ESTIMATION OF OZONE EXPOSURES
EXPERIENCED BY OUTDOOR WORKERS IN
NINE URBAN AREAS USING A
PROBABILISTIC VERSION OF NEW
by
Ted Johnson, Jim Capel, Mike McCoy, and Jill Warnasch Mozier
International Technology Air Quality Services
South Square Corporate Centre One
3710 University Drive, Suite 201
Durham, North Carolina 27707-6208
Contract No. 63-D-30094
Work Assignment Nos. 0-1 and 1-4
JTN 453207-1
Harvey Richmond, Work Assignment Manager
Nancy Riley, Project Manager
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF AIR QUALITY PLANNING AND STANDARDS
RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711
April 1996
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CONTENTS
Figures vi
Tables vii
Acknowledgment xvi
1. Introduction 1
2. Overview of the Methodology 4
Define study area, population-of-interest,
subdivisions of study area, and exposure period 4
Divide the population-of-interest into an exhaustive
set of cohorts 6
Develop an exposure event sequence for each cohort for
the exposure period 7
Estimate the pollutant concentration and ventilation
rate associated with each exposure event 12
Extrapolate the cohort exposures to the population-of-interest 21
3. The Mass-Balance Model 27
Theoretical basis and assumptions 28
Simulation of microenvironmental ozone concentrations 35
Air exchange rate distributions 38
Window status algorithm 41
4. Preparation of Air Quality Data 45
Selection of representative data sets 45
Treatment of missing values and descriptive statistics 46
III
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CONTENTS (continued)
5. Adjustment of Ozone Data to Simulate Compliance with Alternative
Air Quality Standards 73
Specification of AQI and estimation of baseline AQI values 75
Estimation of AQI's under attainment conditions 81
Adjustment of one-hour ozone data sets 85
Application of the AQAP's to Philadelphia 88
Special Adjustment Procedures Applied in Selected
Attainment Scenarios 96
6. Preparation of Outdoor Worker Data Bases 99
Selection of time/activity data 99
Processing of time/activity data 112
City-specific outdoor worker populations 128
7. Ozone Exposure Estimates for Nine Urban Areas 151
Regulatory scenarios 151
Formats of the exposure summary tables 152
Results of analyses 154
Estimates of maximum dose exposures 175
8. Principal Limitations of the pNEM/O3 Methodology 220
Time/activity patterns 221
Equivalent ventilation rates 222
The air quality adjustment procedures 224
Estimation of cohort populations 225
The mass balance model 226
References 228
Appendices
A. Ten Time/Activity Bases Generally Applicable to Air
Pollution Exposure Assessments A-1
B. Occupation Groups Defined for the Outdoor
Worker Exposure Analysis B-1
IV
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CONTENTS (continued)
C. Potential Outdoor Occupations Not Included in
Outdoor Worker Exposure Analysis C-1
D. Sample Output of pNEM/OS Applied to Outdoor
Workers (Houston, 1-Hour, Daily Maximum 0.12 ppm
Standard [Current NAAQS]) D-1
E. One-Hour Exposure Distributions E-1
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FIGURES
Number
1 Page From the Activity Diary Used in the Cincinnati Study 9
2a Eight-Hour Daily Maximum Dose Exposure Distributions for
Outdoor Workers Exposed on One or More Days Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
Philadelphia, PA 215
2b Eight-Hour Daily Maximum Dose Exposure Distributions of
Total Occurrences for Outdoor Worker Exposure Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
Philadelphia, PA 215
3a Eight-Hour Daily Maximum Dose Exposure Distributions for
Outdoor Workers Exposed on One or More Days Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
Houston, TX 216
3b Eight-Hour Daily Maximum Dose Exposure Distributions of
Total Occurrences for Outdoor Worker Exposure Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
Houston, TX 216
4a Eight-Hour Daily Maximum Dose Exposure Distributions for
Outdoor Workers Exposed on One or More Days Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
New York, NY 217
4b Eight-Hour Daily Maximum Dose Exposure Distributions of
Total Occurrences for Outdoor Worker Exposure Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
New York, NY 217
5a Eight-Hour Daily Maximum Dose Exposure Distributions for
Outdoor Workers Exposed on One or More Days Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
Washington, D.C. 218
5b Eight-Hour Daily Maximum Dose Exposure Distributions of
Total Occurrences for Outdoor Worker Exposure Under
Moderate Exertion (EVR 13-27 Liters/Min-M2) in
Washington, D.C. 218
VI
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TABLES
Number
1 Characteristics of Study Areas 6
2 Characteristics of Studies Providing Time/Activity Data
for Outdoor Workers 10
3 Parameters Associated with Algorithms Used to Estimate
Ozone Concentrations in Microenvironments 14
4 Parameter Values of Lognormal Distributions Used to
Characterize Equivalent Ventilation Rate 18
5 Algorithm for Determining Upper Limit for EVR 19
6 Parameter Values for Algorithm Used to Determine Limits for
Equivalent Ventilation Rates for Outdoor Workers 20
7 Estimated Fraction of Houston Workers Within Each
Home District That Commute to Each Work District 25
8 Means, Standard Deviations, and Confidence Intervals
for Estimates of MA/V) Provided by Weschler 34
9 Distributions of Air Exchange Rate Values Used in the
pNEM/03 Mass Balance Model 38
10 Probability of Window Status for Day by Air Conditioning
System and Temperature Range 43
11 Probability of Windows Being Open by Clock Hour, Temperature
Range, and Window Status of Preceding Hour (PH) for Residences
With Central Air Conditioning 43
VII
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TABLES (continued)
Number
12 Probability of Windows Being Open by Clock Hour, Temperature
Range, and Window Status of Preceding Hour (PH) for Residences
With Window Air Conditioning Units 44
13 Probability of Windows Being Open by Clock Hour, Temperature
Range, and Window Status of Preceding Hour (PH) for Residences
With No Air Conditioning System 44
14 Characteristics of Ozone Study Areas and Monitoring Sites 47
15 Descriptive Statistics for 1991 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the Chicago Study Area 48
16 Descriptive Statistics for 1990 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the Denver Study Area 50
17 Descriptive Statistics for 1990 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the Houston Study Area 51
18 Descriptive Statistics for 1991 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the Los Angeles Study Area 53
19 Descriptive Statistics for 1991 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the Miami Study Area 55
20 Descriptive Statistics for 1991 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the New York Study Area 56
21 Descriptive Statistics for 1991 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the Philadelphia Study Area 58
VIII
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TABLES (continued)
Number
22 Descriptive Statistics for 1990 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the St. Louis Study Area 60
23 Descriptive Statistics for 1991 Data Sets Containing
Hourly-Average Ozone Concentrations Obtained From
Selected Monitoring Sites in the Washington Study Area 62
24 Descriptive Statistics for 1991 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the Chicago Study Area 64
25 Descriptive Statistics for 1990 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the Denver Study Area 65
26 Descriptive Statistics for 1990 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the Houston Study Area 66
27 Descriptive Statistics for 1991 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the Los Angeles Study Area 67
28 Descriptive Statistics for 1991 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the Miami Study Area 68
29 Descriptive Statistics for 1991 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the New York Study Area 69
30 Descriptive Statistics for 1991 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the Philadelphia Study Area 70
IX
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TABLES (continued)
Number
31 Descriptive Statistics for 1990 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the St. Louis Study Area 71
32 Descriptive Statistics for 1991 Data Sets Containing
Eight-Hour Ozone Concentrations Obtained From
Selected Monitoring Sites in the Washington Study Area 72
33 Baseline Air Quality Indicators for Nine Cities 78
34 Air Quality Adjustment Procedure Used to Simulate Attainment
of 1H1EX NAAQS (The Expected Number of Daily Maximum
One-Hour Ozone Concentrations Exceeding the Specified Value
Shall Not Exceed One) 82
35 Air Quality Adjustment Procedure Used to Simulate Attainment
of 8H1EX NAAQS (The Expected Number of Daily Maximum
Eight-Hour Ozone Concentrations Exceeding the Specified Value
Shall Not Exceed One) 83
36 Air Quality Adjustment Procedure Used to Simulate Attainment
of 8H5EX NAAQS (The Expected Number of Daily Maximum
Eight-Hour Ozone Concentrations Exceeding the Specified Value
Shall Not Exceed Five) 84
37 Values for Equivalence Relationships 87
38 Determination of Adjustment Coefficients for One-Hour
NAAQS Attainment (1H1EX-120) in Philadelphia 89
39 Descriptive Statistics for Hourly-Hour Data (ppb) for Site
34-005-3001 (District 1, Philadelphia): Baseline and Attainment
of Three Ozone Standards 91
40 Determination of Adjustment Coefficients for Eight-Hour
NAAQS Attainment (8H1EX-80) in Philadelphia 92
41 Determination of Adjustment Coefficients for Eight-Hour
NAAQS Attainment (EH6LDM = 80 ppb) in Philadelphia 95
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TABLES (continued)
Number Page
42 Activity Codes From the Seven Activity Studies
Indicating a Person at Work 101
43 Characteristics of Activity Data for Initial Set of
Potential Outdoor Workers 102
44 Subjects Selected From the Cincinnati Diary Study
for Inclusion in the Outdoor Worker Time/Activity Database 103
45 Subjects Selected From the Denver Diary Study for
Inclusion in the Outdoor Worker Time/Activity Database 104
46 Subjects Selected From the Washington Diary Study for
Inclusion in the Outdoor Worker Time/Activity Database 104
47 Subjects Selected From the California Diary Study for
Inclusion in the Outdoor Worker Time/Activity Database 105
48 Subjects Selected From the Los Angeles Outdoor Worker
Diary Study for Inclusion in the Outdoor Worker
Time/Activity Database 108
49 Subjects Selected From the Los Angeles Construction Worker
Diary Study for Inclusion in the Outdoor Worker
Time/Activity Database 109
50 Subjects Selected From the Valdez Diary Study for Inclusion
in the Outdoor Worker Time/Activity Database 111
51 Breathing Rate Categories of Activities in the Cincinnati
Study 114
52 Cumulative Breathing Rate Category Probabilities
From the Cincinnati Activity-Diary Study by
Activity Class, Microenvironment, Time of Day Category,
and Event Duration Category 118
53 Activity Classes Assigned to Activity Codes Used in
the California Diary Study 122
54 Activity Classes Assigned to Activity Codes Used in
the Denver Diary Study 125
XI
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TABLES (continued)
Number
55 Activity Classes Assigned to Activity Codes Used in
the Valdez Diary Study 126
56 Activity Classes Assigned to Activity Codes Used in
the Washington Diary Study 127
57 1990 Bureau of Census Occupations and Occupation
Groups Represented in the Outdoor Worker
Time/Activity Data Base 129
58 Percentages of "Outdoor Workers" Within Each ITAQS Group 141
59 Estimated Outdoor Worker Populations by 1990
Bureau of Census Occupation Group and Study Area 144
60 Total Worker Populations by Occupation Group and
Study Area 146
61 Estimated Outdoor Worker Percentages by Occupation
Group and Study Area 149
62 Number and Percent of Outdoor Workers Experiencing
One or More One-Hour Daily Maximum Ozone Exposures
Above 120 ppb at Any Ventilation Rate 155
63 Number and Percent of Outdoor Workers Experiencing
One or More Eight-Hour Daily Maximum Ozone Exposures
Above 60 ppb at Any Ventilation Rate 160
64 Number and Percent of Outdoor Workers Experiencing
One or More Eight-Hour Daily Maximum Ozone Exposures
Above 80 ppb at Any Ventilation Rate 155
65 Number and Percent of Outdoor Workers Experiencing
One or More Eight-Hour Daily Maximum Ozone Exposures
Above 100 ppb at Any Ventilation Rate 170
XII
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TABLES (continued)
Number
66a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Chicago During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min"1- M"2 176-177
67a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Chicago During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min"1- M"2 to 27
Liters • Min"1-M"2 178-179
68a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Denver During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min"1 • M"2 180-181
69a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Denver During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min"1- M"2 to 27
Liters • Min"1-M"2 182-183
70a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Houston During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min"1- M"2 184-185
71a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Houston During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min"1- M"2 to 27
Liters -Min"1-M"2 186-187
72a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Los Angeles During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min"1- M"2 188-189
XIII
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TABLES (continued)
Number
73a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Los Angeles During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min'1- M'2 to 27
Liters • Min'1 • M'2 190-191
74a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Miami During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min'1- M"2 192-193
75a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Miami During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min'1- M*2 to 27
Liters • Min1-M-2 194-195
76a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in New York During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min'1 • M'2 196-197
77a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in New York During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min'1- M'2 to 27
Liters • Min'1-M'2 198-199
78a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Philadelphia During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min'1- M"2 200-201
79a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Philadelphia During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min'1- M'2 to 27
Liters • Min"1 • M"2 202-203
XIV
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TABLES (continued)
Number
80a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in St. Louis During
Which Ozone Concentration Exceeded 0.12 ppm and
EVR Equaled or Exceeded 30 Liters • Min'1- M'2 204-205
81a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in St. Louis During
Which Ozone Concentration Exceeded 0.08 ppm and
EVR Ranged From 13 Liters • Min'1- M'2 to 27
Liters -Min'1-M'2 206-207
82a,b Estimates of One-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Washington, D.C.
During Which Ozone Concentration Exceeded 0'.12
ppm and EVR Equaled or Exceeded 30 Liters • Min'1 • M'2 208-209
83a,b Estimates of Eight-Hour Maximum Dosage Exposures
Experienced by Outdoor Workers in Washington, D.C.
During Which Ozone Concentration Exceeded 0.08
ppm and EVR Ranged From 13 Liters - Min"1- M"2 to 27
13 Liters • Min'1-M'2 210-211
xv
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ACKNOWLEDGMENT
In evaluating alternative National Ambient Air Quality Standards (NAAQS), the
U.S. Environmental Protection Agency (EPA) assesses the risks to human health of
air quality meeting each of the standards under consideration. This assessment of
risk requires estimates of the number of persons exposed at various pollutant
concentrations for specified periods of time. Since 1979, IT Air Quality Services
(ITAQS) has assisted EPA in developing various versions of the NAAQS Exposure
Model (NEM) to assist in this process. In 1993, ITAQS developed a probabilistic
version of NEM applicable to ozone (pNEM/03) and applied it to the general
population residing in each of nine urban areas. In 1994, EPA directed ITAQS to
develop a special version of pNEM/O3 applicable to outdoor workers and to use it to
estimate the ozone exposures of outdoor workers residing in the nine urban areas.
This report summarizes the results of this research effort.
The outdoor worker project was managed by Mr. Mike McCoy of ITAQS with
technical direction provided by Mr. Ted Johnson. Mr. Jim Capel of ITAQS developed
the special version of pNEM/O3 and performed all computer runs of the model. An
ITAQS project team composed of Mr. Roy Paul, Mr. Doug Brinson, Mr. Mike McCoy,
and Ms. Jill Warnasch Mozier developed the special input databases required by
pNEM/O3, including a database containing time/activity data representative of outdoor
workers and a database containing estimates of the number of outdoor workers in
each of the nine urban areas.
Mr. Mike McCoy and Ms. Jill Warnasch Mozier were the principal authors of
Section 6 of this report. Mr. Ted Johnson was the principal author of the remaining
sections. Ms. Joan Abemethy typed the report and created many of the graphs in
Section 7.
ITAQS' work on this project was funded under Work Assignment Nos. 0-1, 1-4,
and 1-5 of EPA Contract No. 63-D-30094. Mr. Harvey Richmond served as the EPA
Work Assignment Manager and provided guidance throughout the project. Mr.
Thomas McCurdy provided technical direction and guidance in the development of
pNEM/O3 through March 1994 and has provided technical review since that time. Ms.
Nancy Riley was the EPA Project Manager.
The authors would like to express their appreciation to Ms. Mary Anne Simpson
and Ms. Margaret Brill for their assistance with obtaining 1990 Census Data at Perkins
Library on Duke University campus.
XVI
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SECTION 1
INTRODUCTION
Within the U.S. Environmental Protection Agency (EPA), the Office of Air
Quality Planning and Standards (OAQPS) has responsibility for establishing and
revising national ambient air quality standards (NAAQS). In evaluating alternative
NAAQS proposed for a particular pollutant, OAQPS assesses the risks to human
health of air quality meeting each of the standards under consideration.1 This
assessment of risk requires estimates of the number of persons exposed at various
pollutant concentrations for specified periods of time. The estimates may be specific
to an urbanized area such as Los Angeles or apply to the entire nation.
Several researchers2'3 have recommended that such estimates be obtained by
simulating the movements of people through zones of varying air quality so as to
approximate the actual exposure patterns of people living within a defined area.
OAQPS has implemented this approach through an evolving methodology referred to
as the NAAQS Exposure Model (NEM). An early overview of the NEM methodology is
provided in a paper by Biller et al.4 From 1979 to 1988, IT Air Quality Services
(formerly PEI Associates, Inc.) assisted OAQPS in developing and applying pollutant-
specific versions of NEM to ozone,5 particulate matter,6 and CO.7 These versions of
NEM are referred to as "deterministic" versions in that no attempt was made to model
random processes within the exposure simulation.
The deterministic versions of NEM were similar in that each was capable of
simulating the movements of selected segments of an urban population through a set
of environmental settings. Each environmental setting was defined by a geographic
area and a microenvironment. The size and distribution of the geographic areas were
determined according to the ambient characteristics of the pollutant. Ambient
(outdoor) pollutant levels in each geographic area were estimated from either fixed-site
1
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monitoring data or dispersion model estimates. To better utilize fixed-site monitoring
data, researchers developed special time series techniques to fill in missing values
and special roll-back techniques to adjust the monitoring data to simulate conditions
under attainment of a particular NAAQS.
Additional details concerning the evolution of the deterministic version of NEM
are provided by Paul et al.8 Critiques of deterministic NEM are included in surveys of
exposure models by Pandian9 and Ryan.10 Two staff papers11-12 prepared by EPA
discuss the use of NEM in evaluating alternative NAAQS for CO and ozone.
In 1988, OAQPS began to incorporate probabilistic elements into the NEM
methodology and to apply the resulting model (pNEM) to the criteria pollutants. The
initial result of this work was an early version of pNEM applicable to ozone
(pNEM/O3). This model used a regression-based relationship to estimate indoor
ozone concentrations from outdoor concentrations. A report by Johnson et al.
describes this model and its application to Houston, Texas13.
An advanced version of pNEM applicable to carbon monoxide (pNEM/CO) was
developed in 1991. This model marked the first time in the evolution of NEM that a
mass balance model was used to estimate indoor pollutant concentrations. The
application of pNEM/CO to Denver, Colorado, has been described by Johnson et al14.
A new version of pNEM/O3 was developed in early 1992. Unlike the earlier
version of pNEM/O3, the new model uses a mass balance model to estimate indoor
ozone concentrations. A report by Johnson et al.15 describes the new version of
pNEM/O3 and summarizes the results of an initial application of the model to 10 cities.
Subsequent to the February 1993 report, ITAQS made the following
enhancements to pNEM/O3 and its input data bases.
Use of more recent (1990-91) fixed-site monitoring data for estimating
ambient ozone concentrations. The earlier analysis was based on 1981-
84 monitoring data.
An increase in the number of fixed-site monitors used to represent each
urban area.
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Use of more recent (1990) census data for estimating cohort populations.
The earlier analysis used 1980 census data.
A new methodology for adjusting ambient ozone data to simulate
attainment of one-hour and eight-hour NAAQS.
Revision of the algorithm used to determine limiting values for equivalent
ventilation rate.
Development of origin/destination tables through the use of a new
commuting algorithm.
A report by Johnson et al.16 describes these enhancements and summarizes the
results of applying the enhanced model to nine of the ten cities included in the
previous exposure assessment. Tacoma, Washington, was excluded from the
analysis because of insufficient monitoring data.
In 1994, EPA directed ITAQS to develop a special version of pNEM/03
applicable to outdoor workers and to use it to estimate the ozone exposures of
outdoor workers residing in each of the nine areas. This report summarizes the
results of this effort. The report is divided into eight sections. Section 2 provides an
overview of the pNEM/OS methodology and describes in detail how the model was
applied to outdoor workers in a specific city (Houston). Section 3 describes the mass
balance model incorporated into pNEM/O3. Section 4 describes the process by which
ambient ozone data sets were selected for use in pNEM/O3. It also describes the
methods used to fill in missing values in these data sets. Section 5 presents the
method used to adjust ambient ozone data to simulate the attainment of proposed air
quality standards. Section 6 describes the methods used to identify time/activity data
representative of outdoor workers and to estimate the number of outdoor workers in
each urban area. Section 7 provides ozone exposure estimates for each of the nine
cities. The principal limitations of the model are discussed in Section 8.
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SECTION 2
OVERVIEW OF THE METHODOLOGY
The general NEM methodology consists of five steps.
1. Define a study area, a population-of-interest, appropriate subdivisions of
the study area, and an exposure period.
2. Divide the population-of-interest into an exhaustive set of cohorts.
3. Develop an exposure event sequence for each cohort for the exposure
period.
4. Estimate the pollutant concentration, ventilation rate, and physiological
indicator (if applicable) associated with each exposure event.
5. Extrapolate the cohort exposures to the population-of-interest and to
individual sensitive groups.
This approach has been followed in developing a probabilistic version of NEM
applicable to ozone (pNEM/03). The remainder of this section provides an overview of
the pNEM/O3 methodology as applied to outdoor workers. The application of
pNEM/O3 to outdoor workers in Houston is used as a means of demonstrating various
features of the methodology.
2.1 Define Study Area, Population-of-lnterest, Subdivisions of Study Area, and
Exposure Period
The pNEM/03 methodology provides estimates of the distribution of ozone
exposures within a defined population (the population-of-interest) for a specified
exposure period. The population-of-interest is typically defined as 1) all residents of a
defined study area or 2) the residents of the study area which belong to a specific
sensitive population. The study area is defined as an aggregation of exposure
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districts. Each exposure district is defined as a contiguous set of census tracts or
block numbering areas Qointly referred to as "census units") as defined by the Bureau
of Census for the 1990 U.S. census.
All census units assigned to a particular exposure district are located within a
specified radius of a fixed-site ozone monitor. The pNEM/O3 methodology is based
on the assumption that the ambient ozone concentration throughout each exposure
district can be estimated by ozone data provided by the associated fixed-site monitor.
Table 1 lists the nine study areas defined for the exposure analyses. Each
study area is associated with a major urban area. The table lists the number of
exposure districts and the exposure period for each study area. In each case, the
exposure period is defined as a series of months within a particular calendar year.
The specified months conform to the "ozone season" specified for the urban area by
EPA. The ozone season is the annual period when high ambient ozone levels are
likely to occur. Three ozone seasons appear in Table 1: January through December,
March through September, and April through October. The specified calendar year is
either 1990 or 1991, the selected year being the higher year with respect to reported
hourly ambient ozone concentrations.
In the application of pNEM/O3 to Houston, eleven fixed-site monitors were
selected to represent ambient ozone concentrations (see Section 4). An exposure
district was constructed around each monitor through the use of a special computer
program ("DIST90"). This program identified all census units having population
centroids located within 15 km of the monitor. When a census unit was paired with
more than one monitor, the program assigned it to the nearest monitor.
The sum of all census units assigned to the eleven exposure districts defined
the Houston study area. In 1990, the study area consisted of 532 census units and
contained 2,370,512 residents17. A subset of this population, outdoor workers, were
designated as the principal population-of-interest.
The Houston ozone season spans the entire calendar year. Consequently, the
Houston exposure period was defined as calendar year 1990.
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TABLE 1. CHARACTERISTICS OF STUDY AREAS
Study area
Chicago
Denver
Houston
Los Angeles
Miami
New York
Philadelphia
St. Louis
Washington
Number of
exposure
districts
12
7
11
16
6
12
10
11
11
1990
population3
6,175,121
1 ,484,798
2,370,512
10,371,115
1,941,994
10,657,873
3,785,810
1 ,706,778
3,085,419
Exposure period
Year
1991
1990
1990
1991
1991
1991
1991
1990
1991
Months
Apr-Oct
Mar-Sep
Jan-Dec
Jan-Dec
Jan-Dec
Apr-Oct
Apr-Oct
Apr-Oct
Apr-Oct
Number of
outdoor
worker
cohorts
432
147
363
768
108
432
300
363
363
aTotal population residing in the exposure districts which comprise the study area.
2.2 Divide the Population-of-lnterest Into an Exhaustive Set of Cohorts
In a pNEM analysis, the population-of-interest is divided into a set of cohorts
such that each person is assigned to one and only one cohort. Each cohort is
assumed to contain persons with identical exposures during the specified exposure
period. Cohort exposure is typically assumed to be a function of demographic group,
location of residence, and location of work place. Specifying the home and work
district of each cohort provides a means of linking cohort exposure to ambient
pollutant concentrations. Specifying the demographic group provides a means of
linking cohort exposure to activity patterns that vary with age, work status, and other
demographic variables. In some analyses, cohorts are further distinguished according
to factors relating to characteristics of the residence, proximity to emission sources, or
to time spent in particular microenvironments.
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In the pNEM/03 analyses described in this report, each cohort was identified as
a distinct combination of 1) home district, 2) demographic group, 3) work district, and
4) residential air conditioning system. All cohorts were associated with a single
demographic group -- outdoor workers. The home district and work district of each
cohort were identified according to the exposure districts defined for the study area.
The decision to identify cohorts with respect to the residential air conditioning
system was based on the results of two supplemental analyses by ITAQS. An
analysis18 of data on window openings provided by the Cincinnati Activity Diary Study
(CADS)19 suggested that the time per day that windows are open in a residence is a
function of outdoor temperature and air conditioning system, when the later is
characterized as 1) no air conditioning, 2) room units, or 3) central air. An analysis20
of data collected by Stock21 during a study of asthmatics in Houston suggested that
indoor ozone levels are significantly higher when windows are open than when
windows are closed. For example, the median ratio of indoor ozone to outdoor ozone
for residences in the Sunnyside section of Houston was 0.89 when windows were
open and 0.09 when windows were closed. The importance of outdoor ozone
concentrations in determining indoor ozone concentrations has also been reported by
Weschler et al.22
The cohort identification process produced 363 distinct cohorts for the Houston
study area -- one for each combination of home district (11 possibilities), work district
(11 possibilities), and air conditioning system (3 possibilities). Table 1 lists the number
of cohorts defined for each of the nine study areas.
2.3 Develop an Exposure Event Sequence for Each Cohort for the Exposure
Period
In the pNEM/03 methodology, the exposure of each cohort is determined by an
exposure event sequence (EES) specific to the cohort. Each EES consists of a series
of events with durations from 1 to 60 minutes. To permit the analyst to determine
average exposures for specific clock hours, the exposure events are defined such that
no event falls within more than one clock hour. Each exposure event assigns the
-------
cohort to a particular combination of geographic area and microenvironment. Each
event also provides an indication of respiration rate. In typical applications, this
indicator is a classification of slow - sleeping, slow - awake, medium, or fast.
The EESs are determined by assembling activity diary records relating to
individual 24-hour periods into a series of records spanning the ozone season of the
associated study area. Because each subject of a typical activity diary study provides
data for only a few days, the construction of a multi-month EES requires either the
repetition of data from one subject or the use of data from multiple subjects. The
latter approach is used in pNEM analyses to better represent the variability of
exposure that is expected to occur among the persons included in each cohort.
Previous applications of pNEM/03 have employed activity diary data obtained
from the CADS19. During this study over 900 subjects completed three-day activity
diaries and detailed background questionnaires. Figure 1 presents a page from the
Cincinnati diary. Each subject was instructed to complete a new diary page whenever
he or she changed location or began a new activity.
In the outdoor worker exposure analysis, analysts augmented the CADS data
with diary data from six other time/activity studies (see Table 2 and Appendix A).
Section 6 of this report describes how the data from all seven studies were assembled
and processed to produce a unified time/activity database representative of outdoor
workers. The data within this special database were organized by study subject and
24-hour (midnight-to-midnight) time period. The diary records for one subject for one
24-hour period were designated a "person-day." The data base contained 136
person-days, each of which was indexed by the following factors:
1. Season: summer or winter
2. Temperature classification: cool or warm
3. Day type: weekday or weekend.
The season and day type indices were based on the calendar date of the person-day.
8
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TIME
PH
A. ACTIVITY (please specify)
B. LOCATION
In transit, car 01
In transit, other vehicle . . 02
Specify .
Indoors, your residence .... 03
Indoors, other residence. . . 04
Indoors, office 05
Indoors, manufacturing
facility 06
Indoors, school 07
Indoors, store 08
Indoors, other 09
Specify
Outdoors, within 10 yards of
road or street 10
Outdoors, other 11
Specify
Uncertain 12
*£nter MIDN for midnight and NOON for noon. Otherwise enter four-digit
time (e.g., 0930 for 9:30 and 1217 for 12:17) and check a.m. or p.m.
C. BREATHING RATE
Slow (e.g., sitting) 13
Medium (e.g., brisk walk). . . 14
Fast (e.g., running) 15
Breathing problem 16
Specify
D. SMOKING
I am smoking 17
Others are smoking 18
'No one is smoking 19
I. ONLY IF INDOORS
(1) Fireplace in use?
Yes 20
No 21
(2) Woodstove in use?
Yes 22
No 23
(3) Windows open?
Yes 24
No 25
Uncertain 26
Figure 1. Page from the activity diary used in the Cincinnati study.19
-------
TABLE 2. CHAR/
.
Database name
California - 12
and over
Cincinnati
Denver
Los Angeles -
construction
Los Angeles -
outdoor worker
Valdez
Washington
\CTERISTICS OF STUDIES PROVIDING TIME/ACTIVITY DATA FOR OUTDOOR WORKERS
Reference
number
23
19
24
25
26
27
28
Characteristics
of subjects
Ages 12 to 94
Ages 0 to 86
Ages 1 8 to 70
Construction workers
(ages 23 to 42)
Adult outdoor workers
(ages 1 9 to 50)
Ages 10 to 72
Ages 18 to 70
Number of
subject-
days
1762
2800
859
19
60
405
705
Study
calendar
periods
Oct. 1987-
July 1988
March and
August 1985
Nov. 1982-
Feb. 1983
July - Nov.
1991
Summer
1989
Nov. 1990-
Oct. 1991
Nov. 1982 -
Feb. 1983
Diary type
Retrospective
Real-time*
Real-time
Real-time"
Real-time8
Retrospective
Real-time
^- —
Diary time
period
Midnight to
midnight
Midnight to
midnight
7 p.m to
7 p.m.
(nominal)
Subject
wakeup to
subject
returns home
from work
Midnight to
midnight
Varying
24- h period
7 p.m. to
7 p.m.
(nominal)
Breathing
rates
reported?
No
Yes
No
Yes
Yes
No
No
aStudy employed the Cincinnati diary format.
-------
The temperature classification was based on the daily maximum temperature reported
for the diary study area on that date. The cool range was defined as daily maximum
temperatures below 55° F in winter and temperatures below 84° F in summer.
A distinct EES was developed for each cohort in each of the nine study areas
by applying a computerized sampling algorithm to the time/activity data base. The
algorithm was provided with the sequence of daily maximum temperatures reported for
the associated study area and exposure period (Table 1) and with the list of cohorts
defined for the study area. The temperature data were used to assign each calendar
day in the exposure period to one of the temperature ranges used in classifying the
time/activity data. To construct the EES for a particular cohort, the algorithm selected
a person-day from the time/activity data base for each calendar day in the specified
exposure period according to the demographic group of the cohort and the season,
day type, and temperature classification associated with the calendar day.
Each exposure event within an EES was defined by 1) district, 2) micro-
environment, and 3) breathing rate category. The district was either the home or the
work district associated with the cohort. The home/work determination was based on
a decision rule which was applied to the activity diary data associated with the
exposure event.
Seven microenvironments were defined:
1. Indoors - residence - central air conditioning system
2. Indoors - residence - window air conditioning units
3. Indoors - residence - no air conditioning system
4. Indoors - nonresidential locations
5. Outdoors - near road
6. Outdoors - other
7. In vehicle.
Location codes appearing in the time/activity data base were used to determine the
primary microenvironment location of each exposure event (indoors - residence,
indoors - nonresidential locations, outdoors- near road, outdoors - other, or in vehicle).
11
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The indoors - residence location was subdivided into three microenvironments
according to air conditioning (AC) system: central system, window unit(s), or none.
This classification was based on the AC system specified for the cohort's residence.
For example, a cohort designated as residing in a home with central AC would always
be assigned to the microenvironment defined as "indoors - residence - central AC"
when activity diary data indicated the cohort was inside a residence.
Four breathing rate categories were defined according to codes appearing in
the time/activity data base: slow - sleeping, slow - awake, medium, and fast. Each
exposure event was assigned to one of these categories.
2.4 Estimate the Pollutant Concentration and Ventilation Rate Associated With
Each Exposure Event
In the general pNEM methodology, the EES defined for each cohort is used to
determine a corresponding sequence of exposures, event by event. Each exposure is
defined by a pollutant concentration and a ventilation rate indicator.
2.4.1 Estimation of Pollutant Concentration
In the pNEM/03 analysis, the pollutant concentration during each exposure
event was assumed to be a function of the microenvironment and district associated
with the event. Consequently a continuous season or year-long sequence of hourly
average ozone concentrations was developed for each combination of
microenvironment and district. When an exposure event assigned a cohort to a
particular combination of microenvironment and district, the cohort was assigned the
ozone concentration specified for the corresponding clock hour in the appropriate
microenvironment/district sequence.
Each year-long sequence of hourly average ozone values for the indoor and in-
vehicle microenvironments was generated by the mass-balance algorithm described in
Section 3. Briefly, this algorithm estimated the hourly average indoor ozone
concentrations during hour h as a function of the indoor ozone concentration at the
end of the preceding hour (i.e., hour h - 1), the ozone concentration outdoors during
12
-------
hour h, the air exchange rate during hour h (v), and an ozone decay factor (Fd).
Values for the air exchange rate and the ozone decay factor were sampled from
appropriate distributions on a daily basis (Subsections 3.1 and 3.3). Air exchange rate
was permitted to change hourly in the three residential microenvironments depending
on whether windows were assigned a status of "open" or "closed". This assignment
was determined through the use of a probabilistic model (Subsection 3.4) in which the
status during each clock hour was assumed to be a function of AC system,
temperature range, and window status during the previous clock hour.
The outdoor ozone concentration associated with microenvironment m in district
d during hour h was determined by an expression having the general form
Cm,r(m.d,t,s)=b(ni) x Cmon(d, t, s) +e(t), (1)
where Cout(m,d,t,s) is the outdoor (or ambient) ozone concentration in
microenvironment m in district d at time t under regulatory scenario s, Cmon(d,t,s) is the
ozone concentration estimated to occur at the monitor representing district d at time t
under regulatory scenario s, b(m) is a constant specific to microenvironment m, and
e(t) is a random normal variable with mean = 0 and standard deviation = a(m). A
value for e(t) was selected from a normal distribution with mean = 0 and standard
deviation = a(m) every hour. The value of Cmon(d,t,s) was constant over each clock
hour.
In the application of pNEM/O3 described in this report, b(m) was set equal to
1.056 for all microenvironments. A value of 5.3 ppb (0.0053 ppm) was used as the
value of a(m) for all microenvironments (Table 3). Consequently, each sequence of
hourly ozone values was generated by the expression
Couc(m, d, t,s) =1.056 x Cmon(d, t,s) +e(t), (2)
where e(t) is a random normal variate with mean = 0 and standard deviation = 5.3
ppb.
13
-------
TABLE 3. PARAMETERS ASSOCIATED WITH ALGORITHMS USED
TO ESTIMATE OZONE CONCENTRATIONS IN MICROENVIRONMENTS
Parameter
b(m)
o(m)
Air exchange
rate
Ozone decay
factor
Equation(s)
containing
parameter
1
1
38
38
Microenvironment3
All
All
1-4,7
1 -4
7
Parameter value
1.056
5.3 ppb
See Table 9
Normal distribution
• Arith. mean = 4.04 h'1
• Std. dev. = 1 .35 IV1
• Minimum = 1.44 h"1
• Maximum = 8.09 h'1
72.0 h'1
aMicroenvironments:
1 = Indoors - residence - central air conditioning
2 = Indoors - residence - window units
3 = Indoors - residence - none
4 = Indoors - nonresidential locations
5 = Outdoors - near road
6 = Outdoors - other
7 = In vehicle
The expression is based on the results of regression analyses13 performed by
ITAQS analysts on personal exposure data collected by T. Stock during the Houston
Asthmatic Study21. In these analyses, the dependent variable was five-minute ozone
concentration measured outdoors by a personal exposure monitor (PEM). The
independent variable was the simultaneous ozone concentration (hourly average
value) reported by the nearest fixed-site monitor.
An initial regression analysis of 327 paired values yielded an intercept of 0.81
ppb, a slope of 1.042, and set of regression residuals with a standard deviation of
18.5 ppb. The R2 value was 0.544. Because the regression intercept value was
14
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found to be non-significant (p = 0.76), a second regression analysis was performed in
which the regression line was forced through the origin (i.e., intercept = 0). This
analysis yielded a slope of 1.056 and a set of regression residuals with a standard
deviation of 18.5 ppb. The residuals were found to be approximately normal
(skewness = -0.32, kurtosis = 0.87).
Attempts were made to fit more complex regression models to the Stock data.
These models included regression equations using logarithmic transformations of the
variables and regression equations which included the previous PEM value as an
independent variable. These alternative models were found to offer no significant
improvement in performance over the model specified above. Some of the alternative
models were found to be unstable.
The results of this analysis suggested that Equation 2 could be used as a
means of generating five-minute values of Cout(d,t,s), given that e(t) values were
selected every five minutes from a normal distribution with mean equal to 0 and
standard deviation equal to 18.5 ppb. A procedure based on this expression was
used in a previous version of pNEM/O3 to generate five-minute ozone concentrations
for the outdoor microenvironment13.
As the new version of pNEM/OS required hourly-average outdoor ozone
concentrations rather than five-minute values, the procedure used in the earlier model
was modified so that an hourly-average value of e(t) was selected for each hour from
a normal distribution with mean equal to 0 and standard deviation equal to 5.3 ppb.
The use of a smaller standard deviation (5.3 ppb versus 18.5 ppb) for the hourly-
average e(t) terms was based on the statistical principle that the standard deviation of
the average of n values drawn from a distribution with standard deviation equal to a
will tend to have a standard deviation equal to a/m, where m is the square root of n.
As there are 12 five-minute values in one hour, the value of n is 12. The
corresponding value of m is 3.5, and 18.5 ppb/3.5 = 5.3 ppb.
The current version of pNEM/O3 provides for two outdoor microenvironments:
No. 5 (outdoors - near road) and No. 6 (outdoors - other). In the pNEM/O3 analyses
described in this report, these microenvironments were treated identically; that is,
15
-------
Equation 2 was used to determine the hourly ozone concentrations in each outdoor
microenvironment. This approach is likely to over-estimate ozone concentrations in
microenvironment No. 5 (outdoors - near road) because it does not account for
potential ozone scavenging by nitric oxides emitted from motor vehicles. The
magnitude of this bias is difficult to quantify because of the scarcity of research in this
area and the inconsistency of research findings. For example, a study by Rhodes and
Holland29 of a single freeway in San Diego found that downwind ozone concentrations
measured near the roadway were less than 28 percent of the ozone concentrations
measured simultaneously at more distant outdoor locations judged to be unaffected by
the roadway. However, an analysis20 of outdoor personal exposure data obtained
from the Stock study found that the average ratio of personal ozone concentration to
fixed-site ozone concentration was approximately 1 .0 in areas of both low and high
traffic density.
2.4.2 The Air Quality Adjustment Model
In Equation 1 , Cmon(d,t,s) is the monitor-derived value for district d at time t
under scenario s. The value for this variable was determined by adjusting monitoring
data representing baseline conditions (i.e., 1990 or 1991 air quality) according to the
equation
r(d,t,s) = (a) [Cmon(d,t,e
b
-mon
where Cmon(d,t,e) is the monitor-derived value for district d under baseline conditions.
The multiplicative factor (a) and the exponent (b) are specific to district and scenario.
Section 5 describes the derivation of Equation 3 and provides examples of its
application to Philadelphia monitoring data.
Equation 3 requires a complete (gapless) year of hourly average Cmon(d,t,e)
values for each district. These data sets were prepared by applying a special
interpolation program to the hourly average ozone data reported by each fixed-site
monitor. The interpolation program provided an estimate of each missing value. The
16
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resulting filled-in data sets were assumed to represent baseline conditions at each
monitor.
The interpolation program provides estimates of missing values through the use
of a time series model developed by Johnson and Wijnberg30. The time series model
is based on the assumption that hourly average air quality values can be represented
by a combination of cyclical, autoregressive, and noise processes. The parameter
values of these processes are determined by a statistical analysis of the reported
data.
2.4.3 Equivalent Ventilation Rate
In addition to ozone concentration, an equivalent ventilation rate (EVR) value
was estimated for each exposure event. EVR is defined as ventilation rate divided by
body surface area (BSA). Clinical research by EPA suggests that EVR exhibits less
inter-person variability than ventilation rate for a given level of exertion.31
The algorithm used to estimate EVR was employed previously in applications of
the pNEM methodology to ozone13 and carbon monoxide.14 This algorithm is based
on an analysis32 of activity diary data provided by Dr. Jack Hackney. The data were
obtained from 36 subjects in Los Angeles who completed activity diaries identical to
those used in the Cincinnati study. The heart rate of each subject was monitored
during the period reported in the diary. Separate clinical trials were conducted to
determine a relationship between ventilation rate and heart rate for each subject.
These relationships and subject-specific BSA values were used to convert the one-
minute heart rate data associated with each diary activity to an average EVR value for
the activity. The resulting EVR estimates were then grouped by breathing rate
category (slow - sleeping, slow - awake, medium, fast). Statistical analysis indicated
that a two-parameter lognormal distribution provided a good fit to the EVR values in
each group. Table 4 lists the geometric mean and standard deviation of each fitted
distribution.
The appropriate adult distribution in Table 4 was randomly sampled to provide
an EVR value for each exposure event in the pNEM/03 simulation. EVR values were
17
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TABLE 4. PARAMETER VALUES OF LOGNORMAL DISTRIBUTIONS USED TO
CHARACTERIZE EQUIVALENT VENTILATION RATE
Age group
Children
Adults
Breathing rate
Slow-sleeping
Slow-awake
Medium
Fast
Slow-sleeping
Slow-awake
Medium
Fast
Parameter values of fitted lognormal
distribution
Geometric mean3
8.1
10.0
12.3
14.8
5.4
7.1
8.6
18.9
Geometric
standard deviation
J.60
1.46
1.44
1.62
1.22
1.36
1.34
1.92
3Liters/min per m2
not permitted to exceed an upper limit (EVRLIM) which varied with event duration, in
all cases, the value of EVRLIM was set at a level estimated to be achievable by
members of the cohort who 1) exercised regularly, 2) were motivated to attain high
exertion levels, and 3) were not professional athletes. Joggers would be included in
this group; professional basketball players would not be included.
Table 5 presents the algorithm used to determine EVRLIM. This algorithm is a
variation of "Algorithm B" proposed by Johnson and Adams.33 The algorithm accounts
for the following research findings reported by Erb,34 Astrand and Rodahl,35 and
McArdle et al.36
1. Ventilation rate (VE), oxygen uptake rate (VO2), and the ratio of VE to VO2
increase with increasing work rate.
2. A person's maximum VE is determined by his or her maximum oxygen
uptake rate (VO2MAX) and the VE/VO2 ratio in effect under maximum
oxygen uptake conditions (MAXRATIO) such that
'2max
) (MAXRATIO) .
18
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TABLE 5. ALGORITHM FOR DETERMINING UPPER LIMIT FOR EVR
1. Obtain values for the following quantities from Table 6.
VO2max: maximum oxygen uptake rate
MAXRATIO: maximum ratio of ventilation rate to oxygen
uptake rate
SUBRATIO: submaxirnal ratio of ventilation rate to oxygen
uptake rate
BSA: body surface area
2. Determine duration of event (t).
3. If t <= 5 minutes, determine the upper limit for EVR (EVRLIM) by the
expression
EVRLIM = (1.2)(VO2mJ(MAXRATIO)/BSA.
4. If 5 minutes < t <= 162 minutes, determine the percentage of maximum
oxygen uptake rate that can be maintained for duration t by the expression
PCTV02max = 116.19-(10.06)[ln(t)].
Next determine the ratio of ventilation rate to oxygen uptake rate by the
expression
RATIO = SUBRATIO +
(MAXRATIO-SUBRATIO)(PCTVO2max - 65)/35.
Finally determine EVRLIM by the expression
EVRLIM = (1.2)(V02mJ(PCTV02max)(RATIO)/(100)(BSA).
5. If t > 162 minutes, determine PCTV02max by the expression presented in
Step 4 and EVRLIM by the expression
EVRLIM =
19
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TABLE 6. PARAMETER .VALUES FOR ALGORITHM USED TO DETERMINE
LIMITS FOR EQUIVALENT VENTILATION RATES FOR OUTDOOR WORKERS
Parameter acronym
BSA
V02MAX
MAXRATIO
SUBRATIO
Definition
Body surface area
Maximum oxygen uptake rate (VO2MAX)
Ratio of ventilation rate (VE) to oxygen
uptake rate (V02) under maximum uptake
conditions
Ratio of ventilation rate (VE) to oxygen
uptake rate (V02) under submaximal
conditions
Parameter value
1 .90 m2
3.69 liters/min
32.5
21.0
3- VOamax and MAXRATIO are functions of age, gender, and training,
among other factors.
4. Individuals cannot maintain oxygen uptake rates equal to V02max for more
than about five minutes.
5. For activity durations greater than five minutes (i.e., t > 5 min), the
percentage of V02max that can be maintained continuously (PCTVO2max)
decreases as the natural logarithm of the activity duration [ln(t)j
increases.
In determining the EVRLIM value applicable to a particular combination of
cohort and event duration, the algorithm uses estimates of V02max, MAXRATIO,
SUBRATIO, and BSA specific to adult males between 18 and 24 years of age (Table
6). Estimates of EVRLIM provided by Johnson and Adams33 suggest that adults in
this category are likely to experience the highest EVR values of all adults included in-
the outdoor worker classification. The parameter values listed in Table 6 are based
on estimates developed by Johnson and Adams33 for males 18 to 24 years of age.
The population-of-interest selected for analysis, outdoor workers, is not Kmfted
to males 18 to 24 years of age. It also includes female outdoor workers and older
20
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male outdoor workers. The application of the parameters listed in Table 6 to these
other groups is likely to produce a small number of unrealistically high EVR values in
the pNEM/O3 simulation. This potential bias may be corrected in future versions of
the model by dividing outdoor workers into various demographic groups according to
age and gender. A separate set of EVRLIM parameters would have to be developed
for each group.
2.4.5 Hourly Average Exposure Sequences
Algorithms within pNEM/03 provided three estimates for each exposure event:
average ozone concentration, average EVR, and the product of average ozone
concentration and EVR (ozone x EVR). These estimates were processed to produce
time-weighted estimates of ozone concentration, EVR, and ozone x EVR for each
clock hour. The result was a year-long sequence of hourly values for each of three
exposure indicators for each cohort. These sequences can be further processed to
determine cohort-specific values for various multihour exposure indicators. Examples
of such indicators include the largest eight-hour daily maximum ozone concentration
and the number of times the hourly-average ozone concentration exceeds 0.12 ppm.
2.5 Extrapolate the Cohort Exposures to the Population-of-lnterest
The cohort-specific exposure estimates developed in Step 4 of the pNEM
methodology (Subsection 2.4) were extrapolated to the general outdoor worker
population of each study area by estimating the population size of each cohort.
Cohort populations were estimated by the following four-step procedure. In Step 1 ,
the number of outdoor workers residing in each census unit was estimated by the
formula
POPOW(c) = (P(g) xPOP(g,c)] (4)
where POPOW(c) is the number of outdoor workers in census unit c, POP(g,c) is the
number of workers in occupation group g who reside in census unit c, and P(g) is the
estimated percentage of workers in occupation group g who are outdoor workers.
Values for POP(g.c) were obtained directly from 1990 Bureau of Census data files17
21
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that list population data for 13 occupational groups defined by the Bureau of Census
by census unit. Section 6 describes the method used to estimate a value of P(g) for
each occupation group.
In Step 2, the fraction of homes falling into each of the three air conditioning
categories was estimated by census unit. The fractions associated with each census
unit were determined using 1980 census data, as the 1990 census did not collect air
conditioning data. In cases where the boundaries of a 1990 census unit did not
coincide with 1980 census units, analysts used the fractions associated with the 1980
census unit located nearest to the 1990 census unit. In Step 3, the outdoor worker
population of each census unit was multiplied by the air conditioning fractions to
provide an estimate of the number of outdoor workers in each air conditioning
category. The estimation equation was
POPOW(c.a) = F(c,a} x POPOW(c) , (5)
where POPOW(c,a) is the population of outdoor workers associated with census unit c
and air conditioning system a. F(c,a) is the fraction of housing units in census unit c
with air conditioning system a, and POPOW(c) is the number of outdoor workers
residing in census unit c. The values of POPOW(c.a) were summed over each home
district to yield estimates of POPOW(h.a), the number of outdoor workers within home
district h assigned to air conditioning category a.
In Step 4, the populations of the individual cohorts were determined by the
expression
COM(h,a,w) = POPOW(h,a) x COM(h, w) /WORK(h) . (6)
COM(h,a,w) is the number of persons in the outdoor worker cohort associated with
home district h, AC system a, and work district w; COM(h,w) is the number of outdoor
workers that commute from home district h to work district w; and WORK(h) is the
total number of workers in home district h. Estimates of WORK(h) were developed
from census data specific to each district. Estimates of COM(h.w) were obtained from
an origin-destination (O-D) table produced by a special commuting model.
22
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The pNEM/03 commuting model is an enhanced version of a model developed
by Johnson et al.37 The pNEM/O3 commuting model has been previously described
by Johnson et al.16
In applying the commuting model to Houston, analysts first identified all
counties which were located within 50 km of the center of Houston. The 819 census
units located within these counties were assigned to a commute modeling zone. Each
census unit within the modeling zone was assumed to be both a potential home
location and a potential work location. Using commuting data from the 1990 census,
analysts applied the commuting model to the census units included in the modeling
zone and developed an origin-destination table. This table listed the number of
persons associated with each of the 670,761 (819 x 819) possible pairings of home
and work locations.
Analysts next defined 12 subdivisions of the commute modeling zone - one for
each of the 11 Houston exposure districts and an additional district (District #12)
containing all leftover census units. The 670,761 pairings of home and work census
units were aggregated into an origin-destination table listing the number of persons
associated with each of the 144 possible pairings of the 12 districts. This table was
used to estimate values of COM(h,w)/Work(h) - the fraction of workers residing in
exposure district h who commuted to exposure district w. Only persons who lived and
worked in one of the 11 Houston exposure districts were included in the exposure
assessment. Persons who lived or worked in the remaining district (i.e., District #12)
were excluded from the exposure analysis. These persons were assumed to spend a
significant part of each week in an area not included in one of the 11 Houston
exposure districts. The pNEM/03 methodology does not provide a means for
estimating the exposures of people during periods when they are not within the
boundaries of an exposure district.
Table 7 lists the values of the quantity COM(h,w)/Work(h) determined by this
method for each of the 144 combinations of home and work district in Houston. Of
23
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these, 13 combinations include District 12 as either the home or work location. As
indicated above, persons associated with these 13 entries were not included in the
exposure assessment.
A special tabulation program in pNEM/03 combined the cohort-specific
estimates of exposure and population to produce histograms and cumulative
frequency tables for various population exposure indicators and averaging times.
Section 7 provides exposure estimates based on existing conditions in each study
area, the attainment of the current NAAQS, and the attainment of each of seven
alternative NAAQS.
24
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TABLE 7. ESTIMATED FRACTION OF HOUSTON
WORKERS WITHIN EACH HOME DISTRICT THAT
COMMUTE TO EACH WORK DISTRICT
District Identifier
Home
1
2
Work
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Com (h,w)
Work (h)
0.360
0.004
0.049
0.245
0.013
0.005
0.003
0.005
0.015
0.010
0.145
0.1 06a
0.027
0.286
0.000
0.333 '
0.007
0.000
0.000
0.000
0.000
0.000
0.012
0.2573
District Identifier
Home
3
4
Work
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Com (h,w)
Work (h)
0.190
0.000
0.150
0.072
0.015
0.038
0.008
0.015
0.120
0.073
0.244
0.01 9a
0.052
0.019
0.002
0.572
0.114
0.002
0.002
0.001
0.001
0.002
0.135
0.0783
District Identifier
Home
5
6
Work
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Com (h.w)
Work (h)
0.006
0.001
0.001
0.230
0.476
0.011
0.013
0.002
0.002
0.003
0.181
0.0593
0.007
0.001
0.016
0.036
0.053
0.186
0.137
0.068
0.047
0.103
0.302
0.0093
(continued)
25
-------
Table 7 (continued)
District Identifier
Home
7
8
Work
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Com fh.w)
Work (h)
0.002
0.000
0.003
0.015
0.043
0.124
0.357
0.188
0.026
0.057
0.097
0.058a
0.000
0.000
0.004
0.001
0.002
0.055
0.150
0.508
0.061
0.057
0.019
0.1 19a
District Identifier
Home
9
10
Work
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Com (h,w)
Work (h)
0.023
0.000
0.088
0.011
0.004
0.059
0.021
0.178
0.363
0.145
0.052
0.029a
0.015
0.000
0.046
0.027
0.016
0.162
0.107
0.135
0.115
0.143
0.197
0.0063
District Identifier
Home
11
12
Work
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
/"N-._— /L_ ...\
Com (n.w)
Work (h)
0.033
0.001
0.015
0.180
0.123
0.041
0.013
0.006
0.008
0.019
0.524
0.009a
0.041a
0.01 7a
0.0043
0.0983
0.046a
0.002a
0.01 8a
0.041a
0.008*
0.001a
0.012*
0.568a
3Persons associated with this entry were not included in the exposure assessment.
26
-------
SECTION 3
THE MASS-BALANCE MODEL
In the pNEM/O3 simulation, the ozone concentration in a particular
microenvironment during a particular clock hour is assumed to be constant. For
indoor and in-vehicle microenvironments, this value is determined by using a mass
balance model to calculate the average ozone concentration for the clock hour
expected under the following conditions:
1. There are no indoor sources of ozone.
2. The indoor ozone concentration at the end of the preceding hour is
specified.
3. The outdoor ozone concentration during the clock hour is constant at a
specified value.
4. The air exchange rate during the clock hour is constant at a specified
value.
5. Ozone decays at a rate that is proportional to the indoor ozone
concentration. The proportionality factor is constant at a specified value.
The mass balance model employed in these calculations is based on a generalized
mass balance model described by Nagda et al.,38 hereafter referred to as the Nagda
model. As originally proposed, this model assumed that pollutant concentration
decays indoors at a constant rate. For use in pNEM/O3, the Nagda model was
revised to incorporate the alternative assumption that the indoor decay rate is
proportional to the indoor concentration. The Nagda model was further revised to
incorporate ozone-specific assumptions concerning various parameter values
suggested by Weschler39 and others.
27
-------
Subsection 3.1 presents the theoretical basis for the pNEM/O3 mass balance
model and the principal model assumptions. Subsection 3.2 describes the algorithms
which were used to generate hourly values of ozone for the indoor and in-vehicle
microenvironments. Subsection 3.3 presents the procedure used to determine air
exchange rate for the mass balance model. An algorithm for simulating the opening
and closing of windows is described in Subsection 3.4.
3.1 Theoretical Basis and Assumptions
The Nagda model can be expressed by the differential equation
where Cin = Indoor concentration (units: mass/volume)
FB = Fraction of outdoor concentration intercepted by the enclosure
(dimensionless fraction)
v = Air exchange rate (1/time)
Cout = Outdoor concentration (mass/volume)
S = Indoor generation rate (mass/time)
cV = Effective indoor volume where c is a dimensionless fraction
(volume)
m = Mixing factor (dimensionless fraction)
X = Decay rate (mass/time)
q = Flow rate through air cleaning device (volume/time)
F = Efficiency of the air cleaning device (dimensionless fraction).
In this model, the pollutant decay rate (A,) is assumed to be constant. Research by
Nazaroff and Cass40 and by Hayes41 suggests that the decay rate for ozone should be
proportional to Cin. Consequently, the pNEM/03 mass balance equation substitutes
28
-------
the term Fd Cin for the term X/cV in Equation 7. The coefficient Fd is expressed in
units of 1/time.
The following notational changes were made to simplify the equation:
(8)
V9 = CV,
FB is the "penetration factor," and Ve is the "effective volume." The resulting equation
p
is
If the three terms that are proportional to Cin are collected into one term, the equation
can be expressed as
ve
(11)
where
«.rrr
(12)
It can be shown that Equation 11 has the following approximate solution:
29
-------
Cin(t)=kiCin(t-*t)+k2£ouC+k2, (13)
where
(15)
and Coy, is the average value of the outdoor concentration over the interval t to t + At.
If C^y, is constant over the interval, then Equation 13 is an exact solution.
The average indoor concentration for hour h, Cjn (h), is given by the expression
+ a3 (17)
where Cin(h-1) is the instantaneous indoor concentration at the end of the preceding
hour, Coy, (h) is the average outdoor concentration for hour h,
3i = z(h) , (18
30
-------
a2 = (FV/v') (l-z(h)} , (19)
(20)
and
A steady-state version of the mass balance model can be developed by solving
Equation 1 1 under the conditions that
(22
and Cout is constant. In this case, the mass balance equation is
0 = F v C + -^- -v'C- , (23 )
which can be rearranged as
C = (F V /v1) C + ——— . ( 9 A.'
*-in » * n ' ' OUt / T, V ^ - .
^^* x^ ***••- »»/TT
31
-------
The ratio of indoor concentration to outdoor concentration is
. = (F v/V) +_—2. . (25:
Weschler39 has developed a steady-state equation for the indoor/outdoor ratio
which is expressed in his notation as
I/O = Ex/[Ex + kd(A/V} } , (26)
where I = indoor concentration, 0 = outdoor concentration, Ex = air exchange rate, kd
= deposition velocity, A = surface area, and V = volume. With respect to Equation 10,
Weschler's model implies that there are no indoor sources (S = zero), no air cleaning
devices (F = zero), the penetration factor is unity (Fp = 1), c = 1, and m = 1. Under
these conditions, Equation 10 becomes
~^Cin = vCout-(v+Fd)Cin (27)
and Equation 25 becomes
,r = —^r • (28)
Weschler's model (Equation 26) and Equation 28 are equivalent if the following
substitutions are made:
Cin = I (29!
32
-------
(so;
31!
(32;
Equation 32 is a particularly useful relationship, as Weschler has identified a number
of studies which suggest that kd(A/V) is relatively constant from building to building.
He suggests that 1.0 x 10'3 sec'1 is a good general estimate of this quantity.
Weschler et al.39 present 14 estimates of kd(A/V) based on data obtained from
specific studies. Nine of these values are based on the observed first-order decay of
ozone in isolated rooms. The remaining five values are based on reported I/O values
and air exchange rates. Table 8 presents means and standard deviations for the first
nine estimates, for the last five estimates, and for all 14 estimates. Two-sided 95
percent confidence intervals for the means are also provided.
The values in Table 8 can be converted to units of h"1 by multiplying each value
by 3600. Expressed in these units, the mean and standard deviation for the 14
estimates are 4.04 h'1 and 1.35 h"1, respectively. A normal distribution with these
parameters was assumed to represent the distribution of Fd values for the non-vehicle
indoor microenvironments. The value of Fd was not permitted to be less than 1.44 h
or more than 8.09 h"1. The lower bound was based on the smallest value cited by
Weschler39 which was measured in a stainless steel room. The upper bound
corresponds to the 99.87 percentile (i.e., z = 3) of a normal distribution with mean
equal to 4.04 and standard deviation equal to 1.35. The largest value cited by
Weschler et al.39 was 7.2 h"1.
-1
33
-------
The mass balance model was also used to simulate ozone concentrations for
the in-vehicle microenvironment. Ideally, the in-vehicle microenvironment would have
been represented by a distribution of Fd values based on ozone decay rates measured
in a representative sample of motor vehicles. Because of the scarcity of research •
concerning ozone decay rates in motor vehicles, ITAQS analysts were not able to
develop such a distribution. Instead, a point estimate of 72.0 h'1 was assumed for the
Fd of the in-vehicle microenvironment. This value was derived by Hayes41 from an
analysis of data for one vehicle presented by Petersen and Sabersky42. Hayes has
used this value in applications of the PAQM exposure model41.
The use of a point estimate based on a single motor vehicle is likely to produce
a bias in the ozone concentrations estimated for the in-vehicle microenvironment. The
direction of this bias is uncertain.
TABLE 8. MEANS, STANDARD DEVIATIONS, AND CONFIDENCE INTERVALS
FOR ESTIMATES OF kd(A/V) PROVIDED BY WESCHLER
Parameter
Sample size
' Mean, sec'1
Standard
deviation, sec"1
Two-sided 95%
confidence
interval, sec'1
Source of kd (A/V) estimate
Observed first-order
decay
9
1.133x 10"3
0.447 x 10'3
(0.789, 1.477)x10'3
Reported
I/O values
5
1.098x 10'3
0.143 x 10'3
(0.920, 1.276)x 10'3
All
14
1.121 x 10'3
0.374 x 10'3
(0.906, 1.335)x10'3
34
-------
3.2 Simulation of Microenvironmental Ozone Concentrations
Consistent with the theoretical considerations discussed in Subsection 3,1, the
following equation was used to estimate the hourly average ozone concentration in a
particular indoor or in-vehicle microenvironment during hour h:
£.n(h) = a,Cin(h-l)+a2£ouC(h) (33)
where Cjn (h) is the average indoor ozone concentration during hour h, Cin (h-1) is the
instantaneous ozone concentration at the end of the preceding hour, C^, (h) is the
outdoor ozone concentration during hour h,
ax = z(h), (34)
a, = (v/vO [l-z(h)l , (35)
and
z(h] = (l-e-v/)/v', (36
v' = v+Fd (37)
The instantaneous ozone concentration at the end of a particular hour, Cin (h),
was estimated by the equation
cin(h) = k^cin(h-i) +k2£oac(h), (38)
where
35
-------
= e
k2 = (v/v')
and v' is determined by Equation 37.
The following algorithm was used to generate a sequence of hourly-average
ozone concentrations for each combination of microenvironment and district.
1. Go to first/next day.
2. Select value of air exchange rate for day from appropriate distribution or
use point estimate. If microenvironment is residential, select one air
exchange value for hours when windows are open and one for hours
when windows are closed. If microenvironment is a nonresidential
building or vehicle, then one air exchange rate is used for all hours of the
day.
3. Select value of decay rate (Fd) for day from appropriate distribution or
use point estimate. If microenvironment is non-vehicular enclosure,
select value of Fd from normal distribution with mean = 4.04 h"1 and
standard deviation = 1.35 h'1. Value is not permitted to be less than 1.44
h"1 or more than 8.09 h"1. If microenvironment is "in vehicle", use point
estimate of 72.0 h"1.
4. Go to first/next clock hour.
5. If microenvironment is residential, use supplementary window algorithm
to determine window status for current hour (open or closed). Window
status determines which air exchange rate determined in Step 2 applies
to current hour.
6. Use Equation 33 to determine ozone concentration for current hour
based on air exchange rate specified for hour, outdoor ozone
concentration during hour, and ozone concentration at end of preceding
hour.
7. Use Equation 38 to determine instantaneous ozone concentration at end
of current hour based on air exchange rate specified for hour, outdoor
36
-------
ozone concentration during hour, and instantaneous ozone concentration
at end of preceding hour. This value is saved for input into Equation 33
during the next hour.
8. If end of day, go to Step 1. Otherwise, go to Step 4.
Step 2 requires the random selection of an air exchange rate from a specified
distribution. Four enclosure categories were established for this purpose.
Residential buildings - windows open
Residential buildings - windows closed
Nonresidential buildings
Vehicles.
A survey of the scientific literature determined that there were sufficient data available
to define distributions for only two of the four enclosure categories: "residential
building - windows closed" or "nonresidential building". In each case, a two-parameter
lognormal distribution was found to provide a good fit to the data. Point (single-
valued) estimates were developed for the remaining two enclosure categories.
Each of the two lognormal distributions was defined by the expression
AER = GM x GSDZ (41>
where AER is the air exchange rate, GM is the geometric mean, and GSD is the
geometric standard deviation. The values for GM and GSD were determined by fitting
lognormal distributions to representative data sets (Subsection 3.3). A value of AER
was selected at random from a particular lognormal distribution by randomly selecting
a value of Z from the unit normal distribution [N(0,1)] and substituting it into Equation
41. Table 9 lists the values of GM and GSD for the two lognormal distributions and
the values of the point estimates.
The distributions used to determine AER are discussed in more detail in
Subsection 3.3. Subsection 3.4 provides a description of the algorithm used to
determine window status in the residential microenvironments (Step 4).
37
-------
TABLE 9. DISTRIBUTIONS OF AIR EXCHANGE RATE VALUES USED
IN THE pNEM/03 MASS BALANCE MODEL
Enclosure category
Residential building-
windows closed
Residential building-
windows open
Nonresidential building
Vehicle
Air exchange rate distribution
Lognormal distribution
0 Geometric mean = 0.53
0 Geometric standard deviation =
0 Lower bound = 0.063
0 Upper bound = 4.47
1.704
Point estimate: 6.4
Lognormal distribution
0 Geometric mean = 1 .285
0 Geometric standard deviation =
0 Lower bound = 0.19
0 Upper bound = 8.69
1.891
Point estimate: 36
3.3 Air Exchange Rate Distributions
A review of the scientific literature relating to air exchange rates identified 31
relevant references (list available on request). Of these, only a few were found to
contain sufficient data to construct a distribution of air exchange rates relating to a
particular building type such as residence or office. The two most useful studies were
conducted by Grimsrud et al.43 and by Turk et al.44
Residential Locations
Grimsrud et al.43 measured AER's in 312 residences. Reported AER values
ranged from 0.08 to 3.24. ITAQS analyzed these data to determine which of two
distributions (normal versus lognormal) better characterized the data. The lognormal
distribution was found to yield a better fit, as the data were highly skewed. The fitted
lognormal parameters were geometric mean = 0.53 and geometric standard deviation
= 1.704. This distribution was used in pNEM/03 to represent the distribution of AER's
38
-------
in residences with windows closed. Upper and lower limits of 4.47 and 0.063 air
changes per hour were established to prevent the selection of unusually extreme
values of AER. These limits corresponded to the substitution of Z = 4 and Z = -4 in
Equation 41 when GM = 0.53 and GSD = 1.704. The upper bound was 38 percent
larger than the largest reported AER (3.24). The lower bound was 21 percent smaller
than the smallest reported AER (0.08).
No comparable data bases were identified which were considered
representative of residences where windows are open. Hayes has used 6.4 h"1 as the
AER value for open windows in applications of the PAQM model.41 This value was
based on an analysis by Hayes45 of a hypothetical building plan with an assumed
"orifice coefficient." Orifice coefficient is defined as the ratio of the equivalent area of
all openings in a building to the building's volume. In support of this approach, Hayes
cites a report by Moschandreas et al.46 which suggests that infiltration is proportional
to a building's orifice coefficient.
ITAQS analysts considered Hayes's estimate to be the best available estimate
of AER for residences with windows open. Consequently, the AER for residences with
windows open was treated as a point estimate (6.4 h'1) in the pNEM/O3 analyses
described here. Note that the use of an AER estimate representing a single set of
conditions is likely to produce a bias in the ozone concentrations estimated for this
microenvironment. The direction of this bias is uncertain.
Nonresidential Locations
Turk et al.44 measured AER's in 40 public buildings identified as schools (n = 7),
offices (n = 25), libraries (n = 3), and multipurpose buildings (n = 5). The minimum
reported AER was 0.3; the maximum was 4.1. ITAQS analysts fit normal and
lognormal distributions to the data for all 40 buildings and found that the lognormal
distribution produced a slightly better fit, although it had a tendency to over-predict
high values. The fitted lognormal parameters were geometric mean = 1.285 and
geometric standard deviation = 1.891.
39
-------
The buildings can be grouped as offices (n = 25) and nonoffices (n = 15).
Lognormal fits to these data sets yielded geometric means and standard deviations of
1.30 and 1.93 for offices and 1.27 and 1.87 for nonoffices. ITAQS performed a two-
sample t test on the two data sets and found no significant difference in the means or
standard deviations of the data. Consequently, a single lognormal distribution
(geometric mean = 1.285, geometric standard deviation = 1.891) was used in
pNEM/03 for all nonresidential buildings. To prevent the over-prediction of high AER
values, an upper bound of 8.69 was established. This value results when Z = 3 is
substituted into Equation 41 with GM = 1.285 and GSD = 1.891. This value is over
twice the largest AER value (4.1) reported for the 40 buildings and corresponds to the
99.87 percentile of the specified lognormal distribution, A lower bound of 0.19 was
also established. This value corresponds to a Z value of -3 and represents the 0.13
percentile of the lognormal distribution.
ITAQS analysts consider the AER data obtained from Turk et al.44 to be
generally representative of buildings with closed windows. Consequently, the
lognormal AER distribution derived from these data may not be applicable to non-
residential buildings which are ventilated by open windows. As comparable data were
not available for non-residential buildings with open windows, analysts applied the
lognormal AER distribution for closed windows to all non-residential buildings. This
approach is likely to under-estimate the ozone exposures of people who frequently
occupy buildings with open windows.
In Vehicle Locations
A point estimate of 36 air changes per hour was used for in-vehicle locations.
This value was obtained from Hayes47 based on his analysis of data for a single
vehicle presented by Peterson and Sabersky42. Hayes notes that the greater AER
observed in vehicles, even with the windows closed, is due to wind effects on the
moving vehicle and the "leakiness" of typical automobiles.
ITAQS analysts considered Hayes's estimate to be the best available estimate
of AER for the in-vehicle microenvironment. Consequently, in-vehicle AER was
40
-------
treated as a point estimate (36 IT1) in the pNEM/03 analyses described here. It
should be noted that the use of an AER estimate representing a single set of
conditions is likely to produce a bias in the ozone concentrations estimated for this
microenvironment. The direction of this bias is uncertain.
3.4 Window Status Algorithm
The opening and closing of windows in the three residential microenvironments
was simulated by an algorithm which specified a window status (open or closed) for
each clock hour. The algorithm consisted of the following eight-step procedure.
1. Identify air conditioning system associated with cohort (central, window
units, none).
2. Go to first/next day.
3. Determine average temperature for day from supplementary file. Identify
temperature range which contains this value (below 32, 32 to below 63,
63 to 75, above 75).
4. Select random number between zero and 1. Compare random number
with probabilities listed in Table 10 for specified air conditioning system
and temperature range. Determine window status for day. If day status
is "windows open all day" or "windows closed all day", set window status
for all clock hours of day as indicated and go to Step 2. If day status is
"windows open part of day", go to Step 5.
5. Go to first/next clock hour.
6. Determine window status of preceding clock hour.
7. Select random number between zero and 1. Compare random number
with probabilities listed in Table 11, 12, or 13 for specified air
conditioning system, clock hour, temperature range, and window status
for preceding hour. If the random number is less than the specified
probability, the window will be open during the clock hour. Otherwise,
the window will be closed.
8. If end of day, go to Step 2. Otherwise, go to Step 5.
41
-------
This algorithm assigns each day to one of three categories: 1) windows closed all
day, 2) windows open all day, and 3) windows open part of day. These assignments
are made according to the air conditioning system associated with the cohort and the
average temperature of the day. If the day assignment is "windows open part of day",
the algorithm assigns window status on an hourly basis for each of the 24 clock hours
in the day. These hourly assignments are made according to the 1) cohort's air
conditioning system, 2) clock hour, 3) average temperature for the day, and 4) window
status of the preceding hour. Both the daily and hourly assignments are made
probabilistically by comparing random numbers to the probabilities that the specified
window status will occur under the stated conditions.
The window status probabilities listed in Tables 10, 11, 12, and 13 were
developed through a statistical analysis of data on window openings obtained from the
CADS.19 This analysis indicated that air conditioning system, temperature, clock hour,
and window status of preceding hour were statistically significant factors affecting
window status.
42
-------
TABLE 10. PROBABILITY OF WINDOW STATUS FOR DAY BY AIR
CONDITIONING SYSTEM AND TEMPERATURE RANGE
Air
conditioning
system
Central
Room units
None
Temperature
range, °F
Below 32
32 to 62
63 to 75 '
Above 75
Below 32
32 to 62
63 to 75
Above 75
Below 32
32 to 62
63 to 75
Above 75
Probability of window status for day
Closed all day
1.000
0.851
0.358
0.633
1 .000
0.734
0.114
0.160
1.000
0.812
0.095
0.016
Open all day
0
0.009
0.343
0.167
0
0.028
0.505
0.380
0
0.011
0.672
0.823
Open part of day
0
0.140
0.299
0.200
0
0.238
0.381
0.460
0
0.177
0.233
0.161
TABLE 11. PROBABILITY OF WINDOWS BEING OPEN BY CLOCK HOUR,
TEMPERATURE RANGE, AND WINDOW STATUS OF PRECEDING HOUR (PH) FOR
RESIDENCES WITH CENTRAL AIR CONDITIONING
Clock
hour
1-3
4-6
7-9
10-12
13-15
16-18
19-21
22-24
Probability of windows being open
32°F to 62° F
PH=open
1.000
1.000
0.837
0.679
0.857
0.932
0.646
0.811
PH=closed
0.000
0.005
0.038
0.126
0.149
0.131
0.043
0.036
63°F to 75°F
PH=open
0.978
0.989
0.932
0.865
0.912
0.935
0.892
0.913
PH=closed
0.011
0.000
0.074
0.235
0.240
0.161
0.136
0.101
Above 75°F
PH=open
0.986
1.000
0.961
0.860
0.923
0.912
0.893
0.909
PH=closed
0.020
0.017
0.094
0.174
0.263
0.000
0.047
0.066
43
-------
TABLE 12. PROBABILITY OF WINDOWS BEING OPEN BY CLOCK HOUR,
TEMPERATURE RANGE, AND WINDOW STATUS OF PRECEDING HOUR (PH) FOR
RESIDENCES WITH WINDOW AIR CONDITIONING UNITS
S^t _ _ 1
Clock
hour
1-3
4-6
7-9
10-12
13-15
16-18
19-21
22-24
Probability of windows being open
32°F to 62°F
PH=open
0.970
0.975
0.864
0.929
0.860
0.859
0.684
0.919
PH=closed
0.006
0.000
0.040
0.121
0.244
0.103
0.063
0.042
63°F to 75°F
PH=open
0.947
0.994
0.934
0.917
0.969
0.956
0.925
0.851
PH=closed
0.007
0.016
0.101
0.303
0.400
0.125
0.176
0.064
Above 75°F
PH=open
0.974
0.989
0.989
0.849
0.819
0.930
0.902
0.865
PH=closed
0.010
0.017
0.092
0.351
0.152
0.043
0.056
0.121
TABLE 13. PROBABILITY OF WINDOWS BEING OPEN BY CLOCK HOUR,
TEMPERATURE RANGE, AND WINDOW STATUS OF PRECEDING HOUR (PH) FOR
RESIDENCES WITH NO AIR CONDITIONING SYSTEM
Clock
hour
1-3
4-6
7-9
10-12
13-15
16-18
19-21
22-24
Probability of windows being open
32°F to 62°F
PH=open
1.000
1.000
0.950
0.889
0.923
0.848
0.609
0.684
PH=closed
0.015
0.000
0.000
0.200
0.130
0.200
0.067
0.043
63°F to 75°F
PH=open
0.974
1.000
0.868
0.933
1.000
0.964
0.909
0.800
PH=closed
0.031
0.000
0.057
0.400
0.286
0.000
0.500
0.167
Above 75°F
PH=open
1.000
1.000
1.000
0.875
0.917
0.818
1.000
0.769
PH=closed
0.000
0.000
0.000
0.500
0.000
0.667
0.200
0.500
44
-------
SECTION 4
PREPARATION OF AIR QUALITY DATA
The pNEM/03 mass balance model requires representative ambient air quality
data for each exposure district in the form of a time series containing one value for
each hour in the specified ozone season. This section describes the procedures used
to select appropriate data sets for the nine study areas. It also describes the
procedure used for filling in missing values in these data sets.
4.1 Selection of Representative Data Sets
To simplify the computer simulation, the ambient ozone concentration
throughout an exposure district was assumed to be a function of the ozone
concentration measured at a single, representative monitoring site located within the
district. Based on guidance from EPA, analysts defined the shape of each exposure
district by first drawing a circle of radius = 15 km with the monitoring site at the center.
If the centroid of a census unit (census tract or block numbering area) was located
within this circle, the census unit was assigned to the exposure district. If a centroid
was located within more than one circle, the census unit was assigned to the nearest
monitor. Note that the monitoring sites selected to represent a city directly determined
the location and shape of the city's exposure districts.
With one exception, the monitoring sites selected for the pNEM/O3 analysis of
outdoor workers were identical to those used in an earlier pNEM/O3 analysis of the
ozone exposure within the general population of the nine study areas. Section 4 of
the report by Johnson et al.16 describes the selection process employed in the earlier
analysis. The exception concerns one of the 12 monitoring sites selected to represent
ambient ozone conditions in the New York study area. This site (identified by EPA as
Site No. 36-061-0063) was selected to represent an exposure district centered on the
45
-------
southern end of Manhattan Island. Site No. 36-061-0063 was later judged to be
unrepresentative of ground-level ozone concentrations in this area of New York due to
the site's high elevation. Consistent with guidance from EPA, researchers selected
the next nearest ozone monitor (No. 36-061-0010) to represent the Manhattan
exposure district in the pNEM/03 analysis of outdoor workers. Monitor No. 36-061-
0010 also represents another exposure district which is centered on the northern end
of Manhattan Island, the actual location of this monitor.
Table 14 lists the number of ozone monitoring sites selected for each study
area. The table also indicates the largest value for the second highest daily maximum
hourly ozone concentration reported by the selected monitors for the indicated ozone
season. It should be noted that the omission of Monitor No. 36-061-0063 from the
New York study area does not affect the value of this air quality indicator (175 ppb).
4.2 Treatment of Missing Values and Descriptive Statistics
Hourly average ozone data reported by each site were used to estimate the
ambient ozone levels within the associated exposure district. Gaps in the hourly
average ozone data sets were filled in by using a time series model developed by
Johnson and Wijnberg30. The model contains cyclical, autoregressive, and noise
components whose parameters were determined from a statistical analysis of the
reported data.
Tables 15 through 23 provide descriptive statistics for each hourly-average data
set before and after application of the fill-in program. In general, the fill-in program
has little or no effect on the listed percentiies or high values. Whenever there is a
difference in the values for a particular percentile, the filled-in value is usually lower.
It should be noted that the data sets differ in terms of concentration resolution.
The reported ozone concentration values for all 11 Houston sites and for 15 of the 16
Los Angeles sites are rounded to the nearest 10 ppb. The data for the other seven
cities are rounded to the nearest 1 ppb. All other factors being equal, the algorithm
used to fill in missing values generally performs better when applied to air quality data
of high resolution.
46
-------
TABLE 14. CHARACTERISTICS OF OZONE STUDY AREAS AND MONITORING SITES
Study area
Chicago
Denver
Houston
Los Angeles
Miami
New York City
Philadelphia
St. Louis
Washington, D.C.
Designated
exposure period
Ozone
season
Apr - Oct
Mar - Sep
Jan - Dec
Jan - Dec
Jan - Dec
Apr - Oct
Apr - Oct
Apr - Oct
Apr - Oct
Year
1991
1990
1990
1991
1991
1991
1991
1990
1991
Number of
counties3
in area
7
6
5
4
2
18
13
7
13
Number of
monitoring
sites
selected
12
7
11
16
6
11b
10
11
11
Largest reported
second high daily
maximum ozone
concentration, ppb
129
110
220
310
123
175
156
125
144
aCounties are geographic areas assigned a county code by the Bureau of Census in
Summary Tape File 3 (STF3). A county is counted if any portion is within the study area.
"Monitor No. 36-061-0010 represents two exposure districts.
ITAQS analysts also constructed a data set for each monitor listing eight-hour
running average ozone concentrations based on the filled-in data sets. These data
were used to determine each site's status with respect to various eight-hour NAAQS
under consideration by EPA. Tables 24 through 32 provide eight-hour descriptive
statistics for the monitors selected to represent each city.
47
-------
TABLE 15. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE CHICAGO STUDY AREA
Monitor ID
17-031-0001
17-031-0032
17-031-1003
17-031-1601
17-031-4002
17-031-4003
17-031-7002
Monitor
location
Alsip
Chicago
Chicago
Lemont
Cicero
Des Plaines
Evanston
Dist-
rict
code
1
2
3
4
5
6
7
Filled'
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4903
5136
4985
5136
4895
5136
4799
5136
5033
5136
4936
5136
4876
5136
Percentiles, ppb
50
19
19
28
28
19
19
28
28
18
18
23
23
30
30
90
51
50
58
59
51
50
61
60
49
49
53
52
59
58
95
61
61
69
69
63
61
71
71
60
59
63
63
69
69
99
77
77
87
87
81
81
89
89
78
78
80
80
90
90^
99.5
83
83
92
92
88
87
98
97
86
86
85
86
97
96
High values,
ppb
Second
104
104
116
116
129
129
126
126
120
120
105
105
115
115
First
108
108
120
120
134
134
152
152
125
125
119
119
123
123
00
(continued)
-------
TABLE 1.5 (Continued)
Monitor ID
17-031-8003
17-043-6001
17-089-0005
17-097-0001
17-097-1002
Monitor
location
Calumet City
Lisle
Elgin
Deerfield
Waukegan
Dist-
rict
code
8
9
10
11
12
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4856
5136
5100
5136
5041
5136
5011
5136
5038
5136
Percentiles, ppb
50
23
24
19
20
26
26
26
26
30
30
90
54
54
49
50
54
54
56
56
61
61
95
64
64
59
59
63
63
67
67
71
71
99
81
81
78
78
82
82
85
85
92
92
99.5
86
86
87
87
91
90
90
90
102
102
High values,
ppb
Second
97
97
116
116
126 .
126
116
116
119
119
First
109
109
118
118
128
128
124
124
126
126
CO
"Number of hourly-average ozone concentrations during designated ozone season.
-------
TABLE 16. DESCRIPTIVE STATISTICS FOR 1990 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE DENVER STUDY AREA
Monitor ID
08-001-3001
08-005-0002
08-005-0003
08-013-1001
08-031-0002
08-031-0014
08-059-0002
Monitor
location
Adams Co.
Arapaho Co.
Englewood
Boulder Co.
Denver
Denver
Arvada
Dist-
rict
code
1
2
3
4
5
6
7
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4322
5136
4047
5136
5036
5136
4458
5136
5063
5136
4453
5136
4908
5136
Percentiles, ppb
50
26
26
40
39
23
23
33
32
17
17
22
22
26
26
90
54
53
63
60
53
54
55
54
40
40
54
53
56
55
95
59
58
70
67
62
62
64
63
47
46
62
61
64
64
99
69
68
88
86
76
76
78
77
59
59
77
75
79
79
99.5
72
72
93
91
83
83
83
80
64
64
83
81
83
83
High values,
ppb
Second
87
87
109
109
110
110
102
102
104
104
107
107
115
115
First
99
99
111
110
111
111
106
106
120
120
120
120
115
115
aNumber of hourly-average ozone concentrations during designated ozone season.
en
o
-------
01
TABLE 17. DESCRIPTIVE STATISTICS FOR 1990 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE HOUSTON STUDY AREA
— - *---' ' - - • • — --.-. -_-.._ -
Monitor ID
48-201-0024
48-201-0029
48-201-0046
48-201-0047
48-201-0051
48-201-0059
48-201-0062
Monitor
location
Harris Co.
Harris Co.
Houston
Houston
Houston
Houston
Houston
Dist-
rict
code
1
2
3
4
5
6
7
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
6865
8760
7689
8760
8138
8760
7970
8760
7999
8760
6941
8760
8072
8760
Percentiles, ppb
50
20
20
20
20
10
10
10
10
20
20
10
10
20
20
90
60
60
50
50
50
50
50
50
50
50
40
40
50
46
95
80
70
70
70
60
60
60
60
70
70
50
50
60
60
99
110
110
100
100
100
100
100
100
110
110 -.
80
70
100
90
99.5
130
120
120
110
120
120
120
120
130
130
90
90
110
110
High values,
ppb
Second
220
220
160
160
200
200
210
210
200
200
140
140
180
180
First
220
220
180
180
230
230
240
240
220
220
190
190
230
230
(continued)
-------
TABLE 17 (Continued)
Monitor ID
48-201-1003
48-201-1034
48-201-1035
48-201-1037
Monitor
location
Deer Park
Houston
Houston
Houston
Dist-
rict
code
8
9
10
.11
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
na
7685
8760
8098
8760
8300
8760
8086
8760
Percentiles, ppb
50
20
20
10
10
10
10
10
10
90
50
50
50
45
50
50
40
40
95
60
60
60
60
60
60
60
60
99
100
100
90
90
100
100
100
100
99.5
110
110
120
110
120
120
120
120
High values,
ppb
Second
230
230
200
200
230
230
220
220
First
230
230
210
210
230
230
220
220
"Number of hourly-average ozone concentrations during designated ozone season.
01
-------
TABLE 18. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE LOS ANGELES STUDY AREA
Monitor ID
06-037-0016
06-037-1103
06-037-1301
06-037-1601
06-037-1902
06-037-2005
06-037-4002
06-037-5001
Monitor
location
Glendora
Los Angeles
Lynwood
Pico Rivera
Santa Monica
Pasadena
Long Beach
Hawthorne
Dist-
rict
code
1
2
3
4
5
6
7
8
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
8416
8760
8356
8760
8478
8760
8523
8760
8179
8760
8344
8760
8377
8760
8465
8760
Percentiles, ppb
50
20
20
10
10
10
10
10
10
26
25
10
10
20
20
20
20
90
80
80
50
50
40
40
60
60
65
64
70 i
70
40
40
50
50
95
110
110
70
70
50
50
80
80
80
79
100
100
50
50
60
60
99
180
180
120
110
80
80
130
130
114
112
160
160
70
70
80
80
99.5
200
200
130
130
90
90
160
160
131
128
170
170
80
80
90
90
High values,
ppb
Second
310
310
170
170
130
130
250
250
191
191
220
220
100
100
110
110
First
320
320
190
190
160
160
260
260
191
191
230
230
110
110
110
115
en
CO
(continued)
-------
TABLE 18 (Continued)
Monitor ID
06-059-0001
06-059-1003
06-059-3002
06-059-5001
06-065-8001
06-071-1004
06-071-4003
06-071-9004
Monitor
location
Anaheim
Costa Mesa
Los Alamitos
La Habra
Rubidoux
Upland
Redlands
San Bernardino
Dist-
rict
code
9
10
11
12
13
14
15
16
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
8473
8760
8358
8760
8442
8760
8492
8760
8521
8760
8408
8760
8374
8760
8514
8760
Percentiles, ppb
50
10
10
30
30
20
20
20
15
20
20
10
10
30
30
20
13
90
50
50
50
50
50
50
60
53
90
80
70
70
90
90
80
80
95
60
60
60
60
60
60
70
70
110
110
100
90
120
120
110
110
99
100
100
80
80
90
90
110
110
160
160
160
160
180
180
160
160
99.5
110
110
90
90
100
100
130
130
180
180
180
180
190
190
170
170
High values,
ppb
Second
200
200
140
140
150
150
190
190
240
240
240
240
250
250
240
240
aNumber of hourly-average ozone concentrations during designated ozone season.
First
250
250
170
170
170
170
210
210
240
240
270
270
250
250
250
250
Ol
-------
TABLE 19. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE MIAMI STUDY AREA
Monitor ID
12-011-0003
12-011-2003
12-011-8002
12-025-0021
12-025-0027
12-025-0029
Monitor
location
Broward Co.
Pompano
Beach
Dania
Dade Co.
Dade Co.
Dade Co.
Dist-
rict
code
1
2
3
4
5
6
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
8624
8760
8664
8760
8732
8760
8470
8760
8486
8760
8576
8760
Percentiles, ppb
50
22
22
23
23
26
26
21
21
28
28
21
21
90
42
42
41
41
43
43
41
41
44
44
39
39
95
48
48
46
46
49
49
46
46
• 49
49
45
44
99
59
59
58
58
61
61
57
57
58
57
54
54
99.5
63
63
64
63
64
64
64
63
65
64
58
58
High values,
PPb
Second
93
93
91
91
95
95
123
123
90
90
85
85
First
94
94
96
96
100
100
124
124
95
95
90
90
"Number of hourly-average ozone concentrations during designated ozone season.
Ol
Ol
-------
TABLE 20. DESCRIPTIVE STATISTICS FOR 1991
CONCENTRATIONS OBTAINED FROM SELECTED
DATA SETS CONTAINING HOURLY-AVERAGE OZONE
MONITORING SITES IN THE NEW YORK STUDY AREA
Monitor ID
09-001-0017
34-013-0011
34-017-0006
34-027-3001
34-039-5001
36-001-0080
36-061-0010
Monitor
location
Greenwich
Newark
Bayonne
Morris Co.
Plainfield
Bronx Co.
New York
City
Dist-
rict
code
1
2
3
i
4
5
6
7, 8
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4882
5136
5033
5136
4968
5136
4691
5136
4986
5136
4422
5136
4893
5136
Percentiles, ppb
50
29
29
18
18
24
24
39
39
19
20
12
13
14
14
90
61
60
52
52
64
64
75
73
55
55
36
36
43
42
95
75
74
67
67
81
80
88
86
69
68
47
45
58
57
99
110
110
92
92
109
108
111
111
90
90
68
67
87
87
99.5
120
118
97
97
116
116
118
118
97
96
72
72
95
95
High values,
ppb
Secon
H
147
147
123
123
166
166
137
137
115
115
92
92
151
151
First
161
161
132
132
167
167
139
139
120
120
94
94
155
155
en
O)
(continued)
-------
TABLE 20 (Continued)
en
-4
Monitor ID
36-061-0063
36-081-0004
36-085-0067
36-103-0002
36-119-2004
"Number of hou
bOriginally assig
Monitor
location
New York
City
Queens Co.
Richmond Co.
Babylon
White Plains
-
rly-average ozon
ned to District 8.
Dist-
rict
code
b
9
10
11
12
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4912
5136
4912
5136
4086
5136
4884
5136
4975
5136
Percentiles, ppb
50
41
41
20
20
28
29
30
30
27
27
90
82
82
57
57
67
62
67
67
62
61
95
96
95
72
72
81
77
81
80
78
78
99
122
122
105
105
106
103
111
110
107
107
99.5
130
130
115
115
116
111
121
120
116
116
High values,
ppb
Second
175
175
162
162
169
169
175
175
145
145
First
177
177
174
174
178
178
217
217
152
152
e concentrations during designated ozone season.
Replaced by Monitor No. 36-061-0010.
(continued)
-------
TABLE 21. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE PHILADELPHIA STUDY AREA
Monitor ID
34-005-3001
34-007-0003
34-007-1001
34-015-0002
42-017-0012
42-045-0002
42-091-0013
Monitor
location
McGuire AFB
Camden
Camden
Gloucester
Bristol
Chester
Norristown
Dist-
rict
code
1
2
3
4
5
6
7
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4939
5136
4998
5136
4989
5136
5001
5136
4986
5136
5085
5136
4907
5136
Percentiles, ppb
50
35
34
28
28
36
36
33
33
28
28
30
30
26
26
90
72
72
70
70
76
76
74
73
70
70
67
67
67
66
95
88
88
84
84
89
89
87
87
84
84
78
78
78
77
99
117
117
115
114
112
112
115
115
111
110
103
103
99
98
99.5
126
124
120
120
117
117
125
125
119
118
108
108
106
105
High values,
ppb
Second
156
156
143
143
146
146
151
151
139
139
125
125
125
125
First
156
156
148
148
149
149
151
151
144
144
135
135
127
127
en
oo
(continued)
-------
TABLE 21 (Continued)
Monitor ID
42-101-0014
42-101-0023
42-101-0024
Monitor
location
Philadelphia
Philadelphia
Philadelphia
Dist-
rict
code
8
9
10
Filled
in?
No
Yes
No
Yes
No
Yes
na
4900
5136
4786
5136
4984
5136
Percentiles, ppb
50
30
30
20
20
30
30
90
70
70
50
50
70
70
95
80
80
70
70
80
80
99
100
100
90
90
110
110
99.5
110
110
100
100
110
110
High values,
ppb
Second
140
140
130
130
130 ...
130
First
140
140
130
130
140
140
en
CD
"Number of hourly-average ozone concentrations during designated ozone season.
-------
TABLE 22. DESCRIPTIVE STATISTICS FOR 1990 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE ST. LOUIS STUDY AREA
Monitor ID
17-163-0010
29-183-1002
29-189-0001
29-1 89-0006
29-189-3001
29-189-5001
29-189-7001
Monitor
location
East St. Louis
St. Charles
Affton
St. Louis Co.
Clayton
Ferguson
St. Ann
Dist-
rict
code
1
2
3
4
5
6
7
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4963
5136
4587
5136
4218
5136
5038
5136
5042
5136
5026
5136
5036
5136
Percentiles, ppb
50
19
19
23
27
L28
29
24
24 '
24
24
18
18
29
29
90
48
48
55
55
62
59
48
48
53
54
42
42
58
58
95
57
57
66
66
75
72
55
55
65
65
48
47
70
70
99
73
73
90
90
93
90
70
69
83
83
61
61
92
92
99.5
83
82
102
98
100
99
75
75
93
92
64
64
96
96
High values,
ppb
Second
116
116
125
125
120
120
99
99
125
125
75
75
130
130
First
124
124
125
125
127
127
100
100
127
127
80
80
135
135
en
o
(continued)
-------
TABLE 22 (Continued)
Monitor ID
29-510-0007
29-510-0062
29-510-0072
29-510-0080
Monitor
location
St. Louis
St. Louis
St. Louis
St. Louis
Dist-
rict
code
8
9
10
11
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
na
5008
5136
4928
5136
4830
5136
5044
5136
Percentiles, ppb
50
18
18
24
24
18
18
24
24
90
44
44
53
53
40
40
53
53
95
52
52
63
63
48
48
64
65
99
69
69
82
82
64
64
86
86
99.5
74
74
89
89
72
72
94
94
High values,
ppb
Second
96
96
108
108
100
100
117
117
First
96
96
111
111
110
110
129
129
aNumber of hourly-average ozone concentrations during designated ozone season.
O)
-------
TABLE 23. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING HOURLY-AVERAGE OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE WASHINGTON STUDY AREA
Monitor ID
11-001-0017
11-001-0025
24-031-3001
24-033-0002
24-033-8001
51-013-0020
51-059-0018
Monitor
location
Washington
Washington
Rockville
Greenbelt
Suitland-
Silver Hills
Arlington Co.
Mt. Vernon
Dist-
rict
code
1
2
3
4
5
6
7
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
na
4928
5136
5031
5136
4881
5136
5034
5136
4997
5136
5034
5136
4897
5136
Percentiles, ppb
50
19
19
24
24
29
29
30
30
31
31
28
28
30
30
90
54
54
61
60
69
68
74
74
69
68
68
68
71
71
95
64
64
72
71
79
79
87
87
81
81
80
79
83
83
99
82
82
90
90
100
99
110
109
102
102
102
102
106
105
99.5
91
90
99
99
103
103
115
114
108
108
107
107
111
111
High values,
ppb
Second
137
137
144
144
135
135
148
148
139
139
142
142
126
126
First
147
147
148
148
137
137
153
153
144
144
148
148
142
142
O)
ro
(continued)
-------
TABLE 23 (Continued)
Monitor ID
51-059-1004
51-059-5001
51-510-0009
51-600-0005
Monitor
location
Seven
Corners
McLean
Alexandria
Fairfax
Dist-
rict
code
8
9
10
11~
Filled
in?
No
Yes
No
Yes
No
Yes
No
Yes
na
4951
5136
5037
5136
4916
5136
4947
5136
Percentiles, ppb
50
33
33
27
27
22
22
33
32
90
71
71
63
63
54
54
66
66
95
86
86
73
74
65
65
77
76
99
110
109
95
95
84
84
97
96
99.5
119
116
104
101
95
94
107
106
High values,
ppb
Second
174
174
137
137
131
131
131
131
First
178
178
138
138
132
132
132
132
o>
CO
aNumber of hourly-average ozone concentrations during designated ozone season.
-------
TABLE 24. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE CHICAGO STUDY AREA
Monitor ID
17-031-0001
17-031-002
17-031-1003
17-031-1601
17-031-4002
17-031-4003
17-031-7002
17-031-8003
17-043-6001
17-089-0005
17-097-0001
17-097-1002
Monitor
location
Alsip
Chicago
Chicago
Lemont
Cicero
Des Plaines
Evanston
Calumet City
Lisle
Elgin
Deerfield
Waukegan
District
code
1
2
3
4
5
6
7
8
9
10
11
12
Percentiles, ppb
50
20
28
19
28
18
24
30
24
20
26
26
31
90
46
54
46
57
45
48
55
49
45
50
52
58
95
54
63
55
66
54
57
64
58
53
58
61
66
99
69
80
71
82
70
72
83
74
70
74
77
84
99.5
75
84
76
88
75
77
86
78
79
82
83
88
High values, ppb
Second
94
106
101
108
95
93
101
90
98
106
101
104
First
95
107
101
109
95
95
102
90
98
106
103
106
-------
TABLE 25. DESCRIPTIVE STATISTICS FOR 1990 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE DENVER STUDY AREA
Monitor ID
08-001-3001
08-005-0002
08-005-0003
08-013-1001
08-031-0002
08-031-0014
08-059-0002
Monitor
location
Adams Co.
Arapaho Co.
Englewood
Boulder Co.
Denver
Denver
Arvada
District
code
1
2
3
4
5
6
7
Percentiles, ppb
50
26
38
24
33
18
23
26
90
47
56
48
50
35
47
50
95
52
62
54
57
41
52
57
99
60
76
65
68
51
62
68
99.5
63
80
70
71
54
64
72
High values, ppb
Second
72
87
83
83
84
77
95
First
74
87
83
85
85
80
96
CD
01
-------
TABLE 26. DESCRIPTIVE STATISTICS FOR 1990 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE HOUSTON STUDY AREA
Monitor ID
48-201-0024
48-201-0029
48-201-0046
48-201-0047
48-201-0051
48-201-0059
48-201-0062
48-201-1003
48-201-1034
48-201-1035
48-201-1037
Monitor
location
Harris Co.
Harris Co.
Houston
Houston
Houston
Houston
Houston
Deer Park
Houston
Houston
Houston
District
code
1
2
3
4
5
6
7
8
9
10
11
Percentiles, ppb
50
21
21
14
15
21
14
17
19
16
15
12
90
50
49
42
42
48
33
41
46
41
42
39
95
64
61
53
55
61
41
52
56
54
56
51
99
92
89
84
82
92
60
79
84
81
86
81
99.5
104
96
95
96
105
71
90
92
90
97
92
High values, ppb
Second
149
124
151
156
167
110
154
139
144
156
160
First
150
124
152
164
170
112
155
140
146
157
164
O)
CD
-------
TABLE 27. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE LOS ANGELES STUDY AREA
Monitor ID
06-037-0016
06-037-1103
06-037-1301
06-037-1601
06-037-1902
06-037-2005
06-037-4002
06-037-5001
06-059-0001
06-059-1003
06-059-3002
06-059-5001
06-065-8001
06-071-1004
06-071-4003
06-071-9004
jiaViVi'..')' •• ^ .mi.iiHfvu.-i"
Monitor
location
Glendora
Los Angeles
Lynwood
Pico Rivera
Santa Monica
Pasadena
Long Beach
Hawthorne
Anaheim
Costa Mesa
Los Alamitos
La Habra
Rubidoux
Upland
Redlands
San
Bernardino
l'.l '.!..! ...... u. , .nj.'.i^.jl.j,.. =
District
code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Percentiles, ppb
50
24
14
12
12
27
18
17
21
17
25
25
17
24
16
30
19
90
70
47
34
51
58
62
35
46
42
47
50
50
76
61
86
74
95
95
60
41
67
69
84
42
51
52
55
59
62
97
84
110
96
99
135
85
62
97
93
120
56
67
77
71
75
90
139
124
152
135
99.5
150
92
67
111
101
130
61
76
85
76
80
100
155
134
162
146
High values, ppb
Second
181
120
86
142
155
165
82
96
119
101
97
129
194
164
197
192
First
182
120
89
146
155
166
83
99
119
102
99
132
196
165
197
192
CD
-------
TABLE 28. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE MIAMI STUDY AREA
Monitor ID
12-011-0003
12-011-2003
12-011-8002
12-025-0021
12-025-0027
12-025-0029
'
Monitor location
Broward Co.
Pompano Beach
Dania
Dade Co.
Dade Co.
Dade Co.
' - - . 1
District
code
1
2
3
4
5
6
--"'•-- ' '-- _•— —
50
22
22
25
21
27
21
— -
Percentiles, ppb
90
39
39
42
37
43
37
95
44
44
47
43
47
42
99
54
52
56
52
55
51
99.5
56
54
59
55
58
53
=;
High values, ppb
Second
76
71
71
77
77
73
First
77
72
72
79
80
73
O)
CD
-------
TABLE 29. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE
EIGHT-HOUR OZONE
NEW YORK STUDY AREA
Monitor ID
09-001-0017
34-013-0011
34-017-0006
34-027-3001
34-039-5001
36-001-0080
36-061-0010
36-061-0063
36-081-0004
36-085-0067
36-103-0002
36-119-2004
Monitor
location
Grenwich
Newark
Bayonne
Morris Co.
Plainfield
Bronx Co.
New York City
New York City
Queens Co.
Richmond Co.
Babylon
White Plains
District
code
1
2
3
4
5
6
7,8
a
9
10
11
12
Percentiles, ppb
50
29
19
25
39
21
14
15
41
21
29
30
27
90
57
46
58
70
50
32
39
79
51
58
62
58
95
67
59
72
82
61
41
50
90
64
71
73
70
-••- -- - ~~"
99
95
82
95
100
80
56
73
113
90
95
97
94
99.5
103
89
103
109
88
59
79
122
99
101
104
105
•
High values, ppb
Second
125
102
112
125
109
69
102
133
119
135
129
125
First
126
103
112
125
109
71
102
135
119
136
129
127
o
-------
TABLE 30. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE PHILADELPHIA STUDY AREA
Monitor ID
34-005-3001
34-007-0003
34-007-1001
34-015-0002
42-017-0012
42-045-0002
42-091-0013
42-101-0014
42-101-0023
42-101-0024
Monitor
location
McGuire
AFB
Camden
Camden Co.
Gloucester
Bristol
Chester
Norristown
Philadelphia
Philadelphia
Philadelphia
District
code
1
2
3
4
5
6
7
8
9
10
Percentiles, ppb
50
34
28
37
3.3
28
30
26
31
21
27
90
67
64
71
68
64
62
60
65
49
61
95
80
76
81
80
76
72
70
76
60
72
99
107
101
103
105
100
92
92
96
79
97
99.5
114
109
107
113
104
98
98
100
86
103
High values, ppb
Second
138
129
124
135
115
113
118
125
112
116
First
141
131
125
135
116
114
118
127
114
116
-------
TABLE 31. DESCRIPTIVE STATISTICS FOR 1990 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE ST. LOUIS STUDY AREA
Monitor ID
17-163-0010
29-183-1002
29-189-0001
29-189-0006
29-189-3001
29-189-5001
29-189-7001
29-510-0007
29-510-0062
29-510-0072
29-510-0080
Monitor
location
East St.
Louis
St. Charles
Affton
St. Louis
Co.
Clayton
Ferguson
St. Ann
St. Louis
St. Louis
St. Louis
St. Louis
District
code
1
2
3
4
5
6
7
8
9
10
11
Percentiles, ppb
50
20
26
30
24
25
19
29
19
25
19
25
90
43
50
54
44
49
39
54
40
48
37
50
95
51
59
64
50
58
44
64
48
57
43
60
99
66
78
80
62
76
54
81
60
73
56
76
99.5
70
85
85
67
80
56
84
65
77
61
83
High values, ppb
Second
98
110
100
85
93
62
101
76
89
83
99
First
99
110
103
86
94
63
104
77
91
85
100
-------
TABLE 32. DESCRIPTIVE STATISTICS FOR 1991 DATA SETS CONTAINING EIGHT-HOUR OZONE
CONCENTRATIONS OBTAINED FROM SELECTED MONITORING SITES IN THE WASHINGTON STUDY AREA
Monitor ID
11-001-0017
11-001-0025
24-031-3001
24-033-0002
24-033-8001
51-013-0020
51-059-0018
51-059-1004
51-059-5001
51-510-0009
51-600-0005
Monitor
location
Washington
Washington
Rockville
Greenbelt
Suitland S.H.
Arlington Co.
Mt. Vernon
Seven
Corners
McLean
Alexandria
Fairfax
District
code
1
2
3
4
5
6
7
8
9
10
11
Percentiles, ppb
50
20
25
30
31
32
29
30
33
28
23
33
90
48
55
62
68
63
61
65
66
56
50
61
95
58
64
71
79
73
72
75
77
65
59
70
99
73
79
88
96
90
91
92
97
81
75
88
99.5
78
85.
93
102
94
97
99
102
89
82
96
High values, ppb |
Second
120
114
113
129
124
127
110
147
115
111
110
First
120 I
117
113
131 1
125
128
112 I
147
115 I
111 I
111
-------
SECTION 5
ADJUSTMENT OF OZONE DATA TO SIMULATE COMPLIANCE
WITH ALTERNATIVE AIR QUALITY STANDARDS
In applying pNEM/OS to a particular study area, the analyst typically defines the
air quality conditions within the area as representing (1) baseline conditions or (2)
conditions in which the area just attains a specific NAAQS. A report by Johnson et
al.16 describes the procedures used in an earlier pNEM/O3 analysis to develop
monitor-specific ozone data sets representing baseline and attainment conditions for
each of the nine study areas. These same data sets were employed in the pNEM/O3
analysis of outdoor workers described in the present report. The remainder of this
section presents the applicable material from the earlier report concerning the
development of these data sets.
Fixed-site monitoring data for the years 1990 and 1991 were used to represent
baseline conditions for each of the nine study areas. Special air quality adjustment
procedures (AQAP's) were used to adjust the baseline data to simulate conditions in
which each study area just attains a specific NAAQS. EPA identified the following
NAAQS formulations for assessment:
1. One hour daily maximum - one expected exceedance (1H1 EX): the
expected number of daily maximum one-hour ozone concentrations
exceeding the specified value shall not exceed one.
Standard levels: 100 ppb, 120 ppb (the current NAAQS for ozone)
2. Eight-hour daily maximum - one expected exceedance (8H1EX): the
expected number of daily maximum eight-hour ozone concentrations
exceeding the specified value shall not exceed one.
Standard levels: 70 ppb, 80 ppb, 90 ppb, 100 ppb
73
-------
3. Eight-hour daily maximum - five expected exceedances (8H5EX): the
expected number of daily maximum eight-hour ozone concentrations
exceeding the specified value shall not exceed five.
Standard levels: 80 ppb, 90 ppb
A separate AQAP was developed for each of the three classes of NAAQS (1H1EX,
8H1EX, andSHSEX).
Each AQAP consisted of the following four steps:
1. Specify an air quality indicator (AQI) to be used in evaluating the status
of a monitoring site with respect to the NAAQS of interest.
2. Determine the value of the AQI for each site within the study area under
baseline conditions.
3. Determine the value of the AQI under conditions in which the air pollution
levels within the study area have been reduced or increased until the site
with the highest pollution levels just attains a specified NAAQS.
4. Adjust the one-hour values of the baseline data set associated with each
site to yield the AQI value determined in Step 3. The adjusted data set
should retain the temporal profile of the baseline data set.
Subsection 5.1 discusses the specification of appropriate AQI's (Step 1) and the
determination of baseline AQI values (Step 2). Subsection 5.2 presents the methods
used to estimate AQI's under attainment conditions (Step 3). Subsection 5.3
describes the procedures used in Step 4 to adjust one-hour data to simulate
significant reductions in ozone levels within a study area. More detailed descriptions
of these procedures can be found in Appendices A and B of a report by Johnson et
a!.16 Subsection 5.4 provides examples in which the procedures described in
Subsection 5.3 were applied to Philadelphia. Subsection 5.5 presents an alternative
procedure which analysts used to adjust one-hour data to simulate small changes
(decreases or increases) in ozone levels within a study area. This procedure was
applied to Denver, Chicago, and Miami for all NAAQS formulations.
74
-------
5.1 Specification of AQI and Estimation of Baseline AQI Values
The following AQI's were selected for evaluating the 1H1EX, 8H1EX, and
8H5EX standards.
1H1 EX: the characteristic largest daily maximum one-hour ozone
concentration
8H1EX: the characteristic largest daily maximum eight-hour ozone
concentration (except for Denver, in which the observed second
highest daily maximum was used, as explained in Subsection 5.5)
8H5EX: the observed sixth largest daily maximum eight-hour ozone
concentration.
Note that a statistical AQI (the characteristic largest value) was generally specified for
the 1H1EX and 8H1EX standards, whereas a deterministic AQI (the observed sixth
largest value) was used for the 8H5EX standards. Analysts elected to use statistical
AQI's for the 1H1EX and 8H1EX standards because such indicators are less affected
by anomalous high values than the corresponding deterministic AQI (the second
highest observed value). A statistical indicator was not considered necessary for the
8H5EX standards, as the sixth highest observed value is relatively unaffected by
anomalous high values.
The characteristic largest value (CLV) of a distribution is that value expected to
be exceeded once in n observations. If F(x) is the cumulative distribution of x, then
F(x) =1-1 (42)
n
when x is the CLV.
Selection of an appropriate cumulative distribution to fit data is important in
determining a reasonable CLV. Two distributions that often provide close fits to
ambient air quality data are the Weibull and the lognormal. The Weibull distribution is
defined as
F(x) = 1 - exp [-(*)*] ' (43)
75
-------
where 8 is the scale parameter and k is the shape parameter. The lognormal
distribution is defined as
F(x) = _!_ f^exp (-t*/2) dt (44
™~~ — oo
where
and In x is distributed normally with mean ji and variance a2. As discussed in
previous reports, the Weibull distribution generally provides a better fit to hourly
average ozone data.15
The hourly average values reported by a single monitoring site during a
specified ozone season form a time series xt (t = 1, 2, 3, ..., n). If the hourly average
time series is complete, it will contain n = (24)(N) values, where N is the number of
days in the ozone season. From this time series a second time series of daily
maximum 1-hour values can be constructed.
Assume that a Weibull distribution with parameters 5 and k provides a good fit
to the empirical distribution of hourly average values. If one disregards
autocorrelation, the value expected to be exceeded once in n = (24)(N) hours can be
estimated as
CLVOH = 5 [ln(24) (N) ]I/k. (46)
This is the characteristic largest one-hour value. If we again disregard autocorrelation,
the daily maximum 1-hour value expected to be exceeded once in N days can be
estimated as
CLVOHDM = 6{-ln[l - (J^ll) 1/24 ]}i/*. (47)
N
This is the characteristic largest daily maximum one-hour value. For 7-month and 12-
month ozone seasons, N is equal to 214 and 365, respectively. For these values of
N, CLVOH and CLVOHDM are virtually indistinguishable in value over the range in k
values typically found in ozone data (0.6 < k < 2.5). For example, the following values
were calculated using 8 = 40 ppb.
76
-------
0.6
1.4
2.5
0.6
1.4
2.5
1428
185
94
1580
193
97
1428
185
94
1580
193
97
N k CLVOH CLVOHDM
214
365
The CLVOH and CLVOHDM values match to the nearest ppb. Consequently,
the expression
CLVOHDM = 6 [In(24) (N) ]l/k <48}
can be used as an alternative to Equation 47 for calculating CLVOHDM. The quantity
calculated by Equation 48, hereafter denoted by CLV1, was selected as the AQI to be
used in evaluating the status of a monitoring site with respect to a particular 1H1EX
standard.
A data set containing one-hour concentration values can be processed to
determine a corresponding data set containing eight-hour running average values. If a
Weibull distribution is fit to the eight-hour data, one can determine a characteristic
largest eight-hour value by the equation
CLVEH = 6[ln(24)W)]1/k, <49}
where 5 and k are the Weibull parameters for the eight-hour fit. Based on the
argument made above for one-hour data, this value should be approximately equal to
the characteristic largest daily maximum eight-hour value (CLVEHDM) of the data set.
For simplicity, the term CLV8 is hereafter used to refer to the quantity calculated by
Equation 49. CLV8 was selected as the AQI to be used in evaluating attainment
status with respect to a particular 8H1EX standard.
Table 33 lists the data sets selected to represent baseline conditions in each of
the nine cities under analysis. Table 33 also provides estimates of CLV1 and CLV8
77
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TABLE 33. BASELINE AIR QUALITY INDICATORS FOR NINE CITIES
City
Chicago
Denver
Houston
Year
1991
1990
1990
District
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
1
2
3
4
5
6
7
8
9
10
11
Ozone concentration, ppb
CLV1
109
124
123
134
120
111
119
104
122
127
122
131
91
116
114
103
98
117
109
224
182
241
224
227
180
208
207
231
235
232
CLV8
94
107
106
114
99
97
106
92
106
111
106
111
74
94
85
86
79
78
94
162
137
161
171
179
131
165
143
154
171
167
EH6LDM
78
86
77
90
78
79
89
78
82
83
85
91
67
84
73
74
56
65
75
116
110
110
107
124
86
104
99
101
116
107
(Continued)
78
-------
Table 33 (Continued)
City
Los Angeles
Miami
New York
Year
1991
1991
1991
District
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1
2
3
4
5
6
1
2
3
4
5
6
7a
8a
9
10
11
12
Ozone concentration, ppb
CLV1
321
185
148
271
215
248
116
136
198
153
167
216
264
266
261
249
90
97
93
105
96
87
158
121
153
143
123
97
141
141
162
170
183
148
CLV8
207
133
99
166
162
172
85
104
121
101
100
134
209
184
215
204
74
74
72
82
80
72
135
112
133
134
113
75
108
108
131
143
140
137
EH6LDM
170
109
75
129
115
146
64
84
94
81
87
110
167
146
180
165
60
60
64
59
65
57
108
91
113
105
88
64
83
83
104
101
107
105
(Continued)
79
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Table 33 (Continued)
City
Philadelphia
St. Louis
Washington
Year
1991
1990
1991
District
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
11
1
2
3
4
5
6
7
8
9
10
11
Ozone concentration, ppb
CLV1
167
149
153
162
145
134
135
140
131
141
124
141
131
103
122
78
124
100
114
103
119
134
135
130
143
141
143
135
169
141
134
145
CLV8
142
136
128
138
120
118
123
128
116
126
100
116
106
87
97
65
103
79
91
• 84
104
110
113
113
128
119
123
118
143
120
112
123
EH6LDM
116
113
111
115
107
101
102
104
90
102
73
88
87
68
81
59
87
67
80
64
86
80
88
95
106
98
100
104
102
91
85
100
Districts 7 and 8 in New York are represented by the same ozone monitor (Monitor
No. 36-061-0010).
80
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based on Weibull fits to the upper two percent of each data set. These values were
used as estimates of CLV1 and CLV8 representing baseline conditions.
As previously indicated, the sixth largest daily maximum 8 hour value (denoted
EH6LDM) was used to evaluate the status of a monitoring site with respect to a
particular 8H5EX standards. Table 33 lists the baseline value of this AQI for each site
in the nine cities under analysis.
5.2 Estimation of AQI's Under Attainment Conditions
Tables 34, 35, and 36 provide the step-by-step procedures followed in
implementing the AQAP's developed respectively for 1H1EX, 8H1EX, and 8H5EX
NAAQS. In general, analysts assumed that the i-th ranked site (ranking determined
by baseline AQI) will undergo a change in its AQI value proportional to the change
required for the highest ranked site to exactly attain the specified standard. The
ranking assigned to a particular site under attainment conditions was determined by
the site's average ranking over five years, rather than the site's ranking under baseline
conditions. Consequently, the site ranked highest under baseline conditions was not
necessarily the highest ranked site under attainment conditions. Evaluation of
representative ozone data suggested that a site's future ranking could be better
predicted from its long-term average rank than from a single year's ranking.
Steps 1 through 4 in each table comprise the procedures used to estimate the
value of an attainment AQI value for each site in a particular city. Each attainment
AQI was converted to a corresponding characteristic one-hour largest value under
attainment (ACLV1). For 1H1EX standards (Table 34), the value of ACLV1
determined by Step 4 was used without further adjustment as the value of ACLV1
required in subsequent steps. For 8H1 EX standards (Table 35), the value of ACLV8
determined in Step 4 was converted to the required ACLV1 value through the use of
an equivalence relationship (Step 5). The equivalence relationship was
ACLVI = (RATI01) (ACLV8) (53}
where RATIO1 varied with urban area (Table 37).
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TABLE 34. AIR QUALITY ADJUSTMENT PROCEDURE USED TO SIMULATE
ATTAINMENT OF 1H1EX NAAQS (THE EXPECTED NUMBER OF
DAILY MAXIMUM ONE-HOUR OZONE CONCENTRATIONS EXCEEDING THE
SPECIFIED VALUE SHALL NOT EXCEED ONE)
1. Determine the following quantities.
CLV1(i,j): the CLV1 of i-th ranked site in City j for the "baseline" or
"start" year.
MAXCLV1(j): the largest CLV1 of all sites in City j for the
baseline year.
AMAXCLV1G): the largest CLV1 value permitted under the
proposed 1-hr NAAQS.
2. Select five years prior to the baseline year and determine the value of
CLV1 (or related air quality indicator) at each site m in City j for each
year. Rank these values by city and year. Let RANK(m,j,y) indicate
the rank of site m in city j in year y. Let MEANRANK(mj) indicate the
mean value of RANK(m,j,y) over the five years. Rank the
MEANRANK(mJ) values and let RELRANK(m,j) indicate the relative
rank of MEANRANK(mj).
3. Calculate an adjusted CLV1 for the i-th ranked site in City j by the
expression
ACLVKi.j) = [CLVl(i,j}][AMAXCLVl(j)]/[MAXCLVl(j)}. (50)
4. If RELRANK(m,j) = i, then m will be the i-th ranked site in City j under
attainment. That is,
ACLV1(m,j) = ACLV1(j,j) if RELRANK(mJ) = i.
5. The 1-hour data at Site m under attainment will be determined by
adjusting the 1-hour data at Site m in the baseline year. A Weibull
distribution fit to the adjusted data will have a CLV1 equal to
ACLV1(i,j) where i = RELRANK(m.j). Subsection 5.3 provides a
method for estimating the parameters of this distribution and for
making the adjustment.
82
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TABLE 35. AIR QUALITY ADJUSTMENT PROCEDURE USED TO SIMULATE
ATTAINMENT OF 8H1EX NAAQS (THE EXPECTED NUMBER OF DAILY
MAXIMUM EIGHT-HOUR OZONE CONCENTRATIONS EXCEEDING
THE SPECIFIED VALUE SHALL NOT EXCEED ONE)
1. Determine the following quantities.
CLV8(i,j): the eight-hour CLV of i-th ranked site in City j for
the "baseline" or "start" year.
MAXCLV8(j): the largest CLV8 of all sites in City j for the
baseline year.
AMAXCLV8(j): the largest CLV8 value permitted under the
proposed 8-hr NAAQS.
2. Select five years prior to the baseline year and determine the value of
CLV8 (or related air quality indicator) at each site m in City j for each
year. Rank these values by city and year. Let RANK(m,j,y) indicate
the rank of site m in city j in year y. Let MEANRANK(mj) indicate the
mean value of RANK(m,j,y) over the five years. Rank the
MEANRANK(mj) values and let RELRANK(m,j) indicate the relative
rank of MEANRANK(m.j).
3. Calculate an adjusted CLV8 for the i-th ranked site in City j by the
expression
ACLV8(i,j) = [CLV8(i,j)][AMAXCLV8(j)]/(MAXCLV8(j)l. (51)
4. If RELRANK(m,j) = i, then m will be the i-th ranked site in City j under
attainment. That is,
ACLV8(m,j) = ACLV8(i,j) if RELRANK(mj) = i.
5. Using Equation 53, estimate the CLV1 associated with each
ACLV8(m,j) value. Denote this value as ACLV1(m,j).
6. The 1-hour data for Site m under attainment of the 8-hr NAAQS will
be determined by adjusting the 1-hour data for Site m in the baseline
year. A Weibull distribution fit to the adjusted data will have a CLV1
equal to ACLV1(i,j) where i = RELRANK(mJ). Subsection 5.3
provides a method for estimating the parameters of this distribution
and for making the adjustment.
83
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TABLE 36. AIR QUALITY ADJUSTMENT PROCEDURE USED TO SIMULATE
ATTAINMENT OF 8H5EX NAAQS (THE EXPECTED NUMBER OF DAILY
MAXIMUM EIGHT-HOUR OZONE CONCENTRATIONS EXCEEDING THE
SPECIFIED VALUE SHALL NOT EXCEED FIVE)
1. Determine the following quantities.
EH6LDM(i,j): the EH6LDM of the i-th ranked site in City j for
the baseline year,
MAXEH6LDM(j): the largest EH6LDM of all sites in City j for the
baseline year.
AMAXEH6LDM(j): the largest EH6LDM value permitted under the
proposed 1-hr NAAQS.
2. Select five years prior to the baseline year and determine the value of
EH6LDM (or related air quality indicator) at each site m in City j for
each year. Rank these values by city and year. Let RANK(m,j,y)
indicate the rank of site m in city j in year y. Let MEANRANK(m,j)
indicate the mean value of RANK(m,j,y) over the n years. Rank the
MEANRANK(m,j) values and let RELRANK(m,j) indicate the relative
rank of MEANRANK(m,j).
3. Calculate an adjusted EH6LDM for the i-th ranked site in City j by the
expression
AEH6LDM(i,j) = [EH6LDM(i,j)][(AMAXEH6LDM(j)/[MAXEH6LDM(j)]. (52)
4. If RELRANK(mJ) = i, then m will be the i-th ranked site in City j under
attainment. That is,
AEH6LDM(m,j) = AEH6LDM(i,j) if RELRANK(mJ) = i.
5. Using Equation 54, estimate the CLV1 associated with each
AEH6LDM(m,j) value. Denote this value as ACLV1(m,j).
6. The 1-hour data for Site m under attainment of the 8H5EX NAAQS
will be determined by adjusting the 1-hour data for Site m in the
baseline year. A Weibull distribution fit to the adjusted data will have
a CLV1 equal to ACLV1(i,j) where i = RELRANK(m,j). Subsection 5.3
provides a method for estimating the parameters of this distribution
and for making the adjustment.
84
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A similar method was employed for 8H5EX standards (Table 36). The value of
AEH6LDM determined in Step 4 was converted to the required ACLV1 value through
the use of an equivalence relationship (Step 5). In this case, the equivalence
relationship was
ACLVI = (RATI02) (AEH6LDM) (54>
where RATIO2 varied with city (Table 37).
Through these procedures, a distinct ACLV1 value was assigned to each site
for each standard under evaluation. This ACLV1 value was subsequently used to
construct an attainment one-hour data set using the procedures described in
Subsection 5.3.
5.3 Adjustment of One-Hour Ozone Data Sets
After a site's attainment ACLV1 value was determined, the baseline one-hour
data set associated with the site was adjusted hour-by-hour to create an attainment
one-hour data set. A two-stage adjustment procedure was employed. In the first
stage, the baseline one-hour data were adjusted to produce an initial attainment data
set that had the specified ACLV1 value. In the second stage, the initial data set was
"fine-tuned" to produce a final attainment data set having the exact AQI value
specified for the site.
5.3.1 Initial Adjustment for All Standards
The initial adjustment equation was
yr = (a) (xc)b (55)
where xt was the baseline ozone concentration for hour t and y, was the attainment
ozone concentration for hour t. The terms a and b were "adjustment coefficients"
specific to the site and to the standard being attained.
The adjustment equation was based on the general assumption that Weibull
distributions would provide good fits to the one-hour data sets under baseline and
85
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attainment conditions. A Weibull distribution can be completely characterized through
the use of a shape parameter (k) and a scale parameter (8). The baseline values of k
and delta were determined by applying a special maximum likelihood fitting algorithm
to each one-hour baseline data set. The attainment value of k (k') was estimated by
the empirically-derived equation
l/k' = -0.2389 + (0.003367) (ACL VI) + (0.4726) (I/A:) (56)
where ACLV1 was the estimated value of CLV1 under attainment conditions and k
was the baseline k value. The attainment value of 8 (8') was then determined by the
identity equation
8; = (ACL VI) /[ln(n) ]l/* (57
where n was the number of one-hour values in the exposure period.
The unadjusted data set was treated as a time series where x, represented the
one-hour value at time t. The corresponding adjusted data set was constructed
through the use of the expression
yc = (Sx) (xe/5)*/Jc/ (58)
where y, was the adjusted one-hour value at time t. This expression incorporates the
assumption that the time series y, at a site after attainment is related to the original
time series x, in such a way that 1) the rank of the one-hour value at each time t is
unchanged, 2) the x, values follow a Weibull distribution with parameters 8 and k, and
3) the y, values follow a Weibull distribution with parameters 8' and k'. These
assumptions are discussed in Appendix A of the report by Johnson et al.16 Equation
58 can be restated as Equation 55 above with the substitutions
a = (5/)/(5)*/*/ (59)
b = k/k' . (60)
86
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TABLE 37. VALUES FOR EQUIVALENCE RELATIONSHIPS
City
Chicago
Denver
Houston
Los Angeles
Miami
New York
Philadelphia
St. Louis
Washington
RATIO 1a
1.155
1.234
1.374
1.444
1.248
1.178
1.132
1.226
1.179
RATIO2"
1.441
1.453
2.091
1.846
1.513
1.436
1.367
1.506
1.450
aRATIO1 = (ACLV1)/(ACLV8).
bRAT!O2 = (ACLV1)/(EH6LDM).
5.3.2 Final adjustment for Eight-hour Standards
When applied to the 8H1EX standards, the initial adjustment procedure
described above produced a one-hour data set with a CLV1 value that exactly
matched the specified CLV1. Because the assumed relationship between CLV1 and
CLV8 was only an approximation, the CLV8 value of the adjusted data set did not
always match the attainment CLV8 value specified for the site. Consequently,
analysts made a final "fine-tuning" adjustment to the one-hour data to obtain the exact
CLV8 value specified. The following final adjustment equation was used.
Adjusted y, = (y,)(Target attainment CLV8)/(lnitial attainment CLV8) (61)
In this equation, y, is the one-hour value for hour t after the initial adjustment
procedure (Equation 55). The "initial attainment CLV8" is the CLV8 value of this data
set. The "target attainment CLV8" is the attainment CLV8 value assigned to the site
by the procedure summarized in Table 35.
A similar fine-tuning procedure was employed for the 8H5EX standards. The
final adjustment equation was
Adjusted y, = (y,)(Target attainment EH6LDM)/(lnitial attainment EH6LDM) (62)
87
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The "initial attainment EH6LDM" is the EH6LDM value of the site after the initial
adjustment (Equation 55). The "target attainment EH6LDM" is the attainment
EH6LDM value assigned to the site by the procedure summarized in Table 36.
5.4 Application of the AQAP's to Philadelphia
To test the reasonableness of the AQAP's described above, each was initially
applied to Philadelphia. Three attainment scenarios were evaluated:
1H1EX-120: One-hour daily maximum, one expected exceedance of 120 ppb
8H1EX-80: Eight-hour daily maximum, one expected exceedance of 80 ppb
8H5EX-80: Eight-hour daily maximum, five expected exceedances of 80 ppb.
In each case, baseline conditions were represented by filled-in 1991 ozone data
obtained from the 10 monitoring sites listed in Table 21.
5.4.1 Attainment of 1H1EX-120 Standard
The AQAP summarized in Table 34 was applied to Philadelphia for the purpose
of simulating the attainment of the 1H1EX-120 ppb standard. Table 38 presents the
results of each step. In this example, baseline conditions in Philadelphia were
assumed to be represented by 1991 ozone data as reported by the 10 monitoring
sites listed for Philadelphia in Table 21.
Analysts initiated the AQAP by fitting a Weibull distribution to the filled-in 1991
one-hour data set associated with each Philadelphia monitoring site. Each fit
produced estimates of the Weibull parameters (k and 5) and the CLV1. The largest
CLV1 for 1991 was associated with District 1 (167 ppb).
To exactly attain the specified NAAQS, the largest CLV1 must equal 120 ppb.
Consequently, Equation 48 (Step 3, Table 34) was implemented as
88
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TABLE 38. DETERMINATION OF ADJUSTMENT COEFFICIENTS FOR ONE-HOUR NAAQS
ATTAINMENT (1H1EX-120) IN PHILADELPHIA
District
1
2
3
4
5
6
7
8
9
10
Weibullfitto 1991 1-hrdata
k
1.69
2.21
1.96
1.81
2.28
2.23
1.93
2.14
1.74
2.26
8
46.9
56.4
51.0
49.3
56.6
51.2
44.3
51.2
38.1
54.5
CLV1
167
149
153
162
145
134
135
140
131
141
1-hr NAAQS attainment parameters3
Adjusted
CLV1
120
107
110
116
104
96
97
101
94
101
Reassigned
CLV1
107
110
116
120
104
101
94
101
96
97
k'
2.494
2.896
2.546
2.346
3.139
3.194
3.101
3.106
2.809
3.369
5'
45.27
52.44
49.95
48.09
52.51
51.60
47.06
50.62
44.74
51.31
Adjustment
coefficients
a
3.336
2.417
2.420
2.377
2.800
3.305
4.447
3.361
4.694
3.511
b
0.678
0.763
0.770
0.772
0.726
0.698
0.622
0.689
0.619
0.671
CD
CD
aAssumes maximum CLV1 equals 120 ppb.
-------
ACLVl{i,j) = (CLVl(i,j)] (120/167 ) = [CLV1 (i, j) ] (0.719) . (63)
Applying this expression to each 1991 CLV1 produced 10 ACLVI's representing
attainment conditions. These values are listed in the column labeled "adjusted CLV1."
These values were then reassigned to the Philadelphia districts according to the five-
year ranking determined for each district. Thus, the largest adjusted CLV1 (120 ppb)
was assigned to District 4 because District 4 had the highest five-year ranking.
Similarly, the second largest adjusted CLV1 (116 ppb) was assigned to District 3
because District 3 had the second highest five-year ranking.
In this example, the five-year ranking of each site was determined by analyzing
second-high daily maximum one-hour ozone concentrations reported by the site over a
recent five-year period. Second-high daily maximum values were used in this step
rather than CLVTs because they were easier to obtain from standard EPA reports.
Analysts next used Equations 56 and 57 to estimate site-specific values for k'
and 5', the values of the Weibull parameters under attainment conditions. For District
1, the substitution of k = 1.69, ACLV1 = 107 ppb, and n = 5136 produced the
estimates k' = 2.494 and 8' = 45.27 ppb. These values were substituted into
Equations 59 and 60 to produce the values of the adjustment coefficients listed in
Table 38 for District 1 (a = 3.336 and b = 0.678).
A one-hour ozone data set representing attainment conditions was constructed
for each site by applying Equation 55 to the baseline one-hour data set for the site.
Table 39 provides descriptive statistics for the baseline and attainment data sets
associated with District 1.
5.4.2 Attainment of 8H1EX-80 Standard
To evaluate the AQAP for 8H1EX standards, the procedure summarized in
Table 35 was applied to Philadelphia for the purpose of simulating the attainment of
the 8H1EX-80 standard. The results are presented in Table 40. As in the previous
example, baseline conditions for Philadelphia were represented by 1991 ozone data.
90
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TABLE 39. DESCRIPTIVE STATISTICS FOR HOURLY-HOUR DATA (PPB)
FOR SITE 34-005-3001 (DISTRICT 1, PHILADELPHIA): BASELINE AND
ATTAINMENT OF THREE OZONE STANDARDS
Statistic
Number of values
Mean
Standard deviation
Minimum
5th percentile
10th percentile
25th percentile
50th percentile
75th percentile
90th percentile
95th percentile
99th percentile
99.5 percentile
99.8 percentile
99.9 percentile
Maximum
Baseline
5136
38
25
0
4
8
19
34
51
72
87
117
124
137
143
156
Attainment of indicated standard
1H1EX-120
5136
37
18
0
9
14
25
36
48
61
69
84
88
94
97
102
8H1EX-80
5136
36
14
0
12
17
27
36
45
54
60
71
73
77
78
82
8H5EX-80
5136
34
16
0
7
13
23
34
43
54
61
74
77
82
84
89
Analysts initiated the AQAP by fitting a Weibull distribution to the filled-in 1991
one-hour data set associated with each Philadelphia monitoring site. Each fit
produced estimates of the Weibull parameters (k and 8) and the CLV1. As in the
previous example, the largest CLV1 for 1991 was associated with District 1 (167 ppb).
Analysts next estimated a baseline CLV8 for each site by fitting a Weibull
distribution to the running-average eight-hour data associated with each Philadelphia
monitoring site. The largest CLV8 was 142 ppb (District 1).
To exactly attain the specified NAAQS, the largest CLV8 must equal 80 ppb.
Consequently, Equation 49 (Step 3, Table 35) was implemented as
ACLV8(i,j) = [CLVSU,j)J (80/142) = [CLV8(i, j) ] (0 . 563) . (64)
91
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TABLE 40.
DETERMINATION OF ADJUSTMENT COEFFICIENTS FOR EIGHT-HOUR NAAQS
ATTAINMENT (8H1EX-80) IN PHILADELPHIA
District
1
2
3
4
5
6
7
8
9
10
Weibull fits to 1991
1-hk
1.69
2.21
1.96
1.8lH
2.28
2.23
1.93
2.14
1.74
2.26
1-h8
46.9
56.4
51.0
49.3
56.6
51.2
44.3
51.2
38.1
54.5
CLV1
167
149
153
162
145
134
135
140
131
141
' — -•""• •••
data
CLV8
142
136
128
138
120
118
123
128
116
126
8-hr NAAQS attainment parameters3
Adjusted
CLV8
80
77
72
78
68
66
69
72
65
71
Reassigned
CLV8
72
77
78
80
72
69
65
71
66
68
Equivalent
CLV1
82
87
88
91
82
78
74
80
75
77
—
1-h Weibull
parameters
k'
3.173
3.725
3.339
3.057
4.119
4.237
3.941
3.960
3.518
4.359
8'
41.45
49.01
46.44
44.89
48.41
47.08
42.70
46.75
40.60
47.06
Adjustment
coefficients
a
5.339
4.481
4.618
4.465
5.183
5.932
6.671
5.572
6.708
5.922
•-
b
0.533
0.593
0.587
0.592
0554
0.526
0 490
0540
0.495
0.518
1
-------
Analysts applied this expression to each 1991 CLV8 to obtain 10 ACLVS's
representing attainment conditions. These values are listed in the column labeled
"adjusted CLV8." These values were then reassigned to the Philadelphia districts
according to the five-year ranking determined for each district. The resulting
assignments are listed in Table 40 under the heading "reassigned CLV8."
Each reassigned CLV8 was then converted into an equivalent attainment CLV1
using Equation 53 with the RATIO1 value for Philadelphia (1.132). For example, the
reassigned CLV8 for District 1 (72 ppb) was multiplied by 1.132 to produce an
equivalent attainment CLV1 of 82 ppb.
Analysts next used Equations 56 and 57 to estimate site-specific values for kj
and 8', the values of the Weibull parameters for one-hour data under attainment
conditions. For District 1, the substitution of k = 1.69, ACLV1 = 82 ppb, and n = 5136
produced the estimates k! = 3.173 and 5' = 41.45 ppb. These values were substituted
into Equations 59 and 60 to produce the values of the adjustment coefficients listed in
Table 40 for District 1 (a = 5.339 and b = 0.533). These coefficients were then
substituted into Equation 55 to produce an initial one-hour data set approximating
attainment conditions.
The one-hour data were processed to produce a corresponding 8-hour running
average data set. A Weibull distribution was next fit to the adjusted eight-hour data
for the site to determine an initial attainment CLV8. Analysts then used Equation 61
to make the final "fine-tuning" adjustment to the one-hour data necessary to achieve
the target CLV8 specified for the site (72 ppb). The resulting one-hour data set was
assumed to represent attainment conditions for District 1. Table 39 provides
descriptive statistics for this data set. Attainment data sets were developed in a
similar manner for each of the other Philadelphia monitoring sites.
5.4.3 Attainment of 8H5EX-80 Standard
The AQAP for 8H5EX standards (Table 36) was applied to Philadelphia for the
purpose of simulating the attainment of the 8H5EX-80 standard. The results are
presented in Table 41.
93
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As in the two previous examples, baseline conditions for Philadelphia were
represented by 1991 ozone data. Analysts began the AQAP by fitting a Weibull
distribution to the filled-in 1991 one-hour data set associated with each Philadelphia
monitoring site. Each fit produced estimates of the Weibull parameters (k and 5) and
the CLV1. The largest CLV1 for 1991 was associated with District 1 (167 ppb).
Analysts next determined a baseline EH6LDM value for each site by first
calculating all eight-hour daily maximum concentrations in the associated one-hour
data set and then identifying the sixth largest value. The largest EH6LDM was 116
ppb (District 1).
The largest EH6LDM value permitted under the 8H5EX-80 standard is 80 ppb.
As the largest baseline EH6LDM was 116 ppb, Equation 52 (Table 36) was expressed
as
AEH6LDM(ilj} = [EH6LDM(i,j)} (80/116 )= (EH6LDM(i, j) ] (0.563) . (65)
Analysts applied this expression to each 1991 EH6LDM to obtain 10 AEH6LDM's
representing attainment conditions. These values are listed in the Table 41 column
labeled "adjusted EH6LDM." Analysts next reassigned the values to the Philadelphia
districts according to the five-year ranking determined for each district. The resulting
assignments are listed in Table 41 under the heading "reassigned EH6LDM."
Each reassigned EH6LDM was then converted into an equivalent attainment
CLV1 using Equation 54 with the RATI02 value for Philadelphia (1.367). In the case
of District 1, the reassigned EH6LDM (74 ppb) was multiplied by 1.367 to produce an
equivalent attainment CLV1 of 101 ppb.
Analysts next used Equations 56 and 57 to estimate site-specific values for k'
and 5', the values of the Weibull parameters for one-hour data under attainment
94
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TABLE 41. DETERMINATION OF ADJUSTMENT COEFFICIENTS FOR EIGHT-HOUR NAAQS
ATTAINMENT (EH6LDM = 80 ppb) IN PHILADELPHIA
District
1
2
3
4
5
6
7
8
9
10
Parameters of 1991 data
1-hk
1.69
2.21
1.96
1.81
2.28
2.23
1.93
2.14
1.74
2.26
1-hS
46.9
56.4
51.0
49.3
56.6
51.2
44.3
51.2
38.1
54.5
CLV1
167
149
153
162
145
134
135
140
131
141
EH6LDM
116
113
111
115
107
101
102
104
90
102
8-hour NAAQS attainment parameters
Adjusted
EH6LDM
80
78
77
79
74
70
70
72
62
70
Rank
5
2
1
3
4
6
8
9
10
7
Reassigned
EH6LDM
74
79
80
78
77
72
70
70
62
70
Equivalent
CLV1
101
108
109
107
105
98
96
96
85
96
1-hourWeibull
parameters
k1
2.626
2.954
2.708
2.615
3.106
3.300
3.038
3.277
3.136
3.408
8'
44.62
52.24
49.37
47.10
52.64
51.16
47.38
49.88
42.89
51.15
Adjustment
coefficients
a
3.750
2.556
2.869
3.171
2.721
3.581
4.261
3.817
5.689
3.608
b
0.644
0.748
0.724
0.692
0.734
0.676
0.635
0.653
0.555
0.663
'Assumes maximum EH6LDM equals 80 ppb.
CD
Ol
-------
conditions. For District 1, the substitution of k = 1.69, ACLV1 = 101 ppb, and n =
5136 produced the estimates k1 = 2.626 and 8' = 44.62 ppb. These values were
substituted into Equations 59 and 60 to produce the values of the adjustment
coefficients listed in Table 41 for District 1 (a = 3.750 and b = 0.644). These
coefficients were then substituted into Equation 55 to produce an initial one-hour data
set approximating attainment conditions.
The one-hour data were processed to produce a corresponding 8-hour running
average data set. These data were analyzed to determine an initial attainment
EH6LDM. Analysts then employed Equation 62 to make the final "fine-tuning"
adjustment to the one-hour data necessary to achieve the target attainment EH6LDM
specified for the district (101 ppb). The resulting tuned data set was assumed to
represent attainment conditions for District 1. Table 39 presents descriptive statistics
for this data set. Attainment data sets were developed in a similar manner for each of
the other Philadelphia monitoring sites.
5.5 Special Adjustment Procedures Applied in Selected Attainment Scenarios
The AQAP's described above were developed by comparing the ozone data
reported by a site in a high ozone year with ozone data reported by the same site in a
low ozone year. Consequently, the AQAP's are expected to perform best when used
to simulate a significant reduction in the ozone levels at a site. The results of an
analysis of AQAP performance by ITAQS suggested that the AQAP's described above
may produce unrealistic data sets for Denver, Chicago, and Miami when used to
simulate a small reduction in ozone levels or when used to simulate an increase in
ozone levels. For this reason, ITAQS used a different set of AQAP's for all attainment
scenarios in the Chicago, Denver, and Miami study areas. The Chicago scenarios
generally required small increases in ozone levels to exactly meet the specified
attainment conditions. The Denver and Miami scenarios required small changes in
both directions.
In the alternative AQAP's for the 1H1EX-120 and the 1H1EX-100 scenarios, the
procedures summarized in Table 34 were followed to the point in Step 5 where the
96
-------
reader is directed to Section 5.3. The procedures in Section 5.3 were not employed to
adjust the one-hour data; instead, each value of the adjusted data set was estimated
by the expression
yc = (c) (xt) (66)
where xt was the baseline ozone concentration for hour t and y, was the attainment
ozone concentration for hour t. The value of c was determined by the expression
C = (ACLVl) / (CLVl) (67)
where ACLV1 is the characteristic largest one-hour value of the site before adjustment
and ACLV1 is the characteristic largest one-hour value assigned to the site in Step 4
to represent attainment conditions.
In a similar manner, the alternative AQAP's for the 8H1EX-70, 8H1EX-80,
8H1EX-90, and 8H1EX-100 scenarios followed the procedures summarized in Table
35 to the point in Step 6 where the reader is directed to Section 5.3. Again, the
procedures in Section 5.3 were not employed to adjust the one-hour data. Instead, an
initial estimate of each value of the adjusted data set was estimated according to
Equations 66 and 67. For Chicago and Miami, ACLV1 was the characteristic largest
one-hour value assigned to the site in Step 5 of Table 35. For Denver, however, the
obsen/ed second highest daily maximum value was assigned to the site in Step 5 of
Table 35, instead of the ACLVL The observed second highest daily maximum was
used as the air quality indicator in Denver because it provided a better representation
of the data than the characteristic largest one-hour value provided. The alternative
adjustment procedure for all three cities was completed by applying Equation 61 to the
data to make a final "fine-tuning" adjustment.
The alternative AQAP's for the 8H5EX-80 and 8H5EX-90 scenarios followed the
steps listed in Table 36 to the point in Step 6 where the reader is directed to Section
5.3. The applicable procedures in Section 5.3 were again omitted; instead, Equations
97
-------
66 and 67 were employed to make an initial estimate of each value of the adjusted
data set. In this procedure, ACV1 was the characteristic largest one-hour value
assigned to the site in Step 5 of Table 36. The adjustment procedure was completed
by using Equation 62 to make the final fine tuning adjustment.
98
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SECTION 6
PREPARATION OF OUTDOOR WORKER DATA BASES
As previously described in Section 2 of this report, a special version of
pNEM/O3 was used to estimate the exposures of outdoor workers residing in nine
study areas under various air quality scenarios. In these exposure assessments, the
outdoor workers in each study area were represented by a collection of cohorts. The
distribution of ozone exposures across the outdoor worker population of each study
area was equal to the sum of the exposures of the individual cohorts.
To simulate the ozone exposures of a particular cohort, the pNEM/O3 model
required an exposure event sequence for the cohort and an estimate of the number of
people represented by the cohort. The exposure event sequence was constructed by
sampling a special time/activity database containing activity diary data obtained from
seven studies. Analysts estimated the population of each cohort through the use of
occupation-specific census data and the informed judgment of an expert panel. This
section describes the procedures employed to create the time/activity database, to
construct an exposure event sequence for each cohort, and to estimate the number of
people in each cohort.
6.1 Selection of Time/Activity Data
Previous applications of pNEM/O3 have employed activity diary data obtained
from the CADS19. In the outdoor worker exposure analysis, analysts augmented the
CADS data with diary data from six other time/activity studies (see Table 2). These
seven studies are a subset of 10 studies identified by Johnson et al.48 as generally
appropriate for use in exposure assessments. The remaining three studies49-50 listed
99
-------
by Johnson et al. focused on the activity patterns of children and did not employ adult
subjects. Appendix A provides a brief description of each of the 10 studies.
Under the direction of EPA, ITAQS developed a procedure for identifying
outdoor workers among the subjects of the seven time/activity studies listed in Table
2. The procedure consisted of four steps.
In Step 1, analysts identified the codes used in each study to indicate diary
entries associated with work-related activities. Table 42 lists these codes by study.
In Step 2, analysts created an initial pool of "potential" outdoor workers. A
subject was designated a potential outdoor worker if the subject was associated with
at least one person-day of diary data in which the person spent time outdoors while at
work. All person-days associated with each designated outdoor worker were selected
for possible inclusion in the outdoor worker time/activity database. This procedure
produced a pool containing 338 possible outdoor workers with 408 person-days of
activity diary data (Table 43). ITAQS and EPA reviewed the characteristics of the 338
potential outdoor workers and determined that the subject selection criteria were too
lenient.
In Step 3, ITAQS and EPA established stricter criteria for identifying potential
outdoor workers among the diary study subjects. According to these criteria, each
selected subject must be associated with at least one day during which he or she (1)
spent at least four hours at work and (2) spent at least 50 percent of the work time
outdoors. These selection criteria reduced the size of the pool to 109 subjects with
160 person-days of diary data.
In the fourth step of the procedure, ITAQS and EPA examined the job title of
each of the 109 selected subjects and identified a number of occupations appearing in
the database which were unlikely to represent true outdoor workers (e.g., office
manager). Subjects with these occupations were removed from the pool of potential
outdoor workers. This step yielded a final pool containing 89 outdoor workers with
136 person-days of diary data. Tables 44 through 50 provide a list of the occupations
associated with the outdoor workers selected from each study.
100
-------
TABLE 42. ACTIVITY CODES FROM THE SEVEN ACTIVITY STUDIES
INDICATING A PERSON AT WORK
Study ID
1
2
3
5
8
9
10
Study name
Cincinnati
Denver
Washington, D.C.
California Adults
Los Angeles
Outdoor Workers
Los Angeles
Construction
Workers
Valdez
Activity code
2
2
2
22
1
3
5
6
8
2
2
80
81
82
83
18
Activity description
Income-related work
Income-related work
Work or business meeting
Work or business meeting
Main job
Traveling during work
Second job
Eating (at work)
Breaks
Income-related work
Income-related work
Sitting, standing, or driving on job
site
Walking
Carrying building materials or
equipment
Working at trade (hammering,
sawing, framing, etc.)
At Work
101
-------
TABLE 43. CHARACTERISTICS OF ACTIVITY DATA FOR INITIAL SET OF
POTENTIAL OUTDOOR WORKERS
Study
Denver
Cincinnati
Washington, D.C.
California Adults
Los Angeles Outdoor
Workers
Los Angeles
Construction
Workers
Valdez
Total
Number of
activity-days
41
105
33
156
29
19
25
408
Number of
persons
32
60
33
156
16
19
22
338
Type of occupation
codes
Bureau of Census,
1980
Bureau of Census,
1980
Bureau of Labor, 1970
Unique to study
Bureau of Census,
1980
Bureau of Census,
1980
Unique to study
102
-------
TABLE 44. SUBJECTS SELECTED FROM THE CINCINNATI DIARY STUDY FOR
IN THE OUTDOOR WORKER TIME/ACTIVITY DATABASE
INCLUSION
Study ID
Number
1
1
1
1
1
1
1
1
1
1
1
1
1
1
PID
6820131
6830766
690250
6741382
1820529
5740749
5910852
1490984
5350937
5181818
9331679
7322019
6521602
1711416
Occupation
876: Stevedore
563: Brickmason, stomemason
889: Laborers, except construction
486: Groundskeeper, gardener
883: Freight, stock, material movers
595: Roofer
589: Glazier
575: Electrician
57: Mechanical engineer
694: Water and sewer treatment plant operator
873: Production helper
859: Miscellaneous material moving equipment operator
587: Plumber, pipefitter apprentice
36: Inspector and compliance officer
Total
Number of
events
(person days)
148 (3)
140 (3)
109 (3)
138 (3)
158 (3)
143 (3)
122 (3)
68(2)
135 (3)
174 (3)
152 (3)
106 (3)
100 (2)
146 (3)
1 ,839 (40)
o
OJ
-------
TABLE 45. SUBJECTS SELECTED FROM THE DENVER DIARY STUDY FOR INCLUSION
IN THE OUTDOOR WORKER TIME/ACTIVITY DATABASE
Study ID
Number
2
2
2
2
2
2
PID
2128734
2138899
2120186
2125185
2157535
2165785
Occupation
885: Garage and service station related
889: Laborer, except construction
577: Electrical power installer/repairer
555: Supervisor, electricians/power install
856: Industrial truck and tractor operator
426: Guards and police, except public service
Total
Number of
events
(person days)
107 (2)
63(2)
61 (2)
78(2)
74(2)
61 (2)
444 (12)
TABLE 46. SUBJECTS SELECTED FROM THE WASHINGTON DIARY STUDY FOR INCLUSION
IN THE OUTDOOR WORKER TIME/ACTIVITY DATABASE
Study ID
Number
3
3
PID
2003929
2026417
Occupation
780: Miscellaneous laborer
424: Craneman, derrickman, hoistman
Total
Number of
events
(person days)
30(1)
35(1)
65(2)
-------
TABLE 47. SUBJECTS SELECTED FROM THE CALIFORNIA DIARY STUDY FOR INCLUSION
IN THE OUTDOOR WORKER TIME/ACTIVITY DATABASE
Study ID
Number
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
PID
1943
3291
4641
5771
6541
7253
9361
9841
12341
15791
20601
23501
24351
24701
25161
28171
Occupation
95: Service station attendant
72: Supervisor, construction trades
76: Construction trades
75: Mechanic and repairer of machinery
76: Construction trades
75: Mechanic and repairer of machinery
63: Farm worker
71: Supervisor, mechanics and repairers
76: Construction trades
90: Fabricators, assemblers, handiworkers
76: Construction trades
85: Machine operator
61 : Farmer, manager of farm
95: Service station attendant
76: Construction trades
72: Supervisor, construction trades
Number of
events
(person days)
29(1)
27(1)
29(1)
35(1)
39(1)
27(1)
28(1)
30(1)
31(1)
32(1)
39(1)
34(1)
30(1)
44(1)
29(1)
37(1)
o
en
(continued)
-------
TABLE 47 (Continued)
Study ID
Number
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
PID
28551
28801
29901
30451
31321
31791
32341
42063
42461
43051
45911
48241
48311
48671
61823
62491
Occupation
72: Supervisor, construction trades
96: Garbage collector
75: Mechanic and repairer of machinery
75: Mechanic and repairer of machinery
76: Construction trades
76: Construction trades
96: Garbage collector
75: Mechanic and repairer of machinery
55: Cook, waiter
67: Groundskeeper and gardener
72: Supervisor, construction trades
76: Construction trades
76: Construction trades
90: Fabricators, assemblers, handiworkers
60: Fireman, policeman
72: Supervisor, construction trades
Number of
events
(person days)
27(1)
32(1)
31 (1)
29(1)
38(1)
28 (1)
37(1)
39(1)
31 (1)
43(1)
34(1)
26(1)
29(1)
34(1)
37(1)
33(1)
o
CD
(continued)
-------
TABLE 47 (Continued)
Study ID
Number
5
5
5
5
5
5
5
PID
64321
64861
64921
65631
66431
66471
67271
Occupation
75: Mechanic and repairer of machinery
63: Farm worker
37: Sales representative
72: Supervisor, construction trades
71: Supervisor, mechanics and repairers
28: Engineer, scientist, and architect
67: Groundskeeper and gardener
Total
Number of
events
(person days)
33(1)
31(1)
25(1)
30(1)
27(1)
26(1)
30(1)
1 ,250 (39)
-------
TABLE 48. SUBJECTS SELECTED FROM THE LOS ANGELES OUTDOOR WORKER
DIARY STUDY FOR INCLUSION IN THE OUTDOOR WORKER TIME/ACTIVITY DATABASE
Study ID
Number
8
8
8
8
8
8
PID
1290
1299
1304
1350
1372
1404
Occupation
479: Municipal irrigation worker
477: Municipal water system foreman
869: Construction worker
355: Mail carrier
554: Construction foreman
579: Painter
Total
Number of
events
(person days)
191 (3)
193(3)
96(3)
163 (3)
123 (3)
153 (3)
919(18)
o
CD
-------
TABLE 49. SUBJECTS SELECTED FROM THE LOS ANGELES CONSTRUCTION WORKER
DIARY STUDY FOR INCLUSION IN THE OUTDOOR WORKER TIME/ACTIVITY DATABASE
Study ID
Number
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
PID
1761
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1778
Occupation
869: General construction worker
869: General construction worker
567: Carpenter
869: General construction worker
567: Carpenter
567: Carpenter
869: General construction worker
567: Carpenter
567: Carpenter
567: Carpenter
567: Carpenter
597: Ironworker
567: Carpenter
597: Ironworker
567: Carpenter
567: Carpenter
Number of events
(person days)
87(1)
135(1)
108(1)
49(1)
89(1)
40(1)
36(1)
134(1)
85(1)
128(1)
109(1)
110(1)
106(1)
113(1)
84(1)
87(1)
o
CD
(continued)
-------
TABLE 49 (Continued)
Study ID
Number
9
9
9
PID
1779
1780
1781
Occupation
567: Carpenter
597: Ironworker
869: Laborer
Total
Number of events
(person days)
136 (1)
104(1)
75(1)
1,815(19)
-------
TABLE 50. SUBJECTS SELECTED FROM THE VALDEZ DIARY STUDY FOR INCLUSION IN
THE OUTDOOR WORKER TIME/ACTIVITY DATABASE
Study ID
Number
10
10
10
PID
98008
98106
98138
Occupation
2110: Boating
2120: Equipment/hardware
2110: Boating
Total
Number of events
(person days)
74(2)
85(3)
29(1)
188 (6)
-------
6.2 Processing of Time/Activity Data
In a typical pNEM analysis, the ozone exposure of each cohort is determined
by the cohort's exposure event sequence. An exposure event sequence consists of a
series of person-days with each person-day further divided into a series of exposure
events. Each exposure event specifies a start time, an event duration, a
microenvironment, a breathing rate category, and a district location (home or work).
Exposure event sequences are constructed by sampling person-days from a prepared
time/activity database according to a set of selection rules.
In the special pNEM/03 analysis of outdoor workers described here, each
exposure event sequence was constructed by sampling a time/activity database
containing 136 person-days of diary data drawn from seven studies. To create this
database, analysts first defined a standard data format which met the input
requirements of the exposure model. The diary data obtained from each study were
then converted into an equivalent data set with the specified format.
The standard format was designed to easily accommodate CADS data, as data
from this study had been used in the majority of previous pNEM analyses. As three of
the seven studies selected for the outdoor worker analysis employed the CADS diary
(Cincinnati, Los Angeles - Outdoor Workers, and Los Angeles - Construction
Workers), data from these studies required minimal processing to be included in the
time/activity database. The data obtained from the remaining four studies (Denver,
Washington, California - Adults, and Valdez) required significant processing. None of
these four studies characterized diary entries according to a breathing rate category.
Consequently, researchers developed a Monte Carlo technique to assign breathing
rate categories to diary entries obtained from these four studies.
The Monte Carlo technique employed assignment probabilities which varied
according to four event descriptors: activity type, microenvironment, time of day, and
duration. These descriptors were identified by Johnson et al.51 as influencing exertion
levels associated with diary events. To estimate assignment probabilities relative to
112
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these descriptors, each event in the CADS database was categorized according to the
following indices:
Activity class
A: high probability of fast breathing rate
B: moderate probability of fast breathing rate
C: low probability of fast breathing rate
D: sleeping
Microenvironment
1: Indoors - residence
2: Indoors - other
3: Outdoors
4: In vehicle
Time of day
1: 0700 to 1659
2: 1700 to 0659
Duration
1: 0 to 20 minutes
2: Greater than 20 minutes
The microenvironment classification was determined by the location code (e.g., office)
- associated with the event in the CADS database. The time of day classification was
determined by the start time of the event.
The activity classification consisted of three waking classes (A, B, and C) and
one sleeping class (D). Activities were assigned to these classes based on the
likelihood that the activity would be associated with a fast breathing rate, with
Classification D being reserved for sleeping activities. Table 51 matches each CADS
activity code to one of the four activity classes (A, B, C, or D). These matchups are
based primarily on the results of an analysis of the CADS database performed by
Johnson in 199214.
113
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TABLE 51. BREATHING RATE CATEGORIES OF ACTIVITIES IN THE
CINCINNATI STUDY
Activity
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Description of Activity
All destination - oriented travel
(including walking)
Income - related work
Day - care
Kindergarten - 12th grade
College or trade school
Adult education and special training
Homework
Meal Preparation and cleanup
Laundry
Other indoor chores
Yard work and outdoor chores
Child care and child - centered activities
Errands and shopping
Personal care outside home (doctor,
hair dresser)
Eating
Sleeping
Other personal needs
Religious activities
Meetings of clubs, organizations,
committees, etc.
Other collective participation
Breathing
Rate
Category
B
B
C
C
C
C
C
C
B
B
A
C
C
C
C
D
C
C
C
C
(continued)
114
-------
Table 51 (continued)
Activity
Code
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
.37
38
39
40
Description of Activity
Spectator sports events
Movies, concerts, and other
entertainment events outside home
Cafe, bar, tea room
Museums and exhibitions
Parties and receptions
Visiting friends
Recess and physical education
Active sports and games outside
school, including exercises and
aerobics
Hunting, fishing, hiking
Jogging or bicycling
Taking a walk
Artistic creations, music, and hobbies
Other active leisure
Reading
Television or radio
Conversation and correspondence
Relaxing, reflecting, thinking (no visible
activity)
Other passive leisure
Asthma attack
Other sudden illness or injury
Breathing
Rate
Category
B
C
C
B
B
C
A
A
A
A
A
C
A
C
C
C
C
C
C
C
(continued)
115
-------
Table 51 (continued)
Activity
Code
43
44
45
Description of Activity
Interview
Wakeup
Baby crying
Breathing
Rate
Category
C
C
A
116
-------
Slow
Moderate
Fast
262
122
34
0.63
0.29
0.08
ITAQS created a data group for each of the 48 combinations of activity class,
microenvironment, time of day, and duration which could be specified using only the
three non-sleeping activity classes (A, B, and C). Each diary entry in the CADS
database was assigned to one of the 48 groups. Within each data group, the diary
entries were further identified by breathing rate category (slow, medium, or fast).
Table 52 lists the number of diary entries in each of the 48 groups which were
placed in each of the three breathing rate categories and the corresponding
cumulative fractions. For example, the group identified as Activity Class = A,
Microenvironment = 1, Time of Day = 1, and Duration = 1 contained 418 events (see
first entry in Table 52). These 418 events were apportioned among the three
breathing rate categories as follows:
Breathing Rate Number Fraction Cumulative Fraction
0.63
0.92
1.00
In this example, 63 percent of the events were characterized as slow, 29 percent as
moderate, and 8 percent as fast.
Researchers developed a Monte Carlo algorithm to assign breathing rate
categories to events obtained from the four diary studies which did not report
breathing rate categories. Each event from one of these studies was indexed
according to activity class (Tables 53 through 56), microenvironment, time of day, and
duration. The algorithm generated a random number for each event which was
compared to the cumulative fractions listed in Table 52 for the particular combination
of indices.
For example, the random number generated for an event identified as Activity
Class = A, Microenvironment = 1, Time of Day = 1, and Duration = 1 would be
compared to the cumulative fractions listed in the first row of Table 52. If the random
number was between 0 and 0.63, the algorithm would assign a slow breathing rate to
the event. The algorithm would assign a moderate breathing rate to events with
117
-------
TABLE 52. CUMULATIVE BREATHING RATE CATEGORY PROBABILITIES FROM THE CINCINNATI
ACTIVITY-DIARY STUDY BY ACTIVITY CLASS,
MICROENVIRONMENT, TIME OF DAY CATEGORY, AND EVENT DURATION CATEGORY
Activity
class
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Micro-
environment
1
1
1
1
2
2
2
2
3
3
3
3
4
4
Time of day
category
1
1
2
2
1
1
2
2
1
1
2
2
1
1
Event
duration
category
1
2
1
2
1
2
1
2
1
2
1
2
1
2
Cumulative probability of assigning breathing rate
categories (number of events used to determine
percentage)
Low (2)
0.63 (262)
0.78 (589)
0.60(152)
0.66 (327)
0.20 (25)
0.25 (56)
0.20 (10)
0.24 (47)
0.33 (367)
0.29 (536)
0.32 (235)
0.27 (336)
1 .00 (2)
1.00(3)
Medium (3)
0.92 (122)
0.97 (141)
0.89 (74)
0.93 (138)
0.63 (55)
0.64 (90)
0.80 (29)
0.79 (105)
0.86 (599)
0.88(1,071)
0.88 (413)
0.86 (757)
NAa (0)
NA(0)
High (4)
1.00(34)
1 .00 (26)
1.00(28) I
1.00(34)
1.00(48)
1.00(81) I
1.00(10) I
1 .00 (40)
1.00(163)
1.00(229)
1 .00 (87)
1.00(173)
NA(0)
NA(0)
CD
(continued)
-------
TABLE 52 (Continued)
Activity
class
A
A
B
B
B
B
B
B
B
B
B
B
B
B
Micro-
environment
4
4
1
1
1
1
2
2
2
2
3
3
3
3
Time of day
category
2
2
1
1
2
2
1
1
2
2
1
1
2
2
Event
duration
category
1
2
1
2
1
2
1
2
1
2
1
2
1
2
Cumulative probability of assigning breathing rate
categories (number of events used to determine
percentage)
Low (2)
0.00 (0)
1.00(1)
0.71 (757)
0.80(1,582)
0.75 (448)
0.86 (691)
0.68 (970)
0.90 (3,762)
0.80 (313)
0.81 (824)
0.63 (5,854)
0.53(361)
0.67 (3,876)
0.72 (330)
Medium (3)
NA(0)
NA(0)
0.99 (298)
1 .00 (382)
1.00(150)
1.00(110)
0.99 (449)
1.00(401)
0.99 (74)
0.99 (184)
0.99 (3,366)
0.96 (298)
0.99(1,902)
0.98(118)
High (4)
NA(0)
NA(0)
1.00(7)
NA(4)
NA(1)
NA(3)
1.00(12)
NA (14)
1.00(5)
1.00(7)
1 .00 (87)
1 .00 (25)
1.00(41)
1.00(8)
CO
(continued)
-------
TABLE 52 (Continued)
Activity
class
B
B
B
B
C
C
C
C
C
C
C
C
C
C
Micro-
environment
4
4
4
4
1
1
1
1
2
2
2
2
3
3
Time of day
category
1
1
2
2
1
1
2
2
1
1
2
2
1
1
Event
duration
category
1
2
1
2
1
2
1
2
1
2
1
2
1
2
Cumulative probability of assigning breathing rate
categories (number of events used to determine
percentage)
Low (2)
1 .00 (5,264)
1.00(1,848)
1.00(3,358)
1.00(1,266)
0.99 (7,807)
0.99 (8,024)
0.99 (6,644)
1.00(10,861)
0.96 (2,559)
0.98 (4,032)
0.97 (894)
0.99 (1,236)
0.91 (505)
0.95 (419)
Medium (3)
NA(5)
NA(1)
NA(0)
NA(2)
1.00(87)
1 .00 (48)
1.00(75)
NA (41)
1.00(117)
1 .00 (88)
1.00(32)
1.00(17)
1.00(46)
0.99 (17)
High (4)
NA(0)
NA(0)
NA(0)
NA(0)
NA(0)
NA(0)
NA(1)
NA(1)
NA(2)
NA(0)
NA(0)
NA(0)
NA(2)
1.00(3)
to
o
(continued)
-------
TABLE 52 (Continued)
Activity
class
C
C
C
C
C
C
Micro-
environment
3
3
4
4
4
4
Time of day
category
2
2
1
1
2
2
Event
duration
category
1
2
1
2
1
2
Cumulative probability of assigning breathing rate
categories (number of events used to determine
percentage)
Low (2)
0.94 (331)
0.94 (480)
1.00(12)
1.00(10)
1.00(13)
1 .00 (5)
Medium (3)
0.99 (18)
1.00(30)
NA(0)
NA(0)
NA(0)
NA(0)
High (4)
1.00(2)
NA(2)
NA(0)
NA(0)
NA(0)
NA(0)
aNot applicable.
-------
TABLE 53. ACTIVITY CLASSES ASSIGNED TO ACTIVITY CODES USED
IN THE CALIFORNIA DIARY STUDY
Activity
code
1
2
3
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
Description of activity
Work - income related at- and away-from-home
Unemployment - job search, welfare activities
Travel during work
Other paid work - second job, part-time youth job
Eating at work - lunch, coffee while working
Activities at work - before and after work day - i.e.
conversations
Breaks - coffee breaks
Travel to/from work or job-search travel
Food preparation - cooking, serving, preserving
Food cleanup - cleaning table, dishes
Cleaning house - mainly indoor
Outdoor cleaning - yard work, garbage, snow, etc.
Clothes care - laundry, other clothes care
Car repair/maintenance - oil, tires, body work, etc.
General repairs: indoor, outdoor, carpentry, painting
Plant care - outdoor garden, houseplants
Pet and animal care - domestic, feeding livestock
Other household - garage sale, packing, groceries, chores
Baby care - feeding, etc. to children < 4
Child care - children between 5 and 17
Helping/teaching - children with homework, hobbies
Talking/reading - discipline (to children), conversing,
listening
Indoor playing with baby, children
Outdoor playing - playing, coaching children
Medical care - child
Other child care - coordinating non-school activities,
Babysitting
Dry cleaning activities - pick up/drop off
Travel related to child care (including walking)
Everyday shopping
Durable good/house shopping
Personal care services
Medical appointments
Government/financial services (errands too)
Car repair services - buying gas, etc.
Other repairs - errands for: clothes, appliances '
Activity
class
B
B
B
B
C
C
C
B
C
C
B
A
B
B
B
B
B
B
C
C
C
C
C
B
C
C
C
B
C
C
C
C
C
C
C
(continued)
122
-------
TABLE 53 (Continued)
Activity
code
Description of activity
Activity
class
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
54
55
56
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
Other services - lawyer, video pick up, etc. (errand related)
Errands
Travel related to goods and services
Washing - personal hygiene
Medical care - at home
Help and care - to relatives, i.e., moving neighbors
Meals at home
Meals out (friends' or at restaurant)
Night sleep
Naps/sleep
Dressing, grooming
Not ascertained activities
Travel related to personal care
Students' classes
Other classes - lectures, professional, tutor
Doing homework - reading, studying, research
Using library
Other education
Travel related to education
Work for professional/union organizations
Work for special interest identity organizations
Work for political party and civic participation
Work for volunteer/helping organizations
Work for religious groups
Religious practice
Work for fraternal organizations
Work for chiid/youth/family organizations
Work for other organizations
Travel related to organizational activity
Sports events - attending as spectator
Miscellaneous events - circus, fairs, rock concerts
Movies
Attending theater
Visiting museums
Visiting - socializing with friends
C
C
B
C
C
B
C
C
D
D
C
C
B
C
C
C
C
C
B
C
C
C
C
C
C
C
C
C
B
B
B
C
C
B
C
(continued)
123
-------
TABLE 53 (Continued)
Activity
code
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
Description of activity
Parties and picnicking
Bars/lounges
Other social events
Travel related to event/social activities
Active sports
Outdoor leisure - hunting, fishing, boating, camping, etc.
Walking/biking/hiking/jogging, etc.
Hobbies - photography, scrapbooks, etc.
Domestic crafts - knitting, sewing, quilting
Art - sculpture, painting, potting drawing
Music/drama/dance/active leisure
Games - card, board, computer
Computer use
Travel related to active leisure
Radio use
TV use
Records/tapes
Read books
Reading magazines/not ascertained
Reading a newspaper
Conversations
Letters, writing, paperwork
Other passive leisure
Travel related to passive leisure
Activity
class
B
C
C
B
A
B
A
C
C
C
A
C
C
B
C
C
C
C
C
C
C
C
C
C
124
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TABLE 54. ACTIVITY CLASSES ASSIGNED TO ACTIVITY CODES USED
IN THE DENVER DIARY STUDY
Activity
code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Description of activity
All travel
Work (income-related) and study
Cooking
Laundry
Other indoor chores and child care
Yard work and other outdoor activities
Errands and shopping
Eating
Sleeping
Other personal needs
Social, political or religious activities
Cafe or pub
Walking, bicycling, or jogging (not in transit)
Other leisure activities
Uncertain of applicable code
No entry in diary
Interview
Final entry
Autolog value (i.e., hourly value automatically logged by
PEM)
Begin breath sample
End breath sample
Activity
class
B
C
C
B
B
A
C
C
D
C
C
C
A
C
C
NA
C
C
C
C
C
125
-------
TABLE 55. ACTIVITY CLASSES ASSIGNED TO ACTIVITY CODES USED
IN THE VALDEZ DIARY STUDY
Activity
code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
99
Description of activity
Cooking
Eating
Driving car, truck, bus
Driving boat
Driving plane
Driving other
Biking
Sedentary activity
Physical activity
At school
Grooming, dressing
Socializing
Shopping, errands
Going to bed
Getting out of bed
Exercising
Walking
At work
Fishing
Pumping gasoline
Not specified
Playing
At dock
Interview
Activity
class
C
C
B
B
B
B
A
C
A
C
C
C
C
D
C
A
B
B
B
B
C
B
B
C
126
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TABLE 56. ACTIVITY CLASSES ASSIGNED TO ACTIVITY CODES USED
IN THE WASHINGTON DIARY STUDY
Activity
code
1
2
3
4
5
6
7
8
9
11
12
13
14
15
16
17
18
19
21-36
77
86
87
88
89
Description of activity
Transit, travel
Work, business meeting
Cooking
Laundry
Inside house - chores
Outside house - chores
Errands, shopping, etc.
Personal activities
Leisure activities
Sleeping
School, study
Eating, drinking
Sports and exercise
Church, political meetings, etc.
Inside house - miscellaneous
In parking garage or lot
Outside, not otherwise specified
Doctor or dentist office
Same as activities 1-16 including suspected sleep
Same as activity 87 including suspected sleep
Dummy start diary
Start diary
End diary
Any other activity
Activity
class
B
B
C
B
B
A
C
C
C
D
C
C
A
C
C
B
B
C
C
C
C
C
random numbers between 0.63 and 0.92; similarly, fast breathing rates would be
assigned to events with random numbers between 0.92 and 1.00.
The cumulative fractions listed in Table 52 were used by the Monte Carlo
algorithm to process all diary events associated with waking activities. When the
activity code for a diary entry indicated that the subject was sleeping during the event
(i.e., activity class = D), the algorithm always assigned the fourth breathing rate
category (sleeping) to the event.
127
-------
As indicated above, 136 person-days of diary data representing 89 outdoor
workers were processed and combined into a database suitable for input into
pNEM/03. Subsection 2.3 describes the algorithm used by pNEM/03 to sample this
database and construct an exposure event sequence for each cohort.
6.3 City-Specific Outdoor Worker Populations
To apply pNEM/O3 to a study area, the number of outdoor workers in that area
must be estimated. The 1990 Census Report17 lists the number of persons in each
occupation by Metropolitan Statistical Area (MSA). The occupations listed in the
Census Report are defined by the Bureau of Census (BOC) and are represented by a
single number or code. These single occupations are grouped together into 13 BOC-
defined occupation groups (Table 57) based on more generally conceived
occupational categories. Each group contains numerous single occupations.
The occupational census data provides the total worker populations in each
MSA. It does not, however, provide an estimate of the number of outdoor workers.
ITAQS used the following procedure to estimate the city- or MSA- specific outdoor
worker populations using 1990 Census data:
1. Identify the BOC-defined occupations likely to contain outdoor workers,
based on the occupational descriptions of the 89 outdoor workers in the
seven activity-diary data bases.
2. Estimate the percentage of persons in each occupation (identified in Step
1) who would be considered outdoor workers, using a consensus from a
three member panel of researchers.
3. For each MSA, multiply the total number of workers (provided in the
1990 Census Report) in each BOC-defined occupation by the respective
percentage of persons estimated to be outdoor workers.
4. Sum the number of outdoor workers in each BOC occupation
(determined in Step 3) by the 13 BOC occupation groups.
5. For each MSA and BOC occupation group, determine the percentage of
outdoor workers by dividing the number of outdoor workers (determined
in Step 4) by the corresponding number of total workers.
128
-------
TABLE 57. 1990 BUREAU OF CENSUS OCCUPATIONS AND OCCUPATION GROUPS REPRESENTED
IN THE OUTDOOR WORKER TIME/ACTIVITY DATA BASE
Occupation Group
Code
1
2
Title
Executive, administrative, and managerial
Professional specialty
Occupation
Code
35
36
37
43
44
45
46
47
48
49
53
54
55
56
57
Title
Construction inspectors
Inspectors and compliance officers, except
construction
Management related occupations, n.e.c.3
Architects
Aerospace engineers
Metallurgical and materials engineers
Mining engineers
Petroleum engineers
Chemical engineers
Nuclear engineers
Civil engineers
Agricultural engineers
Electrical engineers
Industrial engineers
Mechanical engineers
to
CD
(continued)
-------
TABLE 57 (Continued)
Occupation Group
Code
2
Title
Professional specialty (continued)
Occupation
Code
58
59
63
69
73
74
75
76
77
78
79
83
183
184
188
194
Title
Marine and naval engineers
Engineers, n.e.c.
Surveyors and mapping scientists
Physicists and astronomers
Chemists, except biochemists
Atmospheric and space scientists
Geologists and geodesists
Physical scientists, n.e.c.
Agricultural and food scientists
Biological and life scientists
Forestry and conservation scientists
Medical scientists
Authors
Technical writers
Painters, sculptors, craft-artists, and artist
printmakers
Artists, performers, and related workers, n.e.c.
CJ
o
(continued)
-------
TABLE 57 (Continued)
Occupation Group
Code
2
7
8
Title
Professional specialty (continued)
Protective service
Service, except protective and household
Occupation
Code
195
416
417
418
423
424
426
427
435
436
438
439
443
444
Title
Editors and reporters
Fire inspection and fire prevention occupations
Firefighting occupations
Police and detectives, public service
Sheriffs, bailiffs, and other law enforcement
officers
Correctional institution officers
Guards and police, except public service
Protective service occupations, n.e.c
Waiters and waitresses
Cooks
Food counter, fountain, and related
occupations
Kitchen workers, food preparation
Waiters'/waitresses' assistants
Miscellaneous food preparation occupations
(continued)
-------
TABLE 57 (Continued)
Code
9
10
Occupation Group
Title
Farming, forestry, and fishing
Precision production, craft, and repair
Occupation
Code
473
474
475
476
477
479
484
486
489
503
505
506
507
508
509
514
Title
Farmers, except horticultural
Horticultural specialty farmers
Managers, farms, except horticultural
Managers, horticultural specialty farms
Supervisors, farm workers
Farm workers
Nursery workers
Groundskeepers and gardeners, except farm
Inspectors, agricultural products
Supervisors, mechanics and repairers
Automobile mechanics
Automobile mechanics apprentices
Bus, truck, and stationary engine mechanics
Aircraft engine mechanics
Small engine repairers
Automobile body and related repairers
CO
ro
(continued)
-------
TABLE 57 (Continued)
Occupation Group
Code
10
Title
Precision production, craft, and repair
(continued)
Occupation
Code
515
516
517
518
519
523
525
526
527
529
533
534
535
536
Title
Aircraft mechanics, except engines
Heavy equipment repairers
Farm equipment mechanics
Industrial machinery repairers
Machinery maintenance occupations
Electronic repairers, communication and
industrial equipment
Data processing equipment repairers
Household appliance and power tool repairers
Telephone line installers and repairers
Telephone installers and repairers
Miscellaneous electrical and electronic
equipment repairers
Heating, air conditioning, and refrigeration
mechanics
Camera, watch, and musical instrument
repairers
Locksmiths and safe repairers
w
CO
(continued)
-------
TABLE 57 (Continued)
Code
10
Occupation Group
Title
Precision production, craft, and repair
(continued)
Occupation
Code
538
539
543
544
547
549
555
558
563
564
567
569
575
576
577
585
Title
Office machine repairers
Mechanical controls and valve repairers
Elevator installers and repairers
Millwrights
Specified mechanics and repairers
Not specified mechanics and repairers
Supervisors, electricians and power
transmission installers
Supervisors, construction trades, n.e.c.
Brickmasons, stonemasons
Brickmason and stonemason apprentices
Carpenters
Carpenter apprentices
Electricians
Electrician apprentices
Electrical power installers and repairers
Plumbers, pipefitters, and steamfitters
CO
(continued)
-------
TABLE 57 (Continued)
Occupation Group
Code
10
11
Title
Precision production, craft, and repair
(continued)
Machine operators, assemblers, and
inspectors
Occupation
Code
587
588
589
594
595
597
599
694
715
753
754
755
756
757
Title
Plumber, pipefitter, and steamfitter
apprentices
Concrete and terrazzo finishers
Glaziers
Paving, surfacing, and tamping equipment
operators
Roofers
Structural metal workers
Construction trades, n.e.c.
Water and sewer treatment plant operators
Miscellaneous metal, plastic, stone, and glass
working machine operators
Cementing and gluing machine operators
Packaging and filling machine operators
Extruding and forming machine operators
Mixing and blending machine operators
Separating, filtering, and clarifying machine
operators
OJ
Ol
(continued)
-------
TABLE 57 (Continued)
Occupation Group
Code
11
Title
Machine operators, assemblers, and
inspectors (continued)
Occupation
Code
758
759
763
764
765
766
768
769
773
774
777
779
783
784
785
Title
Compressing and compacting machine
operators
Painting and paint spraying machine operators
Roasting and baking machine operators, food
Washing, cleaning, and pickling machine
operators
Folding machine operators
Furnace, kiln, and oven operators, except food
Crushing and grinding machine operators
Slicing and cutting machine operators
Motion picture projectionists
Photographic process machine operators
Miscellaneous machine operators, n.e.c.
Machine operators, not specified
Welders and cutters
Solderers and brazers
Assemblers
OJ
en
(continued)
-------
TABLE 57 (Continued)
Occupation Group
Code
11
12
Title
Machine operators, assemblers, and
inspectors (continued)
Transportation and material moving
occupations
Occupation
Code
786
787
789
793
795
843
848
849
853
855
856
859
Title
Hand cutting and trimming occupations
Hand molding, casting, and forming
occupations
Hand painting, coating, and decorating
occupations
Hand engraving and printing occupations
Miscellaneous hand working occupations
Supervisors, material moving equipment
operators
Hoist and winch operators
Crane and tower operators
Excavating and loading machine operators
Grader, dozer, and scraper operators
Industrial truck and tractor equipment
operators
Miscellaneous material moving equipment
operators
o>
(continued)
-------
TABLE 57 (Continued)
Occupation Group
Occupation
Code
Title
Code
Title
13
Handlers, equipment cleaners, helpers, and
laborers
866
867
869
874
875
876
877
CO
CD
878
883
885
889
Helpers, construction trades
Helpers, surveyor
Construction laborers
Production helpers
Garbage collectors
Stevedores
Stock handlers and baggers
Machine feeders and offbearers
Freight, stock, and material handlers, n.e.c
Garage and service station related occupations
Laborers, except construction
an.e.c: not elsewhere classified.
-------
6. Determine the outdoor worker populations for each exposure district and
MSA by multiplying the BOG occupation group- and MSA-specific
outdoor worker percentages (determined in Step 5) by the corresponding
total populations of each BOC occupation group within each exposure
district and MSA.
In Step 1, ITAQS examined the occupational descriptions52 of the 89 outdoor workers
identified earlier in the seven activity diary studies. A total of 143 equivalent BOC
occupations were selected as occupations containing an unknown percentage of
outdoor workers. In Step 2, a three member panel, consisting of two ITAQS analysts
and one EPA scientist, was selected to determine the outdoor worker percentages of
the BOC occupations.
To minimize the panel members evaluation time, ITAQS analysts grouped the
143 individual BOC occupations into 37 groups of similar occupations. This grouping
allowed occupations of a similar nature to be assigned the same outdoor worker
percentage by the panel members. The 37 ITAQS-defined occupation groups were
intentionally smaller (i.e., contained fewer individual occupations) than the 13 BOC-
defined occupations groups. This narrower scope of the ITAQS groupings allowed the
panel members to determine group percentages that closely approximated individual
occupation percentages. Each of the 37 ITAQS occupation groups were subsets of
the 13 BOC occupation groups. (This was true for all individual occupations except
one. BOC occupation Code 489, Inspectors of Agricultural Products, was included in
ITAQS occupation group one, which was not a subset of BOC occupation group nine.
ITAQS analysts decided that the percentage of outdoor workers in this occupation was
better represented by the percentages applied to other inspector occupations including
product inspectors. For this reason, BOC Occupation Code 489 was placed within
ITAQS occupation group one.)
Each panel member was provided with detailed job descriptions of the 37
ITAQS occupation groups and asked to provide a range of outdoor worker
percentages for each group. The panel members were instructed to specify each
range so that it would have a 90 percent probability of containing the actual percent of
139
-------
outdoor workers in the indicated group. The resulting estimates were reported using
the form included in Appendix B.
After the panel members individually assigned their percentage ranges, they
met to discuss the ranges and determine, by consensus, a final outdoor worker
percentage for each of the 37 ITAQS occupation groups (Table 58). The group's
consensus percentages were then applied to the 143 individual BOC occupations
(Aocc).
ITAQS analysts collected 1990 Census data listing the total number of workers
by MSA in each of the 143 BOC occupations (BoccMSA) and the 13 BOC occupation
groups (CoccgrpMSA). Steps 3 through 5, in the procedure outlined above, can be
summarized by the following equation:
2 ((Aocc) (BOCCiMSA) ]
_occgip (68)
where
PER-OWoccgrpMSA = The percentage of outdoor workers in each BOC
occupation group and MSA.
Aocc = The panel-determined percentage of outdoor workers
for the 143 BOC occupations.
BoccMSA = The total number of workers in each of the 143 BOC
occupations, specific to each MSA.
The total number of workers in ead
occupation groups, specific to each MSA.
COCcgrp,MSA = Tne total num':)er °f workers in each of the 13 BOC
Using Equation 68, the outdoor worker percentages were applied by MSA to the total
worker populations of each BOC occupation. This produced estimates of outdoor
workers by MSA. However, pNEM/O3 requires estimates of outdoor workers for the
exposure districts in each MSA.
The occupation specific data used by pNEM/O3 to determine exposure district
populations is based on the 13 BOC occupation groups. Analysts, therefore, summed
the outdoor worker populations of each BOC occupation group. For each MSA, the
140
-------
TABLE 58. PERCENTAGES OF "OUTDOOR WORKERS" WITHIN EACH
ITAQS GROUP
ITAQS
Group
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
1 990 Bureau of Census
Occupation Codes
Included in Group
35, 36, and 489
37
43 through 63 and
69 through 83
183, 184, 188, 194, and
195
41 6 through 424
426 and 427
435 through 444
473 through 476
477 through 484
486
503
505 through 549
555
558
563 and 564
567 and 569
575 and 576
577
585 and 587
589
595 -
597
588, 594, and 599
Panel determined percentages of
population who are "outdoor
workers"
35
10
15
5
25
25
5
85
90
90
20
15
15
50
95
55
25
30
25
40
95
90
85
(continued)
141
-------
TABLE 58 (Continued)
ITAQS
Group
Number
24
25
26
27
28
29
30
31
32
33
34
35
36
37
1990 Bureau of Census
Occupation Codes
Included in Group
694
715
753 through 779
783 through 795
848 and 849
856
843, 853, 855, and 859
866, 867, and 869
874
875
876
877, 878, and 883
885
889
Panel determined percentages of
population who are "outdoor
workers"
25
10
5
10
50
40
80
85
15
90
35
35
70
25
142
-------
percentage of outdoor workers in each BOC occupation group (PER-OWoccgrpMSA) was
determined by dividing the outdoor worker population values (Table 59) by the
corresponding total worker populations for that BOC occupation group (Table 60).
In Step 6, the BOC occupation group- and MSA-specific percentages (Table 61)
were applied to the corresponding occupation group populations of each exposure
district within each MSA. To obtain the outdoor workers estimates in their final form,
the estimates were summed across all occupation groups within each exposure
district. This provided exposure district- and MSA-specific outdoor worker estimates.
Finally, it should be noted that not every occupation potentially containing
outdoor workers was included in the model. Appendix C contains a list of 42 omitted
BOC occupations that include some unknown percentage of outdoor workers. These
occupations were not considered in this analysis, because no-representative activity
patterns were present in the seven-city activity-diary data base of outdoor workers. All
other occupations, those occupations which are not part of either the 143 occupations
included in the study or the 42 omitted occupations, were considered to have a zero
percentage of outdoor workers and were therefore not relevant to this analysis.
143
-------
TABLE 59. ESTIMATED OUTDOOR WORKER POPULATIONS BY
OCCUPATION GROUP AND STUDY AREA
1 990 Bureau of Census
Occupation Groups
Code
1
2
3
4
5
6
7
8
Title
Executive,
administrative,
and managerial
Professional
specialty
Technicians
and related
support
Sales
Administrative
support
including
clerical
Private
household
Protective
service
Service, except
for protective
and household
Estimated Outdoor Worker Populations by Study Area
Philadelphia
3,197
10,922
0
0
0
0
13,317
5,297
Chicago
3,518
12,747
0
0
0
0
17,243
7,570
Washington
D.C.
4,197
12,518
0
0
0
0
10,551
3,527
Denver
1,081
5,598
0
0
0
0
3,209
2,033
Houston
1,845
9,585
0
0
0
0
7,860
3,588
New
York
10,747
28,543
0
0
0
0
49,464
14,104
Miami
1,665
2,917
0
0
0
0
8,589
3,316
Los
Angeles
6,891
29,049
0
0
0
0
27,304
13,615
St.
Louis
1,117
4,636
0
0
0
0
4,290
2,716
(continued)
-------
TABLE 59 (Continued)
1 990 Bureau of Census
Occupation Groups
Code
9
10
11
12
13
Title
Farming,
forestry,
and fishing
Precision
production,
craft,
and repair
Machine
operators,
assemblers,
and inspectors
Transportation
and material
moving
Handlers,
equipment
cleaners,
helpers, and
laborers
Estimated Outdoor Worker Populations by Study Area
Philadelphia
23,990
64,580
7,377
8,156
47,807
Chicago
21,622
80,956
13,118
11,712
69,983
Washington
D.C.
14,556
44,774
1,449
2,787
28.649
Denver
7,705
17,463
1,578
1,894
13,684
Houston
16,405
45,271
5,998
5,408
43,311
New
York
46,829
171,102
18,457
14,756
137,048
Miami
19,338
37,589
2,907
3,069
28,678
Los
Angeles
101,923
155,383
23,763
17,119
136,924
St.
Louis
10,292
28,174
3,931
3,427
21,077
en
-------
TABLE 60. TOTAL WORKER POPULATIONS BY OCCUPATION GROUP AND STUDY AREA
1990 Bureau of
Census Occupation
Groups
Code
1
2
3
4
5
6
7
Title
Executive,
adminis-
trative,
and
managerial
Pro-
fessional
specialty
Tech-
nicians
and
related
support
Sales
Adminis-
trative
support
including
clerical
Private
household
Protective
service
Total Worker Populations by Study Area
Philadelphia
392,917
456,413
121,260
348,474
559,067
8,692
60,104
Chicago
553,470
568,133
139,426
515,848
758,155
12,170
76,908
Washington
D.C.
447,116
445,448
114,274
223,056
409,498
15,595
46,275
Denver
154,976
167,965
48,398
130,424
181,768
3,902
13,881
Houston
251,666
271,329
82,400
244,111
308,249
14,291
33,638
New York
1,349,526
1,483,652
310,062
1 ,094.703
1,781,414
50,579
222,740
Miami
202,571
186,838
53,804
228,977
278,015
13,157
38,116
Los
Angeles
973,246
999.754
243,749
890,551
1,229,590
63,137
119,906
St.
Louis
150,709
173,530
47,637
148,897
214,790
4,187
18,610
O)
(continued)
-------
TABLE 60 (Continued)
1990 Bureau of
Census Occupation
Groups
Code
8
I
I
I 9
|
|
I 10
11
Title
Service,
except
for
protective
and
household
Farming,
forestry,
and
fishing
Precision
pro-
duction,
craft,
and repair
Machine
operators,
assem-
blers,
and
inspectors
Total Worker Populations by Study Area
Philadelphia
298,833
32,403
311,204
164,102
Chicago
410,988
30,473
436,616
286,375
Washington
D.C.
203,315
19,906
175,414
41,950
Denver
110,768
10,504
92,227
43,257
Houston
198,706
22,738
229,233
86,103
New York
940,192
65,258
845,247
463,173
Miami
189,929
26,610
177.473
76,569
Los
Angeles
719.865
131,220
850,573
542,532
St.
Louis
146,567
13,441
130.004
76,426
(continued)
-------
TABLE 60 (Continued)
1990 Bureau of
Census Occupation
Groups
Code
12
13
Title
Trans-
portation
and
material
moving
Handlers,
equipment
cleaners,
helpers,
and
laborers
Total Worker Populations by Study Area
Philadelphia
108,565
113,019
Chicago
157,672
183,855
Washington
D.C.
61,398
55,952
Denver
33,747
33,798
Houston
75,819
82,791
New York
333,560
312,006
Miami
58,604
62,133
Los
Angeles
255,384
307,003
St.
Louis
47,369
50,953
CO
-------
TABLE 61. ESTIMATED OUTDOOR WORKER PERCENTAGES BY
OCCUPATION GROUP AND STUDY AREA
1990 Bureau of Census
Occupation Groups
Code
1
2
3
4
5
6
7
8
9
10
Title
Executive,
administrative,
and managerial
Professional
specialty
Technicians and
related support
Sales
Administrative
support including
clerical
Private household
Protective service
Service, except
for protective and
household
Farming, forestry,
and fishing
Precision
production, craft,
and repair
Estimated Percentage of Occupation Group Population Who are Outdoor Workers
Philadelphia
0.81
2.39
0.00
0.00
0.00
0.00
22.16
1.77
74.04
20.75
Chicago
0.64
2.24
0.00
0.00
0.00
0.00
22.42
1.84
70.95
18.54
Washington
' D.C.
0.94
2.81
0.00
0.00
0.00
0.00
22.80
1.73
73.13
25.52
Denver
0.70
3.33
0.00
0.00
0.00
0.00
23.12
1.84
73.35
18.93
Houston
0.73
3.53
0.00
0.00
0.00
0.00
23.37
1.81
72.15
19.75
New
York
0.80
1.92
0.00
0.00
0.00
0.00
22.21
1.50
71.76
20.24
Miami
0.82
1.56
0.00
0.00
0.00
0.00
22.53
1.75
72.67
21.18
Los
Angeles
0.71
2.91
0.00
0.00
0.00
0.00
22.77
1.89
77.67
18.27
St.
Louis
0.74
2.67
0.00
0.00
0.00
0.00
23.05
1.85
76.57
21.67
CD
(continued)
-------
TABLE 61 (Continued)
1990 Bur^of Census
Occupation Groups
Code
11
12
13
; Title
Machine
operators,
assemblers,
and inspectors
Transportation
and material
moving
Handlers,
equipment
cleaners,
helpers, and
laborers
Estimated Percentage of Occupation Group Population Who are Outdoor Workers |
Philadelphia
4.50
7.51
42.30
Chicago
4.58
7.43
38.06
1 . ii '.
Washington
D.C.
3.45
4.54
51.20
• 1 ii;l
Denver
3.65
5.61
40.49
Houston
6.97
7.13
52.31
New
York
3.98
4.42
43.92
Miami
3.80
5.24
46.16
Los
Angeles
4.38
6.70
44.60
St. I
Louis I
5.14
7.23 I
41.37 I
en
o
-------
SECTION 7
OZONE EXPOSURE ESTIMATES FOR NINE URBAN AREAS
The enhanced pNEM/O3 methodology described in this report was applied to
the nine urban areas listed earlier in Table 1. The result of each application was a set
of 18 exposure summary tables for each regulatory scenario under evaluation. This
section describes the scenarios that were analyzed, provides a guide to the interpre-
tation of output tables, and summarizes the principal results of each exposure
assessment.
7.1 Regulatory Scenarios
The following regulatory scenarios were examined in applying pNEM/O3
to each study area.
Baseline Ambient ozone conditions were represented by unadjusted fixed-
site monitoring data as reported for the exposure period listed in
Table 1. These data were assumed to represent ambient ozone
levels typical of "as is" air quality conditions.
1H1EX One-hour daily maximum - one expected exceedance: the
expected number of daily maximum one-hour ozone
concentrations exceeding the specified value shall not exceed
one.
Standard levels: 100 ppb, 120 ppb (the current NAAQS for
ozone)
8H1EX Eight-hour daily maximum - one expected exceedance: the
expected number of daily maximum one-hour ozone
concentrations exceeding the specified value shall not exceed
one.
Standard levels: 70 ppb, 80 ppb, 90 ppb, 100 ppb
151
-------
8H5EX Eight-hour daily maximum - five expected exceedance: the
expected number of daily maximum one-hour ozone
concentrations exceeding the specified value shall not exceed five.
Standard levels: 80 ppb, 90 ppb
Section 5 describes the procedures used to adjust baseline data to simulate
attainment of 1H1EX, 8H1EX, and 8H5EX standards,
7.2 Formats of the Exposure Summary Tables
The application of pNEM/O3 to a study area produced a set of 18 exposure
summary tables listing exposure estimates for the outdoor worker population. The
outdoor worker population included that percentage of the population in each study
area determined to be "outdoor workers," as defined in Section 6.1.
Appendix D contains exposure summary tables for the outdoor worker
population obtained from a sample application of pNEM/O3 to Houston. The tables
are organized according to the following table formats. (Note that the table numbers
listed under each format refer to the tables in Appendix D.)
Number of people -- cumulative exposures (or doses) by EVR range
These tables list estimates by ozone concentration and EVR range. Each table
entry lists the number of outdoor workers who experienced one or more ozone
exposures (or doses) during which the ozone concentration was at or above the level
indicated by the row label and the average EVR was within the range indicated by the
column heading. Separate tables provide estimates for one-hour exposures (Table 1
in Appendix D), one-hour daily maximum exposures (Table 1A), one-hour daily
maximum doses (Table 1B), eight-hour daily maximum exposures (Table 4), and
eight-hour daily maximum doses (Table 4A).
Number of people -- cumulative seasonal mean exposures
Table 7 in Appendix D lists estimates by ozone concentration only. Each entry
lists the number of outdoor workers who were associated with a seasonal mean
exposure at or above the ozone level indicated by the row label. The seasonal mean
is calculated as the average of the eight-hour daily maximum ozone exposures
occurring from April to October, inclusive.
152
-------
Number of occurrences -- exposures for doses) by EVR range
These tables list estimates arranged by ozone concentration range and EVR
range. Each table entry lists the number of times an outdoor worker experienced an
ozone exposure during which the ozone concentration was within the range indicated
by the row label and the average EVR was within the range indicated by the column
heading. There are separate tables for one-hour exposures (Table 2 in Appendix D),
one-hour daily maximum exposures (Table 2A), one-hour daily maximum doses (Table
2B), eight-hour daily maximum exposures (Table 5), and eight-hour daily maximum
doses (Table 5A).
Number of occurrences -- seasonal mean exposures
Table 8 in Appendix D presents estimates by ozone range only. Each entry
lists the number of times an outdoor worker experienced a seasonal mean exposure
at or above the ozone level indicated by the row label. The seasonal mean is
calculated as the average of the eight-hour daily maximum ozone exposures occurring
from April to October, inclusive.
Number of people -- highest exposures (or doses) by EVR range
Each of these tables lists estimates arranged by ozone concentration and EVR
range. Each entry indicates the number of outdoor workers who experienced their
maximum ozone exposure under conditions in which the ozone concentration was at
or above the level indicated by the row label and the average EVR was within the
range indicated by the column heading. There are separate tables for one-hour daily
maximum exposures (Table 3 in Appendix D) and eight-hour daily maximum
exposures (Table 6).
Number of people -- cumulative daily maximum doses by number of days
These tables provide estimates arranged by ozone concentration and number
of days per year. Each entry lists the number of outdoor workers who experienced a
daily maximum dose at or above the indicated ozone concentration for the specified
number of days. Separate tables are provided for daily maximum one-hour doses
(Table 9 in Appendix D), daily maximum eight-hour doses (Table 10), daily maximum
one-hour doses with EVR of 30 liters x min"1 x m"2 or greater (Table 11), and dally
maximum eight-hour doses with EVR ranging from 13 liters x min"1 x m"2 to 27 liters x
min'1 x m'2 (Table 12).
153
-------
Regardless of format, each table in Appendix D provides footnotes identifying
the study area and regulatory scenario. The footnotes also indicate the number of
exposure districts in the study area, the first and last days of the ozone season, and
the number of days in the ozone season.
7.3 Results of Analyses
The pNEM/O3 model incorporates a number of stochastic (random) elements
which directly affect the exposure estimates produced by the model. Consequently,
exposure estimates are likely to vary from run to run. To better characterize this
variability, ITAQS ran the model 10 times for each combination of study area and
regulatory scenario. Tables 62 through 65 provide means and ranges for selected
exposure indicators based on these runs.
Table 62 illustrates the general format used in Tables 62 through 65. This table
presents estimates for the number (and percentage) of outdoor workers experiencing
one or more one-hour daily maximum ozone exposures above 120 ppb at any
ventilation rate. The first row in the table lists results for the Chicago study area under
the baseline scenario. Of the estimated 141,204 outdoor workers in the Chicago
study area, 75,027 (53.13 percent) are estimated to have experienced the specified
exposure conditions based on the mean of the 10 runs. The estimates associated
with individual runs range from 65,597 (46.46 percent) to 88,165 (62.44 percent).
Tables 63, 64, and 65 employ the same format to present estimates for the number
(and percentage) of outdoor workers who experience one or more eight-hour daily
maximum ozone exposures above 60 ppb, 80 ppb, and 100 ppb, respectively, at any
ventilation rate.
A review of the estimates in Tables 62 through 65 indicates that exposures are
generally higher under baseline conditions than under any one of the standards.
Denver and Miami are the main exceptions to this general rule; exposures under the
current NAAQS, the 8H1EX-100, and the 8H1EX-90 scenarios tend to be higher than
154
-------
TABLE 62. NUMBER AND PERCENT OF OUTDOOR WORKERS EXPERIENCING ONE OR MORE
ONE-HOUR DAILY MAXIMUM OZONE EXPOSURES ABOVE 120 PPB AT ANY VENTILATION RATE
Study Area
Chicago
Denver
Number of
Persons at Risk
141,204
36,093
Regulatory
Scenario
Baseline
Current NAAQS "
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
75,027
27,419
3
67,888
10,956
0
0
71,507
17,044
12,846
15,634
0
29,312
19,708
6,137
79
23,752
5,465
Percent of
Total
53.13
19.42
0.00
48.08
7.76
0.00
0.00
50.64
12.07
35.59
43.32
0.00
81.21
54.60
17.00
0.22
65.81
15.14
Range
Number of Persons
Exposed
65,597 - 88,165
14,668 - 42,504
0-26
56,657-79,121
6,920 - 16,670
0-0
0-0
49,696 - 86,648
8,830-30,571
8,957 - 19,097
10,662 - 20,264
0-0
21,844 - 33,536
15,790-22,790
2,661 - 9,082
0-794
20,134 - 26,408
1,982 - 7,696
Percent of Total
46.46 - 62.44
10.39 - 30.10
0.00 - 0.02
40.12- 56.03
4.90- 11.81
0.00 - 0.00
0.00 - 0.00
35.19-61.36
6.25-21.65
24.82 - 52.91
29.54 - 56.14
0.00 - 0.00
60.52 - 92.92
43.75 - 63.14
7.37 - 25.16
0.00 - 2.20
55.78-73.17
5.49 -21.32
Ol
Ol
(continued)
-------
TABLE 62 (Continued)
Study Area
Houston
Los
Angeles
Number of
Persons at Risk
71,735
294,008
Regulatory
Scenario
Baseline
Current NAAQS
IHIEX-IOO
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
71,735
16,071
0
42,988
22,672
2,186
0
58,766
23,168
275,659
13,743
0
87,506
18,185
14,549
0
30,110
20,769
Percent of
Total
100.00
22.40
0.00
59.93
31.61
3.05
0.00
81.92
32.30
93.76
4.67
0.00
29.76
6.19
4.95
0.00
10.24
7.06
Range
Number of Persons
Exposed
71,735 - 71,735
12,054 - 20,731
0-0
30,402 - 49,034
17,058 - 31,794
202 - 4,968
0-0
54,929 - 63,080
16,144 - 30,834
270,935 - 286,906
2,004 - 21,441
0-0
76,663 -.103,243
10,952- 24,811
2,214 - 21,424
0-0
23,539 - 39,209
11,627 - 22,966
Percent of Total
100- 100
16.80- 28.90
0.00 - 0.00
42.38 - 68.35
23.78 - 44.32
0.28 - 6.93
0.00 - 0.00
76.57 - 87.93
22.51 - 42.98
92.15 - 97.58
0.68 - 7.29
0.00 - 0.00
26.08 - 35.12
3.73 - 8.44
0.75 - 7.29
0.00 - 0.00
8.01 - 13.34
3.95 - 7.81
Ol
01
(continued)
-------
TABLE 62 (Continued)
Study Area
Miami
New
York
Number of
Persons at Risk
47,187
195,735
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
8,502
9,211
789
35,970
12,453
1,231
241
42,705
16,616
173,139
13,282
8
33,639
6,705
1,015
0
34,283
7,875
Percent of
Total
18.02
19.52
1.67
76.23
26.39
2.61
0.51
90.50
35.21
88.46
6.79
0.00
17.19
3.43
0.52
0.00
17.52
4.02
Range
Number of Persons
Exposed
2,659 - 10,762
3,154 - 18,771
58 - 1,248
32,919 - 39,384
4,240 - 20,669
404 - 1,952
37- 1,092
37,588 - 46,077
11,174-26,430
158,169- 182,670
6,044 - 21,293
0-75
19,063 - 60,295
788 - 12,564
0 - 7,907
0-0
28,758 - 40,606
729 - 12,761
Percent of Total
5.64 - 22.81
6.68 - 39.78
0.12 - 2.64
69.76 - 83.46
8.99 - 43.80
0.86-4.14
0.08 - 2.31
79.66 - 97.65
23.68 - 56.01
80.81 - 93.33
3.09 - 10.88
0.00 - 0.04
9.74 - 30.80
0.40 - 6.42
0.00 - 4.04
0.00 - 0.00
14.69 - 20.75
0.37 - 6.52
en
(continued)
-------
TABLE 62 (Continued)
Study Area
Philadelphia
St. Louis
Number of
Persons at Risk
98,745
40,843
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
IHIEX-IOO
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
97,897
6,404
0
10,375
63
0
0
9,514
0
18,497
6,391
48
4,739
1,235
0
0
15,592
1,059
Percent of
Total
99.14
6.49
0.00
10.51
0.06
0.00
0.00
9.63
0.00 ,
45.29
15.65
0.12
11.60
3.02
0.00
0.00
38.18
2.59
Range
Number of Persons
Exposed
94,923 - 98,745
2,992- 11,805
0-0
6,441 - 17,800
0- 167
0-0
0-0
5,640 - 17,659
0-0
15,909 - 20,785
2,930 - 8,190
0-366
2,166 - 9,920
1 10 - 3,769
0-0
0 -0
11,479-20,403
118-4,345
Percent of Total
96.13 - 100.00
3.03 - 11.96
0.00 - 0.00
6.52 - 18.03
0.00- 0.17
0.00 - 0.00
0.00 - 0.00
5.71 - 17.88
0.00 - 0.00
38.95 - 50.89
7.17-20.05
0.00 - 0.90
5.30 - 24.29
0.27 - 9.23
0.00 - 0.00
0.00 - 0.00
28.11 - 49.95
0.29 - 10.64
Ol
00
(continued)
-------
TABLE 62 (Continued)
Study Area
Washington
D.C.
Number of
Persons at Risk
76,173
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
72,500
4,699
52
6,659
904
0
0
16,886
1,014
Percent of
Total
95.18
6.17
0.07
8.74
1.19
0.00
0.00
22.17
1.33
Range
Number of Persons
Exposed
66,835 - 74,507
915 - 7,269
0-492
5,407 - 8,217
268 - 4,216
0-0
0-0
11,699-21,500
55 - 2,348
Percent of Total
87.74- 97.81
1.20-9.54
0.00 - 0.65
7.10- 10.79
0.35 - 5.53
0.00 - 0.00
0.00 - 0.00
15.36 - 28.23
0.07 - 3.08
en
CD
-------
TABLE 63. NUMBER AND PERCENT OF OUTDOOR WORKERS EXPERIENCING ONE OR MORE
EIGHT-HOUR DAILY MAXIMUM OZONE EXPOSURES ABOVE 60 PPB AT ANY VENTILATION RATE
Study Area
Chicago
Denver
Number of
Persons at Risk
141,204
36,093
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
139,485
140,109
82,381
141,204
137,226
101,745
19,158
141,204
138,755
32,169
33,204
20,973
36,061
35,243
29,486
14,197
35,616
31,401
Percent of
Total
98.78
99.22
58.34
100.00
97.18
72.06
13.57
100.00
98.27
89.13
92.00
58.11
99.91
97.64
81.69
39.33
98.68
87.00
Range
Number of
Persons Exposed
135,085- 141,204
137,872 - 141,204
68,842 - 97,345
141,204- 141,204
133,330 - 140,672
84,250- 112,246
11,641 -26,220
141,204 - 141,204
134,620- 141,204
30,344-33,911
30,406 - 35,344
17,645 - 23,725
35,776 - 36,093
33,768 - 36,093
26,704 - 32,275
9,990 - 19,616
32,880 - 36,093
28,512-33,078
— — _
Percent of Total
95.67 - 100.00
97.64 - 100.00
48.75 - 68.94
100.00 - 100.00
94.42 - 99.62
59.67 - 79.49
8.24 - 18.57
100.00 - 100.00
95.34 - 100.00
84.07 - 93.95
84.24 - 97 92
48.89 - 65.73
99.12 - 100.00
93.56 - 100.00
73.99 - 89 42
27.68 - 54 35
91.10 - 100.00
79.00-91.65
• —
o>
o
(continued)
-------
TABLE 63 (Continued)
Study Area
Houston
Los
Angeles
Number of
Persons at Risk
71,735
294,008
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
71,735
69,225
57,325
70,937
70,009
55,275
29,507
71,645
67,845
294,008
124,007
54,209
178,543
107,288
109,898
45,502
116,247
94,314
Percent of
Total
100.00
96.50
79.91
98.89
97.59
77.05
41.13
99.87
94.58
100.00
42.18
18.44
60.73
36.49
37.38
15.48
39.54
32.08
Range
Number of
Persons Exposed
71,735-71,735
68,200 - 70,814
51,337 - 62,887
69,932 - 71,735
67,751 - 71,665
46,832 - 58,663
21,501 - 37,804
71,283 -71,735
65,025 - 69,630
294,008 - 294,008
117,506- 130,372
49,172 - 65,133
166,840 - 189,145
100,876- 115,636
100,055 - 122,204
35,261 - 52.855
102,684 - 135,175
83,666 - 105,666
Percent of Total
100.00 - 100.00
95.07 - 98.72
71.56 - 87.67
97.49 - 100.00
94.45 - 99.00
65.28 - 81.78
29.97 - 52.70
99.37 - 100.00
90.65 - 97.07
100.00 - 100.00
39.97 - 44.34
16.72 - 22.15
56.75 - 64.33
34.31 - 39.33
34.03 -41.56
11.99 - 17.98
34.93 - 45.98
28.46 - 35.94
o>
(continued)
-------
TABLE 63 (Continued)
a
Study Area
Miami
New
York
Number of
Persons at Risk
47,187
195,735
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1 EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
37,493
46,663
30,361
47,187
47,187
43,782
19,964
47,187
47,187
195,735
143,486
85,843
174,509
152,498
90,710
35,313
157,752
100,734
Percent of
Total
79.46
98.89
64.34
100.00
100.00
92.78
42.31
100.00
100.00
100.00
73.31
43.86
89.16
77.91
46.34
18.04
80.59
51.46
Range
Number of
Persons Exposed
32,706 - 42,006
44,924-47,187
23,714 - 39,884
47,187 - 47,187
47,187 - 47,187
39,466 - 46,581
9,679 - 29,163
47,187 - 47,187
47,187 - 47,187
195,735 - 195,735
137,555 - 149,557
77,809 - 92,754
170,004- 177,318
140,796 - 160,148
85,650 - 101,264
15,332 - 43,705
149,719 - 168,015
89,789 - 1 14,797
Percent of Total
69.31 - 89.02
95.20 - 100.00
50.26 - 84.52
100.00 - 100.00
100.00 - 100.00
83.64 - 98.72
20.51 - 61.80
100.00 - 100.00
100.00 - 100.00
100.00 - 100.00
70.28 - 76.41
39.75 - 47.39
86.85 - 90.59
71.93-81.82
43.76 - 51.74
7.83 - 22.33
76.49 - 85.84
45.87 - 58.65
(continued)
-------
TABLE 63 (Continued)
Study Area
Philadelphia
St. Louis
Number of
Persons at Risk
98,745
40,843
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
98,745
98,745
96,730
98,745
98,537
80,778
19,541
98,745
95,289
39,572
40,201
35,915
38,361
37,099
27,661
12,663
40,432
36,091
Percent of
Total
100.00
100.00
97.96
100.00
99.79
81.80
19.79
100.00
96.50
96.89
98.43
87.93
93.92
90.83
67.73
31.00
98.99
88.36
Range
Number of
Persons Exposed
98,745 - 98,745
98,745 - 98,745
94,462 - 98,644
98,745 - 98,745
97,361 - 98,745
73,761 - 90,346
10,154 - 25,348
98,745 - 98,745
90,616 - 97,830
38,532 - 40,434
39,684 - 40,593
35,179 - 37,284
37,529 - 39,044
36,259 - 38,178
25,940 - 29,030
9,319 - 14,722
40,091 - 40,724
34,090 - 37,255
Percent of Total
100.00 - 100.00
100.00 - 100.00
95.66 - 99.90
100.00 - 100.00
98.60 - 100.00
74.70-91.49
10.28 - 25.67
100.00 - 100.00
91.77-99.07
94.34 . 99.00
97.16- 99.39
86.13 - 91.29.
91.89 - 95.60
88.78 - 93.48
63.51 - 71.08
22.82 - 36.05
98.16 - 99.71
83.47 - 91.22
o>
CO
(continued)
-------
TABLE 63 (Continued)
Study Area
Washington
D.C.
Number of
Persons at Risk
76,173
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
75,598
74,974
66,218
75,806
75,604
42,715
8,242
76,173
68,392
Percent of
Total
99.24
98.43
86.93
99.52
99.25
56.08
10.82
100.00
89.78
Range
Number of
Persons Exposed
75,108 - 76,173
74,603 - 75,647
63,615 - 69,750
75,142 - 76,173
74,655 - 76,028
37,404 - 50,538
7,536 - 9,175
76,173-76,173
65,795 - 71,269
Percent of Total
98.60 - 100.00
97.94 - 99.31
83.51 -91.57
98.65 - 100.00
98.01 - 99.81
49.10-66.35
9.89 - 12.04
100.00 - 100.00
86.38 - 93.56
o>
-------
TABLE 64. NUMBER AND PERCENT OF OUTDOOR WORKERS EXPERIENCING ONE OR MORE
EIGHT-HOUR DAILY MAXIMUM OZONE EXPOSURES ABOVE 80 PPB AT ANY VENTILATION RATE
Study Area
Chicago
Denver
Number of
Persons at Risk
141,204
36,093
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
82,740
23,145
194
74,002
7,482
388
0
84,236
17,296
4,930
7,872
0
23,017
11,860
1,201
0
11,692
1,275
Percent of
Total
58.60
16.39
0.14
52.41
5.30
0.27
0.00
59.66
12.25
13.66
21.81
0.00
63.77
32.86
3.33
0.00
32.39
3.53
Range
Number of Persons
Exposed
71,701 - 88,934
15,702 - 33,300
0 - 1 ,008
51,403 - 87,654
2,945 - 14,386
0 - 1,297
0-0
68,695 -99,111
7,376-25,681
2,293 - 7,620
5,274 - 9,802
0-0
20,838 - 26,528
9,562 - 15,388
126 - 5,036
0-0
8,392 - 15,792
192 - 3,267
Percent of
Total
50.78 - 62.98
11.12-23.58
0.00 - 0.71
36.40 - 62.08
2.09 - 10.19
0.00 - 0.92
0.00 - 0.00
48.65 - 70.19
5.22 - 18.19
6.35 -21.11
14.61 - 27.16
0.00 - 0.00
57.73 - 73.50
26.49 - 42.63
0.35 - 13.95
0.00 - 0.00
23.25 - 43.75
0.53 - 9.05
Oi
Ol
(continued)
-------
TABLE 64 (Continued)
Study Area
Houston
Los
Angeles
Number of
Persons at Risk
71,735
294,008
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
71,308
15,187
3,350
42,078
19,717
5,319
2,086
43,779
17,451
266,395
11,405
0
81,701
33,559
7,359
0
31,131
16,316
Percent of
Total
99.40
21.17
4.67
58.66
27.49
7.41
2.91
61.03
24.33
90.61
3.88
0.00
27.79
11.41
2.50
0.00
10.59
5.55
Range
Number of Persons
Exposed
69,932 - 71,735
6,554 - 25,436
632 - 8,587
29,562 - 49,405
11,126-26,333
1,790 - 10,743
103 - 4,612
28,533 - 56,106
6,222 - 31,383
260,353 - 270,882
3,088 - 25,641
0-0
69,147 - 93,213
26,833 -41,044
1,233 - 17,735
0-0
23,098 - 40,024
3,148 - 27,014
Percent of
Total
97.49 - 100.00
9.14 - 35.46
0.88- 11.97
41.21 - 68.87
15.51 - 36.71
2.50 - 14.98
0.14 - 6.43
39.78 - 78.21
8.67 - 43.75
88.55 - 92.13
1.05 - 8.72
0.00 - 0.00
23.52- 31.70
9.13 - 13.96
0.42 - 6.03
0.00 - 0.00
7.86 - 13.61
1.07-9.19
en
CD
(continued)
-------
TABLE 64 (Continued)
Study Area
Miami
New
York
Number of
Persons at Risk
47,187
195,735
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
1,240
13,197
39
37,834
13,730
3,056
0
41,816
18,732
168,890
16,079
30
63,765
8,350
0
0
43,253
3,997
Percent of
Total
2.63
27.97
0.08
80.18
29.10
6.48
0.00
88.62
39.70
86.29
8.21
0.02
32.58
4.27
0.00
0.00
22.10
2.04
Range
Number of Persons
Exposed
8 - 6,062
9,963 - 15,695
0- 388
31,911 -42,473
7,249 - 20,712
658 - 7,137
0-0
38,133 - 46,180
13,827 - 24,470
155,174- 183,401
12,611 -21,558
0-301
52,806 - 75,032
4,983 - 12,683
0-0
0-0
26,990 - 56,292
203 - 9,150
Percent of
Total
0.02 - 12.85
21.11 -33.26
0.00 - 0.82
67.63 - 90.01
15.36 - 43.89
1.39- 15.12
0.00 - 0.00
80.81 - 97.87
29.30- 51.86
79.28 - 93.70
6.44- 11.01
0.00 - 0.15
26.98 - 38.33
2.55 - 6.48
0.00 - 0.00
0.00 - 0.00
13.79 - 28.76
0.10 - 4.67
05
(continued)
-------
TABLE 64 (Continued)
Study Area
Philadelphia
St. Louis
Number of
Persons at Risk
98,745
40,843
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
98,467
34,519
457
40,853
7,191
255
0
28,277
1,204
20,859
17,573
2,789
15,357
5,855
209
0
23,034
5,776
Percent of
Total
99.72
34.96
0.46
41.37
7.28
0.26
0.00
28.64
1.22
51.07
43.03
6.83
37.60
14.34
0.51
0.00
56.40
14.14
Range
Number of Persons
Exposed
96,638 - 98,745
27,146 - 44,365
0 - 1,991
34,872 - 46,943
2,291 - 14,782
0 - 2,134
0-0
23,021 - 33,072
36 - 3,275
18,501 - 23,842
13,226-20,153
1,338 - 4,490
8,210 - 20,107
4,028 - 9,868
0-619
0-0
20,356 - 24,932
3,434 - 8,584
Percent of
Total
97.87 - 100.00
27.49 - 44.93
0.00 - 2.02
35.32 - 47.54
2.32 - 14.97
0.00-2.16
0.00 - 0.00
23.31 - 33.49
0.04 - 3.32
45.30 - 58.37
32.38 - 49.34
3.28 - 10.99
20.10 - 49.23
9.86 - 24.16
0.00 - 1.52
0.00 - 0.00
49.84- 61.04
8.41 -21.02
CD
CO
(continued)
-------
TABLE 64 (Continued)
Study Area
Washington
D.C.
Number of
Persons at Risk
76,173
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
70,904
11,848
196
16,554
2,374
0
0
26,625
806
Percent of
Total
93.08
15.55
0.26
21.73
3.12
0.00
0.00
34.95
0.01
Range
Number of Persons
Exposed
68,263 - 73,717
8,647 - 15,573
0-506
11,904-23,595
440 - 5,368
0-0
0-0
21,442 - 33,233
2 - 2,516
Percent of
Total
89.62 - 96.78
11.35-20.44
0.00 - 0.66
15.63 - 30.98
0.58 - 7.05
0.00 - 0.00
0.00 - 0.00
28.15 - 43.63
0.00 - 3.30
O)
CD
-------
m
EIGHT-
65> NUMBER AND PERCENT OF OUTDOOR WORKERS EXPERIENCING ONE OR MORE
HOUR DAILY MAXIMUM OZONE EXPOSURES ABOVE 100 PPB AT ANY VENTILATION RATE
• — - --
_ Study Area
Chicago
Denver
=============
Number of
Persons at Risk
141,204
36,093
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
360
0
0
252
0
0
0
309
0
0
0
0
1,735
0
0
0
0
0
Percent of
Total
0.25
0.00
0.00
0.18
0.00
0.00
0.00
0.22
0.00
0.00
0.00
0.00
4.81
0.00
0.00
0.00
0.00
0.00
Rang
Number of
Persons Exposed
0 - 1,674
0-0
0 -0
0 - 1,264
0-0
0-0
0-0
0- 1,258
0-0
0-0
0-0
0-0
224 - 3,462
0-0
0-0
0-0
0-0
0-0
. __ .
1 ^ IVi-Y I !_.
e
Percent of
Total
0.00- 1.19
0.00 - 0.00
0.00 - 0.00
0.00 - 0.90
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.89
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.62 - 9.59
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
—
0.00 - 0 00
_ .
(continued)
-------
TABLE 65 (Continued)
Study Area
Houston
Los
Angeles
Number of
Persons at Risk
71,735
294,008
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
65,276
991
0
4,258
2,189
135
0
10,485
935
205,145
0
0
4,659
0
0
0
110
0
Percent of
Total
91.00
1.38
0.00
5.94
3.05
0.19
0.00
14.62
1.30
69.78
0.00
0.00
1.58
0.00
0.00
0.00
0.04
0.00
Range
Number of
Persons Exposed
62,997 - 68,214
0 - 4,428
0-0
1,393 - 10,658
179 - 5,278
0-704
0-0
4,947 - 19,190
5 - 2,988
195,749 - 220,455
0-0
0-0
554 - 16,137
0-0
0-0
0-0
0- 544
0-0
Percent of
Total
87.82 - 95.09
0.00- 6.17
0.00 - 0.00
1.94 - 14.86
0.25 - 7.36
0.00 - 0.98
0.00 - 0.00
6.90 -26.75
0.01 -4.17
66.58 - 74.98
0.00 - 0.00
0.00 - 0.00
0.19 - 5.49
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.19
0.00 - 0.00
(continued)
-------
TABLE 65 (Continued)
Study Area
Miami
New
Yrvrlf
Number of
Persons at Risk
47,187
195,735
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
0
0
0
3,804
0
0
0
7,774
1,168
72,067
0
0
0
0
0
0
34
0
Percent of
Total
0.00
0.00
0.00
8.06
0.00
0.00
0.00
16.47
2.48
36.82
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
Rang
Number of
Persons Exposed
0-0
0-0
0-0
1,106- 10,824
0-0
0-0
0-0
2,123- 11,923
122 - 4,498
61,174 - 90,297
0-0
0-0
0-0
0-0
0-0
0-0
0- 158
0-0
e
Percent of
Total
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
2.34 - 22.94
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
4.50 - 25.27
0.26 - 9.53
31.25 -46.13
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.08
0.00 - 0.00
(continued)
-------
TABLE 65 (Continued)
Study Area
Philadelphia
St. Louis
Number of
Persons at Risk
98,745
40,843
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
46,855
0
0
0
0
0
0
0
0
347
267
0
97
0
0
0
1,602
0
Percent of
Total
47.45
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
84.84
0.65
0.00
0.24
0.00
0.00
0.00
3.92
0.00
Range
Number of
Persons Exposed
35,526 - 55,480
0-0
0-0
0-0
0-0
0-0
0-0
0-0
0-0
46 - 954
0- 1,397
0-0
0-250
0-0
0-0
0-0
445 - 3,760
0-0
Percent of
Total
35.98 -56.19
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.11 - 2.34
0.00 - 3.42
0.00 - 0.00
0.00 - 0.61
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
1.09 - 9.21
0.00 - 0.00
CJ
(continued)
-------
TABLE 65 (Continued)
Study Area
Washington
D.C.
Number of
Persons at Risk
76,173
Regulatory
Scenario
Baseline
Current NAAQS
1H1EX-100
8H1EX-100
8H1EX-90
8H1EX-80
8H1EX-70
8H5EX-90
8H5EX-80
Mean
Number of
Persons Exposed
13,237
0
0
0
0
0
0
1
0
Percent of
Total
17.38
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Range
Number of
Persons Exposed
10,156 - 17,946
0-0
0-0
0-0
0-0
0-0
0-0
0-7
0-0
Percent of
Total
13.33 - 23.56
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.00
0.00 - 0.01
0.00 - 0.00
-------
exposures under baseline conditions, for all exposure indicators. Chicago, St. Louis,
and Washington, D.C. also tend to exhibit this exception at the eight-hour daily
maximum above 60 ppb ozone exposure indicator. In these cases, the ambient ozone
levels permitted by the regulatory scenarios are higher than the ambient levels which
occur under baseline conditions. Consequently, the adjustment of baseline data to
exactly meet the current NAAQS, for example, produces an increase in ozone
exposure.
7.4 Estimates of Maximum Dose Exposures
Each ozone exposure estimated by pNEM/O3 includes a value for ozone
concentration and a value for EVR. The product of ozone concentration and EVR
provides an indication of ozone dose. The "daily maximum dose" is assumed to occur
each day during the period when this product is highest. Consistent with this concept,
pNEM/OS provides dose estimates for two averaging times: the one-hour maximum
daily dose and the eight-hour daily maximum dose. Analysts selected two specific
exposure indicators from these model outputs for further evaluation:
The number of outdoor workers who experienced one or more one-hour
maximum daily dosage exposures during which the ozone concentration
exceeded 0.12 ppm (120 ppb) and the EVR equaled or exceeded 30
liters • min"1- m'2.
The number of outdoor workers who experienced one or more eight-hour
maximum daily dosage exposures during which the ozone concentration
exceeded 0.08 ppm (80 ppb) and the EVR ranged from 13 liters • min"1
• m"2 to 27 liters • min'1 • m"2.
Tables 66a through 83b present a summary of the exposure estimates based on these
two indicators. The tables are grouped in pairs by study area; for example, Table 66a,
66b, 67a and 67b present the one-hour and eight-hour dose estimates, respectively,
for Chicago. Note that the values listed in each table consist of mean values and
ranges based on 1Q runs of pNEM/03. Each table provides a separate set of
estimates for each of the nine air quality scenarios discussed previously.
175
-------
Statistic"
Baseline
1H1EX-1200
1H1EX-100
O)
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
146
0.10
0.00-0.47
146
d
e
1.00
100.00
0.00
0.00
"Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
°Current NAAQS.
dLess than 0.01 percent.
"All values less than 0.01 percent.
4
d
0.00-0.03
4
d
e
1.00
100.00
0.00
0.00
0
0.00
0
0.00
-------
TABLE 66b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN CHICAGO DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic6
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
203
0.14
0.00-0.67
203
d
e
1.00
100.00
0.00
0.00
8H1EX-90
0
0.00
0
0.00
-
-
8H1EX-80
0
0.00
0
0.00
-
-
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
282
0.20
0.00-0.85
282
d
e
1.00
100.00
0.00
0.00
8H5EX-80
0
0.00
0
0.00
-
-
Equivalent ventilation rate = (ventilation rate)/(body surface area).
"Mean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 67a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN CHICAGO DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1f"\«» *
Day
2 Days
3 Days
>3 Days
Regulatory scenario
Baseline
4
d
0.00-0.02
4
d
e
1.00
100.00
0.00
0.00
0.00
1H1EX-1200
36
0.03
0.00-0.24
36
d
e
1.00
100.00
0.00
0 00
\J,\J\J
Ono
,\j\j
1H1EX-100
0
0.00
-
0
0.00
-
-
-
-
-
CO
"Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
dLess than 0.01 percent.
8AII values less than 0.01 percent.
-------
TABLE 67b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN CHICAGO DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
122
0.09
0.00-0.43
122
d
e
1.00
100.00
0.00
0.00
0.00
8H1EX-90
0
0.00
-
0
0.00
-
-
.
.
.
-
8H1EX-80
0
0.00
-
0
0.00
-
-
-
_
.
-
8H1EX-70
0
0.00
-
0
0.00
-
-
.
.
_
-
8H5EX-90
11
0.01
0.00-0.08
11
d
e
1.00
100.00
0.00
0.00
0.00
8H5EX-80
0
0.00
-
0
0.00
-
-
.
-
CO
"Equivalent ventilation rate = (ventilation rate)/(body surface area).
"Mean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 68a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN DENVER DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
Baseline
541
1.50
0.10-3.75
541
0.01
0.00-0.02
1.00
100.00
0.00
0.00
1H1EX-120C
268
0.74
0.00-2.20
268
d
0.00-0.01
1.00
100.00
0.00
0.00
1H1EX-100
0
0.00
0
0.00
-
-
00
o
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
dLess than 0.01 percent.
-------
TABLE 68b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN DENVER DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
1,733
4.80
0.70-14.87
1,736
0.02
0.00-0.07
1.00
99.72
0.28
0.00
8H1EX-90
676
1.87
0.10-7.54
676
0.01
0.00-0.04
1.00
100.00
0.00
0.00
8H1EX-80
12
0.03
0.00-0.24
12
d
e
1.00
100.00
0.00
0.00
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
1,167
3.23
0.87-8.46
1,167
0.02
0.00-0.04
1.00
100.00
0.00
0.00
8H5EX-80
146
0.40
'0.00-1.91
146
d
0.00-0.01
1.00
100.00
0.00
0.00
co
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
CO
ro
TABLE 69a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN DENVER DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
Baseline
0
0.00
-
0
0.00
-
-
.
.
_
1H1EX-1200
4
0.01
0.00-0.07
4
d
e
1.00
100.00
0.00
0.00
0.00
1H1EX-100
o
0.00
o
0.00
.
-
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
less than 0.01 percent.
°AII values less than 0.01 percent.
-------
TABLE 69b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN DENVER DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic1*
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
492
1.36
0.00-4.29
492
0.01
0.00-0.02
1.00
100.00
0.00
0.00
0.00
8H1EX-90
249
0.69
0.00-3.19
249
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
8H1EX-80
0
0.00
-
0
0.00
-
-
_
_
*
-
8H1EX-70
0
0.00
0
0.00
-
'
-
8H5EX-90
17
0.05
0.00-0.27
17
d
e
1.00
100.00
0.00
0.00
0.00
8H5EX-80
o
0.00
o
0.00
_
-
CO
CO
"Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
CO
TABLE 70a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN HOUSTON DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR
._.
' . J_=: — =====
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 1 0 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 1 0 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
Baseline
9,383
13.08
5.56-22.66
10,027
0.04
0.02-0.06
1.07
93.03
6.73
0.25
1H1EX-120C
90
0 13
0.00-1.00
90
d
e
1.00
100.00
0.00
0.00
1H1EX-100
Onn
Onrv
.UU
-
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
"Mean or range for 10 runs of pNEM/O3.
Current NAAQS.
dl_ess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 70b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN HOUSTON DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M2
Statistic15
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
101
0.14
0.00-0.55
101
d
e
1.00
100.00
0.00
0.00
8H1EX-90
15
0.02
0.00-0.20
15
d
e
1.00
100.00
0.00
0.00
8H1 EX-80
0
0.00
0
0.00
-
-
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
458
0.64
0.05-3.07
458
d
0.00-0.01
1.00
100.00
0.00
0.00
8H5EX-80
3
d
0.00-0.02
3
d
e
1.00
100.00
0.00
0.00
00
01
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 71 a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN HOUSTON DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
j
2 Days
j
3 Days
>3 Days
Regulatory scenario
Baseline
6,385
8.90
3.98-14.09
6,562
0.03
0.01-0.04
1.03
97.36
2.56
0.08
0.00
1H1EX-1200
108
0.15
0.00-1.07
108
d
e
1.00
100.00
0.00
0.00
0.00
1H1EX-100
2
d
0.00-0.03
2
d
e
1.00
100.00
0.00
0.00
0.00
co
en
Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
'Current NAAQS.
dLess than 0.01 percent.
°AII values less than 0.01 percent.
-------
TABLE 71 b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN HOUSTON DURING WHICH OZONE CONCENTRATION
EXCEEDED O.Q8 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 1 0 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
528
0.74
0.00-2.30
528
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
8H1EX-90
236
0.33
0.00-1.38
270
d
e
1.14
91.81
8.19
0.00
0.00
8H1EX-80
0
0.00
-
0
0.00
-
-
,
.
.
-
8H1EX-70
0
0.00
-
0
0.00
-
-
.
-
8H5EX-90
350
0.49
0.00-1.56
350
d
e
1.00
100.00
0.00
0.00
0.00
8H5EX-80
98
0.14
• 0.00-1.19
98
d
e
1.00
100.00
0.00
0.00
0.00
00
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 72a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN LOS ANGELES DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Regulatory scenario
Baseline
1H1EX-120C
1H1EX-100
oo
co
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
46,425
15.79
9.26-24.89
61,763
0.06
0.03-0.10
1.33
76.32
19.02
4.66
'Equivalent ventilation rate = (ventilation rate)/(body surface area).
"Mean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
dLess than 0.01 percent.
"All values less than 0.01 percent.
o
o.oo
o
o.oo
o
o.oo
o
o.oo
-------
TABLE 72b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN LOS ANGELES DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
1,818
0.62
0.00-4.61
1,818
d
0.00-0.01
1.00
100.00
0.00
0.00
8H1EX-90
1
d
e
1
d
8
1.00
100.00
0.00
0.00
8H1EX-80
0
0.00
-
0
0.00
-
-
-
-
-
8H1EX-70
0
0.00
-
0
0.00
-
-
.
_
-
8H5EX-90
419
0.14
0.00-1.07
419
d
e
1.00
100.00
0.00
0.00
8H5EX-80
0
0.00
-
0
0.00
-
.
.
-
CO
CO
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 73a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN LOS ANGELES DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
Baseline
48.276
16.42
10.99-27.17
63,690
0.06
0.03-0.10
1.32
73.81
21.74
4.20
0.25
1H1EX-1200
0
0.00
-
0
0.00
-
.
-
-
-
-
1H1EX-100
0
0.00
-
0
0.00
-
-
-
-
-
-
(0
o
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
"Current NAAQS.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 73b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN LOS ANGELES DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
641
0.22
0.00-1.18
641
d
e
1.00
100.00
0.00
0.00
0.00
8H1EX-90
43
0.01
0.00-0.11
43
d
e
1.00
100.00
0.00
0.00
0.00
8H1EX-80
0
0.00
-
0
0.00
-
-
.
_
_
-
8H1EX-70
0
0.00
-
0
0.00
-
-
-
8H5EX-90
994
0.34
0.00-2.03
994
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
8H5EX-80
0
0.00
0
0.00
-
-
CO
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 74a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN MIAMI DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
Baseline
7
0.01
0.00-0.13
7
d
e
1.00
100.00
0.00
0.00
1H1EX-1200
112
0.24
0.00-1.79
112
d
e
1.00
100.00
0.00
0.00
1H1EX-100
0
0.00
0
0.00
-
-
CD
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
less than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 74b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN MIAMI DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS-M1N'1-M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
841
1.78
0.00-12.12
841
d
0.00-0.03
1.00
100.00
0.00
0.00
8H1EX-90
205
0.43
0.00-2.50
205
d
0.00-0.01
1.00
100.00
0.00
0.00
8H1EX-80
0
0.00
0
0.00
-
:
8H1EX-70
6
0.01
0.00-0.13
6
d
e
1.00
100.00
0.00
0.00
8H5EX-90
933
1.98
0.00-6.14
933
0.01
0.00-0.02
1.00
100.00
0.00
0.00
8H5EX-80
68
0.14
0.00-0.34
68
d
e
1.00
100.00
0.00
0.00
CD
CO
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 75a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN MIAMI DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN 1- M'2 TO 27 LITERS- MIN'1- M'2
-^I1"!11-1"--"--'--1-- • ---mi; , .,
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
:
'
Regulatory scenario
Baseline
0
0.00
-
0
0.00
-
-
-
-
.
=========
1H1EX-1200
277
0.59
0.00-5.73
277
d
0.00-0.02
1.00
100.00
0.00
0.00
0.00
1H1EX-100
o
0.00
o
0 00
\Jt\lf \f
.
-
Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
"Less than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 75b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN MIAMI DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
1,016
2.15
0.00-8.48
1,016
0.01
0.00-0.02
1.00
100.00
0.00
0.00
0.00
8H1EX-90
281
0.60
0.00-5.76
281
d
0.00-0.02
1.00
100.00
0.00
0.00
0.00
8H1EX-80
7
0.01
0.00-0.14
7
d
e
1.00
100.00
0.00
0.00
0.00
8H1EX-70
0
0.00
-
0
0.00
-
0.00
0.00
0.00
0.00
8H5EX-90
2,051
4.35
0.19-13.15
2,051
0.01
0.00-0.04
1.00
100.00
0.00
0.00
0.00
8H5EX-80
141
0.30
0.00-1.79
141
d
e
1.00
100.00
0.00
0.00
0.00
co
en
aEquivaleht ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 76a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN NEW YORK DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 1 0 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
Baseline
10,226
5.22
1.94-13.29
10,226
0.02
0.01-0.06
1.00
100.00
0.00
0.00
1H1EX-120C
0
0.00
0
0.00
-
-
1H1EX-100
0
0.00
0
0.00
-
-
CD
O)
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
°Current NAAQS.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 76b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN NEW YORK DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic13
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
274
0.14
0.00-1.32
274
d
0.00-0.01
1.00
100.00
0.00
0.00
8H1EX-90
0
0.00
0
0.00
-
-
8H1EX-80
0
0.00
0
0.00
-
-
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
0
0.00
0
0.00
-
-
8H5EX-80
0
0.00
0
0.00
-
-
CO
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 77a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN NEW YORK DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN1- M2 TO 27 LITERS- MIN'1- M'2
Statistic15
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
Baseline
2,283
1.17
0.00-3.88
2,283
0.01
0.00-0.02
1.00
100.00
0.00
0.00
0.00
1H1EX-1200
78
0.04
0.00-0.40
78
d
e
1.00
100.00
0.00
0.00
0.00
1H1EX-100
0
0.00
-
0
0.00
-
-
_
_
.
-
CO
CD
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
°Current NAAQS.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 77b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN NEW YORK DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS-MIN'1-M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
14
0.01
0.00-0.05
14
d
e
1.00
100.00
o on
\J.\J\J
o on
\J.\J\J
ono
\J\J\J
8H1EX-90
0
0.00
-
0
o.oo
-
-
-
-
"
8H1EX-80
0
0.00
-
0
0.00
-
-
-
-
"
8H1EX-70
0
0.00
-
0
0.00
-
-
-
-
~
8H5EX-90
0
0.00
-
0
0.00
-
-
-
-
—
8H5EX-80
0
0.00
"
0
0.00
-
-
-
-
"
co
CD
Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
"Less than 0.01 percent.
°AII values less than 0.01 percent.
-------
TABLE 78a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN PHILADELPHIA DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
Baseline
7,172
7.26
2.82-15.08
7,175
0.03
0.01-0.07
1.00
99.92
0.08
0.00
1H1EX-1200
221
0.22
0.00-1.08
221
d
0.00-0.01
1.00
100.00
0.00
0.00
1H1EX-100
0
0.00
0
0.00
-
-
10
o
o
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
dLess than 0.01 percent.
-------
TABLE 78b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN PHILADELPHIA DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
288
0.29
0.00-0.97
288
d
e
1.00
100.00
0.00
0.00
8H1EX-90
0
0.00
0
0.00
-
-
8H1EX-80
0
0.00
0
0.00
-
-
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
330
0.33
0.00-1.66
330
d
0.00-0.01
1.00
100.00
0.00
0.00
8H5EX-80
0
0.00
0
0.00
-
-
O
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
"Mean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
N)
O
TABLE 79a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN PHILADELPHIA DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
Baseline
3,860
3.91
0.41-8.52
3,869
0.02
0.00-0.04
1.00
99.85
0.15
0.00
0.00
1H1EX-1200
323
0.33
0.00-1.08
323
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
:
1H1EX-100
1
d
0.00-0.01
1
d
e
1.00
100.00
0.00
0.00
0.00
Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
"Less than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 79b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN PHILADELPHIA DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
252
0.26
0.00-1.38
252
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
8H1EX-90
120
0.12
0.00-1.08
120
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
8H1EX-80
95
0.10
0.00-0.97
95
d
e
1.00
100.00
0.00
0.00
0.00
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
22
0.02
0.00-0.15
22
d
e
1.00
100.00
0.00
0.00
0.00
8H5EX-80
0
0.00
0
0.00
-
-
ro
o
CO
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
"Mean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 80a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN ST. LOUIS DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
Baseline
116
0.28
0.00-0.80
116
d
e
1.00
100.00
0.00
0.00
1H1EX-1200
100
0.24
0.00-2.44
100
d
0.00-0.01
1.00
100.00
0.00
0.00
1H1EX-100
0
0.00
0
0.00
-
-
ro
o
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
dLess than 0.01 percent.
eAII values less than 0.01 percent.
-------
TABLE 80b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN ST. LOUIS DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
Statistic6
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
0
0.00
0
0.00
-
-
-
8H1EX-90
0
0.00
0
0.00
-
-
-
8H1EX-80
0
0.00
0
0.00
-
-
-
8H1EX-70
0
0.00
0
0.00
-
-
-
8H5EX-90
26
0.06
0.00-0.50
26
d
e
1.00
100.00
0.00
0.00
8H5EX-80
0
0.00
0
0.00
-
-
-
M
O
cn
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 81 a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN ST. LOUIS DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
Baseline
381
0.93
0.09-2.14
381
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
1H1EX-1200
265
0.65
0.00-2.02
265
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
1H1EX-100
2
d
0.00-0.05
2
d
e
1.00
100.00
0.00
0.00
0.00
O
Oi
""Equivalent ventilation rate = (ventilation rate)/(body surface area).
. bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
less than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 81 b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN ST. LOUIS DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M* TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
375
0.92
0.00-6.91
375
d
0.00-0.03
1.00
100.00
0.00
0.00
0.00
8H1EX-90
12
0.03
0.00-0.26
12
d
e
1.00
100.00
0.00
0.00
0.00
8H1EX-80
6
0.01
0.00-0.10
6
d
e
1.00
100.00
0.00
0.00
0.00
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
635
1.55
0.01-5.15
635
0.01
0.00-0.02
1.00
100.00
0.00
0.00
0.00
8H5EX-80
41
0.10
0.00-0.90
41
d
e
1.00
100.00
0.00
0.00
0.00
to
o
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
"Mean or range for 10 runs of pNEM/O3.
less than 0.01 percent.
eAII values less than 0.01 percent.
-------
o
co
TABLE 82a. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN WASHINGTON D.C. DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M'2
'"* -" '—... """"" _ . •-• —•• -.. —
=====================^
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
—
Regulatory scenario
Baseline
3,270
4.29
2.03-8.04
3,283
0.02
0.01-0.04
1.00
99.65
0.35
0.00
1H1EX-1200
19
0.02
0.00-0.14
19
d
a
1.00
100.00
0.00
0.00
-•^-___ - - --
1H1EX-100
n
v/
0 00
\J ,\J\J
o
\J
0 00
\Jf\J\J
.
-
"Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
cCurrent NAAQS.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 82b. ESTIMATES OF ONE-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN WASHINGTON D.C. DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.12 ppm AND EVRa EQUALED OR EXCEEDED 30 LITERS- MIN'1- M2
NJ
O
CO
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
>2 Days
Regulatory scenario
8H1EX-100
59
0.08
0.00-0.66
59
d
e
1.00
100.00
0.00
0.00
—
8H1EX-90
0
0.00
-
0
0.00
-
-
.
.
-
8H1EX-80
0
0.00
-
0
0.00
-
-
_
_
-
8H1 EX-70
0
0.00
0
0.00
-
•
8H5EX-90
160
0.21
0.00-1.13
160
d
0.00-0.01
1.00
100.00
0.00
0.00
—
8H5EX-80
o
000
o
0 00
_
=====
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
"All values less than 0.01 percent.
-------
TABLE 83a. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN WASHINGTON D.C. DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic*1
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
Baseline
947
1.24
0.13-2.63
952
0.01
0.00-0.01
1.01
99.38
0.62
0.00
0.00
1H1EX-120C
113
0.15
0.00-0.98
113
d
a
1.00
100.00
0.00
0.00
0.00
"™ - ° — .1
1H1EX-100
0
0.00
0
0.00
.
-
NJ
O
aEquivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
°Current NAAQS.
dLess than 0.01 percent.
6AII values less than 0.01 percent.
-------
TABLE 83b. ESTIMATES OF EIGHT-HOUR MAXIMUM DOSAGE EXPOSURES EXPERIENCED
BY OUTDOOR WORKERS IN WASHINGTON D.C. DURING WHICH OZONE CONCENTRATION
EXCEEDED 0.08 ppm AND EVRa RANGED FROM 13 LITERS- MIN'1- M'2 TO 27 LITERS- MIN'1- M'2
Statistic"
Mean Estimate of the Number of Outdoor Workers
Percent of Total Outdoor Worker Population
Range in this percentage for 10 runs
Mean Estimate of Person-Occurrences
Percent of Total Person-Occurrences
Range in this percentage for 10 runs
Mean Estimate of Occurrences/Person Exposed
Percentage exposed for indicated number of days
1 Day
2 Days
3 Days
>3 Days
Regulatory scenario
8H1EX-100
346
0.45
0.00-2.76
346
d
0.00-0.01
1.00
100.00
0.00
0.00
0.00
=
8H1EX-90
0
0.00
-
0
0.00
-
-
_
.
.
-
8H1EX-80
0
0.00
-
0
0.00
-
-
.
_
_
-
8H1EX-70
0
0.00
0
0.00
-
-
8H5EX-90
86
0.11
0.00-0.70
86
d
e
1.00
100.00
0.00
0.00
0.00
8H5EX-80
o
0.00
o
0.00
_
•
ro
"Equivalent ventilation rate = (ventilation rate)/(body surface area).
bMean or range for 10 runs of pNEM/O3.
dLess than 0.01 percent.
8AII values less than 0.01 percent.
-------
Table 70a and 70b illustrate the general format used in all of the one-hour
tables. The statistics in the first row are the 10-run mean estimates (by scenario) for
the number of outdoor workers in Houston who experienced one or more one-hour
maximum daily dosage exposures during which the ozone concentration exceeded
0.12 ppm and the EVR equaled or exceeded 30 liters • min"1- rrf2. Under baseline
conditions, 9,383 outdoor workers are estimated to have experienced the specified
exposure. According to the value listed in the second row, 9,383 workers comprise
13.08 percent of the total outdoor worker population in Houston. Entries in the third
row indicate that the percentage values ranged from 5.56 to 22.66 percent over the 10
runs.
The fourth row in Table 70a and 70b lists 10-run mean estimates for the
number of person-occurrences in which an outdoor worker in Houston experienced a
one-hour maximum daily dosage exposure during which the ozone concentration
exceeded 0.12 ppm and the EVR equaled or exceeded 30 liters • rnin"1- m"2. As each
worker can experience more than one person-occurrence during the Houston ozone
season, the estimated number of person-occurrences can exceed the number of
persons exposed at the specified levels. Under baseline line conditions, for example,
the 10-run mean for person-occurrences (10,027) is larger than the number of
exposed workers listed in the first row (9,383).
The total possible number of one-hour person-occurrences is equal to
26,184,370 -- the product of the number of Houston outdoor workers (71,738) and the
number of days in the Houston ozone season (365). According to the value listed in
the fifth row of Table 70, 10,027 person-occurrences is 0.04 percent of the total
possible number of person-occurrences; that is, 10,027/26,184,370 = 0.04 percent.
Entries in the six row of Table 70 indicate that the percentage values ranged from
0.02 to 0.06 percent over the 10 runs.
The seventh row in the table lists the ratio of person-occurrences to people
exposed based on 10-run means. Under baseline conditions, the ratio is 10,027/9,383
or 1.07.
The last three rows in Table 70a and 70b provide exposure frequency statistics
for outdoor workers who experienced the specified exposure conditions on at least
212
-------
one day. Of the 9,383 outdoor workers exposed under baseline conditions, 93.03
percent were exposed for one day only while 6.73 percent were exposed for exactly
two days. The remainder (0.25 percent) were exposed for more than two days.
Table 71 a and 71 b use this same general table format to present eight-hour
daily maximum dosage estimates for Houston. The statistics in the first row are the
10-run mean estimates (by scenario) for the number of outdoor workers in Houston
who experienced one or more eight-hour maximum daily dosage exposures during
which the ozone concentration exceeded 0.08 ppm and the EVR ranged from 13 liters
• min"1- m"2 to 27 liters • min'1- m"2. Under baseline conditions, 6,385 outdoor
workers are estimated to have experienced the specified exposure. This value is
equivalent to 8.90 percent of the total outdoor worker population in Houston. The
percentage values ranged from 3.98 to 14.09 percent over the 10 runs.
The fourth row in Table 71 a and 71 b lists 10-run mean estimates for the
number of person-occurrences in which an outdoor worker in Houston experienced an
eight-hour maximum daily dosage exposure during which the ozone concentration
exceeded 0.08 ppm and the EVR ranged from 13 liters • min"1- m"2 to 27 liters • min"
1- m"2. The 10-run mean for person-occurrences under baseline conditions (6,562) is
slightly larger than the number of exposed workers listed in the first row (6,385).
Consistent with the one-hour analysis, the total possible number of eight-hour
person-occurrences is equal to 26,184,370 - the product of the number of Houston
outdoor workers (71,738) and the number of days in the Houston ozone season (365).
According to the value listed in the fifth row of Table 71 a, 6,562 person-occurrences is
0.03 percent of the total possible number of person-occurrences. The baseline
percentage values ranged from 0.01 to 0.04 percent over the 10 runs.
The seventh row in Table 71 a and 71 b lists the ratio of person-occurrences to
people exposed based on 10-run means. Under baseline conditions, the ratio is
6,562/6,385 or 1.03.
Table 71 a and 71 b conclude with four rows listing exposure frequency statistics
for outdoor workers who experienced the specified exposure conditions on at least
one day. Of the 6,385 outdoor workers exposed under baseline conditions, 97.36
213
-------
percent were exposed for one day only while the remaining 2.56 percent were
exposed for exactly two days. None of the outdoor workers were exposed for more
than two days.
Figures 2a through 5b are graphs showing eight-hour daily maximum dose
exposures for outdoor workers under various air quality scenarios. Two graphs are
provided for each of four study areas (Philadelphia, Houston, New York, and
Washington). The graphs use two indicators to characterize ozone exposure:
Number of workers experiencing eight-hour daily maximum-dose
exposures on one or more days under moderate exertion conditions,
Number of occurrences in which a worker experiences an eight-hour
daily maximum dose exposure under moderate exertion conditions.
Moderate exertion conditions are defined as an EVR level between 13 and 27 liters-
min"1- m'2.
Figure 2a presents results for the first indicator (number of workers) based on
applications of pNEM/03 to Philadelphia. Nine distributions are plotted on the graph:
one for baseline ("as is") conditions; two for one-hour, one-exceedance standards
(1112 and 1110); four for eight-hour, one-exceedance standards; and two for eight-
hour, five-exceedance standards. The first digit in the code for each standard
indicates the averaging time; the second digit specifies the number of exceedances.
The last two digits indicate the ozone concentration of the standard expressed in
pphm. For example, 8508 indicates an eight-hour five exceedance standard with
ozone concentration equal to 8 pphm or 0.08 ppm.
The ordinate (y coordinate) of each point on the graph shows the number of
workers with one or more eight-hour daily maximum dose exposures equal to or
above the ozone concentration indicated by the point's abscissa (x coordinate). In
Figure 2a, the "as is" curve is associated with the highest number of workers exposed
when the specified ozone concentration falls between 0.05 ppm and 0.10 ppm. The
214
-------
FIGURE 2a. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS FOR
OUTDOOR WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN PHILADELPHIA, PA
120
ASIS
:m
1112
->-
8109
8108
8110
8508
-*K-
8107
-e-
1110
8509
NX
0.02 0.04
0.16 0.18 0.2
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
FIGURE 2b. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS OF
TOTAL OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN PHILADELPHIA, PA
350
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18
215
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FIGURE 3a. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS FOR
OUTDOOR WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN HOUSTON, TX
80
0
0.02 0.04
0.16 0.18
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
FIGURE 3b. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS OF
TOTAL OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN HOUSTON, TX
500
ASIS
*
1112
+
8109
-*-
8108
•&
8110
-0-
8508
-*-
8107
-e-
1110
—-»—
8509
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
216
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FIGURE 4a. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS FOR
OUTDOOR WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN NEW YORK, NY
200
oo150
ASIS
1112
8109
8108
—i
8110
XN
8508
->£-
8107
1110
8509
0.02 0.04
0.16 0.18 0.2
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
FIGURE 4b. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS OF
TOTAL OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN NEW YORK, NY
700
CO
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18
217
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FIGURE 5a. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS FOR
OUTDOOR WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN WASHINGTON, D.C.
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18
0.2
FIGURE 5b. EIGHT-HOUR DAILY MAXIMUM DOSE EXPOSURE DISTRIBUTIONS OF
TOTAL OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 13-27 LITERS/MIN-M2) IN WASHINGTON, D.C.
300
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
218
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nine curves tend to converge at lower and higher ozone concentrations. In a similar
manner, the 8107 standard tends to be associated with lowest number of children
exposed when the specified ozone concentration falls between 0.03 and 0.06 ppm.
Figures 2a through 5b provide eight-hour daily maximum dose distributions for
exposures occurring under moderate exertion conditions (EVR values between 13 and
27 liters- min"1- m~2). Appendix E provides graphs for one-hour exposures for two
other EVR ranges of interest to EPA: 16 to 30 liters- min"1- m"2 and 30+ liters- min'1-
m'2.
219
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SECTION 8
PRINCIPAL LIMITATIONS OF THE pNEM/O3 METHODOLOGY
The pNEM/C-3 methodology was developed specifically to meet the
requirements of OAQPS for a computer-based model capable of simulating the ozone
exposures of specific population groups under alternative NAAQS. In addition to
meeting these needs, the designers of pNEM/O3 have attempted to create a model
which is flexible in application and easy to upgrade. The model was deliberately
constructed as a collection of stand-alone algorithms organized within a modular
framework. For this reason, analysts can revise individual algorithms without the need
to make major changes to other parts of the model.
The structure of each algorithm in pNEM/O3 is largely determined by the
characteristics of the available input data. For example, the algorithm used to
construct a season-long exposure event sequence for each cohort is constrained by
the fact that none of the available time/activity studies provides more than three days
of diary data for any one subject. To make maximum use of the available diary data,
the pNEM/O3 sequencing algorithm constructs each exposure event sequence by
sampling data from more than one subject. The other pNEM/O3 algorithms are
similarly designed to make best use of available data bases.
In evaluating the exposure estimates presented in this report, the reader should
note that the model has a number of limitations which may affect its accuracy. These
limitations are usually the result of limitations in the input data bases. The available
data were typically collected for purposes other than use in a population exposure
model. Consequently, these data frequently represent special sets of conditions which
differ from those assumed by pNEM/O3. In these situations, analysts must exercise a
certain degree of judgement in adapting the data for use in pNEM/O3.
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This section presents a brief discussion of the principal limitations in the
pNEM/OS methodology as applied to outdoor workers. The limitations are organized
according to five major components of the model: time/activity patterns, equivalent
ventilation rates, air quality adjustment, the mass balance model, and the estimation of
cohort populations.
8.1 Time/Activity Patterns
In the general pNEM/O3 methodology, the exposure-related activities of each
cohort are represented by a multi-day exposure event sequence which spans a
specified ozone season. Each sequence is constructed by an algorithm which selects
24-hour (midnight-to-midnight) activity patterns from a specially prepared database.
This database contains data from one or more time/activity studies in which subjects
recorded their daily activities in diaries.
In the application of pNEM/O3 to outdoor workers, the time/activity database
consisted of diary data obtained from 89 subjects identified as outdoor workers. The
database contained only 136 person-days of data, an average of less than two days
per subject. These data are unlikely to fully characterize the spectrum of activity
patterns associated with outdoor workers.
The subjects who contributed to this database may not provide a balanced
representation of U.S. outdoor workers. The majority of subjects (88 percent) resided
in either the State of California (64 subjects) or in Cincinnati (14 subjects). Six
subjects resided in Denver, CO; three resided in Valdez, AK; and two lived in
Washington, DC.
Random selection protocols were used in the selection of the 64 subjects who
participated in the Cincinnati, Denver, Washington, Valdez, and California Adults
studies. The remaining 25 subjects participated in the two Los Angeles studies and
were solicited using non-random protocols.
Analysts used time/activity data obtained from these 89 subjects to represent
the activities of outdoor workers in nine study areas. Only three of these study areas
(Denver, Washington, and Los Angeles) were locations of diary studies which
221
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contributed time/activity data to the analysis. Although the algorithm which constructs
exposure event sequences attempts to account for effects of local climate on activity,
it is unlikely that this adjustment procedure corrects for all inter-city differences in
people's activities. Time/activity patterns are likely to be affected by a variety of local
factors, including topography, land-use, traffic patterns, mass transit systems, and
.recreational opportunities.
As discussed previously, the average subject provided less than two
days of diary data. For this reason, the construction of each season-long exposure
event sequence required either the repetition of data from one subject or the use of
data from multiple subjects. The latter approach was used in the outdoor worker
pNEM/O3 analyses to better represent the variability of exposure expected to occur
among the workers included in each cohort. The principal deficiency of this approach
is that it may not adequately account for the day-to-day repetition of activities common
to individual workers. Using activities from different subjects may underestimate
multiple occurrences of high exertion and/or outdoor exposure for those segments of
the population who engage in repetitive outdoor activities.
8.2 Equivalent Ventilation Rates
In the general pNEM/O3 methodology, the EVR associated with each exposure
event is determined by an algorithm which randomly selects the value from one of the
eight lognormal distributions presented in Table 4. Each distribution is specific to age
group (children or adults) and breathing rate category (sleeping, slow, medium, or
fast). The distributions are based on EVR data obtained from two diary studies
conducted by J. Hackney and associates in Los Angeles.
A total of 36 subjects participated in the Los Angeles studies. Because of the
small sample size, the resulting EVR database may not accurately represent the
variability of EVR across the population. In addition, the database does not provide
sufficient data to adequately characterize age-specific differences in EVR. For
example, none of the Los Angeles subjects was below the age of 10 or above the age
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of 50. The lognormal distributions of EVR developed for children and adults are likely
to over-estimate EVR when applied to pre-school children or older adults.
The distributions developed for adults are expected to yield relatively good
estimates of EVR when applied to outdoor workers, as these distributions are based
on data acquired primarily from subjects who were outdoor workers. If the resulting
EVR estimates are biased, they are likely to be biased high because of the operation
of the EVR limiting algorithm. This algorithm determines the maximum EVR that can
be maintained for a specified duration by a subject who is (1) male, (2) between the
ages of 18 and 24, (3) exercising regularly, and (4) motivated to reach a high
ventilation rate. The EVR value assigned by pNEM/OS to an event of the specified
duration is not permitted to exceed this value.
The four conditions assumed by the EVR limiting algorithm do not apply to all
members of the outdoor worker population. Consequently, the EVR limiting algorithm
may permit more high EVR values to occur in the pNEM/O3 simulation than would
occur in the actual outdoor worker population. This potential bias may be corrected in
future versions of pNEM/O3 by distinguishing outdoor worker cohorts by gender, age,
and physical conditioning. The parameters of the EVR limiting algorithm would be
varied according to these factors to yield a reasonable upper EVR limit for each
cohort.
The algorithm used to estimate EVR requires that each exposure event be
assigned to one of the four breathing rate categories. These assignments were
readily available in the time/activity data obtained from the Cincinnati and Los Angeles
studies, as the subjects of these studies entered this information directly into their
diaries. Information on breathing rate category was not provided by the diaries used
in the remaining time/activity studies. Consequently, the Monte Carlo procedure
described in Subsection 6.2 was used to assign breathing rate categories to the
time/activity data obtained from these studies. The Monte Carlo procedure was based
on the untested assumption that the probabilistic relationships between activity type
(e.g., yard work) and breathing rate category calculated from the Cincinnati
time/activity data could be applied to time/activity data from other studies.
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8.3 The Air Quality Adjustment Procedures
Section 5 presents a summary of the procedures used to adjust baseline ozone
monitoring data to simulate conditions expected when a study area just attains a
specified NAAQS. These procedures assume that 1) the Weibull distribution provides
a good fit to most ozone data, and 2) the parameters of the Weibull distribution fitting
data from a particular monitoring site will change over time in a predictable fashion.
The adjustment procedures include equations for predicting the values of the WeibulJ
parameters under future attainment conditions.
The prediction equations were developed through a statistical analysis of ozone
data obtained from selected monitoring sites which have experienced moderate
reductions in ozone levels during the 1980's. It should be noted that none of the
selected monitoring sites reported ozone reductions of the magnitude required to bring
Los Angeles into compliance with any one of the NAAQS under evaluation. For this
reason, the prediction equations may not produce accurate estimates for the Weibull
distribution parameters when applied to Los Angeles ozone data.
Researchers have recently performed a series of tests to evaluate the air
quality adjustment procedures with respect to moderate reductions in ozone levels. In
a technical letter, Johnson52 describes the general test procedure and its application to
six pNEM/O3 study areas (Chicago, Washington, Houston, Los Angeles, New York,
and Philadelphia). Analysts first selected a year representing relatively low ozone
concentrations for each city. The air quality adjustment procedure for each of the
three NAAQS formulations (1H1EX, 8H1EX, and 8H5EX) was applied to the baseline
ozone data for the study area with the goal of simulating the ozone levels observed
during the "low ozone" year. The resulting "estimated" ozone concentrations for the
low ozone year were compared with the actual ozone levels reported for the low
ozone year. The comparisons were performed using selected percentiles of the
cumulative ozone distributions (estimated and observed) associated with each fixed-
site monitor.
224
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The test results suggest that the air quality adjustment procedures perform
adequately in the upper-tail region (90th percentile and above) of the ozone
distribution, the region that determines the exposures of most concern in p.NEM/03
analyses. The results also show that the air quality adjustment procedures may
significantly over-estimate ozone concentrations in the lower portions of the
distribution. This problem is probably the result of using a somewhat "stiff1 two-
parameter distribution (the Weibull) to characterize one-hour ozone data.
Researchers may achieve better results by using a more flexible three-parameter
distribution, although this approach would likely require a more complicated air
quality adjustment procedure.
The air quality adjustment procedure is based on an assumption that the
attainment status of a particular city can be determined by a single year of
monitoring data. For example, the current status of Philadelphia is determined by
ozone monitoring data for 1991. This single year of monitoring data is then adjusted
to exactly meet a specified NAAQS. It should be noted that the pNEM/OS approach
to determining attainment status differs somewhat from the actual method used by
EPA to determine attainment status. EPA typically examines three recent years of
monitoring data for a particular city and calculates a multi-year air quality indicator
(e.g., the fourth highest daily maximum one-hour ozone concentration for the three-
year period). The air quality indicator determined by this method is likely to differ
from the air quality indicator determined in a pNEM/O3 analysis from a single year
of data. As the direction of the difference is random, the degree of adjustment
applied to a city by pNEM/O3 may be greater than or less than the adjustment
required to bring the city into compliance based on three years of data.
8.4 Estimation of Cohort Populations
Subsection 6.3 of this report describes the procedure used to estimate cohort
populations. As part of this procedure, a panel of analysts reviewed material
describing the general duties associated with selected occupational classifications.
Based on this review, each panel member provided a series of "best estimates" as
225
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to the percentages of workers in specified occupational classes who could be
classified as working outdoors. The panel then discussed the estimates as a group
and developed a final set of "consensus estimates". The consensus estimates were
then combined with census-derived data to calculate the number of outdoor workers
who resided in each exposure district of each study area.
ITAQS used this indirect approach because analysts were unable to find
reliable data on the number of outdoor workers in each occupational classification.
In essence, the approach substitutes informed personal judgement for the required,
but unavailable, occupational data. The validity of this approach cannot be
evaluated until reliable census data on outdoor workers are available. It should also
be noted that the absolute number of outdoor workers exposed is biased low in the
estimations. This underestimation is due to the inability to-estimate exposures for
the unknown percentage of outdoor workers in the 42 occupations listed in Appendix
C (see Subsection 6.3). '
8.5 The Mass Balance Model
The pNEM/O3 methodology uses the mass balance model described in
Section 3 to estimate ozone concentrations for the following enclosure categories:
Residential buildings - windows closed
Residential buildings - windows open
Nonresidential buildings
Vehicles.
The mass balance model provides hourly average ozone concentrations for each
enclosure category as a function of outdoor ozone concentration, AER, and ozone
decay rate.
In the application of pNEM/O3 to outdoor workers, the outdoor ozone
concentration required by the mass balance model was always derived from fixed-
226
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site monitoring data. These data were representative of local conditions and were
considered to be relatively reliable.
The AER values for residential buildings with closed windows were obtained
from a lognormal distribution fit to AER data from 312 residences. These data were
considered to be generally representative of housing in urban areas in the U.S.
No comparable databases were identified which were considered
representative of residences with open windows. Consequently, analysts
represented this enclosure category with a point estimate developed by Hayes45.
Analysts are uncertain as to the accuracy and general applicability of this estimate.
The AER values for nonresidential buildings were obtained from a lognormal
distribution fit to AER data from 40 buildings provided by Turk et al.44 This sample
may be too small to adequately characterize nonresidential buildings in the U.S. It
should also be noted that the Turk data are likely to represent only buildings with
closed windows. Consequently, the lognormal distribution derived from the Turk
data is likely to under-estimate the ozone exposures of people who frequently
occupy nonresidential buildings with open windows.
A point estimate of 36 air changes per hour was used for the AER of
vehicles. This value was obtained from Hayes47 based on his analysis of data
reported by Peterson and Sabersky42 for a single vehicle. The use of a point
estimate is considered unrealistic as it does not account for varying ventilation
conditions within a particular motor vehicle or the variability in AER from vehicle to
vehicle.
Analysts also used a point estimate for the ozone decay rate of vehicles.
This value was based on data from a single automobile and may be biased.
Ozone decay rates for residential and nonresidential buildings were sampled
from a normal distribution. This distribution was based on decay rate data for a
relatively small number of buildings assembled by Weschler et al.39 These data may
not adequately represent the variability of ozone decay rates among urban buildings
in the U.S.
227
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51. T. Johnson and M. McCoy, 1994, "A Monte Carlo Approach to Generating
Equivalent Ventilation Rates in Population Exposure Assessments," available
from Dr. Will Ollison, American Petroleum Institute, 1220 L Street, N.W.,
Washington, D.C. 20005.
52. Johnson, T. R., 1995, Letter to Harvey Richmond, Office of Air Quality Planning
and Standards, U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina, August 4.
53. U.S. Department of Labor, 1990, Occupational Outlook Handbook 1990-91
Edition. April.
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APPENDIX A
TEN TIME/ACTIVITY BASES GENERALLY APPLICABLE
TO AIR POLLUTION EXPOSURE ASSESSMENTS
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In 1992, Johnson et al.48 conducted a literature review to identify all time/activity
databases which would be appropriate for use in pNEM exposure assessments. The
survey identified ten databases with adequate data characteristics. Eight of the
databases relate to five individual urban areas: Cincinnati, Ohio; Denver, Colorado;
Los Angeles, California; Valdez, Alaska; and Washington, D.C. The remaining two
databases relate to the entire State of California. In the discussion that follows, each
database will be identified according to the associated geographical area. When a
geographical area is associated with more than one database, each database will be
further distinguished according to the sampled population (e.g., Los Angeles - outdoor
workers).
California
The California Air Resources Board conducted two state-wide time/activity
studies23'49 to provide a large pool of activity pattern data suitable for use in estimating
environmental exposures. The first study, referred to hereafter as the "California - 12
and over" study, was conducted between October 1987 and July 1988. During the
study, interviewers collected one day of activity data from each of 1762 California
residents over the age of 11. The second study ("California - 11 and under") was
conducted from April 1989 through February 1990. The study gathered one day of
activity data from each of 1200 children ages 11 and under. Both studies employed
retrospective telephone interviews to obtain a record of each subject's activities during
the preceding day.
Cincinnati
The Cincinnati Activity Diary Study19 was conducted by the Electric Power
Research Institute during March and August 1985 to provide an extensive database
for evaluating human exposure to air pollution. The sampled population included all
residents of a three-county area in and around Cincinnati, Ohio. Each subject
recorded his or her activities over a three-day period in a real-time diary and
completed a detailed background questionnaire. The 487 March subjects provided
1401 subject-days of diary data; the 486 August subjects provided 1399 subject-days.
Activity diary data collected during the Cincinnati study have been used by the U.S.
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Environmental Protection -Agency (EPA) in various applications of the pNEM/CO14 and
pNEM/O316 exposure models.
Denver and Washington
The U.S. Environmental Protection Agency conducted studies of adults (18 to
70 years) in Denver, Colorado, and Washington, D.C., during the winter of 1982 -
1983 for the purpose of collecting representative data on personal exposure to carbon
monoxide. In the Denver study24, each of 454 subjects carried a personal exposure
monitor (PEM) and a real-time time/activity diary for two 24-hour periods. Each
subject also provided a breath CO sample at the end of each monitored period and
completed a detailed background questionnaire. The Washington study28 employed a
similar protocol to obtain data for a single 24-hour period from each of 908 subjects.
Activity diary data from these two studies have been used in conjunction with data
from the Cincinnati study in applications of EPA's pNEM/CO exposure model14.
Los Angeles
Between 1989 and 1991, a research team headed by Dr. Jack Hackney
conducted four activity diary studies in Los Angeles with funding provided by the
American Petroleum Institute. The first of these, the "Los Angeles - outdoor worker
study", was conducted during the summer of 1989.26 Each of 20 outdoor workers
wore a heart rate monitor for a three-day period during which .the worker recorded his
or her activities in a real-time diary identical to that used in the Cincinnati study.
In October of 1989, the outdoor worker study was expanded to include 20
healthy elementary school children. During this phase of the Los Angeles study,
referred to here as the "Los Angeles - elementary school" study, each child wore a
heart-rate monitor for two or three days and recorded his or her activities in the real-
time Cincinnati diary. Approximately 58 subject-days of activity data were collected.50
A third phase of the Los Angeles study (the "Los Angeles - high school" study)
was conducted during September and October 1990.50 During this phase, 66 subject-
days of real-time activity data were collected from 19 students between the ages 13
and 17 using the Cincinnati diary.
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The Hackney research team conducted a fourth study in Los Angeles between
July and November 1991. Each of 19 construction workers between the ages of 23
and 42 wore a heart rate monitor during a typical work day. The Cincinnati diary was
used to record each subject's activities during this period. The study protocol differed
from the other Los Angeles studies in that each diary was completed by a trained
observer rather than by the subject. The observer monitored each subject's activities
visually and by two-way radio. This approach produced unusually detailed diaries of
high accuracy.25
Valdez
The Valdez Air Health Study27 was undertaken by the Alyeska Pipeline Service
Company in response to concerns expressed by the citizens of Valdez, Alaska,
regarding their potential exposure to certain volatile organic compounds (VOCs).
Between November 1990 and October 1991, 405 subjects aged 10 to 72 years were
interviewed and requested to report their daily activities for an earlier 24-hour period.
In addition to the activity data, researchers collected extensive data on personal
exposures to VOC's, ambient air quality, and meteorological conditions.
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APPENDIX B
OCCUPATION GROUPS DEFINED FOR THE
OUTDOOR WORKER EXPOSURE ANALYSIS
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Occupational Descriptions
The numbered codes listed after each occupational group title are the 1990 Bureau of
Census occupation codes. The occupational group titles are generic categories that
include all of the codes listed after each title. After each occupational group title and
associated census codes is a list of related (or in some cases identical) occupational
titles and brief descriptions of the nature of the work and the working conditions for
these occupations (from the 1990-91 Occupational Outlook Handbook3 published by
the U.S. Department of Labor). These descriptions give an overview of the nature of
the work generally performed for each occupational grouping, and focus on any
information pertaining to the extent to which each occupational grouping may entail
outdoor work. It is hoped that these descriptions will assist in determining the
estimated percentage of persons in each occupational grouping who are outdoor
workers.
1. Inspector and compliance officer, including construction (Codes 35, 36
and 489):
Inspectors and Compliance Officers, Except Construction (OOH pp. 41-44):
Inspectors and compliance officers enforce adherence to a wide range of laws,
regulations, policies, and procedures that protect the public on matters such as
health, safety, food, immigration, licensing, interstate commerce, and
international trade. Depending upon their employer, inspectors vary widely in
title and responsibilities. Examples of different kinds of inspectors include:
health inspectors, food inspectors, agricultural quarantine inspectors,
environmental health inspectors, agricultural commodity graders, immigration
inspectors, customs inspectors, postal inspectors, aviation safety inspectors,
railroad inspectors, motor vehicle inspectors, traffic inspectors, occupational
safety and health inspectors, mine inspectors, wage-hour compliance
inspectors, equal opportunity representatives, alcohol, tobacco, and firearms
inspectors, securities and real estate directors, revenue officers, attendance
officers, dealer compliance representatives, logging operations inspectors, travel
accommodations raters, and quality control inspectors.
Inspectors and compliance officers live an active life; they meet many people
and work in a variety of environments. Their jobs often involve considerable
fieldwork, and some inspectors travel frequently. At times, inspectors have
unfavorable working conditions. For example, mine inspectors often are
exposed to the same hazards as miners. Many inspectors work long and
irregular hours.
Similar occupations include revenue agents, construction and building
inspectors, fire marshals, State and local police, customs patrol officers,
customs special agents, and fish and game wardens.
Construction and Building Inspectors (OOH pp.19-20): Construction and
building inspectors examine the construction, alteration, or repair of highways,
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streets, sewer and water systems, dams, bridges, buildings, and other
structures to insure compliance with building codes and ordinances, zoning
regulations, and contract specifications. Initial inspections are made during
construction, and followup inspections are conducted periodically to monitor
continuing compliance with regulations. In areas subject to unusually severe
natural hazards - such as earthquakes or hurricanes - inspectors monitor
compliance with additional regulations. Inspectors generally specialize in one
particular type of construction work. Specializations include: building
inspectors, electrical inspectors, elevator inspectors, mechanical inspectors,
plumbing inspectors, public works inspectors, and home inspectors.
Construction and building inspectors increasingly use computers to help them
monitor the status of construction inspection activities and the issuance of
permits. Details about construction projects, building and occupancy permits,
and other information can be stored and easily retrieved.
Construction and building inspectors usually work alone. They may spend
much of their time in a field office reviewing blueprints, answering letters or
telephone calls, writing reports, and scheduling inspections. The rest of their
time is spent inspecting construction and building sites.
Workers in other occupations involving a combination of similar skills are
drafters, estimators, industrial engineering technicians, and surveyors.
2. Management related occupation (Code 37):
OOH lists the following management related occupations (in addition to the
Administrative Services Manager, whose description follows): Construction
Managers; Engineering, science, and data processing managers; Financial
managers; General managers and top executives; Government chief executives
and legislators; Health services managers; Hotel managers and assistants;
Industrial production managers; Marketing, advertising, and public relations
managers; Personnel, training, and labor relations specialists and managers;
Property and real estate managers; Purchasing agents and managers; and
Restaurant and food services managers. Except for construction managers and
property and real estate managers, the working conditions of these
management related occupations is generally described as indoor, office work -
and even construction managers and property and real estate managers are
described as spending a significant portion of each day indoors.
Administrative Services Manager (OOH pp.15-16): Administrative services
managers - who work throughout private industry and government - coordinate
and direct supportive services such as secretarial and correspondence;
conference planning and travel; information processing; personnel and financial
records processing; communication; mail; materials scheduling and distribution;
printing and reproduction; personal property procurement, supply, and disposal;
data processing; library; food; and transportation. They work within the same
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managerial hierarchy as other managers. Supervisory level managers directly
oversee supervisors or staffs involved in supportive services. Mid-level
administrative services managers develop overall plans, set goals and
deadlines, develop procedures to direct and improve supportive services, define
supervisory level mangers' responsibilities, and delegate authority. They are
generally found in larger firms. Administrative services managers often are
involved in the hiring and dismissal of employees but generally have no role in
the formulation of policy. In small firms, one administrative services manager
may oversee all supportive services. As the size of the firm increases,
however, administrative services managers increasingly specialize in one or
more of these activities. Administrative services managers also work as
contract administrators, directing contract development related to the purchase
or sale of equipment, materials, supplies, products, or services. In small firms,
the allocation, use, and security of building space also is an administrative
services management function, but is often the responsibility of facilities
managers in larger companies. Other administrative services managers are
engaged in surplus property disposal, and increasingly important source of
revenue, while others oversee unclaimed property disposal. In State
government, the activities include locating owners of unclaimed liquid assets -
such as stocks, bonds, savings accounts, and the contents of safe deposit
boxes - and in local government, locating owners or auctioning off unclaimed
personal property - such as motor vehicles.
Administrative services managers generally work in comfortable offices.
However, in small firms, these managers may work alongside the supervisors
and staff they oversee, and the office area may be crowded and noisy. Since
their duties involve a wide range of activities, they must maintain regular
contact with personnel in other departments. Managers involved in personal
property procurement, utilization, and disposal may travel extensively between
home offices, branch offices, vendors' offices, and property sales sites.
Occupations with similar functions include administrative assistants, appraisers,
buyers, clerical supervisors, contract specialists, cost estimators, procurement
services managers, project directors, property and real estate managers,
purchasing managers, and sales managers.
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See also Construction Managers listed under Supervisor, construction trades.
3. Engineer, scientist, and architect (Codes 43 through 63 and 69 through
83):
Engineers (OOH pp.62-64): Engineers apply the theories and principles of
science and mathematics to the economical solution of practical technical
problems. Often their work is the link between a scientific discovery and its
application. Engineers design machinery, products, systems, and processes for
efficient and economical performance. They develop electric power, water
supply, and waste disposal systems. They design industrial machinery and
equipment for manufacturing goods, and heating, air-conditioning, and
ventilation equipment for more comfortable living. Engineers also develop
equipment to probe outer space and the ocean depths; design defense and
weapons systems for the Armed Forces; and design, plan, and supervise the
construction of buildings, highways, and rapid transit systems. They also
design and develop consumer products such as automobiles, home appliances,
electronic home entertainment equipment, and systems for control and
automation of manufacturing, business, and management processes. In
addition to design and development, many engineers work in testing,
production, operations, or maintenance. They supervise production in factories,
determine the causes of breakdowns, and test manufactured products to
maintain quality. They also estimate the time and cost to complete projects.
Some work in engineering administration and management or in sales, where
an engineering background enables them to discuss the technical aspects of a
product and assist in planning its installation or use. Most engineers specialize;
more than 25 major specialties are recognized by professional societies.
Many engineers work in an office almost all of the time but others work in
laboratories, industrial plants, or construction sites, where they inspect,
supervise, or solve on-site problems. Engineers in branches such as civil
engineering may work outdoors part of the time. A few engineers travel
extensively to plants or construction sites.
Related occupations include physical scientists, life scientists, mathematicians,
engineering and science technicians, and architects.
Biological Scientists (OOH pp.86-88): Biological scientists study living
organisms and their relationship to their environment. About two-fifths of all
biological scientists work in research and development. Others, in applied
research, use knowledge provided by basic research to develop new medicines,
increase crop yields, and improve the environment. Biological scientists may
work in laboratories and use laboratory animals or greenhouse plants, electron
microscopes, computers, electronic instruments, or a wide variety of other
equipment to conduct their research. A good deal of research, however, is
performed outside of laboratories. For example, a botanist may do research in
the volcanic valleys of Alaska to see what plants grow there, or an ecologist
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may study how a forest area recovers after a fire. Different biological
disciplines include aquatic biologists, biochemists, botanists, microbiologists,
physiologists, zoologists, and ecologists.
Biological scientists generally work regular hours in offices, laboratories, or
classrooms and usually are not exposed to unsafe or unhealthy conditions.
Related occupations include forester, forestry technician, range manager, soil
conservationist, agricultural scientist, soil scientist, life science technician,
farmer, farm manager, animal breeder, landscape contractor, florist, nursery
manager, and greenskeeper.
Agricultural Scientists (OOH pp.85-86): Agricultural scientists study farm crops
and animals and develop ways of improving their quantity and quality. They
look for ways to improve crop yield and quality with less labor, control pests
and weeds more safely and effectively, and conserve soil and water.
Agricultural scientists generally work regular hours in offices and laboratories.
Some spend much time outdoors conducting research on farms or agricultural
research stations.
Foresters and Conservation Scientists (OOH pp.88-89): Foresters and
conservation scientists manage, develop, and help protect forests and
rangelands and other natural resources. Working conditions for foresters and
conservation scientists vary considerably. Their image as solitary horseback
riders singlehandedly protecting large areas of land far from civilization no
longer holds true. Modern foresters and conservation scientists spend a great
deal of time working with people. They deal regularly with land owners,
loggers, forestry technicians and aides, farmers, ranchers, government officials,
special interest groups, and the public in general. The work can still be
physically demanding, though. Many foresters and conservation scientists often
work outdoors in all kinds of weather, sometimes in remote areas. To get to
these areas, they use airplanes, helicopters, four-wheel drive vehicles, and
horses, or walk. Foresters and conservation scientists also may work long
hours fighting fires or in other emergencies.
Chemists (OOH pp.90-91): Chemists search for and put to practical use new
knowledge about chemicals. Many chemists work in research and
development. Much research is performed in laboratories, but research
chemists also work in offices when they do theoretical research or plan, record,
and report on their research. Chemists may also do some of their research in a
chemical plant or outdoors - while gathering samples of pollutants, for example.
Chemists usually work regular hours in offices and laboratories.
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Geologists and Geophysicists (OOH pp.91-92): Geologists and geophysicists
study the physical aspects and history of the earth. Most geologists and
geophysicists divide their time between fieldwork and office or laboratory work.
Geologists often travel to remote field sites by helicopter or jeep.and cover
large areas by foot. Exploration geologists and geophysicists often work
overseas or in remote areas, and geological and physical oceanographers may
spend considerable time at sea.
Meteorologists (OOH pp.93-94): Meteorologists study the atmosphere's
physical characteristics, motions, and processes, and the way the atmosphere
affects the rest of our environment. Jobs in weather stations, most of which
operate around the clock 7 days a week, often involve night work and rotating
shifts. Weather stations are at airports, in or near cities, and in isolated and
remote areas. Meteorologists in smaller weather offices generally work alone;
in larger ones, they work as a team. Meteorologists not doing forecasting work
regular hours, usually in offices.
Physicists and Astronomers (OOH pp.94-94): Physicists attempt to discover
basic principles governing the structure and behavior of matter, the generation
and transfer of energy, and the interaction of matter and energy. Astronomers
use the principles of physics and mathematics to learn about the fundamental
nature of the universe and the sun, moon, planets, stars, and galaxies.
Physicists usually work regular hours in laboratories and offices. Most do not
encounter unusual hazards in their work. Some physicists work away from
home temporarily at national or international facilities with unique equipment
such as particle accelerators. Astronomers who make observations may travel
to observatories, which are usually in remote locations, and frequently work at
night.
\
Architects (OOH pp.71-72): Architects design a wide variety of buildings, such
as office and apartment buildings, schools, churches, factories, hospitals,
houses and airport terminals. They also design multibuilding complexes such
as urban centers, college campuses, industrial parks, and entire communities.
In addition to designing buildings, architects may advise on the selection of
building sites, prepare cost and land-use studies, and do long-range planning
for land development.
Architects generally work in a comfortable environment. Most of their time is
spent in offices advising clients, developing reports and drawings, and working
with other architects and engineers. However, they also often work at
construction sites reviewing the progress of projects.
4. Writer, artist (Codes 183, 184, 188, 194 and 195):
Writers and Editors (OOH pp.173-174): Writers develop original fiction and
nonfiction for books, magazines, trade journals, newspapers, technical studies
and reports, company newsletters, radio and television broadcasts, and
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advertisements. Editors supervise writers and select and prepare material for
publication or broadcasting. Writers start by selecting a topic or being assigned
one by an editor. They then gather information through personal observation,
library research, and interviews. Established writers may work on a freelance
basis, where they sell their work to publishers or publication units,
manufacturing firms, and public relations and advertising departments or
agencies.
Working conditions for writers and editors vary with the kind of publication they
work on and the kind of articles they produce. Some work in comfortable,
private offices; others work in noisy rooms filled with the sound of keyboards
and computer printers as well as the voices of other writers tracking down
information over the telephone. The search for information sometimes requires
travel and visits to diverse workplaces, such as factories, offices, laboratories,
the ball-park, or the theater, but many have to be content with telephone
interviews and the library.
Visual Artists (OOH pp.180-181): Visual artists use an almost limitless variety
of methods and materials to communicate ideas, thoughts, and feelings. They
may use oils, watercolors, acrylics, pastels, magic markers, pencils, pen and
ink, silkscreen, plaster, clay, or any of a number of other media, including
computers, to create abstract works or images of objects, people, nature,
topography, or events. Visual artists generally fall into one of two categories -
"graphic artists" and "fine artists" - depending not so much on the medium, but
on the artist's purpose in creating a work of art. Graphic artists put their artistic
skills and vision at the service of commercial clients, such as major
corporations, retail stores, advertising firms, and production companies. Fine
artists, on the other hand, often create art to satisfy their own inner need for
self-expression, and may display their work in museums, art galleries, and
homes. Fine artists usually work independently, choosing the subject matter
and medium they deem fit.
Graphic and fine artists generally work in art studios located in office buildings
or their own homes. While their surroundings are usually well lighted and
ventilated, odors from glues, paint, ink, or other materials may be present.
5. Fireman, Policeman (Codes 416 through 424):
Firefighting Occupations (OOH pp.286-288): [Note - This description applies to
career firefighters, not volunteers]. During duty hours, firefighters must be
prepared to respond to a fire and handle any emergency that arises. Because
firefighting is dangerous and complicated, it requires organization and
teamwork. At every fire,~fifefighters perform specific duties assigned by an
officer such as a lieutenant, captain, or chief. They may connect hose lines to
hydrants, operate a pump, or position ladders. Their duties may change
several times while the company is in action. They may rescue victims and
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administer emergency medical aid, ventilate smoke-filled areas, operate
equipment, and salvage the contents of buildings.
The job of firefighter has become more complex in recent years due to the use
of increasingly complex equipment. In addition, many firefighters have
assumed additional responsibilities - for example, working with ambulance
services that provide emergency medical treatment, assisting in the recovery
from natural disasters such as earthquakes and tornadoes, and becoming
involved with the control and cleanup of oil spills and other hazardous chemical
incidents. Most fire departments are also responsible for fire prevention. They
may provide specially trained personnel to inspect public buildings for conditions
that might cause a fire. They may check building plans, the number and
working condition of fire escapes and fire doors, the storage of flammable
materials, and other possible hazards. In addition, firefighters educate the
public about fire prevention and safety measures. They frequently speak on
this subject before school assemblies and civic groups.
Between alarms, they have classroom training, clean and maintain equipment,
conduct practice drills and fire inspections, and participate in physical fitness
activities. Firefighters also prepare written reports on fire incidents and review
fire science literature to keep abreast of technological developments and
administrative practices and policies.
Firefighters spend much of their time at fire stations, which usually have
facilities for dining and sleeping. When an alarm comes in, firefighters must
respond rapidly, regardless of the weather or hour. They may spend long
periods at fires, hazardous chemical incidents, and other emergencies on their
feet and outdoors, sometimes in adverse weather. Duty hours may include
some time when firefighters are free to read and study.
Police, Detectives, and Special Agents (OOH pp.290-292): The safety of our
Nation's cities, towns, and highways greatly depends on the work of police,
detectives, and special agents, whose responsibilities range from controlling
traffic to preventing and investigating crimes. In most jurisdictions, whether on
or off duty, these officers are expected to exercise their authority whenever
necessary. Police and detectives who work in small communities and rural
areas have many duties. In the course of a day's work, they may direct traffic
at the scene of a fire, investigate a burglary, and give first aid to an accident
victim. In a large police department, by contrast, officers usually are assigned
to a specific type of duty. Most officers are detailed either to patrol or to traffic
duty; smaller numbers are assigned to special work such as accident
prevention. Others are experts in chemical and microscopic analysis, firearms
identification, and handwriting and fingerprint identification. In very large cities,
a few officers may work with special units such as mounted and motorcycle
police, harbor patrols, helicopter patrols, canine corps, mobile rescue teams,
and youth aid services.
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State police officers (sometimes called State troopers or highway patrol officers)
patrol highways and enforce laws and regulations that govern their use.
Police, detectives, and special agents also write reports and maintain police
records. They may testify in courts when their arrests result in legal action.
Some officers, such as division or bureau chiefs, are responsible for training or
certain kinds of criminal investigations, and those who command police
operations in an assigned area have administrative and supervisory duties.
Police, detectives, and special agents may have to work outdoors for long
periods in all kinds of weather. The injury rate among police, detectives, and
special agents is higher than in many occupations and reflects the risks taken
in pursuing speeding motorists, apprehending criminals, and dealing with public
disorders.
6. Guards and police, except public service (Codes 426 and 427):
Guards (OOH pp.288-289): Guards, also called security officers, patrol and
inspect property to protect against fire, theft, vandalism, and illegal entry. Their
duties vary with the size, type, and location of their employer. Guards work in
office buildings, banks, hospitals, department stores, ports, airports, railroads,
museums, art galleries, factories, laboratories, government buildings, data
processing centers, military bases, universities, parks, golf courses, social
affairs, sports events, conventions, nightclubs, and in armored cars.
Patrolling usually is done on foot, but if the property is large, guards may make
their rounds by car or motor scooter. As more businesses purchase advance
electronic security systems to protect their property, more guards are being
assigned to stations where they monitor these systems. Guards work indoors
and outdoors patrolling buildings, industrial plants, and grounds.
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7. Cook, Waiter (Codes 435 through 444):
Chefs, Cooks, and Other Kitchen Workers (OOH pp.294-296): Chefs and
cooks are responsible for preparing meals that are tasty and attractively
presented. Chefs are the most highly skilled, trained, and experienced kitchen
workers. Although the terms chef and cook are still sometimes used
interchangeably, cooks generally have more limited skills.
Institutional chefs and cooks work in the kitchens of schools, industrial
cafeterias, hospitals, and other institutions. Restaurant chefs and cooks
generally prepare a wider selection of dishes for each meal, cooking most
individual servings to order. Bread and pastry bakers produce baked goods for
restaurants, institutions, and retail bakery shops. Short-order cooks prepare
foods to order in restaurants and coffee shops that emphasize fast service.
Specialty fast-food cooks prepare a limited selection of menu items in fast-food
restaurants.
Many restaurant and institutional kitchens have modern equipment, convenient
work areas, and air-conditioning; but others, particularly in older and smaller
eating places, are frequently not as well equipped. Other variations in working
conditions depend on the type and quantity of food being prepared and the
local laws governing food service operations. Workers generally must withstand
the pressure and strain of working in close quarters during busy periods, stand
for hours at a time, lift heavy pots and kettles, and work near hot ovens and
grills.
Food and Beverage Service Workers (OOH pp.296-298): Waiters and
waitresses all take customers' orders, serve food and beverages, prepare
itemized checks, and sometimes accept payments - but the manner in which
they perform these tasks varies considerably, depending on where they work.
Bartenders fill the drink orders that waiters and waitresses take from customers
seated in the restaurant or lounge, as well as orders from customers seated at
the bar. Hosts and hostesses try to evoke a good impression of the restaurant
by warmly welcoming guests. Dining room attendants and bartender helpers
assist waiters, waitresses, and bartenders by keeping the serving area stocked
with supplies, cleaning tables, and removing dirty dishes to the kitchen.
Counter attendants take orders and serve food at counters. Fast-food workers
take orders from customers standing at counters at fast-food restaurants.
Food and beverage service workers are on their feet most of the time and often
have to carry heavy trays of food, dishes, and glassware. During busy dining
periods, they are under pressure to serve customers quickly and efficiently.
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8. Farmer, manager of farm (Codes 473 through 476):
Farm Operators and Managers (OOH pp.318-320): Farm operators may be
farmer owners or tenant farmers (renters). Their specific tasks are determined
by the type of farm they operate. On crop farms - farms growing grain, fiber,
fruit, and vegetables - farm operators are responsible for planning, tilling,
planting, fertilizing, cultivating, spraying, and harvesting. After the harvest, they
make sure that the crops are packaged, loaded, and promptly marketed or
stored for resale. On livestock, dairy, and poultry farms, farm operators must
plan, feed, and care for the animals and keep barns, pens, coops, and other
farm buildings clean and in repair. They also oversee breeding, some
slaughtering, and marketing activities. On horticultural specialty farms, farm
operators oversee the production of ornamental plants, nursery products - such
as flowers, bulbs, shrubbery, and sod - and fruits and vegetables grown in
greenhouses. Farm operators perform tasks ranging from setting up and
operating machinery to erecting fences and sheds. The size of the farm often
determines which of these tasks operators will handle themselves. Operators
of large farms have employees who do much of the physical work that small-
farm operators do themselves. Although employment on most farms is limited
to the farm operator and one or two family workers or hired employees, some
large farms have 100 or more full-time and seasonal workers. Some of these
workers are in nonfarm occupations, such as truckdriver, sales representative,
bookkeeper, and computer specialist.
Farming is attractive to persons who prefer a controlled pace and the more
wholesome rural life to urban living. Many types of farming are seasonal in
nature. Although many farm operators and managers on crop farms work from
sunup to sundown during the planting and harvesting seasons, they often work
on the farm only 6 to 7 months a year, and many have second jobs off the
farm. On farms that raise animals for meat or dairy products, work goes on
constantly throughout the year. Because animals must be fed and watered
every day and cows milked twice daily, operators of these farms rarely get the
chance to be away. On very large farms, farm operators spend substantial time
meeting with farm managers or farm supervisors in charge of various activities.
Professional farm managers overseeing several farms may divide their time
between traveling to meet with farm operators and planning and scheduling
farm operations while in their offices.
9. Farm worker (Codes 477 through 484):
Farm workers (OOH p.454): Farm workers perform a variety of the following
duties: plant, cultivate, harvest, and store crops; tend livestock and poultry;
operate and maintain farm machinery; and maintain structures. Farm workers
may also haul livestock and produce to market or terminal shipping point.
See also Farmer, manager of farm.
B-12
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10. Groundskeeper and Gardener (Code 486):
Gardeners and Groundskeepers (OOH pp.311-312): All gardeners plant and
care for trees, plants, and lawns, but their duties vary by specialty, with some
jobs encompassing a much wider array of responsibilities than others. A large
commercial project might entail landscaping the interior and exterior of a
shopping mall. For a residential customer, gardeners might terrace a hillside,
build retaining walls, and install a patio, as well as plant trees and shrubs.
They may also contract to care for the landscape after it is completed.
Gardeners working exclusively for homeowners, estates, and public gardens
are responsible for the overall care of the property, ranging from feeding,
watering, and pruning the flowering plants and trees to mowing and watering
the lawn.
Groundskeepers have even more varied duties than do gardeners, frequently
combining the work of a gardener with that of a maintenance mechanic. They
may work on athletic fields, golf courses, cemeteries, or parks.
Many of the jobs for gardeners and groundskeepers are seasonal, mainly in the
spring and summer, when cleanup, planting and mowing and trimming take
place. Gardeners and groundskeepers work outdoors in all kinds of weather.
11. Supervisor, mechanic, and repairer (Code 503): ,
Blue-Collar Worker Supervisors (OOH pp.387-388): For the millions of workers
who assemble manufactured goods, service motor vehicles, lay bricks, load
trucks, or perform thousands of other activities, a blue-collar worker supervisor
is the boss. These supervisors ensure that workers, equipment, and materials
are used properly and efficiently. They make sure machinery is set up correctly
and schedule or perform repairs and maintenance work. Supervisors tell other
workers what to do and make sure it is done safely, correctly, and on time.
Many blue-collar worker supervisors work in a normal shop environment. They
may be on their feet much of the time overseeing the work of subordinates and
may be subjected to the noise and grime of machinery. Other supervisors,
such as those is construction and oil exploration and production, may work
outdoors and are subject to all kinds of weather conditions. Supervisors may
be on the job before other workers arrive and stay after they leave.
See also Mechanic and repairer of machinery.
B-13
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12. Mechanic and repairer of machinery (Codes 505 through 549):
General Maintenance Mechanics (OOH pp.344-345): Most craft workers
specialize in one kind of work; general maintenance mechanics are jacks-of-all-
trades. They repair and maintain machines, mechanical equipment, and
buildings, and work on plumbing, electrical, and air-conditioning and heating
systems. They build partitions, make plaster and dry wall repairs, and fix or
paint roofs, windows, doors, floors, woodwork, and other parts of building
structures. They also install, maintain, and repair specialized equipment and
machinery found in cafeterias, laundries, hospitals, stores, offices, and factories.
Typical duties include replacing faulty electrical switches, repairing air-
conditioning motors, and unclogging drains. General maintenance mechanics
often do a variety of tasks in a single day, generally at a number of different
locations in a building, or in several buildings.
Automotive Mechanics (OOH pp.329-331): Automotive mechanics, often called
automotive service technicians, repair and service automobiles and occasionally
light trucks, such as vans and pickups, with gasoline engines. Generally,
mechanics work indoors. Most repair shops are well ventilated and lighted, but
some are drafty and noisy.
13. Supervisor, electricians/power install. (Code 555):
See Electrician; and Blue Collar Worker Supervisor listed under Supervisor,
mechanic, and repairer.
14. Supervisor, construction trades (Code 558):
Construction Managers (OOH pp.21-22): (Information on construction
"supervisors" was not available, but note the mentioning of construction
supervisor, in relation to construction manager in the following). Construction
managers may assume various levels of responsibility and are known by a wide
range of job titles that are often used interchangeably - for example,
construction superintendent, constructor, production manager, project manager,
general construction manager, executive construction manager, contractor,
subcontractor, and general contractor. Construction managers determine the
appropriate construction methods and schedule all required construction
activities in logical, discrete steps, each leading to an intermediate objective.
They estimate the time required to complete each step in an effort to meet
established budgets and deadlines for particular construction projects.
Construction managers determine the labor requirements and, if necessary,
supervise or monitor the hiring and dismissal of engineers, cost estimators,
clerks, construction supervisors, craft workers, machinery and equipment
operators, and other construction workers. Computers are used to evaluate
various construction methods and determine the most cost-efficient and
timesaving plan.
B-14
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On the job, construction managers direct construction supervisors and monitor
the progress of construction activities including the delivery and use of supplies,
tools, machinery, equipment, and vehicles. They are responsible for all
necessary permits and licenses and, depending upon the contractual
arrangements, direct or monitor compliance with safety codes and other labor
or union regulations.
Construction managers regularly review engineering and architectural drawings
and specifications and confer with construction engineers to maintain the rate of
construction activity. They meet with cost estimators to monitor construction
costs and avoid overruns. Based upon direct observation and reports by
subordinate supervisors, construction managers may prepare daily reports of
progress and requirements for labor, material, and machinery and equipment at
the construction site. Construction managers meet regularly with owners, other
construction managers, and design professionals to monitor and synchronize all
phases of the construction project.
Construction managers work out of a central office - often spacious and orderly,
where the overall construction is monitored - and the construction site office -
usually small and crowded with workers streaming in and out, where
management decisions regarding daily construction activities are made. In
addition, construction managers are always "on call" to deal with accidents,
delays, or complications caused by bad weather at the site. Although the work
generally is not considered dangerous, construction managers must be alert
while touring construction sites, especially when machinery, equipment, and
vehicles are operating.
Others whose work entails similar functions include architects, builders, civil
engineers, construction supervisors, cost engineers, cost estimators,
developers, electrical engineers, industrial engineers, landscape architects, and
mechanical engineers.
See also Construction Trades; and Blue Collar Worker Supervisors listed under
Supervisor, mechanic, and repairer.
15. Brickmason and Stonemason (Codes 563 and 564):
Bricklayers and Stonemasons (OOH pp.362-363): Bricklayers and
stonemasons work in closely related trades that produce attractive, durable
surfaces. Bricklayers build walls, floors, partitions, fireplaces, and other
structures with brick, cinder or concrete block, and other masonry materials.
They also install firebrick linings in industrial furnaces. Stonemasons build
stone walls as well as set stone exteriors and floors. Because stone is
expensive, stonemasons work mostly on high-cost buildings, such as churches,
hotels, and office buildings.
B-15
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Bricklayers and stonemasons usually work outdoors. They stand, kneel, and
bend for long periods and may have to lift heavy materials. They are also
subject to injuries from tools and falls from scaffolds.
16. Carpenter (Codes 567 and 569):
Carpenters (OOH pp.363-364): Almost all construction projects employ
carpenters, the largest group of building trade workers. The duties of
carpenters vary by type of employer. A carpenter employed by a special trade
contractor, for example, may specialize in setting forms for concrete
construction, while one who is employed by a general building contractor may
perform many tasks, such as framing walls and partitions, putting in doors and
windows, and installing paneling and tile ceilings.
As in other building trades, carpentry work is active and sometimes strenuous.
Prolonged standing, climbing, bending, and kneeling often are necessary.
Many carpenters work outdoors.
17. Electrician (Codes 575 and 576):
Electrician (OOH pp.369-371): Electricians install and maintain electrical
systems for a variety of purposes, including climate control, security, and
communications. They may also install and maintain the electronic controls for
machines in business and industry. Although most electricians specialize in
either construction or maintenance, a growing number do both. Electricians
work with blueprints when they install electrical systems in factories, office
buildings, homes, and other structures. In factories and offices, they first place
conduit (pipe or tubing) inside designated partitions, walls, or other concealed
areas. They then pull insulated wires or cables through the conduit to complete
circuits between these boxes. In lighter construction, such as residential,
plastic-covered wire usually is used rather than conduit.
Electricians' work is sometimes strenuous. They may stand for long periods
and frequently work on ladders and scaffolds. They often work in awkward or
cramped positions.
18. Electrical power installer/repairer (Code 577):
See Electrician.
19. Plumber, pipefitter apprentice (Codes 585 and 587):
Plumbers and Pipefitters (OOH pp.377-378): Plumbers and pipefitters install,
maintain, and repair many different types of pipe systems. For example, some
systems move water to a municipal water treatment plant, and then to
residential, commercial, and public buildings. Others dispose of waste. Some
bring in gas for stoves and furnaces. Others supply air-conditioning. Pipe
B-16
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systems in power plants carry the steam that powers huge turbines. Pipes also
are used in manufacturing plants to move material through the production
process. Plumbers also install plumbing fixtures - bathtubs, sinks, and toilets -
and appliances such as dishwashers and water heaters.
Physical stamina is required for plumbing and pipefitting work because these
workers frequently must lift heavy pipes, stand for long periods, and sometimes
work in uncomfortable or cramped positions. They also may have to work
outdoors in inclement weather. These maintenance workers may spend quite a
bit of time traveling to and from work sites.
20. Glaziers (Code 589):
Glaziers (OOH pp.371-372): Glaziers select, cut, install, and remove all types
of glass as well as plastics and similar material used as glass substitutes. They
also install mirrors, shower doors and bathtub enclosures, and glass for table
tops and display cases. They may mount steel and aluminum sashes or
frames and attach locks and hinges to glass doors.
For most jobs, the glass is precut and mounted in frames at a factory or a
contractor's shop. It arrives at the job site ready to be positioned and secured
in place by glaziers. These workers may use a crane or hoist with suction cups
to lift large, heavy pieces of glass. They then gently guide the glass into
position by hand. Once glaziers have the glass in place, they secure it with
mastic or a pastelike cement, bolts, rubber gaskets, glazing compound, metal
clips, or metal or wood molding. For some jobs, the glazier must cut the glass
manually at the work site.
Glaziers often work outdoors - sometimes in inclement weather. Sometimes
they work on scaffolds at great heights.
21. Roofers (Code 595):
Roofers (OOH pp.378-379): Roofers repair and install roofs of tar or asphalt
and gravel, rubber, thermoplastic, and metal; and shingles made of slate,
asphalt, fiberglass, wood, or tile. Repair and reroofing provide many work
opportunities for these workers. Roofers may also waterproof foundation walls
and floors.
Roofers' work is strenuous. It involves heavy lifting, as well as climbing,
bending, and kneeling. Of all construction industries, the roofing industry has
one of the highest accident rates. Roofers work outdoors in all types of
weather, particularly when making repairs. Roofs are extremely hot during the
summer.
22. Structural metal worker (Code 597):
B-17
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Structural and Reinforcing Ironworkers (OOH pp.383-384): Structural and
reinforcing ironworkers fabricate, assemble, and install such products as steel
columns, beams, and girders in the frames of large buildings, power
transmission towers, bridges, and highways; the steel bars or wire fabric in
reinforced concrete; and metal stairways, catwalks, floor gratings, ladders, and
window frames, as well as lampposts, railings, fences, and decorative ironwork.
These workers also repair, renovate, and maintain older buildings and
structures such as steel mills, utility plants, automobile factories, highways, and
bridges.
Before construction can begin, ironworkers must erect the steel frames and
assemble the cranes and derricks that move structural steel, reinforcing bars,
buckets of concrete, lumber, and other materials and equipment around the
construction site. Once this job has been completed, ironworkers at the
construction site begin to connect steel columns, beams, and girders according
to blueprints and instruction from supervisors.
Structural and reinforcing ironworkers usually work outside in all kinds of
weather. However, those who work at great heights do not work when it is wet,
icy, or extremely windy.
23. Construction trades (Codes 588, 594 and 599):
Handlers, Equipment Cleaners, Helpers, and Laborers (OOH pp.445-446):
Construction laborers provide much of the routine physical labor at building
sites. They supply tools, materials, and equipment to carpenters, electricians,
masons, plumbers, and other construction workers. Some have job titles that
indicate the work they do. Tenders for bricklayers and plasterers mix and
supply materials, set up and move scaffolding, and provide other services.
Laborers dig trenches, set braces to support the sides of excavations, and
clean up rubble and debris. They may operate jackhammers, earth tampers,
cement mixers, buggies, front-end loaders, "walk-behind" ditchdiggers, small
mechanical hoists, and laser beam equipment to align and grade ditches and
tunnels.
Most handlers, equipment cleaners, helpers, and laborers do repetitive,
physically demanding work. They may lift and carry heavy objects, and stoop,
kneel, crouch, and crawl in awkward positions. Some work at great heights, or
outdoors in all weather conditions.
24. Water and Sewer Treatment Plant Operator (Code 694):
Water and Wastewater Treatment Plant Operators (OOH pp.409-410): Water
treatment plant operators treat water so that it is safe to drink. Wastewater
treatment plant operators remove harmful domestic and industrial pollution from
wastewater. Operators in both types of plants control processes and equipment
to remove solid materials, chemicals, and micro-organisms from the water or to
B-18
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render them harmless. By operating and maintaining the pumps, pipes, valves,
and processing equipment of the treatment facility, operators move the water or
wastewater through the various treatment processes. Operators read and
interpret meters and gauges to make sure plant equipment and processes are
working properly and adjust controls as needed. These workers are
increasingly relying on computers to help them monitor equipment and
processes. Occasionally operators must work under emergency conditions.
The specific duties of plant operators depend on the type and size of plant In
smaller plants, one operator may control all machinery, perform tests, keep
records, handle complaints, and do repairs and maintenance. Some operators
may handle both a water treatment and a wastewater treatment plant. In larger
plants with many employees, operators may be assigned to one process or one
station, and the staff may include chemists, engineers, laboratory technicians,
mechanics, helpers, supervisors, and a superintendent.
Water and wastewater treatment plant operators work both indoors and
outdoors and may be exposed to noise from machinery and unpleasant odors,
although chlorine and other chemicals are used to minimize these.
25. Misc. plastic, metal, stone mach. oper. (Code 715):
Metalworking and Plastic-Working Machine Operators (OOH pp.396-397):
Metalworking and plastic-working machine operators run the machines that
produce, for example, metal ball bearings, the plastic knobs on a radio, the
steel hood of an automobile, and thousands of other parts that are used in
automobiles and nearly every other manufactured product.
Most metalworking and plastic-working machine shops are well lighted and well
ventilated. Older steel mills, on the other hand, tend to be hot, poorly lit, and
not well ventilated. However, modern mills are well designed and are equipped
with the most advanced engineering and computer-controlled operations. The
work requires stamina because the operators are on their feet much of the day
and may do moderately heavy lifting.
See also Machine Operator.
26. Machine Operator (Codes 753 through 779):
Machinists (OOH pp.394-395): Precision metal parts are essential for the
production of industrial machinery, aircraft, automobiles, and other durable and
nondurable goods. Machinists are skilled workers who produce metal goods
that are made in numbers too small to produce with automated machinery.
They set up and operate most types of machine tools, and they also must know
the working properties of metals such as steel, cast iron, aluminum, and brass.
Using their skill with machine tools and their knowledge of metals, machinists
B-19
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plan and carry out the operations needed to make machined products that meet
precise specifications.
Most machine shops are well lighted and well ventilated. Working around high-
speed machine tools, however, presents certain dangers, and workers must
follow safety procedures. The job requires stamina because machinists stand
most of the day and may lift moderately heavy workpieces.
See also Misc. plastic, metal, stone mach. open
27. Fabricator, assembler, and handiwork (Codes 783 through 795):
Precision Assemblers (OOH pp.386-387): Workers who put together the parts
of manufactured articles are called assemblers. In some instances, hundreds
of assemblers work on a single product; in others, a single assembler is
responsible for each product. Assembly work varies from simple, repetitive jobs
that are relatively easy to learn to those requiring great precision and many
months of experience and training.
The work of precision assemblers requires a high degree of accuracy. Workers
must be able to interpret detailed specifications and instructions and apply
independent judgement. Some experienced assemblers work with engineers
and technicians, assembling prototypes or test products. Precision assemblers
involved in product development must know how to read blueprints and
engineering specifications and how to use a variety of tools and precision
measuring instruments.
The conditions under which precision assemblers work depend on the
manufacturing plant where they are employed. Electronics and watch
assemblers .sit at tables in rooms that are clean, well lighted, and free from
dust. Assemblers of aircraft and industrial machinery, however, usually come in
contact with oil and grease, and their working areas may be quite noisy. They
may have to lift and fit heavy objects.
Machine Assemblers (OOH pp.457): Machine assemblers perform work at a
level less than that required of the precision level. It includes such occupations
as air-conditioning coil assemblers, ball bearing ring assemblers, fuel injection
assemblers, and subassemblers.
28. Crane and Derrick and Hoist and Winch operator (Codes 848 and 849):
Material Moving Equipment Operators (OOH pp.436-437): Crane and tower
operators operate mechanical boom and cable or tower and cable equipment to
lift and move materials, machinery, or other heavy objects. Although many
cranes are used on construction sites, others are used in manufacturing and
other industries. Hoist and winch operators operate or tend machines which lift
and pull loads using power-operated cable equipment.
B-20
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Many material moving equipment operators work outdoors, in hot and cold
weather, and sometimes in rain or snow.
29. Industrial truck and tractor operator (Code 856):
Material Moving Equipment Operators (OOH pp.436-437): Industrial truck and
tractor operators drive and control industrial trucks or tractors. A typical
industrial truck, often called a forkiift, has a hydraulic lifting mechanism and
forks. Industrial truck operators use them to carry loads on a skid or pallet
around a factory or warehouse. Industrial tractor operators pull trailers loaded
with materials, goods, or equipment between factories, warehouses, and
outdoor storage areas.
Many material moving equipment operators work outdoors, in hot and cold
weather, and sometimes in rain or snow. Industrial truck and tractor operators
work mainly indoors, in warehouses or manufacturing plants.
30. Misc. material moving equip, operator (Codes 843, 853, 855 and 859):
Material Moving Equipment Operators (OOH pp.436-437): Material moving
equipment operators use machinery to move construction materials and other
manufactured goods, earth, logs, petroleum products, grain, coal, and other
heavy materials. Generally they move materials over short distances - around
a factory, construction site, or on or off trucks and ships. They may also set up
and inspect equipment and make adjustments and minor repairs.
Excavation and loading machine operators operate and tend machinery
equipped with scoops, shovels, or buckets to excavate earth at construction
sites and to load and move loose materials, mainly in the mining and
construction industries. Grader, dozer, and scraper operators operate vehicles
equipped with blades to remove, distribute, level, and grade earth. Other
material moving equipment operators tend air compressors or pumps at
construction sites. Some operate oil or natural gas pumps and compressors at
oil and gas wells and on oil and gas pipelines, and others operate ship loading
and unloading equipment, conveyors, hoists, and other kinds of specialized
material handling equipment such as mine or railroad tank car unloading
equipment.
Many material moving equipment operators work outdoors, in hot and cold
weather, and sometimes in rain or snow.
31. Construction worker (Codes 866, 867 and 869):
See Construction trades.
32. Production helper (Code 874):
B-21
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Handlers, Equipment Cleaners, Helpers, and Laborers (OOH pp.445-446):
Helpers and laborers must be familiar with the duties of workers they help, as
well as with the materials, tools, and machinery they use, in order to perform
their jobs effectively. In factories, they may move raw materials, components,
and finished goods to and from machines, storage areas, and loading docks.
They aid machine operators and tenders by moving materials, supplies and
tools to and from work areas. They help set up and adjust machines, and may
tend the machines during operation if an operator is out. Helpers may sort
finished products, keep records of machine processes, report malfunctions to
operators, and clean machinery after use.
In factories, handlers, equipment cleaners, helpers, and laborers may work
evening or "night-owl" shifts.
33. Garbage collector (Code 875):
Handlers, Equipment Cleaners, Helpers, and Laborers (OOH pp.445-446):
Refuse collectors collect trash and garbage and drive garbage trucks. They
must work outdoors in all weather conditions.
34. Stevedore (Code 876):
Handlers, Equipment Cleaners, Helpers, and Laborers (OOH pp.445-446):
Freight, stock, and material movers include stock handlers and baggers,
machine feeders and offbearers, stevedores, and related occupations. They
move materials to and from storage areas, loading docks, delivery vehicles,
ships' holds, machines, and containers either manually or with forklifts, dollies,
handtrucks, or carts. Their specific duties vary by industry and work setting. In
factories, they move raw materials, components, and finished goods to and
from machines, storage areas, and loading docks. They receive and sort
materials and supplies and prepare them according to work orders for delivery
to work or storage areas. In grocery stores, they stock shelves, bag groceries,
carry packages to customers' cars, and return shopping carts to designated
areas. In film production companies, handlers may unload cameras and other
film equipment from vans and trucks and move the equipment into position for
filming.
Most handlers, equipment cleaners, helpers, and laborers do repetitive,
physically demanding work. They may lift and carry heavy objects, and stoop,
kneel, crouch, and crawl in awkward positions. Some work at great heights, or
outdoors in all weather conditions. Some jobs expose workers to harmful
chemicals, fumes, odors, or dangerous machinery, so these employees may
need to wear safety clothing and observe safety procedures.
35. Freight, stock, and material movers (Codes 877, 878 and 883):
See Stevedore.
B-22
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36. Garage and service station related occ. (Code 885):
Handlers, Equipment Cleaners, Helpers, and Laborers (OOH pp.445-446):
Service station attendants fill fuel tanks, wash windshields, change oil, repair
tires, and replace belts, lights, windshield wipers, and other accessories on
automobiles. They work outdoors in all weather conditions, excepting the time
spent indoors between customers.
See also Automotive Mechanic, listed above under Mechanic and repairer of
machinery.
37. Laborers, except construction (Code 889):
See Stevedore and Production Helper.
B-23
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APPENDIX C
POTENTIAL OUTDOOR OCCUPATIONS NOT
INCLUDED IN OUTDOOR WORKER EXPOSURE ANALYSIS
C-1
-------
Potential Outdoor Occupations Not Included in
Outdoor Worker Exposure Analysis
Executive. Administrative, and Managerial Occupations:
Funeral Directors (Code 019)
Veterinarians (Code 086)
Professional Specialty Occupations:
Physical Education Teachers (Code 138)
Teachers, prekindergarten and kindergarten (Code 155)
Recreation workers (Code 175)
Athletes (Code 199)
Technicians and Related Support Occupations:
Surveying and mapping technicians (Code 218)
Sales Occupations:
Real estate sales occupations (Code 254)
News vendors (Code 278)
Administrative Support Occupations, Including Clerical:
Mail carriers, postal service (Code 355)
Messengers (Code 357)
Meter readers (Code 366)
Private Household Occupations:
Child care workers, private household (Code 406)
Protective Service Occupations:
Crossing guards (Code 425)
Service Occupations. Except Protective and Household:
Pest control occupations (Code 455)
Attendants, amusement and recreation facilities (Code 459)
Guides (Code 461)
Public transportation attendants (Code 463)
Baggage porters and bellhops (Code 464)
Child care workers, not elsewhere classified (Code 468)
Farming. Forestry, and Fishing Occupations:
Animal caretakers, except farm (Code 487)
Graders and sorters, agricultural products (Code 488)
Supervisors, forestry, and logging workers (Code 494)
C-2
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Forestry workers, except logging (Code 495)
Timber cutting and logging occupations (Code 496)
Captains and other officers, fishing vessels (Code 497)
Fishers (Code 498)
Hunters and trappers (Code 499)
Precision Production. Craft, and Repair Occupations:
Painters, construction and maintenance (Code 579)
Drillers, earth (Code 598)
Supervisors, extractive occupations (Code 613)
Drillers, oil well (Code 614)
Explosives workers (Code 615)
Mining machine operators (Code 616)
Mining occupations, not elsewhere classified (Code 617)
Transportation and Material Moving Occupations:
Ship captains and mates, except fishing boats (Code 828)
Sailors and deckhands (Code 829)
Marine Engineers (Code 833)
Operating Engineers (Code 844)
Longshore equipment operators (Code 845)
Handlers. Equipment Cleaners. Helpers, and Laborers:
Helpers, extractive occupations (Code 868)
Vehicle washers and equipment cleaners (Code 887)
C-3
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APPENDIX D
SAMPLE OUTPUT OF pNEM/03 APPLIED TO
OUTDOOR WORKERS (HOUSTON, 1-HOUR, DAILY
MAXIMUM 0.12 PPM STANDARD [CURRENT NAAQS])
D-1
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Table 1.
Cumulative Numbers of People at Hourly 03 Exposures
during 03 Season by Equivalent Ventilation Rate
03 Level
Equalled or
Exceeded , ppo
.401+
.381
.361
.341
.321
.301
.281
.261
.241
.221
.201
.181
.161
.141
.121
.101
.081
.061
.041
.021
.001
0.000
Equivalent Ventilation Rate, l/min-m**2
<15 15-24 25-29 30-34 35+
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16092
60874
71735
71735
71735
71735
71735
71735
0
0
0
0
0
0
0
0
0
0
0
0
0
0
204
5306
33411
67684
69932
71735
71735
71735
0
0
0
. 0
0
0
0
0
0
0
0
0
0
0
0
67
2804
15492
53206
69294
71705
71705
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
3298
12454
39480
64717
71732
71732
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5165
26099
64177
68319
70776
ANT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16296
60874
71735
71735
71735
71735
71735
71735 71735
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season =
No. days in 03 season -
Outdoor Workers
11
1
365
365
D-2
-------
Table 2.
Occurrences of People at Hourly Exposures
During 03 Season by Equivalent Ventilation Rate
03 Interval
ppm
.401+
.381 -.400
.361-. 380
.341 -.360
.321 -.340
.301-. 320
.281 -.300
.261 -.280
.241-. 260
.221 -.240
.201 -.220
.181 -.200
.1S1-.180
.141-. 160
.121 -.140
.101-. 120
.081-. 100
.061-. 080
.041 -.060
.021 -.040
.001-. 020
0.000
Equivalent Ventilation Rate, l/min-m**2
<15 15-24 25-29 30-34
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
19088.
167243.
1399616.
6872617.
37138333.
114915145.
417622899.
42203372.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
204.
5140.
37017.
162964.
829540.
1979116.
2695620.
386103.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
67.
3275.
15053.
87949 .
301680.
302889 .
48707.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
10.
3288.
9671.
63329.
141850.
161119.
35713.
35+
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
5969.
26437.
120900.
349768.
237983 .
48926.
ANT
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
19292.
172460.
1449165.
7086742.
38240051.
117687559.
421020510.
42722821.
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season -
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
628398600.
D-3
-------
Table 1A.
Cumulative Numbers of People at Ihr Dally Max. Exposure
During 03 Season by Equivalent Ventilation Rate
03 Level
Equalled or Equivalent Ventilation Rate, l/min-m**2
Exceeded, ppn <15 15-24 25-29 30-34 35+
.401+
.381
.361
.341
.321
.301
.281
.261
.241
.221
.201
.181
.161
.141
.121
.101
.081
.061
.041
.021
.001
0.000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16092
60750
71735
71735
71735
71735
71735
71735
0
0
0
0
0
0
0
0
0
0
0
0
0
0
204
5250
2S990
56661
69263
69655
714S8
71458
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
67
1461
7820
24823
35983
39029
39029
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2023
4836
19844
24377
2S386
25386
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3621
8881
33959
45177
45487
45487
ANY
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16296
60874
71735
71735
71735
71735
71735
71735
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-4
-------
Table 2A.
Occurrences of People at Ihr Dally Max. Exposure
During 03 Season by Equivalent Ventilation Rate
03 Interval,
ppm
.401 +
.381 -.400
.361 -.380
.341 -.360
.321 -.340
.301 -.320
.281 -.300
.261-. 280
.241 -.260
.221 -.240
.201 -.220
.181 -.200
.161-. 180
.141-. 160
.121-. 140
.101-. 120
.081-. 100
.061 -.080
.041-. 060
.021 -.040
.001 -.020
0.000
Equivalent Ventilation Rate
<15 15-24 25-29
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
18511.
108574.
808028.
2715046.
10156527.
9814010.
1895436.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
204.
5062.
21735.
64626.
231879.
176492.
9472.
0.
• 0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
67.
1394.
6796.
24897.
18474.
3046.
0.
, l/mln-n**2
30-34
. . 0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
2023.
2824.
18717.
4784.
1168.
0.
35+
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
3709.
6971.
39862.
21765.
1176.
0.
ANT
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
18715.
113703.
836889.
2796263.
10471882.
10035525.
1910298.
0.
26183275.
Study Area = HOUSTON 1 1H NAAQS
Ho. exposure districts =
First day of 03 season =
Last day of 03 season -
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-5
-------
Table IB.
Cumulative Numbers of People at 1-Hr Daily Max. Dose
During 03 Season by 1-Hr 03 and EVR.
03 Level
Equalled or Equivalent Ventilation Rate, l/min-m**2
Exceeded, ppm <15 15-24 25-29 30-34 35+
.401 +
.381
.361
.341
.321
.301
.281
.251
.241
.221
.201
.181
.161
.141
.121
.101
.081
.061
.041
.021
.001
0.000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10652
49014
71432
71735
7173S
71735
71735
71735
0
0
0
0
0
0
0
0
0
0
0
0
0
0
204
5085
31237
67237
69932
71735
71735
71735
0
0
0
. 0
0
0
0
0
0
0
0
0
0
0
0
67
2547
13338
46753
69289
69593
69593
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2572
10922
36247
62600
66158
66158
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5165
26099
63637
68319
68973
68973
ANT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10856
50710
71432
71735
71735
71735
71735
71735
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season -
No. days in 03 season -
Outdoor Workers
11
1
365
365
0-6
-------
Table 2B.
Occurrences of People at 1-Hr Daily Max. Dose
During 03 Season by 1-Hr 03 and EVR.
========================================-===========--— _______
03 Interval, Equivalent Ventilation Rate, l/min-m**2
PPo <1S 15-24 25-29 30-34
.401+
.381 -.400
.361-. 380
.341-.3SO
.321 -.340
.301 -.320
.281 -.300
.261 -.280
.241 -.260
.221 -.240
.201-. 220
.181 -.200
.161-. 180
.141-. 160
.121-. 140
.101-. 120
.081-. 100
.061 -.080
.041 -.060
.021 -.040
.001 -.020
0.000
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
10974.
72722.
471528.
1825465.
7628903.
10181324.
2497124.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
204.
4897.
33912.
131604.
625280.
1203966.
361675.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
67.
2480.
11715.
53451 .
207092.
102932.
0.
0,
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
2572.
8819.
46941.
96670.
61550.
0.
35+
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
5969.
26256.
112585.
277155.
117443.
0.
ANY
0.
0.
0.
0.
0.
0.
0.
0.
0.
• o.
0.
0.
0.
0.
11178.
77686 .
516461.
2003859.
8467160.
11966207.
3140724.
0.
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts -
First day of 03 season =
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
26183275.
D-7
-------
Table 3.
Number of People at Their Highest Ihr Daily Max. Exposure
During 03 Season by Ventilation Rate Categories
03 Level
Equalled or
Exceeded , ppm
.401+
.381 -.400
.361-. 380
.341 -.360
.321 -.340
.301-. 320
.281-. 300
.261-. 280
.241 -.260
.221 -.240
.201 -.220
.181 -.200
.161-. 180
.141 -.160
.121 -.140
.101-. 120
.081 -.100
.061 -.080
.041 -.060
.021 -.040
.001 -.020
0.000
Equivalent Ventilation Rate, l/min-m**2
<15 15-24 25-29 30-34 35+
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16092
44658
10985
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
204
5046
20740
30671
12602
392
1803
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
67
1394
6359
17003
11160
3046
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2023
2813
15008
4533
1009
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3621
5260
25078
11218
310
0
ANY,
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16296
44578
10861
0
0
0
0
0
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season =
No. days in 03 season -
Outdoor Workers
11
1
365
365
D-8
-------
Table 4.
Cumulative numbers of People at 8-Hr Daily Max. Exposure
During 03 Season by 8-Hr Equivalent Ventilation Rate
03 Level
Equalled
Exceeded,
.201+
.191
.181
.171
.161
.151
.141
.131
.121
.111
.101
.091
.081
.071
.061
.041
.021
.001
0.000
or 8hr
. ppm <15
0
0
0
0
0
0
0
0
0
0
0
604
6554
42363
69018
71735
71735
71735
71735
Equivalent
15-24
0
0
0
0
0
0
0
0
0
0
0
0
0
3276
5196
36033
65618
66181
66181
: Ventilatioi
25-29
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1215
7333
7634
7634
3 Rate, ':
30-34
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
217
217
217
========
L/min-m**2
35+
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ANY
0
0
0
0
0
0
0
0
0
0
0
604
6554
45388
69018
71735
71735
71735
71735
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts -
First day of 03 season =
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-9
-------
Table 5.
Occurrences of People at 8-Hr Daily Max. Exposure
During 03 Season by 8-Hr Equivalent Ventilation Kate
03 Interval,
ppm
.201+
.191-. 200
.181-. 190
.171-. 180
.161-. 170
.151-. 160
.141 -.150
.131-. 140
.121-. 130
.111-. 120
.101 -.110
.091-. 100
.081 -.090
.071-. 080
.061 -.070
.041 -.060
.021 -.040
.001 -.020
0.000
8hr
<15
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
604.
6139.
55557.
317613.
3959617.
15172886.
6428285 .
3606.
Equivalent Ventilation Rate, l/min-a**2
15-24 25-29 30-34 35+
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
3276.
2640.
43798.
157828.
22669.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
1215.
7024.
301.
0.
0.
0.
0.
0.
0.
0.
o. •
0.
0.
0.
0.
0.
0.
0.
0.
9.
208.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
AHI
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
604.
6139.
58833.
320253.
4004639.
15337946.
6451255.
3606.
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season =
No. days in 03 season -
Outdoor Workers
11
1
365
365
26183275.
D-10
-------
Table 4A.
Cumulative Numbers of People at 8-Hr Daily Max. Dose
During 03 Season by 8-Hr 03 and 8-Hr EVR.
03 Level
Equalled or
Exceeded, ppm
.201 +
.191
.181
.171
.161
.151
.141
.131
.121
.111
.101
.091
.081
.071
.061
.041
.021
.001 .
0.000
================
8hr Equivalent Ventilation Rate, l/min-m**2
<1S 15-24 25-29 30-34 35+ ANT
0
0
0
0
0
0
0
0
0
0
0
604
6554
40217
69007
71735
71735
71735
71735
=========
0
0
0
0
0
0
0
0
0
0
0
0
0
3276
5196
35879
65997
66634
66634
==========
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2753
10083
12733
12733
==========
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
236
236
236
================
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
604
6554
43242
69007
71735
' 71735
71735
71735
===========
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season -
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-11
-------
Table 5A.
Occurrences of People at 8-Hr Daily Max. Dose
During 03 Season by 8-Hr 03 and 8-Hr EVR
03 Interval,
ppm
.201+
.131-. 200
.181 -.190
.171-. 180
.161-. 170
.151-. 160
.141-. 150
.131-. 140
.121-. 130
.111-. 120
.101-. 110
.091-. 100
.081 -.090
.071 -.080
.061-. 070
.041 -.060
.021 -.040
.001 -.020
0.000
8hr
<15
0.
0.
0.
0.
0.
0.
0.
0.
0..
0.
0.
604.
6129.
50587.
300164.
3837899 .
15043126.
6676560.
3606.
Equivalent Ventilation Rate, l/min-m**2
15-24 25-29 30-34 35+
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
3276.
2640.
45436.
168712.
28648.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
2753.
9903.
2996.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
9.
227.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
ANY
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
604.
6129.
53863.
302804.
3886097 -
15221968.
6708204.
3606.
33B:XSSS3ES:3: 3B 3E
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts -
First day of 03 season =
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
26183275.
D-12
-------
Table 6.
Number of People at Their Highest 8-Hr Daily Max. Exposure
During 03 Season by 8-Hr Ventilation Rate Categories
03 Level
Equalled or
Exceeded, ppo
.201+
.191-. 200
.181-. 190
.171 -.180
.161-. 170
.151-. ISO
.141-. ISO
.131-. 140
.121-. 130
.111-. 120
.101-. 110
.091 -.100
.081 -.090
.071 -.080
.061-. 070
.041 -.060
.021 -.040
.001 -.020
0.000
8hr 1
<15
0
0
0
0
0
0
0
0
0
0
0
604
S9SO
35809
26655
2717
0
0
0
Equivalent
15-24
0
0
0
0
0
0
0
0
0
0
0
0
0
3276
1920
30837
29585
563
0
Ventilatioi
25-29
0
• 0
0
0
0
0
0
0
0
0
0
0
0
0
0
1215
6118
301
0
n Kate,
30-34
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
208
0
0
l/min-n**2
35+
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ANY
0
0
0
0
0
0
0
0
0
0
0
604
5950
38834
23630
2717
0
0
0
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season -
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-13
-------
Table 7.
Cumulative Numbers of People at 8-Hr Dally Max.
Seasonal Mean (April to October) Exposure
03 Level
Equalled or
Exceeded, ppm
.071 +
.066
.061
.056
.051
.046
.041
.036
.031
.026
.021
.011
.001
0.000
0
0
0
0
0
0
0
5250
65946
68664
69932
71735
71735
71735
Study Area = HOUSTON 1 1H NAAQS Outdoor Workers
No. exposure districts = 11
First day of 03 season = 1
Last day of 03 season - 365
No. days in 03 season = 365
D-14
-------
Table 8.
Occurrences of People at 8-Hr Daily Max.
Seasonal Mean (April to October) Exposure
03 Interval,
ppm
.071+
.066 -.070
.061-. 065
.056-. 060
.051-. 055
.046-. 050
.041 -.045
.036 -.040
.031-. 035
.026-. 030
.021-. 025
.011-. 020
.001-. 010
0.000
0
0
0
0
0
0
0
5250
60696
2718
1268
1803
0
0
Study Area = HOUSTON 1 1H HAAQS Outdoor WorXers
No. exposure districts = 11
First day of 03 season = 1
Last day of 03 season = 365
No. days in 03 season = 365
D-15
-------
Table 9.
Xuraber of People at Daily Max Dose that Exceed
Specified 1-HR 03 Levels 1 or More Times per Tear
03 Level
Equalled or
Exceeded, ppm
.401+
.381
.361
.341
.321
.301
.281
.261
.241
.221
.201
.181
.161
.141
.121
.101
.081
.061
.041
.021
.001
0.000
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10534
23949
1869
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
.0
0
0
322
21017
2027
0
0
0
0
0
Days / Year
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3835
2421
0
0
0
0
0
BX————— ——-—-——-
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
28
2172
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
22
4760
0
0
0
0
0
===========
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1859
58183
71735
71735
71735
71735
71735
=======
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season =
No. days in 03 season =
Outdoor WorXers
11
1
365
365
D-16
-------
Table 10.
Number of People at Dally Hax 8-HR Dose that Exceed
Specified 8-hr 03 Levels 1 or More Times per lear
03 Level
Equalled or
Exceeded, ppm
.201+
.191
.181
.171
.161
.151
.141
.131
.121
.111
.101
.091
.081
.071
.061
.041
.021
.001
0.000
1
0
0
0
0
0
0
0
0
0
0
0
604
6410
29164
1735
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
109
11370
4697
0
0
0
0
Days / It
3
0
0
0
0
0
0
0
0
0
0
0
0
35
2150
3874
0
0
0
0
:ar
4
0
0
0
0
0
0
0
0
0
0
0
0
0
548
19605
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
10
8787
0
0
0
0
== = ===SS5S=
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30309
71735
71735
71735
71735
Study Area = HOUSTON '1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season -
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-17
-------
Table 11.
Number of People that Exceed Specified 03 Levels
at 1-HR Daily Max Dose 1 or Mora Times per Tear
with Ventilation Rates of 30 or Higher
03 Level
Equalled or
Exceeded, ppm
.401+
.381
.351
.341
.321
.301
.281
.261
.241
.221
.201
.181
.161
.141
.121
.101
.081
.061
.041
.021
.001
0.000
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6333
24096
13856
131
3071
3071
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
804
6757
20192
94
0
0
Days / lear
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0.
0
0
0
1818
6974
593
476
476
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
138
9669
9500
2996
2996
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10532
3422
7308
7308
>5
0
0
0
0
0
0
0
0.
0
0
0
0
0
0
0
0
.0
0
5910
54579
57884
57884
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts -
First day of 03 season =
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-18
-------
Table 12.
Number of People that Exceed Specified 8 HR 03 Levels
at Daily Max 8-HR Dose 1 or More limes per Year
with 8 Hour Ventilation Rates from 13 through 27
03 Level
Squalled or
Exceeded , ppm
.201+
.191
.181
.171
.161
.151
.141
.131
.121
-111
.101
.091
.081
.071
.061
.041
.021
.001
0.000
1
0
0
0
0
0
0
0
0
0
0
0
0
50
3604
5756
26263
898
539
539
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
720
11259
13155
8383
8383
Days / Zear
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4551
10866
8012
8012
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
268
5751
4149
4149
5 '
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1441
5827
9209
9209
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
91
31825
38372
38372
Study Area = HOUSTON 1 1H NAAQS
No. exposure districts =
First day of 03 season =
Last day of 03 season =
No. days in 03 season =
Outdoor Workers
11
1
365
365
D-19
-------
APPENDIX E
ONE-HOUR EXPOSURE DISTRIBUTIONS
E-1
-------
FIGURE E-1. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN PHILADELPHIA, PA
120
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
FIGURE E-2. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN PHILADELPHIA, PA
1,000
CO
Q
CO
^
o
CO
LU
O
LU
Z3
O
O
9
2
O
CO
cc.
LU
a.
800
600 -
400 -
200 -
0.02 0.04 0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
E-2
-------
FIGURE E-3. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN HOUSTON, TX
ASIS
1112
8109
8108
-B-
8110
/~\
8508
-*-
8107
s~\r
1110
8509
\ /
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION. PPM
0.16 0.18
0.2
FIGURE E-4. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN HOUSTON, TX
1,400
Q 1,200
ASIS
1112
8109
8108
i—i
±±
8110
8508
8107
1110
8509
-X-
0 0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
E-3
-------
FIGURE E-5. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN NEW YORK, NY
250
ASIS
1112
8109
8108
-B-
8110
8508
8107
1110
•
8509
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18
0.2
FIGURE E-6. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN NEW YORK, NY
2,000
0.02 0.04 0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
E-4
-------
FIGURE E-7. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN WASHINGTON, D.C.
ASIS
1112
8109
8108
i—i
i—i
8110
8508
8107
1110
8509
0
0.2
0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18
CONCENTRATION, PPM
FIGURE E-8. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER HEAVY
EXERTION (EVR 30+ LITERS/MIN-M2) IN WASHINGTON, D.C.
800
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
E-5
-------
FIGURE E-9. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN PHILADELPHIA, PA
120
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION. PPM
0.16 0.18
FIGURE E-10. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN PHILADELPHIA, PA
4,000
AS IS
1112
-4
8109
8108
i—i
8110
^
8508
-Xr
8107
_ s-\
1110
8509
0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
CONCENTRATION, PPM
E-6
-------
FIGURE E-11. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN HOUSTON, TX
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
FIGURE E-12. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN HOUSTON, TX
6,000
AS IS
Hi
1112
-+
8109
-*
8108
-B
8110
-e-
8508
8107
-e-
1110
•
8509
0 0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION. PPM
0.16 0.18 0.2
E-7
-------
FIGURE E-13. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN NEW YORK, NY
250
ASIS
1112
8109
8108
i—i
8110
8508
8107
1110
8509
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
FIGURE E-14. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN NEW YORK, NY
8,000
co
Q
CO
O
I
t
CO
111
o
LU
01
o
CJ
o
2
O
CO
OL
111
CL
6,000
4,000 -
2,000 -
0.02 0.04 0.06 0.08 0.1 0.12 0.14
CONCENTRATION. PPM
0.16 0.18 0.2
E-8
-------
FIGURE E-15. ONE-HOUR EXPOSURE DISTRIBUTIONS FOR OUTDOOR
WORKERS EXPOSED ON ONE OR MORE DAYS UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN WASHINGTON, D.C.
0.02 0.04
0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18
0.2
FIGURE E-16. ONE-HOUR EXPOSURE DISTRIBUTIONS OF TOTAL
OCCURRENCES FOR OUTDOOR WORKER EXPOSURE UNDER MODERATE
EXERTION (EVR 16-30 LITERS/MIN-M2) IN WASHINGTON, D.C.
3,500
C/3
Q 3,000
ASIS
1112
8109
8108
i — i
8110
-e-
8508
8107
-e-
1110
8509
0.02 0.04 0.06 0.08 0.1 0.12 0.14
CONCENTRATION, PPM
0.16 0.18 0.2
E-9
------- |