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 20460DISCLAIMER

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.

iiCONTENTS

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

iiiCONTENTS (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

### 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-vA-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-1Central 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-2that 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-3structure) 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-4Modification 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-5A 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-6have 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-7Figure 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-8Table 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-9Table 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-10ventilation 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-11A-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-15V 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-16Johnson 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-17A-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-199. 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-20only 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-21Study 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-22A-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 ProfessorSecond 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-24with 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-253. 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-267. 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-27PREDICTION 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-28subsequently 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-29A-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-31o 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-32A-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-35A-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-maxA-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 D

RESPONSE PREPARED BY S. GRAHAM (U.S. EPA) TO PEER-REVIEW COMMENTS ON APPENDIX ATABLE 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-2LIST 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-31. 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-4postexercise 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-5are 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-10Figure 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-11mind, 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-128. 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-14D-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

.