# EPA/600/R-06/129F

May 2009



# METABOLICALLY DERIVED HUMA N 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 accord ance with U.S. Environmental Protection

Agency policy and approved for publication. Men tion 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 envi ronmental 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. Previ ous 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 Huma n 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 ABBREVIAT IONS 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 CALCULAT ION: 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 ESTI MATES 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 VALU E FOR EACH ACTIVITY WITHIN

THE SIMULATED 24-HOUR ACTIVITY PATTERN FOR EACH NHANES

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

3.5. STEP 5: CALCULATE EN ERGY EXPENDITURE AND VO 2 

# FOR EACH

ACTIVITY WITHIN AN INDIVI DUAL’S SIMULATED 24-HOUR

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

3.6. STEP 6: CALCULATE VENTILAT ION 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 VENTILATI ON RATE (VE) EQUATIONS FOR USE IN

INHALATION-ORIENTED EX POSURE MODELS ............................................................... A-1

APPENDIX B: STATISTICAL DISTRI BUTIONS 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 indivi duals 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 as signed 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-we ight adjusted ventilation rate

( V

&

E 

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

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

regression model for predicting body-we ight adjusted ventilation rate

( V

&

E 

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

4-1a. Descriptive statistics for daily averag e 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 performi ng activities within specified intensity

categories, and average ventilation rates a ssociated with these activity categories,

for males according to age category ................................................................................ 4-5

4-2b. Average time spent per day performi ng activities within specified intensity

categories, and average ventilation rates a ssociated 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

VO 2 

# oxygen consumption rate

VQ ventilator y equivalents

vi

# PREFACE

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

Office of Research and Developm ent (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 a nd needs in consultation with clie nts; and (3) develop companion

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

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

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

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

documents provide summaries of available statis tical data on various factors impacting an

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

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

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

Based Upon Oxygen Consumption Rate s, 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 A ssessment (NCEA), Office of Research and

Development was responsible for th e 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 directi on, 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 Ce nter 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 Ce nter for Environmental Assessment

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

the general public, reviewed th is 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 Emer itus, 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 primar y purposes of these handbooks are (1) to

summarize data on human behaviors and charac teristics that can affect exposure to

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

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

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

literature and other reliable data made availa ble through research. Ma ny 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 presente d in the handbooks have been

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

scientific literature. Among the exposure f actors addressed by thes e handbooks are drinking

water consumption, soil ingesti on, inhalation rates, dermal f actors, food consumption, breast

milk intake, human activity factors, consumer produc t 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 gene ral population.

One important determinant of a person’s expos ure 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 consum ed. Past methodologies have

not taken into account the variabil ity in ventilatory equivalent w ith regard to age, gender, and

fitness level.

ix

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

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

E 

# s directly

from an individual’s oxygen consumption rate (VO 2 

# ) 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 Hu man Activity Database (CHAD). This new

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

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

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

metabolic equivalent (METS) value based on statis tical sampling of the di stribution assigned by

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

lognormal, exponential, triangular, and uniform di stributions 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 VO 2 

# was calculated by multiplying EE by H, the volume of

oxygen consumed per unit of energy. VO 2 

# was calculated both as volume per time and as

volume per time per unit body weight.

The inhalation rate for each act ivity within the 24-hour simula ted activity pattern for each

individual was estimated as a function of VO 2 

# , body weight, age, and gender. Following this, the

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

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

than or equal to 1.5], light intensity [METS gr eater than 1.5 and less th an or equal to 3.0],

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

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

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

presented as daily rates for childr en and adults, ranged from 8.76-20.93 m 3 

# /day for males

(Table C-2a) and 8.53-16.20 m 3 

# /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 child ren and adults performing various activities,

ranged from 3.0×10 -03 

# - 5.8×10 -02 

# m 3 

# /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 hi gh-intensity activities.

x

# xi A validation exercise was conducted to compar e results presented in this document with

values published in th e literature using two other methodologies. Th ese methodologies include

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

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

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

document in Appendix A within the inhalati on modules of populati on-based probabilistic

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

model and the Air Pollution Exposure (APEX) m odel. 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 es timates 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) esti mates—particularly when corr ecting the Brochu et al. (2006a)

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

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

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

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

methods. Although the three methodol ogies available to estimate V &

E 

# s (i.e., doubly labeled

water, food-energy consumpti on, and metabolic ally-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 es timated 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/pa ssive, light intensity, moderate

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

females, is unique and is not currently avai lable 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 Agen cy (EPA) and its program offices conduct

various types of exposure assessment activitie s 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 f actors that can impact a pers on’s exposure to environmental

contaminants. The two primary purposes of the handbooks are

• to summarize data on human behaviors and ch aracteristics 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 f actors, consumer product use, and residential

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

general population as well as sp ecific target popul ations. The handbooks are a compilation of

information obtained from a vari ety of different sources and st udies that are presented in a

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

used in their publications. Exposure assesso rs are the primary in tended audience—with a

particular focus placed on researchers requiri ng 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 resear ch 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 expos ure to toxic chemicals via inhalation of

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

E 

# ), the volume of air i nhaled in a specified

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

E 

# is often

1-1

# abbreviated V &

E 

# (with the dot above the V indicating that the abbrevia tion represents ventilation

“rate” rather than “volume”) and has occasionall y 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 fr om several published studies,

each having considered different appr oaches and target populations.

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

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

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

(1993) was the first to demonstrate how EE could be used to charac terize 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 i nhaled air volume to H ). Layton (1993)

used a constant value for H (equal to 0.05 L O 2 

# /KJ or 0.21 L O 2 

# /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 e xpended 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 differen t levels of physical activity that a person

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

Activity-specific EEs were calculated as th e 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 durat ion of time spent performing the activity.

Among the data sources used by Layton (1993) in th ese 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 le vels. The VQ value of 27 used

in Layton (1993) may be appropriate for adults, bu t 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. Thes e limitations introduce

bias to the results. As a resu lt, staff at U.S. EPA's Nationa l 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

( VO 2 

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

modules of population-based probabilistic exposu re models, including th e Stochastic Human

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

The methodology features linear re gression 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 Da tabase (CHAD). The data were analyzed for

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

Guidance on Selecting Age Groups for Moni toring 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 nati onwide food intake surveys to estimate EEs (Layton, 1993;

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

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

# H 2 

# O and H 18

2 

# O).

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

isotopes deuterium ( 2 

# H) and heavy oxygen-18 ( 18 

# O) 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 n ecessary 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 Con tinuing Survey of Food Intake by Individuals

(USDA, 2000). The methodology presented in th is 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 appro ach provides reliable

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

period of time. The methodology is not useful , however, for estimati ng 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 lim itations 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 e quivalents (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 inte nsity or exertion associ ated with recorded

activities for the specific pur pose 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 valida tion of the metabolically derived V &

E 

# s presented in this

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

provides a summary of these an alyses and additional descript ion 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 subpopula tion uses the following information on that

person:

• Body weight;

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

of activity requiring exertion), primarily to pe rform 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 fo r these different types of

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

provides a specific type of in formation for an individual.

2.1. SOURCE OF BODY WEIGHT DATA: 1999

− 2002 NHANES

The Centers for Disease Control and Preven tion’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 peri odic series of surveys focusing on different

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

interviews and physical examinations.

Beginning in 1999, the NHANES became a conti nuous, 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 releas es public-use data files every 2 years. Data

used in this document originated from pub lic-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 comb ined 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 us ing measuring equipment

that was consistent from year to year. With in this database, a tota l 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 co nsidered 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 Category a 

## 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 CALCULATIO N: 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 rela ted. BMR is a function of such attributes as

body weight, height, age, and gender.

U.S. EPA has identified several sets of ma thematical 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 candida te equations were those propos ed by Schofield (1985), which

express BMR (in megajoules 1 

# 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 bett er representation of the gene ral population. (Most alternative

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

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

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

They were used by Layton (1993) and are includ ed 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 presen ted 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 Category a 

# 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 predicti ons generated by the Sc hofield equations for

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

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

necessary revisions to the pred iction process will be incorporat ed into the intake dose rate

modeling procedures used by th e 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 a ll ages and genders and which

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

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

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

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

a monitoring period of at least 24 hours. Th is 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 st udies within the database), location,

duration expended, and an estimate of the metabolic cost of performing th e 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 ac tivity levels. An activity’s METS

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

metabolic rate.

2-4

# The CHAD assigns a METS value to an activ ity according to the standardized ID code

that it assigned to the activity (see Appendix B). However, for mo st 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 a ssigned a statistical distributio n 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 fr om the code’s assigned distribution to serve

as the METS value for that particular activit y. The statistical dist ributions 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 distributiona l 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 code s, the CHAD occasionally

assigned a different form of the distributi on to different age cat egories (<25 years, 25

− 40 years,

>40 years), in order to account for different ra nges 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 lowe r 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 distribu tions 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 dist ributions were assigned to a particular activity

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

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

presents more information on the specific appr oach 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 sa mple within a certain age range, such as

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

national scale: the U.S. EPA-sponsored Natio nal 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 ac tivities performed over a 24-hour day, along with

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

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

regions (Northeast, Midwest, Sout h, 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 acr oss 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, NHA PS data records were distinguished by the type

of questionnaire that the survey provided to th e 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 da ta, according to the

age and gender categories considered in th is 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 th e CHAD, the database

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

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

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

body-weight measurements could not be downloaded with the other study da ta 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 C HAD also provided the

approach for assigning METS valu es to specific activities.

2-6

# 2-7 Table 2-3. Numbers of individuals from the NHAPS study by age and

gender categories

Age Category a 

# 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 house holds 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, thes e sample weights are not available within

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

reviewers noted that the telephone/ retrospective approach to colle ct 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-tim e diary was kept by the

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

data in real time and woul d be nationally representati ve of the U.S. population.

# 3. APPROACH

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

E 

# from VO 2 

# using a

series of regression-based equatio ns derived from data collected fr om 25 years of clinical studies

by Dr. William C. Adams of the University of California at Da vis (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 repres ented in an NHANES individual’s simulated

24-hour activity pattern.

5. Calculate EE and VO 2 

# for each activity within an NHANES individual’s simulated

24-hour activity pattern.

6. Calculate activity-specific V &

E 

# values for an NHANES indi vidual 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 VO 2 

# (adjusted for body weight), age, and gender.

7. Calculate average daily V & &

E 

# , as well as average V

E 

# 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 obtaine d 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 we re placed in a single

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

given in U.S. EPA (2005) with the following exce ption: 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 woul d have had insufficient sample sizes for the

analyses presented in this document.

Tables 2-1 and 2-3, in Secti on 2, list the age and gender catego ries used in this analysis,

along with the numbers of individuals within the NHANES and NHAPS da ta 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 ESTI MATES 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 estimat es 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 partic ipants had activity pattern data available for a single 24-hour

3-2

# monitoring period. For a give n age/gender category, let N correspond to the number of NHAPS

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

assigned a unique group ID number from 1 to N

.

For each of the 19,022 individuals in the NHANE S 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, wi th replacement, from the set of integers

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

age/gender category). The number of random in tegers 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 tim es 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 identifie d in this simulated set, and for each code,

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

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

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

the given activity. The proportions associ ated 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 in formation 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 wa s assigned to a given NHANES individual, it

was necessary to assign a METS value to each activ ity 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 statis tical distributions to each activity ID code.

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

single distribution, a few codes we re assigned different distributi ons for different age ranges,

apparently to account for different ranges of inte nsity 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 distribu tion 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 di stribution types represented within Appendix B

used random number generator func tions 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 dist ribution on the interval (0, 1) with

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

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

The random number generation pr ocedure depended not only on th e 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 l ogarithmic 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 

# + std 2 

# )]}

+ sqrt { log [1 + ( std/mean ) 2 

# ]} × RANNOR

• Normal distribution : METS = mean + std × RANNOR

• Triangular distribution : The generated METS value depe nds 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 distributi on was specified as a “point estimate,” the

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

an activity ID code, the METS value was al ways assigned to equa l this single value.

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

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

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

assigned whenever the randomly-generat ed METS value fell above the bound.

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

possible METS values” that could be assigne d 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 e xperience a METS value ab ove a certain threshold

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

gender. When METS values were generated fr om 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 value s 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 EN ERGY EXPENDITURE AND VO 2 

# FOR EACH

ACTIVITY WITHIN AN INDIVIDUAL’S SIMULATED 24-HOUR ACTIVITY

PATTERN

Once the METS values were generated, EE (e xpressed 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 VO 2 

# (expressed in L O 2 

# /min) were calculated from

these values according to the approach given in the document in Appendix A. VO 2 

# was

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

energy (L O 2 

# /kcal): VO 2 

# = EE × H.

In each application of this equation, the value of H is obtained by randomly sampling

from the uniform distribution over the inte rval (0.20, 0.22) for males and (0.19, 0.21) for

females. (These two distributions were obt ained from Table A-1 of Appendix A and differ

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

3-6

# distribution did not differ according to age.) VO 2 

# values were normalized by body weight by

dividing VO 2 

# 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. Fo r each activity within an individual’s activity

pattern, the model predicted V &

E 

# as a function of VO 2 

# 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 )

= b 0 

# + b 1 

# × log ( VO 2 

# / BW ) + b 2 

# × log ( age ) + b 3 

# × gender + ε where “ log ”

indicates the natural l ogarithmic transformation, BW corresponds to the in dividual’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 deviati on between the actual and predicted value of the

left-hand side of the equation for individuals having the same age, gender, and ( VO 2 

# /BW ) value

and is assumed to originate from a normal di stribution with a mean of 0 and a standard

deviation of σ . Estimated values of the in tercept and slope parameters ( b 0 

# , b 1 

# , b 2



# , and b 3 

# ) σ

and were provided for specified age ranges and are given in Tabl e 3-2. These age ranges were

determined based on prior usage (such as in John son, 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 fo r predicting body-weight adjusted

ventilation rate ( V &

E 

# /BW; L/min/kg)

Age b 0 

# b 1 

# b 2 

# b 3 

# 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 si mulated 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 VO 2 

# for that activity to the individual’s body weight, the

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

( b 0 

# , b 1 

# , b 2



# , and b 3 

# , from Table 3-2) that are rele vant 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 subs tituted for the error term in the

regression equation.

• The equation was then calculated, and the result wa s 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, ε b 

# and ε w 

# , representing between-person and

within-person variability, respectively: log ( V &

E 

# / BW)

= b 0 

# + b 1 

# × log ( VO 2 

# / BW ) + b 2



# × log ( age ) + b 3 

# × gender + ( ε b 

# + ε w 

# ), where all other terms are as defined in the multiple linear

regression model. Both ε b 

# and ε w 

# are assumed to originate from normal distributions with mean

0, but with different standard deviations σ b 

# and σ w 

# , respectively. Estimated values of the

intercept and slope parameters ( b 0 

# , b 1 

# , b 2



# , and b 3 

# ), σ b 

# , and σ w 

# are given in Table 3-3 for the same

age ranges given in Table 3-2. Note that be cause 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 B 0 

# b 1 

# b 2 

# b 3 

# σ b 

# σ

w



# <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 VE NTILATION RATE FOR TIME SPENT

PERFORMING ACTIVITIES WITHIN SP ECIFIED METS CATEGORIES, AS

WELL AS 24-HOUR AVERAGE VENTILAT ION RATE, FOR EACH NHANES

PARTICIPANT

Once values of V &

E 

# and V &

E 

# /BW were predicted for each reporte d activity ID code within

an individual’s simulated 24-hour activit y 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 simulate d 24-hour activity patter n, each activity was classified into one

of four activ ity categories that were, in turn, associat ed with intensity level:

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

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

than 3.0.

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

than 6.0.

• High Intensity Activities : Activities with M ETS 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 activi ties within the indi vidual’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 V E,i 

# represent the individual’s V &

E 

# calculated

in Step 6 for the i th 

# activity within this activity category, and let T i 

# 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 calcu lated 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 individua l’s simulated 24-hour

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

equaling the total number of activities w ithin the 24-hour acti vity 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 indi vidual’s body weight.

3.8. STEP 8: CALCULATE SUMMA RY 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 acro ss individuals for each of the four METS

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

(i.e., activity code 14500). These summaries corr esponded to weighted de scriptive statistics,

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

NHANES 1999

− 2002 database. The descriptive statisti cs, which were calculated using the

UNIVARIATE procedure within the SAS ® 

# System, included the mean, maximum, and selected

percentiles of the observed distri bution among the 19,022 NHANES participants.

3-10

# 4. RESULTS

This section presents tables that summar ize 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 re presented within the NHANES 1999

− 2002 data base (Section 2).

The results in this section were generate d using Version 9 (Rel ease 9.1.3) of the SAS ® 

# System

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

information that accompanies the re sults presented in this section.

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

a function of VO 2 

# , age, and gender for each activity assigned to each individual in the NHANES

data set. Section 3.6 also cites a mixed effect s 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, wh ile the mixed effects model separated random

error into two additive terms that repres ented between-individual and within-individual

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

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

to the multiple linear regression model predicti on 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 pres ented in this section. (It should be noted,

however, that if the prediction process did not incorporate a realizati on 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 an d Appendix C include the

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

characterize the central tendency and the general ra nge of the predicted da ta distribution. While

no parametric distributional assumptions were pla ced on the observed data distributions before

these statistics were calculated, the 4-year samp ling 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 descriptiv e 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 re presented 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, VO 2



# , 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 average V &

E 

# , both adjusted and unadjusted for body

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

E 

# s entering

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

represent an average rate take n over a 24-hour period (and, therefor e, its typical activity pattern)

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

information, but expressed in m 3 

# /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 in tensity, and high intensity ac tivities. In addition, average V &

E



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

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

napping are included within the se dentary/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. Thes e 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 fa lling within the sp ecified category, as

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

performing these activities within th eir simulated activity patterns.

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



# 4-3Age CategoryDaily Average Ventilation Rate, Unadjusted for Body Weight(;L/min)EV&Daily Average Ventilation Rate, Adjusted for Body Weight(/BW: L/min-kg)EV&MeanPercentilesMaxi-mumMeanPercentilesMaxi-mum5th10th25th50th75th90th95th5th10th25th50th75th90th95thBirth to <1year6.083.323.964.976.047.248.288.8111.840.760.630.650.700.750.810.870.901.031 year9.376.767.238.099.1110.4311.8212.4316.830.820.670.710.760.810.880.951.031.202 years9.196.567.097.949.1610.0711.3012.3019.560.660.540.570.610.650.700.760.780.943 to <6 years8.787.247.557.918.749.4710.1610.7013.560.490.360.390.430.480.540.610.640.756 to <11 years9.327.007.428.159.0910.2311.5012.3117.340.310.220.240.260.300.350.380.400.5611 to <16 years10.647.928.419.2210.2711.6813.5714.7319.820.200.140.150.170.190.220.250.270.3516 to <21 years11.958.759.3110.0611.5513.3115.2316.2327.230.160.120.130.140.160.180.190.210.2721 to <31 years13.078.819.4210.7612.6214.7517.0618.8430.150.160.110.120.130.160.180.210.220.3631 to <41 years14.099.7210.3911.7813.7715.9818.5920.0728.280.170.110.120.140.160.190.220.240.3241 to <51 years14.5410.1810.7912.1514.3016.5918.5519.7031.930.170.120.120.140.160.190.220.230.3251 to <61 years14.5210.4111.1612.2214.1716.0818.7620.2026.510.170.110.120.140.170.190.210.230.3061 to <71 years12.469.6610.0711.0312.2213.5715.1216.3219.510.140.120.120.130.140.150.170.180.2271 to <81 years11.359.109.4510.1811.2712.2013.4914.1817.030.140.120.120.130.140.150.160.170.2281 years andolder10.528.308.739.6010.3511.3312.5112.9815.720.140.120.120.130.140.150.160.170.19Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics inthis table. Ventilation rate was estimated using the multiple linear regression model in Section 3.6.

# 4-4Table 4-1b. Descriptive statistics for daily average ventilation rate (L/min) infemales, by age categoryAge CategoryDaily Average Ventilation Rate, Unadjusted for BodyWeight(; L/min)EV&Daily Average Ventilation Rate, Adjusted for Body WeightV&(/BW: L/min-kg)EMeanPercentilesMaximumMeanPercentilesMaximum5th10th25th50th75th90th95th5th10th25th50th75th90th95thBirth to <1year5.923.363.814.755.846.798.098.7918.230.790.630.670.720.780.860.920.961.111 year9.246.317.037.819.0510.1712.1212.9317.200.830.680.700.770.820.900.981.021.202 years8.856.196.997.908.759.6910.8211.3615.980.660.570.580.620.660.700.740.770.863 to <6 years8.456.867.217.788.359.049.7410.3713.710.480.330.370.410.480.530.610.640.776 to <11 years8.626.947.197.658.309.3210.5111.3514.460.300.190.210.250.300.340.380.400.5211 to <16 years9.337.277.728.369.0810.1011.2912.0918.460.170.130.140.150.170.190.220.240.3316 to <21 years9.446.857.378.189.1710.4311.8912.7020.910.150.110.120.130.140.160.190.200.2521 to <31 years10.127.057.418.299.7911.5413.4214.6820.990.140.100.110.120.140.160.180.190.2831 to <41 years10.407.698.209.0410.2011.3312.8514.2019.640.140.100.110.120.140.160.190.210.3041 to <51 years11.258.418.739.8311.0312.4713.8314.8224.920.150.100.110.130.150.170.200.210.2951 to <61 years11.248.569.009.7711.0412.3613.8414.7317.850.150.110.110.130.150.170.190.210.2861 to <71 years9.027.227.488.188.979.6610.6911.2114.120.120.100.100.110.120.130.150.160.1971 to <81 years8.366.877.087.568.219.009.8010.5512.290.120.100.100.110.120.130.150.160.2381 years andolder7.746.386.577.047.658.248.929.6811.760.120.100.100.110.120.140.150.150.20Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics inthis table.V&Ewas 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 aver age 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 Activity a



# 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 aver age 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 Activity a



# 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 aver age 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 Activity a



# 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 th e 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 corr esponding to the number of minutes spent performing

the activity. Numbers in these two columns represen t averages, calculated across individuals in the

specified age category, of these we ighted 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 aver age 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 Activity a



# 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 Activity a



# 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 Activity a



# 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 weight ed average of the individual’s activity-specific



# V &

E 

## s for activities falling within the category, estimated usin g the multiple linear regression model in Section 3.6,

with weights corresponding to the number of minutes sp ent performing the activity. Numbers in these two columns

represent averages, calculated across individuals in the spec ified 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 Append ix C. These five tables summarize the

following:

• Duration of time spent perf orming activities (hr/day)

• Average V &

E 

# (L/min and m 3 

# /min), unadjusted for body weight

• Average V &

E 

# (L/min/kg and m 3 

# /min/kg), adjusted for body weight

4.1. STRENGTHS AND LIMITATIONS

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

Appendix A) are that it a ccounts 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 VO 2 

# rather than an indirect meas ure 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, ca n introduce bias and additional variability.) The

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

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

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

weight and activity patterns that can repres ent everyone in an ag e/gender subpopulation.

However, in the prediction of V &

E 

# from VO 2 

# , 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 act ivity 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 indi vidual’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 we ight, 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 func tion of body weight, gender, and age category.

While this set of equations was deemed to repr esent 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 repr esentative of current popu lations (especially in

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

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

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

subject to independent tec hnical 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 assigne d 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 ac ross the simulated activity patterns appear to

be within reason for each age category.

The approach does not specifically account fo r 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 C HAD identified appropriate distri butions based upon a review of

the exercise physiology and clin ical nutrition literature. Th e METS randomization process

allows for different METS values to be assi gned 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 M ETS 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 w ith the METS randomization process.

4-12

# By using the NHANES sampling weights in th e calculation of the statistics in this

document, the goal of this effort was to generate statistics that could repr esent 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 st udy, 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 a ny given 24-hour period may be more variable

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

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

previous activities, V &

E 

# s may be overestimated as a result.

Data from the NHAPS were used to characteri ze 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 s ubpopulations, especially those se eing greater trends toward

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

sedentary nature of the populat ion as a whole may be affectin g the continued relevance of

NHAPS activity data to the contemporary U.S. popul ation. METS distributi ons also may not be

adequately characterized when activities are cond ucted 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 studie s 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 advant ages associated with the NHAPS data remained

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

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

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

other methodologies. These methodologies in clude the use of doubl y labeled water for

estimating EE (Brochu et al., 2006a, b) and the use of food-energy intake s 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 resulti ng 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. Th ese 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 strengt hs 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 relatio n of pulmonary ventilation and heart rate in leg work

alone, arm work alone, and in co mbined arm and leg work. Paper WA-84A.04 presented at the 88 th 

## 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 Re sources Board. Final Report, Contract No. A033-205. Available online at

http://www.arb.ca.gov/researc h/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) Physio logical 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 asse ssment. Hum Ecol Risk Assess 12(4):675

− 701.

Brochu, P; Ducré-Robitaille, J; Brodeur, J. (2006b) Physiological daily inhala tion 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. Hu m Ecol Risk Assess 12(4):702

− 735.

CDC (Centers for Disease Control and Prevention). (2 000) 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). (2 002) 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. D ecember 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 Prev ention). (2004) NHANES analytic guidelines

. National Center for

Health Statistics, Centers for Disease Control and Preven tion, Hyattsville, MD. June 2004. Available online at

http://www.cdc.gov/nchs/data/nhanes/nhan es_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 usef ul method to determine aerobic fitness

in children? Med Sci Sports Exerc 32(11):1964

− 1969.

Hebestreit, H; Kriemler, S; Hughson, RL; et al. (1998) Ki netics 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 na tional 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 an d 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/da ta/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 Resear ch 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 Tria ngle 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) CH AD 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 Na tional Exposure Research Laboratory, U.S. Environmental

Protection Agency. Research Triangle Park, NC. Available online at

http://www.epa.gov/chadnet1/r eports/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/r ecordisplay.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/cf m/recordisplay.cfm?deid=199243.

# APPENDIX A:

Revised Ventilation Rate ( V E 

# ) 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 accord ance with U.S. Environmental Protection

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

does not constitute endorsement or recommendation for use.



# Abstract to Appendix A

Using data compiled from 32 clinical exerci se studies, algorithms were developed to

estimate body mass-normalized ventilation rate (V E 

# , 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 gr oups, and variation both between

and within individuals. A multiple linear regr ession (MLR) model was first used to estimate

significant explanatory parameters (p<0.01) fo llowing natural log (Ln) transformation of body

mass (BM) normalized oxygen consumption rate (VO 2 

# ). Log transformed age (Ln(age)), gender

(-1 for males, 1 for females), and Ln(VO 2 

# /BM) served as independent variables and regressed on

multiple V E 

# measurements that were collected during incremental exercise to obtain regression

parameter estimates. The (MLR) model show ed marginal statistical improvement (R 2 

# +5%) in

comparison with a previous simple li near regression model for estimating V E 

# , however the MLR

can estimate population V E 

# with one-half the equations formerly used and can be used to address

uncertainty in V E 

# estimations. A mixed-effects regre ssion (MER) model was then constructed

utilizing the independent variab les as fixed parameters and re taining individuals and study of

origin as random effects variables. The ME R model was used to allocate the random error ( ε ) to

between-person residuals distributions (inter-individual variabili ty) 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 classifi cation (e.g., 45-55 years old) and

estimated ventilation rates for each model were compared at their respective 50 th 

# , 95 th 

# and 99 th



# percentiles. U.S. EPA’s Air Pollution Exposure (APEX) model was used to estimate population

ventilation rates using a variety of ventilati on algorithms for comparison with the MLR and

MER at individual years in age. V E 

# estimations from the MLR and MER algorithms were

similar across all ages and provided reasonable ve ntilation rates at all percentiles and ages,

suggesting either approach is re asonable 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 [V E 

# /BSA]

F i 

# Conversion Factors

HR Heart rate

HT Height

LBM Lean Body Mass (equiv alent to fat-free mass)

METS Metabolic equivalents of work

NAAQS National Ambient Air Quality Standard

NERL National Exposure Research Laboratory

OAQPS Office of Air Qualit y Planning and Standards

Pa CO2 

# partial pressure of arterial carbon dioxide

• • 

# RQ Respiratory quotient ( V

CO 2 

# / V

O 2 

# )

SHEDS Stochastic Human Exposure a nd Dose Simulation model (NERL)

• 

# V

A 

# Alveolar ventilation rate (due to formatting issues, V A 

# is used in report)

• 

# V

CO

s 

# Carbon dioxide expiration rate

V D 

# Dead space volume of the lung

• 

# V

E 

# Total ventilation rate (due to formatting issues, V E 

# is primarily used here)

V T 

# Tidal volume of the lung

• 

# V

O 2 

# Oxygen consumption rate (due to formatting issues, VO 2 

# is primarily used here)

• • 

# VQ Ventilatory equivalent ( V

E 

# / V

O 2 

# )

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 coll eagues who shared thei r 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 inform ation on past practices regarding uptake dose

modeling. Finally, we thank our U.S EPA colleag ue, Dr. James Starr who reviewed this report

and discussed ventilat ion 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-3 1

Attachment A3 ........................................................................................................................ A-355



# List of Tables

Table A-1. Parameter estimates used to estimate activity-specific VO 2 

# for males and

females of different age groups ....................................................................... A-5

Table A-2. Parameter and resi duals 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 (e i 

# ) using Adams data and

assuming equation (3) or (4) ........................................................................... A-9

Table A-4. Residual di stributions standard deviation estimates (e b 

# and e

w 

# ) using

data characterized by percentage of maximum VO 2 

# (VO 2 

# m) assuming

equation (3) ................................................................................................... A-10

Table A-5. Residual di stributions standard deviation estimates (e b 

# and e

w 

# ) using

data categorized by pe rcentage of maximum VO 2 

# (VO 2 

# m) assuming

equation (4) ................................................................................................... A-10

Table A-6. Recommended inhalation rates (L/m in) from U.S. EPA (1997) Table 5-23 A-12

Table A1-1. Total subjects for each study, ge nder, 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 (V E 

# , L/min) for females (left) and males

(right) while performing low-le vel (top), moderate (middle), and

vigorous (bottom) activities .......................................................................... A-13

Figure A-4. Estimated populat ion ventilation rates (V E 

# , L/min) for 20,000 persons

using APEX and the mixed eff ects regression (MER) algorithm

(Equation 4 and Table 3) ............................................................................... A-14

Figure A2-1. Relationship between total ventilation rate (V E 

# ) and oxygen

consumption rate (VO 2 

# ) during exercise ....................................................... A-33

Figure A2-2. Relationship between body mass normalized total ventilation rate

(V E 

# /BM) to oxygen consumption rate (VO 2 

# /BM) during exercise ............... A-33

Figure A2-3. Relationship between the natural logarithm of total ventilation rate

Ln(V E 

# ) and oxygen consumption rate Ln(VO 2 

# ) during exercise .................. A-34

Figure A2-4. Relationship between body mass normalized total ventilation rate

Ln(V E 

# /BM) to oxygen consumption rate Ln(VO 2 

# /BM) during exercise ...... A-34

Figure A3-1. Comparison of selected pe rcentiles of estimated event-based

ventilation rates from 20,000 pe rson 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) (r ight) ventilation algorithms ..................... A-38

Figure A3-3. Percent difference of estimate d 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 (V E 

# , commonly in L min -1 

# ). Some of the early,

more advanced human exposure models were de veloped 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 pr obabilistic NEM (pNEM) models. The

ventilation algorithm became more detailed over time, culminati ng with equivalent ventilation

rate (EVR; V E 

# normalized to body surface area (BSA)) and alveolar ventilation rate (V A 

# )

estimations used by a number of the pNEM models that are described in numerous

OAQPS-sponsored papers and reports (Johns on, 2002; Johnson and Adams, 1994; Johnson and

Capel, 2002; Johnson et al., 1995, 1996; J ohnson and McCoy, 1995; McCurdy, 1994b; and

McCurdy, 1997a). More recently, the National Exposure Research Laboratory (NERL) has

developed the Stochastic Huma n Exposure and Dose Simulation (SHEDS) model, essentially

adopting the ventilation algorithm used in OAQP S’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 th ese fairly complex models, a standard but

flexible algorithm is required. One that not on ly 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 ch emical uptake characteristics (e.g., absorption

across the alveolar membrane ve rsus total absorption). Using current U.S. EPA exposure model

approaches for approximating vent ilation rates and cons idering 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

V

D 

### /V

T



### V

E 

### . 

### F

1 (EE Æ VO

2 )



### V

A 

### . 

### V

O 2 

### .



### RQ/Pa

CO 2 

### F

2 

### BMR METS

VQ 

# * *

=

BM

HT BSA

EVR CHAD

V

D 

### /V

T



### V

E 

### . 

### F

1 (EE Æ VO

2 )



### V

A 

### . 

### V

O 2 

### .



### RQ/Pa

CO 2 

### F

2



## Figure A-1. Pathways for estimating various ventilation parameters and metrics.



# A-1

# Central to the framework is the U.S. EP A’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 develope d from a review of the exercise physiology and clinical nutrition

literatures (McCurdy, 2000) and re present the ratio of the en ergy 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) t ogether. Then activityspecific METS (METS A 

# ) 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 (EE E i 

# ) is defined as:

EE E i 

# = BMR i 

# * METS A



# Estimated EE E i 

# can then be converted to an ac tivity-specific oxygen consumption rate

(VO 2E i 

# ) using a gender-specific relationship expressed as a uniform distribution (F 1 i 

# , L-O 2 

# /kilocalorie) (McCurdy, 2000) as follows:

VO 2E i 

# = EE E i 

# * F 1 i



# VO 2 

# , however, is not the final phys iological process to be simu lated since most air pollution

clinical studies do not use it as the end-point ventilation metric. Most of these studies use V E 

# or

EVR, and some exposure models, particular ly OAQPS’s APEX model for carbon monoxide

(APEX-CO), need V A 

# (commonly in L min -1 

# ) for their inhalation modeling approach. By

definition, V A 

# is a fraction of V E 

# and is important in estimati ng respiratory uptake of gases

(e.g., O 2 

# , CO, CO 2 

# ) and chemicals that likely act as gases (e.g., benzene, 1-3-butadiene

[Lin, et al., 2001]). Regardless, all three mentioned ventilation metrics (V E 

# , EVR, V A 

# ) can be

obtained from VO 2 

# , either directly or indirectly, thus VO 2 

# is fundamental to the development of

each of these ventilation algorithms.

The pathway from VO 2 

# to V E 

# can be direct or indirect, with the indirect approach itself

having a few options: from VO 2 

# to V A 

# and then to V E 

# , or from VO 2 

# to V E 

# using the ventilatory

quotient (VQ or alternatively, the ventilatory equivalent). VQ is simply the unitless ratio of V E



# to VO 2 

# when both metrics are in the same units. Th is ratio is non-linear w ith 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 nuanc es among the many ways

A-2

# that V E 

# and EVR have been estimated over the years, in general the approach taken has been the

VQ pathway depicted in Figure A-1. V A 

# has been estimated by Johnson (2002) using a direct

relationship between VO 2 

# and V A 

# 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 descri bes an approach to estimating V E 

# directly from VO 2 

# using a series of regression-based equations deri ved from 25 years of c linical studies conducted

by Dr. William C. Adams of the University of Ca lifornia at Davis. Much of the work cited

above has been predicated upon past work and da ta 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 hi s laboratory at the University of California-Davis. These data

have been extensively analyzed by OAQPS cont ractors, particularly Ted Johnson of TRJ

Environmental (and previously wi th IT Technology). In additi on 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 Bran ch of NERL review

the literature on calculating V A 

# 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 V E 

# algorithm for use in both th e APEX and SHEDS inhalation

modules. It is this work that is described below.



# 2. Methods

Data Set Description



# The data set acquired is li sted 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 obt ained in electronic format. The number of

subjects included within these studies was n early one-thousand, undoubtedly one of the largest

datasets of its kind. The data se t used was a Microsoft ® Excel (. xls) file obtaine d 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 th is disc was included an ASCII te xt version of the file and the

memo from Dr. Adams de scribing the data set.

The raw data required physical manipulati on 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 form at of the original study data sets, a file was

created containing a single vector for each indi vidual ventilation parameter. Data were then

screened for erroneous and potentially ex treme values. Ventilation parameters (V E 

# and VO 2 

# )

were normalized to body mass and followed with a natural logarithm (Ln) transformation.

Statistical Analysis



# All statistical analyses were performe d using SAS® software, version 8.2.1 (SAS

Institute, Cary, NC). Parameters considered us eful 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 si gnificance (p<0.01) using an analysis of variance

(ANOVA). Then, a simple li near regression (SLR) model was developed of the form y i 

# = b 0 

# +

b 1 

# x i 

# + ε i 

# to estimate parameter coefficients for use in predictive equations:

Ln(V E 

## /BM) i 

## = b 0 

## + (b 1 

## * Ln(VO 2 

## /BM i 

## )) + e i 

## Eq. (1)



# where b 0 

# = the regression intercept, b 1 

# = the regression slope coefficient, and e i 

# representing

individual variability in ventilation rate. The coefficient of determination (R 2 

# ) was used in

evaluating the regression model since it repres ents the proportion of total variance of the

dependent variable “explained” by the independent variables.

The approach was modified slightly for pr edictive purposes to re flect 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 b 0 

# = the intercept and b 1 

# = the

slope regression coefficient:

Ln(V E 

## /BM) i 

## = b 0 

## + (b 1 

## * Ln(VO 2 

## /BM i 

## )) + e bi 

## + e wi 

## Eq. (2)



# It was assumed here that the predictive re gression equation represents a mixed-effects

regression (MER) model containing both fi xed and random effects variables. VO 2 

# was

considered a fixed parameter and subject and st udy were random effects variables used to

estimate the between-person (inter-indivi dual variability) residuals distribution (e b 

# ) and withinperson (intra-individual variabil ity) residuals distribution (e

w 

# ) rather than simply random error

( ε ) alone. Each of the residuals are no rmally 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 pur pose 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; y i 

# = b 0 

# + b 1 

# x i1 

# + b 2 

# x i2 

# +...+ b i 

# x ip 

# + ε i 

# ) was

implemented to include both age and gender as independent variables:

Ln(VE/BM) i 

## = b 0 

## + (b 1 

## * Ln(VO 2 

## /BM i 

## )) + (b 2 

## * Ln(age i 

## )) + (b 3 

## * gender i 

## ) + e i 

## Eq. (3)



# The age of each study subject was transformed by the natural logarithm. Gender was used as a

classification variable, with males represente d 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 

## = b 0 

## + (b 1 

## * Ln(VO 2 

## /BM i 

## )) + (b 2 

## * Ln(age i 

## )) + (b 3 

## * gender i 

## ) + e bi 

## + e wi 

## Eq. (4)



# Statistical significance of estimated coefficien ts and the regression model was assessed at

p<0.01.

A-4

# Modification of the age groupings orig inally developed by Johnson (2002) was

performed to determine if the statistical perf ormance of the predictive equations could be

improved. Criteria for the model developmen t included individual regression coefficient

significance ( p - or t -value), total model explanatory power (R 2 

# ), and stability of the regression

coefficients. For this last crite rion, it was desired that coefficien ts neither greatly increase nor

decrease in the individual regr ession equations compared with previous coefficient estimates

while expanding/compressing age classificati ons. Age groupings were varied by one-year

increments until the evaluation crite ria described above was optimized, that is, models containing

the greatest R 2 

# , with statistically significant coefficients that varied minimally were retained.

Algorithm Evaluation



# Each of the algorithms for estimating vent ilation were evaluated using one or both

methods described below to determine the ra nge 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 additiona l evaluations are provided in Attachment A3.

Ventilation rates were first estimated using Crystal BallTM software (Decisioneering, Inc.,

Denver Colorado). Age- and gender-specific bo dy weights for simulate d individuals were

estimated by probabilistic sampling of distributions provided by Burmaster and Crouch (1997).

Basal metabolic rate was estimated using age- and gender-specific e quations presented in

Schoefield (1985), with age itself being sample d from uniform distributions within the age

groupings used in our analyses. Activity-specific VO 2 

# was generated using METS distributions

for low, moderate, and vigorous in tensity activities combined with the unit conversions given in

Table A-1. Ventilation rates we re estimated for 5,000 hypothetical persons within each age (or

age grouping) and gender categor y using predictive equations (3 ) and (4) and their respective

parameters. To estimate variability in ventilatio n 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), 95 th 

# (p95), and 99 th 

# (p99) percentiles of the hypothetical population

distribution of estimated ventil ation rates were compiled by age. The output represents the

possible range of expected ventilation rate s across the population at a moment in time.

Table A-1

. Parameter estimates used to estimate activity specific VO 2 

## for males and females

of different age groups. 1



## Age group Gender METS-Activity Level 2 

## Conversion Factors

Low Moderate Vigorous Energy to

Oxygen

(L-O 2 

## /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 deviati on of the METS for each distribution was 17% (see

McCurdy and Graham, 2004)



# A-5

# A second method for evaluation was conduc ted 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 comparis on of selected ventilat ion algorithms developed

as would be used in an actual exposure model. Activity-specific ventilat ion 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 mu ch of the data are further divided such that

within one hour there may be multiple activit ies performed or multiple locations inhabited,

upwards to 1 minute in duration. Since every simulated individua l had multiple estimations for

ventilation rate depending on thei r 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 (genera lly between 1 and 400 persons were simulated for each year of

age). The median (p50), 95 th 

# (p95), and 99 th 

# (p99) percentiles and maximum ventilation rates

estimated with the APEX model represent the variability in th e mid-upper range of ventilation

rates for individuals within a population. It shoul d be noted that the maxi mum for all individuals

is the same as the 99 th 

# 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 de velopment of several regression equations

derived from the Adams data set and summari zed in Table 9-1 of Johnson (2002); however

significance of these variables wa s not reported there. An anal ysis of variance was performed

here on V E 

# , utilizing the 4 age groups (i.e., <18, 18-44, 45-64, >65 years old) and two genders as

classification variables indi cated by Johnson (2002). VO 2 

# normalized to body mass was

included as an additional indepe ndent variable. Age group, gender, their interaction term (age

group by gender), and VO 2 

# were each significant explanat ory parameters (all p<0.003).

Results of the simple linear regression anal ysis, 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. Regressi on 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 th is work; both the interc epts and slopes were

systematically lower for the simple regression. The results from the simple mixed model and

Johnson (2002) were nearly identi cal with the most notable diffe rences seen in the residuals

distributions, albeit at a minimal level.

Following this single variable model comp arison, 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 a nd, since its use as a parameter in a multiple

linear regression would halv e the number of equations needed for ventilation simulations, was to

be included as a parameter in the regression m odel. For age, it was hypothesized that it would

A-6

# have a statistically significant e ffect on the relationship between V E 

# and VO 2 

# , not just among the

different age groups but also within a given age group. Figur e A-2 shows the relationship

between VQ and age, with the most notable vari ation 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 th e females, although not st atistically 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 furt her culling of the data set was warranted.

Observations of VQ in excess of 50 were removed based on a review of th e 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 cu lling was negligible (not reported).

Table A-2. Parameter and residuals distri bution estimates derived from two different

statistical techniques and reported from Johnson (2002) for use in predictive

equation (1) or (2).

Age

group n Gender Method a 

## Ln(VO 2 

## /BM) Residuals

R 2 

## b 0 

## se b0 

## b 1 

## se b1 

## e b 

## e

w



## <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 RE G in SAS) when using equation (1); MER: mixed

effects regression model (PROC MIXED in SAS) when using equat ion (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 fi nal 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, resp ectively) were first evaluated. Based on the

criteria described above, the lo wer and upper age groups were rede fined 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 crite ria), 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 othe r age groups. It is not apparent whether this

response is physiologically represen tative 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 us e in equations (3) or (4) following age group

optimization are presented in Table A-2. Sli ghtly 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 ea rlier analyses (on average 85%). Each of the

regression models and all estimat ed coefficients were statisti cally significant (p<0.01) except

where noted.

A-8

## Table A-3. Ventilation parameter estimates (b i 

## ), standard errors (se), and residual

distributions standard deviation estimates (e i 

## ) using Adams data and assuming

equation (3) or (4).

Age

group n Method a 

### Ln(VO 2 

### /BM) Ln(age) Gender Residuals

R 2 

### b 0 

### se b 0 

### b 1 

### se b 1 

### b 2 

### se b 2 

### b 3 

### se b 3 

### e b 

### e

w



### <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 0.0380 b 

### 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 -0.02081 c 

### 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 evaluati on, an analysis of the residua ls distributions was first

undertaken in a manner that mimicked the wa y 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 ra nge. 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 slee ping. Since resting measurements were not

collected by Dr. Adams for most of his subjects, an evaluation of the data bracketed by percent

of maximum VO 2 

# (VO 2 

# m) was decidedly appropriate in dete rmining whether the data could be

extrapolated downward to reasonably simulate lo w energy-expenditure activ ities. Typically VO 2 

# reserve (VO 2 

# res) is used; however, this was not measur ed in the Adams’ studies. A tripartite

categorization of the measured VO 2 

# for a step relative to the VO 2 

# m of each subject was

undertaken using <33.3%, 33.3-66.6%, >66.6% of VO 2 

# m as the category boundary values. This

categorization has been done previously based on intervals of low, moderate, and vigorous

exercise and recently summarized from the exercise physiology lit erature (McCurdy and

Graham, 2004). Residuals distributions were es timated using the multiple linear regression and

mixed models as was done above [equations (3 ) and (4)], but now account ing 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 indica tes there is less variability in ventilation rate at

the low and moderate exercise levels.

A-9

## Table A-4. Residual distributions standard deviation estimates (e b 

## and e

w 

## ) using data

categorized by percentage of maximum VO 2 

## (VO 2 

## m) assuming equation (3).

Age

Group <33.3% V0 2 

## m 33.3-66.6% V0 2 

## m >66.6% V0 2 

## m

e i 

## x n e i 

## x s e i 

## 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 V0 2 

## samples subjects had within each age group and tripartite

categorization.



# For the mixed model, between-person residuals (e b 

# ) were generally hi gher and the withinperson variability was lower for all age groups using the tripartite br eakdown (Table A-5)

compared to the residuals distri butions 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 in tuitively 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 s ubjects in each exercise

category because some of the individuals di d not attain a level of exercise >66.6% VO 2 

# m;

however, this was not the principal reason for the observed residual differences since

consistently even fewer individuals were measured at exercise <33.3% VO 2 

# m (Tables 4 and 5).

In addition, more measurements were cons istently obtained for exercise >66.6% VO 2 

# m on

average per person than at the low or moderate levels of exercise.

Table A-5. Residual distributions standard deviation estimates (e b 

## and e

w 

## ) using data

categorized by percentage of maximum VO 2 

## (VO 2 

## m) assuming equation (4).

Age Group <33.3% V0 2 

## m 33.3-66.6% V0 2 

## m >66.6% V0 2 

## m

e b 

## e

w 

## e b 

## e

w 

## e b 

## e

w



## <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 i ndividual’s ventilation rate over al l 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, ag e groups, or genders. Usi ng 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 lo wer 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 reduc tion in efficiency (i.e., underdevelopment) of

their respiratory systems. However, since the natu ral 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 paramete rs (evaluation method 1) and/or by using the

algorithm in an actual exposure model (evalu ation method 2). When simulating multiple

activities for one individual and for a popul ation, alternative sampling strategies are

recommended below for estimating variability and uncertainty. Ventilati on rates estimated using

the general input parameter distributions (evalu ation 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 ex ertion levels and at the various mid to upper

percentiles, however some trends were note d. Even though each simulation is independent,

comparisons of the average percent difference at selected percentiles fo r 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 (avera ge percent difference of

between 3.5-4.0%) than those estim ated 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 estimate d 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 eith er approach is acceptable for use in estimating

ventilation rate, but that the ME R model may be slightly more re sponsive to changes in activity

level and better capture variability in ventilation rates, specifi cally when using the intra- and

inter-personal residuals. Overall female ve ntilation 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. Ventilati on rates for males ranged higher for the varying

activities, with 10 to 35 L/min, 25 to 110 L/mi n, 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 AP EX model. Results for the 20,000 person

simulation of both genders are presented in Figure A-4. At any given percen tile, ventilation rates

increase rapidly with age for persons less than 20 years old, stabilize fr om 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 indivi duals spans by about a f actor of 5 or more,

A-11

# A-12 depending primarily on age. Values at older ag es 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 summa rized by U.S. EPA (1997), but much of the

data presented here are in fact approximations to that report utilizi ng similar approaches.

Table 5-6 in U.S. EPA (1997) contains somewh at 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 extens ively 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 re flective of "average" or likely inhalation rates

and are generally comparable to the me dians 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 53.3Percentiles of Estimated V E for Females Performing Low-Exertion Activities Using Selected

Parameter Distributions

0 5 10 15 20 25

0 2 0 4 0 6 0 8 0 1 0 0

Age (years)VE(L/Min) p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER Percentiles of Estimated V E for Males Performing Low-Exertion Activities Using Selected

Parameter Distributions

0 5 10 15 20 25 30 35 40

0 2 0 4 0 6 0 8 0 1 0 0

Age (years)VE(L/Min) p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Percentiles of Estimated V E for Females Performing Moderate-Exertion Activities Using

Selected Parameter Distributions

0 25 50 75

0 2 0 4 0 6 0 8 0 1 0 0

Age (years)VE(L/Min) p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER Percentiles of Estimated V E for Males Performing Moderate-Exertion Activities Using Selected

Parameter Distributions

0 25 50 75 100 125

0 2 0 4 0 6 0 8 0 1 0 0

Age (years)VE(L/Min) p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER

Percentiles of Estimated V E for Females Performing Vigorous-Exertion Activities Using

Selected Parameter Distributions

0 25 50 75 100 125

0 2 0 4 0 6 0 8 0 1 0 0

Age (years)VE(L/Min) p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER Percentiles of Estimated V E for Males Performing Vigorous-Exertion Activities Using Selected

Parameter Distributions

0 25 50 75 100 125 150 175

0 2 0 4 0 6 0 8 0 1 0 0

Age (years)VE(L/Min) p50-MLR

p95-MLR

p99-MLR

p50-MER

p95-MER

p99-MER



## Figure A-3. Estimated ventilation rates (V E 

## , L/min) for females (left) and males (right) while

performing low-level (top), moderate (middl e), 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-13p99 V E 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 V E 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 V E 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 (V E 

## , L/min) for 20,000 persons using APEX

and the mixed effects regression (MER) algor ithm (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 exposur e modeling purposes, the regression equation

coefficients listed in Table A-3 be used with equation (3) or (4) to es timate activity-specific body

mass-adjusted V E 

# for simulated individuals in the ag e 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 V E 

# 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 ye ar old are not to be

approximated (i.e., persons with age<1 can be estim ated as one year old or using an alternative

approximation). In addition, we suggest that indi vidual 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 individua l variability, with the MER model used when

addressing inter- and intra-individual variability . Inter-individual variability is addressed

through selection of between-person residuals (e b 

# ) once per simulated individual. Intraindividual variability is addressed through selection of within-person residuals (e

w 

# ) every time an

individual undertakes an activity. To addre ss uncertainty, we recomm end that additional

simulations should be undertaken using the standa rd errors (se) of the regression coefficients

themselves to address measuremen t error and unobser ved variability.



# 5. Future Research

As mentioned earlier, a method for estimating V A 

# to remain consistent with the V E 

# estimation is currently being investigated by both NERL and OAQPS. Currently, the pathway

from VO 2 

# to V A 

# is considered as a direct linear propor tionality (i.e., a constant value of 19.63)

and estimated independently from V E 

# . A preliminary literature review indicates that the

approximation is reasonable and may be linear fo r low to moderate exercise levels, but at a

minimum, there is variability in V A 

# at all exercise levels that is not accounted for by the point

estimate used to modify VO 2 

# . Further investigation is ne eded to determine if the VO 2 

# to V A 

# relationship is maintained for vigorous activity levels. In addition, the lack of a direct

computational link with V E 

# potentially can lead to simulated values of V A 

# in excess of V E 

# , a

physiological impossibility.

One potential method would be to estimate V A 

# from V E 

# by using another physiological

relationship: the ratio of dead space volume-to-tidal volume (V D 

# /V T 

# , see Figure A-1).

Physiological dead space is the vol ume of the lung that does not take part in gas exchange and is

comprised of basic anatomic dead space (e.g. vo lume 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 vo lume is the total amount of air breathed upon

inspiration, not all of which come s in contact with the alveolar region of the lung due to the

presence of physiologic dead sp ace. It has been found that V D 

# /V T 

# does not remain constant over

varying exercise levels, with V T 

# increasing at a greater rate than V D 

# during increasing exercise

level. The effect of this non- linear relationship in simulating V A 

# (does V A 

# increase linearly with

increasing VO 2 

# at all exercise levels?) has not yet b een determined. The relationships of V E 

# ,

A-15

# V D 

# /V T 

# , and VO 2 

# with V A 

# and other ventilation parameters (e.g., the respirator y quotient or RQ)

will be explored in greater detail and integrated in a second report.



# 6. References

Adams WC. 1993. Measurement of Breathing Rate and Volume in Routinely Performed Daily

Activities. Davis CA: University of California.

Adams WC, Shaffrath JD and Ollison WM. 199 5. The relation of pulmonary ventilation and

heart rate in leg work alone, arm work alone, and in combined arm and leg work. Paper WA84A.04 presented at the Annual Meeting of the Air & Waste Management Assoc.

Baba R, Mori E, et al. 2002. Simple exponent ial regression model to describe the relation

between minute ventilation and oxygen uptake du ring incremental exercise. Nagoya J Med

Sci. 65: 95-102.

Burmaster DE and Crouch EAC. 1997. Lognormal di stributions for body weight as a fucntion of

age for males and females in the United States, 1976-1980. Risk Anal. 17(4): 499-505.

Galletti PM. 1959. Les echanges respiratoires pendant l'exercice musculaire. Helv Physiol

Acta. 17:34-61.

Johnson T. 1995. Recent advances in the estim ation of population exposure to mobile source

pollutants. J Exp Anal Environ Epidemiol. 5: 551-571.

Johnson T. 2002. A Guide to Selected Algorit hms, Distributions, and Databases Used in

Exposure Models Developed by the Office of Ai r Quality Planning and Standards. Chapel

Hill: TRJ Environmental.

Johnson T and Adams WC. 1994. An algorithm for determining maximum sustainable

ventilation rate according to gender, age, and exercise duratio n. Unpublished paper.

Johnson T and Capel J. 2002. User’s Guide: Soft ware for Estimating Ventilation (Respiration)

Rates for Use in Dosimetry Models. Chapel Hill NC: TRJ Environmental.

Johnson T, Capel J, McCoy M, and Warnasch J. 1996. Estimation of Ozone Exposures

Experienced by Outdoor Childre n in Nine Urban Areas Usin g a Probabilistic Version of

NEM. Durham NC: IT Corporation.

Johnson T and McCoy M. 1995. A Monte Carlo Approach to Generating Equivalent

Ventilation Rates in Population Exposure A ssessments. Washington DC: American

Petroleum Institute (API Publication # 4617).

Johnson T, McCoy Jr. M, and Ollison W. 1995. A Monte Carlo approach to generating

equivalent ventilation rates in populati on exposure assessments.” Paper 95-TA42.05

presented at the annual meeting of the Ai r & Manage. Waste Assoc.; San Antonio TX.

A-16

# Johnson T and Mihlan G. 1998. Analysis of c linical data provided by Dr. William Adams and

revisions to proposed probabili stic algorithm for estimating ventilation rate in the 1998

version of pNEM/CO. Memo to A. Rosenba um, Systems Applications International.

Lin Y-S, Smith TJ, et al. 2001. Human physio logic factors in respiratory uptake of 1,3-

butadiene. Environ Health Perspect. 109(9):921-926.

McArdle WD, Katch FI, and Katch VL. 1991. Exercise Physiology . 3rd Ed. Philadelphia: Lea

& 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,

Research Triangle Park NC (April 20).

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

disaggregated, sequential approach and alte rnative recommended approaches. Paper

presented at the 7th Annual Meeting of the International Society of Exposure Analysis.

McCurdy T. 1997b. Human activities that may l ead to high inhaled intake doses in children

aged 6-13. Environ Tox Pharm. 4: 251-260.

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 An al Environ Epidemiol. 10: 86-97.

McCurdy T. 2001. Research Note: Analyses to underst and 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 Databa se. J Exp Anal Environ Epidemiol. 10:

566-578.

McCurdy TR and Graham SE. 2004. Analyses to understand relationships among physiological

parameters in children and adoles cents aged 6-16. EPA/600/X-04/092.

Nieman DC. 1999. Exercise Testing and Pr escription. A Health-Related Approach. 4 th 

# edition.

Mayfield Publishing Comp any, Mountain View, CA.

A-17

# A-18 Schofield WN. 1985. Predicting basal metabolic rate, new standards and review of previous

work. Human Nutrition: Clini cal Nutrition. 39C(Supp1):5-41.

U.S. EPA. 1997. Exposure Factors Handbook. Nati onal Center for Environmental Assessment,

Office of Research and De velopment, 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 electro nic 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 phy siological data that should be very useful for estimating VE

and respiratory intake dose. It is in an Excel (5.0a) spread sh eet 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 pr oposal 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 cy cle ergometer or on a motor driven treadmill.

Missing study numbers from the original propos al 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 subj ects exercised on a treadmill (21.2).

The total number of subjects with mu lti-stage, steady-state corresponding VO 2 

# and V E 

# values,

including those at VO 2 

# max, 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 runni ng protocol. In a ddition, steady-state VO 2 

# and V E 

# values at several

submaximal workloads on the treadmill were ava ilable 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. Ho wever, each (except for Study 30) was designed

to obtain at least near steady-st ate physiologic response at progre ssively intensified work rates

ranging from light, or modera te, 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= tr ained 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 VO 2 

# max (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. V E 

# (l/min BTPS) measured during the last minute of each step

d. VO 2 

# (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 1 4 8 1 4 8 0 0 0 1 4 8 0

2 4 2 4 2 0 0 0 4 2 0

5 60 30 30 0 0 30 30

6 1 2 6 6 0 0 6 6

7 4 0 4 0 4 0 0

8 6 6 0 6 0 0 0

9 1 0 1 0 0 1 0 0 0 0

1 0 8 8 0 8 0 0 0

12 10 10 0 10 0 0 0

1 3 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

2 3 1 7 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

3 1 2 0 0 2 0 0 0 0 2 0

32 10 10 0 10 0 0 0

33 40 20 20 20 20 0 0

3 4 1 0 6 4 6 4 0 0

3 5 6 3 3 3 3 0 0

3 6 1 2 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 m easurement for all entr ies were used throughout the file. For machine

setting, two columns were needed for treadmill test s, one each for speed and percent grade, while

A-20

# only one (work rate in Watts) was required for Qu inton 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 onl y 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 valu es (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 identifi ed, they were eliminated from transfer to the

electronic data base. I also noted any missing da ta 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 electron ic data base was thoroug hly 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 wa s being said was what

appeared on the spreadsheet. Erro rs 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 i n, 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 de veloping a publishable paper or two.

Best regards,

William C. Adams

Professor

# Second memo from Dr. Adams to T. Mc Curdy describing additional data from

study #30

8 October 2001

Dear Tom:

Pursuant to the U.S. EPA Order for Supplie s and Services, No. 1D-5590-NATX, approved for

the period, 1 August - 1 Novemb er 2001, I believe that I have now completed all professional

services stipulated. Specifically, it was requested that I provide certain “r aw” 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 Br eathing Rate and Volume in

Routinely Performed Daily Activities (Adams, 1993). The professional services stipulated

included: 1) providing a complete listing of all variab les that were obtaine d during the study in

accordance with the attached Statem ent of Work; 2) the development of an electronic data base

of selected physiological information for child ren and adolescents from the aforementioned

study, again in accordance with the attached Stat ement of Work; and 3) the submittance of a

transcribed data file for the aforementioned stu dy in ASC II format, together with a description

of data quality objectives that were establishe d in accordance with the attached Statement of

Work.

The subject pool of interest included 132 individua ls, 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 ad olescents, 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 activ ity 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 rest ed in a lying position). The 12 young children each

did two spontaneous play protocols of 20 minut es 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 adolescen t subjects were not asked to

perform a spontaneous play protocol. In add ition, each subject (or their parent/guardian)

completed an 11-item health history questionnaire.

Enclosed is a 3.75 ZIP disk which includes the elect ronic data base contai ning 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 f ile with headin gs. Consistent

units of measurement for all entr ies 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 VO 2max 

# values from measured submaximal HR and VO 2



# 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 VO 2max 

# (l/min, STPD) for the test - NOTE: VO 2max 

# 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 fo r treadmill in speed (m/min) and one for

percent grade). The latter was always zero.

c. V E 

# (l/min BTPS) measured during the last two minutes of each step

d. VO 2 

# (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 mete rs; 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. V E 

# (l/min BTPS) measured during the 5 minutes of each test

b. VO 2 

# (l/min, STPD) measured for the 5 minute of the test

c. average of five HR (b/min) measuremen ts 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. V E 

# (l/min BTPS) measured during the 5 minutes

b. average of five HR (b/min) measurem ents taken each minute of the 5 minute

period

c. average of five breathing frequenc y (breaths/min) measurements taken each

minute of the 5 minute period. NOTE: Because these data were obtained on a tape

cassette that rather routinely malfunc tioned, 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 we re to indicate in th e comments column;

e.g., any problems with the equipment, wh at the subject was playing, and/or an

estimation of the intensity of activity. The occasio nal noted problems with

equipment were dealt with as descri bed 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 implem ent, 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 V E 

# 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. Nume rals in column 4 correspond

to which of 5 choices were circled. In si x 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 circ led. No one circled No. 1 (none). Most

subjects circled more than one choice, whic h 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 ot her activities they e ngaged in (10, play; 11,

dance; 12, horseback riding; 13, gymnastics; 14, rollerblading; 15, karate; 16, ice skating;

17, aerobics (high impact); 18, aerobics (machin es 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 onl y one choice was circled. If more than one

choice was indicated, higher numbers were us ed as follows: 13, c hoices 7, 9, and 10; 14,

choices 9, 10, and 11; and 15, choices 10 and 11.

A-27

# PREDICTION OF VO 2MAX 

# FROM SUBMAXIMAL MEASURED HR AND VO 2 

# 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 HR

max



# 6. VO 2max 

# y intercept

7. VO 2max 

# b exponent

8. predicted VO 2max 

# (l/min)

9. predicted VO 2max 

# (l/min/kgBM)

The rationale for predicting percent VO 2max 

# at any given percent HR

max 

# is developed in brief on

p. 403 of McArdle et al.’s exercise physiology text (4th ed., 1996) a nd 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 cl osely similar submaximal % VO 2max 

# values as a function of %

HR

max 

# 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 VO 2max 

# prediction from measured

submaximal HR and VO 2 

# , see Fig.10-4 (line A), p. 346, in As trand and Rodahl. To use this

procedure, it is first nece ssary to obtain a valid HR

max 

# value which decreases an average of 1

b/min each year of age from 10 years on. Th e best data I’m aware of on young children and

adolescents that had HR and VO 2 

# measured in both submaximal and maximal treadmill exercise

is that of Astrand ( Experimental Studies of Physical Work ing Capacity in Relation to Sex and

Age , 1952, Ejnar Munksgaard, Copenhagen). Between th e ages of 4 and 10 years, there was no

significant relationship between HR

max 

# 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 girl s averaging 210 b/min. Accordingly, in File #5,

the estimated HR

max 

# 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 VO 2max 

# were obtained by calculating, via simp le regression analyses, individual

subject values from measured submaximal HR and VO 2 

# values taken from File #1. Predicted

VO 2max 

# (in l/min), given in column 8 for each subject, was obtained by multiplying the b

exponent value (column 7) times the estimated HR

max 

# value (column 5) for each subject, and

then subtracting their y intercept value (column 6). Each subject’s VO 2max 

# value in ml/min/kg

(column 9) was calculated by dividing th e column 8 value by body mass (column 3).

Accuracy of the data in the enclosed electroni c 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 ba ses, 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 pl ay protocols (due to the result of momentary saliva blockage in

the Harvard respirometer, Heart Watch heart rate arti facts, 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, f B data. This occurred because there was no

way to determine whether the expiration electr onic 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 f B was not of such prime concern as HR and V E , 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 tr anslated 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 origin al CARB study computer data file s 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 fi ve files differed somewhat and are described in

detail here. For the active file , a copy of the 1998 Excel file wa s made and all data not from

Study #30 for the 132 subjects of inte rest were deleted. A search of the original 1998 Excel file

was done, and a print out of these data obtain ed (i.e., pp. 14-18, 36- 40, 58-62, and 80-84). All

entries in the 2001 file were double-checked against the 1998 pr int-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 tr ansferring data from

summary CARB study electronic data fi les (in a similar, bu t 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 differe nce seen. As a further cross-check, I then

calculated entire group (N = 132) m eans for each posture in the pres ent file, and compared these

values to weighted tabular mean values in th e 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) en tailed entering V E 

# , HR, and breathing

frequency data from handwritt en 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 pe riod, and compared these values to weighted

tabular mean values in the original Final Repor t. 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 co rrect subject and time period.

Procedures used for establishing the health hist ory data base from handwritten responses to a

questionnaire, together with how data was entere d in each column, are described above. The data

were typed directly into the el ectronic file (#4) for each subject from the handwritten responses

on the questionnaire. The numerical values ente red were double-checked for each question (#s 1,

2, 3, 7, and 11) for each subject immediat ely after each subject’s data entry.

Procedures used for pred icting each subject’s VO 2max 

# from submaximal HR and VO 2 

# 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 transfe rred directly from File #1 and a mean, with

standard deviation, calculated fo r each column which matched those previously calculated in File

#1. The individual submaximal HR and VO 2 

# values entered into a STATVIEW simple

regression analysis were each double-checked before each individual analysis was done. The

resultant y intercept and b expo nent values were written on a printout of the subjects’

submaximal HR and VO 2 

# 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 VO 2max 

# value < 33 or > 66 ml/min/kg was double-checked. In no case was

an error found. Please note that 18 s ubjects only had 3 sets of submaxim al 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 VO 2 

# values was sufficient (in my estima tion) to obtain valid predicted VO 2max 

# values. Thus,

I recommend deleting the predicted VO 2max 

# values for these four subj ects. If this is done, the

mean VO 2max 

# for the group is 47.63 ml/min/kg, a value th at 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 repor t letter contains additional info rmation beyond the electronic data

base that you wanted and clar ifies 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. Principall y, the row and column structure of the file had to be altered to put them into

proper alignment. Row headings we re scattered within rows of th e 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 V E 

# ) over multiple

stages of the test (V E1 

# , V E2 

# , V E3 

# , etc.) were carried across the dataset in multiple columns. It was

desired to have the multiple measurements as a si ngle 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 head ing 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 rem oved since there were no parameters

supplied for this step (e.g. V E14 

# , VO 2 14 

# , etc.).

o Common data were recoded in to 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., s p13). 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 ch aracters. 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., V E1 

# , V E2 

# , V E3 

# , etc.) were combined

under a single vector (e.g., V E 

# ) to create a second SAS data file: adams2.sas7dbat. A new

variable was created to account for the mu ltiple measurements for a given parameter

termed ‘step’ (e.g., step=1 is for where V E 

# and VO 2 

# were first recorded; step=2 for the

second measurement of V E 

# , etc.).

• This data set contained a total of 19 variables:

o Step Step or stage measurem ent 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 VO 2 

# (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 V E 

# (L min -1 

# )

o VO 2 

# m Observed or estimated VO 2 

# maximum for the test (L min -1 

# STPD)

o Wk Watts (power setting for the cycle ergometer)

Maximum VO 2 

# (VO 2 

# m) was reported for all of the studies but one. Study 30 contained estimates

of VO 2 

# m for some of the data (individuals < 18.9 y ears old) however the study also contained 79

individuals where VO 2 

# m was neither measured nor estima ted. The method reported by Adams

(see Appendix B) to estimate VO 2 

# m 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 VO 2



# . The slope ( m ) and intercept ( b ) estimates were then used to approximate VO 2 

# m from the HRm

estimate and added to the final data set.

Quality Assurance. Data values--mostly V E 

# , VO 2 

# , 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 f ound in either of the SAS data sets. The number

of individuals in the newly created data sets wa s each 956, equivalent to that reported by Dr.

Adams upon transfer of the data set (in Appendi x A) and the total number of measurements of

V E 

# and VO 2 

# for individuals >18 years old was equivalent (n=5,681) to that reported by Johnson

(2002).

A simple plot of the body mass-normalized to tal 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 consumpti on levels during one sample collection. These

data were considered to be que stionable, and upon inspection s eemed 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 re flect this assumption rather than delete the

datapoints altogether, even though there is no dir ect evidence that the decimal was misplaced.

Due to the number of samples for a given paramete r 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.sas 7dbat’) and ‘adams4.sas7dbat’(from data set

‘adams2.sas7dbat’).

A-32

# A-33 Data Transformation . Figure A2-1 shows the relationshi p between total ventilation and oxygen

consumption rates. In general, the relationshi p 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 resear chers (e.g., Baba et al. 2 002). Normalization of V E 

# and VO 2 

# by body

mass is commonly done to account for a portion of the variability inhe rent between the two

physiological measures (Figure A2-2).

Figure A2-1. Relationship between total ventilati on Figure A2-2. Relationship between body

rate (V ) and oxygen consumption rate (VO 2 

## ) mass normalized total ventilation rate E 

## during exercise. (V /BM) to oxygen consumption rate E 

## (VO 2 

## /BM) during exercise.



# Due to the non-linear relationship between V E 

# and VO 2 

# , a number of the parameters were

transformed by taking the natural logarithm (Ln) of the variable. These include:

• Ln(V E 

# ) natural log of V E



# • Ln(VO 2 

# ) natural log of VO 2



# • Ln(V E 

## /BM) 

# natural log of body mass normalized V E



# • Ln(VO 2 

## /BM) 

# natural log of body mass normalized VO 2



# • VQ ventilatory equivalent or V E 

# / VO 2



# • Ln(age) natural log of age

A logarithmic transformation direct ly 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 (Figur e A2-4), that is, it better demonstrates a

degree of homoscadisticity. It should be noted that this lin earity and balanced dispersion was

also demonstrated among different age groups investigated in th e body of the report.

## Figure A2-3. Relationship between the natural Figure A2-4. Relationship between the

logarithm of total ventilation rate Ln(V ) and natural logarithm of body mass E 

## oxygen consumption rate Ln(VO 2 

## ) during normalized total ventilation rate

exercise. Ln(V /BM) and oxygen consumption rate E 

## Ln(VO 2 

## /BM) during exercise.



# A-34

# Attachment A3 to Appendix A

Selected ventilation algorithms were eval uated 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 pa rameters in Tables 2 and 3. Model output was

nearly 800,000 event-based ventilation rates, typi cally 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 ra tes 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 eq uation 4 and parameters from Table A-3.

MLR+MER : regression coefficients from MLR c oupled with variance components estimated

from the MER model.

Johnson : Johnson (2002) regression model using equa tion 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:

b 0 

## Ln(VO 2 

## /BM) Residuals

b 1 

## e b 

## e

w



## 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 gi ven percentile, ventilati on 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 va riability at ages greater than 75 is also evident,

a function of both the limited amount of data ava ilable for the development of the algorithm and

the limited number of persons simulated at th ese ages from the population of 20,000. At each of

the percentiles, the Johnson (2002) algorithm gene rated lower ventilation estimates for persons

under age 5, a function of the method of the algorithm derivation, wher eas the intercept was

modified based on published literature VE/VO2 re lationships while the re siduals were assumed

the same as those greater than 18 years of ag e. 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 child ren that resulted from ignoring age as an

independent variable in developm ent of the regression parameters.

Figure A3-2 presents the full range of percentile s for the event-based ve ntilation rates generated

from the APEX model using the mixed effect s regression (MER) mode l and the Johnson (2002)

model. Results are very similar, however at young ages (<5 years old), the Johnson (2002)

model estimates lower ventilati on 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 th e 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 a bout 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 ventilationrates from 20,000 person APEX model simulationusing different ventilation algorithms.



# A-37max VEfrom events of 20,000 person APEX simulation0204060801001201401600 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)MLR+MER-maxMER-maxJohnson-maxMLR-maxSMER-maxp99 VEfrom events of 20,000 person APEX simulation01020304050600 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)MLR+MER-p99MER-p99Johnson-p99MLR-p99SMER-p99p95 VEfrom events of 20,000 person APEX simulation0510152025303540450 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)MLR+MER-p95MER-p95Johnson-p95MLR-p95SMER-p95p50 VEfrom events of 20,000 person APEX simulation02468101214160 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)MLR&MER-p50MER-p50Johnson-p50MLR-p50SMER-p50Figure A3-2. Comparison of estimated event-based ventilationrate percentiles from 20,000 person APEX model simulation using mixedeffects regression (MER-left) and Johnson (2002) (right) ventilation algorithms.



# A-38Mixed Model Estimates of VEfrom for 796,714 events from 20,000 person APEX simulationLower Percentiles05101520250 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)MER-p50MER-meanMER-p5MER-p1MER-minJohnson Model Estimates of VEfrom for 799,355 events from 20,000 person APEX simulationLower Percentiles05101520250 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)Johnson-p50Johnson-meanJohnson-p5Johnson-p1Johnson-minMixed Model Estimates of VEfrom for 796,714 events from 20,000 person APEX simulationUpper Percentiles0204060801001201400 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)MER-p50MER-meanMER-p95MER-p99MER-maxJohnson Model Estimates of VEfrom for 799,355 events from 20,000 person APEX simulationUpper Percentiles0204060801001201400 10 20 30 40 50 60 70 80 90 100age (years)

V E (L/min)Johnson-p50Johnson-meanJohnson-p95Johnson-p99Johnson-maJohnson-max

# A-39Figure A3-3. Percent difference of estimated event-based ventilation rate percentiles from 20,000 person APEX model simulationusingmixed effects regression (MER-left) and Johnson (2002) (right) ventilation algorithms.-140-120-100-80-60-40-200204060800 10 20 30 40 50 60 70 80 90 100Age (years)

% difference (Johnson-MER)/(Johnson+MER)*200maxp99p95p50p5p1min

# APPENDIX B

STATISTICAL DISTRIBUTIONS A SSIGNED TO ACTIVITY CODES

FOR USE IN SIMULATING METS VALUES

# LIST OF TABLES

B-1. METS Distributions assigned to ac tivity ID codes within CHAD ..................................B-4

B-2. Activity codes whose METS Distribu tions 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 code s 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 C HAD 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 di stribution. For a given activity ID code, the CHAD randomly

generates a METS value from the specified distri bution. If “Truncate Left Tail?” equals “Y,”

then any METS value falling below the distribut ion’s specified minimum was set to equal the

minimum. Likewise, if “Truncat e Right Tail?” equals “Y,” thenany METS value falling above

the distribution’s specified maximu m was set to equal the maximum. Truncation of the left and

right tails occurred with the normal and lognormal distributions, while trun cation 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 valu es. Note that truncat ion did not affect the

initial random generatio n of METS values (i.e., randomi zation did not occur on truncated

distributions).

Activity ID codes followed by “*” in Tabl e 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 se t. 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 distribut ion to the activity.

Because the occupation of the NHAPS participants was not specified in the activity data records

within CHAD, the available information with in CHAD was not suffici ent 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 “sim ilar” in description to the code and assign that

activity’s METS distribution to the code. Table B-2 specifi es 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

j og (not in

transit) 17131 20 LogNormal 5.8 5.5 1.8 1.8 11.3 Y Y

Walk, bike, or

j og (not in

transit) 17131 30 Normal 5.7 5.7 1.8 2.1 9.3 Y Y

Walk, bike, or

j og (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?

17220 Listen to

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 = ex ecutive/administrative/managerial; PROF = professional;

TECH = technicians; SALE = sales; ADMSUP = admi nistrative support; HSHLD = private household;

PROTECT = protective services; SERV = service; FA RM = farming/forestry/fishing; PREC = precision

production/craft/repair; MACH = mach ine 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 162 00 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 Ot her 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 wei ght (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 (m 3 

# /day) in males, by age

category ...................................................................................................................... ......C-5

C-2b. Descriptive statistics for da ily average ventilation rate (m 3 

# /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 cate gory, by age and gender categories ..............................C-7

C-4. Descriptive statistics for average ve ntilation 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 vent ilation 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 fo r daily ventilation rate (m 3 

# /min), unadjusted for body

weight, while performing activities within the specified activity category, by age

and gender categories .....................................................................................................C-22

C-7. Descriptive statistics fo r daily ventilation rate (m 3 

# /min-kg), adjusted for body

weight, while performing activities within the specified activity category, by age

and gender categories .....................................................................................................C-27

C-2Table C-1a. Descriptive statistics of bodyweight (kg) and BMR (kcal/min) across NHANESmaleparticipants,by age group



# C-3Body Weight (kg)BMR (kcal/min)Age CategoryMeanPercentilesMaxi-mumPercentilesMeanMaxi-mum5th10th25th50th75th90th95th5th10th25th50th75th90th95thBirth to <1year8.04.85.56.78.19.410.410.813.40.310.180.210.260.310.370.410.420.531 year11.49.19.810.311.312.313.213.716.10.450.350.380.400.450.490.520.540.642 years13.911.111.712.513.815.316.217.223.30.550.440.460.500.550.610.650.690.943 to <6 years18.513.414.516.017.820.223.325.242.00.640.560.580.600.630.670.720.751.016 to <11 years31.819.921.924.829.636.345.450.086.90.850.660.700.740.820.911.041.111.5711 to <16 years56.432.835.243.353.865.779.992.5143.61.150.860.890.991.121.261.441.592.2216 to <21 years76.554.357.663.972.283.6102.8111.2176.01.331.081.111.181.281.421.601.732.6221 to <31 years83.856.860.969.580.893.7108.7123.4196.81.351.071.121.211.321.451.621.742.5431 to <41 years87.161.065.673.983.496.3112.6126.7193.31.301.091.131.191.271.371.501.612.1441 to <51 years88.464.067.776.785.497.8111.8121.2188.31.311.121.141.221.291.381.501.572.1151 to <61 years89.062.667.476.686.699.6110.5120.3179.01.301.071.121.201.291.391.481.552.0361 to <71 years87.663.466.776.185.797.1111.2119.0162.81.120.920.951.031.101.201.311.381.7371 to <81 years82.460.664.472.581.092.0101.1108.8132.71.080.900.931.001.071.161.231.291.4981 years andolder75.457.961.867.074.682.091.6100.5111.81.020.880.910.951.011.071.151.221.32Individual measures have been weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.The numbers of male NHANES participants with data entering into these statistics are given in Table 2-1.Table C-1b. Descriptive statistics of bodyweight (kg) and BMR (kcal/min) across NHANESfemaleparticipants,by age group



# C-4Body Weight (kg)BMR (kcal/min)Age CategoryMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thBirth to <1 year7.44.64.96.37.58.69.610.420.20.280.160.180.230.280.330.370.400.801 year11.18.89.19.910.912.113.113.818.90.430.330.350.380.420.470.510.540.742 years13.311.011.212.013.114.415.616.822.70.520.420.430.460.510.560.610.660.903 to <6 years18.213.314.215.517.419.523.026.938.60.590.520.540.560.580.610.660.720.886 to <11 years30.918.920.623.328.136.244.750.487.00.760.600.630.670.740.840.951.021.5611 to <16 years55.635.638.145.053.162.475.386.2134.41.000.810.830.900.971.061.181.281.7316 to <21 years65.246.247.854.361.372.589.996.2156.41.040.830.870.931.011.111.271.351.9521 to <31 years72.447.551.458.369.082.598.3109.6159.11.070.830.870.931.031.171.331.451.9731 to <41 years74.751.054.660.769.784.0103.8112.8191.11.010.870.890.930.981.061.171.221.6641 to <51 years76.651.354.260.772.787.5102.8117.2182.81.020.880.890.931.001.081.171.251.6251 to <61 years77.053.156.262.873.687.7104.6113.4150.11.010.860.890.931.001.071.171.221.4361 to <71 years75.551.755.963.873.183.999.9109.2138.70.930.780.810.860.920.991.091.151.3371 to <81 years70.346.852.059.468.580.391.897.7127.60.900.750.780.830.890.961.041.071.2681 years andolder63.945.247.454.562.671.479.491.4120.00.860.740.760.800.850.910.961.031.21Individual measures have been weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.The numbers of female NHANES participants with data entering into these statistics are given in Table 2-1.Table C-2a. Descriptive statistics for daily average ventilation rate (m3/day) inmales, by age category



# C-5Daily Average Ventilation Rate, Unadjusted for Body WeightDaily Average Ventilation Rate, Adjusted for Body Weight(;m3/day)EV&(/BW; m3/day-kg)EV&Age CategoryMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thBirth to <1 year8.764.775.707.168.7010.4311.9312.6917.051.090.910.941.001.091.161.261.291.481 year13.499.7310.4111.6513.1115.0217.0317.8924.241.190.961.021.091.171.261.371.481.732 years13.239.4510.2011.4313.1914.4916.2717.7128.170.950.780.820.870.941.011.091.131.363 to <6 years12.6510.4210.8711.4012.5813.6414.6315.4119.520.700.520.560.610.690.780.870.921.086 to <11 years13.4210.0810.6911.7313.0914.7316.5617.7224.970.440.320.340.380.430.500.550.580.8111 to <16 years15.3211.4112.1113.2714.7916.8119.5421.2128.540.280.210.220.250.280.320.360.380.5016 to <21 years17.2212.6013.4114.4816.6319.1621.9423.3839.210.230.170.180.200.230.250.280.300.3921 to <31 years18.8212.6913.5715.4918.1821.2324.5727.1443.420.230.160.170.190.220.260.300.320.5131 to <41 years20.2914.0014.9716.9619.8323.0226.7728.9040.720.240.160.180.200.230.270.310.340.4641 to <51 years20.9314.6615.5417.5020.6023.8926.7128.3745.980.240.170.180.200.230.280.320.340.4751 to <61 years20.9114.9816.0717.6020.4123.1627.0129.0938.170.240.160.180.200.240.270.300.340.4361 to <71 years17.9413.9214.5015.8817.6019.5421.7823.5028.090.210.170.180.190.200.220.240.250.3271 to <81 years16.3513.1013.6114.6716.2317.5719.4320.4224.530.200.170.180.190.200.210.230.240.3181 years andolder15.1511.9512.5713.8214.9016.3118.0218.6822.630.200.170.180.190.200.220.230.250.28Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.Ventilation rate was estimated using the multiple linear regression model in Section 3.6.Table C-2b. Descriptive statisticsfor daily average ventilation rate (m3/day) infemales, by age category



# C-6Daily Average Ventilation Rate, Unadjusted for Body Weight; m3/day)Daily Average Ventilation Rate, Adjusted for Body Weight(/BW; m3/day-kg)(EV&EV&Age CategoryMeanPercentilesMaxMeanPercentilesMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thBirth to <1year8.534.845.486.838.419.7811.6512.6626.261.140.910.971.041.131.241.331.381.601 year13.319.0810.1211.2413.0314.6417.4518.6224.771.200.971.011.101.181.301.411.461.732 years12.748.9110.0711.3812.6013.9615.5816.3723.010.950.820.840.890.961.011.071.111.233 to <6 years12.169.8710.3811.2012.0213.0114.0314.9319.740.690.480.540.600.680.770.880.921.126 to <11 years12.419.9910.3511.0111.9513.4215.1316.3420.820.430.280.310.360.430.490.550.580.7511 to <16 years13.4410.4711.1112.0413.0814.5416.2517.4126.580.250.190.200.220.240.280.310.340.4716 to <21 years13.599.8610.6111.7813.2015.0217.1218.2930.110.210.160.170.190.210.240.270.280.3621 to <31 years14.5710.1510.6711.9314.1016.6219.3221.1430.230.210.140.160.180.200.230.260.280.4031 to <41 years14.9811.0711.8013.0214.6816.3218.5120.4528.280.210.140.150.180.200.230.270.300.4341 to <51 years16.2012.1012.5814.1615.8817.9519.9121.3535.890.220.150.160.190.210.250.280.310.4151 to <61 years16.1812.3312.9614.0815.9017.8119.9321.2225.700.220.150.160.180.210.240.280.300.4061 to <71 years12.9910.4010.7711.7812.9213.9015.4016.1520.340.180.140.150.160.170.190.210.220.2771 to <81 years12.049.9010.2010.8911.8212.9614.1115.2017.700.180.140.140.160.170.190.210.230.3481 years andolder11.149.199.4510.1311.0211.8712.8513.9416.930.180.140.150.160.180.200.210.220.28Individual daily averages are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.Ventilation rate was estimated using the multiple linear regression model in Section 3.6.Table C-3. Descriptive statistics forduration of time (hr/day) spent performing activities within the specifiedactivity category, by age and gender categories



# C-7Duration (hr/day) Spent at Activity - MalesDuration (hr/day) Spent at Activity - FemalesAge CategoryMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSleep or nap (Activity ID = 14500)Birth to <1 year13.5112.6312.7813.1913.5313.8814.2414.4615.0312.9912.0012.1612.5312.9613.4413.8214.0714.821 year12.6111.8912.1512.3412.6112.8913.1313.2913.7912.5811.5911.8812.2912.6312.9613.1613.3114.552 years12.0611.1911.4511.8012.0712.3912.6512.7513.4012.0911.4511.6811.8612.0812.3412.5712.6613.483 to <6 years11.1810.5710.7010.9411.1811.4511.6311.8212.3911.1310.4510.7010.9211.1211.3811.5811.7512.236 to <11 years10.189.659.759.9310.1910.3910.5910.7211.2410.269.559.7310.0110.2710.5410.7410.9111.4311 to <16 years9.388.848.949.159.389.619.839.9510.339.578.828.979.279.559.8710.1710.3111.5216 to <21 years8.697.918.088.368.679.039.349.5010.449.088.268.448.749.089.399.7910.0211.1121 to <31 years8.367.547.708.028.368.679.039.239.778.607.897.998.268.598.909.209.3810.3531 to <41 years8.067.367.507.778.068.368.598.769.828.317.547.707.988.288.598.929.1710.2241 to <51 years7.897.157.307.587.888.178.488.689.388.327.587.757.998.318.638.939.1310.0251 to <61 years7.967.297.517.697.968.238.488.669.048.127.367.537.818.118.438.738.859.2961 to <71 years8.317.657.788.018.308.608.839.019.668.407.677.888.158.408.688.939.099.8071 to <81 years8.517.808.028.278.538.748.999.109.898.587.858.018.268.558.899.199.4610.3481 years andolder9.248.488.648.979.259.549.749.9610.699.118.358.538.849.109.349.7310.0410.55Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDuration (hr/day) Spent at Activity - Males



# C-8Duration (hr/day) Spent at Activity - FemalesMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSedentary & Passive Activities (METS<1.5―Includes Sleep or Nap)Birth to <1 year14.9513.8214.0314.4914.8815.4415.9016.1217.4814.0712.8613.0513.5314.0814.5415.0815.4916.141 year14.2713.2213.3313.7614.2514.7415.0815.3816.4514.3213.0213.2513.7314.3114.8815.3615.8016.402 years14.6213.5213.6714.1114.5415.1115.6015.7717.2814.8613.8113.9514.4414.8115.3215.7816.0316.913 to <6 years14.1213.0113.1813.5414.0314.5315.2615.6217.2914.2712.8813.1513.5614.2314.8215.4315.8517.966 to <11 years13.5112.1912.4512.8613.3013.8514.8215.9419.2113.9712.4912.7413.2213.8214.5015.3416.3618.6811 to <16 years13.8512.3912.6513.0613.6114.3015.4116.7618.7914.1912.3812.7613.3414.0514.8215.8716.8119.2716 to <21 years13.2111.3911.7212.3213.0813.9714.8315.4418.7013.5811.8012.1712.7913.5214.2915.0815.6716.9621 to <31 years12.4110.6911.0611.7412.3913.0913.7514.1615.3512.5910.9711.2911.8812.6013.2113.7514.1916.2431 to <41 years12.3110.7310.9811.6112.2412.9813.6314.0515.5812.2910.9111.1411.6112.2412.9113.5013.9015.1841 to <51 years12.3210.5611.0011.6712.3012.9513.6713.9815.4812.2210.7811.0811.5612.1812.8213.4013.7915.1751 to <61 years13.0611.4711.8612.3613.0313.7214.3814.7615.9512.6611.0811.4012.0812.6413.3013.8914.1215.8061 to <71 years14.4912.9613.2413.7614.4815.1615.7216.2417.5014.2512.8913.1613.6814.2214.8615.3815.6917.1471 to <81 years15.9014.2214.6715.2515.9416.6517.1117.4618.4715.3813.6614.2014.7615.4116.0516.6216.9417.9081 years andolder16.5815.1315.4515.9216.6417.2117.7018.0618.7616.4814.8715.0915.8016.5917.1517.7118.0719.13Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDuration (hr/day) Spent at Activity - Males



# C-9Duration (hr/day) Spent at Activity - FemalesMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thLight Intensity Activities (1.5 < METS<3.0)Birth to <1 year5.302.973.253.714.527.298.088.509.916.003.493.704.265.018.439.319.7710.531 year5.522.682.893.374.318.239.049.7310.905.612.832.943.464.398.289.039.3910.572 years5.483.063.263.854.587.588.839.049.925.783.203.544.295.337.488.468.749.933 to <6 years6.603.864.255.166.208.269.319.7010.746.253.784.104.795.847.868.849.3810.326 to <11 years7.625.075.576.637.638.729.7810.1211.597.274.635.466.337.178.349.429.7911.0611 to <16 years7.504.485.596.757.678.519.199.6310.917.554.895.626.757.678.559.279.5710.8516 to <21 years7.134.374.976.007.028.299.4310.0311.506.984.605.085.916.857.969.169.5712.2921 to <31 years6.093.153.504.205.088.499.9610.4712.256.423.664.094.845.828.189.5610.1412.1131 to <41 years5.722.803.123.704.648.349.8710.4912.106.514.064.335.065.988.149.469.9313.1241 to <51 years6.072.973.413.924.828.5610.1910.7912.686.563.994.304.975.908.409.7510.1811.8351 to <61 years5.643.213.444.034.797.598.949.7512.096.524.094.425.196.057.959.129.4311.5861 to <71 years5.493.503.824.585.296.417.407.9510.236.234.404.745.476.236.967.678.1711.1371 to <81 years4.963.453.754.294.815.596.266.599.905.964.224.515.245.926.637.467.919.4381 years andolder4.863.543.714.174.745.396.336.597.565.303.673.964.635.166.006.707.018.78Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDuration (hr/day) Spent at Activity - Males



# C-10Duration (hr/day) Spent at Activity - FemalesMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS<6.0)Birth to <1 year3.670.630.971.744.205.205.806.217.523.910.530.741.104.875.776.276.547.681 year4.040.450.591.145.296.066.616.947.684.020.520.731.085.146.107.007.378.072 years3.830.590.761.234.745.375.826.157.403.270.500.781.224.014.885.355.576.933 to <6 years3.150.550.751.303.804.525.115.326.303.350.700.891.613.884.715.295.657.586 to <11 years2.660.650.921.652.683.574.364.795.952.570.650.951.822.663.413.954.326.1011 to <16 years2.350.881.091.662.303.023.623.895.902.010.891.081.451.962.513.033.284.9616 to <21 years3.351.131.422.193.454.375.245.596.833.261.271.482.213.394.244.745.076.6821 to <31 years5.241.151.582.526.017.157.958.399.944.801.621.942.785.376.427.197.529.2131 to <41 years5.691.261.652.846.677.758.458.909.875.001.712.063.095.416.607.317.589.5941 to <51 years5.401.211.552.396.467.578.408.8510.525.051.752.002.975.486.667.507.9710.1651 to <61 years5.001.291.632.725.686.757.608.019.944.581.712.133.104.795.986.897.148.9761 to <71 years3.731.621.972.813.704.675.456.017.453.311.651.972.563.344.014.615.016.9071 to <81 years2.871.561.832.282.863.453.954.315.442.481.191.361.822.482.993.644.015.6381 years andolder2.351.321.451.792.292.853.283.614.372.061.011.251.551.992.513.073.444.68Table C-3. Descriptive statistics for duration of time (hr/day) spent performing activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDuration (hr/day) Spent at Activity - Males



# C-11Duration (hr/day) Spent at Activity - FemalesMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thHigh Intensity (METS > 6.0)Birth to <1 year0.200.000.000.010.140.280.500.590.960.170.030.050.090.140.210.330.400.581 year0.310.010.010.030.220.560.780.931.520.220.030.050.090.180.350.400.430.482 years0.100.000.010.030.050.140.250.330.480.150.000.010.030.080.160.480.651.013 to <6 years0.270.020.030.040.130.330.751.161.480.190.010.020.050.100.220.460.731.436 to <11 years0.320.010.010.030.130.381.101.503.200.240.020.030.060.120.260.670.981.7111 to <16 years0.380.030.040.100.210.471.031.342.350.300.030.040.080.190.400.660.963.1616 to <21 years0.400.030.040.140.270.530.991.292.590.240.010.030.080.180.340.510.601.6121 to <31 years0.330.020.050.110.270.450.690.851.950.260.030.050.100.190.360.560.671.4031 to <41 years0.380.030.070.140.280.510.831.031.770.250.030.050.090.190.330.520.721.4041 to <51 years0.340.030.050.090.230.500.781.002.400.260.030.040.090.200.360.550.681.4951 to <61 years0.410.030.050.130.340.590.871.131.950.340.030.040.120.280.500.740.851.5861 to <71 years0.370.030.050.130.280.490.801.082.210.320.030.040.100.230.460.680.891.7771 to <81 years0.390.010.030.100.290.570.901.112.060.290.030.050.100.250.430.600.711.2481 years andolder0.320.020.030.080.250.470.710.881.760.260.020.030.090.210.380.590.711.23Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.Ventilation rate was estimated using the multiple linear regression model in Section 3.6.Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, whileperforming activities within the specified activity category, by age and gender categories



# C-12Average Ventilation Rate (L/min) - Males,Average Ventilation Rate (L/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightAge CategoryMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSleep or nap (Activity ID = 14500)Birth to <1year3.081.661.912.453.003.684.354.777.192.921.541.722.272.883.504.044.408.691 year4.503.113.273.784.354.955.906.4410.024.593.023.283.764.565.325.966.379.592 years4.613.013.363.944.495.216.056.738.964.563.003.303.974.525.215.766.159.483 to <6 years4.363.063.303.764.294.865.545.927.674.182.903.203.624.104.715.225.737.386 to <11 years4.613.143.393.834.465.216.016.549.944.362.973.173.694.244.935.676.088.4211 to <16 years5.263.533.784.345.065.916.947.8111.494.813.343.573.994.665.396.396.999.3916 to <21 years5.313.553.854.355.156.096.927.6012.824.402.782.963.584.265.055.896.6312.2521 to <31 years4.733.163.353.844.565.426.266.9111.173.892.542.743.133.684.445.366.019.5831 to <41 years5.163.373.624.235.015.846.817.4610.864.002.662.863.313.894.545.285.778.1041 to <51 years5.653.744.094.735.536.477.417.8410.844.403.003.233.694.254.955.666.258.9751 to <61 years5.783.964.204.785.576.547.748.2611.814.563.123.303.724.415.196.076.638.9661 to <71 years5.984.364.575.135.816.687.457.9312.274.473.223.353.784.384.995.726.379.5771 to <81 years6.074.264.555.176.006.777.658.3310.504.523.313.473.894.405.115.676.067.3581 years andolder5.974.204.495.235.906.687.367.769.984.493.173.493.824.394.915.616.168.27Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min) - Males,



# C-13Average Ventilation Rate (L/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSedentary & Passive Activities (METS<1.5―Includes Sleep or Nap)Birth to <1year3.181.741.992.503.103.804.404.887.093.001.601.802.322.973.584.114.449.591 year4.623.173.503.914.495.035.956.449.914.713.263.443.984.735.305.956.639.502 years4.793.253.664.104.695.356.056.719.094.733.343.534.194.675.255.756.229.423 to <6 years4.583.473.634.074.565.035.585.827.604.403.313.493.954.344.845.295.737.086 to <11 years4.873.553.784.184.725.406.036.589.474.643.413.674.044.515.065.886.288.3111 to <16 years5.644.034.304.795.436.267.207.8711.085.213.904.164.535.095.686.537.069.0716 to <21 years5.764.174.424.935.606.437.157.7613.454.763.263.564.034.695.326.056.6011.8221 to <31 years5.113.763.994.335.005.646.426.9810.304.193.043.193.554.004.635.386.029.2231 to <41 years5.573.994.424.865.456.176.997.439.984.333.223.453.774.244.805.335.797.7041 to <51 years6.114.654.925.376.026.657.467.7710.534.753.603.824.184.655.195.746.268.7051 to <61 years6.274.685.065.506.166.897.608.1410.394.963.784.004.364.875.446.066.448.3061 to <71 years6.545.025.315.856.477.127.878.2210.864.893.814.024.344.815.305.866.298.1871 to <81 years6.655.265.555.966.597.187.818.269.924.954.074.134.414.895.425.896.157.5981 years andolder6.445.095.375.826.437.017.577.909.134.893.934.104.394.795.255.716.127.46Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min) - Males,



# C-14Average Ventilation Rate (L/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thLight Intensity Activities (1.5 < METS<3.0)Birth to <1year7.944.155.066.167.959.5710.7611.9015.507.323.794.635.737.198.739.8210.8016.971 year11.568.668.999.8911.4212.9114.3915.7621.1211.628.598.8010.0311.2012.9415.1715.8020.222 years11.678.529.149.9611.3713.0214.6615.3118.9811.998.749.4010.2711.6913.1715.6316.3423.613 to <6 years11.369.209.5510.2311.1212.2813.4014.0019.6510.928.839.049.8710.6911.7412.8513.8116.436 to <11 years11.648.959.3310.2011.2612.7914.6015.6021.8311.078.519.029.7910.7911.9813.4714.6722.2211 to <16 years13.229.7810.2611.3412.8414.6516.4218.6526.8612.029.409.7310.6311.7613.0914.6615.8222.1016 to <21 years13.4110.0110.5411.5312.9514.9516.9518.0029.0711.088.318.739.6410.7612.2713.8014.9221.4021 to <31 years12.979.6810.1811.2512.4214.0416.4617.7427.2210.557.758.249.0510.2411.6713.4014.2621.4631 to <41 years13.6410.6311.0511.9913.3314.8316.4618.1025.5011.078.849.309.9610.9411.9313.1113.8717.4041 to <51 years14.3811.1611.8112.9514.1115.6117.3918.2523.0111.789.6410.0010.6711.6112.6613.8514.5417.6751 to <61 years14.5611.0811.5812.9714.3515.9017.9619.3725.4812.029.7610.1710.8711.7912.9714.2314.8717.9461 to <71 years14.1211.0711.7412.6913.8715.3716.9117.9720.5410.828.879.289.8510.6411.6712.6213.2117.4071 to <81 years13.8711.1711.6812.7313.6914.9616.2316.8920.0210.838.849.239.9410.7411.6912.5213.0117.5981 years andolder13.7611.0211.7112.5613.7514.7016.0316.7220.7110.408.698.849.3610.2911.3712.0612.6316.05Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min) - Males,



# C-15Average Ventilation Rate (L/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS<6.0)Birth to <1year14.497.418.8111.4614.3516.9520.0822.5030.5413.987.919.0011.1513.5316.3219.4122.3040.871 year21.3514.4815.8818.0320.6224.0626.9428.9039.8720.9815.6216.3017.9220.1423.5127.0929.2534.532 years21.5415.3716.7118.4220.8224.0726.8729.6850.9321.3414.2115.5718.1721.4523.9227.6128.7637.583 to <6 years21.0316.3117.1618.7220.5522.9425.6027.0634.8820.0115.2616.3217.8419.7621.6123.8325.8932.866 to <11 years22.2816.3617.2319.3421.6425.0027.5929.5043.3921.0015.9816.8318.4720.3922.9826.0628.0843.1311 to <16 years26.4019.3320.4522.6025.4129.1933.7736.9355.0223.5518.1619.4720.8323.0425.3828.4231.4142.4216 to <21 years29.0220.3021.6924.5227.9731.7438.1542.1467.3523.2216.6017.6119.6222.3926.1330.2831.9852.4721 to <31 years29.1919.6520.9724.1627.9233.0038.7943.1171.7122.9315.5616.6818.9821.9426.0230.0232.8454.1831 to <41 years30.3021.4022.7025.0829.0934.1039.6043.4857.6922.7016.8717.5719.5021.9524.8128.9431.1047.2741 to <51 years31.5822.5824.4427.2130.4435.1140.2844.9763.3624.4917.6018.8820.7923.9427.4130.7933.5850.6751 to <61 years32.7122.3624.0127.9531.4036.9641.6645.7770.4825.2418.8319.8021.7824.3028.1131.8735.0246.1861 to <71 years29.7622.4724.0426.0529.2232.2736.9339.9852.2621.4216.9017.7019.2220.8623.2225.7227.3235.4571 to <81 years29.2922.8123.9226.1428.7832.0435.6537.3244.8621.0916.8617.6118.8720.6822.8524.9426.3534.4181 years andolder28.5322.4523.3625.4728.1931.0333.4435.5241.1120.8716.5117.5319.0920.6222.5124.5926.0129.27Table C-4. Descriptive statistics for average ventilation rate (L/min), unadjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min) - Males,



# C-16Average Ventilation Rate (L/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thHigh Intensity (METS > 6.0)Birth to <1year27.4715.0717.2620.6327.7932.4738.4142.2457.9024.1912.3613.2617.1522.4529.2735.5940.6774.551 year40.2528.3331.6834.6639.8044.3451.6255.9260.6636.4825.9426.2430.4236.1141.9747.2848.6476.972 years40.4528.1529.7434.4540.5746.1751.9055.0692.0137.5828.9930.5132.3336.4340.8148.0751.3673.013 to <6 years39.0429.4631.3534.0137.8043.2348.9352.2266.1734.5327.0028.2129.9833.3337.6343.2244.7256.626 to <11 years43.6230.6632.7635.7741.9449.5256.5862.4089.8639.3928.5930.1333.6638.0244.0850.4854.6082.8811 to <16 years50.8234.3136.8441.5349.1257.4066.2572.92122.9146.5631.0633.7638.7645.3452.9060.8166.32102.3716 to <21 years53.1735.9638.3343.5150.5159.3371.4583.03129.8844.0928.6930.6136.5142.7150.2358.1563.44108.8321 to <31 years53.9133.5537.9544.8351.5161.6372.3882.07111.9445.6828.8431.1836.6543.1052.2261.9368.91107.8931 to <41 years54.2737.7940.3645.4352.0561.2171.4277.35103.8844.4430.2732.9337.0242.2350.4559.5465.2689.5141 to <51 years57.3138.3142.4748.2955.2064.4575.6184.39110.2846.9831.0434.0238.3545.6154.0661.5267.4088.7251 to <61 years58.4238.9541.5748.6555.9065.9578.5786.46140.7447.3531.5434.8239.3845.6954.0762.3068.7584.4061 to <71 years54.1336.2839.5145.1752.4160.8171.9675.23102.1640.0227.5630.6334.5938.7145.3050.8156.4271.3471 to <81 years52.4636.9939.5044.1249.9558.9567.5676.4597.3440.6428.4930.0834.2539.5646.9851.9654.0775.2581 years andolder53.3135.3539.1745.5150.9361.1869.5577.0596.7641.8828.4830.0934.3541.3847.5755.5858.3372.12Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.Ventilation rate was estimated using the multiple linear regression model in Section 3.6.Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, whileperforming activities within the specified activity category, by age and gender categories



# C-17Average Ventilation Rate (L/min-kg) - Males,Average Ventilation Rate (L/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightAge CategoryMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSleep or nap (Activity ID = 14500)Birth to <1year0.380.280.300.340.380.430.460.500.670.390.280.300.340.390.430.480.520.741 year0.400.300.310.350.380.440.490.520.630.410.310.330.360.410.460.520.540.662 years0.330.250.260.290.330.360.400.440.540.340.260.270.290.330.390.430.450.493 to <6 years0.240.160.170.200.240.280.310.350.480.240.140.160.200.230.280.320.350.526 to <11 years0.150.100.110.130.150.170.200.220.300.150.090.100.120.150.180.210.230.3011 to <16 years0.100.070.070.080.090.110.130.140.210.090.060.070.070.090.100.120.130.1816 to <21 years0.070.050.050.060.070.080.090.100.150.070.040.050.060.070.080.090.100.1521 to <31 years0.060.040.040.050.060.070.080.080.130.060.040.040.040.050.060.070.080.1031 to <41 years0.060.040.040.050.060.070.080.090.130.060.030.040.040.050.060.080.080.1141 to <51 years0.070.040.050.050.060.070.090.090.140.060.040.040.050.060.070.080.090.1151 to <61 years0.070.050.050.050.060.080.090.090.140.060.040.040.050.060.070.080.090.1361 to <71 years0.070.050.050.060.070.080.090.090.120.060.040.050.050.060.070.080.080.1071 to <81 years0.070.050.060.060.070.080.090.100.130.070.050.050.060.060.070.080.090.1381 years andolder0.080.060.060.070.080.090.100.110.120.070.050.060.060.070.080.090.100.12Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min-kg) - Males,



# C-18Average Ventilation Rate (L/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSedentary & Passive Activities (METS<1.5―Includes Sleep or Nap)Birth to <1year0.400.300.320.350.390.440.470.500.660.400.300.320.350.400.450.480.520.721 year0.410.320.330.360.400.450.490.520.620.430.340.350.380.420.470.510.540.642 years0.340.270.290.310.340.370.410.450.510.360.290.300.320.350.390.420.440.483 to <6 years0.250.180.190.210.250.290.330.350.450.250.160.180.210.250.280.330.360.496 to <11 years0.160.110.120.140.160.180.210.220.290.160.100.110.130.160.190.210.230.2911 to <16 years0.100.080.080.090.100.120.130.140.200.100.070.070.080.090.110.120.130.1716 to <21 years0.080.050.060.070.080.090.090.100.130.070.050.060.060.070.080.100.100.1421 to <31 years0.060.050.050.060.060.070.080.080.120.060.040.050.050.060.070.070.080.1031 to <41 years0.070.050.050.060.070.070.080.090.120.060.040.040.050.060.070.080.080.1141 to <51 years0.070.050.060.060.070.080.090.090.130.060.040.050.050.060.070.080.090.1151 to <61 years0.070.050.060.060.070.080.090.090.140.070.050.050.060.070.080.080.090.1261 to <71 years0.080.060.060.070.080.080.090.090.110.070.050.050.060.070.070.080.080.1071 to <81 years0.080.070.070.080.080.090.090.100.110.070.060.060.070.070.080.090.090.1581 years andolder0.090.070.080.080.090.090.100.110.110.080.060.070.070.080.090.090.100.11Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min-kg) - Males,



# C-19Average Ventilation Rate (L/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thLight Intensity Activities (1.5 < METS<3.0)Birth to <1year0.990.790.830.900.971.071.171.201.430.980.790.820.880.961.051.181.231.651 year1.020.840.860.921.011.101.221.301.481.050.850.870.951.041.141.251.271.642 years0.840.680.720.760.830.891.001.031.180.900.730.760.820.890.961.041.101.263 to <6 years0.630.440.480.540.630.710.790.871.080.620.450.480.540.600.700.780.831.026 to <11 years0.380.270.290.320.380.440.490.530.710.380.250.270.310.380.440.500.540.7111 to <16 years0.250.180.190.210.240.280.310.330.440.230.160.170.200.220.250.280.310.4016 to <21 years0.180.140.140.160.180.200.220.230.330.170.130.140.150.170.190.210.220.2921 to <31 years0.160.120.130.140.150.170.190.210.290.150.120.120.130.150.160.180.190.2331 to <41 years0.160.120.130.140.160.180.200.210.280.150.110.120.130.150.180.190.200.2741 to <51 years0.170.130.130.150.160.180.200.210.330.160.110.120.140.160.180.200.220.2851 to <61 years0.170.130.130.150.160.180.200.220.290.160.120.130.140.160.180.200.210.2661 to <71 years0.160.140.140.150.160.180.190.200.270.150.120.120.130.140.160.170.180.2471 to <81 years0.170.140.150.160.170.180.190.200.260.160.120.130.140.160.170.190.200.2881 years andolder0.180.150.160.170.180.200.210.220.250.170.130.140.150.160.180.200.210.23Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min-kg) - Males,



# C-20Average Ventilation Rate (L/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS<6.0)Birth to <1year1.801.391.491.621.781.942.182.283.011.871.471.521.671.852.012.252.402.831 year1.881.411.501.651.822.012.332.533.231.901.521.621.731.872.022.242.373.242 years1.551.211.281.401.541.661.842.022.291.601.271.311.441.581.751.922.022.593 to <6 years1.170.800.881.001.121.311.561.682.101.140.790.850.961.111.311.451.561.936 to <11 years0.740.500.550.620.710.830.961.041.430.720.460.510.600.710.840.941.011.3711 to <16 years0.490.360.380.420.470.550.640.681.060.440.320.340.380.430.490.550.610.9916 to <21 years0.390.280.300.330.380.430.490.520.710.360.270.280.310.350.410.460.490.6521 to <31 years0.360.240.260.300.340.400.470.510.820.330.240.250.280.320.360.420.450.6631 to <41 years0.360.240.260.300.340.400.470.520.760.320.210.230.270.300.350.410.460.7141 to <51 years0.370.250.270.310.350.410.470.520.720.330.220.240.280.320.380.440.490.6251 to <61 years0.380.260.280.310.370.430.480.550.760.340.240.250.280.330.380.440.490.6461 to <71 years0.340.270.280.310.340.370.400.420.570.290.220.240.260.280.320.350.370.5171 to <81 years0.360.290.310.330.360.390.420.440.550.310.240.250.270.300.340.380.410.6881 years andolder0.380.310.320.350.380.420.450.470.530.330.250.270.300.330.370.400.420.52Table C-5. Descriptive statistics for average ventilation rate (L/min-kg), adjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryAverage Ventilation Rate (L/min-kg) - Males,



# C-21Average Ventilation Rate (L/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thHigh Intensity (METS > 6.0)Birth to <1year3.482.702.933.103.463.814.144.325.083.262.532.622.893.233.633.964.085.021 year3.522.522.893.223.573.914.114.344.863.382.572.752.973.243.714.164.874.882 years2.892.172.342.582.873.203.433.544.302.802.202.312.482.813.123.353.483.883 to <6 years2.171.551.661.812.112.502.732.983.621.981.361.511.691.902.192.502.993.246 to <11 years1.410.941.031.191.381.591.831.932.681.330.890.971.121.331.521.721.812.2211 to <16 years0.950.630.700.790.911.091.271.361.980.880.590.630.710.851.011.181.312.0516 to <21 years0.710.480.530.600.690.800.921.001.940.700.450.500.570.690.790.921.001.5021 to <31 years0.660.450.470.540.640.750.850.971.270.650.420.460.550.630.730.880.941.3031 to <41 years0.640.440.470.530.620.730.850.931.230.610.380.420.500.590.710.830.901.5541 to <51 years0.660.440.480.550.630.740.860.941.770.650.380.440.520.640.760.880.951.6151 to <61 years0.680.450.480.550.640.770.911.021.310.630.390.430.510.610.750.850.931.3761 to <71 years0.620.440.470.530.610.700.790.851.080.540.360.400.450.530.610.720.801.1171 to <81 years0.650.470.500.550.630.720.850.911.040.590.390.440.500.580.680.780.831.2681 years andolder0.720.500.540.600.700.800.940.991.350.670.450.480.540.630.770.930.971.22Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.Ventilation rate was estimated using the multiple linear regression model in Section 3.6.Table C-6. Descriptive statisticsfor daily ventilation rate (m3/min), unadjusted for body weight, whileperforming activities within the specified activity category, by age and gender categories



# C-22Daily Ventilation Rate (m3/min) - Males,Daily Ventilation Rate (m3/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightAge CategoryMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSleep or nap (Activity ID = 14500)Birth to <1year3.1E-031.7E-031.9E-032.5E-033.0E-033.7E-034.4E-034.8E-037.2E-032.9E-031.5E-031.7E-032.3E-032.9E-033.5E-034.0E-034.4E-038.7E-031 year4.5E-033.1E-033.3E-033.8E-034.3E-034.9E-035.9E-036.4E-031.0E-024.6E-033.0E-033.3E-033.8E-034.6E-035.3E-036.0E-036.4E-039.6E-032 years4.6E-033.0E-033.4E-033.9E-034.5E-035.2E-036.1E-036.7E-039.0E-034.6E-033.0E-033.3E-034.0E-034.5E-035.2E-035.8E-036.1E-039.5E-033 to <6 years4.4E-033.1E-033.3E-033.8E-034.3E-034.9E-035.5E-035.9E-037.7E-034.2E-032.9E-033.2E-033.6E-034.1E-034.7E-035.2E-035.7E-037.4E-036 to <11 years4.6E-033.1E-033.4E-033.8E-034.5E-035.2E-036.0E-036.5E-039.9E-034.4E-033.0E-033.2E-033.7E-034.2E-034.9E-035.7E-036.1E-038.4E-0311 to <16 years5.3E-033.5E-033.8E-034.3E-035.1E-035.9E-036.9E-037.8E-031.1E-024.8E-033.3E-033.6E-034.0E-034.7E-035.4E-036.4E-037.0E-039.4E-0316 to <21 years5.3E-033.6E-033.9E-034.3E-035.2E-036.1E-036.9E-037.6E-031.3E-024.4E-032.8E-033.0E-033.6E-034.3E-035.1E-035.9E-036.6E-031.2E-0221 to <31 years4.7E-033.2E-033.3E-033.8E-034.6E-035.4E-036.3E-036.9E-031.1E-023.9E-032.5E-032.7E-033.1E-033.7E-034.4E-035.4E-036.0E-039.6E-0331 to <41 years5.2E-033.4E-033.6E-034.2E-035.0E-035.8E-036.8E-037.5E-031.1E-024.0E-032.7E-032.9E-033.3E-033.9E-034.5E-035.3E-035.8E-038.1E-0341 to <51 years5.7E-033.7E-034.1E-034.7E-035.5E-036.5E-037.4E-037.8E-031.1E-024.4E-033.0E-033.2E-033.7E-034.2E-035.0E-035.7E-036.2E-039.0E-0351 to <61 years5.8E-034.0E-034.2E-034.8E-035.6E-036.5E-037.7E-038.3E-031.2E-024.6E-033.1E-033.3E-033.7E-034.4E-035.2E-036.1E-036.6E-039.0E-0361 to <71 years6.0E-034.4E-034.6E-035.1E-035.8E-036.7E-037.5E-037.9E-031.2E-024.5E-033.2E-033.3E-033.8E-034.4E-035.0E-035.7E-036.4E-039.6E-0371 to <81 years6.1E-034.3E-034.6E-035.2E-036.0E-036.8E-037.6E-038.3E-031.1E-024.5E-033.3E-033.5E-033.9E-034.4E-035.1E-035.7E-036.1E-037.3E-0381 years andolder6.0E-034.2E-034.5E-035.2E-035.9E-036.7E-037.4E-037.8E-031.0E-024.5E-033.2E-033.5E-033.8E-034.4E-034.9E-035.6E-036.2E-038.3E-03Table C-6. Descriptive statisticsfor daily ventilation rate (m3/min), unadjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min) - Males,



# C-23Daily Ventilation Rate (m3/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSedentary & Passive Activities (METS<1.5―Includes Sleep or Nap)Birth to <1year3.2E-031.7E-032.0E-032.5E-033.1E-033.8E-034.4E-034.9E-037.1E-033.0E-031.6E-031.8E-032.3E-033.0E-033.6E-034.1E-034.4E-039.6E-031 year4.6E-033.2E-033.5E-033.9E-034.5E-035.0E-036.0E-036.4E-039.9E-034.7E-033.3E-033.4E-034.0E-034.7E-035.3E-036.0E-036.6E-039.5E-032 years4.8E-033.2E-033.7E-034.1E-034.7E-035.4E-036.1E-036.7E-039.1E-034.7E-033.3E-033.5E-034.2E-034.7E-035.3E-035.8E-036.2E-039.4E-033 to <6 years4.6E-033.5E-033.6E-034.1E-034.6E-035.0E-035.6E-035.8E-037.6E-034.4E-033.3E-033.5E-033.9E-034.3E-034.8E-035.3E-035.7E-037.1E-036 to <11 years4.9E-033.6E-033.8E-034.2E-034.7E-035.4E-036.0E-036.6E-039.5E-034.6E-033.4E-033.7E-034.0E-034.5E-035.1E-035.9E-036.3E-038.3E-0311 to <16 years5.6E-034.0E-034.3E-034.8E-035.4E-036.3E-037.2E-037.9E-031.1E-025.2E-033.9E-034.2E-034.5E-035.1E-035.7E-036.5E-037.1E-039.1E-0316 to <21 years5.8E-034.2E-034.4E-034.9E-035.6E-036.4E-037.1E-037.8E-031.3E-024.8E-033.3E-033.6E-034.0E-034.7E-035.3E-036.0E-036.6E-031.2E-0221 to <31 years5.1E-033.8E-034.0E-034.3E-035.0E-035.6E-036.4E-037.0E-031.0E-024.2E-033.0E-033.2E-033.6E-034.0E-034.6E-035.4E-036.0E-039.2E-0331 to <41 years5.6E-034.0E-034.4E-034.9E-035.4E-036.2E-037.0E-037.4E-031.0E-024.3E-033.2E-033.4E-033.8E-034.2E-034.8E-035.3E-035.8E-037.7E-0341 to <51 years6.1E-034.7E-034.9E-035.4E-036.0E-036.7E-037.5E-037.8E-031.1E-024.8E-033.6E-033.8E-034.2E-034.6E-035.2E-035.7E-036.3E-038.7E-0351 to <61 years6.3E-034.7E-035.1E-035.5E-036.2E-036.9E-037.6E-038.1E-031.0E-025.0E-033.8E-034.0E-034.4E-034.9E-035.4E-036.1E-036.4E-038.3E-0361 to <71 years6.5E-035.0E-035.3E-035.8E-036.5E-037.1E-037.9E-038.2E-031.1E-024.9E-033.8E-034.0E-034.3E-034.8E-035.3E-035.9E-036.3E-038.2E-0371 to <81 years6.6E-035.3E-035.6E-036.0E-036.6E-037.2E-037.8E-038.3E-039.9E-034.9E-034.1E-034.1E-034.4E-034.9E-035.4E-035.9E-036.1E-037.6E-0381 years andolder6.4E-035.1E-035.4E-035.8E-036.4E-037.0E-037.6E-037.9E-039.1E-034.9E-033.9E-034.1E-034.4E-034.8E-035.3E-035.7E-036.1E-037.5E-03Table C-6. Descriptive statisticsfor daily ventilation rate (m3/min), unadjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min) - Males,



# C-24Daily Ventilation Rate (m3/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thLight Intensity Activities (1.5 < METS<3.0)Birth to <1year7.9E-034.1E-035.1E-036.2E-037.9E-039.6E-031.1E-021.2E-021.5E-027.3E-033.8E-034.6E-035.7E-037.2E-038.7E-039.8E-031.1E-021.7E-021 year1.2E-028.7E-039.0E-039.9E-031.1E-021.3E-021.4E-021.6E-022.1E-021.2E-028.6E-038.8E-031.0E-021.1E-021.3E-021.5E-021.6E-022.0E-022 years1.2E-028.5E-039.1E-031.0E-021.1E-021.3E-021.5E-021.5E-021.9E-021.2E-028.7E-039.4E-031.0E-021.2E-021.3E-021.6E-021.6E-022.4E-023 to <6 years1.1E-029.2E-039.5E-031.0E-021.1E-021.2E-021.3E-021.4E-022.0E-021.1E-028.8E-039.0E-039.9E-031.1E-021.2E-021.3E-021.4E-021.6E-026 to <11 years1.2E-029.0E-039.3E-031.0E-021.1E-021.3E-021.5E-021.6E-022.2E-021.1E-028.5E-039.0E-039.8E-031.1E-021.2E-021.3E-021.5E-022.2E-0211 to <16 years1.3E-029.8E-031.0E-021.1E-021.3E-021.5E-021.6E-021.9E-022.7E-021.2E-029.4E-039.7E-031.1E-021.2E-021.3E-021.5E-021.6E-022.2E-0216 to <21 years1.3E-021.0E-021.1E-021.2E-021.3E-021.5E-021.7E-021.8E-022.9E-021.1E-028.3E-038.7E-039.6E-031.1E-021.2E-021.4E-021.5E-022.1E-0221 to <31 years1.3E-029.7E-031.0E-021.1E-021.2E-021.4E-021.6E-021.8E-022.7E-021.1E-027.8E-038.2E-039.1E-031.0E-021.2E-021.3E-021.4E-022.1E-0231 to <41 years1.4E-021.1E-021.1E-021.2E-021.3E-021.5E-021.6E-021.8E-022.5E-021.1E-028.8E-039.3E-031.0E-021.1E-021.2E-021.3E-021.4E-021.7E-0241 to <51 years1.4E-021.1E-021.2E-021.3E-021.4E-021.6E-021.7E-021.8E-022.3E-021.2E-029.6E-031.0E-021.1E-021.2E-021.3E-021.4E-021.5E-021.8E-0251 to <61 years1.5E-021.1E-021.2E-021.3E-021.4E-021.6E-021.8E-021.9E-022.5E-021.2E-029.8E-031.0E-021.1E-021.2E-021.3E-021.4E-021.5E-021.8E-0261 to <71 years1.4E-021.1E-021.2E-021.3E-021.4E-021.5E-021.7E-021.8E-022.1E-021.1E-028.9E-039.3E-039.8E-031.1E-021.2E-021.3E-021.3E-021.7E-0271 to <81 years1.4E-021.1E-021.2E-021.3E-021.4E-021.5E-021.6E-021.7E-022.0E-021.1E-028.8E-039.2E-039.9E-031.1E-021.2E-021.3E-021.3E-021.8E-0281 years andolder1.4E-021.1E-021.2E-021.3E-021.4E-021.5E-021.6E-021.7E-022.1E-021.0E-028.7E-038.8E-039.4E-031.0E-021.1E-021.2E-021.3E-021.6E-02Table C-6. Descriptive statisticsfor daily ventilation rate (m3/min), unadjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min) - Males,



# C-25Daily Ventilation Rate (m3/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS<6.0)Birth to <1year1.4E-027.4E-038.8E-031.1E-021.4E-021.7E-022.0E-022.3E-023.1E-021.4E-027.9E-039.0E-031.1E-021.4E-021.6E-021.9E-022.2E-024.1E-021 year2.1E-021.4E-021.6E-021.8E-022.1E-022.4E-022.7E-022.9E-024.0E-022.1E-021.6E-021.6E-021.8E-022.0E-022.4E-022.7E-022.9E-023.5E-022 years2.2E-021.5E-021.7E-021.8E-022.1E-022.4E-022.7E-023.0E-025.1E-022.1E-021.4E-021.6E-021.8E-022.1E-022.4E-022.8E-022.9E-023.8E-023 to <6 years2.1E-021.6E-021.7E-021.9E-022.1E-022.3E-022.6E-022.7E-023.5E-022.0E-021.5E-021.6E-021.8E-022.0E-022.2E-022.4E-022.6E-023.3E-026 to <11 years2.2E-021.6E-021.7E-021.9E-022.2E-022.5E-022.8E-022.9E-024.3E-022.1E-021.6E-021.7E-021.8E-022.0E-022.3E-022.6E-022.8E-024.3E-0211 to <16 years2.6E-021.9E-022.0E-022.3E-022.5E-022.9E-023.4E-023.7E-025.5E-022.4E-021.8E-021.9E-022.1E-022.3E-022.5E-022.8E-023.1E-024.2E-0216 to <21 years2.9E-022.0E-022.2E-022.5E-022.8E-023.2E-023.8E-024.2E-026.7E-022.3E-021.7E-021.8E-022.0E-022.2E-022.6E-023.0E-023.2E-025.2E-0221 to <31 years2.9E-022.0E-022.1E-022.4E-022.8E-023.3E-023.9E-024.3E-027.2E-022.3E-021.6E-021.7E-021.9E-022.2E-022.6E-023.0E-023.3E-025.4E-0231 to <41 years3.0E-022.1E-022.3E-022.5E-022.9E-023.4E-024.0E-024.3E-025.8E-022.3E-021.7E-021.8E-021.9E-022.2E-022.5E-022.9E-023.1E-024.7E-0241 to <51 years3.2E-022.3E-022.4E-022.7E-023.0E-023.5E-024.0E-024.5E-026.3E-022.4E-021.8E-021.9E-022.1E-022.4E-022.7E-023.1E-023.4E-025.1E-0251 to <61 years3.3E-022.2E-022.4E-022.8E-023.1E-023.7E-024.2E-024.6E-027.0E-022.5E-021.9E-022.0E-022.2E-022.4E-022.8E-023.2E-023.5E-024.6E-0261 to <71 years3.0E-022.2E-022.4E-022.6E-022.9E-023.2E-023.7E-024.0E-025.2E-022.1E-021.7E-021.8E-021.9E-022.1E-022.3E-022.6E-022.7E-023.5E-0271 to <81 years2.9E-022.3E-022.4E-022.6E-022.9E-023.2E-023.6E-023.7E-024.5E-022.1E-021.7E-021.8E-021.9E-022.1E-022.3E-022.5E-022.6E-023.4E-0281 years andolder2.9E-022.2E-022.3E-022.5E-022.8E-023.1E-023.3E-023.6E-024.1E-022.1E-021.7E-021.8E-021.9E-022.1E-022.3E-022.5E-022.6E-022.9E-02Table C-6. Descriptive statisticsfor daily ventilation rate (m3/min), unadjusted for body weight, whileperforming activities within the specified activitycategory, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min) - Males,



# C-26Daily Ventilation Rate (m3/min) - Females,Unadjusted for Body WeightUnadjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thHigh Intensity (METS > 6.0)Birth to <1year2.7E-021.5E-021.7E-022.1E-022.8E-023.2E-023.8E-024.2E-025.8E-022.4E-021.2E-021.3E-021.7E-022.2E-022.9E-023.6E-024.1E-027.5E-021 year4.0E-022.8E-023.2E-023.5E-024.0E-024.4E-025.2E-025.6E-026.1E-023.6E-022.6E-022.6E-023.0E-023.6E-024.2E-024.7E-024.9E-027.7E-022 years4.0E-022.8E-023.0E-023.4E-024.1E-024.6E-025.2E-025.5E-029.2E-023.8E-022.9E-023.1E-023.2E-023.6E-024.1E-024.8E-025.1E-027.3E-023 to <6 years3.9E-022.9E-023.1E-023.4E-023.8E-024.3E-024.9E-025.2E-026.6E-023.5E-022.7E-022.8E-023.0E-023.3E-023.8E-024.3E-024.5E-025.7E-026 to <11 years4.4E-023.1E-023.3E-023.6E-024.2E-025.0E-025.7E-026.2E-029.0E-023.9E-022.9E-023.0E-023.4E-023.8E-024.4E-025.0E-025.5E-028.3E-0211 to <16 years5.1E-023.4E-023.7E-024.2E-024.9E-025.7E-026.6E-027.3E-021.2E-014.7E-023.1E-023.4E-023.9E-024.5E-025.3E-026.1E-026.6E-021.0E-0116 to <21 years5.3E-023.6E-023.8E-024.4E-025.1E-025.9E-027.1E-028.3E-021.3E-014.4E-022.9E-023.1E-023.7E-024.3E-025.0E-025.8E-026.3E-021.1E-0121 to <31 years5.4E-023.4E-023.8E-024.5E-025.2E-026.2E-027.2E-028.2E-021.1E-014.6E-022.9E-023.1E-023.7E-024.3E-025.2E-026.2E-026.9E-021.1E-0131 to <41 years5.4E-023.8E-024.0E-024.5E-025.2E-026.1E-027.1E-027.7E-021.0E-014.4E-023.0E-023.3E-023.7E-024.2E-025.0E-026.0E-026.5E-029.0E-0241 to <51 years5.7E-023.8E-024.2E-024.8E-025.5E-026.4E-027.6E-028.4E-021.1E-014.7E-023.1E-023.4E-023.8E-024.6E-025.4E-026.2E-026.7E-028.9E-0251 to <61 years5.8E-023.9E-024.2E-024.9E-025.6E-026.6E-027.9E-028.6E-021.4E-014.7E-023.2E-023.5E-023.9E-024.6E-025.4E-026.2E-026.9E-028.4E-0261 to <71 years5.4E-023.6E-024.0E-024.5E-025.2E-026.1E-027.2E-027.5E-021.0E-014.0E-022.8E-023.1E-023.5E-023.9E-024.5E-025.1E-025.6E-027.1E-0271 to <81 years5.2E-023.7E-024.0E-024.4E-025.0E-025.9E-026.8E-027.6E-029.7E-024.1E-022.8E-023.0E-023.4E-024.0E-024.7E-025.2E-025.4E-027.5E-0281 years andolder5.3E-023.5E-023.9E-024.6E-025.1E-026.1E-027.0E-027.7E-029.7E-024.2E-022.8E-023.0E-023.4E-024.1E-024.8E-025.6E-025.8E-027.2E-02Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.Ventilation rate was estimated using the multiple linear regression model in Section 3.6.Table C-7. Descriptive statisticsfor daily ventilation rate (m3/min-kg), adjusted for body weight, while performingactivities within the specified activitycategory, by age and gender categories



# C-27Daily Ventilation Rate (m3/min-kg) - Males,Daily Ventilation Rate (m3/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightAge CategoryMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSleep or nap (Activity ID = 14500)Birth to <1year3.8E-042.8E-043.0E-043.4E-043.8E-044.3E-044.6E-045.0E-046.7E-043.9E-042.8E-043.0E-043.4E-043.9E-044.3E-044.8E-045.2E-047.4E-041 year4.0E-043.0E-043.1E-043.5E-043.8E-044.4E-044.9E-045.2E-046.3E-044.1E-043.1E-043.3E-043.6E-044.1E-044.6E-045.2E-045.4E-046.6E-042 years3.3E-042.5E-042.6E-042.9E-043.3E-043.6E-044.0E-044.4E-045.4E-043.4E-042.6E-042.7E-042.9E-043.3E-043.9E-044.3E-044.5E-044.9E-043 to <6 years2.4E-041.6E-041.7E-042.0E-042.4E-042.8E-043.1E-043.5E-044.8E-042.4E-041.4E-041.6E-042.0E-042.3E-042.8E-043.2E-043.5E-045.2E-046 to <11 years1.5E-041.0E-041.1E-041.3E-041.5E-041.7E-042.0E-042.2E-043.0E-041.5E-048.9E-059.7E-051.2E-041.5E-041.8E-042.1E-042.3E-043.0E-0411 to <16 years9.8E-056.7E-057.2E-058.1E-059.4E-051.1E-041.3E-041.4E-042.1E-049.0E-055.9E-056.5E-057.5E-058.7E-051.0E-041.2E-041.3E-041.8E-0416 to <21 years7.1E-054.7E-055.2E-056.1E-056.9E-058.0E-059.0E-059.8E-051.5E-046.9E-054.4E-054.7E-055.7E-056.7E-058.0E-059.3E-051.0E-041.5E-0421 to <31 years5.8E-053.8E-054.2E-054.8E-055.6E-056.6E-057.6E-058.3E-051.3E-045.5E-053.5E-053.8E-054.5E-055.4E-056.5E-057.4E-058.2E-059.8E-0531 to <41 years6.1E-053.8E-054.3E-055.0E-056.0E-057.0E-058.0E-058.6E-051.3E-045.6E-053.4E-053.7E-054.5E-055.4E-056.5E-057.6E-058.2E-051.1E-0441 to <51 years6.5E-054.4E-054.7E-055.4E-056.4E-057.4E-058.6E-059.2E-051.4E-046.0E-053.9E-054.1E-054.8E-055.7E-057.0E-058.4E-059.0E-051.1E-0451 to <61 years6.6E-054.5E-054.9E-055.5E-056.4E-057.6E-058.6E-059.3E-051.4E-046.1E-053.9E-054.2E-055.0E-055.9E-057.1E-058.3E-058.8E-051.3E-0461 to <71 years6.9E-055.1E-055.4E-056.0E-056.8E-057.6E-058.6E-059.3E-051.2E-046.1E-054.3E-054.6E-055.2E-055.9E-056.7E-057.6E-058.1E-051.0E-0471 to <81 years7.5E-055.5E-055.8E-056.4E-057.3E-058.3E-059.3E-059.9E-051.3E-046.6E-054.7E-055.1E-055.6E-056.4E-057.4E-058.4E-059.0E-051.3E-0481 years andolder8.0E-056.1E-056.4E-057.1E-057.8E-058.8E-059.7E-051.1E-041.2E-047.2E-055.1E-055.6E-056.3E-057.0E-057.9E-059.1E-059.6E-051.2E-04Table C-7. Descriptive statisticsfor daily ventilation rate (m3/min-kg), adjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min-kg) - Males,



# C-28Daily Ventilation Rate (m3/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thSedentary & Passive Activities (METS<1.5―Includes Sleep or Nap)Birth to <1year4.0E-043.0E-043.2E-043.5E-043.9E-044.4E-044.7E-045.0E-046.6E-044.0E-043.0E-043.2E-043.5E-044.0E-044.5E-044.8E-045.2E-047.2E-041 year4.1E-043.2E-043.3E-043.6E-044.0E-044.5E-044.9E-045.2E-046.2E-044.3E-043.4E-043.5E-043.8E-044.2E-044.7E-045.1E-045.4E-046.4E-042 years3.4E-042.7E-042.9E-043.1E-043.4E-043.7E-044.1E-044.5E-045.1E-043.6E-042.9E-043.0E-043.2E-043.5E-043.9E-044.2E-044.4E-044.8E-043 to <6 years2.5E-041.8E-041.9E-042.1E-042.5E-042.9E-043.3E-043.5E-044.5E-042.5E-041.6E-041.8E-042.1E-042.5E-042.8E-043.3E-043.6E-044.9E-046 to <11 years1.6E-041.1E-041.2E-041.4E-041.6E-041.8E-042.1E-042.2E-042.9E-041.6E-049.9E-051.1E-041.3E-041.6E-041.9E-042.1E-042.3E-042.9E-0411 to <16 years1.0E-047.7E-058.0E-058.8E-051.0E-041.2E-041.3E-041.4E-042.0E-049.7E-057.1E-057.5E-058.3E-059.5E-051.1E-041.2E-041.3E-041.7E-0416 to <21 years7.7E-055.5E-056.0E-056.8E-057.6E-058.5E-059.5E-051.0E-041.3E-047.5E-055.3E-055.7E-056.3E-057.4E-058.5E-059.6E-051.0E-041.4E-0421 to <31 years6.2E-054.7E-054.9E-055.5E-056.1E-056.9E-057.7E-058.2E-051.2E-046.0E-054.3E-054.5E-055.1E-055.9E-056.7E-057.5E-058.0E-059.9E-0531 to <41 years6.6E-054.6E-055.0E-055.7E-056.5E-057.4E-058.2E-058.6E-051.2E-046.0E-054.0E-054.2E-055.1E-055.9E-056.9E-057.8E-058.3E-051.1E-0441 to <51 years7.1E-055.4E-055.7E-056.2E-057.0E-057.8E-058.6E-059.1E-051.3E-046.5E-054.4E-054.8E-055.5E-056.3E-057.3E-058.3E-059.1E-051.1E-0451 to <61 years7.2E-055.5E-055.8E-056.3E-057.1E-057.9E-058.8E-059.2E-051.4E-046.7E-054.6E-055.1E-055.7E-056.5E-057.6E-058.3E-059.0E-051.2E-0461 to <71 years7.6E-056.1E-056.4E-056.9E-057.5E-058.1E-058.9E-059.4E-051.1E-046.6E-055.2E-055.4E-055.9E-056.6E-057.2E-057.8E-058.4E-051.0E-0471 to <81 years8.2E-056.7E-057.0E-057.5E-058.1E-058.8E-059.4E-059.8E-051.1E-047.2E-055.5E-056.0E-056.5E-057.1E-057.8E-058.8E-059.2E-051.5E-0481 years andolder8.6E-057.1E-057.5E-058.0E-058.6E-059.2E-059.9E-051.1E-041.1E-047.8E-056.3E-056.5E-057.0E-057.7E-058.6E-059.3E-059.6E-051.1E-04Table C-7. Descriptive statisticsfor daily ventilation rate (m3/min-kg), adjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min-kg) - Males,



# C-29Daily Ventilation Rate (m3/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightMeanPercentilesMaxPercentilesMeanMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thLight Intensity Activities (1.5 < METS<3.0)Birth to <1year9.9E-047.9E-048.3E-049.0E-049.7E-041.1E-031.2E-031.2E-031.4E-039.8E-047.9E-048.2E-048.8E-049.6E-041.0E-031.2E-031.2E-031.7E-031 year1.0E-038.4E-048.6E-049.2E-041.0E-031.1E-031.2E-031.3E-031.5E-031.0E-038.5E-048.7E-049.5E-041.0E-031.1E-031.2E-031.3E-031.6E-032 years8.4E-046.8E-047.2E-047.6E-048.3E-048.9E-041.0E-031.0E-031.2E-039.0E-047.3E-047.6E-048.2E-048.9E-049.6E-041.0E-031.1E-031.3E-033 to <6 years6.3E-044.4E-044.8E-045.4E-046.3E-047.1E-047.9E-048.7E-041.1E-036.2E-044.5E-044.8E-045.4E-046.0E-047.0E-047.8E-048.3E-041.0E-036 to <11 years3.8E-042.7E-042.9E-043.2E-043.8E-044.4E-044.9E-045.3E-047.1E-043.8E-042.5E-042.7E-043.1E-043.8E-044.4E-045.0E-045.4E-047.1E-0411 to <16 years2.5E-041.8E-041.9E-042.1E-042.4E-042.8E-043.1E-043.3E-044.4E-042.3E-041.6E-041.7E-042.0E-042.2E-042.5E-042.8E-043.1E-044.0E-0416 to <21 years1.8E-041.4E-041.4E-041.6E-041.8E-042.0E-042.2E-042.3E-043.3E-041.7E-041.3E-041.4E-041.5E-041.7E-041.9E-042.1E-042.2E-042.9E-0421 to <31 years1.6E-041.2E-041.3E-041.4E-041.5E-041.7E-041.9E-042.1E-042.9E-041.5E-041.2E-041.2E-041.3E-041.5E-041.6E-041.8E-041.9E-042.3E-0431 to <41 years1.6E-041.2E-041.3E-041.4E-041.6E-041.8E-042.0E-042.1E-042.8E-041.5E-041.1E-041.2E-041.3E-041.5E-041.8E-041.9E-042.0E-042.7E-0441 to <51 years1.7E-041.3E-041.3E-041.5E-041.6E-041.8E-042.0E-042.1E-043.3E-041.6E-041.1E-041.2E-041.4E-041.6E-041.8E-042.0E-042.2E-042.8E-0451 to <61 years1.7E-041.3E-041.3E-041.5E-041.6E-041.8E-042.0E-042.2E-042.9E-041.6E-041.2E-041.3E-041.4E-041.6E-041.8E-042.0E-042.1E-042.6E-0461 to <71 years1.6E-041.4E-041.4E-041.5E-041.6E-041.8E-041.9E-042.0E-042.7E-041.5E-041.2E-041.2E-041.3E-041.4E-041.6E-041.7E-041.8E-042.4E-0471 to <81 years1.7E-041.4E-041.5E-041.6E-041.7E-041.8E-041.9E-042.0E-042.6E-041.6E-041.2E-041.3E-041.4E-041.6E-041.7E-041.9E-042.0E-042.8E-0481 years andolder1.8E-041.5E-041.6E-041.7E-041.8E-042.0E-042.1E-042.2E-042.5E-041.7E-041.3E-041.4E-041.5E-041.6E-041.8E-042.0E-042.1E-042.3E-04Table C-7. Descriptive statisticsfor daily ventilation rate (m3/min-kg), adjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min-kg) - Males,



# C-30Daily Ventilation Rate (m3/min-kg) - Females,Adjusted for Body WeightAdjusted for Body WeightMeanPercentilesMaxi-mumPercentilesMeanMaximum5th10th25th50th75th90th95th5th10th25th50th75th90th95thModerate Intensity Activities (3.0 < METS<6.0)Birth to <1year1.8E-031.4E-031.5E-031.6E-031.8E-031.9E-032.2E-032.3E-033.0E-031.9E-031.5E-031.5E-031.7E-031.9E-032.0E-032.3E-032.4E-032.8E-031 year1.9E-031.4E-031.5E-031.7E-031.8E-032.0E-032.3E-032.5E-033.2E-031.9E-031.5E-031.6E-031.7E-031.9E-032.0E-032.2E-032.4E-033.2E-032 years1.5E-031.2E-031.3E-031.4E-031.5E-031.7E-031.8E-032.0E-032.3E-031.6E-031.3E-031.3E-031.4E-031.6E-031.7E-031.9E-032.0E-032.6E-033 to <6 years1.2E-038.0E-048.8E-041.0E-031.1E-031.3E-031.6E-031.7E-032.1E-031.1E-037.9E-048.5E-049.6E-041.1E-031.3E-031.5E-031.6E-031.9E-036 to <11 years7.4E-045.0E-045.5E-046.2E-047.1E-048.3E-049.6E-041.0E-031.4E-037.2E-044.6E-045.1E-046.0E-047.1E-048.4E-049.4E-041.0E-031.4E-0311 to <16 years4.9E-043.6E-043.8E-044.2E-044.7E-045.5E-046.4E-046.8E-041.1E-034.4E-043.2E-043.4E-043.8E-044.3E-044.9E-045.5E-046.1E-049.9E-0416 to <21 years3.9E-042.8E-043.0E-043.3E-043.8E-044.3E-044.9E-045.2E-047.1E-043.6E-042.7E-042.8E-043.1E-043.5E-044.1E-044.6E-044.9E-046.5E-0421 to <31 years3.6E-042.4E-042.6E-043.0E-043.4E-044.0E-044.7E-045.1E-048.2E-043.3E-042.4E-042.5E-042.8E-043.2E-043.6E-044.2E-044.5E-046.6E-0431 to <41 years3.6E-042.4E-042.6E-043.0E-043.4E-044.0E-044.7E-045.2E-047.6E-043.2E-042.1E-042.3E-042.7E-043.0E-043.5E-044.1E-044.6E-047.1E-0441 to <51 years3.7E-042.5E-042.7E-043.1E-043.5E-044.1E-044.7E-045.2E-047.2E-043.3E-042.2E-042.4E-042.8E-043.2E-043.8E-044.4E-044.9E-046.2E-0451 to <61 years3.8E-042.6E-042.8E-043.1E-043.7E-044.3E-044.8E-045.5E-047.6E-043.4E-042.4E-042.5E-042.8E-043.3E-043.8E-044.4E-044.9E-046.4E-0461 to <71 years3.4E-042.7E-042.8E-043.1E-043.4E-043.7E-044.0E-044.2E-045.7E-042.9E-042.2E-042.4E-042.6E-042.8E-043.2E-043.5E-043.7E-045.1E-0471 to <81 years3.6E-042.9E-043.1E-043.3E-043.6E-043.9E-044.2E-044.4E-045.5E-043.1E-042.4E-042.5E-042.7E-043.0E-043.4E-043.8E-044.1E-046.8E-0481 years andolder3.8E-043.1E-043.2E-043.5E-043.8E-044.2E-044.5E-044.7E-045.3E-043.3E-042.5E-042.7E-043.0E-043.3E-043.7E-044.0E-044.2E-045.2E-04

# C-31Table C-7. Descriptive statisticsfor daily ventilation rate (m3/min-kg), adjusted for body weight, while performingactivities within the specified activity category, by age and gender categories (continued)Age CategoryDaily Ventilation Rate (m3/min-kg) - Males,Adjusted for Body WeightDaily Ventilation Rate (m3/min-kg) - Females,Adjusted for Body WeightMeanPercentilesMaxMeanPercentilesMax5th10th25th50th75th90th95th5th10th25th50th75th90th95thHigh Intensity (METS > 6.0)Birth to <1year3.5E-032.7E-032.9E-033.1E-033.5E-033.8E-034.1E-034.3E-035.1E-033.3E-032.5E-032.6E-032.9E-033.2E-033.6E-034.0E-034.1E-035.0E-031 year3.5E-032.5E-032.9E-033.2E-033.6E-033.9E-034.1E-034.3E-034.9E-033.4E-032.6E-032.7E-033.0E-033.2E-033.7E-034.2E-034.9E-034.9E-032 years2.9E-032.2E-032.3E-032.6E-032.9E-033.2E-033.4E-033.5E-034.3E-032.8E-032.2E-032.3E-032.5E-032.8E-033.1E-033.4E-033.5E-033.9E-033 to <6 years2.2E-031.5E-031.7E-031.8E-032.1E-032.5E-032.7E-033.0E-033.6E-032.0E-031.4E-031.5E-031.7E-031.9E-032.2E-032.5E-033.0E-033.2E-036 to <11 years1.4E-039.4E-041.0E-031.2E-031.4E-031.6E-031.8E-031.9E-032.7E-031.3E-038.9E-049.7E-041.1E-031.3E-031.5E-031.7E-031.8E-032.2E-0311 to <16 years9.5E-046.3E-047.0E-047.9E-049.1E-041.1E-031.3E-031.4E-032.0E-038.8E-045.9E-046.3E-047.1E-048.5E-041.0E-031.2E-031.3E-032.0E-0316 to <21 years7.1E-044.8E-045.3E-046.0E-046.9E-048.0E-049.2E-041.0E-031.9E-037.0E-044.5E-045.0E-045.7E-046.9E-047.9E-049.2E-041.0E-031.5E-0321 to <31 years6.6E-044.5E-044.7E-045.4E-046.4E-047.5E-048.5E-049.7E-041.3E-036.5E-044.2E-044.6E-045.5E-046.3E-047.3E-048.8E-049.4E-041.3E-0331 to <41 years6.4E-044.4E-044.7E-045.3E-046.2E-047.3E-048.5E-049.3E-041.2E-036.1E-043.8E-044.2E-045.0E-045.9E-047.1E-048.3E-049.0E-041.5E-0341 to <51 years6.6E-044.4E-044.8E-045.5E-046.3E-047.4E-048.6E-049.4E-041.8E-036.5E-043.8E-044.4E-045.2E-046.4E-047.6E-048.8E-049.5E-041.6E-0351 to <61 years6.8E-044.5E-044.8E-045.5E-046.4E-047.7E-049.1E-041.0E-031.3E-036.3E-043.9E-044.3E-045.1E-046.1E-047.5E-048.5E-049.3E-041.4E-0361 to <71 years6.2E-044.4E-044.7E-045.3E-046.1E-047.0E-047.9E-048.5E-041.1E-035.4E-043.6E-044.0E-044.5E-045.3E-046.1E-047.2E-048.0E-041.1E-0371 to <81 years6.5E-044.7E-045.0E-045.5E-046.3E-047.2E-048.5E-049.1E-041.0E-035.9E-043.9E-044.4E-045.0E-045.8E-046.8E-047.8E-048.3E-041.3E-0381 years andolder7.2E-045.0E-045.4E-046.0E-047.0E-048.0E-049.4E-049.9E-041.4E-036.7E-044.5E-044.8E-045.4E-046.3E-047.7E-049.3E-049.7E-041.2E-03Individual measures are weighted by their 4-year sampling weights as assigned within NHANES 1999−2002 when calculating the statistics in this table.Ventilation rate was estimated using the multiple linear regression model in Section 3.6.

# 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 wi th 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 Blaisd ell (2007) ..................... D-12

8. Results of Compariso of APEX Estimat es 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 expe nditure comparison ................................................................ D-8

D-3. Comparison of body mass normalized mean basal energy expenditure (BEE-kg). ....... D-8

D-4. Comparison of mean total dail y energy expenditure (EE) .............................................. D-9

D-5. Comparison of mean physic al activity level (PAL) ...................................................... D-10

D-6. Comparison of mean daily ventilation rate (V E 

# ) ........................................................... D-10

D-7. Comparison of body mass normalized mean daily ventilation rate (V E 

# -kg) ................ D-11

D-8. Comparison of body mass normaliz ed mean ventilation rates (V E 

# -kg) correcting

Brochu et al. (2006a) results with ch ild appropriate VQ estimates .............................. D-12

D-9. Comparison of mean daily ventilation rate (V E 

# ) in children ........................................ D-14

D-10. Comparison of body mass normaliz ed mean ventilation rates (V E 

# -kg) in children ...... D-14

D-3

## 1. 

# Purpose

This appendix addresses comments provided by external peer revi ewers on Appendix A

of this document. In particular, clarificati on of how U.S. EPA’s Air Pollutants Exposure

(APEX) model can be used to estimate ventil ation 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 desi gned to estimate exposur e to air pollutants and

inhalation dose, accounting for the expected variability in both human behavior and physiology

through consideration of important influential ch aracteristics. 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 concentrati on with the time series of

activity-specific ventila tion rates that could be as s hort as 1 minute in duration.

Briefly, any number of indivi duals 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., bod y 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-loca tion-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 distributio n of Metabolic Equivalents of wo rk (METS) (e.g., point, lognormal,

normal) that is sampled to estimate activity-sp ecific 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) re sult in activity-specific

VO 2 

# (typically in L-O 2 

# /min). These data are used as input s to the regression equations reported

in Appendix A (along with age, gender, and body mass) to estimate ventilation rate (V E 

# , L/min).

There are greater details in the approach such as those regarding adjusting the MET time-series

to account for fatigue therefor e 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 activ ities. See U.S. EPA (2008a, b) for more details.

3. Ventilation Algorithm Evaluation

Two recent publications were id entified 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 comparis on, derived from data reported in studies that used the

doubly-labeled water (DLW) method to estimate en ergy 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 ex ample, overweight indi viduals were defined

as having body mass indexes (BMI) above the 97 th 

# percentiles for infants and toddlers <3 years,

>85 th 

# percentile for children under 20 , and BMI >25 for adults above 19 years old. Estimates of

energy expended were combined wi th a fixed oxygen uptake factor ( H = 0.21 ) and a fixed

ventilatory equivalent ( VQ = V E 

# /VO 2 

# = 27 ), while also accounting for stored daily energy cost

for growth up to age 24, although this cost is ge nerally 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 usef ul for estimating variability in an individuals ventilation rate (or

other parameter) over shorter time periods. Reported data are av erages 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 consum ed) 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 es timates 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 othe r 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 so me 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 reasonabl e number of persons within each year of age

and other potential catego rical 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 ag e and body mass are important infl uential variables. Body mass

and height estimates were used to calculat e 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 97 th 

# percentile for 2-year olds of the same

gender (there were no data repo rted by CDC for ages less than 2 years), while children aged

3

− 20 years were classified as ove rweight if they exceeded the 85 th 

# 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 an alysis. Multi-day pa rameter 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 Br ochu et al. (2006a).

5. Results of Comparison of APEX Esti mates with Brochu et al. (2008a, b)

Figure D-1 compares the mean body mass (B M, kg) estimates of APEX simulated

individuals by one year age inte rvals 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 popul ation reported by Brochu et al. (2006a) and the APEX simulated

population regarding body mass.

D-60 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 in tervals 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 nor malized basal energy expenditure (BEE-kg,

Kcal/day-kg) estimates of APEX simulated indi viduals by one year inte rvals 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 inte rvals and those reported in Table Web-3, page 3

of Brochu et al. (2006b) for several age groupi ngs 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-70 200 400 600 800 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-80 500 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 activ ity levels (PAL, unitless) estimated by

APEX simulated individuals by one year age inte rvals and those reported in Table Web-3, page 3

of Brochu et al. (2006b) for several age groupi ngs 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 a nd 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 (V E 

# , m 3 

# /day) estimates of APEX

simulated individuals by one year age intervals with those repo rted 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 estima tions were higher for ch ildren (age <11) and

the elderly (age >64), while Broc hu 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-90.0 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 me an daily ventilation rate (V E 

# ).

D-10

# Figure D-7 compares the body mass normali zed mean daily ventilation rate (V E 

# -kg,

m 3 

# /day-kg) estimates of APEX simulated indivi duals by one year age intervals with those

reported in Table 2, page 684 of Brochu et al. (2006a) for seve ral 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 Br ochu et al., [2006 a]). 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 (V E 

# -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 approxima tion 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 estimate s were modified here using the VQ estimates

offered by Arcus-Arth and Blaisdell (2007). Fi gure D-8 illustrates th e comparison of APEX

body mass normalized mean daily ven tilation 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 corre cting 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

(V E 

# -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. (200 6a) 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 co mpare 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 variab le in Arcus-Arth

and Blaisdell (2007), therefore all APEX simulate d individuals were used , regardless of whether

they could be classified as overw eight or of normal weight. In a ddition, data were obtained from

Tables 3 and 4 of Brochu et al. (2006a) for a fe w 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 wi th 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 sim ilar at each year of age, with slightly higher

estimates by Arcus-Arth and Blai sdell (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), pos sibly influenced by differences in body mass between the two

sample populations, particularly between ages 0 and 1.

10. Conclusions of Arcus-Arth a nd 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-130 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 me an daily ventilation rate (V E 

# ) 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 (V E 

# -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) Ph ysiological 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 an d health risk assessment. Hu m Ecol Risk Assess 12(4):675

− 701.

Brochu, P; Ducre-Robitaille, J-F; Brodeur, J. (2006b) Suppl emental 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: Un ited States. BMI-for-age

charts. National Center for Health Sta tistics, 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 Resear ch 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) Consol idated Human Activities Database. Office of Research

and Development, National Exposure Research La boratory, 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 Qu ality 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

.