. EPA rely on the data from these handbooks to conduct their 

exposure and risk assessments. 

 The Exposure Factors Handbook was first published in 1997 and the Child-Specific 

Exposure Factors Handbook in 2008, and the data presented in the handbooks have been 

compiled from various sources, including government reports and information presented in the 

scientific literature. Among the exposure factors addressed by these handbooks are drinking 

water consumption, soil ingestion, inhalation rates, dermal factors, food consumption, breast 

milk intake, human activity factors, consumer product use, and residential characteristics. These 

exposure factors represent the general population as well as specific target populations that may 

have differing characteristics from those of the general population. 

 One important determinant of a person’s exposure to contaminants in air is the ventilation 

rate (V& ), or the volume of air that is inhaled by an individual in a specified time period. V& E E s, 

also known as breathing or inhalation rates, are given in Chapter 6 of the Exposure Factors 

Handbook and the Child-Specific Exposure Factors Handbook. Ventilation rates have been 

calculated in the past indirectly using estimates of “ventilatory equivalent,” or the ratio of the 

volume of air ventilating the lungs to the volume of oxygen consumed. Past methodologies have 

not taken into account the variability in ventilatory equivalent with regard to age, gender, and 

fitness level. 

 ix
 This document, Metabolically Derived Human Ventilation Rates: A Revised Approach 

Based Upon Oxygen Consumption Rates presents a revised approach that calculates V&

E s directly 

from an individual’s oxygen consumption rate (VO2) and applies this method to data provided 

from more recent sources—the 1999−2002 National Health and Nutrition Examination Survey 

(NHANES) and U.S. EPA’s Consolidated Human Activity Database (CHAD). This new 

approach considers variability due to age, gender, and activities. Data were grouped into age 

categories and a simulated 24-hour activity pattern was generated by randomly sampling activity 

patterns from the set of participants with the same gender and age. Each activity was assigned a 

metabolic equivalent (METS) value based on statistical sampling of the distribution assigned by 

CHAD to each activity code. Using statistical software, equations for METS based on normal, 

lognormal, exponential, triangular, and uniform distributions were generated as needed for the 

various activity codes. The METS values were then translated into energy expenditure (EE) by 

multiplying the METS by the basal metabolic rate (BMR), which was calculated as a linear 

function of body weight. The VO2 was calculated by multiplying EE by H, the volume of 

oxygen consumed per unit of energy. VO2 was calculated both as volume per time and as 

volume per time per unit body weight. 

The inhalation rate for each activity within the 24-hour simulated activity pattern for each 

individual was estimated as a function of VO2, body weight, age, and gender. Following this, the 

average inhalation rate was calculated for each individual for the entire 24-hour period, as well 

as for four separate classes of activities based on METS value (sedentary/passive [METS less 

than or equal to 1.5], light intensity [METS greater than 1.5 and less than or equal to 3.0], 

moderate intensity [METS greater than 3.0 and less than or equal to 6.0], and high intensity 

[METS greater than 6.0]). Data for individuals were then used to generate summary tables with 

distributional data based on gender and age categories. Mean long-term inhalation rates, 

presented as daily rates for children and adults, ranged from 8.76-20.93 m3

/day for males 

(Table C-2a) and 8.53-16.20 m3/day for females (Table C-2b) with the lowest value 

corresponding to children birth to <1 year and the highest value to adults 41 to <51 years. Mean 

short-term inhalation rates, determined for children and adults performing various activities, 

ranged from 3.0×10-03 - 5.8×10-02 m3

/minute (Table C-6) with the lowest value corresponding to 

male children birth to <1 year sleeping or napping and the highest value for male adults 51 to 

<61 years of age during high-intensity activities. 

 x
 xi

A validation exercise was conducted to compare results presented in this document with 

values published in the literature using two other methodologies. These methodologies include 

the use of doubly labeled water for estimating EE (Brochu et al., 2006a, b) and the use of 

food-energy intakes from nationwide food intake surveys to estimate EEs (Layton, 1993; 

Arcus-Arth and Blaisdell, 2007). U.S. EPA has implemented the methodology presented in this 

document in Appendix A within the inhalation modules of population-based probabilistic 

exposure models, including the Stochastic Human Exposure and Dose Simulation (SHEDS) 

model and the Air Pollution Exposure (APEX) model. The APEX model results were then 

compared with the values reported by Brochu et al. (2006a, b) and Arcus-Arth and Blaisdell 

(2007) and are presented in Appendix D. Mean estimates for all of the physiological parameters 

generated by APEX, including V&

E s, are reasonably correlated with independent measures from 

the Brochu et al. (2006a, b) estimates—particularly when correcting the Brochu et al. (2006a) 

ventilation estimates for children using a more appropriate estimate of ventilatory equivalents 

(VQ) for children. The comparisons offer a validation of the methodology presented in this 

document and show that despite the different methodologies and data sources, the resulting 

APEX-derived mean ventilation estimates compare favorably to those of these two other 

methods. Although the three methodologies available to estimate V&

E s (i.e., doubly labeled 

water, food-energy consumption, and metabolically-derived V&

E s) have different strengths and 

limitations, they complement each other in providing useful information on V&

E s. It should be 

noted, however, that upper percentile values estimated using the metabolically derived daily V&

E s 

methodology may be more uncertain. These values tend to equate to unusually high estimates of 

caloric intake per day and are unlikely to represent an average individual. 

The comprehensive analysis of V&

E s presented in this document for the four separate 

classes of activities based on METS value (i.e., sedentary/passive, light intensity, moderate 

intensity, and high intensity) for the age categories for both children and adults, males and 

females, is unique and is not currently available in the literature. These estimates of V&

E s and 

their variability with age and gender are important parameters in the estimation of the inhaled 

dose and deposition of contaminants along the respiratory tract. 
1. BACKGROUND AND OBJECTIVES 

 The U.S. Environmental Protection Agency (EPA) and its program offices conduct 

various types of exposure assessment activities to characterize human exposure to toxic 

chemicals. To assist in these efforts, U.S. EPA’s National Center for Environmental Assessment 

has developed the Exposure Factors Handbook (U.S. EPA, 1997) and the Child-Specific 

Exposure Factors Handbook (U.S. EPA, 2008), documents that provide a summary of available 

statistical data on various factors that can impact a person’s exposure to environmental 

contaminants. The two primary purposes of the handbooks are 

• to summarize data on human behaviors and characteristics that can affect exposure to 

environmental contaminants, and 

• to recommend values for specific exposure factors when included within an exposure 

assessment. 

The exposure factors addressed by the handbooks include drinking water consumption, soil 

ingestion, inhalation rates, dermal factors including skin area and soil adherence factors, food 

consumption, breast milk intake, human activity factors, consumer product use, and residential 

characteristics. Values documented in the handbooks for these exposure factors represent the 

general population as well as specific target populations. The handbooks are a compilation of 

information obtained from a variety of different sources and studies that are presented in a 

consistent manner, while retaining much of the original formats that the individual study authors 

used in their publications. Exposure assessors are the primary intended audience—with a 

particular focus placed on researchers requiring data on standard factors to calculate human 

exposure to toxic chemicals. 

 U.S. EPA maintains the handbooks and periodically updates them using current literature 

and data available through U.S. EPA's research and other reliable sources. The handbooks are 

available on U.S. EPA’s Web site at www.epa.gov/ncea. 

 One important determinant of human exposure to toxic chemicals via inhalation of 

contaminants in air is a person’s ventilation rate (V&

E ), the volume of air inhaled in a specified 

time period (e.g., liters per minute, hour, or day). In the scientific literature, V&

E is often 

 1-1 
abbreviated V&

E (with the dot above the V indicating that the abbreviation represents ventilation 

“rate” rather than “volume”) and has occasionally been referred to as “breathing rate” or 

“inhalation rate,” among other terms. Values of V&

E s for both adults and children are given 

within Chapter 6 (Inhalation) of the handbooks and originate from several published studies, 

each having considered different approaches and target populations. 

In multiroute exposure assessments, U.S. EPA recognizes that metabolism “can become 

the systemic organizing principle for simplifying intake/uptake dose modeling” (McCurdy, 

2000). Metabolism can be quantifiably measured through energy expenditure (EE). Layton 

(1993) was the first to demonstrate how EE could be used to characterize inhalation exposures 

by estimating metabolically consistent V&

E s for different age/gender cohorts. Layton’s approach, 

as cited in the handbooks, calculated V&

E as the product of EE (expressed in energy units per unit 

time―typically on a daily basis), oxygen uptake (H; the volume of oxygen consumed per energy 

unit), and ventilatory equivalent (VQ; a unitless ratio of inhaled air volume to H). Layton (1993) 

used a constant value for H (equal to 0.05 L O2/KJ or 0.21 L O2/kcal) and VQ (equal to 27) while 

representing average daily EE by each of the following three approaches: 

 1) EE = average daily intake of food energy as determined from dietary survey data, 

adjusting for the under-reporting of foods. 

 2) EE = basal metabolic rate (BMR; energy expended per day, determined as a function of 

body weight) multiplied by the ratio of total daily EE to BMR that is reported in earlier 

publications. 

 3) EE = average EE associated with different levels of physical activity that a person 

experiences in an average day, as determined from time-activity survey data. 

Activity-specific EEs were calculated as the product of a person’s BMR, the activity’s 

metabolic equivalent (METS) score (i.e., a measure of the activity’s metabolic rate 

relative to a person’s BMR), and the duration of time spent performing the activity. 

Among the data sources used by Layton (1993) in these calculations are the U.S. Department of 

Agriculture (USDA) 1977−78 Nationwide Food Consumption Survey, the Second National 

Health and Nutrition Examination Survey (NHANES), and various exposure and activity studies 

published primarily in the 1980s. 

 1-2 
 One limitation of Layton’s approach to calculating V&

E is its dependence on ventilatory 

equivalent (VQ), which relies on an individual’s fitness and EE levels. The VQ value of 27 used 

in Layton (1993) may be appropriate for adults, but not necessarily for children. In addition, the 

relationship between oxygen consumption and V&

E has been documented to be nonlinear 

(Hebestreit et al., 1998, 2000), even among equally fit individuals. These limitations introduce 

bias to the results. As a result, staff at U.S. EPA's National Exposure Research Laboratory 

(NERL) have developed a revised approach, documented in the U.S. EPA document within 

Appendix A, which calculates V&

E as a direct function of a person’s oxygen consumption rate 

(VO2). U.S. EPA has implemented the methodology in Appendix A within the inhalation 

modules of population-based probabilistic exposure models, including the Stochastic Human 

Exposure and Dose Simulation (SHEDS) model and the Air Pollution Exposure (APEX) model. 

The methodology features linear regression models that predict V&

E s that are normalized for body 

mass and account for activity level, variability within age groups, and variation both between and 

within individuals. This document presents metabolically derived human V&

E s that were 

calculated by applying this methodology to data from such sources as the 1999−2002 NHANES 

and U.S. EPA’s Consolidated Human Activity Database (CHAD). The data were analyzed for 

various age categories and gender. Age categories for children were based on U.S. EPA’s 

Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures to 

Environmental Contaminants (U.S. EPA, 2005). Infants under 1 year of age were grouped into 

one category because of sample-size limitations. 

 There are two other methodologies found in the literature for estimation of V&

E s. One is 

the doubly labeled water method for estimating EE (Brochu et al., 2006a, b), while the other uses 

food-energy intakes from nationwide food intake surveys to estimate EEs (Layton, 1993; 

Arcus-Arth and Blaisdell, 2007). Doubly labeled water is water in which both the oxygen and 

hydrogen atoms are replaced with nonradioactive isotopes of these elements (2

H2O and H 18

2 O). 

The methodology is used to measure metabolic rate by measuring the disappearance rate of the 

isotopes deuterium (2H) and heavy oxygen-18 (18O) in urine, saliva, or blood samples over time 

following the ingestion of predetermined doses of doubly labeled water (Brochu et al., 2006a, b). 

This methodology is the most accurate measurement of the total daily EEs and the stored daily 

 1-3 
 1-4 

energy cost for growth, parameters necessary for the estimation of daily V&

E s. Arcus-Arth and 

Blaisdell (2007) used the methodology developed by Layton (1993) and updated the estimates of 

V&

E s by using food intake data from USDA’s Continuing Survey of Food Intake by Individuals 

(USDA, 2000). The methodology presented in this document and the other two methodologies 

found in the literature all have strengths and limitations. The doubly labeled methodology has a 

disadvantage: it assumes a fixed H and ventilatory equivalent. In reality, these are known to 

vary with age and fitness level. However, the doubly labeled approach provides reliable 

estimates of average daily inhalation rates because direct measurements are taken for a long 

period of time. The methodology is not useful, however, for estimating variability in an 

individual’s V&

E (or other parameter) over shorter time periods (i.e., V&E s for various levels of 

exertion). 

The food-energy consumption methodology has limitations resulting from the collection 

of data from a recall survey instrument. Food intake, especially for children, may be 

underreported. It also relies on accurate estimates of both H and ventilatory equivalents (VQ). 

 One advantage of the metabolically derived V&

E s is that it does not require any 

assumptions about VQ. The estimates derived using this methodology add to the current body of 

knowledge regarding V&

E s. The methodology presented in this document also has some 

limitations. These are described in more detailed in Section 4.1. For example, there may be 

biases introduced by the assumed physical intensity or exertion associated with recorded 

activities for the specific purpose of estimating EEs. In addition, the methodology requires 

reliable estimates of the BMR, for which data are limited for some age groups. The 

methodology also may be more appropriate for estimating activity-specific V&

E s rather than 

long-term daily averages. 

 In order to provide some validation of the metabolically derived V&

E s presented in this 

document, U.S. EPA conducted analyses comparing the three methodologies. Appendix D 

provides a summary of these analyses and additional description of how U.S. EPA’s APEX 

model uses the methodology in Appendix A to estimate V&

E s. It also compares the estimates 

generated by the methodology (as applied within the APEX model) with those of two other 

recently reported methodologies. 
2. DATA SOURCES 

 The approach presented in this document for calculating metabolically based V&

E for a 

person within a specific age and gender subpopulation uses the following information on that 

person: 

• Body weight; 

• BMR, a measure of a person’s EE while at physical and mental rest (i.e., in the absence 

of activity requiring exertion), primarily to perform basic brain, liver, and skeletal muscle 

function (McCurdy, 2000); 

• Typical 24-hour activity pattern (i.e., types of activities performed in a given day and the 

duration for which each activity was conducted); and 

• METS values associated with each activity type. 

After carefully identifying and evaluating various sources for these different types of 

information, U.S. EPA selected the data sources below for use in this effort. Each data source 

provides a specific type of information for an individual. 

2.1. SOURCE OF BODY WEIGHT DATA: 1999−2002 NHANES 

The Centers for Disease Control and Prevention’s (CDC) National Center for Health 

Statistics operates the NHANES program of studies. NHANES is designed to assess the health 

and nutritional status of adults and children in the United States. Begun in the 1960s, the 

NHANES program originally consisted of a periodic series of surveys focusing on different 

population groups or health topics. Data collected within the NHANES originates from personal 

interviews and physical examinations. 

 Beginning in 1999, the NHANES became a continuous, annual survey rather than the 

periodic survey that it had been in the past. The survey examines a nationally representative 

sample of persons each year. The CDC now releases public-use data files every 2 years. Data 

used in this document originated from public-use data files labeled as “NHANES 1999−2000” 

and “NHANES 2001−2002,” upon CDC’s recommendation that NHANES data collected from 

1999 to 2002 should be considered as originating from a single survey (CDC, 2005). A total of 

 2-1 
21,004 individuals were represented in the combined data set, which is comprised of 

(CDC, 2004): 

• 1999−2000: Interview sample size = 9,965; examination sample size = 9,282 

• 2001−2002: Interview sample size = 11,039; examination sample size = 10,477 

U.S. EPA selected the NHANES 1999−2002 database because it is the most complete and 

recent nationally representative source of body weight data for the U.S. population and for 

subcategories determined by age and gender (CDC, 2000, 2002). Reported body weights were 

measured by trained health professionals during an interview process using measuring equipment 

that was consistent from year to year. Within this database, a total of 19,022 individuals had 

recorded data for age, gender, and body weight. Table 2-1 presents the number of individuals 

according to the age and gender categories considered in this document. In addition, 

Tables C-1a and C-1b of Appendix C presents a summary of body weight data for the 

NHANES subjects represented in Table 2-1 by gender and age category. 

Table 2-1. Numbers of individuals from NHANES 1999−2002 with available 

age, gender, and body weight data, by age and gender categories 

Age Categorya Gender Category

Male Female Total

Birth to <1 year 419 415 834 

1 year 308 245 553 

2 years 261 255 516 

3 to <6 years 540 543 1,083 

6 to <11 years 940 894 1,834 

11 to <16 years 1,337 1,451 2,788 

16 to <21 years 1,241 1,182 2,423 

21 to <31 years 701 1,023 1,724 

31 to <41 years 728 869 1,597 

41 to <51 years 753 763 1,516 

51 to <61 years 627 622 1,249 

61 to <71 years 678 700 1,378 

71 to <81 years 496 470 966 

81 years and older 255 306 561 

Total 9,284 9,738 19,022 a

An age category labeled as “x to <y years” denotes the first day of x years of age to the last day of 

(y − 1) years of age. “1 year” represents 12 to 23 months, and “2 years” represents 24 to 35 months. 

 2-2 
2.2. SOURCE OF BMR CALCULATION: SCHOFIELD (1985) 

 As noted earlier, a person’s basal metabolic rate, or BMR, is a measurement of energy 

required to maintain the body’s normal body functions while at rest. Thus, it serves as a baseline 

to which the EE of specific activities can be related. BMR is a function of such attributes as 

body weight, height, age, and gender. 

U.S. EPA has identified several sets of mathematical equations that researchers have 

published for calculating BMR as a function of one or more attributes of a person. Each such 

equation typically represented some subset of the population determined by age, gender, and 

ethnic origin. Among the candidate equations were those proposed by Schofield (1985), which 

express BMR (in megajoules1 per day) as a linear function of body weight (in kg) based upon a 

person’s gender and age category. Although these equations tend to be most representative of 

primarily Caucasian individuals descended from European regions, no other source of BMR 

estimates was judged to be a better representation of the general population. (Most alternative 

BMR prediction equations tend to be based on small sample sizes involving a narrowly-defined 

cohort of individuals.) Furthermore, the Schofield equations have been frequently cited in 

refereed publications, and they are currently coded in U.S. EPA’s APEX and SHEDS models. 

They were used by Layton (1993) and are included in Appendix 5A of the Exposure Factors 

Handbook (U.S. EPA, 1997). U.S. EPA subsequently determined that the Schofield equations 

would continue to be used for the analyses presented in this document. Table 2-2 presents these 

equations. 

Table 2-2. Equations from Schofield (1985) that predict BMR (MJ/day) as a 

function of body weight (BW, kg) 

Age Categorya Male Female

Birth to <3 years BMR = 0.249 × BW − 0.127 BMR = 0.244 × BW − 0.130 

3 to <10 years BMR = 0.095 × BW + 2.110 BMR = 0.085 × BW + 2.033 

10 to <18 years BMR = 0.074 × BW + 2.754 BMR = 0.056 × BW + 2.898 

18 to <30 years BMR = 0.063 × BW + 2.896 BMR = 0.062 × BW + 2.036 

30 to <60 years BMR = 0.048 × BW + 3.653 BMR = 0.034 × BW + 3.538 

60 years and older BMR = 0.049 × BW + 2.459 BMR = 0.038 × BW + 2.755 

a

An age category labeled as “x to <y years” denotes the first day of x years of age to the last day of (y − 1)

years of age. 

 

1

A megajoule (MJ) equals 1 million joules, or, approximately, 238.846 kilocalories (kcal).

 2-3 
 U.S. EPA recognizes that since the Schofield equations were derived, increased rates of 

obesity, overweight incidence, and sedentariness have been observed in some sectors of the U.S. 

population, especially children and adolescents (e.g., Derumeaux-Burel et al., 2004). This may 

impact the representativeness of BMR predictions generated by the Schofield equations for 

certain age groups, such as children under 4 years of age. U.S. EPA continues to reinvestigate 

the issue of revising BMR predictions to account for more recently published information. Any 

necessary revisions to the prediction process will be incorporated into the intake dose rate 

modeling procedures used by the APEX and SHEDS models. 

2.3. SOURCE OF ACTIVITY AND METS DATA: CONSOLIDATED HUMAN 

ACTIVITY DATABASE (CHAD) 

CHAD is the central source of information on activity patterns and METS values for 

individuals within various age and gender categories. Available from 

http://www.epa.gov/chadnet1 and documented in U.S. EPA (2002), CHAD contains data from 

12 preexisting human activity studies that were conducted within the U.S. at the city, state, and 

national levels. It is intended for use by exposure assessors and modelers as a source of activity 

data for exposure/intake dose modeling and/or statistical analysis. CHAD contains nearly 

23,000 person-days of time-location-activity data representing all ages and genders and which 

can be used for exposure modeling purposes (McCurdy et al., 2000). 

 U.S. EPA’s NERL has developed and maintained CHAD since 1997. CHAD 

incorporates various human activity databases that U.S. EPA has used over the years. Each of 

these databases contains information on each activity undertaken by a given study subject during 

a monitoring period of at least 24 hours. This activity-specific information includes the 

activity’s ID code (taken from the list of activity codes in Appendix B that corresponded to the 

set of standardized activities that were applied across all studies within the database), location, 

duration expended, and an estimate of the metabolic cost of performing the activity. Metabolic 

cost is given in units of “METS” or “metabolic equivalents of work,” an EE metric used by 

exercise physiologists and clinical nutritionists to represent activity levels. An activity’s METS 

value represents a dimensionless ratio of its metabolic rate (EE) to a person’s resting, or basal, 

metabolic rate. 

 2-4 
 The CHAD assigns a METS value to an activity according to the standardized ID code 

that it assigned to the activity (see Appendix B). However, for most activities, it does not always 

assign the same single point METS value to each occurrence of the same activity within the 

database. Instead, the CHAD assigned a statistical distribution to each activity ID code 

(McCurdy et al., 2000) representing the distribution of possible METS values associated with 

that activity. Whenever a specific activity ID code was encountered within a study respondent’s 

data records, the CHAD generated a random value from the code’s assigned distribution to serve 

as the METS value for that particular activity. The statistical distributions that the CHAD 

assigned to each activity ID code were specified in Appendix 1 of U.S. EPA (2002) and are 

presented in Appendix B. The distributional forms included normal, lognormal, uniform, 

triangular, and exponential distributions, as well as point estimates (i.e., when the same METS 

value was to be assigned for all occurrences). For some activity codes, the CHAD occasionally 

assigned a different form of the distribution to different age categories (<25 years, 25−40 years, 

>40 years), in order to account for different ranges of intensity levels that may occur among 

these age groups when performing the specified activity. Appendix B also lists lower and upper 

bounds for certain distributions, where the lower bound was assigned in lieu of the randomly 

generated METS value when the latter fell below the bound, and the upper bound was assigned 

whenever the randomly generated METS value fell above the bound. 

For this analysis, U.S. EPA utilized the distributions that had previously been assigned to 

each activity code as specified in Appendix 1 of U.S. EPA (2002). No documentation was 

available within CHAD to justify why certain distributions were assigned to a particular activity 

code, why different distributions were assigned to different age categories within an activity 

code, or why the age categories within these distributions were defined as they were. Section 3 

presents more information on the specific approach used in this document to assign METS 

values to activities prior to calculating V&

E . 

2.3.1. The National Human Activity Pattern Survey 

 Many of the studies in CHAD focused their sample within a certain age range, such as 

children or senior citizens, and/or a single region or city. Only one study was conducted on a 

national scale: the U.S. EPA-sponsored National Human Activity Pattern Survey (NHAPS). 

Conducted from 1992 to 1994 by the University of Maryland Survey Research Center, the 

 2-5 
NHAPS was a probability based national telephone interview survey of 9,386 respondents that 

collected retrospective diary information on activities performed over a 24-hour day, along with 

personal and exposure-related data (Klepeis et al., 2001). Participants were selected using a 

stratified sampling approach, with stratification corresponding to the four major U.S. census 

regions (Northeast, Midwest, South, West) within the 48 contiguous states (Klepeis et al., 2001). 

U.S. EPA adopted the method used in the NHAPS study for assigning activity codes as the 

common method for coding activities across all studies within the CHAD. 

 Based upon the NHAPS study’s more general representation of the U.S. population 

compared to the other studies within CHAD, activity data from the NHAPS study were selected 

for use in characterizing activity patterns and obtaining METS values when calculating V&

E

estimates for this document. Within CHAD, NHAPS data records were distinguished by the type 

of questionnaire that the survey provided to the study subjects. Because this discernment did not 

affect the recording of information on activities performed in the previous 24-hour period and on 

the duration spent performing each of these activities, all data records were utilized in the 

analyses within this document regardless of the questionnaire type used. Tables 2-3 presents a 

breakdown of the number of NHAPS respondents with available activity data, according to the 

age and gender categories considered in this document. A total of 9,196 respondents had 

available age and gender information, and, therefore, contributed information to this analysis. 

(Each of these respondents contributed 24 hours worth of activity pattern data.) 

One major limitation to the use of the NHAPS study data in this document was the lack 

of body-weight measurements within the CHAD data records for the study respondents. When 

an NHAPS respondent’s data records are accessed interactively within the CHAD, the database 

assigns a simulated body-weight measurement to that respondent by sampling randomly from a 

lognormal distribution that is specific to the respondent’s age and gender. (Details on the 

lognormal distributions are not provided within U.S. EPA, 2002.) However, these simulated 

body-weight measurements could not be downloaded with the other study data for use in this 

document. Therefore, the NHAPS data were used only for characterizing the activity patterns of 

an individual within a given age and gender category, while the CHAD also provided the 

approach for assigning METS values to specific activities. 

 2-6 
 2-7 

Table 2-3. Numbers of individuals from the NHAPS study by age and 

gender categories 

Age Categorya Gender Category

Male Female Total

Birth to <1 year 53 30 83 

1 year 67 64 131 

2 years 63 61 124 

3 to <6 years 184 169 353 

6 to <11 years 261 225 486 

11 to <16 years 234 239 473 

16 to <21 years 234 227 461 

21 to <31 years 755 748 1,503 

31 to <41 years 737 848 1,585 

41 to <51 years 588 736 1,324 

51 to <61 years 453 548 1,001 

61 to <71 years 354 536 890 

71 to <81 years 199 380 579 

81 years and older 59 144 203 

Total 4,241 4,955 9,196

a

An age category labeled as “x to <y years” denotes the first day of x years of age to the last day of 

(y − 1) years of age. “1 year” represents 12 to 23 months, and “2 years” represents 24 to 35 months. 

 Although the NHAPS study features a probabilistic sampling design, it does not select 

respondents or their 24-hour monitoring periods purely randomly. For example, weekend days 

were over sampled, while, in selected households having children, a child had a higher 

probability for selection than an adult. While the NHAPS study team assigned sample weights to 

respondents to account for the sampling design, these sample weights are not available within 

CHAD, and, therefore, are not utilized in the analyses presented in this report. Also, certain 

reviewers noted that the telephone/retrospective approach to collect activity data in the NHAPS, 

which involved recounting of activities done in the previous 24 hours, can, occasionally, result in 

less accurate or less complete information than if a prospective/real-time diary was kept by the 

study subjects. However, no comparable data set could be identified that both recorded activity 

data in real time and would be nationally representative of the U.S. population. 
3. APPROACH 

 The document in Appendix A describes an approach for estimating V&

E from VO2 using a 

series of regression-based equations derived from data collected from 25 years of clinical studies 

by Dr. William C. Adams of the University of California at Davis (Adams, 1993; Adams et al., 

1995). The multistep approach presented in this section applies these equations to the data 

sources cited within Section 2 to estimate V&

E . An overview of the steps involved in this 

approach is as follows: 

1. Categorize individuals in the NHANES 1999−2002 and NHAPS data sets by age and 

gender. 

2. Calculate BMR for NHANES individuals as a function of body weight. 

3. Obtain a simulated 24-hour activity pattern for each NHANES individual. 

4. Assign a METS value to each activity represented in an NHANES individual’s simulated 

24-hour activity pattern. 

5. Calculate EE and VO2 for each activity within an NHANES individual’s simulated 

24-hour activity pattern. 

6. Calculate activity-specific V&

E values for an NHANES individual using the equations 

derived in the U.S. EPA document (see Appendix A) that express V&

E (adjusted for body 

weight) as a function of VO2 (adjusted for body weight), age, and gender. 

7. Calculate average daily V& & E , as well as average VE for activities sharing a similar 

intensity level, for each NHANES individual. 

8. Summarize average V&

E values across individuals for each age and gender category. 

Each step is further discussed in the subsequent sections of this document. 

 3-1 
3.1. STEP 1: GROUP NHANES AND NHAPS PARTICIPANTS BY AGE AND 

GENDER CATEGORIES 

Once the NHANES and NHAPS data were obtained for this analysis, the individuals 

represented in each data set were grouped into age and gender categories using information 

stored within the data records. Adults from 21 to 80 years were divided into six groups, each of 

size 10 years (21-30 years, 31-40 years, etc.), while adults above 80 years were placed in a single 

group. Children (<21 years) were divided into seven age categories according to groupings 

given in U.S. EPA (2005) with the following exception: children less than 1 year old were 

placed into a single group rather than further divided by age. If these children were further 

segregated, the resulting age-related groups would have had insufficient sample sizes for the 

analyses presented in this document. 

 Tables 2-1 and 2-3, in Section 2, list the age and gender categories used in this analysis, 

along with the numbers of individuals within the NHANES and NHAPS data sets, respectively, 

that were grouped into each category. A total of 19,022 NHANES participants and 

9,196 NHAPS participants were grouped into these categories, corresponding to those 

individuals having sufficient data to allow the grouping and to contribute to this analysis. 

3.2. STEP 2: CALCULATE BMR ESTIMATES FOR NHANES PARTICIPANTS 

 As noted in Section 2, body-weight data were available for individuals in the NHANES 

data set (originating from data collected during the survey’s medical examinations) but not for 

NHAPS participants; therefore, BMR estimates could be calculated only for the 

19,022 NHANES individuals. The Schofield equations given in Table 2-2 of Section 2 were 

used to calculate these estimates as a function of age, gender, and body weight. However, the 

approach in Appendix A assumes that BMR is expressed in kcal/min, while the Schofield 

equations calculate BMR in MJ/day. Given that 1 MJ equals 238.846 kcal, BMR was converted 

from MJ/day to kcal/min as follows: BMR (kcal/min) = 0.16587 × [BMR (MJ/day)]. 

3.3. STEP 3: GENERATE A SIMULATED 24-HOUR ACTIVITY PATTERN FOR 

EACH NHANES PARTICIPANT 

 Table 2-3 of Section 2 gives the number of NHAPS participants within each age/gender 

category. Each of these participants had activity pattern data available for a single 24-hour 

 3-2 
monitoring period. For a given age/gender category, let N correspond to the number of NHAPS 

participants in that category, as given in Table 2-3. Each participant in this category was then 

assigned a unique group ID number from 1 to N. 

 For each of the 19,022 individuals in the NHANES data set, the following procedure was 

performed to generate a simulated 24-hour activity pattern for that individual: 

• The individual’s age/gender category was noted. 

• Twenty (20) random integers were generated, with replacement, from the set of integers 

ranging from 1 to N (i.e., N = number of NHAPS participants within the individual’s 

age/gender category). The number of random integers to select (20) was arbitrarily 

determined. 

• For each random integer that was generated, data on the recorded 24-hour activity pattern 

(activity ID codes and the duration of time spent performing each activity) were obtained 

for the NHAPS participant whose group ID number within the given age/gender category 

matched the random integer. This resulted in assigning a “simulated” set of activity data 

to the NHANES individual that represented a total of 20 × 24 = 480 hours. (Because an 

integer could occur multiple times within the generated set of 20 random integers, a given 

set of 24-hour activity pattern data could likewise be represented multiple times within 

the simulated set of activity data.) 

• The different activity ID codes were identified in this simulated set, and for each code, 

the duration of time (in minutes) spent performing that activity was totaled across all 

records within this set. This total duration was then divided by 28,800 (i.e., the number 

of minutes in 480 hours) to estimate the proportion of this total time that is represented by 

the given activity. The proportions associated with each activity were then each 

multiplied by 24 to yield a simulated number of hours that the given NHANES individual 

was deemed to perform the activity within a 24-hour period. 

Note that activities could not be assigned to NHANES participants based on prior knowledge of 

their preferences and lifestyles because this information was unavailable. Furthermore, because 

no body weight data were available on NHAPS participants, it is not possible to account for body 

weight in assigning an activity pattern to NHANES participants. 

 3-3 
3.4. STEP 4: GENERATE A METS VALUE FOR EACH ACTIVITY WITHIN THE 

SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES 

PARTICIPANT 

 Once a simulated 24-hour activity pattern was assigned to a given NHANES individual, it 

was necessary to assign a METS value to each activity ID code represented within that activity 

pattern. METS values were assigned following the same approach used in the CHAD. As first 

noted in Section 2.3, the CHAD has assigned statistical distributions to each activity ID code. 

Appendix B lists these statistical distributions. While most activity ID codes were assigned a 

single distribution, a few codes were assigned different distributions for different age ranges, 

apparently to account for different ranges of intensity levels that may occur among different age 

groups performing the same type of activity. 

 As is done in the CHAD, for each activity ID code encountered within the simulated 

24-hour activity pattern for an NHANES individual, a METS value was assigned to that activity 

by randomly sampling from the statistical distribution that CHAD has assigned to that code (and, 

when necessary, to the age range in which the individual falls). The procedure developed to 

generate random numbers from each of the distribution types represented within Appendix B 

used random number generator functions available within the SAS® System (SAS, 2005). These 

functions yield the following: 

• RANEXP, a random number from a standard exponential distribution (scale 

parameter = 1). 

• RANNOR, a random number from a standard normal distribution (mean = 0, standard 

deviation = 1). 

• RANTRI, a random number from a triangular distribution on the interval (0, 1) with 

parameter H, a number between 0 and 1 which represents the distribution’s modal value. 

• RANUNI, a random number from a uniform distribution on the interval (0, 1). 

The random number generation procedure depended not only on the particular distributional 

form (e.g., uniform, normal, lognormal, exponential, triangular), but, also, on specific parameters 

associated with the distribution, such as the mean (mean), standard deviation (std), minimum 

(min), and maximum (max), which are specified along with the distributions in Appendix B. The 

exp denotes the exponentiation function, log denotes the natural logarithmic function, and sqrt

 3-4 
denotes the square root function. Then random numbers for the distributions in Appendix B 

were generated as follows: 

• Exponential distribution: METS = min + std × RANEXP

• Lognormal distribution: METS = exp {log [mean

2

/sqrt (mean

2 + std2

)]} 

+ sqrt {log [1 + (std/mean)

2

]} × RANNOR

• Normal distribution: METS = mean + std × RANNOR

• Triangular distribution: The generated METS value depends on the value of the mode of 

the triangular distribution, which equals 3 × mean − min − max. 

- If mode = min, then METS = max − sqrt [(1 − RANUNI) × (max − min) 

× (max − mode)] 

- If mode = max, then METS = min + sqrt [RANUNI × (max − min) 

× (mode − min)]

- If min < mode < max, then METS = min + (max − min) × RANTRI, 

where the value of H used to determine RANTRI equals (mode − min)/(max −

min).

• Uniform distribution: METS = min + (max − min) × RANUNI. 

 Whenever an activity ID code’s distribution was specified as a “point estimate,” the 

distribution consisted of a single value that occurred with 100% probability. Therefore, for such 

an activity ID code, the METS value was always assigned to equal this single value. 

 The distributions for some activity ID codes were accompanied by a specified lower and 

upper bound (see Appendix B). In these situations, the lower bound was assigned in lieu of the 

randomly generated METS value when the latter fell below the bound, and the upper bound was 

assigned whenever the randomly-generated METS value fell above the bound. 

 In November 2003, the CHAD incorporated a new feature which identified “maximum 

possible METS values” that could be assigned to children aged 16 years and younger when 

performing an activity that is 5 minutes or more in duration. This feature was implemented due 

to U.S. EPA’s finding that a child does not experience a METS value above a certain threshold 

(McCurdy and Graham, 2004). Table 3-1 presents these maximum possible values by age and 

gender. When METS values were generated from the statistical distributions specified in 

 3-5 
Appendix B, those values exceeding the maximum specified in Table 3-1 were replaced by the 

maximum. 

Table 3-1. Maximum possible METS values assigned to children, by age and 

gender 

Age (years) Gender

Males Females

6 and younger 7.2 6.4 

7 7.7 6.8 

8 8.2 7.3 

9 8.7 7.7 

10 9.2 8.2 

11 9.8 8.7 

12 10.5 9.3 

13 11.1 10.0 

14 11.8 10.6 

15 12.6 11.3 

16 13.4 12.2

Source: http://oaspub.epa.gov/chad/recent_additions$.startup 

3.5. STEP 5: CALCULATE ENERGY EXPENDITURE AND VO2 FOR EACH 

ACTIVITY WITHIN AN INDIVIDUAL’S SIMULATED 24-HOUR ACTIVITY 

PATTERN 

 Once the METS values were generated, EE (expressed in kcal/min) associated with a 

given activity was calculated by multiplying the activity’s assigned METS value by the BMR 

value assigned to the individual within Step 2: EE = BMR × METS. 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 VO2 (expressed in L O2/min) were calculated from 

these values according to the approach given in the document in Appendix A. VO2 was 

calculated as the product of EE (kcal/min) and H, the volume of oxygen consumed per unit of 

energy (L O2/kcal): VO2 = EE × H. 

In each application of this equation, the value of H is 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 A-1 of Appendix A and differ 

slightly from the distribution given in McCurdy, 2000. For a given gender, the specified uniform 

 3-6 
distribution did not differ according to age.) VO2 values were normalized by body weight by 

dividing VO2 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, the multiple linear regression model presented in Section 2 of Appendix 

A was applied to data on each activity within the simulated 24-hour activity pattern of an 

NHANES participant in order to predict that person’s V&

E (expressed in L/min), adjusted for body 

weight, when performing the given activity. For each activity within an individual’s activity 

pattern, the model predicted V&

E as a function of VO2 estimated within Step 5 (also after adjusting 

for body weight), age, and gender. The multiple linear regression model took the following 

form: log (V&

E /BW) = b0 + b1 × log (VO2/BW) + b2 × log (age) + b3 × gender + ε where “log” 

indicates the natural logarithmic transformation, BW corresponds 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 a mean of 0 and a standard 

deviation of σ. Estimated values of the intercept and slope parameters (b0, b1, b2, and b3) σ

and 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 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 (V&

E /BW; L/min/kg) 

Age b0 b1 b2 b3 σ

<20 years 4.4329 1.0864 −0.2829 0.0513 0.1444 

20−33 years 3.5718 1.1702 0.1138 0.0450 0.1741 

34−60 years 3.1876 1.1224 0.1762 0.0415 0.1727 

>60 years 2.4487 1.0437 0.2681 −0.0298 0.1277

Source: Table A-3 of Appendix A. 

 3-7 
For each activity within an individual’s simulated 24-hour activity pattern, the predicted 

values of V&

E /BW were determined as follows: 

• The following information was entered into the regression equation: the ratio of the 

individual’s calculated VO2 for that activity to the individual’s body weight, the 

individual’s age and gender codes, and estimates of the intercept and slope parameter 

(b0, b1, b2, and b3, from Table 3-2) that are relevant to the individual’s age. 

• For the model’s random error term , a random number was generated from a normal 

distribution with mean zero and standard deviation equal to the estimate given in 

Table 3-2 for σ. This random number was then substituted for the error term in the 

regression equation. 

• The equation was then calculated, and the result was exponentiated. 

The predicted value of V&

E that is unadjusted for body weight was determined by multiplying this 

result by the individual’s body weight. 

 In developing this step, U.S. EPA had also considered an alternate form of the regression 

model for predicting V&

E /BW, which was also presented in Appendix A. In this alternate model, 

called a mixed-effects regression model, the random error term of the multiple linear regression 

model is divided into two additive components, εb and εw, representing between-person and 

within-person variability, respectively: log (V&

E /BW) = b0 + b1 × log (VO2/BW) + b2

× log (age) + b3 × gender + (εb + εw), where all other terms are as defined in the multiple linear 

regression model. Both εb and εw are assumed to originate from normal distributions with mean 

0, but with different standard deviations σb and σw, respectively. Estimated values of the 

intercept and slope parameters (b0, b1, b2, and b3), σb, and σw are given in Table 3-3 for the same 

age ranges given in Table 3-2. Note that because the two models differ in their random 

component, their parameter estimates differ as well. 

Appendix A provides more details on the derivation of the multiple linear-regression 

model and the mixed-effects regression model, along with their parameter estimates. Upon 

observing how V&

E estimates compare between the two methods, which is discussed in Section 4, 

the multiple linear-regression model was used as the basis for the V&

E estimates presented in this 

document. 

 3-8 
Table 3-3. Estimated values, by age range, of the parameters within the 

mixed effects regression model for predicting body-weight adjusted 

ventilation rate (V&

E /BW; L/min-kg) 

Age B0 b1 b2 b3 σb σw

<20 years 4.3675 1.0751 −0.2714 0.0479 0.0955 0.1117 

20−33 years 3.7603 1.2491 0.1416 0.0533 0.1217 0.1296 

34−60 years 3.2440 1.1464 0.1856 0.0380 0.1260 0.1152 

>60 years 2.5828 1.0840 0.2766 −0.0208 0.1064 0.0676

Source: Table A-3 of Appendix A. 

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 V&

E and V&E /BW were predicted for each reported activity ID code within 

an individual’s simulated 24-hour activity pattern (Step 6), an average daily V&

E 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 values). 

Within the individual’s simulated 24-hour activity pattern, each activity was classified into one 

of four activity categories that were, in turn, associated with intensity level: 

• Sedentary/Passive Activities: Activities with METS values no higher than 1.5. 

• Light Intensity Activities: Activities with METS values exceeding 1.5, but no higher 

than 3.0. 

• Moderate Intensity Activities: Activities with METS values exceeding 3.0, but no higher 

than 6.0. 

• High Intensity Activities: Activities with METS values exceeding 6.0. 

(These categories were defined based on general information in the scientific literature on how 

researchers have grouped activities according to intensity level.) Within an activity category, let 

A represent the number of activities within the individual’s simulated 24-hour activity pattern 

 3-9 
that fall within the category, and let T equal the total duration of time (in minutes) that the 

individual spent performing these A activities. Let VE,i represent the individual’s V&

E calculated 

in Step 6 for the i

th activity within this activity category, and let Ti correspond to the duration of 

time spent by the individual performing this activity (i = 1, ..., A). Then the individual’s average 

daily V&

E for that METS activity group was calculated as a weighted average of the 

activity-specific V&

E values, with weights corresponding to time spent performing the activities: 

∑

A

( ) T V i E × ,i

V& = i = 1

E T

For each NHANES individual, this average V&

E statistic was calculated within each of the four 

activity categories, as well as across all activities within the individual’s simulated 24-hour 

activity pattern. The latter average was calculated using the same formula as above, with A

equaling the total number of activities within the 24-hour activity pattern, and T equaling 

1,440 minutes (i.e., the total number of minutes in a 24-hour period). These average daily V&

E

values were adjusted for body weight by dividing by the individual’s body weight. 

3.8. STEP 8: CALCULATE SUMMARY TABLES ACROSS INDIVIDUALS 

 For each age and gender category noted in Tables 2-1 and 2-3, individual-specific 

average V&

E values from Step 7 were summarized across individuals for each of the four METS 

activity categories, for a 24-hour period, and for sleeping and napping activities only 

(i.e., activity code 14500). These summaries corresponded to weighted descriptive statistics, 

with the weights corresponding to the individuals’ 4-year sampling weights stored within the 

NHANES 1999−2002 database. The descriptive statistics, which were calculated using the 

UNIVARIATE procedure within the SAS® System, included the mean, maximum, and selected 

percentiles of the observed distribution among the 19,022 NHANES participants. 

 3-10 
4. RESULTS 

 This section presents tables that summarize the results of the eight-step statistical 

technique described in Section 3, which predict ventilatory rate from simulated 24-hour activity 

data assigned to individuals represented within the NHANES 1999−2002 data base (Section 2). 

The results in this section were generated using Version 9 (Release 9.1.3) of the SAS® System 

(SAS, 2005). Appendix C provides supplemental summary tables that provide more detailed 

information that accompanies the results presented in this section. 

 The multiple linear regression model in Section 3.6 was used to predict ventilatory rate as 

a function of VO2, age, and gender for each activity assigned to each individual in the NHANES 

data set. Section 3.6 also cites a mixed effects model which differed from the multiple linear 

regression model in how its random component was specified (i.e., the multiple linear regression 

model included a single random error term, while the mixed effects model separated random 

error into two additive terms that represented between-individual and within-individual 

variability). The extent to which predictions differed between the two types of models was 

minimal; the median percentage change in the mixed effect regression model prediction relative 

to the multiple linear regression model prediction was a two percentage point decline. The 

multiple linear regression model predicted higher ventilatory rate estimates 53% of the time 

compared to the mixed effect regression model, and this percentage did not deviate much 

between the two genders or among different METS categories. Because no model tended to 

consistently produce higher predictions compared to the other, the choice of models was not 

expected to impact the types of summaries presented in this section. (It should be noted, 

however, that if the prediction process did not incorporate a realization of the random error 

term(s), then the multiple linear regression model led to higher ventilatory rate predictions 

compared to the mixed effect regression model more frequently―about 62% of the time.) 

 Descriptive statistics presented in tables within this section and Appendix C include the 

observed mean and selected percentiles of the analyzed data. These statistics were selected to 

characterize the central tendency and the general range of the predicted data distribution. While 

no parametric distributional assumptions were placed on the observed data distributions before 

these statistics were calculated, the 4-year sampling weights assigned to the individuals within 

 4-1 
NHANES 1999−2002 were used to weight each individual’s data values in the calculations of 

these statistics. 

 Table C-1 in Appendix C contains descriptive statistics on body weight and BMR for the 

NHANES individuals by gender and age category. This table serves to summarize the reported 

body weights of the individuals represented in these analyses, as well as the outcome of the 

BMR calculations (using the Schofield equations and conversion to kcal/min), both of which 

enter into calculation of EE, VO2, and V&

E . Sample sizes within each age/gender category were 

provided in Table 2-1 of Section 2. 

 Tables 4-1a and 4-1b summarize daily averageV&

E , both adjusted and unadjusted for body 

weight, by age category for males and females, respectively. The daily average V&

E s entering 

into these summaries, in L/min, were calculated in Step 7 (Section 3.7). The summaries 

represent an average rate taken over a 24-hour period (and, therefore, its typical activity pattern) 

among individuals in the specified category. Table C-2, in Appendix C, presents the same 

information, but expressed in m3

/day, as is currently done in the Exposure Factors Handbook. 

 As noted in Section 3.7, average daily V&

E was also calculated for each of four groups of 

activities defined according to specified ranges of METS values representing sedentary/passive 

activity, light intensity, middle intensity, and high intensity activities. In addition, average V&

E

was calculated for the period of time when an individual is sleeping or napping. This activity 

occurs more than any other and represents the lowest intensity activity. Thus, while sleeping and 

napping are included within the sedentary/passive activity category for this data analysis, it is 

also treated as a separate activity in the calculations. Table 4-2a (for males) and Table 4-2b (for 

females) summarize average V&

E , both adjusted and unadjusted for body weight, within each 

activity category by gender and age category. These results are presented in L/min, representing 

an average rate while performing the activity. 

Tables 4-2a and 4-2b also summarize the number of NHANES participants whose 

simulated 24-hour activity pattern included activities falling within the specified category, as 

well as the average number of hours per day (across individuals) that individuals spent 

performing these activities within their simulated activity patterns. 

 4-2 
Table 4-1a. Descriptive statistics for daily average ventilation rate (L/min) in males, by age categor

 4-3 Age Category Daily Average Ventilation Rate, Unadjusted for Body Weight ( ; L/min) VE& Daily Average Ventilation Rate, Ad

( /BW: L/min

VE& Mean Percentiles Maxi- mum Mean Percentiles 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75

Birth to <1 year 6.08 3.32 3.96 4.97 6.04 7.24 8.28 8.81 11.84 0.76 0.63 0.65 0.70 0.75 0.

1 year 9.37 6.76 7.23 8.09 9.11 10.43 11.82 12.43 16.83 0.82 0.67 0.71 0.76 0.81 0.

2 years 9.19 6.56 7.09 7.94 9.16 10.07 11.30 12.30 19.56 0.66 0.54 0.57 0.61 0.65 0.

3 to <6 years 8.78 7.24 7.55 7.91 8.74 9.47 10.16 10.70 13.56 0.49 0.36 0.39 0.43 0.48 0.

6 to <11 years 9.32 7.00 7.42 8.15 9.09 10.23 11.50 12.31 17.34 0.31 0.22 0.24 0.26 0.30 0.

11 to <16 years 10.64 7.92 8.41 9.22 10.27 11.68 13.57 14.73 19.82 0.20 0.14 0.15 0.17 0.19 0.16 to <21 years 11.95 8.75 9.31 10.06 11.55 13.31 15.23 16.23 27.23 0.16 0.12 0.13 0.14 0.16 0.21 to <31 years 13.07 8.81 9.42 10.76 12.62 14.75 17.06 18.84 30.15 0.16 0.11 0.12 0.13 0.16 0.31 to <41 years 14.09 9.72 10.39 11.78 13.77 15.98 18.59 20.07 28.28 0.17 0.11 0.12 0.14 0.16 0.41 to <51 years 14.54 10.18 10.79 12.15 14.30 16.59 18.55 19.70 31.93 0.17 0.12 0.12 0.14 0.16 0.51 to <61 years 14.52 10.41 11.16 12.22 14.17 16.08 18.76 20.20 26.51 0.17 0.11 0.12 0.14 0.17 0.61 to <71 years 12.46 9.66 10.07 11.03 12.22 13.57 15.12 16.32 19.51 0.14 0.12 0.12 0.13 0.14 0.71 to <81 years 11.35 9.10 9.45 10.18 11.27 12.20 13.49 14.18 17.03 0.14 0.12 0.12 0.13 0.14 0.

81 years and 

older 10.52 8.30 8.73 9.60 10.35 11.33 12.51 12.98 15.72 0.14 0.12 0.12 0.13 0.14 0.

Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when cathis table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.
 4-4 Table 4-1b. Descriptive statistics for daily average ventilation rate (L/min) in females, by age categ

Age Category Daily Average Ventilation Rate, Unadjusted for Body Weight 

( ; L/min)

VE

&

Daily Average Ventilation Rate, Adju

V& ( /BW: L/min-k

E

Mean

Percentiles Maximum Mean Percentiles 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 75

Birth to <1 

year

5.92 3.36 3.81 4.75 5.84 6.79 8.09 8.79 18.23 0.79 0.63 0.67 0.72 0.78 0.8

1 year 9.24 6.31 7.03 7.81 9.05 10.17 12.12 12.93 17.20 0.83 0.68 0.70 0.77 0.82 0.9

2 years 8.85 6.19 6.99 7.90 8.75 9.69 10.82 11.36 15.98 0.66 0.57 0.58 0.62 0.66 0.7

3 to <6 years 8.45 6.86 7.21 7.78 8.35 9.04 9.74 10.37 13.71 0.48 0.33 0.37 0.41 0.48 0.5

6 to <11 years 8.62 6.94 7.19 7.65 8.30 9.32 10.51 11.35 14.46 0.30 0.19 0.21 0.25 0.30 0.3

11 to <16 years 9.33 7.27 7.72 8.36 9.08 10.10 11.29 12.09 18.46 0.17 0.13 0.14 0.15 0.17 0.116 to <21 years 9.44 6.85 7.37 8.18 9.17 10.43 11.89 12.70 20.91 0.15 0.11 0.12 0.13 0.14 0.121 to <31 years 10.12 7.05 7.41 8.29 9.79 11.54 13.42 14.68 20.99 0.14 0.10 0.11 0.12 0.14 0.131 to <41 years 10.40 7.69 8.20 9.04 10.20 11.33 12.85 14.20 19.64 0.14 0.10 0.11 0.12 0.14 0.141 to <51 years 11.25 8.41 8.73 9.83 11.03 12.47 13.83 14.82 24.92 0.15 0.10 0.11 0.13 0.15 0.151 to <61 years 11.24 8.56 9.00 9.77 11.04 12.36 13.84 14.73 17.85 0.15 0.11 0.11 0.13 0.15 0.161 to <71 years 9.02 7.22 7.48 8.18 8.97 9.66 10.69 11.21 14.12 0.12 0.10 0.10 0.11 0.12 0.171 to <81 years 8.36 6.87 7.08 7.56 8.21 9.00 9.80 10.55 12.29 0.12 0.10 0.10 0.11 0.12 0.1

81 years and 

older 7.74 6.38 6.57 7.04 7.65 8.24 8.92 9.68 11.76 0.12 0.10 0.10 0.11 0.12 0.1

Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999

−2002 when ca

this table. 

V&

E was estimated using the multiple linear regression model in Section 3.6. 
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 

Participants 

Reporting 

Activity

Average 

Duration 

(hr/day) Spent 

at Activity

Ventilation Rate During this Activitya

Unadjusted for Body 

Weight (L/min)

Adjusted for Body 

Weight (L/min-kg)

Sleep or Nap (Activity ID = 14500) 

Birth to <1 year 419 13.5 3.08 0.38 

1 year 308 12.6 4.50 0.40 

2 years 261 12.1 4.61 0.33 

3 to <6 years 540 11.2 4.36 0.24 

6 to <11 years 940 10.2 4.61 0.15 

11 to <16 years 1,337 9.4 5.26 0.10 

16 to <21 years 1,241 8.7 5.31 0.07 

21 to <31 years 701 8.4 4.73 0.06 

31 to <41 years 728 8.1 5.16 0.06 

41 to <51 years 753 7.9 5.65 0.07 

51 to <61 years 627 8.0 5.78 0.07 

61 to <71 years 678 8.3 5.98 0.07 

71 to <81 years 496 8.5 6.07 0.07 

81 years and older 255 9.2 5.97 0.08 

Sedentary & Passive Activities (METS < 1.5―Includes Sleep or Nap) 

Birth to <1 year 419 15.0 3.18 0.40 

1 year 308 14.3 4.62 0.41 

2 years 261 14.6 4.79 0.34 

3 to <6 years 540 14.1 4.58 0.25 

6 to <11 years 940 13.5 4.87 0.16 

11 to <16 years 1,337 13.8 5.64 0.10 

16 to <21 years 1,241 13.2 5.76 0.08 

21 to <31 years 701 12.4 5.11 0.06 

31 to <41 years 728 12.3 5.57 0.07 

41 to <51 years 753 12.3 6.11 0.07 

51 to <61 years 627 13.1 6.27 0.07 

61 to <71 years 678 14.5 6.54 0.08 

71 to <81 years 496 15.9 6.65 0.08 

81 years and older 255 16.6 6.44 0.09 

 4-5 
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 (continued) 

Age Category

# NHANES 

Participants 

Reporting 

Activity

Average 

Duration 

(hr/day) Spent 

at Activity

Ventilation Rate During this Activitya

Unadjusted for Body 

Weight (L/min)

Adjusted for Body 

Weight (L/min-kg)

Light-Intensity Activities (1.5 < METS < 3.0) 

Birth to <1 year 419 5.3 7.94 0.99 

1 year 308 5.5 11.56 1.02 

2 years 261 5.5 11.67 0.84 

3 to <6 years 540 6.6 11.36 0.63 

6 to <11 years 940 7.6 11.64 0.38 

11 to <16 years 1,337 7.5 13.22 0.25 

16 to <21 years 1,241 7.1 13.41 0.18 

21 to <31 years 701 6.1 12.97 0.16 

31 to <41 years 728 5.7 13.64 0.16 

41 to <51 years 753 6.1 14.38 0.17 

51 to <61 years 627 5.6 14.56 0.17 

61 to <71 years 678 5.5 14.12 0.16 

71 to <81 years 496 5.0 13.87 0.17 

81 years and older 255 4.9 13.76 0.18 

Moderate-Intensity Activities (3.0 < METS < 6.0) 

Birth to <1 year 419 3.7 14.49 1.80 

1 year 308 4.0 21.35 1.88 

2 years 261 3.8 21.54 1.55 

3 to <6 years 540 3.2 21.03 1.17 

6 to <11 years 940 2.7 22.28 0.74 

11 to <16 years 1,337 2.3 26.40 0.49 

16 to <21 years 1,241 3.3 29.02 0.39 

21 to <31 years 701 5.2 29.19 0.36 

31 to <41 years 728 5.7 30.30 0.36 

41 to <51 years 753 5.4 31.58 0.37 

51 to <61 years 627 5.0 32.71 0.38 

61 to <71 years 678 3.7 29.76 0.34 

71 to <81 years 496 2.9 29.29 0.36 

81 years and older 255 2.3 28.53 0.38 

4-6 
4-7 

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 (continued) 

Age Category

# NHANES 

Participants 

Reporting 

Activity

Average 

Duration 

(hr/day) Spent 

at Activity

Ventilation Rate During this Activitya

Unadjusted for Body 

Weight (L/min)

Adjusted for Body 

Weight (L/min-kg)

High-Intensity Activities (METS > 6.0) 

Birth to <1 year 183 0.2 27.47 3.48 

1 year 164 0.3 40.25 3.52 

2 years 162 0.1 40.45 2.89 

3 to <6 years 263 0.3 39.04 2.17 

6 to <11 years 637 0.3 43.62 1.41 

11 to <16 years 1,111 0.4 50.82 0.95 

16 to <21 years 968 0.4 53.17 0.71 

21 to <31 years 546 0.3 53.91 0.66 

31 to <41 years 567 0.4 54.27 0.64 

41 to <51 years 487 0.3 57.31 0.66 

51 to <61 years 452 0.4 58.42 0.68 

61 to <71 years 490 0.4 54.13 0.62 

71 to <81 years 343 0.4 52.46 0.65 

81 years and older 168 0.3 53.31 0.72 

a

An individual’s V&

E for the given activity category equals the weighted average of the individual’s 

activity-specific V&

E s 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. 
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 

Participants 

Reporting 

Activity

Average 

Duration 

(hr/day) Spent 

at Activity

Ventilation Rate During this Activitya

Unadjusted for Body 

Weight (L/min)

Adjusted for Body 

Weight (L/min-kg)

Sleep or Nap (Activity ID = 14500) 

Birth to <1 year 415 13.0 2.92 0.39 

1 year 245 12.6 4.59 0.41 

2 years 255 12.1 4.56 0.34 

3 to <6 years 543 11.1 4.18 0.24 

6 to <11 years 894 10.3 4.36 0.15 

11 to <16 years 1,451 9.6 4.81 0.09 

16 to <21 years 1,182 9.1 4.40 0.07 

21 to <31 years 1,023 8.6 3.89 0.06 

31 to <41 years 869 8.3 4.00 0.06 

41 to <51 years 763 8.3 4.40 0.06 

51 to <61 years 622 8.1 4.56 0.06 

61 to <71 years 700 8.4 4.47 0.06 

71 to <81 years 470 8.6 4.52 0.07 

81 years and older 306 9.1 4.49 0.07 

Sedentary & Passive Activities (METS < 1.5―Includes Sleep or Nap) 

Birth to <1 year 415 14.1 3.00 0.40 

1 year 245 14.3 4.71 0.43 

2 years 255 14.9 4.73 0.36 

3 to <6 years 543 14.3 4.40 0.25 

6 to <11 years 894 14.0 4.64 0.16 

11 to <16 years 1,451 14.2 5.21 0.10 

16 to <21 years 1,182 13.6 4.76 0.07 

21 to <31 years 1,023 12.6 4.19 0.06 

31 to <41 years 869 12.3 4.33 0.06 

41 to <51 years 763 12.2 4.75 0.06 

51 to <61 years 622 12.7 4.96 0.07 

61 to <71 years 700 14.3 4.89 0.07 

71 to <81 years 470 15.4 4.95 0.07 

81 years and older 306 16.5 4.89 0.08 

 4-8 
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 (continued)

Age Category

# NHANES 

Participants 

Reporting 

Activity

Average 

Duration 

(hr/day) Spent 

at Activity

Ventilation Rate During this Activitya

Unadjusted for Body 

Weight (L/min)

Adjusted for Body 

Weight (L/min-kg)

Light-Intensity Activities (1.5 < METS < 3.0) 

Birth to <1 year 415 6.0 7.32 0.98 

1 year 245 5.6 11.62 1.05 

2 years 255 5.8 11.99 0.90 

3 to <6 years 543 6.3 10.92 0.62 

6 to <11 years 894 7.3 11.07 0.38 

11 to <16 years 1,451 7.6 12.02 0.23 

16 to <21 years 1,182 7.0 11.08 0.17 

21 to <31 years 1,023 6.4 10.55 0.15 

31 to <41 years 869 6.5 11.07 0.15 

41 to <51 years 763 6.6 11.78 0.16 

51 to <61 years 622 6.5 12.02 0.16 

61 to <71 years 700 6.2 10.82 0.15 

71 to <81 years 470 6.0 10.83 0.16 

81 years and older 306 5.3 10.40 0.17 

Moderate-Intensity Activities (3.0 < METS < 6.0) 

Birth to <1 year 415 3.9 13.98 1.87 

1 year 245 4.0 20.98 1.90 

2 years 255 3.3 21.34 1.60 

3 to <6 years 543 3.4 20.01 1.14 

6 to <11 years 894 2.6 21.00 0.72 

11 to <16 years 1,451 2.0 23.55 0.44 

16 to <21 years 1,182 3.3 23.22 0.36 

21 to <31 years 1,023 4.8 22.93 0.33 

31 to <41 years 869 5.0 22.70 0.32 

41 to <51 years 763 5.0 24.49 0.33 

51 to <61 years 622 4.6 25.24 0.34 

61 to <71 years 700 3.3 21.42 0.29 

71 to <81 years 470 2.5 21.09 0.31 

81 years and older 306 2.1 20.87 0.33 

4-9 
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 (continued)

Age Category

# NHANES 

Participants 

Reporting 

Activity

Average 

Duration 

(hr/day) Spent 

at Activity

Ventilation Rate During this Activitya

Unadjusted for Body 

Weight (L/min)

Adjusted for Body 

Weight (L/min-kg)

High-Intensity Activities (METS > 6.0) 

Birth to <1 year 79 0.2 24.19 3.26 

1 year 55 0.2 36.48 3.38 

2 years 130 0.2 37.58 2.80 

3 to <6 years 347 0.2 34.53 1.98 

6 to <11 years 707 0.2 39.39 1.33 

11 to <16 years 1,170 0.3 46.56 0.88 

16 to <21 years 887 0.2 44.09 0.70 

21 to <31 years 796 0.3 45.68 0.65 

31 to <41 years 687 0.2 44.44 0.61 

41 to <51 years 515 0.3 46.98 0.65 

51 to <61 years 424 0.3 47.35 0.63 

61 to <71 years 465 0.3 40.02 0.54 

71 to <81 years 304 0.3 40.64 0.59 

81 years and older 188 0.3 41.88 0.67 

a

An individual’s V&

E for the given activity category equals the weighted average of the individual’s activity-specific 

V&

E s 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. 

4-10 
 Additional descriptive statistics to accompany the results in Tables 4-2a and 4-2b can be 

found in Table C-3 through Table C-7 in Appendix C. These five tables summarize the 

following: 

• Duration of time spent performing activities (hr/day) 

• Average V&

E (L/min and m3

/min), unadjusted for body weight 

• Average V&

E (L/min/kg and m3

/min/kg), adjusted for body weight 

4.1. STRENGTHS AND LIMITATIONS 

 The major strengths of the approach applied in this document (and detailed in 

Appendix A) are that it accounts for differences in V&

E that occur due to activity level, the effect 

of age and gender, and variation between individuals. The approach yields an estimate of V&

E

that is a function of VO2 rather than an indirect measure of oxygen consumption such as VQ. 

(While other researchers have estimated V&

E given VQ, the appropriate value of VQ to use can 

depend on an individual’s work rate, and thus, can introduce bias and additional variability.) The 

primary sources of input data to this approach, the NHANES and NHAPS data sets, are each 

nationally representative data sets with a large sample size, even within the age and gender 

categories considered in this document, thereby allowing for improved characterization of body 

weight and activity patterns that can represent everyone in an age/gender subpopulation. 

However, in the prediction of V&

E from VO2, there is, admittedly, limited data available in the 

literature on diverse groups of people varying in age, gender, body weight, and other relevant 

factors. 

By simulating an individual’s 24-hour activity pattern based on information for a 

subpopulation with the same age and gender range, this approach attempted to address the 

correlation that is present between an individual’s BMR measure and the METS values 

associated with the activities that the individual performs. However, because the NHAPS 

database within CHAD does not include body weight, information on both METS values and 

BMR were not available for an individual that would allow a more rigorous characterization, 

such as taking into account correlation among the incidence and duration of certain activity 

4-11 
types. This was one limitation of the analysis outcome. While other data sources within CHAD, 

which did include body weight were considered, they were deemed to have limited target 

populations that would have limited the ability to infer findings to larger populations. 

To determine an individual’s BMR, EPA utilized a series of equations proposed by 

Schofield (1985) that predicted BMR as a function of body weight, gender, and age category. 

While this set of equations was deemed to represent the general U.S. population more completely 

than any other available BMR estimating procedures, there are some limitations associated with 

their use. For example, the equations are based on studies that have become considerably dated 

in recent years, and, therefore, may be less representative of current populations (especially in 

young children). Some researchers question the extent of uncertainty that may be introduced by 

expressing BMR as a strictly linear function of body weight, as the Schofield equations do. 

Despite these limitations, the derivation and application of the Schofield equations have been 

subject to independent technical review in scientific publications. They remain the best available 

tool for U.S. EPA in calculating BMR from body weight for the general population. 

 The simulated 24-hour activity pattern assigned to an NHANES participant is likely to 

contain a greater variety of different types of activities than one person may typically experience 

in a day. Furthermore, a particular activity may be represented within an activity pattern for a 

shorter duration than what may be typical for a person. The durations of different intensity 

levels summarized in Table C-3 of Appendix C across the simulated activity patterns appear to 

be within reason for each age category. 

 The approach does not specifically account for uncertainty that is introduced by assigning 

a random METS value to an activity that originates from a pre-specified statistical distribution. 

In addition, a potential bias may be introduced if this distribution is not appropriate in reality for 

a given activity, although the CHAD identified appropriate distributions based upon a review of 

the exercise physiology and clinical nutrition literature. The METS randomization process 

allows for different METS values to be assigned to the same activity being performed by the 

same individual at a given moment in time. There is both variability associated with this METS 

randomization process as well as variability in METS values that is present from one individual 

to another. The variability in METS values that is present from one individual to another 

confounds the variability associated with the METS randomization process. 

4-12 
 By using the NHANES sampling weights in the calculation of the statistics in this 

document, the goal of this effort was to generate statistics that could represent national estimates. 

In the calculation, use of the sample weights is preferable to ignoring them. However, because 

the 24-hour activity pattern assigned to each NHANES individual was simulated using activity 

information from the NHAPS study, the observed distribution of V&

E values across individuals 

can only approximate a national distribution. In addition, because the simulated 24-hour activity 

patterns are limited to the set of activities reported within the NHAPS database, and because 

each simulated pattern represented an average of multiple patterns observed within the NHAPS 

database, an individual’s true activity pattern in any given 24-hour period may be more variable 

than that considered in this exercise. Furthermore, because the simulated activity profiles did not 

consider possible limits on the “maximum possible METS value” that would account for 

previous activities, V&

E s may be overestimated as a result. 

 Data from the NHAPS were used to characterize activity levels for individuals in the U.S. 

population. Because the NHAPS was conducted over 10 years ago, it may not accurately portray 

activity profiles in certain subpopulations, especially those seeing greater trends toward 

overweight incidence and obesity (e.g., children and adolescents). In addition, the growing 

sedentary nature of the population as a whole may be affecting the continued relevance of 

NHAPS activity data to the contemporary U.S. population. METS distributions also may not be 

adequately characterized when activities are conducted by children, due to the more frequent and 

sudden movement by children from one activity to another compared to other subpopulations. 

Lastly, the survey’s practice of retrospectively providing activity information may result in less 

accurate and less detailed information than studies that use prospective, real-time diaries that 

subjects would complete after participation in each activity. While U.S. EPA recognizes and 

considered these limitations, the several advantages associated with the NHAPS data remained 

important, and, therefore, led to accepting the data for use in this analysis. 

In order to assess the impact of these limitations, a validation exercise was conducted to 

compare results presented in this document with values published in the literature using two 

other methodologies. These methodologies include the use of doubly labeled water for 

estimating EE (Brochu et al., 2006a, b) and the use of food-energy intakes from nationwide food 

intake surveys to estimate EEs (Layton, 1993; Arcus-Arth and Blaisdell, 2007). Appendix D 

shows how the results from the metabolically derived methodology compares with the values 

4-13 
reported by Brochu et al. (2006a, b) and Arcus-Arth and Blaisdell (2007). Mean estimates for all 

of the physiological parameters generated by APEX including V&

E s are reasonably correlated 

with independent measures from the Brochu et al. (2006a, b) estimates, particularly when 

correcting the Brochu et al. (2006a) ventilation estimates for children using a more appropriate 

estimate of VQ for children. The results of this exercise suggest that despite the different 

methodologies and data sources, the resulting APEX-derived mean ventilation estimates 

compare favorably to those of these two other methods. It should be noted, however, that upper 

percentile values may be more uncertain. These values tend to equate to unusually high 

estimates of caloric intake per day and are unlikely to represent an average individual. Although 

the three methodologies available to estimate V&

E s (i.e., doubly labeled water, food-energy 

consumption, and metabolically-derived V&

E s) have different strengths and limitations, they 

complement each other in providing useful information on V&

E s. 

4-14 
REFERENCES 

Adams, WC; Shaffrath, JD; Ollison, WM. (1995) The relation of pulmonary ventilation and heart rate in leg work 

alone, arm work alone, and in combined arm and leg work. Paper WA-84A.04 presented at the 88th Annual Air & 

Waste Management Association Conference. 21 June 1995. 

Adams, WC. (1993) Measurement of breathing rate and volume in routinely performed daily activities. California 

Environmental Protection Agency, Air Resources Board. Final Report, Contract No. A033-205. Available online at 

http://www.arb.ca.gov/research/apr/past/a033-205.pdf 

Arcus-Arth, A; Blaisdell, RJ. (2007) Statistical distributions of daily breathing rates for narrow age groups of infants 

and children. Risk Anal 27(1):97−110. 

Brochu, P; Ducré-Robitaille, J; Brodeur, J. (2006a) Physiological daily inhalation rates for free-living individuals 

aged 1 month to 96 years, using data from doubly labeled water measurements: a proposal for air quality criteria, 

standard calculations and health risk assessment. Hum Ecol Risk Assess 12(4):675−701. 

Brochu, P; Ducré-Robitaille, J; Brodeur, J. (2006b) Physiological daily inhalation rates for free-living pregnant and 

lactating adolescents and women aged 11 to 55 years, using data from doubly labeled water measurements for use in 

health risk assessment. Hum Ecol Risk Assess 12(4):702−735. 

CDC (Centers for Disease Control and Prevention). (2000) National Health and Nutrition Examination Survey 

(NHANES) 1999-2000. U.S. Department of Health and Human Services, National Center for Health Statistics 

(NCHS), Hyattsville, MD. Available online at http://www.cdc.gov/nchs/about/major/nhanes/nhanes99_00.htm 

CDC (Centers for Disease Control and Prevention). (2002) National Health and Nutrition Examination Survey 

(NHANES) 1999-2000. U.S. Department of Health and Human Services, National Center for Health Statistics 

(NCHS), Hyattsville, MD. Available online at http://www.cdc.gov/nchs/about/major/nhanes/nhanes01-02.htm. 

CDC (Centers for Disease Control and Prevention). (2005) Analytic and reporting guidelines The national health 

and nutrition examination survey (NHANES). National Center for Health Statistics, Centers for Disease Control 

and Prevention, Hyattsville, MD. December 2005. Available online at 

http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_2005.pdf. 

CDC (Centers for Disease Control and Prevention). (2004) NHANES analytic guidelines. National Center for 

Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD. June 2004. Available online at 

http://www.cdc.gov/nchs/data/nhanes/nhanes_general_guidelines_june_04.pdf. 

Derumeaux-Burel, H; Meyer, M; Morin, L; et al. (2004) Prediction of resting energy expenditure in a large 

population of obese children. Am J Clin Nutr 80(6):1544−1550. 

Hebestreit, H; Staschen, B; Hebestreit, A. (2000) Ventilatory threshold: a useful method to determine aerobic fitness 

in children? Med Sci Sports Exerc 32(11):1964−1969. 

Hebestreit, H; Kriemler, S; Hughson, RL; et al. (1998) Kinetics of oxygen uptake at the onset of exercise in boys 

and men. J Appl Physiol 85(5):1833−1841. 

Klepeis, NE; Nelson, WC; Ott, WR; et al. (2001) The national human activity pattern survey (NHAPS): a resource 

for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol 11(3):231−252. 

Johnson, T. (2002) A guide to selected algorithms, distributions, and databases used in exposure models developed 

by the office of air quality planning and standards. Prepared for the U.S. Environmental Protection Agency, Office 

of Research and Development, RTP, NC. Prepared by TRJ Environmental, Chapel Hill, NC. Available online at 

http://www.epa.gov/ttn/fera/data/human/report052202.pdf. 

R-1 
R-2 

Layton, DW. (1993) Metabolically consistent breathing rates for use in dose assessments. Health Phys 64(1):23−36. 

McCurdy, TR. (2000) Conceptual basis for multi-route intake dose modeling using an energy expenditure approach. 

J Expo Anal Environ Epidemiol 10:86−97. 

McCurdy, TR; Glen, G; Smith, L; et al. (2000) The National Exposure Research Laboratory’s consolidated human 

activity database. J Expo Anal Environ Epidemiol 10:566−578. 

McCurdy TR; Graham, SE. (2004) Analyses to understand relationships among physiological parameters in children 

and adolescents aged 6−16. U.S. Environmental Protection Agency, Office of Research and Development, National 

Exposures Research Laboratory, Research Triangle Park, N. Carolina. EPA/600/X-04/092. 

SAS (2005) SAS OnlineDoc® 9.1.3. Cary, NC: SAS Institute, Inc. Available online at 

http://support.sas.com/onlinedoc/913/docMainpage.jsp. 

Schofield, WN. (1985) Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin 

Nutr. 39(Suppl.):5−41. 

USDA (Department of Agriculture). (2000) Continuing survey of food intake by individuals (CSFII) 1994-96, 98. 

U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD. Available from the National 

Technical Information Service (NTIS), Springfield Va, PB2000-500027. 

U.S. EPA (Environmental Protection Agency. (1997) Exposure factors handbook. Office of Research and 

Development, National Center for Environmental Assessment, Research Triangle Park, NC. Available online at 

http://www.epa.gov/ncea/pdfs/efh/front.pdf. 

U.S. EPA (Environmental Protection Agency). (2002) CHAD user’s guide: extracting human activity information 

from CHAD on the PC. Prepared by ManTech Environmental Technologies and modified March 22 2002 by 

Science Applications International Corporation for the National Exposure Research Laboratory, U.S. Environmental 

Protection Agency. Research Triangle Park, NC. Available online at 

http://www.epa.gov/chadnet1/reports/CHAD_Manual.pdf. 

U.S. EPA (Environmental Protection Agency). (2005) Guidance on selecting age groups for monitoring and 

assessing childhood exposures to environmental contaminants. Risk Assessment Forum, Washington, DC. 

EPA/630/P-03/003F. November 2005. Available online at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=146583. 

U.S. EPA (Environmental Protection Assessment). (2008) Child-specific exposure factors handbook (CSEFH). 

National Center for Environmental Assessment, Washington, DC, EPA/600/R-06/096F, October, 2008. Available 

online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199243. 
APPENDIX A: 

Revised Ventilation Rate (VE) Equations for Use 

 in Inhalation-Oriented Exposure Models 

by 

S. Graham and T. McCurdy 

EPA/600/X-05/008
Disclaimer to Appendix A 

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

Abstract to Appendix A 

Using data compiled from 32 clinical exercise studies, algorithms were developed to 

estimate body mass-normalized ventilation rate (VE, L/min-kg) for 4 age groups (<20, 20-<34, 

34-<61, 61+ years of age) and both genders. The algorithms account for differences in 

ventilation rate due to activity level, variability within age groups, and variation both between 

and within individuals. A multiple linear regression (MLR) model was first used to estimate 

significant explanatory parameters (p<0.01) following natural log (Ln) transformation of body 

mass (BM) normalized oxygen consumption rate (VO2). Log transformed age (Ln(age)), gender 

(-1 for males, 1 for females), and Ln(VO2/BM) served as independent variables and regressed on 

multiple VE measurements that were collected during incremental exercise to obtain regression 

parameter estimates. The (MLR) model showed marginal statistical improvement (R2

 +5%) in 

comparison with a previous simple linear regression model for estimating VE, however the MLR 

can estimate population VE with one-half the equations formerly used and can be used to address 

uncertainty in VE estimations. A mixed-effects regression (MER) model was then constructed 

utilizing the independent variables as fixed parameters and retaining individuals and study of 

origin as random effects variables. The MER model was used to allocate the random error (ε) to 

between-person residuals distributions (inter-individual variability) and within-person residuals 

distributions (intra-individual variability). Predictive equations were executed for 5,000 

iterations at a given age (e.g., 5 year olds) or age group classification (e.g., 45-55 years old) and 

estimated ventilation rates for each model were compared at their respective 50th, 95th and 99th

percentiles. U.S. EPA’s Air Pollution Exposure (APEX) model was used to estimate population 

ventilation rates using a variety of ventilation algorithms for comparison with the MLR and 

MER at individual years in age. VE estimations from the MLR and MER algorithms were 

similar across all ages and provided reasonable ventilation rates at all percentiles and ages, 

suggesting either approach is reasonable for stochastic modeling exercises where simulation of 

activity-specific person-oriented ventilation rates is desired. 

 A-ii 
Keywords / Acronyms 

APEX Air Pollution Exposure model (OAQPS) 

BMR Basal metabolism rate 

BM Body mass 

BMI Body mass index 

BSA Body surface area 

CHAD Consolidated Human Activity Database 

EE Energy expenditure 

EVR Equivalent ventilation rate [VE/BSA] 

Fi Conversion Factors 

HR Heart rate 

HT Height 

LBM Lean Body Mass (equivalent to fat-free mass) 

METS Metabolic equivalents of work 

NAAQS National Ambient Air Quality Standard 

NERL National Exposure Research Laboratory 

OAQPS Office of Air Quality Planning and Standards 

PaCO2 partial pressure of arterial carbon dioxide • •

RQ Respiratory quotient (VCO2 /VO2 ) 

SHEDS Stochastic Human Exposure and Dose Simulation model (NERL) •

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

•

VCOs Carbon dioxide expiration rate 

VD Dead space volume of the lung 

•

VE Total ventilation rate (due to formatting issues, VE is primarily used here) 

VT Tidal volume of the lung 

•

VO2 Oxygen consumption rate (due to formatting issues, VO2 is primarily used here) 

• •

VQ Ventilatory equivalent (VE /VO2 ) 

 A-iii 
Acknowledgments 

 The authors are indebted to a number of people who invested time in improving this 

report. Special thanks are due our OAQPS colleagues who shared their expertise in human 

exposure modeling and risk assessment which helped focus our efforts; they are, in particular: 

John Langstaff, Ted Palma, and Harvey Richmond. Gratitude is also due to Ted Johnson of TRJ 

Environmental, who provided us with information on past practices regarding uptake dose 

modeling. Finally, we thank our U.S EPA colleague, Dr. James Starr who reviewed this report 

and discussed ventilation issues with us. 

 A-iv 
Table of Contents 

1. Introduction ............................................................................................................................ A-1 

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-15 

6. References ............................................................................................................................ A-16 

Attachment A1 ........................................................................................................................ A-199 

Attachment A2 .......................................................................................................................... A-31 

Attachment A3 ........................................................................................................................ A-355

List of Tables 

Table A-1. Parameter estimates used to estimate activity-specific VO2 for males and 

 females of different age groups ....................................................................... A-5 

Table A-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 A-3. Ventilation parameter estimates (bi), standard errors (se), and residual 

 distributions standard deviation estimates (ei) using Adams data and 

 assuming equation (3) or (4) ........................................................................... A-9 

Table A-4. Residual distributions standard deviation estimates (eb and ew) using 

 data characterized by percentage of maximum VO2 (VO2m) assuming 

 equation (3) ................................................................................................... A-10 

Table A-5. Residual distributions standard deviation estimates (eb and ew) using 

 data categorized by percentage of maximum VO2 (VO2m) assuming 

 equation (4) ................................................................................................... A-10 

Table A-6. Recommended inhalation rates (L/min) from U.S. EPA (1997) Table 5-23 A-12 

Table A1-1. Total subjects for each study, gender, and exercise ergometry used ............ A-20 

 A-v 
 A-vi 

List of Figures 

Figure A-1. Pathways for estimating various ventilation parameters and metrics ............. A-1 

Figure A-2. Ventilatory quotient (VQ) as a function of age during exercise ..................... A-8 

Figure A-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 A-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 A2-1. Relationship between total ventilation rate (VE) and oxygen 

 consumption rate (VO2) during exercise ....................................................... A-33 

Figure A2-2. Relationship between body mass normalized total ventilation rate 

 (VE/BM) to oxygen consumption rate (VO2/BM) during exercise ............... A-33 

Figure A2-3. Relationship between the natural logarithm of total ventilation rate 

 Ln(VE) and oxygen consumption rate Ln(VO2) during exercise .................. A-34 

Figure A2-4. Relationship between body mass normalized total ventilation rate 

 Ln(VE/BM) to oxygen consumption rate Ln(VO2/BM) during exercise ...... A-34 

Figure A3-1. Comparison of selected percentiles of estimated event-based 

 ventilation rates from 20,000 person APEX model simulation using 

 different ventilation algorithms ..................................................................... A-37 

Figure A3-2. 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-38 

Figure A3-3. 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-39 
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 U.S. 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 (NERL) has 

developed the Stochastic Human Exposure and Dose Simulation (SHEDS) 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 U.S. 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 A-1. 

BMR METS

VQ

* * =

BM

HT

BSA

EVR

CHAD

VD/VT

VE

.

F1

(EE Æ VO2)

VA

.

VO2

.

RQ/PaCO2

F2

BMR METS

VQ

* * =

BM

HT

BSA

EVR

CHAD

VD/VT

VE

.

F1

(EE Æ VO2)

VA

.

VO2

.

RQ/PaCO2

F2

Figure A-1. Pathways for estimating various ventilation parameters and metrics. 

 A-1 
 Central to the framework is the U.S. EPA’s Consolidated Human Activity Database 

(CHAD), a database of nearly 23,000 person-days of time-location-activity data useful for 

exposure modeling purposes (McCurdy et al., 2000). Distributions of metabolic equivalents 

(METS) are assigned in CHAD to every activity that respondents participated in. These METS 

distributions have been developed from a review of the exercise physiology and clinical nutrition 

literatures (McCurdy, 2000) and represent the ratio of the energy needed for the activity 

performed to the energy needed to sustain life (basal metabolism). The METS are fundamental 

to simulating an individual’s breathing rate while the person is performing a variety of activities 

(e.g., running, walking, sleeping). 

 To estimate activity-specific ventilation rates, first a prediction equation for basal 

metabolic rate (BMR, in kilocalories min-1) is used to estimate the simulated individual’s resting 

metabolic rate from their body mass (BM), or from BM and height (HT) together. Then activityspecific METS (METSA) are sampled via Monte-Carlo techniques and multiplied by a person’s 

estimated BMR to obtain a single realization of the energy expenditure rate (EE, kilocalories 

min-1). This rate of energy expenditure is retained over the duration of the activity (termed here 

as an “event”), which can be as short as 1 minute or as long as one hour (due to the structure of 

CHAD). 

 Thus mathematically, event-specific EE for an individual (EEEi) is defined as: 

EEEi = BMRi * METSA

 Estimated EEEi can then be converted to an activity-specific oxygen consumption rate 

(VO2Ei) using a gender-specific relationship expressed as a uniform distribution (F1i,

L-O2/kilocalorie) (McCurdy, 2000) as follows: 

VO2Ei = EEEi * F1i

VO2, however, is not the final physiological process to be simulated since most air pollution 

clinical studies do not use it as the end-point ventilation metric. Most of these studies use VE or 

EVR, and some exposure models, particularly OAQPS’s APEX model for carbon monoxide 

(APEX-CO), need VA (commonly in L min-1) for their inhalation modeling approach. By 

definition, VA is a fraction of VE and is important in estimating respiratory uptake of gases 

(e.g., O2, CO, CO2) and chemicals that likely act as gases (e.g., benzene, 1-3-butadiene 

[Lin, et al., 2001]). Regardless, all three mentioned ventilation metrics (VE, EVR, VA) can be 

obtained from VO2, either directly or indirectly, thus VO2 is fundamental to the development of 

each of these ventilation algorithms. 

 The pathway from VO2 to VE can be direct or indirect, with the indirect approach itself 

having a few options: from VO2 to VA and then to VE, or from VO2 to VE using the ventilatory 

quotient (VQ or alternatively, the ventilatory equivalent). VQ is simply the unitless ratio of VE

to VO2 when both metrics are in the same units. This ratio is non-linear with work rate however, 

varying between 20 and 32 in healthy people at low-to-moderate work rates while higher at more 

extreme exercise levels (McArdle et al., 1991). While there are nuances among the many ways 

 A-2 
that VE and EVR have been estimated over the years, in general the approach taken has been the 

VQ pathway depicted in Figure A-1. VA has been estimated by Johnson (2002) using a direct 

relationship between VO2 and VA originally described by Galletti (1959). For a more complete 

discussion of how ventilation rate has been modeled by OAQPS, see Section 9 of Johnson 

(2002). 

 This NERL Research Report describes an approach to estimating VE directly from VO2

using a series of regression-based equations derived from 25 years of clinical studies conducted 

by Dr. William C. Adams of the University of California at Davis. Much of the work cited 

above has been predicated upon past work and data provided by Dr. Adams, particularly Adams 

(1993) and Adams et al. (1995). OAQPS and NERL at different times acquired independent 

(non-overlapping) data sets from his laboratory at the University of California-Davis. These data 

have been extensively analyzed by OAQPS contractors, particularly Ted Johnson of TRJ 

Environmental (and previously with IT Technology). In addition to the citations noted above 

regarding analysis of Dr. Adams’ data, see also Johnson et al. (1998). 

 OAQPS requested that staff in the Exposure Modeling Research Branch of NERL review 

the literature on calculating VA since a previous review of the algorithms used in pNEM/CO 

indicated that a constant in the equation possibly varied non-linearly with exercise rate. That 

review has not been completed as of this date, but as an outgrowth of this work NERL staff 

decided to first investigate a VE algorithm for use in both the APEX and SHEDS inhalation 

modules. It is this work that is described below. 

2. Methods 

Data Set Description 

 The data set acquired is listed and briefly described in communication memos authored 

by Dr. Adams and provided in Attachment A1. Data from 32 panel studies collected over a 

25-year period by the same laboratory were obtained in electronic format. The number of 

subjects included within these studies was nearly one-thousand, undoubtedly one of the largest 

datasets of its kind. The data set used was a Microsoft ® Excel (.xls) file obtained from a disk 

labeled “Converted Adams Data”. The file used in this analysis (adam2.xls) was considered as 

the raw data file, since also on this disc was included an ASCII text version of the file and the 

memo from Dr. Adams describing the data set. 

 The raw data required physical manipulation and mathematical transformation to allow 

for statistical analyses. Details of the procedures used as part of this research are described 

further in Attachment A2. Briefly, due to the format of the original study data sets, a file was 

created containing a single vector for each individual ventilation parameter. Data were then 

screened for erroneous and potentially extreme values. Ventilation parameters (VE and VO2) 

were normalized to body mass and followed with a natural logarithm (Ln) transformation. 

Statistical Analysis 

 All statistical analyses were performed using SAS® software, version 8.2.1 (SAS 

Institute, Cary, NC). Parameters considered useful in model simulations (i.e., those that could 

capture a significant degree of variability and are consistent with current exposure modeling 

 A-3 
structure) were first evaluated for statistical significance (p<0.01) using an analysis of variance 

(ANOVA). Then, a simple linear regression (SLR) model was developed of the form yi = b0 + 

b1xi + εi to estimate parameter coefficients for use in predictive equations: 

 Ln(VE/BM)i = b0 + (b1 * Ln(VO2/BMi)) + ei Eq. (1) 

where b0 = the regression intercept, b1 = the regression slope coefficient, and ei representing 

individual variability in ventilation rate. The coefficient of determination (R2

) was used in 

evaluating the regression model since it represents the proportion of total variance of the 

dependent variable “explained” by the independent variables. 

 The approach was modified slightly for predictive purposes to reflect additional test 

factors contributing to variance in the ventilation rate. The model presented here was given as 

Equation 9-6 in Johnson (2002) and interpreted as follows, where b0 = the intercept and b1 = the 

slope regression coefficient: 

 Ln(VE/BM)i = b0 + (b1 * Ln(VO2/BMi)) + ebi + ewi Eq. (2) 

 It was assumed here that the predictive regression equation represents a mixed-effects 

regression (MER) model containing both fixed and random effects variables. VO2 was 

considered a fixed parameter and subject and study were random effects variables used to 

estimate the between-person (inter-individual variability) residuals distribution (eb) and withinperson (intra-individual variability) residuals distribution (ew) rather than simply random error 

(ε) alone. Each of the residuals are normally distributed, with a mean of 0 and an estimated 

standard deviation of σ

2

 (i.e., N{0, σ

2

}). Statistical significance of estimated coefficients and the 

regression model was assessed at p<0.01. The purpose of this regression analysis was to 

duplicate the model presented by Johnson (2002) and provide standard errors associated with the 

parameter estimates. 

Finally, multiple linear regression (MLR; yi = b0 + b1xi1 + b2xi2 +...+ bixip + εi) was 

implemented to include both age and gender as independent variables: 

 Ln(VE/BM)i = b0 + (b1 * Ln(VO2/BMi)) + (b2 * Ln(agei)) + (b3 * genderi) + ei Eq. (3)

The age of each study subject was transformed by the natural logarithm. Gender was used as a 

classification variable, with males represented by -1 and females represented by 1. The 

regression was set in this manner to provide for reasonable estimation of ventilation rates even if 

gender was unknown (gender=0). Random error (ε) can also be allocated to two variance 

components as described above for equation (2) using a MER model that includes age and 

gender as additional variables. This new model is represented as: 

 Ln(VE/BM)i = b0 + (b1 * Ln(VO2/BMi)) + (b2 * Ln(agei)) + (b3 * genderi) + ebi + ewi Eq. (4) 

Statistical significance of estimated coefficients and the regression model was assessed at 

p<0.01. 

 A-4 
 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 t-value), 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 Attachment A3. 

 Ventilation rates were first estimated using Crystal BallTM 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 A-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 A-1. Parameter estimates used to estimate activity specific VO2 for males and females 

of different age groups.1

Age group Gender 

METS-Activity Level2 Conversion Factors 

Low Moderate Vigorous 

Energy to 

Oxygen 

(L-O2/kcal) 

Unit 

(MJ/kcal)/ 

(min/day) 

Child 

(0-18 yrs) 

Male N{2.0,0.34} N{5.0,0.85} N{9.0,1.5} U{0.20-0.22} 

239/1440 Female N{1.5,0.26} N{4.5,0.77} N{8.0,1.4} U{0.19-0.21} 

Adult 

(>18 yrs) 

Male N{2.5,0.43} N{6.5,1.1} N{10,1.7} U{0.20-0.22} 

Female N{2.0,0.34} N{5.0,0.85} N{9.0,1.5} U{0.19-0.21} 

1

 Distribution type and parameters used: N=normal {arithmetic mean, standard deviation}; U=Uniform 

{min,max}. 

2

 It was assumed that the relative standard deviation of the METS for each distribution was 17% (see 

McCurdy and Graham, 2004) 

 A-5 
 A second method for evaluation was conducted using OAQPS’s APEX model, version 

4.0 (see U.S. EPA, 2005 for details on the model algorithms). Twenty thousand individuals were 

simulated for one day to allow for the comparison of selected ventilation algorithms developed 

as would be used in an actual exposure model. Activity-specific ventilation rates were generated 

by APEX using human activity diaries from CHAD and the general approach described above 

and outlined in Figure A-1. Diaries in CHAD are at a minimum disaggregated to hourly 

components, that is, the maximum time step for an activity or location inhabited could be one 

hour, thus up to 24 events in a day. However much of the data are further divided such that 

within one hour there may be multiple activities performed or multiple locations inhabited, 

upwards to 1 minute in duration. Since every simulated individual had multiple estimations for 

ventilation rate depending on their activities performed (generally ranging from 30-40 events in a 

day), distributions were first calculated for each person followed by an estimate of the population 

distribution at each age (generally between 1 and 400 persons were simulated for each year of 

age). The median (p50), 95th (p95), and 99th (p99) percentiles and maximum ventilation rates 

estimated with the APEX model represent the variability in the mid-upper range of ventilation 

rates for individuals within a population. It should be noted that the maximum for all individuals 

is the same as the 99th percentile unless there was more than 99 events (rare if occurs at all). 

3. Results and Discussion 

Statistical Analysis 

 Both age and gender were used in the development of several regression equations 

derived from the Adams data set and summarized in Table 9-1 of Johnson (2002); however 

significance of these variables was not reported there. An analysis of variance was performed 

here on VE, utilizing the 4 age groups (i.e., <18, 18-44, 45-64, >65 years old) and two genders as 

classification variables indicated by Johnson (2002). VO2 normalized to body mass was 

included as an additional independent variable. Age group, gender, their interaction term (age 

group by gender), and VO2 were each significant explanatory parameters (all p<0.003). 

 Results of the simple linear regression analysis, the simple mixed model addressing fixed 

and random effects, and parameter coefficients reported by Johnson (2002) assuming equations 

(1) or (2) are presented in Table A-2. Regression model intercept and slope were statistically 

significant parameters in each of the regression models. 

 There were marginal differences between the simple regression coefficients and the 

simple mixed model coefficients developed in this work; both the intercepts and slopes were 

systematically lower for the simple regression. The results from the simple mixed model and 

Johnson (2002) were nearly identical with the most notable differences seen in the residuals 

distributions, albeit at a minimal level. 

 Following this single variable model comparison, age and gender were investigated as 

additional independent variables for use in a multiple linear regression model. Gender was 

already deemed significant based on the ANOVA and, since its use as a parameter in a multiple 

linear regression would halve the number of equations needed for ventilation simulations, was to 

be included as a parameter in the regression model. For age, it was hypothesized that it would 

 A-6 
have a statistically significant effect on the relationship between VE and VO2, not just among the 

different age groups but also within a given age group. Figure A-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 A-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 A-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 n Gender Methoda

Ln(VO2/BM) Residuals

R2 b0 seb0 b1 seb1 eb ew

<18 

315 F 

SLR 3.214 0.089 0.941 0.022 0.1609 0.8504 

MER 3.263 0.050 0.950 0.012 0.1427 0.0735 

Johnson Not Performed

288 M 

SLR 3.054 0.103 0.913 0.026 0.1715 0.8069 

MER 3.180 0.052 0.941 0.012 0.1600 0.0722 

Johnson Not Performed

18-44 

1473 F 

SLR 4.021 0.040 1.182 0.011 0.1736 0.8790 

MER 4.358 0.034 1.276 0.009 0.1351 0.1176 

Johnson 4.357 1.276 0.1351 0.1182 

3145 M 

SLR 3.758 0.023 1.130 0.007 0.1826 0.8965 

MER 3.983 0.022 1.194 0.006 0.1219 0.1382 

Johnson 3.991 1.197 0.1228 0.1395 

45-64 

60 F 

SLR 3.360 0.239 0.998 0.055 0.1401 0.8498 

MER 3.462 0.153 1.023 0.034 0.1152 0.0774 

Johnson 3.454 1.021 0.1106 0.0769 

641 M 

SLR 3.824 0.060 1.117 0.016 0.1584 0.8884 

MER 4.019 0.047 1.166 0.012 0.1172 0.1073 

Johnson 4.018 1.165 0.1107 0.1112 

65+ 

45 F 

SLR 2.687 0.297 0.846 0.068 0.0960 0.7820 

MER 2.958 0.143 0.908 0.032 0.0920 0.0341 

Johnson 2.956 0.908 0.0886 0.0338 

317 M 

SLR 3.686 0.090 1.060 0.023 0.1280 0.8729 

MER 3.731 0.055 1.071 0.013 0.1092 0.0632 

Johnson 3.730 1.071 0.1082 0.0632 

a 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)

 A-7 
Figure A-2. 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 A-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. 

 A-8 
Table A-3. Ventilation parameter estimates (bi), standard errors (se), and residual 

distributions standard deviation estimates (ei) using Adams data and assuming 

equation (3) or (4).

Age 

group n Methoda

Ln(VO2/BM) Ln(age) Gender Residuals 

R2 b0 se b0 b1 se b1 b2 se b2 b3 se b3 eb ew

<20 1085 

MLR 4.4329 0.0579 1.0864 0.0097 -0.2829 0.0124 0.0513 0.0045 0.1444 0.9250 

MER 4.3675 0.0650 1.0751 0.0087 -0.2714 0.0190 0.0479 0.0077 0.0955 0.1117 

20-<34 3646 

MLR 3.5718 0.0792 1.1702 0.0067 0.1138 0.0243 0.0450 0.0031 0.1741 0.8927 

MER 3.7603 0.1564 1.2491 0.0061 0.1416 0.0493 0.0533 0.0061 0.1217 0.1296 

34-<61 1083 

MLR 3.1876 0.1271 1.1224 0.0120 0.1762 0.0335 0.0415 0.0095 0.1727 0.8925 

MER 3.2440 0.2578 1.1464 0.0088 0.1856 0.0674 0.0380b 0.0172 0.1260 0.1152 

61+ 457 

MLR 2.4487 0.3646 1.0437 0.0195 0.2681 0.0834 -0.0298 0.0100 0.1277 0.8932 

MER 2.5826 0.7013 1.0840 0.0122 0.2766 0.1652 -0.02081c 0.0149 0.1064 0.0676 

a MLR: multiple linear regression model (PROC REG in SAS) when using equation (3); MER: mixed-effects regression 

(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 VO2 (VO2m) was decidedly appropriate in determining whether the data could be 

extrapolated downward to reasonably simulate low energy-expenditure activities. Typically VO2

reserve (VO2res) is used; however, this was not measured in the Adams’ studies. A tripartite 

categorization of the measured VO2 for a step relative to the VO2m of each subject was 

undertaken using <33.3%, 33.3-66.6%, >66.6% of VO2m 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 

A-4) were generally lower at the lower and moderate level exercise levels compared with the 

estimated total residuals in Table A-3. This indicates there is less variability in ventilation rate at 

the low and moderate exercise levels. 

 A-9 
Table A-4. Residual distributions standard deviation estimates (eb and ew) using data 

categorized by percentage of maximum VO2 (VO2m) assuming equation (3). 

Age 

Group 

<33.3% V02m 33.3-66.6% V02m >66.6% V02m 

ei x n ei x s ei x s 

<20 0.1233 123 2.0 0.1007 179 2.5 0.1523 137 2.8 

20-<34 0.1486 127 1.9 0.1184 428 2.9 0.1734 521 4.1 

34-<61 0.1954 74 1.8 0.1568 144 3.2 0.1592 139 3.5 

61+ 0.0974 9 1.9 0.1144 78 2.7 0.1344 67 3.4 

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 withinperson variability was lower for all age groups using the tripartite breakdown (Table A-5) 

compared to the residuals distributions estimated using all of the data combined (Table A-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% VO2m; 

however, this was not the principal reason for the observed residual differences since 

consistently even fewer individuals were measured at exercise <33.3% VO2m (Tables 4 and 5). 

In addition, more measurements were consistently obtained for exercise >66.6% VO2m on 

average per person than at the low or moderate levels of exercise. 

Table A-5. Residual distributions standard deviation estimates (eb and ew) using data 

categorized by percentage of maximum VO2 (VO2m) assuming equation (4).

Age Group 

<33.3% V02m 33.3-66.6% V02m >66.6% V02m 

eb ew eb ew eb ew

<20 0.1217 0.0506 0.0951 0.0456 0.1637 0.0741 

20-<34 0.1291 0.0728 0.1088 0.0524 0.2190 0.0740 

34-<61 0.1522 0.0938 0.1444 0.0581 0.1936 0.0710 

61+ 0.1244 0.0164 0.1112 0.0362 0.1422 0.0563 

Numbers of individuals and samples collected per individual are the same as indicated in Table A-4. 

 These results in Tables A-4 and A-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 

 A-10 
ventilation estimations for all exercise levels, age groups, or genders. Using the data provided in 

Table A-3 and implementation of either equation (3) or equation (4) should not have a large 

impact on a population-based exposure analyses. 

 It should be noted that in extrapolating lower than the age range of the original data 

(e.g., <3.6 years old), it is assumed that regression equations are suitable for these children and 

infants. The trend for VQ illustrated in Figure A-2 is likely to be continued upward for younger 

children and infants due to the anticipated reduction in efficiency (i.e., underdevelopment) of 

their respiratory systems. However, since the natural log for age <1 is negative [i.e., ln(1)=0; for 

x<1, ln(x)<0], the equations are inappropriate for infants <1. 

Performance Evaluation 

 The algorithms underwent a probabilistic evaluation using either representative 

distributions of exposure model input parameters (evaluation method 1) and/or by using the 

algorithm in an actual exposure model (evaluation method 2). When simulating multiple 

activities for one individual and for a population, alternative sampling strategies are 

recommended below for estimating variability and uncertainty. Ventilation rates estimated using 

the general input parameter distributions (evaluation method 1) are summarized in Figure A-3 

for females and males separately from using either the MLR or MER models. 

 Ventilation estimates for both the MLR and MER models are comparable to one another, 

particularly for young persons at each of the exertion levels and at the various mid to upper 

percentiles, however some trends were noted. Even though each simulation is independent, 

comparisons of the average percent difference at selected percentiles for each of the 5,000 person 

simulations are considered appropriate. Female ventilation rates estimated using the MLR 

tended to be slightly higher on average at each of the percentiles (average percent difference of 

between 3.5-4.0%) than those estimated using the MER for low exertion activities. This trend 

was also consistent with the results for males, whereas the MLR estimated ventilation rates were 

on average 1.6-2.9% higher than those estimated using the MER algorithm. Moderate exertion 

activities yielded the most similar results in both males and females (-0.6 to 0.1% and -0.1 

to 0.9%, respectively). However, ventilation rates associated with vigorous activity levels were 

1.3-1.8% lower in females, and 1.8-2.3% lower in males when comparing the MLR with the 

MER algorithm. These results suggest that either approach is acceptable for use in estimating 

ventilation rate, but that the MER model may be slightly more responsive to changes in activity 

level and better capture variability in ventilation rates, specifically when using the intra- and 

inter-personal residuals. Overall female ventilation rates ranged from 5 to 20 L/min, 

20 to 50 L/min, and 40 to 100 L/min for low, moderate, and vigorous exertion activities, 

respectively using either algorithm. Ventilation rates for males ranged higher for the varying 

activities, with 10 to 35 L/min, 25 to 110 L/min, and 50 to 175 L/min estimated for low, 

moderate and vigorous exertion, respectively using either algorithm. 

 Additional evaluations were performed on the MER algorithm by estimating potential 

population-based ventilation rates with the APEX model. Results for the 20,000 person 

simulation of both genders are presented in Figure A-4. At any given percentile, ventilation rates 

increase rapidly with age for persons less than 20 years old, stabilize from ages 20 to about 60, 

then gradually decline with further increases in age. The distribution of these selected mid to 

upper percentiles for ventilation rate in individuals spans by about a factor of 5 or more, 

 A-11 
 A-12 

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 U.S. EPA (1997), but much of the 

data presented here are in fact approximations to that report utilizing similar approaches. 

Table 5-6 in U.S. EPA (1997) contains somewhat comparable data disaggregated by age and 

gender, adults only, for average inhalation rates. The origin of the U.S. EPA (1997) data, 

however, is Adams (1993), which is used extensively in this report. Recommended inhalation 

rates from Table 5-23 in U.S. EPA (1997), based on measured and approximated data, are 

presented in Table A-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 A-6. Recommended inhalation rates (L/min) from U.S. EPA (1997) Table 5-23.

Rest Sedentary Low Medium High 

Children 5.0 6.7 16.7 20 31.7 

Adults 6.7 8.3 16.7 26.7 53.3 
Percentiles of Estimated VE for Females Performing Low-Exertion Activities Using Selected 

Parameter Distributions

0

5

10

15

20

25

0 20 40 60 80 100

Age (years)

VE (L/Min)

p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Percentiles of Estimated VE for Males Performing Low-Exertion Activities Using Selected 

Parameter Distributions

0

5

10

15

20

25

30

35

40

0 20 40 60 80 100

Age (years)

VE (L/Min)

p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Percentiles of Estimated VE for Females Performing Moderate-Exertion Activities Using 

Selected Parameter Distributions

0

25

50

75

0 20 40 60 80 100

Age (years)

VE (L/Min)

p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Percentiles of Estimated VE for Males Performing Moderate-Exertion Activities Using Selected 

Parameter Distributions

0

25

50

75

100

125

0 20 40 60 80 100

Age (years)

VE (L/Min)

p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Percentiles of Estimated VE for Females Performing Vigorous-Exertion Activities Using 

Selected Parameter Distributions

0

25

50

75

100

125

0 20 40 60 80 100

Age (years)

VE (L/Min)

p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Percentiles of Estimated VE for Males Performing Vigorous-Exertion Activities Using Selected 

Parameter Distributions

0

25

50

75

100

125

150

175

0 20 40 60 80 100

Age (years)

VE (L/Min)

p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Figure A-3. Estimated ventilation rates (VE, L/min) 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. 

 A-13 
p99 VE using 20,000 persons from APEX and using Mixed Model

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60 70 80 90 100

Age (years)

VE (L/min)

p99mean

p99p50

p99p95

p99p99

p99max

p99p5

p99p1

p99min

p95 VE using 20,000 persons from APEX and using Mixed Model

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90 100

Age (years)

VE (L/min)

p95mean

p95p50

p95p95

p95p99

p95max

p95p5

p95p1

p95min

p50 VE using 20,000 persons from APEX and using Mixed Model

0

5

10

15

20

25

30

35

0 10 20 30 40 50 60 70 80 90 100

Age (years)

VE (L/min)

p50mean

p50p50

p50p95

p50p99

p50max

p50p5

p50p1

p50min

Figure A-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 A-3). The 

full distribution of the median (p50-top), 95th (p95-middle) and 99th (p99-bottom) 

percentiles are represented for each age. 

 A-14 
4. Recommendations 

 We recommend that for inhalation exposure modeling purposes, the regression equation 

coefficients listed in Table A-3 be used with equation (3) or (4) to estimate activity-specific body 

mass-adjusted VE for simulated individuals in the age groups listed. Estimated regression 

coefficients and output from each of the algorithms were very similar, however gender within the 

MER algorithm was not considered statistically significant for the older age group compared 

with the MLR. 

 To obtain estimates of VE in units of L min-1, the antilog of the predicted value multiplied 

by the subject’s body mass (BM in kg) would be taken. Ages less than one year old are not to be 

approximated (i.e., persons with age<1 can be estimated as one year old or using an alternative 

approximation). In addition, we suggest that individual variability be addressed by “fixing” the 

regression parameter estimates and using random sampling from each of the residual 

distributions {N: 0, sd} to account for individual variability, with the MER model used when 

addressing inter- and intra-individual variability. Inter-individual variability is addressed 

through selection of between-person residuals (eb) once per simulated individual. Intraindividual variability is addressed through selection of within-person residuals (ew) every time an 

individual undertakes an activity. To address uncertainty, we recommend that additional 

simulations should be undertaken using the standard errors (se) of the regression coefficients 

themselves to address measurement error and unobserved variability. 

5. Future Research 

 As mentioned earlier, a method for estimating VA to remain consistent with the VE

estimation is currently being investigated by both NERL and OAQPS. Currently, the pathway 

from VO2 to VA is considered as a direct linear proportionality (i.e., a constant value of 19.63) 

and estimated independently from VE. A preliminary literature review indicates that the 

approximation is reasonable and may be linear for low to moderate exercise levels, but at a 

minimum, there is variability in VA at all exercise levels that is not accounted for by the point 

estimate used to modify VO2. Further investigation is needed to determine if the VO2 to VA

relationship is maintained for vigorous activity levels. In addition, the lack of a direct 

computational link with VE potentially can lead to simulated values of VA in excess of VE, a 

physiological impossibility. 

 One potential method would be to estimate VA from VE by using another physiological 

relationship: the ratio of dead space volume-to-tidal volume (VD/VT, see Figure A-1). 

Physiological dead space is the volume of the lung that does not take part in gas exchange and is 

comprised of basic anatomic dead space (e.g. volume of trachea and bronchioles) and areas of 

lung with reduced functionality (e.g., damaged alveolar regions, increased dead space due to 

bronchiole expansion during exercise). Tidal volume is the total amount of air breathed upon 

inspiration, not all of which comes in contact with the alveolar region of the lung due to the 

presence of physiologic dead space. It has been found that VD/VT does not remain constant over 

varying exercise levels, with VT increasing at a greater rate than VD during increasing exercise 

level. The effect of this non-linear relationship in simulating VA (does VA increase linearly with 

increasing VO2 at all exercise levels?) has not yet been determined. The relationships of VE, 

 A-15 
VD/VT, and VO2 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. 

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Activities. Davis CA: University of California. 

Adams WC, Shaffrath JD and Ollison WM. 1995. The relation of pulmonary ventilation and 

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Baba R, Mori E, et al. 2002. Simple exponential regression model to describe the relation 

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Burmaster DE and Crouch EAC. 1997. Lognormal distributions for body weight as a fucntion of 

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Galletti PM. 1959. Les echanges respiratoires pendant l'exercice musculaire. Helv Physiol 

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Johnson T and McCoy M. 1995. A Monte Carlo Approach to Generating Equivalent 

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Johnson T and Mihlan G. 1998. Analysis of clinical data provided by Dr. William Adams and 

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Lin Y-S, Smith TJ, et al. 2001. Human physiologic factors in respiratory uptake of 1,3-

butadiene. Environ Health Perspect. 109(9):921-926. 

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& Febiger. 

McCurdy T. 1994a. Human exposure to ambient ozone. pp. 85-127 in: D.J. McKee (ed.) 

Tropospheric Ozone. Ann Arbor MI: Lewis Publishers. 

McCurdy T. 1994b. Repackaging Adams (1993) breathing rate data. U.S. EPA memo, 

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McCurdy T. 1995. Estimating human exposure to selected motor vehicle pollutants using the 

NEM series of models: Lessons to be learned. J Exp Anal Environ Epidemiol. 5: 533-550. 

McCurdy T. 1997a. Comparison of cumulative inhaled ozone dose estimates using a 

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McCurdy T. 1997c. Modeling the dose profile in human exposure assessments: ozone as an 

example. Rev Tox: In Vivo Tox Risk Assess. 1: 3-23. 

McCurdy T. 2000. Conceptual basis for multi-route intake dose modeling using an energy 

expenditure approach. J Expos Anal Environ Epidemiol. 10: 86-97. 

McCurdy T. 2001. Research Note: Analyses to understand relationships among physiological 

parameters in children and adolescents aged 6-16. RTP: U.S. Environmental Protection 

Agency (being reviewed). 

McCurdy T, Glen G, Smith L, and Lakkadi Y. 2000. The National Exposure Research 

Laboratory’s Consolidated Human Activity Database. J Exp Anal Environ Epidemiol. 10: 

566-578. 

McCurdy TR and Graham SE. 2004. Analyses to understand relationships among physiological 

parameters in children and adolescents aged 6-16. EPA/600/X-04/092. 

Nieman DC. 1999. Exercise Testing and Prescription. A Health-Related Approach. 4th edition. 

Mayfield Publishing Company, Mountain View, CA. 

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Schofield WN. 1985. Predicting basal metabolic rate, new standards and review of previous 

work. Human Nutrition: Clinical Nutrition. 39C(Supp1):5-41. 

U.S. EPA. 1997. Exposure Factors Handbook. National Center for Environmental Assessment, 

Office of Research and Development, Washington D.C. EPA/600/P-95/002Fa. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12464 

U.S. EPA. 2005. Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4) Volume I: User’s Guide. Office of Air 

Quality Planning and Standards, US EPA. November 2005. 

http://www.epa.gov/ttn/fera/data/apex/APEX4UG120505.pdf 
Attachment A1 to Appendix A 

First memo from Dr. Adams to T. McCurdy describing major data set

 21 August 1998 

Dear Tom: 

Enclosed is a diskette which includes the electronic data base containing data my graduate 

students and I have collected over the last 25 years on a large number of subjects of varying ages 

that includes VE, VO2, and other physiological data that should be very useful for estimating VE 

and respiratory intake dose. It is in an Excel (5.0a) spread sheet format, as well as an ASCII 

format, blank delimited file with headings. 

A description of the subjects for which data were potentially available was detailed in a list of 37 

studies (pages 5-8) in my proposal dated 28 April 1998. Table A1-1 details the 31 studies for 

which valid physiologic data were available, together with the total number of subjects, their 

gender, and whether they were tested on a cycle ergometer or on a motor driven treadmill. 

Missing study numbers from the original proposal list denotes that no valid body composition 

and multi-stage VO2max data were available. In Study 21, 16 male subjects exercised on a cycle 

ergometer (21.1), while 22 male subjects exercised on a treadmill (21.2). 

The total number of subjects with multi-stage, steady-state corresponding VO2 and VE values, 

including those at VO2max, was 521 males and 224 females. Most were obtained on a cycle 

ergometer test (262 males and 158 females), with the remainder on a treadmill, utilizing a 

walking and/or running protocol. In addition, steady-state VO2 and VE values at several 

submaximal workloads on the treadmill were available on 211 other subjects as described in 

Study 30, above. Time at each work level was usually two or three minutes, except at the 

maximal work level, which sometimes was as short as 15 sec. (with the physiologic data 

extrapolated to per minute values). A variety of progressive increment protocols were used on 

both the cycle ergometer and the treadmill. However, each (except for Study 30) was designed 

to obtain at least near steady-state physiologic response at progressively intensified work rates 

ranging from light, or moderate, through very heavy, ending with voluntary exhaustion. 

In the electronic data base, the array of data for each subject is arranged horizontally in the 

following order: 

1. study ID number (1=Study 1, 2=Study 2, etc.) 

2. subject ID number 

3. subject gender (0=male, 1=female) 

4. subject age (years) 

5. special characteristics of the subject (e.g., 1= trained athlete, 2= trained non-athlete, 3= 

normally active, 4= sedentary, and 5= obese) 

6. subject height (cm) 

7. subject body mass (kg) 

8. subject lean body mass (kg) 

 A-19 
9. machine used (1= cycle ergometer, 2= treadmill) 

10. total test time (min) 

11. observed VO2max (l/min, STPD) for the test 

12. for each step of the test for each subject, the following sequence was used: 

a. cumulative test time at end of step 

b. machine setting (cycle ergometer in Watts, treadmill in speed (m/min) and percent 

grade) 

c. VE (l/min BTPS) measured during the last minute of each step 

d. VO2 (l/min, STPD) measured during the last minute of each step 

e. HR (b/min) measured during the last minute of each step 

Table A1-1. Total subjects for each study, gender, and exercise ergometry used. 

Cycle Ergometer Tests Treadmill Tests

Study Total Subjects Males Females Males Females Males Females

1 148 148 0 0 0 148 0

2 42 42 0 0 0 42 0

5 60 30 30 0 0 30 30

6 12 6 6 0 0 6 6

7 4 0 4 0 4 0 0

8 6 6 0 6 0 0 0

9 10 10 0 10 0 0 0

10 8 8 0 8 0 0 0

12 10 10 0 10 0 0 0

13 8 8 0 8 0 0 0

14 32 0 32 0 32 0 0

16 10 10 0 10 0 0 0

18 25 25 0 25 0 0 0

19 15 0 15 0 15 0 0

20 39 18 21 18 21 0 0

21.1 16 16 0 16 0 0 0

21.2 22 22 0 0 0 22 0

23 17 9 8 9 8 0 0

24 13 13 0 13 0 0 0

25 37 37 0 37 0 0 0

26 13 13 0 13 0 0 0

27 21 11 10 0 0 11 10

28 40 20 20 20 20 0 0

29 11 0 11 0 11 0 0

30 211 105 106 0 0 105 106

31 20 0 20 0 0 0 20

32 10 10 0 10 0 0 0

33 40 20 20 20 20 0 0

34 10 6 4 6 4 0 0

35 6 3 3 3 3 0 0

36 12 6 6 6 6 0 0

37 28 14 14 14 14 0 0

Total 956 626 330 262 158 364 172

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 

 A-20 
only one (work rate in Watts) was required for Quinton electronically braked cycle ergometer 

tests. A Monark cycle ergometer was used in Studies 9 and 33-37. Calibration of the Monark 

device displayed on the ergometer itself only accounts for braking force produced by the 

flywheel friction strap, and does not include internal friction produced in the drive train. 

Therefore, work rate values displayed on the ergometer were converted to Watts and then 

increased by 9% in order to obtain corrected values (E. Harman, Medicine and Science in Sports 

and Exercise 21(4):487, 1989). 

Quality assurance of the basic data, including that from handwritten records and computer print 

outs, was initiated by my review of each subject’s data. Where apparent spurious data appeared, 

or notably aberrant subject responses were identified, they were eliminated from transfer to the 

electronic data base. I also noted any missing data for any subject, so that it was clear to the 

graduate student transferring the data which were valid and what data were missing. The 

graduate student transferring data to the electronic data base was thoroughly trained as to what 

data were to be entered and the format that they were to be entered in. After data were entered 

for a study, the graduate student read the data appearing on the original data record for each 

subject’s protocol, while another graduate student verified that what was being said was what 

appeared on the spreadsheet. Errors identified by this procedure proved to be relatively small in 

number, non-systematic, and easily correctable. I have great confidence that the data furnished 

you are a valid representation of what appears in our original handwritten or computer print-out 

records. 

A list of subjects who participated in more than one study is given below in ascending Study 

Number (and subject number) for the first study they participated in, and then the other 

study(ies), with their subject number(s), that they participated in. 

Study 1 

Subject #2 also subject #2 in study 2. 

Subject #6 also subject #10 in study 18. 

Subject #25 also subject #7 in study 2, and #3 in study 5. 

Subject #29 also subject #18 in study 18. 

Subject #30 also subject #23 in study 18. 

Subject #43 also subject #3 in study 18. 

Subject #52 also subject #2 in study 18. 

Subject #54 also subject #17 in study 18. 

Subject #55 also subject #20 in study 2. 

Subject #56 also subject #19 in study 2, and #5 in study 19. 

Subject #60 also subject #13 in study 2. 

Subject #61 also subject #19 in study 18. 

Subject #63 also subject #18 in study 2, and #5 in study 8. 

Subject #69 also subject #16 in study 18. 

Subject #88 also subject #21 in study 18. 

Subject #89 also subject #14 in study 18. 

Subject #91 also subject #22 in study 18. 

Subject #97 also subject #11 in study 18. 

 A-21 
Study 2 

Subject #17 also subject #6 in study 18. 

Subject #32 also subject #30 in study 5. 

Subject #33 also subject #26 in study 5. 

Subject #34 also subject #1 in study 8. 

Subject #35 also subject #3 in study 8. 

Study 5 

Subject #18 also subject #1 in study 6. 

Subject #19 also subject #3 in study 6. 

Subject #21 also subject #6 in study 6, #1 in study 9, #1 in study 12, #2 in study 20, #17 in 

study 21.2, and #23 in study #25. 

Subject #27 also subject #2 in study 9. 

Subject #43 also subject #10 in study 6. 

Subject #48 also subject #11 in study 6. 

Study 9 

Subject #9 also subject #15 in study 21.2. 

Study 10 

Subject #1 also subject #7 in study 13, #3 in study 20, and #34 in study 25. 

Subject #2 also subject #4 in study 13 and #1 in study 20. 

Subject #7 also subject #8 in study 13. 

Study 12 

Subject #10 also subject #5 in study 20. 

Study 13 

Subject #2 also subject #5 in study 16. 

Study 20 

Subject #7 also subject #16 in study 21.1 and #8 in study 25. 

Study 21.1 

Subject #3 also subject #3 in study 24 and #33 in study 25. 

Study 21.2 

Subject #18 also subject #18 in study 25. 

Study 23 

Subject #1 also subject #10 in study 28. 

Subject #5 also subject #12 in study 24. 

Study 24 

Subject #13 also subject #21 in study 25. 

 A-22 
 A-23 

Study 28 

Subject #12 also subject #3 in study 32. 

Subject #28 also subject #20 in study 31. 

Study 31 

Subject #10 also subject #40 in study 33. 

Subject #15 also subject #3 in study 34. 

Study 32 

Subject #2 also subject #12 in study 33. 

Study 33 

Subject #3 also subject #7 in study 34. 

Subject #7 also subject #4 in study 35. 

Subject #9 also subject #10 in study 34, and #5 in study 35. 

Subject #35 also subject #4 in study 34. 

Study 34 

Subject #1 also subject #2 in study 35. 

Study 35 

Subject #3 also subject #3 in study 36. 

Study 36 

Subject #12 also subject #26 in study 37. 

I believe that this final report letter contains additional information beyond the electronic data 

base that you wanted and clarifies the format that was used. If you have questions, however, 

please do not hesitate to give me a call or drop me a note by FAX. I look forward to hearing from 

you and working with you and Ted on developing a publishable paper or two. 

Best regards, 

William C. Adams 

Professor 
Second memo from Dr. Adams to T. McCurdy describing additional data from 

study #30

8 October 2001 

Dear Tom: 

Pursuant to the U.S. EPA Order for Supplies and Services, No. 1D-5590-NATX, approved for 

the period, 1 August - 1 November 2001, I believe that I have now completed all professional 

services stipulated. Specifically, it was requested that I provide certain “raw” data on a group of 

children and adolescents who were part of the subject pool utilized in a California State Air 

Resources Board sponsored study, entitled: Measurement of Breathing Rate and Volume in 

Routinely Performed Daily Activities (Adams, 1993). The professional services stipulated 

included: 1) providing a complete listing of all variables that were obtained during the study in 

accordance with the attached Statement of Work; 2) the development of an electronic data base 

of selected physiological information for children and adolescents from the aforementioned 

study, again in accordance with the attached Statement of Work; and 3) the submittance of a 

transcribed data file for the aforementioned study in ASC II format, together with a description 

of data quality objectives that were established in accordance with the attached Statement of 

Work. 

The subject pool of interest included 132 individuals, half female and half male, including 12 

young children, age 3.6-5.8 yrs., 80 children, age 6.0-12.9 yrs., and 40 adolescents, age 13.2-18.9 

yrs. All subjects were apparently healthy. In all cases, subject identification, including age and 

gender, as well as body weight, height, and activity habitus, were obtained. Body composition, as 

assessed by gender/age specific skinfold formulae, were used to calculate lean body mass. All 

subjects completed a laboratory treadmill walk (usually three different speeds, i.e., steps) and 

jog (ranging form 1 to 3 different speeds) protocol. The treadmill grade was horizontal 

throughout. Each subject completed a laboratory resting protocol (40 of the children did only 

sitting and standing, while the others also rested in a lying position). The 12 young children each 

did two spontaneous play protocols of 20 minutes duration, while 40 children also did two 

spontaneous play protocols, but of 30 minutes duration. The other 40 children did a single 

spontaneous play protocol of 35 minutes duration. The 40 adolescent subjects were not asked to 

perform a spontaneous play protocol. In addition, each subject (or their parent/guardian) 

completed an 11-item health history questionnaire. 

Enclosed is a 3.75 ZIP disk which includes the electronic data base containing data described in 

general above. It is in an Excel (5.0a) spread sheet format produced on a Macintosh Performa 

6214CD hard drive, as well as an ASCII format, blank delimited file with headings. Consistent 

units of measurement for all entries were used throughout this file. In the electronic data base, 

the array of data for each subject is separated into five distinct files: 1) active (treadmill) 

protocol; 2) resting protocol; 3) spontaneous play protocol; 4) health history responses to 

selected questions; and 5) predicted VO2max values from measured submaximal HR and VO2

values contained in File #1. Details of what items, variables, time periods, etc., and their order, 

which are arranged horizontally in each file, is as we agreed on via my FAX of 22 August 2001, 

 A-24 
with minor modifications we agreed on by phone the next day. The order for each file is given 

below: 

ACTIVE (File #1) 

1. File ID number (#1) 

2. subject ID number (same number for each subject as identified for Study #30 in 1998 

data base) 

3. subject gender (0=male, 1=female) 

4. subject age (years) 

5. special characteristics of the subject (viz., 1= trained athlete, 2= trained non-athlete, 3= 

normally active, 4= sedentary, and 5= obese) 

6. subject height (cm) 

7. subject body mass (kg) 

8. subject lean body mass (kg) 

9. machine used (1= cycle ergometer, 2= treadmill) - NOTE: TREADMILL ONLY 

USED IN THIS STUDY. 

10. total test time (min) 

11. observed VO2max (l/min, STPD) for the test - NOTE: VO2max NOT MEASURED IN 

THIS STUDY.

12. for each step of the test for each subject, the following sequence was used: 

a. cumulative test time at end of step 

b. machine setting (two columns: one for treadmill in speed (m/min) and one for 

percent grade). The latter was always zero. 

c. VE (l/min BTPS) measured during the last two minutes of each step 

d. VO2 (l/min, STPD) measured during the last two minutes of each step 

e. HR (b/min) measured during the last two minutes of each step 

RESTING (File #2) 

1. File ID number (#2) 

2. subject ID number (same number for each subject as identified for Study #30 in 1998 

data base) 

3. subject’s body surface area in square meters; from measured body height and body mass, 

using the standard DuBois and DuBois formula 

4. for each resting posture for each subject, the following sequence was used: 

a. VE (l/min BTPS) measured during the 5 minutes of each test 

b. VO2 (l/min, STPD) measured for the 5 minute of the test 

c. average of five HR (b/min) measurements taken each minute of the 5 minute test 

d. average of five breathing frequency (breaths/min) measurements taken each 

minute of the 5 minute test 

SPONTANEOUS PLAY (File #3) 

1. File ID number (#3) 

2. subject ID number (same number for each subject as identified for Study #30 in 1998 

data base) 

 A-25 
3. for each 5 minutes data collection period for each subject, the following sequence was 

used: 

a. VE (l/min BTPS) measured during the 5 minutes 

b. average of five HR (b/min) measurements taken each minute of the 5 minute 

period 

c. average of five breathing frequency (breaths/min) measurements taken each 

minute of the 5 minute period. NOTE: Because these data were obtained on a tape 

cassette that rather routinely malfunctioned, valid data were obtained in only 

~75% of the subject 5-minute time periods 

d. activity intensity rating by the technician. NOTE: There was some confusion 

among the technicians as to what they were to indicate in the comments column; 

e.g., any problems with the equipment, what the subject was playing, and/or an 

estimation of the intensity of activity. The occasional noted problems with 

equipment were dealt with as described on pp. 38-39 of the CARB Final Report 

(Adams, 1993). While the play activity was occasionally recorded, it was not 

systematic (i.e., estimated at between 15-20%). Intensity of play was recorded 

~55% of the time. The intensity scale devised and used for the first time in the 

enclosed data base was: 1 = standing, or just “hanging out”; 2 = moderate 

intensity, i.e., walking, swinging an implement, kicking or throwing a ball, etc.; 

and 3 = vigorous, or very active. Ratings of 1.5 and 2.5 were used to indicate 

activity intensity somewhere in-between the absolute number categories. The 

mean value for each 5-minute period was near 2.0, moderate, which closely 

agrees with the observed VE estimated intensity discussed on p. 110 of the CARB 

Final Report. 

HEALTH HISTORY (File #4) 

1. File ID number (#4) 

2. subject ID number (same number for each subject as identified for Study #30 in 1998 

data base) 

3. Re question #1, how often do you exercise? Numerals in column 3 correspond to which 

of 5 choices were circled. 

4. Re question #2, describe the intensity of your exercise. Numerals in column 4 correspond 

to which of 5 choices were circled. In six cases, two adjoining numbers (e.g., 2 and 3) 

were circled, and the mean entered (in this case, 2.5). 

5. Re question #3, what types of exercise do you engage in? Numerals in column 5 

correspond to which of 9 choices were circled. No one circled No. 1 (none). Most 

subjects circled more than one choice, which is reflected by the numerals 2 through 8 in 

column 5 for each subject. If the subject circled 9 (other), the following numerals were 

entered in column 5 to indicate which other activities they engaged in (10, play; 11, 

dance; 12, horseback riding; 13, gymnastics; 14, rollerblading; 15, karate; 16, ice skating; 

17, aerobics (high impact); 18, aerobics (machines at fitness club); 19, hockey; and 20, 

boxing 

6. Re question #7, any medical complaints? 1 = yes; 2 = no. If yes, 1 was not entered, but 

what “caused” the yes answer was entered in column 6 as follows: 3, asthma; 4, ear, 5, 

scoliosis; 6, cerebral palsy; 7, allergies 

 A-26 
7. Re question #11, do you have, or have you ever had, any of the following? Numerals 

from 1 through 12 in column 7 indicate that only one choice was circled. If more than one 

choice was indicated, higher numbers were used as follows: 13, choices 7, 9, and 10; 14, 

choices 9, 10, and 11; and 15, choices 10 and 11. 

 A-27 
PREDICTION OF VO2MAX FROM SUBMAXIMAL MEASURED HR AND VO2 VALUES 

OBTAINED FROM FILE #1 (File #5) 

1. File ID number (#5) 

2. subject ID number (same number for each subject as identified for Study #30 in 1998 

data base) 

3. subject body mass (kg) 

4. subject age (years) 

5. estimated HRmax

6. VO2max y intercept 

7. VO2max b exponent 

8. predicted VO2max (l/min) 

9. predicted VO2max (l/min/kgBM) 

The rationale for predicting percent VO2max at any given percent HRmax is developed in brief on 

p. 403 of McArdle et al.’s exercise physiology text (4th ed., 1996) and in more detail in Astrand 

and Rodahl’s Textbook of Work Physiology (2nd ed., 1977), pp. 344-348. Using data from both 

sources, I calculated very closely similar submaximal % VO2max values as a function of % 

HRmax values (i.e., never more than 2%, and usually the same or only 1% difference). To get a 

clear visual perspective overview of the estimated VO2max prediction from measured 

submaximal HR and VO2, see Fig.10-4 (line A), p. 346, in Astrand and Rodahl. To use this 

procedure, it is first necessary to obtain a valid HRmax value which decreases an average of 1 

b/min each year of age from 10 years on. The best data I’m aware of on young children and 

adolescents that had HR and VO2 measured in both submaximal and maximal treadmill exercise 

is that of Astrand (Experimental Studies of Physical Working Capacity in Relation to Sex and 

Age, 1952, Ejnar Munksgaard, Copenhagen). Between the ages of 4 and 10 years, there was no 

significant relationship between HRmax and age for either sex, averaging 205 b/min. Thereafter, 

up to 33 years, there was the now widely accepted decrease of 1 b/min per year of age for both 

males and females, with 10 year-old boys and girls averaging 210 b/min. Accordingly, in File #5, 

the estimated HRmax in column 5 is 205 b/min for subjects less than 10 years of age and 220 

minus age in years for subjects 10 to 18.9 years of age. The y intercept and b exponent values for 

predicting VO2max were obtained by calculating, via simple regression analyses, individual 

subject values from measured submaximal HR and VO2 values taken from File #1. Predicted 

VO2max (in l/min), given in column 8 for each subject, was obtained by multiplying the b 

exponent value (column 7) times the estimated HRmax value (column 5) for each subject, and 

then subtracting their y intercept value (column 6). Each subject’s VO2max value in ml/min/kg 

(column 9) was calculated by dividing the column 8 value by body mass (column 3). 

Accuracy of the data in the enclosed electronic files began with data management and quality 

control procedures employed in the original CARB study, and which are described in detail on 

pages 38-39 of the Final Report (Adams, 1993). In summary, very few problems were 

encountered in the acquisition of active and resting protocol data. Accuracy assurance 

procedures for the transfer of the data from handwritten records to master data sheets, and 

 A-28 
subsequently to electronic spreadsheet data bases, is described in the aforementioned Final 

Report. The retrospective quality control program for all field protocol data bases, including 

spontaneous play, revealed that 5 children needed to repeat a protocol. Elimination of aberrant 

bits of data obtained during the play protocols (due to the result of momentary saliva blockage in 

the Harvard respirometer, Heart Watch heart rate artifacts, etc.), which rarely included more than 

one or two 1-min "glitches" in any one protocol, were part of the aforementioned quality control 

program. When this was done, the remaining data for the 5-min period was used to calculate an 

average for the full time period (i.e., 20, 30, or 35 min). A significant number of play protocols 

(~35%) were completed with incomplete, or no, fB data. This occurred because there was no 

way to determine whether the expiration electronic pulse from the Harvard respirometer was 

being recorded on the tape cassette until after the protocol was completed. However, since these 

were random occurrences, and fB was not of such prime concern as HR and VE, these protocols 

were not repeated. 

Per the Statement of Work for this project, to ensure that an accurate translation of the data was 

accomplished, all data entries were checked by me. The data quality objectives described in 

detail below were developed before data were translated to the enclosed electronic data files. 

These objectives were applied against 100% of the entries transcribed, including file column 

headings, for the first 500 datum points. In each of the five files, this objective was met, and 

double-checking procedures described in detail below were employed to achieve the highest 

accuracy possible. I have great confidence that the data furnished you are a valid representation 

of what appears in our original CARB study computer data files and the original handwritten 

records used to transfer data to electronic files for the first time in this project. 

The specific procedures used for each of the five files differed somewhat and are described in 

detail here. For the active file, a copy of the 1998 Excel file was made and all data not from 

Study #30 for the 132 subjects of interest were deleted. A search of the original 1998 Excel file 

was done, and a print out of these data obtained (i.e., pp. 14-18, 36-40, 58-62, and 80-84). All 

entries in the 2001 file were double-checked against the 1998 print-out for the first 12 subjects, 

and for subjects 13, 45, 46, 58, 59, 66, 107, 131, 132, 150, 151, and 152. Finally, the values on 

the last page of data for all subjects was verified. In no case was any difference seen. 

Formulation of the file for the resting protocol (#2) was initiated by transferring data from 

summary CARB study electronic data files (in a similar, but not exact format for each subject) to 

the present electronic data file. Individual data values for all variables in each posture were 

double-checked against a print-out of the 1998 data for the first 12 subjects, and for every 10 

subjects thereafter. In no case was any difference seen. As a further cross-check, I then 

calculated entire group (N = 132) means for each posture in the present file, and compared these 

values to weighted tabular mean values in the original Final Report, and found no difference 

greater than 0.7%, i.e., within the range of rounding error. 

Formulation of the file for the play protocol (#3) entailed entering VE, HR, and breathing 

frequency data from handwritten data summary sheets. All values were double-checked 

immediately after entry for each time period (4 to 7) for each subject (N=92). In addition, I then 

calculated an entire group mean for each time period, and compared these values to weighted 

tabular mean values in the original Final Report. Again, I found very close agreement. Intensity 

 A-29 
 A-30 

values available for each time period for each subject were entered from handwritten data 

acquisition sheets into the electronic data base (File #3). As I entered them, I double-checked 

these values against that read from the data sheet, and that the adjacent HR and breathing 

frequency data were for the correct subject and time period. 

Procedures used for establishing the health history data base from handwritten responses to a 

questionnaire, together with how data was entered in each column, are described above. The data 

were typed directly into the electronic file (#4) for each subject from the handwritten responses 

on the questionnaire. The numerical values entered were double-checked for each question (#s 1, 

2, 3, 7, and 11) for each subject immediately after each subject’s data entry. 

Procedures used for predicting each subject’s VO2max from submaximal HR and VO2 data (the 

latter obtained from File #1), together with how data was entered in each column, are described 

above. The data for columns 1-4 were transferred directly from File #1 and a mean, with 

standard deviation, calculated for each column which matched those previously calculated in File 

#1. The individual submaximal HR and VO2 values entered into a STATVIEW simple 

regression analysis were each double-checked before each individual analysis was done. The 

resultant y intercept and b exponent values were written on a printout of the subjects’ 

submaximal HR and VO2 values, with each set double-checked as they were entered in the File 

#5 Excel spread sheet. In addition to recalculating all values for the first 10 subjects, any subject 

who had a predicted VO2max value < 33 or > 66 ml/min/kg was double-checked. In no case was 

an error found. Please note that 18 subjects only had 3 sets of submaximal values (i.e., all at three 

walking speeds). In all but 4 cases (subjects # 3, 29, 108, and 142), the spread of observed HR 

and VO2 values was sufficient (in my estimation) to obtain valid predicted VO2max values. Thus, 

I recommend deleting the predicted VO2max values for these four subjects. If this is done, the 

mean VO2max for the group is 47.63 ml/min/kg, a value that I consider highly likely in a group 

of healthy children and adolescents of probable slightly greater fitness than the average 

population. 

I believe that this final report letter contains additional information beyond the electronic data 

base that you wanted and clarifies the format and procedure that were used. If you have 

questions, however, please do not hesitate to give me a call or drop me a note by FAX. I look 

forward to hearing from you and working again with you in the future. 

Best regards, 

William C. Adams, Ph.D. 
Attachment A2 to Appendix A 

Data Set Manipulation. The data file needed significant manipulation to facilitate statistical 

analysis. Principally, the row and column structure of the file had to be altered to put them into 

proper alignment. Row headings were scattered within rows of the data set due to two different 

test protocols (cycle and treadmill) that required different parameter measurements. In addition, 

within-person measurements for the same parameter (e.g., total ventilation or VE) over multiple 

stages of the test (VE1, VE2, VE3, etc.) were carried across the dataset in multiple columns. It was 

desired to have the multiple measurements as a single vector for a given parameter. Therefore, 

the following changes were made to the data set: 

• 11 separate data sets were created in Excel by the 11 heading groupings within the raw 

data set (more than one study could be combined under previous headers) 

• A master list of parameters was created such that the 11 data sets could be combined 

under one heading having 102 unique designations. Specific changes made were: 

o Parameter heading for step 14 was removed since there were no parameters 

supplied for this step (e.g. VE14, VO2 14, etc.). 

o Common data were recoded into vectors having a common descriptor. Originally 

identical names were not used to describe the same parameter at different steps 

(e.g., the speed parameter for the cycle ergonometer used “spd” for steps under 10 

(e.g., spd1) and “sp” for steps >9 (e.g., sp13). It was assumed that “sp”=“spd”, 

and for grade, “gr”=“grd”). 

o Removed inconsistent coding. Spd12 on one instance was mislabeled as Spd11 in 

Study #1. This was corrected. 

o Cleaned up variable name conventions. Both “Age” and “LBM” parameters 

contained a space after the label characters. This space was removed. 

• These 11 Excel data sets were combined in SAS to create a SAS data set 

(adams.sas7dbat). 

• In SAS, multiple measurements for a parameter (e.g., VE1, VE2, VE3, etc.) were combined 

under a single vector (e.g., VE) to create a second SAS data file: adams2.sas7dbat. A new 

variable was created to account for the multiple measurements for a given parameter 

termed ‘step’ (e.g., step=1 is for where VE and VO2 were first recorded; step=2 for the 

second measurement of VE, etc.). 

• This data set contained a total of 19 variables: 

o Step Step or stage measurement taken within an individual 

o Age Subjects age in years (yrs) 

o BM Body mass (kg) 

o Char A characteristic of an individual acting as a surrogate for fitness level 

• 1= Trained athlete 

• 2= Trained non-athlete 

• 3= Active individual 

• 4= Sedentary individual 

• 5= Obese 

o ET Cumulative test time at the end of each step (min) 

o Gend Gender: ♂ = -1; ♀ = 1 

 A-31 
o Grd Grade on treadmill (in percent) 

o HR Heart rate (bpm or beats min-1) 

o HT Height (cm) 

o LBM Lean body mass (kg) 

o Mach Machine used: Cycle Ergometer = 1; Treadmill = 2 

o VO2 VO2 (L min-1 BTPS) 

o Spd Speed of the Subject on Treadmill (m min-1) 

o stud Study number 

o Subj Subject number 

o TT Total test time (min) 

o VE VE (L min-1) 

o VO2m Observed or estimated VO2 maximum for the test (L min-1 STPD) 

o Wk Watts (power setting for the cycle ergometer) 

Maximum VO2 (VO2m) was reported for all of the studies but one. Study 30 contained estimates 

of VO2m for some of the data (individuals < 18.9 years old) however the study also contained 79 

individuals where VO2m was neither measured nor estimated. The method reported by Adams 

(see Appendix B) to estimate VO2m for the younger individuals was duplicated here for the 

missing data. Briefly, maximum heart rate (HRm) was estimated using an equation provided in 

Nieman (1999) (i.e., HRm=220-age). A simple linear regression analysis followed for each 

individual (of the form y=mx+b) where HR measurements were regressed on concomitant VO2. 

The slope (m) and intercept (b) estimates were then used to approximate VO2m from the HRm 

estimate and added to the final data set. 

Quality Assurance. Data values--mostly VE, VO2, and BM, since these were the principal 

analytical parameters-- were spot-checked by hand from the original Excel spreadsheet to both 

newly created SAS data sets. No errors were found in either of the SAS data sets. The number 

of individuals in the newly created data sets was each 956, equivalent to that reported by Dr. 

Adams upon transfer of the data set (in Appendix A) and the total number of measurements of 

VE and VO2 for individuals >18 years old was equivalent (n=5,681) to that reported by Johnson 

(2002). 

A simple plot of the body mass-normalized total ventilation versus the body mass-normalized 

oxygen consumption revealed that two individuals (i.e., stud=1 subj=25 step=8; stud=31 subj=9 

step=8) had exceptionally large oxygen consumption levels during one sample collection. These 

data were considered to be questionable, and upon inspection seemed to be the result of a 

misplaced decimal point (30.8 and 28.5 should be 3.08 and 2.85, respectively). 

Data were replaced in the SAS data sets to reflect this assumption rather than delete the 

datapoints altogether, even though there is no direct evidence that the decimal was misplaced. 

Due to the number of samples for a given parameter in the data set (>5,000), the impact of this 

change on the analyses presented here is negligible. The new dataset was saved as 

‘adams3.sas7dbat’ (from data set ‘adams.sas7dbat’) and ‘adams4.sas7dbat’(from data set 

‘adams2.sas7dbat’). 

 A-32 
 A-33 

Data Transformation. Figure A2-1 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 A2-2). 

Figure A2-1. Relationship between total ventilation Figure A2-2. Relationship between body 

rate (VE) and oxygen consumption rate (VO2) mass normalized total ventilation rate 

during exercise. (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: 

• Ln(VE) natural log of VE

• Ln(VO2) natural log of VO2

• Ln(VE/BM) natural log of body mass normalized VE

• Ln(VO2/BM) natural log of body mass normalized VO2

• VQ ventilatory equivalent or VE / VO2

• Ln(age) natural log of age 

A logarithmic transformation directly applied to the parameters allows for a significant reduction 

in the dispersion (Figure A2-3 compared to Figure A2-1), 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 A2-4), 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. 
Figure A2-3. Relationship between the natural Figure A2-4. Relationship between the 

logarithm of total ventilation rate Ln(VE) and natural logarithm of body mass 

oxygen consumption rate Ln(VO2) during normalized total ventilation rate 

exercise. Ln(VE/BM) and oxygen consumption rate 

 Ln(VO2/BM) during exercise. 

 A-34 
Attachment A3 to Appendix A 

Selected ventilation algorithms were evaluated using the APEX model by adjusting the 

ventilation.txt file (see U.S. EPA, 2005). 20,000 persons were simulated for one day using the 

algorithms described in the main body text and parameters in Tables 2 and 3. Model output was 

nearly 800,000 event-based ventilation rates, typically around 40 events per individual simulated. 

Figure A3-1 presents the mid to upper range percentiles based on these 800,000 events to 

encompass the possible maximum ventilation rates generated by each simulation. Algorithms 

evaluated included the following: 

MLR: multiple linear regression algorithm using equation 3 and parameters from Table A-3. 

MER: mixed-effects regression model using equation 4 and parameters from Table A-3. 

MLR+MER: regression coefficients from MLR coupled with variance components estimated 

from the MER model. 

Johnson: Johnson (2002) regression model using equation 2 and parameters from Table A-2. 

SMER: a simplified mixed effects regression model using equation 2 and parameters derived for 

all age groups from the Adams data set as follows: 

b0

Ln(VO2/BM) Residuals

b1 eb ew

Females 4.1017 1.1904 0.1408 0.1186 

Males 3.9332 1.1638 0.1445 0.1277 

Results are very similar for each of the algorithms, not surprisingly since they were for the most 

part derived from the same data set. At any given percentile, ventilation rates increase rapidly 

with age for those less than 20 years old, stabilize from ages 20 to about 60, then gradually 

decline with further increases in age. Increased variability at ages greater than 75 is also evident, 

a function of both the limited amount of data available for the development of the algorithm and 

the limited number of persons simulated at these ages from the population of 20,000. At each of 

the percentiles, the Johnson (2002) algorithm generated lower ventilation estimates for persons 

under age 5, a function of the method of the algorithm derivation, whereas the intercept was 

modified based on published literature VE/VO2 relationships while the residuals were assumed 

the same as those greater than 18 years of age. When considering a simple mixed effects 

regression (SMER) algorithm, flattening out of the percentiles occurs across the ages, mostly due 

to elevation of ventilation rates of young children that resulted from ignoring age as an 

independent variable in development of the regression parameters. 

Figure A3-2 presents the full range of percentiles for the event-based ventilation rates generated 

from the APEX model using the mixed effects regression (MER) model and the Johnson (2002) 

model. Results are very similar, however at young ages (<5 years old), the Johnson (2002) 

model estimates lower ventilation rates at both the lower and upper percentiles. The percent 

difference between the two model estimates is large, ranging from about 40-120% lower (Figure 

A3-3). The lower percentiles (min, p1, p5) for all ages >5 are moderately different, the Johnson 

(2002) ventilation estimates are less than the MER by about 20-40% for ages 10-45, then 10-

 A-35 
 A-36 

20% greater than the MER estimates for ages above 45. The MER algorithm estimates higher 

ventilation rates for persons above age 60 by about 20% considering the upper percentiles (p95, 

p99, max), with greater differences at age 90 and older (20-60%). 
Figure A3-1. Comparison of selected percentiles of estimated event-based ventilation rates from 20,000 person AP

using different ventilation algorithms. 

 A-37 max VE from events of 20,000 person APEX simulation 020406080 100 120 140 160 0 10 20 30 40 50 60 70 80 90 100 age (years) VE (L/min) MLR+MER-max MER-max Johnson-max MLR-max SMER-max p99 VE from events of 20,000 person APEX simulation 0102030405060 0 10 20 30 40 50 60 70 80 

age (years) VE (L/min) MLR

MERJohMLRSME

p95 VE from events of 20,000 person APEX simulation

05

1015202530354045

0 10 20 30 40 50 60 70 80 90 100

age (years)

V

E (L/min)

MLR+MER-p95MER-p95Johnson-p95MLR-p95SMER-p95

p50 VE from events of 20,000 person APEX simulation

02468

10121416

0 10 20 30 40 50 60 70 80 

age (years)

V

E (L/min)

MLRMERJohMLRSME
Figure A3-2. Comparison of estimated event-based ventilation rate percentiles from 20,000 person APEX model si

effects regression (MER-left) and Johnson (2002) (right) ventilation algorithms. 

 A-38 Mixed Model Estimates of VE from for 796,714 events from 20,000 person APEX simulation Lower Percentiles

05

10152025

0 10 20 30 40 50 60 70 80 90 100

age (years)

VE (L/min)

MER-p50MER-meanMER-p5MER-p1MER-min

Johnson Model Estimates of VE from for 799,355 events 

Lower Percentiles

05

10152025

0 10 20 30 40 50 60 

age (years)

VE (L/min)

Mixed Model Estimates of VE from for 796,714 events from 20,000 person APEX simulation

Upper Percentiles

0

20406080

100120140

0 10 20 30 40 50 60 70 80 90 100

age (years)

VE (L/min)

MER-p50MER-meanMER-p95MER-p99MER-max

Johnson Model Estimates of VE from for 799,355 events f

Upper Percentiles

0

20406080

100120140

0 10 20 30 40 50 60 

age (years)

VE (L/min)
 A-39 Figure A3-3. Percent difference of estimated event-based ventilation rate percentiles from 20,000 person APEX mo

mixed effects regression (MER-left) and Johnson (2002) (right) ventilation algorithms. 

-140-120-100

-80-60-40-20

0

20406080

0 10 20 30 40 50 60 70 80 90 10

Age (years)

% difference (Johnson-MER)/(Johnson+MER)*200 max p99 p95 p50 p5p1

min
APPENDIX B 

STATISTICAL DISTRIBUTIONS ASSIGNED TO ACTIVITY CODES 

FOR USE IN SIMULATING METS VALUES 
LIST OF TABLES 

B-1. METS Distributions assigned to activity ID codes within CHAD ..................................B-4 

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-11 

B-2 
 Table B-1 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 have been documented in Appendix 1 of the CHAD User’s Guide (U.S. 

EPA, 2002). 

 The last two columns of Table B-1 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-1 were encountered within the NHAPS 

data set. 

 A total of ten activity ID codes that did not have a METS distribution assigned to them 

within CHAD were encountered in the NHAPS data set. These codes, listed in Table B-2, were 

occupation-related activity codes that appeared to represent subcodes to code 10000 (general 

work and other income-producing activities). Such subcodes 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 subcodes 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. 

B-3 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

Activity 

ID 

Code 

Activity 

Description Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Work, 

general 

10000 ADMIN LogNormal 1.7 1.7 0.3 1.4 2.7 Y Y 

Work, 

general 

10000 ADMSUP LogNormal 1.7 1.7 0.3 1.4 2.7 Y Y 

Work, 

general 

10000 FARM LogNormal 7.5 7.0 3.0 3.6 17.0 Y Y 

Work, 

general 

10000 HSHLD LogNormal 3.6 3.5 0.8 2.5 6.0 Y Y 

Work, 

general 

10000 MACH Uniform 5.3 5.3 0.7 4.0 6.5 

Work, 

general 

10000 PREC Triangle 3.3 3.3 0.4 2.5 4.5 Y Y 

Work, 

general 

10000 PROF Triangle 2.9 2.7 1.0 1.2 5.6 

Work, 

general 

10000 PROTECT Triangle 2.9 2.7 1.0 1.2 5.6 

Work, 

general 

10000 SALE Triangle 2.9 2.7 1.0 1.2 5.6 

Work, 

general 

10000 SERV Triangle 5.2 5.3 1.4 1.6 8.4 

Work, 

general 

10000 TECH Triangle 3.3 3.3 0.4 2.5 4.5 

Work, 

general 

10000 TRANS LogNormal 3.3 3.0 1.5 1.3 8.4 Y Y 

Breaks 10300* Uniform 1.8 1.8 0.4 1.0 2.5 

General 

household 

activities 

11000 Triangle 4.7 4.6 1.3 1.5 8.0 

Prepare 

food 

11100* LogNormal 2.6 2.5 0.5 2.0 4.0 Y Y 

Prepare and 

clean-up 

food 

11110 Exponential 2.8 2.5 0.9 1.9 4.0 Y 

Indoor 

chores 

11200 Exponential 3.4 3.0 1.4 2.0 5.0 Y 

Clean-up 

food 

11210* Uniform 2.5 2.5 0.1 2.3 2.7 

Clean house 11220* Exponential 4.1 3.5 1.9 2.2 5.0 Y 

B-4 
Outdoor 

chores 

11300* Normal 5.0 5.0 1.0 2.0 7.0 Y Y 

Clean 

outdoors 

11310 Exponential 5.3 4.5 2.7 2.6 6.0 Y 

Care of 

clothes 

11400* Exponential 2.2 2.0 0.7 1.5 4.0 Y 

Wash 

clothes 

11410 Point Est. 2.0 2.0 2.0 2.0 

Build a fire 11500 Point Est. 2.0 2.0 2.0 2.0 

Repair, 

general 

11600 Normal 4.5 4.5 1.5 2.0 8.0 Y Y 

Repair of 

boat 

11610 Point Est. 4.5 4.5 4.5 4.5 

Paint home 

/ room 

11620 Exponential 4.9 4.5 1.4 3.5 6.0 Y 

Repair / 

maintain car 

11630* Triangle 3.5 3.4 0.4 3.0 4.5 

Home 

repairs 

11640 Exponential 4.7 4.5 0.7 4.0 6.0 Y 

Other 

repairs 

11650* Uniform 4.5 4.5 1.4 2.0 7.0 

Care of 

plants 

11700* Uniform 3.5 3.5 0.9 2.0 5.0 

Care for 

pets/animals 

11800* Uniform 3.3 3.3 0.1 3.0 3.5 

Other 

household 

11900* Exponential 6.6 5.5 3.6 3.0 9.0 Y 

B-5 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

ID 

Code 

Activity 

Description Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Child care, 

general 

12000 LogNormal 3.1 3.0 0.7 2.5 5.0 Y Y 

Care of baby 12100* Uniform 3.3 3.3 0.1 3.0 3.5 

Care of child 12200* Uniform 3.3 3.3 0.1 3.0 3.5 

Help / teach 12300* Uniform 2.8 2.8 0.1 2.5 3.0 

Talk /read 12400* Uniform 2.8 2.8 0.1 2.5 3.0 

Play indoors 12500* Uniform 2.8 2.8 0.1 2.5 3.0 

Play outdoors 12600* Uniform 4.5 4.5 0.3 4.0 5.0 

Medical 

care-child 

12700* Uniform 3.2 3.2 0.1 3.0 3.3 

Other child 

care 

12800* Uniform 3.0 3.0 0.3 2.5 3.5 

Obtain goods 

and services, 

general 

13000 Triangle 3.8 3.7 0.8 2.0 6.0 

Dry clean 13100* Uniform 3.3 3.3 0.4 2.5 4.0 

Shop / run 

errands 

13200 Triangle 3.7 3.6 0.8 2.0 6.0 

Shop for food 13210* Triangle 3.9 3.8 0.8 2.2 6.0 

Shop for 

clothes or 

household 

goods 

13220* Uniform 3.4 3.4 0.6 2.3 4.5 

Run errands 13230* Uniform 3.5 3.5 0.6 2.5 4.5 

Obtain 

personal care 

service 

13300* Uniform 3.5 3.5 0.6 2.5 4.5 

Obtain 

medical 

service 

13400* Uniform 3.5 3.5 0.6 2.5 4.5 

Obtain 

government / 

financial 

services 

13500* Uniform 3.5 3.5 0.6 2.5 4.5 

Obtain car 

services 

13600* Uniform 3.5 3.5 0.6 2.5 4.5 

Other repairs 13700* Uniform 3.5 3.5 0.6 2.5 4.5 

B-6 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

Description Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Activity 

ID 

Code 

Other 

services 

13800* Uniform 3.5 3.5 0.6 2.5 4.5 

Personal 

needs and 

care, general 

14000 Uniform 2.0 2.0 0.6 1.0 3.0 

Shower, 

bathe, pers. 

hygiene 

14100 Normal 2.0 2.0 0.3 1.0 4.0 Y Y 

Shower, bathe 14110* Uniform 3.0 3.0 0.6 2.0 4.0 

Personal 

hygiene 

14120* Uniform 1.8 1.8 0.4 1.0 2.5 

Medical care 14200* Uniform 1.8 1.8 0.4 1.0 2.5 

Help and care 14300* LogNormal 3.1 3.0 0.7 2.5 5.0 Y Y 

Eat 14400* Uniform 1.8 1.8 0.1 1.5 2.0 

Sleep or nap 14500* LogNormal 0.9 0.9 0.1 0.8 1.1 Y Y 

Dress, groom 14600* Point Est. 2.5 2.5 2.5 2.5 

Other 

personal 

needs 

14700* Triangle 2.0 2.0 0.4 1.0 2.9 

General educ. 

and pro. 

training 

15000 LogNormal 1.9 1.8 0.7 1.4 4.0 Y Y 

Attend 

full-time 

school 

15100* Uniform 2.1 2.1 0.4 1.4 2.8 

Attend 

day-care 

15110 Uniform 2.3 2.3 0.4 1.5 3.0 

Attend K−12 15120 Uniform 2.1 2.1 0.4 1.4 2.8 

Attend 

college or 

trade school 

15130 Uniform 2.0 2.0 0.3 1.4 2.5 

Adult 

education and 

special 

training 

15140 Uniform 1.8 1.8 0.2 1.4 2.2 

Attend other 

classes 

15200* Uniform 2.2 2.2 0.5 1.4 3.0 

B-7 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

Description 

Activity 

ID 

Code Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Do 

homework 

15300* Point Est. 1.8 1.8 1.8 1.8 

Use library 15400* Uniform 2.3 2.3 0.4 1.5 3.0 

Other 

education 

15500* Uniform 2.8 2.8 0.7 1.5 4.0 

General 

entertainment 

/ social 

activities 

16000 LogNormal 2.2 2.0 1.1 1.0 6.0 Y Y 

Attend sports 

events 

16100* Uniform 2.7 2.7 0.8 1.4 4.0 

Participate in 

social, 

political, or 

religious 

activities 

16200 Uniform 1.7 1.7 0.2 1.4 2.0 

Practice 

religion 

16210* Uniform 1.7 1.7 0.2 1.4 2.0 

Watch movie 16300* Uniform 1.3 1.3 0.2 1.0 1.6 

Attend theater 16400* Uniform 1.7 1.7 0.4 1.0 2.3 

Visit 

museums 

16500* Uniform 2.5 2.5 0.3 2.0 2.9 

Visit 16600* Uniform 1.5 1.5 0.3 1.0 1.9 

Attend a party 16700* LogNormal 3.3 3.0 1.4 1.5 8.0 Y Y 

Go to bar / 

lounge 

16800* LogNormal 3.3 3.0 1.4 1.5 8.0 Y Y 

Other 

entertainment 

/ social events 

16900* Uniform 3.8 3.8 1.3 1.5 6.0 

Leisure, 

general 

17000 20 LogNormal 5.7 5.0 3.0 1.4 16.0 Y Y 

Leisure, 

general 

17000 30 Normal 5.0 5.0 2.0 1.0 9.0 Y Y 

Leisure, 

general 

17000 40 Normal 4.5 4.5 1.4 1.7 7.3 Y Y 

Sports and 

active leisure 

17100 20 LogNormal 5.7 5.0 3.0 1.4 16.0 Y Y 

B-8 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

Description 

Activity 

ID 

Code Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Sports and 

active leisure 

17100 30 Normal 5.0 5.0 2.0 1.0 9.0 Y Y 

Sports and 

active leisure 

17100 40 Normal 4.5 4.5 1.4 1.7 7.3 Y Y 

Participate in 

sports 

17110* 20 LogNormal 3.6 3.2 1.9 1.4 10.0 Y Y 

Participate in 

sports 

17110* 30 LogNormal 3.6 3.2 1.9 1.4 10.0 Y Y 

Participate in 

sports 

17110* 40 LogNormal 3.4 3.0 1.7 1.4 9.0 Y Y 

Hunting, 

fishing, 

hiking 

17111 20 Normal 5.6 5.6 2.1 1.4 9.8 Y Y 

Hunting, 

fishing, 

hiking 

17111 30 Normal 5.8 5.8 2.4 1.0 10.6 Y Y 

Hunting, 

fishing, 

hiking 

17111 40 Normal 4.7 4.7 1.8 1.1 8.3 Y Y 

Golf 17112 20 Uniform 3.8 3.8 1.0 2.0 5.5 

Golf 17112 30 Uniform 3.8 3.8 1.0 2.0 5.5 

Golf 17112 40 Uniform 3.5 3.5 0.9 2.0 5.0 

Bowling / 

pool / ping 

pong / pinball 

17113 Uniform 3.0 3.0 0.6 2.0 4.0 

Yoga 17114 Triangle 3.1 3.2 0.6 1.4 4.0 

Participate in 

outdoor 

leisure 

17120 20 LogNormal 4.2 3.9 1.5 2.0 9.0 Y Y 

Participate in 

outdoor 

leisure 

17120 30 LogNormal 4.2 3.9 1.5 2.0 9.0 Y Y 

Participate in 

outdoor 

leisure 

17120 40 Point Est. 3.5 3.5 0.0 0.0 

Play, 

unspecified 

17121 20 LogNormal 4.2 3.9 1.5 2.0 9.0 Y Y 

Play, 

unspecified 

17121 30 LogNormal 4.2 3.9 1.5 2.0 9.0 Y Y 

B-9 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

Description 

Activity 

ID 

Code Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Play, 

unspecified 

17121 40 Point Est. 3.5 3.5 0.0 0.0 

Passive, 

sitting 

17122* Uniform 1.5 1.5 0.2 1.2 1.8 

Exercise 17130* 20 LogNormal 5.8 5.5 1.8 1.8 11.3 Y Y 

Exercise 17130* 30 Normal 5.7 5.7 1.8 2.1 9.3 Y Y 

Exercise 17130* 40 Normal 4.7 4.7 1.2 2.3 7.1 Y Y 

Walk, bike, or 

jog (not in 

transit) 

17131 20 LogNormal 5.8 5.5 1.8 1.8 11.3 Y Y 

Walk, bike, or 

jog (not in 

transit) 

17131 30 Normal 5.7 5.7 1.8 2.1 9.3 Y Y 

Walk, bike, or 

jog (not in 

transit) 

17131 40 Normal 4.7 4.7 1.2 2.3 7.1 Y Y 

Create art, 

music, work 

on hobbies 

17140 20 Normal 5.3 5.3 1.8 1.7 8.9 Y Y 

Create art, 

music, work 

on hobbies 

17140 30 Normal 5.2 5.2 1.7 1.7 8.9 Y Y 

Create art, 

music, work 

on hobbies 

17140 40 Normal 3.8 3.8 1.0 1.8 5.8 Y Y 

Participate in 

hobbies 

17141* Triangle 2.8 2.7 0.8 1.5 5.0 

Create 

domestic 

crafts 

17142* Triangle 2.0 1.9 0.4 1.5 3.0 

Create art 17143* Uniform 2.5 2.5 0.3 2.0 3.0 

Perform 

music / drama 

/ dance 

17144* 20 Normal 5.3 5.3 1.8 1.7 8.9 Y Y 

Perform 

music / drama 

/ dance 

17144* 30 Normal 5.2 5.2 1.7 1.7 8.9 Y Y 

B-10 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

Description 

Activity 

ID 

Code Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Perform 

music / drama 

/ dance 

17144* 40 Normal 3.8 3.8 1.0 1.8 5.8 Y Y 

Play games 17150* Triangle 3.3 3.2 0.6 2.4 5.0 

Use of 

computers 

17160* Uniform 1.6 1.6 0.2 1.2 2.0 

Recess and 

physical 

education 

17170 Uniform 5.0 5.0 1.7 2.0 8.0 

Other sports 

and active 

leisure 

17180 20 LogNormal 6.6 5.9 3.2 2.0 17.4 Y Y 

Other sports 

and active 

leisure 

17180 30 Normal 6.0 6.0 2.0 2.0 10.0 Y Y 

Other sports 

and active 

leisure 

17180 40 Normal 4.8 4.8 1.4 2.0 7.6 Y Y 

Participate in 

passive 

leisure 

17200 LogNormal 1.3 1.3 0.3 1.0 2.3 Y Y 

Watch 17210 Uniform 1.5 1.5 0.2 1.2 1.8 

Watch adult 

at work 

17211 Uniform 0.0 0.0 0.0 1.2 0.0 

Watch 

someone 

provide 

childcare 

17212 Uniform 0.0 0.0 0.0 1.2 0.0 

Watch 

personal care 

17213 Uniform 0.0 0.0 0.0 1.2 0.0 

Watch 

education 

17214 Uniform 0.0 0.0 0.0 1.2 0.0 

Watch 

organizational 

activities 

17215 Uniform 0.0 0.0 0.0 1.2 0.0 

Watch 

recreation 

17216 Uniform 2.7 2.7 0.8 1.4 4.0 

B-11 
Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

Description 

Activity 

ID 

Code Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Listen to 17220 

radio / 

recorded 

music / watch 

T.V. 

LogNormal 1.2 1.2 0.4 0.9 2.3 Y Y 

Listen to 

radio 

17221* Uniform 1.2 1.2 0.1 1.0 1.3 

Listen to 

recorded 

music 

17222* Uniform 1.9 1.9 0.2 1.5 2.3 

Watch TV 17223* Point Est. 1.0 1.0 1.0 1.0 

Read, general 17230 Uniform 1.3 1.3 0.2 1.0 1.6 

Read books 17231* Uniform 1.3 1.3 0.2 1.0 1.6 

Read 

magazines / 

not 

ascertained 

17232* Uniform 1.3 1.3 0.2 1.0 1.6 

Read 

newspaper 

17233* Uniform 1.3 1.3 0.2 1.0 1.6 

Converse / 

write 

17240 Uniform 1.4 1.4 0.2 1.0 1.8 

Converse 17241* Uniform 1.4 1.4 0.2 1.0 1.8 

Write for 

leisure / 

pleasure / 

paperwork 

17242* Uniform 1.4 1.4 0.2 1.0 1.8 

Think and 

relax 

17250* Uniform 1.2 1.2 0.1 1.0 1.3 

Other passive 

leisure 

17260 Uniform 1.9 1.9 0.2 1.5 2.3 

Other leisure 17300 Uniform 1.5 1.5 0.2 1.2 1.8 

Travel, 

general 

18000 LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel during 

work 

18100* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel 

to/from work 

18200* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

child care 

18300* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

B-12 
B-13 

Table B-1. METS Distributions assigned to activity ID codes within CHAD 

(continued) 

Activity 

Description 

Activity 

ID 

Code Age(a) Occupation(b)

Distribution 

Type Mean Median

Std 

Dev Min Max 

Truncate 

Left 

Tail? 

Truncate 

Right 

Tail? 

Travel for 

goods and 

services 

18400* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

personal care 

18500* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

education 

18600* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

organ. 

activity 

18700* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

event / social 

act 

18800* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

leisure 

18900 LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

active leisure 

18910* LogNormal 2.3 2.0 1.3 1.0 7.0 Y Y 

Travel for 

passive 

leisure 

18920* LogNormal 2.3 2.0 1.3 1.8 7.0 Y Y 

a

Age Group (“20" = <25 years; “30" = 25−39 years; “40" = >40 years) 

b

Occupation (activity ID code = 1000 only): ADMIN = executive/administrative/managerial; PROF = professional; 

TECH = technicians; SALE = sales; ADMSUP = administrative support; HSHLD = private household; 

PROTECT = protective services; SERV = service; FARM = farming/forestry/fishing; PREC = precision 

production/craft/repair; MACH = machine operators/assemblers/inspectors; TRANS = transportation and material 

moving; LABOR = handling/equipment cleaners/helpers/laborers 

*Activity ID codes encountered within the NHAPS data set. 
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 Whose METS Distribution Was 

Assigned to the Code in the First Column

Activity 

Code Activity Description

Activity 

Code Activity Description

10111 Work for professional/union 

organizations 

10000 

(PROF) 

Work and other income producing 

activities, general—professional 

positions 

10112 Work for special interest identity 

organizations 

16200 Participate in social, political, or 

religious activities 

10113 Work for political party and civic 

participation 

16200 Participate in social, political, or 

religious activities 

10114 Work for volunteer/helping 

organizations 

14300 Help and care 

10115 Work of/for religious groups 16200 Participate in social, political, or 

religious activities 

10116 Work for fraternal organizations 16200 Participate in social, political, or 

religious activities 

10117 Work for child/youth/family 

organizations 

12800 Other child care 

10118 Work for other organizations 10000 

(ADMIN) 

Work and other income producing 

activities, general—executive, 

administrative, and managerial 

positions 

10120 Work, income-related only 16900 Other entertainment/social events 

10200 Unemployment 13500 Obtain government/financial services 

B-14 
APPENDIX C 

ADDITIONAL ANALYSIS TABLES 
LIST OF TABLES 

C-1a. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES 

male participants, by age group .......................................................................................C-3 

C-1b. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES 

female participants, by age group ....................................................................................C-4 

C-2a. Descriptive statistics for daily average ventilation rate (m3/day) in males, by age 

category ............................................................................................................................C-5 

C-2b. Descriptive statistics for daily average ventilation rate (m3/day) in females, by age 

category ............................................................................................................................C-6 

C-3. Descriptive statistics for duration of time (hr/day) spent performing activities 

within the specified activity category, by age and gender categories ..............................C-7 

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-12 

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-17 

C-6. Descriptive statistics for daily ventilation rate (m3

/min), unadjusted for body 

weight, while performing activities within the specified activity category, by age 

and gender categories .....................................................................................................C-22 

C-7. Descriptive statistics for daily ventilation rate (m3

/min-kg), adjusted for body 

weight, while performing activities within the specified activity category, by age 

and gender categories .....................................................................................................C-27 

C-2 
Table C-1a. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES male p

by age group 

 C-3 Body Weight (kg) BMR (kcal/min

Age Category Mean Percentiles Maxi- mum Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75

Birth to <1 year 8.0 4.8 5.5 6.7 8.1 9.4 10.4 10.8 13.4 0.31 0.18 0.21 0.26 0.31 0.

1 year 11.4 9.1 9.8 10.3 11.3 12.3 13.2 13.7 16.1 0.45 0.35 0.38 0.40 0.45 0.

2 years 13.9 11.1 11.7 12.5 13.8 15.3 16.2 17.2 23.3 0.55 0.44 0.46 0.50 0.55 0.

3 to <6 years 18.5 13.4 14.5 16.0 17.8 20.2 23.3 25.2 42.0 0.64 0.56 0.58 0.60 0.63 0.

6 to <11 years 31.8 19.9 21.9 24.8 29.6 36.3 45.4 50.0 86.9 0.85 0.66 0.70 0.74 0.82 0.

11 to <16 years 56.4 32.8 35.2 43.3 53.8 65.7 79.9 92.5 143.6 1.15 0.86 0.89 0.99 1.12 1.16 to <21 years 76.5 54.3 57.6 63.9 72.2 83.6 102.8 111.2 176.0 1.33 1.08 1.11 1.18 1.28 1.21 to <31 years 83.8 56.8 60.9 69.5 80.8 93.7 108.7 123.4 196.8 1.35 1.07 1.12 1.21 1.32 1.31 to <41 years 87.1 61.0 65.6 73.9 83.4 96.3 112.6 126.7 193.3 1.30 1.09 1.13 1.19 1.27 1.41 to <51 years 88.4 64.0 67.7 76.7 85.4 97.8 111.8 121.2 188.3 1.31 1.12 1.14 1.22 1.29 1.51 to <61 years 89.0 62.6 67.4 76.6 86.6 99.6 110.5 120.3 179.0 1.30 1.07 1.12 1.20 1.29 1.61 to <71 years 87.6 63.4 66.7 76.1 85.7 97.1 111.2 119.0 162.8 1.12 0.92 0.95 1.03 1.10 1.71 to <81 years 82.4 60.6 64.4 72.5 81.0 92.0 101.1 108.8 132.7 1.08 0.90 0.93 1.00 1.07 1.

81 years and 

older 75.4 57.9 61.8 67.0 74.6 82.0 91.6 100.5 111.8 1.02 0.88 0.91 0.95 1.01 1.

Individual measures have been weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating thThe numbers of male NHANES participants with data entering into these statistics are given in Table 2-1. 
Table C-1b. Descriptive statistics of body weight (kg) and BMR (kcal/min) across NHANES female 

by age group 

 C-4 Body Weight (kg) BMR (kcal/min

Age Category Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Birth to <1 year 7.4 4.6 4.9 6.3 7.5 8.6 9.6 10.4 20.2 0.28 0.16 0.18 0.23 0.28 0.

1 year 11.1 8.8 9.1 9.9 10.9 12.1 13.1 13.8 18.9 0.43 0.33 0.35 0.38 0.42 0.

2 years 13.3 11.0 11.2 12.0 13.1 14.4 15.6 16.8 22.7 0.52 0.42 0.43 0.46 0.51 0.

3 to <6 years 18.2 13.3 14.2 15.5 17.4 19.5 23.0 26.9 38.6 0.59 0.52 0.54 0.56 0.58 0.

6 to <11 years 30.9 18.9 20.6 23.3 28.1 36.2 44.7 50.4 87.0 0.76 0.60 0.63 0.67 0.74 0.

11 to <16 years 55.6 35.6 38.1 45.0 53.1 62.4 75.3 86.2 134.4 1.00 0.81 0.83 0.90 0.97 1.16 to <21 years 65.2 46.2 47.8 54.3 61.3 72.5 89.9 96.2 156.4 1.04 0.83 0.87 0.93 1.01 1.21 to <31 years 72.4 47.5 51.4 58.3 69.0 82.5 98.3 109.6 159.1 1.07 0.83 0.87 0.93 1.03 1.31 to <41 years 74.7 51.0 54.6 60.7 69.7 84.0 103.8 112.8 191.1 1.01 0.87 0.89 0.93 0.98 1.41 to <51 years 76.6 51.3 54.2 60.7 72.7 87.5 102.8 117.2 182.8 1.02 0.88 0.89 0.93 1.00 1.51 to <61 years 77.0 53.1 56.2 62.8 73.6 87.7 104.6 113.4 150.1 1.01 0.86 0.89 0.93 1.00 1.61 to <71 years 75.5 51.7 55.9 63.8 73.1 83.9 99.9 109.2 138.7 0.93 0.78 0.81 0.86 0.92 0.71 to <81 years 70.3 46.8 52.0 59.4 68.5 80.3 91.8 97.7 127.6 0.90 0.75 0.78 0.83 0.89 0.

81 years and 

older 63.9 45.2 47.4 54.5 62.6 71.4 79.4 91.4 120.0 0.86 0.74 0.76 0.80 0.85 0.

Individual measures have been weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating thThe numbers of female NHANES participants with data entering into these statistics are given in Table 2-1. 
Table C-2a. Descriptive statistics for daily average ventilation rate (m3/day) in males, by age catego

 C-5 Daily Average Ventilation Rate, Unadjusted for Body Weight Daily Average Ventilation Rate, Ad

( ; m3/day)

VE

& ( /BW; m3/day

VE

&

Age Category

Mean

Percentiles

Max

Percentiles

Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 7

Birth to <1 year 8.76 4.77 5.70 7.16 8.70 10.43 11.93 12.69 17.05 1.09 0.91 0.94 1.00 1.09 1

1 year 13.49 9.73 10.41 11.65 13.11 15.02 17.03 17.89 24.24 1.19 0.96 1.02 1.09 1.17 1

2 years 13.23 9.45 10.20 11.43 13.19 14.49 16.27 17.71 28.17 0.95 0.78 0.82 0.87 0.94 1

3 to <6 years 12.65 10.42 10.87 11.40 12.58 13.64 14.63 15.41 19.52 0.70 0.52 0.56 0.61 0.69 0

6 to <11 years 13.42 10.08 10.69 11.73 13.09 14.73 16.56 17.72 24.97 0.44 0.32 0.34 0.38 0.43 0

11 to <16 years 15.32 11.41 12.11 13.27 14.79 16.81 19.54 21.21 28.54 0.28 0.21 0.22 0.25 0.28 016 to <21 years 17.22 12.60 13.41 14.48 16.63 19.16 21.94 23.38 39.21 0.23 0.17 0.18 0.20 0.23 021 to <31 years 18.82 12.69 13.57 15.49 18.18 21.23 24.57 27.14 43.42 0.23 0.16 0.17 0.19 0.22 031 to <41 years 20.29 14.00 14.97 16.96 19.83 23.02 26.77 28.90 40.72 0.24 0.16 0.18 0.20 0.23 041 to <51 years 20.93 14.66 15.54 17.50 20.60 23.89 26.71 28.37 45.98 0.24 0.17 0.18 0.20 0.23 051 to <61 years 20.91 14.98 16.07 17.60 20.41 23.16 27.01 29.09 38.17 0.24 0.16 0.18 0.20 0.24 061 to <71 years 17.94 13.92 14.50 15.88 17.60 19.54 21.78 23.50 28.09 0.21 0.17 0.18 0.19 0.20 071 to <81 years 16.35 13.10 13.61 14.67 16.23 17.57 19.43 20.42 24.53 0.20 0.17 0.18 0.19 0.20 0

81 years and 

older 15.15 11.95 12.57 13.82 14.90 16.31 18.02 18.68 22.63 0.20 0.17 0.18 0.19 0.20 0

Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999

−2002 when calculating the 

Ventilation rate was estimated using the multiple linear regression model in Section 3.6. 
Table C-2b. Descriptive statistics for daily average ventilation rate (m3/day) in females, by age cate

 C-6 Daily Average Ventilation Rate, Unadjusted for Body Weight ; m3/day) Daily Average Ventilation Rate, Adj

( /BW; m3 ( /day

VE

& VE

&

Age Category

Mean

Percentiles

Max Mean

Percentiles

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 75

Birth to <1 

year

8.53 4.84 5.48 6.83 8.41 9.78 11.65 12.66 26.26 1.14 0.91 0.97 1.04 1.13 1.

1 year 13.31 9.08 10.12 11.24 13.03 14.64 17.45 18.62 24.77 1.20 0.97 1.01 1.10 1.18 1.

2 years 12.74 8.91 10.07 11.38 12.60 13.96 15.58 16.37 23.01 0.95 0.82 0.84 0.89 0.96 1.

3 to <6 years 12.16 9.87 10.38 11.20 12.02 13.01 14.03 14.93 19.74 0.69 0.48 0.54 0.60 0.68 0.

6 to <11 years 12.41 9.99 10.35 11.01 11.95 13.42 15.13 16.34 20.82 0.43 0.28 0.31 0.36 0.43 0.

11 to <16 years 13.44 10.47 11.11 12.04 13.08 14.54 16.25 17.41 26.58 0.25 0.19 0.20 0.22 0.24 0.16 to <21 years 13.59 9.86 10.61 11.78 13.20 15.02 17.12 18.29 30.11 0.21 0.16 0.17 0.19 0.21 0.21 to <31 years 14.57 10.15 10.67 11.93 14.10 16.62 19.32 21.14 30.23 0.21 0.14 0.16 0.18 0.20 0.31 to <41 years 14.98 11.07 11.80 13.02 14.68 16.32 18.51 20.45 28.28 0.21 0.14 0.15 0.18 0.20 0.41 to <51 years 16.20 12.10 12.58 14.16 15.88 17.95 19.91 21.35 35.89 0.22 0.15 0.16 0.19 0.21 0.51 to <61 years 16.18 12.33 12.96 14.08 15.90 17.81 19.93 21.22 25.70 0.22 0.15 0.16 0.18 0.21 0.61 to <71 years 12.99 10.40 10.77 11.78 12.92 13.90 15.40 16.15 20.34 0.18 0.14 0.15 0.16 0.17 0.71 to <81 years 12.04 9.90 10.20 10.89 11.82 12.96 14.11 15.20 17.70 0.18 0.14 0.14 0.16 0.17 0.

81 years and 

older 11.14 9.19 9.45 10.13 11.02 11.87 12.85 13.94 16.93 0.18 0.14 0.15 0.16 0.18 0.

Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999

−2002 when calculating the 

Ventilation rate was estimated using the multiple linear regression model in Section 3.6. 
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within th

activity category, by age and gender categories 

 C-7 Duration (hr/day) Spent at Activity - Males Duration (hr/day) Spent at 

Age Category Mean Percentiles Max Percentile

Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 

Sleep or nap (Activity ID = 14500) Birth to <1 year 13.51 12.63 12.78 13.19 13.53 13.88 14.24 14.46 15.03 12.99 12.00 12.16 12.53 12.96 

1 year 12.61 11.89 12.15 12.34 12.61 12.89 13.13 13.29 13.79 12.58 11.59 11.88 12.29 12.63 

2 years 12.06 11.19 11.45 11.80 12.07 12.39 12.65 12.75 13.40 12.09 11.45 11.68 11.86 12.08 

3 to <6 years 11.18 10.57 10.70 10.94 11.18 11.45 11.63 11.82 12.39 11.13 10.45 10.70 10.92 11.12 

6 to <11 years 10.18 9.65 9.75 9.93 10.19 10.39 10.59 10.72 11.24 10.26 9.55 9.73 10.01 10.27 

11 to <16 years 9.38 8.84 8.94 9.15 9.38 9.61 9.83 9.95 10.33 9.57 8.82 8.97 9.27 9.55 16 to <21 years 8.69 7.91 8.08 8.36 8.67 9.03 9.34 9.50 10.44 9.08 8.26 8.44 8.74 9.08 21 to <31 years 8.36 7.54 7.70 8.02 8.36 8.67 9.03 9.23 9.77 8.60 7.89 7.99 8.26 8.59 31 to <41 years 8.06 7.36 7.50 7.77 8.06 8.36 8.59 8.76 9.82 8.31 7.54 7.70 7.98 8.28 41 to <51 years 7.89 7.15 7.30 7.58 7.88 8.17 8.48 8.68 9.38 8.32 7.58 7.75 7.99 8.31 51 to <61 years 7.96 7.29 7.51 7.69 7.96 8.23 8.48 8.66 9.04 8.12 7.36 7.53 7.81 8.11 61 to <71 years 8.31 7.65 7.78 8.01 8.30 8.60 8.83 9.01 9.66 8.40 7.67 7.88 8.15 8.40 71 to <81 years 8.51 7.80 8.02 8.27 8.53 8.74 8.99 9.10 9.89 8.58 7.85 8.01 8.26 8.55 

81 years and 

older 9.24 8.48 8.64 8.97 9.25 9.54 9.74 9.96 10.69 9.11 8.35 8.53 8.84 9.10 
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within th

category, by age and gender categories (continued) 

Age Category

Duration (hr/day) Spent at Activity - Males

 C-8 Duration (hr/day) Spent at 

Mean Percentiles Max Percentile

Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 

Sedentary & Passive Activities (METS < 1.5―Includes Sleep or Nap) Birth to <1 year 14.95 13.82 14.03 14.49 14.88 15.44 15.90 16.12 17.48 14.07 12.86 13.05 13.53 14.08 

1 year 14.27 13.22 13.33 13.76 14.25 14.74 15.08 15.38 16.45 14.32 13.02 13.25 13.73 14.31 

2 years 14.62 13.52 13.67 14.11 14.54 15.11 15.60 15.77 17.28 14.86 13.81 13.95 14.44 14.81 

3 to <6 years 14.12 13.01 13.18 13.54 14.03 14.53 15.26 15.62 17.29 14.27 12.88 13.15 13.56 14.23 

6 to <11 years 13.51 12.19 12.45 12.86 13.30 13.85 14.82 15.94 19.21 13.97 12.49 12.74 13.22 13.82 

11 to <16 years 13.85 12.39 12.65 13.06 13.61 14.30 15.41 16.76 18.79 14.19 12.38 12.76 13.34 14.05 16 to <21 years 13.21 11.39 11.72 12.32 13.08 13.97 14.83 15.44 18.70 13.58 11.80 12.17 12.79 13.52 21 to <31 years 12.41 10.69 11.06 11.74 12.39 13.09 13.75 14.16 15.35 12.59 10.97 11.29 11.88 12.60 31 to <41 years 12.31 10.73 10.98 11.61 12.24 12.98 13.63 14.05 15.58 12.29 10.91 11.14 11.61 12.24 41 to <51 years 12.32 10.56 11.00 11.67 12.30 12.95 13.67 13.98 15.48 12.22 10.78 11.08 11.56 12.18 51 to <61 years 13.06 11.47 11.86 12.36 13.03 13.72 14.38 14.76 15.95 12.66 11.08 11.40 12.08 12.64 61 to <71 years 14.49 12.96 13.24 13.76 14.48 15.16 15.72 16.24 17.50 14.25 12.89 13.16 13.68 14.22 71 to <81 years 15.90 14.22 14.67 15.25 15.94 16.65 17.11 17.46 18.47 15.38 13.66 14.20 14.76 15.41 

81 years and 

older 16.58 15.13 15.45 15.92 16.64 17.21 17.70 18.06 18.76 16.48 14.87 15.09 15.80 16.59 
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within th

category, by age and gender categories (continued) 

Age Category

Duration (hr/day) Spent at Activity - Males

 C-9 Duration (hr/day) Spent at 

Mean Percentiles Max Percentile

Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 

Light Intensity Activities (1.5 < METS < 3.0) Birth to <1 year 5.30 2.97 3.25 3.71 4.52 7.29 8.08 8.50 9.91 6.00 3.49 3.70 4.26 5.01 

1 year 5.52 2.68 2.89 3.37 4.31 8.23 9.04 9.73 10.90 5.61 2.83 2.94 3.46 4.39 

2 years 5.48 3.06 3.26 3.85 4.58 7.58 8.83 9.04 9.92 5.78 3.20 3.54 4.29 5.33 

3 to <6 years 6.60 3.86 4.25 5.16 6.20 8.26 9.31 9.70 10.74 6.25 3.78 4.10 4.79 5.84 

6 to <11 years 7.62 5.07 5.57 6.63 7.63 8.72 9.78 10.12 11.59 7.27 4.63 5.46 6.33 7.17 

11 to <16 years 7.50 4.48 5.59 6.75 7.67 8.51 9.19 9.63 10.91 7.55 4.89 5.62 6.75 7.67 16 to <21 years 7.13 4.37 4.97 6.00 7.02 8.29 9.43 10.03 11.50 6.98 4.60 5.08 5.91 6.85 21 to <31 years 6.09 3.15 3.50 4.20 5.08 8.49 9.96 10.47 12.25 6.42 3.66 4.09 4.84 5.82 31 to <41 years 5.72 2.80 3.12 3.70 4.64 8.34 9.87 10.49 12.10 6.51 4.06 4.33 5.06 5.98 41 to <51 years 6.07 2.97 3.41 3.92 4.82 8.56 10.19 10.79 12.68 6.56 3.99 4.30 4.97 5.90 51 to <61 years 5.64 3.21 3.44 4.03 4.79 7.59 8.94 9.75 12.09 6.52 4.09 4.42 5.19 6.05 61 to <71 years 5.49 3.50 3.82 4.58 5.29 6.41 7.40 7.95 10.23 6.23 4.40 4.74 5.47 6.23 71 to <81 years 4.96 3.45 3.75 4.29 4.81 5.59 6.26 6.59 9.90 5.96 4.22 4.51 5.24 5.92 

81 years and 

older 4.86 3.54 3.71 4.17 4.74 5.39 6.33 6.59 7.56 5.30 3.67 3.96 4.63 5.16 
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within th

category, by age and gender categories (continued) 

Age Category

Duration (hr/day) Spent at Activity - Males

 C-10 Duration (hr/day) Spent at 

Mean Percentiles Max Percentile

Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 

Moderate Intensity Activities (3.0 < METS < 6.0) Birth to <1 year 3.67 0.63 0.97 1.74 4.20 5.20 5.80 6.21 7.52 3.91 0.53 0.74 1.10 4.87 

1 year 4.04 0.45 0.59 1.14 5.29 6.06 6.61 6.94 7.68 4.02 0.52 0.73 1.08 5.14 

2 years 3.83 0.59 0.76 1.23 4.74 5.37 5.82 6.15 7.40 3.27 0.50 0.78 1.22 4.01 

3 to <6 years 3.15 0.55 0.75 1.30 3.80 4.52 5.11 5.32 6.30 3.35 0.70 0.89 1.61 3.88 

6 to <11 years 2.66 0.65 0.92 1.65 2.68 3.57 4.36 4.79 5.95 2.57 0.65 0.95 1.82 2.66 

11 to <16 years 2.35 0.88 1.09 1.66 2.30 3.02 3.62 3.89 5.90 2.01 0.89 1.08 1.45 1.96 16 to <21 years 3.35 1.13 1.42 2.19 3.45 4.37 5.24 5.59 6.83 3.26 1.27 1.48 2.21 3.39 21 to <31 years 5.24 1.15 1.58 2.52 6.01 7.15 7.95 8.39 9.94 4.80 1.62 1.94 2.78 5.37 31 to <41 years 5.69 1.26 1.65 2.84 6.67 7.75 8.45 8.90 9.87 5.00 1.71 2.06 3.09 5.41 41 to <51 years 5.40 1.21 1.55 2.39 6.46 7.57 8.40 8.85 10.52 5.05 1.75 2.00 2.97 5.48 51 to <61 years 5.00 1.29 1.63 2.72 5.68 6.75 7.60 8.01 9.94 4.58 1.71 2.13 3.10 4.79 61 to <71 years 3.73 1.62 1.97 2.81 3.70 4.67 5.45 6.01 7.45 3.31 1.65 1.97 2.56 3.34 71 to <81 years 2.87 1.56 1.83 2.28 2.86 3.45 3.95 4.31 5.44 2.48 1.19 1.36 1.82 2.48 

81 years and 

older 2.35 1.32 1.45 1.79 2.29 2.85 3.28 3.61 4.37 2.06 1.01 1.25 1.55 1.99 
Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within th

category, by age and gender categories (continued) 

Age Category

Duration (hr/day) Spent at Activity - Males

 C-11 Duration (hr/day) Spent at 

Mean Percentiles Max Percentile

Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 

High Intensity (METS > 6.0) Birth to <1 year 0.20 0.00 0.00 0.01 0.14 0.28 0.50 0.59 0.96 0.17 0.03 0.05 0.09 0.14 

1 year 0.31 0.01 0.01 0.03 0.22 0.56 0.78 0.93 1.52 0.22 0.03 0.05 0.09 0.18 

2 years 0.10 0.00 0.01 0.03 0.05 0.14 0.25 0.33 0.48 0.15 0.00 0.01 0.03 0.08 

3 to <6 years 0.27 0.02 0.03 0.04 0.13 0.33 0.75 1.16 1.48 0.19 0.01 0.02 0.05 0.10 

6 to <11 years 0.32 0.01 0.01 0.03 0.13 0.38 1.10 1.50 3.20 0.24 0.02 0.03 0.06 0.12 

11 to <16 years 0.38 0.03 0.04 0.10 0.21 0.47 1.03 1.34 2.35 0.30 0.03 0.04 0.08 0.19 16 to <21 years 0.40 0.03 0.04 0.14 0.27 0.53 0.99 1.29 2.59 0.24 0.01 0.03 0.08 0.18 21 to <31 years 0.33 0.02 0.05 0.11 0.27 0.45 0.69 0.85 1.95 0.26 0.03 0.05 0.10 0.19 31 to <41 years 0.38 0.03 0.07 0.14 0.28 0.51 0.83 1.03 1.77 0.25 0.03 0.05 0.09 0.19 41 to <51 years 0.34 0.03 0.05 0.09 0.23 0.50 0.78 1.00 2.40 0.26 0.03 0.04 0.09 0.20 51 to <61 years 0.41 0.03 0.05 0.13 0.34 0.59 0.87 1.13 1.95 0.34 0.03 0.04 0.12 0.28 61 to <71 years 0.37 0.03 0.05 0.13 0.28 0.49 0.80 1.08 2.21 0.32 0.03 0.04 0.10 0.23 71 to <81 years 0.39 0.01 0.03 0.10 0.29 0.57 0.90 1.11 2.06 0.29 0.03 0.05 0.10 0.25 

81 years and 

older 0.32 0.02 0.03 0.08 0.25 0.47 0.71 0.88 1.76 0.26 0.02 0.03 0.09 0.21 

Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the stati

Ventilation rate was estimated using the multiple linear regression model in Section 3.6. 
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, w

performing activities within the specified activity category, by age and gender categories 

 C-12 Average Ventilation Rate (L/min) - Males, Average Ventilation Rate (L

Unadjusted for Body Weight Unadjusted for Bod

Age Category Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Sleep or nap (Activity ID = 14500) Birth to <1 year 3.08 1.66 1.91 2.45 3.00 3.68 4.35 4.77 7.19 2.92 1.54 1.72 2.27 2.88 3

1 year 4.50 3.11 3.27 3.78 4.35 4.95 5.90 6.44 10.02 4.59 3.02 3.28 3.76 4.56 5

2 years 4.61 3.01 3.36 3.94 4.49 5.21 6.05 6.73 8.96 4.56 3.00 3.30 3.97 4.52 5

3 to <6 years 4.36 3.06 3.30 3.76 4.29 4.86 5.54 5.92 7.67 4.18 2.90 3.20 3.62 4.10 4

6 to <11 years 4.61 3.14 3.39 3.83 4.46 5.21 6.01 6.54 9.94 4.36 2.97 3.17 3.69 4.24 4

11 to <16 years 5.26 3.53 3.78 4.34 5.06 5.91 6.94 7.81 11.49 4.81 3.34 3.57 3.99 4.66 516 to <21 years 5.31 3.55 3.85 4.35 5.15 6.09 6.92 7.60 12.82 4.40 2.78 2.96 3.58 4.26 521 to <31 years 4.73 3.16 3.35 3.84 4.56 5.42 6.26 6.91 11.17 3.89 2.54 2.74 3.13 3.68 431 to <41 years 5.16 3.37 3.62 4.23 5.01 5.84 6.81 7.46 10.86 4.00 2.66 2.86 3.31 3.89 441 to <51 years 5.65 3.74 4.09 4.73 5.53 6.47 7.41 7.84 10.84 4.40 3.00 3.23 3.69 4.25 451 to <61 years 5.78 3.96 4.20 4.78 5.57 6.54 7.74 8.26 11.81 4.56 3.12 3.30 3.72 4.41 561 to <71 years 5.98 4.36 4.57 5.13 5.81 6.68 7.45 7.93 12.27 4.47 3.22 3.35 3.78 4.38 471 to <81 years 6.07 4.26 4.55 5.17 6.00 6.77 7.65 8.33 10.50 4.52 3.31 3.47 3.89 4.40 5

81 years and 

older 5.97 4.20 4.49 5.23 5.90 6.68 7.36 7.76 9.98 4.49 3.17 3.49 3.82 4.39 4
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued)

Age Category

Average Ventilation Rate (L/min) - Males, 

 C-13 Average Ventilation Rate (L

Unadjusted for Body Weight Unadjusted for Bod

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Sedentary & Passive Activities (METS < 1.5―Includes Sleep or Nap) Birth to <1 year 3.18 1.74 1.99 2.50 3.10 3.80 4.40 4.88 7.09 3.00 1.60 1.80 2.32 2.97 3

1 year 4.62 3.17 3.50 3.91 4.49 5.03 5.95 6.44 9.91 4.71 3.26 3.44 3.98 4.73 5

2 years 4.79 3.25 3.66 4.10 4.69 5.35 6.05 6.71 9.09 4.73 3.34 3.53 4.19 4.67 5

3 to <6 years 4.58 3.47 3.63 4.07 4.56 5.03 5.58 5.82 7.60 4.40 3.31 3.49 3.95 4.34 4

6 to <11 years 4.87 3.55 3.78 4.18 4.72 5.40 6.03 6.58 9.47 4.64 3.41 3.67 4.04 4.51 5

11 to <16 years 5.64 4.03 4.30 4.79 5.43 6.26 7.20 7.87 11.08 5.21 3.90 4.16 4.53 5.09 516 to <21 years 5.76 4.17 4.42 4.93 5.60 6.43 7.15 7.76 13.45 4.76 3.26 3.56 4.03 4.69 521 to <31 years 5.11 3.76 3.99 4.33 5.00 5.64 6.42 6.98 10.30 4.19 3.04 3.19 3.55 4.00 431 to <41 years 5.57 3.99 4.42 4.86 5.45 6.17 6.99 7.43 9.98 4.33 3.22 3.45 3.77 4.24 441 to <51 years 6.11 4.65 4.92 5.37 6.02 6.65 7.46 7.77 10.53 4.75 3.60 3.82 4.18 4.65 551 to <61 years 6.27 4.68 5.06 5.50 6.16 6.89 7.60 8.14 10.39 4.96 3.78 4.00 4.36 4.87 561 to <71 years 6.54 5.02 5.31 5.85 6.47 7.12 7.87 8.22 10.86 4.89 3.81 4.02 4.34 4.81 571 to <81 years 6.65 5.26 5.55 5.96 6.59 7.18 7.81 8.26 9.92 4.95 4.07 4.13 4.41 4.89 5

81 years and 

older 6.44 5.09 5.37 5.82 6.43 7.01 7.57 7.90 9.13 4.89 3.93 4.10 4.39 4.79 5
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued)

Age Category

Average Ventilation Rate (L/min) - Males, 

 C-14 Average Ventilation Rate (L

Unadjusted for Body Weight Unadjusted for Bod

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Light Intensity Activities (1.5 < METS < 3.0) Birth to <1 year 7.94 4.15 5.06 6.16 7.95 9.57 10.76 11.90 15.50 7.32 3.79 4.63 5.73 7.19 8

1 year 11.56 8.66 8.99 9.89 11.42 12.91 14.39 15.76 21.12 11.62 8.59 8.80 10.03 11.20 1

2 years 11.67 8.52 9.14 9.96 11.37 13.02 14.66 15.31 18.98 11.99 8.74 9.40 10.27 11.69 1

3 to <6 years 11.36 9.20 9.55 10.23 11.12 12.28 13.40 14.00 19.65 10.92 8.83 9.04 9.87 10.69 1

6 to <11 years 11.64 8.95 9.33 10.20 11.26 12.79 14.60 15.60 21.83 11.07 8.51 9.02 9.79 10.79 1

11 to <16 years 13.22 9.78 10.26 11.34 12.84 14.65 16.42 18.65 26.86 12.02 9.40 9.73 10.63 11.76 116 to <21 years 13.41 10.01 10.54 11.53 12.95 14.95 16.95 18.00 29.07 11.08 8.31 8.73 9.64 10.76 121 to <31 years 12.97 9.68 10.18 11.25 12.42 14.04 16.46 17.74 27.22 10.55 7.75 8.24 9.05 10.24 131 to <41 years 13.64 10.63 11.05 11.99 13.33 14.83 16.46 18.10 25.50 11.07 8.84 9.30 9.96 10.94 141 to <51 years 14.38 11.16 11.81 12.95 14.11 15.61 17.39 18.25 23.01 11.78 9.64 10.00 10.67 11.61 151 to <61 years 14.56 11.08 11.58 12.97 14.35 15.90 17.96 19.37 25.48 12.02 9.76 10.17 10.87 11.79 161 to <71 years 14.12 11.07 11.74 12.69 13.87 15.37 16.91 17.97 20.54 10.82 8.87 9.28 9.85 10.64 171 to <81 years 13.87 11.17 11.68 12.73 13.69 14.96 16.23 16.89 20.02 10.83 8.84 9.23 9.94 10.74 1

81 years and 

older 13.76 11.02 11.71 12.56 13.75 14.70 16.03 16.72 20.71 10.40 8.69 8.84 9.36 10.29 1
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued)

Age Category

Average Ventilation Rate (L/min) - Males, 

 C-15 Average Ventilation Rate (L

Unadjusted for Body Weight Unadjusted for Bod

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Moderate Intensity Activities (3.0 < METS < 6.0) Birth to <1 year 14.49 7.41 8.81 11.46 14.35 16.95 20.08 22.50 30.54 13.98 7.91 9.00 11.15 13.53 1

1 year 21.35 14.48 15.88 18.03 20.62 24.06 26.94 28.90 39.87 20.98 15.62 16.30 17.92 20.14 2

2 years 21.54 15.37 16.71 18.42 20.82 24.07 26.87 29.68 50.93 21.34 14.21 15.57 18.17 21.45 2

3 to <6 years 21.03 16.31 17.16 18.72 20.55 22.94 25.60 27.06 34.88 20.01 15.26 16.32 17.84 19.76 2

6 to <11 years 22.28 16.36 17.23 19.34 21.64 25.00 27.59 29.50 43.39 21.00 15.98 16.83 18.47 20.39 2

11 to <16 years 26.40 19.33 20.45 22.60 25.41 29.19 33.77 36.93 55.02 23.55 18.16 19.47 20.83 23.04 216 to <21 years 29.02 20.30 21.69 24.52 27.97 31.74 38.15 42.14 67.35 23.22 16.60 17.61 19.62 22.39 221 to <31 years 29.19 19.65 20.97 24.16 27.92 33.00 38.79 43.11 71.71 22.93 15.56 16.68 18.98 21.94 231 to <41 years 30.30 21.40 22.70 25.08 29.09 34.10 39.60 43.48 57.69 22.70 16.87 17.57 19.50 21.95 241 to <51 years 31.58 22.58 24.44 27.21 30.44 35.11 40.28 44.97 63.36 24.49 17.60 18.88 20.79 23.94 251 to <61 years 32.71 22.36 24.01 27.95 31.40 36.96 41.66 45.77 70.48 25.24 18.83 19.80 21.78 24.30 261 to <71 years 29.76 22.47 24.04 26.05 29.22 32.27 36.93 39.98 52.26 21.42 16.90 17.70 19.22 20.86 271 to <81 years 29.29 22.81 23.92 26.14 28.78 32.04 35.65 37.32 44.86 21.09 16.86 17.61 18.87 20.68 2

81 years and 

older 28.53 22.45 23.36 25.47 28.19 31.03 33.44 35.52 41.11 20.87 16.51 17.53 19.09 20.62 2
Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued)

Age Category

Average Ventilation Rate (L/min) - Males, 

 C-16 Average Ventilation Rate (L

Unadjusted for Body Weight Unadjusted for Bod

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 7

High Intensity (METS > 6.0)

Birth to <1 

year

27.47 15.07 17.26 20.63 27.79 32.47 38.41 42.24 57.90 24.19 12.36 13.26 17.15 22.45 2

1 year 40.25 28.33 31.68 34.66 39.80 44.34 51.62 55.92 60.66 36.48 25.94 26.24 30.42 36.11 4

2 years 40.45 28.15 29.74 34.45 40.57 46.17 51.90 55.06 92.01 37.58 28.99 30.51 32.33 36.43 4

3 to <6 years 39.04 29.46 31.35 34.01 37.80 43.23 48.93 52.22 66.17 34.53 27.00 28.21 29.98 33.33 3

6 to <11 years 43.62 30.66 32.76 35.77 41.94 49.52 56.58 62.40 89.86 39.39 28.59 30.13 33.66 38.02 4

11 to <16 years 50.82 34.31 36.84 41.53 49.12 57.40 66.25 72.92 122.91 46.56 31.06 33.76 38.76 45.34 516 to <21 years 53.17 35.96 38.33 43.51 50.51 59.33 71.45 83.03 129.88 44.09 28.69 30.61 36.51 42.71 521 to <31 years 53.91 33.55 37.95 44.83 51.51 61.63 72.38 82.07 111.94 45.68 28.84 31.18 36.65 43.10 531 to <41 years 54.27 37.79 40.36 45.43 52.05 61.21 71.42 77.35 103.88 44.44 30.27 32.93 37.02 42.23 541 to <51 years 57.31 38.31 42.47 48.29 55.20 64.45 75.61 84.39 110.28 46.98 31.04 34.02 38.35 45.61 551 to <61 years 58.42 38.95 41.57 48.65 55.90 65.95 78.57 86.46 140.74 47.35 31.54 34.82 39.38 45.69 561 to <71 years 54.13 36.28 39.51 45.17 52.41 60.81 71.96 75.23 102.16 40.02 27.56 30.63 34.59 38.71 471 to <81 years 52.46 36.99 39.50 44.12 49.95 58.95 67.56 76.45 97.34 40.64 28.49 30.08 34.25 39.56 4

81 years and 

older 53.31 35.35 39.17 45.51 50.93 61.18 69.55 77.05 96.76 41.88 28.48 30.09 34.35 41.38 4

Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999

−2002 when calculating the stati

Ventilation rate was estimated using the multiple linear regression model in Section 3.6. 
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, 

performing activities within the specified activity category, by age and gender categories 

 C-17 Average Ventilation Rate (L/min-kg) - Males, Average Ventilation Rate (L/m

Adjusted for Body Weight Adjusted for Body 

Age Category Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Sleep or nap (Activity ID = 14500) Birth to <1 year 0.38 0.28 0.30 0.34 0.38 0.43 0.46 0.50 0.67 0.39 0.28 0.30 0.34 0.39 0

1 year 0.40 0.30 0.31 0.35 0.38 0.44 0.49 0.52 0.63 0.41 0.31 0.33 0.36 0.41 0

2 years 0.33 0.25 0.26 0.29 0.33 0.36 0.40 0.44 0.54 0.34 0.26 0.27 0.29 0.33 0

3 to <6 years 0.24 0.16 0.17 0.20 0.24 0.28 0.31 0.35 0.48 0.24 0.14 0.16 0.20 0.23 0

6 to <11 years 0.15 0.10 0.11 0.13 0.15 0.17 0.20 0.22 0.30 0.15 0.09 0.10 0.12 0.15 0

11 to <16 years 0.10 0.07 0.07 0.08 0.09 0.11 0.13 0.14 0.21 0.09 0.06 0.07 0.07 0.09 016 to <21 years 0.07 0.05 0.05 0.06 0.07 0.08 0.09 0.10 0.15 0.07 0.04 0.05 0.06 0.07 021 to <31 years 0.06 0.04 0.04 0.05 0.06 0.07 0.08 0.08 0.13 0.06 0.04 0.04 0.04 0.05 031 to <41 years 0.06 0.04 0.04 0.05 0.06 0.07 0.08 0.09 0.13 0.06 0.03 0.04 0.04 0.05 041 to <51 years 0.07 0.04 0.05 0.05 0.06 0.07 0.09 0.09 0.14 0.06 0.04 0.04 0.05 0.06 051 to <61 years 0.07 0.05 0.05 0.05 0.06 0.08 0.09 0.09 0.14 0.06 0.04 0.04 0.05 0.06 061 to <71 years 0.07 0.05 0.05 0.06 0.07 0.08 0.09 0.09 0.12 0.06 0.04 0.05 0.05 0.06 071 to <81 years 0.07 0.05 0.06 0.06 0.07 0.08 0.09 0.10 0.13 0.07 0.05 0.05 0.06 0.06 0

81 years and 

older 0.08 0.06 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.07 0.05 0.06 0.06 0.07 0
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, 

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Average Ventilation Rate (L/min-kg) - Males, 

 C-18 Average Ventilation Rate (L/m

Adjusted for Body Weight Adjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Sedentary & Passive Activities (METS < 1.5―Includes Sleep or Nap) Birth to <1 year 0.40 0.30 0.32 0.35 0.39 0.44 0.47 0.50 0.66 0.40 0.30 0.32 0.35 0.40 0

1 year 0.41 0.32 0.33 0.36 0.40 0.45 0.49 0.52 0.62 0.43 0.34 0.35 0.38 0.42 0

2 years 0.34 0.27 0.29 0.31 0.34 0.37 0.41 0.45 0.51 0.36 0.29 0.30 0.32 0.35 0

3 to <6 years 0.25 0.18 0.19 0.21 0.25 0.29 0.33 0.35 0.45 0.25 0.16 0.18 0.21 0.25 0

6 to <11 years 0.16 0.11 0.12 0.14 0.16 0.18 0.21 0.22 0.29 0.16 0.10 0.11 0.13 0.16 0

11 to <16 years 0.10 0.08 0.08 0.09 0.10 0.12 0.13 0.14 0.20 0.10 0.07 0.07 0.08 0.09 016 to <21 years 0.08 0.05 0.06 0.07 0.08 0.09 0.09 0.10 0.13 0.07 0.05 0.06 0.06 0.07 021 to <31 years 0.06 0.05 0.05 0.06 0.06 0.07 0.08 0.08 0.12 0.06 0.04 0.05 0.05 0.06 031 to <41 years 0.07 0.05 0.05 0.06 0.07 0.07 0.08 0.09 0.12 0.06 0.04 0.04 0.05 0.06 041 to <51 years 0.07 0.05 0.06 0.06 0.07 0.08 0.09 0.09 0.13 0.06 0.04 0.05 0.05 0.06 051 to <61 years 0.07 0.05 0.06 0.06 0.07 0.08 0.09 0.09 0.14 0.07 0.05 0.05 0.06 0.07 061 to <71 years 0.08 0.06 0.06 0.07 0.08 0.08 0.09 0.09 0.11 0.07 0.05 0.05 0.06 0.07 071 to <81 years 0.08 0.07 0.07 0.08 0.08 0.09 0.09 0.10 0.11 0.07 0.06 0.06 0.07 0.07 0

81 years and 

older 0.09 0.07 0.08 0.08 0.09 0.09 0.10 0.11 0.11 0.08 0.06 0.07 0.07 0.08 0
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, 

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Average Ventilation Rate (L/min-kg) - Males, 

 C-19 Average Ventilation Rate (L/m

Adjusted for Body Weight Adjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Light Intensity Activities (1.5 < METS < 3.0) Birth to <1 year 0.99 0.79 0.83 0.90 0.97 1.07 1.17 1.20 1.43 0.98 0.79 0.82 0.88 0.96 1

1 year 1.02 0.84 0.86 0.92 1.01 1.10 1.22 1.30 1.48 1.05 0.85 0.87 0.95 1.04 1

2 years 0.84 0.68 0.72 0.76 0.83 0.89 1.00 1.03 1.18 0.90 0.73 0.76 0.82 0.89 0

3 to <6 years 0.63 0.44 0.48 0.54 0.63 0.71 0.79 0.87 1.08 0.62 0.45 0.48 0.54 0.60 0

6 to <11 years 0.38 0.27 0.29 0.32 0.38 0.44 0.49 0.53 0.71 0.38 0.25 0.27 0.31 0.38 0

11 to <16 years 0.25 0.18 0.19 0.21 0.24 0.28 0.31 0.33 0.44 0.23 0.16 0.17 0.20 0.22 016 to <21 years 0.18 0.14 0.14 0.16 0.18 0.20 0.22 0.23 0.33 0.17 0.13 0.14 0.15 0.17 021 to <31 years 0.16 0.12 0.13 0.14 0.15 0.17 0.19 0.21 0.29 0.15 0.12 0.12 0.13 0.15 031 to <41 years 0.16 0.12 0.13 0.14 0.16 0.18 0.20 0.21 0.28 0.15 0.11 0.12 0.13 0.15 041 to <51 years 0.17 0.13 0.13 0.15 0.16 0.18 0.20 0.21 0.33 0.16 0.11 0.12 0.14 0.16 051 to <61 years 0.17 0.13 0.13 0.15 0.16 0.18 0.20 0.22 0.29 0.16 0.12 0.13 0.14 0.16 061 to <71 years 0.16 0.14 0.14 0.15 0.16 0.18 0.19 0.20 0.27 0.15 0.12 0.12 0.13 0.14 071 to <81 years 0.17 0.14 0.15 0.16 0.17 0.18 0.19 0.20 0.26 0.16 0.12 0.13 0.14 0.16 0

81 years and 

older 0.18 0.15 0.16 0.17 0.18 0.20 0.21 0.22 0.25 0.17 0.13 0.14 0.15 0.16 0
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, 

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Average Ventilation Rate (L/min-kg) - Males, 

 C-20 Average Ventilation Rate (L/m

Adjusted for Body Weight Adjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

Moderate Intensity Activities (3.0 < METS < 6.0) Birth to <1 year 1.80 1.39 1.49 1.62 1.78 1.94 2.18 2.28 3.01 1.87 1.47 1.52 1.67 1.85 2

1 year 1.88 1.41 1.50 1.65 1.82 2.01 2.33 2.53 3.23 1.90 1.52 1.62 1.73 1.87 2

2 years 1.55 1.21 1.28 1.40 1.54 1.66 1.84 2.02 2.29 1.60 1.27 1.31 1.44 1.58 1

3 to <6 years 1.17 0.80 0.88 1.00 1.12 1.31 1.56 1.68 2.10 1.14 0.79 0.85 0.96 1.11 1

6 to <11 years 0.74 0.50 0.55 0.62 0.71 0.83 0.96 1.04 1.43 0.72 0.46 0.51 0.60 0.71 0

11 to <16 years 0.49 0.36 0.38 0.42 0.47 0.55 0.64 0.68 1.06 0.44 0.32 0.34 0.38 0.43 016 to <21 years 0.39 0.28 0.30 0.33 0.38 0.43 0.49 0.52 0.71 0.36 0.27 0.28 0.31 0.35 021 to <31 years 0.36 0.24 0.26 0.30 0.34 0.40 0.47 0.51 0.82 0.33 0.24 0.25 0.28 0.32 031 to <41 years 0.36 0.24 0.26 0.30 0.34 0.40 0.47 0.52 0.76 0.32 0.21 0.23 0.27 0.30 041 to <51 years 0.37 0.25 0.27 0.31 0.35 0.41 0.47 0.52 0.72 0.33 0.22 0.24 0.28 0.32 051 to <61 years 0.38 0.26 0.28 0.31 0.37 0.43 0.48 0.55 0.76 0.34 0.24 0.25 0.28 0.33 061 to <71 years 0.34 0.27 0.28 0.31 0.34 0.37 0.40 0.42 0.57 0.29 0.22 0.24 0.26 0.28 071 to <81 years 0.36 0.29 0.31 0.33 0.36 0.39 0.42 0.44 0.55 0.31 0.24 0.25 0.27 0.30 0

81 years and 

older 0.38 0.31 0.32 0.35 0.38 0.42 0.45 0.47 0.53 0.33 0.25 0.27 0.30 0.33 0
Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, 

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Average Ventilation Rate (L/min-kg) - Males, 

 C-21 Average Ventilation Rate (L/m

Adjusted for Body Weight Adjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 7

High Intensity (METS > 6.0) Birth to <1 year 3.48 2.70 2.93 3.10 3.46 3.81 4.14 4.32 5.08 3.26 2.53 2.62 2.89 3.23 3

1 year 3.52 2.52 2.89 3.22 3.57 3.91 4.11 4.34 4.86 3.38 2.57 2.75 2.97 3.24 3

2 years 2.89 2.17 2.34 2.58 2.87 3.20 3.43 3.54 4.30 2.80 2.20 2.31 2.48 2.81 3

3 to <6 years 2.17 1.55 1.66 1.81 2.11 2.50 2.73 2.98 3.62 1.98 1.36 1.51 1.69 1.90 2

6 to <11 years 1.41 0.94 1.03 1.19 1.38 1.59 1.83 1.93 2.68 1.33 0.89 0.97 1.12 1.33 1

11 to <16 years 0.95 0.63 0.70 0.79 0.91 1.09 1.27 1.36 1.98 0.88 0.59 0.63 0.71 0.85 116 to <21 years 0.71 0.48 0.53 0.60 0.69 0.80 0.92 1.00 1.94 0.70 0.45 0.50 0.57 0.69 021 to <31 years 0.66 0.45 0.47 0.54 0.64 0.75 0.85 0.97 1.27 0.65 0.42 0.46 0.55 0.63 031 to <41 years 0.64 0.44 0.47 0.53 0.62 0.73 0.85 0.93 1.23 0.61 0.38 0.42 0.50 0.59 041 to <51 years 0.66 0.44 0.48 0.55 0.63 0.74 0.86 0.94 1.77 0.65 0.38 0.44 0.52 0.64 051 to <61 years 0.68 0.45 0.48 0.55 0.64 0.77 0.91 1.02 1.31 0.63 0.39 0.43 0.51 0.61 061 to <71 years 0.62 0.44 0.47 0.53 0.61 0.70 0.79 0.85 1.08 0.54 0.36 0.40 0.45 0.53 071 to <81 years 0.65 0.47 0.50 0.55 0.63 0.72 0.85 0.91 1.04 0.59 0.39 0.44 0.50 0.58 0

81 years and 

older 0.72 0.50 0.54 0.60 0.70 0.80 0.94 0.99 1.35 0.67 0.45 0.48 0.54 0.63 0

Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the stati

Ventilation rate was estimated using the multiple linear regression model in Section 3.6. 
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, wh

performing activities within the specified activity category, by age and gender categories 

 C-22 Daily Ventilation Rate (m3/min) - Males, Daily Ventilation Rate (m3/m

Unadjusted for Body Weight Unadjusted for Body 

Age Category Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 7

Sleep or nap (Activity ID = 14500)

Birth to <1 

year

3.1E-03 1.7E-03 1.9E-03 2.5E-03 3.0E-03 3.7E-03 4.4E-03 4.8E-03 7.2E-03 2.9E-03 1.5E-03 1.7E-03 2.3E-03 2.9E-03 3.5

1 year 4.5E-03 3.1E-03 3.3E-03 3.8E-03 4.3E-03 4.9E-03 5.9E-03 6.4E-03 1.0E-02 4.6E-03 3.0E-03 3.3E-03 3.8E-03 4.6E-03 5.3

2 years 4.6E-03 3.0E-03 3.4E-03 3.9E-03 4.5E-03 5.2E-03 6.1E-03 6.7E-03 9.0E-03 4.6E-03 3.0E-03 3.3E-03 4.0E-03 4.5E-03 5.2

3 to <6 years 4.4E-03 3.1E-03 3.3E-03 3.8E-03 4.3E-03 4.9E-03 5.5E-03 5.9E-03 7.7E-03 4.2E-03 2.9E-03 3.2E-03 3.6E-03 4.1E-03 4.7

6 to <11 years 4.6E-03 3.1E-03 3.4E-03 3.8E-03 4.5E-03 5.2E-03 6.0E-03 6.5E-03 9.9E-03 4.4E-03 3.0E-03 3.2E-03 3.7E-03 4.2E-03 4.9

11 to <16 years 5.3E-03 3.5E-03 3.8E-03 4.3E-03 5.1E-03 5.9E-03 6.9E-03 7.8E-03 1.1E-02 4.8E-03 3.3E-03 3.6E-03 4.0E-03 4.7E-03 5.416 to <21 years 5.3E-03 3.6E-03 3.9E-03 4.3E-03 5.2E-03 6.1E-03 6.9E-03 7.6E-03 1.3E-02 4.4E-03 2.8E-03 3.0E-03 3.6E-03 4.3E-03 5.121 to <31 years 4.7E-03 3.2E-03 3.3E-03 3.8E-03 4.6E-03 5.4E-03 6.3E-03 6.9E-03 1.1E-02 3.9E-03 2.5E-03 2.7E-03 3.1E-03 3.7E-03 4.431 to <41 years 5.2E-03 3.4E-03 3.6E-03 4.2E-03 5.0E-03 5.8E-03 6.8E-03 7.5E-03 1.1E-02 4.0E-03 2.7E-03 2.9E-03 3.3E-03 3.9E-03 4.541 to <51 years 5.7E-03 3.7E-03 4.1E-03 4.7E-03 5.5E-03 6.5E-03 7.4E-03 7.8E-03 1.1E-02 4.4E-03 3.0E-03 3.2E-03 3.7E-03 4.2E-03 5.051 to <61 years 5.8E-03 4.0E-03 4.2E-03 4.8E-03 5.6E-03 6.5E-03 7.7E-03 8.3E-03 1.2E-02 4.6E-03 3.1E-03 3.3E-03 3.7E-03 4.4E-03 5.261 to <71 years 6.0E-03 4.4E-03 4.6E-03 5.1E-03 5.8E-03 6.7E-03 7.5E-03 7.9E-03 1.2E-02 4.5E-03 3.2E-03 3.3E-03 3.8E-03 4.4E-03 5.071 to <81 years 6.1E-03 4.3E-03 4.6E-03 5.2E-03 6.0E-03 6.8E-03 7.6E-03 8.3E-03 1.1E-02 4.5E-03 3.3E-03 3.5E-03 3.9E-03 4.4E-03 5.1

81 years and 

older 6.0E-03 4.2E-03 4.5E-03 5.2E-03 5.9E-03 6.7E-03 7.4E-03 7.8E-03 1.0E-02 4.5E-03 3.2E-03 3.5E-03 3.8E-03 4.4E-03 4.9
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, wh

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Daily Ventilation Rate (m3/min) - Males, 

 C-23 Daily Ventilation Rate (m3/m

Unadjusted for Body Weight Unadjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 7

Sedentary & Passive Activities (METS 

< 1.5

―Includes Sleep or Nap)

Birth to <1 

year

3.2E-03 1.7E-03 2.0E-03 2.5E-03 3.1E-03 3.8E-03 4.4E-03 4.9E-03 7.1E-03 3.0E-03 1.6E-03 1.8E-03 2.3E-03 3.0E-03 3.6

1 year 4.6E-03 3.2E-03 3.5E-03 3.9E-03 4.5E-03 5.0E-03 6.0E-03 6.4E-03 9.9E-03 4.7E-03 3.3E-03 3.4E-03 4.0E-03 4.7E-03 5.3

2 years 4.8E-03 3.2E-03 3.7E-03 4.1E-03 4.7E-03 5.4E-03 6.1E-03 6.7E-03 9.1E-03 4.7E-03 3.3E-03 3.5E-03 4.2E-03 4.7E-03 5.3

3 to <6 years 4.6E-03 3.5E-03 3.6E-03 4.1E-03 4.6E-03 5.0E-03 5.6E-03 5.8E-03 7.6E-03 4.4E-03 3.3E-03 3.5E-03 3.9E-03 4.3E-03 4.8

6 to <11 years 4.9E-03 3.6E-03 3.8E-03 4.2E-03 4.7E-03 5.4E-03 6.0E-03 6.6E-03 9.5E-03 4.6E-03 3.4E-03 3.7E-03 4.0E-03 4.5E-03 5.1

11 to <16 years 5.6E-03 4.0E-03 4.3E-03 4.8E-03 5.4E-03 6.3E-03 7.2E-03 7.9E-03 1.1E-02 5.2E-03 3.9E-03 4.2E-03 4.5E-03 5.1E-03 5.716 to <21 years 5.8E-03 4.2E-03 4.4E-03 4.9E-03 5.6E-03 6.4E-03 7.1E-03 7.8E-03 1.3E-02 4.8E-03 3.3E-03 3.6E-03 4.0E-03 4.7E-03 5.321 to <31 years 5.1E-03 3.8E-03 4.0E-03 4.3E-03 5.0E-03 5.6E-03 6.4E-03 7.0E-03 1.0E-02 4.2E-03 3.0E-03 3.2E-03 3.6E-03 4.0E-03 4.631 to <41 years 5.6E-03 4.0E-03 4.4E-03 4.9E-03 5.4E-03 6.2E-03 7.0E-03 7.4E-03 1.0E-02 4.3E-03 3.2E-03 3.4E-03 3.8E-03 4.2E-03 4.841 to <51 years 6.1E-03 4.7E-03 4.9E-03 5.4E-03 6.0E-03 6.7E-03 7.5E-03 7.8E-03 1.1E-02 4.8E-03 3.6E-03 3.8E-03 4.2E-03 4.6E-03 5.251 to <61 years 6.3E-03 4.7E-03 5.1E-03 5.5E-03 6.2E-03 6.9E-03 7.6E-03 8.1E-03 1.0E-02 5.0E-03 3.8E-03 4.0E-03 4.4E-03 4.9E-03 5.461 to <71 years 6.5E-03 5.0E-03 5.3E-03 5.8E-03 6.5E-03 7.1E-03 7.9E-03 8.2E-03 1.1E-02 4.9E-03 3.8E-03 4.0E-03 4.3E-03 4.8E-03 5.371 to <81 years 6.6E-03 5.3E-03 5.6E-03 6.0E-03 6.6E-03 7.2E-03 7.8E-03 8.3E-03 9.9E-03 4.9E-03 4.1E-03 4.1E-03 4.4E-03 4.9E-03 5.4

81 years and 

older 6.4E-03 5.1E-03 5.4E-03 5.8E-03 6.4E-03 7.0E-03 7.6E-03 7.9E-03 9.1E-03 4.9E-03 3.9E-03 4.1E-03 4.4E-03 4.8E-03 5.3
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, wh

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Daily Ventilation Rate (m3/min) - Males, 

 C-24 Daily Ventilation Rate (m3/m

Unadjusted for Body Weight Unadjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 7

Light Intensity Activities (1.5 < METS 

< 3.0)

Birth to <1 

year

7.9E-03 4.1E-03 5.1E-03 6.2E-03 7.9E-03 9.6E-03 1.1E-02 1.2E-02 1.5E-02 7.3E-03 3.8E-03 4.6E-03 5.7E-03 7.2E-03 8.7

1 year 1.2E-02 8.7E-03 9.0E-03 9.9E-03 1.1E-02 1.3E-02 1.4E-02 1.6E-02 2.1E-02 1.2E-02 8.6E-03 8.8E-03 1.0E-02 1.1E-02 1.3

2 years 1.2E-02 8.5E-03 9.1E-03 1.0E-02 1.1E-02 1.3E-02 1.5E-02 1.5E-02 1.9E-02 1.2E-02 8.7E-03 9.4E-03 1.0E-02 1.2E-02 1.3

3 to <6 years 1.1E-02 9.2E-03 9.5E-03 1.0E-02 1.1E-02 1.2E-02 1.3E-02 1.4E-02 2.0E-02 1.1E-02 8.8E-03 9.0E-03 9.9E-03 1.1E-02 1.2

6 to <11 years 1.2E-02 9.0E-03 9.3E-03 1.0E-02 1.1E-02 1.3E-02 1.5E-02 1.6E-02 2.2E-02 1.1E-02 8.5E-03 9.0E-03 9.8E-03 1.1E-02 1.2

11 to <16 years 1.3E-02 9.8E-03 1.0E-02 1.1E-02 1.3E-02 1.5E-02 1.6E-02 1.9E-02 2.7E-02 1.2E-02 9.4E-03 9.7E-03 1.1E-02 1.2E-02 1.316 to <21 years 1.3E-02 1.0E-02 1.1E-02 1.2E-02 1.3E-02 1.5E-02 1.7E-02 1.8E-02 2.9E-02 1.1E-02 8.3E-03 8.7E-03 9.6E-03 1.1E-02 1.221 to <31 years 1.3E-02 9.7E-03 1.0E-02 1.1E-02 1.2E-02 1.4E-02 1.6E-02 1.8E-02 2.7E-02 1.1E-02 7.8E-03 8.2E-03 9.1E-03 1.0E-02 1.231 to <41 years 1.4E-02 1.1E-02 1.1E-02 1.2E-02 1.3E-02 1.5E-02 1.6E-02 1.8E-02 2.5E-02 1.1E-02 8.8E-03 9.3E-03 1.0E-02 1.1E-02 1.241 to <51 years 1.4E-02 1.1E-02 1.2E-02 1.3E-02 1.4E-02 1.6E-02 1.7E-02 1.8E-02 2.3E-02 1.2E-02 9.6E-03 1.0E-02 1.1E-02 1.2E-02 1.351 to <61 years 1.5E-02 1.1E-02 1.2E-02 1.3E-02 1.4E-02 1.6E-02 1.8E-02 1.9E-02 2.5E-02 1.2E-02 9.8E-03 1.0E-02 1.1E-02 1.2E-02 1.361 to <71 years 1.4E-02 1.1E-02 1.2E-02 1.3E-02 1.4E-02 1.5E-02 1.7E-02 1.8E-02 2.1E-02 1.1E-02 8.9E-03 9.3E-03 9.8E-03 1.1E-02 1.271 to <81 years 1.4E-02 1.1E-02 1.2E-02 1.3E-02 1.4E-02 1.5E-02 1.6E-02 1.7E-02 2.0E-02 1.1E-02 8.8E-03 9.2E-03 9.9E-03 1.1E-02 1.2

81 years and 

older 1.4E-02 1.1E-02 1.2E-02 1.3E-02 1.4E-02 1.5E-02 1.6E-02 1.7E-02 2.1E-02 1.0E-02 8.7E-03 8.8E-03 9.4E-03 1.0E-02 1.1
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, wh

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Daily Ventilation Rate (m3/min) - Males, 

 C-25 Daily Ventilation Rate (m3/m

Unadjusted for Body Weight Unadjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 7

Moderate Intensity Activities (3.0 < METS 

< 6.0)

Birth to <1 

year

1.4E-02 7.4E-03 8.8E-03 1.1E-02 1.4E-02 1.7E-02 2.0E-02 2.3E-02 3.1E-02 1.4E-02 7.9E-03 9.0E-03 1.1E-02 1.4E-02 1.6

1 year 2.1E-02 1.4E-02 1.6E-02 1.8E-02 2.1E-02 2.4E-02 2.7E-02 2.9E-02 4.0E-02 2.1E-02 1.6E-02 1.6E-02 1.8E-02 2.0E-02 2.4

2 years 2.2E-02 1.5E-02 1.7E-02 1.8E-02 2.1E-02 2.4E-02 2.7E-02 3.0E-02 5.1E-02 2.1E-02 1.4E-02 1.6E-02 1.8E-02 2.1E-02 2.4

3 to <6 years 2.1E-02 1.6E-02 1.7E-02 1.9E-02 2.1E-02 2.3E-02 2.6E-02 2.7E-02 3.5E-02 2.0E-02 1.5E-02 1.6E-02 1.8E-02 2.0E-02 2.2

6 to <11 years 2.2E-02 1.6E-02 1.7E-02 1.9E-02 2.2E-02 2.5E-02 2.8E-02 2.9E-02 4.3E-02 2.1E-02 1.6E-02 1.7E-02 1.8E-02 2.0E-02 2.3

11 to <16 years 2.6E-02 1.9E-02 2.0E-02 2.3E-02 2.5E-02 2.9E-02 3.4E-02 3.7E-02 5.5E-02 2.4E-02 1.8E-02 1.9E-02 2.1E-02 2.3E-02 2.516 to <21 years 2.9E-02 2.0E-02 2.2E-02 2.5E-02 2.8E-02 3.2E-02 3.8E-02 4.2E-02 6.7E-02 2.3E-02 1.7E-02 1.8E-02 2.0E-02 2.2E-02 2.621 to <31 years 2.9E-02 2.0E-02 2.1E-02 2.4E-02 2.8E-02 3.3E-02 3.9E-02 4.3E-02 7.2E-02 2.3E-02 1.6E-02 1.7E-02 1.9E-02 2.2E-02 2.631 to <41 years 3.0E-02 2.1E-02 2.3E-02 2.5E-02 2.9E-02 3.4E-02 4.0E-02 4.3E-02 5.8E-02 2.3E-02 1.7E-02 1.8E-02 1.9E-02 2.2E-02 2.541 to <51 years 3.2E-02 2.3E-02 2.4E-02 2.7E-02 3.0E-02 3.5E-02 4.0E-02 4.5E-02 6.3E-02 2.4E-02 1.8E-02 1.9E-02 2.1E-02 2.4E-02 2.751 to <61 years 3.3E-02 2.2E-02 2.4E-02 2.8E-02 3.1E-02 3.7E-02 4.2E-02 4.6E-02 7.0E-02 2.5E-02 1.9E-02 2.0E-02 2.2E-02 2.4E-02 2.861 to <71 years 3.0E-02 2.2E-02 2.4E-02 2.6E-02 2.9E-02 3.2E-02 3.7E-02 4.0E-02 5.2E-02 2.1E-02 1.7E-02 1.8E-02 1.9E-02 2.1E-02 2.371 to <81 years 2.9E-02 2.3E-02 2.4E-02 2.6E-02 2.9E-02 3.2E-02 3.6E-02 3.7E-02 4.5E-02 2.1E-02 1.7E-02 1.8E-02 1.9E-02 2.1E-02 2.3

81 years and 

older 2.9E-02 2.2E-02 2.3E-02 2.5E-02 2.8E-02 3.1E-02 3.3E-02 3.6E-02 4.1E-02 2.1E-02 1.7E-02 1.8E-02 1.9E-02 2.1E-02 2.3
Table C-6. Descriptive statistics for daily ventilation rate (m3/min), unadjusted for body weight, wh

performing activities within the specified activity category, by age and gender categories (continued

Age Category

Daily Ventilation Rate (m3/min) - Males, 

 C-26 Daily Ventilation Rate (m3/m

Unadjusted for Body Weight Unadjusted for Body 

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 7

High Intensity (METS > 6.0)

Birth to <1 

year

2.7E-02 1.5E-02 1.7E-02 2.1E-02 2.8E-02 3.2E-02 3.8E-02 4.2E-02 5.8E-02 2.4E-02 1.2E-02 1.3E-02 1.7E-02 2.2E-02 2.9

1 year 4.0E-02 2.8E-02 3.2E-02 3.5E-02 4.0E-02 4.4E-02 5.2E-02 5.6E-02 6.1E-02 3.6E-02 2.6E-02 2.6E-02 3.0E-02 3.6E-02 4.2

2 years 4.0E-02 2.8E-02 3.0E-02 3.4E-02 4.1E-02 4.6E-02 5.2E-02 5.5E-02 9.2E-02 3.8E-02 2.9E-02 3.1E-02 3.2E-02 3.6E-02 4.1

3 to <6 years 3.9E-02 2.9E-02 3.1E-02 3.4E-02 3.8E-02 4.3E-02 4.9E-02 5.2E-02 6.6E-02 3.5E-02 2.7E-02 2.8E-02 3.0E-02 3.3E-02 3.8

6 to <11 years 4.4E-02 3.1E-02 3.3E-02 3.6E-02 4.2E-02 5.0E-02 5.7E-02 6.2E-02 9.0E-02 3.9E-02 2.9E-02 3.0E-02 3.4E-02 3.8E-02 4.4

11 to <16 years 5.1E-02 3.4E-02 3.7E-02 4.2E-02 4.9E-02 5.7E-02 6.6E-02 7.3E-02 1.2E-01 4.7E-02 3.1E-02 3.4E-02 3.9E-02 4.5E-02 5.316 to <21 years 5.3E-02 3.6E-02 3.8E-02 4.4E-02 5.1E-02 5.9E-02 7.1E-02 8.3E-02 1.3E-01 4.4E-02 2.9E-02 3.1E-02 3.7E-02 4.3E-02 5.021 to <31 years 5.4E-02 3.4E-02 3.8E-02 4.5E-02 5.2E-02 6.2E-02 7.2E-02 8.2E-02 1.1E-01 4.6E-02 2.9E-02 3.1E-02 3.7E-02 4.3E-02 5.231 to <41 years 5.4E-02 3.8E-02 4.0E-02 4.5E-02 5.2E-02 6.1E-02 7.1E-02 7.7E-02 1.0E-01 4.4E-02 3.0E-02 3.3E-02 3.7E-02 4.2E-02 5.041 to <51 years 5.7E-02 3.8E-02 4.2E-02 4.8E-02 5.5E-02 6.4E-02 7.6E-02 8.4E-02 1.1E-01 4.7E-02 3.1E-02 3.4E-02 3.8E-02 4.6E-02 5.451 to <61 years 5.8E-02 3.9E-02 4.2E-02 4.9E-02 5.6E-02 6.6E-02 7.9E-02 8.6E-02 1.4E-01 4.7E-02 3.2E-02 3.5E-02 3.9E-02 4.6E-02 5.461 to <71 years 5.4E-02 3.6E-02 4.0E-02 4.5E-02 5.2E-02 6.1E-02 7.2E-02 7.5E-02 1.0E-01 4.0E-02 2.8E-02 3.1E-02 3.5E-02 3.9E-02 4.571 to <81 years 5.2E-02 3.7E-02 4.0E-02 4.4E-02 5.0E-02 5.9E-02 6.8E-02 7.6E-02 9.7E-02 4.1E-02 2.8E-02 3.0E-02 3.4E-02 4.0E-02 4.7

81 years and 

older 5.3E-02 3.5E-02 3.9E-02 4.6E-02 5.1E-02 6.1E-02 7.0E-02 7.7E-02 9.7E-02 4.2E-02 2.8E-02 3.0E-02 3.4E-02 4.1E-02 4.8

Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999

−2002 when calculating the stati

Ventilation rate was estimated using the multiple linear regression model in Section 3.6. 
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, w

activities within the specified activity category, by age and gender categories 

 C-27 Daily Ventilation Rate (m3/min-kg) - Males, Daily Ventilation Rate (m3/min

Adjusted for Body Weight Adjusted for Body W

Age Category Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 75t

Sleep or nap (Activity ID = 14500)

Birth to <1 

year

3.8E-04 2.8E-04 3.0E-04 3.4E-04 3.8E-04 4.3E-04 4.6E-04 5.0E-04 6.7E-04 3.9E-04 2.8E-04 3.0E-04 3.4E-04 3.9E-04 4.3E-

1 year 4.0E-04 3.0E-04 3.1E-04 3.5E-04 3.8E-04 4.4E-04 4.9E-04 5.2E-04 6.3E-04 4.1E-04 3.1E-04 3.3E-04 3.6E-04 4.1E-04 4.6E-

2 years 3.3E-04 2.5E-04 2.6E-04 2.9E-04 3.3E-04 3.6E-04 4.0E-04 4.4E-04 5.4E-04 3.4E-04 2.6E-04 2.7E-04 2.9E-04 3.3E-04 3.9E-

3 to <6 years 2.4E-04 1.6E-04 1.7E-04 2.0E-04 2.4E-04 2.8E-04 3.1E-04 3.5E-04 4.8E-04 2.4E-04 1.4E-04 1.6E-04 2.0E-04 2.3E-04 2.8E-

6 to <11 years 1.5E-04 1.0E-04 1.1E-04 1.3E-04 1.5E-04 1.7E-04 2.0E-04 2.2E-04 3.0E-04 1.5E-04 8.9E-05 9.7E-05 1.2E-04 1.5E-04 1.8E-

11 to <16 years 9.8E-05 6.7E-05 7.2E-05 8.1E-05 9.4E-05 1.1E-04 1.3E-04 1.4E-04 2.1E-04 9.0E-05 5.9E-05 6.5E-05 7.5E-05 8.7E-05 1.0E-16 to <21 years 7.1E-05 4.7E-05 5.2E-05 6.1E-05 6.9E-05 8.0E-05 9.0E-05 9.8E-05 1.5E-04 6.9E-05 4.4E-05 4.7E-05 5.7E-05 6.7E-05 8.0E-21 to <31 years 5.8E-05 3.8E-05 4.2E-05 4.8E-05 5.6E-05 6.6E-05 7.6E-05 8.3E-05 1.3E-04 5.5E-05 3.5E-05 3.8E-05 4.5E-05 5.4E-05 6.5E-31 to <41 years 6.1E-05 3.8E-05 4.3E-05 5.0E-05 6.0E-05 7.0E-05 8.0E-05 8.6E-05 1.3E-04 5.6E-05 3.4E-05 3.7E-05 4.5E-05 5.4E-05 6.5E-41 to <51 years 6.5E-05 4.4E-05 4.7E-05 5.4E-05 6.4E-05 7.4E-05 8.6E-05 9.2E-05 1.4E-04 6.0E-05 3.9E-05 4.1E-05 4.8E-05 5.7E-05 7.0E-51 to <61 years 6.6E-05 4.5E-05 4.9E-05 5.5E-05 6.4E-05 7.6E-05 8.6E-05 9.3E-05 1.4E-04 6.1E-05 3.9E-05 4.2E-05 5.0E-05 5.9E-05 7.1E-61 to <71 years 6.9E-05 5.1E-05 5.4E-05 6.0E-05 6.8E-05 7.6E-05 8.6E-05 9.3E-05 1.2E-04 6.1E-05 4.3E-05 4.6E-05 5.2E-05 5.9E-05 6.7E-71 to <81 years 7.5E-05 5.5E-05 5.8E-05 6.4E-05 7.3E-05 8.3E-05 9.3E-05 9.9E-05 1.3E-04 6.6E-05 4.7E-05 5.1E-05 5.6E-05 6.4E-05 7.4E-

81 years and 

older 8.0E-05 6.1E-05 6.4E-05 7.1E-05 7.8E-05 8.8E-05 9.7E-05 1.1E-04 1.2E-04 7.2E-05 5.1E-05 5.6E-05 6.3E-05 7.0E-05 7.9E-
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued) 

Age Category

Daily Ventilation Rate (m3/min-kg) - Males, 

 C-28 Daily Ventilation Rate (m3/min

Adjusted for Body Weight Adjusted for Body W

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 75t

Sedentary & Passive Activities (METS 

< 1.5

―Includes Sleep or Nap)

Birth to <1 

year

4.0E-04 3.0E-04 3.2E-04 3.5E-04 3.9E-04 4.4E-04 4.7E-04 5.0E-04 6.6E-04 4.0E-04 3.0E-04 3.2E-04 3.5E-04 4.0E-04 4.5E-

1 year 4.1E-04 3.2E-04 3.3E-04 3.6E-04 4.0E-04 4.5E-04 4.9E-04 5.2E-04 6.2E-04 4.3E-04 3.4E-04 3.5E-04 3.8E-04 4.2E-04 4.7E-

2 years 3.4E-04 2.7E-04 2.9E-04 3.1E-04 3.4E-04 3.7E-04 4.1E-04 4.5E-04 5.1E-04 3.6E-04 2.9E-04 3.0E-04 3.2E-04 3.5E-04 3.9E-

3 to <6 years 2.5E-04 1.8E-04 1.9E-04 2.1E-04 2.5E-04 2.9E-04 3.3E-04 3.5E-04 4.5E-04 2.5E-04 1.6E-04 1.8E-04 2.1E-04 2.5E-04 2.8E-

6 to <11 years 1.6E-04 1.1E-04 1.2E-04 1.4E-04 1.6E-04 1.8E-04 2.1E-04 2.2E-04 2.9E-04 1.6E-04 9.9E-05 1.1E-04 1.3E-04 1.6E-04 1.9E-

11 to <16 years 1.0E-04 7.7E-05 8.0E-05 8.8E-05 1.0E-04 1.2E-04 1.3E-04 1.4E-04 2.0E-04 9.7E-05 7.1E-05 7.5E-05 8.3E-05 9.5E-05 1.1E-16 to <21 years 7.7E-05 5.5E-05 6.0E-05 6.8E-05 7.6E-05 8.5E-05 9.5E-05 1.0E-04 1.3E-04 7.5E-05 5.3E-05 5.7E-05 6.3E-05 7.4E-05 8.5E-21 to <31 years 6.2E-05 4.7E-05 4.9E-05 5.5E-05 6.1E-05 6.9E-05 7.7E-05 8.2E-05 1.2E-04 6.0E-05 4.3E-05 4.5E-05 5.1E-05 5.9E-05 6.7E-31 to <41 years 6.6E-05 4.6E-05 5.0E-05 5.7E-05 6.5E-05 7.4E-05 8.2E-05 8.6E-05 1.2E-04 6.0E-05 4.0E-05 4.2E-05 5.1E-05 5.9E-05 6.9E-41 to <51 years 7.1E-05 5.4E-05 5.7E-05 6.2E-05 7.0E-05 7.8E-05 8.6E-05 9.1E-05 1.3E-04 6.5E-05 4.4E-05 4.8E-05 5.5E-05 6.3E-05 7.3E-51 to <61 years 7.2E-05 5.5E-05 5.8E-05 6.3E-05 7.1E-05 7.9E-05 8.8E-05 9.2E-05 1.4E-04 6.7E-05 4.6E-05 5.1E-05 5.7E-05 6.5E-05 7.6E-61 to <71 years 7.6E-05 6.1E-05 6.4E-05 6.9E-05 7.5E-05 8.1E-05 8.9E-05 9.4E-05 1.1E-04 6.6E-05 5.2E-05 5.4E-05 5.9E-05 6.6E-05 7.2E-71 to <81 years 8.2E-05 6.7E-05 7.0E-05 7.5E-05 8.1E-05 8.8E-05 9.4E-05 9.8E-05 1.1E-04 7.2E-05 5.5E-05 6.0E-05 6.5E-05 7.1E-05 7.8E-

81 years and 

older 8.6E-05 7.1E-05 7.5E-05 8.0E-05 8.6E-05 9.2E-05 9.9E-05 1.1E-04 1.1E-04 7.8E-05 6.3E-05 6.5E-05 7.0E-05 7.7E-05 8.6E-
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued) 

Age Category

Daily Ventilation Rate (m3/min-kg) - Males, 

 C-29 Daily Ventilation Rate (m3/min

Adjusted for Body Weight Adjusted for Body W

Mean Percentiles Max Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 75t

Light Intensity Activities (1.5 < METS 

< 3.0)

Birth to <1 

year

9.9E-04 7.9E-04 8.3E-04 9.0E-04 9.7E-04 1.1E-03 1.2E-03 1.2E-03 1.4E-03 9.8E-04 7.9E-04 8.2E-04 8.8E-04 9.6E-04 1.0E-

1 year 1.0E-03 8.4E-04 8.6E-04 9.2E-04 1.0E-03 1.1E-03 1.2E-03 1.3E-03 1.5E-03 1.0E-03 8.5E-04 8.7E-04 9.5E-04 1.0E-03 1.1E-

2 years 8.4E-04 6.8E-04 7.2E-04 7.6E-04 8.3E-04 8.9E-04 1.0E-03 1.0E-03 1.2E-03 9.0E-04 7.3E-04 7.6E-04 8.2E-04 8.9E-04 9.6E-

3 to <6 years 6.3E-04 4.4E-04 4.8E-04 5.4E-04 6.3E-04 7.1E-04 7.9E-04 8.7E-04 1.1E-03 6.2E-04 4.5E-04 4.8E-04 5.4E-04 6.0E-04 7.0E-

6 to <11 years 3.8E-04 2.7E-04 2.9E-04 3.2E-04 3.8E-04 4.4E-04 4.9E-04 5.3E-04 7.1E-04 3.8E-04 2.5E-04 2.7E-04 3.1E-04 3.8E-04 4.4E-

11 to <16 years 2.5E-04 1.8E-04 1.9E-04 2.1E-04 2.4E-04 2.8E-04 3.1E-04 3.3E-04 4.4E-04 2.3E-04 1.6E-04 1.7E-04 2.0E-04 2.2E-04 2.5E-16 to <21 years 1.8E-04 1.4E-04 1.4E-04 1.6E-04 1.8E-04 2.0E-04 2.2E-04 2.3E-04 3.3E-04 1.7E-04 1.3E-04 1.4E-04 1.5E-04 1.7E-04 1.9E-21 to <31 years 1.6E-04 1.2E-04 1.3E-04 1.4E-04 1.5E-04 1.7E-04 1.9E-04 2.1E-04 2.9E-04 1.5E-04 1.2E-04 1.2E-04 1.3E-04 1.5E-04 1.6E-31 to <41 years 1.6E-04 1.2E-04 1.3E-04 1.4E-04 1.6E-04 1.8E-04 2.0E-04 2.1E-04 2.8E-04 1.5E-04 1.1E-04 1.2E-04 1.3E-04 1.5E-04 1.8E-41 to <51 years 1.7E-04 1.3E-04 1.3E-04 1.5E-04 1.6E-04 1.8E-04 2.0E-04 2.1E-04 3.3E-04 1.6E-04 1.1E-04 1.2E-04 1.4E-04 1.6E-04 1.8E-51 to <61 years 1.7E-04 1.3E-04 1.3E-04 1.5E-04 1.6E-04 1.8E-04 2.0E-04 2.2E-04 2.9E-04 1.6E-04 1.2E-04 1.3E-04 1.4E-04 1.6E-04 1.8E-61 to <71 years 1.6E-04 1.4E-04 1.4E-04 1.5E-04 1.6E-04 1.8E-04 1.9E-04 2.0E-04 2.7E-04 1.5E-04 1.2E-04 1.2E-04 1.3E-04 1.4E-04 1.6E-71 to <81 years 1.7E-04 1.4E-04 1.5E-04 1.6E-04 1.7E-04 1.8E-04 1.9E-04 2.0E-04 2.6E-04 1.6E-04 1.2E-04 1.3E-04 1.4E-04 1.6E-04 1.7E-

81 years and 

older 1.8E-04 1.5E-04 1.6E-04 1.7E-04 1.8E-04 2.0E-04 2.1E-04 2.2E-04 2.5E-04 1.7E-04 1.3E-04 1.4E-04 1.5E-04 1.6E-04 1.8E-
Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued) 

Age Category

Daily Ventilation Rate (m3/min-kg) - Males, 

 C-30 Daily Ventilation Rate (m3/min

Adjusted for Body Weight Adjusted for Body W

Mean Percentiles Maxi- mum Percentiles Mean 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 75t

Moderate Intensity Activities (3.0 < METS 

< 6.0)

Birth to <1 

year

1.8E-03 1.4E-03 1.5E-03 1.6E-03 1.8E-03 1.9E-03 2.2E-03 2.3E-03 3.0E-03 1.9E-03 1.5E-03 1.5E-03 1.7E-03 1.9E-03 2.0E-

1 year 1.9E-03 1.4E-03 1.5E-03 1.7E-03 1.8E-03 2.0E-03 2.3E-03 2.5E-03 3.2E-03 1.9E-03 1.5E-03 1.6E-03 1.7E-03 1.9E-03 2.0E-

2 years 1.5E-03 1.2E-03 1.3E-03 1.4E-03 1.5E-03 1.7E-03 1.8E-03 2.0E-03 2.3E-03 1.6E-03 1.3E-03 1.3E-03 1.4E-03 1.6E-03 1.7E-

3 to <6 years 1.2E-03 8.0E-04 8.8E-04 1.0E-03 1.1E-03 1.3E-03 1.6E-03 1.7E-03 2.1E-03 1.1E-03 7.9E-04 8.5E-04 9.6E-04 1.1E-03 1.3E-

6 to <11 years 7.4E-04 5.0E-04 5.5E-04 6.2E-04 7.1E-04 8.3E-04 9.6E-04 1.0E-03 1.4E-03 7.2E-04 4.6E-04 5.1E-04 6.0E-04 7.1E-04 8.4E-

11 to <16 years 4.9E-04 3.6E-04 3.8E-04 4.2E-04 4.7E-04 5.5E-04 6.4E-04 6.8E-04 1.1E-03 4.4E-04 3.2E-04 3.4E-04 3.8E-04 4.3E-04 4.9E-16 to <21 years 3.9E-04 2.8E-04 3.0E-04 3.3E-04 3.8E-04 4.3E-04 4.9E-04 5.2E-04 7.1E-04 3.6E-04 2.7E-04 2.8E-04 3.1E-04 3.5E-04 4.1E-21 to <31 years 3.6E-04 2.4E-04 2.6E-04 3.0E-04 3.4E-04 4.0E-04 4.7E-04 5.1E-04 8.2E-04 3.3E-04 2.4E-04 2.5E-04 2.8E-04 3.2E-04 3.6E-31 to <41 years 3.6E-04 2.4E-04 2.6E-04 3.0E-04 3.4E-04 4.0E-04 4.7E-04 5.2E-04 7.6E-04 3.2E-04 2.1E-04 2.3E-04 2.7E-04 3.0E-04 3.5E-41 to <51 years 3.7E-04 2.5E-04 2.7E-04 3.1E-04 3.5E-04 4.1E-04 4.7E-04 5.2E-04 7.2E-04 3.3E-04 2.2E-04 2.4E-04 2.8E-04 3.2E-04 3.8E-51 to <61 years 3.8E-04 2.6E-04 2.8E-04 3.1E-04 3.7E-04 4.3E-04 4.8E-04 5.5E-04 7.6E-04 3.4E-04 2.4E-04 2.5E-04 2.8E-04 3.3E-04 3.8E-61 to <71 years 3.4E-04 2.7E-04 2.8E-04 3.1E-04 3.4E-04 3.7E-04 4.0E-04 4.2E-04 5.7E-04 2.9E-04 2.2E-04 2.4E-04 2.6E-04 2.8E-04 3.2E-71 to <81 years 3.6E-04 2.9E-04 3.1E-04 3.3E-04 3.6E-04 3.9E-04 4.2E-04 4.4E-04 5.5E-04 3.1E-04 2.4E-04 2.5E-04 2.7E-04 3.0E-04 3.4E-

81 years and 

older 3.8E-04 3.1E-04 3.2E-04 3.5E-04 3.8E-04 4.2E-04 4.5E-04 4.7E-04 5.3E-04 3.3E-04 2.5E-04 2.7E-04 3.0E-04 3.3E-04 3.7E-
 C-31 Table C-7. Descriptive statistics for daily ventilation rate (m3/min-kg), adjusted for body weight, w

activities within the specified activity category, by age and gender categories (continued) Age Category Daily Ventilation Rate (m3/min-kg) - Males, Adjusted for Body Weight Daily Ventilation Rate (m3/min

Adjusted for Body W

Mean Percentiles Max Mean Percentiles 

5th 10th 25th 50th 75th 90th 95th

5th 10th 25th 50th 75t

High Intensity (METS > 6.0)

Birth to <1 

year

3.5E-03 2.7E-03 2.9E-03 3.1E-03 3.5E-03 3.8E-03 4.1E-03 4.3E-03 5.1E-03 3.3E-03 2.5E-03 2.6E-03 2.9E-03 3.2E-03 3.6E-

1 year 3.5E-03 2.5E-03 2.9E-03 3.2E-03 3.6E-03 3.9E-03 4.1E-03 4.3E-03 4.9E-03 3.4E-03 2.6E-03 2.7E-03 3.0E-03 3.2E-03 3.7E-

2 years 2.9E-03 2.2E-03 2.3E-03 2.6E-03 2.9E-03 3.2E-03 3.4E-03 3.5E-03 4.3E-03 2.8E-03 2.2E-03 2.3E-03 2.5E-03 2.8E-03 3.1E-

3 to <6 years 2.2E-03 1.5E-03 1.7E-03 1.8E-03 2.1E-03 2.5E-03 2.7E-03 3.0E-03 3.6E-03 2.0E-03 1.4E-03 1.5E-03 1.7E-03 1.9E-03 2.2E-

6 to <11 years 1.4E-03 9.4E-04 1.0E-03 1.2E-03 1.4E-03 1.6E-03 1.8E-03 1.9E-03 2.7E-03 1.3E-03 8.9E-04 9.7E-04 1.1E-03 1.3E-03 1.5E-

11 to <16 years 9.5E-04 6.3E-04 7.0E-04 7.9E-04 9.1E-04 1.1E-03 1.3E-03 1.4E-03 2.0E-03 8.8E-04 5.9E-04 6.3E-04 7.1E-04 8.5E-04 1.0E-16 to <21 years 7.1E-04 4.8E-04 5.3E-04 6.0E-04 6.9E-04 8.0E-04 9.2E-04 1.0E-03 1.9E-03 7.0E-04 4.5E-04 5.0E-04 5.7E-04 6.9E-04 7.9E-21 to <31 years 6.6E-04 4.5E-04 4.7E-04 5.4E-04 6.4E-04 7.5E-04 8.5E-04 9.7E-04 1.3E-03 6.5E-04 4.2E-04 4.6E-04 5.5E-04 6.3E-04 7.3E-31 to <41 years 6.4E-04 4.4E-04 4.7E-04 5.3E-04 6.2E-04 7.3E-04 8.5E-04 9.3E-04 1.2E-03 6.1E-04 3.8E-04 4.2E-04 5.0E-04 5.9E-04 7.1E-41 to <51 years 6.6E-04 4.4E-04 4.8E-04 5.5E-04 6.3E-04 7.4E-04 8.6E-04 9.4E-04 1.8E-03 6.5E-04 3.8E-04 4.4E-04 5.2E-04 6.4E-04 7.6E-51 to <61 years 6.8E-04 4.5E-04 4.8E-04 5.5E-04 6.4E-04 7.7E-04 9.1E-04 1.0E-03 1.3E-03 6.3E-04 3.9E-04 4.3E-04 5.1E-04 6.1E-04 7.5E-61 to <71 years 6.2E-04 4.4E-04 4.7E-04 5.3E-04 6.1E-04 7.0E-04 7.9E-04 8.5E-04 1.1E-03 5.4E-04 3.6E-04 4.0E-04 4.5E-04 5.3E-04 6.1E-71 to <81 years 6.5E-04 4.7E-04 5.0E-04 5.5E-04 6.3E-04 7.2E-04 8.5E-04 9.1E-04 1.0E-03 5.9E-04 3.9E-04 4.4E-04 5.0E-04 5.8E-04 6.8E-

81 years and 

older 7.2E-04 5.0E-04 5.4E-04 6.0E-04 7.0E-04 8.0E-04 9.4E-04 9.9E-04 1.4E-03 6.7E-04 4.5E-04 4.8E-04 5.4E-04 6.3E-04 7.7E-

Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999

−2002 when calculating the stati

Ventilation rate was estimated using the multiple linear regression model in Section 3.6. 
APPENDIX D 

RESPONSE PREPARED BY S. GRAHAM (U.S. EPA) 

TO PEER-REVIEW COMMENTS ON APPENDIX A 
TABLE OF CONTENTS 

1. Purpose ........................................................................................................................... D-4 

2. APEX Model Description .............................................................................................. D-4 

3. Ventilation Algorithm Evaluation .................................................................................. D-5 

4. Comparison of APEX Estimates with Brochu et al. (2006a, b) ..................................... D-6 

5. Results of Comparison of APEX Estimates with Brochu et al. (2006a, b) .................. D-6 

6. Concluisons of Brochu et al. (2006a, b) Comparison .................................................. D-11 

7. Comparison of APEX Estimates with Arcus-Arth and Blaisdell (2007) ..................... D-12 

8. Results of Compariso of APEX Estimates with Arcus-Arth and Brochu et al. 

(2006a, b) ..................................................................................................................... D-13 

9. Issues ............................................................................................................................. D-13 

10. Conclusions of Arcus-Arth and Blaisdell (2007) Comparison .................................... D-13 

11. References to Appendix D ........................................................................................... D-15 

D-2 
LIST OF FIGURES 

D-1. Body mass comparison ................................................................................................... D-7 

D-2. Mean resting energy expenditure comparison ................................................................ D-8 

D-3. Comparison of body mass normalized mean basal energy expenditure (BEE-kg). ....... D-8 

D-4. Comparison of mean total daily energy expenditure (EE) .............................................. D-9 

D-5. Comparison of mean physical activity level (PAL) ...................................................... D-10 

D-6. Comparison of mean daily ventilation rate (VE) ........................................................... D-10 

D-7. Comparison of body mass normalized mean daily ventilation rate (VE-kg) ................ D-11 

D-8. Comparison of body mass normalized mean ventilation rates (VE-kg) correcting 

Brochu et al. (2006a) results with child appropriate VQ estimates .............................. D-12 

D-9. Comparison of mean daily ventilation rate (VE) in children ........................................ D-14 

D-10. Comparison of body mass normalized mean ventilation rates (VE-kg) in children ...... D-14 

D-3 
1. Purpose 

This appendix addresses comments provided by external peer reviewers on Appendix A 

of this document. In particular, clarification of how U.S. EPA’s Air Pollutants Exposure 

(APEX) model can be used to estimate ventilation rates is provided, and model ventilation 

estimates are compared with recently published estimates of ventilation rate. 

2. APEX Model Description 

U.S. EPA (2008a, b) provides more details on the approach to estimating ventilation rates 

using APEX. To summarize, the model is designed to estimate exposure to air pollutants and 

inhalation dose, accounting for the expected variability in both human behavior and physiology 

through consideration of important influential characteristics. One noteworthy feature of the 

model is its ability to estimate a time-series of exposure and dose for simulated individuals by 

correlating the time-series of microenvironmental concentration with the time series of 

activity-specific ventilation rates that could be as short as 1 minute in duration. 

Briefly, any number of individuals can be simulated in a model run to estimate exposure 

and exposure-related metrics (e.g., ventilation rate, body mass). Personal attributes of a 

simulated individual are first estimated (e.g., body mass, basal energy expenditure [BEE]) using 

either measurement data distributions (e.g., body mass distributions from CDC) or equations 

derived from measurement data and reported in the peer-reviewed literature (e.g., EE from 

Schoefield, 1985). Human time-location-activity diaries in U.S. EPA’s Consolidated Human 

Activity Database (CHAD) (McCurdy et al., 2000; U.S. EPA, 2002) are used to generate activity 

profiles for the simulated individuals for periods as short as one day upwards to a year. Each 

activity has a distribution of Metabolic Equivalents of work (METS) (e.g., point, lognormal, 

normal) that is sampled to estimate activity-specific METS for the individual performing the 

given activity. That combined with body mass dependent EE and a conversion for energy 

expenditure to oxygen consumption (e.g., H in Brochu et al., 2006a) result in activity-specific 

VO2 (typically in L-O2/min). These data are used as inputs to the regression equations reported 

in Appendix A (along with age, gender, and body mass) to estimate ventilation rate (VE, L/min). 

There are greater details in the approach such as those regarding adjusting the MET time-series 

to account for fatigue therefore regulating the duration of vigorous activities and excess 

D-4 
postexercise oxygen consumption whereby oxygen consumption is increased to repay oxygen 

debt that may have incurred from vigorous activities. See U.S. EPA (2008a, b) for more details. 

3. Ventilation Algorithm Evaluation 

Two recent publications were identified as potentially useful in the evaluation of the 

ventilation algorithm. Brochu et al. (2006a) presents data for ventilation rates, body mass, and 

energy expenditure for comparison, derived from data reported in studies that used the 

doubly-labeled water (DLW) method to estimate energy expenditure. Important reported subject 

characteristics include both genders, ages from 1 month to 96 years, and disaggregated by body 

mass (overweight/obese, normal body mass). For example, overweight individuals were defined 

as having body mass indexes (BMI) above the 97th percentiles for infants and toddlers <3 years, 

>85th percentile for children under 20, and BMI >25 for adults above 19 years old. Estimates of 

energy expended were combined with a fixed oxygen uptake factor (H = 0.21) and a fixed 

ventilatory equivalent (VQ = VE/VO2 = 27), while also accounting for stored daily energy cost 

for growth up to age 24, although this cost is generally only about 2% of total energy expended 

for ages above 1 year old. The DLW measurement generally extended from 7−21 days, resulting 

in time-averaged metrics that provide reasonable estimates for the mean (e.g., mean daily 

ventilation rate), but are not useful for estimating variability in an individuals ventilation rate (or 

other parameter) over shorter time periods. Reported data are averages for several age groupings 

(e.g., 1−<2, 2−<5, 5−<7, etc.) with derived percentiles assuming a normal distribution. 

Arcus-Arth and Blaisdell (2007) provide ventilation estimates for children <19 years of age 

using energy intake (EI, or calories consumed) and body mass data provided from the U.S. 

Department of Agriculture’s (USDA) Continuing Survey of Food Intake for Individuals (CSFII), 

adjusted for underreporting of food intakes (x 1.2 for children >8 years) and stored energy in 

infants alone (<1 years old). Two-day daily average EIs were combined with H (i.e., 0.22 for 

infants, 0.21 for non infants) and VQ (i.e., 33.5 for children 0−8, 30.6 for boys 9−18, 31.5 for 

girls 9−18 years old). Again, time-averaging of the data provide reasonable estimates of the 

mean, but offer no variability in ventilation estimates for time periods of shorter duration. 

Furthermore, data for both genders are combined and reported by age, with gender differences 

reported only for aggregated age groups (males and females, 9−18 years old). Additional 

gender-specific age groupings are reported for comparison with other literature values, however 

D-5 
are limited in number to be of great use here. An APEX model simulation was performed to 

generate estimates of relevant parameters for comparison with some of the estimates reported in 

these two publications. 

4. Comparison of APEX Estimates with Brochu et al. (2006a, b) 

A 14-day simulation was performed (i.e., the median of 7−21 days for the DLW data) for 

comparison with the time averaged Brochu et al. (2006a) data. Twenty-five thousand persons 

were simulated by APEX to generate a reasonable number of persons within each year of age 

and other potential categorical variables (e.g., 100−200, although some age groups have only 

1−5 persons). It is important when comparing the two types of data to have them as similar as 

possible, particularly since age and body mass are important influential variables. Body mass 

and height estimates were used to calculate BMIs for use in identifying normal versus 

overweight individuals as was done for the Brochu et al. (2006a) data. Percentiles for BMI of 

children ages 2−20 were obtained from CDC (2000). Children <2 years old were classified as 

overweight based on whether or not they exceeded the 97th percentile for 2-year olds of the same 

gender (there were no data reported by CDC for ages less than 2 years), while children aged 

3−20 years were classified as overweight if they exceeded the 85th percentile. Adults were 

classified as overweight if their BMI > 25.0. A total of 9,613 normal-weight individuals were 

simulated by APEX and used for the following analysis. Multi-day parameter estimates (e.g., 

ventilation rates, physical activity level, or PAL) were time averaged across the 14-day 

simulation period, yielding a mean daily value for each person to best represent the DLW time 

averaging done in Brochu et al. (2006a). 

5. Results of Comparison of APEX Estimates with Brochu et al. (2008a, b) 

Figure D-1 compares the mean body mass (BM, kg) estimates of APEX simulated 

individuals by one year age intervals with the mean body mass reported in Table 2, page 684 of 

Brochu et al. (2006a) for several age groupings of normal-weight individuals. There are no 

apparent differences in the population reported by Brochu et al. (2006a) and the APEX simulated 

population regarding body mass. 

D-6 
0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90 10

Age (years)

Mean Body Mass (kg) 

0

APEX Females

Brochu et al. (2006) Females

APEX Males

Brochu et al. (2006) Males

Figure D-1. Body mass comparison. 

Figure D-2 compares the mean basal energy expenditure (BEE, Kcal/day) estimates of 

APEX simulated individuals by one year age intervals with the mean daily BEE reported in 

Table Web-3, page 3 of Brochu et al. (2006b) for several age groupings of normal-weight 

individuals. Little differences exist in the population reported by Brochu et al. (2006b) and the 

APEX simulated population regarding BEE. 

Figure D-3 compares the mean body mass normalized basal energy expenditure (BEE-kg, 

Kcal/day-kg) estimates of APEX simulated individuals by one year intervals and those reported 

in Table Web-3m, page 3 of Brochu et al. (2006b) for several age groupings of normal-weight 

individuals. Little difference exists in the population reported by Brochu et al. (2006b) and the 

APEX simulated population regarding BEE-kg. 

Figure D-4 compares the mean daily energy expenditure (EE, Kcal/day) estimates of 

APEX simulated individuals by one year age intervals and those reported in Table Web-3, page 3 

of Brochu et al. (2006b) for several age groupings of normal-weight individuals. Little 

difference exists in the population reported by Brochu et al. (2006b) and the APEX simulated 

population regarding EE for ages less than 15, APEX estimates of EE are higher by about 10% 

for both genders at greater ages. 

D-7 
0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 10 20 30 40 50 60 70 80 90 100

Age (years)

Mean Basal Energy Expenditure (Kcal/day) 

APEX Females

Brochu et al. (2006) Females

APEX Males

Brochu et al. (2006) Males

Figure D-2. Mean resting energy expenditure comparison. 

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 100

Age (years)

Body Mass Normalized Basal Energy Expenditure (Kcal/kg-day)

APEX Females

Brochu et al. (2006) Females

APEX Males

Brochu et al. (2006) Males

Figure D-3. Comparison of body mass normalized mean basal energy 

expenditure (BEE-kg). 

D-8 
0

500

1000

1500

2000

2500

3000

3500

4000

0 10 20 30 40 50 60 70 80 90 100

Age (years)

Mean Daily Energy Expenditure (kcal/day)

APEX Females

Brochu et al. (2006) Females

APEX Males

Brochu et al. (2006) Males

Figure D-4. Comparison of mean total daily energy expenditure (EE). 

Figure D-5 compares the mean physical activity levels (PAL, unitless) estimated by 

APEX simulated individuals by one year age intervals and those reported in Table Web-3, page 3 

of Brochu et al. (2006b) for several age groupings of normal-weight individuals. Little 

difference exists in the population reported by Brochu et al. (2006b) and the APEX simulated 

population regarding PAL for ages between 10 and 20. APEX estimates of PAL are higher for 

both genders at other ages when compared with similar age PAL estimates from Brochu et al. 

(2006a, b), most notably at ages above 64. 

Figure D-6 compares the mean daily ventilation rate (VE, m3

/day) estimates of APEX 

simulated individuals by one year age intervals with those reported in Table 2, page 684 of 

Brochu et al. (2006a) for several age groupings of normal-weight individuals. The results were 

mixed for several ages and both genders when comparing those reported by Brochu et al. (2006a) 

and the APEX simulated population. APEX estimations were higher for children (age <11) and 

the elderly (age >64), while Brochu et al. (2006a) estimates were generally higher for males age 

12−40 and females age 18−40. For the young children, this may be a function of the fixed VQ 

used by Brochu et al. (2006 a, b) (see below). 

D-9 
0.0

0.5

1.0

1.5

2.0

2.5

0 10 20 30 40 50 60 70 80 90 100

Age (years)

Mean Physical Activity Level I

APEX Females

Brochu et al. (2006) Females

APEX Males

Brochu et al. (2006) Males

Figure D-5. Comparison of mean physical activity level (PAL). 

0

5

10

15

20

25

0 10 20 30 40 50 60 70 80 90 100

Age (years)

Mean Daily Ventilation (m3/day) 

APEX Females

Brochu et al. (2006) Females

APEX Males

Brochu et al. (2006) Males

Figure D-6. Comparison of mean daily ventilation rate (VE). 

D-10 
Figure D-7 compares the body mass normalized mean daily ventilation rate (VE-kg, 

m3/day-kg) estimates of APEX simulated individuals by one year age intervals with those 

reported in Table 2, page 684 of Brochu et al. (2006a) for several age groupings of 

normal-weight individuals. The two largest differences appear for children of both genders less 

than age 10 (Brochu et al., [2006a] estimates are systematically lower than APEX estimates) and 

ages between 16−33 (APEX estimates are lower than Brochu et al., [2006a]). Body mass 

normalized ventilation rates also appear to be slightly higher using APEX for ages above 64, 

both genders. 

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 1

Age (years)

Body Mass Normalized Mean Daily Ventilation (m3/day-kg)M

00

APEX Females

Brochu et al. (2006) Females

APEX MAles

Brochu et al. (2006) Males

Figure D-7. Comparison of body mass normalized mean daily ventilation 

rate (VE-kg). 

6. Issues 

One principal issue identified as responsible for some of the noted differences in 

ventilation estimates is in the VQ used by Brochu et al. (2006a). A single value of 27 was used 

in estimating ventilation rates for both children and adults, however it is widely recognized that 

while a VQ of 27 may be a reasonable approximation for estimating mean ventilation rates of 

adults, it is not appropriate for use in estimating mean ventilation rates in children. With this in 

D-11 
mind, the Brochu et al. (2006a) ventilation estimates were modified here using the VQ estimates 

offered by Arcus-Arth and Blaisdell (2007). Figure D-8 illustrates the comparison of APEX 

body mass normalized mean daily ventilation rates with that of Brochu et al. (2006a) corrected 

ventilation estimates. The body mass normalized ventilation estimates for children are more 

similar to those generated by APEX when correcting the Brochu et al. (2006a) VQ parameter. 

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 1

Age (years)

Body Mass Normalized Mean Daily Ventilation (m3/day-kg)M

00

APEX Females

Brochu et al. (2006) Females - Modified VQ

APEX MAles

Brochu et al. (2006) Males - Modified VQ

Figure D-8. Comparison of body mass normalized mean ventilation rates 

(VE-kg) correcting Brochu et al. (2006a) results with child appropriate 

VQ estimates. 

7. Conclusions of Brochu et al. (2006a, b) Comparison 

Mean estimates for all of the physiological parameters generated by APEX including 

ventilation rates are reasonably correlated with independent measures from the Brochu et al. 

(2006a, b) estimates, particularly when correcting the Brochu et al. (2006a) ventilation estimates 

for children using a more appropriate estimate of VQ for children. 

D-12 
8. Comparison of APEX Estimates with Arcus-Arth and Blaisdell (2007) 

A 2-day model simulation was performed using APEX to generate ventilation estimates 

(total ventilation and body mass normalized) for children to compare with results of Arcus-Arth 

and Blaisdell (2007). Table II (page 102) of Arcus-Arth and Blaisdell (2007) provided daily 

ventilation estimates, while Table III (page 103) provided body mass normalized ventilation 

rates. APEX ventilation estimates were time-averaged to generate mean daily values, and since 

the data reported in Arcus-Arth and Blaisdell (2007) were not separated by gender (outside of 

broad age categories), the APEX estimates were also combined to provide a mean estimate for 

each year of age (0−18). Body mass was also not used as a categorical variable in Arcus-Arth 

and Blaisdell (2007), therefore all APEX simulated individuals were used, regardless of whether 

they could be classified as overweight or of normal weight. In addition, data were obtained from 

Tables 3 and 4 of Brochu et al. (2006a) for a few age categories and considering both estimates 

for normal and overweight individuals (there were no combined data available). The Brochu et 

al. (2006 a, b) results have been corrected for VQ as noted above using VQ estimates of 

Arcus-Arth and Blaisdell (2007). 

9. Results of Comparison of APEX Estimates with Arcus-Arth and Brochu et al. 

 (2006a, b)

Figures D-9 and D-10 illustrate ventilation rate estimates from the APEX simulation, 

along with relevant data for children (ages 0−18) obtained from the two papers. Mean daily 

ventilation estimates (Figure D-9) are quite similar at each year of age, with slightly higher 

estimates by Arcus-Arth and Blaisdell (2007) at ages 9 and above, particularly when compared 

with APEX ventilation estimates. When ventilation rate is normalized by body mass 

(Figure D-10), the largest difference occurs at ages less than 4, whereas APEX estimates are 

higher than Arcus-Arth (2007), possibly influenced by differences in body mass between the two 

sample populations, particularly between ages 0 and 1.

10. Conclusions of Arcus-Arth and Blaisdell (2007) Comparison 

Ventilation estimates are remarkably similar for children for all three sources of data, 

particularly when considering the differences in the type of input data used and the varied 

approaches of APEX, Brochu et al. (2006 a, b), and Arcus-Arth and Blaisdell (2007). 

D-13 
0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18

Age (years)

Mean Daily Ventilation m3/day 

APEX Mean

Brochu et al. (2006) Normals Mean - Modified VQ

Arcus-Arth and Blaiedell (2007) Mean

Brochu et al. (2006) OW/OB Mean - Modified VQ

Figure D-9. Comparison of mean daily ventilation rate (VE) in children. 

0.0

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10 12 14 16 18

Age (years)

Body Mass Normalized Ventilation (m3/day-kg) 

APEX Mean

Brochu et al. (2006) Normals Mean - Modified VQ

Arcus-Arth and Blaiedell (2007) Mean

Brochu et al. (2006) OW/OB Mean - Modified VQ

Figure D-10. Comparison of body mass normalized mean ventilation 

rates (VE-kg) in children. 

D-14 
D-15 

11. References to Appendix D 

Arcus-Arth, A; Blaisdell, J. (2007). Statistical distributions of daily breathing rates for narrow age groups of infants 

and children. Risk Anal 27(1):97−110. 

Brochu, P; Ducre-Robitaille, J-F ; Brodeur, J. (2006a) Physiological daily inhalation rates for free-living individuals 

aged 1 month to 96 years, using data from doubly labeled water measurements: a proposal for air quality 

criteria, standard calculations and health risk assessment. Hum Ecol Risk Assess 12(4):675−701. 

Brochu, P; Ducre-Robitaille, J-F; Brodeur, J. (2006b) Supplemental Material for Physiological daily inhalation rates 

for free-living individuals aged 1 month to 96 years, using data from doubly labeled water measurements: a 

proposal for air quality criteria, standard calculations and health risk assessment. Hum Ecol Risk Assess 

12(4):1−12. 

CDC (Centers for Disease Control and Prevention). (2000). CDC Growth Charts: United States. BMI-for-age 

charts. National Center for Health Statistics, Hyattsville. Available online at 

http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/datafiles.htm. 

McCurdy, T; Glen, G; Smith, L; et al. (2000) The National Exposure Research Laboratory’s consolidated human 

activity database. J Expo Anal Environ Epidemiol 10:566−578. 

Schofield, W.N. 1985. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr. 

Clin. Nutr., 39C (suppl. 1): 5-41. 

U.S. EPA Environmental Protection Agency) (2002) Consolidated Human Activities Database. Office of Research 

and Development, National Exposure Research Laboratory, Washington, DC. Available online at 

http://www.epa.gov/chadnet1/. 

U.S. EPA (2008a). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model Documentation 

(TRIM.Expo / APEX, Version 4) Volume I: User’s Guide. Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. June 2006. Available online at 

http://www.epa.gov/ttn/fera/human_apex.html. 

U.S. EPA (2008b). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model Documentation 

(TRIM.Expo / APEX, Version 4) Volume II: Technical Support Document. Office of Air Quality Planning 

and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. June 2006. Available 

online at http://www.epa.gov/ttn/fera/human_apex.html.