Risk and Exposure Assessment to Support 

the Review of the Carbon Monoxide Primary 

National Ambient Air Quality Standards: 

First External Review Draft 

EPA-452/P-09-008 

October 2009 

Risk and Exposure Assessment to Support the Review of the 

Carbon Monoxide Primary National Ambient Air Quality 

Standards: 

First 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 Souad Benromdhane, U.S. 

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

Research Triangle Park, North Carolina 27711 (email: benromdhane.souad@epa.gov). 

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

by Abt Associates, Inc. in partial fulfillment of Contract No. EP-D-08-100, Work Assignment 0-

08. 

ACKNOWLEDGEMENTS 

In addition to EPA staff, personnel from Abt Associates, Inc. contributed to the writing of 

this document. Specific chapters and appendices where Abt Associates, Inc. made significant 

contributions include: chapter 5 (Approach to Exposure and Dose Assessment for the Current 

Review), chapter 6 (Exposure/dose assessment), and appendices B (Mass Balance Model in 

APEX), and C (COHb Module for pNEM/CO). 
Table of Contents 

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

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

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

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

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

1.3 CURRENT ASSESSMENT ............................................................................ 1-5 

1.4 REFERENCES ................................................................................................ 1-6 

2 OVERVIEW OF EXPOSURE AND DOSE ASSESSMENT CONCEPTUAL 

MODEL .................................................................................................................... 2-1 

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

2.2 EXPOSURE PATHWAYS AND RELEVANT MICROENVIRONMENTS ..... 

 .......................................................................................................................... 2-1 

2.3 AT-RISK-POPULATIONS ............................................................................. 2-2 

2.4 EXPOSURE AND DOSE METRICS ............................................................. 2-4 

2.5 RISK CHARACTERIZATION METRIC....................................................... 2-4 

2.6 REFERENCES ................................................................................................ 2-6 

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

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

3.2 STUDY AREAS FOR CURRENT ASSESSMENTS................................... 3-12 

3.3 KEY OBSERVATIONS................................................................................ 3-16 

3.4 REFERENCES .............................................................................................. 3-17 

4 APPROACH TO RISK CHARACTERIZATION FOR THE CURRENT 

REVIEW................................................................................................................... 4-1

4.1 CARDIOVASSCULAR DISEASE RELATED EFFECTS ............................ 4-1 

4.2 HEALTH EFFECTS BENCHMARKS ........................................................... 4-2 

4.3 KEY OBSERVATIONS.................................................................................. 4-4 

4.4 REFERENCES ................................................................................................ 4-5 

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5 APPROACH TO EXPOSURE AND DOSE ASSESSMENT FOR CURRENT 

REVIEW................................................................................................................... 5-1

5.1 MODEL OVERVIEW..................................................................................... 5-1 

5.2 MODEL HISTORY AND EVOLUTION ....................................................... 5-2 

5.3 MODEL SIMULATION PROCESS ............................................................... 5-3 

5.3.1 Characterize the Study Area .................................................................... 5-5 

5.3.2 Generate Simulated Individuals............................................................... 5-5 

5.3.3 Construct Activity Sequences.................................................................. 5-5 

5.3.4 Calculate Microenvironmental Concentrations .......................................5-7 

5.3.5 Estimate Energy Expenditure and Ventilation Rates............................... 5-9 

5.3.6 Calculate Exposure ................................................................................ 5-11 

5.3.7 Calculate Dose ....................................................................................... 5-11 

5.3.8 Model Output......................................................................................... 5-13 

5.3.9 Model limitations................................................................................... 5-13 

5.4 PERSONAL EXPOSURE AND THE IN-VEHICLE MICROENVIRONMENT 

 ........................................................................................................................ 5-15 

5.4.1 Personal Exposure Monitoring Studies..................................................5-15 

5.4.2 In-Vehicle Concentrations ..................................................................... 5-17 

5.5 STRATEGY FOR CO EXPOSURE/DOSE ASSESSMENT FOR THE 

CURRENT REVIEW .................................................................................... 5-23 

5.5.1 Background for Current Assessment Strategy....................................... 5-23 

5.5.2 Selected Approach for Current Review ................................................. 5-24 

5.6 KEY OBSERVATIONS................................................................................ 5-30 

5.7 REFERENCES .............................................................................................. 5-31 

6 EXPOSURE/DOSE ASSESSMENT AND RISK CHARACTERIZATION...... 6-1 

6.1 APPLICATION OF APEX4.3 TO CARBON MONOXIDE.......................... 6-1 

6.1.1 Study Areas and Exposure Periods..........................................................6-2 

6.1.2 Exposure Scenarios.................................................................................. 6-6 

6.1.3 Populations-at-Risk..................................................................................6-6 

6.1.4 Air Quality and Meteorological Data ...................................................... 6-8 

6.1.5 Microenvironments................................................................................ 6-13 

6.1.6 Time/Activity Patterns........................................................................... 6-14 

6.2 EXPOSURE AND DOSE ESTIMATES AND RISK CHARACTERIZATION. 

 ........................................................................................................................ 6-14 

6.2.1 Denver – Scenarios A and B..................................................................6-14 

6.2.2 Los Angeles – Scenarios A and B.......................................................... 6-21 

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6.2.3 Comparison of Denver and Los Angeles Estimates for End-of-hour COHb 

Levels.....................................................................................................6-26 

6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000 

PNEM/CO AND 2009 APEX/CO ASSESSMENTS .................................... 6-26 

6.3.1 Important Differences Between the 2000 pNEM/CO and 2009 APEX/CO 

Exposure Assessments........................................................................... 6-27 

6.3.2 Comparison of Estimated COHb Levels in Adults with Coronary Heart 

Disease using the 2000 pNEM/CO and 2009 APEX/CO Assessments........

................................................................................................................ 6-28 

6.4 VARIABILITY ANALYSIS AND UNCERTAINTY CHARACTERIZATION 

 ........................................................................................................................ 6-34 

6.4.1 Analysis of Variability...........................................................................6-34 

6.4.2 Characterization of Uncertainty.............................................................6-36 

6.5 KEY OBSERVATIONS................................................................................ 6-42 

6.6 REFERENCES .............................................................................................. 6-44 

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, Years 1993 – 

2008 ...........................................................................................................................3-5 

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

(bottom) CO Ambient Monitoring Concentrations in Los Angeles, California, Years 

1993 – 2008 ............................................................................................................... 3-6 

Figure 3-3. Diurnal Distribution of 1-hour CO Concentrations in Denver (Monitor 08-031-0002) 

by Day-type (weekdays-left; weekends-right), Years 1995 (top) and 2006 (bottom) ... 

...................................................................................................................................3-8 

Figure 3-4. Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor 06-037-

1301) by day-type (weekdays-left; weekends-right), years 1997 (top) and 2006 

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

Figure 5-1. Figure 5-1. ....... Locations of active ambient CO monitors meeting 75% completeness 

criterion in 2006 along with locations of inactive ambient CO monitors, within the 

metropolitan Denver (top) and metropolitan Los Angeles (bottom).......................5-27 

Figure 5-2. Figure 5-2. . Distribution of 1-hour (top) and 8-hour average daily maximum (bottom) 

CO concentrations at ambient CO monitors in Denver County, year 2006 ............ 5-27 

Figure 5-3. Figure 5-3. . Distribution of 1-hour (top) and 8-hour average daily maximum (bottom) 

CO concentrations at ambient CO monitors in Los Angeles County, year 2006.... 5-29 

Figure 6-1. Map of the Denver Study Area Defined as a Circle with Radius = 20 km Centered on 

Fixed-Site Monitor ID 080310002............................................................................6-4 

October 2009 iii Draft – Do Not Quote or Cite 
Figure 6-2. Map of the Los Angeles Study Area Defined as a Circle with radius = 20 km 

Centered on Fixed-Site Monitor No. 060371301...................................................... 6-5 

Figure 6-3. Percentage of Los Angeles and Denver Adults with Coronary Heart Disease (CHD) 

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

the Specified Percentage for Air Quality Adjusted to Just Meeting the Current 

Standard. Data taken from Tables 6-22 and 6-23...................................................6-33 

 

List of Tables 

Table 3-1. Descriptive Statistics for CO Concentrations Measured at Selected Fixed-Site 

Monitors in the Denver Metropolitan Area for the Years 2005 – 2007 ..................3-13 

Table 3-2. Descriptive Statistics for CO Concentrations Measured at Selected Fixed-Site 

Monitors in the Los Angeles Metropolitan Area for the Years 2005 – 2007..........3-14 

Table 5-1. Parameters of the Factors Model............................................................................... 5-9 

Table 5-2. Carbon Monoxide Concentrations Inside and Immediately Outside Vehicles, and 

Indoor/Outdoor Vehicle Ratios ...............................................................................5-21 

Table 5-3. Descriptive Statistics for CO Concentrations Measured Inside Vehicles and at FixedSite Monitors (from Shikiya et al., 1989)................................................................ 5-23 

Table 6-1. National Prevalence Rates for Coronary Heart Disease by Age Range.................... 6-7 

Table 6-2. National Prevalence Rates for Coronary Heart Disease by Gender..........................6-7 

Table 6-3. National Prevalence Rates for Coronary Heart Disease Used in APEX, Stratified by 

Age and Gender......................................................................................................... 6-8 

Table 6-4. Site Characteristics of Fixed-site CO Monitors Selected to Represent the Denver and 

Los Angeles Study Areas .......................................................................................... 6-8 

Table 6-5. Descriptive Statistics for 1-hour Carbon Monoxide Concentrations Reported by the 

Selected Denver and Los Angeles Monitors Before and After Estimation of Missing 

Values........................................................................................................................ 6-9 

Table 6-6. Descriptive Statistics for 1-hour Carbon Monoxide Concentrations Reported by the 

Denver and Los Angeles Monitors Before and After Adjustment to Simulate Just 

Meeting the Current 8-Hour CO NAAQS............................................................... 6-11 

Table 6-7. Site Characteristics of Meteorological Monitoring Stations Selected to Represent the 

Denver and Los Angeles Study Areas..................................................................... 6-11 

Table 6-8. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 

Denver Study Area Estimated to Experience a 1-hour Daily Maximum CO Exposure 

at or Above the Specified Concentration ................................................................6-17 

Table 6-9. Number of Adults with Coronary Heart Disease (CHD) in the Denver Study Area 

Estimated to Experience a 1-hour Daily Maximum CO Exposure at or Above the 

Specified Concentration ..........................................................................................6-17 

Table 6-10. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 

Denver Study Area Estimated to Experience an 8-hour Daily Maximum CO 

Exposure at or Above the Specified Concentration ................................................ 6-18 

October 2009 iv Draft – Do Not Quote or Cite 
October 2009 v Draft – Do Not Quote or Cite 

Table 6-11. Number of Adults with Coronary Heart Disease (CHD) in the Denver Study Area 

Estimated to Experience an 8-hour Daily Maximum CO Exposure at or Above the 

Specified Concentration ..........................................................................................6-18 

Table 6-12. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 

Denver Study Area Estimated to Experience a Daily Maximum End-of-hour COHb 

Level at or Above the Specified Concentration ......................................................6-19 

Table 6-13. Number of Adults with Coronary Heart Disease (CHD) in the Denver Study Area 

Estimated to Experience a Daily Maximum End-of-hour COHb Level at or Above 

the Specified Concentration ....................................................................................6-19 

Table 6-14. Estimated Average Number of Days with a Daily Maximum End-of-hour COHb 

Level At or Above the Specified Concentration Per Adult With Coronary Heart 

Disease (CHD) in the Denver Study Area............................................................... 6-20 

Table 6-15. Number of Person-Days for Adults with Coronary Heart Disease (CHD) in the Los 

Angeles Study Area Estimated to Experience a 1-hour Daily Maximum CO Exposure 

At or Above the Specified Concentration ...............................................................6-22 

Table 6-16. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles Study 

Area Estimated to Experience a 1-hour Daily Maximum CO Exposure At or Above 

the Specified Concentration ....................................................................................6-22 

Table 6-17. Number Of Person-Days For Adults with Coronary Heart Disease (CHD) in the Los 

Angeles Study Area Estimated to Experience an 8-hour Daily Maximum CO 

Exposure At or Above the Specified Concentration ............................................... 6-23 

Table 6-18. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles Estimated 

to Experience an 8-hour Daily Maximum CO Exposure At or Above the Specified 

Concentration .......................................................................................................... 6-24 

Table 6-19. Number of Person-Days For Adults With Coronary Heart Disease (CHD) in the Los 

Angeles Study Area Estimated to Experience a Daily Maximum End-of-hour COHb 

Level At or Above the Specified Concentration .....................................................6-24 

Table 6-20. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles Study 

Area Estimated to Experience a Daily Maximum End-of-hour COHb Level At or 

Above the Specified Concentration.........................................................................6-25 

Table 6-21. Estimated Average Number of Days with a Daily Maximum End-of-hour COHb 

Level At or Above the Specified Concentration Per Adult with Coronary Heart 

Disease (CHD) in the Los Angeles Study Area ...................................................... 6-25 

Table 6-22. Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to 

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

Percentage Under Specified Air Quality Conditions for 1995................................ 6-31 

Table 6-23. 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 Under “Just Meeting”Conditions for 1997 ...........................................6-32 

Table 6-24. Summary of How Variability Was Incorporated Into the Exposure Assessment .. 6-35 

Table 6-25. Characterization of Key Uncertainties in the Draft Assessment for Denver and Los 

Angeles Areas.......................................................................................................... 6-38 
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1. INTRODUCTION 

1.1 BACKGROUND 

The U. S. Environmental Protection Agency (EPA) is presently conducting a review of 

the national ambient air quality standards (NAAQS) for carbon monoxide (CO). Sections 108 

and 109 of the Clean Air Act (Act) govern the establishment and periodic review of the NAAQS. 

These standards are established for pollutants that may reasonably be anticipated to endanger 

public health and welfare, and whose presence in the ambient air results from numerous or 

diverse mobile or stationary sources. The NAAQS are to be based on air quality criteria, which 

are to accurately reflect the latest scientific knowledge useful in indicating the kind and extent of 

identifiable effects on public health or welfare that may be expected from the presence of the 

pollutant in ambient air. The EPA Administrator is to promulgate and periodically review, at 

five-year intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for such 

pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator 

is to make revisions in the criteria and standards, and promulgate any new standards, as may be 

appropriate. The Act also requires that an independent scientific review committee advise the 

Administrator as part of this NAAQS review process, a function performed by the Clean Air 

Scientific Advisory Committee (CASAC). 

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

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

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

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

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

modeling relating CO exposures to COHb levels. The review completed in 1994 also reaffirmed 

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

FR 38906). 

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

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

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

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

impact of meteorology and topography on ambient CO concentrations in high altitude and 
October, 2009 1-2 Draft – Do Not Cite or Quote

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

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

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

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

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

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

conduct rulemaking to complete the review. 

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

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

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

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

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

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

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

documents that would be developed in the review in a draft Integrated Review Plan for the 

National Ambient Air Quality Standards for Carbon Monoxide (US EPA, 2008a). After CASAC 

and public input on the draft plan, EPA made the final plan available in August 2008 (US EPA, 

2008b). EPA is currently completing the process of assessing the latest available policyrelevant scientific information to inform the review of the CO standards. The latest draft of this 

assessment is contained in the second external review draft of the Integrated Science Assessment 

for Carbon Monoxide (hereafter, “draft ISA”) (US EPA, 2009c) which was released in 

September 2009 for review by the CASAC and for public comments. The draft ISA includes an 

evaluation of the scientific evidence on the health effects of CO, including information on 

exposure, physiological mechanisms by which CO might adversely impact human health, an 

evaluation of the clinical evidence for CO-related morbidity, and an evaluation of the 

epidemiological evidence for CO-related morbidity and mortality associations.1

 

Building upon the health effects evidence presented in the draft ISA as well as CASAC 

advice (Brain and Samet, 2009) and public comments on a scope and methods planning 

document for the exposure/risk assessment (hereafter, “Scope and Methods Plan”) (US EPA, 

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 The draft 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. 
October, 2009 1-3 Draft – Do Not Cite or Quote

2009a), EPA’s Office of Air Quality Planning and Standards (OAQPS) has developed this first 

draft Risk/Exposure Assessment describing the initial quantitative assessments being conducted 

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

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

associated with the quantitative analyses performed. The final REA will draw upon the final 

ISA and will reflect consideration of CASAC and public comments on this draft REA. 

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The schedule for completion of this review is governed by a court order that specifies that 

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

CO NAAQS no later than October 28, 2010 and May 13, 2011, respectively. The order also sets 

dates for the following interim milestones: release of a first draft ISA by March 14, 2009 

(completed), a first draft risk/exposure assessment by October 29, 2009, a final ISA by January 

29, 2010, and a final risk/exposure assessment by May 28, 2010. 

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

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

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

basis for alternative policy options for consideration by senior EPA management prior to 

rulemaking. The PA will integrate and interpret information from the ISA and the REA to frame 

policy options for consideration by the Administrator. The PA is intended to help “bridge the 

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

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

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

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

existing standards as may be appropriate. OAQPS currently plans to release a draft PA in late 

February 2010 for review by CASAC, as well as for public comment, in conjunction with 

CASAC review and public comment of the second draft REA (US EPA, 2009c). 

1.2 ASSESSMENTS FROM PREVIOUS REVIEWS 

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

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

characterize risks for at-risk populations (50 FR 37484; 59 FR 38906). These prior risk 

characterizations compared the numbers of at-risk individuals and percent of the at-risk 

population exceeding several potential health effect benchmarks, expressed in terms of COHb 
October, 2009 1-4 Draft – Do Not Cite or Quote

levels. This characterization was based on COHb levels observed in several controlled human 

exposure studies reporting aggravation of angina associated with short-term (< 8-hr) CO 

exposures and described in EPA’s Air Quality Criteria Document (AQCD) (US EPA, 1979; US 

EPA, 1984; US EPA, 1991). 

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In the review completed in 1994, this characterization was performed for the at-risk 

population in the city of Denver, Colorado (US EPA, 1992). That analysis indicated that if the 

current 8-hr standard were just met, the proportion of the nonsmoking population with 

cardiovascular disease experiencing exposures at or above 9 ppm for 8 hrs decreased by an order 

of magnitude or more as compared to the proportion under then-existing CO levels, down to less 

than 1 percent of the total person-days in that population. Likewise, meeting the current 8-hr 

standard reduced the proportion of the nonsmoking cardiovascular-disease population person 

days at or above COHb levels of concern by an order of magnitude or more relative to thenexisting CO levels. More specifically, upon meeting the 8-hr standard, EPA estimated that less 

than 0.1% of the nonsmoking cardiovascular-disease population would experience a COHb level 

of about 2.1%. A smaller percentage of the at-risk population was estimated to exceed higher 

COHb percentages. The analysis also took into account that certain indoor sources (e.g., passive 

smoking, gas stove usage) contributed to total CO exposure but could not be effectively 

mitigated by setting more stringent ambient air quality standards. 

In the subsequent review, initiated in 1997, EPA consulted with CASAC on a draft 

exposure analysis methodology document, Estimation of Carbon Monoxide Exposures and 

Associated Carboxyhemoglobin Levels in Denver Residents using pNEM/CO (Version 2.0) 

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

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

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

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

exposure and dose modeling methodology (and version 2.1 of pNEM/CO) for two study areas 

(Denver and Los Angeles). This report was subjected to an external peer review by three 

exposure modeling experts convened by Science Applications International Corporation (SAIC, 

2001). 
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1 1.3 CURRENT ASSESSMENT 

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In preparing the Scope and Methods Plan for the current health risk/exposure assessment, 

we considered the scientific evidence presented in the first draft ISA (US EPA, 2009b) and the 

key science policy issues raised in the IRP (US EPA, 2008b). EPA held a consultation with 

CASAC to solicit comments on the Scope and Methods Plan during a May 2009 CASAC 

meeting at which CASAC also provided comments on the first draft of the ISA. Public 

comments were also requested (74 FR 15265). CASAC and public comments were considered 

in advance of the conduct of the analyses and results presented in this draft REA. The design of 

the current risk assessment builds upon information presented in the second draft ISA (US EPA, 

2009c) with particular attention to conclusions regarding the adequacy of the air quality data for 

the purposes of exposure assessment. 

In this draft assessment we are relying on generally similar methodology and focusing on 

the same two urban areas (Denver and Los Angeles) as that used in the assessment for the 

previous review. Although improvements have been made to the exposure model since the time 

of the last review, we recognize significant data limitations in the current review. In CASAC’s 

comments on the first draft ISA, the Committee stated that the “current ambient monitoring 

network is not well designed to characterize spatial and temporal variability in ambient 

concentrations” and that “it does not adequately support detailed assessments of human 

exposure” (Brain and Samet, 2009). As a result, the draft assessment that we describe in this 

document has implemented a much-simplified, screening-level approach focused on a single 

monitor and an exposure situation of particular interest for ambient CO (as described in detail in 

chapters 5 and 6). Based on the concerns raised by CASAC regarding the adequacy of the 

current monitoring data for this purpose, staff decided not to perform a detailed analysis 

involving multiple monitors and comprehensive estimation of exposure concentrations in 

multiple microenvironments, as has been done in the past. In presenting this draft, screeninglevel assessment, however, we recognize that the simplifications in this approach contribute to 

limitations and uncertainties in the interpretation of the results. One purpose of this draft 

document is to seek CASAC views, and public comment, regarding our characterization of the 

results in light of uncertainties associated with the assessment design and inputs, and CASAC’s 

advice on the role of this assessment in informing the current review of the CO NAAQS. 
October, 2009 1-6 Draft – Do Not Cite or Quote

1 1.4 REFERENCES

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Brain JD and Samet JM (2009). Letter to EPA Administrator Lisa Jackson: Clean Air Scientific Advisory 

Committee’s (CASAC) Peer Review of the Agency’s 1st Draft Carbon Monoxide Integrated Science 

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

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

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

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

No. 68-D6-0064, WA Nos. 1-19 and 2-24. 

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

Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), prepared by 

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

No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQ-OAR-2008-0015-DRAFT-0009. 

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

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

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

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

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

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

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

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

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

Triangle Park, NC. 

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

Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-90/045F. 

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

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

EPA/452/R-92-004. 

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

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

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

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

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

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

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

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

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

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

001. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html. 

US EPA (2008b). Integrated Review Plan for the National Ambient Air Quality Standards for Carbon Monoxide. 

U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452R-08-005. 

Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html. 
October, 2009 1-7 Draft – Do Not Cite or Quote

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

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

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

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

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

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

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

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

at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 
October, 2009 2-1 Draft – Do Not Cite or Quote

2. OVERVIEW OF EXPOSURE AND DOSE ASSESSMENT 

CONCEPTUAL MODEL 

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In order to help inform the discussion of the CO assessment presented in chapters 5 and 

6, staff has briefly summarized the conceptual model for the consideration of exposure to 

ambient CO and associated health risk, from key sources through the identification of at-risk 

population groups, dose metric, and the risk characterization approach. 

7 2.1. SOURCES OF CARBON MONOXIDE 

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

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

emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the 

combustion zone. CO production relative to CO2 generally decreases with any increase in fuel 

oxygen (O2) content, burn temperature, or mixing time in the combustion zone (draft ISA, 

section 3.2). As a result, CO emissions from large fossil-fueled power plants are typically very 

low because of the boilers highly efficient combustion and optimized fuel consumption. In 

contrast, internal combustion engines used in many mobile sources have widely varying 

operating conditions. Therefore, higher and more varying CO formation results from the 

operation of these mobile sources (draft ISA, section 3.2). In 2002, CO emissions from on-road 

vehicles accounted for 63% of total emissions by individual source sectors in the U.S. (draft ISA, 

Figure 3-1).1

 As with previous reviews, mobile sources continue to be a significant source sector 

for CO in ambient air. 

Sources of indoor CO include infiltration of ambient air indoors, as well as, where 

present, indoor (nonambient) sources such as gas stoves and environmental tobacco smoke. 

(draft ISA, section 3.6.5.2). 

2.2. EXPOSURE PATHWAYS AND RELEVANT MICROENVIRONMENTS 

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

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

Studies of personal exposure to ambient CO have shown that the largest percentage of the time in 

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EPA, 2006), which included data from various sources such as industries and state, tribal, and local air agencies 

(draft ISA, p. 3-2). 
October, 2009 2-2 Draft – Do Not Cite or Quote

which an individual is exposed to ambient CO occurs indoors (draft ISA, section 2.3). As a 

result of people spending a significant amount of their time indoors (whether at home, school, 

workplace or elsewhere), CO concentrations in indoor microenvironments are an important 

determinant of an individual’s CO exposures. Microenvironments that may influence CO 

exposures typically include residential indoor environments and other indoor locations, neartraffic outdoor environments and other outdoor locations, and inside vehicles. As is summarized 

further in section 5.4, the highest exposure concentrations to ambient CO are experienced by 

individuals in transit on or near roadways (draft ISA, section 2.3). Ambient concentrations near 

roadways are generally influenced by vehicle traffic densities (draft ISA, section 3.5.2.2). As a 

consequence, near-road and in-vehicle exposure to CO will be much higher during commuting 

times. Thus, exposure to CO near roadway and in vehicle microenvironments are of concern in 

this review and are a focus of this draft assessment. 

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

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

exposure. For example, some assessments performed for previous reviews have included 

modeling simulations both without and with indoor sources (gas stoves and environmental 

tobacco smoke) to provide context for the assessment of ambient CO exposure and dose (e.g., 

USEPA, 1994; Johnson et al., 2000).2

 As noted in section 5.5, this draft assessment does not 

include a simulation with indoor sources on. 

2.3. AT-RISK-POPULATIONS 

In considering populations for inclusion in this exposure/risk assessment, we considered 

the evidence regarding those with increased susceptibility or vulnerability. The term 

‘susceptibility’ has been used to characterize populations that have a greater likelihood of 

experiencing effects related to ambient CO exposure, and the term ‘vulnerability’ has been used 

to identify those periods during an individual’s life when they are more susceptible to 

environmental exposures (draft ISA, section 5.7). In reviewing and setting NAAQS, EPA is 

required to establish a primary standard that provides protection for population groups that may 

be at greater risk due to increased susceptibility and/or increased vulnerability. 

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

more stringent ambient air quality standards and are therefore not a focus of this assessment of ambient CO 

exposure and dose. 
October, 2009 2-3 Draft – Do Not Cite or Quote

The draft ISA states that the strongest evidence regarding CO induced health effects 

relates to cardiovascular morbidity indicating that a causal relationship is likely to exist between 

relevant short-term CO exposures and cardiovascular morbidity, particularly in individuals with 

coronary artery disease (CAD), also referred to as coronary heart disease (CHD) (draft ISA, 

section 5.8). This evidence comes from human exposure studies of individuals with CAD, along 

with coherent results from recent epidemiologic studies reporting associations between shortterm CO exposure and increased risk of emergency department visits and hospital admissions for 

individuals affected with ischemic heart disease (IHD) and related outcomes (draft ISA, section 

5.7). Other subpopulations potentially at risk include individuals with diseases such as chronic 

obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late 

life stages, such as older adults or the developing young (draft ISA, section 2.6.1). There is 

limited evidence available from controlled human exposure, epidemiologic, or toxicological 

studies characterizing the nature of specific health effects of CO in these subpopulations. 

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The ISA notes that the most compelling evidence of a CO-induced effect on the 

cardiovascular system at COHb levels relevant to the current NAAQS comes from a series of 

controlled human exposure studies among individuals with CHD (draft ISA, section 2.5.1). The 

draft ISA indicates that these studies demonstrate consistent decreases in the time to onset of 

exercise induced angina and ST-segment changes (as indicators of myocardial ischemia) 

following CO exposures resulting in COHb levels of 3-6%, with one multicenter study reporting 

similar effects at COHb levels as low as 2.0-2.4%. It also recognizes that no human clinical 

studies have evaluated the effect of controlled exposures to CO resulting in COHb levels lower 

than 2% (draft ISA, section 5.2.6). Furthermore, 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 (draft ISA, section 2.5.1) 

Therefore, the primary target population for the assessment described in this document 

will be adults with CHD (also known as ischemic heart disease (IHD) or CAD). This is the same 

population group that was the focus of the exposure/dose assessments conducted for previous 

CO NAAQS reviews. Coronary heart disease includes those who have angina pectoris (cardiac 

chest pain), as well as those who have experienced a heart attack. Approximately 13.7 million 

people were diagnosed with CHD in 2007, which represent a large population that may be more 
October, 2009 2-4 Draft – Do Not Cite or Quote

susceptible to ambient CO exposure when compared to the general population (draft ISA, section 

5.7). 

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3 2.4. EXPOSURE AND DOSE METRICS 

Upon inhalation, CO diffuses through the respiratory zone (alveoli) to the blood where it 

binds to a number of heme-containing molecules, mainly hemoglobin (Hb), forming 

carboxyhemoglobin (COHb). Inhaled ambient CO elicits various health effects through this 

binding and associated alteration of the function of a number of heme-containing molecules, 

mainly Hb (draft ISA, section 4.1). The dosimetry and pharmacokinetics of CO are discussed in 

detail in chapter 4 of the draft ISA (US EPA, 2009). The best characterized health effect 

associated with CO levels of concern is hypoxia (reduced O2 availability) induced by increased 

COHb levels in blood (draft ISA, section 5.1.2). Thus, the dose metric used to characterize 

health risks associated with exposure to ambient CO in this assessment is the level of COHb in 

the blood. The Coburn-Forster-Kane (CFK) model (draft ISA, section 4.2.1) has been used to 

estimate dose (blood levels of COHb) for the exposure/dose modeling in this assessment (see 

section 5.3.7 of this document). 

2.5. RISK CHARACTERIZATION METRIC 

The category of health endpoints on which we focused in the risk and exposure 

assessment are those associated with coronary heart disease (see chapter 4). Similar to the 

approach used in prior CO NAAQS reviews, we have estimated CO exposures and resulting 

doses (i.e., COHb levels) for the defined at-risk population (people with CHD) and characterized 

the risk for this population in urban study areas associated with CO levels representing recent air 

quality and air quality adjusted to simulate just meeting the current CO NAAQS. In previous 

reviews, the COHb estimates were compared to potential health benchmarks (see section 1.2 

above). Although the draft ISA has described epidemiologic findings from a group of studies, 

many of which were conducted since the 2000 CO AQCD, that observe associations between 

short term ambient CO exposures and increases in emergency department visits and hospital 

admissions for cardiovascular effects (draft ISA, section 5.2.1.9), a number of issues complicate 

the use of these studies in a quantitative risk assessment (draft ISA, section 5.2.3). In 

consideration of these issues and CASAC views on the Scope and Methods Plan (Brain and 
October, 2009 2-5 Draft – Do Not Cite or Quote

Samet, 2009; US EPA, 2009), risk has been characterized in this assessment using the potential 

health effect benchmark level approach.

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3

 More specifically, we have estimated the number of 

persons and percent of the at-risk population (i.e., individuals with CHD) that exceed potential 

health effect benchmark levels, derived from the evaluation of the controlled human exposure 

studies mentioned above and specified in terms of COHb levels, associated with various CO air 

quality scenarios. 

The range of potential health effects benchmarks that we have used extends to lower 

levels than the range where controlled human exposure studies reported CO-related health effects 

(i.e., 3-6% COHb with one multicenter study reporting effects at 2.0 to 2.4% COHb using gas 

chromatography (GC)) to take into consideration both the uncertainty about the actual COHb 

levels experienced in the controlled human exposure studies due to the use of different 

measurement methods and that these studies did not include individuals with more severe CHD 

who may respond at lower COHb levels relative to the subjects tested. In addition, there were no 

studies evaluating effects of CO below 2.0-2.4% COHb levels. Based on these considerations, 

staff has included 1.5-, 2.0-, 2.5-, and 3.0 % COHb as potential health effect benchmark levels in 

the current CO risk characterization. 

Two metrics in this CO exposure/dose assessment are considered: (1) estimates of the 

number of people, percentage of the at risk population, and total number of person days for CO 

at-risk populations exposed to CO at concentrations that exceed selected benchmarks (1.5, 2.0, 

2.5, and 3.0 % COHb) for various CO air quality scenarios; (2) estimates of the number of 

people and the total number of occurrences for which potential health effect COHb benchmark 

levels are exceeded for the various CO air quality scenarios. 

 

3

 While not used for the purposes of this quantitative assessment, EPA will fully consider the health evidence, 

including the epidemiological studies, in the Policy Assessment Document, along with considerations based on the 

risk and exposure assessment findings. 
October, 2009 2-6 Draft – Do Not Cite or Quote

1 2.6. REFERENCES 

Brain JD and Samet JM (2009). Letter to EPA Administrator Lisa Jackson: Clean Air Scientific Advisory 

Committee’s (CASAC) Peer Review of the Agency’s 1st Draft Carbon Monoxide Integrated Science 

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

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CDC (2007). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007, series 10, 

number 240, May 2009. 

Coburn R, Forster R, Kane P (1965). Consideration of the physiological variables that determine the blood 

carboxyhemoglobin concentrations in man. J. CLin. Invest. 44:1899-1910. 

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

Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), prepared by 

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

No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQ-OAR-2008-0015-DRAFT-0009. 

US EPA. (1991). Air quality criteria for carbon monoxide. Research Triangle Park, NC: Office of Health and 

Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA/600/8-90/045F. 

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

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

EPA/452/R-92-004. 

US EPA. (2006). 2002 National Emissions Inventory Data and Documentation. U.S. Environmental Protection 

Agency, Office of Air and Radiation, Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html. 

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

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

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

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

US EPA. (2009). Draft Integrated Science Assessment for Carbon Monoxide – Health Criteria (Second External 

Review Draft). U.S. Environmental Protection Agency, Research Triangle Park, NC, Report no 600/R09/019B. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 
October, 2009 3-1 Draft – Do Not Cite or Quote

1 3. AIR QUALITY CONSIDERATIONS 

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Ambient air quality data can be used as an indicator of exposure or used in conjunction 

with other information to estimate exposure concentrations. How well the ambient air quality is 

represented in a particular location is dependent on the ambient monitoring network design 

relative to the spatial and temporal characteristics of the pollutant as well understanding the 

concentration contribution from important local source emissions. This chapter summarizes 

findings about the current air quality conditions and their spatial distribution, with particular 

focus on aspects informative to the design and conduct of this assessment and including 

descriptions of CO measurement methods, monitor siting requirements, and monitor locations 

(section 3.1).1

 Section 3.2 then draws upon the information presented in sections 3.1, among 

other data, to select ambient air quality/study locations most useful in meeting the objectives of 

the REA. Finally, key observations of the chapter are presented in section 3.3. 

3.1 AMBIENT CO MONITORING 

In this section, a broad overview of the monitoring network is provided (section 3.1.1) 

and is followed by a summary of analytical detection issues (section 3.1.2). Ambient CO 

concentrations and their spatial and temporal variability are characterized in section 3.1.3. 

Lastly, estimates of policy-relevant background (PRB) concentrations which are defined as those 

ambient concentrations that would occur in the U.S. in the absence of anthropogenic emissions in 

continental North America are presented in section 3.1.4 of this document. 

3.1.1 Monitoring Network 

Ambient CO concentrations are measured by monitoring networks that are operated by 

state and local monitoring agencies in the U.S., and are funded in part by the EPA. The main 

network providing ambient data for use in comparison to the NAAQS is the State and Local Air 

Monitoring Stations (SLAMS) network. The subsections below provide specific information 

regarding the methods used for obtaining ambient CO measurements and the requirements that 

apply to states in the design of the CO network. 

Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient 

monitoring requirements (see 71 FR 61236, October 17, 2006). This action was made to allow 

for reductions in measurements of some pollutants (CO, SO2, NO2, and Pb) where measured 

levels were well below the applicable NAAQS and air quality problems were not expected. CO 

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of other NAAQS pollutants such as NO2 and SO2 follow the same general spatial scales. 
October, 2009 3-2 Draft – Do Not Cite or Quote

monitoring activities have been maintained at some SLAMS and these measurements of CO are 

required to continue until discontinuation is approved by the EPA Regional Administrator. 

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CO monitors are typically sited to reflect one of the following spatial scales2: 

 Microscale: Data represents concentrations within a 100 m radius of the monitor. For 

CO, microscale monitors are sited 2 – 10 m from a roadway. Measurements are 

intended to represent the near-road or street canyon environment. 

 Middle scale: Data represents concentrations averaged over areas defined by 100 – 500 

m radii. Measurements are intended to represent several city blocks. 

 Neighborhood scale: Data represents concentrations averaged over areas defined by 

0.5 – 4.0 km radii. Measurements are intended to represent extended portions of a city. 

In addition to monitoring required for determining compliance with the NAAQS, the 

EPA is currently in the process of implementing plans for a new network of multi-pollutant 

stations called NCore that is intended to meet multiple monitoring objectives. A subset of the 

SLAMS network, NCore stations are intended to address integrated air quality management 

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.3 

States were required to submit to EPA Annual Monitoring Network Plans (AMNP) describing 

their candidate NCore stations by July 1, 2009. EPA is reviewing these plans and intends to 

provide station approvals later in 2009. The complete NCore network, required to be fully 

implemented by January 1, 2011, will consist of approximately 63 urban and 20 rural stations 

and will include some existing SLAMS sites that have been modified to include additional 

measurements. Each state will contain at least one NCore station, and 46 of the states plus 

Washington, D.C. will have at least one urban station. CO will be measured using trace-level 

monitors, as will SO2, NO, and NOy.

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 The majority of NCore stations will be sited to represent 

neighborhood, urban, and regional scales, consistent with the NCore network design objective of 

representing exposure expected across urban and rural areas in locations that are not dominated 

by local sources. 

3.1.2 Analytical Sensitivity 

To promote uniform enforcement of the air quality standards set forth under the CAA, 

EPA has established provisions in the Code of Federal Regulations (CFR) under which analytical 

3

 (http://www.epa.gov/ttn/amtic/ncore/index.html). 4

 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). 
October, 2009 3-3 Draft – Do Not Cite or Quote

methods can be designated as federal reference methods (FRMs) or federal equivalent methods 

(FEMs). Measurements for determinations of NAAQS compliance must be made with FRMs or 

FEMs.

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5

 Specifications for CO monitoring are designed to help states utilize equipment that has 

met performance criteria utilized in the FRM or FEM approval process; operational parameters 

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), a 1.0 ppm lower detectable limit (LDL) is well below the NAAQS levels 

and is therefore sufficient for demonstration of compliance. However, with ambient CO levels 

now routinely near 1 ppm, there is greater uncertainty in a larger portion of the distribution of 

monitoring data because a large percentage of these measurements are below the LDL of 

conventional monitors. For this reason, a new generation of ambient CO monitors has been 

designed that provides trace-level measurements with improved sensitivity at or below the 

typical ambient CO levels measured in most urban and all rural locations. Additionally, tracelevel CO measurements are needed to support additional objectives such as validating the inputs 

to chemical transport models and assessing the role of transport between urban and rural areas 

because background CO concentrations on the order of 0.1 ppm are well below the LDL of 

conventional monitors. Newer GFC instruments have been designed for automatic zeroing to 

minimize drift (US EPA, 2000). 

Currently, a total of 13 approved FRMs are in use in the SLAMS network, based on a 

retrieval of data reported between 2005 and 2009. Among these methods, nine are “legacy” 

monitors with a federal method detection limit (MDL) listed as 0.5 ppm according to records in 

EPA’s Air Quality System (AQS).6

 As discussed in the draft ISA, many of the reported 

concentrations in recent years are near or below these MDLs (draft ISA, p. 3-43). Four of these 

methods are newer trace-level methods with a federal MDL of 0.02 ppm and a growing body of 

ambient data from trace-level CO instruments is becoming available. Among newer GFC tracelevel instruments, manufacturer-declared LDLs range from 0.02 – 0.04 ppm, with 24-hour zero 

drift varying between 0.5% within 1 ppm and 0.1 ppm, and precision varying from 0.5% to 0.1 

ppm. EPA performed MDL testing on several trace-level CO monitors in 2005 and 2006 

following the 40 CFR Part 136 procedures. Those tests demonstrated MDLs of approximately 

0.017 – 0.018 ppm (17 – 18 ppb), slightly below the stated LDL of 0.02 – 0.04 ppm. 

5

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operate on the principle of non-dispersive infrared (NDIR) detection and can include the gas filter correlation (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). 

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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 (Mitchie et al., 

1983). 
October, 2009 3-4 Draft – Do Not Cite or Quote

Based on a retrieval of data reported between 2005 and 2009 to AQS, a total of 36 tracelevel CO monitors have reported data with the majority of these monitors currently active. The 

majority of these active monitors are associated with the implementation of the NCore network. 

The extent to which trace-level monitors become integrated into non-NCore SLAMS stations, 

however, will depend on the availability of funding for states to replace well-operating legacy 

CO monitors as well as the possibility that monitoring requirements for CO might either 

encourage or require such technological improvements. 

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3.1.3 Patterns of CO Concentrations 

As discussed in the draft ISA, the spatial and temporal patterns of ambient CO 

concentrations are heavily influenced by the patterns associated with mobile source emissions 

(draft ISA, chapter 3). Based on the 2002 National Emissions Inventory (NEI), on-road mobile 

sources comprise about half of the total anthropogenic CO emissions, though in metropolitan 

areas of the U.S. the contribution can be as high a 75% of all CO emissions due to greater motor 

vehicle density. For example, emissions in Denver county originating from on-road mobile 

sources is about 71% of total CO emissions (draft ISA, section 3.2). When considering all 

mobile sources (non-road and on-road combined), the contribution to total CO emissions can be 

over 80%. Again using Denver County as an example, all mobile sources contribute to about 

98% of total CO emissions. Temporally, the national-scale anthropogenic CO emissions have 

decreased 35% between 1990 and 2002. Nearly all the national-level CO reductions since 1990 

are the result of emission reductions in on-road vehicles (draft ISA, Figure 3.-2). 

Nearly 400 ambient monitoring stations report continuous hourly averages of CO 

concentrations across the U.S. Over the period 2005-2007, 291 out of 376 monitors met a 75% 

completeness requirement, spread among 243 counties, cities, or municipalities (draft ISA, 

section 3.4.2.2). All CO concentrations measured at these monitoring sites are well below the 

current NAAQS. For example, in 2007, none of the monitors reported a second-highest 1-hour 

CO concentration above 35 ppm, the level of the current 1-hour NAAQS, while only two sites 

reported a 2nd highest 1-hour CO concentrations between 15.1 and 35.0 ppm. Only five counties 

reported 2nd highest 8-hour CO concentration 5.0 ppm or higher. 
October, 2009 3-5 Draft – Do Not Cite or Quote

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1993199419951996199719981999200020012002200320042005200620072008

Year

2nd Highest 1-hour Average CO (ppm)

080310002

080310013

080310014

080310019

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Year

2nd Highest 8-hour Average CO (ppm)

080310002

060370113

060371002

080310019

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

Average (bottom) CO Ambient Monitoring Concentrations in Denver, 

Colorado, Years 1993 – 2008.
October, 2009 3-6 Draft – Do Not Cite or Quote

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1993199419951996199719981999200020012002200320042005200620072008

Year

2nd Highest 1-hour Average CO (ppm)

060370002

060370113

060371002

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060371201

060371301

060371601

060371701

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1993199419951996199719981999200020012002200320042005200620072008

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2nd Highest 8-hour Average CO (ppm)

060370002

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060371002

060371103

060371201

060371301

060371601

060371701

060372005

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060375001

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

Average (bottom) CO Ambient Monitoring Concentrations in Los Angeles, 

California, Years 1993 – 2008. 
October, 2009 3-7 Draft – Do Not Cite or Quote

The current levels of ambient CO across the U.S. reflect the steady declines in ambient 

concentrations that have occurred over the past several years. On average across the U.S. the 

decline has been on the order of 50% since the early 1990s (draft ISA, Figure 3-31). As an 

example, Figures 3-1 and 3-2 illustrate the trends observed in Denver and Los Angeles, 

respectively, for the period from 1993 through 2008. Both the 2nd highest 1-hour and 8-hour 

concentrations are plotted for each year from all existing monitors in those metropolitan areas 

respectively. Note, these figures indicate both a significant decrease in the 2nd highest 1-hour 

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and 8-hour average CO concentrations and a relative decrease in spatial variability in ambient 

CO concentrations since the last review. 

Carbon monoxide also exhibits hourly variability within a day, with two distinct temporal 

patterns noted for weekdays and weekends (draft ISA, section 3.5.2.2). The diel variation is 

inherently linked to the typical commute times-of-day that occurs within urban locations. In 

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

percentile hourly concentrations generally exhibit early-morning and late afternoon peak CO 

concentrations during weekdays (draft ISA, Figure 3-33). The weekend diel variation in ambient 

CO concentrations was much lower than that occurring during weekdays as expected due to the 

relative absence of commuter vehicle traffic during the morning and evening hours of the day. 

Most urban areas have relatively stable concentrations throughout weekend days at each of the 

selected percentiles, though a few locations (e.g., Phoenix, Los Angeles, Seattle) did have a more 

pronounced late afternoon peak (draft ISA, Figure 3-34). 

Staff investigated local hourly variation at two separate CO monitors located in Denver 

and Los Angeles to illustrate similar trends. Figure 3-3 indicates that on average, peak ambient 

CO concentrations that occur during typical commute times in Denver ranged from about 1 to 5 

ppm during weekdays in 1995, while, currently, ambient CO concentrations during morning and 

afternoon commutes range from about 1 to 2 ppm. Weekends tend to exhibit less variability 

throughout the day. On average, CO ambient concentrations generally ranged from 1 to 3 ppm 

throughout the day in 1995, while current weekend concentrations are less than 1 ppm for most 

hours of the day. In Los Angeles, both the concentration levels and variability are greater than 

when compared with similar years and times of day in Denver (Figure 3-4). Peak ambient CO 

concentrations are more prominent during morning commutes and generally ranged from 2 to 10 

ppm in 1995, while currently (year 2006) most commuting times are associated with 

concentrations ranging from between 1 and 5 ppm. The weekend profile exhibits some variation 

when considering either year, with maximum concentration levels and variability exhibited 

during the overnight hours. 
October, 2009 3-8 Draft – 

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Figure 3-3. Diurnal Distribution of 1-hour CO Concentrations in Denver (Monitor 08-031-0002) by Day-

weekends-right), Years 1995 (top) and 2006 (bottom). The box encompasses concentrations fropercentiles or Interquartile range (IQR), the line bisecting the box is the median, the solid dot withmean, the whiskers represent 1.5 times the IQR, and concentrations outside the whiskers are indica
October, 2009 3-9 Draft – 

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Figure 3-4. Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor 06-037-1301) by d

left; weekends-right), years 1997 (top) and 2006 (bottom). The box encompasses concentration

percentiles or IQR, the line bisecting the box is the median, the solid dot within the box is the meanrepresent 1.5 times the IQR, and concentrations outside the whiskers are indicated by open circles. 
October, 2009 3-10 Draft – Do Not Cite or Quote 

Ambient monitor siting characteristics can also influence ambient CO concentration 

observations. Microscale and middle scale monitors are commonly used to measure significant 

source impacts, while neighborhood and urban scale monitors are designated for populationoriented monitoring (40 CFR Part 58 Appendix D). As CO concentrations primarily originate 

from vehicle emissions, the microscale and middle scale data can be a useful indicator of nearroad air quality. Such data analyzed in the draft ISA were concluded to be consistent with hourly 

concentrations reported in the literature for the near road environment in the U.S. (draft ISA, p. 

3-63). Further, when considering monitoring scale across ambient monitors in the U.S., the 

median hourly CO concentration measured at microscale monitors was about 25% higher than at 

middle scale monitors and 67% higher than at neighborhood scale monitors (draft ISA, Table 3-

12). In general, similar patterns were present in the 1-hour daily max, 1-hour daily average, and 

8-hour daily max distributions (draft ISA, Table 3-12). These patterns are also consistent with 

findings presented by other researchers regarding the relative decrease in concentration with 

increasing distance from roadways, though the magnitude of the relationship can vary. Two 

studies summarized in the draft ISA (Zhu et. al., 2002; Baldauf et. al., 2008) indicate that nearroad CO concentrations (i.e., measured within 20 m of an interstate highway) can range from 2 – 

10 times greater than CO concentrations measured as far as 300 m from a major road (draft ISA, 

Figures 3-26 and 3-27). 

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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, 7

 the draft 

ISA also analyzed the information available for network monitors on average annual daily traffic 

(AADT). The ISA noted that only two microscale monitors and two middle scale monitors in 

the existing network are sited at roads with ≥100,000 AADT, although it is not uncommon for 

roadways within CSAs to have several roads with AADT > 100,000. The AADT ranged from 

160,000-178,000 for the near-road monitors used in the aforementioned 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. Existing microscale sites near roads having only moderate traffic count data 

(<100,000AADT) may record concentrations that are not substantially different from those 

obtained from neighborhood scale measurements (draft ISA, section 3.5.1.3). 

Within a specific urban area however, consideration of only monitor scale or other 

attributes reported in AQS, such as AADT estimates may be of limited use in efforts to 

characterize the monitoring data as to its representation of local near-road CO concentrations. 

For example, of the five monitors meeting a 75% completeness criterion in the Denver 

7

Note that 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 (draft ISA, Figure 3-20). 
October, 2009 3-11 Draft – Do Not Cite or Quote 

Consolidated Statistical Area (CSA), three were microscale and two were neighborhood scale 

(draft ISA, section 3.5.1.2). While one of the microscale monitors sited within downtown 

Denver measured the highest hourly ambient CO concentrations (ID 080310002), another 

microscale monitor located outside the urban core measured the lowest hourly ambient CO 

concentrations (draft ISA, Figure 3-18). Further, the AADT estimate for a major road near the 

microscale monitor within the urban core (ID 080310002, AADT=17,200) was lower than that 

listed for the microscale monitor outside the urban core (ID 080130009, AADT=20,000) (draft 

ISA, Table A-2). And, a third microscale monitor located 1.3 km from monitor ID 080310002, 

within the urban core, and measuring somewhat lower CO concentrations (but not lower than the 

monitor outside the urban core) had only 500 AADT listed for the nearest major road. It is likely 

that the higher CO concentrations measured at the downtown monitor reflect influences of the 

denser roadway network surrounding that monitor in the downtown Denver area (Figure 3-17).

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Thus, to better characterize the representation of near-road CO concentrations for many 

of the existing ambient monitors, additional analyses, beyond consideration of AQS attributes 

such as monitoring scale and traffic count, would likely need to be performed (e.g., using GIS to 

determine monitor distance from roads, the number and type of roads within close proximity of 

the monitor, and obtaining current traffic count data for all roads). 

3.1.4 Policy-Relevant Background Concentrations 

EPA has generally conducted NAAQS risk assessments that focus on the risks associated 

with ambient levels of a pollutant that are in excess of policy-relevant background (PRB). 

Policy-relevant background levels are defined, for purposes of this document, as concentrations 

of a pollutant that would occur in the U.S. in the absence of anthropogenic emissions in the U.S., 

Canada, and Mexico. Over the continental U.S. (CONUS), the 3-year (2005- 2007) average CO 

PRB concentration is estimated to range from 118 to 146 ppb (draft ISA, section 3.5.4). Outside 

the CONUS, the 3-year average CO PRB in three Alaskan sites is estimated to range from 127 to 

135 ppb, and from 95 to 103 ppb in two Hawaiian monitoring locations. The estimated PRB 

concentrations exhibit significant within-location seasonal variation, with minimum 

concentrations observed in the summer and fall and maximum concentrations occurring in the 

winter and spring. For example, PRB in two California sites is estimated to range from about 85 

to 170 ppb, and one site in Colorado, ranged from about 80 to 140 ppb (Figure 3-40 of the draft 

ISA). 

Given that ambient concentrations of interest in this REA are well above the estimated 

PRB levels discussed above and, thus the contribution of PRB to overall ambient CO 

8

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monitor site. 
October, 2009 3-12 Draft – Do Not Cite or Quote 

concentrations is very small, EPA is characterizing risks associated with ambient CO levels 

without regard to estimated PRB levels. 

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3.2 STUDY AREAS FOR CURRENT ASSESSMENT 

Staff identified several criteria to select the exposure assessment study areas drawing 

from information discussed in the earlier sections of this Chapter and additional scientific 

evidence in the draft ISA. We selected Denver and Los Angeles as areas to focus the current 

assessment because (1) both cities have been included in prior CO NAAQS exposure 

assessments and thus serve as an important connection with past assessments, (2) they have 

historically had the highest CO ambient concentrations among urban areas in the U.S., and (3) 

Denver is at high altitude and represents an important risk scenario due to the increased 

susceptibility of individuals at high altitude from exposure to CO. In addition, of 10 urban areas 

across the U.S. having monitors meeting a 75% completeness criteria, the two locations were 

ranked 1st (Los Angeles) and 2nd (Denver) regarding percent of elderly population within 5, 10, 

and 15 km of monitor locations, and ranked 1st (Los Angeles) and 5th (Denver) regarding number 

of 1- and 8-hour daily maximum CO concentration measurements (draft ISA, section 3.5.1.1). 

Maximum and 2nd highest 1-hour and 8-hour average CO concentrations are provided in 

Table 3-1 for all monitors located within four Denver-area counties (i.e., Adams, Boulder, 

Jefferson, and Denver) having at least one year of ambient monitoring data for years 2005 

through 2007.9 Table 3-2 provides a similar concentration summary for the Los Angeles 

monitors in four counties (i.e., Los Angeles, Orange, Riverside, and San Bernardino) that 

reported CO concentrations for at least one year between 2005 and 2007. Additional discussion 

regarding specific sites and monitoring data used in the exposure modeling is provided in 

sections 5.5 and 6.1.2. 

In order to investigate ambient CO concentrations in each study area, EPA initially 

considered the sites listed in Tables 3-1 and 3-2 that reported data for at least one year between 

2005 and 2007 with 75% completeness. As shown in these tables, maximum concentrations as 

well as 2nd highest 1-hour and 8-hour average CO concentrations generally are quite 

homogeneous and do not exhibit great variability. Focusing on Denver and Los Angeles 

Counties, however, the sites show higher concentrations for the year 2006 in specific sites within 

each county. 

Considering the spatial scale and location of monitoring sites in these two areas, staff 

recognizes limitations related to the coverage provided by the available sites, particularly in the 

Denver area, to support development of a comprehensive population exposure assessment for 

these urban areas in this time period. This issue is discussed further in chapter 5. 

9

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October, 2009 3-13 Draft – Do Not Cite or Quote 

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Table 3-1. Descriptive Statistics for CO Concentrations Measured at Selected Fixed-Site 

Monitors in the Denver Metropolitan Area for the Years 2005 - 2007. 

CO concentration (ppm) 

1-hour 8-hour average 

County 

Site ID 

(location) Year 

Number 

of 1-hour 

values Maximum 2nd Highest Maximum 

2nd

Highest 

2005 8693 3.4 3.3 2.5 2.2 

Adams 2006 8633 3.8 3.8 2.6 2.5 080013001 

(Welby) 2007 8663 3.1 3 2.3 2.1 

2005 8509 5 4.8 2.5 2.4 

2006 8531 3.9 2.8 2.2 1.8 080130009 

(440 Main St) Boulder 2007 8588 3.8 3.4 2.3 1.9 

080130010 

(2150 28th St.) 

2005 2978 3.6 3.2 2 1.9 

2005 8680 4.6 4.3 2.9 2.5 

2006 8672 6.4 4.6 3.4 3.1 080310002 

(CAMP) 2007 8676 6 5.9 3.2 2.8 

080310013 2005 8674 5.3 3.6 2.5 2.4 

(NJE-E) 2006 8635 4.4 3.9 2.9 2.5 

080310014 2005 8121 3.9 3.4 2.3 2.1 

(Carriage) 2006 8557 3.9 3.5 3 3 

2005 8640 5.6 4.2 2.4 2.3 

2006 8569 9.3 5.7 3.1 2.6 

Denver 

080310019 

(Firehouse #6) 2007 8412 4.2 4.1 2.5 2.4 

2005 8461 4.1 3.6 2.1 2 Jefferson 080590002 2006 8603 3.6 3.5 2 2 
October, 2009 3-14 Draft – Do Not Cite or Quote 

Table 3-2. Descriptive Statistics for CO Concentrations Measured at Selected Fixed-Site 

Monitors in the Los Angeles Metropolitan Area for the Years 2005 - 2007. 

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CO concentration (ppm) 

1-hour 8-hour average

County 

Site ID 

(location) Year 

Number 

of 1-hour 

values Maximum

2nd

Highest Maximum

2nd

Highest

2005 8355 2.5 2.3 1.7 1.6 

2006 8368 2.2 2.2 1.7 1.6 060370002 

(Azusa) 2007 8344 2.6 2.4 1.8 1.7 

2005 8350 3.4 3.1 2.1 2.0 

2006 8365 2.9 2.8 2.0 1.9 

060370113 

(West Los 

Angeles) 2007 8267 2.7 2.5 2.0 1.6 

2005 8279 4.4 4.0 3.4 3.2 

2006 8345 4.3 4.2 3.4 3.4 060371002 

(Burbank) 2007 8334 3.7 3.7 2.8 2.7 

2005 8298 3.9 3.4 3.1 2.7 

2006 8265 3.5 3.3 2.7 2.5 060371103 

(Los Angeles) 2007 8148 3.2 3.1 2.2 2.1 

2005 8018 5.1 4.9 3.5 3.4 

2006 8375 4.8 4.7 3.5 3.4 060371201 

(Reseda) 2007 7954 3.7 3.5 2.8 2.7 

2005 8331 7.4 7.2 5.9 5.6 

2006 8275 8.4 8.2 6.2 5.6 060371301 

(Lynwood) 2007 8284 7.8 7.0 5.3 4.9 

2005 2538 3.3 3.2 2.4 2.4 

2006 4698 3.1 3.1 2.7 2.7 060371601 

(Pico Rivera) 2007 8318 4.8 3.7 2.9 2.8 

2005 8350 4.2 4.0 2.5 2.4 

2006 8335 3.3 3.1 2.2 2.2 060371701 

(Pomona) 2007 8293 3.3 3.1 2.0 2.0 

2005 8274 4.3 4.3 2.8 2.8 

2006 8258 4.1 3.7 2.8 2.7 060372005 

(Pasadena) 2007 8338 3.3 3.2 2.3 2.2 

2005 8340 4.2 4.0 3.5 3.1 

2006 8216 4.2 4.0 3.4 3.3 060374002 

(Long Beach) 2007 7769 3.3 3.2 2.6 2.4 

2005 8364 2.8 2.7 2.1 2.1 

2006 8356 2.8 2.7 2.3 2.1 060375005 

(Los Angeles) 2007 8311 3.3 3.2 2.4 2.1 

2005 8248 2.2 2.0 1.3 1.2 

2006 8339 2.0 2.0 1.3 1.1 060376012 

(Santa Clara) 2007 8339 1.9 1.9 1.2 1.2 

2005 8265 2.9 2.5 1.5 1.5 

2006 7710 3.2 2.8 1.6 1.6 

Los 

Angeles 

060379033 

(Lancaster) 2007 8226 2.5 2.3 1.3 1.2 

Orange 060590007 2005 8307 4.1 4 3.3 3.1 
October, 2009 3-15 Draft – Do Not Cite or Quote 

CO concentration (ppm) 

1-hour 8-hour average

County 

Site ID 

(location) Year 

Number 

of 1-hour 

values Maximum

2nd

Highest Maximum

2nd

Highest

(Anaheim) 2006 8342 4.5 4.3 2.9 2.9 

2007 7681 3.6 3.6 2.9 2.3 

2005 8308 4.7 4.1 3.2 3.1 

2006 8358 3.5 3.3 3.0 2.5 060591003 

(Costa Mesa) 2007 8160 4.5 4.4 3.1 2.6 

2005 8265 2.2 2.2 1.6 1.6 

2006 8336 1.9 1.9 1.6 1.3 060592022 

(Mission Viejo) 2007 8296 2.9 2.7 2.2 2.0 

2005 8333 6.8 6.7 3.1 3.0 

2006 8227 6.0 6.0 2.9 2.9 060595001 

(La Habra) 2007 8211 6.3 6.3 2.9 2.7 

2005 8190 4.0 3.7 2.4 2.2 

2006 8385 3.8 3.8 2.4 2.1 060651003 

(Riverside) 2007 8376 3.7 3.4 2.2 2.0 

2005 8296 2.1 2.0 0.8 0.7 

2006 8357 2.3 1.8 0.9 0.8 060655001 

(Palm Springs) 2007 8351 1.5 1.5 0.8 0.7 

2005 8216 3.4 3.3 2.5 2.3 

2006 8348 2.7 2.7 2.3 2.1 060658001 

(Rubidoux) 2007 8280 3.8 3.6 2.9 2.5 

2005 8312 1.7 1.5 1.0 1.0 

2006 8256 1.4 1.4 1.0 1.0 

Riverside 

060659001 

(Lake Elsinore) 2007 8290 1.6 1.5 1.4 1.3 

2005 8106 3.3 2.2 1.3 1.2 

2006 7847 3.5 2.6 1.2 1.1 060710001 

(Barstow) 2007 8217 1.4 1.3 0.7 0.6 

2005 8289 2.5 2.1 1.6 1.4 

2006 8225 2.2 2.2 1.6 1.5 060710306 

(Victorville) 2007 8348 2.1 2.0 1.6 1.5 

2005 8314 2.5 2.4 1.9 1.7 

2006 8210 2.7 2.6 1.9 1.8 060711004 

(Upland) 2007 8309 2.4 2.2 1.7 1.6 

2005 8240 3.8 3.3 2.5 2.2 

2006 8340 2.8 2.8 2.2 2.0 

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Bernardino 

060719004 

(San 

Bernardino) 2007 8330 3.7 3.1 2.3 2.1 

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October, 2009 3-16 Draft – Do Not Cite or Quote 

1 3.3 KEY OBSERVATIONS 

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Presented below are key observations resulting from the air quality considerations. 

 Automobiles are the primary contributor to CO emissions, particularly in urban areas 

due to greater vehicle and roadway densities. 

 Recent (2005-2007) ambient CO concentrations across the U.S. are lower than those 

reported in the previous CO NAAQS review and are also well below the current CO 

NAAQS levels. Further, a large proportion of the reported concentrations are below 

the conventional instrument detection limit of 1 ppm. 

 Ambient CO concentrations are highest at monitors sited closest to roadways (i.e., 

microscale and middle scale monitors) and exhibit a diel variation linked to the 

typical commute times of day, with peaks generally observed during early morning 

and late afternoon during weekdays. 

 The currently available information for CO monitors indicates that siting of 

microscale and middle scale monitors in the current network is primarily limited to 

roads where traffic density described for them is moderate (<100,000 AADT), 

however, factors other than reported AADT (e.g., orientation with regard to dense 

urban roadway networks) can contribute to sites reporting higher CO concentrations. 

 Due to the limited number of existing ambient monitors and the monitor site 

characteristics, it is difficult to fully characterize the current spatial and temporal 

variability in CO ambient concentrations across the two urban areas that are the focus 

for this assessment, Denver and Los Angeles. 
October, 2009 3-17 Draft – Do Not Cite or Quote 

1 3.4 REFERENCES 

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Baldauf R; Thoma E; Hays M; Shores R; Kinsey J; Gullett B; Kimbrough S; Isakov V; Long T; Snow R; Khlystov 

A; Weinstein J; Chen FL; Seila R; Olson D; Gilmour I; Cho SH; Watkins N; Rowley P; Bang J. (2008). 

Traffic and meteorological impacts on near-road air quality: Summary of methods and trends from the 

Raleigh near-road study. J Air Waste Manag Assoc, 58: 865-878. 

US EPA (2000). Air Quality Criteria for Carbon Monoxide. EPA 600/P-99/001F. U.S. Environmental Protection 

Agency, Research Triangle Park, NC. 

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

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

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

Zhu Y; Hinds WC; Kim S; Shen S; Sioutas C. (2002). Study of ultrafine particles near a major highway with heavyduty diesel traffic. Atmos Environ, 36: 4323-4335 
4. APPROACH TO RISK CHARACTERIZATION FOR 

CURRENT REVIEW 

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This section describes the health effects evidence, dose metric of interest and 

approach to characterization of risk in support of the current review of the CO primary 

NAAQS. Similar to the approach used in prior CO NAAQS reviews, the approach to risk 

characterization presented in this section is based on the estimation of CO exposures and 

resulting doses (an internal biomarker) for a defined at-risk population within urban study 

areas associated with CO levels representing recent air quality and air quality adjusted to 

simulate just meeting the current CO NAAQS. 

Carbon monoxide can elicit a broad range of effects in multiple tissues and organ 

systems that are dependent upon concentration and duration of exposure, and that may 

involve multiple mechanisms including hypoxic stress and others such as free radical 

production and the initiation of cell signaling. However, binding of CO to reduced iron in 

heme proteins with subsequent alteration of heme protein function is the common 

mechanism underlying the biological responses to CO (draft ISA, section 5.1). Similarly, 

based on the health effects evidence summarized in the draft ISA, the best characterized 

dose metric for estimating exposure to CO associated with adverse health effects is blood 

carboxyhemoglobin (COHb) levels. As described in the draft ISA, the most compelling 

evidence of a CO-induced effect on the cardiovascular system at COHb levels relevant to 

the current NAAQS comes from a series of controlled human exposure studies among 

individuals with coronary heart disease (CHD) (draft ISA, section 5.2). Specifically for 

the current analysis, we characterize risk for the population of interest (CHD population) 

by using a potential health effects benchmark level approach, in combination with shortterm CO exposure and dose modeling, to estimate the number and percent of the 

population with CHD that would potentially exceed COHb levels of concern, upon just 

meeting various CO air quality scenarios. Section 4.1 presents a brief summary of the 

health effects evidence from controlled human exposure studies (draft ISA, section 5.2.4) 

and section 4.2 describes the rationale for the selection of potential health effects 

benchmarks and their use in the characterization of risk for adults with CHD. Section 4.3 

presents key observations relevant to the approach for the risk characterization. 

4.1. CARDIOVASCULAR DISEASE RELATED EFFECTS 

Controlled human studies provide strong evidence for an association between 

short-term exposure to CO and exacerbation of preexisting coronary heart disease. 

Several controlled human exposure studies discussed in the 2000 CO AQCD (section 

October 2009 4-1 Draft – Do Not Cite or Quote 
6.2.2, US EPA, 2000) showed that short-term exposure to CO and subsequent elevation 

of COHb levels enhance exercise-induced myocardial ischemia. 

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Among those studies the draft ISA places emphasis on the work of Allred et al., a 

large multi-laboratory study designed to evaluate myocardial ischemia, as documented by 

electrocardiogram ST-segment changes and time to onset of angina, during a standard 

treadmill test, at CO exposures targeted to result in COHb levels of 2% and 4%. As 

described in the draft ISA (draft ISA, section 5.2.4), other controlled human exposure 

studies (Adams et al. 1988, Anderson et al. 1973, Kleinman et al 1989, Kleinman et al., 

1998) involving individuals with stable angina have also demonstrated the capacity of 

CO to decrease the time to onset of angina, as well as to reduce the duration of exercise at 

COHb concentrations between 3 and 6% (as measured by CO-oximeter). A single study 

by Sheps et al. (1987) observed no change in time to onset of angina or maximal exercise 

time following a 1 h exposure to 100 ppm CO (targeted COHb of 4%) among a group of 

30 patients with CHD. In a subsequent study conducted by the same laboratory, a 

significant increase in number of ventricular arrhythmias during exercise was observed 

relative to room air among individuals with CHD following a 1-hr exposure to 200 ppm 

CO (targeted COHb of 6%), but not following a 1-hr exposure to 100 ppm CO (targeted 

COHb of 4%) (Sheps et al., 1990). The draft ISA notes that although the subjects 

evaluated in the studies described above are not necessarily representative of the most 

sensitive population, the level of disease in these individuals was relatively severe, with 

the majority either having a history of myocardial infarction or having ≥ 70% occlusion 

of one or more of the coronary arteries. 

The draft ISA (draft ISA, section 5.2.4) states that no new human clinical studies 

involving controlled short-term CO exposures among subjects with coronary artery 

disease have been published since the 2000 CO AQCD. However, a number of new 

studies have investigated the effects of CO in healthy adults. Adir et. al., (1999) showed 

that short-term exposure to CO at concentrations targeted to produce 4-6% COHb, 

followed by a treadmill test (at maximal exercise capacity), caused a decrease in the 

duration of exercise and in the metabolic equivalent units (indicative of the oxygen 

consumed by the body during exercise). The draft ISA notes that these results are in 

agreement with the findings of several studies cited in the 2000 CO AQCD which 

observed decreases in exercise duration and maximal aerobic capacity among healthy 

adults at COHb levels ≥ 3% (draft ISA, section 5.2.4), which provides coherence with the 

observed effects of short-term exposure to CO on exercise-induced myocardial ischemia 

among patients with CHD. 

October 2009 4-2 Draft – Do Not Cite or Quote 
1 4.2. HEALTH EFFECT BENCHMARKS 

As in the review completed in 1994 and in the CO exposure/dose assessment 

completed in 2000 (section 6.3.2), a health effect benchmark level approach is used in the 

current analysis to estimate the number and percent of the population with CHD that 

would potentially exceed COHb levels of concern for specific CO air quality scenarios. 

Since the ISA has not identified new studies that demonstrate CO effects at COHb levels 

lower than those described in the 2000 AQCD, we are relying in the same studies as we 

did on the review completed in 1994. As mentioned above, a number of studies, 

described in detail in the 2000 CO AQCD (section 6.2.2, US EPA, 2000), showed 

statistically significant group mean responses, measured in terms of reduced time to onset 

of exercise-induced angina, in the range of 3 to 6 %COHb (measured by CO-oximeter) in 

subjects with coronary heart disease. We note that the lowest COHb level at which 

reduced time to onset of angina was observed was in the range of 2.0 to 2.4% COHb 

(measured by gas chromatography), in a multi-center CO exposure study (Allred et al., 

1989a,b, 1991; draft ISA, section 5.2.6). This range (2.0-2.4%) is representative of the 

two individual COHb level averages obtained post-exposure (2.4%) and post-exercise 

test (2.0%). However, there was no clear pattern across the different studies with respect 

to the magnitude of the decreased time to onset of angina versus dose level. In addition, 

these studies do not address the fraction of the population experiencing a specified health 

effect at various dose levels. Thus, based on information in the draft ISA, staff 

concluded that at this time there is insufficient controlled human exposure data to support 

the development of quantitative dose-response relationships which would be required in 

order to conduct a quantitative risk assessment for this health endpoint. 

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Potential health effect benchmark values used in the risk characterization linked 

to the exposure/dose analyses were derived solely based on the controlled human 

exposure literature. This is primarily because CO concentrations reported in controlled 

human exposure studies represent actual personal exposures rather than concentrations 

measured at fixed site ambient monitors. In addition, controlled human exposure studies 

can examine the health effects of short-term exposure to CO in the absence of copollutants that can confound results in epidemiologic analyses; thus, health effects 

observed in controlled human exposure studies can confidently be attributed to a defined 

COHb dose level associated with ambient short-term CO exposures. 

In identifying the potential health effect benchmark levels for the risk 

characterization, staff considered a number of factors in drawing on the results of 

controlled human exposure studies. As noted above, the lowest group mean COHb level 

October 2009 4-3 Draft – Do Not Cite or Quote 
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at which reduced time to onset of angina was observed was in the range of 2.0 to 2.4% 

COHb (measured by gas chromatography(GC)) in a multi-center CO exposure study 

(Allred et al., 1989a,b, 1991; draft ISA, section 5.2.6 and 2.5.1). Similar effects have not 

been evaluated below this range. 

Staff identified potential health effects benchmarks of 1.5%, 2.0%, 2.5% and 3% 

COHb levels based on the consideration of the studies reporting adverse effects at COHb 

levels as low as 2 to 2.4% (using GC) discussed above. These levels reflect comments 

from the CASAC CO panel on the draft Analysis Plan (Brain and Samet, 2009) and 

include the range of levels considered in the review completed in 1994 (US EPA, 1992). 

The potential health effects benchmarks extend lower than the range where controlled 

human exposure studies reported CO-related health effects to take into consideration both 

the uncertainty about the actual COHb levels experienced in the controlled human 

exposure studies due to the use of different measurement methods and that these studies 

did not include individuals with more severe cardiovascular disease who may respond at 

lower COHb levels relative to the subjects tested. 

4.3. KEY OBSERVATIONS 

Presented below are key observations relevant to the risk characterization approach in 

support of the current CO NAAQS review. 

 An important at-risk population for short-term exposure to is the adult CHD 

population. 

 The data from controlled human studies do not support the development of a 

quantitative risk assessment due to lack of sufficient information to 

characterize the dose-response relationship within the range of interest. 

Instead, risk will be characterized in the current assessment using a potential 

health effect benchmark levels approach (as in previous assessments). 

 Evaluation of health effects evidence reported in controlled human exposure 

studies of short-term CO exposure in the adult CHD population is the basis for 

the selection of potential health effects benchmarks of 1.5, 2.0, 2.5, and 3.0% 

COHb on the consideration of studies reporting adverse effects at COHb 

levels in the range of 2 to 2.4%. 

October 2009 4-4 Draft – Do Not Cite or Quote 
1 4.4. REFERENCES 

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Adams, K.F.; Koch, G.; Chatterjee, B.; Goldstein, G.M.; O'Neil, J.J.; Bromberg, P.A.; Sheps, D.S.; 

McAllister, S.; Price, C.J.; Bissette, J. (1988) Acute elevation of blood carboxyhemoglobin to 6% 

impairs exercise performance and aggravates symptoms in patients with ischemic heart disease. J. 

Am. Coll. Cardiol. 12: 900-909 

Adir Y, Merdler A, Ben Haim S, Front A, Harduf R & Bitterman H ( 1999) Effects of Exposure to Low 

Concentrations of Carbon Monoxide on Exercise Performance and Myocardial Perfusion in 

Young Healthy Men. Occup Environm Med, 56: 535-538. 

Allred, E.N.; Bleecker, E.R.; Chaitman, B.R.; Dahms, T.E.; Gottlieb, S.O.; Hackney, J.D.; Pagano, M.; 

Selvester, R.H.; Walden, S.M.; Warren, J. (1989a) Short-term effects of carbon monoxide 

exposure on the exercise performance of subjects with coronary artery disease. N. Engl. J. Med. 

321: 1426-1432. 

Allred, E.N.; Bleecker, E.R.; Chaitman, B.R.; Dahms, T.E.; Gottlieb, S.O.; Hackney, J.D.; Hayes, D.; 

Pagano, M.; Selvester, R.H.; Walden, S.M.; Warren, J. (1989b) Acute effects of carbon monoxide 

exposure on individuals with coronary artery disease. Cambridge, MA: Health Effects Institute; 

research report no. 25. 

Anderson EW, Andelman RJ, Strauch JM, Fortuin NJ and Knelson, JH (1973). Effect of low level carbon 

monoxide exposure on onset and duration of angina pectoris. Annals of Internal Medicine 79: 46-

50. 

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

and Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), 

prepared by ICF Kaiser Consulting Group for U.S. EPA, Office of Air Quality Planning and 

Standards, under Contract No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQOAR-2008-0015-DRAFT-0009. 

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

and Environmental Assessment, Environmental Criteria and Assessment Office; report no. 

EPA/600/8-90/045F. 

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

Assessment, Office of Research and Development, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 27711; report no. EPA/600/P-99/001F. June 2000. Available: 

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

Sheps, D.S.; Herbst, M.C.; Hinderliter, A.L.; Adams, K.F.; Ekelund, L.G.; O'Neil, J.J.; Goldstein, G.M.; 

Bromberg, P.A.; Dalton, J.L.; Ballenger, M.N.; Davis, S.M.; Koch, G.G. (1990) Production of 

arrythmias by elevated carboxyhemoglobin in patients with coronary artery disease. Ann. Intern. 

Med. 113: 343-351. 

Kleinman M T, Davidson DM, Vandagriff RB, Caiozzo VJ, Whittenberger JL.(1989). Effects of short-term 

exposure to carbon monoxide in subjects with coronary artery disease. Arch. Environ. Health 44: 

361-369. 

Kleinman MT, Leaf DA, Kelly E, Caiozzo V, Osann K, O'Niell T. (1998). Urban angina in the mountains: 

effects of carbon monoxide and mild hypoxemia on subjects with chronic stable angina. Arch. 

Environ.Health 53: 388-397. 

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US EPA (2009). Integrated Science Assessment for Carbon Monoxide – Second External Review Draft. 

U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/600/R09/019B. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html. 
5. APPROACH TO EXPOSURE AND DOSE ASSESSMENT FOR 

CURRENT REVIEW 

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This chapter presents an overview and description of the overall approach to estimating 

human exposure and dose for past and recent assessments and presents a strategy for the current 

exposure and dose assessments for the CO NAAQS review. Section 5.1 provides a brief 

overview of the exposure model, followed by a short history that explains the evolution of the 

exposure and dose models used in NAAQS reviews in section 5.2. Section 5.3 provides a 

description of the exposure and dose models that have been used by OAQPS to conduct prior 

exposure and dose assessments for CO NAAQS and which serve as the basic modeling tools to 

be used for the current assessment. Section 5.4 briefly summarizes information about personal 

exposure and key microenvironments for CO. Section 5.5 discusses the current monitoring 

network in the two study areas selected for the current assessment and the limitations of the 

existing monitoring network for purposes of conducting an exposure and dose assessment in 

these areas. This final section also presents the strategy for the current exposure and dose 

analysis. 

5.1 MODEL OVERVIEW 

The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program 

designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and 

consolidated metropolitan levels. APEX, also known as TRIM.Expo, is the human inhalation 

exposure module of EPA’s Total Risk Integrated Methodology (TRIM) model framework (US 

EPA, 1999), a modeling system with multimedia capabilities for assessing human health and 

ecological risks from hazardous and criteria air pollutants.1

APEX estimates human exposure to criteria and 

toxic air pollutants at the local, urban, or consolidated 

metropolitan area levels using a stochastic, 

“microenvironmental” approach. The model randomly 

selects data for a sample of hypothetical individuals from 

an actual population database and simulates each 

hypothetical individual’s movements through time and 

space (e.g., indoors at home, inside vehicles) to estimate 

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. 

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(U.S. EPA 2008a, 2008b), and other supporting documentation, can be found at http://www.epa.gov/ttn/fera. 

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his or her exposure to a pollutant. APEX can account for travel to and from work locations (i.e., 

commuting) and provide estimates of exposures at both home and work locations for individuals 

who work away from home. 

5.2 MODEL HISTORY AND EVOLUTION 

APEX was derived from the National Ambient Air Quality Standards (NAAQS) 

Exposure Model (NEM) series of models. The NEM series was developed to estimate 

population exposures to the criteria pollutants (e.g., CO, ozone). In 1988, OAQPS first 

incorporated probabilistic elements into the NEM methodology and used activity pattern data 

based on available human activity diary studies to create an early version of probabilistic NEM 

for ozone (i.e., pNEM/O3). In 1991, a probabilistic version of NEM was developed for CO 

(pNEM/CO) that included a one-compartment mass-balance model to estimate CO 

concentrations in indoor microenvironments. The application of this model to Denver, Colorado 

is summarized in Johnson et al. (1992). Between 1999 and 2001, updated versions of pNEM/CO 

(versions 2.0 and 2.1) were developed that rely on detailed activity diary data compiled in EPA’s 

Consolidated Human Activities Database (CHAD) (McCurdy et al., 2000; US EPA, 2002) and 

enhanced algorithms for simulating gas stove usage, estimating alveolar ventilation rate (a 

measure of human respiration), and modeling home-to-work commuting patterns. A draft report 

by Johnson et al. (2000) describes the application of Version 2.1 of pNEM/CO to Denver and 

Los Angeles. 

The first version of APEX was essentially identical to pNEM/CO (version 2.0) except 

that it ran on a PC instead of a mainframe. The next version, APEX2, was substantially 

different, particularly in the use of a personal profile approach rather than a cohort simulation 

approach. APEX3 introduced a number of new features including automatic site selection from 

national databases, a series of new output tables providing summary exposure and dose statistics, 

and a thoroughly reorganized method of describing microenvironments and their parameters. 

Johnson and Capel (2003) describe a case study in which Version 3.1 of APEX was used to 

estimate population exposure to CO in Denver and Los Angeles. 

The current version of APEX (Version 4.3) (US EPA, 2008a; 2008b) was used to 

estimate CO exposure and dose as described in chapter 6 of this document. This version was 

also recently used to estimate ozone (O3) exposures in 12 urban areas for the O3 NAAQS review 

(US EPA, 2007), in estimating population exposures to nitrogen dioxide (NO2) in Atlanta as part 

of the NO2 NAAQS review (EPA, 2008c), and in estimating sulfur dioxide (SO2) exposures for 

asthmatics and asthmatic children in two study areas in Missouri as part of the SO2 NAAQS 

review (US EPA, 2009a). There have been several recent enhancements to APEX since the prior 

1994 CO NAAQS review, including: 

October, 2009 5-2 Draft – Do Not Cite or Quote
1  Algorithms for the assembly of multi-day (longitudinal) activity diaries that model intraindividual variance, inter-individual variance, and day-to-day autocorrelation in diary 

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4  Methods for adjusting diary-based energy expenditures for fatigue and excess postexercise oxygen (EPOC) consumption; 

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7  The ability to model commuters leaving the study area; 

8  The ability to model air quality and exposure for flexible time scales; 

9  New output files containing diary event-level, time-step level, and hourly-level exposure, 

dose, and ventilation data, and hourly-level microenvironmental data; 

 The ability to model the prevalence of disease states such as asthma or coronary heart 

disease (CHD); 

 New output exposure tables that report exposure statistics for population groups and 

lifestages such as children and active people under different ventilation levels; 

 The inclusion of commuting data from the 2000 census; and 

 Expanded options for modeling microenvironments. 

As discussed below in section 5.3, due to limitations in the CO ambient monitoring data, 

the current exposure/dose assessment does not take advantage of a number of the recent 

advances listed above. 

5.3 MODEL SIMULATION PROCESS 

APEX is designed to simulate population exposure to criteria and air toxic pollutants at 

22 local, urban, and regional scales. The user specifies the geographic area to be modeled and the 

23 number of individuals to be simulated to represent this population. APEX then generates a 

24 personal profile for each simulated person that specifies various parameter values required by the 

25 model. The model next uses diary-derived time/activity data matched to each personal profile to 

26 generate an exposure event sequence (also referred to as “activity pattern” or “composite diary”) 

27 for the modeled individual that spans a specified time period, such as a calendar year. Each 

28 event in the sequence specifies a start time, exposure duration, a geographic location, a 

29 microenvironment, and an activity. Probabilistic algorithms are used to estimate the pollutant 

30 concentration and ventilation (respiration) rate associated with each exposure event. The 

31 estimated pollutant concentrations account for the effects of ambient (outdoor) pollutant 

32 concentration, penetration factor, air exchange rate, decay/deposition rate, and proximity to 

33 emission sources, depending on the microenvironment, available data, and the estimation method 

34 selected by the user. The ventilation rate is derived from an energy expenditure rate estimated 

35 for each individual and specified activity performed. Because the modeled individuals represent 

October, 2009 5-3 Draft – Do Not Cite or Quote
1 a random sample of the population of interest, the distribution of modeled individual exposures 

2 can then be extrapolated to the larger population of interest. 

3 The model simulation generally includes up to seven steps. 

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1. Characterize the study area: APEX selects sectors (e.g., census tracts) within a 

study area—and thus identifies the potentially exposed population — usually based on 

the user-defined center and radius of the study area and availability of air quality and 

weather input data for the area (section 5.3.1). 

2. Generate simulated individuals: APEX stochastically generates a sample of 

simulated individuals based on the census data for the study area and human profile 

distribution data (such as age-specific employment probabilities). (section 5.3.2) 

3. Construct activity sequences: APEX constructs an exposure event sequence (activity 

pattern) spanning the simulation period for each of the simulated persons based on the 

CHAD activity pattern data (section 5.3.3). 

4. Calculate microenvironmental concentrations: APEX enables the user to define 

microenvironments that people in a study area would visit (e.g., by grouping location 

codes included in the activity pattern database). The model then calculates timeaveraged concentrations (e.g., hourly) of each pollutant in each of the 

microenvironments for each simulated person for the period of simulation, based on the 

user-provided ambient air quality data (section 5.3.4). 

5. Estimate energy expenditure and ventilation rates: APEX constructs a time-series 

of energy expenditures for each profile based on the activity event sequence. These 

expenditures are adjusted to ensure that they are physiologically realistic, and then 

used to estimate a number of ventilation metrics that are later used in estimating dose 

(section 5.3.5). 

6. Calculate exposure: APEX assigns a concentration to each exposure event based on 

the microenvironment occupied during the event and the person’s activity. These 

values are time-averaged (e.g., hourly) to produce a sequence of exposures spanning 

the specified exposure period (typically one year). The hourly values may be further 

aggregated to produce 8-hour, daily, monthly, and annual average exposure values 

(section 5.3.6). 

7. Calculate dose: APEX optionally calculates hourly, daily, monthly, and annual 

average dose values for each of the simulated individuals. For the application of 

APEX to CO, a module within the model estimates the percent COHb level in the 

blood at the end of each hour based on the time-series of CO concentrations and 

alveolar ventilation rates experienced by the simulated person (section 5.3.7). 

The model simulation continues until exposures (and associated COHb levels) are 

37 calculated for the user-specified number of simulated individuals. Figure A-1 in Appendix A 

38 presents a conceptual model and simplified data flow of APEX used in this assessment. The 

39 following sections provide additional details on the general procedures and algorithms used in 

October, 2009 5-4 Draft – Do Not Cite or Quote
1 each of the simulation steps listed above. The specific inputs and algorithms used in applying 

2 APEX to CO for the current assessment are further described in section 6.1 

3 5.3.1 Characterize the Study Area 

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An initial study area in an APEX analysis consists of a set of basic geographic units 

5 called sectors, typically defined as census tracts. The user may provide the geographic center 

6 (latitude/longitude) and radius of the study area and then APEX calculates the distances to the 

7 center of the study area of all the sectors included in the sector location database, and finally 

8 selects the sectors within the radius of the study area. APEX then maps the user-provided air 

9 quality and meteorological data for specified monitoring districts to the selected sectors. The 

10 sectors identified as having acceptable air quality and meteorological data within the radius of 

11 the study area are selected to comprise a final study area for the APEX simulation analysis. This 

12 final study area determines the population make-up of the simulated persons (profiles) to be 

13 modeled. 

5.3.2 Generate Simulated Individuals 

APEX stochastically generates a user-specified number of simulated (hypothetical) 

16 persons to represent the population in the study area. Each simulated person is represented by a 

17 “personal profile.” APEX generates the simulated person or profile by probabilistically selecting 

18 values for a set of profile variables. The profile variables include: 

 Demographic variables (e.g., age, gender, home sector, work sector) that are generated 

based on the census data; 

 Residential variables (e.g., air conditioning prevalence) which are generated based on 

sets of distribution data; 

 Physiological variables (e.g., blood volume, body mass, resting metabolic rate) that are 

generated based on age- and gender-specific distribution data; and 

 Daily varying variables (e.g., daily work status) which are generated based on 

distribution data that change daily during the simulation period. 

APEX first selects and calculates demographic, residential, and physiological variables 

(except for daily values) for each of the specified number of simulated individuals. APEX then 

follows each simulated individual over time and calculates exposures (and optionally doses) for 

the individual over the specified time period. The profile variables are listed and described in 

detail in section 5 of US EPA (2008b). 

5.3.3 Construct Activity Sequences 

APEX probabilistically creates a composite diary for each of the simulated persons by 

34 selecting a 24-hour diary record – or diary day – from an activity database for each day of the 

October, 2009 5-5 Draft – Do Not Cite or Quote
1 simulation period. (CHAD data are supplied with APEX for this purpose.) A composite diary is 

2 a sequence of events that simulates the movement of a modeled person through geographical 

3 locations and microenvironments during the simulation period. Each event is defined by 

4 geographic location, start time, duration, microenvironment visited, and an activity performed. 

The activity database input to APEX contains the following information for each diary 

day: age, gender, employment status, occupation, day-of-week (day-type), and maximum hourly 

average temperature. This information enables APEX to select data from the activity database 

that tend to match the characteristics of the simulated person, the study area, and the specified 

time period. APEX develops a composite diary for each of the simulated individuals according 

to the following steps. 

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1. Divide diary days in the CHAD database into user-defined activity pools, based on 

day-type and temperature. 

2. Assign an activity pool number to each day of the simulation period, based on the userprovided daily maximum/average temperature data. 

3. Calculate a selection probability for each of the diary days in each of the activity pools, 

based on age/gender/employment similarity of a simulated person to a diary day. 

4. Probabilistically select a diary day from available diary days in the activity pool 

assigned to each day of the simulation period. 

5. Estimate a metabolic value for each activity performed while in a CHAD location, 

based on the activity-specific metabolic distribution data. This value is used to 

calculate a ventilation rate for the simulated person performing the activity. 

6. Map the CHAD locations in the selected diary to the user-defined modeled 

microenvironments. 

7. Concatenate the selected diary days into a sequential longitudinal diary for a simulated 

individual covering all days in the simulated period. 

APEX provides an optional longitudinal diary assembly algorithm that enables the user to 

27 create composite diaries that reflect the tendency of individuals to repeat activities on a day-to28 day basis. The user specifies values for two statistical variables (i.e., D and A) that relate to a 

29 key daily variable, typically the time spent per day in a particular microenvironment (e.g., in a 

30 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. APEX 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 in section 6.3 of US EPA (2008b). 

October, 2009 5-6 Draft – Do Not Cite or Quote
1 5.3.4 Calculate Microenvironmental Concentrations 

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Probabilistic algorithms are used to estimate the pollutant concentration associated with 

3 each exposure event. The estimated pollutant concentrations account for the effects of ambient 

4 (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, 

5 and proximity to emission sources, depending on the microenvironment, available data, and the 

6 estimation method selected by the user. 

APEX calculates air concentrations in the various microenvironments visited by the 

simulated person by using the ambient air data for the relevant census tracts and the userspecified method and parameters that are specific to each microenvironment. In typical 

applications, APEX calculates hourly concentrations in all the microenvironments at each hour 

of the simulation for each of the simulated individuals, based on the hourly ambient air quality 

data specific to the geographic locations visited by the individual. APEX provides two methods 

for calculating microenvironmental concentrations: the mass balance method and the transfer 

factors method (described briefly below). The user is required to specify a calculation method 

for each of the microenvironments; there are no restrictions on the method specified for each 

microenvironment (e.g., some microenvironments can use the transfer factors method while the 

others can use the mass balance method). As discussed in section 5.4, the current draft 

assessment employed a simplified approach relying on the factors model approach with 

particular focus on the in-vehicle microenvironment. 

20 Mass Balance Model

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The mass balance method models an enclosed microenvironment as a well-mixed volume 

in which the air concentration is assumed to be spatially uniform at any specific time. The 

concentration of an air pollutant in such a microenvironment is estimated using the following 

four processes: 

 Inflow of air into the microenvironment; 

 Outflow of air from the microenvironment; 

 Removal of a pollutant from the microenvironment due to deposition, filtration, and/or 

chemical degradation; and 

 Emissions from sources of a pollutant inside the microenvironment (if indoor sources are 

modeled). 

The mass balance model feature of APEX (see Appendix B) has not been used in the 

APEX application for CO described in this draft REA. 

34 Factors Model

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The factors model approach is conceptually simpler than the mass balance method and 

has fewer user-specified parameters. It estimates the concentration in a microenvironment as a 

linear function of ambient concentration of that hour, regardless of the concentration in the 

microenvironment during the preceding hour. Table 5-1 lists the parameters required by the 

factors model approach to calculate concentrations in a microenvironment without emissions 

sources. 

7 Table 5-1. Parameters of the Factors Model 

Variable Definition Units Value Range

f proximity Proximity factor N/A f proximity > 0 

f penetration Penetration factor N/A 0 ≤ f penetration ≤ 1 

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The factors model approach uses the following equation to calculate hourly mean 

concentration in a microenvironment from the user-provided hourly air quality data: 

 (5-1) ambient proximity npenetratio

hourlymean

ME C  ffC

where: 

= Hourly concentration in a microenvironment (ppm) hourlymean CME

Cambient = Hourly concentration in ambient environment (ppm) 

fproximity = Proximity factor (unitless) 

fpenetration = Penetration factor (unitless) 

The proximity factor (fproximity) is used to account for differences in ambient 

concentrations between the geographic location represented by the ambient air quality data (e.g., 

a regional fixed-site monitor) and the geographic location of the particular microenvironment. 

For example, a residence might be located near a heavily-trafficked roadway, whereby the 

ambient air outside the house would likely have elevated levels of mobile source pollutants such 

as carbon monoxide. In this case, a value greater than one for the proximity factor would be 

appropriate to represent the increase in concentrations outside the home relative to the ambient 

monitor. Additionally, for some pollutants the process of infiltration may remove a fraction of 

the pollutant from the air. The fraction that is retained in the indoor air is given by the 

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penetration factor (fpenetration) and is dependent on the particular pollutant’s physical and chemical 

removal rates. Typically, the value of the penetration factor ranges from 0 to 1. 

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5.3.5 Estimate Energy Expenditure and Ventilation Rates 

APEX includes a module that estimates COHb levels in the blood as a function of 

alveolar ventilation rate, the CO concentration of the respired air, endogenous CO production 

rate, and various physiological variables such as blood volume and pulmonary CO diffusion rate. 

Alveolar ventilation rate is estimated as a function of oxygen uptake rate, which in turn is 

estimated as a function of energy expenditure rate. This section provides a brief summary of the 

algorithm used to estimate alveolar ventilation rate. A detailed description of the algorithm, 

together with the distributions and estimating equations used in determining the value of each 

parameter in the algorithm, can be found in Appendix C. 

13 Energy Expenditure 

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McCurdy (2000) has recommended that measures of human ventilation (respiration) rate 

be estimated as functions of energy expenditure rate. The energy expended by an individual 

during a particular activity can be expressed as 

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in which EE is the average energy expenditure rate (kcal min-1) during the activity and 

RMR is the resting metabolic rate of the individual expressed in terms of number of energy units 

expended per unit of time (kcal min-1). METS (i.e., metabolic equivalent of work) is a ratio 

specific to the activity and is dimensionless. 

The METS concept provides a means for estimating the alveolar ventilation rate 

associated with each activity. For convenience, let EE(i,j,k) indicate the energy expenditure rate 

associated with the i

th activity on day j for person k. Equation 5-2 can now be expressed as 

25 EE(i,j,k) = [METS(i,j,k)]  [RMR(k)] (5 3) 

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in which RMR(k) is the average value for resting metabolic rate specific to person k. 

Note that METS(i,j,k) is specific to a particular activity performed by person k. 

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

ECF(k) represents an energy conversion factor defined as the volume of oxygen required to 

produce one kilocalorie of energy in person k. The oxygen uptake rate (VO2) associated with a 

particular activity can be expressed as 

34 VO2(i,j,k) = [ECF(k)]  [EE(i,j,k)], (5 4) 

October, 2009 5-9 Draft – Do Not Cite or Quote
in which VO2(i,j,k) has units of liters oxygen min-1, ECF(k) has units of liters oxygen 

kcal-1, and EE(i,j,k) has units of kcal min-1. The value of VO2(i,j,k) can now be determined from 

MET(i,j,k) by substituting Equation 5-3 into Equation 5-4 to produce the relationship 

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At the beginning of exercise, there is a lag between work expended and oxygen 

consumption. During this work/ventilation mismatch, an individual’s energy needs are met by 

anaerobic processes. The magnitude of the mismatch between expenditure and consumption is 

termed the oxygen deficit. During heavy exercise, further oxygen deficit (in addition to that 

associated with the start of exercise) may be accumulated. At some point, oxygen deficit reaches 

a maximum value, and performance and energy expenditure deteriorate. After exercise ceases, 

ventilation and oxygen consumption will remain elevated above baseline levels. This increased 

oxygen consumption was historically labeled the “oxygen debt” or “recovery oxygen 

consumption.” However, the term “excess post-exercise oxygen consumption” (EPOC) has been 

adopted for this phenomenon. APEX has an algorithm for adjusting the MET values to account 

for EPOC. This algorithm is described in detail in section 7.2 of US EPA (2008b). 

19 Alveolar Ventilation Rate

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Alveolar ventilation (VA) represents the portion of the minute ventilation that is involved 

in gaseous exchange with the blood. VO2 is the oxygen uptake that occurs during this exchange. 

The absolute value of VA is known to be affected by total lung volume, lung dead space, and 

respiration frequency -- parameters which vary according to person and/or exercise rate. 

However, it is reasonable to assume that the ratio of VA to VO2 is relatively constant regardless 

of a person’s physiological characteristics or energy expenditure rate. Consistent with this 

assumption, APEX converts each estimate of VO2(i,j,k) to an estimate of VA(i,j,k) by the 

proportional relationship 

28 VA(i,j,k) = (19.63)  [VO2(i,j,k)] (5-6) 

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in which both VA and VO2 are expressed in units of liters min-1. This relationship was 

obtained from Joumard et al. (1981), who based it on research by Galetti (1959). Equation 5-6 

can also be expressed by the equivalent equation 

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If ECF and RMR are specified for an individual, then Equation 5-7 requires only an 

activity-specific estimate of METS to produce an estimate of the energy expenditure rate for a 

given activity. APEX processes time/activity data obtained from the CHAD to create a sequence 

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of activity-specific METS values for each simulated individual. APEX estimates RMR as a 

function of body mass based on probabilistic equations specific to age and gender using 

equations reported by Schofield (1985). A value of ECF is selected for each individual from a 

uniform distribution (minimum = 0.20, maximum = 0.21) based on data provided by Esmail et 

al. (1995). Using Equation 5-7 and these inputs, APEX calculates a sequence of VA values for 

each simulated individual. These values are provided to the algorithm that estimates the percent 

COHb in the blood resulting from the simulated exposure (see section 5.3.7 and Appendix C). 

5.3.6 Calculate Exposure 

APEX calculates exposure as a time series of exposure concentrations that a simulated 

individual experiences during the simulation period. APEX determines the exposure using 

hourly ambient air concentrations, calculated concentrations in each microenvironment based on 

these ambient air concentrations, and the minutes spent in a sequence of microenvironments 

visited according to the composite diary. The hourly exposure concentration at any clock hour 

during the simulation period is determined using the following equation: 

T

tC

C

N

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j

hourlymean

jME

i

  1

)()(

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where: 

Ci = Hourly exposure concentration at clock hour i of the simulation period 

 (ppm) 

N = Number of events (i.e., varied microenvironments visited/activities 

 performed) in clock hour i of the simulation period. 

hourlymean C jME )( = Hourly mean concentration in microenvironment j (ppm) 

t(j) = Time spent in microenvironment j (minutes) 

T = 60 minutes 

From the hourly exposures, APEX calculates time series of 8-hour and daily average 

exposure concentrations that a simulated individual would experience during the simulation 

period. APEX then statistically summarizes and tabulates the hourly, 8-hour, and daily 

exposures in a series of output tables. 

5.3.7 Calculate Dose 

Using time/activity data obtained from several diary studies, APEX constructs a 

composite diary for each simulated person in the specified at-risk population. The composite 

diary consists of a sequence of events spanning the specified period of the exposure assessment 

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(typically one calendar year). Each event is defined by a start time, duration, a geographic 

location, a microenvironment, and an activity. Using the algorithms described above in sections 

5.3.5 and 5.3.6, APEX provides estimates of CO concentration and alveolar ventilation rate for 

each event in the composite diary, for each simulated individual. APEX then uses these data, 

together with estimates of various physiological parameters specific to the simulated individual, 

to estimate the percent COHb in the blood at the end of each event. The percent COHb 

calculation is based on the solution to the non-linear Coburn, Forster, Kane (CFK) equation, as 

detailed in Appendix C. 

Briefly, the CFK model describes the rate of change in COHb blood levels as a function 

of the following quantities: 

 Inspired CO pressure; 

 COHb level; 

 Oxyhemoglobin (O2Hb) level; 

 Hemoglobin (Hb) content of blood; 

 Blood volume; 

 Alveolar ventilation rate; 

 Endogenous CO production rate; 

 Mean pulmonary capillary oxygen pressure; 

 Pulmonary diffusion rate of CO; 

 Haldane coefficient (M); 

 Barometric pressure; and 

 Vapor pressure of water at body temperature (47 torr). 

If all of the listed quantities except COHb level are constant over some time interval, the 

CFK equation has a linear form over the interval and is readily integrated. The solution to the 

linear form gives reasonably accurate results for lower levels of COHb. However, CO and 

oxygen compete for the available hemoglobin and are, therefore, not independent of each other. 

If this dependency is taken into account, the resulting differential equation is no longer linear. 

Peterson and Stewart (1975) proposed a heuristic approach to account for 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 if one assumes a particular relationship between COHb and O2Hb. 

October, 2009 5-12 Draft – Do Not Cite or Quote
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-Menten kinetic model 

which is analytically integrable. 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. 

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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 in the Muller 

and Barton paper, a combination of the binary search and Newton-Raphson root finding methods 

was used to solve for COHb (Press et al., 1986). 

As mentioned above, the current COHb module included in APEX is based on the 

solution to the non-linear CFK equation using the assumption adopted by Muller and Barton 

(1987) which employs a linear relationship between O2Hb and COHb. The CFK equation does 

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 fourth-order Taylor’s 

series with subintervals. This method, first incorporated in APEX3, is described in detail by 

Glen (2002) and summarized in Appendix C. 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 percent COHb at both low and high levels of CO 

exposure. 

5.3.8 Model Output 

All of the output files written by APEX are ASCII text files. Appendix D lists each of 

24 the output data files written for these simulations and provides descriptions of their content. 

25 Additional output files that can be produced by APEX are listed in Table 5-1 of the APEX 

26 User’s Guide (US EPA, 2008a). These include tabulations of hourly exposure, ventilation, and 

27 energy expenditures. Detailed event-level information can also be output. Specific outputs 

28 generated for the purposes of the current CO exposure and dose assessment are discussed in 

29 section 6.1. 

5.3.9 Model Limitations 

APEX attempts to reasonably represent a sample of individuals who reside within a 

32 specific geographic area, and estimates the contact with the air pollutant given the inherent 

33 variability in peoples’ locations and activities. This sample of individuals is a “virtual” sample, 

34 created by the model according to the relative frequencies of various demographic variables and 

October, 2009 5-13 Draft – Do Not Cite or Quote
1 census data, with the goal of obtaining a representative sample (to the extent possible) of the 

2 actual population of interest in the study area. The activity patterns of the sampled individuals 

3 (e.g., the specification of indoor and other microenvironments visited and the time spent in each) 

4 are assumed by the model to be comparable to individuals with similar demographic 

5 characteristics, and are represented by actual time-location-activity patterns compiled in CHAD 

6 (US EPA, 2002; McCurdy et al., 2000). The air pollutant exposure concentrations are estimated 

7 by the model using a set of user-input ambient outdoor concentrations and information on the 

8 physical factors that relate ambient pollutant to concentrations expected in various 

9 microenvironments. Although this aspect of APEX is not fully employed in the current 

10 simplified, screening-level assessment, the model structure would allow one to account for the 

11 most significant factors contributing to inhalation exposure – the temporal and spatial 

12 distribution of people and pollutant concentrations throughout the study area and among the 

13 microenvironments, providing there is sufficient input data to characterize these distributions – 

14 while also providing the flexibility to adjust some of these factors to meet the exposure 

15 assessment objectives. This may include exposure scenarios where ambient air quality is 

16 adjusted to simulate just meeting the current or alternative standards under consideration. 

While APEX is designed to represent the most important personal attributes and physical 

factors that influence human exposure, all models have limitations and require the use of 

assumptions. Some of the general limitations of APEX are associated with the 

representativeness of the data distributions input to the model (e.g., human activity patterns) and 

assumptions made within various model algorithms including the following. 

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 The population activity pattern data used in APEX (i.e., CHAD) are compiled from 

studies conducted in a variety of geographic areas and during time periods that differed 

as to season and calendar year, though a large portion of CHAD is from studies of 

national scope. Consequently, the data base may not have data diaries available that fully 

represent a particular study scenario. However, to better match the activity pattern data 

to the simulated population residing in a particular location, diary pools can be created by 

APEX that are for specific seasons and temperature ranges. 

 Commuting pattern data were derived from the 2000 U.S. Census. The commuting data 

address only home-to-work travel. The population not employed outside the home is 

assumed to always remain in the residential census tract. Furthermore, although several 

of the APEX microenvironments account for time spent in travel, the travel activity is 

typically assumed to occur in a composite of the home and work tract. No provision is 

made for the possibility of passing through other tracts during travel. 

 APEX creates seasonal or annual sequences of daily activities for a simulated individual 

by sampling human activity data from more than one subject. While there are input 

variables (e.g., time spent outdoors) used to simulate the correlation of day-to-day 

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3  The model currently does not capture certain correlations among human activities that 

can impact microenvironmental concentrations (for example, cigarette smoking leading 

to an individual opening a window, which in turn affects the rate that outdoor air enters 

the residence or vehicle). 

7  Certain aspects of the personal profiles (e.g., weight) are held constant, though in reality 

they change as an individual ages. This is generally only an issue for simulations with 

long timeframes. 

These and other uncertainties in model inputs and algorithms, and how they may affect 

the estimated exposures and dose, are discussed in section 6.4. 

5.4 PERSONAL EXPOSURE AND THE IN-VEHICLE MICROENVIRONMENT 

This section summarizes key findings from personal exposure studies with particular 

14 attention to microenvironments of importance to ambient CO exposures. 

5.4.1 Personal Exposure Monitoring Studies 

This section summarizes some of the findings from personal exposure studies, in 

17 particular, through identifying the important microenvironments in assessing CO exposure and 

18 providing context for measured CO exposure levels relevant to this draft REA. Details regarding 

19 personal exposure measurement studies of target populations are discussed in section 8.2 of the 

20 1991 CO AQCD (US EPA, 1991), chapter 4 of the 2000 CO AQCD (US EPA, 2000), and 

21 section 3.6 of the draft ISA. 

As ambient CO concentrations have decreased dramatically over time, so have personal 

CO exposure levels.2 However, while CO concentrations have declined over the past few 

decades, some general patterns in the relationship between ambient, microenvironmental, and 

exposure concentration still remain. 

First, as a result of the significant time people spend indoors - whether at home, at 

school, workplace, or other indoor location (section 3.6.2, draft ISA) - indoor CO concentrations 

are an important determinant of an individual’s CO exposures. Recent population exposure 

studies conducted in Milan, Italy support this conclusion (Bruinen de Bruin et al., 2004), 

indicating that over 80% of the population exposure to CO can occur in indoor 

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

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 Many recently-conducted personal exposure studies in the U.S. have not included CO as an analyte, 

possibly due to high detection limits of personal exposure monitoring devices relative to ambient concentrations. 

October, 2009 5-15 Draft – Do Not Cite or Quote
indoors, indoor CO concentrations are similarly an important determinant in an individual’s 

exposure to ambient CO. 

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Second, there is variability in the relationship between personal exposure and ambient 

concentrations, particularly when considering microenvironmental exposures. For example, the 

draft ISA summarized the relationship between personal CO exposures in five broadly defined 

microenvironments (i.e., indoor residence, indoor other, outdoor near road, outdoor other, and 

in-vehicle) and ambient CO concentrations in Baltimore, MD based on data provided in Chang 

et al. (2000). On average, the indoor-to-ambient and outdoor-to-ambient ratios were about one, 

though most of the ratios observed across this set of indoor and outdoor microenvironments were 

less than one. With the exception of those for the in-vehicle microenvironments, which as a 

group were generally above one, few ratios were above unity (draft ISA, p. 3-102, Figure 3-43). 

Given the expected stability of CO as it infiltrates indoor microenvironments from outdoor air 

and the lack of significant removal mechanisms of CO in outdoor microenvironments, it is likely 

that the variability in personal/microenvironmental-to-ambient and outdoor-to-ambient ratios is 

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

simultaneously measured ambient concentrations at fixed-site monitors, and also reflects the 

impact of lag time associated with attaining steady state relationships, as well as potential 

presence of non-ambient sources. 

Third, because motor vehicles remain important contributors to ambient CO 

concentrations, both the time spent in motor vehicles and the presence of elevated on-road CO 

concentrations continue to be important contributors to personal exposures. For example, in the 

same study summarized by the draft ISA on personal exposures occurring within particular 

microenvironments (i.e., Chang et al., 2000), in-vehicle exposures were, on average, a factor of 3 

to 4 greater than ambient concentrations (distance of ambient monitor to roadway not specified), 

with most in-vehicle exposure-to-ambient concentration ratios greater than one (median of 

approximately 2.5). Given this relationship, it should not be surprising that while about 8% of a 

person’s time per day is spent in transit, 13-17% of their total daily exposure occurs within an invehicle microenvironment (e.g., Bruinen de Bruin et al., 2004; Scotto di Marco et al., 2005). 

And finally, as for CO population exposure studies conducted in the U.S., two pertinent 

studies could be found: one conducted in Denver CO and the other in Washington, DC during 

the winter of 1982 and 1983 (Akland et al., 1985). Both studies collected measurements and 

activity pattern diaries from a random sample of the population, defined as including noninstitutionalized, non-smoking residents, 18 to 70 years of age, who lived in each respective 

city’s metropolitan area. In both cities, when comparing the distribution of measured CO 

concentrations from the monitoring network to measured personal exposures, two common 

October, 2009 5-16 Draft – Do Not Cite or Quote
phenomena were observed. At the lowest percentiles of each distribution, ambient CO 

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

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

(US EPA, 2000). Again, ambient concentrations may be a reasonable indicator of exposure for a 

portion of the population, but given spatial and temporal variability in ambient concentrations 

and exposures associated with high concentration microenvironments, there will likely be a 

combination of exposures that are under- and over-estimated when considering ambient 

concentrations alone. As an example of the potential to underestimate exposure concentration 

when solely relying on ambient fixed-site concentrations as an indicator of exposure, over 10% 

of the daily maximum 8-hour personal exposures in Denver exceeded the NAAQS of 9 ppm, and 

about 4% did so in Washington (Akland et al., 1985). This is in contrast to simultaneous CO 

measurements at ambient fixed-site monitors where CO concentrations exceeded 9 ppm about 

3% of the time in Denver and never exceeded 9 ppm in Washington D.C. (Akland et al., 1985). 

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Consistent with the above discussion, the Denver and Washington studies determined 

that the highest average CO concentrations occurred when subjects were in a mobile source 

influenced microenvironment (e.g., inside parking garages, in-vehicles). Commute time was 

also a factor; those who commuted 6 hours or more per week had higher average exposures than 

those who commuted fewer hours per week. Furthermore, mean CO concentrations within invehicle microenvironments (ranging from 7.0 to 9.8 ppm) were greater than common outdoor 

locations (ranging from 1.4 to 3.2 ppm) (US EPA, 2000). In considering the results from the 

Denver and Washington personal exposure studies it is important to recognize that CO emissions 

from motor vehicle sources have declined dramatically since the early 1980’s when these studies 

were conducted. Consequently, both ambient fixed-site CO concentrations and in-vehicle CO 

concentrations have also been reduced significantly since that time period. 

5.4.2 In-Vehicle CO Concentrations 

Given the contribution of in-vehicle exposures to total CO exposure and our focus on 

27 exposure to ambient CO, consideration of the contribution of ambient CO to in-vehicle 

28 concentrations is important to CO exposure assessment. Information useful to this consideration 

29 includes the relationship between CO concentrations within vehicles to concentrations 

30 simultaneously measured outside of vehicles and also at nearby fixed-site monitors. The utility 

31 of such data that has been reported in the extant literature to the assessment conducted here can 

32 be determined by broadly evaluating the fundamental study design and by considering potential 

33 influential factors that might affect measured CO concentrations (e.g., fleet characteristics, 

34 monitor siting). Accordingly, staff evaluated data reported in several U.S. and non-U.S. studies 

35 that measured CO concentrations inside vehicles, immediately outside vehicles, at roadside 

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1 locations, and at fixed-site monitors. Particular attention is given to data available within the 

2 published literature that may be most appropriate for the purposes of the current exposure 

3 assessment. The research findings from a few of the more recent studies (i.e., since 1991) are 

4 summarized below. In addition, discussion regarding these and supporting information from 

5 studies conducted in the 1980’s follows. 

6 5.4.2.1 Studies Comparing CO Concentrations Inside and Outside Motor Vehicles 

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Table 3-1 summarizes four relevant studies selected by staff that provided data 

8 comparing CO concentrations inside a motor vehicle with concentrations immediately outside 

9 the vehicle. Two of the studies reviewed were conducted in the U.S. (Chan et al., 1991; Rodes et 

10 al., 1998). Given the low-reactivity of CO, it is expected that the ratio of the two concentrations 

11 (inside-vehicle versus outside-vehicle or I/O) would be equivalent to one (in the absence of in12 vehicle sources). 

Boulter and McCrae (2005) measured CO concentrations inside vehicles, immediately 

outside the vehicles, and under a range of vehicle ventilation conditions within two tunnels: one 

in Graz, Austria and the other in Liverpool, England. On average the I/O ratio ranged from one 

to slightly above unity. Statistical analysis indicated that the air conditioning (AC), fan, and 

window operating conditions did not have a statistically significant affect on the I/O ratios. 

Chan et al. (1991) measured inside-vehicle concentrations of volatile organic compounds 

(VOCs) and three criteria air pollutants (ozone, CO, and NO2) during the summer of 1988 in 

Raleigh, North Carolina. Two four-door sedans of different ages were used to evaluate 

in-vehicle concentrations of these compounds under different driving conditions. The study 

evaluated a variety of factors that could influence driver exposure, including varying traffic 

patterns, car models, vehicle ventilation conditions, and driving periods. The median I/O ratio 

for these two vehicles operated under a variety of conditions was 1.1 (Table 3.1). Chan et al. 

(1991) note that the slightly higher in-vehicle concentration may be a function of differences in 

interior and exterior sampling locations, and engine running loss emissions that contributed CO 

to the interior of the vehicle. 

During September and October 1997, Rodes et al. (1998) collected 2-hour pollutant 

concentration measurements inside two vehicles during scripted commutes in Sacramento and 

Los Angeles. Similar measurements were made simultaneously outside the vehicles, along the 

roadways, and at the nearest ambient monitoring stations. A variety of scenarios were studied 

based on variables such as roadway type, traffic congestion, ventilation setting, and vehicle type. 

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scenario.3

 On average, all I/O ratios were less than one in both locations, though within the 

range of other researchers reporting this ratio. 

Sharp and Tight (1997) measured inside- and outside-vehicle CO concentrations using a 

single automobile and considering four different ventilation settings. The averaging time for CO 

measurements was one minute, thus the authors calculated I/O ratios based on both average data 

and peak data (Table 3-1). The I/O ratios were consistent with those of similar studies, with 

in-vehicle CO levels being slightly higher than those measured directly outside the vehicle. Peak 

concentration (inside and outside of the vehicle) comparisons did span a wider range of values 

that included I/O ratios both below and above unity. The choice of ventilation setting (i.e., 

window open or mechanical ventilation) had an effect on average and peak CO concentrations; 

in general, conditions associated with the lowest air exchange rates (i.e., windows closed and no 

mechanical ventilation) had the lowest I/O ratios in these studies. 

The findings reported in each of the above four studies are supported by a review by 

Flachsbart (1999) regarding other studies published between 1982 and 1992 that measured 

interior and exterior CO concentrations simultaneously during motor vehicle trips. The I/O ratio 

was similar for two studies: Petersen and Allen (1982) reported a ratio of 0.92 for a study in Los 

Angeles, California; and Koushi et al. (1992) reported a ratio of 0.84 for a study in Riyadh, 

Saudi Arabia. Both of these research studies reported no affect from altering ventilation 

conditions. 

In contrast, one study reported indicated I/O ratios could exceed unity with the 

ventilation set to recirculate vehicle air (Abi Esber and El-Fadel, 2008). It is possible that this 

was the result of a gradual build-up of CO concentrations within the vehicle cabin (ISA, section 

3.6.6.2). In addition, Colwill and Hickman (1980) reported that internal CO levels were about 

30 – 80% of exterior concentrations for a study conducted in London. However, the large 

difference in these I/O ratios when compared with those reported by most other researchers 

could be explained by the location of the exterior probe (i.e., at bumper height compared with 

probes commonly placed higher on the vehicle) (Flachsbart, 1999).

3

 The study design also included several other driving scenarios: (1) a California school bus following a 

student route in Sacramento, (2) comparison of a sedan traveling in a Los Angeles carpool lane versus one traveling 

in a congested right hand lane, and (3) a sedan encountering situations to maximize the in-vehicle pollutant 

concentration levels. These data are not included in this summary. 
Table 5-2. Carbon Monoxide Concentrations Inside and Immediately Outside Vehicles, and Indoor/Outd

CO concentration (ppm) 

Study Inside 

Vehicle 

Outside 

Vehicle 

Indoor/Outdoor 

(I/O) Ratio Drive Conditions Av

Boulter and McCrae (2005) 

6.19 ± 2.08 4.94 ± 1.80 (mean ± std) 

7.13 ± 1.92 4.88 ± 1.86 (mean ± std) 

0.99 ± 0.07 1.10 ± 0.07 (mean ± std) 

Plabutsch Tunnel in Graz, Austria  Kingsway Tunnel in Liverpool, England 

StatisticCO conthrough 

Chan et al. (1991) 

11.0 (median) 

10.0 (median) 

1.10 (median) 

Two sedans were driven on three road types (urban, interstate, rural) in Raleigh, NC during summer 1988 

Statisticsamples periods

Rodes et al. (1998) 

Sacramento: 1.4 – 3.5 Los Angeles: 3.5 – 5.4 (mean range) 

Sacramento: 2.2 – 4.2 Los Angeles: 4.4 – 5.6 (mean range) 

Sacramento: 0.73 – 0.90 Los Angeles: 0.88 – 0.96 (mean range) 

Two vehicles (lead and following) were driven on various road types in Sacramento and Los Angeles during September and October 1997. Note, minimum quantitation limit was 2 ppm for Draeger Model 190 monitors used. 

Statisticof 120 omeasurmorning periods

Sharp and Tight (1997) 

1.19 to 1.43 (mean range) 0.65 to 1.38  (max range) 

Nine test runs were conducted in Leeds, England on three road types under four ventilation conditions. Lowest values associated with windows closed and no mechanical ventilation. 

Statisticand maconcenteach m

October, 2009 20 Draft – Do Not Cite or Quote
In general, the above results suggest that the I/O ratio tends toward unity when there are 

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

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

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

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

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

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

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

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

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

is generally equivalent to penetration).5

 Under these stated conditions, the I/O ratio would 

ultimately converge to unity. 

5.4.2.2 Studies Comparing CO Concentrations Inside Motor Vehicles to 

Concentrations at Fixed-Site Monitors and Roadside Locations 

A report by Shikiya et al. (1989) describes an in-vehicle study conducted in the South 

Coast Air Basin of California during the summer of 1987 and winter of 1988. Participants were 

randomly-selected home-to-work commuters from a non-industrial business park. 

Measurements of hazardous air pollutants (HAPs), CO, and Pb were collected from within 

vehicles and contrasted with measurements at existing fixed-site monitoring stations. A total of 

192 CO measurements were made each representing the average concentration of the round-trip 

commute.6

 

On average, CO concentrations were 8.6 ppm, and even though the maximum observed 

CO concentration in a vehicle was as high as 46 ppm, only 3 percent of the in-vehicle 

commuting concentration measurements were greater than 20 ppm (Table 3-2). Shikiya et al. 

(1989) also investigated several potentially influential variables. Statistical differences in 

concentration were noted for season (p = 0.01), and age of vehicle (p = 0.05). Mean 

concentrations using several other classification variables did not differ significantly at the p = 

0.05 level (e.g., ventilation status, vehicle speed, freeway density during commute). 

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 Interior sources of CO to in-vehicle concentrations may include self-pollution such as that associated with 

defective exhaust systems or inadequate internal ventilation (draft ISA, p. 3-105). While automobile technology has 

advanced with improvements in these areas (e.g., Flachsbart et al., 1999), interior sources may contribute in some 

instances (e.g., older school buses, draft ISA, p. 3-105). 

5

 See section 3.6.2 of the draft ISA. 

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 The average one-way commute time was 33 minutes. 

October 2009 5-21 Draft – Do Not Quote or Cite 
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Table 3-2 also presents descriptive statistics for averages of individual 4-hour integrated 

samples collected during peak commuting hours at three fixed-site monitors that encompassed 

the general routes of the commuters: Long Beach (a coastal location), Los Angeles (a central 

location), and Pomona (an inland location). On average, CO concentrations were 3.7 ppm, 

though maximum CO concentrations were as high as 8.7 ppm. Other monitoring sites located in 

Los Angeles, Rubidoux, and Upland reported ambient 24-hour average CO concentrations of 2.2 

ppm during the summer. This group of monitors were designated by Shikiya et al. (1987) to be 

less influenced by local roadway emissions compared with the three other fixed-site monitors. 

Using the mean for all in-vehicle CO concentrations during round-trip commutes (8.6 ppm) and 

dividing by the mean of the integrated fixed-site concentrations measured during peak 

commuting hours (3.7 ppm) gives an in-vehicle to ambient monitor ratio of about 2.4. 

12 Table 5-3. Descriptive Statistics for CO Concentrations Measured Inside Vehicles and at 

Fixed-Site Monitors (from Shikiya et al., 1989). 

CO concentration (ppm)a

Measurement 

Grouping 

variable Category 

Number of 

samples Mean Std Max 

All -- 192 8.6 5.0 46.4 

Season Summer 80 6.5 2.2 14.6 b

Winter 112 10.1 5.8 46.4 

1973 - 83 9.4 

Average in-vehicle CO 

concentration during 

round-trip commute 

Vehicle yearc

1984 - 88 7.8 

Integrated fixed-site 

concentration during peak 

commuting hours (Long 

Beach, Los Angeles, and 

Pomona stations) 

All -- 19 3.7 2.1 8.6 

Notes: 

a Mean, std and max are the arithmetic mean, arithmetic standard deviation, and maximum CO concentrations. 

b Statistically significant at p = 0.01 level.c

 Summer: May – October, Winter: November – March. 

c

 Statistically significant at p = 0.05 level. 

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Further, the draft ISA notes that studies summarized in the 2000 CO AQCD found that 

in-vehicle CO concentrations were generally two to five times higher than ambient CO 

concentrations obtained at fixed-site monitors within the cities studied. However, several of 

these studies were conducted when CO vehicle emissions were much higher and/or under 

situations that are less relevant to the two urban areas of the U.S. included in the current 

exposure and dose assessment, discussed below in section 5.5. As described above, the findings 

October 2009 5-22 Draft – Do Not Quote or Cite 
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reported by Shikiya et al. (1989) in a study conducted in Los Angles supports a ratio of about 

two. We note, however, that based on several factors discussed above, such as the traffic 

characteristics of the roadway and the site characteristics of the fixed-site monitor, the 

relationship can vary (e.g., higher ratios would be obtained using more remotely sited monitors, 

and the size of relationship may vary with absolute magnitude of roadway concentrations). 

5.5 STRATEGY FOR CO EXPOSURE/DOSE ASSESSMENT FOR THE 

CURRENT REVIEW 

5.5.1 Background for Current Assessment Strategy 

The draft Scope and Methods Plan for the current review of the primary CO NAAQS (US 

EPA, 2009b) described an approach based on the application of APEX to estimate human 

exposures to CO and the resulting dose and to characterize the potential health risks that are 

associated with recent ambient levels of CO and with ambient levels that just meet the existing 

standards in two urban study areas (Denver and Los Angeles). The characterization of health 

risks focused on development of estimates of COHb levels and the number of people and the 

total number of occurrences for which potential COHb benchmark levels are exceeded. 

In consideration of information on current locations of CO monitors discussed in chapter 

3 of this document and in the draft ISA (sections 3.5-3.7), and CASAC comments on both the 

draft Scope and Methods Plan and the first draft ISA, however, staff notes significant limitations 

of the currently available CO monitors related to their use in detailed population exposure 

assessment. 

 The number of CO monitors in Denver and Los Angeles counties has decreased since the 

previous review, from 9 to 3 or 4 monitors operating in Denver (depending on the year 

considered) and from 21 to 12 in Los Angeles. 

 The current levels of ambient CO concentrations are much lower than in the last review, 

and a significant number of the measurements are near or below detection limits. 

 Concentrations of ambient CO occurring in key microenvironments are not reflected by 

ambient monitors. As stated by the CASAC CO Panel, “Relying only on EPA’s fixed 

monitoring network CO measurements may underestimate CO exposures for specific 

vulnerable populations such as individuals residing near heavily trafficked roads and who 

commute to work on a daily basis” (Brain and Samet, 2009, p.2). 

 As discussed chapter 3 above, the currently available CO monitors pose significant 

limitations in our ability to fully characterize the current spatial and temporal variability 

in CO ambient concentrations across the two urban areas of focus for this assessment, 

Denver and Los Angeles. These limitations affect our ability to derive detailed 

relationships about CO concentrations in ambient air across the study area from which 

detailed microenvironmental concentrations can be estimated. More broadly, the 

October 2009 5-23 Draft – Do Not Quote or Cite 
CASAC CO Panel expressed the view that “the current ambient monitoring network is 

not well designed to characterize spatial and temporal variability in ambient 

concentrations. Thus, it does not adequately support detailed assessments of human 

exposure or air quality modeling such as for photochemical oxidants” (Brain and Samet, 

2009, p.11). 

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In light of these limitations in the air quality data, including CASAC’s concerns about the 

adequacy of the monitoring network to perform detailed exposure analyses, and in light of the 

findings from the CO assessments completed for the 1994 review (USEPA, 1994) and 

subsequently (Johnson, et al., 2000), as well as the lack of new evidence in the draft ISA to 

support a quantitative risk characterization approach different from past assessments, staff 

decided not to perform a detailed analysis involving multiple monitors and comprehensive 

estimation of exposure concentrations in multiple microenvironments, as has been done in the 

past. Rather than develop such a detailed analysis using the available air quality data that has 

been recognized as limited by CASAC for such a purpose, we developed an alternative 

simplified, screening-level approach. We recognize that there are uncertainties associated with 

the revised approach and, thus, its utility is primarily as a screening assessment to provide some 

perspective on current ambient CO concentrations and associated CO exposure, dose and risk. 

One purpose of this draft document is to seek CASAC views on the extent to which this 

assessment design provides information useful to this current CO NAAQS review. 

The following section presents the approach used to develop the current CO exposures 

and COHb estimates for the two urban study areas presented in this document. 

5.5.2 Selected Approach for Current Review 

As discussed in section 5.5.1 above, despite the capabilities of the APEX model, staff felt 

24 that the lack of spatial and temporal variability in available ambient monitoring data precluded 

25 the development of a credible broad-scale urban exposure assessment such as that conducted 

26 recently for the O3 NAAQS (US EPA, 2007). Therefore, staff decided to perform a limited 

27 exposure analysis using APEX and ambient data at a single monitor each in Denver and Los 

28 Angeles counties. 

In developing the approach, staff evaluated the monitoring data available in the selected 

locations (i.e., Denver County and Los Angeles County) for years 2005 through 2007 (see Table 

3-1 for sites IDs, locations, and number of sample-hours). Staff noted that, following the 

examination of the existing and active monitors in Denver and Los Angeles counties and using a 

75% completeness criterion, only two of the Denver County monitors had data for 2007. Three 

of the six Denver sites used in the exposure assessment conducted in 2000 (Johnson et al. 2000) 

have been removed from the monitoring network and are no longer reporting CO concentrations 

in Denver (Figure 5-1). Based on these observations, 2006 was chosen to be the most recent 

October 2009 5-24 Draft – Do Not Quote or Cite 
year to be analyzed. As a result of this choice, there were four monitors in Denver County and 

twelve monitors in Los Angeles County (Figure 5-1) with complete air quality data available. 

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The monitor siting characteristics and breadth of spatial coverage are also important 

features to consider in representing the air quality in an area. Most of the monitors in Denver 

County are clustered within the central portion of the Denver County, with two monitors (IDs 

080310002 and 080310019) within 1 mile of one another and generally having a similar 

concentration distribution when considering their 1-hour and 8-hour average CO concentrations 

(Figure 5-2). There is limited spatial variation among the twelve Los Angeles County monitors 

(Figure 5-3). Further, of the monitors available in Denver County to use in an exposure 

assessment, one monitor appears to best represent the highest population density in Denver 

County (draft ISA, Figure 3.4-3). This monitor (ID 08-031-0002) is located at 2105 Broadway. 

This particular monitor was included in the previous reviews and continues to report the some of 

the highest concentrations in the area (Figure 5-2 and Table 3-1). It is described as a micro-scale 

site, within 6 meters from a roadway having 17,200 vehicles/day traffic volume, 7 meters from a 

road with 10,000 vehicles/day, and 16 meters from a road with 1,000 vehicles/day. Based on the 

same criteria (i.e., to envelop a study area that captures the population centers and where ambient 

CO levels tend to be high), a single monitor in Los Angeles County (ID 060371301) was 

selected for use in the exposure assessment for the Los Angeles study area. This monitor is 

described as representing a middle scale, and it is near to an arterial road, but 350 m from a 

major freeway (the I-105) with a traffic count close to 35,000 vehicles/day. Staff note, however, 

that a study of ambient CO concentrations related to motor vehicle traffic in Los Angeles and 

Sacramento (Rodes et al., 1998) observed little difference in CO concentrations between arterial 

roads and freeways for Los Angeles (draft ISA, p. 3-65). 

October 2009 5-25 Draft – Do Not Quote or Cite 
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3 Figure 5-1. Locations of active ambient CO monitors meeting 75% completeness 

criterion in 2006 along with locations of inactive ambient CO monitors, 

within the metropolitan Denver (top) and metropolitan Los Angeles (bottom). 

October 2009 5-26 Draft – Do Not Quote or Cite 
1-hour CO (ppm)

0

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080310002 080310013 080310014 080310019

Site ID

1-hour CO (ppm)

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080310002 080310013 080310014 080310019

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8-hour daily maximum CO (ppm)

080310002 080310013 080310014 080310019

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8-hour daily maximum CO (ppm)

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3 Figure 5-2. Distribution of 1-hour (top) and 8-hour average daily maximum (bottom) CO 

concentrations at ambient CO monitors in Denver County, year 2006. 

October 2009 5-27 Draft – Do Not Quote or Cite 
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060370002 = A

060370113 = B

060371002 = C

060371103 = D

060371201 = E

060371301 = F

060371701 = G

060372005 = H

060374002 = I

060375005 = J

060376012 = K

060379033 = L

060371002 060371201 060371701 060374002 060376012

0

1-hour CO (ppm)

A B C LKJIHGFED

Site ID

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060370002 = A

060370113 = B

060371002 = C

060371103 = D

060371201 = E

060371301 = F

060371701 = G

060372005 = H

060374002 = I

060375005 = J

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060371002 060371201 060371701 060374002 060376012

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1-hour CO (ppm)

A B C LKJIHGFED

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060370002 = A

060370113 = B

060371002 = C

060371103 = D

060371201 = E

060371301 = F

060371701 = G

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060374002 = I

060375005 = J

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060371002 060371201 060371701 060374002 060376012

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060370002 060371002 060371201 060371701 060374002 060376012

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060370002 = A

060370113 = B

060371002 = C

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060371201 = E

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A B C LKJIHGFED

Site ID

060370002 060371002 060371201 060371701 060374002 060376012

0

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060370002 = A

060370113 = B

060371002 = C

060371103 = D

060371201 = E

060371301 = F

060371701 = G

060372005 = H

060374002 = I

060375005 = J

060376012 = K

060379033 = L

8-hour daily maximum CO (ppm)

A B C LKJIHGFED 060370002 060371002 060371201 060371701 060374002 060376012

0

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060370002 = A

060370113 = B

060371002 = C

060371103 = D

060371201 = E

060371301 = F

060371701 = G

060372005 = H

060374002 = I

060375005 = J

060376012 = K

060379033 = L

8-hour daily maximum CO (ppm)

A B C LKJIHGFED

2 Site ID

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3 Figure 5-3. Distribution of 1-hour (top) and 8-hour average daily maximum (bottom) CO 

concentrations at ambient CO monitors in Los Angeles County, year 2006. 

October 2009 5-28 Draft – Do Not Quote or Cite 
In each of the two study areas, two broadly defined air quality scenarios were 

investigated. In the first scenario (“Scenario A”), all microenvironmental concentrations are set 

equal to the ambient concentrations measured at the single fixed-site monitor selected to 

represent each exposure modeling domain. Staff used APEX to estimate the COHb levels in 

blood of the at-risk population (i.e., adults with CHD) by assuming that the population is 

exposed to the ambient concentrations measured at the selected near-road monitor for each study 

area throughout the entire simulation period. This general assumption likely results in overestimates of CO exposure and COHb levels for much of the population because CO peak hourly 

concentrations are typically somewhat lower indoors than outdoors due to consideration of air 

exchange (in the absence of indoor sources of CO). This scenario, however, may underestimate 

CO exposure for some small portion of the population that may live in close proximity to heavily 

trafficked roadways and spend appreciable time in transit on such roadways. These individuals, 

based on the analysis of air quality relationships and personal exposure measurements, would 

likely have periods of higher exposures than represented by Scenario A since CO concentrations 

in vehicles, and exposure concentrations for individuals in transit on roadways, are typically 

higher than the concentrations measured at a near-road monitor. The impact of such higher 

exposure periods on an individual’s COHb levels will vary depending on the magnitude and 

pattern of exposures in the prior and subsequent hours. 

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In the second scenario (“Scenario B”), we assume that the concentration outside a motor 

vehicle is twice the ambient concentration and that the concentration inside the vehicle is the 

same as the concentration immediately outside the vehicle; that is, the in-vehicle 

microenvironment is set equal to twice the ambient monitor concentrations. For Scenario B, all 

other microenvironmental concentrations are set equal to the ambient concentration based on the 

fixed-site monitor concentration, consistent with their treatment in Scenario A. The intent of this 

scenario is to determine the magnitude of the change in exposure and COHb levels when 

incorporating a rough estimate of the greater exposure concentrations occurring inside motor 

vehicles. Further details regarding the air quality scenarios and specific exposure modeling 

input data used for the assessment are given in chapter 6. 

 

October 2009 5-29 Draft – Do Not Quote or Cite 
1 5.6 KEY OBSERVATIONS 

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Presented below are key observations related to the approach for the population 

3 assessment of CO exposure and dose. 

4  APEX, an EPA human exposure and dose model, has a long history of use in estimating 

exposure and dose for many of the criteria pollutants including CO, O3, SO2, and NO2. 

Over time, staff have improved and developed new model algorithms, incorporated 

newer available input data and parameter distributions, as well as performed several 

model evaluations, sensitivity analyses, and uncertainty characterizations for the same 

pollutants. Based on this analysis, APEX was judged to be an appropriate model to use 

for assessing CO exposure and dose. 

 Personal CO exposure studies indicate that in general, indoor exposures contribute the 

greatest portion of an individual’s total daily exposure, though variability in exposure 

concentrations may be driven largely by exposure in certain microenvironments, such as, 

with regard to ambient CO, inside motor vehicles or when outdoors near roadways. 

Accordingly, in estimating CO exposures and associated COHb levels an approach is 

needed to estimate the generally higher in-vehicle and in-transit exposure concentrations 

compared to the generally lower ambient concentrations concurrently reported by fixed 

site ambient monitors.

 Given the limitations in the number of ambient monitors currently in operation, the 

limited spatial and temporal representation of ambient concentrations provided by the 

current monitoring network, and limited number of CO concentrations at or above the 

instrument detection limit, the simplified, screening-level approach used in this exposure 

assessment does not employ detailed microenvironmental concentration modeling and 

uses a single fixed-site monitor in each study area. The single monitoring site selected in 

each location typically reported a higher range of CO concentrations when compared 

with other monitors in each area, and thus, when used as an input to an exposure model, 

is generally considered likely to generate conservative (i.e., higher) estimates of exposure 

for the large majority of the population. 

 

October 2009 5-30 Draft – Do Not Quote or Cite 
1 5.7 REFERENCES 

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

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Biller WF, Feagans TB, Johnson TR, Duggan GR, Paul RA, McCurdy T, and Thomas HC. (1981). A General 

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Chan, C., Ozkaynak, H., Spengler, J., and Sheldon, L. (1991). ADriver Exposure to Volatile Organic Compounds, 

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Chang LT; Koutrakis P; Catalano PJ; Suh HH. (2000). Hourly personal exposures to fine particles and gaseous 

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Colwill, D., and Hickman, A. (1980). “Exposure of Drivers to Carbon Monoxide.” Journal of the Air Pollution 

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Esmail S, Bhambhani Y, Brintnell S. (1995). Gender Differences in Work Performance on the Baltimore 

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Flachsbart, P. (1999). “Human Exposure to Carbon Monoxide from Mobile Sources.” Chemosphere – Global 

Change Science. Volume 1, pp. 301 – 329. 

Flachsbart, P., Mack, G., Howes, J., and Rodes, C. (1987). “Carbon Monoxide Exposures of Washington 

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33 

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at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html.. 
October, 2009 Draft – Do Not Cite or Quote 6-1

1 6 EXPOSURE/DOSE ASSESSMENT AND RISK 

2 CHARACTERIZATION 

3 This chapter describes the draft assessment of ambient CO exposure and dose and 

4 associated characterization of risk and uncertainty. Section 6.1 provides an overview of the 

5 application of the APEX model in this assessment. In section 6.2, we present the exposure and 

6 dose estimates and describe the estimates in the context of potential health benchmarks. The 

7 current assessment and associated exposure and dose estimates are described in comparison to 

8 estimates from an earlier CO assessment (Johnson et al., 2000) in section 6.3. The final section 

9 presents an analysis of how variability was considered by staff in this assessment followed by a 

10 qualitative characterization of uncertainty (section 6.4). 

11 6.1 APPLICATION OF APEX4.3 TO CARBON MONOXIDE 

12 The previous analysis of population exposure to carbon monoxide (CO) employed the 

13 pNEM/CO model (summarized in section 5.2 above) and focused on Denver and Los Angeles 

14 study areas, comprising the majority of census tracts within those metropolitan areas (Johnson et 

15 al., 2000). Air quality data were obtained from multiple fixed-site monitors within the study 

16 areas, and the exposure assessment accounted for the effects of geographic location, a diverse set 

17 of microenvironments, commuting within the study area, and selected indoor sources (e.g., 

18 environmental tobacco smoke, gas stoves). 

19 In the specific application of APEX4.3 described in this report, a different approach has 

20 been taken. The simplified approach presented is intended to emphasize the contribution of CO 

21 exposures occurring in vehicles to overall CO exposures. The contribution of in-vehicle 

22 exposures is thought to be influential in producing the highest exposures to ambient CO and 

23 resulting COHb levels (draft ISA, section 3.6.6.2). The ambient CO concentration throughout 

24 the study areas and all microenvironmental concentrations are generally assumed to equal the 

25 hourly-average concentrations measured by a single high CO concentration monitor in one of the 

26 two scenarios (Scenario A). In a second scenario, in-vehicle concentrations are assumed to equal 

27 twice the ambient monitor concentrations (Scenario B). Therefore, the locations visited by 

28 simulated persons in the study area are represented by at most two microenvironments (in29 vehicle and all other). The study areas and scenarios are briefly described in sections 6.1.1 and 

30 6.1.2 below. The study area population of interest was limited to adults with coronary heart 

31 disease (CHD) that reside within a defined distance of a single downtown fixed-site monitor (as 

32 discussed in sections 5.5.2 and 6.1.3). Given simplified assumptions in the approach, the 

33 generated results would tend to represent population exposures experienced by persons 

34 residing/travelling in high CO (of ambient origin) concentration microenvironments. However, 
October, 2009 Draft – Do Not Cite or Quote 6-2

1 note that this assessment does not include the contribution of indoor sources to total CO 

2 exposure, which in prior assessments have been shown to be important contributors to total 

3 exposures (see US EPA, 1992; Johnson et al., 2000). 

4 A general description of APEX4.3 and its capabilities, as well as the history of the 

5 pNEM/APEX series of exposure models can be found in chapter 5. This section focuses on the 

6 development of the specific input files and parameters used in the current application of 

7 APEX4.3 to CO in the Denver and Los Angeles study areas. In particular, this section (and 

8 associated appendices) describes the 

9  Geographic areas and time periods defined for the exposure analyses, 

10  Exposure scenarios under evaluation, 

11  Populations-at-risk and the associated prevalence rates for CHD, 

12  Air quality and meteorological data used for each study area, 

13  Microenvironments defined for each exposure scenario, and 

14  Methods used to construct a composite diary for each simulated individual. 

15 In addition to the application-specific input data bases described in this section, we used a 

16 number of default databases provided with APEX4.3 as inputs to the model. These included 

17 national data files obtained from the U.S. Census Bureau (i.e., the 2000 Census data) for the 

18 following types of information (http://www.epa.gov/ttn/fera/apex_download.html#input): 

19  Population data by race, gender, age, and census tract; 

20  Employment probabilities by gender, age, and census tract; 

21  Locations of census tracts (latitude and longitude); and 

22  Commuting flows for combinations of home and work census tracts. 

23 Another default input file provided tables of age- and gender-specific physiological 

24 parameters (e.g., weight). The contents of these default files will not be described in this section; 

25 they are described in detail in the APEX Users Guide (US EPA, 2008a) and the APEX Technical 

26 Support Document (US EPA, 2008b). 

27 6.1.1 Study Areas and Exposure Periods 

As discussed in section 3.2, EPA selected areas within Denver, Colorado, and Los 

Angeles, California, for the exposure assessment. Briefly, considerations in selection of these 

areas include: the prior analysis of these locations in CO NAAQS reviews, the areas having 

historically elevated CO concentrations, and the areas currently having some of the most 

complete ambient monitoring data available. The actual study areas were defined as including 

all census tracts within 20 km of the following fixed-site monitors. 
October, 2009 Draft – Do Not Cite or Quote 6-3

1 Denver: Monitor No. 080310002; 2105 Broadway, Denver, CO (CAMP site). 

2 Los Angeles: Monitor No. 060371301; 11220 Long Beach Blvd., Lynwood, CA. 

3 The Denver study area includes most of the urbanized area inside the arc defined by 

4 Highway 470 (Figure 6-1) within Denver County. The Los Angeles study area is centered at 

5 Lynwood, CA and includes large portions of Los Angeles and Long Beach within Los Angles 

6 County (Figure 6-2). 

7 EPA selected the following calendar years as the study periods for each area. 

8 Denver: 1995 and 2006 

9 Los Angeles: 1997 and 2006 

10 The year 2006 was selected for both cities because it was the most recent year of monitoring data 

11 that met the 75% completeness requirement for the fixed-site monitors listed above. The CO 

12 levels reported for 2006 were well below the 8-hour NAAQS and were considered representative 

13 of current conditions in each study area. The year 1995 for Denver and the year 1997 for Los 

14 Angeles were selected as periods for which the monitoring data indicated higher CO conditions 

15 near or exceeding, the 8-hour CO NAAQS (9 ppm). As discussed in section 6.1.4.3, staff 

16 applied an adjustment to the monitoring data reported for these years to simulate ambient CO 

17 levels that would just meet the current 8-hour NAAQS. 
October, 2009 Draft – Do Not Cite or Quote 6-4

1 

2 Figure 6-1. Map of the Denver Study Area Defined as a Circle with Radius = 20 km 

3 Centered on Fixed-Site Monitor ID 080310002. 

4 
October, 2009 Draft – Do Not Cite or Quote 6-5

1 

2 Figure 6-2. Map of the Los Angeles Study Area Defined as a Circle with radius = 20 km 

3 Centered on Fixed-Site Monitor No. 060371301. 

4 

5 
October, 2009 Draft – Do Not Cite or Quote 6-6

1 6.1.2 Exposure Scenarios 

2 Two exposure scenarios were evaluated for each study area, designated as Scenario A 

3 and Scenario B. The scenarios differed according to the modeling factors assumed for the two 

4 broadly defined microenvironments modeled (i.e., in-vehicle and all others). See section 6.1.5 

5 for additional details regarding microenvironments. Both Scenario A and B were focused on the 

6 adult CHD population living within each study area and are described in more detail below. 

7 6.1.2.1 Scenario A 

8 In this scenario, the sequence of concentrations in each microenvironment was set equal 

9 to the ambient concentrations derived from the fixed-site monitor selected to represent the study 

10 area. Essentially, the microenvironment assignments in each diary entry for each simulated 

11 individual did not affect exposure concentration levels (i.e., the individual was exposed to the 

12 ambient concentrations as measured at the fixed-site monitor for all hours in the exposure 

13 period). 

14 The time/activity database that was used as the input for this scenario included all adult 

15 diaries in CHAD and thus was not necessarily specific to the study area. Note however that the 

16 APEX model samples from this pool of diaries to reflect the actual population distribution based 

17 on the specific age and gender residing in each census tract. In addition, the sampling from the 

18 broad diary pool is also guided by several temperature ranges and applied to observed 

19 temperatures for the specific geographic region. 

20 6.1.2.2 Scenario B 

21 Scenario B assumed that the concentrations outside a motor vehicle were greater than that 

22 measured at the ambient fixed-site monitor and that the concentrations inside the vehicle were 

23 the same as the concentrations immediately outside the vehicle. The CO concentrations in all 

24 other microenvironments were set equal to the ambient concentrations as measured at the fixed25 site monitor, consistent with their treatment in Scenario A. As in Scenario A, the input 

26 time/activity database included all diaries in CHAD. 

27 6.1.3 Populations-at-Risk 

28 Staff defined the population group at risk within each study area to include adults ages 18 

29 or older with CHD. Coronary heart disease is caused by inadequate circulation of the blood to 

30 the heart muscle, which is a result of the coronary arteries being blocked by cholesterol deposits 

31 (ISA, section 5.2.1.9). The focus on adults with CHD is consistent with the previous (2000) 

32 review of the CO NAAQS. The current and previous assessments focused on adults as the 

33 incidence of CHD in younger individuals is extremely small. 
October, 2009 Draft – Do Not Cite or Quote 6-7

1 At the time of the review completed in 1994, the estimated number of individuals with 

2 CHD represented about 3% of the entire (all ages) U.S. population (US EPA, 1992). More 

3 recently however, the National Health Interview Survey for 2007 reported a prevalence rate for 

4 CHD of about 6 percent for the population above 18 years of age (CDC, 2009; draft ISA, section 

5 5.7.2.1). The current exposure/dose assessment requires estimates (by age and gender) of the 

6 fraction of the population in the Denver and Los Angeles study areas who have CHD. Given the 

7 general similarity in regional rates for CHD, staff decided to apply national prevalence rates for 

8 CHD to each of the two study areas. Table 6-1 provides national prevalence data for CHD by 

9 age obtained from the National Health Interview Survey of 2007 (CDC, 2009). Table 6-2 

10 provides national prevalence rates for CHD by gender obtained from the same source. These 

11 data were used to estimate gender-specific adjustment factors of 1.31 = males/total = 0.080/0.061 

12 for males and 0.74 = females/total = 0.045/0.061 for females. Table 6-3 provides estimated 

13 national prevalence rates for CHD by age range adjusted for gender differences using these 

14 values. 

15 Table 6-1. 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 a

Source: Coronary heart disease statistics in Table 2, “Summary Health 

Statistics for U.S. Adults: National Health Interview Survey, 2007,” U.S. 

Department of Health and Human Services, Center for Disease Control, 

Hyattsville, MD, May 2009. 

16 Table 6-2. 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 a

Source: Coronary heart disease statistics in Table 2, “Summary Health Statistics 

for U.S. Adults: National Health Interview Survey, 2007,” U.S. Department of Health 

and Human Services, Center for Disease Control, Hyattsville, MD, May 2009. 

17 
October, 2009 Draft – Do Not Cite or Quote 6-8

1 Table 6-3. National Prevalence Rates for Coronary Heart Disease Used in APEX, 

2 Stratified by Age and 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 a

Source: Values listed in Table 6-2 were multiplied by 1.31 (= 

0.080/0.061) for males and 0.74 (= 0.045/0.061) for females. 

3 

4 6.1.4 Air Quality and Meteorological Data 

5 6.1.4.1 Selection of Fixed-Site Monitors 

6 Based on considerations described in sections 3.2 and 5.5.2, staff selected the downtown 

7 “CAMP” monitor (ID 080310002) to represent ambient CO concentrations in the Denver, CO 

8 study area and monitor 060371301 in Lynwood, CA to represent ambient CO concentrations in 

9 the Los Angeles study area. Details regarding each monitor’s site characteristics are given in 

10 (Table 6-4). Note that the ambient monitor in Denver is a microscale monitor sited within the 

11 urban core and generally records the highest hourly CO concentrations within the county (Table 

12 3-1). Similarly, the middle scale monitor in the Los Angeles study also reported the highest CO 

13 concentration levels in the Los Angeles study area (Table 3-2). 
October, 2009 Draft – Do Not Cite or Quote 6-9

1 Table 6-4. Site Characteristics of Fixed-site CO Monitors Selected to Represent the 

2 Denver and Los Angeles Study Areas. 

Site Characteristic Denver Los Angeles 

Site ID 080310002 060371301 

Street address 2105 Broadway 11220 Long Beach Blvd. 

Town Denver, CO Lynwood, CA 

Local site name CAMP 

Latitude 39.751184 33.928990 

Longitude -104.987625 -118.210710 

Elevation (above sea level), meters 1593 27 

Monitor start date January 1, 1971 January 1, 1973 

Measurement scale Microscale Middle scale 

Sample collection frequency 1 hour 1 hour 

Sample analysis method Non-dispersive infrared Non-dispersive infrared 

Monitor type SLAMS SLAMS 

Reporting agency 

Colorado Department of 

Public health and 

Environment 

South Coast Air Quality 

Management District 

3 6.1.4.2 Estimation of Missing Air Quality Values 

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 three 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 6 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 

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 models were available for a 

17 particular time-of-day) and the gap was less than 9 hours, the missing values were 

18 estimated by linear interpolation between the valid values at the ends of the gap. 

19 Table 6-5 provides descriptive statistics for 1-hour CO concentrations in each data set, 

20 before and after estimating missing values. The agreement between these statistics indicates that 

21 the addition of the estimated missing-value concentrations did not significantly affect the 

22 distribution of the hourly CO data. 
October, 2009 Draft – Do Not Cite or Quote 6-10

1 Table 6-5. Descriptive Statistics for 1-hour CO Concentrations Reported by the Selected 

2 Denver and Los Angeles Monitors Before and After Estimation of Missing 

3 Values. 

1-hour CO concentrations (ppm) 

Percentile 

Area Year 

Missing 

value 

substitution

Samples 

(n) Mean Std 50 90 95 99 99.9 

2nd

highest Max 

No 8697 1.5 1.2 1.2 2.7 3.4 6.1 13.1 16.4 24.5 1995 Yes 8760 1.5 1.2 1.2 2.7 3.4 6.1 13.1 16.4 24.5 

No 8672 0.6 0.4 0.5 1.0 1.3 2.2 4.1 4.6 6.4 

Denvera

2006 Yes 8760 0.6 0.4 0.5 1.0 1.3 2.1 4.1 4.6 6.4 

No 8302 2.4 2.2 1.7 4.9 6.8 11.2 17.2 18.8 19.2 1997 

Yes 8760 2.3 2.2 1.7 4.9 6.7 11.2 17.2 18.8 19.2 

No 8275 1.0 0. 9 0.7 2.0 2.9 4.7 6.8 8.2 8.4 

Los 

Angelesb

2006 Yes 8760 1.0 0.90 0.7 2.0 2.9 4.6 6.8 8.2 8.4 

aSite ID 080310002 

bSite ID 060371301

4 6.1.4.3 Air Quality Adjustment to Simulate Just Meeting NAAQS 

5 In addition to modeling exposures based on recent air quality, exposures and resulting 

6 dose were estimated for air quality conditions that just meet the current 8-hour CO NAAQS of 9 

ppm.

1 7 Because CO concentrations in recent years were significantly lower than the current 

8 NAAQS, staff first selected an earlier year for each city (1995 for Denver and 1997 for Los 

9 Angeles) to represent air quality conditions that were near the 8-hour CO standard. Consistent 

10 with the data adjustment approach employed in the previous draft CO exposure assessment 

11 (Johnson et al., 2000) and risk and exposure assessments for other pollutants conducted in 

12 support of other recent NAAQS reviews (e.g., US EPA, 2008c), as discussed in section 3.1.4 

13 staff concluded (1) that the policy-relevant background levels of CO were negligible in each area 

14 and (2) that the fixed-site monitoring data could be adjusted to simulate just meeting the current 

15 CO standards by use of a simple proportional adjustment of all hourly values. Consequently, the 

16 following adjustment equation was employed: 

17 

18 COadj(m,h) = (NAAQS/DV) x CO(m,h). (6-1) 

19 

20 CO(m,h) is the 1-hour CO concentration at hour h for monitor m. It follows that COadj(m,h) is 

21 the adjusted CO concentration for hour h at monitor m through the use of the specific design 

22 value (DV) for monitor m. Although the current 8-hour NAAQS for CO specifies a maximum 

 1

 The 8-hour CO NAAQS of 9 ppm was selected for purposes of simulating just meeting the CO NAAQS because it 

is the controlling standard from a control strategy development viewpoint. 
October, 2009 Draft – Do Not Cite or Quote 6-11

1 concentration of 9 ppm, which is not to be exceeded more than 1 time in a year, the NAAQS 

2 term in Equation 6-1 is equivalent to 9.4 ppm due to the application of a standard data rounding 

convention used in calculating design values2 3 (DVs) for CO (Laxton, 1990). 

4 The DVs for Denver for the year 1995 and for Los Angeles for 1997 were 9.5 ppm and 

5 15 ppm, respectively. The Denver DV is calculated as the second-highest 8-hour CO 

6 concentration reported by monitor ID 080310002 for 1995. The adjustment factor (or 

7 NAAQS/DV) that was applied equally to all 8,760 hourly ambient CO concentrations at that 

8 monitor is thus 9.4/9.5, or 0.99. In a similar manner, the DV used in Los Angeles is the second9 highest 8-hour CO concentration reported at monitor 060371301 for 1997, giving an ambient 

10 concentration adjustment factor of 9.4/15, or 0.63 which was applied equally to all 8,760 hourly 

11 ambient CO concentrations from the Los Angeles monitor. 

12 Table 6-6 lists descriptive statistics for the Denver and Los Angeles 1-hour data sets 

13 before and after adjustment. As expected, the adjusted data set for Denver 1995 is very similar 

14 to the unadjusted data set given that the adjustment factor used was close to unity. For example, 

15 the maximum concentration was reduced from 24.5 ppm to 24.2 ppm. The change in CO 

16 concentrations was greater as a result of adjusting the Los Angeles ambient data. For example, 

17 the maximum CO concentration was reduced from 19.2 ppm to 12.0 ppm. The adjusted data 

18 sets, representing air quality simulated to just meet the current 8-hour CO NAAQS,for Denver 

19 and Los Angeles, exhibit their greatest differences at the extreme upper percentiles of the 

distribution (i.e., the 99.9th 20 percentile and above). 

21 Table 6-6. Descriptive Statistics for 1-hour Carbon Monoxide Concentrations Reported 

22 by the Denver and Los Angeles Monitors Before and After Adjustment to 

23 Simulate Just Meeting the Current 8-Hour CO NAAQS. 

1-hour CO concentrations (ppm) 

Percentile 

Area Year 

Adjusted 

to just 

meeting 

NAAQS Mean Std 50 90 95 99 99.9 

2nd

highest Max 

No 1.5 1.2 1.2 2.7 3.4 6.1 13.1 16.4 24.5 Denvera

 1995 Yes 1.5 1.2 1.2 2.7 3.4 6.0 12.9 16.2 24.2 

Los No 2.3 2.2 1.7 4.9 6.7 11.2 17.2 18.8 19.2 

Angelesb 1997 Yes 1.5 1.4 1.1 3.1 4.2 7.0 10.8 11.8 12.0 a

Site ID 080310002 

b

Site ID 060371301 

24 

 2

 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 standard, the design value is the second highest daily, non-overlapping, maximum 8-

hour average concentration over a year. The design value for the 1-hour standard is the second highest daily 

maximum 1-hour average concentration over a year. 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
October, 2009 Draft – Do Not Cite or Quote 6-12

1 6.1.4.4 Meteorological Stations 

2 A few algorithms within APEX require meteorological data (primarily temperature) from 

3 stations located within the study area. For the analyses described in this report, hourly 

4 temperature data were obtained from meteorological stations located at or near the fixed-site CO 

5 monitor specified for each study area. Table 6-7 identifies the meteorological stations used and 

6 selected site characteristics. 

7 Table 6-7. Site Characteristics of Meteorological Monitoring Stations Selected to 

8 Represent the Denver and Los Angeles Study Areas. 

Site Characteristic Denver Los Angeles Site 1 Los Angeles Site 2 

Site ID 080310002 060374002 

Street address 2105 Broadway 

3648 N. Long Beach 

Blvd. Daugherty Field 

Town Denver Long Beach, CA Long Beach, CA 

Latitude 39.751184 33.823760 33.81667 

Longitude -104.987625 -118.189210 -118.15 

Elevation (above sea 

level), meters 1593 6 9.4 

Sample collection 

frequency 1 hour 1 hour 1 hour 

Reporting agency 

Colorado Department 

of Public Health and 

Environment 

South Coast Air 

Quality Management 

District 

9 

10 The procedure used for generating a complete meteorological data set was as follows. 

11  Staff first checked on the availability of hourly temperature data for the specified years at 

12 each CO fixed-site monitor specified for each study area. 

13  For Los Angeles, temperature data were not available for the specified CO monitoring 

14 site (Site ID 060371301). Consequently, we evaluated two alternative sites: Site 1 

15 (located at CO monitoring site 060374002) and site 2 (located at Daugherty Field), which 

16 are approximately 12 km and 15 km, respectively, from site 060371301. These sites 

17 were separated from each other by a distance of only 3.6 km. Temperature data for the 

18 two years considered for the exposure analysis (i.e., 1997 and 2006) were reported by 

19 both sites. Because Site 1 had fewer missing values for 2006, it was selected as the 

20 primary meteorological site to represent the Los Angeles area for that year. Temperature 

21 data from Site 2 were used to fill the single missing value in the Site 1 data set for year 

22 2006. However, the 1997 data contained 2,263 missing values for Site 1 and only 9 

23 missing values for Site 2. Consequently, Site 2 was selected as the primary 

24 meteorological site to represent the Los Angeles area for 1997. Two of the nine missing 

25 values from Site 2 were available from Site 1; staff replaced these two missing values 

26 with corresponding values from Site 1. A linear interpolation, using the values at the end 
October, 2009 Draft – Do Not Cite or Quote 6-13

1 of existing gaps, was used to fill in the seven remaining missing values in the 1997 data 

set in Site 2.3 2 

3  For Denver, temperature data were available for the CO ambient monitoring site used 

4 (i.e., Site ID: 080310002) and both years considered in the exposure assessment (1995 

5 and 2006). Linear interpolation was used to fill 18 of 41 missing values considering the 

6 1995 data and 11 of 11 missing values considering the 2006 data. In one instance, the 

7 gap in hourly temperature data was 23 continuous hours. Staff considered a linear 

8 interpolation to be inappropriate in this situation because it would likely not produce 

9 reasonable estimates of the variability in temperature (particularly the daily maximum) 

10 occurring during the 23-hour gap. An alternative approach was used in which the 

11 temperature data for corresponding hours in the previous day were substituted for the 

12 missing data. 

13 6.1.5 Microenvironments 

14 As mentioned earlier, two general microenvironments were defined for the exposure 

15 analyses: in vehicle and all other. Each microenvironment was defined as an aggregation of the 

16 location codes used in CHAD to specify where each exposure event occurred. Note that location 

17 is interpreted here as referring to the microenvironmental characteristics of a place (e.g., indoors 

18 at school), rather than the particular geographic location. Appendix E provides the mapping of 

19 the CHAD location codes to the two APEX modeled microenvironments. 

20 The factors approach was used to estimate a CO concentration in the two 

21 microenvironments for each hour of the specified study period (Equation 5-1, section 5.3.4). The 

22 penetration factor for all microenvironments was set equal to 1 for both scenarios (see draft ISA, 

23 section 3.6.5.1 for all indoor microenvironments and section 5.4.2.1 for the in-vehicle 

24 microenvironment). The proximity factor was set equal to 1 for scenario A in both 

25 microenvironments modeled (i.e., in vehicle and all other) and equal to 2 for the in-vehicle 

26 microenvironment and 1 for the all other microenvironment in scenario B. The values used in 

27 representing in-vehicle concentrations for scenario B were based on staff’s evaluation of 

28 measurement studies that simultaneously measured CO concentrations within motor vehicles and 

29 at nearby fixed-site monitors (see section 5.4.2.2). 

30 Staff did not adjust ambient concentrations to estimate near-road microenvironmental CO 

31 concentrations. This was because the ambient CO concentrations from the two monitors used in 

32 this assessment had the highest hourly CO concentrations recorded in each study area, and based 

33 on the AQS noted monitoring scale, were already designated to capture near road CO 

34 concentrations (i.e., microscale and middle scale). While higher near-road CO concentrations 

35 are possible, staff judged that these ambient data would already represent upper percentile 

 

3

 We used PROC EXPAND along with the JOIN option in SAS. The JOIN option fits a continuous curve 

to the data by connecting successive straight line segments. 
October, 2009 Draft – Do Not Cite or Quote 6-14

1 ambient CO concentrations experienced by most persons residing or spending time near 

2 roadways in each study area. 

3 6.1.6 Time/Activity Patterns 

4 APEX constructs a 365-day longitudinal diary for each simulated individual by selecting 

5 24-hour diaries from those available in CHAD. In performing the exposure assessments 

6 described in this report, all available diaries for persons above age 17 in the CHAD database 

7 were used regardless of particular commuting patterns. 

8 6.1.6.1 Construction of Longitudinal Diaries 

9 As discussed in section 5.3.3, APEX provides a longitudinal diary assembly algorithm 

10 that enables the user to create composite diaries that reflect the tendency of individuals to repeat 

11 day-to-day activities. The user specifies values for two statistical variables (D and A) that relate 

12 to a key daily variable, typically the time spent per day in a particular microenvironment (e.g., in 

13 a motor vehicle). The D statistic reflects the relative importance of intra- and inter-personal 

14 variance within the selected key daily variable. The A variable quantifies the day-to-day 

15 autocorrelation in the selected key daily variable. APEX then constructs composite diaries that 

16 exhibit the statistical properties defined by the specified values of D and A. 

17 In this exposure assessment, we used the longitudinal diary algorithm to construct year18 long activity patterns for each simulated individual to reflect the day-to-day correlation of time 

19 spent inside motor vehicles. Each diary day in the CHAD database was tagged with the number 

20 of minutes spent in the vehicle microenvironment. Parameter settings of D = 0.31 and A = 0.19 

21 were specified to control the day-to-day repetition of time spent in motor vehicles in the 

22 constructed composite diaries. These particular D and A values were obtained from Isaacs et al. 

23 (2009) (see Appendix F). 

24 In selecting particular diaries to represent the simulated population, the CHAD data are 

25 categorized or separated by APEX into data pools. In Scenario A and B, the pools were defined 

by three ranges for the maximum temperature of the diary day (< 55.0 ◦ 26 F, between 55.0 and 83.9 

◦

F, and ≥84.0 ◦ 27 F) and two day-types (i.e., weekend and week day); thus, there were 3 x 2 = 6 

28 diary pools. The window for age was set at 15%. For example, diaries can be selected for a 

29 simulated individual of age 60 from CHAD individuals ranging from ages 51 though 69. 

30 6.2 EXPOSURE AND DOSE ESTIMATES AND RISK CHARACTERIZATION 

31 6.2.1 Denver – Scenarios A and B 

32 Output files for APEX4.3 runs were generated for various combinations of calendar year 

33 (1995 and 2006), exposure scenario (A or B), and air quality condition (as is or just meeting the 
October, 2009 Draft – Do Not Cite or Quote 6-15

1 8-hour NAAQS) in the Denver study area. These results are summarized in a series of tables that 

2 follow (Tables 6-8 through Table 6-14). 

3 Table 6-8 presents estimates for the number of person-days during the calendar year in 

4 which members of the Denver population-at-risk experienced a 1-hour daily maximum CO 

5 exposure at or above each of the indicated CO concentrations. Results are presented for 

6 Scenarios A and B for each of two air quality conditions: as is conditions represented by 2006 

7 monitoring data and just meeting conditions as represented by 1995 monitoring data adjusted to 

8 simulate just meeting the 8-hour NAAQS. The maximum possible value for person-days of 

9 exposure is about 23.4 million person-days – the product of the estimated population-at-risk 

10 (about 64,000) and the number of days in the specified exposure period (365). 

11 Using a format similar to Table 6-8, Table 6-9 presents estimates of the number of 

12 persons in the population-at risk that experienced at least one 1-hour daily maximum CO 

13 exposure at or above each of the indicated CO concentrations. In this table, the maximum 

14 possible value is about 64,000 people – the estimated number of people in the population-at-risk. 

15 Thus, each person can be counted no more than once in determining the value in Table 6-9. 

16 Table 6-10 and Table 6-11 are comparable to Table 6-8 and Table 6-9, respectively 

17 though they provide estimates for 8-hour daily maximum exposures rather than 1-hour daily 

18 maximum exposures. Again, the maximum possible value for person-days of exposure in Table 

19 6-10 is about 23.4 million person-days; the maximum possible value of persons exposed in Table 

20 6-11 is about 64,000 people. 

21 Table 6-12 and 6-13 are also analogous to the prior tables, though they present estimates 

22 of the number of person-days in which members of the Denver population-at-risk experienced a 

23 daily maximum end-of-hour COHb level at or above each of the indicated levels. Again, the 

24 maximum possible value for person-days is about 23.4 million for Table 6-12 and the maximum 

25 number of persons experiencing a maximum COHb level in the year in the population-at-risk is 

26 about 64,000 for Table 6-13. 

27 Table 6-14 provides estimates for the mean number of days per person in which the 

28 person experienced a daily maximum end-of-hour COHb level at or above each of the indicated 

29 levels. These values were calculated by dividing the values listed in Table 6-12 (total number of 

30 person-days) by the comparable values in Table 6-13 (number of people); hence the maximum 

31 possible value is 365. 

32 Table 6-9 through Table 6-15 exhibit general patterns that are consistent with the input 

33 data and parameter settings specified for the associated model runs. The Scenario B values are 

34 greater than the comparable Scenario A values because Scenario B specifically accounts for in35 vehicle microenvironmental CO concentrations, while Scenario A assumes in-vehicle CO 

36 concentrations (and all other microenvironments) are equal to the ambient fixed-site monitor 
October, 2009 Draft – Do Not Cite or Quote 6-16

1 concentrations. However, the effect of better accounting for in-vehicle exposures (Scenario B) 

2 to the overall estimated population exposures and doses is primarily limited to differences 

3 observed in the upper percentiles of the distribution. For example, about 4.8% of simulated 

4 individuals are estimated to experience an end-of-hour COHb concentration at or above 1.0% 

5 when considering Scenario A and the as is air quality (Table 6-13). When considering Scenario 

6 B and as is air quality, about 7.0% of the population experiences an end-of-hour COHb level at 

7 or above 1.0%. Note however that when considering either scenario A or B, these data also 

8 indicate that between 93 and 95% of the population experienced an end-of-hour COHb 

9 concentration below 1.0%, suggesting that only an additional 2% of the population was affected 

10 by the addition of the in-vehicle microenvironment. The effect of accounting for in-vehicle 

11 concentrations (i.e, Scenario A results compared with Scenario B) was greater when considering 

12 the air quality adjusted to just meeting the current standard. For example, approximately 11% of 

13 simulated individuals were estimated to experience at least one end-of-hour COHb above 2.5% 

14 when considering Scenario B compared with over an order of magnitude fewer persons when 

15 considering Scenario A. 

16 The estimated number of person-days and persons considering air quality just meeting the 

17 current standard is greater than that estimated considering as is air quality at comparable target 

18 concentrations. For example, the entire simulated population was estimated to experience at 

19 least one end-of-hour COHb concentration at or above 1.5% when considering the air quality just 

20 meeting the current standard and Scenario A. This same COHb level was only experienced by 

21 approximately 0.2% of the population when considering the as is air quality and Scenario A 

22 (Table 6-13). This of course is because the monitoring data used to represent ambient as is air 

23 quality have significantly lower CO levels than the data used to represent just meeting the 8-hour 

24 standard conditions in Denver (i.e., the adjusted 1995 air quality simulation). 

25 As described in sections 2.6 and 4.2, our characterization of health risk for CO in this 

26 assessment focuses on several risk metrics involving comparison of estimated COHb levels in 

27 the adult CHD population to potential health benchmarks (1.5-3.0% COHb). Assessment results 

28 involving this comparison for the Denver study area are emphasized in bold type in Tables 6-12 

29 through 6-14. Well below 1 percent of the at-risk population was estimated to reach COHb 

30 levels at or above 1.5% under as is conditions in both scenarios. Under air quality conditions 

31 just meeting the current standard, substantially greater percentages of the population were 

32 estimated to reach COHb levels at or above all of the potential health benchmark levels, with 

33 100% of the at-risk population estimated to reach COHb levels ≥ 1.5% in Scenario B for these 

34 conditions (Table 6-13). 
October, 2009 Draft – Do Not Cite or Quote 6-17

1 Table 6-8. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 

2 Denver Study Area Estimated to Experience a 1-hour Daily Maximum CO 

3 Exposure at or Above the Specified Concentration. 

Number of person-days 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1995)b

CO 

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 2.8E+03 

30 0 0 0 1.5E+04 

20 0 0 6.4E+04 1.0E+05 

15 0 0 1.3E+05 2.4E+05 

12 0 2.0E+03 3.2E+05 5.9E+05 

9 0 1.1E+04 8.3E+05 1.1E+06 

6 6.4E+04 1.4E+05 2.3E+06 3.2E+06 

3 9.6E+05 1.4E+06 1.0E+07 1.3E+07 

0 2.3E+07 2.3E+07 2.3E+07 2.3E+07 a

 “As Is” air quality data are for the year 2006. b

 Air quality data for the year 1995 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Denver study area is estimated to be about 64,000. 

4 Table 6-9. Number of Adults with Coronary Heart Disease (CHD) in the Denver Study 

5 Area Estimated to Experience a 1-hour Daily Maximum CO Exposure at or 

6 Above the Specified Concentration. 

Number of persons 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1995)b

CO 

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 2,800 

30 0 0 0 14,000 

20 0 0 64,000 64,000 

15 0 0 64,000 64,000 

12 0 2,000 64,000 64,000 

9 0 9,700 64,000 64,000 

6 64,000 64,000 64,000 64,000 

3 64,000 64,000 64,000 64,000 

0 64,000 64,000 64,000 64,000 a

 “As Is” air quality data is for the year 2006. 

b Air quality data for the year 1995 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Denver study area is estimated to be about 64,000. 
October, 2009 Draft – Do Not Cite or Quote 6-18

1 Table 6-10. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 

2 Denver Study Area Estimated to Experience an 8-hour Daily Maximum CO 

3 Exposure at or Above the Specified Concentration. 

Number of person-days 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1995)b

CO 

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 0 

30 0 0 0 0 

20 0 0 0 3.4E+02 

15 0 0 0 2.4E+03 

12 0 0 0 2.0E+04 

9 0 0 1.3E+05 1.5E+05 

6 0 7.4E+01 4.5E+05 6.1E+05 

3 1.3E+05 2.1E+05 4.1E+06 5.0E+06 

0 2.3E+07 2.3E+07 2.3E+07 2.3E+07 a “As Is” air quality data is for the year 2006. 

b

 Air quality data for the year 1995 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Denver study area is estimated to be about 64,000. 

4 Table 6-11. Number of Adults with Coronary Heart Disease (CHD) in the Denver Study 

5 Area Estimated to Experience an 8-hour Daily Maximum CO Exposure at or 

6 Above the Specified Concentration. 

Number of persons 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1995)b

CO 

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 0 

30 0 0 0 0 

20 0 0 0 340 

15 0 0 0 2,400 

12 0 0 0 17,000 

9 0 0 64,000 64,000 

6 0 74 64,000 64,000 

3 64,000 64,000 64,000 64,000 

0 64,000 64,000 64,000 64,000 a

 “As Is” air quality data is for the year 2006. 

b

 Air quality data for the year 1995 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Denver study area is estimated to be about 64,000. 
October, 2009 Draft – Do Not Cite or Quote 6-19

1 Table 6-12. Number of Person-days for Adults with Coronary Heart Disease (CHD) in the 

2 Denver Study Area Estimated to Experience a Daily Maximum End-of-hour 

3 COHb Level at or Above the Specified Concentration. 

Number of person-days 

“As Is” Air Quality (2006)a 

“Just Meeting” Air Quality 

(1995)b

COHb concentration 

(percent) 

Scenario A Scenario B Scenario A Scenario B 

8.0 0 0 0 0 

6.0 0 0 0 0 

5.0 0 0 0 0 

4.0 0 0 0 2.5E+01 

3.0 0 0 4.9E+01 1.8E+03 

2.5 1.9E+03 1.9E+03 3.3E+03 1.1E+04 

2.0 3.9E+03 3.9E+03 2.6E+04 5.8E+04 

1.5 9.2E+03 9.3E+03 1.8E+05 2.4E+05 

1.0 2.0E+05 2.1E+05 1.4E+06 1.7E+06 

0 2.3E+07 2.3E+07 2.3E+07 2.3E+07 a

 “As Is” air quality data is for the year 2006. 

b Air quality data for the year 1995 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Denver study area is estimated to be about 64,000. 

4 Table 6-13. Number (and Percent) of Adults with Coronary Heart Disease (CHD) in the 

5 Denver Study Area Estimated to Experience a Daily Maximum End-of-hour 

6 COHb Level at or Above the Specified Concentration. 

Number of persons (percentc) 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1995)b

COHb 

concentration 

(percent) Scenario A Scenario B Scenario A Scenario B 

8.0 0 0 0 0 

6.0 0 0 0 0 

5.0 0 0 0 0 

4.0 0 0 0 25 (<0.1) 

3.0 0 0 12 (<0.1) 1,700 (3) 

2.5 12 (<0.1) 12 (<0.1) 250 (0.4) 7,100 (11) 

2.0 12 (<0.1) 12 (<0.1) 16,000 (26) 36,000 (56) 

1.5 160 (0.2) 160 (0.2) 64,000 (100) 64,000 (100) 

1.0 3,100 (5) 4,500 (7) 64,000 (100) 64,000 (100) 

0 64,000 (100) 64,000 (100) 64,000 (100) 64,000 (100) a “As Is” air quality data are for the year 2006. b

 Air quality data for the year 1995 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. c

 Percent of adult CHD population. 

Note: Total adult population with CHD in the Denver study area is estimated to be about 64,000. 
October, 2009 Draft – Do Not Cite or Quote 6-20

1 Table 6-14. Estimated Average Number of Days with a Daily Maximum End-of-hour 

2 COHb Level At or Above the Specified Concentration Per Adult With 

3 Coronary Heart Disease (CHD) in the Denver Study Area. 

Average of person-days/person 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1995)b

COHb 

concentration 

(percent) Scenario A Scenario B Scenario A Scenario B 

8.0 0 0 0 0 

6.0 0 0 0 0 

5.0 0 0 0 0 

4.0 0 0 0 0 

3.0 0 0 <0.1 <0.1 

2.5 <0.1 <0.1 <0.1 0.2 

2.0 <0.1 <0.1 0.4 0.9 

1.5 0.1 0.1 2.9 3.8 

1.0 3.1 3.2 22 27 

0 365 365 365 365 a

 “As Is” air quality data is for the year 2006. 

b

 Air quality data for the year 1995 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Denver study area is estimated to be about 64,000. 

4 
October, 2009 Draft – Do Not Cite or Quote 6-21

1 6.2.2 Los Angeles – Scenarios A and B 

2 Output files for APEX4.3 runs were generated for various combinations of calendar year 

3 (1997 and 2006), exposure scenario (A or B), and air quality condition (as is or just meeting the 

4 8-hour NAAQS) in the Los Angeles study area. These results are summarized in a series of 

5 tables that follow (Tables 6-15 through Table 6-21). 

6 The same general patterns identified above using the Denver results were observed in the 

7 Los Angles results when considering either modeling scenario and air quality condition. For 

8 example in Scenario A, approximately 3% of the simulated population experienced an end-of9 hour COHb level at or above 1.5% considering the as is air quality compared with about 67% of 

10 the population having the same or greater COHb level when using air quality adjusted to just 

11 meeting the current standard (Table 6-20). This is as expected given the fixed-site input data for 

12 2006 used to represent ambient as is conditions in Los Angeles having lower CO concentrations 

13 than the data used to represent just meeting the current standard (Table 6-5 and 6-6). Similarly, a 

14 greater number of persons and person-days are estimated when considering scenario B compared 

15 with scenario A. For example, when considering the as is air quality, approximately 3% of the 

16 simulated population experienced an end-of-hour COHb level at or above 1.5% considering 

17 Scenario A compared with about 8% of the simulated population when separately accounting for 

18 in-vehicle exposures (Scenario B). This is also as expected because, as in the case of Denver, 

19 Scenario B assumes CO concentrations in the Los Angeles in-vehicle microenvironment were 

20 twice the ambient concentrations, while Scenario A assumes the in-vehicle concentrations (and 

21 all other microenvironments) are equal to the ambient concentrations measured at the fixed-site 

22 monitor. 

23 As described in sections 2.6 and 4.2, our characterization of health risk for CO in this 

24 assessment focuses on several risk metrics involving comparison of COHb levels in adults with 

25 CHD to potential health benchmarks (1.5-3.0% COHb). Assessment results involving this 

26 comparison for the Los Angeles study area are emphasized in bold type in Tables 6-19 – 6-21. 

27 Fewer than 1 percent of the at-risk population was estimated to reach COHB levels at or above 

28 2.0% under as is conditions when considering both scenarios, with 3% and 8% at or above 1.5% 

29 COHb in Scenarios A and B, respectively. Under air quality conditions just meeting the current 

30 standards, substantially greater percentages of the population were estimated to reach COHb 

31 levels at or above all of the potential health benchmark levels, with 67% and 80% of the at-risk 

32 population estimated reach COHb levels ≥ 1.5% in Scenarios A and B, respectively, for these 

33 conditions (Table 6-20). 

34 
October, 2009 Draft – Do Not Cite or Quote 6-22

1 Table 6-15. Number of Person-Days for Adults with Coronary Heart Disease (CHD) in the 

2 Los Angeles Study Area Estimated to Experience a 1-hour Daily Maximum 

3 CO Exposure At or Above the Specified Concentration. 

Number of person-days 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1997)b

CO 

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 0 

30 0 0 0 0 

20 0 0 0 1.3E+04 

15 0 9.2E+03 0 1.4E+05 

12 0 6.3E+04 1.6E+05 6.1E+05 

9 0 3.3E+05 2.4E+06 3.2E+06 

6 1.9E+06 2.8E+06 7.9E+06 9.2E+06 

3 1.5E+07 1.7E+07 2.4E+07 2.5E+07 

0 5.7E+-7 5.7E+07 5.7E+07 5.7E+07 a

 “As Is” air quality data are for the year 2006. b

 Air quality data for the year 1997 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Los Angeles study area is estimated to be about 160,000. 

4 Table 6-16. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles 

5 Study Area Estimated to Experience a 1-hour Daily Maximum CO Exposure 

6 At or Above the Specified Concentration. 

Number of persons 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1997) CO b

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 0 

30 0 0 0 0 

20 0 0 0 12,000 

15 0 8,800 0 75,000 

12 0 44,000 160,000 160,000 

9 0 110,000 160,000 160,000 

6 160,000 160,000 160,000 160,000 

3 160,000 160,000 160,000 160,000 

0 160,000 160,000 160,000 160,000 a

 “As Is” air quality data is for the year 2006. 

b

 Air quality data for the year 1997 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Los Angeles study area is estimated to be 160,000. 
October, 2009 Draft – Do Not Cite or Quote 6-23

1 Table 6-17. Number Of Person-Days For Adults with Coronary Heart Disease (CHD) in 

2 the Los Angeles Study Area Estimated to Experience an 8-hour Daily 

3 Maximum CO Exposure At or Above the Specified Concentration. 

Number of person-days 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1997)b

CO 

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 0 

30 0 0 0 0 

20 0 0 0 0 

15 0 0 0 6.8E+02 

12 0 3.2E+01 0 8.5E+03 

9 0 2.9E+03 3.1E+05 3.7E+05 

6 1.6E+05 2.3E+05 2.2E+06 2.7E+06 

3 6.3E+06 7.0E+06 1.6E+07 1.6E+07 

0 5.7E+07 5.7E+07 5.7E+07 5.7E+07 a

 “As Is” air quality data is for the year 2006. 

b

 Air quality data for the year 1997 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Los Angeles study area is estimated to be about 160,000. 

4 
October, 2009 Draft – Do Not Cite or Quote 6-24

1 

2 Table 6-18. Number of Adults with Coronary Heart Disease (CHD) in the Los Angeles 

3 Estimated to Experience an 8-hour Daily Maximum CO Exposure At or 

4 Above the Specified Concentration. 

Number of persons 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1997)b

CO 

Concentration 

(ppm) Scenario A Scenario B Scenario A Scenario B 

80 0 0 0 0 

60 0 0 0 0 

40 0 0 0 0 

30 0 0 0 0 

20 0 0 0 0 

15 0 0 0 610 

12 0 32 0 6,900 

9 0 2,300 160,000 160,000 

6 160,000 160,000 160,000 160,000 

3 160,000 160,000 160,000 160,000 

0 160,000 160,000 160,000 160,000 a

 “As Is” air quality data is for the year 2006. 

b

 Air quality data for the year 1997 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Los Angeles study area is estimated to be about 160,000. 

5 Table 6-19. Number of Person-Days For Adults With Coronary Heart Disease (CHD) in 

6 the Los Angeles Study Area Estimated to Experience a Daily Maximum End7 of-hour COHb Level At or Above the Specified Concentration. 

Number of person-days 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1997)b

COHb 

concentration 

(percent) Scenario A Scenario B Scenario A Scenario B 

8.0 0 0 0 0 

6.0 0 0 0 0 

5.0 0 0 0 0 

4.0 0 0 0 0 

3.0 0 0 0 0 

2.5 0 0 6.5E+01 6.8E+02 

2.0 1.6E+02 3.2E+02 6.1E+03 1.6E+04 

1.5 2.8E+04 4.1E+04 3.2E+05 4.8E+05 

1.0 1.6E+06 1.8E+06 5.1E+06 5.9E+0 

0 5.7E+07 5.7E+07 5.7E+07 5.7E+07 a

 “As Is” air quality data is for the year 2006. 

b

 Air quality data for the year 1997 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 

Note: Total adult population with CHD in the Los Angeles study area is estimated to be about 160,000. 
October, 2009 Draft – Do Not Cite or Quote 6-25

1 Table 6-20. Number (and Percent) of Adults with Coronary Heart Disease (CHD) in the 

2 Los Angeles Study Area Estimated to Experience a Daily Maximum End-of3 hour COHb Level At or Above the Specified Concentration. 

Number of persons (percent)c

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1997)b

COHb 

concentration 

(percent) Scenario A Scenario B Scenario A Scenario B 

8.0 0 0 0 0 

6.0 0 0 0 0 

5.0 0 0 0 0 

4.0 0 0 0 0 

3.0 0 0 0 0 

2.5 0 0 65 (<0.1) 680 (0.4) 

2.0 130 (<0.1) 290 (0.2) 3,900 (2) 10,000 (7) 

1.5 4,600 (3) 13,000 (8) 110,000 (67) 127,000 (80) 

1.0 147,000 (93) 150,000 (97) 160,000 (100) 160,000 (100) 

0 160,000 (100) 160,000 (100) 160,000 (100) 160,000 (100) 

a “As Is” air quality data are for the year 2006. b Air quality data for the year 1997 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. c

 Percent of adult CHD population. 

Note: Total adult population with CHD in the Los Angeles study area is estimated to be about 160,000. 

4 Table 6-21 Estimated Average Number of Days with a Daily Maximum End-of-hour 

5 COHb Level At or Above the Specified Concentration Per Adult with 

6 Coronary Heart Disease (CHD) in the Los Angeles Study Area. 

Average of person-days/person 

“As Is” Air Quality (2006)a “Just Meeting” Air Quality (1997)b

COHb 

concentration 

(percent) Scenario A Scenario B Scenario A Scenario B 

8.0 0 0 0 0 

6.0 0 0 0 0 

5.0 0 0 0 0 

4.0 0 0 0 0 

3.0 0 0 0 0 

2.5 0 0 0 0 

2.0 <0.1 <0.1 <0.1 0.1 

1.5 0.2 0.3 2.1 3.0 

1.0 9.9 12 32 37 

0 365 365 365 365 a

 “As Is” air quality data are for the year 2006. b

 Air quality data for the year 1997 adjusted downwards to just meet 9 ppm, 8-hour NAAQS. 
October, 2009 6-26 Draft – Do Not Cite or Quote

1 6.2.3 Comparison of Denver and Los Angeles Estimates for End-of-hour COHb 

2 Levels 

3 We can best compare Denver and Los Angeles estimates using the results provided in 

4 Tables 6-13 and 6-20. These tables provide estimates for the percentage of people in the 

5 population-at-risk that are estimated to experience a daily maximum end-of-hour COHb level at 

6 or above the specified value. For either scenario, the percentage of people with COHb levels at 

7 or above 1.5 percent is greater for Los Angeles than for Denver when considering the as is air 

8 quality. For example, in Los Angeles it was estimated that 3 and 8 percent of persons 

9 experienced an end-of hour COHb level at or above this level for Scenarios A and B, 

10 respectively, for the as is case. In Denver, virtually no persons (0.2%) experienced an end-of11 hour COHb at or above 1.5% associated with the as is case. This pattern is consistent with the 

12 fact that the monitoring data used to represent as is conditions in Los Angeles exhibited higher 

13 CO levels than Denver (Table 6-5). 

14 The pattern noted above is reversed when considering air quality that just meets the 

15 current standard. For example, the estimated percent of persons having a COHb level at or 

16 above 2 percent for Scenarios A and B are 26 percent and 56 percent, respectively for Denver 

17 (Table 6-13); the comparable estimates for Los Angeles are about 2 percent for Scenario A and 7 

18 percent for Scenario B (Table 6-20). This pattern is also expected, since the air quality data used 

19 to represent just meeting the current standard in Denver has higher CO levels at the upper 

20 percentiles of the distribution than the Los Angeles data (Table 6-6). 

21 6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000 

22 PNEM/CO AND DRAFT 2009 APEX/CO ASSESSMENTS 

23 As part of the review of the CO NAAQS initiated in 1997, a draft CO exposure 

24 assessment was prepared (Johnson, et al., 1999). Subsequent to the discontinuation of that CO 

25 NAAQS review, a revised document was completed (Johnson et al., 2000). The 2000 document 

26 was subsequently subject to peer review by several exposure modeling experts (SAIC, 2001). 

27 The 2000 CO population exposure assessment was conducted for Denver using air quality data 

28 for 1995 and for Los Angeles using air quality data for 1997. The exposure and dose estimates 

29 were obtained by applying pNEM/CO, a predecessor to APEX, to adults with ischemic heart 

30 disease residing in a defined study area within each city (Johnson et al., 2000). As part of 

31 current (2009) draft exposure assessment described in section 6.1, staff has again used APEX to 

32 estimate CO exposures and resulting COHb levels in a portion of Denver using 1995 air quality 

33 data and in a portion of Los Angeles using 1997 air quality data. In this case, the population-at34 risk was defined as adults with CHD which is approximately equivalent to the ischemic heart 

35 disease definition used in the prior review. 
October, 2009 6-27 Draft – Do Not Cite or Quote

1 In comparing the earlier pNEM/CO results with the exposure and dose estimates obtained 

2 from the current draft APEX/CO assessment for the same cities and years, it is important to 

3 understand the differences between the methodologies employed in the two assessments. The 

4 methods and results associated with the 2000 pNEM/CO analysis are described in detail in a 

5 report by Johnson et al. (2000). The methods used in the current (2009) draft APEX exposure 

6 assessment are described above in section 6.1. Section 6.3.1 provides a brief discussion of the 

7 important differences between the two assessments that may account for some of the observed 

8 differences in the exposure estimates. Section 6.3.2 presents estimates of COHb levels in adults 

9 with CHD obtained from the two assessments. 

10 6.3.1 Important Differences Between the 2000 pNEM/CO and 2009 draft APEX/CO 

11 Exposure/Dose Assessments 

12 In the 2000 pNEM/CO assessment, the Denver study area was defined to include the 

13 census tracts located within 10 km of each of six fixed-site monitors in the Denver metropolitan 

14 area. Air quality data for 1995 reported by these fixed-site monitors were used to represent 

15 “existing conditions” in the study area. Because the second non-overlapping 8-hour maximum 

16 CO concentration (design value) equaled 9.5 ppm, the existing conditions in Denver for 1995 

17 were considered to approximate just meeting the 8-hour standard in which the DV equals 9.4 

18 ppm. 

19 In a similar manner, the Los Angeles study area was defined to include all census tracts 

20 within 10 km of ten fixed-site monitors in the Los Angeles metropolitan area. Air quality data 

21 for 1997 reported by these fixed-site monitors were used to represent “existing conditions” in the 

22 study area. Because the 1997 CO levels in Los Angeles exceeded the 8-hour NAAQS, the 

23 concentrations at each monitoring site were adjusted downwards so that the concentrations 

24 associated with the DV site exactly met the 8-hour NAAQS (i.e., the adjusted maximum CO 

25 concentration at the DV site equaled 9.4 ppm). 

26 Note that the air quality data used in the pNEM/CO assessments for each city included 

27 data from multiple sites (6 in Denver, 10 in Los Angeles) that represented areas of varying CO 

28 levels. The monitoring data associated with the DV (highest CO) site were only applied to those 

people who resided in the circular area within 10 km of that particular monitor.4 29 Data from other 

30 (lower CO) sites were applied to the people in the study area who resided within the 10 km 

31 circular areas centered on those sites. 

32 In the 2009 draft APEX/CO assessment, the Denver and Los Angeles study areas are 

33 each defined to include all census tracts within 20 km of a single fixed-site monitor. This 

 

4

 In Denver, there was one instance where two monitors in close proximity to each other were 

geographically combined to represent a single composite monitoring location. 
October, 2009 6-28 Draft – Do Not Cite or Quote

1 monitor is the DV site for the specified year of the assessment (1995 for Denver, 1997 for Los 

2 Angeles). Consequently, monitoring data associated with the DV (highest CO) site are applied 

3 to all people within the surrounding study area. This focus on high concentration CO monitors 

4 in the current assessment would tend to produce a greater percentage of persons exposed to 

5 higher CO levels than would the approach used in the 2000 pNEM/CO assessment. 

6 In the 2000 exposure assessment, pNEM/CO was set up to account for 15 distinct 

7 microenvironments. Each of the 12 enclosed microenvironments (including 3 motor vehicle 

8 microenvironments) was modeled using a sophisticated mass balance model. This model used 

9 probabilistic techniques to account for outdoor (ambient) air quality, air exchange rate, and 

10 indoor emissions. When applied to building microenvironments characterized by relatively low 

11 air exchange rates, the mass balance model in pNEM/CO yields hourly-average CO 

12 concentrations in the building that tend to have less variance than the corresponding hourly13 average ambient concentrations outside the building. Relative to the ambient concentrations 

14 outside the building, the indoor concentrations have lower peak values and the peaks are delayed 

15 in time. This effect is not significant for in-vehicle microenvironments that are characterized by 

16 relatively high air exchange rates. In addition, two indoor sources of CO were evaluated in the 

17 2000 pNEM/CO assessment for residential microenvironments: gas stoves and passive smoking. 

18 The model was set up so that these sources could be turned on and off within the model. The 

19 estimated number of people with COHb levels above 2.5 percent was noticeably higher when 

20 pNEM/CO accounted for the specified indoor sources. 

21 The 2009 draft APEX/CO assessment specifies only two microenvironments (i.e., in 

22 vehicle and all other). The CO concentrations in these microenvironments are modeled using a 

23 simple proportionality factor (the proximity factor) applied to the corresponding ambient air 

24 quality concentrations based on the fixed-site monitor values. For the in-vehicle 

25 microenvironment, the proximity factor equals 1 for Scenario A and 2 for Scenario B. The 

26 proximity factor for all other microenvironments equals 1 for both scenarios. And finally, no 

27 provision has been made to account for the effects of indoor sources in this draft of the current 

28 CO assessment given the simplified approach used. 

29 6.3.2 Comparison of Estimated COHb Levels in Adults with Coronary Heart 

30 Disease using the 2000 pNEM/CO and 2009 Draft APEX/CO Assessments 

31 Table 6-22 presents estimates for the percentage of Denver adults with CHD who would 

32 experience a daily maximum end-of-hour COHb level at or above the specified level under the 

33 specified air quality conditions for 1995. Table 6-23 presents similar estimates for Los Angeles. 

34 Each table provides two sets of estimates for the 2000 pNEM/CO assessment (indoor sources 

35 “on” and “off”) and two sets for the current (2009) draft APEX/CO assessment (Scenarios A and 
October, 2009 6-29 Draft – Do Not Cite or Quote

1 B). See section 6.3.1 above for a brief discussion of the modeling assumptions used in 

2 developing each set of estimates. 

3 As expected, the COHb levels estimated by the 2000 pNEM/CO assessment are higher 

4 when internal sources are turned on. As stated above, it was also expected that estimated COHb 

5 levels would be higher for Scenario B than for Scenario A in the current assessment, since 

6 Scenario B uses a larger proximity factor for the in-vehicle microenvironment. 

7 Because of the significant differences in modeling approaches employed by the two 

8 assessments, it is difficult to make a direct comparison of the results for pNEM/CO 2000 with 

9 the results for current APEX/CO draft assessment. As discussed in section 6.3.1, the two 

10 assessments differ according to: 

11  Boundaries of the study area defined for each city, 

12  Monitors used to represent ambient CO levels, 

13  Defined microenvironments, 

14  Microenvironmental modeling approach used, and 

15  Treatment of indoor sources. 

16 With these caveats in mind, we observe that the estimated percentage of Denver adults 

17 with CHD that experience end-of-hour COHb levels at or above 2 percent is higher for the 2009 

18 draft APEX/CO assessment (Scenario A or Scenario B for the just meeting the current 8-hour 

19 standard case) than for the 2000 pNEM/CO assessment (as is case which was very close to just 

20 meeting the 9 ppm CO NAAQS), regardless of whether internal sources are turned on or off in 

21 the pNEM assessment (Table 6-22). The corresponding results for Los Angeles (Table 6-23) for 

22 the just meeting the standard case show values for the 2009 draft APEX/CO assessment 

23 (Scenario A or Scenario B) that are lower than the 2000 pNEM/CO assessment for the just 

24 meeting the standard case when internal sources are turned on and higher when the sources are 

25 turned off. Note that these patterns are not consistent across all COHb levels. For example, the 

26 estimate listed for either study area for COHb levels at or above 4 percent is higher for 2000 

27 pNEM/CO (sources on) than for the corresponding values for 2009 draft APEX/CO assessment 

28 (Scenarios A and B). 

29 In the current assessment, assuming the ambient concentrations contributing to all 

30 microenvironments are equal to the concentrations reported at the fixed-site monitor (for 

31 Scenario A) indicates that there would not be any spatial heterogeneity in CO concentrations 

32 across the study area, that is, the single monitor used in each study area is assumed to represent 

33 all outdoor CO concentrations. However, there are other ambient monitors within the 20 km 

34 study area having lower CO concentrations and these were used in the previous 2000 assessment. 

35 Therefore, the assumption of spatial homogeneity would tend to contribute to the greater CO 
October, 2009 6-30 Draft – Do Not Cite or Quote

1 population exposures estimated in the current assessment compared to those estimated in the 

2 previous 2000 assessment, when holding all other factors constant. Staff also assumed in the 

3 current assessment that the penetration of CO into all microenvironments was equivalent to one. 

4 This assumption would lead to a lack of attenuation of peak outdoor ambient CO concentrations 

5 that is expected to occur in indoor microenvironments when not accounting for physical 

6 processes described above. Therefore, greater population exposures would be estimated for the 

7 current assessment when compared with the exposures estimated in the prior 2000 assessment, 

8 holding all other factors constant. The impact of these simplifying assumptions is best illustrated 

9 in Figure 6-3 using the data provided in Tables 6-22 and 6-23 for the situation where no indoor 

10 sources were modeled. Clearly, in the current assessment nearly all of the simulated population 

11 reaches a higher estimated daily maximum COHb level (at least once per year) for both areas 

12 when compared with the previous 2000 assessment. Once accounting for this higher population 

13 distribution at COHb levels up to about 1% COHb, the general shape of the population 

14 distribution is very similar for both the locations when compared with results in the previous 

15 assessment, particularly when including the contribution of the in-vehicle microenvironment 

16 separately (Scenario B) in estimating exposures. 
October, 2009 6-31 Draft – Do Not Cite or Quote

1 Table 6-22. Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated 

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

3 Specified Percentage Under Specified Air Quality Conditions for 1995. 

Percentage of adults with coronary heart diseasea estimated 

to experience a daily maximum end-of-hour COHb level 

at or above the specified percentage 

2000 pNEM/CO assessment for 

“existing” conditionsb

2009 draft APEX/CO assessment 

for “just meeting” conditionsc

Internal sources off 

COHb 

concentration 

(percent) 

Internal sources 

on 

Internal sources 

off Scenario A Scenario B 

6.0 0.2 0 0 0 

5.0 0.6 0 0 0 

4.0 1.6 0 0 <0.1 

3.0 5.5 < 0.1 <0.1 2.7 

2.5 10.4 0.2 0.4 11.2 

2.0 19.9 0.5 25.6 56.5 

1.5 37.6 6.7 99.7 99.8 

1.0 83.2 65.0 100 100 

0 100 100 100 100 a

 Characterized as “ischemic heart disease” in the 2000 pNEM/CO exposure assessment. b

 “Existing” conditions: Denver CO conditions during 1995 with no adjustment. Second non-overlapping 

8-hour maximum CO concentration (design value) equals 9.5 ppm. These conditions approximate “just 

meeting” conditions for Denver (i.e., design value equals 9.4 ppm). 

c

 “Just meeting” conditions: 1995 CO levels in Denver adjusted to simulate conditions when the second 

non-overlapping 8-hour maximum CO concentration at the design value site equals 9.4 ppm. 

4 

5 
October, 2009 6-32 Draft – Do Not Cite or Quote

Table 6-23. Percentage of Los Angeles Adults with Coronary Heart Disease (CHD)a 1 

2 Estimated to Experience a Daily Maximum End-of-hour COHb Level At or 

Above the Specified Percentage Under “Just Meeting”Conditionsb 3 for 1997. 

Percentage of adults with coronary heart diseasea estimated 

to experience a daily maximum end-of-hour COHb level 

at or above the specified percentage 

2000 pNEM/CO assessment 2009 draft APEX/CO assessment 

Internal sources off 

COHb 

concentration 

(percent) 

Internal sources 

on 

Internal sources 

off Scenario A Scenario B 

6.0 0.2 0 0 0 

5.0 0.8 0 0 0 

4.0 2.2 0 0 0 

3.0 5.1 <0.1 0 0 

2.5 9.0 <0.1 <0.1 0.4 

2.0 16.8 0.5 2.4 6.6 

1.5 32.3 5.2 67.0 80.5 

1.0 79.0 58.1 100 100 

0 100 100 100 100 a

 Characterized as “ischemic heart disease” in the 2000 pNEM/CO exposure assessment. b “Just meeting” conditions: 1997 CO levels in Los Angeles adjusted to simulate conditions when the 

second non-overlapping 8-hour maximum CO concentration at the design value site equals 9.4 ppm. 

4 
October, 2009 6-33 Draft – Do Not Cite or Quote

Denver - Just Meeting the Current Standard

0

10

20

30

40

50

60

70

80

90

100

01234567

COHb level (%)

Population at or Above Selected 

Level (%)

2000 pNEM/CO assessment

Scenario A - Current Assessment

Scenario B - Current Assessment

1 

Los Angeles - Just Meeting the Current Standard

0

10

20

30

40

50

60

70

80

90

100

01234567

COHb level (%)

Population at or Above Selected 

Level (%)

2000 pNEM/CO assessment

Scenario A - Current Assessment

Scenario B - Current Assessment

2 

3 Figure 6-3. Percentage of Los Angeles and Denver Adults with Coronary Heart Disease 

4 (CHD) Estimated to Experience a Daily Maximum End-of-hour COHb Level 

5 At or Above the Specified Percentage for Air Quality Adjusted to Just 

6 Meeting the Current Standard. Data taken from Tables 6-22 and 6-23. 

7 
October, 2009 6-34 Draft – Do Not Cite or Quote

1 6.4 VARIABILITY ANALYSIS AND UNCERTAINTY CHARACTERIZATION 

2 An important issue associated with any population exposure or risk assessment is the 

3 characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in 

4 a population or variable of interest (e.g., residential air exchange rates) and cannot be reduced 

5 through further research, only better characterized with additional measurement. Uncertainty

6 refers to the lack of knowledge regarding the values of model input variables (i.e., parameter 

7 uncertainty), the physical systems or relationships used (i.e., use of input variables to estimate 

8 exposure or risk or model uncertainty), and in specifying the scenario that is consistent with 

9 purpose of the assessment (i.e., scenario uncertainty). Uncertainty is, ideally, reduced to the 

10 maximum extent possible through improved measurement of key parameters and iterative model 

11 refinement. The approaches used to assess variability and to characterize uncertainty in this 

12 REA are discussed in the following two sections. Each section also contains a concise summary 

13 of the identified components contributing to uncertainty and how each source may affect the 

14 estimated exposures. 

15 6.4.1 Analysis of Variability 

16 The purpose for addressing variability in this REA is to ensure that the estimates of 

17 exposure and risk reflect the variability of ambient CO concentrations and associated CO 

18 exposure and health risk across the study locations and population. In this draft assessment, 

19 there are several algorithms that account for variability of input data when generating the number 

20 of estimated benchmark exceedances or health risk outputs. For example, variability may arise 

21 from differences in the population residing within census tracts (e.g., age distribution) and the 

22 activities that may affect CO population exposure and dose (e.g., time spent inside vehicles, 

23 moderate or greater exertion outdoors). A complete range of potential exposure levels and 

24 associated risk estimates can be generated when appropriately addressing variability in exposure 

25 and risk assessments; note however that the range of values obtained would be within the 

26 constraints of the input parameters, algorithms, or modeling system used, not the complete range 

27 of the true exposure or risk values. 

28 Where possible, staff identified and incorporated the observed variability in input data 

29 sets and estimated parameters within the exposure and dose assessment performed rather than 

30 employing standard default assumptions and/or using point estimates to describe model inputs. 

31 The details regarding any variability distributions used in data inputs are described in section 6.1. 

32 To the extent possible given the data available for the assessment, staff accounted for variability 

33 within the exposure and dose modeling. APEX has been designed to account for variability in 

34 some of the input data, including the physiological variables that are important inputs to 

35 determining ventilation rates and COHb dose levels. As a result, APEX addresses much of the 
October, 2009 Draft – Do Not Cite or Quote 6-35

Table 6-24. Summary of How Variability Was Incorporated Into the Exposure and Dose 

Assessment. 

Component Variability Source Comment 

Population data 

Individuals are randomly sampled from U.S. census 

tracts used in model domains, by age (single years) and 

gender. 

Activity patterns 

Data diaries are randomly selected from CHAD 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). 

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. 

Measured ambient CO 

concentrations 

Temporal: 1-hour CO for an entire year predicted using 

ambient monitoring data. Ambient Input 

Meteorological data Spatial: Local surface NWS stations used. 

Temporal: 1-hour NWS temperature data for each year. 

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) (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. 

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 C for 

details) 

Blood volume 

Values determined according to gender using equations 

based on work by Allen et al (1956) (see Appendix C for 

details). 

Hemoglobin content of 

the blood 

Values randomly selected from distributions developed 

by gender and age categories based on NHANES study 

(US DHHS, 1982) (see Appendix C for details). 

Pulmonary CO 

diffusion rate 

Values selected according to gender, height, and age 

based on equations adapted from Salorinne (1976) (see 

Appendix C 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 (see Appendix C for details). 
October, 2009 Draft – Do Not Cite or Quote 6-36

1 variability in exposure and dose estimates given variability in factors that affect human exposure 

2 and dose. The variability accounted for in this analysis is summarized in Table 6-24. 

3 6.4.2 Characterization of Uncertainty 

4 While it may be possible to capture a range of exposure or risk values by accounting for 

5 variability inherent to influential factors, the true exposure or risk for any given individual is 

6 largely unknown. To characterize health risks, exposure and risk assessors commonly use an 

7 iterative process of gathering data, developing models, and estimating exposures and risks, given 

8 the goals of the assessment, scale of the assessment performed, and the limitations of the input 

9 data available. However, significant uncertainty often remains and emphasis is then placed on 

10 characterizing the nature of that uncertainty and its impact on exposure and risk estimates. 

11 The characterization of uncertainty can include either qualitative or quantitative 

12 evaluations, or perhaps a combination of both. The approach can also be tiered, that is, the 

13 analysis can begin with a simple qualitative uncertainty characterization then progress to a 

14 complex probabilistic uncertainty analysis. This may follow when a lower tier analysis indicates 

15 there is a high degree of uncertainty for certain identified sources, the sources of uncertainty are 

16 highly influential variables in estimating the exposure and risk, and sufficient information and 

17 other resources are available to conduct a quantitative uncertainty assessment. This is not to 

18 suggest that quantitative uncertainty analyses should always be performed in all exposure and 

19 risk assessments. The decision regarding the type of uncertainty characterization performed is 

20 also informed by the intended scope and purpose of the assessment, whether the selected analysis 

21 will provide additional information to the overall decision regarding health protection, whether 

22 sufficient data are available to conduct a complex quantitative analysis, and whether time and 

23 resources are available for higher tier characterizations (US EPA, 2004; WHO, 2008). 

24 The primary purpose of the uncertainty characterization approach selected in this draft 

25 REA is to identify and compare the relative impact important sources of uncertainty may have on 

26 the estimated potential health effect endpoints. The approach used to evaluate uncertainty was 

27 adapted from guidelines outlining how to conduct a qualitative uncertainty characterization 

28 (WHO, 2008) and applied in the most recent NO2 (US EPA, 2008c) and SO2 NAAQS reviews 

29 (US EPA, 2009). While it may be considered ideal to follow a tiered approach in the REA to 

30 quantitatively characterize all identified uncertainties, staff selected the mainly qualitative 

31 approach given the extremely limited data available to inform probabilistic analyses. 

32 The qualitative approach used in this REA varies from that of WHO (2008) in that a 

33 greater focus of the characterization performed was placed on evaluating the direction and the 
October, 2009 Draft – Do Not Cite or Quote 6-37

magnitude5 1 of the uncertainty; that is, qualitatively rating how the source of uncertainty, in the 

2 presence of alternative information, may affect the estimated exposures and health risk results. 

3 In addition and consistent with the WHO (2008) guidance, staff discuss the uncertainty in the 

4 knowledge-base (e.g., the accuracy of the data used, acknowledgement of data gaps) and 

5 decisions made where possible (e.g., selection of particular model forms), though qualitative 

6 ratings were assigned only to uncertainty regarding the knowledge-base. 

7 First, staff identified the key aspects of the assessment approach that may contribute to 

8 uncertainty in the exposure and risk estimates and provide the rationale for their inclusion. Then, 

9 staff characterized the magnitude and direction of the influence on the assessment results for 

10 each of these identified sources of uncertainty. Consistent with the WHO (2008) guidance, staff 

11 subjectively scaled the overall impact of the uncertainty by considering the degree of severity of 

12 the uncertainty as implied by the relationship between the source of the uncertainty and the 

13 output of the air quality characterization. Where the magnitude of uncertainty was rated low, it 

14 was judged that large changes within the source of uncertainty would have only a small effect on 

15 the exposure results. A designation of medium implies that a change within the source of 

16 uncertainty would likely have a moderate (or proportional) effect on the results. A 

17 characterization of high implies that a small change in the source would have a large effect on 

18 results. Staff also included the direction of influence, indicating how the source of uncertainty 

19 was judged to affect estimated exposures or risk estimates; either the estimated values were 

20 likely over- or under-estimated. In the instance where the component of uncertainty can affect 

21 the assessment endpoint in either direction, the influence was judged as both. Staff characterized 

22 the direction of influence as unknown when there was no evidence available to judge the 

23 directional nature of uncertainty associated with the particular source. Staff also subjectively 

24 scaled the knowledge-base uncertainty associated with each identified source using a three level 

25 scale: low indicated significant confidence in the data used and its applicability to the assessment 

26 endpoints, medium implied that there were some limitations regarding consistency and 

27 completeness of the data used or scientific evidence presented, and high indicated the 

28 knowledge-base was extremely limited. 

29 The output of the uncertainty characterization was a summary describing, for each 

30 identified source of uncertainty, the magnitude of the impact and the direction of influence the 

31 uncertainty may have on the exposure and risk characterization results. There are several 

32 sources of uncertainty associated with this simplified approach for modeling CO population 

33 exposure/dose and associated potential health risk, each summarized and discussed in Table 6-

34 25.

 

5

 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2. 
October, 2009 Draft – Do Not Cite or Quote 6-38

1 2 Table 6-25. Characterization of Key Uncertainties in the Draft Assessment for Denver and Los Angeles Areas. 

Sources of Uncertainty 

Influence of Uncertainty 

on Exposure/Dose or 

Risk 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. 

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 

Over Medium High 

INF: Use of a single fixed-site ambient CO monitor likely does not adequate

ambient CO levels throughout each study area. Given that typical in-vehicle 2 to 4 and on-road sources tend to dominate CO emissions, it is likely that tconcentrations across a region would be less than this value. Given that the exposure assessment had generally greater concentrations than other monis likely that exposures are overestimated for most simulated individuals. 

KB: In the absence of 1) a monitoring network designed to measure spatial 

performing air quality modeling to estimate spatial and temporal variability in existing and representative monitoring data that can potentially indicate spauncertainty in the knowledge-base as high. 

Ambient CO 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 

Historical Data Used Unknown Medium High 

INF & KB: Even though the historical data represent a real air quality conditi

levels expected to just meet the 8-hour current standard, the condition simuhow influential factors such as emission levels per vehicle, vehicular traffic, earlier period of time and the hypothetical condition of just meeting the curre
October, 2009 Draft – Do Not Cite or Quote 6-39

Sources of Uncertainty 

Influence of Uncertainty 

on Exposure/Dose or 

Risk Estimates 

Category Element Direction Magnitude

KnowledgeBase 

Uncertainty Commentsa 

Current 8-Hour Standard 

Proportional Approach Used Both Low Low 

INF: The magnitude of the adjustment applied to historical ambient concent

0.99 was the adjustment applied), though greater for Los Angeles (0.63). In CO concentrations (Table 6-5), a linear relationship exists between the rangtime periods in both locations. More importantly, a strong proportional relatirecent and historic CO concentrations measured at the Los Angeles monito

KB: A similar proportional approach was judged adequate in simulating air q

CO NAAQS in prior assessments (US EPA, 1992; Johnson et al., 2000). A

distributions comparing 1995 and 1997 CO air quality data in the Denver anwith more recent CO air quality data (i.e., 2006) in these same two areas shthroughout the distribution. 

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. 

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.the patterns of typical commuting in CHAD versus the urban locations mode

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. 

Longitudinal Profile Algorithm Both Low – Medium Medium 

INF: The magnitude of potential influence would be mostly directed toward enumber or percent of persons having at least one exposure or dose above a 

KB: In developing the longitudinal method, the evaluation indicated that bot

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. 

APEX Inputs and Algorithms 

Meteorological Data Both Low Low INF & KB: Data are from the National Weather Service, a well-known and qu

temperatures are only used when selecting appropriate diaries to simulate i
October, 2009 Draft – Do Not Cite or Quote 6-40

Sources of Uncertainty 

Influence of Uncertainty 

on Exposure/Dose or 

Risk Estimates 

Category Element Direction Magnitude

KnowledgeBase 

Uncertainty Commentsa 

Algorithm and Input Data for In-Vehicle CO Concentrations 

Both Medium Medium 

INF: Given that on-road and in-vehicle CO concentrations are typically highScenario A likely underestimates in-vehicle exposures. There is variability bconcentrations that is not accounted for by using a single value to represent as traffic density, local meteorology, driving conditions, and differences in veoperation. KB: While most studies reviewed indicate that, on average, there may be a ambient and in-vehicle CO concentrations, there are a limited number of stuand even fewer were located in the U.S. It is largely unknown how this and influence the true relationship between in-vehicle and ambient CO concentr

Algorithm and Input Data for All Other Microenvironmental CO Concentrations 

Over Low-Medium Medium 

INF & KB: Even though CO is considered relatively inert, it is likely that the indoor microenvironments are overestimated. This is a result of not consideinfiltration of outdoor CO concentrations, thereby reducing indoor microenviof ambient origin. It is also possible that the residential indoor or outdoor msimulated individuals residing in close proximity to major roads may be undeambient monitors are unlikely to reflect the higher CO concentrations expecsection 3.6.6.2). The simplified approach used an ambient monitor that may CO concentrations (i.e., microscale and middle scale monitors) experienced study area. However, given the larger portion of time spent in locations otheand the limited difference in exposures estimated when comparing scenario and doses have an overall tendency to be overestimated for most of the sim

Commuting 

Algorithm Not Used Over Medium Low 

INF & KB: In using the ambient monitor that has the greatest CO concentraan area, it is assumed that these concentrations would represent conservatwould lead to overestimates in exposures, particularly when not considering concentration locations. 

CHD Prevalence Both Low Low 

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. 

Potential Health Effect Benchmark Levels 

Susceptible 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 COHstudy has evaluated the effect of exposure to CO resulting in COHb levels badverse effect level and, thus there is greater uncertainty about the lowest buncertainty about whether individuals with the most severe CHD are adequasupporting the choice of benchmark levels is based on controlled human exuncertainty on the risk characterization as being low. 

aINF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating. 
October, 2009 6-41 Draft – Do Not Cite or Quote

Based on the qualitative judgments made by staff for a range of sources of uncertainty 

and their characterization as to direction and magnitude of influence on exposures and doses, the 

exposure and dose estimates are possibly overestimated for a larger portion of the population the 

assessment is intended to represent (i.e., those residing/travelling in high CO concentration 

microenvironments). This is because: 

1  Of the four sources of uncertainty associated with potential overestimation (i.e., 

2 spatial/temporal representation of monitoring data, data base quality, and absence of 

3 movement among different air quality districts during commutes, algorithm and input 

4 data for all other microenvironmental CO concentrations), three were estimated as having 

5 medium magnitude of influence, while the remaining source (i.e., ambient monitoring 

6 database quality) was ranked as having a low or a negligible magnitude of influence. 

7  The one source of uncertainty associated with potential underestimation (i.e., missing 

8 data substitution) was judged to have a low magnitude of influence on estimated 

9 exposures and doses. 

10  Of the remaining identified sources of uncertainty judged by staff to have either 

11 bidirectional influence (six sources) or unknown (three sources) direction, five sources 

12 were judged to have a low magnitude of influence on estimated exposures and doses. 

While there was a wide-ranging level of uncertainty in the knowledge-base for the 

identified sources, there is relatively less uncertainty in staff judgments regarding the sources 

associated with potential overestimation of CO exposure and resulting COHb levels. 

13  A high degree of uncertainty in the knowledge-base was assigned to two sources: the 

14 spatial/temporal representation of monitoring data (direction of influence characterized as 

15 over, with a medium rated magnitude) and the use of historical data in representing air 

16 quality that just meet the current standard (direction of influence characterized as 

17 unknown, with magnitude rated as medium). 

18  The knowledge-base uncertainty was low for three of the five sources identified above as 

19 being associated with either under- or overestimating exposures (the rating for the 

20 remaining two sources was medium and high). 

21  The knowledge-base uncertainty for sources with unknown or bidirectional influence was 

22 low (five sources), medium (four sources), and high (one source). 

23 
October, 2009 6-42 Draft – Do Not Cite or Quote

1 6.5 KEY OBSERVATIONS 

2 Presented below are key observations resulting from the exposure and dose assessment 

3 for ambient CO. 

4  An important limitation in the assessment for this review is the lack of detailed spatial 

5 representation of the current ambient monitoring data, which creates challenges for 

6 estimating the spatial variability of CO concentrations across a study area. This 

7 limitation contributed in part to the reasoning for the development of the simplified 

8 approach used in this assessment. 

9 o In this simplified approach, staff used a single monitor recording the highest CO 

10 concentrations to represent the ambient air quality in each area. This was done to 

11 accommodate the potentially greater CO exposures expected to occur to persons 

12 residing in areas with higher CO concentrations (i.e., those occurring on or 

13 immediately near major roadways). 

14 o Using a single monitor in each study area, however, still posed difficulties in 

15 characterizing the full range of microenvironmental CO concentrations, such as 

16 CO levels in vehicles or near major roadways, concentrations outside residences, 

17 as well as those occurring within indoor microenvironments for the simulated 

18 population. This is a result of the limited information across a broad geographic 

19 area regarding the relationships between specific ambient monitor concentrations 

20 and microenvironmental concentrations. 

21  One-hour and 8-hour average daily maximum exposures and the daily maximum end-of22 hour COHb blood levels were estimated using a simplified exposure modeling approach 

23 involving two scenarios in two study areas: urban areas of Denver and Los Angeles 

24 counties. In Scenario A, CO concentrations in all microenvironments were set equal to 

25 the ambient monitor concentrations and in Scenario B, the CO concentration for the in26 vehicle microenvironment were increased over those of the ambient monitor and all other 

27 microenvironments were set equal to the ambient monitor concentrations. The two air 

28 quality conditions investigated by staff included as is air quality, and air quality for 

29 higher CO levels, adjusted to simulate just meeting the current 8-hour CO NAAQS 

30 (section 6.2). 

31 o Fewer than 1% of the study population in each study area (< 0.2%) were 

32 estimated to experience a daily maximum end-of-hour COHB level at or above 

33 2.0% under as is air quality conditions in either scenario. 

34 o Results for the two study areas differed appreciably for air quality adjusted to just 

35 meet the current standard. For these conditions, the estimates of percent of 

36 population experiencing a daily maximum end-of-hour COHB level at or above 

37 potential health benchmarks were substantially greater for the Denver study area 

38 (e.g., differing by a factor of 8 or more for the 2% COHb benchmark). 

39  Results generated in the current assessment for the air quality conditions just meeting the 

40 current NAAQS were compared with estimates from the assessment conducted in 2000 

41 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study areas 
October, 2009 6-43 Draft – Do Not Cite or Quote

1 (section 6.3). While focused on similar air quality conditions, the two assessments 

2 employed different versions of the exposure model (APEX vs pNEM) and there were 

3 significant differences in the approach used in each assessment. For example, as 

4 compared to the current assessment, the 2000 assessment employed more monitors to 

5 represent ambient CO levels, differentially treated a much greater number of 

6 microenvironments, and encompassed larger study areas. 

7 o The estimated percent of persons with daily maximum end-of-hour COHb blood 

8 levels when using air quality adjusted to just meet the current standard in both 

9 Denver and Los Angeles was substantially greater in the current assessment when 

10 compared to that estimated in the 2000 assessment (e.g., a difference of a factor of 

11 10 or more at the 2% COHb benchmark). 

12  Based on an overall qualitative judgment of the identified sources of uncertainty in the 

13 assessment approach, selections made regarding input data, and algorithms used, and 

14 their characterization as to direction and magnitude of influence, the exposure and dose 

15 estimates for much of the simulated population represented by either scenario in this 

16 assessment are likely overestimated (section 6.4, Table 6-25). There may be a smaller 

17 fraction of the simulated population (e.g., those residing in close proximity to major 

18 roads, persons regularly commuting for extended periods of time) where some periods of 

19 exposure are underestimated due to the simplified assumptions made in estimating in20 vehicle and near-road CO exposures, although likely less so and for a yet smaller portion 

21 of the population in scenario B. The impact of such potentially higher exposure periods 

22 on the population COHb levels will vary depending on the overall pattern of exposures. 

23  Given the considerations described above regarding the characterization of uncertainty 

24 and the tendency of the assessment approach to overestimate exposure and dose, staff 

25 finds the utility of this assessment for the purpose of considering the adequacy of the 

26 current standards to be limited. 
October, 2009 6-44 Draft – Do Not Cite or Quote

1 6.6 REFERENCES 

2 CDC. (2009). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007. Series 10, 

3 Number 240. U.S. Department of Health and Human Services, Hyattsville, MD, May 2009. 

4 Isaacs K., McCurdy, T Errickson A, Forbes S, Glen G, Graham S, McCurdy L, Nysewander, M, Smith L, 

5 Tulve N, and Vallero D. (2009). Statistical properties of longitudinal time-activity data for use in EPA 

6 exposure models. Poster presented at the American Time Use Research Conference; College Park MD, 

7 June 26, 2009. 

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

9 and Associated Carboxyhemoglobin Levels in Denver Residents Using pNEM/CO (Version 2.0). Report 

10 prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No. 68-D6-0064. U.S. 

11 Environmental Protection Agency, Research Triangle Park, North Carolina. March 1999. 

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

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

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

15 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available 

16 at: http://www.epa.gov/ttn/fera/human_related.html.. June 2000. 

17 SAIC. 2001. Technical Peer Review of “Estimation of Carbon Monoxide Exposures and Associated 

18 Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO (version 2.1)” 

19 Prepared by Science Applications International Corporation under EPA Contract No. 68-D-98-113. 

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

21 US Census Bureau. (2009). American Fact Finder. Census Summary File 3 (SF3) – custom tables. Available at: 

22 www.factfinder.census.gov. 

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

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

25 EPA/452/R-92-004. 

26 US EPA. (2004). An Examination of EPA Risk Assessment Principles and Practices. Staff Paper prepared by the 

27 US EPA Risk Assessment Task Force. EPA/100/B-04/001. Available at: 

28 http://www.epa.gov/OSA/pdfs/ratf-final.pdf. 

29 US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 

30 (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-001a. Office of 

31 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

32 US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation 

33 (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support Document. Report No. EPA-452/B-08-

34 001b. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research 

35 Triangle Park, NC. 

36 US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient 

37 Air Quality Standard. EPA-452/R-08-008a. November 2008. Available at: 

38 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 

39 US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air 

40 Quality Standard. EPA-452/R-09-007. August 2009. Available 

41 athttp://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 
October, 2009 6-45 Draft – Do Not Cite or Quote

1 WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on characterizing and 

2 communicating uncertainty in exposure assessment. Available at: 

3 http://www.who.int/ipcs/methods/harmonization/areas/exposure/en/. 
Appendix A 

Conceptual Model and Simplified Data Flow of APEX.

October 2009 A-1 Draft – Do Not Cite or Quote 
Air Exchange Rates & 

Building Volumes 

Emission Rates and 

Use Patterns for 

Indoor Sources 

(e.g., gas appliances, 

passive smoking) 

Ambient Fixed-Site 

Concentrations 

Air Quality Specification 

 Mass balance Model 

or Factors Approach

for Indoor and in Vehicle

Microenvironments

Exposure Algorithms

Seasonal Considerations

(Temperature)

Human Activity and

Exertion Patterns

Population &

Commuting Data

Distribution of People and

Occurrences of Exposures

Linked with Breathing Rate

Outdoor

Microenvironment

Concentrations

Di

 a

D

Appendix A. Conceptual Model and Simplified Data Flow of APEX. 

September 2009 A-2 Draft – Do Not Cite or Quote 
Appendix B 

Mass Balance Model in APEX 

The mass balance method models an enclosed microenvironment as a well-mixed volume 

in which the air concentration is spatially uniform at any specific time. The concentration of an 

air pollutant in such a microenvironment is estimated using the following four processes: 

 Inflow of air into the microenvironment; 

 Outflow of air from the microenvironment; 

 Removal of a pollutant from the microenvironment due to deposition, filtration, 

and/or chemical degradation; and 

 Emissions from sources of a pollutant inside the microenvironment. 

Table B-1 lists the parameters required by the mass balance method to calculate 

concentrations in a microenvironment. The proximity factor (fproximity) is used to account for 

differences in ambient concentrations between the geographic location represented by the 

ambient air quality data (e.g., a regional fixed-site monitor) and the geographic location of the 

microenvironment (e.g., near a roadway). This factor could take a value either greater than or 

less than 1. Emission source (ES) represents the emission rate for the emission source, and 

concentration source (CS) is the mean air concentration resulting from the source. Rremoval is 

defined as the removal rate of a pollutant from a microenvironment due to deposition, filtration, 

and chemical reaction. The air exchange rate (Rair exchange) is expressed in air changes per hour. 

October 2009 B-1 Draft – Do Not Cite or Quote
 Table B-1. Parameters of the Mass Balance Model 

Variable Definition Units Value Range

f proximity Proximity factor N/A 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 

The mass balance equation for a pollutant in a microenvironment is described by the 

differential equation 

in out CCC removal  Csource dt

ME (t)dC (B-1) 

where: 

 dCME(t) = Change in concentration in a microenvironment at time t (ppm), 

)Cin = Rate of change in microenvironmental concentration due to influx 

of air (ppm/hour), 

)Cout = Rate of change in microenvironmental concentration due to outflux 

of air (ppm/hour), 

)Cremoval = Rate of change in microenvironmental concentration due to 

removal processes (ppm/hour), and 

)Csource = Rate of change in microenvironmental concentration due to an 

emission source inside the microenvironment (ppm/hour). 

Within the time period of an hour each of the rates of change, )Cin, )Cout, )Cremoval, and 

)Csource, is assumed to be constant. 

October 2009 B-2 Draft – Do Not Cite or Quote
The change in microenvironmental concentration due to influx of air is represented by the 

following equation: 

ambient proximity airnpenetratio exchange

in

in RxfxfxC dt

tdC C )( (B-2) 

where: 

Cambient = Ambient hourly outdoor concentration (ppm) 

fproximity = Proximity factor 

 fpenetration = Penetration factor 

Rair exchange = Air exchange rate (1/hour) 

The change in microenvironmental concentration due to outflux of air is described by: 

 )( )( R tC

dt

tdC C air exchange ME

out

out   (B-3) 

The change in concentration due to deposition, filtration, and chemical degradation in a 

microenvironment is simulated by the first-order equation: 

(t)CR(t))CRR(R dt

(t)dC C deposition filtration chemical ME remova ME

removal

removal    (B-4) 

where: 

Rdeposition = Removal rate of a pollutant from a microenvironment due to 

deposition (1/hour) 

Rfiltration = Removal rate of a pollutant from a microenvironment due to 

filtration (1/hour) 

Rchemical = Removal rate of a pollutant from a microenvironment due to 

chemical degradation (1/hour) 

Rremoval = Removal rate of a pollutant from a microenvironment due to 

overall removal (1/hour) 

As discussed in Section 6.2, EPA decided not to model indoor emissions of CO in the 

current exposure assessment; consequently, the optional term )Csource was uniformly set equal to 

0.0 for this study. 

Combining Equation B-1 with Equations B-2, B-3, and B-4 yields 

)( )( dt

ME (t)dC  RC airin exchange ME removal  ME tCRtC (B-5) 

October 2009 B-3 Draft – Do Not Cite or Quote
The solution to this differential equation is 

)( exp())0(( tR ) R

C C

R

C tC combined

combined

in

ME

combined

in

ME  



  (B-6) 

where: 

CME(0) = Concentration of a pollutant in a microenvironment at the 

beginning of a hour (ppm) 

CME(t) = Concentration of a pollutant in a microenvironment at time t within 

the time period of a hour (ppm) 

Rcombined = Rair exchange + Rremoval (1/hour) 

Based on Equation B-6, the following three hourly concentrations in a microenvironment 

are calculated: 

 

combined

in

ME

equil

ME R

C tCC

 )(  (B-7) 

)(exp))0(( (B-8) combined

equil

ME ME

equil

ME

endhourly

ME  CCCC R

combined

equil combined

ME ME

equil

ME

hourlymean

ME R

R

CCC

dt

dttC

C )(exp1))0((

)(

1

0

1

0  





 (B-9) 

where: 

= Equilibrium concentration in a microenvironment (ppm) equil CME

CME(0) = Concentration in a microenvironment at the beginning of an hour 

(ppm) 

= Concentration in a microenvironment at the end of an hour (ppm) endhourly CME

= Hourly mean concentration in a microenvironment (ppm) hourlymean CME

At each hour time step of the simulation period, APEX uses Equations B-7, B-8, and B-9 

to calculate the hourly equilibrium, hourly ending, and hourly mean concentrations. APEX 

reports hourly mean concentration as hourly concentration for a specific hour. The calculation 

continues to the next hour by using for the previous hour as CME(0). endhourly CME

October 2009 B-4 Draft – Do Not Cite or Quote
October 2009 C-1 Draft – Do Not Cite or Quote 

Appendix C 

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). 

C.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 5 

of this report, 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. 
October 2009 C-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). 

C.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. C-1) 

where, 

 

A

B H O

Lco V

P P

D

B 

1 2    (Eq. C-2) 
October 2009 C-3 Draft – Do Not Cite or Quote 

Table C-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)
October 2009 C-4 Draft – Do Not Cite or Quote 

 If the only quantity in equation (C-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. C-3) 

The following equation, the Haldane relationship, applies approximately at equilibrium 

conditions. 

 M

Pc O Hb

Pc COHb

CO

O  [ ]

[ ]

2

2 (Eq. C-4) 

The Haldane coefficient, M, is the chemical equilibrium constant for reaction (C-3). The 

above reaction can also be viewed as the difference between two competing chemical reactions: 

 CO  RHb  COHb (Eq. C-5) 

 

 O RHb O Hb 2   2 (Eq. C-6) 

 

Subtracting (C-6) from (C-5) yields (C-3). If (C-3) is in equilibrium, then (C-5) and (C6) are in equilibrium. If k represents the equilibrium constant for (C-6) then: 

 k

Pc RHb

O Hb

O

 [ ]

[ ]

2

2 (Eq. C-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 (C-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. C-8) 

Eliminating [RHb] between (C-7) and (C-8) and solving for [O2Hb] yields: 
October 2009 C-5 Draft – Do Not Cite or Quote 

 ([ ] [ ]) 1 [ ] 2 0

2

2 THb COHb

kPc

kPc

O Hb

O

O    (Eq. C-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 (C-9) involving the mean pulmonary capillary oxygen 

pressure and the equilibrium constant k. Substituting (C-9) into (C-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. C-10) 

In working with the CFK model it is convenient to express COHb as a percent of [RHb]0. 

Multiplying (C-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. C-11) 

 

Equation (C-11) can be written in the form suggested by Muller and Barton (1987): 

100 %[ ]

%[ ] %[ ]

1 COHb

COHb C C

dt

d COHb

O    (Eq.C-12) 

where, 

 ( ) [ ]

100

b

I

b

CO

O

O BV

P

V

V

THb

C CO    (Eq. C-13) 

 

O b

CO

k THb MBV

kP

C

[ ]

100(1 ) 2

1

  (Eq. C-14) 

 Given values for the atmospheric pressure and the physiological variables in equations 

(C-12) through (C-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 (C-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. C-15) 

The equation for %[COHb]∞ is obtained by setting equation (C-12) equal to zero and 

solving for %[COHb], which is now equal to %[COHb]∞: 
October 2009 C-6 Draft – Do Not Cite or Quote 

 ( )

100 %[ ] C C1C COHb

O

O

   (Eq. C-16) 

Equation (C-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. C-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 (C-12) above, except that (C-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. C-18) 

 A0 = C0 / (C0 + C1) (Eq. C-19) 

 A1 = C1 / (C0 + C1) (Eq. C-20) 

 D = D0 - A1 (Eq. C-21) 

 

z = (C0 + C1) t / (100*D0*D0) (Eq. C-22) 

The z variable is a re-scaled time variable that is dimensionless. It is used as the 
October 2009 C-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 (C-17) as a function of z yields the expression 

 N`(z) = D0 2 A0 - D0 2 A1 N(z) / ( 100 - N(z) ) (Eq. C-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. C-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. C-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. C-26) 

Tests showed that the fourth-order Taylor series expansion (C-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 (C-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. 

C.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 C.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 
October 2009 C-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 5.3.3 of this report. 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 (C-12) through (C-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 C.4 describes the equations and procedures used by the APEX4.3 COHb module 

to obtain the values of the input variables for equations (C-2) and (C-13) through (C-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. 
October 2009 C-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 5.3.5 and 5.3.7 of the main body of 

this report, the algorithms used to estimate VE and COHb require values for various 

physiological parameters such as body mass, blood volume, and pulmonary diffusion rate. Table 

C-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 C-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 
October 2009 C-10 Draft – Do Not Cite or Quote 

 Haldane Coefficient 

 Equilibrium constant for the reaction of O2 

 Atmospheric pressure, torr 

 Blood volume, ml 

 Total potential reduced hemoglobin content of blood, ml CO/ml STPD 

 Pulmonary CO diffusion rate, ml/min/torr STPD 

 Endogenous CO production rate, ml/min STPD 
October 2009 C-11 Draft – Do Not Cite o

Table C-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
October 2009 C-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. (Isaacs and Smith, 20

per deciliter of blood 
October 2009 C-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


October 2009 C-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 5.3.5 of this report and Chapter 5 of US EPA (20

Energy conversion factor (ECF) 

Ventilation rate 

Gender See Section 5.3.5 of this report and Chapter 5 of US EPA (20

NVO2max Ventilation 

rate 

Gender Age

See Section 5.3.5 of this report and Chapter 5 of US EPA (20

VO2max Ventilation 

rate 

NVO2max

Body Weight 

See Section 5.3.5 of this report and Chapter 5 of US EPA (20
October 2009 C-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 C-1. Flow Diagram for Physiological Profile Generator. Input data is supplied at the start 

of the APEX4.3 computation. 
October 2009 C-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. C-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. C-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 (C-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 
October 2009 C-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. C-29) 

where HbAlt is the percent increase in Hb due to exposure to altitude and is given by (EPA 

1978): 

Altitude HbAlt e0.0001249  2.76

Hb in equation (C-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 C-2 for the distributions of Hb by age and gender). Given the hemoglobin content 

of the blood based on the distributions listed in Table C-2, [THb]0 is calculated using equation 

(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. 
October 2009 C-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 C-30 and C-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. C-30) 

 females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(z) (Eq. C-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. C-32) 

 Women: 97 D  0.556 height  0.115 age  5. LCO (Eq. C-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 C-32 and C-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 C-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 C-2 presents these distributions 

classified by class, gender, and menstrual phase. 
October 2009 C-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 C-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. C-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. C-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. 
October 2009 C-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 5.3.5 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. C-36) 

Table C-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 
October 2009 C-21 Draft – Do Not Cite or Quote 

C.5 References

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. 

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. 

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 
October 2009 C-22 Draft – Do Not Cite or Quote 

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. 

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 
October 2009 C-23 Draft – Do Not Cite or Quote 

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. 
Appendix D 

Apex Output Files 

October, 2009 D-1 Draft – Do Not Cite or Quote
October, 2009 D-2 Draft – Do Not Cite or Quote

Appendix D. Apex Output Files. 

Output File Type Description

Log The Log file contains the record of the APEX model simulation as it 

progresses. If the simulation completes successfully, the log file 

indicates the input files and parameter settings used for the simulation 

and reports on a number of different factors. If the simulation ends 

prematurely, the log file contains error messages describing the critical 

errors that caused the simulation to end.

Profile Summary The Profile Summary file provides a summary of each profile modeled in 

the simulation. Each line lists the person’s age, gender and race, in 

addition to a number of other personal profile variables that the model 

uses to simulate exposure.

Sites The Sites file lists the sectors, air districts, and zones in the study area, 

and identifies the mapping between them.

Hourly The Hourly file provides an hour-by-hour time series of exposures, 

doses, and other variables for each modeled profile.

Daily The Daily file provides a day-by-day time series of exposures, doses, and 

other variables for each modeled profile.

Events The Events file contains event-level information (including MET, 

exposure, ventilation, and dose) for individuals in the simulation. 

Settings in the Control file allow the user to write this information for all 

persons, every Nth person, or for a set of specified profile IDs.

Microenvironment 

Summary

The Microenvironment Summary file provides a summary of the time and 

exposure by microenvironment for each profile modeled in the 

simulation.

Microenvironment 

Results

The Microenvironment Results file provides an hour-by-hour time series 

of microenvironment concentrations and parameters for a pollutant for 

each modeled profile for each location (“Home”, “Work”, and “Other”). 

A Microenvironment Results file is generated for each pollutant.

Output Tables The Output Tables file contains a series of tables summarizing the 

exposure (and dose, if calculated) results of the simulation for a pollutant. 

The percentiles and exposure/dose cut-off points used in these tables are 

defined in the Control file. A Tables file is generated for each pollutant.
Appendix E 

Mapping of CHAD Location Codes to Microenvironments Defined for 

Application of APEX4.3 to Carbon Monoxide. 

October, 2009 E-1 Draft – Do Not Cite or Quote
Appendix E. Mapping Of Chad Location Codes To Microenvironments Defined For 

Application Of Apex4.3 To Carbon Monoxide. 

! Mapping of CHAD activity locations to two APEX microenvironments: in-vehicle (2) and other (1) 

CHAD Loc. Description APEX 

--------- ---------------------------------------------- 

U Uncertain of correct code = 1 H 

X No data = 1 H 

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 = 1 H 

30210 Your residence, outdoor = 1 H 

30211 ..., pool or spa = 1 H 

30219 ..., other outdoor = 1 H 

30220 Other residence, outdoor = 1 H 

30221 ..., pool or spa = 1 H 

30229 ..., other outdoor = 1 H 

30300 Residential garage or carport = 1 H 

30310 ..., indoor = 1 H 

30320 ..., outdoor = 1 H 

30330 Your garage or carport = 1 H 

30331 ..., indoor = 1 H 

30332 ..., outdoor = 1 H 

30340 Other residential garage or carport = 1 H 

30341 ..., indoor = 1 H 

30342 ..., outdoor = 1 H 

30400 Residence, none of the above = 1 H 

31000 Travel, general = 1 H 

31100 Motorized travel = 2 H 

31110 Car = 2 H 

31120 Truck = 2 H 

31121 Truck (pickup or van) = 2 H 

31122 Truck (not pickup or van) = 2 H 

31130 Motorcycle or moped = 2 H 

31140 Bus = 2 H 

31150 Train or subway = 1 H 

31160 Airplane = 1 H 

31170 Boat = 1 H 

31171 Boat, motorized = 1 H 

31172 Boat, other = 1 H 

31900 Travel, other = 2 H 

31910 ..., other vehicle = 2 H 

31200 Non-motorized travel = 1 H 

31210 Walk = 1 H 

31220 Bicycle or inline skates/skateboard = 1 H 

31230 In stroller or carried by adult = 1 H 

31300 Waiting for travel = 1 H 

October, 2009 E-2 Draft – Do Not Cite or Quote
31310 ..., bus or train stop = 1 H 

31320 ..., indoors = 1 H 

31900 Travel, other = 2 H 

31910 ..., other vehicle = 2 H 

32000 Non-residence indoor, general = 1 H 

32100 Office building/ bank/ post office = 1 H 

32200 Industrial/ factory/ warehouse = 1 H 

32300 Grocery store/ convenience store = 1 H 

32400 Shopping mall/ non-grocery store = 1 H 

32500 Bar/ night club/ bowling alley = 1 H 

32510 Bar or night club = 1 H 

32520 Bowling alley = 1 H 

32600 Repair shop = 1 H 

32610 Auto repair shop/ gas station = 1 H 

32620 Other repair shop = 1 H 

32700 Indoor gym /health club = 1 H 

32800 Childcare facility = 1 H 

32810 ..., house = 1 H 

32820 ..., commercial = 1 H 

32900 Large public building = 1 H 

32910 Auditorium/ arena/ concert hall = 1 H 

32920 Library/ courtroom/ museum/ theater = 1 H 

33100 Laundromat = 1 H 

31200 Non-motorized travel = 1 H 

31210 Walk = 1 H 

31220 Bicycle or inline skates/skateboard = 1 H 

31230 In stroller or carried by adult = 1 H 

31300 Waiting for travel = 1 H 

31310 ..., bus or train stop = 1 H 

31320 ..., indoors = 1 H 

31900 Travel, other = 2 H 

31910 ..., other vehicle = 2 H 

32000 Non-residence indoor, general = 1 H 

32100 Office building/ bank/ post office = 1 H 

32200 Industrial/ factory/ warehouse = 1 H 

32300 Grocery store/ convenience store = 1 H 

32400 Shopping mall/ non-grocery store = 1 H 

32500 Bar/ night club/ bowling alley = 1 H 

32510 Bar or night club = 1 H 

32520 Bowling alley = 1 H 

32600 Repair shop = 1 H 

32610 Auto repair shop/ gas station = 1 H 

32620 Other repair shop = 1 H 

32700 Indoor gym /health club = 1 H 

32800 Childcare facility = 1 H 

32810 ..., house = 1 H 

32820 ..., commercial = 1 H 

32900 Large public building = 1 H 

32910 Auditorium/ arena/ concert hall = 1 H 

32920 Library/ courtroom/ museum/ theater = 1 H 

33100 Laundromat = 1 H 

33200 Hospital/ medical care facility = 1 H 

33300 Barber/ hair dresser/ beauty parlor = 1 H 

33400 Indoors, moving among locations = 1 H 

33500 School = 1 H 

33600 Restaurant = 1 H 

33700 Church = 1 H 

33800 Hotel/ motel = 1 H 

33900 Dry cleaners = 1 H 

34100 Indoor parking garage = 1 H 

34200 Laboratory = 1 H 

34300 Indoor, none of the above = 1 H 

35000 Non-residence outdoor, general = 1 H 

35100 Sidewalk, street = 1 H 

35110 Within 10 yards of street = 1 H 

35200 Outdoor public parking lot /garage = 1 H 

35210 ..., public garage = 1 H 

35220 ..., parking lot = 1 H 

35300 Service station/ gas station = 1 H 

35400 Construction site = 1 H 

35500 Amusement park = 1 H 

October, 2009 E-3 Draft – Do Not Cite or Quote
October, 2009 E-4 Draft – Do Not Cite or Quote

35600 Playground = 1 H 

35610 ..., school grounds = 1 H 

35620 ..., public or park = 1 H 

35700 Stadium or amphitheater = 1 H 

35800 Park/ golf course = 1 H 

35810 Park = 1 H 

35820 Golf course = 1 H 

35900 Pool/ river/ lake = 1 H 

36100 Outdoor restaurant/ picnic = 1 H 

36200 Farm = 1 H 

36300 Outdoor, none of the above = 1 H 
Appendix F 

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 presentation is included at the end of the Appendix in its entirety. 

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, 3School of Information and 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, 

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. 

October 2009 F-1 Draft – Do Not Cite or Quote 
INTRODUCTION 

Recently, new methods of assembling multi-day diaries in human exposure models from crosssectional 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. 

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 

2

w

2

b

2

b

2

2

b

σσ

σ

σ

σ D

 

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 

     









N

1j

2

1j j

1N

1j

j yy/yyyyA

October 2009 F-2 Draft – Do Not Cite or Quote 
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., 2007). 

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 (ie. 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. 

October 2009 F-3 Draft – Do Not Cite or Quote 
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]. 

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. 

October 2009 F-4 Draft – Do Not Cite or Quote 
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 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-to-day 

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. 

October 2009 F-5 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 boxedbut 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 

nonworkday) 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/nonworkday was a better discriminator of time spent outside than a weekday/weekend 

split. As such, further comparisons are also presented for both workdays and nonworkdays. 

October 2009 F-6 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 nonworkdays). 

The effect of season on time spent in locations/activities is shown in Figure 4. Seasonal effects 

were apparent for time spent outdoors on nonworkdays, 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. 

October 2009 F-7 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 daytype 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 daytype. 

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, daytype 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 from 

cross-sectional diary studies indicating that workdays/nonworkdays 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 sprnt outdoors, especially when daytype was considered. Such 

breakdowns by temperature and daytype may eliminate the need for diary pools different 

seasons, providing larger pools for diary sampling on a given day. Further analysis with 

other time-activity data can confirm this trend. 

October 2009 F-8 Draft – Do Not Cite or Quote 
October 2009 F-9 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 A.S., Xue J., Ozkaynak H., and Spengler J.D. (2000). The Harvard Southern California 

Chronic Ozone Exposure study: assessing ozone exposure of gradeschool-age children in two 

southern California communities. Environ Health Perspect. 108:265–270. 

Glen G., Smith L., Isaacs K., McCurdy T., and Langstaff J. (2008). A new method of 

longitudinal diary assembly for human exposure modeling. J Expo Sci Environ Epidemiol.

18(3):299-311. 

Xue J., McCurdy T., Spengler J., and Ozkaynak H. (2004). Understanding variability in time 

spent in selected locations for 7-12-year old children. J Expo Sci Environ Epdemiol. 14:222-233. 

McCurdy T., Glen G., Smith L., and Lakkadi L. (2000). The National Exposure Research 

Laboratory's Consolidated Human Activity Database. J Expo Anal Environ Epidemiol. 10:566-

78. 
Isaacs et al. (2009) in original 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 quartResu

versutime/asectiodata ndiscricompThe eeffecthard wrelateThe e

definemaximas godayty

REFEGeyh assesPerspGlen GmodelXue Jyear o

McCuActivit

2

w

2

b

2

b

22

b

σσ

σ

σσ D





    









 

N

1j

2

1j j

1N

1j

j yy/yyyyA

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 quartResu

versutime/asectiodata ndiscricompThe eeffecthard wrelateThe e

definemaximas godayty

REFEGeyh assesPerspGlen GmodelXue Jyear o

McCuActivit

2

w

2

b

2

b

22

b

σσ

σ

σσ D





    









 

N

1j

2

1j j

1N

1j

j yy/yyyyA

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

October 2009 F-10 Draft – Do Not Cite or Quote 
Appendix G 

Analysis of CHAD Diaries for Time Spent in Vehicles. 

The U. S. Census Bureau (2009) provides an on-line facility for accessing the 

detailed census data included in their Summary File 3 (SF3). Using this resource, we 

obtained information on travel time to work for “workers 16 years and over” (US Census 

Bureau, 2009, Table P31) specific to Denver County, Colorado and Los Angeles, CA. 

The counts in Table P31 for trips to work places other than home were converted into the 

percentages listed in Columns 2 and 3 of Table G-1. Although the P31 statistics applied 

to people 16 years or older, we assumed that the statistics were generally applicable to 

people 18 years or older. 

We next determined the number of 24-hour diaries in CHAD 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 G-1 are listed in Column 4 in 

and the values were converted to the percentage values listed in Column 5. 

Table G-1. Representation of Denver and LA Commuting Characteristics in 

CHAD Diaries. 

 

a

Subjects are 18+ years of age. Diaries include 1+ minute in motor vehicle between 6 am and 9 am. 

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 10.28 7.75 563 9.79 

10 to 19 31.96 25.92 1,676 29.16 

20 to 29 24.15 21.04 1,068 18.58 

30 to 39 18.60 21.37 1,111 19.33 

40 to 59 9.29 13.57 665 11.57 

60 to 89 3.80 6.99 407 7.08 

90+ 1.73 3.35 258 4.49 

Total 100.00 100.00 5,748 100.00 

United States Office of Air Quality Planning and Standards Publication No. EPA-452/D-09-008 

Environmental Protection Health and Environmental Impacts Division October 2009 Agency Research Triangle Park, NC