ESTIMATION OF CARBON MONOXIDE
 

EXPOSURES AND ASSOCIATED
 

CARBOXYHEMOGLOBIN LEVELS IN DENVER RESIDENTS
 

USING pNEM/CO (VERSION 2.0)
 

by
 

Ted Johnson, Gary Mihlan, Jacky LaPointe, and Kris Fletcher
 

TRJ Environmental, Inc.
 

713 Shadylawn Road
 

Chapel Hill, NC 27514
 

Jim Capel
 

1005 Demerius Street
 

Durham, NC 27701
 

Contract No. 68-D6-0064
 

Work Assignment No. 1-19 and 2-24
 

Arlene Rosenbaum, Project Manager
 

ICF Kaiser Consulting Group
 

Systems Applications Division
 

101 Lucas Valley Road
 

San Rafael, CA 94903
 

Prepared for
 

Harvey Richmond, EPA Work Assignment Manager
 

U.S. Environmental Protection Agency
 

Office of Air Quality Planning and Standards
 

Research Triangle Park, North Carolina 27711
 

March 15, 1999 (Draft) 
DISCLAIMER
 

This draft report is being furnished to the U.S. Environmental Protection Agency 

by TRJ Environmental, Inc.; Jim Capel; and ICF Kaiser in partial fulfillment of Contract 

No. 68-D-0064, Work Assignment Nos. 1-19 and 2-24. The opinions, findings, and 

conclusions expressed are those of the authors and are not necessarily those of the 

Environmental Protection Agency. Comments should be addresses to Mr. Harvey 

Richmond, U.S. EPA, Office of Air Quality Planning and Standards, MD-15, Research 

Triangle Park, North Carolina 27711. 

ii 
CONTENTS
 

Disclaimer ........................................................... ii 
 

Figures ............................................................. vi 
 

Tables ............................................................. vii 
 

Acknowledgments ..................................................... xi

 Section	 Page 

1.	 Introduction .............................................. 1-1 
 

1.1 	 Applications of pNEM/CO to Denver, Colorado ............. 1-2 
 

1.2	 Report Organization .................................. 1-2 
 

1.3 	 References for Section 1 .............................. 1-3 
 

2.	 Overview of the Methodology ................................ 2-1 
 

2.1	 Define Study Area, Populations-of-Interest, Subdivisions of 

Study Area, and Exposure Period ....................... 2-1 
 

2.2	 Divide the Population-of-Interest into an Exhaustive Set 

of Cohorts .......................................... 2-5 
 

2.3	 Develop an Exposure Event Sequence for Each Cohort for 

the Exposure Period .................................. 2-7 
 

2.4	 Estimate the Pollutant Concentration, Ventilation Rate, and 

Carboxyhemoglobin Level Associated with Each Exposure 

Event ............................................ 2-16 
 

2.4.1 Estimation of Pollutant Concentration .............. 2-17 
 

2.4.2 The Air Quality Adjustment Model ................. 2-24 
 

2.4.3 Ventilation Rate ............................... 2-24 
 

2.4.4 Carboxyhemoglobin Level ....................... 2-27 
 

2.4.5 The Physiological Profile Generator ................ 2-27 
 

2.4.6 Hourly Average Exposure Estimates ............... 2-28 
 

2.5	 Extrapolate the Cohort Exposures to the Population-of-Interest 

and to Individual Sensitive Populations .................. 2-28 
 

2.5.1 General Population ............................ 2-28 
 

2.5.2 Persons with Ischemic Heart Disease .............. 2-29 
 

2.6 	 References for Section 2 ............................. 2-31 
 

iii 
CONTENTS (continued) 

3.	 Preparation of Fixed-Site Monitoring Data and Creation of 

Exposure Districts ......................................... 3-1 
 

3.1	 Selection of Monitoring Sites and Definition of Exposure 

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

3.2 	 Estimation of Missing Values ........................... 3-6 
 

3.3 	 References for Section 3 ............................. 3-15 
 

4.	 The Mass-Balance Model ................................... 4-1 
 

4.1	 Overview of the Model ................................ 4-1 
 

4.2	 The Air Exchange Rate Algorithm ....................... 4-5 
 

4.3	 Air Exchange Rate Distributions ......................... 4-9 
 

4.3.1 Residential Locations ........................... 4-10 
 

4.3.2 Nonresidential Locations ........................ 4-11 
 

4.3.3 Vehicle Locations .............................. 4-12 
 

4.4	 Probability of Stove Use .............................. 4-13 
 

4.5 	 Gas Stove Emission Rate ............................. 4-17 
 

4.6	 Residential Volume .................................. 4-20 
 

4.7 	 References for Section 4 ............................. 4-21 
 

5. 	 Estimation of Ventilation Rate ................................ 5-1 
 

5.1 The Metabolic Equivalence Concept ..................... 5-1 
 

5.2	 Oxygen Requirements for Energy Expenditure ............. 5-2 
 

5.3	 Estimating Ventilation Rate from Oxygen Uptake Rate ....... 5-2 
 

5.4	 Tests to Identify Unrealistic Estimates of Oxygen Uptake 

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

5.5	 The Probabilistic Algorithm ............................. 5-8 
 

5.6 	 Effect of Cardiovascular Disease on Exertion Levels ......... 5-8 
 

5.7 	 References for Section 5 ............................. 5-21 
 

6. 	 Estimation of Commuting Patterns ............................ 6-1 
 

6.1 The Bureau of Census Commuting Database .............. 6-1 
 

6.2 The Denver Exposure Districts .......................... 6-3 
 

6.3 Origin-Destination Table for Denver ...................... 6-5 
 

6.4 	 Data Quality ........................................ 6-7 
 

6.5 	 References for Section 6 .............................. 6-8 
 

iv 
CONTENTS (continued) 

7.	 Exposure Estimates for Denver Residents with Ischemic
 

Heart Disease ............................................ 7-1 
 

8. 	 Principal Limitations of the pNEM/CO Methodology ............... 8-1 
 

8.1 	 Time/Activity Patterns ................................. 8-2 
 

8.2	 Ventilation (Inhalation) Rates ........................... 8-3 
 

8.3	 Estimation of Cohort Populations ........................ 8-4 
 

8.4	 The Mass Balance Model .............................. 8-6 
 

8.5 	 The Carboxyhemoglobin Algorithm ...................... 8-8 
 

8.6 	 References ......................................... 8-8

 Appendices 

A Distributions of Energy Expenditure Rates Associated with CHAD Location
 

Descriptors ............................................. A-1 
 

B Fixed-Site Monitors in Colorado Reporting CO Data to AIRS for
 

One or More Years Between 1993 and 1997 ................... B-1 
 

C Distributions and Equations Used in the Ventilation Rate Algorithm . . C-1
 

D Analysis of Clinical Data Provided by Dr. William Adams .......... D-1 
 

E Algorithm for Estimating Carboxyhemoglobin Levels ............. E-1 
 

v 
FIGURES
 

Figure Page

 2-1 Conceptual Overview and Data Flow of pNEM/CO ..................... 2-2

 3-1 Monitoring Sites in the Greater Denver Area .......................... 3-2

 3-2 Monitoring Sites in the Denver Area Which Reported Data for 1995 

through 1997 .................................................. 3-5

 6-1 Fixed-Site CO Monitoring Sites Used to Define Denver Exposure Districts . . . 6-4
 

vi 
TABLES
 

Table Page

 2-1 Descriptive Statistics for Hourly Average Values in Data Sets Selected to
 

Represent Denver Exposure Districts After Estimation of Missing Values .... 2-4

 2-2 Demographic Groups Defined for the Denver pNEM/CO Analyses and
 

Number of Associated Cohorts .................................... 2-6

 2-3 Assignment of CHAD Location Codes to pNEM/CO Microenvironments .... 2-10

 2-4 Microenvironments Defined for the Denver pNEM/CO Analysis .......... 2-15

 2-5 Non-Zero Passive Smoking Increments for the Indicated CHAD Location
 

Codes ...................................................... 2-18

 2-6 Initial Values for Previous Outdoor Carbon Monoxide Concentration and
 

2-7 Algorithm Used to Simulate a Year-Long Sequence of Hourly-Average
 

2-8 Parameters Included in Physiological Profile for Adults in Version 2.0
 

2-9 Percentage of Persons with Ischemic Heart Disease (IHD) by Demographic
 

Upper Bounds of Indexing Intervals ................................ 2-21

Outdoor CO Concentrations for Each “Method A” Microenvironment ...... 2-22

of pNEM/CO .................................................. 2-30

Group ....................................................... 2-32

 2-10 Estimates of Population Residing in Each Denver Exposure District ....... 2-34

 3-1 Fixed-Site Monitors Reporting Carbon Monoxide Data for the Denver Area
 

Between 1993 and 1997 ......................................... 3-3

 3-2 Three-Year Average Value of Second Largest Daily Maximum 8-Hour CO
 

3-3 Characterization of Monitoring Sites Used in pNEM/CO for the Denver
 

3-4 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Littleton “Highlands” Site (005-0002, M) Before and After Estimation of
 

3-5 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Denver “Broadway” Site (031-0002, A) Before and After Estimation of
 

Concentration for Denver Area Monitors ............................. 3-4

Urban Area .................................................... 3-7

Missing Values ................................................. 3-8

Missing Values ................................................. 3-9 
 

vii 
TABLES (continued)

 3-6	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Denver “Albion” Site (031-0013, C) Before and After Estimation of
 

Missing Values ................................................ 3-10

 3-7	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Denver “Julian” Site (031-0014, B) Before and After Estimation of
 

Missing Values ................................................ 3-11

 3-8	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Arvada Site (059-0002, L) Before and After Estimation of Missing 

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

 3-9	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Boulder “28th Street” Site (013-0010) Before and After Estimation of
 

Missing Values ................................................ 3-13

 3-10 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Boulder “Marine Street” Site (013-1001) Before and After Estimation of
 

Missing Values ................................................ 3-14

 3-11 Comparison of Descriptive Statistics for 1-Hour Carbon Monoxide Concentrations
 

Reported by 28th Street, Marine Street, and Composite
 

Sites After Estimation of Missing Values ............................ 3-16

 3-12 Descriptive Statistics for Carbon Monoxide Concentrations Reported by
 

Boulder “Composite” After Estimation of Missing Values ................ 3-17

 4-1	 Distributions of Parameter Values Used in the pNEM/CO Mass-Balance
 

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

 4-2	 Distributions for Air Exchange Rate for Enclosed, Nonresidential 

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

 4-3	 Percentage of Person-Days with Indicated Window Ratio by Air 

Conditioning System and Temperature Range ....................... 4-10

 4-4	 Statistics on Gas Stove Use Obtained from a Survey by Koontz et al.
 

(1992) ...................................................... 4-13

 4-5	 Probability of Gas Stove Use by Clock Hour and Assumed Burner Operation
 

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

 4-6	 Proportion of PEM Values in Gas Stove Residences with Stove in Operation
 

by Clock Hour and Work Status ................................... 4-16 
 

viii 
TABLES (continued)

 4-7 Statistics on Square Footage of Occupied Units in Denver, Colorado 

(Bureau of Census, 1995) ....................................... 4-21

 5-1	 Regression-Based Estimates for Parameters of Equation 5-5 ............. 5-4

 5-2	 Values of the Upper Limit of PCTVO2max for Specified Averaging Times ..... 5-7

 5-3	 Algorithm Used in Version 2.0 of pNEM/CO to Estimate Ventilation Rates
 

as a Function of Energy Expenditure Rate ............................ 5-9

 5-4	 Parameters Used in Probablistic Algorithm for Estimating Ventilation Rate
 

in Version 2.0 of pNEM/CO ...................................... 5-10

 5-5	 Parameters for Lognormal Distributions Fitted to Body Mass (BM) Data in
 

Kilograms Organized by Age and Gender ........................... 5-11

 5-6	 Descriptive Statistics for VO2 and VER Measured at Maximal Exertion
 

by Various Researchers ......................................... 5-12

 5-7	 Estimates of the Energy Conversion Factor (ECF) Based on Data in 

Esmail, Bhambhani, and Brintnell (1995) ............................ 5-14

 5-8	 Regression Equations for Predicting Basal Metabolic Rate Provided by
 

Schofield (1985) as Compiled by McCurdy (1998) ..................... 5-15

 5-9	 Results of Statistical Tests Performed on CHAD Data Evaluating the 

Association Between Angina and Various Variables Representing
 

Physical Exertion and Time Spent in Selected Microenvironments
 

(Data for Males Only) ........................................... 5-17

 5-10 Results of Statistical Tests Performed on CHAD Data Evaluating the 

Association Between Angina and Various Variables Representing
 

Physical Exertion and Time Spent in Selected Microenvironments
 

(Data for Females Only) ......................................... 5-19

 6-1	 Format of Commuting Database Obtained from the Bureau of Census ...... 6-2

 6-2	 Exposure Districts Defined for Denver pNEM/CO Analysis ............... 6-5

 6-3	 Number and Fraction of Commuters Associated with Each Combination
 

of Home and Work District ........................................ 6-6

 6-4	 Percentage of Commuters (WR) Assigned by Bureau of Census to Each
 

Home-Work Combination Based on Supplemental Data ................. 6-9 
 

ix 
TABLES (continued)

 7-1 Number of Person-Days Under Existing Conditions in Which Denver 

Adults with Ischemic Heart Disease Were Estimated to Experience 

a 1-Hour Daily Maximum Carbon Monoxide Exposure At or Above 

the Specified Concentration Under Each of Two Scenarios . . . . . . . . . . . . . . . 7-2

 7-2 Number of Person-Days Under Existing Conditions in Which Denver 

Adults with Ischemic Heart Disease Were Estimated to Experience 

an 8-Hour Daily Maximum Carbon Monoxide Exposure At or Above 

the Specified Concentration Under Each of Two Scenarios . . . . . . . . . . . . . . . 7-3

 7-3 Number of Person-Days Under Existing Conditions in Which Denver 

Adults with Ischemic Heart Disease Were Estimated to Experience 

an End-of-Hour Carboxyhemoglobin Level At or Above the Specified 

Percentage Under Each of Two Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4 

x 
ACKNOWLEDGMENTS 

In 1992, the U.S. Environmental Protection Agency (EPA) funded the 

development of a probabilistic version of NEM applicable to carbon monoxide 

(pNEM/CO). This report describes an updated version of pNEM/CO developed in 1998 

(hereafter referred to as Version 2.0) through the efforts of the ICF Kaiser Consulting 

Group (ICF), IT Air Quality Services (ITAQS), TRJ Environmental, Inc. (TRJ), and Jim 

Capel. This report also presents the results of applying the updated model to residents 

of Denver, Colorado. 

The updated version of pNEM/CO consists of three principal parts: a main 

program which estimates carbon monoxide (CO) exposures within a defined population, 

a special module which estimates the carboxyhemoglobin (COHb) levels which result 

from these exposures, and a program which tabulates the exposure and COHb 

estimates. Supplementary programs are used to process air quality and population 

data for input into the main program. 

Mr. Ted Johnson of TRJ was the principal author of this report. He developed 

the general pNEM/CO methodology described in Section 2 and many of the specific 

algorithms used to simulate various exposure factors within the model. Mr. Gary Mihlan 

of TRJ processed commuting data provided by ICF to create the origin-destination table 

for Denver. Ms. Jacky LaPointe and Ms. Kristen Fletcher reviewed the scientific 

literature on selected indoor sources of CO and provided recommendations for 

modifying pNEM/CO to account for these sources. 

Mr. Jim Capel wrote the main pNEM/CO program, the COHb module, and the 

majority of supplementary programs. He also converted the Comprehensive Human 

Activity Database (CHAD) into an input database suitable for use with pNEM/CO. In 

addition, he set up all input data files and run streams required for application of 

pNEM/CO to Denver, ran the model, and tabulated the results. 

Mr. Dib Paul served as the project manager for ITAQS. He also performed a 

literature review to identify data useful in implementing the ventilation rate algorithm. 

Mr. Andy Law of ITAQS was coauthor of Section 3 and developed input data for the 

mass-balance and gas-stove models described in that section. Mr. Sri Pangaluri of 

xi 
ITAQS assisted Mr. Jim Capel in converting the CHAD database into an input database 

suitable for pNEM/CO. 

Ms. Arlene Rosenbaum served as the project manager for ICF. Ms. Pat Stiefer 

of ICF prepared the census and commuting data files for Denver. Dr. Jonathan Cohen, 

Mr. Sergey Nikiforov, and Ms. Rosenbaum performed the statistical comparison of 

exertion levels for persons with and without heart disease described in Section 5.6. 

The COHb module is based on algorithms developed by Dr. William F. Biller for 

the 1992 version of pNEM/CO under an earlier contract with EPA’s Office of Air Quality 

Planning and Standards. Dr. Biller also developed the hourly average version of the 

mass balance model in the 1992 version of pNEM/CO which continues to be used in the 

current version. Mr. Harvey Richmond of EPA assisted in developing the COHb 

algorithm and in characterizing the distributions of many of the variables contained in 

the COHb algorithm. 

The method for estimating ventilation (inhalation) rates described in Section 5 

was developed by Mr. Ted Johnson, Dr. Gary Mihlan, Ms. Jill Mozier, and Mr. Mark 

Weaver of TRJ. The method is based on an approach suggested by Mr. Tom McCurdy 

of EPA’s Office of Research and Development. Mr. McCurdy also provided estimates 

for various parameters used in implementing the method. The CHAD database was 

created jointly by Mr. McCurdy and by Mr. Yeshpal Lakkadi, Mr. Graham Glen, Mr. 

Luther Smith, Ms. Jo Ann Tippett, and Ms. Maria del Valle-Torres of ManTech 

Environmental Technology, Inc. The clinical data used to determine the relationship 

between ventilation rate and oxygen uptake rate were provided by Dr. William Adams 

through EPA Delivery Order No. 8D-0810-NAEX to the Regents of the University of 

California. Dr. Jonathan Cohen of ICF performed the statistical analyses of the Adams 

data which produced the parameter estimates listed in Table 5-1. 

The authors gratefully acknowledge the assistance of Dr. Irving Billick and the 

Gas Research Institute, who provided references and reports containing useful 

information on gas stove emission rates and operating conditions. 

The development of Version 2.0 of pNEM/CO and its application to Denver were 

funded by EPA under Contract Nos. 68-D3-0094 and 68-D6-0064. Mr. Harvey 

Richmond of EPA’s Health and Ecosystem Effects Group was the EPA Work 

Assignment Manager for all work assignments associated with this effort. Mr. 

xii 
Richmond also developed the prevalence estimates for ischemic heart disease 

presented in Section 2.5.2. 

xiii
 
SECTION 1
 

INTRODUCTION
 

In 1997, the U.S. Environmental protection Agency (EPA) initiated its periodic 

review of the national ambient air quality standards (NAAQS) for carbon monoxide (CO) 

as required under the Clean Air Act Amendments of 1990. As part of its review, EPA 

will prepare a Staff Paper that provides the EPA Administrator, the Clean Air Scientific 

Advisory Committee (CASAC), and the public with the staff’s interpretation of the 

scientific evidence reviewed in the revised Air Quality Criteria Document, also prepared 

by EPA, as well as the results of technical air quality and human exposure analyses 

conducted as part of this review. EPA presented a development plan for the CO 

NAAQS review (EPA, 1998) to the CASAC at a public meeting on November 16, 1998, 

which describes the scope of the review and presents the status and schedule for 

various aspects of the review, including the development of human exposure estimates 

to CO. 

One of the key inputs to the review is the estimation of human exposure. 

Exposure is being characterized using an updated version of the probabilistic NAAQS 

Exposure Model for CO (pNEM/CO) which was previously developed in 1992 as part of 

the last review of the CO NAAQS (see Johnson et al., 1992). This report presents a 

description of the revised version of pNEM/CO (hereafter referred to as Version 2.0) 

and a preliminary application of the model to the Denver urban area. The purpose of 

this report is to obtain technical review of the proposed methodology from the CASAC 

and the general public. As described in its development plan, EPA then plans to make 

any necessary revisions to the model and to apply the model to study areas in both 

Denver and Los Angeles. EPA selected Denver as one of the study areas to provide a 

basis for comparison with the previous review and because it is one of the few areas 

where a personal exposure study has been conducted that can be used to provide a 

limited evaluation of the model. Los Angeles is being included because (1) Los Angeles 

poses the largest public health burden in terms of ambient CO levels and potential 

population exposure, (2) the city has an extensive ambient monitoring network, and (3) 

1-1
 
there is a study of personal, indoor, and ambient concentrations in Los Angeles that can 

be used to provide a limited evaluation of the model. 

1.1 Applications of pNEM/CO to Denver, Colorado 

The original version of pNEM applicable to CO (pNEM/CO) was developed for 

EPA in 1991. Unlike the pNEM/O3 model which provides only exposure estimates, 

pNEM/CO also provides an estimate of internal dose [the carboxyhemoglobin (COHb) 

level] associated with each exposure. Johnson et. al. (1992) have described the use of 

pNEM/CO to estimate CO exposures and resulting COHb levels in the residents of 

Denver, Colorado. In this 1992 application, researchers estimated exposures expected 

under recent air quality conditions and under conditions in which a specific NAAQS was 

just attained in the city. 

Each version of pNEM has a modular structure, with separate computer 

subroutines being used to prepare input databases, calculate exposures, and tabulate 

results. This modular feature permits researchers to construct a new version of pNEM 

by combining features from existing versions with new components that address 

application-specific modeling needs. EPA has recently updated the pNEM/CO 

methodology to permit the use of 1990 census data. Analysts have also enhanced the 

algorithms in the model which simulate gas stove use, determine indoor CO 

concentrations, account for passive smoking, estimate ventilation (inhalation) rate, and 

model home-to-work commuting patterns. This report describes Version 2.0 of 

pNEM/CO and presents preliminary results of applying the model to the Denver area 

under recent air quality conditions. Once the methodology is reviewed and any 

necessary revisions are made, the model will be applied to both the Denver and Los 

Angeles study areas. A future technical report will include the estimated exposures 

under recent air quality conditions and under conditions in which the current CO 

NAAQS is just attained in each of these urban areas. 

1.2 Report Organization 

1-2
 
This report is divided into eight sections. Section 2 presents an overview of the 

updated pNEM/CO methodology. Section 3 outlines the methods used to select and 

process the fixed-site monitoring data used in applying pNEM/CO to Denver. Section 4 

provides a detailed description of the mass balance model used to estimate CO 

concentrations for indoor and in-vehicle microenvironments. Section 5 provides a 

summary of the procedure used to estimate ventilation rate. Section 6 describes the 

development of origin-destination tables for home-to-work commuting trips in Denver. 

Section 7 presents the results of applying pNEM/CO to special populations within the 

Denver metropolitan area. A discussion of the limitations of Version 2.0 of pNEM/CO 

can be found in Section 8. Section 8 also provides recommendations for further 

research. 

1.3 References for Section 1 

Johnson, T., J. Capel, R. Paul, and L. Wijnberg. 1992. Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver Residents 

Using a Probabilistic Version of NEM. Report prepared by International Technology 

Air Quality Services under EPA Contract No. 68-D0-0062. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina. 

U.S. Environmental Protection Agency. 1998. Carbon Monoxide NAAQS Review 

Development Plan. Office of Air Quality Planning and Standards, Research Triangle 

Park, North Carolina, November. 

1-3
 
SECTION 2
 

OVERVIEW OF THE METHODOLOGY
 

Version 2.0 of pNEM/CO follows the same general approach used in the original 

1992 version (Johnson et al., 1992). Figure 2-1 shows the conceptual overview of the 

logic and data flow of the model. The various inputs to the model (e.g., activity patterns, 

ambient monitoring data, air exchange rates, commuting data, population census data) 

are shown in the rounded boxes and the model calculations take place in the 

rectangular boxes (e.g., mass balance model for indoor microenvironments). The 

general pNEM methodology can be viewed as the following five steps: 

1.	 Define a study area, one or more populations-of-interest, appropriate 

subdivisions of the study area, and an exposure period. 

2.	 Divide the population-of interest into an exhaustive set of cohorts. 

3.	 Develop an exposure event sequence for each cohort for the exposure 

period. 

4.	 Estimate the pollutant concentration, ventilation rate, and physiological 

indicator (if applicable) associated with each exposure event. 

5.	 Extrapolate the cohort exposures to each population-of-interest. 

The remainder of this section describes how Version 2.0 implements each step of the 

pNEM/CO methodology. The preliminary application of the methodology to the Denver 

study area is used to illustrate the various aspects of the model. 

2.1	 Define Study Area, Populations-of-Interest, Subdivisions of Study Area, and 

Exposure Period 

The pNEM/CO methodology provides estimates of the distribution of CO 

exposures and associated carboxyhemoglobin (COHb) levels within a defined 

population (the population-of-interest) for a specified exposure period. The exposure 

2-1
 
Air Exchange Rates & 

Building Volumes 

Emission Rates and 

Use Patterns for 

Indoor Sources 

(e.g., gas appliances, 

passive smoking) 

Ambient Fixed-Site 

Concentrations 

Air Quality Specification

 Mass Balance Model 

for Indoor 

Microenvironments 

Exposure Algorithms 

Seasonal Considerations 

(Temperature) 

Human Activity and 

Exertion Patterns 

Population 

Commuting D

Distribution of Peop

Occurrences of Expo

Linked with Breathin

Outdoor 

Microenvironment 

Concentrations 

Figure 2-1. Conceptual Model and Data Flow of pNEM/CO 

2-2
 
period is usually a recent calendar year for which good data are available with respect 

to ambient CO levels. The population-of interest is typically defined as people with 

specific demographic characteristics (e.g., adults with ischemic heart disease) who live 

and work within a defined set of exposure districts. Each exposure district is a 

contiguous set of census units surrounding one or more fixed-site CO monitors selected 

as representative of the district. 

Analysts defined six exposure districts for the application of pNEM/CO to Denver. 

Section 3.1 describes in detail the process used to develop these districts. Briefly, 

analysts identified seven fixed-site monitors which (1) were located within 50 km of the 

center of Denver, (2) were located in areas of appropriate urban land use, and (3) 

reported sufficient air quality data for 1995 through 1997. Five of the seven sites were 

identical to sites used in the 1992 Denver analysis; the remaining two sites were 

located in downtown Boulder (28th Street and Marine Street). The locations of five sites 

used in the 1992 analysis were considered appropriate for defining five separate 

exposure districts with 10 km radii. However, the Boulder sites were considered too 

close together to support separate exposure districts. Consequently, analysts defined 

six exposure districts -- one for each of the 1992 Denver sites and a “composite” 

Boulder site. For purposes of constructing the associated exposure district, the 

composite site was assigned a location midway between the two Boulder sites. 

The focus of the previous review of the CO NAAQS was on the population with 

ischemic heart disease. As the incidence of ischemic heart disease for individuals 

younger than age 18 is extremely small, EPA has chosen to define the population of 

interest as adults (18 and older) with ischemic heart disease who lived and worked 

within the six exposure districts. 

The exposure period was defined as calendar years 1995, 1996, and 1997. 

Consequently, 21 “site-years” of fixed-site monitoring data were selected for the new 

Denver analysis (7 sites x three years/site). Each of the selected site-years of data was 

at least 96.6 percent complete. Table 2-1 provides descriptive statistics for these data 

sets after missing values were estimated. Section 3 outlines the methods used to select 

and process the Denver monitoring data. 

2-3
 
Table 2-1. Descriptive Statistics for Hourly Average Values in Data Sets Selected to 

Represent Denver Exposure Districts After Estimation of Missing Values 

Location Monitor ID Year 

Descriptive statistics for hourly-average CO concentrations, 

ppm 

50th 90th 95th 99th 99.5th Maximum 

Littleton 005-0002 95 0.3 0.7 1.0 1.8 2.2 3.6 

96 0.3 0.7 1.0 1.8 2.2 4.3 

97 0.3 0.7 1.0 1.7 2.0 4.3 

Broadway 031-0002 95 1.2 2.7 3.4 6.1 7.7 24.5 

96 1.1 2.5 3.2 5.6 6.9 21.6 

97 1.0 2.4 3.0 5.4 6.3 11.4 

Albion 031-0013 95 0.9 2.5 3.4 5.5 6.4 14.6 

96 0.8 2.2 3.0 5.0 5.7 14.6 

97 0.8 2.1 2.8 4.7 5.4 11.6 

Julian 031-0014 95 0.7 2.3 3.2 5.3 6.5 10.4 

96 0.6 2.0 2.9 4.7 5.5 9.1 

97 0.6 2.1 2.9 5.0 5.8 9.5 

Arvada 059-0002 95 0.6 2.0 2.7 4.8 5.8 11.9 

96 0.5 1.7 2.4 4.1 4.9 7.9 

97 0.6 1.8 2.4 4.2 5.1 9.2 

Boulder 

28th St. 

013-0010 95 0.8 2.1 2.8 4.8 5.5 10.6 

96 0.7 1.8 2.4 3.9 4.7 8.5 

97 0.7 1.7 2.4 3.9 4.5 9.0 

Boulder 

Marine St. 

013-1001 95 0.4 0.9 1.3 2.3 2.9 8.3 

96 0.4 0.9 1.1 2.0 2.5 4.5 

97 0.4 0.9 1.2 2.4 3.0 7.1 

Composite 

Boulder 

monitor 

--­ 95 0.7 1.5 2.0 3.3 3.8 9.5 

96 0.6 1.3 1.7 2.8 3.3 6.0 

97 0.6 1.3 1.7 3.0 3.6 7.3 

2-4
 
2.2 Divide the Population-of-Interest into an Exhaustive Set of Cohorts 

In a pNEM analysis, the population-of-interest is divided into a set of cohorts 

such that each person is assigned to one and only one cohort. Each cohort is assumed 

to contain persons with identical exposures during the specified exposure period. 

Cohort exposure is typically assumed to be a function of demographic group, location of 

residence, and location of work place. Specifying the home and work district of each 

cohort provides a means of linking cohort exposure to ambient CO concentrations. 

Specifying the demographic group provides a means of linking cohort exposure to 

activity patterns which vary with age, work status, and other demographic variables. In 

some analyses, cohorts are further distinguished according to factors relating to 

proximity to emission sources or time spent in particular microenvironments. 

In the application of pNEM/CO (Version 2.0) to Denver, each cohort was 

identified as a distinct combination of (1) home district, (2) demographic group, (3) work 

district (if applicable), (4) residential cooking fuel, and (5) replicate number. The home 

district and work district of each cohort were identified according to the districts defined 

above. Table 2-2 lists 11 demographic groups defined for the Denver pNEM/CO 

analysis. Four of the demographic groups are identified as workers. Each cohort 

associated with one of these groups was identified by both home and work district. The 

remaining cohorts were identified only by home district. Note that although children 

were included within the demographic groups defined for the Denver analysis, the 

exposure estimates summarized in Section 7 are limited to the adult demographic 

groups. 

The residential cooking fuel of each cohort was identified as either “natural gas” 

or “other.” This cohort index was used because a personal monitoring study (Johnson, 

1984) conducted in Denver suggested that proximity to operating natural gas stoves 

contributed significantly to CO exposure. A review of the scientific literature concerning 

five other sources (kerosene space heaters, gas space heaters, wood stoves, 

fireplaces, and attached garages) indicated that each source either did not contribute 

significantly to indoor CO levels or was used in less than 1 percent of the Denver 

residences (Fletcher and LaPointe, 1998). 

2-5
 
Table 2-2. Demographic Groups Defined for the Denver pNEM/CO Analyses 

and Number of Associated Cohorts 

Demographic group 

Includes commuting 

cohorts? 

Number of cohorts 

associated with 

demographic group

 1. Children, 0 to 17 no 60

 2. Males, 18 to 44, working yes 360

 3. Males, 18 to 44, nonworking no 60

 4. Males, 45 to 64, working yes 360

 5. Males, 45 to 64, nonworking no 60

 6. Males, 65+ no 60

 7. Females, 18 to 44, working yes 360

 8. Females, 18 to 44, nonworking no 60

 9. Females, 45 to 64, working yes 360 

10. Females, 45 to 64, nonworking no 60 

11. Females, 65+ no 60 

Total 1,860 

2-6
 
Earlier versions of pNEM/CO have used defined cohorts solely according to home 

district, demographic group, work district (if applicable), and residential cooking fuel. A 

new feature was installed in pNEM/CO (Version 2.0) which permitted the user to specify 

a “replication” value (n) such that the model will produce n cohorts for each combination 

of these four indices. Because pNEM/CO uses a Monte Carlo process to construct an 

activity pattern for each cohort, each of the n cohorts associated with a particular 

combination of home district, demographic group, work district, and residential cooking 

fuel is associated with a distinct exposure sequence. The replication feature permits the 

analyst to divide the population-of-interest into a larger number of smaller cohorts -- a 

process which decreases the “lumpiness” of the exposure simulation. A replication 

value of five (n = 5) was specified for the Denver exposure analyses described in this 

report. Consequently, the pNEM/CO model analyzed five times the number of cohorts it 

would have considered if the cohorts had been defined solely by home district, 

demographic group, work district, and residential cooking fuel. 

Table 2-2 lists the number of Denver cohorts associated with each demographic 

group. Each of the seven nonworking demographic groups is associated with 60 

cohorts, one for each combination of home district, residential cooking fuel, and 

replicate number (6 x 2 x 5 = 60). Each of the four working demographic groups is 

associated with 360 cohorts, one for each combination of home district, work district, 

residential cooking fuel, and replicate number (6 x 2 x 6 x 5 = 360). The total number of 

Denver cohorts is thus (7 x 60) + (4 x 360) or 1,860. 

2.3	 Develop an Exposure Event Sequence for Each Cohort for the Exposure 

Period 

In the pNEM/CO methodology, the exposure of each cohort is determined by an 

exposure event sequence (EES) specific to the cohort. Each EES consists of a series 

of events with durations from 1 to 60 minutes. To permit the analyst to determine 

average exposures for specific clock hours, the exposure events are defined such that 

no event falls within more than one clock hour. Each exposure event assigns the cohort 

to a particular combination of geographic area and microenvironment. In addition, each 

event specifies whether or not the cohort is in the presence of smokers. Each event 

2-7
 
also provides an indication of respiration rate. In the original (1992) version of pNEM, 

this indicator was a classification of breathing rate as sleeping, slow, medium, or fast. 

In Version 2.0 of pNEM/CO, this indicator is a specific activity descriptor such as 

“raking” or “playing baseball.” 

In typical pNEM applications, the EESs are determined by assembling activity 

diary records relating to individual 24-hour periods into a year-long series of records. 

Because each subject of a typical activity diary study provides data for only a few days, 

the construction of a year-long EES requires either the repetition of data from one 

subject or the use of data from multiple subjects. The latter approach is used in pNEM 

analyses to better represent the variability of exposure that is expected to occur among 

the persons included in the cohort. 

In the pNEM/CO (Version 2.0) analysis, activity diary data were obtained from 

the Consolidated Human Activity Database (CHAD). CHAD is comprised of 

approximately 17,000 person-days of 24-hour time/activity data developed from eight 

surveys (Tippett et al., 1997). The surveys include probability-based recall studies 

conducted by EPA and the California Air Resources Board, as well as real-time diary 

studies conducted in individual U.S. metropolitan studies using both probability-based 

and volunteer subject panels. All ages of both genders are represented in CHAD. The 

data for each subject consist of one or more days of sequential activities, in which each 

activity is defined by start time, duration, activity type (140 categories), and 

microenvironment classification (110 categories). Activities vary from one minute to one 

hour in duration, with longer activities being subdivided into clock-hour durations to 

facilitate exposure modeling. A distribution of values for the ratio of oxygen uptake rate 

to body mass (referred to as metabolic equivalents or “METs”) is provided for each 

activity type listed in CHAD. The forms and parameters of these distributions were 

determined through an extensive review of the exercise and nutrition literature. The 

primary source of distributional data was Ainsworth et al. (1993), a compendium 

developed specifically to “facilitate the coding of physical activities and to promote 

comparability across studies.” 

The CHAD database was processed to create a special database appropriate for 

input in pNEM/CO. This database consisted of diary records organized by study subject 

and calendar day. The diary records for one subject for one calendar day were 

2-8
 
designated a “person-day.” The CHAD-derived database contained 14,048 usable 

person-days, each of which was indexed by the following factors: 

1. Demographic group 

2. Season: summer or winter 

3. Temperature classification: cool or warm 

4. Day type: weekday or weekend. 

The demographic group index was determined by the demographic group to which the 

subject filling out the diary belonged. The season and day indices were based on the 

date of the calendar day. The temperature classification was based on the daily 

maximum temperature (in °F) of the associated geographic location on that date. The 

cool range was defined as temperatures below 55° in winter and temperatures below 

84° in summer. 

The EES for each cohort was determined by a computerized sampling algorithm. 

The algorithm was provided with the sequence of daily maximum temperatures reported 

by Denver in each of three years (1995, 1996, 1997) and with a list of cohorts. The 

temperature data were used to assign each calendar day in these years to one of the 

temperature ranges used in classifying the activity diary data. To construct the EES for 

a particular cohort, the algorithm selected a person-day from the CHAD-derived 

database for each calendar day in the three-year period according to the demographic 

group of the cohort and the season, day type, and temperature classification associated 

with the time period. 

Each exposure event within an EES was defined by (1) district, (2) CHAD 

location descriptor, (3) microenvironment, (4) CHAD activity descriptor, and (5) passive 

smoking status. The district was either the home or work district associated with the 

cohort. The home/work determination was based on a decision rule which was applied 

to the activity diary data associated with the exposure event. 

The CHAD location descriptor refers to the location code associated with the 

event in the CHAD database. The location descriptor was used to assign the event to a 

microenvironment and to determine the contribution of passive smoking (if applicable) to 

the CO concentration experienced during the event. Table 2-3 lists the 120 codes used 

to define the location descriptors of exposure events. 

Table 2-4 lists the 13 microenvironments used for event assignments. Each 

2-9
 
microenvironment is identified as to a general location (e.g., outdoors) and a specific 

location (e.g., near road). The list includes two indoor microenvironments related to 

residences, five indoor microenvironments related to nonresidential buildings, three 

outdoor microenvironments, and three vehicle microenvironments. The majority of 

these microenvironments are aggregates of two or more of the CHAD location 

descriptors. Only location descriptions associated with similar average CO exposures 

were combined in defining the aggregate microenvironments. Researchers determined 

these similarities through an analysis of personal CO monitoring data obtained from the 

Denver activity diary study (Johnson, 1984). Table 2-3 shows the assignment of CHAD 

location descriptors to microenvironments. 

Activity descriptors were defined according to activity classifications appearing in 

CHAD. CHAD provides a distribution of energy expenditure rate for each activity 

classification which was used in a later step to estimate a ventilation rate for each 

activity (see Section 5). Appendix A lists the CHAD descriptors and associated 

distributions for energy expenditure rate. 

The effects of active smoking on CO exposure were not addressed in the 

exposure analysis described here. Because of the coding conventions used in the 

CHAD diary studies, passive smoking patterns could be determined for nonsmoking 

subjects only. Consequently, the activity diaries sampled in constructing EESs were 

limited to those of nonsmokersa

. The diary record associated with each exposure event 

provided information on whether or not the subject was in the presence of smokers. 

This information was used to assign a passive smoking status to each event. a(ALL 

CHAD DATA ARE CURRENTLY USED -- CHAD DATA WILL BE LIMITED TO 

NONSMOKERS IN NEXT VERSION). 

2-10
 
Table 2-3. Assignment of CHAD Location Codes to pNEM/CO Microenvironments 

(MAY BE REVISED) 

CHAD Location Code 

pNEM/CO 

Microenvironment 

Code 

<30> Home 

30000: residence, general 1 

30010: your residence 1

 30020: other’s residence 1 

30100: residence, indoor 1

 30120: your residence, indoor 1

 30121: kitchen 1

 30122: living room/ family room 1

 30123: dining room 1

 30124: bathroom 1

 30125: bedroom 1

 30126: study/ office 1

 30127: basement 1

 30128: utility room/ laundry room 1

 30129: other indoor 1

 30130: other’s residence, indoor 1

 30131: kitchen 1

 30132: living room/ family room 1

 30133: dining room 1

 30134: bathroom 1

 30135: bedroom 1

 30136: study/ office 1

 30137: basement 1

 30138: utility room/ laundry room 1 

2-11
 
CHAD Location Code 

pNEM/CO 

Microenvironment 

Code 

30139: other indoor 1 

30200: residence, outdoor 9

 30210: your residence, outdoor 9

 30211: pool, spa 9

 30219: other outdoor 9

 30220: other’s residence, outdoor 9

 30221: pool, spa 9

 30229: other outdoor 9 

30300: garage 7

 30310: indoor garage 7

 30320: outdoor garage 9

 30330: your garage 7

 30331: indoor garage 7

 30332: outdoor garage 9

 30340: other’s garage 7

 30341: indoor garage 7

 30342: outdoor garage 9 

30400: other, residence 1 

<31> Travel 

31000: travel, general 10 

31100: motorized travel

 31110: car 10

 31120: truck 11

 31121: truck (pick-up or van) 11

 31122: truck (other than pick-up or van) 11

 31130: motorcycle/ moped/ motorized scooter 11 

2-12
 

 

CHAD Location Code 

pNEM/CO 

Microenvironment 

Code 

31140: bus 11

 31150: train/ subway/ rapid transit 11

 31160: airplane 99

 31170: boat 9

 31171: motorized boat 9

 31172: unmotorized boat 9 

31200: non-motorized travel 9

 31210: walk 9

 31220: bicycle/ skateboard/ roller-skates 9

 31230: in a stroller or carried by an adult 9 

31300: waiting 9

 31310: wait for bus, train, ride (at stop) 8

 31320: wait for travel, indoors 5 

31900: other travel 11

 31910: other vehicle 11 

<32-34> Other Indoor 

32000: other indoor, general 5 

32100: office building/ bank/ post office 5 

32200: industrial plant/ factory/ warehouse 6 

32300: grocery store/ convenience store 5 

32400: shopping mall/ non-grocery store 3 

32500: bar/ night club/ bowling alley 4

 32510: bar/ night club

 32520: bowling alley 

32600: repair shop

 32610: auto repair shop/ gas station 2 

2-13
CHAD Location Code 

pNEM/CO 

Microenvironment 

Code 

32620: other repair shop 3 

32700: indoor gym/ sports or health club 6 

32800: childcare facility

 32810: childcare facility, house 1

 32820: childcare facility, commercial 6 

32900: public building/ library/ museum/ theater 5

 32910: auditorium, sport’s arena, concert hall 4

 32920: library, courtroom, museum, theater 5 

33100: laundromat 5 

33200: hospital/ health care facility/ doctor’s office 6 

33300: beauty parlor/ barber shop/ hair dresser’s 5 

33400: at work: no specific location, moving among locations 5 

33500: school 6 

33600: restaurant 4 

33700: church 6 

33800: hotel/ motel 5 

33900: dry cleaners 6 

34100: parking garage 12 

34200: laboratory 5 

34300: other, indoor (specify) 5 

<35-36> Other Outdoor 

35000: other outdoor, general 9 

35100: sidewalk/ street/ neighborhood 8

 35110: within 10 yards of street 8 

35200: public garage/ parking lot 12

 35210: public garage 12 

2-14
CHAD Location Code 

pNEM/CO 

Microenvironment 

Code 

35220: parking lot 12 

35300: service station/ gas station 12 

35400: construction site 9 

35500: amusement park 9 

35600: school grounds/ playground 9

 35610: school grounds

 35620: playground 

35700: sports stadium and amphitheater 9 

35800: park/ golf course 9

 35810: park

 35820: golf course 

35900: pool, river, lake 9 

36100: restaurant, picnic 9 

36200: farm 9 

36300: other outdoor (specify) 9 

2-15
Table 2-4. Microenvironments Defined for the Denver 

pNEM/CO Analysis 

Microenvironment 

Activity diary locations included in 

microenvironment 

Code 

General 

location Specific location 

1 Indoors Residence Indoors - residence 

2 Indoors Nonresidence A Service station or auto repair 

3 Indoors Nonresidence B Other repair shop 

Shopping mall 

4 Indoors Nonresidence C Restaurant 

Other indoor location 

Auditorium 

5 Indoors Nonresidence D Store 

Office 

Other public building 

6 Indoors Nonresidence E Health care facility 

School 

Church 

Manufacturing facility 

7 Indoors Residential garage Residential garage 

8 Outdoors Near road Near road 

9 Outdoors Other locations Outdoor residential garage 

Construction site 

Residential grounds 

School grounds 

Sports arena 

Park or golf course 

Other outdoor location 

10 Vehicle Automobile Automobile 

11 Vehicle Other Bus 

Truck 

Bicycle 

Motorcycle 

Train/subway 

Other vehicle 

12 Outdoor Public parking or fueling facility Indoor parking garage 

Outdoor parking garage 

Outdoor parking lot 

Outdoor service station 

99 Vehicle Airplane Airplane 

2-16
 
2.4	 Estimate the Pollutant Concentration, Ventilation Rate, and COHb Level 

Associated with Each Exposure Event 

In the general pNEM methodology, the EES defined for each cohort is used to 

determine a corresponding sequence of exposures, event by event. Each exposure is 

defined by a pollutant concentration and a ventilation rate indicator. In some 

applications, a biokinetics model is used to determine the status of a physiological 

indicator at the end of each exposure event, based on the status of the indicator at the 

beginning of each event and the pollutant dose delivered during the event. The 

delivered dose is a function of the pollutant concentration and ventilation rate values 

assigned to the event and the demographic characteristics of the cohort. 

2.4.1	 Estimation of Pollutant Concentration 

In the pNEM/CO analysis, each exposure event within a particular EES was 

indexed according to district d, microenvironment m, person-day p, clock hour h, and 

start time t. The exposure associated with a particular event, CEXP(d,m,p,h,t) was 

estimated by the expression 

CEXP(d,m,p,h,t) = CME(d,m,p,h) + SMOKE(m,t).	 (2-1) 

CME(d,m,p,h) is the CO concentration determined for microenvironment m in district d 

for person-day p and hour h. SMOKE(m,t) is an assumed contribution from passive 

smoking specific to microenvironment and event. 

The SMOKE(m,t) term represented the short-term contribution of passive 

smoking to CO exposure. The operation of this term was a function of two event 

descriptors: passive smoking status and CHAD location descriptor. If (1) the passive 

smoking status for an indoor event indicated the presence of smokers and (2) the 

location was one of the three microenvironments listed in Table 2-5, then the value of 

SMOKE(m,t) was set equal to the value specified in the table for the duration of the 

event. Otherwise, SMOKE(m,t) was set equal to zero. The values listed in Table 2-5 

2-17
 
were based on statistical analyses (Johnson, Memorandum No. 7, 1998) of data 

obtained from a personal monitoring study conducted in Denver (Johnson, 1984) and a 

microenvironmental monitoring study performed in northern California (Ott et al., in 

press). 

The three microenvironments listed in Table 2-5 include the indoor 

microenvironments that are expected to be most impacted by passive smoking. Version 

2.0 of pNEM/CO currently treats the remaining ten microenvironments as being free 

from the effects of passive smoking. This assumption may underestimate CO levels in 

some of these microenvironments. However, analysts were unable to find sufficient 

data to develop realistic estimates for the contribution of passive smoking to these 

microenvironments. 

The CME(d,m,p,h) term represented the component of exposure contributed by 

ambient (outdoor) CO concentrations and by the operation of residential gas stoves. An 

array of CME values was created for each cohort. Each array consisted of a set of 

year-long sequences of hourly-average CME values, one for each combination of 

microenvironment and district. The district was either the home or work district 

specified for the cohort. When an exposure event occurring during hour h assigned a 

cohort to a particular combination of microenvironment and district, the cohort was 

assigned the CO concentration specified for hour h in the designated 

microenvironment/district sequence. 

Each year-long sequence of hourly average CME values for the indoor and motor 

vehicle microenvironments was generated by the mass-balance algorithm described in 

Section 4. Briefly, this algorithm estimated the hourly average indoor CO 

concentrations during hour h as a function of the indoor CO concentration during the 

preceding hour (i.e., hour h - 1), the CO concentration outdoors during hour h, the air 

exchange rate during hour h, and the indoor emissions of CO from gas stoves during 

hour h (if applicable). Values for the air exchange rate and gas stove emission rate 

were sampled from appropriate distributions on a daily basis. During each clock hour, 

gas stoves were probabilistically determined as “on” for 30 minutes, “on” for 60 minutes, 

or “off’ for the entire hour. The probability of being on varied with time of day according 

to use patterns observed during the Denver activity diary study. 

2-18
 
Table 2-5. Non-Zero Passive Smoking Increments for Indicated CHAD 

Location Codes. 

pNEM/CO 

microenvironment 

Denver location 

category 

CHAD location categories 

with non-zero passive 

smoking increments 

Passive 

smoking 

increment, 

ppm 

1: indoors, 

residence 

2: indoors - residence All CHAD codes assigned 

to pNEM/CO microenvironment no. 1 

0.4 

4: indoors, 

nonresidence C 

5: restaurant 33600: restaurant 1.1 

bara 32500: bar, night club, or 

bowling alley 

32510: bar or nightclub 

32520: bowling alley 

5.3 

56: auditorium 32910: auditorium, sports 

arena, or concert hall 

2.3 

62: other indoor 

locations 

32000: other indoor, 

general 

34300: other indoor, 

specify 

1.2 

5: indoors, 

nonresidence D 

3: office 32100: office building, 

bank, or post office 

0.6 

a

The location “bar” does not have a distinct location code in the Denver PEM database. 

2-19
 
Ideally, passive smoking would have been modeled within the mass balance 

model described above rather than represented by a separate additive factor. Law 

(1998) attempted to account for passive smoking in the mass balance model, but found 

that there were insufficient data available for one of the key parameters required for the 

model, namely, the number of smokers actively smoking in various microenvironments. 

Consequently, EPA elected to treat passive smoking as an additive factor representing 

the average contribution of passive smoking to CO levels in each of three 

microenvironments listed in Table 5-2. 

The outdoor CO concentration required by the mass-balance algorithm was 

determined for each hour through a Monte Carlo process. According to this process, 

the outdoor CO concentration estimated for a particular hour h was influenced by (1) the 

microenvironment, (2) the outdoor CO concentration estimated for the preceding hour 

for the specified microenvironment, and (3) the CO concentration estimated for hour h 

at the fixed-site monitor representing district d. 

For a particular combination of microenvironment and district, the process 

consisted of stepping through the hours in the calendar year and selecting an outdoor 

CO concentration for each hour. The selection for a particular hour was made by 

randomly selecting a value from one of a set of empirical distributions established for 

the micoenvironment. Two methods were employed in making these selections. In 

Method A, the selection procedure accounted for both the current fixed-site CO 

concentration and the outdoor CO concentration determined for the preceding hour. In 

Method B, the selection procedure accounted only for the current fixed-site CO 

concentration. Method A was applied to the four microenvironments listed in Table 2-6. 

Method B was applied to the remaining microenvironments. 

Twenty-five empirical distributions were established for each of the Method A 

microenvironments. Each distribution represented the distribution of outdoor CO 

concentrations expected to occur for a defined pair of events: (1) the fixed-site CO 

concentration falls within interval i and (2) the outdoor CO concentration of the previous 

hour falls within interval j. 

2-20
 
The following five intervals were established for fixed-site concentrations. 

CO concentration, ppm 

Interval (i) Lower bound Upper bound 

1 0 1 

2 1 2 

3 2 4 

4 4 8 

5 8 na 

The first interval includes 0 and 1. Each of the remaining intervals excludes the 

specified lower bound and includes the specified upper bound. 

Five intervals were established for the previous outdoor CO concentration 

(interval j) for each of the four microenvironments. Table 2-6 lists the upper bounds of 

these intervals. 

Table 2-6. Initial Values for Previous Outdoor Carbon Monoxide Concentration and 

Upper Bounds of Indexing Intervals (Interval j) 

Microenvironment Carbon monoxide concentration, ppm 

Code General 

location 

Specific location Initial 

valuea 

Upper bound of indicated interval 

1 2 3 4 5 

1 Indoors Residence 1.1 0 1.1 3.1 6.0 b 

4 Indoors Nonresidence C 3.1 1.2 3.1 6.1 10.6 b 

5 Indoors Nonresidence D 2.3 0.5 2.3 4.8 8.3 b 

10 Vehicle Automobile 5.0 1.8 5.0 10.0 16.3 b a

For outdoor concentration during previous hour. Value set equal to upper bound of
 

second interval. 

b

Not bounded.
 

Each distribution was expressed as a cumulative probability distribution. Values 

were selected randomly from a distribution by first selecting a random number between 

zero and one and then determining the CO concentration associated with the random 

number on the cumulative distribution. For example, the random number 0.87 would 

select the concentration value associated with the 87th percentile of the cumulative 

distribution. 

2-21
 
Table 2-7 presents the algorithm used to simulate a year-long sequence of 

hourly-average outdoor CO concentrations for each of the Method A microenvironments. The empirical distributions used in Step 7 were developed through a statistical 

analysis of data obtained from the 1982 - 1983 Denver activity diary study (Johnson, 

1984). During this study, each of approximately 450 subjects carried a personal 

exposure monitor (PEM) for two 24-hour periods. Each PEM measured CO 

concentration continuously. The PEM readings were averaged by exposure event such 

that each event was associated with a single microenvironment and a single clock hour. 

Event durations ranged from one minute to one hour. The microenvironment assigned 

to each PEM was determined from entries made in the subject’s activity diary. 

The goal of the statistical analysis was to develop distributions representing the 

outdoor CO concentrations associated with each microenvironment. Originally, these 

distributions were to be developed using only outdoor PEM values. The Denver study 

2-22
 
Table 2-7. Algorithm Used to Simulate a Year-Long Sequence of Hourly-Average 

Outdoor CO Concentrations for Each “Method A” Microenvironment 

1.	 Identify microenvironment and district associated with simulation. 

2.	 Go to first hour. 

3.	 Set previous outdoor CO concentration equal to initial value listed in Table 2-6 

for microenvironment. 

4.	 See lookup table (Table 2-6) for microenvironment to determine interval index 

(j) for previous outdoor concentration. 

5.	 Determine fixed-site CO concentration associated with hour for specified 

district and microenvironment. 

6.	 See lookup table to determine interval index (i) for fixed-site CO concentration. 

7.	 Locate cumulative distribution associated with indices i,j in supplemental array. 

If no cumulative distribution has been determined for (i,j), go back to previous 

hour and redo Steps 8 through 12 (requires drawing a new random number in 

Step 8). 

8.	 Select random number between zero and one. Compare random number 

(RN) with probabilities listed in cumulative distribution identified in Step 7. RN 

will fall within one of the specified intervals, such that 

LBCF < RN # UBCF 

where LBCF is the lower bound cumulative fraction and UBCF is the upper 

bound cumulative fraction. 

9.	 Associated with LBCF is a lower bound pollutant concentration (LBPC). 

Associated with the UBCF is an upper bound pollutant concentration (UBPC). 

Use the following interpolation equation to estimate the current outdoor CO 

concentration value. 

CO = LBPC + (RN - LBCF)(UBPC - LBPC)/(UBCF - LBCF) 

10.	 Go to the next hour. 

11.	 The current outdoor CO concentration as determined by Step 9 becomes the 

previous outdoor CO concentration. 

12.	 Go to Step 4. 

2-23
 
was found to contain relatively few outdoor PEM values, however, and these values 

were difficult to associate with specific indoor microenvironments. 

An alternative approach was subsequently implemented in which each indoor 

PEM was assumed to represent the outdoor concentration at the same location one 

hour earlier, given that the indoor PEM value was not measured during a period when a 

gas stove was in operation. Consistent with this assumption, an indoor PEM value 

reported for hour h was indexed according to a fixed-site monitor concentration reported 

for h - 1. For outdoor and motor vehicle microenvironments, each PEM value was 

assumed to represent the current outdoor concentration. Consequently, each PEM 

value was indexed according to the fixed-site concentration associated with the current 

hour. 

In indexing the indoor and outdoor PEM values according to fixed-site 

concentration, analysts limited the indexing procedure to PEM values which occurred 

within 10 km of at least one fixed-site monitor. In each case, the index was determined 

by the fixed-site concentration reported by the nearest monitor. As it was difficult to 

determine the nearest fixed-site monitor for PEM values associated with vehicle 

microenvironments, analysts used the average of the concentrations reported by all of 

the operating fixed-site monitors to index these PEM values. 

The PEM values reported by a particular subject of the Denver study consisted of 

a sequence of CO concentrations which could be indexed by microenvironment. 

Whenever a particular PEM value was preceded by a PEM value reported for the same 

microenvironment, the value of the preceding PEM value was used to determine the 

preceding hour index (j). No index could be determined in cases where a particular 

PEM value was not preceded by a PEM value associated with the same 

microenvironment. 

The initial result of this approach was a file listing PEM values indexed according 

to microenvironment, fixed-site CO interval (i), and previous PEM CO interval (j). This 

file was subsequently analyzed to determine the distribution of PEM values for each 

combination of Method A microenvironment, i value, and j value. The resulting 

distributions were accessed in Step 7 of the 12-step procedure described in Table 2-7. 

Method B was applied to the nine microenvironments not listed in Table 2-6. In 

each of these cases, the Denver PEM data available for the microenvironment were 

2-24
 
judged to be insufficient for the purpose of accounting for both the current fixed-site CO 

concentration and the preceding outdoor CO concentration. An analysis of the activity 

diary data for these microenvironments indicated that people tended not to occupy 

these microenvironments for long time periods. Consequently, the decision was made 

to ignore the effects of the preceding outdoor concentration in the selection process. 

Five empirical distributions were established for each of the Method B 

microenvironments, one for each of the five ranges used in Method B for classifying 

fixed-site concentrations. The outdoor value selected for each hour was randomly 

selected from one of these five distributions according to the fixed-site concentration 

associated with the hour. Apart from this use of the five rather than 25 empirical 

distributions, Method B was identical to Method A. 

Methods A and B each require a complete (gapless) year of hourly average 

fixed-site monitoring values for each district. Section 3.2 describes the method used to 

fill in missing hourly-average values. The resulting filled-in data sets were assumed to 

represent existing conditions at each monitor. 

2.4.2 The Air Quality Adjustment Procedure 

The next application of version 2.0 of pNEM/CO will include exposure estimates 

associated with air quality just meeting the current 8-hour NAAQS for CO. It is 

anticipated that the approach used to adjust current CO ambient air quality levels to 

represent air quality under the current NAAQS will be similar to the approach described 

in Section 2.4.2 of Johnson et al. (1992). 

2.4.3 Ventilation Rate 

In addition to CO concentration, a ventilation rate (VE) value was estimated for 

each exposure event. VE is expressed as liters of air respired per minute (liters min-1). 

The algorithm used to estimate VE was developed for Version 2.0 of pNEM/CO and has 

not been used previously in pNEM analyses. Section 5 provides a detailed description 

of the algorithm. 

2-25
 
Briefly, the CHAD database provided an activity indicator for each exposure 

event. Each activity type was assigned a distribution of values for the metabolic 

equivalent of work (MET). MET is a dimensionless quantity defined by the ratio 

MET = EE/RMR, (2-2) 

where EE is the rate of energy expenditure during a particular activity (expressed in 

kcal/min), and RMR is a person’s typical resting metabolic rate (also expressed in 

kcal/min). For example, activity no. 11300 -- “outdoor chores” -- was represented by a 

normal distribution of MET values with mean equal to 5 and a standard deviation equal 

to 1. Appendix A lists the distribution assigned to each of the CHAD activity codes. 

A probabilistic procedure was used to assign a RMR value to each cohort for the 

entire 365-day exposure period. An EE value was calculated for each exposure event 

by the equation 

EEa(i,j,k) = [MET(i,j,k)][RMR(k)], (2-3) 

in which EEa(i,j,k) was the average energy expenditure rate (kcal min-1) for cohort k 

during exposure event i on day j; MET(i,j,k) was a value for MET randomly selected 

from the distribution associated with activity type a; a was the activity type associated 

with exposure event e; and RMR(c,d) was the RMR value randomly generated for 

cohort k. Section 5.5 describes the methods used to randomly select or generate the 

required parameter values. 

Energy expenditure requires oxygen which is supplied by ventilation (respiration). 

Let ECF(k) indicate an energy conversion factor defined as the volume of oxygen 

required to produce one kilocalorie of energy in person k. The oxygen uptake rate 

(VO2) associated with a particular activity can be expressed as 

VO2(i,j,k) = [ECF(k)][EEa(i,j,k)], (2-4) 

in which VO2(i,j,k) has units of liters oxygen min-1, ECF(k) has units of liters oxygen 

kcal-1, and EE(i,j,k) has units of kcal min-1. In pNEM/CO, the value of VO2(i,j,k) is 

2-26
 
determined from MET(i,j,k) by substituting Equation 2-3 into Equation 2-4 to produce the 

relationship 

VO2(i,j,k) = [ECF(k)][MET(i,j,k)][RMR(k)]. (2-5) 

Section 5-5 describes the probabilistic methods used to estimate values of ECF(k) and 

RMR(k) for person k. 

Ventilation rate (VE) tends to increase as VO2 increases up to the point of 

maximum oxygen uptake (VO2max). The relationship is known to be non-linear, with the 

slope of the relationship usually increasing at higher values of VO2. In Version 2.0 of 

pNEM/CO, the relationship between VE(i,j,k) and VO2(i,j,k) is modeled by the generic 

equation 

ln[VE(i,j,k)/BM(k)] = a + (b){ln[VO2(i,j,k)/BM(k)]} + d(k) + e(i,j,k) (2-6) 

in which VE(i,j,k) is the VE value associated with the ith event of day j for person k, BM(k) 

is the body mass assigned to person k, and a and b are constants determined by the 

age and gender of person k. The term d(k) is a random variable selected for each 

person from a normal distribution with mean equal to zero and standard deviation equal 

to Fd. The term e(i,j,k) is a random variable selected for each individual event from a 

normal distribution with mean equal to zero and standard deviation equal to Fe. 

Section 5.3 provides values for each of the parameters appearing in Equation 2­

6. The values of a, b, Fd, and Fe were determined through a statistical analysis of data 

relating VE to VO2 obtained from 32 clinical studies performed by Dr. William Adams. 

The data were stratified into six groups according to age and gender. 

The VE algorithm included a method for identifying “impossible” values which 

were occasionally generated by the estimation process. This method determined a 

maximum VO2 value for each exposure event which accounted for the duration of the 

activity and for the age, weight, and gender of the person. No estimate of VO2 (and the 

corresponding estimate of VE) was permitted to exceed this limit. Section 5.4 provides 

a more detailed description of this procedure. 

2-27
 
2.4.4 Carboxyhemoglobin Level 

An algorithm developed by Biller and Richmond (included as an appendix in 

Johnson et al., 1992) was used by pNEM/CO to estimate the COHb level at the end of 

each exposure event. The algorithm is based on a differential equation proposed by 

Coburn, Forster, and Kane (1963). Inputs to the algorithm include 

Percent COHb at the start of the event
 

Average CO exposure concentration during the event, ppm
 

Time duration of the event, min
 

Alveolar ventilation rate, ml/min
 

Haldane Constant
 

Atmospheric pressure at sea level, torr
 

Altitude above sea level, feet
 

Blood volume, ml
 

Total hemoglobin content of blood, gm/100 ml
 

Pulmonary CO diffusion rate, ml/min per torr
 

Endogenous CO production rate, ml/min
 

An updated version of the Biller and Richmond Appendix appears as Appendix E to this 

report and provides a detailed description of the COHb algorithm, the various 

physiological parameters that are inputs to the COHb algorithm, and a list of related 

references. 

2.4.5 The Physiological Profile Generator 

As discussed in Sections 2.4.3 and 2.4.4, the algorithms used to estimate VE and COHb 

required values for various physiological parameters such as body mass, blood volume, 

and RMR. Appendix E provides a complete list of these parameters. A special 

algorithm within pNEM probabilistically generated a value for each parameter on the list 

(collectively referred to as a “physiological profile”) for each combination of cohort and 

calendar day processed by pNEM/CO. Each of the generated physiological profiles 

was internally consistent, in that the functional relationships among the various 

parameters were maintained. For example, blood volume was determined as a function 

of weight and height, where height was estimated as a function of weight. Weight was 

2-28
 
in turn selected from a distribution specific to gender and age. Appendix E describes 

the method used to estimate values for each parameter in the application of pNEM/CO 

to Denver. 

2.4.6	 Hourly Average Exposure Estimates 

Algorithms within pNEM/CO provided four estimates for each exposure event: 

average CO concentration, average VE, the product of average CO concentration and 

VE (represented as “CO x VE”), and the COHb level at the end of the event. These 

estimates were processed to produce time-weighted estimates of CO concentration, VE, 

and CO x VE for each clock hour, as well as end-of-hour estimates of COHb. The result 

was a year-long sequence of hourly values for CO, VE, CO x VE, and COHb for each of 

the 1860 Denver cohorts. These sequences were statistically analyzed to determine 

the value of various multihour exposure indicators of interest, including the largest eighthour daily maximum CO concentration occurring each year and the number of times the 

end-of-hour COHb level exceeded a specified percentage value. 

2.5	 Extrapolate the Cohort Exposures to the Population-of-Interest and to 

Individual Sensitive Groups 

2.5.1	 General Population 

The cohort-specific exposure estimates developed in Step 4 of the pNEM 

methodology (Section 2.4) were extrapolated to the general Denver population by 

estimating the population size of each cohort. Cohort populations were estimated in 

three steps. The population of each demographic group within a particular home district 

[Pop(d,h)] was first estimated from census data specific to that district. Each of these 

groups was subdivided into a group residing in homes with gas stoves and a group 

residing in homes with other cooking fuels. The population of each of these groups was 

determined by the expression 

Pop(d,h,f) = F(h,f) x Pop(d,h)	 (2-7) 

2-29
 
where Pop(d,h,f) is the population of a group associated with demographic group d, 

home district h, and cooking fuel f. F(h,f) is the fraction of homes in Home district h that 

use cooking fuel f. F(h,f) was determined from BOC census data specific to each 

district (Bureau of Census, 1992). 

The Pop(d,h,f) values provided an estimate of the population of each noncommuting cohort residing within home district h. The populations of the commuting 

cohorts (assumed to include all working cohorts) were determined by the expression 

Com(d,h,f,w) = Pop(d,h,f) x Com(h,w)/Work(h). (2-8) 

Com(d,h,f,w) is the number of persons in the commuting cohort associated with 

demographic group d, home district h, cooking fuel f, and work district w; Com(h,w) is 

the number of workers in all demographic groups that commute from home district h to 

work district w; and Work(h) is the total number of workers in home district h. Estimates 

of Work(h) were developed from census data specific to each district. Section 6 

describes the method used to estimate Com(h,w) from origin-destination data provided 

by the BOC. 

2.5.2 Persons with Ischemic Heart Disease 

The cohort-specific exposure estimates developed in Step 4 were also 

extrapolated to the sensitive population defined as persons with diagnosed and 

undiagnosed ischemic heart disease (IHD). The extrapolation was performed using the 

procedure described in Subsection 2.5.1 with a single variation: the following equation 

was substituted for Equation 2-8. 

Pop(d,h,f) = IHD(d) x F(h,f) x Pop(d,h). (2-9) 

The term IHD(d) is the fraction of persons in demographic group d with IHD. 

Estimates of the prevalence of IHD by demographic group were provided by H. 

Richmond (memorandum, August 25, 1998). Table 2-8 lists these estimates as 

percentages. In each case, a total prevalence rate is provided which is the sum of a 

2-30
 
prevalence rate for diagnosed IHD and a prevalence rate for undiagnosed IHD. 

Estimates of diagnosed IHD were obtained from the National Health Interview Survey 

(Adams and Marano, 1995), in which U.S. prevalence rates were disaggregated by age 

and gender. The estimated prevalence of diagnosed IHD for children (age 0 to 17) is 

0.01 percent. According to the National Health Interview Survey, approximately 8.0 

million individuals are estimated to have diagnosed IHD in the civilian, noninstitutionalized population. These estimates do not include individuals in the military or 

individuals in nursing homes or other institutions. 

Table 2-9 lists the resulting population estimates by exposure district for (1) all 

adults and (2) adults with IHD. The total number of adults with IHD in the six-district 

study area is approximately 45,000. District No. 3 has the largest number of adults with 

IHD (about 14,000), accounting for 30 percent of the total. On average, 5.7 percent of 

the adults are estimated to have IHD. 

In extrapolating the cohort-specific exposure estimates developed in Step 4 to 

persons with IHD, analysts assumed the activity patterns of IHD were similar to those of 

the general population. Section 5.5 presents the results of a statistical analysis 

performed to evaluate the reasonableness of this assumption. 

The estimates of undiagnosed IHD in Table 2-8 were based on two assumptions: 

(1) there are 3.5 million persons in the U.S. with undiagnosed IHD and (2) persons with 

undiagnosed IHD are distributed within the population in the same proportions as 

persons with diagnosed IHD. The 3.5 million statistic was based on an estimate by the 

American Heart Association (1990) that there are between three and four million 

persons with undiagnosed IHD. 

2-31
 
Table 2-8. Percentage of Persons with Ischemic Heart Disease (IHD) by 

Demographic Group 

Demographic group 

Percentage of persons with IHD 

Diagnosed Undiagnosed Total 

1. Children, 0 to 17 0.01 0.004 0.014 

2. Males, 18 to 44, working 0.38 0.17 0.55 

3. Males, 18 to 44, nonworking 0.38 0.17 0.55 

4. Males, 45 to 64, working 8.19 3.60 11.8 

5. Males, 45 to 64, nonworking 8.19 3.60 11.8 

6. Males, 65+ 19.2 8.45 27.7 

7. Females, 18 to 44, working 0.13 0.06 0.19 

8. Females, 18 to 44, nonworking 0.13 0.06 0.19 

9. Females, 45 to 64, working 3.25 1.43 4.68 

10. Females, 45 to 64, working 3.25 1.43 4.68 

11. Females, 65+ 12.3 5.41 17.7 

Table 2-10. Estimates of Population Residing in Each Denver Exposure District. 

Denver 

exposure 

district 

All adults Adults with ischemic heart disease 

Number Percent of total Number Percent of total 

1 109,552 14.0 5,915 (5.4)a 13.3 

2 77,098 9.9 4,360 (5.7) 9.8 

3 218,083 28.0 13,527 (6.2) 30.3 

4 148,997 19.1 9,811 (6.6) 22.0 

5 142,036 18.2 7,699 (5.4) 17.3 

6 84,253 10.8 3,266 (3.9) 7.3 

All 780,019 100.0 44,578 (5.7) 100.0 a

 Number in parentheses is percentage of adults with ischemic heart disease. 

2-32 
2.6 References for Section 2 

Adams, P. F., and M. A. Marano. 1995. Current Estimates from the National Health 

Interview Survey, 1994. National Center for Health Statistics, Vital Health Statistics 10 

(193). 

Ainsworth, B. E., W. L. Haskell, A. S. Leon, D. R. Jacobs, Jr., et al. 1993. 

“Compendium of Physical Activities: Classification of Energy Costs of Human Physical 

Activities,” Med. Sci. Sports Exer. Volume 25, pp. 71 - 80. 

American Heart Association. 1990. 1990 Heart and Stroke Facts. American Heart 

Association, Dallas, Texas. p. 13. 

Bureau of Census. 1992. “Census of Population and Housing, 1990: Summary Tape 

File 3 on CD-ROM (Colorado).” U.S. Bureau of the Census, Washington, DC. 

Bureau of Census. 1994. “1990 Census of Population, STP154 -- Census Tract for 

Work by Census Tract of Residence,” U.S. Bureau of the Census, Journey to Work and 

Migration Statistics Branch, Population Division, Washington, DC. 

Coburn, R. F., R. E. Forster, and R. B. Kane. 1963. “Considerations of the Physiology 

and Variables that Determine Blood Caboxyhemoglobin Concentration in Man,” Journal 

of Clinical Investigations, Vol. 44, pp. 1899-1910. 

Fletcher, K. and J. LaPointe. November 22, 1998. “Review of Documents on Indoor 

Sources of Carbon Monoxide. (Attachment).” Johnson, Ted. December 2, 1998. 

“Memorandum No. 2: Evaluation of Five Potential Indoors Sources of Carbon 

Monoxide,” Report prepared by TRJ Environmental, Inc., under EPA Contract No. 68­

D6-0064. 

Tippett, J. A., M. del Valle-Torres, G. Glen, and Y. Lakkadi. 1997. Development of 

NERL/CHAD: The National Exposure Research Laboratory Consolidated Human 

Activity Database. Report prepared by ManTech Environmental Technology, Inc., 

under EPA Contract No. 68-D5-0049. 

Johnson, T. R. 1984. A Study of Personal Exposure to Carbon Monoxide in 

Denver, Colorado. Report No. EPA-600/54-84-014, U. S. Environmental Protection 

Agency, Research Triangle Park, North Carolina. 

Johnson, T. September, 8, 1998. “Memorandum No. 7: Additional Analyses 

Supporting Modifications to the Passive Smoking Algorithm in pNEM/CO,” Prepared by 

TRJ Environmental, Inc., under EPA Contract No. 68-D6-0064. 

Johnson, T., J. Capel, R. Paul, and L. Wijnberg. 1992. Estimation of Carbon 

Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver 

Residents Using a probabilistic Version of NEM. Report prepared by International 

Technology Air Quality Services under EPA Contract No. 68-D0-0062, U. S. 

2-33
 
Environmental Protection Agency, Office of Air Quality Planning and Standards, 

Research Triangle Park, North Carolina 27711. 

Law, A. 1998. “Letter Report: Literature Review for Updating Carbon Monoxide 

Exposure Model pNEM/CO. Work Assignment 4-19, Contract 68-D3-0094. Task 5 ­

Passive Smoking.” Prepared by International Technology Air Quality Services under 

EPA Contract No. 68-D3-0094. 

Ott, W., P. Switzer, L. Hildemann, P. Flaschbart, N. Klepeis, and D. Hallerbach. 1999. 

“Measurement of Indoor and Outdoor Fine Particle and Carbon Monoxide 

Concentrations in 199 Public Access Locations in Three Cities.” Draft Technical 

Progress Report, Department of Statistics, Stanford University, Stanford, CA 94305. 

Richmond, H. August 25, 1998. “Memorandum: Sensitive Population Estimates for 

Use in Carbon Monoxide Exposure Analysis.” U. S. Environmental Protection Agency, 

Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina 

27711. 

2-34
 
SECTION 3
 

PREPARATION OF FIXED-SITE MONITORING DATA 

AND CREATION OF EXPOSURE DISTRICTS
 

3.1 Selection of Monitoring Sites and Definition of Exposure Districts 

Analysts began the process of selecting CO monitoring sites for the current 

pNEM analysis by obtaining a “Quick Look” report for Colorado CO sites from the EPA 

Aerometric Information Retrieval System (AIRS) for the years 1993 through 1997. 

Appendix B contains a facsimile of this report. Analysts designated all sites within 

Adams, Arapahoe, Boulder, Denver, and Jefferson Counties which reported CO data 

between 1993 and 1997 as potential sites for the pNEM/CO analysis. Figure 3-1 shows 

the locations of the sites which met these criteria plotted on a map indicating population 

density. (The Greeley site did not qualify as a potential site, as it was located in Weld 

County. It is included in Figure 3-1 to show its relative location to the other sites). 

Table 3-1 lists the CO sites initially under evaluation and indicates the number of 1-hour 

values reported by each site during each year in the 1993-1997 period. Note that the 

list includes all five of the monitoring sites used in 1992 pNEM/CO analysis. These 

sites are identified by the codes A, B, C, L, and M. Site descriptions for these monitors 

are provided in Appendix A of the report by Johnson et al. (1992). 

EPA specified that the pNEM/CO analysis for Denver must use three years of 

data for each monitoring site and that preference should be given to recent years. 

Table 3-2 lists the second highest daily maximum 8-hour concentration reported by 

each site in Table 3-1 for 1995, 1996, and 1997. Five of the sites did not meet the 75% 

completeness criterion for each of the three years: Commerce City, Englewood, Denver 

031-0018, Denver 031-0019, and Denver 031-0020. These sites were dropped from 

further consideration. 

3-1
 
Figure 3-1. Monitoring Sites in the Greater Denver Metropolitan Area. 

3-2 
Table 3-1. Fixed-Site Monitors Reporting Carbon Monoxide Data for the Denver Area Between 1993 a

County 

Monitor Description 1992 

pNEM/CO 

monitor ID 

Number of one-hour v

Site ID City Address 1993 1994 19

Adams 001-3001 Welby 78th Ave. and Steele Street - 8632 8687 86

001-7015 Commerce City Rocky Mountain Arsenal - 7919 3595 0 

Arapahoe 005-0002 Littleton 8100 So. University Blvd. 

(Highlands) 

M 8589 8705 86

005-0003 Englewood 3300 S. Huron Street - 8717 7126 0 

Boulder 013-0009 Longmont 440 Main Street - 8701 8557 86

013-0010 Boulder 2150 28th Street - 353 8639 86

013-1001 Boulder 2320 Marine Street - 8708 8565 86

Denver 031-0002 Denver 2105 Broadway (Broadway) A 8687 8700 86

031-0013 Denver 14th and Albion St. (Albion) C 8675 8665 86

031-0014 Denver 23rd and Julian (Julian) B 8676 8543 87

031-0018 Denver Blake St. side of Speer - 1021 1591 0 

031-0019 Denver Speer and Auraria Parkway - 997 3049 36031-0020 Denver 935 Colorado Blvd., UCHS - 0 1370 21

Jefferson 059-0002 Arvada W. 57th Ave and Garrison L 8723 8525 86

3-3
 
Table 3-2. Three-Year Average Value of Second Largest Daily Maximum 8-Hour CO Concentration 

Monitors. 

County 

Monitor Description 1992 

pNEM/CO 

monitor ID 

Second high max 8-ho

Site ID City Address 1995 1996 19

Adams 001-3001 Adams County 78th Ave. And Steele Street - 5.1 3.9 4.

001-7015 Commerce City Rocky Mountain Arsenal - - - - 

Arapahoe 005-0002 Littleton 8100 So. University Blvd. 

(Highlands) 

M 2.1 2.6 2.

005-0003 Englewood 3300 S. Huron Street - - - - 

Boulder 013-0009 Longmont 440 Main Street - 4.7 5.5 5.

013-0010 Boulder 2150 28th Street - 5.2 4.3 3.

013-1001 Boulder 2320 Marine Street - 3.7 2.5 3.

Denver 031-0002 Denver 2105 Broadway (Broadway) A 9.5 7.3 5.

031-0013 Denver 14th and Albion St. (Albion) C 6.2 5.2 4.

031-0014 Denver 23rd and Julian (Julian) B 5.9 5.7 6.

031-0018 Denver Blake St. side of Speer - - - - 

031-0019 Denver Speer and Auraria Parkway - (7.1)a 7.0 6.

031-0020 Denver 935 Colorado Blvd., UCHS - (6.0)b - - 

Jefferson 059-0002 Arvada W. 57th Ave and Garrison L 4.6 4.3 4.

a Number of values = 3658 b Number of values = 2141 

3-4
 
Fixed-Site Monitors with 1995-1997 Data
 

Figure 3-2. Monitoring Sites in the Denver Area Which Reported Data for 1995
 

Through 1997.
 

3-5
 
Figure 3-2 shows the locations of the remaining nine sites. Consistent with 

guidance received from EPA, analysts next omitted the 78th Avenue (Welby) and 

Longmont sites. The 78th Avenue site is located in a predominately agricultural area 

which was considered unrepresentative of urban residential locations. The Longmont 

site was considered to be too distant from other sites. 

Seven sites remained at this stage of the selection procedure: the five sites used 

in the 1992 Denver analysis and two Boulder sites (28th Street and Marine Street). The 

locations of five sites used in the 1992 analysis were considered appropriate for defining 

five separate exposure districts with 10 km radii. However, the Boulder sites were 

considered too close together to support separate exposure districts. With EPA’s 

approval, analysts defined six exposure districts -- one for each of the 1992 Denver 

sites and a “composite” Boulder site. For purposes of constructing the associated 

exposure district, the composite site was assigned a location midway between the two 

Boulder sites (UTM Zone 13: Northing 4429.6495, Easting 477.625). The outdoor CO 

concentration for hour h in this district was defined as the average of values reported by 

the two Boulder sites for hour h. 

The exposure period was defined as calendar years 1995, 1996, and 1997. 

Consequently, 21 “site-years” of data were selected for the current Denver analysis (7 

sites x three years/site). The statistics listed in Table 3-1 indicate that each of the 

selected site-years of data was at least 96.6 percent complete. 

Table 3-3 provides site characteristics for each of the seven Denver-area 

monitors included in the exposure analysis. The information in the table was obtained 

from the Aerometric Information Retrieval System (AIRS) by Mr. David Lutz of EPA’s 

Environmental Monitoring and Analysis Division. The table will be updated when 

additional information is provided by the State of Colorado. 

3.2 Estimation of Missing Values 

The pNEM/CO model requires that each input site-year of monitoring data be 

complete (gapless). The missing values in each data set were estimated using a time 

series model developed by Johnson and Wijnberg (1981). The time series model is 

based on the assumption that hourly average air quality values can be represented by a 

3-6
 
Table 3-3. Characterization of Monitoring Sites Used in pNEM/CO for the Denver Urban Area. 

City 

Site Name and Address 

AIRS ID 

Land Use 

Spatial Scale 

Elevation (meters) 

Monitor Height (meters) 

Distanto Roa(meter

Littleton Highlands 005-0002 site --­ --­ --­ --­ 

8100 University terminated Blvd. 12/31/97 

Denver Camp 

2105 Broadway 

031-0002 commercial microscale 1591 3 #1 - 6 

#2 - 16 #3 - 7 

Denver NJHE 

14th and Albion 

St. 

031-0013 residential neighborhood 1615 3 --­ 

Denver Carriage 

23rd and Julian 

031-0014 residential neighborhood 1609 4 #1 - 5

#2 - 59 

Arvada W. 57th Ave. 059-0002 residential --­ 1641 5 179 

and Garrison 

Boulder 2150 28th St. 013-0010 commercial microscale --­ 3 9 

Boulder 2320 Marine St. 013-1001 residential --­ 1619 4 #1 - 67 

#2 - 17#3 - 2

aWhen the monitoring site is near more than one roadway, the distance is provided for each roadway sbWhen the monitoring site is near more than one roadway, the traffic volume is provided for each roadw

3-7 
combination of cyclical, autoregressive, and random processes. The parameter values 

of these processes are determined by a statistical analysis of the reported data. 

Tables 3-4 through 3-10 provide descriptive statistics by monitoring site for the 1­

hour CO concentrations in each data set before and after estimation of the missing 

values. The statistics indicate that the addition of missing-value estimates did not 

significantly affect the distribution of any data set. Each table also provides descriptive 

statistics for running-average 8-hour concentrations after estimation of missing values. 

Table 3-11 provides a comparison of the descriptive statistics (after estimation of 

missing values) for the 1-hour CO concentrations associated with each of the Boulder 

sites and with the composite site. Table 3-12 provides both 1-hour and 8-hour 

descriptive statistics for the Boulder “composite” site after estimation of missing values. 

3-8
 
Table 3-4.	 Descriptive Statistics for Carbon Monoxide Concentrations 

Reported by Littleton “Highlands” Site (005-0002, M) Before 

and After Estimation of Missing Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (o) 8670 0.3 0.7 1.0 1.8 2.2 3.3 3.6 

1 h (s) 8760 0.3 0.7 1.0 1.8 2.2 3.3 3.6 

8 h (s) 8760 0.3 0.7 0.8 1.4 1.7 2.5 2.6 

1996 1 h (o) 8677 0.3 0.7 1.0 1.8 2.2 4.1 4.3 

1 h (s) 8784 0.3 0.7 1.0 1.8 2.2 4.1 4.3 

8 h (s) 8784 0.3 0.6 0.8 1.5 1.6 2.9 2.9 

1997 1 h (o) 8463 0.3 0.7 1.0 1.8 2.0 4.0 4.3 

1 h (s) 8760 0.3 0.7 1.0 1.7 2.0 4.0 4.3 

8 h (s) 8760 0.3 0.6 0.8 1.3 1.5 2.8 3.0 

a

 1 h (o): original 1-hour data set as down-loaded from AIRS. 

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3-9
 
Table 3-5.	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by 

Denver “Broadway” Site (031-0002, A) Before and After Estimation of 

Missing Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (o) 8697 1.2 2.7 3.4 6.1 7.7 16.4 24.5 

1 h (s) 8760 1.2 2.7 3.4 6.1 7.7 16.4 24.5 

8 h (s) 8760 1.3 2.4 3.0 4.7 5.8 10.8 11.0 

1996 1 h (o) 8673 1.1 2.5 3.2 5.7 6.9 16.7 21.6 

1 h (s) 8784 1.1 2.5 3.2 5.6 6.9 16.7 21.6 

8 h (s) 8784 1.2 2.2 2.8 4.3 4.9 8.7 9.0 

1997 1 h (o) 8687 1.0 2.4 3.0 5.4 6.3 10.0 11.4 

1 h (s) 8760 1.0 2.4 3.0 5.4 6.3 10.0 11.4 

8 h (s) 8760 1.1 2.1 2.7 4.1 4.6 5.7 5.7 

a

 1 h (o): original 1-hour data set as down-loaded from AIRS. 

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3-10
 
Table 3-6.	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by 

Denver “Albion” Site (031-0013, C) Before and After Estimation of Missing 

Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (o) 8647 0.9 2.5 3.4 5.5 6.4 13.6 14.6 

1 h (s) 8760 0.9 2.5 3.4 5.5 6.4 13.6 14.6 

8 h (s) 8760 1.1 2.2 2.7 3.7 4.3 8.5 8.5 

1996 1 h (o) 8516 0.8 2.2 3.0 5.1 5.7 9.4 14.6 

1 h (s) 8784 0.8 2.2 3.0 5.0 5.7 9.4 14.6 

8 h (s) 8784 1.0 1.9 2.4 3.6 4.2 5.4 5.6 

1997 1 h (o) 8690 0.8 2.1 2.8 4.7 5.4 10.6 11.6 

1 h (s) 8760 0.8 2.1 2.8 4.7 5.4 10.6 11.6 

8 h (s) 8760 0.9 1.9 2.3 3.4 3.9 4.8 4.8 

a

 1 h (o): original 1-hour data set as down-loaded from AIRS. 

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3-11
 
Table 3-7.	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by 

Denver “Julian” Site (031-0014, B) Before and After Estimation of Missing 

Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (o) 8701 0.7 2.3 3.2 5.3 6.4 9.9 10.4 

1 h (s) 8760 0.7 2.3 3.2 5.3 6.5 9.9 10.4 

8 h (s) 8760 0.8 2.1 2.7 4.1 4.8 7.2 7.3 

1996 1 h (o) 8736 0.6 2.0 2.9 4.7 5.5 8.2 9.1 

1 h (s) 8784 0.6 2.0 2.9 4.7 5.5 8.2 9.1 

8 h (s) 8784 0.7 1.8 2.4 3.6 4.0 7.2 7.3 

1997 1 h (o) 8677 0.6 2.1 2.9 5.0 5.8 8.4 9.5 

1 h (s) 8760 0.6 2.1 2.9 5.0 5.8 8.4 9.5 

8 h (s) 8760 0.7 2.0 2.5 3.8 4.2 6.9 7.0 

a

 1 h (o): original 1-hour data set as down-loaded from AIRS. 

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3-12
 
Table 3-8. Descriptive Statistics for Carbon Monoxide Concentrations Reported by 

Arvada Site (059-0002, L) Before and After Estimation of Missing Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (o) 8680 0.6 2.0 2.7 4.8 5.8 8.9 11.9 

1 h (s) 8760 0.6 2.0 2.7 4.8 5.8 8.9 11.9 

8 h (s) 8760 0.8 1.8 2.3 3.1 3.5 5.0 5.1 

1996 1 h (o) 8724 0.5 1.7 2.4 4.1 4.9 7.2 7.9 

1 h (s) 8784 0.5 1.7 2.4 4.1 4.9 7.2 7.9 

8 h (s) 8784 0.6 1.6 2.0 2.8 3.1 4.3 4.3 

1997 1 h (o) 8697 0.6 1.8 2.4 4.2 5.1 7.7 9.2 

1 h (s) 8760 0.6 1.8 2.4 4.2 5.1 7.7 9.2 

8 h (s) 8760 0.6 1.6 2.1 3.0 3.3 5.0 5.1 

a

 1 h (o): original 1-hour data set as down-loaded from AIRS. 

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3-13
 
Table 3-9.	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by 

Boulder “28th Street” Site (013-0010) Before and After Estimation of 

Missing Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (o) 8608 0.8 2.1 2.8 4.8 5.5 10.3 10.6 

1 h (s) 8760 0.8 2.1 2.8 4.8 5.5 10.3 10.6 

8 h (s) 8760 0.9 1.8 2.2 3.1 3.6 5.2 5.3 

1996 1 h (o) 8576 0.7 1.8 2.4 4.0 4.7 8.4 8.5 

1 h (s) 8784 0.7 1.8 2.4 3.9 4.7 8.4 8.5 

8 h (s) 8784 0.8 1.6 1.9 2.8 3.1 5.4 5.6 

1997 1 h (o) 8697 0.7 1.7 2.4 4.0 4.5 8.2 9.0 

1 h (s) 8760 0.7 1.7 2.4 3.9 4.5 8.2 9.0 

8 h (s) 8760 0.8 1.5 2.0 2.9 3.3 5.5 5.5 

a

 1 h (o): original 1-hour data set as down-loaded from AIRS. 

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3-14
 
Table 3-10.	 Descriptive Statistics for Carbon Monoxide Concentrations Reported by 

Boulder “Marine Street” Site (013-1001) Before and After Estimation of 

Missing Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (o) 8651 0.4 0.9 1.3 2.3 2.9 8.2 8.3 

1 h (s) 8760 0.4 0.9 1.3 2.3 2.9 8.2 8.3 

8 h (s) 8760 0.5 0.9 1.1 1.8 2.1 3.8 3.9 

1996 1 h (o) 8669 0.4 0.9 1.1 2.1 2.5 4.3 4.5 

1 h (s) 8784 0.4 0.9 1.1 2.0 2.5 4.3 4.5 

8 h (s) 8784 0.4 0.8 1.0 1.5 1.8 2.5 2.5 

1997 1 h (o) 8517 0.4 0.8 1.2 2.4 3.0 6.9 7.1 

1 h (s) 8760 0.4 0.9 1.2 2.4 3.0 6.9 7.1 

8 h (s) 8760 0.4 0.8 1.0 2.0 2.3 5.0 5.1 

a

 1 h (o): original 1-hour data set as down-loaded from AIRS. 

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3-15
 
Table 3-11.	 Comparison of Descriptive Statistics for 1-Hour Carbon Monoxide 

Concentrations Reported by 28th Street, Marine Street, and Composite 

Sites in Boulder After Estimation of Missing Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 28th 8760 0.8 2.1 2.8 4.8 5.5 10.3 10.6 

Marine 8760 0.4 0.9 1.3 2.3 2.9 8.2 8.3 

Comp. 8760 0.7 1.5 2.0 3.3 3.8 8.7 9.5 

1996 28th 8784 0.7 1.8 2.4 3.9 4.7 8.4 8.5 

Marine 8784 0.4 0.9 1.1 2.0 2.5 4.3 4.5 

Comp. 8764 0.6 1.3 1.7 2.8 3.3 6.0 6.0 

1997 28th 8760 0.7 1.7 2.4 3.9 4.5 8.2 9.0 

Marine 8760 0.4 0.9 1.2 2.4 3.0 6.9 7.1 

Comp. 8760 0.6 1.3 1.7 3.0 3.6 6.8 7.3 

a

 28th = 28th Street, Marine = Marine Street, Comp. = composite site (hour-by-hour average of 28th Street 

and Marine Street values). 

3-16
 
Table 3-12. Descriptive Statistics for Carbon Monoxide Concentrations Reported by 

Boulder “Composite” Site After Estimation of Missing Values. 

Year 

Data 

seta 

No. of 

obs. 

Descriptive statistics for CO concentration, ppm 

50th 90th 95th 99th 99.5th 

second 

maximum maximum 

1995 1 h (s) 8760 0.7 1.5 2.0 3.3 3.8 8.7 9.5 

8 h (s) 8760 0.7 1.3 1.6 2.3 2.7 4.5 4.6 

1996 1 h (s) 8764 0.6 1.3 1.7 2.8 3.3 6.0 6.0 

8 h (s) 8764 0.6 1.2 1.4 2.0 2.4 3.9 4.0 

1997 1 h (s) 8760 0.6 1.3 1.7 3.0 3.6 6.8 7.3 

8 h (s) 8760 0.6 1.1 1.5 2.3 2.6 5.2 5.3 

a

 1 h (s): supplemented 1-hour data set (includes estimates of missing values) 

8 h (s): supplemented 8-hour running average data set [based on 1 h (s) data]. 

3.3 References for Section 3 

Johnson, T., J. Capel, R. Paul, and L. Wijnberg. 1992. Estimation of Carbon 

Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver 

Residents Using a Probabilistic Version of NEM. Report prepared by International 

Technology Air Quality Services under EPA Contract No. 68-D0-0062. U.S. 

Environmental Protection Agency, Research Triangle Park, North Carolina. 

Johnson, T., and L. Wijnberg. 1981. “Time Series Analysis of Hourly Average Air 

Quality Data,” presented at the 74th Annual meeting of the Air Pollution Control 

Association, Philadelphia, Pennsylvania. 

3-17
 
 

SECTION 4
 

THE MASS-BALANCE MODEL
 

The 1992 application of pNEM/CO to Denver marked a milestone in the evolution 

of the NEM methodology in that it represented the first time that a mass-balance model 

had been incorporated directly into the NEM methodology. Researchers updated the 

mass balance model for use in Version 2.0 of pNEM/CO. This section provides an 

overview of the pNEM/CO mass-balance model together with descriptions of the 

algorithms used in the model to estimate air exchange rates and emissions from gas 

stoves. It also describes the data used for the input parameters to the mass-balance 

model. 

4.1	 Overview of the Model 

The pNEM/CO methodology includes a mass-balance model which is used to 

estimate CO concentrations when a cohort is assigned to an indoor or motor vehicle 

microenvironment. The mass-balance model is based on the generalized massbalance model presented by Nagda, Rector, and Koontz (1987). As originally 

proposed, this model assumed that pollutant concentration decays indoors at a constant 

rate. For use in pNEM/CO, the Nagda model was revised to incorporate an alternative 

assumption that the indoor decay rate is proportional to the indoor concentration. The 

resulting model can be expressed by the differential equation 

(4-1) 

in which 

Cin = Indoor concentration (units: mass/volume) 

FB =	 Fraction of outdoor concentration intercepted by the enclosure 

(dimensionless fraction) 

4-1
 
Fd = Pollutant decay coefficient (1/time) 

< = Air exchange rate (1/time) 

Cout = Outdoor concentration (mass/volume) 

S = Indoor generation rate (mass/time) 

cV = Effective indoor volume where c is a dimensionless fraction (volume) 

m = Mixing factor (dimensionless fraction) 

q = Flow rate through air-cleaning device (volume/time) 

F = Efficiency of the air-cleaning device (dimensionless fraction) 

As CO is a nonreactive pollutant, it is reasonable to assume 1) that the enclosure does 

not intercept any of the CO as it moves indoors, 2) that the CO does not decay once it 

enters the enclosure, and 3) that no CO is removed by air-filtration devices. Under 

these assumptions, the parameters FB, Fd, and F in Equation 4-1 would be set equal to 

zero. If the additional assumptions are made that c and m are each equal to 1, the resulting differential equation is 

(4-2) 

It can be shown that this equation has the following exact solution: 

where 

(4-3) 

(4-4)
 

4-2
 
(4-5) 

(4-6) 

and )t is a fixed time interval. Based on this relationship, the average indoor pollutant 

concentration of hour h [Cin(h)] can be calculated by the expression 

Cin(h) = aiCin(h - 1) + a2Cout(h) + a3 (4-7) 

where Cin(h - 1) is the indoor concentration at the end of the preceding hour and Cout(h) 

is the average outdoor concentration during hour h. The other variables appearing in 

Equation 4-7 are defined by the following equations: 

(4-8) 

(4-9) 

(4-10) 

(4-11) 

Equation 4-7 was used to construct a sequence of hourly average values for each 

combination of microenvironment (indoor and motor vehicle) and exposure district. 

In constructing each sequence, the value of Cout for a particular hour was set 

equal to the value for outdoor concentration determined for that hour by the algorithm 

described in Subsection 2.4.1. A value for air exchange rate (<) was selected from a 

user-specified distribution each day at midnight and held constant for the entire day. 

This procedure was consistent with the procedure used to construct the EES for each 

cohort. As discussed in Section 2, each EES consisted of a series of person-days 

selected from an activity diary data base. Each person-day spanned a 24-hour period 

from midnight to midnight. 

4-3
 
The term S/V represents the contributions of indoor sources to indoor levels. 

This term was included in the mass-balance equation when the microenvironment was 

indoors - residence and the cohort was characterized as using natural gas for cooking. 

The S parameter was assumed to represent emissions from a single gas stove in the 

residence, and the V parameter was assumed to represent the total volume of the residence. 

Gas stove operation within the indoor - residence microenvironment was simulated by assuming a constant emission rate attributable to pilot light operation and a varying emission rate attributable to burner operation. Burner operation was assumed to 

occur in discrete "burner operation periods" (BOPs) of 60 minutes duration during normal dinner hours and of 30 minutes duration at other times. No more than one BOP 

was permitted to occur within a given clock hour, and each BOP began and ended with 

the same clock hour. A Monte Carlo process was used to randomly assign BOPs to 

clock hours throughout the year based on a table listing the probability of a BOP occurring within each hour of a typical day. This table was developed from an analysis of gas 

stove use patterns observed during the Denver Personal Monitoring Study (Johnson, 

1984). 

An average burner emission rate was determined for each midnight-to-midnight 

period by selecting annual gas use and emission factor values from user-specified distributions. Pilot lights were assumed to operate continuously at a constant emission 

rate. The simulated burner and pilot light emissions were summed for each clock hour 

and presented to the mass-balance model as an hourly average value for S. The residential volume (V) receiving the CO emissions was determined for each midnight-tomidnight period by selecting values from a distribution representing the housing stock in 

Denver. 

The following subsections provide descriptions of the algorithms and data bases 

used to determine the air exchange rates, burner operation probabilities, burner emission rates, pilot light emission rates, and residential volumes used in the mass-balance 

model. Many of these algorithms require that values be selected at random from normal or lognormal distributions. This selection was conducted by first defining the distribution of interest by one of the following expressions: 

4-4
 
Normal: X = AM + (ASD)(z) (4-12) 

Lognormal: X = (GM)(GSD)Z (4-13) 

In these expressions, AM is the arithmetic mean, ASD is the arithmetic standard 

deviation, GM is the geometric mean, and GSD is the geometric standard deviation. 

The distribution type (normal vs. lognormal) and the corresponding values for the mean 

and standard deviation were determined by fitting distributions to representative data 

sets. A value for X was selected from a particular distribution by randomly selecting a 

value for Z from the unit normal distribution [N(0, 1)] and substituting it into the 

appropriate equation. Tables 4-1 and 4-2 list the distribution type and parameter values 

for each of the random variables used in the mass-balance model. 

4.2 The Air Exchange Rate Algorithm 

An air exchange rate (AER) value was estimated for each indoor microenvironment (IME) for each 24-hour period (midnight to midnight) of the exposure year. The 

estimation procedure consisted of randomly selecting AER values from a distribution 

specific to each IME. 

Two distinct AER distributions were established for the residential IME: one representing windows open and one representing windows closed. The choice between 

windows open and windows closed was conditioned on the air conditioning (AC) system 

assigned to the residence and the outdoor temperature. For each 24-hour period, an 

AC algorithm was used to probabilistically specify the AC system for the residence (central, window units, or none). A window status algorithm was then used to probabilistically determine window status (closed or open). Based on this determination, a 

value of AER for the 24-hour period was selected from either the closed window distribution or the open window distribution. 

The AC algorithm requires that the user specify the proportion of residences in 

the study area that have central AC, window units, and no AC. According to the 1995 

American Housing Survey for Denver (Bureau of the Census, 1995), the breakdown for 

Denver is 25.3 percent central, 14.3 percent window, and 60.4 percent none. The 

algorithm as applied to Denver can be described as follows: 

4-5
 
Table 4-1. Distributions of Parameter Values Used in the pNEM/CO Mass-Balance 

Model 

Parameter Distribution of parameter Reference 

Air exchange rate, exchanges/h: Lognormal distributions by season Johnson, Memorandum No. 1, 1998 

residence - windows closed Season 1 

N Geometric mean = 0.450 

N Geometric standard deviation = 1.960 

N Lower bound = 0.120 

N Upper bound = 1.683 

Season 2 

N Geometric mean = 0.308 

N Geometric standard deviation = 2.241 

N Lower bound = 0.063 

N Upper bound = 1.498 

Season 3 

N Geometric mean = 0.653 

N Geometric standard deviation = 2.010 

N Lower bound = 0.166 

N Upper bound = 2.566 

Season 4 

N Geometric mean = 0.309 

N Geometric standard deviation = 1.716 

N Lower bound = 0.107 

N Upper bound = 0.890 

Murray and Burmaster, 1995 

Air exchange rate, exchanges/h: Lognormal distribution Johnson, Memorandum No. 1, 1998 

residence - windows open N Geometric mean = 1.34 

N Geometric standard deviation = 1.55 

N Lower bound = 0.57 

N Upper bound = 3.16 

Johnson, Weaver, Mozier, et al., 

1998 

Air exchange rate, exchanges/h: 

nonresidential, enclosed 

microenvironments, including motor 

vehicles 

See Table 4-2 See Table 4-2 

Annual gas usage by burners, 

kilojoules 

Lognormal distribution 

N Geometric mean = 2.11 x 106 

N Geometric standard deviation = 1.48 

N Lower bound = 0.98 x 106 

N Upper bound = 4.55 x 106 

Menkedick et al., 1993 

Annual gas usage by pilot lights, 

kilojoules 

Lognormal distribution 

N Geometric mean = 3.37 x 106 

N Geometric standard deviation = 1.84 

N Lower bound = 1.02 x 106 

N Upper bound = 11.13 x 106 

Menkedick et al., 1993 

Burner emission factor, mg/kilojoule Lognormal distribution 

N Geometric mean = 0.0294 

N Geometric standard deviation = 2.77 

N Upper bound = 0.400 

Davidson et al., 1987 

Residential volume, cubic meters Lognormal distribution 

N Geometric mean = 436 

N Geometric standard deviation = 1.62 

N Lower bound = 169 

N Upper bound = 1122 

Bureau of Census, 1995 

4-6
 
Table 4-2. Distributions for Air Exchange Rate for Enclosed, Nonresidential Microenvironments 

Microenvironment Activity diary locations 

included in microenvironDistribution of Air Exchan

Distribu- Lognormal Parameters Bo

Code 

General 

location 

Specific 

location 

ment tion type GM GSD Lower 

2 Indoors Nonresidence 

A 

Service station or auto repair 

Lognormal 1.24 1.93 0.34 

3 Indoors Nonresidence 

B 

Other repair shop Shopping mall 

Lognormal 1.24 1.93 0.34 

4 Indoors Nonresidence 

C 

Restaurant Other indoor location Auditorium 

Lognormal 1.24 1.93 0.34 

5 Indoors Nonresidence 

D 

Store Office Other public building 

Lognormal 1.24 1.93 0.34 

6 Indoors Nonresidence 

E 

Health care facility School Church Manufacturing facility 

Lognormal 1.36 1.91 0.38 

7 Indoors Residential 

garage 

Residential garage Lognormal 1.24 1.93 0.34 

10 Vehicle Automobile Automobile Lognormal 39.7 1.76 13.1 

4-7
 
Microenvironment Activity diary locations 

included in microenvironDistribution of Air Exchan

Distribu- Lognormal Parameters Bo

Code 

General 

location 

Specific 

location 

ment tion type GM GSD Lower 

11 Vehicle Other Bus 

Truck Bicycle Motorcycle Train/subway Other vehicle 

Lognormal 39.7 1.759 13.1 

99 Vehicle Airplane Airplane NA -­ -­ -­ 

aData set containing all non-school AER values provided by Turk et al. (1989) and CEC (Lagus Applied 1995).bData set containing all AER values provided by Turk et al. (1989) and CEC (Lagus Applied TechnologcOtt, Switzer, and Willis (1994). 

4-8
 
1.	 For each day, select a random number (RN) between zero and 1. 

2.	 If RN < 0.253, the AC system is "central." 

3.	 If 0.253 < RN < 0.396, the AC system is "window units." 

4.	 If 0.396 <RN, the AC system is "none." 

The window status algorithm was originally developed for applications of 

pNEM/O3 and has been described by Johnson et al. (1990). This algorithm determines 

window status based on AC system and the daily average temperature according to the 

probabilities listed in Table 4-3. The combination of window status algorithm and AER 

algorithm can be described as follows: 

1.	 For each day, determine daily average temperature from a supplementary 

temperature file and AC system from AC system algorithm. Select RN 

between zero and 1. 

2.	 Assume Step 1 specified 65 degrees and central AC. RN will be evaluated against percentage values listed in Table 4-3 for central AC - medium 

temperature range (i.e., 35.6, 29.4, and 34.6). 

3.	 If RN < 0.356, windows are closed all day. AER value is selected from the 

"windows closed" AER distribution. 

4.	 If 0.356 < RN < 0.650, windows are open all day. AER value is selected 

from the "windows open" AER distribution. 

5.	 If 0.650 < RN, windows are open for 58.2 percent of the day (see last column). AER is determined by the expression 

AER = (0.582)(open AER) + (0.418)(closed AER) (4-14) 

where open AER is selected from the open window AER distribution and 

closed AER is selected from the closed window AER distribution. 

4.3	 Air Exchange Rate Distributions 

A review of scientific literature was conducted to identify references relating to air 

exchange rates (AERs). Of the references identified, only a few were found to contain 

sufficient data to construct a distribution of AERs relating to a particular building type 

such as residence or office. The three most useful studies were conducted by Murray 

and Burmaster (1995), Turk et al. (1989), and Lagus Applied Technology (1995). 

4-9
 
Table 4-3. Percentage of Person-Days With Indicated Window Ratio by Air 

Conditioning System And Temperature Range 

Air conditioning 

system 

Temperature 

rangea 

Percentage of person-days with indicated window ratiob 

Mean of ratios not 

Ratio = 0 Ratio = 1 0 < Ratio = <1 equal to 0 or 1

Central Low 

Medium 

High 

86.0 

35.6 

62.1 

0.8 

29.4 

12.9 

13.2 

34.6 

25.0 

0.260 

0.582 

0.503 

Room units Low 

Medium 

High 

73.2 

12.0 

17.1 

2.0 

44.2 

34.3 

24.7 

43.8 

48.6 

0.316 

0.618 

0.521 

No air conditioning Low 

Medium 

High 

80.0 

4.7 

1.4 

1.0 

59.1 

70.8 

19.0 

36.2 

27.8 

0.276 

0.716 

0.774 

a Low: 31° to 62°F. 

Medium: 63° to 75°F. 

High: 76°F and above. 

b Ratio = (minutes windows open)/(minutes spent in residence). 

4.3.1 Residential Locations 

An article by Murray and Burmaster (1995) described their analysis of residential 

air exchange rate data compiled by the Brookhaven National Laboratory (BNL). The 

BNL data included AERs for 2,844 residences in the United States, classified according 

to four geographic regions and the four seasons. The data for Denver were included in 

Region 2. The BNL data for Region 2 includes a large number of AER values for winter 

and spring, but small sample sizes for summer and autumn (n = 2 and 23, respectively). 

Statistical methods were used to estimate the geometric mean and standard deviation 

for the seasons with limited data (Johnson, Memorandum No. 1, 1998). The resulting 

seasonal AER distributions for Region 2 (which includes Denver) are included in Table 

4-1. The lower and upper bounds of the distributions are based on the 2.5th and 97.5th 

percentile of the distributions. 

Note that the Region 2 estimates presented by Murray and Burmaster were 

based solely on data derived from the BNL database. Pandian et al. (1998) recently 

identified errors in a version of the BNL database previously used by Pandian, Ott, and 

Behar (1993) and provided corrected estimates of AER for various geographic regions. 

In evaluating other researcher’s use of the BNL database, Pandian et al. concluded that 

4-10
 
 

the errors they identified did not affect the AER statistics presented by Murray and 

Burmaster. This conclusion is supported by the corrected statistics presented by 

Pandian et al. (1998) for a region containing Denver which are consistent with the 

statistics presented by Murray and Burmaster (1995) for Region 2. 

For residences with windows open, the AER distribution in Version 2.0 of 

pNEM/CO is based on a study of a single residence. The American Petroleum Institute 

(API) conducted a study of a typical suburban house over a 24-hour time period 

(Johnson, Weaver, Mozier, et al., 1998). In that study, researchers altered the 

ventilation characteristics of the house each hour according to a prepared script, and 

measured the resulting hourly average AER. Analysts determined that the data for 

hours when windows were open could be characterized by a lognormal distribution with 

a geometric mean of 1.34 air changes per hour, and a geometric standard deviation of 

1.55 (Johnson, Memorandum No. 1, 1998). The upper and lower bounds of the 

distribution have been set at 0.57 and 3.16, which correspond to the 2.5th and 97.5th 

percentiles, respectively. 

4.3.2 Nonresidential Locations 

Two AER distributions are used in pNEM/CO to represent non-residential 

buildings. Microenvironment Nos. 2 through 5 are represented by a lognormal 

distribution with geometric mean = 1.24 and geometric standard deviation = 1.93. 

Microenvironment No. 6 is represented by a lognormal distribution with geometric mean 

= 1.36 and geometric standard deviation = 1.91. These distributions were developed 

from statistical analyses of AER data provided by two studies. The first study, 

conducted by Turk et al. (1989), measured AERs in 40 public buildings identified as 

schools (n = 7), offices (n = 25), libraries (n = 3), and multipurpose buildings (n = 5). 

The second study was conducted by the California Energy Commission (Lagus Applied 

Technology, Inc., 1995), and included 49 public buildings identified as schools (15), 

offices (22), and retail stores (13). 

Microenvironment Nos. 2 through 5 are similar in that each includes various 

types of public buildings but omits schools. To determine a representative distribution 

of AER for these microenvironments, analysts combined all non-school data from the 

Turk and CEC studies into a single data set containing 68 values. These values could 

4-11
 
be well-fit by a lognormal distribution with geometric mean of 1.24 air changes per hour 

and a geometric standard deviation of 1.93. As indicated in Table 4-2, AER values for 

Microenvironments Nos. 2 through 5 were randomly selected from this distribution. 

Values were not permitted to fall below 0.34 or exceed 4.50, corresponding to the 2.5th 

and 97.5th percentiles of the distribution. 

Microenvironment No. 6 differs from Microenvironments Nos. 2 through 5 in that 

it includes school and non-school buildings. Consequently, analysts used the complete 

set of AER values from the Turk and CEC studies to represent this microenvironment. 

The resulting data set could be well fit by a lognormal distribution with geometric mean 

= 1.36 air changes per hour and geometric standard deviation = 1.91. AER values for 

Microenvironment No. 6 were randomly selected from this distribution. Values were not 

permitted to fall below 0.38 or exceed 4.83, corresponding to the 2.5th and 97.5th 

percentiles of the distribution. 

4.3.3 Vehicle Locations 

A point estimate of 36 air changes per hour was used for in-vehicle locations in 

the 1992 version of pNEM/CO. This value was obtained from Hayes (1991) based on 

his analysis of data presented by Peterson and Sabersky (1975). 

In a study reported by Ott, Switzer, and Willis (1994), researchers measured an 

AER value of 13.1 air changes per hour in a car moving at 20 mph with windows closed. 

AER values of 67 to 120 were measured in the car at the same speed with windows 

open. 

Peggy Jenkins (1998) has indicated that additional data on AER in vehicles will 

be available soon from the California Air Resources Board. These data should provide 

a better basis for determining an AER distribution for vehicles. Until these data are 

available, AER values for vehicles will be sampled from a lognormal distribution with 

geometric mean = 39.7 and geometric standard deviation = 1.76. This approach is 

based on the assumption that the AER distribution has a lognormal distribution and that 

the 2.5th and 97.5th percentiles of this distribution correspond to the lowest and highest 

reported AER values for vehicles (13 and 120 air changes per hour, respectively). 

4-12
 
4.4 Probability of Stove Use 

The operation of gas stove burners in residences is simulated in the massbalance model by specifying when the burners are on, the emission rate of the burners 

during operation, and the volume of the residence where it is located. 

As discussed above, burner operation was assumed to occur in discrete BOPs 

such that use always began and ended within a single clock hour. BOP duration was 

assigned a value of either 30 or 60 minutes, depending on the time of day. These 

values were based on responses to a questionnaire administered by GEOMET to 4312 

survey participants. Each participant provided data on the type of cooking facilities in 

the home, frequency of cooking, and average time spent in meal preparation (Koontz, 

Mehegan, and Nagda, 1992). 

Table 4-4 presents a summary of data from this survey by type of meal (breakfast, lunch, and dinner). The values listed for average weekly time spent cooking breakfast, lunch, and dinner are 66, 71, and 288 minutes, respectively. The total time for all 

three meals is 425 minutes per week. The average daily cooking time based on this 

weekly value is 425/7 or 61 minutes. 

Table 4-4.	 Statistics on Gas Stove Use Obtained from a Survey by Koontz et al. 

(1992) 

Data item Breakfast Lunch Dinner Sum 

Weekly duration of gas stove use, minutes 66 71 288 425 

Weekly frequency of gas stove use 2.5 2.2 5.0 9.7 

Average duration of use, minutes 26 32 58 

In addition to duration, the data in Table 4-4 provide an indication as to the frequency that a gas stove is used to prepare meals in the typical residence. In one week, 

the stove will be used to prepare 2.5 breakfasts, 2.2 lunches, and 5.0 dinners -- a total 

of 9.7 meals per week. On an average day, the number of meals prepared on a gas 

stove is 9.7/7 or 1.4. 

Dividing the weekly cooking time associated with each meal type by the average 

frequency of the meal yields average BOPs for breakfast, lunch, and dinner of 26, 32, 

4-13
 
and 58 minutes, respectively. Based on these results, pNEM/CO uses a value of 60 

minutes for BOPs that occur during normal dinner hours and 30 minutes for BOPs that 

occur at other times. 

In pNEM/CO, stove operation is determined on an hourly basis by comparing a 

randomly selected number between 0 and 1 with AP(h), the probability of a gas stove 

being operated during the indicated clock hour h (h = 1, 2, ..., 24). If the random number is less than AP(h), the stove is "on" for a duration of M(h) minutes, where M(h) is 

either 30 or 60 minutes, depending on the value of h. If the random number is greater 

than or equal to AP(h), the gas stove is "off" for the entire hour. 

Table 4-5 lists the values of AP(h) and M(h) used in the pNEM/CO analysis by 

clock hour. These values were developed to (1) reflect diurnal patterns in gas stove 

usage specific to Denver, (2) yield an average daily duration for stove use of 

approximately 61 minutes, and (3) yield an average daily frequency of stove use of 

approximately 1.4. 

Diurnal patterns in stove use were determined through an analysis of data from 

the Denver Personal Monitoring Study (Johnson, 1984). In this analysis, the diary 

entries and background questionnaire provided by each study subject were used to 

determine (1) when the subject was in a residence having a gas stove and (2) whether 

the stove was on. As working subjects would not always be present when other family 

members were operating a gas stove, it was assumed that workers would tend to 

under-report stove use in their residences. It was further assumed that nonworkers 

would use gas stoves more than the average person and that the diaries of nonworkers 

would tend to over-represent typical gas stove use. Consequently, the decision was 

made to average the worker and nonworker data and then adjust these results so that 

the adjusted P(h) values would yield 1.4 hours of stove use "events" per day, on 

average. 

Table 4-6 presents the relevant data. For each clock hour, the table lists values 

of p(h) for workers and nonworkers calculated as 

P(h) = [N(stove on, GSR)]/[N(GSR)] (4-15) 

where N(stove on, GSR) is the number of diary entries indicating the subject was in a 

4-14
 
Table 4-5. Probability of Gas Stove Use by Clock Hour and Assumed Burner 

Operation Period 

Clock hour 

AP(h): probability of gas 

stove operation 

M(h): assumed burner 

operation period, minutes 

Product of AP(h) and 

M(h), minutes 

1 0.025 30 0.76 

2 0.023 30 0.68 

3 0.023 30 0.69 

4 0.023 30 0.70 

5 0.023 30 0.70 

6 0.026 30 0.77 

7 0.049 30 1.46 

8 0.058 30 1.73 

9 0.081 30 2.43 

10 0.073 30 2.20 

11 0.062 30 1.86 

12 0.075 30 2.25 

13 0.085 30 2.54 

14 0.071 30 2.14 

15 0.067 60 4.01 

16 0.064 60 3.86 

17 0.107 60 6.41 

18 0.130 60 7.80 

19 0.091 60 5.49 

20 0.058 60 3.45 

21 0.052 60 3.11 

22 0.047 60 2.79 

23 0.040 30 1.21 

24 0.035 30 1.04 

Total 1.386 60.04 

4-15
 
Table 4-6. Proportion of PEM Values in Gas Stove Residences with Stove in 

Operation by Clock Hour and Work Status 

Clock hour 

Nonworkers Workers 

Average 

P(h) 

AP(h): 

n P(h) n P(h) adjusted P(h)

1 63 0.111 149 0.041 0.076 0.025 

2 59 0.085 139 0.051 0.068 0.023 

3 5 0.086 136 0.052 0.069 0.023 

4 58 0.086 134 0.053 0.070 0.023 

5 58 0.086 133 0.053 0.070 0.023 

6 62 0.097 141 0.057 0.077 0.026 

7 67 0.119 175 0.173 0.146 0.049 

8 84 0.179 151 0.167 0.173 0.058 

9 119 0.269 134 0.216 0.243 0.081 

10 87 0.230 86 0.209 0.220 0.073 

11 80 0.200 70 0.171 0.186 0.062 

12 76 0.253 76 0.197 0.225 0.075 

13 72 0.296 70 0.214 0.255 0.085 

14 62 0.213 80 0.215 0.214 0.071 

15 51 0.216 59 0.186 0.201 0.067 

16 64 0.266 75 0.120 0.193 0.064 

17 96 0.396 122 0.246 0.321 0.107 

18 103 0.456 174 0.326 0.391 0.130 

19 151 0.251 244 0.299 0.275 0.091 

20 148 0.149 341 0.196 0.173 0.058 

21 102 0.147 236 0.165 0.156 0.052 

22 82 0.183 176 0.097 0.140 0.047 

23 82 0.159 193 0.083 0.121 0.040 

24 75 0.133 148 0.075 0.104 0.035 

4.167 1.386 

4-16
 
gas stove residence when the stove was on and N(GSR) is the total number of diary 

entries indicating the subject was in a gas stove residence. In calculating these values, 

a stove was considered on during a particular clock hour if the subject's activity diary 

indicated at least 1 minute of use during the hour. 

The column labeled "average P(h)" lists the arithmetic mean of the worker and 

nonworker P(h) values. These probabilities sum to 4.2 over 24 hours. It is desirable 

that the probabilities sum to 1.4, as this will produce an average of 1.4 BOPs per day. 

The values labeled "adjusted P(h)" were calculated by multiplying the average values by 

0.333 (1.4/4.2). The adjusted values sum to 1.4. 

The AP(h) values are listed also in Table 4-5. To the right of each AP(h) value is 

the assumed value of M(h); that is, the number of minutes the stove will be assumed to 

operate if the stove is determined to be "on" during the hour. The product of AP(h) and 

M(h) is listed in the far right column. Summing these values over all 24 hours provides 

an estimate of the average number of minutes per day that a gas stove will be operated 

according to the algorithm. The sum is approximately 60 minutes, a value very close to 

the desired value of 61 minutes. 

4.5 Gas Stove Emission Rate 

The gas stove algorithm in pNEM/CO is based on the assumption that the mass 

of CO emitted by a gas stove during a particular hour (h) can be estimated by the equation 

MASSCO(h) = (ERBURN)[M(h)]/60 + (ERPILOT)(1 hr) (4-16) 

where MASSCO(h) is expressed in mg, ERBURN is the hourly burn emission rate in mg 

per hour, and ERPILOT is the hourly pilot light emission rate in mg per hour. M(h) is the 

duration of burner use during hour h expressed in minutes. The pilot light is assumed to 

on continuously during the one hour period. 

M(h) is zero for each hour in which the algorithm assigns the stove a status of 

"off." If the stove status is "on" for a particular hour, M(h) is assigned a value of 30 or 

60 minutes according to Table 4-6. 

ERBURN is determined by the equation 

4-17
 
ERBURN = (AUB/365.2)(EFBURN)(Y’) (4-17) 

where AUB is the annual fuel usage of the burners in kilojoules, 365.2 is the number of 

hours per year that the burners are operated assuming 60 minutes of use per day 

(Table 4-6), EFBURN is the burner emission factor in mg of CO per kilojoule, and Y’ is 

an adjustment factor which varies sinusoidally throughout the year. The values of AUB, 

EFBURN, and Y’ are held constant within each 24-hour period. 

Values of AUB are randomly selected from a lognormal distribution with 

geometric mean = 2.11 million kilojoules and geometric standard deviation = 1.48. This 

distribution is based on the distribution of annual burner gas use measured by the 

Northern Illinois Gas Company (NIGAS) in 57 homes (Menkedick et al., 1993). The 

value of AUB is not permitted to exceed 4.55 million kilojoules. This value represents 

the 97.5th percentile of the distribution. 

The seasonal adjustment factor (Y’) is determined by the equation 

Y’(j) = 1.00 - (0.190){sin[-1.616 + (2B)(j)/365]}. (4-18) 

in which j is the Julian date. This equation was derived by Johnson (Memorandum No. 

1, 1998) from a sinusoidal pattern observed in a study conducted by NIGAS (Wilkes and 

Koontz, 1995). The NIGAS data indicate that gas use in the winter is approximately 46 

percent higher than in the summer. 

Values of EFBURN are randomly selected from a lognormal distribution with geometric mean = 0.0294 mg/kilojoule and geometric standard deviation = 2.77. Values of 

EFBURN are not permitted to exceed 0.400 mg/kilojoule. These values are based on 

the results of an analysis of data reported by Davidson et al. (1987) and represent a 

well-adjusted stove. As such, the assumed geometric mean is probably low with 

respect to the overall population of gas stoves. 

Consistent with the above discussion, the following algorithm is used to estimate 

a value of ERBURN for each 24-hour period. 

1. Go to the next day of the current sequence. The Julian date of this day is 

j. 

4-18
 
2.	 Randomly select a value for AUB from a lognormal distribution with 

geometric mean = 2.11 million kilojoules per year and geometric standard 

deviation = 1.48. 

3.	 Randomly select a value of EFBURN from a lognormal distribution with 

geometric mean = 0.0294 mg/kilojoule and geometric standard deviation = 

2.77. 

4.	 Use Equation 4-18 to calculate Y’(j). 

5.	 ERBURN = (EFBURN)(AUB)(Y’)/365.2 for all hours of the day. 

The resulting value of ERBURN is inserted into Equation 4-16 as required. 

The ERPILOT value in Equation 4-16 is determined by the equation 

ERPILOT = (AUP/8760)(EFPILOT)	 (4-19) 

where AUP is the annual fuel usage by all pilot lights in kilojoules, 8760 is the number of 

hours per year that the pilot lights are in operation, and EFPILOT is the pilot light emission factor in mg of CO per kilojoule. The value of AUP is constant over each 24-hour 

period and is randomly selected from a specified lognormal distribution. The value of 

EFPILOT is assumed to be constant within each 24-hour period and is set equal to the 

value determined for EFBURN in Equation 4-17. 

The distribution for gas usage by pilot lights (AUP) is based on data from the 

NIGAS study discussed previously. The total gas usage (burners plus pilot lights) for 33 

stoves had an arithmetic mean of 57.1 therms and a standard deviation of 18.3 therms. 

Total gas use was also measured for 57 stoves that did not have pilot lights; the 

arithmetic mean gas use for stoves without pilot lights was 21.8 therms. The difference 

between the two samples, 57.1 - 21.8 = 35.3 therms, provides an estimate of the mean 

fuel usage for pilot lights only. The square root of the differences in variances, (18.32

 ­

8.92

)

1/2 = 16.0 therms, provides an estimate of the standard deviation. Therefore, AUP 

is assumed to have an arithmetic mean of 35.3 therms (3,500 ft3

) and an arithmetic 

standard deviation of 16.0 therms (1,600 ft3

). 

The ratio of standard deviation to mean is 0.45, indicating that the distribution is 

skewed. Consequently, it was assumed that the underlying distribution is lognormal. 

4-19
 
The corresponding geometric mean and geometric standard deviation for this 

distribution are 3,215 ft3

 = 3.37 million kilojoules per year and 1.84 (dimensionless), 

respectively. The lower and upper bounds for permitted AUP values are 1.02 and 11.13 

million kilojoules per year, respectively, corresponding to the 2.5th and 97.5th percentile 

of the specified lognormal distribution. 

The value of MASSCO(h) determined by Equation 4-16 is used as the value of S 

for hour h in Equation 4-10, regardless of the value of M(h). This approach permits the 

use of the hourly average exact solution (Equation 4-7) to the mass-balance equation 

(Equation 4-2). The practical result of this simplification is a slight smoothing in the 

simulated hour-to-hour variation in indoor CO concentrations with respect to the pattern 

which would be simulated by a model with finer time resolution. 

4.6 Residential Volume 

In the application of pNEM/CO to Denver, residence volume was randomly 

selected from a lognormal distribution based on data from the 1995 American Housing 

Survey for Denver, Colorado (Bureau of the Census, 1995). Table 4-7 lists frequency 

data on residential square footage from the survey specific to Denver. These data can 

be closely fit by a lognormal distribution with a geometric mean of 1926 and a geometric 

standard deviation of 1.62. The geometric mean agrees well with the median listed in 

the survey data (2020). (The geometric mean of a “perfect” lognormal distribution is 

equal to its median). 

Assuming an eight-foot ceiling and using 1 cubic meter = 35.315 cubic feet, the 

residential volumes can be modeled by a lognormal distribution with a geometric mean 

of 436 m3

 and a geometric standard deviation of 1.62. The upper and lower bounds of 

the distribution have been set at 169 and 1,122 m3, respectively, corresponding to the 

2.5th and 97.5th percentiles of the distribution. 

4-20
 
Table 4-7. Statistics on Square Footage of Occupied Units in Denver, Colorado 

(Bureau of the Census, 1995). 

Range of square footage 

for occupied units 

Denver residences 

Number in thousands Cumulative percent 

less than 500 1.0 0.2 

500 to 749 9.3 2.5 

750 to 999 34.1 10.6 

1,000 to 1,499 75.6 28.6 

1,500 to 1,999 86.0 49.2 

2,000 to 2,499 86.7 69.8 

2,500 to 2,999 49.8 81.7 

3,000 to 3,999 53.4 94.5 

4,000+ 23.2 100.00 

Totala 419.1 -­ a

 Omits 38,500 units which did not report square footage values. 

4.7 References for Section 4 

Bureau of Census. 1995. “American Housing Survey for the Denver Metropolitan Area 

in 1995.” Current Housing Reports, Report No. H170/95-46. U. S. Department of 

Commerce. 

Davidson, C. L., J. E. Borrazzo, and C. T. Hendrickson. 1987. Pollutant Emission 

Factors for Gas Stoves. Report No. EPA/600/9-87-005. U. S. Environmental 

Protection Agency. Research Triangle Park, North Carolina. 

Hayes, S. R. 1991. “Use of an Outdoor Air Quality Model (IAQM) to Estimate Indoor 

ozone Levels,” Journal of the Air and Waste Management Association, Vol. 41, pp. 

161 - 170. 

Jenkins, Peggy. 1998. Personal Communication with Ted Johnson. California Air 

Resources Board, Sacramento, California. 

4-21
 
Johnson, T. R., R.A. Paul, J. E. Capel, and T. McCurdy. 1990. “Estimation of Ozone 

Exposure in Houston Using a Probabilistic Version of NEM,” presented at the 83rd 

Annual Meeting of the Air and Waste Management Association, Pittsburgh, 

Pennsylvania. 

Johnson, T., M. Weaver, J. Mozier, et al. 1998. “Variation of Residential Air Exchange 

Rates Under Scripted Ventilation Conditions.” Presented at the International 

Symposium on the Measurements of Toxic and Related Pollutants, Air and Waste 

management Association, September 1 - 3, 1998, Cary, North Carolina. 

Johnson, T. September 9, 1998. “Analyses Supporting Proposed Modifications to the 

Mass Balance Algorithm and Gas Stove Algorithm in pNEM/CO.” Memorandum 

prepared by TRJ Environmental, Inc., under EPA Contract No. 68-D6-0064. 

Koontz, M. D., L. L. Mehegan, and N. L. Nagda. 1992. “Distribution and Use of 

Cooking Appliances That Can Affect Indoor Air Quality,” Report No. GRI-93/0013. Gas 

Research Institute, Chicago. 

Lagus Applied Technology, Inc. 1995. Air Exchange Rates in Non-Residential 

Buildings in California. Document No. P400-91-034. Prepared for the California 

Energy Commission under Contract No. 400-91-034. 

Menkedick, J., N. Niemuth, P. Hartford, and D. K. Landstrom. 1993. Metered Ranges, 

Cooktops, and Ovens in the Northern Illinois Gas Residential Load Study Data 

Base. GRI Report No. 93/0204. Prepared by Batelle for the Gas Research Institute, 

8600 West Bryn Mawr Avenue, Chicago, IL 60631. 

Murray, D. M., and D. E. Burmaster. 1995. “Residential Air Exchange Rates in the 

United States: Empirical and Estimated Parametric Distributions by Season and Climatic 

Region.” Risk Analysis, Vol. 15, No. 4, 459-465. 

Nagda,N. L., H. E. Rector, and M. D. Koontz. 1987. Guidelines for Monitoring 

Indoor Air Quality, Hemisphere Publishing Corporation, Washington, D.C. 

Pandian, D. P., J. V. Behar, W. R. Ott, L. A. Wallace, A. L. Wilson, S. D. Colome, and 

M. Koontz. 1998. “Correcting Errors in the Nationwide Data Base of Residential Air 

Exchange Rates.” Journal of Exposure Analysis and Environmental Epidemiology, 

Vol. 8, No. 4, pp. 577-586. 

Pandian, D.P., W. R. Ott, and J. V. Behar. 1993. “Residential Air Exchange Rates for 

Use in Indoor Air and Exposure Modeling Studies.” Journal of Exposure Analysis 

and Environmental Epidemiology, Vol. 3, No. 4, pp. 407-416. 

Petersen, G. A., and R. H. Sabersky. 1975. “Measurements of Pollutants Inside an 

Automobile,” Journal of the Air Pollution Control Association, Volume 25, No. 10, 

pp. 1028 - 1032. 

4-22
 
Turk, B. H., D. T. Grimsrud, J. T. Brown, K. L. Geisling-Sobotke, J. Harrison, and R. J. 

Prill. 1989. “Commercial Building Ventilation Rates and Particle Concentrations,” 

ASHRAE Transactions, Vol. 95, Part 1. 

4-23
 
SECTION 5
 

ESTIMATION OF VENTILATION RATE 

This section provides a brief summary of the algorithm used to estimate ventilation rate 

in Version 2.0 of pNEM/CO. It also presents distributions and estimating equations for 

each variable parameter in the algorithm. The section also discusses the rationale 

behind the decision not to explicitly account for the potential effects of angina in the 

algorithm. The section begins with a brief discussion of the physiological principles 

incorporated into the new algorithm. 

5.1 The Metabolic Equivalence Concept 

McCurdy (1999) has recommended that ventilation rate be estimated as a 

function of energy expenditure rate. The energy expended by an individual during a 

particular activity can be expressed as 

EE = (MET)(RMR) (5-1) 

in which EE is the average energy expenditure rate (kcal min-1) during the activity and 

RMR is the resting metabolic rate of the individual expressed in terms of number of 

energy units expended per unit of time (kcal min-1) . MET (the “metabolic equivalent of 

work”) is a ratio specific to the activity and is dimensionless. If RMR is specified for an 

individual, then Equation 5-1 requires only an activity-specific estimate of MET to 

produce an estimate of the energy expenditure rate for a given activity. EPA has 

recently developed the Consolidated Human Activity Database (CHAD) which contains 

24-hour sequences of activity-specific values of MET indexed by gender, age, and other 

useful descriptors. As discussed below, equations for estimating RMR as a function of 

body mass (BM) can be obtained from the literature for the demographic groups of 

interest to a particular exposure assessment. 

5-1
 
The MET concept provides a means for estimating the ventilation rate associated 

with each activity. For convenience, let EEa(i,j,k) indicate the energy expenditure rate 

associated with the i-th activity of day j for person k. Equation 5-1 can now be 

expressed as 

EEa(i,j,k) = [MET(i,j,k)][RMR(k)] (5-2) 

in which RMR(k) is the average value for resting metabolic rate specific to person k. 

Note that MET(i,j,k) is specific to a particular activity performed by person k. 

5.2 Oxygen Requirements for Energy Expenditure 

Energy expenditure requires oxygen which is supplied by ventilation (respiration). 

Let ECF(k) indicate an energy conversion factor defined as the volume of oxygen 

required to produce one kilocalorie of energy in person k. The oxygen uptake rate 

(VO2) associated with a particular activity can be expressed as 

VO2(i,j,k) = [ECF(k)][EEa(i,j,k)], (5-3) 

in which VO2(i,j,k) has units of liters oxygen min-1, ECF(k) has units of liters oxygen 

kcal-1, and EE(i,j,k) has units of kcal min-1. The value of VO2(i,j,k) can now be 

determined from MET(i,j,k) by substituting Equation 5-2 into Equation 5-3 to produce the 

relationship 

VO2(i,j,k) = [ECF(k)][MET(i,j,k)][RMR(k)]. (5-4) 

The analyst must provide values of ECF(k) and RMR(k) for person k. Methods for 

estimating these values are provided in Section 5.4. 

5.3 Estimating VE from VO2 

5-2
 
Ventilation rate (VE) tends to increase as VO2 increases up to the point of 

maximum oxygen uptake (VO2max). The relationship is known to be non-linear, with the 

slope of the relationship usually increasing at higher values of VO2. In Version 2.0 of 

pNEM/CO, the relationship between VE(i,j,k) and VO2(i,j,k) is modeled by the generic 

equation 

ln[VE(i,j,k)/BM(k)] = a + (b){ln[VO2(i,j,k)/BM(k)]} + d(k) + e(i,j,k) (5-5) 

in which VE(i,j,k) is the VE value associated with the ith event of day j for person k, BM(k) 

is the body mass assigned to person k, and a and b are constants determined by the 

age and gender of person k according to Table 5-1. The term d(k) is a random variable 

selected for each person from a normal distribution with mean equal to zero and 

standard deviation equal to Fd. The term e(i,j,k) is a random variable selected for each 

individual event from a normal distribution with mean equal to zero and standard 

deviation equal to Fe. Table 5-1 lists values of Fd and Fe by age and gender. 

The values of a, b, Fd, and Fe listed in Table 5-1 were determined through a 

statistical analysis of data obtained from 32 clinical studies performed by Dr. William 

Adams. During each study, a panel of subjects were put through a test sequence of 

increasing levels (steps) of exertion on either a cycle ergometer or a treadmill. At each 

step, researchers measured test time, machine setting, the subject’s ventilation rate 

(VE), the subject’s oxygen uptake rate (VO2), and the subject’s heart rate (HR). In most 

cases, researchers also established a maximum value of VO2 (VO2max) for each 

sequence of steps. Subject-specific data on age, gender, body mass, lean body mass, 

height, and other relevant parameters were also recorded. 

Johnson (1998) performed a series of statistical analyses on these data to 

determine whether the data would support the development of subject-specific 

equations relating VER (the ratio of VE to VO2) to VO2 expressed as a percentage of 

VO2max. The data for most subjects were found to lack sufficient measurements of VE at 

low values of VO2 to properly “anchor” the lower end of the assumed relationship, 

although the relationship between VE and VO2 at high exertion rates was usually well 

characterized. Supplemental analyses of the Adams data by EPA researchers 

suggested that a strong relationship existed between VE/BM and VO2/BM (where BM 

5-3
 
was the body mass of the subject providing the values of VE and VO2) when data from 

multiple subjects were combined according to age and gender. 

Based on these findings, EPA elected to develop an equation relating VE to VO2 

applicable to each of six groups of subjects defined by the age and gender categories 

listed in Table 5-1. Jonathan Cohen of ICF used regression analysis to fit Equation 5-5 

to cross-sectional data for the subjects belonging to each of the six groups. Appendix D 

describes these analyses in detail. The resulting regression coefficients were used as 

estimates of a, b, Fd, and Fe. 

In summary, Equation 5-4 was used to convert event-specific values of MET to 

corresponding values of VO2. Equation 5-5 was then used to convert the VO2 value to a 

value of VE. Section 5.4 describes two tests for reasonableness that were performed on 

the VO2 estimates prior to converting them to VE values. Section 5.5 presents a stepby-step description of the algorithm that was used to probabilistically determine the 

various parameter values required by Equations 5-4 and 5-5. 

Table 5-1. Regression-Based Estimates for Parameters of Equation 5-5. 

Subject 

characteristics Regression resultsa 

Number 

Standard deviation 

Age 

group Gender 

of test 

values Intercept (a) Slope (b) 

Person-level 

errors (Fd) 

Test-level 

errors (Fe) 

18 - 44 Female 1473 4.357 1.276 0.1351 0.1182 

Male 3145 3.991 1.197 0.1228 0.1395 

45 - 64 Female 60 3.454 1.021 0.1106 0.0769 

Male 641 4.018 1.165 0.1107 0.1112 

65+ Female 45 2.956 0.908 0.0886 0.0338 

Male 317 3.730 1.071 0.1082 0.0632 a

Based on regression analysis of data provided by Dr. William Adams. See Appendix D 

for details. 

5.4 Tests to Identify Unrealistic Values of Oxygen Uptake Rate 

5-4 
A person’s maximum ventilation rate is determined by his or her maximum 

oxygen uptake rate (VO2max) and the VE/VO2 ratio in effect under maximum oxygen 

uptake conditions. As work increases, energy is provided primarily by aerobic (oxygenbased) processes up to the point of VO2max, referred to as the point of maximal aerobic 

power (MAP). The additional energy required for higher work rates is provided primarily 

by anaerobic processes. Consequently, the work rate where VO2max is reached is less 

than a person’s maximum work rate. 

Astrand and Rodahl (1977) state that most individuals cannot maintain a work 

rate equal to 100 percent of MAP (i.e., a work rate where VO2 equals VO2max) for more 

than about five minutes. As the duration of work increases, there is a progressive 

decrease in the average VO2 level that can be maintained. Astrand and Rodahl also 

state that a VO2 level equal to 50 percent of VO2max cannot be maintained for a whole 

working day. 

Erb (1981) has developed estimates of the percentage of “maximum work 

capacity” that can be maintained by young and middle-aged adults for durations of one 

to nine hours (Table 5-2). These values -- which apply to normally active, non-trained 

adults -- appear to be functionally equivalent to the percentage of VO2max (designated 

PCTVO2max) that can be maintained for the indicated time period and are so labeled in 

Table 5-2. According to Erb, a person can maintain 64 percent of VO2max for one hour 

and 33 percent of VO2max for nine hours without straining. 

The following expression provides a close fit to values Erb proposed for durations 

of one to nine hours: 

PCTVO2max(t) = 121.2 - (14.0)[ln(t)]. (5-6) 

Note that t is duration in minutes and ln indicates the natural (base e) logarithm. 

Equation 5-6 provides an estimate of approximately 100 percent for t = 5 minutes, 

consistent with the statement by Astrand and Rodahl that 100 percent of VO2max can be 

maintained for up to 5 minutes. These findings suggest that it is reasonable to assume 

that (1) PCTVO2max should not exceed 100 percent for events with durations between 0 

and 5 minutes, (2) Equation 5-6 can be used to determine the upper limit of PCTVO2max 

for events of durations between 5 minutes and 540 minutes (9 hours), and (3) the 

5-5
 
PCTVO2max values in Table 5-2 can be used as upper limits for VO2 averaged over multihour periods from one to nine hours in duration. A conservative assumption (i.e., one 

which may permit unrealistically high VO2 values) is that the value for nine hours (33 

percent) applies to longer time periods. 

The concepts discussed above were the basis of two tests applied to VO2 

estimates produced by Version 2.0 of pNEM/CO. The first test was applied to VO2 

values associated with individual exposure events. The second test was applied to 

running-average values of VO2 with durations of 1 to 24 hours. 

The first test was based on the assumption that the PCTVO2max value associated 

with an individual (event -specific) VO2 value could not exceed an upper limit calculated 

by Equation 5-6. This test was carried out by comparing each event-specific VO2 value 

generated for a cohort with a permitted upper limit (PUL) value obtained from the 

following equation. 

Permitted upper limit of VO2 = (Upper limit of PCTVO2max)(VO2max)/100. (5-7) 

When the event duration ranged between 5 minutes and nine hours, the value for the 

upper limit of PCTVO2max in Equation 5-7 was obtained from Equation 5-6 using the 

value of VO2max assigned to the cohort by the physiological profile generator. Outside 

this range, PCTVO2max was assumed to equal 100 percent for durations less than 5 

minutes and to equal 33 percent for durations greater than nine hours. If the VO2 value 

exceeded the PUL determined by Equation 5-7, the VO2 value was set equal to the 

calcuated PUL. Otherwise, the value of VO2 was not affected by Test 1. 

The second test assumed that “running-average” VO2 values (expressed as a 

percentage of the VO2max value) could not exceed the values specified by Erb in Table 

5-2. This test was implemented by first averaging the event-specific VO2 values 

generated for a cohort by clock hour to produce 24 one-hour VO2 values. Runningaverage VO2 values for all possible sequential periods of 1 to 24 hours in duration were 

then calculated from these one-hour values. Each running-average VO2 value was 

used to calculate the value 

Test Ratio = (running average VO2)/(permitted upper limit of VO2) (5-8) 

5-6
 
in which the “permitted upper limit” (PUL) is a value specific to the indicated averaging 

time. If any test ratio exceeded 1.0, then all event-specific VO2 values for the personday were proportionally reduced so that the largest test ratio equaled exactly 1.0. 

Table 5-2. Values of the Upper Limit of PCTVO2max for Specified Averaging Times. 

Averaging time (hours) Upper limit of PCTVO2max, percent 

1 64 

2 54 

3 48 

4 44 

5 41 

6 39 

7 37 

8 35 

9 33 

10 to 24 33a 

a

Conservative estimate based on nine-hour value proposed by Erb (1981). All other 

values in this column are identical to values proposed by Erb (1981). 

Equation 5-7 was used to determine PULs for running-average VO2 values. In 

this application, the upper limit of PCTVO2max was obtained from Table 5-2 according to 

the period of the running average. 

The value for VO2max required by the two tests was determined by the 

physiological profile generator using the equation

 VO2max = (NVO2max)(BM), (5-9) 

in which BM was body mass in kg and NVO2max was maximum oxygen uptake rate per 

kg of body mass. As discussed in the next section, values of NVO2max and BM were 

randomly sampled from distributions specific to the age and gender of the cohort. 

5-7
 
5.5 The Probabilistic Algorithm 

Table 5-3 presents a probabilistic algorithm for estimating ventilation rate which 

incorporates the physiological principles discussed above. This algorithm was 

programmed into Version 2.0 of pNEM/CO as described in Section 2 of this report. 

Table 5-4 lists the parameters appearing in the algorithm and indicates the functional 

form and source of data for each parameter. 

To run the model, the algorithm requires distributions for BM, NVO2max, and ECF 

specific to age and gender. Table 5-5 lists distributions for BM by age and gender 

based on articles by Brainard and Burmaster (1992) and by Burmaster et al. (1994). 

Table 5-6 lists distributions for NVO2max obtained through a review of the literature. 

Table 5-7 lists distributions of ECF based on data provided by Esmail, Bhambhani, and 

Brintnell (1995). Analysts reviewed these data and selected the most appropriate 

distribution for each parameter for each combination of gender and age (0 < age < 100 

years). These distributions (listed in Appendix C) were incorporated into the ventilation 

rate algorithm. 

The ventilation rate algorithm also requires an equation for estimating RMR for 

each combination of age and gender. Analysts reviewed a list of equations previously 

compiled by McCurdy (1998) and determined that a set of equations developed by 

Schofield (1985) provided good coverage of all age and gender combinations. These 

equations were determined through regression analyses and have the functional form 

RMR = a + (b)(BM) + e, (5-10) 

in which e is assumed to be normally distributed with mean = zero and standard 

deviation = Fe. Table 5-8 lists Schofield’s values of a, b, and Fe for 12 age/gender 

combinations. These values are the basis of the RMR equations listed in the Appendix 

C which have been incorporated into the ventilation rate algorithm. 

5-8
 
 Table 5-3. Algorithm Used in Version 2.0 of pNEM/CO to Estimate Ventilation Rates 

as a Function of Energy Expenditure Rate. 

1.	 Go to first/next cohort. Cohort = k. 

2.	 Obtain age, gender, and height of cohort k from physiological profile generator. 

3.	 Obtain appropriate values of NVO2max(k), BM(k), and ECF(k) for Cohort k by randomly selecting values from 

appropriate distributions according to age and gender determined in Step 2. Substitute BM(k) value in appropriate 

equation to determine RMR(k). Calculate 

VO2max(k) = [NVO2max(k)][BM(k)]. 

4.	 Obtain values for parameters of Equation 5-5 from Table 5-1 according to age and gender determined in Step 2. 

Randomly select a value of z(k) for cohort from a normal distribution with mean equal to zero and standard deviation 

equal to 1. Determine e(k) value for cohort by following expression 

e(k) = [z(k)][Fd]. 

5.	 Go to first/next day. Day = j. 

6.	 Go to first/next exposure event. Event = i. Randomly select a value of z(i,j,k) value for the event from a normal 

distribution with mean equal to zero and standard deviation equal to 1. Determine e(i,j,k) value for event by the 

following expression 

e(i,j,k) = [z(i,j,k)][Fe]. 

7.	 Note activity classification of exposure event. Select MET value from distribution assigned to activity classification. 

This value is denoted MET(i,j,k). 

8.	 Convert the MET(i,j,k) value to a corresponding VO2(i,j,k) value by Equation 5-4, i.e., 

VO2(i,j,k) = [ECF(k)][MET(i,j,k)][RMR(k)]. 

9.	 Determine upper limit of PCTVO2max(i,j,k) by Equation 5-6. If duration is less than 5 minutes, upper limit of 

PCTVO2max(i,j,k) equals 100 percent. If duration is greater than 540 minutes (9 hours), upper limit of PCTVO2max(i,j,k) 

equals 33 percent. 

10.	 Test 1: Determine the permitted upper limit for VO2 by Equation 5-7 using the value of PCTVO2max(i,j,k) determined in 

Step 9. If VO2 for event exceeds upper limit, set VO2 equal to the upper limit. 

11.	 If last exposure event of day, go to Step 12. Otherwise, go to Step 6. 

12.	 Test 2: Average event-specific VO2 values for day by clock hour to produce 24 one-hour VO2 values. Calculate 

running-average VO2 values for all possible sequences of 1 to 24 hours in duration from these one-hour values. 

Obtain PCTVO2max value for each duration from Table 5-2. Use Equation 5-7 to calculate permitted upper limit of VO2 

for each duration. Use Equation 5-8 to determine test ratio for each running-average VO2 value. If any test ratio 

exceeds 1.0, reduce all event-specific VO2 values proportionally so that largest test ratio of resulting adjusted VO2 

values equals exactly 1.0. 

13.	 Following completion of Test 2 (Step 12), use Equation 5-5 and parameter values obtained in Steps 3, 4, and 6 to 

convert each event-specific VO2 value of day to a corresponding VE value. 

14.	 If last day, go to Step 15. Otheriwse, go to Step 5. 

15.	 If last cohort, end. Otherwise, go to Step 1. 

5-9
Table 5-4. Parameters Used in Probabilistic Algorithm for Estimating Ventilation 

Rates in Version 2.0 of pNEM/CO. 

Parameter Abbreviation Functional Form Source of Data 

Body mass BM Lognormal distribution Brainard and 

Burmaster (see Table 

5-5) 

Energy Conversion 

Factor 

ECF Point estimate Esmail et al., 1995 

(see Table 5-7) 

Metabolic 

Equivalence 

MET Distribution specified 

in CHAD Database 

McCurdy, 1998 (see 

Appendix A) 

Resting metabolic 

rate 

RMR Regression equations 

specific to age and 

gender 

Schofield, 1985, as 

compiled by McCurdy, 

1998 (see Table 5-8) 

Normalized oxygen 

uptake rate 

NVO2max Normal distribution Research summarized 

in Table 5-6 

5-10
 
Table 5-5. Parameters for Lognormal Distributions Fitted to Body Mass (BM) Data in 

Kilograms Organized by Age and Gender. 

Age, 

years 

Males Females 

ln(BM) BM ln(BM) BM 

: F GM GSD : F GM GSD 

0.5 - 1 2.23 0.132 9.3 1.141 2.16 0.145 8.7 1.156 

1 2.46 0.119 11.7 1.126 2.38 0.128 10.8 1.137 

2 2.60 0.120 13.5 1.127 2.56 0.112 12.9 1.119 

3 2.75 0.114 15.6 1.121 2.69 0.137 14.7 1.147 

4 2.87 0.133 17.6 1.142 2.83 0.133 16.9 1.142 

5 2.99 0.138 19.9 1.148 2.98 0.163 19.7 1.177 

6 3.13 0.145 22.9 1.156 3.10 0.174 22.2 1.190 

7 3.21 0.151 24.8 1.163 3.19 0.174 24.3 1.190 

8 3.33 0.181 27.9 1.198 3.31 0.156 27.4 1.169 

9 3.43 0.165 30.9 1.179 3.46 0.214 31.8 1.239 

10 3.59 0.195 36.2 1.215 3.57 0.199 35.5 1.220 

11 3.69 0.252 40.0 1.287 3.71 0.226 40.9 1.254 

12 3.78 0.224 43.8 1.251 3.82 0.213 45.6 1.237 

13 3.88 0.215 48.4 1.240 3.92 0.216 50.4 1.241 

14 4.02 0.181 55.7 1.198 3.99 0.187 54.1 1.206 

15 4.09 0.159 59.7 1.172 4.00 0.156 54.6 1.169 

16 4.20 0.168 66.7 1.183 4.06 0.167 58.0 1.182 

17 4.19 0.167 66.0 1.182 4.08 0.165 59.1 1.179 

18 4.25 0.159 70.1 1.172 4.07 0.147 58.6 1.158 

19 4.26 0.154 70.8 1.166 4.10 0.149 60.3 1.161 

18 - 74a 4.34 0.17 76.7 1.19 4.17 0.20 64.7 1.22 a

Derived from Brainard and Burmaster (1992). All other statistics derived from 

Burmaster et al. (1994). 

5-11 
Table 5-6. Descriptive Statistics for VO2 and VER Measured at Maximal Exertion by Various Research

Population group n 

VO2max, liters/minX NVO2max, ml/min per kg Maximum ratio of VE to VO2 

Mean S.D. C.V. 

a Mean S.D. C.V. Mean S.D. C.V

Females, 20-29 8 2.23 0.26 0.12 39.9 4.7 0.12 34.1 6.0 0.18 

Females, 30-39 12 2.13 0.28 0.13 37.3 5.2 0.14 35.2 5.7 0.16 

Females, 40-49 8 2.01 0.19 0.09 32.5 2.7 0.08 31.8 4.7 0.15 

Females, 50-65 16 1.85 0.25 0.14 28.4 2.7 0.10 33.1 4.0 0.12 

Females, 20-25 32 2.88 0.24 0.08 48.4 2.8 0.06 32.3 2.8 0.09 

Males, 20-29 4 4.19 NR NR 52.2 NR NR 31.8 NR NR 

Males, 30-39 13 3.01 0.54 0.18 39.8 7.3 0.18 34.6 6.0 0.17 

Males, 40-49 9 2.99 0.32 0.11 39.2 5.5 0.14 33.8 5.3 0.16 

Males, 50-59 66 2.54 0.36 0.14 33.1 4.9 0.15 26.9 4.6 0.17 

Males, 60-69 8 2.23 0.29 0.13 31.4 5.3 0.17 33.1 5.8 0.18 

Males, 20-33 29 4.16 0.39 0.09 58.6 4.5 0.08 29.4 3.0 0.10 

Males, 11 18 1.65 0.30 0.18 45.4 8.06 0.18 37.4 7.00 0.19 

Males, 12 15 1.85 0.31 0.17 47.4 8.13 0.17 35.0 5.42 0.15 

Males, 13 15 2.26 0.39 0.17 46.0 6.97 0.15 31.0 3.02 0.10 

Males, 14 15 2.62 1.12 0.43 45.7 4.26 0.09 33.2 7.28 0.22 

Males, 15 13 2.70 0.50 0.19 47.5 4.69 0.10 34.4 5.05 0.15 

Males, 21-27 13 3.91 0.52 0.13 54.5 7.61 0.14 NR NR NR 

Males and Females, 20­

29 

80 3.09 0.83 0.27 45.3 7.54 0.17 NR NR NR 

Males and Females, 30­

39 

81 3.19 0.86 0.27 43.8 8.15 0.19 NR NR NR 

5-12
 
Population group n 

VO2max, liters/minX NVO2max, ml/min per kg Maximum ratio of VE to VO2 

Mean S.D. C.V. 

a Mean S.D. C.V. Mean S.D. C.V

Males and Females, 40­

49 

79 3.13 0.92 0.29 42.9 9.04 0.21 NR NR NR 

Males and Females, 50­

59 

78 2.84 0.91 0.32 36.8 8.93 0.24 NR NR NR 

Males and Females, 60­

69 

74 2.31 0.72 0.31 30.7 7.98 0.26 NR NR NR 

Males and Females, 70­

79 

47 1.91 0.56 0.29 27.2 5.67 0.21 NR NR NR 

Males, 20-79 210 3.54 0.71 0.20 44.0 9.42 0.21 NR NR NR 

Females, 20-79 229 2.14 0.51 0.24 33.8 8.65 0.26 NR NR NR 

Males, 10-17 6 NR NR NR 46.6 5.8 0.12 NR NR NR Males, 18-72 15 NR NR NR 45.7 16.7 0.37 NR NR NR 

Females, 7-17 6 NR NR NR 38.0 5.0 0.13 NR NR NR 

Females, 21-72 16 NR NR NR 32.2 8.9 0.28 NR NR NR 

Male, 23-33 20 NR NR NR 48.3 4.9 0.10 NR NR NR 

Male, 9-13 20 NR NR NR 57.9 6.9 0.12 NR NR NR 

aC.V. = (std. dev.)/(mean), dimensionless. 

5-13
 
Table 5-7. Estimates of the Energy Conversion Factor (ECF) Based on Data in 

Esmail, Bhambhani, and Brintnell (1995). 

Group 

Number of 

subjects Test 

Mean VO2, 

liters min-1 

Mean 

GECa

, kcal 

min-1 

Ratio of 

meansb 

Women 20 wheel-turn 0.81 4.1 0.198 

push-pull 0.80 4.0 0.200 

overhead-reach 0.87 4.4 0.198 

Men Not 

reported 

wheel-turn 1.13 5.7 0.198 

push-pull 1.16 5.6 0.207 

overhead-reach 1.13 5.7 0.198 a

GEC: gross energy cost.

b

Data were not available for calculating the mean of subject-specific ratios.
 

5-14
 
 

Table 5-8. Regression Equations for Predicting Basal Metabolic Rate Provided by 

Schofield (1985) as Compiled by McCurdy (1998). 

Gender Age, years 

Regression coefficientsa 

a b Fe 

Female < 3 -0.130 0.244 0.25 

Female 3 - 9.9 2.033 0.085 0.29 

Female 10 - 17.9 2.898 0.056 0.47 

Female 18 - 29.9 2.036 0.062 0.50 

Female 30 - 59.9 3.538 0.034 0.47 

Female $ 60 2.755 0.038 0.45 

Male < 3 -0.127 0.244 0.29 

Male 3 - 9.9 2.110 0.095 0.28 

Male 10 - 17.9 2.754 0.074 0.44 

Male 18 - 29.9 2.896 0.063 0.64 

Male 30 - 59.9 3.653 0.048 0.70 

Male $ 60 2.459 0.049 0.69 a

Regression equation: BMR(MJ/day) = a + (b)(BM) + e, Fe = standard deviation of e. 

Basal metabolic rate (BMR) is assumed to be equivalent to resting metabolic rate 

(RMR). 

5.6 Effect of Cardiovascular Disease on Exertion Levels 

As discussed in Section 2.5.2, analysts assumed the exercise patterns of people 

with IHD were similar to those of the general population. To determine the validity of 

this assumption, Cohen, Nikiforov, and Rosenbaum (1999) analyzed activity/exertion 

data from the National Human Activity Pattern Survey (NHAPS) representing subjects 

with and without heart disease. Subjects were classified as having heart disease if they 

reported that a doctor had told them they had angina. The NHAPS database provided a 

24-hour sequence of activities for each subject as reported in a recall-style diary 

(Robinson and Blair, 1995). A Monte Carlo algorithm based on the METS principle 

described in Section 5.1 was applied 100 times to each 24-hour sequence. In each 

5-15
 
application (iteration), the algorithm generated an energy expenditure rate (EE) in kcal 

min-1 for each activity in the sequence. The resulting distributions of EE values were 

statistically analyzed to determine whether they differed according to the health status 

of the associated NHAPS subjects. Researchers also analyzed differences in time 

spent in outdoor and in-vehicle microenvironments. 

Cohen, Nikiforov, and Rosenbaum (1999) provide detailed results for the 

following activity/exertion indicators: average and 95th percentile of the maximum daily 

8-hour EE value; percentage of time spent outdoors or in a vehicle; average percentage 

of time at light, moderate, and heavy exertion; and occurrence of moderate or high EE 

values. Tables 5-9 and 5-10 are illustrative of the general results of the analysis. The 

tables provide means and standard deviations for each indicator stratified by gender 

and age. The T test is used to test for differences in means; the F test for differences in 

standard deviations. The non-parametric Wilcoxon test is used to compare the central 

tendencies of the angina and non-angina distributions without the normality assumption 

required by the T test. The Kolmogorov-Smirnov test compares the overall distributions 

of each group. 

Although the results in Tables 5-9 and 5-10 suggest that mean values for the 

various indicators tend to be lower for angina subjects in each age and gender group 

than for corresponding non-angina subjects, there are exceptions to this pattern. For 

example, the tables show that angina subjects tend to have lower mean values for the 

average maximum 8-hour exertion than the non-angina subjects. However, the mean is 

actually higher for angina subjects aged 0 - 54 years of either gender and for males 75 

years or older. The mean average maximum 8-hour exertions are consistently higher for 

males of all age groups, with or without angina, compared to females. Similar patterns 

are found for the 95th percentile of the maximum 8-hour exertion. 

The comparisons of the percentages of time spent outdoors or in a vehicle also 

vary across age and gender subgroups. The largest, and most surprising, angina vs. 

non-angina difference is for the mean percentage of time spent outdoors by males aged 

0 - 54 years: angina subjects have a mean of 17% compared to the mean of 9% for 

non-angina subjects. However the angina subjects in the 55 - 64 and 65 - 74 age 

groups of either gender spend less time outdoors, on average, than non-angina 

subjects. 

5-16
 
Table 5-9.	 Results of Statistical Tests Performed on CHAD Data Evaluating the Association Betwee

Various Variables Representing Physical Exertion and Time Spent in Selected Microenvi

Males Only). 

T Test Comparison of Means F Test Comparison of Standard 

Deviations Wilco

Variable Age group Mean p-value Standard deviation p-value Tes

val

Angina 

Nonangina Angina Non- angina 

Average maximum 8 hr exertion (Mcal) 

0 - 54 1.85 1.59 0.00 0.49 0.55 0.34 0.0

55 - 64 1.48 1.77 0.00 0.48 0.48 0.86 0.0

65 - 74 1.39 1.49 0.36 0.47 0.48 0.96 0.4

75+ 1.27 1.20 0.52 0.44 0.42 0.65 0.5

95th percentile of 

maximum 8 hr exertion (Mcal) 

0 - 54 2.94 2.68 0.17 1.06 1.30 0.13 0.2

55 - 64 2.40 2.90 0.04 1.21 1.14 0.61 0.0

65 - 74 1.91 2.17 0.14 0.78 0.94 0.31 0.2

75+ 1.73 1.67 0.71 0.69 0.76 0.68 0.5

Percentage of time spent outdoors 

0 - 54 16.9 8.9 0.02 19.2 13.8 0.00 0.0

55 - 64 9.3 10.0 0.79 14.3 13.9 0.78 0.6

65 - 74 6.4 10.4 0.13 11.4 14.3 0.20 0.1

75+ 8.2 7.1 0.72 12.6 10.1 0.16 0.5

Percentage of time spent in vehicle 

0 - 54 6.0 6.1 0.92 7.6 8.1 0.63 0.8

55 - 64 4.0 6.9 0.00 3.8 9.6 0.00 0.1

5-17
 
Percentage of time 

spent in vehicle (cont.) 

65 - 74 7.2 5.9 0.51 9.0 7.8 0.29 0.8

75+ 2.3 3.3 0.14 2.5 3.9 0.05 0.4

Percentage of time spent outdoors or in vehicle 

0 - 54 22.8 14.9 0.02 19.3 15.5 0.05 0.0

55 - 64 13.3 16.9 0.24 15.3 16.2 0.76 0.1

65 - 74 13.6 16.3 0.45 15.9 15.9 1.00 0.1

75+ 10.5 10.4 0.98 12.9 10.4 0.19 0.6

Average percentage of time with exertion above 

0 - 54 34.8 27.8 0.00 13.3 15.2 0.34 0.0

55 - 64 24.6 30.1 0.06 14.5 12.0 0.14 0.0

2.39 kcal/min = 0.010MJ/min (light) 65 - 74 21.9 23.3 0.63 13.2 12.5 0.64 0.6

75+ 18.2 15.9 0.41 11.1 10.4 0.63 0.3

Average percentage of time with exertion above 

0 - 54 6.6 4.5 0.05 6.4 4.3 0.00 0.0

55 - 64 3.4 5.4 0.01 3.6 4.4 0.17 0.0

5.97 kcal/min = 0.025MJ/min (moderate) 

65 - 74 2.3 3.3 0.08 2.5 3.5 0.06 0.2

75+ 2.0 1.6 0.53 2.4 2.4 0.92 0.4

Average percentage of time with exertion above 

0 - 54 0.66 0.74 0.59 0.79 0.99 0.11 0.5

55 - 64 0.57 0.85 0.19 1.07 1.22 0.40 0.0

9.55 kcal/min = 0.040MJ/min (heavy) 

65 - 74 0.16 0.39 0.01 0.36 0.72 0.00 0.0

75+ 0.13 0.16 0.79 0.33 0.51 0.05 0.5

5-18
 
Table 5-10.	 Results of Statistical Tests Performed on CHAD Data Evaluating the Association Betwee

Various Variables Representing Physical Exertion and Time Spent in Selected MicroenviFemales Only). 

T Test Comparison of Means F Test Comparison of Standard 

Deviations Wilco

Variable Age group Mean p-value Standard deviation p-value Tes

val

Angina 

Nonangina Angina Non- angina 

Average maximum 8 hr exertion (Mcal) 

0 - 54 1.30 1.27 0.69 0.31 0.38 0.34 0.7

55 - 64 1.21 1.27 0.33 0.32 0.33 1.00 0.5

65 - 74 1.05 1.10 0.29 0.30 0.31 0.94 0.3

75+ 0.96 0.98 0.63 0.33 0.30 0.34 0.4

95th percentile of 

maximum 8 hr exertion (Mcal) 

0 - 54 1.98 2.01 0.86 0.82 0.91 0.69 0.9

55 - 64 1.79 1.92 0.41 0.80 0.77 0.68 0.3

65 - 74 1.42 1.51 0.26 0.53 0.57 0.57 0.3

75+ 1.27 1.31 0.59 0.56 0.52 0.43 0.4

Percentage of time spent outdoors 

0 - 54 3.6 5.1 0.42 7.3 9.6 0.20 0.4

55 - 64 4.3 4.6 0.88 9.5 8.2 0.23 0.2

65 - 74 2.8 4.1 0.14 5.5 7.3 0.02 0.4

75+ 3.8 2.3 0.40 12.0 4.6 0.00 0.7

Percentage of time spent in vehicle 

0 - 54 4.5 5.4 0.55 5.5 6.0 0.72 0.4

55 - 64 4.2 5.6 0.35 7.5 7.2 0.71 0.0

5-19
 
Percentage of time 

spent in vehicle (cont.) 

65 - 74 5.3 4.2 0.23 5.9 6.2 0.77 0.1

75+ 2.8 2.9 0.86 4.4 4.3 0.91 0.7

Percentage of time spent outdoors or in vehicle 

0 - 54 8.2 10.5 0.36 9.9 11.3 0.57 0.1

55 - 64 8.5 10.2 0.48 12.2 10.4 0.22 0.0

65 - 74 8.1 8.3 0.88 8.2 9.4 0.26 0.9

75+ 6.6 5.2 0.45 12.4 6.3 0.00 0.7

Average percentage of time with exertion above 

0 - 54 23.5 21.4 0.49 12.3 12.2 0.86 0.4

55 - 64 19.0 21.0 0.33 10.3 10.5 1.00 0.5

2.39 kcal/min = 0.010MJ/min (light) 65 - 74 14.0 15.5 0.26 8.5 9.5 0.31 0.4

75+ 11.3 11.8 0.73 9.8 8.6 0.24 0.5

Average percentage of time with exertion above 

0 - 54 1.44 1.59 0.71 1.66 1.97 0.44 0.7

55 - 64 0.79 1.31 0.05 1.27 2.07 0.00 0.0

5.97 kcal/min = 0.025MJ/min (moderate) 

65 - 74 0.65 0.68 0.87 1.35 1.56 0.22 0.5

75+ 0.75 0.43 0.23 1.73 1.31 0.01 0.8

Average percentage of time with exertion above 

0 - 54 0.10 0.18 0.06 0.17 0.32 0.00 0.6

55 - 64 0.10 0.09 0.96 0.28 0.20 0.01 0.6

9.55 kcal/min = 0.040MJ/min (heavy) 

65 - 74 0.03 0.03 0.89 0.08 0.11 0.00 0.8

75+ 0.03 0.02 0.44 0.08 0.08 0.90 0.2

5-20
 
The comparisons in Tables 5-9 and 5-10 of the mean percentages of time above 

the light, moderate or high exertion levels show a variety of patterns for different age 

groups, genders, and exertion levels. Overall, age and gender were found to have very 

significant effects on the summary statistics under evaluation. As angina patients tend 

to be much older and tend to include more females than the general population, 

researchers performed additional analyses in which the results were adjusted for 

gender and age by stratification (comparing subjects in a given age/gender subgroup), 

or by fitting a general linear model (with separate terms for age, gender, and angina 

effects and their interactions). These analyses showed that, overall, angina subjects 

tended to have less extreme exertion levels. More specifically, the maximum 8-hour 

exertion energies tended to be lower, as did the percentages of time above moderate or 

high exertion rate thresholds. The differences between angina and non-angina subjects 

in percentages of time spent outdoors or in a vehicle were not generally statistically 

significant. 

The large sample of NHAPS subjects produced, in many cases, statistically 

significant differences in the exertion rate summaries between angina and non-angina 

subjects. However, those differences were generally numerically small compared to the 

mean values. EPA has concluded that the differences in activity and exertion between 

angina and non-angina subjects, although statistically significant, are not large enough 

to warrant adjusting the pNEM/CO modeling approach to account for an angina/nonangina difference. 

5.6 References for Section 5 

Astrand, I. 1960. “Aerobic Work Capacity in Men and Women with Special Reference to 

Age. Acta. Physiol. Scand. Volume 49, Supplement 169. 

Astrand, P. O. and K. Rodahl, 1977. Textbook of Work Physiology. 2nd ed. McGrawHill, New York, New York. 

Brainard, J, and D. Burmaster. 1992. “Bivariate Distributions for Height and Weight of 

Men and Women in the United States,” Risk Analysis, Vol. 12, No. 2, pp. 267 - 275. 

5-21
 
Burmaster, D. E., and E. A. C. Crouch. 1997. “Lognormal Distributions of Body Weight 

as a Function of Age for Males and Females in the United States, 1976 - 1980,” Risk 

Analysis, Vol. 17, No. 4. 

Cohen, J. P., S. Nikiforov, and A. S. Rosenbaum. 1999. “Differences in Human Activity 

Patterns Between Individuals With and Without Cardiovascular Disease.” Memorandum 

submitted to the U. S. Environmental Protection Agency under EPA Contract No. 68­

D0-0062. Systems Applications International. 

Erb, B. D., 1981. "Applying Work Physiology to Occupational Medicine," Occupational 

Health Safety, Vol. 50, pp. 20-24. 

Esmail, Bhambhani, and Brintnell. 1995. “Gender Differences in Work Performance on 

the Baltimore Therapeutic Equipment Work Simulator.” Amer. J. Occup. Therapy. 

Volume 49, pp. 405 - 411. 

Heil, D. P., P. S. Freedson, L. E. Ahlquist, J. Price, and J. M. Rippe. 1995. 

“Nonexercise Regression Models to Estimate Peak Oxygen Consumption.” Med. Sci. 

Sports Exer. Volume 27, pp. 599 - 606. 

Johnson, T. 1998. “Analysis of Clinical Data Provided by Dr. William Adams and 

Revisions to Proposed Probabilistic Algorithm for Estimating Ventilation Rate in the 

1998 Version of pNEM/CO.” Memorandum submitted to the U.S. Environmental 

Protection Agency under EPA Contract No. 68-D6-0064. TRJ Environmental, Inc. 

Katch, V. L., and M. W. Park. 1975. “Minute-by-Minute Oxygen Requirement and Work 

Efficiency for Constant-Load Exercise of Increasing Duration.” Res. Q. Volume 46, pp. 

38 - 47. 

McArdle, W. D., F. I. Katch, and V. L. Katch, 1991. Exercise Physiology: Energy, 

Nutrition, and Human Performance, Lea and Febiger, Malvern, Pennsylvania. 

McCurdy, T. 1998. Personal Communication. Information on distributions available 

upon request to author. U.S. Environmental Protection Agency, National Exposure 

Research Laboratory, Research Triangle Park, North Carolina 27711. 

McCurdy, T., 1999. “Conceptual Basis for Multi-Route Intake Dose Modeling Using an 

Energy Expenditure Approach.” Journal of Exposure Analysis and Environmental 

Epidemiology, accepted for publication. 

Mercier, J., A. Varray, M. Ramonatxo, B. Mercier, and C. Préfaut. 1991. “Influence of 

Anthropomorphic Characteristics on Changes in Maximal Exercise Ventilation and 

Breathing Pattern During Growth in Boys.” Eur. J. Appl. Physiol. Volume 63, pp. 235 

- 241. 

5-22
 
Mermier, J., J. M. Samet, W. E. Lambert, and T. W. Chick. 1993. “Evaluation of the 

Relationship Between Heart Rate and Ventilation for Epidemiologic Studies.” Arch. 

Environ. Health. Volume 48, pp. 263 - 269. 

Robinson, J.P., and J. Blair. 1995. Estimating Exposure to Pollutants Through 

Human Activity Pattern Data: The National Micro-Environmental Activity Pattern 

Survey (MAPS). Survey Research Center, University of Maryland, College Park, MD. 

Rowland, T. W., J. A. Auchinachie, T. J. Keenan, and G. M. Green. 1987. “Physiologic 

Responses to Treadmill Running in Adult and Prepubertal Males.” Inter. J. Sports 

Med. Volume 8, pp. 292 - 297. 

Schofield, W. N. 1985. “Predicting Basal Metabolic Rate, New Standards, and Review 

of Previous Work.” Hum. Nutr. Clin. Nutr. Volume 39C, Supplement 1, pp. 5 - 41. 

5-23
 
SECTION 6
 

ESTIMATION OF COMMUTING PATTERNS
 

In applying the previous (1992) version of pNEM/CO to Denver, analysts used an 

iterative algorithm described by Johnson, Capel, and Byrne (1991) to develop an 

“origin-destination” table indicating the pattern of commuting trips made by working 

cohorts among the defined exposure districts. Version 2.0 of pNEM/CO required a 

similar origin-destination table for the Denver study area. Researchers identified a 

special commuting database developed by the Bureau of Census (1994) as a promising 

alternative to the commuting algorithm for creating this table. This section describes the 

method used to develop an origin-destination table for Denver using the Bureau of 

Census (BOC) database. 

6.1 The BOC Commuting Database 

Table 6-1 presents the format of the BOC database. The following two data 

items were used in the analyses which follow. 

C WF: weighted count of workers in specified flow. 

C WA: weighted count of workers allocated to this tract of work. 

WF is the total number of workers estimated by BOC to commute (“flow”) from the 

indicated residence census tract to the indicated work census tract. Each value of WF 

is a weighted estimate based on responses to the 1990 census “long form.” The 

estimate includes both (1) people who explicitly indicated they lived and worked in the 

indicated census tracts and (2) other people who the BOC allocated to the flow based 

on supplemental information. WA indicates only the number of people who fall into the 

second category. As WA increases as a percentage of WF, confidence in the value of 

WF is reduced. 

6-1
 
Table 6-1. Format of Commuting Database Obtained from the Bureau of Census. 

Bytes Data item Explanation of codes 

1 - 2 FIPS state code of place of work 01 - 56 = Alabama - Wyoming 

99 = did not work in U.S. 

3 - 5 FIPS county code of place of work 000 = did not work in U.S. 

NNN = county code 

6 - 11 Census tract/BNA code of place of work 000000 = did not work in U.S. 

000100...999999 = legal tract code range 

12 - 14 Census MCD code of place of work 000 = did not work in New England state 

NNN = MCD code in New England 

(FIPS State = 09, 23, 25, 33, 44, 50) 

15 - 18 Census place code of place of work 0000 = did not work in U.S. 

0001...9998 = census place code 

9999 = did not work in a place 

19 Blank 

20 - 21 FIPS state code of residence 01 - 56 = Alabama - Wyoming 

99 = did not work in U.S. 

22 - 24 FIPS county code of residence NNN = county code 

25 - 30 Census tract/BNA code of residence 000100...999999 = legal tract code range 

31 - 33 Census MCD code of place of residence 000 = did not live in New England state 

NNN = MCD code in New England 

(FIPS State = 09, 23, 25, 33, 44, 50) 

34 - 37 Census place code of residence 0001...9998 = census place code 

9999 = did not live in a place 

38 Blank 

39 - 44 Weighted count of workers in this flow, 

including all place of work allocation 

(everyone forced into a tract of work) 

45 - 50 Weighted count of workers allocated to this 

tract of work 

51 - 56 Weighted count of workers allocated to this 

place of work 

57 - 62 Weighted count of workers allocated to this 

county or MCD (in 6 New England states) 

of work 

6-2
 
To develop an origin-destination table for a particular study area, analysts first 

identify the census tracts included within each exposure district defined within the study 

area. Next, the flows among the individual census tracts in each district are combined 

to determine flows among the districts. Each district-to-district flow originating in a 

particular district is then converted to a fraction of the total workers commuting from the 

district. These fractions are organized by home and work districts to create the required 

origin-destination table. Section 6.2 describes the application of this approach to the 

exposure districts defined for the application of Version 2.0 of pNEM/CO to Denver. 

6.2 The Denver Exposure Districts 

As described in Section 2.1, analysts defined six exposure districts for application 

of the Version 2.0 of pNEM/CO to the Denver study area. Five of the districts are 

centered on individual fixed-site CO monitors in the Denver metropolitan area. The 

sixth district is centered on a point midway between two monitors located in Boulder, 

CO. Figure 6-1 shows the locations of these seven fixed-site monitors. Table 6-2 

identifies the monitor(s) associated with each district. 

Each district was defined as a collection of census tracts as delineated by the 

BOC for the 1990 decennial census. The census tracts were drawn from a 

comprehensive listing of the census tracts located in the following nine counties: 

Adams Clear Creek Gilpin 

Arapahoe Denver Jefferson 

Boulder Douglas Weld. 

In developing the districts, analysts assigned each census tract located within 10 km of 

one or more district centers to the nearest district. People residing in the remaining 

census tracts were excluded from the pNEM/CO analysis, based on the assumption that 

pNEM/CO could not accurately estimate the exposures of people who lived more than 

10 km from a monitoring station. 

Table 6-2 lists the number of census tracts assigned to each of the six exposure 

6-3
 
Monitors Defining Exposure Districts
 

Figure 6-1. Fixed-Site CO Monitoring Sites Used to Define Denver Exposure Districts. 

6-4
 
Table 6-2. Exposure Districts Defined for Denver pNEM/CO Analysis. 

District no. 

Monitor(s) included in district 

Number of 

census tracts 

assigned to AIRS ID City Address district

1 005-0002 Littleton 8100 So. University Blvd. 

(Highlands) 

47 

2 031-0002 Denver 2105 Broadway (CAMP) 44 

3 031-0013 Denver 14th and Albion St. (NJHE) 116 

4 031-0014 Denver 23rd and Julian (Carriage) 61 

5 059-0002 Arvada W. 57th Ave. and Garrison 55 

6 013-0010 

013-1001 

Boulder 2150 28th Street 

2320 Marine Street 

28 

19 Remaining census tracts in nine county region 242 

districts and the number of remaining census tracts (designated “District 19”). Among 

the home districts (1 through 6), the number of census tracts contained within a district 

ranges from 28 (No. 6: Boulder) to 116 (No. 3: 14th and Albion). The six home districts 

account for 351 (59 percent) of the 593 census tracts in the nine-county area. 

6.3 Origin-Destination Table for Denver 

Analysts defined six “home” exposure districts and seven “work” exposure 

districts. The six home districts were the six monitor-derived exposure districts listed in 

Table 6-2. The seven “work” districts included the six districts in Table 6-2 and a 

seventh district (District 19) containing all areas not included in the six home districts. 

Each flow value in the BOC database was assigned to the appropriate combination of 

home and work districts according to the residence and work census tracts listed for the 

flow. Table 6-3 lists the sums of the flows assigned to each combination of home and 

work district. It also presents each sum as a fraction of the total flow originating at the 

6-5
 
Table 6-3. Number and Fraction of Commuters Associated with Each Combination of Home and Work 

Home 

Work District 

Districts containing CO monitors 

D

district Statistic 1 2 3 4 5 6 

1 Commuters 35,045 15,334 14,929 3,974 1,499 470 1

Fractionb 0.391 0.171 0.167 0.044 0.017 0.005 

2 Commuters 2,184 27,440 8,649 3,457 1,789 669 

Fraction 0.045 0.563 0.178 0.071 0.037 0.014 

3 Commuters 12,804 42,602 64,969 7,844 2,651 1,434 2

Fraction 0.083 0.276 0.421 0.051 0.017 0.009 

4 Commuters 5,207 26,728 13,773 28,053 9,283 898 1

Fraction 0.053 0.271 0.140 0.285 0.094 0.009 

5 Commuters 2,811 22,282 11,666 14,765 39,110 3,428 1

Fraction 0.025 0.196 0.103 0.130 0.344 0.030 

6 Commuters 814 4,149 1,771 766 1,305 44,630 

Fraction 0.014 0.069 0.029 0.013 0.022 0.740 

Totals by Work District 58,865 138,535 115,757 58,859 55,637 51,529 8

a District 19 includes all census tracts in the nine-county area that are not located in Districts 1 through b Fraction = (Comij)/(Comi

)

 Com

ij = number of workers commuting from home district i to work district j

 Com i = total number of workers commuting from home district i to all work districts (including Distric

6-6
 
6-7
 
indicated home district. For example, the flow from Home District No. 1 to Work District

 No. 2 is 15,334. Listed under this value is the fraction 0.171, calculated by dividing 

15,334 by the total flow (89,539) from Home District No. 1 to all districts. 

Home District No. 3, the district containing the largest number of census tracts, is 

associated with the largest number of commuters (154,332). Home District No. 2 has 

the smallest number of commuters (48,699). Work District No. 2 is associated with the 

largest number of commuters (138,535); Work District No. 6 (Boulder) has the smallest 

number of commuters (51,529). Note that these values include only those commuters 

which move among the home and work districts listed in Table 6-2. 

Table 6-3 accounts for the commuting patterns of 565,086 workers. Of these, 

85,904 workers (15 percent) reside in one of the six home districts but work in District 

19 (i.e., at a location more than 10 km from the nearest fixed-site monitor). People 

working in District 19 were excluded from the pNEM/CO analysis of Denver, based on 

the assumption that pNEM/CO could not accurately estimate the exposures of people 

who worked more than 10 km from a monitoring station. Consequently, the working 

cohorts are limited to people with residential and work locations within Districts 1 

through 6. 

6.4 Data Quality 

It should be noted that the BOC Commuting Database is not a perfect 

representation of the commuting patterns of Denver residents. The data were acquired 

from the census “long form” which the BOC administered to about one-sixth of the 

Denver residents. The BOC extrapolated the data from this subset of the population to 

the remainder of the population using various assumptions and supplemental 

information. 

Analysts defined WR as the ratio of WA (number of workers allocated to a 

particular home-work combination by indirect methods) to WF (total number of workers 

associated with the home-work combination); i.e., 

WR = (WA)/(WF) (6-1) 

6-8
 
WR provided a crude indication of the uncertainty associated with each WF value in the 

database. 

Table 6-4 presents the population-weighted mean value of WR (expressed as a 

percentage) for each of the 42 combinations of home and work district. The 42 WR 

values range from 10.28 percent (Home District No. 6, Work District No. 3) to 43.90 

percent (Home District No. 1, Work District No. 5). The largest values (indicating the 

highest degree of uncertainty) are associated with Work Districts Nos. 5 and 19. 

Table 6-4 also provides an aggregate WR value for all flows originating in each 

home district. The six values range from 22.14 percent (Home District No. 1) to 28.59 

percent (Home District No. 2). Aggregate WR values for all flows ending in each work 

district can also be found in Table 4. The values range from 18.46 percent (Work 

District No. 1) to 35.27 percent (Work District No. 19). 

The overall aggregate value of WR is 23.98 percent. In general, this analysis 

indicates that less than 25 percent of the commute trips were estimated indirectly using 

supplemental data. 

6.5 References for Section 6 

Bureau of the Census. 1994. “STP154: Census Tract of Work by Census Tract of 

Residence.” Journey-to-Work and Migration Statistics Branch, Population Division, 

U. S. Bureau of the Census. 

Johnson, Ted R., J. E. Capel, and D. M. Byrne. 1991. “The Estimation of Commuting 

Patterns in Applications of the Hazardous Air Pollutant Model (HAPEM).” Paper No. 91­

172.6. Presented at the 84th Annual meeting of the Air and Waste Management 

Association, Vancouver, British Columbia, June 16 - 21. 

6-9
 
Table 6-4. Percentage of Commuters (WR) Assigned by BOC to Each Home-Work 

Combination Based on Supplemental Data. 

Home 

Work District 

All work 

districts 

Districts containing CO monitor(s) 

19

District 1 2 3 4 5 6 

1 15.21 18.47 23.93 23.70 43.90a 21.70 34.93a 22.14 

2 23.99 30.09a 22.68 22.36 29.29a 22.87 38.40a 28.59a 

3 19.31 22.87 16.21 32.66a 35.16a 24.48 35.28a 22.27 

4 30.44a 22.96 35.05a 14.95 33.14a 35.63a 36.14a 25.79a 

5 24.55 17.72 25.12a 21.38 25.73a 33.07a 24.18 24.18 

6 32.56a 21.43 10.28 11.49 31.34a 22.47 38.60a 24.06 

All 

home 

districts 

18.46 22.96 20.74 19.91 26.93a 22.97 35.27a 23.98 

a

 Value exceeds 25 percent. 

6-10
 
SECTION 7
 

EXPOSURE ESTIMATES FOR DENVER RESIDENTS
 

WITH ISCHEMIC HEART DISEASE
 

The pNEM/CO methodology was used to develop estimates of CO exposure and 

resulting COHb levels within a population-of-interest (POI) defined as “all adults with 

ischemic heart disease (IHD) who reside and work within the Denver study area.” 

Adults are defined as persons 18 years and older. The Denver study area is defined as 

the aggregation of the six exposure districts specified in Section 2.1. 

This section presents selected results for two scenarios: 

1. Existing conditions - indoor sources “on” 

2. Existing conditions - indoor sources “off.” 

In these descriptions, the qualifier “existing conditions” refers to the air quality conditions 

reported by the Denver fixed-site monitors during 1995. The term “indoor sources” 

refers to gas stoves and passive smoking only. Indoor sources “on” indicates 

pNEM/CO was run in the standard mode in which CO contributions from gas stoves and 

passive smoking are treated according to the procedures described in Section 2. Indoor 

sources “off” indicates that pNEM/CO was run with the CO contribution from these 

sources set equal to zero. 

Tables 7-1 through 7-3 present modeling results for the POI. This group is 

estimated to contain 44,545 persons. Note that each listed exposure statistic is the 

arithmetic mean of statistics obtained from 10 runs of pNEM/CO. Because pNEM/CO 

contains a variety of stochastic (probabilistic) factors, each run of the model produces a 

different set of exposure estimates. Consequently, the means presented in Tables 7-1 

through 7-3 provide an indication of the central tendency of each exposure statistic. 

The range of values for the 10 runs is listed in parentheses next to each mean value. 

Table 7-1 presents estimates of the number of person-days in which members of 

the POI experienced a 1-hour daily maximum CO exposure at or above each of the 

7-1
 
Table 7-1.	 Number of Person-Days Under Existing Conditions in Which Denver Adults with Ischemic 

Were Estimated to Experience a 1-Hour Daily Maximum Carbon Monoxide Exposure At oSpecified Concentration. DRAFT 

CO concentration, 

ppm 

Indoor sources on Indoor sources o

Number of person-days Percentage Number of person-days 

60 2.95E04 (2.49E04 - 3.33E04)a 0.18 (0.15 - 0.20) 2.95E04 (2.49E04 - 3.31E04) 

50 3.56E04 (3.30E04 - 3.88E04) 0.22 (0.20 - 0.24) 3.56E04 (3.30E04 - 3.88 E04) 

45 4.26E04 (3.78E04 - 4.68E04) 0.26 (0.23 - 0.29) 4.24E04 (3.76E04 - 4.67E04) 

40 7.11E04 (6.73E04 - 7.51E04) 0.44 (0.41 - 0.46) 7.11E04 (6.74E04 - 7.51E04) 

35 9.83E04 (9.07E04 - 1.02E05) 0.60 (0.56 - 0.63) 9.81E04 (9.07E04 - 1.02E05) 30 1.26E05 (1.14E05 - 1.33E05) 0.77 (0.70 - 0.82) 1.25E05 (1.13E05 - 1.32E05) 

25 1.76E05 (1.64E05 - 1.89E05) 1.08 (1.01 - 1.16) 1.75E05 (1.63E05 - 1.89E05) 

20 3.98E05 (3.73E05 - 4.30E05) 2.44 (2.29 - 2.65) 3.23E05 (3.04E05 - 3.50E05) 

15 1.57E06 (1.52E06 - 1.59E06) 9.63 (9.35 - 9.77) 1.43E06 (1.38E06 - 1.45E06) 

10 3.54E06 (3.48E06 - 3.58E06) 21.8 (21.4 - 22.0) 3.48E06 (3.42E06 - 3.52E06) 

0 1.63E07 (1.63E07 - 1.63E07) 100 (100 - 100) 1.63E07 (1.63E07 - 1.63E07) 

a Values in parentheses indicate range of 10 runs. 

7-2
 
Table 7-2.	 Number of Person-Days Under Existing Conditions in Which Denver Adults with Ischemic 

Were Estimated to Experience an 8-Hour Daily Maximum Carbon Monoxide Exposure At Specified Concentration. DRAFT 

CO concentration, 

ppm 

Indoor sources on Indoor sources o

Number of person-days Percentage Number of person-days 

25+ 6.5E01 (9 - 1.19E02) 0.00 (0.00 - 0.00) 5.3E01 (8 - 1.19E02) 

20 2.68E03 (2.03E03 - 3.17E03) 0.02 (0.01 - 0.02) 1.39E03 (7.27E02 - 1.90E03) 

15 3.42E04 (3.16E04 - 3.94E04) 0.21 (0.19 - 0.24) 1.80E04 (1.59E04 - 2.02E04) 

12 1.18E05 (1.14E05 - 1.22E05) 0.73 (0.70 - 0.75) 9.28E04 (9.00E04 - 9.70E04) 

9 3.71E05 (3.56E05 - 3.81E05) 2.28 (2.19 - 2.35) 3.16E05 (3.05E05 - 3.24E05) 

6 1.24E06 (1.23E06 - 1.27E06) 7.65 (7.58 - 7.83) 1.14E06 (1.13E06 - 1.15E06) 

0 1.63E07 (1.63E07 - 1.63E07) 100 (100 - 100) 1.63E07 (1.63E07 - 1.63E07) 

a Values in parentheses indicate range of 10 runs. 

7-3
 
Table 7-3.	 Number of Person-Hours Under Existing Conditions in Which Denver Adults with Ischem

Were Estimated to Experience an End-of-Hour Carboxyhemoglobin Level At or Above thPercentage. DRAFT 

CO concentration, 

ppm 

Indoor sources on Indoor sources o

Number of person-hours Percentage Number of person-hours 

6.0 7 (0 - 34) 0.00 (0.00 - 0.00) 7 (0 - 34) 

5.0 73 (6 - 2.12E02) 0.00 (0.00 - 0.00) 72 (5 - 2.12E02) 4.0 1.01E03 (6.18E02 - 1.97E03) 0.00 (0.00 - 0.00) 1.01E03 (6.1E02 - 1.97E03) 3.0 1.35E03 (1.14E04 - 1.45E04) 0.00 (0.00 - 0.00) 8.46E03 (6.06E03 - 1.02E04) 

2.9 1.87E04 (1.65E04 - 2.14E04) 0.00 (0.00 - 0.01) 1.09E04 (7.97E03 - 1.34E04) 

2.8 2.61E04 (2.36E04 - 2.89E04) 0.01 (0.01 - 0.01) 1.46E04 (1.13E04 - 1.67E04) 

2.7 3.53E04 (3.17E04 - 3.88E04) 0.01 (0.01 - 0.01) 1.91E04 (1.58E04 - 2.24E04) 

2.6 4.92E04 (4.32E04 - 5.49E04) 0.01 (0.01 - 0.01) 2.65E04 (2.23E04 - 3.12E04) 

2.5 6.79E04 (5.93E04 - 7.54E04) 0.02 (0.02 - 0.02) 3.74E04 (3.21E04 - 4.45E04) 

2.4 9.56E04 (8.61E04 - 1.03E05) 0.02 (0.02 - 0.03) 5.40E04 (4.66E04 - 6.24E04) 

2.3 1.36E05 (1.24E05 - 1.45E05) 0.03 (0.03 - 0.04) 8.06E04 (7.18E04 - 9.26E04) 

2.2 1.93E05 (1.83E05 - 2.05E05) 0.05 (0.05 - 0.05) 1.21E05 (1.12E05 - 1.35E05) 

2.1 2.74E05 (2.62E05 - 2.90E05) 0.07 (0.07 - 0.07) 1.78E05 (1.64E05 - 1.95E05) 2.0 3.81E05 (3.64E05 - 3.98E05) 0.10 (0.09 - 0.10) 2.57E05 (2.38E05 - 2.75E05) 1.5 1.88E06 (1.77E06 - 2.02E06) 0.48 (0.45 - 0.52) 1.50E06 (1.37E06 - 1.65E06) 1.0 1.08E07 (9.86E06 - 1.39E07) 2.77 (2.53 - 3.55) 9.78E06 (8.79E06 - 1.28E07) 0.5 1.25E08 (1.16E08 - 1.33E08) 32.0 (29.7 - 34.1) 1.19E08 (1.10E08 - 1.27E08) 

0 3.90E08 (3.90E08 - 3.90E08) 100 (100 - 100) 3.90E08 (3.90E08 - 3.90E08) 

a Values in parentheses indicate range of 10 runs. 

7-4 
indicated CO concentrations. Table 7-2 is similar in format; it presents estimates of the 

number of person-days in which members of the POI experienced an 8-hour daily 

maximum exposure at or above each indicated CO concentration. The maximum 

possible value in each of these tables is about 16. 3 million (denoted 1.63E07 in the 

tables) -- the product of the number of persons in the POI (44,545) and the number of 

days in the exposure period (365). 

Table 7-3 presents estimates of the number of person-hours in which members 

of the POI experienced an end-of-hour COHb level at or above each of the indicated 

levels. The maximum possible value in this table is about 390 million (3.90E08) - the 

product of the number of persons in the POI (44,545) and the number of hours in the 

exposure period (8760). 

The results presented in Tables 7-1 through 7-3 are preliminary results and are 

intended mainly to show the types of exposure estimates that are produced by the 

model. These estimates are subject to revision as further refinements to the model are 

made based on comments from EPA, CASAC, and the general public on the proposed 

Version 2.0 methodology. 

7-5
 
SECTION 8 

PRINCIPAL LIMITATIONS OF THE pNEM/CO METHODOLOGY 

The pNEM/CO methodology was developed specifically to meet the 

requirements of OAQPS for a computer-based model capable of simulating the CO 

exposures and resulting COHb levels of specific population groups under alternative 

NAAQS. In addition to meeting these needs, the designers of pNEM/CO have 

attempted to create a model which is flexible in application and easy to upgrade. The 

model was deliberately constructed as a collection of stand-alone algorithms organized 

within a modular framework. For this reason, analysts can revise individual algorithms 

without the need to make major changes to other parts of the model. 

The structure of each algorithm in pNEM/CO is largely determined by the 

characteristics of the available input data. For example, the algorithm used to construct 

a season-long exposure event sequence for each cohort is constrained by the fact that 

none of the available time/activity studies provides more than three days of diary data 

for any one subject. To make maximum use of the available diary data, the pNEM/CO 

sequencing algorithm constructs each exposure event sequence by sampling data from 

more than one subject. The other pNEM/CO algorithms are similarly designed to make 

best use of available data bases. 

The exposure and COHb estimates presented in this report represent preliminary 

estimates based on the current Version 2.0 methodology. It is anticipated that some 

additional and revised data will become available over the next several months which 

will require running the model again to produce the estimates that will be considered 

during the current review of the CO NAAQS. In addition, the model may be revised 

based on comments from other EPA reviewers, CASAC, and/or the general public. 

This section presents a brief discussion of the principal limitations in the 

pNEM/CO methodology as applied to Denver adults with IHD. These limitations are 

usually the result of limitations in the input data bases. The available data were typically 

collected for purposes other than use in a population exposure model. Consequently, 

8-1
 
these data frequently represent special sets of conditions which differ from those 

assumed by pNEM/CO. In these situations, analysts must exercise a certain degree of 

judgment in adapting the data for use in pNEM/CO. The limitations are organized 

according to six major components of the model: time/activity patterns, ventilation 

(inhalation) rates, air quality adjustment, the mass balance model, estimation of cohort 

populations, and the COHb algorithm. 

8.1 Time/Activity Patterns 

In the general pNEM/CO methodology, the exposure-related activities of each 

cohort are represented by a multi-day exposure event sequence which spans a 

specified time period (typically a calendar year). Each sequence is constructed by an 

algorithm which selects 24-hour (midnight-to-midnight) activity patterns from a specially 

prepared data file. This time/activity data file was derived from CHAD, a database 

developed by EPA which combines data from a variety of diary studies and telephone 

surveys in which subjects reported their daily activities. 

In the application of pNEM/CO to Denver residents with IHD, the special data file 

consisted of diary data from CHAD subjects identified as adults. As these data were 

obtained primarily from healthy subjects, the data should adequately characterize the 

spectrum of activity patterns associated with the general adult population. However, the 

time/activity data may be somewhat unrepresentative of adults with IHD, the sensitive 

population group under analysis. To determine whether the data misrepresents the 

target group, researchers conducted the analysis described in Section 5.6. EPA has 

concluded that the differences in activity and exertion between angina and non-angina 

subjects, although statistically significant, do not appear to be large enough to 

significantly impact the validity of pNEM/CO modeling results which do not adjust for an 

angina/non-angina difference. 

Section 7 presents pNEM/CO results for Denver, Colorado. The majority of 

time/activity data used in these model runs were obtained from locations other than 

Denver. Although the algorithm which constructs exposure event sequences attempts 

to account for effects of local climate on activity, it is unlikely that this adjustment 

corrects for all inter-city differences in people’s activities. Time/activity patterns are 

8-2
 
likely to be affected by a variety of local factors, including topography, land-use, traffic 

patterns, mass transit systems, and recreational opportunities. 

The average subject in the CHAD-derived database provided less than two days 

of diary data. For this reason, the construction of each year-long exposure event 

sequence required either the repetition of data from one subject or the use of data from 

multiple subjects. The latter approach was used in the pNEM/CO analyses described in 

this report to better represent the variability of exposure expected to occur among the 

adults in each cohort. The principal deficiency of this approach is that it may not 

adequately account for the day-to-day repetition of activities common to individual 

adults. Using activities from different subjects may underestimate multiple occurrences 

of high exposure situations (e.g., long commutes) for segments of the population who 

engage in highly repetitive activities. 

8.2 Ventilation (Inhalation) Rates 

One of the advanced features of pNEM/CO is its ability to probabilistically 

estimate a ventilation rate (VE) value for each exposure event, where VE is expressed as 

liters of air respired per minute (liters min-1). The algorithm used to estimate VE was 

developed specifically for Version 2.0 of pNEM/CO and has not been used previously in 

pNEM analyses. As described in Section 5, the exposure event sequence for each 

cohort provides an activity descriptor (e.g., “raking”) for each event. Analysts provide a 

distribution of possible MET values for each activity descriptor type. The ventilation rate 

algorithm selects a value of MET for each exposure event from the appropriate 

distribution and converts it to a corresponding energy expenditure rate according to a 

resting metabolic rate assigned to the cohort. 

The algorithm next converts each energy expenditure rate to a corresponding 

oxygen uptake rate (VO2). Finally, the algorithm converts the resulting VO2 value to VE 

through the use of one of six equations specific to gender and age. In essence, the 

algorithm determines the quantity of air that a person in the cohort must breathe to 

obtain the oxygen needed to burn the calories required by the activity associated with 

each exposure event. 

8-3
 
Most of the steps in converting an activity descriptor for an event to a VE value for 

the event employ equations and parameter values which are relatively well-supported 

by clinical data (see Section 5). Perhaps the weakest link in the algorithm is the step 

which requires the analyst to provide a distribution of possible MET values for each 

event descriptor. These distributions are currently based on distributions provided by 

the developers of CHAD. Because there were often insufficient data available to 

accurately define a distribution for each activity descriptor, the developers tended to 

follow a conservative approach and over-estimate the variability of each distribution. 

Consequently, the VE values produced by the ventilation rate algorithm may exhibit an 

excessive degree of variability. To prevent the occurrence of “impossible” values 

arising from this variability, the ventilation rate algorithm includes test routines which 

identify and adjust VO2 values that exceed limits based on activity duration and the 

physiological characteristics of the cohort. 

The ventilation rate estimated for a particular exposure event is not explicitly 

affected by the ventilation rates estimated for preceding events. Consequently, the 

algorithm does not adequately account for excess post-exercise oxygen consumption 

(EPOC), a condition experienced when individuals are engaged in strenuous exertion 

that results in an oxygen debt that impacts oxygen uptake and ventilation rates after 

cessation of the strenuous exercise. EPA is considering adding an adjustment 

approach to account for EPOC in future versions of pNEM. 

Finally, healthy subjects provided most of the clinical data used to estimate the 

parameters of the ventilation rate algorithm. These data may not be entirely 

representative of adults with IHD, the sensitive population group included in this 

exposure analysis. 

8.3 Estimation of Cohort Populations 

Subsection 2.5.1 of this report describes the procedure used to estimate cohort 

populations for the general Denver population. Each cohort is defined by demographic 

group, cooking fuel, and work location. Ideally, the population of each cohort would be 

estimated from census data specific to each combination of these factors. In actuality, 

census data are available for each factor separately. Consequently, the population8-4
 
estimation routines within pNEM/CO assume that the factors are independent in 

developing cohort estimates. However, it is likely that some factors are correlated. For 

example, pNEM/CO applies the same commuting pattern to all working demographic 

groups residing in a particular district because commuting data specific to the pNEM/CO 

demographic groups are not available from BOC. This assumption ignores the probable 

correlations between gender and work location. 

Version 2.0 of pNEM/CO marks the first use of a new commuting database 

provide by the BOC. Although an improvement over earlier sources of commuting data, 

the BOC commuting database is not a perfect representation of the commuting patterns 

of Denver residents. The data were acquired from the census “long form” which the 

BOC administered to about one-sixth of the Denver residents. The BOC extrapolated 

the data from this subset of the population to the remainder of the population using 

various assumptions and supplemental information. 

Section 2.5.2 describes the method used to estimate cohort populations for the 

sensitive population defined as persons with diagnosed and undiagnosed IHD. The 

method used the procedure applied to the general population with an additional step 

which accounted for the fraction of each demographic group who had IHD (diagnosed 

and undiagnosed). Table 2-9 lists estimates of these fractions. The estimates for 

diagnosed IHD are considered relatively reliable on a national scale, as they were 

obtained from data disaggregated by age and gender obtained from the National Health 

Interview Survey. According to the National Health Interview Survey, approximately 8.0 

million individuals are estimated to have diagnosed IHD in the civilian, noninstitutionalized population. These estimates do not include individuals in the military or 

individuals in nursing homes or other institutions. There is likely to be some geographic 

variation in the fraction of persons with IHD, but here is insufficient information available 

to account for this variation. 

The estimates of undiagnosed IHD are considered less certain. These estimates 

are based on two assumptions: (1) there are 3.5 million persons in the U.S. with 

undiagnosed IHD and (2) persons with undiagnosed IHD are distributed by age and 

gender within the population in the same proportions as persons with diagnosed IHD. 

The 3.5 million statistic is based on an estimate by the American Heart Association 

(1990) that there are between three and four million persons with undiagnosed IHD. 

8-5
 
8.4 The Mass-Balance Model 

The pNEM/CO methodology uses the mass-balance model described in Section 

4 to estimate CO concentrations in various enclosure types. The mass-balance model 

provides hourly average CO concentrations for each enclosure category as a function of 

outdoor CO concentration, air exchange rate (AER), and indoor emissions of CO from 

gas stoves. 

The outdoor CO concentrations were derived from fixed-site monitoring data 

through a Monte Carlo process. According to this process, the outdoor CO 

concentration estimated for a particular hour h was influenced by (1) the microenvironment, (2) the outdoor CO concentration estimated for the preceding hour for the 

specified microenvironment, and (3) the CO concentration estimated for hour h at the 

fixed-site monitor representing district d. The probabilities incorporated into the process 

were developed through a statistical analysis of data obtained from the 1982 - 1983 

Denver personal monitoring study (Johnson, 1984). Although these data are specific to 

the city analyzed in this report (Denver), they were collected more than a decade ago 

and may not be representative of current conditions in Denver. More recent personal 

monitoring data for Denver are not available. 

The mass-balance model uses hourly-average CO concentrations measured by 

a relatively small number of fixed-site monitors (six individual sites plus one composite 

site). This monitoring network is unlikely to fully represent the geographically variability 

of outdoor CO concentrations in the Denver study area. In addition, some of these sites 

may be affected by specific CO sources which are not typical of general CO emission 

patterns. (However, there are no known major CO point sources near any of the fixedsite monitors used in this exposure analysis.) These potential deficiencies in the fixedsite data may be somewhat mitigated by the use of probabilistic relationships in 

pNEM/CO to estimate outdoor CO concentrations for the mass-balance model. The 

relationships are based on personal monitoring data representing a wide assortment of 

locations within the Denver study area. 

The AER values for residential buildings with closed windows were obtained from 

lognormal distributions fit to seasonal AER data collected by Brookhaven National 

Laboratory (BNL). The BNL data for the region containing Denver included a large 

8-6
 
number of AER values for winter and spring, but small sample sizes for summer and 

winter. Consequently, analysts were required to use statistical methods to estimate the 

geometric mean and standard deviation for the seasons with limited data. 

The AER distribution for residences with windows open is based on data 

collected in a single test residence in North Carolina over a single 24-hour period. 

These data may not be representative of Denver residences with open windows. 

AER distributions for non-residential buildings are based on statistical analysis of 

data for 89 buildings. As none of the buildings was located in Denver, the distributions 

may not accurately represent non-residential buildings in Denver. 

The AER distribution for vehicles is based on very limited data from two studies. 

The California Air Resources Board has indicated that additional data on AER in 

vehicles will soon be available. These data are expected to provide an improved basis 

for determining the AER for vehicles. 

The mass-balance model simulates the operation of gas stove burners in 

residences by specifying when the burners are on, the emission rate of the burners 

during operation, and the volume of the residence where it is located. The probabilistic 

burner use patterns used in pNEM/CO are considered relatively reliable, as they are 

based on survey data for 4312 users and on diaries completed by several hundred 

subjects of the Denver personal monitoring study. Burner emission rates are based on 

fuel use patterns observed in 57 Illinois homes and emission factors representing a 

well-adjusted stove. It is not known whether Denver fuel use patterns differ significantly 

from those of Illinois residents. The use of emission factor data representing a welltuned stove probably underestimates emissions from the overall population of gas 

stoves. 

Residential volumes for Denver homes are randomly selected from a lognormal 

distribution based on Denver square footage data from the 1995 American Housing 

Survey. These data are considered to be relatively reliable. 

Version 2.0 of pNEM/CO does not directly account for certain indoor sources 

(e.g., kerosene heaters, woodstoves, fireplaces, charcoal grills and hibachis used in the 

living area of homes, or motor vehicle operation in attached garages) which are likely to 

produce some high-end CO personal exposures. However, it should be noted that 

these situations are not directly related to the CO contribution from the ambient air. The 

8-7
 
model also does not capture high-end CO exposures that are due to malfunctioning gas 

stoves, ovens, or other gas appliances or the improper use of these gas appliances. 

The fact that some of these potentially high-end CO exposure scenarios are not 

considered within pNEM/CO may at least partially explain why past comparisons of the 

model predictions with actual personal exposure data collected in Denver showed the 

model under-predicting the upper tail of CO exposure distribution for the population 

(Law et al., 1997). 

8.5 The COHb Algorithm 

This algorithm provides an estimate of the COHb level at the end of each 

exposure event. The algorithm is based on a differential equation proposed by Coburn, 

Foster, and Kane (1963). EPA’s draft updated criteria document (EPA, 1999, p.5-35) 

states that the nonlinear CFK equation is the most widely used predictive model of 

COHb formation, and it still is considered the best all-around model for COHb 

prediction. Various tests of the CFK equation indicate that the model predicts COHb 

levels accurately unless one is dealing with very high CO exposures of short duration 

(e.g., hundreds of ppm CO in a few minutes). 

A special algorithm within pNEM/CO probabilistically generates a value for each 

parameter of the CFK equation (collectively referred to as a “physiological profile”). The 

algorithm ensures that the functional relationships among the various parameters are 

maintained. A report by Richmond and Johnson (Appendix E of this report) discusses 

the limitations of data used to estimate the distributions and predictive equations 

associated with each of the parameters appearing in the CFK equation. 

8.6 References 

American Heart Association. 1990. 1990 Heart and Stroke Facts. American Heart 

Association, Dallas, TX, p. 13. 

8-8
 
Coburn, R. F., R. E. Forster, and R. B. Kane. 1963. “Considerations of the Physiology 

and Variables that Determine Blood Caboxyhemoglobin Concentration in Man,” Journal 

of Clinical Investigations, Vol. 44, pp. 1899-1910. 

Johnson, T. R. 1984. A Study of Personal Exposure to Carbon Monoxide in 

Denver, Colorado. Report No. EPA-600/54-84-014, U. S. Environmental Protection 

Agency, Research Triangle Park, North Carolina. 

U. S. Environmental Protection Agency. 1999. Air Quality Criteria for Carbon 

Monoxide (External Review Draft). Report No. EPA/600/P-99/001. U. S. 

Environmental Protection Agency, National Center for Environmental Assessment, 

Research Triangle Park, NC. 

Johnson, T., J. Capel, R. Paul, and L. Wijnberg. 1992. Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver Residents 

Using a Probabilistic Version of NEM. Report prepared by International Technology 

Air Quality Services under EPA Contract No. 68-D0-0062. U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina. 

Law, P. L., P. J. Lioy, M. P. Zelenka, A. H. Huber. and T. R. McCurdy. 1997. 

“Evaluation of a Probabilistic Exposure Model Applied to Carbon Monoxide (pNEM/CO) 

Using Denver Personal Exposure Data.” Journal of the Air and Waste Management 

Association. Vol. 47, pp. 491 - 500. 

8-9
 
Appendix A
 

Distribution of Energy Expenditure Rates
 

Associated with CHAD Location Descriptors
 

A-1
 
Distributions for Energy Expenditure Rates By Activity Code, Age, and Occupation (if applicable). 

Notes: 

1. Activities coded as 10... are activities with codes beginning with 10. 

2. OCC: occupational categories. 

3. DN: distribution number 

4. DL: distribution type (T = triangular, N = normal, U = uniform, E = exponential, P = point) 

5. Activities starting with 17... are calculated based on age. 

Age = 1 if respondent < 25 years 

Age = 2 if respondent 25 - 39 years 

Age = 3 if respondent > 40 years 

ACTIVI 

TY AGE OCC DN DL MEAN MED SD MIN MAX FLAG LEFT RIGHT 

10... X ADMIN 4 L 1.7 1.7 0.3 1.4 2.7 0 0.16 0.01 

10... X PROF 5 T 2.9 2.7 1 1.2 5.6 0 0 0 

10... X 

ADMSU 

P 4 L 1.7 1.7 0.3 1.4 2.7 0 0.16 0.01 

10... X TECH 5 T 3.3 3.3 0.4 2.5 4.5 1 0 0 

10... X TRANS 4 L 3.3 3 1.5 1.3 8.4 1 0.03 0.01 

10... X SALE 5 T 2.9 2.7 1 1.2 5.6 0 0 0 

10... X SERV 5 T 5.2 5.3 1.4 1.6 8.4 1 0 0 

10... X HSHLD 4 L 3.6 3.5 0.8 2.5 6 1 0.07 0.01 

10... X 

PROTE 

CT 5 T 2.9 2.7 1 1.2 5.6 0 0 0 

10... X PREC 5 T 3.3 3.3 0.4 2.5 4.5 1 0 0 

10... X MACH 2 U 5.3 5.3 0.7 4 6.5 1 0 0 

10... X FARM 4 L 7.5 7 3 3.6 17 1 0.04 0.01 

10... X LABOR 5 T 8.5 8.4 2.1 3.6 13.8 1 0 0 

17100 1 X 4 L 5.7 5 3 1.4 16 1 0.01 0.01 

17100 2 X 1 N 5 5 2 1 9 1 0.02 0.02 

17100 3 X 1 N 4.5 4.5 1.4 1.7 7.3 1 0.02 0.02 

17110 1 X 4 L 3.6 3.2 1.9 1.4 10 1 0.05 0.01 

17110 2 X 4 L 3.6 3.2 1.9 1.4 10 1 0.05 0.01 

17110 3 X 4 L 3.4 3 1.7 1.4 9 1 0.05 0.01 

17111 1 X 1 N 5.6 5.6 2.1 1.4 9.8 1 0.02 0.02 

17111 2 X 1 N 5.8 5.8 2.4 1 10.6 1 0.02 0.02 

17111 3 X 1 N 4.7 4.7 1.8 1.1 8.3 1 0.02 0.02 

17112 1 X 2 U 3.8 3.8 1 2 5.5 1 0 0 

17112 2 X 2 U 3.8 3.8 1 2 5.5 1 0 0 

17112 3 X 2 U 3.5 3.5 0.9 2 5 1 0 0 

17120 1 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01 

17120 2 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01 

17120 3 X 6 P 3.5 3.5 . . . 1 . . 

17121 1 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01 

17121 2 X 4 L 4.2 3.9 1.5 2 9 1 0.03 0.01 

17121 3 X 6 P 3.5 3.5 . . . 1 . . 

17130 1 X 4 L 5.8 5.5 1.8 1.8 11.3 1 0 0.01 

17130 2 X 1 N 5.7 5.7 1.8 2.1 9.3 1 0.02 0.02 

17130 3 X 1 N 4.7 4.7 1.2 2.3 7.1 1 0.02 0.02 

17131 1 X 4 L 5.8 5.5 1.8 1.8 11.3 1 0 0.01 

17131 2 X 1 N 5.7 5.7 1.8 2.1 9.3 1 0.02 0.02 

17131 3 X 1 N 4.7 4.7 1.2 2.3 7.1 1 0.02 0.02 

17140 1 X 1 N 5.3 5.3 1.8 1.7 8.9 1 0.02 0.02 

A-2
 
17140 2 X 1 N 5.2 5.2 1.7 1.7 8.9 1 0.02 0.01 

17140 3 X 1 N 3.8 3.8 1 1.8 5.8 1 0.02 0.02 

17144 1 X 1 N 5.3 5.3 1.8 1.7 8.9 1 0.02 0.02 

17144 2 X 1 N 5.2 5.2 1.7 1.7 8.9 1 0.02 0.01 

17144 3 X 1 N 3.8 3.8 1 1.8 5.8 1 0.02 0.02 

17180 1 X 4 L 6.6 5.9 3.2 2 17.4 1 0.01 0.01 

17180 2 X 1 N 6 6 2 2 10 1 0.02 0.02 

17180 3 X 1 N 4.8 4.8 1.4 2 7.6 1 0.02 0.02 

10200 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0 

10300 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0 

11000 X X 5 T 4.7 4.6 1.3 1.5 8 1 0 0 

11100 X X 4 L 2.6 2.5 0.5 2 4 0 0.13 0.01 

11110 X X 3 E 2.8 2.5 0.9 1.9 4 0 0 0.02 

11200 X X 3 E 3.4 3 1.4 2 5 1 0 0.01 

11210 X X 2 U 2.5 2.5 0.1 2.3 2.7 0 0 0 

11220 X X 3 E 4.1 3.5 1.9 2.2 5 1 0 0.01 

11300 X X 1 N 5 5 1 2 7 1 0 0.02 

11310 X X 3 E 5.3 4.5 2.7 2.6 6 1 0 0 

11400 X X 3 E 2.2 2 0.7 1.5 4 0 0 0.02 

11410 X X 6 P 2 2 . . . 0 . . 

11500 X X 6 P 2 2 . . . 0 . . 

11600 X X 1 N 4.5 4.5 1.5 2 8 1 0.05 0.01 

11610 X X 6 P 4.5 4.5 . . . 1 . . 

11620 X X 3 E 4.9 4.5 1.4 3.5 6 1 0 0 

11630 X X 5 T 3.5 3.4 0.4 3 4.5 1 0 0 

11640 X X 3 E 4.7 4.5 0.7 4 6 1 0 0 

11650 X X 2 U 4.5 4.5 1.4 2 7 1 0 0 

11700 X X 2 U 3.5 3.5 0.9 2 5 1 0 0 

11800 X X 2 U 3.3 3.3 0.1 3 3.5 1 0 0 

11900 X X 3 E 6.6 5.5 3.6 3 9 1 0 0 

12000 X X 4 L 3.1 3 0.7 2.5 5 1 0.2 0.01 

12100 X X 2 U 3.3 3.3 0.1 3 3.5 1 0 0 

12200 X X 2 U 3.3 3.3 0.1 3 3.5 1 0 0 

12300 X X 2 U 2.8 2.8 0.1 2.5 3 0 0 0 

12400 X X 2 U 2.8 2.8 0.1 2.5 3 0 0 0 

12500 X X 2 U 2.8 2.8 0.1 2.5 3 0 0 0 

12600 X X 2 U 4.5 4.5 0.3 4 5 1 0 0 

12700 X X 2 U 3.2 3.2 0.1 3 3.3 1 0 0 

12800 X X 2 U 3 3 0.3 2.5 3.5 1 0 0 

13000 X X 5 T 3.8 3.7 0.8 2 6 1 0 0 

13100 X X 2 U 3.3 3.3 0.4 2.5 4 1 0 0 

13200 X X 5 T 3.7 3.6 0.8 2 6 1 0 0 

13210 X X 5 T 3.9 3.8 0.8 2.2 6 1 0 0 

13220 X X 2 U 3.4 3.4 0.6 2.3 4.5 1 0 0 

13230 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0 

13300 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0 

13400 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0 

13500 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0 

13600 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0 

13700 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0 

13800 X X 2 U 3.5 3.5 0.6 2.5 4.5 1 0 0 

14000 X X 2 U 2 2 0.6 1 3 0 0 0 

A-3
 
14100 X X 1 N 2 2 0.3 1 4 0 0 0 

14110 X X 2 U 3 3 0.6 2 4 1 0 0 

14120 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0 

14200 X X 2 U 1.8 1.8 0.4 1 2.5 0 0 0 

14300 X X 4 L 3.1 3 0.7 2.5 5 1 0.2 0.01 

14400 X X 2 U 1.8 1.8 0.1 1.5 2 0 0 0 

14500 X X 4 L 0.9 0.9 0.1 0.8 1.1 0 0.09 0.01 

14600 X X 6 P 2.5 2.5 . . . 0 . . 

14700 X X 5 T 2 2 0.4 1 2.9 0 0 0 

15000 X X 4 L 1.9 1.8 0.7 1.4 4 0 0.23 0.01 

15100 X X 2 U 2.1 2.1 0.4 1.4 2.8 0 0 0 

15110 X X 2 U 2.3 2.3 0.4 1.5 3 0 0 0 

15120 X X 2 U 2.1 2.1 0.4 1.4 2.8 0 0 0 

15130 X X 2 U 2 2 0.3 1.4 2.5 0 0 0 

15140 X X 2 U 1.8 1.8 0.2 1.4 2.2 0 0 0 

15200 X X 2 U 2.2 2.2 0.5 1.4 3 0 0 0 

15300 X X 6 P 1.8 1.8 . . . 0 . . 

15400 X X 2 U 2.3 2.3 0.4 1.5 3 0 0 0 

15500 X X 2 U 2.8 2.8 0.7 1.5 4 0 0 0 

16000 X X 4 L 2.2 2 1.1 1 6 0 0.07 0.01 

16100 X X 2 U 2.7 2.7 0.8 1.4 4 0 0 0 

16200 X X 2 U 1.7 1.7 0.2 1.4 2 0 0 0 

16210 X X 2 U 1.7 1.7 0.2 1.4 2 0 0 0 

16300 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0 

16400 X X 2 U 1.7 1.7 0.4 1 2.3 0 0 0 

16500 X X 2 U 2.5 2.5 0.3 2 2.9 0 0 0 

16600 X X 2 U 1.5 1.5 0.3 1 1.9 0 0 0 

16700 X X 4 L 3.3 3 1.4 1.5 8 1 0.05 0.01 

16800 X X 4 L 3.3 3 1.4 1.5 8 1 0.05 0.01 

16900 X X 2 U 3.8 3.8 1.3 1.5 6 1 0 0 

17113 X X 2 U 3 3 0.6 2 4 1 0 0 

17114 X X 5 T 3.1 3.2 0.6 1.4 4 1 0 0 

17122 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 0 0 

17141 X X 5 T 2.8 2.7 0.8 1.5 5 0 0 0 

17142 X X 5 T 2 1.9 0.4 1.5 3 0 0 0 

17143 X X 2 U 2.5 2.5 0.3 2 3 0 0 0 

17150 X X 5 T 3.3 3.2 0.6 2.4 5 1 0 0 

17160 X X 2 U 1.6 1.6 0.2 1.2 2 0 0 0 

17170 X X 2 U 5 5 1.7 2 8 1 0 0 

17200 X X 4 L 1.3 1.3 0.3 1 2.3 0 0.14 0.01 

17210 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 0 0 

17211 X X 2 U . . . 1.2 . 0 0 . 

17212 X X 2 U . . . 1.2 . 0 0 . 

17213 X X 2 U . . . 1.2 . 0 0 . 

17214 X X 2 U . . . 1.2 . 0 0 . 

17215 X X 2 U . . . 1.2 . 0 0 . 

17216 X X 2 U 2.7 2.7 0.8 1.4 4 0 0 0 

17220 X X 4 L 1.2 1.2 0.4 0.9 2.3 0 0.15 0.01 

17221 X X 2 U 1.2 1.2 0.1 1 1.3 0 0 0 

17222 X X 2 U 1.9 1.9 0.2 1.5 2.3 0 0 0 

17223 X X 6 P 1 1 . . . 0 . . 

17230 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0 

A-4
 
17231 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0 

17232 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0 

17233 X X 2 U 1.3 1.3 0.2 1 1.6 0 0 0 

17240 X X 2 U 1.4 1.4 0.2 1 1.8 0 0 0 

17241 X X 2 U 1.4 1.4 0.2 1 1.8 0 0 0 

17242 X X 2 U 1.4 1.4 0.2 1 1.8 0 0 0 

17250 X X 2 U 1.2 1.2 0.1 1 1.3 0 0 0 

17260 X X 2 U 1.9 1.9 0.2 1.5 2.3 0 0 0 

17300 X X 2 U 1.5 1.5 0.2 1.2 1.8 0 0 0 

18000 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18100 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18200 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18300 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18400 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18500 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18600 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18700 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18800 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18900 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18910 X X 4 L 2.3 2 1.3 1 7 0 0.1 0.01 

18920 X X 4 L 2.3 2 1.3 1.8 7 0 0.42 0.01 

A-5
 
CHAD Activity Codes 

<10> Work and Other Income Producing Activities 

10000: work and other income producing activities, general 

10100: work, general

 10110: work, general, for organizational activities

 10111: work for professional/union organizations

 10112: work for special interest identity organizations

 10113: work for political party and civic participation

 10114: work for volunteer/ helping organizations

 10115: work of/ for religious groups

 10116: work for fraternal organizations

 10117: work for child/ youth/ family organizations

 10118: work for other organizations

 10120: work, income-related only 

 10130: work, secondary (income-related) 

10200: unemployment 

10300: breaks 

<11> Household Activities 

11000: general household activities 

11100: prepare food

 11110: prepare and clean-up food 

11200: indoor chores

 11210: clean-up food

 11220: clean house 

11300: outdoor chores

 11310: clean outdoors 

11400: care of clothes

 11410: wash clothes 

11500: build a fire 

11600: repair, general

 11610: repair of boat

 11620: paint home/ room

 11630: repair/ maintain car

 11640: home repairs

 11650: other repairs
 

11700: care for plants 

11800: care for pets/ animals 

11900: other household 

<12> Child Care 

12000: child care, general 

12100: care of baby 

12200: care of child 

12300: help/teach 

12400: talk/read 

12500: play indoors 

A-6
 
12600: play outdoors 

12700: medical care-child 

12800: other child care 

<13> Obtain Goods and Services 

13000: obtain goods and services, general 

13100: dry clean 

13200: shop/ run errands, general

 13210: shop for food

 13220: shop for clothes or household goods

 13230: run errands 

13300: obtain personal care service 

13400: obtain medical service 

13500: obtain government/ financial services 

13600: obtain car service 

13700: other repairs 

13800: other services 

<14> Personal Needs and Care 

14000: personal needs and care, general 

14100: shower, bathe, personal hygiene

 14110: shower, bathe

 14120: personal hygiene 

14200: medical care 

14300: help and care 

14400: eat 

14500: sleep or nap 

14600: dress, groom 

14700: other personal needs 

<15> Education and Professional Training 

15000: general education and professional training 

15100: attend full-time school

 15110: attend day-care

 15120: attend K-12

 15130: attend college or trade school

 15140: attend adult education and special training 

15200: attend other classes 

15300: do homework 

15400: use library 

15500: other education 

<16> Entertainment/ Social Activities 

16000: general entertainment/ social activities 

16100: attend sports events 

16200: participate in social, political, or religious activities

 16210: practice religion 

16300: view movie 

16400: attend theater 

16500: visit museums 

16600: visit 

A-7
 
16700: attend a party 

16800: go to bar/ lounge 

16900: other entertainment/ social events 

<17> Leisure 

17000: leisure, general 

17100: participate in sports and active leisure

 17110: participate in sports

 17111: hunting, fishing, hiking

 17112: golf

 17113: bowling/ pool/ ping pong/ pinball

 17114: yoga

 17120: participate in outdoor leisure

 17121: play, unspecified 

17122: passive, sitting

 17130: exercise

 17131: walk, bike, or jog (not in transit)

 17140: create art, music, participate in hobbies

 17141: participate in hobbies

 17142: create domestic crafts

 17143: create art

 17144: perform music/ drama/ dance

 17150: play games

 17160: use of computer 

17170: participate in recess and physical education

 17180: other sports and active leisure 

17200: participate in passive leisure

 17210: watch

 17211: watch adult at work

 17212: watch someone provide childcare

 17213: watch personal care

 17214: watch education

 17215: watch organizational activities

 17216: watch recreation

 17220: listen to radio/ listen to recorded music/ watch t.v.

 17221: listen to radio

 17222: listen to recorded music

 17223: watch t.v.

 17230: read, general

 17231: read books

 17232: read magazine/ not ascertained

 17233: read newspaper

 17240: converse/ write

 17241: converse

 17242: write for leisure/ pleasure/ paperwork

 17250: think and relax

 17260: other passive leisure 

17300: other leisure 

A-8
 
<18> Travel 

18000: travel, general 

18100: travel during work 

18200: travel to/from work 

18300: travel for child care 

18400: travel for goods and services 

18500: travel for personal care 

18600: travel for education 

18700: travel for organizational activity 

18800: travel for event/ social activity 

18900: travel for leisure

 18910: travel for active leisure

 18920: travel for passive leisure 

A-9
 
Appendix B 

Fixed-Site Monitors in Colorado Reporting CO Data to AIRS
 

for One or More Years Between 1993 and 1997
 

B-1
 
1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP450 AIR QUALITY SUBSYSTEM
 QUICK LOOK REPORT
 0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM
 0 P
 O M 

REP MAX 1-HR OBS> MAX 8

 SITE ID 

C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 

 ________________________________________________________________________________________________________________008-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 93 001 8632 9.0 8.2 0 6.6  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 94 001 8687 9.0 8.8 0 6.4  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 95 001 8681 8.5 8.1 0 5.7  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 96 001 8712 6.2 6.2 0 3.9  08-001-3001 1 2 ADAMS CO 78TH AVE & STEELE ST - W 97 001 8661 8.3 6.6 0 5.0  08-001-7015 1 4 COMMERCE CITY ADAMS CO ROCKY MOUNTAIN ARSENAL 93 830 7919 7.7 6.6 0 4.3  08-001-7015 1 4 COMMERCE CITY ADAMS CO ROCKY MOUNTAIN ARSENAL 94 830 3595 3.2 2.5 0 1.2  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 93 001 8589 7.0 6.5 0 4.9  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 94 001 8705 4.6 4.2 0 2.9  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 95 001 8670 3.6 3.3 0 2.6  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 96 001 8677 4.3 4.1 0 2.9  08-005-0002 1 2 LITTLETON ARAPAHOE CO 8100 SO UNIVERSITY BLVD- 97 001 8463 4.3 4.0 0 3.0  08-005-0003 1 2 ENGLEWOOD ARAPAHOE CO 3300 S. HURON ST. 93 001 8717 11.5 10.9 0 8.4  08-005-0003 1 2 ENGLEWOOD ARAPAHOE CO 3300 S. HURON ST. 94 001 7126 7.5 7.3 0 4.9  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 93 001 8701 9.5 9.0 0 7.0  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 94 001 8557 12.6 12.2 0 6.7  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 95 001 8690 14.0 8.8 0 5.6  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 96 001 8735 11.3 9.2 0 5.5  08-013-0009 1 2 LONGMONT BOULDER CO 440 MAIN ST. LONGMONT 97 001 8617 9.6 9.1 0 5.7  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 93 001 353 7.6 7.6 0 5.1  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 94 001 8639 12.4 10.5 0 6.6  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 95 001 8608 10.6 10.3 0 5.3  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 96 001 8576 8.5 8.4 0 5.6  08-013-0010 1 2 BOULDER BOULDER CO 2150 28TH STREET 97 001 8697 9.0 8.2 0 5.5  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 93 001 8708 10.2 8.4 0 5.5  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 94 001 8565 7.8 6.0 0 2.8  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 95 001 8651 8.3 8.2 0 4.1  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 96 001 8669 4.5 4.3 0 2.5  08-013-1001 1 2 BOULDER BOULDER CO 2320 MARINE ST., BOULDER 97 001 8517 7.1 6.9 0 5.1  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 93 001 8687 19.4 18.2 0 10.4  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 94 001 8700 20.4 17.1 0 9.9  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 95 001 8697 24.5 16.4 0 11.0  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 96 001 8673 21.6 16.7 0 9.0  08-031-0002 2 1 DENVER DENVER CO 2105 BROADWAY - CAMP 97 001 8687 11.4 10.0 0 5.7  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 93 001 8675 18.1 14.9 0 9.1  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 94 001 8665 12.4 12.2 0 8.0  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 95 001 8647 14.6 13.6 0 8.5  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 96 001 8516 14.6 9.4 0 5.6  08-031-0013 1 2 DENVER DENVER CO 14TH AND ALBION ST. NJH- 97 001 8690 11.6 10.6 0 4.8  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 93 001 8676 14.1 12.1 0 8.5  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 94 001 8543 11.2 10.9 0 9.3  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 95 001 8701 10.4 9.9 0 7.3  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 96 001 8736 9.1 8.2 0 7.3  08-031-0014 1 1 DENVER DENVER CO 23 RD AND JULIAN_(CARRIA 97 001 8677 9.5 8.4 0 7.0 1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP450 AIR QUALITY SUBSYSTEM
 QUICK LOOK REPORT
 0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM
 0 P
 O M 

REP MAX 1-HR OBS> MAX 8

 SITE ID 

C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 

 ________________________________________________________________________________________________________________

B-2
 
008-031-0018 1 3 DENVER DENVER CO BLAKE ST. SIDE OF SPEER 93 001 1021 16.2 15.3 0 10.4 

 08-031-0018 1 3 DENVER DENVER CO BLAKE ST. SIDE OF SPEER 94 001 1591 12.2 11.6 0 7.8  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 93 001 997 16.2 16.1 0 10.4  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 94 001 3049 13.9 13.4 0 9.0  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 95 001 3658 15.0 14.0 0 9.7  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 96 001 8694 15.7 12.5 0 9.2  08-031-0019 1 2 DENVER DENVER CO SPEER SIDE OF SPEER & AU 97 001 8354 11.2 11.2 0 6.6  08-031-0020 1 3 DENVER DENVER CO 935 COLORADO BLVD., UCHS 94 001 1370 12.8 11.7 0 7.6  08-031-0020 1 3 DENVER DENVER CO 935 COLORADO BLVD., UCHS 95 001 2141 11.9 10.3 0 7.4  08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 93 001 8716 11.7 10.7 0 5.6 

 08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 94 001 8716 12.5 10.9 0 4.4 

 08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 95 001 8698 10.4 9.6 0 5.2 

 08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 96 001 8688 10.7 9.1 0 4.7 

 08-041-0004 1 2 COLORADO SPRING EL PASO CO 712 S TEJON ST 97 001 4658 10.4 8.2 0 4.7 

 08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 93 001 8660 13.6 11.6 0 6.1 

 08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 94 001 8578 11.7 11.4 0 5.4 

 08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 95 001 8716 10.8 10.3 0 7.2 

 08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 96 001 8746 11.3 11.3 0 7.6 

 08-041-0006 1 2 COLORADO SPRING EL PASO CO UINTAH & I-25 97 001 8577 13.1 10.6 0 5.9 

 08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 93 026 8341 4.1 3.9 0 2.1  08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 94 026 8709 4.0 3.9 0 2.3  08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 95 026 8724 4.2 3.8 0 2.3  08-041-6004 1 3 COLORADO SPRING EL PASO CO 6000 PULPIT ROCK DRIVE. 96 026 8544 3.8 3.4 0 2.1  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 93 026 8508 7.0 7.0 0 4.5  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 94 026 8655 6.8 6.2 0 4.6  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 95 026 8515 5.9 5.6 0 3.2  08-041-6005 1 3 EL PASO CO 4940 S. HIGHWAY 85/87 96 026 3064 6.1 5.6 0 3.0  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 93 026 8257 2.0 1.8 0 1.1  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 94 026 7620 2.0 1.9 0 1.2  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 95 026 7793 3.0 2.9 0 2.4  08-041-6006 1 3 EL PASO CO 9400 CHIPITA PARK ROAD 96 026 4148 2.1 2.1 0 1.8  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 93 026 6003 1.9 1.7 0 1.0  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 94 026 8702 2.2 1.9 0 1.3  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 95 026 8707 2.4 2.2 0 1.3  08-041-6009 1 3 EL PASO CO R.D.NIXON POWER PLANT EX 96 026 4328 1.5 1.4 0 .8  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 93 026 8584 8.7 7.9 0 4.5  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 94 026 8318 7.9 6.8 0 3.3  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 95 026 8500 7.4 7.0 0 4.0  08-041-6011 1 3 COLORADO SPRING EL PASO CO 130 WEST CACHE LA POUDRE 96 026 8469 8.1 8.1 0 4.9  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 93 026 8619 11.0 10.1 0 5.3  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 94 026 8703 10.8 9.3 0 4.6  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 95 026 8429 9.4 8.7 0 5.0  08-041-6013 1 3 COLORADO SPRING EL PASO CO 1699 S. CORONA AVE 96 026 4310 11.9 9.6 0 5.4  08-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 93 026 8593 15.1 14.2 0 6.5 1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP450 AIR QUALITY SUBSYSTEM
 QUICK LOOK REPORT
 0 CARBON MONOXIDE (42101) COLORADO UNITS: 007 PPM
 0 P
 O M 

REP MAX 1-HR OBS> MAX 8

 SITE ID 

C T CITY COUNTY ADDRESS YR ORG #OBS 1ST 2ND 35 1ST 

 ________________________________________________________________________________________________________________008-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 94 026 8574 16.6 14.5 0 4.9  08-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 95 026 8477 15.0 12.4 0 4.9  08-041-6016 1 3 COLORADO SPRING EL PASO CO 3730 MEADOWLAND BLVD. 96 026 8682 11.0 11.0 0 4.6  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 93 001 8723 11.2 10.4 0 5.1  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 94 001 8525 10.8 10.0 0 5.2  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 95 001 8680 11.9 8.9 0 5.1  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 96 001 8724 7.9 7.2 0 4.3  08-059-0002 1 2 ARVADA JEFFERSON CO W 57TH AVENUE AND GARRIS 97 001 8697 9.2 7.7 0 5.1 

B-3
 
 08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 93 001 8698 17.3 13.8 0 7.4 

 08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 94 001 8703 13.6 12.1 0 7.3 

 08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 95 001 8699 10.6 9.8 0 5.6 

 08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 96 001 8597 12.7 10.9 0 5.5 

 08-069-1004 1 2 FORT COLLINS LARIMER CO 708 S. MASON FT COLLINS 97 001 8708 10.3 9.2 0 5.3 

 08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 93 001 8383 12.0 11.2 0 6.9 

 08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 94 001 8367 11.6 11.6 0 7.5 

 08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 95 001 8209 10.0 8.7 0 5.4 

 08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 96 001 8754 10.5 10.5 0 7.5 

 08-077-0014 1 2 GRAND JUNCTION MESA CO STOCKER STADIUM (12TH & 97 001 8550 8.5 7.8 0 6.4 

 08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 93 001 8688 10.9 10.2 0 6.1 

 08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 94 001 8707 11.3 11.1 0 6.4 

 08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 95 001 8703 10.3 9.6 0 5.7 

 08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 96 001 8739 12.3 10.7 0 7.5 

 08-123-0007 1 2 GREELEY WELD CO 811 15TH ST - GREELEY 97 001 8717 9.6 8.6 0 5.3 

B-4
1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP450 AIR QUALITY SUBSYSTEM QUICK LOOK REPORT 0 CARBON MONOXIDE (42101) -METHODS: CODE COLLECTION METHOD ANALYSIS METHOD ==== 

============================== =================================== 

0 000 MULTIPLE METHODS MULTIPLE METHODS 048 

INSTRUMENTAL NON DISPERSIVE INFRA-RED

 051 

INSTRUMENTAL NON DISPERSIVE INFRA-RED

 054 

INSTRUMENTAL NON DISPERSIVE INFRA-RED

 093 

INSTRUMENTAL GAS FILTER COORELATION CO ANALYZER 

1DATE 98/06/25 EPA AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) 

 AMP217P AIR QUALITY SUBSYSTEM AQ CLEANUP PROCESSING SUMMARY SHEET -- REPORT 0 * * * PROGRAM AMP217 TERMINATED SUCCESSFULLY ON 98/06/25 AT 09:40:17 * * * 

B-5
 
Appendix C
 

Distributions and Equations Used in the Ventilation Rate Algorithm
 

C-1
 
Each table in Appendix C is specific to parameter and gender (e.g., NVO2max values for 

males). The tables which list distributions include the following data items 

Age: age of person in years 

Source: source of data (see Table 5-4) 

Distr: distribution of data [normal, lognormal (LN), or uniform] 

Mean: arithmetic mean for normal distributions SD: arithmetic standard deviation 

GM: geometric mean of lognormal distribution 

GSD: geometric standard deviation of lognormal distribution 

Lower bound: smallest value permitted 

Upper bound: largest value permitted 

Assumptions: special assumptions used in developing distribution parameters 

The tables which provide equations for estimating RMR include the following data items 

Age: age of person in years 

Source: source of data (see Table 5-9) 

DV: dependent variable of regression equation 

IV: independent variable of regression equation
 

Slope: slope of regression equation (estimate of “a” in Equation 5-10)
 

Interc: intercept of regression equation (estimate of “b” in Equation 5-10)
 

SE: standard error of regression residuals (estimate of Fe in Equation 5-10)
 

Assumptions: special assumptions used in developing equation parameters
 

The codes listed under “source” are informal identification codes developed by analysts. 

The following table relates these codes to tables provided in Section 5. 

C-2
 
Table C-1. Explanation of Codes Listed Under “Source” in Appendix C Tables. 

Code Listed in “Source” 

Column of Table in 

Appendix C 

Referenced Table in This 

Report Original Reference 

3a Table 5-6 Astrand (1960) 

3b Table 5-6 Mercier et al. (1991) 

3c Table 5-6 Katch and Park (1975) 

4 Table 5-5 Brainard and Burmaster 

(1992), Burmaster et al. 

(1994). 

5 Table 5-7 Esmail, Bhambhani, and 

Brintnell (1995). 

R47a - R47l Table 5-8 Schofield (1985) 

C-3
 
NVO2max - Males 

Males (last revised 6-11-98) 

Age Source 

NVO2max 

distribution 

Distr Mean SD Lower Upper Assumptions 

0 1 Normal 44.0 5.2 33.7 54.3 2-yr-old mean, CV = 6.9/57.9 

1 1 Normal 44.0 5.2 33.7 54.3 2-yr-old mean, CV = 6.9/57.9 

2 1 Normal 44.0 5.2 33.7 54.3 CV = 6.9/57.9 

3 1 Normal 46.0 5.5 35.3 56.7 CV = 6.9/57.9 

4 1 Normal 48.0 5.7 36.8 59.2 CV = 6.9/57.9 

5 1 Normal 50.0 6.0 38.3 61.7 CV = 6.9/57.9 

6 1 Normal 52.0 6.2 39.9 64.1 CV = 6.9/57.9 

7 1 Normal 54.0 6.4 41.4 66.6 CV = 6.9/57.9 

8 1 Normal 56.0 6.7 42.9 69.1 CV = 6.9/57.9 

9 3g Normal 57.9 6.9 44.4 71.4 

10 3g Normal 57.9 6.9 44.4 71.4 

11 3b Normal 45.4 8.1 29.6 61.2 

12 3b Normal 47.4 8.1 31.5 63.3 

13 3b Normal 46.0 7.0 32.3 59.7 

14 3b Normal 45.7 4.3 37.4 54.0 

15 3b Normal 47.5 4.7 38.3 56.7 

16 1 Normal 55.0 5.4 44.4 65.6 CV = 4.69/47.5 

17 1 Normal 53.0 5.2 42.7 63.3 

18 1 Normal 50.0 4.9 40.3 59.7 

19 1 Normal 50.0 4.9 40.3 59.7 

20 3a Normal 58.6 4.5 49.8 67.4 

21 3c Normal 54.5 7.6 39.6 69.4 

22 3c Normal 54.5 7.6 39.6 69.4 

23 3c Normal 54.5 7.6 39.6 69.4 

24 3c Normal 54.5 7.6 39.6 69.4 

25 3c Normal 54.5 7.6 39.6 69.4 

26 3c Normal 54.5 7.6 39.6 69.4 

27 3c Normal 54.5 7.6 39.6 69.4 

28 3a Normal 58.6 4.5 49.8 67.4 

29 3a Normal 58.6 4.5 49.8 67.4 

30 3a Normal 39.8 7.3 25.5 54.1 

31 3a Normal 39.8 7.3 25.5 54.1 

32 3a Normal 39.8 7.3 25.5 54.1 

33 3a Normal 39.8 7.3 25.5 54.1 

34 3a Normal 39.8 7.3 25.5 54.1 

35 3a Normal 39.8 7.3 25.5 54.1 

36 3a Normal 39.8 7.3 25.5 54.1 

37 3a Normal 39.8 7.3 25.5 54.1 

38 3a Normal 39.8 7.3 25.5 54.1 

39 3a Normal 39.8 7.3 25.5 54.1 

40 3a Normal 39.2 5.5 28.4 50.0 

41 3a Normal 39.2 5.5 28.4 50.0 

C-4
 
42 3a Normal 39.2 5.5 28.4 50.0 

43 3a Normal 39.2 5.5 28.4 50.0 

44 3a Normal 39.2 5.5 28.4 50.0 

45 3a Normal 39.2 5.5 28.4 50.0 

46 3a Normal 39.2 5.5 28.4 50.0 

47 3a Normal 39.2 5.5 28.4 50.0 

48 3a Normal 39.2 5.5 28.4 50.0 

49 3a Normal 39.2 5.5 28.4 50.0 

50 3a Normal 33.1 4.9 23.5 42.7 

51 3a Normal 33.1 4.9 23.5 42.7 

52 3a Normal 33.1 4.9 23.5 42.7 

53 3a Normal 33.1 4.9 23.5 42.7 

54 3a Normal 33.1 4.9 23.5 42.7 

55 3a Normal 33.1 4.9 23.5 42.7 

56 3a Normal 33.1 4.9 23.5 42.7 

57 3a Normal 33.1 4.9 23.5 42.7 

58 3a Normal 33.1 4.9 23.5 42.7 

59 3a Normal 33.1 4.9 23.5 42.7 

60 3a Normal 31.4 5.3 21.0 41.8 

61 3a Normal 31.4 5.3 21.0 41.8 

62 3a Normal 31.4 5.3 21.0 41.8 

63 3a Normal 31.4 5.3 21.0 41.8 

64 3a Normal 31.4 5.3 21.0 41.8 

65 3a Normal 31.4 5.3 21.0 41.8 

66 3a Normal 31.4 5.3 21.0 41.8 

67 3a Normal 31.4 5.3 21.0 41.8 

68 3a Normal 31.4 5.3 21.0 41.8 

69 3a Normal 31.4 5.3 21.0 41.8 

70 3d Normal 27.2 5.7 16.1 38.3 

71 3d Normal 27.2 5.7 16.1 38.3 

72 3d Normal 27.2 5.7 16.1 38.3 

73 3d Normal 27.2 5.7 16.1 38.3 

74 3d Normal 27.2 5.7 16.1 38.3 

75 3d Normal 27.2 5.7 16.1 38.3 

76 3d Normal 27.2 5.7 16.1 38.3 

77 3d Normal 27.2 5.7 16.1 38.3 

78 3d Normal 27.2 5.7 16.1 38.3 

79 3d Normal 27.2 5.7 16.1 38.3 

80 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

81 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

82 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

83 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

84 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

85 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

86 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

87 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

88 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

89 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

90 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

91 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

92 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

C-5
 
93 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

94 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

95 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

96 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

97 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

98 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

99 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

100 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

NVO2max - Females 

C-6 
Females (last revised 6-11-98) 

Age Source 

NVO2max 

distribution 

Distr Mean SD Lower Upper Assumptions 

0 2 Normal 43.0 5.1 33.1 52.9 2-yr-old mean, CV = 4.7/39.9 

1 2 Normal 43.0 5.1 33.1 52.9 2-yr-old mean, CV = 4.7/39.9 

2 2 Normal 43.0 5.1 33.1 52.9 CV = 4.7/39.9 

3 2 Normal 44.0 5.2 33.8 54.2 CV = 4.7/39.9 

4 2 Normal 46.0 5.4 35.4 56.6 CV = 4.7/39.9 

5 2 Normal 47.0 5.5 36.1 57.9 CV = 4.7/39.9 

6 2 Normal 50.0 5.9 38.5 61.5 CV = 4.7/39.9 

7 2 Normal 52.0 6.1 40.0 64.0 CV = 4.7/39.9 

8 2 Normal 53.0 6.2 40.8 65.2 CV = 4.7/39.9 

9 2 Normal 52.0 6.1 40.0 64.0 CV = 4.7/39.9 

10 2 Normal 51.0 6.0 39.2 62.8 CV = 4.7/39.9 

11 2 Normal 50.0 5.9 38.5 61.5 CV = 4.7/39.9 

12 2 Normal 49.0 5.8 37.7 60.3 CV = 4.7/39.9 

13 2 Normal 47.0 5.5 36.1 57.9 CV = 4.7/39.9 

14 2 Normal 46.0 5.4 35.4 56.6 CV = 4.7/39.9 

15 2 Normal 46.0 5.4 35.4 56.6 CV = 4.7/39.9 

16 2 Normal 45.0 5.3 34.6 55.4 CV = 4.7/39.9 

17 2 Normal 44.0 5.2 33.8 54.2 CV = 4.7/39.9 

18 2 Normal 41.0 4.8 31.5 50.5 CV = 4.7/39.9 

19 2 Normal 41.0 4.8 31.5 50.5 CV = 4.7/39.9 

20 3a Normal 39.9 4.7 30.7 49.1 

21 3a Normal 39.9 4.7 30.7 49.1 

22 3a Normal 39.9 4.7 30.7 49.1 

23 3a Normal 39.9 4.7 30.7 49.1 

24 3a Normal 39.9 4.7 30.7 49.1 

25 3a Normal 39.9 4.7 30.7 49.1 

26 3a Normal 39.9 4.7 30.7 49.1 

27 3a Normal 39.9 4.7 30.7 49.1 

28 3a Normal 39.9 4.7 30.7 49.1 

29 3a Normal 39.9 4.7 30.7 49.1 

30 3a Normal 37.3 5.2 27.1 47.5 

31 3a Normal 37.3 5.2 27.1 47.5 

32 3a Normal 37.3 5.2 27.1 47.5 

33 3a Normal 37.3 5.2 27.1 47.5 

34 3a Normal 37.3 5.2 27.1 47.5 

35 3a Normal 37.3 5.2 27.1 47.5 

36 3a Normal 37.3 5.2 27.1 47.5 

37 3a Normal 37.3 5.2 27.1 47.5 

38 3a Normal 37.3 5.2 27.1 47.5 

39 3a Normal 37.3 5.2 27.1 47.5 

40 3a Normal 32.5 2.7 27.2 37.8 

41 3a Normal 32.5 2.7 27.2 37.8 

42 3a Normal 32.5 2.7 27.2 37.8 

43 3a Normal 32.5 2.7 27.2 37.8 

44 3a Normal 32.5 2.7 27.2 37.8 

45 3a Normal 32.5 2.7 27.2 37.8 

C-7
 
46 3a Normal 32.5 2.7 27.2 37.8 

47 3a Normal 32.5 2.7 27.2 37.8 

48 3a Normal 32.5 2.7 27.2 37.8 

49 3a Normal 32.5 2.7 27.2 37.8 

50 3a Normal 28.4 2.7 23.1 33.7 

51 3a Normal 28.4 2.7 23.1 33.7 

52 3a Normal 28.4 2.7 23.1 33.7 

53 3a Normal 28.4 2.7 23.1 33.7 

54 3a Normal 28.4 2.7 23.1 33.7 

55 3a Normal 28.4 2.7 23.1 33.7 

56 3a Normal 28.4 2.7 23.1 33.7 

57 3a Normal 28.4 2.7 23.1 33.7 

58 3a Normal 28.4 2.7 23.1 33.7 

59 3a Normal 28.4 2.7 23.1 33.7 

60 3a Normal 30.7 8.0 15.1 46.3 

61 3a Normal 30.7 8.0 15.1 46.3 

62 3a Normal 30.7 8.0 15.1 46.3 

63 3a Normal 30.7 8.0 15.1 46.3 

64 3a Normal 30.7 8.0 15.1 46.3 

65 3a Normal 30.7 8.0 15.1 46.3 

66 3d Normal 30.7 8.0 15.1 46.3 

67 3d Normal 30.7 8.0 15.1 46.3 

68 3d Normal 30.7 8.0 15.1 46.3 

69 3d Normal 30.7 8.0 15.1 46.3 

70 3d Normal 27.2 5.7 16.1 38.3 

71 3d Normal 27.2 5.7 16.1 38.3 

72 3d Normal 27.2 5.7 16.1 38.3 

73 3d Normal 27.2 5.7 16.1 38.3 

74 3d Normal 27.2 5.7 16.1 38.3 

75 3d Normal 27.2 5.7 16.1 38.3 

76 3d Normal 27.2 5.7 16.1 38.3 

77 3d Normal 27.2 5.7 16.1 38.3 

78 3d Normal 27.2 5.7 16.1 38.3 

79 3d Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

80 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

81 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

82 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

83 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

84 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

85 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

86 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

87 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

88 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

89 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

90 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

91 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

92 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

93 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

94 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

95 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

96 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

97 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

C-8
 
98 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

99 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

100 (3d) Normal 27.2 5.7 16.1 38.3 Assumes data for age 70-79 applies 

C-9
 
Body Mass - Males 

Males (last revised 6-11-98) 

Age Source 

Body mass 

distribution 

, kg 

Distr GM GSD Lower Upper Assumptions 

0 4 LN 9.3 1.141 7.2 12.0 

1 4 LN 11.7 1.126 9.3 14.8 

2 4 LN 13.5 1.127 10.7 17.1 

3 4 LN 15.6 1.121 12.5 19.5 

4 4 LN 17.6 1.142 13.6 22.8 

5 4 LN 19.9 1.148 15.2 26.1 

6 4 LN 22.9 1.156 17.2 30.4 

7 4 LN 24.8 1.163 18.4 33.3 

8 4 LN 27.9 1.198 19.6 39.8 

9 4 LN 30.9 1.179 22.4 42.7 

10 4 LN 36.2 1.215 24.7 53.0 

11 4 LN 40.0 1.287 24.4 65.6 

12 4 LN 43.8 1.251 28.2 67.9 

13 4 LN 48.4 1.240 31.7 73.8 

14 4 LN 55.7 1.198 39.1 79.4 

15 4 LN 59.7 1.172 43.7 81.5 

16 4 LN 66.7 1.183 48.0 92.7 

17 4 LN 66.0 1.182 47.6 91.6 

18 4 LN 70.1 1.172 51.4 95.7 

19 4 LN 70.8 1.166 52.4 95.7 

20 4 LN 76.7 1.190 54.5 107.9 

21 4 LN 76.7 1.190 54.5 107.9 

22 4 LN 76.7 1.190 54.5 107.9 

23 4 LN 76.7 1.190 54.5 107.9 

24 4 LN 76.7 1.190 54.5 107.9 

25 4 LN 76.7 1.190 54.5 107.9 

26 4 LN 76.7 1.190 54.5 107.9 

27 4 LN 76.7 1.190 54.5 107.9 

28 4 LN 76.7 1.190 54.5 107.9 

29 4 LN 76.7 1.190 54.5 107.9 

30 4 LN 76.7 1.190 54.5 107.9 

31 4 LN 76.7 1.190 54.5 107.9 

32 4 LN 76.7 1.190 54.5 107.9 

33 4 LN 76.7 1.190 54.5 107.9 

34 4 LN 76.7 1.190 54.5 107.9 

35 4 LN 76.7 1.190 54.5 107.9 

36 4 LN 76.7 1.190 54.5 107.9 

37 4 LN 76.7 1.190 54.5 107.9 

38 4 LN 76.7 1.190 54.5 107.9 

39 4 LN 76.7 1.190 54.5 107.9 

40 4 LN 76.7 1.190 54.5 107.9 

41 4 LN 76.7 1.190 54.5 107.9 

42 4 LN 76.7 1.190 54.5 107.9 

43 4 LN 76.7 1.190 54.5 107.9 

44 4 LN 76.7 1.190 54.5 107.9 

C-10
 
45 4 LN 76.7 1.190 54.5 107.9 

46 4 LN 76.7 1.190 54.5 107.9 

47 4 LN 76.7 1.190 54.5 107.9 

48 4 LN 76.7 1.190 54.5 107.9 

49 4 LN 76.7 1.190 54.5 107.9 

50 4 LN 76.7 1.190 54.5 107.9 

51 4 LN 76.7 1.190 54.5 107.9 

52 4 LN 76.7 1.190 54.5 107.9 

53 4 LN 76.7 1.190 54.5 107.9 

54 4 LN 76.7 1.190 54.5 107.9 

55 4 LN 76.7 1.190 54.5 107.9 

56 4 LN 76.7 1.190 54.5 107.9 

57 4 LN 76.7 1.190 54.5 107.9 

58 4 LN 76.7 1.190 54.5 107.9 

59 4 LN 76.7 1.190 54.5 107.9 

60 4 LN 76.7 1.190 54.5 107.9 

61 4 LN 76.7 1.190 54.5 107.9 

62 4 LN 76.7 1.190 54.5 107.9 

63 4 LN 76.7 1.190 54.5 107.9 

64 4 LN 76.7 1.190 54.5 107.9 

65 4 LN 76.7 1.190 54.5 107.9 

66 4 LN 76.7 1.190 54.5 107.9 

67 4 LN 76.7 1.190 54.5 107.9 

68 4 LN 76.7 1.190 54.5 107.9 

69 4 LN 76.7 1.190 54.5 107.9 

70 4 LN 76.7 1.190 54.5 107.9 

71 4 LN 76.7 1.190 54.5 107.9 

72 4 LN 76.7 1.190 54.5 107.9 

73 4 LN 76.7 1.190 54.5 107.9 

74 4 LN 76.7 1.190 54.5 107.9 

75 4 LN 76.7 1.190 54.5 107.9 

76 4 LN 76.7 1.190 54.5 107.9 

77 4 LN 76.7 1.190 54.5 107.9 

78 4 LN 76.7 1.190 54.5 107.9 

79 4 LN 76.7 1.190 54.5 107.9 

80 4 LN 76.7 1.190 54.5 107.9 

81 4 LN 76.7 1.190 54.5 107.9 

82 4 LN 76.7 1.190 54.5 107.9 

83 4 LN 76.7 1.190 54.5 107.9 

84 4 LN 76.7 1.190 54.5 107.9 

85 4 LN 76.7 1.190 54.5 107.9 

86 4 LN 76.7 1.190 54.5 107.9 

87 4 LN 76.7 1.190 54.5 107.9 

88 4 LN 76.7 1.190 54.5 107.9 

89 4 LN 76.7 1.190 54.5 107.9 

90 4 LN 76.7 1.190 54.5 107.9 

91 4 LN 76.7 1.190 54.5 107.9 

92 4 LN 76.7 1.190 54.5 107.9 

93 4 LN 76.7 1.190 54.5 107.9 

94 4 LN 76.7 1.190 54.5 107.9 

95 4 LN 76.7 1.190 54.5 107.9 

96 4 LN 76.7 1.190 54.5 107.9 

C-11
 
97 4 LN 76.7 1.190 54.5 107.9 

98 4 LN 76.7 1.190 54.5 107.9 

99 4 LN 76.7 1.190 54.5 107.9 

100 4 LN 76.7 1.190 54.5 107.9 

C-12
 
Body Mass - Females 

Females (last revised 6-11-98) 

Age Source 

Body 

mass 

distributi 

on, kg 

Distr GM GSD Lower Upper Assumptions 

0 4 LN 8.7 1.156 6.5 11.6 

1 4 LN 10.8 1.137 8.4 13.9 

2 4 LN 12.9 1.119 10.3 16.1 

3 4 LN 14.7 1.147 11.2 19.2 

4 4 LN 16.9 1.142 13.0 21.9 

5 4 LN 19.7 1.177 14.3 27.1 

6 4 LN 22.2 1.190 15.8 31.2 

7 4 LN 24.3 1.190 17.3 34.2 

8 4 LN 27.4 1.169 20.2 37.2 

9 4 LN 31.8 1.239 20.9 48.4 

10 4 LN 35.5 1.220 24.0 52.4 

11 4 LN 40.9 1.254 26.2 63.7 

12 4 LN 45.6 1.237 30.1 69.2 

13 4 LN 50.4 1.241 33.0 77.0 

14 4 LN 54.1 1.206 37.5 78.1 

15 4 LN 54.6 1.169 40.2 74.1 

16 4 LN 58.0 1.182 41.8 80.5 

17 4 LN 59.1 1.179 42.8 81.6 

18 4 LN 58.6 1.158 44.0 78.1 

19 4 LN 60.3 1.161 45.0 80.8 

20 4 LN 64.7 1.220 43.8 95.5 

21 4 LN 64.7 1.220 43.8 95.5 

22 4 LN 64.7 1.220 43.8 95.5 

23 4 LN 64.7 1.220 43.8 95.5 

24 4 LN 64.7 1.220 43.8 95.5 

25 4 LN 64.7 1.220 43.8 95.5 

26 4 LN 64.7 1.220 43.8 95.5 

27 4 LN 64.7 1.220 43.8 95.5 

28 4 LN 64.7 1.220 43.8 95.5 

29 4 LN 64.7 1.220 43.8 95.5 

30 4 LN 64.7 1.220 43.8 95.5 

31 4 LN 64.7 1.220 43.8 95.5 

32 4 LN 64.7 1.220 43.8 95.5 

33 4 LN 64.7 1.220 43.8 95.5 

34 4 LN 64.7 1.220 43.8 95.5 

35 4 LN 64.7 1.220 43.8 95.5 

C-13
 
36 4 LN 64.7 1.220 43.8 95.5 

37 4 LN 64.7 1.220 43.8 95.5 

38 4 LN 64.7 1.220 43.8 95.5 

39 4 LN 64.7 1.220 43.8 95.5 

40 4 LN 64.7 1.220 43.8 95.5 

41 4 LN 64.7 1.220 43.8 95.5 

42 4 LN 64.7 1.220 43.8 95.5 

43 4 LN 64.7 1.220 43.8 95.5 

44 4 LN 64.7 1.220 43.8 95.5 

45 4 LN 64.7 1.220 43.8 95.5 

46 4 LN 64.7 1.220 43.8 95.5 

47 4 LN 64.7 1.220 43.8 95.5 

48 4 LN 64.7 1.220 43.8 95.5 

49 4 LN 64.7 1.220 43.8 95.5 

50 4 LN 64.7 1.220 43.8 95.5 

51 4 LN 64.7 1.220 43.8 95.5 

52 4 LN 64.7 1.220 43.8 95.5 

53 4 LN 64.7 1.220 43.8 95.5 

54 4 LN 64.7 1.220 43.8 95.5 

55 4 LN 64.7 1.220 43.8 95.5 

56 4 LN 64.7 1.220 43.8 95.5 

57 4 LN 64.7 1.220 43.8 95.5 

58 4 LN 64.7 1.220 43.8 95.5 

59 4 LN 64.7 1.220 43.8 95.5 

60 4 LN 64.7 1.220 43.8 95.5 

61 4 LN 64.7 1.220 43.8 95.5 

62 4 LN 64.7 1.220 43.8 95.5 

63 4 LN 64.7 1.220 43.8 95.5 

64 4 LN 64.7 1.220 43.8 95.5 

65 4 LN 64.7 1.220 43.8 95.5 

66 4 LN 64.7 1.220 43.8 95.5 

67 4 LN 64.7 1.220 43.8 95.5 

68 4 LN 64.7 1.220 43.8 95.5 

69 4 LN 64.7 1.220 43.8 95.5 

70 4 LN 64.7 1.220 43.8 95.5 

71 4 LN 64.7 1.220 43.8 95.5 

72 4 LN 64.7 1.220 43.8 95.5 

73 4 LN 64.7 1.220 43.8 95.5 

74 4 LN 64.7 1.220 43.8 95.5 

75 4 LN 64.7 1.220 43.8 95.5 

76 4 LN 64.7 1.220 43.8 95.5 

77 4 LN 64.7 1.220 43.8 95.5 

78 4 LN 64.7 1.220 43.8 95.5 

79 4 LN 64.7 1.220 43.8 95.5 

C-14
 
80 4 LN 64.7 1.220 43.8 95.5 

81 4 LN 64.7 1.220 43.8 95.5 

82 4 LN 64.7 1.220 43.8 95.5 

83 4 LN 64.7 1.220 43.8 95.5 

84 4 LN 64.7 1.220 43.8 95.5 

85 4 LN 64.7 1.220 43.8 95.5 

86 4 LN 64.7 1.220 43.8 95.5 

87 4 LN 64.7 1.220 43.8 95.5 

88 4 LN 64.7 1.220 43.8 95.5 

89 4 LN 64.7 1.220 43.8 95.5 

90 4 LN 64.7 1.220 43.8 95.5 

91 4 LN 64.7 1.220 43.8 95.5 

92 4 LN 64.7 1.220 43.8 95.5 

93 4 LN 64.7 1.220 43.8 95.5 

94 4 LN 64.7 1.220 43.8 95.5 

95 4 LN 64.7 1.220 43.8 95.5 

96 4 LN 64.7 1.220 43.8 95.5 

97 4 LN 64.7 1.220 43.8 95.5 

98 4 LN 64.7 1.220 43.8 95.5 

99 4 LN 64.7 1.220 43.8 95.5 

100 4 LN 64.7 1.220 43.8 95.5 

C-15
 
ECF - Males 

Males (last revised 6-11-98) 

Age Source 

ECF 

Distr Lower Upper Assumptions

0 5 Uniform 0.20 0.21 

1 5 Uniform 0.20 0.21 

2 5 Uniform 0.20 0.21 

3 5 Uniform 0.20 0.21 

4 5 Uniform 0.20 0.21 

5 5 Uniform 0.20 0.21 

6 5 Uniform 0.20 0.21 

7 5 Uniform 0.20 0.21 

8 5 Uniform 0.20 0.21 

9 5 Uniform 0.20 0.21 

10 5 Uniform 0.20 0.21 

11 5 Uniform 0.20 0.21 

12 5 Uniform 0.20 0.21 

13 5 Uniform 0.20 0.21 

14 5 Uniform 0.20 0.21 

15 5 Uniform 0.20 0.21 

16 5 Uniform 0.20 0.21 

17 5 Uniform 0.20 0.21 

18 5 Uniform 0.20 0.21 

19 5 Uniform 0.20 0.21 

20 5 Uniform 0.20 0.21 

21 5 Uniform 0.20 0.21 

22 5 Uniform 0.20 0.21 

23 5 Uniform 0.20 0.21 

24 5 Uniform 0.20 0.21 

25 5 Uniform 0.20 0.21 

26 5 Uniform 0.20 0.21 

27 5 Uniform 0.20 0.21 

28 5 Uniform 0.20 0.21 

29 5 Uniform 0.20 0.21 

30 5 Uniform 0.20 0.21 

31 5 Uniform 0.20 0.21 

32 5 Uniform 0.20 0.21 

33 5 Uniform 0.20 0.21 

34 5 Uniform 0.20 0.21 

35 5 Uniform 0.20 0.21 

36 5 Uniform 0.20 0.21 

37 5 Uniform 0.20 0.21 

38 5 Uniform 0.20 0.21 

C-16
 
39 5 Uniform 0.20 0.21 

40 5 Uniform 0.20 0.21 

41 5 Uniform 0.20 0.21 

42 5 Uniform 0.20 0.21 

43 5 Uniform 0.20 0.21 

44 5 Uniform 0.20 0.21 

45 5 Uniform 0.20 0.21 

46 5 Uniform 0.20 0.21 

47 5 Uniform 0.20 0.21 

48 5 Uniform 0.20 0.21 

49 5 Uniform 0.20 0.21 

50 5 Uniform 0.20 0.21 

51 5 Uniform 0.20 0.21 

52 5 Uniform 0.20 0.21 

53 5 Uniform 0.20 0.21 

54 5 Uniform 0.20 0.21 

55 5 Uniform 0.20 0.21 

56 5 Uniform 0.20 0.21 

57 5 Uniform 0.20 0.21 

58 5 Uniform 0.20 0.21 

59 5 Uniform 0.20 0.21 

60 5 Uniform 0.20 0.21 

61 5 Uniform 0.20 0.21 

62 5 Uniform 0.20 0.21 

63 5 Uniform 0.20 0.21 

64 5 Uniform 0.20 0.21 

65 5 Uniform 0.20 0.21 

66 5 Uniform 0.20 0.21 

67 5 Uniform 0.20 0.21 

68 5 Uniform 0.20 0.21 

69 5 Uniform 0.20 0.21 

70 5 Uniform 0.20 0.21 

71 5 Uniform 0.20 0.21 

72 5 Uniform 0.20 0.21 

73 5 Uniform 0.20 0.21 

74 5 Uniform 0.20 0.21 

75 5 Uniform 0.20 0.21 

76 5 Uniform 0.20 0.21 

77 5 Uniform 0.20 0.21 

78 5 Uniform 0.20 0.21 

79 5 Uniform 0.20 0.21 

80 5 Uniform 0.20 0.21 

81 5 Uniform 0.20 0.21 

82 5 Uniform 0.20 0.21 

C-17
 
83 5 Uniform 0.20 0.21 

84 5 Uniform 0.20 0.21 

85 5 Uniform 0.20 0.21 

86 5 Uniform 0.20 0.21 

87 5 Uniform 0.20 0.21 

88 5 Uniform 0.20 0.21 

89 5 Uniform 0.20 0.21 

90 5 Uniform 0.20 0.21 

91 5 Uniform 0.20 0.21 

92 5 Uniform 0.20 0.21 

93 5 Uniform 0.20 0.21 

94 5 Uniform 0.20 0.21 

95 5 Uniform 0.20 0.21 

96 5 Uniform 0.20 0.21 

97 5 Uniform 0.20 0.21 

98 5 Uniform 0.20 0.21 

99 5 Uniform 0.20 0.21 

100 5 Uniform 0.20 0.21 

C-18
 
ECF - Females 

Females (last revised 6-11-98) 

Age Source 

ECF 

Distr Lower Upper Assumptions

0 5 Uniform 0.20 0.21 

1 5 Uniform 0.20 0.21 

2 5 Uniform 0.20 0.21 

3 5 Uniform 0.20 0.21 

4 5 Uniform 0.20 0.21 

5 5 Uniform 0.20 0.21 

6 5 Uniform 0.20 0.21 

7 5 Uniform 0.20 0.21 

8 5 Uniform 0.20 0.21 

9 5 Uniform 0.20 0.21 

10 5 Uniform 0.20 0.21 

11 5 Uniform 0.20 0.21 

12 5 Uniform 0.20 0.21 

13 5 Uniform 0.20 0.21 

14 5 Uniform 0.20 0.21 

15 5 Uniform 0.20 0.21 

16 5 Uniform 0.20 0.21 

17 5 Uniform 0.20 0.21 

18 5 Uniform 0.20 0.21 

19 5 Uniform 0.20 0.21 

20 5 Uniform 0.20 0.21 

21 5 Uniform 0.20 0.21 

22 5 Uniform 0.20 0.21 

23 5 Uniform 0.20 0.21 

24 5 Uniform 0.20 0.21 

25 5 Uniform 0.20 0.21 

26 5 Uniform 0.20 0.21 

27 5 Uniform 0.20 0.21 

28 5 Uniform 0.20 0.21 

29 5 Uniform 0.20 0.21 

30 5 Uniform 0.20 0.21 

31 5 Uniform 0.20 0.21 

32 5 Uniform 0.20 0.21 

33 5 Uniform 0.20 0.21 

34 5 Uniform 0.20 0.21 

35 5 Uniform 0.20 0.21 

36 5 Uniform 0.20 0.21 

37 5 Uniform 0.20 0.21 

38 5 Uniform 0.20 0.21 

C-19
 
39 5 Uniform 0.20 0.21 

40 5 Uniform 0.20 0.21 

41 5 Uniform 0.20 0.21 

42 5 Uniform 0.20 0.21 

43 5 Uniform 0.20 0.21 

44 5 Uniform 0.20 0.21 

45 5 Uniform 0.20 0.21 

46 5 Uniform 0.20 0.21 

47 5 Uniform 0.20 0.21 

48 5 Uniform 0.20 0.21 

49 5 Uniform 0.20 0.21 

50 5 Uniform 0.20 0.21 

51 5 Uniform 0.20 0.21 

52 5 Uniform 0.20 0.21 

53 5 Uniform 0.20 0.21 

54 5 Uniform 0.20 0.21 

55 5 Uniform 0.20 0.21 

56 5 Uniform 0.20 0.21 

57 5 Uniform 0.20 0.21 

58 5 Uniform 0.20 0.21 

59 5 Uniform 0.20 0.21 

60 5 Uniform 0.20 0.21 

61 5 Uniform 0.20 0.21 

62 5 Uniform 0.20 0.21 

63 5 Uniform 0.20 0.21 

64 5 Uniform 0.20 0.21 

65 5 Uniform 0.20 0.21 

66 5 Uniform 0.20 0.21 

67 5 Uniform 0.20 0.21 

68 5 Uniform 0.20 0.21 

69 5 Uniform 0.20 0.21 

70 5 Uniform 0.20 0.21 

71 5 Uniform 0.20 0.21 

72 5 Uniform 0.20 0.21 

73 5 Uniform 0.20 0.21 

74 5 Uniform 0.20 0.21 

75 5 Uniform 0.20 0.21 

76 5 Uniform 0.20 0.21 

77 5 Uniform 0.20 0.21 

78 5 Uniform 0.20 0.21 

79 5 Uniform 0.20 0.21 

80 5 Uniform 0.20 0.21 

81 5 Uniform 0.20 0.21 

82 5 Uniform 0.20 0.21 

C-20
 
83 5 Uniform 0.20 0.21 

84 5 Uniform 0.20 0.21 

85 5 Uniform 0.20 0.21 

86 5 Uniform 0.20 0.21 

87 5 Uniform 0.20 0.21 

88 5 Uniform 0.20 0.21 

89 5 Uniform 0.20 0.21 

90 5 Uniform 0.20 0.21 

91 5 Uniform 0.20 0.21 

92 5 Uniform 0.20 0.21 

93 5 Uniform 0.20 0.21 

94 5 Uniform 0.20 0.21 

95 5 Uniform 0.20 0.21 

96 5 Uniform 0.20 0.21 

97 5 Uniform 0.20 0.21 

98 5 Uniform 0.20 0.21 

99 5 Uniform 0.20 0.21 

100 5 Uniform 0.20 0.21 

C-21
 
RMR - Males 

Males (last revised 6-11-98) 

Age Source 

Regre 

ssion 

equati 

on 

Estimate 

for 

median 

DV IV Slope Interc SE Units weight Assumptions

0 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.1 

equation for age = 1 yr 

applies 

1 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.7 

2 R47g BMR BM 0.244 -0.127 0.280 MJ/day 3.2 

3 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.6 

4 R47h BMR BM 0.095 2.110 0.280 MJ/day 3.8 

5 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.0 

6 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.3 

7 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.5 

8 R47h BMR BM 0.095 2.110 0.280 MJ/day 4.8 

9 R47h BMR BM 0.095 2.110 0.280 MJ/day 5.0 

10 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.4 

11 R47i BMR BM 0.074 2.754 0.440 MJ/day 5.7 

12 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.0 

13 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.3 

14 R47i BMR BM 0.074 2.754 0.440 MJ/day 6.9 

15 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.2 

16 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.7 

17 R47i BMR BM 0.074 2.754 0.440 MJ/day 7.6 

18 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.3 

19 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.4 

20 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

21 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

22 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

23 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

24 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

25 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

26 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

27 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

28 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

29 R47j BMR BM 0.063 2.896 0.640 MJ/day 7.7 

30 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

31 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

32 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

33 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

C-22
 
34 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

35 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

36 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

37 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

38 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

39 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

40 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

41 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

42 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

43 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

44 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

45 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

46 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

47 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

48 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

49 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

50 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

51 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

52 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

53 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

54 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

55 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

56 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

57 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

58 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

59 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

60 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

61 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

62 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

63 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

64 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

65 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

66 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

67 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

68 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

69 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

70 R47k BMR BM 0.048 3.653 0.700 MJ/day 7.3 

71 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

72 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

73 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

74 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

75 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

76 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

77 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

C-23
 
78 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

79 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

80 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

81 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

82 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

83 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

84 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

85 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

86 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

87 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

88 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

89 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

90 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

91 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

92 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

93 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

94 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

95 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

96 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

97 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

98 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

99 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

100 R47l BMR BM 0.049 2.459 0.690 MJ/day 6.2 

C-24
 
RMR - Females 

Females 

(last revised 

6-11-98) 

Age Source 

Regression 

equation Estimate for 

median 

DV IV Slope Interc SE Units weight Assumptions

0 R47a BMR BM 0.244 -0.130 0.250 MJ/day 2.0 

1 R47a BMR BM 0.244 -0.130 0.250 MJ/day 2.5 

2 R47a BMR BM 0.244 -0.130 0.250 MJ/day 3.0 

3 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.3 

4 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.5 

5 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.7 

6 R47b BMR BM 0.085 2.033 0.290 MJ/day 3.9 

7 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.1 

8 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.4 

9 R47b BMR BM 0.085 2.033 0.290 MJ/day 4.7 

10 R47c BMR BM 0.056 2.898 0.470 MJ/day 4.9 

11 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.2 

12 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.5 

13 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.7 

14 R47c BMR BM 0.056 2.898 0.470 MJ/day 5.9 

15 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.0 

16 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.1 

17 R47c BMR BM 0.056 2.898 0.470 MJ/day 6.2 

18 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.7 

19 R47d BMR BM 0.062 2.036 0.500 MJ/day 5.8 

20 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

21 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

22 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

23 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

24 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

25 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

26 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

27 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

28 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

29 R47d BMR BM 0.062 2.036 0.500 MJ/day 6.0 

30 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

31 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

32 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

33 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

34 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

35 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

C-25
 
36 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

37 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

38 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

39 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

40 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

41 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

42 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

43 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

44 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

45 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

46 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

47 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

48 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

49 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

50 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

51 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

52 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

53 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

54 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

55 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

56 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

57 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

58 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

59 R47e BMR BM 0.034 3.538 0.470 MJ/day 5.7 

60 R47e BMR BM 0.038 2.755 0.450 MJ/day 5.2 

61 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

62 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

63 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

64 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

65 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

66 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

67 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

68 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

69 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

70 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

71 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

72 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

73 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

74 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

75 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

76 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

77 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

78 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

79 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

C-26
 
80 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

81 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

82 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

83 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

84 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

85 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

86 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

87 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

88 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

89 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

90 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

91 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

92 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

93 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

94 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

95 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

96 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

97 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

98 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

99 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

100 R47f BMR BM 0.038 2.755 0.450 MJ/day 5.2 

C-27
 
Appendix D
 

Analysis of Clinical Data Provided by Dr. William Adams
 

D-1
 
Two alternative models were considered for the relationship between ventilation rate (VE, liters 

air per minute) and oxygen uptake rate (VO2, liters oxygen per minute). The “RAW” model 

assumes that the ventilation rate to body mass ratio is a linear function of the oxygen uptake rate 

to body mass ratio. The “LOG” model assumes that the logarithm of the ventilation rate to body 

mass ratio is a linear function of the logarithm of the oxygen uptake rate to body mass ratio. Let 

BM be the body mass. The model equations are 

RAW MODEL: VE/BM = a + b VO2/BM + error(person) + error(test) 

LOG MODEL: log(VE/BM) = a + b log(VO2/BM) + error(person) + error(test) 

In this statistical mixed model, there are assumed to be two independent sources of error, 

representing the variability between persons and the variability between different activities by 

the same person. Each person has a value of error(person) independently drawn from a normal 

distribution with mean zero and variance F2(person); the same error(person) value applies to 

every activity for that person. Each combination of person and activity has a value of error(test) 

independently drawn from a normal distribution with mean zero and variance F2(test); each new 

activity has a different error(test) value. A major advantage of this formulation is that it accounts 

for the correlations between different measurements on the same person. The intercept (a), slope 

(b), and error variances (F2(person) and F2(test)) depend on the gender and age group (18-44, 45-

64, or 65+). All logarithms are natural logarithms (base e). 

These two models were fitted to data supplied by Dr. Adams compiling the results of 32 clinical 

studies, where VE and VO2 were measured during the last minute of each step of a test sequence 

on either a cycle ergometer or a treadmill (up to 14 steps per study subject). If the same person 

was tested in two or more studies, then the value of error(person) randomly assigned to that 

person was assumed to be the same for all studies. Before fitting the more complicated, but more 

realistic, mixed model formulations, we first used simple linear regression to fit the special case 

where the error(person) terms are ignored, so that all test results are assumed to be independent, 

even if they are measured on the same person. The two regression models were compared using 

standard goodness-of-fit measures and regression diagnostics, as summarized below. The 

regression model comparison showed the LOG model fitted better, as expected from Tom 

McCurdy’s preliminary analysis, and so the mixed model version of the LOG model was used in 

the pNEM/CO simulation model. Rewriting the LOG model as a function of VE, we get 

eerror(person) eerror(test) VE = ea

 (VO2)

b (BM)1-b . 

For each simulated person-day in PNEM/CO, the value of error(person) is randomly drawn from 

a normal distribution with mean zero and variance F2(person); the values of error(person) and 

BM are assumed to be the same throughout the simulated day. For each activity, the value of 

error(test) is randomly drawn from a normal distribution with mean zero and variance F2(test). 

For each activity, VE is computed from the simulated value of VO2 using the above equation. 

The values of a, b, F2

(person), and F2(test), estimated from the LOG model, are given in Table 1. 

The two regression models are defined by the above models RAW and LOG, with error(person) 

= 0. These regression models were compared using standard goodness-of-fit measures and 

regression diagnostics, summarized as follows. Conditioning on the observed values of BM, 

D-2
 
VO2, age group, and gender, the overall log-likelihood is defined as the logarithm of the fitted 

probability density function for VE (assumed to be normal for the RAW model and log-normal 

for the LOG model). The overall log-likelihood was 9536.36 for the RAW model and 10927.67 

for the LOG model, showing a much better fit for the LOG model. The log-likelihoods by age 

group and gender showed that the LOG model fit better for five of the six age group and gender 

combinations; the RAW model fit just slightly better for the females aged 65 and older. Scatter 

plots of the standardized deletion residuals against the VO2/BM ratio showed some patterns 

incompatible with the assumed statistical models (the residuals and explanatory variables should 

show no obvious associations) and there were some very high residuals for both models. 

However, these scatter plots showed a much better fit for the LOG model formulation. 

Furthermore, although the standardized residuals should be approximately normally distributed, 

a normality test was failed for five of the six strata using the RAW model, but was passed for 

five of the six strata using the LOG model. For five of the six strata, refitting the two models 

after removing points with extremely high residuals and/or points expected to strongly influence 

the fitted model coefficients did not significantly improve the fit or change the estimated 

coefficients. For the females aged 65 and older stratum, there were only 45 pairs, and one pair 

had unusually high values of both VE (39.97 l/min) and VO2 (1.41 l/min). Although removal of 

this unusual pair changed the fitted parameters by about 10 percent (e.g., for the LOG model the 

intercept in this stratum was 2.69 before removal and 2.38 after removal), these changes were 

within the standard errors of the fitted parameters. Therefore it was decided to use the statistical 

model fitted to all the points for the pNEM/CO model. 

Since the regression model is a special case of the mixed model (with F2(person) = 0), the mixed 

models will have higher values of the log-likelihood. Based on the detailed regression 

diagnostics for the regression case, the final statistical model was the mixed model version of the 

LOG model, which was fitted by the maximum likelihood method. 

D-3
 
Table 1. Mixed model parameters for LOG model: log(VE/BM) = a + b log(VO2/BM) + 

error(person) + error(test). 

Age Group Gender Intercept 

(a) 

Slope 

(b) 

Variance 

Between 

Persons 

(F2(person)) 

Variance 

Between 

Activities 

(F2(test)) 

18 - 44 Female 4.3568 1.2756 0.01825 0.01397 

Male 3.9908 1.1969 0.01509 0.01945 

45 - 64 Female 3.4543 1.0208 0.01224 0.00591 

Male 4.0183 1.1646 0.01225 0.01237 

65 - Female 2.9562 0.9077 0.00785 0.00114 

Male 3.7304 1.0709 0.01170 0.00399 

D-4
 
Appendix E
 

Algorithm for Estimating Carboxyhemoglobin Levels 

E-1
 
COHB MODULE FOR pNEM/CO 

H. M. Richmond and T. R. Johnson 

February 1999 

This appendix describes the probabilistic COHb module and discusses its basis. The 

approach described here is based primarily on the COHb module described by Biller and 

Richmond in an Appendix to the 1992 version of pNEM/CO (Johnson et al., 1992). 

I. THE BASE PHYSIOLOGICAL MODEL FOR COMPUTING COHb LEVELS 

The COHB module in the original CO-NEM (Johnson and Paul, 1983) used as its basic 

model the differential equation derived by Coburn, Forster, and Kane (1965) which described the 

dynamic relationship between instantaneous blood levels of COHb, inspired CO, and other 

physiological variables. This model, which will be referred to here as the CFK model, continues 

to be the most widely-used method for estimating COHb and is the basic model for COHb 

computations in pNEM/CO. The CFK model is described in Section II. 

The CFK model describes the rate of change of COHb blood levels as a function of the 

following quantities: 

1. Inspired CO pressure

 2. COHb level

 3. Oxyhemoglobin (O2Hb) level

 4. Hemoglobin (Hb) content of blood

 5. Blood volume

 6. Alveolar ventilation rate

 7. Endogenous CO production rate

 8. Mean pulmonary capillary oxygen pressure

 9. Pulmonary diffusion rate of CO 

10. Haldane coefficient (M) 

11. Barometric pressure 

12. Vapor pressure of water at body temperature (47 torr) 

If all of the listed quantities except COHb level are constant over some time interval, the CFK 

equation has a linear form over the interval and is readily integrated. The solution to the linear 

form gives reasonably accurate results for lower levels of COHb. However, CO and oxygen 

compete for the available hemoglobin and are, therefore, not independent of each other. If this 

dependency is taken into account, the resulting differential equation is no longer linear. Peterson 

and Stewart (1975) proposed a heuristic approach to account for with this dependency which 

assumed the linear form and then adjusted the O2Hb level iteratively based on the assumption of 

a linear relationship between COHB and O2Hb. This approach was used in the COHb module of 

the original CO-NEM. Alternatively, it is possible to determine COHb at any time by numerical 

integration of the nonlinear CFK equation (e.g. by use of the Runge-Kutta method) if one 

assumes a particular relationship between COHb and O2Hb. Muller and Barton (1987) 

E-2
 
demonstrated that assuming a linear relationship between COHb and O2Hb leads to a form of the 

CFK equation equivalent to the Michaelis-Menton kinetic model which is analytically 

integrable. However, the analytical solution in this case cannot be solved explicitly for COHb. 

Muller and Barton demonstrated a binary search method for determining the COHb value. 

The COHb module for pNEM/CO employs a linear relationship between COHb and 

O2Hb which is consistent with the basic assumptions of the CFK model but differs from the 

linear forms used by other modelers. The Muller and Barton (1987) solution is employed. 

However, instead of the simple binary search described in the Muller and Barton paper, a 

combination of the binary search and Newton-Raphson root finding methods was used to solve 

for COHb (Press et al., 1986). Using the Muller and Barton solution increased computation time 

compared to the Peterson-Stewart method but was shown to be faster than fourth order RungeKutta numerical integration. 

II. The CFK Model For Estimation Of Carboxyhemoglobin 

Table 1 defines the variables which appear in the equations of this section. Coburn, 

Forster, and Kane (1965) derived the following differential equation governing COHb levels in 

the blood upon exposure to CO. 

d COHb [ ] V& CO PICO PCO2[COHb] (Eq. 1) = + −

dt V b BVb MBVb[O Hb 2 ]

where 

1 ( PB − PH2 O) (Eq. 2) B = +

D & LCO VA

If the only quantity in this equation that can vary with time is [COHb], the CFK equation 

is linear and can be readily integrated. However, since oxygen (O2) and CO compete for the 

available HB, [COHb] and [O2Hb] must be related. Increasing [COHb] will result in decreasing 

[O2Hb]. Thus the CFK equation is not linear and requires the relationship between the two 

quantities to be known if it is to be accurately integrated over a wide range of COHb levels. 

Various linear relationships between [COHb] and [O2Hb] have been used (See Marcus, 

1980; McCartney, 1990; Muller and Barton, 1987; and Tikuisis et al., 1987). A relationship not 

previously used follows directly from the basic assumptions of the CFK model. The CFK model 

employs the Haldane coefficient, which is the equilibrium constant associated with the following 

reaction representing the replacement of O2 in O2Hb by CO: 

(Eq. 3) CO + O2Hb = O2 + COHb 

E-3
 
 

 

Table 1. Definitions Of CFK Model Variables 

Variable 

t 

[COHb] 

[O2Hb] 

[RHb] 

[COHb]o 

[THb]0 

%[COHb] 

%[O2Hb] 

%[COHb]0 

%[COHB]4

PIco 

PCCO

PCO2 

PB

PH O2 

V&A

V&CO

Definition 

Time from start of an exposure event, min 

Concentration of carboxyhemoglobin (COHb) in blood at 

time, t, ml CO per ml blood at STPD 

Concentration of oxyhemoglobin (O2Hb) in blood at time t, 

ml O2 per ml blood at STPD 

Concentration of reduced hemoglobin in blood as equivalent 

ml CO per ml of blood at STPD 

[COHb] at t = 0 

[RHb] + [COHb] + [O2Hb] 

[COHb] expressed as percent of [RHb]0 

[O2Hb] expressed as percent of [RHb]0 

%[COHb] at t = 0 

%[COHb] at t = 4

Pressure of inspired CO in air saturated with water vapor at 

body temperature, torr 

Mean pulmonary capillary CO pressure, torr 

Mean pulmonary capillary O2 pressure, torr 

Barometric pressure, torr 

Vapor pressure of water at body temperature, torr (47 torr) 

Alveolar ventilation rate, ml/min STPD 

Endogenous CO production rate, ml/min STPD 

E-4
 
 

Table 1. Definitions Of CFK Model Variables (Continued) 

Variable Definition 

DL Pulmonary CO diffusion rate, ml/min/torr STPD CO

M Haldane coefficient 

k Equilibrium constant for reaction O2 + RHb = O2Hb 

Vb Blood volume, ml 

Hb Total hemoglobin in blood, g/100ml 

%MetHb Methemoglobin as weight percent of Hb 

E-5
 
The following equation, the Haldane relationship, applies approximately at equilibrium 

conditions. 

PCO [COHb]

(Eq. 4) 2 

= M

PCO[O Hb 2 ]

The Haldane coefficient, M, is the chemical equilibrium constant for reaction (3) 

The above reaction can also be viewed as the difference between two competing 

chemical reactions: 

(Eq. 5) CO + RHb = COHb 

(Eq. 6) O2 + RHb = O2Hb 

Subtracting (6) from (5) yields (3). If (3) is in equilibrium, then (5) and (6) are in equilibrium. 

If k is the equilibrium constant for (6) then: 

[O Hb 2 ] (Eq. 7) = k

PCO [RHb] 2 

It is known that an individual breathing air free of CO for an extended period will have 

about 97% of the reactive hemoglobin tied up as O2Hb and the rest (3%) as RHb. It is also 

known that at one atmosphere barometric pressure the mean pulmonary capillary oxygen 

pressure is approximately 100 torr. Substituting into (7) yields 0.32 as the approximate value of 

k at body temperature. From mass balance considerations: 

(Eq. 8) [O2Hb] + [COHb] + [RHb] = [THb]o 

Eliminating [RHb] between (7) and (8) and solving for [O2Hb] yields: 

kPCO2 (Eq. 9) [O Hb 2 ] = ([THb]0 − [COHb]) 1+ kPCO2 

This equation is the desired linear relationship. It has the same form as a relationship 

given without explanation by McCartney (1990), but replaces the constant in the McCartney 

equation by the term in (9) involving the mean pulmonary capillary oxygen pressure and the 

equilibrium constant k. Substituting (9) into (1) yields a CFK equation free of [O2Hb] and fully 

consistent with Coburn, Forster, and Kane’s original derivation. 

[ ] &CO P [COHb] 1+ kPC d COHb V O I

(Eq. 10) = + CO

− 2 

dt V&b BVb [THb]o − [COHb] kMBVb

E-6
 
 

In working with the CFK model it is convenient to express COHb as a percent of [RHb]0. 

Multiplying (10) by 100 and dividing by [RHb]0: 

d%[COHb] ⎛ & PI ] 100 VCO 100 1 + kPC ) CO ⎞ %[COHb ( 2

(Eq.11) = ⎜ + ⎟ − O

dt [ ] THb [ ] [ ] o ⎝ Vb BVb ⎠ 100 − % COHb k RHb o MBVb

Equation (11) can be written in the form suggested by Muller and Barton (1987): 

d%[COHb] %[COHb] (Eq.12) = C C1

dt o −

100 − %[COHb]

where 

100 ⎛ V&CO PI

CO ⎞

(Eq.13) Co = ⎜ + ⎟ [THb]o ⎝ Vb BVb ⎠

100 1 ( + kPc ) 2

(Eq.14) C1 = O

[ ]o k THb MBVb

Given values for the atmospheric pressure and the physiological variables in equations 

(12) - (14), the value of %[COHb] at time t can be found by numerical integration using such 

techniques as the fourth order Runge-Kutta method (Press et al., 1986). 

Muller and Barton (1987) demonstrated that an equation of the form of (12) is equivalent 

to a Michaelis-Menton kinetics model which is integrable. Integration yields: 

(%[COHb]∞ − %[COHb]) (Eq.15) − (C0 + C1)t + %[COHb] − %[COHb]0 − (100 − %[COHb]∞ ) ln = 0

%[COHb]∞ − %[COHb]0 

The equation for %[COHb]4 is obtained by setting equation (12) equal to zero and solving for 

%[COHb] which is now equal to %[COHb]4: 

100C0 

(Eq.16) %[COHb]∞ = (C0 + C1)

E-7
 
Equation (15) cannot be solved explicitly for %[COHb]. The Muller and Barton paper suggests 

the binary search method as one way to find the value of %[COHb]. Press and coauthors (1986) 

contend a combination of the binary search and Newton-Raphson methods is faster on average. 

III. Application of the Basic COHb Model in pNEM/CO 

Description of pNEM/CO 

pNEM/CO follows the daily activities over an extended period of a finite set of cohorts 

residing within a given geographic area. The period may be a single season or a calendar year. 

Each cohort is defined as a group of people with similar demographic and physiological 

characteristics who are likely to follow similar activity patterns. (Smokers are typically omitted 

from the cohorts included in pNEM/CO assessments, as cigarette smoking dominates their 

exposure to CO.) The exposure of each cohort is represented by a continuous sequence of 

exposure events which span the time period of interest. Each exposure event represents a time 

interval of 60 minutes or less during which the individual resides in a single environment and 

engages in a single activity. To permit calculation of hourly average exposures, exposure events 

are not permitted to fall in more than one clock hour. Consequently, the passage from one 

exposure event to the next is indicated by a change in microenvironment, activity, or clock hour. 

Algorithms within pNEM/CO calculates an average CO concentration for each exposure event 

according to the time, district, and microenvironment specified for the event. As the exposure 

events for a cohort are contiguous, the model can combine these concentrations to output 

distributions of one-hour and running eight-hour exposures for each cohort. The exposures 

calculated for individual cohorts can then be weighted according to their estimated populations 

to produce exposure distributions for larger population groups of particular interest. 

To treat the daily behavior of cohorts probabilistically, each cohort is identified 

according to home district, work district, demographic group, and the use of cooking fuel in the 

residence. Currently seven demographic groups are used to distinguish cohorts by sex and age. 

A set of pools of 24-hour activity patterns is used based on activity data drawn from the 

Consolidated Human Activity Data Base (CHAD) which is described in Section 2.3 of this 

report. These patterns are twenty-four hour samplings of the behavior of real people. The 

patterns in each pool represent the same demographic cohort group, day type (weekday, weekend 

day), and ambient temperature range. Each pattern consists of a 24-hour set of contiguous 

exposure events defined by event start time, duration, microenvironment, and activity. For a 

given cohort a daily pattern is randomly sampled from the appropriate pool each day during the 

period of the computation. 

From the description in the preceding paragraph, it is apparent that while the cohort 

represents a single demographic group, the activity pattern selected to represent each 24-hour 

period is obtained from a different member of the group. This feature has an impact on the 

design of the COHb module. 

The COHb Module 

E-8
 
The COHb module of pNEM/CO employs the Muller and Barton (1987) integration of 

the CFK model as represented by equations (12)-(14) to compute the COHb level of a cohort at 

the end of each exposure event. To perform this computation, the COHb module requires 

information on each of the quantities listed in the section describing the CFK model. In addition, 

the COHb level at the beginning of the exposure event must be known. This latter quantity is 

usually the COHb level computed at the end of the previous contiguous exposure event. To 

obtain the initial COHb at the start of the exposure period, the computation is started one day 

before the beginning of the period. The effect of the initial COHb value on the end value is 

negligible after about 15 hours. The program stores the COHb levels at the end of each clock 

hour and outputs distributions of COHb levels for the sensitive population. 

Assignment of CFK Model Input Data for an Exposure Event 

Section IV describes the equations and procedures used by the pNEM/CO COHb module 

to obtain the values of the input variables for equations (2) and (13) through (16). A brief 

overview is given here. 

The actual inspired CO level can change significantly during an exposure event. The 

model supplies an average exposure concentration for the event, which is used as the CO input. 

The time constant for the change in COHb is sufficiently large that the use of concentrations 

based on averaging times up to one hour can be used in place of the instantaneous concentrations 

over the averaging time period with little loss of accuracy in estimating the COHb level at the 

end of the exposure event. Furthermore, applying the average concentrations to a contiguous 

sequence of exposure events does not cause an accumulation of error. 

The COHb model presently used in pNEM/CO does not account for changing barometric 

pressure. It uses a constant barometric pressure which is a function of the average elevation of 

an area above sea level. The pressure at sea level is taken to be 760 torr. 

The remaining input variables to the CFK model are all physiological parameters. While 

the Haldane coefficient, the equilibrium constant k, and average pulmonary capillary oxygen 

pressure are treated as having the same constant values for all cohorts, the remaining 

physiological input variables will vary among individuals. The next section describes the 

methods used to generate the various physiological input variables for each combination of 

cohort and calendar day processed by pNEM/CO. 

IV. Computation of Input Data for the COHb Module 

As discussed in the previous section and in Sections 2.43 and 2.44 of the main body of 

this report, the algorithms used to estimate VE and COHb require values for various 

physiological parameters such as body mass, blood volume, and pulmonary diffusion rate. Table 

2 provides a complete list of these parameters. A special algorithm within pNEM 

probabilistically generates a value for each parameter on the list (collectively referred to as a 

“physiological profile”) for each combination of cohort and calender day processed by 

pNEM/CO. Figure 1 is a flow diagram showing the process by which each physiological profile 

was generated. Each of the generated physiological profiles is internally consistent, in that the 

E-9
 
functional relationships among the various parameters are maintained. For example, blood 

volume is determined as a function of weight and height, where height is estimated as a function 

of weight. Weight in turn is selected from a distribution specific to gender and age. Table 2 

provides a brief summary of the method used to estimate values for each parameter in the 

application of pNEM/CO to Denver. 

For each cohort, as defined above, pNEM/CO computes exposure for a contiguous 

sequence of exposure events spanning the total time period of the computation. This multi-day 

sequence of exposure events is determined by random sampling day-long event sequences from a 

set of pools of 24-hour activity patterns. An individual 24-hour pattern in one of these pools is 

referred to as a unit exposure sequence (UES). Each pool consists of a collection of UESs which 

are specific to the cohort demographic group, day type, and average daily temperature. 

A UES is a contiguous set of exposure events spanning 24 hours. Each event is 

characterized by start time, duration in minutes, home/work status, microenvironment, and 

activity. All exposure events are constrained to occur entirely within a clock hour. The UESs 

start at 7:00 p.m. and end at 7:00 p.m. the following day. 

The CFK model within the COHb module is called for each exposure event. For each 

event it requires the following data. 

Time duration of event, min
 

Inspired CO partial pressure averaged over the event, torr
 

Percent COHb at the start of the event
 

Alveolar ventilation rate, ml/min STPD
 

Average pulmonary capillary oxygen pressure, torr
 

Haldane Coefficient
 

Equilibrium constant for the reaction of O2
 

Atmospheric pressure, torr
 

Blood volume, ml
 

Total potential reduced hemoglobin content of blood, ml CO/ml STPD
 

Pulmonary CO diffusion rate, ml/min/torr STPD
 

Endogenous CO production rate, ml/min STPD
 

Given these data as inputs, the module computes the percent COHb at the end of the exposure 

event. This value is used by the module as the initial percent COHb for the next contiguous 

exposure event. The main program retains only those COHb values at the end of each clock 

hour. 

Some of the above data do not change during a pNEM/CO computer run and, therefore, 

need to be supplied to the computer program only once at the start. Some of the data vary with 

the cohort and therefore need to be supplied at the beginning of each activity day. Other data 

tend to change with the exposure event and therefore need to be supplied for each new exposure 

event. 

E-10
 
Table 2. Parameters Included in Physiological Profile for Adults in Version 2.0 of pNEM/CO. 

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter 

Method Used to Estimate Parameter 

Age COHb 

Ventilation rate 

Demographic group Randomly selected from population-weighted distribution s

group 

Gender COHb 

Ventilation rate 

Demographic group Randomly selected from population-weighted distribution s

group 

Weight (body mass) COHb 

Ventilation rate 

Gender Age 

Randomly selected from population-weighted distribution s

based on Brainard and Burmaster (1992). 

Height COHb Weight 

Gender 

Estimated by the following equations: males: height = 34.43 inches + (6.67)[ln(weight)] + (2 females: height = 48.07 inches + (3.07)[ln(weight)] + (2The z term was randomly selected from a unit normal [N(0,height (inches), weight (lbs). The estimation equations are based on the results of a statis(1998) of height and weight data provided by Brainard and 

Menstrual phase COHb Gender 

Age 

If gender = female, menstrual phase was randomly assigned following age-specific probabilities. Age < 65: 

50% premenstrual, 50% postmenstrual.

 Age 65+: 

100% premenstrual. 

E-11
 
Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter 

Method Used to Estimate Parameter 

Blood volume COHb Gender 

Weight Height 

Blood volume (Vb) was determined according to gender by 

which are based on work by Allen et al. (1956) which was munits used for height and weight. Men: 

Vb = (20.4)(weight) + (0.00683)(H3) - 30

 Women: Vb = (14.6)(weight) + (0.00678)(H3) - 30 

Units: blood volume (ml), weight (lbs), height (inches). 

Hemoglobin content of COHb Gender Randomly selected from normal distribution with arithmetic 

the blood, Hb Age arithmetic standard deviation (ASD) determined by gender 

obtained from the 1976-1980 NHANES study (USDHHS, 1 Males, 18 - 44: AM = 15.3, ASD = 1.0 Males, 45 - 64: AM = 15.1, ASD = 1.2 Males, 65+: AM = 14.8, ASD = 1.4 Females, 18 - 44: AM = 13.3, ASD = 1.1 Females, 45 - 64: AM = 13.6, ASD = 1.2 Females, 65+: AM = 13.7, ASD = 1.2 Units: grams of Hb per deciliter of blood 

Pulmonary CO diffusion COHb Gender Pulmonary CO diffusion rate (DL) was determined accordinrate, DL Height age according to the following equations obtained from a pa

CO Age and modified to conform to the units used in the COHb mod

 Males:

DL = (0.361)(height) - (0.232)(age) + 16.3 ml

CO

 Females: = (0.556)(height) - (0.115)(age) - 5.97 m

DLCO

Units: (ml/min/torr), height (inches), age (years). DLCO

E-12
 
Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter 

Method Used to Estimate Parameter 

Endogenous CO production rate 

COHb Gender 

Age Menstrual phase 

Endogenous CO production rate was randomly selected from distribution with geometric mean (GM) and geometric standdetermined according to the following equations specific to menstrual phase. Males, 18 - 64: GM = 0.473, GSD = 1.316 Males, 65+: GM = 0.473, GSD = 1.316 Females, 18 - 64, premenstrual: GM = 0.497, GSD = 1.45 Females, 18 - 64, postmenstrual: GM = 0.311, GSD = 1.4 Females, 65+: GM = 0.497, GSD = 1.459 Units: GM (ml/hr), GSD (dimensionless). 

Resting metabolic rate (RMR) 

Ventilation rate Gender 

Age Weight (body mass) 

See Section 5.4 of the main body of this report. 

Energy conversion factor (ECF) 

Ventilation rate Gender See Section 5.4 of the main body of this report. 

Ventilatory Equivalence Ratio (VER) 

Ventilation rate Gender 

Age Height 

See Section 5.4 of the main body of this report. 

NVO2max Ventilation rate Gender 

Age 

See Section 5.4 of the main body of this report. 

VO2max Ventilation rate NVO2max 

Weight (body mass) 

See Section 5.4 of the main body of this report. 

E-13
 
Demographic Group 

Gender Weight Age 

Height 

Menstrual Phase 

Endogenous CO
 

Production Rate
 

Blood Volume 

Total Hemoglobin
 

Content
 

of the Blood
 

Pulmonary CO
 

Diffusion Rate
 

Resting Metabolic
 

Rate (RMR)
 

Energy Conversion
 

Factor (ECF)
 

Maximum Normalized 

Oxygen Uptake Rate 

(NVO2max) 

Maximum Oxygen
 

Uptake Rate (VO2max)
 

Figure 1. Flow Diagram for Physiological Profile Generator. 

Input Data Supplied at Start of the pNEM/CO Computation 

E-14 
 

Barometric Pressure 

A constant barometric pressure is assumed for the study area based on the average height 

above sea level: 

(Eq. 17) PB = ∗ 760 exp(− 0 0000386 . ∗ Altitude)

where altitude is the average height (in feet) of the study area above sea level (USEPA, 1978). 

Average Pulmonary Capillary Oxygen Pressure 

The equation employed is based on an approximation used by Peterson and Stewart 

(1975) in which the 49 torr is subtracted from the partial pressure of inspired oxygen. This leads 

to the following approximate relationship: 

(Eq.18) PC = 0 209 . ( PB − 47) − 49 O2 

The constant 0.209 is the mole fraction of O2 in dry air. The constant 47 is the vapor pressure of 

water at body temperature. This expression was used in an investigation of the CFK equation by 

Tikuisis et al. (1987). Modelers have tended to use the value 100 torr. Equation (18) gives the 

value 100 torr for a barometric pressure of 760 torr. 

Haldane Coefficient 

The value of 218 has been used for the Haldane coefficient. Measured values in the 

range 210 to 270 have been reported in the literature. Modelers have tended to use values in the 

range 210 to 240. In the early 1980's, the Clean Air Scientific Advisory Committee (CASAC) 

expressed the opinion to EPA (Friedlander, 1982) that the most careful work done in this area 

was that by Rodkey (1969), who determined a value of 218. This value was used in the COHb 

module of the earlier CO NEM version. Other modelers using values in the range 218 to 220 are 

Peterson and Stewart, 1970; Marcus, 1980; Collier and Goldsmith, 1983; Muller and Barton, 

1987. As the value 218 falls within the range currently used by modelers, EPA analysts have 

decided to continue using value in pNEM/CO. 

Equilibrium Constant for the Reaction of O2 and RHb 

This quantity was estimated in Section II to have the value 0.32 based on the observation 

that %[RHb] is about 3% in individuals breathing air which is free of CO and a value of 100 torr 

for PC . O2 

Total Reduced Hemoglobin in the Absence of O2 and CO 

The quantity [THb]0 is expressed as equivalent milliliters of O2 or CO at STPD per 

milliliter of blood. Total Hb blood levels are customarily expressed as grams per deciliter of 

blood. The total Hb level in the absence of COHb and O2Hb would consist principally of RHb 

E-15
 
 

which can react with O2 or CO and MetHb which cannot. Total Hb blood levels also tend to be 

higher in people living at higher altitudes. To relate [THb]0 to Hb, it is therefore necessary to 

correct for the MetHb present, adjust for the effect of altitude, and convert to equivalent 

milliliters of CO at STPD. The later conversion is based on the observation that a gram of 

reduced Hb can react with a maximum of 1.39 ml of O2 or CO at STPD. The application of 

these three factors yields the equation: 

(1+ HbAlt∗ Altitude) (Eq. 23) [THb] = 139 Hb(100 − % MetHb) 0 . ∗ ∗

100 

where HbAlt is a regression constant. Hb in equation (23) is a sea level value. Hb level in a 

human population is normally distributed with the mean Hb and standard deviation both 

dependent on gender and age class (see entry in Table 2 for the distributions of Hb by age and 

gender). Given the hemoglobin content of the blood based on the distributions listed in Table 2, 

[THb]0 is calculated using equation (23). The weight percent MetHB, %MetHB, is taken to be 

0.5% of the weight of Hb (Muller and Barton, 1987). 

The altitude correction factor, HbAlt, was developed by application of simple regression 

analyses to Hb data obtained in 17 U.S. cities (USEPA, 1973). 

Men: 0.000161 S.E. = 0.000064
 

Women: 0.000115 S.E. = 0.000043
 

Two cities (Phoenix and Houston) were eliminated in the regression analysis because the 

measured Hb levels were substantially below that of the other cities. The altitude factor is small. 

It predicts about a 5% increase in Hb for residents of Denver over that for people living at sea 

level. 

Determination of Weight 

Body mass or weight (in kg) was determined by fitting lognormal distributions to data 

organized by age and gender based on work by Brainard and Burmaster (1992) and Burmaster et 

al. (1994). Table 5-5 in the main body of this report summarizes the parameters for the 

lognormal distributions obtained. 

Determination of Height 

The following equations were used to estimate height as a function of gender and weight. 

(Eq. 24) males: height = 34.43 inches + (6.67)[ln(weight)] + (2.38 inches)(z) 

(Eq. 25) females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(z) 

The z term was randomly selected from a unit normal [N(0,1)] distribution. 

Equations 24 and 25 are based on the results of a statistical analysis by Johnson (1998) of height 

and weight data provided by Brainard and Burmaster (1992). 

E-16
 
Base Pulmonary Diffusion Rate of CO 

A base lung diffusivity of CO for the cohort is calculated as follows: 

(Eq. 26) Men: DL = 0 361 . ∗ height − 0 232 . ∗ age = 16 3 . CO

(Eq. 27) Women: DL = 0 556 . ∗ height − 0115 . ∗ age − 5 97 . CO

where height is in inches and age is in years. 

The regression equations were obtained from a paper by Salorinne (1976) and modified 

to conform to the units used in the COHb module. The Salorinne data were obtained for nonexercising individuals. Tikuisis et al. (1992), working with eleven male subjects at various 

exercise levels, showed significant increase in lung diffusivity of CO with increasing alveolar 

ventilation rate. Regression analyses of data provided by Tikuisis for the individual subjects in 

the study showed the relationship to be linear. From this relationship and the heights and ages of 

the subjects in the Tikuisis et al. study, it was determined that the Salorinne equations for male 

subjects correspond to an alveolar ventilation rate of 6.69 l/min STPD. In the absence of other 

data it is assumed that this same value applies to women. Thus, for each twenty-four hour period 

equations (26) and (27) are used to compute lung diffusion rates of CO for a base case alveolar 

ventilation rate of 6.69 1/min STPD. As will be seen, this value is adjusted to account for the 

actual ventilation rate experienced by the cohort during each individual exposure event. 

INPUT DATA SUPPLIED AT START OF EACH 24-HOUR PERIOD 

Endogenous Rate of CO Production 

The endogenous CO production rates taken from a number of sources show the rate to be 

distributed lognormally in the population (see Appendix for data and sources). The distribution 

is different for men and women. For a woman there is a further difference depending on whether 

she is in her premenstrual or postmenstrual phase. The parameters of the lognormal distribution 

are given in Table 2 above. 

The calculation is as follows: If the cohort is female, a random number is sampled from a 

uniform distribution of real numbers in the range 0-1. If the number is less than 0.5, the cohort is 

considered to be in the premenstrual half of the month. Otherwise she is considered to be in the 

postmenstrual period. If the woman is in the 65+ age category she is treated as premenstrual. 

The appropriate combination of geometric mean and geometric standard deviation are obtained 

from the above table depending on the age class, sex, and menstrual phase. As in the previous 

cases a random number, z, is sampled from the standardized normal distribution, N(0,1). The 

appropriate endogenous CO production rate is then obtained from: 

& z

(Eq. 28) VCO = 0 01667 . ∗ (geom. mean)∗ (geom S.. D. )

E-17
 
The constant term converts ml/hr to ml/min. 

INPUT DATA SUPPLIED WITH EACH EXPOSURE EVENT 

Duration of Exposure Event 

The duration of the exposure event in minutes is supplied by the main program to the 

COHb module. 

Partial Pressure of Inspired Carbon Monoxide 

The main program supplies the inspired CO concentration averaged over the duration of 

the exposure expressed as ppm. This quantity is converted to pressure via: 

(Eq. 29) P = (CO)∗ ( Pb − 47)∗10−6 

ICO

Initial Percent COHb Level at Start of Exposure Event 

The program retains the percent COHb computed at the end of the previous exposure 

event and uses this value as the initial percent COHb for the present event. The starting COHb 

at the beginning of an activity day is the final COHb level at the end of the preceding activity 

day. This latter procedure is used for the first activity day of the overall computation since the 

program starts the day before the overall period covered by the pNEM/CO computation. 

Alveolar Ventilation Rate 

The main program supplies the COHb module with ventilation rate derived from the 

algorithm discussed in Section 2.4.3 in the main body of this report. 

Adjusted Pulmonary Diffusion Rate of CO 

Given the alveolar ventilation rate for the exposure event the associated adjusted 

pulmonary diffusion rate can be calculated from: 

& (Eq. 30) DL ( Adjusted = DL ) (Base) + 0 000845 . VA − 5 65 . CO CO

(See discussion of base pulmonary diffusion rate.) 

E-18
 
V. REFERENCES 

Biller, W. F. and H.M. Richmond. 1982. “Sensitivity Analysis on Coburn Model Predictions of 

COHb Levels Associated with Alternative CO Standards.” Report to Strategies and Air 

Standards Division of the Office of Air quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC, November, 1982. 

Brouillard, R. P., M.E. Contrad, and T.A. Bensinger. 1975. Blood Vol.45, pp. 67-69. 

Brainard, J. and D. Burmaster. 1992. “Bivariate Distributions for Height and Weight of Men 

and Women in the United States,” Risk Analysis, Vol. 12, No.2, pp. 267-275. 

Burke, P. D., F.L. Rodkey, T.F. Blaschke, H.A. Collison, and J.G. Waggoner. 1974. 

“Comparison of Plasma Bilirubin Turnover and Carbon Monoxide Production in Man.” J. Lab. 

Clin. Med. Vol. 83, pp. 29-37. 

Coburn, R. F., R.E. Forster, and R.B. Kane. 1965. “Considerations of the Physiology and 

Variables That Determine Blood Carboxyhemoglobin Concentration in Man.” J. Clin. Invest., 

Vol. 44, pp. 1899-1910. 

Coburn, R. F., W.S. Blakemore, and R.E. Forster. 1963. “Endogenous Carbon Monoxide 

Production in Man.” J. Clin. Invest., Vol. 42, pp.1172-1178. 

Coltman, C. A., and G.M. Dudley III. 1969. “The Relationship Between Endogenous Carbon 

Monoxide and Total Heme Mass in Normal and Abnormal Subjects. Am. J. Med. Sci. Vol. 

258, pp. 374-385. 

Collier, C. and J.R. Goldsmith. 1983. “Interactions of Carbon Monoxide and Hemoglobin at 

High Altitude.” Atmos. Environ. Vol. 17, pp. 723-728. 

Delivoria-Papadoppulos, M., R.F. Coburn, and R.E. Forster. 1974. “Cyclic Variation of Rate of 

Carbon Monoxide Production in Normal Women.” J. Appl. Physiol. Vol. 36, pp. 49-71. 

Friedlander, S. K. 1982. “Letter from The Clean Air Scientific Advisory Committee to USEPA 

Administrator.” August 31, 1982. 

Hartwell, T. D., C.A. Clayton, R.M. Richie, R.W. Whitmore, H.S. Zelon, S.M. Jones, and D.A. 

Whitehurst. 1984. “A Study of Carbon Monoxide Exposure of Residents of Washington, D. C. 

and Denver, Colorado.” EPA-600/s4-84-031. U.S. Environmental Protection Agency, Research 

Triangle Park, NC. 

Johnson, T. and R.A. Paul. 1983. “The NAAQS Model (NEM) Applied to Carbon Monoxide.” 

EPA 450/5-83-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

Johnson, T. 1987. “A Study of Human Activity Patterns in Cincinnati, Ohio.” Prepared by PEI 

Associates, Inc. for the Electric Power Research Institute, Palo Alto, CA. 

E-19
 
Johnson, T. 1998. “Memo No. 5: Equations for Converting Weight to Height Proposed for the 

1998 Version of pNEM/CO.” Memorandum Submitted to U.S. Environmental Protection 

Agency. TRJ Environmental, Inc., 713 Shadylawn Road, Chapel Hill, North Carolina 27514. 

Luomanmaki, K. and R.F. Coburn. 1969. “Effects of Metabolism and Distribution of Carbon 

Monoxide on Blood and Body Stores.” Am. J. Physiol. Vol. 217, No.2, pp. 354-362. 

Lynch, S. R. and A.L. Moede. 1972. “Variation in the Rate of Endogenous Carbon Monoxide 

Production in Normal Human Beings.” J. Lab. Clin. Med. Vol. 79, pp. 85-95. 

Marcus, A. H. 1980. “Mathematical Models for Carboxyhemoglobin.” Atmos. Environ. Vol. 

14, pp. 841-844. 

McCartney, M. L. 1990. “Sensitivity Analysis Applied to Coburn-forster-kane Models of 

Carboxhemoglobin Formation.” Am. Ind. Hyg. Assoc. J. Vol. 51, No. 3, pp.169-177. 

Merke, C., E. Cavallin-Stahl, and B. Lundh. 1975. “Carbon Monoxide Production and 

Reticulocyte Count in Normal Women: Effect of Contraceptive Drugs and Smoking.” Acta 

Med. Scan. Vol. 198, pp.155-160. 

Muller, K. E. and C.N. Barton. 1987. “A Nonlinear Version of the Coburn, Forster and Kane 

Model of Blood Carboxyhemoglobin.” Atmos. Environ. Vol. 21, pp.1963-1967. 

Peterson, J. E. and R.D. Stewart. 1975. “Predicting the Carboxhemoglobin Levels Resulting 

from Carbon Monoxide Exposures.” J. Appl. Physiol. Vol. 39, pp. 633-638. 

Press, W. H., B.P. Flannery, S.A. Teukolsky, and W.T. Vettering. 1986. NUMERICAL 

RECIPES, Cambridge University Press. 

Salorinne, Y. 1976. “Single-breath Pulmonary Diffusing Capacity.” Scand. J. Resp. Diseases 

Supplementum 96. 

Tikuisis, P., H.D. Madill., B.J. Gill, W.F. Lewis, K.M. Cox, and D.M. Kane, D. M. (1987) A 

critical analysis of the use of the CFK equation in predicting COHb formation. Am. Ind. Hyg. 

Assoc. J. 48(3):208-213. 

Tikuisis, P., Kane, D. M., McLellan, T. M., Buick, F., and S.M. Fairburn. 1992. “Rate of 

Formation of Carboxyhemoglobin in Exercising Humans Exposed to Carbon Monoxide.” J. 

Appli. Physiol. Vol. 72, No. 4. 

U.S. Department of Health and Human Services. 1982. “Hematological and Nutritional 

Biochemistry Reference Data for Persons 6 Months-74 Years of Age: United States, 1976-80.” 

DHHS Publication No. (PHS)83-1682. 

U.S. Environmental Protection Agency. 1978. “Altitude as a Factor in Air Pollution.” EPA600/9-78-015, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

E-20
 
U. S. Environmental Protection Agency. 1990. “Air Quality Criteria for Carbon Monoxide.” 

EPA-600/8-90-045A, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

Werner, B. and J. Lindahl. 1980. “Endogenous Carbon Monoxide Production after Bicycle 

Exercise in Healthy Subjects and in Patients with Hereditary Spherocytosis.” Scan. J. Lab. 

Invest. Vol. 40, pp. 319-324. 

E-21
 
Table 3. Literature Data Used to Derive Geometric Mean and Standard Deviation 

Lognormal Distribution of Endogenous Co Production Rate 

ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco 

(ml/hr) REFERENCE 

0.35 Coburn et al., 1963 

0.35 “ 

0.4 “ 

0.39 “ 

0.43 “ 

0.35 “ 

0.51 “ 

0.42 “ 

0.57 “ 

0.45 “ 

0.4 Lynch and Moede, 1972 

0.81 “ 

0.26 “ 

0.65 “ 

0.51 “ 

0.62 “ 

0.44 “ 

0.43 Berk et al., 1974 

0.58 “ 

0.52 “ 

0.59 “ 

0.8 “ 

0.72 “ 

0.54 “ 

E-22
 
ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco 

(ml/hr) REFERENCE 

0.45 Delivoria-Papadopoules et al., 1974 

0.26 “ 

0.6 “ 

0.45 “ 

0.39 “ 

0.4 “ 

0.81 Brouillard et al., 1975 

0.57 “ 

0.33 “ 

0.7 “ 

0.58 “ 

0.38 “ 

0.51 “ 

0.55 “ 

0.37 “ 

0.49 “ 

0.45 “ 

0.5 “ 

0.33 “ 

0.45 “ 

0.36 “ 

0.54 Werner and Lindahl, 1980 

0.76 “ 

0.48 “ 

0.31 “ 

0.7 “ 

E-23
 
ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco 

(ml/hr) REFERENCE 

0.36 “ 

0.65 “ 

0.38 Luomanmaki and Coburn, 1969 

0.42 “ 

0.41 “ 

0.54 “ 

0.38 “ 

0.72 Lynch and Moede, 1972 

0.37 “ 

0.23 “ 

0.33 “ 

0.42 “ 

0.44 “ 

0.29 “ 

0.48 “ 

0.57 Delivoria-Papadopoulos et al., 1974 

0.54 “ 

0.72 “ 

0.99 “ 

0.48 “ 

0.53 “ 

0.43 “ 

0.64 Merke et al., 1975 

0.86 “ 

0.35 “ 

0.52 “ 

E-24
 
ENDOGENOUS CO PRODUCTION RATE FOR MEN

 Vco 

(ml/hr) REFERENCE 

0.8 “ 

0.54 “ 

0.68 “ 

0.28 “ 

0.48 Lynch and Moede, 1972 

0.23 “ 

0.25 “ 

0.2 “ 

0.22 “ 

0.15 “ 

0.21 “ 

0.23 Delivoria-Papadopoulos et al., 1974 

0.51 “ 

0.34 “ 

0.41 “ 

0.26 “ 

0.16 “ 

0.3 “ 

0.4 Merke et al., 1975 

0.47 “ 

0.23 “ 

0.24 “ 

0.55 “ 

0.32 “ 

0.43 “ 

0.35 “ 

E-25
 
E-26