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Risk and Exposure Assessment to Support
the Review of the Carbon Monoxide Primary
National Ambient Air Quality Standards:
Second External Review Draft
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EPA-452/P-10-004
February, 2010
Risk and Exposure Assessment to Support the Review of the
Carbon Monoxide Primary National Ambient Air Quality
Standards:
Second External Review Draft
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, North Carolina
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DISCLAIMER
This document has been reviewed by the Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency (EPA), and approved for publication. This draft
document has been prepared by staff from the Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or
recommendations are those of the authors and do not necessarily reflect the views of the EPA
Mention of trade names or commercial products is not intended to constitute endorsement or
recommendation for use. This document is being provided to the Clean Air Scientific Advisory
Committee for their review, and made available to the public for comment. Any questions or
comments concerning this document should be addressed to Dr. Stephen Graham, U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-06,
Research Triangle Park, North Carolina 27711 (email: graham.stephen@epa.gov).
Elements of this report have been provided to the U.S. Environmental Protection Agency (EPA)
by Abt Associates, Inc. and TRI Environmental, Inc. in partial fulfillment of Contract No. EP-D-
08-100, Work Assignments 0-08 and 1-15.
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Table of Contents
List of Figures iv
List of Tables iv
I INTRODUCTION 1-1
1.1 BACKGROUND 1-1
1.2 PREVIOUS REVIEWS AND ASSESSMENTS 1-3
1.3 CURRENT REVIEW, CAS AC ADVICE AND PUBLIC COMMENT 1 -6
1.4 REFERENCES 1-8
2 CONCEPTUAL OVERVIEW: ASSESSING AMBIENT CARBON
MONOXIDE EXPOSURE AND RISK 2-1
2.1 SOURCES OF CARBON MONOXIDE 2-1
2.2 EXPOSURE PATHWAYS AND IMPORTANT
MICROENVIRONMENTS 2-2
2.3 EXPOSURE AND DOSE METRICS 2-6
2.4 AT-RISK POPULATIONS 2-7
2.5 HEALTH ENDPOINTS 2-9
2.5.1 Cardiovascular Disease-related Effects 2-10
2.5.2 Other Effects 2-12
2.6 RISK CHARACTERIZATION APPROACH 2-13
2.7 KEY OBSERVATIONS 2-17
2.8 REFERENCES 2-19
3 AIR QUALITY CONSIDERATIONS 3-1
3.1 AMBIENT CO MONITORING 3-1
3.1.1 Monitoring Network 3-1
3.1.2 Analytical Sensitivity 3-2
3.1.3 General Patterns of CO Concentrations 3-4
3.1.4 Policy-Relevant Background Concentrations 3-11
3.1.5 Within-Monitor CO Concentration Trends 3-11
3.2 STUDY AREAS SELECTED FOR CURRENT ASSESSMENT 3-18
3.3 KEY OBSERVATIONS 3-18
3.4 REFERENCES 3-20
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4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING CO
EXPOSURE AND COHB DOSE LEVELS 4-1
4.1 PURPOSE 4-1
4.2 MODEL OVERVIEW 4-2
4.3 MODEL HISTORY AND EVOLUTION 4-2
4.4 MODEL SIMULATION PROCESS 4-3
4.4.1 Characterize Study Area 4-6
4.4.2 Generate Simulated Individuals 4-6
4.4.3 Construct Activity Sequences 4-8
4.4.4 Calculate Microenvironmental Concentrations 4-13
4.4.5 Estimate Energy Expenditure and Ventilation Rates 4-30
4.4.6 Calculate Exposure 4-32
4.4.7 Calculate Dose 4-33
4.4.8 Model Output 4-34
4.6 KEY OBSERVATIONS 4-34
4.7 REFERENCES 4-35
5 APPLICATION OF APEX4.3 IN THIS ASSESSMENT 5-1
5.1 PURPOSE 5-1
5.2 OVERVIEW 5-2
5.3 STUDY AREAS 5-3
5.4 EXPOSURE PERIODS 5-3
5.5 STUDY POPULATION 5-7
5.5.1 Total and Simulated Population 5-7
5.5.2 Selected at-Risk Subpopulation 5-7
5.5.3 Time-Location-Activity Patterns 5-10
5.5.4 Construction of Longitudinal Diaries 5-10
5.6 EXPOSURE SCENARIOS 5-10
5.7 AMBIENT AIR QUALITY DATA 5-11
5.7.1 Unadjusted 1-Hour Ambient Concentrations 5-11
5.7.2 Method for Estimating of Missing 1-Hour Ambient Concentrations. 5-12
5.7.3 Adjusted 1-Hour Ambient Concentrations 5-16
5.8 METEOROLOGICAL DATA 5-20
5.8.1 Method for Estimating of Missing 1-Hour Temperature Data 5-21
5.9 MICROENVIRONMENTS MODELED 5-22
5.9.1 The Micronenvironmental Model as Implemented by APEX4.3 5-22
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5.9.2 Microenvironmental Mapping 5-25
5.9.3 Selection of Microenvironmental Method Used 5-26
5.9.4 Air Exchange Rates and Air Conditioning Prevalence 5-26
5.10 KEY OBSERVATIONS 5-28
5.11 REFERENCES 5-30
6 SIMULATED EXPOSURE AND COHB DOSE RESULTS 6-1
6.1 ESTIMATED EXPOSURES 6-2
6.1.1 Air quality as is 6-2
6.1.2 Air quality adjusted to just meet the current 8-hour standard 6-4
6.1.3 Air quality adjusted to just meet alternative air quality scenarios 6-6
6.2 ESTIMATED COHB DOSE LEVELS 6-10
6.2.1 Air quality as is 6-10
6.2.2 Air quality adjusted to just meet the current 8-hour standard 6-11
6.2.3 Air quality adjusted to just meet alternative air quality scenarios 6-13
6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000
PNEM/CO AND DRAFT 2009 APEX/CO ASSESSMENTS 6-14
6.4 EVALUATION OF ENDOGENOUS CO CONTRIBUTION TO COHB
LEVELS IN APEX SIMULATED INDIVIDUALS 6-17
6.4.1 Estimation of Endogenous CO Contribution to Population COHb
Levels 6-17
6.4.2 Contribution of Endogenous CO Production and Ambient Exposures to
COHb Level 6-19
6.5 KEY OBSERVATIONS 6-23
6.6 REFERENCES 6-25
7 VARIABILITY ANALYSIS AND UNCERTAINTY
CHARACTERIZATION 7-1
7.1 ANALYSIS OF VARIABILITY 7-1
7.2 CHARACTERIZATION OF UNCERTAINTY 7-3
7.3 KEY OBSERVATIONS 7-10
7.4 REFERENCES 7-11
8 SUMMARY OF KEY OBSERVATIONS 8-1
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List of Figures
Figure 3-1 Spatial and Temporal Trends in the 2nd Highest 1-hour (top) and 8-hour Average
(bottom) CO Ambient Monitoring Concentrations in Denver, Colorado(left) and
Los Angeles, California (right),Years 1993 - 2008 3-6
Figure 3-2 Diurnal Distribution of 1-hour CO Concentrations in Denver (Monitor
080310002) by Day-type (weekdays-left; weekends-right), Years 1995 (top) and
2006 (bottom) 3-9
Figure 3-3 Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor
060371301) by day-type (weekdays-left; weekends-right), years 1997 (top) and
2006 (bottom) 3-10
Figure 3-4 Comparison of high concentration year (1997) with low concentration year
(2006) ambient air quality in Los Angeles. The 0 through 100th percentiles of the
quality distribution are plotted for each monitor-year 3-13
Figure 4-1 Conceptual model and simplified data flow for estimating population exposure
and dose using APEX 4-5
Figure 5-1 Ambient monitor locations, air districts (black circles), meteorological zones
(blue circles), and study area (red circle) for the Denver exposure modeling
domain 5-4
Figure 5-2 Ambient monitor locations, air districts (black circles), meteorological zones
(blue and pink circles), and study area (red circle) for the Los Angeles exposure
modeling domain 5-5
Figure 6-1 Histogram of the percent COHb ambient contribution estimated using Denver
1995 ambient concentrations adjusted to just meet the current standard 6-20
Figure 6-2 The contribution of endogenous CO production relative to an individual's
maximum end-of-hour COHb level using 1995 Denver ambient concentrations
adjusted to just meet the current standard 6-21
Figure 6-3 Comparison of endogenous CO production relative to an individual's maximum
COHb ambient contribution using 1995 Denver ambient concentrations adjusted
to just meet the current standard 6-22
Figure 6-4. Comparison of endogenous CO production relative to an individual's maximum
COHb ambient contribution using 1995 Denver ambient concentrations adjusted
to just meet the current standard 6-23
List of Tables
Table 3-1. Within monitor temporal variability in Denver using historical (1995-97) and
recent (2005-07) air quality data - 2nd highest 8-hour average 3-15
Table 3-2. Within monitor temporal variability in Los Angeles using historical (1995-97) and
recent (2005-07) air quality data - 2nd highest 8-hour average 3-15
Table 3-3. Within monitor temporal variability in Denver using historical (1995-97) and
recent (2005-07) air quality data - 99th percentile 1-hour daily maximum 3-16
Table 3-4. Within monitor temporal variability in Los Angeles using historical (1995-97)
and recent (2005-07) air quality data - 99th percentile 1-hour daily
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maximum 3-16
Table 3-5. Within monitor temporal variability in Denver using historical (1995-97) and
recent (2005-07) air quality data - 99th percentile 8-hour daily maximum 3-17
Table 3-6. Within monitor temporal variability in Los Angeles using historical (1995-97)
and recent (2005-07) air quality data - 99th percentile 8-hour daily
maximum 3-17
Table 4-1. Summary of activity pattern studies comprising the recent version of CHAD 4-10
Table 4-2. Variables used by APEX4.3 in the mass balance model 4-15
Table 4-3. Variables used by APEX4.3 in the factors model 4-16
Table 4-4. Estimated values of distribution parameters and variables in equation 4-11 as
implemented in the application of pNEM/CO to Denver and Los Angeles
(Johnson et al., 2000) 4-21
Table 4-5. Parameters of Bounded Lognormal Distributions Defined for Proximity Factors
Used in Applications of APEX3.1 to Los Angeles (Johnson and Capel,
2003) 4-29
Table 5-1. Attributes of fixed-site monitors selected for the Denver study area 5-3
Table 5-2. Attributes of fixed-site monitors selected for the Los Angeles study area 5-4
Table 5-3. National prevalence rates for coronary heart disease by age range 5-8
Table 5-4 National prevalence rates for coronary heart disease by gender 5-8
Table 5-5 National prevalence rates for coronary heart disease stratified by age range and
gender 5-8
Table 5-6 National prevalence rates for coronary heart disease, including diagnosed and
undiagnosed cases, stratified by age and gender 5-9
Table 5-7 Descriptive statistics for hourly carbon monoxide concentrations before and after
estimation of missing values -Denver 1995 5-13
Table 5-8 Descriptive statistics for hourly carbon monoxide concentrations before and after
estimation of missing values -Denver 2006 5-13
Table 5-9 Descriptive statistics for hourly carbon monoxide concentrations before and after
estimation of missing values - Los Angeles 1997 5-14
Table 5-10 Descriptive statistics for hourly carbon monoxide concentrations before and after
estimation of missing values - Los Angeles 2006 5-15
Table 5-11 Design values and adjustment factors used to represent air quality just meeting
the current and potential alternative standards 5-17
Table 5-12 Descriptive statistics for hourly carbon monoxide concentrations after adjusting
to just meet the current 8-hour standard - Denver (adjusted 1995 data) 5-19
Table 5-13 Descriptive statistics for hourly carbon monoxide concentrations after adjusting
to just meet the current 8-hour standard - Los Angeles (adjusted 1997 data). 5-19
Table 5-14 Locations of meteorological stations selected for Denver 5-20
Table 5-15 Locations of meteorological stations selected for Los Angeles 5-21
Table 5-16 Parameters of Bounded Lognormal Distributions Defined for Proximity Factors
to be Used in the Proposed Application of APEX4.3 to Los Angeles and
Denver 5-24
Table 5-17 List of microenvironments modeled and calculation methods used 5-26
Table 5-18 Lognormal distributions of indoor air exchange rates used in Los Angeles.... 5-27
Table 5-19 Lognormal distributions of indoor air exchange rates used in Denver 5-28
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Table 6-1. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Denver Study Area - "As Is" Air
Quality 6-3
Table 6-2. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Los Angeles Study Area - "As Is"
Air Quality 6-4
Table 6-3 Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Denver Study Area - Air Quality Just
Meeting the Current 8-Hour Standard 6-5
Table 6-4 Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Los Angeles Study Area - Air
Quality Just Meeting the Current 8-Hour Standard 6-5
Table 6-5 Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour
Exposure At or Above the Specified Level - Adults With Coronary Heart Disease
(CHD) in the Denver Study Area - Air Quality Just Meeting Potential Alternative
Standards 6-6
Table 6-6 Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour
Exposure At or Above the Specified Level - Adults With Coronary Heart Disease
(CHD) in the Los Angeles Area - Air Quality Just Meeting Potential Alternative
Standards 6-7
Table 6-7. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour
Exposure At or Above the Specified Level - Adults With Coronary Heart Disease
(CHD) in the Denver Study Area - Air Quality Just Meeting Potential Alternative
Standards 6-9
Table 6-8. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour
Exposure At or Above the Specified Level - Adults With Coronary Heart Disease
(CHD) in the Los Angeles Area - Air Quality Just Meeting Potential Alternative
Standards 6-9
Table 6-9. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Denver Study Area - Air Quality As
Is 6-11
Table 6-10.Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Los Angeles Study Area - Air
Quality As Is 6-11
Table 6-11 Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Denver Study Area - Air Quality Just
Meeting the Current 8-hour Standard 6-12
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Table 6-12 Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Los Angeles Study Area - Air
Quality Just Meeting the Current 8-hour Standard 6-12
Table 6-13 Estimated Number (and Percentage) of Persons with a Daily Maximum End-of-
hour COHb Level At or Above the Specified Level - Adults With Coronary
Heart Disease (CHD) in the Denver Study Area - Air Quality Just Meeting
Potential Alternative Standards 6-13
Table 6-14 Estimated Number (and Percentage) of Persons with a Daily Maximum End-of-
hour COHb Level At or Above the Specified Level - Adults With Coronary
Heart Disease (CHD) in the Los Angeles Study Area - Air Quality Just Meeting
Potential Alternative Standards 6-14
Table 6-15 Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to
Experience a Daily Maximum End-of-hour COHb Level At or Above the
Specified Percentage - Air Quality Just Meeting the Current Standard 6-16
Table 6-16 Percentage of Los Angeles Adults with Coronary Heart Disease (CHD) Estimated
to Experience a Daily Maximum End-of-hour COHb Level At or Above the
Specified Percentage - Air Quality Just Meeting the Current Standard 6-16
Table 6-17 Estimated Number (and Percentage) of Persons with a Daily Maximum End-of-
hour COHb Level At or Above the Specified Level - Adults With Coronary
Heart Disease (CHD) in the Denver and Los Angeles Study Areas - Zero
Ambient Exposures 6-18
Table 6-18 Descriptive Statistics for the Percent COHb Ambient Contribution Estimated
Using Denver 1995 Ambient Concentrations Adjusted to Just Meet the Current
Standard 6-19
Table 7-1 Summary of How Variability Was Incorporated Into the Second Draft REA. 7-2
Table 7-2 Characterization of Key Uncertainties in the Second Draft REA for Denver and
Los Angeles Areas 7-7
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1 1 INTRODUCTION
2 This draft document describes the quantitative human exposure assessment and risk
3 characterization being conducted to inform the U.S. Environmental Protection Agency's (EPA's)
4 current review of the National Ambient Air Quality Standards (NAAQS) for carbon monoxide
5 (CO). This draft report, Risk and Exposure Assessment to Support the Review of the Carbon
6 Monoxide Primary National Ambient Air Quality Standards: Second External Review Draft, is
7 being provided to the Clean Air Scientific Advisory Committee (CASAC) CO Panel and the
8 public for review in advance of a public meeting of the CASAC CO panel planned for March 22-
9 23, 2010. Following that meeting, we will take CASAC and public comments into account in
10 preparing the final document. We plan to complete the final Risk and Exposure Assessment
11 report in May 2010. Given the significant time constraints of this review,1 results of the
12 assessment are provided in this document without substantial interpretation. Rather,
13 interpretative discussion of these results is provided in the draft Policy Assessment.
14 1.1 BACKGROUND
15 The EPA is presently conducting a review of the national ambient air quality standards
16 for CO. Sections 108 and 109 of the Clean Air Act (Act) govern the establishment and periodic
17 review of the NAAQS. These standards are established for certain pollutants that may
18 reasonably be anticipated to endanger public health and welfare, and whose presence in the
19 ambient air results from numerous or diverse mobile or stationary sources. The NAAQS are to
20 be based on air quality criteria, which are to accurately reflect the latest scientific knowledge
21 useful in indicating the kind and extent of identifiable effects on public health or welfare that
22 may be expected from the presence of the pollutant in ambient air. Based on periodic reviews of
23 the air quality criteria and standards, the Administrator is to make revisions in the criteria and
24 standards, and promulgate any new standards, as may be appropriate. The Act also requires that
25 an independent scientific review committee advise the Administrator as part of this NAAQS
26 review process, a function performed by the CASAC.
27 The current NAAQS for CO includes two primary standards to provide protection for
28 exposures to carbon monoxide. In 1994, EPA retained the primary standards at 9 parts per
29 million (ppm), 8-hour average and 35 ppm, 1-hour average, neither to be exceeded more than
30 once per year (59 FR 38906). These standards were based primarily on the clinical evidence
31 relating carboxyhemoglobin (COHb) levels to various adverse health endpoints and exposure
32 modeling relating CO exposures to COFtb levels. With the 1994 decision, EPA also reaffirmed
1 As noted below, the schedule for this review is governed by the terms of a court order.
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1 an earlier decision that the evidence did not support the need for a secondary standard for CO (59
2 FR38906).
3 A subsequent review of the CO NAAQS was initiated in 1997, which led to the
4 completion of the 2000 Air Quality Criteria Document for Carbon Monoxide (US EPA, 2000)
5 and a draft exposure analysis methodology document (US EPA, 1999). EPA put on hold the
6 NAAQS review when Congress requested that the National Research Council (NRC) review the
7 impact of meteorology and topography on ambient CO concentrations in high altitude and
8 extreme cold regions of the U.S. In response, the NRC convened the Committee on Carbon
9 Monoxide Episodes in Meteorological and Topographical Problem Areas, which focused on
10 Fairbanks, Alaska as a case-study. A final report, "Managing Carbon Monoxide Pollution in
11 Meteorological and Topographical Problem Areas" (NRC, 2003), offered a wide range of
12 recommendations regarding management of CO air pollution, cold start emissions standards,
13 oxygenated fuels, and CO monitoring. Following completion of this NRC report, EPA did not
14 conduct rulemaking to complete the review.
15 EPA initiated the current review of the NAAQS for CO on September 13, 2007, with a
16 call for information from the public (72 FR 52369) requesting the submission of recent scientific
17 information on specified topics. A workshop was held on January 28-29, 2008 (73 FR 2490) to
18 discuss policy-relevant scientific and technical information to inform EPA's planning for the CO
19 NAAQS review. Following the workshop, EPA outlined the science-policy questions that would
20 frame this review, outlined the process and schedule that the review would follow, and provided
21 more complete descriptions of the purpose, contents, and approach for developing the key
22 documents for the review in a draft Plan for Review of the National Ambient Air Quality
23 Standards for Carbon Monoxide (US EPA, 2008a). After CASAC and public input on the draft
24 plan, EPA made the final plan available in August 2008 (US EPA, 2008b). In January, 2010,
25 EPA completed the process of assessing the latest available policy-relevant scientific information
26 to inform the review of the CO standards. This assessment, the Integrated Science Assessment
27 for Carbon Monoxide (hereafter, "ISA") (US EPA, 2010a), includes an evaluation of the
28 scientific evidence on the health effects of CO, including information on exposure, physiological
29 mechanisms by which CO might adversely impact human health, an evaluation of the clinical
30 evidence for CO-related morbidity, and an evaluation of the epidemiological evidence for CO-
31 related morbidity and mortality associations.2
32 EPA's Office of Air Quality Planning and Standards (OAQPS) has developed this second
33 draft Risk and Exposure Assessment (REA) describing the quantitative assessment conducted by
2 The ISA also evaluates scientific evidence for the effects of CO on public welfare which EPA will
consider in its review of the need for a secondary standard. EPA is not intending to do a quantitative risk
assessment for the secondary standard review.
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1 the Agency to support the review of the primary CO standards. This second draft document is a
2 concise presentation of the methods, key results, observations, and related uncertainties
3 associated with the quantitative analyses performed. This REA builds upon the health effects
4 evidence presented in the final ISA, as well as CASAC advice (Brain and Samet, 2009; Brain
5 and Samet, 2010) and public comments on a scope and methods planning document for the REA
6 (hereafter, "Scope and Methods Plan") (US EPA, 2009a) and on the first draft REA (US EPA,
7 2009b). The final REA will reflect consideration of CASAC and public comments on this
8 second draft REA. The final REA will be completed by May 28, 2010, consistent with the court
9 order governing the schedule for completion of this review. The court order also specifies that
10 EPA sign for publication notices of proposed and final rulemaking concerning its review of the
11 CO NAAQS no later than October 28, 2010 and May 13, 2011, respectively.
12 The final ISA and final REA will inform the policy assessment and rulemaking steps that
13 will lead to final decisions on the CO NAAQS. The policy assessment will be described in a
14 Policy Assessment (hereafter, "PA") document, which will include staff analysis of the scientific
15 basis for alternative policy options for consideration by the Administrator prior to rulemaking.
16 The PA will integrate and interpret information from the ISA and the REA to frame policy
17 options for consideration by the Administrator. The PA is intended to help "bridge the gap"
18 between the Agency's scientific and technical assessments, presented in the ISA and REA and
19 the judgments required of the Administrator in determining whether it is appropriate to retain or
20 revise the standards. The PA is also intended to facilitate CASAC's advice to the Administrator
21 on the adequacy of existing standards, and any new standards or revisions to existing standards
22 as may be appropriate. A draft PA is being prepared (USEPA, 201 Ob) for release for review by
23 CASAC, as well as for public comment, in conjunction with CASAC review and public
24 comment of this document.
25 1.2 PREVIOUS REVIEWS AND ASSESSMENTS
26 Reviews of the CO NAAQS completed in 1985 and 1994 included analyses of exposure
27 to ambient CO and associated internal dose, in terms of COHb levels, which were used to
28 characterize risks for populations of interest (50 FR 37484; 59 FR 38906). These prior risk
29 characterizations compared the numbers and percent of the modeled population that exceeded
30 several potential health effect benchmarks, expressed in terms of COHb levels. The COHb
31 levels of interest in these reviews were drawn from the evidence of COHb levels associated with
32 exercise-induced aggravation of angina in controlled human exposure studies involving short-
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1 term (shorter than 8 hours) exposures of patients with diagnosed ischemic heart disease (IHD)3 to
2 elevated CO concentrations (US EPA, 1979; US EPA, 1984; US EPA, 1991).
3 In the review completed in 1994, this characterization was performed for the population
4 of interest in the city of Denver, Colorado (US EPA, 1992; Johnson et al., 1992). That analysis
5 indicated that if the current 8-hour standard were just met, the proportion of the nonsmoking
6 population with cardiovascular disease4 experiencing exposures at or above 9 ppm for 8 hours
7 decreased by an order of magnitude or more as compared to the proportion under then-existing
8 ambient CO levels, down to less than 1 percent of the total person-days in that population.
9 Likewise, just meeting the current 8-hour standard reduced the proportion of the nonsmoking
10 cardiovascular-disease population person days at or above COHb levels of concern by an order
11 of magnitude or more relative to then-existing ambient CO levels. More specifically, upon just
12 meeting the 8-hour standard, EPA estimated that less than 0.1% of the nonsmoking
13 cardiovascular-disease population would experience a COHb level of about 2.1%. A smaller
14 percentage of the at-risk population was estimated to exceed higher COHb levels. The analysis
15 also considered additional exposure scenarios that included certain indoor sources (e.g., passive
16 smoking, gas stove usage). While these indoor sources were shown to contribute to total CO
17 exposure, it was determined to be of limited utility in the risk assessment because these source
18 emissions would not be effectively mitigated by setting more stringent ambient air quality
19 standards (59 FR 38914).
20 In the review initiated in 1997, EPA consulted with CAS AC on a draft exposure analysis
21 methodology document, Estimation of Carbon Monoxide Exposures and Associated
22 Carboxyhemoglobin Levels in Denver Residents using pNEM/CO (Version 2.0) (Mauderly,
23 1999; Johnson, 1999). Although the EPA did not complete the review initiated in 1997, OAQPS
24 continued work on the CO exposure assessment to further develop the exposure assessment
25 modeling component of the Total Risk Integrated Methodology (TRIM) system. A subsequent
26 draft technical report (Johnson et al., 2000) was produced documenting the application of the CO
3 Ischemic heart disease is a category of cardiovascular disease associated with narrowed heart arteries; it is
often also called coronary artery disease (CAD) and coronary heart disease (CHD). Individuals with CHD have
myocardial ischemia, which occurs when the heart muscle receives insufficient oxygen delivered by the blood.
Exercise-induced angina pectoris (chest pain) occurs in many of them. Among all patients with diagnosed CAD, the
predominant type of ischemia, such as that indicated by ST segment depression, is asymptomatic (i.e., silent). Also,
patients who experience angina typically have additional ischemic episodes that are asymptomatic (2000 AQCD,
section 7.7.2.1).
4 In characterizing the population of interest with regard to demographics (age and sex), the assessment for
the review completed in 1994 drew from estimates of the prevalence of ischemic heart disease (IHD) provided by
the National Health Interview Survey and corresponding estimates of undiagnosed ischemia developed by EPA.
Estimates of undiagnosed IHD were based on two assumptions: (1) there are 3.5 million persons inU.S. with
undiagnosed IHD (drawn from estimate by American Heart Association) and (2) persons with undiagnosed IHD are
distributed within the population in the same manner as persons with diagnosed IHD (USEPA, 1992).
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1 exposure and dose modeling methodology for two study areas (Denver and Los Angeles). The
2 exposure and dose estimates were obtained by applying pNEM/CO version 2.1, a predecessor to
3 APEX, to adults with IHD residing within each urban area.5 This report was subjected to an
4 external peer review by three exposure modeling experts and convened by Science Applications
5 International Corporation (SAIC, 2001).
6 In the 2000 pNEM/CO assessment, the Denver study area was defined as all census tracts
7 located within 10 km of each of six fixed-site monitors within the Denver metropolitan area. Air
8 quality data for 1995 reported by these monitors were used to represent existing conditions in the
9 study area. Because the second highest non-overlapping 8-hour average CO concentration
10 equaled 9.5 ppm, the existing conditions in Denver for 1995 were considered to approximate just
11 meeting the 8-hour average CO standard.6 In a similar manner, the Los Angeles study area was
12 defined as all census tracts within 10 km often fixed-site monitors in the Los Angeles area,
13 though air quality data for 1997 were adjusted downwards so that the concentrations associated
14 with the design monitor just met the 8-hour NAAQS. A total of 15 distinct microenvironments
15 were modeled using a mass balance model accounting for the infiltration of outdoor (ambient)
16 concentrations, air exchange rates, as well as CO emissions from two indoor sources (residential
17 gas stoves and passive cigarette smoke).
18 In the 2000 pNEM/CO assessment, approximately 0.5% of the non-smoking IHD
19 population in both urban areas was estimated to experience a maximum end-of-hour COHb level
20 of about 2.0% upon just meeting the current 8-hour standard.7 A smaller percentage of the at-
21 risk population was estimated to exceed higher COHb levels (e.g., <0.1% of persons were
22 estimated to have COHb levels at or above 3.0% in either location). Indoor CO sources were an
23 important contributor to COHb though these impacted a much larger portion of the simulated
24 population at the higher COHb levels (i.e., those persons with >1% COHb). For example, in
25 Denver with indoor sources included, nearly 20% of persons with IHD were estimated to have a
26 maximum end-of-hour COHb level of about 2.0%. In Los Angeles with indoor sources included,
27 the estimated percent of persons having a COHb level at or above 2.0% was lower (i.e., about
28 17%), though still a much greater percentage than that estimated in the absence of indoor sources
29 (i.e., <1%).
5 This is consistent with the demographic group modeled in the 1992 assessment described above (Johnson
et al., 1992; USEPA, 1994), and drew from updated information with regard to prevalence demographics (Johnson
et al., 2000, p. section 2.5.2).
6 A rounding convention allows the second highest 8-hour average CO concentration (i.e., the design value
(DV)) to be as high as 9.4 ppm for the 8-hour CO NAAQS of 9 ppm.
7 Note that the contemporaneous design value for Denver was 0.1 ppm above just meeting the current 8-
hour standard (9.5 versus 9.4 ppm).
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1 1.3 CURRENT REVIEW, CASAC ADVICE AND PUBLIC COMMENT
2 In preparing the Scope and Methods Plan for the REA, we considered the scientific
3 evidence presented in the first draft ISA (US EPA, 2009c) and the key science policy issues
4 raised in the IRP (US EPA, 2008b). EPA held a consultation with CASAC to solicit comments
5 on the Scope and Methods Plan during a May 2009 CASAC meeting at which CASAC also
6 provided comments on the first draft ISA (Brain, 2009). Public comments were also requested
7 (74 FR 15265). Those CASAC and public comments were considered in advance of the conduct
8 of the analyses and results presented in the first draft REA (US EPA, 2009b).
9 As a result of the notable limitations in available ambient monitoring and
10 microenvironmental concentration data, staff implemented a much-simplified, screening-level
11 approach to assess population exposure and dose for the first draft APEX/CO REA (US EPA,
12 2009b). Two urban study areas, Denver and Los Angeles, were defined as all census tracts
13 within 20 km of a single fixed-site monitor, using the design value monitor (i.e., the monitor
14 recording the highest concentration in each area) for the specified year of the assessment.
15 Therefore, ambient monitoring data associated with the site measuring the highest CO
16 concentrations were applied to all people within the surrounding study area. This was the case
17 for scenarios that included as is air quality (year 2006 for either location) and air quality adjusted
18 to just meeting the current standard (adjusted from 1995 for Denver and from 1997 for Los
19 Angeles). In the first draft REA, no adjustment was made for spatial variability in ambient
20 concentrations across each area and at most two microenvironments were included in the
21 exposure model simulations (in-vehicle and all others). The in-vehicle microenvironmental
22 factor was constrained to a point estimate of 2.0, that is, these microenvironmental
23 concentrations would always be twice that observed at the single ambient monitor. In the design
24 of the assessment, it was noted that this focus on high concentration CO monitors and other
25 model simplifications would have a tendency to produce higher CO exposures in most simulated
26 persons than results generated from the 2000 pNEM/CO assessment. The results were consistent
27 with this statement. The estimated percent of the population at any COHb level was greater in
28 the first draft REA than that estimated by Johnson et al. (2000).
29 On November 16-17, 2009, the CASAC CO panel met to discuss the first draft REA.
30 The final written comments and recommendations were provided to EPA in February 2010
31 (Brain and Samet, 2010). The design of the current second draft REA builds upon these
32 recommendations from CASAC, information presented in the final ISA (US EPA, 2010a), as
33 well as comments made by the public.8 Specifically in this assessment, EPA has:
8 Public Comments on the first draft REA were submitted to the docket for this review and also presented
in November, 2009 at the CASAC meeting.
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1 • Expanded each of the modeling domains to include a greater number of
2 ambient monitors used as input to APEX;
3 • Increased the number of microenvironments modeled from two to eight;
4 • Improved the representation of variability in estimated
5 microenvironmental concentrations, including in-vehicles;
6 • Included an algorithm that adjusts for spatial heterogeneity in estimated
7 outdoor concentrations across each model domain;
8 • Implemented the mass-balance model for estimating concentrations in all
9 indoor microenvironments;
10 • Implemented the algorithm that allows commuters to experience home-
11 tract and work-tract ambient concentrations; and
12 • Expanded the at-risk population to address the undiagnosed persons with
13 CHD.
14 The purpose of this second draft REA is to seek CASAC review and public comment
15 regarding our characterization of the results presented considering the improvements made to the
16 assessment design and inputs used, and CASAC's advice on the role of this assessment in
17 informing the current review of the CO NAAQS.
18
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1 1.4 REFERENCES
2 Brain JD. (2009). Letter from Dr. Joseph Brain to Administrator Lisa Jackson. Re: Consultation on EPA's Carbon
3 Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure
4 Assessment. CASAC-09-012. July 14, 2009.
5 Brain JD and Samet JM. (2009). Letter from Drs. J.D. Brain and J.M. Samet to Administrator Lisa Jackson. Re:
6 Review of the EPA's Integrated Science Assessment for Carbon Monoxide ( First External Review Draft).
1 EPA-CASAC-09-011. June 24, 2009.
8 Brain JD and Samet JM. (2010). Letter from Drs. J.D. Brain and J.M. Samet to Administrator Lisa Jackson. Re:
9 Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO) Primary
10 National Ambient Air Quality Standards: First External Review Draft. EPA-CASAC-10-006. February 12,
11 2010.
12 Johnson T, Capel J, Paul R, Wijnberg L. (1992). Estimation of Carbon Monoxide Exposure and associated
13 Carboxyhemoglobin levels in Denver Residents Using a Probabalistic verion of NEM, prepared by
14 International Technology for U.S. EPA, Office of Air Quality Planning and Standards, Durham, NC,
15 Contract No. 68-DO-0062,
16 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (1999). Estimation of Carbon Monoxide Exposures and
17 Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.0) prepared by
18 ICF Kaiser Consulting Group for U.S. EPA, Office of Air Quality Planning and Standards, under Contract
19 No. 68-D6-0064, WA Nos. 1-19 and 2-24. Available at: http://www.epa.gov/ttn/fera/human_related.html
20 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and
21 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO
22 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No.
23 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available
24 at: http://www.epa.gov/ttn/fera/human_related.html..
25 Mauderly J. (1999). Letter from Dr. Joe Mauderly, Chair, Clean Air Scientific Advisory Committee, to
26 Administrator Carole M. Browner. Re: Notification of a Consultation on the Estimation of Carbon
27 Monoxide Exposures and Associated Carboxyhemoglobin Levels in Denver Residents using pNEM/CO
28 (Ver. 2.0). July 12, 1999.
29 National Research Council. (2003). Managing Carbon Monoxide Pollution in Meteorological and Topographical
30 Problem Areas. Washington, D.C. The National Academies Press.
31 SAIC. (2001). Memo to Harvey Richmmond, EPA, Technical Peer Review (including reviewers comments) of
32 "Estimation of Carbon Monoxide Exposures and Associated Carboxyhemoglobin Levels for Residents of
33 Denver and Los Angeles Using pNEM/CO (version 2.1)", Docket EPA-HQ-OAR-2008-0015. Available
34 at: http://www.epa.gov/ttn/fera/human_related.html
35 US EPA. (1979). AirQuality Criteria for Carbon Monoxide. Research Triangle Park, NC: Office of Health and
36 Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-79-022.
37 US EPA. (1984). Review of the NAAQS for Carbon Monoxide: Reassessment of Scientific and Technical
3 8 Information. Office of Air Quality Planning and Standards, report no. EPA-450/584-904. Research
39 Triangle Park, NC.
40 US EPA. (1991). Air Quality Criteria for Carbon Monoxide. Research Triangle Park, NC: Office of Health and
41 Environmental Assessment, Environmental Criteria and Assessment Office, report no. EPA/600/8-90/045F.
February, 2010 1-8 Draft - Do Not Cite or Quote
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1 US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of
2 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no.
3 EPA/452/R-92-004.
4 US EPA. (1999). Total Risk Integrated Methodology - TRIM.Expo Technical Support Document, External Review
5 Draft, November 1999. Office of Air Quality Planning and Standards, U.S. Environmental Protection
6 Agency, Research Triangle Park, NC, report no. EPA-453/D-99-001. Available at:
7 http://www.epa.gov/ttn/fera/trim_fate.html#1999historical.
8 US EPA. (2000). Air Quality Criteria for Carbon Monoxide. National Center for Environmental Assessment,
9 Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC,
10 report no. EPA/600/P-99/00IF. June 2000. Available at:
11 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=18163.
12 US EPA. (2008a). Draft Plan for Review of the National Ambient Air Quality Standards for Carbon Monoxide.
13 U.S. Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452D-08-001. Also
14 known as Draft Integrated Review Plan. Available at:
15 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html.
16 US EPA. (2008b). Plan for Review of the National Ambient Air Quality Standards for Carbon Monoxide. U.S.
17 Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/452R-08-005. Also known
18 as Integrated Review Plan. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html.
19 US EPA. (2009a). Carbon Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health
20 Risk and Exposure Assessment U. S. Environmental Protection Agency, Research Triangle Park, NC,
21 report no. EPA-452/R-09-004. Available at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_pd.html.
22 US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary
23 National Ambient Air Quality Standards: First External Review Draft. U.S. Environmental Protection
24 Agency, Research Triangle Park, NC, report no. EPA-452/P-09-008. Available at:
25 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_rea.html
26 US EPA. (2009c). Integrated Science Assessment for Carbon Monoxide-First External Review Draft. U.S.
27 Environmental Protection Agency, Research Triangle Park, NC, report no. EPA/600/R-09/019. Available
28 at: http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html.
29 US EPA. (2010a). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency,
3 0 Washington, DC, EPA/600/R-09/019F. Available at:
31 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html.
32 US EPA. (2010b). Policy Assessment to Support the Review of the Carbon Monoxide Primary National Ambient
33 Air Quality Standards: External Review Draft. U.S. Environmental Protection Agency, Research Triangle
34 Park, NC, report no. EPA-452/P-10-005. Available at:
3 5 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_rea.html.
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1 2 CONCEPTUAL OVERVIEW: ASSESSING AMBIENT CARBON
2 MONOXIDE EXPOSURE AND RISK
3 In this chapter, we have summarized the conceptual model for assessing exposure to
4 ambient CO and associated health risk. Subsections focus on different components of the model
5 including identification of the key emission sources to ambient concentrations (section 2.1),
6 exposure pathways and relevant microenvironments (section 2.2), identification of at-risk
7 populations (section 2.3), the dose metric (section 2.4), health endpoints (section 2.5), and the
8 risk characterization approach (section 2.6). Section 2.7 presents the key observations for this
9 chapter.
10 2.1 SOURCES OF CARBON MONOXIDE
11 Carbon monoxide in ambient air is formed primarily by the incomplete combustion of
12 carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO
13 emitted from these reactions, relative to the amount of carbon dioxide (CO2) generated, is
14 sensitive to conditions in the combustion zone. CO production relative to CO2 generally
15 decreases with any increase in fuel oxygen (©2) content, burn temperature, or mixing time in the
16 combustion zone (ISA, section 3.2). As a result, CO emissions from large fossil-fueled power
17 plants are typically very low because optimized fuel consumption conditions make boiler
18 combustion highly efficient. In contrast, internal combustion engines commonly used to power
19 mobile sources have widely varying operating conditions. Therefore, higher and more variable
20 CO emission levels result from the operation of these mobile sources (ISA, section 3.2). In
21 2002, CO emissions from on-road vehicles accounted for a substantial majority of total
22 emissions by individual source sectors in the U.S. (ISA, Figure 3-1).l As in previous NAAQS
23 reviews, mobile sources continue to be a significant emission source of CO to ambient air.
24 Sources of CO inside buildings include infiltration of ambient air indoors, as well as,
25 where present, indoor (nonambient) sources such as gas stoves and tobacco smoke (ISA, section
26 3.6.5.2). In addition to infiltration of ambient air, CO inside motor vehicles may also receive
27 contributions from nonambient sources in the cabin, which can be substantial under air
28 ventilation modes that limit inflow from outside the vehicle (ISA, p. 3-89). However, the focus
29 of this REA, which is conducted to inform the current review of the CO NAAQS, is on sources
30 of ambient CO. We provide quantitative estimates of population exposure and dose originating
31 from ambient CO in two urban areas (details on site selection are provided in chapter 3 below).
1 The 2002 National Emissions Inventory (NEI) was the most recently available NEI meeting data quality
objectives for the ISA. The NEI includes data from various sources such as industries and state, tribal, and local air
agencies (ISA, p. 3-1).
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1 The exposure modeling in this assessment does not quantitatively estimate the contribution of
2 indoor sources to an individual's total exposure and dose. This assessment does however
3 qualitatively draw upon available information regarding potential indoor source contributions to
4 estimated population exposure and dose (described in section 2.2 below).
5 2.2 EXPOSURE PATHWAYS AND IMPORTANT MICROENVIRONMENTS
6 Human exposure to CO involves the contact (via inhalation) between a person and the
7 pollutant in the various locations (or microenvironments) in which people spend their time.
8 Studies of personal exposure have generally found that the largest portion of the day is generally
9 spent indoors and the largest percentage of the time in which an individual is exposed to ambient
10 CO occurs indoors (ISA, sections 2.3 and 3.6). As a result, CO concentrations in indoor
11 microenvironments are an important determinant of an individual's total CO exposure. Recent
12 population exposure studies conducted in Milan, Italy support this conclusion (Bruinen de Bruin
13 et al., 2004), indicating that over 80% of the population exposure to CO can occur in indoor
14 microenvironments (ISA, Table 3-13). Taking into account the infiltration of ambient CO
15 indoors, indoor CO concentrations are similarly an important determinant in an individual's
16 exposure to ambient CO.
17 Microenvironments that may influence CO exposures typically include residential indoor
18 environments and other indoor locations, near-traffic outdoor microenvironments and other
19 outdoor locations, and inside vehicles. Consideration of microenvironmental exposures
20 illustrates the variability in the relationship between personal exposure and ambient
21 concentrations. For example, one study summarized the relationship between personal CO
22 exposure concentrations in five broadly defined microenvironments (i.e., indoor residence,
23 indoor other, outdoor near road, outdoor other, and in-vehicle) and ambient CO concentrations in
24 Baltimore, MD (ISA, section 3.6.5.2; Chang et al., 2000). On average, the indoor-to-ambient
25 and outdoor-to-ambient concentration ratios were about one, though most of the ratios observed
26 across this set of indoor and outdoor microenvironments were less than one. With the exception
27 of ratios for the in-vehicle microenvironments, which as a group were generally above one, few
28 ratios were above unity (ISA, p. 3-85, Figure 3-46). Given the expected stability of CO as it
29 infiltrates indoor microenvironments from outdoor air and the lack of significant removal
30 mechanisms of CO in outdoor microenvironments, it is likely that the variability in
31 personal/microenvironmental-to-ambient and outdoor-to-ambient concentration ratios is the
32 result of spatial and temporal variability in outdoor concentrations with respect to simultaneously
33 measured ambient concentrations at fixed-site monitors, and also reflects the impact of lag time
34 associated with attaining steady state relationships, as well as potential presence of non-ambient
35 sources.
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1 Typically the highest CO exposure concentrations are experienced while inside vehicles.
2 Because motor vehicles continue to be important contributors to ambient CO concentrations,
3 both the time spent in motor vehicles and the elevated CO concentrations occurring on and near
4 heavily trafficked roads continue to be important contributors to personal exposures. For
5 example, in the study summarized above on personal exposures occurring within particular
6 microenvironments (i.e., Chang et al., 2000), most in-vehicle CO exposure-to-ambient
7 concentration ratios were greater than one, with the median being approximately 2.5. The
8 average ratio was approximately 2.5 in summer, but a few somewhat higher in-vehicle
9 measurements in the winter period, contributed to a winter average of approximately 4 (ISA,
10 section 3.6.5.2, Figure 3-46; Chang et al., 2000 Figure 5).2 Given this relationship, it should not
11 be surprising that while about 8% of a person's time per day is spent in transit, 13-17% of their
12 total daily exposure occurs within an in-vehicle microenvironment (e.g., Bruinen de Bruin et al.,
13 2004; Scotto di Marco et al., 2005).
14 A similar influence of mobile source-influenced microenvironments was observed in the
15 CO population exposure studies conducted in Denver CO and Washington, DC during the winter
16 of 1982 and 1983 (Akland et al., 1985).3 In both cities, when comparing the distribution of
17 measured CO concentrations from the monitoring network to measured personal exposures, two
18 common phenomena were observed. At the lowest percentiles of each distribution, ambient CO
19 concentrations were consistently greater than the personal exposures. At the highest percentiles
20 of each distribution, ambient concentrations were consistently lower than the personal exposures
21 (US EPA, 2000). These studies determined that the highest average CO concentrations occurred
22 when subjects were in a mobile source-influenced microenvironment (e.g., inside parking
23 garages, in-vehicles). Commute time was also a factor; those who commuted 6 hours or more
24 per week had higher average exposures than those who commuted fewer hours per week.
25 Furthermore, mean CO concentrations within in-vehicle microenvironments (ranging from 7.0 to
26 9.8 ppm) were greater than common outdoor locations (ranging from 1.4 to 3.2 ppm) (US EPA,
27 2000). In considering the results from the Denver and Washington personal exposure studies it
28 is important to recognize that CO emissions from motor vehicle sources have declined
29 dramatically since the early 1980's when these studies were conducted. Consequently, both
30 ambient fixed-site CO concentrations and in-vehicle CO concentrations have also been reduced
31 significantly since that time period.
2 Information on the distance of the ambient monitors from highly trafficked roadways or potential for in-
vehicle (nonambient) sources was not provided.
3 Both studies collected measurements and activity pattern diaries from a random sample of the population,
defined as including non-institutionalized, non-smoking residents, 18 to 70 years of age, who lived in each
respective city's metropolitan area (Akland et al., 1985).
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1 Given their influence on ambient exposures, exposures to CO near roadways and in
2 vehicle microenvironments are of particular importance in this assessment. Data from several
3 studies that have compared concentrations inside vehicles to concentrations immediately outside
4 vehicles, indicates that indoor/outdoor concentration (I/O) ratios on average range from just
5 above to just below unity (Chan et al., 1991; Rodes et al., 1998; Boulter and McCrae, 2005;
6 Sharp and Tight, 1997). These studies are supported by a review by Flachsbart (1999) regarding
7 other studies published between 1982 and 1992 that measured interior and exterior CO
8 concentrations simultaneously during motor vehicle trips and reported I/O ratios just below unity
9 (Petersen and Allen, 1982; Koushi et al., 1992). Some studies reported no effect of ventilation
10 setting on I/O ratios, while others reported an effect. For example, one study described in the
11 ISA indicated I/O ratios could exceed unity with the ventilation set to re-circulate vehicle air
12 (Abi Esber and El-Fadel, 2008). However, the study authors attributed this finding to
13 unaccounted sources of CO that caused increases in CO concentrations within the vehicle cabin
14 under those conditions (ISA, section 3.6.6.2; Abi Esber and El-Fadel, 2008).
15 In general, the above results suggest that the I/O ratio tends toward unity when there are
16 no interior sources of CO, the automobile engine does not contribute directly to its own interior
17 concentrations, and the measurement probes are properly installed on the vehicle. This
18 conclusion is consistent with theoretical expectations for a non-reactive pollutant. For example,
19 CO concentrations inside vehicles can be estimated as a function of outside CO concentration,
20 air exchange rate, a penetration factor, and the emission rates of indoor sources (e.g., exhaust
21 leaks, smoking). If one assumes that (1) steady-state ventilation conditions exist, (2) the indoor
22 removal rate (K) is zero (i.e., no loss of CO as it moves from outside to inside the vehicle), and
23 (3) there are zero emissions from interior sources, then the CO concentration inside a vehicle can
24 be simplified to a function of outside CO concentrations and the penetration rate (i.e., infiltration
25 is generally equivalent to penetration).4 Under these stated conditions, the I/O ratio would
26 ultimately converge to unity.
27 There are a few studies that have measured both in-vehicle and fixed-site monitoring
28 concentrations. The data from these studies can also inform the development of
29 microenvironmental factors used for estimating in-vehicle CO exposures. The ISA notes that
30 studies summarized in the 2000 CO AQCD found that in-vehicle CO concentrations were
31 generally two to five times higher than ambient CO concentrations obtained at fixed-site
32 monitors within the cities studied. For example, Shikiya et al. (1989) reported such
33 concentrations measured as part of a southern California study. When using the reported in-
34 vehicle CO measurements, one could estimate concentration ratios ranging from 1.8 to 2.7, a
4 See section 3.6.2 of the ISA.
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1 range of ratios dependent on the time-of-year measurements were collected. Note however that
2 there are several factors that could contribute to variability in reported or calculated
3 concentration ratios. For example, often times in these measurement studies, the averaging time
4 associated with the companion measurements differ, that is there may be a much shorter
5 sampling interval for the in-vehicle measurement when compared with that of the ambient
6 monitor. More specifically, Shikiya et al. (1989) measured in-vehicle CO concentrations during
7 commutes lasting, on average, 33 minutes, while fixed site monitoring values averaged over 4-
8 hours. It is likely that the time-averaged concentrations are less than that of the true fixed-site
9 concentrations that occurred during the 33 minute commute, perhaps resulting in an
10 overestimation of the concentration ratios when using this data. Furthermore, Shikiya et al.
11 (1989) reported seasonal differences for the in-vehicle CO concentrations (winter averaged 10.1
12 ppm; summer averaged 6.5 ppm), but not for the fixed-site monitor (average for both seasons
13 was 3.7 ppm). Typically ambient concentrations are greater in winter (e.g., ISA Figure 3-22 for
14 Los Angeles). Therefore, when using the fixed-site seasonal average and in-vehicle seasonally
15 stratified measurements from Shikiya et al. (1989) to calculate the ratios as was done above, the
16 winter value may be overestimated while the summer value could be underestimated. In addition
17 to the factors mentioned above, this relationship can vary based on several other factors that may
18 influence the fixed-site monitor concentration, such as the nearby roadway traffic density, the
19 monitor siting characteristics (e.g., proximity to the roadway), and local scale meteorology (e.g.,
20 downwind), with each described in greater detail in chapter 3. Of the few studies reporting in-
21 vehicle and companion fixed-site measurements, most do not measure all of the potentially
22 influential factors or provide the data stratified by such factors. Thus, a general range of two to
23 five may be adequate to represent the total variability for this particular relationship, recognizing
24 that there are limitations in the available measurement data to define this relationship.
25 Although not the focus of this review, indoor sources such as gas stoves and
26 environmental tobacco smoke can, where present, also be important contributors to total CO
27 exposure and may be of concern for such at-risk populations as individuals with cardiovascular
28 disease, among others (see section 2.3 below). For example, some assessments performed for
29 previous reviews have included modeling simulations both without and with indoor sources (gas
30 stoves and tobacco smoke) to provide context for the assessment of ambient CO exposure and
31 dose (e.g., US EPA, 1992; Johnson et al., 2000). The 2000 pNEM/CO simulations with indoor
32 sources indicated that the impact of such sources on the proportion of the population
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1 experiencing higher exposures and COHb levels can be substantial, as summarized in section 1.2
2 above.5
3 2.3 EXPOSURE AND DOSE METRICS
4 Exposure concentration over a time period of interest (e.g., one hour or eight hours) is a
5 common exposure metric which reflects the integration of exposures to pollutant concentrations
6 that occur in each microenvironment in which time is spent (see section 4.4.6 below). In the case
7 of CO, for which the common mechanism underlying biological response is binding to heme
8 proteins, COHb level in blood is well recognized as an important internal dose metric used in
9 evaluating CO exposure and the potential for health effects (ISA, p. 2-4, sections 4.1, 4.2, 5.1.1).
10 Accordingly, COHb levels are used in this assessment.
11 Carboxyhemoglobin occurs in the blood due to endogenous CO production from
12 biochemical reactions associated with normal breakdown of heme proteins, as well as in
13 response to inhaled (exogenous) ambient and nonambient6 CO exposures (ISA, section 4.5).7
14 Levels of endogenous COHb in healthy individuals have been described to range down to 0.3%
15 and generally be less than 1% (ISA, pp. 4-9, 4-23, 2-6). However, the production of endogenous
16 CO and levels of endogenous COHb vary with several physiological characteristics (e.g., slower
17 COHb elimination with increasing age), as well as some disease states, which can lead to higher
18 endogenous levels in some individuals (ISA, section 4.5). Other factors affecting CO uptake and
19 elimination include physical activity and altitude (ISA, section 4.4).
20 The amount of COHb formed in response to exogenous CO is dependent on the CO
21 concentration and duration of exposure, exercise (which increases the amount of air removed and
22 replaced per unit of time for gas exchange), the pulmonary diffusing capacity for CO, ambient
23 pressure, health status, and the specific metabolism of the exposed individual. The formation of
24 COHb is a reversible process, but the high affinity of CO for Hb, which affects the elimination
25 half-time for COHb, can lead to accumulation of COHb in some circumstances. Fortunately,
26 mechanisms exist in normal, healthy individuals to compensate for the reduction in tissue oxygen
27 caused by increasing levels of COHb. Cardiac output increases and blood vessels dilate to carry
28 more blood so that the tissue can extract adequate amounts of oxygen from the blood (ISA,
29 chapter 4). As discussed in sections 2.4 and 2.5 below, however, there are several medical
5 As has been recognized in previous CO NAAQS reviews, such sources cannot be effectively mitigated by
setting more stringent ambient air quality standards (59 FR 38914), and are therefore not a focus of this assessment.
6 A significant source of nonambient CO long recognized as contributing to elevated COHb levels is
tobacco smoking (e.g., ISA, Figure 4-12).
7 The dosimetry and pharmacokinetics of CO are discussed in detail in chapter 4 of the ISA.
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1 disorders that can make an individual more susceptible to the potential adverse effects of low
2 levels of CO, especially during exercise.
3 As described in section 4.4.7 below, blood levels of COHb have been estimated in this
4 REA using a nonlinear solution of the Coburn-Forster-Kane (CFK) model (Coburn et al., 1965),
5 which remains "the most extensively validated and applied model for COFIb prediction (ISA,
6 section 4.2.3).
7 2.4 AT-RISK POPULATIONS
8 The term 'susceptibility' (and the term "at-risk") has been used to recognize populations
9 that have a greater likelihood of experiencing effects related to ambient CO exposure (ISA,
10 section 5.7). This increased likelihood of response to CO can potentially result from many
11 factors, including pre-existing medical disorders or disease states, age, gender, lifestyle or
12 increased exposures (ISA, section 5.7). For example, medical disorders that limit the flow of
13 oxygenated blood to the tissues have the potential to make an individual more susceptible to the
14 potential adverse effects of low levels of CO, especially during exercise. Based on the available
15 evidence in the current review, coronary artery disease (CAD), also known as coronary heart
16 disease (CUD) is the "most important susceptibility characteristic for increased risk due to CO
17 exposure" (ISA, p. 2-11). While persons with a normal cardiovascular system can tolerate
18 substantial concentrations of CO if they vasodilate or increase cardiac output in response to the
19 hypoxia produced by CO, those that are unable to vasodilate in response to CO exposure may
20 show evidence of ischemia at low concentrations of COHb (ISA, p. 2-10). There is strong
21 evidence for this in controlled human exposure studies of exercising individuals with CAD,
22 which is supported by results from recent epidemiologic studies reporting associations between
23 short-term CO exposure and increased risk of emergency department visits and hospital
24 admissions for individuals affected with ischemic heart disease (IHD)8 and related outcomes
25 (ISA, section 5.7). This combined evidence, briefly summarized in section 2.5.1 below and
26 described in more detail in the ISA, supports the conclusion that individuals with CAD represent
27 the population most susceptible to increased risk of CO-induced health effects (ISA, sections
28 5.7.1.1 and 5.7.8). The 2007 estimate of the size of the U.S. population with coronary heart
29 disease, inclusive of those with angina pectoris (cardiac chest pain) and those who have
Ischemic heart disease is a category of cardiovascular disease associated with narrowed heart arteries,
which is often also called CAD (coronary artery disease) and CHD (coronary heart disease). Individuals with CHD
have myocardial ischemia, which occurs when the heart muscle receives insufficient oxygen delivered by the blood.
Exercise-induced angina pectoris (chest pain) occurs in many of them. Among all patients with diagnosed CAD, the
predominant type of ischemia, as identified by ST segment depression, is asymptomatic (i.e., silent). Also, patients
who experience angina typically have additional ischemic episodes that are asymptomatic (2000 AQCD, section
7.7.2.1). In addition to such chronic conditions, CHD can include myocardial infarction (ISA, p. 5-24).
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1 experienced a heart attack (ISA, Table 5-26) is 13.7 million people, some fraction of whom have
2 IHD (ISA, pp.5-117). Further, there are estimated to be several million additional people with
3 silent ischemia or undiagnosed IHD (AHA, 2003). In combination this represents a large
4 population that is more susceptible to ambient CO exposure when compared to the general
5 population (ISA, section 5.7).
6 Other types of cardiovascular disease9 may also potentially contribute to increased
7 susceptibility to the adverse effects of low levels of CO, especially during exercise (ISA, section
8 5.7.1.1). For example, some evidence with regard to other types of cardiovascular disease such
9 as congestive heart failure, arrhythmia, and non-specific cardiovascular disease, although more
10 limited for peripheral vascular and cerebrovascular disease, indicates that "the continuous nature
11 of the progression of CAD and its close relationship with other forms of cardiovascular disease
12 suggest that a larger population than just those individuals with a prior diagnosis of CAD may be
13 susceptible to health effects from CO exposure" (ISA, p. 5-117).
14 Other populations considered to be potentially at increased risk relative to the general
15 population due to gender differences, aging, or preexisting disease or because of the use of
16 medications or alterations in their environment, include fetuses and young infants; the elderly,
17 especially those with compromised cardiovascular function; individuals with hematologic
18 diseases (e.g., anemia) that affect oxygen-carrying capacity or transport in the blood; those with
19 chronic obstructive pulmonary disease; people using medicinal or recreational drugs with central
20 nervous system depressant properties; individuals exposed to other chemical substances that
21 increase endogenous formation of CO; individuals who have not adapted to high altitude and are
22 exposed to a combination of high altitude and CO; and people that spend a substantial amount of
23 time on or near heavily traveled which may contribute to higher CO exposures (ISA, section
24 5.7). For example, although the effects of CO on maternal-fetal relationships are not well
25 understood, fetal circulation is likely to have a higher COHb level than the maternal circulation
26 because of differences in uptake and elimination of CO from fetal Hb, which may contribute to
27 an enhanced sensitivity to CO exposure during gestation (ISA, section 5.7.2.2). Additionally,
28 although there are no controlled human exposure or epidemiological studies examining potential
29 CO-induced effects in people suffering with anemia, it is reasonable to assume that the potential
30 combination of hypoxic effects of CO together with reduced oxygen availability and/or elevated
9 Cardiovascular disease comprises many types of medical disorders, including heart disease,
cerebrovascular disease (e.g., stroke), hypertension (high blood pressure), and peripheral vascular diseases. Heart
disease, in turn, comprises several types of disorders, including ischemic heart disease (i.e., coronary heart disease
[CHD], CAD, myocardial infarction, and angina), congestive heart failure, and disturbances of cardiac rhythm
(dysrhythmias and arryhthmias) (2000 AQCD, p. 7-7).
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1 baseline COHb levels in people suffering with anemia10 may make this population susceptible to
2 CO-induced effects (ISA, section 5.7.1.4). Asthma and COPD are other oxygen-limiting
3 diseases which may be exacerbated by CO-related oxygen limitation. Other individuals that may
4 be potentially susceptible to CO are those that may have increased endogenous production of CO
5 and potentially higher endogenous COHb levels such as diabetics, for which a few
6 epidemiological studies provide suggestive evidence of increased risk for cardiovascular
7 emergency department visits and hospital admissions compared to non-diabetics in response to
8 short-term CO concentrations (ISA, section 5.7.1.3).
9 Based on the current evidence, most particularly with regard to quantitative information
10 of COHb levels and association with specific health effects, the primary target population for
11 purposes of the quantitative assessment described in this document is adults with CHD (also
12 known as ischemic heart disease IHD or CAD), both diagnosed and undiagnosed.11 Little
13 empirical evidence is available by which to specify health effects associated with CO exposures
14 in the other, potentially at-risk groups identified above. Such evidence characterizing the nature
15 of specific health effects of CO in these populations is limited and does not include COHb levels
16 related to health effects in these groups. As a result, while we continue to recognize the potential
17 susceptibility of the larger cardiovascular disease population to health effects of CO, as has been
18 recognized in past reviews, as well as the potential susceptibility of several other populations
19 identified above (ISA, section 5.7), the at-risk population simulated in this assessment is
20 individuals with CAD (diagnosed and undiagnosed and inclusive of individuals with angina
21 pectoris and heart attacks). We note, however, that the larger cardiovascular disease population
22 and the potential susceptibility of other populations is further considered with regard to the
23 review of the CO NAAQS in the draft Policy Assessment document (US EPA, 201 Ob).
24 2.5 HEALTH ENDPOINTS
25 Carbon monoxide elicits various health effects by binding to reduced iron in heme
26 proteins and altering the functioning of a number of heme proteins (ISA, sections 4.6 and 5.1).
27 The level of CO bound to hemoglobin as carboxyhemoglobin (COHb) in the blood is the best
28 characterized dose metric for evaluating CO exposure and the potential for associated health
29 effects, as described in section 2.3 above.
10 Individuals affected with anemias of different etiologies may have low hematocrit, reduced capacity of
the blood to carry oxygen, or increased COHb levels, all of which would decrease the oxygen available for organs
and tissues (ISA, pp. 118-119).
11 As described in section 1.2 above, this is the same population group that was the focus of the
exposure/dose assessments conducted previously (e.g., US EPA, 1992; Johnson et al., 2000).
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1 The best characterized health effect associated with CO levels of concern is hypoxia
2 (reduced oxygen availability) induced by increased COHb levels in blood (ISA, section 5.1.2).
3 The formation of COHb reduces the oxygen carrying capacity of the blood and impairs the
4 release of oxygen from oxy-hemoglobin complexes to the tissues. Accordingly, CO is especially
5 harmful in individuals with impaired cardiovascular systems (as discussed in section 2.4 above)
6 and the clearest evidence of causal relationships with CO exists for cardiovascular effects. In
7 characterizing the combined evidence, the ISA concluded that cardiovascular effects are likely
8 causally related to short-term exposures to CO at relevant concentrations, with "relevant CO
9 concentrations" defined in the ISA as "generally within one or two orders of magnitude of
10 ambient CO concentrations" (ISA, p. 2-5). The "most compelling evidence of CO-induced
11 effects on the cardiovascular system comes from a series of controlled human exposure studies
12 among individuals with coronary heart disease (CHD) (ISA, sections 5.2.4 and 5.2.6).
13 Other potential effects of CO which are less well characterized at relevant exposure
14 concentrations are those on the central nervous system, reproduction and prenatal development,
15 and the respiratory system (ISA, section 2.5). These additional health endpoints, for which the
16 limited available evidence is suggestive of causal relationships (ISA, sections 5.3, 5.4 and 5.5),
17 are also considered in this review and are discussed in detail in the ISA and summarized briefly
18 in section 2.5.2 below. Across the health endpoints identified here, however, the focus of the
19 quantitative analysis described in this document is on cardiovascular disease-related effects that
20 have been observed in adults with CHD, most specifically decreased time to exercise-induced
21 angina and changes to the "ST" segment of an electrocardiogram that are indicative of
22 myocardial ischemia. This focus is based on the strength of the evidence and availability of
23 quantitative information from human studies of controlled CO exposures in which the resulting
24 COHb levels were associated with these effects (as discussed in sections 2.5.1 and 2.6 below).
25 2.5.1 Cardiovascular Disease-related Effects
26 The best characterized cardiovascular disease-related effects associated with CO are
27 markers of myocardial ischemia observed in studies of controlled CO exposures of CHD
28 patients12 and effects on exercise duration and maximal aerobic capacity observed in controlled
29 exposure studies of healthy adults.13 As noted in the ISA, the decreases in exercise duration
12 Epidemiological studies have consistently shown associations between ambient CO measurements and
emergency department visits and hospital admissions for IHD, which is coherent with the effects observed in
controlled human exposure studies of CAD patients. Additional studies have shown associations between ambient
CO and hospital admissions for congestive heart failure and cardiovascular disease as a whole (which includes
IHD), although this evidence is not as consistent among studies as the IHD evidence.
13 Human clinical studies of individuals without diagnosed heart disease that were conducted since the 2000
CO AQCD did not report an association between CO and ST-segment changes or arrhythmia (ISA, section 2.5.1).
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1 among healthy adults (associated with COHb levels from 3 up to 20%) were relatively small and
2 only likely to be noticed by competing athletes, although they are considered to provide
3 coherence with the exercise-induced cardiovascular effects of greater concern that have been
4 demonstrated in CHD patients. The controlled human exposure studies involving individuals
5 with preexisting CHD provide strong evidence for an association between short-term exposure to
6 CO and measures of ischemia (US EPA, 2000, section 6.2.2; ISA, section 5.2.4). Multiple
7 controlled human exposure studies have shown that short-term exposure to CO and subsequent
8 elevation of COHb to levels of approximately 2-6% reduces time to onset of exercise-induced
9 myocardial ischemia in individuals with preexisting CAD, with no evidence of a threshold at the
10 lowest levels tested (ISA, section 5.2.4).
11 The controlled exposure study of principal importance is a large multi-laboratory study
12 designed to evaluate myocardial ischemia, as documented by reductions in time to change in the
13 ST-segment of an electrocardiogram14 and in time to onset of angina, during a standard treadmill
14 test, at CO exposures targeted to result in mean subject COHb levels of 2% and 4%, as measured
15 by gas chromatographic technique15 (ISA, section 5.2.4, from Allred et al., 1989a, 1989b, 1991).
16 In this study, subjects on three separate occasions underwent an initial graded exercise treadmill
17 test, followed by 50- to 70-min exposures under resting conditions to average CO concentrations
18 of 0.7 ppm (room air concentration range 0-2 ppm), 117 ppm (range 42-202 ppm) and 253 ppm
19 (range 143-357 ppm). After the 50- to 70-min exposures, subjects underwent a second graded
20 exercise treadmill test, and the percent change in time to onset of angina and time to ST endpoint
21 between the first and second exercise tests was determined. Relative to clean-air exposure that
22 resulted in a mean COHb level of 0.6% (post-exercise), exposures to CO resulting in post-
23 exercise mean COHb concentrations of 2.0% and 3.9%16 were shown to decrease the time
24 required to induce ST-segment changes by 5.1% (p=0.01) and 12.1% (p<0.001), respectively.
25 These changes were well correlated with the onset of exercise-induced angina the time to which
26 was shortened by 4.2% (p=0.027) and 7.1% (p=0.002), respectively, for the two CO exposures
27 (ISA, section 5.2.4; (Allred et al., 1989a,1989b, 1991).
14 The S-T segment is a portion of the electrocardiogram, depression of which is an indication of
insufficient oxygen supply to the heart muscle tissue
15 As stated in the ISA, the gas chromatographic technique for measuring COHb levels "is known to be
more accurate than spectrophotometric measurements, particularly for samples containing COHb concentrations <
5%" (ISA, p. 5-41). CO-oximetry is a spectrophotometric method commonly used to rapidly provide approximate
concentrations of COHb during controlled exposures (ISA, p. 5-41). At the low concentrations of COHb (<5%)
more relevant to exposures to ambient CO, co-oximeters are reported to overestimate COHb levels compared to GC
measurements, while at higher concentrations, this method is reported to produce underestimates (ISA, p.4-18).
16 The corresponding co-oximeter measured post-exercise levels were 2.7% and 4.7%. The post-exposure,
pre-exercise COHb levels for the two CO exposures were 2.4% and 4.7% by GC and 3.2% and 5.6% by co-oximetry
(ISA, p. 5-41).
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1 No human clinical studies have been specifically designed to evaluate the effect of
2 controlled exposures to CO resulting in study mean COHb levels lower than 2% (ISA, section
3 5.2.6). However, an important finding of the multi-lab oratory study was the dose-response
4 relationship observed between COHb and ischemia without evidence of a measurable threshold
5 effect (Allred et al., 1991; Allred et al., 1989b). As reported by the authors, the results
6 comparing "the effects of increasing COHb from baseline levels (0.6%) to 2 and 3.9% COHb
7 showed that each produced further changes in objective ECG measures of ischemia" implying
8 that "small increments in COHb could adversely affect myocardial function and produce
9 ischemia" (Allred et al., 1991; Allred et al., 1989b). For each 1% increase in COHb resulting
10 from the experimentally increased CO exposure concentrations the dose-response analysis
11 performed by the authors indicated decreases of 1.9% in time to exercise-induced angina and
12 3.9% in time to exercised-induced ST-segment change in persons with pre-existing CAD (ISA,
13 section 5.2.4; Allred et al., 1989a,1989b, 1991).
14 Other controlled human exposure studies (Adams et al. 1988, Anderson et al. 1973,
15 Kleinman et al. 1989, Kleinman et al., 1998) involving individuals with stable angina have
16 confirmed the Allred et al findings at COHb concentrations between 3 and 6% (as measured by
17 CO-oximeter) (ISA, section 5.2.4). Among the evidence is also a study of a small group of
18 patients with CAD which reported no change in time to onset of angina or maximal exercise time
19 following a 1 hour exposure targeted to result in 4% COHb. A subsequent study conducted by
20 the same laboratory reported a significant increase in number of ventricular arrhythmias during
21 exercise relative to room air among individuals with CAD following a 1-hr CO exposure targeted
22 to yield 6% COHb, but not following a 1-hr exposure targeted to yield a COHb level of 4% (ISA,
23 p. 5-42; Sheps et al., 1990; Sheps et al., 1987). Although there was no clear pattern across the
24 different studies with respect to the magnitude of the decreased time to onset of angina versus
25 dose level, differences in study protocols and analytical methods do not allow for an informative
26 pooled or quantitative metaanalysis of the dose-response relationship across studies (ISA, section
27 5.2.4).
28 Although the subjects evaluated in the controlled human exposure studies described
29 above are not necessarily representative of the most sensitive population, the level of disease in
30 these individuals ranged from moderate to severe, with the majority either having a history of
31 myocardial infarction or having > 70% occlusion of one or more of the coronary arteries (ISA, p.
32 5-43).
33 2.5.2 Other Effects
34 Other health effects for which the evidence is suggestive of causal relationships with
35 short-term CO exposures include some effects on the central nervous system, reproduction and
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1 prenatal development, and the respiratory system (ISA, section 2.5). High CO exposures have
2 "long been known to adversely affect central nervous system (CNS) function", although a
3 relationship close to ambient levels is less clear (ISA, pp. 5-49). Further, the evidence indicates
4 that healthy adults may be protected against such effects at ambient levels through compensatory
5 responses such as increased cardiac output and cerebral blood flow, although these compensatory
6 mechanisms may be impaired among susceptible groups, including those with reduced
7 cardiovascular function (ISA, section 5.3.3). Limited evidence indicates an association of CO
8 exposure during early pregnancy with pre-term births and birth defects (ISA, p. 2-8). New
9 epidemiologic studies report positive associations for CO-induced lung-related outcomes,
10 although interpretation is affected by uncertainties including with regard to the biological
11 mechanism that could explain CO-induced respiratory outcomes (ISA, section 5.5.5).
12 2.6 RISK CHARACTERIZATION APPROACH
13 In identifying an approach to characterize the risk of cardiovascular effects of exposures
14 to ambient CO, we considered 1) approaches employed in previous assessments, 2) the currently
15 available evidence regarding associations between CO concentrations and cardiovascular
16 outcomes, and 3) advice from CAS AC (Brain, 2009; Brain and Samet, 2009; Brain and Samet,
17 2010). As summarized in section 1.2 above, the last CO NAAQS review included analyses of
18 exposure to ambient CO and associated internal dose, in terms of COHb levels, which were used
19 to characterize risks for the population of interest (USEPA, 1992). The prior risk
20 characterization considered the percent of the modeled population that exceeded COHb levels of
21 interest which were drawn from the evidence of COHb levels associated with exercise-induced
22 aggravation of angina in controlled human exposure studies involving short-term (shorter than 8
23 hours) exposures of patients with diagnosed CAD17 to elevated CO concentrations (US EPA,
24 1991).
25 In the current review, the controlled human exposure studies among individuals with
26 CAD continue to provide the clearest evidence of CO-induced effects on the cardiovascular
27 system as the most sensitive endpoint. In contrast to epidemiological studies, human exposure
28 studies also provide quantitative information linking CO exposures through COHb levels with
29 these effects. Among these studies, the multilaboratory study of Allred et al (1989a, 1989b,
30 1991) continues to be the principal study informing our understanding of the effects of CO on
31 individuals with pre-existing CAD at the low end of the range of COHb levels studied (USEP A,
32 1991, 2000, 2010). The strength of the evidence more broadly continues to support the use of
17 Study subjects met certain criteria with respect to evidence of coronary artery disease, often also called
CHD or IHD.
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1 COHb level as the internal dose metric for assessing exposure to ambient levels of CO and
2 characterizing associated potential for health risk. Thus, based on the strength of the evidence
3 and the availability of quantitative information from controlled human exposure studies, this
4 REA also focuses on estimates of the percent of the simulated population expected to experience
5 maximum end-of-hour COHb levels of interest based on findings of those studies.
6 We also note that, in the current review, a number of epidemiological studies are now
7 available that investigate associations of cardiovascular morbidity with ambient measurements of
8 CO (ISA, sections 5.2.4 and 5.2.5).18 These studies have observed associations between ambient
9 monitor CO concentrations and increases in emergency department visits and hospital
10 admissions for cardiovascular effects (ISA, sections 5.2.1.9). While these studies are coherent
11 with the controlled human exposure studies (ISA, section 5.2.6), we recognize a number of
12 uncertainties that complicate their use for our purposes in a quantitative risk assessment (ISA,
13 pp. 2-14 to 2-17, section 5.2.3).
14 • "This [epidemiological] evidence for ischemia-related outcomes is coherent with
15 effects observed in controlled human exposure studies, although uncertainty regarding
16 the extent of reduced O2 delivery to tissues following exposure to ambient CO
17 concentrations contributes to the uncertainty in quantitative interpretation of effect
18 estimates." [ISA, p. 2-14]
19 • The correlation between concentrations of CO and other combustion-related pollutants
20 "complicates the quantitative interpretation of effect estimates in these studies to
21 apportion the relative extent to which CO at ambient concentrations is independently
22 associated with cardiovascular or other effects, and the extent to which CO acts as a
23 marker for the effects of another combustion-related pollutant or mix of pollutants."
24 [ISA, p. 2-16]
25 • Although the epidemiological evidence indicates that CO associations generally remain
26 robust in copollutant models, uncertainty associated with the use of these models
27 "complicates quantitative interpretation of the effect estimates reported in
28 epidemiologic studies" [ISA, p. 2-16]
29 • "Some of these uncertainties [identified in 2000 AQCD] remain and complicate the
30 quantitative interpretation of the epidemiologic findings, particularly regarding the
31 biological plausibility of health effects occurring at COHb levels resulting from
32 exposures to ambient CO concentrations measured at AQS monitors." [ISA, p. 2-17]
33 Given these uncertainties in the quantitative interpretation of epidemiological studies for
34 CO, and the longstanding body of evidence that links exposures to effects through an internal
35 dose metric, we have characterized health risk of ambient CO exposures in this assessment using
18 One additional controlled exposure study in CHD patients is available since the last review. It involved
higher COHb levels than the study by Allred et al (1989a, 1989b, 1991) and is not a focus here (2000 AQCD,
section 6.2.2).
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1 estimates of associated COHb levels and a benchmark level approach, with benchmarks
2 identified in consideration of the controlled human exposure literature.19 This is supported by
3 the fact that COHb levels reported in controlled human exposure studies are a better indicator of
4 personal exposure and dose than concentrations measured at fixed site ambient monitors. In
5 addition, controlled human exposure studies can examine the health effects of short-term
6 exposure to CO in the absence of co-pollutants that can confound results in epidemiologic
7 analyses; thus, health effects observed in controlled human exposure studies can confidently be
8 attributed to a defined COHb dose level resulting from short-term CO exposures.
9 In drawing from the results of the controlled human exposure studies to inform the
10 characterization of potential CO risk in this assessment, staff considered a number of factors,
11 listed below.
12 • Myocardial ischemic effects, as documented by reductions in times to exercise-induced
13 change in the ST-segment of an electrocardiogram and to exercise-induced onset of
14 angina, were observed in response to CO exposures involving subjects with pre-
15 existing CAD. Staff gives primary focus here to the multi-laboratory study in which
16 COHb was analyzed by the more accurate GC method. (Allred et al., 1989a,b, 1991).
17 • Relative to clean-air exposure that resulted in a mean level of 0.6% COHb (post-
18 exercise), exposures to CO resulting in post-exercise mean COHb levels of 2.0% and
19 3.9%20 were shown to decrease the time required to induce ST-segment changes by
20 5.1% (p=0.01) and 12.1% (p<0.001), respectively. These changes were well correlated
21 with the onset of exercise-induced angina, the time to which was shortened by 4.2%
22 (p=0.027) and 7.1% (p=0.002), respectively, for the two CO exposures (Allred et al.,
23 1989a, 1989b, 1991).
24 • There is no evidence of a threshold for the measures assessed at the lowest levels
25 tested, with incremental additions of COHb from baseline mean levels of 0.6% to 2 and
26 3.9% COHb showing changes in the monitored measures of ischemia (Allred et al.,
27 1989b, 1991). The average of the regressions of the individual study subject data for
28 these measures at baseline COHb and the two COHb levels resulting from the two
29 controlled CO exposures was summarized by the authors as indicating decreases of
30 roughly 1.9% in time to exercise-induced angina and 3.9% in time to exercised-
31 induced ST-segment change per 1% increase in COHb concentration in persons with
32 pre-existing CAD (ISA, section 5.2.4; Allred et al., 1989a,1989b, 1991).
19 While not used for the purposes of this quantitative assessment, EPA is considering all of the current
health evidence, including the epidemiological studies, in the Policy Assessment, along with considerations based on
the risk and exposure assessment findings.
20 Subjects were exposed to two levels of CO exposure, resulting in COHb levels in the range of 2.0 to
2.4% and 3.9 to 4.7%, respectively. The upper end of each range is the average COHb level obtained post-exposure
and the lower end is the average COHb level obtained after the subsequent exercise test (Allred et al., 1989a,b,
1991).
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1 • Studies have not been designed to evaluate similar effects of exposures to increased
2 CO concentrations eliciting average COHb levels below the 2% target level of Allred
3 et al (1989a, 1989b, 1991). In addition, these studies do not address the fraction of the
4 population experiencing a specified health effect at various dose levels. These aspects
5 of the evidence contributed to EPA's conclusion that at this time there are insufficient
6 controlled human exposure data to support the development of quantitative dose-
7 response relationships which would be required in order to conduct a quantitative risk
8 assessment for this health endpoint, rather than the benchmark level approach.
9 In drawing on this information, staff recognize the uncertainty associated with
10 interpretation of COHb levels estimated to result from CO exposure concentrations in this
11 assessment that are much lower than the CO exposure concentrations used in the clinical studies
12 to elicit increases in participant's COHb levels to target levels for the study.
13 We have reviewed COHb estimates developed in this assessment with attention to both
14 COHb in absolute terms and also based on consideration of the contribution to COHb associated
15 with ambient CO exposures. With regard to COHb in absolute terms, staff identified benchmark
16 levels of 1.5%, 2.0%, 2.5% and 3% COHb based on consideration of the evidence from
17 controlled human studies of CHD patients discussed above, and is inclusive of the range of levels
18 considered in the review completed in 1994 (USEPA, 1992). This range extends below the
19 lowest mean COHb level (e.g., 2.0% post-exercise in Allred et al., 1989b) resulting from
20 controlled exposure to increased CO concentration in the clinical evidence. This extension
21 reflects comments from the CAS AC CO panel on the draft Analysis Plan (Brain and Samet,
22 2009) and consideration of the uncertainties regarding the actual COHb levels experienced in the
23 controlled human exposure studies; that these studies did not include individuals with most
24 severe cardiovascular disease; the lack of studies evaluating effects of controlled short-term CO
25 exposures resulting in COHb levels below study mean 2.0-2.4% and the lack of evidence of an
26 effect threshold at these levels. We note that CASAC comments on the first draft REA
27 recommended the addition of a benchmark at 1% COHb and staff has presented results for this
28 COHb level in this draft REA. In considering this advice, we recognize, however, that a level of
29 1% COHb overlaps with the upper part of the range of endogenous levels in health individuals as
30 characterized in the ISA (ISA, p. 2-6) and with the upper part of the range of baseline COHb
31 levels in the study by Allred et al (1989b, Appendix B). As a result, while noting population
32 dose estimates in relation to this level, we have not placed weight on this level as a potential
33 health effects benchmark in discussions of the results below and in the draft Policy Assessment
34 document. We additionally consider, however, the observations of the multi-laboratory clinical
35 study with regard to response per 1% increase in COHb concentration resulting from short-term
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1 controlled CO exposure exposures of persons with pre-existing CAD (ISA, section 5.2.4; Allred
2 etal., 1989a, 1989b, 1991).21
3 The benchmark levels identified are used to interpret COHb levels estimated to occur in
4 the modeled population in response to exposures to ambient CO in different air quality scenarios
5 in light of the evidence for cardiovascular effects in individuals with CHD when exposed to CO.
6 That is, we have estimated the number of persons and percent of the simulated at-risk population
7 expected to experience COHb levels below each of these potential health effect benchmark
8 levels as a result of ambient CO exposures associated with a set of air quality scenarios
9 employed to inform the current review of the CO NAAQS (see chapter 5 below). As noted in
10 chapter 1 above, given the significant time constraints of this review, results are provided in this
11 document without substantial interpretation. Rather, discussion of health risk and public health
12 implications of these results in the context of the NAAQS review is provided in the draft Policy
13 Assessment.
14 2.7 KEY OBSERVATIONS
15 Presented below are key observations regarding the current evidence for health effects
16 associated with exposures to ambient CO.
17 • Carbon monoxide in ambient air is formed primarily by the incomplete combustion of
18 carbon-containing fuels and photochemical reactions in the atmosphere, with on-road
19 mobile sources representing significant sources of CO to ambient air.
20 • Microenvironments influenced by on-road mobile sources are important contributors to
21 ambient CO exposures, particularly in urban areas.
22 • The formation of COHb is a key step in the elicitation of various health effects by CO.
23 Further, COHb level is commonly used in exposure assessment and is considered the
24 best biomarker for CO health effects of concern.
25 • Individuals with CHD are the population with greatest susceptibility to short-term
26 exposure to CO, and the population for which the current evidence indicates health
27 effects occurring at the lowest exposures. The evidence further indicates a potential for
28 other underlying cardiovascular conditions to contribute susceptibility to CO effects.
29 Other populations potentially at risk include individuals with diseases such as chronic
21 Relative to clean-air exposure that resulted in a mean COHb level of 0.6% (post-exercise), exposures to
CO resulting in post-exercise mean COHb concentrations of 2.0% and 3.9% were shown to decrease the time
required to induce ST-segment changes by 5.1% (p=0.01)and 12.1%(p<0.001), respectively. These changes were
well correlated with the onset of exercise-induced angina the time to which was shortened by 4.2% (p=0.027) and
7.1% (p=0.002), respectively, for the two CO exposures. A dose-response analysis in which the individual
regressions of study subject responses at baseline COHb and at the two increased COHb levels were averaged was
summarized as indicating decreases of roughly 1.9% in time to exercise-induced angina and 3.9% in time to
exercised-induced ST-segment change per 1% increase in COHb concentration in persons with pre-existing CAD
(ISA, section 5.2.4; Allred et al., 1989a,1989b, 1991).
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1 obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in
2 prenatal or elderly life stages.
3 • Cardiovascular effects are the health endpoint for which the evidence is strongest and
4 indicative of a likely causal relationship with CO exposures. Other endpoints for
5 which the evidence is suggestive of such a relationship include effects on the central
6 nervous system, reproduction and prenatal development, and the respiratory system.
7 • Risk is characterized in this REA through evaluation of COHb estimated to result from
8 ambient CO exposure in individuals with CHD (including undiagnosed persons)
9 considering potential health effect benchmarks for daily maximum COHb levels.
10 Results are reported in terms of percent of population expected to experience daily
11 maximum COHb levels at or above a series of levels that range as low as 1%. These
12 results are considered in the Policy Assessment document in light of potential health
13 effects benchmarks ranging from 1.5%, which is below the lowest study mean COHb
14 level resulting from experimental CO exposure in controlled human exposures of
15 subjects with CAD, up to 3.0%, a level associated with adverse effects in those studies.
February, 2010 2-18 Draft - Do Not Cite or Quote
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1 2.8 REFERENCES
2 Abi-Esber L and El-Fadel M. (2008). In-vehicle CO ingression: Validation through field measurements and mass
3 balance simulations. Sci Total Environ. 394:75-89.
4 Adams KF, Koch G, Chatterjee B, Goldstein GM, O'Neil JJ, Bromberg PA, Sheps DS, McAllister S, Price CJ,
5 Bissette J. (1988). Acute elevation of blood carboxyhemoglobin to 6% impairs exercise performance and
6 aggravates symptoms in patients with ischemic heart disease. J Am Coll Car dial. 12:900-909
7 Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to carbon monoxide in
8 Washington, DC, and Denver, Colorado, during the winter of 1982-1983. Environ Sci Technol. 19:911-
9 918.
10 Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD, Pagano M, Selvester RH, Walden
11 SM, Warren J. (1989a). Short-term effects of carbon monoxide exposure on the exercise performance of
12 subjects with coronary artery disease. NEnglJMed. 321:1426-1432.
13 Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD, Hayes D, Pagano M, Selvester RH,
14 Walden SM, Warren J. (1989b). Acute effects of carbon monoxide exposure on individuals with coronary
15 artery disease. Cambridge, MA: Health Effects Institute; research report no. 25.
16 Allred EN, Bleecker ER, Chaitman BR, Dahms TE, Gottlieb SO, Hackney JD, Pagano M, Selvester RH, Walden
17 SM, Warren J. (1991). Effects of carbon monoxide on myocardial ischemia. Environ Health Perspect
18 91:89-132.
19 AHA. (2003). Heart and Stroke Facts. American Heart Association, Dallas, TX. Available at:
20 http://www.americanheart.org/downloadable/heart/1056719919740HSFacts2003text.pdf
21 Anderson EW, Andelman RJ, Strauch JM, Fortuin NJ, Knelson JH. (1973). Effect of low level carbon monoxide
22 exposure on onset and duration of angina pectoris. Annals of Internal Medicine. 79:46-50.
23 Boulter P and McCrae I. (2005). Carbon Monoxide Inside Vehicles: Implications for Road Tunnel Ventilation. In:
24 Annual Research Review 2005. TRL Academy.
25 Brain JD. (2009). Letter from Dr. Joseph Brain to Administrator Lisa Jackson. Re: Consultation on EPA's Carbon
26 Monoxide National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure
27 Assessment. CASAC-09-012. July 14, 2009.
28 Brain JD and Samet JM. (2009). Letter to EPA Administrator Lisa Jackson: Clean Air Scientific Advisory
29 Committee's (CASAC) Peer Review of the Agency's 1st Draft Carbon Monoxide Integrated Science
30 Assessment. EPA-CASAC-09-011. June 24, 2009.
31 Brain JD and Samet JM. (2010). Letter from Drs. J.D. Brain and J.M. Samet to Administrator Lisa Jackson. Re:
32 Review of the Risk and Exposure Assessment to Support the Review of the Carbon Monoxide (CO)
33 Primary National Ambient Air Quality Standards: First External Review Draft. CASAC-XX-XXX.
34 January XX, 2010.
3 5 Bruinen de BruinY, Hanninen O, Carter P, Maroni M, Kephalopoulos S, Scotto De Marco G, Jantunen M. (2004).
3 6 Personal carbon monoxide exposure levels: Contribution of local sources to exposures and
37 microenvironmental concentrations in Milan. J Expo Anal Environ Epidemiol. 14:312-322.
38 Chan C, OzkaynakH, SpenglerJ, Sheldon L. (1991). Driver exposure to volatile organic compounds, CO, ozone,
39 and NO2 under different driving conditions. Environ Sci Technol. 25(5) :964-972.
February, 2010 2-19 Draft - Do Not Cite or Quote
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1 Chang LT, Koutrakis P, Catalano PJ, Suh HH. (2000). Hourly personal exposures to fine particles and gaseous
2 pollutants - Results from Baltimore, Maryland. J Air Waste Manage Assoc. 50:1223-1235.
3 Coburn R, Forster R, Kane P. (1965). Consideration of the physiological variables that determine the blood
4 carboxyhemoglobin concentrations in man. J Clin Invest. 44:1899-1910.
5 Davies DM and Smith DJ. (1980). Electrocardiographic changes in healthy men during continuous low-level
6 carbon monoxide exposure. EnvironRes. 21:197-206.
7 FlachsbartP. (1999). Human exposure to carbon monoxide from mobile sources. Chemosphere - Global Change
8 Science. 1:301-329.
9 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and
10 Associated Carboxyhemoglobin levels in Denver Residents Using pNEM/CO (Version 2.1), prepared by
11 ICF Kaiser Consulting Group for U.S. EPA, Office of Air Quality Planning and Standards, under Contract
12 No. 68-D6-0064, WA Nos. 1-19, 2-24, 2-30, and 3-3, Docket EPA-HQ-OAR-2008-0015-0010.
13 Kleinman MT, Davidson DM, Vandagriff RB, Caiozzo VJ, Whittenberger JL. (1989). Effects of short-term
14 exposure to carbon monoxide in subjects with coronary artery disease. Arch Environ Health. 44:361-369.
15 Kleinman MT, Leaf DA, Kelly E, Caiozzo V, Osann K, O'Niell T. (1998). Urban angina in the mountains: effects
16 of carbon monoxide and mild hypoxemia on subjects with chronic stable angina. Arch Environ.Health.
17 53:388-397.
18 Koushi P, Al-Dhowalis K, Niazi S. (1992). Vehicle occupant exposure to carbon monoxide. J Air Waste Manag.
19 42:1603-1608.
20 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S, Frazier C. (1998).
21 Measuring Concentrations of Selected Air Pollutants Inside California Vehicles. Final Report. Prepared
22 by Research Triangle Institute under Contract No. 95-339. California Air Resources Board. Sacramento,
23 California.
24 Scotto di Marco G, Kephalopoulos S, Ruuskanene J, Jantunene M. (2005). Personal carbon monoxide exposure in
25 Helsinki, Finland. Atmos Environ. 39Z:2697-2707.
26 Sharp D and Tight M. (1997). Vehicle occupant exposure to air pollution. In: Policy, Planning, and Sustainability:
27 Proceedings of Seminars C and D Held at PTRC European Transport Forum, Brunei University. Pages
28 481-492.
29 Sheps DS, Adams KF Jr, Bromberg PA, Goldstein GM, O'Neil JJ, Horstman D, Koch G. (1987). Lack of effect of
3 0 low levels of carboxyhemoglobin on cardiovascular function in patients with ischemic heart disease. Arch
31 Environ Health. 42:108-116.
32 Sheps DS, Herbst MC, Hinderliter AL, Adams KF, Ekelund LG, O'Neil JJ, Goldstein GM, Bromberg PA, Dalton
33 JL, Ballenger MN, Davis SM, Koch GG. (1990). Production of arrythmias by elevated
34 carboxyhemoglobin in patients with coronary artery disease. AnnlnternMed. 113:343-351.
3 5 Shikiya D, Liu C, Kahn M, Juarros J, Barcikowski W. (1989). In-Vehicle Air Toxics Characterization Study in the
36 South Coast Air Basin. Office of Planning and Rules, South Coast Air Quality Management District. May.
37 US EPA. (1991). Air Quality Criteria For Carbon Monoxide. Research Triangle Park, NC: Office of Health and
38 Environmental Assessment, Environmental Criteria and Assessment Office; report no. EPA/600/8-90/045F.
February, 2010 2-20 Draft - Do Not Cite or Quote
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1 US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of
2 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no.
3 EPA/452/R-92-004.
4 US EPA. (2000). Air Quality Criteria for Carbon Monoxide. National Center for Environmental Assessment,
5 Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
6 27711; report no. EPA/600/P-99/001F. June 2000. Available:
7 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=18163.
8 US EPA. (2006). 2002 National Emissions Inventory Data and Documentation. U.S. Environmental Protection
9 Agency, Office of Air and Radiation, Office of Air Quality Planning and Standards, Research Triangle
10 Park, NC. http://www.epa.gov/ttn/chief/net/2002inventory.html.
11 US EPA. (2010a). Integrated Science Assessment for Carbon Monoxide. U.S. Environmental Protection Agency,
12 Washington, DC, EPA/600/R-09/019F. Available at:
13 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html.
14 US EPA. (20 lOb). Policy Assessment for the Review of the Carbon Monoxide National Ambient Air Quality
15 Standards, External Review Draft. Office of Air Quality Planning and Standards Staff Paper, report no.
16 EPA-452/P-10-005.
February, 2010 2-21 Draft - Do Not Cite or Quote
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1 3 AIR QUALITY CONSIDERATIONS
2 Ambient air quality data can be used as an indicator of exposure or used in conjunction
3 with other information to estimate exposure concentrations. How well the ambient air quality is
4 represented in a particular location is dependent on the ambient monitoring network design
5 relative to the spatial and temporal characteristics of the pollutant as well understanding the
6 concentration contribution from important local source emissions. This chapter summarizes
7 findings about the current air quality conditions and their spatial distribution, with particular
8 focus on aspects informative to the design and conduct of this assessment and including
9 descriptions of CO measurement methods, monitor siting requirements, and monitor locations
10 (section 3.1). Section 3.2 then draws upon the information presented in sections 3.1, among
11 other data, to select ambient air quality/study locations most useful in meeting the objectives of
12 the REA. Finally, key observations of the chapter are presented in section 3.3.
13 3.1 AMBIENT CO MONITORING
14 In this section, a broad overview of the monitoring network is provided (section 3.1.1)
15 and is followed by a summary of analytical detection issues (section 3.1.2). Ambient CO
16 concentrations and their spatial and temporal variability are characterized in section 3.1.3.
17 Estimates of policy-relevant background (PRB) concentrations which are defined as those
18 ambient concentrations that would occur in the US in the absence of anthropogenic emissions in
19 continental North America are presented in section 3.1.4. And finally, section 3.1.5 presents an
20 analysis of the specific CO concentration trends observed in individual monitors.
21 3.1.1 Monitoring Network
22 Ambient CO concentrations are measured by monitoring networks that are operated by
23 state and local monitoring agencies in the US, and are funded in part by the EPA. The main
24 network providing ambient data for use in comparison to the NAAQS is the State and Local Air
25 Monitoring Stations (SLAMS) network. The subsections below provide specific information
26 regarding the methods used for obtaining ambient CO measurements and the requirements that
27 apply to states in the design of the CO network.
28 Minimum monitoring requirements for CO were revoked in the 2006 revisions to ambient
29 monitoring requirements (see 71 FR 61236, October 17, 2006). This action was made to allow
30 for reductions in measurements of some pollutants (CO, SO2, NO2, and Pb) where measured
31 levels were well below the applicable NAAQS and air quality problems were not expected. CO
32 monitoring activities have been maintained at some SLAMS and these measurements of CO are
33 required to continue until discontinuation is approved by the EPA Regional Administrator.
February 2010 3-1 Draft - Do Not Quote or Cite
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1 CO monitors are typically sited to reflect one of the following spatial scales.1
2 • Microscale: Data represent concentrations within a 100 m radius of the monitor. For
3 CO, microscale monitors are sited 2 - 10 m from a roadway. Measurements are
4 intended to represent the near-road or street canyon environment.
5 • Middle scale: Data represent concentrations averaged over areas defined by 100 - 500
6 m radii. Measurements are intended to represent several city blocks.
7 • Neighborhood scale: Data represent concentrations averaged over areas defined by 0.5
8 - 4.0 km radii. Measurements are intended to represent extended portions of a city.
9 In addition to monitoring required for determining compliance with the NAAQS, the
10 EPA is currently in the process of implementing plans for a new network of multi-pollutant
11 stations called NCore that is intended to meet multiple monitoring objectives. A subset of the
12 SLAMS network, NCore stations are intended to address integrated air quality management
13 needs to support long-term trends analysis, model evaluation, health and ecosystem studies, as
14 well as the more traditional objectives of NAAQS compliance and Air Quality Index reporting.2
15 The complete NCore network, required to be fully implemented by January 1, 2011, will consist
16 of approximately 63 urban and 20 rural stations and will include some existing SLAMS sites that
17 have been modified to include additional measurements. Each state will contain at least one
18 NCore station, and 46 of the states plus Washington, D.C. will have at least one urban station.
19 CO will be measured using high sensitivity monitors, as will SO2, NO, and NOy.3 The majority
20 of NCore stations will be sited to represent neighborhood, urban, and regional scales, consistent
21 with the NCore network design objective of representing exposure expected across urban and
22 rural areas in locations that are not dominated by local sources.
23 3.1.2 Analytical Sensitivity
24 To promote uniform enforcement of the air quality standards set forth under the C AA,
25 EPA has established provisions in the Code of Federal Regulations (CFR) under which analytical
26 methods can be designated as federal reference methods (FRMs) or federal equivalent methods
27 (FEMs). Measurements for determinations of NAAQS compliance must be made with FRMs or
28 FEMs.4 Specifications for CO monitoring are designed to help states utilize equipment that has
1 A complete description of spatial scales is listed in 40 CFR Part 58 Appendix D, section 1.2. Ambient
monitoring of other NAAQS pollutants such as NO2 and SO2 follow the same general spatial scales.
2 (http://www.epa.gov/ttn/amtic/ncore/index.html).
3 NCore sites must measure, at a minimum, PM2 5 particle mass using continuous and integrated/filter-based
samplers, speciated PM25, PM10.25 particle mass, speciatedPM10_25, O3, SO2, CO, NO/NOY, wind speed, wind
direction, relative humidity, and ambient temperature (http://www.epa.gov/ttn/amtic/ncore/index.html).
4 As of August 2009, twenty automated FRMs had been approved for CO measurement. All EPA FRMs
for CO operate on the principle of non-dispersive infrared (NDIR) detection and can include the gas filter correlation
February 2010 3-2 Draft - Do Not Quote or Cite
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1 met performance criteria utilized in the FRM or FEM approval process; operational parameters
2 are documented in 40 CFR Part 53, Table B-l. Given the levels of the CO NAAQS (35 ppm, 1-
3 hour; 9 ppm, 8-hour average), the required 1.0 ppm lower detectable limit (LDL)5 is well below
4 the NAAQS levels and is therefore sufficient for demonstration of compliance. However, with
5 ambient CO levels now routinely near or below 1 ppm, there is greater uncertainty in a larger
6 portion of the distribution of monitoring data because a large percentage of these measurements
7 are below the LDL of conventional monitors. For this reason, a new generation of ambient CO
8 monitors has been designed that provides measurements with improved sensitivity at or below
9 the typical ambient CO levels measured in most urban and all rural locations. Additionally, the
10 higher sensitivity CO measurements are needed to support additional objectives such as
11 validating the inputs to chemical transport models and assessing the role of transport between
12 urban and rural areas because policy relevant background CO concentrations on the order of 0.1
13 ppm are well below the LDL of conventional monitors. Newer GFC instruments have been
14 designed for automatic zeroing to minimize drift (US EPA, 2000).
15 Currently, a total of 13 approved FRMs are in use in the SLAMS network, based on a
16 retrieval of data reported between 2005 and 2009. Among these methods, nine are "legacy"
17 monitors with a federal method detection limit (MDL)6 given as 0.5 ppm according to records in
18 EPA's Air Quality System (AQS).7 As discussed in the ISA, many of the reported
19 concentrations in recent years are near or below these MDLs (ISA, section 3.5.1.2). Four of
20 these new methods are high sensitivity methods with a federal MDL of 0.02 ppm and there are a
21 growing body of ambient data from high sensitivity CO instruments is becoming available.
22 Among newer GFC high sensitivity instruments, manufacturer-declared LDLs range from 0.02 -
23 0.04 ppm, with 24-hour zero drift varying between 0.5% within 1 ppm and 0.1 ppm, and
24 precision varying from 0.5% to 0.1 ppm. EPA performed MDL testing on several high
25 sensitivity CO monitors in 2005 and 2006 following the 40 CFR Part 136 procedures. Those
(GFC) methodology. An extensive and comprehensive review of NDIR, GFC, and alternative, non-FRM techniques
for CO detection was included in the 2000 CO AQCD (US EPA, 2000).
5 Defined in 40 CFR Part 53.23 as the minimum pollutant concentration which produces a signal of twice
the noise level.
6 Defined in 40 CFR Part 136 as the minimum concentration of a substance that can be measured and
reported with 99% confidence that the analyte concentration is greater than zero and is determined from analysis of
a sample in a given matrix containing the analyte.
7 Among several of the older instruments (Federal Reference Method codes 008, 012, 018, 033, 041, 050,
051, and 054), performance testing has shown LDLs of 0.62 - 1.05 ppm, with 24-hour drift ranging from 0.044 -
0.25 ppm and precision ranging from 0.022 - 0.067 ppm at 20% of the upper range limit of the instrument (Michie
etal., 1983).
February 2010 3-3 Draft - Do Not Quote or Cite
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1 tests demonstrated MDLs of approximately 0.017 - 0.018 ppm, slightly below the stated LDL of
2 0.02 - 0.04 ppm.
3 Based on a retrieval of data reported to AQS for the time period between 2005 and 2009,
4 a total of 36 high sensitivity CO monitors have reported data with the majority of these monitors
5 currently active. The majority of these active monitors are associated with the implementation of
6 the NCore network. The extent to which high sensitivity monitors become integrated into non-
7 NCore SLAMS stations, however, will depend on the availability of funding for states to replace
8 well-operating legacy CO monitors as well as the possibility that monitoring requirements for
9 CO might either encourage or require such technological improvements.
10 3.1.3 General Patterns of CO Concentrations
11 As discussed in the ISA, the spatial and temporal patterns of ambient CO concentrations
12 are heavily influenced by the patterns associated with mobile source emissions (ISA, section
13 3.2.1). Based on the 2002 National Emissions Inventory (NEI), on-road mobile sources
14 comprise about half of the total anthropogenic CO emissions, though in metropolitan areas of the
15 US the contribution is as high as 75% of all CO emissions due to greater motor vehicle density.
16 For example, emissions in Denver county originating from on-road mobile sources is about 71%
17 of total CO emissions (ISA, section 3.2). When considering all mobile sources (non-road and
18 on-road combined), the contribution to total CO emissions is roughly 80% nationwide and can be
19 higher in some metropolitan areas. Again using Denver County as an example, all mobile
20 sources contribute about 98% of total CO emissions. Temporally, the national-scale
21 anthropogenic CO emissions have decreased 35% between 1990 and 2002. Nearly all the
22 national-level CO reductions since 1990 are the result of emission reductions in on-road vehicles
23 (ISA, Figure 3-2).
24 Nearly 400 ambient monitoring stations report continuous hourly averages of CO
25 concentrations across the US. Over the period 2005-2007, 291 out of 376 monitors met a 75%
26 completeness requirement, spread among 243 counties, cities, or municipalities (ISA, section
27 3.4.2.2). No violations of the NAAQS were reported at these monitoring sites during this time
28 period. For example, in 2007, none of the monitors reported a second-highest 1-hour CO
29 concentration above 35 ppm, the level of the current 1-hour NAAQS, while only two sites
30 reported 2nd highest 1-hour CO concentrations between 15.1 and 35.0 ppm (ISA section 3.5.1.1).
31 Only five counties reported a 2nd highest 8-hour CO concentration of 5.0 ppm or higher.
32 The current levels of ambient CO across the U. S. reflect the steady declines in ambient
33 concentrations that have occurred over the past several years. On average across the US the
34 decline has been on the order of 50% since the early 1990s (ISA, Figure 3-34). As an example,
35 Figures 3-1 illustrate the trends observed in Denver and Los Angeles ambient concentrations, for
February 2010 3-4 Draft - Do Not Quote or Cite
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1 several selected monitors within the urban core of each area during 1993 through 2008. Note
2 that there is a significant decrease in the 2nd highest 1-hour and 8-hour average CO
3 concentrations since the last review.
4 Ambient monitor siting characteristics can influence ambient CO concentrations.
5 Microscale and middle scale monitors are commonly used to measure significant source impacts,
6 while neighborhood and urban scale monitors are designated for population-oriented monitoring
7 (40 CFR Part 58 Appendix D). As CO concentrations primarily originate from vehicle
8 emissions, the microscale and middle scale data can be a useful indicator of near-road air quality.
9 Such data analyzed in the ISA were concluded to be consistent with hourly concentrations
10 reported in the literature for the near road environment in the US (ISA, p. 3-57). Further, when
11 considering monitoring scale across ambient monitors in the US, the median hourly CO
12 concentration measured at microscale monitors was about 25% higher than at middle scale
13 monitors and 67% higher than at neighborhood scale monitors (ISA, Table 3-12). In general,
14 similar patterns were present in the 1-hour daily max, 1-hour daily average, and 8-hour daily
15 max distributions (ISA, Table 3-12). These patterns are also consistent with findings presented
16 by other researchers regarding the relative decrease in concentration with increasing distance
17 from roadways, though the magnitude of the relationship can vary. Two studies summarized in
18 the ISA (Zhu et al., 2002; Baldauf et al., 2008) indicate that near-road CO concentrations (i.e.,
19 measured within 20 meters of an interstate highway) can range from 2-10 times greater than
20 CO concentrations measured as far as 300 meters from a major road possibly influenced by wind
21 direction and on-road vehicle density (ISA, Figures 3-29 and 3-30).
22 While recognizing that monitoring site attributes are not available for all monitors in the
23 current network and that data for some attributes may not reflect current conditions,8 the ISA
24 also evaluated the average annual daily traffic (AADT) data available for each ambient monitor.
25 The ISA noted that only two microscale monitors and two middle scale monitors in the existing
26 network are sited at roads with >100,000 AADT, although it is not uncommon for roadways
27 within Consolidated Statistical Areas (CSAs) to have several roads with AADT > 100,000. The
28 AADT ranged from 160,000-178,000 for the near-road monitors used in the aforementioned
29 study by Zhu et al. (2002) where CO concentrations were up to 10 times greater than monitors
30 sited at 300 m from a major road.9 Existing microscale sites near roads having only moderate
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 CS A, AQS had no
information regarding monitoring scale for 16 (ISA, Figure 3-22).
9 Local-scale meteorology may have also contributed to the heightened concentrations, given that the Zhu
et al. (2000) study was designed to capture CO concentrations downwind of the roadway.
February 2010 3-5 Draft - Do Not Quote or Cite
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1 traffic count data (<100,000 AADT) may record concentrations that are not substantially
2 different from those obtained from neighborhood scale measurements (ISA, section 3.5.1.3).
3 Within a specific urban area, however, consideration of only monitor scale or other
4 attributes reported in AQS, such as AADT estimates, may be of limited use in efforts to
5 characterize the monitoring data as to its representation of local near-road CO concentrations.
6 For example, of the five monitors meeting a 75% completeness criterion in the Denver CSA,
7 three were microscale and two were neighborhood scale (ISA, section 3.5.1.2). While one of the
8 microscale monitors sited within downtown Denver measured the highest hourly ambient CO
9 concentrations (ID 080310002), another microscale monitor (ID 080130009) located outside the
10 urban core measured the lowest hourly ambient CO concentrations (ISA, Figure 3-19). Further,
11 the AADT estimate for a major road near the microscale monitor within the urban core (ID
12 080310002, AADT=17,200) was lower than that listed for the microscale monitor outside the
13 urban core (ID 080130009, AADT=20,000) (ISA, Table A-2). And, a third microscale monitor
14 located 1.3 km from monitor ID 080310002, within the urban core, and measuring somewhat
15 lower CO concentrations (but not lower than the monitor outside the urban core) had only 500
16 AADT listed for the nearest major road. It is likely that the higher CO concentrations measured
17 at the downtown monitor reflect influences of the denser roadway network surrounding that
18 monitor in the downtown Denver area (ISA, Figure 3-17).10
19 Thus, to better characterize the representation of near-road CO concentrations for many
20 of the existing ambient monitors, additional analyses, beyond consideration of AQS attributes
21 such as monitoring scale and traffic count, local meteorology, would likely need to be performed
22 (e.g., using GIS to determine monitor distance from roads, the number and type of roads within
23 close proximity of the monitor, and obtaining current traffic count data for all roads).
24 Carbon monoxide also exhibits hourly variability within a day, with two distinct temporal
25 patterns noted for weekdays and weekends (ISA, section 3.5.2.2). The diurnal variation is
26 inherently linked to the typical commute times-of-day that occur within urban locations. In
27 general, in recent years observed mean and median concentrations for all hours of the day and
28 across all monitors within urban areas demonstrated limited variability, however 90th and 95th
29 percentile hourly concentrations generally exhibit early-morning and late afternoon peak CO
30 concentrations during weekdays (ISA, Figure 3-36). The weekend diurnal variation in ambient
31 CO concentrations was much lower than that occurring during weekdays as expected due to the
32 relative absence of commuter vehicle traffic during the morning and evening hours of the day.
33 Most urban areas have relatively stable concentrations throughout weekend days at each of the
10 Staff also recognizes some uncertainty in how well the AQS AADT estimates reflect current conditions
at this monitor site.
February 2010 3-7 Draft - Do Not Quote or Cite
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1 selected percentiles, though a few locations (e.g., Phoenix, Los Angeles, Seattle) did have a more
2 pronounced late afternoon peak (ISA, Figure 3-37).
3 Staff investigated local hourly variation at two separate CO monitors located in Denver
4 and Los Angeles to illustrate similar trends. The monitor in Denver is a microscale monitor
5 located in downtown Denver and expected to reflect concentrations resulting from dense
6 downtown traffic in that city; it is the monitor measuring the highest ambient CO concentrations
7 in the Denver area. The monitor in Los Angeles is a middle scale monitor located in Lynwood;
8 it is also the monitor measuring the highest ambient CO concentrations in the Los Angeles area
9 Figure 3-2 indicates that on average, peak ambient CO concentrations that occur during typical
10 commute times in Denver ranged from about 1 to 5 ppm during weekdays in 1995, while,
11 currently, ambient CO concentrations during morning and afternoon commutes range from about
12 1 to 2 ppm. Weekends tend to exhibit less variability throughout the day. On average, CO
13 ambient concentrations generally ranged from 1 to 3 ppm throughout the day in 1995, while
14 current weekend concentrations are less than 1 ppm for most hours of the day. In Los Angeles,
15 both the concentration levels and variability are greater than when compared with similar years
16 and times of day in Denver (Figure 3-3). Peak ambient CO concentrations are more prominent
17 during morning commutes and generally ranged from 2 to 10 ppm in 1995, while currently (year
18 2006) most commuting times are associated with concentrations ranging from between 1 and 5
19 ppm. The weekend profile exhibits some variation when considering either year, with
20 maximum concentration levels and variability exhibited during the overnight hours.
February 2010 3-8 Draft - Do Not Quote or Cite
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Weekdays in 1995
Weekends in 1995
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2 Figure 3-2. Diurnal Distribution of 1-hour CO Concentrations in Denver (Monitor 080310002) by Day-type (weekdays-left;
3 weekends-right), Years 1995 (top) and 2006 (bottom). The box encompasses concentrations from the 25th to 75th
4 percentiles or Interquartile range (IQR), the line bisecting the box is the median, the solid dot within the box is the
5 mean, the whiskers represent 1.5 times the IQR, and concentrations outside the whiskers are indicated by open circles.
6 Note there are differences in the y-axis scale for the two time periods.
February 2010
3-9
Draft - Do Not Quote or Cite
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Weekdays in 1997
Weekends in 1997
o
o
o
\ I I I I I I I I I I I I I I I I I I I I I I I
0123456789 11 13 15 17 19 21 23
Clock Hour
o
o
o
\ I I I I I I I I I I I I I I I I I I I I I I I
0123456789 11 13 15 17 19 21 23
Clock Hour
o
o
Weekdays in 2006
Qiifl
I I I I I I I I I I
01 23456789
11
Clock Hour
\ I
13
15
I I
17
I I I
19 21
23
o
o
Weekends in 2006
i i i i i i i i i i
0123456789
\ I I I
11 13
Clock Hour
15 17
I I \
19 21
I i
23
2 Figure 3-3. Diurnal distribution of 1-hour CO concentrations in Los Angeles (Monitor 060371301) by day-type (weekdays-
3 left; weekends-right), years 1997 (top) and 2006 (bottom). The box encompasses concentrations from the 25th to
4 75th percentiles or IQR, the line bisecting the box is the median, the solid dot within the box is the mean, the whiskers
5 represent 1.5 times the IQR, and concentrations outside the whiskers are indicated by open circles. Note there are
6 differences in the y-axis scale for the two time periods.
February 2010
3-10
Draft - Do Not Quote or Cite
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1 3.1.4 Policy-Relevant Background Concentrations
2 EPA has generally conducted NAAQS risk assessments that focus on the risks associated
3 with ambient levels of a pollutant that are in excess of policy-relevant background (PRB).
4 Policy-relevant background levels are defined, for purposes of this document, as concentrations
5 of a pollutant that would occur in the US in the absence of anthropogenic emissions in the US,
6 Canada, and Mexico.
7 Over the continental US (CONUS), the 3-year (2005-2007) average CO PRB
8 concentration is estimated to range from 0.118 to 0.146 ppm (ISA, section 3.5.4). Outside the
9 CONUS, the 3-year average CO PRB in three Alaskan sites is estimated to range from 0.127 to
10 0.135 ppm, and from 0.095 to 0.103 ppm in two Hawaiian monitoring locations. The estimated
11 PRB concentrations exhibit significant within-location seasonal variation, with minimum
12 concentrations observed in the summer and fall and maximum concentrations occurring in the
13 winter and spring. For example, PRB in two California sites is estimated to range from about
14 0.085 to 0.170 ppm, and one site in Colorado, ranged from about 0.080 to 0.140 ppm (ISA,
15 Figure 3-43).
16 Given that ambient concentrations of interest in this REA are well above the estimated
17 PRB levels discussed above and, thus the contribution of PRB to overall ambient CO
18 concentrations is very small, EPA is characterizing risks associated with ambient CO levels
19 without regard to estimated PRB levels.
20 3.1.5 Within-Monitor CO Concentration Trends
21 The previous section addressed general trends in ambient concentrations. Of particular
22 interest in this assessment is how concentrations have changed within a specific monitor over
23 time. This is an important consideration in determining how best to address alternative air
24 quality conditions. These alternative air quality conditions are useful in evaluating how varying
25 distributions of air quality might affect different exposure scenarios. In other recent NAAQS
26 reviews for NO2 (US EPA, 2008) and SO2 (US EPA, 2009) it was determined the relationship
27 between high concentration and low concentration years of ambient monitoring data was mainly
28 proportional (Rizzo, 2008; 2009), that is all concentrations across the entire distribution at a
29 single monitor changed in equivalent amounts over time. Staff needed the relationship to adjust
30 current air quality because, at the time of the NAAQS reviews, the current ambient NO2 and SO2
31 concentrations were far below that expected to just meet the current standards.
32 Knowledge of this relationship for ambient CO concentrations is also needed to develop
33 alternative air quality conditions for use in some of the exposure scenarios investigated in this
34 draft REA. Ambient CO concentration data were obtained for several monitors in Los Angeles
35 for two years: 1997 - representing a high concentration year and 2006 - representing a low
February 2010 3-11 Draft - Do Not Quote or Cite
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1 concentration year. First, all reported hourly CO concentrations were used to calculate the full
2 percentiles of the distribution (0-100 by 1 pet increments) for each year. Then the percentiles for
3 the high concentration year were plotted against that of the low concentration year for each
4 individual monitor (Figure 3-4). A simple linear regression was also plotted, along with the
5 regression slope, intercept, and fit statistic (R2). As shown by the relationships, there is a very
6 strong linear relationship when comparing each year of data within each monitor, and the
7 regression intercepts for most of the monitors are small, indicating there is adequate support for
8 adjusting air quality by a proportional method.
9 Staff was also interested in estimating the within-monitor temporal variability for three
10 air quality metrics. The first air quality metric was the current design value, that is, the 2nd
11 highest 8-hour average concentration in a year. The next two air quality metrics compared by
12 staff were the 99th percentile 1-hour and 8-hour daily maximum CO concentrations.
February 2010 3-12 Draft - Do Not Quote or Cite
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0600370113
0600371103
Q_
i 7
O
O
S 5
o!4
I3
« ^
o
a 1
23456
1997 (0-100) Percentile CO (PPM)
0600371301
y = 0.4232x + 0.0129
R2 = 0.9963
3
2
01
Q.
S. 1
-------
1 Staff evaluated the within-monitor temporal variability using two comparisons: one using
2 historical versus current air quality data and the other comparing year-to-year variability of these
3 upper percentile concentrations within the air quality distribution. Two three-year periods
4 (1995-1997 and 2005-2007) were chosen by staff to represent historical and recent air quality,
5 respectively. Staff limited the analysis to four monitors within the Denver CSA and ten monitors
6 within the Los Angeles CSA, with all monitor data meeting standard requirements for data
7 completeness. In addition to the temporal evaluation of the air quality metrics, a limited analysis
8 of the spatial variability across the two periods is also provided for the selected monitors in each
9 area.
10 Tables 3-1 and 3-2 provide results for the first air quality metric in Denver and Los
11 Angeles, respectively. As shown by the Tables, there is a wide range in the temporal variability
12 of the 2nd highest 8-hour average CO concentration in both locations, however, the relative
13 variability, as indicated by the coefficient of variation (COV),11 is generally less for the recent air
14 quality when compared with the historical air quality. For example, in Denver the COV ranges
15 from 4-27 percent (mean = 13%) for the historical data, while the recent data temporal COV
16 ranges from 3-23 percent (mean = 10%) (Table 3-1). In addition, the magnitude of the spatial
17 variability tends to vary from year-to-year as indicated by the COV, though there are differences
18 in the historical versus recent air quality pattern by location. In Denver, there was generally less
19 spatial variability in the 2nd highest 8-hour concentration when comparing the recent and
20 historical air quality data. There was no apparent trend in year-to-year spatial variability for Los
21 Angeles as both air quality periods had a mean COV of about 31% (Table 3-2).
22 Similar temporal trends are observed with the 99th percentile 1-hour daily maximum
23 concentrations when comparing historical versus recent air quality (Tables 3-3 and 3-4 for
24 Denver and Los Angeles, respectively). The temporal variability in the recent air quality was
25 also less than that of the prior air quality metric (i.e., the 2nd highest 8-hour average), averaging
26 about 4% COV in Denver and 7% COV in Los Angeles across that 3-year period. The year-to-
27 year spatial variability for this metric is consistent with that stated above. In Denver, the COV
28 on average was less for the recent air quality when compared with the historical data. There was
29 little difference in the year-to-year spatial variability in Los Angeles when considering the two
30 air quality periods. Results for the 99th percentile 8-hour daily maximum concentrations were
31 more similar to the results for the 2nd highest 8-hour average concentration than the 99th
32 percentile 1-hour daily maximum (Tables 3-5 and 3-6, for Denver and Los Angeles,
33 respectively).
11 The COV is calculated here by dividing the standard deviation (std) by the mean, then multiplying by
100.
February 2010 3-14 Draft - Do Not Quote or Cite
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1
2
Table 3-1. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) air quality data -
»nd
2 highest 8-hour average.
Monitor
31-0002
31-0013
31-0014
59-0002
mean
std
cov
Historical Air Quality - 2nd highest 8-hour average
1995
9.5
6.2
5.9
4.6
6.6
2.1
32
1995
7.3
5.2
5.7
4.3
5.6
1.3
22
1997
5.5
4.7
6.2
4.9
5.3
0.6
12
mean
7.4
5.4
5.9
4.6
5.8
1.2
20
std
2.0
0.8
0.2
0.3
COV
27
14
4
7
Recent Air Quality - 2nd highest 8-hour average
2005
2.6
2.4
2.1
1.8
2.2
0.3
14
2006
3.1
2.5
3.0
2.0
2.6
0.5
19
2007
2.8
2.8
mean
2.8
2.4
2.5
1.9
2.4
0.4
16
std
0.3
0.1
0.6
0.1
COV
9
3
23
6
3 Table 3-2. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (2005-07) air quality
»nd
data - 2 highest 8-hour average.
Monitor
37-0113
37-1002
37-1103
37-1201
37-1301
37-2005
37-4002
59-0001/7
59-1003
59-5001
mean
std
COV
Historical Air Quality - 2nd highest 8-hour average
1995
9.4
10.9
7.9
9.4
11.7
8.6
6.3
7.3
5.3
6.4
8.3
2.1
25
1996
8.5
8.5
7.5
6.7
14.3
6.9
6.2
6.1
6.5
6.3
7.7
2.5
32
1997
4.1
7.2
5.9
7.7
15.0
5.4
6.4
5.4
5.0
5.7
6.8
3.1
45
mean
7.3
8.9
7.1
7.9
13.6
7.0
6.3
6.3
5.6
6.1
7.6
2.3
31
std
2.8
1.9
1.1
1.3
1.7
1.6
0.1
1.0
0.8
0.4
COV
39
21
15
17
13
23
2
16
14
6
Recent Air Quality - 2nd highest 8-hour average
2005
1.9
3.2
2.6
3.4
5.6
2.8
2.9
3.1
3.1
2.9
3.1
0.9
30
2006
1.9
3.4
2.5
3.4
5.6
2.7
3.3
2.9
2.5
2.9
3.1
1.0
32
2007
1.6
2.7
2.1
2.7
4.9
2.2
2.5
2.3
2.5
2.5
2.6
0.9
33
mean
1.8
3.1
2.4
3.2
5.3
2.6
2.9
2.8
2.7
2.8
3.0
0.9
31
std
0.2
0.4
0.3
0.4
0.4
0.3
0.4
0.4
0.3
0.2
COV
10
12
12
12
8
13
15
15
12
8
February 2010
3-15
Draft - Do Not Quote or Cite
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1 Table 3-3. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) air quality data
2 99th percentile 1-hour daily maximum.
Monitor
31-0002
31-0013
31-0014
59-0002
mean
std
cov
Historical Air Quality - 99th percentile 1-hour daily maximum
1995
13.5
11.1
8.2
8.6
10.4
2.5
24
1995
13.4
9.0
7.3
6.8
9.1
3.0
32
1997
9.1
8.6
7.8
7.2
8.2
0.9
10
mean
12.0
9.6
7.8
7.5
9.2
2.1
22
std
2.5
1.3
0.5
0.9
COV
21
14
6
12
Recent Air Quality - 99th percentile 1-hour daily maximum
2005
3.8
3.5
3.3
3.4
3.5
0.2
6
2006
4.5
3.7
3.2
3.4
3.7
0.6
16
2007
4.4
4.4
mean
4.2
3.6
3.2
3.4
3.6
0.4
12
std
0.4
0.1
0.1
0.0
COV
9
3
3
0
Table 3-4. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (2005-07) air quality
data - 99th percentile 1-hour daily maximum.
Monitor
37-0113
37-1002
37-1103
37-1201
37-1301
37-2005
37-4002
59-0001/7
59-1003
59-5001
mean
std
COV
Historical Air Quality - 99th percentile 1-hour daily maximum
1995
13.9
11.6
9.0
10.6
16.2
10.3
7.6
9.1
7.3
10.7
10.6
2.8
26
1996
7.5
9.7
9.4
8.4
20.2
8.8
8.4
8.2
8.4
11.6
10.1
3.7
37
1997
6.1
8.2
7.4
8.4
18.5
6.2
7.6
7.7
6.9
10.3
8.7
3.6
42
mean
9.2
9.9
8.6
9.1
18.3
8.4
7.9
8.4
7.5
10.9
9.8
3.1
32
std
4.2
1.7
1.1
1.3
2.0
2.0
0.5
0.7
0.8
0.7
COV
45
17
13
14
11
24
6
9
11
6
Recent Air Quality - 99th percentile 1-hour daily maximum
2005
2.6
3.9
3.1
4.0
7.1
3.4
3.8
3.6
3.6
5.2
4.0
1.3
32
2006
2.7
4.1
2.9
3.9
7.4
3.3
3.8
3.6
3.2
5.4
4.0
1.4
35
2007
2.1
3.6
2.6
3.4
6.8
3.0
3.1
3.2
3.2
5.2
3.6
1.4
38
mean
2.5
3.9
2.9
3.8
7.1
3.2
3.6
3.5
3.3
5.3
3.9
1.3
34
std
0.3
0.3
0.3
0.3
0.3
0.2
0.4
0.2
0.2
0.1
COV
13
7
9
8
4
6
12
6
6
2
February 2010
3-16
Draft - Do Not Quote or Cite
-------
1 Table 3-5. Within monitor temporal variability in Denver using historical (1995-97) and recent (2005-07) air quality data
2 99th percentile 8-hour daily maximum.
Monitor
31-0002
31-0013
31-0014
59-0002
mean
std
cov
Historical Air Quality - 99th percentile 8-hour daily maximum
1995
7.3
5.4
5.7
4.1
5.6
1.3
24
1995
7.2
5.2
5.5
3.8
5.4
1.4
26
1997
5.2
4.7
5.8
4.8
5.1
0.5
10
mean
6.6
5.1
5.7
4.2
5.4
1.0
18
std
1.2
0.4
0.1
0.5
COV
18
7
2
12
Recent Air Quality - 99th percentile 8-hour daily maximum
2005
2.4
2.2
2.1
1.8
2.1
0.3
12
2006
2.8
2.1
2.8
1.8
2.4
0.5
21
2007
2.7
2.7
mean
2.6
2.2
2.4
1.8
2.3
0.4
16
std
0.2
0.0
0.5
0.0
COV
9
2
22
2
Table 3-6. Within monitor temporal variability in Los Angeles using historical (1995-97) and recent (2005-07) air quality
data - 99th percentile 8-hour daily maximum.
Monitor
37-0113
37-1002
37-1103
37-1201
37-1301
37-2005
37-4002
59-0001
59-1003
59-5001
mean
std
COV
Historical Air Quality - 99th percentile 8-hour daily maximum
1995
8.6
9.7
7.5
9.0
11.2
8.5
5.9
6.5
4.7
6.3
7.8
2.0
25
1996
5.2
8.3
7.0
6.7
13.9
6.8
6.2
5.7
6.4
5.9
7.2
2.5
35
1997
3.7
6.8
5.6
7.3
13.1
5.0
5.9
5.1
4.9
5.3
6.3
2.6
42
mean
5.8
8.3
6.7
7.6
12.7
6.7
6.0
5.8
5.3
5.8
7.1
2.2
31
std
2.5
1.4
1.0
1.2
1.4
1.8
0.2
0.7
0.9
0.5
COV
43
17
15
15
11
26
3
12
17
9
Recent Air Quality - 99th percentile 8-hour daily maximum
2005
1.9
3.0
2.6
3.2
4.9
2.8
2.9
2.7
3.0
2.6
3.0
0.8
25
2006
1.8
3.2
2.4
3.1
5.1
2.6
2.7
2.7
2.2
2.7
2.9
0.9
31
2007
1.5
2.6
2.0
2.6
4.5
2.1
2.4
2.1
2.4
2.5
2.5
0.8
31
mean
1.8
3.0
2.4
2.9
4.8
2.5
2.7
2.5
2.6
2.6
2.8
0.8
29
std
0.2
0.3
0.3
0.3
0.3
0.3
0.2
0.3
0.4
0.1
COV
12
11
13
12
7
14
9
14
16
5
February 2010
3-17
Draft - Do Not Quote or Cite
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1 3.2 STUDY AREAS SELECTED FOR CURRENT CO REA
2 Staff identified several criteria to select the exposure assessment study areas drawing
3 from information discussed in the earlier sections of this Chapter and additional scientific
4 evidence in the ISA. We selected Denver and Los Angeles as areas to focus the current
5 assessment because (1) both cities have been included in prior CO NAAQS exposure
6 assessments and thus serve as an important connection with past assessments, (2) they have
7 historically had among the highest CO ambient concentrations among urban areas in the U.S.,
8 and (3) Denver is at high altitude and represents a scenario of interest due to the potentially
9 increased susceptibility of visitors to high altitude from exposure to CO. In addition, of 10 urban
10 areas across the US having monitors meeting a 75% completeness criteria, the two locations
11 were ranked 1st (Los Angeles) and 2nd (Denver) regarding percent of elderly population within 5,
12 10, and 15 km of monitor locations, and ranked 1st (Los Angeles) and 5th (Denver) regarding
13 number of 1-hour and 8-hour daily maximum CO concentration measurements (ISA, section
14 3.5.1.1).
15 3.3 KEY OBSERVATIONS
16 Presented below are key observations resulting from the air quality considerations.
17 • Mobile sources (i.e., gasoline powered vehicles) are the primary contributor to CO
18 emissions, particularly in urban areas due to greater vehicle and roadway densities.
19 • Recent (2005-2007) ambient CO concentrations across the US are lower than those
20 reported in the previous CO NAAQS review and are also well below the current CO
21 NAAQS levels. Further, a large proportion of the reported concentrations are below
22 the conventional instrument lower detectable limit of 1 ppm.
23 • The currently available information for CO monitors indicates that siting of microscale
24 and middle scale monitors in the current network is primarily limited to roads where
25 traffic density described for them is moderate (<100,000 AADT), however, factors
26 other than reported AADT (e.g., orientation with regard to dense urban roadway
27 networks) can contribute to sites reporting higher CO concentrations.
28 • Ambient CO concentrations are highest at monitors sited closest to roadways (i.e.,
29 microscale and middle scale monitors) and exhibit a diurnal variation linked to the
30 typical commute times of day, with peaks generally observed during early morning and
31 late afternoon during weekdays.
32 • Policy relevant background (PRB) concentrations across the US are generally less than
33 0.2 ppm, far below that of interest in this REA with regard to ambient CO exposures.
34 • Historical trends in ambient monitoring data indicate that at individual sites, ambient
35 concentrations have generally decreased in a proportional manner. This comparison
36 included air quality distributions with concentrations at or above the current standard
37 and those reflecting current (as is) conditions.
February 2010 3-18 Draft-Do Not Quote or Cite
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1 • The temporal variability in selected upper percentile ambient concentrations (e.g., 99th
2 percentile 1-hour daily maximum) at individual monitors is relatively small across a
3 three year monitoring period, particularly when considering recent air quality.
February 2010 3-19 Draft - Do Not Quote or Cite
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1 3.4 REFERENCES
2
3 Baldauf R, Thoma E, Hays M, Shores R, Kinsey J, Gullett B, Kimbrough S, Isakov V, Long T, Snow R, Khlystov
4 A, Weinstein J, Chen FL, Seila R, Olson D, Gilmour I, Cho SH, Watkins N, Rowley P, Bang J. (2008).
5 Traffic and meteorological impacts on near-road air quality: Summary of methods and trends from the
6 Raleigh near-road study. J Air Waste ManagAssoc. 58:865-878.
7 Michie Jr RM, McElroy FF, Sokash JA, Thompson VL, Dayton DP, Sutcliffe CR. (1983). Performance Test
8 Results and Comparative for Designated Equivalence Methods for Carbon Monoxide. EPA-600/S4-83-
9 013.
10 Rizzo M. (2008). Investigation of how distributions of hourly nitrogen dioxide concentrations have changed over
11 time in six cities. Nitrogen Dioxide NAAQS Review Docket (EPA-HQ-OAR-2006-0922 ). Available at
12 http://www.epa.gOv/ttn/naaqs/standards/nox/s noxcrrea.html.
13 Rizzo M. (2009). Investigation of How Distributions of Hourly Sulfur Dioxide Concentrations Have Changed Over
14 Time in Six Cities. Sulfur Dioxide Review Docket. Docket ID No. EPA-HQ-OAR-2007-0352. Available
15 at: http://www.regulations.gov.
16 US EPA. (2000). Air Quality Criteria for Carbon Monoxide. EPA 600/P-99/001F. US Environmental Protection
17 Agency, Research Triangle Park, NC.
18 US EPA. (2009). Integrated Science Assessment for Carbon Monoxide-Final Draft. U.S. Environmental
19 Protection Agency, Research Triangle Park, NC, report no. EPA/600/R-09/019F. Available at:
20 http://www.epa.gov/ttn/naaqs/standards/co/s_co_cr_isa.html.
21 Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C. (2002). Study of ultrafine particles near a major highway with
22 heavy-duty diesel traffic. Atmos Environ. 36:4323-4335.
23
February 2010 3-20 Draft - Do Not Quote or Cite
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1 4 OVERVIEW OF APEX MODELING SYSTEM FOR ESTIMATING
2 CO EXPOSURES AND COHB DOSE LEVELS
3 4.1 PURPOSE
4 This chapter presents an overview and description of the overall approach to estimating
5 human exposure and dose for past and recent NAAQS reviews. Section 4.2 provides a brief
6 overview of EPA's Air Pollutants Exposure model (APEX), the model used in this assessment to
7 estimate population exposure and dose. This overview is followed by a short history that
8 explains the evolution of exposure and dose models used by OAQPS to conduct exposure and
9 dose assessments for CO and other NAAQS reviews (section 4.3). Section 4.4 provides a
10 generalized description of the APEX simulation process, though having detailed focus on a few
11 of the important approaches used for modeling CO exposure and dose. This includes expanded
12 discussion on the approach used to estimate microenvironmental concentrations (section 4.4.4)
13 and COHb dose levels (section 4.4.7).
14 4.2 MODEL OVERVIEW
15 The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program
16 designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and
17 consolidated metropolitan levels. APEX, also known as TREVI.Expo, is the human inhalation
18 exposure module of EPA's Total Risk Integrated Methodology (TRIM) model framework (US
19 EPA, 1999), a modeling system with multimedia capabilities for assessing human health and
20 ecological risks from hazardous and criteria air pollutants.1
21 APEX estimates human exposure using a
. . A microenvironment is a three-
22 stochastic, microenvironmental approach (see caption).
dimensional space in which human
23 The model randomly selects data for a sample of , , ... . , . „ , ,
contact with an environmental pollutant
24 hypothetical individuals from an actual population
takes place and which can be treated as
25 database and simulates each individual's movements a well characterized relatively
26 through time and space (e.g., indoors at home, inside
27 vehicles) to estimate his or her exposure to a pollutant.
28 APEX can account for travel to and from work locations
29 (i.e., commuting) and provide estimates of exposures at
homogeneous location with respect to
pollutant concentrations for a specified
time period.
1 Additional information on the TRIM modeling system, as well as downloads of the APEX Model, user guides
(U.S. EPA 2008a, 2008b), and other supporting documentation, can be found at http://www.epa.gov/ttn/fera.
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1 both home and work locations for individuals who work away from home.
2 4.3 MODEL HISTORY AND EVOLUTION
3 APEX was derived from the National Ambient Air Quality Standards (NAAQS)
4 Exposure Model (NEM) series of models. The NEM series was developed to estimate
5 population exposures to the criteria pollutants (e.g., CO, ozone). In 1988, OAQPS first
6 incorporated probabilistic elements into the NEM methodology and used activity pattern data
7 based on available human activity diary studies to create an early version of probabilistic NEM
8 for ozone (i.e., pNEM/Os). In 1991, a probabilistic version of NEM was developed for CO
9 (pNEM/CO) that included a one-compartment mass-balance model to estimate CO
10 concentrations in indoor microenvironments. The application of this model to Denver, Colorado
11 is summarized in Johnson et al. (1992). Between 1999 and 2001, updated versions of pNEM/CO
12 (versions 2.0 and 2.1) were developed that rely on detailed activity diary data compiled in EPA's
13 Consolidated Human Activities Database (CHAD) (McCurdy et al., 2000; US EPA, 2002) and
14 enhanced algorithms for simulating gas stove usage, estimating alveolar ventilation rate (a
15 measure of human respiration), and modeling home-to-work commuting patterns. A draft report
16 by Johnson et al. (2000) describes the application of Version 2.1 of pNEM/CO to Denver and
17 Los Angeles.
18 The first version of APEX was essentially identical to pNEM/CO (version 2.0) except
19 that it ran on a PC instead of a mainframe. The next version, APEX2, was substantially
20 different, particularly in the use of a personal profile approach rather than a cohort simulation
21 approach. APEX3 introduced a number of new features including automatic site selection from
22 national databases, a series of new output tables providing summary exposure and dose statistics,
23 and a thoroughly reorganized method of describing microenvironments and their parameters.
24 Johnson and Capel (2003) describe a case study in which Version 3.1 of APEX was used to
25 estimate population exposure to CO in Los Angeles.
26 The current version of APEX (Version 4.3) (US EPA, 2008a; 2008b) was used to
27 estimate CO exposure and dose as described in chapter 5 of this document. This version was
28 also recently used to estimate ozone (Os) exposures in 12 urban areas for the Os NAAQS review
29 (US EPA, 2007), to estimate population exposures to nitrogen dioxide (NO2) in Atlanta as part of
30 the NO2 NAAQS review (US EPA, 2008c), and to estimate sulfur dioxide (802) exposures for
31 asthmatics and asthmatic children in two study areas in Missouri as part of the SO2 NAAQS
32 review (US EPA, 2009a). There have been several recent enhancements to APEX since the prior
33 1994 CO NAAQS review, including:
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1 • Algorithms for the assembly of multi-day (longitudinal) activity diaries that model intra-
2 individual variance, inter-individual variance, and day-to-day autocorrelation in diary
3 properties;
4 • Methods for adjusting diary-based energy expenditures for fatigue and excess post-
5 exercise oxygen (EPOC) consumption;
6 • New equations for estimation of ventilation (i.e., breathing rate);
7 • The ability to use air quality data and model exposures over flexible time scales;
8 • New output files containing diary event-level, time-step level, and hourly-level exposure,
9 dose, and ventilation data, and hourly-level microenvironmental data;
10 • The ability to model the prevalence of disease states such as asthma or coronary heart
11 disease (CHD);
12 • New output exposure tables that report exposure statistics for population groups and life-
13 stages such as children and active people at varying ventilation rates;
14 • The inclusion of tract-level commuting data from the 2000 census; and
15 • Expanded options for modeling microenvironments.
16 4.4 MODEL SIMULATION PROCESS
17 APEX4.3 is designed to simulate population exposure to criteria and air toxic pollutants
18 at local, urban, and regional scales. The user specifies the geographic area to be modeled and the
19 number of individuals to be simulated to represent this population. APEX4.3 then generates a
20 personal profile for each simulated person that specifies various parameter values required by the
21 model. The model next uses diary-derived time/activity data matched to each personal profile to
22 generate an exposure event sequence (also referred to as a time-location-activity pattern or
23 composite diary) for the modeled individual that spans a specified time period, such as a calendar
24 year. Each event in the sequence specifies a start time, exposure duration, a geographic location,
25 a microenvironment inhabited, and an activity performed. Probabilistic algorithms are used to
26 estimate the pollutant concentration and ventilation (respiration) rate associated with each
27 exposure event. The estimated pollutant concentrations account for the effects of ambient
28 (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate,
29 and proximity to emission sources, each depending on the microenvironment, available data, and
30 the estimation method selected by the user. The ventilation rate is derived from an energy
31 expenditure rate estimated for each individual when performing the specified activity. Because
32 the simulated individuals represent a random sample of the population of interest, the distribution
33 of modeled individual exposures can then be extrapolated to the larger population of interest.
34 The model simulation generally includes up to seven steps as follows:
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1 • Characterize study area: APEX4.3 selects sectors (e.g., census tracts) within a study
2 area—and thus identifies the potentially exposed population — usually based on the
3 user-defined center and radius of the study area and availability of air quality and
4 weather input data for the area (section 4.4.1).
5 • Generate simulated individuals: APEX4.3 stochastically generates a sample of
6 simulated individuals based on the census data for the study area and human profile
7 distribution data (such as age-specific employment probabilities or disease prevalence)
8 (section 4.4.2)
9 • Construct activity sequences: APEX4.3 constructs an exposure event sequence (time-
10 location-activity pattern) spanning the simulation period for each of the simulated
11 persons based on the CHAD diaries (section 4.4.3).
12 • Calculate microenvironmental concentrations: APEX4.3 enables the user to define
13 microenvironments that people in a study area would visit (e.g., by grouping location
14 codes included in the activity pattern database). The model then calculates time-
15 averaged concentrations (e.g., hourly) of each pollutant in each of the
16 microenvironments for each simulated person for the period of simulation based on the
17 user-provided ambient air quality data (section 4.4.4).
18 • Estimate energy expenditure and ventilation rates: APEX4.3 constructs a time-
19 series of energy expenditures for each individual's exposure profile based on the
20 sequence of activities performed. The sequence of energy expenditures are adjusted to
21 ensure that they are physiologically realistic and then used to estimate activity-specific
22 alveolar ventilation rates (section 4.4.5).
23 • Calculate exposure: APEX4.3 assigns a concentration to each exposure event based
24 on the microenvironment occupied during the event and the person's activity. These
25 values are time-averaged (e.g., hourly) to produce a sequence of exposures spanning
26 the specified exposure period (typically one year). The hourly values may be further
27 aggregated to produce 8-hour, daily, monthly, and annual average exposure values
28 (section 4.4.6).
29 • Calculate dose: APEX4.3 optionally calculates hourly, daily, monthly, and annual
30 average dose values for each of the simulated individuals. For the application of
31 APEX to CO, a module within the model estimates the percent COHb level in the
32 blood at the end of each hour based on the time-series of CO concentrations and
33 alveolar ventilation rates experienced by the simulated person (section 4.4.7).
34 The model simulation continues until exposures (and associated COHb dose levels) are
35 calculated for the user-specified number of simulated individuals. Figure 4-1 presents a
36 conceptual model and simplified data flow diagram illustrating the implementation of APEX4.3
37 to estimate CO exposure and dose. The following sections provide additional details on the
38 general procedures and algorithms used in each of the seven simulation steps listed above,
39 though more complete discussion can be found in US EPA (2008a, b). The specific input data
40 and microenvironmental factors used in applying APEX4.3 to CO for the current assessment are
41 further described in section 5.1.
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Air Exchange Rates
(AER) & Prevalence
US Census
Tract-Level Population
& Commuting Data
Meteorological Data
Temperature
Outdoor CO Concentration
Algorithm
(spatial & temporal adjustment)
Ambient Air Quality
1 -Hour Ambient Monitor
CO Concentrations
USDHHS/CDC
Disease Prevalence
CHAD
Time-Location-Activity
Patterns
Exposure Algorithms
(mass balance or factors)
Microenvironmental
Factors/Distributions
J
Persons & Person-Days
At or Above
Exposure Levels
Energy Expenditure &
Ventilation Algorithm
(alveolar respiration rate)
Dose Algorithm
(CFK model)
J
Persons & Person-Days
At or Above
Benchmark Dose Levels
2 Figure 4-1. Conceptual model and simplified data flow for estimating population exposure and dose using APEX4.3.
February 2010
4-5
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1 4.4.1 Characterize Study Area
2 An initial study area in an APEX4.3 assessment consists of a set of basic geographic units
3 called sectors, typically defined by US census data reported at the census tract level. The user
4 may provide the geographic center (latitude/longitude) and radius of the study area and then
5 APEX4.3 calculates the distances to the center of the study area of all the sectors included in the
6 sector location database, and finally selects the sectors within the radius of the study area.
7 APEX4.3 then maps the user-provided air quality and meteorological data for specified
8 monitoring districts to the selected sectors. The sectors identified as having acceptable air
9 quality and meteorological data within the radius of the study area are selected to comprise a
10 final study area for the APEX4.3 simulation analysis. This final study area determines the
11 population make-up of the simulated persons (profiles) to be modeled.
12 4.4.2 Generate Simulated Individuals
13 APEX4.3 stochastically generates a user-specified number of simulated persons to
14 represent the population in the study area. Each simulated person is represented by a personal
15 profile. APEX4.3 generates the simulated person by probabilistically selecting values for a set of
16 profile variables. The profile variables include:
17 • Demographic variables that are generated based on US census data (e.g., age, gender,
18 home sector, work sector);
19 • Residential variables that are generated based on sets of distribution data (e.g., air
20 conditioning prevalence);
21 • Physiological variables that are generated based on age- and gender-specific distribution
22 data (e.g., blood volume, body mass, resting metabolic rate); and
23 • Daily varying variables that are generated based on distribution data that change daily
24 during the simulation period (e.g., daily work status).
25 APEX4.3 first selects and calculates demographic, residential, and physiological
26 variables (except for daily values) for each of the user-specified number of simulated individuals.
27 APEX4.3 then follows each simulated individual over time and calculates exposures (and
28 optionally doses) for the individual over the duration of the assessment period. The complete
29 listing of profile variables used by APEX4.3 and detailed description can be found in section 5 of
30 US EPA (2008b). An overview of the data sources used and their implementation in APEX4.3 is
31 provided below.
32 4.4.2.1 Population Demographics
33 APEX4.3 takes population characteristics into account to develop accurate
34 representations of study area demographics. Specifically, population counts by area and
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1 employment probability estimates are used to develop representative profiles of hypothetical
2 individuals for the simulation.
3 APEX4.3 is flexible in the resolution of population data provided. As long as the data are
4 available, any resolution can be used (e.g., county, census tract, census block). For this
5 application of the model, census tract level data were used. Tract-level population counts are
6 obtained from the 2000 Census of Population and Housing Summary File 1 (SF-1). This file
7 contains data compiled from the questions asked of all respondents and about every housing unit.
8 As part of the population demographics inputs, it is important to integrate working
9 patterns into the assessment. In the 2000 US Census, estimates of employment were developed
10 by census information (US Census Bureau, 2007). The employment statistics are broken down
11 by gender and age group, so that each gender/age group combination is given an employment
12 probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are:
13 16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75.
14 Children under 16 years of age were assumed to be not employed.
15 4.4.2.2 Commuting Database
16 In addition to using estimates of employment by tract, APEX4.3 also incorporates home-
17 to-work commuting data. Commuting data were derived from the 2000 Census and were
18 collected as part of the Census Transportation Planning Package (CTPP) (US DOT, 2007). The
19 data used contain counts of individuals commuting from home to work locations at a number of
20 geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to
21 create the national commuting data distributed with APEX4.3. This database contains
22 commuting data for each of the 50 states and Washington, D.C.
23 Several assumptions were made in the development of the database and with the
24 modeling of a person's commute in this assessment as follows.
25 • Commutes within tracts and home workers: There is no differentiation between
26 people that work at home from those that commute within their home tract.
27 • Commute distance cutoff: All persons in home-work flows up to 120 km are assumed
28 to be daily commuters and no persons in more widely separated flows would commute
29 daily. This means that the list of destinations for each home tract was restricted to only
30 those work tracts that are within 120 km of the home tract. This distance is based on
31 the presence of a near-constant relationship between commute flows and distance
32 traveled up to 120 km.
33 • Eliminated Records: Tract-to-tract pairs representing workers who either worked
34 outside of the US (9,631 tract pairs with 107,595 workers) or worked in an unknown
35 location (120,830 tract pairs with 8,940,163 workers) were eliminated from the
36 database. An additional 515 workers in the commuting database whose data were
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1 missing from the original files, possibly due to privacy concerns or errors, were also
2 deleted.
3 • Commuting outside the study area: APEX4.3 allows for some flexibility in the
4 treatment of persons in the modeled population who commute to destinations outside
5 the study area. Users can either retain these persons and include them as part of the
6 population exposed or have them eliminated from the model simulation. In the first
7 instance (i.e., "KeepLeavers = Yes"), APEX4.3 can assign input concentrations based
8 on the available ambient concentration data within the model domain. For the second
9 option (i.e., "KeepLeavers = No"), people who work inside the study area but live
10 outside of it are not modeled, nor are people who live in the study area but work
11 outside of it.
12 4.4.2.3 Profile Functions File
13 A Profile Functions file contains settings used to generate results for variables related to
14 simulated individuals. While certain settings for individuals are generated automatically by
15 APEX4.3 based on other input files, including demographic characteristics, others can be
16 specified using this file. For example, the file may contain settings for determining whether the
17 profiled individual's residence has an air conditioner, a gas stove, etc.
18 4.4.2.4 Physiology File
19 The APEX4.3 physiology.txt file contains age- and gender-based information for several
20 physiological parameters used in human exposure modeling. This information includes various
21 equations, distributional shapes, and parameters for all age and gender cohorts from age 0 to 100
22 years for variables such as normalized maximal oxygen uptake, body mass, resting metabolic
23 rate (RMR), and blood hemoglobin content. Appendix A provides an evaluation of a few
24 important variables used by APEX4.3 in this exposure and dose assessment as well as their
25 updated values or distributions (e.g., new age-gender body mass distributions derived from 1999-
26 2004 National Health and Nutrition Examination Survey data). Details regarding any other
27 physiology variable distributions and their parameters not discussed in this draft CO REA and
28 associated appendices can be found in US EPA (2008a, b).
29 4.4.3 Construct Activity Sequences
30 Different human activities, such as spending time outdoors, indoors, or driving, will be
31 associated with varying pollutant concentrations. Therefore, to accurately model individuals and
32 their exposure to pollutants, it is critical to understand people's daily activities and use such data
33 in the exposure model. EPA's Consolidated Human Activity Database (CHAD) provides diary -
34 derived data indicating where people spend time and the activities they perform at each location
35 (US EPA, 2002). CHAD was designed to provide a basis for conducting multi-route, multi-
36 media exposure assessments (McCurdy et al., 2000). The data contained within CHAD originate
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1 from multiple activity pattern surveys with varied structures (Table 4-1), however the surveys
2 have commonality in containing daily diaries of human activities performed, locations visited,
3 and personal attributes (e.g., age and gender).
4 There are four CHAD-related input files used in APEX4.3. The first three can be
5 considered standard input files for most model simulations; the user typically does not modify
6 their contents. These include the human activity diaries file, the personal data file, and a
7 metabolic information file, each of which are discussed briefly below. The fourth CHAD-related
8 input file maps the five-digit location codes used in the diary file to APEX4.3
9 microenvironments; this file is commonly modified by the user and is discussed in section 5.8
10 (i.e., specific microenvironments modeled in this CO assessment). And finally, section 4.4.3.4
11 discusses how these diaries are linked together to form a continuous time-location-activity
12 pattern for each individual across the entire simulation period.
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1 Table 4-1. Summary of activity pattern studies comprising the recent version of CHAD.
Study Name
Baltimore
GARB: Adults
GARB: Adolescents
GARB: Children
Cincinnati (EPRI)
Denver (EPA)
Los Angeles:
Elementary
Los Angeles: High
School
NHAPS A
NHAPS B
PSID 1
(U Michigan I)
PSID 2
(U Michigan II)
Valdez
Washington, DC
RTI Ozone Averting
Behavior
RTP Panel Study
Seattle Study
Internal EPA Study
2006-2007
EPA Longitudinal 1
EPA Longitudinal 2
EPA Longitudinal 3
CHAD
Prefix
BAL
CAA
CAY
CAC
CIN
DEN
LAE
LAH
NHA
NHW
UMC
ISR
VAL
WAS
OAB
RTP
SEA
EPA
EPA
EPA
EPA
Study
Years
1997-1998
1987-1988
1987-1988
1989-1990
1985
1982-1983
1989
1990
1992-1994
1992-1994
1997
2002-2003
1990-1991
1982-1983
2002-2003
2000-2001
1999-2002
2006-2007
1999,2002
2000
2008
Number of
Diary Days
391
1579
183
1200
2614
805
51
43
4723
4663
5616
4782
397
699
2907
1003
1693
434
736
197
62
Reference
Williams et al. (2000)
Wiley etal. (1991 a)
Wiley etal. (1991 a)
Wiley etal. (1991b)
Johnson (1989)
Johnson (1984); Akland etal. (1985)
Spier etal. (1992)
Spier etal. (1992)
Klepeis et al. (1996); Tsang and Klepeis (1996)
Klepeis et al. (1996); Tsang and Klepeis (1996)
University of Michigan (2010)
University of Michigan (2010)
Goldstein et al. (1992)
Hartwell et al. (1984); Akland et al. (1985)
Mansfield and Corey (2003); Mansfield et al.
(2004; 2006)
Williams et al. (2003a,b)
Liu et al. (2003)
Isaacs et al. (2009)
Isaacs et al. (2009)
Isaacs et al. (2009)
Isaacs et al. (2009)
3 4.4.3.1 Personal Information file
4 Personal attribute data are contained in the CHAD questionnaire file that is distributed
5 withAPEX4.3. This file also has information for each day individuals have diaries. The
6 different variables in this file are:
7 • The study, person, and diary day identifiers
8 • Day of week
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1 • Gender
2 • Employment status
3 • Age in years
4 • Maximum temperature in degrees Celsius for the diary day
5 • Mean temperature in degrees Celsius for the diary day
6 • Occupation code (if requested)
7 • Time, in minutes, during this diary day for which no data are included in the database
8 4.4.3.2 Diary Events File
9 The human activity diary data are contained in the events file that is distributed with
10 APEX4.3. This file contains the locations visited and the activities performed for the nearly
11 35,000 person-days of data with event intervals ranging from a minimum of one minute upwards
12 to a one hour maximum duration. A study individuals' diary can vary in length from one to 15
13 days (i.e., referring to the number of person-days). The diary events file contains the following
14 variables:
15 • The study, person, and diary day identifiers
16 • Start time of the event
17 • Number of minutes for the event
18 • Activity code (a record of what the individual was doing)
19 • Location code (a record of where the individual was)
20 4.4.3.3 Activity-Specific Metabolic File
21 The metabolic file contains the distributional forms and parameters for the activity -
22 specific metabolic equivalents (METs) used to quantitatively assign exertion levels to each
23 activity performed by simulated individuals (McCurdy, 2000). Some activities are specified as a
24 single point value (for instance, sleep), while others, such as athletic endeavors or manual labor,
25 are represented by normal, lognormal, or similar statistical distributions. APEX4.3 samples from
26 these distributions and calculates values to simulate the variable nature of activity levels among
27 different people. The CHAD User's guide provides details on the distributions used, parameters,
28 and sources for each activity (US EPA, 2002).
29 4.4.3.4 Longitudinal Diary Processing
30 APEX4.3 probabilistically creates a composite longitudinal diary for each of the
31 simulated persons by selecting a 24-hour diary record - or diary day - from an activity database
32 for each day of the simulation period. The EPA's CHAD data (US EPA, 2002) are supplied with
33 APEX4.3 for this purpose. A composite diary is a sequence of events that simulates the
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1 movement of a modeled person through varying geographical locations and microenvironments
2 for the duration of the simulation period. Each diary event is defined by geographic location,
3 start time, duration, microenvironment visited, and an activity performed.
4 The activity database input to APEX4.3 contains the following information for each diary
5 day: age, gender, employment status, occupation, day-of-week (or day-type), and maximum
6 hourly average temperature. This information enables APEX4.3 to select data from the activity
7 database that tend to match the characteristics of the simulated person, the study area, and the
8 specified time period. APEX4.3 develops a composite diary for each of the simulated
9 individuals according to the following steps.
10 • Divide diary days in the CHAD database into user-defined activity pools, based on
11 day-type and temperature.
12 • Assign an activity pool number to each day of the simulation period, based on the user-
13 provided daily maximum/average temperature data.
14 • Calculate a selection probability for each of the diary days in each of the activity pools,
15 based on age/gender/employment similarity of a simulated person to a diary day.
16 • Probabilistically select a diary day from available diary days in the activity pool
17 assigned to each day of the simulation period.
18 • Estimate a MET value for each activity performed while in a location, based on a
19 random sampling of the particular distribution of each specific activity. The METs
20 values are used to calculate an activity-specific ventilation rate (see section 4.4.5) for
21 the simulated person.
22 • Map the CHAD locations in the selected diary to the user-defined modeled
23 microenvironments.
24 • Concatenate the selected diary days into a sequential longitudinal diary for a simulated
25 individual covering all days in the simulated period.
26 APEX4.3 provides an optional longitudinal diary-assembly algorithm that enables the
27 user to create composite diaries that reflect the tendency of individuals to repeat activities on a
28 day-to-day basis. The user specifies values for two statistical variables (i.e., D and A) that relate
29 to a key daily variable, typically the time spent per day in a particular microenvironment (e.g., in
30 a motor vehicle). The D statistic reflects the relative importance of within-person variance and
31 between-person variance in the key variable. The A statistic quantifies the lag-one (day-to-day)
32 variable autocorrelation. APEX4.3 then constructs composite diaries that exhibit the statistical
33 properties defined by the specified values ofD and A. The longitudinal diary assembly
34 algorithm is described in greater detail by Glen et al. (2008) and in section 6.3 of US EPA
35 (2008b).
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1 4.4.4 Calculate Microenvironmental Concentrations
2 Probabilistic algorithms are used by APEX4.3 to estimate the pollutant concentration
3 associated with each exposure event. The estimated pollutant concentrations account for the
4 effects of ambient (outdoor) pollutant concentration, penetration factor, air exchange rate,
5 decay/deposition rate, and proximity to emission sources, depending on the microenvironment,
6 available data, and the estimation method selected by the user.
7 APEX4.3 calculates air concentrations in the various microenvironments visited by the
8 simulated person by using the ambient air data for the relevant census tracts and the user-
9 specified method and parameters that are specific to each microenvironment. In typical
10 applications, APEX4.3 calculates hourly concentrations in all the microenvironments at each
11 hour of the simulation for each of the simulated individuals, based on the hourly ambient air
12 quality data specific to the geographic locations visited by the individual. APEX4.3 provides
13 two methods for calculating microenvironmental concentrations: the mass balance method and
14 the transfer factors method (described briefly below). The user is required to specify a
15 calculation method for each of the microenvironments; there are no restrictions on the method
16 specified for each microenvironment (e.g., some microenvironments can use the mass balance
17 method while the others can use the transfer factors method). Each of these approaches is
18 described in sections 4.4.4.1 and 4.4.4.2, respectively.
19 When using an exposure model to estimate population exposures to CO it is best to
20 estimate the outdoor (ambient) CO concentration in the immediate vicinity of each
21 microenvironment. This is because concentrations measured at a fixed-site monitor may not
22 adequately represent the spatial and temporal heterogeneity in concentrations expected with
23 distance from the ambient monitor location. There can be different ways to accomplish this. For
24 example, one can use an emission-based dispersion model to estimate ambient concentrations at
25 a fine temporal (e.g., hourly) and spatial scale (e.g., census block-level or 500 meter grids).
26 Another method is to use a statistically-based approach that addresses the variability in
27 concentrations in a similar manner as a dispersion model, only that important physical factors
28 that influence concentration levels are represented by and/or possibly combined with a series of
29 regression equation coefficients and are related to an ambient monitor CO concentration.
30 Ultimately, it is this estimated outdoor CO concentration that is then used as input to the
31 algorithm (either the mass balance model or factors method) employed to estimate CO
32 microenvironmental concentrations.
33 Staff selected a statistically-based approach to adjust ambient monitor concentrations.
34 The approach was designed to reflect both the spatial and temporal variability expected to occur
35 outside microenvironments, while also appropriately linking the estimated microenvironmental
36 concentrations to observed concentrations at a fixed-site ambient monitor. The approach was
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1 developed using personal exposure, fixed-site monitor, and outdoor concentration measurement
2 data and first implemented in the pNEM/CO model for use in the most recent CO exposure
3 assessment (Johnson et al., 2000). This approach was proposed as a method to address spatial
4 and temporal variability in outdoor and microenvironmental concentrations in the draft scope and
5 methods plan (US EPA, 2009b), though not fully described there as is done here.
6 The microenvironmental algorithm and data used by pNEM/CO to estimate variable
7 parameters is described in section 4.4.4.3. The pNEM/CO approach was then adapted and
8 implemented in APEX3.1, a model more similar in structure to the current version of APEX
9 (version 4.3) than pNEM/CO. This approach is described in section 4.4.4.4. The details
10 regarding selection of microenvironments and parameters used by APEX4.3 in this assessment is
11 provided in section 5.9.
12 4.4.4.1 Overview of the Mass Balance Model
13 The mass balance method models an enclosed microenvironment as a well-mixed volume
14 in which the air concentration is spatially uniform at any specific time. The concentration of an
15 air pollutant in such a microenvironment is estimated using the following four processes:
16 • Inflow of air into the microenvironment;
17 • Outflow of air from the microenvironment;
18 • Removal of a pollutant from the microenvironment due to deposition, filtration, and/or
19 chemical degradation; and
20 • Emissions from sources of a pollutant inside the microenvironment.
21 Table 4-2 lists the parameters required by the mass balance method to calculate
22 concentrations in a microenvironment. The proximity factor (fpr0ximity) is used to account for
23 differences in ambient concentrations between the geographic location represented by the
24 ambient air quality data (e.g., a fixed-site monitor) and the geographic location of the
25 microenvironment (e.g., near a roadway). This factor could take a value either greater than or
26 less than 1. Emission source (ES) represents the emission rate for the emission source, and
27 concentration source (CS) is the mean air concentration resulting from the source (these are not
28 used in the current assessment. The factor Rremovai is defined as the removal rate of a pollutant
29 from a microenvironment due to deposition, filtration, and chemical reaction. The air exchange
30 rate (Rair exchange) is expressed in air changes per hour.
February 20 JO 4-14 Draft - Do Not Quote or Cite
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1 Table 4-2. Variables used by APEX4.3 in the mass balance model.
2
3
Variable
' proximity
cs
ES
" removal
" air exchange
V
Definition
Proximity factor
Concentration source
Emission source
Removal rate due to
deposition, filtration, and
chemical reaction
Air exchange rate
Volume of
microenvironment
Units
unitless
ppm
ug/hr
1/hr
1/hr
m3
Value Range
' proximity ^ U
CS>0
ES>0
^removal — 0
"air exchange — "
V>0
The mass balance equation for a pollutant in a microenvironment is described by the
differential equation
5
6
7
8
9
10
11
12
13
14
15
19
20
dt
where:
dCME(t)
- removal
(4-1)
Change in concentration in a microenvironment at time t (ppm),
Rate of change in microenvironmental concentration due to influx
of air (ppm/hour),
Rate of change in microenvironmental concentration due to outflux
of air (ppm/hour),
Rate of change in microenvironmental concentration due to
removal processes (ppm/hour), and
Rate of change in microenvironmental concentration due to an
emission source inside the microenvironment (ppm/hour).
16 Within the time period of an hour each of the rates of change, AC!n, AC0!d, ACremova/, and
17 AC 'source-, is assumed to be constant. The change in microenvironmental concentration due to
1 8 influx of air is represented by the following equation:
. „
A ( =
j.
- = C x f x f xR
ambient J proximity J penetration airexchan;
ige
(4-2)
where:
February 2010
4-15
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
73
f~*
^ambient
Jproximity
Jpenetration
t^air exchange
The change
ACC
The change
microenvironment
removal
where:
^deposition
^filtration
t^chemical
-^removal
= Ambient hourly outdoor concentration (ppm)
= Proximity factor
= Penetration factor
= Air exchange rate (I/hour)
in microenvironmental concentration due to outflux of air is described by
dCout(t}
_ ^ v'-p „(-< (f\ (A-
mt , ^ air exchange ^^ ME \L J V* •
in concentration due to deposition, filtration, and chemical degradation in
is simulated by a first-order equation:
a^removal(t)
, (lv deposition ' lv filtration ' "* ^chemical / ^ ME\ l/ ^remova A ^ ME\ l/ V*
= Removal rate of a pollutant from a microenvironment due to
deposition (I/hour)
= Removal rate of a pollutant from a microenvironment due to
filtration (I/hour)
= Removal rate of a pollutant from a microenvironment due to
chemical degradation (I/hour)
= Removal rate of a pollutant from a microenvironment due to
overall removal (I/hour)
3)
a
4)
As discussed in Section 2.2, EPA has not modeled indoor emissions of CO in the current
exposure assessment; consequently, the optional term ACSOurce was uniformly set equal to 0.0
this study.
Combining
dC,
Equation 4-1 with Equations 4-2, 4-3, and 4-4 yields
^•(>-l\r P . yT ff\ R . yC (t\ (4-
for
S^>
dt
air exchange
24
25
The solution to this differential equation is
February 2010
4-16
Draft - Do Not Quote or Cite
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1
2
O
4
5
6
7
where:
cw0;
cw#
^combined
combined
Cc
be;
Cc
the
= Rai
combined
(4-6)
Concentration of a pollutant in a microenvironment at the
beginning of a hour (ppm)
Concentration of a pollutant in a microenvironment at time t within
the time period of a hour (ppm)
air exchange ' -^removal
8 Based on Equation 4-6, the following three hourly concentrations in a microenvironment
9 are calculated:
10
11
11
combined
_„ N
^combined)
(4-7)
f T,
f equil \ -*- PX-P V ^combined )
'-MB /
„
ombined
13
14
1 5
16
17
18
where:
CME(O)
^hourly end
Equilibrium concentration in a microenvironment (ppm)
Concentration in a microenvironment at the beginning of an hour
(ppm)
Concentration in a microenvironment at the end of an hour (ppm)
Hourly mean concentration in a microenvironment (ppm)
19 At each hour time step of the simulation period, APEX4.3 uses Equations 4-7, 4-8, and 4-
20 9 to calculate the hourly equilibrium, hourly ending, and hourly mean concentrations. APEX4.3
21 reports hourly mean concentration as hourly concentration for a specific hour. The calculation
22 continues to the next hour by using c^rlyend for the previous hour as CME(O).
February 20 JO
4- 1 7
Draft - Do Not Quote or Cite
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1 4.4.4.2 Overview of the Factors Model
2 The factors model approach is conceptually simpler than the mass balance method and
3 has fewer user-specified parameters. It estimates the concentration in a microenvironment as a
4 linear function of ambient concentration of that hour, regardless of the concentration in the
5 microenvironment during the preceding hour. Table 4-3 lists the parameters required by the
6 factors model approach to calculate concentrations in a microenvironment without emissions
7 sources.
8 Table 4-3. Variables used by APEX4.3 in the factors model.
Variable
' proximity
' penetration
Definition
Proximity factor
Penetration factor
Units
unitless
unitless
Value Range
' proximity ^ "
v — 1 penetration — •
9
10 The factors model approach uses the following equation to calculate hourly mean
11 concentration in a microenvironment from the user-provided hourly air quality data:
s~i hourlymean (~* \< f V f
1-^ ME ambient J proximity J penetration \*~*-")
13 where
14 ^hauriymean = Hourly concentration in a microenvironment (ppm)
15 Cambient = Hourly concentration in ambient environment (ppm)
16 /proximity = Proximity factor (unitless)
17 /penetration = Penetration factor (unitless)
18
19 The proximity factor (/proximity) is used to account for differences in ambient
20 concentrations between the geographic location represented by the ambient air quality data (e.g.,
21 a fixed-site monitor) and the geographic location of the particular microenvironment. For
22 example, persons travelling inside motor vehicles may be located on a heavily-trafficked
23 roadway, whereby the ambient air outside the vehicle would likely have elevated levels of
24 mobile source pollutants such as carbon monoxide relative to the ambient monitor. In this case,
25 a value greater than one for the proximity factor would be appropriate to represent the increase in
26 concentrations outside the vehicle relative to the ambient monitor. Additionally, for some
27 pollutants the process of infiltration may remove a fraction of the pollutant from the air. The
28 fraction that is retained in the indoor/enclosed microenvironment is given by the penetration
29 factor (/penetration) and is dependent on the particular pollutant's physical and chemical removal
30 rates.
February 2010 4-18 Draft - Do Not Quote or Cite
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1 4.4.4.3 Description of the Original pNEM/CO Microenvironmental Model
2 Version 2.1 of pNEM/CO determined the hourly outdoor CO concentration applicable to
3 each microenvironment through a Monte Carlo process based on the following equation
4
5 COout(c,m,d,h) = M(m) x L(c, m, d) x T(c,m,d,h) x [COmon(d,h)]A (4-11)
6
7 where,
8
9 COout(c,m,d,h) = outdoor CO concentration (ppm) for cohort c with respect to
10 microenvironment m in district d during hour h,
11 M(m) = multiplier (> 0) specific to microenvironment m,
12 L(c,m,d) = location multiplier (> 0) specific to cohort c, microenvironment m,
13 and district d (held constant for all hours),
14 T(c,m,d,h) = time-of-day multiplier (> 0) specific to cohort c, microenvironment
15 m, district d, and hour h,
16 COmon(d,h) = ambient monitor-derived CO concentration (ppm) for hour h in
17 district d, and
18 A = exponent (A > 0).
19
20 This equation was used to generate a year-long sequence of outdoor one-hour CO
21 concentrations for each combination of cohort (c), microenvironment (m), and district (d) by
22 Johnson et al. (2000). The exponent^ was set equal to 0.621 and held constant for all
23 sequences. The value ofM(m) varied only with microenvironment as indicated in Table 4-4 [and
24 is identical to Table 2-6 in Johnson et al. (2000)].
25 A value of the location factor L(c, m, d) was specified for each individual sequence and
26 held constant for all hours in the sequence. The value was randomly selected from a lognormal
27 distribution with geometric mean (GML) equal to 1.0 and geometric standard deviation (GSDiJ
28 equal to 1.5232. The natural logarithms of this distribution can be characterized by a normal
29 distribution with an arithmetic mean (UL) equal to zero and an arithmetic standard deviation (GL)
30 equal to 0.4208.
31 A value of the time-of-day factor T(c, m, d, h) was randomly selected for each hour
32 within a sequence from a lognormal distribution with geometric mean (GMx) equal to 1.0 and
33 geometric standard deviation (GSDT) equal to 1.6289. The natural logarithms of this distribution
34 follow a normal distribution with an arithmetic mean (UT) equal to zero and an arithmetic
35 standard deviation (GT) equal to 0.4879.
February 2010 4-19 Draft - Do Not Quote or Cite
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1 The COout(c, m, d, h) term is interpreted as the outdoor CO concentration in the
2 immediate vicinity of microenvironment m in district d during hour h. COmon(d, h) is the CO
3 concentration reported for hour h by a nearby fixed-site monitor selected to represent district d.
4 The mass balance model in pNEM/CO included a penetration factor that was set equal to
5 1.0 for CO. Consequently, there is no change in CO concentration as ambient (outdoor) air
6 moves into a microenvironment, though the CO concentration within the microenvironment will
7 be affected by air exchange rate and the presence of indoor sources.
February 20 JO 4-20 Draft - Do Not Quote or Cite
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1 Table 4-4. Estimated values of distribution parameters and variables in equation 4-11 as
2 implemented in the application of pNEM/CO to Denver and Los Angeles
3 (Johnson et al., 2000).
Microenvironment3
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
General
location
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Outdoors
Outdoors
Vehicle
Vehicle
Vehicle
Outdoor
Specific location
Residence
Nonresidence A
Nonresidence B
Nonresidence C
Nonresidence D
Nonresidence E
Nonresidence F
Nonresidence G
Residential garage
Near road
Other locations
Automobile
Truck
Mass transit vehicles
Public parking or
fueling facility
Activity diary
locations included in
microenvironment
Indoors - residence
Service station
Auto repair
Other repair shop
Shopping mall
Restaurant
Bar
Other indoor location
Auditorium
Store
Office
Other public building
Health care facility
School
Church
Manufacturing facility
Residential garage
Near road
Bicycle
Motorcycle
Outdoor res. garage
Construction site
Residential grounds
School grounds
Sports arena
Park or golf course
Other outdoor
Automobile
Truck
Bus
Train/subway
Other vehicle
Indoor parking garage
Outdoor parking garage
Outdoor parking lot
Outdoor service station
Parameter Estimates for Equation 4-1 1
A
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
0.621
O-L
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
0.4208
CTT
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
0.4879
M(m)
1.034
2.970
1.213
1.213
1.213
1.213
1.213
0.989
1.034
1.607
1.436
3.020
3.020
3.020
2.970
Notes:
a Aggregate microenvironments defined for statistical analysis of Denver PEM data: residence (1 and 9), service/parking (2 and 15), commercial (3
through 7), and vehicle (12 through 14).
February 2010
4-21
Draft - Do Not Quote or Cite
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1
2 4.4.4.3.1 Data Used To Estimate pNEM/CO Microenvironmental Model Parameters
3 During a residential monitoring study described by Wilson, Colome, and Tian (1995),
4 researchers measured 10-minute CO concentrations outside 293 residences throughout California
5 in 1992. These residences were customers of Pacific Gas and Electricity (PG&E) (129
6 residences in Northern California), San Diego Gas and Electric Company (89 residences in the
7 San Diego area), and Southern California Gas Corporation (75 residences in the Los Angeles
8 area). After excluding the PG&E data (not part of the Los Angeles study area) and homes for
9 which valid CO data were not available, analysts used the remaining subset of 156 residences, 70
10 from Los Angeles and 86 from San Diego, as the basis for estimating values of GL, OT, and A
11 applicable to the Los Angeles study area.2 The data subset contained 44,726 valid 10-minute
12 averages measured outside of residences, of which less than 1% were negative (smallest value =
13 -1.0 ppm), 14,817 (33 %) were equal to 0 ppm, and the remainder were positive (maximum =
14 68.7 ppm). The valid 10-minute values were then averaged by clock hour to permit comparison
15 with hourly-average CO concentrations reported by nearby fixed-site monitors.
16 Analysts determined that the negative values in the data set were most likely caused by
17 the subtraction of an offset from all measured values to account for monitor drift. To adjust for
18 this offset and to prevent the occurrence of negative and zero values (which could not be used in
19 fitting equation 4-11), analysts added a constant offset of 0.5 ppm to each hourly-average value
20 measured outside a residence. In addition, seventeen (0.2%) of the original hourly averages less
21 than or equal to -0.5 ppm were discarded. Each of the resulting one-hour outdoor CO
22 concentrations was paired with the one-hour CO concentration measured simultaneously at the
23 nearest fixed-site monitor [based on data obtained from EPA's Aerometric Information Retrieval
24 System (AIRS)]. This approach yielded a final database containing 6,330 pairs of hourly
25 average concentrations, in which each pair was indexed by date, time, residence identifier, fixed-
26 site monitor identifier, and fixed-site monitor scale (e.g., neighborhood).
27 The M(m) values of equation 4-11 were based on data provided by the Denver Personal
28 Monitoring Study (Akland et al, 1985; Johnson, 1984). During this study, each of approximately
29 450 subjects carried a personal exposure monitor (PEM) for two 24-hour periods. Each PEM
30 measured CO concentration continuously. The PEM readings were averaged by exposure event
31 such that each event was associated with a single microenvironment and a single clock hour
32 (e.g., 1 pm to 2 pm). Event durations ranged from one minute to one hour. The
2 Note these same coefficient values were also applied to estimate exposures in the Denver study area, as
researchers were unable to identify a usable data set specific to Denver.
February 2010 4-22 Draft - Do Not Quote or Cite
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1 microenvironment assigned to each PEM reading was determined from entries made in a real-
2 time diary carried by the subject.
3 Researchers created a data base in which each PEM value was matched to the
4 corresponding hourly-average CO concentration reported by the nearest fixed-site monitor. The
5 data were first processed by excluding cases with missing measurements, cases in which
6 measurements failed a quality control check, and cases in which applicable diary data indicated
7 the potential presence of smokers or gas stoves. Each PEM CO concentration was then assigned
8 to a microenvironment, TO, based on entries in the activity dairy. In some cases, data for two or
9 more similar microenvironments were aggregated to provide more stable estimates than those
10 based on the very limited amount of data available for specific microenvironments (see Table 4-4
11 footnote). For consistency with the Wilson, Colome, and Tian (1995) database, all cases with a
12 zero measurement from the personal exposure monitor were excluded, as were all cases in which
13 the fixed site monitor concentration was zero after rounding to the nearest integer ppm. Note
14 that the Denver fixed-site data were recorded to the nearest 0.1 ppm, whereas the Los Angeles
15 fixed-site data were only recorded to the nearest integer.
16 4.4.4.3.2 Development of the pNEM/CO Microenvironmental Model Equation
17 Equation 4-11 was based on the results of data analyses that suggested that the
18 relationship between COout(c, m, d, h) and COmon(d, h) should account for the specific
19 microenvironment, the geographic location of the microenvironment, and the time-of-day.
20 Analysts recognized that numerous statistical models could have been developed. In specifying
21 the model that was ultimately used (i.e., equation 4-11), analysts attempted to balance the need
22 for simplicity and parsimony with the need to represent the patterns in concentration variability
23 observed in the available data. Most of the model development was based on a comparison of
24 hourly averages of 10-minute CO concentrations measured outside residences in southern
25 California (Wilson, Colome, and Tian, 1995) with hourly average CO concentrations measured
26 at the nearest fixed-site monitor. For this case, m represented the residence microenvironment in
27 the district d. The district d was initially taken to be the entire study region where measurements
28 were collected (i.e., San Diego and Los Angeles areas).
29 Analysts began by considering a simple linear regression model of the form
30
31 COout(c,m,d,h) = a(m,d) + A x [COmon(d,h)] + e(c,m,d,h) (4-12)
32
33 where the residual term e(c,m,d,h) was assumed to be independent and normally distributed with
34 a mean of zero. For simplicity and parsimony, the slope coefficient^ was assumed to be the
35 same for all microenvironments (m) and districts (d).
February 2010 4-23 Draft - Do Not Quote or Cite
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1 Although the coefficient of determination (R2) for this linear regression model was
2 moderate (0.53),3 the model was found to be unacceptable because it does not properly reflect
3 the strong correlations that were observed between concentrations measured outside the same
4 location. Instead, this form of regression model assumes that the residuals associated with a
5 particular residential location are independent. In other words, this model does not properly
6 separate out the variation between locations from the variation within locations. Analysts
7 identified two other deficiencies in this model: (1) large negative values of the randomly-selected
8 e(c,m,d,h) term could produce impossible negative outdoor concentrations, and (2) the model did
9 not generate outdoor concentrations characterized by lognormal distributions. Various
10 researchers (e.g., Ott, 1995) have demonstrated that ambient CO concentrations tend to be
11 characterized by lognormal distributions rather than normal distributions.
12 To better address these latter concerns, analysts evaluated an alternative model where the
13 natural logarithm of outdoor concentration was expressed as a linear function of the natural
14 logarithm of monitor concentration:
15
16 LN[COout(c,m,d,h)] = a(m,d) + A x LN[COmon(d,h)] + e(c,m,d,h), (4-13)
17
18 In this equation and those that follow, LN[ ] indicates the natural logarithm of the
19 quantity in brackets. To properly separate the variability between and within locations, the
20 intercept term a(m,d) was also permitted to vary with the cohort location, c, leading to the final
21 selected model:
22
23 LN[COout(c,m,d,h)] = a(c,m,d) + A x LN[COmon(d,h)] + e(c,m,d,h). (4-14)
24
25 Exponentiating both sides of equation 4-14 yields the equivalent formulation to that
26 presented above in equation 4-11:
27
28 COout(c,m,d,h) = M(m) x L(c,m,d) x T(c,m,d,h) x [COmon(d,h)]A, (4-15)
29
30 where
31 M(m) = expjmean [a(c,m,d)]}, averaged over cohorts,
32
3 Note that the R2 goodness-of-fit statistic is not an appropriate measure of model adequacy when the true,
underlying errors are highly correlated.
February 2010 4-24 Draft - Do Not Quote or Cite
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1 L(c,m,d) = exp{a(c,m,d)-mean [a(c,m,d)]}, and
2
3 T(c,m,d,h) = exp[e(c,m,d,h)].
4
5 Several alternative statistical models were considered by analysts during the development
6 of the selected model formulation. Early in the process, analysts evaluated a series of
7 autoregressive time series models, in which model predictions were influenced by the past
8 history of CO concentrations at the monitor and outdoors of the microenvironment. These
9 models were rejected for several reasons: (1) they were inherently complex, (2) they yielded a
10 wide variation in model coefficients which did not always produce reasonable estimates when
11 applied to specific California residences, and (3) they required microenvironment-specific time
12 series data for coefficient estimation which were not readily available for non-residential
13 microenvironments.
14 Analysts also evaluated models similar to equation 4-11 in which the exponent^ varied
15 with microenvironment. These models were rejected due to the need for parsimony and the lack
16 of sufficient, suitable data for estimating microenvironment-specific values of A. A simpler
17 model in which the exponent^ is fixed at 1 was rejected because fits of equation 4-11 to the
18 California data indicated that A differed significantly from 1 (p<0.01). In addition, the
19 assumption that^4 = 1 produced unrealistically high predictions for outdoor CO concentrations
20 when the model was applied to monitoring data obtained from the Denver Broadway site (ID 31-
21 0002). These high values were found to be a direct result of setting A = I, which forced the
22 geometric standard deviation of the estimated outdoor concentrations to significantly exceed the
23 geometric standard deviation of the monitor values.
24 Analysts ultimately arrived at equation 4-11 (equivalent to Equation 4-15), which permits
25 the A exponent to differ from 1.0. The model was fitted using statistical software for a mixed
26 (random and fixed effects) model which employed restricted maximum likelihood estimation.
27 The fit yielded estimates of OL = 0.4208, OT = 0.4879, and A = 0.621, the values subsequently
28 used in the pNEM/CO runs described by Johnson et al. (2000). The fitted value ofM(m),
29 representing residences in Los Angeles during 1992, was actually 0.9706. An alternative value
30 (1.034), based on the additional analyses described below, was applied to the indoor-residence
31 microenvironment in the pNEM/CO runs (see Table 4-4).
32 This model, considered a reasonable compromise between model simplicity and
33 performance, is completely specified by four parameters [M(m), OL, OT, and A]. Note that OL, OT,
34 and A are defined to be independent of the microenvironment, whereas M(m) is
35 microenvironment-specific. At the time of the initial model development, researchers were
36 unable to find a single data source capable of providing estimates of all four parameters.
February 2010 4-25 Draft - Do Not Quote or Cite
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1 Consequently, values for GL, GT, and A were estimated by analyzing data obtained from the
2 California study conducted by Wilson, Colome, and Tian (1995), whereas the specific M(m)
3 values were based on data provided by the Denver Personal Monitoring Study (Akland et al,
4 1985; Johnson, 1984).
5 Researchers conducted a series of sensitivity analyses to evaluate the potential effects on
6 parameter estimates of variations in the regional location and scale of the fixed-site monitor.
7 Equation 4-11 was fitted to a series of data subsets defined by region (Los Angeles or San
8 Diego) or by the scale of the fixed-site monitor (based on the estimated maximum distance from
9 the monitor represented by the measured concentrations: micro, middle, neighborhood, or urban
10 scale). The fitted values of GL, GT, A, and M(m) were very similar across the different subsets,
11 supporting the assumption that these parameters can be assumed to be representative of
12 concentration patterns outside residences in other regions and for other time periods, and can be
13 chosen to be the same value for all monitoring scales. Due to a lack of additional suitable data,
14 the values of GL, GT, and^4 are also assumed to be applicable to concentrations outside all other
15 microenvironments, although M(m) varies with the particular microenvironment (see below).
16 In equation 4-11, the COout(c, m, d, h) term represents the outdoor CO concentration
17 associated with a particular microenvironment m., even when the microenvironment is an indoor
18 location. Few of the outdoor personal exposure measurement (PEM) values reported by the
19 Denver study could be reliably associated with particular indoor microenvironments.
20 Consequently, researchers employed a simplified procedure for estimating M(m) values which
21 assumed that the mean of the indoor PEM values associated with each indoor microenvironment
22 was approximately equal to the mean of the outdoor concentration for the microenvironment.4
23 This assumption is consistent with the results of applying mass-balance modeling to non-reactive
24 pollutants in enclosed spaces where the only source of the pollutant is the outside air. In such
25 cases, the mean indoor concentration approximates the mean outdoor concentration, with the
26 instantaneous indoor concentration exhibiting a lower degree of variability than the
27 corresponding outdoor concentration.
28 When equation 4-11 is expressed in a logarithmic form (i.e., as in equation 4-14) and
29 averaged over cohorts, one obtains the equation
30
4 Because the simplified approach was also less sensitive to the wide variation in averaging times exhibited
by the PEM values (i.e., one minute to 60 minutes), analysts were able to use the majority of PEM values in the
statistical analysis. Limiting the analysis to one-hour PEM values would have significantly reduced the pool of
usable data.
February 2010 4-26 Draft - Do Not Quote or Cite
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1 Mean{LN[COout(c, m, d, h)}
2 = Mean[a(c, m, d)] + A x Mean{LN[COmon(d, h)]} + Mean[e(c, m, d, h)]
3 = LN[M(m)] + A x Mean{LN[COmon(d, h)]}.
4
5 Therefore, the value ofM(m) equals
6
7 M(m) = exp{MeanLN[COout(c, m, d, h)] - A x Mean LN[COmon(d, h)]} (4-16)
8
9 where A = 0.621 (as above). This equation was then used to obtain estimates oiM(m) for each
10 particular microenvironment, or aggregate of microenvironments, as indicated in Table 4-4 using
11 the available Denver PEM study data (Akland et al, 1985; Johnson, 1984). The same value of
12 M(m) was applied to each specific microenvironment within an aggregate.
13 4.4.4.4 The Micronenvironmental Model as Implemented by APEX3.1
14 As discussed in section 4-3, the pNEM/CO model effectively evolved into what is known
15 today as the APEX model. In APEX3.1, the portion of the outdoor concentration affecting the
16 indoor concentration is determined by the formula
17
18 COout = Ambient * Proximity * Penetration (4-17)
19
20 Note that we can represent Proximity and Penetration as distributions in APEX3.1.
21 These distributions can be sampled hourly, daily, or yearly. Let us make the following
22 substitutions of the variables used to estimate the outdoor concentrations:
23
24 Ambient = [COmon(d,h)]A (4-18)
25 Proximity = M(m) x L(c, m, d) (4-19)
26 Penetration = T(c,m,d,h). (4-20)
27
28 which gives
29
30 COout = M(m) x L(c, m, d) x T(c,m,d,h) x [COmon(d,h)]A (4-21)
31
32 and is identical to equation 4-11 above.
33 To obtain results from APEX3.1 that are comparable to that generated by pNEM/CO,
34 Johnson and Capel (2003) preprocessed the hourly ambient monitor data assigned to the district
35 containing the microenvironment using the formula
February 2010 4-27 Draft - Do Not Quote or Cite
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1
2 Ambient = [COmon(d,h)]a621 (4-22)
O
4 where COmon(d,h) is expressed in ppm. For each profile, a value for the Proximity term was
5 selected for each microenvironment from a lognormal distribution with geometric mean equal to
6 M(m) and geometric standard deviation equal to 1.5232. The natural logarithms of this
7 distribution were characterized by a normal distribution with an arithmetic mean (UL) equal to
8 ln[M(m)] and an arithmetic standard deviation (GL) equal to 0.4208. Consistent with the
9 pNEM/CO algorithm, Proximity values were not permitted to fall below the 5th percentile of the
10 specified distribution or above the 95th percentile of the distribution. Table 4-5 lists the
11 parameter values applicable to each of the 15 microenvironments defined by Johnson and Capel
12 (2003).
13 Penetration values were randomly selected for each hour from a lognormal distribution
14 with geometric mean (GMT) equal to 1.0 and geometric standard deviation (GSDT) equal to
15 1.6289. As indicated above, the natural logarithms of this distribution followed a normal
16 distribution with an arithmetic mean (UT) equal to zero and an arithmetic standard deviation (GT)
17 equal to 0.4879. In agreement with the pNEM/CO algorithm, Penetration values were not
18 permitted to fall below the 5th percentile of the specified distribution (0.4482) or above the 95th
19 percentile of the distribution (2.2313).5
5 Note the Penetration factor was not used according to its intended purpose in the APEX model. As
discussed in Volume I of the APEX3.1 User's Guide (US EPA, 2003), the Penetration factor is typically used to
account for removal of pollutants during the transfer of outdoor air to a microenvironment. The Penetration factor
was used to represent the T(c,m,d,h) term in equation 4-11 because Penetration is the only APEX3.1 parameter
available for this purpose, given that the Proximity factor is being used to represent the product of M(m) and
L(c,m,d). The product of M(m), L(c,m,d), and T(c,m,d,h) could not be represented by a single term because
L(c,m,d) and T(c,m,d,h) have different averaging times (day vs. hour).
February 2010 4-28 Draft - Do Not Quote or Cite
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1 Table 4-5. Parameters of Bounded Lognormal Distributions Defined for Proximity
2 Factors Used in Applications of APEX3.1 to Los Angeles (Johnson and Capel,
3 2003).
Microenvironment
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
General
Location
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Indoors
Outdoors
Outdoors
Vehicle
Vehicle
Vehicle
Outdoor
Specific location
Residence
Nonresidence A
Nonresidence B
Nonresidence C
Nonresidence D
Nonresidence E
Nonresidence F
Nonresidence G
Residential
garage
Near road
Other locations
Automobile
Truck
Mass transit
vehicles
Public parking or
fueling facility
Activity diary
locations included in
microenvironment
Indoors - residence
Service station
Auto repair
Other repair shop
Shopping mall
Restaurant
Bar
Other indoor location
Auditorium
Store
Office
Other public building
Health care facility
School
Church
Manufacturing facility
Residential garage
Near road
Bicycle
Motorcycle
Outdoor res. garage
Construction site
Residential grounds
School grounds
Sports arena
Park or golf course
Other outdoor
Automobile
Truck
Bus
Train/subway
Other vehicle
Indoor parking garage
Outdoor parking
garage
Outdoor parking lot
Outdoor service station
Parameters of bounded lognormal
distribution
GM
1.034
2.970
1.213
1.213
1.213
1.213
1.213
0.989
1.034
1.607
1.436
3.020
3.020
3.020
2.970
GSD
1.5232
1.5232
1.5232
1 .5232
1.5232
1.5232
1.5232
1.5232
1.5232
1.5232
1.5232
1.5232
1.5232
1.5232
1.5232
Minimum
(5th pet)
0.5175
1 .4864
0.6071
0.6071
0.6071
0.6071
0.6071
0.4950
0.5175
0.8042
0.7187
1.5114
1.5114
1.5114
1 .4864
Maximum
(95th pet)
2.0661
5.9345
2.4237
2.4237
2.4237
2.4237
2.4237
1 .9762
2.0661
3.2110
2.8693
6.0344
6.0344
6.0344
5.9345
February 2010
4-29
Draft - Do Not Quote or Cite
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1 4.4.5 Estimate Energy Expenditure and Ventilation Rates
2 APEX4.3 includes a module that estimates COHb levels in the blood as a function of
3 alveolar ventilation rate, the CO concentration of the respired air, endogenous CO production
4 rate, and various physiological variables such as blood volume and pulmonary CO diffusion rate.
5 Alveolar ventilation rate is estimated as a function of oxygen uptake rate, which in turn is
6 estimated as a function of energy expenditure rate. This section provides a brief summary of the
7 algorithm used to estimate alveolar ventilation rate. A detailed description of the algorithm,
8 based on the nonlinear solution to the Coburn-Forster-Kane (CFK) equation (Coburn et al.,
9 1965), together with the distributions and estimating equations used in determining the value of
10 each parameter in the algorithm, can be found in Appendix B.
11 4.4.5.1 Energy Expenditure
12 McCurdy (2000) has recommended that measures of human ventilation (respiration) rate
13 be estimated as functions of energy expenditure rate. The energy expended by an individual
14 during a particular activity can be expressed as
15 EE = (METS) x (RMR) (4-23)
16 where EE is the average energy expenditure rate (kcal min"1) during the activity and RMR is the
17 resting metabolic rate of the individual expressed in terms of number of energy units expended
18 per unit of time (kcal min"1). METS (i.e., metabolic equivalent of work) is a ratio specific to the
19 activity and is dimensionless.
20 The METS concept provides a means for estimating the alveolar ventilation rate
21 associated with each activity. For convenience, let EE(iJ,k) indicate the energy expenditure rate
22 associated with the ith activity on dayy for person k. Equation 4-23 can now be expressed as
23 EE(iJ,k) = [METS(/j,£)] x [RMR(£)] (4-24)
24 where RMR(A:) is the average value for resting metabolic rate specific to person k. Note that
25 METS(/j,A:) is specific to a particular activity performed by person k.
26 4.4.5.2 Oxygen Requirements for Energy Expenditure
27 Energy expenditure requires oxygen which is supplied by ventilation (respiration).
28 ECF(&) represents an energy conversion factor defined as the volume of oxygen required to
29 produce one kilocalorie of energy in person k. The oxygen uptake rate (VO2) associated with a
30 particular activity can be expressed as
31 V02(i,j,k) = [EC?(k)]x[EE(iJ,k)] (4-25)
February 20 JO 4-30 Draft - Do Not Quote or Cite
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1 where VO2(i,j,K) has units of liters oxygen min"1, ECF(&) has units of liters oxygen kcal"1, and
2 EE(iJ,k) has units of kcal min"1. The value ofVO2(i,j,k) can now be determined from MET(i,j,k)
3 by substituting Equation 4-24 into Equation 4-25 to produce the relationship
4 VO2(iJ,K) = [ECF(£)] x [METS(/j,£)] x [RMR(£)] (4-26)
5 4.4.5.3 Excess Post-Exercise Oxygen Consumption
6 At the beginning of exercise, there is a lag between work expended and oxygen
7 consumption. During this work/ventilation mismatch, an individual's energy needs are met by
8 anaerobic processes. The magnitude of the mismatch between expenditure and consumption is
9 termed the oxygen deficit. During heavy exercise, further oxygen deficit (in addition to that
10 associated with the start of exercise) may be accumulated. At some point, oxygen deficit reaches
11 a maximum value, and performance and energy expenditure deteriorate. After exercise ceases,
12 ventilation and oxygen consumption will remain elevated above baseline levels. This increased
13 oxygen consumption was historically labeled the oxygen debt or recovery oxygen consumption.
14 However, the term excess post-exercise oxygen consumption (EPOC) has been adopted here to
15 represent this phenomenon. APEX4.3 has an algorithm for adjusting the MET values to account
16 for EPOC. This algorithm is described in detail in section 7.2 of US EPA (2008b).
17 4.4.5.4 Alveolar Ventilation Rate
18 Alveolar ventilation (VA) represents the portion of the minute ventilation that is involved
19 in gaseous exchange with the blood. VO2 is the oxygen uptake that occurs during this exchange.
20 The absolute value of VA is known to be affected by total lung volume, lung dead space, and
21 respiration frequency - parameters that vary according to the person and/or exercise rate.
22 However, it is reasonable to assume that the ratio of VA to VO2 is relatively constant regardless
23 of a person's physiological characteristics or energy expenditure rate. Consistent with this
24 assumption, APEX4.3 converts each estimate of VO2(i,j,K) to an estimate of VA(/J,£) by the
25 proportional relationship
26 VA(JJ,K) = (19.63) x [VO2(iJ,k)] (4-27)
27 where both VA and VO2 are expressed in units of liters min"1. This relationship was obtained
28 from Joumard et al. (1981), who based it on research by Galetti (1959). Equation 4-15 can also
29 be expressed by the equivalent equation
30 VA(iJ,K) = (19.63) x [METS(/j,£)] x [ECF(£)] x [RMR(£)] (4-28)
February 20 JO 4-31 Draft - Do Not Quote or Cite
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1 If ECF and RMR are specified for an individual, then Equation 4-28 requires only an
2 activity-specific estimate of METS to produce an estimate of the energy expenditure rate for a
3 given activity. APEX4.3 processes time-location-activity data obtained from the CHAD to
4 create a sequence of activity-specific METS values for each simulated individual. APEX4.3
5 estimates RMR as a function of body mass based on probabilistic equations specific to age and
6 gender using equations reported by Schofield (1985). A value of ECF is selected for each
7 individual from a uniform distribution (minimum = 0.20, maximum = 0.21) based on data
8 provided by Esmail et al. (1995). Using Equation 4-28 and these inputs, APEX4.3 calculates a
9 sequence of VA values for each simulated individual. These values are provided to the algorithm
10 that estimates the percent COFIb in the blood resulting from the simulated exposure (see section
11 4.4.7 and Appendix B).
12 4.4.6 Calculate Exposure
13 APEX4.3 calculates exposure as a time series of exposure concentrations that a simulated
14 individual experiences during the simulation period. APEX4.3 determines the exposure using
15 hourly ambient air concentrations, calculated concentrations in each microenvironment based on
16 these ambient air concentrations, and the minutes spent in a sequence of microenvironments
17 visited according to the composite diary. The hourly exposure concentration at any clock hour
18 during the simulation period is determined using the following equation:
N
Zs~i hourly-mean ,
^ME(j) T (j)
19 Ci = -^ (4-29)
20 where
21 C;• = Hourly exposure concentration at clock hour /' of the simulation period
22 (ppm)
23 N = Number of events (i.e., varied microenvironments visited/activities
24 performed) in clock hour /' of the simulation period.
25 C^'j™ean = Hourly mean concentration in microenvironment y (ppm)
26 i(j) = Time spent in microenvironment y' (minutes)
27 T 60 minutes
28 From the hourly exposures, APEX4.3 calculates time series of 8-hour and daily average
29 exposure concentrations that a simulated individual would experience during the simulation
30 period. APEX4.3 then statistically summarizes and tabulates the number of persons and person-
31 days at or above selected hourly, 8-hour, and daily average exposure concentrations in a series of
32 output tables.
February 20 JO 4-32 Draft - Do Not Quote or Cite
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1 4.4.7 Calculate Dose
2 Using time-location-activity pattern data obtained from several diary studies, APEX4.3
3 constructs a composite diary for each simulated person in the specified population. The
4 composite diary consists of a sequence of events spanning the specified period of the exposure
5 assessment (typically one calendar year). Each event is defined by a start time, duration, a
6 geographic location, a microenvironment, and an activity. Using the algorithms described above
7 in sections 4.4.4, 4.4.5, and 4.4.6, APEX4.3 provides estimates of CO microenvironmental
8 concentrations and the persons' alveolar ventilation rate for each event in the composite diary,
9 for each simulated individual. APEX4.3 then uses these data, together with estimates of various
10 physiological parameters specific to the simulated individual, to estimate the percent COHb in
11 the blood at the end of each event. The percent COHb calculation is based on the solution to the
12 nonlinear Coburn-Forster-Kane (CFK) equation (Coburn et al., 1965), as detailed in Appendix B.
13 Briefly, the CFK module in APEX4.3 describes the rate of change in COHb blood levels as a
14 function of the following quantities:
15 • Inspired CO pressure;
16 • COHb level;
17 • Oxyhemoglobin (O2Hb) level;
18 • Hemoglobin (Hb) content of blood;
19 • Blood volume;
20 • Alveolar ventilation rate;
21 • Endogenous CO production rate;
22 • Mean pulmonary capillary oxygen pressure;
23 • Pulmonary diffusion rate of CO;
24 • Haldane coefficient (M);
25 • Barometric pressure; and
26 • Vapor pressure of water at body temperature (47 torr).
27 If all of the listed quantities except COHb level are constant over some time interval, the
28 CFK equation has a linear form over the interval and is readily integrated. The solution to the
29 linear form gives reasonably accurate results for lower levels of COHb (ISA section 4.2.1).6
30 However, CO and oxygen can compete for binding with the available hemoglobin and, therefore,
6 US EPA (2009b).
February 2010 4-33 Draft - Do Not Quote or Cite
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1 are not independent of each other. If this dependency is taken into account, the resulting
2 differential equation is no longer linear. Peterson and Stewart (1975) proposed a heuristic
3 approach to account for this dependency which assumed the linear form and then adjusted the
4 O2Hb level iteratively based on the assumption of a linear relationship between COHb and
5 O2Hb. This approach was used in the COHb module of the original CO-NEM exposure model
6 (Biller and Richmond, 1982; Johnson and Paul, 1983).
7 Alternatively, it is possible to determine COHb at any time by numerical integration of
8 the nonlinear CFK equation if one assumes a particular relationship between COHb and O2Hb.
9 Muller and Barton (1987) demonstrated that assuming a linear relationship between COHb and
10 O2Hb leads to a form of the CFK equation equivalent to the Michaelis-Menten kinetic model that
11 can be analytically integrated. However, the analytical solution in this case cannot be solved
12 explicitly for COHb. Muller and Barton (1987) demonstrated a binary search method for
13 determining the COHb value.
14 The COHb module used in pNEM/CO employed a linear relationship between COHb and
15 O2Hb which was consistent with the basic assumptions of the CFK model. The approach
16 differed from the linear forms used by other modelers in that the Muller and Barton (1987)
17 solution was employed. However, instead of the simple binary search described in the Muller
18 and Barton paper, a combination of the binary search and Newton-Raphson root finding methods
19 was used to solve for COHb (Press et al., 1986).
20 As mentioned above, the current COHb module included in APEX4.3 is based on the
21 solution to the nonlinear CFK equation using the assumption adopted by Muller and Barton
22 (1987) which employs a linear relationship between O2Hb and COHb. The CFK equation does
23 not have an explicit solution, so an iterative solution or approximation is needed to calculate each
24 percent COHb value. APEX4.3 solves the CFK equation using a 4th-order Taylor's series with
25 subintervals. This method, first incorporated in APEX3 (Glen, 2002), is summarized in
26 Appendix B. The selected method (4th-order Taylor series with subintervals) was chosen
27 because of its simplicity, fast execution speed, and ability to produce relatively accurate
28 estimates of percent COHb at both low and high levels of CO exposure. While there may be
29 other approaches available (e.g., Bruce and Bruce (2003) multi-compartment model), both the
30 nonlinear and linear CFK models remain the most widely accepted and validated approaches
31 used to estimate COHb levels (ISA, section 4.2.3)
32 4.4.8 Model Output
33 All of the output files written by APEX4.3 are ASCII text files; the complete list and
34 their descriptions can be found in Table 5-1 of the APEX4.3 User's Guide (US EPA, 2008a). In
35 general, the simulation output files most relevant to results generated for the assessment include
February 2010 4-34 Draft - Do Not Quote or Cite
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1 tabulations of hourly exposure, ventilation, and energy expenditure. Detailed event-level
2 information can also be output. However, given the potential size of the files that can be
3 generated for a large population and assessment duration, it is not common to generate event-
4 level files outside of research purposes. Specific outputs generated for the purposes of the
5 current CO exposure and dose assessment are discussed in section 6.1.
6 4.5 KEY OBSERVATIONS
7 Presented below are key observations related to the modeling system used for the
8 population assessment of CO exposure and dose.
9 • APEX, an EPA human exposure and dose model, has a long history of use in estimating
10 exposure and dose for many of the criteria pollutants including CO, Os, SO2, and NO2.
11 Over time, staff have improved and developed new model algorithms, incorporated
12 newer available input data and parameter distributions, as well as performed several
13 model evaluations, sensitivity analyses, and uncertainty characterizations for the above
14 pollutants. Based on this analysis, APEX was judged to be an appropriate model to use
15 for assessing CO exposure and dose.
16
17
February 2010 4-3 5 Draft - Do Not Quote or Cite
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1 5 APPLICATION OF APEX4.3 IN THIS ASSESSMENT
2 5.1 PURPOSE
3 This chapter presents detailed information regarding the varied input data sources, the
4 APEX model settings, and input variable parameterizations used in estimating population
5 exposure and dose in the Denver and Los Angeles study areas. In particular, this chapter (and its
6 associated appendices) describes the:
7 • geographic study areas and time periods defined for the exposure and dose analyses,
8 • method and parameters used to construct a composite diary for each simulated individual,
9 • study area population, the modeled at-risk population and associated CHD prevalence
10 rates,
11 • exposure scenarios under evaluation,
12 • air quality and meteorological data used for each study area and exposure scenario,
13 • method used to estimate local outdoor and microenvironmental CO concentrations.
14 Note that the APEX model version used in this assessment was APEX4.3, but for
15 simplicity will be referred to as APEX in much of the discussion that follows.
16 5.2 OVERVIEW
17 As summarized above in section 1.3, the previous analysis of population exposure to
18 carbon monoxide (CO) employed the pNEM/CO model in Denver and Los Angeles study areas,
19 comprising the majority of census tracts within those metropolitan areas (Johnson et al., 2000).
20 In this earlier exposure assessment, air quality data were obtained from multiple fixed-site
21 monitors within the study areas, and the exposure assessment accounted for the effects of
22 geographic location, a diverse set of microenvironments, commuting within the study area, and
23 selected indoor sources (e.g., passive smoking, gas stoves). In the specific application of APEX
24 described in this second draft CO REA, a similar exposure and dose modeling approach has been
25 developed by staff, though without inclusion of indoor source emissions. The detailed approach
26 presented here was designed to include the major comments and recommendations made by the
27 CASAC and public regarding the geographically constricted and simplified exposure modeling
28 approach used in the first draft CO REA (US EPA, 2009a).
29 The general description of APEX, the standard databases used, modeling capabilities, as
30 well as the history of the pNEM/APEX series of exposure models, can be found in chapter 4.
31 This includes use of the national data files obtained from the US Census Bureau (i.e., the 2000
32 Census data) for the following types of information:
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1 • Population data and employment probabilities by gender, age, and census tract;
2 • Locations of census tracts (latitude and longitude); and
3 • Commuting flows for combinations of home and work census tracts.
4 Other default input files provided within APEX include tables of age- and gender-specific
5 physiological parameters (e.g., body weight) and activity-specific metabolic equivalents (METs).
6 The contents of each of these default files and their use were summarized in chapter 4. They are
7 described in greater detail in the APEX Users Guide (US EPA, 2008a) and the APEX Technical
8 Support Document (US EPA, 2008b).
9 5.3 STUDY AREAS
10 As discussed in section 3.2, staff selected areas within Denver, Colorado, and Los
11 Angeles, California, for the current exposure and dose assessment. Briefly, considerations in
12 selection of these areas included: the prior analysis of these locations in CO NAAQS reviews,
13 the areas having historically elevated CO concentrations, and the areas currently having some of
14 the most complete ambient monitoring data available. The monitors selected for use in defining
15 the air quality in each urban area are listed in Tables 5-1 (Denver) and 5-2 (Los Angeles).
16 The actual study areas were defined as including all census tracts within 10 km of these
17 selected fixed-site monitors. These areas are illustrated in Figures 5-1 and 5-2, which indicate
18 the locations of the fixed-site monitors and the circular 10-km region surrounding each ambient
19 monitor. Each 10 km region defines the aforementioned air district that includes the geographic
20 area (i.e., the census tracts) represented by data from the associated CO monitor. Note that all air
21 districts have the same radius (10 km), a value specified by the "AirRadius" input parameter of
22 APEX.
23 In addition to defining the air districts, the model user must specify a location for the
24 center of the study area and a value for "CityRadius." The circular area defined by the city
25 center location and the value of "CityRadius" must be large enough to include all census tracts
26 included in the air districts. For Denver, staff used the location of monitor ID 31-0014 (Denver -
27 Julian) for the city center and set the "CityRadius" equal to 20 km (Figure 5-1). Staff used the
28 location of monitor ID 37-1103 (Los Angeles) for the center city of Los Angeles and set the
29 "CityRadius" equal to 65 km (Figure 5-2).
30 5.4 EXPOSURE PERIODS
31 EPA selected the following calendar years as the study periods for each area:
32 Denver: 1995 and 2006
33 Los Angeles: 1997 and 2006
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1
2
3
4
5
6
7
8
9
10
11
The year 2006 was selected for both cities because it was the most recent year of
monitoring data that met the 75% completeness requirement for the ambient monitors listed
above. Note, the CO levels reported for 2006 were well below the 8-hour NAAQS (see Tables
5-1 and 5-2) and are considered representative of the as is air quality in each study area for
purposes of this assessment. The year 1995 for Denver and the year 1997 for Los Angeles were
selected as periods for which the ambient monitoring concentrations were near or exceeding the
8-hour average CO NAAQS of 9 ppm. Staff judged that these historical monitoring data would
be most useful in representing air quality that just meets the current or alternative CO standards
and, following an appropriate concentration level adjustment, would represent a particular air
quality scenario (see sections 5.6 and 5.7.3).
Table 5-1. Attributes of fixed-site monitors selected for the Denver study area.
Monitor ID
City
Local Name
Latitude
Longitude
Elevation (m)
Scale
Objective
19952™
Highest 8-hour
avg CO (ppm)
2006 2na
Highest 8-hour
avg CO (ppm)
031-0002a
Denver
CAMP
39.751184
-104.987625
1593
Microscale
Highest
Concentration
9.5
3.1
031-00133
Denver
NJH-E
39.738578
-104.939925
1620
Neighborhood
Population
Exposure
6.2
2.5
031 -001 4a
Denver
Carriage
39.800333
-105.099973
1640
-
Unknown
5.9
3
059-00023
Arvada
-
39.751761
-105.030681
1621
Neighborhood
Population
Exposure
4.6
2
Notes:
3 Identified monitor was used in the 2000 pNEM/CO analysis (Johnson et al., 2000).
12
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Table 5-2. Attributes of fixed-site monitors selected for the Los Angeles study area.
Monitor ID
City
Local Name
Latitude
Longitude
Elevation (m)
Scale
Objective
19972"°
Highest 8-hour
avg CO (ppm)
2006 2na
Highest 8-hour
avg CO (ppm)
037-01 13a
West LA
-
34.05111
-118.45636
91
-
Unknown
4.1
1.9
037-1 002a
Burbank
-
34.17605
-118.31712
168
-
Unknown
7.2
3.4
037-1 103a
Los
Angeles
-
34.06659
-118.22688
87
-
Unknown
5.9
2.5
037-1201
Reseda
-
34.19925
-118.53276
226
-
Unknown
7.7
3.4
037-13013
Lynwood
-
33.92899
-118.21071
27
Middle
Highest
Cone.
15
5.6
037-20053
Pasadena
-
34.1326
-118.1272
250
-
Unknown
5.4
2.7
037-40023
Long
Beach
-
33.82376
-118.18921
6
-
Unknown
6.4
3.3
059-0001 /7a'b
Anaheim
-
33.83062
-117.93845
45
Neighborhood
Population
Exposure
5.4
2.9
059-1003
Costa
Mesa
-
33.67464
-117.92568
0
Middle
Unknown
5
2.5
059-5001 a
La Habra
-
33.92513
-117.95264
82
-
Population
Exposure
5.7
2.9
Notes:
3 Identified monitor was used in the 2000 pNEM/CO analysis (Johnson et al., 2000).
b When considering the two monitoring periods (1997 and 2006), two separate ambient monitor IDs were noted (059-0001 and 059-0007) though
effectively the locations of both monitors were the same.
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.: 08-031-0014
x. vr ^ [.
Legend
• Study Area Center
O Selected MET Station
*** Selected CO Monitor
J Circle Radius (Study Area Center) = 20 km
Population (2007) Density
(people per square km)
0-900
900-1,900
'1,900-3,100
Circle Radius (Selected MET Station) = 15.5 km
j "j Circle Radius (Selected CO Monitor) = 10 km
3,100-5,300
5,300 - 9,500
2 Figure 5-1. Ambient monitor locations, air districts (black circles), meteorological zones
3 (blue circles), and study area (red circle) for the Denver exposure modeling
4 domain.
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No: 06-037-1301
No: 06-037-4002
No: 06-059-5001
06-059-0001/0007
No: 06-059-1003
Legend
• Study Area Center
Selected MET Station for 2006
Selected MET Station for 1997
Population (2007) Density
(people per square km)
0 - 2,500
A, 2,500 - 5,500
*"* Selected CO Monitor 5,500-10,300
Circle Radius (Selected MET Station for 1997) = 70.5 km ^| 10,300 - 20,400
Circle Radius (Selected MET Station for 2006) = 70.5 km • 20,400 - 38,400
Circle Radius (Study Area Center) = 65 km
L... J circ'e Radius (Selected CO Monitor) = 10 km
100 Kilometers
2 Figure 5-2. Ambient monitor locations, air districts (black circles), meteorological zones
3 (blue and pink circles), and study area (red circle) for the Los Angeles
4 exposure modeling domain.
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1 5.5 STUDY POPULATION
2 5.5.1 Total and Simulated Population
3 The population estimates obtained from the 2000 US Census were used "as is" for each
4 study area and scenario modeled; there were no adjustments made for modeling the most recent
5 air quality data (2006) or for each of the alternative exposure scenarios modeled (a hypothetical
6 year). The total population in this study was restricted to those aged 18 years or older. Based on
7 the census tracts included and removal of residents commuting outside of the study area, the total
8 modeled population was 617,020 persons (or 81.1% of the total population). The corresponding
9 figure for Los Angeles was 5,017,551 persons (or 88.5% of the total population within the
10 modeled census tracts). To obtain adequate representation of the simulated population while also
11 keeping the model runs tractable, fifty-thousand exposure profiles (or simulated individuals)
12 were run by APEX for each study area and exposure scenario.
13 5.5.2 Selected at-Risk Subpopulation
14 The at-risk population simulated within each study area is comprised of adults ages 18
15 and older with CHD (diagnosed and undiagnosed). This focus on adults is consistent with the
16 previous CO exposure assessment (Johnson et. al, 2000) and the completed 1994 CO NAAQS
17 review (US EPA, 1992), as the incidence of CHD in younger individuals is extremely small
18 (CDC, 2009). In this assessment, the term CHD is used consistent with its use in the National
19 Health Interview Survey (NHIS) where it is inclusive of coronary heart disease, angina pectoris
20 and heart attack (CDC, 2009).
21 For estimates of adults with diagnosed CHD, staff obtained CHD prevalence data from
22 the NHIS for 2007 (CDC, 2009). The estimated CHD prevalence for the population above 18
23 years of age is about 6% (ISA, section 5.7.2.1).1 Staff assumed the national prevalence rates for
24 CHD were appropriate to use in each of the two study areas because there was a general
25 similarity in the reported regional rates. Staff desired the prevalence rates to be stratified by age
26 and gender, though the available data were stratified by age or gender. Table 5-3 provides
27 national prevalence data for CHD by age and Table 5-4 provides CHD stratified by gender. The
28 gender-only data were used to estimate gender-specific adjustment factors to apply to the age-
29 only data set. For males, the adjustment factor = 0.080/0.061 = 1.31; for females, the adjustment
30 factor = 0.045/0.061 = 0.74. Table 5-5 provides the estimated national prevalence rates for CHD
31 by age range adjusted for gender using these adjustment factors.
1 Note that in the last CO NAAQS review completed in 1994, the estimated number of individuals with
CHD represented about 3% of the entire (all ages) US population (US EPA, 1992).
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1 Table 5-3. National prevalence rates for coronary heart disease by age range.
4
5
6
7
Age range
18 to 44
45 to 64
65 to 74
75+
Prevalence rate (fraction) for coronary heart disease3
0.009
0.067
0.187
0.236
Notes:
3 Source: Coronary heart disease statistics in Table 2 of NHIS (CDC,
2009), which include coronary heart disease, angina pectoris and heart
attack.
2 Table 5-4. National prevalence rates for coronary heart disease by gender.
Age range
18+
Prevalence rate (fraction) for coronary heart
disease3
Total
0.061
Males
0.080
Females
0.045
Notes:
3 Source: Coronary heart disease statistics in Table 2 of NHIS (CDC,
2009), which include coronary heart disease, angina pectoris and heart
attack.
Table 5-5. National prevalence rates for coronary heart disease, stratified by age and
gender.
Age range
18 to 44
45 to 64
65 to 74
75+
Prevalence rate (fraction) for coronary heart
disease3
Males
0.012
0.088
0.244
0.310
Females
0.007
0.050
0.138
0.175
Notes:
3 Values listed in Table 5-3 were multiplied by 1
and 0.74 for females using data from Table 5-4.
.31 for males
Staff has expanded the selected at-risk population to also include undiagnosed cases of
coronary heart disease using a method similar to that developed by OAQPS for use in the 2000
exposure assessment (see Appendix F of Johnson et al., 2000). Briefly, in the prior assessment
the prevalence estimates of diagnosed IHD2 were stratified by age and sex (Adams and Marano,
2 The NHIS prevalence rates used in the 2000 assessment used the term IHD, rather than CHD (Adams and
Marano, 1995).
5-8
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1995) and constituted approximately 8.0 million individuals in the civilian, non-institutionalized
population.3 In addition, as many as three to four million persons were estimated to have silent
ischemia or undiagnosed IHD (American Heart Association, 1990). Staff used this information
to provide estimates of the undiagnosed IHD population for use in the pNEM/CO model. Staff
assumed 3.5 million persons had undiagnosed IHD and assumed the prevalence to be distributed
by age and gender in the same manner as diagnosed IHD. These data yield an adjustment factor
of 0.438 (i.e., 3.5 million/8.0 million) to apply to the diagnosed prevalence for use in estimating
the undiagnosed prevalence. Consequently, this factor can be interpreted as the undiagnosed
cases may be 43.8% of the diagnosed prevalence.
Table 5-6 lists the results of applying the 0.438 factor to the age and gender stratified
prevalence rates listed in Table 5-5. This assumes that CHD and IHD are identical with respect
to the ratio of undiagnosed cases to diagnosed cases and this ratio has not changed since 1990.
The total prevalence listed for each gender (diagnosed and undiagnosed combined) was used by
APEX in estimating the selected at-risk population.
When using these prevalence rates in the APEX model runs, there were 383,040
simulated persons (or 7.6% of the total simulated population) with either diagnosed or
undiagnosed CHD in the Los Angeles study area, while in Denver there were 53,656 simulated
persons (or 8.7% of the total simulated population) within the same selected at-risk population.
Table 5-6. National prevalence rates for coronary heart disease, including diagnosed and
undiagnosed cases, stratified by age and gender.
Age range
18 to 44
45 to 64
65 to 74
75+
Prevalence rate (fraction) for coronary heart disease
Males
Diagnosed
0.012
0.088
0.244
0.310
Undiagnosed3
0.005
0.039
0.107
0.136
Total
0.017
0.127
0.351
0.446
Females
Diagnosed
0.007
0.050
0.138
0.175
Undiagnosed3
0.003
0.022
0.060
0.077
Total
0.010
0.072
0.198
0.252
Notes:
3 Values listed in Table 5-5 (diagnosed CHD) were multiplied by 0.438 to estimate the undiagnosed
prevalence. Staff assumed CHD and IHD are identical with respect to the ratio of undiagnosed cases
(3.5 million) to diagnosed cases (8.0 million) and that this ratio has not changed since 1990 (see
Appendix F of Johnson et al. (2000)).
21
3 These estimates did not include individuals in the military or individuals in nursing homes or other
institutions.
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1 5.5.3 Time-Location-Activity Patterns
2 APEX constructs a 365-day longitudinal diary for each simulated individual by selecting
3 24-hour diaries from those available in CHAD. In performing the exposure assessments
4 described in this report, all available diaries for persons above age 17 in the CHAD database
5 were used.
6 5.5.4 Construction of Longitudinal Diaries
7 As discussed in section 4.4.3.4, APEX provides a longitudinal diary assembly algorithm
8 that enables the user to create composite diaries that reflect the tendency of individuals to repeat
9 day-to-day activities (Glen et al., 2008). The user specifies values for two statistical variables (D
10 and A) that relate to a key daily variable, typically the time spent per day in a particular
11 microenvironment (e.g., in a motor vehicle). The D statistic reflects the relative importance of
12 intra- and inter-personal variance within the selected key daily variable. The A variable
13 quantifies the day-to-day autocorrelation in the selected key daily variable. APEX then
14 constructs composite diaries that exhibit the statistical properties defined by the specified values
15 ofDandA.
16 In this exposure assessment, we used the longitudinal diary algorithm to construct year-
17 long activity patterns for each simulated individual to reflect the day-to-day correlation of time
18 spent inside motor vehicles. Each diary day in the CHAD database was tagged with the number
19 of minutes spent in the vehicle microenvironment. Parameter settings ofD = 0.31 and A = 0.19
20 were specified to control the day-to-day repetition of time spent in motor vehicles in the
21 constructed composite diaries. These particular D and A values were obtained from Isaacs et al.
22 (2009) (see Appendix C).
23 In selecting particular diaries to represent the simulated population, the CHAD data are
24 categorized or separated by APEX into data pools. The pools were defined by three ranges for
o o o
25 the maximum temperature of the diary day (< 55.0 F, between 55.0 and 83.9 F, and>84.0 F)
26 and two day-types (i.e., weekend and weekday); thus, there were 3x2 = 6 diary pools. The
27 window for age was set at 15%. For example, diaries can be selected for a simulated individual
28 of age 60 from CHAD individuals ranging from ages 51 though 69 (i.e., 60 +/- 15 percent).
29 5.6 EXPOSURE SCENARIOS
30 In this second draft CO REA, the exposure scenario refers to the air quality conditions
31 considered for each APEX simulation. Staff evaluated five exposure scenarios for each study
32 area. The first exposure scenario used unadjusted 2006 ambient air quality as input to APEX;
33 this is designated as the as is air quality exposure scenario. The purpose of this scenario is to
34 determine the number of persons that may experience COHb levels at or above selected
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1 benchmarks when considering current air quality conditions. The next four exposure scenarios
2 used adjusted high concentration year ambient data in each location (i.e., the 1995 monitoring
3 data in Denver and the 1997 monitoring data in Los Angeles). The purpose of these scenarios is
4 to determine the number of persons that may experience COHb levels at or above selected
5 benchmark levels when considering air quality conditions that just meet a selected level, form,
6 and averaging time of interest. This is not the same as considering exposures associated with the
7 as is air quality conditions.
8 The first of these adjusted air quality exposure scenarios considered ambient
9 concentrations adjusted to just meeting the 8-hour average CO NAAQS of 9 ppm. This
10 particular form was selected when considering the two current standard forms (8-hour average
11 and 1-hour) because it is the controlling standard.4 The second of these exposure scenarios using
12 the historical monitoring data also considered the current form of the current 8-hour CO
13 standard, but with the ambient concentrations in each study adjusted to meet an alternative
14 standard level of 5 ppm. The next two scenarios considered percentile forms of potential
15 alternative standards, consistent with the alternative standards investigated for other criteria
16 pollutants (e.g., NO2 (US EPA, 2008c); and SO2 (US EPA, 2009b)). The first of these potential
17 percentile forms considered the 99th percentile daily maximum 8-hour average CO
18 concentrations, while the second considered the same form though with a 1-hour averaging time.
19 Details regarding the concentration adjustments associated with each of the current and potential
20 alternative standards are provided in section 5.7.3.
21 5.7 AMBIENT AIR QUALITY DATA
22 5.7.1 Unadjusted 1-Hour Ambient Concentrations
23 Ambient monitoring data serve as an important input in estimating CO exposure and
24 dose. Descriptive statistics were generated for the hourly CO concentrations measured at the
25 identified ambient monitors in each location and monitoring year (Tables 5-7 to 5-10). As
26 expected, CO concentrations are about a factor of two or greater when comparing the high
27 concentration year (1995 or 1997) to the more recent year (2006) of ambient monitoring data in
28 either location. In general, there is similarity in the concentration distribution for both locations
29 within a given year, with the following exceptions. There is one monitor in Los Angeles (ID 37-
30 1301) reporting exceptionally high concentrations at each of the percentiles of the distribution in
31 this location when compared with the other Los Angeles monitors for either year. In addition,
4 The controlling standard by definition would be the standard that allows air quality to have either a 2nd highest 8-
hour average concentration of < 9.4 ppm (i.e., the 8-hour standard is the controlling standard) or to have a 2nd
highest 1-hour concentration of < 35.4 ppm (i.e., the 1-hour standard is the controlling standard).
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1 there is a sharper rate of increase in the upper percentile concentrations (i.e., >95th percentiles) in
2 Denver when compared with the Los Angeles ambient concentration distribution, for either year.
3 5.7.2 Method for Estimating of Missing 1-Hour Ambient Concentrations
4 APEX requires that each site-year of monitoring data be complete (i.e., it is free of hourly
5 gaps in concentration levels). The missing values in each data set were estimated by the
6 sequential application of the following four methods.
7 1) If the data gap was less than six continuous missing values, the missing values were
8 estimated by linear interpolation using the valid values at the ends of the gap.
9 2) Where possible, data gaps of at least six hours were estimated as linear functions of
10 hourly values reported by other ambient CO monitors in the area. Linear regression
11 was used to develop a set of models that were specific to a time-of-day and at each
12 monitor. The model selected to estimate missing values for a particular time of day
13 was the model that maximized the variance explained (R2) for that hour, subject to the
14 constraints that regression model R2 was greater than 0.5 and the number of available
15 measurements used in constructing the model was at least 50.
16 3) In cases where method 2 (above) could not be used (i.e., no regression models were
17 available for a particular time-of-day) and the gap was less than nine hours, the missing
18 values were estimated by linear interpolation between the valid values at the ends of
19 the gap.
20 4) All remaining missing values were substituted with the 1-hour concentration from the
21 same day and hour as the nearest monitor. The hourly concentration used was
22 normalized to the respective monitors' monthly mean concentrations.
23 Tables 5-7 to 5-10 provide the descriptive statistics for 1-hour CO concentrations in each
24 data set, before and after estimating missing values. The agreement between these statistics
25 indicates that the addition of the estimated missing-value concentrations did not significantly
26 affect the overall distribution of the hourly CO concentrations in either year or location.
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1
2
Table 5-7. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing
values - Denver 1995.
Monitor
ID
31-0002
31-0013
31-0014
59-0002
Missing
values
filled?
No
Yes
No
Yes
No
Yes
No
Yes
1 -hour values
(n)
Present
8697
8760
8647
8760
8701
8760
8680
8760
Missing
63
0
113
0
59
0
80
0
CO concentration (ppm)
Mean
1.50
1.50
1.25
1.25
1.09
1.09
0.96
0.96
SD
1.20
1.20
1.08
1.08
1.05
1.05
0.93
0.93
Min
0.0
0.0
0.1
0.1
0.0
0.0
0.1
0.1
Percentile
25th
0.8
0.8
0.6
0.6
0.5
0.5
0.4
0.4
50th
1.2
1.2
0.9
0.9
0.7
0.7
0.6
0.6
75th
1.8
1.8
1.5
1.5
1.3
1.3
1.1
1.1
90th
2.7
2.7
2.5
2.5
2.3
2.3
2.0
2.0
95th
3.4
3.4
3.4
3.4
3.2
3.2
2.7
2.7
99th
6.1
6.1
5.5
5.5
5.3
5.3
4.8
4.8
99.9th
13.1
13.1
8.9
8.8
7.7
7.8
7.5
7.5
Max
24.5
24.5
14.6
14.6
10.4
10.4
11.9
11.9
Table 5-8. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing
values - Denver 2006.
Monitor
ID
31-0002
31-0013
31-0014
59-0002
Missing
values
filled?
No
Yes
No
Yes
No
Yes
No
Yes
1 -hour values
(n)
Present
8672
8760
8635
8760
8557
8760
8603
8760
Missing
88
0
125
0
203
0
57
0
CO concentration (ppm)
Mean
0.62
0.62
0.49
0.49
0.47
0.47
0.40
0.40
SD
0.39
0.39
0.36
0.36
0.38
0.38
0.37
0.37
Min
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Percentile
25th
0.4
0.4
0.3
0.3
0.3
0.3
0.2
0.2
50th
0.5
0.5
0.4
0.4
0.4
0.4
0.3
0.3
75th
0.7
0.7
0.6
0.6
0.5
0.5
0.5
0.5
90th
1.0
1.0
0.9
0.9
0.9
0.9
0.8
0.8
95th
1.3
1.3
1.2
1.2
1.2
1.2
1.1
1.1
99th
2.2
2.1
1.8
1.8
2.0
2.0
1.9
1.9
99.9th
4.1
4.1
3.4
3.4
3.1
3.1
2.8
2.8
Max
6.4
6.4
4.4
4.4
3.9
3.9
3.6
3.6
February 2010
5-13
Draft - Do Not Quote or Cite
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1
2
Table 5-9. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing
values - Los Angeles 1997.
Monitor
ID
37-0113
37-1002
37-1103
37-1201
37-1301
37-2005
37-4002
59-0001/7
59-1003
59-5001
Missing
values
filled?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
1 -hour values
(n)
Present
8360
8760
8025
8760
8292
8760
8245
8760
8302
8760
8250
8760
8347
8760
8354
8760
8325
8760
8230
8760
Missing
400
0
735
0
468
0
515
0
458
0
510
0
413
0
406
0
435
0
530
0
CO concentration (ppm)
Mean
0.84
0.84
1.75
1.73
1.36
1.36
1.15
1.17
2.35
2.34
1.11
1.10
1.11
1.11
1.11
1.11
0.74
0.74
1.36
1.36
SD
0.86
0.85
1.27
1.24
1.19
1.17
1.25
1.24
2.19
2.17
0.84
0.83
1.10
1.11
0.91
0.90
1.01
1.00
1.21
1.19
Min
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Percentile
25th
0.2
0.2
0.9
0.9
0.5
0.5
0.4
0.4
1.1
1.1
0.6
0.6
0.4
0.4
0.6
0.6
0.2
0.2
0.6
0.6
50th
0.6
0.6
1.4
1.4
0.9
1.0
0.7
0.7
1.7
1.7
0.9
0.9
0.7
0.7
0.8
0.8
0.3
0.3
1.0
1.0
75th
1.2
1.2
2.2
2.1
1.9
1.9
1.5
1.5
2.8
2.8
1.4
1.4
1.3
1.4
1.4
1.4
0.9
0.9
1.7
1.7
90th
2.0
2.0
3.5
3.5
3.1
3.0
2.8
2.8
4.9
4.9
2.1
2.1
2.7
2.7
2.3
2.3
2.1
2.1
2.8
2.8
95th
2.6
2.6
4.5
4.4
3.9
3.8
3.8
3.8
6.8
6.7
2.8
2.8
3.6
3.6
2.9
2.9
3.0
3.0
3.7
3.7
99th
3.7
3.6
6.1
6.0
5.4
5.4
6.0
5.9
11.3
11.2
4.2
4.2
5.2
5.2
4.6
4.6
4.7
4.6
6.2
6.2
99.9th
5.1
5.1
7.8
7.7
7.2
7.1
8.4
8.3
17.2
17.2
6.1
6.0
7.3
7.2
6.9
6.9
6.3
6.2
9.9
9.9
Max
7.3
7.3
8.8
8.8
8.9
8.9
11.7
11.7
19.2
19.2
8.1
8.1
9.0
9.0
8.4
8.4
7.3
7.3
11.9
11.9
February 2010
5-14
Draft - Do Not Quote or Cite
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1
2
Table 5-10. Descriptive statistics for hourly carbon monoxide concentrations before and after estimation of missing
values - Los Angeles 2006.
Monitor
37-0113
37-1002
37-1103
37-1201
37-1301
37-2005
37-4002
59-0001/7
59-1003
59-5001
Missing
values
filled?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
1 -hour values
(n)
Present
8365
8760
8345
8760
8265
8760
8375
8760
8275
8760
8258
8760
8216
8760
8342
8760
8358
8760
8227
8760
Missing
395
0
415
0
495
0
385
0
485
0
502
0
544
0
418
0
402
0
533
0
CO concentration (ppm)
Mean
0.42
0.43
0.67
0.67
0.55
0.56
0.55
0.56
1.00
1.01
0.73
0.73
0.74
0.75
0.43
0.43
0.33
0.33
0.64
0.64
SD
0.37
0.37
0.61
0.61
0.50
0.50
0.54
0.53
0.89
0.90
0.49
0.49
0.55
0.54
0.47
0.47
0.45
0.45
0.57
0.56
Min
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Percentile
25th
0.2
0.2
0.3
0.3
0.2
0.2
0.2
0.2
0.5
0.5
0.4
0.4
0.4
0.4
0.1
0.1
0.1
0.1
0.3
0.3
50th
0.3
0.3
0.5
0.5
0.4
0.4
0.4
0.4
0.7
0.7
0.6
0.6
0.6
0.6
0.3
0.3
0.1
0.1
0.4
0.4
75th
0.6
0.6
0.8
0.8
0.7
0.8
0.6
0.7
1.1
1.1
1.0
1.0
0.9
0.9
0.5
0.5
0.4
0.4
0.7
0.7
90th
0.9
0.9
1.5
1.5
1.3
1.3
1.2
1.2
2.0
2.0
1.4
1.3
1.5
1.5
1.0
1.0
0.9
0.9
1.3
1.3
95th
1.2
1.2
2.0
2.0
1.6
1.6
1.7
1.7
2.9
2.9
1.7
1.7
1.9
1.9
1.4
1.4
1.4
1.4
1.8
1.8
99th
1.7
1.7
2.9
2.9
2.3
2.2
2.7
2.7
4.7
4.6
2.4
2.4
2.7
2.7
2.3
2.3
2.1
2.1
3.0
2.9
99.9th
2.5
2.5
4.0
3.9
2.9
2.9
3.8
3.7
6.9
6.8
3.2
3.1
3.7
3.7
3.4
3.4
3.1
3.0
4.7
4.6
Max
2.9
2.9
4.3
4.3
3.5
3.5
4.8
4.8
8.4
8.4
4.1
4.1
4.2
4.2
4.5
4.5
3.5
3.5
6.0
6.0
4
5
February 2010
5-15
Draft - Do Not Quote or Cite
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1 5.7.3 Adjusted 1-Hour Ambient Concentrations
2 In addition to modeling exposures based on recent as is air quality (i.e., ambient
3 monitoring data for year 2006), exposures and resulting dose were estimated for air quality
4 conditions that just meet the current 8-hour CO NAAQS and the potential alternative standards.
5 Because CO concentrations in recent years were significantly lower than the current NAAQS,
6 staff first selected an earlier year for each city (1995 for Denver and 1997 for Los Angeles) to
7 represent air quality conditions that were near the current 8-hour CO standard. Consistent with
8 the data adjustment approach employed in the previous draft CO exposure assessment (Johnson
9 et al., 2000), and approaches used in prior REAs supporting other pollutant NAAQS reviews
10 (e.g., US EPA, 2008c; US EPA, 2009b), staff concluded (1) that the policy-relevant background
11 levels of CO were negligible in each area (section 3.1.4), and (2) that the fixed-site monitoring
12 data could be adjusted to simulate just meeting the current CO standards by use of a simple
13 proportional adjustment of all hourly values (section 3.1.5). Consequently, the following
14 adjustment equation was employed:
15
16 COadj(w,/0 = (NAAQS/DV) x CO(m,h). (5-1)
17
18 CO(m,h) is the 1-hour CO concentration at hour h for monitor m. It follows that COa
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Staff evaluated three additional air quality scenarios considering potential alternative
standard levels, averaging times, and forms. Assuming a similar form and averaging time of the
current 8-hour standard (2nd highest non-overlapping 8-hour average CO concentration), staff
selected a level of 5 ppm for the first potential alternative standard.6 As was done for other
recent NAAQS reviews (US EPA, 2008c; US EPA, 2009b), staff selected percentiles of the air
quality distribution and averaging times to identifying potential levels associated with alternative
standards. The second potential alternative standard considered by staff also uses an 8-hour
average concentration, though having a 99th percentile daily maximum CO concentration of 5.0
ppm.7 The final potential alternative standard that staff evaluated was a 99th percentile daily
maximum 1-hour CO concentration of 8.0 ppm. Table 5-11 summarizes the adjustment factors
that were developed from equation 5-1 and used to adjust the high concentration year air quality
data in each study area.
Table 5-11. Design values and adjustment factors used to represent air quality just
meeting the current and potential alternative standards.
Study Area
Denver
Los Angeles
Standard
Averaging
Time
8-hour
1-hour
8-hour
1-hour
Form
2nd highest
99th pet daily max
99th pet daily max
2nd highest
99th pet daily max
99th pet daily max
Level
(ppm)
9
5
5.0
8.0
9
5
5.0
8.0
Design Value3
(ppm)
9.5
7.3
13.5
15
13.1
18.5
Adjustment
Factor
0.989b
0.568
0.685
0.593
0.627 b
0.360
0.380
0.432
Notes:
3 All design values were obtained from monitor ID monitor ID 080310002 in Denver (1995 data) and
monitor ID 060371301 in Los Angeles (1997 data).
b Adjustment factor for just meeting the current 8-hour average CO standard.
15
16
17
6 Note that this would allow a 2nd highest non-overlapping 8-hour concentration up to 5.4 ppm (hence the
design value).
7 It was assumed that there are an infinite number of zeros, that is, the level is exactly 5.0 ppm. This
rounding convention applies to the other potential alternative standard selected, the level is exactly 8.0.
5-17
February 2010
Draft - Do Not Quote or Cite
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1 Table 5-12 and 5-13 provides the descriptive statistics for the Denver and Los Angeles
2 ambient monitor 1-hour CO concentrations, respectively, after applying the appropriate
3 adjustment factor to simulate just meeting the current standard. As expected, the adjusted
4 monitoring concentrations for Denver 1995 are very similar to the unadjusted data set given that
5 the adjustment factor used was close to unity. For example, the maximum concentration at the
6 design monitor was reduced from 24.5 ppm to 24.2 ppm. The change in CO concentrations was
7 much greater in Los Angeles compared with that of Denver as a result of differences in the
8 adjustment factor used in each study area. For example, the maximum CO concentration at the
9 design monitor in Los Angeles was reduced from 19.2 ppm to 12.0 ppm. Considering the
10 patterns described above in section 5.7.1 for the unadjusted air quality and given that the
11 concentration adjustment was proportional, additional remarks can be made regarding
12 differences in the air quality adjusted to just meet the current 8-hour CO NAAQS. When
13 comparing the adjusted concentrations in Denver and Los Angeles, there is still a sharper rate of
14 increase in CO concentrations at and above the 95th percentiles of the distribution, only now all
15 of the Denver monitors have greater CO concentrations at these upper percentiles when
16 compared with concentrations observed at all of the Los Angeles monitors (excluding
17 concentrations at the Los Angeles design monitor).
18 Given the proportional approach used to adjust ambient concentrations for each of the
19 other exposure scenarios (e.g., 99th percentile daily maximum 1-hour concentration of 8.0);
20 similar patterns in concentrations were expected and are therefore not summarized here.
February 2010 5-18 Draft - Do Not Quote or Cite
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1
2
Table 5-12. Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet the current 8-
hour standard - Denver (adjusted 1995 data).
Monitor
ID
31-0002
31-0013
31-0014
59-0002
Hourly-average CO concentration (ppm)
Mean
1.5
1.2
1.1
1.0
SD
1.2
1.1
1.0
0.9
25.0
0.8
0.6
0.5
0.4
50.0
1.2
0.9
0.7
0.6
75.0
1.8
1.5
1.3
1.1
90.0
2.7
2.5
2.3
2.0
95.0
3.4
3.4
3.2
2.7
99.0
6.0
5.4
5.3
4.8
99.5
7.6
6.4
6.4
5.7
99.9
13.0
8.7
7.7
7.4
Max
24.2
14.4
10.3
11.8
DV
(ppm)
9.4
6.1
5.8
4.5
4
5
Table 5-13. Descriptive statistics for hourly carbon monoxide concentrations after adjusting to just meet the current 8-
hour standard - Los Angeles (adjusted 1997 data).
Monitor
ID
37-0113
37-1002
37-1103
37-1201
37-1301
37-2005
37-4002
59-0001
59-1003
59-5001
Hourly-average CO concentration (ppm)
Mean
0.5
1.1
0.9
0.7
1.5
0.7
0.7
0.7
0.5
0.9
SD
0.5
0.8
0.7
0.8
1.4
0.5
0.7
0.6
0.6
0.7
25.0
0.1
0.6
0.3
0.3
0.7
0.4
0.3
0.4
0.1
0.4
50.0
0.4
0.9
0.6
0.4
1.1
0.6
0.4
0.5
0.2
0.6
75.0
0.8
1.3
1.2
0.9
1.7
0.9
0.8
0.9
0.6
1.1
90.0
1.3
2.2
1.9
1.8
3.1
1.3
1.7
1.4
1.3
1.8
95.0
1.6
2.8
2.4
2.4
4.2
1.8
2.3
1.8
1.9
2.3
99.0
2.3
3.8
3.4
3.7
7.0
2.6
3.3
2.9
2.9
3.8
99.5
2.6
4.1
3.6
4.3
8.5
2.9
3.7
3.4
3.2
4.5
99.9
3.2
4.8
4.5
5.2
10.8
3.8
4.5
4.3
3.9
6.1
Max
4.6
5.5
5.6
7.3
12.0
5.1
5.6
5.3
4.6
7.5
DV
(ppm)
2.6
4.5
3.7
4.8
9.4
3.4
4.0
3.4
3.1
3.6
February 2010
5-19
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
5.8 METEOROLOGICAL DATA
A few algorithms within APEX require meteorological data (primarily temperature) from
stations located within the study area. For example, in selecting a CHAD diary to simulate an
individual's daily activities, a range of daily maximum temperatures is used to categorize diaries
for sampling purposes so as to best match the temperature observed on the simulation day within
the study area (section 5.5.4). In addition, mean temperatures are used by APEX to select from
an appropriate air exchange rate distribution to estimate indoor microenvironmental
concentrations (section 5.8). For the analyses described in this report, hourly temperature data
were obtained from meteorological stations located at or near the fixed-site CO monitor specified
for each study area.
Tables 5-14 and 5-15 list the meteorological stations staff selected for use in modeling
the Denver and Los Angeles study areas, respectively. Ideally, staff would have used the same
station (Long Beach: 37-4002) matched for both monitoring years (1997 and 2006) in Los
Angeles. Because this station did not report a complete year of data for 1997, we have
substituted data reported by the Long Beach Daugherty Field station located approximately 3.6
km from the 37-4002 station. The same two stations (31-0002 and 59-0002) will be used for the
Denver study area for 1995 and Denver 2006, because there were adequate data for both years
for both sites.
To run APEX, a "ZoneRadius" is specified by the user as the maximum radius for the
region surrounding each meteorological station that will be represented by the temperature data
provided by the station. In this assessment, staff set this at a value that includes all census tracts
within the air districts. A radius of 15.5 km met this requirement for Denver (Figure 5-1), while
Los Angeles required a larger radius of 70.5 km (Figure 5-2).
Table 5-14. Locations of meteorological stations selected for Denver.
Meteorological station
Monitor ID
31-0002
59-0002
County
Denver
Jefferson
Location coordinates
Latitude
39.751184
39.800333
Longitude
-104.987625
-105.099973
Monitoring Year
1995
1-hour
values
(n)
8742
8702
Mean
temp
(°F)
53.3
49.7
2006
1-hour
values
(n)
8749
8758
Mean
temp
(°F)
55.2
51.5
25
February 2010
5-20
Draft - Do Not Quote or Cite
-------
Table 5-15. Locations of meteorological stations selected for Los Angeles.
Meteorological station
Monitor ID
Daugherty
Field
37-4002
County
Long Beach
Long Beach
Coordinates
Latitude
33.81667
33.82376
Longitude
-118.15
-118.18921
Monitoring Year
1997
1-hour
values
(n)
8751
~
Mean
temp
(°F)
65.8
~
2006
1-hour
values
(n)
~
8759
Mean
temp
(°F)
~
63.8
3 5.8.1 Method for Estimating of Missing 1-Hour Temperature Data
4 APEX also requires a complete (full) meteorological data set to run properly. In
5 checking the meteorological data for completeness, staff noted all stations and years had at least
6 one missing hourly value for temperature (Tables 5-14 and 5-15). To generate the full year of
7 temperature data set, we estimated the missing values for the selected meteorological (MET)
8 stations in Denver and Los Angeles as follows.
9 For the Denver study area, staff selected two MET stations for use in 1995 and 2006. All
10 missing values in year 2006 were filled using linear interpolation. For the missing values in
11 1995, staff used linear interpolation to fill in short gaps. Where there were long gaps in the data
12 (e.g., more than 16 continuous hours of missing values), linear interpolation was judged as
13 inappropriate because this method would likely not produce reasonable estimates of the potential
14 variability in temperature (particularly the daily maximum) that might occur during this gap. In
15 these instances, staff applied an alternative approach whereas the average temperature of the
16 previous day and the latter day were averaged and then substituted for the corresponding hours.
17 For example, if the temperature data was missing from 1AM to 11pm on 2/8/1995, staff
18 averaged the hourly temperature of 2/7/1995 and 2/9/1995 for 1AM, 2AM ..., 11PM to fill the
19 missing hours (all eleven hours have an individual value).8
20 For Los Angeles, staff evaluated the two sites noted here as site 1 (ID 037-4002) and site
21 2 (located at Daugherty Field). Both locations reported temperature in both years of interest;
22 however, the degree of completeness for each varied. Given their close proximity to one another
23 (3.6 km), staff decided that a complete data set would be best generated by using a composite of
24 the two monitors, using the monitor with the greatest number of measurements as the primary
8 Calculating the average temperature using this method does not apply if 1) the long gap occurs on January
1 or December 31, or if 2) the temperature data in the previous day or the latter day are not available. In such cases,
we used the non-missing values in the previous day or the latter day, whichever was available.
February 20 JO 5-21 Draft - Do Not Quote or Cite
-------
1 data set. Because site 1 had fewer missing values than site 2 for 2006, site 1 was selected as the
2 primary meteorological site to represent the Los Angeles area for that year. For the one missing
3 value on Site 1 in 2006, the corresponding temperature from Site 2 was used to fill the missing
4 value for 2006. For 1997, there were 2,263 missing values on Site 1 while only 9 missing values
5 on Site 2. As a result, Site 2 was selected by staff as the primary meteorological station for 1997.
6 Two of the nine missing values from Site 2 were available from Site 1. Therefore, these
7 temperatures were directly substituted with values from the corresponding hours of the Site 1
8 data set. To fill the remaining seven missing values, we used linear interpolation by connecting
9 successive straight line segments and fitting a continuous curve to the data.9
10 The temperature distributions before and after filling missing values were compared at
11 for each station in each year to assess the impact (if any) of the substitution method. Given the
12 limited number of missing values in the original data sets, there were negligible differences when
13 comparing mean, median, variance and percentile statistics.
14 5.9 MICROENVIRONMENTS MODELED
15 This section briefly discusses the approach and specific factors used to estimate CO
16 microenvironmental concentrations in the current assessment. As described in section 4.4.4.3,
17 the approach was originally developed for pNEM/CO and used the previous assessment (Johnson
18 etal.,2000).
19 5.9.1 The Micronenvironmental Model as Implemented by APEX4.3
20 Section 8.2.2 of US EPA (2008b) indicates that the mass balance model in APEX4.3
21 models the portion of outdoor air that enters the microenvironment as
22
^•5 '-''-'out Iproximity X Ipenetration X ^'Jambient \-^~^)
24
25 Since this is effectively equivalent to the method used by APEX3.1 described in section
26 4.4.4 A, we used the same method here with respect to application of the proximity and
27 penetration factors in APEX4.3 to implement equation 4-11. First, to obtain the appropriate CO
28 concentrations outside each microenvironment, ambient CO concentration were adjusted by an
29 exponential factor of 0.621 (see equation 4-22). Then for each profile, a value for fproximity term
30 would be sampled for each microenvironment from a lognormal distribution with geometric
31 mean (GM) equal to M(m) and geometric standard deviation (GSD) equal to 1.5232. A value for
32 /penetration for each hour would also be sampled from a lognormal distribution with geometric
33 mean (GMx) equal to 1.0 and geometric standard deviation (GSDx) equal to 1.6289.
9 This was done in SAS using a procedure "PROC EXPAND" with "JOIN" option.
February 2010 5-22 Draft - Do Not Quote or Cite
-------
1 Table 5-16 presents the algorithm parameters proposed for the eight microenvironments
2 currently defined for the application of APEX to Los Angeles and Denver. These eight
3 microenvironments were selected rather than the fifteen selected in earlier assessments (see REA
4 Table 4-4 and 4-5) based on the locations having the same proximity factors and air exchange
5 rates distributions, or when using a similar microenvironmental approach (see section 5.9.5).
6 Note that when this algorithm is implemented within the APEX framework, the
7 application of Equation 4-11 produces a "compression" effect in which the ratio of COout to
8 COmon tends to become smaller (on average) as COmon increases. This effect is consistent with
9 data reported by field studies such as Wilson, Colome, and Tian (1995) which have compared
10 outdoor concentrations with simultaneously measured fixed-site concentrations.
February 2010 5-23 Draft - Do Not Quote or Cite
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1
2
Table 5-16. Parameters of Bounded Lognormal Distributions Defined for Proximity
Factors to be Used in the Proposed Application of APEX4.3 to Los
Angeles and Denver.
Microenvironment
Code
1
2
3
4
5
Q
7
8
General
location
Indoors
Indoors
Indoors
Indoors
Outdoors
Outdoor
Outdoors
Vehicle
Specific
location
Residence
Service
station and
auto repair
Other
indoor
locations A
Other
indoor
locations B
Near road
locations
Public
parking or
fueling
facility
Other
outdoor
locations
Automobile
and mass
transit
Activity diary
locations
included in
microenvironment
Indoors - residence
Service station
Auto repair
Other re pair shop
Shopping mall
Other indoor location
Auditorium
Store
Office
Other public building
Bars
Restaurants
Health care facility
School
Church
Manufacturing facility
Bus stop
Bicycle
Motorcycle
Other near road
Indoor parking
garage
Outdoor parking
garage
Outdoor parking lot
Outdoor service
station
Outdoor res. garage
Construction site
Residential grounds
School grounds
Sports arena
Park or golf course
Other outdoor
Automobile
Truck
Bus
Train/subway
Other vehicle
Parameters of bounded lognormal
distribution for proximity factor
GM
1.034
2.970
1.213
0.989
1.607
2.970
1.436
3.020
GSD
1 .5232
1 .5232
1 .5232
1 .5232
1 .5232
1 .5232
1 .5232
1 .5232
Minimum
(5th pet)
0.5175
1 .4864
0.6071
0.4950
0.8042
1 .4864
0.7187
1.5114
Maximum
(95th pet)
2.0661
5.9345
2.4237
1 .9762
3.2110
5.9345
2.8693
6.0344
February 2010
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1 5.9.2 Microenvironmental Mapping
2 In APEX, microenvironments represent the exposure locations for simulated individuals.
3 For exposures to be estimated accurately, it is important to have realistic microenvironments that
4 match closely to the locations where actual people spend time on a daily basis. It is necessary to
5 map the CHAD location codes to one of the eight specific microenvironments selected for this
6 exposure assessment or to a supplemental category (either -1 or 0). As a reminder, these eight
7 microenvironments were selected based on having suitable data to use for proximity factors and
8 air exchange rates (when using a mass balance approach). The -1 code is assigned to events
9 where the location code is missing (X) or the location is classified as uncertain (U); the -1 code
10 instructs APEX to use the last known microenvironment for that persons diary in determining the
11 exposure concentration. The 0 code is assigned to an airplane microenvironment (CHAD
12 location code: 31160) and instructs APEX to set the exposure concentration equal to 0 ppm. See
13 Appendix D Figure D-l that describes the specific mapping of CHAD codes to
14 microenvironments.
15 The microenvironment mapping file also permits the user to assign a home/work/other
16 (H/W/O) location to each CHAD location code. The home/work/other location determines the
17 source of the hourly-average monitoring data that will represent the ambient CO concentration
18 for the microenvironment: the home district monitor, the work district monitor, or other.
19 The initial APEX assignments of H/W/O to the CHAD location codes were used as a
20 starting point (see Appendix D Figure D-l) and modified using a few of the options available in
21 APEX. First, staff overrode the H/W/O designations listed in the microenvironment mapping
22 file for selected activities by compiling a list of CHAD activity codes that will always be
23 associated with the work district (regardless of the CHAD location code). This list is inserted in
24 the "CustomWork" parameter found in the simulation control file. The default list of work
25 activity codes, which were used in this application, includes codes 10000 through 10300 (see
26 Appendix D Table D-l). As a result of using this option, APEX will assign the simulated person
27 to the work district whenever the activity code falls between 10000 and 10300. This assignment
28 will override the home/work/assignment associated with the applicable CHAD location code.
29 There will still be exposure events in which the simulated person is assigned to the
30 "other" location. In the default mode, APEX uses an average of all monitor values to determine
31 the ambient concentration for these events. Note that this averaging approach will tend to
32 smooth the data; that is, it will produce ambient CO concentrations that have slightly less
33 variance than a comparable set of ambient concentrations obtained from a single monitor. To
34 avoid this effect, staff chose to specify the option OtherDistricts = 1, so that only one monitor is
35 used to represent "other." The monitor used in the model application is randomly selected from
36 the set of all monitors.
February 2010 5-25 Draft - Do Not Quote or Cite
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5.9.3 Selection of Microenvironmental Method Used
As discussed in chapter 4, the two methods available in APEX for calculating pollutant
levels within microenvironments are mass balance or a factors approach. Table 5-17 lists the
microenvironments used in this study and the calculation method used.
Table 5-17. List of microenvironments modeled and calculation methods used.
Microenvironment
Code
1
2
3
4
5
6
7
8
Location
Indoors
Indoors
Indoors
Indoors
Outdoors
Outdoor
Outdoors
Vehicle
Name
Residence
Service station and auto repair
Other indoor locations A
Other indoor locations B
Near road locations
Public parking or fueling facility
Other outdoor locations
Automobile and mass transit
Calculation
Method
Mass balance
Mass balance
Mass balance
Mass balance
Factors
Factors
Factors
Factors
7 5.9.4 Air Exchange Rates and Air Conditioning Prevalence
8 For the microenvironments using the mass balance method (i.e., all indoor
9 microenvironments), air exchange rate (AER) and air conditioning prevalence data are needed to
10 estimate microenvironmental concentrations. Air exchange rate data used for the indoor
11 residential microenvironment were the same used in APEX for the most recent Oj NAAQS
12 review (US EPA, 2007). As part of that earlier review, AER data were reviewed, compiled and
13 evaluated from the extant literature to generate location-specific AER distributions10 categorized
14 by influential factors, namely temperature and presence of air conditioning. In general,
15 lognormal distributions provided the best fit, and are defined by a geometric mean (GM) and
16 standard deviation (GSD). To avoid unusually extreme simulated AER values, bounds of 0.1
17 and 10 were selected for minimum and maximum AER, respectively. Tables 5-18 and 5-19
There were AER measurement data specific to the Los Angeles study area; these were used by US EPA
(2007) to develop AER distributions. Denver was not a location of interest in US EPA (2007); therefore there were
no Denver-specific AER developed for this study area. Consistent with what was done in US EPA (2007) for cities
not having location-specific AER data available, the composite AER distributions developed using data from cities
outside California were applied in this study to Denver.
5-26
February 2010
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1 summarize the AER distributions used in modeling indoor exposures, classified by A/C
2 prevalence and temperature categories. For all other indoor microenvironments, the AER
3 distributions used here (Tables 5-18 and 5-19) were based data provided by an indoor air quality
4 study (Persily et al., 2005). These are the same AER distributions used for the APEX
5 assessments in the most recent O3 NAAQS review (US EPA, 2007), NO2 REA (EPA, 2008c) and
6 SO2 REA (US EPA, 2009b).
7 Because the selection of an air exchange rate distribution is conditioned on the presence
8 or absence of an air-conditioner (A/C), the air conditioning status of the residential
9 microenvironments in each modeled area is simulated randomly using the probability that a
10 residence has an air conditioner. A value of 55% was used to represent the A/C prevalence rate
11 in Los Angeles, based on data obtained from US EPA (2007). For Denver, residential A/C
12 prevalence was estimated to be 69% of homes, a value obtained from AHS (2005). Air
13 conditioning prevalence is noted as being distinct from usage rate, the latter being represented by
14 the air exchange rate distribution and is dependent on temperature.
15 Table 5-18. Lognormal distributions of indoor air exchange rates used in Los
16 Angeles.
Micro-
environment
Indoors -
residence
Indoors - other
Classification category
A/C present?
Yesb
No
-
Mean Temp
(degrees F)
<50
50-67
68-76
77-85
86+
<50
50-67
68-76
77-85
86+
-
Parameters of bounded lognormal distribution3
GM
0.589
0.589
1.100
0.813
0.266
0.543
0.747
1.372
0.988
0.988
1.109
GSD
1.894
1.894
2.365
2.415
2.790
3.087
2.085
2.283
1.967
1.967
3.015
Minimum
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1C
Maximum
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10. Oc
Notes:
3 Obtained from Table D-4 of US EPA (2007).
b Estimated air conditioning prevalence rate for Los Angeles = 55 percent (see page 47 and Table A-3
of US EPA, 2007).
c Assumed here to be consistent with other approximated lower and upper bounds.
February 2010
5-27
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Table 5-19. Lognormal distributions of indoor air exchange rates used in Denver.
Micro-
environment
Indoors -
residence
Indoors - other
Classification category
A/C present?
Yesb
No
-
Mean Temp
(degrees F)
<50
50-68
68-77
77-86
86+
<50
50-68
68+
-
Parameters of bounded lognormal distribution3
GM
0.9185
0.5636
0.4676
0.4235
0.5667
0.9258
0.7333
1.3782
1.109
GSD
1.8589
1.9396
2.2011
2.0373
1 .9447
2.0836
2.3299
2.2757
3.015
Minimum
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Maximum
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
Notes:
3 Obtained from Table D-4 of US EPA (2007) and derived from locations outside California.
b Estimated air conditioning prevalence rate for Denver = 69% (see Table 1-4 in AHS, 2005).
c Assumed here to be consistent with other approximated lower and upper bounds.
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
5.10 KEY OBSERVATIONS
The following presents the key observations for this chapter:
• Two exposure model domains (Denver and Los Angeles study areas) were defined by
overlaying ambient monitor locations having 10 km radii with US census tract
population data. Monitors selected comprised the bulk of the urban core in each
location, where ambient monitoring data exist.
• The selected at-risk population was simulated by combining the tract-specific age and
gender population distribution and the CHD prevalence, also stratified by age and
gender. In using this approach, staff can represent the variability that exists in the
CHD population that resides in each census tract and within each study area.
• Staff expanded the selected at-risk population to include an estimate of persons with
undiagnosed CHD.
• Compared with the single-monitor approach used for the first draft CO REA, staff
expanded the number of ambient monitors used in this second draft CO REA to better
capture the spatial variability in ambient concentrations. In Denver, a total of four
monitors were used, in Los Angeles, the total number of monitors was ten.
• Compared with the two microenvironments modeled in the first draft CO REA, staff
has expanded the number modeled in each location to eight. This approach is designed
to better represent the expected variability in microenvironmental CO concentrations.
February 2010
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1 • Compared with the approach used to estimate microenvironmental concentrations in
2 the first draft CO REA (factors approach only), all indoor microenvironments were
3 modeled using a mass balance model in this second draft assessment. Use of the mass
4 balance model will better represent temporal variability in indoor CO concentrations
5 with respect to the outdoor CO concentration variability. In addition, distributions of
6 microenvironmental factors were used in this second draft CO REA for all
7 microenvironments rather than using point estimates (as was done for the first draft CO
8 REA). Using distributions of microenvironmental factors will better represent both
9 spatial and temporal variability in estimated microenvironmental CO concentrations.
February 2010 5-29 Draft - Do Not Quote or Cite
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1 5.11 REFERENCES
2
3 AHS. (2005). American Housing Survey for the Denver Metropolitan Area: 2004. Available at:
4 http://www.census.gov/prod/2005pubs/hl70-04-46.pdf
5 Adams PF and Marano MA. (1995). Current Estimates from the National Health Interview Survey, 1994. National
6 Center for Health Statistics. Vital Health Stat. 10(193).
7 American Heart Association. (1990). Heart and Stroke Facts. American Heart Association, Dallas, TX. p. 13.
8 CDC. (2009). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007. Series 10,
9 Number 240. U.S. Department of Health and Human Services, Hyattsville, MD, May 2009.
10 GlenG, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal diary assembly for
11 human exposure modeling. JExpos Sci Environ Epidem. 18:299-311.
12 Isaacs K, McCurdy T, Errickson A, Forbes S, Glen G, Graham S, McCurdy L, Nysewander, M, Smith L, Tulve N,
13 Vallero D. (2009). Statistical properties of longitudinal time-activity data for use in EPA exposure models.
14 Poster presented at the American Time Use Research Conference; College Park MD, June 26, 2009.
15 Johnson TR. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Report No. EPA-
16 600/54-84-014, U. S. Environmental Protection Agency, Research Triangle Park, North Carolina.
17 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and
18 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO
19 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No.
20 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available
21 at: http://www.epa.gov/ttn/fera/human_related.html. June 2000.
22 Persily A, Gorfain J, Brunner G. (2005). Ventilation design and performance in U.S. Office buildings. ASHRAE
23 Journal. April 2005, 30-35.
24 US EPA. (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of
25 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no.
26 EPA/452/R-92-004.
27 US EPA. (2003). Total Risk Integrated Methodology TRIM.ExpoInhalation User's Document. Volume I: Air
28 Pollutants Exposure Model (APEX, version 3). Available at:
29 http://www.epa.gov/ttn/fera/data/apex322/apexusersguidevoli4-24-03.pdf.
30 US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. Office of Air Quality Planning
31 and Standards, Research Triangle Park, NC. July 2007. EPA-452/R-07-010. Available at:
32 http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007 07 o3 exposure tsd.pdf.
33 US EPA. (2008a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation
34 (TRIM.Expo/APEX, Version 4.3). Volume 1: Users Guide. Report No. EPA-452/B-08-00la. Office of
3 5 Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC.
36 US EPA. (2008b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model Documentation
37 (TRIM.Expo/APEX, Version 4.3). Volume 2: Technical Support DocumenLReport No. EPA-452/B-08-
38 00Ib. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research
39 Triangle Park, NC.
February 2010 5-30 Draft - Do Not Quote or Cite
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1 US EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient
2 Air Quality Standard. EPA-452/R-08-008a. November 2008. Available at:
3 http://www.epa.gov/ttn/naaqs/standards/nox/data/2008112 l_NO2_REA_final.pdf.
4 US EPA. (2009a). Risk and Exposure Assessment to Support the Review of the Carbon Monoxide Primary
5 National Ambient Air Quality Standard. First External Review Draft. EPA-452/P-09-008. Available at:
6 http://www.epa.gov/ttn/naaqs/standards/co/data/COREAlstDraftOct2009.pdf
7 US EPA. (2009b). Risk and Exposure Assessment to Support the Review of the SO2 Primary National Ambient Air
8 Quality Standard. EPA-452/R-09-007. August 2009. Available at
9 http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf.
10 Wilson AL, Colome SD, Tian Y. (1995). California Residential Indoor Air Quality Study. Volume III: Ancillary
11 and Exploratory Analysis. Integrated Environmental Services, Irvine, California.
February 2010 5-31 Draft - Do Not Quote or Cite
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1 6 SIMULATED EXPOSURE AND COHB DOSE RESULTS
2 This chapter summarizes the CO exposure and dose results for the Denver and Los
3 Angeles study areas that were generated using EPA's APEX model described in chapters 4 and
4 5. Staff considered exposures associated with five air quality scenarios; air quality (1) as is, (2)
5 adjusted to just meet the current 8-hour standard of 9 ppm, (3) adjusted to just meet a 2nd highest
6 8-hour average concentration of 5.4 ppm, (4) adjusted to just meet a 99th percentile daily
7 maximum 8-hour average of 5.0 ppm, and (5) adjusted to just meet a 99th percentile daily
8 maximum 8-hour average of 8.0 ppm.
9 The chapter is divided into five main sections. The first section (6.1) summarizes the
10 estimated exposures associated with each of the five air quality scenarios. The simulated at-risk
11 population includes individuals with diagnosed CHD as well as those persons with potentially
12 undiagnosed CHD.1 For simplicity, they will be combined and referred to as the CHD
13 population in this chapter. The exposure metrics of interest in this REA and generated by APEX
14 include the number and percent of persons at or above staff-selected exposure levels and the
15 corresponding number of person-days.2 Two exposure averaging times were also selected: 1-
16 hour and 8-hour daily maximum exposures. Section 6.2 summarizes the estimated COHb dose
17 levels for persons in the simulated at-risk population residing in each study area. The dose
18 metric of interest in this REA and generated by APEX includes the number and percent of
19 persons at or above staff selected COHb levels and the corresponding number of person-days.
20 Consistent with prior CO exposure assessments, the daily maximum end-of-hour COHb level
21 was recorded. In section 6.3, staff compares the dose estimates in this second draft CO REA
22 with those estimated in the 2000 exposure assessment (Johnson et al., 2000). The fourth section
23 (6.4) presents an evaluation of endogenous CO production for the APEX simulated individuals.
24 This includes analysis of the COHb ambient contribution attributed to ambient CO in a select
25 group of simulated persons. Finally, key observations are presented in the final section (6.5). As
26 mentioned in Chapter 1, exposure and risk results are provided here without substantial
27 interpretation. Rather, interpretative discussion of these results is provided in the CO Policy
28 Assessment.
1 As described in section 5.5 above, in characterizing the population of interest with regard to
demographics (age and gender), the assessment drew from estimates of the prevalence of coronary heart disease
(CHD) provided by the National Health Interview Survey (which includes CHD, angina pectoris and heart attack)
and corresponding estimates of undiagnosed ischemia developed by EPA.
2 Because the duration of the exposure assessment is one year, there are opportunities for individuals to
experience more than one day in the year above a selected exposure concentration, hence use of the term person-
days.
February 20 JO 6-1 Draft - Do Not Quote or Cite
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1 6.1 ESTIMATED EXPOSURES
2 The section summarizes the estimated exposures for the simulated individuals in a series
3 of tables, separated by the five air quality scenarios and study areas considered.
4 6.1.1 Air Quality "As Is"
5 As described in section 5.6, ambient monitoring data from each location for year 2006
6 were used to represent the as is air quality. Table 6-1 summarizes the distribution of the 1-hour
7 and 8-hour daily maximum CO exposures experienced by the CHD population in the Denver
8 Study area. About 80% of the simulated CHD population did not experience a 1-hour daily
9 maximum exposure above 9 ppm; 99.9% did not experience a 1-hour daily maximum exposure
10 concentration above 20 ppm. Of the nearly 20 million person-days, over 99% were associated
11 with a 1-hour daily maximum exposure below 6 ppm. Very few individuals were estimated to
12 experience an 8-hour daily maximum exposure above 8 ppm (0.4% of the CHD population).
13 Approximately 99% of simulated person-days were associated with 8-hour daily maximum
14 exposure concentrations of less than 3 ppm. These results are consistent with the ambient
15 concentration distribution used to represent this scenario, where upper percentile concentrations
16 range from about 2 to 6.4 ppm (see Table 5-8). Note also that the highest estimated 1-hour daily
17 maximum exposures are likely a function of microenvironmental concentrations (e.g., in-
18 vehicles or near-roads) that, in general, may be a factor of two to five times higher than ambient
19 CO concentrations.
20 In Los Angeles, there were a greater number of individuals experiencing exposures at
21 each of the selected exposure levels (Table 6-2) when compared with Denver (Table 6-1), given
22 that the overall exposure modeling domain extended over a larger area with a higher total
23 population. The estimated percentage of persons exposed in Los Angeles is also greater when
24 compared with the corresponding exposure levels evaluated for the Denver study area. For
25 example, approximately 32% of the population was estimated to experience a 1-hour daily
26 maximum exposure of at least 9 ppm in Los Angeles (Table 6-2) while in Denver this same level
27 was experienced by approximately 20% of the CHD population (Table 6-1). This result is likely
28 driven by the differences noted in the as is air quality data, where in Los Angeles, the 2006
29 ambient concentrations were generally higher than those observed for Denver (section 5.7.1).
30 In addition, the maximum 1-hour daily maximum exposure was estimated to be at or
31 above 30 ppm but less than 40 ppm in the Los Angeles study area, though limited to a small
32 fraction of the population (<0.1%). The corresponding maximum 1-hour daily maximum
33 exposure in the Denver study area was at or above 20 ppm but less than 25 ppm, and was
34 experienced by approximately 0.1% of the CHD population. Therefore, the overall range of the
35 exposure distribution was wider in Los Angeles when compared with that of Denver when
February 20 JO 6-2 Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
considering the as1 /s air quality scenario. Similar to Denver, over 98% of the person-days in Los
Angeles were associated with 1-hour daily maximum exposures below 6 ppm and very few
persons (0.8%) experienced 8-hour daily maximum exposures above 8 ppm. These exposure
results are also consistent with the distribution of ambient air quality used to represent this
scenario, where upper percentile concentrations extend from about 2 to 8.4 ppm (Table 5-10).
Table 6-1. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level -
Adults With Coronary Heart Disease (CHD) in the Denver Study Area -"As
Is" Air Quality.
Daily
Maximum
Exposure
(ppm)
0
3
6
9
12
15
20
25
30
40
1-Hour
Persons
Number
53,656
53,397
32,517
10,662
3,048
876
62
0
0
0
Percent
100
100
60.6
19.9
5.7
1.6
0.1
0
0
0
Person-days
Number
19,580,000
2,188,000
170,400
24,560
4,677
1,061
62
0
0
0
Percent
100
11.2
0.9
0.1
<0.1
<0.1
<0.1
0
0
0
8-Hour
Persons
Number
53,656
31,036
1,715
62
12
0
0
0
0
0
Percent
100
58
3.2
0.1
<0.1
0
0
0
0
0
Person-days
Number
19,580,000
189,500
2,851
86
12
0
0
0
0
0
Percent
100
1.0
<0.1
<0.1
<0.1
0
0
0
0
0
Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air
quality scenario.
10
February 2010
6-3
Draft - Do Not Quote or Cite
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1 Table 6-2. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
2 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level -
3 Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area -
4 Air Quality As Is.
Daily
Maximum
Exposure
(ppm)
0
3
6
9
12
15
20
25
30
40
1-Hour
Persons
Number
383,040
382,739
287,606
122,428
42,850
13,949
2,208
401
100
0
Percent
100
100
75.1
32.0
11.2
3.6
0.6
0.1
<0.1
0
Person-days
Number
139,800,000
23,620,000
2,423,000
408,300
83,990
20,170
2,509
502
100
0
Percent
100
16.9
1.7
0.3
0.1
<0.1
<0.1
<0.1
<0.1
0
8-Hour
Persons
Number
383,040
294,430
36,528
3,011
301
0
0
0
0
0
Percent
100
77
9.5
0.8
0.1
0
0
0
0
0
Person-days
Number
139,800,000
3,793,000
72,150
3,412
301
0
0
0
0
0
Percent
100
2.7
0.1
<0.1
<0.1
0
0
0
0
0
Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As Is air
quality scenario.
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
6.1.2 Air quality adjusted to just meet the current 8-hour standard
As described in section 5.6, historical ambient monitoring data from each study area were
adjusted to represent air quality that just meets the current 8-hour standard. For both Denver
(year 1995) and Los Angeles (year 1997), air quality needed to be adjusted downwards to meet a
2nd highest 8-hour average concentration of 9.4 ppm. Note that even with a downward
proportional adjustment, these ambient concentrations remain higher than as is ambient air
quality. Table 6-3 summarizes the CHD population exposure results generated for the Denver
study area when using these adjusted ambient CO concentrations as an input to APEX and using
the same modeling assumptions and parameter distributions described in chapters 4 and 5. Over
half of the Denver CHD population was estimated to experience a 1-hour daily maximum
exposure at or above 12 ppm. This is nearly a factor of 10 greater than that estimated when
using the as is air quality (Table 6-1). The maximum 1-hour daily maximum exposure was
estimated to be at or above 40 ppm when considering air quality adjusted to just meet the current
standard, though only experienced by 0.2% of the CHD population. Thus, there is a wider range
in the exposure levels experienced by the CHD population. The number and percent of persons
experiencing 8-hour daily maximum exposures is also greater for this scenario when compared
with corresponding levels using the as is air quality. Nearly 10% of the CHD population was
estimated to experience an 8-hour daily maximum exposure at or above 9 ppm (Table 6-3) when
considering air quality just meeting the current 8-hour standard. Most of the CHD population
February 2010
6-4
Draft - Do Not Quote or Cite
-------
1 (99.6%) would not experience an 8-hour daily maximum concentration at that same level when
2 considering the as is air quality scenario (Table 6-1).
3 Similarly in Los Angeles, the number and percent of persons exposed above selected
4 exposure concentrations is greater when considering the air quality adjusted to just meet the
5 current standard than when using as is air quality. For example, nearly 50% of the CHD
6 population was estimated to experience a 1-hour daily maximum exposure of 9 ppm when
7 considering air quality just meeting the current standard (Table 6-4), while only 32% were
8 estimated to experience a similar concentration using as is air quality (Table 6-2). The range of
9 the 1-hour daily maximum exposure distribution extends upward to 40 ppm, but less than 60
10 ppm for this scenario. This estimate of an upper level is consistent with the maximum in-vehicle
11 concentration of 46 ppm measured by Shikiya (1989) during 112 southern California commutes
12 in wintertime. However, Rodes et al. (1998) reported maximum in-vehicle and on road CO
13 concentrations of only 7.6 and 9.0 ppm during Los Angeles commutes in 1997. Note though the
14 scripted commutes in this study were time-averaged for two hours, the sample size was limited
15 (about 30 total samples), and conducted over a nine days in the fall.
16 When comparing the overall population distribution for Los Angeles to Denver for this
17 exposure scenario, there are a greater percentage of persons and person-days estimated for the
18 Denver CHD population at the same exposure level. For example, only 2.7% of the CHD
19 population was estimated to experience an 8-hour daily maximum exposure at or above 9 ppm in
20 Los Angeles (Table 6-4), while in Denver, the estimated percent of the CHD population exposed
21 at this level was over a factor of three greater (9.4%) (Table 6-3). This result is likely driven by
22 differences observed at the upper tails of the air quality distribution noted in section 5.7.3, even
23 though both study areas have ambient concentrations adjusted to just meet the same 8-hour
24 average CO concentration of 9.4 ppm.
February 2010 6-5 Draft - Do Not Quote or Cite
-------
1 Table 6-3. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
2 Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level -
3 Adults With Coronary Heart Disease (CHD) in the Denver Study Area - Air
4 Quality Just Meeting the Current 8-Hour Standard.
Daily
Maximum
Exposure
(ppm)
0
3
6
9
12
15
20
30
40
60
1-Hour
Persons
Number
53,656
53,656
53,039
44,598
28,469
16,610
6,022
691
86
0
Percent
100
100
98.9
83.1
53.1
31.0
11.2
1.3
0.2
0
Person-days
Number
19,580,000
8,638,000
1,625,000
404,800
127,300
46,710
10,290
802
86
0
Percent
100
44.1
8.3
2.1
0.7
0.2
0.1
<0.1
<0.1
0
8-Hour
Persons
Number
53,656
52,706
23,879
5,060
1,037
309
37
0
0
0
Percent
100
98
44.5
9.4
1.9
0.6
0.1
0
0
0
Person-days
Number
19,580,000
2,690,000
97,760
9,724
1,382
346
37
0
0
0
Percent
100
13.7
0.5
<0.1
<0.1
<0.1
<0.1
0
0
0
Ambient concentrations from 1995 were adjusted to just meet a 2na highest 8-hour average
concentration of 9.4 ppm.
6
7
Table 6-4. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum 1-Hour or 8-hour Exposure At or Above the Specified Level -
Adults With Coronary Heart Disease (CHD) in the Los Angeles Study Area -
Air Quality Just Meeting the Current 8-Hour Standard.
Daily
Maximum
Exposure
(ppm)
0
3
6
9
12
15
20
30
40
60
1-Hour
Persons
Number
383,040
383,040
335,975
189,563
83,693
36,126
8,731
803
0
0
Percent
100
100
87.7
49.5
21.8
9.4
2.3
0.2
0
0
Person-days
Number
139,800,000
36,430,000
4,826,000
982,300
257,200
80,180
14,450
803
0
0
Percent
100
26.1
3.5
0.7
0.2
0.1
<0.1
<0.1
0
0
8-Hour
Persons
Number
383,040
342,598
75,966
10,336
1,505
301
100
0
0
0
Percent
100
89
19.8
2.7
0.4
0.1
<0.1
0
0
0
Person-days
Number
139,800,000
8,655,000
262,600
18,670
2,308
401
100
0
0
0
Percent
100
6.2
0.2
<0.1
<0.1
<0.1
<0.1
0
0
0
Ambient concentrations from 1997 were adjusted to just meet a 2na highest 8-hour average concentration
of 9.4 ppm.
10
11
12
6.1.3 Air quality adjusted to just meet alternative air quality scenarios
Three potential alternative air quality scenarios were investigated to observe how the
selected averaging times, forms and target levels would affect the estimated exposure
February 2010
6-6
Draft - Do Not Quote or Cite
-------
1 concentrations (section 5.7.3). The data for the 1-hour and 8-hour daily maximum exposure
2 concentrations are presented here, only with a focus on the number and percent of persons
3 exposed at selected concentrations. Table 6-5 summarizes the 1-hour exposure results for each
4 of the three alternative standards scenarios in the Denver study area, while the following table
5 presents the same information for the Los Angeles study area (Table 6-6). Tables 6-7 and 6-8
6 contain the corresponding 8-hour exposure distribution for the potential alternative standard
7 scenarios in each respective location.
8 In comparing the exposure results for each potential alternative scenario within each
9 study area and exposure averaging time, generally similar numbers of persons and their
10 respective percentages of the CHD population are observed at the same level. This was by
11 general design, that is, to investigate differing forms of the potential alternative standards that
12 would generate potentially similar exposure (and dose) results. Again, there is a wider range in
13 the 1-hour exposure levels experienced by the CHD population in Denver (Table 6-5) when
14 compared with that of Los Angeles (Table 6-6) when considering the same potential alternative
15 standard. This is also consistent patterns in the estimated distribution of 8-hour daily maximum
16 exposures experienced by the CHD population, though the upper range of that 8-hour maximum
17 exposure is of course less than that of the 1-hour daily maximum in each respective location
18 (Tables 6-7 and 6-8). There is some variability in the percent of persons exposed when
19 considering a particular level, form, and study area. For example, the 2nd highest 8-hour CO
20 concentration of 5.4 ppm best limited the number and percent of exposed persons in each
21 location when compared to results for the other potential alternative standard, though in Denver
22 there were still a few persons estimated to experience a 1-hour daily maximum at or above 30
23 ppm (Table 6-7). In Los Angeles, the upper level of the 1-hour daily maximum exposure
24 concentration experienced by the simulated CHD population was just at or above 20 ppm and
25 below 30 ppm.
February 2010 6-7 Draft - Do Not Quote or Cite
-------
1 Table 6-5. Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour
2 Exposure At or Above the Specified Level - Adults With Coronary Heart
3 Disease (CHD) in the Denver Study Area - Air Quality Just Meeting Potential
4 Alternative Standards.
Daily
Maximum
1-hour
Exposure
(ppm)
0
3
6
9
12
15
20
30
40
60
2na highest 8-hour
average of 5.4 ppm
Persons
Number
53,656
53,656
47,264
25,174
11,082
4,850
975
62
0
0
Percent
100
100
88.1
46.9
20.7
9.0
1.8
0.1
0
0
99tn pet 8-hour Daily
Max of 5.0 ppm
Persons
Number
53,656
53,656
50,534
32,147
16,326
7,676
1,888
136
0
0
Percent
100
100
94.2
59.9
30.4
14.3
3.5
0.3
0
0
99tn pet 1 -hour Daily
Max of 8.0 ppm
Persons
Number
53,656
53,656
48,239
26,877
12,192
5,282
1,222
74
0
0
Percent
100
100
89.9
50.1
22.7
9.8
2.3
0.1
0
0
Ambient concentrations from 1995 were adjusted to just meet the level of the potential
alternative standard indicated.
6
7
Table 6-6. Estimated Number (and Percentage) of Persons with a Daily Maximum 1-Hour
Exposure At or Above the Specified Level - Adults With Coronary Heart
Disease (CHD) in the Los Angeles Area - Air Quality Just Meeting Potential
Alternative Standards.
10
11
Daily
Maximum
1-hour
Exposure
(ppm)
0
3
6
9
12
15
20
30
40
60
2na highest 8-hour
average of 5.4 ppm
Persons
Number
383,040
378,624
215,454
68,540
19,769
6,322
903
0
0
0
Percent
100
99.0
56.2
17.9
5.2
1.7
0.2
0
0
0
99tn pet Daily 8-hour
Max of 5.0 ppm
Persons
Number
383,040
379,728
229,302
77,571
24,285
7,827
1,305
0
0
0
Percent
100
99.0
59.9
20.3
6.3
2.0
0.3
0
0
0
99tn pet Daily 1-hour
Max of 8.0 ppm
Persons
Number
383,040
381,535
260,913
98,244
34,721
10,738
2,709
0
0
0
Percent
100
99.6
68.1
25.6
9.1
2.8
0.7
0
0
0
Ambient concentrations from 1997 were adjusted to just meet the level of the potential
alternative standard indicated.
February 2010
6-8
Draft - Do Not Quote or Cite
-------
1 Table 6-7. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour
2 Exposure At or Above the Specified Level - Adults With Coronary Heart
3 Disease (CHD) in the Denver Study Area - Air Quality Just Meeting Potential
4 Alternative Standards.
Daily
Maximum
8-hour
Exposure
(ppm)
0
3
6
9
12
15
20
30
40
60
2na highest 8-hour
average of 5.4 ppm
Persons
Number
53,656
44,574
6,590
839
111
25
0
0
0
0
Percent
100
83.1
12.3
1.6
0.2
<0.1
0
0
0
0
99tn pet 8-hour Daily
Max of 5.0 ppm
Persons
Number
53,656
48,819
10,650
1,555
296
49
0
0
0
0
Percent
100
91.0
19.8
2.9
0.6
<0.1
0
0
0
0
99tn pet 1 -hour Daily
Max of 8.0 ppm
Persons
Number
53,656
45,808
7,380
926
123
25
0
0
0
0
Percent
100
85.4
13.8
1.7
0.2
<0.1
0
0
0
0
Ambient concentrations from 1995 were adjusted to just meet the level of the potential
alternative standard indicated.
6
7
Table 6-8. Estimated Number (and Percentage) of Persons with a Daily Maximum 8-Hour
Exposure At or Above the Specified Level - Adults With Coronary Heart
Disease (CHD) in the Los Angeles Area - Air Quality Just Meeting Potential
Alternative Standards
Daily
Maximum
8-hour
Exposure
(ppm)
0
3
6
9
12
15
20
30
40
60
2na highest 8-hour
average of 5.4 ppm
Persons
Number
383,040
214,149
17,060
903
301
0
0
0
0
0
Percent
100
55.9
4.4
0.2
<0.1
0
0
0
0
0
99tn pet Daily 8-hour
Max of 5.0 ppm
Persons
Number
383,040
230,807
20,672
1,204
301
100
Percent
100
60.3
5.4
0.3
<0.1
<0.1
0
0
0
0
99tn pet Daily 1-hour
Max of 8.0 ppm
Persons
Number
383,040
264,425
28,801
2,007
301
100
Percent
100
69.0
7.5
0.5
<0.1
<0.1
0
0
0
0
Ambient concentrations from 1997 were adjusted to just meet the level of the potential
alternative standard indicated.
10
February 2010
6-9
Draft - Do Not Quote or Cite
-------
1 6.2 ESTIMATED COHB DOSE LEVELS
2 Consistent with section 6.2, this section summarizes the estimated COHb levels for the
3 simulated CHD population in a series of tables, separated by the air quality scenarios and study
4 areas considered. In addition to reporting the number and percentage of persons and person-days
5 associated with the dose metric of interest (daily maximum end-of-hour COHb level), staff
6 provides the person-days per person at or above the selected COHb levels. This dose metric can
7 be calculated in two manners. The first, termed on average, is the number of person-days at a
8 given level divided by the total number of CHD persons in each model simulation. Therefore,
9 this metric gives an estimate of, on average, the number of days an individual in the entire CHD
10 population might experience a daily maximum end-of-hour COHb concentration at or above the
11 selected COHb level. The second, termed at level, is calculated by dividing number of person-
12 days estimated for a given level by the number of persons estimated for the same COHb level.
13 Therefore, this 2nd metric will provide an estimate of, for persons that experience a selected
14 COHb level, the average number of days in the year they may experience that selected level.
15 This second metric (at level) will always be larger than the first (on average) because it only
16 includes the persons experiencing a selected COHb dose level.3
17 6.2.1 Air Quality "As Is"
18 Table 6-9 provides the COHb levels (%) for the simulated COHb population in Denver,
19 when considering the as is air quality. No persons were estimated to have a daily maximum end-
20 of-hour COHb level at or above 2.0%, while only a few (<0.1%) were estimated to have a COHb
21 dose level >1.8%. Over 99% of the CHD population had a daily maximum end-of-hour COHb
22 level below 1.5%. Most of the person-days were associated with daily maximum end-of-hour
23 COHb levels below 1.0%. It follows that the majority of the person-days per person on average
24 were also limited to COHb levels at or below 1.0%. When individuals did have an estimated
25 daily maximum end-of-hour COHb level at or above 1.5%, it occurred on multiple days (i.e.,
26 between 8 and 14 (Table 6-9), depending on the level).
27 Similarly in Los Angeles, very few persons (98.5%) had an estimated daily maximum
28 end-of-hour COHb level at or above 1.5% when considering the as is air quality (Table 6-10).
29 There were, however, a few persons (0.1%) estimated to have daily maximum end-of-hour
30 COHb levels at or above 2.0% in this study area. Of these 301 simulated individuals, all were
31 estimated to have only one person-day per person at that level. The majority of the person-days
32 and person-days per person on average were limited to COHb levels at or below 1.0%.
3 This averaging of person-days would underestimate the number of person-days any one simulated
individual might experience above a given benchmark in a year. For example, 10 exceedances occurring in one
individual would give the same average as 1 exceedance occurring in 10 individuals.
February 2010 6-10 Draft - Do Not Quote or Cite
-------
1 Table 6-9. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
2 Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
3 With Coronary Heart Disease (CHD) in the Denver Study Area - Air Quality
4 As Is.
COHb
Level
(%)
0.0
1.0
1.5
1.8
2.0
2.5
3.0
4.0
Persons
Number
53,656
7,873
333
12
0
0
0
0
Percent
100
14.7
0.6
<0.1
0
0
0
0
Person-days
Number
19,580,000
293,000
4,652
99
0
0
0
0
Percent
100
1.5
<0.1
<0.1
0
0
0
0
Person-days/person
On Average
365
5.5
<0.1
<0.1
0
0
0
0
At Level
365
37.2
14.0
8.2
0
0
0
0
Unadjusted ambient concentrations from four monitors in 2006 were used to represent the As
Is air quality scenario.
6
7
10
11
12
13
14
15
16
17
Table 6-10. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Los Angeles Study Area - Air
Quality As Is.
COHb
Level
(%)
0.0
1.0
1.5
1.8
2.0
2.5
3.0
4.0
Persons
Number
383,040
98,043
5,820
1,505
301
0
0
0
Percent
100
25.6
1.5
0.4
0.1
0
0
0
Person-days
Number
139,800,000
1,645,000
86,800
8,630
301
0
0
0
Percent
100
1.2
0.1
<0.1
<0.1
0
0
0
Person-days/person
On
Average
365
4.3
0.2
<0.1
<0.1
0
0
0
At Level
365
16.8
14.9
5.7
1.0
0
0
0
Unadjusted ambient concentrations from ten monitors in 2006 were used to represent the As
Is air quality scenario.
6.2.2 Air Quality Adjusted to Just Meet the Current 8-hour Standard
Consistent with the estimated exposure concentrations, dose levels experienced by the
CHD population in each study area were greater when considering simulated exposures
associated with air quality adjusted to just meet the current standard than when using as is air
quality. For example, in Denver, just over 3% of the CHD population was estimated to have a
daily maximum end-of-hour COHb level at or above 2.0% (Table 6-11). There were no persons
in Denver estimated above this COHb level based on estimated ambient exposures associated
with as is air quality. A similar pattern is observed for the CHD population in Los Angeles
February 2010
6-11
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
10
11
12
13
14
(Table 6-12), though a lower percentage of persons (0.5%) was estimated to have daily
maximum end-of-hour COHb levels at or above 2.0% when compared results for Denver. In
both study areas, a few persons had daily maximum end-of-hour COHb levels extending
upwards to 3.0%. Most of the persons that did experience these higher COHb levels (>2.0%)
however, experienced them for fewer than 2 days in a year (Table 6-12).
Table 6-11. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Denver Study Area - Air Quality
Just Meeting the Current 8-hour Standard.
COHb
Level
(%)
0.0
1.0
1.5
2.0
2.5
3.0
4.0
Persons
Number
53,656
40,921
10,267
1,814
346
86
0
Percent
100
76.3
19.1
3.4
0.6
0.2
0
Person-days
Number
19,580,000
829,300
35,520
2,480
370
86
0
Percent
100
4.2
0.2
<0.1
<0.1
<0.1
0
Person-days/person
On
Average
365
15.5
0.7
<0.1
<0.1
<0.1
0
At Level
365
20.3
3.5
1.4
1.1
1.0
0
Ambient concentrations from 1995 were adjusted to just meet a 2na highest 8-hour
average concentration of 9.4 ppm.
Table 6-12. Estimated Number (and Percentage) of Persons and Person-Days with a Daily
Maximum End-of-hour COHb Level At or Above the Specified Level - Adults
With Coronary Heart Disease (CHD) in the Los Angeles Study Area - Air
Quality Just Meeting the Current 8-hour Standard.
COHb
Level
(%)
0.0
1.0
1.5
2.0
2.5
3.0
4.0
Persons
Number
383,040
155,243
17,561
2,007
301
100
0
Percent
100
40.5
4.6
0.5
0.1
<0.1
0
Person-days
Number
139,800,000
2,472,000
120,100
3,111
401
100
0
Percent
100
1.8
0.1
<0.1
<0.1
<0.1
0
Person-days/person
On
Average
365
6.4
0.3
<0.1
<0.1
<0.1
0
At Level
365
15.9
6.8
1.6
1.3
1.0
0
Ambient concentrations from 1997 were adjusted to just meet a 2na highest 8-hour
average concentration of 9.4 ppm.
15
February 2010
6-12
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
6.2.3 Air Quality Adjusted to Just Meet Alternative Air Quality Scenarios
Consistent with results described for the exposure, the percentage of persons estimated to
experience maximum end-of-hour COHb at selected levels are general similar across the three
potential alternative standard scenarios. For example, in Denver most of the population (>99%)
were estimated to not experience a daily maximum end-of-hour COHb level above 2.0% (Table
6-13). There are a few study area differences worthy of note. As expected, the corresponding
estimated percent of the CHD population in Denver is greater than that estimated for Los
Angeles, even when considering the same potential alternative standard form and air quality
level (Table 6-14). For example, when considering a 99th percentile daily maximum 8-hour
average CO concentration of 5.0 ppm, 0.8% of the CHD population had an estimated daily
maximum end-of-hour COHb level at or above 2.0%; in Los Angeles this dose level was
estimated for only 0.1% of the CHD population.
Table 6-13. Estimated Number (and Percentage) of Persons with a Daily Maximum End-
of-hour COHb Level At or Above the Specified Level - Adults With Coronary
Heart Disease (CHD) in the Denver Study Area - Air Quality Just Meeting
Potential Alternative Standards.
COHb Level
(%)
0.0
1.0
1.5
1.8
2.0
2.5
3.0
4.0
2na highest 8-hour
average of 5.4 ppm
Persons
Number
53,656
19,560
2,061
444
197
62
0
0
Percent
100
36.5
3.8
0.8
0.4
0.1
0
0
99tn pet Daily Max 8-
hour of 5.0 ppm
Persons
Number
53,656
26,692
3,826
1,012
407
86
12
0
Percent
100
49.7
7.1
1.9
0.8
0.2
<0.1
0
99tn pet Daily Max 1-
hour of 8.0 ppm
Persons
Number
53,656
21,040
2,271
568
234
62
0
0
Percent
100
39.2
4.2
1.1
0.4
0.1
0
0
Ambient concentrations from 1995 were adjusted to just meet the level of the potential
alternative standard indicated.
17
February 2010
6-13
Draft - Do Not Quote or Cite
-------
1 Table 6-14. Estimated Number (and Percentage) of Persons with a Daily Maximum End-
2 of-hour COHb Level At or Above the Specified Level - Adults With Coronary
3 Heart Disease (CHD) in the Los Angeles Study Area - Air Quality Just
4 Meeting Potential Alternative Standards.
COHb Level
(%)
0.0
1.0
1.5
1.8
2.0
2.5
3.0
4.0
2na highest 8-hour
average of 5.4 ppm
Persons
Number
383,040
53,086
2,408
602
301
0
0
0
Percent
100
13.9
0.6
0.2
0.1
0
0
0
99tn pet Daily Max 8-
hour of 5.0 ppm
Persons
Number
383,040
60,913
3,312
803
301
0
0
0
Percent
100
15.9
0.9
0.2
0.1
0
0
0
99tn pet Daily Max 1-hour
of 8.0 ppm
Persons
Number
383,040
77,772
5,319
1,204
401
0
0
0
Percent
100.0
20.3
1.4
0.3
0.1
0
0
0
Ambient concentrations from 1997 were adjusted to just meet the level of the potential
alternative standard indicated.
6 6.3 COMPARISON OF COHB ESTIMATES OBTAINED FROM THE 2000
7 PNEM/CO AND DRAFT 2010 APEX/CO ASSESSMENTS
8 As described above in chapters 2 and 4, population exposure and dose were estimated in
9 2000 using pNEM/CO, a predecessor to APEX, for adults with ischemic heart disease residing in
10 a defined study area within the same two urban areas (Johnson et al., 2000). As described in
11 section 1.2 above, IHD is also called CHD and with regard to characterizing the population of
12 interest with regard to demographics (age and sex), the 2000 assessment, like the current
13 assessment, drew from estimates of the prevalence provided by the NHIS (which includes CHD
14 or IHD, angina pectoris and heart attack) and corresponding estimates of undiagnosed ischemia
15 developed by EPA. As part of this current (2010) second draft REA, staff has used APEX to
16 estimate CO exposures and resulting COHb levels using a largely similar approach, modeling
17 domains, years of ambient concentration data,4 and defined at-risk population. There are some
18 differences that exist when comparing the specific methodologies:
19 • number of ambient monitors used (e.g., previously 6 in Denver versus 4 used here),
20 • location of ambient monitors used (e.g., only 7 of the same monitors used previously
21 were used here for Los Angeles),
22 • number of microenvironments modeled (previously 15 versus the 8 modeled here)
' When considering the exposure scenario that uses air quality just meeting the current standard.
February 2010
6-14
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1 • use of mass balance modeling (previously all 12 enclosed MEs used mass balance, here
2 only indoor MEs use mass balance)
3 • cohort approach (pNEM) versus individual approach (APEX) , and
4 • two indoor sources of CO included in the 2000 pNEM/CO assessment for residential
5 microenvironments: gas stoves and passive smoking.
6 Despite these differences and others not listed, staff still did not expect to see greatly
7 different results when comparing the two assessments given the similarities in the likely
8 influential variables (i.e., ambient concentrations, microenvironmental approach, CFK module
9 used, etc.). Table 6-15 presents estimates for the percentage of Denver adults with CHD
10 estimated to experience a daily maximum end-of-hour COHb level at or above the specified level
11 under the specified air quality conditions for 1995. Table 6-16 presents similar estimates for Los
12 Angeles using the adjusted 1997 ambient air quality to just meet the current 8-hour standard.
13 Each table provides two sets of estimates for the 2000 pNEM/CO assessment (indoor sources
14 "on" and "off) and one set generated for the current (2010) second draft APEX/CO REA.
15 As expected, the COHb levels estimated by the 2000 pNEM/CO assessment are higher
16 when internal sources are turned on, though in the absence of indoor sources, the range of dose
17 estimates are generally similar in both study areas. However at selected COHb levels in Denver,
18 the current approach estimated a higher percent of the CHD population than when compared
19 with the previous Johnson et al. (2000) assessment. For example, approximately 3.4% of the
20 CHD population was estimated to have a daily maximum end-of-hour COHb level at or above
21 2.0% in this current assessment. The corresponding value estimated in the Johnson et al. (2000)
22 assessment was approximately 0.5% of the IHD population.
February 2010 6-15 Draft - Do Not Quote or Cite
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1
2
Table 6-15. Percentage of Denver Adults with Coronary Heart Disease (CHD) Estimated to
Experience a Daily Maximum End-of-hour COHb Level At or Above the
Specified Percentage - Air Quality Just Meeting the Current Standard.
4
5
COHb
Level
(%)
6.0
5.0
4.0
3.0
2.5
2.0
1.5
1.0
0
Percentage of CHD Adults at or Above COHb Level
Johnson et al. (2000) pNEM/COa
Internal sources on
0.2
0.6
1.6
5.5
10.4
19.9
37.6
83.2
100
Internal sources off
0
0
0
<0.1
0.2
0.5
6.7
65.0
100
201 02nd draft REA
APEX/COb
Internal sources off
0
0
0
0.2
0.6
3.4
19.1
76.3
100
3 Used Denver 1995 CO ambient concentrations with no adjustment (2nd highest 8-hour
CO concentration was 9.5 ppm, close in value to the design value of 9.4 ppm).
b Denver 1995 ambient CO concentrations adjusted to just meet the current 8-hour
standard (9.4 ppm).
6 Table 6-16. Percentage of Los Angeles Adults with Coronary Heart Disease (CHD)
7 Estimated to Experience a Daily Maximum End-of-hour COHb Level At or
8 Above the Specified Percentage - Air Quality Just Meeting the Current
9 Standard.
COHb
Level
(%)
6.0
5.0
4.0
3.0
2.5
2.0
1.5
1.0
0
Percentage of CHD Adults at or Above COHb Level
Johnson et al. (2000) pNEM/COa
Internal sources on
0.2
0.8
2.2
5.1
9.0
16.8
32.3
79.0
100
Internal sources off
0
0
0
<0.1
<0.1
0.5
5.2
58.1
100
201 02nd draft REA
APEX/COb
Internal sources off
0
0
0
<0.1
0.1
0.5
4.6
40.5
100
3 Los Angeles 1997 ambient CO concentrations adjusted to just meet the current 8-
hour standard (9.4 ppm).
February 2010
6-16
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1 6.4 EVALUATION OF ENDOGENOUS CO CONTRIBUTION TO COHB
2 LEVELS IN APEX SIMULATED INDIVIDUALS
3 As summarized in section 4.4.7 and described fully in Appendix B, staff estimated COHb
4 levels in each simulated individual using the CFK dose module within APEX. Theoretically, in
5 the absence of ambient concentrations or other sources of CO, one can perform an APEX
6 simulation to estimate endogenous CO production and its effect on COHb levels. Staff has
7 performed such a simulation using APEX for both study locations. The results of these
8 simulations, conducted in a similar manner as the above exposure scenarios (i.e., a 50,000 person
9 simulation and the random assignment of the CHD population from this simulation) only in the
10 absence of environmental CO concentrations, are provided in section 6.3.1. A second set of
11 simulations was performed to better characterize the endogenous CO production rate and its
12 impact on estimated COHb levels. This second set of simulations considered both the estimated
13 COHb levels associated with and without ambient exposures for an identical, yet smaller, set of
14 individuals. The purpose of this focused analysis was to determine the contribution of the
15 endogenous CO and ambient exposure to total COHb levels. Because the generation of these
16 data required hourly concentration output and multiple model runs, the sample size was restricted
17 to less than 100 simulated persons to maintain a manageable data file. In addition, this
18 subpopulation was a random sample from the entire adult population residing within the Denver
19 study area. Details of this second set of simulations are provided in section 6.4.2.
20 6.4.1 Estimation of Endogenous CO Contribution to Population COHb Levels
21 As mentioned above, these two additional simulations were conducted in each the Denver
22 and Los Angeles study areas. Fifty thousand persons were simulated, as was done when
23 considering the five exposure scenarios. The only difference between these APEX simulations
24 and those for the five air quality scenarios was the ambient concentration input files used. In
25 these two model runs, all ambient CO concentrations equaled zero. The same output described
26 above for the five air quality scenarios were generated, only in this instance, the distribution of
27 population exposures effectively equals zero. We evaluated smaller bins of the maximum end-
28 of-hour COHb level below 1.0% in these simulations (i.e., 0.25%) given that even when using as
29 is air quality, most persons had COHb levels at or below 1.0%. We report the daily maximum
30 end-of-hour COHb levels for the simulated CHD population and, given this, the results can be
31 considered as a single view of population-level endogenous CO production.5 We caution against
32 assuming that there is a direct correlation between the endogenous CO production estimated here
33 and the total COHb levels estimated for each study area in the prior sections. That is, the daily
34 maximum end-of hour COHb level estimated to result from endogenous CO production
5 Theoretically the APEX model can estimate all hourly values as was done in section 6.4.2.
February 2010 6-17 Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
exclusively may not necessarily be correlated with the daily maximum end-of hour COHb level
for the sum of endogenous CO production and ambient CO exposures. Nevertheless, it still
provides some information regarding the potential effect of the endogenous CO production on
estimated COHb levels in the simulated population.
Table 6-17 summarizes the daily maximum end-of-hour COHb levels estimated for the
CHD population in Denver and Los Angeles, with no ambient CO exposure contribution. In
general, the population distributions for the two areas are very similar, with the majority of the
population (about 99%) having a daily maximum end-of-hour COHb level of less than 1.0%.
The mean and median COHb level would approximately fall between 0.25 and 0.50% in both
populations, with the percent population estimated to experience maximum end-of-hour COHb
levels at or above 1.0% greater in the Denver study area (1.5%) than in the LA area (0.8%).
These estimated distributions do not appear outside of what might be expected given some of the
available data reported in the extant literature. However, as a reminder, these output data are for
the maximum value that occurred in an entire year simulation. The distribution for the average
end-of-hour COHb level associated with endogenous CO production would surely be less than
that indicated here and likely more comparable to any available measurement data. Staff notes
that these distributions represent a population sample and is unique in its own right; even if a
mean estimate could be constructed it may not be comparable to measurements performed on a
smaller (and possibly not random) population of limited study subjects.
Table 6-17. Estimated Number (and Percentage) of Persons with a Daily Maximum End-
of-hour COHb Level At or Above the Specified Level - Adults With Coronary
Heart Disease (CHD) in the Denver and Los Angeles Study Areas - Zero
Ambient Exposures.
COHb
Level
(%)
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.80
2.00
Denver
Persons
53,656
47,362
13,537
3,209
790
140
12
0
0
Percent
100
88.3
25.2
6.0
1.5
0.3
<0.1
0
0
Los Angeles
Persons
383,040
327,546
85,098
17,260
3,211
803
301
100
0
Percent
100
85.5
22.2
4.5
0.8
0.2
<0.1
<0.1
0
24
February 2010
6-18
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1 6.4.2 Contribution of Endogenous CO Production and Ambient Exposures to
2 COHb Level in Limited Simulations
3 Two APEX model simulations were performed for this second evaluation: one using
4 1995 Denver ambient concentrations adjusted to just meet the current standard (9.4. ppm) and
5 the second using ambient concentrations equal to zero. Each of the new runs simulated 8,760
6 hours of exposure for each of 92 persons (n = 92 people x 8,760 hrs/person = 805,920 person-
7 hours per run). By design, the simulated persons in each of these two model runs line up
8 perfectly in terms of physiology and activities performed, enabling staff to compare the COHb
9 levels across the two runs hour by hour. We first calculated all 805,920 hour-by-hour ambient
10 contributions (COHb ambient contribution) in corresponding COHb levels (i.e, COHb ambient
11 contribution = % COHb with ambient exposure minus % COHb for zero exposure), effectively
12 giving the ambient contribution to estimated COHb levels. Table 6-18 provides the descriptive
13 statistics for the COHb ambient contribution experienced by the selected population. Below are
14 listed selected statistics for COHb ambient contribution based on the entire hourly data set (N =
15 805,920). Figure 6-1 provides the same information only in graphical form; note that most of the
16 ambient contribution to COHb levels values fall between 0.1 and 0.4% COHb and can be well
17 represented by a lognormal distribution (GM 0.205, GSD 1.57}. The complete distribution of
18 the endogenous contribution to COHb levels is also provided in Table 6-18. Most of the
19 simulated hours for the population had an endogenous end-of-hour COHb level contribution of
20 less than 0.5%, though for a limited number of hours, the endogenous contribution could be over
21 1.0% COHb. On average, this limited population was estimated to have just over half of their
22 hourly total COHb level attributed to endogenous COHb production.6
23 Table 6-18. Descriptive statistics for the % COHb ambient contribution estimated using
24 Denver 1995 ambient concentrations adjusted to just meet the current
25 standard.
Statistic
(N = 805,920)
Arithmetic Mean
Arithmetic SD
Geometric Mean
Geometric SD*
Minimum
10th percentile
20th percentile
COHb Ambient
Contribution
(% COHb)
0.2264
0.109
0.2046
1 .5682
0.003
0.1155
0.1416
COHb
Endogenous
(% COHb)
0.255
0.1476
0.2206
1.7085
0.0329
0.1106
0.1373
COHb Total
(% COHb)
0.4814
0.1757
0.4529
1.4151
0.0988
0.2912
0.3378
6 One should not go beyond comparing the means or 50th percentiles of the ambient and endogenous
contribution as the other percentiles of the distribution are likely not correlated.
February 2010
6-19
Draft - Do Not Quote or Cite
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Statistic
(N = 805,920)
30th percentile
40th percentile
50th percentile
60th percentile
70th percentile
80th percentile
90th percentile
95th percentile
98th percentile
99th percentile
99.5th percentile
99.8th percentile
99.9th percentile
Maximum
COHb Ambient
Contribution
(% COHb)
0.1628
0.1833
0.2046
0.2284
0.2573
0.2965
0.3615
0.4268
0.5192
0.5949
0.6759
0.8006
0.9086
1.9158
COHb
Endogenous
(% COHb)
0.1631
0.1882
0.2177
0.2544
0.299
0.3526
0.4404
0.5343
0.6405
0.778
0.9006
1.07
1.2
1.541
COHb Total
(% COHb)
0.3766
0.413
0.4504
0.4915
0.5407
0.6063
0.7098
0.8034
0.9332
1.062
1.188
1.319
1.394
2.323
Notes:
*dimensionless
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0.0 0
1 0
2 0
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3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0
COHb (percent)
2 Difference in hourly COHb (Denver 1995 rollback to 9.4 - Denver 1995 zero)
3 Figure 6-1. Histogram of the % COHb ambient contribution estimated using Denver
4 1995 ambient CO concentrations adjusted to just meet the current standard.
5 Two other metrics were calculated for each individual using the same hourly output for
6 the limited simulation run. First, staff calculated the 1-hour COHb ambient contribution
February 2010
6-20
Draft - Do Not Quote or Cite
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1
2
O
4
5
6
7
8
9
10
11
12
13
14
15
associated with the maximum total 1-hour COHb (i.e., endogenous and ambient contribution)
and second, the 1-hour total COHb associated with the maximum COHb ambient contribution.
The purpose of these metrics was to determine the relative contribution the endogenous CO
production and ambient exposure have on the maximum hourly COHb level.
Figure 6-2 illustrates the contribution of endogenous CO production relative to that of
each person's maximum end-of-hour COHb level. As can be seen in the figure, there is not a
strong relationship between the two variables, that is, the endogenous CO production does little
to influence a persons maximum end-of-hour COHb level, given this air quality scenario (i.e.,
just meeting the current standard in Denver). Consistent with the population distribution
described above (section 6.4.1), there are very few individuals that were estimated to have
maximum end-of-hour COHb levels at or above 1.0% in the absence of ambient CO exposure.
As described earlier, it was expected that there would not be a relationship between the
contribution of endogenous CO production to COHb level and the ambient exposure contribution
to COHb. This expectation is confirmed by the results presented in Figure 6-3.
ja 16
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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2
Maximum End-of Hour (Total) COHb Level (% COHb)
2.4
16 Figure 6-2. The contribution of endogenous CO production relative to an individual's
17 maximum end-of-hour COHb level using 1995 Denver ambient
18 concentrations adjusted to just meet the current standard.
February 2010
6-21
Draft - Do Not Quote or Cite
-------
o
o 2T
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Maximum Ambient End-of-Hour COHb Contribution (% COHb)
2 Figure 6-3. Comparison of endogenous CO production relative to an individual's
3 maximum COHb ambient contribution using 1995 Denver ambient
4 concentrations adjusted to just meet the current standard.
February 2010
6-22
Draft - Do Not Quote or Cite
-------
1 Alternatively, one can see the strong relationship between the maximum end-of-hour
2 COHb level and the contribution from ambient exposure (Figure 6-4). For the majority of the 92
3 simulated persons, the maximum end-of-hour total COHb level (i.e., the combined contribution
4 from both endogenous CO production and ambient CO exposure) is largely driven by the
5 contribution from ambient CO exposure.
6
7
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
o on
I 2.0
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Figure 6-4.
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
Maximum Ambient End-of-Hour COHb Contribution (% COHb)
Comparison of endogenous CO production relative to an individual's
maximum COHb ambient contribution using 1995 Denver ambient
concentrations adjusted to just meet the current standard.
6.5 KEY OBSERVATIONS
Presented below are key observations resulting from the exposure and dose assessment
for ambient CO.
• Ambient CO exposures and resulting COHb levels in the blood of exposed individuals
were estimated for populations in two study areas in the Los Angeles and Denver areas
under five air quality scenarios: as is air quality, air quality adjusted to simulate just
meeting the current 8-hour CO NAAQS, and air quality adjusted to just meet three
potential alternative standards.
• More than 98% of the simulated at-risk population in each study area was estimated to
experience a daily maximum end-of-hour COFtb level below 1.5% over the course of a
year considering as is air quality in either study area, with more than 99.9% of the
selected at-risk population in both areas having daily maximums COHb levels below
2%.
• The distribution of maximum end-of-hour COHb levels extended slightly higher for the
Los Angeles population when using as is air quality, while the Denver population was
February 2010
6-23
Draft - Do Not Quote or Cite
-------
1 estimated to experience higher levels under conditions of air quality adjusted to just
2 meet the current standard.
3 • More than 95% of the simulated at-risk population in the Los Angeles study area was
4 estimated to experience an annual daily maximum end-of-hour COHB level below
5 1.5%, and 99.5% with maximum COHb less than 2%, with air quality adjusted to just
6 meet the current standard. In contrast, 80.1% of the simulated at-risk population in the
7 Denver study area was estimated to experience a daily maximum end-of-hour COHB
8 level below 1.5%, and greater than 95% with maximum COHb less than 2%, in air
9 quality conditions adjusted to just meet the current standard.
10 • Alternative standards that we considered in this document included two potential
11 alternative forms for the 8-hour standard and one potential alternative form for thel-
12 hour standard. Beyond the results for alternative forms presented in this document,
13 results for alternative standard levels for any given combination of averaging time and
14 form are presented and discussed in the draft Policy Assessment.
15 • The three potential alternative standards considered in this document resulted in
16 generally similar percentages of individuals exposed at selected concentrations and
17 maximum end-of hour COHb levels. Each of these potential alternative standards
18 generated fewer persons and a lower percent of the CHD population at or above
19 selected COHb levels (e.g., <1% at a 2.0% COHb level in Denver) when compared
20 with corresponding COHb levels associated with air quality adjusted to just meet the
21 current 8-hour standard (e.g., 3.4% at a 2.0% COHb level in Denver). When
22 considering the potential alternative standards in Los Angeles, only 0.1% of the CHD
23 population was estimated to experience a maximum end-of-hour COHb level at or
24 above 2.0 % COHb, compared to 0.5% at that same COHb level associated with air
25 quality adjusted to just meet the current standard.
26 • A few simulations with a small number of individuals in which no external CO sources
27 were included provided limited information regarding the contribution of endogenous
28 COHb to the total COHb estimates produced. This quite limited information indicates
29 that most simulated individuals have endogenous COHb levels below 1% and that the
30 upper end of total COHb (reflecting ambient plus endogenous contributions) may arise
31 as a result of an individual receiving high external CO exposures rather than having
32 higher endogenous levels.
33 • Results generated in the current assessment for the air quality conditions just meeting
34 the current NAAQS were compared with estimates from the assessment conducted in
35 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study
36 areas (section 6.3). While the two assessments employed similar approaches and
37 similar, although not identical, air quality data for this scenario, they used different
38 versions of the exposure model (APEX vs. pNEM). Results were quite similar for the
39 1.5% and 2% COHb level in Los Angeles study area and somewhat different in the
40 Denver study area.
41
February 2010 6-24 Draft - Do Not Quote or Cite
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1 6.6 REFERENCES
2 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and
3 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO
4 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No.
5 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available
6 at: http://www.epa.gov/ttn/fera/human_related.html.. June 2000.
7 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S, Frazier C. (1998).
8 Measuring Concentrations of Selected Air Pollutants Inside California Vehicles. Final Report. Prepared
9 by Research Triangle Institute under Contract No. 95-339. California Air Resources Board. Sacramento,
10 California.
11 Shikiya D, Liu C, Kahn M, Juarros J, Barcikowski W. (1989). In-Vehicle Air Toxics Characterization Study in the
12 South Coast Air Basin. Office of Planning and Rules, South Coast Air Quality Management District. May.
13
February 2010 6-25 Draft - Do Not Quote or Cite
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1 7 VARIABILITY ANALYSIS AND UNCERTAINTY
2 CHARACTERIZATION
3 An important issue associated with any population exposure or risk assessment is the
4 characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in
5 a population or variable of interest (e.g., residential air exchange rates). The degree of variability
6 cannot be reduced through further research, only better characterized with additional
7 measurement. Uncertainty refers to the lack of knowledge regarding the values of model input
8 variables (i.e., parameter uncertainty), the physical systems or relationships used (i.e., use of
9 input variables to estimate exposure or risk or model uncertainty), and in specifying the scenario
10 that is consistent with purpose of the assessment (i.e., scenario uncertainty). Uncertainty is,
11 ideally, reduced to the maximum extent possible through improved measurement of key
12 parameters and iterative model refinement. The approaches used to assess variability and to
13 characterize uncertainty in this REA are discussed in the following two sections. Each section
14 also contains a concise summary of the identified components contributing to uncertainty and
15 how each source may affect the estimated exposures.
16 7.1 ANALYSIS OF VARIABILITY
17 The purpose for addressing variability in this REA is to ensure that the estimates of
18 exposure and risk reflect the variability of ambient CO concentrations and associated CO
19 exposure and health risk across the study locations and population. In this second draft CO
20 REA, there are several algorithms that account for variability of input data when generating the
21 number of estimated benchmark exceedances or health risk outputs. For example, variability
22 may arise from differences in the population residing within census tracts (e.g., age distribution)
23 and the activities that may affect CO population exposure and dose (e.g., time spent inside
24 vehicles, moderate or greater exertion outdoors). A complete range of potential exposure levels
25 and associated risk estimates can be generated when appropriately addressing variability in
26 exposure and risk assessments; note however that the range of values obtained would be within
27 the constraints of the input parameters, algorithms, or modeling system used, not necessarily the
28 complete range of the true exposure or risk values.
29 Where possible, staff identified and incorporated the observed variability in input data
30 sets and estimated parameters within the exposure and dose assessment performed rather than
31 employing standard default assumptions and/or using point estimates to describe model inputs.
32 The details regarding any variability distributions used in data inputs are described in chapter 5.
33 To the extent possible given the data available for the assessment, staff accounted for variability
34 within the exposure and dose modeling. APEX has been designed to account for variability in
35 some of the input data, including the physiological variables that are important inputs to
February, 2010 7-1 Draft - Do Not Cite or Quote
-------
1 determining ventilation rates and COHb dose levels. As a result, APEX addresses much of the
2 variability in factors that affect human exposure and dose. The variability accounted for in this
3 analysis is summarized in Table 7-1.
4 Table 7-1. Summary of How Variability Was Incorporated Into the Second Draft CO
5 REA.
Component
Simulated
Individuals
Ambient Input
Microenvironmental
Approach
Variability Source
Population data
Commuting data
Activity patterns
Longitudinal profiles
Coronary heart
disease (CHD)
prevalence
Measured ambient CO
concentrations
Meteorological data
Microenvironments
Proximity factors
Mass balance model
Comment
Individuals are randomly sampled from US census tracts
used in model domains, by age (single years) and
gender (US Census Bureau, 2007).
Individuals are probabilistically assigned ambient
concentrations originating from either their home or work
tract based on US Census derived commuter data (US
Census Bureau, 2007).
Data diaries are randomly selected from CHAD master
(35,000 diaries) using six diary pools stratified by two
day-types (weekday, weekend) and three temperature
ranges (< 55.0 °F, between 55.0 and 83.9°F, and >84.0
F). The CHAD diaries capture real locations persons
visit and activities performed, ranging from 1 minute to 1-
hour in duration (US EPA, 2002).
A sequence of diaries is linked together for each
individual that preserves both the inter- and intra-
personal variability in human activities (Glen et al.,
2008).
CHD prevalence is stratified by four age groups (18-44,
45-64, 65-74, and 75+) and both genders (CDC, 2009)
Temporal: 1 -hour CO for an entire year predicted using
ambient monitoring data.
Spatial: Four monitors were used to represent ambient
conditions in Denver; ten monitors used in Los Angeles;
each monitor was assigned a 10 km zone of influence.
Spatial: Local surface NWS stations used.
Temporal: 1-hour NWS temperature data for each year.
Eight total microenvironments were represented,
including those expected to be associated with high
exposure concentrations (i.e., in-vehicle and near-road).
This results in differential exposure estimates for each
individual (and event) when spending time within each.
In the current APEX approach, microenvironmental
concentrations were estimated using proximity factors to
adjust the outdoor CO concentrations. All proximity
factors were represented by lognormal distributions
whose values are randomly selected for every individual
exposure event.
For the indoor microenvironments, using a mass balance
model accounts for CO concentrations occurring during a
previous hour (and of ambient origin) to calculate current
indoor CO concentrations.
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Component
Physiological
Factors Relevant to
Ventilation Rate and
Estimation of COHb
Levels
Variability Source
Air exchange rates
Resting metabolic
rate
Metabolic equivalents
by activity (METS)
Oxygen uptake per
unit of energy
expended
Weight (body mass)
Height
Blood volume
Hemoglobin content of
the blood
Pulmonary CO
diffusion rate
Endogenous CO
production rate
Comment
Several lognormal distributions are sampled based on
five daily mean temperature ranges, two regions, and
location specific A/C prevalence rates.
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).
Values randomly sampled from distributions developed
for specific activities (some age-specific) (McCurdy,
2000; US EPA, 2002).
Values randomly sampled from a uniform distribution
(Johnson etal., 2000).
Randomly selected from population-weighted lognormal
distribution with geometric mean (GM) and geometric
standard deviation (GSD) distribution specific to age and
gender derived from data from the National Health and
Nutrition Examination Survey (NHANES), for the years
1999-2004 (Isaacs and Smith (2005) in Appendix A).
Values randomly sampled from distribution based on
equations developed for each gender developed from
analyses (Johnson, 1998) of height and weight data
(Brainard and Burmaster, 1992) (see Appendix B for
details).
Values determined according to gender using equations
based on work by Allen et al. (1956) (see Appendix B for
details).
Values randomly selected from distributions developed
by gender and age categories based on NHANES study
(see Isaacs and Smith (2005) in Appendix A).
Values selected according to gender, height, and age
based on equations adapted from Salorinne (1976) (see
Appendix B for details).
Values randomly selected from lognormal distributions
according to equations specific to age, gender, and
menstrual phase (data obtained from eight independent
studies; see Appendix B for details).
1 7.2 CHARACTERIZATION OF UNCERTAINTY
2 While it may be possible to capture a range of exposure or risk values by accounting for
3 variability inherent to influential factors, the true exposure or risk for any given individual is
4 largely unknown. To characterize health risks, exposure and risk assessors commonly use an
5 iterative process of gathering data, developing models, and estimating exposures and risks, given
6 the goals of the assessment, scale of the assessment performed, and limitations of the input data
7 available. However, significant uncertainty often remains and emphasis is then placed on
8 characterizing the nature of that uncertainty and its impact on exposure and risk estimates.
9 Staff has used such an iterative process in characterizing the uncertainty associated with
10 the approach and data used in the 1st draft REA. Following a review of that 1st draft REA by
11 CAS AC, a few sources of uncertainty were identified as most important in improving the
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1 approach used to estimate exposure and dose for the second draft CO REA. This included 1)
2 expanding the number of monitors used to better address spatial variability in ambient CO
3 concentrations, 2) increasing the number of microenvironments modeled from two to eight, 3)
4 using distributions of proximity factors to estimate all microenvironmental concentrations rather
5 than simple point estimates, and 4) additional analysis of historical trends in ambient CO
6 concentrations at individual monitors. These additional analyses and approaches used are not
7 without their own uncertainties, and following this iterative process, also need to be
8 characterized.
9 The characterization of uncertainty can include either qualitative or quantitative
10 evaluations, or a combination of both. The approach can also be tiered, that is, the analysis can
11 begin with a simple qualitative uncertainty characterization then progress to a complex
12 probabilistic uncertainty analysis. This second level of analysis may be appropriate when a
13 lower tier analysis indicates there is a high degree of uncertainty for certain identified sources,
14 the sources of uncertainty are highly influential variables in estimating the exposure and risk, and
15 sufficient information and other resources are available to conduct a quantitative uncertainty
16 assessment. This is not to suggest that quantitative uncertainty analyses should always be
17 performed in all exposure and risk assessments. The decision regarding the type of uncertainty
18 characterization performed is also informed by the intended scope and purpose of the
19 assessment, whether the selected analysis will provide additional information to the overall
20 decision regarding health protection, whether sufficient data are available to conduct a complex
21 quantitative analysis, and whether time and resources are available for higher tier
22 characterizations (US EPA, 2004; WHO, 2008).
23 The primary purpose of the uncertainty characterization approach selected in this second
24 draft CO REA is to identify and compare the relative impact that important sources of
25 uncertainty may have on the estimated potential health effect endpoints. The approach used to
26 evaluate uncertainty was adapted from guidelines outlining how to conduct a qualitative
27 uncertainty characterization (WHO, 2008) and applied in the most recent NO2 (US EPA, 2008)
28 and SO2 NAAQS reviews (US EPA, 2009). While it may be considered ideal to follow a tiered
29 approach in the REA to quantitatively characterize all identified uncertainties, staff selected the
30 mainly qualitative approach given the extremely limited data available to inform probabilistic
31 analyses.
32 The qualitative approach used in this REA varies from that of WHO (2008) in that a
33 greater focus was placed on evaluating the direction and the magnitude1 of the uncertainty; that
34 is, qualitatively rating how the source of uncertainty, in the presence of alternative information,
1 This is synonymous with the "level of uncertainty" discussed in WHO (2008), section 5.1.2.2.
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1 may affect the estimated exposures and health risk results. In addition and consistent with the
2 WHO (2008) guidance, staff discuss the uncertainty in the knowledge-base (e.g., the accuracy of
3 the data used, acknowledgement of data gaps) and decisions made where possible (e.g., selection
4 of particular model forms), though qualitative ratings were assigned only to uncertainty
5 regarding the knowledge-base.
6 First, staff identified the key aspects of the assessment approach that may contribute to
7 uncertainty in the exposure and risk estimates and provide the rationale for their inclusion. Then,
8 staff characterized the magnitude and direction of the influence on the assessment results for
9 each of these identified sources of uncertainty. Consistent with the WHO (2008) guidance, staff
10 subjectively scaled the overall impact of the uncertainty by considering the degree of severity of
11 the uncertainty as implied by the relationship between the source of the uncertainty and the
12 exposure concentrations and COHb dose levels.
13 Where the magnitude of uncertainty was rated low, it was judged that large changes
14 within the source of uncertainty would have only a small effect on the exposure results. For
15 example, a statistical procedure was used to substitute missing ambient concentrations in each
16 ambient data set. Staff compared the air quality distributions and found negligible differences
17 between the substituted data set and the one with missing values (e.g., Tables 5-7 through 5-10).
18 There is still uncertainly in the approach used, since there are a variety of methods available to
19 use. However, staff judged that the quantitative comparison of the data sets indicates that there
20 would likely be little influence on exposure estimates by the data substitution procedure.
21 A magnitude designation of medium implies that a change within the source of
22 uncertainty would likely have a moderate (or proportional) effect on the results. For example,
23 the magnitude of uncertainty associated with using the historical data to represent a hypothetical
24 future scenario was rated as low-medium. While we do not have information regarding how the
25 ambient CO concentration distribution might look in the future, we do know however what the
26 distribution might look like based on historical trends and the primary emission sources. If these
27 trends in observed concentrations and emissions remain consistent in the future, then the
28 magnitude of the impact to estimated exposures in this assessment would be judged as likely low
29 or having negligible impact on the exposure and dose estimates. However, if there are new
30 emission sources, the magnitude of influence might be greater. When adjusting air quality in
31 each location to simulate the various exposure scenarios, staff observed mainly proportional
32 differences (e.g., a factor of two or three) in the estimated exposure and dose levels. Assuming
33 that these types of ambient concentration adjustments could reflect the addition of a new source
34 in each area carries its own uncertainties, however based on this information, staff also judged
35 the magnitude of influence in using the historical air quality data to represent a hypothetical
36 future scenario as high as medium. A characterization of high implies that a small change in the
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1 source would have a large effect on results. This rating would be used where model was
2 extremely sensitive to the identified source of uncertainty.
3 Staff also included the direction of influence, indicating how the source of uncertainty
4 was judged to affect estimated exposures or risk estimates; either the estimated values were
5 likely over- or under-estimated. In the instance where the component of uncertainty can affect
6 the assessment endpoint in either direction, the influence was judged as both. Staff characterized
7 the direction of influence as unknown when there was no evidence available to judge the
8 directional nature of uncertainty associated with the particular source. Staff also subjectively
9 scaled the knowledge-base uncertainty associated with each identified source using a three level
10 scale: low indicated significant confidence in the data used and its applicability to the assessment
11 endpoints, medium implied that there were some limitations regarding consistency and
12 completeness of the data used or scientific evidence presented, and high indicated the extent of
13 the knowledge-base was extremely limited.
14 The output of the uncertainty characterization was a summary describing, for each
15 identified source of uncertainty, the magnitude of the impact and the direction of influence the
16 uncertainty may have on the exposure and risk characterization results. There are several
17 sources of uncertainty associated with this simplified approach for modeling CO population
18 exposure/dose and associated potential health risk, each summarized and discussed in Table 7-2.
19 As mentioned in section 1 above, given the significant time constraints of this review,
20 results of the assessment are provided in this document without substantial interpretation.
21 Rather, interpretative discussion of these results, including further consideration of public health
22 implications, is provided in the draft Policy Assessment.
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1 Table 7-2. Characterization of Key Uncertainties in the Second Draft CO REA for Denver and Los Angeles Areas.
Sources of Uncertainty
Category
Element
Influence of
Uncertainty on
Exposure/Dose
Estimates
Direction
Magnitude
Knowledge-
Base
Uncertainty
Comments
Database Quality
Over
Low
Low
INF: There may be a limited number of poor quality high concentration data within the analytical data sets, potentially
influencing the number of benchmark dose level exceedances. Note also that the uncertainty regarding low
concentration data near the monitor detection limit is unlikely to influence the number of benchmark exceedances.
KB: EPA's Air Quality System data used in the analyses are of high quality. There is no other source of monitoring
data as comprehensive. Data are being used in a manner consistent with one of the defined objectives of ambient
monitoring.
Ambient CO
Monitor
Concentrations
Spatial and
Temporal
Representation
Both
Low- Medium
Medium
INF: Use of several ambient monitors better represents spatial-temporal variability in ambient CO levels throughout
each study area when compared with the simplified approach used in the 1st draft CO REA. Analysis of the monitoring
concentrations indicates there is spatial variability in monitoring concentrations across each area, but that it is relatively
limited, particularly for more recent ambient concentrations (low magnitude). In comparing results generated using the
simplified approach used in the 1st draft CO REA however, selection of the particular monitor(s) used may have a
medium magnitude of influence on estimated exposures.
KB: Each ambient monitor has specific objectives and monitoring scale that may not appropriately capture the true
spatial and temporal variability in CO concentrations. In the absence of 1) a monitoring network designed to measure
spatial variability in CO concentrations, 2) performing air quality modeling to estimate fine scale spatial and temporal
variability in CO concentrations and, 3) analysis of additional monitoring data that can potentially indicate spatial
concentration gradients, staff judge the uncertainty in the knowledge-base as medium.
Missing Data
Substitution
Under
Low
Low
INF: Assuming there is an equal probability of missing low and high concentration hourly values, and that substituted
data are limited by the bounds of the algorithm (i.e., as defined by limits in the measurement data), there may be a few
missing high concentration data that could lead to underestimation in exposure concentrations and doses. This
assumes that the substitution of low-level concentration data with potentially higher concentrations (within the bounds
of the algorithm) does not affect exposure results.
KB: All available measurement data are quality assured. Very few data values were substituted with respect to the
number of measured values available in each location.
Adjustment of Air
Quality to Simulate
Just Meeting the
Current and
Potential Alternative
Standards
Historical Data Used
Unknown
Low - Medium
Medium
INF & KB: Even though the historical data represent a real air quality condition that may be similar to concentrations
levels expected to just meet the 8-hour current and potential alternative standards, the condition simulated is
hypothetical. Based on observed trends in air quality over time and the results generated using adjusted ambient
concentrations, staff judges that, at most, the magnitude of influence would be a medium level. However, there is
uncertainty in how influential factors such as emission levels per vehicle, vehicular traffic, and meteorology compare
between an earlier period of time and the hypothetical scenario of just meeting the current standard some time in the
future. It is possible that these historical patterns can serve as a reasonable basis for predicting future air quality
scenarios, though these patterns would not account for the influence of a new CO emission source(s). Therefore, staff
judges the knowledge base uncertainty as medium.
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Sources of Uncertainty
Category
Element
Influence of
Uncertainty on
Exposure/Dose
Estimates
Direction Magnitude
Knowledge-
Base
Uncertainty
Comments3
Proportional
Approach Used
Both
Low
Low
INF: The magnitude of the adjustment applied to historical ambient concentration data was wide ranging. For example,
in Denver, to just meet the current standard 0.99 was the adjustment applied. In comparison, to just meet a 2nd highest
8-hour average CO concentration in Los Angeles, a greater adjustment was needed (0.36). However, in comparing
recent and historical ambient CO concentrations for several ambient monitors in Los Angles (Figure 3-4), a strong
proportional relationship is present when comparing the recent and historic CO concentrations.
KB: A similar proportional approach was judged adequate in simulating air quality conditions just meeting the 8-hour
CO NAAQS in prior assessments (US EPA, 1992; Johnson et al., 2000). In addition, little difference was observed
here when comparing exposure results using the1997 LA data adjusted downwards to level similar to that observed
with the as is air quality with exposure results using the 2006 air quality.
APEX Inputs and
Algorithms
Population Database
Both
Low
Low
INF & KB: Population data are from the US Census Bureau, a reliable and quality assured source. Data used are
specifically for census tracts modeled in Denver and Los Angeles. Staff assumed any remaining uncertainties in the
database 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 performing specific activities in particular
locations. While not specific to a particular area, the activity patterns of a population are generally well represented by
the mainly population-based and nationally-representative survey data (e.g., see Table E-1 in Appendix E regarding the
patterns of typical commuting in CHAD versus the urban locations modeled in this assessment). CHAD is comprised of
data from individuals that may or may not have had an identified health condition and are assumed to represent the
activities of persons with normal health status as well as those with certain health conditions that may not affect general
activity patterns. A statistical analysis was performed on a subset of the CHAD data where persons were specifically
asked whether they had angina (see Appendix F of CO REA and Johnson et al., 2000). Activity patterns for persons
with angina were compared to those individuals not having angina using various exertion level metrics and time spent
outdoors or inside-vehicles. The percentages of time spent outdoors or in a vehicle were generally not statistically
significantly different between angina and non-angina subjects While there were statistically significant differences in
the exertion rate between angina and non-angina subjects, it was likely a function of the large sample size for the non-
angina subjects since actual differences were generally numerically small compared to the mean values. The
differences in activity and exertion between angina and non-angina subjects, although statistically significant, were
judged not large enough to severely impact the validity of APEX (or pNEM/CO) modeling results that do not adjust for
an angina/non-angina difference.
KB: Data are from a reliable and quality assured source (CHAD) and are from surveys of real persons. Features of an
individual's activity pattern are well represented, adjustments are made to represent the population distribution in a
specific area (using age and gender), and temperature is used to link CHAD diaries with the simulated individuals
residing in a specific area. However, there are several assumptions made that contribute to uncertainty in its use. For
example, activity patterns of persons surveyed over 30 years ago are assumed to represent a current persons activity
patterns.
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Sources of Uncertainty
Category
Element
Influence of
Uncertainty on
Exposure/Dose
Estimates
Direction
Magnitude
Knowledge-
Base
Uncertainty
Comments3
Longitudinal Profile
Algorithm
Both
Low - Medium
Medium
INF: This assessment focused on persons having at least one exposure or dose above a selected level (low
magnitude), however when considering multi-day exposures, the magnitude of potential influence is judged as medium.
KB: In developing the longitudinal method, the evaluation indicated that both the £>and A statistics are reasonably
reproduced for the population. In addition, the approach was compared to two other independent methods used for
constructing longitudinal activity patterns (see Appendix B, Attachment 5 of US EPA, 2009). Note however, long-term
diary profiles (i.e., monthly, annual) do not exist for a population.
Meteorological Data
Both
Low
Low
INF & KB: Data are from the National Weather Service, a well-known and quality-assured source. Daily maximum
temperatures are used when selecting appropriate diaries to simulate individuals. The bin ranges used are wide such
that erroneous temperature data would likely have limited impact to exposure results. Daily mean temperatures are
used when selecting air exchange rates. Given the overlap of the AER distributions and the wide temperature ranges
used to categorize them, there is likely limited impact by erroneous temperature data.
Microenvironmental
Algorithm and Input
Data
Unknown
Medium
Medium
INF & KB: In this second draft CO REA, the number of microenvironments selected captures the likely locations
persons spend time and where CO exposures would occur. Using distributions of proximity factors derived from
measurement data in Denver and applied to estimate microenvironmental concentrations is reasonable. However, how
these data derived from a Denver study reflect similar relationships in Los Angeles has greater uncertainty.
Additionally, the Denver measurement data were collected in the 1980's, therefore there is also uncertainty as to how
these data might reflect relationships observed for other years modeled in this assessment (i.e., 2006). However, for
most of the distributions, in particular those used to estimate high exposure microenvironments, there are other
comparable measurement data and relationships available (albeit limited in number) to generally support the
distributions applied in this assessment.
Commuting
Algorithm
Both
Low
Low
INF & KB: In this second draft REA, the commuting algorithm within APEX was implemented. Use of this algorithm
better represents individual exposures across each modeling domain. The data are derived from the US Census, a
well-known and quality-assured source.
CHD Prevalence
Both
Low
Medium
INF & KB: Data are from the Centers for Disease Control, a well-known and quality-assured source. Though
prevalence data are not specific for each region, the national prevalence data were stratified by selected age-groups
and gender. Staff used gender-specific ratios and applied them to all age groups uniformly even though there may be
uncertainty in the accuracy of the prevalence estimates for specific age and gender groups. In addition, potentially
undiagnosed individuals with CHD were included to expand the total CHD population considered. This was based on
several assumptions including using 1990 estimates of the population with undiagnosed IHD.
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Sources of Uncertainty
Category
Element
Influence of
Uncertainty on
Exposure/Dose
Estimates
Direction Magnitude
Knowledge-
Base
Uncertainty
Comments3
Physiological
Factors
Unknown
Low-Medium
Medium
INF & KB: Many of the parameters used to estimate the physiological attributes of the CHD population were developed
from healthy individuals; there were no adjustments made to account for a particular health condition. While the ISA
notes some variability in some parameters in individuals with specific health conditions that might affect CO uptake and
elimination, most conditions may not necessarily be associated with the simulated at-risk population, i.e., CHD
individuals. In addition, some of the parameters used to estimate COHb levels are based on older publications (some
dating back to the mid 20th century). It is possible that most of the relationships still remain appropriate in modeling the
current population however, in the absence of conducting a comprehensive review and comparing the historical data to
recent measurements, staff judges the knowledge-base uncertainty as medium.
Potential Health
Effect Benchmark
Levels
Simulated At-Risk
Population
Unknown
Low
Medium
INF & KB: Data from a well-conducted multi-center controlled human exposure study demonstrate cardiovascular
effects in subjects with moderate to severe coronary artery disease at study mean COHb levels as low as 2.0-2.4%
which were increased from a baseline mean of 0.6-0.7%.as a result of short (~1 hour) experimentally controlled
increases in CO exposures (study mean of 117 ppm CO). No laboratory study has been specifically designed to
evaluate the effect of experimentally increased exposure to CO resulting in an increase in COHb levels to a study
mean below 2.0%. However, based on analysis of individual study subject responses at baseline and at the two
increased COHb levels, study authors concluded that each increase in COHb produced further changes in the study
response metric, without evidence of a measurable threshold effect. There is no established no adverse effect level
and, thus there is greater uncertainty about the lowest benchmark level identified (i.e., 1.5%) and uncertainty about
whether individuals with the most severe CHD are adequately represented. Additionally the COHb levels estimated in
this assessment result from CO exposure concentrations much lower than the experimental exposure concentrations
used to increase study subject COHb levels to the study targets (e.g., 2.0%) and with which the responses were
associated. Given that the evidence supporting the choice of benchmark levels is based on controlled human
exposure data, staff judged the influence of this uncertainty on the risk characterization as being low.
Notes:
aINF refers to comments associated with the influence rating; KB refers to comments associated with the knowledge-base rating.
bThis entry focuses on the uncertainty associated with the benchmark levels in their application to estimated COHb levels for the simulated at-risk population (i.e., individuals with diagnosed/undiagnosed CHD, inclusive
of angina pectoris and heart attacks). With regard to other potentially susceptible populations (as described in section 2.4 above), we additionally note the lack of studies that describe COHb levels and health effects that
might be expected as a result of short-term elevations in CO exposure in those populations.
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1 7.3 KEY OBSERVATIONS
Based on an overall qualitative judgment of the identified sources of uncertainty in the
assessment approach, selections made regarding input data, and algorithms used, and their
characterization as to direction and magnitude of influence on exposures and doses, staff
consider the exposure and dose estimates reasonable for the simulated population the assessment
is intended to represent (i.e., the CHD population residing within the urban core of each study
area). This is because:
2 • Only two sources of uncertainty were associated with a potential directional influence:
3 data base quality (overestimation) and missing data substitution (underestimation), and
4 both were judged to have a low magnitude of influence on estimated exposures and
5 doses.
6 • Twelve of the identified sources of uncertainty were judged by staff to have either
7 bidirectional influence (eight sources) or unknown (four sources) direction:
8 - One source of uncertainty (i.e., microenvironmental algorithm and data inputs)
9 was judged as having a potentially medium magnitude of influence on exposure
10 and dose estimates.
11 - Five of the remaining eleven sources (i.e., spatial and temporal representation,
12 historical data used, activity pattern database, longitudinal profile algorithm,
13 physiological factors) were judged as having low to medium magnitude of
14 influence, the level of which varied based on whether an identified condition
15 existed.
16 - Six of the sources were judged to have a low magnitude of influence on estimated
17 exposures and doses (i.e., proportional approach used, population database,
18 meteorological data, commuting data, CHD prevalence, and benchmark levels for
19 the simulated at-risk population).
There was a wide-ranging level of uncertainty in the knowledge-base for the identified
sources:
20 • Eight sources were judged by staff as having medium knowledge-base uncertainty
21 including: spatial and temporal representation, historical data used, activity pattern
22 database, longitudinal profile algorithm, microenvironmental algorithm and input data,
23 CHD prevalence, physiological factors, and the benchmark levels for the simulated at-
24 risk population.
25 • The knowledge-base uncertainty was judged as low for four of the identified sources
26 having either unknown or bidirectional influence. This included the proportional
27 approach used, population database, meteorological data, commuting data.
28 • The knowledge-base uncertainty was also judged as low for the two sources identified
29 above as being associated with either under- or overestimating exposures.
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1 The ratings of the knowledge-base uncertainty can indicate the need for additional data or
2 analyses to better characterize the uncertainty. When combined with the potential magnitude of
3 influence associated with each identified source, a prioritization can be given to the higher rated
4 influential sources. Based on the results of this uncertainty characterization, staff judges that six
5 sources (i.e., the spatial and temporal representation, historical data used, activity pattern
6 database, longitudinal profile algorithm, microenvironmental algorithm and input data, and
7 physiological factors) remain as the most important uncertainties in this assessment.
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1 7.4 REFERENCES
2
3 Allen TH, Peng MT, Chen KP, Huang TF, Chang C, Fang HS. (1956). Prediction of blood volume and adiposity in
4 man from body weight and cube of height. Metabolism. 5:328-345.
5 Brainard J and Burmaster D. (1992). Bivariate distributions for height and weight of men and women in the United
6 States. RiskAnalysis. 12(2):267-275.
7 CDC. (2009). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2007. Series 10,
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9 Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal diary assembly for
10 human exposure modeling. JExpos Sci Environ Epidem. 18:299-311.
11 Issacs K and Smith L. (2005). New Values for Physiological Parameters for the Exposure Model Input File
12 Physiology.txt. Technical memorandum to Tom McCurdy, NERL WA10. December 20, 2005. Provided
13 in Appendix A of the second draft CO REA.
14 Johnson T. (1998). Memo No. 5: Equations for Converting Weight to Height Proposed for the 1998 Version of
15 pNEM/CO. Memorandum Submitted to U.S. Environmental Protection Agency. TRJ Environmental, Inc.,
16 713 Shadylawn Road, Chapel Hill, North Carolina 27514.
17 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J. (2000). Estimation of Carbon Monoxide Exposures and
18 Associated Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO
19 (Version 2.1). Report prepared by ICF Consulting and TRJ Environmental, Inc., under EPA Contract No.
20 68-D6-0064. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Available
21 at: http://www.epa.gov/ttn/fera/human_related.html.. June 2000.
22 McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy expenditure approach.
23 J Expos Anal Environ Epidemiol. 10:1-12.
24 SAIC. 2001. Technical Peer Review of "Estimation of Carbon Monoxide Exposures and Associated
25 Carboxyhemoglobin Levels for Residents of Denver and Los Angeles Using pNEM/CO (version 2.1)"
26 Prepared by Science Applications International Corporation under EPA Contract No. 68-D-98-113.
27 Available at: http://www.epa.gov/ttn/fera/human related.html.
28 SalorinneY. (1976). Single-breath pulmonary diffusing capacity. ScandJResp Diseases. Supplemental 96.
29 US Census Bureau. (2007). Employment Status: 2000- Supplemental Tables. Available at:
30 http://www.census.gov/population/www/cen2000/phc-t28.html.
31 US EPA (1992). Review of the National Ambient Air Quality Standards for Carbon Monoxide: Assessment of
32 Scientific and Technical Information. Office of Air Quality Planning and Standards Staff Paper, report no.
33 EPA/452/R-92-004.
34 US EPA. (2002). EPA's Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/.
35 US EPA. (2004). An Examination of EPA Risk Assessment Principles and Practices. Staff Paper prepared by the
36 US EPA Risk Assessment Task Force. EPA/ 100/B-04/001. Available at:
37 http://www.epa.gov/OSA/pdfs/ratf-final.pdf.
February 20 JO 1-13 Draft - Do Not Quote or Cite
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1 US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NC>2 Primary National Ambient Air
2 Quality Standard. EPA-452/R-08-008a. November 2008. Available at:
3 http://www.epa.gov/ttn/naaqs/standards/nox/data/2008112 l_NO2_REA_final.pdf.
4 US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SC>2 Primary National Ambient Air
5 Quality Standard. EPA-452/R-09-007. August 2009. Available
6 athttp://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf.
7 WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on characterizing and
8 communicating uncertainty in exposure assessment. Available at:
9 http://www.who.int/ipcs/methods/harmonization/areas/exposure/en/.
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1 8 SUMMARY OF KEY OBSERVATIONS
2 Presented together below are the key observations made in each of the chapters.
3
4 Introduction
5 • This draft document describes the quantitative human exposure assessment and risk
6 characterization being conducted to inform the U.S. Environmental Protection
7 Agency's (EPA's) current review of the National Ambient Air Quality Standards
8 (NAAQS) for carbon monoxide (CO). The previous review of the CO NAAQS was
9 concluded in 1994 with confirmation of the current standards. An assessment of
10 ambient CO exposure/dose was developed in an earlier phase of this review in the late
11 1990s. The design of this 2nd draft REA builds upon recommendations from CAS AC,
12 information presented in the final ISA, as well as comments made by the public.
13
14 Conceptual Overview: Assessing Ambient Carbon Monoxide Exposure and Risk
15 • Carbon monoxide in ambient air is formed primarily by the incomplete combustion of
16 carbon-containing fuels and photochemical reactions in the atmosphere, with on-road
17 mobile sources representing significant sources of CO to ambient air.
18 • Microenvironments influenced by on-road mobile sources are important contributors to
19 ambient CO exposures, particularly in urban areas.
20 • The formation of COHb is a key step in the elicitation of various health effects by CO.
21 Further, COHb level is commonly used in exposure assessment and is considered the
22 best biomarker for CO health effects of concern.
23 • Individuals with CHD are the population with greatest susceptibility to short-term
24 exposure to CO, and the population for which the current evidence indicates health
25 effects occurring at the lowest exposures. The evidence further indicates a potential for
26 other underlying cardiovascular conditions to contribute susceptibility to CO effects.
27 Other populations potentially at risk include individuals with diseases such as chronic
28 obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in
29 prenatal or elderly life stages.
30 • Cardiovascular effects are the health endpoint for which the evidence is strongest and
31 indicative of a likely causal relationship with CO exposures. Other endpoints for
32 which the evidence is suggestive of such a relationship include effects on the central
33 nervous system, reproduction and prenatal development, and the respiratory system.
34 • Risk is characterized in this REA through evaluation of COHb estimated to result from
35 ambient CO exposure in individuals with CHD (including undiagnosed persons)
36 considering potential health effect benchmarks for daily maximum COHb levels.
37 Results are reported in terms of percent of population expected to experience daily
38 maximum COHb levels at or above a series of levels that range as low as 1%. These
39 results are considered in the Policy Assessment document in light of potential health
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1 effects benchmarks ranging from 1.5%, which is below the lowest study mean COHb
2 level resulting from experimental CO exposure in controlled human exposures of
3 subjects with CAD, up to 3.0%, a level associated with adverse effects in those studies.
4
5 Air Quality Considerations
6 • Mobile sources (i.e., gasoline powered vehicles) are the primary contributor to CO
7 emissions, particularly in urban areas due to greater vehicle and roadway densities.
8 • Recent (2005-2007) ambient CO concentrations across the US are lower than those
9 reported in the previous CO NAAQS review and are also well below the current CO
10 NAAQS levels. Further, a large proportion of the reported concentrations are below
11 the conventional instrument lower detectable limit of 1 ppm.
12 • The currently available information for CO monitors indicates that siting of microscale
13 and middle scale monitors in the current network is primarily limited to roads where
14 traffic density described for them is moderate (<100,000 AADT), however, factors
15 other than reported AADT (e.g., orientation with regard to dense urban roadway
16 networks) can contribute to sites reporting higher CO concentrations.
17 • Ambient CO concentrations are highest at monitors sited closest to roadways (i.e.,
18 microscale and middle scale monitors) and exhibit a diurnal variation linked to the
19 typical commute times of day, with peaks generally observed during early morning and
20 late afternoon during weekdays.
21 • Policy relevant background (PRB) concentrations across the US are generally less than
22 0.2 ppm, far below that of interest in this REA with regard to ambient CO exposures.
23 • Historical trends in ambient monitoring data indicate that at individual sites, ambient
24 concentrations have generally decreased in a proportional manner. This comparison
25 included air quality distributions with concentrations at or above the current standard
26 and those reflecting current (as is) conditions.
27 • The temporal variability in selected upper percentile ambient concentrations (e.g., 99th
28 percentile 1-hour daily maximum) at individual monitors is relatively small across a
29 three year monitoring period, particularly when considering recent air quality.
30
31 Overview Of Approach Used for Estimating Co Exposure and COHb Dose Levels
32 • APEX, an EPA human exposure and dose model, has along history of use in
33 estimating exposure and dose for many of the criteria pollutants including CO, Os,
34 SO2, and NO2. Over time, staff have improved and developed new model algorithms,
35 incorporated newer available input data and parameter distributions, as well as
36 performed several model evaluations, sensitivity analyses, and uncertainty
37 characterizations for the above pollutants. Based on this analysis, APEX was judged to
38 be an appropriate model to use for assessing CO exposure and dose.
39
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1 Application of APEX4.3 in this Assessment
2 • Two exposure model domains (Denver and Los Angeles study areas) were defined by
3 overlaying ambient monitor locations having 10 km radii with US census tract
4 population data. Monitors selected comprised the bulk of the urban core in each
5 location, where ambient monitoring data exist.
6 • The selected at-risk population was simulated by combining the tract-specific age and
7 gender population distribution and the CHD prevalence, also stratified by age and
8 gender. In using this approach, staff can represent the variability that exists in the
9 CHD population that resides in each census tract and within each study area.
10 • Staff expanded the selected at-risk population to include an estimate of persons with
11 undiagnosed CHD.
12 • Compared with the single-monitor approach used for the first draft CO REA, staff
13 expanded the number of ambient monitors used in this second draft CO REA to better
14 capture the spatial variability in ambient concentrations. In Denver, a total of four
15 monitors were used, in Los Angeles, the total number of monitors was ten.
16 • Compared with the two microenvironments modeled in the first draft CO REA, staff
17 has expanded the number modeled in each location to eight. This approach is designed
18 to better represent the expected variability in microenvironmental CO concentrations.
19 • Compared with the approach used to estimate microenvironmental concentrations in
20 the first draft CO REA (factors approach only), all indoor microenvironments were
21 modeled using a mass balance model in this second draft assessment. Use of the mass
22 balance model will better represent temporal variability in indoor CO concentrations
23 with respect to the outdoor CO concentration variability. In addition, distributions of
24 microenvironmental factors were used in this second draft CO REA for all
25 microenvironments rather than using point estimates (as was done for the first draft CO
26 REA). Using distributions of microenvironmental factors will better represent both
27 spatial and temporal variability in estimated microenvironmental CO concentrations
28
29 Simulated Exposure and COHb Dose Results
30 • Ambient CO exposures and resulting COHb levels in the blood of exposed individuals
31 were estimated for populations in two study areas in the Los Angeles and Denver areas
32 under five air quality scenarios: as is air quality, air quality adjusted to simulate just
33 meeting the current 8-hour CO NAAQS, and air quality adjusted to just meet three
34 potential alternative standards.
35 • More than 98% of the simulated at-risk population in each study area was estimated to
36 experience a daily maximum end-of-hour COHb level below 1.5% over the course of a
37 year considering as is air quality in either study area, with more than 99.9% of the
38 selected at-risk population in both areas having daily maximums COHb levels below
39 2%.
40 • The distribution of maximum end-of-hour COHb levels extended slightly higher for the
41 Los Angeles population when using as is air quality, while the Denver population was
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1 estimated to experience higher levels under conditions of air quality adjusted to just
2 meet the current standard.
3 • More than 95% of the simulated at-risk population in the Los Angeles study area was
4 estimated to experience an annual daily maximum end-of-hour COHB level below
5 1.5%, and 99.5% with maximum COHb less than 2%, with air quality adjusted to just
6 meet the current standard. In contrast, 80.1% of the simulated at-risk population in the
7 Denver study area was estimated to experience a daily maximum end-of-hour COHB
8 level below 1.5%, and greater than 95% with maximum COHb less than 2%, in air
9 quality conditions adjusted to just meet the current standard.
10 • Alternative standards that we considered in this document included two potential
11 alternatives for the 8-hour standard and one potential alternative 1-hour standard. The
12 results looking at alternative levels for any given combination of averaging time and
13 form are presented and discussed in the draft Policy Assessment.
14 • For the three potential alternative standard scenarios for which results are presented in
15 this document provided generally similar percent of individuals exposed at selected
16 concentration levels and maximum end-of hour COHb levels. Each of these potential
17 alternative standards generated fewer persons and a lower percent of the CHD
18 population at or above selected COHb levels (e.g., <1% at a 2.0% COHb level in
19 Denver) when compared with corresponding COHb levels and using air quality
20 adjusted to just meeting the current standard (e.g., 3.4% at a 2.0% COHb level in
21 Denver). When considering the potential alternative standard scenarios in Los
22 Angeles, only 0.1% of the CHD population was estimated to experience a maximum
23 end-of-hour COHb level of 2.0 % COHb compared to 0.5% at that same COHb level
24 and using air quality adjusted to just meet the current standard.
25 • A few simulations with a small number of individuals in which no external CO sources
26 were included provided limited information regarding the contribution of endogenous
27 COHb to the total COHb estimates produced. This quite limited information indicates
28 that most simulated individuals have endogenous COHb levels below 1% and that the
29 upper end of total COHb (reflecting ambient plus endogenous contributions) may arise
30 as a result of an individual receiving high external CO exposures rather than having
31 higher endogenous levels.
32 • Results generated in the current assessment for the air quality conditions just meeting
33 the current NAAQS were compared with estimates from the assessment conducted in
34 2000 (Johnson et al., 2000) for similar conditions in the Denver and Los Angeles study
35 areas (section 6.3). While the two assessments employed similar approaches and
36 similar, although not identical, air quality data for this scenario, they used different
37 versions of the exposure model (APEX vs. pNEM). Results were quite similar for the
38 1.5% and 2% COHb level in Los Angeles study area and somewhat different in the
39 Denver study area.
40
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1 Variability Analysis and Uncertainty Characterization
2 • Based on an overall qualitative judgment of the identified sources of uncertainty in the
3 assessment approach, selections made regarding input data, and algorithms used, and
4 their characterization as to direction and magnitude of influence on exposures and
5 doses, staff consider the exposure and dose estimates reasonable for the simulated
6 population the assessment is intended to represent (i.e., the CHD population residing
7 within the urban core of each study area). This is because:
8 • Only two sources of uncertainty were associated with a potential directional
9 influence: data base quality (overestimation) and missing data substitution
10 (underestimation), and both were judged to have a low magnitude of influence
11 on estimated exposures and doses.
12 • Twelve of the identified sources of uncertainty were judged by staff to have
13 either bidirectional influence (eight sources) or unknown (four sources)
14 direction:
15 - One source of uncertainty (i.e., microenvironmental algorithm and
16 data inputs) was judged as having a potentially medium magnitude
17 of influence on exposure and dose estimates.
18 - Five of the remaining eleven sources (i.e., spatial and temporal
19 representation, historical data used, activity pattern database,
20 longitudinal profile algorithm, physiological factors) were judged
21 as having low to medium magnitude of influence, the level of
22 which varied based on whether an identified condition existed.
23 - Six of the sources were judged to have a low magnitude of
24 influence on estimated exposures and doses (i.e., proportional
25 approach used, population database, meteorological data,
26 commuting data, CHD prevalence, and benchmark levels for the
27 simulated at-risk population).
28 • There was a wide-ranging level of uncertainty in the knowledge-base for the identified
29 sources:
30 • Eight sources were judged by staff as having medium knowledge-base
31 uncertainty including: spatial and temporal representation, historical data used,
32 activity pattern database, longitudinal profile algorithm, microenvironmental
33 algorithm and input data, CHD prevalence, physiological factors, and the
34 benchmark levels for the simulated at-risk population.
35 • The knowledge-base uncertainty was judged as low for four of the identified
36 sources having either unknown or bidirectional influence. This included the
37 proportional approach used, population database, meteorological data,
38 commuting data.
39 • The knowledge-base uncertainty was also judged as low for the two sources
40 identified above as being associated with either under- or overestimating
41 exposures.
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1 • The ratings of the knowledge-base uncertainty can indicate the need for additional data
2 or analyses to better characterize the uncertainly. When combined with the potential
3 magnitude of influence associated with each identified source, a prioritization can be
4 given to the higher rated influential sources. Based on the results of this uncertainty
5 characterization, staff judges that six sources (i.e., the spatial and temporal
6 representation, historical data used, activity pattern database, longitudinal profile
7 algorithm, microenvironmental algorithm and input data, and physiological factors)
8 remain as the most important uncertainties in this assessment.
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Appendix A
Technical Memorandum on Updates To APEX Physiology.Txt File
(Isaacs And Smith, 2005)
The following contains a technical memo provided by Isaacs and Smith (2005) in its
original format. Staff included page numbers and performed some minor formatting to
text and table headers for the purposes of inclusion into the draft CO REA appendices.
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TECHNICAL MEMORANDUM
TO: Tom McCurdy, WA-COR, NERL WA 10
FROM: Kristin Isaacs and Luther Smith, Alion Science and Technology
DATE: December 20, 2005
SUBJECT: New Values for Physiological Parameters for the Exposure Model Input
File Physiology.txt.
Table of Contents
List of Figures 3
1. Introduction 4
2. Evaluation of the Current Physiology File Data 4
2.1 Normalized Maximal Oxygen Uptake (nvo2max) 4
2.2 Body Mass 5
2.3 Resting Metabolic Rate 5
2.4 Hemoglobin Content and Blood Volume Factor 5
2.5 Summary of Findings 5
3. Derivation of New Distributions for Body Mass 6
3.1 The NHANES Body Mass Dataset 6
3.2 Calculation of the New Sampling Weights for the Combined NHANES Dataset.
6
3.3 Fitting the Body Mass Data 7
4. Derivation of New Distributions for Normalized Vo2max 13
4.1 The Nvo2max Data 13
4.2 Determining the NVo2max Distributions 17
5. Derivation of New Distributions for Hemoglobin Content (Hemoglobin Density) 25
6. Blood Volume as a Function of Height and Weight 28
References 29
Appendix A. SAS Code for Estimating the Body Mass Distributions 40
Appendix B. SAS Code for Estimating the Normalized Vo2Max Distributions 41
Appendix C. SAS Code for Estimating the Hemoglobin Content Data 42
Appendix D. TheNewPhysiology.txt file 43
Appendix E. All Derived Physiological Parameters 54
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LIST OF FIGURES
Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a
Function of Age, Derived from NHANES 1999-2004 Study Data 9
Figure 2. Geometric Standard Deviations for the Best-fit Lognormal Distributions for
Body Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data 10
Figure 3. Minimums (1st Percentile) for Body Mass as a Function of Age, Derived from
NHANES 1999-2004 Study Data 11
Figure 4. Maximums (99th Percentile) for Body Mass as a Function of Age, Derived
from NHANES 1999-2004 Study Data 12
Figure 5. Individual Nvo2max Measurements for Males and Females, Derived from
Literature Studies and Experimental Measurements 14
Figure 6. Grouped Mean Nvo2max Measurements for Males and Females, Derived from
Literature Studies 15
Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature
Studies 16
Figure 8. Combined Nvo2max Group Means for Males and Females 19
Figure 9. Combined Nvo2max Group Standard Deviations 20
Figure 10. Nvo2max Normal Distribution Fits: Raw Fit Means and Smoothed Fits 21
Figure 11. Nvo2max Normal Distribution Fits: Raw Fit Standard Deviations and
Smoothed Fits 22
Figure 12. Nvo2max Minimums. 1st Percentile of the Best-fit Normal Distribution 23
Figure 13. Nvo2max Maximums. 99th Percentile of the Best-fit Normal Distribution... 24
Figure 14. Mean Values of Hemoglobin Content as Derived from the 1999-2002
NHANES Dataset, with Comparison to Current Physiology.txt Values 26
Figure 15. Values of Hemoglobin Content Standard Deviation as Derived from the 1999-
2002 NHANES Dataset, with Comparison to Current Physiology.txt Values 27
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1. INTRODUCTION
The purpose of this memo is to present an updated version of the physiological
parameters input file (Physiology.txt) for the APEX model. Portions of this file are also
used as input for SHEDS-PM and SHEDS-AirToxics.
The physiology file contains age- and gender-based information for several physiological
parameters used in human exposure modeling. This information includes distributional
shapes and parameters for all age and gender cohorts from age 0 to 100 years for
normalized maximal oxygen uptake (nvo2max), body mass, resting metabolic rate
(RMR), and blood hemoglobin content. In addition, a parameter called blood volume
factor (BVF), which is a cohort-dependent parameter in the equation for blood volume as
a function of body mass, is present in the file as well.
New age- and gender-dependent distributions were developed based the best available
physiological data from the literature. In this report, a summary of the current state of the
physiology file is presented, followed by the derivation of new physiological data for
body mass, normalized vo2max, and hemoglobin content. Portions of the SAS code used
for analysis are included (Appendices A-C), as is the new Physiology.txt file (Appendix
D). The final appendix (Appendix E) contains tables of all the derived physiological
parameters.
2. EVALUATION OF THE CURRENT PHYSIOLOGY FILE DATA
The physiology.txt file was originally generated for the PNEM model by T. Johnson. It
was last updated 6/11/1998, as documented in the report User's Guide: Software for
Estimating Ventilation (Respiration) Rates for Use in Dosimetry Models, (T. Johnson and
J. Capel). In that report, the original references for the data in the file were provided. An
evaluation of the data in the file was included in a previous memo to the WA-COR under
this work assignment. A summary of those findings is repeated here.
2.1 Normalized Maximal Oxygen Uptake (nvo2max).
The nvo2max data were derived from a number of sources. The data for males,
especially, were pieced together from a variety of studies (a total of 6), leading to
discontinuities in the distributional parameters. However, in each age and gender cohort,
the distributions parameters were derived from a single published study. Additionally,
much of the nvo2max data is quite old. The data for males at age 20 and at 28-69 came
from a study from 1960 [1]. Data for males aged 0-8 and 16-19, and females 0-19 came
from a figure in a textbook from 1977 [2], which in turn was based on limited earlier
data. An additional issue with the 1977 data is (according to the report mentioned above)
that values for certain ages (very young or elderly) were acquired by simple tangential
extrapolation of the data in the figure.
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In addition, in some cases it was not clear how the parameters were derived from the
referenced studies. For example, Heil et al. [3] was referenced as the source of the values
for females aged 66-100. However, an examination of that study provided no clues as to
how the values were actually determined. As far as can be determined, in no place did
the authors break down the means and SDs of their data into groups separated by both
gender and age simultaneously.
2.2 Body Mass.
The current body mass data were derived from an in-depth analysis [4, 5] of the second
CDC National Health and Nutrition Examination Survey (NHANES II) body mass data
[6]. The data were relatively comprehensive, and the methods used to generate the
lognormal distributions were sound. However, the NHANES II data were compiled for
the years 1976-1980, so an analysis of more recent data is necessary to accurately
account for changes in human activity patterns in adults and especially children.
2.3 Resting Metabolic Rate.
Not included for evaluation, per discussion with WA-COR.
2.4 Hemoglobin Content and Blood Volume Factor.
The original references for the hemoglobin content or blood volume factor values given
in the current physiology.txt file could not be identified. Therefore, their validity could
not be evaluated and it was desirable that new statistics be calculated.
2.5 Summary of Findings
• In some cases, especially for nvo2max, the data are unnecessarily and confusingly
disjointed across ages.
• It is also unclear how some of the nvo2max values were derived from the referenced
studies.
• With the exception of the Schofield equations for the BM/RMR regression, parameter
distributions at each age and gender cohort were derived from data from a single
study.
• Many of the studies used are very old (ex. 1960, 1977).
• Some the data is of questionable validity (for example, the extrapolation of a textbook
figure is used), although it may have been the best available at the time of the
compilation of the file.
• The original source of the hemoglobin content and blood volume factor data could
not be identified.
• Given these conclusions, we recommended a full review and update of the current
physiology.txt file data. Specifically, we recommended that where possible, new
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distributions or equations should be developed based on thorough, compiled data
from appropriate studies.
3. DERIVATION OF NEW DISTRIBUTIONS FOR BODY MASS
3.1 The NHANES Body Mass Dataset.
New body mass distributions were generated from data from the National Health and
Nutrition Examination Survey (NHANES). This survey is an ongoing study carried out
by the National Center for Health Statistics of the Centers for Disease Control. EPA
recognizes the utility of this dataset in characterizing the American population for risk
assessment and policy support purposes [7].
Older NHANES data (for the years 1976-1980) have been used previously to develop
population estimates of body mass distributions [4,5]. The current Physiology.txt file
body mass distributions are based on this work. However, the analysis presented here is
based on the most recent NHANES data, for the years 1999-2004 [8].
Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES
studies were downloaded from the NHANES website. The files were downloaded as
SAS xpt datasets. The downloaded files were as follows:
1999-2000 2001-2002 2003-2004
BMX.xpt BMX b r.xpt BMX c.xpt
Demo.xpt Demo_b.xpt Demo_c.xpt
The Demographic datasets contained the age and gender values for each survey
participant, while the Body Measurement datasets contained the body weights for each
subject. The combined dataset comprised 31,126 individuals. This resulted in
approximately 400-500 persons in each age 0-18 year cohort, and approximately 80-150
persons in each age 19-85 year cohort (the NHANES studies more heavily sampled
children).
3.2 Calculation of the New Sampling Weights for the Combined NHANES Dataset.
In the analysis of the NHANES data, sampling weights must be used to ensure that the
data are weighted to appropriately represent the national population. Sampling weights
for the combined NHANES body mass dataset were derived as recommended by the
documentation provided with the most recent NHANES release [9]. Specifically, the
sampling weight for each subject was calculated as:
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combined ,, 2003-2004
-
~7
J
--
combined ~ ~7 ^1999-2002
where wCOmbined is the sampling weight for the combined dataset, W2003-2004 is the weight
for the subjects in the most recent study, and Wi999_2oo2 is the weight for subjects in
combined 4-year (1999-2000 and 2001-2002) NHANES dataset. (Both weights are
provided with the appropriate NHANES release. The combined 1999-2002 weight,
which is not a simply half of that for the corresponding 2-year periods, was explicitly
calculated for researcher use by CDC since the two 2-year periods use different census
data.)
By using the sampling weights, once can consider any 2-year NHANES dataset or any
combination of datasets as a nationally representative sample.
3.3 Fitting the Body Mass Data.
In the current physiology file, body mass is modeled as a two-parameter lognormal
distribution. The NHANES body mass data were fit to several types of distributions
(including normal, beta, and three-parameter lognormal distributions). It was determined
that overall, the distribution that provided the best combination of good behavior over
ages and good fit to the data was a two-parameter lognormal distribution.
The data were fit to the lognormal distributions using the SAS PROC UNIVARIATE
procedure. The FREQ option of the procedure was used to apply the sampling weights.
The SAS code used to generate the body mass distributions is provided in Appendix A.
As the NHANES 1999-2003 studies only covered persons up to age 85, linear forecasts
were made for ages 86-100, as based on the data for ages 60 and greater.
3.4 Body Mass Results.
Geometric means and standard deviations (SD) for the best-fit lognormal distributions for
body mass are given in Figures 1 and 2. The means behaved fairly smoothly across ages.
Note that for children age 0-18, the values of the new fits are similar, but slightly higher
than those in the current Physiology.txt file, which were derived from earlier NHANES
studies. The new means also capture the trend towards decreasing body weight in older
persons that was previously neglected in the Physiology.txt file.
The maximum and minimum values for the distributions are presented in Figures 3 and 4.
The minimums and maximums were calculated as the 1st and 99th percentile of the raw
body mass data for the cohort. (Note that these values differ from the 1st and 99th
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percentiles of the fitted lognormals.) While the minimum value is consistent with the
current Physiology.txt (which was based on earlier NHANES studies), the new cohort
maximums are generally higher than before.
The behavior of several of the body mass parameters (especially the SD) is fairly noisy,
especially for adults. This is most likely due to the smaller number of samples for adults
as compared to children. Therefore, it may desirable to use age-grouped data or running
averages over years in these age ranges. While the attached prepared Physiology.txt file
uses the "raw" parameters, smoothed results using 5-year running averages are provided
in the attached data tables (Appendix E, plots not shown). These could be used at the
direction of EPA; changing the "official" release Physiology.txt file would be trivial.
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MALES: Body Mass Geometric Mean
an
100.000 -,
90.000 -
80.000 -
70.000 -
60.000 -
50.000 -
40.000 -
30.000 -
20.000 -
10.000
0.000
0
Current Physiology
File
20
40 60
Age (years)
80
100
FEMALES: Body Mass Geometric Mean
New Values
Current Physiology
File
20
40 60
Age (years)
80
100
Figure 1. Geometric Means for the Best-fit Lognormal Distributions for Body Mass as a
Function of Age, Derived from NHANES 1999-2004 Study Data.
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MALES: Body Mass GSD
20
New Values
Current Physiology
File
40 60
Age (years)
80
100
1.000
FEMALES: Body Mass GSD
20
New Values
Current Physiology
File
40 60
Age (years)
80
100
Figure 2. Geometric Standard Deviations for the Best-fit Lognormal Distributions for Body
Mass as a Function of Age, Derived from NHANES 1999-2004 Study Data.
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MALES: Body Mass Minimum
20
Current Physiology
File
40 60
Age (years)
80
100
ctf
FEMALES: Body Mass Minimum
20
New Values
Current Physiology File
40 60
Age (years)
80
100
Figure 3. Minimums (1 Percentile) for Body Mass as a Function of Age, Derived from
NHANES 1999-2004 Study Data.
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MALES: Body Mass Maximum
250 n
200 -
150 -
I 100
50 -
0
0
20
•New Values
• Current Physiology
File
40 60
Age (years)
80
100
FEMALES: Body Mass Maximum
200 n
180 -
160 -
140 -
'55 120 -
¥ loo-
sj
S 80 -
60 -
40 -
20 -
0 -
0
20
•New Values
• Current Physiology
File
40 60
Age (years)
80
100
nth
Figure 4. Maximums (99 Percentile) for Body Mass as a Function of Age, Derived from
NHANES 1999-2004 Study Data.
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4. DERIVATION OF NEW DISTRIBUTIONS FOR NORMALIZED
VO2MAX
4.1 The Nvo2max Data
The NHANES studies do report data for vo2max in individuals. However, the NHANES
vo2max values are estimated values, i.e. they are not measured directly. Such estimated
values are not appropriate for use in this context (as per discussion with the WA-COR).
Therefore, nvo2max distributional shapes were determined from a large database of
experimental and literature vo2max measurements for different age/gender cohorts.
A PubMed-based literature search located a number of studies in which vo2max was
directly measured. In addition, a large number of scientific papers (-350) reporting
vo2max were also provided to Alion by the WA-COR. All the studies were evaluated for
use by determining if: 1) any normalized vo2max data for individuals were reported or 2)
any group means for narrow age-gender cohorts were reported. Studies in which the
studied age group was very broad or contained both males and females were discarded.
Also discarded were any studies in which vo2max was not normalized by body mass, or
for which no age data were reported. Data for ill or highly-trained individuals were not
used; however, studies in which subjects underwent mild or moderate exercise training
were included. Two large databases, one of individual vo2max data and one of grouped
means and SDs, were constructed from the valid studies.
The database of individual data comprised age versus nvo2max data for 1949 men and
1558 women. The data were pulled from either tables or graphs in 20 published studies
[11-30]. Additional raw experimental data were provided by the WA-COR [31]. In the
case of the graphical data, the original source was digitized and the data points were
pulled from the digital figure using graphics software. (This was accomplished by
calibrating the pixels of the digitized image with the range of age and nvo2max values.)
The individual nvo2max data for males and females are shown in Figure 5.
The grouped mean and SD data were derived from 136 studies [32-167]. These data
comprised approximately 550 means and SDs for different age/gender cohorts. Single
age/gender cohort means and SD values for the Adams data [31] were also included in
this dataset. Only data for subject groups having an age SD of less than approximately 2-
3 years were considered. The grouped mean values for men and women are shown in
Figure 6, while the group SD values are shown in Figure 7.
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MALES :Nvo2max
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MALES: Study Means, Nvo2max
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MALES: Study Standard Deviations: Nvo2max
x
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(N
O
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8.0 -
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60.0
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Figure 7. Nvo2max Standard Deviations for Males and Females, Derived from Literature
Studies.
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4.2 Determining the NVo2max Distributions
Both the grouped mean and the individual datasets were evaluated for use in deriving the
nvo2max parameters.
The group means and SD were combined into single age/gender cohort values. The
combined means were calculated as mean of the group means, weighted by the number of
subjects. The group SD were calculated by transforming each group SD to a group
variance, calculating the mean variance (weighted by the number of subjects in each
study) and retransforming the variances to SDs. The combined group means and SDs are
given in Figures 8 and 9.
The combined group means were fairly well-behaved across age and gender cohorts (see
Figure 8), while the SD data (Figure 9) were noisier. These data may be appropriate for
use in the Physiology.txt file; however, it was noted that the group mean data, while
plentiful for children, were not very well represented in the adult (30+ years) age range
(especially for women). This is mainly due to the fact that very few investigators use
narrow age cohorts when studying adults, rather, it was far more common for broader age
groups to be used. These data were not included in the grouped mean analysis, as the
mean nvo2max for a broad age group cannot be assumed valid for the cohort represented
by the study age mean. Therefore, we opted to use the database of individual nvo2max
measurements to develop new distributions for the Physiology.txt file.
The individual nvo2max data were fit to several types of distributions (including normal,
beta, and lognormal distributions). It was determined that the normal distribution fit the
data best. The parameters (means and standard deviations) of the best-fit distributions
were obtained using the SAS PROC UNIVARIATE procedure. The SAS code used to fit
the data is given in Appendix B.
Both raw and smoothed nvo2max fits were calculated. Calculating 5-year running
averages did not smooth the data considerably. Therefore, the smoothed fits were
determined by choosing a best-fit functional form for the nv02max data. The data were
fit to functions as follows:
Mean (Age 0-20): Linear function
Mean (Age 21-100): Parabolic function
SD (Age 0-26): Linear function
SD(Age27-100): Parabolic function
Fitting the data in this manner also allowed for all age/gender cohorts to be represented.
Since only cohorts having N>10 were fit to distributions, there were some cohorts for
which no parameters were calculated. The raw and smoothed fits for means are given in
Figure 10; analogous data for SD is given in Figure 11. The raw nvo2max parameters
were not as clean across ages as the body mass data (probably due to the much smaller
sample size), and thus the smoothed fits were selected for use in the attached
Physiology.txt file. As with body mass, the raw fits may be used at the direction of EPA.
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The results for the nvo2max means were in fact quite close to those in the current file.
However, the values exhibited much more consistent behavior across ages, and the values
for elderly persons were lower than previously. The SD values were also in the same
range as the current values, yet they no longer demonstrate nonsensical discontinuities
across ages.
The minimum and maximum nvo2max values were assumed to be the 1st and 99th
percentile of the best-fit lognormal distribution. (Note: this is different from the method
used for estimating the body mass limits. In that case, the samples were large enough
that the percentiles of the raw data were appropriate for use as minimum and maximum.
As the nvo2max data cohorts had much smaller N than the NHANES studies, the raw
percentiles were less appropriate.) The maximum and minimum values are shown in
Figures 12 and 13.
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MALES: Nvo2max, Combined Group Means
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Figure 8. Combined Nvo2max Group Means for Males and Females
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MALES: Nvo2max, Combined Group SD
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Figure 9. Combined Nvo2max Group Standard Deviations.
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MALES: MEAN Nvo2max
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20
40 60
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FEMALES: Nvo2max Standard Deviation
Raw Values
Fit Values
Current Physiology.txt
20
40 60
Age (Years)
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Figure 11. Nvo2max Normal Distribution Fits: Raw Fit Standard Deviations and Smoothed
Fits.
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oo
CN
60 -
50 -
40 -
30 -
20 -
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Figure 13. Nvo2max Maximums. 99 Percentile of the Best-fit Normal Distribution.
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5. DERIVATION OF NEW DISTRIBUTIONS FOR HEMOGLOBIN
CONTENT (HEMOGLOBIN DENSITY)
The new hemoglobin content values were derived from the combined NHANES 1999-
2000 and 2001-2002 datasets. As of December 2005, hemoglobin data had not yet been
released for the 2003-2004 study. The age data was provided in the Demographic
datasets (Demo.xpt and Demo_b.xpt, previously downloaded for the body mass analysis)
for the two survey periods, while hemoglobin content (in g/dL) was provided in the
Laboratory #25 (Complete Blood Count) datasets (Iab25.xpt and 125_b.xpt, which were
downloaded for this analysis). The dataset comprised 20,321 individuals; appropriate
sample weights were used for the combined 4-year (1999-2002) dataset as provided with
the NHANES 2001-2002 data release. Similarly to the body mass data, the hemoglobin
content values were analyzed in SAS. The age and hemoglobin datasets were merged
and fit to normal distributions using the SAS PROC UNIVARIATE procedure. The
FREQ option of the procedure was used to apply the sampling weights. The SAS code in
provided in the Appendix C.
Hemoglobin content statistics were estimated for single-year age and gender cohorts for
ages 1-19, as the behavior of the means were smooth in this age range. For persons 20
and over, the data were grouped in 5-year cohorts (20-24, 25-29, etc.) No blood count
data were available for subjects under 1 year of age or greater than 90. The age 0 mean
values were obtained by a linear regression of ages 1-20 (males) or 1 to 11 (females) back
to age 0. These were the ages for which the hemoglobin content demonstrated an
increase with age. The 91-95 and 96-100 mean values were obtained by a linear
regression of the 61-65 and older age groups. As the standard deviations did not appear
to behave as smoothly with age as did the mean values, the age 0 value was assumed
equal to the age 1 value, and the age 91-95 and 96-100 value was assumed equal to the
age 90-94 value.
The resulting means and standard deviations for the best-for normal distributions for
hemoglobin content are given in Figures 14 and 15. The current hemoglobin content
values are shown for comparison.
The main conclusions that can be made is that the current Physiology.txt input file
overestimates mean hemoglobin content in children and in older persons. The standard
deviation values in the current physiology.txt file are fairly close to those found in this
analysis. The new values are not very smooth over ages; EPA may elect to continue to
use the current values. It should be noted that the original reference for the current
hemoglobin statistics is unknown.
Note: In the current implementation of APEX, the hemoglobin content statistics affect
only the CO dose algorithm calculations.
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Figure 14. Mean Values of Hemoglobin Content as Derived from the 1999-2002 NHANES
Dataset, with Comparison to Current Physiology.txt Values
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MALES: Hemoglobin Content Standard Deviation
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Figure 15. Values of Hemoglobin Content Standard Deviation as Derived from the 1999-
2002 NHANES Dataset, with Comparison to Current Physiology.txt Values
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6. BLOOD VOLUME AS A FUNCTION OF HEIGHT AND WEIGHT
In APEX, blood volume is estimated as a function of height and weight by the following
equation:
vuood = BVF*Weight+ K*Height3 - 30
where Vbiood is the blood volume (ml), Weight is in pounds, and height is in inches. BVF
is the blood volume factor that is read in from the physiology file, and K is a gender-
dependent constant (0.00683 for males, 0.00678 for females). This is a modification of
Allen's equation [168] to include the age/gender dependent BVF and adjusted for the
given units.
As previously mentioned, the data upon which the BVF values in the physiology file
were based could not be identified. The available documentation for pNEM documents a
non-age-dependent use of these equations.
In addition, no appropriate data were found for deriving new estimates for the BVF
variable as a function of age and gender for use with the Allen equations. It should be
noted however, that these equations were modified by Nadler [169]. These equations
seem to be used somewhat more often than the originals in the literature.
In addition, other (more recent) equations exist for estimation of blood volume from
height and weight specifically in children [170,171] or body surface area [172]. In
particular, Linderkamp et al. [170] derived prediction equations for blood volume as a
function of a number of physiological parameters for children in three different age
groups. It is recommended that further analysis of this study and others be undertaken.
However, inclusion of new blood volume equations in APEX would require changes
beyond the current physiology file (i.e. other, more intensive, code changes would be
needed). Thus, at the present time, no specific improvements to the current BVF values
in the physiology file can be made.
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Appendix A. SAS Code for Estimating the Body Mass Distributions
Data weight;
merge Demo Demo b Demo c Bmx Bmx b r Bmx c;
by SEQN;
mass=BMXWT;
gen=RIAGENDR;
ageyrs=RIDAGEYR;
agemonths=RIDAGEEX;
wt = (2/3)*WTMEC4YR;
if (SEQN>2100«) THEN wt=(l/3)*WTMEC2YR;
if agemonths<12 and agemonths>0 THEN ageyrs=0;
keep SEQN mass gen ageyrs agemonths wt;
run;
proc sort data=weight;
by gen ageyrs;
run;
Proc univariate data=weight;
by gen ageyrs;
var mass;
freg wt;
histogram mass / lognormal;
run;
February 2010
A-40
Draft - Do Not Cite or Quote
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Appendix B. SAS Code for Estimating the Normalized Vo2max
Distributions
proc sort data=alldata;
by gender age;
run;
Proc univariate data=alldata;
by gender age;
var nvo2max;
histogram nvo2max / normal;
output out=outputdatal N=samplesize mean=Mean
std=StdDeviation ProbN=NormalFit;
run;
Proc export data=outputdatal outfile="H:\kki-05-PHYSIOLOGY_10\Alldata_vo2max.csv"
replace;
run;
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Appendix C. SAS Code for Estimating the Hemoglobin Content Data
Data Hb;
merge Demo Lab25 Demo b L25 b;
by SEQN;
Hb=LBXHGB;
gen=RIAGENDR;
ageyrs=RIDAGEYR;
agemonths=RIDAGEEX;
wt = WTMEC4YR;
if agemonths<12 and agemonths>0 THEN ageyrs= ;
if ageyrs>20 then ageyrs=(floor(ageyrs/ )+ )4 ; ;
keep SEQN Hb gen ageyrs agemonths wt;
run;
proc sort data=Hb;
by gen ageyrs;
run;
Proc univariate data=Hb;
by gen ageyrs;
var Hb;
reg wt;
histogram Hb / normal;
output out=outputs N=samplesize mean=Mean
std=StdDeviation ProbN=NormalFit;
run;
Proc export data=outputs outfile="H:\kki-05-PHYSIOLOGY_10\Hemoglobin\HbFitswt.csv"
replace;
run;
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Appendix D. The New Physiology.Txt File
Note: The values contained in the file conform to the current APEX read formats. That
is, the number of decimal places for each parameter is dictated by the APEX code. It is
likely that this will change in the future, at which point more significant digits could be
added to the Physiology.txt file.
Males
age 0-100, then females age
NVO2max distribution
0-100 (last revised 12-20-05)
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
Source Distr
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Mean
48
48
48
49
49
49
50
50
50
51
51
51
52
52
52
53
53
53
53
54
54
54
53
52
51
51
50
49
48
48
47
46
46
45
44
44
43
42
42
41
40
40
39
39
38
38
37
36
36
35
35
34
34
34
33
33
32
32
31
31
31
30
30
30
29
.3
.6
.9
.2
.5
.8
.1
.4
.8
.1
.4
.7
.0
.3
.6
.0
.3
.6
.9
.2
.5
.2
.4
.6
.8
.1
.3
.6
.8
.1
.4
.7
.0
.3
.6
.0
.3
.7
.1
.4
.8
.2
.7
.1
.5
.0
.4
.9
.4
.9
.4
.9
.5
.0
.6
.1
.7
.3
.9
.5
.1
.7
.4
.0
.7
SD
1.
2 .
2 .
2 .
3 .
3 .
3 .
4 .
4 .
4 .
5.
5.
5.
5.
6 .
6 .
6 .
7 .
7.
7 .
8 .
8 .
8 .
9.
9.
9.
10
10
10
10
9.
9.
9.
9.
9.
9.
8 .
8 .
8 .
7.
5.
5.
5.
5.
5.
5.
5.
5.
5.
5.
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
5.
5.
5.
5.
5.
7
0
4
7
0
3
7
0
3
6
0
3
6
9
2
6
9
2
5
9
2
5
8
2
5
8
.7
.5
.3
.1
9
7
6
4
2
0
9
7
6
3
5
5
5
5
5
5
5
5
5
5
9
9
9
9
9
9
9
9
9
9
3
3
3
3
3
Lower
44
43
43
43
42
42
41
41
40
40
39
39
39
38
38
37
37
36
36
35
35
34
32
31
29
28
25
25
24
24
24
24
23
23
23
23
22
22
22
25
28
28
28
28
28
28
28
28
28
28
23
23
23
23
23
23
23
23
23
23
21
21
21
21
21
.3
.8
.4
.0
.5
.1
.6
.2
.8
.3
.9
.4
.0
.6
.1
.7
.3
.8
.4
.9
.5
.5
.9
.4
.8
.3
.5
.2
.9
.6
.3
.0
.8
.5
.2
.0
.7
.4
.2
.5
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.0
.0
.0
.0
.0
Upper
52
53
54
55
56
57
58
59
60
61
62
64
65
66
67
68
69
70
71
72
73
74
74
73
73
73
75
74
72
71
70
69
68
67
66
65
64
62
61
54
50
50
50
50
50
50
50
50
50
50
42
42
42
42
42
42
42
42
42
42
41
41
41
41
41
.2
.3
.4
.4
.5
.6
.6
.7
.8
.8
.9
.0
.0
.1
.2
.2
.3
.4
.4
.5
.6
.0
.0
.9
.9
.9
.2
.0
.8
.6
.4
.3
.2
.1
.0
.0
.0
.9
.9
.1
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.8
.8
.8
.8
.8
Assumptions
February 2010
A-43
Draft - Do Not Cite or Quote
-------
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
29
29
28
28
28
27
27
27
27
27
26
26
26
26
26
25
25
25
25
25
25
25
25
25
24
24
24
24
24
24
24
24
24
25
25
25
35
36
36
36
37
37
37
38
38
38
39
39
39
40
40
40
41
41
41
42
42
42
41
40
40
39
39
38
37
37
36
36
35
34
34
33
33
32
32
31
31
31
30
30
29
.4
.1
.8
.5
.2
.9
.7
.4
.2
.0
.7
.5
.4
.2
.0
.8
.7
.6
.4
.3
.2
.1
.1
.0
.9
.9
.9
.8
.8
.8
.8
.9
.9
.0
.0
.1
.9
.2
.5
.9
.2
.5
.9
.2
.5
.9
.2
.5
.9
.2
.5
.9
.2
.5
.8
.2
.5
.1
.5
.8
.2
.6
.0
.4
.8
.2
.6
.0
.5
.9
.4
.9
.4
.9
.4
.9
.4
.0
.5
.1
.6
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
7
7
7
7
7
7
6
6
6
6
6
6
6
5
5
5
.3
.3
.3
.3
.3
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.9
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
.0
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
.0
.1
.2
.3
.4
.4
.1
.9
.7
.6
.4
.2
.0
.8
.7
.5
.4
.2
.1
.0
.8
.7
.6
21
21
21
21
21
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
22
22
22
22
22
22
22
22
23
23
23
23
23
23
23
23
23
24
24
24
24
23
22
22
21
20
19
18
18
18
18
18
18
18
18
18
17
17
17
17
17
17
17
16
16
.0
.0
.0
.0
.0
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.2
.3
.4
.5
.6
.7
.8
.9
.0
.1
.3
.4
.5
.6
.7
.8
.9
.0
.1
.2
.4
.7
.9
.0
.1
.3
.5
.9
.8
.7
.6
.5
.4
.2
.1
.0
.8
.7
.6
.4
.3
.1
.0
.8
.6
41
41
41
41
41
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
49
50
50
51
51
52
52
53
54
54
55
55
56
56
57
57
58
59
59
60
60
60
60
59
59
58
58
57
56
55
54
53
52
51
50
49
48
48
47
46
45
44
44
43
42
.8
.8
.8
.8
.8
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.6
.2
.7
.3
.8
.4
.9
.5
.0
.6
.1
.7
.2
.8
.3
.9
.5
.0
.6
.1
.7
.5
.1
.6
.2
.8
.4
.8
.7
.6
.6
.6
.6
.7
.7
.8
.9
.0
.2
.4
.6
.8
.0
.3
.6
February 2010
A-44
Draft - Do Not Cite or Quote
-------
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Males
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
age 0-100,
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
Normal
29.
28 .
28 .
28 .
27.
27.
26 .
26 .
26 .
25.
25.
25.
24 .
24 .
24 .
24 .
23 .
23 .
23 .
23 .
22 .
22 .
22 .
22 .
21.
21.
21.
21.
21.
21.
21.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
then females
2
8
4
0
6
2
8
5
1
8
5
2
9
6
3
0
7
5
2
0
7
5
3
1
9
7
6
4
3
1
0
9
8
7
6
5
4
3
3
3
2
2
2
2
2
2
2
3
3
4
4
5
6
7
8
9
age
Body mass distribution,
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Febn
Source
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
iary 2010
Distr
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
GM
7.8
11.
13 .
16 .
18 .
21.
23 .
27.
31.
34 .
38 .
44 .
48 .
55.
62 .
67.
72 .
73 .
75.
77 .
78 .
78 .
4
9
0
5
6
1
1
7
7
3
1
0
4
8
7
5
1
1
2
0
2
5.
5.
5.
5.
5.
5.
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
4 .
0-
kg
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
1.
5
4
3
2
1
0
9
8
8
7
7
6
6
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
100
GSD
301
143
146
154
165
234
213
216
302
265
280
308
315
340
293
255
267
248
243
245
250
297
16 .5
16 .3
16 .1
16 .0
15.8
15.6
15.4
15.2
15.1
14 .9
14 .7
14 .5
14 .3
14 .1
13 .9
13 .6
13 .4
13 .2
13 .0
12 .8
12 .5
12 .3
12 .1
11.9
11.7
11.5
11.4
11.2
11.1
10.9
10.8
10.7
10.6
10.4
10.4
10.3
10.2
10.1
10.1
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.1
10.1
10.2
10.2
10.3
10.4
10.5
10.6
10.7
41.9
41.2
40.6
40.0
39.4
38 .8
38 .3
37.7
37.2
36 .7
36 .3
35.9
35.4
35.1
34 .7
34 .3
34 .0
33 .7
33 .4
33 .2
33 .0
32 .7
32 .5
32 .3
32 .1
32 .0
31.8
31.6
31.5
31.3
31.2
31.1
31.0
30.9
30.8
30.7
30.6
30.6
30.5
30.5
30.4
30.4
30.4
30.4
30.4
30.4
30.4
30.5
30.5
30.6
30.6
30.7
30.8
30.9
31.0
31.1
(last revised 12-20-05)
Lower
3 .6
8 .2
9.8
11.7
11.1
13 .7
16 .1
19.3
19.1
24 .0
24 .3
26 .2
27 .7
27 .7
35.7
41.5
45.8
49.9
51.2
52 .6
50.5
46 .8
A-45
Upper Assumptions
11.8
16 .1
20.9
23 .7
28 .1
42 .4
41.1
46 .8
66 .2
69.9
72 .9
83 .8
94 .8
106 .6
121.0
117.9
139.1
136 .6
144 .2
134 .5
130.0
199.2
Draft -Do
-------
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
0
1
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
83
80
81
84
81
85
84
82
81
81
84
88
81
87
83
85
84
84
90
87
88
88
88
87
88
86
84
86
84
88
89
89
90
88
84
87
85
84
87
89
84
89
90
89
86
86
85
87
82
79
82
85
83
84
78
79
79
77
79
75
76
74
75
71
74
73
72
72
71
70
70
69
69
68
67
67
66
65
65
7.
11
.8
.6
.7
.8
.8
.2
.3
.1
.6
.3
.7
.2
.2
.2
.4
.8
.1
.6
.1
.4
.3
.4
.5
.1
.2
.5
.8
.2
.7
.0
.9
.0
.1
.3
.8
.5
.1
.2
.0
.0
.8
.1
.0
.9
.8
.2
.2
.1
.8
.6
.0
.6
.0
.5
.7
.4
.9
.6
.9
.4
.8
.6
.3
.8
.0
.4
.7
.1
.5
.9
.3
.6
.0
.4
.8
.1
.5
.9
.3
4
.1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.292
.222
.251
.206
.273
.249
.272
.236
.262
.249
.235
.231
.221
.251
.228
.241
.260
.196
.246
.173
.205
.233
.200
.205
.243
.229
.186
.240
.179
.208
.216
.228
.216
.222
.195
.253
.266
.182
.232
.207
.228
.262
.193
.215
.228
.207
.191
.222
.210
.240
.204
.196
.217
.185
.207
.170
.195
.155
.174
.157
.180
.158
.205
.191
.170
.170
.160
.160
.160
.160
.160
.150
.150
.150
.150
.140
.140
.140
.140
.304
.163
53
50
50
50
48
50
51
50
52
48
49
64
53
61
45
59
52
61
58
61
62
54
56
60
54
49
56
47
53
57
55
58
64
55
45
58
51
58
57
49
56
56
59
58
54
43
61
50
46
51
51
56
53
56
55
58
41
56
56
55
54
53
41
46
50
50
50
50
49
49
49
49
49
48
48
48
48
48
47
3 .
7 .
.3
.5
.6
.2
.9
.0
.0
.6
.5
.8
.7
.8
.1
.0
.8
.3
.8
.2
.5
.3
.2
.0
.6
.6
.2
.9
.3
.0
.4
.9
.2
.2
.1
.1
.0
.3
.6
.7
.3
.9
.0
.3
.1
.1
.0
.1
.2
.7
.5
.0
.9
.2
.3
.5
.9
.7
.1
.4
.0
.8
.4
.2
.5
.9
.6
.4
.2
.0
.8
.6
.4
.3
.1
.9
.7
.5
.3
.1
.9
7
4
155.4
137.6
132 .6
136 .1
164 .5
153 .9
167.2
147.2
139.0
170.6
135.8
146 .3
136 .9
193 .3
140.5
150.9
149.7
140.6
154 .0
117.7
144 .0
145.3
128 .9
160.2
154 .3
188 .3
128 .3
171.3
124 .4
143 .6
144 .9
143 .3
155.2
138 .6
110.3
160.0
179.0
112 .4
141.7
162 .8
152 .1
171.6
119.0
126 .3
150.1
127.5
163 .2
127.2
125.5
122 .8
132 .7
128 .3
120.0
133 .5
121.1
109.3
115.1
107.8
111.9
111.9
111.8
107.0
109.5
105.8
101.1
99.1
97.2
95.2
93 .2
91.3
89.3
87.4
85.4
83 .4
81.5
79.5
77.6
75.6
73 .6
12 .1
15.3
February 2010
A-46
Draft - Do Not Cite or Quote
-------
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
CDC
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
LN
13
15
18
20
22
26
30
35
40
46
50
56
57
60
61
61
64
66
67
67
66
69
70
66
73
70
74
69
70
73
72
72
69
73
73
70
75
72
72
73
73
73
75
76
77
72
74
72
75
72
74
74
72
76
77
72
74
80
75
77
73
72
75
72
73
75
73
74
69
69
69
71
70
70
69
70
66
67
62
65
64
.3
.6
.0
.4
.5
.5
.5
.2
.6
.6
.7
.6
.2
.1
.6
.2
.6
.2
.0
.2
.8
.7
.3
.3
.0
.6
.4
.1
.6
.0
.9
.7
.8
.0
.5
.0
.6
.3
.9
.4
.7
.4
.7
.8
.5
.8
.6
.8
.2
.9
.5
.7
.4
.0
.3
.4
.5
.6
.8
.1
.3
.3
.4
.9
.1
.8
.2
.4
.0
.1
.9
.4
.4
.5
.5
.1
.4
.8
.2
.4
.8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.158
.160
.171
.229
.194
.239
.315
.271
.304
.302
.274
.275
.248
.249
.255
.248
.281
.274
.262
.262
.273
.304
.289
.283
.281
.281
.312
.250
.305
.278
.281
.307
.230
.306
.289
.284
.295
.251
.289
.268
.270
.314
.266
.308
.304
.298
.303
.261
.292
.240
.283
.259
.281
.231
.315
.252
.267
.277
.260
.240
.198
.238
.281
.254
.242
.266
.250
.225
.188
.232
.240
.240
.277
.216
.199
.240
.211
.200
.255
.184
.260
10
11
12
12
15
16
19
20
22
27
27
33
37
34
40
41
42
41
41
39
42
40
47
44
45
41
44
39
42
43
41
44
46
44
44
48
43
41
45
50
47
45
49
41
46
47
44
45
48
42
45
46
44
53
45
48
45
50
51
50
49
46
41
35
48
47
39
48
45
45
40
47
37
46
48
40
44
46
41
42
40
.1
.0
.8
.6
.9
.9
.8
.3
.7
.7
.8
.4
.7
.9
.9
.5
.4
.6
.5
.7
.0
.3
.5
.8
.3
.4
.3
.3
.1
.7
.5
.9
.6
.2
.6
.1
.7
.6
.5
.5
.1
.6
.5
.6
.6
.8
.2
.1
.4
.5
.7
.2
.3
.6
.6
.6
.0
.9
.3
.7
.7
.9
.1
.9
.4
.2
.3
.0
.9
.5
.7
.4
.4
.8
.8
.3
.1
.2
.2
.7
.6
20.4
27.9
29.1
40.4
36 .7
51.0
60.8
58 .6
71.2
84 .6
93 .3
99.5
110.0
108 .4
113 .8
133 .1
123 .6
118 .5
122 .6
123 .7
123 .5
143 .0
144 .5
131.8
128 .9
140.9
142 .1
116 .3
151.5
125.9
139.7
135.2
115.3
138 .4
150.1
152 .1
151.7
123 .1
137.4
156 .9
146 .1
159.5
153 .0
141.5
145.8
130.6
166 .0
125.5
175.7
120.2
146 .6
176 .6
123 .1
125.6
134 .9
122 .6
117.7
133 .0
128 .3
125.6
121.1
119.9
132 .5
113 .7
113 .3
123 .8
120.7
118 .0
102 .8
108 .1
103 .8
127.6
106 .4
117.4
101.7
119.8
109.8
98 .4
121.4
91.4
120.0
February 2010
A-47
Draft - Do Not Cite or Quote
-------
83 CDC LN 62.9 1.196 44.7 101.2
84 CDC LN 62.2 1.216 43.5 108.4
85 CDC LN 61.5 1.209 42.3 93.2
86 CDC LN 62.4 1.210 41.9 101.2
8 7 CDC LN 61.8 1.210 41.7 100.3
8 8 CDC LN 61.3 1.210 41.5 99.4
89 CDC LN 60.7 1.210 41.3 98.4
90 CDC LN 60.2 1.210 41.1 97.5
91 CDC LN 59.6 1.200 40.9 96.6
92 CDC LN 59.1 1.200 40.7 95.7
93 CDC LN 58.5 1.200 40.5 94.8
94 CDC LN 58.0 1.200 40.3 93.9
95 CDC LN 57.4 1.200 40.1 93.0
96 CDC LN 56.9 1.200 39.9 92.1
97 CDC LN 56.3 1.200 39.7 91.2
98 CDC LN 55.8 1.190 39.5 90.3
99 CDC LN 55.2 1.190 39.3 89.4
100 CDC LN 54.7 1.190 39.1 88.5
Males age 0-100 then females age 0-100 (last revised 6-11-98)
Regression equation Estimate for RMR
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Source
R47g
R47g
R47g
R47h
R47h
R47h
R47h
R47h
R47h
R47h
R47i
R47i
R47i
R47i
R47i
R47i
R47i
R47i
R47J
R47J
R47J
R47J
R47J
R47J
R47J
R47J
R47J
R47J
R47J
R47J
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
DV
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
IV
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
Slope
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.244
.244
.244
.095
.095
.095
.095
.095
.095
.095
.074
.074
.074
.074
.074
.074
.074
.074
.063
.063
.063
.063
.063
.063
.063
.063
.063
.063
.063
.063
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
Interc
-
-
-
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
0.127
0.127
0.127
.110
.110
.110
.110
.110
.110
.110
.754
.754
.754
.754
.754
.754
.754
.754
.896
.896
.896
.896
.896
.896
.896
.896
.896
.896
.896
.896
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
SE
.290
.290
.280
.280
.280
.280
.280
.280
.280
.280
.440
.440
.440
.440
.440
.440
.440
.440
.640
.640
.640
.640
.640
.640
.640
.640
.640
.640
.640
.640
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
Units med. wgt
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
2
2
3
3
3
4
4
4
4
5
5
5
6
6
6
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
.1
.7
.2
.6
.8
.0
.3
.5
.8
.0
.4
.7
.0
.3
.9
.2
.7
.6
.3
.4
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
February 2010
A-48
Draft - Do Not Cite or Quote
-------
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R47k
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R471
R47a
R47a
R47a
R47b
R47b
R47b
R47b
R47b
R47b
R47b
R47c
R47C
R47C
R47C
R47C
R47C
R47C
R47C
R47d
R47d
R47d
R47d
R47d
R47d
R47d
R47d
R47d
R47d
R47d
R47d
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.048
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.049
.244
.244
.244
.085
.085
.085
.085
.085
.085
.085
.056
.056
.056
.056
.056
.056
.056
.056
.062
.062
.062
.062
.062
.062
.062
.062
.062
.062
.062
.062
.034
.034
.034
.034
.034
.034
.034
.034
.034
.034
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
-
-
-
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.653
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
.459
0.130
0.130
0.130
.033
.033
.033
.033
.033
.033
.033
.898
.898
.898
.898
.898
.898
.898
.898
.036
.036
.036
.036
.036
.036
.036
.036
.036
.036
.036
.036
.538
.538
.538
.538
.538
.538
.538
.538
.538
.538
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.700
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.690
.250
.250
.250
.290
.290
.290
.290
.290
.290
.290
.470
.470
.470
.470
.470
.470
.470
.470
.500
.500
.500
.500
.500
.500
.500
.500
.500
.500
.500
.500
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
7
7
7
7
7
7
7
7
7
7
7
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
2
2
3
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
5
5
6
6
6
6
6
6
6
6
6
6
5
5
5
5
5
5
5
5
5
5
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.0
.5
.0
.3
.5
.7
.9
.1
.4
.7
.9
.2
.5
.7
.9
.0
.1
.2
.7
.8
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
February 2010
A-49
Draft - Do Not Cite or Quote
-------
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Males
Blood
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47e
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
R47f
age 0-100
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BMR
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
BM
then females age
Volume factor and
BLDFAC
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
HGMN
11.
12 .
12 .
12 .
12 .
13 .
13 .
13 .
13 .
13 .
13 .
13 .
14 .
14 .
14 .
15.
15.
9
2
4
7
8
0
2
5
4
6
6
7
0
3
7
1
4
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.034
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0.038
0-100
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
3 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
2 .
(HG
538
538
538
538
538
538
538
538
538
538
538
538
538
538
538
538
538
538
538
538
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
755
last
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.470
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
.450
revised
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
MJ/day
12-20-05)
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.7
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
Hemoglobin content
HGSTD
1.0
1.0
0.8
0.8
0.8
0.9
0.9
0.8
0.8
1.0
0.9
0.7
1.0
1.0
1.0
1.0
1.0
February 2010
A-50
Draft - Do Not Cite or Quote
-------
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
17
17
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
.0
.0
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
13
13
13
13
13
13
13
.5
.7
.8
.8
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.6
.6
.6
.6
.6
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.4
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.1
.1
.1
.1
.1
.0
.0
.0
.0
.0
.7
.7
.7
.7
.7
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.0
.0
.0
.0
.0
.8
.8
.8
.8
.8
.5
.5
1
1
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.0
.0
.8
.9
.9
.9
.9
.9
.9
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.0
.0
.0
.0
.0
.2
.2
.2
.2
.2
.2
.2
.2
.2
.2
.4
.4
.4
.4
.4
.5
.5
.5
.5
.5
.4
.4
.4
.4
.4
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
February 2010
A-51
Draft - Do Not Cite or Quote
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98
99
100
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
20
20
20
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
14
.4
.4
.4
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
13
13
13
12
12
12
12
12
12
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
13
.5
.5
.5
.2
.3
.6
.5
.8
.9
.0
.1
.3
.4
.6
.5
.6
.5
.6
.5
.5
.5
.5
.4
.5
.5
.5
.5
.5
.5
.3
.3
.3
.3
.3
.3
.3
.3
.3
.3
.5
.5
.5
.5
.5
.5
.5
.5
.5
.5
.6
.6
.6
.6
.6
.7
.7
.7
.7
.7
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
.8
1
1
1
0
0
0
1
0
1
0
0
0
0
1
0
0
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.8
.8
.8
.7
.7
.8
.0
.8
.0
.8
.8
.8
.8
.0
.9
.9
.0
.0
.9
.1
.1
.2
.1
.1
.2
.2
.2
.2
.2
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.2
.2
.2
.2
.2
.3
.3
.3
.3
.3
.2
.2
.2
.2
.2
.1
.1
.1
.1
.1
.2
.2
.2
.2
.2
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.1
.3
.3
February 2010
A-52
Draft - Do Not Cite or Quote
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78 14.6 13.8 1.3
79 14.6 13.8 1.3
80 14.6 13.8 1.3
81 14.6 13.6 1.2
82 14.6 13.6 1.2
83 14.6 13.6 1.2
84 14.6 13.6 1.2
85 14.6 13.6 1.2
86 14.6 13.4 1.6
87 14.6 13.4 1.6
88 14.6 13.4 1.6
89 14.6 13.4 1.6
90 14.6 13.4 1.6
91 14.6 13.2 1.6
92 14.6 13.2 1.6
93 14.6 13.2 1.6
94 14.6 13.2 1.6
95 14.6 13.2 1.6
96 14.6 13.0 1.6
97 14.6 13.0 1.6
98 14.6 13.0 1.6
99 14.6 13.0 1.6
100 14.6 13.0 1.6
February 2010 A-53 Draft - Do Not Cite or Quote
-------
Appendix E. All Derived Physiological Parameters
Table 1. Nv02max Values for Males: Raw and Smoothed Fits.
Age
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
18.00
19.00
20.00
21.00
22.00
23.00
24.00
25.00
26.00
27.00
28.00
29.00
30.00
31.00
32.00
33.00
34.00
35.00
36.00
37.00
38.00
39.00
40.00
MEAN
Raw
Fit Values
51.37
53.46
51.10
51.28
50.13
50.70
52.74
52.93
53.18
49.46
49.77
51.98
59.88
56.80
54.60
54.61
53.76
57.23
50.90
50.06
46.38
48.32
51.02
45.59
45.86
46.90
42.08
44.48
38.63
42.63
40.41
39.70
40.62
39.02
MEAN
Smoothed
Fit Values
48.25
48.56
48.88
49.19
49.50
49.82
50.13
50.44
50.76
51.07
51.39
51.70
52.01
52.33
52.64
52.95
53.27
53.58
53.90
54.21
54.52
54.23
53.42
52.63
51.84
51.07
50.31
49.56
48.82
48.10
47.38
46.67
45.98
45.30
44.63
43.97
43.32
42.68
42.05
41.44
40.83
MALES
SD SD
Raw Smoothed
Fit Values Fit Values
2.86
2.86
6.26
5.87
6.04
7.13
5.13
4.72
5.57
6.06
6.93
7.48
9.65
9.31
8.17
8.40
9.60
10.44
10.63
9.66
8.95
10.47
12.31
9.91
10.14
11.03
9.08
8.95
10.10
7.11
8.81
6.22
8.01
8.28
1.71
2.04
2.36
2.68
3.01
3.33
3.65
3.98
4.30
4.62
4.95
5.27
5.59
5.92
6.24
6.56
6.89
7.21
7.53
7.86
8.18
8.50
8.83
9.15
9.47
9.80
10.69
10.49
10.29
10.10
9.92
9.73
9.55
9.38
9.20
9.03
8.87
8.71
8.55
8.40
8.25
MIN
(IstPctl)
44.26
43.82
43.39
42.95
42.51
42.07
41.63
41.19
40.76
40.32
39.88
39.44
39.00
38.56
38.13
37.69
37.25
36.81
36.37
35.93
35.50
34.45
32.89
31.35
29.81
28.29
25.45
25.16
24.88
24.60
24.32
24.04
23.76
23.49
23.22
22.95
22.69
22.42
22.16
21.90
21.64
MAX
(99th Pctl)
52.24
53.30
54.37
55.43
56.50
57.56
58.63
59.70
60.76
61.83
62.89
63.96
65.02
66.09
67.16
68.22
69.29
70.35
71.42
72.48
73.55
74.01
73.95
73.91
73.88
73.86
75.17
73.96
72.77
71.59
70.44
69.31
68.20
67.10
66.03
64.98
63.95
62.94
61.94
60.97
60.02
February 2010
A-54
Draft - Do Not Cite or Quote
-------
Age
41.00
42.00
43.00
44.00
45.00
46.00
47.00
48.00
49.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
61.00
62.00
63.00
64.00
65.00
66.00
67.00
68.00
69.00
70.00
71.00
72.00
73.00
74.00
75.00
76.00
77.00
78.00
79.00
80.00
81.00
82.00
83.00
84.00
85.00
86.00
MEAN
Raw
Fit Values
39.72
35.58
39.98
38.65
40.15
40.67
41.51
38.92
34.65
33.85
32.52
36.31
36.23
33.91
33.40
31.68
32.47
33.24
33.05
29.02
31.68
29.72
30.90
30.65
29.86
28.60
29.47
28.95
31.13
27.12
28.56
27.62
27.84
25.05
23.74
23.68
MEAN
Smoothed
Fit Values
40.24
39.66
39.09
38.53
37.98
37.44
36.92
36.40
35.90
35.41
34.92
34.45
34.00
33.55
33.11
32.69
32.27
31.87
31.48
31.10
30.73
30.37
30.02
29.69
29.36
29.05
28.75
28.46
28.18
27.91
27.65
27.41
27.17
26.95
26.74
26.54
26.35
26.17
26.00
25.84
25.70
25.57
25.44
25.33
25.23
25.14
MALES
SD SD
Raw Smoothed
Fit Values Fit Values
9.96
9.85
6.46
7.60
6.59
7.89
9.68
10.52
7.68
6.49
4.51
7.08
7.31
5.29
5.08
6.52
6.33
6.32
6.45
3.59
6.95
5.09
8.06
5.32
6.90
5.51
5.25
5.63
6.43
3.44
5.71
5.03
6.27
6.68
4.99
5.88
8.10
7.96
7.82
7.69
7.56
7.43
7.31
7.19
7.07
6.96
6.86
6.75
6.65
6.56
6.46
6.37
6.29
6.21
6.13
6.06
5.99
5.92
5.86
5.80
5.75
5.70
5.65
5.61
5.57
5.53
5.50
5.47
5.45
5.43
5.41
5.40
5.39
5.38
5.38
5.39
5.39
5.39
5.39
5.39
5.39
5.39
MIN
(IstPctl)
21.39
21.14
20.89
20.64
20.40
20.16
19.91
19.68
19.44
19.21
18.98
18.75
18.52
18.30
18.08
17.86
17.64
17.43
17.22
17.01
16.80
16.60
16.40
16.20
16.00
15.80
15.61
15.42
15.23
15.05
14.86
14.68
14.50
14.33
14.15
13.98
13.81
13.65
13.48
13.32
13.17
13.04
12.92
12.81
12.70
12.62
MAX
(99th Pctl)
59.09
58.18
57.28
56.41
55.56
54.73
53.92
53.12
52.35
51.60
50.87
50.16
49.47
48.79
48.14
47.51
46.90
46.31
45.74
45.19
44.66
44.14
43.65
43.18
42.73
42.30
41.89
41.50
41.13
40.78
40.45
40.13
39.84
39.57
39.32
39.09
38.88
38.69
38.52
38.37
38.22
38.09
37.97
37.86
37.76
37.67
February 2010
A-55
Draft - Do Not Cite or Quote
-------
Age
87.00
88.00
89.00
90.00
91.00
92.00
93.00
94.00
95.00
96.00
97.00
98.00
99.00
100.00
MEAN MEAN
Raw Smoothed
Fit Values Fit Values
25.06
25.00
24.94
24.90
24.86
24.84
24.83
24.83
24.84
24.87
24.90
24.95
25.00
25.07
MALES
SD SD
Raw Smoothed
Fit Values Fit Values
5.39
5.39
5.39
5.39
5.39
5.39
5.39
5.39
5.39
5.39
5.39
5.39
5.39
5.39
MIN
(IstPctl)
12.54
12.47
12.42
12.37
12.34
12.32
12.31
12.31
12.32
12.34
12.37
12.42
12.48
12.54
MAX
(99th Pctl)
37.59
37.52
37.47
37.42
37.39
37.37
37.36
37.36
37.37
37.39
37.43
37.47
37.53
37.60
Table 2. Nv02max Values for Females: Raw and Smoothed Fits
Age
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
18.00
19.00
20.00
MEAN
Raw
Fit Values
30.56
45.53
43.88
43.03
42.00
37.57
39.57
35.51
38.22
45.67
43.87
42.52
MEAN
Smoothed
Fit
Values
35.88
36.21
36.54
36.87
37.20
37.54
37.87
38.20
38.53
38.86
39.19
39.52
39.85
40.18
40.51
40.85
41.18
41.51
41.84
42.17
42.50
FEMALES
SD SD
Raw Smoothed
Fit Fit
Values Values
9.90
6.27
5.26
6.88
7.48
6.79
5.43
5.36
8.86
8.53
7.83
7.69
5.90
6.00
6.09
6.19
6.28
6.38
6.47
6.57
6.66
6.76
6.85
6.95
7.04
7.14
7.23
7.33
7.42
7.52
7.61
7.71
7.80
MIN
(1st
Pctl)
22.15
22.26
22.37
22.48
22.59
22.70
22.81
22.92
23.03
23.14
23.25
23.36
23.47
23.58
23.69
23.80
23.91
24.02
24.13
24.24
24.35
MAX
(99th Pctl)
49.61
50.17
50.72
51.27
51.82
52.37
52.93
53.48
54.03
54.58
55.13
55.69
56.24
56.79
57.34
57.89
58.45
59.00
59.55
60.10
60.65
February 2010
A-56
Draft - Do Not Cite or Quote
-------
Age
21.00
22.00
23.00
24.00
25.00
26.00
27.00
28.00
29.00
30.00
31.00
32.00
33.00
34.00
35.00
36.00
37.00
38.00
39.00
40.00
41.00
42.00
43.00
44.00
45.00
46.00
47.00
48.00
49.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
61.00
62.00
63.00
64.00
65.00
MEAN
Raw
Fit Values
43.45
43.22
43.87
41.14
38.20
38.98
34.94
38.08
35.13
35.79
35.22
36.06
34.95
38.13
32.63
33.59
31.11
33.12
28.80
29.06
29.54
30.90
27.60
29.33
28.53
29.41
30.49
27.92
26.48
29.80
27.49
28.95
23.77
25.34
26.05
26.30
26.06
23.67
24.70
21.63
26.64
23.84
20.26
MEAN
Smoothed
Fit
Values
42.10
41.45
40.81
40.18
39.56
38.95
38.35
37.75
37.17
36.60
36.04
35.48
34.94
34.41
33.88
33.37
32.87
32.37
31.89
31.42
30.95
30.50
30.05
29.62
29.19
28.78
28.37
27.97
27.59
27.21
26.84
26.49
26.14
25.80
25.48
25.16
24.85
24.55
24.27
23.99
23.72
23.46
23.21
22.97
22.74
FEMALES
SD SD
Raw Smoothed
Fit Fit
Values Values
8.51
7.59
10.13
8.22
7.09
11.12
8.02
9.80
6.30
9.10
7.89
6.93
9.51
7.08
4.88
6.17
5.13
3.76
5.14
5.74
8.00
6.82
4.32
4.17
4.90
6.00
7.15
6.05
5.36
5.13
3.66
5.83
3.56
4.61
4.29
4.91
4.07
4.81
4.65
4.99
7.38
3.77
3.83
7.90
7.99
8.09
8.18
8.28
8.37
8.35
8.14
7.94
7.74
7.55
7.37
7.19
7.01
6.84
6.68
6.52
6.37
6.22
6.08
5.95
5.82
5.70
5.58
5.47
5.36
5.26
5.16
5.07
4.99
4.91
4.83
4.77
4.70
4.65
4.60
4.55
4.51
4.48
4.45
4.43
4.41
4.40
4.39
4.39
MIN
(1st
Pctl)
23.73
22.86
21.99
21.14
20.30
19.47
18.93
18.82
18.71
18.59
18.47
18.35
18.23
18.10
17.97
17.83
17.70
17.55
17.41
17.26
17.11
16.96
16.80
16.64
16.48
16.31
16.14
15.97
15.79
15.61
15.43
15.24
15.06
14.86
14.67
14.47
14.27
14.06
13.85
13.64
13.43
13.21
12.99
12.76
12.53
MAX
(99th Pctl)
60.48
60.05
59.63
59.22
58.82
58.43
57.76
56.69
55.64
54.61
53.60
52.62
51.66
50.72
49.80
48.91
48.04
47.19
46.37
45.57
44.79
44.03
43.30
42.59
41.90
41.24
40.60
39.98
39.38
38.81
38.26
37.73
37.23
36.74
36.29
35.85
35.44
35.05
34.68
34.33
34.01
33.71
33.44
33.18
32.95
February 2010
A-57
Draft - Do Not Cite or Quote
-------
Age
66.00
67.00
68.00
69.00
70.00
71.00
72.00
73.00
74.00
75.00
76.00
77.00
78.00
79.00
80.00
81.00
82.00
83.00
84.00
85.00
86.00
87.00
88.00
89.00
90.00
91.00
92.00
93.00
94.00
95.00
96.00
97.00
98.00
99.00
100.00
MEAN
Raw
Fit Values
20.38
20.49
22.05
21.92
20.38
25.30
21.21
20.46
20.63
20.60
20.91
22.27
19.93
22.80
23.19
19.29
13.44
28.03
17.00
18.69
18.18
27.15
18.18
MEAN
Smoothed
Fit
Values
22.52
22.31
22.11
21.92
21.74
21.57
21.41
21.26
21.12
20.99
20.87
20.76
20.65
20.56
20.48
20.41
20.34
20.29
20.25
20.21
20.19
20.18
20.17
20.18
20.20
20.22
20.26
20.30
20.36
20.42
20.50
20.58
20.67
20.78
20.89
FEMALES
SD SD
Raw Smoothed
Fit Fit
Values Values
4.39
4.39
3.90 4.39
4.56 4.39
4.15 4.39
4.39
4.39
4.59 4.39
4.39
3.80 4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
4.39
MIN
(1st
Pctl)
12.31
12.10
11.90
11.71
11.53
11.36
11.20
11.05
10.91
10.78
10.66
10.55
10.44
10.35
10.27
10.20
10.13
10.08
10.04
10.00
9.98
9.97
9.96
9.97
9.98
10.01
10.05
10.09
10.15
10.21
10.28
10.37
10.46
10.57
10.68
MAX
(99th Pctl)
32.73
32.52
32.32
32.13
31.95
31.78
31.62
31.47
31.33
31.20
31.08
30.97
30.86
30.77
30.69
30.62
30.55
30.50
30.46
30.42
30.40
30.39
30.38
30.39
30.41
30.43
30.47
30.51
30.57
30.63
30.71
30.79
30.88
30.99
31.10
Table 3. Body Mass Raw Fits.
February 2010
A-58
Draft - Do Not Cite or Quote
-------
Age
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
18.00
19.00
20.00
21.00
22.00
23.00
24.00
25.00
26.00
27.00
28.00
29.00
30.00
31.00
32.00
33.00
34.00
35.00
36.00
37.00
38.00
39.00
40.00
41.00
42.00
43.00
44.00
45.00
Geometric
Mean
7.767
11.440
13.932
15.967
18.475
21.618
23.142
27.072
31.651
34.656
38.329
44.149
47.988
55.364
62.832
67.650
72.460
73.081
75.060
77.182
77.952
78.239
83.845
80.607
81.706
84.818
81.812
85.166
84.321
82.144
81.581
81.275
84.715
88.188
81.163
87.192
83.404
85.759
84.132
84.611
90.071
87.425
88.290
88.423
88.528
87.102
MALES
GSD
1.301
1.143
1.146
1.154
1.165
1.234
1.213
1.216
1.302
1.265
1.280
1.308
1.315
1.340
1.293
1.255
1.267
1.248
1.243
1.245
1.250
1.297
1.292
1.222
1.251
1.206
1.273
1.249
1.272
1.236
1.262
1.249
1.235
1.231
1.221
1.251
1.228
1.241
1.260
1.196
1.246
1.173
1.205
1.233
1.200
1.205
Min
3.6
8.2
9.8
11.7
11.1
13.7
16.1
19.3
19.1
24
24.3
26.2
27.7
27.7
35.7
41.5
45.8
49.9
51.2
52.6
50.5
46.8
53.3
50.5
50.6
50.2
48.9
50
51
50.6
52.5
48.8
49.7
64.8
53.1
61
45.8
59.3
52.8
61.2
58.5
61.3
62.2
54
56.6
60.6
Max
11.8
16.1
20.9
23.7
28.1
42.4
41.1
46.8
66.2
69.9
72.9
83.8
94.8
106.6
121
117.9
139.1
136.6
144.2
134.5
130
199.2
155.4
137.6
132.6
136.1
164.5
153.9
167.2
147.2
139
170.6
135.8
146.3
136.9
193.3
140.5
150.9
149.7
140.6
154
117.7
144
145.3
128.9
160.2
Geometric
Mean
7
11
13
15
18
20
22
26
30
35
40
46
50
56
57
60
61
61
64
66
66
67
66
69
70
66
72
70
74
69
70
73
72
72
69
73
73
70
75
72
72
73
73
73
75
76
429
119
258
587
005
353
454
483
534
235
550
579
673
649
214
091
582
229
591
156
981
218
823
721
284
300
973
604
363
110
616
039
938
710
773
044
547
019
587
295
888
363
697
438
742
795
FEMALES
GSD Min
1.304
1.163
1.158
1.160
1.171
1.229
1.194
1.239
1.315
1.271
1.304
1.302
1.274
1.275
1.248
1.249
1.255
1.248
1.281
1.274
1.262
1.262
1.273
1.304
1.289
1.283
1.281
1.281
1.312
1.250
1.305
1.278
1.281
1.307
1.230
1.306
1.289
1.284
1.295
1.251
1.289
1.268
1.270
1.314
1.266
1.308
3.7
7.4
10.1
11
12.8
12.6
15.9
16.9
19.8
20.3
22.7
27.7
27.8
33.4
37.7
34.9
40.9
41.5
42.4
41.6
41.5
39.7
42
40.3
47.5
44.8
45.3
41.4
44.3
39.3
42.1
43.7
41.5
44.9
46.6
44.2
44.6
48.1
43.7
41.6
45.5
50.5
47.1
45.6
49.5
41.6
Max
12.1
15.3
20.4
27.9
29.1
40.4
36.7
51
60.8
58.6
71.2
84.6
93.3
99.5
110
108.4
113.8
133.1
123.6
118.5
122.6
123.7
123.5
143
144.5
131.8
128.9
140.9
142.1
116.3
151.5
125.9
139.7
135.2
115.3
138.4
150.1
152.1
151.7
123.1
137.4
156.9
146.1
159.5
153
141.5
February 2010
A-59
Draft - Do Not Cite or Quote
-------
Age
46.00
47.00
48.00
49.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
61.00
62.00
63.00
64.00
65.00
66.00
67.00
68.00
69.00
70.00
71.00
72.00
73.00
74.00
75.00
76.00
77.00
78.00
79.00
80.00
81.00
82.00
83.00
84.00
85.00
86.00
87.00
88.00
89.00
90.00
91.00
Geometric
Mean
88.157
86.547
84.793
86.235
84.659
87.975
89.886
89.012
90.098
88.268
84.796
87.501
85.116
84.190
87.044
89.007
84.788
89.137
89.974
89.891
86.814
86.207
85.172
87.116
82.775
79.630
82.011
85.590
83.001
84.465
78.733
79.376
79.909
77.629
79.866
75.405
76.798
74.611
75.325
71.776
73.986494
73.364276
72.742058
72.11984
71 .497622
70.875404
MALES
GSD Min
1.243
1.229
1.186
1.240
1.179
1.208
1.216
1.228
1.216
1.222
1.195
1.253
1.266
1.182
1.232
1.207
1.228
1.262
1.193
1.215
1.228
1.207
1.191
1.222
1.210
1.240
1.204
1.196
1.217
1.185
1.207
1.170
1.195
1.155
1.174
1.157
1.180
1.158
1.205
1.191
1.17
1.17
1.16
1.16
1.16
1.16
54.2
49.9
56.3
47
53.4
57.9
55.2
58.2
64.1
55.1
45
58.3
51.6
58.7
57.3
49.9
56.04
56.3
59.1
58.1
54
43.1
61.2
50.7
46.5
51
51.9
56.2
53.3
56.5
55.9
58.7
41.1
56.4
56
55.8
54.4
53.2
41.5
46.9
50.57
50.38
50.19
50
49.81
49.62
Max
154.3
188.3
128.3
171.3
124.4
143.6
144.9
143.3
155.2
138.6
110.3
160
179
112.4
141.7
162.8
152.1
171.6
119
126.3
150.1
127.5
163.2
127.2
125.5
122.8
132.7
128.3
120
133.5
121.1
109.3
115.1
107.8
111.9
111.9
111.8
107
109.5
105.8
101.07
99.113
97.154
95.194
93.235
91.276
Geometric
Mean
77.544
72.849
74.646
72.844
75.217
72.941
74.472
74.733
72.413
75.951
77.322
72.378
74.548
80.638
75.777
77.121
73.347
72.308
75.440
72.910
73.101
75.835
73.207
74.368
68.977
69.083
69.898
71.360
70.410
70.526
69.549
70.128
66.375
67.780
62.214
65.397
64.755
62.886
62.215
61.453
62.400356
61.847614
61.294872
60.74213
60.189388
59.636646
FEMALES
GSD Min
1.304
1.298
1.303
1.261
1.292
1.240
1.283
1.259
1.281
1.231
1.315
1.252
1.267
1.277
1.260
1.240
1.198
1.238
1.281
1.254
1.242
1.266
1.250
1.225
1.188
1.232
1.240
1.240
1.277
1.216
1.199
1.240
1.211
1.200
1.255
1.184
1.260
1.196
1.216
1.209
1.21
1.21
1.21
1.21
1.21
1.2
46.6
47.8
44.2
45.1
48.4
42.5
45.7
46.2
44.3
53.6
45.6
48.6
45
50.9
51.3
50.7
49.7
46.9
41.1
35.9
48.4
47.2
39.3
48
45.9
45.5
40.7
47.4
37.4
46.8
48.8
40.3
44.1
46.2
41.2
42.7
40.6
44.7
43.5
42.3
41.85
41.66
41.47
41.27
41.08
40.88
Max
145.8
130.6
166
125.54
175.7
120.2
146.6
176.6
123.1
125.6
134.9
122.6
117.7
133
128.3
125.6
121.1
119.9
132.5
113.7
113.3
123.8
120.7
118
102.8
108.1
103.8
127.6
106.4
117.4
101.7
119.8
109.8
98.4
121.4
91.4
120
101.2
108.4
93.2
101.16
100.26
99.351
98.445
97.538
96.632
February 2010
A-60
Draft - Do Not Cite or Quote
-------
Age
92.00
93.00
94.00
95.00
96.00
97.00
98.00
99.00
100.00
MALES
Geometric
Mean
70.253186
69.630968
69.00875
68.386532
67.764314
67.142096
66.519878
65.89766
65.275442
GSD
1.16
1.15
1.15
1.15
1.15
1.14
1.14
1.14
1.14
Min
49.44
49.25
49.06
48.87
48.68
48.49
48.3
48.11
47.92
Max
89.317
87.358
85.399
83.44
81.481
79.522
77.563
75.604
73.645
FEMALES
Geometric
Mean
59.083904
58.531162
57.97842
57.425678
56.872936
56.320194
55.767452
55.21471
54.661968
GSD
1.2
1.2
1.2
1.2
1.2
1.2
1.19
1.19
1.19
Min
40.69
40.49
40.3
40.1
39.91
39.71
39.52
39.32
39.13
Max
95.726
94.82
93.914
93.008
92.102
91.195
90.289
89.383
88.477
**
Dark shading (age 86+) designates linear forecast.
Table 4. Body Mass Smoothed Fits (5-Year Running Averages).
Age
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
18.00
19.00
20.00
21.00
22.00
23.00
24.00
Geometric
Mean
7.767209794
1 1 .44008024
13.93227373
15.96664726
18.47458493
21.61756114
23.14243627
27.07246068
31.6505017
34.65600448
38.32939135
44.14863459
47.98795299
55.36374737
62.83159173
67.65031426
72.45980541
73.08089659
75.06031573
77.18236513
77.95205826
78.45564692
79.56489519
80.46958232
81 .84267254
MALES
GSD Min
1.300901
1.143324
1.145566
1.153689
1.164972
1 .233822
1.213499
1.215834
1.301873
1.265317
1.279707
1.30753
1.314848
1 .33952
1.292533
1.254999
1 .267468
1 .248405
1 .243204
1 .244928
1 .250326
1.265585
1.261251
1 .262527
1.253588
3.6
8.2
9.8
11.7
11.1
13.7
16.1
19.3
19.1
24
24.3
26.2
27.7
27.7
35.7
41.5
45.8
49.9
51.2
52.6
50.5
50.88
50.74
50.34
50.28
Max
11.8
16.1
20.9
23.7
28.1
42.4
41.1
46.8
66.2
69.9
72.9
83.8
94.8
106.6
121
117.9
139.1
136.6
144.2
134.5
130
152.66
151.34
150.96
152.18
Geometric
Mean
7.428916349
11.11947416
13.25797158
15.58684049
18.00506307
20.35285099
22.45431948
26.48323788
30.53391399
35.23472141
40.54996835
46.57910267
50.67329267
56.64881107
57.21362103
60.09135575
61.58214656
61.22931022
64.59054256
66.15556407
66.98146906
66.35375002
67.37976393
68.20537834
68.06901959
FEMALES
GSD Min
1 .304229
1.162608
1.158434
1.159883
1.17108
1 .229237
1.194119
1 .23892
1.315137
1.271364
1 .303997
1.302182
1 .273946
1 .275455
1 .24795
1 .24897
1.255162
1 .248057
1.281298
1 .274083
1.261822
1 .270386
1 .274844
1.277813
1.282127
3.7
7.4
10.1
11
12.8
12.6
15.9
16.9
19.8
20.3
22.7
27.7
27.8
33.4
37.7
34.9
40.9
41.5
42.4
41.6
41.5
41.44
41.02
42.2
42.86
Max
12.1
15.3
20.4
27.9
29.1
40.4
36.7
51
60.8
58.6
71.2
84.6
93.3
99.5
110
108.4
113.8
133.1
123.6
118.5
122.6
122.38
126.26
131.46
133.3
February 2010
A-61
Draft - Do Not Cite or Quote
-------
Age
25.00
26.00
27.00
28.00
29.00
30.00
31.00
32.00
33.00
34.00
35.00
36.00
37.00
38.00
39.00
40.00
41.00
42.00
43.00
44.00
45.00
46.00
47.00
48.00
49.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
57.00
58.00
59.00
60.00
61.00
62.00
63.00
64.00
65.00
66.00
67.00
68.00
69.00
70.00
Geometric
Mean
82.55729313
82.82151847
83.56439112
83.65195203
83.00459482
82.89721864
82.80701235
83.58034187
83.38418057
84.50647805
84.9321819
85.14102649
84.32994666
85.01958212
85.59524544
86.39949423
86.90564401
87.76379051
88.54719729
87.95342484
88.09985934
87.751282
87.02523405
86.56661258
86.07815707
86.04175058
86.70964624
87.55345712
88.32616726
89.04784314
88.4120991
87.93495739
87.15584772
85.97418819
85.72952642
86.57173577
86.0292098
86.83331368
87.99005122
88.55927286
88.12051692
88.40439667
87.61146369
87.03986775
85.61667034
84.17987726
MALES
GSD Min
1 .248802
1 .240222
1.250399
1 .247428
1.258753
1.253937
1.251132
1 .242848
1.239735
1.237533
1.233184
1 .234298
1.240177
1.235131
1.233983
1 .223065
1.215924
1.210495
1.211458
1.203416
1.217379
1.222211
1.212835
1 .220669
1.215489
1.208607
1.206031
1.2144
1 .209663
1.218268
1.215526
1 .222906
1.230391
1.223617
1 .22558
1 .228074
1 .222944
1 .22222
1 .224363
1 .220869
1 .225034
1.220898
1.206714
1.212565
1.211601
1.213949
50.7
50.04
50.14
50.14
50.6
50.58
50.52
53.28
53.78
55.48
54.88
56.8
54.4
56.02
55.52
58.62
59.2
59.44
58.52
58.94
57.52
55.06
55.52
53.6
52.16
52.9
53.96
54.34
57.76
58.1
55.52
56.14
54.82
53.74
54.18
55.16
54.708
55.648
55.728
55.888
56.708
54.12
55.1
53.42
51.1
50.5
Max
145.24
144.94
150.86
153.78
154.36
155.58
151.96
147.78
145.72
156.58
150.56
153.58
154.26
155
147.14
142.58
141.2
140.32
137.98
139.22
146.54
155.4
152
160.48
153.32
151.18
142.5
145.5
142.28
145.12
138.46
141.48
148.62
140.06
140.68
151.18
149.6
148.12
149.44
146.36
143.82
138.9
137.22
138.86
138.7
133.24
Geometric
Mean
69.21992781
69.97607936
70.90453453
70.66975978
71.53295767
71.54621552
72.01313142
71 .6826276
71.81523165
72.30094254
72.40264379
71.81884258
72.3941641
72.89859355
72.86733489
72.830387
73.56585153
73.13604869
73.82543503
74.60684165
75.44302619
75.27348935
75.51517243
74.93569966
74.62001355
73.69947055
74.02400492
74.04127315
73.95491798
74.10188224
74.97813364
74.55937637
74.52242942
76.16748501
76.13252691
76.09221736
76.28599922
75.83796229
74.79845832
74.22522224
73.42130739
73.91902253
74.09892054
73.88448789
73.09783811
72.29417333
FEMALES
GSD Min
1 .285979
1 .287735
1.289413
1.28161
1 .285847
1.285108
1 .28495
1.283915
1 .280002
1 .280205
1 .282492
1.283151
1 .280709
1 .284821
1.281407
1.277165
1 .274483
1 .278433
1.281514
1 .285327
1 .292445
1.29795
1 .295658
1 .29459
1.291492
1 .278764
1 .27574
1 .266893
1 .270898
1.25873
1 .273724
1 .267547
1 .269258
1 .268509
1 .274205
1.259138
1.248419
1 .242552
1 .243365
1 .242246
1 .242692
1 .256261
1 .258602
1 .247351
1 .234088
1.23216
43.98
43.86
44.66
43.02
42.48
42.16
42.18
42.3
43.76
44.18
44.36
45.68
45.44
44.44
44.7
45.88
45.68
46.06
47.64
46.86
46.08
46.22
45.94
45.06
46.42
45.6
45.18
45.58
45.42
46.46
47.08
47.66
47.42
48.74
48.28
49.3
49.52
49.9
47.94
44.86
44.4
43.9
42.38
43.76
45.76
45.18
Max
134.34
137.82
137.64
132
135.94
135.34
135.1
133.72
133.52
130.9
135.74
138.22
141.52
143.08
142.88
144.24
143.04
144.6
150.58
151.4
149.18
146.08
147.38
141.888
148.728
143.608
146.808
148.928
148.44
138.42
141.36
136.56
124.78
126.76
127.3
125.44
125.14
125.58
125.48
122.56
120.1
120.64
120.8
117.9
115.72
114.68
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Age
71.00
72.00
73.00
74.00
75.00
76.00
77.00
78.00
79.00
80.00
81.00
82.00
83.00
84.00
85.00
86.00
87.00
88.00
89.00
90.00
91.00
92.00
93.00
94.00
95.00
96.00
97.00
98.00
99.00
100.00
Geometric
Mean
83.34052655
83.42413149
82.60108145
82.93914453
82.75981666
82.23282472
81.09670348
80.02242628
79.10265965
78.43698141
77.92142176
76.86173441
76.40090269
74.7828307
74.4992137
73.81250877
73.43871959
72.79767378
72.74205786
72.11983988
71.4976219
70.87540393
70.25318595
69.63096798
69.00875
68.38653203
67.76431405
67.14209607
66.5198781
66.20876911
MALES
GSD Min
1.213444
1.214423
1.213508
1.208433
1.201809
1.19492
1.194698
1.182282
1.180128
1.170309
1.17229
1.164819
1.174796
1.17822
1.180574
1.177977
1.179377
1.170756
1.164535
1.162177
1.159818
1.157459
1.1551
1.152742
1.150383
1.148024
1.145665
1.143307
1.140948
1.139769
52.26
51.26
51.78
53.78
54.76
56.12
53.1
53.72
53.62
53.6
52.74
55.16
52.18
50.36
49.31362
48.50949
47.90759
49.60794
50.19052
50.00172
49.81292
49.62412
49.43532
49.24652
49.05772
48.86892
48.68012
48.49132
48.30252
48.20812
Max
134.28
127.3
125.86
127.46
127.12
122.44
119.8
117.36
113.04
111.2
111.7
110.08
110.42
109.2
107.0343
104.4969
102.5276
99.66646
97.15354
95.19446
93.23538
91.27631
89.31723
87.35815
85.39908
83.44
81 .48092
79.52185
77.56277
76.58323
Geometric
Mean
71.10679374
70.73734313
69.94568967
70.25540111
70.34868617
70.39465694
69.39757689
68.87151054
67.20924709
66.37891246
65.30424541
64.60647334
63.49351577
63.34131978
62.74189138
62.16041309
61 .84223228
61.5476723
61.29487188
60.74212987
60.18938785
59.63664584
59.08390383
58.53116181
57.9784198
57.42567778
56.87293577
56.32019375
55.76745174
55.49108073
FEMALES
GSD Min
1 .227009
1.225132
1 .235452
1.241
1 .23444
1 .234265
1.228511
1.213229
1.221048
1.217987
1 .222093
1.219074
1 .22226
1.213219
1.218822
1 .208932
1.211505
1.209819
1.209189
1 .207753
1.206317
1 .20488
1 .203444
1 .202008
1.200571
1.199135
1.197699
1.196263
1.194826
1.194108
43.88
45.5
43.38
43.56
44.22
44.14
43.48
45.24
44.12
42.9
42.96
43.08
42.54
42.76
42.59095
42.80295
42.15598
41.71005
41.46516
41.27036
41.07556
40.88075
40.68595
40.49115
40.29634
40.10154
39.90674
39.71193
39.51713
39.41973
Max
110.68
112.06
109.74
112.66
111.38
114.58
111.02
109.42
110.22
108.16
108.2
106.48
108.48
102.84
104.7926
100.844
100.4742
98.48308
99.35077
98.44462
97.53846
96.63231
95.72615
94.82
93.91385
93.00769
92.10154
91.19538
90.28923
89.83615
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Table 5. Hemoglobin Content.
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
MALES
MEAN STD
11.927 0.993545
12.20959 1.013091
12.42075 0.823171
12.69015 0.83159
12.8006 0.80152
12.95822 0.878515
13.19574 0.893008
13.46198 0.836639
13.35161 0.833121
13.59742 0.971019
13.63062 0.906785
13.66 0.726155
13.9727 0.955869
14.28293 1.036749
14.70654 1.020254
15.13583 1.04546
15.36442 1.021623
15.45945 0.979296
15.7487 1.02514
15.76812 0.831813
15.79371 0.880956
15.71703 0.91072
15.70837 1.045808
15.55635 0.959964
15.43525 1.021741
15.44038 1.105939
15.41492 1.096952
15.31983 1.123792
15.27653 0.97796
15.07274 1.192645
14.96193 1.24457
14.72786 1.418355
14.51 1.476879
14.52915 1.352814
13.97647 1.757686
13.801 1.757686
13.534 1.757686
FEMALES
MEAN STD
12.209 0.729499905
12.27307 0.719158646
12.55018 0.843436666
12.4519 0.965868504
12.83442 0.773409545
12.87154 0.969254536
13.01866 0.828912341
13.09899 0.754370806
13.25291 0.826349227
13.36671 0.808377267
13.58919 1.034306588
13.52681 0.90041802
13.6273 0.884271668
13.46986 0.97623121
13.58878 1.034527514
13.47154 0.856131982
13.50562 1.088863466
13.49842 1.117860417
13.46091 1.18250671
13.35445 1.090493585
13.5016 1.072791517
13.47168 1.170602542
13.2967 1.145254677
13.34583 1.134192006
13.4881 1.163867696
13.48617 1.348669176
13.61113 1.193756618
13.67737 1.106237392
13.83717 1.237714453
13.76529 1.093354796
13.81911 1.093565513
13.79013 1.056812752
13.84426 1.30818261
13.57546 1.238910845
13.43767 1.552685662
13.2085 1.552685662
13.005 1.552685662
February 2010
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Appendix B
COHb Module for APEX4.3
This appendix describes the probabilistic carboxyhemoglobin (COHb) module used in the
current APEX4.3 model. The approach described here is based primarily on the COHb module
originally described by Biller and Richmond in two reports (Johnson et al., 1992; Johnson et al.,
2000) and used in EPA probabilistic NAAQS exposure model for CO (pNEM/CO), a
predecessor of APEX4.3. This appendix also describes the principal changes made to the COHb
module when it was incorporated into APEX4.3, including a change in the method used to solve
the Coburn-Forster-Kane (CFK) equation (Coburn et al., 1965).
B.I The Base Physiological Model for Computing COHb Levels
Using time/activity data obtained from various diary studies, APEX constructs a
composite diary for each simulated person in the specified population at risk. The composite
diary consists of a sequence of events spanning the specified period of the exposure assessment
(typically one calendar year). Each event is defined by a start time, a duration, a geographic
location, a microenvironment, and an activity. Using various algorithms described in Section 4
of the draft CO REA, APEX4.3 provides estimates of CO concentration and alveolar ventilation
rate for each event in the composite diary. APEX4.3 then uses these data, together with
estimates of various physiological parameters specific to the simulated individual, to estimate the
percent COHb in the blood (%COHb) as an average %COHb value over the duration of each
exposure event and as an instantaneous %COHb level at the end of each event.
The %COHb calculation is based on the solution to the non-linear CFK equation,
previously described in Appendix E of Johnson et al. (2000). The CFK model describes the rate
of change of COHb blood levels as a function of the following quantities:
1. Inspired CO pressure
2. COHb level
3. Oxyhemoglobin (O2Hb) level
4. Hemoglobin (Hb) content of blood
5. Blood volume
6. Alveolar ventilation rate
7. Endogenous CO production rate
8. Mean pulmonary capillary oxygen pressure
9. Pulmonary diffusion rate of CO
10. Haldane coefficient (M)
11. Barometric pressure
12. Vapor pressure of water at body temperature (i.e., 47 torr).
If all of the listed quantities except COHb level are constant over some time interval, the
CFK equation has a linear form over the interval and is readily integrated. The solution to the
linear form gives reasonably accurate results for lower levels of COHb. However, CO and
oxygen compete for the available hemoglobin and are, therefore, not independent of each other.
February 2010 B-1 Draft - Do Not Cite or Quote
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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 ChHb level iteratively based on the assumption of
a linear relationship between COHb and O2Hb. This approach was used in the COHb module of
the original CO-NEM exposure model (Biller and Richmond, 1982, Johnson and Paul, 1983).
Alternatively, it is possible to determine COHb at any time by numerical integration of
the nonlinear CFK equation (e.g., by use of the Runge-Kutta method) if one assumes a particular
relationship between COHb and O2Hb. Muller and Barton (1987) demonstrated that assuming a
linear relationship between COHb and O2Hb leads to a form of the CFK equation equivalent to
the Michaelis-Menton kinetic model which can be analytically integrated. However, the
analytical solution in this case cannot be solved explicitly for COHb. Muller and Barton (1987)
demonstrated a binary search method for determining the COHb value.
The COHb module used in pNEM/CO employed a linear relationship between COHb and
O2Hb which was consistent with the basic assumptions of the CFK model. The approach
differed from the linear forms used by other modelers in that the Muller and Barton (1987)
solution was employed. However, instead of the simple binary search described by Muller and
Barton (1987), a combination of the binary search and Newton-Raphson root-finding methods
was used to solve for COHb (Press et al., 1986). Using the Muller and Barton (1987) solution
increased computation time compared to the Peterson and Stewart (1975) method but was shown
to be faster than fourth-order Runge-Kutta numerical integration.
APEX4.3 employs a different approach in which the CFK equation is solved using a
fourth-order Taylor's series expansion with subintervals. This method, first incorporated in
Version 3 of APEX, is described in Section C.2 of this appendix. A more detailed description
can be found in the Programmer's Guide for the APEX3 model (Glen, 2002).
B.2 The CFK Model for Estimation of Carboxyhemoglobin
Table C-l defines the variables which appear in the equations of this section. Coburn,
Forster, and Kane (1965) derived the following differential equation governing COHb levels in
the blood upon exposure to CO.
d[COHb} = Vco } PIco Pc2
dt Vb BVb MBVb[O2Hb} l q' " '
where,
1 P -P
B = —— + B . H2° (Eq. B-2)
n TT~ \ ~i /
DLco VA
February 2010 B-2 Draft - Do Not Cite or Quote
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Table B-l. Definitions of CFK Model Variables.
Variable
t
[COHb]
[02Hb]
[RHb]
[COHb]0
[THb]0
%[COHb]
%[02Hb]
%[COHb]0
%[COHB],
Definition
Time from start of an exposure event
Concentration of carboxyhemoglobin (COHb) in blood at
time t
Concentration of oxyhemoglobin (O2Hb) in blood at time t
Concentration of reduced hemoglobin in blood
[COHb] at t = 0
[RHb] + [COHb] + [02Hb]
[COHb] expressed as percent of [RHb]0
[O2Hb] expressed as percent of [RHb]0
[COHb] at t = 0
[COHb] at t = -
Pressure of inspired CO in air saturated with water vapor
at body temperature
Mean pulmonary capillary CO pressure
Mean pulmonary capillary O2 pressure
Barometric pressure
Vapor pressure of water at body temperature, or 47
Alveolar ventilation rate
Endogenous CO production rate
Pulmonary CO diffusion rate
Haldane coefficient
Equilibrium constant for reaction O2 + RHb = O2Hb
Blood volume
Total hemoglobin in blood
Methemoglobin as weight percent of Hb
PB
PHO
VA
Vco
DL
CO
M
k
Vb
Hb
%MetHb
Notes:
1 Standard Temperature Pressure, and Dry (STPD)
Units
minutes
ml CO per ml blood at STPD
ml O2 per ml blood at STPD
equivalent ml CO per ml of
blood at STPD
ml CO per ml blood at STPD
torr
torr
torr
torr
torr
ml/min STPD
ml/min STPD
ml/min/torr, STPD
ml
g/100ml
February 2010
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If the only quantity in equation (B-l) that can vary with time is [COHb], the CFK
equation is linear and can be readily integrated. However, since oxygen (02) 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 + O2Hb ^O2 + COHb (Eq. B-3)
The following equation, the Haldane relationship, applies approximately at equilibrium
conditions.
Pc0 [COHb}
^^ = M (Eq. B-4)
Pcco[02Hb]
The Haldane coefficient, M, is the chemical equilibrium constant for reaction (B-3). The
above reaction can also be viewed as the difference between two competing chemical reactions:
CO + RHb ^ COHb (Eq. B-5)
O2 + RHb ^ O2Hb (Eq. B-6)
Subtracting (B-6) from (B-5) yields (B-3). If (B-3) is in equilibrium, then (B-5) and (B-
6) are in equilibrium. If A; represents the equilibrium constant for (B-6) then:
t (Eq.B-7)
It is known that an individual breathing air free of CO for an extended period will have
about 97% of their reactive Hb bound with oxygen (O2Hb) and the remainder (3%) as the
reduced form (RHb). It is also known that at one atmosphere barometric pressure, the mean
pulmonary capillary oxygen pressure is approximately 100 torr. Substituting into (B-7) yields
0.32 as the approximate value of & at body temperature. From mass balance considerations:
[O2Hb] + [COHb] + [RHb] = [THb]0 (Eq. B-8)
Eliminating [RHb] between (B-7) and (B-8) and solving for [O2Hb] yields:
February 20 JO B-4 Draft - Do Not Cite or Quote
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kPc
[02Hb] = °2 ([THb]0 - [COHb}} (Eq. B-9)
\ + kPc
This equation represents the aforementioned linear form of the CFK equation. It has the
same form as a relationship given by McCartney (1990), but replaces the constant in the
McCartney equation by the term in (B-9) involving the mean pulmonary capillary oxygen
pressure and the equilibrium constant k. Substituting (B-9) into (B-l) yields a CFK equation free
of [C^Hb] and fully consistent with Coburn, Forster, and Kane's original derivation.
d[COHb] = Vco | Plco [COHb] l + kPct
dt ~ Vb + BVh [THb]0-[COHb]X kMBV,
(Eq. B-10)
In working with the CFK model it is convenient to express COHb as a percent of [RHb]o.
Multiplying (B-10) by 100 and dividing by [RHb]o yields the expression
d%[COHb]_ 100 Vco | Plc %[COHb] 100(1 + ^C02) B
dt [THb]0 Vb BVb WO-%[COHb] k[RHb]0MBVb
Equation (B-l 1) can be written in the form suggested by Muller and Barton (1987):
dt WO-%[COHb]
where,
C0 =-— P^ + ) (Eq.B-13)
yV BV}
= - - _ B
k[THb}0MBVb
Given values for the atmospheric pressure and the physiological variables in equations
(B-l 2) through (B-l 4), the value of %[COHb] at time t can be found by numerical integration
using such techniques as the fourth-order Runge-Kutta method (Press et al., 1986). Muller and
Barton (1987) demonstrated that an equation of the form of (B-12) is equivalent to a Michaelis-
Menton kinetics model which can be integrated. The integration yields:
_(c0 + cl)t+%[coHb]-%[coHb]0 -(m-%[coHb] }ln- = (E B_15)
01 ° ™ %[COHb]x-%[COHb]0
The equation for %[COHb]oo is obtained by setting equation (B-12) equal to zero and
solving for %[COHb], which is now equal to %[COHb]oo:
February 2010 B-5 Draft - Do Not Cite or Quote
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%[COHb]m = 10°C° (Eq. B-16)
Equation (B-15) cannot be solved explicitly for %[COHb]. Muller and Barton (1987)
suggest the binary search method as one way to find the value of %[COHb]. Press et al. (1986)
contend a combination of the binary search and Newton-Raphson methods is faster on average.
Consequently, the pNEM/CO version of the COHb module used a combination of the binary
search and Newton-Raphson root finding methods to solve for COHb (Press et al., 1986). Using
the Muller and Barton (1987) solution increased the computation time when compared with the
Peterson and Stewart (1975) method, however it was still shown to be faster than the fourth-
order 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 %COFIb 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:
]ST(t) = Co - Ci N(t) / (100 - N(t) ) (Eq. B-17)
where Co and Ci are constants (at least over the duration of one event) that depend on physical
and physiological parameters and on the CO concentration in the air. Equation (C-17) is
equivalent to (B-12) above, except that (B-12) uses the symbol %[COHb] instead of N(t).
The task of expanding N(t) in a Taylor's series becomes simpler if the following new
variables are defined:
D0= 1-N(0)/100 (Eq. B-18)
A0= C0/(C0 + Ci) (Eq.B-19)
Ai = Ci / (Co + Ci) (Eq. B-20)
D = Do-Ai (Eq. B-21)
z = (Co + Ci) t / (100*D0*D0) (Eq. B-22)
The z variable is a re-scaled time variable that is dimensionless. It is used as the
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independent variable for the Taylor's series expansion. In equations expressed as functions of z
rather than t, any primes will indicate the derivatives with respect to z.
Expressing (B-17) as a function of z yields the expression
1ST (z) = Do 2 Ao - Do 2 AI N(z) / ( 100 - N(z) ) (Eq. B-23)
The Taylor's series about the origin (z = 0) for N(z) is given by
N(z) = N(0) + 1ST(0) z + 1ST'(0) z2 / 2 + 1ST'(0) z3 / 6 + Niv(0) z4/ 24 + ... (Eq. B-24)
Through a series of algebraic substitutions, Glen (2002) shows that the Taylor series
expansion of N(z) truncated to the fourth order can be represented by
T4(z) = T3(z) - 100 AI Do D (Ai2 - 8 D AI + 6 D 2) z4 / 24 (Eq. B-25)
where
T3(z) = N(0) +100 Do D z -100 AI D0 D z2 / 2 + 100 AI D0 D (Ai - 2D) z3 / 6 (Eq. B-26)
Tests showed that the fourth-order Taylor series expansion (B-25) provided greater
accuracy than the third-order expansion for z values close to one. Glen (2002) found that z
values below one generally correspond to N(0) values below forty to fifty percent for one-hour
exposure events.
The z value for a given event depends on the event duration, the initial %COHb level
N(0), and on the physiological parameters, and can be directly evaluated at the start of each
event. For events with a z value above some threshold, it is possible to improve the performance
of (B-25) by dividing the event into smaller events ("subintervals"), each with a shorter duration
and hence smaller z value. As the subinterval duration decreases, accuracy increases at the
expense of program execution time. APEX4.3 enables the user to select a limit on z which in
turn determines the number of subintervals to be used in applying the fourth-order Taylor
expansion. Glen (2002) recommends that the limit on z be set at 0.4 or 0.5.
B.3 Application of the COHb Model in APEX4.3
Description of APEX4.3 for CO
APEX4.3 follows the daily activities over an extended period of a finite set of simulated
individuals residing within a given geographic area. The period may be a single season or a
calendar year. Each simulated individual is defined by a set of general demographic
characteristics that includes age, gender, and body weight. The values of these factors are used
to derive values for blood volume, menstrual phase, endogenous CO production rate, and other
factors required by the COHb module (see Section B.4). The exposure of each individual is
represented by a continuous sequence of exposure events which span the time period of interest.
Each exposure event represents a time interval of 60 minutes or less during which the individual
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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 eight-
hour exposures for each individual. The exposures calculated for the simulated individuals can
then be population-weighted to produce exposure distributions for population groups of
particular interest (e.g., people with coronary heart disease).
APEX4.3 constructs a year-long time/activity pattern for each simulated individual by
sampling 24-hour activity patterns from the Consolidated Human Activity Data Base (CHAD),
which is described in Section 4.4.3 of the draft CO REA. The sampling approach attempts to
match the 24-hour activity patterns to the simulated individual and exposure period according to
the demographic characteristics of the individual and the season, day type (weekday/weekend),
and maximum temperature of each day in the specified exposure period.
The COHb Module
The COHb module in APEX4.3 currently employs the version of the CFK model
represented by equations (B-12) through (B-14) to compute an average COHb value over the
duration of each exposure event and an instantaneous COHb level at the end of each event. To
perform these computations, the COHb module requires information on each of the quantities
listed in the section describing the CFK model. In addition, the COHb level at the beginning of
the exposure event must be known. This latter quantity is usually the COHb level computed at
the end of the previous contiguous exposure event. To obtain the initial COHb at the start of the
exposure period, the computation is started one day before the beginning of the period. The
effect of the initial COHb value on the end value is negligible after about 15 hours. The program
stores the calculated COHb values for each exposure event and outputs distributions of COHb
levels by population group for averaging times ranging from one hour to one day.
Assignment of CFK Model Input Data for an Exposure Event
Section B.4 describes the equations and procedures used by the APEX4.3 COHb module
to obtain the values of the input variables for equations (B-2) and (B-13) through (B-16). A brief
overview is given here.
The actual inspired CO level can change significantly during an exposure event. The
model supplies an average exposure concentration for the event, which is used as the CO input.
The time constant for the change in COHb is sufficiently large that the use of concentrations
based on averaging times up to one hour can be used in place of the instantaneous concentrations
over the averaging time period with little loss of accuracy in estimating the COHb level at the
end of the exposure event. Furthermore, applying the average concentrations to a contiguous
sequence of exposure events does not cause an accumulation of error.
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The COHb model presently used in APEX4.3 does not account for changing barometric
pressure. It uses a constant barometric pressure which is a function of the average elevation of
an area above sea level. The pressure at sea level is taken to be 760 torr.
The remaining input variables to the CFK model are all physiological parameters. While
the Haldane coefficient, the equilibrium constant k, and average pulmonary capillary oxygen
pressure are treated as having the same constant values for all individuals, the remaining
physiological input variables will vary among individuals. The next section describes the
methods used to generate the various physiological input variables for each combination of
individual and calendar day processed by APEX.
C.4 Computation of Input Data for the COHb Module
As discussed in the previous section and in Sections 4.4.5 of the draft CO REA, the
algorithms used to estimate VE and COHb require values for various physiological parameters
such as body mass, blood volume, and pulmonary diffusion rate. Table B-2 provides a list and
description of the principal parameters; additional parameters are listed and described in Chapter
5 of US EPA (2008). An algorithm within APEX4.3 probabilistically generates a value for each
parameter on the list (collectively referred to as a physiological profile) for each simulated
individual. Figure B-l 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, ton-
Hal dane Coefficient
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Equilibrium constant for the reaction of Ch
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
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Table B-2. Principal Parameters Included in the Physiological Profile for Adults for Applications of APEX4.3.
Parameter
Algorithm(s)
Containing
Parameter
Other Parameters
Required for
Calculating
Parameter
Method Used to Estimate Parameter Value
Age
COHb
Ventilation
rate
Demographic group
Randomly selected from population-weighted distribution specific to demographic
group
Gender
COHb
Ventilation
rate
Demographic group
Randomly selected from population-weighted distribution specific to demographic
group
Body Weight
COHb
Ventilation
rate
Gender
Age
Randomly selected from population-weighted lognormal distribution with geometric
mean (GM) and geometric standard deviation (GSD) distribution specific to age and
gender derived from data from the National Health and Nutrition Examination
Survey (NHANES), for the years 1999-2004 (Isaacs and Smith, 2005)
Height
COHb
Weight
Gender
Estimated using equations developed 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)
Females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(z)
The z term is randomly selected from a unit normal [N(0,1)] distribution. Units:
height (inches), weight (Ibs).
Menstrual phase
COHb
Gender
Age
If gender = female, menstrual phase was randomly assigned in alternating 14-day
cycles according to the following age-specific probabilities.
Age < 12 or >50: 100% premenstrual
Age 12 through 50: 50% premenstrual, 50% postmenstrual.
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Parameter
Algorithm(s)
Containing
Parameter
Other Parameters
Required for
Calculating
Parameter
Method Used to Estimate Parameter Value
Blood volume
COHb
Gender
Weight
Height
Blood volume (Vb) was determined according to gender by the following equations
which are based on work by Allen et al. (1956) which was modified to accept the
units 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 (Ibs), height (inches).
Hemoglobin
content of the
blood, Hb
COHb
Gender
Age
Randomly selected from normal distribution with arithmetic mean (AM) and
arithmetic standard deviation (ASD) determined by gender and age based
obtained from data from the National Health and Nutrition Examination Survey
(NHANES), for the years 1999-2004 (see Isaacs and Smith, 2005 in Appendix A)
Units: grams of Hb per deciliter of blood
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Parameter
Algorithm(s)
Containing
Parameter
Other Parameters
Required for
Calculating
Parameter
Method Used to Estimate Parameter Value
Pulmonary CO
diffusion rate,
DL
COHb
Gender
Height
Age
Pulmonary CO diffusion rate (DL) was determined according to gender, height, and
age according to the following equations obtained from a paper by Salorinne (1976)
and modified to conform to the units used in the COHb module.
Males:
= (0.361)(height) - (0.232)(age) + 16.3 ml/min/torr
Females:
DLCQ = (0.556)(height) - (0.115)(age) - 5.97 ml/min/torr
Units:
DL (ml/min/torr), height (inches), age (years).
Given the alveolar ventilation rate for the exposure event the associated adjusted
pulmonary diffusion rate is calculated as:
DL (Adjusted) = DL (Base) + 0.000845F, - 5.7
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Parameter
Endogenous CO
production rate
Resting
metabolic rate
(RMR)
Energy
conversion factor
(ECF)
NV02max
VO2max
Algorithm(s)
Containing
Parameter
COHb
Ventilation
rate
Ventilation
rate
Ventilation
rate
Ventilation
rate
Other Parameters
Required for
Calculating
Parameter
Gender
Age
Menstrual phase
Gender
Age
Body Weight
Gender
Gender
Age
NV02max
Body Weight
Method Used to Estimate Parameter Value
Endogenous CO production rate was randomly selected from a lognormal
distribution with geometric mean (GM) and geometric standard deviation (GSD)
determined according to the following equations specific to age, gender, and
menstrual phase.
Males, 18+: GM = 0.473, GSD = 1.316
Females, 18+, premenstrual: GM = 0.497, GSD = 1.459
Females, 1 8+, postmenstrual: GM = 0.31 1 , GSD = 1 .459
Units: GM (ml/hr), GSD (dimensionless).
See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA (2008).
See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA (2008).
See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA (2008).
See Section 4.4.5 of draft CO REA and Chapter 5 of US EPA (2008).
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Gender
Demographic Profile of
Simulated Individual
Weight
Height
Menstrual Phase
Endogenous CO
Production Rate
Blood Volume
Total Hemoglobin
Content
of the Blood
Pulmonary CO
Diffusion Rate
Resting Metabolic Rate
(RMR)
Energy Conversion Factor
(ECF)
Maximum Normalized
Oxygen Uptake Rate
(NV02mJ
Maximum Oxygen Uptake
Rate(V02mJ
Age
Figure B-l. Flow Diagram for Physiological Profile Generator. Input data is supplied at the start
of the APEX4.3 computation.
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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:
PB = 760 x exp(-0.0000386 x Altitude) (Eq. B-27)
where altitude is the average height (in feet) of the study area above sea level (US EPA, 1978).
The altitude was set at 5,183 feet for Denver and 328 feet for Los Angeles.
Average Pulmonary Capillary Oxygen Pressure
The equation employed is based on an approximation used by Peterson and Stewart
(1975) in which 49 torr is subtracted from the partial pressure of inspired oxygen. This leads to
the following approximate relationship:
Pc02 = 0.209(PB - 47) - 49 (Eq. B-28)
where 0.209 is the mole fraction of O2 in dry air and 47 is the vapor pressure of water at body
temperature. This expression was used in an investigation of the CFK equation by Tikuisis et al.
(1987). Often times a value 100 torr is commonly used as Equation (B-28) generates this value
for a barometric pressure equivalent to 760 torr.
Haldane Coefficient
The value of 218 was used for the Haldane coefficient. While measured values in the
range 210 to 270 have been reported in the extent literature, most researchers use values within
the range of 210 to 240. In the early 1980's, the Clean Air Scientific Advisory Committee
(CASAC) expressed the opinion to EPA (Friedlander, 1982) that the most careful work done in
this area was that by Rodkey (1969), who determined a value of 218. This value was selected for
use in the COHb module of the earlier CO-NEM exposure model. Other researchers using
values in the range 218 to 220 include Peterson and Stewart, 1970; Marcus, 1980; Collier and
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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 O? 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 Pco .
Total Reduced Hemoglobin in the Absence of O? and CO
The quantity [THb]o 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 Q^ or CO and MetHb which cannot. Total Hb blood levels also tend to be
higher in people living at higher altitudes. To relate [THb]o 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 Q^ or CO at STPD. The application of
these three factors yields the equation:
[RHb]0 = 1 .39 x #6(100 - %MetHb) x 1 + j (£q. B-29)
where HbAlt is the percent increase in Hb due to exposure to altitude and is given by (US EPA,
1978):
HbAlt = 2.76e°-ooon49Altitude
Hb in equation (B-29) is a sea level value. Hb level in a human population is normally
distributed with the mean Hb and standard deviation both dependent on gender and age class (see
entry in Table B-2 for the distributions of Hb by age and gender). Given the hemoglobin content
of the blood based on the distributions listed in Table B-2, [THb]o is calculated using equation
(B-29). The weight percent MetHB, %MetHB, is taken to be 0.5% of the weight of Hb (Muller
and Barton, 1987).
Determination of Weight
Body mass or weight (in kg) was determined by fitting lognormal distributions to data
organized by age and gender from the National Health and Nutrition Examination Survey for the
years 1999-2004 (Isaacs and Smith, 2005). Distribution parameters were estimated for single-
year 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.
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Determination of Height
The following equations were used to estimate height as a function of gender and weight.
Equations B-30 and B-31 were derived by Johnson (1998) using height and weight data provided
by Brainard and Burmaster (1992).
males: height = 34.43 inches + (6.67)[ln(weight)] + (2.38 inches)(z) (Eq. B-30)
females: height = 48.07 inches + (3.07)[ln(weight)] + (2.48 inches)(z) (Eq. B-31)
where the z term was randomly selected from a unit normal [N(0,l)] distribution.
Base Pulmonary Diffusion Rate of CO
A base lung diffusivity of CO for the individual is calculated as follows:
Men: DLm = 0.361 xheight-0.232xage +16.3 (Eq. B-32)
Women: DLm = 0.556 x height-O.I \5xage-5.97 (Eq. B-33)
where height is in inches and age is in years.
The regression equations were obtained from a paper by Salorinne (1976) and modified
to conform to the units used in the COHb module. The Salorinne data were obtained for non-
exercising 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 1/min STPD. In the absence of other
data it is assumed that this same value applies to women. Thus, for each twenty-four hour period
equations B-32 and B-33 are used to compute lung diffusion rates of CO for a base case alveolar
ventilation rate of 6.69 1/min STPD. As will be seen, this value is adjusted to account for the
actual ventilation rate experienced by the simulated individual during each individual exposure
event.
Endogenous Rate of CO Production
The endogenous CO production rates taken from a number of sources show the rate to be
distributed lognormally in the population (see Table B-3 for data and sources). The distribution
is different for men and women. For a woman there is a further difference depending on whether
she is in her premenstrual or postmenstrual phase. Table B-2 presents these distributions
classified by class, gender, and menstrual phase.
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For each male individual, APEX4.3 specifies a single value for endogenous CO
production rate and uses it for all days of the year. For each female individual between 18 and
64 years of age, APEX4.3 specifies one value of endogenous CO production rate to represent
premenstrual days and one value to represent postmenstrual days. Female individuals under 12
years and older than 50 are assumed to be premenstrual; consequently, APEX4.3 specifies a
single value for endogenous CO production rate to be used for all days of the year. The specified
values are randomly selected from the appropriate distributions presented in Table B-2. A
random number, z, is sampled from the standardized normal distribution, N(0,l) to make each
selection. The appropriate endogenous CO production rate is then obtained from:
Vco = 0.01667 x (geom.mean) x (geom.S.D.y (Eq. B-34)
The constant term converts ml/hr to ml/min.
A probabilistic algorithm within APEX4.3 assigns a menstrual phase to each day of the
year for female individuals aged 12 to 50 years. The algorithm randomly assigns a number
between 1 and 28 to January 1. The number is increased by one for each successive day until
number 28 is reached. The next day is numbered 1 and the 28-day numbering cycle is repeated
until each day of the year has been assigned a number between 1 and 28. Days numbered 1
through 14 are identified as post-menstrual days; days numbered 15 through 28 are identified as
pre-menstrual days.
INPUT DATA SUPPLIED WITH EACH EXPOSURE EVENT
Duration of Exposure Event
The duration of the exposure event in minutes is supplied by the main program to the
COHb module.
Partial Pressure of Inspired Carbon Monoxide
The main program supplies the inspired CO concentration averaged over the duration of
the exposure expressed as ppm. This quantity is converted to pressure via:
Plco = (CO) x (Pb - 47) x 10-6 (Eq. B-35)
Initial Percent COHb Level at Start of Exposure Event
The program retains the percent COHb computed at the end of the previous exposure
event and uses this value as the initial percent COHb for the present event. The starting COHb at
the beginning of an activity day is the final COHb level at the end of the preceding activity day.
This latter procedure is used for the first activity day of the overall computation since the
program starts the day before the overall period covered by the APEX4.3 computation.
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Alveolar Ventilation Rate
The main program supplies the COHb module with ventilation rate derived from the
algorithm discussed in Section 4.4.5 of this report.
Adjusted Pulmonary Diffusion Rate of CO
Given the alveolar ventilation rate for the exposure event the associated adjusted
pulmonary diffusion rate can be calculated from:
DLm (Adjusted) = DLm (Base} + 0.000845F,, - 5.7
(Eq. B-36)
Table B-3. Literature Data Used to Derive Geometric Mean and Standard Deviation
Lognormal Distribution of Endogenous CO Production Rate.
Study Author
Brouillard et al.
(1975)
Burke et al.
(1974)
Coburn et al.
(1963)
Delivoria-
Papadopoules
etal. (1974)
Luomanmaki
and Coburn
(1969)
Lynch and
Moede(1972)
Merke et al.
(1975)
Werner and
Lindahl(1980)
Values for Endogenous CO Production Rate
0.81
0.37
0.43
0.35
0.45
0.45
0.57
0.23
0.38
0.4
0.72
0.48
0.64
0.4
0.54
0.57
0.49
0.58
0.4
0.26
0.54
0.51
0.42
0.81
0.37
0.23
0.86
0.47
0.76
0.33
0.45
0.52
0.39
0.6
0.72
0.34
0.41
0.26
0.23
0.25
0.35
0.23
0.48
0.7
0.5
0.59
0.43
0.45
0.99
0.41
0.54
0.65
0.33
0.2
0.52
0.24
0.31
0.58
0.33
0.8
0.35
0.39
0.48
0.26
0.38
0.51
0.42
0.22
0.8
0.55
0.7
0.38
0.45
0.72
0.51
0.4
0.53
0.16
0.62
0.44
0.15
0.54
0.32
0.36
0.51
0.36
0.54
0.42
0.43
0.3
0.44
0.29
0.21
0.68
0.43
0.65
0.55
0.57
0.48
0.28
0.35
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B.5 References
Allen TH, Peng MT, Chen KP, Huang TF, Chang C, Fang HS. (1956). Prediction of blood
volume and adiposity in man from body weight and cube of height. Metabolism. 5:328-
345.
Biller WF and Richmond HM. (1982). Sensitivity Analysis on Coburn Model Predictions of
COHb Levels Associated with Alternative CO Standards. Report to Strategies and Air
Standards Division of the Office of Air quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC, November, 1982.
Brouillard RP, Contrad ME, Bensinger TA. (1975). Effect of blood in the gut on measurements
of endogenous carbon monoxide production. Blood. 45:67-69.
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women in the United States. Risk Analysis. 12(2):267-275.
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determine blood carboxyhemoglobin concentration in man. J din Invest. 44:1899-1910.
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man. JClin Invest. 42:1172-1178.
Collier C and Goldsmith JR. (1983). Interactions of carbon monoxide and hemoglobin at high
altitude. Atmos Environ. 17:723-728.
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monoxide production in normal women. J ApplPhysiol. 36:49-71.
Friedlander SK. (1982). Letter from the Clean Air Scientific Advisory Committee to US EPA
Administrator. August 31, 1982.
Glen G. (2002). Programmer's Guide for the APEX3 Model Prepared by ManTech
Environmental Technology, Inc., for the U.S. Environmental Protection Agency,
Research Triangle Park, NC. April 30.
Issacs K and Smith L. (2005). New Values for Physiological Parameters for the Exposure
Model Input File Physiology.txt. Technical memorandum to Tom McCurdy, NERL
WA10. December 20, 2005. Provided in Appendix A of the 2nd draft CO REA.
Johnson T and Paul RA. (1983). The NAAQS Model (MEM) Applied to Carbon Monoxide.
EPA 450/5-83-003, U.S. Environmental Protection Agency, Research Triangle Park, NC.
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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
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and Associated Carboxyhemoglobin Levels in Denver Residents Using a Probabilistic
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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.
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monoxide on blood and body stores. Am JPhysiol. 217(2):354-362.
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production in normal human beings. J Lab ClinMed. 79:85-95.
Marcus AH. (1980). Mathematical models for Carboxyhemoglobin. Atmos Environ. 14:841-
844.
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carboxhemoglobin rormation. Am IndHyg 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. Ada Med Scan.
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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. JApplPhysiol. 39:633-638.
Press WH, Flannery BP, Teukolsky SA, Vettering WT. (1986). Numerical Recipes: The Art of
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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.
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Salorinne Y. (1976). Single-breath pulmonary diffusing capacity. ScandJResp Diseases.
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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 COFIb formation. Am IndHygAssoc J.
48(3):208-213.
Tikuisis P, Kane DM, McLellan TM, Buick F, Fairburn SM. (1992). Rate of formation of
carboxyhemoglobin in exercising humans exposed to carbon monoxide. J ApplPhysiol.
72(4):1311-1319.
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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
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Invest. 40:319-324.
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Appendix C
Isaacs et al. (2009) Reference Used in Developing D and A
Statistics Input to APEX Model
The following presents a reformatted version of the Isaacs et al. (2009) presentation to allow for
easier reading. The poster, included at the end of this Appendix in its entirety, was originally
presented at the American Time Use Research Conference, June 25-26, 2009, University of
Maryland, College Park, MD.
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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,
5Homemaker, Durham, NC.
ABSTRACT
Realistic simulation of longitudinal activity patterns is necessary for appropriately
reproducing the frequency and duration of pollutant exposures in human exposure models. In
EPA's exposure models, longitudinal activity diaries for simulated persons are constructed from
the 1-day cross sectional activity diaries in the Consolidated Human Activity Database (CHAD).
Recently, new algorithms have been developed to construct longitudinal diaries from CHAD
diaries based on realistic variance and autocorrelation properties of diary characteristics relevant
to pollutant exposure. Characteristics of particular interest include time spent in particular
microenvironments and time spent in activities that produce high ventilation rates. However,
few multi-day data are currently available for estimating accurate statistical properties for these
quantities. Results from a recent time-activity study of 10 adults and one newborn child are
presented here. The participants recorded their personal location and activity for two-week
periods in each of four seasons in 2006 and 2007. The data were recorded 24 hours a day, in
increments as small as one minute. Additional recording periods for these same individuals are
expected in the future. The diaries for all subjects were assessed to calculate the between-person
variance, the within-person variance, and the autocorrelation for various lags in the time spent in
outdoor, residence, indoor (non-residence), and vehicle microenvironments, as well as for time
spent performing high-METS activities. The effectiveness of various day-type definitions (for
example, weekend versus weekday, or workday versus non-workday) for grouping similar diary
days is examined. Seasonal variation in activity patterns is analyzed. These data have the
potential to aid in the development of improved input variance and autocorrelation statistics for
longitudinal diary assembly algorithms in EPA's human exposure models.
INTRODUCTION
Recently, new methods of assembling multi-day diaries in human exposure models from
cross-sectional single-day diaries have been proposed that are based on the variance and
autocorrelation statistics of the simulated population (Glen et al. 2008). Appropriately modeling
intra- and interindividual variability using such algorithms may be essential in producing
appropriate estimates of exposure. In addition, reproducing realistic autocorrelations in key diary
properties may be required for the modeling of episodic exposure patterns.
Previously, longitudinal time activity-location data collected in children in the Southern
California Chronic Ozone Exposure Study (Geyh et al. 2000) have been analyzed to obtain
estimates of appropriate measures of variance and autocorrelation for use in the longitudinal
algorithm. Data from a new study in adults are now presented.
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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/chadnetl), 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 ow2 and c\,2 by specifying the D statistic,
defined to be:
D pertains to the population as a whole and is bounded by zero and one. A value of zero implies
all persons have the same average behavior, whereas a value of one implies the greatest possible
difference in mean behavior that is consistent with the total variance.
In addition to targeting the within-person and between-person variances through setting
the D statistic, the new diary assembly method optionally allows targeting of the day-to-day
autocorrelation. This is a measure of the tendency for similar diaries to occur on consecutive
days. The lag-one autocorrelation in a variable y is for a person defined as:
N-1
The population autocorrelation A is the mean of the A values for all individuals.
Autocorrelation could be of interest to the exposure modeler if the concentration time series were
strongly episodic, for example. In the diary assembly, a positive autocorrelation indicates a
tendency for diaries with x-scores near each other to be used on consecutive days, while a
negative autocorrelation indicates a tendency for dissimilar x-scores to be used on consecutive
days. Some preliminary values of A have been derived from the same data that were used to
estimate D (Glen et al., 2008).
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METHODS
Activity Diary Study
Activity-location data were collected from 10 adults. Nine of the adults were working
professionals; one was a stay-at-home parent. Nine of the adults recorded their personal location
and activity for two-week periods in each of four seasons in 2006 and 2007. Additional data
were collected in one of the male subjects in 1999, another male (the 10th adult) in 2002, and in
one of the females in 2008 (collected during maternity leave). The data were recorded 24 hours
a day, in increments as small as one minute. In this preliminary analysis, the time spent
outdoors, indoors, in travel, and performing hard work each day were calculated from the diaries.
"Hard work" was self-reported by each individual, as defined as activities requiring heavy
breathing and/or sweating. Daily high temperatures and precipitation amounts were acquired for
each day of the study.
Variance and Autocorrelation Statistics
Variance and lag-one autocorrelation statistics were calculated for the studied
individuals. Variance statistics were estimated for both the raw measured variables (i.e. time in
minutes) and the scaled ranks of the variable for each person on a given day. The ratio of the
between-person variance to the total variance (the sum of the between- and within-person
variance) was calculated for the population. This ratio, calculated using the raw variables, is the
intraclass correlation coefficient (ICC), while the same ratio, calculated using the ranks, is D, the
diversity statistic. The autocorrelation A was also calculated using both the raw variables and
the scaled ranks of the variables on each day for each person in the study.
Analysis of Time Spent in Locations/Activities
The longitudinal data were assessed to support decisions on optimal diary pools for
exposure modeling. Time spent in each of the examined locations/activities were assessed as a
function of day of the week (weekday versus weekend), day type (workday versus non-
workday), season, temperature, precipitation, and gender. These analyses were undertaken to
assess the utility of different diary pool definitions. Optimal definitions of diary pools can
adequately capture temporal patterns in activities while maximizing the number of activity
diaries available for sampling on a given day for a simulated individual. Differences between
groups were assessed with the Wilcoxon signed rank test (for 2 groups) or the Kruskal-Wallis
test (for more than 2 groups). The Wilcoxon rank sum (two-sample) test was used to test
differences between genders.
RESULTS AND DISCUSSION
Individual Variability
Figure 1 shows an example of the individual variability in time spent in different
locations/activities for a single male subject; a 367-day period from this subject is depicted.
Distributions of time for this subject are also shown. These figures demonstrate the large amount
of intra-individual variability that can be seen in longitudinal activity studies. Distributions of
time spent in locations/activities for the population is shown in Figure 4[sic 2].
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Examples of Day-to-Day Variability For a Single Subject (M)
Time Spent Outdoors
Time Spent in Travel
100200300400500600 0 100200300400500600
Distribution Df Time Spent in travel lor an Individual Cumu!.i1i.c Dmnhntion of Time SpenrinTr.tvrllai .in Individu.t
400
350
. 300
250
1(B 300 300 «0 500 800
Of Tim* Spent Doing Hard Woik lot M Individual
Time Spent Doing Hard Work
I i i
I
jjuiLyu
300
275
IfTS
: 150
| 125
| 100
1 75
50
25
0
1W 200 200 400 500
mm
400
350
300
I 250
I 20°
I 150
100
50
0
100 200 100 400 500 630
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.
T!nwS[.!i* Doing H*il Weil
Time Spoil Moots
TlnuSpoM Doing H«(JWoik
Tune Spent Inducts
130
« 1000
3 MO
600
I 400
z 200
0
too
Tine Sf «il Outdoors
an w M w
Tkn* Spent In Travel
Tkne Spill
-------
The D and ranked A values were compared to those calculated for children from the
Southern California Chronic Ozone Exposure Study (SCCOES). The diversity (D) for this group
of adults for outdoor time were higher than those calculated for the children (0.38 versus 0.19).
The D values for travel time in the current study were also higher (0.18 in children versus 0.36 in
this study). These differences reflect the increased heterogeneity in these variables in the studied
adults versus the (relatively homogenous) studied children. The A values calculated for outdoor
time in this study were virtually identical to those estimated using data from SCCOES.
In general, differences between D by temperature and day types were notable, even considering
the small number subjects in this study. There were gender differences observed in D; the
mechanism of these differences is unclear, but is likely influenced by the activity patterns of the
female who was not a worker.
There were observed differences in A by temperature, but especially by day type. This is
not unexpected, as it is reasonable that the behavior of working adults is more consistent day-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.
Table 1. Variance and Autocorrelation Statistics: All Days/Subjects
Location/ Activity
Indoors
Outdoors
Travel
Hard Work
ICC
0.26
0.16
0.14
0.18
D
0.33
0.38
0.31
0.22
A (Raw)
0.23
0.22
0.12
0.17
A (Ranks)
0.34
0.31
0.19
0.19
Table 2. Variance and Autocorrelation Statistics: By Gender
Location/Activity
Males
Indoors
Outdoors
Travel
Hard Work
Females
Indoors
Outdoors
Travel
Hard Work
ICC
0.36
0.14
0.36
-0.01
0.08
0.07
0.05
0.15
D
0.54
0.22
0.46
0.15
0.09
0.27
0.16
0.24
A (Raw)
0.25
0.24
0.17
0.22
0.37
0.35
0.15
0.16
A (Ranks)
0.16
0.22
0.08
0.20
0.25
0.18
0.11
0.21
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Table 3. Variance and Autocorrelation Statistics: By Temperature
Location/Activity
Days with max temp
less than 50 degrees
Indoors
Outdoors
Travel
Hard Work
Days with max temp
greater or equal to 50 degrees
Indoors
Outdoors
Travel
Hard Work
ICC
0,37
0,20
0,23
0,21
0.12
0,09
0,10
0.01
D
0,37
0,27
0,37
0.31
0,26
0.24
0.24
0.20
A (Raw)
0.23
0.33
0.20
0.14
0.45
0.39
0.34
0.35
A (Ranks)
0.19
0.18
0.09
0.14
0.23
0.20
0.09
0.14
Table 4. Variance and Autocorrelation Statistics: By Daytype
Location/Activity
Workday
Indoors
Outdoors
Travel
Hard Work
NonWorkday
Indoors
Outdoors
Travel
Hard Work
ICC
0.37
0.19
0.45
0.20
0.12
0.11
0.09
0.06
D
0.47
0.31
0.47
0.25
0.21
0.14
0.24
0.07
A (Raw)
0.56
0.78
0.30
0.53
0.59
0.60
0.38
0.43
A (Ranks)
0.05
0.07
0.01
-0.12
0.24
0.19
0.08
0.18
Time Spent in Different Locations/Activities
The time spent in different locations/activities for different day types, seasons,
temperature categories are presented in Figures 3-6. The effects of gender and precipitation
were also studied. There were no significant differences for these categories, and thus plots are
not shown. The plotted data represent all days for all subjects. The medians are represented by
the midline of the boxes, the first and third quartiles by the ends of the boxes, and the means by
the stars. The whiskers extend to cover data that lies beyond the boxed but within the quartiles
plus 1.5 times the interquartile range. Points outside this range are plotted.
Results by day of the week and day type are presented in Figure 3. Day type (workday
versus non-workday) was at least as good as day of the week in categorizing time/activities. This
trend is similar to that seen in a recent analysis of the larger, cross-sectional database of diaries
from The National Human Activity Pattern Survey (NHAPS, data not shown). That analysis
indicated that a workday/non-workday was a better discriminator of time spent outside than a
weekday/weekend split. As such, further comparisons are also presented for both workdays and
non-workdays.
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Time Spent Outdoors By Cay of ine Week
Time Spent in Travel B, Day of the Week
Time Spent Doing Hard Work By Day of the W»
Time Spent Outdoors By Day Type
Time Spent in Travel By Day Type
Time Spent Doing Hard Work By Day Type
Figure 3. Time spent in different locations/activities as a function of day of the week, and
daytype (workday versus non-workdays).
The effect of season on time spent in locations/activities is shown in Figure 4. Seasonal
effects were apparent for time spent outdoors on non-workdays, and for time spent doing hard
work. Travel was also affected by season, likely due to the large number of work-related travel
days in the fall for this particular group of workers.
Time Spent Outdoors By Season
Time Spent in Travel By Season
Tim* Spent in Doing Hard Wort, By Season
Time Spent Outdoors By Season and Day Typ«
Time Spent in Trawl By Season and Day Type
Time Spent Doing Haid Work By Season and Day Type
Figure 4. Time spent in different locations/activities as a function of season and daytype.
February 2010
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The effect of temperature category is shown in Figure 5. The temperature category was
defined as warmer = maximum temperature greater than or equal to 75 degrees, colder=
maximum temperature less than 75 degrees. Temperature category was better than or as good as
season in discriminating behavior in time spent outdoors, even when day type was considered.
Time Spent Outdoors By Tempeiature Category
Time Spent in Travel By Temperature Categoiy
Time Spent Doing Hard Work By Temperature Category
Time Spent Outdoors By Temperature Category and Day Type
Time Spent in Travel By Temperature Category and Day Type Time Spent Doing Hard Work By Temperature Category and Day Type
Figure 5. Time spent in different locations/activities as a function of temperature category
(colder: max temp< 75 degrees, warmer: max temp> 75 degrees) and day type.
CONCLUSIONS
• The diversity (D) and autocorrelation (A) for this group of adults for outdoor time were
higher than those calculated for children in a previous study. Thus these data provide
some justification for considering age when considering D and A input values for EPA's
exposure models.
• While the current data suggest possible effects of temperature, day type and gender on
diversity (D) and autocorrelation (A), more data from this and other studies are needed to
confirm these findings. Such results could aid in the fine-tuning of the longitudinal diary
algorithm.
• The analysis of the time spent in locations was consistent with recent findings from cross-
sectional diary studies indicating that workdays/non-workdays may be a better grouping
for diary pools than weekdays/weekends.
Temperature category was at least as good as season in discriminating behavior for this
population for time spent outdoors, especially when day type was considered. Such
breakdowns by temperature and day type may eliminate the need for diary pools for
different seasons, providing larger pools for diary sampling on a given day. Further
analysis with other time-activity data can confirm this trend.
February 2010
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FUTURE WORK
We plan to repeat this type of study periodically. Data will be compared to/combined
with analyses of other available longitudinal time/location/activity studies.
DISCLAIMER
The information in this document has been funded wholly (or in part) by the U. S.
Environmental Protection Agency (EPA contract 68-D-00-206). It has been subjected to review
by the EPA and approved for publication. Approval does not signify that the contents
necessarily reflect the views of the Agency, nor does mention of trade names or commercial
products constitute endorsement or recommendation for use.
REFERENCES
Geyh AS, Xue J, Ozkaynak H, Spengler JD. (2000). The Harvard Southern California Chronic
Ozone Exposure study: assessing ozone exposure of grade school-age children in two southern
California communities. Environ Health Perspect. 108:265-270.
Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal
diary assembly for human exposure modeling. J Expo Sci Environ Epidemiol. 18(3):299-311.
McCurdy T, Glen G, Smith L, Lakkadi L. (2000). The National Exposure Research
Laboratory's Consolidated Human Activity Database. J Expo Anal Environ Epidemiol. 10:566-
78.
February 2010 C-10 Draft - Do Not Cite or Quote
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Isaacs et al. (2009) in original poster format:
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
1Alion 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, 5Homemaker, Durham, NC.
Activity Diary Study
category (colder: maxtemp<75 degrees, warmer: max tempi 75 degrees) and
daytype.
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Appendix D
Microenvironmental Mapping
Figure D-l presents how CHAD codes are mapped to the eight microenvironments used to model exposure
in the draft CO REA. Table D-l provides the CHAD activity codes used to identify when a simulated
individual was in a work air district.
Figure D-l. Microenvironmental Mapping Input File Showing Mapping of CFIAD Location Codes
to the Eight Microenvironments for Application of APEX4.3 to Carbon Monoxide.
! Mapping of CHAD location codes to nine APEX
! by Option 4 of Memorandum dated 12/8/2009.
CHAD Loc. Description
U
X
30000
30010
30020
30100
30120
30121
30122
30123
30124
30125
30126
30127
30128
30129
30130
30131
30132
30133
30134
30135
30136
30137
30138
30139
30200
30210
30211
30219
30220
30221
30229
Uncertain of correct code
No data
Residence, general
Your residence
Other residence
Residence, indoor
Your residence, indoor
. . . , kitchen
. . . , living room or family room
. . . , dining room
. . . , bathroom
. . . , bedroom
. . . , study or office
. . . , basement
. . . , utility or laundry room
. . . , other indoor
Other residence, indoor
. . . , kitchen
. . . , living room or family room
. . . , dining room
. . . , bathroom
. . . , bedroom
. . . , study or office
. . . , basement
. . . , utility or laundry room
. . . , other indoor
Residence, outdoor
Your residence, outdoor
. . . , pool or spa
. . . , other outdoor
Other residence, outdoor
. . . , pool or spa
. . . , other outdoor
microenvironments defined
APEX
= -1
= -1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
7
7
7
7
7
7
7
U
U
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
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30300
30310
30320
30330
30331
30332
30340
30341
30342
30400
31000
31100
31110
31120
31121
31122
31130
31140
31150
31160
31170
31171
31172
31200
31210
31220
31230
31300
31310
31320
31900
31910
32000
32100
32200
32300
32400
32500
32510
32520
32600
32610
32620
32700
32800
32810
32820
32900
32910
32920
33100
Residential garage or carport =
. . . , indoor =
. . . , outdoor =
Your garage or carport =
. . . , indoor =
. . . , outdoor =
Other residential garage or carport =
. . . , indoor =
. . . , outdoor =
Residence, none of the above =
Travel, general =
Motorized travel =
Car
Truck
Truck (pickup or van) =
Truck (not pickup or van) =
Motorcycle or moped =
Bus
Train or subway =
Airplane =
Boat
Boat, motorized =
Boat, other =
Non-motorized travel =
Walk
Bicycle or inline skates/skateboard =
In stroller or carried by adult =
Waiting for travel =
. . . , bus or train stop =
. . . , indoors =
Travel, other =
. . . , other vehicle =
Non-residence indoor, general =
Office building/ bank/ post office =
Industrial/ factory/ warehouse =
Grocery store/ convenience store =
Shopping mall/ non-grocery store =
Bar/ night club/ bowling alley =
Bar or night club =
Bowling alley =
Repair shop =
Auto repair shop/ gas station =
Other repair shop =
Indoor gym /health club =
Childcare facility =
. . . , house =
. . . , commercial =
Large public building =
Auditorium/ arena/ concert hall =
Library/ courtroom/ museum/ theater =
Laundromat =
1
1
7
1
1
7
1
1
7
1
8
8
8
8
8
8
5
8
8
0
7
7
7
7
7
7
7
7
5
4
8
8
3
3
4
3
3
3
3
3
3
2
3
3
4
1
4
3
3
3
3
H
H
H
H
H
H
H
H
H
H
O
O
O
O
O
O
O
O
O
O
O
0
0
0
0
0
0
0
0
0
0
0
0
0
O
H
O
O
O
O
O
O
O
O
O
O
0
0
0
0
H
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33200
33300
33400
33500
33600
33700
33800
33900
34100
34200
34300
35000
35100
35110
35200
35210
35220
35300
35400
35500
35600
35610
35620
35700
35800
35810
35820
35900
36100
36200
36300
Hospital/ medical care facility =
Barber/ hair dresser/ beauty parlor =
Indoors, moving among locations =
School
Restaurant =
Church
Hotel/ motel
Dry cleaners =
Indoor parking garage =
Laboratory =
Indoor, none of the above =
Non-residence outdoor, general =
Sidewalk, street =
Within 10 yards of street =
Outdoor public parking lot /garage =
. . . , public garage =
. . . , parking lot =
Service station/ gas station =
Construction site =
Amusement park =
Playground =
. . . , school grounds =
. . . , public or park =
Stadium or amphitheater =
Park/ golf course =
Park
Golf course =
Pool/ river/ lake =
Outdoor restaurant/ picnic =
Farm =
Outdoor, none of the above =
4
3
3
4
3
4
3
3
6
3
3
7
5
5
6
6
6
2
7
7
7
7
7
7
7
7
7
7
7
7
7
0
H
0
0
0
H
0
H
O
O
O
O
O
O
O
O
O
O
O
O
H
0
H
0
0
0
0
0
0
0
0
Table D-l. CHAD Work Related Activity Codes Used To identify Work Air Districts.
<10> Work and Other Income Producing Activities
10000: work and other income producing activities, general
10100: work, general
10110: work, general, for organizational activities
10111: work for professional/union organizations
10112: work for special interest identity organizations
10113: work for political party and civic participation
10114: work for volunteer/ helping organizations
10115: work of/ for religious groups
10116: work for fraternal organizations
10117: work for child/ youth/ family organizations
10118: work for other organizations
10120: work, income-related only
10130: work, secondary (income-related)
10200: unemployment
10300: breaks
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Appendix E
Analysis of CHAD Diaries for Time Spent in Vehicles.
The US Census Bureau (2009) provides an on-line facility for accessing the detailed
census data included in their Summary File 3 (SF3). We obtained information on travel time to
work for workers ages 16 years and over specific to Denver County, Colorado and Los Angeles,
CA (US Census Bureau, 2009, Table P31). Staff converted the counts listed in Table P31 for
trips to work places other than home into the percentages listed in Columns 2 and 3 of Table E-l.
Although the P31 statistics apply to people 16 years or older, staff assumed that the statistics
were generally applicable to people 18 years or older.
We next determined the number of 24-hour diaries in EPA's Consolidated Human
Activity Database (CHAD) (US EPA, 2002) that met the following criteria: the subject was >18
years of age and the diary reported at least one minute in a motor vehicle between 6 am and 9
am. The number of these diaries that had in-vehicle times corresponding to the bins listed in
Table E-l are given in Column 4 and were converted to the percentages listed in Column 5.
Table E-l. Comparison of Denver and LA commuting characteristics (US Census, 2009)
to time spent in motor vehicles using CHAD Diaries (US EPA, 2002).
Travel time
(minutes)
(1)
1 to 9
10to19
20 to 29
30 to 39
40 to 59
60 to 89
90+
Total
Percent of commuters
according to SF3
census data for Denver
County
(2)
10.3
32.0
24.2
18.6
9.3
3.8
1.7
100
Percent of commuters
according to SF3
census data for Los
Angeles County
(3)
7.8
25.9
21.0
21.4
13.6
7.0
3.4
100
24-hour diaries meeting
inclusion criteria3
Number in
CHAD
(4)
563
1,676
1,068
1,111
665
407
258
5,748
Percent in
CHAD
(5)
9.79
29.16
18.58
19.33
11.57
7.08
4.49
100
Notes:
a Subjects are 1 8+ years of age. Diaries are those having >one minute in motor vehicle time spent
between 6 AM and 9 AM.
References
US Census Bureau. (2009). American Fact Finder. Census Summary File 3 (SF3) - custom tables. Available at:
www.factfinder.census.gov.
US EPA. (2002). EPA's Consolidated Human Activities Database. Available at: http://www.epa.gov/chad/.
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Appendix F
Differences in Human Activity Patterns Between Individuals
With and Without Cardiovascular Disease
The following presents a memorandum by Cohen et al. (1999) that was included in
the Johnson et al. (2000) CO exposure assessment (see Appendix J of that report).
It is in its original form, with some minor editing performed by staff for inclusion
into this second draft CO REA.
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MEMORANDUM
TO: Harvey Richmond
FROM: Jonathan Cohen, Sergey Nikiforov, and Arlene Rosenbaum
DATE: January 15, 1999
SUBJECT: EPA 68-DO-0062 Work Assignment 2-24: Task 2: Evaluation of
Differences in Human Activity Patterns Between Individuals With or
Without Cardiovascular Disease
EVALUATION OF DIFFERENCES IN HUMAN ACTIVITY PATTERNS BETWEEN
INDIVIDUALS WITH OR WITHOUT CARDIOVASCULAR DISEASE
SUMMARY
Activity pattern data from the National Human Activity Pattern Survey were used to compare
activity patterns and exertion distributions between subjects with or without angina. The diary
survey provided a 24-hour diary of activities. Exertion rates for each person in the survey were
simulated 100 times. For each person, the body weight was simulated from a log-normal
distribution specific to the age and gender. The resting metabolic rate was simulated using a
regression against body weight, with coefficients depending on age and gender. Finally, the
exertion rate was simulated for each activity and person by multiplying the simulated resting
metabolic rate by a MET exertion ratio with a distribution specific to each type of activity. The
current version of the probabilistic NAAQS Exposure Model for Carbon Monoxide (pNEM/CO),
described in Johnson (1998), begins with the same set of physiological equations and statistical
distributions for probabilistic simulation of exposure. The pNEM/CO model uses the much
broader Consolidated Human Activity Data Base (CHAD) and simulates additional
physiological variables, such as the ventilation rate. The description of the relevant probabilistic
and physiological equations in this memorandum is largely based on Johnson (1998); see that
memorandum for more detailed information.
Differences between angina and non-angina subjects were evaluated for several summary
statistics: average and 95th percentile of the maximum daily 8-hour exertion, percentage of time
spent outdoors or in a vehicle, average percentage of time at light, moderate or heavy exertion
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levels. Age and gender have very significant effects on these summary statistics of activity and
exertion. Since angina patients tend to be much older and tend to include more females than the
general population, it is very important to adjust for age and gender effects when comparing
angina and non-angina groups. Otherwise, one cannot distinguish between the angina effect and
the effects of age and gender. Statistical analyses comparing angina to non-angina subjects were
performed, adjusting for age and gender either by stratification (comparing subjects in a given
age/gender subgroup), or by fitting a general linear model (with separate terms for age, gender,
and angina effects and their interactions). These analyses showed that, overall, angina subjects
tended to have less extreme exertion levels. More specifically, the maximum 8-hour exertion
energies tended to be lower, as did the percentages of time above moderate or high exertion rate
thresholds. The percentages of time spent outdoors or in a vehicle were generally not statistically
significantly different between angina and non-angina subjects.
The large sample of NHAPS subjects produced, in many cases, statistically significant
differences in the exertion rate summaries between angina and non-angina subjects. However,
those differences were generally numerically small compared to the mean values. Therefore we
conclude that the differences in activity and exertion between angina and non-angina subjects,
although statistically significant, are not large enough to severely impact the validity of
pNEM/CO modeling results that do not adjust for an angina/non-angina difference.
METHODOLOGY
For these analyses we used the National Human Activity Pattern Survey (NHAPS) database, a
telephone survey of human activity patterns conducted for the USEPA between October 1992
and September 1994 by the Survey Research Center at the University of Maryland. See Klepeis
et al. (1996, 1998) and Tsang and Klepeis (1996) for more details about the NHAPS study and
various statistical analyses of those data. The NHAPS data (Triplett, 1996) are included in
CHAD. (Other CHAD studies did not include questions about cardiovascular disease and so
could not be used for these analyses comparing angina and non-angina respondents.) A
nationally representative sample of 9,386 respondents completed a detailed diary listing all their
activities and locations over a 24-hour period (either from the previous day or a previous
weekend day). A few respondents did not state their age and/or gender and their data was not
used in our analysis. Our analysis used 9,149 of the surveys. Respondents were also asked
demographic questions, including age and gender, and health questions, including whether or not
they have been told by a doctor that they have angina: 243 respondents (2.6 percent) had angina.
Respondents were asked about employment status (e.g. full-time, part-time, or unemployed) but
not about their occupation. Other follow-up questions (not used in our analyses) related to the
respondent's exposure to either water or air pollution on the diary day. For each household, the
respondent was randomly selected to be either the adult or child (under 18) with the next
birthday; an adult provided proxy responses for a child.
The EPA report (Klepeis, Tsang and Behar, 1996), Section 3, shows that the sample is
reasonably representative of the national population with respect to gender and age distributions.
The NHAPS population slightly underrepresented males (46 % NHAPS compared to 49 % from
the 1990 Census). The fraction of weekend (Saturday or Sunday) respondents was 33 %, close to
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the desired ratio of 2:7, but Thursdays, Fridays and Saturdays were underrepresented. The Fall
season was significantly underrepresented. The database includes weights to adjust for varying
selection probabilities, due to differences in the numbers of adults or children in a selected
household, the numbers of non-business phones in a household, the numbers of non-business
telephones in each census region, and to the survey stratification between weekend or weekdays
and between children and adults. Based on discussions with the EPA WAM, it was decided that
the weights would not be used in these analyses; the raw, unweighted data would be treated as an
approximately simple random sample. Note that the statistical weights: 1) were not used in the
pNEM/CO exposure modeling effort, 2) could not be used to accurately estimate standard errors
of weighted means, and 3) were close to 1 for most respondents.
In pNEM/CO, each activity is assigned a probability distribution of the exertion rate (kilo-
calories per minute). For this analysis, the 24-hour sequence of exertion rates was simulated 100
times for each person in the NHAPS sample; the sequence of activities is fixed but the simulated
exertion rates vary. Following both CHAD and the exposure modeling methodology currently
used in pNEM/CO, a constant simulated exertion rate is assumed throughout the time period of
each listed activity in the 24-hour diary. If the individual repeats the same activity at a later time,
with other activities intervening, the exertion rate is simulated again. SAS statistical software
was used for the simulations and for the statistical analysis.
The assigned exertion rate distribution depends upon the type of activity, and the occupation,
age, gender, and body weight of the respondent. The exertion rate (kilo-calories/minute =
kcal/min), also referred to as average energy expenditure rate, EE, is defined as the product
EE = MET x RMR.
MET is the metabolic equivalent of work, a dimensionless ratio (i.e., exertion compared to the
resting metabolic rate) specific to each activity, and, in some cases, to an age group. RMR is the
resting metabolic rate (kcal/min), approximately equal to the basal metabolic rate. We used the
same set of MET statistical distributions supplied by Tom McCurdy that are currently used in
pNEM/CO (and CHAD). For the work activity "at main job," the MET distribution depends on
the occupation. Since occupation was not recorded in NHAPS, we followed the pNEM
methodology and randomly selected the occupation based on census fractions of persons in each
activity. The same occupation is assumed throughout a simulated person-day (in case the person
repeats the work activity), but is randomly selected again for the next simulated person-day.
Note that this procedure may bias the comparison between angina and non-angina subjects, since
the distribution of occupation is expected to differ between angina subjects and the general
population.
A single RMR value was simulated to represent each person-day. Thus the same person would
have 100 simulated RMRs, one for each of the 100 days simulated. This reflects the assumption
that each person represents the activity pattern for a group of persons with the same age and
gender. As in pNEM/CO, RMR was simulated from a normal distribution where the mean is of
the form a + b (Body Mass), and the standard deviation is the constant _. The values of a, b, and
_ are the values derived by Schofield (1985) for 12 age/gender combinations (this assumes basal
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metabolic rate is equivalent to resting metabolic rate). In turn, the body mass was simulated
using the log-normal distributions estimated by Brainard and Burmaster (1992) and Burmaster
and Crouch (1994). The parameters of the log-normal distributions depend on age and gender.
The statistical analysis used the following summary statistics of the activity and simulated
exertion patterns for each person in the NHAPS study. The selection of these summary statistics
was based on recommendations from the EPA WAM:
Average maximum 8-hour energy expenditure. For each 8-hour period in a simulated person-
day, starting every 10 minutes, integrate the simulated EE to give the energy expenditure in
Meal (millions of calories), i.e. sum the products of activity time and energy expenditure
rate. For each simulated day, compute the maximum 8-hour energy expenditure, treating the
simulated day in circular fashion so that the respondent is assumed to repeat exactly the same
activity and exertion rate patterns on the day after the diary day. For example, the simulated
activities for the period starting at 10 pm are assumed to follow the reported sequence of
activities for the diary day from 10 pm to midnight and then the reported sequence from the
beginning of the diary day until 6 am. To represent a typical value for the selected person,
compute the average maximum 8-hour energy expenditure across the 100 simulations.
95th percentile maximum 8-hour energy expenditure. As in the last bullet, compute the
maximum 8-hour energy expenditure for each simulated day. To represent an extreme value
for the selected person, compute the fifth highest maximum 8-hour energy expenditure
among the 100 simulations.
_ Percentage time spent outdoors. This number is the same for all simulations, since the
activity patterns are held constant.
_ Percentage time spent in a vehicle. This number is the same for all simulations, since the
activity patterns are held constant.
_ Percentage time spent outdoors or in a vehicle. This number is the same for all simulations,
since the activity patterns are held constant.
_ Average percentage time with exertion rate above 2.39 kcal/min. For each simulated person-
day, the percentage of that day with an EE (rate) above the threshold level of 2.39 kcal/min
was computed; then, this percentage was averaged over the 100 simulations for that person.
The statistic estimates the percentage time spent at or above the threshold exertion rate level
over a long period, assuming the daily activity pattern was the same every day. The threshold
of 2.39 kcal/min, which equals 0.010 MJ/min, represents "light" exertion (see below).
_ Average percentage time with exertion rate above 5.97 kcal/min. The threshold of 5.97
kcal/min, which equals 0.025 MJ/min, represents "moderate" exertion (see below).
_ Average percentage time with exertion rate above 9.55 kcal/min. The threshold of 9.55
kcal/min, which equals 0.040 MJ/min, represents "heavy" exertion (see below).
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The exertion rate thresholds used for these analysis were originally defined as 0.010, 0.025, and
0.040 mega-joules per minute, but were converted into the more commonly used calorie units (1
joule equals 0.2388 calories). For purposes of exposure assessment, exertion categories (i.e.,
light, moderate, or heavy exertion) are more usefully defined by the ventilation rate VE (liters air
per minute) rather than the energy expenditure rate EE (kilo-calories per minute). For the EPA's
Ozone Criteria Document, the Environmental Criteria and Assessment Office categorized VE
into ranges of 0-23, 24-43, 44-63, and 64+ liters of air per minute to define light, moderate,
heavy, and very heavy exertion, respectively (based on a reference male adult with body weight
70 kg). To convert from EE to VE, EE is first multiplied by an energy conversion factor, ECF, to
give the oxygen uptake rate VO2 (liters of oxygen per minute). ECF varies across the
population, but is approximately 0.2 liters oxygen per kcal (Esmail, Bhambhani, and Brintnell,
1995). The "ventilatory equivalent rate" (VER) is the dimensionless ratio of VE (liters per
minute) divided by VO2 (liters per minute) and has typical values from about 24 for light
exertion to about 32 for peak exertion. Thus the selected energy expenditure rates are
approximately equivalent to the following ventilation rates:
EE = 0.010 MJ/min = 2.39 kcal/min:
VE = EE _ ECF _ VER = 2.39 _ 0.2 _ 24 = 11.5 liters/min = light exertion
EE = 0.025 MJ/min = 5.97 kcal/min:
VE = EE _ ECF _ VER = 5.97 _ 0.2 _ 28 = 33.4 liters/min = moderate exertion
EE = 0.040 MJ/min = 9.55 kcal/min:
VE = EE _ ECF _ VER = 9.55 _ 0.2 _ 32 = 61.1 liters/min = heavy exertion
The selected summary statistics were computed for each of the 243 angina subjects and 8,906
non-angina subjects in the NHAPS study. A statistical analysis compared the distributions of
these summary statistics for persons with and without angina. For each summary statistic we
compared the mean values between the angina and non-angina groups using standard t tests. The
significance level (p-value) for the difference in means was computed using the Smith-
Satterthwaite procedure, that tests for no difference in population means assuming that the two
populations are normally distributed but may have different variances. P-values at or below 0.05
denote significant differences at the five percent level of significance. By the central limit
theorem, the p-values for the t test comparisons should be reasonably accurate for the large
samples used in the overall analyses, even if the normality assumption does not hold, but the p-
values will be less accurate for the analyses of specific gender and age subgroups. We also
compared variances using a standard F test, that assumes normality of the two populations.
Since the normality assumption may not be a sufficiently good approximation, we also applied
two non-parametric tests that do not require specific parametric distributions. The non-
parametric Wilcoxon test, also known as the Mann-Whitney-Wilcoxon test or the Rank Sum
Test, was used to compare the central tendencies of the two distributions. This test assumes only
that the populations have the same distributional shape, which may or may not be the normal
distribution, but the distribution of values for angina population might be shifted by some
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constant value, and thus might have a different median than the non-angina population. The
Kolmogorov-Smirnov test was used to evaluate any possible differences between the two
distributions, whether due to differences in means, medians, variances, or any other features of
the distribution. This test uses the maximum absolute difference between the two cumulative
distribution functions, assuming only that these distributions are continuous.
The mean, variance, median, and distribution function comparisons were made for all persons
combined, separately for males and females, and then separately for four age groups within the
male and female subgroups. Age groupings were chosen to include approximately 25 percent of
angina subjects in each group. Separate comparisons for males and females are needed to
distinguish whether any overall differences in exertion or activity are explained by the fact that
angina subjects are more likely to be female than in the general population. Since activity
patterns and exertion rates differ between males and females, any overall difference between the
angina and non-angina groups might be explained by the greater propensity for females to get
angina, rather than the direct effect of angina. Similarly, the subsetting by age group evaluates
the effect of the different age distributions for angina subjects compared to the general
population (angina subjects tend to be much older). This statistical analysis does not, and cannot,
address questions as to whether the angina causes the change in exertion or activity patterns, or
vice versa. We only examine whether or not the summary statistics of activity and exertion
patterns are different for the two populations.
A general linear model approach was also used as an alternative method of adjusting for the
effects of age and gender on the angina/non-angina comparison. We focused attention on a
relatively simple statistical model with cubic terms in age (a simple linear function of age fitted
poorly), gender, interactions between age and gender, and a single term for the effect of angina:
Summary Statistic = I(male){_ + _(age) + _(age)2 + _(age)3}
+ I(female){_ + _(a|
+ _I(angina) + error
I(female){_ + _(age) + _(age)2 + _(age)3}
where: I(male) = 1 for males, 0 for females; I(female) = 1 for females, 0 for males; I(angina) = 1
for persons having angina, 0 for persons not having angina. The errors are assumed to be
normally distributed, statistically independent, and have mean zero and some constant variance.
This statistical model assumes that the expected value of the summary statistic is a cubic
function of age, but is a different function for males and females. The selected model has the
same coefficient for the cubic term for males and females, but different coefficients for the
intercept, linear, and quadratic effects. The model also assumes that having angina changes the
mean by a constant amount, which is the same factor for all age groups and both genders. A
more sophisticated model might allow for interactions between angina and the age and gender
variables, to allow for the possibility that the angina effect varies by gender and/or age. Note,
however, that our statistical analysis clearly showed that age and gender were much more
significant predictors of exertion patterns than the angina indicator, explaining most of the
variability in the summary statistics.
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Project resources were insufficient for a detailed exploration of alternative statistical models. We
tried using logarithmic transformations to improve the model fit, but could not reasonably use
such models in view of the large number of cases where the observed summary statistic was zero
(the logarithm is then undefined). The model fit for the selected model (without taking
logarithms) varied with the summary statistic. R squared goodness-of-fit statistics were
extremely low, less than 0.05, for the percentages of time spent outdoors and/or in a vehicle. For
the summary statistics based on the maximum 8-hour exertion and the percentages of time above
exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a fairly good fit,
0.48. The cases of poor fitting models may be because the selected statistical models poorly
represent the relationship between age, gender, and angina and the activity/exertion summary
statistic and/or because the activity/exertion pattern varies substantially between people of the
same age, gender, and angina status.
RESULTS
Age, Gender, and Angina Disease Distributions
Table 1 shows the number of subjects with or without angina by gender and by age group. The
four age groups were chosen to have approximately the same numbers of angina subjects. The
strong association between angina and age is illustrated by the fact that 52/243 =21 % of angina
subjects are under 55 but 6877/8906 = 77 % of non-angina subjects are under 55. Angina
subjects tend to be significantly older than the general population. The association between
angina and gender is weaker. 103/243 = 42.3 % of angina subjects are male, but 4116/8906 =
46.2 % of non-angina subjects are male.
Overall Comparisons of Activity and Exertion Summary Statistics between Angina and
Non-Angina Subjects
Table 2 compares the means between the angina and non-angina subjects, without stratification
by age or gender. The average and 95th percentile of the maximum eight hour exertion has a
statistically significantly lower mean for angina subjects. Furthermore, for each of the exertion
levels 2.39, 5.97, and 9.55 kcal/min (0.010, 0.025, and 0.040 MJ/min), the mean percentage of
time above each level was statistically significantly lower for the angina subjects. Non-angina
subjects spend an average of 2.8 percent of their time doing activities requiring moderate or
higher levels of exertion, defined by exertion rates above 5.97 kcal/min (0.025 MJ/min); angina
subjects spend an average of 2.2 percent of their time doing such activities. All subjects spend
over 75 percent of time in light or sedentary activities, with extertion rates below 2.39 kcal/min,
including sleeping. All these exertion distribution comparisons show that angina subjects tend to
do activities with less exertion than the general population. However, since the summary
analyses in Table 2 do not take into account the marked differences between the age and gender
distributions of angina and non-angina subjects, the lower exertion rates could be associated with
the tendency for angina subjects to be older (and female) rather than the disease itself. The
average percentages of time spent outdoors are nearly identical, and are not statistically
significantly different between angina and non-angina subjects, but angina subjects spend
statistically significantly less time in vehicles (4.5 % rather than 5.5 %, on average).
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Table 2 compares the standard deviations using a F-test based on the variance ratio for angina vs.
non-angina subjects. In most cases the F tests show statistically significantly different variances
(and, therefore, standard deviations).
Table 2 also uses the non-parametric Wilcoxon test to compare the central tendencies of the two
distributions without the normality assumption required by the T test. Corresponding to the T
test comparisons, the Wilcoxon test finds that the angina and non-angina distributions are
significantly different in almost all cases; the angina subjects have a lower median value for each
of the selected summary statistics. Exceptions are for the average maximum 8-hour exertion, just
significant at the 7 % level, and the percentage of time spent outdoors, which has a non-
significant p-value of 22 %.
Finally, Table 2 compares the distribution functions using the Kolmogorov-Smironov test. The
distributions are statistically significantly different at the five and one percent levels in all cases
except for the percentage of time spent outdoors, which shows no significant difference. For that
variable, the T and Wilcoxon tests showed no statistically significant differences in central
tendency although the F test showed a statistically significant difference in the population
variances. If the population variances are different, so are the two distribution functions. The
discrepancy between the F and Kolmogorov-Smirnov tests is partly explained by the fact that the
F test is very sensitive to the assumption of normal distributions, whereas the Kolmogorov-
Smirnov test only requires the distributions to be continuous. (Both tests assume that the mean
and variances are constant for each population, which is inconsistent with the variation of the
means and variances with age and gender shown in the stratified analyses in Tables 3 and 4.) The
discrepancy is also partly explained by the fact that the Kolmogorov-Smirnov test is less
powerful (less likely to detect a difference) than the other tests, because it makes the fewest
assumptions and considers the widest class of alternative hypotheses.
Stratified Comparisons of Activity and Exertion Summary Statistics between Angina and
Non-Angina Subjects
Tables 3 and 4 provide the same statistical comparisons as Table 2, stratified by gender and age
group. The results show the mean values for the selected summary statistics are not consistently
lower for each age and gender subgroup of angina subjects. For example, Table 2 showed that
the angina subjects had a lower overall mean value of the average maximum 8-hour exertion
than the non-angina subjects. Tables 3 and 4 show the mean is actually higher for angina
subjects 0-54 of either gender and for males 75 or older. The mean average maximum 8-hour
exertions are consistently higher for males of all age groups, with or without angina, compared
to females. Similar patterns are found for the 95th percentile of the maximum 8-hour exertion.
The comparisons of the percentages of time spent outdoors or in a vehicle also vary across age
and gender subgroups. The largest, and most surprising, angina vs. non-angina difference is for
the mean percentage of time spent outdoors by 0-54 year old males: angina subjects have a mean
of 17 % compared to the mean of 9 % for non-angina subjects. However the angina subjects in
the 55-64 and 65-74 age groups of either gender spend less time outdoors, on average, than non-
February 2010 F-9 Draft - Do Not Cite or Quote
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angina subjects.
The Table 3 and 4 comparisons of the mean percentages of time above the light, moderate or
high exertion levels show a variety of patterns for different age groups, genders, and exertion
levels.
Comparisons of Activity and Exertion Summary Statistics between Angina and Non-
Angina Subjects Adjusted for Age and Gender Differences
Table 5 gives the results of the fitted general linear model. As explained above, the fitted model
assumes that for each gender, the average value of the summary statistic is a cubic function of
age. Furthermore, having angina changes the expected value by a fixed amount, which is
assumed to be the same value for every age and gender. This angina effect is the coefficient
reported in the table, together with its standard error and p-value. P-values less than or equal to
0.05 indicate summary statistics where the angina effect was statistically significant at the 5
percent significance level. The angina coefficient can be thought of as the effect of angina after
adjusting for age and gender. The effects of age and gender are not reported, but in all cases were
extremely statistically significant compared to the angina effect.
Table 5 also reports the R squared goodness-of-fit statistic, which is the squared correlation
between the observed and predicted values. R squared values vary from 0 (the worst possible fit)
to 1 (a perfect fit), and are often interpreted as the fraction of the variability in the dependent
variable (summary statistic) that is explained by the regression model.
The first two rows of Table 5 show that the angina effect on the average and 95th percentile
maximum 8-hour exertion is a statistically significant reduction (at the 6 and 1 % levels,
respectively) for angina subjects compared to non-angina subjects. However, these reductions of
0.04 Meal and 0.16 Meal are small when compared to the overall mean values of 1.4 and 2.3
Meal (non-angina subjects) reported in Table 2. The next three rows show that angina subjects
tend to spend a little more time (0.7 percentage points) outdoors and a little less time (0.5
percentage points) in a vehicle compared to non-angina subjects; those differences are not
statistically significant. The last four rows show that angina subjects tend to spend less time at
moderate or high levels of exertion, after adjusting for age and gender, although the differences
are at most 1 percentage point and are not statistically significant. For example, the unadjusted
average percentage time above 2.39 kcal/min (0.010 MJ/min) was 23.5 % for non-angina
subjects (Table 2), and the effect of angina is to reduce the expected percentage of time by 0.7.
As shown in Tables 3 and 4, this is due to average reductions of up to 5 percentage points for
ages 55 and older but increases of 6 (males) and 2 (females) percentage points for the 0-54 age
group.
R squared goodness-of-fit statistics were extremely low, 0.05 or less, for the percentages of time
spent outdoors and/or in a vehicle. Thus the regression models for those percentages give very
poor predictions. There are two possible reasons for this. First, the combination of age, gender,
and angina status may be strongly associated with the percentages of time spent outdoors or in a
vehicle but the assumed form of the regression model may poorly represent the functional
February 2010 F-10 Draft - Do Not Cite or Quote
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relationship. Second, the combination of age, gender, and angina status may be poorly associated
with the percentages of time spent outdoors or in a vehicle so that those activity percentages vary
mainly with the effects of factors other than age, gender, and angina status. In either case, those
regression models are not recommended for use in predicting the activity percentages.
For the summary statistics based on the maximum 8-hour exertion and the percentages of time
above exertion rate thresholds, the R squared statistics ranged from a poor fit, 0.25, to a
reasonably good fit, 0.48. As above, the cases of poor fitting models may be because the selected
statistical models poorly represent the relationship between age, gender, and angina and the
activity/exertion summary statistic and/or because the activity/exertion pattern varies
substantially between people of the same age, gender, and angina status. Alternative general
linear models, or the more sophisticated generalized linear models, could be developed to
improve the predictive ability of the statistical models.
February 2010 F-l 1 Draft - Do Not Cite or Quote
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REFERENCES
Brainard, J., and Burmaster, D. E. 1992. "Bivariate Distributions for Height and Weight of Men
and Women in the United States." Risk Analysis 12:2, pp. 267-275.
Burmaster, D. E., and Crouch, E. A. C. 1994. "Lognormal Distributions of Body Weight as a
Function of Age for Males and Females in the United States." Risk Analysis 17:4, pp. 499-
508.
Esmail, Bhambhani, and Brintnell. 1995. "Gender Differences in Work Performance on the
Baltimore Therapeutic Equipment Work Simulator." Amer. J. Occup. Therapy 49, pp. 405-
411.
Johnson, T. R, 1998. Proposed Probabilistic Algorithm for Estimating Ventilation Rate in the
1998 Version ofpNEM/CO. Memorandum submitted to EPA (September 25, 1998).
Klepeis, N. E., Tsang, A. E., and Behar, J. V. 1996. Analysis of the National Human Activity
Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment.
EPA/600/R-96/074.
Klepeis, N. E., Nelson, W. C., Tsang, A. M., Robinson, J. P., Hern, S. C., Engelmann, W. H.,
and Behar, J. V. 1998. "The National Human Activity Pattern Survey (NHAPS): Data
Collection Methodology and Selected Results." Submitted to Journal of Exposure Analysis
and Environmental Epidemiology.
Schofield, W. N. 1985. "Predicting Basal Metabolic Rate, New Standards, and Review of
Previous Work." HumNutr ClinNutr. 39C (Supp 1), pp. 5-41.
Triplett, T. 1996. National Human Activity Pattern Survey, CD-ROM Version 2.0. University of
Maryland Survey Research Center.
Tsang, A. M. and Klepeis, N. E. 1996. Descriptive Statistics Tables from a Detailed Analysis of
the National Human Activity Pattern Survey (NHAPS) Data. EPA/600/R-96/148.
February 2010 F-12 Draft - Do Not Cite or Quote
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Table 1. Distribution of subjects according to their age, gender and disease status
Age Group
0-54
55-64
65-74
75+
Total
Males
Angina (%)
35 (1 .0)
28 (6.5)
23 (7.9)
17(10.7)
103(2.4)
Non-angina
(%)
3307 (98.9)
400 (93.5)
267 (92.1)
142(89.3)
4116(97.6)
All
3342
428
290
159
4219
Females
Angina (%)
17(0.5)
28 (5.4 )
48 (9.6)
47(14.4)
140(2.8)
Non-angina
(%)
3570 (95.5)
491 (94.6)
450 (91 .4)
279 (85.6)
4790 (97.2)
All
3587
519
498
326
4930
All
Angina (%)
52 (0.8)
56 (5.9)
71 (9.0)
64(13.2)
243 (2.6)
Non-angina
(%)
6877 (99.2)
891 (94.1)
717(91.0)
421 (86.8)
8906 (97.4)
All
6929
947
788
485
9149
This table was modified by staff on 2-22-1 0 from the below original version due to issues related to the conversion from Word Perfect to Microsoft
Word.
Gender
Males
Females
All
Age
group
Angina(%)
35(1.0)
3342
28 (6.5)
428
23 (7.9)
290
17(10.7)
159
103(2.4)
4219
Non-angina(%) All
3307 (98.9)
400 (93.5)
267(92.1)
142(89.3)
4116(97.6)
Angina(%)
17(0.5)
3587
28 (5.4 )
519
48 (9.6)
498
47 (14.4)
326
140(2.8)
4930
Non-angina(%) All
3570 (95.5)
491 (94.6)
450(91.4)
279 (85.6)
4790 (97.2)
Angina(%)
52 (0.8)
6929
56 (5.9)
947
71 (9.0)
788
64(13.2)
485
243 (2.6)
9149
Non-angina(%) All
6877 (99.2)
891 (94.1)
717(91.0)
421 (86.8)
8906 (97.4)
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Table 2. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. All
Variable
Average maximum 8hr
exertion (Meal)
Ninety fifth percentile of
maximum 8hr exertion
(Meal)
Percentage of time spent
outdoors
Percentage of time spent in
vehicle
Percentage of time spent
outdoors or in vehicle
Average percentage of time
with exertion above 2.39
kcal/min
= 0.01 OMJ/min (light)
Average percentage of time
with exertion above 5.97
kcal/min
= 0.025 MJ/ min (moderate)
Average percentage of time
with exertion above 9.55
kcal/min
T Test Comparison of
Means
Mean
Angina
1.28
1.87
6.73
4.55
11.27
19.98
2.17
0.213
Mean P-value
Non-angina
1.40 0.00
2.25 0.00
6.74 0.99
5.55 0.01
12.29 0.27
23.53 0.00
2.78 0.01
0.406 0.00
F Test Comparison of
Standard Deviations
St. Dev.
value
Angina
0.48
0.97
12.87
6.19
14.33
13.56
3.68
0.554
St. Dev. P-
Non-angina
0.49 0.68
1.13 0.00
11.63 0.02
7.13 0.00
13.45 0.15
13.78 0.75
3.56 0.46
0.761 0.00
Wilcoxon Kolmogorov
Test -Smirnov
Test
P-value
P-value
0.00 0.00
0.00 0.00
0.22 0.23
0.00 0.00
0.00 0.00
0.00 0.00
0.00 0.00
0.00 0.00
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= 0.040 MJ/min (heavy)
Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. Males
Variable
Average maximum
8hr exertion (Meal)
Ninety fifth
percentile of
maximum 8hr
exertion (Meal)
Percentage of time
spent outdoors
February 2010
Age
Group
0-54
55-64
65-74
75+
0-54
55-64
65-74
75+
0-54
55-64
65-74
T Test Comparison
Means
Mean Mean P-
value
Angina
1.85
0.00
1.48
0.00
1.39
0.36
1.27
0.52
2.94
0.17
2.40
0.04
1.91
0.14
1.73
0.71
16.86
0.02
9.28
F-15
of F Test Comparison of Wilcoxon
Standard Deviations Test
St. Dev. St. Dev. P- P-value
value
Non-angina Angina
1.59
1.77
1.49
1.20
2.68
2.90
2.17
1.67
8.85
10.02
0.49
0.34
0.48
0.86
0.47
0.96
0.44
0.65
1.06
0.13
1.21
0.61
0.78
0.31
0.69
0.68
19.16
0.00
14.26
Non-angina
0.55
0.48
0.48
0.42
1.30
1.14
0.94
0.76
13.75
13.86
0.02
0.01
0.41
0.51
0.22
0.02
0.26
0.55
0.01
0.65
0.12
Kolmogorov
-Smirnov
Test
P-value
0.02
0.02
0.82
0.95
0.06
0.03
0.63
0.88
0.01
1.00
0.13
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Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. Males
Variable
Percentage of time
spent in vehicle
Percentage of time
spent outdoors or
in vehicle
Average
Age
Group
75+
0-54
55-64
65-74
75+
0-54
55-64
65-74
75+
0-54
T Test Comparison of F Test Comparison of
Means Standard Deviations
Mean Mean P- St. Dev. St. Dev. P-
value value
Angina Non-angina Angina Non-angina
0.79
6.43
0.13
8.23
0.72
5.96
0.92
3.99
0.00
7.20
0.51
2.29
0.14
22.83
0.02
13.27
0.24
13.63
0.45
10.51
0.98
34.76
10.40
7.09
6.09
6.87
5.88
3.34
14.94
16.89
16.29
10.43
27.78
0.78
11.38
0.20
12.63
0.16
7.55
0.63
3.78
0.00
9.04
0.29
2.54
0.05
19.28
0.05
15.30
0.76
15.87
1.00
12.86
0.19
13.33
14.34
10.05
8.10
9.63
7.80
3.94
15.52
16.18
15.91
10.39
15.18
Wilcoxon
Test
P-value
0.58
0.89
0.18
0.82
0.44
0.02
0.17
0.19
0.69
0.02
Kolmogorov
-Smirnov
Test
P-value
0.66
0.52
0.19
0.93
0.54
0.02
0.22
0.29
0.41
0.05
February 2010
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Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. Males
Variable
percentage of time
with exertion
above 2.39
kcal/min
= 0.010MJ/min
(light)
Average
percentage of time
with exertion
above 5.97
kcal/min
= 0.025 MJ/m in
(moderate)
Average
percentage of time
with exertion
above 9.55
kcal/min
= 0.040 MJ/min
(heavy)
Age
Group
55-64
65-74
75+
0-54
55-64
65-74
75+
0-54
55-64
65-74
75+
T Test Comparison of F Test Comparison of
Means Standard Deviations
Mean Mean P- St. Dev. St. Dev. P-
value value
Angina
0.00
24.60
0.06
21.92
0.63
18.24
0.41
6.63
0.05
3.43
0.01
2.27
0.08
2.02
0.53
0.662
0.59
0.565
0.19
0.155
0.01
0.132
Non-angina
30.07
23.32
15.87
4.46
5.44
3.27
1.62
0.735
0.846
0.388
0.157
Angina
0.34
14.51
0.14
13.23
0.64
11.13
0.63
6.37
0.00
3.55
0.17
2.47
0.06
2.42
0.92
0.792
0.11
1.068
0.40
0.361
0.00
0.331
Non-angina
12.03
12.48
10.37
4.31
4.41
3.46
2.41
0.986
1.222
0.716
0.512
Wilcoxon
Test
P-value
0.06
0.67
0.39
0.02
0.01
0.20
0.43
0.55
0.05
0.06
0.55
Kolmogorov
-Smirnov
Test
P-value
0.14
0.84
0.74
0.01
0.01
0.40
0.59
0.15
0.17
0.04
0.96
February 2010
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Table 3. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. Males
Variable
Table 4. Statistical
Exertion. Females
Variable
Average maximum
8hr exertion (Meal)
Ninety fifth
Age
Group
Tests for
Age
Group
0-54
55-64
65-74
75+
0-54
T Test Comparison of
Means
Mean Mean P-
value
Angina Non-angina
0.79
the Association between
T Test Comparison of
Means
Mean Mean P-
value
Angina Non-angina
1.30 1.27
0.69
1.21 1.27
0.33
1.05 1.10
0.29
0.96 0.98
0.63
1.98 2.01
F Test Comparison of
Standard Deviations
St. Dev. St. Dev. P-
value
Angina Non-angina
0.05
Wilcoxon
Test
P-value
Kolmogorov
-Smirnov
Test
P-value
Angina and Various Variables Representing Physical
F Test Comparison of
Standard Deviations
St. Dev. St. Dev. P-
value
Angina Non-angina
0.31 0.38 0.34
0.32 0.33 1.00
0.30 0.31 0.94
0.33 0.30 0.34
0.82 0.91
Wilcoxon
Test
P-value
0.72
0.56
0.31
0.44
0.99
Kolmogorov
-Smirnov
Test
P-value
0.73
0.22
0.44
0.66
0.86
February 2010
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Table 4. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. Females
Variable
Age
Group
T Test Comparison
Means
Mean Mean P-
value
of F Test Comparison of
Standard Deviations
St. Dev. St. Dev. P-
value
Angina Non-angina Angina
percentile of
maximum 8hr
exertion (Meal)
Percentage of time
spent outdoors
Percentage of time
spent in vehicle
Percentage of time
55-64
65-74
75+
0-54
55-64
65-74
75+
0-54
55-64
65-74
75+
0-54
0.86
1.79
0.41
1.42
0.26
1.27
0.59
3.64
0.42
4.31
0.88
2.84
0.14
3.79
0.40
4.54
0.55
4.21
0.35
5.26
0.23
2.82
0.86
8.18
1.92
1.51
1.31
5.11
4.59
4.14
2.27
5.35
5.60
4.15
2.94
10.46
0.69
0.80
0.68
0.53
0.57
0.56
0.43
7.29
0.20
9.53
0.23
5.51
0.02
12.04
0.00
5.48
0.72
7.53
0.71
5.94
0.77
4.37
0.91
9.93
Non-angina
0.77
0.57
0.52
9.58
8.22
7.34
4.60
5.98
7.23
6.18
4.34
11.27
Wilcoxon
Test
P-value
0.33
0.31
0.43
0.43
0.23
0.49
0.76
0.40
0.06
0.15
0.72
0.18
Kolmogorov
-Smirnov
Test
P-value
0.28
0.62
0.47
0.91
0.63
0.53
1.00
0.35
0.12
0.19
0.96
0.27
February 2010
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Table 4. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. Females
Variable
spent outdoors or
in vehicle
Average
percentage of time
with exertion
above 2.39
kcal/min
= 0.010MJ/min
(light)
Average
percentage of time
with exertion
above 5.97
kcal/min
= 0.025 MJ/m in
(moderate)
Average
Age
Group
55-64
65-74
75+
0-54
55-64
65-74
75+
0-54
55-64
65-74
75+
0-54
T Test Comparison of F Test Comparison of
Means Standard Deviations
Mean Mean P- St. Dev. St. Dev. P-
value value
Angina
0.36
8.52
0.48
8.10
0.88
6.61
0.45
23.50
0.49
19.04
0.33
13.97
0.26
11.31
0.73
1.44
0.71
0.79
0.05
0.65
0.87
0.75
0.23
0.101
Non-angina
10.18
8.29
5.21
21.40
21.04
15.46
11.84
1.59
1.31
0.68
0.43
0.184
Angina
0.57
12.15
0.22
8.21
0.26
12.36
0.00
12.28
0.86
10.34
1.00
8.45
0.31
9.75
0.24
1.66
0.44
1.27
0.00
1.35
0.22
1.73
0.01
0.170
Non-angina
10.42
9.38
6.25
12.15
10.47
9.53
8.61
1.97
2.07
1.56
1.31
0.324
Wilcoxon
Test
P-value
0.04
0.99
0.77
0.41
0.51
0.43
0.51
0.73
0.04
0.51
0.84
0.63
Kolmogorov
-Smirnov
Test
P-value
0.05
0.86
0.97
0.69
0.54
0.76
0.89
0.74
0.06
0.88
0.67
0.80
February 2010
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Table 4. Statistical Tests for the Association between Angina and Various Variables Representing Physical
Exertion. Females
Variable
Age T Test Comparison of
Group Means
Mean Mean P-
value
Angina Non-angina
F Test Comparison of
Standard Deviations
St. Dev. St. Dev. P-
value
Angina Non-angina
Wilcoxon
Test
P-value
Kolmogorov
-Smirnov
Test
P-value
percentage of time
with exertion
above 9.55
kcal/min
= 0.040 MJ/min
(heavy)
55-64
65-74
75+
0.06
0.095
0.96
0.028
0.89
0.025
0.44
0.093
0.030
0.016
0.00
0.277
0.01
0.076
0.00
0.075
0.90
0.198
0.110
0.077
0.67
0.85
0.28
1.00
0.99
0.83
February 2010
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Table 5. General Linear Models for the Association between Angina and Various Variables Representing Physical
Exertion.
Variable
Average maximum 8hr exertion (Meal)
Ninety fifth percentile of maximum 8hr exertion
(Meal)
Percentage of time spent outdoors
Percentage of time spent in vehicle
Percentage of time spent outdoors or in vehicle
Average percentage of time with exertion above
2.39 kcal/min = 0.010 MJ/min (light)
Average percentage of time with exertion above
5.97 kcal/min = 0.025 MJ/min (moderate)
Average percentage of time with exertion above
9.55 kcal/min = 0.040 MJ/min (heavy)
Angina
Coefficient1
-0.0445
-0.1553
+0.6975
-0.4805
+0.2170
-0.7359
-0.1730
-0.0933
Standard
Error
0.0237
0.0581
0.7648
0.4679
0.8777
0.6996
0.1910
0.0439
P-value
0.0608
0.0075
0.3618
0.3045
0.8047
0.2929
0.3650
0.0334
R squared
0.4819
0.4114
0.0388
0.0325
0.0520
0.4239
0.3570
0.2494
1. The angina coefficient is the expected difference (angina minus non-angina) between the summary statistic for angina and
non-angina subjects of the same age and gender.
February 2010
F-22
Draft - Do Not Cite or Quote
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/P-10-004
Environmental Protection Health and Environmental Impacts Division February, 2010
Agency Research Triangle Park, NC
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