Risk and Exposure Assessment to Support 

the Review of the Carbon Monoxide Primary 

National Ambient Air Quality Standards: 

Second External Review Draft 

EPA-452/P-10-004 

February, 2010 

Risk and Exposure Assessment to Support the Review of the 

Carbon Monoxide Primary National Ambient Air Quality 

Standards: 

Second External Review Draft 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, North Carolina 
DISCLAIMER

This document has been reviewed by the Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency (EPA), and approved for publication. This draft 

document has been prepared by staff from the Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or 

recommendations are those of the authors and do not necessarily reflect the views of the EPA 

Mention of trade names or commercial products is not intended to constitute endorsement or 

recommendation for use. This document is being provided to the Clean Air Scientific Advisory 

Committee for their review, and made available to the public for comment. Any questions or 

comments concerning this document should be addressed to Dr. Stephen Graham, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06, 

Research Triangle Park, North Carolina 27711 (email: graham.stephen@epa.gov ). 

Elements of this report have been provided to the U.S. Environmental Protection Agency (EPA) 

by Abt Associates, Inc. and TRI Environmental, Inc. in partial fulfillment of Contract No. EP-D08-100, Work Assignments 0-08 and 1-15. 
February 2010 Draft -Do Not Quote or Cite i

Table of Contents 

 List of Figures........................................................................................................ iv

 List of Tables ......................................................................................................... iv

1 INTRODUCTION................................................................................................. 1-1 

1.1 BACKGROUND .................................................................................... 1-1 

1.2 PREVIOUS REVIEWS AND ASSESSMENTS.................................... 1-3 

1.3 CURRENT REVIEW, CASAC ADVICE AND PUBLIC COMMENT 1-6 

1.4 REFERENCES ....................................................................................... 1-8 

2 CONCEPTUAL OVERVIEW: ASSESSING AMBIENT CARBON 

MONOXIDE EXPOSURE AND RISK .............................................................. 2-1 

2.1 SOURCES OF CARBON MONOXIDE................................................ 2-1 

2.2 EXPOSURE PATHWAYS AND IMPORTANT 

 MICROENVIRONMENTS................................................................... 2-2 

2.3 EXPOSURE AND DOSE METRICS .................................................... 2-6 

2.4 AT-RISK POPULATIONS .................................................................... 2-7 

2.5 HEALTH ENDPOINTS ......................................................................... 2-9 

2.5.1 Cardiovascular Disease-related Effects .............................................. 2-10

2.5.2 Other Effects ....................................................................................... 2-12

2.6 RISK CHARACTERIZATION APPROACH........................................ 2-13 

2.7 KEY OBSERVATIONS......................................................................... 2-17 

2.8 REFERENCES ....................................................................................... 2-19 

3 AIR QUALITY CONSIDERATIONS ................................................................ 3-1 

3.1 AMBIENT CO MONITORING............................................................. 3-1 

3.1.1 Monitoring Network ........................................................................... 3-1 

3.1.2 Analytical Sensitivity.......................................................................... 3-2 

3.1.3 General Patterns of CO Concentrations.............................................. 3-4 

3.1.4 Policy-Relevant Background Concentrations ..................................... 3-11 

3.1.5 Within-Monitor CO Concentration Trends......................................... 3-11 

3.2 STUDY AREAS SELECTED FOR CURRENT ASSESSMENT......... 3-18 

3.3 KEY OBSERVATIONS......................................................................... 3-18 

3.4 REFERENCES ....................................................................................... 3-20 
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4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING CO 

EXPOSURE AND COHB DOSE LEVELS........................................................ 4-1 

4.1 PURPOSE............................................................................................... 4-1 

4.2 MODEL OVERVIEW............................................................................ 4-2 

4.3 MODEL HISTORY AND EVOLUTION .............................................. 4-2 

4.4 MODEL SIMULATION PROCESS...................................................... 4-3 

4.4.1 Characterize Study Area ..................................................................... 4-6 

4.4.2 Generate Simulated Individuals.......................................................... 4-6 

4.4.3 Construct Activity Sequences ............................................................. 4-8 

4.4.4 Calculate Microenvironmental Concentrations .................................. 4-13 

4.4.5 Estimate Energy Expenditure and Ventilation Rates.......................... 4-30 

4.4.6 Calculate Exposure ............................................................................. 4-32 

4.4.7 Calculate Dose .................................................................................... 4-33 

4.4.8 Model Output ...................................................................................... 4-34 

4.6 KEY OBSERVATIONS......................................................................... 4-34 

4.7 REFERENCES ....................................................................................... 4-35 

5 APPLICATION OF APEX4.3 IN THIS ASSESSMENT.................................. 5-1 

5.1 PURPOSE............................................................................................... 5-1 

5.2 OVERVIEW ........................................................................................... 5-2 

5.3 STUDY AREAS..................................................................................... 5-3 

5.4 EXPOSURE PERIODS .......................................................................... 5-3 

5.5 STUDY POPULATION......................................................................... 5-7 

5.5.1 Total and Simulated Population.......................................................... 5-7 

5.5.2 Selected at-Risk Subpopulation .......................................................... 5-7 

5.5.3 Time-Location-Activity Patterns ........................................................ 5-10 

5.5.4 Construction of Longitudinal Diaries ................................................. 5-10 

5.6 EXPOSURE SCENARIOS..................................................................... 5-10 

5.7 AMBIENT AIR QUALITY DATA ....................................................... 5-11 

5.7.1 Unadjusted 1-Hour Ambient Concentrations...................................... 5-11 

5.7.2 Method for Estimating of Missing 1-Hour Ambient Concentrations . 5-12 

5.7.3 Adjusted 1-Hour Ambient Concentrations ......................................... 5-16 

5.8 METEOROLOGICAL DATA ............................................................... 5-20 

5.8.1 Method for Estimating of Missing 1-Hour Temperature Data ........... 5-21 

5.9 MICROENVIRONMENTS MODELED ............................................... 5-22 

5.9.1 The Micronenvironmental Model as Implemented by APEX4.3 ....... 5-22 
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5.9.2 Microenvironmental Mapping ............................................................ 5-25 

5.9.3 Selection of Microenvironmental Method Used................................. 5-26 

5.9.4 Air Exchange Rates and Air Conditioning Prevalence....................... 5-26 

5.10 KEY OBSERVATIONS......................................................................... 5-28 

5.11 REFERENCES ....................................................................................... 5-30 

6 SIMULATED EXPOSURE AND COHB DOSE RESULTS............................ 6-1 

6.1 ESTIMATED EXPOSURES.................................................................. 6-2 

6.1.1 Air quality as is ................................................................................... 6-2 

6.1.2 Air quality adjusted to just meet the current 8-hour standard............. 6-4 

6.1.3 Air quality adjusted to just meet alternative air quality scenarios ...... 6-6 

6.2 ESTIMATED COHB DOSE LEVELS .................................................. 6-10 

6.2.1 Air quality as is ................................................................................... 6-10 

6.2.2 Air quality adjusted to just meet the current 8-hour standard............. 6-11 

6.2.3 Air quality adjusted to just meet alternative air quality scenarios ...... 6-13 

6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000 

PNEM/CO AND DRAFT 2009 APEX/CO ASSESSMENTS ............... 6-14 

6.4 EVALUATION OF ENDOGENOUS CO CONTRIBUTION TO COHB 

LEVELS IN APEX SIMULATED INDIVIDUALS.............................. 6-17 

6.4.1 Estimation of Endogenous CO Contribution to Population COHb 

 Levels.................................................................................................. 6-17 

6.4.2 Contribution of Endogenous CO Production and Ambient Exposures to 

COHb Level ........................................................................................ 6-19 

6.5 KEY OBSERVATIONS......................................................................... 6-23 

6.6 REFERENCES ....................................................................................... 6-25 

7 VARIABILITY ANALYSIS AND UNCERTAINTY 

 CHARACTERIZATION ..................................................................................... 7-1 

7.1 ANALYSIS OF VARIABILITY............................................................ 7-1 

7.2 CHARACTERIZATION OF UNCERTAINTY..................................... 7-3 

7.3 KEY OBSERVATIONS......................................................................... 7-10 

7.4 REFERENCES ....................................................................................... 7-11 

8 SUMMARY OF KEY OBSERVATIONS .......................................................... 8-1
February 2010 Draft -Do Not Quote or Cite iv

List of Figures 

Figure 3-1 Spatial and Temporal Trends in the 2nd Highest 1-hour (top) and 8-hour Average 

(bottom) CO Ambient Monitoring Concentrations in Denver, Colorado(left) and 

Los Angeles, California (right),Years 1993 – 2008 ......................................... 3-6 

Figure 3-2 Diurnal Distribution of 1-hour CO Concentrations in Denver (Monitor 

080310002) by Day-type (weekdays-left; weekends-right), Years 1995 (top) and 

2006 (bottom). ................................................................................................ 3-9 

Figure 3-3 Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor 

060371301) by day-type (weekdays-left; weekends-right), years 1997 (top) and 

2006 (bottom). .................................................................................................. 3-10 

Figure 3-4 Comparison of high concentration year (1997) with low concentration year 

(2006) ambient air quality in Los Angeles. The 0 through 100th percentiles of the 

quality distribution are plotted for each monitor-year...................................... 3-13 

Figure 4-1 Conceptual model and simplified data flow for estimating population exposure 

and dose using APEX....................................................................................... 4-5 

Figure 5-1 Ambient monitor locations, air districts (black circles), meteorological zones 

(blue circles), and study area (red circle) for the Denver exposure modeling 

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

Figure 5-2 Ambient monitor locations, air districts (black circles), meteorological zones 

(blue and pink circles), and study area (red circle) for the Los Angeles exposure 

modeling domain.............................................................................................. 5-5 

Figure 6-1 Histogram of the percent COHb ambient contribution estimated using Denver 

1995 ambient concentrations adjusted to just meet the current standard. ........ 6-20 

Figure 6-2 The contribution of endogenous CO production relative to an individual’s 

maximum end-of-hour COHb level using 1995 Denver ambient concentrations 

adjusted to just meet the current standard. ....................................................... 6-21 

Figure 6-3 Comparison of endogenous CO production relative to an individual’s maximum 

COHb ambient contribution using 1995 Denver ambient concentrations adjusted 

to just meet the current standard....................................................................... 6-22 

Figure 6-4. Comparison of endogenous CO production relative to an individual’s maximum 

COHb ambient contribution using 1995 Denver ambient concentrations adjusted 

to just meet the current standard....................................................................... 6-23 

List of Tables 

Table 3-1. Within monitor temporal variability in Denver using historical (1995-97) and 

recent (2005-07) air quality data - 2nd highest 8-hour average ....................... 3-15 

Table 3-2. Within monitor temporal variability in Los Angeles using historical (1995-97) and 

recent (2005-07) air quality data - 2nd highest 8-hour average. ..................... 3-15 

Table 3-3. Within monitor temporal variability in Denver using historical (1995-97) and 

recent (2005-07) air quality data – 99th percentile 1-hour daily maximum..... 3-16 

 Table 3-4. Within monitor temporal variability in Los Angeles using historical (1995-97) 

and recent (2005-07) air quality data – 99th percentile 1-hour daily 
February 2010 Draft -Do Not Quote or Cite v

 maximum.......................................................................................................... 3-16 

 Table 3-5. Within monitor temporal variability in Denver using historical (1995-97) and 

recent (2005-07) air quality data – 99th percentile 8-hour daily maximum..... 3-17 

 Table 3-6. Within monitor temporal variability in Los Angeles using historical (1995-97) 

and recent (2005-07) air quality data – 99th percentile 8-hour daily 

 maximum.......................................................................................................... 3-17 

Table 4-1. Summary of activity pattern studies comprising the recent version of CHAD 4-10 

Table 4-2. Variables used by APEX4.3 in the mass balance model.................................. 4-15 

Table 4-3. Variables used by APEX4.3 in the factors model ............................................ 4-16 

Table 4-4. Estimated values of distribution parameters and variables in equation 4-11 as 

implemented in the application of pNEM/CO to Denver and Los Angeles 

(Johnson et al., 2000). ...................................................................................... 4-21 

Table 4-5. Parameters of Bounded Lognormal Distributions Defined for Proximity Factors 

Used in Applications of APEX3.1 to Los Angeles (Johnson and Capel, 

 2003)................................................................................................................. 4-29 

Table 5-1. Attributes of fixed-site monitors selected for the Denver study area............... 5-3 

Table 5-2. Attributes of fixed-site monitors selected for the Los Angeles study area....... 5-4 

Table 5-3. National prevalence rates for coronary heart disease by age range.................. 5-8 

Table 5-4 National prevalence rates for coronary heart disease by gender ...................... 5-8 

Table 5-5 National prevalence rates for coronary heart disease stratified by age range and 

gender ............................................................................................................... 5-8 

Table 5-6 National prevalence rates for coronary heart disease, including diagnosed and 

undiagnosed cases, stratified by age and gender.............................................. 5-9 

Table 5-7 Descriptive statistics for hourly carbon monoxide concentrations before and after 

estimation of missing values – Denver 1995.................................................... 5-13 

Table 5-8 Descriptive statistics for hourly carbon monoxide concentrations before and after 

estimation of missing values – Denver 2006.................................................... 5-13 

Table 5-9 Descriptive statistics for hourly carbon monoxide concentrations before and after 

estimation of missing values – Los Angeles 1997 ........................................... 5-14 

Table 5-10 Descriptive statistics for hourly carbon monoxide concentrations before and after 

estimation of missing values – Los Angeles 2006 ........................................... 5-15 

Table 5-11 Design values and adjustment factors used to represent air quality just meeting 

the current and potential alternative standards ................................................. 5-17 

Table 5-12 Descriptive statistics for hourly carbon monoxide concentrations after adjusting 

to just meet the current 8-hour standard – Denver (adjusted 1995 data). ........ 5-19 

Table 5-13 Descriptive statistics for hourly carbon monoxide concentrations after adjusting 

to just meet the current 8-hour standard – Los Angeles (adjusted 1997 data). 5-19 

Table 5-14 Locations of meteorological stations selected for Denver ............................... 5-20 

Table 5-15 Locations of meteorological stations selected for Los Angeles ....................... 5-21 

Table 5-16 Parameters of Bounded Lognormal Distributions Defined for Proximity Factors 

to be Used in the Proposed Application of APEX4.3 to Los Angeles and 

 Denver .............................................................................................................. 5-24 

Table 5-17 List of microenvironments modeled and calculation methods used ................ 5-26 

Table 5-18 Lognormal distributions of indoor air exchange rates used in Los Angeles .... 5-27 

Table 5-19 Lognormal distributions of indoor air exchange rates used in Denver ............ 5-28 
February 2010 Draft -Do Not Quote or Cite vi

Table 6-1. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Denver Study Area – “As Is” Air 

Quality.............................................................................................................. 6-3 

Table 6-2. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Los Angeles Study Area – “As Is” 

Air Quality........................................................................................................ 6-4 

Table 6-3 Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality Just 

Meeting the Current 8-Hour Standard.............................................................. 6-5 

Table 6-4 Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air 

Quality Just Meeting the Current 8-Hour Standard.......................................... 6-5 

Table 6-5 Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour 

Exposure At or Above the Specified Level – Adults With Coronary Heart Disease 

(CHD) in the Denver Study Area – Air Quality Just Meeting Potential Alternative 

Standards .......................................................................................................... 6-6 

Table 6-6 Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour 

Exposure At or Above the Specified Level – Adults With Coronary Heart Disease 

(CHD) in the Los Angeles Area – Air Quality Just Meeting Potential Alternative 

Standards .......................................................................................................... 6-7 

Table 6-7. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour 

Exposure At or Above the Specified Level – Adults With Coronary Heart Disease 

(CHD) in the Denver Study Area – Air Quality Just Meeting Potential Alternative 

Standards. ......................................................................................................... 6-9 

Table 6-8. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour 

Exposure At or Above the Specified Level – Adults With Coronary Heart Disease 

(CHD) in the Los Angeles Area – Air Quality Just Meeting Potential Alternative 

Standards .......................................................................................................... 6-9 

Table 6-9. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality As 

Is ..................................................................................................................... 6-11 

Table 6-10. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air 

Quality As Is..................................................................................................... 6-11 

Table 6-11 Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality Just 

Meeting the Current 8-hour Standard............................................................... 6-12 
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Table 6-12 Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air 

Quality Just Meeting the Current 8-hour Standard .......................................... 6-12 

Table 6-13 Estimated Number (and Percentage) of Persons with a Daily Maximum End-ofhour COHb Level At or Above the Specified Level – Adults With Coronary 

Heart Disease (CHD) in the Denver Study Area – Air Quality Just Meeting 

Potential Alternative Standards........................................................................ 6-13 

Table 6-14 Estimated Number (and Percentage) of Persons with a Daily Maximum End-ofhour COHb Level At or Above the Specified Level – Adults With Coronary 

Heart Disease (CHD) in the Los Angeles Study Area – Air Quality Just Meeting 

Potential Alternative Standards........................................................................ 6-14 

Table 6-15 Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to 

Experience a Daily Maximum End-of-hour COHb Level At or Above the 

Specified Percentage – Air Quality Just Meeting the Current Standard .......... 6-16 

Table 6-16 Percentage of Los Angeles Adults with Coronary Heart Disease (CHD) Estimated 

to Experience a Daily Maximum End-of-hour COHb Level At or Above the 

Specified Percentage – Air Quality Just Meeting the Current Standard .......... 6-16 

Table 6-17 Estimated Number (and Percentage) of Persons with a Daily Maximum End-ofhour COHb Level At or Above the Specified Level – Adults With Coronary 

Heart Disease (CHD) in the Denver and Los Angeles Study Areas – Zero 

Ambient Exposures .......................................................................................... 6-18 

Table 6-18 Descriptive Statistics for the Percent COHb Ambient Contribution Estimated 

Using Denver 1995 Ambient Concentrations Adjusted to Just Meet the Current 

Standard............................................................................................................ 6-19 

Table 7-1 Summary of How Variability Was Incorporated Into the Second Draft REA. 7-2 

Table 7-2 Characterization of Key Uncertainties in the Second Draft REA for Denver and 

Los Angeles Areas............................................................................................ 7-7 
February, 2010 1-1 Draft – Do Not Cite or Quote

1 1 INTRODUCTION 

2 This draft document describes the quantitative human exposure assessment and risk 

3 characterization being conducted to inform the U.S. Environmental Protection Agency’s (EPA’s) 

4 current review of the National Ambient Air Quality Standards (NAAQS) for carbon monoxide 

5 (CO). This draft report, Risk and Exposure Assessment to Support the Review of the Carbon 

6 Monoxide Primary National Ambient Air Quality Standards: Second External Review Draft, is 

7 being provided to the Clean Air Scientific Advisory Committee (CASAC) CO Panel and the 

8 public for review in advance of a public meeting of the CASAC CO panel planned for March 22-

9 23, 2010. Following that meeting, we will take CASAC and public comments into account in 

10 preparing the final document. We plan to complete the final Risk and Exposure Assessment 

report in May 2010. Given the significant time constraints of this review,1 11 results of the 

12 assessment are provided in this document without substantial interpretation. Rather, 

13 interpretative discussion of these results is provided in the draft Policy Assessment. 

14 1.1 BACKGROUND 

15 The EPA is presently conducting a review of the national ambient air quality standards 

16 for CO. Sections 108 and 109 of the Clean Air Act (Act) govern the establishment and periodic 

17 review of the NAAQS. These standards are established for certain pollutants that may 

18 reasonably be anticipated to endanger public health and welfare, and whose presence in the 

19 ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are to 

20 be based on air quality criteria, which are to accurately reflect the latest scientific knowledge 

21 useful in indicating the kind and extent of identifiable effects on public health or welfare that 

22 may be expected from the presence of the pollutant in ambient air. Based on periodic reviews of 

23 the air quality criteria and standards, the Administrator is to make revisions in the criteria and 

24 standards, and promulgate any new standards, as may be appropriate. The Act also requires that 

25 an independent scientific review committee advise the Administrator as part of this NAAQS 

26 review process, a function performed by the CASAC. 

27 The current NAAQS for CO includes two primary standards to provide protection for 

28 exposures to carbon monoxide. In 1994, EPA retained the primary standards at 9 parts per 

29 million (ppm), 8-hour average and 35 ppm, 1-hour average, neither to be exceeded more than 

30 once per year (59 FR 38906). These standards were based primarily on the clinical evidence 

31 relating carboxyhemoglobin (COHb) levels to various adverse health endpoints and exposure 

32 modeling relating CO exposures to COHb levels. With the 1994 decision, EPA also reaffirmed 

 

1

 As noted below, the schedule for this review is governed by the terms of a court order. 
February, 2010 1-2 Draft – Do Not Cite or Quote

1 an earlier decision that the evidence did not support the need for a secondary standard for CO (59 

2 FR 38906). 

3 A subsequent review of the CO NAAQS was initiated in 1997, which led to the 

4 completion of the 2000 Air Quality Criteria Document for Carbon Monoxide (US EPA, 2000) 

5 and a draft exposure analysis methodology document (US EPA, 1999). EPA put on hold the 

6 NAAQS review when Congress requested that the National Research Council (NRC) review the 

7 impact of meteorology and topography on ambient CO concentrations in high altitude and 

8 extreme cold regions of the U.S. In response, the NRC convened the Committee on Carbon 

9 Monoxide Episodes in Meteorological and Topographical Problem Areas, which focused on 

10 Fairbanks, Alaska as a case-study. A final report, “Managing Carbon Monoxide Pollution in 

11 Meteorological and Topographical Problem Areas” (NRC, 2003), offered a wide range of 

12 recommendations regarding management of CO air pollution, cold start emissions standards, 

13 oxygenated fuels, and CO monitoring. Following completion of this NRC report, EPA did not 

14 conduct rulemaking to complete the review. 

15 EPA initiated the current review of the NAAQS for CO on September 13, 2007, with a 

16 call for information from the public (72 FR 52369) requesting the submission of recent scientific 

17 information on specified topics. A workshop was held on January 28–29, 2008 (73 FR 2490) to 

18 discuss policy-relevant scientific and technical information to inform EPA’s planning for the CO 

19 NAAQS review. Following the workshop, EPA outlined the science-policy questions that would 

20 frame this review, outlined the process and schedule that the review would follow, and provided 

21 more complete descriptions of the purpose, contents, and approach for developing the key 

22 documents for the review in a draft Plan for Review of the National Ambient Air Quality 

23 Standards for Carbon Monoxide (US EPA, 2008a). After CASAC and public input on the draft 

24 plan, EPA made the final plan available in August 2008 (US EPA, 2008b). In January, 2010, 

25 EPA completed the process of assessing the latest available policy-relevant scientific information 

26 to inform the review of the CO standards. This assessment, the Integrated Science Assessment 

27 for Carbon Monoxide (hereafter, “ISA”) (US EPA, 2010a), includes an evaluation of the 

28 scientific evidence on the health effects of CO, including information on exposure, physiological 

29 mechanisms by which CO might adversely impact human health, an evaluation of the clinical 

30 evidence for CO-related morbidity, and an evaluation of the epidemiological evidence for COrelated morbidity and mortality associations.2 31 

32 EPA’s Office of Air Quality Planning and Standards (OAQPS) has developed this second 

33 draft Risk and Exposure Assessment (REA) describing the quantitative assessment conducted by 

 

2

 The ISA also evaluates scientific evidence for the effects of CO on public welfare which EPA will 

consider in its review of the need for a secondary standard. EPA is not intending to do a quantitative risk 

assessment for the secondary standard review. 
February, 2010 1-3 Draft – Do Not Cite or Quote

1 the Agency to support the review of the primary CO standards. This second draft document is a 

2 concise presentation of the methods, key results, observations, and related uncertainties 

3 associated with the quantitative analyses performed. This REA builds upon the health effects 

4 evidence presented in the final ISA, as well as CASAC advice (Brain and Samet, 2009; Brain 

5 and Samet, 2010) and public comments on a scope and methods planning document for the REA 

6 (hereafter, “Scope and Methods Plan”) (US EPA, 2009a) and on the first draft REA (US EPA, 

7 2009b). The final REA will reflect consideration of CASAC and public comments on this 

8 second draft REA. The final REA will be completed by May 28, 2010, consistent with the court 

9 order governing the schedule for completion of this review. The court order also specifies that 

10 EPA sign for publication notices of proposed and final rulemaking concerning its review of the 

11 CO NAAQS no later than October 28, 2010 and May 13, 2011, respectively. 

12 The final ISA and final REA will inform the policy assessment and rulemaking steps that 

13 will lead to final decisions on the CO NAAQS. The policy assessment will be described in a 

14 Policy Assessment (hereafter, “PA”) document, which will include staff analysis of the scientific 

15 basis for alternative policy options for consideration by the Administrator prior to rulemaking. 

16 The PA will integrate and interpret information from the ISA and the REA to frame policy 

17 options for consideration by the Administrator. The PA is intended to help “bridge the gap” 

18 between the Agency’s scientific and technical assessments, presented in the ISA and REA and 

19 the judgments required of the Administrator in determining whether it is appropriate to retain or 

20 revise the standards. The PA is also intended to facilitate CASAC’s advice to the Administrator 

21 on the adequacy of existing standards, and any new standards or revisions to existing standards 

22 as may be appropriate. A draft PA is being prepared (USEPA, 2010b) for release for review by 

23 CASAC, as well as for public comment, in conjunction with CASAC review and public 

24 comment of this document. 

25 1.2 PREVIOUS REVIEWS AND ASSESSMENTS 

26 Reviews of the CO NAAQS completed in 1985 and 1994 included analyses of exposure 

27 to ambient CO and associated internal dose, in terms of COHb levels, which were used to 

28 characterize risks for populations of interest (50 FR 37484; 59 FR 38906). These prior risk 

29 characterizations compared the numbers and percent of the modeled population that exceeded 

30 several potential health effect benchmarks, expressed in terms of COHb levels. The COHb 

31 levels of interest in these reviews were drawn from the evidence of COHb levels associated with 

32 exercise-induced aggravation of angina in controlled human exposure studies involving short-
February, 2010 1-4 Draft – Do Not Cite or Quote

term (shorter than 8 hours) exposures of patients with diagnosed ischemic heart disease (IHD)3 1 to 

2 elevated CO concentrations (US EPA, 1979; US EPA, 1984; US EPA, 1991). 

3 In the review completed in 1994, this characterization was performed for the population 

4 of interest in the city of Denver, Colorado (US EPA, 1992; Johnson et al., 1992). That analysis 

5 indicated that if the current 8-hour standard were just met, the proportion of the nonsmoking 

population with cardiovascular disease4 6 experiencing exposures at or above 9 ppm for 8 hours 

7 decreased by an order of magnitude or more as compared to the proportion under then-existing 

8 ambient CO levels, down to less than 1 percent of the total person-days in that population. 

9 Likewise, just meeting the current 8-hour standard reduced the proportion of the nonsmoking 

10 cardiovascular-disease population person days at or above COHb levels of concern by an order 

11 of magnitude or more relative to then-existing ambient CO levels. More specifically, upon just 

12 meeting the 8-hour standard, EPA estimated that less than 0.1% of the nonsmoking 

13 cardiovascular-disease population would experience a COHb level of about 2.1%. A smaller 

14 percentage of the at-risk population was estimated to exceed higher COHb levels. The analysis 

15 also considered additional exposure scenarios that included certain indoor sources (e.g., passive 

16 smoking, gas stove usage). While these indoor sources were shown to contribute to total CO 

17 exposure, it was determined to be of limited utility in the risk assessment because these source 

18 emissions would not be effectively mitigated by setting more stringent ambient air quality 

19 standards (59 FR 38914). 

20 In the review initiated in 1997, EPA consulted with CASAC on a draft exposure analysis 

21 methodology document, Estimation of Carbon Monoxide Exposures and Associated 

22 Carboxyhemoglobin Levels in Denver Residents using pNEM/CO (Version 2.0) (Mauderly, 

23 1999; Johnson, 1999). Although the EPA did not complete the review initiated in 1997, OAQPS 

24 continued work on the CO exposure assessment to further develop the exposure assessment 

25 modeling component of the Total Risk Integrated Methodology (TRIM) system. A subsequent 

26 draft technical report (Johnson et al., 2000) was produced documenting the application of the CO 

 

3

 Ischemic heart disease is a category of cardiovascular disease associated with narrowed heart arteries; it is 

often also called coronary artery disease (CAD) and coronary heart disease (CHD). Individuals with CHD have 

myocardial ischemia, which occurs when the heart muscle receives insufficient oxygen delivered by the blood. 

Exercise-induced angina pectoris (chest pain) occurs in many of them. Among all patients with diagnosed CAD, the 

predominant type of ischemia, such as that indicated by ST segment depression, is asymptomatic (i.e., silent). Also, 

patients who experience angina typically have additional ischemic episodes that are asymptomatic (2000 AQCD, 

section 7.7.2.1). 

4

 In characterizing the population of interest with regard to demographics (age and sex), the assessment for 

the review completed in 1994 drew from estimates of the prevalence of ischemic heart disease (IHD) provided by 

the National Health Interview Survey and corresponding estimates of undiagnosed ischemia developed by EPA. 

Estimates of undiagnosed IHD were based on two assumptions: (1) there are 3.5 million persons in U.S. with 

undiagnosed IHD (drawn from estimate by American Heart Association) and (2) persons with undiagnosed IHD are 

distributed within the population in the same manner as persons with diagnosed IHD (USEPA, 1992). 
February, 2010 1-5 Draft – Do Not Cite or Quote

1 exposure and dose modeling methodology for two study areas (Denver and Los Angeles). The 

2 exposure and dose estimates were obtained by applying pNEM/CO version 2.1, a predecessor to 

APEX, to adults with IHD residing within each urban area.5 3 This report was subjected to an 

4 external peer review by three exposure modeling experts and convened by Science Applications 

5 International Corporation (SAIC, 2001). 

6 In the 2000 pNEM/CO assessment, the Denver study area was defined as all census tracts 

7 located within 10 km of each of six fixed-site monitors within the Denver metropolitan area. Air 

8 quality data for 1995 reported by these monitors were used to represent existing conditions in the 

9 study area. Because the second highest non-overlapping 8-hour average CO concentration 

10 equaled 9.5 ppm, the existing conditions in Denver for 1995 were considered to approximate just 

meeting the 8-hour average CO standard.6 11 In a similar manner, the Los Angeles study area was 

12 defined as all census tracts within 10 km of ten fixed-site monitors in the Los Angeles area, 

13 though air quality data for 1997 were adjusted downwards so that the concentrations associated 

14 with the design monitor just met the 8-hour NAAQS. A total of 15 distinct microenvironments 

15 were modeled using a mass balance model accounting for the infiltration of outdoor (ambient) 

16 concentrations, air exchange rates, as well as CO emissions from two indoor sources (residential 

17 gas stoves and passive cigarette smoke). 

18 In the 2000 pNEM/CO assessment, approximately 0.5% of the non-smoking IHD 

19 population in both urban areas was estimated to experience a maximum end-of-hour COHb level 

of about 2.0% upon just meeting the current 8-hour standard.7 20 A smaller percentage of the at21 risk population was estimated to exceed higher COHb levels (e.g., <0.1% of persons were 

22 estimated to have COHb levels at or above 3.0% in either location). Indoor CO sources were an 

23 important contributor to COHb though these impacted a much larger portion of the simulated 

24 population at the higher COHb levels (i.e., those persons with >1% COHb). For example, in 

25 Denver with indoor sources included, nearly 20% of persons with IHD were estimated to have a 

26 maximum end-of-hour COHb level of about 2.0%. In Los Angeles with indoor sources included, 

27 the estimated percent of persons having a COHb level at or above 2.0% was lower (i.e., about 

28 17%), though still a much greater percentage than that estimated in the absence of indoor sources 

29 (i.e., <1%). 

 

5

 This is consistent with the demographic group modeled in the 1992 assessment described above (Johnson 

et al., 1992; USEPA, 1994), and drew from updated information with regard to prevalence demographics (Johnson 

et al., 2000, p. section 2.5.2). 

6

 A rounding convention allows the second highest 8-hour average CO concentration (i.e., the design value 

(DV)) to be as high as 9.4 ppm for the 8-hour CO NAAQS of 9 ppm. 

7

 Note that the contemporaneous design value for Denver was 0.1 ppm above just meeting the current 8-

hour standard (9.5 versus 9.4 ppm). 
February, 2010 1-6 Draft – Do Not Cite or Quote

1 1.3 CURRENT REVIEW, CASAC ADVICE AND PUBLIC COMMENT 

2 In preparing the Scope and Methods Plan for the REA, we considered the scientific 

3 evidence presented in the first draft ISA (US EPA, 2009c) and the key science policy issues 

4 raised in the IRP (US EPA, 2008b). EPA held a consultation with CASAC to solicit comments 

5 on the Scope and Methods Plan during a May 2009 CASAC meeting at which CASAC also 

6 provided comments on the first draft ISA (Brain, 2009). Public comments were also requested 

7 (74 FR 15265). Those CASAC and public comments were considered in advance of the conduct 

8 of the analyses and results presented in the first draft REA (US EPA, 2009b). 

9 As a result of the notable limitations in available ambient monitoring and 

10 microenvironmental concentration data, staff implemented a much-simplified, screening-level 

11 approach to assess population exposure and dose for the first draft APEX/CO REA (US EPA, 

12 2009b). Two urban study areas, Denver and Los Angeles, were defined as all census tracts 

13 within 20 km of a single fixed-site monitor, using the design value monitor (i.e., the monitor 

14 recording the highest concentration in each area) for the specified year of the assessment. 

15 Therefore, ambient monitoring data associated with the site measuring the highest CO 

16 concentrations were applied to all people within the surrounding study area. This was the case 

17 for scenarios that included as is air quality (year 2006 for either location) and air quality adjusted 

18 to just meeting the current standard (adjusted from 1995 for Denver and from 1997 for Los 

19 Angeles). In the first draft REA, no adjustment was made for spatial variability in ambient 

20 concentrations across each area and at most two microenvironments were included in the 

21 exposure model simulations (in-vehicle and all others). The in-vehicle microenvironmental 

22 factor was constrained to a point estimate of 2.0, that is, these microenvironmental 

23 concentrations would always be twice that observed at the single ambient monitor. In the design 

24 of the assessment, it was noted that this focus on high concentration CO monitors and other 

25 model simplifications would have a tendency to produce higher CO exposures in most simulated 

26 persons than results generated from the 2000 pNEM/CO assessment. The results were consistent 

27 with this statement. The estimated percent of the population at any COHb level was greater in 

28 the first draft REA than that estimated by Johnson et al. (2000). 

29 On November 16-17, 2009, the CASAC CO panel met to discuss the first draft REA. 

30 The final written comments and recommendations were provided to EPA in February 2010 

31 (Brain and Samet, 2010). The design of the current second draft REA builds upon these 

32 recommendations from CASAC, information presented in the final ISA (US EPA, 2010a), as 

well as comments made by the public.8 33 Specifically in this assessment, EPA has: 

 

8

 Public Comments on the first draft REA were submitted to the docket for this review and also presented 

in November, 2009 at the CASAC meeting. 
February, 2010 1-7 Draft – Do Not Cite or Quote

1  Expanded each of the modeling domains to include a greater number of 

2 ambient monitors used as input to APEX; 

3  Increased the number of microenvironments modeled from two to eight; 

4  Improved the representation of variability in estimated 

5 microenvironmental concentrations, including in-vehicles; 

6  Included an algorithm that adjusts for spatial heterogeneity in estimated 

7 outdoor concentrations across each model domain; 

8  Implemented the mass-balance model for estimating concentrations in all 

9 indoor microenvironments; 

10  Implemented the algorithm that allows commuters to experience home11 tract and work-tract ambient concentrations; and 

12  Expanded the at-risk population to address the undiagnosed persons with 

13 CHD. 

14 The purpose of this second draft REA is to seek CASAC review and public comment 

15 regarding our characterization of the results presented considering the improvements made to the 

16 assessment design and inputs used, and CASAC’s advice on the role of this assessment in 

17 informing the current review of the CO NAAQS. 

18 
February, 2010 1-8 Draft – Do Not Cite or Quote

1 1.4 REFERENCES 

2 Brain JD. (2009). Letter from Dr. Joseph Brain to Administrator Lisa Jackson. Re: Consultation on EPA’s Carbon 

3 Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure 

4 Assessment. CASAC-09-012. July 14, 2009. 

5 Brain JD and Samet JM. (2009). Letter from Drs. J.D. Brain and J.M. Samet to Administrator Lisa Jackson. Re: 

6 Review of the EPA’s Integrated Science Assessment for Carbon Monoxide ( First External Review Draft).

7 EPA-CASAC-09-011. June 24, 2009. 

8 Brain JD and Samet JM. (2010). Letter from Drs. J.D. Brain and J.M. Samet to Administrator Lisa Jackson. Re: 

9 Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) Primary 

10 National Ambient Air Quality Standards: First External Review Draft. EPA-CASAC-10-006. February 12, 

11 2010. 

12 Johnson T, Capel J, Paul R, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposure and associated 

13 Carboxyhemoglobin levels in Denver Residents Using a Probabalistic verion of NEM, prepared by 

14 International Technology for U.S. EPA, Office of Air Quality Planning and Standards, Durham, NC, 

15 Contract No. 68-D0-0062, 

16 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (1999). Estimation of Carbon Monoxide Exposures and 

17 Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.0) prepared by 

18 ICF Kaiser Consulting Group for U.S. EPA, Office of Air Quality Planning and Standards, under Contract 

19 No. 68-D6-0064, WA Nos. 1-19 and 2-24. Available at: http://www.epa.gov/ttn/fera/human_related.html 

20 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 

21 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 

22 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 

23 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 

24 at: http://www.epa.gov/ttn/fera/human_related.html.. 

25 Mauderly J. (1999). Letter from Dr. Joe Mauderly, Chair, Clean Air Scientific Advisory Committee, to 

26 Administrator Carole M. Browner. Re: Notification of a Consultation on the Estimation of Carbon 

27 Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver Residents using pNEM/CO 

28 (Ver. 2.0). July 12, 1999. 

29 National Research Council. (2003). Managing Carbon Monoxide Pollution in Meteorological and Topographical 

30 Problem Areas. Washington, D.C. The National Academies Press. 

31 SAIC. (2001). Memo to Harvey Richmmond, EPA, Technical Peer Review (including reviewers comments) of 

32 “Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels for Residents of 

33 Denver and Los Angeles Using pNEM/CO (version 2.1)”, Docket EPA-HQ-OAR-2008-0015. Available 

34 at: http://www.epa.gov/ttn/fera/human_related.html 

35 US EPA. (1979). Air Quality Criteria for Carbon Monoxide. Research Triangle Park, NC: Office of Health and 

36 Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-79-022. 

37 US EPA. (1984). Review of the NAAQS for Carbon Monoxide: Reassessment of Scientific and Technical 

38 Information. Office of Air Quality Planning and Standards, report no. EPA-450/584-904. Research 

39 Triangle Park, NC. 

40 US EPA. (1991). Air Quality Criteria for Carbon Monoxide. Research Triangle Park, NC: Office of Health and 

41 Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-90/045F. 
February, 2010 1-9 Draft – Do Not Cite or Quote

1 US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of 

2 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 

3 EPA/452/R-92-004. 

4 US EPA. (1999). Total Risk Integrated Methodology - TRIM.Expo Technical Support Document, External Review 

5 Draft, November 1999. Office of Air Quality Planning and Standards, U.S. Environmental Protection 

6 Agency, Research Triangle Park, NC, report no. EPA-453/D-99-001. Available at: 

7 http://www.epa.gov/ttn/fera/trim_fate.html#1999historical. 

8 US EPA. (2000). Air Quality Criteria for Carbon Monoxide. National Center for Environmental Assessment, 

9 Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 

10 report no. EPA/600/P-99/001F. June 2000. Available at: 

11 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=18163. 

12 US EPA. (2008a). Draft Plan for Review of the National Ambient Air Quality Standards for Carbon Monoxide. 

13 U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452D-08-001. Also 

14 known as Draft Integrated Review Plan. Available at: 

15 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html. 

16 US EPA. (2008b). Plan for Review of the National Ambient Air Quality Standards for Carbon Monoxide. U.S. 

17 Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452R-08-005. Also known 

18 as Integrated Review Plan. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html. 

19 US EPA. (2009a). Carbon Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health 

20 Risk and Exposure Assessment U. S. Environmental Protection Agency, Research Triangle Park, NC, 

21 report no. EPA-452/R-09-004. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html. 

22 US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary 

23 National Ambient Air Quality Standards: First External Review Draft. U.S. Environmental Protection 

24 Agency, Research Triangle Park, NC, report no. EPA-452/P-09-008. Available at: 

25 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_rea.html 

26 US EPA. (2009c). Integrated Science Assessment for Carbon Monoxide – First External Review Draft. U.S. 

27 Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/600/R-09/019. Available 

28 at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 

29 US EPA. (2010a). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency, 

30 Washington, DC, EPA/600/R-09/019F. Available at: 

31 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 

32 US EPA. (2010b). Policy Assessment to Support the Review of the Carbon Monoxide Primary National Ambient 

33 Air Quality Standards: External Review Draft. U.S. Environmental Protection Agency, Research Triangle 

34 Park, NC, report no. EPA-452/P-10-005. Available at: 

35 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_rea.html. 
February, 2010 2-1 Draft – Do Not Cite or Quote

1 2 CONCEPTUAL OVERVIEW: ASSESSING AMBIENT CARBON 

2 MONOXIDE EXPOSURE AND RISK 

3 In this chapter, we have summarized the conceptual model for assessing exposure to 

4 ambient CO and associated health risk. Subsections focus on different components of the model 

5 including identification of the key emission sources to ambient concentrations (section 2.1), 

6 exposure pathways and relevant microenvironments (section 2.2), identification of at-risk 

7 populations (section 2.3), the dose metric (section 2.4), health endpoints (section 2.5), and the 

8 risk characterization approach (section 2.6). Section 2.7 presents the key observations for this 

9 chapter. 

10 2.1 SOURCES OF CARBON MONOXIDE 

11 Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

12 carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO 

13 emitted from these reactions, relative to the amount of carbon dioxide (CO2) generated, is 

14 sensitive to conditions in the combustion zone. CO production relative to CO2 generally 

15 decreases with any increase in fuel oxygen (O2) content, burn temperature, or mixing time in the 

16 combustion zone (ISA, section 3.2). As a result, CO emissions from large fossil-fueled power 

17 plants are typically very low because optimized fuel consumption conditions make boiler 

18 combustion highly efficient. In contrast, internal combustion engines commonly used to power 

19 mobile sources have widely varying operating conditions. Therefore, higher and more variable 

20 CO emission levels result from the operation of these mobile sources (ISA, section 3.2). In 

21 2002, CO emissions from on-road vehicles accounted for a substantial majority of total 

emissions by individual source sectors in the U.S. (ISA, Figure 3-1).1 22 As in previous NAAQS 

23 reviews, mobile sources continue to be a significant emission source of CO to ambient air. 

24 Sources of CO inside buildings include infiltration of ambient air indoors, as well as, 

25 where present, indoor (nonambient) sources such as gas stoves and tobacco smoke (ISA, section 

26 3.6.5.2). In addition to infiltration of ambient air, CO inside motor vehicles may also receive 

27 contributions from nonambient sources in the cabin, which can be substantial under air 

28 ventilation modes that limit inflow from outside the vehicle (ISA, p. 3-89). However, the focus 

29 of this REA, which is conducted to inform the current review of the CO NAAQS, is on sources 

30 of ambient CO. We provide quantitative estimates of population exposure and dose originating 

31 from ambient CO in two urban areas (details on site selection are provided in chapter 3 below). 

 

1

 The 2002 National Emissions Inventory (NEI) was the most recently available NEI meeting data quality 

objectives for the ISA. The NEI includes data from various sources such as industries and state, tribal, and local air 

agencies (ISA, p. 3-1). 
February, 2010 2-2 Draft – Do Not Cite or Quote

1 The exposure modeling in this assessment does not quantitatively estimate the contribution of 

2 indoor sources to an individual’s total exposure and dose. This assessment does however 

3 qualitatively draw upon available information regarding potential indoor source contributions to 

4 estimated population exposure and dose (described in section 2.2 below). 

5 2.2 EXPOSURE PATHWAYS AND IMPORTANT MICROENVIRONMENTS 

6 Human exposure to CO involves the contact (via inhalation) between a person and the 

7 pollutant in the various locations (or microenvironments) in which people spend their time. 

8 Studies of personal exposure have generally found that the largest portion of the day is generally 

9 spent indoors and the largest percentage of the time in which an individual is exposed to ambient 

10 CO occurs indoors (ISA, sections 2.3 and 3.6). As a result, CO concentrations in indoor 

11 microenvironments are an important determinant of an individual’s total CO exposure. Recent 

12 population exposure studies conducted in Milan, Italy support this conclusion (Bruinen de Bruin 

13 et al., 2004), indicating that over 80% of the population exposure to CO can occur in indoor 

14 microenvironments (ISA, Table 3-13). Taking into account the infiltration of ambient CO 

15 indoors, indoor CO concentrations are similarly an important determinant in an individual’s 

16 exposure to ambient CO. 

17 Microenvironments that may influence CO exposures typically include residential indoor 

18 environments and other indoor locations, near-traffic outdoor microenvironments and other 

19 outdoor locations, and inside vehicles. Consideration of microenvironmental exposures 

20 illustrates the variability in the relationship between personal exposure and ambient 

21 concentrations. For example, one study summarized the relationship between personal CO 

22 exposure concentrations in five broadly defined microenvironments (i.e., indoor residence, 

23 indoor other, outdoor near road, outdoor other, and in-vehicle) and ambient CO concentrations in 

24 Baltimore, MD (ISA, section 3.6.5.2; Chang et al., 2000). On average, the indoor-to-ambient 

25 and outdoor-to-ambient concentration ratios were about one, though most of the ratios observed 

26 across this set of indoor and outdoor microenvironments were less than one. With the exception 

27 of ratios for the in-vehicle microenvironments, which as a group were generally above one, few 

28 ratios were above unity (ISA, p. 3-85, Figure 3-46). Given the expected stability of CO as it 

29 infiltrates indoor microenvironments from outdoor air and the lack of significant removal 

30 mechanisms of CO in outdoor microenvironments, it is likely that the variability in 

31 personal/microenvironmental-to-ambient and outdoor-to-ambient concentration ratios is the 

32 result of spatial and temporal variability in outdoor concentrations with respect to simultaneously 

33 measured ambient concentrations at fixed-site monitors, and also reflects the impact of lag time 

34 associated with attaining steady state relationships, as well as potential presence of non-ambient 

35 sources. 
February, 2010 2-3 Draft – Do Not Cite or Quote

1 Typically the highest CO exposure concentrations are experienced while inside vehicles. 

2 Because motor vehicles continue to be important contributors to ambient CO concentrations, 

3 both the time spent in motor vehicles and the elevated CO concentrations occurring on and near 

4 heavily trafficked roads continue to be important contributors to personal exposures. For 

5 example, in the study summarized above on personal exposures occurring within particular 

6 microenvironments (i.e., Chang et al., 2000), most in-vehicle CO exposure-to-ambient 

7 concentration ratios were greater than one, with the median being approximately 2.5. The 

8 average ratio was approximately 2.5 in summer, but a few somewhat higher in-vehicle 

9 measurements in the winter period, contributed to a winter average of approximately 4 (ISA, 

section 3.6.5.2, Figure 3-46; Chang et al., 2000 Figure 5).2 10 Given this relationship, it should not 

11 be surprising that while about 8% of a person’s time per day is spent in transit, 13-17% of their 

12 total daily exposure occurs within an in-vehicle microenvironment (e.g., Bruinen de Bruin et al., 

13 2004; Scotto di Marco et al., 2005). 

14 A similar influence of mobile source-influenced microenvironments was observed in the 

15 CO population exposure studies conducted in Denver CO and Washington, DC during the winter 

of 1982 and 1983 (Akland et al., 1985).3 16 In both cities, when comparing the distribution of 

17 measured CO concentrations from the monitoring network to measured personal exposures, two 

18 common phenomena were observed. At the lowest percentiles of each distribution, ambient CO 

19 concentrations were consistently greater than the personal exposures. At the highest percentiles 

20 of each distribution, ambient concentrations were consistently lower than the personal exposures 

21 (US EPA, 2000). These studies determined that the highest average CO concentrations occurred 

22 when subjects were in a mobile source-influenced microenvironment (e.g., inside parking 

23 garages, in-vehicles). Commute time was also a factor; those who commuted 6 hours or more 

24 per week had higher average exposures than those who commuted fewer hours per week. 

25 Furthermore, mean CO concentrations within in-vehicle microenvironments (ranging from 7.0 to 

26 9.8 ppm) were greater than common outdoor locations (ranging from 1.4 to 3.2 ppm) (US EPA, 

27 2000). In considering the results from the Denver and Washington personal exposure studies it 

28 is important to recognize that CO emissions from motor vehicle sources have declined 

29 dramatically since the early 1980’s when these studies were conducted. Consequently, both 

30 ambient fixed-site CO concentrations and in-vehicle CO concentrations have also been reduced 

31 significantly since that time period. 

 

2

 Information on the distance of the ambient monitors from highly trafficked roadways or potential for invehicle (nonambient) sources was not provided. 

3

 Both studies collected measurements and activity pattern diaries from a random sample of the population, 

defined as including non-institutionalized, non-smoking residents, 18 to 70 years of age, who lived in each 

respective city’s metropolitan area (Akland et al., 1985). 
February, 2010 2-4 Draft – Do Not Cite or Quote

1 Given their influence on ambient exposures, exposures to CO near roadways and in 

2 vehicle microenvironments are of particular importance in this assessment. Data from several 

3 studies that have compared concentrations inside vehicles to concentrations immediately outside 

4 vehicles, indicates that indoor/outdoor concentration (I/O) ratios on average range from just 

5 above to just below unity (Chan et al., 1991; Rodes et al., 1998; Boulter and McCrae, 2005; 

6 Sharp and Tight, 1997). These studies are supported by a review by Flachsbart (1999) regarding 

7 other studies published between 1982 and 1992 that measured interior and exterior CO 

8 concentrations simultaneously during motor vehicle trips and reported I/O ratios just below unity 

9 (Petersen and Allen, 1982; Koushi et al., 1992). Some studies reported no effect of ventilation 

10 setting on I/O ratios, while others reported an effect. For example, one study described in the 

11 ISA indicated I/O ratios could exceed unity with the ventilation set to re-circulate vehicle air 

12 (Abi Esber and El-Fadel, 2008). However, the study authors attributed this finding to 

13 unaccounted sources of CO that caused increases in CO concentrations within the vehicle cabin 

14 under those conditions (ISA, section 3.6.6.2; Abi Esber and El-Fadel, 2008). 

15 In general, the above results suggest that the I/O ratio tends toward unity when there are 

16 no interior sources of CO, the automobile engine does not contribute directly to its own interior 

17 concentrations, and the measurement probes are properly installed on the vehicle. This 

18 conclusion is consistent with theoretical expectations for a non-reactive pollutant. For example, 

19 CO concentrations inside vehicles can be estimated as a function of outside CO concentration, 

20 air exchange rate, a penetration factor, and the emission rates of indoor sources (e.g., exhaust 

21 leaks, smoking). If one assumes that (1) steady-state ventilation conditions exist, (2) the indoor 

22 removal rate (k) is zero (i.e., no loss of CO as it moves from outside to inside the vehicle), and 

23 (3) there are zero emissions from interior sources, then the CO concentration inside a vehicle can 

24 be simplified to a function of outside CO concentrations and the penetration rate (i.e., infiltration 

is generally equivalent to penetration).4 25 Under these stated conditions, the I/O ratio would 

26 ultimately converge to unity. 

27 There are a few studies that have measured both in-vehicle and fixed-site monitoring 

28 concentrations. The data from these studies can also inform the development of 

29 microenvironmental factors used for estimating in-vehicle CO exposures. The ISA notes that 

30 studies summarized in the 2000 CO AQCD found that in-vehicle CO concentrations were 

31 generally two to five times higher than ambient CO concentrations obtained at fixed-site 

32 monitors within the cities studied. For example, Shikiya et al. (1989) reported such 

33 concentrations measured as part of a southern California study. When using the reported in34 vehicle CO measurements, one could estimate concentration ratios ranging from 1.8 to 2.7, a 

 

4

 See section 3.6.2 of the ISA. 
February, 2010 2-5 Draft – Do Not Cite or Quote

1 range of ratios dependent on the time-of-year measurements were collected. Note however that 

2 there are several factors that could contribute to variability in reported or calculated 

3 concentration ratios. For example, often times in these measurement studies, the averaging time 

4 associated with the companion measurements differ, that is there may be a much shorter 

5 sampling interval for the in-vehicle measurement when compared with that of the ambient 

6 monitor. More specifically, Shikiya et al. (1989) measured in-vehicle CO concentrations during 

7 commutes lasting, on average, 33 minutes, while fixed site monitoring values averaged over 4-

8 hours. It is likely that the time-averaged concentrations are less than that of the true fixed-site 

9 concentrations that occurred during the 33 minute commute, perhaps resulting in an 

10 overestimation of the concentration ratios when using this data. Furthermore, Shikiya et al. 

11 (1989) reported seasonal differences for the in-vehicle CO concentrations (winter averaged 10.1 

12 ppm; summer averaged 6.5 ppm), but not for the fixed-site monitor (average for both seasons 

13 was 3.7 ppm). Typically ambient concentrations are greater in winter (e.g., ISA Figure 3-22 for 

14 Los Angeles). Therefore, when using the fixed-site seasonal average and in-vehicle seasonally 

15 stratified measurements from Shikiya et al. (1989) to calculate the ratios as was done above, the 

16 winter value may be overestimated while the summer value could be underestimated. In addition 

17 to the factors mentioned above, this relationship can vary based on several other factors that may 

18 influence the fixed-site monitor concentration, such as the nearby roadway traffic density, the 

19 monitor siting characteristics (e.g., proximity to the roadway), and local scale meteorology (e.g., 

20 downwind), with each described in greater detail in chapter 3. Of the few studies reporting in21 vehicle and companion fixed-site measurements, most do not measure all of the potentially 

22 influential factors or provide the data stratified by such factors. Thus, a general range of two to 

23 five may be adequate to represent the total variability for this particular relationship, recognizing 

24 that there are limitations in the available measurement data to define this relationship. 

25 Although not the focus of this review, indoor sources such as gas stoves and 

26 environmental tobacco smoke can, where present, also be important contributors to total CO 

27 exposure and may be of concern for such at-risk populations as individuals with cardiovascular 

28 disease, among others (see section 2.3 below). For example, some assessments performed for 

29 previous reviews have included modeling simulations both without and with indoor sources (gas 

30 stoves and tobacco smoke) to provide context for the assessment of ambient CO exposure and 

31 dose (e.g., US EPA, 1992; Johnson et al., 2000). The 2000 pNEM/CO simulations with indoor 

32 sources indicated that the impact of such sources on the proportion of the population 
February, 2010 2-6 Draft – Do Not Cite or Quote

1 experiencing higher exposures and COHb levels can be substantial, as summarized in section 1.2 

above.5 2 

3 2.3 EXPOSURE AND DOSE METRICS 

4 Exposure concentration over a time period of interest (e.g., one hour or eight hours) is a 

5 common exposure metric which reflects the integration of exposures to pollutant concentrations 

6 that occur in each microenvironment in which time is spent (see section 4.4.6 below). In the case 

7 of CO, for which the common mechanism underlying biological response is binding to heme 

8 proteins, COHb level in blood is well recognized as an important internal dose metric used in 

9 evaluating CO exposure and the potential for health effects (ISA, p. 2-4, sections 4.1, 4.2, 5.1.1). 

10 Accordingly, COHb levels are used in this assessment. 

11 Carboxyhemoglobin occurs in the blood due to endogenous CO production from 

12 biochemical reactions associated with normal breakdown of heme proteins, as well as in 

response to inhaled (exogenous) ambient and nonambient6

 CO exposures (ISA, section 4.5).7 13 

14 Levels of endogenous COHb in healthy individuals have been described to range down to 0.3% 

15 and generally be less than 1% (ISA, pp. 4-9, 4-23, 2-6). However, the production of endogenous 

16 CO and levels of endogenous COHb vary with several physiological characteristics (e.g., slower 

17 COHb elimination with increasing age), as well as some disease states, which can lead to higher 

18 endogenous levels in some individuals (ISA, section 4.5). Other factors affecting CO uptake and 

19 elimination include physical activity and altitude (ISA, section 4.4). 

20 The amount of COHb formed in response to exogenous CO is dependent on the CO 

21 concentration and duration of exposure, exercise (which increases the amount of air removed and 

22 replaced per unit of time for gas exchange), the pulmonary diffusing capacity for CO, ambient 

23 pressure, health status, and the specific metabolism of the exposed individual. The formation of 

24 COHb is a reversible process, but the high affinity of CO for Hb, which affects the elimination 

25 half-time for COHb, can lead to accumulation of COHb in some circumstances. Fortunately, 

26 mechanisms exist in normal, healthy individuals to compensate for the reduction in tissue oxygen 

27 caused by increasing levels of COHb. Cardiac output increases and blood vessels dilate to carry 

28 more blood so that the tissue can extract adequate amounts of oxygen from the blood (ISA, 

29 chapter 4). As discussed in sections 2.4 and 2.5 below, however, there are several medical 

 

5

 As has been recognized in previous CO NAAQS reviews, such sources cannot be effectively mitigated by 

setting more stringent ambient air quality standards (59 FR 38914), and are therefore not a focus of this assessment. 

6

 A significant source of nonambient CO long recognized as contributing to elevated COHb levels is 

tobacco smoking (e.g., ISA, Figure 4-12). 

7

 The dosimetry and pharmacokinetics of CO are discussed in detail in chapter 4 of the ISA. 
February, 2010 2-7 Draft – Do Not Cite or Quote

1 disorders that can make an individual more susceptible to the potential adverse effects of low 

2 levels of CO, especially during exercise. 

3 As described in section 4.4.7 below, blood levels of COHb have been estimated in this 

4 REA using a nonlinear solution of the Coburn-Forster-Kane (CFK) model (Coburn et al., 1965), 

5 which remains “the most extensively validated and applied model for COHb prediction (ISA, 

6 section 4.2.3). 

7 2.4 AT-RISK POPULATIONS 

8 The term ‘susceptibility’ (and the term “at-risk”) has been used to recognize populations 

9 that have a greater likelihood of experiencing effects related to ambient CO exposure (ISA, 

10 section 5.7). This increased likelihood of response to CO can potentially result from many 

11 factors, including pre-existing medical disorders or disease states, age, gender, lifestyle or 

12 increased exposures (ISA, section 5.7). For example, medical disorders that limit the flow of 

13 oxygenated blood to the tissues have the potential to make an individual more susceptible to the 

14 potential adverse effects of low levels of CO, especially during exercise. Based on the available 

15 evidence in the current review, coronary artery disease (CAD), also known as coronary heart 

16 disease (CHD) is the “most important susceptibility characteristic for increased risk due to CO 

17 exposure” (ISA, p. 2-11). While persons with a normal cardiovascular system can tolerate 

18 substantial concentrations of CO if they vasodilate or increase cardiac output in response to the 

19 hypoxia produced by CO, those that are unable to vasodilate in response to CO exposure may 

20 show evidence of ischemia at low concentrations of COHb (ISA, p. 2-10). There is strong 

21 evidence for this in controlled human exposure studies of exercising individuals with CAD, 

22 which is supported by results from recent epidemiologic studies reporting associations between 

23 short-term CO exposure and increased risk of emergency department visits and hospital 

admissions for individuals affected with ischemic heart disease (IHD)8 24 and related outcomes 

25 (ISA, section 5.7). This combined evidence, briefly summarized in section 2.5.1 below and 

26 described in more detail in the ISA, supports the conclusion that individuals with CAD represent 

27 the population most susceptible to increased risk of CO-induced health effects (ISA, sections 

28 5.7.1.1 and 5.7.8). The 2007 estimate of the size of the U.S. population with coronary heart 

29 disease, inclusive of those with angina pectoris (cardiac chest pain) and those who have 

 

8

 Ischemic heart disease is a category of cardiovascular disease associated with narrowed heart arteries, 

which is often also called CAD (coronary artery disease) and CHD (coronary heart disease). Individuals with CHD 

have myocardial ischemia, which occurs when the heart muscle receives insufficient oxygen delivered by the blood. 

Exercise-induced angina pectoris (chest pain) occurs in many of them. Among all patients with diagnosed CAD, the 

predominant type of ischemia, as identified by ST segment depression, is asymptomatic (i.e., silent). Also, patients 

who experience angina typically have additional ischemic episodes that are asymptomatic (2000 AQCD, section 

7.7.2.1). In addition to such chronic conditions, CHD can include myocardial infarction (ISA, p. 5-24). 
February, 2010 2-8 Draft – Do Not Cite or Quote

1 experienced a heart attack (ISA, Table 5-26) is 13.7 million people, some fraction of whom have 

2 IHD (ISA, pp.5-117). Further, there are estimated to be several million additional people with 

3 silent ischemia or undiagnosed IHD (AHA, 2003). In combination this represents a large 

4 population that is more susceptible to ambient CO exposure when compared to the general 

5 population (ISA, section 5.7). 

Other types of cardiovascular disease9 6 may also potentially contribute to increased 

7 susceptibility to the adverse effects of low levels of CO, especially during exercise (ISA, section 

8 5.7.1.1). For example, some evidence with regard to other types of cardiovascular disease such 

9 as congestive heart failure, arrhythmia, and non-specific cardiovascular disease, although more 

10 limited for peripheral vascular and cerebrovascular disease, indicates that “the continuous nature 

11 of the progression of CAD and its close relationship with other forms of cardiovascular disease 

12 suggest that a larger population than just those individuals with a prior diagnosis of CAD may be 

13 susceptible to health effects from CO exposure” (ISA, p. 5-117). 

14 Other populations considered to be potentially at increased risk relative to the general 

15 population due to gender differences, aging, or preexisting disease or because of the use of 

16 medications or alterations in their environment, include fetuses and young infants; the elderly, 

17 especially those with compromised cardiovascular function; individuals with hematologic 

18 diseases (e.g., anemia) that affect oxygen-carrying capacity or transport in the blood; those with 

19 chronic obstructive pulmonary disease; people using medicinal or recreational drugs with central 

20 nervous system depressant properties; individuals exposed to other chemical substances that 

21 increase endogenous formation of CO; individuals who have not adapted to high altitude and are 

22 exposed to a combination of high altitude and CO; and people that spend a substantial amount of 

23 time on or near heavily traveled which may contribute to higher CO exposures (ISA, section 

24 5.7). For example, although the effects of CO on maternal-fetal relationships are not well 

25 understood, fetal circulation is likely to have a higher COHb level than the maternal circulation 

26 because of differences in uptake and elimination of CO from fetal Hb, which may contribute to 

27 an enhanced sensitivity to CO exposure during gestation (ISA, section 5.7.2.2). Additionally, 

28 although there are no controlled human exposure or epidemiological studies examining potential 

29 CO-induced effects in people suffering with anemia, it is reasonable to assume that the potential 

30 combination of hypoxic effects of CO together with reduced oxygen availability and/or elevated 

 

9

 Cardiovascular disease comprises many types of medical disorders, including heart disease, 

cerebrovascular disease (e.g., stroke), hypertension (high blood pressure), and peripheral vascular diseases. Heart 

disease, in turn, comprises several types of disorders, including ischemic heart disease (i.e., coronary heart disease 

[CHD], CAD, myocardial infarction, and angina), congestive heart failure, and disturbances of cardiac rhythm 

(dysrhythmias and arryhthmias) (2000 AQCD, p. 7-7). 
February, 2010 2-9 Draft – Do Not Cite or Quote

baseline COHb levels in people suffering with anemia10 1 may make this population susceptible to 

2 CO-induced effects (ISA, section 5.7.1.4). Asthma and COPD are other oxygen-limiting 

3 diseases which may be exacerbated by CO-related oxygen limitation. Other individuals that may 

4 be potentially susceptible to CO are those that may have increased endogenous production of CO 

5 and potentially higher endogenous COHb levels such as diabetics, for which a few 

6 epidemiological studies provide suggestive evidence of increased risk for cardiovascular 

7 emergency department visits and hospital admissions compared to non-diabetics in response to 

8 short-term CO concentrations (ISA, section 5.7.1.3). 

9 Based on the current evidence, most particularly with regard to quantitative information 

10 of COHb levels and association with specific health effects, the primary target population for 

11 purposes of the quantitative assessment described in this document is adults with CHD (also 

known as ischemic heart disease IHD or CAD), both diagnosed and undiagnosed.11 12 Little 

13 empirical evidence is available by which to specify health effects associated with CO exposures 

14 in the other, potentially at-risk groups identified above. Such evidence characterizing the nature 

15 of specific health effects of CO in these populations is limited and does not include COHb levels 

16 related to health effects in these groups. As a result, while we continue to recognize the potential 

17 susceptibility of the larger cardiovascular disease population to health effects of CO, as has been 

18 recognized in past reviews, as well as the potential susceptibility of several other populations 

19 identified above (ISA, section 5.7), the at-risk population simulated in this assessment is 

20 individuals with CAD (diagnosed and undiagnosed and inclusive of individuals with angina 

21 pectoris and heart attacks). We note, however, that the larger cardiovascular disease population 

22 and the potential susceptibility of other populations is further considered with regard to the 

23 review of the CO NAAQS in the draft Policy Assessment document (US EPA, 2010b). 

24 2.5 HEALTH ENDPOINTS 

25 Carbon monoxide elicits various health effects by binding to reduced iron in heme 

26 proteins and altering the functioning of a number of heme proteins (ISA, sections 4.6 and 5.1). 

27 The level of CO bound to hemoglobin as carboxyhemoglobin (COHb) in the blood is the best 

28 characterized dose metric for evaluating CO exposure and the potential for associated health 

29 effects, as described in section 2.3 above. 

 

10 Individuals affected with anemias of different etiologies may have low hematocrit, reduced capacity of 

the blood to carry oxygen, or increased COHb levels, all of which would decrease the oxygen available for organs 

and tissues (ISA, pp. 118-119). 

11 As described in section 1.2 above, this is the same population group that was the focus of the 

exposure/dose assessments conducted previously (e.g., US EPA, 1992; Johnson et al., 2000). 
February, 2010 2-10 Draft – Do Not Cite or Quote

1 The best characterized health effect associated with CO levels of concern is hypoxia 

2 (reduced oxygen availability) induced by increased COHb levels in blood (ISA, section 5.1.2). 

3 The formation of COHb reduces the oxygen carrying capacity of the blood and impairs the 

4 release of oxygen from oxy-hemoglobin complexes to the tissues. Accordingly, CO is especially 

5 harmful in individuals with impaired cardiovascular systems (as discussed in section 2.4 above) 

6 and the clearest evidence of causal relationships with CO exists for cardiovascular effects. In 

7 characterizing the combined evidence, the ISA concluded that cardiovascular effects are likely 

8 causally related to short-term exposures to CO at relevant concentrations, with “relevant CO 

9 concentrations” defined in the ISA as “generally within one or two orders of magnitude of 

10 ambient CO concentrations” (ISA, p. 2-5). The “most compelling evidence of CO-induced 

11 effects on the cardiovascular system comes from a series of controlled human exposure studies 

12 among individuals with coronary heart disease (CHD) (ISA, sections 5.2.4 and 5.2.6). 

13 Other potential effects of CO which are less well characterized at relevant exposure 

14 concentrations are those on the central nervous system, reproduction and prenatal development, 

15 and the respiratory system (ISA, section 2.5). These additional health endpoints, for which the 

16 limited available evidence is suggestive of causal relationships (ISA, sections 5.3, 5.4 and 5.5), 

17 are also considered in this review and are discussed in detail in the ISA and summarized briefly 

18 in section 2.5.2 below. Across the health endpoints identified here, however, the focus of the 

19 quantitative analysis described in this document is on cardiovascular disease-related effects that 

20 have been observed in adults with CHD, most specifically decreased time to exercise-induced 

21 angina and changes to the “ST” segment of an electrocardiogram that are indicative of 

22 myocardial ischemia. This focus is based on the strength of the evidence and availability of 

23 quantitative information from human studies of controlled CO exposures in which the resulting 

24 COHb levels were associated with these effects (as discussed in sections 2.5.1 and 2.6 below). 

25 2.5.1 Cardiovascular Disease-related Effects 

26 The best characterized cardiovascular disease-related effects associated with CO are 

27 markers of myocardial ischemia observed in studies of controlled CO exposures of CHD 

patients12 28 and effects on exercise duration and maximal aerobic capacity observed in controlled 

exposure studies of healthy adults.13 29 As noted in the ISA, the decreases in exercise duration 

 

12 Epidemiological studies have consistently shown associations between ambient CO measurements and 

emergency department visits and hospital admissions for IHD, which is coherent with the effects observed in 

controlled human exposure studies of CAD patients. Additional studies have shown associations between ambient 

CO and hospital admissions for congestive heart failure and cardiovascular disease as a whole (which includes 

IHD), although this evidence is not as consistent among studies as the IHD evidence. 

13 Human clinical studies of individuals without diagnosed heart disease that were conducted since the 2000 

CO AQCD did not report an association between CO and ST-segment changes or arrhythmia (ISA, section 2.5.1). 
February, 2010 2-11 Draft – Do Not Cite or Quote

1 among healthy adults (associated with COHb levels from 3 up to 20%) were relatively small and 

2 only likely to be noticed by competing athletes, although they are considered to provide 

3 coherence with the exercise-induced cardiovascular effects of greater concern that have been 

4 demonstrated in CHD patients. The controlled human exposure studies involving individuals 

5 with preexisting CHD provide strong evidence for an association between short-term exposure to 

6 CO and measures of ischemia (US EPA, 2000, section 6.2.2; ISA, section 5.2.4). Multiple 

7 controlled human exposure studies have shown that short-term exposure to CO and subsequent 

8 elevation of COHb to levels of approximately 2-6% reduces time to onset of exercise-induced 

9 myocardial ischemia in individuals with preexisting CAD, with no evidence of a threshold at the 

10 lowest levels tested (ISA, section 5.2.4). 

11 The controlled exposure study of principal importance is a large multi-laboratory study 

12 designed to evaluate myocardial ischemia, as documented by reductions in time to change in the 

ST-segment of an electrocardiogram14 13 and in time to onset of angina, during a standard treadmill 

14 test, at CO exposures targeted to result in mean subject COHb levels of 2% and 4%, as measured 

by gas chromatographic technique15 15 (ISA, section 5.2.4, from Allred et al., 1989a, 1989b, 1991). 

16 In this study, subjects on three separate occasions underwent an initial graded exercise treadmill 

17 test, followed by 50- to 70-min exposures under resting conditions to average CO concentrations 

18 of 0.7 ppm (room air concentration range 0-2 ppm), 117 ppm (range 42-202 ppm) and 253 ppm 

19 (range 143-357 ppm). After the 50- to 70-min exposures, subjects underwent a second graded 

20 exercise treadmill test, and the percent change in time to onset of angina and time to ST endpoint 

21 between the first and second exercise tests was determined. Relative to clean-air exposure that 

22 resulted in a mean COHb level of 0.6% (post-exercise), exposures to CO resulting in postexercise mean COHb concentrations of 2.0% and 3.9%16 23 were shown to decrease the time 

24 required to induce ST-segment changes by 5.1% (p=0.01) and 12.1% (p<0.001), respectively. 

25 These changes were well correlated with the onset of exercise-induced angina the time to which 

26 was shortened by 4.2% (p=0.027) and 7.1% (p=0.002), respectively, for the two CO exposures 

27 (ISA, section 5.2.4; (Allred et al., 1989a,1989b, 1991). 

 

14 The S-T segment is a portion of the electrocardiogram, depression of which is an indication of 

insufficient oxygen supply to the heart muscle tissue 

15 As stated in the ISA, the gas chromatographic technique for measuring COHb levels “is known to be 

more accurate than spectrophotometric measurements, particularly for samples containing COHb concentrations < 

5%” (ISA, p. 5-41). CO-oximetry is a spectrophotometric method commonly used to rapidly provide approximate 

concentrations of COHb during controlled exposures (ISA, p. 5-41). At the low concentrations of COHb (<5%) 

more relevant to exposures to ambient CO, co-oximeters are reported to overestimate COHb levels compared to GC 

measurements, while at higher concentrations, this method is reported to produce underestimates (ISA, p.4-18). 

16 The corresponding co-oximeter measured post-exercise levels were 2.7% and 4.7%. The post-exposure, 

pre-exercise COHb levels for the two CO exposures were 2.4% and 4.7% by GC and 3.2% and 5.6% by co-oximetry 

(ISA, p. 5-41). 
February, 2010 2-12 Draft – Do Not Cite or Quote

1 No human clinical studies have been specifically designed to evaluate the effect of 

2 controlled exposures to CO resulting in study mean COHb levels lower than 2% (ISA, section 

3 5.2.6). However, an important finding of the multi-laboratory study was the dose-response 

4 relationship observed between COHb and ischemia without evidence of a measurable threshold 

5 effect (Allred et al., 1991; Allred et al., 1989b). As reported by the authors, the results 

6 comparing “the effects of increasing COHb from baseline levels (0.6%) to 2 and 3.9% COHb 

7 showed that each produced further changes in objective ECG measures of ischemia” implying 

8 that “small increments in COHb could adversely affect myocardial function and produce 

9 ischemia” (Allred et al., 1991; Allred et al., 1989b). For each 1% increase in COHb resulting 

10 from the experimentally increased CO exposure concentrations the dose-response analysis 

11 performed by the authors indicated decreases of 1.9% in time to exercise-induced angina and 

12 3.9% in time to exercised-induced ST-segment change in persons with pre-existing CAD (ISA, 

13 section 5.2.4; Allred et al., 1989a,1989b, 1991). 

14 Other controlled human exposure studies (Adams et al. 1988, Anderson et al. 1973, 

15 Kleinman et al. 1989, Kleinman et al., 1998) involving individuals with stable angina have 

16 confirmed the Allred et al findings at COHb concentrations between 3 and 6% (as measured by 

17 CO-oximeter) (ISA, section 5.2.4). Among the evidence is also a study of a small group of 

18 patients with CAD which reported no change in time to onset of angina or maximal exercise time 

19 following a 1 hour exposure targeted to result in 4% COHb. A subsequent study conducted by 

20 the same laboratory reported a significant increase in number of ventricular arrhythmias during 

21 exercise relative to room air among individuals with CAD following a 1-hr CO exposure targeted 

22 to yield 6% COHb, but not following a 1-hr exposure targeted to yield a COHb level of 4% (ISA, 

23 p. 5-42; Sheps et al., 1990; Sheps et al., 1987). Although there was no clear pattern across the 

24 different studies with respect to the magnitude of the decreased time to onset of angina versus 

25 dose level, differences in study protocols and analytical methods do not allow for an informative 

26 pooled or quantitative metaanalysis of the dose-response relationship across studies (ISA, section 

27 5.2.4). 

28 Although the subjects evaluated in the controlled human exposure studies described 

29 above are not necessarily representative of the most sensitive population, the level of disease in 

30 these individuals ranged from moderate to severe, with the majority either having a history of 

31 myocardial infarction or having ≥ 70% occlusion of one or more of the coronary arteries (ISA, p. 

32 5-43). 

33 2.5.2 Other Effects 

34 Other health effects for which the evidence is suggestive of causal relationships with 

35 short-term CO exposures include some effects on the central nervous system, reproduction and 
February, 2010 2-13 Draft – Do Not Cite or Quote

1 prenatal development, and the respiratory system (ISA, section 2.5). High CO exposures have 

2 “long been known to adversely affect central nervous system (CNS) function”, although a 

3 relationship close to ambient levels is less clear (ISA, pp. 5-49). Further, the evidence indicates 

4 that healthy adults may be protected against such effects at ambient levels through compensatory 

5 responses such as increased cardiac output and cerebral blood flow, although these compensatory 

6 mechanisms may be impaired among susceptible groups, including those with reduced 

7 cardiovascular function (ISA, section 5.3.3). Limited evidence indicates an association of CO 

8 exposure during early pregnancy with pre-term births and birth defects (ISA, p. 2-8). New 

9 epidemiologic studies report positive associations for CO-induced lung-related outcomes, 

10 although interpretation is affected by uncertainties including with regard to the biological 

11 mechanism that could explain CO-induced respiratory outcomes (ISA, section 5.5.5). 

12 2.6 RISK CHARACTERIZATION APPROACH 

13 In identifying an approach to characterize the risk of cardiovascular effects of exposures 

14 to ambient CO, we considered 1) approaches employed in previous assessments, 2) the currently 

15 available evidence regarding associations between CO concentrations and cardiovascular 

16 outcomes, and 3) advice from CASAC (Brain, 2009; Brain and Samet, 2009; Brain and Samet, 

17 2010). As summarized in section 1.2 above, the last CO NAAQS review included analyses of 

18 exposure to ambient CO and associated internal dose, in terms of COHb levels, which were used 

19 to characterize risks for the population of interest (USEPA, 1992). The prior risk 

20 characterization considered the percent of the modeled population that exceeded COHb levels of 

21 interest which were drawn from the evidence of COHb levels associated with exercise-induced 

22 aggravation of angina in controlled human exposure studies involving short-term (shorter than 8 

hours) exposures of patients with diagnosed CAD17 23 to elevated CO concentrations (US EPA, 

24 1991). 

25 In the current review, the controlled human exposure studies among individuals with 

26 CAD continue to provide the clearest evidence of CO-induced effects on the cardiovascular 

27 system as the most sensitive endpoint. In contrast to epidemiological studies, human exposure 

28 studies also provide quantitative information linking CO exposures through COHb levels with 

29 these effects. Among these studies, the multilaboratory study of Allred et al (1989a, 1989b, 

30 1991) continues to be the principal study informing our understanding of the effects of CO on 

31 individuals with pre-existing CAD at the low end of the range of COHb levels studied (USEPA, 

32 1991, 2000, 2010). The strength of the evidence more broadly continues to support the use of 

 

17 Study subjects met certain criteria with respect to evidence of coronary artery disease, often also called 

CHD or IHD. 
February, 2010 2-14 Draft – Do Not Cite or Quote

1 COHb level as the internal dose metric for assessing exposure to ambient levels of CO and 

2 characterizing associated potential for health risk. Thus, based on the strength of the evidence 

3 and the availability of quantitative information from controlled human exposure studies, this 

4 REA also focuses on estimates of the percent of the simulated population expected to experience 

5 maximum end-of-hour COHb levels of interest based on findings of those studies. 

6 We also note that, in the current review, a number of epidemiological studies are now 

7 available that investigate associations of cardiovascular morbidity with ambient measurements of 

CO (ISA, sections 5.2.4 and 5.2.5).18 8 These studies have observed associations between ambient 

9 monitor CO concentrations and increases in emergency department visits and hospital 

10 admissions for cardiovascular effects (ISA, sections 5.2.1.9). While these studies are coherent 

11 with the controlled human exposure studies (ISA, section 5.2.6), we recognize a number of 

12 uncertainties that complicate their use for our purposes in a quantitative risk assessment (ISA, 

13 pp. 2-14 to 2-17, section 5.2.3). 

14  “This [epidemiological] evidence for ischemia-related outcomes is coherent with 

15 effects observed in controlled human exposure studies, although uncertainty regarding 

16 the extent of reduced O2 delivery to tissues following exposure to ambient CO 

17 concentrations contributes to the uncertainty in quantitative interpretation of effect 

18 estimates.“ [ISA, p. 2-14] 

19  The correlation between concentrations of CO and other combustion-related pollutants 

20 “complicates the quantitative interpretation of effect estimates in these studies to 

21 apportion the relative extent to which CO at ambient concentrations is independently 

22 associated with cardiovascular or other effects, and the extent to which CO acts as a 

23 marker for the effects of another combustion-related pollutant or mix of pollutants.” 

24 [ISA, p. 2-16] 

25  Although the epidemiological evidence indicates that CO associations generally remain 

26 robust in copollutant models, uncertainty associated with the use of these models 

27 “complicates quantitative interpretation of the effect estimates reported in 

28 epidemiologic studies” [ISA, p. 2-16] 

29  “Some of these uncertainties [identified in 2000 AQCD] remain and complicate the 

30 quantitative interpretation of the epidemiologic findings, particularly regarding the 

31 biological plausibility of health effects occurring at COHb levels resulting from 

32 exposures to ambient CO concentrations measured at AQS monitors.” [ISA, p. 2-17] 

33 Given these uncertainties in the quantitative interpretation of epidemiological studies for 

34 CO, and the longstanding body of evidence that links exposures to effects through an internal 

35 dose metric, we have characterized health risk of ambient CO exposures in this assessment using 

 

18 One additional controlled exposure study in CHD patients is available since the last review. It involved 

higher COHb levels than the study by Allred et al (1989a, 1989b, 1991) and is not a focus here (2000 AQCD, 

section 6.2.2). 
February, 2010 2-15 Draft – Do Not Cite or Quote

1 estimates of associated COHb levels and a benchmark level approach, with benchmarks 

identified in consideration of the controlled human exposure literature.19 2 This is supported by 

3 the fact that COHb levels reported in controlled human exposure studies are a better indicator of 

4 personal exposure and dose than concentrations measured at fixed site ambient monitors. In 

5 addition, controlled human exposure studies can examine the health effects of short-term 

6 exposure to CO in the absence of co-pollutants that can confound results in epidemiologic 

7 analyses; thus, health effects observed in controlled human exposure studies can confidently be 

8 attributed to a defined COHb dose level resulting from short-term CO exposures. 

9 In drawing from the results of the controlled human exposure studies to inform the 

10 characterization of potential CO risk in this assessment, staff considered a number of factors, 

11 listed below. 

12  Myocardial ischemic effects, as documented by reductions in times to exercise-induced 

13 change in the ST-segment of an electrocardiogram and to exercise-induced onset of 

14 angina, were observed in response to CO exposures involving subjects with pre15 existing CAD. Staff gives primary focus here to the multi-laboratory study in which 

16 COHb was analyzed by the more accurate GC method. (Allred et al., 1989a,b, 1991). 

17  Relative to clean-air exposure that resulted in a mean level of 0.6% COHb (post18 exercise), exposures to CO resulting in post-exercise mean COHb levels of 2.0% and 

3.9%20 19 were shown to decrease the time required to induce ST-segment changes by 

20 5.1% (p=0.01) and 12.1% (p<0.001), respectively. These changes were well correlated 

21 with the onset of exercise-induced angina, the time to which was shortened by 4.2% 

22 (p=0.027) and 7.1% (p=0.002), respectively, for the two CO exposures (Allred et al., 

23 1989a, 1989b, 1991). 

24  There is no evidence of a threshold for the measures assessed at the lowest levels 

25 tested, with incremental additions of COHb from baseline mean levels of 0.6% to 2 and 

26 3.9% COHb showing changes in the monitored measures of ischemia (Allred et al., 

27 1989b, 1991). The average of the regressions of the individual study subject data for 

28 these measures at baseline COHb and the two COHb levels resulting from the two 

29 controlled CO exposures was summarized by the authors as indicating decreases of 

30 roughly 1.9% in time to exercise-induced angina and 3.9% in time to exercised31 induced ST-segment change per 1% increase in COHb concentration in persons with 

32 pre-existing CAD (ISA, section 5.2.4; Allred et al., 1989a,1989b, 1991). 

 

19 While not used for the purposes of this quantitative assessment, EPA is considering all of the current 

health evidence, including the epidemiological studies, in the Policy Assessment, along with considerations based on 

the risk and exposure assessment findings. 

20 Subjects were exposed to two levels of CO exposure, resulting in COHb levels in the range of 2.0 to 

2.4% and 3.9 to 4.7%, respectively. The upper end of each range is the average COHb level obtained post-exposure 

and the lower end is the average COHb level obtained after the subsequent exercise test (Allred et al., 1989a,b, 

1991). 
February, 2010 2-16 Draft – Do Not Cite or Quote

1  Studies have not been designed to evaluate similar effects of exposures to increased 

2 CO concentrations eliciting average COHb levels below the 2% target level of Allred 

3 et al (1989a, 1989b, 1991). In addition, these studies do not address the fraction of the 

4 population experiencing a specified health effect at various dose levels. These aspects 

5 of the evidence contributed to EPA’s conclusion that at this time there are insufficient 

6 controlled human exposure data to support the development of quantitative dose7 response relationships which would be required in order to conduct a quantitative risk 

8 assessment for this health endpoint, rather than the benchmark level approach. 

9 In drawing on this information, staff recognize the uncertainty associated with 

10 interpretation of COHb levels estimated to result from CO exposure concentrations in this 

11 assessment that are much lower than the CO exposure concentrations used in the clinical studies 

12 to elicit increases in participant’s COHb levels to target levels for the study. 

13 We have reviewed COHb estimates developed in this assessment with attention to both 

14 COHb in absolute terms and also based on consideration of the contribution to COHb associated 

15 with ambient CO exposures. With regard to COHb in absolute terms, staff identified benchmark 

16 levels of 1.5%, 2.0%, 2.5% and 3% COHb based on consideration of the evidence from 

17 controlled human studies of CHD patients discussed above, and is inclusive of the range of levels 

18 considered in the review completed in 1994 (USEPA, 1992). This range extends below the 

19 lowest mean COHb level (e.g., 2.0% post-exercise in Allred et al., 1989b) resulting from 

20 controlled exposure to increased CO concentration in the clinical evidence. This extension 

21 reflects comments from the CASAC CO panel on the draft Analysis Plan (Brain and Samet, 

22 2009) and consideration of the uncertainties regarding the actual COHb levels experienced in the 

23 controlled human exposure studies; that these studies did not include individuals with most 

24 severe cardiovascular disease; the lack of studies evaluating effects of controlled short-term CO 

25 exposures resulting in COHb levels below study mean 2.0-2.4% and the lack of evidence of an 

26 effect threshold at these levels. We note that CASAC comments on the first draft REA 

27 recommended the addition of a benchmark at 1% COHb and staff has presented results for this 

28 COHb level in this draft REA. In considering this advice, we recognize, however, that a level of 

29 1% COHb overlaps with the upper part of the range of endogenous levels in health individuals as 

30 characterized in the ISA (ISA, p. 2-6) and with the upper part of the range of baseline COHb 

31 levels in the study by Allred et al (1989b, Appendix B). As a result, while noting population 

32 dose estimates in relation to this level, we have not placed weight on this level as a potential 

33 health effects benchmark in discussions of the results below and in the draft Policy Assessment 

34 document. We additionally consider, however, the observations of the multi-laboratory clinical 

35 study with regard to response per 1% increase in COHb concentration resulting from short-term 
February, 2010 2-17 Draft – Do Not Cite or Quote

1 controlled CO exposure exposures of persons with pre-existing CAD (ISA, section 5.2.4; Allred 

et al., 1989a, 1989b, 1991).21 2 

3 The benchmark levels identified are used to interpret COHb levels estimated to occur in 

4 the modeled population in response to exposures to ambient CO in different air quality scenarios 

5 in light of the evidence for cardiovascular effects in individuals with CHD when exposed to CO. 

6 That is, we have estimated the number of persons and percent of the simulated at-risk population 

7 expected to experience COHb levels below each of these potential health effect benchmark 

8 levels as a result of ambient CO exposures associated with a set of air quality scenarios 

9 employed to inform the current review of the CO NAAQS (see chapter 5 below). As noted in 

10 chapter 1 above, given the significant time constraints of this review, results are provided in this 

11 document without substantial interpretation. Rather, discussion of health risk and public health 

12 implications of these results in the context of the NAAQS review is provided in the draft Policy 

13 Assessment. 

14 2.7 KEY OBSERVATIONS 

15 Presented below are key observations regarding the current evidence for health effects 

16 associated with exposures to ambient CO. 

17  Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

18 carbon-containing fuels and photochemical reactions in the atmosphere, with on-road 

19 mobile sources representing significant sources of CO to ambient air. 

20  Microenvironments influenced by on-road mobile sources are important contributors to 

21 ambient CO exposures, particularly in urban areas. 

22  The formation of COHb is a key step in the elicitation of various health effects by CO. 

23 Further, COHb level is commonly used in exposure assessment and is considered the 

24 best biomarker for CO health effects of concern. 

25  Individuals with CHD are the population with greatest susceptibility to short-term 

26 exposure to CO, and the population for which the current evidence indicates health 

27 effects occurring at the lowest exposures. The evidence further indicates a potential for 

28 other underlying cardiovascular conditions to contribute susceptibility to CO effects. 

29 Other populations potentially at risk include individuals with diseases such as chronic 

 

21 Relative to clean-air exposure that resulted in a mean COHb level of 0.6% (post-exercise), exposures to 

CO resulting in post-exercise mean COHb concentrations of 2.0% and 3.9% were shown to decrease the time 

required to induce ST-segment changes by 5.1% (p=0.01) and 12.1% (p<0.001), respectively. These changes were 

well correlated with the onset of exercise-induced angina the time to which was shortened by 4.2% (p=0.027) and 

7.1% (p=0.002), respectively, for the two CO exposures. A dose-response analysis in which the individual 

regressions of study subject responses at baseline COHb and at the two increased COHb levels were averaged was 

summarized as indicating decreases of roughly 1.9% in time to exercise-induced angina and 3.9% in time to 

exercised-induced ST-segment change per 1% increase in COHb concentration in persons with pre-existing CAD 

(ISA, section 5.2.4; Allred et al., 1989a,1989b, 1991). 
February, 2010 2-18 Draft – Do Not Cite or Quote

1 obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in 

2 prenatal or elderly life stages. 

3  Cardiovascular effects are the health endpoint for which the evidence is strongest and 

4 indicative of a likely causal relationship with CO exposures. Other endpoints for 

5 which the evidence is suggestive of such a relationship include effects on the central 

6 nervous system, reproduction and prenatal development, and the respiratory system. 

7  Risk is characterized in this REA through evaluation of COHb estimated to result from 

8 ambient CO exposure in individuals with CHD (including undiagnosed persons) 

9 considering potential health effect benchmarks for daily maximum COHb levels. 

10 Results are reported in terms of percent of population expected to experience daily 

11 maximum COHb levels at or above a series of levels that range as low as 1%. These 

12 results are considered in the Policy Assessment document in light of potential health 

13 effects benchmarks ranging from 1.5%, which is below the lowest study mean COHb 

14 level resulting from experimental CO exposure in controlled human exposures of 

15 subjects with CAD, up to 3.0%, a level associated with adverse effects in those studies. 

 
February, 2010 2-19 Draft – Do Not Cite or Quote

1 2.8 REFERENCES 

2 Abi-Esber L and El-Fadel M. (2008). In-vehicle CO ingression: Validation through field measurements and mass 

3 balance simulations. Sci Total Environ. 394:75-89. 

4 Adams KF, Koch G, Chatterjee B, Goldstein GM, O'Neil JJ, Bromberg PA, Sheps DS, McAllister S, Price CJ, 

5 Bissette J. (1988). Acute elevation of blood carboxyhemoglobin to 6% impairs exercise performance and 

6 aggravates symptoms in patients with ischemic heart disease. J Am Coll Cardiol. 12:900-909 

7 Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to carbon monoxide in 

8 Washington, DC, and Denver, Colorado, during the winter of 1982-1983. Environ Sci Technol. 19:911-

9 918. 

10 Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD, Pagano M, Selvester RH, Walden 

11 SM, Warren J. (1989a). Short-term effects of carbon monoxide exposure on the exercise performance of 

12 subjects with coronary artery disease. N Engl J Med. 321:1426-1432. 

13 Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD, Hayes D, Pagano M, Selvester RH, 

14 Walden SM, Warren J. (1989b). Acute effects of carbon monoxide exposure on individuals with coronary 

15 artery disease. Cambridge, MA: Health Effects Institute; research report no. 25. 

16 Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD, Pagano M, Selvester RH, Walden 

17 SM, Warren J. (1991). Effects of carbon monoxide on myocardial ischemia. Environ Health Perspect

18 91:89-132. 

19 AHA. (2003). Heart and Stroke Facts. American Heart Association, Dallas, TX. Available at: 

20 http://www.americanheart.org/downloadable/heart/1056719919740HSFacts2003text.pdf 

21 Anderson EW, Andelman RJ, Strauch JM, Fortuin NJ, Knelson JH. (1973). Effect of low level carbon monoxide 

22 exposure on onset and duration of angina pectoris. Annals of Internal Medicine. 79:46-50. 

23 Boulter P and McCrae I. (2005). Carbon Monoxide Inside Vehicles: Implications for Road Tunnel Ventilation. In: 

24 Annual Research Review 2005. TRL Academy. 

25 Brain JD. (2009). Letter from Dr. Joseph Brain to Administrator Lisa Jackson. Re: Consultation on EPA’s Carbon 

26 Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure 

27 Assessment. CASAC-09-012. July 14, 2009. 

28 Brain JD and Samet JM. (2009). Letter to EPA Administrator Lisa Jackson: Clean Air Scientific Advisory 

29 Committee’s (CASAC) Peer Review of the Agency’s 1st Draft Carbon Monoxide Integrated Science 

30 Assessment. EPA-CASAC-09-011. June 24, 2009. 

31 Brain JD and Samet JM. (2010). Letter from Drs. J.D. Brain and J.M. Samet to Administrator Lisa Jackson. Re: 

32 Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) 

33 Primary National Ambient Air Quality Standards: First External Review Draft. CASAC-XX-XXX. 

34 January XX, 2010. 

35 Bruinen de BruinY, Hanninen O, Carter P, Maroni M, Kephalopoulos S, Scotto De Marco G, Jantunen M. (2004). 

36 Personal carbon monoxide exposure levels: Contribution of local sources to exposures and 

37 microenvironmental concentrations in Milan. J Expo Anal Environ Epidemiol. 14:312-322. 

38 Chan C, Ozkaynak H, Spengler J, Sheldon L. (1991). Driver exposure to volatile organic compounds, CO, ozone, 

39 and NO2 under different driving conditions. Environ Sci Technol. 25(5) :964-972. 
February, 2010 2-20 Draft – Do Not Cite or Quote

1 Chang LT, Koutrakis P, Catalano PJ, Suh HH. (2000). Hourly personal exposures to fine particles and gaseous 

2 pollutants - Results from Baltimore, Maryland. J Air Waste Manage Assoc. 50:1223-1235. 

3 Coburn R, Forster R, Kane P. (1965). Consideration of the physiological variables that determine the blood 

4 carboxyhemoglobin concentrations in man. J Clin Invest. 44:1899-1910. 

5 Davies DM and Smith DJ. (1980). Electrocardiographic changes in healthy men during continuous low-level 

6 carbon monoxide exposure. Environ Res. 21:197-206. 

7 Flachsbart P. (1999). Human exposure to carbon monoxide from mobile sources. Chemosphere – Global Change 

8 Science. 1:301-329. 

9 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 

10 Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), prepared by 

11 ICF Kaiser Consulting Group for U.S. EPA, Office of Air Quality Planning and Standards, under Contract 

12 No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQ-OAR-2008-0015-0010. 

13 Kleinman MT, Davidson DM, Vandagriff RB, Caiozzo VJ, Whittenberger JL. (1989). Effects of short-term 

14 exposure to carbon monoxide in subjects with coronary artery disease. Arch Environ Health. 44:361-369. 

15 Kleinman MT, Leaf DA, Kelly E, Caiozzo V, Osann K, O'Niell T. (1998). Urban angina in the mountains: effects 

16 of carbon monoxide and mild hypoxemia on subjects with chronic stable angina. Arch Environ.Health. 

17 53:388-397. 

18 Koushi P, Al-Dhowalis K, Niazi S. (1992). Vehicle occupant exposure to carbon monoxide. J Air Waste Manag. 

19 42:1603-1608. 

20 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S, Frazier C. (1998). 

21 Measuring Concentrations of Selected Air Pollutants Inside California Vehicles. Final Report. Prepared 

22 by Research Triangle Institute under Contract No. 95-339. California Air Resources Board. Sacramento, 

23 California. 

24 Scotto di Marco G, Kephalopoulos S, Ruuskanene J, Jantunene M. (2005). Personal carbon monoxide exposure in 

25 Helsinki, Finland. Atmos Environ. 39Z:2697–2707. 

26 Sharp D and Tight M. (1997). Vehicle occupant exposure to air pollution. In: Policy, Planning, and Sustainability: 

27 Proceedings of Seminars C and D Held at PTRC European Transport Forum, Brunel University. Pages 

28 481-492. 

29 Sheps DS, Adams KF Jr, Bromberg PA, Goldstein GM, O'Neil JJ, Horstman D, Koch G. (1987). Lack of effect of 

30 low levels of carboxyhemoglobin on cardiovascular function in patients with ischemic heart disease. Arch 

31 Environ Health. 42:108-116. 

32 Sheps DS, Herbst MC, Hinderliter AL, Adams KF, Ekelund LG, O'Neil JJ, Goldstein GM, Bromberg PA, Dalton 

33 JL, Ballenger MN, Davis SM, Koch GG. (1990). Production of arrythmias by elevated 

34 carboxyhemoglobin in patients with coronary artery disease. Ann Intern Med. 113:343-351. 

35 Shikiya D, Liu C, Kahn M, Juarros J, Barcikowski W. (1989). In-Vehicle Air Toxics Characterization Study in the 

36 South Coast Air Basin. Office of Planning and Rules, South Coast Air Quality Management District. May. 

37 US EPA. (1991). Air Quality Criteria For Carbon Monoxide. Research Triangle Park, NC: Office of Health and 

38 Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA/600/8-90/045F. 
February, 2010 2-21 Draft – Do Not Cite or Quote

1 US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of 

2 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 

3 EPA/452/R-92-004. 

4 US EPA. (2000). Air Quality Criteria for Carbon Monoxide. National Center for Environmental Assessment, 

5 Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 

6 27711; report no. EPA/600/P-99/001F. June 2000. Available: 

7 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=18163. 

8 US EPA. (2006). 2002 National Emissions Inventory Data and Documentation. U.S. Environmental Protection 

9 Agency, Office of Air and Radiation, Office of Air Quality Planning and Standards, Research Triangle 

10 Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html. 

11 US EPA. (2010a). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency, 

12 Washington, DC, EPA/600/R-09/019F. Available at: 

13 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 

14 US EPA. (2010b). Policy Assessment for the Review of the Carbon Monoxide National Ambient Air Quality 

15 Standards, External Review Draft. Office of Air Quality Planning and Standards Staff Paper, report no. 

16 EPA-452/P-10-005. 
February 2010 Draft – Do Not Quote or Cite 3-1

1 3 AIR QUALITY CONSIDERATIONS 

2 Ambient air quality data can be used as an indicator of exposure or used in conjunction 

3 with other information to estimate exposure concentrations. How well the ambient air quality is 

4 represented in a particular location is dependent on the ambient monitoring network design 

5 relative to the spatial and temporal characteristics of the pollutant as well understanding the 

6 concentration contribution from important local source emissions. This chapter summarizes 

7 findings about the current air quality conditions and their spatial distribution, with particular 

8 focus on aspects informative to the design and conduct of this assessment and including 

9 descriptions of CO measurement methods, monitor siting requirements, and monitor locations 

10 (section 3.1). Section 3.2 then draws upon the information presented in sections 3.1, among 

11 other data, to select ambient air quality/study locations most useful in meeting the objectives of 

12 the REA. Finally, key observations of the chapter are presented in section 3.3. 

13 3.1 AMBIENT CO MONITORING 

14 In this section, a broad overview of the monitoring network is provided (section 3.1.1) 

15 and is followed by a summary of analytical detection issues (section 3.1.2). Ambient CO 

16 concentrations and their spatial and temporal variability are characterized in section 3.1.3. 

17 Estimates of policy-relevant background (PRB) concentrations which are defined as those 

18 ambient concentrations that would occur in the US in the absence of anthropogenic emissions in 

19 continental North America are presented in section 3.1.4. And finally, section 3.1.5 presents an 

20 analysis of the specific CO concentration trends observed in individual monitors. 

21 3.1.1 Monitoring Network 

22 Ambient CO concentrations are measured by monitoring networks that are operated by 

23 state and local monitoring agencies in the US, and are funded in part by the EPA. The main 

24 network providing ambient data for use in comparison to the NAAQS is the State and Local Air 

25 Monitoring Stations (SLAMS) network. The subsections below provide specific information 

26 regarding the methods used for obtaining ambient CO measurements and the requirements that 

27 apply to states in the design of the CO network. 

28 Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient 

29 monitoring requirements (see 71 FR 61236, October 17, 2006). This action was made to allow 

30 for reductions in measurements of some pollutants (CO, SO2, NO2, and Pb) where measured 

31 levels were well below the applicable NAAQS and air quality problems were not expected. CO 

32 monitoring activities have been maintained at some SLAMS and these measurements of CO are 

33 required to continue until discontinuation is approved by the EPA Regional Administrator. 
February 2010 Draft – Do Not Quote or Cite 3-2

CO monitors are typically sited to reflect one of the following spatial scales.1 1 

2  Microscale: Data represent concentrations within a 100 m radius of the monitor. For 

3 CO, microscale monitors are sited 2 – 10 m from a roadway. Measurements are 

4 intended to represent the near-road or street canyon environment. 

5  Middle scale: Data represent concentrations averaged over areas defined by 100 – 500 

6 m radii. Measurements are intended to represent several city blocks. 

7  Neighborhood scale: Data represent concentrations averaged over areas defined by 0.5 

8 – 4.0 km radii. Measurements are intended to represent extended portions of a city. 

9 In addition to monitoring required for determining compliance with the NAAQS, the 

10 EPA is currently in the process of implementing plans for a new network of multi-pollutant 

11 stations called NCore that is intended to meet multiple monitoring objectives. A subset of the 

12 SLAMS network, NCore stations are intended to address integrated air quality management 

13 needs to support long-term trends analysis, model evaluation, health and ecosystem studies, as 

well as the more traditional objectives of NAAQS compliance and Air Quality Index reporting.2 14 

15 The complete NCore network, required to be fully implemented by January 1, 2011, will consist 

16 of approximately 63 urban and 20 rural stations and will include some existing SLAMS sites that 

17 have been modified to include additional measurements. Each state will contain at least one 

18 NCore station, and 46 of the states plus Washington, D.C. will have at least one urban station. 

CO will be measured using high sensitivity monitors, as will SO2, NO, and NOY.

3 19 The majority 

20 of NCore stations will be sited to represent neighborhood, urban, and regional scales, consistent 

21 with the NCore network design objective of representing exposure expected across urban and 

22 rural areas in locations that are not dominated by local sources. 

23 3.1.2 Analytical Sensitivity 

24 To promote uniform enforcement of the air quality standards set forth under the CAA, 

25 EPA has established provisions in the Code of Federal Regulations (CFR) under which analytical 

26 methods can be designated as federal reference methods (FRMs) or federal equivalent methods 

27 (FEMs). Measurements for determinations of NAAQS compliance must be made with FRMs or 

FEMs.4 28 Specifications for CO monitoring are designed to help states utilize equipment that has 

 

1

 A complete description of spatial scales is listed in 40 CFR Part 58 Appendix D, section 1.2. Ambient 

monitoring of other NAAQS pollutants such as NO2 and SO2 follow the same general spatial scales. 

2

 (http://www.epa.gov/ttn/amtic/ncore/index.html). 

3

 NCore sites must measure, at a minimum, PM2.5 particle mass using continuous and integrated/filter-based 

samplers, speciated PM2.5, PM10-2.5 particle mass, speciated PM10–2.5, O3, SO2, CO, NO/NOY, wind speed, wind 

direction, relative humidity, and ambient temperature (http://www.epa.gov/ttn/amtic/ncore/index.html). 

4

 As of August 2009, twenty automated FRMs had been approved for CO measurement. All EPA FRMs 

for CO operate on the principle of non-dispersive infrared (NDIR) detection and can include the gas filter correlation 
February 2010 Draft – Do Not Quote or Cite 3-3

1 met performance criteria utilized in the FRM or FEM approval process; operational parameters 

2 are documented in 40 CFR Part 53, Table B-1. Given the levels of the CO NAAQS (35 ppm, 1-

hour; 9 ppm, 8-hour average), the required 1.0 ppm lower detectable limit (LDL)5 3 is well below 

4 the NAAQS levels and is therefore sufficient for demonstration of compliance. However, with 

5 ambient CO levels now routinely near or below 1 ppm, there is greater uncertainty in a larger 

6 portion of the distribution of monitoring data because a large percentage of these measurements 

7 are below the LDL of conventional monitors. For this reason, a new generation of ambient CO 

8 monitors has been designed that provides measurements with improved sensitivity at or below 

9 the typical ambient CO levels measured in most urban and all rural locations. Additionally, the 

10 higher sensitivity CO measurements are needed to support additional objectives such as 

11 validating the inputs to chemical transport models and assessing the role of transport between 

12 urban and rural areas because policy relevant background CO concentrations on the order of 0.1 

13 ppm are well below the LDL of conventional monitors. Newer GFC instruments have been 

14 designed for automatic zeroing to minimize drift (US EPA, 2000). 

15 Currently, a total of 13 approved FRMs are in use in the SLAMS network, based on a 

16 retrieval of data reported between 2005 and 2009. Among these methods, nine are “legacy” 

monitors with a federal method detection limit (MDL)6 17 given as 0.5 ppm according to records in 

EPA’s Air Quality System (AQS).7 18 As discussed in the ISA, many of the reported 

19 concentrations in recent years are near or below these MDLs (ISA, section 3.5.1.2). Four of 

20 these new methods are high sensitivity methods with a federal MDL of 0.02 ppm and there are a 

21 growing body of ambient data from high sensitivity CO instruments is becoming available. 

22 Among newer GFC high sensitivity instruments, manufacturer-declared LDLs range from 0.02 – 

23 0.04 ppm, with 24-hour zero drift varying between 0.5% within 1 ppm and 0.1 ppm, and 

24 precision varying from 0.5% to 0.1 ppm. EPA performed MDL testing on several high 

25 sensitivity CO monitors in 2005 and 2006 following the 40 CFR Part 136 procedures. Those 

 

(GFC) methodology. An extensive and comprehensive review of NDIR, GFC, and alternative, non-FRM techniques 

for CO detection was included in the 2000 CO AQCD (US EPA, 2000). 

5

 Defined in 40 CFR Part 53.23 as the minimum pollutant concentration which produces a signal of twice 

the noise level. 

6

 Defined in 40 CFR Part 136 as the minimum concentration of a substance that can be measured and 

reported with 99% confidence that the analyte concentration is greater than zero and is determined from analysis of 

a sample in a given matrix containing the analyte. 

7

 Among several of the older instruments (Federal Reference Method codes 008, 012, 018, 033, 041, 050, 

051, and 054), performance testing has shown LDLs of 0.62 – 1.05 ppm, with 24-hour drift ranging from 0.044 – 

0.25 ppm and precision ranging from 0.022 – 0.067 ppm at 20% of the upper range limit of the instrument (Michie 

et al., 1983). 
February 2010 Draft – Do Not Quote or Cite 3-4

1 tests demonstrated MDLs of approximately 0.017 – 0.018 ppm, slightly below the stated LDL of 

2 0.02 – 0.04 ppm. 

3 Based on a retrieval of data reported to AQS for the time period between 2005 and 2009, 

4 a total of 36 high sensitivity CO monitors have reported data with the majority of these monitors 

5 currently active. The majority of these active monitors are associated with the implementation of 

6 the NCore network. The extent to which high sensitivity monitors become integrated into non7 NCore SLAMS stations, however, will depend on the availability of funding for states to replace 

8 well-operating legacy CO monitors as well as the possibility that monitoring requirements for 

9 CO might either encourage or require such technological improvements. 

10 3.1.3 General Patterns of CO Concentrations 

11 As discussed in the ISA, the spatial and temporal patterns of ambient CO concentrations 

12 are heavily influenced by the patterns associated with mobile source emissions (ISA, section 

13 3.2.1). Based on the 2002 National Emissions Inventory (NEI), on-road mobile sources 

14 comprise about half of the total anthropogenic CO emissions, though in metropolitan areas of the 

15 US the contribution is as high as 75% of all CO emissions due to greater motor vehicle density. 

16 For example, emissions in Denver county originating from on-road mobile sources is about 71% 

17 of total CO emissions (ISA, section 3.2). When considering all mobile sources (non-road and 

18 on-road combined), the contribution to total CO emissions is roughly 80% nationwide and can be 

19 higher in some metropolitan areas. Again using Denver County as an example, all mobile 

20 sources contribute about 98% of total CO emissions. Temporally, the national-scale 

21 anthropogenic CO emissions have decreased 35% between 1990 and 2002. Nearly all the 

22 national-level CO reductions since 1990 are the result of emission reductions in on-road vehicles 

23 (ISA, Figure 3-2). 

24 Nearly 400 ambient monitoring stations report continuous hourly averages of CO 

25 concentrations across the US. Over the period 2005-2007, 291 out of 376 monitors met a 75% 

26 completeness requirement, spread among 243 counties, cities, or municipalities (ISA, section 

27 3.4.2.2). No violations of the NAAQS were reported at these monitoring sites during this time 

28 period. For example, in 2007, none of the monitors reported a second-highest 1-hour CO 

29 concentration above 35 ppm, the level of the current 1-hour NAAQS, while only two sites 

reported 2nd 30 highest 1-hour CO concentrations between 15.1 and 35.0 ppm (ISA section 3.5.1.1). 

Only five counties reported a 2nd 31 highest 8-hour CO concentration of 5.0 ppm or higher. 

32 The current levels of ambient CO across the U.S. reflect the steady declines in ambient 

33 concentrations that have occurred over the past several years. On average across the US the 

34 decline has been on the order of 50% since the early 1990s (ISA, Figure 3-34). As an example, 

35 Figures 3-1 illustrate the trends observed in Denver and Los Angeles ambient concentrations, for 
February 2010 Draft – Do Not Quote or Cite 3-5

1 several selected monitors within the urban core of each area during 1993 through 2008. Note 

that there is a significant decrease in the 2nd 2 highest 1-hour and 8-hour average CO 

3 concentrations since the last review. 

4 Ambient monitor siting characteristics can influence ambient CO concentrations. 

5 Microscale and middle scale monitors are commonly used to measure significant source impacts, 

6 while neighborhood and urban scale monitors are designated for population-oriented monitoring 

7 (40 CFR Part 58 Appendix D). As CO concentrations primarily originate from vehicle 

8 emissions, the microscale and middle scale data can be a useful indicator of near-road air quality. 

9 Such data analyzed in the ISA were concluded to be consistent with hourly concentrations 

10 reported in the literature for the near road environment in the US (ISA, p. 3-57). Further, when 

11 considering monitoring scale across ambient monitors in the US, the median hourly CO 

12 concentration measured at microscale monitors was about 25% higher than at middle scale 

13 monitors and 67% higher than at neighborhood scale monitors (ISA, Table 3-12). In general, 

14 similar patterns were present in the 1-hour daily max, 1-hour daily average, and 8-hour daily 

15 max distributions (ISA, Table 3-12). These patterns are also consistent with findings presented 

16 by other researchers regarding the relative decrease in concentration with increasing distance 

17 from roadways, though the magnitude of the relationship can vary. Two studies summarized in 

18 the ISA (Zhu et al., 2002; Baldauf et al., 2008) indicate that near-road CO concentrations (i.e., 

19 measured within 20 meters of an interstate highway) can range from 2 – 10 times greater than 

20 CO concentrations measured as far as 300 meters from a major road possibly influenced by wind 

21 direction and on-road vehicle density (ISA, Figures 3-29 and 3-30). 

22 While recognizing that monitoring site attributes are not available for all monitors in the 

current network and that data for some attributes may not reflect current conditions, 8 23 the ISA 

24 also evaluated the average annual daily traffic (AADT) data available for each ambient monitor. 

25 The ISA noted that only two microscale monitors and two middle scale monitors in the existing 

26 network are sited at roads with ≥100,000 AADT, although it is not uncommon for roadways 

27 within Consolidated Statistical Areas (CSAs) to have several roads with AADT > 100,000. The 

28 AADT ranged from 160,000-178,000 for the near-road monitors used in the aforementioned 

29 study by Zhu et al. (2002) where CO concentrations were up to 10 times greater than monitors 

sited at 300 m from a major road.9 30 Existing microscale sites near roads having only moderate 

 

8

 Recorded AQS monitoring site attributes are not always available for each monitor or may not always 

reflect potential source influences. For example, of 24 CO monitors in the Los Angeles CSA, AQS had no 

information regarding monitoring scale for 16 (ISA, Figure 3-22). 

9

 Local-scale meteorology may have also contributed to the heightened concentrations, given that the Zhu 

et al. (2000) study was designed to capture CO concentrations downwind of the roadway. 
Februaryr 2010 3-6 Draft – 

1 

Figure 3-1. Spatial and Temporal Trends in the 2nd 2 Highest 1-hour (top) and 8-hour Average (bottom) C

3 Monitoring Concentrations in Denver, Colorado (left) and Los Angeles, California (right), Y

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February 2010 Draft – Do Not Quote or Cite 3-7

1 traffic count data (<100,000 AADT) may record concentrations that are not substantially 

2 different from those obtained from neighborhood scale measurements (ISA, section 3.5.1.3). 

3 Within a specific urban area, however, consideration of only monitor scale or other 

4 attributes reported in AQS, such as AADT estimates, may be of limited use in efforts to 

5 characterize the monitoring data as to its representation of local near-road CO concentrations. 

6 For example, of the five monitors meeting a 75% completeness criterion in the Denver CSA, 

7 three were microscale and two were neighborhood scale (ISA, section 3.5.1.2). While one of the 

8 microscale monitors sited within downtown Denver measured the highest hourly ambient CO 

9 concentrations (ID 080310002), another microscale monitor (ID 080130009) located outside the 

10 urban core measured the lowest hourly ambient CO concentrations (ISA, Figure 3-19). Further, 

11 the AADT estimate for a major road near the microscale monitor within the urban core (ID 

12 080310002, AADT=17,200) was lower than that listed for the microscale monitor outside the 

13 urban core (ID 080130009, AADT=20,000) (ISA, Table A-2). And, a third microscale monitor 

14 located 1.3 km from monitor ID 080310002, within the urban core, and measuring somewhat 

15 lower CO concentrations (but not lower than the monitor outside the urban core) had only 500 

16 AADT listed for the nearest major road. It is likely that the higher CO concentrations measured 

17 at the downtown monitor reflect influences of the denser roadway network surrounding that 

monitor in the downtown Denver area (ISA, Figure 3-17).10 18 

19 Thus, to better characterize the representation of near-road CO concentrations for many 

20 of the existing ambient monitors, additional analyses, beyond consideration of AQS attributes 

21 such as monitoring scale and traffic count, local meteorology, would likely need to be performed 

22 (e.g., using GIS to determine monitor distance from roads, the number and type of roads within 

23 close proximity of the monitor, and obtaining current traffic count data for all roads). 

24 Carbon monoxide also exhibits hourly variability within a day, with two distinct temporal 

25 patterns noted for weekdays and weekends (ISA, section 3.5.2.2). The diurnal variation is 

26 inherently linked to the typical commute times-of-day that occur within urban locations. In 

27 general, in recent years observed mean and median concentrations for all hours of the day and 

across all monitors within urban areas demonstrated limited variability, however 90th and 95th 28 

29 percentile hourly concentrations generally exhibit early-morning and late afternoon peak CO 

30 concentrations during weekdays (ISA, Figure 3-36). The weekend diurnal variation in ambient 

31 CO concentrations was much lower than that occurring during weekdays as expected due to the 

32 relative absence of commuter vehicle traffic during the morning and evening hours of the day. 

33 Most urban areas have relatively stable concentrations throughout weekend days at each of the 

 

10 Staff also recognizes some uncertainty in how well the AQS AADT estimates reflect current conditions 

at this monitor site. 
February 2010 Draft – Do Not Quote or Cite 3-8

1 selected percentiles, though a few locations (e.g., Phoenix, Los Angeles, Seattle) did have a more 

2 pronounced late afternoon peak (ISA, Figure 3-37). 

3 Staff investigated local hourly variation at two separate CO monitors located in Denver 

4 and Los Angeles to illustrate similar trends. The monitor in Denver is a microscale monitor 

5 located in downtown Denver and expected to reflect concentrations resulting from dense 

6 downtown traffic in that city; it is the monitor measuring the highest ambient CO concentrations 

7 in the Denver area. The monitor in Los Angeles is a middle scale monitor located in Lynwood; 

8 it is also the monitor measuring the highest ambient CO concentrations in the Los Angeles area 

9 Figure 3-2 indicates that on average, peak ambient CO concentrations that occur during typical 

10 commute times in Denver ranged from about 1 to 5 ppm during weekdays in 1995, while, 

11 currently, ambient CO concentrations during morning and afternoon commutes range from about 

12 1 to 2 ppm. Weekends tend to exhibit less variability throughout the day. On average, CO 

13 ambient concentrations generally ranged from 1 to 3 ppm throughout the day in 1995, while 

14 current weekend concentrations are less than 1 ppm for most hours of the day. In Los Angeles, 

15 both the concentration levels and variability are greater than when compared with similar years 

16 and times of day in Denver (Figure 3-3). Peak ambient CO concentrations are more prominent 

17 during morning commutes and generally ranged from 2 to 10 ppm in 1995, while currently (year 

18 2006) most commuting times are associated with concentrations ranging from between 1 and 5 

19 ppm. The weekend profile exhibits some variation when considering either year, with 

20 maximum concentration levels and variability exhibited during the overnight hours. 
February 2010 Draft – Do Not Quote or Cite 3-9

1 2 Figure 3-2. Diurnal Distribution of 1-hour CO Concentrations in Denver (Monitor 080310002) by Day-ty3 weekends-right), Years 1995 (top) and 2006 (bottom). The box encompasses concentrations fro4 percentiles or Interquartile range (IQR), the line bisecting the box is the median, the solid dot with

5 mean, the whiskers represent 1.5 times the IQR, and concentrations outside the whiskers are indica6 Note there are differences in the y-axis scale for the two time periods. 
February 2010 Draft – Do Not Quote or Cite 3-10

1 

2 Figure 3-3. Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor 060371301) by da3 left; weekends-right), years 1997 (top) and 2006 (bottom). The box encompasses concentration

4 75th percentiles or IQR, the line bisecting the box is the median, the solid dot within the box is the 5 represent 1.5 times the IQR, and concentrations outside the whiskers are indicated by open circles. 6 differences in the y-axis scale for the two time periods.
February 2010 Draft – Do Not Quote or Cite 3-11

1 3.1.4 Policy-Relevant Background Concentrations 

2 EPA has generally conducted NAAQS risk assessments that focus on the risks associated 

3 with ambient levels of a pollutant that are in excess of policy-relevant background (PRB). 

4 Policy-relevant background levels are defined, for purposes of this document, as concentrations 

5 of a pollutant that would occur in the US in the absence of anthropogenic emissions in the US, 

6 Canada, and Mexico. 

7 Over the continental US (CONUS), the 3-year (2005-2007) average CO PRB 

8 concentration is estimated to range from 0.118 to 0.146 ppm (ISA, section 3.5.4). Outside the 

9 CONUS, the 3-year average CO PRB in three Alaskan sites is estimated to range from 0.127 to 

10 0.135 ppm, and from 0.095 to 0.103 ppm in two Hawaiian monitoring locations. The estimated 

11 PRB concentrations exhibit significant within-location seasonal variation, with minimum 

12 concentrations observed in the summer and fall and maximum concentrations occurring in the 

13 winter and spring. For example, PRB in two California sites is estimated to range from about 

14 0.085 to 0.170 ppm, and one site in Colorado, ranged from about 0.080 to 0.140 ppm (ISA, 

15 Figure 3-43). 

16 Given that ambient concentrations of interest in this REA are well above the estimated 

17 PRB levels discussed above and, thus the contribution of PRB to overall ambient CO 

18 concentrations is very small, EPA is characterizing risks associated with ambient CO levels 

19 without regard to estimated PRB levels. 

20 3.1.5 Within-Monitor CO Concentration Trends 

21 The previous section addressed general trends in ambient concentrations. Of particular 

22 interest in this assessment is how concentrations have changed within a specific monitor over 

23 time. This is an important consideration in determining how best to address alternative air 

24 quality conditions. These alternative air quality conditions are useful in evaluating how varying 

25 distributions of air quality might affect different exposure scenarios. In other recent NAAQS 

26 reviews for NO2 (US EPA, 2008) and SO2 (US EPA, 2009) it was determined the relationship 

27 between high concentration and low concentration years of ambient monitoring data was mainly 

28 proportional (Rizzo, 2008; 2009), that is all concentrations across the entire distribution at a 

29 single monitor changed in equivalent amounts over time. Staff needed the relationship to adjust 

30 current air quality because, at the time of the NAAQS reviews, the current ambient NO2 and SO2

31 concentrations were far below that expected to just meet the current standards. 

32 Knowledge of this relationship for ambient CO concentrations is also needed to develop 

33 alternative air quality conditions for use in some of the exposure scenarios investigated in this 

34 draft REA. Ambient CO concentration data were obtained for several monitors in Los Angeles 

35 for two years: 1997 – representing a high concentration year and 2006 – representing a low 
February 2010 Draft – Do Not Quote or Cite 3-12

1 concentration year. First, all reported hourly CO concentrations were used to calculate the full 

2 percentiles of the distribution (0-100 by 1 pct increments) for each year. Then the percentiles for 

3 the high concentration year were plotted against that of the low concentration year for each 

4 individual monitor (Figure 3-4). A simple linear regression was also plotted, along with the 

regression slope, intercept, and fit statistic (R2 5 ). As shown by the relationships, there is a very 

6 strong linear relationship when comparing each year of data within each monitor, and the 

7 regression intercepts for most of the monitors are small, indicating there is adequate support for 

8 adjusting air quality by a proportional method. 

9 Staff was also interested in estimating the within-monitor temporal variability for three 

air quality metrics. The first air quality metric was the current design value, that is, the 2nd 10 

11 highest 8-hour average concentration in a year. The next two air quality metrics compared by 

staff were the 99th 12 percentile 1-hour and 8-hour daily maximum CO concentrations.
February 2010 Draft – Do Not Quote or Cite 3-13

1 

2 Figure 3-4. Comparison of a high concentration year (1997) with a low concentration year (2006) in Los 

through 100th 3 percentiles of the hourly air quality distribution are plotted for each monitor-y

0600370113

y = 0.4078x + 0.0783

R2 = 0.9903

01234

012345678

1997 (0-100) Percentile CO (PPM)

2006 (0-100) Percentile CO (PPM)

0600371103

01234

0 2 4 6 

1997 (0-100) Percentile C

2006 (0-100) Percentile CO (PPM)

0600371301

y = 0.4232x + 0.0129

R2 = 0.9963

0123456789

0 5 10 15 20 25

1997 (0-100) Percentile CO (PPM)

2006 (0-100) Percentile CO (PPM)

060591003

01234

01234

1997 (0-100) Percentile C

2006 (0-100) Percentile CO (PPM)
February 2010 Draft – Do Not Quote or Cite 3-14

1 Staff evaluated the within-monitor temporal variability using two comparisons: one using 

2 historical versus current air quality data and the other comparing year-to-year variability of these 

3 upper percentile concentrations within the air quality distribution. Two three-year periods 

4 (1995-1997 and 2005-2007) were chosen by staff to represent historical and recent air quality, 

5 respectively. Staff limited the analysis to four monitors within the Denver CSA and ten monitors 

6 within the Los Angeles CSA, with all monitor data meeting standard requirements for data 

7 completeness. In addition to the temporal evaluation of the air quality metrics, a limited analysis 

8 of the spatial variability across the two periods is also provided for the selected monitors in each 

9 area. 

10 Tables 3-1 and 3-2 provide results for the first air quality metric in Denver and Los 

11 Angeles, respectively. As shown by the Tables, there is a wide range in the temporal variability 

of the 2nd 12 highest 8-hour average CO concentration in both locations, however, the relative 

variability, as indicated by the coefficient of variation (COV),11 13 is generally less for the recent air 

14 quality when compared with the historical air quality. For example, in Denver the COV ranges 

15 from 4-27 percent (mean = 13%) for the historical data, while the recent data temporal COV 

16 ranges from 3-23 percent (mean = 10%) (Table 3-1). In addition, the magnitude of the spatial 

17 variability tends to vary from year-to-year as indicated by the COV, though there are differences 

18 in the historical versus recent air quality pattern by location. In Denver, there was generally less 

spatial variability in the 2nd 19 highest 8-hour concentration when comparing the recent and 

20 historical air quality data. There was no apparent trend in year-to-year spatial variability for Los 

21 Angeles as both air quality periods had a mean COV of about 31% (Table 3-2). 

Similar temporal trends are observed with the 99th 22 percentile 1-hour daily maximum 

23 concentrations when comparing historical versus recent air quality (Tables 3-3 and 3-4 for 

24 Denver and Los Angeles, respectively). The temporal variability in the recent air quality was 

also less than that of the prior air quality metric (i.e., the 2nd 25 highest 8-hour average), averaging 

26 about 4% COV in Denver and 7% COV in Los Angeles across that 3-year period. The year-to27 year spatial variability for this metric is consistent with that stated above. In Denver, the COV 

28 on average was less for the recent air quality when compared with the historical data. There was 

29 little difference in the year-to-year spatial variability in Los Angeles when considering the two 

air quality periods. Results for the 99th 30 percentile 8-hour daily maximum concentrations were 

more similar to the results for the 2nd highest 8-hour average concentration than the 99th 31 

32 percentile 1-hour daily maximum (Tables 3-5 and 3-6, for Denver and Los Angeles, 

33 respectively).

 

11 The COV is calculated here by dividing the standard deviation (std) by the mean, then multiplying by 

100. 
February 2010 Draft – Do Not Quote or Cite 3-15

1 Table 3-1. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) 

2nd 2 highest 8-hour average. 

Historical Air Quality – 2nd highest 8-hour average Recent Air Quality – 2nd highest 8-ho

Monitor 1995 1995 1997 mean std COV 2005 2006 2007 mean 31-0002 9.5 7.3 5.5 7.4 2.0 27 2.6 3.1 2.8 2.8 31-0013 6.2 5.2 4.7 5.4 0.8 14 2.4 2.5 2.4 31-0014 5.9 5.7 6.2 5.9 0.2 4 2.1 3.0 2.5 59-0002 4.6 4.3 4.9 4.6 0.3 7 1.8 2.0 1.9 

mean 6.6 5.6 5.3 5.8 2.2 2.6 2.8 2.4 

std 2.1 1.3 0.6 1.2 0.3 0.5 0.4 

COV 32 22 12 20 14 19 16 

3 Table 3-2. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (200

data - 2nd 4 highest 8-hour average. 

Historical Air Quality – 2nd highest 8-hour average Recent Air Quality – 2nd highest 8-ho

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 37-0113 9.4 8.5 4.1 7.3 2.8 39 1.9 1.9 1.6 1.8 37-1002 10.9 8.5 7.2 8.9 1.9 21 3.2 3.4 2.7 3.1 37-1103 7.9 7.5 5.9 7.1 1.1 15 2.6 2.5 2.1 2.4 37-1201 9.4 6.7 7.7 7.9 1.3 17 3.4 3.4 2.7 3.2 37-1301 11.7 14.3 15.0 13.6 1.7 13 5.6 5.6 4.9 5.3 37-2005 8.6 6.9 5.4 7.0 1.6 23 2.8 2.7 2.2 2.6 37-4002 6.3 6.2 6.4 6.3 0.1 2 2.9 3.3 2.5 2.9 

59-0001/7 7.3 6.1 5.4 6.3 1.0 16 3.1 2.9 2.3 2.8 

59-1003 5.3 6.5 5.0 5.6 0.8 14 3.1 2.5 2.5 2.7 59-5001 6.4 6.3 5.7 6.1 0.4 6 2.9 2.9 2.5 2.8 

mean 8.3 7.7 6.8 7.6 3.1 3.1 2.6 3.0 

std 2.1 2.5 3.1 2.3 0.9 1.0 0.9 0.9 

COV 25 32 45 31 30 32 33 31 
February 2010 Draft – Do Not Quote or Cite 3-16

1 Table 3-3. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) 

99th 2 percentile 1-hour daily maximum. 

Historical Air Quality – 99th percentile 1-hour daily maximum Recent Air Quality – 99th percentile 1-hou

Monitor 1995 1995 1997 mean std COV 2005 2006 2007 mean 31-0002 13.5 13.4 9.1 12.0 2.5 21 3.8 4.5 4.4 4.2 31-0013 11.1 9.0 8.6 9.6 1.3 14 3.5 3.7 3.6 31-0014 8.2 7.3 7.8 7.8 0.5 6 3.3 3.2 3.2 59-0002 8.6 6.8 7.2 7.5 0.9 12 3.4 3.4 3.4 

mean 10.4 9.1 8.2 9.2 3.5 3.7 4.4 3.6 

std 2.5 3.0 0.9 2.1 0.2 0.6 0.4 

COV 24 32 10 22 6 16 12 

3 Table 3-4. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (200

data – 99th 4 percentile 1-hour daily maximum. 

Historical Air Quality – 99th percentile 1-hour daily maximum Recent Air Quality – 99th percentile 1-hou

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 37-0113 13.9 7.5 6.1 9.2 4.2 45 2.6 2.7 2.1 2.5 37-1002 11.6 9.7 8.2 9.9 1.7 17 3.9 4.1 3.6 3.9 37-1103 9.0 9.4 7.4 8.6 1.1 13 3.1 2.9 2.6 2.9 37-1201 10.6 8.4 8.4 9.1 1.3 14 4.0 3.9 3.4 3.8 37-1301 16.2 20.2 18.5 18.3 2.0 11 7.1 7.4 6.8 7.1 37-2005 10.3 8.8 6.2 8.4 2.0 24 3.4 3.3 3.0 3.2 37-4002 7.6 8.4 7.6 7.9 0.5 6 3.8 3.8 3.1 3.6 

59-0001/7 9.1 8.2 7.7 8.4 0.7 9 3.6 3.6 3.2 3.5 

59-1003 7.3 8.4 6.9 7.5 0.8 11 3.6 3.2 3.2 3.3 59-5001 10.7 11.6 10.3 10.9 0.7 6 5.2 5.4 5.2 5.3 

mean 10.6 10.1 8.7 9.8 4.0 4.0 3.6 3.9 

std 2.8 3.7 3.6 3.1 1.3 1.4 1.4 1.3 

COV 26 37 42 32 32 35 38 34 
February 2010 Draft – Do Not Quote or Cite 3-17

1 Table 3-5. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) 

99th 2 percentile 8-hour daily maximum. 

Historical Air Quality – 99th percentile 8-hour daily maximum Recent Air Quality – 99th percentile 8-hou

Monitor 1995 1995 1997 mean std COV 2005 2006 2007 mean 31-0002 7.3 7.2 5.2 6.6 1.2 18 2.4 2.8 2.7 2.6 31-0013 5.4 5.2 4.7 5.1 0.4 7 2.2 2.1 2.2 31-0014 5.7 5.5 5.8 5.7 0.1 2 2.1 2.8 2.4 59-0002 4.1 3.8 4.8 4.2 0.5 12 1.8 1.8 1.8 

mean 5.6 5.4 5.1 5.4 2.1 2.4 2.7 2.3 

std 1.3 1.4 0.5 1.0 0.3 0.5 0.4 

COV 24 26 10 18 12 21 16 

3 Table 3-6. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (200

data – 99th 4 percentile 8-hour daily maximum. 

Historical Air Quality – 99th percentile 8-hour daily maximum Recent Air Quality – 99th percentile 8-hou

Monitor 1995 1996 1997 mean std COV 2005 2006 2007 mean 37-0113 8.6 5.2 3.7 5.8 2.5 43 1.9 1.8 1.5 1.8 37-1002 9.7 8.3 6.8 8.3 1.4 17 3.0 3.2 2.6 3.0 37-1103 7.5 7.0 5.6 6.7 1.0 15 2.6 2.4 2.0 2.4 37-1201 9.0 6.7 7.3 7.6 1.2 15 3.2 3.1 2.6 2.9 37-1301 11.2 13.9 13.1 12.7 1.4 11 4.9 5.1 4.5 4.8 37-2005 8.5 6.8 5.0 6.7 1.8 26 2.8 2.6 2.1 2.5 37-4002 5.9 6.2 5.9 6.0 0.2 3 2.9 2.7 2.4 2.7 59-0001 6.5 5.7 5.1 5.8 0.7 12 2.7 2.7 2.1 2.5 59-1003 4.7 6.4 4.9 5.3 0.9 17 3.0 2.2 2.4 2.6 59-5001 6.3 5.9 5.3 5.8 0.5 9 2.6 2.7 2.5 2.6 

mean 7.8 7.2 6.3 7.1 3.0 2.9 2.5 2.8 

std 2.0 2.5 2.6 2.2 0.8 0.9 0.8 0.8 

COV 25 35 42 31 25 31 31 29 
February 2010 Draft – Do Not Quote or Cite 3-18

1 3.2 STUDY AREAS SELECTED FOR CURRENT CO REA 

2 Staff identified several criteria to select the exposure assessment study areas drawing 

3 from information discussed in the earlier sections of this Chapter and additional scientific 

4 evidence in the ISA. We selected Denver and Los Angeles as areas to focus the current 

5 assessment because (1) both cities have been included in prior CO NAAQS exposure 

6 assessments and thus serve as an important connection with past assessments, (2) they have 

7 historically had among the highest CO ambient concentrations among urban areas in the U.S., 

8 and (3) Denver is at high altitude and represents a scenario of interest due to the potentially 

9 increased susceptibility of visitors to high altitude from exposure to CO. In addition, of 10 urban 

10 areas across the US having monitors meeting a 75% completeness criteria, the two locations 

were ranked 1st (Los Angeles) and 2nd 11 (Denver) regarding percent of elderly population within 5, 

10, and 15 km of monitor locations, and ranked 1st (Los Angeles) and 5th 12 (Denver) regarding 

13 number of 1-hour and 8-hour daily maximum CO concentration measurements (ISA, section 

14 3.5.1.1). 

15 3.3 KEY OBSERVATIONS 

16 Presented below are key observations resulting from the air quality considerations. 

17  Mobile sources (i.e., gasoline powered vehicles) are the primary contributor to CO 

18 emissions, particularly in urban areas due to greater vehicle and roadway densities. 

19  Recent (2005-2007) ambient CO concentrations across the US are lower than those 

20 reported in the previous CO NAAQS review and are also well below the current CO 

21 NAAQS levels. Further, a large proportion of the reported concentrations are below 

22 the conventional instrument lower detectable limit of 1 ppm. 

23  The currently available information for CO monitors indicates that siting of microscale 

24 and middle scale monitors in the current network is primarily limited to roads where 

25 traffic density described for them is moderate (<100,000 AADT), however, factors 

26 other than reported AADT (e.g., orientation with regard to dense urban roadway 

27 networks) can contribute to sites reporting higher CO concentrations. 

28  Ambient CO concentrations are highest at monitors sited closest to roadways (i.e., 

29 microscale and middle scale monitors) and exhibit a diurnal variation linked to the 

30 typical commute times of day, with peaks generally observed during early morning and 

31 late afternoon during weekdays. 

32  Policy relevant background (PRB) concentrations across the US are generally less than 

33 0.2 ppm, far below that of interest in this REA with regard to ambient CO exposures. 

34  Historical trends in ambient monitoring data indicate that at individual sites, ambient 

35 concentrations have generally decreased in a proportional manner. This comparison 

36 included air quality distributions with concentrations at or above the current standard 

37 and those reflecting current (as is) conditions. 
February 2010 Draft – Do Not Quote or Cite 3-19

 The temporal variability in selected upper percentile ambient concentrations (e.g., 99th 1 

2 percentile 1-hour daily maximum) at individual monitors is relatively small across a 

3 three year monitoring period, particularly when considering recent air quality. 
February 2010 Draft – Do Not Quote or Cite 3-20

1 3.4 REFERENCES 

2 

3 Baldauf R, Thoma E, Hays M, Shores R, Kinsey J, Gullett B, Kimbrough S, Isakov V, Long T, Snow R, Khlystov 

4 A, Weinstein J, Chen FL, Seila R, Olson D, Gilmour I, Cho SH, Watkins N, Rowley P, Bang J. (2008). 

5 Traffic and meteorological impacts on near-road air quality: Summary of methods and trends from the 

6 Raleigh near-road study. J Air Waste Manag Assoc. 58: 865-878. 

7 Michie Jr RM, McElroy FF, Sokash JA, Thompson VL, Dayton DP, Sutcliffe CR. (1983). Performance Test 

8 Results and Comparative for Designated Equivalence Methods for Carbon Monoxide. EPA-600/S4-83-

9 013. 

10 Rizzo M. (2008). Investigation of how distributions of hourly nitrogen dioxide concentrations have changed over 

11 time in six cities. Nitrogen Dioxide NAAQS Review Docket (EPA–HQ–OAR–2006–0922 ). Available at 

12 http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_rea.html. 

13 Rizzo M. (2009). Investigation of How Distributions of Hourly Sulfur Dioxide Concentrations Have Changed Over 

14 Time in Six Cities. Sulfur Dioxide Review Docket. Docket ID No. EPA-HQ-OAR-2007-0352. Available 

15 at: http://www.regulations.gov. 

16 US EPA. (2000). Air Quality Criteria for Carbon Monoxide. EPA 600/P-99/001F. US Environmental Protection 

17 Agency, Research Triangle Park, NC. 

18 US EPA. (2009). Integrated Science Assessment for Carbon Monoxide –Final Draft. U.S. Environmental 

19 Protection Agency, Research Triangle Park, NC, report no. EPA/600/R-09/019F. Available at: 

20 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 

21 Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C. (2002). Study of ultrafine particles near a major highway with 

22 heavy-duty diesel traffic. Atmos Environ. 36:4323-4335. 

23 
February 2010 Draft – Do Not Quote or Cite 4-1

1 4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING 

2 CO EXPOSURES AND COHB DOSE LEVELS 

3 4.1 PURPOSE 

4 This chapter presents an overview and description of the overall approach to estimating 

5 human exposure and dose for past and recent NAAQS reviews. Section 4.2 provides a brief 

6 overview of EPA’s Air Pollutants Exposure model (APEX), the model used in this assessment to 

7 estimate population exposure and dose. This overview is followed by a short history that 

8 explains the evolution of exposure and dose models used by OAQPS to conduct exposure and 

9 dose assessments for CO and other NAAQS reviews (section 4.3). Section 4.4 provides a 

10 generalized description of the APEX simulation process, though having detailed focus on a few 

11 of the important approaches used for modeling CO exposure and dose. This includes expanded 

12 discussion on the approach used to estimate microenvironmental concentrations (section 4.4.4) 

13 and COHb dose levels (section 4.4.7). 

14 4.2 MODEL OVERVIEW 

15 The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program 

16 designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and 

17 consolidated metropolitan levels. APEX, also known as TRIM.Expo, is the human inhalation 

18 exposure module of EPA’s Total Risk Integrated Methodology (TRIM) model framework (US 

19 EPA, 1999), a modeling system with multimedia capabilities for assessing human health and 

ecological risks from hazardous and criteria air pollutants.1 20 

21 APEX estimates human exposure using a 

22 stochastic, microenvironmental approach (see caption). 

23 The model randomly selects data for a sample of 

24 hypothetical individuals from an actual population 

25 database and simulates each individual’s movements 

26 through time and space (e.g., indoors at home, inside 

27 vehicles) to estimate his or her exposure to a pollutant. 

28 APEX can account for travel to and from work locations 

29 (i.e., commuting) and provide estimates of exposures at 

 

1

 Additional information on the TRIM modeling system, as well as downloads of the APEX Model, user guides 

(U.S. EPA 2008a, 2008b), and other supporting documentation, can be found at http://www.epa.gov/ttn/fera. 

A microenvironment is a threedimensional space in which human 

contact with an environmental pollutant 

takes place and which can be treated as 

a well characterized, relatively 

homogeneous location with respect to 

pollutant concentrations for a specified 

time period. 
February 2010 Draft – Do Not Quote or Cite 4-2

1 both home and work locations for individuals who work away from home. 

2 4.3 MODEL HISTORY AND EVOLUTION 

3 APEX was derived from the National Ambient Air Quality Standards (NAAQS) 

4 Exposure Model (NEM) series of models. The NEM series was developed to estimate 

5 population exposures to the criteria pollutants (e.g., CO, ozone). In 1988, OAQPS first 

6 incorporated probabilistic elements into the NEM methodology and used activity pattern data 

7 based on available human activity diary studies to create an early version of probabilistic NEM 

8 for ozone (i.e., pNEM/O3). In 1991, a probabilistic version of NEM was developed for CO 

9 (pNEM/CO) that included a one-compartment mass-balance model to estimate CO 

10 concentrations in indoor microenvironments. The application of this model to Denver, Colorado 

11 is summarized in Johnson et al. (1992). Between 1999 and 2001, updated versions of pNEM/CO 

12 (versions 2.0 and 2.1) were developed that rely on detailed activity diary data compiled in EPA’s 

13 Consolidated Human Activities Database (CHAD) (McCurdy et al., 2000; US EPA, 2002) and 

14 enhanced algorithms for simulating gas stove usage, estimating alveolar ventilation rate (a 

15 measure of human respiration), and modeling home-to-work commuting patterns. A draft report 

16 by Johnson et al. (2000) describes the application of Version 2.1 of pNEM/CO to Denver and 

17 Los Angeles. 

18 The first version of APEX was essentially identical to pNEM/CO (version 2.0) except 

19 that it ran on a PC instead of a mainframe. The next version, APEX2, was substantially 

20 different, particularly in the use of a personal profile approach rather than a cohort simulation 

21 approach. APEX3 introduced a number of new features including automatic site selection from 

22 national databases, a series of new output tables providing summary exposure and dose statistics, 

23 and a thoroughly reorganized method of describing microenvironments and their parameters. 

24 Johnson and Capel (2003) describe a case study in which Version 3.1 of APEX was used to 

25 estimate population exposure to CO in Los Angeles. 

26 The current version of APEX (Version 4.3) (US EPA, 2008a; 2008b) was used to 

27 estimate CO exposure and dose as described in chapter 5 of this document. This version was 

28 also recently used to estimate ozone (O3) exposures in 12 urban areas for the O3 NAAQS review 

29 (US EPA, 2007), to estimate population exposures to nitrogen dioxide (NO2) in Atlanta as part of 

30 the NO2 NAAQS review (US EPA, 2008c), and to estimate sulfur dioxide (SO2) exposures for 

31 asthmatics and asthmatic children in two study areas in Missouri as part of the SO2 NAAQS 

32 review (US EPA, 2009a). There have been several recent enhancements to APEX since the prior 

33 1994 CO NAAQS review, including: 
February 2010 Draft – Do Not Quote or Cite 4-3

1  Algorithms for the assembly of multi-day (longitudinal) activity diaries that model intra2 individual variance, inter-individual variance, and day-to-day autocorrelation in diary 

3 properties; 

4  Methods for adjusting diary-based energy expenditures for fatigue and excess post5 exercise oxygen (EPOC) consumption; 

6  New equations for estimation of ventilation (i.e., breathing rate); 

7  The ability to use air quality data and model exposures over flexible time scales; 

8  New output files containing diary event-level, time-step level, and hourly-level exposure, 

9 dose, and ventilation data, and hourly-level microenvironmental data; 

10  The ability to model the prevalence of disease states such as asthma or coronary heart 

11 disease (CHD); 

12  New output exposure tables that report exposure statistics for population groups and life13 stages such as children and active people at varying ventilation rates; 

14  The inclusion of tract-level commuting data from the 2000 census; and 

15  Expanded options for modeling microenvironments. 

16 4.4 MODEL SIMULATION PROCESS 

17 APEX4.3 is designed to simulate population exposure to criteria and air toxic pollutants 

18 at local, urban, and regional scales. The user specifies the geographic area to be modeled and the 

19 number of individuals to be simulated to represent this population. APEX4.3 then generates a 

20 personal profile for each simulated person that specifies various parameter values required by the 

21 model. The model next uses diary-derived time/activity data matched to each personal profile to 

22 generate an exposure event sequence (also referred to as a time-location-activity pattern or 

23 composite diary) for the modeled individual that spans a specified time period, such as a calendar 

24 year. Each event in the sequence specifies a start time, exposure duration, a geographic location, 

25 a microenvironment inhabited, and an activity performed. Probabilistic algorithms are used to 

26 estimate the pollutant concentration and ventilation (respiration) rate associated with each 

27 exposure event. The estimated pollutant concentrations account for the effects of ambient 

28 (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, 

29 and proximity to emission sources, each depending on the microenvironment, available data, and 

30 the estimation method selected by the user. The ventilation rate is derived from an energy 

31 expenditure rate estimated for each individual when performing the specified activity. Because 

32 the simulated individuals represent a random sample of the population of interest, the distribution 

33 of modeled individual exposures can then be extrapolated to the larger population of interest. 

34 The model simulation generally includes up to seven steps as follows: 
February 2010 Draft – Do Not Quote or Cite 4-4

1  Characterize study area: APEX4.3 selects sectors (e.g., census tracts) within a study 

2 area—and thus identifies the potentially exposed population — usually based on the 

3 user-defined center and radius of the study area and availability of air quality and 

4 weather input data for the area (section 4.4.1). 

5  Generate simulated individuals: APEX4.3 stochastically generates a sample of 

6 simulated individuals based on the census data for the study area and human profile 

7 distribution data (such as age-specific employment probabilities or disease prevalence) 

8 (section 4.4.2) 

9  Construct activity sequences: APEX4.3 constructs an exposure event sequence (time10 location-activity pattern) spanning the simulation period for each of the simulated 

11 persons based on the CHAD diaries (section 4.4.3). 

12  Calculate microenvironmental concentrations: APEX4.3 enables the user to define 

13 microenvironments that people in a study area would visit (e.g., by grouping location 

14 codes included in the activity pattern database). The model then calculates time15 averaged concentrations (e.g., hourly) of each pollutant in each of the 

16 microenvironments for each simulated person for the period of simulation based on the 

17 user-provided ambient air quality data (section 4.4.4). 

18  Estimate energy expenditure and ventilation rates: APEX4.3 constructs a time19 series of energy expenditures for each individual’s exposure profile based on the 

20 sequence of activities performed. The sequence of energy expenditures are adjusted to 

21 ensure that they are physiologically realistic and then used to estimate activity-specific 

22 alveolar ventilation rates (section 4.4.5). 

23  Calculate exposure: APEX4.3 assigns a concentration to each exposure event based 

24 on the microenvironment occupied during the event and the person’s activity. These 

25 values are time-averaged (e.g., hourly) to produce a sequence of exposures spanning 

26 the specified exposure period (typically one year). The hourly values may be further 

27 aggregated to produce 8-hour, daily, monthly, and annual average exposure values 

28 (section 4.4.6). 

29  Calculate dose: APEX4.3 optionally calculates hourly, daily, monthly, and annual 

30 average dose values for each of the simulated individuals. For the application of 

31 APEX to CO, a module within the model estimates the percent COHb level in the 

32 blood at the end of each hour based on the time-series of CO concentrations and 

33 alveolar ventilation rates experienced by the simulated person (section 4.4.7). 

34 The model simulation continues until exposures (and associated COHb dose levels) are 

35 calculated for the user-specified number of simulated individuals. Figure 4-1 presents a 

36 conceptual model and simplified data flow diagram illustrating the implementation of APEX4.3 

37 to estimate CO exposure and dose. The following sections provide additional details on the 

38 general procedures and algorithms used in each of the seven simulation steps listed above, 

39 though more complete discussion can be found in US EPA (2008a, b). The specific input data 

40 and microenvironmental factors used in applying APEX4.3 to CO for the current assessment are 

41 further described in section 5.1.
February 2010 Draft – Do Not Quote or C

4-5

Air Exchange Rates

(AER) & Prevalence

Time

US Census

Tract-Level Population

& Commuting Data

Ambient Air Quality

1-Hour Ambient Monitor

CO Concentrations

Microenvironmental

Factors/Distributions

Exposure Algorithms

(mass balance or factors)

Dose Al

(CFK 

Outdoor CO Concentration

Algorithm

(spatial & temporal adjustment)

Persons & Person-Days

At or Above

Exposure Levels

Persons & P

At or A

Benchmark 

USDHHS/CDC

Disease Prevalence

Meteorological Data

Temperature

Energy Exp

Ventilation 

(alveolar res

Air Exchange Rates

(AER) & Prevalence

Time

US Census

Tract-Level Population

& Commuting Data

Ambient Air Quality

1-Hour Ambient Monitor

CO Concentrations

Microenvironmental

Factors/Distributions

Exposure Algorithms

(mass balance or factors)

Dose Al

(CFK 

Outdoor CO Concentration

Algorithm

(spatial & temporal adjustment)

Persons & Person-Days

At or Above

Exposure Levels

Persons & P

At or A

Benchmark 

USDHHS/CDC

Disease Prevalence

Meteorological Data

Temperature

Energy Exp

Ventilation 

(alveolar res

1 2 Figure 4-1. Conceptual model and simplified data flow for estimating population exposure and dose usin3 
February 2010 Draft – Do Not Quote or Cite 4-6

1 4.4.1 Characterize Study Area 

2 An initial study area in an APEX4.3 assessment consists of a set of basic geographic units 

3 called sectors, typically defined by US census data reported at the census tract level. The user 

4 may provide the geographic center (latitude/longitude) and radius of the study area and then 

5 APEX4.3 calculates the distances to the center of the study area of all the sectors included in the 

6 sector location database, and finally selects the sectors within the radius of the study area. 

7 APEX4.3 then maps the user-provided air quality and meteorological data for specified 

8 monitoring districts to the selected sectors. The sectors identified as having acceptable air 

9 quality and meteorological data within the radius of the study area are selected to comprise a 

10 final study area for the APEX4.3 simulation analysis. This final study area determines the 

11 population make-up of the simulated persons (profiles) to be modeled. 

12 4.4.2 Generate Simulated Individuals 

13 APEX4.3 stochastically generates a user-specified number of simulated persons to 

14 represent the population in the study area. Each simulated person is represented by a personal 

15 profile. APEX4.3 generates the simulated person by probabilistically selecting values for a set of 

16 profile variables. The profile variables include: 

17  Demographic variables that are generated based on US census data (e.g., age, gender, 

18 home sector, work sector); 

19  Residential variables that are generated based on sets of distribution data (e.g., air 

20 conditioning prevalence); 

21  Physiological variables that are generated based on age- and gender-specific distribution 

22 data (e.g., blood volume, body mass, resting metabolic rate); and 

23  Daily varying variables that are generated based on distribution data that change daily 

24 during the simulation period (e.g., daily work status). 

25 APEX4.3 first selects and calculates demographic, residential, and physiological 

26 variables (except for daily values) for each of the user-specified number of simulated individuals. 

27 APEX4.3 then follows each simulated individual over time and calculates exposures (and 

28 optionally doses) for the individual over the duration of the assessment period. The complete 

29 listing of profile variables used by APEX4.3 and detailed description can be found in section 5 of 

30 US EPA (2008b). An overview of the data sources used and their implementation in APEX4.3 is 

31 provided below. 

32 4.4.2.1 Population Demographics 

33 APEX4.3 takes population characteristics into account to develop accurate 

34 representations of study area demographics. Specifically, population counts by area and 
February 2010 Draft – Do Not Quote or Cite 4-7

1 employment probability estimates are used to develop representative profiles of hypothetical 

2 individuals for the simulation. 

3 APEX4.3 is flexible in the resolution of population data provided. As long as the data are 

4 available, any resolution can be used (e.g., county, census tract, census block). For this 

5 application of the model, census tract level data were used. Tract-level population counts are 

6 obtained from the 2000 Census of Population and Housing Summary File 1 (SF-1). This file 

7 contains data compiled from the questions asked of all respondents and about every housing unit. 

8 As part of the population demographics inputs, it is important to integrate working 

9 patterns into the assessment. In the 2000 US Census, estimates of employment were developed 

10 by census information (US Census Bureau, 2007). The employment statistics are broken down 

11 by gender and age group, so that each gender/age group combination is given an employment 

12 probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are: 

13 16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75. 

14 Children under 16 years of age were assumed to be not employed. 

15 4.4.2.2 Commuting Database 

16 In addition to using estimates of employment by tract, APEX4.3 also incorporates home17 to-work commuting data. Commuting data were derived from the 2000 Census and were 

18 collected as part of the Census Transportation Planning Package (CTPP) (US DOT, 2007). The 

19 data used contain counts of individuals commuting from home to work locations at a number of 

20 geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to 

21 create the national commuting data distributed with APEX4.3. This database contains 

22 commuting data for each of the 50 states and Washington, D.C. 

23 Several assumptions were made in the development of the database and with the 

24 modeling of a person’s commute in this assessment as follows. 

25  Commutes within tracts and home workers: There is no differentiation between 

26 people that work at home from those that commute within their home tract. 

27  Commute distance cutoff: All persons in home-work flows up to 120 km are assumed 

28 to be daily commuters and no persons in more widely separated flows would commute 

29 daily. This means that the list of destinations for each home tract was restricted to only 

30 those work tracts that are within 120 km of the home tract. This distance is based on 

31 the presence of a near-constant relationship between commute flows and distance 

32 traveled up to 120 km. 

33  Eliminated Records: Tract-to-tract pairs representing workers who either worked 

34 outside of the US (9,631 tract pairs with 107,595 workers) or worked in an unknown 

35 location (120,830 tract pairs with 8,940,163 workers) were eliminated from the 

36 database. An additional 515 workers in the commuting database whose data were 
February 2010 Draft – Do Not Quote or Cite 4-8

1 missing from the original files, possibly due to privacy concerns or errors, were also 

2 deleted. 

3  Commuting outside the study area: APEX4.3 allows for some flexibility in the 

4 treatment of persons in the modeled population who commute to destinations outside 

5 the study area. Users can either retain these persons and include them as part of the 

6 population exposed or have them eliminated from the model simulation. In the first 

7 instance (i.e., “KeepLeavers = Yes”), APEX4.3 can assign input concentrations based 

8 on the available ambient concentration data within the model domain. For the second 

9 option (i.e., “KeepLeavers = No”) , people who work inside the study area but live 

10 outside of it are not modeled, nor are people who live in the study area but work 

11 outside of it. 

12 4.4.2.3 Profile Functions File 

13 A Profile Functions file contains settings used to generate results for variables related to 

14 simulated individuals. While certain settings for individuals are generated automatically by 

15 APEX4.3 based on other input files, including demographic characteristics, others can be 

16 specified using this file. For example, the file may contain settings for determining whether the 

17 profiled individual’s residence has an air conditioner, a gas stove, etc. 

18 4.4.2.4 Physiology File 

19 The APEX4.3 physiology.txt file contains age- and gender-based information for several 

20 physiological parameters used in human exposure modeling. This information includes various 

21 equations, distributional shapes, and parameters for all age and gender cohorts from age 0 to 100 

22 years for variables such as normalized maximal oxygen uptake, body mass, resting metabolic 

23 rate (RMR), and blood hemoglobin content. Appendix A provides an evaluation of a few 

24 important variables used by APEX4.3 in this exposure and dose assessment as well as their 

25 updated values or distributions (e.g., new age-gender body mass distributions derived from 1999-

26 2004 National Health and Nutrition Examination Survey data). Details regarding any other 

27 physiology variable distributions and their parameters not discussed in this draft CO REA and 

28 associated appendices can be found in US EPA (2008a, b). 

29 4.4.3 Construct Activity Sequences 

30 Different human activities, such as spending time outdoors, indoors, or driving, will be 

31 associated with varying pollutant concentrations. Therefore, to accurately model individuals and 

32 their exposure to pollutants, it is critical to understand people’s daily activities and use such data 

33 in the exposure model. EPA’s Consolidated Human Activity Database (CHAD) provides diary34 derived data indicating where people spend time and the activities they perform at each location 

35 (US EPA, 2002). CHAD was designed to provide a basis for conducting multi-route, multi36 media exposure assessments (McCurdy et al., 2000). The data contained within CHAD originate 
February 2010 Draft – Do Not Quote or Cite 4-9

1 from multiple activity pattern surveys with varied structures (Table 4-1), however the surveys 

2 have commonality in containing daily diaries of human activities performed, locations visited, 

3 and personal attributes (e.g., age and gender). 

4 There are four CHAD-related input files used in APEX4.3. The first three can be 

5 considered standard input files for most model simulations; the user typically does not modify 

6 their contents. These include the human activity diaries file, the personal data file, and a 

7 metabolic information file, each of which are discussed briefly below. The fourth CHAD-related 

8 input file maps the five-digit location codes used in the diary file to APEX4.3 

9 microenvironments; this file is commonly modified by the user and is discussed in section 5.8 

10 (i.e., specific microenvironments modeled in this CO assessment). And finally, section 4.4.3.4 

11 discusses how these diaries are linked together to form a continuous time-location-activity 

12 pattern for each individual across the entire simulation period. 
February 2010 Draft – Do Not Quote or Cite 4-10

1 Table 4-1. Summary of activity pattern studies comprising the recent version of CHAD. 

Study Name 

CHAD 

Prefix Study 

Years 

Number of 

Diary Days Reference 

Baltimore BAL 1997-1998 391 Williams et al. (2000) 

CARB: Adults CAA 1987-1988 1579 Wiley et al. (1991a) 

CARB: Adolescents CAY 1987-1988 183 Wiley et al. (1991a) 

CARB: Children CAC 1989-1990 1200 Wiley et al. (1991b) 

Cincinnati (EPRI) CIN 1985 2614 Johnson (1989) 

Denver (EPA) DEN 1982-1983 805 Johnson (1984); Akland et al. (1985) 

Los Angeles: 

Elementary 

LAE 1989 51 Spier et al. (1992) 

Los Angeles: High 

School 

LAH 1990 43 Spier et al. (1992) 

NHAPS A NHA 1992-1994 4723 Klepeis et al. (1996); Tsang and Klepeis (1996)

NHAPS B NHW 1992-1994 4663 Klepeis et al. (1996); Tsang and Klepeis (1996)

PSID 1 

(U Michigan I) 

UMC 1997 5616 University of Michigan (2010) 

PSID 2 

(U Michigan II) 

ISR 2002-2003 4782 University of Michigan (2010) 

Valdez VAL 1990-1991 397 Goldstein et al. (1992) 

Washington, DC WAS 1982-1983 699 Hartwell et al. (1984); Akland et al. (1985) 

RTI Ozone Averting 

Behavior 

OAB 2002-2003 2907 Mansfield and Corey (2003); Mansfield et al. 

(2004; 2006) 

RTP Panel Study RTP 2000-2001 1003 Williams et al. (2003a,b) 

Seattle Study SEA 1999-2002 1693 Liu et al. (2003) 

Internal EPA Study 

2006-2007 

EPA 2006-2007 434 Isaacs et al. (2009) 

EPA Longitudinal 1 EPA 1999,2002 736 Isaacs et al. (2009) 

EPA Longitudinal 2 EPA 2000 197 Isaacs et al. (2009) 

EPA Longitudinal 3 EPA 2008 62 Isaacs et al. (2009) 

2 

3 4.4.3.1 Personal Information file 

4 Personal attribute data are contained in the CHAD questionnaire file that is distributed 

5 with APEX4.3. This file also has information for each day individuals have diaries. The 

6 different variables in this file are: 

7  The study, person, and diary day identifiers 

8  Day of week 
February 2010 Draft – Do Not Quote or Cite 4-11

1  Gender 

2  Employment status 

3  Age in years 

4  Maximum temperature in degrees Celsius for the diary day 

5  Mean temperature in degrees Celsius for the diary day 

6  Occupation code (if requested) 

7  Time, in minutes, during this diary day for which no data are included in the database 

8 4.4.3.2 Diary Events File 

9 The human activity diary data are contained in the events file that is distributed with 

10 APEX4.3. This file contains the locations visited and the activities performed for the nearly 

11 35,000 person-days of data with event intervals ranging from a minimum of one minute upwards 

12 to a one hour maximum duration. A study individuals’ diary can vary in length from one to 15 

13 days (i.e., referring to the number of person-days). The diary events file contains the following 

14 variables: 

15  The study, person, and diary day identifiers 

16  Start time of the event 

17  Number of minutes for the event 

18  Activity code (a record of what the individual was doing) 

19  Location code (a record of where the individual was) 

20 4.4.3.3 Activity-Specific Metabolic File 

21 The metabolic file contains the distributional forms and parameters for the activity22 specific metabolic equivalents (METs) used to quantitatively assign exertion levels to each 

23 activity performed by simulated individuals (McCurdy, 2000). Some activities are specified as a 

24 single point value (for instance, sleep), while others, such as athletic endeavors or manual labor, 

25 are represented by normal, lognormal, or similar statistical distributions. APEX4.3 samples from 

26 these distributions and calculates values to simulate the variable nature of activity levels among 

27 different people. The CHAD User’s guide provides details on the distributions used, parameters, 

28 and sources for each activity (US EPA, 2002). 

29 4.4.3.4 Longitudinal Diary Processing 

30 APEX4.3 probabilistically creates a composite longitudinal diary for each of the 

31 simulated persons by selecting a 24-hour diary record – or diary day – from an activity database 

32 for each day of the simulation period. The EPA’s CHAD data (US EPA, 2002) are supplied with 

33 APEX4.3 for this purpose. A composite diary is a sequence of events that simulates the 
February 2010 Draft – Do Not Quote or Cite 4-12

1 movement of a modeled person through varying geographical locations and microenvironments 

2 for the duration of the simulation period. Each diary event is defined by geographic location, 

3 start time, duration, microenvironment visited, and an activity performed. 

4 The activity database input to APEX4.3 contains the following information for each diary 

5 day: age, gender, employment status, occupation, day-of-week (or day-type), and maximum 

6 hourly average temperature. This information enables APEX4.3 to select data from the activity 

7 database that tend to match the characteristics of the simulated person, the study area, and the 

8 specified time period. APEX4.3 develops a composite diary for each of the simulated 

9 individuals according to the following steps. 

10  Divide diary days in the CHAD database into user-defined activity pools, based on 

11 day-type and temperature. 

12  Assign an activity pool number to each day of the simulation period, based on the user13 provided daily maximum/average temperature data. 

14  Calculate a selection probability for each of the diary days in each of the activity pools, 

15 based on age/gender/employment similarity of a simulated person to a diary day. 

16  Probabilistically select a diary day from available diary days in the activity pool 

17 assigned to each day of the simulation period. 

18  Estimate a MET value for each activity performed while in a location, based on a 

19 random sampling of the particular distribution of each specific activity. The METs 

20 values are used to calculate an activity-specific ventilation rate (see section 4.4.5) for 

21 the simulated person. 

22  Map the CHAD locations in the selected diary to the user-defined modeled 

23 microenvironments. 

24  Concatenate the selected diary days into a sequential longitudinal diary for a simulated 

25 individual covering all days in the simulated period. 

26 APEX4.3 provides an optional longitudinal diary-assembly algorithm that enables the 

27 user to create composite diaries that reflect the tendency of individuals to repeat activities on a 

28 day-to-day basis. The user specifies values for two statistical variables (i.e., D and A) that relate 

29 to a key daily variable, typically the time spent per day in a particular microenvironment (e.g., in 

30 a motor vehicle). The D statistic reflects the relative importance of within-person variance and 

31 between-person variance in the key variable. The A statistic quantifies the lag-one (day-to-day) 

32 variable autocorrelation. APEX4.3 then constructs composite diaries that exhibit the statistical 

33 properties defined by the specified values of D and A. The longitudinal diary assembly 

34 algorithm is described in greater detail by Glen et al. (2008) and in section 6.3 of US EPA 

35 (2008b). 
February 2010 Draft – Do Not Quote or Cite 4-13

1 4.4.4 Calculate Microenvironmental Concentrations 

2 Probabilistic algorithms are used by APEX4.3 to estimate the pollutant concentration 

3 associated with each exposure event. The estimated pollutant concentrations account for the 

4 effects of ambient (outdoor) pollutant concentration, penetration factor, air exchange rate, 

5 decay/deposition rate, and proximity to emission sources, depending on the microenvironment, 

6 available data, and the estimation method selected by the user. 

7 APEX4.3 calculates air concentrations in the various microenvironments visited by the 

8 simulated person by using the ambient air data for the relevant census tracts and the user9 specified method and parameters that are specific to each microenvironment. In typical 

10 applications, APEX4.3 calculates hourly concentrations in all the microenvironments at each 

11 hour of the simulation for each of the simulated individuals, based on the hourly ambient air 

12 quality data specific to the geographic locations visited by the individual. APEX4.3 provides 

13 two methods for calculating microenvironmental concentrations: the mass balance method and 

14 the transfer factors method (described briefly below). The user is required to specify a 

15 calculation method for each of the microenvironments; there are no restrictions on the method 

16 specified for each microenvironment (e.g., some microenvironments can use the mass balance 

17 method while the others can use the transfer factors method). Each of these approaches is 

18 described in sections 4.4.4.1 and 4.4.4.2, respectively. 

19 When using an exposure model to estimate population exposures to CO it is best to 

20 estimate the outdoor (ambient) CO concentration in the immediate vicinity of each 

21 microenvironment. This is because concentrations measured at a fixed-site monitor may not 

22 adequately represent the spatial and temporal heterogeneity in concentrations expected with 

23 distance from the ambient monitor location. There can be different ways to accomplish this. For 

24 example, one can use an emission-based dispersion model to estimate ambient concentrations at 

25 a fine temporal (e.g., hourly) and spatial scale (e.g., census block-level or 500 meter grids). 

26 Another method is to use a statistically-based approach that addresses the variability in 

27 concentrations in a similar manner as a dispersion model, only that important physical factors 

28 that influence concentration levels are represented by and/or possibly combined with a series of 

29 regression equation coefficients and are related to an ambient monitor CO concentration. 

30 Ultimately, it is this estimated outdoor CO concentration that is then used as input to the 

31 algorithm (either the mass balance model or factors method) employed to estimate CO 

32 microenvironmental concentrations. 

33 Staff selected a statistically-based approach to adjust ambient monitor concentrations. 

34 The approach was designed to reflect both the spatial and temporal variability expected to occur 

35 outside microenvironments, while also appropriately linking the estimated microenvironmental 

36 concentrations to observed concentrations at a fixed-site ambient monitor. The approach was 
February 2010 Draft – Do Not Quote or Cite 4-14

1 developed using personal exposure, fixed-site monitor, and outdoor concentration measurement 

2 data and first implemented in the pNEM/CO model for use in the most recent CO exposure 

3 assessment (Johnson et al., 2000). This approach was proposed as a method to address spatial 

4 and temporal variability in outdoor and microenvironmental concentrations in the draft scope and 

5 methods plan (US EPA, 2009b), though not fully described there as is done here. 

6 The microenvironmental algorithm and data used by pNEM/CO to estimate variable 

7 parameters is described in section 4.4.4.3. The pNEM/CO approach was then adapted and 

8 implemented in APEX3.1, a model more similar in structure to the current version of APEX 

9 (version 4.3) than pNEM/CO. This approach is described in section 4.4.4.4. The details 

10 regarding selection of microenvironments and parameters used by APEX4.3 in this assessment is 

11 provided in section 5.9. 

12 4.4.4.1 Overview of the Mass Balance Model 

13 The mass balance method models an enclosed microenvironment as a well-mixed volume 

14 in which the air concentration is spatially uniform at any specific time. The concentration of an 

15 air pollutant in such a microenvironment is estimated using the following four processes: 

16  Inflow of air into the microenvironment; 

17  Outflow of air from the microenvironment; 

18  Removal of a pollutant from the microenvironment due to deposition, filtration, and/or 

19 chemical degradation; and 

20  Emissions from sources of a pollutant inside the microenvironment. 

21 Table 4-2 lists the parameters required by the mass balance method to calculate 

22 concentrations in a microenvironment. The proximity factor (fproximity) is used to account for 

23 differences in ambient concentrations between the geographic location represented by the 

24 ambient air quality data (e.g., a fixed-site monitor) and the geographic location of the 

25 microenvironment (e.g., near a roadway). This factor could take a value either greater than or 

26 less than 1. Emission source (ES) represents the emission rate for the emission source, and 

27 concentration source (CS) is the mean air concentration resulting from the source (these are not 

28 used in the current assessment. The factor Rremoval is defined as the removal rate of a pollutant 

29 from a microenvironment due to deposition, filtration, and chemical reaction. The air exchange 

30 rate (Rair exchange) is expressed in air changes per hour. 
February 2010 Draft – Do Not Quote or Cite 4-15

1 Table 4-2. Variables used by APEX4.3 in the mass balance model. 

Variable Definition Units Value Range

f proximity Proximity factor unitless f proximity > 0 

CS Concentration source ppm CS ≥ 0 

ES Emission source μg/hr ES ≥ 0 

R removal

Removal rate due to 

deposition, filtration, and 

chemical reaction 

1/hr Rremoval ≥ 0 

R air exchange Air exchange rate 1/hr Rair exchange ≥ 0 

V Volume of 

microenvironment 

m3 V > 0 

2 

3 The mass balance equation for a pollutant in a microenvironment is described by the 

4 differential equation 

 Cin  Cout  Cremoval  Csource dt

dC ME (t) 5 (4-1) 

6 where: 

7 dCME(t) = Change in concentration in a microenvironment at time t (ppm), 

8 Cin = Rate of change in microenvironmental concentration due to influx 

9 of air (ppm/hour), 

10 Cout = Rate of change in microenvironmental concentration due to outflux 

11 of air (ppm/hour), 

12 Cremoval = Rate of change in microenvironmental concentration due to 

13 removal processes (ppm/hour), and 

14 Csource = Rate of change in microenvironmental concentration due to an 

15 emission source inside the microenvironment (ppm/hour). 

16 Within the time period of an hour each of the rates of change, Cin, Cout, Cremoval, and 

17 Csource, is assumed to be constant. The change in microenvironmental concentration due to 

18 influx of air is represented by the following equation: 

ambient proximity penetration air exchange

in

in C x f x f x R

dt

dC t C   ( ) 19 (4-2) 

20 where: 
February 2010 Draft – Do Not Quote or Cite 4-16

1 Cambient = Ambient hourly outdoor concentration (ppm) 

2 fproximity = Proximity factor 

3 fpenetration = Penetration factor 

4 Rair exchange = Air exchange rate (1/hour) 

5 The change in microenvironmental concentration due to outflux of air is described by: 

 ( ) ( ) R C t

dt

dC t C air exchange ME

out 6  out    (4-3) 

7 The change in concentration due to deposition, filtration, and chemical degradation in a 

8 microenvironment is simulated by a first-order equation: 

(R R R )C (t) R C (t) dt

dC (t) C deposition filtration chemical ME remova MEremoval 9  removal       (4-4) 

10 where: 

11 Rdeposition = Removal rate of a pollutant from a microenvironment due to 

12 deposition (1/hour) 

13 Rfiltration = Removal rate of a pollutant from a microenvironment due to 

14 filtration (1/hour) 

15 Rchemical = Removal rate of a pollutant from a microenvironment due to 

16 chemical degradation (1/hour) 

17 Rremoval = Removal rate of a pollutant from a microenvironment due to 

18 overall removal (1/hour) 

19 As discussed in Section 2.2, EPA has not modeled indoor emissions of CO in the current 

20 exposure assessment; consequently, the optional term Csource was uniformly set equal to 0.0 for 

21 this study. 

22 Combining Equation 4-1 with Equations 4-2, 4-3, and 4-4 yields 

( ) ( ) dt

dC ME (t) C R C t R C t   in  air exchange  ME  removal  ME 23 (4-5) 

24 

25 The solution to this differential equation is 
February 2010 Draft – Do Not Quote or Cite 4-17

( ) ( (0) ) exp( R t) R

C C

R

C C t combined

combined

in

ME

combined

in

ME  

   1  (4-6) 

2 where: 

3 CME(0) = Concentration of a pollutant in a microenvironment at the 

4 beginning of a hour (ppm) 

5 CME(t) = Concentration of a pollutant in a microenvironment at time t within 

6 the time period of a hour (ppm) 

7 Rcombined = Rair exchange + Rremoval (1/hour) 

8 Based on Equation 4-6, the following three hourly concentrations in a microenvironment 

9 are calculated: 

 

combined

in

ME

equil

ME R

C C C t

 10  (  )  (4-7) 

( (0) ) exp( ) combined

equil

ME ME

equil

ME

hourly end 11 CME C  C C R (4-8) 

combined

equil combined

ME ME

equil

ME

hourlymean

ME R

R C C C

dt

C t dt

C 1 exp( ) ( (0) )

( )

1

0

1

0      





12 (4-9) 

13 where: 

equil CME 14 = Equilibrium concentration in a microenvironment (ppm) 

15 CME(0) = Concentration in a microenvironment at the beginning of an hour 

16 (ppm) 

hourly end CME 17 = Concentration in a microenvironment at the end of an hour (ppm) 

hourlymean 18 CME = Hourly mean concentration in a microenvironment (ppm) 

19 At each hour time step of the simulation period, APEX4.3 uses Equations 4-7, 4-8, and 4-

20 9 to calculate the hourly equilibrium, hourly ending, and hourly mean concentrations. APEX4.3 

21 reports hourly mean concentration as hourly concentration for a specific hour. The calculation 

continues to the next hour by using hourly end C ME 22 for the previous hour as CME(0). 
February 2010 Draft – Do Not Quote or Cite 4-18

1 4.4.4.2 Overview of the Factors Model 

2 The factors model approach is conceptually simpler than the mass balance method and 

3 has fewer user-specified parameters. It estimates the concentration in a microenvironment as a 

4 linear function of ambient concentration of that hour, regardless of the concentration in the 

5 microenvironment during the preceding hour. Table 4-3 lists the parameters required by the 

6 factors model approach to calculate concentrations in a microenvironment without emissions 

7 sources. 

8 Table 4-3. Variables used by APEX4.3 in the factors model. 

Variable Definition Units Value Range

f proximity Proximity factor unitless f proximity > 0 

f penetration Penetration factor unitless 0 ≤ f penetration ≤ 1 

9 

10 The factors model approach uses the following equation to calculate hourly mean 

11 concentration in a microenvironment from the user-provided hourly air quality data: 

ambient proximity penetration

hourlymean

ME 12 C C  f  f (4-10) 

13 where 

hourlymean 14 CME = Hourly concentration in a microenvironment (ppm) 

15 Cambient = Hourly concentration in ambient environment (ppm) 

16 fproximity = Proximity factor (unitless) 

17 fpenetration = Penetration factor (unitless) 

18 

19 The proximity factor (fproximity) is used to account for differences in ambient 

20 concentrations between the geographic location represented by the ambient air quality data (e.g., 

21 a fixed-site monitor) and the geographic location of the particular microenvironment. For 

22 example, persons travelling inside motor vehicles may be located on a heavily-trafficked 

23 roadway, whereby the ambient air outside the vehicle would likely have elevated levels of 

24 mobile source pollutants such as carbon monoxide relative to the ambient monitor. In this case, 

25 a value greater than one for the proximity factor would be appropriate to represent the increase in 

26 concentrations outside the vehicle relative to the ambient monitor. Additionally, for some 

27 pollutants the process of infiltration may remove a fraction of the pollutant from the air. The 

28 fraction that is retained in the indoor/enclosed microenvironment is given by the penetration 

29 factor (fpenetration) and is dependent on the particular pollutant’s physical and chemical removal 

30 rates. 
February 2010 Draft – Do Not Quote or Cite 4-19

1 4.4.4.3 Description of the Original pNEM/CO Microenvironmental Model 

2 Version 2.1 of pNEM/CO determined the hourly outdoor CO concentration applicable to 

3 each microenvironment through a Monte Carlo process based on the following equation 

4 

 COout(c,m,d,h) = M(m) x L(c, m, d) x T(c,m,d,h) x [COmon(d,h)]A 5 (4-11) 

6 

7 where, 

8 

9 COout(c,m,d,h) = outdoor CO concentration (ppm) for cohort c with respect to 

10 microenvironment m in district d during hour h, 

11 M(m) = multiplier (> 0) specific to microenvironment m, 

12 L(c,m,d) = location multiplier (> 0) specific to cohort c, microenvironment m, 

13 and district d (held constant for all hours), 

14 T(c,m,d,h) = time-of-day multiplier (> 0) specific to cohort c, microenvironment 

15 m, district d, and hour h, 

16 COmon(d,h) = ambient monitor-derived CO concentration (ppm) for hour h in 

17 district d, and 

18 A = exponent (A > 0). 

19 

20 This equation was used to generate a year-long sequence of outdoor one-hour CO 

21 concentrations for each combination of cohort (c), microenvironment (m), and district (d) by 

22 Johnson et al. (2000). The exponent A was set equal to 0.621 and held constant for all 

23 sequences. The value of M(m) varied only with microenvironment as indicated in Table 4-4 [and 

24 is identical to Table 2-6 in Johnson et al. (2000)]. 

25 A value of the location factor L(c, m, d) was specified for each individual sequence and 

26 held constant for all hours in the sequence. The value was randomly selected from a lognormal 

27 distribution with geometric mean (GML) equal to 1.0 and geometric standard deviation (GSDL) 

28 equal to 1.5232. The natural logarithms of this distribution can be characterized by a normal 

29 distribution with an arithmetic mean (μL) equal to zero and an arithmetic standard deviation (σL) 

30 equal to 0.4208. 

31 A value of the time-of-day factor T(c, m, d, h) was randomly selected for each hour 

32 within a sequence from a lognormal distribution with geometric mean (GMT) equal to 1.0 and 

33 geometric standard deviation (GSDT) equal to 1.6289. The natural logarithms of this distribution 

34 follow a normal distribution with an arithmetic mean (μT) equal to zero and an arithmetic 

35 standard deviation (σT) equal to 0.4879. 
February 2010 Draft – Do Not Quote or Cite 4-20

1 The COout(c, m, d, h) term is interpreted as the outdoor CO concentration in the 

2 immediate vicinity of microenvironment m in district d during hour h. COmon(d, h) is the CO 

3 concentration reported for hour h by a nearby fixed-site monitor selected to represent district d. 

4 The mass balance model in pNEM/CO included a penetration factor that was set equal to 

5 1.0 for CO. Consequently, there is no change in CO concentration as ambient (outdoor) air 

6 moves into a microenvironment, though the CO concentration within the microenvironment will 

7 be affected by air exchange rate and the presence of indoor sources. 
February 2010 Draft – Do Not Quote or Cite 4-21

1 Table 4-4. Estimated values of distribution parameters and variables in equation 4-11 as 

2 implemented in the application of pNEM/CO to Denver and Los Angeles 

3 (Johnson et al., 2000). 

Microenvironmenta Parameter Estimates for Equation 4-11 

Code 

General 

location Specific location 

Activity diary 

locations included in 

microenvironment A σL σT M(m) 

1 Indoors Residence Indoors - residence 0.621 0.4208 0.4879 1.034 

2 Indoors Nonresidence A Service station 

Auto repair 

0.621 0.4208 0.4879 2.970 

3 Indoors Nonresidence B Other repair shop 

Shopping mall 

0.621 0.4208 0.4879 1.213 

4 Indoors Nonresidence C Restaurant 0.621 0.4208 0.4879 1.213 

5 Indoors Nonresidence D Bar 0.621 0.4208 0.4879 1.213 

6 Indoors Nonresidence E Other indoor location 

Auditorium 

0.621 0.4208 0.4879 1.213 

7 Indoors Nonresidence F Store 

Office 

Other public building 

0.621 0.4208 0.4879 1.213 

8 Indoors Nonresidence G Health care facility 

School 

Church 

Manufacturing facility 

0.621 0.4208 0.4879 0.989 

9 Indoors Residential garage Residential garage 0.621 0.4208 0.4879 1.034 

10 Outdoors Near road Near road 

Bicycle 

Motorcycle 

0.621 0.4208 0.4879 1.607 

11 Outdoors Other locations Outdoor res. garage 

Construction site 

Residential grounds 

School grounds 

Sports arena 

Park or golf course 

Other outdoor 

0.621 0.4208 0.4879 1.436 

12 Vehicle Automobile Automobile 0.621 0.4208 0.4879 3.020 

13 Vehicle Truck Truck 0.621 0.4208 0.4879 3.020 

14 Vehicle Mass transit vehicles Bus 

Train/subway 

Other vehicle 

0.621 0.4208 0.4879 3.020 

15 Outdoor Public parking or 

fueling facility 

Indoor parking garage 

Outdoor parking garage 

Outdoor parking lot 

Outdoor service station 

0.621 0.4208 0.4879 2.970 

Notes: 

a Aggregate microenvironments defined for statistical analysis of Denver PEM data: residence (1 and 9), service/parking (2 and 15), commercial (3 

through 7), and vehicle (12 through 14).
February 2010 Draft – Do Not Quote or Cite 4-22

1 

2 4.4.4.3.1 Data Used To Estimate pNEM/CO Microenvironmental Model Parameters 

3 During a residential monitoring study described by Wilson, Colome, and Tian (1995), 

4 researchers measured 10-minute CO concentrations outside 293 residences throughout California 

5 in 1992. These residences were customers of Pacific Gas and Electricity (PG&E) (129 

6 residences in Northern California), San Diego Gas and Electric Company (89 residences in the 

7 San Diego area), and Southern California Gas Corporation (75 residences in the Los Angeles 

8 area). After excluding the PG&E data (not part of the Los Angeles study area) and homes for 

9 which valid CO data were not available, analysts used the remaining subset of 156 residences, 70 

10 from Los Angeles and 86 from San Diego, as the basis for estimating values of σL, σT, and A

applicable to the Los Angeles study area.2 11 The data subset contained 44,726 valid 10-minute 

12 averages measured outside of residences, of which less than 1% were negative (smallest value = 

13 -1.0 ppm), 14,817 (33 %) were equal to 0 ppm, and the remainder were positive (maximum = 

14 68.7 ppm). The valid 10-minute values were then averaged by clock hour to permit comparison 

15 with hourly-average CO concentrations reported by nearby fixed-site monitors. 

16 Analysts determined that the negative values in the data set were most likely caused by 

17 the subtraction of an offset from all measured values to account for monitor drift. To adjust for 

18 this offset and to prevent the occurrence of negative and zero values (which could not be used in 

19 fitting equation 4-11), analysts added a constant offset of 0.5 ppm to each hourly-average value 

20 measured outside a residence. In addition, seventeen (0.2%) of the original hourly averages less 

21 than or equal to -0.5 ppm were discarded. Each of the resulting one-hour outdoor CO 

22 concentrations was paired with the one-hour CO concentration measured simultaneously at the 

23 nearest fixed-site monitor [based on data obtained from EPA’s Aerometric Information Retrieval 

24 System (AIRS)]. This approach yielded a final database containing 6,330 pairs of hourly 

25 average concentrations, in which each pair was indexed by date, time, residence identifier, fixed26 site monitor identifier, and fixed-site monitor scale (e.g., neighborhood). 

27 The M(m) values of equation 4-11 were based on data provided by the Denver Personal 

28 Monitoring Study (Akland et al, 1985; Johnson, 1984). During this study, each of approximately 

29 450 subjects carried a personal exposure monitor (PEM) for two 24-hour periods. Each PEM 

30 measured CO concentration continuously. The PEM readings were averaged by exposure event 

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

32 (e.g., 1 pm to 2 pm). Event durations ranged from one minute to one hour. The 

 

2

 Note these same coefficient values were also applied to estimate exposures in the Denver study area, as 

researchers were unable to identify a usable data set specific to Denver. 
February 2010 Draft – Do Not Quote or Cite 4-23

1 microenvironment assigned to each PEM reading was determined from entries made in a real2 time diary carried by the subject. 

3 Researchers created a data base in which each PEM value was matched to the 

4 corresponding hourly-average CO concentration reported by the nearest fixed-site monitor. The 

5 data were first processed by excluding cases with missing measurements, cases in which 

6 measurements failed a quality control check, and cases in which applicable diary data indicated 

7 the potential presence of smokers or gas stoves. Each PEM CO concentration was then assigned 

8 to a microenvironment, m, based on entries in the activity dairy. In some cases, data for two or 

9 more similar microenvironments were aggregated to provide more stable estimates than those 

10 based on the very limited amount of data available for specific microenvironments (see Table 4-4 

11 footnote). For consistency with the Wilson, Colome, and Tian (1995) database, all cases with a 

12 zero measurement from the personal exposure monitor were excluded, as were all cases in which 

13 the fixed site monitor concentration was zero after rounding to the nearest integer ppm. Note 

14 that the Denver fixed-site data were recorded to the nearest 0.1 ppm, whereas the Los Angeles 

15 fixed-site data were only recorded to the nearest integer. 

16 4.4.4.3.2 Development of the pNEM/CO Microenvironmental Model Equation 

17 Equation 4-11 was based on the results of data analyses that suggested that the 

18 relationship between COout(c, m, d, h) and COmon(d, h) should account for the specific 

19 microenvironment, the geographic location of the microenvironment, and the time-of-day. 

20 Analysts recognized that numerous statistical models could have been developed. In specifying 

21 the model that was ultimately used (i.e., equation 4-11), analysts attempted to balance the need 

22 for simplicity and parsimony with the need to represent the patterns in concentration variability 

23 observed in the available data. Most of the model development was based on a comparison of 

24 hourly averages of 10-minute CO concentrations measured outside residences in southern 

25 California (Wilson, Colome, and Tian, 1995) with hourly average CO concentrations measured 

26 at the nearest fixed-site monitor. For this case, m represented the residence microenvironment in 

27 the district d. The district d was initially taken to be the entire study region where measurements 

28 were collected (i.e., San Diego and Los Angeles areas). 

29 Analysts began by considering a simple linear regression model of the form 

30 

31 COout(c,m,d,h) = a(m,d) + A x [COmon(d,h)] + e(c,m,d,h) (4-12) 

32 

33 where the residual term e(c,m,d,h) was assumed to be independent and normally distributed with 

34 a mean of zero. For simplicity and parsimony, the slope coefficient A was assumed to be the 

35 same for all microenvironments (m) and districts (d). 
February 2010 Draft – Do Not Quote or Cite 4-24

Although the coefficient of determination (R2 1 ) for this linear regression model was 

moderate (0.53),3 2 the model was found to be unacceptable because it does not properly reflect 

3 the strong correlations that were observed between concentrations measured outside the same 

4 location. Instead, this form of regression model assumes that the residuals associated with a 

5 particular residential location are independent. In other words, this model does not properly 

6 separate out the variation between locations from the variation within locations. Analysts 

7 identified two other deficiencies in this model: (1) large negative values of the randomly-selected 

8 e(c,m,d,h) term could produce impossible negative outdoor concentrations, and (2) the model did 

9 not generate outdoor concentrations characterized by lognormal distributions. Various 

10 researchers (e.g., Ott, 1995) have demonstrated that ambient CO concentrations tend to be 

11 characterized by lognormal distributions rather than normal distributions. 

12 To better address these latter concerns, analysts evaluated an alternative model where the 

13 natural logarithm of outdoor concentration was expressed as a linear function of the natural 

14 logarithm of monitor concentration: 

15 

16 LN[COout(c,m,d,h)] = a(m,d) + A x LN[COmon(d,h)] + e(c,m,d,h), (4-13) 

17 

18 In this equation and those that follow, LN[ ] indicates the natural logarithm of the 

19 quantity in brackets. To properly separate the variability between and within locations, the 

20 intercept term a(m,d) was also permitted to vary with the cohort location, c, leading to the final 

21 selected model: 

22 

23 LN[COout(c,m,d,h)] = a(c,m,d) + A x LN[COmon(d,h)] + e(c,m,d,h). (4-14) 

24 

25 Exponentiating both sides of equation 4-14 yields the equivalent formulation to that 

26 presented above in equation 4-11: 

27 

COout(c,m,d,h) = M(m) x L(c,m,d) x T(c,m,d,h) x [COmon(d,h)]A 28 , (4-15) 

29 

30 where 

31 M(m) = exp{mean [a(c,m,d)]}, averaged over cohorts, 

32 

 

3

 Note that the R2 goodness-of-fit statistic is not an appropriate measure of model adequacy when the true, 

underlying errors are highly correlated. 
February 2010 Draft – Do Not Quote or Cite 4-25

1 L(c,m,d) = exp{a(c,m,d) - mean [a(c,m,d)]}, and 

2 

3 T(c,m,d,h) = exp[e(c,m,d,h)]. 

4 

5 Several alternative statistical models were considered by analysts during the development 

6 of the selected model formulation. Early in the process, analysts evaluated a series of 

7 autoregressive time series models, in which model predictions were influenced by the past 

8 history of CO concentrations at the monitor and outdoors of the microenvironment. These 

9 models were rejected for several reasons: (1) they were inherently complex, (2) they yielded a 

10 wide variation in model coefficients which did not always produce reasonable estimates when 

11 applied to specific California residences, and (3) they required microenvironment-specific time 

12 series data for coefficient estimation which were not readily available for non-residential 

13 microenvironments. 

14 Analysts also evaluated models similar to equation 4-11 in which the exponent A varied 

15 with microenvironment. These models were rejected due to the need for parsimony and the lack 

16 of sufficient, suitable data for estimating microenvironment-specific values of A. A simpler 

17 model in which the exponent A is fixed at 1 was rejected because fits of equation 4-11 to the 

18 California data indicated that A differed significantly from 1 (p<0.01). In addition, the 

19 assumption that A = 1 produced unrealistically high predictions for outdoor CO concentrations 

20 when the model was applied to monitoring data obtained from the Denver Broadway site (ID 31-

21 0002). These high values were found to be a direct result of setting A = 1, which forced the 

22 geometric standard deviation of the estimated outdoor concentrations to significantly exceed the 

23 geometric standard deviation of the monitor values. 

24 Analysts ultimately arrived at equation 4-11 (equivalent to Equation 4-15), which permits 

25 the A exponent to differ from 1.0. The model was fitted using statistical software for a mixed 

26 (random and fixed effects) model which employed restricted maximum likelihood estimation. 

27 The fit yielded estimates of σL = 0.4208, σT = 0.4879, and A = 0.621, the values subsequently 

28 used in the pNEM/CO runs described by Johnson et al. (2000). The fitted value of M(m), 

29 representing residences in Los Angeles during 1992, was actually 0.9706. An alternative value 

30 (1.034), based on the additional analyses described below, was applied to the indoor-residence 

31 microenvironment in the pNEM/CO runs (see Table 4-4). 

32 This model, considered a reasonable compromise between model simplicity and 

33 performance, is completely specified by four parameters [M(m), σL, σT, and A]. Note that σL, σT, 

34 and A are defined to be independent of the microenvironment, whereas M(m) is 

35 microenvironment-specific. At the time of the initial model development, researchers were 

36 unable to find a single data source capable of providing estimates of all four parameters. 
February 2010 Draft – Do Not Quote or Cite 4-26

1 Consequently, values for σL, σT, and A were estimated by analyzing data obtained from the 

2 California study conducted by Wilson, Colome, and Tian (1995), whereas the specific M(m)

3 values were based on data provided by the Denver Personal Monitoring Study (Akland et al, 

4 1985; Johnson, 1984). 

5 Researchers conducted a series of sensitivity analyses to evaluate the potential effects on 

6 parameter estimates of variations in the regional location and scale of the fixed-site monitor. 

7 Equation 4-11 was fitted to a series of data subsets defined by region (Los Angeles or San 

8 Diego) or by the scale of the fixed-site monitor (based on the estimated maximum distance from 

9 the monitor represented by the measured concentrations: micro, middle, neighborhood, or urban 

10 scale). The fitted values of σL, σT, A, and M(m) were very similar across the different subsets, 

11 supporting the assumption that these parameters can be assumed to be representative of 

12 concentration patterns outside residences in other regions and for other time periods, and can be 

13 chosen to be the same value for all monitoring scales. Due to a lack of additional suitable data, 

14 the values of σL, σT, and A are also assumed to be applicable to concentrations outside all other 

15 microenvironments, although M(m) varies with the particular microenvironment (see below). 

16 In equation 4-11, the COout(c, m, d, h) term represents the outdoor CO concentration 

17 associated with a particular microenvironment m, even when the microenvironment is an indoor 

18 location. Few of the outdoor personal exposure measurement (PEM) values reported by the 

19 Denver study could be reliably associated with particular indoor microenvironments. 

20 Consequently, researchers employed a simplified procedure for estimating M(m) values which 

21 assumed that the mean of the indoor PEM values associated with each indoor microenvironment 

was approximately equal to the mean of the outdoor concentration for the microenvironment.4 22 

23 This assumption is consistent with the results of applying mass-balance modeling to non-reactive 

24 pollutants in enclosed spaces where the only source of the pollutant is the outside air. In such 

25 cases, the mean indoor concentration approximates the mean outdoor concentration, with the 

26 instantaneous indoor concentration exhibiting a lower degree of variability than the 

27 corresponding outdoor concentration. 

28 When equation 4-11 is expressed in a logarithmic form (i.e., as in equation 4-14) and 

29 averaged over cohorts, one obtains the equation 

30 

 

4

 Because the simplified approach was also less sensitive to the wide variation in averaging times exhibited 

by the PEM values (i.e., one minute to 60 minutes), analysts were able to use the majority of PEM values in the 

statistical analysis. Limiting the analysis to one-hour PEM values would have significantly reduced the pool of 

usable data. 
February 2010 Draft – Do Not Quote or Cite 4-27

1 Mean{LN[COout(c, m, d, h)} 

2 = Mean[a(c, m, d)] + A x Mean{LN[COmon(d, h)]} + Mean[e(c, m, d, h)] 

3 = LN[M(m)] + A x Mean{LN[COmon(d, h)]}. 

4 

5 Therefore, the value of M(m) equals 

6 

7 M(m) = exp{Mean LN[COout(c, m, d, h)] − A x Mean LN[COmon(d, h)]} (4-16) 

8 

9 where A = 0.621 (as above). This equation was then used to obtain estimates of M(m) for each 

10 particular microenvironment, or aggregate of microenvironments, as indicated in Table 4-4 using 

11 the available Denver PEM study data (Akland et al, 1985; Johnson, 1984). The same value of 

12 M(m) was applied to each specific microenvironment within an aggregate. 

13 4.4.4.4 The Micronenvironmental Model as Implemented by APEX3.1 

14 As discussed in section 4-3, the pNEM/CO model effectively evolved into what is known 

15 today as the APEX model. In APEX3.1, the portion of the outdoor concentration affecting the 

16 indoor concentration is determined by the formula 

17 

18 COout = Ambient * Proximity * Penetration (4-17) 

19 

20 Note that we can represent Proximity and Penetration as distributions in APEX3.1. 

21 These distributions can be sampled hourly, daily, or yearly. Let us make the following 

22 substitutions of the variables used to estimate the outdoor concentrations: 

23 

 Ambient = [COmon(d,h)]A 24 (4-18) 

25 Proximity = M(m) x L(c, m, d) (4-19) 

26 Penetration = T(c,m,d,h). (4-20) 

27 

28 which gives 

29 

 COout = M(m) x L(c, m, d) x T(c,m,d,h) x [COmon(d,h)]A 30 (4-21) 

31 

32 and is identical to equation 4-11 above. 

33 To obtain results from APEX3.1 that are comparable to that generated by pNEM/CO, 

34 Johnson and Capel (2003) preprocessed the hourly ambient monitor data assigned to the district 

35 containing the microenvironment using the formula 
February 2010 Draft – Do Not Quote or Cite 4-28

1 

 Ambient = [COmon(d,h)]0.621 2 (4-22) 

3 

4 where COmon(d,h) is expressed in ppm. For each profile, a value for the Proximity term was 

5 selected for each microenvironment from a lognormal distribution with geometric mean equal to 

6 M(m) and geometric standard deviation equal to 1.5232. The natural logarithms of this 

7 distribution were characterized by a normal distribution with an arithmetic mean (μL) equal to 

8 ln[M(m)] and an arithmetic standard deviation (σL) equal to 0.4208. Consistent with the 

pNEM/CO algorithm, Proximity values were not permitted to fall below the 5th 9 percentile of the 

specified distribution or above the 95th 10 percentile of the distribution. Table 4-5 lists the 

11 parameter values applicable to each of the 15 microenvironments defined by Johnson and Capel 

12 (2003). 

13 Penetration values were randomly selected for each hour from a lognormal distribution 

14 with geometric mean (GMT) equal to 1.0 and geometric standard deviation (GSDT) equal to 

15 1.6289. As indicated above, the natural logarithms of this distribution followed a normal 

16 distribution with an arithmetic mean (μT) equal to zero and an arithmetic standard deviation (σT) 

17 equal to 0.4879. In agreement with the pNEM/CO algorithm, Penetration values were not 

permitted to fall below the 5th percentile of the specified distribution (0.4482) or above the 95th 18 

percentile of the distribution (2.2313).5 19 

 

5

 Note the Penetration factor was not used according to its intended purpose in the APEX model. As 

discussed in Volume I of the APEX3.1 User’s Guide (US EPA, 2003), the Penetration factor is typically used to 

account for removal of pollutants during the transfer of outdoor air to a microenvironment. The Penetration factor 

was used to represent the T(c,m,d,h) term in equation 4-11 because Penetration is the only APEX3.1 parameter 

available for this purpose, given that the Proximity factor is being used to represent the product of M(m) and 

L(c,m,d). The product of M(m), L(c,m,d), and T(c,m,d,h) could not be represented by a single term because 

L(c,m,d) and T(c,m,d,h) have different averaging times (day vs. hour). 
February 2010 Draft – Do Not Quote or Cite 4-29

1 Table 4-5. Parameters of Bounded Lognormal Distributions Defined for Proximity 

2 Factors Used in Applications of APEX3.1 to Los Angeles (Johnson and Capel, 

3 2003). 

Microenvironment

Parameters of bounded lognormal 

distribution

Code

General 

Location Specific location

Activity diary 

locations included in 

microenvironment GM GSD 

Minimum

(5th pct)

Maximum

(95th pct)

1 Indoors Residence Indoors - residence 1.034 1.5232 0.5175 2.0661 

2 Indoors Nonresidence A Service station 

Auto repair 

2.970 1.5232 1.4864 5.9345 

3 Indoors Nonresidence B Other repair shop 

Shopping mall 

1.213 1.5232 0.6071 2.4237 

4 Indoors Nonresidence C Restaurant 1.213 1.5232 0.6071 2.4237 

5 Indoors Nonresidence D Bar 1.213 1.5232 0.6071 2.4237 

6 Indoors Nonresidence E Other indoor location 

Auditorium 

1.213 1.5232 0.6071 2.4237 

7 Indoors Nonresidence F Store 

Office 

Other public building 

1.213 1.5232 0.6071 2.4237 

8 Indoors Nonresidence G Health care facility 

School 

Church 

Manufacturing facility 

0.989 1.5232 0.4950 1.9762 

9 Indoors Residential 

garage 

Residential garage 1.034 1.5232 0.5175 2.0661 

10 Outdoors Near road Near road 

Bicycle 

Motorcycle 

1.607 1.5232 0.8042 3.2110 

11 Outdoors Other locations Outdoor res. garage 

Construction site 

Residential grounds 

School grounds 

Sports arena 

Park or golf course 

Other outdoor 

1.436 1.5232 0.7187 2.8693 

12 Vehicle Automobile Automobile 3.020 1.5232 1.5114 6.0344 

13 Vehicle Truck Truck 3.020 1.5232 1.5114 6.0344 

14 Vehicle Mass transit 

vehicles 

Bus 

Train/subway 

Other vehicle 

3.020 1.5232 1.5114 6.0344 

15 Outdoor Public parking or 

fueling facility 

Indoor parking garage 

Outdoor parking 

garage 

Outdoor parking lot 

Outdoor service station

2.970 1.5232 1.4864 5.9345 
February 2010 Draft – Do Not Quote or Cite 4-30

1 4.4.5 Estimate Energy Expenditure and Ventilation Rates 

2 APEX4.3 includes a module that estimates COHb levels in the blood as a function of 

3 alveolar ventilation rate, the CO concentration of the respired air, endogenous CO production 

4 rate, and various physiological variables such as blood volume and pulmonary CO diffusion rate. 

5 Alveolar ventilation rate is estimated as a function of oxygen uptake rate, which in turn is 

6 estimated as a function of energy expenditure rate. This section provides a brief summary of the 

7 algorithm used to estimate alveolar ventilation rate. A detailed description of the algorithm, 

8 based on the nonlinear solution to the Coburn-Forster-Kane (CFK) equation (Coburn et al., 

9 1965), together with the distributions and estimating equations used in determining the value of 

10 each parameter in the algorithm, can be found in Appendix B. 

11 4.4.5.1 Energy Expenditure 

12 McCurdy (2000) has recommended that measures of human ventilation (respiration) rate 

13 be estimated as functions of energy expenditure rate. The energy expended by an individual 

14 during a particular activity can be expressed as 

15 EE = (METS)  (RMR) (4-23) 

where EE is the average energy expenditure rate (kcal min-1 16 ) during the activity and RMR is the 

17 resting metabolic rate of the individual expressed in terms of number of energy units expended 

per unit of time (kcal min-1 18 ). METS (i.e., metabolic equivalent of work) is a ratio specific to the 

19 activity and is dimensionless. 

20 The METS concept provides a means for estimating the alveolar ventilation rate 

21 associated with each activity. For convenience, let EE(i,j,k) indicate the energy expenditure rate 

associated with the i

th 22 activity on day j for person k. Equation 4-23 can now be expressed as 

23 EE(i,j,k) = [METS(i,j,k)]  [RMR(k)] (4-24) 

24 where RMR(k) is the average value for resting metabolic rate specific to person k. Note that 

25 METS(i,j,k) is specific to a particular activity performed by person k. 

26 4.4.5.2 Oxygen Requirements for Energy Expenditure 

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

28 ECF(k) represents an energy conversion factor defined as the volume of oxygen required to 

29 produce one kilocalorie of energy in person k. The oxygen uptake rate (VO2) associated with a 

30 particular activity can be expressed as 

31 VO2(i,j,k) = [ECF(k)]  [EE(i,j,k)] (4-25) 
February 2010 Draft – Do Not Quote or Cite 4-31

where VO2(i,j,k) has units of liters oxygen min-1, ECF(k) has units of liters oxygen kcal-1 1 , and 

EE(i,j,k) has units of kcal min-1 2 . The value of VO2(i,j,k) can now be determined from MET(i,j,k) 

3 by substituting Equation 4-24 into Equation 4-25 to produce the relationship 

4 VO2(i,j,k) = [ECF(k)]  [METS(i,j,k)]  [RMR(k)] (4-26) 

5 4.4.5.3 Excess Post-Exercise Oxygen Consumption 

6 At the beginning of exercise, there is a lag between work expended and oxygen 

7 consumption. During this work/ventilation mismatch, an individual’s energy needs are met by 

8 anaerobic processes. The magnitude of the mismatch between expenditure and consumption is 

9 termed the oxygen deficit. During heavy exercise, further oxygen deficit (in addition to that 

10 associated with the start of exercise) may be accumulated. At some point, oxygen deficit reaches 

11 a maximum value, and performance and energy expenditure deteriorate. After exercise ceases, 

12 ventilation and oxygen consumption will remain elevated above baseline levels. This increased 

13 oxygen consumption was historically labeled the oxygen debt or recovery oxygen consumption. 

14 However, the term excess post-exercise oxygen consumption (EPOC) has been adopted here to 

15 represent this phenomenon. APEX4.3 has an algorithm for adjusting the MET values to account 

16 for EPOC. This algorithm is described in detail in section 7.2 of US EPA (2008b). 

17 4.4.5.4 Alveolar Ventilation Rate 

18 Alveolar ventilation (VA) represents the portion of the minute ventilation that is involved 

19 in gaseous exchange with the blood. VO2 is the oxygen uptake that occurs during this exchange. 

20 The absolute value of VA is known to be affected by total lung volume, lung dead space, and 

21 respiration frequency – parameters that vary according to the person and/or exercise rate. 

22 However, it is reasonable to assume that the ratio of VA to VO2 is relatively constant regardless 

23 of a person’s physiological characteristics or energy expenditure rate. Consistent with this 

24 assumption, APEX4.3 converts each estimate of VO2(i,j,k) to an estimate of VA(i,j,k) by the 

25 proportional relationship 

26 VA(i,j,k) = (19.63)  [VO2(i,j,k)] (4-27) 

where both VA and VO2 are expressed in units of liters min-1 27 . This relationship was obtained 

28 from Joumard et al. (1981), who based it on research by Galetti (1959). Equation 4-15 can also 

29 be expressed by the equivalent equation 

30 VA(i,j,k) = (19.63)  [METS(i,j,k)]  [ECF(k)]  [RMR(k)] (4-28) 
February 2010 Draft – Do Not Quote or Cite 4-32

1 If ECF and RMR are specified for an individual, then Equation 4-28 requires only an 

2 activity-specific estimate of METS to produce an estimate of the energy expenditure rate for a 

3 given activity. APEX4.3 processes time-location-activity data obtained from the CHAD to 

4 create a sequence of activity-specific METS values for each simulated individual. APEX4.3 

5 estimates RMR as a function of body mass based on probabilistic equations specific to age and 

6 gender using equations reported by Schofield (1985). A value of ECF is selected for each 

7 individual from a uniform distribution (minimum = 0.20, maximum = 0.21) based on data 

8 provided by Esmail et al. (1995). Using Equation 4-28 and these inputs, APEX4.3 calculates a 

9 sequence of VA values for each simulated individual. These values are provided to the algorithm 

10 that estimates the percent COHb in the blood resulting from the simulated exposure (see section 

11 4.4.7 and Appendix B). 

12 4.4.6 Calculate Exposure 

13 APEX4.3 calculates exposure as a time series of exposure concentrations that a simulated 

14 individual experiences during the simulation period. APEX4.3 determines the exposure using 

15 hourly ambient air concentrations, calculated concentrations in each microenvironment based on 

16 these ambient air concentrations, and the minutes spent in a sequence of microenvironments 

17 visited according to the composite diary. The hourly exposure concentration at any clock hour 

18 during the simulation period is determined using the following equation: 

T

C t

C

N

j

j

hourlymean

ME j

i

  1

( ) ( )

19 (4-29) 

20 where 

21 Ci = Hourly exposure concentration at clock hour i of the simulation period 

22 (ppm) 

23 N = Number of events (i.e., varied microenvironments visited/activities 

24 performed) in clock hour i of the simulation period. 

hourlymean 25 CME( j) = Hourly mean concentration in microenvironment j (ppm) 

26 t(j) = Time spent in microenvironment j (minutes) 

27 T = 60 minutes 

28 From the hourly exposures, APEX4.3 calculates time series of 8-hour and daily average 

29 exposure concentrations that a simulated individual would experience during the simulation 

30 period. APEX4.3 then statistically summarizes and tabulates the number of persons and person31 days at or above selected hourly, 8-hour, and daily average exposure concentrations in a series of 

32 output tables. 
February 2010 Draft – Do Not Quote or Cite 4-33

1 4.4.7 Calculate Dose 

2 Using time-location-activity pattern data obtained from several diary studies, APEX4.3 

3 constructs a composite diary for each simulated person in the specified population. The 

4 composite diary consists of a sequence of events spanning the specified period of the exposure 

5 assessment (typically one calendar year). Each event is defined by a start time, duration, a 

6 geographic location, a microenvironment, and an activity. Using the algorithms described above 

7 in sections 4.4.4, 4.4.5, and 4.4.6, APEX4.3 provides estimates of CO microenvironmental 

8 concentrations and the persons’ alveolar ventilation rate for each event in the composite diary, 

9 for each simulated individual. APEX4.3 then uses these data, together with estimates of various 

10 physiological parameters specific to the simulated individual, to estimate the percent COHb in 

11 the blood at the end of each event. The percent COHb calculation is based on the solution to the 

12 nonlinear Coburn-Forster-Kane (CFK) equation (Coburn et al., 1965), as detailed in Appendix B. 

13 Briefly, the CFK module in APEX4.3 describes the rate of change in COHb blood levels as a 

14 function of the following quantities: 

15  Inspired CO pressure; 

16  COHb level; 

17  Oxyhemoglobin (O2Hb) level; 

18  Hemoglobin (Hb) content of blood; 

19  Blood volume; 

20  Alveolar ventilation rate; 

21  Endogenous CO production rate; 

22  Mean pulmonary capillary oxygen pressure; 

23  Pulmonary diffusion rate of CO; 

24  Haldane coefficient (M); 

25  Barometric pressure; and 

26  Vapor pressure of water at body temperature (47 torr). 

27 If all of the listed quantities except COHb level are constant over some time interval, the 

28 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 (ISA section 4.2.1).6 29 

30 However, CO and oxygen can compete for binding with the available hemoglobin and, therefore, 

 

6

 US EPA (2009b). 
February 2010 Draft – Do Not Quote or Cite 4-34

1 are not independent of each other. If this dependency is taken into account, the resulting 

2 differential equation is no longer linear. Peterson and Stewart (1975) proposed a heuristic 

3 approach to account for this dependency which assumed the linear form and then adjusted the 

4 O2Hb level iteratively based on the assumption of a linear relationship between COHb and 

5 O2Hb. This approach was used in the COHb module of the original CO-NEM exposure model 

6 (Biller and Richmond, 1982; Johnson and Paul, 1983). 

7 Alternatively, it is possible to determine COHb at any time by numerical integration of 

8 the nonlinear CFK equation if one assumes a particular relationship between COHb and O2Hb. 

9 Muller and Barton (1987) demonstrated that assuming a linear relationship between COHb and 

10 O2Hb leads to a form of the CFK equation equivalent to the Michaelis-Menten kinetic model that 

11 can be analytically integrated. However, the analytical solution in this case cannot be solved 

12 explicitly for COHb. Muller and Barton (1987) demonstrated a binary search method for 

13 determining the COHb value. 

14 The COHb module used in pNEM/CO employed a linear relationship between COHb and 

15 O2Hb which was consistent with the basic assumptions of the CFK model. The approach 

16 differed from the linear forms used by other modelers in that the Muller and Barton (1987) 

17 solution was employed. However, instead of the simple binary search described in the Muller 

18 and Barton paper, a combination of the binary search and Newton-Raphson root finding methods 

19 was used to solve for COHb (Press et al., 1986). 

20 As mentioned above, the current COHb module included in APEX4.3 is based on the 

21 solution to the nonlinear CFK equation using the assumption adopted by Muller and Barton 

22 (1987) which employs a linear relationship between O2Hb and COHb. The CFK equation does 

23 not have an explicit solution, so an iterative solution or approximation is needed to calculate each 

percent COHb value. APEX4.3 solves the CFK equation using a 4th 24 -order Taylor’s series with 

25 subintervals. This method, first incorporated in APEX3 (Glen, 2002), is summarized in 

Appendix B. The selected method (4th 26 -order Taylor series with subintervals) was chosen 

27 because of its simplicity, fast execution speed, and ability to produce relatively accurate 

28 estimates of percent COHb at both low and high levels of CO exposure. While there may be 

29 other approaches available (e.g., Bruce and Bruce (2003) multi-compartment model), both the 

30 nonlinear and linear CFK models remain the most widely accepted and validated approaches 

31 used to estimate COHb levels (ISA, section 4.2.3) 

32 4.4.8 Model Output 

33 All of the output files written by APEX4.3 are ASCII text files; the complete list and 

34 their descriptions can be found in Table 5-1 of the APEX4.3 User’s Guide (US EPA, 2008a). In 

35 general, the simulation output files most relevant to results generated for the assessment include 
February 2010 Draft – Do Not Quote or Cite 4-35

1 tabulations of hourly exposure, ventilation, and energy expenditure. Detailed event-level 

2 information can also be output. However, given the potential size of the files that can be 

3 generated for a large population and assessment duration, it is not common to generate event4 level files outside of research purposes. Specific outputs generated for the purposes of the 

5 current CO exposure and dose assessment are discussed in section 6.1. 

6 4.5 KEY OBSERVATIONS 

7 Presented below are key observations related to the modeling system used for the 

8 population assessment of CO exposure and dose. 

9  APEX, an EPA human exposure and dose model, has a long history of use in estimating 

10 exposure and dose for many of the criteria pollutants including CO, O3, SO2, and NO2. 

11 Over time, staff have improved and developed new model algorithms, incorporated 

12 newer available input data and parameter distributions, as well as performed several 

13 model evaluations, sensitivity analyses, and uncertainty characterizations for the above 

14 pollutants. Based on this analysis, APEX was judged to be an appropriate model to use 

15 for assessing CO exposure and dose. 

16 

17 
February 2010 Draft – Do Not Quote or Cite 4-36

1 4.7 REFERENCES 

2 

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9 Bruce EN and Bruce MC. (2003). A multicompartment model of carboxyhemoglobin and carboxymyoglobin 

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23 Carbon Monoxide Exposure of Residents of Washington, DC and Denver, Colorado. Research Triangle 

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26 Isaacs K, McCurdy T, Errickson A, Forbes S, Glen G, Graham S, McCurdy L, Nysewander M, Smith L, 

27 Tulve N, Vallero D. (2009). Statistical properties of longitudinal time-activity data for use in EPA 

28 exposure models. Poster presented at the American Time Use Research Conference; College Park MD, 

29 June 26, 2009. 

30 Joumard R, Chiron M, Vidon R, Maurin M, Rouzioux JM. (1981). Mathematical models of the uptake of carbon 

31 monoxide on hemoglobin at low carbon monoxide levels. Environ Health Persp. 41:277-289. 

32 Johnson T and Capel J. (2003). Total Risk Integrated Methodology. TRIM ExpoInhalation User’s Document. 

33 Volume III: Air Pollutant Exposure Model Criteria Air Pollutants Case Study (Carbon Monoxide 

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35 Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. 

36 Johnson T and Paul R. (1983). The NAAQS Exposure Model (NEM) Applied to Carbon Monoxide. EPA Report 

37 No. 450/5-83-003. Prepared by PEDCo Environmental, Inc. for the Office of Air Quality Planning and 

38 Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 
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1 Johnson T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Research Triangle 

2 Park, NC: U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory. EPA3 600/4-84-014. 

4 Johnson T. (1989). Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric Power Research Institute. 

5 EPRI EN-6204. 

6 Johnson T, Capel J, Olaguer E, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposures and Associated 

7 Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic Version of NEM. Report prepared 

8 for the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research 

9 Triangle Park, North Carolina. 

10 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 

11 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 

12 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 

13 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 

14 at: http://www.epa.gov/ttn/fera/human_related.html. June 2000. 

15 Ott W. (1995). Environmental Statistics and Data Analysis. CRC Press, Boca Raton. 

16 Klepeis NE, Tsang AM, Behar JV. (1996). Analysis of the National Human Activity Pattern Survey (NHAPS) 

17 Respondents from a Standpoint of Exposure Assessment. Washington, DC: U.S. Environmental Protection 

18 Agency, Office of Research and Development. EPA/600/R-96/074. 

19 Liu L-JS, Box M, Kalman D, Kaufman J, Koenig J, Larson T, Lumley T, Sheppard L, Wallace L. (2003). Exposure 

20 assessment of particulate matter for susceptible populations in Seattle. Environ Health Persp. 111: 909-

21 918. 

22 Mansfield CA and Corey C. (2003). Analysis of Survey Data on Ozone Alert Days. Research Triangle Park: 

23 Research Triangle Institute. (Task 4 Report) 

24 Mansfield C, Johnson FR, Van Houtven G. (2006). The missing piece: averting behavior for children’s ozone 

25 exposures. Resource Energy Econ. 28:215-228. 

26 Mansfield C, Van Houtven G, et al. (2004). Parental Averting Behavior With Respect To Ozone Alerts. Research 

27 Triangle Park NC: Research Triangle Institute. 

28 McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy expenditure approach. 

29 J Expos Anal Environ Epidemiol. 10:1-12. 

30 McCurdy T, Glen G, Smith L, and Lakkadi Y. (2000). The National Exposure Research Laboratory’s Consolidated 

31 Human Activity Database. J Expos Anal and Environ Epidemiol. 10:566-578. 

32 Muller KE and Barton CN. (1987). A nonlinear version of the Coburn, Forster and Kane model of blood 

33 carboxyhemoglobin. Atmos Environ. 21:1963-1967. 

34 Peterson JE and Stewart RD. (1975). Predicting the carboxyhemoglobin levels resulting from carbon monoxide 

35 exposures. J Appl Physiol. 39(4): 633-638. 

36 Press WH, Flannery BP, Teukolsky SA, Vettering WT. (1986). Numerical Recipes. Cambridge University Press. 

37 Schofield WN. (1985). Predicting basal metabolic rate, new standards, and review of previous work. Hum Nutr 

38 Clin Nutr. 39C(S1): 5-41. 
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1 Spier CE, Little DE, Trim SC, Johnson TR, Linn WS, Hackney JD. (1992). Activity patterns in elementary and 

2 high school students exposed to oxidant pollution. J Expo Anal Environ Epidemiol. 2:277-293. 

3 Tsang AM and Klepeis NE. (1996). Descriptive Statistics Tables from a Detailed Analysis of the National Human 

4 Activity Pattern Survey (NHAPS) Data. U.S. Environmental Protection Agency. EPA/600/R-96/148. 

5 US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at: 

6 http://www.census.gov/population/www/cen2000/phc-t28.html. 

7 US DOT. (2007). Part 3-The Journey To Work files. Bureau of Transportation Statistics (BTS). Available at: 

8 http://transtats.bts.gov/. 

9 US EPA. (1999). Total Risk Integrated Methodology. Available at: 

10 http://www.epa.gov/ttnatw01/urban/trim/trimpg.html.

11 US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/. 

12 US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas (July 2007). Office of Air 

13 Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-07-010. Available at: 

14 http://epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html. 

15 US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 

16 (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-001a. Office of 

17 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

18 Available at: http://www.epa.gov/ttn/fera/human_apex.html

19 US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 

20 (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08-

21 001b. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research 

22 Triangle Park, NC. Available at: http://www.epa.gov/ttn/fera/human_apex.html

23 US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient 

24 Air Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 

25 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 

26 US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air 

27 Quality Standard. EPA-452/R-09-007. August 2009. Available at: 

28 http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 

29 US EPA. (2009b). Carbon Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health 

30 Risk and Exposure Assessment, EPA-452/R-09-004. Available at: 

31 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html

32 

33 University of Michigan. (2010). The Panel Study of Income Dynamics (PSID). Data and documentation Available 

34 at: http://www.psidonline.isr.umich.edu/Data/. 

35 Wiley JA, Robinson JP, Piazza T, Garrett K, Cirksena K, Cheng Y-T, Martin G. (1991a). Activity Patterns of 

36 California Residents: Final Report. California Air Resources Board, Sacramento, CA. ARB/R93/487. 

37 Available from: NTIS, Springfield, VA., PB94-108719. 

38 Wiley JA, Robinson JP, Cheng Y-T, Piazza T, Stork L, Pladsen K. (1991b). Study of Children's Activity Patterns: 

39 Final Report. California Air Resources Board, Sacramento, CA. ARB-R-93/489. 
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1 Williams R, Suggs J, Creason J, Rodes C, Lawless P, Kwok R, Zweidinger R, Sheldon L. (2000). The 1998 

2 Baltimore particulate matter epidemiology-exposure study: Part 2. Personal exposure associated with an 

3 elderly population. J Expo Anal Environ Epidemiol. 10(6):533-543. 

4 Williams R, Suggs J, Rea A, Leovic K, et al. (2003). The Research Triangle particulate panel study: PM mass 

5 concentrations relationships. Atmos Environ. 37:5349-5363. 

6 Williams R, Suggs J, Rea A, Sheldon L, et al. (2003). The Research Triangle particulate panel study: modeling 

7 ambient source contributions to personal and residential PM mass concentrations. Atmos Environ. 

8 37:5365-5378. 

9 Wilson AL, Colome SD, Tian Y. (1995). California Residential Indoor Air Quality Study. Volume III: Ancillary 

10 and Exploratory Analysis. Integrated Environmental Services, Irvine, California. 
February 2010 Draft – Do Not Quote or Cite 5-1

1 5 APPLICATION OF APEX4.3 IN THIS ASSESSMENT 

2 5.1 PURPOSE 

3 This chapter presents detailed information regarding the varied input data sources, the 

4 APEX model settings, and input variable parameterizations used in estimating population 

5 exposure and dose in the Denver and Los Angeles study areas. In particular, this chapter (and its 

6 associated appendices) describes the: 

7  geographic study areas and time periods defined for the exposure and dose analyses, 

8  method and parameters used to construct a composite diary for each simulated individual, 

9  study area population, the modeled at-risk population and associated CHD prevalence 

10 rates, 

11  exposure scenarios under evaluation, 

12  air quality and meteorological data used for each study area and exposure scenario, 

13  method used to estimate local outdoor and microenvironmental CO concentrations. 

14 Note that the APEX model version used in this assessment was APEX4.3, but for 

15 simplicity will be referred to as APEX in much of the discussion that follows. 

16 5.2 OVERVIEW 

17 As summarized above in section 1.3, the previous analysis of population exposure to 

18 carbon monoxide (CO) employed the pNEM/CO model in Denver and Los Angeles study areas, 

19 comprising the majority of census tracts within those metropolitan areas (Johnson et al., 2000). 

20 In this earlier exposure assessment, air quality data were obtained from multiple fixed-site 

21 monitors within the study areas, and the exposure assessment accounted for the effects of 

22 geographic location, a diverse set of microenvironments, commuting within the study area, and 

23 selected indoor sources (e.g., passive smoking, gas stoves). In the specific application of APEX 

24 described in this second draft CO REA, a similar exposure and dose modeling approach has been 

25 developed by staff, though without inclusion of indoor source emissions. The detailed approach 

26 presented here was designed to include the major comments and recommendations made by the 

27 CASAC and public regarding the geographically constricted and simplified exposure modeling 

28 approach used in the first draft CO REA (US EPA, 2009a). 

29 The general description of APEX, the standard databases used, modeling capabilities, as 

30 well as the history of the pNEM/APEX series of exposure models, can be found in chapter 4. 

31 This includes use of the national data files obtained from the US Census Bureau (i.e., the 2000 

32 Census data) for the following types of information: 
February 2010 Draft – Do Not Quote or Cite 5-2

1  Population data and employment probabilities by gender, age, and census tract; 

2  Locations of census tracts (latitude and longitude); and 

3  Commuting flows for combinations of home and work census tracts. 

4 Other default input files provided within APEX include tables of age- and gender-specific 

5 physiological parameters (e.g., body weight) and activity-specific metabolic equivalents (METs). 

6 The contents of each of these default files and their use were summarized in chapter 4. They are 

7 described in greater detail in the APEX Users Guide (US EPA, 2008a) and the APEX Technical 

8 Support Document (US EPA, 2008b). 

9 5.3 STUDY AREAS 

10 As discussed in section 3.2, staff selected areas within Denver, Colorado, and Los 

11 Angeles, California, for the current exposure and dose assessment. Briefly, considerations in 

12 selection of these areas included: the prior analysis of these locations in CO NAAQS reviews, 

13 the areas having historically elevated CO concentrations, and the areas currently having some of 

14 the most complete ambient monitoring data available. The monitors selected for use in defining 

15 the air quality in each urban area are listed in Tables 5-1 (Denver) and 5-2 (Los Angeles). 

16 The actual study areas were defined as including all census tracts within 10 km of these 

17 selected fixed-site monitors. These areas are illustrated in Figures 5-1 and 5-2, which indicate 

18 the locations of the fixed-site monitors and the circular 10-km region surrounding each ambient 

19 monitor. Each 10 km region defines the aforementioned air district that includes the geographic 

20 area (i.e., the census tracts) represented by data from the associated CO monitor. Note that all air 

21 districts have the same radius (10 km), a value specified by the “AirRadius” input parameter of 

22 APEX. 

23 In addition to defining the air districts, the model user must specify a location for the 

24 center of the study area and a value for “CityRadius.” The circular area defined by the city 

25 center location and the value of “CityRadius” must be large enough to include all census tracts 

26 included in the air districts. For Denver, staff used the location of monitor ID 31-0014 (Denver - 

27 Julian) for the city center and set the “CityRadius” equal to 20 km (Figure 5-1). Staff used the 

28 location of monitor ID 37-1103 (Los Angeles) for the center city of Los Angeles and set the 

29 “CityRadius” equal to 65 km (Figure 5-2). 

30 5.4 EXPOSURE PERIODS 

31 EPA selected the following calendar years as the study periods for each area: 

32 Denver: 1995 and 2006 

33 Los Angeles: 1997 and 2006 
February 2010 Draft – Do Not Quote or Cite 5-3

1 The year 2006 was selected for both cities because it was the most recent year of 

2 monitoring data that met the 75% completeness requirement for the ambient monitors listed 

3 above. Note, the CO levels reported for 2006 were well below the 8-hour NAAQS (see Tables 

4 5-1 and 5-2) and are considered representative of the as is air quality in each study area for 

5 purposes of this assessment. The year 1995 for Denver and the year 1997 for Los Angeles were 

6 selected as periods for which the ambient monitoring concentrations were near or exceeding the 

7 8-hour average CO NAAQS of 9 ppm. Staff judged that these historical monitoring data would 

8 be most useful in representing air quality that just meets the current or alternative CO standards 

9 and, following an appropriate concentration level adjustment, would represent a particular air 

10 quality scenario (see sections 5.6 and 5.7.3). 

11 Table 5-1. Attributes of fixed-site monitors selected for the Denver study area. 

Monitor ID 031-0002a

 031-0013a 031-0014a 059-0002a

City Denver Denver Denver Arvada 

Local Name CAMP NJH-E Carriage - 

Latitude 39.751184 39.738578 39.800333 39.751761 

Longitude -104.987625 -104.939925 -105.099973 -105.030681 

Elevation (m) 1593 1620 1640 1621 

Scale Microscale Neighborhood - Neighborhood 

Objective 

Highest 

Concentration 

Population 

Exposure Unknown 

Population 

Exposure 

1995 2nd

Highest 8-hour 

avg CO (ppm) 9.5 6.2 5.9 4.6 

2006 2nd

Highest 8-hour 

avg CO (ppm) 3.1 2.5 3 2 

Notes: 

a

 Identified monitor was used in the 2000 pNEM/CO analysis (Johnson et al., 2000). 

12 
February 2010 Draft – Do Not Quote or C

5-4

1 Table 5-2. Attributes of fixed-site monitors selected for the Los Angeles study area. 

Monitor ID 037-0113a 037-1002a 037-1103a 037-1201 037-1301a 037-2005a 037-4002a 059-0001/7a,City West LA Burbank 

Los 

Angeles Reseda Lynwood Pasadena 

Long 

Beach Anaheim 

Local Name - - - - - - - - Latitude 34.05111 34.17605 34.06659 34.19925 33.92899 34.1326 33.82376 33.83062 Longitude -118.45636 -118.31712 -118.22688 -118.53276 -118.21071 -118.1272 -118.18921 -117.93845 Elevation (m) 91 168 87 226 27 250 6 45 Scale - - - - Middle - - NeighborhooObjective Unknown Unknown Unknown Unknown 

Highest 

Conc. Unknown Unknown 

Population 

Exposure 

1997 2nd

Highest 8-hour avg CO (ppm) 4.1 7.2 5.9 7.7 15 5.4 6.4 5.4 

2006 2nd

Highest 8-hour avg CO (ppm) 1.9 3.4 2.5 3.4 5.6 2.7 3.3 2.9 

Notes: a Identified monitor was used in the 2000 pNEM/CO analysis (Johnson et al., 2000). b When considering the two monitoring periods (1997 and 2006), two separate ambient monitor IDs were noted (059-0001 

effectively the locations of both monitors were the same. 
February 2010 Draft – Do Not Quote or Cite 5-5

1 

2 Figure 5-1. Ambient monitor locations, air districts (black circles), meteorological zones 

3 (blue circles), and study area (red circle) for the Denver exposure modeling 

4 domain. 
February 2010 Draft – Do Not Quote or Cite 5-6

1 

2 Figure 5-2. Ambient monitor locations, air districts (black circles), meteorological zones 

3 (blue and pink circles), and study area (red circle) for the Los Angeles 

4 exposure modeling domain. 
February 2010 Draft – Do Not Quote or Cite 5-7

1 5.5 STUDY POPULATION 

2 5.5.1 Total and Simulated Population 

3 The population estimates obtained from the 2000 US Census were used “as is” for each 

4 study area and scenario modeled; there were no adjustments made for modeling the most recent 

5 air quality data (2006) or for each of the alternative exposure scenarios modeled (a hypothetical 

6 year). The total population in this study was restricted to those aged 18 years or older. Based on 

7 the census tracts included and removal of residents commuting outside of the study area, the total 

8 modeled population was 617,020 persons (or 81.1% of the total population). The corresponding 

9 figure for Los Angeles was 5,017,551 persons (or 88.5% of the total population within the 

10 modeled census tracts). To obtain adequate representation of the simulated population while also 

11 keeping the model runs tractable, fifty-thousand exposure profiles (or simulated individuals) 

12 were run by APEX for each study area and exposure scenario. 

13 5.5.2 Selected at-Risk Subpopulation 

14 The at-risk population simulated within each study area is comprised of adults ages 18 

15 and older with CHD (diagnosed and undiagnosed). This focus on adults is consistent with the 

16 previous CO exposure assessment (Johnson et. al, 2000) and the completed 1994 CO NAAQS 

17 review (US EPA, 1992), as the incidence of CHD in younger individuals is extremely small 

18 (CDC, 2009). In this assessment, the term CHD is used consistent with its use in the National 

19 Health Interview Survey (NHIS) where it is inclusive of coronary heart disease, angina pectoris 

20 and heart attack (CDC, 2009). 

21 For estimates of adults with diagnosed CHD, staff obtained CHD prevalence data from 

22 the NHIS for 2007 (CDC, 2009). The estimated CHD prevalence for the population above 18 

years of age is about 6% (ISA, section 5.7.2.1).1 23 Staff assumed the national prevalence rates for 

24 CHD were appropriate to use in each of the two study areas because there was a general 

25 similarity in the reported regional rates. Staff desired the prevalence rates to be stratified by age 

26 and gender, though the available data were stratified by age or gender. Table 5-3 provides 

27 national prevalence data for CHD by age and Table 5-4 provides CHD stratified by gender. The 

28 gender-only data were used to estimate gender-specific adjustment factors to apply to the age29 only data set. For males, the adjustment factor = 0.080/0.061 = 1.31; for females, the adjustment 

30 factor = 0.045/0.061 = 0.74. Table 5-5 provides the estimated national prevalence rates for CHD 

31 by age range adjusted for gender using these adjustment factors. 

 

1

 Note that in the last CO NAAQS review completed in 1994, the estimated number of individuals with 

CHD represented about 3% of the entire (all ages) US population (US EPA, 1992). 
February 2010 Draft – Do Not Quote or Cite 5-8

1 Table 5-3. National prevalence rates for coronary heart disease by age range. 

Age range Prevalence rate (fraction) for coronary heart diseasea

18 to 44 0.009 

45 to 64 0.067 

65 to 74 0.187 

75+ 0.236 

Notes: 

a

 Source: Coronary heart disease statistics in Table 2 of NHIS (CDC, 

2009), which include coronary heart disease, angina pectoris and heart 

attack. 

2 Table 5-4. National prevalence rates for coronary heart disease by gender. 

Prevalence rate (fraction) for coronary heart 

diseasea

Age range Total Males Females 

18+ 0.061 0.080 0.045 

Notes: 

a

 Source: Coronary heart disease statistics in Table 2 of NHIS (CDC, 

2009), which include coronary heart disease, angina pectoris and heart 

attack. 

3 Table 5-5. National prevalence rates for coronary heart disease, stratified by age and 

4 gender. 

Prevalence rate (fraction) for coronary heart 

diseasea

Age range Males Females 

18 to 44 0.012 0.007 

45 to 64 0.088 0.050 

65 to 74 0.244 0.138 

75+ 0.310 0.175 

Notes: 

a

 Values listed in Table 5-3 were multiplied by 1.31 for males 

and 0.74 for females using data from Table 5-4. 

5 

6 Staff has expanded the selected at-risk population to also include undiagnosed cases of 

7 coronary heart disease using a method similar to that developed by OAQPS for use in the 2000 

8 exposure assessment (see Appendix F of Johnson et al., 2000). Briefly, in the prior assessment 

the prevalence estimates of diagnosed IHD2 9 were stratified by age and sex (Adams and Marano, 

 

2

 The NHIS prevalence rates used in the 2000 assessment used the term IHD, rather than CHD (Adams and 

Marano, 1995). 
February 2010 Draft – Do Not Quote or Cite 5-9

1 1995) and constituted approximately 8.0 million individuals in the civilian, non-institutionalized 

population.3 2 In addition, as many as three to four million persons were estimated to have silent 

3 ischemia or undiagnosed IHD (American Heart Association, 1990). Staff used this information 

4 to provide estimates of the undiagnosed IHD population for use in the pNEM/CO model. Staff 

5 assumed 3.5 million persons had undiagnosed IHD and assumed the prevalence to be distributed 

6 by age and gender in the same manner as diagnosed IHD. These data yield an adjustment factor 

7 of 0.438 (i.e., 3.5 million/8.0 million) to apply to the diagnosed prevalence for use in estimating 

8 the undiagnosed prevalence. Consequently, this factor can be interpreted as the undiagnosed 

9 cases may be 43.8% of the diagnosed prevalence. 

10 Table 5-6 lists the results of applying the 0.438 factor to the age and gender stratified 

11 prevalence rates listed in Table 5-5. This assumes that CHD and IHD are identical with respect 

12 to the ratio of undiagnosed cases to diagnosed cases and this ratio has not changed since 1990. 

13 The total prevalence listed for each gender (diagnosed and undiagnosed combined) was used by 

14 APEX in estimating the selected at-risk population. 

15 When using these prevalence rates in the APEX model runs, there were 383,040 

16 simulated persons (or 7.6% of the total simulated population) with either diagnosed or 

17 undiagnosed CHD in the Los Angeles study area, while in Denver there were 53,656 simulated 

18 persons (or 8.7% of the total simulated population) within the same selected at-risk population. 

19 Table 5-6. National prevalence rates for coronary heart disease, including diagnosed and 

20 undiagnosed cases, stratified by age and gender. 

Prevalence rate (fraction) for coronary heart disease 

Males Females 

Age range Diagnosed Undiagnoseda Total Diagnosed Undiagnoseda

 Total 

18 to 44 0.012 0.005 0.017 0.007 0.003 0.010 

45 to 64 0.088 0.039 0.127 0.050 0.022 0.072 

65 to 74 0.244 0.107 0.351 0.138 0.060 0.198 

75+ 0.310 0.136 0.446 0.175 0.077 0.252 

Notes: 

a

 Values listed in Table 5-5 (diagnosed CHD) were multiplied by 0.438 to estimate the undiagnosed 

prevalence. Staff assumed CHD and IHD are identical with respect to the ratio of undiagnosed cases 

(3.5 million) to diagnosed cases (8.0 million) and that this ratio has not changed since 1990 (see 

Appendix F of Johnson et al. (2000)). 

21 

 

3

 These estimates did not include individuals in the military or individuals in nursing homes or other 

institutions. 
February 2010 Draft – Do Not Quote or Cite 5-10

1 5.5.3 Time-Location-Activity Patterns 

2 APEX constructs a 365-day longitudinal diary for each simulated individual by selecting 

3 24-hour diaries from those available in CHAD. In performing the exposure assessments 

4 described in this report, all available diaries for persons above age 17 in the CHAD database 

5 were used. 

6 5.5.4 Construction of Longitudinal Diaries 

7 As discussed in section 4.4.3.4, APEX provides a longitudinal diary assembly algorithm 

8 that enables the user to create composite diaries that reflect the tendency of individuals to repeat 

9 day-to-day activities (Glen et al., 2008). The user specifies values for two statistical variables (D

10 and A) that relate to a key daily variable, typically the time spent per day in a particular 

11 microenvironment (e.g., in a motor vehicle). The D statistic reflects the relative importance of 

12 intra- and inter-personal variance within the selected key daily variable. The A variable 

13 quantifies the day-to-day autocorrelation in the selected key daily variable. APEX then 

14 constructs composite diaries that exhibit the statistical properties defined by the specified values 

15 of D and A. 

16 In this exposure assessment, we used the longitudinal diary algorithm to construct year17 long activity patterns for each simulated individual to reflect the day-to-day correlation of time 

18 spent inside motor vehicles. Each diary day in the CHAD database was tagged with the number 

19 of minutes spent in the vehicle microenvironment. Parameter settings of D = 0.31 and A = 0.19 

20 were specified to control the day-to-day repetition of time spent in motor vehicles in the 

21 constructed composite diaries. These particular D and A values were obtained from Isaacs et al. 

22 (2009) (see Appendix C). 

23 In selecting particular diaries to represent the simulated population, the CHAD data are 

24 categorized or separated by APEX into data pools. The pools were defined by three ranges for 

the maximum temperature of the diary day (< 55.0 ◦

F, between 55.0 and 83.9 ◦F, and ≥84.0 ◦ 25 F) 

26 and two day-types (i.e., weekend and weekday); thus, there were 3 x 2 = 6 diary pools. The 

27 window for age was set at 15%. For example, diaries can be selected for a simulated individual 

28 of age 60 from CHAD individuals ranging from ages 51 though 69 (i.e., 60 +/- 15 percent). 

29 5.6 EXPOSURE SCENARIOS 

30 In this second draft CO REA, the exposure scenario refers to the air quality conditions 

31 considered for each APEX simulation. Staff evaluated five exposure scenarios for each study 

32 area. The first exposure scenario used unadjusted 2006 ambient air quality as input to APEX; 

33 this is designated as the as is air quality exposure scenario. The purpose of this scenario is to 

34 determine the number of persons that may experience COHb levels at or above selected 
February 2010 Draft – Do Not Quote or Cite 5-11

1 benchmarks when considering current air quality conditions. The next four exposure scenarios 

2 used adjusted high concentration year ambient data in each location (i.e., the 1995 monitoring 

3 data in Denver and the 1997 monitoring data in Los Angeles). The purpose of these scenarios is 

4 to determine the number of persons that may experience COHb levels at or above selected 

5 benchmark levels when considering air quality conditions that just meet a selected level, form, 

6 and averaging time of interest. This is not the same as considering exposures associated with the 

7 as is air quality conditions. 

8 The first of these adjusted air quality exposure scenarios considered ambient 

9 concentrations adjusted to just meeting the 8-hour average CO NAAQS of 9 ppm. This 

10 particular form was selected when considering the two current standard forms (8-hour average 

and 1-hour) because it is the controlling standard.4 11 The second of these exposure scenarios using 

12 the historical monitoring data also considered the current form of the current 8-hour CO 

13 standard, but with the ambient concentrations in each study adjusted to meet an alternative 

14 standard level of 5 ppm. The next two scenarios considered percentile forms of potential 

15 alternative standards, consistent with the alternative standards investigated for other criteria 

16 pollutants (e.g., NO2 (US EPA, 2008c); and SO2 (US EPA, 2009b)). The first of these potential 

percentile forms considered the 99th 17 percentile daily maximum 8-hour average CO 

18 concentrations, while the second considered the same form though with a 1-hour averaging time. 

19 Details regarding the concentration adjustments associated with each of the current and potential 

20 alternative standards are provided in section 5.7.3. 

21 5.7 AMBIENT AIR QUALITY DATA 

22 5.7.1 Unadjusted 1-Hour Ambient Concentrations 

23 Ambient monitoring data serve as an important input in estimating CO exposure and 

24 dose. Descriptive statistics were generated for the hourly CO concentrations measured at the 

25 identified ambient monitors in each location and monitoring year (Tables 5-7 to 5-10). As 

26 expected, CO concentrations are about a factor of two or greater when comparing the high 

27 concentration year (1995 or 1997) to the more recent year (2006) of ambient monitoring data in 

28 either location. In general, there is similarity in the concentration distribution for both locations 

29 within a given year, with the following exceptions. There is one monitor in Los Angeles (ID 37-

30 1301) reporting exceptionally high concentrations at each of the percentiles of the distribution in 

31 this location when compared with the other Los Angeles monitors for either year. In addition, 

 4

 The controlling standard by definition would be the standard that allows air quality to have either a 2nd highest 8-

hour average concentration of ≤ 9.4 ppm (i.e., the 8-hour standard is the controlling standard) or to have a 2nd

highest 1-hour concentration of ≤ 35.4 ppm (i.e., the 1-hour standard is the controlling standard). 
February 2010 Draft – Do Not Quote or Cite 5-12

there is a sharper rate of increase in the upper percentile concentrations (i.e., ≥95th 1 percentiles) in 

2 Denver when compared with the Los Angeles ambient concentration distribution, for either year. 

3 5.7.2 Method for Estimating of Missing 1-Hour Ambient Concentrations 

4 APEX requires that each site-year of monitoring data be complete (i.e., it is free of hourly 

5 gaps in concentration levels). The missing values in each data set were estimated by the 

6 sequential application of the following four methods. 

7 1) If the data gap was less than six continuous missing values, the missing values were 

8 estimated by linear interpolation using the valid values at the ends of the gap. 

9 2) Where possible, data gaps of at least six hours were estimated as linear functions of 

10 hourly values reported by other ambient CO monitors in the area. Linear regression 

11 was used to develop a set of models that were specific to a time-of-day and at each 

12 monitor. The model selected to estimate missing values for a particular time of day 

was the model that maximized the variance explained (R2 13 ) for that hour, subject to the 

constraints that regression model R2 14 was greater than 0.5 and the number of available 

15 measurements used in constructing the model was at least 50. 

16 3) In cases where method 2 (above) could not be used (i.e., no regression models were 

17 available for a particular time-of-day) and the gap was less than nine hours, the missing 

18 values were estimated by linear interpolation between the valid values at the ends of 

19 the gap. 

20 4) All remaining missing values were substituted with the 1-hour concentration from the 

21 same day and hour as the nearest monitor. The hourly concentration used was 

22 normalized to the respective monitors’ monthly mean concentrations. 

23 Tables 5-7 to 5-10 provide the descriptive statistics for 1-hour CO concentrations in each 

24 data set, before and after estimating missing values. The agreement between these statistics 

25 indicates that the addition of the estimated missing-value concentrations did not significantly 

26 affect the overall distribution of the hourly CO concentrations in either year or location. 
February 2010 5-13 Draft – Do Not 

1 Table 5-7. Descriptive statistics for hourly carbon monoxide concentrations before and after estima2 values – Denver 1995. 

1-hour values CO concentration (ppm) 

Monitor (n) Percentile 

ID 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 

No 8697 63 1.50 1.20 0.0 0.8 1.2 1.8 2.7 3.4 

31-0002 

Yes 8760 0 1.50 1.20 0.0 0.8 1.2 1.8 2.7 3.4 

No 8647 113 1.25 1.08 0.1 0.6 0.9 1.5 2.5 3.4 

31-0013 

Yes 8760 0 1.25 1.08 0.1 0.6 0.9 1.5 2.5 3.4 

No 8701 59 1.09 1.05 0.0 0.5 0.7 1.3 2.3 3.2 

31-0014 

Yes 8760 0 1.09 1.05 0.0 0.5 0.7 1.3 2.3 3.2 

No 8680 80 0.96 0.93 0.1 0.4 0.6 1.1 2.0 2.7 

59-0002 

Yes 8760 0 0.96 0.93 0.1 0.4 0.6 1.1 2.0 2.7 

3 Table 5-8. Descriptive statistics for hourly carbon monoxide concentrations before and after estima4 values – Denver 2006. 

1-hour values CO concentration (ppm) 

Monitor (n) Percentile 

ID 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 

No 8672 88 0.62 0.39 0.0 0.4 0.5 0.7 1.0 1.3 

31-0002 

Yes 8760 0 0.62 0.39 0.0 0.4 0.5 0.7 1.0 1.3 

No 8635 125 0.49 0.36 0.0 0.3 0.4 0.6 0.9 1.2 

31-0013 

Yes 8760 0 0.49 0.36 0.0 0.3 0.4 0.6 0.9 1.2 

No 8557 203 0.47 0.38 0.0 0.3 0.4 0.5 0.9 1.2 

31-0014 

Yes 8760 0 0.47 0.38 0.0 0.3 0.4 0.5 0.9 1.2 

No 8603 57 0.40 0.37 0.0 0.2 0.3 0.5 0.8 1.1 

59-0002 

Yes 8760 0 0.40 0.37 0.0 0.2 0.3 0.5 0.8 1.1 
February 2010 5-14 Draft – Do Not 

1 Table 5-9. Descriptive statistics for hourly carbon monoxide concentrations before and after estima2 values – Los Angeles 1997. 

1-hour values CO concentration (ppm) 

Monitor (n) Percentile 

ID 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 

No 8360 400 0.84 0.86 0.0 0.2 0.6 1.2 2.0 2.6 

37-0113 

Yes 8760 0 0.84 0.85 0.0 0.2 0.6 1.2 2.0 2.6 

No 8025 735 1.75 1.27 0.0 0.9 1.4 2.2 3.5 4.5 

37-1002 

Yes 8760 0 1.73 1.24 0.0 0.9 1.4 2.1 3.5 4.4 

No 8292 468 1.36 1.19 0.0 0.5 0.9 1.9 3.1 3.9 

37-1103 

Yes 8760 0 1.36 1.17 0.0 0.5 1.0 1.9 3.0 3.8 

No 8245 515 1.15 1.25 0.0 0.4 0.7 1.5 2.8 3.8 

37-1201 

Yes 8760 0 1.17 1.24 0.0 0.4 0.7 1.5 2.8 3.8 

No 8302 458 2.35 2.19 0.0 1.1 1.7 2.8 4.9 6.8 

37-1301 

Yes 8760 0 2.34 2.17 0.0 1.1 1.7 2.8 4.9 6.7 

No 8250 510 1.11 0.84 0.0 0.6 0.9 1.4 2.1 2.8 

37-2005 

Yes 8760 0 1.10 0.83 0.0 0.6 0.9 1.4 2.1 2.8 

No 8347 413 1.11 1.10 0.0 0.4 0.7 1.3 2.7 3.6 

37-4002 

Yes 8760 0 1.11 1.11 0.0 0.4 0.7 1.4 2.7 3.6 

No 8354 406 1.11 0.91 0.0 0.6 0.8 1.4 2.3 2.9 

59-0001/7 

Yes 8760 0 1.11 0.90 0.0 0.6 0.8 1.4 2.3 2.9 

No 8325 435 0.74 1.01 0.0 0.2 0.3 0.9 2.1 3.0 

59-1003 

Yes 8760 0 0.74 1.00 0.0 0.2 0.3 0.9 2.1 3.0 

No 8230 530 1.36 1.21 0.0 0.6 1.0 1.7 2.8 3.7 

59-5001 

Yes 8760 0 1.36 1.19 0.0 0.6 1.0 1.7 2.8 3.7 

3 
February 2010 Draft – Do Not Quote or C

5-15

1 Table 5-10. Descriptive statistics for hourly carbon monoxide concentrations before and after estima2 values – Los Angeles 2006. 

1-hour values CO concentration (ppm) 

(n) Percentile 

Monitor 

Missing 

values 

filled? Present Missing Mean SD Min 25th 50th 75th 90th 95th 9

No 8365 395 0.42 0.37 0.0 0.2 0.3 0.6 0.9 1.2 

37-0113 

Yes 8760 0 0.43 0.37 0.0 0.2 0.3 0.6 0.9 1.2 

No 8345 415 0.67 0.61 0.0 0.3 0.5 0.8 1.5 2.0 2

37-1002 

Yes 8760 0 0.67 0.61 0.0 0.3 0.5 0.8 1.5 2.0 2

No 8265 495 0.55 0.50 0.0 0.2 0.4 0.7 1.3 1.6 2

37-1103 

Yes 8760 0 0.56 0.50 0.0 0.2 0.4 0.8 1.3 1.6 2

No 8375 385 0.55 0.54 0.0 0.2 0.4 0.6 1.2 1.7 2

37-1201 

Yes 8760 0 0.56 0.53 0.0 0.2 0.4 0.7 1.2 1.7 2

No 8275 485 1.00 0.89 0.0 0.5 0.7 1.1 2.0 2.9 4

37-1301 

Yes 8760 0 1.01 0.90 0.0 0.5 0.7 1.1 2.0 2.9 4

No 8258 502 0.73 0.49 0.0 0.4 0.6 1.0 1.4 1.7 2

37-2005 

Yes 8760 0 0.73 0.49 0.0 0.4 0.6 1.0 1.3 1.7 2

No 8216 544 0.74 0.55 0.0 0.4 0.6 0.9 1.5 1.9 2

37-4002 

Yes 8760 0 0.75 0.54 0.0 0.4 0.6 0.9 1.5 1.9 2

No 8342 418 0.43 0.47 0.0 0.1 0.3 0.5 1.0 1.4 2

59-0001/7 

Yes 8760 0 0.43 0.47 0.0 0.1 0.3 0.5 1.0 1.4 2

No 8358 402 0.33 0.45 0.0 0.1 0.1 0.4 0.9 1.4 2

59-1003 

Yes 8760 0 0.33 0.45 0.0 0.1 0.1 0.4 0.9 1.4 2

No 8227 533 0.64 0.57 0.0 0.3 0.4 0.7 1.3 1.8 

59-5001 

Yes 8760 0 0.64 0.56 0.0 0.3 0.4 0.7 1.3 1.8 2

3 4 5 
February 2010 Draft – Do Not Quote or Cite 5-16

1 5.7.3 Adjusted 1-Hour Ambient Concentrations 

2 In addition to modeling exposures based on recent as is air quality (i.e., ambient 

3 monitoring data for year 2006), exposures and resulting dose were estimated for air quality 

4 conditions that just meet the current 8-hour CO NAAQS and the potential alternative standards. 

5 Because CO concentrations in recent years were significantly lower than the current NAAQS, 

6 staff first selected an earlier year for each city (1995 for Denver and 1997 for Los Angeles) to 

7 represent air quality conditions that were near the current 8-hour CO standard. Consistent with 

8 the data adjustment approach employed in the previous draft CO exposure assessment (Johnson 

9 et al., 2000), and approaches used in prior REAs supporting other pollutant NAAQS reviews 

10 (e.g., US EPA, 2008c; US EPA, 2009b), staff concluded (1) that the policy-relevant background 

11 levels of CO were negligible in each area (section 3.1.4), and (2) that the fixed-site monitoring 

12 data could be adjusted to simulate just meeting the current CO standards by use of a simple 

13 proportional adjustment of all hourly values (section 3.1.5). Consequently, the following 

14 adjustment equation was employed: 

15 

16 COadj(m,h) = (NAAQS/DV) x CO(m,h). (5-1) 

17 

18 CO(m,h) is the 1-hour CO concentration at hour h for monitor m. It follows that COadj(m,h) is 

19 the adjusted CO concentration for hour h at monitor m through the use of the specific design 

20 value (DV) for monitor m. Although the current 8-hour NAAQS for CO specifies a maximum 

21 concentration of 9 ppm, which is not to be exceeded more than one time in a year, the NAAQS 

22 term in Equation 5-1 is equivalent to 9.4 ppm due to the application of a standard data rounding 

convention used in calculating design values (DVs) for CO. 5 23 

24 The DVs for Denver (1995) and for Los Angeles (1997) were 9.5 ppm and 15 ppm, 

25 respectively. The Denver DV is calculated as the second-highest 8-hour average CO 

26 concentration reported by monitor ID 080310002 for 1995. The adjustment factor (or 

27 NAAQS/DV) that was applied equally to all 8,760 hourly ambient CO concentrations at that 

28 monitor is thus 9.4/9.5, or 0.989. In a similar manner, the DV used in Los Angeles is the second29 highest 8-hour average CO concentration reported at monitor ID 060371301 for 1997, giving an 

30 ambient concentration adjustment factor of 9.4/15, or 0.627 which was applied equally to all 

31 8,760 hourly ambient CO concentrations from the Los Angeles monitor. 

 5

 A design value is a statistic that describes the air quality status of a given area or monitor relative to the level of the 

NAAQS. For the CO 8-hour NAAQS, the design value is the highest annual second maximum non-overlapping 8-

hour concentration during the most recent two years. The design value for the 1-hour CO NAAQS is the highest 

annual second maximum 1-hour concentration during the most recent two years. The latest update (2007-2008) on 

the CO design values can be found at: http://www.epa.gov/airtrends/pdfs/dv_co_2006_2008.pdf
February 2010 Draft – Do Not Quote or Cite 5-17

1 Staff evaluated three additional air quality scenarios considering potential alternative 

2 standard levels, averaging times, and forms. Assuming a similar form and averaging time of the 

current 8-hour standard (2nd 3 highest non-overlapping 8-hour average CO concentration), staff 

selected a level of 5 ppm for the first potential alternative standard.6 4 As was done for other 

5 recent NAAQS reviews (US EPA, 2008c; US EPA, 2009b), staff selected percentiles of the air 

6 quality distribution and averaging times to identifying potential levels associated with alternative 

7 standards. The second potential alternative standard considered by staff also uses an 8-hour 

average concentration, though having a 99th 8 percentile daily maximum CO concentration of 5.0 

ppm.7 The final potential alternative standard that staff evaluated was a 99th 9 percentile daily 

10 maximum 1-hour CO concentration of 8.0 ppm. Table 5-11 summarizes the adjustment factors 

11 that were developed from equation 5-1 and used to adjust the high concentration year air quality 

12 data in each study area. 

13 Table 5-11. Design values and adjustment factors used to represent air quality just 

14 meeting the current and potential alternative standards. 

Standard 

Study Area Averaging 

Time Form 

Level 

(ppm) 

Design Valuea

(ppm) 

Adjustment 

Factor 

9 0.989b

2nd highest 5 9.5 

8-hour 0.568 

99th pct daily max 5.0 7.3 0.685 

Denver 

1-hour 99th pct daily max 8.0 13.5 0.593 

9 0.627 b

2nd highest 5 15 

8-hour 0.360 

99th pct daily max 5.0 13.1 0.380 Los Angeles 

1-hour 99th pct daily max 8.0 18.5 0.432 

Notes: 

a

 All design values were obtained from monitor ID monitor ID 080310002 in Denver (1995 data) and 

monitor ID 060371301 in Los Angeles (1997 data). 

b

 Adjustment factor for just meeting the current 8-hour average CO standard. 

15 

16 

17 

 

6

 Note that this would allow a 2nd highest non-overlapping 8-hour concentration up to 5.4 ppm (hence the 

design value). 

7

 It was assumed that there are an infinite number of zeros, that is, the level is exactly 5.0 ppm. This 

rounding convention applies to the other potential alternative standard selected, the level is exactly 8.0. 
February 2010 Draft – Do Not Quote or Cite 5-18

1 Table 5-12 and 5-13 provides the descriptive statistics for the Denver and Los Angeles 

2 ambient monitor 1-hour CO concentrations, respectively, after applying the appropriate 

3 adjustment factor to simulate just meeting the current standard. As expected, the adjusted 

4 monitoring concentrations for Denver 1995 are very similar to the unadjusted data set given that 

5 the adjustment factor used was close to unity. For example, the maximum concentration at the 

6 design monitor was reduced from 24.5 ppm to 24.2 ppm. The change in CO concentrations was 

7 much greater in Los Angeles compared with that of Denver as a result of differences in the 

8 adjustment factor used in each study area. For example, the maximum CO concentration at the 

9 design monitor in Los Angeles was reduced from 19.2 ppm to 12.0 ppm. Considering the 

10 patterns described above in section 5.7.1 for the unadjusted air quality and given that the 

11 concentration adjustment was proportional, additional remarks can be made regarding 

12 differences in the air quality adjusted to just meet the current 8-hour CO NAAQS. When 

13 comparing the adjusted concentrations in Denver and Los Angeles, there is still a sharper rate of 

increase in CO concentrations at and above the 95th 14 percentiles of the distribution, only now all 

15 of the Denver monitors have greater CO concentrations at these upper percentiles when 

16 compared with concentrations observed at all of the Los Angeles monitors (excluding 

17 concentrations at the Los Angeles design monitor). 

18 Given the proportional approach used to adjust ambient concentrations for each of the 

other exposure scenarios (e.g., 99th 19 percentile daily maximum 1-hour concentration of 8.0); 

20 similar patterns in concentrations were expected and are therefore not summarized here. 
February 2010 Draft – Do Not Quote or C

5-19

1 Table 5-12. Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just m2 hour standard – Denver (adjusted 1995 data). 

Hourly-average CO concentration (ppm) 

Monitor 

ID Mean SD 25.0 50.0 75.0 90.0 95.0 99.0 99.5 99.9 

31-0002 1.5 1.2 0.8 1.2 1.8 2.7 3.4 6.0 7.6 13.0 31-0013 1.2 1.1 0.6 0.9 1.5 2.5 3.4 5.4 6.4 8.7 31-0014 1.1 1.0 0.5 0.7 1.3 2.3 3.2 5.3 6.4 7.7 59-0002 1.0 0.9 0.4 0.6 1.1 2.0 2.7 4.8 5.7 7.4 

3 4 Table 5-13. Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just m5 hour standard – Los Angeles (adjusted 1997 data). 

Hourly-average CO concentration (ppm) 

Monitor 

ID Mean SD 25.0 50.0 75.0 90.0 95.0 99.0 99.5 99.9 

37-0113 0.5 0.5 0.1 0.4 0.8 1.3 1.6 2.3 2.6 3.2 37-1002 1.1 0.8 0.6 0.9 1.3 2.2 2.8 3.8 4.1 4.8 37-1103 0.9 0.7 0.3 0.6 1.2 1.9 2.4 3.4 3.6 4.5 37-1201 0.7 0.8 0.3 0.4 0.9 1.8 2.4 3.7 4.3 5.2 37-1301 1.5 1.4 0.7 1.1 1.7 3.1 4.2 7.0 8.5 10.8 37-2005 0.7 0.5 0.4 0.6 0.9 1.3 1.8 2.6 2.9 3.8 37-4002 0.7 0.7 0.3 0.4 0.8 1.7 2.3 3.3 3.7 4.5 59-0001 0.7 0.6 0.4 0.5 0.9 1.4 1.8 2.9 3.4 4.3 59-1003 0.5 0.6 0.1 0.2 0.6 1.3 1.9 2.9 3.2 3.9 59-5001 0.9 0.7 0.4 0.6 1.1 1.8 2.3 3.8 4.5 6.1 
February 2010 Draft – Do Not Quote or Cite 5-20

1 5.8 METEOROLOGICAL DATA 

2 A few algorithms within APEX require meteorological data (primarily temperature) from 

3 stations located within the study area. For example, in selecting a CHAD diary to simulate an 

4 individual’s daily activities, a range of daily maximum temperatures is used to categorize diaries 

5 for sampling purposes so as to best match the temperature observed on the simulation day within 

6 the study area (section 5.5.4). In addition, mean temperatures are used by APEX to select from 

7 an appropriate air exchange rate distribution to estimate indoor microenvironmental 

8 concentrations (section 5.8). For the analyses described in this report, hourly temperature data 

9 were obtained from meteorological stations located at or near the fixed-site CO monitor specified 

10 for each study area. 

11 Tables 5-14 and 5-15 list the meteorological stations staff selected for use in modeling 

12 the Denver and Los Angeles study areas, respectively. Ideally, staff would have used the same 

13 station (Long Beach: 37-4002) matched for both monitoring years (1997 and 2006) in Los 

14 Angeles. Because this station did not report a complete year of data for 1997, we have 

15 substituted data reported by the Long Beach Daugherty Field station located approximately 3.6 

16 km from the 37-4002 station. The same two stations (31-0002 and 59-0002) will be used for the 

17 Denver study area for 1995 and Denver 2006, because there were adequate data for both years 

18 for both sites. 

19 To run APEX, a “ZoneRadius” is specified by the user as the maximum radius for the 

20 region surrounding each meteorological station that will be represented by the temperature data 

21 provided by the station. In this assessment, staff set this at a value that includes all census tracts 

22 within the air districts. A radius of 15.5 km met this requirement for Denver (Figure 5-1), while 

23 Los Angeles required a larger radius of 70.5 km (Figure 5-2). 

24 Table 5-14. Locations of meteorological stations selected for Denver. 

Monitoring Year 

Meteorological station Location coordinates 1995 2006 

Monitor ID County Latitude Longitude 

1-hour 

values 

(n) 

Mean 

temp

(◦F) 

1-hour 

values 

(n) 

Mean 

temp

(◦F) 

31-0002 Denver 39.751184 -104.987625 8742 53.3 8749 55.2 

59-0002 Jefferson 39.800333 -105.099973 8702 49.7 8758 51.5 

25 
February 2010 Draft – Do Not Quote or Cite 5-21

1 

2 Table 5-15. Locations of meteorological stations selected for Los Angeles. 

Monitoring Year 

Meteorological station Coordinates 1997 2006 

Monitor ID County Latitude Longitude 

1-hour 

values 

(n) 

Mean 

temp

(◦F) 

1-hour 

values 

(n) 

Mean 

temp

(◦F) 

Daugherty 

Field Long Beach 33.81667 -118.15 8751 65.8 -- -- 

37-4002 Long Beach 33.82376 -118.18921 -- -- 8759 63.8 

3 5.8.1 Method for Estimating of Missing 1-Hour Temperature Data 

4 APEX also requires a complete (full) meteorological data set to run properly. In 

5 checking the meteorological data for completeness, staff noted all stations and years had at least 

6 one missing hourly value for temperature (Tables 5-14 and 5-15). To generate the full year of 

7 temperature data set, we estimated the missing values for the selected meteorological (MET) 

8 stations in Denver and Los Angeles as follows. 

9 For the Denver study area, staff selected two MET stations for use in 1995 and 2006. All 

10 missing values in year 2006 were filled using linear interpolation. For the missing values in 

11 1995, staff used linear interpolation to fill in short gaps. Where there were long gaps in the data 

12 (e.g., more than 16 continuous hours of missing values), linear interpolation was judged as 

13 inappropriate because this method would likely not produce reasonable estimates of the potential 

14 variability in temperature (particularly the daily maximum) that might occur during this gap. In 

15 these instances, staff applied an alternative approach whereas the average temperature of the 

16 previous day and the latter day were averaged and then substituted for the corresponding hours. 

17 For example, if the temperature data was missing from 1AM to 11pm on 2/8/1995, staff 

18 averaged the hourly temperature of 2/7/1995 and 2/9/1995 for 1AM, 2AM ..., 11PM to fill the 

missing hours (all eleven hours have an individual value).8 19 

20 For Los Angeles, staff evaluated the two sites noted here as site 1 (ID 037-4002) and site 

21 2 (located at Daugherty Field). Both locations reported temperature in both years of interest; 

22 however, the degree of completeness for each varied. Given their close proximity to one another 

23 (3.6 km), staff decided that a complete data set would be best generated by using a composite of 

24 the two monitors, using the monitor with the greatest number of measurements as the primary 

 

8

 Calculating the average temperature using this method does not apply if 1) the long gap occurs on January 

1 or December 31, or if 2) the temperature data in the previous day or the latter day are not available. In such cases, 

we used the non-missing values in the previous day or the latter day, whichever was available. 
February 2010 Draft – Do Not Quote or Cite 5-22

1 data set. Because site 1 had fewer missing values than site 2 for 2006, site 1 was selected as the 

2 primary meteorological site to represent the Los Angeles area for that year. For the one missing 

3 value on Site 1 in 2006, the corresponding temperature from Site 2 was used to fill the missing 

4 value for 2006. For 1997, there were 2,263 missing values on Site 1 while only 9 missing values 

5 on Site 2. As a result, Site 2 was selected by staff as the primary meteorological station for 1997. 

6 Two of the nine missing values from Site 2 were available from Site 1. Therefore, these 

7 temperatures were directly substituted with values from the corresponding hours of the Site 1 

8 data set. To fill the remaining seven missing values, we used linear interpolation by connecting 

successive straight line segments and fitting a continuous curve to the data.9 9 

10 The temperature distributions before and after filling missing values were compared at 

11 for each station in each year to assess the impact (if any) of the substitution method. Given the 

12 limited number of missing values in the original data sets, there were negligible differences when 

13 comparing mean, median, variance and percentile statistics. 

14 5.9 MICROENVIRONMENTS MODELED 

15 This section briefly discusses the approach and specific factors used to estimate CO 

16 microenvironmental concentrations in the current assessment. As described in section 4.4.4.3, 

17 the approach was originally developed for pNEM/CO and used the previous assessment (Johnson 

18 et al., 2000). 

19 5.9.1 The Micronenvironmental Model as Implemented by APEX4.3 

20 Section 8.2.2 of US EPA (2008b) indicates that the mass balance model in APEX4.3 

21 models the portion of outdoor air that enters the microenvironment as 

22 

23 COout= fproximity x fpenetration x COambient (5-2) 

24 

25 Since this is effectively equivalent to the method used by APEX3.1 described in section 

26 4.4.4.4, we used the same method here with respect to application of the proximity and 

27 penetration factors in APEX4.3 to implement equation 4-11. First, to obtain the appropriate CO 

28 concentrations outside each microenvironment, ambient CO concentration were adjusted by an 

29 exponential factor of 0.621 (see equation 4-22). Then for each profile, a value for fproximity term 

30 would be sampled for each microenvironment from a lognormal distribution with geometric 

31 mean (GM) equal to M(m) and geometric standard deviation (GSD) equal to 1.5232. A value for 

32 fpenetration for each hour would also be sampled from a lognormal distribution with geometric 

33 mean (GMT) equal to 1.0 and geometric standard deviation (GSDT) equal to 1.6289. 

 

9

 This was done in SAS using a procedure “PROC EXPAND” with “JOIN” option. 
February 2010 Draft – Do Not Quote or Cite 5-23

1 Table 5-16 presents the algorithm parameters proposed for the eight microenvironments 

2 currently defined for the application of APEX to Los Angeles and Denver. These eight 

3 microenvironments were selected rather than the fifteen selected in earlier assessments (see REA 

4 Table 4-4 and 4-5) based on the locations having the same proximity factors and air exchange 

5 rates distributions, or when using a similar microenvironmental approach (see section 5.9.5). 

6 Note that when this algorithm is implemented within the APEX framework, the 

7 application of Equation 4-11 produces a “compression” effect in which the ratio of COout to 

8 COmon tends to become smaller (on average) as COmon increases. This effect is consistent with 

9 data reported by field studies such as Wilson, Colome, and Tian (1995) which have compared 

10 outdoor concentrations with simultaneously measured fixed-site concentrations. 
February 2010 Draft – Do Not Quote or Cite 5-24

1 Table 5-16. Parameters of Bounded Lognormal Distributions Defined for Proximity 

2 Factors to be Used in the Proposed Application of APEX4.3 to Los 

3 Angeles and Denver. 

Microenvironment

Parameters of bounded lognormal 

distribution for proximity factor 

Code

General 

location

Specific 

location

Activity diary 

locations 

included in 

microenvironment GM GSD

Minimum 

(5th pct)

Maximum 

(95th pct)

1 Indoors Residence Indoors - residence 1.034 1.5232 0.5175 2.0661 

2 Indoors Service 

station and 

auto repair 

Service station 

Auto repair 

2.970 1.5232 1.4864 5.9345 

3 Indoors Other 

indoor 

locations A 

Other repair shop 

Shopping mall 

Other indoor location 

Auditorium 

Store 

Office 

Other public building 

Bars 

Restaurants 

1.213 1.5232 0.6071 2.4237 

4 Indoors Other 

indoor 

locations B 

Health care facility 

School 

Church 

Manufacturing facility

0.989 1.5232 0.4950 1.9762 

5 Outdoors Near road 

locations 

Bus stop 

Bicycle 

Motorcycle 

Other near road 

1.607 1.5232 0.8042 3.2110 

6 Outdoor Public 

parking or 

fueling 

facility 

Indoor parking 

garage 

Outdoor parking 

garage 

Outdoor parking lot 

Outdoor service 

station 

2.970 1.5232 1.4864 5.9345 

7 Outdoors Other 

outdoor 

locations 

Outdoor res. garage 

Construction site 

Residential grounds 

School grounds 

Sports arena 

Park or golf course 

Other outdoor 

1.436 1.5232 0.7187 2.8693 

8 Vehicle Automobile 

and mass 

transit 

Automobile 

Truck 

Bus 

Train/subway 

Other vehicle 

3.020 1.5232 1.5114 6.0344 

4 
February 2010 Draft – Do Not Quote or Cite 5-25

1 5.9.2 Microenvironmental Mapping 

2 In APEX, microenvironments represent the exposure locations for simulated individuals. 

3 For exposures to be estimated accurately, it is important to have realistic microenvironments that 

4 match closely to the locations where actual people spend time on a daily basis. It is necessary to 

5 map the CHAD location codes to one of the eight specific microenvironments selected for this 

6 exposure assessment or to a supplemental category (either -1 or 0). As a reminder, these eight 

7 microenvironments were selected based on having suitable data to use for proximity factors and 

8 air exchange rates (when using a mass balance approach). The -1 code is assigned to events 

9 where the location code is missing (X) or the location is classified as uncertain (U); the -1 code 

10 instructs APEX to use the last known microenvironment for that persons diary in determining the 

11 exposure concentration. The 0 code is assigned to an airplane microenvironment (CHAD 

12 location code: 31160) and instructs APEX to set the exposure concentration equal to 0 ppm. See 

13 Appendix D Figure D-1 that describes the specific mapping of CHAD codes to 

14 microenvironments. 

15 The microenvironment mapping file also permits the user to assign a home/work/other 

16 (H/W/O) location to each CHAD location code. The home/work/other location determines the 

17 source of the hourly-average monitoring data that will represent the ambient CO concentration 

18 for the microenvironment: the home district monitor, the work district monitor, or other. 

19 The initial APEX assignments of H/W/O to the CHAD location codes were used as a 

20 starting point (see Appendix D Figure D-1) and modified using a few of the options available in 

21 APEX. First, staff overrode the H/W/O designations listed in the microenvironment mapping 

22 file for selected activities by compiling a list of CHAD activity codes that will always be 

23 associated with the work district (regardless of the CHAD location code). This list is inserted in 

24 the “CustomWork” parameter found in the simulation control file. The default list of work 

25 activity codes, which were used in this application, includes codes 10000 through 10300 (see 

26 Appendix D Table D-1). As a result of using this option, APEX will assign the simulated person 

27 to the work district whenever the activity code falls between 10000 and 10300. This assignment 

28 will override the home/work/assignment associated with the applicable CHAD location code. 

29 There will still be exposure events in which the simulated person is assigned to the 

30 “other” location. In the default mode, APEX uses an average of all monitor values to determine 

31 the ambient concentration for these events. Note that this averaging approach will tend to 

32 smooth the data; that is, it will produce ambient CO concentrations that have slightly less 

33 variance than a comparable set of ambient concentrations obtained from a single monitor. To 

34 avoid this effect, staff chose to specify the option OtherDistricts = 1, so that only one monitor is 

35 used to represent “other.” The monitor used in the model application is randomly selected from 

36 the set of all monitors. 
February 2010 Draft – Do Not Quote or Cite 5-26

1 5.9.3 Selection of Microenvironmental Method Used 

2 As discussed in chapter 4, the two methods available in APEX for calculating pollutant 

3 levels within microenvironments are mass balance or a factors approach. Table 5-17 lists the 

4 microenvironments used in this study and the calculation method used. 

5 Table 5-17. List of microenvironments modeled and calculation methods used. 

Microenvironment 

Code Location Name 

Calculation 

Method 

1 Indoors Residence Mass balance 

2 Indoors Service station and auto repair Mass balance 

3 Indoors Other indoor locations A Mass balance 

4 Indoors Other indoor locations B Mass balance 

5 Outdoors Near road locations Factors 

6 Outdoor Public parking or fueling facility Factors 

7 Outdoors Other outdoor locations Factors

8 Vehicle Automobile and mass transit Factors 

6 

7 5.9.4 Air Exchange Rates and Air Conditioning Prevalence 

8 For the microenvironments using the mass balance method (i.e., all indoor 

9 microenvironments), air exchange rate (AER) and air conditioning prevalence data are needed to 

10 estimate microenvironmental concentrations. Air exchange rate data used for the indoor 

11 residential microenvironment were the same used in APEX for the most recent O3 NAAQS 

12 review (US EPA, 2007). As part of that earlier review, AER data were reviewed, compiled and 

evaluated from the extant literature to generate location-specific AER distributions10 13 categorized 

14 by influential factors, namely temperature and presence of air conditioning. In general, 

15 lognormal distributions provided the best fit, and are defined by a geometric mean (GM) and 

16 standard deviation (GSD). To avoid unusually extreme simulated AER values, bounds of 0.1 

17 and 10 were selected for minimum and maximum AER, respectively. Tables 5-18 and 5-19 

 

10 There were AER measurement data specific to the Los Angeles study area; these were used by US EPA 

(2007) to develop AER distributions. Denver was not a location of interest in US EPA (2007); therefore there were 

no Denver-specific AER developed for this study area. Consistent with what was done in US EPA (2007) for cities 

not having location-specific AER data available, the composite AER distributions developed using data from cities 

outside California were applied in this study to Denver. 
February 2010 Draft – Do Not Quote or Cite 5-27

1 summarize the AER distributions used in modeling indoor exposures, classified by A/C 

2 prevalence and temperature categories. For all other indoor microenvironments, the AER 

3 distributions used here (Tables 5-18 and 5-19) were based data provided by an indoor air quality 

4 study (Persily et al., 2005). These are the same AER distributions used for the APEX 

5 assessments in the most recent O3 NAAQS review (US EPA, 2007), NO2 REA (EPA, 2008c) and 

6 SO2 REA (US EPA, 2009b). 

7 Because the selection of an air exchange rate distribution is conditioned on the presence 

8 or absence of an air-conditioner (A/C), the air conditioning status of the residential 

9 microenvironments in each modeled area is simulated randomly using the probability that a 

10 residence has an air conditioner. A value of 55% was used to represent the A/C prevalence rate 

11 in Los Angeles, based on data obtained from US EPA (2007). For Denver, residential A/C 

12 prevalence was estimated to be 69% of homes, a value obtained from AHS (2005). Air 

13 conditioning prevalence is noted as being distinct from usage rate, the latter being represented by 

14 the air exchange rate distribution and is dependent on temperature. 

15 Table 5-18. Lognormal distributions of indoor air exchange rates used in Los 

16 Angeles. 

Classification category Parameters of bounded lognormal distributiona

Microenvironment A/C present? 

Mean Temp 

(degrees F) GM GSD Minimum Maximum 

≤ 50 0.589 1.894 0.1 10.0 

50 – 67 0.589 1.894 0.1 10.0 

68 – 76 1.100 2.365 0.1 10.0 

77 – 85 0.813 2.415 0.1 10.0 

Yesb

86+ 0.266 2.790 0.1 10.0 

< 50 0.543 3.087 0.1 10.0 

50 – 67 0.747 2.085 0.1 10.0 

68 – 76 1.372 2.283 0.1 10.0 

77 – 85 0.988 1.967 0.1 10.0 

Indoors - 

residence 

No 

86+ 0.988 1.967 0.1 10.0 

Indoors - other - - 1.109 3.015 0.1c 10.0 c

Notes: 

a

 Obtained from Table D-4 of US EPA (2007). 

b

 Estimated air conditioning prevalence rate for Los Angeles = 55 percent (see page 47 and Table A-3 

of US EPA, 2007). 

c

 Assumed here to be consistent with other approximated lower and upper bounds. 
February 2010 Draft – Do Not Quote or Cite 5-28

1 Table 5-19. Lognormal distributions of indoor air exchange rates used in Denver. 

Classification category Parameters of bounded lognormal distributiona

Microenvironment A/C present? 

Mean Temp 

(degrees F) GM GSD Minimum c Maximum c

≤ 50 0.9185 1.8589 0.1 10.0 

50 – 68 0.5636 1.9396 0.1 10.0 

68 – 77 0.4676 2.2011 0.1 10.0 

77 – 86 0.4235 2.0373 0.1 10.0 

Yesb

86+ 0.5667 1.9447 0.1 10.0 

≤ 50 0.9258 2.0836 0.1 10.0 

50 – 68 0.7333 2.3299 0.1 10.0 

Indoors - 

residence 

No 

68+ 1.3782 2.2757 0.1 10.0 

Indoors - other - - 1.109 3.015 0.1 10.0 

Notes: 

a

 Obtained from Table D-4 of US EPA (2007) and derived from locations outside California. 

b

 Estimated air conditioning prevalence rate for Denver = 69% (see Table 1-4 in AHS, 2005). c

 Assumed here to be consistent with other approximated lower and upper bounds. 

2 

3 5.10 KEY OBSERVATIONS 

4 The following presents the key observations for this chapter: 

5  Two exposure model domains (Denver and Los Angeles study areas) were defined by 

6 overlaying ambient monitor locations having 10 km radii with US census tract 

7 population data. Monitors selected comprised the bulk of the urban core in each 

8 location, where ambient monitoring data exist. 

9  The selected at-risk population was simulated by combining the tract-specific age and 

10 gender population distribution and the CHD prevalence, also stratified by age and 

11 gender. In using this approach, staff can represent the variability that exists in the 

12 CHD population that resides in each census tract and within each study area. 

13  Staff expanded the selected at-risk population to include an estimate of persons with 

14 undiagnosed CHD. 

15  Compared with the single-monitor approach used for the first draft CO REA, staff 

16 expanded the number of ambient monitors used in this second draft CO REA to better 

17 capture the spatial variability in ambient concentrations. In Denver, a total of four 

18 monitors were used, in Los Angeles, the total number of monitors was ten. 

19  Compared with the two microenvironments modeled in the first draft CO REA, staff 

20 has expanded the number modeled in each location to eight. This approach is designed 

21 to better represent the expected variability in microenvironmental CO concentrations. 
February 2010 Draft – Do Not Quote or Cite 5-29

1  Compared with the approach used to estimate microenvironmental concentrations in 

2 the first draft CO REA (factors approach only), all indoor microenvironments were 

3 modeled using a mass balance model in this second draft assessment. Use of the mass 

4 balance model will better represent temporal variability in indoor CO concentrations 

5 with respect to the outdoor CO concentration variability. In addition, distributions of 

6 microenvironmental factors were used in this second draft CO REA for all 

7 microenvironments rather than using point estimates (as was done for the first draft CO 

8 REA). Using distributions of microenvironmental factors will better represent both 

9 spatial and temporal variability in estimated microenvironmental CO concentrations. 
February 2010 Draft – Do Not Quote or Cite 5-30

1 5.11 REFERENCES 

2 

3 AHS. (2005). American Housing Survey for the Denver Metropolitan Area: 2004. Available at:

4 http://www.census.gov/prod/2005pubs/h170-04-46.pdf

5 Adams PF and Marano MA. (1995). Current Estimates from the National Health Interview Survey, 1994. National 

6 Center for Health Statistics. Vital Health Stat. 10(193). 

7 American Heart Association. (1990). Heart and Stroke Facts. American Heart Association, Dallas, TX. p.13. 

8 CDC. (2009). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007. Series 10, 

9 Number 240. U.S. Department of Health and Human Services, Hyattsville, MD, May 2009. 

10 Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal diary assembly for 

11 human exposure modeling. J Expos Sci Environ Epidem. 18:299-311. 

12 Isaacs K, McCurdy T, Errickson A, Forbes S, Glen G, Graham S, McCurdy L, Nysewander, M, Smith L, Tulve N, 

13 Vallero D. (2009). Statistical properties of longitudinal time-activity data for use in EPA exposure models. 

14 Poster presented at the American Time Use Research Conference; College Park MD, June 26, 2009. 

15 Johnson TR. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Report No. EPA16 600/54-84-014, U. S. Environmental Protection Agency, Research Triangle Park, North Carolina. 

17 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 

18 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 

19 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 

20 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 

21 at: http://www.epa.gov/ttn/fera/human_related.html. June 2000. 

22 Persily A, Gorfain J, Brunner G. (2005). Ventilation design and performance in U.S. Office buildings. ASHRAE 

23 Journal. April 2005, 30-35. 

24 US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of 

25 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 

26 EPA/452/R-92-004. 

27 US EPA. (2003). Total Risk Integrated Methodology TRIM.ExpoInhalation User's Document. Volume I: Air 

28 Pollutants Exposure Model (APEX, version 3). Available at: 

29 http://www.epa.gov/ttn/fera/data/apex322/apexusersguidevoli4-24-03.pdf. 

30 US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. Office of Air Quality Planning 

31 and Standards, Research Triangle Park, NC. July 2007. EPA-452/R-07-010. Available at: 

32 http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_o3_exposure_tsd.pdf. 

33 US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 

34 (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-001a. Office of 

35 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

36 US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 

37 (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08-

38 001b. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research 

39 Triangle Park, NC. 
February 2010 Draft – Do Not Quote or Cite 5-31

1 US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient 

2 Air Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 

3 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 

4 US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary 

5 National Ambient Air Quality Standard. First External Review Draft. EPA-452/P-09-008. Available at: 

6 http://www.epa.gov/ttn/naaqs/standards/co/data/COREA1stDraftOct2009.pdf

7 US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air 

8 Quality Standard. EPA-452/R-09-007. August 2009. Available at 

9 http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 

10 Wilson AL, Colome SD, Tian Y. (1995). California Residential Indoor Air Quality Study. Volume III: Ancillary 

11 and Exploratory Analysis. Integrated Environmental Services, Irvine, California. 
 February 2010 Draft – Do Not Quote or Cite 6-1

1 6 SIMULATED EXPOSURE AND COHB DOSE RESULTS 

2 This chapter summarizes the CO exposure and dose results for the Denver and Los 

3 Angeles study areas that were generated using EPA’s APEX model described in chapters 4 and 

4 5. Staff considered exposures associated with five air quality scenarios; air quality (1) as is, (2) 

adjusted to just meet the current 8-hour standard of 9 ppm, (3) adjusted to just meet a 2nd 5 highest 

8-hour average concentration of 5.4 ppm, (4) adjusted to just meet a 99th 6 percentile daily 

maximum 8-hour average of 5.0 ppm, and (5) adjusted to just meet a 99th 7 percentile daily 

8 maximum 8-hour average of 8.0 ppm. 

9 The chapter is divided into five main sections. The first section (6.1) summarizes the 

10 estimated exposures associated with each of the five air quality scenarios. The simulated at-risk 

11 population includes individuals with diagnosed CHD as well as those persons with potentially 

undiagnosed CHD.1 12 For simplicity, they will be combined and referred to as the CHD 

13 population in this chapter. The exposure metrics of interest in this REA and generated by APEX 

14 include the number and percent of persons at or above staff-selected exposure levels and the 

corresponding number of person-days.2 15 Two exposure averaging times were also selected: 1-

16 hour and 8-hour daily maximum exposures. Section 6.2 summarizes the estimated COHb dose 

17 levels for persons in the simulated at-risk population residing in each study area. The dose 

18 metric of interest in this REA and generated by APEX includes the number and percent of 

19 persons at or above staff selected COHb levels and the corresponding number of person-days. 

20 Consistent with prior CO exposure assessments, the daily maximum end-of-hour COHb level 

21 was recorded. In section 6.3, staff compares the dose estimates in this second draft CO REA 

22 with those estimated in the 2000 exposure assessment (Johnson et al., 2000). The fourth section 

23 (6.4) presents an evaluation of endogenous CO production for the APEX simulated individuals. 

24 This includes analysis of the COHb ambient contribution attributed to ambient CO in a select 

25 group of simulated persons. Finally, key observations are presented in the final section (6.5). As 

26 mentioned in Chapter 1, exposure and risk results are provided here without substantial 

27 interpretation. Rather, interpretative discussion of these results is provided in the CO Policy 

28 Assessment. 

 

1

 As described in section 5.5 above, in characterizing the population of interest with regard to 

demographics (age and gender), the assessment drew from estimates of the prevalence of coronary heart disease 

(CHD) provided by the National Health Interview Survey (which includes CHD, angina pectoris and heart attack) 

and corresponding estimates of undiagnosed ischemia developed by EPA. 

2

 Because the duration of the exposure assessment is one year, there are opportunities for individuals to 

experience more than one day in the year above a selected exposure concentration, hence use of the term persondays. 
 February 2010 Draft – Do Not Quote or Cite 6-2

1 6.1 ESTIMATED EXPOSURES 

2 The section summarizes the estimated exposures for the simulated individuals in a series 

3 of tables, separated by the five air quality scenarios and study areas considered. 

4 6.1.1 Air Quality “As Is” 

5 As described in section 5.6, ambient monitoring data from each location for year 2006 

6 were used to represent the as is air quality. Table 6-1 summarizes the distribution of the 1-hour 

7 and 8-hour daily maximum CO exposures experienced by the CHD population in the Denver 

8 Study area. About 80% of the simulated CHD population did not experience a 1-hour daily 

9 maximum exposure above 9 ppm; 99.9% did not experience a 1-hour daily maximum exposure 

10 concentration above 20 ppm. Of the nearly 20 million person-days, over 99% were associated 

11 with a 1-hour daily maximum exposure below 6 ppm. Very few individuals were estimated to 

12 experience an 8-hour daily maximum exposure above 8 ppm (0.4% of the CHD population). 

13 Approximately 99% of simulated person-days were associated with 8-hour daily maximum 

14 exposure concentrations of less than 3 ppm. These results are consistent with the ambient 

15 concentration distribution used to represent this scenario, where upper percentile concentrations 

16 range from about 2 to 6.4 ppm (see Table 5-8). Note also that the highest estimated 1-hour daily 

17 maximum exposures are likely a function of microenvironmental concentrations (e.g., in18 vehicles or near-roads) that, in general, may be a factor of two to five times higher than ambient 

19 CO concentrations. 

20 In Los Angeles, there were a greater number of individuals experiencing exposures at 

21 each of the selected exposure levels (Table 6-2) when compared with Denver (Table 6-1), given 

22 that the overall exposure modeling domain extended over a larger area with a higher total 

23 population. The estimated percentage of persons exposed in Los Angeles is also greater when 

24 compared with the corresponding exposure levels evaluated for the Denver study area. For 

25 example, approximately 32% of the population was estimated to experience a 1-hour daily 

26 maximum exposure of at least 9 ppm in Los Angeles (Table 6-2) while in Denver this same level 

27 was experienced by approximately 20% of the CHD population (Table 6-1). This result is likely 

28 driven by the differences noted in the as is air quality data, where in Los Angeles, the 2006 

29 ambient concentrations were generally higher than those observed for Denver (section 5.7.1). 

30 In addition, the maximum 1-hour daily maximum exposure was estimated to be at or 

31 above 30 ppm but less than 40 ppm in the Los Angeles study area, though limited to a small 

32 fraction of the population (<0.1%). The corresponding maximum 1-hour daily maximum 

33 exposure in the Denver study area was at or above 20 ppm but less than 25 ppm, and was 

34 experienced by approximately 0.1% of the CHD population. Therefore, the overall range of the 

35 exposure distribution was wider in Los Angeles when compared with that of Denver when 
 February 2010 Draft – Do Not Quote or Cite 6-3

1 considering the as is air quality scenario. Similar to Denver, over 98% of the person-days in Los 

2 Angeles were associated with 1-hour daily maximum exposures below 6 ppm and very few 

3 persons (0.8%) experienced 8-hour daily maximum exposures above 8 ppm. These exposure 

4 results are also consistent with the distribution of ambient air quality used to represent this 

5 scenario, where upper percentile concentrations extend from about 2 to 8.4 ppm (Table 5-10). 

6 Table 6-1. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

7 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 

8 Adults With Coronary Heart Disease (CHD) in the Denver Study Area –“As 

9 Is” Air Quality. 

1-Hour 8-Hour 

Persons Person-days Persons Person-days 

Daily 

Maximum 

Exposure 

(ppm) Number Percent Number Percent Number Percent Number Percent

0 53,656 100 19,580,000 100 53,656 100 19,580,000 100 

3 53,397 100 2,188,000 11.2 31,036 58 189,500 1.0 

6 32,517 60.6 170,400 0.9 1,715 3.2 2,851 <0.1 

9 10,662 19.9 24,560 0.1 62 0.1 86 <0.1 

12 3,048 5.7 4,677 <0.1 12 <0.1 12 <0.1 

15 876 1.6 1,061 <0.1 0 0 0 0 

20 62 0.1 62 <0.1 0 0 0 0 

25 0 0 0 0 0 0 0 0 

30 0 0 0 0 0 0 0 0 

40 0 0 0 0 0 0 0 0 

Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air 

quality scenario. 

10 
 February 2010 Draft – Do Not Quote or Cite 6-4

1 Table 6-2. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

2 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 

3 Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – 

4 Air Quality As Is. 

1-Hour 8-Hour 

Persons Person-days Persons Person-days 

Daily 

Maximum 

Exposure 

(ppm) Number Percent Number Percent Number Percent Number Percent

0 383,040 100 139,800,000 100 383,040 100 139,800,000 100 

3 382,739 100 23,620,000 16.9 294,430 77 3,793,000 2.7 

6 287,606 75.1 2,423,000 1.7 36,528 9.5 72,150 0.1 

9 122,428 32.0 408,300 0.3 3,011 0.8 3,412 <0.1 

12 42,850 11.2 83,990 0.1 301 0.1 301 <0.1 

15 13,949 3.6 20,170 <0.1 0 0 0 0 

20 2,208 0.6 2,509 <0.1 0 0 0 0 

25 401 0.1 502 <0.1 0 0 0 0 

30 100 <0.1 100 <0.1 0 0 0 0 

40 0 0 0 0 0 0 0 0 

Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air 

quality scenario. 

5 

6 6.1.2 Air quality adjusted to just meet the current 8-hour standard 

7 As described in section 5.6, historical ambient monitoring data from each study area were 

8 adjusted to represent air quality that just meets the current 8-hour standard. For both Denver 

9 (year 1995) and Los Angeles (year 1997), air quality needed to be adjusted downwards to meet a 

2nd 10 highest 8-hour average concentration of 9.4 ppm. Note that even with a downward 

11 proportional adjustment, these ambient concentrations remain higher than as is ambient air 

12 quality. Table 6-3 summarizes the CHD population exposure results generated for the Denver 

13 study area when using these adjusted ambient CO concentrations as an input to APEX and using 

14 the same modeling assumptions and parameter distributions described in chapters 4 and 5. Over 

15 half of the Denver CHD population was estimated to experience a 1-hour daily maximum 

16 exposure at or above 12 ppm. This is nearly a factor of 10 greater than that estimated when 

17 using the as is air quality (Table 6-1). The maximum 1-hour daily maximum exposure was 

18 estimated to be at or above 40 ppm when considering air quality adjusted to just meet the current 

19 standard, though only experienced by 0.2% of the CHD population. Thus, there is a wider range 

20 in the exposure levels experienced by the CHD population. The number and percent of persons 

21 experiencing 8-hour daily maximum exposures is also greater for this scenario when compared 

22 with corresponding levels using the as is air quality. Nearly 10% of the CHD population was 

23 estimated to experience an 8-hour daily maximum exposure at or above 9 ppm (Table 6-3) when 

24 considering air quality just meeting the current 8-hour standard. Most of the CHD population 
 February 2010 Draft – Do Not Quote or Cite 6-5

1 (99.6%) would not experience an 8-hour daily maximum concentration at that same level when 

2 considering the as is air quality scenario (Table 6-1). 

3 Similarly in Los Angeles, the number and percent of persons exposed above selected 

4 exposure concentrations is greater when considering the air quality adjusted to just meet the 

5 current standard than when using as is air quality. For example, nearly 50% of the CHD 

6 population was estimated to experience a 1-hour daily maximum exposure of 9 ppm when 

7 considering air quality just meeting the current standard (Table 6-4), while only 32% were 

8 estimated to experience a similar concentration using as is air quality (Table 6-2). The range of 

9 the 1-hour daily maximum exposure distribution extends upward to 40 ppm, but less than 60 

10 ppm for this scenario. This estimate of an upper level is consistent with the maximum in-vehicle 

11 concentration of 46 ppm measured by Shikiya (1989) during 112 southern California commutes 

12 in wintertime. However, Rodes et al. (1998) reported maximum in-vehicle and on road CO 

13 concentrations of only 7.6 and 9.0 ppm during Los Angeles commutes in 1997. Note though the 

14 scripted commutes in this study were time-averaged for two hours, the sample size was limited 

15 (about 30 total samples), and conducted over a nine days in the fall. 

16 When comparing the overall population distribution for Los Angeles to Denver for this 

17 exposure scenario, there are a greater percentage of persons and person-days estimated for the 

18 Denver CHD population at the same exposure level. For example, only 2.7% of the CHD 

19 population was estimated to experience an 8-hour daily maximum exposure at or above 9 ppm in 

20 Los Angeles (Table 6-4), while in Denver, the estimated percent of the CHD population exposed 

21 at this level was over a factor of three greater (9.4%) (Table 6-3). This result is likely driven by 

22 differences observed at the upper tails of the air quality distribution noted in section 5.7.3, even 

23 though both study areas have ambient concentrations adjusted to just meet the same 8-hour 

24 average CO concentration of 9.4 ppm. 
 February 2010 Draft – Do Not Quote or Cite 6-6

1 Table 6-3. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

2 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 

3 Adults With Coronary Heart Disease (CHD) in the Denver Study Area – Air 

4 Quality Just Meeting the Current 8-Hour Standard. 

1-Hour 8-Hour 

Persons Person-days Persons Person-days 

Daily 

Maximum 

Exposure 

(ppm) Number Percent Number Percent Number Percent Number Percent

0 53,656 100 19,580,000 100 53,656 100 19,580,000 100 

3 53,656 100 8,638,000 44.1 52,706 98 2,690,000 13.7 

6 53,039 98.9 1,625,000 8.3 23,879 44.5 97,760 0.5 

9 44,598 83.1 404,800 2.1 5,060 9.4 9,724 <0.1 

12 28,469 53.1 127,300 0.7 1,037 1.9 1,382 <0.1 

15 16,610 31.0 46,710 0.2 309 0.6 346 <0.1 

20 6,022 11.2 10,290 0.1 37 0.1 37 <0.1 

30 691 1.3 802 <0.1 0 0 0 0 

40 86 0.2 86 <0.1 0 0 0 0 

60 0 0 0 0 0 0 0 0 

Ambient concentrations from 1995 were adjusted to just meet a 2nd highest 8-hour average 

concentration of 9.4 ppm. 

5 

6 Table 6-4. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

7 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level – 

8 Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area – 

9 Air Quality Just Meeting the Current 8-Hour Standard. 

1-Hour 8-Hour 

Persons Person-days Persons Person-days 

Daily 

Maximum 

Exposure 

(ppm) Number Percent Number Percent Number Percent Number Percent

0 383,040 100 139,800,000 100 383,040 100 139,800,000 100 

3 383,040 100 36,430,000 26.1 342,598 89 8,655,000 6.2 

6 335,975 87.7 4,826,000 3.5 75,966 19.8 262,600 0.2 

9 189,563 49.5 982,300 0.7 10,336 2.7 18,670 <0.1 

12 83,693 21.8 257,200 0.2 1,505 0.4 2,308 <0.1 

15 36,126 9.4 80,180 0.1 301 0.1 401 <0.1 

20 8,731 2.3 14,450 <0.1 100 <0.1 100 <0.1 

30 803 0.2 803 <0.1 0 0 0 0 

40 0 0 0 0 0 0 0 0 

60 0 0 0 0 0 0 0 0 

Ambient concentrations from 1997 were adjusted to just meet a 2nd highest 8-hour average concentration 

of 9.4 ppm. 

10 6.1.3 Air quality adjusted to just meet alternative air quality scenarios 

11 Three potential alternative air quality scenarios were investigated to observe how the 

12 selected averaging times, forms and target levels would affect the estimated exposure 
 February 2010 Draft – Do Not Quote or Cite 6-7

1 concentrations (section 5.7.3). The data for the 1-hour and 8-hour daily maximum exposure 

2 concentrations are presented here, only with a focus on the number and percent of persons 

3 exposed at selected concentrations. Table 6-5 summarizes the 1-hour exposure results for each 

4 of the three alternative standards scenarios in the Denver study area, while the following table 

5 presents the same information for the Los Angeles study area (Table 6-6). Tables 6-7 and 6-8 

6 contain the corresponding 8-hour exposure distribution for the potential alternative standard 

7 scenarios in each respective location. 

8 In comparing the exposure results for each potential alternative scenario within each 

9 study area and exposure averaging time, generally similar numbers of persons and their 

10 respective percentages of the CHD population are observed at the same level. This was by 

11 general design, that is, to investigate differing forms of the potential alternative standards that 

12 would generate potentially similar exposure (and dose) results. Again, there is a wider range in 

13 the 1-hour exposure levels experienced by the CHD population in Denver (Table 6-5) when 

14 compared with that of Los Angeles (Table 6-6) when considering the same potential alternative 

15 standard. This is also consistent patterns in the estimated distribution of 8-hour daily maximum 

16 exposures experienced by the CHD population, though the upper range of that 8-hour maximum 

17 exposure is of course less than that of the 1-hour daily maximum in each respective location 

18 (Tables 6-7 and 6-8). There is some variability in the percent of persons exposed when 

considering a particular level, form, and study area. For example, the 2nd 19 highest 8-hour CO 

20 concentration of 5.4 ppm best limited the number and percent of exposed persons in each 

21 location when compared to results for the other potential alternative standard, though in Denver 

22 there were still a few persons estimated to experience a 1-hour daily maximum at or above 30 

23 ppm (Table 6-7). In Los Angeles, the upper level of the 1-hour daily maximum exposure 

24 concentration experienced by the simulated CHD population was just at or above 20 ppm and 

25 below 30 ppm. 
 February 2010 Draft – Do Not Quote or Cite 6-8

1 Table 6-5. Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour 

2 Exposure At or Above the Specified Level – Adults With Coronary Heart 

3 Disease (CHD) in the Denver Study Area – Air Quality Just Meeting Potential 

4 Alternative Standards. 

2nd highest 8-hour 

average of 5.4 ppm 

99th pct 8-hour Daily 

Max of 5.0 ppm 

99th pct 1-hour Daily 

Max of 8.0 ppm 

Persons Persons Persons 

Daily 

Maximum 

1-hour 

Exposure 

(ppm) Number Percent Number Percent Number Percent 

0 53,656 100 53,656 100 53,656 100 

3 53,656 100 53,656 100 53,656 100 

6 47,264 88.1 50,534 94.2 48,239 89.9 

9 25,174 46.9 32,147 59.9 26,877 50.1 

12 11,082 20.7 16,326 30.4 12,192 22.7 

15 4,850 9.0 7,676 14.3 5,282 9.8 

20 975 1.8 1,888 3.5 1,222 2.3 

30 62 0.1 136 0.3 74 0.1 

40 0 0 0 0 0 0 

60 0 0 0 0 0 0 

Ambient concentrations from 1995 were adjusted to just meet the level of the potential 

alternative standard indicated. 

5 

6 Table 6-6. Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour 

7 Exposure At or Above the Specified Level – Adults With Coronary Heart 

8 Disease (CHD) in the Los Angeles Area – Air Quality Just Meeting Potential 

9 Alternative Standards. 

2nd highest 8-hour 

average of 5.4 ppm 

99th pct Daily 8-hour 

Max of 5.0 ppm 

99th pct Daily 1-hour 

Max of 8.0 ppm 

Persons Persons Persons 

Daily 

Maximum 

1-hour 

Exposure 

(ppm) Number Percent Number Percent Number Percent 

0 383,040 100 383,040 100 383,040 100 

3 378,624 99.0 379,728 99.0 381,535 99.6 

6 215,454 56.2 229,302 59.9 260,913 68.1 

9 68,540 17.9 77,571 20.3 98,244 25.6 

12 19,769 5.2 24,285 6.3 34,721 9.1 

15 6,322 1.7 7,827 2.0 10,738 2.8 

20 903 0.2 1,305 0.3 2,709 0.7 

30 0 0 0 0 0 0 

40 0 0 0 0 0 0 

60 0 0 0 0 0 0 

Ambient concentrations from 1997 were adjusted to just meet the level of the potential 

alternative standard indicated. 

10 

11 
 February 2010 Draft – Do Not Quote or Cite 6-9

1 Table 6-7. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour 

2 Exposure At or Above the Specified Level – Adults With Coronary Heart 

3 Disease (CHD) in the Denver Study Area – Air Quality Just Meeting Potential 

4 Alternative Standards. 

2nd highest 8-hour 

average of 5.4 ppm 

99th pct 8-hour Daily 

Max of 5.0 ppm 

99th pct 1-hour Daily 

Max of 8.0 ppm 

Persons Persons Persons 

Daily 

Maximum 

8-hour 

Exposure 

(ppm) Number Percent Number Percent Number Percent 

0 53,656 100 53,656 100 53,656 100 

3 44,574 83.1 48,819 91.0 45,808 85.4 

6 6,590 12.3 10,650 19.8 7,380 13.8 

9 839 1.6 1,555 2.9 926 1.7 

12 111 0.2 296 0.6 123 0.2 

15 25 <0.1 49 <0.1 25 <0.1 

20 0 0 0 0 0 0 

30 0 0 0 0 0 0 

40 0 0 0 0 0 0 

60 0 0 0 0 0 0 

Ambient concentrations from 1995 were adjusted to just meet the level of the potential 

alternative standard indicated. 

5 

6 Table 6-8. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour 

7 Exposure At or Above the Specified Level – Adults With Coronary Heart 

8 Disease (CHD) in the Los Angeles Area – Air Quality Just Meeting Potential 

9 Alternative Standards 

2nd highest 8-hour 

average of 5.4 ppm 

99th pct Daily 8-hour 

Max of 5.0 ppm 

99th pct Daily 1-hour 

Max of 8.0 ppm 

Persons Persons Persons 

Daily 

Maximum 

8-hour 

Exposure 

(ppm) Number Percent Number Percent Number Percent 

0 383,040 100 383,040 100 383,040 100 

3 214,149 55.9 230,807 60.3 264,425 69.0 

6 17,060 4.4 20,672 5.4 28,801 7.5 

9 903 0.2 1,204 0.3 2,007 0.5 

12 301 <0.1 301 <0.1 301 <0.1 

15 0 0 100 <0.1 100 <0.1 

20 0 0 0 0 

30 0 0 0 0 

40 0 0 0 0 

60 0 0 0 0 

Ambient concentrations from 1997 were adjusted to just meet the level of the potential 

alternative standard indicated. 

10 
 February 2010 Draft – Do Not Quote or Cite 6-10

1 6.2 ESTIMATED COHB DOSE LEVELS 

2 Consistent with section 6.2, this section summarizes the estimated COHb levels for the 

3 simulated CHD population in a series of tables, separated by the air quality scenarios and study 

4 areas considered. In addition to reporting the number and percentage of persons and person-days 

5 associated with the dose metric of interest (daily maximum end-of-hour COHb level), staff 

6 provides the person-days per person at or above the selected COHb levels. This dose metric can 

7 be calculated in two manners. The first, termed on average, is the number of person-days at a 

8 given level divided by the total number of CHD persons in each model simulation. Therefore, 

9 this metric gives an estimate of, on average, the number of days an individual in the entire CHD 

10 population might experience a daily maximum end-of-hour COHb concentration at or above the 

11 selected COHb level. The second, termed at level, is calculated by dividing number of person12 days estimated for a given level by the number of persons estimated for the same COHb level. 

Therefore, this 2nd 13 metric will provide an estimate of, for persons that experience a selected 

14 COHb level, the average number of days in the year they may experience that selected level. 

15 This second metric (at level) will always be larger than the first (on average) because it only 

includes the persons experiencing a selected COHb dose level.3 16 

17 6.2.1 Air Quality “As Is” 

18 Table 6-9 provides the COHb levels (%) for the simulated COHb population in Denver, 

19 when considering the as is air quality. No persons were estimated to have a daily maximum end20 of-hour COHb level at or above 2.0%, while only a few (<0.1%) were estimated to have a COHb 

21 dose level ≥1.8%. Over 99% of the CHD population had a daily maximum end-of-hour COHb 

22 level below 1.5%. Most of the person-days were associated with daily maximum end-of-hour 

23 COHb levels below 1.0%. It follows that the majority of the person-days per person on average

24 were also limited to COHb levels at or below 1.0%. When individuals did have an estimated 

25 daily maximum end-of-hour COHb level at or above 1.5%, it occurred on multiple days (i.e., 

26 between 8 and 14 (Table 6-9), depending on the level). 

27 Similarly in Los Angeles, very few persons (98.5%) had an estimated daily maximum 

28 end-of-hour COHb level at or above 1.5% when considering the as is air quality (Table 6-10). 

29 There were, however, a few persons (0.1%) estimated to have daily maximum end-of-hour 

30 COHb levels at or above 2.0% in this study area. Of these 301 simulated individuals, all were 

31 estimated to have only one person-day per person at that level. The majority of the person-days 

32 and person-days per person on average were limited to COHb levels at or below 1.0%. 

 

3

 This averaging of person-days would underestimate the number of person-days any one simulated 

individual might experience above a given benchmark in a year. For example, 10 exceedances occurring in one 

individual would give the same average as 1 exceedance occurring in 10 individuals. 
 February 2010 Draft – Do Not Quote or Cite 6-11

1 Table 6-9. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

2 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

3 With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality 

4 As Is. 

COHb Persons Person-days Person-days/person Level 

(%) Number Percent Number Percent On Average At Level 

0.0 53,656 100 19,580,000 100 365 365 

1.0 7,873 14.7 293,000 1.5 5.5 37.2 

1.5 333 0.6 4,652 <0.1 <0.1 14.0 

1.8 12 <0.1 99 <0.1 <0.1 8.2 

2.0 0 0 0 0 0 0 

2.5 0 0 0 0 0 0 

3.0 0 0 0 0 0 0 

4.0 0 0 0 0 0 0 

Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As 

Is air quality scenario.

5 

6 Table 6-10. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

7 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

8 With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air 

9 Quality As Is. 

COHb Persons Person-days Person-days/person 

Level 

(%) Number Percent Number Percent 

On 

Average At Level 

0.0 383,040 100 139,800,000 100 365 365 

1.0 98,043 25.6 1,645,000 1.2 4.3 16.8 

1.5 5,820 1.5 86,800 0.1 0.2 14.9 

1.8 1,505 0.4 8,630 <0.1 <0.1 5.7 

2.0 301 0.1 301 <0.1 <0.1 1.0 

2.5 0 0 0 0 0 0 

3.0 0 0 0 0 0 0 

4.0 0 0 0 0 0 0 

Unadjusted ambient concentrations from ten monitors in 2006 were used to represent the As 

Is air quality scenario.

10 6.2.2 Air Quality Adjusted to Just Meet the Current 8-hour Standard 

11 Consistent with the estimated exposure concentrations, dose levels experienced by the 

12 CHD population in each study area were greater when considering simulated exposures 

13 associated with air quality adjusted to just meet the current standard than when using as is air 

14 quality. For example, in Denver, just over 3% of the CHD population was estimated to have a 

15 daily maximum end-of-hour COHb level at or above 2.0% (Table 6-11). There were no persons 

16 in Denver estimated above this COHb level based on estimated ambient exposures associated 

17 with as is air quality. A similar pattern is observed for the CHD population in Los Angeles 
 February 2010 Draft – Do Not Quote or Cite 6-12

1 (Table 6-12), though a lower percentage of persons (0.5%) was estimated to have daily 

2 maximum end-of-hour COHb levels at or above 2.0% when compared results for Denver. In 

3 both study areas, a few persons had daily maximum end-of-hour COHb levels extending 

4 upwards to 3.0%. Most of the persons that did experience these higher COHb levels (≥2.0%) 

5 however, experienced them for fewer than 2 days in a year (Table 6-12). 

6 Table 6-11. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

7 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

8 With Coronary Heart Disease (CHD) in the Denver Study Area – Air Quality 

9 Just Meeting the Current 8-hour Standard. 

COHb Persons Person-days Person-days/person 

Level 

(%) Number Percent Number Percent 

On 

Average At Level 

0.0 53,656 100 19,580,000 100 365 365 

1.0 40,921 76.3 829,300 4.2 15.5 20.3 

1.5 10,267 19.1 35,520 0.2 0.7 3.5 

2.0 1,814 3.4 2,480 <0.1 <0.1 1.4 

2.5 346 0.6 370 <0.1 <0.1 1.1 

3.0 86 0.2 86 <0.1 <0.1 1.0 

4.0 0 0 0 0 0 0 

Ambient concentrations from 1995 were adjusted to just meet a 2nd highest 8-hour 

average concentration of 9.4 ppm. 

10 

11 Table 6-12. Estimated Number (and Percentage) of Persons and Person-Days with a Daily 

12 Maximum End-of-hour COHb Level At or Above the Specified Level – Adults 

13 With Coronary Heart Disease (CHD) in the Los Angeles Study Area – Air 

14 Quality Just Meeting the Current 8-hour Standard. 

COHb Persons Person-days Person-days/person 

Level 

(%) Number Percent Number Percent 

On 

Average At Level 

0.0 383,040 100 139,800,000 100 365 365 

1.0 155,243 40.5 2,472,000 1.8 6.4 15.9 

1.5 17,561 4.6 120,100 0.1 0.3 6.8 

2.0 2,007 0.5 3,111 <0.1 <0.1 1.6 

2.5 301 0.1 401 <0.1 <0.1 1.3 

3.0 100 <0.1 100 <0.1 <0.1 1.0 

4.0 0 0 0 0 0 0 

Ambient concentrations from 1997 were adjusted to just meet a 2nd highest 8-hour 

average concentration of 9.4 ppm. 

15 
 February 2010 Draft – Do Not Quote or Cite 6-13

1 6.2.3 Air Quality Adjusted to Just Meet Alternative Air Quality Scenarios 

2 Consistent with results described for the exposure, the percentage of persons estimated to 

3 experience maximum end-of-hour COHb at selected levels are general similar across the three 

4 potential alternative standard scenarios. For example, in Denver most of the population (>99%) 

5 were estimated to not experience a daily maximum end-of-hour COHb level above 2.0% (Table 

6 6-13). There are a few study area differences worthy of note. As expected, the corresponding 

7 estimated percent of the CHD population in Denver is greater than that estimated for Los 

8 Angeles, even when considering the same potential alternative standard form and air quality 

level (Table 6-14). For example, when considering a 99th 9 percentile daily maximum 8-hour 

10 average CO concentration of 5.0 ppm, 0.8% of the CHD population had an estimated daily 

11 maximum end-of-hour COHb level at or above 2.0%; in Los Angeles this dose level was 

12 estimated for only 0.1% of the CHD population. 

13 Table 6-13. Estimated Number (and Percentage) of Persons with a Daily Maximum End14 of-hour COHb Level At or Above the Specified Level – Adults With Coronary 

15 Heart Disease (CHD) in the Denver Study Area – Air Quality Just Meeting 

16 Potential Alternative Standards. 

2nd highest 8-hour 

average of 5.4 ppm 

99th pct Daily Max 8-

hour of 5.0 ppm 

99th pct Daily Max 1-

hour of 8.0 ppm 

COHb Level Persons Persons Persons 

(%) Number Percent Number Percent Number Percent 

0.0 53,656 100 53,656 100 53,656 100 

1.0 19,560 36.5 26,692 49.7 21,040 39.2 

1.5 2,061 3.8 3,826 7.1 2,271 4.2 

1.8 444 0.8 1,012 1.9 568 1.1 

2.0 197 0.4 407 0.8 234 0.4 

2.5 62 0.1 86 0.2 62 0.1 

3.0 0 0 12 <0.1 0 0 

4.0 0 0 0 0 0 0 

Ambient concentrations from 1995 were adjusted to just meet the level of the potential 

alternative standard indicated. 

17 
 February 2010 Draft – Do Not Quote or Cite 6-14

1 Table 6-14. Estimated Number (and Percentage) of Persons with a Daily Maximum End2 of-hour COHb Level At or Above the Specified Level – Adults With Coronary 

3 Heart Disease (CHD) in the Los Angeles Study Area – Air Quality Just 

4 Meeting Potential Alternative Standards. 

2nd highest 8-hour 

average of 5.4 ppm 

99th pct Daily Max 8-

hour of 5.0 ppm 

99th pct Daily Max 1-hour 

of 8.0 ppm 

COHb Level Persons Persons Persons 

(%) Number Percent Number Percent Number Percent 

0.0 383,040 100 383,040 100 383,040 100.0 

1.0 53,086 13.9 60,913 15.9 77,772 20.3 

1.5 2,408 0.6 3,312 0.9 5,319 1.4 

1.8 602 0.2 803 0.2 1,204 0.3 

2.0 301 0.1 301 0.1 401 0.1 

2.5 0 0 0 0 0 0 

3.0 0 0 0 0 0 0 

4.0 0 0 0 0 0 0 

Ambient concentrations from 1997 were adjusted to just meet the level of the potential 

alternative standard indicated. 

5 

6 6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000 

7 PNEM/CO AND DRAFT 2010 APEX/CO ASSESSMENTS 

8 As described above in chapters 2 and 4, population exposure and dose were estimated in 

9 2000 using pNEM/CO, a predecessor to APEX, for adults with ischemic heart disease residing in 

10 a defined study area within the same two urban areas (Johnson et al., 2000). As described in 

11 section 1.2 above, IHD is also called CHD and with regard to characterizing the population of 

12 interest with regard to demographics (age and sex), the 2000 assessment, like the current 

13 assessment, drew from estimates of the prevalence provided by the NHIS (which includes CHD 

14 or IHD, angina pectoris and heart attack) and corresponding estimates of undiagnosed ischemia 

15 developed by EPA. As part of this current (2010) second draft REA, staff has used APEX to 

16 estimate CO exposures and resulting COHb levels using a largely similar approach, modeling 

domains, years of ambient concentration data,4 17 and defined at-risk population. There are some 

18 differences that exist when comparing the specific methodologies: 

19  number of ambient monitors used (e.g., previously 6 in Denver versus 4 used here), 

20  location of ambient monitors used (e.g., only 7 of the same monitors used previously 

21 were used here for Los Angeles), 

22  number of microenvironments modeled (previously 15 versus the 8 modeled here) 

 

4

 When considering the exposure scenario that uses air quality just meeting the current standard. 
 February 2010 Draft – Do Not Quote or Cite 6-15

1  use of mass balance modeling (previously all 12 enclosed MEs used mass balance, here 

2 only indoor MEs use mass balance) 

3  cohort approach (pNEM) versus individual approach (APEX) , and 

4  two indoor sources of CO included in the 2000 pNEM/CO assessment for residential 

5 microenvironments: gas stoves and passive smoking. 

6 Despite these differences and others not listed, staff still did not expect to see greatly 

7 different results when comparing the two assessments given the similarities in the likely 

8 influential variables (i.e., ambient concentrations, microenvironmental approach, CFK module 

9 used, etc.). Table 6-15 presents estimates for the percentage of Denver adults with CHD 

10 estimated to experience a daily maximum end-of-hour COHb level at or above the specified level 

11 under the specified air quality conditions for 1995. Table 6-16 presents similar estimates for Los 

12 Angeles using the adjusted 1997 ambient air quality to just meet the current 8-hour standard. 

13 Each table provides two sets of estimates for the 2000 pNEM/CO assessment (indoor sources 

14 “on” and “off”) and one set generated for the current (2010) second draft APEX/CO REA. 

15 As expected, the COHb levels estimated by the 2000 pNEM/CO assessment are higher 

16 when internal sources are turned on, though in the absence of indoor sources, the range of dose 

17 estimates are generally similar in both study areas. However at selected COHb levels in Denver, 

18 the current approach estimated a higher percent of the CHD population than when compared 

19 with the previous Johnson et al. (2000) assessment. For example, approximately 3.4% of the 

20 CHD population was estimated to have a daily maximum end-of-hour COHb level at or above 

21 2.0% in this current assessment. The corresponding value estimated in the Johnson et al. (2000) 

22 assessment was approximately 0.5% of the IHD population. 
 February 2010 Draft – Do Not Quote or Cite 6-16

1 Table 6-15. Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to 

2 Experience a Daily Maximum End-of-hour COHb Level At or Above the 

3 Specified Percentage – Air Quality Just Meeting the Current Standard. 

Percentage of CHD Adults at or Above COHb Level 

Johnson et al. (2000) pNEM/COa

2010 2nd draft REA 

APEX/COb

COHb 

Level 

(%) Internal sources on Internal sources off Internal sources off 

6.0 0.2 0 0 

5.0 0.6 0 0 

4.0 1.6 0 0 

3.0 5.5 < 0.1 0.2 

2.5 10.4 0.2 0.6 

2.0 19.9 0.5 3.4 

1.5 37.6 6.7 19.1 

1.0 83.2 65.0 76.3 

0 100 100 100 a

 Used Denver 1995 CO ambient concentrations with no adjustment (2nd highest 8-hour 

CO concentration was 9.5 ppm, close in value to the design value of 9.4 ppm). 

b

 Denver 1995 ambient CO concentrations adjusted to just meet the current 8-hour 

standard (9.4 ppm). 

4 

5 

6 Table 6-16. Percentage of Los Angeles Adults with Coronary Heart Disease (CHD) 

7 Estimated to Experience a Daily Maximum End-of-hour COHb Level At or 

8 Above the Specified Percentage – Air Quality Just Meeting the Current 

9 Standard. 

Percentage of CHD Adults at or Above COHb Level 

Johnson et al. (2000) pNEM/COa

2010 2nd draft REA 

APEX/COb

COHb 

Level 

(%) Internal sources on Internal sources off Internal sources off 

6.0 0.2 0 0 

5.0 0.8 0 0 

4.0 2.2 0 0 

3.0 5.1 <0.1 <0.1 

2.5 9.0 <0.1 0.1 

2.0 16.8 0.5 0.5 

1.5 32.3 5.2 4.6 

1.0 79.0 58.1 40.5 

0 100 100 100 a

 Los Angeles 1997 ambient CO concentrations adjusted to just meet the current 8-

hour standard (9.4 ppm). 
 February 2010 Draft – Do Not Quote or Cite 6-17

1 6.4 EVALUATION OF ENDOGENOUS CO CONTRIBUTION TO COHB 

2 LEVELS IN APEX SIMULATED INDIVIDUALS 

3 As summarized in section 4.4.7 and described fully in Appendix B, staff estimated COHb 

4 levels in each simulated individual using the CFK dose module within APEX. Theoretically, in 

5 the absence of ambient concentrations or other sources of CO, one can perform an APEX 

6 simulation to estimate endogenous CO production and its effect on COHb levels. Staff has 

7 performed such a simulation using APEX for both study locations. The results of these 

8 simulations, conducted in a similar manner as the above exposure scenarios (i.e., a 50,000 person 

9 simulation and the random assignment of the CHD population from this simulation) only in the 

10 absence of environmental CO concentrations, are provided in section 6.3.1. A second set of 

11 simulations was performed to better characterize the endogenous CO production rate and its 

12 impact on estimated COHb levels. This second set of simulations considered both the estimated 

13 COHb levels associated with and without ambient exposures for an identical, yet smaller, set of 

14 individuals. The purpose of this focused analysis was to determine the contribution of the 

15 endogenous CO and ambient exposure to total COHb levels. Because the generation of these 

16 data required hourly concentration output and multiple model runs, the sample size was restricted 

17 to less than 100 simulated persons to maintain a manageable data file. In addition, this 

18 subpopulation was a random sample from the entire adult population residing within the Denver 

19 study area. Details of this second set of simulations are provided in section 6.4.2. 

20 6.4.1 Estimation of Endogenous CO Contribution to Population COHb Levels 

21 As mentioned above, these two additional simulations were conducted in each the Denver 

22 and Los Angeles study areas. Fifty thousand persons were simulated, as was done when 

23 considering the five exposure scenarios. The only difference between these APEX simulations 

24 and those for the five air quality scenarios was the ambient concentration input files used. In 

25 these two model runs, all ambient CO concentrations equaled zero. The same output described 

26 above for the five air quality scenarios were generated, only in this instance, the distribution of 

27 population exposures effectively equals zero. We evaluated smaller bins of the maximum end28 of-hour COHb level below 1.0% in these simulations (i.e., 0.25%) given that even when using as 

29 is air quality, most persons had COHb levels at or below 1.0%. We report the daily maximum 

30 end-of-hour COHb levels for the simulated CHD population and, given this, the results can be 

considered as a single view of population-level endogenous CO production.5 31 We caution against 

32 assuming that there is a direct correlation between the endogenous CO production estimated here 

33 and the total COHb levels estimated for each study area in the prior sections. That is, the daily 

34 maximum end-of hour COHb level estimated to result from endogenous CO production 

 

5

 Theoretically the APEX model can estimate all hourly values as was done in section 6.4.2. 
 February 2010 Draft – Do Not Quote or Cite 6-18

1 exclusively may not necessarily be correlated with the daily maximum end-of hour COHb level 

2 for the sum of endogenous CO production and ambient CO exposures. Nevertheless, it still 

3 provides some information regarding the potential effect of the endogenous CO production on 

4 estimated COHb levels in the simulated population. 

5 Table 6-17 summarizes the daily maximum end-of-hour COHb levels estimated for the 

6 CHD population in Denver and Los Angeles, with no ambient CO exposure contribution. In 

7 general, the population distributions for the two areas are very similar, with the majority of the 

8 population (about 99%) having a daily maximum end-of-hour COHb level of less than 1.0%. 

9 The mean and median COHb level would approximately fall between 0.25 and 0.50% in both 

10 populations, with the percent population estimated to experience maximum end-of-hour COHb 

11 levels at or above 1.0% greater in the Denver study area (1.5%) than in the LA area (0.8%). 

12 These estimated distributions do not appear outside of what might be expected given some of the 

13 available data reported in the extant literature. However, as a reminder, these output data are for 

14 the maximum value that occurred in an entire year simulation. The distribution for the average 

15 end-of-hour COHb level associated with endogenous CO production would surely be less than 

16 that indicated here and likely more comparable to any available measurement data. Staff notes 

17 that these distributions represent a population sample and is unique in its own right; even if a 

18 mean estimate could be constructed it may not be comparable to measurements performed on a 

19 smaller (and possibly not random) population of limited study subjects. 

20 Table 6-17. Estimated Number (and Percentage) of Persons with a Daily Maximum End21 of-hour COHb Level At or Above the Specified Level – Adults With Coronary 

22 Heart Disease (CHD) in the Denver and Los Angeles Study Areas – Zero 

23 Ambient Exposures. 

COHb Denver Los Angeles 

Level 

(%) Persons Percent Persons Percent 

0.00 53,656 100 383,040 100 

0.25 47,362 88.3 327,546 85.5 

0.50 13,537 25.2 85,098 22.2 

0.75 3,209 6.0 17,260 4.5 

1.00 790 1.5 3,211 0.8 

1.25 140 0.3 803 0.2 

1.50 12 <0.1 301 <0.1 

1.80 0 0 100 <0.1 

2.00 0 0 0 0 

24 
 February 2010 Draft – Do Not Quote or Cite 6-19

1 6.4.2 Contribution of Endogenous CO Production and Ambient Exposures to 

2 COHb Level in Limited Simulations 

3 Two APEX model simulations were performed for this second evaluation: one using 

4 1995 Denver ambient concentrations adjusted to just meet the current standard (9.4. ppm) and 

5 the second using ambient concentrations equal to zero. Each of the new runs simulated 8,760 

6 hours of exposure for each of 92 persons (n = 92 people x 8,760 hrs/person = 805,920 person7 hours per run). By design, the simulated persons in each of these two model runs line up 

8 perfectly in terms of physiology and activities performed, enabling staff to compare the COHb 

9 levels across the two runs hour by hour. We first calculated all 805,920 hour-by-hour ambient 

10 contributions (COHb ambient contribution) in corresponding COHb levels (i.e, COHb ambient 

11 contribution = % COHb with ambient exposure minus % COHb for zero exposure), effectively 

12 giving the ambient contribution to estimated COHb levels. Table 6-18 provides the descriptive 

13 statistics for the COHb ambient contribution experienced by the selected population. Below are 

14 listed selected statistics for COHb ambient contribution based on the entire hourly data set (N = 

15 805,920). Figure 6-1 provides the same information only in graphical form; note that most of the 

16 ambient contribution to COHb levels values fall between 0.1 and 0.4% COHb and can be well 

17 represented by a lognormal distribution {GM 0.205, GSD 1.57}. The complete distribution of 

18 the endogenous contribution to COHb levels is also provided in Table 6-18. Most of the 

19 simulated hours for the population had an endogenous end-of-hour COHb level contribution of 

20 less than 0.5%, though for a limited number of hours, the endogenous contribution could be over 

21 1.0% COHb. On average, this limited population was estimated to have just over half of their 

hourly total COHb level attributed to endogenous COHb production.6 22 

23 Table 6-18. Descriptive statistics for the % COHb ambient contribution estimated using 

24 Denver 1995 ambient concentrations adjusted to just meet the current 

25 standard. 

Statistic 

(N = 805,920) 

COHb Ambient 

Contribution 

(% COHb) 

COHb 

Endogenous 

(% COHb) 

COHb Total 

(% COHb) 

Arithmetic Mean 0.2264 0.255 0.4814 

Arithmetic SD 0.109 0.1476 0.1757 

Geometric Mean 0.2046 0.2206 0.4529 

Geometric SD* 1.5682 1.7085 1.4151 

Minimum 0.003 0.0329 0.0988 

10th percentile 0.1155 0.1106 0.2912 

20th percentile 0.1416 0.1373 0.3378 

 

6

 One should not go beyond comparing the means or 50th percentiles of the ambient and endogenous 

contribution as the other percentiles of the distribution are likely not correlated. 
 February 2010 Draft – Do Not Quote or Cite 6-20

Statistic 

(N = 805,920) 

COHb Ambient 

Contribution 

(% COHb) 

COHb 

Endogenous 

(% COHb) 

COHb Total 

(% COHb) 

30th percentile 0.1628 0.1631 0.3766 

40th percentile 0.1833 0.1882 0.413 

50th percentile 0.2046 0.2177 0.4504 

60th percentile 0.2284 0.2544 0.4915 

70th percentile 0.2573 0.299 0.5407 

80th percentile 0.2965 0.3526 0.6063 

90th percentile 0.3615 0.4404 0.7098 

95th percentile 0.4268 0.5343 0.8034 

98th percentile 0.5192 0.6405 0.9332 

99th percentile 0.5949 0.778 1.062 

99.5th percentile 0.6759 0.9006 1.188 

99.8th percentile 0.8006 1.07 1.319 

99.9th percentile 0.9086 1.2 1.394 

Maximum 1.9158 1.541 2.323 

Notes: 

* dimensionless 

1 

2 

3 Figure 6-1. Histogram of the % COHb ambient contribution estimated using Denver 

4 1995 ambient CO concentrations adjusted to just meet the current standard. 

5 Two other metrics were calculated for each individual using the same hourly output for 

6 the limited simulation run. First, staff calculated the 1-hour COHb ambient contribution 
 February 2010 Draft – Do Not Quote or Cite 6-21

1 associated with the maximum total 1-hour COHb (i.e., endogenous and ambient contribution) 

2 and second, the 1-hour total COHb associated with the maximum COHb ambient contribution. 

3 The purpose of these metrics was to determine the relative contribution the endogenous CO 

4 production and ambient exposure have on the maximum hourly COHb level. 

5 Figure 6-2 illustrates the contribution of endogenous CO production relative to that of 

6 each person’s maximum end-of-hour COHb level. As can be seen in the figure, there is not a 

7 strong relationship between the two variables, that is, the endogenous CO production does little 

8 to influence a persons maximum end-of-hour COHb level, given this air quality scenario (i.e., 

9 just meeting the current standard in Denver). Consistent with the population distribution 

10 described above (section 6.4.1), there are very few individuals that were estimated to have 

11 maximum end-of-hour COHb levels at or above 1.0% in the absence of ambient CO exposure. 

12 As described earlier, it was expected that there would not be a relationship between the 

13 contribution of endogenous CO production to COHb level and the ambient exposure contribution 

14 to COHb. This expectation is confirmed by the results presented in Figure 6-3. 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

Maximum End-of Hour (Total) COHb Level (% COHb)

Endogenous End-of Hour COHb 

Contribution (% COHb)

15 

16 Figure 6-2. The contribution of endogenous CO production relative to an individual’s 

17 maximum end-of-hour COHb level using 1995 Denver ambient 

18 concentrations adjusted to just meet the current standard. 
 February 2010 Draft – Do Not Quote or Cite 6-22

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

Maximum Ambient End-of-Hour COHb Contribution (% COHb)

Endogenous End-of Hour COHb 

Contribution (% COHb)

1 

2 Figure 6-3. Comparison of endogenous CO production relative to an individual’s 

3 maximum COHb ambient contribution using 1995 Denver ambient 

4 concentrations adjusted to just meet the current standard. 
 February 2010 Draft – Do Not Quote or Cite 6-23

1 Alternatively, one can see the strong relationship between the maximum end-of-hour 

2 COHb level and the contribution from ambient exposure (Figure 6-4). For the majority of the 92 

3 simulated persons, the maximum end-of-hour total COHb level (i.e., the combined contribution 

4 from both endogenous CO production and ambient CO exposure) is largely driven by the 

5 contribution from ambient CO exposure. 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

Maximum Ambient End-of-Hour COHb Contribution (% COHb)

Maximum End-of Hour (Total) COHb Level 

(% COHb)

6 

7 Figure 6-4. Comparison of endogenous CO production relative to an individual’s 

8 maximum COHb ambient contribution using 1995 Denver ambient 

9 concentrations adjusted to just meet the current standard. 

10 6.5 KEY OBSERVATIONS 

11 Presented below are key observations resulting from the exposure and dose assessment 

12 for ambient CO. 

13  Ambient CO exposures and resulting COHb levels in the blood of exposed individuals 

14 were estimated for populations in two study areas in the Los Angeles and Denver areas 

15 under five air quality scenarios: as is air quality, air quality adjusted to simulate just 

16 meeting the current 8-hour CO NAAQS, and air quality adjusted to just meet three 

17 potential alternative standards. 

18  More than 98% of the simulated at-risk population in each study area was estimated to 

19 experience a daily maximum end-of-hour COHb level below 1.5% over the course of a 

20 year considering as is air quality in either study area, with more than 99.9% of the 

21 selected at-risk population in both areas having daily maximums COHb levels below 

22 2%. 

23  The distribution of maximum end-of-hour COHb levels extended slightly higher for the 

24 Los Angeles population when using as is air quality, while the Denver population was 
 February 2010 Draft – Do Not Quote or Cite 6-24

1 estimated to experience higher levels under conditions of air quality adjusted to just 

2 meet the current standard. 

3  More than 95% of the simulated at-risk population in the Los Angeles study area was 

4 estimated to experience an annual daily maximum end-of-hour COHB level below 

5 1.5%, and 99.5% with maximum COHb less than 2%, with air quality adjusted to just 

6 meet the current standard. In contrast, 80.1% of the simulated at-risk population in the 

7 Denver study area was estimated to experience a daily maximum end-of-hour COHB 

8 level below 1.5%, and greater than 95% with maximum COHb less than 2%, in air 

9 quality conditions adjusted to just meet the current standard. 

10  Alternative standards that we considered in this document included two potential 

11 alternative forms for the 8-hour standard and one potential alternative form for the1-

12 hour standard. Beyond the results for alternative forms presented in this document, 

13 results for alternative standard levels for any given combination of averaging time and 

14 form are presented and discussed in the draft Policy Assessment. 

15  The three potential alternative standards considered in this document resulted in 

16 generally similar percentages of individuals exposed at selected concentrations and 

17 maximum end-of hour COHb levels. Each of these potential alternative standards 

18 generated fewer persons and a lower percent of the CHD population at or above 

19 selected COHb levels (e.g., <1% at a 2.0% COHb level in Denver) when compared 

20 with corresponding COHb levels associated with air quality adjusted to just meet the 

21 current 8-hour standard (e.g., 3.4% at a 2.0% COHb level in Denver). When 

22 considering the potential alternative standards in Los Angeles, only 0.1% of the CHD 

23 population was estimated to experience a maximum end-of-hour COHb level at or 

24 above 2.0 % COHb, compared to 0.5% at that same COHb level associated with air 

25 quality adjusted to just meet the current standard. 

26  A few simulations with a small number of individuals in which no external CO sources 

27 were included provided limited information regarding the contribution of endogenous 

28 COHb to the total COHb estimates produced. This quite limited information indicates 

29 that most simulated individuals have endogenous COHb levels below 1% and that the 

30 upper end of total COHb (reflecting ambient plus endogenous contributions) may arise 

31 as a result of an individual receiving high external CO exposures rather than having 

32 higher endogenous levels. 

33  Results generated in the current assessment for the air quality conditions just meeting 

34 the current NAAQS were compared with estimates from the assessment conducted in 

35 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study 

36 areas (section 6.3). While the two assessments employed similar approaches and 

37 similar, although not identical, air quality data for this scenario, they used different 

38 versions of the exposure model (APEX vs. pNEM). Results were quite similar for the 

39 1.5% and 2% COHb level in Los Angeles study area and somewhat different in the 

40 Denver study area. 

41 
 February 2010 Draft – Do Not Quote or Cite 6-25

1 6.6 REFERENCES 

2 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 

3 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 

4 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 

5 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 

6 at: http://www.epa.gov/ttn/fera/human_related.html.. June 2000. 

7 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S, Frazier C. (1998). 

8 Measuring Concentrations of Selected Air Pollutants Inside California Vehicles. Final Report. Prepared 

9 by Research Triangle Institute under Contract No. 95-339. California Air Resources Board. Sacramento, 

10 California. 

11 Shikiya D, Liu C, Kahn M, Juarros J, Barcikowski W. (1989). In-Vehicle Air Toxics Characterization Study in the 

12 South Coast Air Basin. Office of Planning and Rules, South Coast Air Quality Management District. May. 

13 
February, 2010 Draft – Do Not Cite or Quote 7-1

1 7 VARIABILITY ANALYSIS AND UNCERTAINTY 

2 CHARACTERIZATION 

3 An important issue associated with any population exposure or risk assessment is the 

4 characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in 

5 a population or variable of interest (e.g., residential air exchange rates). The degree of variability 

6 cannot be reduced through further research, only better characterized with additional 

7 measurement. Uncertainty refers to the lack of knowledge regarding the values of model input 

8 variables (i.e., parameter uncertainty), the physical systems or relationships used (i.e., use of 

9 input variables to estimate exposure or risk or model uncertainty), and in specifying the scenario 

10 that is consistent with purpose of the assessment (i.e., scenario uncertainty). Uncertainty is, 

11 ideally, reduced to the maximum extent possible through improved measurement of key 

12 parameters and iterative model refinement. The approaches used to assess variability and to 

13 characterize uncertainty in this REA are discussed in the following two sections. Each section 

14 also contains a concise summary of the identified components contributing to uncertainty and 

15 how each source may affect the estimated exposures. 

16 7.1 ANALYSIS OF VARIABILITY 

17 The purpose for addressing variability in this REA is to ensure that the estimates of 

18 exposure and risk reflect the variability of ambient CO concentrations and associated CO 

19 exposure and health risk across the study locations and population. In this second draft CO 

20 REA, there are several algorithms that account for variability of input data when generating the 

21 number of estimated benchmark exceedances or health risk outputs. For example, variability 

22 may arise from differences in the population residing within census tracts (e.g., age distribution) 

23 and the activities that may affect CO population exposure and dose (e.g., time spent inside 

24 vehicles, moderate or greater exertion outdoors). A complete range of potential exposure levels 

25 and associated risk estimates can be generated when appropriately addressing variability in 

26 exposure and risk assessments; note however that the range of values obtained would be within 

27 the constraints of the input parameters, algorithms, or modeling system used, not necessarily the 

28 complete range of the true exposure or risk values. 

29 Where possible, staff identified and incorporated the observed variability in input data 

30 sets and estimated parameters within the exposure and dose assessment performed rather than 

31 employing standard default assumptions and/or using point estimates to describe model inputs. 

32 The details regarding any variability distributions used in data inputs are described in chapter 5. 

33 To the extent possible given the data available for the assessment, staff accounted for variability 

34 within the exposure and dose modeling. APEX has been designed to account for variability in 

35 some of the input data, including the physiological variables that are important inputs to 
February, 2010 Draft – Do Not Cite or Quote 7-2

1 determining ventilation rates and COHb dose levels. As a result, APEX addresses much of the 

2 variability in factors that affect human exposure and dose. The variability accounted for in this 

3 analysis is summarized in Table 7-1. 

4 Table 7-1. Summary of How Variability Was Incorporated Into the Second Draft CO 

5 REA. 

Component Variability Source Comment 

Population data 

Individuals are randomly sampled from US census tracts 

used in model domains, by age (single years) and 

gender (US Census Bureau, 2007). 

Commuting data 

Individuals are probabilistically assigned ambient 

concentrations originating from either their home or work 

tract based on US Census derived commuter data (US 

Census Bureau, 2007). 

Activity patterns 

Data diaries are randomly selected from CHAD master 

(35,000 diaries) using six diary pools stratified by two 

day-types (weekday, weekend) and three temperature 

ranges (< 55.0 ◦

F, between 55.0 and 83.9◦F, and ≥84.0 ◦

F). The CHAD diaries capture real locations persons 

visit and activities performed, ranging from 1 minute to 1-

hour in duration (US EPA, 2002). 

Longitudinal profiles 

A sequence of diaries is linked together for each 

individual that preserves both the inter- and intrapersonal variability in human activities (Glen et al., 

2008). 

Simulated 

Individuals 

Coronary heart 

disease (CHD) 

prevalence 

CHD prevalence is stratified by four age groups (18-44, 

45-64, 65-74, and 75+) and both genders (CDC, 2009) 

Measured ambient CO 

concentrations 

Temporal: 1-hour CO for an entire year predicted using 

ambient monitoring data. 

Spatial: Four monitors were used to represent ambient 

conditions in Denver; ten monitors used in Los Angeles; 

each monitor was assigned a 10 km zone of influence. 

Ambient Input 

Meteorological data Spatial: Local surface NWS stations used. 

Temporal: 1-hour NWS temperature data for each year. 

Microenvironments 

Eight total microenvironments were represented, 

including those expected to be associated with high 

exposure concentrations (i.e., in-vehicle and near-road). 

This results in differential exposure estimates for each 

individual (and event) when spending time within each. 

Proximity factors 

In the current APEX approach, microenvironmental 

concentrations were estimated using proximity factors to 

adjust the outdoor CO concentrations. All proximity 

factors were represented by lognormal distributions 

whose values are randomly selected for every individual 

exposure event. 

Microenvironmental 

Approach 

Mass balance model 

For the indoor microenvironments, using a mass balance 

model accounts for CO concentrations occurring during a 

previous hour (and of ambient origin) to calculate current 

indoor CO concentrations. 
February, 2010 Draft – Do Not Cite or Quote 7-3

Component Variability Source Comment 

Air exchange rates 

Several lognormal distributions are sampled based on 

five daily mean temperature ranges, two regions, and 

location specific A/C prevalence rates. 

Resting metabolic 

rate 

Three age-group (18-29, 30-59, and 60+) by gender 

specific regression equations were used with body mass 

as the independent variable (Johnson et al., 2000). 

Metabolic equivalents 

by activity (METS) 

Values randomly sampled from distributions developed 

for specific activities (some age-specific) (McCurdy, 

2000; US EPA, 2002). 

Oxygen uptake per 

unit of energy 

expended 

Values randomly sampled from a uniform distribution 

(Johnson et al., 2000). 

Weight (body mass) 

Randomly selected from population-weighted lognormal 

distribution with geometric mean (GM) and geometric 

standard deviation (GSD) distribution specific to age and 

gender derived from data from the National Health and 

Nutrition Examination Survey (NHANES), for the years 

1999-2004 (Isaacs and Smith (2005) in Appendix A). 

Height 

Values randomly sampled from distribution based on 

equations developed for each gender developed from 

analyses (Johnson, 1998) of height and weight data 

(Brainard and Burmaster, 1992) (see Appendix B for 

details). 

Blood volume 

Values determined according to gender using equations 

based on work by Allen et al. (1956) (see Appendix B for 

details). 

Hemoglobin content of 

the blood 

Values randomly selected from distributions developed 

by gender and age categories based on NHANES study 

(see Isaacs and Smith (2005) in Appendix A). 

Pulmonary CO 

diffusion rate 

Values selected according to gender, height, and age 

based on equations adapted from Salorinne (1976) (see 

Appendix B for details). 

Physiological 

Factors Relevant to 

Ventilation Rate and 

Estimation of COHb 

Levels 

Endogenous CO 

production rate 

Values randomly selected from lognormal distributions 

according to equations specific to age, gender, and 

menstrual phase (data obtained from eight independent 

studies; see Appendix B for details). 

1 7.2 CHARACTERIZATION OF UNCERTAINTY 

2 While it may be possible to capture a range of exposure or risk values by accounting for 

3 variability inherent to influential factors, the true exposure or risk for any given individual is 

4 largely unknown. To characterize health risks, exposure and risk assessors commonly use an 

5 iterative process of gathering data, developing models, and estimating exposures and risks, given 

6 the goals of the assessment, scale of the assessment performed, and limitations of the input data 

7 available. However, significant uncertainty often remains and emphasis is then placed on 

8 characterizing the nature of that uncertainty and its impact on exposure and risk estimates. 

9 Staff has used such an iterative process in characterizing the uncertainty associated with 

the approach and data used in the 1st draft REA. Following a review of that 1st 10 draft REA by 

11 CASAC, a few sources of uncertainty were identified as most important in improving the 
February, 2010 Draft – Do Not Cite or Quote 7-4

1 approach used to estimate exposure and dose for the second draft CO REA. This included 1) 

2 expanding the number of monitors used to better address spatial variability in ambient CO 

3 concentrations, 2) increasing the number of microenvironments modeled from two to eight, 3) 

4 using distributions of proximity factors to estimate all microenvironmental concentrations rather 

5 than simple point estimates, and 4) additional analysis of historical trends in ambient CO 

6 concentrations at individual monitors. These additional analyses and approaches used are not 

7 without their own uncertainties, and following this iterative process, also need to be 

8 characterized. 

9 The characterization of uncertainty can include either qualitative or quantitative 

10 evaluations, or a combination of both. The approach can also be tiered, that is, the analysis can 

11 begin with a simple qualitative uncertainty characterization then progress to a complex 

12 probabilistic uncertainty analysis. This second level of analysis may be appropriate when a 

13 lower tier analysis indicates there is a high degree of uncertainty for certain identified sources, 

14 the sources of uncertainty are highly influential variables in estimating the exposure and risk, and 

15 sufficient information and other resources are available to conduct a quantitative uncertainty 

16 assessment. This is not to suggest that quantitative uncertainty analyses should always be 

17 performed in all exposure and risk assessments. The decision regarding the type of uncertainty 

18 characterization performed is also informed by the intended scope and purpose of the 

19 assessment, whether the selected analysis will provide additional information to the overall 

20 decision regarding health protection, whether sufficient data are available to conduct a complex 

21 quantitative analysis, and whether time and resources are available for higher tier 

22 characterizations (US EPA, 2004; WHO, 2008). 

23 The primary purpose of the uncertainty characterization approach selected in this second 

24 draft CO REA is to identify and compare the relative impact that important sources of 

25 uncertainty may have on the estimated potential health effect endpoints. The approach used to 

26 evaluate uncertainty was adapted from guidelines outlining how to conduct a qualitative 

27 uncertainty characterization (WHO, 2008) and applied in the most recent NO2 (US EPA, 2008) 

28 and SO2 NAAQS reviews (US EPA, 2009). While it may be considered ideal to follow a tiered 

29 approach in the REA to quantitatively characterize all identified uncertainties, staff selected the 

30 mainly qualitative approach given the extremely limited data available to inform probabilistic 

31 analyses. 

32 The qualitative approach used in this REA varies from that of WHO (2008) in that a 

greater focus was placed on evaluating the direction and the magnitude1 33 of the uncertainty; that 

34 is, qualitatively rating how the source of uncertainty, in the presence of alternative information, 

 

1

 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2. 
February, 2010 Draft – Do Not Cite or Quote 7-5

1 may affect the estimated exposures and health risk results. In addition and consistent with the 

2 WHO (2008) guidance, staff discuss the uncertainty in the knowledge-base (e.g., the accuracy of 

3 the data used, acknowledgement of data gaps) and decisions made where possible (e.g., selection 

4 of particular model forms), though qualitative ratings were assigned only to uncertainty 

5 regarding the knowledge-base. 

6 First, staff identified the key aspects of the assessment approach that may contribute to 

7 uncertainty in the exposure and risk estimates and provide the rationale for their inclusion. Then, 

8 staff characterized the magnitude and direction of the influence on the assessment results for 

9 each of these identified sources of uncertainty. Consistent with the WHO (2008) guidance, staff 

10 subjectively scaled the overall impact of the uncertainty by considering the degree of severity of 

11 the uncertainty as implied by the relationship between the source of the uncertainty and the 

12 exposure concentrations and COHb dose levels. 

13 Where the magnitude of uncertainty was rated low, it was judged that large changes 

14 within the source of uncertainty would have only a small effect on the exposure results. For 

15 example, a statistical procedure was used to substitute missing ambient concentrations in each 

16 ambient data set. Staff compared the air quality distributions and found negligible differences 

17 between the substituted data set and the one with missing values (e.g., Tables 5-7 through 5-10). 

18 There is still uncertainty in the approach used, since there are a variety of methods available to 

19 use. However, staff judged that the quantitative comparison of the data sets indicates that there 

20 would likely be little influence on exposure estimates by the data substitution procedure. 

21 A magnitude designation of medium implies that a change within the source of 

22 uncertainty would likely have a moderate (or proportional) effect on the results. For example, 

23 the magnitude of uncertainty associated with using the historical data to represent a hypothetical 

24 future scenario was rated as low-medium. While we do not have information regarding how the 

25 ambient CO concentration distribution might look in the future, we do know however what the 

26 distribution might look like based on historical trends and the primary emission sources. If these 

27 trends in observed concentrations and emissions remain consistent in the future, then the 

28 magnitude of the impact to estimated exposures in this assessment would be judged as likely low

29 or having negligible impact on the exposure and dose estimates. However, if there are new 

30 emission sources, the magnitude of influence might be greater. When adjusting air quality in 

31 each location to simulate the various exposure scenarios, staff observed mainly proportional 

32 differences (e.g., a factor of two or three) in the estimated exposure and dose levels. Assuming 

33 that these types of ambient concentration adjustments could reflect the addition of a new source 

34 in each area carries its own uncertainties, however based on this information, staff also judged 

35 the magnitude of influence in using the historical air quality data to represent a hypothetical 

36 future scenario as high as medium. A characterization of high implies that a small change in the 
February, 2010 Draft – Do Not Cite or Quote 7-6

1 source would have a large effect on results. This rating would be used where model was 

2 extremely sensitive to the identified source of uncertainty. 

3 Staff also included the direction of influence, indicating how the source of uncertainty 

4 was judged to affect estimated exposures or risk estimates; either the estimated values were 

5 likely over- or under-estimated. In the instance where the component of uncertainty can affect 

6 the assessment endpoint in either direction, the influence was judged as both. Staff characterized 

7 the direction of influence as unknown when there was no evidence available to judge the 

8 directional nature of uncertainty associated with the particular source. Staff also subjectively 

9 scaled the knowledge-base uncertainty associated with each identified source using a three level 

10 scale: low indicated significant confidence in the data used and its applicability to the assessment 

11 endpoints, medium implied that there were some limitations regarding consistency and 

12 completeness of the data used or scientific evidence presented, and high indicated the extent of 

13 the knowledge-base was extremely limited. 

14 The output of the uncertainty characterization was a summary describing, for each 

15 identified source of uncertainty, the magnitude of the impact and the direction of influence the 

16 uncertainty may have on the exposure and risk characterization results. There are several 

17 sources of uncertainty associated with this simplified approach for modeling CO population 

18 exposure/dose and associated potential health risk, each summarized and discussed in Table 7-2. 

19 As mentioned in section 1 above, given the significant time constraints of this review, 

20 results of the assessment are provided in this document without substantial interpretation. 

21 Rather, interpretative discussion of these results, including further consideration of public health 

22 implications, is provided in the draft Policy Assessment.
February, 2010 Draft – Do Not Cite or Quote 7-7

1 Table 7-2. Characterization of Key Uncertainties in the Second Draft CO REA for Denver and Los Angeles Are

Sources of Uncertainty 

Influence of 

Uncertainty on 

Exposure/Dose 

Estimates 

Category Element Direction Magnitude 

KnowledgeBase 

Uncertainty Commentsa 

Database Quality Over Low Low 

INF: There may be a limited number of poor quality high concentration data 

influencing the number of benchmark dose level exceedances. Note also thconcentration data near the monitor detection limit is unlikely to influence th

KB: EPA’s Air Quality System data used in the analyses are of high quality. 

data as comprehensive. Data are being used in a manner consistent with omonitoring. 

Spatial and Temporal Representation 

Both Low - Medium Medium 

INF: Use of several ambient monitors better represents spatial-temporal var

each study area when compared with the simplified approach used in the 1s

concentrations indicates there is spatial variability in monitoring concentratiolimited, particularly for more recent ambient concentrations (low magnitude)simplified approach used in the 1st draft CO REA however, selection of the p

medium magnitude of influence on estimated exposures. 

KB: Each ambient monitor has specific objectives and monitoring scale tha

spatial and temporal variability in CO concentrations. In the absence of 1) a spatial variability in CO concentrations, 2) performing air quality modeling to variability in CO concentrations and, 3) analysis of additional monitoring datconcentration gradients, staff judge the uncertainty in the knowledge-base a

Ambient CO Monitor Concentrations 

Missing Data Substitution Under Low Low 

INF: Assuming there is an equal probability of missing low and high concentdata are limited by the bounds of the algorithm (i.e., as defined by limits in thmissing high concentration data that could lead to underestimation in exposassumes that the substitution of low-level concentration data with potentially of the algorithm) does not affect exposure results. 

KB: All available measurement data are quality assured. Very few data valu

number of measured values available in each location. 

Adjustment of Air Quality to Simulate Just Meeting the Current and Potential Alternative Standards 

Historical Data Used Unknown Low - Medium Medium 

INF & KB: Even though the historical data represent a real air quality conditi

levels expected to just meet the 8-hour current and potential alternative stanhypothetical. Based on observed trends in air quality over time and the resuconcentrations, staff judges that, at most, the magnitude of influence would uncertainty in how influential factors such as emission levels per vehicle, vebetween an earlier period of time and the hypothetical scenario of just meetfuture. It is possible that these historical patterns can serve as a reasonable scenarios, though these patterns would not account for the influence of a nejudges the knowledge base uncertainty as medium. 
February, 2010 Draft – Do Not Cite or Quote 7-8

Sources of Uncertainty 

Influence of 

Uncertainty on 

Exposure/Dose 

Estimates 

Category Element Direction Magnitude 

KnowledgeBase 

Uncertainty Commentsa 

Proportional Approach Used Both Low Low 

INF: The magnitude of the adjustment applied to historical ambient concent

in Denver, to just meet the current standard 0.99 was the adjustment applie8-hour average CO concentration in Los Angeles, a greater adjustment was recent and historical ambient CO concentrations for several ambient monitoproportional relationship is present when comparing the recent and historic 

KB: A similar proportional approach was judged adequate in simulating air q

CO NAAQS in prior assessments (US EPA, 1992; Johnson et al., 2000). In 

here when comparing exposure results using the1997 LA data adjusted dowwith the as is air quality with exposure results using the 2006 air quality. 

Population Database Both Low Low 

INF & KB: Population data are from the US Census Bureau, a reliable and q

specifically for census tracts modeled in Denver and Los Angeles. Staff assdatabase would have negligible influence on exposure and dose results. 

APEX Inputs and Algorithms 

Activity Pattern Database Unknown Low – Medium Medium 

INF: Data are actual records of the time spent in specific locations while per

locations. While not specific to a particular area, the activity patterns of a pothe mainly population-based and nationally-representative survey data (e.g.patterns of typical commuting in CHAD versus the urban locations modeled data from individuals that may or may not have had an identified health condactivities of persons with normal health status as well as those with certain hactivity patterns. A statistical analysis was performed on a subset of the CHasked whether they had angina (see Appendix F of CO REA and Johnson ewith angina were compared to those individuals not having angina using varoutdoors or inside-vehicles. The percentages of time spent outdoors or in a significantly different between angina and non-angina subjects While there the exertion rate between angina and non-angina subjects, it was likely a fuangina subjects since actual differences were generally numerically small codifferences in activity and exertion between angina and non-angina subjectsjudged not large enough to severely impact the validity of APEX (or pNEM/Can angina/non-angina difference. 

KB: Data are from a reliable and quality assured source (CHAD) and are fro

individual’s activity pattern are well represented, adjustments are made to respecific area (using age and gender), and temperature is used to link CHAD residing in a specific area. However, there are several assumptions made texample, activity patterns of persons surveyed over 30 years ago are assumpatterns. 
February, 2010 Draft – Do Not Cite or Quote 7-9

Sources of Uncertainty 

Influence of 

Uncertainty on 

Exposure/Dose 

Estimates 

Category Element Direction Magnitude 

KnowledgeBase 

Uncertainty Commentsa 

Longitudinal Profile Algorithm Both Low – Medium Medium 

INF: This assessment focused on persons having at least one exposure or d

magnitude), however when considering multi-day exposures, the magnitude 

KB: In developing the longitudinal method, the evaluation indicated that both 

reproduced for the population. In addition, the approach was compared to tconstructing longitudinal activity patterns (see Appendix B, Attachment 5 of 

diary profiles (i.e., monthly, annual) do not exist for a population. 

Meteorological Data Both Low Low 

INF & KB: Data are from the National Weather Service, a well-known and qutemperatures are used when selecting appropriate diaries to simulate individthat erroneous temperature data would likely have limited impact to exposurused when selecting air exchange rates. Given the overlap of the AER distrused to categorize them, there is likely limited impact by erroneous tempera

Microenvironmental Algorithm and Input Data 

Unknown Medium Medium 

INF & KB: In this second draft CO REA, the number of microenvironments spersons spend time and where CO exposures would occur. Using distributimeasurement data in Denver and applied to estimate microenvironmental cthese data derived from a Denver study reflect similar relationships in Los AAdditionally, the Denver measurement data were collected in the 1980’s, thethese data might reflect relationships observed for other years modeled in thmost of the distributions, in particular those used to estimate high exposure comparable measurement data and relationships available (albeit limited in distributions applied in this assessment. 

Commuting 

Algorithm Both Low Low 

INF & KB: In this second draft REA, the commuting algorithm within APEX better represents individual exposures across each modeling domain. The well-known and quality-assured source. 

CHD Prevalence Both Low Medium 

INF & KB: Data are from the Centers for Disease Control, a well-known and prevalence data are not specific for each region, the national prevalence daand gender. Staff used gender-specific ratios and applied them to all age guncertainty in the accuracy of the prevalence estimates for specific age and undiagnosed individuals with CHD were included to expand the total CHD p

several assumptions including using 1990 estimates of the population with u
February, 2010 Draft – Do Not Cite or Quote 7-10

Sources of Uncertainty 

Influence of 

Uncertainty on 

Exposure/Dose 

Estimates 

Category Element Direction Magnitude 

KnowledgeBase 

Uncertainty Commentsa 

Physiological Factors Unknown Low – Medium Medium 

INF & KB: Many of the parameters used to estimate the physiological attribu

from healthy individuals; there were no adjustments made to account for a pnotes some variability in some parameters in individuals with specific health elimination, most conditions may not necessarily be associated with the simindividuals. In addition, some of the parameters used to estimate COHb levdating back to the mid 20th century). It is possible that most of the relationsh

current population however, in the absence of conducting a comprehensive recent measurements, staff judges the knowledge-base uncertainty as med

Potential Health Effect Benchmark Levels 

Simulated At-Risk Population Unknown Low Medium 

INF & KB: Data from a well-conducted multi-center controlled human expos

effects in subjects with moderate to severe coronary artery disease at study which were increased from a baseline mean of 0.6-0.7%.as a result of short increases in CO exposures (study mean of 117 ppm CO). No laboratory stuevaluate the effect of experimentally increased exposure to CO resulting in mean below 2.0%. However, based on analysis of individual study subject increased COHb levels, study authors concluded that each increase in COHresponse metric, without evidence of a measurable threshold effect. There iand, thus there is greater uncertainty about the lowest benchmark level idenwhether individuals with the most severe CHD are adequately represented. 

this assessment result from CO exposure concentrations much lower than tused to increase study subject COHb levels to the study targets (e.g., 2.0%) associated. Given that the evidence supporting the choice of benchmark levexposure data, staff judged the influence of this uncertainty on the risk chara

Notes: aINF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating. bThis entry focuses on the uncertainty associated with the benchmark levels in their application to estimated COHb levels for the simulated at-risk population (i.e., individuals w

of angina pectoris and heart attacks). With regard to other potentially susceptible populations (as described in section 2.4 above), we additionally note the lack of studies that d

might be expected as a result of short-term elevations in CO exposure in those populations. 
February 2010 Draft – Do Not Quote or Cite 7-11

1 7.3 KEY OBSERVATIONS 

Based on an overall qualitative judgment of the identified sources of uncertainty in the 

assessment approach, selections made regarding input data, and algorithms used, and their 

characterization as to direction and magnitude of influence on exposures and doses, staff 

consider the exposure and dose estimates reasonable for the simulated population the assessment 

is intended to represent (i.e., the CHD population residing within the urban core of each study 

area). This is because: 

2  Only two sources of uncertainty were associated with a potential directional influence: 

3 data base quality (overestimation) and missing data substitution (underestimation), and 

4 both were judged to have a low magnitude of influence on estimated exposures and 

5 doses. 

6  Twelve of the identified sources of uncertainty were judged by staff to have either 

7 bidirectional influence (eight sources) or unknown (four sources) direction: 

8  One source of uncertainty (i.e., microenvironmental algorithm and data inputs) 

9 was judged as having a potentially medium magnitude of influence on exposure 

10 and dose estimates. 

11  Five of the remaining eleven sources (i.e., spatial and temporal representation, 

12 historical data used, activity pattern database, longitudinal profile algorithm, 

13 physiological factors) were judged as having low to medium magnitude of 

14 influence, the level of which varied based on whether an identified condition 

15 existed. 

16  Six of the sources were judged to have a low magnitude of influence on estimated 

17 exposures and doses (i.e., proportional approach used, population database, 

18 meteorological data, commuting data, CHD prevalence, and benchmark levels for 

19 the simulated at-risk population). 

There was a wide-ranging level of uncertainty in the knowledge-base for the identified 

sources: 

20  Eight sources were judged by staff as having medium knowledge-base uncertainty 

21 including: spatial and temporal representation, historical data used, activity pattern 

22 database, longitudinal profile algorithm, microenvironmental algorithm and input data, 

23 CHD prevalence, physiological factors, and the benchmark levels for the simulated at24 risk population. 

25  The knowledge-base uncertainty was judged as low for four of the identified sources 

26 having either unknown or bidirectional influence. This included the proportional 

27 approach used, population database, meteorological data, commuting data. 

28  The knowledge-base uncertainty was also judged as low for the two sources identified 

29 above as being associated with either under- or overestimating exposures. 
February 2010 Draft – Do Not Quote or Cite 7-12

1 The ratings of the knowledge-base uncertainty can indicate the need for additional data or 

2 analyses to better characterize the uncertainty. When combined with the potential magnitude of 

3 influence associated with each identified source, a prioritization can be given to the higher rated 

4 influential sources. Based on the results of this uncertainty characterization, staff judges that six 

5 sources (i.e., the spatial and temporal representation, historical data used, activity pattern 

6 database, longitudinal profile algorithm, microenvironmental algorithm and input data, and 

7 physiological factors) remain as the most important uncertainties in this assessment. 
February 2010 Draft – Do Not Quote or Cite 7-13

1 7.4 REFERENCES 

2 

3 Allen TH, Peng MT, Chen KP, Huang TF, Chang C, Fang HS. (1956). Prediction of blood volume and adiposity in 

4 man from body weight and cube of height. Metabolism. 5:328-345. 

5 Brainard J and Burmaster D. (1992). Bivariate distributions for height and weight of men and women in the United 

6 States. Risk Analysis. 12(2):267-275. 

7 CDC. (2009). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007. Series 10, 

8 Number 240. U.S. Department of Health and Human Services, Hyattsville, MD, May 2009. 

9 Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal diary assembly for 

10 human exposure modeling. J Expos Sci Environ Epidem. 18:299-311. 

11 Issacs K and Smith L. (2005). New Values for Physiological Parameters for the Exposure Model Input File 

12 Physiology.txt. Technical memorandum to Tom McCurdy, NERL WA10. December 20, 2005. Provided 

13 in Appendix A of the second draft CO REA. 

14 Johnson T. (1998). Memo No. 5: Equations for Converting Weight to Height Proposed for the 1998 Version of 

15 pNEM/CO. Memorandum Submitted to U.S. Environmental Protection Agency. TRJ Environmental, Inc., 

16 713 Shadylawn Road, Chapel Hill, North Carolina 27514. 

17 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and 

18 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO 

19 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 

20 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 

21 at: http://www.epa.gov/ttn/fera/human_related.html.. June 2000. 

22 McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy expenditure approach. 

23 J Expos Anal Environ Epidemiol. 10:1-12. 

24 SAIC. 2001. Technical Peer Review of “Estimation of Carbon Monoxide Exposures and Associated 

25 Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO (version 2.1)” 

26 Prepared by Science Applications International Corporation under EPA Contract No. 68-D-98-113. 

27 Available at: http://www.epa.gov/ttn/fera/human_related.html. 

28 Salorinne Y. (1976). Single-breath pulmonary diffusing capacity. Scand J Resp Diseases. Supplementum 96. 

29 US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at: 

30 http://www.census.gov/population/www/cen2000/phc-t28.html. 

31 US EPA (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of 

32 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no. 

33 EPA/452/R-92-004. 

34 US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/. 

35 US EPA. (2004). An Examination of EPA Risk Assessment Principles and Practices. Staff Paper prepared by the 

36 US EPA Risk Assessment Task Force. EPA/100/B-04/001. Available at: 

37 http://www.epa.gov/OSA/pdfs/ratf-final.pdf. 
February 2010 Draft – Do Not Quote or Cite 7-14

1 US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient Air 

2 Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 

3 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 

4 US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air 

5 Quality Standard. EPA-452/R-09-007. August 2009. Available 

6 athttp://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 

7 WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on characterizing and 

8 communicating uncertainty in exposure assessment. Available at: 

9 http://www.who.int/ipcs/methods/harmonization/areas/exposure/en/. 
February 2010 Draft – Do Not Quote or Cite 8-1

1 8 SUMMARY OF KEY OBSERVATIONS 

2 Presented together below are the key observations made in each of the chapters. 

3 

4 Introduction

5  This draft document describes the quantitative human exposure assessment and risk 

6 characterization being conducted to inform the U.S. Environmental Protection 

7 Agency’s (EPA’s) current review of the National Ambient Air Quality Standards 

8 (NAAQS) for carbon monoxide (CO). The previous review of the CO NAAQS was 

9 concluded in 1994 with confirmation of the current standards. An assessment of 

10 ambient CO exposure/dose was developed in an earlier phase of this review in the late 

1990s. The design of this 2nd 11 draft REA builds upon recommendations from CASAC, 

12 information presented in the final ISA, as well as comments made by the public. 

13 

14 Conceptual Overview: Assessing Ambient Carbon Monoxide Exposure and Risk 

15  Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

16 carbon-containing fuels and photochemical reactions in the atmosphere, with on-road 

17 mobile sources representing significant sources of CO to ambient air. 

18  Microenvironments influenced by on-road mobile sources are important contributors to 

19 ambient CO exposures, particularly in urban areas. 

20  The formation of COHb is a key step in the elicitation of various health effects by CO. 

21 Further, COHb level is commonly used in exposure assessment and is considered the 

22 best biomarker for CO health effects of concern. 

23  Individuals with CHD are the population with greatest susceptibility to short-term 

24 exposure to CO, and the population for which the current evidence indicates health 

25 effects occurring at the lowest exposures. The evidence further indicates a potential for 

26 other underlying cardiovascular conditions to contribute susceptibility to CO effects. 

27 Other populations potentially at risk include individuals with diseases such as chronic 

28 obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in 

29 prenatal or elderly life stages. 

30  Cardiovascular effects are the health endpoint for which the evidence is strongest and 

31 indicative of a likely causal relationship with CO exposures. Other endpoints for 

32 which the evidence is suggestive of such a relationship include effects on the central 

33 nervous system, reproduction and prenatal development, and the respiratory system. 

34  Risk is characterized in this REA through evaluation of COHb estimated to result from 

35 ambient CO exposure in individuals with CHD (including undiagnosed persons) 

36 considering potential health effect benchmarks for daily maximum COHb levels. 

37 Results are reported in terms of percent of population expected to experience daily 

38 maximum COHb levels at or above a series of levels that range as low as 1%. These 

39 results are considered in the Policy Assessment document in light of potential health 
February 2010 Draft – Do Not Quote or Cite 8-2

1 effects benchmarks ranging from 1.5%, which is below the lowest study mean COHb 

2 level resulting from experimental CO exposure in controlled human exposures of 

3 subjects with CAD, up to 3.0%, a level associated with adverse effects in those studies. 

4 

5 Air Quality Considerations 

6  Mobile sources (i.e., gasoline powered vehicles) are the primary contributor to CO 

7 emissions, particularly in urban areas due to greater vehicle and roadway densities. 

8  Recent (2005-2007) ambient CO concentrations across the US are lower than those 

9 reported in the previous CO NAAQS review and are also well below the current CO 

10 NAAQS levels. Further, a large proportion of the reported concentrations are below 

11 the conventional instrument lower detectable limit of 1 ppm. 

12  The currently available information for CO monitors indicates that siting of microscale 

13 and middle scale monitors in the current network is primarily limited to roads where 

14 traffic density described for them is moderate (<100,000 AADT), however, factors 

15 other than reported AADT (e.g., orientation with regard to dense urban roadway 

16 networks) can contribute to sites reporting higher CO concentrations. 

17  Ambient CO concentrations are highest at monitors sited closest to roadways (i.e., 

18 microscale and middle scale monitors) and exhibit a diurnal variation linked to the 

19 typical commute times of day, with peaks generally observed during early morning and 

20 late afternoon during weekdays. 

21  Policy relevant background (PRB) concentrations across the US are generally less than 

22 0.2 ppm, far below that of interest in this REA with regard to ambient CO exposures. 

23  Historical trends in ambient monitoring data indicate that at individual sites, ambient 

24 concentrations have generally decreased in a proportional manner. This comparison 

25 included air quality distributions with concentrations at or above the current standard 

26 and those reflecting current (as is) conditions. 

 The temporal variability in selected upper percentile ambient concentrations (e.g., 99th 27 

28 percentile 1-hour daily maximum) at individual monitors is relatively small across a 

29 three year monitoring period, particularly when considering recent air quality. 

30 

31 Overview Of Approach Used for Estimating Co Exposure and COHb Dose Levels 

32  APEX, an EPA human exposure and dose model, has a long history of use in 

33 estimating exposure and dose for many of the criteria pollutants including CO, O3, 

34 SO2, and NO2. Over time, staff have improved and developed new model algorithms, 

35 incorporated newer available input data and parameter distributions, as well as 

36 performed several model evaluations, sensitivity analyses, and uncertainty 

37 characterizations for the above pollutants. Based on this analysis, APEX was judged to 

38 be an appropriate model to use for assessing CO exposure and dose. 

39 
February 2010 Draft – Do Not Quote or Cite 8-3

1 Application of APEX4.3 in this Assessment

2  Two exposure model domains (Denver and Los Angeles study areas) were defined by 

3 overlaying ambient monitor locations having 10 km radii with US census tract 

4 population data. Monitors selected comprised the bulk of the urban core in each 

5 location, where ambient monitoring data exist. 

6  The selected at-risk population was simulated by combining the tract-specific age and 

7 gender population distribution and the CHD prevalence, also stratified by age and 

8 gender. In using this approach, staff can represent the variability that exists in the 

9 CHD population that resides in each census tract and within each study area. 

10  Staff expanded the selected at-risk population to include an estimate of persons with 

11 undiagnosed CHD. 

12  Compared with the single-monitor approach used for the first draft CO REA, staff 

13 expanded the number of ambient monitors used in this second draft CO REA to better 

14 capture the spatial variability in ambient concentrations. In Denver, a total of four 

15 monitors were used, in Los Angeles, the total number of monitors was ten. 

16  Compared with the two microenvironments modeled in the first draft CO REA, staff 

17 has expanded the number modeled in each location to eight. This approach is designed 

18 to better represent the expected variability in microenvironmental CO concentrations. 

19  Compared with the approach used to estimate microenvironmental concentrations in 

20 the first draft CO REA (factors approach only), all indoor microenvironments were 

21 modeled using a mass balance model in this second draft assessment. Use of the mass 

22 balance model will better represent temporal variability in indoor CO concentrations 

23 with respect to the outdoor CO concentration variability. In addition, distributions of 

24 microenvironmental factors were used in this second draft CO REA for all 

25 microenvironments rather than using point estimates (as was done for the first draft CO 

26 REA). Using distributions of microenvironmental factors will better represent both 

27 spatial and temporal variability in estimated microenvironmental CO concentrations 

28 

29 Simulated Exposure and COHb Dose Results

30  Ambient CO exposures and resulting COHb levels in the blood of exposed individuals 

31 were estimated for populations in two study areas in the Los Angeles and Denver areas 

32 under five air quality scenarios: as is air quality, air quality adjusted to simulate just 

33 meeting the current 8-hour CO NAAQS, and air quality adjusted to just meet three 

34 potential alternative standards. 

35  More than 98% of the simulated at-risk population in each study area was estimated to 

36 experience a daily maximum end-of-hour COHb level below 1.5% over the course of a 

37 year considering as is air quality in either study area, with more than 99.9% of the 

38 selected at-risk population in both areas having daily maximums COHb levels below 

39 2%. 

40  The distribution of maximum end-of-hour COHb levels extended slightly higher for the 

41 Los Angeles population when using as is air quality, while the Denver population was 
February 2010 Draft – Do Not Quote or Cite 8-4

1 estimated to experience higher levels under conditions of air quality adjusted to just 

2 meet the current standard. 

3  More than 95% of the simulated at-risk population in the Los Angeles study area was 

4 estimated to experience an annual daily maximum end-of-hour COHB level below 

5 1.5%, and 99.5% with maximum COHb less than 2%, with air quality adjusted to just 

6 meet the current standard. In contrast, 80.1% of the simulated at-risk population in the 

7 Denver study area was estimated to experience a daily maximum end-of-hour COHB 

8 level below 1.5%, and greater than 95% with maximum COHb less than 2%, in air 

9 quality conditions adjusted to just meet the current standard. 

10  Alternative standards that we considered in this document included two potential 

11 alternatives for the 8-hour standard and one potential alternative 1-hour standard. The 

12 results looking at alternative levels for any given combination of averaging time and 

13 form are presented and discussed in the draft Policy Assessment. 

14  For the three potential alternative standard scenarios for which results are presented in 

15 this document provided generally similar percent of individuals exposed at selected 

16 concentration levels and maximum end-of hour COHb levels. Each of these potential 

17 alternative standards generated fewer persons and a lower percent of the CHD 

18 population at or above selected COHb levels (e.g., <1% at a 2.0% COHb level in 

19 Denver) when compared with corresponding COHb levels and using air quality 

20 adjusted to just meeting the current standard (e.g., 3.4% at a 2.0% COHb level in 

21 Denver). When considering the potential alternative standard scenarios in Los 

22 Angeles, only 0.1% of the CHD population was estimated to experience a maximum 

23 end-of-hour COHb level of 2.0 % COHb compared to 0.5% at that same COHb level 

24 and using air quality adjusted to just meet the current standard. 

25  A few simulations with a small number of individuals in which no external CO sources 

26 were included provided limited information regarding the contribution of endogenous 

27 COHb to the total COHb estimates produced. This quite limited information indicates 

28 that most simulated individuals have endogenous COHb levels below 1% and that the 

29 upper end of total COHb (reflecting ambient plus endogenous contributions) may arise 

30 as a result of an individual receiving high external CO exposures rather than having 

31 higher endogenous levels. 

32  Results generated in the current assessment for the air quality conditions just meeting 

33 the current NAAQS were compared with estimates from the assessment conducted in 

34 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study 

35 areas (section 6.3). While the two assessments employed similar approaches and 

36 similar, although not identical, air quality data for this scenario, they used different 

37 versions of the exposure model (APEX vs. pNEM). Results were quite similar for the 

38 1.5% and 2% COHb level in Los Angeles study area and somewhat different in the 

39 Denver study area. 

40 
February 2010 Draft – Do Not Quote or Cite 8-5

1 Variability Analysis and Uncertainty Characterization

2  Based on an overall qualitative judgment of the identified sources of uncertainty in the 

3 assessment approach, selections made regarding input data, and algorithms used, and 

4 their characterization as to direction and magnitude of influence on exposures and 

5 doses, staff consider the exposure and dose estimates reasonable for the simulated 

6 population the assessment is intended to represent (i.e., the CHD population residing 

7 within the urban core of each study area). This is because: 

8  Only two sources of uncertainty were associated with a potential directional 

9 influence: data base quality (overestimation) and missing data substitution 

10 (underestimation), and both were judged to have a low magnitude of influence 

11 on estimated exposures and doses. 

12  Twelve of the identified sources of uncertainty were judged by staff to have 

13 either bidirectional influence (eight sources) or unknown (four sources) 

14 direction: 

15 - One source of uncertainty (i.e., microenvironmental algorithm and 

16 data inputs) was judged as having a potentially medium magnitude 

17 of influence on exposure and dose estimates. 

18 - Five of the remaining eleven sources (i.e., spatial and temporal 

19 representation, historical data used, activity pattern database, 

20 longitudinal profile algorithm, physiological factors) were judged 

21 as having low to medium magnitude of influence, the level of 

22 which varied based on whether an identified condition existed. 

23 - Six of the sources were judged to have a low magnitude of 

24 influence on estimated exposures and doses (i.e., proportional 

25 approach used, population database, meteorological data, 

26 commuting data, CHD prevalence, and benchmark levels for the 

27 simulated at-risk population). 

28  There was a wide-ranging level of uncertainty in the knowledge-base for the identified 

29 sources: 

30  Eight sources were judged by staff as having medium knowledge-base 

31 uncertainty including: spatial and temporal representation, historical data used, 

32 activity pattern database, longitudinal profile algorithm, microenvironmental 

33 algorithm and input data, CHD prevalence, physiological factors, and the 

34 benchmark levels for the simulated at-risk population. 

35  The knowledge-base uncertainty was judged as low for four of the identified 

36 sources having either unknown or bidirectional influence. This included the 

37 proportional approach used, population database, meteorological data, 

38 commuting data. 

39  The knowledge-base uncertainty was also judged as low for the two sources 

40 identified above as being associated with either under- or overestimating 

41 exposures. 
February 2010 Draft – Do Not Quote or Cite 8-6

1  The ratings of the knowledge-base uncertainty can indicate the need for additional data 

2 or analyses to better characterize the uncertainty. When combined with the potential 

3 magnitude of influence associated with each identified source, a prioritization can be 

4 given to the higher rated influential sources. Based on the results of this uncertainty 

5 characterization, staff judges that six sources (i.e., the spatial and temporal 

6 representation, historical data used, activity pattern database, longitudinal profile 

7 algorithm, microenvironmental algorithm and input data, and physiological factors) 

8 remain as the most important uncertainties in this assessment. 

9 
February 2010 A-1 Draft – Do Not Cite or Quote 

Appendix A 

Technical Memorandum on Updates To APEX Physiology.Txt File 

(Isaacs And Smith, 2005) 

The following contains a technical memo provided by Isaacs and Smith (2005) in its 

original format. Staff included page numbers and performed some minor formatting to 

text and table headers for the purposes of inclusion into the draft CO REA appendices. 
February 2010 A-2 Draft – Do Not Cite or Quote 

TECHNICAL MEMORANDUM 

TO: Tom McCurdy, WA-COR, NERL WA 10

FROM: Kristin Isaacs and Luther Smith, Alion Science and Technology

DATE: December 20, 2005 

SUBJECT: New Values for Physiological Parameters for the Exposure Model Input 

File Physiology.txt.

Table of Contents 

List of Figures..................................................................................................................... 3 

1. Introduction.................................................................................................................... 4 

2. Evaluation of the Current Physiology File Data............................................................ 4 

2.1 Normalized Maximal Oxygen Uptake (nvo2max)............................................... 4 

2.2 Body Mass. .......................................................................................................... 5 

2.3 Resting Metabolic Rate......................................................................................... 5 

2.4 Hemoglobin Content and Blood Volume Factor. ................................................ 5 

2.5 Summary of Findings............................................................................................ 5 

3. Derivation of New Distributions for Body Mass........................................................... 6 

3.1 The NHANES Body Mass Dataset...................................................................... 6 

3.2 Calculation of the New Sampling Weights for the Combined NHANES Dataset.

....................................................................................................................... 6 

3.3 Fitting the Body Mass Data. ................................................................................. 7 

4. Derivation of New Distributions for Normalized Vo2max .......................................... 13 

4.1 The Nvo2max Data ............................................................................................. 13 

4.2 Determining the NVo2max Distributions........................................................... 17 

5. Derivation of New Distributions for Hemoglobin Content (Hemoglobin Density) ..... 25 

6. Blood Volume as a Function of Height and Weight.................................................... 28 

References......................................................................................................................... 29 

Appendix A. SAS Code for Estimating the Body Mass Distributions ............................. 40 

Appendix B. SAS Code for Estimating the Normalized Vo2Max Distributions ............. 41 

Appendix C. SAS Code for Estimating the Hemoglobin Content Data ........................... 42 

Appendix D. The New Physiology.txt file....................................................................... 43 

Appendix E. All Derived Physiological Parameters........................................................ 54 
February 2010 A-3 Draft – Do Not Cite or Quote 

LIST OF FIGURES 

Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a 

Function of Age, Derived from NHANES 1999-2004 Study Data. ................................... 9 

Figure 2. Geometric Standard Deviations for the Best-fit Lognormal Distributions for 

Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data........ 10 

Figure 3. Minimums (1st Percentile) for Body Mass as a Function of Age, Derived from 

NHANES 1999-2004 Study Data. .................................................................................... 11 

Figure 4. Maximums (99th Percentile) for Body Mass as a Function of Age, Derived 

from NHANES 1999-2004 Study Data. ........................................................................... 12 

Figure 5. Individual Nvo2max Measurements for Males and Females, Derived from 

Literature Studies and Experimental Measurements. ....................................................... 14 

Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from 

Literature Studies.............................................................................................................. 15 

Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature 

Studies............................................................................................................................... 16 

Figure 8. Combined Nvo2max Group Means for Males and Females ............................ 19 

Figure 9. Combined Nvo2max Group Standard Deviations............................................. 20 

Figure 10. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits. ..... 21 

Figure 11. Nvo2max Normal Distribution Fits: Raw Fit Standard Deviations and 

Smoothed Fits. .................................................................................................................. 22 

Figure 12. Nvo2max Minimums. 1st Percentile of the Best-fit Normal Distribution. .... 23 

Figure 13. Nvo2max Maximums. 99th Percentile of the Best-fit Normal Distribution. .. 24 

Figure 14. Mean Values of Hemoglobin Content as Derived from the 1999-2002 

NHANES Dataset, with Comparison to Current Physiology.txt Values.......................... 26 

Figure 15. Values of Hemoglobin Content Standard Deviation as Derived from the 1999-

2002 NHANES Dataset, with Comparison to Current Physiology.txt Values................. 27 
February 2010 A-4 Draft – Do Not Cite or Quote 

1. INTRODUCTION 

The purpose of this memo is to present an updated version of the physiological 

parameters input file (Physiology.txt) for the APEX model. Portions of this file are also 

used as input for SHEDS-PM and SHEDS-AirToxics. 

The physiology file contains age- and gender-based information for several physiological 

parameters used in human exposure modeling. This information includes distributional 

shapes and parameters for all age and gender cohorts from age 0 to 100 years for 

normalized maximal oxygen uptake (nvo2max), body mass, resting metabolic rate 

(RMR), and blood hemoglobin content. In addition, a parameter called blood volume 

factor (BVF), which is a cohort-dependent parameter in the equation for blood volume as 

a function of body mass, is present in the file as well. 

New age- and gender-dependent distributions were developed based the best available 

physiological data from the literature. In this report, a summary of the current state of the 

physiology file is presented, followed by the derivation of new physiological data for 

body mass, normalized vo2max, and hemoglobin content. Portions of the SAS code used 

for analysis are included (Appendices A-C), as is the new Physiology.txt file (Appendix 

D). The final appendix (Appendix E) contains tables of all the derived physiological 

parameters. 

2. EVALUATION OF THE CURRENT PHYSIOLOGY FILE DATA 

The physiology.txt file was originally generated for the PNEM model by T. Johnson. It 

was last updated 6/11/1998, as documented in the report User’s Guide: Software for 

Estimating Ventilation (Respiration) Rates for Use in Dosimetry Models, (T. Johnson and 

J. Capel). In that report, the original references for the data in the file were provided. An 

evaluation of the data in the file was included in a previous memo to the WA-COR under 

this work assignment. A summary of those findings is repeated here. 

2.1 Normalized Maximal Oxygen Uptake (nvo2max). 

The nvo2max data were derived from a number of sources. The data for males, 

especially, were pieced together from a variety of studies (a total of 6), leading to 

discontinuities in the distributional parameters. However, in each age and gender cohort, 

the distributions parameters were derived from a single published study. Additionally, 

much of the nvo2max data is quite old. The data for males at age 20 and at 28-69 came 

from a study from 1960 [1]. Data for males aged 0-8 and 16-19, and females 0-19 came 

from a figure in a textbook from 1977 [2], which in turn was based on limited earlier 

data. An additional issue with the 1977 data is (according to the report mentioned above) 

that values for certain ages (very young or elderly) were acquired by simple tangential 

extrapolation of the data in the figure. 
February 2010 A-5 Draft – Do Not Cite or Quote 

In addition, in some cases it was not clear how the parameters were derived from the 

referenced studies. For example, Heil et al. [3] was referenced as the source of the values 

for females aged 66-100. However, an examination of that study provided no clues as to 

how the values were actually determined. As far as can be determined, in no place did 

the authors break down the means and SDs of their data into groups separated by both 

gender and age simultaneously. 

2.2 Body Mass. 

The current body mass data were derived from an in-depth analysis [4, 5] of the second 

CDC National Health and Nutrition Examination Survey (NHANES II) body mass data 

[6]. The data were relatively comprehensive, and the methods used to generate the 

lognormal distributions were sound. However, the NHANES II data were compiled for 

the years 1976-1980, so an analysis of more recent data is necessary to accurately 

account for changes in human activity patterns in adults and especially children. 

2.3 Resting Metabolic Rate. 

Not included for evaluation, per discussion with WA-COR. 

2.4 Hemoglobin Content and Blood Volume Factor.

The original references for the hemoglobin content or blood volume factor values given 

in the current physiology.txt file could not be identified. Therefore, their validity could 

not be evaluated and it was desirable that new statistics be calculated. 

2.5 Summary of Findings 

 In some cases, especially for nvo2max, the data are unnecessarily and confusingly 

disjointed across ages. 

 It is also unclear how some of the nvo2max values were derived from the referenced 

studies. 

 With the exception of the Schofield equations for the BM/RMR regression, parameter 

distributions at each age and gender cohort were derived from data from a single 

study. 

 Many of the studies used are very old (ex. 1960, 1977). 

 Some the data is of questionable validity (for example, the extrapolation of a textbook 

figure is used), although it may have been the best available at the time of the 

compilation of the file. 

 The original source of the hemoglobin content and blood volume factor data could 

not be identified. 

 Given these conclusions, we recommended a full review and update of the current 

physiology.txt file data. Specifically, we recommended that where possible, new 
February 2010 A-6 Draft – Do Not Cite or Quote 

distributions or equations should be developed based on thorough, compiled data 

from appropriate studies. 

3. DERIVATION OF NEW DISTRIBUTIONS FOR BODY MASS 

3.1 The NHANES Body Mass Dataset. 

New body mass distributions were generated from data from the National Health and 

Nutrition Examination Survey (NHANES). This survey is an ongoing study carried out 

by the National Center for Health Statistics of the Centers for Disease Control. EPA 

recognizes the utility of this dataset in characterizing the American population for risk 

assessment and policy support purposes [7]. 

Older NHANES data (for the years 1976-1980) have been used previously to develop 

population estimates of body mass distributions [4,5]. The current Physiology.txt file 

body mass distributions are based on this work. However, the analysis presented here is 

based on the most recent NHANES data, for the years 1999-2004 [8]. 

Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES 

studies were downloaded from the NHANES website. The files were downloaded as 

SAS xpt datasets. The downloaded files were as follows: 

1999-2000 

BMX.xpt 

Demo.xpt 

2001-2002

BMX_b_r.xpt 

Demo_b.xpt 

2003-2004 

BMX_c.xpt 

Demo_c.xpt 

The Demographic datasets contained the age and gender values for each survey 

participant, while the Body Measurement datasets contained the body weights for each 

subject. The combined dataset comprised 31,126 individuals. This resulted in 

approximately 400-500 persons in each age 0-18 year cohort, and approximately 80-150 

persons in each age 19-85 year cohort (the NHANES studies more heavily sampled 

children). 

3.2 Calculation of the New Sampling Weights for the Combined NHANES Dataset. 

In the analysis of the NHANES data, sampling weights must be used to ensure that the 

data are weighted to appropriately represent the national population. Sampling weights 

for the combined NHANES body mass dataset were derived as recommended by the 

documentation provided with the most recent NHANES release [9]. Specifically, the 

sampling weight for each subject was calculated as: 
February 2010 A-7 Draft – Do Not Cite or Quote 

2003 2004 3

1

wcombined  w  (1) 

1999 2002 3

2

wcombined  w  (2) 

where wcombined is the sampling weight for the combined dataset, w2003-2004 is the weight 

for the subjects in the most recent study, and w1999-2002 is the weight for subjects in 

combined 4-year (1999-2000 and 2001-2002) NHANES dataset. (Both weights are 

provided with the appropriate NHANES release. The combined 1999-2002 weight, 

which is not a simply half of that for the corresponding 2-year periods, was explicitly 

calculated for researcher use by CDC since the two 2-year periods use different census 

data.) 

By using the sampling weights, once can consider any 2-year NHANES dataset or any 

combination of datasets as a nationally representative sample. 

3.3 Fitting the Body Mass Data. 

In the current physiology file, body mass is modeled as a two-parameter lognormal 

distribution. The NHANES body mass data were fit to several types of distributions 

(including normal, beta, and three-parameter lognormal distributions). It was determined 

that overall, the distribution that provided the best combination of good behavior over 

ages and good fit to the data was a two-parameter lognormal distribution. 

The data were fit to the lognormal distributions using the SAS PROC UNIVARIATE 

procedure. The FREQ option of the procedure was used to apply the sampling weights. 

The SAS code used to generate the body mass distributions is provided in Appendix A. 

As the NHANES 1999-2003 studies only covered persons up to age 85, linear forecasts 

were made for ages 86-100, as based on the data for ages 60 and greater. 

3.4 Body Mass Results. 

Geometric means and standard deviations (SD) for the best-fit lognormal distributions for 

body mass are given in Figures 1 and 2. The means behaved fairly smoothly across ages. 

Note that for children age 0-18, the values of the new fits are similar, but slightly higher 

than those in the current Physiology.txt file, which were derived from earlier NHANES 

studies. The new means also capture the trend towards decreasing body weight in older 

persons that was previously neglected in the Physiology.txt file. 

The maximum and minimum values for the distributions are presented in Figures 3 and 4. 

The minimums and maximums were calculated as the 1st and 99th percentile of the raw 

body mass data for the cohort. (Note that these values differ from the 1st and 99th
February 2010 A-8 Draft – Do Not Cite or Quote 

percentiles of the fitted lognormals.) While the minimum value is consistent with the 

current Physiology.txt (which was based on earlier NHANES studies), the new cohort 

maximums are generally higher than before. 

The behavior of several of the body mass parameters (especially the SD) is fairly noisy, 

especially for adults. This is most likely due to the smaller number of samples for adults 

as compared to children. Therefore, it may desirable to use age-grouped data or running 

averages over years in these age ranges. While the attached prepared Physiology.txt file 

uses the “raw” parameters, smoothed results using 5-year running averages are provided 

in the attached data tables (Appendix E, plots not shown). These could be used at the 

direction of EPA; changing the “official” release Physiology.txt file would be trivial. 
February 2010 A-9 Draft – Do Not Cite or Quote 

MALES: Body Mass Geometric Mean

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Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a 

Function of Age, Derived from NHANES 1999-2004 Study Data.
February 2010 A-10 Draft – Do Not Cite or Quote 

MALES: Body Mass GSD

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Figure 2. Geometric Standard Deviations for the Best-fit Lognormal Distributions for Body 

Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.
February 2010 A-11 Draft – Do Not Cite or Quote 

FEMALES: Body Mass Minimum

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Figure 3. Minimums (1st Percentile) for Body Mass as a Function of Age, Derived from 

NHANES 1999-2004 Study Data.
February 2010 A-12 Draft – Do Not Cite or Quote 

MALES: Body Mass Maximum

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Figure 4. Maximums (99th Percentile) for Body Mass as a Function of Age, Derived from 

NHANES 1999-2004 Study Data. 
February 2010 A-13 Draft – Do Not Cite or Quote 

4. DERIVATION OF NEW DISTRIBUTIONS FOR NORMALIZED 

VO2MAX 

4.1 The Nvo2max Data 

The NHANES studies do report data for vo2max in individuals. However, the NHANES 

vo2max values are estimated values, i.e. they are not measured directly. Such estimated 

values are not appropriate for use in this context (as per discussion with the WA-COR). 

Therefore, nvo2max distributional shapes were determined from a large database of 

experimental and literature vo2max measurements for different age/gender cohorts. 

A PubMed-based literature search located a number of studies in which vo2max was 

directly measured. In addition, a large number of scientific papers (~350) reporting 

vo2max were also provided to Alion by the WA-COR. All the studies were evaluated for 

use by determining if: 1) any normalized vo2max data for individuals were reported or 2) 

any group means for narrow age-gender cohorts were reported. Studies in which the 

studied age group was very broad or contained both males and females were discarded. 

Also discarded were any studies in which vo2max was not normalized by body mass, or 

for which no age data were reported. Data for ill or highly-trained individuals were not 

used; however, studies in which subjects underwent mild or moderate exercise training 

were included. Two large databases, one of individual vo2max data and one of grouped 

means and SDs, were constructed from the valid studies. 

The database of individual data comprised age versus nvo2max data for 1949 men and 

1558 women. The data were pulled from either tables or graphs in 20 published studies 

[11-30]. Additional raw experimental data were provided by the WA-COR [31]. In the 

case of the graphical data, the original source was digitized and the data points were 

pulled from the digital figure using graphics software. (This was accomplished by 

calibrating the pixels of the digitized image with the range of age and nvo2max values.) 

The individual nvo2max data for males and females are shown in Figure 5. 

The grouped mean and SD data were derived from 136 studies [32-167]. These data 

comprised approximately 550 means and SDs for different age/gender cohorts. Single 

age/gender cohort means and SD values for the Adams data [31] were also included in 

this dataset. Only data for subject groups having an age SD of less than approximately 2-

3 years were considered. The grouped mean values for men and women are shown in 

Figure 6, while the group SD values are shown in Figure 7. 
February 2010 A-14 Draft – Do Not Cite or Quote 

MALES: Nvo2max

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Figure 5. Individual Nvo2max Measurements for Males and Females, Derived from 

Literature Studies and Experimental Measurements. 

N=1558 

N=1949 
February 2010 A-15 Draft – Do Not Cite or Quote 

MALES: Study Means, Nvo2max

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Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from 

Literature Studies. 
February 2010 A-16 Draft – Do Not Cite or Quote 

MALES: Study Standard Deviations: Nvo2max

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Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature 

Studies. 
February 2010 A-17 Draft – Do Not Cite or Quote 

4.2 Determining the NVo2max Distributions

Both the grouped mean and the individual datasets were evaluated for use in deriving the 

nvo2max parameters. 

The group means and SD were combined into single age/gender cohort values. The 

combined means were calculated as mean of the group means, weighted by the number of 

subjects. The group SD were calculated by transforming each group SD to a group 

variance, calculating the mean variance (weighted by the number of subjects in each 

study) and retransforming the variances to SDs. The combined group means and SDs are 

given in Figures 8 and 9. 

The combined group means were fairly well-behaved across age and gender cohorts (see 

Figure 8), while the SD data (Figure 9) were noisier. These data may be appropriate for 

use in the Physiology.txt file; however, it was noted that the group mean data, while 

plentiful for children, were not very well represented in the adult (30+ years) age range 

(especially for women). This is mainly due to the fact that very few investigators use 

narrow age cohorts when studying adults, rather, it was far more common for broader age 

groups to be used. These data were not included in the grouped mean analysis, as the 

mean nvo2max for a broad age group cannot be assumed valid for the cohort represented 

by the study age mean. Therefore, we opted to use the database of individual nvo2max 

measurements to develop new distributions for the Physiology.txt file. 

The individual nvo2max data were fit to several types of distributions (including normal, 

beta, and lognormal distributions). It was determined that the normal distribution fit the 

data best. The parameters (means and standard deviations) of the best-fit distributions 

were obtained using the SAS PROC UNIVARIATE procedure. The SAS code used to fit 

the data is given in Appendix B. 

Both raw and smoothed nvo2max fits were calculated. Calculating 5-year running 

averages did not smooth the data considerably. Therefore, the smoothed fits were 

determined by choosing a best-fit functional form for the nv02max data. The data were 

fit to functions as follows: 

Mean (Age 0-20): Linear function 

Mean (Age 21-100): Parabolic function 

SD (Age 0-26): Linear function 

SD (Age 27-100): Parabolic function 

Fitting the data in this manner also allowed for all age/gender cohorts to be represented. 

Since only cohorts having N>10 were fit to distributions, there were some cohorts for 

which no parameters were calculated. The raw and smoothed fits for means are given in 

Figure 10; analogous data for SD is given in Figure 11. The raw nvo2max parameters 

were not as clean across ages as the body mass data (probably due to the much smaller 

sample size), and thus the smoothed fits were selected for use in the attached 

Physiology.txt file. As with body mass, the raw fits may be used at the direction of EPA. 
February 2010 A-18 Draft – Do Not Cite or Quote 

The results for the nvo2max means were in fact quite close to those in the current file. 

However, the values exhibited much more consistent behavior across ages, and the values 

for elderly persons were lower than previously. The SD values were also in the same 

range as the current values, yet they no longer demonstrate nonsensical discontinuities 

across ages. 

The minimum and maximum nvo2max values were assumed to be the 1st and 99th

percentile of the best-fit lognormal distribution. (Note: this is different from the method 

used for estimating the body mass limits. In that case, the samples were large enough 

that the percentiles of the raw data were appropriate for use as minimum and maximum. 

As the nvo2max data cohorts had much smaller N than the NHANES studies, the raw 

percentiles were less appropriate.) The maximum and minimum values are shown in 

Figures 12 and 13. 
February 2010 A-19 Draft – Do Not Cite or Quote 

MALES: Nvo2max, Combined Group Means

0

10

20

30

40

50

60

0 20 40 60 80

Age (Years)

nv02max (ml/k

g

FEMALES: Nvo2max, Combined Group Means

0

10

20

30

40

50

0 10 20 30 40 50 60 70

Age (Years)

nv02max (ml/k

g

Figure 8. Combined Nvo2max Group Means for Males and Females 
February 2010 A-20 Draft – Do Not Cite or Quote 

MALES: Nvo2max, Combined Group SD

0

2

4

6

8

10

12

14

16

0 20 40 60 80

Age (Years)

nv02max (ml/k

g

FEMALES: Nvo2max, Combined Group SD

0

2

4

6

8

10

0 10 20 30 40 50 60 70

Age (Years)

nv02max (ml/k

g

Figure 9. Combined Nvo2max Group Standard Deviations. 
February 2010 A-21 Draft – Do Not Cite or Quote 

MALES: MEAN Nvo2max

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Fit Values

Smoothed Fit Values

Current Physiology.txt

FEMALES: MEAN Nvo2max

0

10

20

30

40

50

60

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Fit Values

Smoothed Fit Values

Current Physiology.txt

Figure 10. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits. 
February 2010 A-22 Draft – Do Not Cite or Quote 

MALES: Nvo2max Standard Deviation

0

2

4

6

8

10

12

14

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

Raw Values

Fit Values

Current Physiology.txt

FEMALES: Nvo2max Standard Deviation

0

2

4

6

8

10

12

0 20 40 60 80 100

Age (Years)

Nvo2max (ml/kg

Raw Values

Fit Values

Current Physiology.txt

Figure 11. Nvo2max Normal Distribution Fits: Raw Fit Standard Deviations and Smoothed 

Fits. 
February 2010 A-23 Draft – Do Not Cite or Quote 

MALES: Nvo2max Minimum

0

10

20

30

40

50

60

0 20 40 60 80 100

Age (Years)

nvo2max (ml/kg

New Values

Current Physiology.txt

FEMALES: Nvo2max Minimum

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100

Age (Years)

Nvo2max (ml/kg

New Values

Current Physiology.txt

Figure 12. Nvo2max Minimums. 1st Percentile of the Best-fit Normal Distribution. 
February 2010 A-24 Draft – Do Not Cite or Quote 

MALES: Nvo2max Maximum

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100

Age (Years)

nvo2max (ml/k

g

New Values

Current Physiology.txt

FEMALES: Nvo2max Maximum

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Age (Years)

Nvo2max (ml/k

g

New Values

Current Physiology.txt

Figure 13. Nvo2max Maximums. 99th Percentile of the Best-fit Normal Distribution. 
February 2010 A-25 Draft – Do Not Cite or Quote 

5. DERIVATION OF NEW DISTRIBUTIONS FOR HEMOGLOBIN 

CONTENT (HEMOGLOBIN DENSITY)

The new hemoglobin content values were derived from the combined NHANES 1999-

2000 and 2001-2002 datasets. As of December 2005, hemoglobin data had not yet been 

released for the 2003-2004 study. The age data was provided in the Demographic 

datasets (Demo.xpt and Demo_b.xpt, previously downloaded for the body mass analysis) 

for the two survey periods, while hemoglobin content (in g/dL) was provided in the 

Laboratory #25 (Complete Blood Count) datasets (lab25.xpt and l25_b.xpt, which were 

downloaded for this analysis). The dataset comprised 20,321 individuals; appropriate 

sample weights were used for the combined 4-year (1999-2002) dataset as provided with 

the NHANES 2001-2002 data release. Similarly to the body mass data, the hemoglobin 

content values were analyzed in SAS. The age and hemoglobin datasets were merged 

and fit to normal distributions using the SAS PROC UNIVARIATE procedure. The 

FREQ option of the procedure was used to apply the sampling weights. The SAS code in 

provided in the Appendix C. 

Hemoglobin content statistics were estimated for single-year age and gender cohorts for 

ages 1-19, as the behavior of the means were smooth in this age range. For persons 20 

and over, the data were grouped in 5-year cohorts (20-24, 25-29, etc.) No blood count 

data were available for subjects under 1 year of age or greater than 90. The age 0 mean 

values were obtained by a linear regression of ages 1-20 (males) or 1 to 11 (females) back 

to age 0. These were the ages for which the hemoglobin content demonstrated an 

increase with age. The 91-95 and 96-100 mean values were obtained by a linear 

regression of the 61-65 and older age groups. As the standard deviations did not appear 

to behave as smoothly with age as did the mean values, the age 0 value was assumed 

equal to the age 1 value, and the age 91-95 and 96-100 value was assumed equal to the 

age 90-94 value. 

The resulting means and standard deviations for the best-for normal distributions for 

hemoglobin content are given in Figures 14 and 15. The current hemoglobin content 

values are shown for comparison. 

The main conclusions that can be made is that the current Physiology.txt input file 

overestimates mean hemoglobin content in children and in older persons. The standard 

deviation values in the current physiology.txt file are fairly close to those found in this 

analysis. The new values are not very smooth over ages; EPA may elect to continue to 

use the current values. It should be noted that the original reference for the current 

hemoglobin statistics is unknown.

Note: In the current implementation of APEX, the hemoglobin content statistics affect 

only the CO dose algorithm calculations. 
February 2010 A-26 Draft – Do Not Cite or Quote 

MALES: Mean Hemoglobin Content

10

11

12

13

14

15

16

17

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

New Values

Current Physiology.txt

FEMALES: Mean Hemoglobin Content

10

11

12

13

14

15

16

17

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

Figure 14. Mean Values of Hemoglobin Content as Derived from the 1999-2002 NHANES 

Dataset, with Comparison to Current Physiology.txt Values 
February 2010 A-27 Draft – Do Not Cite or Quote 

 

MALES: Hemoglobin Content Standard Deviation

0

0.5

1

1.5

2

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

New Values

Current Physiology.txt

FEMALES: Hemoglobin Content Standard Deviation

0

0.5

1

1.5

2

0 20 40 60 80 100

Age (years)

Hb Content (g/dL)

Figure 15. Values of Hemoglobin Content Standard Deviation as Derived from the 1999-

2002 NHANES Dataset, with Comparison to Current Physiology.txt Values 
February 2010 A-28 Draft – Do Not Cite or Quote 

6. BLOOD VOLUME AS A FUNCTION OF HEIGHT AND WEIGHT 

In APEX, blood volume is estimated as a function of height and weight by the following 

equation: 

30 3 V BVF*Weight+ K*Height - blood 

where Vblood is the blood volume (ml), Weight is in pounds, and height is in inches. BVF 

is the blood volume factor that is read in from the physiology file, and K is a genderdependent constant (0.00683 for males, 0.00678 for females). This is a modification of 

Allen’s equation [168] to include the age/gender dependent BVF and adjusted for the 

given units. 

As previously mentioned, the data upon which the BVF values in the physiology file 

were based could not be identified. The available documentation for pNEM documents a 

non-age-dependent use of these equations. 

In addition, no appropriate data were found for deriving new estimates for the BVF 

variable as a function of age and gender for use with the Allen equations. It should be 

noted however, that these equations were modified by Nadler [169]. These equations 

seem to be used somewhat more often than the originals in the literature. 

In addition, other (more recent) equations exist for estimation of blood volume from 

height and weight specifically in children [170,171] or body surface area [172]. In 

particular, Linderkamp et al. [170] derived prediction equations for blood volume as a 

function of a number of physiological parameters for children in three different age 

groups. It is recommended that further analysis of this study and others be undertaken. 

However, inclusion of new blood volume equations in APEX would require changes 

beyond the current physiology file (i.e. other, more intensive, code changes would be 

needed). Thus, at the present time, no specific improvements to the current BVF values 

in the physiology file can be made. 
February 2010 A-29 Draft – Do Not Cite or Quote 

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February 2010 A-40 Draft – Do Not Cite or Quote 

Appendix A. SAS Code for Estimating the Body Mass Distributions 

/* This program calculates lognormal distributions for BM from the NHANES 1999-2004 Data 

K K Isaacs 10/2005 

Alion Science and Technology 

Distributions are derived from raw body mass and age data downloaded from the CDC site at 

http://www.cdc.gov/nchs/about/major/nhanes/ 

The data are stored in the downloaded datasets: 

1999-2000 (SAS export files) 

BMX.xpt (NHANES Body Measurement Data, contains body wt in kg) 

Demo.xpt (NHAMES Demographic Data, contains age in years or months) 

2001-2002 (SAS export files) 

BMX_b_r.xpt (NHANES Body Measurement Data, contains body wt in kg) 

Demo_b.xpt (NHAMES Demographic Data, contains age in years or months) 

2003-2004 (SAS export files) 

BMX_c.xpt (NHANES Body Measurement Data, contains body wt in kg) 

Demo_c.xpt (NHAMES Demographic Data, contains age in years or months) 

*/

* Merge the Body Measurement and Demographics datasets;

Data weight; 

 merge Demo Demo_b Demo_c Bmx Bmx_b_r Bmx_c; 

 by SEQN; 

 mass=BMXWT; 

 gen=RIAGENDR; 

 ageyrs=RIDAGEYR; 

 agemonths=RIDAGEEX; 

 wt = (2/3)*WTMEC4YR; 

 if (SEQN>21004) THEN wt=(1/3)*WTMEC2YR; 

 if agemonths<12 and agemonths>0 THEN ageyrs=0; 

 keep SEQN mass gen ageyrs agemonths wt; 

run; 

proc sort data=weight; 

 by gen ageyrs; 

run; 

Proc univariate data=weight; 

by gen ageyrs; 

var mass; 

freq wt; 

histogram mass / lognormal; 

run;
February 2010 A-41 Draft – Do Not Cite or Quote 

Appendix B. SAS Code for Estimating the Normalized Vo2max 

Distributions 

/***********************************************************************/

/* This is a program to fit the V02Max (Adams and others) data to different 

distributional shapes. 

Adams experimental data provided in Excel form by Stephen Graham and Tom McCurdy, EPA 

Other data collected by Alion Science and Tech. 

This work was performed for WA 10, APEX/SHEDS Physiology File Update 

K. K. Isaacs October 2005 

Alion Science and Technology 

/***********************************************************************/; 

*load datasets;

Data alldata ; 

 infile 'H:\kki-05-PHYSIOLOGY_10\NVO2MAX\vo2max.csv' DLM="," END=eof; 

 input age nvo2max gender; 

 output alldata; 

 

proc sort data=alldata; 

by gender age; 

run; 

Proc univariate data=alldata; 

by gender age; 

var nvo2max; 

histogram nvo2max / normal; 

output out=outputdata1 N=samplesize mean=Mean 

 std=StdDeviation ProbN=NormalFit; 

run; 

Proc export data=outputdata1 outfile="H:\kki-05-PHYSIOLOGY_10\Alldata_vo2max.csv"

replace; 

run; 
February 2010 A-42 Draft – Do Not Cite or Quote 

Appendix C. SAS Code for Estimating the Hemoglobin Content Data 

/* This program calculates best fit normal distributions for hemoglobin content 

from the NHANES 1999-2000 and 2001-2002 datasets. 

Alion Science and Technology 

K K Isaacs 12/2005 

Distributions are derived from hemoglobin content and age data downloaded from the CDC 

site at 

http://www.cdc.gov/nchs/about/major/nhanes/nhanes99-00.htm 

and 

http://www.cdc.gov/nchs/about/major/nhanes/nhanes01-02.htm 

The data are stored in the downloaded datasets: 

1999-2000 

lab25.xpt (NHANES Lab dataset #25) 

Demo.xpt (NHANES Demographic Data, contains age in years or months) 

2001-2002 

l25_b.xpt (NHANES Lab dataset #25) 

Demo_b.xpt (NHANES Demographic Data, contains age in years or months) 

*/

*Data are read into SAS by loading the xpt files. 

* Merge the Laboratory and Demographics datasets;

Data Hb; 

 merge Demo Lab25 Demo_b L25_b; 

 by SEQN; * Sample number;

 Hb=LBXHGB; * Hb content g/dL;

 gen=RIAGENDR; * Gender;

 ageyrs=RIDAGEYR; * Age in years; 

 agemonths=RIDAGEEX; * Age in months;

 wt = WTMEC4YR; * 4-year sample weights;

 if agemonths<12 and agemonths>0 THEN ageyrs=0; * Age 0;

 if ageyrs>20 then ageyrs=(floor(ageyrs/5)+1)*5; * Bin in 5-year incs;

 keep SEQN Hb gen ageyrs agemonths wt; 

run; 

proc sort data=Hb; 

 by gen ageyrs; 

run; 

Proc univariate data=Hb; 

by gen ageyrs; 

var Hb; 

req wt; * Apply sample weights;

histogram Hb / normal; * Fit to Normal;

output out=outputs N=samplesize mean=Mean 

 std=StdDeviation ProbN=NormalFit; 

run; 

Proc export data=outputs outfile="H:\kki-05-PHYSIOLOGY_10\Hemoglobin\HbFitswt.csv"

replace; 

run;
February 2010 A-43 Draft – Do Not Cite or Quote 

Appendix D. The New Physiology.Txt File 

Note: The values contained in the file conform to the current APEX read formats. That 

is, the number of decimal places for each parameter is dictated by the APEX code. It is 

likely that this will change in the future, at which point more significant digits could be 

added to the Physiology.txt file. 

Males age 0-100, then females age 0-100 (last revised 12-20-05) 

 NVO2max distribution 

 Age Source Distr Mean SD Lower Upper Assumptions 

 0 NA Normal 48.3 1.7 44.3 52.2 

 1 NA Normal 48.6 2.0 43.8 53.3 

 2 NA Normal 48.9 2.4 43.4 54.4 

 3 NA Normal 49.2 2.7 43.0 55.4 

 4 NA Normal 49.5 3.0 42.5 56.5 

 5 NA Normal 49.8 3.3 42.1 57.6 

 6 NA Normal 50.1 3.7 41.6 58.6 

 7 NA Normal 50.4 4.0 41.2 59.7 

 8 NA Normal 50.8 4.3 40.8 60.8 

 9 NA Normal 51.1 4.6 40.3 61.8 

 10 NA Normal 51.4 5.0 39.9 62.9 

 11 NA Normal 51.7 5.3 39.4 64.0 

 12 NA Normal 52.0 5.6 39.0 65.0 

 13 NA Normal 52.3 5.9 38.6 66.1 

 14 NA Normal 52.6 6.2 38.1 67.2 

 15 NA Normal 53.0 6.6 37.7 68.2 

 16 NA Normal 53.3 6.9 37.3 69.3 

 17 NA Normal 53.6 7.2 36.8 70.4 

 18 NA Normal 53.9 7.5 36.4 71.4 

 19 NA Normal 54.2 7.9 35.9 72.5 

 20 NA Normal 54.5 8.2 35.5 73.6 

 21 NA Normal 54.2 8.5 34.5 74.0 

 22 NA Normal 53.4 8.8 32.9 74.0 

 23 NA Normal 52.6 9.2 31.4 73.9 

 24 NA Normal 51.8 9.5 29.8 73.9 

 25 NA Normal 51.1 9.8 28.3 73.9 

 26 NA Normal 50.3 10.7 25.5 75.2 

 27 NA Normal 49.6 10.5 25.2 74.0 

 28 NA Normal 48.8 10.3 24.9 72.8 

 29 NA Normal 48.1 10.1 24.6 71.6 

 30 NA Normal 47.4 9.9 24.3 70.4 

 31 NA Normal 46.7 9.7 24.0 69.3 

 32 NA Normal 46.0 9.6 23.8 68.2 

 33 NA Normal 45.3 9.4 23.5 67.1 

 34 NA Normal 44.6 9.2 23.2 66.0 

 35 NA Normal 44.0 9.0 23.0 65.0 

 36 NA Normal 43.3 8.9 22.7 64.0 

 37 NA Normal 42.7 8.7 22.4 62.9 

 38 NA Normal 42.1 8.6 22.2 61.9 

 39 NA Normal 41.4 7.3 25.5 54.1 

 40 NA Normal 40.8 5.5 28.4 50.0 

 41 NA Normal 40.2 5.5 28.4 50.0 

 42 NA Normal 39.7 5.5 28.4 50.0 

 43 NA Normal 39.1 5.5 28.4 50.0 

 44 NA Normal 38.5 5.5 28.4 50.0 

 45 NA Normal 38.0 5.5 28.4 50.0 

 46 NA Normal 37.4 5.5 28.4 50.0 

 47 NA Normal 36.9 5.5 28.4 50.0 

 48 NA Normal 36.4 5.5 28.4 50.0 

 49 NA Normal 35.9 5.5 28.4 50.0 

 50 NA Normal 35.4 4.9 23.5 42.7 

 51 NA Normal 34.9 4.9 23.5 42.7 

 52 NA Normal 34.5 4.9 23.5 42.7 

 53 NA Normal 34.0 4.9 23.5 42.7 

 54 NA Normal 33.6 4.9 23.5 42.7 

 55 NA Normal 33.1 4.9 23.5 42.7 

 56 NA Normal 32.7 4.9 23.5 42.7 

 57 NA Normal 32.3 4.9 23.5 42.7 

 58 NA Normal 31.9 4.9 23.5 42.7 

 59 NA Normal 31.5 4.9 23.5 42.7 

 60 NA Normal 31.1 5.3 21.0 41.8 

 61 NA Normal 30.7 5.3 21.0 41.8 

 62 NA Normal 30.4 5.3 21.0 41.8 

 63 NA Normal 30.0 5.3 21.0 41.8 

 64 NA Normal 29.7 5.3 21.0 41.8 
February 2010 A-44 Draft – Do Not Cite or Quote 

 65 NA Normal 29.4 5.3 21.0 41.8 

 66 NA Normal 29.1 5.3 21.0 41.8 

 67 NA Normal 28.8 5.3 21.0 41.8 

 68 NA Normal 28.5 5.3 21.0 41.8 

 69 NA Normal 28.2 5.3 21.0 41.8 

 70 NA Normal 27.9 5.7 16.1 38.3 

 71 NA Normal 27.7 5.7 16.1 38.3 

 72 NA Normal 27.4 5.7 16.1 38.3 

 73 NA Normal 27.2 5.7 16.1 38.3 

 74 NA Normal 27.0 5.7 16.1 38.3 

 75 NA Normal 26.7 5.7 16.1 38.3 

 76 NA Normal 26.5 5.7 16.1 38.3 

 77 NA Normal 26.4 5.7 16.1 38.3 

 78 NA Normal 26.2 5.7 16.1 38.3 

 79 NA Normal 26.0 5.7 16.1 38.3 

 80 NA Normal 25.8 5.7 16.1 38.3 

 81 NA Normal 25.7 5.7 16.1 38.3 

 82 NA Normal 25.6 5.7 16.1 38.3 

 83 NA Normal 25.4 5.7 16.1 38.3 

 84 NA Normal 25.3 5.7 16.1 38.3 

 85 NA Normal 25.2 5.7 16.1 38.3 

 86 NA Normal 25.1 5.7 16.1 38.3 

 87 NA Normal 25.1 5.7 16.1 38.3 

 88 NA Normal 25.0 5.7 16.1 38.3 

 89 NA Normal 24.9 5.7 16.1 38.3 

 90 NA Normal 24.9 5.7 16.1 38.3 

 91 NA Normal 24.9 5.7 16.1 38.3 

 92 NA Normal 24.8 5.7 16.1 38.3 

 93 NA Normal 24.8 5.7 16.1 38.3 

 94 NA Normal 24.8 5.7 16.1 38.3 

 95 NA Normal 24.8 5.7 16.1 38.3 

 96 NA Normal 24.9 5.7 16.1 38.3 

 97 NA Normal 24.9 5.7 16.1 38.3 

 98 NA Normal 25.0 5.7 16.1 38.3 

 99 NA Normal 25.0 5.7 16.1 38.3 

 100 NA Normal 25.1 5.7 16.1 38.3 

 0 NA Normal 35.9 5.9 22.2 49.6 

 1 NA Normal 36.2 6.0 22.3 50.2 

 2 NA Normal 36.5 6.1 22.4 50.7 

 3 NA Normal 36.9 6.2 22.5 51.3 

 4 NA Normal 37.2 6.3 22.6 51.8 

 5 NA Normal 37.5 6.4 22.7 52.4 

 6 NA Normal 37.9 6.5 22.8 52.9 

 7 NA Normal 38.2 6.6 22.9 53.5 

 8 NA Normal 38.5 6.7 23.0 54.0 

 9 NA Normal 38.9 6.8 23.1 54.6 

 10 NA Normal 39.2 6.9 23.3 55.1 

 11 NA Normal 39.5 7.0 23.4 55.7 

 12 NA Normal 39.9 7.0 23.5 56.2 

 13 NA Normal 40.2 7.1 23.6 56.8 

 14 NA Normal 40.5 7.2 23.7 57.3 

 15 NA Normal 40.9 7.3 23.8 57.9 

 16 NA Normal 41.2 7.4 23.9 58.5 

 17 NA Normal 41.5 7.5 24.0 59.0 

 18 NA Normal 41.8 7.6 24.1 59.6 

 19 NA Normal 42.2 7.7 24.2 60.1 

 20 NA Normal 42.5 7.8 24.4 60.7 

 21 NA Normal 42.1 7.9 23.7 60.5 

 22 NA Normal 41.5 8.0 22.9 60.1 

 23 NA Normal 40.8 8.1 22.0 59.6 

 24 NA Normal 40.2 8.2 21.1 59.2 

 25 NA Normal 39.6 8.3 20.3 58.8 

 26 NA Normal 39.0 8.4 19.5 58.4 

 27 NA Normal 38.4 8.4 18.9 57.8 

 28 NA Normal 37.8 8.1 18.8 56.7 

 29 NA Normal 37.2 7.9 18.7 55.6 

 30 NA Normal 36.6 7.7 18.6 54.6 

 31 NA Normal 36.0 7.6 18.5 53.6 

 32 NA Normal 35.5 7.4 18.4 52.6 

 33 NA Normal 34.9 7.2 18.2 51.7 

 34 NA Normal 34.4 7.0 18.1 50.7 

 35 NA Normal 33.9 6.8 18.0 49.8 

 36 NA Normal 33.4 6.7 17.8 48.9 

 37 NA Normal 32.9 6.5 17.7 48.0 

 38 NA Normal 32.4 6.4 17.6 47.2 

 39 NA Normal 31.9 6.2 17.4 46.4 

 40 NA Normal 31.4 6.1 17.3 45.6 

 41 NA Normal 31.0 6.0 17.1 44.8 

 42 NA Normal 30.5 5.8 17.0 44.0 

 43 NA Normal 30.1 5.7 16.8 43.3 

 44 NA Normal 29.6 5.6 16.6 42.6 
February 2010 A-45 Draft – Do Not Cite or Quote 

 45 NA Normal 29.2 5.5 16.5 41.9 

 46 NA Normal 28.8 5.4 16.3 41.2 

 47 NA Normal 28.4 5.3 16.1 40.6 

 48 NA Normal 28.0 5.2 16.0 40.0 

 49 NA Normal 27.6 5.1 15.8 39.4 

 50 NA Normal 27.2 5.0 15.6 38.8 

 51 NA Normal 26.8 4.9 15.4 38.3 

 52 NA Normal 26.5 4.8 15.2 37.7 

 53 NA Normal 26.1 4.8 15.1 37.2 

 54 NA Normal 25.8 4.7 14.9 36.7 

 55 NA Normal 25.5 4.7 14.7 36.3 

 56 NA Normal 25.2 4.6 14.5 35.9 

 57 NA Normal 24.9 4.6 14.3 35.4 

 58 NA Normal 24.6 4.5 14.1 35.1 

 59 NA Normal 24.3 4.5 13.9 34.7 

 60 NA Normal 24.0 4.5 13.6 34.3 

 61 NA Normal 23.7 4.4 13.4 34.0 

 62 NA Normal 23.5 4.4 13.2 33.7 

 63 NA Normal 23.2 4.4 13.0 33.4 

 64 NA Normal 23.0 4.4 12.8 33.2 

 65 NA Normal 22.7 4.4 12.5 33.0 

 66 NA Normal 22.5 4.4 12.3 32.7 

 67 NA Normal 22.3 4.4 12.1 32.5 

 68 NA Normal 22.1 4.4 11.9 32.3 

 69 NA Normal 21.9 4.4 11.7 32.1 

 70 NA Normal 21.7 4.4 11.5 32.0 

 71 NA Normal 21.6 4.4 11.4 31.8 

 72 NA Normal 21.4 4.4 11.2 31.6 

 73 NA Normal 21.3 4.4 11.1 31.5 

 74 NA Normal 21.1 4.4 10.9 31.3 

 75 NA Normal 21.0 4.4 10.8 31.2 

 76 NA Normal 20.9 4.4 10.7 31.1 

 77 NA Normal 20.8 4.4 10.6 31.0 

 78 NA Normal 20.7 4.4 10.4 30.9 

 79 NA Normal 20.6 4.4 10.4 30.8 

 80 NA Normal 20.5 4.4 10.3 30.7 

 81 NA Normal 20.4 4.4 10.2 30.6 

 82 NA Normal 20.3 4.4 10.1 30.6 

 83 NA Normal 20.3 4.4 10.1 30.5 

 84 NA Normal 20.3 4.4 10.0 30.5 

 85 NA Normal 20.2 4.4 10.0 30.4 

 86 NA Normal 20.2 4.4 10.0 30.4 

 87 NA Normal 20.2 4.4 10.0 30.4 

 88 NA Normal 20.2 4.4 10.0 30.4 

 89 NA Normal 20.2 4.4 10.0 30.4 

 90 NA Normal 20.2 4.4 10.0 30.4 

 91 NA Normal 20.2 4.4 10.0 30.4 

 92 NA Normal 20.3 4.4 10.1 30.5 

 93 NA Normal 20.3 4.4 10.1 30.5 

 94 NA Normal 20.4 4.4 10.2 30.6 

 95 NA Normal 20.4 4.4 10.2 30.6 

 96 NA Normal 20.5 4.4 10.3 30.7 

 97 NA Normal 20.6 4.4 10.4 30.8 

 98 NA Normal 20.7 4.4 10.5 30.9 

 99 NA Normal 20.8 4.4 10.6 31.0 

 100 NA Normal 20.9 4.4 10.7 31.1 

Males age 0-100, then females age 0-100 (last revised 12-20-05) 

 Body mass distribution, kg 

 Age Source Distr GM GSD Lower Upper Assumptions 

 0 CDC LN 7.8 1.301 3.6 11.8 

 1 CDC LN 11.4 1.143 8.2 16.1 

 2 CDC LN 13.9 1.146 9.8 20.9 

 3 CDC LN 16.0 1.154 11.7 23.7 

 4 CDC LN 18.5 1.165 11.1 28.1 

 5 CDC LN 21.6 1.234 13.7 42.4 

 6 CDC LN 23.1 1.213 16.1 41.1 

 7 CDC LN 27.1 1.216 19.3 46.8 

 8 CDC LN 31.7 1.302 19.1 66.2 

 9 CDC LN 34.7 1.265 24.0 69.9 

 10 CDC LN 38.3 1.280 24.3 72.9 

 11 CDC LN 44.1 1.308 26.2 83.8 

 12 CDC LN 48.0 1.315 27.7 94.8 

 13 CDC LN 55.4 1.340 27.7 106.6 

 14 CDC LN 62.8 1.293 35.7 121.0 

 15 CDC LN 67.7 1.255 41.5 117.9 

 16 CDC LN 72.5 1.267 45.8 139.1 

 17 CDC LN 73.1 1.248 49.9 136.6 

 18 CDC LN 75.1 1.243 51.2 144.2 

 19 CDC LN 77.2 1.245 52.6 134.5 

 20 CDC LN 78.0 1.250 50.5 130.0 

 21 CDC LN 78.2 1.297 46.8 199.2 
February 2010 A-46 Draft – Do Not Cite or Quote 

 22 CDC LN 83.8 1.292 53.3 155.4 

 23 CDC LN 80.6 1.222 50.5 137.6 

 24 CDC LN 81.7 1.251 50.6 132.6 

 25 CDC LN 84.8 1.206 50.2 136.1 

 26 CDC LN 81.8 1.273 48.9 164.5 

 27 CDC LN 85.2 1.249 50.0 153.9 

 28 CDC LN 84.3 1.272 51.0 167.2 

 29 CDC LN 82.1 1.236 50.6 147.2 

 30 CDC LN 81.6 1.262 52.5 139.0 

 31 CDC LN 81.3 1.249 48.8 170.6 

 32 CDC LN 84.7 1.235 49.7 135.8 

 33 CDC LN 88.2 1.231 64.8 146.3 

 34 CDC LN 81.2 1.221 53.1 136.9 

 35 CDC LN 87.2 1.251 61.0 193.3 

 36 CDC LN 83.4 1.228 45.8 140.5 

 37 CDC LN 85.8 1.241 59.3 150.9 

 38 CDC LN 84.1 1.260 52.8 149.7 

 39 CDC LN 84.6 1.196 61.2 140.6 

 40 CDC LN 90.1 1.246 58.5 154.0 

 41 CDC LN 87.4 1.173 61.3 117.7 

 42 CDC LN 88.3 1.205 62.2 144.0 

 43 CDC LN 88.4 1.233 54.0 145.3 

 44 CDC LN 88.5 1.200 56.6 128.9 

 45 CDC LN 87.1 1.205 60.6 160.2 

 46 CDC LN 88.2 1.243 54.2 154.3 

 47 CDC LN 86.5 1.229 49.9 188.3 

 48 CDC LN 84.8 1.186 56.3 128.3 

 49 CDC LN 86.2 1.240 47.0 171.3 

 50 CDC LN 84.7 1.179 53.4 124.4 

 51 CDC LN 88.0 1.208 57.9 143.6 

 52 CDC LN 89.9 1.216 55.2 144.9 

 53 CDC LN 89.0 1.228 58.2 143.3 

 54 CDC LN 90.1 1.216 64.1 155.2 

 55 CDC LN 88.3 1.222 55.1 138.6 

 56 CDC LN 84.8 1.195 45.0 110.3 

 57 CDC LN 87.5 1.253 58.3 160.0 

 58 CDC LN 85.1 1.266 51.6 179.0 

 59 CDC LN 84.2 1.182 58.7 112.4 

 60 CDC LN 87.0 1.232 57.3 141.7 

 61 CDC LN 89.0 1.207 49.9 162.8 

 62 CDC LN 84.8 1.228 56.0 152.1 

 63 CDC LN 89.1 1.262 56.3 171.6 

 64 CDC LN 90.0 1.193 59.1 119.0 

 65 CDC LN 89.9 1.215 58.1 126.3 

 66 CDC LN 86.8 1.228 54.0 150.1 

 67 CDC LN 86.2 1.207 43.1 127.5 

 68 CDC LN 85.2 1.191 61.2 163.2 

 69 CDC LN 87.1 1.222 50.7 127.2 

 70 CDC LN 82.8 1.210 46.5 125.5 

 71 CDC LN 79.6 1.240 51.0 122.8 

 72 CDC LN 82.0 1.204 51.9 132.7 

 73 CDC LN 85.6 1.196 56.2 128.3 

 74 CDC LN 83.0 1.217 53.3 120.0 

 75 CDC LN 84.5 1.185 56.5 133.5 

 76 CDC LN 78.7 1.207 55.9 121.1 

 77 CDC LN 79.4 1.170 58.7 109.3 

 78 CDC LN 79.9 1.195 41.1 115.1 

 79 CDC LN 77.6 1.155 56.4 107.8 

 80 CDC LN 79.9 1.174 56.0 111.9 

 81 CDC LN 75.4 1.157 55.8 111.9 

 82 CDC LN 76.8 1.180 54.4 111.8 

 83 CDC LN 74.6 1.158 53.2 107.0 

 84 CDC LN 75.3 1.205 41.5 109.5 

 85 CDC LN 71.8 1.191 46.9 105.8 

 86 CDC LN 74.0 1.170 50.6 101.1 

 87 CDC LN 73.4 1.170 50.4 99.1 

 88 CDC LN 72.7 1.160 50.2 97.2 

 89 CDC LN 72.1 1.160 50.0 95.2 

 90 CDC LN 71.5 1.160 49.8 93.2 

 91 CDC LN 70.9 1.160 49.6 91.3 

 92 CDC LN 70.3 1.160 49.4 89.3 

 93 CDC LN 69.6 1.150 49.3 87.4 

 94 CDC LN 69.0 1.150 49.1 85.4 

 95 CDC LN 68.4 1.150 48.9 83.4 

 96 CDC LN 67.8 1.150 48.7 81.5 

 97 CDC LN 67.1 1.140 48.5 79.5 

 98 CDC LN 66.5 1.140 48.3 77.6 

 99 CDC LN 65.9 1.140 48.1 75.6 

 100 CDC LN 65.3 1.140 47.9 73.6 

 0 CDC LN 7.4 1.304 3.7 12.1 

 1 CDC LN 11.1 1.163 7.4 15.3 
February 2010 A-47 Draft – Do Not Cite or Quote 

 2 CDC LN 13.3 1.158 10.1 20.4 

 3 CDC LN 15.6 1.160 11.0 27.9 

 4 CDC LN 18.0 1.171 12.8 29.1 

 5 CDC LN 20.4 1.229 12.6 40.4 

 6 CDC LN 22.5 1.194 15.9 36.7 

 7 CDC LN 26.5 1.239 16.9 51.0 

 8 CDC LN 30.5 1.315 19.8 60.8 

 9 CDC LN 35.2 1.271 20.3 58.6 

 10 CDC LN 40.6 1.304 22.7 71.2 

 11 CDC LN 46.6 1.302 27.7 84.6 

 12 CDC LN 50.7 1.274 27.8 93.3 

 13 CDC LN 56.6 1.275 33.4 99.5 

 14 CDC LN 57.2 1.248 37.7 110.0 

 15 CDC LN 60.1 1.249 34.9 108.4 

 16 CDC LN 61.6 1.255 40.9 113.8 

 17 CDC LN 61.2 1.248 41.5 133.1 

 18 CDC LN 64.6 1.281 42.4 123.6 

 19 CDC LN 66.2 1.274 41.6 118.5 

 20 CDC LN 67.0 1.262 41.5 122.6 

 21 CDC LN 67.2 1.262 39.7 123.7 

 22 CDC LN 66.8 1.273 42.0 123.5 

 23 CDC LN 69.7 1.304 40.3 143.0 

 24 CDC LN 70.3 1.289 47.5 144.5 

 25 CDC LN 66.3 1.283 44.8 131.8 

 26 CDC LN 73.0 1.281 45.3 128.9 

 27 CDC LN 70.6 1.281 41.4 140.9 

 28 CDC LN 74.4 1.312 44.3 142.1 

 29 CDC LN 69.1 1.250 39.3 116.3 

 30 CDC LN 70.6 1.305 42.1 151.5 

 31 CDC LN 73.0 1.278 43.7 125.9 

 32 CDC LN 72.9 1.281 41.5 139.7 

 33 CDC LN 72.7 1.307 44.9 135.2 

 34 CDC LN 69.8 1.230 46.6 115.3 

 35 CDC LN 73.0 1.306 44.2 138.4 

 36 CDC LN 73.5 1.289 44.6 150.1 

 37 CDC LN 70.0 1.284 48.1 152.1 

 38 CDC LN 75.6 1.295 43.7 151.7 

 39 CDC LN 72.3 1.251 41.6 123.1 

 40 CDC LN 72.9 1.289 45.5 137.4 

 41 CDC LN 73.4 1.268 50.5 156.9 

 42 CDC LN 73.7 1.270 47.1 146.1 

 43 CDC LN 73.4 1.314 45.6 159.5 

 44 CDC LN 75.7 1.266 49.5 153.0 

 45 CDC LN 76.8 1.308 41.6 141.5 

 46 CDC LN 77.5 1.304 46.6 145.8 

 47 CDC LN 72.8 1.298 47.8 130.6 

 48 CDC LN 74.6 1.303 44.2 166.0 

 49 CDC LN 72.8 1.261 45.1 125.5 

 50 CDC LN 75.2 1.292 48.4 175.7 

 51 CDC LN 72.9 1.240 42.5 120.2 

 52 CDC LN 74.5 1.283 45.7 146.6 

 53 CDC LN 74.7 1.259 46.2 176.6 

 54 CDC LN 72.4 1.281 44.3 123.1 

 55 CDC LN 76.0 1.231 53.6 125.6 

 56 CDC LN 77.3 1.315 45.6 134.9 

 57 CDC LN 72.4 1.252 48.6 122.6 

 58 CDC LN 74.5 1.267 45.0 117.7 

 59 CDC LN 80.6 1.277 50.9 133.0 

 60 CDC LN 75.8 1.260 51.3 128.3 

 61 CDC LN 77.1 1.240 50.7 125.6 

 62 CDC LN 73.3 1.198 49.7 121.1 

 63 CDC LN 72.3 1.238 46.9 119.9 

 64 CDC LN 75.4 1.281 41.1 132.5 

 65 CDC LN 72.9 1.254 35.9 113.7 

 66 CDC LN 73.1 1.242 48.4 113.3 

 67 CDC LN 75.8 1.266 47.2 123.8 

 68 CDC LN 73.2 1.250 39.3 120.7 

 69 CDC LN 74.4 1.225 48.0 118.0 

 70 CDC LN 69.0 1.188 45.9 102.8 

 71 CDC LN 69.1 1.232 45.5 108.1 

 72 CDC LN 69.9 1.240 40.7 103.8 

 73 CDC LN 71.4 1.240 47.4 127.6 

 74 CDC LN 70.4 1.277 37.4 106.4 

 75 CDC LN 70.5 1.216 46.8 117.4 

 76 CDC LN 69.5 1.199 48.8 101.7 

 77 CDC LN 70.1 1.240 40.3 119.8 

 78 CDC LN 66.4 1.211 44.1 109.8 

 79 CDC LN 67.8 1.200 46.2 98.4 

 80 CDC LN 62.2 1.255 41.2 121.4 

 81 CDC LN 65.4 1.184 42.7 91.4 

 82 CDC LN 64.8 1.260 40.6 120.0 
February 2010 A-48 Draft – Do Not Cite or Quote 

 83 CDC LN 62.9 1.196 44.7 101.2 

 84 CDC LN 62.2 1.216 43.5 108.4 

 85 CDC LN 61.5 1.209 42.3 93.2 

 86 CDC LN 62.4 1.210 41.9 101.2 

 87 CDC LN 61.8 1.210 41.7 100.3 

 88 CDC LN 61.3 1.210 41.5 99.4 

 89 CDC LN 60.7 1.210 41.3 98.4 

 90 CDC LN 60.2 1.210 41.1 97.5 

 91 CDC LN 59.6 1.200 40.9 96.6 

 92 CDC LN 59.1 1.200 40.7 95.7 

 93 CDC LN 58.5 1.200 40.5 94.8 

 94 CDC LN 58.0 1.200 40.3 93.9 

 95 CDC LN 57.4 1.200 40.1 93.0 

 96 CDC LN 56.9 1.200 39.9 92.1 

 97 CDC LN 56.3 1.200 39.7 91.2 

 98 CDC LN 55.8 1.190 39.5 90.3 

 99 CDC LN 55.2 1.190 39.3 89.4 

 100 CDC LN 54.7 1.190 39.1 88.5 

Males age 0-100 then females age 0-100 (last revised 6-11-98) 

 Regression equation Estimate for RMR 

 Age Source DV IV Slope Interc SE Units med. wgt 

 0 R47g BMR BM 0.244 -0.127 0.290 MJ/day 2.1 

 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 

 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 
February 2010 A-49 Draft – Do Not Cite or Quote 

 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 

 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 

 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 

 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 
February 2010 A-50 Draft – Do Not Cite or Quote 

 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 

 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 

Males age 0-100 then females age 0-100 (HG last revised 12-20-05) 

Blood Volume factor and Hemoglobin content 

 Age BLDFAC HGMN HGSTD 

 0 17.0 11.9 1.0 

 1 17.0 12.2 1.0 

 2 17.0 12.4 0.8 

 3 17.0 12.7 0.8 

 4 17.0 12.8 0.8 

 5 17.0 13.0 0.9 

 6 17.0 13.2 0.9 

 7 17.0 13.5 0.8 

 8 17.0 13.4 0.8 

 9 17.0 13.6 1.0 

 10 17.0 13.6 0.9 

 11 17.0 13.7 0.7 

 12 17.0 14.0 1.0 

 13 17.0 14.3 1.0 

 14 17.0 14.7 1.0 

 15 17.0 15.1 1.0 

 16 17.0 15.4 1.0 
February 2010 A-51 Draft – Do Not Cite or Quote 

 17 17.0 15.5 1.0 

 18 17.0 15.7 1.0 

 19 20.4 15.8 0.8 

 20 20.4 15.8 0.9 

 21 20.4 15.7 0.9 

 22 20.4 15.7 0.9 

 23 20.4 15.7 0.9 

 24 20.4 15.7 0.9 

 25 20.4 15.7 0.9 

 26 20.4 15.7 1.0 

 27 20.4 15.7 1.0 

 28 20.4 15.7 1.0 

 29 20.4 15.7 1.0 

 30 20.4 15.7 1.0 

 31 20.4 15.6 1.0 

 32 20.4 15.6 1.0 

 33 20.4 15.6 1.0 

 34 20.4 15.6 1.0 

 35 20.4 15.6 1.0 

 36 20.4 15.4 1.0 

 37 20.4 15.4 1.0 

 38 20.4 15.4 1.0 

 39 20.4 15.4 1.0 

 40 20.4 15.4 1.0 

 41 20.4 15.4 1.1 

 42 20.4 15.4 1.1 

 43 20.4 15.4 1.1 

 44 20.4 15.4 1.1 

 45 20.4 15.4 1.1 

 46 20.4 15.4 1.1 

 47 20.4 15.4 1.1 

 48 20.4 15.4 1.1 

 49 20.4 15.4 1.1 

 50 20.4 15.4 1.1 

 51 20.4 15.3 1.1 

 52 20.4 15.3 1.1 

 53 20.4 15.3 1.1 

 54 20.4 15.3 1.1 

 55 20.4 15.3 1.1 

 56 20.4 15.3 1.0 

 57 20.4 15.3 1.0 

 58 20.4 15.3 1.0 

 59 20.4 15.3 1.0 

 60 20.4 15.3 1.0 

 61 20.4 15.1 1.2 

 62 20.4 15.1 1.2 

 63 20.4 15.1 1.2 

 64 20.4 15.1 1.2 

 65 20.4 15.1 1.2 

 66 20.4 15.0 1.2 

 67 20.4 15.0 1.2 

 68 20.4 15.0 1.2 

 69 20.4 15.0 1.2 

 70 20.4 15.0 1.2 

 71 20.4 14.7 1.4 

 72 20.4 14.7 1.4 

 73 20.4 14.7 1.4 

 74 20.4 14.7 1.4 

 75 20.4 14.7 1.4 

 76 20.4 14.5 1.5 

 77 20.4 14.5 1.5 

 78 20.4 14.5 1.5 

 79 20.4 14.5 1.5 

 80 20.4 14.5 1.5 

 81 20.4 14.5 1.4 

 82 20.4 14.5 1.4 

 83 20.4 14.5 1.4 

 84 20.4 14.5 1.4 

 85 20.4 14.5 1.4 

 86 20.4 14.0 1.8 

 87 20.4 14.0 1.8 

 88 20.4 14.0 1.8 

 89 20.4 14.0 1.8 

 90 20.4 14.0 1.8 

 91 20.4 13.8 1.8 

 92 20.4 13.8 1.8 

 93 20.4 13.8 1.8 

 94 20.4 13.8 1.8 

 95 20.4 13.8 1.8 

 96 20.4 13.5 1.8 

 97 20.4 13.5 1.8 
February 2010 A-52 Draft – Do Not Cite or Quote 

 98 20.4 13.5 1.8 

 99 20.4 13.5 1.8 

 100 20.4 13.5 1.8 

 0 17.0 12.2 0.7 

 1 17.0 12.3 0.7 

 2 17.0 12.6 0.8 

 3 17.0 12.5 1.0 

 4 17.0 12.8 0.8 

 5 17.0 12.9 1.0 

 6 17.0 13.0 0.8 

 7 17.0 13.1 0.8 

 8 17.0 13.3 0.8 

 9 17.0 13.4 0.8 

 10 17.0 13.6 1.0 

 11 17.0 13.5 0.9 

 12 17.0 13.6 0.9 

 13 17.0 13.5 1.0 

 14 17.0 13.6 1.0 

 15 17.0 13.5 0.9 

 16 17.0 13.5 1.1 

 17 17.0 13.5 1.1 

 18 17.0 13.5 1.2 

 19 14.6 13.4 1.1 

 20 14.6 13.5 1.1 

 21 14.6 13.5 1.2 

 22 14.6 13.5 1.2 

 23 14.6 13.5 1.2 

 24 14.6 13.5 1.2 

 25 14.6 13.5 1.2 

 26 14.6 13.3 1.1 

 27 14.6 13.3 1.1 

 28 14.6 13.3 1.1 

 29 14.6 13.3 1.1 

 30 14.6 13.3 1.1 

 31 14.6 13.3 1.1 

 32 14.6 13.3 1.1 

 33 14.6 13.3 1.1 

 34 14.6 13.3 1.1 

 35 14.6 13.3 1.1 

 36 14.6 13.5 1.2 

 37 14.6 13.5 1.2 

 38 14.6 13.5 1.2 

 39 14.6 13.5 1.2 

 40 14.6 13.5 1.2 

 41 14.6 13.5 1.3 

 42 14.6 13.5 1.3 

 43 14.6 13.5 1.3 

 44 14.6 13.5 1.3 

 45 14.6 13.5 1.3 

 46 14.6 13.6 1.2 

 47 14.6 13.6 1.2 

 48 14.6 13.6 1.2 

 49 14.6 13.6 1.2 

 50 14.6 13.6 1.2 

 51 14.6 13.7 1.1 

 52 14.6 13.7 1.1 

 53 14.6 13.7 1.1 

 54 14.6 13.7 1.1 

 55 14.6 13.7 1.1 

 56 14.6 13.8 1.2 

 57 14.6 13.8 1.2 

 58 14.6 13.8 1.2 

 59 14.6 13.8 1.2 

 60 14.6 13.8 1.2 

 61 14.6 13.8 1.1 

 62 14.6 13.8 1.1 

 63 14.6 13.8 1.1 

 64 14.6 13.8 1.1 

 65 14.6 13.8 1.1 

 66 14.6 13.8 1.1 

 67 14.6 13.8 1.1 

 68 14.6 13.8 1.1 

 69 14.6 13.8 1.1 

 70 14.6 13.8 1.1 

 71 14.6 13.8 1.1 

 72 14.6 13.8 1.1 

 73 14.6 13.8 1.1 

 74 14.6 13.8 1.1 

 75 14.6 13.8 1.1 

 76 14.6 13.8 1.3 

 77 14.6 13.8 1.3 
February 2010 A-53 Draft – Do Not Cite or Quote 

 78 14.6 13.8 1.3 

 79 14.6 13.8 1.3 

 80 14.6 13.8 1.3 

 81 14.6 13.6 1.2 

 82 14.6 13.6 1.2 

 83 14.6 13.6 1.2 

 84 14.6 13.6 1.2 

 85 14.6 13.6 1.2 

 86 14.6 13.4 1.6 

 87 14.6 13.4 1.6 

 88 14.6 13.4 1.6 

 89 14.6 13.4 1.6 

 90 14.6 13.4 1.6 

 91 14.6 13.2 1.6 

 92 14.6 13.2 1.6 

 93 14.6 13.2 1.6 

 94 14.6 13.2 1.6 

 95 14.6 13.2 1.6 

 96 14.6 13.0 1.6 

 97 14.6 13.0 1.6 

 98 14.6 13.0 1.6 

 99 14.6 13.0 1.6 

 100 14.6 13.0 1.6
February 2010 A-54 Draft – Do Not Cite or Quote 

Appendix E. All Derived Physiological Parameters

Table 1. Nv02max Values for Males: Raw and Smoothed Fits. 

 MALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit Values

Raw 

Fit Values

Smoothed

 Fit Values (1st Pctl) (99th Pctl)

0.00 48.25 1.71 44.26 52.24

1.00 48.56 2.04 43.82 53.30

2.00 48.88 2.36 43.39 54.37

3.00 49.19 2.68 42.95 55.43

4.00 49.50 3.01 42.51 56.50

5.00 49.82 3.33 42.07 57.56

6.00 50.13 3.65 41.63 58.63

7.00 51.37 50.44 2.86 3.98 41.19 59.70

8.00 53.46 50.76 2.86 4.30 40.76 60.76

9.00 51.10 51.07 6.26 4.62 40.32 61.83

10.00 51.28 51.39 5.87 4.95 39.88 62.89

11.00 50.13 51.70 6.04 5.27 39.44 63.96

12.00 50.70 52.01 7.13 5.59 39.00 65.02

13.00 52.74 52.33 5.13 5.92 38.56 66.09

14.00 52.93 52.64 4.72 6.24 38.13 67.16

15.00 53.18 52.95 5.57 6.56 37.69 68.22

16.00 49.46 53.27 6.06 6.89 37.25 69.29

17.00 49.77 53.58 6.93 7.21 36.81 70.35

18.00 51.98 53.90 7.48 7.53 36.37 71.42

19.00 59.88 54.21 9.65 7.86 35.93 72.48

20.00 56.80 54.52 9.31 8.18 35.50 73.55

21.00 54.60 54.23 8.17 8.50 34.45 74.01

22.00 54.61 53.42 8.40 8.83 32.89 73.95

23.00 53.76 52.63 9.60 9.15 31.35 73.91

24.00 57.23 51.84 10.44 9.47 29.81 73.88

25.00 50.90 51.07 10.63 9.80 28.29 73.86

26.00 50.06 50.31 9.66 10.69 25.45 75.17

27.00 46.38 49.56 8.95 10.49 25.16 73.96

28.00 48.32 48.82 10.47 10.29 24.88 72.77

29.00 51.02 48.10 12.31 10.10 24.60 71.59

30.00 45.59 47.38 9.91 9.92 24.32 70.44

31.00 45.86 46.67 10.14 9.73 24.04 69.31

32.00 46.90 45.98 11.03 9.55 23.76 68.20

33.00 42.08 45.30 9.08 9.38 23.49 67.10

34.00 44.48 44.63 8.95 9.20 23.22 66.03

35.00 38.63 43.97 10.10 9.03 22.95 64.98

36.00 42.63 43.32 7.11 8.87 22.69 63.95

37.00 40.41 42.68 8.81 8.71 22.42 62.94

38.00 39.70 42.05 6.22 8.55 22.16 61.94

39.00 40.62 41.44 8.01 8.40 21.90 60.97

40.00 39.02 40.83 8.28 8.25 21.64 60.02
February 2010 A-55 Draft – Do Not Cite or Quote 

 MALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit Values

Raw 

Fit Values

Smoothed

 Fit Values (1st Pctl) (99th Pctl)

41.00 39.72 40.24 9.96 8.10 21.39 59.09

42.00 35.58 39.66 9.85 7.96 21.14 58.18

43.00 39.98 39.09 6.46 7.82 20.89 57.28

44.00 38.65 38.53 7.60 7.69 20.64 56.41

45.00 40.15 37.98 6.59 7.56 20.40 55.56

46.00 40.67 37.44 7.89 7.43 20.16 54.73

47.00 41.51 36.92 9.68 7.31 19.91 53.92

48.00 38.92 36.40 10.52 7.19 19.68 53.12

49.00 34.65 35.90 7.68 7.07 19.44 52.35

50.00 33.85 35.41 6.49 6.96 19.21 51.60

51.00 32.52 34.92 4.51 6.86 18.98 50.87

52.00 36.31 34.45 7.08 6.75 18.75 50.16

53.00 36.23 34.00 7.31 6.65 18.52 49.47

54.00 33.91 33.55 5.29 6.56 18.30 48.79

55.00 33.40 33.11 5.08 6.46 18.08 48.14

56.00 31.68 32.69 6.52 6.37 17.86 47.51

57.00 32.47 32.27 6.33 6.29 17.64 46.90

58.00 33.24 31.87 6.32 6.21 17.43 46.31

59.00 33.05 31.48 6.45 6.13 17.22 45.74

60.00 29.02 31.10 3.59 6.06 17.01 45.19

61.00 31.68 30.73 6.95 5.99 16.80 44.66

62.00 29.72 30.37 5.09 5.92 16.60 44.14

63.00 30.90 30.02 8.06 5.86 16.40 43.65

64.00 30.65 29.69 5.32 5.80 16.20 43.18

65.00 29.86 29.36 6.90 5.75 16.00 42.73

66.00 28.60 29.05 5.51 5.70 15.80 42.30

67.00 29.47 28.75 5.25 5.65 15.61 41.89

68.00 28.95 28.46 5.63 5.61 15.42 41.50

69.00 31.13 28.18 6.43 5.57 15.23 41.13

70.00 27.12 27.91 3.44 5.53 15.05 40.78

71.00 27.65 5.50 14.86 40.45

72.00 28.56 27.41 5.71 5.47 14.68 40.13

73.00 27.62 27.17 5.03 5.45 14.50 39.84

74.00 27.84 26.95 6.27 5.43 14.33 39.57

75.00 26.74 5.41 14.15 39.32

76.00 25.05 26.54 6.68 5.40 13.98 39.09

77.00 23.74 26.35 4.99 5.39 13.81 38.88

78.00 26.17 5.38 13.65 38.69

79.00 26.00 5.38 13.48 38.52

80.00 25.84 5.39 13.32 38.37

81.00 23.68 25.70 5.88 5.39 13.17 38.22

82.00 25.57 5.39 13.04 38.09

83.00 25.44 5.39 12.92 37.97

84.00 25.33 5.39 12.81 37.86

85.00 25.23 5.39 12.70 37.76

86.00 25.14 5.39 12.62 37.67
February 2010 A-56 Draft – Do Not Cite or Quote 

 MALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit Values

Raw 

Fit Values

Smoothed

 Fit Values (1st Pctl) (99th Pctl)

87.00 25.06 5.39 12.54 37.59

88.00 25.00 5.39 12.47 37.52

89.00 24.94 5.39 12.42 37.47

90.00 24.90 5.39 12.37 37.42

91.00 24.86 5.39 12.34 37.39

92.00 24.84 5.39 12.32 37.37

93.00 24.83 5.39 12.31 37.36

94.00 24.83 5.39 12.31 37.36

95.00 24.84 5.39 12.32 37.37

96.00 24.87 5.39 12.34 37.39

97.00 24.90 5.39 12.37 37.43

98.00 24.95 5.39 12.42 37.47

99.00 25.00 5.39 12.48 37.53

100.00 25.07 5.39 12.54 37.60

Table 2. Nv02max Values for Females: Raw and Smoothed Fits 

 FEMALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit 

Values 

Raw 

Fit 

Values 

Smoothed

 Fit 

Values 

(1st 

Pctl) (99th Pctl) 

0.00 35.88 5.90 22.15 49.61

1.00 36.21 6.00 22.26 50.17

2.00 36.54 6.09 22.37 50.72

3.00 36.87 6.19 22.48 51.27

4.00 37.20 6.28 22.59 51.82

5.00 37.54 6.38 22.70 52.37

6.00 37.87 6.47 22.81 52.93

7.00 38.20 6.57 22.92 53.48

8.00 38.53 6.66 23.03 54.03

9.00 30.56 38.86 9.90 6.76 23.14 54.58

10.00 45.53 39.19 6.27 6.85 23.25 55.13

11.00 43.88 39.52 5.26 6.95 23.36 55.69

12.00 43.03 39.85 6.88 7.04 23.47 56.24

13.00 42.00 40.18 7.48 7.14 23.58 56.79

14.00 37.57 40.51 6.79 7.23 23.69 57.34

15.00 39.57 40.85 5.43 7.33 23.80 57.89

16.00 35.51 41.18 5.36 7.42 23.91 58.45

17.00 38.22 41.51 8.86 7.52 24.02 59.00

18.00 45.67 41.84 8.53 7.61 24.13 59.55

19.00 43.87 42.17 7.83 7.71 24.24 60.10

20.00 42.52 42.50 7.69 7.80 24.35 60.65
February 2010 A-57 Draft – Do Not Cite or Quote 

 FEMALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit 

Values 

Raw 

Fit 

Values 

Smoothed

 Fit 

Values 

(1st 

Pctl) (99th Pctl) 

21.00 43.45 42.10 8.51 7.90 23.73 60.48

22.00 43.22 41.45 7.59 7.99 22.86 60.05

23.00 43.87 40.81 10.13 8.09 21.99 59.63

24.00 41.14 40.18 8.22 8.18 21.14 59.22

25.00 38.20 39.56 7.09 8.28 20.30 58.82

26.00 38.98 38.95 11.12 8.37 19.47 58.43

27.00 34.94 38.35 8.02 8.35 18.93 57.76

28.00 38.08 37.75 9.80 8.14 18.82 56.69

29.00 35.13 37.17 6.30 7.94 18.71 55.64

30.00 35.79 36.60 9.10 7.74 18.59 54.61

31.00 35.22 36.04 7.89 7.55 18.47 53.60

32.00 36.06 35.48 6.93 7.37 18.35 52.62

33.00 34.95 34.94 9.51 7.19 18.23 51.66

34.00 38.13 34.41 7.08 7.01 18.10 50.72

35.00 32.63 33.88 4.88 6.84 17.97 49.80

36.00 33.59 33.37 6.17 6.68 17.83 48.91

37.00 31.11 32.87 5.13 6.52 17.70 48.04

38.00 33.12 32.37 3.76 6.37 17.55 47.19

39.00 28.80 31.89 5.14 6.22 17.41 46.37

40.00 29.06 31.42 5.74 6.08 17.26 45.57

41.00 29.54 30.95 8.00 5.95 17.11 44.79

42.00 30.90 30.50 6.82 5.82 16.96 44.03

43.00 27.60 30.05 4.32 5.70 16.80 43.30

44.00 29.33 29.62 4.17 5.58 16.64 42.59

45.00 28.53 29.19 4.90 5.47 16.48 41.90

46.00 29.41 28.78 6.00 5.36 16.31 41.24

47.00 30.49 28.37 7.15 5.26 16.14 40.60

48.00 27.92 27.97 6.05 5.16 15.97 39.98

49.00 26.48 27.59 5.36 5.07 15.79 39.38

50.00 29.80 27.21 5.13 4.99 15.61 38.81

51.00 27.49 26.84 3.66 4.91 15.43 38.26

52.00 28.95 26.49 5.83 4.83 15.24 37.73

53.00 23.77 26.14 3.56 4.77 15.06 37.23

54.00 25.34 25.80 4.61 4.70 14.86 36.74

55.00 26.05 25.48 4.29 4.65 14.67 36.29

56.00 26.30 25.16 4.91 4.60 14.47 35.85

57.00 26.06 24.85 4.07 4.55 14.27 35.44

58.00 24.55 4.51 14.06 35.05

59.00 24.27 4.48 13.85 34.68

60.00 23.67 23.99 4.81 4.45 13.64 34.33

61.00 24.70 23.72 4.65 4.43 13.43 34.01

62.00 21.63 23.46 4.99 4.41 13.21 33.71

63.00 26.64 23.21 7.38 4.40 12.99 33.44

64.00 23.84 22.97 3.77 4.39 12.76 33.18

65.00 20.26 22.74 3.83 4.39 12.53 32.95
February 2010 A-58 Draft – Do Not Cite or Quote 

 FEMALES

 MEAN MEAN SD SD MIN MAX 

Age 

Raw 

Fit Values 

Smoothed

 Fit 

Values 

Raw 

Fit 

Values 

Smoothed

 Fit 

Values 

(1st 

Pctl) (99th Pctl) 

66.00 20.38 22.52 4.39 12.31 32.73

67.00 20.49 22.31 4.39 12.10 32.52

68.00 22.05 22.11 3.90 4.39 11.90 32.32

69.00 21.92 21.92 4.56 4.39 11.71 32.13

70.00 20.38 21.74 4.15 4.39 11.53 31.95

71.00 25.30 21.57 4.39 11.36 31.78

72.00 21.21 21.41 4.39 11.20 31.62

73.00 20.46 21.26 4.59 4.39 11.05 31.47

74.00 20.63 21.12 4.39 10.91 31.33

75.00 20.60 20.99 3.80 4.39 10.78 31.20

76.00 20.91 20.87 4.39 10.66 31.08

77.00 22.27 20.76 4.39 10.55 30.97

78.00 19.93 20.65 4.39 10.44 30.86

79.00 22.80 20.56 4.39 10.35 30.77

80.00 23.19 20.48 4.39 10.27 30.69

81.00 19.29 20.41 4.39 10.20 30.62

82.00 13.44 20.34 4.39 10.13 30.55

83.00 28.03 20.29 4.39 10.08 30.50

84.00 17.00 20.25 4.39 10.04 30.46

85.00 18.69 20.21 4.39 10.00 30.42

86.00 18.18 20.19 4.39 9.98 30.40

87.00 20.18 4.39 9.97 30.39

88.00 27.15 20.17 4.39 9.96 30.38

89.00 20.18 4.39 9.97 30.39

90.00 18.18 20.20 4.39 9.98 30.41

91.00 20.22 4.39 10.01 30.43

92.00 20.26 4.39 10.05 30.47

93.00 20.30 4.39 10.09 30.51

94.00 20.36 4.39 10.15 30.57

95.00 20.42 4.39 10.21 30.63

96.00 20.50 4.39 10.28 30.71

97.00 20.58 4.39 10.37 30.79

98.00 20.67 4.39 10.46 30.88

99.00 20.78 4.39 10.57 30.99

100.00 20.89 4.39 10.68 31.10

Table 3. Body Mass Raw Fits. 
February 2010 A-59 Draft – Do Not Cite or Quote 

 

 MALES FEMALES

Age 

Geometric 

Mean GSD Min Max 

Geometric

 Mean GSD Min Max 

0.00 7.767 1.301 3.6 11.8 7.429 1.304 3.7 12.1

1.00 11.440 1.143 8.2 16.1 11.119 1.163 7.4 15.3

2.00 13.932 1.146 9.8 20.9 13.258 1.158 10.1 20.4

3.00 15.967 1.154 11.7 23.7 15.587 1.160 11 27.9

4.00 18.475 1.165 11.1 28.1 18.005 1.171 12.8 29.1

5.00 21.618 1.234 13.7 42.4 20.353 1.229 12.6 40.4

6.00 23.142 1.213 16.1 41.1 22.454 1.194 15.9 36.7

7.00 27.072 1.216 19.3 46.8 26.483 1.239 16.9 51

8.00 31.651 1.302 19.1 66.2 30.534 1.315 19.8 60.8

9.00 34.656 1.265 24 69.9 35.235 1.271 20.3 58.6

10.00 38.329 1.280 24.3 72.9 40.550 1.304 22.7 71.2

11.00 44.149 1.308 26.2 83.8 46.579 1.302 27.7 84.6

12.00 47.988 1.315 27.7 94.8 50.673 1.274 27.8 93.3

13.00 55.364 1.340 27.7 106.6 56.649 1.275 33.4 99.5

14.00 62.832 1.293 35.7 121 57.214 1.248 37.7 110

15.00 67.650 1.255 41.5 117.9 60.091 1.249 34.9 108.4

16.00 72.460 1.267 45.8 139.1 61.582 1.255 40.9 113.8

17.00 73.081 1.248 49.9 136.6 61.229 1.248 41.5 133.1

18.00 75.060 1.243 51.2 144.2 64.591 1.281 42.4 123.6

19.00 77.182 1.245 52.6 134.5 66.156 1.274 41.6 118.5

20.00 77.952 1.250 50.5 130 66.981 1.262 41.5 122.6

21.00 78.239 1.297 46.8 199.2 67.218 1.262 39.7 123.7

22.00 83.845 1.292 53.3 155.4 66.823 1.273 42 123.5

23.00 80.607 1.222 50.5 137.6 69.721 1.304 40.3 143

24.00 81.706 1.251 50.6 132.6 70.284 1.289 47.5 144.5

25.00 84.818 1.206 50.2 136.1 66.300 1.283 44.8 131.8

26.00 81.812 1.273 48.9 164.5 72.973 1.281 45.3 128.9

27.00 85.166 1.249 50 153.9 70.604 1.281 41.4 140.9

28.00 84.321 1.272 51 167.2 74.363 1.312 44.3 142.1

29.00 82.144 1.236 50.6 147.2 69.110 1.250 39.3 116.3

30.00 81.581 1.262 52.5 139 70.616 1.305 42.1 151.5

31.00 81.275 1.249 48.8 170.6 73.039 1.278 43.7 125.9

32.00 84.715 1.235 49.7 135.8 72.938 1.281 41.5 139.7

33.00 88.188 1.231 64.8 146.3 72.710 1.307 44.9 135.2

34.00 81.163 1.221 53.1 136.9 69.773 1.230 46.6 115.3

35.00 87.192 1.251 61 193.3 73.044 1.306 44.2 138.4

36.00 83.404 1.228 45.8 140.5 73.547 1.289 44.6 150.1

37.00 85.759 1.241 59.3 150.9 70.019 1.284 48.1 152.1

38.00 84.132 1.260 52.8 149.7 75.587 1.295 43.7 151.7

39.00 84.611 1.196 61.2 140.6 72.295 1.251 41.6 123.1

40.00 90.071 1.246 58.5 154 72.888 1.289 45.5 137.4

41.00 87.425 1.173 61.3 117.7 73.363 1.268 50.5 156.9

42.00 88.290 1.205 62.2 144 73.697 1.270 47.1 146.1

43.00 88.423 1.233 54 145.3 73.438 1.314 45.6 159.5

44.00 88.528 1.200 56.6 128.9 75.742 1.266 49.5 153

45.00 87.102 1.205 60.6 160.2 76.795 1.308 41.6 141.5
February 2010 A-60 Draft – Do Not Cite or Quote 

 

 MALES FEMALES

Age 

Geometric 

Mean GSD Min Max 

Geometric

 Mean GSD Min Max 

46.00 88.157 1.243 54.2 154.3 77.544 1.304 46.6 145.8

47.00 86.547 1.229 49.9 188.3 72.849 1.298 47.8 130.6

48.00 84.793 1.186 56.3 128.3 74.646 1.303 44.2 166

49.00 86.235 1.240 47 171.3 72.844 1.261 45.1 125.54

50.00 84.659 1.179 53.4 124.4 75.217 1.292 48.4 175.7

51.00 87.975 1.208 57.9 143.6 72.941 1.240 42.5 120.2

52.00 89.886 1.216 55.2 144.9 74.472 1.283 45.7 146.6

53.00 89.012 1.228 58.2 143.3 74.733 1.259 46.2 176.6

54.00 90.098 1.216 64.1 155.2 72.413 1.281 44.3 123.1

55.00 88.268 1.222 55.1 138.6 75.951 1.231 53.6 125.6

56.00 84.796 1.195 45 110.3 77.322 1.315 45.6 134.9

57.00 87.501 1.253 58.3 160 72.378 1.252 48.6 122.6

58.00 85.116 1.266 51.6 179 74.548 1.267 45 117.7

59.00 84.190 1.182 58.7 112.4 80.638 1.277 50.9 133

60.00 87.044 1.232 57.3 141.7 75.777 1.260 51.3 128.3

61.00 89.007 1.207 49.9 162.8 77.121 1.240 50.7 125.6

62.00 84.788 1.228 56.04 152.1 73.347 1.198 49.7 121.1

63.00 89.137 1.262 56.3 171.6 72.308 1.238 46.9 119.9

64.00 89.974 1.193 59.1 119 75.440 1.281 41.1 132.5

65.00 89.891 1.215 58.1 126.3 72.910 1.254 35.9 113.7

66.00 86.814 1.228 54 150.1 73.101 1.242 48.4 113.3

67.00 86.207 1.207 43.1 127.5 75.835 1.266 47.2 123.8

68.00 85.172 1.191 61.2 163.2 73.207 1.250 39.3 120.7

69.00 87.116 1.222 50.7 127.2 74.368 1.225 48 118

70.00 82.775 1.210 46.5 125.5 68.977 1.188 45.9 102.8

71.00 79.630 1.240 51 122.8 69.083 1.232 45.5 108.1

72.00 82.011 1.204 51.9 132.7 69.898 1.240 40.7 103.8

73.00 85.590 1.196 56.2 128.3 71.360 1.240 47.4 127.6

74.00 83.001 1.217 53.3 120 70.410 1.277 37.4 106.4

75.00 84.465 1.185 56.5 133.5 70.526 1.216 46.8 117.4

76.00 78.733 1.207 55.9 121.1 69.549 1.199 48.8 101.7

77.00 79.376 1.170 58.7 109.3 70.128 1.240 40.3 119.8

78.00 79.909 1.195 41.1 115.1 66.375 1.211 44.1 109.8

79.00 77.629 1.155 56.4 107.8 67.780 1.200 46.2 98.4

80.00 79.866 1.174 56 111.9 62.214 1.255 41.2 121.4

81.00 75.405 1.157 55.8 111.9 65.397 1.184 42.7 91.4

82.00 76.798 1.180 54.4 111.8 64.755 1.260 40.6 120

83.00 74.611 1.158 53.2 107 62.886 1.196 44.7 101.2

84.00 75.325 1.205 41.5 109.5 62.215 1.216 43.5 108.4

85.00 71.776 1.191 46.9 105.8 61.453 1.209 42.3 93.2

86.00 73.986494 1.17 50.57 101.07 62.400356 1.21 41.85 101.16

87.00 73.364276 1.17 50.38 99.113 61.847614 1.21 41.66 100.26

88.00 72.742058 1.16 50.19 97.154 61.294872 1.21 41.47 99.351

89.00 72.11984 1.16 50 95.194 60.74213 1.21 41.27 98.445

90.00 71.497622 1.16 49.81 93.235 60.189388 1.21 41.08 97.538

91.00 70.875404 1.16 49.62 91.276 59.636646 1.2 40.88 96.632
February 2010 A-61 Draft – Do Not Cite or Quote 

 

 MALES FEMALES

Age 

Geometric 

Mean GSD Min Max 

Geometric

 Mean GSD Min Max 

92.00 70.253186 1.16 49.44 89.317 59.083904 1.2 40.69 95.726

93.00 69.630968 1.15 49.25 87.358 58.531162 1.2 40.49 94.82

94.00 69.00875 1.15 49.06 85.399 57.97842 1.2 40.3 93.914

95.00 68.386532 1.15 48.87 83.44 57.425678 1.2 40.1 93.008

96.00 67.764314 1.15 48.68 81.481 56.872936 1.2 39.91 92.102

97.00 67.142096 1.14 48.49 79.522 56.320194 1.2 39.71 91.195

98.00 66.519878 1.14 48.3 77.563 55.767452 1.19 39.52 90.289

99.00 65.89766 1.14 48.11 75.604 55.21471 1.19 39.32 89.383

100.00 65.275442 1.14 47.92 73.645 54.661968 1.19 39.13 88.477

 **Dark shading (age 86+) designates linear forecast. 

Table 4. Body Mass Smoothed Fits (5-Year Running Averages). 

 

 MALES FEMALES 

Age 

Geometric 

 Mean GSD Min Max 

Geometric 

 Mean GSD Min Max 

0.00 7.767209794 1.300901 3.6 11.8 7.428916349 1.304229 3.7 12.1

1.00 11.44008024 1.143324 8.2 16.1 11.11947416 1.162608 7.4 15.3

2.00 13.93227373 1.145566 9.8 20.9 13.25797158 1.158434 10.1 20.4

3.00 15.96664726 1.153689 11.7 23.7 15.58684049 1.159883 11 27.9

4.00 18.47458493 1.164972 11.1 28.1 18.00506307 1.17108 12.8 29.1

5.00 21.61756114 1.233822 13.7 42.4 20.35285099 1.229237 12.6 40.4

6.00 23.14243627 1.213499 16.1 41.1 22.45431948 1.194119 15.9 36.7

7.00 27.07246068 1.215834 19.3 46.8 26.48323788 1.23892 16.9 51

8.00 31.6505017 1.301873 19.1 66.2 30.53391399 1.315137 19.8 60.8

9.00 34.65600448 1.265317 24 69.9 35.23472141 1.271364 20.3 58.6

10.00 38.32939135 1.279707 24.3 72.9 40.54996835 1.303997 22.7 71.2

11.00 44.14863459 1.30753 26.2 83.8 46.57910267 1.302182 27.7 84.6

12.00 47.98795299 1.314848 27.7 94.8 50.67329267 1.273946 27.8 93.3

13.00 55.36374737 1.33952 27.7 106.6 56.64881107 1.275455 33.4 99.5

14.00 62.83159173 1.292533 35.7 121 57.21362103 1.24795 37.7 110

15.00 67.65031426 1.254999 41.5 117.9 60.09135575 1.24897 34.9 108.4

16.00 72.45980541 1.267468 45.8 139.1 61.58214656 1.255162 40.9 113.8

17.00 73.08089659 1.248405 49.9 136.6 61.22931022 1.248057 41.5 133.1

18.00 75.06031573 1.243204 51.2 144.2 64.59054256 1.281298 42.4 123.6

19.00 77.18236513 1.244928 52.6 134.5 66.15556407 1.274083 41.6 118.5

20.00 77.95205826 1.250326 50.5 130 66.98146906 1.261822 41.5 122.6

21.00 78.45564692 1.265585 50.88 152.66 66.35375002 1.270386 41.44 122.38

22.00 79.56489519 1.261251 50.74 151.34 67.37976393 1.274844 41.02 126.26

23.00 80.46958232 1.262527 50.34 150.96 68.20537834 1.277813 42.2 131.46

24.00 81.84267254 1.253588 50.28 152.18 68.06901959 1.282127 42.86 133.3
February 2010 A-62 Draft – Do Not Cite or Quote 

 

 MALES FEMALES 

Age 

Geometric 

 Mean GSD Min Max 

Geometric 

 Mean GSD Min Max 

25.00 82.55729313 1.248802 50.7 145.24 69.21992781 1.285979 43.98 134.34

26.00 82.82151847 1.240222 50.04 144.94 69.97607936 1.287735 43.86 137.82

27.00 83.56439112 1.250399 50.14 150.86 70.90453453 1.289413 44.66 137.64

28.00 83.65195203 1.247428 50.14 153.78 70.66975978 1.28161 43.02 132

29.00 83.00459482 1.258753 50.6 154.36 71.53295767 1.285847 42.48 135.94

30.00 82.89721864 1.253937 50.58 155.58 71.54621552 1.285108 42.16 135.34

31.00 82.80701235 1.251132 50.52 151.96 72.01313142 1.28495 42.18 135.1

32.00 83.58034187 1.242848 53.28 147.78 71.6826276 1.283915 42.3 133.72

33.00 83.38418057 1.239735 53.78 145.72 71.81523165 1.280002 43.76 133.52

34.00 84.50647805 1.237533 55.48 156.58 72.30094254 1.280205 44.18 130.9

35.00 84.9321819 1.233184 54.88 150.56 72.40264379 1.282492 44.36 135.74

36.00 85.14102649 1.234298 56.8 153.58 71.81884258 1.283151 45.68 138.22

37.00 84.32994666 1.240177 54.4 154.26 72.3941641 1.280709 45.44 141.52

38.00 85.01958212 1.235131 56.02 155 72.89859355 1.284821 44.44 143.08

39.00 85.59524544 1.233983 55.52 147.14 72.86733489 1.281407 44.7 142.88

40.00 86.39949423 1.223065 58.62 142.58 72.830387 1.277165 45.88 144.24

41.00 86.90564401 1.215924 59.2 141.2 73.56585153 1.274483 45.68 143.04

42.00 87.76379051 1.210495 59.44 140.32 73.13604869 1.278433 46.06 144.6

43.00 88.54719729 1.211458 58.52 137.98 73.82543503 1.281514 47.64 150.58

44.00 87.95342484 1.203416 58.94 139.22 74.60684165 1.285327 46.86 151.4

45.00 88.09985934 1.217379 57.52 146.54 75.44302619 1.292445 46.08 149.18

46.00 87.751282 1.222211 55.06 155.4 75.27348935 1.29795 46.22 146.08

47.00 87.02523405 1.212835 55.52 152 75.51517243 1.295658 45.94 147.38

48.00 86.56661258 1.220669 53.6 160.48 74.93569966 1.29459 45.06 141.888

49.00 86.07815707 1.215489 52.16 153.32 74.62001355 1.291492 46.42 148.728

50.00 86.04175058 1.208607 52.9 151.18 73.69947055 1.278764 45.6 143.608

51.00 86.70964624 1.206031 53.96 142.5 74.02400492 1.27574 45.18 146.808

52.00 87.55345712 1.2144 54.34 145.5 74.04127315 1.266893 45.58 148.928

53.00 88.32616726 1.209663 57.76 142.28 73.95491798 1.270898 45.42 148.44

54.00 89.04784314 1.218268 58.1 145.12 74.10188224 1.25873 46.46 138.42

55.00 88.4120991 1.215526 55.52 138.46 74.97813364 1.273724 47.08 141.36

56.00 87.93495739 1.222906 56.14 141.48 74.55937637 1.267547 47.66 136.56

57.00 87.15584772 1.230391 54.82 148.62 74.52242942 1.269258 47.42 124.78

58.00 85.97418819 1.223617 53.74 140.06 76.16748501 1.268509 48.74 126.76

59.00 85.72952642 1.22558 54.18 140.68 76.13252691 1.274205 48.28 127.3

60.00 86.57173577 1.228074 55.16 151.18 76.09221736 1.259138 49.3 125.44

61.00 86.0292098 1.222944 54.708 149.6 76.28599922 1.248419 49.52 125.14

62.00 86.83331368 1.22222 55.648 148.12 75.83796229 1.242552 49.9 125.58

63.00 87.99005122 1.224363 55.728 149.44 74.79845832 1.243365 47.94 125.48

64.00 88.55927286 1.220869 55.888 146.36 74.22522224 1.242246 44.86 122.56

65.00 88.12051692 1.225034 56.708 143.82 73.42130739 1.242692 44.4 120.1

66.00 88.40439667 1.220898 54.12 138.9 73.91902253 1.256261 43.9 120.64

67.00 87.61146369 1.206714 55.1 137.22 74.09892054 1.258602 42.38 120.8

68.00 87.03986775 1.212565 53.42 138.86 73.88448789 1.247351 43.76 117.9

69.00 85.61667034 1.211601 51.1 138.7 73.09783811 1.234088 45.76 115.72

70.00 84.17987726 1.213949 50.5 133.24 72.29417333 1.23216 45.18 114.68
February 2010 A-63 Draft – Do Not Cite or Quote 

 

 MALES FEMALES 

Age 

Geometric 

 Mean GSD Min Max 

Geometric 

 Mean GSD Min Max 

71.00 83.34052655 1.213444 52.26 134.28 71.10679374 1.227009 43.88 110.68

72.00 83.42413149 1.214423 51.26 127.3 70.73734313 1.225132 45.5 112.06

73.00 82.60108145 1.213508 51.78 125.86 69.94568967 1.235452 43.38 109.74

74.00 82.93914453 1.208433 53.78 127.46 70.25540111 1.241 43.56 112.66

75.00 82.75981666 1.201809 54.76 127.12 70.34868617 1.23444 44.22 111.38

76.00 82.23282472 1.19492 56.12 122.44 70.39465694 1.234265 44.14 114.58

77.00 81.09670348 1.194698 53.1 119.8 69.39757689 1.228511 43.48 111.02

78.00 80.02242628 1.182282 53.72 117.36 68.87151054 1.213229 45.24 109.42

79.00 79.10265965 1.180128 53.62 113.04 67.20924709 1.221048 44.12 110.22

80.00 78.43698141 1.170309 53.6 111.2 66.37891246 1.217987 42.9 108.16

81.00 77.92142176 1.17229 52.74 111.7 65.30424541 1.222093 42.96 108.2

82.00 76.86173441 1.164819 55.16 110.08 64.60647334 1.219074 43.08 106.48

83.00 76.40090269 1.174796 52.18 110.42 63.49351577 1.22226 42.54 108.48

84.00 74.7828307 1.17822 50.36 109.2 63.34131978 1.213219 42.76 102.84

85.00 74.4992137 1.180574 49.31362 107.0343 62.74189138 1.218822 42.59095 104.7926

86.00 73.81250877 1.177977 48.50949 104.4969 62.16041309 1.208932 42.80295 100.844

87.00 73.43871959 1.179377 47.90759 102.5276 61.84223228 1.211505 42.15598 100.4742

88.00 72.79767378 1.170756 49.60794 99.66646 61.5476723 1.209819 41.71005 98.48308

89.00 72.74205786 1.164535 50.19052 97.15354 61.29487188 1.209189 41.46516 99.35077

90.00 72.11983988 1.162177 50.00172 95.19446 60.74212987 1.207753 41.27036 98.44462

91.00 71.4976219 1.159818 49.81292 93.23538 60.18938785 1.206317 41.07556 97.53846

92.00 70.87540393 1.157459 49.62412 91.27631 59.63664584 1.20488 40.88075 96.63231

93.00 70.25318595 1.1551 49.43532 89.31723 59.08390383 1.203444 40.68595 95.72615

94.00 69.63096798 1.152742 49.24652 87.35815 58.53116181 1.202008 40.49115 94.82

95.00 69.00875 1.150383 49.05772 85.39908 57.9784198 1.200571 40.29634 93.91385

96.00 68.38653203 1.148024 48.86892 83.44 57.42567778 1.199135 40.10154 93.00769

97.00 67.76431405 1.145665 48.68012 81.48092 56.87293577 1.197699 39.90674 92.10154

98.00 67.14209607 1.143307 48.49132 79.52185 56.32019375 1.196263 39.71193 91.19538

99.00 66.5198781 1.140948 48.30252 77.56277 55.76745174 1.194826 39.51713 90.28923

100.00 66.20876911 1.139769 48.20812 76.58323 55.49108073 1.194108 39.41973 89.83615
February 2010 A-64 Draft – Do Not Cite or Quote 

Table 5. Hemoglobin Content. 

 

 MALES FEMALES 

Age MEAN STD MEAN STD 

0 11.927 0.993545 12.209 0.729499905 

1 12.20959 1.013091 12.27307 0.719158646 

2 12.42075 0.823171 12.55018 0.843436666 

3 12.69015 0.83159 12.4519 0.965868504 

4 12.8006 0.80152 12.83442 0.773409545 

5 12.95822 0.878515 12.87154 0.969254536 

6 13.19574 0.893008 13.01866 0.828912341 

7 13.46198 0.836639 13.09899 0.754370806 

8 13.35161 0.833121 13.25291 0.826349227 

9 13.59742 0.971019 13.36671 0.808377267 

10 13.63062 0.906785 13.58919 1.034306588 

11 13.66 0.726155 13.52681 0.90041802 

12 13.9727 0.955869 13.6273 0.884271668 

13 14.28293 1.036749 13.46986 0.97623121 

14 14.70654 1.020254 13.58878 1.034527514 

15 15.13583 1.04546 13.47154 0.856131982 

16 15.36442 1.021623 13.50562 1.088863466 

17 15.45945 0.979296 13.49842 1.117860417 

18 15.7487 1.02514 13.46091 1.18250671 

19 15.76812 0.831813 13.35445 1.090493585 

20 15.79371 0.880956 13.5016 1.072791517 

25 15.71703 0.91072 13.47168 1.170602542 

30 15.70837 1.045808 13.2967 1.145254677 

35 15.55635 0.959964 13.34583 1.134192006 

40 15.43525 1.021741 13.4881 1.163867696 

45 15.44038 1.105939 13.48617 1.348669176 

50 15.41492 1.096952 13.61113 1.193756618 

55 15.31983 1.123792 13.67737 1.106237392 

60 15.27653 0.97796 13.83717 1.237714453 

65 15.07274 1.192645 13.76529 1.093354796 

70 14.96193 1.24457 13.81911 1.093565513 

75 14.72786 1.418355 13.79013 1.056812752 

80 14.51 1.476879 13.84426 1.30818261 

85 14.52915 1.352814 13.57546 1.238910845 

90 13.97647 1.757686 13.43767 1.552685662 

95 13.801 1.757686 13.2085 1.552685662 

100 13.534 1.757686 13.005 1.552685662 

 
February 2010 B-1 Draft – Do Not Cite or Quote 

Appendix B 

COHb Module for APEX4.3 

 This appendix describes the probabilistic carboxyhemoglobin (COHb) module used in the 

current APEX4.3 model. The approach described here is based primarily on the COHb module 

originally described by Biller and Richmond in two reports (Johnson et al., 1992; Johnson et al., 

2000) and used in EPA probabilistic NAAQS exposure model for CO (pNEM/CO), a 

predecessor of APEX4.3. This appendix also describes the principal changes made to the COHb 

module when it was incorporated into APEX4.3, including a change in the method used to solve 

the Coburn-Forster-Kane (CFK) equation (Coburn et al., 1965). 

B.1 The Base Physiological Model for Computing COHb Levels 

 Using time/activity data obtained from various diary studies, APEX constructs a 

composite diary for each simulated person in the specified population at risk. The composite 

diary consists of a sequence of events spanning the specified period of the exposure assessment 

(typically one calendar year). Each event is defined by a start time, a duration, a geographic 

location, a microenvironment, and an activity. Using various algorithms described in Section 4 

of the draft CO REA, APEX4.3 provides estimates of CO concentration and alveolar ventilation 

rate for each event in the composite diary. APEX4.3 then uses these data, together with 

estimates of various physiological parameters specific to the simulated individual, to estimate the 

percent COHb in the blood (%COHb) as an average %COHb value over the duration of each 

exposure event and as an instantaneous %COHb level at the end of each event. 

The %COHb calculation is based on the solution to the non-linear CFK equation, 

previously described in Appendix E of Johnson et al. (2000). 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 (i.e., 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. 
February 2010 B-2 Draft – Do Not Cite or Quote 

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 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 exposure model (Biller and Richmond, 1982, Johnson and Paul, 1983). 

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) 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 can be analytically integrated. However, the 

analytical solution in this case cannot be solved explicitly for COHb. Muller and Barton (1987) 

demonstrated a binary search method for determining the COHb value. 

The COHb module used in pNEM/CO employed a linear relationship between COHb and 

O2Hb which was consistent with the basic assumptions of the CFK model. The approach 

differed from the linear forms used by other modelers in that the Muller and Barton (1987) 

solution was employed. However, instead of the simple binary search described by Muller and 

Barton (1987), 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 (1987) solution 

increased computation time compared to the Peterson and Stewart (1975) method but was shown 

to be faster than fourth-order Runge-Kutta numerical integration. 

APEX4.3 employs a different approach in which the CFK equation is solved using a 

fourth-order Taylor’s series expansion with subintervals. This method, first incorporated in 

Version 3 of APEX, is described in Section C.2 of this appendix. A more detailed description 

can be found in the Programmer’s Guide for the APEX3 model (Glen, 2002). 

B.2 The CFK Model for Estimation of Carboxyhemoglobin 

 Table C-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. 

 [ ]

[ ] [ ]

2

2

MBV O Hb

P COHb

BV

P

V

V

dt

d COHb

b

CO

b

Ico

b

co     (Eq. B-1) 

where, 

 

A

B H O

Lco V

P P

D

B 

1 2    (Eq. B-2) 
February 2010 B-3 Draft – Do Not Cite or Quote 

Table B-1. Definitions of CFK Model Variables. 

Variable Definition Units 

t Time from start of an exposure event minutes 

[COHb] Concentration of carboxyhemoglobin (COHb) in blood at 

time t 

ml CO per ml blood at STPD 

[O2Hb] Concentration of oxyhemoglobin (O2Hb) in blood at time t ml O2 per ml blood at STPD 

[RHb] Concentration of reduced hemoglobin in blood equivalent ml CO per ml of 

blood at STPD 

[COHb]0 [COHb] at t = 0 ml CO per ml blood at STPD 

[THb]0 [RHb] + [COHb] + [O2Hb] 

%[COHb] [COHb] expressed as percent of [RHb]0 % 

%[O2Hb] [O2Hb] expressed as percent of [RHb]0 % 

%[COHb]0 [COHb] at t = 0 % 

%[COHB]∞ [COHb] at t = ∞ % 

PICO

Pressure of inspired CO in air saturated with water vapor 

at body temperature 

torr 

PCCO

Mean pulmonary capillary CO pressure torr 

PCO2

Mean pulmonary capillary O2 pressure torr 

PB Barometric pressure torr 

P H O2

Vapor pressure of water at body temperature, or 47 torr 

VA

Alveolar ventilation rate ml/min STPD 

VCO Endogenous CO production rate ml/min STPD 

DLCO

Pulmonary CO diffusion rate ml/min/torr, STPD 

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 % 

Notes: 

1

 Standard Temperature Pressure, and Dry (STPD)
February 2010 B-4 Draft – Do Not Cite or Quote 

 If the only quantity in equation (B-1) 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: 

 CO  O Hb  O  COHb 2 2 (Eq. B-3) 

The following equation, the Haldane relationship, applies approximately at equilibrium 

conditions. 

 M

Pc O Hb

Pc COHb

CO

O  [ ]

[ ]

2

2 (Eq. B-4) 

The Haldane coefficient, M, is the chemical equilibrium constant for reaction (B-3). The 

above reaction can also be viewed as the difference between two competing chemical reactions: 

 CO  RHb  COHb (Eq. B-5) 

 

 O RHb O Hb 2   2 (Eq. B-6) 

 

Subtracting (B-6) from (B-5) yields (B-3). If (B-3) is in equilibrium, then (B-5) and (B6) are in equilibrium. If k represents the equilibrium constant for (B-6) then: 

 k

Pc RHb

O Hb

O

 [ ]

[ ]

2

2 (Eq. B-7) 

 It is known that an individual breathing air free of CO for an extended period will have 

about 97% of their reactive Hb bound with oxygen (O2Hb) and the remainder (3%) as the 

reduced form (RHb). It is also known that at one atmosphere barometric pressure, the mean 

pulmonary capillary oxygen pressure is approximately 100 torr. Substituting into (B-7) yields 

0.32 as the approximate value of k at body temperature. From mass balance considerations: 

 O Hb COHb RHb THb O [ ] [ ] [ ] [ ] 2    (Eq. B-8) 

Eliminating [RHb] between (B-7) and (B-8) and solving for [O2Hb] yields: 
February 2010 B-5 Draft – Do Not Cite or Quote 

 ([ ] [ ]) 1 [ ] 2 0

2

2 THb COHb

kPc

kPc

O Hb

O

O    (Eq. B-9) 

 This equation represents the aforementioned linear form of the CFK equation. It has the 

same form as a relationship given by McCartney (1990), but replaces the constant in the 

McCartney equation by the term in (B-9) involving the mean pulmonary capillary oxygen 

pressure and the equilibrium constant k. Substituting (B-9) into (B-1) yields a CFK equation free 

of [O2Hb] and fully consistent with Coburn, Forster, and Kane’s original derivation. 

 

b

O

b O

I

b

CO

kMBV

kPc

THb COHb

COHb

BV

P

V

V

dt

d COHb CO 2 1

[ ] [ ]

[ ] [ ] 

     

 (Eq. B-10) 

In working with the CFK model it is convenient to express COHb as a percent of [RHb]0. 

Multiplying (B-10) by 100 and dividing by [RHb]0 yields the expression 

O b

CO

b

I

b

CO

O k RHb MBV

kP

COHb

COHb

BV

P

V

V

dt THb

d COHb CO

[ ]

100(1 )

100 %[ ]

%[ ] ( ) [ ]%[ ] 100 2 

      (Eq. B-11) 

 

Equation (B-11) can be written in the form suggested by Muller and Barton (1987): 

100 %[ ]

%[ ] %[ ]

1 COHb

COHb C C

dt

d COHb

O    (Eq. B-12) 

where, 

 ( ) [ ]

100

b

I

b

CO

O

O BV

P

V

V

THb

C CO    (Eq. B-13) 

 

O b

CO

k THb MBV

kP

C

[ ]

100(1 ) 2

1

  (Eq. B-14) 

 Given values for the atmospheric pressure and the physiological variables in equations 

(B-12) through (B-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 (B-12) is equivalent to a MichaelisMenton kinetics model which can be integrated. The integration yields: 

0

%[ ] %[ ]

(%[ ] %[ ]) ( 1 ) %[ ] %[ ] (100 %[ ] )ln        







O

O O COHb COHb

COHb COHb C C t COHb COHb COHb (Eq. B-15) 

The equation for %[COHb]∞ is obtained by setting equation (B-12) equal to zero and 

solving for %[COHb], which is now equal to %[COHb]∞: 
February 2010 B-6 Draft – Do Not Cite or Quote 

 ( )

100 %[ ] C C1C COHb

O

O

   (Eq. B-16) 

Equation (B-15) cannot be solved explicitly for %[COHb]. Muller and Barton (1987) 

suggest the binary search method as one way to find the value of %[COHb]. Press et al. (1986) 

contend a combination of the binary search and Newton-Raphson methods is faster on average. 

Consequently, the pNEM/CO version of the COHb module used a combination of the binary 

search and Newton-Raphson root finding methods to solve for COHb (Press et al., 1986). Using 

the Muller and Barton (1987) solution increased the computation time when compared with the 

Peterson and Stewart (1975) method, however it was still shown to be faster than the fourthorder Runge-Kutta numerical integration. 

The current version of APEX (APEX4.3) employs an alternative approach in which the 

CFK equation is solved using a fourth-order Taylor’s series expansion with subintervals. This 

method, first incorporated in Version 3 of APEX, is described in detail in the Programmer’s 

Guide for the APEX3 Model by Glen (2002). This reference also includes the results of various 

tests conducted on 10 candidate methods for solving the CFK equation. The selected method 

(fourth-order Taylor series with subintervals) was chosen because of its simplicity, fast execution 

speed, and ability to produce relatively accurate estimates of %COHb at both low and high levels 

of CO exposure. Additional information concerning the %COHb calculation method and its 

theoretical basis can be found in Section 10.2 of US EPA (2008). 

In developing the fourth-order Taylor Series expansion approach, Glen (2002) began by 

defining N(t) as the %COHb level in the blood at time t, a quantity that is mathematically 

restricted to range between 0 and 100 (percent). N(t) satisfies the following differential equation: 

 N`(t) = C0 - C1 N(t) / (100 - N(t) ) (Eq. B-17) 

where C0 and C1 are constants (at least over the duration of one event) that depend on physical 

and physiological parameters and on the CO concentration in the air. Equation (C-17) is 

equivalent to (B-12) above, except that (B-12) uses the symbol %[COHb] instead of N(t). 

The task of expanding N(t) in a Taylor’s series becomes simpler if the following new 

variables are defined: 

 D0 = 1- N(0) / 100 (Eq. B-18) 

 A0 = C0 / (C0 + C1) (Eq. B-19) 

 A1 = C1 / (C0 + C1) (Eq. B-20) 

 D = D0 - A1 (Eq. B-21) 

 

z = (C0 + C1) t / (100*D0*D0) (Eq. B-22) 

The z variable is a re-scaled time variable that is dimensionless. It is used as the 
February 2010 B-7 Draft – Do Not Cite or Quote 

independent variable for the Taylor’s series expansion. In equations expressed as functions of z

rather than t, any primes will indicate the derivatives with respect to z. 

Expressing (B-17) as a function of z yields the expression 

 N`(z) = D0 2 A0 - D0 2 A1 N(z) / ( 100 - N(z) ) (Eq. B-23) 

The Taylor’s series about the origin (z = 0) for N(z) is given by 

 N(z) = N(0) + N`(0) z + N``(0) z2

 / 2 + N```(0) z3 / 6 + Niv(0) z4/ 24 + ... (Eq. B-24) 

Through a series of algebraic substitutions, Glen (2002) shows that the Taylor series 

expansion of N(z) truncated to the fourth order can be represented by 

 T4(z) = T3(z) - 100 A1 D0 D (A1

 2 - 8 D A1 + 6 D 2) z4

 / 24 (Eq. B-25) 

where 

 T3(z) = N(0) +100 D0 D z -100 A1 D0 D z2

 / 2 + 100 A1 D0 D (A1 - 2D) z3 / 6 (Eq. B-26) 

Tests showed that the fourth-order Taylor series expansion (B-25) provided greater 

accuracy than the third-order expansion for z values close to one. Glen (2002) found that z

values below one generally correspond to N(0) values below forty to fifty percent for one-hour 

exposure events. 

The z value for a given event depends on the event duration, the initial %COHb level 

N(0), and on the physiological parameters, and can be directly evaluated at the start of each 

event. For events with a z value above some threshold, it is possible to improve the performance 

of (B-25) by dividing the event into smaller events (“subintervals”), each with a shorter duration 

and hence smaller z value. As the subinterval duration decreases, accuracy increases at the 

expense of program execution time. APEX4.3 enables the user to select a limit on z which in 

turn determines the number of subintervals to be used in applying the fourth-order Taylor 

expansion. Glen (2002) recommends that the limit on z be set at 0.4 or 0.5. 

B.3 Application of the COHb Model in APEX4.3 

Description of APEX4.3 for CO

 APEX4.3 follows the daily activities over an extended period of a finite set of simulated 

individuals residing within a given geographic area. The period may be a single season or a 

calendar year. Each simulated individual is defined by a set of general demographic 

characteristics that includes age, gender, and body weight. The values of these factors are used 

to derive values for blood volume, menstrual phase, endogenous CO production rate, and other 

factors required by the COHb module (see Section B.4). The exposure of each individual 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 
February 2010 B-8 Draft – Do Not Cite or Quote 

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 APEX4.3 calculate 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 simulated individual are contiguous, the 

model can combine these concentrations to output distributions of one-hour and running eighthour exposures for each individual. The exposures calculated for the simulated individuals can 

then be population-weighted to produce exposure distributions for population groups of 

particular interest (e.g., people with coronary heart disease). 

 APEX4.3 constructs a year-long time/activity pattern for each simulated individual by 

sampling 24-hour activity patterns from the Consolidated Human Activity Data Base (CHAD), 

which is described in Section 4.4.3 of the draft CO REA. The sampling approach attempts to 

match the 24-hour activity patterns to the simulated individual and exposure period according to 

the demographic characteristics of the individual and the season, day type (weekday/weekend), 

and maximum temperature of each day in the specified exposure period. 

The COHb Module

 The COHb module in APEX4.3 currently employs the version of the CFK model 

represented by equations (B-12) through (B-14) to compute an average COHb value over the 

duration of each exposure event and an instantaneous COHb level at the end of each event. To 

perform these computations, 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 calculated COHb values for each exposure event and outputs distributions of COHb 

levels by population group for averaging times ranging from one hour to one day. 

Assignment of CFK Model Input Data for an Exposure Event

 Section B.4 describes the equations and procedures used by the APEX4.3 COHb module 

to obtain the values of the input variables for equations (B-2) and (B-13) through (B-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. 
February 2010 B-9 Draft – Do Not Cite or Quote 

 The COHb model presently used in APEX4.3 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 individuals, 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 

individual and calendar day processed by APEX. 

C.4 Computation of Input Data for the COHb Module 

 As discussed in the previous section and in Sections 4.4.5 of the draft CO REA, 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 B-2 provides a list and 

description of the principal parameters; additional parameters are listed and described in Chapter 

5 of US EPA (2008). An algorithm within APEX4.3 probabilistically generates a value for each 

parameter on the list (collectively referred to as a physiological profile) for each simulated 

individual. Figure B-1 is a flow diagram showing the process by which each physiological 

profile is generated. Each of the generated physiological profiles is internally consistent, in that 

the 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. 

 For each simulated individual, APEX4.3 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 

here as a unit exposure sequence (UES). Each pool consists of a collection of UESs that are 

specific to selected demographic characteristics of the individual (e.g., age and gender), season, 

day type (weekday/weekend), and maximum 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 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 
February 2010 B-10 Draft – Do Not Cite or Quote 

 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 
February 2010 B-11 Draft – Do Not Cite o

Table B-2. Principal Parameters Included in the Physiological Profile for Adults for Applications of APEX4.3. 

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter 

Method Used to Estimate Parameter Va

Age COHb 

Ventilation rate 

Demographic group Randomly selected from population-weighted distribution spe

group 

Gender COHb 

Ventilation rate 

Demographic group Randomly selected from population-weighted distribution spe

group 

Body Weight COHb 

Ventilation rate 

Gender Age 

Randomly selected from population-weighted lognormal distr

mean (GM) and geometric standard deviation (GSD) distributgender derived from data from the National Health and NutriSurvey (NHANES), for the years 1999-2004 (Isaacs and Smi

Height COHb Weight 

Gender 

Estimated using equations developed by Johnson (1998) usindata provided by Brainard and Burmaster (1992).  Males: height = 34.43 inches + (6.67)[ln(weight)] + (2.3 Females: height = 48.07 inches + (3.07)[ln(weight)] + (2.4The z term is randomly selected from a unit normal [N(0,1)] d

height (inches), weight (lbs). 

Menstrual phase COHb Gender 

Age 

If gender = female, menstrual phase was randomly assigned cycles according to the following age-specific probabilities.  Age < 12 or >50: 100% premenstrual  Age 12 through 50: 50% premenstrual, 50% postmenstrua
February 2010 B-12 Draft – Do Not Cite o

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter 

Method Used to Estimate Parameter Va

Blood volume COHb Gender 

Weight Height 

Blood volume (Vb) was determined according to gender by th

which are based on work by Allen et al. (1956) which was mounits used for height and weight.

 Males: Vb = (20.4)(weight) + (0.00683)(H3) - 30 

 Females: Vb = (14.6)(weight) + (0.00678)(H3) - 30 

Units: blood volume (ml), weight (lbs), height (inches). 

Hemoglobin content of the blood, Hb 

COHb Gender 

Age 

Randomly selected from normal distribution with arithmetic marithmetic standard deviation (ASD) determined by gender an

obtained from data from the National Health and Nutrition Ex(NHANES), for the years 1999-2004 (see Isaacs and Smith, 2Units: grams of Hb per deciliter of blood 
February 2010 B-13 Draft – Do Not Cite o

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter 

Method Used to Estimate Parameter Va

Pulmonary CO diffusion rate, 

DL

CO

COHb Gender 

Height Age 

Pulmonary CO diffusion rate (DL) was determined according age according to the following equations obtained from a pap

and modified to conform to the units used in the COHb modu Males:

DL

CO = (0.361)(height) - (0.232)(age) + 16.3 ml/min/torr 

 Females:

DL

CO = (0.556)(height) - (0.115)(age) - 5.97 ml/min/torr 

Units:

DL

CO (ml/min/torr), height (inches), age (years). 

Given the alveolar ventilation rate for the exposure event the pulmonary diffusion rate is calculated as: 

( )  ( )  0.000845  5.7 DLCO Adjusted DLCO Base VA


February 2010 B-14 Draft – Do Not Cite o

Parameter 

Algorithm(s) 

Containing 

Parameter 

Other Parameters 

Required for 

Calculating 

Parameter 

Method Used to Estimate Parameter Va

Endogenous CO production rate 

COHb Gender 

Age Menstrual phase 

Endogenous CO production rate was randomly selected from distribution with geometric mean (GM) and geometric standa

determined according to the following equations specific to agmenstrual phase.  Males, 18+: GM = 0.473, GSD = 1.316  Females, 18+, premenstrual: GM = 0.497, GSD = 1.459  Females, 18+, postmenstrual: GM = 0.311, GSD = 1.459 Units: GM (ml/hr), GSD (dimensionless). 

Resting metabolic rate (RMR) 

Ventilation rate 

Gender Age Body Weight 

See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA 

Energy conversion factor (ECF) 

Ventilation rate 

Gender See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA 

NVO2max Ventilation 

rate 

Gender Age

See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA 

VO2max Ventilation 

rate 

NVO2max

Body Weight 

See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA 
February 2010 B-15 Draft – Do Not Cite or Quote 

Demographic Profile of

Simulated Individual

Gender Weight Age

Height

Menstrual Phase

Endogenous CO

Production Rate

Blood Volume

Pulmonary CO

Diffusion Rate

Maximum Normalized

Oxygen Uptake Rate

(NVO2max)

Maximum Oxygen Uptake

Rate (VO2max)

Resting Metabolic Rate

(RMR)

Total Hemoglobin

Content

of the Blood

Energy Conversion Factor

(ECF)

Figure B-1. Flow Diagram for Physiological Profile Generator. Input data is supplied at the start 

of the APEX4.3 computation. 
February 2010 B-16 Draft – Do Not Cite or Quote 

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 module also computes the average percent COHb value for each 

exposure event. The main program retains these values and uses them to calculate percent 

COHb values for averaging times ranging from one hour to one day. 

 Some of the above data do not change during an APEX4.3 computer run and, therefore, 

need to be supplied to the computer program only once at the start. Some of the data vary with 

the individual 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. 

Barometric Pressure

 A constant barometric pressure is assumed for the study area based on the average height 

above sea level: 

P 760 exp( 0.0000386 Altitude) B     (Eq. B-27) 

where altitude is the average height (in feet) of the study area above sea level (US EPA, 1978). 

The altitude was set at 5,183 feet for Denver and 328 feet for Los Angeles. 

Average Pulmonary Capillary Oxygen Pressure

 The equation employed is based on an approximation used by Peterson and Stewart 

(1975) in which 49 torr is subtracted from the partial pressure of inspired oxygen. This leads to 

the following approximate relationship: 

 0.209( 47) 49 2 PcO  PB   (Eq. B-28) 

where 0.209 is the mole fraction of O2 in dry air and 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). Often times a value 100 torr is commonly used as Equation (B-28) generates this value 

for a barometric pressure equivalent to 760 torr. 

Haldane Coefficient

 The value of 218 was used for the Haldane coefficient. While measured values in the 

range 210 to 270 have been reported in the extent literature, most researchers use values within 

the range of 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 selected for 

use in the COHb module of the earlier CO-NEM exposure model. Other researchers using 

values in the range 218 to 220 include Peterson and Stewart, 1970; Marcus, 1980; Collier and 
February 2010 B-17 Draft – Do Not Cite or Quote 

Goldsmith, 1983; and Muller and Barton, 1987. As the value 218 falls within the range currently 

used by researchers, EPA analysts have elected to continue using this value in APEX4.3. 

Equilibrium Constant for the Reaction of O2 and RHb

 This quantity was estimated in Section C.2 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 CO2 P . 

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 

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: 





 



     

100 [ ] 1.39 (100 % ) 1 HbAlt RHb O Hb MetHb (Eq. B-29) 

where HbAlt is the percent increase in Hb due to exposure to altitude and is given by (US EPA, 

1978): 

Altitude HbAlt e0.0001249  2.76

Hb in equation (B-29) 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 B-2 for the distributions of Hb by age and gender). Given the hemoglobin content 

of the blood based on the distributions listed in Table B-2, [THb]0 is calculated using equation 

(B-29). The weight percent MetHB, %MetHB, is taken to be 0.5% of the weight of Hb (Muller 

and Barton, 1987). 

Determination of Weight

 Body mass or weight (in kg) was determined by fitting lognormal distributions to data 

organized by age and gender from the National Health and Nutrition Examination Survey for the 

years 1999-2004 (Isaacs and Smith, 2005). Distribution parameters were estimated for singleyear age cohorts for both genders for ages 0-85. As the NHANES 1999-2004 studies only 

covered persons up to age 85, linear forecasts for the parameters were made for ages 86-100, as 

based on the data for ages 60 and greater. 
February 2010 B-18 Draft – Do Not Cite or Quote 

Determination of Height

The following equations were used to estimate height as a function of gender and weight. 

Equations B-30 and B-31 were derived by Johnson (1998) using height and weight data provided 

by Brainard and Burmaster (1992). 

 males: height = 34.43 inches + (6.67)[ln(weight)] + (2.38 inches)(z) (Eq. B-30) 

 females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(z) (Eq. B-31) 

where the z term was randomly selected from a unit normal [N(0,1)] distribution. 

Base Pulmonary Diffusion Rate of CO

 A base lung diffusivity of CO for the individual is calculated as follows: 

 Men: D  0.361 height  0.232 age 16.3 LCO (Eq. B-32) 

 Women: 97 D  0.556 height  0.115 age  5. LCO (Eq. B-33) 

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 B-32 and B-33 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 simulated individual during each individual exposure 

event. 

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 Table B-3 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. Table B-2 presents these distributions 

classified by class, gender, and menstrual phase. 
February 2010 B-19 Draft – Do Not Cite or Quote 

 For each male individual, APEX4.3 specifies a single value for endogenous CO 

production rate and uses it for all days of the year. For each female individual between 18 and 

64 years of age, APEX4.3 specifies one value of endogenous CO production rate to represent 

premenstrual days and one value to represent postmenstrual days. Female individuals under 12 

years and older than 50 are assumed to be premenstrual; consequently, APEX4.3 specifies a 

single value for endogenous CO production rate to be used for all days of the year. The specified 

values are randomly selected from the appropriate distributions presented in Table B-2. A 

random number, z, is sampled from the standardized normal distribution, N(0,1) to make each 

selection. The appropriate endogenous CO production rate is then obtained from: 

 

 z VCO  0.01667  (geom.mean) (geom.S.D.)  (Eq. B-34) 

The constant term converts ml/hr to ml/min. 

 A probabilistic algorithm within APEX4.3 assigns a menstrual phase to each day of the 

year for female individuals aged 12 to 50 years. The algorithm randomly assigns a number 

between 1 and 28 to January 1. The number is increased by one for each successive day until 

number 28 is reached. The next day is numbered 1 and the 28-day numbering cycle is repeated 

until each day of the year has been assigned a number between 1 and 28. Days numbered 1 

through 14 are identified as post-menstrual days; days numbered 15 through 28 are identified as 

pre-menstrual days. 

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: 

 6 ( ) ( 47) 10 PI  CO  Pb   CO (Eq. B-35) 

 

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 APEX4.3 computation. 
February 2010 B-20 Draft – Do Not Cite or Quote 

Alveolar Ventilation Rate

 The main program supplies the COHb module with ventilation rate derived from the 

algorithm discussed in Section 4.4.5 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: 

( )  ( )  0.000845  5.7 DLCO Adjusted DLCO Base VA

 (Eq. B-36) 

Table B-3. Literature Data Used to Derive Geometric Mean and Standard Deviation 

Lognormal Distribution of Endogenous CO Production Rate. 

Study Author Values for Endogenous CO Production Rate 

Brouillard et al. 0.81 0.57 0.33 0.7 0.58 0.38 0.51 0.55 

(1975) 0.37 0.49 0.45 0.5 0.33 0.45 0.36 

Burke et al. 

(1974) 

0.43 0.58 0.52 0.59 0.8 0.72 0.54 

Coburn et al. 0.35 0.4 0.39 0.43 0.35 0.51 0.42 0.57 

(1963) 0.45 

0.45 0.26 0.6 0.45 0.39 0.4 

0.57 0.54 0.72 0.99 0.48 0.53 0.43 

DelivoriaPapadopoules 

et al. (1974) 0.23 0.51 0.34 0.41 0.26 0.16 0.3 

Luomanmaki 

and Coburn 

(1969) 

0.38 0.42 0.41 0.54 0.38 

0.4 0.81 0.26 0.65 0.51 0.62 0.44 

0.72 0.37 0.23 0.33 0.42 0.44 0.29 0.48 

Lynch and 

Moede (1972) 

0.48 0.23 0.25 0.2 0.22 0.15 0.21 

Merke et al. 0.64 0.86 0.35 0.52 0.8 0.54 0.68 0.28 

(1975) 0.4 0.47 0.23 0.24 0.55 0.32 0.43 0.35 

Werner and 

Lindahl (1980) 

0.54 0.76 0.48 0.31 0.7 0.36 0.65 
February 2010 B-21 Draft – Do Not Cite or Quote 

B.5 References

Allen TH, Peng MT, Chen KP, Huang TF, Chang C, Fang HS. (1956). Prediction of blood 

volume and adiposity in man from body weight and cube of height. Metabolism. 5:328-

345. 

Biller WF and Richmond HM. (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 RP, Contrad ME, Bensinger TA. (1975). Effect of blood in the gut on measurements 

of endogenous carbon monoxide production. Blood. 45:67-69. 

Brainard J and Burmaster D. (1992). Bivariate distributions for height and weight of men and 

women in the United States. Risk Analysis. 12(2):267-275. 

Burke PD, Rodkey FL, Blaschke TF, Collison HA, Waggoner JG. (1974). Comparison of 

plasma bilirubin turnover and carbon monoxide production in man. J Lab Clin Med. 

83:29-37. 

Coburn RF, Forster RE, Kane RB. (1965). Considerations of the physiology and variables that 

determine blood carboxyhemoglobin concentration in man. J Clin Invest. 44:1899-1910. 

Coburn RF, Blakemore WS, Forster RE. (1963). Endogenous carbon monoxide production in 

man. J Clin Invest. 42:1172-1178. 

Collier C and Goldsmith JR. (1983). Interactions of carbon monoxide and hemoglobin at high 

altitude. Atmos Environ. 17:723-728. 

Delivoria-Papadoppulos M, Coburn RF, Forster RE. (1974). Cyclic variation of rate of carbon 

monoxide production in normal women. J Appl Physiol. 36:49-71. 

Friedlander SK. (1982). Letter from the Clean Air Scientific Advisory Committee to US EPA 

Administrator. August 31, 1982. 

Glen G. (2002). Programmer’s Guide for the APEX3 Model. Prepared by ManTech 

Environmental Technology, Inc., for the U.S. Environmental Protection Agency, 

Research Triangle Park, NC. April 30. 

Issacs K and Smith L. (2005). New Values for Physiological Parameters for the Exposure 

Model Input File Physiology.txt. Technical memorandum to Tom McCurdy, NERL 

WA10. December 20, 2005. Provided in Appendix A of the 2nd draft CO REA. 

Johnson T and Paul RA. (1983). The NAAQS Model (NEM) Applied to Carbon Monoxide. 

EPA 450/5-83-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 
February 2010 B-22 Draft – Do Not Cite or Quote 

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. 

Johnson T, Capel J, Olaguer E, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposures 

and Associated Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic 

Version of NEM. Prepared by TRJ Environmental, Inc., for the Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. 

Johnson T, Mihlan G, LaPointe J, et al. (2000). Estimation of Carbon Monoxide Exposures and 

Associated Carboxyhemoglobin Levels in Residents of Denver and Los Angeles Using 

pNEM/CO (Version 2.1). Prepared by TRJ Environmental, Inc., for the Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research 

Triangle Park, NC. 

Luomanmaki K and Coburn RF. (1969). Effects of metabolism and distribution of carbon 

monoxide on blood and body stores. Am J Physiol. 217(2):354-362. 

Lynch SR and Moede AL. (1972). Variation in the rate of endogenous carbon monoxide 

production in normal human beings. J Lab Clin Med. 79:85-95. 

Marcus AH. (1980). Mathematical models for carboxyhemoglobin. Atmos Environ. 14:841-

844. 

McCartney ML. (1990). Sensitivity analysis applied to Coburn-Forster-Kane models of 

carboxhemoglobin rormation. Am Ind Hyg Assoc J. 51(3):169-177. 

Merke C, Cavallin-Stahl E, Lundh B. (1975). Carbon monoxide production and reticulocyte 

count in normal women: effect of contraceptive drugs and smoking. Acta Med Scan. 

198:155-160. 

Muller KE. and Barton CN. (1987). A nonlinear version of the Coburn, Forster and Kane model 

of blood carboxyhemoglobin. Atmos Environ. 21:1963-1967. 

Peterson JE and Stewart RD. (1970). Absorption and elimination of carbon monoxide by 

inactive young men. Arch Environ Health. 21:165-171. 

Peterson JE and Stewart RD. (1975). Predicting the carboxhemoglobin levels resulting from 

carbon monoxide exposures. J Appl Physiol. 39:633-638. 

Press WH, Flannery BP, Teukolsky SA, Vettering WT. (1986). Numerical Recipes: The Art of 

Scientific Computing. 1st edition. Cambridge University Press, Cambridge MA. 

Rodkey FL, O’Neal JD, Collison HA. (1969). Oxygen and carbon monoxide equilibria of 

human adult hemoglobin at atmospheric and elevated pressure. Blood. 33:57-65. 
February 2010 B-23 Draft – Do Not Cite or Quote 

Salorinne Y. (1976). Single-breath pulmonary diffusing capacity. Scand J Resp Diseases. 

Supplementum 96. 

Tikuisis P, Madill HD, Gill BJ, Lewis WF, Cox KM, Kane DM. (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 DM, McLellan TM, Buick F, Fairburn SM. (1992). Rate of formation of 

carboxyhemoglobin in exercising humans exposed to carbon monoxide. J Appl Physiol. 

72(4):1311-1319. 

US EPA. (1978). Altitude as a Factor in Air Pollution. EPA-600/9-78-015. U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

US EPA. (2008). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model 

Documentation (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support 

Document. Report No. EPA-452/B-08-001b. Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

Werner B and Lindahl J. (1980). Endogenous carbon monoxide production after bicycle 

exercise in healthy subjects and in patients with hereditary spherocytosis. Scan J Lab 

Invest. 40:319-324. 
February 2010 C-1 Draft – Do Not Cite or Quote 

Appendix C 

Isaacs et al. (2009) Reference Used in Developing D and A 

Statistics Input to APEX Model 

The following presents a reformatted version of the Isaacs et al. (2009) presentation to allow for 

easier reading. The poster, included at the end of this Appendix in its entirety, was originally 

presented at the American Time Use Research Conference, June 25-26, 2009, University of 

Maryland, College Park, MD. 
February 2010 C-2 Draft – Do Not Cite or Quote 

Statistical Properties of Longitudinal Time-Activity Data for Use in EPA Exposure Models

Kristin Isaacs1

, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen 

Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle Tulve2, and Daniel 

Vallero2

 

1

Alion Science and Technology, Research Triangle Park, NC, 2Human Exposure and 

Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental 

Protection Agency, Research Triangle Park, NC, 3

School of Information and Library Science, 

University of North Carolina at Chapel Hill, Chapel Hill, NC, 4

Office of Air Quality Planning 

and Standards, US Environmental Protection Agency, Research Triangle Park, NC, 

5

Homemaker, Durham, NC. 

ABSTRACT 

Realistic simulation of longitudinal activity patterns is necessary for appropriately 

reproducing the frequency and duration of pollutant exposures in human exposure models. In 

EPA’s exposure models, longitudinal activity diaries for simulated persons are constructed from 

the 1-day cross sectional activity diaries in the Consolidated Human Activity Database (CHAD). 

Recently, new algorithms have been developed to construct longitudinal diaries from CHAD 

diaries based on realistic variance and autocorrelation properties of diary characteristics relevant 

to pollutant exposure. Characteristics of particular interest include time spent in particular 

microenvironments and time spent in activities that produce high ventilation rates. However, 

few multi-day data are currently available for estimating accurate statistical properties for these 

quantities. Results from a recent time-activity study of 10 adults and one newborn child are 

presented here. The participants recorded their personal location and activity for two-week 

periods in each of four seasons in 2006 and 2007. The data were recorded 24 hours a day, in 

increments as small as one minute. Additional recording periods for these same individuals are 

expected in the future. The diaries for all subjects were assessed to calculate the between-person 

variance, the within-person variance, and the autocorrelation for various lags in the time spent in 

outdoor, residence, indoor (non-residence), and vehicle microenvironments, as well as for time 

spent performing high-METS activities. The effectiveness of various day-type definitions (for 

example, weekend versus weekday, or workday versus non-workday) for grouping similar diary 

days is examined. Seasonal variation in activity patterns is analyzed. These data have the 

potential to aid in the development of improved input variance and autocorrelation statistics for 

longitudinal diary assembly algorithms in EPA’s human exposure models. 

INTRODUCTION 

Recently, new methods of assembling multi-day diaries in human exposure models from 

cross-sectional single-day diaries have been proposed that are based on the variance and 

autocorrelation statistics of the simulated population (Glen et al. 2008). Appropriately modeling 

intra- and interindividual variability using such algorithms may be essential in producing 

appropriate estimates of exposure. In addition, reproducing realistic autocorrelations in key diary 

properties may be required for the modeling of episodic exposure patterns. 

Previously, longitudinal time activity-location data collected in children in the Southern 

California Chronic Ozone Exposure Study (Geyh et al. 2000) have been analyzed to obtain 

estimates of appropriate measures of variance and autocorrelation for use in the longitudinal 

algorithm. Data from a new study in adults are now presented. 
February 2010 C-3 Draft – Do Not Cite or Quote 

BACKGROUND 

Exposure models require construction of human activity diaries that cover the entire 

simulation period of a model run. This period is often several months, a year, or even longer. In 

EPA’s models, human activity diaries are usually drawn from EPA’s CHAD (Consolidated 

Human Activity Database; McCurdy et al., 2000; http://www.epa.gov/chadnet1), which typically 

includes just one day (24 hours) of activities from each person. A “longitudinal” diary is one 

that covers the same person over a long period of time. While the SHEDS modeling period may 

be of user-specified duration, it is assumed in this section to be one year, to provide a concrete 

example. 

Recently, a new longitudinal diary assembly algorithm has been developed (Glen et al. 

2007) based on the variance and autocorrelation properties of the modeled simulation. The new 

method requires the user to: 

 1) Select the diary property most relevant to exposure for the current application (such as 

outdoor time or time spent in vehicles) 

 2) Specify the D statistic, which relates the within-person and between-person variances 

for this diary property; and 

 3) Specify the 1-day lag autocorrelation in this diary property. 

The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxics 

models. The new method allows the modeler to apportion the total variance in the key diary 

property into the within- and between-person variances σw2 and σb2 by specifying the D statistic, 

defined to be: 

D pertains to the population as a whole and is bounded by zero and one. A value of zero implies 

all persons have the same average behavior, whereas a value of one implies the greatest possible 

difference in mean behavior that is consistent with the total variance. 

In addition to targeting the within-person and between-person variances through setting 

the D statistic, the new diary assembly method optionally allows targeting of the day-to-day 

autocorrelation. This is a measure of the tendency for similar diaries to occur on consecutive 

days. The lag-one autocorrelation in a variable y is for a person defined as: 

The population autocorrelation A is the mean of the A values for all individuals. 

Autocorrelation could be of interest to the exposure modeler if the concentration time series were 

strongly episodic, for example. In the diary assembly, a positive autocorrelation indicates a 

tendency for diaries with x-scores near each other to be used on consecutive days, while a 

negative autocorrelation indicates a tendency for dissimilar x-scores to be used on consecutive 

days. Some preliminary values of A have been derived from the same data that were used to 

estimate D (Glen et al., 2008). 

2

w

2

b

2

b

2

2

b

σ σ

σ

σ

σ D

  

     







   

N

j 1

2

j 1 j

N 1

j 1

j A y y y y / y y
February 2010 C-4 Draft – Do Not Cite or Quote 

METHODS

Activity Diary Study

Activity-location data were collected from 10 adults. Nine of the adults were working 

professionals; one was a stay-at-home parent. Nine of the adults recorded their personal location 

and activity for two-week periods in each of four seasons in 2006 and 2007. Additional data 

were collected in one of the male subjects in 1999, another male (the 10th adult) in 2002, and in 

one of the females in 2008 (collected during maternity leave). The data were recorded 24 hours 

a day, in increments as small as one minute. In this preliminary analysis, the time spent 

outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries. 

“Hard work” was self-reported by each individual, as defined as activities requiring heavy 

breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for 

each day of the study. 

Variance and Autocorrelation Statistics

Variance and lag-one autocorrelation statistics were calculated for the studied 

individuals. Variance statistics were estimated for both the raw measured variables (i.e. time in 

minutes) and the scaled ranks of the variable for each person on a given day. The ratio of the 

between-person variance to the total variance (the sum of the between- and within-person 

variance) was calculated for the population. This ratio, calculated using the raw variables, is the 

intraclass correlation coefficient (ICC), while the same ratio, calculated using the ranks, is D, the 

diversity statistic. The autocorrelation A was also calculated using both the raw variables and 

the scaled ranks of the variables on each day for each person in the study. 

Analysis of Time Spent in Locations/Activities

The longitudinal data were assessed to support decisions on optimal diary pools for 

exposure modeling. Time spent in each of the examined locations/activities were assessed as a 

function of day of the week (weekday versus weekend), day type (workday versus nonworkday), season, temperature, precipitation, and gender. These analyses were undertaken to 

assess the utility of different diary pool definitions. Optimal definitions of diary pools can 

adequately capture temporal patterns in activities while maximizing the number of activity 

diaries available for sampling on a given day for a simulated individual. Differences between 

groups were assessed with the Wilcoxon signed rank test (for 2 groups) or the Kruskal-Wallis 

test (for more than 2 groups). The Wilcoxon rank sum (two-sample) test was used to test 

differences between genders. 

RESULTS AND DISCUSSION 

Individual Variability

Figure 1 shows an example of the individual variability in time spent in different 

locations/activities for a single male subject; a 367-day period from this subject is depicted. 

Distributions of time for this subject are also shown. These figures demonstrate the large amount 

of intra-individual variability that can be seen in longitudinal activity studies. Distributions of 

time spent in locations/activities for the population is shown in Figure 4[sic 2]. 
February 2010 C-5 Draft – Do Not Cite or Quote 

Figure 1. Time series and distributions of time spent in locations/activities for 367 days of 

data from a single male subject. Note high degree of interpersonal variability in behavior. 

Figure 2. Distributions of time spent in different activities for all days for all subjects. 

Variance and Autocorrelation Statistics

D, ICC, and A values for the population for time spent in different locations/activities are 

given in Table 1. Values of the ICC are lower than D; while A for the raw variables were higher 

than A for the scaled ranks. These trends were also consistent with observed tends in the 

Southern California data. Values were also calculated by gender (Table 2), temperature 

categories (Table 3), and day types (Table 4) where possible. 
February 2010 C-6 Draft – Do Not Cite or Quote 

The D and ranked A values were compared to those calculated for children from the 

Southern California Chronic Ozone Exposure Study (SCCOES). The diversity (D) for this group 

of adults for outdoor time were higher than those calculated for the children (0.38 versus 0.19). 

The D values for travel time in the current study were also higher (0.18 in children versus 0.36 in 

this study). These differences reflect the increased heterogeneity in these variables in the studied 

adults versus the (relatively homogenous) studied children. The A values calculated for outdoor 

time in this study were virtually identical to those estimated using data from SCCOES. 

In general, differences between D by temperature and day types were notable, even considering 

the small number subjects in this study. There were gender differences observed in D; the 

mechanism of these differences is unclear, but is likely influenced by the activity patterns of the 

female who was not a worker. 

There were observed differences in A by temperature, but especially by day type. This is 

not unexpected, as it is reasonable that the behavior of working adults is more consistent day-today on workdays. These trends should be confirmed by analysis of other longitudinal data. Note 

however, that such differences in are only important when strongly episodic behavior or 

exposure is of interest. In general, the values of D are much more relevant to exposure. 
February 2010 C-7 Draft – Do Not Cite or Quote 

Time Spent in Different Locations/Activities

The time spent in different locations/activities for different day types, seasons, 

temperature categories are presented in Figures 3-6. The effects of gender and precipitation 

were also studied. There were no significant differences for these categories, and thus plots are 

not shown. The plotted data represent all days for all subjects. The medians are represented by 

the midline of the boxes, the first and third quartiles by the ends of the boxes, and the means by 

the stars. The whiskers extend to cover data that lies beyond the boxed but within the quartiles 

plus 1.5 times the interquartile range. Points outside this range are plotted. 

Results by day of the week and day type are presented in Figure 3. Day type (workday 

versus non-workday) was at least as good as day of the week in categorizing time/activities. This 

trend is similar to that seen in a recent analysis of the larger, cross-sectional database of diaries 

from The National Human Activity Pattern Survey (NHAPS, data not shown). That analysis 

indicated that a workday/non-workday was a better discriminator of time spent outside than a 

weekday/weekend split. As such, further comparisons are also presented for both workdays and 

non-workdays. 
February 2010 C-8 Draft – Do Not Cite or Quote 

Figure 3. Time spent in different locations/activities as a function of day of the week, and 

daytype (workday versus non-workdays). 

The effect of season on time spent in locations/activities is shown in Figure 4. Seasonal 

effects were apparent for time spent outdoors on non-workdays, and for time spent doing hard 

work. Travel was also affected by season, likely due to the large number of work-related travel 

days in the fall for this particular group of workers. 

Figure 4. Time spent in different locations/activities as a function of season and daytype. 
February 2010 C-9 Draft – Do Not Cite or Quote 

The effect of temperature category is shown in Figure 5. The temperature category was 

defined as warmer = maximum temperature greater than or equal to 75 degrees, colder= 

maximum temperature less than 75 degrees. Temperature category was better than or as good as 

season in discriminating behavior in time spent outdoors, even when day type was considered. 

Figure 5. Time spent in different locations/activities as a function of temperature category 

(colder: max temp< 75 degrees, warmer: max temp 75 degrees) and day type. 

CONCLUSIONS 

• The diversity (D) and autocorrelation (A) for this group of adults for outdoor time were 

higher than those calculated for children in a previous study. Thus these data provide 

some justification for considering age when considering D and A input values for EPA’s 

exposure models. 

• While the current data suggest possible effects of temperature, day type and gender on 

diversity (D) and autocorrelation (A), more data from this and other studies are needed to 

confirm these findings. Such results could aid in the fine-tuning of the longitudinal diary 

algorithm. 

• The analysis of the time spent in locations was consistent with recent findings from crosssectional diary studies indicating that workdays/non-workdays may be a better grouping 

for diary pools than weekdays/weekends. 

• Temperature category was at least as good as season in discriminating behavior for this 

population for time spent outdoors, especially when day type was considered. Such 

breakdowns by temperature and day type may eliminate the need for diary pools for 

different seasons, providing larger pools for diary sampling on a given day. Further 

analysis with other time-activity data can confirm this trend. 
February 2010 C-10 Draft – Do Not Cite or Quote 

FUTURE WORK 

We plan to repeat this type of study periodically. Data will be compared to/combined 

with analyses of other available longitudinal time/location/activity studies. 

DISCLAIMER 

The information in this document has been funded wholly (or in part) by the U. S. 

Environmental Protection Agency (EPA contract 68-D-00-206). It has been subjected to review 

by the EPA and approved for publication. Approval does not signify that the contents 

necessarily reflect the views of the Agency, nor does mention of trade names or commercial 

products constitute endorsement or recommendation for use. 

REFERENCES 

Geyh AS, Xue J, Ozkaynak H, Spengler JD. (2000). The Harvard Southern California Chronic 

Ozone Exposure study: assessing ozone exposure of grade school-age children in two southern 

California communities. Environ Health Perspect. 108:265–270. 

Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal 

diary assembly for human exposure modeling. J Expo Sci Environ Epidemiol. 18(3):299-311. 

McCurdy T, Glen G, Smith L, Lakkadi L. (2000). The National Exposure Research 

Laboratory's Consolidated Human Activity Database. J Expo Anal Environ Epidemiol. 10:566-

78. 
February 2010 C-11 Draft – Do Not Cite or Quote 

Isaacs et al. (2009) in original poster format: 

BACKGROUNDExposure models require construction of human activity diaries that cover the entire simulation period of a model run. This period is often several months, a year, or even longer. In EPA’s models, human activity diaries are usually drawn from EPA’s CHAD (Consolidated Human Activity Database; McCurdy et al., 2000; http://www.epa.gov/chadnet1), which typically includes just one day (24 hours) of activities from each person. A “longitudinal” diary is one that covers the same person over a long period of time. While the SHEDS modeling period may be of user-specified duration, it is assumed in this section to be one year, to provide a concrete example.Recently, a new longitudinal diary assembly algorithm has been developed (Glen et al. 2007) based on the variance and autocorrelation properties of the modeled simulation. The new method requires the user to: 

1) select the diary property most relevant to exposure for the current 

application (such as outdoor time or time spent in vehicles) 

2) specify the D statistic, which relates the within-person and betweenperson variances for this diary property; and 3) specify the 1-day lag autocorrelation in this diary property.

The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxics models.The new method allows the modeler to apportion the total variance in the key diary property into the within- and between-person variances σw2 and σb2 by specifying the D statistic, defined to be

D pertains to the population as a whole and is bounded by zero and one. A value of zero implies all persons have the same average behavior, whereas a value of one implies the greatest possible difference in mean behavior that is consistent with the total variance.In addition to targeting the within-person and between-person variances through setting the D statistic, the new diary assembly method optionally allows targeting of the day-to-day autocorrelation. This is a measure of the tendency for similar diaries to occur on consecutive days. The lag-one autocorrelation in a variable y is for a person defined asThe population autocorrelation A is the mean of the A values for all individuals. Autocorrelation could be of interest to the exposure modeler if the concentration time series were strongly episodic, for example. In the diary assembly, a positive autocorrelation indicates a tendency for diaries with x-scores near each other to be used on consecutive days, while a negative autocorrelation indicates a tendency for dissimilar x-scores to be used on consecutive days. Some preliminary values of A have been derived from the same data that were used to estimate D (Glen et al., 2007). 

INTRODUCTIONRecently, new methods of assembling multi-day diaries in human exposure models from cross-sectional single-day diaries have been proposed that are based on the variance and autocorrelation statistics of the simulated population (Glen et al. 2007). Appropriately modeling intra- and interindividual variability using such algorithms may be essential in producing appropriate estimates of exposure. In addition, reproducing realistic autocorrelations in key diary properties may be required for the modeling of episodic exposure patterns.Previously, longitudinal time activity-location data collected in children in the Southern California Chronic Ozone Exposure Study (Geyh et al. 1999) have been analyzed to obtain estimates of appropriate measures of variance and autocorrelation for use in the longitudinal algorithm. Data from a new study in adults are now presented.

CO• The highesome expo•Whidiverconfialgor• The crossgrou•Tempopubreaseasother FU• We other 

Statistical Properties of Longitudinal Time-Activity Data for Use in EPA Exposu

Kristin Isaacs1, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle 

1Alion Science and Technology, Research Triangle Park, NC, 2Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle 

Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 4Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC, 5Home

ABSTRACTRealistic simulation of longitudinal activity patterns is necessary for appropriately reproducing the frequency and duration of pollutant exposures in human exposure models. In EPA’s exposure models, longitudinal activity diaries for simulated persons are constructed from the 1-day cross sectional activity diaries in the Consolidated Human Activity Database (CHAD). Recently, new algorithms have been developed to construct longitudinal diaries from CHAD diaries based on realistic variance and autocorrelation properties of diary characteristics relevant to pollutant exposure. Characteristics of particular interest include time spent in particular microenvironments and time spent in activities that produce high ventilation rates. However, few multi-day data are currently available for estimating accurate statistical properties for these quantities. Results from a recent time-activity study of 10 adults and one newborn child are presented here. The participants recorded their personal location and activity for two-week periods in each of four seasons in 2006 and 2007. The data were recorded 24 hours a day, in increments as small as one minute. Additional recording periods for these same individuals are expected in the future. The diaries for all subjects were assessed to calculate the between-person variance, the within-person variance, and the autocorrelation for various lags in thetime spent in outdoor, residence, indoor (non-residence), and vehicle microenvironments, as well as for time spent performing high-METS activities. The effectiveness of various day-type definitions (for example, weekend versus weekday, or workday versus non-workday) for grouping similar diary days is examined. Seasonal variation in activity patterns is analyzed. These data have the potential to aid in the development of improved input variance and autocorrelation statistics for longitudinal diary assembly algorithms in EPA’s human exposure models.

DISThe incontrathe coendor

METHODSActivity Diary StudyActivity-location data were collected from 10 adults. Nine of the adults were working professionals; one was a stay-at-home parent. Nine of the adults recorded their personal location and activity for two-week periods in each of four seasons in 2006 and 2007. Additional data were collected in one of the male subjects in 1999, another male (the 10th adult) in 2002, and in one of the 

females in 2008 (collected during maternity leave). The data were recorded 24 hours a day, in increments as small as one minute. In this preliminary analysis, the time spent outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries. “Hard work” was self-reported by each individual, as defined as activities requiring heavy breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for each day of the study. Variance and Autocorrelation StatisticsVariance and lag-one autocorrelation statistics were calculated for the studied individuals. Variance statistics were estimated for both the raw measured variables (ie. time in minutes) and the scaled ranks of the variable for eachperson on a given day. The ratio of the between-person variance to the total variance (the sum of the between- and within-person variance) was calculated for the population. This ratio, calculated using the raw variables, is the intraclass correlation coefficient (ICC), while the same ratio, calculated using the ranks, is D, the diversity statistic. The autocorrelation A was also calculated using both the raw variables and the scaled ranks of the variables on each day for each person in the study.Analysis of Time Spent in Locations/ActivitiesThe longitudinal data were assessed to support decisions on optimal diary pools for exposure modeling. Time spent in each of the examinedlocations/activities were assessed as a function of day of the week (weekday versus weekend), day type (workday versus nonworkday), season, temperature, precipitation, and gender. These analyses were undertaken to assess the utility of different diary pool definitions. Optimal definitions of diary pools can adequately capture temporal patterns in activities while maximizing the number of activity diaries available for sampling on a given day for a simulated individual.Differences between groups were assessed with the Wilcoxon signed rank test (for 2 groups) or the Kruskal-Wallis test (for more than 2 groups). The Wilcoxonrank sum (two-sample) test was used to test differences between genders.

Time The ttempe

precipcategThe pmidlinby the quartResuversutime/asectiodata ndiscricompThe eeffecthard wrelateThe e

definemaximas godayty

REFEGeyh assesPerspGlen GmodelXue Jyear o

McCuActivit

2

w

2

b

2

b

22

b

σ σ

σ

σσ D



 

    









   

N

j 1

2

j 1 j

N 1

j 1

A y j y y y / y y

Figure 1. Time series and distributions of time spent in locations/activities for 367 days of data from a single male subject. Note high degree of interpersonal variability in behavior.

Figure 2. Distributions of time spent in different activities for all days for all subjects. Figure 3. Time spent in different locations/activities as a function of day of the week, and daytype (workday versus nonworkdays).

Figure 4. Time spent in different locations/activities as a function of season and daytype.

Figure 5. Time spent in different locations/activities as a function of temperature category (colder: max temp< 75 degrees, warmer: max temp 75 degrees) and 

daytype.

RESULTS AND DISCUSSIONIndividual VariabilityFigure 1 shows an example of the individual variability in time spent in different locations/activities for a single male subject; a 367-day period from this subject is depicted. Distributions of time for this subject are also shown. These figures demonstrate the large amount of intra-individual variability that can be seen in longitudinal activity studies. Distributions of time spent in locations/activities for the population is shown in Figure 4. 

Variance and Autocorrelation StatisticsD, ICC, and A values for the population for time spent in different locations/activities are given in Table 1. Values of the ICC are lower than D; while A for the raw variables were higher 

than A for the scaled ranks. These trends were also consistent with observed tends in the Southern California data. Values were also calculated by gender (Table 2), temperature categories (Table 3), and daytypes (Table 4) where possible.

The D and ranked A values were compared to those calculated for children from the Southern California Chronic Ozone Exposure Study (SCCOES). The diversity (D) for this group of adults for outdoor time were higher than those calculated for the children (0.38 versus 0.19). The D values for travel time in the current study were also higher (0.18 in children versus 0.36 in this study). These differences reflect the increased heterogeneity in these variable in the studied adults versus the (relatively homogenous) studied children. The A values calculated for outdoor time in this study were virtually identical to those estimated using data from SCCOES. In general, differences between D by temperature and daytypes were notable, even considering the small number subjects in this study. There were gender differences observed in D; the mechanism of these differences is unclear, but are likely influenced by the activity patterns of the female who was not a worker. There were observed differences in A by temperature, but especially by daytype. This is not unexpected, as it is reasonable that the behavior of working adults is more consistent day-today on workdays. These trends should be confirmed by analysis of other longitudinal data. Note however, that such differences in are only important when strongly episodic behavior or exposure is of interest. In general, the values of D are much more relevant to exposure.

P<0.002 P<0.04 NS

P<0.02 P<0.006 NS NS NS NS

P<0.003 P<0.001 NS

P<0.001 P<0.001 NS

NS NS P<0.0001

P<0.02 NS NS P<0.001 P<0.005 P<0.001

BACKGROUNDExposure models require construction of human activity diaries that cover the entire simulation period of a model run. This period is often several months, a year, or even longer. In EPA’s models, human activity diaries are usually drawn from EPA’s CHAD (Consolidated Human Activity Database; McCurdy et al., 2000; http://www.epa.gov/chadnet1), which typically includes just one day (24 hours) of activities from each person. A “longitudinal” diary is one that covers the same person over a long period of time. While the SHEDS modeling period may be of user-specified duration, it is assumed in this section to be one year, to provide a concrete example.Recently, a new longitudinal diary assembly algorithm has been developed (Glen et al. 2007) based on the variance and autocorrelation properties of the modeled simulation. The new method requires the user to: 

1) select the diary property most relevant to exposure for the current 

application (such as outdoor time or time spent in vehicles) 

2) specify the D statistic, which relates the within-person and betweenperson variances for this diary property; and 3) specify the 1-day lag autocorrelation in this diary property.

The new method is currently implemented in EPA’s APEX and SHEDS-Air Toxics models.The new method allows the modeler to apportion the total variance in the key diary property into the within- and between-person variances σw2 and σb2 by specifying the D statistic, defined to be

D pertains to the population as a whole and is bounded by zero and one. A value of zero implies all persons have the same average behavior, whereas a value of one implies the greatest possible difference in mean behavior that is consistent with the total variance.In addition to targeting the within-person and between-person variances through setting the D statistic, the new diary assembly method optionally allows targeting of the day-to-day autocorrelation. This is a measure of the tendency for similar diaries to occur on consecutive days. The lag-one autocorrelation in a variable y is for a person defined asThe population autocorrelation A is the mean of the A values for all individuals. Autocorrelation could be of interest to the exposure modeler if the concentration time series were strongly episodic, for example. In the diary assembly, a positive autocorrelation indicates a tendency for diaries with x-scores near each other to be used on consecutive days, while a negative autocorrelation indicates a tendency for dissimilar x-scores to be used on consecutive days. Some preliminary values of A have been derived from the same data that were used to estimate D (Glen et al., 2007). 

INTRODUCTIONRecently, new methods of assembling multi-day diaries in human exposure models from cross-sectional single-day diaries have been proposed that are based on the variance and autocorrelation statistics of the simulated population (Glen et al. 2007). Appropriately modeling intra- and interindividual variability using such algorithms may be essential in producing appropriate estimates of exposure. In addition, reproducing realistic autocorrelations in key diary properties may be required for the modeling of episodic exposure patterns.Previously, longitudinal time activity-location data collected in children in the Southern California Chronic Ozone Exposure Study (Geyh et al. 1999) have been analyzed to obtain estimates of appropriate measures of variance and autocorrelation for use in the longitudinal algorithm. Data from a new study in adults are now presented.

CO• The highesome expo•Whidiverconfialgor• The crossgrou•Tempopubreaseasother FU• We other 

Statistical Properties of Longitudinal Time-Activity Data for Use in EPA Exposu

Kristin Isaacs1, Thomas McCurdy2, April Errickson3, Susan Forbes3, Graham Glen1, Stephen Graham4, Lisa McCurdy5, Melissa Nysewander1, Luther Smith1, Nicolle 

1Alion Science and Technology, Research Triangle Park, NC, 2Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory, US Environmental Protection Agency, Research Triangle 

Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 4Office of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, NC, 5Home

ABSTRACTRealistic simulation of longitudinal activity patterns is necessary for appropriately reproducing the frequency and duration of pollutant exposures in human exposure models. In EPA’s exposure models, longitudinal activity diaries for simulated persons are constructed from the 1-day cross sectional activity diaries in the Consolidated Human Activity Database (CHAD). Recently, new algorithms have been developed to construct longitudinal diaries from CHAD diaries based on realistic variance and autocorrelation properties of diary characteristics relevant to pollutant exposure. Characteristics of particular interest include time spent in particular microenvironments and time spent in activities that produce high ventilation rates. However, few multi-day data are currently available for estimating accurate statistical properties for these quantities. Results from a recent time-activity study of 10 adults and one newborn child are presented here. The participants recorded their personal location and activity for two-week periods in each of four seasons in 2006 and 2007. The data were recorded 24 hours a day, in increments as small as one minute. Additional recording periods for these same individuals are expected in the future. The diaries for all subjects were assessed to calculate the between-person variance, the within-person variance, and the autocorrelation for various lags in thetime spent in outdoor, residence, indoor (non-residence), and vehicle microenvironments, as well as for time spent performing high-METS activities. The effectiveness of various day-type definitions (for example, weekend versus weekday, or workday versus non-workday) for grouping similar diary days is examined. Seasonal variation in activity patterns is analyzed. These data have the potential to aid in the development of improved input variance and autocorrelation statistics for longitudinal diary assembly algorithms in EPA’s human exposure models.

DISThe incontrathe coendor

METHODSActivity Diary StudyActivity-location data were collected from 10 adults. Nine of the adults were working professionals; one was a stay-at-home parent. Nine of the adults recorded their personal location and activity for two-week periods in each of four seasons in 2006 and 2007. Additional data were collected in one of the male subjects in 1999, another male (the 10th adult) in 2002, and in one of the 

females in 2008 (collected during maternity leave). The data were recorded 24 hours a day, in increments as small as one minute. In this preliminary analysis, the time spent outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries. “Hard work” was self-reported by each individual, as defined as activities requiring heavy breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for each day of the study. Variance and Autocorrelation StatisticsVariance and lag-one autocorrelation statistics were calculated for the studied individuals. Variance statistics were estimated for both the raw measured variables (ie. time in minutes) and the scaled ranks of the variable for eachperson on a given day. The ratio of the between-person variance to the total variance (the sum of the between- and within-person variance) was calculated for the population. This ratio, calculated using the raw variables, is the intraclass correlation coefficient (ICC), while the same ratio, calculated using the ranks, is D, the diversity statistic. The autocorrelation A was also calculated using both the raw variables and the scaled ranks of the variables on each day for each person in the study.Analysis of Time Spent in Locations/ActivitiesThe longitudinal data were assessed to support decisions on optimal diary pools for exposure modeling. Time spent in each of the examinedlocations/activities were assessed as a function of day of the week (weekday versus weekend), day type (workday versus nonworkday), season, temperature, precipitation, and gender. These analyses were undertaken to assess the utility of different diary pool definitions. Optimal definitions of diary pools can adequately capture temporal patterns in activities while maximizing the number of activity diaries available for sampling on a given day for a simulated individual.Differences between groups were assessed with the Wilcoxon signed rank test (for 2 groups) or the Kruskal-Wallis test (for more than 2 groups). The Wilcoxonrank sum (two-sample) test was used to test differences between genders.

Time The ttempe

precipcategThe pmidlinby the quartResuversutime/asectiodata ndiscricompThe eeffecthard wrelateThe e

definemaximas godayty

REFEGeyh assesPerspGlen GmodelXue Jyear o

McCuActivit

2

w

2

b

2

b

22

b

σ σ

σ

σσ D



 

    









   

N

j 1

2

j 1 j

N 1

j 1

A y j y y y / y y

Figure 1. Time series and distributions of time spent in locations/activities for 367 days of data from a single male subject. Note high degree of interpersonal variability in behavior.

Figure 2. Distributions of time spent in different activities for all days for all subjects. Figure 3. Time spent in different locations/activities as a function of day of the week, and daytype (workday versus nonworkdays).

Figure 4. Time spent in different locations/activities as a function of season and daytype.

Figure 5. Time spent in different locations/activities as a function of temperature category (colder: max temp< 75 degrees, warmer: max temp 75 degrees) and 

daytype.

RESULTS AND DISCUSSIONIndividual VariabilityFigure 1 shows an example of the individual variability in time spent in different locations/activities for a single male subject; a 367-day period from this subject is depicted. Distributions of time for this subject are also shown. These figures demonstrate the large amount of intra-individual variability that can be seen in longitudinal activity studies. Distributions of time spent in locations/activities for the population is shown in Figure 4. 

Variance and Autocorrelation StatisticsD, ICC, and A values for the population for time spent in different locations/activities are given in Table 1. Values of the ICC are lower than D; while A for the raw variables were higher 

than A for the scaled ranks. These trends were also consistent with observed tends in the Southern California data. Values were also calculated by gender (Table 2), temperature categories (Table 3), and daytypes (Table 4) where possible.

The D and ranked A values were compared to those calculated for children from the Southern California Chronic Ozone Exposure Study (SCCOES). The diversity (D) for this group of adults for outdoor time were higher than those calculated for the children (0.38 versus 0.19). The D values for travel time in the current study were also higher (0.18 in children versus 0.36 in this study). These differences reflect the increased heterogeneity in these variable in the studied adults versus the (relatively homogenous) studied children. The A values calculated for outdoor time in this study were virtually identical to those estimated using data from SCCOES. In general, differences between D by temperature and daytypes were notable, even considering the small number subjects in this study. There were gender differences observed in D; the mechanism of these differences is unclear, but are likely influenced by the activity patterns of the female who was not a worker. There were observed differences in A by temperature, but especially by daytype. This is not unexpected, as it is reasonable that the behavior of working adults is more consistent day-today on workdays. These trends should be confirmed by analysis of other longitudinal data. Note however, that such differences in are only important when strongly episodic behavior or exposure is of interest. In general, the values of D are much more relevant to exposure.

P<0.002 P<0.04 NS

P<0.02 P<0.006 NS NS NS NS

P<0.003 P<0.001 NS

P<0.001 P<0.001 NS

NS NS P<0.0001

P<0.02 NS NS P<0.001 P<0.005 P<0.001
February 2010 D-1 Draft – Do Not Cite or Quote 

Appendix D 

Microenvironmental Mapping 

Figure D-1 presents how CHAD codes are mapped to the eight microenvironments used to model exposure 

in the draft CO REA. Table D-1 provides the CHAD activity codes used to identify when a simulated 

individual was in a work air district. 

Figure D-1. Microenvironmental Mapping Input File Showing Mapping of CHAD Location Codes 

to the Eight Microenvironments for Application of APEX4.3 to Carbon Monoxide. 

! Mapping of CHAD location codes to nine APEX microenvironments defined 

! by Option 4 of Memorandum dated 12/8/2009. 

CHAD Loc. Description APEX 

--------- ---------------------------------------------- 

U Uncertain of correct code = -1 U 

X No data = -1 U 

30000 Residence, general = 1 H 

30010 Your residence = 1 H 

30020 Other residence = 1 H 

30100 Residence, indoor = 1 H 

30120 Your residence, indoor = 1 H 

30121 ..., kitchen = 1 H 

30122 ..., living room or family room = 1 H 

30123 ..., dining room = 1 H 

30124 ..., bathroom = 1 H 

30125 ..., bedroom = 1 H 

30126 ..., study or office = 1 H 

30127 ..., basement = 1 H 

30128 ..., utility or laundry room = 1 H 

30129 ..., other indoor = 1 H 

30130 Other residence, indoor = 1 H 

30131 ..., kitchen = 1 H 

30132 ..., living room or family room = 1 H 

30133 ..., dining room = 1 H 

30134 ..., bathroom = 1 H 

30135 ..., bedroom = 1 H 

30136 ..., study or office = 1 H 

30137 ..., basement = 1 H 

30138 ..., utility or laundry room = 1 H 

30139 ..., other indoor = 1 H 

30200 Residence, outdoor = 7 H 

30210 Your residence, outdoor = 7 H 

30211 ..., pool or spa = 7 H 

30219 ..., other outdoor = 7 H 

30220 Other residence, outdoor = 7 H 

30221 ..., pool or spa = 7 H 

30229 ..., other outdoor = 7 H 
February 2010 D-2 Draft – Do Not Cite or Quote 

30300 Residential garage or carport = 1 H 

30310 ..., indoor = 1 H 

30320 ..., outdoor = 7 H 

30330 Your garage or carport = 1 H 

30331 ..., indoor = 1 H 

30332 ..., outdoor = 7 H 

30340 Other residential garage or carport = 1 H 

30341 ..., indoor = 1 H 

30342 ..., outdoor = 7 H 

30400 Residence, none of the above = 1 H 

31000 Travel, general = 8 O 

31100 Motorized travel = 8 O 

31110 Car = 8 O 

31120 Truck = 8 O 

31121 Truck (pickup or van) = 8 O 

31122 Truck (not pickup or van) = 8 O 

31130 Motorcycle or moped = 5 O 

31140 Bus = 8 O 

31150 Train or subway = 8 O 

31160 Airplane = 0 O 

31170 Boat = 7 O 

31171 Boat, motorized = 7 O 

31172 Boat, other = 7 O 

31200 Non-motorized travel = 7 O 

31210 Walk = 7 O 

31220 Bicycle or inline skates/skateboard = 7 O 

31230 In stroller or carried by adult = 7 O 

31300 Waiting for travel = 7 O 

31310 ..., bus or train stop = 5 O 

31320 ..., indoors = 4 O 

31900 Travel, other = 8 O 

31910 ..., other vehicle = 8 O 

32000 Non-residence indoor, general = 3 O 

32100 Office building/ bank/ post office = 3 O 

32200 Industrial/ factory/ warehouse = 4 O 

32300 Grocery store/ convenience store = 3 H 

32400 Shopping mall/ non-grocery store = 3 O 

32500 Bar/ night club/ bowling alley = 3 O 

32510 Bar or night club = 3 O 

32520 Bowling alley = 3 O 

32600 Repair shop = 3 O 

32610 Auto repair shop/ gas station = 2 O 

32620 Other repair shop = 3 O 

32700 Indoor gym /health club = 3 O 

32800 Childcare facility = 4 O 

32810 ..., house = 1 O 

32820 ..., commercial = 4 O 

32900 Large public building = 3 O 

32910 Auditorium/ arena/ concert hall = 3 O 

32920 Library/ courtroom/ museum/ theater = 3 O 

33100 Laundromat = 3 H 
February 2010 D-3 Draft – Do Not Cite or Quote 

33200 Hospital/ medical care facility = 4 O 

33300 Barber/ hair dresser/ beauty parlor = 3 H 

33400 Indoors, moving among locations = 3 O 

33500 School = 4 O 

33600 Restaurant = 3 O 

33700 Church = 4 H 

33800 Hotel/ motel = 3 O 

33900 Dry cleaners = 3 H 

34100 Indoor parking garage = 6 O 

34200 Laboratory = 3 O 

34300 Indoor, none of the above = 3 O 

35000 Non-residence outdoor, general = 7 O 

35100 Sidewalk, street = 5 O 

35110 Within 10 yards of street = 5 O 

35200 Outdoor public parking lot /garage = 6 O 

35210 ..., public garage = 6 O 

35220 ..., parking lot = 6 O 

35300 Service station/ gas station = 2 O 

35400 Construction site = 7 O 

35500 Amusement park = 7 O 

35600 Playground = 7 H 

35610 ..., school grounds = 7 O 

35620 ..., public or park = 7 H 

35700 Stadium or amphitheater = 7 O 

35800 Park/ golf course = 7 O 

35810 Park = 7 O 

35820 Golf course = 7 O 

35900 Pool/ river/ lake = 7 O 

36100 Outdoor restaurant/ picnic = 7 O 

36200 Farm = 7 O 

36300 Outdoor, none of the above = 7 O 

Table D-1. CHAD Work Related Activity Codes Used To identify Work Air Districts. 

<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 
Appendix E 

Analysis of CHAD Diaries for Time Spent in Vehicles. 

The US Census Bureau (2009) provides an on-line facility for accessing the detailed 

census data included in their Summary File 3 (SF3). We obtained information on travel time to 

work for workers ages 16 years and over specific to Denver County, Colorado and Los Angeles, 

CA (US Census Bureau, 2009, Table P31). Staff converted the counts listed in Table P31 for 

trips to work places other than home into the percentages listed in Columns 2 and 3 of Table E-1. 

Although the P31 statistics apply to people 16 years or older, staff assumed that the statistics 

were generally applicable to people 18 years or older. 

We next determined the number of 24-hour diaries in EPA’s Consolidated Human 

Activity Database (CHAD) (US EPA, 2002) that met the following criteria: the subject was ≥18 

years of age and the diary reported at least one minute in a motor vehicle between 6 am and 9 

am. The number of these diaries that had in-vehicle times corresponding to the bins listed in 

Table E-1 are given in Column 4 and were converted to the percentages listed in Column 5. 

Table E-1. Comparison of Denver and LA commuting characteristics (US Census, 2009) 

to time spent in motor vehicles using CHAD Diaries (US EPA, 2002). 

 

References 

US Census Bureau. (2009). American Fact Finder. Census Summary File 3 (SF3) – custom tables. Available at: 

www.factfinder.census.gov. 

US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/. 

24-hour diaries meeting 

inclusion criteriaa

Travel time 

(minutes) 

(1) 

Percent of commuters 

according to SF3 

census data for Denver

County 

(2) 

Percent of commuters 

according to SF3 

census data for Los 

Angeles County 

(3)

Number in 

CHAD 

(4)

Percent in 

CHAD 

(5)

1 to 9 10.3 7.8 563 9.79 

10 to 19 32.0 25.9 1,676 29.16 

20 to 29 24.2 21.0 1,068 18.58 

30 to 39 18.6 21.4 1,111 19.33 

40 to 59 9.3 13.6 665 11.57 

60 to 89 3.8 7.0 407 7.08 

90+ 1.7 3.4 258 4.49 

Total 100 100 5,748 100 

Notes: 

a

 Subjects are 18+ years of age. Diaries are those having ≥one minute in motor vehicle time spent 

between 6 AM and 9 AM. 
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Appendix F

Differences in Human Activity Patterns Between Individuals 

 With and Without Cardiovascular Disease

The following presents a memorandum by Cohen et al. (1999) that was included in 

the Johnson et al. (2000) CO exposure assessment (see Appendix J of that report). 

It is in its original form, with some minor editing performed by staff for inclusion 

into this second draft CO REA. 
February 2010 F-2 Draft – Do Not Cite or Quote 

 M E M O R A N D U M 

TO: Harvey Richmond 

FROM: Jonathan Cohen, Sergey Nikiforov, and Arlene Rosenbaum 

DATE: January 15, 1999 

SUBJECT: EPA 68-DO-0062 Work Assignment 2-24: Task 2: Evaluation of 

Differences in Human Activity Patterns Between Individuals With or 

Without Cardiovascular Disease 

EVALUATION OF DIFFERENCES IN HUMAN ACTIVITY PATTERNS BETWEEN 

INDIVIDUALS WITH OR WITHOUT CARDIOVASCULAR DISEASE

SUMMARY 

Activity pattern data from the National Human Activity Pattern Survey were used to compare 

activity patterns and exertion distributions between subjects with or without angina. The diary 

survey provided a 24-hour diary of activities. Exertion rates for each person in the survey were 

simulated 100 times. For each person, the body weight was simulated from a log-normal 

distribution specific to the age and gender. The resting metabolic rate was simulated using a 

regression against body weight, with coefficients depending on age and gender. Finally, the 

exertion rate was simulated for each activity and person by multiplying the simulated resting 

metabolic rate by a MET exertion ratio with a distribution specific to each type of activity. The 

current version of the probabilistic NAAQS Exposure Model for Carbon Monoxide (pNEM/CO), 

described in Johnson (1998), begins with the same set of physiological equations and statistical 

distributions for probabilistic simulation of exposure. The pNEM/CO model uses the much 

broader Consolidated Human Activity Data Base (CHAD) and simulates additional 

physiological variables, such as the ventilation rate. The description of the relevant probabilistic 

and physiological equations in this memorandum is largely based on Johnson (1998); see that 

memorandum for more detailed information. 

Differences between angina and non-angina subjects were evaluated for several summary 

statistics: average and 95th percentile of the maximum daily 8-hour exertion, percentage of time 

spent outdoors or in a vehicle, average percentage of time at light, moderate or heavy exertion 
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levels. Age and gender have very significant effects on these summary statistics of activity and 

exertion. Since angina patients tend to be much older and tend to include more females than the 

general population, it is very important to adjust for age and gender effects when comparing 

angina and non-angina groups. Otherwise, one cannot distinguish between the angina effect and 

the effects of age and gender. Statistical analyses comparing angina to non-angina subjects were 

performed, adjusting for age and gender either 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 percentages of time spent outdoors or in a vehicle were generally not statistically 

significantly different between angina and non-angina subjects. 

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. Therefore we 

conclude that the differences in activity and exertion between angina and non-angina subjects, 

although statistically significant, are not large enough to severely impact the validity of 

pNEM/CO modeling results that do not adjust for an angina/non-angina difference. 

METHODOLOGY 

For these analyses we used the National Human Activity Pattern Survey (NHAPS) database, a 

telephone survey of human activity patterns conducted for the USEPA between October 1992 

and September 1994 by the Survey Research Center at the University of Maryland. See Klepeis 

et al. (1996, 1998) and Tsang and Klepeis (1996) for more details about the NHAPS study and 

various statistical analyses of those data. The NHAPS data (Triplett, 1996) are included in 

CHAD. (Other CHAD studies did not include questions about cardiovascular disease and so 

could not be used for these analyses comparing angina and non-angina respondents.) A 

nationally representative sample of 9,386 respondents completed a detailed diary listing all their 

activities and locations over a 24-hour period (either from the previous day or a previous 

weekend day). A few respondents did not state their age and/or gender and their data was not 

used in our analysis. Our analysis used 9,149 of the surveys. Respondents were also asked 

demographic questions, including age and gender, and health questions, including whether or not 

they have been told by a doctor that they have angina: 243 respondents (2.6 percent) had angina. 

Respondents were asked about employment status (e.g. full-time, part-time, or unemployed) but 

not about their occupation. Other follow-up questions (not used in our analyses) related to the 

respondent’s exposure to either water or air pollution on the diary day. For each household, the 

respondent was randomly selected to be either the adult or child (under 18) with the next 

birthday; an adult provided proxy responses for a child. 

The EPA report (Klepeis, Tsang and Behar, 1996), Section 3, shows that the sample is 

reasonably representative of the national population with respect to gender and age distributions. 

The NHAPS population slightly underrepresented males (46 % NHAPS compared to 49 % from 

the 1990 Census). The fraction of weekend (Saturday or Sunday) respondents was 33 %, close to 
February 2010 F-4 Draft – Do Not Cite or Quote 

the desired ratio of 2:7, but Thursdays, Fridays and Saturdays were underrepresented. The Fall 

season was significantly underrepresented. The database includes weights to adjust for varying 

selection probabilities, due to differences in the numbers of adults or children in a selected 

household, the numbers of non-business phones in a household, the numbers of non-business 

telephones in each census region, and to the survey stratification between weekend or weekdays 

and between children and adults. Based on discussions with the EPA WAM, it was decided that 

the weights would not be used in these analyses; the raw, unweighted data would be treated as an 

approximately simple random sample. Note that the statistical weights: 1) were not used in the 

pNEM/CO exposure modeling effort, 2) could not be used to accurately estimate standard errors 

of weighted means, and 3) were close to 1 for most respondents. 

In pNEM/CO, each activity is assigned a probability distribution of the exertion rate (kilocalories per minute). For this analysis, the 24-hour sequence of exertion rates was simulated 100 

times for each person in the NHAPS sample; the sequence of activities is fixed but the simulated 

exertion rates vary. Following both CHAD and the exposure modeling methodology currently 

used in pNEM/CO, a constant simulated exertion rate is assumed throughout the time period of 

each listed activity in the 24-hour diary. If the individual repeats the same activity at a later time, 

with other activities intervening, the exertion rate is simulated again. SAS statistical software 

was used for the simulations and for the statistical analysis. 

The assigned exertion rate distribution depends upon the type of activity, and the occupation, 

age, gender, and body weight of the respondent. The exertion rate (kilo-calories/minute = 

kcal/min), also referred to as average energy expenditure rate, EE, is defined as the product 

EE = MET x RMR. 

MET is the metabolic equivalent of work, a dimensionless ratio (i.e., exertion compared to the 

resting metabolic rate) specific to each activity, and, in some cases, to an age group. RMR is the 

resting metabolic rate (kcal/min), approximately equal to the basal metabolic rate. We used the 

same set of MET statistical distributions supplied by Tom McCurdy that are currently used in 

pNEM/CO (and CHAD). For the work activity “at main job,” the MET distribution depends on 

the occupation. Since occupation was not recorded in NHAPS, we followed the pNEM 

methodology and randomly selected the occupation based on census fractions of persons in each 

activity. The same occupation is assumed throughout a simulated person-day (in case the person 

repeats the work activity), but is randomly selected again for the next simulated person-day. 

Note that this procedure may bias the comparison between angina and non-angina subjects, since 

the distribution of occupation is expected to differ between angina subjects and the general 

population. 

A single RMR value was simulated to represent each person-day. Thus the same person would 

have 100 simulated RMRs, one for each of the 100 days simulated. This reflects the assumption 

that each person represents the activity pattern for a group of persons with the same age and 

gender. As in pNEM/CO, RMR was simulated from a normal distribution where the mean is of 

the form a + b (Body Mass), and the standard deviation is the constant . The values of a, b, and 

 are the values derived by Schofield (1985) for 12 age/gender combinations (this assumes basal 
February 2010 F-5 Draft – Do Not Cite or Quote 

metabolic rate is equivalent to resting metabolic rate). In turn, the body mass was simulated 

using the log-normal distributions estimated by Brainard and Burmaster (1992) and Burmaster 

and Crouch (1994). The parameters of the log-normal distributions depend on age and gender. 

The statistical analysis used the following summary statistics of the activity and simulated 

exertion patterns for each person in the NHAPS study. The selection of these summary statistics 

was based on recommendations from the EPA WAM: 

 Average maximum 8-hour energy expenditure. For each 8-hour period in a simulated personday, starting every 10 minutes, integrate the simulated EE to give the energy expenditure in 

Mcal (millions of calories), i.e. sum the products of activity time and energy expenditure 

rate. For each simulated day, compute the maximum 8-hour energy expenditure, treating the 

simulated day in circular fashion so that the respondent is assumed to repeat exactly the same 

activity and exertion rate patterns on the day after the diary day. For example, the simulated 

activities for the period starting at 10 pm are assumed to follow the reported sequence of 

activities for the diary day from 10 pm to midnight and then the reported sequence from the 

beginning of the diary day until 6 am. To represent a typical value for the selected person, 

compute the average maximum 8-hour energy expenditure across the 100 simulations. 

 95th percentile maximum 8-hour energy expenditure. As in the last bullet, compute the 

maximum 8-hour energy expenditure for each simulated day. To represent an extreme value 

for the selected person, compute the fifth highest maximum 8-hour energy expenditure 

among the 100 simulations. 

 Percentage time spent outdoors. This number is the same for all simulations, since the 

activity patterns are held constant. 

 Percentage time spent in a vehicle. This number is the same for all simulations, since the 

activity patterns are held constant. 

 Percentage time spent outdoors or in a vehicle. This number is the same for all simulations, 

since the activity patterns are held constant. 

 Average percentage time with exertion rate above 2.39 kcal/min. For each simulated personday, the percentage of that day with an EE (rate) above the threshold level of 2.39 kcal/min 

was computed; then, this percentage was averaged over the 100 simulations for that person. 

The statistic estimates the percentage time spent at or above the threshold exertion rate level 

over a long period, assuming the daily activity pattern was the same every day. The threshold 

of 2.39 kcal/min, which equals 0.010 MJ/min, represents “light” exertion (see below). 

 Average percentage time with exertion rate above 5.97 kcal/min. The threshold of 5.97 

kcal/min, which equals 0.025 MJ/min, represents “moderate” exertion (see below). 

_ Average percentage time with exertion rate above 9.55 kcal/min. The threshold of 9.55 

kcal/min, which equals 0.040 MJ/min, represents “heavy” exertion (see below). 
February 2010 F-6 Draft – Do Not Cite or Quote 

The exertion rate thresholds used for these analysis were originally defined as 0.010, 0.025, and 

0.040 mega-joules per minute, but were converted into the more commonly used calorie units (1 

joule equals 0.2388 calories). For purposes of exposure assessment, exertion categories (i.e., 

light, moderate, or heavy exertion) are more usefully defined by the ventilation rate VE (liters air 

per minute) rather than the energy expenditure rate EE (kilo-calories per minute). For the EPA’s 

Ozone Criteria Document, the Environmental Criteria and Assessment Office categorized VE 

into ranges of 0-23, 24-43, 44-63, and 64+ liters of air per minute to define light, moderate, 

heavy, and very heavy exertion, respectively (based on a reference male adult with body weight 

70 kg). To convert from EE to VE, EE is first multiplied by an energy conversion factor, ECF, to 

give the oxygen uptake rate VO2 (liters of oxygen per minute). ECF varies across the 

population, but is approximately 0.2 liters oxygen per kcal (Esmail, Bhambhani, and Brintnell, 

1995). The “ventilatory equivalent rate” (VER) is the dimensionless ratio of VE (liters per 

minute) divided by VO2 (liters per minute) and has typical values from about 24 for light 

exertion to about 32 for peak exertion. Thus the selected energy expenditure rates are 

approximately equivalent to the following ventilation rates: 

EE = 0.010 MJ/min = 2.39 kcal/min: 

VE = EE  ECF  VER = 2.39  0.2  24 = 11.5 liters/min = light exertion 

EE = 0.025 MJ/min = 5.97 kcal/min: 

VE = EE  ECF  VER = 5.97  0.2  28 = 33.4 liters/min = moderate exertion 

EE = 0.040 MJ/min = 9.55 kcal/min: 

VE = EE  ECF  VER = 9.55  0.2  32 = 61.1 liters/min = heavy exertion

The selected summary statistics were computed for each of the 243 angina subjects and 8,906 

non-angina subjects in the NHAPS study. A statistical analysis compared the distributions of 

these summary statistics for persons with and without angina. For each summary statistic we 

compared the mean values between the angina and non-angina groups using standard t tests. The 

significance level (p-value) for the difference in means was computed using the SmithSatterthwaite procedure, that tests for no difference in population means assuming that the two 

populations are normally distributed but may have different variances. P-values at or below 0.05 

denote significant differences at the five percent level of significance. By the central limit 

theorem, the p-values for the t test comparisons should be reasonably accurate for the large 

samples used in the overall analyses, even if the normality assumption does not hold, but the pvalues will be less accurate for the analyses of specific gender and age subgroups. We also 

compared variances using a standard F test, that assumes normality of the two populations. 

Since the normality assumption may not be a sufficiently good approximation, we also applied 

two non-parametric tests that do not require specific parametric distributions. The nonparametric Wilcoxon test, also known as the Mann-Whitney-Wilcoxon test or the Rank Sum 

Test, was used to compare the central tendencies of the two distributions. This test assumes only 

that the populations have the same distributional shape, which may or may not be the normal 

distribution, but the distribution of values for angina population might be shifted by some 
February 2010 F-7 Draft – Do Not Cite or Quote 

constant value, and thus might have a different median than the non-angina population. The 

Kolmogorov-Smirnov test was used to evaluate any possible differences between the two 

distributions, whether due to differences in means, medians, variances, or any other features of 

the distribution. This test uses the maximum absolute difference between the two cumulative 

distribution functions, assuming only that these distributions are continuous. 

The mean, variance, median, and distribution function comparisons were made for all persons 

combined, separately for males and females, and then separately for four age groups within the 

male and female subgroups. Age groupings were chosen to include approximately 25 percent of 

angina subjects in each group. Separate comparisons for males and females are needed to 

distinguish whether any overall differences in exertion or activity are explained by the fact that 

angina subjects are more likely to be female than in the general population. Since activity 

patterns and exertion rates differ between males and females, any overall difference between the 

angina and non-angina groups might be explained by the greater propensity for females to get 

angina, rather than the direct effect of angina. Similarly, the subsetting by age group evaluates 

the effect of the different age distributions for angina subjects compared to the general 

population (angina subjects tend to be much older). This statistical analysis does not, and cannot, 

address questions as to whether the angina causes the change in exertion or activity patterns, or 

vice versa. We only examine whether or not the summary statistics of activity and exertion 

patterns are different for the two populations. 

A general linear model approach was also used as an alternative method of adjusting for the 

effects of age and gender on the angina/non-angina comparison. We focused attention on a 

relatively simple statistical model with cubic terms in age (a simple linear function of age fitted 

poorly), gender, interactions between age and gender, and a single term for the effect of angina: 

Summary Statistic = I(male){ + (age) + (age)2 + (age)3} 

+ I(female){ + (age) + (age)2 + (age)3} 

+ I(angina) + error 

where: I(male) = 1 for males, 0 for females; I(female) = 1 for females, 0 for males; I(angina) = 1 

for persons having angina, 0 for persons not having angina. The errors are assumed to be 

normally distributed, statistically independent, and have mean zero and some constant variance. 

This statistical model assumes that the expected value of the summary statistic is a cubic 

function of age, but is a different function for males and females. The selected model has the 

same coefficient for the cubic term for males and females, but different coefficients for the 

intercept, linear, and quadratic effects. The model also assumes that having angina changes the 

mean by a constant amount, which is the same factor for all age groups and both genders. A 

more sophisticated model might allow for interactions between angina and the age and gender 

variables, to allow for the possibility that the angina effect varies by gender and/or age. Note, 

however, that our statistical analysis clearly showed that age and gender were much more 

significant predictors of exertion patterns than the angina indicator, explaining most of the 

variability in the summary statistics. 
February 2010 F-8 Draft – Do Not Cite or Quote 

Project resources were insufficient for a detailed exploration of alternative statistical models. We 

tried using logarithmic transformations to improve the model fit, but could not reasonably use 

such models in view of the large number of cases where the observed summary statistic was zero 

(the logarithm is then undefined). The model fit for the selected model (without taking 

logarithms) varied with the summary statistic. R squared goodness-of-fit statistics were 

extremely low, less than 0.05, for the percentages of time spent outdoors and/or in a vehicle. For 

the summary statistics based on the maximum 8-hour exertion and the percentages of time above 

exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a fairly good fit, 

0.48. The cases of poor fitting models may be because the selected statistical models poorly 

represent the relationship between age, gender, and angina and the activity/exertion summary 

statistic and/or because the activity/exertion pattern varies substantially between people of the 

same age, gender, and angina status. 

RESULTS 

Age, Gender, and Angina Disease Distributions 

Table 1 shows the number of subjects with or without angina by gender and by age group. The 

four age groups were chosen to have approximately the same numbers of angina subjects. The 

strong association between angina and age is illustrated by the fact that 52/243 = 21 % of angina 

subjects are under 55 but 6877/8906 = 77 % of non-angina subjects are under 55. Angina 

subjects tend to be significantly older than the general population. The association between 

angina and gender is weaker. 103/243 = 42.3 % of angina subjects are male, but 4116/8906 = 

46.2 % of non-angina subjects are male. 

Overall Comparisons of Activity and Exertion Summary Statistics between Angina and 

Non-Angina Subjects 

Table 2 compares the means between the angina and non-angina subjects, without stratification 

by age or gender. The average and 95th percentile of the maximum eight hour exertion has a 

statistically significantly lower mean for angina subjects. Furthermore, for each of the exertion 

levels 2.39, 5.97, and 9.55 kcal/min (0.010, 0.025, and 0.040 MJ/min), the mean percentage of 

time above each level was statistically significantly lower for the angina subjects. Non-angina 

subjects spend an average of 2.8 percent of their time doing activities requiring moderate or 

higher levels of exertion, defined by exertion rates above 5.97 kcal/min (0.025 MJ/min); angina 

subjects spend an average of 2.2 percent of their time doing such activities. All subjects spend 

over 75 percent of time in light or sedentary activities, with extertion rates below 2.39 kcal/min, 

including sleeping. All these exertion distribution comparisons show that angina subjects tend to 

do activities with less exertion than the general population. However, since the summary 

analyses in Table 2 do not take into account the marked differences between the age and gender 

distributions of angina and non-angina subjects, the lower exertion rates could be associated with 

the tendency for angina subjects to be older (and female) rather than the disease itself. The 

average percentages of time spent outdoors are nearly identical, and are not statistically 

significantly different between angina and non-angina subjects, but angina subjects spend 

statistically significantly less time in vehicles (4.5 % rather than 5.5 %, on average). 
February 2010 F-9 Draft – Do Not Cite or Quote 

Table 2 compares the standard deviations using a F-test based on the variance ratio for angina vs. 

non-angina subjects. In most cases the F tests show statistically significantly different variances 

(and, therefore, standard deviations). 

Table 2 also uses the non-parametric Wilcoxon test to compare the central tendencies of the two 

distributions without the normality assumption required by the T test. Corresponding to the T 

test comparisons, the Wilcoxon test finds that the angina and non-angina distributions are 

significantly different in almost all cases; the angina subjects have a lower median value for each 

of the selected summary statistics. Exceptions are for the average maximum 8-hour exertion, just 

significant at the 7 % level, and the percentage of time spent outdoors, which has a nonsignificant p-value of 22 %. 

Finally, Table 2 compares the distribution functions using the Kolmogorov-Smironov test. The 

distributions are statistically significantly different at the five and one percent levels in all cases 

except for the percentage of time spent outdoors, which shows no significant difference. For that 

variable, the T and Wilcoxon tests showed no statistically significant differences in central 

tendency although the F test showed a statistically significant difference in the population 

variances. If the population variances are different, so are the two distribution functions. The 

discrepancy between the F and Kolmogorov-Smirnov tests is partly explained by the fact that the 

F test is very sensitive to the assumption of normal distributions, whereas the KolmogorovSmirnov test only requires the distributions to be continuous. (Both tests assume that the mean 

and variances are constant for each population, which is inconsistent with the variation of the 

means and variances with age and gender shown in the stratified analyses in Tables 3 and 4.) The 

discrepancy is also partly explained by the fact that the Kolmogorov-Smirnov test is less 

powerful (less likely to detect a difference) than the other tests, because it makes the fewest 

assumptions and considers the widest class of alternative hypotheses. 

Stratified Comparisons of Activity and Exertion Summary Statistics between Angina and 

Non-Angina Subjects 

Tables 3 and 4 provide the same statistical comparisons as Table 2, stratified by gender and age 

group. The results show the mean values for the selected summary statistics are not consistently 

lower for each age and gender subgroup of angina subjects. For example, Table 2 showed that 

the angina subjects had a lower overall mean value of the average maximum 8-hour exertion 

than the non-angina subjects. Tables 3 and 4 show the mean is actually higher for angina 

subjects 0-54 of either gender and for males 75 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 0-54 year old males: 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-
February 2010 F-10 Draft – Do Not Cite or Quote 

angina subjects. 

The Table 3 and 4 comparisons 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. 

Comparisons of Activity and Exertion Summary Statistics between Angina and NonAngina Subjects Adjusted for Age and Gender Differences 

Table 5 gives the results of the fitted general linear model. As explained above, the fitted model 

assumes that for each gender, the average value of the summary statistic is a cubic function of 

age. Furthermore, having angina changes the expected value by a fixed amount, which is 

assumed to be the same value for every age and gender. This angina effect is the coefficient 

reported in the table, together with its standard error and p-value. P-values less than or equal to 

0.05 indicate summary statistics where the angina effect was statistically significant at the 5 

percent significance level. The angina coefficient can be thought of as the effect of angina after 

adjusting for age and gender. The effects of age and gender are not reported, but in all cases were 

extremely statistically significant compared to the angina effect. 

Table 5 also reports the R squared goodness-of-fit statistic, which is the squared correlation 

between the observed and predicted values. R squared values vary from 0 (the worst possible fit) 

to 1 (a perfect fit), and are often interpreted as the fraction of the variability in the dependent 

variable (summary statistic) that is explained by the regression model. 

The first two rows of Table 5 show that the angina effect on the average and 95th percentile 

maximum 8-hour exertion is a statistically significant reduction (at the 6 and 1 % levels, 

respectively) for angina subjects compared to non-angina subjects. However, these reductions of 

0.04 Mcal and 0.16 Mcal are small when compared to the overall mean values of 1.4 and 2.3 

Mcal (non-angina subjects) reported in Table 2. The next three rows show that angina subjects 

tend to spend a little more time (0.7 percentage points) outdoors and a little less time (0.5 

percentage points) in a vehicle compared to non-angina subjects; those differences are not 

statistically significant. The last four rows show that angina subjects tend to spend less time at 

moderate or high levels of exertion, after adjusting for age and gender, although the differences 

are at most 1 percentage point and are not statistically significant. For example, the unadjusted 

average percentage time above 2.39 kcal/min (0.010 MJ/min) was 23.5 % for non-angina 

subjects (Table 2), and the effect of angina is to reduce the expected percentage of time by 0.7. 

As shown in Tables 3 and 4, this is due to average reductions of up to 5 percentage points for 

ages 55 and older but increases of 6 (males) and 2 (females) percentage points for the 0-54 age 

group.

R squared goodness-of-fit statistics were extremely low, 0.05 or less, for the percentages of time 

spent outdoors and/or in a vehicle. Thus the regression models for those percentages give very 

poor predictions. There are two possible reasons for this. First, the combination of age, gender, 

and angina status may be strongly associated with the percentages of time spent outdoors or in a 

vehicle but the assumed form of the regression model may poorly represent the functional 
February 2010 F-11 Draft – Do Not Cite or Quote 

relationship. Second, the combination of age, gender, and angina status may be poorly associated 

with the percentages of time spent outdoors or in a vehicle so that those activity percentages vary 

mainly with the effects of factors other than age, gender, and angina status. In either case, those 

regression models are not recommended for use in predicting the activity percentages. 

For the summary statistics based on the maximum 8-hour exertion and the percentages of time 

above exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a 

reasonably good fit, 0.48. As above, the cases of poor fitting models may be because the selected 

statistical models poorly represent the relationship between age, gender, and angina and the 

activity/exertion summary statistic and/or because the activity/exertion pattern varies 

substantially between people of the same age, gender, and angina status. Alternative general 

linear models, or the more sophisticated generalized linear models, could be developed to 

improve the predictive ability of the statistical models. 
February 2010 F-12 Draft – Do Not Cite or Quote 

REFERENCES 

Brainard, J., and Burmaster, D. E. 1992. “Bivariate Distributions for Height and Weight of Men 

and Women in the United States.” Risk Analysis 12:2, pp. 267-275. 

Burmaster, D. E., and Crouch, E. A. C. 1994. “Lognormal Distributions of Body Weight as a 

Function of Age for Males and Females in the United States.” Risk Analysis 17:4, pp. 499-

508. 

Esmail, Bhambhani, and Brintnell. 1995. “Gender Differences in Work Performance on the 

Baltimore Therapeutic Equipment Work Simulator.” Amer. J. Occup. Therapy 49, pp. 405-

411. 

Johnson, T. R, 1998. Proposed Probabilistic Algorithm for Estimating Ventilation Rate in the 

1998 Version of pNEM/CO. Memorandum submitted to EPA (September 25, 1998). 

Klepeis, N. E., Tsang, A. E., and Behar, J. V. 1996. Analysis of the National Human Activity 

Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. 

EPA/600/R-96/074. 

Klepeis, N. E., Nelson, W. C., Tsang, A. M., Robinson, J. P., Hern, S. C., Engelmann, W. H., 

and Behar, J. V. 1998. “The National Human Activity Pattern Survey (NHAPS): Data 

Collection Methodology and Selected Results.” Submitted to Journal of Exposure Analysis 

and Environmental Epidemiology. 

Schofield, W. N. 1985. “Predicting Basal Metabolic Rate, New Standards, and Review of 

Previous Work.” Hum Nutr Clin Nutr. 39C (Supp 1), pp. 5-41. 

Triplett, T. 1996. National Human Activity Pattern Survey, CD-ROM Version 2.0. University of 

Maryland Survey Research Center. 

Tsang, A. M. and Klepeis, N. E. 1996. Descriptive Statistics Tables from a Detailed Analysis of 

the National Human Activity Pattern Survey (NHAPS) Data. EPA/600/R-96/148. 
February 2010 F-13 Draft – Do Not Cite or Quote 

Table 1. Distribution of subjects according to their age, gender and disease status 

Males Females 

Age Group Angina (%) Non-angina 

(%)

All Angina (%) Non-angina 

(%)

All Angina (%) No

(%)

0-54 35 (1.0) 3307 (98.9) 3342 17 (0.5) 3570 (95.5) 3587 52 (0.8) 68

55-64 28 (6.5) 400 (93.5) 428 28 (5.4 ) 491 (94.6) 519 56 (5.9) 8965-74 23 (7.9) 267 (92.1) 290 48 (9.6) 450 (91.4) 498 71 (9.0) 71

75+ 17 (10.7) 142 (89.3) 159 47 (14.4) 279 (85.6) 326 64 (13.2) 42

Total 103 (2.4) 4116 (97.6) 4219 140 (2.8) 4790 (97.2) 4930 243 (2.6) 89

This table was modified by staff on 2-22-10 from the below original version due to issues related to the conversion from WoWord. 

 Gender Age group 

 Males Angina(%) Non-angina(%) All 

 Females Angina(%) Non-angina(%) All 

 All Angina(%) Non-angi

35 (1.0) 3307 (98.9) 3342 

17 (0.5) 3570 (95.5) 3587 

52 (0.8) 6877 (996929 

28 (6.5) 400 (93.5) 428 

28 (5.4 ) 491 (94.6) 519 

56 (5.9) 891 (94947 

23 (7.9) 267 (92.1) 290 

48 (9.6) 450 (91.4) 498 

71 (9.0) 717 (91788 

17 (10.7) 142 (89.3) 159 

47 (14.4) 279 (85.6) 326 

64 (13.2) 421 (86485 

103 (2.4) 4116 (97.6) 4219 

140 (2.8) 4790 (97.2) 4930 

243 (2.6) 8906 (979149 
February 2010 F-14 Draft – Do Not Cite or Quote 

Table 2. Statistical Tests for the Association between Angina and Various Variables Representi

Exertion. All 

Variable T Test Comparison of 

Means Mean Mean P-valueAngina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

Average maximum 8hr exertion (Mcal) 

1.28 1.40 0.00 0.48 0.49 0.68 0.00 

Ninety fifth percentile of maximum 8hr exertion (Mcal) 

1.87 2.25 0.00 0.97 1.13 0.00 0.00 

Percentage of time spent outdoors 

6.73 6.74 0.99 12.87 11.63 0.02 0.22 

Percentage of time spent in vehicle 

4.55 5.55 0.01 6.19 7.13 0.00 0.00 

Percentage of time spent outdoors or in vehicle 

11.27 12.29 0.27 14.33 13.45 0.15 0.00 

Average percentage of time with exertion above 2.39 kcal/min  = 0.010MJ/min (light) 

19.98 23.53 0.00 13.56 13.78 0.75 0.00 

Average percentage of time with exertion above 5.97 kcal/min  = 0.025 MJ/ min (moderate) 

2.17 2.78 0.01 3.68 3.56 0.46 0.00 

Average percentage of time with exertion above 9.55 kcal/min 

0.213 0.406 0.00 0.554 0.761 0.00 0.00 
February 2010 F-15 Draft – Do Not Cite or Quote 

 = 0.040 MJ/ min (heavy) 

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

Average maximum 8hr exertion (Mcal) 

0-54 55-64 65-74 75+ 

1.85 1.59 0.00 1.48 1.77 0.00 1.39 1.49 0.36 1.27 1.20 0.52 

0.49 0.55 0.34 0.48 0.48 0.86 0.47 0.48 0.96 0.44 0.42 0.65 

 0.02  0.01  0.41  0.51 

Ninety fifth percentile of maximum 8hr exertion (Mcal) 

0-54 55-64 65-74 75+ 

2.94 2.68 0.17 2.40 2.90 0.04 1.91 2.17 0.14 1.73 1.67 0.71 

1.06 1.30 0.13 1.21 1.14 0.61 0.78 0.94 0.31 0.69 0.76 0.68 

 0.22  0.02  0.26  0.55 

Percentage of time spent outdoors 

0-54 55-64 65-74 

16.86 8.85 0.02 9.28 10.02 

19.16 13.75 0.00 14.26 13.86 

 0.01  0.65  0.12 
February 2010 F-16 Draft – Do Not Cite or Quote 

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

75+ 0.79 

6.43 10.40 0.13 8.23 7.09 0.72 

0.78 11.38 14.34 0.20 12.63 10.05 0.16 

 0.58 

Percentage of time spent in vehicle 

0-54 55-64 65-74 75+ 

5.96 6.09 0.92 3.99 6.87 0.00 7.20 5.88 0.51 2.29 3.34 0.14 

7.55 8.10 0.63 3.78 9.63 0.00 9.04 7.80 0.29 2.54 3.94 0.05 

 0.89  0.18  0.82  0.44 

Percentage of time spent outdoors or in vehicle 

0-54 55-64 65-74 75+ 

22.83 14.94 0.02 13.27 16.89 0.24 13.63 16.29 0.45 10.51 10.43 0.98 

19.28 15.52 0.05 15.30 16.18 0.76 15.87 15.91 1.00 12.86 10.39 0.19 

 0.02  0.17  0.19  0.69 

Average 0-54 34.76 27.78 13.33 15.18 0.02 
February 2010 F-17 Draft – Do Not Cite or Quote 

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

percentage of time with exertion above 2.39 kcal/min = 0.010MJ/min (light) 

55-64 65-74 75+ 

0.00 24.60 30.07 0.06 21.92 23.32 0.63 18.24 15.87 0.41 

0.34 14.51 12.03 0.14 13.23 12.48 0.64 11.13 10.37 0.63 

 0.06  0.67  0.39 

Average percentage of time with exertion above 5.97 kcal/min = 0.025 MJ/ min (moderate) 

0-54 55-64 65-74 75+ 

6.63 4.46 0.05 3.43 5.44 0.01 2.27 3.27 0.08 2.02 1.62 0.53 

6.37 4.31 0.00 3.55 4.41 0.17 2.47 3.46 0.06 2.42 2.41 0.92 

 0.02  0.01  0.20  0.43 

Average percentage of time with exertion above 9.55 kcal/min = 0.040 MJ/ min (heavy) 

0-54 55-64 65-74 75+ 

0.662 0.735 0.59 0.565 0.846 0.19 0.155 0.388 0.01 0.132 0.157 

0.792 0.986 0.11 1.068 1.222 0.40 0.361 0.716 0.00 0.331 0.512 

 0.55  0.05  0.06  0.55 
February 2010 F-18 Draft – Do Not Cite or Quote 

Table 3. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. MalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

0.79 0.05 

Table 4. Statistical Tests for the Association between Angina and Various Variables RepresenExertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

Average maximum 8hr exertion (Mcal) 

0-54 55-64 65-74 75+ 

1.30 1.27 0.69 1.21 1.27 0.33 1.05 1.10 0.29 0.96 0.98 0.63 

0.31 0.38 0.340.32 0.33 1.000.30 0.31 0.940.33 0.30 0.34 

 0.72  0.56  0.31  0.44 

Ninety fifth 0-54 1.98 2.01 0.82 0.91 0.99 
February 2010 F-19 Draft – Do Not Cite or Quote 

Table 4. Statistical Tests for the Association between Angina and Various Variables Represen

Exertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

percentile of maximum 8hr exertion (Mcal) 

55-64 65-74 75+ 

0.86 1.79 1.92 0.41 1.42 1.51 0.26 1.27 1.31 0.59 

0.69 0.80 0.77 0.68 0.53 0.57 0.57 0.56 0.52 0.43 

 0.33  0.31  0.43 

Percentage of time spent outdoors 

0-54 55-64 65-74 75+ 

3.64 5.11 0.42 4.31 4.59 0.88 2.84 4.14 0.14 3.79 2.27 0.40 

7.29 9.58 0.20 9.53 8.22 0.23 5.51 7.34 0.02 12.04 4.60 0.00 

 0.43  0.23  0.49  0.76 

Percentage of time spent in vehicle 

0-54 55-64 65-74 75+ 

4.54 5.35 0.55 4.21 5.60 0.35 5.26 4.15 0.23 2.82 2.94 0.86 

5.48 5.98 0.72 7.53 7.23 0.71 5.94 6.18 0.77 4.37 4.34 0.91 

 0.40  0.06  0.15  0.72 

Percentage of time 0-54 8.18 10.46 9.93 11.27 0.18 
February 2010 F-20 Draft – Do Not Cite or Quote 

Table 4. Statistical Tests for the Association between Angina and Various Variables Represen

Exertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

spent outdoors or in vehicle 

55-64 65-74 75+ 

0.36 8.52 10.18 0.48 8.10 8.29 0.88 6.61 5.21 0.45 

0.57 12.15 10.42 0.22 8.21 9.38 0.26 12.36 6.25 0.00 

 0.04  0.99  0.77 

Average percentage of time with exertion above 2.39 kcal/min = 0.010MJ/min (light) 

0-54 55-64 65-74 75+ 

23.50 21.40 0.49 19.04 21.04 0.33 13.97 15.46 0.26 11.31 11.84 0.73 

12.28 12.15 0.86 10.34 10.47 1.00 8.45 9.53 0.31 9.75 8.61 0.24 

 0.41  0.51  0.43  0.51 

Average percentage of time with exertion above 5.97 kcal/min = 0.025 MJ/ min (moderate) 

0-54 55-64 65-74 75+ 

1.44 1.59 0.71 0.79 1.31 0.05 0.65 0.68 0.87 0.75 0.43 0.23 

1.66 1.97 0.44 1.27 2.07 0.00 1.35 1.56 0.22 1.73 1.31 0.01 

 0.73  0.04  0.51  0.84 

Average 0-54 0.101 0.184 0.170 0.324 0.63 
February 2010 F-21 Draft – Do Not Cite or Quote 

Table 4. Statistical Tests for the Association between Angina and Various Variables Represen

Exertion. FemalesVariable Age 

Group 

T Test Comparison of Means Mean Mean Pvalue Angina Non-angina 

F Test Comparison of Standard Deviations St. Dev. St. Dev. Pvalue Angina Non-angina 

Wilcoxon Test  P-value 

percentage of time with exertion above 9.55 kcal/min = 0.040 MJ/ min (heavy) 

55-64 65-74 75+ 

0.06 0.095 0.093 0.96 0.028 0.030 0.89 0.025 0.016 0.44 

0.00 0.277 0.198 0.01 0.076 0.110 0.00 0.075 0.077 0.90 

 0.67  0.85  0.28 
 February 2010 F-22 Draft – Do Not 

Table 5. General Linear Models for the Association between Angina and Various Variables RepresentiExertion. 

Variable 

Angina Coefficient1 

Standard Error 

P-value R s

Average maximum 8hr exertion (Mcal) -0.0445 0.0237 0.0608 0Ninety fifth percentile of maximum 8hr exertion (Mcal) 

-0.1553 0.0581 0.0075 0

Percentage of time spent outdoors +0.6975 0.7648 0.3618 0Percentage of time spent in vehicle -0.4805 0.4679 0.3045 0Percentage of time spent outdoors or in vehicle +0.2170 0.8777 0.8047 0Average percentage of time with exertion above 2.39 kcal/min = 0.010 MJ/min (light) 

-0.7359 0.6996 0.2929 0

Average percentage of time with exertion above 5.97 kcal/min = 0.025 MJ/min (moderate) 

-0.1730 0.1910 0.3650 0

Average percentage of time with exertion above 9.55 kcal/min = 0.040 MJ/min (heavy) 

-0.0933 0.0439 0.0334 0

1. The angina coefficient is the expected difference (angina minus non-angina) between the summary statistic non-angina subjects of the same age and gender.
United States Office of Air Quality Planning and Standards Publication No

Environmental Protection Health and Environmental Impacts Division February, 2010 Agency Research Triangle Park, NC