&EPA
United State
EirviroiiwiU Protection
Agnncy
Health Risk and Exposure Assessment
for Ozone
Second External Review Draft
Chapter 5 Appendices
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DISCLAIMER
This draft document has been prepared by staff from the Risk and Benefits Group, Health
and Environmental Impacts Division, Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency. Any findings and conclusions are those of the authors and do
not necessarily reflect the views of the Agency. This draft document is being circulated to
facilitate discussion with the Clean Air Scientific Advisory Committee to inform the EPA's
consideration of the ozone National Ambient Air Quality Standards.
This information is distributed for the purposes of pre-dissemination peer review under
applicable information quality guidelines. It has not been formally disseminated by EPA. It
does not represent and should not be construed to represent any Agency determination or policy.
Questions related to this preliminary draft document should be addressed to Dr. Bryan
Hubbell, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards,
C539-07, Research Triangle Park, North Carolina 27711 (email: hubbell.bryan@epa.gov).
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EPA-452/P-14-004c
February 2014
Health Risk and Exposure Assessment for Ozone
Second External Review Draft
Chapter 5 Appendices
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Risk and Benefits Group
Research Triangle Park, North Carolina 27711
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Appendix 5-A
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Description of the Air Pollutants Exposure Model (APEX)
Table of Contents
5A-1. OVERVIEW
5A-2. MODEL INPUTS
5A-3. DEMOGRAPHIC CHARACTERISTICS
5A-4. ATTRIBUTES OF INDIVIDUALS
5A-5. CONSTRUCTION OF LONGITUDINAL DIARY SEQUENCE
5A-6. KEY PHYSIOLOGICAL PROCESSES MODELED
5A-7. ESTIMATING MICROENVIRONMENTAL CONCENTRATIONS
5A-7.1. MASS BALANCE MODEL
5A-7 2 FACTORS MODEL
5A-8. EXPOSURE AND DOSE TIME SERIES CALCULATIONS
5A-9. MODEL OUTPUT
5A-10. REFERENCES
List of Tables
Table 5A-1. Ventilation coefficient parameter estimates (&,) and residuals distributions (e\)
Graham and McCurdy (2005).
List of Figures
Figure 5A-1 Illustration of the mass balance model used by APEX
Figure 5A-2. Example of microenvironmental and exposure concentrations for a simulated
individual over a 48 hours simulation. (H: home, A: automobile, S: school, P:
playground O' outdoors at home)
Figure 5A-3. The percent of simulated children (ages 5-18) at or above 8-hour average 63
exposures while at moderate or greater exertion
2
4
5
5
6
8
10
10
16
17
19
21
from
10
11
18
..20
5A-1
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1 This Appendix briefly describes the EPAs Air Pollutants Exposure (APEX) model.
2 5A-1. OVERVIEW
3 APEX is the human inhalation exposure model within the Total Risk Integrated
4 Methodology (TRIM) framework (US EPA 2012a,b). APEX is conceptually based on the
5 probabilistic NAAQS Exposure Model (pNEM) that was used to estimate population exposures
6 for the 1996 O3 NAAQS review (Johnson et al., 1996a; 1996b; 1996c). Since that time the
7 model has been restructured, improved, and expanded to reflect conceptual advances in the
8 science of exposure modeling and newer input data available for the model. Key improvements
9 to algorithms include replacement of the cohort approach with a probabilistic sampling approach
10 focused on individuals, accounting for fatigue and oxygen debt after exercise in the calculation
11 of ventilation rates (Isaacs et al., 2008), and new approaches for construction of longitudinal
12 activity patterns for simulated persons (Glen et al. 2008; Rosenbaum et al., 2008). Major
13 improvements to data input to the model include updated air exchange rates (AERs), population
14 census and commuting data, and the daily time-location-activities database. These
15 improvements are described later in this chapter.
16 APEX estimates human exposure to criteria and toxic air pollutants at local, urban, or
17 regional scales using a stochastic, microenvironmental approach. That is, the model randomly
18 selects data on a sample of hypothetical individuals in an actual population database and
19 simulates each individual's movements through time and space (e.g., at home, in vehicles) to
20 estimate their exposure to the pollutant. APEX can assume people live and work in the same
21 general area (i.e., that the ambient air quality is the same at home and at work) or optionally can
22 model commuting and thus exposure at the work location for individuals who work.
23 The APEX model is a microenvironmental, longitudinal human exposure model for
24 airborne pollutants. It is applied to a specified study area, which is typically a metropolitan area.
25 The time period of the simulation is typically one year, but can easily be made either longer or
26 shorter. APEX uses census data, such as gender and age, to generate the demographic
27 characteristics of simulated individuals. It then assembles a composite activity diary to represent
28 the sequence of activities and microenvironments that the individual experiences. Each
29 microenvironment has a user-specified method for determining air quality. The inhalation
30 exposure in each microenvironment is simply equal to the air concentration in that
31 microenvironment. When coupled with breathing rate information and a physiological model,
32 various measures of dose can also be calculated.
33 The term microenvironment is intended to represent the immediate surroundings of an
34 individual, in which the pollutant of interest is assumed to be well-mixed. Time is modeled as a
35 sequence of discrete time steps called events. In APEX, the concentration in a microenvironment
5A-2
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1 may change between events. For each microenvironment, the user specifies the method of
2 concentration calculation (either mass balance or regression factors, described later in this
3 paper), the relationship of the microenvironment to the ambient air, and the strength of any
4 pollutant sources specific to that microenvironment. Because the microenvironments that are
5 relevant to exposure depend on the nature of the target chemical and APEX is designed to be
6 applied to a wide range of chemicals, both the total number of microenvironments and the
7 properties of each are free to be specified by the user.
8 The ambient air data are provided as input to the model in the form of time series at a
9 list of specified locations. Typically, hourly air concentrations are used, although temporal
10 resolutions as small as one minute may be used. The spatial range of applicability of a given
11 ambient location is called an air district. Any number of air districts can be accommodated in a
12 model run, subject only to computer hardware limitations. In principle, any microenvironment
13 could be found within a given air district. Therefore, to estimate exposures as an individual
14 engages in activities throughout the period it is necessary to determine both the
15 microenvironment and the air district that apply for each event.
16 An exposure event is determined by the time reported in the activity diary; during any
17 event the district, microenvironment, ambient air quality, and breathing rate are assumed to
18 remain fixed. Since the ambient air data change every hour, the maximum duration of an event
19 is limited to one hour. The event duration may be less than this (as short as one minute) if the
20 activity diary indicates that the individual changes microenvironments or activities performed
21 within the hour.
22 An APEX simulation includes the following steps:
23 1. Characterize the study area - APEX selects sectors (e.g., census tracts) within a study area
24 based on user-defined criteria and thus identifies the potentially exposed population and
25 defines the air quality and weather input data required for the area.
26 2. Generate simulated individuals - APEX stochastically generates a sample of simulated
27 individuals based on the census data for the study area and human profile distribution data
28 (such as age-specific employment probabilities). The user must specify the size of the
29 sample. The larger the sample, the more representative it is of the population in the study
30 area and the more stable the model results are (but also the longer the computing time).
31 3. Construct a long-term sequence of activity events and determine breathing rates - APEX
32 constructs an event sequence (activity pattern) spanning the period of simulation for each
33 simulated person. The model then stochastically assigns breathing rates to each event, based
34 on the type of activity and the physical characteristics of the simulated person.
35 4. Calculate pollutant concentrations in microenvironments - APEX enables the user to define
36 any microenvironment that individuals in a study area would visit. The model then
37 calculates concentrations of each pollutant in each of the microenvironments.
5A-3
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1 5. Calculate pollutant exposures for each simulated individual - Microenvironmental
2 concentrations are time weighted based on individuals' events (i.e., time spent in the
3 microenvironment) to produce a sequence of time-averaged exposures (or minute by minute
4 time series) spanning the simulation period.
5 6. Estimate dose - APEX can also calculate the dose time series for each of the simulated
6 individuals based on the exposures and breathing rates for each event. For Os, the adverse
7 health metric of interest is decrement in forced expiratory volume occurring in one second
8 (FEVi). This algorithm responsible for combining the time series of APEX estimated
9 exposure and breathing rates for individuals is discussed in greater detail in the main body of
10 the RE A, Chapter 6.
11
12 The model simulation continues until exposures are determined for the user-specified
13 number of simulated individuals. APEX then calculates population exposure statistics (such as
14 the number of exposures exceeding user-specified levels) for the entire simulation and writes out
15 tables of distributions of these statistics.
16 5A-2. MODEL INPUTS
17 APEX requires certain inputs from the user. The user specifies the geographic area and
18 the range of ages and age groups to be used for the simulation. Hourly (or shorter) ambient air
19 quality and hourly temperature data must be furnished for the entire simulation period. Other
20 hourly meteorological data (humidity, wind speed, wind direction, precipitation) can be used by
21 the model to estimate microenvironmental concentrations, but are optional.
22 In addition, most variables used in the model algorithms are represented by user-specified
23 probability distributions which capture population variability. APEX provides great flexibility in
24 defining model inputs and parameters, including options for the frequency of selecting new
25 values from the probability distributions. The model also allows different distributions to be
26 used at different times of day or on different days, and the distribution can depend conditionally
27 on values of other parameters. The probability distributions available in APEX include beta,
28 binary, Cauchy, discrete, exponential, extreme value, gamma, logistic, lognormal, loguniform,
29 normal, off/on, Pareto, point (constant), triangle, uniform, Weibull, and nonparametric
30 distributions. Minimum and maximum bounds can be specified for each distribution if a
31 truncated distribution is appropriate. There are two options for handling truncation. The
32 generated samples outside the truncation points can be set to the truncation limit; in this case,
33 samples "stack up" at the truncation points. Alternatively, new random values can be selected, in
34 which case the probability outside the limits is spread over the specified range, and thus the
35 probabilities inside the truncation limits will be higher than the theoretical untruncated
36 distribution.
5A-4
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1 5A-3. DEMOGRAPHIC CHARACTERISTICS
2 The starting point for constructing a simulated individual is the population census
3 database; this contains population counts for each combination of age, gender, race, and sector.
4 The user may decide what spatial area is represented by a sector, but the default input file defines
5 a sector as a census tract. Census tracts are variable in both geographic size and population
6 number, though usually have between 1,500 and 8,000 persons. Currently, the default file
7 contains population counts from the 2000 census for every census tract in the United States, thus
8 the default file should be sufficient for most exposure modeling purposes. The combination of
9 age, gender, race, and sector are selected first. The sector becomes the home sector for the
10 individual, and the corresponding air district becomes the home district. The probabilistic
11 selection of individuals is based on the sector population and demographic composition, and
12 taken collectively, the set of simulated individuals constitutes a random sample from the study
13 area.
14 The second step in constructing a simulated individual is to determine their employment
15 status. This is determined by a probability which is a function of age, gender, and home sector.
16 An input file is provided which contains employment probabilities from the 2000 census for
17 every combination of age (16 and over), gender, and census tract. APEX assumes that persons
18 under age 16 do not commute. For persons who are determined to be workers, APEX then
19 randomly selects a work sector, based on probabilities determined from the commuting matrix.
20 The work sector is used to assign a work district for the individual that may differ from the home
21 district, and thus different ambient air quality may be used when the individual is at work.
22 The commuting matrix contains data on flows (number of individuals) traveling from a
23 given home sector to a given work sector. Based on commuting data from the 2000 census, a
24 commuting data base for the entire United States has been prepared. This permits the entire list
25 of non-zero flows to be specified on one input file. Given a home sector, the number of
26 destinations to which people commute varies anywhere from one to several hundred other tracts.
27 5A-4. ATTRIBUTES OF INDIVIDUALS
28 In addition to the above demographic information, each individual is assigned status and
29 physiological attributes. The status variables are factors deemed important in estimating
30 microenvironmental concentrations, and are specified by the user. Status variables can include,
31 but are not limited to, people's housing type, whether their home has air conditioning, whether
32 they use a gas stove at home, whether the stove has a gas pilot light, and whether their car has air
33 conditioning. Physiological variables are important when estimating pollutant specific dose.
34 These variables could include height, weight, blood volume, pulmonary diffusion rate, resting
5A-5
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1 metabolic rate, energy conversion factor (liters of oxygen per kilocalorie energy expended),
2 hemoglobin density in blood, maximum limit on metabolic equivalents of work (MET) ratios
3 (see below), and endogenous CO production rate. All of these variables are treated
4 probabilistically taking into account interdependences where possible, and reflecting variability
5 in the population.
6 Two key personal attributes determined for each individual in this assessment are body
7 mass (BM) and body surface area (BSA). Each simulated individual's body mass was randomly
8 sampled from age- and gender-specific body mass distributions generated from National Health
9 and Nutrition Examination Survey (NHANES) data for the years 1999-2004.l Details in their
10 development and the parameter values are provided by Isaacs and Smith (2005). Then age- and
11 gender-specific body surface area can be estimated for each simulated individual. Briefly, the
12 BSA calculation is based on logarithmic relationships developed by Burmaster (1998) that use
13 body mass as an independent variable as follows:
14 BSA = e-2'2™ EM0'6™ (5A-1)
15 where,
16 BSA = body surface area (m2)
17 BM = body mass (kg)
18 5A-5. CONSTRUCTION OF LONGITUDINAL DIARY SEQUENCE
19 The activity diary determines the sequence of microenvironments visited by the
20 simulated person. A longitudinal sequence of daily diaries must be constructed for each
21 simulated individual to cover the entire simulation period. The default activity diaries in APEX
22 are derived from those in the EPA's Consolidated Human Activity Database (CHAD) (US EPA,
23 2000; 2002), although the user could provide area specific diaries if available. There are over
24 53,000 CHAD diaries, each covering a 24 hour period, that have been compiled from several
25 studies. CHAD is essentially a cross-sectional database that, for the most part, only has one
26 diary per person. Therefore, APEX must assemble each longitudinal diary sequence for a
27 simulated individual from many single-day diaries selected from a pool of similar people.
28 APEX selects diaries from CHAD by matching gender and employment status, and by
29 requiring that age falls within a user-specified range on either side of the age of the simulated
30 individual. For example, if the user specifies plus or minus 20%, then for a 40 year old
31 simulated individual, the available CHAD diaries are those from persons aged 32 to 48. Each
32 simulated individual therefore has an age window of acceptable diaries; these windows can
1 Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES studies were obtained
fro m http: //w w w. cdc. go v/nc hs/nhane s/nhane s_que stio nnaire s. htm.
5A-6
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1 partially overlap those for other simulated individuals. This differs from a cohort-based
2 approach, where the age windows are fixed and non-overlapping. The user may optionally
3 request that APEX allow a decreased probability for selecting diaries from ages outside the
4 primary age window, and also for selecting diaries from persons of missing gender, age, or
5 employment status. These options allow the model to continue the simulation when diaries are
6 not available within the primary window.
7 The available CHAD diaries are classified into diary pools, based on the temperature and
8 day of the week. The model will select diaries from the appropriate pool for days in the
9 simulation having matching temperature and day type characteristics. The rules for defining
10 these pools are specified by the user. For example, the user could request that all diaries from
11 Monday to Friday be classified together, and Saturday and Sunday diaries in another class.
12 Alternatively, the user could instead create more than two classes of weekdays, combine all
13 seven days into one class, or split all seven days into separate classes.
14 The temperature classification can be based either on daily maximum temperature, daily
15 average temperature, or both. The user specifies both the ranges and numbers of temperatures
16 classes. For example, the user might wish to create four temperature classes and set their ranges
17 to below 50 °F, 50-69 °F, 70-84 °F, and above a daily maximum of 84 °F. Then day type and
18 temperature classes are combined to create the diary pools. For example, if there are four
19 temperature classes and two day type classes, then there will be eight diary pools.
20 APEX then determines the day-type and the applicable temperature for each person's
21 simulated day. APEX allows multiple temperature stations to be used; the sectors are
22 automatically mapped to the nearest temperature station. This may be important for study areas
23 such as the greater Los Angeles area, where the inland desert sectors may have very different
24 temperatures from the coastal sectors. For selected diaries, the temperature in the home sector of
25 the simulated person is used. For each day of the simulation, the appropriate diary pool is
26 identified and a CHAD dairy is randomly drawn. When a diary for every day in the simulation
27 period has been selected, they are concatenated into a single longitudinal diary covering the
28 entire simulation for that individual. APEX contains three algorithms for stochastically selecting
29 diaries from the pools to create the longitudinal diary. The first method selects diaries at random
30 after stratification by age, gender, and diary pool; the second method selects diaries based on
31 metrics related to exposure (e.g., time spent outdoors) with the goal of creating longitudinal
32 diaries with variance properties designated by the user (Glen et al., 2008); and the third method
33 uses a clustering algorithm to obtain more realistic recurring behavioral patterns (Rosenbaum
34 2008).
35 The final step in processing the activity diary is to map the CHAD location codes into the
36 set of APEX microenvironments, supplied by the user as an input file. The user may define the
5A-7
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1 number of microenvironments, from one up to the number of different CHAD location codes
2 (which is currently 115).
3 5A-6. KEY PHYSIOLOGICAL PROCESSES MODELED
4 Ventilation is a general term describing the movement of air into and out of the lungs.
5 The rate of ventilation is determined by the type of activity an individual performs which in turn
6 is related to the amount of oxygen required to perform the activity. Minute or total ventilation
7 rate is used to describe the volume of air moved in or out of the lungs per minute. Quantitatively,
•
8 the volume of air breathed in per minute (VI ) is slightly greater than the volume expired per
•
9 minute (VE ). Clinically, however, this difference is not important, and by convention, the
•
10 ventilation rate is always measured on an expired sample or VE .
•
11 The rate of oxygen consumption (V02) is related to the rate of energy usage in
12 performing activities as follows:
13 Vo2=EExECF (5A-2)
14 where,
•
15 V02 = Oxygen consumption rate (liters CVminute)
16 EE = Energy expenditure (kcal/minute)
17 ECF = Energy conversion factor (liters (Vkcal).
18
19 The ECF shows little variation and typically, commonly a value between 0.20 and 0.21 is
20 used to represent the conversion from energy units to oxygen consumption. APEX can randomly
21 sample from a uniform distribution defined by these lower and upper bounds to estimate an ECF
22 for each simulated individual. The activity-specific energy expenditure is highly variable and
23 can be estimated using metabolic equivalents (METs), or the ratios of the rate of energy
24 consumption for non-rest activities to the resting rate of energy consumption, as follows
25
26 EE=METxRMR (5A-3)
27 where,
28 EE = Energy expenditure (kcal/minute)
29 MET = Metabolic equivalent of work (unitless)
30 RMR = Resting metabolic rate (kcal/minute)
31
5A-8
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1 APEX contains distributions of METs for all activities that might be performed by
2 simulated individuals. APEX randomly samples from the various METs distributions to obtain
3 values for every activity performed by each individual. Age- and gender-specific RMR are
4 estimated once for each simulated individual using a linear regression model (see Johnson et al.,
5 2002)2 as follows
6
7 RMR = [b0 + b, (BM) + s]F (5A-4)
8 where,
9 RMR = Resting metabolic rate (kcal/min)
10 b0 = Regression intercept (MJ/day)
11 bi = Regression slope (MJ/day/kg)
12 BM = body mass (kg)
13 e = randomly sampled error term, N{0, se)3 (MJ/day)
14 F = Factor for converting MJ/day to kcal/min (0.166)
• •
15 Finally, Graham and McCurdy (2005) describe an approach to estimate VE using V02.
16 In that report, a series of age- and gender-specific multiple linear regression equations were
17 derived from data generated in 32 clinical exercise studies. The algorithm accounts for
18 variability in ventilation rate due to variation in oxygen consumption, the variability within age
19 groups, and both inter- and intra-personal and variability. The basic algorithm is
20 \n(VE/BM) = b0+bl\n(Vo2/BM) + b2\n(I+ age) + b3 gender+ eb+ew (5A-5)
21 where,
22 In = natural logarithm of variable
•
23 V E! BM = activity specific ventilation rate, body mass normalized (liter air/kg)
24 bt = see below
•
25 Voi.l BM = activity specific oxygen consumption rate, body mass normalized
26 (liter/O2/kg)
27 age = the age of the individual (years)
28 gender = gender value (-1 for males and +1 for females)
29 et, = randomly sampled error term for between persons N{0, se), (liter air/kg)
30 ew = randomly sampled error term for within persons N{0, se), (liter air/kg)
2 The regression equations were adapted by Johnson (2002) using data reported by Schofield (1985). The regression
coefficients and error terms used by APEX are provided in the APEX physiology input file.
3 The value used for each individual is sampled from a normal distribution (N) having a mean of zero (0) and
variability described by the standard error (se)
5A-9
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
As indicated above, the random error (e) is allocated to two variance components used to
estimate the between-person (inter-individual variability) residuals distribution (eb) and within-
person (intra-individual variability) residuals distribution (ew). The regression parameters bo, bj,
&2, and &j are assumed to be constant over time for all simulated persons, e^ is sampled once per
person, while whereas ew varies from event to event. Point estimates of the regression
coefficients and standard errors of the residuals distributions are given in Table 5A-1.
Table 5A-1. Ventilation coefficient parameter estimates (bj) and residuals distributions (e$
from Graham and McCurdy (2005).
Age
group
<20
20-<34
34-<61
en-
Regression Coefficients1
bo
4.3675
3.7603
3.2440
2.5826
bi
1.0751
1.2491
1.1464
1.0840
b2
-0.2714
0.1416
0.1856
0.2766
b3
0.0479
0.0533
0.0380
-0.0208
Random Error1
eb
0.0955
0.1217
0.1260
0.1064
ew
0.1117
0.1296
0.1152
0.0676
1 The values of the coefficients and residuals distributions described by equation (5A-5).
5A-7. ESTIMATING MICROENVIRONMENTAL CONCENTRATIONS
The user provides rules for determining the pollutant concentration in each
microenvironment. There are two available models for calculating microenvironmental
concentrations: mass balance and regression factors. Any indoor microenvironment may use
either model; for each microenvironment, the user specifies whether the mass balance or factors
model will be used.
5A-7.1.
Mass Balance Model
The mass balance method assumes that an enclosed microenvironment (e.g., a room
within a home) is a single well-mixed volume in which the air concentration is approximately
spatially uniform. The concentration of an air pollutant in such a microenvironment is estimated
using the following four processes (and illustrated in Figure 5A-1):
• Inflow of air into the microenvironment;
• Outflow of air from the microenvironment;
• Removal of a pollutant from the microenvironment due to deposition, filtration, and
chemical degradation; and
• Emissions from sources of a pollutant inside the microenvironment.
5 A-10
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Microenvironment
1
2
4
5
6
7
8
9
10
11
12
13
Air
outflow
Air
inflow
Indoorsources
Removal due to:
•Chemical reactions
•Deposition
•Filtration
Figure 5A-1. Illustration of the mass balance model used by APEX.
Considering the microenvironment as a well-mixed fixed volume of air, the mass balance
equation for a pollutant in the microenvironment can be written in terms of concentration:
cfC(f)
dt
in out removal source
(5A-6)
where,
C(t)
C out
(-1
*-" removal
Concentration in the microenvironment at time t
Rate of change in C(t) due to air entering the microenvironment
Rate of change in C(t) due to air leaving the microenvironment
Rate of change in C(t) due to all internal removal processes
Rate of change in C(t) due to all internal source terms
Concentrations are calculated in the same units as the ambient air quality data, e.g., ppm,
ppb, ppt, or |ig/m3. In the following equations concentration is shown only in |ig/m3 for brevity.
The change in microenvironmental concentration due to influx of air, C in, is given by:
14
15 where,
^outdoor x 'penetration x ^airexchange
(5A-7)
5 A-11
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= Ambient concentration at an outdoor microenvironment or outside
2 an indoor microenvironment (|ig/m3)
3 /penetration = Penetration factor (unitless)
4 Rair exchange = Air ex change rate (hr"1)
5
6 Since the air pressure is approximately constant in microenvironments that are modeled
7 in practice, the flow of outside air into the microenvironment is equal to that flowing out of the
8 microenvironment, and this flow rate is given by the air exchange rate. The air exchange rate
9 (hr"1) can be loosely interpreted as the number of times per hour the entire volume of air in the
10 microenvironment is replaced. For some pollutants (especially particulate matter), the process of
11 infiltration may remove a fraction of the pollutant from the outside air. The fraction that is
12 retained in the air is given by the penetration factor /penetration-
13 A proximity factor (fpmximity) and a local outdoor source term are used to account for
14 differences in ambient concentrations between the geographic location represented by the
15 ambient air quality data (e.g., a regional fixed-site monitor) and the geographic location of the
16 microenvironment. That is, the outdoor air at a particular location may differ systematically
17 from the concentration input to the model representing the air quality district. For example, a
18 playground or house might be located next to a busy road in which case the air at the playground
19 or outside the house would have elevated levels for mobile source pollutants such as carbon
20 monoxide and benzene. The concentration in the air at an outdoor location or directly outside an
21 indoor microenvironment (Coutdoor) is calculated as:
22 ^outdoor = 'proximity^ ambient + ^LocalOutdoorSources (5 A-8)
23 where,
24 Cambient = Ambient air district concentration (|ig/m3)
25 /proximity = Proximity factor (unitless)
26 CLocalOutdoorSources = The contribution to the concentration at this location from local
27 sources not represented by the ambient air district concentration
28 (|ig/m3)
29
30 During exploratory analyses, the user may examine how a microenvironment affects
31 overall exposure by setting the microenvironment's proximity or penetration factor to zero, thus
32 effectively eliminating the specified microenvironment.
33 Change in microenvironmental concentration due to outflux of air is calculated as the
34 concentration in the microenvironment C(t) multiplied by the air exchange rate:
5 A-12
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1 Cout = RelreXchenge * C(t) (5A-9)
2 The third term (C removal) in the mass balance calculation (equation 5A-6) represents
3 removal processes within the microenvironment. There are three such processes in general:
4 chemical reaction, deposition, and filtration. Chemical reactions are significant for Os, for
5 example, but not for carbon monoxide. The amount lost to chemical reactions will generally be
6 proportional to the amount present, which in the absence of any other factors would result in an
7 exponential decay in the concentration with time. Similarly, deposition rates are usually given
8 by the product of a (constant) deposition velocity and a (time-varying) concentration, also
9 resulting in an exponential decay. The third removal process is filtration, usually as part of a
10 forced air circulation or HVAC system. Filtration will normally be more effective at removing
11 particles than gases. In any case, filtration rates are also approximately proportional to
12 concentration. Change in concentration due to deposition, filtration, and chemical degradation in
13 a microenvironment is simulated based on the first-order equation:
•.. ^removal = {^deposition + ^filtration + ^ chemical /X^V / /•<- » i ,y.
= RremovalxC(t)
15 where,
16 C removal = Change in microenvironmental concentration due to removal
17 processes (|ig/m3/hr)
18 ^deposition = Removal rate of a pollutant from a microenvironment due to
19 deposition (hr"1)
20 Rfiitration = Removal rate of a pollutant from a microenvironment due to
21 filtration (hr"1)
22 Rchemicai = Removal rate of a pollutant from a microenvironment due to
23 chemical degradation (hr"1)
24 Rremovai = Removal rate of a pollutant from a microenvironment due to the
25 combined effects of deposition, filtration, and chemical
26 degradation (hr"1)
27 The fourth term in the mass balance calculation represents pollutant sources within the
28 microenvironment. This is the most complicated term, in part because several sources may be
29 present. APEX allows two methods of specifying source strengths: emission sources and
30 concentration sources. Either may be used for mass balance microenvironments, and both can be
31 used within the same microenvironment. The source strength values are used to calculate the
32 term C source (|ig/m3/hr).
5 A-13
-------
1 Emission sources are expressed as emission rates in units of |ig/hr, irrespective of the
2 units of concentration. To determine the rate of change of concentration associated with an
3 emission source SE, it is divided by the volume of the microenvironment:
4 Csource,SE=^ (5A-11)
5 where,
6 C source.SE = Rate of change in C(t) due to the emission source SE (|ig/m3/hr)
7 SE = The emission rate (ng/hr)
8V = The volume of the microenvironment (m3)
9
10 Concentration sources (Sc) however, are expressed in units of concentration. These must
11 be the same units as used for the ambient concentration (e.g., |ig/m3). Concentration sources are
12 normally used as additive terms for microenvironments using the factors model. Strictly
13 speaking, they are somewhat inconsistent with the mass balance method, since concentrations
14 should not be inputs but should be consequences of the dynamics of the system. Nevertheless, a
15 suitable meaning can be found by determining the rate of change of concentration ( C SOUrce) that
16 would result in a mean increase of Sc in the concentration, given constant parameters and
17 equilibrium conditions, in this way:
18 Assume that a microenvironment is always in contact with clean air (ambient = zero), and
19 it contains one constant concentration source. Then the mean concentration over time in this
20 microenvironment from this source should be equal to Sc. The mean source strength expressed
21 in ppm/hr or |ig/m3/hr is the rate of change in concentration (C S0urce,sc)- In equilibrium,
22 Co C source, SC (5A-12)
'S
R, D
alV avt*hizanna ~T~ ** r,
exchange ~*~ ** removal
23 where, Cs is the mean increase in concentration over time in the microenvironment due to
24 the source C SOUrce,sc • Thus, C SOUrce,sc can be expressed as
25 Csource,sc=Csx Rmean (5A-13)
26 where Rmean is the chemical removal rate. From equation (5 A- 13), Rmean is equal to the
27 sum of the air exchange rate and the removal rate (Rair exchange + Removal) under equilibrium
28 conditions. In general, however, the microenvironment will not be in equilibrium, but in such
29 conditions there is no clear meaning to attach to C source.sc since there is no fixed emission rate
30 that will lead to a fixed increase in concentration. The simplest solution is to use Rmean = Rair
3 1 exchange + Rremovai However, the user is given the option of specifically specifying Rmean (see
32 discussion of parameters below). This may be used to generate a truly constant source strength
5 A-14
-------
1 C source,sc by making Sc and Rmean both constant in time. If this is not done, then Rmean is simply
2 set to the sum of (Rair exchange + Removal}. If these parameters change over time, then C SOUrce,sc
3 also changes. Physically, the reason for this is that in order to maintain a fixed elevation of
4 concentration over the base conditions, then the source emission rate would have to rise if the air
5 exchange rate were to rise.
6 Multiple emission and concentration sources within a single microenvironment are
7 combined into the final total source term by combining equations (5A-1 1) and (5A-13):
1 "e "c
8 ^source = ^source,SE + ^source,SC = 77 XI ^S/ + ^mean ^ ^S/ (5A-14)
V i=1 i=1
9 where,
10 SEI = Emission source strength for emission source /' (|ig/hr,
1 1 irrespective of the concentration units)
12 Sa = Emission source strength for concentration source /' (|ig/m3)
13 ne = Number of emission sources in the microenvironment
14 nc = Number of concentration sources in the microenvironment
15
16 In equations (5A-1 1) and (5 A- 14), if the units of air quality are ppm rather than |ig/m3,
17 7/Fis replaced byf/V, where/= ppm / |ig/m3 = gram molecular weight / 24.45. (24.45 is the
18 volume (liters) of a mole of the gas at 25°C and 1 atmosphere pressure.)
19 Equations (5A-7), (5A-9), (5 A- 10), and (5 A- 14) can now be combined with equation (5A-6) to
20 form the differential equation for the microenvironmental concentration C(t). Within the time
21 period of a time step (at most 1 hour), C SOUrce and C in are assumed to be constant. Using
ZZ L, combined *— source ~"~ ^— in leaQS tO.
_
combined air exchange
r(t}-R r(t}
^ \L ' removal^ \L '
~^ .. comne ar excange \ remova \ fs.\ 1 ^^
= ^
combined
24
25 Solving this differential equation leads to:
r ( c ^
26 C(t}= ^combined + C(t0}- ^combined e'R—("°} (5A-16)
mean \ mean )
27 where,
28 Cfto) = Concentration of a pollutant in a microenvironment at the
29 beginning of a time step (|ig/m3)
5 A-15
-------
1
2
3
4
5
C(t)
24
25
26
27
28
29
Concentration of a pollutant in a microenvironment at time t
within the time step (|ig/m3).
Based on equation (5A-16), the following three concentrations in a microenvironment are
calculated:
combined
' source
r~j r~j |^ r~j
r^mean ^ air exchange + ^removal
o + 7-) = Cequ, + (c(t0)-Cequil)e-R
-,-
'
:i +
(5A-17)
(5 A-18)
(5 A-19)
9 where,
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Concentration in a microenvironment (|ig/m3) if t —» oo
(equilibrium state).
Concentration in a microenvironment at the beginning of the
time step (|ig/m3)
Concentration in a microenvironment at the end of the time step
(Hg/m3)
Mean concentration over the time step in a microenvironment
(Hg/m3)
;? +;? n,r-h
•*Vjz> exchange ~r ^removal UU I
At each time step of the simulation period, APEX uses equations (5 A-17), (5 A-18), and
(5A-19) to calculate the equilibrium, ending, and mean concentrations, respectively. The
calculation continues to the next time step by using Cfto+T) for the previous hour as Cfto).
L- egw!'/
c^;
crr0+r;
Cmean
5A-7.2.
Factors Model
The factors model is simpler than the mass balance model. In this method, the value of
the concentration in a microenvironment is not dependent on the concentration during the
previous time step. Rather, this model uses the following equation to calculate the concentration
in a microenvironment from the user-provided hourly air quality data:
(5A-20)
mean ambient proximity penetration
30 where,
5 A-16
-------
1 Cmean = Mean concentration over the time step in a microenvironment (|ig/m3)
2 Cambient = The concentration in the ambient (outdoor) environment (|ig/m3)
3 /proximity = Proximity factor (unitless)
4 /penetration = Penetration factor (unitless)
5 Sa = Mean air concentration resulting from source i (|ig/m3)
6 nc = Number of concentration sources in the microenvironment
7
8 The user may specify distributions for proximity, penetration, and any concentration
9 source terms. All of the parameters in equation (5A-20) are evaluated for each time step,
10 although these values might remain constant for several time steps or even for the entire
11 simulation.
12 The ambient air quality data are supplied as time series over the simulation period at
13 several locations across the modeled region. The other variables in the factors and mass balance
14 equations are randomly drawn from user-specified distributions. The user also controls the
15 frequency and pattern of these random draws. Within a single day, the user selects the number
16 of random draws to be made and the hours to which they apply. Over the simulation, the same
17 set of 24 hourly values may either be reused on a regular basis (for example, each winter
18 weekday), or a new set of values may be drawn. The usage patterns may depend on day of the
19 week, on month, or both. It is also possible to define different distributions that apply if specific
20 conditions are met. The air exchange rate is typically modeled with one set of distributions for
21 buildings with air conditioning and another set of distributions for those which do not. The
22 choice of a distribution within a set typically depends on the outdoor temperature and possibly
23 other variables. In total there are eleven such conditional variables which can be used to select
24 the appropriate distributions for the variables in the mass balance or factors equations.
25 For example, the hourly emissions of CO from a gas stove may be given by the product
26 of three random variables: a binary on/off variable that indicates if the stove is used at all during
27 that hour, a usage duration sampled from a continuous distribution, and an emission rate per
28 minute of usage. The binary on/off variable may have a probability for on that varies by time of
29 day and season of the year. The usage duration could be taken from a truncated normal or
30 lognormal distribution that is resampled for each cooking event, while the emission rate could be
31 sampled just once per stove.
32 5A-8. EXPOSURE AND DOSE TIME SERIES CALCULATIONS
33 The activity diaries provide the time sequence of microenvironments visited by the
34 simulated individual and the activities performed by each individual. The pollutant
35 concentration in the air in each microenvironment is assumed to be spatially uniform throughout
5 A-17
-------
1 the microenvironment and unchanging within each
2 factors or the mass balance method, as specified by
diary event and is calculated by either the
the user. The exposure of the individual is
3 given by the time sequence of airborne pollutant concentrations that are encountered in the
4 microenvironments visited. Figure 5A-2 illustrates
the exposures for one simulated 12-year old
5 child over a 2-day period. On both days the child travels to and from school in an automobile,
6 goes outside to a playground in the afternoon while
7 the eveni
ppm
0.14;
0.12:
0.10:
0.08i
0.06:
0.04^
0.02;
onrv
ng.
O
0
P o
0
HHHHHHHHSSSSS^ "
at school, and spends time outside at home ii
P
0
A 0
A Hu
i i. , nuj
i l~~t L_r i i
J i • | I(-HLJ
00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00
time of day
9 Figure 5A-2. Example of microenvironmental and exposure concentrations for a simulated
10 individual over a 48 hours simulation. (H: home, A: automobile, S: school, P: playground,
11 O: outdoors at home).
12 In addition to exposure, APEX models breathing rates based on the physiology of each
13 individual and the exertion levels associated with the activities performed. For each activity type
14 in CHAD, a distribution is provided for a corresponding normalized metabolic equivalent of
15 work or METs (McCurdy, 2000). METs are derived by dividing the metabolic energy
16 requirements for the specific activity by a persons resting, or basal, metabolic rate. The MET
17 ratios have less interpersonal variation than do the absolute energy expenditures. Based on age
18 and gender, the resting metabolic rate, along with other physiological variables is determined for
19 each individual as part of their anthropometric characteristics. Because the MET ratios are
20 sampled independently from distributions for each diary event, it would be possible to produce
21 time-series of MET ratios that are physiologically unrealistic. APEX employs a MET
5 A-18
-------
1 adjustment algorithm based on a modeled oxygen deficit to prevent such overestimation of MET
2 and breathing rates (Isaacs et al., 2008). The relationship between the oxygen deficit and the
3 applied limits on MET ratios are nonlinear and are derived from published data on work capacity
4 and oxygen consumption. The resulting combination of microenvironmental concentration and
5 breathing ventilation rates provides a time series of inhalation intake dose for most pollutants.
6 5A-9. MODEL OUTPUT
7 APEX calculates the exposure and dose time series based on the events as listed on the
8 activity diary with a minimum of one event per hour but usually more during waking hours.
9 APEX can aggregate the event level exposure and dose time series to output hourly, daily,
10 monthly, and annual averages. The types of output files are selected by the user, and can be as
11 detailed as event-level data for each simulated individual (note, Figure 5 A-2 was produced from
12 the event output file). A set of summary tables are produced for a variety of exposure and dose
13 measures. These include tables of person-minutes at various exposure levels, by
14 microenvironment, a table of person-days at or above each average daily exposure level, and
15 tables describing the distributions of exposures for different groups. An example of how APEX
16 results can be depicted is given in Figure 5A-3, which shows the percent of children with at least
17 one 8-hour average exposure at or above different exposure levels, concomitant with moderate or
18 greater exertion. These are results from a simulation of Os exposures for the greater
19 Washington, D.C. metropolitan area for the year 2002. From this graph ones sees, for example,
20 that APEX estimates 30 percent of the children in this area experience exposures above 0.08
21 ppm-8hr while exercising, at least once during the year.
22
5 A-19
-------
1
2
3
4
0%
0 02
0.04 0.06
Ozone Exposure Level (ppm-Shr)
[I OB
0.12
Figure 5A-3. The percent of simulated children (ages 5-18) at or above 8-hour average
exposures while at moderate or greater exertion.
5A-20
-------
2 5A-10. REFERENCES
3 Burmaster, D.E. (1998). LogNormal distributions for skin area as a function of body weight.
4 Risk Analysis. 18(l):27-32.
5 Glen, G., Smith, L., Isaacs, K., McCurdy, T., Langstaff, J. (2008). A new method of
6 longitudinal diary assembly for human exposure modeling. J Expos Sci Environ Epidem.
1 18:299-311.
8 Graham, S.E., McCurdy, T. (2005). Revised ventilation rate (VE) equations for use in
9 inhalation-oriented exposure models. Report no. EPA/600/X-05/008 is Appendix A of US
10 EPA (2009). Metabolically Derived Human Ventilation Rates: A Revised Approach Based
11 Upon Oxygen Consumption Rates (Final Report). Report no. EPA/600/R-06/129F.
12 Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543.
13 Isaacs, K., Glen, G., McCurdy, T., Smith, L. (2008). Modeling energy expenditure and oxygen
14 consumption in human exposure models: accounting for fatigue and EPOC. J Expos Sci
15 Environ Epidemiol. 18:289-298.
16 Isaacs, K., Smith, L. (2005). New Values for Physiological Parameters for the Exposure Model
17 Input File Physiology.txt. Memorandum submitted to the U.S. Environmental Protection
18 Agency under EPA Contract EP-D-05-065. NERL WA 10. Alion Science and Technology.
19 Found in US EPA . (2009). Risk and Exposure Assessment to Support the Review of the
20 SO2 Primary National Ambient Air Quality Standard. EPA-452/R-09-007. August 2009.
21 Available at
22 http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf
23 Johnson, T., Capel, J., McCoy, M. (1996a). Estimation of Ozone Exposures Experienced by
24 Urban Residents Using a Probabilistic Version of NEM and 1990 Population Data.
25 Prepared by IT Air Quality Services for the Office of Air Quality Planning and Standards,
26 U.S. Environmental Protection Agency, Research Triangle Park, North Carolina,
27 September.
28 Johnson, T., Capel, J., Mozier, J., McCoy, M. (1996b). Estimation of Ozone Exposures
29 Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of
30 NEM. Prepared for the Air Quality Management Division under Contract No. 68-DO-
31 30094, April.
32 Johnson, T., Capel, J., McCoy, M., Mozier, J. (1996c). Estimation of Ozone Exposures
33 Experienced by Outdoor Workers in Nine Urban Areas Using a Probabilistic Version of
34 NEM. Prepared for the Air Quality Management Division under Contract No. 68-DO-
35 30094, April.
36 Johnson, T. (2002). A Guide to Selected Algorithms, Distributions, and Databases Used in
37 Exposure Models Developed By the Office of Air Quality Planning and Standards. Revised
38 Draft. Prepared for U.S. Environmental Protection Agency under EPA Grant No.
39 CR827033.
5A-21
-------
1 McCurdy, T. (2000). Conceptual basis for multi-route intake dose modeling using an energy
2 expenditure approach. JExpo Anal Environ Epidemiol. 10:1-12.
3 McCurdy, T., Glen, G., Smith, L., Lakkadi, Y. (2000). The National Exposure Research
4 Laboratory's Consolidated Human Activity Database. JExp Anal Environ Epidemiol.
5 10:566-578.
6 Rosenbaum, A. S. (2008). The Cluster-Markov algorithm in APEX. Memorandum prepared for
7 Stephen Graham, John Langstaff. USEPA OAQPS by ICF International.
8 Schofield, W. N. (1985). Predicting basal metabolic rate, new standards, and review of previous
9 work. HumNutrClinNutr. 39C(S1):5-41.
10 US EPA. (2002). Consolidated Human Activities Database (CHAD) Users Guide. Database and
11 documentation available at: http://www.epa.gov/chadnetl/.
12 US EPA. (2012a). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model
13 Documentation (TRIM.Expo / APEX, Version 4.4) Volume I: User's Guide. Office of Air
14 Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle
15 Park,NC. EPA-452/B-12-00la. Available at:
16 http://www.epa.gov/ttn/fera/human_apex.html
17 US EPA. (2012b). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model
18 Documentation (TRIM.Expo / APEX, Version 4.4) Volume II: Technical Support
19 Document. Office of Air Quality Planning and Standards, U.S. Environmental Protection
20 Agency, Research Triangle Park, NC. EPA-452/B-12-001b. Available at:
21 http://www.epa.gov/ttn/fera/human_apex.html
22
5A-22
-------
Appendix 5-B
Inputs to the APEX Exposure Model
Table of Contents
5B-1 POPULATION DEMOGRAPHICS 3
5B-2 POPULATION COMMUTING PATTERNS 4
5B-3 ASTHMA PREVALENCE RATES 5
5B-4 HUMAN ACTIVITY DATA 6
5B-4.1 CHAD UPDATES SINCE THE 2007 OZONE NAAQS REVIEW 8
5B-4.2 LONGITUDINAL ACTIVITY PATTERN METHODOLOGY 10
5B-5 PHYSIOLOGICAL AND METABOLIC EQUIVALENTS DATA 13
5B-6 MICROENVIRONMENTS MODELED 14
5B-6.1 AIR EXCHANGE RATES FOR INDOOR RESIDENTIAL
MICROENVIRONMENTS 16
5B-6.2 AIR CONDITIONING PREVALENCE FOR INDOOR RESIDENTIAL
MICROENVIRONMENTS 18
5B-6.3 AER DISTRIBUTIONS FOR OTHER INDOOR ENVIRONMENTS 20
5B-6.4 PROXIMITY AND PENETRATION FACTORS FOR IN-VEHICLE AND
NEAR-ROAD MICROENVIRONMENTS 21
5B-6.5 PROXIMITY AND PENETRATION FACTORS FOR OUTDOOR
MICROENVIRONMENTS 22
5B-6.6 OZONE DECAY AND DEPOSITION RATES 22
5B-7 AMBIENT OZONE CONCENTRATIONS 24
5B-8 METEOROLOGICAL DATA 33
5B-9 REFERENCES 38
5B-1
-------
List of Tables
Table 5B-1. Consolidated Human Activity Database (CHAD) study information and diary-days
used by APEX 11
Table 5B-2. Microenvironments modeled and calculation method used 15
Table 5B-3. AERs for indoor residential microenvironments (ME-1) with A/C by study area and
temperature 17
Table 5B-4. AERs for indoor residential microenvironments (ME-1) without A/C by study area
and temperature 18
Table 5B-5. American Housing Survey A/C prevalence from Current Housing Reports (Table 1-
4) for selected urban areas 19
Table 5B-6. Parameter values for distributions of penetration and proximity factors used for
estimating in-vehicle microenvironmental concentrations 21
Table 5B-7. VMT fractions of interstate, urban, and local roads in the study areas used to select
in-vehicle proximity factor distributions 23
Table 5B-8. Counties and air districts modeled in each study area 26
Table 5B-9. Ambient monitors used to define exposure modeling domain and the population
modeled in each study area 27
Table 5B-10. Study area meteorological stations, locations, and hours of missing data 35
List of Figures
Figure 5B-1. Illustration of APEX exposure modeling domains (2000 US Census tract centroids)
for Atlanta, Boston, Baltimore and Chicago study areas 29
Figure 5B-2. Illustration of APEX exposure modeling domains (2000 US Census tract centroids)
for Cleveland, Dallas, Denver and Detroit study areas 30
Figure 5B-3. Illustration of APEX exposure modeling domains (2000 US Census tract centroids)
for Houston, Los Angeles, New York and Philadelphia study areas 31
Figure 5B-4. Illustration of APEX exposure modeling domains (2000 US Census tract centroids)
for Sacramento, St. Louis and Washington DC study areas 32
5B-2
-------
1 The APEX model inputs require extensive analysis and preparation to ensure the model
2 outputs are appropriate as intended, reasonable, and relevant. This Appendix describes the
3 preparation and the sources of data for the APEX input files.
4 5B-1 POPULATION DEMOGRAPHICS
5 APEX accounts for important population characteristics in representing study area
6 demographics. Population counts and employment probabilities by age and gender are used to
7 develop representative profiles of hypothetical individuals for the simulation. For the main-body
8 results of the REA, we estimated population-based exposures using US Census tract-level
9 population counts stratified by age in one-year increments, from birth to 99 years, and were
10 obtained from the 2000 Census of Population and Housing Summary File 1 (SF1).1 The SF1
11 contains the 100-percent data, which is the information compiled from the questions asked of all
12 people and about every housing unit.
13 Three standard APEX input files are used for the current Os assessment:
14 • pop geo2000011403.txt: census tract ID's, their latitudes and longitudes
15 • pop^fall2000 043003.txt: tract-level population counts for females by age
16 • pop^fall2000 043003.txt: tract-level population counts for males by age
17
18 Census tract employment rates were developed using the Employment Status: 2000-
19 Supplemental Tables.2 The file input to APEX is stratified by gender and age group, so that each
20 gender/age group combination is given an employment probability fraction (ranging from 0 to 1)
21 within each census tract. The age groupings in this employment file are: 16-19, 20-21, 22-24,
22 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75. Children under 16
23 years of age are assumed to not be employed.3
24 One standard APEX input file is used for the current Os assessment:
25 • Employment2000 043003.txt: census tract employment probabilities by age
26 groups
1 http://www.census.gov/census2000/sumfilel.html.
2 http://www.census.gov/population/www/cen2000/phc-t28.html.
3 While children can be employed at ages <16, staff feel that when modeling population-based exposures for these
young children regardless of whether or not they have been designated as being employed, it is likely the overall
study group exposure results would not be significantly affected given the small fraction of the population that may
be employed at these ages and that the principal factor influencing high O3 exposure concentrations is afternoon
time spent outdoors. In such a simulation that included employed children <16, only their home tract to work tract
commuting would be affected and unless they were employed as an outdoor worker (also a further subdivision of
those employed), substantial time spent outdoors is unlikely to occur at work.
5B-3
-------
1 5B-2 POPULATION COMMUTING PATTERNS
2 To more realistically simulate human behavior, APEX incorporates workplace patterns
3 into the assessment by use of home-to-work commuting data. By design, commuting is only
4 used for those simulated individuals who are employed (i.e., > 16 years old). The commuting
5 data were derived from the 2000 Census Transportation Planning Package (CTPP) Part 3-
6 Journey-to-Work (JTW) files.4 These files contain counts of individuals commuting from home
7 to work locations at varying geographic scales. These data were processed to calculate fractions
8 for each tract-to-tract flow to create a national commuting flow file distributed with APEX. This
9 database contains commuting data for each of the 50 states and Washington, D.C. Important
10 processing and application assumptions include the following:
11 • Commuting within the Home Tract: the APEX commuting database does not
12 differentiate people that work at home from those that commute within their home tract.
13 • Commuting Distance Cutoff: all persons in home-work flows up to 120 km are daily
14 commuters and no persons in more widely separated flows commute daily, thus the list of
15 destinations for each home tract was restricted to only those work tracts that are within
16 120 km of the home tract.5
17 • Eliminated Records: tract-to-tract pairs that represented workers who either worked
18 outside of the U.S. (9,631 tract pairs with 107,595 workers) or worked in an unknown
19 location (120,830 tract pairs with 8,940,163 workers) were eliminated. An additional 515
20 workers in the commuting database whose data were missing from the original files,
21 possibly due to privacy concerns or errors, were also deleted.
22 • Simulation of Leavers: we restricted the simulated population to those who do not
23 commute to destinations outside the study area because we have not estimated ambient
24 concentrations of 63 in counties outside of the modeled areas.
4 Files downloaded from http://transtats.bts.gov/.
5 Plotting log(flows) versus log(distance) indicates a near-constant slope out to a distance of approximately 120
kilometers. Beyond that distance, the relationship also had a fairly constant, though less inclined, slope. A simple
interpretation is that for distances up to 120 km, the majority of the flow was due to persons traveling between home
and work tracts daily, with the numbers of such persons decreasing rapidly with increasing distance. Beyond 120
km, the majority of the flow is made up of persons who stay at the workplace for extended times, in which case the
separation distance is not as crucial in determining the flow.
5B-4
-------
1 An additional commuting input file was recently developed as a companion to the APEX
2 commuting flow file. Also derived from the 2000 census are tract-level population counts of
3 one-way commute times, and given in 13 time bins (in minutes): < 5, 5 to 9, 10 to 14, 15 to 19,
4 20 to 24, 25 to 29, 30 to 34, 35 to 39, 40 to 44, 45 to 59, 60 to 89, 90-120, works at home (0
5 minutes commuting time). APEX uses these time bins to create a cumulative probability
6 distribution of commuting times for each tract, which it then uses in conjunction with the
7 distribution of commuting distances to assign a profile-level one-way commuting time variable
8 to each employed person in the population. This commuting time profile variable is then used to
9 select for CHAD diaries having appropriate commute times in their daily activity pattern (i.e., a
10 total time spent in travel locations or activities before and after work activities) to represent the
11 simulated individual.
12 Two standard APEX input files are used for the current Os assessment:
13 • Commuting2000 010505.txt: home/work census tract ID's, cumulative
14 probabilities of commuting to work tract from home tract, distances of home to
15 work tract (km)
16 • CommiitingTimes2000 050610. txt: tract-level counts of all workers, commuters,
17 and commute time bins
18 5B-3 ASTHMA PREVALENCE RATES
19 One of the important study group in the exposure assessment is asthmatic school -
20 age children (ages 5-18). Modeling exposures for this study group with APEX requires the
21 estimation of children's asthma prevalence rates. The estimates are based on children's asthma
22 prevalence data from the 2006-2010 National Health Interview Survey (NHIS). Briefly, 2000
23 US census tract level asthma prevalence was estimated for children (by single age years) and
24 adults (by age groups), also stratified by gender and family income/poverty ratio (i.e., whether
25 the family income was considered below or at/above the US Census estimate of poverty level for
26 the given year). Given the significant differences in asthma prevalence by age, gender, region,
27 and poverty status, the variability in the spatial distribution of poverty status across census tracts
28 (and also stratified by age), and the spatial variability in local scale ambient concentrations of
29 many air pollutants, the goal was to better represent the variability in population-based exposures
30 when accounting for and modeling these newly refined attributes of this study group. A detailed
31 description of how the NHIS data were processed for input to APEX is provided in Appendix
32 5C.
33 One standard APEX input file is used for the current Os assessment:
34 • AsthmaPrevalence053112.txt: tract-level asthma prevalence by age (for ages <18)
35 and age groups (for ages > 17)
5B-5
-------
1 5B-4 HUMAN ACTIVITY DATA
2 Exposure models use human activity pattern data to predict and estimate exposure to
3 pollutants. Different human activities, such as outdoor exercise, indoor reading, or driving,
4 would lead to varying pollutant exposures. In addition, different human activities require
5 different energy expenditures, and thus, higher exposure media consumption rates lead to higher
6 doses received. To accurately model individuals and their exposure to pollutants, it is critical to
7 have a firm understanding of the locations where people spend time and the activities performed
8 in such locations.
9 The Consolidated Human Activity Database (CHAD) provides time series data on human
10 activities through a database system of collected human diaries, or daily time location activity
11 logs (US EPA, 2002). The purpose of CHAD is to provide a basis for conducting multi-route,
12 multi-media exposure assessments (McCurdy et al., 2000). The data contained within CHAD
13 come from multiple surveys with somewhat variable study-specific structure (e.g., minute-by-
14 minute versus time-block averaged sequence of diary events), though common to all studies
15 included, individuals provided information on their locations visited and activities performed for
16 each survey day. Personal attribute data for these surveyed individuals, such as age and gender,
17 are included in CHAD as well. The latest version of CHAD master (071113) contains data for
18 54,373 person-days.
19 The CHAD served as the primary source of time location activity pattern data and was
20 processed to retain appropriate diary data for use by APEX. Diaries with missing personal
21 attribute data (i.e., age, gender), missing diary day information (i.e., either daily mean/ maximum
22 temperature, day-of-week), or having 3-hours or more of missing location and/or activity
23 information are not used by APEX. For the latter case, CHAD diaries were evaluated for
24 instances where a diary may contain enough information for the purposes of this exposure
25 assessment allowing it to be adjusted to reduce the missing information to less than 3 hours on a
26 given day. For example, the diary structure of the ozone averting behavior (OAB) study resulted
27 in nearly all of the diary days (n=2,776) having no diary information between the hours of 8PM
28 and midnight. In processing the CHAD data for this subset of diaries, the location was assumed
29 by staff to be indoors at their residence and persons were engaged in a sleep activity. This
30 substitution was judged by staff as a reasonable approximation based on the limited likelihood of
31 a person's highest 63 exposures occurring at this time of day, while still retaining the relevant
32 activity pattern data of interest (e.g., locations visited and activities performed during the
33 daytime hours).
5B-6
-------
1 The following is a list of adjustments made to CHAD diary data where study specific
2 structure was a factor in missing data or diary information was present in either CHAD location
3 or activity codes to infer specific information where data were missing.
4 • OAB (a children's study) missing location and activity events from 8PM - 12AM
5 were set to 'indoor residence' and 'sleep';
6 • BAL missing activity events at SAM occurring indoors were set to 'personal
7 care';
8 • ISR missing activity events occurring when attending school were set to either
9 'attend K-12' (ages 5-18) or 'attend day-care' (ages <5);
10 • NSA (an adults study) missing activity events at 8PM - 12AM occurring indoor
11 residences were set to 'leisure, general';
12 • Locations missing for a number of staff judged outdoor activities6 were set to
13 'outdoor, general';
14 • Locations missing for a number of staff judged indoor residential activities7 were
15 set to "indoor, residence"; and
16 • Locations missing for a number of staff judged general indoor activities8 were set
17 to "indoor, other".
18 Three standard APEX input files are used for the current 63 assessment:
19 • CHADQuest013013B.txt: personal (e.g., age, gender, employment status, county
20 of residence, etc.) and day (e.g., daily maximum temperature, day-of-week)
21 attribute meta data for each diary day
22 • CHADEvents 013013B. txt: time sequence of locations visited and activities
23 performed by individuals for each diary day
24 • CHADSTATSOutdoor 013013B.txt: time spent outdoors for each diary day
25
26 Table 5B-1 summarizes the studies and number of diary days used by APEX in this
27 modeling analysis, providing over 41,000 diary-days of activity data (nearly 18,000 diary-days
28 for ages 4-18) collected between 1982 and 2010.
29
For CHAD activity codes (US EPA, 2002) "11300", "11630", "17100", "17110", "17112", "17120", "17131" or
"17170".
7 For CHAD activity codes (US EPA, 2002) "11100", "11110", "11200", "11210", "11220", "14000", "14100",
"14110", "14120", "14300", "14400", "14500", "14600", or "17223".
8 For CHAD activity codes (US EPA, 2002) "13300", "13400", "15400", "16300", "16400", or "16500".
5B-7
-------
1 5B-4.1 CHAD Updates Since the 2007 Ozone NAAQS Review
2 Since the time of the prior Os NAAQS review conducted in 2007, there have been
3 a number new data sets incorporated into CHAD and used in our current exposure assessment,
4 most of which were from recently conducted studies. The data from these eight additional
5 studies incorporated in CHAD and available for use by APEX have more than doubled the total
6 activity pattern data used for Os exposure modeling in 2007 and has increased the number of
7 children diaries by a factor of five. The studies from which these new data were derived are
8 briefly described below.
9 • DEA. The diaries are from 2 seasons of the 6-season sampling period (2004-2007) used
10 by EPA in the Detroit Exposure and Aerosol Research Study (DEARS) (Williams et al.,
11 2008). The intent was to obtain environmental samples and time use data for 10 days—5
12 in each of 2 seasons per participant located in 6 areas in Wayne County, Michigan (in and
13 around Detroit). A 15-minute block diary approach was used to collect activity data.
14 Participants were all adults and activity data was collected from Tuesday through
15 Saturday. Just over 300 diary-days from DEARS are used by APEX.
16 • EPA. The diaries were collected as part of an ongoing longitudinal internal EPA study
17 by EPA scientists, and in some cases, their families. This dataset contains two long-term
18 longitudinal diaries: one by a 60 year-old-male in 1999-2000 (McCurdy and Graham,
19 2003), and one by a 35 year old male in 2002. Additional longitudinal diaries were kept
20 for a 35-year-old female and her infant daughter in 2008 (though the infant data are not
21 used here). The remaining diaries are from a study of a group of 9 adults (Isaacs et al.
22 2012). In this portion of the study, all subjects were studied for approximately 17
23 consecutive days in each of 4 seasons in 2006 and 2007. Approximately of 1,400 diary-
24 days are used by APEX.
25 • ISR. The diaries are from phase I (1997), phase II (2002-03), and phase III (2007-08) of
26 the University of Michigan's Panel Study of Income Dynamics (PSID), respectively
27 (University of Michigan, 2012). Nationally representative activity pattern data from
28 nearly 11,000 children ages 0-13 (phase I), ages 5-19 (phase II), and ages 10-19 (phase
29 III) were added to the APEX activity pattern data. For each child, time use data were
30 reported by primary care-givers, school teachers, and/or the children themselves on two
31 nonconsecutive days in a single week, in no particular season, though mostly occurring
32 during the spring and fall (phase I), winter (phase II), and spring, fall and winter (phase
33 III) months.
34 • NSA. The diaries were collected as part of the National-Scale Activity Survey (NSAS),
35 an EPA-funded study of averting behavior related to air quality alerts (Knowledge
36 Networks, 2009). Data were collected from about 1,200 adults aged 35-92 in seven
37 metropolitan areas (Atlanta, St. Louis, Sacramento, Washington DC, Dallas, Houston,
38 and Philadelphia). Data were collected over 1-15 (partially consecutive) days across the
39 2009 ozone season, providing approximately 7,000 person days of data for use by APEX.
40 • OAB. The diaries were collected in a study of children's activities on high and low ozone
41 days during the 2002 ozone season (Mansfield et al., 2009). Children ages 2-12 from 35
42 U.S. metropolitan areas having the worst 63 pollution were studied, and of whom, about
5B-8
-------
1 half of were asthmatics. Activity data were collected on 6 nonconsecutive days from
2 each subject, with some subjects providing fewer days, providing nearly 2,200 persons
3 days of data to APEX.
4 • SEA. The diaries are from a particulate matter (PM) exposure study of susceptible study
5 groups living in Seattle, WA between 1999 and 2002 (Liu et al., 2003). Two cohorts
6 were studied: an older adult group with either chronic obstructive pulmonary disease
7 (COPD) or coronary heart disease and a children's group (ages 6-13) with asthma.
8 Activity data were collected on 10 consecutive days from each subject, with some
9 subjects providing fewer days. Over 1,600 adult diaries and more than 300 children
10 diaries were included in the APEX activity pattern file.
11 • SUP. The diaries are from the SUPERB study (Study of Use of Products and Exposure-
12 Related Behaviors) undertaken by researchers from the University of California at Davis
13 Bennett et al., 2012a; Hertz-Picciotto et al., 2012). The study focused on the use of
14 household and personal care products from 47 California households, 30 with children
15 (ages 1-18) living in 22 counties in northern California, and 17 with an older adult (>55
16 y) living in 3 central California counties. Two days of activity data were obtained via the
17 internet for each participant—a weekday and a weekend day. Approximately 2,500
18 diary-days from SUPERB met appropriate criteria for use in APEX.
19 • RTF. The diaries were collected in a panel study of PM exposure in the Research
20 Triangle Park (RTF), NC area (Williams et al., 2003a, b). Two older adult cohorts (ages
21 55-85) were studied: a cohort having implanted cardiac defibrillators living in Chapel
22 Hill, NC and a second group of 30 people having controlled hypertension and residing in
23 a low-to-moderate SES neighborhood in Raleigh, NC. Data were collected on
24 approximately 8 consecutive days in 4 consecutive calendar seasons in 2000-2001.
25 Approximately 900 diary-days were included from this study.
5B-9
-------
1 5B-4.2 Longitudinal Activity Pattern Methodology
2 An important issue in this assessment is the approach used for creating an Os-season or
3 year-long activity sequence for each simulated individual based on a largely cross-sectional
4 activity database of 24-hour records. The typical subject in the time location activity studies in
5 CHAD provided about two days of diary data. For this reason, the construction of a season-long
6 activity sequence for each individual requires some combination of repeating the same data from
7 one subject and using data from multiple subjects. The best approach would reasonably account
8 for the day-to-day and week-to-week repetition of activities common to individuals (though
9 recognizing even these diary sequences are not entirely correlated) while maintaining realistic
10 variability among individuals comprising each study group.
11 The method currently used in APEX for creating longitudinal diaries was designed to
12 capture the tendency of individuals to repeat activities, based on reproducing realistic variation in
13 a key diary variable, which is a user selected function of diary variables. For this 63 analysis,
14 the key variable selected is the amount of time an individual spends outdoors each day, one of
15 the most important determinants of exposure to high levels of 03. The actual diary construction
16 method targets two statistics, a population diversity statistic (D) and a within-person
17 autocorrelation statistic (^4). The D statistic reflects the relative importance of within- and
18 between-person variance in the key variable. The A statistic quantifies the lag-one (day-to-day)
19 key variable autocorrelation. Further details regarding the longitudinal methodology can be
20 found in US EPA (2013a, b).
21 Desired D and A values for the key variable are selected by the user and set in the APEX
22 parameters file, and the method algorithm constructs longitudinal diaries that preserve these
23 parameters. Longitudinal diary data from a limited field study of children ages 7-12 (Geyh et al.,
24 2000; Xue et al., 2004) estimated values of approximately 0.2 for D and 0.2 for A. In the
25 absence of data for estimating these statistics for younger children and others outside the study
26 age range, and since APEX appears to underestimate repeated activities, values of 0.5 for D and
27 0.2 for A are used for all ages.
5B-10
-------
1 Table 5B-1. Consolidated Human Activity Database (CHAD) study information and diary-days used by APEX.
Study Name (CHAD
Abbreviation)
Baltimore Retirement
Home Study (BAL)
California Youth
Activity Patterns Study
(CAY)
California Adults
Activity Patterns Study
(CAA)
California Children
Activity Patterns Study
(CAC)
Cincinnati Activity
Patterns Study (CIN)
Detroit Exposure and
Aerosol Research
Study (DEA)
Denver CO Personal
Exposure Study (DEN)
EPA Longitudinal
Studies (EPA)
Los Angeles Ozone
Exposure Study:
Elementary School
(LAE)
Los Angeles Ozone
Exposure Study: High
School (LAH)
National Human
Activity Pattern Study:
Air (NHA)
Geographic
Coverage
One building
in Baltimore,
MD
California
California
California
Cincinnati, OH
metro, area
Detroit, Ml
metro, area
Denver, CO
metro, area
RTP, NC
Los Angeles,
CA
Los Angeles,
CA
National
Study Dates
1/1 997 to 2/1 997;
7/1 998 to 8/1 998
10/1 987 to 9/1 988
10/1 987 to 9/1 988
4/1 989 to 2/1 990
3/1 985 to 4/1 985;
8/1985
7/2005 to 8/2005;
7/2006 to 8/2006
11/1 982 to
2/1983
2/1 999 to 2/2000;
2/2002 to 8/2002;
7/2006 to 6/2008
10/1989
9/1 990 to 10/1 990
9/1 992 to 10/1 994
Study
Subject
Ages
72-93
12-17
18-94
<1 -11
<1 -86
18-74
18-70
<1 -60
10-12
13-17
<1 -93
APEX
Diary-days
(ages 4-94)
304
182
1,555
1,195
2,449
331
714
1,417
50
42
4,329
APEX
Diary-days
(ages 4-1 8)
0
182
36
771
727
5
7
0
50
42
693
Diary Type,
Time Format,
Survey Design
Diary, 15 Minute
Block, Panel
Recall, Event,
Random
Recall, Event,
Random
Recall, Event,
Random
Diary, Event,
Random
Recall, 15 Minute
Block, Panel
Diary, Event,
Random
Diary, Event,
Panel
Diary, Event,
Panel
Diary, Event,
Panel
Recall, Event,
Random
Study Reference
Williams et al. (2000)
Robinson et al. (1989),
Wiley etal. (1991 a)
Robinson etal. (1989),
Wiley etal. (1991 a)
Wiley etal. (1991b)
Johnson (1989)
Williams et al. (2008)
Johnson (1984), Akland
etal. (1985)
Isaacs etal. (2012)
Spier etal. (1992)
Spier etal. (1992)
Klepeisetal. (1996),
Tsang and Klepeis
(1996)
5B-11
-------
Study Name (CHAD
Abbreviation)
National Human
Activity Pattern Study:
Water (NHW)
National-Scale Activity
Survey (NSA)
Population Study of
Income Dynamics
PSID 1 (ISR)
Population Study of
Income Dynamics
PSID II (ISR)
Population Study of
Income Dynamics
PSID III (ISR)
RTI Ozone Averting
Behavior (OAB)
RTP Panel (RTP)
Seattle (SEA)
Study of Use of
Products and
Exposure Related
Behavior (SUP)
Washington, D.C.
(WAS)
Totals
Geographic
Coverage
National
7 US metro.
areas
National
National
National
35 US metro.
areas
RTP, NC
Seattle, WA
Broader
Sacramento &
San
Francisco, CA
Counties
Wash. DC
metro, area
Study Dates
9/1 992 to 10/1 994
6/2009 to 9/2009
2/1 997 to 12/1 997
1/2002 to 12/2003
10/2007 to 4/2008
7/2002 to 8/2003
6/2000 to 5/2001
10/1 999 to 3/2002
7/2006 to 3/20 10
11/1 982 to 2/1 983
1982-2010
Study
Subject
Ages
<1 -93
35-92
<1 -13
5- 19
10-19
2-12
55-85
6-91
1 -88
18-71
<1 -94
APEX
Diary-days
(ages 4-94)
4,329
6,825
4,978
4,800
2,650
2,872
871
1,624
3,456
686
41,474
APEX
Diary-days
(ages 4-1 8)
745
0
3,507
4,793
2,614
2,187
0
317
994
10
17,680
Diary Type,
Time Format,
Survey Design
Recall, Event,
Random
Recall, 15 Minute
Block, Random
Recall, 15 Minute
Block,
Random/Panel
Recall, 15 Minute
Block,
Random/Panel
Recall, 15 Minute
Block,
Random/Panel
Recall, 15 Minute
Block, Random
Diary, 15 Minute
Block, Panel
Diary, 15 Minute
Block, Panel
Recall, 15 Minute
Block, Panel
Diary, Event,
Random
Study Reference
Klepeisetal. (1996),
Tsang and Klepeis
(1996)
Knowledge Networks
(2009)
University of Michigan
(2012)
University of Michigan
(2012)
University of Michigan
(2012)
Mansfield et al. (2006,
2009)
Williams et al. (2003a,b)
Liu et al. (2003)
Bennett et al. (201 2a),
Hertz-Picciotto et al.
(2010)
Hartwell et al. (1984),
Aklandetal. (1985)
5B-12
-------
1 5B-5 PHYSIOLOGICAL AND METABOLIC EQUIVALENTS DATA
2 APEX requires several physiological parameters to accurately model processes
3 that affect pollutant intake rate for individuals. This is because differences in physiology may
4 cause people with the same exposure and activity scenarios to have different pollutant intake
5 levels. The physiological parameters file used by APEX contains individual data or data
6 distributions stratified by age and gender for maximum ventilatory capacity (in terms of age- and
7 gender-specific maximum oxygen consumption potential, NVC^max), body mass (BM), resting
8 metabolic rate (RMR), body surface area (BSA), maximum oxygen deficits (MOXD) and
9 associated recovery time (RECTIME), height, and oxygen consumption-to-ventilation rate
10 relationships (ECF), among a few others not used for estimating Os exposure and dose).
11 APEX also uses an input file containing the metabolic equivalents for work (METS) to
12 estimate the specific energy expended for each activity listed in the diary file. These METS
13 values are commonly in the form of distributions and were originally derived as relative to an
14 individual's RMR. Some activities are specified as a single point value (for instance, sleep),
15 while others, such as athletic endeavors or manual labor, are normally, lognormally, or otherwise
16 statistically distributed. APEX samples from these distributions and calculates values to
17 simulate the variable nature of activity levels among different people. These personal- and
18 activity-level physiological variables are ultimately used to estimate ventilation rate (VE) and
19 decrements in forced expiratory volume, in one second (dFEVi).
20 Three standard APEX input files are used for the current 63 assessment:
21 • PhysiologyO 10213 thresholdtxt: NVO2max,BM, RMR, BSA, MOXD,
22 RECTIME, height, ECF, and dFEVi distributions and equation coefficients, by
23 sex and age groups
24 • MET Distributions 030612. txt: statistical form and parameters for METS
25 distributions associated with each activity performed, some by age groups
26 • Ventilation 121106.txt: distributions and equation coefficients to estimate
27 individual activity- specific VE by sex and age groups
28
5B-13
-------
1 5B-6 MICROENVIRONMENTS MODELED
2 In APEX, exposure for simulated individuals occurs in microenvironments. For
3 exposures to be accurately estimated, it is important maintain the spatial and temporal sequence
4 of microenvironments persons inhabit and appropriately represent the time series of
5 concentrations that occur within them. As discussed in Appendix 5A, the two methods available
6 in APEX for calculating pollutant concentrations within microenvironments are a mass balance
7 model and a transfer factor approach, each of which uses an appropriate ambient pollutant
8 concentration to estimate the microenvironmental concentration. Table 5B-2 lists the 28
9 microenvironments selected for this analysis and the exposure calculation method for each. The
10 variables used and their associated parameters to calculate microenvironmental concentrations
11 are described in subsequent subsections below.
12 The CHAD database has 115 locations codes, many of which go beyond the scale
13 of the microenvironmental modeling (e.g., inside at residence in a bedroom). Therefore these
14 more specific locations are aggregated by mapping these 115 location codes to the 28 modeled
15 microenvironments. Further, all microenvironmental concentrations in this exposure
16 assessement are estimated using an ambient concentration (section 5B-7), though these
17 concentrations not only vary temporally but spatially, depending on the particular
18 microenvironment. The mapping of locations to the 28 microenvironments also includes an
19 identifier that designates what ambient concentration is used in the calculation of the
20 microenvironmental concentration for each event. For this assessment, we used ambient
21 concentration for each individual based on either their home (H), work (W), near work (NW),
22 near home (NH), last (L, either NH or NW), other (O, average of all), or unknown (U, last ME
23 determined) tracts.
24 Multiple APEX ME input files are used for the current Os assessment, varying by study
25 area though given in one form. Only one ME mapping file is used:
26 • ME descriptions 28MEsO3 CSA[studyarea.]J^date].txt: defines calculation
27 method, variables and their parameters used to estimate all microenvironmental
28 concentrations
29 • MicroEnv Mapping CHADto_APEX_28MEs 022613. txt: maps 115 CHAD
30 locations to 28 APEX microenvironments and defines tract-level ambient
31 concentrations to use for each location
32
5B-14
-------
1 Table 5B-2. Microenvironments modeled and calculation method used.
Microenvironment (ME)
Indoor- Residence
Indoor - Community Center or Auditorium
Indoor- Restaurant
Indoor- Hotel, Motel
Indoor- Office building, Bank, Post office
Indoor- Bar, Night club, Cafe
Indoor- School
Indoor- Shopping mall, Non-grocery store
Indoor- Grocery store, Convenience store
Indoor- Metro-Subway-Train station
Indoor- Hospital, Medical care facility
Indoor- Industrial, factory, warehouse
Indoor- Other indoor
Outdoor- Residential
Outdoor - Park or Golf course
Outdoor - Restaurant or Cafe
Outdoor - School grounds
Outdoor - Boat
Outdoor - Other outdoor non-residential
Near-road - Metro-Subway-Train stop
Near-road -Within 10 yards of street
Near-road - Parking garage (covered or below
ground)
Near-road - Parking lot (open), Street parking
Near-road - Service station
Vehicle - Cars and Light Duty Trucks
Vehicle - Heavy Duty Trucks
Vehicle - Bus
Vehicle - Train, Subway
AEPX ME
Number
1
2
3
4
5
6
7
8
9
10
11
26
27
12
14
15
16
25
13
17
18
19
20
21
22
28
23
24
Calculation
Method
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Factors
Parameters1
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
AER and DE
None
None
None
None
None
None
PR
PR
PR
PR
PR
PE and PR
PE and PR
PE and PR
PE and PR
AER = air exchange rate, DE = decay-deposition rate, PR = proximity factor, PE = penetration factor.
5B-15
-------
1 5B-6.1 Air Exchange Rates for Indoor Residential Microenvironments
2 Distributions of air exchange rates (AERs) for the indoor residential microenvironments
3 (ME-1) were developed using data from several studies. The analysis of these data and the
4 development of most of the distributions used in the modeling were originally described in detail
5 in US EPA (2007) Appendix A, though recently updated by Cohen et al. (2012) and provided in
6 Appendix 5E.
7 The analyses indicated that the AER distributions for the residential microenvironments
8 depend on the type of air conditioning (A/C) and on the outdoor temperature, among other
9 variables for which we do not have sufficient data to estimate. These analyses demonstrate that
10 the AER distributions vary greatly across cities, A/C types, and temperatures, so that the selected
11 AER distributions for the modeled cities should also depend on these attributes. For example,
12 the mean AER for residences with A/C ranges from 0.38 in Research Triangle Park, NC at
13 temperatures > 25 °C upwards to 1.244 in New York, NY considering the same temperature bin.
14 For each combination of A/C type, city, and temperature with a minimum of 11 AER
15 values, exponential, lognormal, normal, and Weibull distributions were fit to the AER values and
16 compared. Generally, the lognormal distribution was the best-fitting of the four distributions,
17 and so, for consistency, the fitted lognormal distributions are used for all the cases. Los Angeles
18 had an adequate number of samples and identifiers to distinguish the estimated AER
19 distributions by central A/C and room unit A/C for the homes with A/C.
20 There were a number of limitations in generating study-area specific AER stratified by
21 temperature and A/C type. For example, AER data and derived distributions were available only
22 for selected cities, and yet the summary statistics and comparisons demonstrate that the AER
23 distributions depend upon the city as well as the temperature range and A/C type. As a result,
24 city-specific AER distributions were used where possible; otherwise staff selected AER data
25 from a similar city. Another important limitation of the analysis was that distributions were not
26 able to be fitted to all of the temperature ranges due to limited number of available measurement
27 data in these ranges. A description of how these limitations were addressed can be found in
28 Appendix 5E. The AER distributions used for the exposure modeling are given in Table 5B-3
29 (Residences with A/C) and Table 5B-4 (Residences without A/C).
30
5B-16
-------
Table 5B-3. AERs for indoor residential microenvironments (ME-1) with A/C by study area
and temperature.
Study Area
Atlanta, Baltimore,
Washington DC
Boston, New York,
Philadelphia
Chicago, Cleveland,
Detroit
Dallas, Houston
Denver, St. Louis
Los Angeles
(Central A/C)
Los Angeles
(Room Unit A/C)
Sacramento
Daily Mean
Temperature
(°Q
< 10
10-20
20-25
>25
< 10
10-25
>25
< 10
10-20
20-25
>25
<20
20-25
25-30
>30
< 10
10-20
20-25
25-30
>30
<20
20-25
>25
<20
20-25
>25
<25
>25
Lognormal
Distribution
(GM, GSD, min, max}
(0.962, 1.809,0.1, 10}
(0.562, 1.906,0.1, 10}
(0.397, 1.889,0.1, 10}
(0.380, 1.709,0.1, 10}
(0.711,2.108,0.1, 10}
(1.139,2.677,0.1, 10}
(1.244,2.177,0.1, 10}
(0.744, 1.982,0.1, 10}
(0.811,2.653,0.1, 10}
(0.785,2.817,0.1, 10}
(0.916,2.671,0.1, 10}
(0.407,2.113,0.1, 10}
(0.467, 1.938,0.1, 10}
(0.422,2.258,0.1, 10}
(0.499, 1.717,0.1, 10}
(0.921, 1.854,0.1, 10}
(0.573, 1.990,0.1, 10}
(0.530,2.427,0.1, 10}
(0.527,2.381,0.1, 10}
(0.609,2.369,0.1, 10}
(0.577, 1.897,0.1, 10}
(1.084,2.336,0.1, 10}
(0.861,2.344,0.1, 10}
(0.672, 1.863,0.1, 10}
(1.674,2.223,0.1, 10}
(0.949, 1.644,0.1, 10}
(0.503, 1.921,0.1, 10}
(0.830,2.353,0.1, 10}
Original AER Study
Data Used
Research Triangle Park,
NC
New York, NY
Detroit, MI and New
York, NY
Houston, TX
All Cities Outside of CA
Los Angeles, CA
Los Angeles, CA
Sacramento, Riverside,
San Bernardino Counties
5B-17
-------
Table 5B-4. AERs for indoor residential microenvironments (ME-1) without A/C by study area
and temperature.
Study Area
Atlanta, Baltimore,
Denver, St. Louis,
Washington DC
Boston, New York,
Philadelphia
Chicago, Cleveland,
Detroit
Dallas, Houston
Los Angeles
Sacramento
Daily Mean
Temperature
(°Q
< 10
10-20
>20
< 10
10-20
>20
<0
0- 10
10-20
20-25
>25
< 10
10-20
>20
<20
20-25
>25
< 10
10-20
20-25
>25
Lognormal
Distribution
(GM, GSD, min, max}
(0.923, 1.843,0.1, 10}
(0.951,2.708,0.1, 10}
(1.575,2.454,0.1, 10}
(1.016,2.138,0.1, 10}
(0.791,2.042,0.1, 10}
(1.606,2.119,0.1, 10}
(1.074, 1.772,0.1, 10}
(0.760, 1.747,0.1, 10}
(1.447,2.950,0.1, 10}
(1.531,2.472,0.1, 10}
(1.901,2.524,0.1, 10}
(0.656, 1.679,0.1, 10}
(0.625,2.916,0.1, 10}
(0.916,2.451,0.1, 10}
(0.744,2.057,0.1, 10}
(1.448,2.315,0.1, 10}
(0.856,2.018,0.1, 10}
(0.526,3.192,0.1, 10}
(0.665,2.174,0.1, 10}
(1.054, 1.711,0.1, 10}
(0.827,2.265,0.1, 10}
Original AER Study
Data Used
All Cities Outside of CA
New York, NY
Detroit, MI and New
York, NY
Houston, TX
Los Angeles, CA
Sacramento, Riverside,
San Bernardino Counties
1 5B-6.2 Air Conditioning Prevalence for Indoor Residential MicroEnvironments
2 The selection of an AER distribution is conditioned on the presence or absence of
3 A/C. We assigned this housing attribute to indoor residential microenvironments (ME-1) using
4 A/C prevalence data from the American Housing Survey (AHS)9. A/C prevalence is noted as
5 distinct from usage rate, the latter represented by the AER distribution and dependent on
6 temperature. The A/C prevalence data were assigned to our study areas where the AHS data best
7 matched our exposure simulation years (Table 5B-5). Because we were able to stratify the AER
8 distributions by three A/C types in Los Angeles, both the individual central and room unit values
9 were used. In all other study areas, the sum of room unit and central A/C prevalence was used.
' Available at: http://www.census.gov/housing/ahs/data/metro.html.
5B-18
-------
1 Table 5B-5. American Housing Survey A/C prevalence from Current Housing Reports (Table 1-4) for selected urban areas.
Metropolitan
Area
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles3
New York4
Philadelphia
Sacramento
St. Louis
Washington, DC
Area1
MA
MSA
CMSA
PMSA
PMSA
PMSA
MA
PMSA
PMSA
PMSA
PMSA
PMSA
PMSA
MA
MA
Study
Years
2004
2007
2007
2009
2004
2002
2004
2009
2007
2003
2009
2009
2004
2004
2007
Total Occupied
Housing Units
(xlOOO)
1595.8
1012.3
1057.1
3010.7
769.3
1235.3
855.7
1672.5
1872.0
5152.4
4493.3
1916.2
669.4
1139.6
1949.1
Number of Occupied Housing Units (xlOOO) with:
Central
A/C
1473.8
785.4
291.5
2050.6
416.4
1146.3
425.1
1194.3
1682.5
2448.4
872.4
1095.9
549.5
974.4
1729.6
>1 Central
A/C
245.9
44.4
20.3
116.2
14.1
171.6
16.8
46.5
153.7
161.6
38.2
52.3
30.7
53.7
145.7
1 Room
Unit
39.8
55.7
259.4
412.0
132.9
25.5
123.8
192.3
46.0
702.1
1036.9
197.9
57.1
65.8
69.2
2 Room
Units
32.5
72.4
198.7
265.1
47.0
29.8
20.3
82.8
59.9
118.6
1184.1
260.8
12.3
43.5
64.8
3+ Room
Units
18.3
65.4
156.0
124.4
17.6
27.8
3.9
29.2
60.6
46.9
812.6
265.8
2.4
16.6
61.2
% of Occupied Housing Units with:2
Central
A/C
92
78
28
68
54
92.8
50
71
90
47.5
19
57
82
86
89
Window
Units
6
19
58
27
26
6.7
17
18
9
16.8
68
38
11
11
10
Central &
Window A/C
98
97
86
95
80
99.5
67
90
99
64.4
87
95
93
97
99
MA - metropolitan area; CMSA - consolidated metropolitan statistical area; PMSA - primary metropolitan statistical area.
2 Shaded areas indicate final values used in APEX functions files to select AER distributions used for indoor residential microenvironments (ME-1).
3 Los Angeles includes Los Angeles-Long Beach, Riverside-San Bernardino-Ontario, and Anaheim-Santa Ana MSA's.
4 New York is represented by the NY-Nassau-Suffolk-Orange MSA.
5B-19
-------
1 5B-6.3 AER DISTRIBUTIONS FOR OTHER INDOOR ENVIRONMENTS
2 To estimate AER distributions for non-residential, indoor environments (e.g.,
3 offices, libraries), we obtained and analyzed two AER data sets: "Turk" (Turk et al., 1989); and
4 "Persily" (Persily and Gorfain, 2004; Persily et al., 2005). The Turk data set includes 40 AER
5 measurements from offices (25 values), schools (7 values), libraries (3 values), and multi-
6 purpose buildings (5 values), each measured using an SFe tracer over two or four hours in
7 different seasons of the year. The Persily data were derived from the US EPA Building
8 Assessment Survey and Evaluation (BASE) study, which was conducted to assess indoor air
9 quality, including ventilation, in a large number of randomly selected office buildings throughout
10 the US. This data base consists of 390 AER measurements in 96 large, mechanically ventilated
11 offices. AERs were measured both by a volumetric method and by a CC>2 ratio method, and
12 included their uncertainty estimates. For these analyses, we used the recommended "Best
13 Estimates" defined by the values with the lower estimated uncertainty; in the vast majority of
14 cases the best estimate was from the volumetric method.
15 Due to the small sample size of the Turk data, the data were analyzed without
16 stratification by building type and/or season. For the Persily data, the AER values for each office
17 space were averaged, rather using the individual measurements, to account for the strong
18 dependence of the AER measurements for the same office space over a relatively short period.
19 The mean values are similar for the two studies, but the standard deviations are about twice as
20 high for the Persily data. We fitted exponential, lognormal, normal, and Weibull distributions to
21 the 96 office space average AER values from the more recent Persily data, and the best fitting of
22 these was the lognormal. The fitted parameters for this distribution are a geometric mean of
23 1.109, geometric standard deviation of 3.015, and bounded by the lower and upper values of the
24 sample data set (0.07, 13.8}. These are used for AER distributions for several indoor non-
25 residential microenvironments (ME-2, ME-4, ME-5, ME-8, ME-9, ME-10, ME-11, ME-26)
26 except for indoor schools (ME-7) and indoor restaurants, bars, night clubs, and cafes (ME-3 and
27 ME-6).
28 The AER distribution used for indoor schools (ME-7) is a discrete distribution (0.8, 1.3,
29 1.8, 2.19, 2.2, 2.21, 3.0, 0.6, 0.1, 0.6, 0.2, 1.8, 1.3, 1.2, 2.9, 0.9, 0.9, 0.9, 0.9, 0.4, 0.4, 0.4, 0.4,
30 0.9, 0.9, 0.9, 0.9, 0.3, 0.3, 0.3, 0.3} developed using data from Turk et al. (1989) and Shendell et
31 al. (2004).
32 The AER distribution used for indoor restaurants, bars, night clubs, and cafes (ME-3,
33 ME-6) is a fitted lognormal distribution, having a geometric mean = 3.712, geometric standard
34 deviation = 1.855 and bounded by the lower and upper values of the sample data set (1.46,
35 9.07}. This distribution was developed using data from Bennett et al. (2012b), who measured
36 these six values in restaurants (details on derivation provided in Appendix 5E).
5B-20
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
5B-6.4 Proximity and Penetration Factors for In-vehicle and Near-Road
Microenvironments
For the in-vehicle proximity and penetration factors (ME-22, ME-23, ME-24,
ME-28), we use distributions developed from the Cincinnati Ozone Study (American Petroleum
Institute, 1997, Appendix B; Johnson et al., 1995). This field study was conducted in the greater
Cincinnati metropolitan area in August and September, 1994. Vehicle tests were conducted
according to an experimental design specifying the vehicle type, road type, vehicle speed, and
ventilation mode. Vehicle types were defined by the three study vehicles: a minivan, a full-size
car, and a compact car. Road types were interstate highways (interstate), principal urban arterial
roads (urban), and local roads (local). Nominal vehicle speeds (typically met over one minute
intervals within 5 mph) were at 35 mph, 45 mph, or 55 mph. Ozone concentrations were
measured inside the vehicle, outside the vehicle, and at six fixed-site monitors in the Cincinnati
area. Table 5B-6 lists the parameters of the normal distributions developed for penetration and
proximity factors for in-vehicle microenvironments used in this modeling analysis.
Table 5B-6. Parameter values for distributions of penetration and proximity
factors used for estimating in-vehicle microenvironmental concentrations.
Microenvironmental
Factor
Penetration
Proximity
Road type
All
Local
Urban
Interstate
Arithmetic
Mean
0.300
0.755
0.754
0.364
Standard
Deviation
0.232
0.203
0.243
0.165
Lower
Bound1
0.100
0.422
0.355
0.093
Upper
Bound
1.0
1.0
1.0
1.0
18
19
A 5 percentile value estimated using a normal approximation as Mean - 1.64 x standard deviation.
5B-21
-------
1 The Vehicle Miles of Travel (VMT) fractions10 provided by the U.S. Department of
2 Transportation (DOT) are used to generate daily conditional variables that determine the
3 selection of which proximity factor distributions are used to estimate in-vehicle
4 microenvironmental concentrations (Table 5B-7). For local and interstate road types, the VMT
5 for the same DOT categories were used. For urban roads, the VMT for all other DOT road types
6 were summed (i.e., other freeways/expressways, other principal arterial, minor arterial, and
7 collector). At the time of this writing, data were only available for four of our modeled years,
8 2006-2008 and 2010. Staff assumed that values for 2009 would be best represented by averaging
9 2008 and 2010.
10 For all outdoors-near-road microenvironments (ME-17, ME-18, ME-19, ME-20, ME-21)
11 we employed the distribution for local roads (i.e., a normal distribution (0.755, 0.203}, bounded
12 by 0.422 and 1.0), based on the assumption that most of the outdoors-near-road ozone exposures
13 will occur proximal to local roads.
14 5B-6.5 Proximity and Penetration Factors for Outdoor Microenvironments
15 All outdoor microenvironments (ME-12, ME-13, ME-14, ME-15, ME-16, ME-25) are
16 assumed well represented by the census tract level Os concentrations. Therefore, both the
17 penetration factor and proximity factor for this microenvironment were set to equal 1.
18 5B-6.6 Ozone Decay and Deposition Rates
19 A distribution for combined Oj decay and deposition rates was obtained from the
20 analysis of measurements from a study by Lee et al. (1999). This study measured decay rates in
21 the living rooms of 43 residences in Southern California. Measurements of decay rates in a
22 second room were made in 24 of these residences. The 67 decay rates range from 0.95 to 8.05
23 hour"1. A lognormal distribution was fit to the measurements from this study, yielding a
24 geometric mean of 2.51 and a geometric standard deviation of 1.53. These values are
25 constrained to lie between 0.95 and 8.05 hour"1. This distribution was used for all indoor
26 microenvironments.
10 U.S. Department of Transportation, Federal Highway Administration. Annual Highway Statistics, Table HM-71:
Urbanized Areas - Miles And Daily Vehicle Miles Of Travel. For example, 2010 data available at:
www.fhwa.dot.gov/policvinformation/statistics/2010/xls/hm71 .xls
5B-22
-------
1 Table 5B-7. VMT fractions of interstate, urban, and local roads in the study areas used to select in-vehicle proximity factor distributions.
Study Area
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Wash., DC
2006
Inter-
state Urban Local
0.34 0.46 0.20
0.34 0.59 0.07
0.32 0.55 0.13
0.30 0.58 0.12
0.40 0.44 0.16
0.30 0.66 0.04
0.23 0.67 0.10
0.26 0.64 0.10
0.24 0.72 0.04
0.29 0.66 0.05
0.19 0.66 0.15
0.23 0.65 0.12
0.25 0.72 0.03
0.36 0.45 0.19
0.31 0.61 0.08
2007
Inter-
state Urban Local
0.34 0.47 0.19
0.34 0.59 0.07
0.32 0.55 0.13
0.30 0.58 0.12
0.40 0.44 0.16
0.30 0.66 0.04
0.24 0.66 0.10
0.25 0.65 0.10
0.24 0.72 0.04
0.29 0.67 0.04
0.19 0.65 0.16
0.24 0.65 0.11
0.25 0.70 0.05
0.37 0.45 0.18
0.31 0.61 0.08
2008
Inter-
state Urban Local
0.32 0.45 0.23
0.34 0.59 0.07
0.32 0.54 0.14
0.31 0.57 0.12
0.39 0.45 0.16
0.30 0.65 0.05
0.25 0.65 0.10
0.24 0.66 0.10
0.24 0.72 0.04
0.28 0.67 0.05
0.19 0.66 0.15
0.24 0.65 0.11
0.23 0.69 0.08
0.38 0.45 0.17
0.30 0.61 0.09
2009
Inter-
state Urban Local
0.31 0.44 0.25
0.34 0.59 0.07
0.32 0.54 0.14
0.30 0.57 0.13
0.39 0.45 0.16
0.30 0.66 0.04
0.25 0.65 0.10
0.25 0.65 0.10
0.24 0.72 0.04
0.29 0.66 0.05
0.19 0.66 0.15
0.20 0.59 0.21
0.23 0.69 0.08
0.37 0.45 0.18
0.30 0.61 0.09
2010
Inter-
state Urban Local
0.30 0.43 0.27
0.34 0.59 0.07
0.32 0.54 0.14
0.31 0.56 0.13
0.38 0.46 0.16
0.29 0.67 0.04
0.25 0.65 0.10
0.26 0.63 0.11
0.24 0.72 0.04
0.29 0.66 0.05
0.19 0.65 0.16
0.18 0.52 0.30
0.23 0.69 0.08
0.36 0.44 0.20
0.30 0.61 0.09
2 A few individual fractions have been adjusted to yield an annual sum of 1.00.
5B-23
-------
1 5B-7 AMBIENT OZONE CONCENTRATIONS
2 To estimate exposure in this assessment, APEX requires hourly ambient Os
3 concentrations at a set of locations (or air districts) within study area. We used hourly ambient
4 monitoring data along with a statistical approach (VNA) to better approximate spatial
5 heterogeneity (where such heterogeneity might be present) across each study area (Os REA,
6 Chapter 4). General processing steps performed to generate the final APEX ambient
7 concentration input files that were used were as follows.
8 After identifying the 15 study areas to be modeled in this assessment, staff defined a
9 broad air quality modeling domain for each study area, specifically bounding where exposures
10 were to be estimated. We evaluated 1) counties modeled in the previous 2007 Os NAAQS
11 review common to current study areas, 2) political/statistical county aggregations (MSA,
12 PMSA), and 3) if the study area was designated as a non-attainment area (NAA), the counties
13 that were part of the NAA list. A final list of counties was generated using this information
14 (Table 5B-8), then hourly Oj, concentrations were estimated at every census tract within the
15 counties that comprised each study area (63 REA, Chapter 4). These data served as the air
16 quality input to APEX with some exception (see below), though note also, not all of the
17 estimated hourly concentrations would be used in the exposure simulation even if supplied to
18 APEX.
19 As was done in the first draft REA, a 30 km radius of influence was used for each
20 monitoring site within the above county-level defined study. All census tracts that fell within the
21 30 km radius of each ambient monitor used to estimate the air quality concentration fields were
22 selected, then any tracts/monitor radii that were largely outside of the urban core were removed,
23 thus defining a final exposure modeling domain in each study area (Table 5B-8).
24 Because APEX uses 2000 census population data and the air concentrations were
25 modeled to 2010 census tracts, some of the air district locations differed slightly from that of the
26 exposure tracts, resulting in different numbers of air districts when compared with the number of
27 census tracts used in simulating exposures. This difference is expected to have a negligible
28 effect on exposure and risk results because APEX always uses the air district nearest to the tract
29 to be modeled, the distances between any two air district centroids within these urban study areas
30 (census tract level) is expected to be small, and the concentration gradient across that said
31 distance is also expected to not be significant.
5B-24
-------
1 Further, staff had computational difficulty in simulating the large number of tracts and air
2 districts for the Los Angeles, New York, and Chicago study areas (number and size of arrays
3 needed in APEX calculations was beyond the standard PC capabilities); based on simulations
4 that ran to completion, the maximum number of air districts possible using a standard 32-bit PC
5 was estimated as 1,900 - 2,000. Thus, to make the analysis more tractable for these study areas,
6 first staff reduced the number of air districts originally modeled (i.e., all year 2010 US tracts in
7 the broad county domain) to the number needed for the actual year 2000 census tracts in the
8 exposure model domain (i.e., all tracts within 30 km of ambient monitors in the broad county
9 domain). Using this approach, the number of air districts was reduced to the following: Chicago
10 (1,882), Los Angeles (3,268), and New York (4,646). For Los Angeles and New York, the
11 number of air districts was reduced to 2,000 and 1,900 using simple random sampling of these
12 tracts using SAS's SURVEYSELECT procedure; the number of air districts for Chicago
13 remained at 1,882. While we estimated this number of districts would run on a standard PC,
14 these three study areas would only run on a 64-bit PC.
15 The final list of year 2000 census tract IDs where exposure was modeled is within the
16 APEX control files. The final list of 2010 census tract IDs where ambient concentrations were
17 estimated is within the APEX air districts files. Table 5B-9 contains the final list of counties, the
18 number of US census tracts where exposures were estimated, the number air districts ultimately
19 used from the air quality input files, and the population counts represented in each study area.
20 The final list of year 2000 census tract IDs where exposure was modeled is within the APEX
21 control files. The final list of 2010 census tract IDs where ambient concentrations were
22 estimated is within the APEX air districts files. Figure 5B-1 through Figure 5B-4 illustrate the
23 general exposure modeling domains (i.e., the selected census tract centroids falling within 30 km
24 of a ambient monitor) for each of the 15 study areas.
25 Multiple unique APEX input files are used for the current O?, assessment, varying by the
26 air quality scenario, year, and study area, though generally in two forms:
27 • concsCSA[studyarea]S'[scenario]'P[std. avg.periodJYfyearJ.txt: hourly
28 concentrations for each tract, by study area, air quality scenario, standard
29 averaging period, year
30 • districtsCSAfstudyareaJYfyearJ.txt: tract ID's, latitudes and longitudes, start and
31 stop dates of concentrations
5B-25
-------
1 Table 5B-8. Identification of U.S. counties and the number of APEX air districts included each study area.
Study Area (State Abbreviation: List of Counties1)
Atlanta (GA: Barrow, Bartow, Butts, Carroll, Cherokee, Clayton, Cobb, Coweta, Dawson, De Kalb, Douglas, Fayette, Forsyth,
Fulton, Gwinnett, Hall, Haralson, Heard, Henry, Jasper, Lamar, Meriwether, Newton, Paulding, Pickens, Pike, Polk, Rockdale,
Spalding, Troup, Upson, Walton; AL Chambers)
Baltimore (MD: Anne Arundel, Baltimore, Carroll, Harford, Howard, Queen Anne's, Baltimore (City))
Boston (MA: Barnstable, Bristol, Dukes, Essex, Middlesex, Nantucket, Norfolk, Plymouth, Suffolk, Worcester)
Chicago (IL: Cook, DeKalb, DuPage, Grundy, Kane, Kankakee, Kendall, Lake, McHenry, Will; IN: Jasper, Lake, LaPorte,
Newton, Porter, Kenosha)
Cleveland (OH: Ashtabula, Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, Summit)
Dallas (TX: Collin, Dallas, Denton, Ellis, Hunt, Johnson, Kaufman, Parker, Rockwall, Tarrant, Wise)
Denver (CO: Adams, Arapahoe, Boulder, Broomfielcf ', Clear Creek, Denver, Douglas, Elbert, Gilpin, Jefferson, Larimer, Park,
Weld)
Detroit (Ml: Genesee, Lapeer, Livingston, Macomb, Monroe, Oakland, St. Clair, Washtenaw, Wayne)
Houston (TX: Austin, Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, San Jacinto, Waller)
Los Angeles, (CA: Los Angeles, Orange, Riverside, San Bernardino, Ventura)
New York (CT: Fairfield, Middlesex, New Haven; NJ: Bergen, Essex, Hudson, Hunterdon, Mercer, Middlesex, Monmouth,
Morris, Passaic, Somerset, Sussex, Union, Warren; NY: Bronx, Kings, Nassau, New York, Orange, Putnam, Queens,
Richmond, Rockland, Suffolk, Westchester)
Philadelphia (DE: New Castle, MD: Cecil; NJ: Atlantic, Burlington, Camden, Cape May, Cumberland, Gloucester, Ocean,
Salem; PA: Bucks, Chester, Delaware, Montgomery, Philadelphia)
Sacramento (CA: El Dorado, Nevada, Placer, Sacramento, Solano, Sutter, Yolo)
St. Louis (IL: Bond, Calhoun, Clinton, Jersey, Macoupin, Madison, Monroe, Saint Clair, MO: Crawford, Franklin, Jefferson,
Lincoln, Saint Charles, Saint Louis, Warren, Washington, St. Louis City)
Washington, DC (District of Columbia; MD: Calvert, Charles, Frederick, Montgomery, Prince George's, St. Mary's, VA:
Arlington, Clarke, Culpeper, Fairfax, Fauquier, Frederick, Loudoun, Prince William, Spotsylvania, Stafford, Warren, Alexandria
City, Fairfax City, Falls Church City, Fredericksburg City, Manassas City, Manassas Park City, Winchester City, WV: Jefferson)
All AREAS (Counties: 207 Exposure of 215 Air Quality)
APEX Air Districts
(VNA Total)
664(1,019)
603 (679)
1,005(1,276)
1,882(2,267)
802 (830)
1012(1,312)
655 (839)
1,419(1,568)
779 (1 ,074)
2,000 (3,920)
1,900(5,003)
1,452(1,735)
447 (623)
494 (626)
1,013(1,391)
16,127(24,162)
2
O
italicized: in air quality do main but not in exposure modeling domain; considered outside of urban core or no monitors.
2 this county is newly defined in the 2010 census.
5B-26
-------
1 Table 5B-9. Ambient monitors used to define exposure modeling domain and the population modeled in each study area.
Study Area (State Abbreviation: List of Monitors1)
Atlanta (GA: 130590002, 130670003, 130770002, 130850001, 130890002, 130893001, 130970004, 131130001,
131210055, 131350002, 131510002, 132230003, 132319991, 132470001)
Baltimore (MD: 240030014, 240051007, 240053001, 240130001, 240251001, 240259001, 240290002, 240313001,
240330030, 240338003, 240339991 , 245100054)
Boston (MA: 250092006, 250094004, 250094005, 250095005, 250170009, 250171102, 250213003, 250250041,
250250042, 250270015, 250270024; NH: 330111011; Rl: 440071010)
Chicago (IL: 170310001, 170310032, 170310042, 170310064, 170310072, 170310076, 170311003, 170311601,
170314002, 170314007, 170314201, 170317002, 170436001, 170890005, 170971002, 170971007, 171110001,
171971011; IN: 180890022, 180890030, 180892008, 180910005, 180910010, 181270024, 181270026, Wl:
550590019, 551010017, 551270005)
Cleveland (OH: 390071001, 390350034, 390350060, 390350064, 390355002, 390550004, 390850003, 390850007,
390853002, 390930018, 391030003, 391030004, 391331001, 391510016, 391514005, 391530020)
Dallas (TX: 480850005, 481130069, 481130075, 481130087, 481133003, 481210034, 481211032, 481390015,
481390016, 481391044, 482210001 , 482311006, 482510003, 482570005, 483670081, 483970001, 484390075,
484391002, 484392003, 484393009, 484393011)
Denver (CO: 080013001, 080050002, 080050006, 080130007, 080130011, 080137001, 080137002, 080190004,
080190005, 080310002, 080310014, 080310025, 080350004, 080590002, 080590005, 080590006, 080590011,
080590013, 080690007, 080690011 , 080691004, 080699991, 080930001, 081190003, 081230009)
Detroit (Ml: 260490021, 260492001, 260910007, 260990009, 260991003, 261250001, 261470005, 261610008,
261619991, 261630001, 261630015, 261630016, 261630019)
Houston (TX: 480391004, 482010024, 482010026, 482010029, 482010046, 482010047, 482010051, 482010055,
482010062, 482010066, 482010070, 482010075, 482010416, 482011015, 482011034, 482011035, 482011039,
482011050,483390078)
Los Angeles (CA: 060370002, 060370016, 060370113, 060371002, 060371103, 060371201, 060371301, 060371302,
060371602, 060371701, 060372005, 060374002, 060374006, 060375005, 060376012, 060379033, 060590007,
060591003, 060592022, 060595001 , 060650004, 060650009, 060650012, 060651010, 060651016, 060651999,
060652002, 060655001, 060656001 , 060658001, 060658005, 060659001, 060659003, 060710001, 060710005,
060710012, 060710306, 060711004, 060711234, 060712002, 060714001, 060714003, 060719002, 060719004,
061110007, 061110009, 061111004, 061112002, 061112003, 061113001)
Census
Tracts for
Exposure
678
618
1,028
2,055
879
1,036
675
1,454
802
3,352
Population
Represented
3,850,951
2,209,226
4,449,291
8,345,373
2,692,846
4,698,392
2,626,239
4,572,479
3,925,054
14,950,340
5B-27
-------
Study Area (State Abbreviation: List of Monitors1)
New York (CT: 090010017, 090011123, 090013007, 090019003, 090070007, 090090027, 090093002; NJ:
340030005, 340030006, 340130003, 340170006, 340190001, 340210005, 340219991, 340230011, 340250005,
340273001, 340290006, 340315001 ; NY: 360050083, 360050110, 360050133, 360610135, 360790005, 360810098,
360810124, 360850067, 360870005, 361030002, 361030004, 361030009, 361192004)
Philadelphia (DE: 100031007, 100031010, 100031013, MD: 240150003; NJ: 340070003,340071001,340110007,
340150002, 340210005, 340219991 , 340290006; PA: 420170012, 420290100, 420450002, 420910013, 421010004,
421010014, 421010024, 421010136)
Sacramento (CA: 060170010, 060170020, 060570005, 060610002, 060610004, 060610006, 060670002, 060670006,
060670010, 060670011, 060670012, 060670013, 060670014, 060675003, 060953003, 061010003, 061010004,
061131003)
St. Louis (IL: 170831001, 171190008, 171191009, 171193007, 171199991, 171630010; MO: 290990012, 290990019,
291130003, 291831002, 291831004, 291890004, 291890005, 291890014, 295100085, 295100086)
Washington, DC (110010025, 110010041, 110010043; MD: 240030014, 240090011, 240130001, 240170010,
240210037, 240313001, 240330030, 240338003, 240339991, 240430009; VA: 510130020, 510330001, 510590005,
510590018, 510590030, 510591005, 510595001, 510610002, 510690010, 511071005, 511390004, 511530009,
51 1 790001 , 51 51 00009; WV: 540030003)
All AREAS (324 ambient monitors)
Census
Tracts for
Exposure
4,889
1,555
461
518
1,037
21,037
Population
Represented
18,520,868
5,506,954
1,926,598
2,340,325
4,498,374
85,113,310
A 30 km radius for monitors operating anytime during 2006-2010 was used to select census tracts in defining the exposure modeling domain.
5B-28
-------
CSA122-ATL
CSA148-BOS
CSA999-BAL
CSA176-CHI
i
2
3
4
Figure 5B-1. Illustration of APEX exposure modeling domains (2000 US Census tract
centroids) for Atlanta, Boston, Baltimore and Chicago study areas.
5B-29
-------
CSA184-CLE
CSA216-DEN
CSA206-DAL
CSA220-DET
l
2 Figure 5B-2. Illustration of APEX exposure modeling domains (2000 US Census tract
3 centroids) for Cleveland, Dallas, Denver and Detroit study areas.
4
5B-30
-------
CSA288-HOU
CSA408-NY
CSA348-LA
CSA428-PHI
1 Figure 5B-3. Illustration of APEX exposure modeling domains (2000 US Census tract
2 centroids) for Houston, Los Angeles, New York and Philadelphia study areas.
5B-31
-------
CSA472-SAC
CSA548-WAS
i
2
3
CSA476-STL
Figure 5B-4. Illustration of APEX exposure modeling domains (2000 US Census tract
centroids) for Sacramento, St. Louis and Washington DC study areas.
5B-32
-------
5B-8 METEOROLOGICAL DATA
Temperature data are used by APEX in selecting human activity data and in estimating
AERs for indoor residential microenvironments. Hourly surface temperature measurements
were obtained from the National Weather Service ISH data files.n The weather stations used for
each city are given in Table 5B-10. When developing profiles and selecting for am AER, APEX
uses temperature data from the closest weather station to each Census tract.
Missing temperature data were estimated by the following procedure. Where there were
consecutive strings of missing values (data gaps) of 4 or fewer hours, missing values were
estimated by linear interpolation between the valid values at the ends of the gap. Remaining
missing values at a station were estimated by fitting linear regression models for each hour of the
r\
day, with each of the other monitors, and choosing the model which maximizes R , for each hour
of the day, subject to the constraints that R2 be greater than 0.50 and the number of regression
data values (days) is at least 60. If there were any remaining missing values at this point, for
gaps of 6 or fewer hours, missing values were estimated by linear interpolation between the valid
values at the ends of the gap. Any remaining missing values were replaced with the value at the
closest station for that hour.
There were negligible differences between the statistically filled and the original
temperature data with missing values. On average, daily mean temperatures were approximately
0.02 °C greater in the final data set used by APEX, compared with the data set having missing
temperatures. The greatest positive difference occurred at station '2227013864', where the filled
data had a daily average mean of about 0.72 °C greater than that of the data set with missing
values. The greatest negative difference was associated with station '2403603710', where the
filled data had a daily average mean of about -0.27 °C less than that of the data set with missing
values. Given these small differences, the number of stations used to represent meteorological
conditions in each study area and the range of values used by APEX in creating diary pools (e.g.,
50 - 68 °F) or AER distributions (e.g., 55 - 84 °F) , the impact of the filled values to estimated
exposures is assumed negligible.
Multiple unique APEX input files are used for the current Oj assessment, varying by the
year and study area, though generally in two forms:
• METdataCSAfstudyareaJYfyearJ.txt: hourly temperature for each MET station,
by study area and year
• METlocsCSAfstudyareaJYfyearJ.txt: MET station ID's, latitudes and longitudes,
start and stop dates of temperature data
11 http://www.ncdc.noaa.gov/oa/climate/surfaceinventories.html
5B-33
-------
5B-9 CONDITIONAL VARIABLES
APEX has added flexibility in using conditional variables in association with selection of
the distributions used to represent input variables, across several modules (i.e., CHAD diary
selection, microenvironmental concentration calculations). In this 63 assessment, a number of
temperature ranges are used in selecting the particular AER distribution (section 5B-6.1),
maximum daily temperature is also used in diary selection to best match the study area MET data
for the simulated individual (<55, 55-83, and >84; based on Graham and McCurdy, 2004), air
conditionining prevalence data (section 5B-6.2), and designation of roadway type travelled based
on VMT miles (section 5B-6.4).
A single unique APEX input files is used for the current Os assessment, varying by the
year and study area:
• Functions O3 CSA[studyarea]Y[year]J^date].txt: conditional variables and values used
5B-34
-------
Table 5B-10. Study area meteorological stations, locations, and hours of missing data.
Study Area
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
ISH ID1
2217003813
2219013874
2219503888
2225593842
2227013864
2228403892
2228713871
2311013873
2320093801
2406093721
4594013705
2505464710
2506014704
2506454769
2507014765
2509014739
2509594746
4394514710
2530094846
2530594892
2534014819
2535014848
2543094822
2521014895
2524014820
2524504853
2525014852
2258313960
2259003927
2259613961
2466093037
2466693067
2469523036
2476824051
2476994062
2564024018
2565003017
2537094847
2537514822
2537614853
2537714804
2538404888
2539014836
2539514833
2637014826
2241012917
Latitude
32.683
33.633
33.767
32.517
33.917
32.616
33.583
33.95
34.35
39.167
38.817
41.917
41.65
41.917
41.717
42.367
42.267
42.933
41.983
41.917
41.783
41.7
42.2
40.917
41.4
41.517
41.25
32.85
32.9
32.817
38.817
39.567
39.717
40.433
40.45
41.15
39.833
42.217
42.4
42.233
42.617
42.917
42.783
42.267
42.967
29.95
Longitude
-83.65
-84.433
-84.517
-84.95
-84.517
-85.433
-85.85
-83.333
-85.167
-76.683
-76.867
-71.5
-70.517
-70.733
-71.433
-71.017
-71.883
-71.433
-87.917
-88.25
-87.75
-86.333
-89.1
-81.433
-81.85
-81.683
-80.667
-96.85
-97.017
-97.367
-104.717
-104.85
-104.75
-104.633
-105.017
-104.8
-104.65
-83.35
-83
-83.533
-82.833
-82.533
-84.583
-84.467
-83.75
-94.017
2006
15
0
14
16
2502
469
24
4
14
0
34
275
46
5
0
0
34
3
2
71
0
3
1
7
0
12
0
1
0
6
2
2
32
72
19
1
0
1
17
1
11
40
1
4
0
5
2007
2
0
15
4
1611
114
4
4
30
0
86
71
84
10
1
2
15
10
1
12
1
0
0
1
1
116
0
2
0
22
2
1
41
393
532
7
2
1
110
7
27
140
3
35
0
24
2008
140
101
113
170
266
209
168
271
187
101
173
362
126
259
94
97
128
103
126
170
127
91
96
128
82
144
119
93
88
157
108
104
103
876
314
129
91
106
226
148
236
94
148
329
116
119
2009
113
41
103
52
93
77
210
39
59
54
184
741
54
143
51
41
61
49
44
97
44
46
43
56
38
79
45
51
46
84
110
53
52
134
381
46
44
40
104
107
59
32
70
49
52
67
2010
128
18
29
48
73
540
55
45
68
27
42
315
95
285
45
34
53
55
21
64
23
18
18
44
19
68
28
40
30
138
71
45
32
140
164
66
40
27
122
78
94
55
37
144
74
27
5B-35
-------
Study Area
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
ISH ID1
2242012923
2243012960
2243512918
2244503904
2286023119
2288023152
2295023174
2297023129
2381523161
2381603159
2383023187
2391093111
2392623136
4718703104
4718823158
2408454760
2409614706
2502014734
2502594741
2502964707
2503014732
2503504781
2503614757
2503794745
2503814714
2504094702
2505894793
2514554746
2517014737
4486094789
2407093730
2407513735
2408013739
2408454760
2408594732
2408913781
2409614706
2517014737
4596603726
2483023232
2483993225
2584523225
4516023202
2433813802
2434013994
2434503966
2445493996
Latitude
29.3
30
29.65
30.583
33.9
34.2
33.933
33.833
34.85
34.733
34.75
34.117
34.217
33.633
33.617
40.183
40.017
40.717
40.85
41.483
40.783
40.783
41.633
41.067
41.5
41.183
41.167
41.7
40.65
40.65
39.45
39.367
39.867
40.183
40.083
39.667
40.017
40.65
39
38.5
38.7
39.3
38.267
38.55
38.75
38.65
37.767
Longitude
-94.8
-95.367
-95.283
-96.367
-117.25
-118.35
-118.4
-118.167
-116.8
-118.217
-118.717
-119.117
-119.083
-116.167
-114.717
-74.067
-74.6
-74.183
-74.067
-73.133
-73.883
-73.1
-73.883
-73.717
-74.1
-73.15
-71.583
-74.8
-75.45
-73.8
-74.567
-75.083
-75.233
-74.067
-75.017
-75.6
-74.6
-75.45
-74.917
-121.5
-121.583
-120.717
-121.933
-89.85
-90.367
-90.65
-90.4
2006
126
1
0
5
13
2
0
2
69
126
31
2046
21
99
2
251
66
1
0
554
0
2
14
8
565
61
687
1387
5
4
4
20
1
251
0
22
66
5
945
3
0
1
34
40
1
5
186
2007
19
0
0
3
25
12
0
4
4
11
3
16
47
18
15
475
62
0
2
292
0
0
5
0
851
68
349
356
4
0
3
84
0
475
10
1
62
4
500
5
0
1
110
55
0
33
24
2008
2468
173
160
108
103
152
113
99
196
438
375
265
311
134
400
566
131
105
129
569
73
174
406
119
1144
146
586
159
148
101
142
268
122
566
143
156
131
148
1299
115
116
217
152
129
98
189
74
2009
444
57
99
107
48
86
44
173
741
176
276
77
218
161
54
183
83
47
70
842
38
138
388
93
1159
114
2970
375
74
56
112
73
57
183
60
244
83
74
1562
51
52
124
78
68
46
62
134
2010
186
30
33
40
29
37
19
269
241
411
554
792
139
60
159
794
121
22
31
703
17
18
139
34
1358
173
127
582
51
21
161
74
21
794
38
89
121
51
1855
77
42
350
46
41
27
41
402
5B-36
-------
Study Area
Washington DC
ISH ID1
2403093738
2403303706
2403513773
2403603710
2404013721
2405013743
2405303717
2405503714
2406093721
2417713734
4594013705
Latitude
38.933
38.267
38.5
38.717
38.3
38.867
39.15
39.083
39.167
39.4
38.817
Longitude
-77.45
-77.45
-77.3
-77.517
-76.417
-77.033
-78.15
-77.567
-76.683
-77.983
-76.867
2006
3
41
177
725
1414
0
38
28
0
36
34
2007
2
48
113
698
123
1
7
28
0
42
86
2008
102
4587
1937
943
322
96
78
64
101
217
173
2009
52
145
788
1631
83
46
32
38
54
137
184
2010
28
36
610
97
86
21
35
181
27
118
42
From the Federal Climate Complex Integrated Surface Hourly (ISH) global database.
5B-37
-------
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5B-41
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i Appendix 5-C
2
3 Generation of Adult and Child Census-tract Level Asthma
4 Prevalence using NHIS (2006-2010) and US Census (2000) Data
5
6 Table of Contents
7 5C-1. OVERVIEW 2
8 5C-2. RAW ASTHMA PREVALENCE DATA SET DESCRIPTION 3
9 5C-3. LOGISTIC MODELING APPROACH USED TO ESTIMATE ASTHMA
10 PREVALENCE 5
11 5C-4. APPLICATION OF LOESS SMOOTHER TO ASTHMA PREVALENCE
12 ESTIMATION 9
13 5C-5. CENSUS TRACT LEVEL POVERTY RATIO DATA SET DESCRIPTION AND
14 PROCESSING 13
15 5C-6. COMBINED CENSUS TRACT LEVEL POVERTY RATIO AND ASTHMA
16 PREVALENCE DATA 14
17 5C-7. REFERENCES 15
18
19
20 List of Tables
21
22 Table 5C-1. Number of total surveyed persons from NHIS (2006-2010) sample adult and child
23 files and the number of those responding to asthma survey questions 5
24 Table 5C-2. Example of alternative logistic models evaluated to estimate child asthma
25 prevalence using the "EVER" asthma response variable and goodness of fit test results 8
26 Table 5C-3. Top 20 model smoothing fits where residual standard error at or a value of 1.0... 11
27
28 List of Figures
29
30 Figure 5C-1. Normal probability plot of studentized residuals generated using logistic model,
31 smoothing set to 0.7, and the children 'EVER' asthmatic data set 12
32 Figure 5C-2. Studentized residuals versus model predicted betas generated using a logistic
33 model and using the children 'EVER' asthmatic data set, with smoothing set to 0.6 13
34
5C-1
-------
1
2 5C-1 OVERVIEW
3 This appendix describes the generation of our census tract level children and adult asthma
4 prevalence data developed from the 2006-2010 National Health Interview Survey (NHIS) and
5 census tract level poverty information from the 2000 US Census. The approach is, for the most
6 part, a reapplication of work performed by Cohen and Rosenbaum (2005), though here we
7 incorporated a few modifications as described below. Details regarding the earlier asthma
8 prevalence work are documented in Appendix G of US EPA (2007).
9 Briefly in the earlier asthma prevalence development work, Cohen and Rosenbaum
10 (2005) calculated asthma prevalence for children aged 0 to 17 years for each age, gender, and
11 four US regions using 2003 NHIS survey data. The four regions defined by NHIS were
12 'Midwest', 'Northeast', 'South', and 'West'. The asthma prevalence was defined as the
13 probability of a 'Yes' response to the question "EVER been told that [the child] had asthma?"1
9
14 among those persons that responded either 'Yes' or 'No' to this question. The responses were
15 weighted to take into account the complex survey design of the NHIS.3 Standard errors and
16 confidence intervals for the prevalence were calculated using a logistic model (PROC SURVEY
17 LOGISTIC; SAS, 2012). A scatter-plot technique (LOESS SMOOTHER; SAS, 2012) was
18 applied to smooth the prevalence curves and compute the standard errors and confidence
19 intervals for the smoothed prevalence estimates. Logistic analysis of the raw and smoothed
20 prevalence curves showed statistically significant differences in prevalence by gender and
21 region, supporting their use as stratification variables in the final data set. These smoothed
22 prevalence estimates were used as an input to EPA's Air Pollution Exposure Model (APEX) to
23 estimate air pollutant exposure in asthmatic children (US EPA, 2007; 2008; 2009).
24 For the current asthma prevalence data set development, several years of recent NHIS
25 survey data (2006-2010) were combined and used to calculate asthma prevalence. The current
26 approach estimates asthma prevalence for children (by age in years) as was done previously by
27 Cohen and Rosenbaum (2005) but now includes an estimate of adult asthma prevalence (by age
28 groups). In addition, two sets of asthma prevalence for each adults and children were estimated
29 here. The first data set, as was done previously, was based on responses to the question "EVER
30 been told that [the child] had asthma". The second data set was developed using the probability
31 of a 'Yes' response to a question that followed those that answered 'Yes' to the first question
1 The response was recorded as variable "CASHMEV" in the downloaded dataset. Data and documentation are
available at http://www.cdc. gov/nchs/nhis/quest data related 1997 forward.htm.
2 If there were another response to this variable other than "yes" or "no" (i.e., refused, not ascertained, don't know,
and missing), the surveyed individual was excluded from the analysis data set.
3 In the SURVEY LOGISTIC procedure, the variable "WTF_SC" was used for weighting, "PSU" was used for
clustering, and "STRATUM" was used to define the stratum.
5C-2
-------
1 regarding ever having asthma, specifically, do those persons "STILL have asthma?"4 And
2 finally, in addition to the nominal variables region and gender (and age and age groups), the
3 asthma prevalence in this new analysis were further stratified by a family income/poverty ratio
4 (i.e., whether the family income was considered below or at/above the US Census estimate of
5 poverty level for the given year).
6 These new asthma prevalence data sets were linked to the US census tract level poverty
7 ratios probabilities (US Census, 2007), also stratified by age and age groups. Given 1) the
8 significant differences in asthma prevalence by age, gender, region, and poverty status, 2) the
9 variability in the spatial distribution of poverty status across census tracts, stratified by age, and
10 3) the spatial variability in local scale ambient concentrations of many air pollutants, it is hoped
11 that the variability in population exposures is now better represented when accounting for and
12 modeling these newly refined attributes of this susceptible population.
13 5C-2 RAW ASTHMA PREVALENCE DATA SET DESCRIPTION
14 In this section we describe the asthma prevalence data sets used and identify the variables
15 retained for our final data set. First, raw data and associated documentation were downloaded
16 from the Center for Disease Control (CDC) and Prevention's National Health Interview Survey
17 (NHIS) website.5 The 'Sample Child' and 'Sample Adult' files were selected because of the
18 availability of person-level attributes of interest within these files, i.e., age in years ('age_p'),
19 gender ('sex'), US geographic region ('region'), coupled with the response to questions of
20 whether or not the surveyed individual ever had and still has asthma. In total, five years of
21 recent survey data were obtained, comprising over 50,000 children and 120,000 children for
22 years 2006-2010 (Table 5C-1).
23 Information regarding personal and family income and poverty ranking are also provided
24 by the NHIS in separate files. Five files ('INCEVIPx.dat') are available for each survey year,
25 each containing either the actual responses (where recorded or provided by survey participant) or
26 imputed values for the desired financial variable.6 For this current analysis, the ratio of income
27 to poverty was used to develop a nominal variable: either the survey participant was below or
28 at/above a selected poverty threshold. This was done in this manner to be consistent with data
29 generated as part of a companion data set, i.e., census tract level poverty ratio probabilities
30 stratified by age (see section 5C-5 below).
4 While we estimated two separate sets of prevalence using the "STILL" and "EVER" variables, only the
"STILL" data were used as input to our exposure model.
5 See http://www.cdc.gov/nchs/nhis.htm (accessed October 4, 2011).
6 Financial information was not collected from all persons; therefore the NHIS provides imputed data.
Details into the available variables and imputation method are provided with each year's data set. For example see
"Multiple Imputation of Family Income and Personal Earnings in the National Health Interview Survey: Methods
and Examples" at http://www.cdc.gov/nchs/data/nhis/tecdoc 2010.pdf.
5C-3
-------
1 Given the changes in how income data were collected over the five year period of interest
2 and the presence of imputed data, a data processing methodology was needed to conform each of
3 the year's data sets to a compatible nominal variable. Briefly, for survey years 2006-2008,
4 poverty ratios ('RAT_CATI') are provided for each person as a categorical variable, ranging
5 from <0.5 to 5.0 by increments of either 0.25 (for poverty ratios categories between <0.5 - 2.0)
6 and 0.50 (for poverty ratios >5.0). For 2009 and 2010 data, the poverty ratio was provided as a
7 continuous variable ('POVRATI3') rather than a categorical variable.7
8 When considering the number of stratification variables, the level of asthma prevalence,
9 and poverty distribution among the survey population, sample size was an important issue. For
10 the adult data, there were insufficient numbers of persons available to stratify the data by single
11 ages (for some years of age there were no survey persons). Therefore, the adult survey data were
12 grouped as follows: ages 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, and, >75.8 To increase the
13 number of persons within the age, gender, and four region groupings of our characterization of
14 'below poverty' asthmatics persons, the poverty ratio threshold was selected as <1.5, therefore
15 including persons that were within 50% above the poverty threshold. As there were five data
16 sets containing variable imputed poverty ratios (as well as a non varying values for where
17 income information was reported) for each year, the method for determining whether a person
18 was below or above the poverty threshold was as follows. If three or more of the five
19 imputed/recorded values were <1.5, the person's family income was categorized 'below' the
20 poverty threshold, if three or more of the 5 values were >1.5, the person's family income was
21 categorized 'above' the poverty threshold. The person-level income files were then merged with
22 the sample adult and child files using the 'FtHX' (a household identifier), 'FMX' (a family
23 identifier), and 'FPX' (an individual identifier) variables. Note, all persons within the sample
24 adult and child files had corresponding financial survey data.
25 Two asthma survey response variables were of interest in this analysis and were used to
26 develop the two separate prevalence data sets for each children and adults. The response to the
27 first question "Have you EVER been told by a doctor or other health professional that you [or
28 your child] had asthma?" was recorded as variable name 'CASFDVIEV' for children and
29 'AASMEV for adults. Only persons having responses of either 'Yes' or 'No' to this question
7 Actually, the 2009 data had continuous values for the poverty ratios ('POVRATI2') but the quality was determined
by us to be questionable: the value varied among family members by orders of magnitude - however, it should be
a constant. The income data ('FAMINCI2') provided were constant among family members, therefore we
combined these data with poverty thresholds obtained from the US Census (available at:
http://www.census.gov/hhes/www/povertv/data/threshld/thresh08.html') for year 2008 by family size (note,
income is the annual salary from the prior year) and calculated an appropriate poverty ratio for each family
member.
8 These same age groupings were used to create the companion file containing the census tract level poverty ratio
probabilities (section 5C-5).
5C-4
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
were retained to estimate the asthma prevalence. This assumes that the exclusion of those
responding otherwise, i.e., those that 'refused' to answer, instances where it was "not
ascertained', or the person 'does not know', does not affect the estimated prevalence rate if either
'Yes' or 'No' answers could actually be given by these persons. There were very few persons
(<0.3%) that did provide an unusable response (Table 5C-1), thus the above assumption is
reasonable. A second question was asked as a follow to persons responding "Yes" to the first
question, specifically, "Do you STILL have asthma?" and noted as variables 'CAS STILL' and
' AASSTILL' for children and adults, respectively. Again, while only persons responding 'Yes'
and 'No' were retained for further analysis, the representativeness of the screened data set is
assumed unchanged from the raw survey data given the few persons having unusable data
Table 5C-1. Number of total surveyed persons from NHIS (2006-2010) sample adult and child
files and the number of those responding to asthma survey questions.
Study Group/Respondents
Children
All Persons
Yes/No Asthma
Yes/No to Still Have + No Asthma
Adults
All Persons
Yes/No Asthma
Yes/No to Still Have + No Asthma
Number of Surveyed Persons
2010
11,277
1 1 ,256
11,253
2010
27,157
27,157
27,113
2009
11,156
11,142
11,129
2009
27,731
27,715
27,686
2008
8,815
8,800
8,793
2008
21,781
21,766
21,726
2007
9,417
9,404
9,394
2007
23,393
23,372
23,349
2006
9,837
9,815
9,797
2006
24,275
24,242
24,208
TOTAL
50,502
50,417
50,366
TOTAL
124,337
124,252
124,082
15
16
17
18
19
20
21
22
23
24
25
26
5C-3 LOGISTIC MODELING APPROACH USED TO ESTIMATE ASTHMA
PREVALENCE
As described in the previous section, four person-level analytical data sets were created
from the raw NHIS data files, generally containing similar variables: a 'Yes' or 'No' asthma
response variable (either 'EVER' or 'STILL'), an age (or age group for adults), their gender
('male' or 'female'), US geographic region ('Midwest', 'Northeast', 'South', and 'West'), and
poverty status ('below' or above'). One approach to calculate prevalence rates and their
uncertainties for a given gender, region, poverty status, and age is to calculate the proportion of
'Yes' responses among the 'Yes' and 'No' responses for that demographic group, appropriately
weighting each response by the survey weight. This simplified approach was initially used to
develop 'raw' asthma prevalence rates however this approach may not be completely
5C-5
-------
1 appropriate. The two main issues with such a simplified approach are that the distributions of
2 the estimated prevalence rates would not be well approximated by normal distributions and that
3 the estimated confidence intervals based on a normal approximation would often extend outside
4 the [0, 1] interval. A better approach for such survey data is to use a logistic transformation and
5 fit the model:
6
7 Prob(asthma) = exp(beta) / (1 + exp(beta) ),
8
9 where, beta may depend on the explanatory variables for age, gender, poverty status, or region.
10 This is equivalent to the model:
11
12 Beta = logit {prob(asthma) } = log {prob(asthma) / [1 - prob (asthma)] }
13
14 The distribution of the estimated values of beta is more closely approximated by a normal
15 distribution than the distribution of the corresponding estimates of prob(asthma). By applying a
16 logit transformation to the confidence intervals for beta, the corresponding confidence intervals
17 for prob(asthma) will always be inside [0, 1]. Another advantage of the logistic modeling is that
18 it can be used to compare alternative statistical models, such as models where the prevalence
19 probability depends upon age, region, poverty status, and gender, or on age, region, poverty
20 status but not gender.
21 A variety of logistic models were fit and compared to use in estimating asthma
22 prevalence, where the transformed probability variable beta is a given function of age, gender,
23 poverty status, and region. I used the SAS procedure SURVEYLOGISTIC to fit the various
24 logistic models, taking into account the NHIS survey weights and survey design (using both
25 stratification and clustering options), as well as considering various combinations of the selected
26 explanatory variables.
27 As an example, Table 5C-2 lists the models fit and their log-likelihood goodness-of-fit
28 measures using the sample child data and for the "EVER" asthma response variable. A total of
29 32 models were fit, depending on the inclusion of selected explanatory variables and how age
30 was considered in the model. The 'Strata' column lists the eight possible stratifications: no
31 stratification, stratified by gender, by region, by poverty status, by region and gender, by region
32 and poverty status, by gender and poverty status, and by region, gender and poverty status. For
33 example, "5. region, gender" indicates that separate prevalence estimates were made for each
34 combination of region and gender. As another example, "2. gender" means that separate
35 prevalence estimates were made for each gender, so that for each gender, the prevalence is
36 assumed to be the same for each region. Note the prevalence estimates are independently
5C-6
-------
1 calculated for each stratum. The 'Description' column of Table 5C-2 indicates how beta
2 depends upon the age:
O
4 Linear in age Beta = a + |3 x age, where a and |3 vary with strata.
r\
5 Quadratic in age Beta = a + |3 x age + y x age , where a |3 and y vary with strata.
6 Cubic in age Beta = a + |3 x age + y x age2 + 5 x age3, where a, |3, y, and 5 vary
7 with the strata.
8 f(age) Beta = arbitrary function of age, with different functions for
9 different strata
10
11 The category f(age) is equivalent to making age one of the stratification variables, and is
12 also equivalent to making beta a polynomial of degree 16 in age (since the maximum age for
13 children is 17), with coefficients that may vary with the strata.
14 The fitted models are listed in order of complexity, where the simplest model (i.e., model
15 1) is an unstratified linear model in age and the most complex model (model 32) has a
16 prevalence that is an arbitrary function of age, gender, poverty status, and region. Model 32 is
17 equivalent to calculating independent prevalence estimates for each of the 288 combinations of
18 age, gender, poverty status, and region.
19 Table 5C-2 also includes the -2 Log Likelihood statistic, a goodness-of-fit measure, and
20 the associated degrees of freedom (DF), which is the total number of estimated parameters. Any
21 two models can be compared using their -2 Log Likelihood values: models having lower values
22 are preferred. If the first model is a special case of the second model, then the approximate
23 statistical significance of the first model is estimated by comparing the difference in the -2 Log
24 Likelihood values with a chi-squared random variable having r degrees of freedom, where r is
25 the difference in the DF (hence a likelihood ratio test). For all pairs of models from Table 5C-2,
26 all the differences in the -2 Log Likelihood statistic are at least 600,000 and thus significant at p-
27 values well below 1 percent. Based on its having the lowest -2 Log Likelihood value, the last
28 model fit (model 32: retaining all explanatory variables and usingf(age)) was preferred and used
29 to estimate the asthma prevalence.9
30
31
9 Similar results were obtained when estimating prevalence using the 'STILL' have asthma variable as well as when
investigating model fit using the adult data sets. Note that because age was a categorical variable in the adult
data sets it could only be evaluated using f(age_group). See Attachment B, Tables 5CB-1 to 5CB-4 for all model
fit results.
5C-7
-------
1 Table 5C-2. Example of alternative logistic models evaluated to estimate child asthma
2 prevalence using the "EVER" asthma response variable and goodness of fit test results.
Model No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Description
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
Strata
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
- 2 Log Likelihood
288740115.1
287062346.4
288120804.1
287385013.1
286367652.6
286283543.6
285696164.7
284477928.1
286862135.1
285098650.6
286207721.5
285352164
284330346.1
284182547.5
283587631.7
282241318.6
286227019.6
284470413
285546716.1
284688169.9
283662673.5
283404487.5
282890785.3
281407414.3
285821686.2
283843266.2
284761522.8
284045849.2
282099156.1
281929968.5
281963915.7
278655423.1
DF1
2
4
8
4
16
16
8
32
3
6
12
6
24
24
12
48
4
8
16
8
32
32
16
64
18
36
72
36
144
144
72
288
model degrees of freedom.
5C-8
-------
1 The SURVEYLOGISTIC procedure produces estimates of the beta values and their 95%
2 confidence intervals for each combination of age, region, poverty status, and gender. By
3 applying the inverse logit transformation,
4
5 Prob(asthma) = exp( beta) / (1 + exp(beta) ),
6
1 one can convert the beta values and associated 95% confidence intervals into predictions
8 and 95% confidence intervals for the prevalence. The standard error for the prevalence was
9 estimated as
10
11 StdError {Prob(asthma)} = StdError (beta) x exp(- beta) / (1 + exp(beta) )2
12
13 which follows from the delta method (i.e., a first order Taylor series approximation).
14 Estimated asthma prevalence using this approach and termed here as 'unsmoothed' are provided
15 in Attachment A. Asthma prevalence for children is provided in Attachment A, Tables 5CA-1
16 ('EVER' had Asthma) and 5CA-2 ('STILL' have asthma) while adult asthma prevalence is
17 provided in Attachment A, Tables 5CA-3 ('EVER' had Asthma) and 5CA-4 (' STILL' have
18 asthma). Graphical representation of each study group is also provided in a series of plots within
19 Attachment A, Figures 5CA-1 to 5CA-4. The variables provided in the tabular presentation are:
20
21 • Region
22 • Gender
23 • Age (in years) or Age_group (age categories)
24 • Poverty Status
25 • Prevalence = predicted prevalence
26 • SE = standard error of predicted prevalence
27 • LowerCI = lower bound of 95 % confidence interval for predicted prevalence
28 • UpperCI = upper bound of 95 % confidence interval for predicted prevalence
29
30 5C-4 APPLICATION OF LOESS SMOOTHER TO ASTHMA PREVALENCE
31 ESTIMATION
32 The estimated prevalence curves shows that the prevalence is not necessarily a smooth
33 function of age. The linear, quadratic, and cubic functions of age modeled by
34 SURVEYLOGISTIC were identified as a potential method for smoothing the curves, but they
35 did not provide the best fit to the data. One reason for this might be due to the attempt to fit a
36 global regression curve to all the age groups, which means that the predictions for age A are
37 affected by data for very different ages. A local regression approach that separately fits a
5C-9
-------
1 regression curve to each age A and its neighboring ages was used, giving a regression weight of
2 1 to the age A, and lower weights to the neighboring ages using a tri-weight function:
O
4 Weight = {1 - [ \age -A /q] 3}, where age -A <= q
5
6 The parameter q defines the number of points in the neighborhood of the age A. Instead
7 of calling q the smoothing parameter, S AS defines the smoothing parameter as the proportion of
8 points in each neighborhood. A quadratic function of age to each age neighborhood was fit
9 separately for each gender and region combination. These local regression curves were fit to the
10 beta values, the logits of the asthma prevalence estimates, and then converted them back to
11 estimated prevalence rates by applying the inverse logit function exp(beta) / (1 + exp(beta)). In
12 addition to the tri-weight variable, each beta value was assigned a weight of
13 1 / [std error (beta)]2, to account for their uncertainties.
14 In this application of LOESS, weights of 1 / [std error (beta)] 2 were used such that a2 =
15 1. The LOESS procedure estimates a2 from the weighted sum of squares. Because it is assumed
16 a2 = 1, the estimated standard errors are multiplied by 1 / estimated a and adjusted the widths of
17 the confidence intervals by the same factor.
18 One data issue was an overly influential point that needed to be adjusted to avoid
19 imposing wild variation in the "smoothed" curves: for the West region, males, age 0, above
20 poverty threshold, there were 249 children surveyed that all gave 'No' answers to the asthma
21 question, leading to an estimated value of -14.203 for beta with a standard error of 0.09. In this
22 case the raw probability of asthma equals zero, so the corresponding estimated beta would be
23 negative infinity, but SAS's software gives -14.203 instead. To reduce the excessive impact of
24 this single data point, we replaced the estimated standard error by 4, which is approximately four
25 times the maximum standard error for all other region, gender, poverty status, and age
26 combinations.
27 There are several potential values that can be selected for the smoothing parameter; the
28 optimum value was determined by evaluating three regression diagnostics: the residual standard
29 error, normal probability plots, and studentized residuals. To generate these statistics, the
30 LOESS procedure was applied to estimated smoothed curves for beta, the logit of the prevalence,
31 as a function of age, separately for each region, gender, and poverty classification. For the
32 children data sets, curves were fit using the choices of 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 for the
33 smoothing parameter. This selected range of values was bounded using the following
34 observations. With only 18 points (i.e., the number of ages), a smoothing parameter of 0.2
35 cannot be used because the weight function assigns zero weights to all ages except age A, and a
36 quadratic model cannot be uniquely fit to a single value. A smoothing parameter of 0.3 also
5C-10
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
cannot be used because that choice assigns a neighborhood of 5 points only (0.3 x 18 = 5,
rounded down), of which the two outside ages have assigned weight zero, making the local
quadratic model fit exactly at every point except for the end points (ages 0, 1, 16 and 17).
Usually one uses a smoothing parameter below 1 so that not all the data are used for the local
regression at a given x value. Note also that a smoothing parameter of 0 can be used to generate
the unsmoothed prevalence. The selection of the smoothing parameter used for the adult curves
would follow a similar logic, although the lower bound could effectively be extended only to 0.9
given the number of age groups. This limits the selection of smoothing parameter applied to the
two adult data sets to a value of 0.9, though values of 0.8 to 1.0 were nevertheless compared for
good measure.
The first regression diagnostic used was the residual standard error, which is the LOESS
estimate of a. As discussed above, the true value of a equals 1, so the best choice of smoothing
parameter should have residual standard errors as close to 1 as possible. Attachment B, Tables
5CB-5 to 5CB-8 contain the residual standard errors output from the LOESS procedure,
considering region, gender, poverty status and each data set examined. For children 'EVER'
having asthma and when considering the best 20 models (of the 112 possible) using this criterion
(note also within 0.06 RSE units of 1), the best choice varies with gender, region, and poverty
status between smoothing parameters of 0.6, 0.7, and 0.8 (Table 5C-3). Similar results were
observed for the 'STILL' data set, though a value of 0.6 would be slightly preferred. Either adult
data set could be smoothed using a value of 0.8 or 0.9 given the limited selection of smoothing
values, though 0.9 appears a better value for the 'STILL' data set.
Table 5C-3. Top 20 model smoothing fits where residual standard error at or a value of 1.0.
Data
Set
Children
Adults
Asthma
EVER
STILL
EVER
STILL
LOESS Smoothing Parameter
0.4
2
2
n/a
n/a
0.5
2
3
n/a
n/a
0.6
5
4
n/a
n/a
0.7
5
2
n/a
n/a
0.8
4
3
6
5
0.9
1
3
6
7
1.0
1
3
8
8
The second regression diagnostic was developed from an approximate studentized
residual. The residual errors from the LOESS model were divided by standard error (beta) to
make their variances approximately constant. These approximately studentized residuals should
be approximately normally distributed with a mean of zero and a variance of a2 = 1. To test this
assumption, normal probability plots of the residuals were created for each smoothing parameter,
combining all the studentized residuals across genders, regions, poverty status, and ages. These
5C-11
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
normal probability plots are provided in Attachment B, Figures CB-1 to CB-4. The results for
the children data indicate little distinction or affect by the selection of a particular smoothing
parameter (e.g., see Figure 5C-1 below), although linearity in the plotted curve is best expressed
with smoothing parameters at or above values of 0.6. When considering the adult data sets,
again the appropriate value would be 0.9, as Attachment B, Figures 5CB-3 and 5CB-4 supports
this conclusion.
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
S moothingParameter=0.7
-2
0.1
10 25 50 75
Normal Percentiles
90 95
99
99.9
Figure 5C-1. Normal probability plot of studentized residuals generated using logistic model,
smoothing set to 0.7, and the children 'EVER' asthmatic data set.
The third regression diagnostic, presented in Attachment B, Figures 5CB-5 to 5CB-8 are
plots of the studentized residuals against the smoothed beta values. All the studentized residuals
for a given smoothing parameter are plotted together within the same graph. Also plotted is a
LOESS smoothed curve fit to the same set of points, with SAS's optimal smoothing parameter
choice, to indicate the typical pattern. Ideally there should be no obvious pattern and an average
studentized residual close to zero with no regression slope (e.g., see Figure 5C-2). For the
children data sets, these plots generally indicate no unusual patterns, and the results for
smoothing parameters 0.4 through 0.6 indicate a fit LOESS curve closest to the studentized
residual equals zero line. When considering the adult data sets, again the appropriate value
would be 0.9, as Attachment B, Figures 5CB-7 and 5CB-8 supports this conclusion.
5C-12
-------
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
SmoothingParameter=0.6
student
5-
4-
3-
2-
1 -
0 :
-2:
-3-
-4:
-5 -
x ° -*
x m
I
-5.00000
reggendpov ^~
o
0
0
X
X
X
1
-0-
o
0
0
X
X
X
1
-0
o
0
0
X
X
X
1 ' 1 1
-4.00000 -3.00000
Predicted lo|
All: LOESS Smoothed
Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
Northeast-Female-BelowPovertyL
Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
South-Male-BelowPovertyLevel
West-Female-BelowPovertyLevel
West-Male-BelowPovertyLevel
1 '
-2
1 1
1 '
.00000
1
-1.00000
1
0
jitprev
0
o
+
X
X
0
o
+
X
X
0
o
+
X
X
Midwest-Female-Abo vePovertyLev
Midwest-Male-Abo vePovertyLevel
Northeast-Female-Abo vePovertyL
Northeast-Male-AbovePovertyLev
South-Female-Abo vePovertyLevel
S outh-Male-Abo vePo verty Level
West-Female-AbovePovertyLevel
West-Male-Abo vePovertyLevel
1
2 Figure 5C-2. Studentized residuals versus model predicted betas generated using a logistic
3 model and using the children 'EVER' asthmatic data set, with smoothing set to 0.6.
4
5 When considering both children asthma prevalence responses evaluated, the residual
6 standard error (estimated values for sigma) suggests the choice of smoothing parameter as 0.6 to
7 0.8. The normal probability plots of the Studentized residuals suggest preference for smoothing
8 at or above 0.6. The plots of residuals against smoothed predictions suggest the choices of 0.4
9 through 0.6. We therefore chose the final value of 0.6 to use for smoothing the children's asthma
10 prevalence. For the adults, 0.9 was selected for smoothing.
11 Smoothed asthma prevalence and associated graphical presentation are provided in
12 Attachment C, following a similar format as the unsmoothed data provided in Attachment A.
13 5C-5 CENSUS TRACT LEVEL POVERTY RATIO DATA SET DESCRIPTION
14 AND PROCESSING
15 This section describes the approach used to generate census tract level poverty ratios for
16 all US census tracts, stratified by age and age groups where available. The data set generation
17 involved primarily two types of data downloaded from the 2000 US Census, each are described
18 below.
5C-13
-------
1 First, individual state level SF3 geographic data ("geo") .ufi files and associated
2 documentation were downloaded10 and, following import by SAS (SAS, 2012), were screened
3 for tract level information using the "sumlev" variable equal to ' 140'. For quality control
4 purposes and ease of matching with the poverty level data, our geo data set retained the
5 following variables: stusab, sumlev, logrecno, state, county, tract, name, latitude, and longitude.
6 Second, the individual state level SF3 files ("30") were downloaded, retaining the
7 number of persons across the variable "PCT50" for all state "logrecno".u The data provided by
8 the PCT50 variable is stratified by age or age groups (ages <5, 5, 6-11, 12-14, 15, 16-17, 18-24,
9 25-34, 35-44, 45-54, 55-64, 65-74, and >75) and income/poverty ratios, given in increments of
10 0.25. We calculated two new variables for each state logrecno using the number of persons from
11 the PCT50 stratifications; the fraction of those persons having poverty ratios < 1.5 and > 1.5 by
12 summing the appropriate PCT50 variable and dividing by the total number of persons in that
13 age/age group. Finally the poverty ratio data were combined with the above described census
14 tract level geographic data using the "stusab" and "logrecno" variables. The final output was a
15 single file containing relevant tract level poverty probabilities by age groups for all US census
16 tracts (where available).
17 5C-6 COMBINED CENSUS TRACT LEVEL POVERTY RATIO AND ASTHMA
18 PREVALENCE DATA
1 9
19 Because the prevalence data are stratified by standard US Census defined regions, we
20 first mapped the tract level poverty level data to an appropriate region based on the State.
21 Further, as APEX requires the input data files to be complete, additional processing of the
22 poverty probability file was needed. For where there was missing tract level poverty
23 information,13 we substituted an age-specific value using the average for the particular county
24 the tract was located within. The frequency of missing data substitution comprised 1.7% of the
25 total poverty probability data set. The two data sets were merged and the final asthma
26 prevalence was calculated using the following weighting scheme:
27
28 prevalence=round((pov_prob *prev_poor) + ((l-pov_prob) *prev_notpoor), 0,0001);
10 Geographic data were obtained from http://www2.census.gov/census 2000/datasets/Summary File 3/.
Information regarding variable names is given in Figure 2-5 of US Census (2007).
11 Poverty ratio data were obtained from http://www2.census.gov/census 2000/datasets/Summary File 3/.
Information regarding poverty ratio names variable names is given in chapter 6 of US Census Bureau (2007).
We used the variable "PCT50", an income to poverty ratio variable stratified by various ages and age groups and
described in chapter 7 of US Census Bureau (2007).
12 For example, see http://www.cdc.gov/std/stats 10/census.htm.
13 Whether there were no data collected by the Census or whether there were simply no persons in that age group is
relatively inconsequential to estimating the asthmatic persons exposed, particularly considering latter case as no
persons in that age group would be modeled.
5C-14
-------
1
2 whereas each US census tract value now expresses a tract specific poverty-weighted
3 prevalence, stratified by ages (children 0-17), age groups (adults), and two genders. These final
4 prevalence data are found within the APEX asthmaprevalence. txt file.
5
6 5C-7 REFERENCES
7 Cohen, J., and Rosenbaum, A. (2005). Analysis of NHIS Asthma Prevalence Data.
8 Memorandum to John Langstaff by ICF Incorporated. For US EPA Work Assignment 3-
9 08 under EPA contract 68D01052.
10 SAS. (2012). SAS/STAT 9.2 User's Guide, Second Edition. Available at:
11 http ://support. sas.com/documentation/cdl/en/statug/63 03 3/PDF/default/statug.pdf.
12 US Census Bureau. (2007). 2000 Census of Population and Housing. Summary File 3 (SF3)
13 Technical Documentation, available at: http://www.census.gov/prod/cen2000/doc/sf3.pdf
14 Individual SF3 files '30' (for income/poverty variables pctSO) for each state were
15 downloaded from: http://www2.census.gov/census_2000/datasets/Summary_File_3/.
16 US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas (July 2007).
17 Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R-
18 07-010. Available at: http://epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html.
19 US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary
20 National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November
21 2008. Available at:
22 http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121 NO2REA final.pdf
23 US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary
24 National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009.
25 Available at:
26 http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf
5C-15
-------
1
2
3
4
Appendix 5C, Attachment A
Unsmoothed Asthma Prevalence Tables and Figures
Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children "EVER" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
Prevalence
0.0018
0.0387
0.0367
0.0395
0.0815
0.0885
0.0438
0.1374
0.0820
0.1027
0.0995
0.1129
0.1752
0.1331
0.1944
0.1383
0.1731
0.1311
0.0564
0.0585
0.1256
0.1127
0.1746
0.1584
0.1229
0.0867
0.1523
0.2070
0.2293
0.1359
0.1501
0.1527
0.1197
0.2103
0.2054
0.1844
0.0061
0.0258
0.0848
0.0996
0.0876
0.1593
0.0977
0.1793
0.1503
0.1418
0.1569
0.1717
0.2054
0.1846
0.1671
0.1454
0.1557
0.1320
0.0293
0.1051
SE
0.0018
0.0233
0.0148
0.0186
0.0298
0.0207
0.0200
0.0277
0.0246
0.0220
0.0193
0.0277
0.0391
0.0256
0.0477
0.0302
0.0341
0.0256
0.0353
0.0197
0.0487
0.0419
0.0395
0.0447
0.0417
0.0353
0.0392
0.0486
0.1109
0.0470
0.0484
0.0380
0.0462
0.0760
0.0597
0.1134
0.0044
0.0178
0.0231
0.0261
0.0223
0.0313
0.0229
0.0313
0.0356
0.0265
0.0322
0.0371
0.0338
0.0358
0.0291
0.0356
0.0278
0.0233
0.0176
0.0376
LowerCI
0.0002
0.0117
0.0165
0.0155
0.0390
0.0556
0.0176
0.0916
0.0450
0.0669
0.0675
0.0688
0.1112
0.0905
0.1173
0.0890
0.1160
0.0885
0.0160
0.0299
0.0567
0.0529
0.1100
0.0888
0.0616
0.0381
0.0902
0.1275
0.0800
0.0670
0.0774
0.0921
0.0544
0.0980
0.1121
0.0491
0.0015
0.0066
0.0491
0.0588
0.0527
0.1069
0.061 1
0.1259
0.0930
0.0973
0.1035
0.1106
0.1470
0.1244
0.1175
0.0885
0.1087
0.0926
0.0089
0.0509
UpperCI
0.0129
0.1208
0.0797
0.0972
0.1624
0.1382
0.1046
0.2010
0.1450
0.1545
0.1442
0.1797
0.2652
0.1916
0.3049
0.2086
0.2502
0.1898
0.1799
0.1112
0.2552
0.2240
0.2658
0.2664
0.2301
0.1851
0.2456
0.3182
0.5043
0.2562
0.2710
0.2427
0.2431
0.3949
0.3462
0.4976
0.0247
0.0957
0.1426
0.1636
0.1423
0.2306
0.1527
0.2489
0.2340
0.2021
0.2306
0.2568
0.2795
0.2650
0.2322
0.2297
0.2182
0.1848
0.0922
0.2047
5C-16
-------
Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children "EVER" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Age
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
Prevalence
0.1786
0.2066
0.2770
0.2504
0.2186
0.2192
0.2902
0.1242
0.2897
0.2669
0.2589
0.2429
0.1470
0.1965
0.1855
0.3740
0.0055
0.0296
0.0697
0.0723
0.1142
0.1058
0.0933
0.1084
0.0780
0.1362
0.0979
0.1697
0.0535
0.0910
0.1500
0.1733
0.1884
0.1694
0.0315
0.1230
0.0703
0.1860
0.1666
0.2347
0.0682
0.0972
0.2049
0.1695
0.0988
0.2622
0.1377
0.3506
0.1869
0.1965
0.1986
0.1625
0.0256
0.0542
0.0635
0.0835
0.1378
0.1444
0.2175
0.2019
0.1878
0.1286
0.1879
0.2532
SE
0.0652
0.0513
0.0638
0.0499
0.0447
0.0456
0.0649
0.0437
0.0639
0.0613
0.1050
0.0693
0.0490
0.0509
0.0611
0.1042
0.0054
0.0164
0.0252
0.0250
0.0254
0.0296
0.0254
0.0251
0.0221
0.0374
0.0298
0.0382
0.0229
0.0273
0.0207
0.0355
0.0510
0.0395
0.0251
0.0576
0.0277
0.0555
0.0598
0.0636
0.0250
0.0362
0.0604
0.0698
0.0440
0.0734
0.0525
0.0762
0.0537
0.0534
0.0470
0.0602
0.0130
0.0231
0.0220
0.0232
0.0329
0.0357
0.0482
0.0343
0.0373
0.0342
0.0278
0.0420
LowerCI
0.0835
0.1236
0.1703
0.1656
0.1436
0.1428
0.1806
0.0607
0.1815
0.1646
0.1068
0.1329
0.0742
0.1150
0.0935
0.1998
0.0008
0.0099
0.0337
0.0362
0.0731
0.0602
0.0541
0.0681
0.0442
0.0780
0.0530
0.1073
0.0228
0.0499
0.1138
0.1142
0.1077
0.1052
0.0064
0.0469
0.0319
0.1002
0.0791
0.1329
0.0327
0.0458
0.1107
0.0717
0.0400
0.1445
0.0629
0.2188
0.1031
0.1120
0.1221
0.0754
0.0094
0.0231
0.0318
0.0478
0.0849
0.0875
0.1376
0.1429
0.1252
0.0751
0.1394
0.1799
UpperCI
0.3418
0.3247
0.4170
0.3600
0.3184
0.321 1
0.4312
0.2374
0.4285
0.4021
0.5051
0.4017
0.2703
0.3151
0.3345
0.5884
0.0368
0.0854
0.1384
0.1394
0.1741
0.1793
0.1563
0.1682
0.1339
0.2272
0.1738
0.2578
0.1204
0.1604
0.1953
0.2541
0.3085
0.2613
0.1404
0.2852
0.1479
0.3193
0.3175
0.3802
0.1366
0.1944
0.3478
0.3505
0.2240
0.4277
0.2752
0.5100
0.3148
0.3217
0.3065
0.3158
0.0679
0.1218
0.1228
0.1418
0.2158
0.2291
0.3263
0.2774
0.2719
0.2115
0.2485
0.3439
5C-17
-------
Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children "EVER" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Poverty Status
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Age
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
Prevalence
0.1801
0.1581
0.2043
0.1752
0.1798
0.1836
0.0375
0.1649
0.2200
0.1124
0.2651
0.2398
0.3209
0.2651
0.2905
0.3810
0.3382
0.2485
0.2819
0.2961
0.2876
0.2632
0.2407
0.3123
0.0129
0.0191
0.0558
0.0793
0.0834
0.0932
0.1446
0.1439
0.1111
0.1258
0.0626
0.1288
0.1064
0.1387
0.1621
0.1399
0.1362
0.1299
0.0495
0.0734
0.0828
0.0973
0.1578
0.1409
0.1536
0.1658
0.1428
0.2123
0.1408
0.2249
0.1741
0.1463
0.2428
0.1947
0.1285
0.1322
0.0135
0.0782
0.1134
0.1063
SE
0.0233
0.0340
0.0447
0.0287
0.0360
0.0282
0.0275
0.0506
0.0503
0.0445
0.0909
0.0651
0.0432
0.0572
0.0969
0.0773
0.1019
0.0708
0.0705
0.0685
0.0713
0.0661
0.0559
0.0734
0.0080
0.0084
0.0147
0.0200
0.0184
0.0222
0.0226
0.0248
0.0194
0.0222
0.0154
0.0210
0.0182
0.0222
0.0243
0.0169
0.0253
0.0197
0.0216
0.0210
0.0207
0.0271
0.0372
0.0300
0.0381
0.0332
0.0302
0.0413
0.0347
0.0466
0.0519
0.0296
0.0437
0.0399
0.0344
0.0323
0.0065
0.0162
0.0190
0.0211
LowerCI
0.1388
0.1022
0.1303
0.1257
0.1195
0.1346
0.0087
0.0877
0.1371
0.0501
0.1262
0.1355
0.2427
0.1686
0.1401
0.2446
0.1732
0.1359
0.1656
0.1808
0.1695
0.1548
0.1483
0.1885
0.0038
0.0080
0.0330
0.0479
0.0537
0.0579
0.1057
0.1017
0.0784
0.0883
0.0383
0.0928
0.0756
0.1006
0.1198
0.1100
0.0938
0.0959
0.0207
0.0415
0.0503
0.0556
0.0976
0.0917
0.0927
0.1104
0.0931
0.1425
0.0855
0.1467
0.0941
0.0972
0.1675
0.1280
0.0747
0.0807
0.0052
0.0517
0.081 1
0.0714
UpperCI
0.2303
0.2366
0.3056
0.2387
0.2614
0.2454
0.1477
0.2887
0.3337
0.2330
0.4738
0.3885
0.4107
0.3908
0.5070
0.5392
0.5551
0.4102
0.4371
0.4448
0.4440
0.4107
0.3660
0.4701
0.0427
0.0447
0.0928
0.1286
0.1273
0.1467
0.1948
0.1996
0.1550
0.1762
0.1005
0.1759
0.1478
0.1881
0.2156
0.1763
0.1938
0.1737
0.1137
0.1268
0.1336
0.1649
0.2450
0.2103
0.2439
0.2414
0.2126
0.3042
0.2233
0.3288
0.2997
0.2142
0.3382
0.2847
0.2122
0.2092
0.0342
0.1165
0.1563
0.1554
5C-18
-------
Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children "EVER" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Poverty Status
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Age
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Prevalence
0.1679
0.1644
0.1328
0.1542
0.1502
0.1522
0.1485
0.1767
0.1915
0.1939
0.1381
0.1579
0.1698
0.1530
0.0610
0.1005
0.1102
0.1699
0.1642
0.2510
0.2064
0.1588
0.2518
0.2246
0.2022
0.1890
0.2322
0.2345
0.2265
0.1801
0.1286
0.1916
0.0049
0.0390
0.0269
0.0439
0.0232
0.0988
0.0829
0.1065
0.0960
0.1124
0.0978
0.1186
0.1655
0.0855
0.1258
0.1482
0.1394
0.2285
0.0064
0.0443
0.0523
0.0403
0.0346
0.0887
0.1351
0.1364
0.1106
0.1254
0.0585
0.0747
0.0720
0.1898
SE
0.0303
0.0226
0.0212
0.0270
0.0224
0.0232
0.0240
0.0255
0.0236
0.0255
0.0196
0.0246
0.0193
0.0240
0.0181
0.0206
0.0225
0.0324
0.0288
0.0485
0.0339
0.0309
0.0503
0.0381
0.0368
0.0344
0.0383
0.0454
0.0489
0.0371
0.0303
0.0297
0.0037
0.0202
0.0097
0.0153
0.0079
0.0294
0.0223
0.0281
0.0280
0.0296
0.0285
0.0188
0.0352
0.0196
0.0278
0.0213
0.0254
0.0375
0.0064
0.0195
0.0220
0.0140
0.0177
0.0372
0.0432
0.0360
0.0244
0.0405
0.0204
0.0264
0.0279
0.0591
LowerCI
0.1165
0.1247
0.0964
0.1083
0.1114
0.1121
0.1073
0.1322
0.1495
0.1487
0.1039
0.1154
0.1352
0.1117
0.0338
0.0667
0.0732
0.1154
0.1152
0.1682
0.1477
0.1072
0.1663
0.1588
0.1394
0.1305
0.1656
0.1573
0.1448
0.1183
0.0799
0.1399
0.001 1
0.0139
0.0132
0.0219
0.0118
0.0544
0.0484
0.0627
0.0534
0.0662
0.0545
0.0864
0.1074
0.0542
0.0806
0.1111
0.0967
0.1632
0.0009
0.0185
0.0226
0.0202
0.0126
0.0380
0.0703
0.0798
0.071 1
0.0650
0.0292
0.0368
0.0331
0.0993
UpperCI
0.2360
0.2136
0.1802
0.2148
0.1994
0.2033
0.2018
0.2323
0.2419
0.2487
0.1813
0.2122
0.2110
0.2061
0.1076
0.1488
0.1626
0.2431
0.2285
0.3572
0.2808
0.2290
0.3622
0.3078
0.2839
0.2658
0.3153
0.3345
0.3361
0.2645
0.2005
0.2566
0.0216
0.1048
0.0541
0.0858
0.0450
0.1730
0.1384
0.1752
0.1666
0.1846
0.1695
0.1606
0.2463
0.1324
0.1911
0.1949
0.1969
0.3101
0.0441
0.1025
0.1166
0.0788
0.0919
0.1934
0.2439
0.2234
0.1682
0.2283
0.1137
0.1460
0.1496
0.3323
5C-19
-------
Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children "EVER" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Age
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Prevalence
0.1431
0.1168
0.0814
0.0637
0.0000
0.0244
0.0517
0.0601
0.1698
0.1236
0.1376
0.1288
0.1018
0.1884
0.1604
0.2121
0.1833
0.2105
0.1475
0.1641
0.1958
0.2113
0.0135
0.0812
0.0417
0.1182
0.1349
0.1562
0.1853
0.1484
0.1549
0.1275
0.1742
0.1909
0.1678
0.1793
0.1919
0.1410
0.1863
0.2030
SE
0.0431
0.0304
0.0290
0.0235
0.0000
0.0121
0.0155
0.0172
0.0275
0.0288
0.0264
0.0354
0.0223
0.0315
0.0273
0.0298
0.0349
0.0397
0.0309
0.0263
0.0282
0.0289
0.0128
0.0317
0.0131
0.0351
0.0329
0.0401
0.0444
0.0343
0.0343
0.0418
0.0431
0.0554
0.0599
0.0491
0.0454
0.0577
0.0384
0.0493
LowerCI
0.0773
0.0692
0.0398
0.0305
0.0000
0.0092
0.0285
0.0339
0.1224
0.0772
0.0934
0.0738
0.0657
0.1342
0.1138
0.1596
0.1244
0.1431
0.0966
0.1188
0.1463
0.1602
0.0020
0.0370
0.0224
0.0647
0.0823
0.0926
0.1133
0.0928
0.0988
0.0654
0.1049
0.1046
0.0800
0.1021
0.1180
0.0606
0.1223
0.1229
UpperCI
0.2495
0.1906
0.1593
0.1285
0.0000
0.0635
0.0920
0.1041
0.2307
0.1918
0.1980
0.2152
0.1547
0.2579
0.2215
0.2762
0.2618
0.2987
0.2187
0.2224
0.2569
0.2733
0.0832
0.1691
0.0765
0.2061
0.2131
0.2514
0.2883
0.2288
0.2346
0.2338
0.2751
0.3227
0.3185
0.2959
0.2966
0.2946
0.2734
0.3165
5C-20
-------
Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children "STILL" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
Prevalence
0.0018
0.0387
0.0302
0.0395
0.0531
0.0617
0.0386
0.0801
0.0492
0.0789
0.0625
0.0856
0.1269
0.1089
0.1580
0.0863
0.1300
0.0989
0.0564
0.0486
0.0959
0.0697
0.1697
0.0819
0.0809
0.0680
0.1257
0.1394
0.1871
0.0726
0.1101
0.1258
0.0999
0.1648
0.1647
0.1747
0.0061
0.0214
0.0752
0.0692
0.0527
0.1293
0.0710
0.1369
0.1047
0.1096
0.1004
0.1340
0.1093
0.1029
0.1230
0.1007
0.1141
0.0644
0.0274
0.0892
0.1786
0.1620
0.2557
0.1914
0.1432
0.1788
SE
0.0018
0.0233
0.0135
0.0186
0.0214
0.0173
0.0192
0.0239
0.0151
0.0200
0.0162
0.0232
0.0357
0.0264
0.0478
0.0213
0.0319
0.0236
0.0353
0.0183
0.0434
0.0338
0.0387
0.0265
0.0357
0.0325
0.0346
0.0398
0.1071
0.0266
0.0452
0.0354
0.0435
0.0745
0.0576
0.1141
0.0044
0.0175
0.0222
0.0203
0.0201
0.0303
0.0193
0.0301
0.0299
0.0269
0.0281
0.0348
0.0242
0.0210
0.0236
0.0305
0.0268
0.0193
0.0175
0.0369
0.0652
0.0475
0.0634
0.0400
0.0333
0.0378
LowerCI
0.0002
0.0117
0.0125
0.0155
0.0238
0.0354
0.0143
0.0442
0.0267
0.0476
0.0373
0.0498
0.0717
0.0669
0.0849
0.0526
0.0792
0.0613
0.0160
0.0229
0.0383
0.0263
0.1065
0.0428
0.0332
0.0261
0.0719
0.0779
0.0548
0.0349
0.0477
0.071 1
0.0413
0.0640
0.0799
0.0429
0.0015
0.0042
0.0417
0.0385
0.0247
0.0805
0.0413
0.0878
0.0589
0.0669
0.0571
0.0791
0.0700
0.0684
0.0837
0.0548
0.071 1
0.0354
0.0077
0.0386
0.0835
0.0888
0.1517
0.1248
0.0894
0.1162
UpperCI
0.0129
0.1208
0.0715
0.0972
0.1142
0.1055
0.0999
0.1411
0.0888
0.1280
0.1029
0.1433
0.2145
0.1724
0.2751
0.1382
0.2062
0.1556
0.1799
0.1000
0.2206
0.1723
0.2594
0.1512
0.1840
0.1661
0.2105
0.2369
0.4777
0.1451
0.2340
0.2130
0.2226
0.3629
0.3094
0.4997
0.0247
0.1008
0.1319
0.1213
0.1090
0.201 1
0.1193
0.2072
0.1793
0.1745
0.1704
0.2179
0.1665
0.1520
0.1771
0.1780
0.1780
0.1143
0.0925
0.1927
0.3418
0.2772
0.3974
0.2821
0.2215
0.2649
5C-21
-------
Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children "STILL" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Age
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Prevalence
0.2414
0.1114
0.2022
0.1731
0.2271
0.1627
0.0967
0.1509
0.1167
0.3301
0.0055
0.0296
0.0697
0.0470
0.0717
0.0642
0.0709
0.0697
0.0609
0.0996
0.0740
0.1028
0.0386
0.0187
0.0907
0.1270
0.0974
0.1239
0.0078
0.1230
0.0658
0.1700
0.1139
0.2219
0.0583
0.0495
0.0850
0.0652
0.0988
0.2587
0.0882
0.3162
0.1293
0.1798
0.1429
0.1133
0.0131
0.0505
0.0635
0.0582
0.1007
0.1245
0.1990
0.1240
0.1482
0.0980
0.0999
0.1805
0.1204
0.0855
0.1243
0.1249
0.1198
0.0690
SE
0.0604
0.0404
0.0624
0.0406
0.1064
0.0591
0.0413
0.0506
0.0490
0.1005
0.0054
0.0164
0.0252
0.0158
0.0199
0.0196
0.0254
0.0180
0.0209
0.0334
0.0260
0.0305
0.0187
0.0095
0.0181
0.0344
0.0267
0.0375
0.0078
0.0576
0.0272
0.0576
0.0456
0.0583
0.0290
0.0252
0.0368
0.0294
0.0440
0.0734
0.0426
0.0739
0.0372
0.0479
0.0381
0.0426
0.0101
0.0227
0.0220
0.0216
0.0281
0.0318
0.0511
0.0274
0.0321
0.0321
0.0216
0.0342
0.0211
0.0237
0.0351
0.0247
0.0283
0.0173
LowerCI
0.1429
0.0533
0.1061
0.1072
0.0822
0.0767
0.0406
0.0757
0.0495
0.1683
0.0008
0.0099
0.0337
0.0240
0.0413
0.0349
0.0346
0.0416
0.0307
0.0507
0.0366
0.0565
0.0147
0.0069
0.0609
0.0733
0.0562
0.0671
0.001 1
0.0469
0.0287
0.0842
0.0503
0.1282
0.0215
0.0179
0.0354
0.0264
0.0400
0.1416
0.0332
0.1913
0.0722
0.1039
0.0831
0.0527
0.0029
0.0206
0.0318
0.0277
0.0574
0.0742
0.1171
0.0795
0.0956
0.0506
0.0648
0.1229
0.0848
0.0491
0.0702
0.0839
0.0744
0.0418
UpperCI
0.3780
0.2180
0.351 1
0.2675
0.4908
0.3125
0.2129
0.2781
0.2512
0.5456
0.0368
0.0854
0.1384
0.0897
0.1218
0.1151
0.1398
0.1143
0.1171
0.1865
0.1439
0.1797
0.0975
0.0500
0.1330
0.2108
0.1636
0.2177
0.0541
0.2852
0.1436
0.3133
0.2376
0.3561
0.1484
0.1294
0.1903
0.1521
0.2240
0.4249
0.2146
0.4746
0.2209
0.2930
0.2348
0.2269
0.0574
0.1185
0.1228
0.1181
0.1705
0.2013
0.3177
0.1885
0.2227
0.1813
0.1509
0.2573
0.1682
0.1449
0.2108
0.1819
0.1872
0.1117
5C-22
-------
Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children "STILL" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
Prevalence
0.0375
0.1649
0.1621
0.1015
0.2486
0.1479
0.2630
0.1707
0.2056
0.3343
0.2276
0.1643
0.1117
0.1931
0.1714
0.2043
0.1684
0.2140
0.0129
0.0144
0.0452
0.0675
0.0540
0.0572
0.1002
0.0894
0.0762
0.0969
0.0473
0.0847
0.0768
0.0700
0.1059
0.0930
0.0702
0.0867
0.0404
0.0613
0.0704
0.0812
0.1404
0.1276
0.0792
0.1262
0.1185
0.1147
0.1038
0.1461
0.1299
0.1013
0.1699
0.1591
0.0633
0.0975
0.0044
0.0700
0.0911
0.0962
0.1230
0.1321
0.0999
0.1114
0.0946
0.1108
SE
0.0275
0.0506
0.0496
0.0440
0.0909
0.0487
0.0391
0.0507
0.0966
0.0680
0.0786
0.0600
0.0389
0.0430
0.0664
0.0555
0.0501
0.0526
0.0080
0.0076
0.0169
0.0196
0.0150
0.0138
0.0186
0.0191
0.0160
0.0210
0.0135
0.0165
0.0152
0.0158
0.0211
0.0186
0.0156
0.0162
0.0203
0.0183
0.0193
0.0254
0.0367
0.0304
0.0288
0.0305
0.0290
0.0286
0.0301
0.0366
0.0490
0.0262
0.0385
0.0365
0.0273
0.0299
0.0025
0.0162
0.0195
0.0206
0.0259
0.0204
0.0192
0.0214
0.0168
0.0202
LowerCI
0.0087
0.0877
0.0864
0.0420
0.1131
0.0753
0.1939
0.0926
0.0751
0.2162
0.1093
0.0770
0.0552
0.1223
0.0764
0.1162
0.0912
0.1286
0.0038
0.0051
0.0215
0.0379
0.031 1
0.0354
0.0692
0.0584
0.0502
0.0627
0.0269
0.0576
0.0518
0.0447
0.071 1
0.0624
0.0451
0.0597
0.0149
0.0338
0.0408
0.0434
0.0826
0.0789
0.0381
0.0775
0.0724
0.0694
0.0579
0.0879
0.0600
0.0602
0.1071
0.0998
0.0267
0.0526
0.0014
0.0442
0.0595
0.0627
0.0805
0.0970
0.0681
0.0758
0.0664
0.0770
UpperCI
0.1477
0.2887
0.2835
0.2255
0.4621
0.2701
0.3463
0.2935
0.4521
0.4776
0.4145
0.3164
0.2132
0.2914
0.3410
0.3338
0.2901
0.3345
0.0427
0.0402
0.0926
0.1175
0.0920
0.091 1
0.1431
0.1346
0.1141
0.1466
0.0819
0.1231
0.1124
0.1080
0.1550
0.1364
0.1077
0.1242
0.1050
0.1085
0.1189
0.1471
0.2286
0.1997
0.1573
0.1989
0.1881
0.1836
0.1792
0.2331
0.2589
0.1655
0.2590
0.2441
0.1427
0.1737
0.0135
0.1092
0.1373
0.1449
0.1833
0.1774
0.1443
0.1608
0.1330
0.1569
5C-23
-------
Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children "STILL" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Poverty Status
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Above Poverty
Above Poverty
Age
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
Prevalence
0.1010
0.0946
0.1340
0.1122
0.0713
0.0899
0.0871
0.0700
0.0477
0.0859
0.0820
0.1434
0.1320
0.2314
0.1395
0.1207
0.2064
0.1364
0.1473
0.1390
0.1673
0.1684
0.0936
0.1379
0.0816
0.1057
0.0013
0.0353
0.0159
0.0284
0.0183
0.0689
0.0477
0.0469
0.0756
0.0686
0.0791
0.0763
0.1023
0.0571
0.1012
0.0923
0.0787
0.1303
0.0064
0.0443
0.0249
0.0372
0.0114
0.0491
0.1016
0.0908
0.0874
0.0839
0.0275
0.0339
0.0551
0.1028
0.1312
0.0630
0.0758
0.0328
0.0000
0.0039
SE
0.0186
0.0175
0.0207
0.0226
0.0153
0.0158
0.0147
0.0178
0.0162
0.0197
0.0201
0.0319
0.0265
0.0486
0.0302
0.0269
0.0474
0.0279
0.0315
0.0286
0.0339
0.0449
0.0305
0.0353
0.0275
0.0289
0.0013
0.0202
0.0076
0.0132
0.0071
0.0276
0.0166
0.0144
0.0263
0.0196
0.0250
0.0124
0.0260
0.0163
0.0251
0.0207
0.0214
0.0294
0.0064
0.0195
0.0153
0.0137
0.0102
0.0294
0.0419
0.0302
0.0258
0.0267
0.0137
0.0160
0.0254
0.0393
0.0440
0.0247
0.0287
0.0163
0.0000
0.0040
LowerCI
0.0699
0.0655
0.0983
0.0750
0.0466
0.0635
0.0623
0.0421
0.0242
0.0544
0.0503
0.0914
0.0881
0.1498
0.0902
0.0771
0.1285
0.0903
0.0956
0.0917
0.1109
0.0975
0.0485
0.0820
0.0415
0.0609
0.0002
0.0113
0.0062
0.0113
0.0085
0.0308
0.0239
0.0255
0.0376
0.0388
0.0420
0.0553
0.0614
0.0323
0.0615
0.0590
0.0458
0.0827
0.0009
0.0185
0.0074
0.0179
0.0020
0.0148
0.0440
0.0464
0.0484
0.0443
0.0103
0.0133
0.0219
0.0474
0.0662
0.0288
0.0354
0.0122
0.0000
0.0005
UpperCI
0.1438
0.1348
0.1801
0.1646
0.1077
0.1260
0.1206
0.1141
0.0916
0.1330
0.1309
0.2178
0.1931
0.3397
0.2097
0.1840
0.3145
0.2009
0.2203
0.2051
0.2445
0.2752
0.1729
0.2226
0.1544
0.1772
0.0095
0.1045
0.0401
0.0695
0.0389
0.1468
0.0928
0.0846
0.1459
0.1185
0.1440
0.1043
0.1655
0.0989
0.1622
0.1416
0.1322
0.1993
0.0441
0.1025
0.0805
0.0756
0.0638
0.1506
0.2174
0.1698
0.1529
0.1532
0.0715
0.0839
0.1315
0.2089
0.2435
0.1324
0.1546
0.0850
0.0000
0.0289
5C-24
-------
Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children "STILL" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Above Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Below Poverty
Age
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Prevalence
0.0305
0.0384
0.1363
0.0933
0.0803
0.1014
0.0537
0.1120
0.1202
0.1333
0.1258
0.1039
0.0873
0.0881
0.1066
0.1364
0.0135
0.0812
0.0308
0.0944
0.1056
0.0856
0.1277
0.0943
0.1282
0.0883
0.0697
0.0954
0.0759
0.0600
0.1457
0.1099
0.0957
0.1136
SE
0.0113
0.0129
0.0261
0.0268
0.0208
0.0320
0.0182
0.0242
0.0253
0.0271
0.0286
0.0328
0.0217
0.0222
0.0230
0.0284
0.0128
0.0317
0.0080
0.0311
0.0306
0.0256
0.0356
0.0353
0.0343
0.0287
0.0228
0.0365
0.0316
0.0276
0.0391
0.0551
0.0350
0.0421
LowerCI
0.0147
0.0197
0.0927
0.0523
0.0478
0.0537
0.0273
0.0726
0.0788
0.0885
0.0796
0.0549
0.0531
0.0532
0.0692
0.0897
0.0020
0.0370
0.0185
0.0486
0.0588
0.0471
0.0726
0.0443
0.0746
0.0459
0.0363
0.0440
0.0329
0.0239
0.0844
0.0394
0.0458
0.0534
UpperCI
0.0623
0.0735
0.1960
0.1608
0.1317
0.1834
0.1029
0.1689
0.1791
0.1959
0.1934
0.1879
0.1404
0.1425
0.1607
0.2021
0.0832
0.1691
0.0510
0.1755
0.1822
0.1508
0.2149
0.1897
0.2115
0.1632
0.1298
0.1947
0.1655
0.1427
0.2398
0.2713
0.1894
0.2254
5C-25
-------
Appendix 5C, Attachment A, Table CA-3. Unsmoothed prevalence for adults "EVER" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age_grp
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
Prevalence
0.1633
0.1347
0.1214
0.1157
0.1360
0.1104
0.0990
0.1990
0.1896
0.1789
0.1903
0.2760
0.1459
0.1295
0.1658
0.1254
0.0934
0.0659
0.0856
0.0884
0.0808
0.1672
0.1103
0.0945
0.1445
0.1623
0.1474
0.0830
0.1834
0.1375
0.1297
0.1209
0.1306
0.1244
0.0844
0.1642
0.1726
0.1771
0.2140
0.2174
0.1752
0.0941
0.1658
0.1262
0.0773
0.0976
0.091 1
0.0926
0.0689
0.1753
0.1255
0.1317
0.1189
0.1681
0.1383
0.0943
0.1501
0.1290
0.1050
0.1163
0.1279
0.1231
SE
0.0154
0.0096
0.0084
0.0072
0.0103
0.0107
0.0095
0.0156
0.0177
0.0209
0.0180
0.0255
0.0205
0.0202
0.0158
0.0092
0.0083
0.0057
0.0086
0.0106
0.0110
0.0182
0.0156
0.0191
0.0204
0.0203
0.0307
0.0217
0.0199
0.0107
0.0109
0.0095
0.0106
0.0130
0.0101
0.0194
0.0170
0.0172
0.0204
0.0232
0.0186
0.0132
0.0223
0.0126
0.0094
0.0086
0.0096
0.0128
0.0127
0.0200
0.0178
0.0244
0.0162
0.0490
0.0313
0.0265
0.0121
0.0084
0.0074
0.0060
0.0087
0.0102
LowerCI
0.1353
0.1169
0.1059
0.1022
0.1171
0.0910
0.0819
0.1701
0.1573
0.1415
0.1576
0.2289
0.1101
0.0948
0.1371
0.1085
0.0784
0.0555
0.0701
0.0697
0.0617
0.1345
0.0832
0.0632
0.1089
0.1263
0.0968
0.0492
0.1476
0.1178
0.1097
0.1034
0.1113
0.1010
0.0666
0.1296
0.1418
0.1459
0.1767
0.1753
0.1417
0.0712
0.1265
0.1034
0.0607
0.0820
0.0740
0.0704
0.0478
0.1395
0.0945
0.0909
0.0906
0.0923
0.0875
0.0536
0.1279
0.1134
0.0914
0.1051
0.1119
0.1044
UpperCI
0.1958
0.1547
0.1389
0.1306
0.1575
0.1332
0.1193
0.2314
0.2268
0.2237
0.2281
0.3285
0.1908
0.1744
0.1990
0.1446
0.1109
0.0779
0.1040
0.1114
0.1050
0.2060
0.1447
0.1391
0.1893
0.2061
0.2182
0.1367
0.2256
0.1598
0.1527
0.1409
0.1528
0.1523
0.1064
0.2059
0.2084
0.2132
0.2567
0.2664
0.2147
0.1234
0.2142
0.1531
0.0980
0.1158
0.1117
0.1209
0.0982
0.2179
0.1648
0.1872
0.1545
0.2865
0.2118
0.1606
0.1754
0.1464
0.1205
0.1285
0.1459
0.1446
5C-26
-------
Appendix 5C, Attachment A, Table CA-3. Unsmoothed prevalence for adults "EVER" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age grp
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
Prevalence
0.0939
0.1511
0.1336
0.1452
0.1622
0.2039
0.1616
0.1127
0.1438
0.1095
0.0890
0.0704
0.0782
0.0789
0.0893
0.1473
0.0914
0.0972
0.1062
0.1068
0.0966
0.0702
0.1595
0.1387
0.1368
0.1431
0.1478
0.1541
0.1231
0.1522
0.1191
0.1466
0.1874
0.1747
0.1318
0.1370
0.1499
0.1304
0.0984
0.0944
0.0917
0.1168
0.1208
0.1589
0.0846
0.0760
0.1422
0.0979
0.1349
0.0937
SE
0.0092
0.0133
0.0087
0.0125
0.0128
0.0179
0.0163
0.0133
0.0100
0.0078
0.0066
0.0051
0.0071
0.0078
0.0111
0.0152
0.0122
0.0139
0.0138
0.0156
0.0149
0.0130
0.0150
0.0096
0.0109
0.0092
0.0094
0.0130
0.0117
0.0184
0.0118
0.0182
0.0219
0.0181
0.0179
0.0198
0.0188
0.0107
0.0080
0.0081
0.0075
0.0126
0.0160
0.0222
0.0128
0.0135
0.0214
0.0176
0.0323
0.0194
LowerCI
0.0773
0.1269
0.1175
0.1224
0.1386
0.1711
0.1321
0.0891
0.1253
0.0952
0.0769
0.0610
0.0654
0.0649
0.0698
0.1199
0.0701
0.0732
0.0821
0.0799
0.0710
0.0486
0.1323
0.1209
0.1168
0.1261
0.1303
0.1302
0.1020
0.1195
0.0978
0.1145
0.1483
0.1419
0.1005
0.1027
0.1167
0.1108
0.0837
0.0796
0.0780
0.0943
0.0928
0.1201
0.0626
0.0535
0.1052
0.0684
0.0831
0.0620
UpperCI
0.1136
0.1790
0.1515
0.1714
0.1889
0.2413
0.1962
0.1415
0.1645
0.1258
0.1027
0.0811
0.0932
0.0956
0.1135
0.1797
0.1184
0.1280
0.1363
0.1414
0.1301
0.1004
0.1911
0.1586
0.1595
0.1621
0.1671
0.1813
0.1479
0.1920
0.1441
0.1859
0.2341
0.2131
0.1709
0.1806
0.1905
0.1527
0.1153
0.1116
0.1076
0.1438
0.1558
0.2073
0.1133
0.1069
0.1894
0.1381
0.2116
0.1393
5C-27
-------
Appendix 5C, Attachment A, Table CA-4. Unsmoothed prevalence for adults "STILL" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age_grp
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
Prevalence
0.1062
0.0859
0.0859
0.0858
0.0996
0.0755
0.0643
0.1306
0.1329
0.1354
0.1398
0.2110
0.1190
0.1029
0.0790
0.0599
0.0486
0.0447
0.0555
0.0524
0.0477
0.0938
0.0572
0.0731
0.0969
0.1350
0.1349
0.0643
0.1123
0.0917
0.0944
0.0858
0.0945
0.0898
0.0706
0.1232
0.1180
0.1265
0.1745
0.1744
0.1388
0.0488
0.0888
0.0655
0.0409
0.0564
0.0469
0.0641
0.0527
0.0780
0.0847
0.0795
0.0798
0.1322
0.1055
0.0758
0.0893
0.0731
0.0689
0.0716
0.0865
0.0914
SE
0.0133
0.0090
0.0081
0.0061
0.0090
0.0083
0.0073
0.0144
0.0143
0.0187
0.0166
0.0221
0.0180
0.0183
0.0125
0.0066
0.0063
0.0049
0.0059
0.0076
0.0088
0.0143
0.0137
0.0162
0.0208
0.0205
0.0294
0.0213
0.0148
0.0102
0.0092
0.0080
0.0086
0.0106
0.0098
0.0182
0.0147
0.0138
0.0185
0.021 1
0.0148
0.0088
0.0161
0.0093
0.0061
0.0078
0.0085
0.0105
0.0110
0.0129
0.0171
0.0212
0.0196
0.0492
0.0296
0.0247
0.0090
0.0064
0.0051
0.0049
0.0064
0.0090
LowerCI
0.0828
0.0699
0.0713
0.0746
0.0832
0.0608
0.0514
0.1049
0.1073
0.1027
0.1102
0.1709
0.0879
0.0722
0.0577
0.0482
0.0377
0.0360
0.0450
0.0394
0.0331
0.0693
0.0355
0.0470
0.0630
0.0997
0.0869
0.0332
0.0864
0.0735
0.0778
0.0714
0.0790
0.0711
0.0537
0.0918
0.0921
0.1018
0.1412
0.1369
0.1123
0.0341
0.0620
0.0495
0.0304
0.0429
0.0328
0.0463
0.0348
0.0562
0.0566
0.0467
0.0489
0.0617
0.0600
0.0395
0.0732
0.0615
0.0595
0.0626
0.0747
0.0753
UpperCI
0.1354
0.1052
0.1031
0.0986
0.1188
0.0934
0.0802
0.1614
0.1634
0.1764
0.1757
0.2575
0.1590
0.1448
0.1071
0.0743
0.0625
0.0554
0.0683
0.0694
0.0682
0.1258
0.0908
0.1119
0.1461
0.1804
0.2035
0.1208
0.1447
0.1138
0.1141
0.1029
0.1127
0.1128
0.0924
0.1634
0.1499
0.1560
0.2137
0.2196
0.1704
0.0693
0.1257
0.0862
0.0547
0.0738
0.0667
0.0880
0.0789
0.1075
0.1248
0.1322
0.1275
0.2608
0.1789
0.1406
0.1086
0.0866
0.0797
0.0818
0.1000
0.1105
5C-28
-------
Appendix 5C, Attachment A, Table CA-4. Unsmoothed prevalence for adults "STILL" having asthma.
Smoothed
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age grp
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
Prevalence
0.0599
0.0996
0.0867
0.1152
0.1369
0.1780
0.1303
0.0895
0.0608
0.0471
0.0451
0.0359
0.0413
0.0441
0.0636
0.0617
0.0344
0.0488
0.0800
0.0676
0.0687
0.0331
0.0908
0.0819
0.0994
0.0937
0.1013
0.1103
0.0783
0.0901
0.0861
0.1081
0.1391
0.1293
0.1053
0.1061
0.0620
0.0528
0.0582
0.0499
0.0542
0.0756
0.071 1
0.0741
0.0457
0.0344
0.1119
0.0528
0.1159
0.0442
SE
0.0072
0.0119
0.0079
0.0113
0.0123
0.0173
0.0152
0.0118
0.0079
0.0053
0.0048
0.0040
0.0055
0.0057
0.0097
0.0086
0.0064
0.0109
0.0131
0.0122
0.0129
0.0083
0.0143
0.0070
0.0090
0.0095
0.0087
0.0114
0.0092
0.0135
0.0111
0.0143
0.0179
0.0164
0.0166
0.0162
0.0104
0.0068
0.0061
0.0065
0.0072
0.0102
0.0133
0.0132
0.0097
0.0089
0.0198
0.0137
0.0336
0.0131
LowerCI
0.0473
0.0786
0.0725
0.0948
0.1144
0.1467
0.1033
0.0689
0.0471
0.0377
0.0365
0.0288
0.0317
0.0342
0.0470
0.0468
0.0239
0.0314
0.0579
0.0473
0.0473
0.0202
0.0663
0.0691
0.0830
0.0766
0.0854
0.0898
0.0621
0.0669
0.0667
0.0831
0.1075
0.1005
0.0770
0.0782
0.0445
0.0410
0.0473
0.0386
0.0416
0.0579
0.0491
0.0520
0.0301
0.0207
0.0786
0.0316
0.0644
0.0246
UpperCI
0.0756
0.1254
0.1035
0.1393
0.1629
0.2144
0.1631
0.1154
0.0782
0.0587
0.0556
0.0446
0.0535
0.0567
0.0855
0.0810
0.0494
0.0751
0.1097
0.0957
0.0987
0.0539
0.1231
0.0968
0.1186
0.1141
0.1197
0.1347
0.0982
0.1202
0.1105
0.1394
0.1781
0.1648
0.1425
0.1424
0.0858
0.0679
0.0715
0.0642
0.0702
0.0982
0.1019
0.1046
0.0689
0.0568
0.1570
0.0870
0.1996
0.0781
5C-29
-------
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=Midwest pov_rat=Above Poverty Level
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
9 10 11 12 13 14 15 16 17
gender
age
Female
Male
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
prev
•^Hr ± i i i
34567
9 10 11 12 13 14 15 16 17
gender
age
Fern ale
Male
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
prev
0.6-
0.2:
o.i:
o.o:
01234
67
10 11 12 13 14 15 16 17
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
Female
Male
gender
Female
Male
Appendix 5C, Attachment A, Figure CA-1. Unsmoothed prevalence and confidence intervals for children 'EVER' having asthma.
5C-30
-------
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=West pov_rat= Above Poverty Level
prev
0.3
0.2
0.1
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
age
Fern ale
Male
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
gender
Male
345
gender
8 9 10 11 12 13 14 15 16 17
age
Female
Male
Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
prev
345
gender
8 9 10 11 12 13 14 15 16 17
age
Female
Male
Appendix 5C, Attachment A, Figure CA-1, cont. Unsmoothed prevalence and confidence intervals for children 'EVER' having
asthma.
5C-31
-------
Figure 2. Raw asthma 'STILL1 prevalence rates and confidence intervals
region=Midwest pov_rat=Above Poverty Level
Figure 2. Raw asthma 'STILL1 prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
9 10 11 12 13 14 15 16 17
gender
age
Female
Figure 2. Raw asthma 'STILL1 prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
01234567
9 10 11 12 13 14 15 16 17
gender
age
Female
prev
0.6
0.5
0.4
0.3
0.2
0.1
3456
8 9 10 11 12 13 14 15 16 17
gender
age
Female
Male
Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
3456
8 9 10 11 12 13 14 15 16 17
gender
age
Female
Male
Appendix 5C, Attachment A, Figure CA-2. Unsmoothed prevalence and confidence intervals for children 'STILL' having asthma.
5C-32
-------
Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
prev
0.4-
0.3:
0.2:
o.i:
o.o
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
age
Fern ale
Male
Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
prev
0.6-
0.5:
0.4-
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
age
gender Female Male
prev
0.6-
0.5:
0.4:
0.3 :
Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals
region=West pov_rat=Above Poverty Level
1234567
10 11 12 13 14 15 16 17
gender
age
Fern ale
Male
Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
age
Female
Male
Appendix 5C, Attachment A, Figure CA-2, cont. Unsmoothed prevalence and confidence intervals for children 'STILL' having
asthma.
5C-33
-------
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=Midwest pov_rat=Above Poverty Level
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
18-24
25-34
35-44
gender
age_grp
Fern ale
Male
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
prev
0.4:
0.3:
35-44
gender
45-54
age_grp
Female
55-64
Male
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
75+
prev
0.4:
18-24
35-44
gender
45-54
age_grp
Fern ale
55-64
Male
65-74
75+
gender
age_grp
Female
Male
Appendix 5C, Attachment A, Figure CA-3. Unsmoothed prevalence and confidence intervals for adults 'EVER' having asthma.
5C-34
-------
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=West pov_rat=Above Poverty Level
prev
0.4:
0.3:
18-24
25-34
35-44
65-74
gender
age_grp
Fern ale
Male
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
18-24
35-44
45-54 55-64
65-74
gender
age_grp
Fern ale
Male
75+
75+
18-24
gender
45-54
age_grp
Fern ale
55-64
Male
Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
gender
age_grp
Female
Male
Appendix 5C, Attachment A, Figure CA-3, cont. Unsmoothed prevalence and confidence intervals for adults 'EVER' having
asthma.
5C-35
-------
Figure 4. Raw adult asthma 'STILL1 prevalence rates and confidence intervals
region=Midwest pov rat=Above Poverty Level
Figure 4. Raw adult asthma 'STILL1 prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
prev
0.4:
prev
0.4:
gender
age_grp
Female
Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
gender
age_grp
Female
Male
Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
prev
0.4:
gender
age_grp
Female
gender
age_grp
Female
Appendix 5C, Attachment A, Figure CA-4. Unsmoothed prevalence and confidence intervals for adults 'STILL' having asthma.
5C-36
-------
Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals
region=West pov_rat= Above Poverty Level
prev
0.4:
0.3:
18-24
25-34
35-44
gender
age_grp
Fern ale
Male
65-74
Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
75+
prev
0.4:
0.3:
35-44
gender
45-54
age_grp
Female
55-64
Male
Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
75+
18-24
35-44
gender
45-54 55-64
age_grp
Female Male
65-74
75+
18-24
gender
age_grp
Fern ale
Male
Appendix 5C, Attachment A, Figure CA-4, cont. Unsmoothed prevalence and confidence intervals for adults 'STILL' having
asthma.
5C-37
-------
Appendix 5C, Attachment B
Logistic Model Fit Tables and Figures
Appendix 5C, Attachment B, Table 5CB-1. Alternative logistic models for estimating child asthma prevalence using the
"EVER" asthma response variable and goodness of fit test results.
Description
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
Stratification Variable
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
-2 log likelihood
288740115.1
287062346.4
288120804.1
287385013.1
286367652.6
286283543.6
285696164.7
284477928.1
286862135.1
285098650.6
286207721 .5
285352164
284330346.1
284182547.5
283587631 .7
282241318.6
286227019.6
284470413
285546716.1
284688169.9
283662673.5
283404487.5
282890785.3
281407414.3
285821686.2
283843266.2
284761522.8
284045849.2
282099156.1
281929968.5
281963915.7
278655423.1
DF
2
4
8
4
16
16
8
32
3
6
12
6
24
24
12
48
4
8
16
8
32
32
16
64
18
36
72
36
144
144
72
288
Appendix 5C, Attachment B, Table 5CB-2. Alternative logistic models for estimating child asthma prevalence using the
"STILL" asthma response variable and goodness of fit test results.
Description
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
1. logit(prob) = linear in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
Stratification Variable
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
-2 log likelihood
181557347.7
180677544.6
180947344.2
180502490.5
179996184.8
179517528
179637601.4
178567573.9
180752073.1
179771977.6
180088080.5
179611530.4
179004935.6
178519078.1
178640744.8
177414967.2
180247874.1
179235170
179583725.1
DF
2
4
8
4
16
16
8
32
3
6
12
6
24
24
12
48
4
8
16
5C-38
-------
Appendix 5C, Attachment B, Table 5CB-2. Alternative logistic models for estimating child asthma prevalence using the
"STILL" asthma response variable and goodness of fit test results.
Description
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
Stratification Variable
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
-2 log likelihood
179067549.2
178407915.7
177897359.3
178029240
176642073.7
179972765.3
178918713.8
178852704.9
178599743.4
177075815.4
176418872.7
177422457.4
173888684.9
DF
8
32
32
16
64
18
36
72
36
144
144
72
288
Appendix 5C, Attachment B, Table 5CB- 3. Alternative logistic models for estimating adult asthma prevalence using the
"EVER" asthma response variable and goodness of fit test results.
Description
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
Stratification Variable
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
-2 log likelihood
825494282
821614711.2
824598583.4
823443004.3
820520390.7
821958349.1
819560679.9
817723710
DF
7
14
28
14
56
56
28
112
Appendix 5C, Attachment B, Table 5CB-4. Alternative logistic models for estimating adult asthma prevalence using the
"STILL" asthma response variable and goodness of fit test results.
Description
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
4. logit(prob) = f(age_grp)
Stratification Variable
1. none
2. gender
3. region
4. poverty
5. region, gender
6. region, poverty
7. gender, poverty
8. region, gender, poverty
-2 log likelihood
600538044.1
594277797.3
599561222.3
597511872.6
593112157.6
596008068.6
591394271.8
589398969.5
DF
7
14
28
14
56
56
28
112
5C-39
-------
Appendix 5C, Attachment B, Table 5CB-5. Effect on residual standard error by varying LOESS
smoothing parameter while fitting children "EVER" having asthma data set.
Region
South
Northeast
South
Midwest
Midwest
South
South
Midwest
West
South
Midwest
Midwest
Midwest
Midwest
Northeast
South
South
Midwest
West
Northeast
South
Northeast
Midwest
Northeast
Midwest
West
West
South
South
Midwest
Midwest
South
Northeast
Midwest
South
South
Midwest
Midwest
Midwest
Midwest
South
South
Northeast
South
Midwest
West
South
South
South
West
South
West
South
Northeast
Midwest
Midwest
South
Northeast
Gender
Female
Female
Male
Male
Male
Female
Male
Male
Female
Female
Female
Male
Male
Female
Female
Male
Female
Female
Male
Female
Female
Female
Male
Female
Male
Female
Male
Female
Female
Male
Female
Male
Female
Male
Male
Female
Male
Male
Male
Female
Male
Male
Female
Male
Female
Male
Male
Female
Male
Female
Male
Male
Female
Male
Male
Male
Female
Male
Poverty Ratio
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Smoothing Parameter
0.5
0.7
0.6
0.9
0.8
0.8
0.5
1
0.7
0.7
0.7
0.4
0.8
0.6
0.4
0.7
0.6
0.8
0.6
0.6
0.5
0.8
0.7
0.5
0.9
0.8
0.5
0.9
0.4
0.7
0.9
0.6
0.9
0.5
0.7
0.4
0.6
0.4
1
0.5
0.8
0.5
1
1
1
0.4
0.8
0.6
0.9
0.9
0.4
0.4
0.7
0.6
0.6
0.5
0.8
0.5
Residual Standard Error
0.999919
1.00088
1.003839
1.00548
1.010889
1.012178
0.982885
1.023284
0.973279
0.97298
1.028007
0.970948
0.965591
1.038233
0.961444
1.040867
0.954946
1.045107
1.052418
0.946315
0.945525
1.054556
0.940657
0.940383
1.063971
1.066819
1.067075
1.067923
0.930104
0.929292
1.072631
0.927161
1.074984
0.917969
0.912266
1.089646
0.90827
0.906073
1.094737
1.096459
1.099725
0.898228
1.101884
0.896985
1.103976
0.894137
0.893364
0.891551
0.890138
1.111538
0.88551 1
1.115223
0.86999
0.86934
0.86245
0.857982
0.857778
0.857592
5C-40
-------
Appendix 5C, Attachment B, Table 5CB-5. Effect on residual standard error by varying LOESS
smoothing parameter while fitting children "EVER" having asthma data set.
Region
West
West
South
South
Northeast
West
West
West
South
West
West
South
Northeast
South
West
Northeast
West
Northeast
West
Midwest
Northeast
Northeast
Midwest
West
Midwest
Northeast
South
Northeast
West
Northeast
West
Northeast
West
West
Midwest
West
West
Northeast
West
Northeast
Northeast
Northeast
Northeast
Midwest
Northeast
West
Northeast
West
Northeast
Northeast
Midwest
West
Midwest
Midwest
Gender
Female
Female
Female
Female
Male
Male
Female
Female
Male
Female
Female
Male
Male
Female
Female
Male
Female
Male
Male
Female
Male
Male
Female
Male
Female
Male
Male
Male
Male
Male
Female
Male
Male
Female
Female
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Male
Female
Male
Female
Male
Female
Female
Poverty Ratio
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Smoothing Parameter
0.6
1
1
0.9
0.7
0.7
0.9
0.8
0.4
0.7
1
0.9
0.8
1
0.6
1
0.5
0.9
1
1
1
0.7
0.4
0.5
0.9
0.6
1
0.8
0.9
0.9
0.5
0.4
0.6
0.4
0.8
0.8
0.7
0.5
0.8
0.8
0.7
0.9
0.6
0.7
0.5
0.4
1
0.9
0.4
0.4
0.6
1
0.5
0.4
Residual Standard Error
0.852664
1.147894
0.849143
0.847567
0.844668
1.163749
1.163943
1.166005
0.826195
1.174564
1.178045
1.178803
0.820245
1.182254
1.187757
0.811815
0.808706
0.805685
0.804743
0.799988
0.799128
0.798212
1.20612
0.793132
0.788082
0.78547
1.216423
0.78144
0.780843
0.779772
1.224495
0.769037
0.763027
0.762134
0.758775
0.756848
0.752592
0.729776
1.284153
1.292845
1.296274
1.308752
1.309671
0.688366
1.314991
1.31595
1.327129
1.35931
1.37577
0.618785
0.607758
1.395061
0.541466
0.522325
5C-41
-------
Appendix 5C, Attachment B, Table 5CB-6. Effect on residual standard error by varying LOESS
smoothing parameter while fitting children "STILL" having asthma data set.
Region
South
Northeast
Northeast
Northeast
Northeast
Midwest
Midwest
Midwest
Midwest
Northeast
South
Midwest
Northeast
South
Northeast
Northeast
Northeast
Midwest
South
Northeast
Northeast
Midwest
Midwest
Midwest
Northeast
Midwest
South
Northeast
South
West
South
South
South
South
West
South
West
West
South
West
Midwest
West
West
Midwest
Northeast
West
Midwest
West
South
Midwest
West
Midwest
Northeast
West
Midwest
Midwest
Midwest
Gender
Female
Male
Male
Male
Male
Male
Male
Female
Male
Male
Male
Female
Male
Female
Male
Male
Male
Male
Female
Male
Male
Male
Female
Male
Male
Male
Female
Male
Male
Male
Male
Male
Female
Female
Male
Male
Female
Female
Male
Female
Male
Male
Male
Female
Female
Female
Female
Male
Female
Female
Male
Male
Female
Female
Male
Female
Female
Poverty Ratio
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Smoothing
Parameter
1
0.9
0.7
0.9
0.4
0.7
0.8
1
0.6
0.8
0.6
0.5
0.5
0.4
0.6
0.5
0.8
0.9
0.6
1
1
0.5
0.9
1
0.7
0.7
0.7
0.6
1
0.9
0.5
0.7
0.8
0.9
0.8
0.9
0.7
1
0.8
0.9
0.8
0.7
1
0.8
0.4
0.8
0.6
0.7
0.5
0.7
0.6
0.4
0.5
0.7
0.9
0.9
1
Residual Standard Error
1.000117
1.000909
1.000993
0.997502
0.997275
0.996943
0.996544
1.003498
0.995815
0.995723
1.007198
0.99235
1.008536
0.99041
1.009859
1.01048
1.011028
1.011038
1.013156
1.01445
1.016505
1.01692
0.979917
1.020707
1.021388
0.977074
0.976479
1.024042
0.975784
1.025093
1.026184
0.971057
0.965833
0.965238
1.03481
0.964953
1.036384
1.040924
0.957162
1.044522
1.04601
1.04802
1.050309
0.946142
0.94543
1.055218
0.938888
1.063545
1.063816
0.931681
1.079146
1.080605
1.083479
1.084472
1.084476
0.914962
0.913089
5C-42
-------
Appendix 5C, Attachment B, Table 5CB-6. Effect on residual standard error by varying LOESS
smoothing parameter while fitting children "STILL" having asthma data set.
Region
South
Midwest
West
Midwest
Midwest
Northeast
South
Midwest
West
Midwest
Northeast
West
West
South
Midwest
Midwest
West
South
Northeast
South
Northeast
Northeast
West
Northeast
West
South
West
South
West
South
West
West
West
Midwest
West
Midwest
South
South
South
South
West
West
Midwest
South
Northeast
Northeast
West
South
Northeast
West
South
Northeast
Northeast
Northeast
Northeast
Gender
Male
Female
Female
Male
Male
Female
Male
Male
Female
Female
Male
Female
Male
Female
Male
Female
Female
Female
Female
Female
Female
Female
Male
Female
Male
Female
Male
Female
Female
Female
Female
Female
Male
Female
Female
Female
Male
Male
Female
Male
Female
Male
Female
Male
Female
Female
Male
Male
Female
Male
Male
Female
Female
Female
Female
Poverty Ratio
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Smoothing
Parameter
0.4
0.8
0.6
0.6
1
0.6
0.4
0.5
0.6
0.7
0.4
0.5
0.5
0.6
0.4
0.4
0.8
1
0.7
0.5
0.9
1
0.4
0.8
0.4
0.7
0.6
0.9
0.5
0.8
0.4
0.9
0.5
0.6
1
0.4
0.5
0.6
0.4
0.7
0.4
0.8
0.5
0.8
0.7
0.6
0.9
0.9
0.8
1
1
0.5
0.9
1
0.4
Residual Standard Error
1.087093
0.912722
0.912605
0.907737
1.103127
1.103286
1.112998
0.878223
1.124127
0.875579
0.874469
0.873529
1.127032
0.87206
0.869726
1.135372
1.136048
0.863066
1.140006
0.858107
1.147352
1.148471
1.152015
1.153553
0.845979
0.842335
0.8413
0.841106
1.166931
0.830955
0.826586
1.183444
0.815615
0.802622
1.20757
0.78769
1.214019
1.216661
0.781555
1.242272
1.252141
1.254244
0.742493
1.294055
1.32003
1.355219
1.356792
1.365737
1.39015
1.405599
1.408469
1.431367
1.503674
1.574778
1.605
5C-43
-------
Appendix 5C, Attachment B, Table 5CB-7. Effect on residual standard error by varying LOESS smoothing
parameter while fitting adults "EVER" having asthma data set.
Region
Midwest
South
West
West
South
Midwest
West
West
West
Northeast
West
Midwest
Northeast
South
Midwest
Midwest
South
Northeast
South
South
South
Northeast
West
South
South
Northeast
Northeast
Northeast
West
Northeast
Northeast
South
Midwest
West
South
West
Northeast
Midwest
West
West
South
Northeast
Northeast
Midwest
Midwest
Midwest
Midwest
Midwest
Gender
Female
Female
Female
Male
Female
Female
Male
Female
Male
Female
Male
Female
Male
Male
Male
Male
Female
Male
Female
Female
Female
Male
Female
Male
Male
Female
Male
Male
Female
Female
Male
Male
Male
Female
Male
Female
Female
Male
Male
Male
Male
Female
Female
Male
Male
Female
Female
Female
Poverty Ratio
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Smoothing Parameter
1
1
0.9
0.8
1
0.9
0.8
0.8
0.9
1
1
0.8
0.8
1
0.9
1
0.9
1
0.8
0.9
0.8
1
1
0.9
1
0.9
0.9
0.9
1
1
0.8
0.9
1
0.8
0.8
0.9
0.8
0.9
0.9
1
0.8
0.9
0.8
0.8
0.8
1
0.9
0.8
Residual Standard Error
0.983356
1.040607
1.044712
0.937658
1.06598
0.911278
1.095844
0.893319
0.886119
0.875056
0.858542
0.843191
1.177547
0.813689
1.190978
0.785268
0.77381
1.241548
0.751726
0.747912
0.740577
0.732859
1.275049
0.708509
0.706944
0.699107
1.301543
0.677309
0.669638
0.662619
0.646318
0.64328
1.395026
0.597305
0.58427
0.567466
0.528031
0.49517
1.523816
1.537805
0.400237
0.394894
0.362058
0.306085
0.169594
1.910643
1.920542
2.249162
5C-44
-------
Appendix 5C, Attachment B, Table 5CB-8. Effect on residual standard error by varying LOESS smoothing
parameter while fitting adults "STILL" having asthma data set.
Region
South
West
West
West
West
West
West
South
Midwest
Northeast
Midwest
South
Midwest
South
South
West
South
Midwest
Northeast
Northeast
South
South
South
West
South
Northeast
Northeast
Midwest
Northeast
Northeast
Northeast
Midwest
South
Northeast
South
Midwest
Midwest
Northeast
West
Northeast
Northeast
Midwest
Midwest
Midwest
Midwest
West
West
West
Gender
Male
Female
Female
Female
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Female
Male
Female
Male
Male
Female
Male
Female
Female
Male
Female
Male
Male
Male
Male
Male
Female
Male
Male
Female
Female
Male
Male
Female
Female
Male
Female
Male
Male
Male
Poverty Ratio
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Smoothing Parameter
0.8
0.8
0.9
1
1
0.8
0.9
0.9
1
1
0.9
0.8
0.8
1
0.9
1
0.9
1
0.9
1
1
0.9
0.8
0.9
1
0.8
1
1
0.9
0.9
0.8
0.9
1
1
0.8
0.9
0.8
0.8
0.8
0.9
0.8
1
0.9
0.8
0.8
0.8
1
0.9
Residual Standard Error
1.015193
1.045714
1.051807
1.061488
0.92928
0.925921
0.915895
1.097531
0.89825
1.102905
0.876146
1.128781
0.870507
1.130393
0.835583
0.825684
1.192655
0.788217
0.786205
1.21537
1.23752
0.748499
0.717121
0.670751
0.664236
0.65848
0.653985
0.650735
0.630298
1.370134
1.375365
0.620174
1.400273
0.581032
0.568428
0.508247
0.503315
0.478186
0.464598
0.453855
0.396203
1.616706
1.636938
0.295923
1.883863
2.16547
2.200364
2.396381
5C-45
-------
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.4
-s
5 10 25 50 75
Normal Percentiles
90 95 99 99.9
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.6
5 10 25 50 75 90 95 99 99.9
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.5
10 25 50 75 90 95
Normal Percentiles
99 99.9
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.7
1 5 10
25 50 75 90 95 99
Normal Percentiles
Appendix 5C, Attachment B, Figure 5CB-1. Normal probability plots of studentized residuals generated using logistic model and children 'EVER'
asthmatic data set.
5C-46
-------
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.8
10 25 50 75
Normal Percentiles
90 95 99
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.9
5 10 25 50 75 90 95
Normal Percentiles
99 99.9
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter= 1
5 10 25 50 75
Normal Percentiles
90 95 99
Appendix 5C, Attachment B, Figure 5CB-1, cont. Normal probability plots of studentized residuals generated using logistic model and children
'EVER' asthmatic data set.
5C-47
-------
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
S mo othingParameter=0.4
25 50 7
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.6
10 25 50 75
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.5
5 10
75
90 95
99
99.9
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.7
Normal Percentiles
5 10 25 50 75
Normal Percentiles
90 95
99
99.9
Appendix 5C, Attachment B, Figure 5CB-2. Normal probability plots of studentized residuals generated using logistic model and children
'STILL' asthmatic data set.
5C-48
-------
•2 0 -
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
Smoothing? arameter=0.8
1 5 10
25 50
Normal Percentiles
75 90 95 99 99.9
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter= 1
25 50 75 90 95 99
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
All genders, regions, poverty ratios combined
SmoothingParameter=0.9
5 10 25
Normal Percentiles
75 90 95 99 99.9
Appendix 5C, Attachment B, Figure 5CB-2, cont. Normal probability plots of studentized residuals generated using logistic model and children
'STILL' asthmatic data set.
5C-49
-------
Normal probability plot of studentized residuals by smoothing parameter
Adults: All genders, regions, poverty ratios combined
SmoothingParameter=0.8
5 10 25 50 75 90 95 99
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
Adults: All genders, regions, poverty ratios combined
SmoothingParameter= 1
5 10 25 50 75 90 95 99 99.9
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
Adults: All genders, regions, poverty ratios combined
SmoothingParameter=0.9
0.1 1 5 10 25 50 75 90 95 99 99.9
Normal Percentiles
Appendix 5C, Attachment B, Figure 5CB-3. Normal probability plots of studentized residuals generated using logistic model and adult 'EVER'
asthmatic data set.
5C-50
-------
Normal probability plot of studentized residuals by smoothing parameter
Adults Still: All genders, regions, poverty ratios combined
SmoothingParameter=0.8
1.5
1.0
0.5
0 -
-0.5
-1.0
-1.5 -
-2.0 ~
1 5 10 25 50 75 90 95 99
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
Adults Still: All genders, regions, poverty ratios combined
SmoothingParameter= 1
5 10 25 50 75 90 95 99 99.9
Normal Percentiles
Normal probability plot of studentized residuals by smoothing parameter
Adults Still: All genders, regions, poverty ratios combined
SmoothingParameter=0.9
0.1 1 5 10 25 50 75 90 95 99 99.9
Normal Percentiles
Appendix 5C, Attachment B, Figure 5CB-4. Normal probability plots of studentized residuals generated using logistic model and adult 'STILL'
asthmatic data set.
5C-51
-------
Studentized residual versus smoothed logits of prevalence rates by smoothing parameter
S mo othingParameter=0.4
Studentized residual versus smoothed logits of prevalence rates by smoothing parameter
SmoothingParameter=0.6
student
5-
4-
3-
2-
1 -
student
Predicted logitprev
reggendpov ^ ^ ^ All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowP overly Level
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowP overly Lev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Fern ale-BelowPovertyLevel
- - West-Male-BelowPovertyLevel
O O O Midwest-Fern ale-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Fern ale-AboveP overtyL
O Northeast-Male-AbovePovertyLev
~\~ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Fern ale-AbovePovertyLevel
XXX West-Male-AbovePovertyLev el
Predicted logitprev
Studentized residual versus smoothed logits of prevalence rates by smoothing parameter
SmoothingParameter=0.5
reggendpov ^ ^ ^ All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South -Female-Be lowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Fern ale-BelowPovertyLev el
West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AboveP overtyL
Northeast-Male-AbovePovertyLev
~l~ South-Fem ale-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Fern ale-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Studentized residual versus smoothed logits of prevalence rates by smoothing parameter
SmoothingParameter=0.7
student
5 -
4-
3-
reggendpov
-4.00000
-3.00000
Predicted logitprev
0 0 0 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
300 Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Fern ale-Be lowPovertyLevel
X West-Male-BelowPovertyLevel
O O O Midwest-Fern ale-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Fern ale-AboveP overtyL
O Northeast-Male-AbovePovertyLev
~+ ~^ ~^ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Fern ale-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
student
-5.00000
reggendpov
O
-4.00000
-3.00000
Predicted logitprev
000 All: LOESS Smoothed O O C
000 Midwest-Female-BelowPovertyLev O O C
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
+ South-Female-BelowPovertyLevel
X South-Male-BelowPovertyLevel
X West-Female-BelowPovertyLevel
West-Male-BelowPovertyLevel
Midwest-Female-AbovePovertyLev
Midwest-Male-AbovePovertyLevel
Northeast-Female-AboveP overtyL
Northeast-Male-AbovePovertyLev
+ South-F em ale-AbovePoverty Level
South-Male-AbovePovertyLevel
West-Fern ale-AbovePovertyLevel
X West-Male-AbovePovertyLevel
Appendix 5C, Attachment B, Figure 5CB-5. Studentized residuals generated using logistic model versus model predicted betas and the child
'EVER' asthmatic data set.
5C-52
-------
Studentized residual versus smoothed logits of prevalence rates by smoothing parameter
SmoothingParameter=0.8
Studentized residual versus smoothed logits of prevalence rates by smoothing parameter
SmoothingParameter= 1
student
-3-
-4-
reggendpov
Predicted logitprev
0 0 0 All: LOESS Smoothed
3 O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPoverty Level
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
"+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
student
5-
4-
3-
2-
1 -
reggendpov
Predicted logitprev
000 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
XXX West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
"+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Studentized residual versus smoothed logits of prevalence rates by smoothing parameter
SmoothingParameter=0.9
student
reggendpov
-4.00000
-3.00000
Predicted logitprev
0 s 0 All: LOESS Smoothed
000 Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
000 Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
+ + + South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
X West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
•+ + + South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Appendix 5C, Attachment B, Figure 5CB-5, cont. Studentized residuals generated using logistic model versus model predicted betas and the child
'EVER' asthmatic data set.
5C-53
-------
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
S mo othingParameter=0.4
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
SmoothingParameter=0.6
student
5 -
4:
3-
student
reggendpov
-4.00000
-3.00000
Predicted logitprev
000 All: LOESS Smoothed
3 O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPoverty Level
O O O Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
- - West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
^ ^ ^ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
SmoothingParameter=0.5
5 ~
4-
3-
2
1 -
0-
-1 -
-2-
-3 -
-4-
-5 -
.*^*i&4£ferim^%
-gpw^ ^ • • ^ o
-5.00000
reggendpov
-4.00000
-3.00000
Predicted logitprev
000 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
South-Male-BelowPoverty Level
XXX West-Female-BelowPovertyLevel
West-Male-BelowPovertyLevel
O O C
O O C
Midwest-Female-AbovePovertyLev
Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
Northeast-Male-AbovePovertyLev
+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
West-Female-AbovePovertyLevel
x West-Male-AbovePovertyLevel
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
SmoothingParameter=0.7
student
5 -
4:
3-
reggendpov
-4.00000
-3.00000
Predicted logitprev
000 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
X West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
^ ^ ^ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
student
5 :
4-
3:
2
1 -
o-
-1 -
-2-
-3 -
-4-
-5 -
-5.00000
reggendpov
-4.00000
-3.00000
Predicted logitprev
SCO All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
+ + ^ South-Female-BelowPovertyLevel
South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
+ + + South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Appendix 5C, Attachment B, Figure 5CB-6. Studentized residuals generated using logistic model versus model predicted betas and the child
'STILL' asthmatic data set.
5C-54
-------
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
SmoothingParameter=0.8
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
SmoothingParameter= 1
student
5-
4-
3-
2-
1 -
student
Predicted logitprev
reggendpov
000 All: LOESS Smoothed
3 O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPoverty Level
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
- - West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
"+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter
SmoothingParameter=0.9
reggendpov
c
Predicted logitprev
000 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
- - West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
Northeast-Male-AbovePovertyLev
+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
student
5 -
4-
3:
O
reggendpov
-4.00000
-3.00000
Predicted logitprev
0 •' s All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPoverty Level
O O O Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
X West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
^ ^ ^ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Appendix 5C, Attachment B, Figure 5CB-6, cont. Studentized residuals generated using logistic model versus model predicted betas using child
'STILL' asthmatic data set.
5C-55
-------
Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameter
SmoothingParameter=0.8
Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameter
SmoothingParameter= 1
student
3:
Predicted logitprev
reggendpov
0 0 0 All: LOESS Smoothed
3 O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPoverty Level
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
"+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameter
SmoothingParameter=0.9
student
3:
reggendpov
Predicted logitprev
000 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
XXX West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
"+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
student
3-
2
o-
-i -
-3-
o
x- x\-^5v ^ fr -"- '"-^£ '"• • ^^f-
O x' x X* 0
0
-3.00000
reggendpov
-1.00000
Predicted logitprev
0 s 0 All: LOESS Smoothed
000 Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
000 Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
+ + + South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
X West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
•+ 4 4 South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Appendix 5C, Attachment B, Figure 5CB-7. Studentized residuals generated using logistic model versus model predicted betas using adult
'EVER' asthmatic data set.
5C-56
-------
Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameter
SmoothingParameter=0.8
Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameter
SmoothingParameter= 1
student
3:
O
Predicted logitprev
reggendpov
0 0 0 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPoverty Level
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
"+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameter
SmoothingParameter=0.9
student
3:
reggendpov
C
Predicted logitprev
000 All: LOESS Smoothed
O O O Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
O O O Northeast-Female-BelowPovertyL
O O O Northeast-Male-BelowPovertyLev
South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
XXX West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
O O O Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
"+ South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
student
3:
-4.00000
reggendpov
-3.00000
-2.00000
Predicted logitprev
0 s 0 All: LOESS Smoothed
000 Midwest-Female-BelowPovertyLev
Midwest-Male-BelowPovertyLevel
000 Northeast-Female-BelowPovertyL
000 Northeast-Male-BelowPovertyLev
+ + + South-Female-BelowPovertyLevel
XXX South-Male-BelowPovertyLevel
XXX West-Female-BelowPovertyLevel
X West-Male-BelowPovertyLevel
O O O Midwest-Female-AbovePovertyLev
000 Midwest-Male-AbovePovertyLevel
Northeast-Female-AbovePovertyL
O Northeast-Male-AbovePovertyLev
•+ 4 4 South-Female-AbovePovertyLevel
South-Male-AbovePovertyLevel
XXX West-Female-AbovePovertyLevel
XXX West-Male-AbovePovertyLevel
Appendix 5C, Attachment B, Figure 5CB-8. Studentized residuals generated using logistic model versus model predicted betas using adult
'STILL' asthmatic data set.
5C-57
-------
Appendix 5C, Attachment C
Smoothed Asthma Prevalence Tables and Figures
Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children "EVER" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
Prevalence
0.0083
0.0179
0.0327
0.0509
0.0671
0.0854
0.0995
0.1041
0.1024
0.1020
0.1055
0.1192
0.1390
0.1529
0.1603
0.1597
0.1517
0.1374
0.0413
0.0706
0.1047
0.1356
0.1553
0.1488
0.1327
0.1341
0.1535
0.1729
0.1861
0.1691
0.1470
0.1439
0.1541
0.1707
0.1962
0.2323
0.0133
0.0313
0.0585
0.0898
0.1111
0.1256
0.1411
0.1496
0.1502
0.1542
0.1627
0.1760
0.1876
0.1847
0.1764
0.1641
0.1487
0.1318
0.0429
0.0908
0.1530
SE
0.0050
0.0066
0.0076
0.0096
0.0122
0.0134
0.0141
0.0145
0.0132
0.0121
0.0127
0.0137
0.0163
0.0176
0.0176
0.0160
0.0161
0.0229
0.0168
0.0168
0.0173
0.0208
0.0237
0.0229
0.0228
0.0224
0.0239
0.0270
0.0311
0.0300
0.0247
0.0239
0.0244
0.0275
0.0427
0.0813
0.0066
0.0091
0.0102
0.0121
0.0145
0.0149
0.0158
0.0164
0.0161
0.0166
0.0173
0.0181
0.0186
0.0181
0.0170
0.0149
0.0144
0.0201
0.0176
0.0214
0.0235
LowerCI
0.0022
0.0079
0.0195
0.0336
0.0448
0.0602
0.0725
0.0765
0.0769
0.0784
0.0806
0.0922
0.1070
0.1182
0.1254
0.1277
0.1197
0.0945
0.0167
0.0416
0.0724
0.0962
0.1100
0.1053
0.0902
0.0920
0.1080
0.1215
0.1272
0.1131
0.1006
0.0990
0.1078
0.1186
0.1187
0.1002
0.0045
0.0164
0.0398
0.0666
0.0831
0.0964
0.1100
0.1171
0.1182
0.1211
0.1283
0.1397
0.1501
0.1483
0.1422
0.1341
0.1198
0.0937
0.0173
0.0536
0.1084
UpperCI
0.0310
0.0397
0.0541
0.0766
0.0993
0.1198
0.1351
0.1403
0.1352
0.1317
0.1369
0.1527
0.1787
0.1956
0.2026
0.1979
0.1903
0.1956
0.0985
0.1174
0.1491
0.1879
0.2146
0.2062
0.1910
0.1912
0.2136
0.2401
0.2640
0.2451
0.2097
0.2045
0.2156
0.2395
0.3065
0.4512
0.0391
0.0588
0.0851
0.1200
0.1471
0.1621
0.1793
0.1892
0.1891
0.1942
0.2041
0.2193
0.2319
0.2277
0.2167
0.1994
0.1833
0.1823
0.1026
0.1498
0.2118
5C-58
-------
Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children "EVER" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
Prevalence
0.2110
0.2428
0.2458
0.2393
0.2261
0.2225
0.2354
0.2499
0.2553
0.2512
0.2149
0.1941
0.2027
0.2364
0.3045
0.0115
0.0278
0.0533
0.0823
0.1027
0.1066
0.1023
0.0979
0.1010
0.1146
0.1179
0.1170
0.1154
0.1246
0.1405
0.1551
0.1714
0.1883
0.0394
0.0754
0.1188
0.1539
0.1684
0.1503
0.1355
0.1263
0.1322
0.1583
0.1818
0.2030
0.2293
0.2437
0.2368
0.2188
0.1906
0.1572
0.0279
0.0444
0.0668
0.0948
0.1269
0.1665
0.1891
0.1901
0.1858
0.1873
0.1908
0.1926
0.1934
SE
0.0277
0.0303
0.0285
0.0270
0.0268
0.0290
0.0311
0.0339
0.0357
0.0377
0.0355
0.0308
0.0292
0.0390
0.0768
0.0066
0.0095
0.0108
0.0127
0.0152
0.0150
0.0143
0.0137
0.0144
0.0166
0.0171
0.0175
0.0164
0.0148
0.0148
0.0152
0.0209
0.0376
0.0211
0.0229
0.0229
0.0265
0.0295
0.0269
0.0245
0.0231
0.0257
0.0301
0.0342
0.0358
0.0359
0.0366
0.0335
0.0286
0.0298
0.0443
0.0107
0.0103
0.0106
0.0134
0.0174
0.0209
0.0207
0.0204
0.0189
0.0189
0.0180
0.0163
0.0168
LowerCI
0.1566
0.1828
0.1888
0.1853
0.1729
0.1655
0.1741
0.1831
0.1852
0.1779
0.1473
0.1353
0.1462
0.1617
0.1652
0.0032
0.0131
0.0340
0.0584
0.0737
0.0777
0.0749
0.0715
0.0734
0.0828
0.0852
0.0836
0.0838
0.0955
0.1109
0.1245
0.1302
0.1189
0.0119
0.0383
0.0770
0.1043
0.1131
0.1003
0.0902
0.0836
0.0853
0.1029
0.1183
0.1355
0.1600
0.1726
0.1713
0.1625
0.1335
0.0822
0.0119
0.0265
0.0470
0.0692
0.0933
0.1257
0.1478
0.1494
0.1479
0.1494
0.1545
0.1595
0.1592
UpperCI
0.2780
0.3150
0.3133
0.3033
0.2900
0.2924
0.3101
0.331 1
0.3409
0.3423
0.3025
0.2703
0.2741
0.3320
0.4921
0.0402
0.0583
0.0827
0.1150
0.1413
0.1445
0.1383
0.1325
0.1375
0.1566
0.1611
0.1615
0.1568
0.1611
0.1765
0.1916
0.2223
0.2851
0.1222
0.1433
0.1789
0.2214
0.2432
0.2193
0.1987
0.1862
0.1993
0.2358
0.2689
0.2926
0.3172
0.3323
0.3179
0.2879
0.2645
0.2796
0.0639
0.0733
0.0940
0.1284
0.1702
0.2173
0.2387
0.2389
0.2307
0.2322
0.2333
0.2307
0.2329
5C-59
-------
Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children "EVER" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
Prevalence
0.1847
0.1797
0.1781
0.1795
0.1838
0.0946
0.1345
0.1759
0.2132
0.2353
0.2638
0.2909
0.3169
0.3272
0.3238
0.3163
0.3022
0.2846
0.2779
0.2702
0.2698
0.2745
0.2843
0.0137
0.0266
0.0453
0.0687
0.0928
0.1142
0.1298
0.1333
0.1231
0.1095
0.1033
0.1086
0.1212
0.1368
0.1437
0.1448
0.1395
0.1283
0.0496
0.0682
0.0893
0.1111
0.1319
0.1473
0.1553
0.1592
0.1650
0.1766
0.1825
0.1805
0.1837
0.1932
0.1891
0.1760
0.1560
0.1298
0.0335
0.0629
0.0985
0.1306
0.1472
SE
0.0172
0.0168
0.0156
0.0162
0.0251
0.0396
0.0296
0.0264
0.0326
0.0361
0.0316
0.0305
0.0339
0.0405
0.0439
0.0429
0.0412
0.0388
0.0367
0.0343
0.0316
0.0349
0.0575
0.0056
0.0064
0.0068
0.0086
0.0112
0.0123
0.0128
0.0123
0.0117
0.0109
0.0102
0.0103
0.0110
0.0113
0.0111
0.0104
0.0113
0.0172
0.0153
0.0123
0.0116
0.0141
0.0171
0.0181
0.0183
0.0183
0.0188
0.0198
0.0216
0.0219
0.0221
0.0218
0.0202
0.0181
0.0195
0.0271
0.0089
0.0093
0.0094
0.0116
0.0133
LowerCI
0.1499
0.1458
0.1465
0.1467
0.1350
0.0365
0.0817
0.1251
0.1503
0.1653
0.2004
0.2287
0.2475
0.2451
0.2356
0.2304
0.2199
0.2074
0.2048
0.2016
0.2062
0.2048
0.1760
0.0056
0.0156
0.0325
0.0522
0.0710
0.0900
0.1042
0.1085
0.0996
0.0877
0.0830
0.0881
0.0991
0.1138
0.1210
0.1235
0.1166
0.0952
0.0250
0.0458
0.0670
0.0838
0.0987
0.1120
0.1193
0.1231
0.1277
0.1374
0.1398
0.1373
0.1401
0.1499
0.1487
0.1398
0.1178
0.0810
0.0186
0.0453
0.0797
0.1073
0.1204
UpperCI
0.2253
0.2195
0.2149
0.2178
0.2452
0.2240
0.2134
0.2416
0.2932
0.3236
0.3388
0.3621
0.3954
0.4214
0.4265
0.4169
0.3995
0.3769
0.3651
0.3518
0.3445
0.3573
0.4250
0.0334
0.0450
0.0629
0.0901
0.1203
0.1439
0.1605
0.1627
0.1512
0.1359
0.1279
0.1332
0.1475
0.1635
0.1699
0.1690
0.1661
0.1709
0.0962
0.1004
0.1181
0.1459
0.1740
0.1914
0.1997
0.2035
0.2104
0.2241
0.2347
0.2336
0.2371
0.2453
0.2374
0.2192
0.2037
0.2015
0.0596
0.0867
0.1212
0.1581
0.1787
5C-60
-------
Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children "EVER" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Prevalence
0.1523
0.1539
0.1485
0.1461
0.1517
0.1639
0.1772
0.1794
0.1752
0.1705
0.1652
0.1600
0.1562
0.0629
0.0922
0.1253
0.1578
0.1852
0.1975
0.2038
0.2087
0.2078
0.2080
0.2122
0.2137
0.2192
0.2199
0.2059
0.1946
0.1827
0.1709
0.0131
0.0188
0.0264
0.0361
0.0469
0.0647
0.0857
0.1008
0.1032
0.1063
0.1166
0.1181
0.1196
0.1202
0.1241
0.1389
0.1665
0.2118
0.0250
0.0309
0.0387
0.0488
0.0602
0.0843
0.1143
0.1295
0.1195
0.0950
0.0786
0.0812
0.0979
0.1278
0.1324
SE
0.0130
0.0128
0.0125
0.0123
0.0124
0.0129
0.0134
0.0128
0.0127
0.0120
0.0108
0.0118
0.0190
0.0140
0.0118
0.0123
0.0156
0.0186
0.0190
0.0198
0.0204
0.0203
0.0206
0.0203
0.0202
0.0214
0.0220
0.0209
0.0186
0.0177
0.0246
0.0067
0.0057
0.0053
0.0064
0.0083
0.0105
0.0130
0.0144
0.0151
0.0144
0.0140
0.0129
0.0131
0.0130
0.0127
0.0125
0.0152
0.0305
0.0138
0.0099
0.0082
0.0099
0.0129
0.0169
0.0197
0.0191
0.0175
0.0151
0.0139
0.0150
0.0179
0.0221
0.0211
LowerCI
0.1259
0.1278
0.1231
0.1212
0.1265
0.1375
0.1496
0.1530
0.1491
0.1458
0.1428
0.1358
0.1189
0.0383
0.0694
0.1008
0.1265
0.1479
0.1592
0.1639
0.1675
0.1669
0.1664
0.1711
0.1727
0.1759
0.1755
0.1639
0.1571
0.1471
0.1235
0.0042
0.0096
0.0171
0.0245
0.0317
0.0451
0.061 1
0.0733
0.0746
0.0786
0.0893
0.0927
0.0938
0.0945
0.0987
0.1136
0.1358
0.1525
0.0073
0.0152
0.0243
0.0312
0.0374
0.0538
0.0776
0.0930
0.0861
0.0666
0.0530
0.0537
0.0651
0.0866
0.0925
UpperCI
0.1831
0.1842
0.1782
0.1752
0.1810
0.1943
0.2085
0.2093
0.2049
0.1984
0.1902
0.1876
0.2026
0.1016
0.1215
0.1547
0.1951
0.2294
0.2424
0.2506
0.2570
0.2558
0.2567
0.2601
0.2612
0.2698
0.2718
0.2554
0.2385
0.2246
0.2317
0.0400
0.0365
0.0407
0.0531
0.0689
0.0919
0.1189
0.1372
0.1412
0.1424
0.1509
0.1494
0.1513
0.1519
0.1548
0.1687
0.2025
0.2864
0.0819
0.0618
0.0612
0.0757
0.0955
0.1296
0.1652
0.1775
0.1636
0.1338
0.1150
0.1209
0.1447
0.1848
0.1859
5C-61
-------
Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children "EVER" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Prevalence
0.1188
0.0917
0.0600
0.0057
0.0191
0.0479
0.0903
0.1300
0.1437
0.1374
0.1290
0.1365
0.1560
0.1794
0.1980
0.1948
0.1818
0.1771
0.1801
0.1897
0.2081
0.0258
0.0442
0.0700
0.1005
0.1323
0.1609
0.1663
0.1582
0.1536
0.1543
0.1630
0.1746
0.1828
0.1809
0.1800
0.1828
0.1881
0.1964
SE
0.0176
0.0164
0.0186
0.0035
0.0067
0.0092
0.0114
0.0149
0.0158
0.0157
0.0148
0.0148
0.0154
0.0160
0.0175
0.0180
0.0175
0.0164
0.0148
0.0149
0.0248
0.0126
0.0124
0.0119
0.0144
0.0190
0.0218
0.0213
0.0205
0.0204
0.0214
0.0240
0.0270
0.0270
0.0276
0.0259
0.0233
0.0242
0.0396
LowerCI
0.0853
0.0615
0.0300
0.0014
0.0084
0.0306
0.0673
0.0993
0.1110
0.1050
0.0985
0.1058
0.1236
0.1454
0.1608
0.1566
0.1449
0.1423
0.1484
0.1577
0.1567
0.0087
0.0237
0.0479
0.0729
0.0959
0.1186
0.1247
0.1182
0.1140
0.1128
0.1168
0.1230
0.1306
0.1280
0.1298
0.1371
0.1405
0.1234
UpperCI
0.1631
0.1347
0.1163
0.0229
0.0428
0.0743
0.1201
0.1685
0.1842
0.1779
0.1671
0.1743
0.1950
0.2193
0.2413
0.2396
0.2256
0.2183
0.2167
0.2264
0.2709
0.0738
0.0812
0.1013
0.1370
0.1799
0.2147
0.2184
0.2086
0.2040
0.2075
0.2228
0.2420
0.2498
0.2495
0.2440
0.2396
0.2471
0.2978
5C-62
-------
Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children "STILL" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
Prevalence
0.0082
0.0168
0.0289
0.0420
0.0509
0.0573
0.0611
0.0624
0.0629
0.0663
0.0737
0.0889
0.1056
0.1157
0.1191
0.1177
0.1107
0.0999
0.0381
0.0620
0.0875
0.1079
0.1187
0.1117
0.0940
0.0974
0.1144
0.1237
0.1196
0.1074
0.1025
0.1096
0.1236
0.1412
0.1633
0.1906
0.0122
0.0268
0.0480
0.0710
0.0842
0.0934
0.1056
0.1117
0.1111
0.1138
0.1126
0.1108
0.1129
0.1139
0.1128
0.1054
0.0935
0.0782
0.0402
0.0824
0.1338
0.1774
0.1949
0.1867
0.1807
0.1734
SE
0.0051
0.0064
0.0070
0.0086
0.0103
0.0108
0.0109
0.0107
0.0100
0.0096
0.0108
0.0126
0.0151
0.0163
0.0160
0.0144
0.0143
0.0205
0.0164
0.0160
0.0160
0.0183
0.0202
0.0194
0.0188
0.0187
0.0205
0.0220
0.0237
0.0225
0.0199
0.0211
0.0229
0.0266
0.0413
0.0779
0.0064
0.0083
0.0091
0.0113
0.0134
0.0138
0.0144
0.0149
0.0152
0.0155
0.0153
0.0146
0.0137
0.0132
0.0127
0.0118
0.0133
0.0184
0.0177
0.0213
0.0225
0.0255
0.0267
0.0237
0.0222
0.0221
LowerCI
0.0021
0.0073
0.0169
0.0267
0.0326
0.0378
0.0412
0.0427
0.0443
0.0481
0.0533
0.0649
0.0768
0.0845
0.0882
0.0896
0.0831
0.0632
0.0146
0.0349
0.0581
0.0738
0.081 1
0.0758
0.0602
0.0634
0.0765
0.0830
0.0766
0.0672
0.0664
0.0712
0.0815
0.0924
0.0914
0.0722
0.0038
0.0135
0.0315
0.0500
0.0591
0.0673
0.0779
0.0829
0.0820
0.0840
0.0831
0.0826
0.0861
0.0880
0.0878
0.0822
0.0682
0.0462
0.0151
0.0463
0.0917
0.1282
0.1429
0.1402
0.1371
0.1301
UpperCI
0.0319
0.0382
0.0490
0.0655
0.0788
0.0859
0.0897
0.0902
0.0886
0.0907
0.1012
0.1206
0.1435
0.1565
0.1588
0.1530
0.1461
0.1544
0.0956
0.1076
0.1295
0.1550
0.1704
0.1616
0.1439
0.1469
0.1676
0.1805
0.1821
0.1673
0.1551
0.1649
0.1830
0.2099
0.2746
0.4158
0.0384
0.0525
0.0725
0.1001
0.1187
0.1282
0.1416
0.1489
0.1489
0.1525
0.1507
0.1472
0.1466
0.1462
0.1438
0.1343
0.1269
0.1292
0.1028
0.1425
0.1911
0.2401
0.2601
0.2443
0.2344
0.2273
5C-63
-------
Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children "STILL" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Prevalence
0.1739
0.1814
0.1813
0.1749
0.1702
0.1499
0.1366
0.1484
0.1846
0.2590
0.0153
0.0281
0.0437
0.0584
0.0657
0.0668
0.0678
0.0696
0.0737
0.0840
0.0807
0.0710
0.0629
0.0680
0.0786
0.0913
0.1095
0.1328
0.0234
0.0564
0.1040
0.1466
0.1618
0.1441
0.1124
0.0751
0.0633
0.0838
0.1288
0.1778
0.2073
0.2063
0.1929
0.1703
0.1414
0.1108
0.0225
0.0368
0.0562
0.0797
0.1035
0.1289
0.1472
0.1423
0.1290
0.1251
0.1288
0.1262
0.1246
0.1230
0.1207
0.1114
0.0983
0.0823
SE
0.0248
0.0269
0.0282
0.0282
0.0298
0.0296
0.0269
0.0268
0.0359
0.0740
0.0089
0.0096
0.0090
0.0098
0.0112
0.0111
0.0111
0.0114
0.0124
0.0147
0.0144
0.0134
0.0116
0.0113
0.0117
0.0120
0.0165
0.0330
0.0142
0.0190
0.0219
0.0272
0.0304
0.0280
0.0238
0.0174
0.0157
0.0188
0.0270
0.0336
0.0349
0.0328
0.0287
0.0235
0.0234
0.0327
0.0108
0.0105
0.0104
0.0127
0.0162
0.0187
0.0190
0.0181
0.0163
0.0159
0.0155
0.0139
0.0139
0.0149
0.0144
0.0126
0.0124
0.0171
LowerCI
0.1260
0.1297
0.1275
0.1214
0.1143
0.0959
0.0876
0.0987
0.1185
0.1306
0.0042
0.0132
0.0276
0.0402
0.0449
0.0461
0.0471
0.0482
0.0506
0.0569
0.0541
0.0466
0.0416
0.0469
0.0564
0.0681
0.0781
0.0753
0.0061
0.0266
0.0648
0.0964
0.1056
0.0928
0.0698
0.0447
0.0364
0.0507
0.0802
0.1154
0.1410
0.1435
0.1375
0.1248
0.0974
0.0567
0.0078
0.0195
0.0373
0.0559
0.0730
0.0931
0.1102
0.1070
0.0973
0.0943
0.0985
0.0989
0.0971
0.0939
0.0925
0.0868
0.0743
0.0518
UpperCI
0.2350
0.2478
0.2514
0.2454
0.2457
0.2268
0.2066
0.2169
0.2761
0.4484
0.0537
0.0589
0.0683
0.0840
0.0950
0.0958
0.0967
0.0993
0.1062
0.1224
0.1187
0.1068
0.0938
0.0976
0.1085
0.1214
0.1513
0.2234
0.0856
0.1157
0.1627
0.2167
0.2400
0.2168
0.1761
0.1234
0.1078
0.1355
0.2004
0.2638
0.2941
0.2873
0.2637
0.2281
0.2009
0.2051
0.0633
0.0682
0.0838
0.1123
0.1449
0.1757
0.1938
0.1868
0.1690
0.1641
0.1668
0.1598
0.1584
0.1594
0.1560
0.1420
0.1291
0.1285
5C-64
-------
Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children "STILL" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
Prevalence
0.0930
0.1202
0.1475
0.1714
0.1860
0.2060
0.2256
0.2496
0.2727
0.2579
0.2318
0.1902
0.1624
0.1641
0.1699
0.1797
0.1933
0.2097
0.0131
0.0228
0.0352
0.0495
0.0633
0.0740
0.0826
0.0888
0.0860
0.0791
0.0747
0.0736
0.0776
0.0851
0.0871
0.0876
0.0859
0.0819
0.0396
0.0573
0.0772
0.0963
0.1120
0.1206
0.1219
0.1152
0.1131
0.1190
0.1208
0.1195
0.1275
0.1405
0.1394
0.1296
0.1136
0.0923
0.0228
0.0476
0.0793
0.1076
0.1193
0.1194
0.1145
0.1071
0.1011
0.1000
SE
0.0402
0.0280
0.0256
0.0311
0.0335
0.0276
0.0276
0.0317
0.0387
0.0395
0.0366
0.0310
0.0268
0.0254
0.0251
0.0244
0.0276
0.0451
0.0059
0.0063
0.0064
0.0074
0.0089
0.0092
0.0096
0.0099
0.0100
0.0095
0.0088
0.0085
0.0087
0.0093
0.0093
0.0087
0.0091
0.0136
0.0135
0.0113
0.0109
0.0136
0.0165
0.0174
0.0173
0.0162
0.0157
0.0161
0.0175
0.0178
0.0192
0.0197
0.0184
0.0166
0.0184
0.0249
0.0070
0.0082
0.0089
0.0109
0.0123
0.0117
0.0111
0.0105
0.0099
0.0098
LowerCI
0.0347
0.0710
0.0997
0.1134
0.1232
0.1519
0.1708
0.1866
0.1964
0.1810
0.1611
0.1311
0.1116
0.1155
0.1216
0.1321
0.1397
0.1274
0.0048
0.0124
0.0236
0.0355
0.0464
0.0561
0.0638
0.0695
0.0666
0.0606
0.0576
0.0570
0.0606
0.0669
0.0688
0.0702
0.0681
0.0567
0.0186
0.0371
0.0564
0.0704
0.0805
0.0874
0.0888
0.0842
0.0829
0.0880
0.0874
0.0857
0.0910
0.1026
0.1037
0.0973
0.0791
0.0503
0.0116
0.0325
0.0619
0.0859
0.0949
0.0960
0.0924
0.0861
0.0813
0.0806
UpperCI
0.2262
0.1964
0.2130
0.2508
0.2708
0.2732
0.2919
0.3255
0.3653
0.3535
0.3216
0.2678
0.2302
0.2278
0.2323
0.2396
0.2612
0.3253
0.0349
0.0415
0.0522
0.0685
0.0857
0.0969
0.1063
0.1129
0.1105
0.1025
0.0963
0.0944
0.0989
0.1078
0.1099
0.1087
0.1080
0.1169
0.0823
0.0876
0.1048
0.1306
0.1536
0.1641
0.1652
0.1556
0.1524
0.1591
0.1646
0.1642
0.1757
0.1893
0.1848
0.1706
0.1605
0.1634
0.0443
0.0693
0.1011
0.1341
0.1490
0.1475
0.1411
0.1323
0.1251
0.1236
5C-65
-------
Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children "STILL" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Age
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
Prevalence
0.1059
0.1122
0.1103
0.1052
0.0983
0.0899
0.0811
0.0727
0.0499
0.0749
0.1033
0.1305
0.1519
0.1595
0.1598
0.1540
0.1466
0.1457
0.1504
0.1508
0.1506
0.1470
0.1345
0.1215
0.1080
0.0948
0.0077
0.0122
0.0181
0.0248
0.0305
0.0382
0.0482
0.0573
0.0628
0.0697
0.0768
0.0786
0.0808
0.0829
0.0845
0.0908
0.1016
0.1180
0.0244
0.0270
0.0306
0.0354
0.0407
0.0577
0.0807
0.0954
0.0876
0.0648
0.0495
0.0473
0.0606
0.0845
0.0931
0.0846
0.0629
0.0376
0.0007
0.0052
SE
0.0102
0.0106
0.0105
0.0105
0.0094
0.0081
0.0089
0.0136
0.0126
0.0110
0.0116
0.0149
0.0177
0.0180
0.0185
0.0180
0.0170
0.0170
0.0171
0.0171
0.0184
0.0192
0.0179
0.0159
0.0164
0.0227
0.0049
0.0046
0.0045
0.0055
0.0068
0.0077
0.0091
0.0098
0.0106
0.0106
0.0099
0.0094
0.0100
0.0108
0.0111
0.0110
0.0129
0.0236
0.0144
0.0091
0.0074
0.0090
0.0112
0.0146
0.0185
0.0181
0.0159
0.0127
0.0107
0.0110
0.0137
0.0179
0.0180
0.0154
0.0143
0.0146
0.0007
0.0027
LowerCI
0.0855
0.0910
0.0893
0.0843
0.0795
0.0737
0.0636
0.0479
0.0285
0.0542
0.0805
0.1012
0.1171
0.1240
0.1234
0.1186
0.1130
0.1122
0.1167
0.1171
0.1146
0.1097
0.0999
0.0907
0.0770
0.0555
0.0019
0.0053
0.0105
0.0153
0.0186
0.0245
0.0318
0.0393
0.0432
0.0497
0.0577
0.0603
0.0615
0.0621
0.0632
0.0694
0.0766
0.0753
0.0066
0.0128
0.0179
0.0201
0.0221
0.0328
0.0483
0.0624
0.0583
0.0419
0.0306
0.0282
0.0366
0.0526
0.0603
0.0562
0.0379
0.0158
0.0001
0.0014
UpperCI
0.1305
0.1376
0.1356
0.1305
0.1210
0.1093
0.1028
0.1089
0.0860
0.1027
0.1316
0.1666
0.1948
0.2029
0.2045
0.1977
0.1879
0.1870
0.1917
0.1921
0.1955
0.1943
0.1788
0.1607
0.1494
0.1573
0.0306
0.0278
0.0310
0.0401
0.0494
0.0590
0.0724
0.0829
0.0904
0.0970
0.1016
0.1018
0.1056
0.1100
0.1121
0.1179
0.1337
0.1803
0.0862
0.0561
0.0518
0.0615
0.0738
0.0996
0.1319
0.1434
0.1296
0.0989
0.0792
0.0781
0.0988
0.1329
0.1411
0.1253
0.1026
0.0868
0.0067
0.0192
5C-66
-------
Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children "STILL" having asthma.
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Prevalence
0.0225
0.0596
0.0989
0.1070
0.0959
0.0830
0.0877
0.1029
0.1189
0.1292
0.1214
0.1050
0.0981
0.0997
0.1091
0.1290
0.0263
0.0374
0.0518
0.0681
0.0871
0.1074
0.1167
0.1138
0.1073
0.0964
0.0830
0.0745
0.0825
0.1000
0.1074
0.1120
0.1127
0.1084
SE
0.0063
0.0095
0.0140
0.0147
0.0141
0.0126
0.0124
0.0135
0.0140
0.0153
0.0154
0.0139
0.0127
0.0116
0.0128
0.0231
0.0130
0.0101
0.0086
0.0105
0.0143
0.0173
0.0183
0.0186
0.0177
0.0164
0.0149
0.0151
0.0165
0.0197
0.0200
0.0193
0.0222
0.0340
LowerCI
0.0112
0.0398
0.0691
0.0754
0.0660
0.0565
0.0613
0.0737
0.0883
0.0955
0.0879
0.0749
0.0707
0.0742
0.0810
0.0814
0.0088
0.0204
0.0358
0.0483
0.0604
0.0749
0.0820
0.0789
0.0741
0.0659
0.0557
0.0474
0.0527
0.0643
0.0707
0.0760
0.0724
0.0531
UpperCI
0.0447
0.0884
0.1397
0.1496
0.1372
0.1203
0.1239
0.1419
0.1584
0.1724
0.1653
0.1452
0.1346
0.1327
0.1454
0.1984
0.0761
0.0673
0.0742
0.0952
0.1240
0.1517
0.1635
0.1615
0.1529
0.1389
0.1221
0.1152
0.1268
0.1524
0.1600
0.1620
0.1714
0.2088
5C-67
-------
Appendix 5C, Attachment C, Table 5CC-3. Smoothed prevalence for adults "EVER" having asthma
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age group
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
Prevalence
0.1642
0.1341
0.1193
0.1204
0.1246
0.1165
0.0980
0.2014
0.1812
0.1782
0.2104
0.2295
0.1892
0.1176
0.1705
0.1209
0.0886
0.0727
0.0770
0.0828
0.0847
0.1654
0.1143
0.1066
0.1376
0.1643
0.1396
0.0853
0.1791
0.1423
0.1256
0.1246
0.1281
0.1151
0.0879
0.1646
0.1705
0.1842
0.2084
0.2180
0.1695
0.0960
0.1728
0.1163
0.0932
0.0901
0.0963
0.0874
0.0708
0.1734
0.1323
0.1182
0.1254
0.1361
0.1305
0.0988
0.1533
0.1235
0.1114
0.1149
0.1261
0.1188
SE
0.0141
0.0063
0.0058
0.0057
0.0066
0.0062
0.0089
0.0153
0.0114
0.0130
0.0146
0.0164
0.0145
0.0173
0.0149
0.0063
0.0053
0.0046
0.0054
0.0058
0.0106
0.0175
0.0109
0.0122
0.0146
0.0164
0.0160
0.0205
0.0176
0.0076
0.0072
0.0071
0.0076
0.0070
0.0098
0.0182
0.0110
0.0126
0.0143
0.0156
0.0118
0.0125
0.0210
0.0081
0.0070
0.0063
0.0072
0.0073
0.0118
0.0193
0.0138
0.0135
0.0144
0.0198
0.0195
0.0255
0.0114
0.0054
0.0050
0.0047
0.0058
0.0058
LowerCI
0.1219
0.1142
0.1012
0.1025
0.1040
0.0971
0.0719
0.1531
0.1445
0.1370
0.1638
0.1770
0.1435
0.0690
0.1249
0.1008
0.0719
0.0583
0.0602
0.0647
0.0545
0.1122
0.0808
0.0703
0.0936
0.1141
0.0918
0.0353
0.1265
0.1183
0.1029
0.1024
0.1043
0.0934
0.0598
0.1104
0.1356
0.1442
0.1629
0.1684
0.1321
0.0603
0.1126
0.0914
0.0721
0.0710
0.0744
0.0656
0.0398
0.1138
0.0896
0.0772
0.0816
0.0786
0.0743
0.0373
0.1185
0.1065
0.0956
0.0998
0.1077
0.1004
UpperCI
0.2176
0.1568
0.1402
0.1409
0.1486
0.1392
0.1322
0.2603
0.2248
0.2284
0.2662
0.2920
0.2453
0.1933
0.2284
0.1444
0.1087
0.0904
0.0980
0.1053
0.1292
0.2370
0.1593
0.1585
0.1979
0.2309
0.2068
0.1920
0.2474
0.1701
0.1525
0.1509
0.1565
0.1412
0.1273
0.2383
0.2123
0.2323
0.2627
0.2773
0.2149
0.1495
0.2560
0.1469
0.1197
0.1139
0.1237
0.1155
0.1229
0.2552
0.1911
0.1768
0.1879
0.2253
0.2191
0.2366
0.1959
0.1429
0.1295
0.1320
0.1472
0.1400
5C-68
-------
Appendix 5C, Attachment C, Table 5CC-3. Smoothed prevalence for adults "EVER" having asthma
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age group
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
18-24
25-34
35-44
45-54
55-64
65-74
75+
Prevalence
0.0959
0.1491
0.1365
0.1414
0.1686
0.1881
0.1651
0.1125
0.1445
0.1086
0.0860
0.0742
0.0733
0.0790
0.0900
0.1433
0.1031
0.0934
0.1055
0.1072
0.0942
0.0712
0.1571
0.1415
0.1373
0.1423
0.1497
0.1445
0.1266
0.1434
0.1318
0.1440
0.1806
0.1713
0.1511
0.1292
0.1566
0.1233
0.1025
0.0908
0.0955
0.1067
0.1265
0.1521
0.0942
0.0885
0.1133
0.1237
0.1134
0.0961
SE
0.0087
0.0122
0.0066
0.0078
0.0097
0.0115
0.0101
0.0124
0.0095
0.0050
0.0044
0.0040
0.0045
0.0048
0.0102
0.0144
0.0087
0.0090
0.0101
0.0108
0.0092
0.0123
0.0135
0.0067
0.0070
0.0067
0.0071
0.0070
0.0112
0.0164
0.0092
0.0117
0.0144
0.0136
0.0117
0.0177
0.0173
0.0069
0.0060
0.0054
0.0059
0.0068
0.0152
0.0204
0.0095
0.0102
0.0130
0.0156
0.0142
0.0190
LowerCI
0.0701
0.1107
0.1149
0.1159
0.1369
0.1505
0.1325
0.0755
0.1147
0.0926
0.0720
0.0616
0.0594
0.0639
0.0606
0.1000
0.0766
0.0664
0.0751
0.0750
0.0666
0.0385
0.1163
0.1201
0.1150
0.1207
0.1268
0.1220
0.0929
0.0945
0.1026
0.1074
0.1350
0.1284
0.1141
0.0785
0.1067
0.1019
0.0839
0.0741
0.0774
0.0860
0.0834
0.0938
0.0660
0.0590
0.0753
0.0789
0.0726
0.0474
UpperCI
0.1297
0.1978
0.1614
0.1714
0.2059
0.2324
0.2039
0.1644
0.1805
0.1269
0.1025
0.0891
0.0902
0.0974
0.1316
0.2013
0.1376
0.1300
0.1462
0.1510
0.1314
0.1279
0.2089
0.1660
0.1631
0.1670
0.1758
0.1704
0.1702
0.2117
0.1678
0.1903
0.2374
0.2248
0.1974
0.2054
0.2240
0.1485
0.1247
0.1107
0.1174
0.1318
0.1871
0.2373
0.1327
0.1308
0.1670
0.1888
0.1727
0.1849
5C-69
-------
Appendix 5C, Attachment C, Table 5CC-4. Smoothed prevalence for adults "STILL" having asthma
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Poverty Status
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Age group
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
Prevalence
0.1046
0.0888
0.0835
0.0893
0.0909
0.081 1
0.0630
0.1327
0.1280
0.1315
0.1600
0.1777
0.1488
0.0940
0.0807
0.0584
0.0479
0.0472
0.0522
0.0528
0.0481
0.0912
0.0683
0.0694
0.1015
0.1338
0.1202
0.0709
0.1098
0.0965
0.0899
0.0901
0.0917
0.0862
0.0726
0.1212
0.1199
0.1338
0.1655
0.1824
0.1273
0.0529
0.0922
0.0600
0.0488
0.0483
0.0563
0.0576
0.0554
0.0791
0.0800
0.0805
0.0857
0.1064
0.1040
0.0771
0.0891
0.0735
0.0684
0.0732
0.0846
0.0817
SE
0.0121
0.0057
0.0052
0.0050
0.0057
0.0051
0.0067
0.0139
0.0095
0.0114
0.0134
0.0146
0.0128
0.0157
0.0115
0.0045
0.0040
0.0038
0.0042
0.0045
0.0081
0.0136
0.0091
0.0109
0.0141
0.0165
0.0161
0.0210
0.0134
0.0065
0.0063
0.0060
0.0062
0.0059
0.0093
0.0166
0.0093
0.0106
0.0127
0.0143
0.0098
0.0086
0.0154
0.0058
0.0050
0.0051
0.0065
0.0063
0.0106
0.0128
0.0119
0.0135
0.0162
0.0224
0.0200
0.0236
0.0083
0.0039
0.0036
0.0037
0.0046
0.0047
LowerCI
0.0703
0.0714
0.0675
0.0738
0.0736
0.0654
0.0438
0.0907
0.0980
0.0961
0.1181
0.1318
0.1091
0.0513
0.0491
0.0448
0.0359
0.0358
0.0395
0.0393
0.0268
0.0542
0.0430
0.0402
0.0624
0.0866
0.0751
0.0250
0.0721
0.0765
0.0708
0.0718
0.0727
0.0681
0.0467
0.0744
0.0914
0.1013
0.1260
0.1381
0.0972
0.0300
0.0509
0.0428
0.0340
0.0334
0.0376
0.0393
0.0281
0.0430
0.0459
0.0427
0.0419
0.0475
0.0501
0.0241
0.0649
0.0615
0.0571
0.0617
0.0705
0.0674
UpperCI
0.1528
0.1100
0.1030
0.1077
0.1118
0.1002
0.0898
0.1899
0.1656
0.1772
0.2132
0.2352
0.1998
0.1659
0.1299
0.0758
0.0637
0.0620
0.0687
0.0706
0.0847
0.1496
0.1067
0.1173
0.1610
0.2010
0.1869
0.1850
0.1638
0.1210
0.1136
0.1124
0.1151
0.1085
0.1110
0.1915
0.1559
0.1747
0.2143
0.2370
0.1650
0.0917
0.1616
0.0836
0.0696
0.0693
0.0834
0.0837
0.1062
0.1409
0.1360
0.1465
0.1672
0.2211
0.2035
0.2203
0.1212
0.0876
0.0817
0.0866
0.1012
0.0987
5C-70
-------
Appendix 5C, Attachment C, Table 5CC-4. Smoothed prevalence for adults "STILL" having asthma
Smoothed
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Poverty Status
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Above Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Below Poverty Level
Age group
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
18-24
25-34
35-44
45-44
55-64
65-74
75+
Prevalence
0.0641
0.0948
0.0942
0.1086
0.1446
0.1618
0.1379
0.0881
0.0600
0.0490
0.0421
0.0386
0.0384
0.0457
0.0627
0.0583
0.0443
0.0492
0.0720
0.0771
0.0608
0.0353
0.0842
0.0876
0.0931
0.0981
0.1028
0.0984
0.0825
0.0863
0.0934
0.1091
0.1332
0.1292
0.1169
0.1021
0.0597
0.0569
0.0549
0.0525
0.0562
0.0660
0.0783
0.0720
0.0484
0.0539
0.0784
0.0936
0.0758
0.0489
SE
0.0070
0.0105
0.0059
0.0073
0.0095
0.0112
0.0095
0.0109
0.0073
0.0035
0.0033
0.0031
0.0034
0.0038
0.0089
0.0080
0.0053
0.0067
0.0090
0.0096
0.0075
0.0082
0.0115
0.0054
0.0062
0.0065
0.0067
0.0061
0.0090
0.0121
0.0078
0.0100
0.0120
0.0120
0.0104
0.0148
0.0092
0.0046
0.0045
0.0046
0.0053
0.0058
0.0131
0.0125
0.0068
0.0084
0.0115
0.0155
0.0129
0.0136
LowerCI
0.0443
0.0641
0.0758
0.0859
0.1149
0.1267
0.1082
0.0570
0.0392
0.0381
0.0322
0.0292
0.0282
0.0343
0.0382
0.0358
0.0290
0.0303
0.0460
0.0492
0.0390
0.0154
0.0522
0.0708
0.0742
0.0781
0.0820
0.0795
0.0565
0.0524
0.0695
0.0789
0.0967
0.0929
0.0854
0.0609
0.0351
0.0432
0.0414
0.0389
0.0407
0.0487
0.0437
0.0389
0.0294
0.0311
0.0465
0.0517
0.0413
0.0182
UpperCI
0.0920
0.1380
0.1166
0.1365
0.1806
0.2043
0.1742
0.1337
0.0907
0.0629
0.0550
0.0510
0.0520
0.0607
0.1013
0.0937
0.0672
0.0790
0.1112
0.1188
0.0937
0.0787
0.1328
0.1080
0.1163
0.1226
0.1281
0.1213
0.1189
0.1387
0.1243
0.1489
0.1806
0.1770
0.1580
0.1662
0.0998
0.0745
0.0723
0.0704
0.0770
0.0889
0.1364
0.1295
0.0787
0.0919
0.1293
0.1635
0.1350
0.1250
5C-71
-------
Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals
region=Midwest pov_rat=Above Poverty Level
Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
prev
0.6-
4567
9 10 11 12 13 14 15 16 17
9 10 11 12 13 14 15 16 17
gender
age
Female
Male
Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
gender
age
Female
Male
Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
prev
4 5 6 7
9 10 11 12 13 14 15 16 17
4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
age
Female
Male
gender
age
Female
Male
Appendix 5C, Attachment C, Figure 5CC-1. Smoothed prevalence and confidence intervals for children 'EVER' having asthma.
5C-72
-------
Figure 1. Smoothed asthma 'EVER1 prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
Figure 1. Smoothed asthma 'EVER1 prevalence rates and confidence intervals
region=West pov_rat= Above Poverty Level
prev
16 17
age
gender
Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
16 17
gender
Male
345
gender
8 9 10 11 12 13 14 15 16 17
age
Female
Male
Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
prev
345
gender
8 9 10 11 12 13 14 15 16 17
age
Female
Male
Appendix 5C, Attachment C, Figure 5CC-1, cont. Smoothed prevalence and confidence intervals for children 'EVER' having
asthma.
5C-73
-------
Figure 2. Smoothed asthma 'STILL1 prevalence rates and confidence intervals
region=Midwest pov rat=Above Poverty Level
Figure 2. Smoothed asthma 'STILL1 prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
prev
prev
3 4 5 6 7 S 9 10 11 12 13 14 15 16 17
3 4 5 6 7 S 9 10 11 12 13 14 15 16 17
gender
Female
Male
Figure 2. Smoothed asthma 'STILL1 prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
gender
age
Female
Male
Figure 2. Smoothed asthma 'STILL1 prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
3456
9 10 11 12 13 14 15 16 17
gender
age
Female
prev
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
age
Female
Appendix 5C, Attachment C, Figure 5CC-2. Smoothed prevalence and confidence intervals for children 'STILL' having asthma.
5C-74
-------
Figure 2. Smoothed asthma 'STILL1 prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervals
region=West pov_rat=Above Poverty Level
prev
0.6-
0.5 -
0.4:
0.3 -
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
age
Female
Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
prev
0.6-
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
age
gender Female Male
prev
0.6-
0.5-
0.4-
0.3:
0.2:
o.i -
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
gender
age
Female
Male
Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
prev
0.6-
0.4-
I-t
345
gender
8 9 10 11 12 13 14 15 16 17
age
Female
Male
Appendix 5C, Attachment C, Figure 5CC-2, cont. Smoothed prevalence and confidence intervals for children 'STILL' having
asthma.
5C-75
-------
Figure 3. Smoothed adult asthma 'EVER1 prevalence rates and confidence intervals
region=Midwest pov_rat=Above Poverty Level
Figure 3. Smoothed adult asthma 'EVER1 prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
prev
0.4:
0.3 :
18-24
35-44
45-54
55-64
65-74
75+
prev
0.4:
0.3:
35-44
45-54
55-64
75+
gender
age_grp
Female
Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
gender
age_grp
Female Male
Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
prev
0.4:
gender
45-54
age_grp
Female
Male
prev
0.4:
0.3:
18-24
25-34
35-44
gender
45-54
age_grp
Female
55-64
Male
75+
Appendix 5C, Attachment C, Figure 5CC-3. Smoothed prevalence and confidence intervals for Adults 'EVER' having asthma.
5C-76
-------
Figure 3. Smoothed adult asthma 'EVER1 prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
Figure 3. Smoothed adult asthma 'EVER1 prevalence rates and confidence intervals
region=West pov_rat=Above Poverty Level
35-44
45-54
55-64
65-74
75+
gender
age_grp
Female Male
Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
25-34 35-44 45-54 55-64
gender
age_grp
Female Male
prev
0.4:
gender
age_grp
Female
Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
prev
0.4:
18-24 25-34 35-44 45-54
55-64 65-74
gender
age_grp
Female
Appendix 5C, Attachment C, Figure 5CC-3, cont. Smoothed prevalence and confidence intervals for Adults 'EVER' having
asthma.
5C-77
-------
Figure 4. Smoothed adult asthma 'STILL1 prevalence rates and confidence intervals
region=Midwest pov_rat=Above Poverty Level
Figure 4. Smoothed adult asthma 'STILL1 prevalence rates and confidence intervals
region=Northeast pov_rat=Above Poverty Level
prev
0.4:
0.3 :
18-24
35-44
45-54
55-64
65-74
75+
prev
0.4:
0.3:
1=1
35-44
45-54
55-64
75+
gender
age_grp
Female
Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals
region=Midwest pov_rat=Below Poverty Level
gender
age_grp
Female
Male
Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals
region=Northeast pov_rat=Below Poverty Level
gender
45-54
age_grp
Female
Male
prev
0.4:
0.3:
18-24
25-34
35-44
gender
45-54
age_grp
Female
55-64
Male
75+
Appendix 5C, Attachment C, Figure 5CC-4. Smoothed prevalence and confidence intervals for Adults 'STILL' having asthma.
5C-78
-------
Figure 4. Smoothed adult asthma 'STILL1 prevalence rates and confidence intervals
region=South pov_rat=Above Poverty Level
Figure 4. Smoothed adult asthma 'STILL1 prevalence rates and confidence intervals
region=West pov_rat= Above Poverty Level
prev
0.4:
0.3 :
i
18-24
35-44
gender
45-54
age_grp
Female
55-64
65-74
75+
Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals
region=South pov_rat=Below Poverty Level
prev
0.4:
0.3 :
18-24
35-44
gender
45-54
age_grp
Female
55-64
65-74
75+
Male
prev
0.4:
0.3:
i—t
18-24 25-34 35-44 45-54 55-64 65-74
75+
gender
age_grp
Female
Male
Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals
region=West pov_rat=Below Poverty Level
prev
0.4:
0.3:
18-24
25-34 35-44
45-54 55-64
75+
gender
age_grp
Female
Male
Appendix 5C, Attachment C, Figure 5CC-4, cont. Smoothed prevalence and confidence intervals for Adults 'STILL' having
asthma.
5C-79
-------
i Appendix 5-D
2
3 Variability Analysis and Uncertainty Characterization
4
5
6 Table of Contents
7 5D-1. OVERVIEW 2
8 5D-2. TREATMENT OF VARIABILITY AND CO-VARIABILITY 2
9 5D-3. CHARACTERIZATION OF UNCERTAINTY 8
10 5D-4. REFERENCES 11
11
12
13 List of Tables
14 Table 5D-1. Components of exposure variability modeled by APEX 5
15 Table 5D-2. Important components of co-variability in exposure modeling 7
16
17
18
19
20
21
22
5D-1
-------
2 5D-1. OVERVIEW
3 An important issue associated with any population exposure or risk assessment is the
4 characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in
5 a population or variable of interest (e.g., residential air exchange rates). The degree of variability
6 cannot be reduced through further research, only better characterized with additional
7 measurement. Uncertainty refers to the lack of knowledge regarding the values of model input
8 variables (i.e., parameter uncertainty), the physical systems or relationships used (i.e., use of
9 input variables to estimate exposure or risk or model uncertainty), and in specifying the scenario
10 that is consistent with purpose of the assessment (i.e., scenario uncertainty). Uncertainty is,
11 ideally, reduced to the maximum extent possible through improved measurement of key
12 parameters and iterative model refinement. The approaches used to assess variability and to
13 characterize uncertainty in this REA are discussed in the following two sections. The primary
14 purpose of this characterization is to provide a summary of variability and uncertainty
15 evaluations conducted to date regarding our 63 exposure assessments and APEX exposure
16 modeling and to identify the most important elements of uncertainty in need of further
17 characterization. Each section contains a concise tabular summary of the identified components
18 and how, for elements of uncertainty, each source may affect the estimated exposures.
19 5D-2. TREATMENT OF VARIABILITY AND CO-VARIABILITY
20 The purpose for addressing variability in this REA is to ensure that the estimates of
21 exposure and risk reflect the variability of ambient 63 concentrations, population characteristics,
22 associated 63 exposure and intake dose, and potential health risk across the study area and for
23 the simulated at-risk populations. In this REA, there are several algorithms that account for
24 variability of input data when generating the number of estimated benchmark exceedances or
25 health risk outputs. For example, variability may arise from differences in the population
26 residing within census tracts (e.g., age distribution) and the activities that may affect population
27 exposure to 63 and the resulting intake dose estimate (e.g., time spent outdoors, performing
28 moderate or greater exertion level activities outdoors). A complete range of potential exposure
29 levels and associated risk estimates can be generated when appropriately addressing variability in
30 exposure and risk assessments; note however that the range of values obtained would be within
31 the constraints of the input parameters, algorithms, or modeling system used, not necessarily the
32 complete range of the true exposure or risk values.
33 Where possible, staff identified and incorporated the observed variability in input data
34 sets rather than employing standard default assumptions and/or using point estimates to describe
5D-2
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1 model inputs. The details regarding variability distributions used in data inputs are described in
2 Appendix 5B, while details regarding the variability addressed within its algorithms and
3 processes are found in the APEX TSD (US EPA, 2012).
4 Briefly, APEX has been designed to account for variability in most of the input data,
5 including the physiological variables that are important inputs to determining exertion levels and
6 associated ventilation rates. APEX simulates individuals and then calculates Os exposures for
7 each of these simulated individuals. The individuals are selected to represent a random sample
8 from a defined population. The collection of individuals represents the variability of the target
9 population, and accounts for several types of variability, including demographic, physiological,
10 and human behavior. In this assessment, we simulated 200,000 individuals to reasonably capture
11 the variability expected in the population exposure distribution for each study area. APEX
12 incorporates stochastic processes representing the natural variability of personal profile
13 characteristics, activity patterns, and microenvironment parameters. In this way, APEX is able
14 to represent much of the variability in the exposure estimates resulting from the variability of the
15 factors effecting human exposure.
16 We note also that correlations and non-linear relationships between variables input to the
17 model can result in the model producing incorrect results if the inherent relationships between
18 these variables are not preserved. That is why APEX is also designed to account for co-
19 variability, or linear and nonlinear correlation among the model inputs, provided that enough is
20 known about these relationships to specify them. This is accomplished by providing inputs that
21 enable the correlation to be modeled explicitly within APEX. For example, there is a non-linear
22 relationship between the outdoor temperature and air exchange rate in homes. One factor that
23 contributes to this non-linear relationship is that windows tend to be closed more often when
24 temperatures are at either low or high extremes than when temperatures are moderate. This
25 relationship is explicitly modeled in APEX by specifying different probability distributions of air
26 exchange rates for different ambient temperatures. In any event, APEX models variability and
27 co-variability in two ways:
28 • Stochastically. The user provides APEX with probability distributions
29 characterizing the variability of many input parameters. These are treated
30 stochastically in the model and the estimated exposure distributions reflect this
31 variability. For example, the rate of O^ removal in houses can depend on a
32 number of factors which we are not able to explicitly model at this time, due to a
33 lack of data. However, we can specify a distribution of removal rates which
34 reflects observed variations in O^ decay. APEX randomly samples from this
35 distribution to obtain values which are used in the mass balance model. Further,
36 co-variability can be modeled stochastically through the use of conditional
5D-3
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1 distributions. If two or more parameters are related, conditional distributions that
2 depend on the values of the related parameters are input to APEX. For example,
3 the distribution of air exchange rates (AERs) in a house depends on the outdoor
4 temperature and whether or not air conditioning (A/C) is in use. In this case, a set
5 of AER distributions is provided to APEX for different ranges of temperatures
6 and A/C use, and the selection of the distribution in APEX is driven by the
7 temperature and A/C status at that time. The spatial variability of A/C prevalence
8 is modeled by supplying APEX with A/C prevalence for each Census tract in the
9 modeled area.
10 • Explicitly. For some variables used in modeling exposure, APEX models
11 variability and co-variability explicitly and not stochastically. For example,
12 hourly-average ambient Os concentrations and temperatures are used in model
13 calculations. These are input to the model for every hour in the time period
14 modeled at different spatial locations, and in this way the variability and co-
15 variability of hourly concentrations and temperatures are modeled explicitly.
16 Important sources of the variability and co-variability accounted for by APEX and used
17 for this exposure analysis are summarized in Table 5D-1 and Table 5D-2 below, respectively.
18
19
5D-4
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1 Table 5D-1. Components of exposure variability modeled by APEX.
Component
Variability Source
Comment
Population data
Individuals are randomly sampled from US census tracts
used in each model study area, stratified by age (single
years), gender, and employment status probability
distributions (US Census Bureau, 2007a).
Commuting data
Employed individuals are probabilistically assigned ambient
concentrations originating from either their home or work tract
based on US Census derived commuter data (US Census
Bureau, 2007a).
Simulated
Individuals
Activity patterns
Data diaries are randomly selected from CHAD master
(>38,000 diaries) using six diary pools stratified by two day-
types (weekday, weekend) and three temperature ranges (<
55.0 °F, between 55.0 and 83.9'F, and >84.0 °F). The CHAD
diaries capture real locations that people visit and the
activities they perform, ranging from 1 minute to 1 hour in
duration (US EPA, 2002).
Longitudinal profiles
A sequence of diaries is linked together for each individual
that preserves both the inter- and intra-personal variability in
human activities (Glen et al., 2008).
Asthma prevalence
Asthma prevalence is stratified by two genders, single age
years (0-17), seven age groups, (18-24, 25-34, 35-44, 45-54,
55-64, 65-74, and, >75), four regions (Midwest, Northeast,
South, and West), and US census tract level poverty ratios
(CDC, 2011; US Census Bureau, 2007b).
Measured ambient O3
concentrations
Ambient Input
Temporal: 1 -hour concentrations for an entire O3 season or
year predicted using ambient monitoring data.
Spatial: Several monitors are used to represent ambient
conditions within each study area; each monitor was assigned
a 30 km zone of influence, though value from closest monitor
is used for each tract. Four US study areas assess regional
differences in ambient conditions.
Meteorological data
Spatial: Values from closest available local surface National
Weather Service (NWS) station were used.
Temporal: 1-hour temperature data input for each year; daily
values calculated by APEX.
Microenvironmental
Approach
Microenvironments:
General
Twenty-eight total microenvironments are represented,
including those expected to be associated with high exposure
concentrations (i.e., outdoors and outdoor near-road). Where
this type of variability is incorporated within particular
microenvironmental algorithm inputs, this results in
differential exposure estimates for each individual (and event)
as persons spend varying time frequency within each
microenvironment and ambient concentrations vary spatially
within and between study areas.
Microenvironments:
Spatial Variability
Ambient concentrations used in microenvironmental
algorithms vary spatially within (where more than one site
available) and among study areas. Concentrations near
roadways are adjusted to account fortitration by NO.
5D-5
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Component
Physiological
Factors and
Algorithms
Variability Source
Microenvironments:
Temporal Variability
Air exchange rates
Proximity factors for
on- and near roads
Resting metabolic
rate (RMR)
Maximum normalized
oxygen consumption
rate (NVO2)
Maximum oxygen
debt (MOXD)
Recovery time
METS by activity
Oxygen uptake per
unit of energy
expended (UCF)
Body mass
Height
Body surface area
Comment
All exposure calculations are performed at the event-level
when using either factors or mass balance approach
(durations can be as short as one minute). In addition, for the
indoor microenvironments, using a mass balance model
accounts for O3 concentrations occurring during a previous
hour (and of ambient origin) to calculate a current event's
indoor O3 concentrations.
Several lognormal distributions are sampled based on five
daily mean temperature ranges, study area, and study-area
specific A/C prevalence rates.
Three distributions are used, stratified by road-type (urban,
interstate, and rural), selected based on VMT to address
expected ozone titration by NO near roads.
Regression equations for three age-group (18-29, 30-59, and
60+) and two genders were used with body mass as the
independent variable (see Johnson et al. (2000) and section
5.3 of APEX TSD).
Single year age- and gender-specific normal distributions are
randomly sampled for each person (Isaacs and Smith, 2005
and section 7.2 of APEX TSD). This variable is used to
calculate maximum metabolic equivalents (METS).
Normal distributions for maximum obtainable oxygen,
stratified by 3 age groups (ages 0-11, 12-18, 19-100) and two
genders (Isaacs and Smith, 2007 and section 7.2 of APEX
TSD). Used when adjusting METS to address fatigue and
EPOC.
One uniform distribution randomly sampled to estimate the
time required to recover a maximum oxygen deficit (Isaacs
and Smith, 2007 and section 7.2 of APEX TSD).
Values randomly sampled from distributions developed for
specific activities (a few are age-group specific) (McCurdy,
2000; US EPA, 2002).
Values randomly sampled from a uniform distribution to
convert energy expenditure to oxygen consumption (Johnson
et al., 2000 and section 5.3 of APEX TSD).
Randomly selected from population-weighted lognormal
distributions with age- and gender-specific geometric mean
(GM) and geometric standard deviation (GSD) derived from
the National Health and Nutrition Examination Survey
(NHANES) for the years 1999-2004 (Isaacs and Smith (2005)
and section 5.3 of APEX TSD).
Values randomly sampled from distributions used are based
on equations developed for each gender by Johnson (1 998)
using height and weight data from Brainard and Burmaster
(1992) (also see Appendix B of 2010 CO REA).
Point estimates of exponential parameters used for
calculating body surface area as a function of body mass
(Burmaster, 1998)
5D-6
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Component
Variability Source
Comment
Ventilation rate
Event-level activity-specific regression equations stratified by
four age groups, using age, gender, body mass normalized
oxygen consumption rate as independent variables, and
accounting for intra and interpersonal variability (Graham and
McCurdy, 2005).
Fatigue and EPOC
APEX approximates the onset of fatigue, controlling for
unrealistic or excessive exercise events in each persons
activity time-series while also estimating excess post-
exercise oxygen consumption (EPOC) that may occur
following vigorous exertion activities (Isaacs et al., 2007 and
section 7.2 of APEX TSD).
1
2
Table 5D-2. Important components of co-variability in exposure modeling.
Type of Co-variability
Within-person correlations n
Between-person correlations
Correlations between profile variables and
microenvironment parameters
Correlations between demographic
variables (e.g., age, gender) and activities
Correlations between activities and
microenvironment parameters
Correlations among microenvironment
parameters in the same microenvironment
Correlations between demographic
variables and air quality
Correlations between meteorological
variables and activities
Correlations between meteorological
variables and microenvironment
parameters
Correlations between drive times in CHAD
and commute distances traveled
Consistency of occupation/school
microenvironmental time and time spent
commuting/busing for individuals from one
working/school day to the next.
Modeled
by
APEX?
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Treatment in APEX / Comments
Sequence of activities performed,
microenvironments visited, and general
physiological parameters (body mass, height,
ventilation rates).
Judged as not important.
Profiles are assigned microenvironment
parameters.
Age and gender are used in activity diary selection.
Perhaps important, but do not have data. For
example, frequency of opening windows when
cooking or smoking tobacco products.
Modeled with joint conditional variables.
Modeled with the spatially varying demographic
variables and air quality input to APEX.
Temperature is used in activity diary selection.
The distributions of microenvironment parameters
can be functions of temperature.
CHAD diary selection is weighted by commute
times for employed persons during weekdays.
Simulated individuals are assigned activity diaries
longitudinally without regard to occupation or
school schedule (note though, longitudinal variable
used to develop annual profile is time spent
outdoors).
3
4
5
6
The term correlation is used to represent linear and nonlinear relationships.
5D-7
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1 5D-3. CHARACTERIZATION OF UNCERTAINTY
2 While it may be possible to capture a range of exposure or risk values by accounting for
3 variability inherent to influential factors, the true exposure or risk for any given individual within
4 a study area is unknown, though can be estimated. To characterize health risks, exposure and
5 risk assessors commonly use an iterative process of gathering data, developing models, and
6 estimating exposures and risks, given the goals of the assessment, scale of the assessment
7 performed, and limitations of the input data available. However, significant uncertainty often
8 remains and emphasis is then placed on characterizing the nature of that uncertainty and its
9 impact on exposure and risk estimates.
10 In the final 2008 Oj, NAAQS rule,1 EPA staff performed such a characterization and at
11 that time, identified the most important uncertainties affecting the exposure estimates. The key
12 elements of uncertainty were 1) the modeling of human activity patterns over an O^ season, 2)
13 the modeling of variations in ambient 63 concentrations near roadways, 3) the modeling of air
14 exchange rates that affect the amount of Os that penetrates indoors, and 4) the characterization of
15 energy expenditure (and related ventilation rate estimates) for children engaged in various
16 activities. Further, the primary findings of a quantitative Monte Carlo analysis also performed at
17 that time indicated that the overall uncertainty of the APEX estimated exposure distributions was
18 relatively small: the percent of children or asthmatic children with exposures above 0.06, 0.07, or
19 0.08 ppm-8hr under moderate exertion have 95% were estimated by APEX to have uncertainty
20 intervals of at most ±6 percentage points. Details for these previously identified uncertainties are
21 discussed in the 2007 63 Staff Paper (section 4.6) and in a technical memorandum describing the
22 2007 Os exposure modeling uncertainty analysis (Langstaff, 2007).
23 The REA' s conducted for the most recent NO2 (US EPA, 2008), SO2 (US EPA, 2009),
24 and CO (US EPA, 2010) NAAQS reviews also presented characterizations of the uncertainties
25 associated with APEX exposure modeling (among other pollutant specific issues), albeit mainly
26 qualitative evaluations. Conclusions drawn from all of these assessments regarding exposure
27 modeling uncertainty have been integrated here, following the standard approach used by EPA
28 staff since 2008 and outlined by WHO (2008) to identify, evaluate, and prioritize the most
29 important uncertainties relevant to the estimated potential health effect endpoints used in this Os
30 REA. Staff selected the qualitative approach used for this first draft OT, REA as a step towards
31 developing an appropriate probabilistic uncertainty analysis, perhaps similar to that performed at
32 the time of the 2007 O3 REA by Langstaff (2007).
1 Federal Register Vol. 73, No. 60. Available at: http://www.epa.gov/ttn/naaqs/standards/ozone/fr/20080327.pdf
5D-8
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1 The qualitative approach used in this first draft O^ REA varies from that described by
2 WHO (2008) in that a greater focus was placed on evaluating the direction and the magnitude2 of
3 the uncertainty; that is, qualitatively rating how the source of uncertainty, in the presence of
4 alternative information, may affect the estimated exposures and health risk results. In addition
5 and consistent with the WHO (2008) guidance, staff discuss the uncertainty in the knowledge
6 base (e.g., the accuracy of the data used, acknowledgement of data gaps) and decisions made
7 where possible (e.g., selection of particular model forms), although qualitative ratings were
8 assigned only to uncertainty regarding the knowledge base.
9 First, staff identified the key aspects of the assessment approach that may contribute to
10 uncertainty in the exposure and risk estimates and provided the rationale for their inclusion.
11 Then, staff characterized the magnitude and direction of the influence on the assessment results
12 for each of these identified sources of uncertainty. Consistent with the WHO (2008) guidance,
13 staff subjectively scaled the overall impact of the uncertainty by considering the degree of
14 uncertainty as implied by the relationship between the source of uncertainty and the exposure
15 concentrations.
16 Where the magnitude of uncertainty was rated low, it was judged that changes within the
17 source of uncertainty would have only a small effect on the exposure results. For example, we
18 have commonly employed statistical procedure to substitute missing concentration values to
19 complete the meteorological data sets. Staff has consistently compared the air quality
20 distributions and found negligible differences between the substituted data set and the one with
21 missing values (e.g., Tables 5-13 through 5-16 of US EPA, 2010), primarily because of the
22 infrequency of missing value substitutions needed to complete a data set. There is still
23 uncertainty in the approach used, and there may be alternative, and possibly better, methods
24 available to perform such a task. However, in this instance, staff judged that the quantitative
25 comparison of the ambient concentration data sets indicates that there would likely be little
26 influence on exposure estimates by the data substitution procedure used.
27 A magnitude designation of moderate implies that a change within the source of
28 uncertainty would likely have a moderate (or proportional) effect on the results. For example,
29 the magnitude of uncertainty associated with using the quadratic approach to represent a
30 hypothetical future air quality scenario was rated as low-moderate. While we do not have
31 information regarding how the ambient Os concentration distribution might look in the future, we
32 do know however what the distribution might look like based on historical trends and the
33 emission sources. These historical data and trends serve to generate algorithms used to adjust air
34 quality. If these trends in observed concentrations and emissions were to remain constant in the
' This is synonymous with the "level of uncertainty" discussed in WHO (2008), section 5.1.2.2.
5D-9
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1 future, then the magnitude of the impact to estimated exposures in this assessment would be
2 judged as likely low or having negligible impact on the estimated exposures. However, if there
3 are entirely new emission sources in the future or if the approach developed is not equally
4 appropriate across the range of assessed study areas, the magnitude of influence might be judged
5 as greater. For example, when comparing exposure estimates for one year that used three
6 different 3-year periods to adjust that year's air quality levels to just meet the current standard,
7 staff observed mainly proportional differences (e.g., a factor of two or three) in the estimated
8 number of persons exposed in more than half of the twelve study areas (Langstaff, 2007).
9 Assuming that these types of ambient concentration adjustments could reflect the addition of a
10 new or unaccounted for emission source in a particular study area, staff also judged the
11 magnitude of influence in using the quadratic approach to adjust air quality data to represent a
12 hypothetical future scenario as moderate. A characterization of high implies that a small change
13 in the source would have a large affect on results, potentially an order of magnitude or more.
14 This rating would be used where the model estimates were extremely sensitive to the identified
15 source of uncertainty.
16 In addition to characterizing the magnitude of uncertainty, staff also included the
17 direction of influence, indicating how the source of uncertainty was judged to affect estimated
18 exposures or risk estimates; either the estimated values were possibly over- or under-estimated.
19 In the instance where the component of uncertainty can affect the assessment endpoint in either
20 direction, the influence was judged as both. Staff characterized the direction of influence as
21 unknown when there was no evidence available to judge the directional nature of uncertainty
22 associated with the particular source. Staff also subjectively scaled the knowledge-base
23 uncertainty associated with each identified source using a three-level scale: low indicated
24 significant confidence in the data used and its applicability to the assessment endpoints,
25 moderate implied that there were some limitations regarding consistency and completeness of
26 the data used or scientific evidence presented, and high indicated the extent of the knowledge-
27 base was extremely limited.
28 The output of the uncertainty characterization is a summary describing, for each
29 identified source of uncertainty, the magnitude of the impact and the direction of influence the
30 uncertainty may have on the exposure and risk characterization results.
31
5D-10
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1 5D-4. REFERENCES
2 Brainard J and Burmaster D. (1992). Bivariate distributions for height and weight of men and
3 women in the United States. Risk Analysis. 12(2):267-275.
4 Burmaster DE. (1998). Lognormal distributions for skin area as a function of body weight. Risk
5 Analysis. 18(l):27-32.
6 CDC. (2011). Summary Health Statistics for U.S. Adults: National Health Interview Survey,
7 years 2006-10. U.S. Department of Health and Human Services, Hyattsville, MD. Data
8 and documentation available at: http://www.cdc.gov/nchs/nhis.htm (accessed October 4,
9 2011).
10 Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal
11 diary assembly for human exposure modeling. JExpos Sci Environ Epidem. 18:299-311.
12 Graham SE and T McCurdy. (2005). Revised ventilation rate (VE) equations for use in
13 inhalation-oriented exposure models. Report no. EPA/600/X-05/008. Report is found
14 within Appendix A of US EPA (2009). Metabolically Derived Human Ventilation Rates:
15 A Revised Approach Based Upon Oxygen Consumption Rates (Final Report). Report no.
16 EPA/600/R-06/129F. Appendix D contains "Response to peer-review comments on
17 Appendix A", prepared by S. Graham (US EPA). Available at:
18 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543
19 Issacs K and Smith L. (2005). New Values for Physiological Parameters for the Exposure Model
20 Input File Physiology.txt. Technical memorandum to Tom McCurdy, NERL WA10.
21 December 20, 2005. Provided in Appendix A of the CO REA (US EPA, 2010).
22 Isaacs K, Glen G, McCurdy T., and Smith L. (2007). Modeling energy expenditure and oxygen
23 consumption in human exposure models: Accounting for fatigue and EPOC. J Expos Sci
24 Environ Epidemiol. 18(3):289-98.
25 Johnson T. (1998). Memo No. 5: Equations for Converting Weight to Height Proposed for the
26 1998 Version of pNEM/CO. Memorandum Submitted to U.S. Environmental Protection
27 Agency. TRJ Environmental, Inc., 713 Shadylawn Road, Chapel Hill, North Carolina
28 27514.
29 Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J, Rosenbaum A, Cohen J, Stiefer P. (2000).
30 Estimation of carbon monoxide exposures and associated carboxyhemoglobin levels for
31 residents of Denver and Los Angeles using pNEM/CO. Appendices. EPA constract 68-D6-
32 0064.
33 Langstaff JE. (2007). OAQPS Staff Memorandum to Ozone NAAQS Review Docket (OAR-
34 2005-0172). Subject: Analysis of Uncertainty in Ozone Population Exposure Modeling.
35 [January 31,2007]. Available at:
36 http://www.epa.gOv/ttn/naaqs/standards/ozone/s o3 cr td.html
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1 McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy
2 expenditure approach. JExpos Anal Environ Epidemiol. 10:1-12.
3 Schofield WN. (1985). Predicting basal metabolic rate, new standards, and review of previous
4 work. HumNutrClinNutr. 39C(S1):5-41.
5 US Census Bureau. (2007a). Employment Status: 2000- Supplemental Tables. Available at:
6 http://www.census.gov/population/www/cen2000/phc-t28.html.
7 US Census Bureau. (2007b). 2000 Census of Population and Housing. Summary File 3 (SF3)
8 Technical Documentation, available at: http://www.census.gov/prod/cen2000/doc/sf3 .pdf.
9 Individual SF3 files '30' (for income/poverty variables pct49-pct51) for each state were
10 downloaded from: http://www2.census.gov/census 2000/datasets/Summary File 3/.
11 US EPA. (2002). EPA's Consolidated Human Activities Database. Available at:
12 http ://www. epa.gov/chad/.
13 US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. Office of Air
14 Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle
15 Park, NC. Available at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html
16 US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary
17 National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November
18 2008. Available at:
19 http ://www. epa. gov/ttn/naaqs/standards/nox/data/20081121 NO2 REA final .pdf.
20 US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary
21 National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009.
22 Available
23 athttp://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf.
24 US EPA. (2010). Quantitative Risk and Exposure Assessment for Carbon Monoxide -
25 Amended. EPA Office of Air Quality Planning and Standards. EPA-452/R-10-009. July
26 2010. Available at: http://www.epa.gov/ttn/naaqs/standards/co/data/CO-REA-Amended-
27 Julv2010.pdf
28 US EPA. (2012). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model
29 Documentation (TREVI.Expo / APEX, Version 4.4) Volume I: User's Guide. Office of Air
30 Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle
31 Park,NC. EPA-452/B-12-00la. Available at:
32 http://www.epa.gov/ttn/fera/human apex.html
33 WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on
34 characterizing and communicating uncertainty in exposure assessment. Available at:
3 5 http ://www. who. int/ipcs/methods/harmonizati on/areas/exposure/en/.
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Appendix 5-E
Updated Analysis of Air Exchange Rate Data
5E-1
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INTERNATIONAL
MEMORANDUM
To: John Langstaff
From: Jonathan Cohen, Hemant Mallya, Arlene Rosenbaum
Date: 28 December, 2012
Re: Updated Analysis of Air Exchange Rate Data
EPA is planning to use the APEX exposure model to estimate ozone exposure in 16
cities / metropolitan areas: Atlanta, GA; Baltimore, MD; Boston, MA; Chicago, IL;
Cleveland, OH; Dallas, TX: Detroit, Ml; Denver, CO: Houston, TX; Los Angeles, CA;
New York, NY; Philadelphia, PA; Sacramento, CA; Seattle, WA; St. Louis, MO-IL;
Washington, DC. As part of this effort, ICF International has developed distributions of
residential and non-residential air exchange rates (AER) for use as APEX inputs for the
cities to be modeled. This memorandum describes the analysis of the AER data and
the proposed APEX input distributions. Also included in this memorandum are
proposed APEX inputs for penetration and proximity factors for selected
microenvironments.
Residential Air Exchange Rates
Studies. Residential air exchange rate (AER) data were obtained from the following
seven studies and summarized in Table 1:
Avol: Avol et al., 1998. In this study, ozone concentrations and AERs were
measured at 126 residences in the greater Los Angeles metropolitan area
between February and December, 1994. Measurements were taken in four
communities: Lancaster, Lake Gregory, Riverside, and San Dimas. Data
included the daily average outdoor temperature, the presence or absence of an
air conditioner (either central or room), and the presence or absence of a swamp
(evaporative) cooler. Air exchange rates were computed based on the total
house volume and based on the total house volume corrected for the furniture.
These data analyses used the study corrected AERs.
RTP Panel: Williams et al., 2003a, 2003b. In this study particulate matter
concentrations and daily average AERs were measured at 37 residences in
central North Carolina during 2000 and 2001 (averaging about 23 AER
measurements per residence). The residences belong to two specific cohorts: a
mostly Caucasian, non-smoking group aged at least 50 years having cardiac
defibrillators living in Chapel Hill; a group of non-smoking, African Americans
aged at least 50 years with controlled hypertension living in a low-to-moderate
SES neighborhood in Raleigh. Data included the daily average outdoor
temperature, and the number of air conditioner units (either central or room).
Every residence had at least one air conditioner unit.
60 Broadway Street-— San Francisco, CA 94111-1429 —— 415-677-7100—— 415-677-7177 fax— www.icfconsuhing.conn
5E-2
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RIOPA: Meng et al., 2004, Weisel et al., 2004. The Relationship of Indoor,
Outdoor, and Personal Air (RIOPA) study was undertaken to estimate the impact
of outdoor sources of air toxics to indoor concentrations and personal exposures.
Volatile organic compounds, carbonyls, fine particles and AERs were measured
once or twice at 310 non-smoking residences from summer 1999 to spring 2001.
Measurements were made at residences in Elizabeth, NJ, Houst on TX, and Los
Angeles CA. Residences in California were randomly selected. Residences in
New Jersey and Texas were preferentially selected to be close (< 0.5 km) to
sources of air toxics. The AER measurements (generally over 24 hours) used a
PMCH tracer. Data included the daily average outdoor temperature, and the
presence or absence of central air conditioning, room air conditioning, or a
swamp (evaporative) cooler.
TEACH: Chillrud at al., 2004, Kinney et al., 2002, Sax et al., 2004. The Toxic
Exposure Assessment, a Columbia/Harvard (TEACH) study was designed to
characterize levels of and factors influencing exposures to air toxics among high
school students living in inner-city neighborhoods of New York City and Los
Angeles, CA. Volatile organic compounds, aldehydes, fine particles, selected
trace elements, and AER were measured at 87 high school student's residences
in New York City and Los Angeles in 1999 and 2000. Data included the
presence or absence of an air conditioner (central or room) and hourly outdoor
temperatures (which were converted to daily averages for these analyses).
Wilson 1984: Wilson et al., 1986, 1996. In this 1984 study, AER and other data
were collected at about 600 southern California homes with three seven-day
tests (in March and July 1984, and January 1985) for each home. We obtained
the data directly from Mr. Wilson. The available data consisted of the three
seven-day averages, the month, the residence zip code, the presence or
absence of a central air conditioner, and the presence or absence of a room air
conditioner. We matched these data by month and zip code to the
corresponding monthly average temperatures obtained from EPA's SCRAM
website as well as from the archives in www.wunderground.com (personal and
airport meteorological stations). Residences more than 25 miles away from the
nearest available meteorological station were excluded from the analysis. For
our analyses, the city/location was defined by the meteorological station, since
grouping the data by zip code would not have produced sufficient data for most
of the zip codes.
Wilson 1991: Wilson et al., 1996, Colome et al., 1993, 1994. In this 1991 study,
AER and other data were collected at about 300 California homes with one two-
day test in the winter for each home. We obtained the data directly from Mr.
Wilson. The available data consisted of the two-day averages, the date, city
name, the residence zip code, the presence or absence of a central air
conditioner, the presence or absence of a swamp (evaporative) cooler, and the
presence or absence of a room air conditioner. We matched these data by date,
city, and zip code to the corresponding daily average temperatures obtained from
EPA's SCRAM website as well as from the archives in www.wunderground.com
(personal and airport meteorological stations). Residences more than 25 miles
5E-3
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away from the nearest available meteorological station were excluded from the
analysis. For our analyses, the city/location was defined by the meteorological
station, since grouping the data by zip code would not have produced sufficient
data for most of the zip codes.
Murray and Burmaster: Murray and Burmaster (1995). For this article, Murray
and Burmaster corrected and compiled nationwide residential AER data from
several studies conducted between 1982 and 1987. These data were originally
compiled by the Lawrence Berkeley National Laboratory. We acknowledge Mr.
Murray's assistance in obtaining these data for us. The available data consisted
of AER measurements, dates, cities, and degree-days. Information on air
conditioner presence or absence was not available.
DEARS: Sheldon (2007). The Detroit Exposure and Aerosol Research Study
(DEARS) collected air exchange rate data as well as PM2.5 and other air pollutant
data at about 120 homes in Detroit, Michigan, for 3 years starting in the summer
of 2004. Each home was sampled for 5 days in the winter and/or 5 days in the
summer. The available data included AER measurements, dates, average,
minimum, and maximum temperatures, the use or non-use of air/conditioners
during each measurement day, the use or non-use of window fans during each
measurement day, and the number of minutes per day that the windows were
open.
For each of the studies, air conditioner usage, window status (open or closed), and fan
status (on or off) was not part of the experimental design, although some of these
studies included information on whether air conditioners or fans were used (and for how
long) and whether windows were closed during the AER measurements (and for how
long).
As indicated in the above summaries, random selection was not used to identify homes
to include in some of the studies: The RTP Panel study selected two specific cohorts of
older subjects with specific diseases. The RIOPA study was biased towards residences
near air toxics sources. The TEACH study focused on inner-city neighborhoods.
Nevertheless, we included all these studies because we determined that any potential
selection bias would be likely to be small and we preferred to keep as much data as
possible. The DEARS study selected homes from certain neighborhoods in Detroit.
The proportion of the DEARS study homes that used A/C on one or more survey days
was 57%, and the proportion of DEARS study homes with some daily temperatures
above 25 °C that used A/C on one or more of those hot days was 73%. The American
Housing Survey shows that for the Detroit metropolitan area, the proportion of homes
with A/C was 90% (see Table 2 below), but 71 % for the city of Detroit. This suggests
that the DEARS study sample may be representative of an older housing stock than the
overall Detroit metropolitan area.
All data and statistical results are compiled into the attached Excel spreadsheet
Summary_Statistic.Dec 2012.xls.
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Table 1. Summary of Studies of Residential Air Exchange Rates.
Study/
Attribute
Locations
Years
Months/
Seasons
Homes with
AER
Measurements
Total AER
Measurements
Average AER
Measurements
per Home
AER
Measurement
Duration
AER
Measurement
Technique
Min AER (h~1)
Max AER (IT1)
Mean AER (h~1)
Min Temp. (°C)
Max Temp. (°C)
Air Conditioner
Categories
Avol
Lancaster, Lake
Gregory,
Riverside, San
Dimas. All in
Southern CA
1994
Feb; Mar; Apr;
May; Jun; Jul;
Aug; Sep; Oct;
Nov
86
161
1.87
Not Available
Not Available
0.01
2.70
0.80
-0.04
36.25
No A/C; Central
or Room A/C;
Swamp Cooler
only; Swamp +
[Central or
Room]
RTP Panel
Research
Triangle Park, NC
2000; 2001
2000 (Jun; Jul;
Aug; Sep; Oct;
Nov), 2001 (Jan;
Feb; Apr; May)
37
854
23.08
24 hour
Perflourocarbon
tracer
0.02
21.44
0.72
-2.18
30.81
Central or Room
A/C (Y/N)
RIOPA
CA; NJ; TX
1999; 2000;
2001
1999 (July to
Dec); 2000 (all
months); 2001
(Jan and Feb)
284
524
1.85
24 to 96 hours
PMCH tracer
0.08
87.50
1.41
-6.82
32.50
Window A/C
(Y/N); Evap
Coolers (Y/N)
TEACH
Los Angeles, CA;
New York City, NY
1999; 2000
1999 (Feb; Mar;
Apr; Jul; Aug);
2000 (Jan; Feb;
Mar; Sep; Oct)
85
151
1.78
Sample time (hours)
reported. Ranges
from about 1 to 7
days
Perflourocarbon
tracer
0.12
8.87
1.71
-1.36
32.00
Central or Room
A/C (Y/N)
Wilson 1984
Southern CA
1984, 1985
Mar 1984, Jul 1984, Jan
1985
581
1,362
2.34
7 days
Perflourocarbon tracer
0.03
11.77
1.05
11.00
28.00
Central A/C (Y/N); Room
A/C (Y/N);
Wilson 1991
Southern CA
1984
Jan, Mar, Jul
288
316
1.10
7 days
Perflourocarbon tracer
0.01
2.91
0.57
3.00
25.00
Central A/C (Y/N); Room
A/C (Y/N); Swamp
Cooler(Y/N)
Murray and
Burmaster
AZ, CA, CO,
CT, FL, ID,
MD, MN, MT,
NJ
1982-1987
Various
1,884
2,844
1.51
Not available
Not available
0.01
11.77
0.76
Not available
Not available
Not available
DEARS
Detroit, Ml
2004- 2006
Jan. Feb, Mar,
Jul. Aug
127
868
6.83
24 hour
Perflourocarbon
tracer
0.08
13.56
0.80
-12.92
30.79
Central or
Room A/C
(Y/N)
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Study/
Attribute
Air Conditioner
Measurements
Made
Fan
Categories
Fan
Measurements
Made
Window Open/
Closed Data
Comments
Avol
A/C use in
Not available
Time on or off for
various fan types
during sampling
was recorded,
but not included
in database
provided.
Duration open
between times
6am-12 pm;
12pm - 6 pm;
and 6pm - 6am
RTP Panel
Not Available
Fan (Y/N)
Not Available
Windows (open /
closed along with
duration open in
inch-hours units
RIOPA
Duration
measurements in
Mrs and Mins
Fan (Y/N)
measurements in
Hours and
Minutes
Windows (Open /
Closed) along
with window
open duration
measurements
CA sample was
a random sample
were deliberately
near to ambient
sources.
TEACH
Not Available
Not Available
Not Available
Not Available
Restricted to inner-
city homes with high
school students.
Wilson 1984
Not Available
Not Available
Not Available
Not Available
Contemporaneous
temperature data
obtained for these
analyses from SCRAM
and
www.wunderground.com
meteorological data.
Wilson 1991
Not Available
Not Available
Not Available
Not Available
Contemporaneous
temperature data
obtained for these
analyses from SCRAM
and
www.wunderground.com
meteorological data.
Murray and
Burmaster
Not available
Not available
Not available
Not available
DEARS
Not available
Fan (Y/N)
Daily duration
in minutes
Windows (Open
/ Closed) along
with window
open duration
measurements
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We compiled the data from these eight studies to create the following variables, of
which some had missing values:
• Study
• Date
• Time - Time of the day that the AER measurement was made
• HouseJD - Residence identifier
• Measurement^!D - Uniquely identifies each AER measurement for a given study
• AER - Air Exchange Rate (per hour)
• AER_Duration - Length of AER measurement period (hours)
• Have_AC - Indicates if the residence has any type of air conditioner (A/C), either
a room A/C or central A/C or swamp cooler or any of them in combination. "Y" =
"Yes." "N" = "No"
• Type_of_AC1 - Indicates the types of A/C or swamp cooler available in each
house measured. Possible values: "Central A/C" "Central and Room A/C"
"Central or Room A/C" "No A/C" "Swamp + (Central or Room)" "Swamp Cooler
only" "Window A/C" "Window and Evap""" (missing)
• Type_of_AC2 - Indicates if a house measured has either no A/C or some A/C.
Possible values: "No A/C" "Central or Room A/C""" (missing)
• Type_of_AC3 - Indicates if a house measured has either no A/C, central A/C, or
room A/C. If separate A/C information is available for central A/C and for room
A/C, then homes with both central and room A/C are coded as having "Central
A/C." Possible values: "No A/C" "Central A/C" "Room A/C""" (missing)
• Have_Fan - Indicates if the house studied has any fans
• Mean_Temp - Daily average outside temperature (°C)
• Min_Temp - Minimum hourly outside temperature (°C)
• Max_Temp - Maximum hourly outside temperature (°C)
• State
• City
• Location - Two character abbreviation
• Flag - Data status. Murray and Burmaster study: "Used" or "Not Used." Other
studies: "Used"; "Missing" (missing values for AER, Type_of_AC2, and/or
Mean_Temp); "Outlier".
Note that in the Wilson 1991 study, one of the Los Angeles values was recorded as
having an "Unknown" air conditioning type. In this analysis, this measurement is treated
as having missing A/C information and is not included in the main data analyses.
Note that for the DEARS study, the available data did not include the presence or
absence of an air conditioner or window fan. Instead, the data set included a flag
indicating whether or not an air conditioner or window fan was used on that day. For the
data analysis, we assumed that if the air conditioner or window fan was used on at least
one day, then the air conditioner or window fan was present; otherwise the air
conditioner or window fan was absent. Since the maximum observed temperature
during the study days was 31 °C and some of the homes did not have measurements on
hot summer days, some of the homes designated as having no A/C may have been
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mistakenly designated. For the DEARS study the type of air conditioner (central or
room) was not provided.
The compiled data base is the attached PC SAS dataset
complete_database2.sas7bdat.
The main data analysis was based on the seven studies other than the Murray and
Burmaster study. The Murray and Burmaster data were excluded because of the
absence of information on air conditioner presence. (However, a subset of these data
was used for a supplementary analysis described below.)
Based on our review of the AER data we excluded nine values with unrealistically high
AER values - above 10 per hour (see the worksheet "OUTLIERS"). The main data
analysis used all the remaining data that had non-missing values for AER,
Type_of_AC2, and Mean_Temp. For the main data analysis, we decided to base the
A/C type variable on the broad characterization "No A/C" versus "Central or Room A/C"
since this variable could be calculated from all of the studies (excluding Murray and
Burmaster). Information on the presence or absence of swamp coolers was not
available from all the studies, and, also importantly, the corresponding information on
swamp cooler prevalence for the subsequent ozone modeling cities was not available
from the American Housing Survey. It is plausible that AER distributions depend upon
the presence or absence of a swamp cooler. It is also plausible that AER distributions
also depend upon whether the residence specifically has a central A/C, room or window
A/C, or both. However we determined to use the broader A/C type definition, which in
effect assumes that the exact A/C type and the presence of a swamp cooler are
approximately proportionately represented in the surveyed residences. The detailed
A/C type variable Type_of_AC3 was used for the Los Angeles AER distributions.
Most of the studies had more than one AER measurement for the same house. It is
reasonable to assume that the AER varies with the house as well as other factors such
as the temperature. (The A/C type can be assumed to be the same for each
measurement of the same house). We expected the temperature to be an important
factor since the AER will be affected by the use of the available ventilation (air
conditioners, windows, fans), which in turn will depend upon the outside meteorology.
Therefore it is not appropriate to average data for the same house under different
conditions, which might have been one way to account for dependence between
multiple measurements on the same house. To simplify the data analysis, we chose to
ignore possible dependence between measurements on the same house on different
days and treat all the AER values as if they were statistically independent.
Summary Statistics. We computed summary statistics for AER and its natural
logarithm LOG_AER on selected strata defined from the study, city, A/C type, and mean
temperature. Cities were defined as in the original databases, except that for Los
Angeles we combined all the data in the Los Angeles ozone modeling region, i.e., the
counties of Los Angeles, Orange, Ventura, Riverside, and San Bernardino. A/C type
was defined from the Type_of_AC2 variable, which we abbreviated as "NA" = "No A/C"
and "AC" = "Central or Room A/C". The mean temperature was grouped into the
following temperature bins: -15 to 0 °C, 0 to 10 °C, 10 to 20 °C, 20 to 25 °C, 25 to 30 °C,
30 to 40 °C (Values equal to the lower bounds are excluded from each interval). Also
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included were strata defined by study = "AH" and/or city = "All," and/or A/C type = "AH"
and/or temperature bin = "All". The following summary statistics for AER and LOG_AER
respectively are shown in the worksheets "SUMMARY_STATISTICS_AER" and
"SUMMARY_STATISTICS_LOG_AER":
• Number of values
• Arithmetic Mean
• Arithmetic Standard Deviation
• Arithmetic Variance
ith
ith
,th
• Deciles (Min, 10tn, 20tn... 90tn percentiles, Max)
These calculations exclude all nine outliers and results are not shown for strata with 10
or fewer values, since those summary statistics are extremely unreliable.
Examination of these summary tables clearly demonstrates that the AER distributions
vary greatly across cities and A/C types and temperatures, so that the selected AER
distributions for the modeled cities should also depend upon the city, A/C type and
temperature. For example, the mean AER for residences with A/C ranges from 0.39 for
Los Angeles between 30 and 40 °C to 1.73 for New York between 20 and 25 °C. The
mean AER for residences without A/C ranges from 0.46 for San Francisco between 10
and 20 °C upwards to 2.54 for Detroit between 25 and 30 °C. The need to account for
the city as well as the A/C type and temperature is illustrated by the result that for
residences with A/C and between 20 and 25 °C, the mean AER ranges from 0.52 for
Research Triangle Park to 1.73 for New York. Statistical comparisons are described
below.
Statistical Comparisons. Various statistical comparisons between the different strata
are shown in the worksheets COMPARISON_STATISTICS_AER and
COMPARISON_STATISTICS_LOG_AER, for the AER and its logarithm, respectively.
The various strata are defined as in the Summary Statistics section, excluding the "All"
cases. For each analysis, we fixed one or two of the variables Study, City, A/C type,
temperature, and tested for statistically significant differences among other variables.
The comparisons are listed in Table 2.
Table 2. Summary of Com
Comparison
Analysis
Number
1
2
3
4
5
6
Comparison
Variable(s)
"Groups
Compared"
City
Temp. Range
Type of A/C
City
City
Type of A/C AND
Temp. Range
parisons of Means.
Stratification
Variable(s)
(not missing in
worksheet)
Type of A/C AND
Temp. Range
Study AND City
Study AND City
Type of A/C
Temp. Range
Study AND City
Total
Comparisons
12
13
16
2
6
18
Cases with significantly
different means (5 %
level)
AER
8
6
6
2
6
7
Log AER
9
6
6
2
6
7
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For example, the first set of comparisons fix the Type of A/C and the temperature range;
there are twelve such combinations. For each of these twelve combinations, we
compare the AER distributions across different cities. This analysis determines whether
the AER distribution is appropriately defined by the A/C type and temperature range,
without specifying the city. Similarly, for the sixth set of comparisons, the study and city
are held fixed (18 combinations) and in each case we compare AER distributions across
groups defined by the combination of the A/C type and the temperature range.
The F-Statistic comparisons compare the mean values between groups using a one
way analysis of variance (ANOVA). This test assumes that the AER or log(AER) values
are normally distributed with a mean that may vary with the comparison variable(s) and
a constant variance. Shown in the worksheets are the F-Statistic and its p-value. P-
values above 0.05 indicate cases where all the group means are not statistically
significantly different at the 5 percent level. Those results are summarized in the last
two columns of the above table "Summary of Comparisons of Means" which gives the
number of cases where the means are significantly different. Comparison analyses 2,
3, and 6 show that for a given study and city, slightly less than half of the comparisons
show significant differences in the means across temperature ranges, A/C types, or
both. Comparison analyses 1, 4, and 5 show that for the majority of cases, means vary
significantly across cities, whether you first stratify by temperature range, A/C type, or
both.
The Kruskal-Wallis Statistic comparisons are non-parametric tests that are extensions
of the more familiar Wilcoxon tests to two or more groups. The analysis is valid if the
AER minus the group median has the same distribution for each group, and tests
whether the group medians are equal. (The test is also consistent under weaker
assumptions against more general alternatives). The P-values show similar patterns to
the parametric F-test comparisons of the means. Since the logarithm is a strictly
increasing function and the test is non-parametric, the Kruskal-Wallis tests give identical
results for AER and Log (AER).
The Mood Statistic comparisons are non-parametric tests that compare the scale
statistics for two or more groups. The scale statistic measures variation about the
central value, which is a non-parametric generalization of the standard deviation.
Specifically, suppose there is a total of N AER or log(AER) values, summing across all
the groups. These N values are ranked from 1 to N, and the j'th highest value is given a
score of (j - (N+1 )/2}2. The Mood statistic uses a one way ANOVA statistic to compare
the total scores for each group. Generally, the Mood statistics show that in most cases
the scale statistics are not statistically significantly different. Since the logarithm is a
strictly increasing function and the test is non-parametric, the Mood tests give identical
results for AER and Log (AER).
Fitting Distributions. Based on the summary statistics and the statistical comparisons,
the need to fit different AER distributions to each combination of A/C type, city, and
temperature is apparent. For each combination with a minimum of 11 AER values, we
fitted and compared exponential, log-normal, normal, and Weibull distributions to the
AER values.
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The first analysis used the same stratifications as in the above "Summary Statistics" and
"Statistical Comparisons" sections. Results are not reported for all strata because of the
minimum data requirement of 11 values. Results for each combination of A/C type, city,
and temperature (i.e., A, C, and T) are given in the worksheet FIT_STATISTICS_ACT.
Each combination has four rows, one for each fitted distribution. For each distribution
we report the fitted parameters (mean, standard deviation, scale, shape) and the p-
value for three standard goodness-of-fit tests: Kolmogorov-Smirnov (K-S), Cramer-Von-
Mises (C-M), Anderson-Darling (A-D). Each goodness-of-fit test compares the empirical
distribution of the AER values to the fitted distribution. The K-S and C-M tests are
different tests examining the overall fit, while the Anderson-Darling test gives more
weight to the fit in the tails of the distribution. For each combination, the best-fitting of
the four distributions has the highest p-value and is marked by an x in the final three
columns. The mean and standard deviation (Std_Dev) are the values for the fitted
distribution. The scale and shape parameters are defined by:
• Exponential: density = a"1 exp(-x/a), where shape = mean = a
• Log-normal: density = {axV(2:r)}~1 exp{ -(log x - Q2 / (2a2)}, where shape = a and
scale = C,. Thus the geometric mean and geometric standard deviation are given
by exp(Q and exp(a), respectively.
• Normal: density = {aV(2:r)}~1 exp{ -(x - jj,)2 / (2a2)}, where mean = jj, and standard
deviation = a
• Weibull: density = (c/a) (x/a)c~1 exp{-(x/a)c}, where shape = c and scale = a
Generally, the log-normal distribution was the best-fitting of the four distributions, and
so, for consistency, we recommend using the fitted log-normal distributions for all the
cases. For the log-normal distributions, the worksheet includes the geometric mean
and geometric standard deviation.
One limitation of the initial analysis was that distributions were available only for
selected cities, and yet the summary statistics and comparisons demonstrate that the
AER distributions depend upon the city as well as the temperature range and A/C type.
As one option to address this issue, we considered modeling cities not listed in the
FIT_STATISTICS_ACT worksheet by using the AER distributions across all cities and
dates for a given temperature range and A/C type. Those fitted distributions are
presented in the FIT_STATISTICS_AT worksheet.
Another important limitation of the initial analysis was that distributions were not fitted to
all of the temperature ranges due to inadequate data. There are missing values
between temperature ranges, and the temperature ranges are all bounded. To address
this issue, the temperature ranges were regrouped to cover the entire range of
temperatures from minus to plus infinity, although obviously the available data to fit
these ranges have finite temperatures. Stratifying by A/C type, city, and the new
temperature ranges produces the results in the worksheet FIT_STATISTICS_ACT2.
Results are reported for five cities: Detroit, AC and NA; Houston, AC and NA; Los
Angeles, AC and NA; New York, AC and NA; Research Triangle Park, AC. As noted
above the DEARS study sample is likely to be representative of an older housing stock
than for the Detroit metropolitan area as a whole. For this reason, the worksheet
FIT_STATISTICS_ACT2 also reports results for the aggregation of the Detroit and New
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York data ("Detroit or NYC," AC and NA); these results can be used for modeling
Chicago, Cleveland and Detroit.
Corresponding to each of the fitted distributions in FIT_STATISTICS _ACT2, we created
histograms to compare each of the fitted distributions with the empirical distributions.
For these graphs, the A/C type, city, and temperature range combinations were
assigned a letter code, as shown in the first column of the worksheet. The first three
digits are a numerical code used for sorting the graphs, and the remaining characters
code the A/C type, temperature range, and city in a fairly obvious manner. These
graphs are in the attached file Graphs_ACT2.Dec 2012.rtf, which can be read directly
by Word.
AER Distributions for Eight Cities. Based upon the FIT_STATISTICS_ACT2 results
for the five cities and the corresponding graphs, we propose using those fitted
distributions for the two cities Houston and New York. For Los Angeles, more data
were available for the type of air conditioning system, and we decided to use
distributions for the more detailed air conditioning type, Type_of_AC3, as described
below.
For Atlanta, Boston, and Philadelphia, we propose using the distribution for one of the
cities thought to have similar characteristics to the city to be modeled with respect to
factors that might influence AERs. These factors include the age composition of
housing stock, construction methods, and other meteorological variables not explicitly
treated in the analysis, such as humidity and wind speed patterns.
As noted above the DEARS study sample is likely to be representative of an older
housing stock than for the Detroit metropolitan area as a whole. Therefore, we
combined the DEARS study data with the data from New York to create an aggregate
distribution for application to Detroit and other cities with similar characteristics, i.e.,
Chicago and Cleveland.
The distributions proposed for the eight cities are as follows:
• Atlanta, GA: Use log-normal distributions for Research Triangle Park.
Residences with A/C only.
• Boston, MA: Use log-normal distributions for New York.
• Chicago, IL: Use log-normal distributions for "Detroit or NYC."
• Cleveland, OH: Use log-normal distributions for "Detroit or NYC."
• Detroit, Ml: Use log-normal distributions for "Detroit or NYC."
• Houston, TX: Use log-normal distributions for Houston.
• New York, NY: Use log-normal distributions for New York.
• Philadelphia, PA: Use log-normal distributions for New York.
All the above distributions are to be found in the FIT_STATISTICS_ACT2 worksheet.
Since the AER data for Research Triangle Park was only available for residences with
air conditioning, AER distributions for Atlanta residences without air conditioning are
discussed below.
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To avoid unusually extreme simulated AER values, we propose to set a minimum AER
value of 0.01 and a maximum AER value of 10.
Obviously, we would be prefer to model each city using data from the same city, but this
approach was chosen as a reasonable alternative, given the available AER data.
AER Distributions for Sacramento and St. Louis. For these two cities, a direct
mapping to one of the five cities Detroit, Houston, Los Angeles, New York, and
Research Triangle Park is not recommended because the cities are likely to be too
dissimilar. Instead, we decided to use the distribution for the inland parts of Los
Angeles to represent Sacramento and to use the aggregate distributions for all cities
outside of California to represent St. Louis. The results are presented in the worksheet
FIT_STATISTICS_ACT3 and corresponding histogram graphs Graphs_ACT3.Dec
2012.rtf. The results for the city denoted by "Sacramento" were obtained by combining
all the available AER data for Sacramento, Riverside, and San Bernardino counties.
The results for the city denoted by "St. Louis" were obtained by combining all non-
California AER data. Thus our proposal is:
• Sacramento, CA: Use log-normal distributions for "Sacramento" in
FIT_STATISTICS_ACT3
• St. Louis, MO-IL: Use log-normal distributions for "St. Louis" in
FIT_STATISTICS_ACT3
To avoid unusually extreme simulated AER values, we propose to set a minimum AER
value of 0.01 and a maximum AER value of 10.
AER Distributions for Baltimore and Washington DC. Baltimore and Washington
DC were judged likely to have similar characteristics to Detroit, Research Triangle Park
and New York City. To choose between these three cities, we compared the Murray
and Burmaster AER data for Maryland with AER data from each of those cities. The
Murray and Burmaster study included AER data for Baltimore and for Gaithersburg and
Rockville, primarily collected in March. April, and May 1987, although there is no
information on daily mean temperatures or A/C type. We collected all the March, April,
and May AER data for Detroit, Research Triangle Park and New York City, and
compared those three distributions with the Murray and Burmaster Maryland data for
the same three months. The summary statistics for AER and Log (AER) are given in
the worksheets SUM_STAT_MURRAY_AER and SUM_STAT_MURRAY_LOG_AER,
using the same formats as the other summary statistics worksheets. The corresponding
statistical comparisons between Detroit and Maryland, Research Triangle Park and
Maryland, and between New York and Maryland, are shown in the worksheets
COMP_STAT_MURRAY_AER and COMP_STAT_MURRAY_LOG_AER.
The results for the means and central values show significant differences at the 5
percent level between the New York and Maryland distributions and between the Detroit
and Maryland distributions. Between Research Triangle Park and Maryland, the central
values and the mean AER values are not statistically significantly different, and the
differences in the mean log (AER) values are much less statistically significant than
between New York and Maryland. The scale statistic comparisons are not statistically
significantly different between New York and Maryland or between Detroit and
5E-13
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Maryland, but were statistically significantly different between Research Triangle Park
and Maryland. Since matching central and mean values is generally more important
than matching the scales, we propose to model both Baltimore and Washington DC
residences with air conditioning using the Research Triangle Park distributions, stratified
by temperature:
• Washington DC: Use log-normal distributions for Research Triangle Park in
FIT_STATISTICS_ACT2. Residences with A/C only.
• Baltimore, MD: Use log-normal distributions for Research Triangle Park in
FIT_STATISTICS_ACT2. Residences with A/C only.
Since the AER data for Research Triangle Park was only available for residences with
air conditioning, the estimated AER distributions for Baltimore and Washington DC
residences without air conditioning are discussed below.
To avoid unusually extreme simulated AER values, we propose to set a minimum AER
value of 0.01 and a maximum AER value of 10.
AER Distributions for Washington DC, Baltimore MD, and Atlanta GA Residences
With No A/C. For Atlanta, Baltimore, and Washington DC we have proposed to use the
AER distributions for Research Triangle Park. However, all the Research Triangle Park
data (from the RTP Panel study) were from houses with air conditioning, so there are no
available distributions for the "No A/C" cases. For these three cities, one option is to
use AER distributions fitted to all the study data for residences without A/C, stratified by
temperature. These fitted distributions are given in the worksheet
FIT_STATISTICS_AT2. The distributions for "No A/C" and "Central or Room A/C" are
both presented for completeness, although we only propose applying the "No A/C"
distributions for modeling these two cities for residences without A/C. However, since
Atlanta, Baltimore, and Washington DC residences are expected to be better
represented by residences outside of California, we instead propose to use the "No A/C"
AER distributions aggregated across cities outside of California, which is the same as
the recommended choice for the St. Louis "No A/C" AER distributions.
• Washington DC, No A/C: Use log-normal distributions for "St. Louis" in
FIT_STATISTICS_ACT3. Residences without A/C only.
• Atlanta, GA, No A/C: Use log-normal distributions for "St. Louis" in
FIT_STATISTICS_ACT3. Residences without A/C only.
• Baltimore, MD. No A/C: Use log-normal distributions for "St. Louis" in
FIT_STATISTICS_ACT3. Residences without A/C only.
To avoid unusually extreme simulated AER values, we propose to set a minimum AER
value of 0.01 and a maximum AER value of 10.
AER Distributions for Los Angeles. Los Angeles data were collected in the Avol,
RIOPA, TEACH, Wilson 1984, and Wilson 1991 studies, and in most cases the data
included whether or not the residence had central A/C, and whether or not the
residence had room A/C. We decided to evaluate whether to stratify the Los Angeles
air exchange rate distribution by this more detailed A/C type, defined by the variable
Type_of_AC3, as well as by the temperature range. The Avol study included
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information on whether or not the residence had an A/C but not on whether or not the
residence has Central A/C, Room A/C, or both. To avoid potential bias due to location
we decided to exclude all the Avol data from these analyses, although we could have
decided to include the 62 Avol study residences without A/C.
Summary statistics of the AER and its logarithm for Los Angeles, stratified by
Type_of_AC3 and temperature range are shown in the worksheets
SUM_STAT_LOS_ANGELES_AER and SUM_STAT_LOS_ANGELES_LOG_AER,
respectively. The corresponding statistical comparisons between residences with
Central A/C only and Room A/C only are shown in the worksheets COMPARE_LA_AER
and COMPARE_LA_LOG_AER. These comparisons did not include the residences
with no A/C, since the goal was to evaluate the need to further stratify the AER
distribution by the specific A/C types, rather than only by the presence or absence of
any A/C. For each of the temperature ranges 10-20 and 20-25, the statistical
comparisons show statistically significant differences between Central A/C and Room
A/C residences in the means and central values, except for the mean comparison of
AER in the range 10-20 (p-value 0.06), and no statistically significant differences in the
scale statistics. For the temperature range 25-30, the statistical comparisons show no
statistically significant differences between Central A/C and Room A/C residences in the
means and central values, and statistically significant differences in the scale statistics.
For the temperature range 0-10, the statistical comparisons show no statistically
significant differences between Central A/C and Room A/C residences. On this basis,
we decided to stratify the Los Angeles AER distributions by the Type_of_AC3 air
conditioner type variable as well as by the temperature range.
The worksheet FIT_STATISTICS_ACT4 compares exponential, log-normal, normal, and
Weibull distributions fitted to each stratum of the Los Angeles data, defined by
Type_of_AC3 and temperature range, using the temperature ranges "<= 20" "20-25"
and ">25". Histograms of the fitted distributions are in the attached Graphs_ACT4.Dec
2012.rtf file, using the letter codes given in the first column of FIT_STATISTICS_ACT4.
Since the tabulated goodness-of-fit statistics and histograms generally support the
lognormal distribution, we propose to model Los Angeles using the Los Angeles data
stratified by the detailed A/C type, Type_of_AC3, and by the temperature range:
• Los Angeles, CA: Use log-normal distributions for Los Angeles in
FIT_STATISTICS_ACT4
A/C Type and Temperature Distributions. Since the proposed AER distribution is
conditional on the A/C type and temperature range, these values also need to be
simulated using APEX in order to select the appropriate AER distribution. Mean daily
temperatures are one of the available APEX inputs for each modeled city, so that the
temperature range can be determined for each modeled day according to the mean
daily temperature. To simulate the A/C type, we obtained estimates of A/C prevalence
from the American Housing Survey. Thus for each city/metropolitan area other than Los
Angeles, we obtained the estimated fraction of residences with Central or Room A/C
(see Table 3), which gives the probability p for selecting the A/C type "Central or Room
A/C". Obviously, 1-p is the probability for "No A/C".
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For comparison with Atlanta, Baltimore, and Washington DC, we have included the A/C
type percentage for Charlotte, NC (representing Research Triangle Park, NC). As
discussed above, we propose modeling the 96-98% of Atlanta, Baltimore and
Washington DC residences with A/C using the Research Triangle Park AER
distributions, and modeling the 2-4 % of Atlanta, Baltimore, and Washington DC
residences without A/C using the combined study No A/C AER distributions (denoted as
"St Louis" in FIT_STATISTICS_ACT3).
Table 3. Fraction of occupied residences with central or room A/C (from American
Housing Survey).
CITY
Atlanta
Baltimore
Boston
Chicago
Cleveland
Denver
Detroit
Houston
New York
Philadelphia
Sacramento
St. Louis
Washington DC
Research Triangle Park
SURVEY AREA & YEAR
Atlanta, 2004
Baltimore, 2007
Boston, 2007
Chicago, 2009
Cleveland, 2004
Denver, 2004
Detroit, 2009
Houston, 2007
New York, 2009
Philadelphia, 2009
Sacramento, 2004
St. Louis, 2004
Washington DC, 2007
Charlotte NC, 2002
PERCENTAGE
98.03, replace by 100*
96.70, replace by 100*
85.67
94.74
79.80
84.56
89.61
98.77
86.93
95.00
92.81
98.73
98.15, replace by 100*
97.89
* See text
For Los Angeles, we have proposed to use different AER distributions depending upon
whether or not the residence had central A/C, and whether or not the residence had
room A/C. To simulate the A/C type, we obtained estimates of A/C prevalence from the
American Housing Survey as shown in Table 4:
Table 4. Fraction of occupied Los Angeles CMSA residences with central or room
A/C1
TYPE OF A/C
No A/C
Room A/C
Central A/C
PERCENTAGE
33.89
16.44
49.68
American Housing Survey, Los Angeles-Long Beach, 2003, Anaheim-Santa Ana 2002, Riverside-San
Bernardino-Ontario 2002
Other AER Studies
We have not used information from some additional residential and non-residential AER
studies that might provide additional information or data. Indoor/ outdoor ozone and
PAN distributions were studied by Jakobi and Fabian (1997). Liu et al (1995) studied
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residential ozone and AER distributions in Toronto, Canada. Weschler and Shields
(2000) describes a modeling study of ventilation and air exchange rates. Weschler
(2000) includes a useful overview of residential and non-residential AER studies.
AER Distributions for Other Indoor Environments
To estimate AER distributions for non-residential, indoor environments (e.g., offices and
schools), we obtained three AER data sets:
The early "Turk" data set (Turk et al, 1989) includes 40 AER measurements from offices
(25 values), schools (7 values), libraries (3 values), and multi-purpose (5 values), each
measured using an SF6 tracer over two- or four-hours in different seasons of the year.
The more recent "Persily" data (Persily and Gorfain 2004; Persily et al. 2005) were
derived from the US EPA Building Assessment Survey and Evaluation (BASE) study,
which was conducted to assess indoor air quality, including ventilation, in a large
number of randomly selected office buildings throughout the U.S. The data base
consists of a total of 390 AER measurements in 96 large, mechanically ventilated
offices; each office was measured up to four times over two days, Wednesday and
Thursday AM and PM. The office spaces were relatively large, with at least 25
occupants, and preferably 50 to 60 occupants. AERs were measured both by a
volumetric method and by a C02 ratio method, and included their uncertainty estimates.
For these analyses, we used the recommended "Best Estimates" defined by the values
with the lower estimated uncertainty; in the vast majority of cases the best estimate was
from the volumetric method.
The most recent "Bennett" data (Bennett et al 2011) was a field study of 37 small and
medium commercial buildings throughout California conducted in 2008 to 2010. The
data base includes information on ventilation rate, temperature, and heating, ventilating,
and air conditioning (HVAC) system characteristics. The study included: seven retail
establishments; five restaurants; eight offices; two each of gas stations, hair salons,
healthcare facilities, grocery stores, dental offices, and fitness gyms; and five other
buildings. The selection of buildings was semi-randomized, providing a minimum
coverage of the vast variety of SMCBs across space, age, size, and building use
category. Buildings were almost evenly distributed across each of five regions of the
state; north-coastal, north-inland, south-coastal, south-inland, and central-inland.
Measurements were made in summer and winter, and three of the buildings were
sampled twice, once in each season. Thus there were a total of 40 measured AER
values. Whole building ventilation rates were determined with a tracer decay method
using SF6, a well established method for commercial buildings.
The office AER SAS databases used for these analyses are attached:
turkdata.sas7bdat for the Turk study, basedata.sasfbdat for the Persily study, and
bennettdata.sas7bdat for the Bennett study.
Due to the small sample size of the Turk data, the data were analyzed without
stratification by building type and/or season. For the Persily data, the AER values for
each office space were averaged, rather using the individual measurements, to account
for the strong dependence of the AER measurements for the same office space over a
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relatively short period. For the Bennett data, analyses of variance of the AER values
and of their logarithms confirmed the finding of the study authors (Bennett et al 2011)
that the AER varied statistically significantly between restaurants and other buildings.
Analyses of variance using a categorized outdoor temperature variable (15-25, 25-35,
and 35 or more) also showed that the AER did not vary statistically significantly with the
outdoor temperature. Although the study authors also reported significant differences
depending upon whether or not doors were open, the stratification by whether or not
doors are open would not be feasible for APEX modeling since that would require a
model for whether or not doors are kept open (perhaps based on the outdoor
temperature and some building characteristics). The study authors did not find a
significant effect due to the presence or absence of HVAC systems. Therefore the
Bennett data were stratified by whether or not the building is a restaurant.
Summary statistics of AER and log (AER) for the three studies are presented in Table 5.
The overall mean values are similar for the three studies, but the mean value for the
restaurants in the Bennett study is almost four times the mean value for the non-
restaurants. Compared to the Turk study, the standard deviations are about twice as
high for the Persily data and for the Bennett study restaurants, but are much lower for
the Bennett study non-restaurants. The proposed AER distributions were derived only
from the most recent Bennett data study stratified into restaurants and non-restaurants.
Similarly to the analyses of the residential AER distributions, we fitted exponential, log-
normal, normal, and Weibull distributions to the AER values. The results are shown in
Tables 6 and 7.
Table 5. AER summary statistics for offices and other non-residential buildings.
Study
Bennett
Bennett
Bennett
Persily
Turk
Bennett
Bennett
Bennett
Persily
Turk
Variable
AER
AER
AER
AER
AER
Log AER
Log AER
Log AER
Log AER
Log AER
Subgroup
All
Not
restaurant
Restaurant
All
All
All
Not
restaurant
Restaurant
All
All
N
40
34
6
96
40
40
34
6
96
40
Mean
1.622
1.144
4.328
1.962
1.540
0.152
-0.052
1.312
0.104
0.254
Std Dev
1.649
0.768
2.642
2.325
0.881
0.785
0.619
0.618
1.104
0.639
Min
0.300
0.300
1.460
0.071
0.300
-1.204
-1.204
0.378
-2.642
-1.204
25tn
%ile
0.705
0.620
2.640
0.501
0.850
-0.350
-0.478
0.971
-0.694
-0.164
Median
1.035
0.995
3.855
1.080
1.500
0.034
-0.005
1.344
0.077
0.405
75tn
%ile
1.890
1.460
5.090
2.756
2.050
0.637
0.378
1.627
1.012
0.715
Max
9.070
4.020
9.070
13.824
4.100
2.205
1.391
2.205
2.626
1.411
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Table 6. Best fitting restaurant AER distributions from the Bennett et al. (2011).
Scale
4.328
1.312
4.907
Shape
0.618
1.922
Mean
4.328
4.492
4.328
4.353
Std Dev
4.328
3.062
2.642
2.358
Distribution
Exponential
Lognormal
Normal
Weibull
P-Value
Kolmogorov-
Smirnov
0.25
0.15
0.15
P-Value
Cramer-
von
Mises
0.23
0.50
0.25
0.25
P-Value
Anderson-
Darling
0.25
0.50
0.25
0.25
Table 7. Best fitting non-restaurant AER distributions from the Bennett et al.
(2011).
Scale
1.144
-0.052
1.290
Shape
0.619
1.654
Mean
1.144
1.149
1.144
1.153
Std Dev
1.144
0.785
0.768
0.716
Distribution
Exponential
Lognormal
Normal
Weibull
P-Value
Kolmogorov-
Smirnov
0.00
0.15
0.01
P-Value
Cramer-
von
Mises
0.00
0.50
0.01
0.21
P-Value
Anderson-
Darling
0.00
0.50
0.01
0.18
(For an explanation of the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-
Darling P-values see the discussion of residential AER distributions above.) For
restaurants, according to two of the three goodness-of-fit measures the best-fitting
distribution is the log-normal; the best-fitting distribution is exponential for the
Kolmogorov-Smirnov test. For non-restaurants, according to all three goodness-of-fit
measures the best-fitting distribution is the log-normal. Reasonable choices for the
lower and upper bounds are the observed minimum and maximum AER values.
We therefore propose the following indoor, non-residential AER distributions.
• AER distribution for indoor, non-residential, restaurant microenvironments:
Lognormal, with scale and shape parameters 1.312 and 0.618, i.e., geometric
mean = 3.712, geometric standard deviation = 1.855. Lower Bound = 1.46. Upper
bound = 9.07.
• AER distribution for indoor, non-residential, non-restaurant microenvironments:
Lognormal, with scale and shape parameters -0.052 and 0.618, i.e., geometric
mean = 0.949, geometric standard deviation = 1.857. Lower Bound = 0.30. Upper
bound = 4.02.
Application of the proposed distributions in APEX would require estimates of the
proportions of restaurants or similar facilities among the non-residential buildings in
each city.
Proximity and Penetration Factors For Outdoors, In-vehicle, and Mass Transit
For the APEX modeling of the outdoor, in-vehicle, and mass transit micro-environments,
an approach using proximity and penetration factors is proposed, as follows.
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Outdoors Near Road
Penetration factor = 1.
For the Proximity factor, we propose using ratio distributions developed from the
Cincinnati Ozone Study (American Petroleum Institute, 1997, Appendix B; Johnson et
al. 1995). The field study was conducted in the greater Cincinnati metropolitan area in
August and September, 1994. Vehicle tests were conducted according to an
experimental design specifying the vehicle type, road type, vehicle speed, and
ventilation mode. Vehicle types were defined by the three study vehicles: a minivan, a
full-size car, and a compact car. Road types were interstate highways (interstate),
principal urban arterial roads (urban), and local roads (local). Nominal vehicle speeds
(typically met over one minute intervals within 5 mph) were at 35 mph, 45 mph, or 55
mph. Ventilation modes were as follows:
• Vent Open: Air conditioner off. Ventilation fan at medium. Driver's window half
open. Other windows closed.
• Normal A/C. Air conditioner at normal. All windows closed.
• Max A/C: Air conditioner at maximum. All windows closed.
Ozone concentrations were measured inside the vehicle, outside the vehicle, and at six
fixed site monitors in the Cincinnati area.
The proximity factor can be estimated from the distributions of the ratios of the outside-
vehicle ozone concentrations to the fixed-site ozone concentrations, reported in Table 8
of Johnson et al. (1995). Ratio distributions were computed by road type (local, urban,
interstate, all) and by the fixed-site monitor (each of the six sites, as well as the nearest
monitor to the test location). For this analysis we propose to use the ratios of outside-
vehicle concentrations to the concentrations at the nearest fixed site monitor, as shown
in Table 8.
Table 8. Ratio of outside-vehicle ozone to ozone at nearest fixed site
1
Road
Type1
Local
Urban
Interstate
All
Number
of
cases1
191
299
241
731
Mean1
0.755
0.754
0.364
0.626
Standard
Deviation1
0.203
0.243
0.165
0.278
25th
Percentile1
0.645
0.585
0.232
0.417
50th
Percentile1
0.742
0.722
0.369
0.623
75th
Percentile1
0.911
0.896
0.484
0.808
Estimated
5th
Percentile2
0.422
0.355
0.093
0.170
From Table 8 of Johnson et al. (1995). Data excluded if fixed-site concentration < 40 ppb.
2 Estimated using a normal approximation as Mean - 1.64 x Standard Deviation.
For the outdoors-near- road microenvironment, we recommend using the distribution for
local roads, since most of the outdoors-near-road ozone exposure will occur on local
roads. The summary data from the Cincinnati Ozone Study are too limited to allow fitting
of distributions, but the 25th and 75th percentiles appear to be approximately equidistant
from the median (50th percentile). Therefore we propose using a normal distribution with
the observed mean and standard deviation. A plausible upper bound for the proximity
factor equals 1. Although the normal distribution allows small positive values and can
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even produce impossible, negative values (with a very low probability), the titration of
ozone concentrations near a road is limited. Therefore, as an empirical approach, we
recommend a lower bound of the estimated 5th percentile, as shown in the final column
of the above table. Therefore in summary we propose:
• Penetration factor for outdoors, near road: 1.
• Proximity factor for outdoors, near road: Normal distribution. Mean = 0.755.
Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
Outdoors, Public Garage / Parking Lot
This micro-environment is similar to the outdoors-near-road microenvironment. We
therefore recommend the same distributions as for outdoors-near-road:
• Penetration factor for outdoors, public garage / parking lot: 1.
• Proximity factor for outdoors, public garage / parking lot: Normal distribution.
Mean = 0.755. Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound
= 1.
Outdoors, Other
The outdoors, other ozone concentrations should be well represented by the ambient
monitors. Therefore we propose:
• Penetration factor for outdoors, other: 1.
• Proximity factor for outdoors, other: 1.
In-Vehicle
For the proximity factor for in-vehicle, we also recommend using the results of the
Cincinnati Ozone Study presented in Table 5. For this microenvironment, the ratios
depend upon the road type, and the relative prevalences of the road types can be
estimated by the proportions of vehicle miles traveled in each modeled city. The
proximity factors are assumed, as before, to be normally distributed, the upper bound to
be1, and the lower bound to be the estimated 5th percentile.
• Proximity factor for in-vehicle, local roads: Normal distribution. Mean = 0.755.
Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
• Proximity factor for in-vehicle, urban roads: Normal distribution. Mean = 0.754.
Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.
• Proximity factor for in-vehicle, interstates: Normal distribution. Mean = 0.364.
Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.
To complete the specification, the distribution of road type needs to be estimated for
each city to be modeled. Vehicle miles traveled (VMT) by city (defined by the Federal-
AID urbanized area) and road type were obtained from the Federal Highway
Administration. For local and interstate road types, the VMT for the same DOT
categories were used. For urban roads, the VMT for all other road types was summed
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(Other freeways/expressways, Other principal arterial, Minor arterial, Collector). The
computed VMT ratios for each city are shown in Table 9
Table 9. Vehicle Miles Traveled by City and Road Type (2008)1.
FEDERAL-AID URBANIZED
AREA
Atlanta
Baltimore
Boston
Chicago
Cleveland
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
FRACTION VMT BY ROAD
TYPE
INTERSTATE
0.32
0.34
0.32
0.31
0.38
0.25
0.24
0.24
0.29
0.18
0.23
0.24
0.37
0.30
URBAN
0.45
0.59
0.54
0.57
0.45
0.65
0.66
0.73
0.66
0.67
0.65
0.69
0.45
0.62
LOCAL
0.23
0.07
0.14
0.12
0.17
0.10
0.10
0.03
0.05
0.15
0.12
0.07
0.18
0.08
1 http://www.fhwa.dot.gov/policvinformation/statistics/2008/pdf/hm71.pdf
Thus to simulate the proximity factor in APEX, we propose to first select the road type
according to the above probability table of road types, then select the AER distribution
(normal) for that road type as defined in the last set of bullets.
For the penetration factor for in-vehicle, we recommend using the inside-vehicle to
outside-vehicle ratios from the Cincinnati Ozone Study. The ratio distributions were
summarized for all the data and for stratifications by vehicle type, vehicle speed, road
type, traffic (light, moderate, or heavy), and ventilation. The overall results and results
by ventilation type are shown in Table 10.
Table 10. Ratio of inside-vehicle ozone to outside-vehicle ozone
1
Ventilation1
Vent Open
Normal A/C
Maximum
A/C
All
Number
of
cases1
226
332
254
812
Mean1
0.361
0.417
0.093
0.300
Standard
Deviation1
0.217
0.211
0.088
0.232
25th
Percent! le1
0.199
0.236
0.016
0.117
50th
Percentile1
0.307
0.408
0.071
0.251
75th
Percentile1
0.519
0.585
0.149
0.463
Estimated
5th
Percentile2
0.005
0.071
O.OOO3
O.OOO3
1. From Table 7 of Johnson et al.(1995). Data excluded if outside-vehicle concentration <
20 ppb.
2. Estimated using a normal approximation as Mean - 1.64 x Standard Deviation
3. Negative estimate (impossible value) replaced by zero.
Although the data in Table 7 indicate that the inside-to-outside ozone ratios strongly
depend upon the ventilation type, it would be very difficult to find suitable data to
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estimate the ventilation type distributions for each modeled city. Furthermore, since the
Cincinnati Ozone Study was scripted, the ventilation conditions may not represent real-
world vehicle ventilation scenarios. Therefore, we propose to use the overall average
distributions.
• Penetration factor for in-vehicle: Normal distribution. Mean = 0.300. Standard
Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1.
Mass Transit
The mass transit microenvironment is expected to be similar to the in-vehicle
microenvironment. Therefore we recommend using the same APEX modeling
approach:
• Proximity factor for mass transit, local roads: Normal distribution. Mean = 0.755.
Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
• Proximity factor for mass transit, urban roads: Normal distribution. Mean = 0.754.
Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.
• Proximity factor for mass transit, interstates: Normal distribution. Mean = 0.364.
Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.
• Road type distributions for mass transit: See Table 8.
• Penetration factor for mass transit: Normal distribution. Mean = 0.300. Standard
Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1.
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References
American Petroleum Institute (1997). Sensitivity testing ofpNEM/03 exposure to
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5E-25
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1
2
3 Appendix 5-F
4
5 Detailed Exposure Results
6
7 Table of Contents
8 5F-1 Exposure Modeling Results for Base Air Quality 3
9 5F-2 Exposure Modeling Results for Adjusted Air Quality 16
10
11
12 List of Tables
13 Table 5F-1. Percent of all school-age children with Os exposures at or above 60, 70, and 80
14 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality.... 12
15 Table 5F-2. Percent of all school-age children with Os exposures at or above 60, 70, and 80
16 ppb-8hr while at moderate or greater exertion, years 2006-2010, adjusted air quality.
17 24
18 Table 5F-3. Mean and maximum number of all school-age children (and associated days per
19 Os season) with at least one Os exposure at or above 60 ppb-8hr while at moderate or
20 greater exertion 53
21 Table 5F-4. Total number of persons experiencing at least one or two 8-hour exposures in all
22 study areas by year, base air quality and air quality adjusted to just meeting the
23 existing 75 ppb standard 57
24
25 List of Figures
26 Figure 5F-1. Percent of all school-age children with at least one Os exposure at or above 60,
27 70, and 80 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air
28 quality 6
29 Figure 5F-2. Percent of asthmatic school-age children with at least one Os exposure at or
30 above 60, 70, and 80 ppb-8hr while at moderate or greater exertion, years 2006-2010,
31 base air quality 7
32 Figure 5F-3. Percent of asthmatic adults with at least one Os exposure at or above 60, 70, and
33 s 80 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality.
34 8
35 Figure 5F-4. Percent of older adults with at least one Os exposure at or above 60, 70, and 80
36 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality 9
37 Figure 5F-5. Percent of school-age children with multiple Os exposures at or above 60 ppb-
38 8hr per study area 63 season, while at moderate or greater exertion, years 2006-2010,
39 base air quality 10
40 Figure 5F-6. Percent of school-age children with multiple Os exposures at or above 70 ppb-
41 8hr per study area Os season, while at moderate or greater exertion, years 2006-2010,
42 base air quality 11
-------
1 Figure 5F-7. Incremental increases in percent of all school-age children with at least one
2 exposure at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-
3 8hr (bottom panel) using the maximum percent exposed for each study area, year
4 2006-2010 adjusted air quality 17
5 Figure 5F-8. Incremental increases in percent of asthmatic school-age children with at least
6 one exposure at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80
7 ppb-8hr (bottom panel) using the maximum percent exposed for each study area,
8 year 2006-2010 adjusted air quality 18
9 Figure 5F-9. Incremental increases in percent of asthmatic adults with at least one exposure at
10 or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom
11 panel) using the maximum percent exposed for each study area, year 2006-2010
12 adjusted air quality 19
13 Figure 5F-10. Incremental increases in percent of all older adults with at least one exposure at
14 or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom
15 panel) using the maximum percent exposed for each study area, year 2006-2010
16 adjusted air quality 20
17 Figure 5F-11. Incremental increases in percent of all school-age children with at least one
18 exposure at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-
19 8hr (bottom panel) using the mean percent exposed for each study area, year 2006-
20 2010 adjusted air quality 21
21 Figure 5F-12. Incremental increases in percent of all school-age children with at least two
22 exposures at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-
23 8hr (bottom panel) using the maximum percent exposed for each study area, year
24 2006-2010 adjusted air quality 22
25 Figure 5F-13. Incremental increases in percent of all school-age children with at least two
26 exposures at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-
27 8hr (bottom panel) using the mean percent exposed for each study area, year 2006-
28 2010 adjusted air quality 23
29 Figure 5F-14. Average number of persons with at least one 8-hour exposure at or above 60 ppb
30 considering the existing and alternative standards, year 2006-2010 adjusted air
31 quality. All school-age children (top left), asthmatic school-age children (top right),
32 asthmatic adults (bottom left), older adults (bottom right) 55
33 Figure 5F-15. Average total number of days in an Os season where simulated persons
34 experienced 8-hour exposures at or above 60 ppb considering the existing and
35 alternative standards, year 2006-2010 adjusted air quality. All school-age children
36 (top left), asthmatic school-age children (top right), asthmatic adults (bottom left),
37 older adults (bottom right) 56
38
39
40
41
42
-------
1 This appendix contains the detailed results for the primary APEX simulations performed
2 to estimate exposures associated with base air quality (section 5F-1) and for air quality just
3 meeting the existing and alternative standard levels (section 5F-2).
4 5F-1 EXPOSURE MODELING RESULTS FOR BASE AIR QUALITY
5 As described in the main body of the REA, comprehensive multi-panel displays of
6 exposure results are presented for each of the study groups of interest, i.e., all school-age
7 children (ages 5 to 18), asthmatic school-age children, asthmatic adults (ages 19 to 95), and older
8 adults (ages 65 to 95) (Figure 5F-1 to Figure 5F-4, respectively). Included in each display are
9 the three benchmark levels (60, 70, and 80 ppb-8hr), the five years of air quality (2006-2010), for
10 the 15 study areas. Modeled exposures in the 15 study areas and considering each benchmark
11 level are presented on the same scale to allow for direct comparisons across the multi-panel
12 display. The most notable patterns in the exposure results are described here using one study
13 group (i.e., school-age children), as there is a general consistency in the year-to-year variability
14 within each study area across all four study groups. Any deviation from the observed pattern
15 will be discussed for the subsequent study group. Table 5F-1 is also provided and contains the
16 complete exposure output for all study areas and years for school-age children.
17 Figure 5F-1 presents the percent of school-age children experiencing at least one Os
18 exposure at or above the selected benchmark levels while at moderate or greater exertion.
19 Consistent with the previously discussed observations regarding year-to-year variability in
20 ambient concentrations (Chapter 4), most study areas have the greatest percent of school-age
21 children experiencing concentrations at or above the three benchmark levels during 2006 or 2007
22 along with having the lowest percent of school-age children exposed during 2009. Three
23 Western U.S. study areas, Dallas, Los Angeles, and Sacramento, differ slightly from this pattern
24 in that they exhibit a minimum percent of school-age children exposed during 2010, while in
25 Houston and Chicago the minimum exposures occur during year 2008. In general, between 20 to
26 40% of school-age children experience at least one 63 exposure at or above 60 ppb-8hr, 10 to
27 20% experience at least one 63 exposure at or above 70 ppb-8hr, and 0 to 10% experience at
28 least one 03 exposure at or above 80 ppb-8hr, all while at moderate or greater exertion and
29 considering the base air quality (2006-2010).
30 The percent of asthmatic school-age children experiencing at least one Os exposure at or
31 above the selected benchmark levels while at moderate or greater exertion (Figure 5F-2) is
32 virtually indistinguishable from that of all school-age children (Figure 5F-1) regarding both the
33 year-to-year pattern and percent of persons exposed. This is the result of having both simulated
34 study groups use an identical time-location-activity diary pool to construct each simulated
35 individual's time series of activities performed and locations visited. Different however would
36 be the relative number of asthmatic school-age children exposed in each study area if compared
-------
1 with non-asthmatic school-age children, as the asthma prevalence rates vary by U.S. location
2 (REA, Table 5-2) though on average are about 10% of all school-age children.
3 As mentioned above, the overall year-to-year pattern of exposure for asthmatic adults is
4 similar to that observed for school-age children, though the percent of asthmatic adults
5 experiencing exposures at or above the health effect benchmark levels is lower by a factor of
6 about three or more (Figure 5F-3). Having a lower percent of asthmatic adults exposed is
7 expected given that outdoor time expenditure is an important determinant of 63 exposure (REA
8 section 5.3.2) and that adults spend less time outdoors than children (REA section 5.3.1). In
9 general, between 5 to 10% of asthmatic adults experience at least one Os exposure at or above 60
10 ppb-8hr, 0 to 5% experience at least one 63 exposure at or above 70 ppb-8hr, and 0 to 2%
11 experience at least one Os exposure at or above 80 ppb-8hr, all while at moderate or greater.
12 While the percent of asthmatic adults exposed is much lower, the number of asthmatic
13 adults at or above the exposure benchmarks is generally just below that estimated number of
14 asthmatic school-age children. As an example, for year 2006 in Atlanta, approximately 44% of
15 asthmatic school-age children (or about 37,000) were estimated to experience at least one
16 exposure at or above 60 ppb-8hr. Though a much smaller percent of asthmatic adults were
17 estimated to experience a similar exposure for the same year (i.e., about 16%), this is equivalent
18 to nearly 31,000 asthmatic adults exposed, at least one time, to an 8-hr average 63 concentration
19 at or above 60 ppb.
20 The percent of older adults (ages 65 to 95) experiencing exposures at or above the
21 selected benchmark levels (Figure 5F-4) is lower by a fewer percentage points when compared
22 with the results for asthmatic adults. Again, older adults, on average, would tend to spend less
23 time outdoors when compared with both adults and children (REA section 5.3.1), in addition to
24 fewer older adults performing activities at moderate or greater exertion for extended periods of
25 time, thus leading to fewer older adults exposed to concentrations of concern. In general, less
26 than 10% of older adults experience at least one 63 exposure at or above 60 ppb-8hr, less than
27 5% experience at least one Os exposure at or above 70 ppb-8hr, and about 2% or less experience
28 at least one 03 exposure at or above 80 ppb-8hr, all while at moderate or greater exertion
29 considering base air quality.
30 Given the similar year-to-year patterns of the single and multiple exposure occurrences
31 and when considering any of the four study groups, we present the graphic multi-day exposure
32 results here considering school-age children only. All multi-day exposure results are provided in
33 Table 5F-1. Figure 5F-5 illustrates the percent of school-age children having multiple
34 exposures at or above 60 ppb-8hr for each of the 15 study areas, considering base air quality
35 (2006-2010). Depending on the year and study area, about 10 to 25% of school-age children
36 could experience at least two exposures above the 60 ppb benchmark during the ozone season,
-------
1 while about 5 to 10% could experience at least four. Most study areas and years are estimated to
2 have fewer than 5% of school-age children experience six or more exposures above 60 ppb-8hr
3 considering the base air quality. When considering the multi-day exposures for school-age
4 children at or above the 70 ppb benchmark (Figure 5F-6), about 2 to 10% of school-age children
5 could experience at least two exposures during the ozone season, while four or more exposures
6 were generally limited to fewer than 4% of school-age children.
-------
l
2
3
0 &
o <
(/)
re
Chicago
• o
CO ^
0) 0)
c x
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Us Angeles
Philadelphia
Figure 5F-1. Percent of all school-age children with at least one Os exposure at or above 60,
70, and 80 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality.
-------
1
2
3
4
Ctitc«go
Figure 5F-2. Percent of asthmatic school-age children with at least one Os exposure at or
above 60, 70, and 80 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air
quality.
-------
Chicago
2
3
Figure 5F-3. Percent of asthmatic adults with at least one Os exposure at or above 60, 70, and
s 80 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality.
-------
Chicago
0)
< 75
S
X <-"
m o
0) 0)
c -»-
Ore
0)
^-* ^h:
W O
^ 0)
'> "°
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1
2
3
4
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0) o
em
0)
Q.
Figure 5F-4. Percent of older adults with at least one Os exposure at or above 60, 70, and 80
ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality.
-------
1
2
3
4
Figure 5V-5. Percent of school-age children with multiple Os exposures at or above 60 ppb-
8hr per study area Os season, while at moderate or greater exertion, years 2006-2010, base air
quality.
-------
1
2
3
4
5
6
7
=
i
sl
2o
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-------
1 Table 5F-1. Percent of all
2 ppb-8hr while at moderate
school-age children with Os exposures at or above 60, 70, and
or greater exertion, years 2006-2010, base air quality.
80
Study Area
Atlanta
Baltimore
Boston
Chicago
Exposure
Benchmark
(ppb-8hr)
60
70
80
60
70
80
60
70
80
60
70
80
Year
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
% of school-age children experiencing multiple exposures per O3 season
at or above benchmarks, base air quality
>1
42.4
41.8
27.3
16.7
20.3
25.2
23.8
9.4
3.8
4.4
10.6
9.3
1.8
0.5
0.4
36.8
29
22.6
10.1
31
19
11.4
8.4
1.1
11.8
5.8
2.7
2
0.1
2.5
22.7
31.8
18.3
12.3
12.9
7.1
14.9
4.6
2.6
2.3
1
4.8
0.6
0.1
0.1
14.7
26.5
3.6
7.8
15.6
2.7
9.2
0
0.8
1.5
0.1
1.2
0
0.1
0
>2
28.9
28.5
15
6.7
10.1
13
11.9
2.5
0.5
0.8
2.7
2.4
0.1
0
0
23.5
16.5
11.1
2.7
18.6
8.1
3.9
2.1
0.1
3.6
0.9
0.4
0.1
0
0.2
9.6
17.5
7.1
3.2
4
1.2
4.8
0.6
0.2
0.1
0
0.6
0
0
0
4.5
13.1
0.4
1.4
5
0.2
2
0
0
0.1
0
0
0
0
0
>3
21.9
21.5
9.2
2.9
5.6
7.3
6.4
0.7
0.1
0.2
0.8
0.7
0
0
0
16.6
10.9
6
0.8
12.5
3.8
1.7
0.6
0
1.3
0.1
0
0
0
0
4.5
10.8
2.8
0.8
1.3
0.2
1.8
0.1
0
0
0
0.1
0
0
0
1.4
7.1
0.1
0.3
1.7
0
0.4
0
0
0
0
0
0
0
0
>4
17.2
16.8
5.7
1.3
3.2
4.3
3.6
0.2
0
0
0.2
0.2
0
0
0
12.2
7.2
3.6
0.3
8.6
1.8
0.7
0.2
0
0.5
0
0
0
0
0
2.1
6.8
1.2
0.1
0.4
0
0.7
0
0
0
0
0
0
0
0
0.5
3.9
0
0
0.5
0
0.1
0
0
0
0
0
0
0
0
>5
13.8
13.3
3.6
0.5
1.8
2.4
2
0.1
0
0
0
0.1
0
0
0
9
5.1
2.2
0.1
5.8
0.8
0.3
0.1
0
0.2
0
0
0
0
0
0.9
4.2
0.5
0
0.1
0
0.2
0
0
0
0
0
0
0
0
0.1
2.1
0
0
0.1
0
0
0
0
0
0
0
0
0
0
>6
11
10.5
2.3
0.2
1.1
1.3
1.1
0
0
0
0
0
0
0
0
6.7
3.7
1.3
0
4
0.4
0.2
0
0
0.1
0
0
0
0
0
0.4
2.6
0.2
0
0
0
0.1
0
0
0
0
0
0
0
0
0
1.2
0
0
0
0
0
0
0
0
0
0
0
0
0
-------
Study Area
Cleveland
Dallas
Denver
Detroit
Exposure
Benchmark
(ppb-8hr)
60
70
80
60
70
80
60
70
80
60
70
80
Year
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
% of school-age children experiencing multiple exposures per O3 season
at or above benchmarks, base air quality
>1
21.4
29.7
21.5
10.2
20.6
4.2
12
6
0.8
4.1
0.2
2.5
0.6
0
0.3
41.3
27.1
21.9
30
21.1
22.1
10.4
5.6
12.2
5.3
6.3
2.9
0.5
2.5
0.6
31.9
24.1
27.3
14
18.4
11.7
5.2
5.3
1.6
1.6
1.6
0.3
0.6
0
0
22.3
34.4
15.6
12.8
16.8
6.1
16.3
3
2.2
2.6
0.8
4
0
0.1
0.1
>2
9.1
16.4
9.7
2.6
8.9
0.4
3.6
0.9
0
0.6
0
0.2
0
0
0
28.4
14.1
10.3
17
9.6
11.1
2.3
0.9
3.6
0.9
1.3
0.1
0
0.2
0
19.6
13
15.7
5.1
8.6
4
1
0.7
0.1
0.1
0.1
0
0
0
0
9.1
20.1
5.3
3.6
6.3
0.8
6.1
0.2
0.2
0.3
0
0.6
0
0
0
>3
4.2
10.2
4.7
0.8
4.3
0.1
1.2
0.1
0
0.1
0
0
0
0
0
21.7
8
5.4
10.6
4.6
6.1
0.5
0.2
1.1
0.2
0.4
0
0
0
0
13.3
8.1
10.2
2
4.4
1.5
0.3
0.1
0
0
0
0
0
0
0
4.1
13.1
1.8
1
2.6
0.1
2.5
0
0
0
0
0.1
0
0
0
>4
1.8
6.4
2.2
0.2
2.1
0
0.4
0
0
0
0
0
0
0
0
17.2
4.5
2.8
6.7
2.2
3.4
0.1
0
0.3
0
0.1
0
0
0
0
9.5
5.3
6.8
0.8
2.4
0.6
0.1
0
0
0
0
0
0
0
0
1.7
8.5
0.6
0.3
1
0
1
0
0
0
0
0
0
0
0
>5
0.8
3.9
1.1
0.1
1
0
0.2
0
0
0
0
0
0
0
0
13.8
2.5
1.5
4.2
1
1.9
0
0
0.1
0
0
0
0
0
0
7
3.4
4.6
0.3
1.2
0.2
0
0
0
0
0
0
0
0
0
0.7
5.6
0.2
0.1
0.4
0
0.4
0
0
0
0
0
0
0
0
>6
0.3
2.5
0.5
0
0.4
0
0.1
0
0
0
0
0
0
0
0
11
1.3
0.7
2.6
0.4
1
0
0
0
0
0
0
0
0
0
5.2
2.2
3.1
0.1
0.6
0.1
0
0
0
0
0
0
0
0
0
0.3
3.6
0
0
0.2
0
0.1
0
0
0
0
0
0
0
0
-------
Study Area
Houston
Los Angeles
New York
Philadelphia
Exposure
Benchmark
(ppb-8hr)
60
70
80
60
70
80
60
70
80
60
70
80
Year
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
% of school-age children experiencing multiple exposures per O3 season
at or above benchmarks, base air quality
>1
37.2
25.9
20.6
24.7
23.1
20.5
9.6
5.5
11.4
7.8
9.3
1.5
0.7
3.8
1.1
39.3
35
38.8
35.4
23.4
23.2
19.1
21.3
18.2
9.6
11.8
8.2
9.8
7.4
2.2
34.8
35.6
31.9
13.5
33.6
18
16.1
13
1.8
12.8
6.6
4.2
2.5
0
2.4
36.6
40.9
34.1
12.7
33.5
16.1
20.9
14.5
1
13.5
3.4
7
3
0
2.7
>2
23
13.4
8.9
11
11.1
8.6
2.2
0.8
2.2
1.5
2.3
0.1
0
0.2
0
24.1
20.1
23.6
20.4
12.2
11.3
7.8
10
7.4
3.1
3.9
1.8
3
1.5
0.3
19.1
21.1
17.2
4
19.4
5.9
5.6
3.4
0.1
3.8
0.8
0.5
0.2
0
0.2
22.4
26
19.8
3.7
19.8
5.9
8.6
4.6
0
4.3
0.4
1.1
0.3
0
0.3
>3
15.7
7.5
4.3
5.1
6
4
0.6
0.1
0.4
0.3
0.6
0
0
0
0
16.9
13.1
16.5
13.8
7.6
6.4
3.8
5.6
3.8
1.3
1.5
0.4
1.1
0.4
0.1
11.4
13.6
9.8
1.2
12.5
2
2
0.9
0
1.2
0.1
0.1
0
0
0
15.4
18.1
12.7
1.1
13.1
2.3
3.9
1.6
0
1.5
0
0.2
0
0
0
>4
11
4.2
2
2.3
3.2
1.9
0.1
0
0.1
0
0.1
0
0
0
0
12.6
9.4
12.2
10
5.1
3.9
1.9
3.3
2
0.6
0.5
0.1
0.5
0.1
0
7
9
5.7
0.3
8.3
0.6
0.7
0.2
0
0.3
0
0
0
0
0
10.8
13.2
8.4
0.3
8.9
0.9
1.8
0.6
0
0.5
0
0
0
0
0
>5
7.8
2.4
0.9
1
1.6
0.9
0
0
0
0
0
0
0
0
0
9.6
6.9
9.2
7.5
3.7
2.4
1
2.1
1.1
0.3
0.2
0
0.2
0
0
4.1
6.1
3.3
0.1
5.5
0.2
0.2
0
0
0.1
0
0
0
0
0
7.6
9.8
5.5
0.1
6.2
0.4
0.7
0.2
0
0.2
0
0
0
0
0
>6
5.5
1.2
0.4
0.4
0.8
0.4
0
0
0
0
0
0
0
0
0
7.4
5.2
7.2
5.7
2.7
1.5
0.5
1.3
0.6
0.1
0.1
0
0.1
0
0
2.5
4
1.8
0
3.6
0.1
0.1
0
0
0
0
0
0
0
0
5.4
7.1
3.7
0
4.3
0.1
0.3
0.1
0
0.1
0
0
0
0
0
-------
Study Area
Sacramento
St. Louis
Washington DC
Exposure
Benchmark
(ppb-8hr)
60
70
80
60
70
80
60
70
80
Year
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
% of school-age children experiencing multiple exposures per O3 season
at or above benchmarks, base air quality
>1
40.9
24.7
36.3
35
20.8
22.8
9.2
19.1
16.7
7.4
8.8
2.7
7.6
5.2
2
32.4
38.4
11
11.3
22.9
12.7
20.9
1.4
1.9
4.9
2.2
6.8
0
0.2
0.1
35.4
35.7
26.2
8.9
32.1
18.4
16.6
10.3
0.7
12.5
5.7
3.9
2.5
0.1
2.4
>2
26.9
12.4
22.8
21.4
9.9
11.7
2.3
8.8
6.8
2
2.7
0.2
2.3
1
0.3
19.7
24.8
3
2.8
11
3.9
9.9
0.1
0
0.7
0.1
1.4
0
0
0
22.1
22.8
14.1
2
19.8
7.7
6.4
2.7
0
4.2
1
0.5
0.2
0
0.3
>3
20
7.2
16.1
14.5
5.6
6.8
0.7
4.7
3.2
0.7
0.9
0
0.9
0.3
0
13.2
17.8
0.9
0.8
5.8
1.3
5
0
0
0.1
0
0.3
0
0
0
15.3
16.1
8.4
0.5
13.5
3.5
2.7
0.7
0
1.6
0.1
0.1
0
0
0
>4
15.3
4.3
11.7
10.4
3.5
4.2
0.2
2.7
1.7
0.3
0.3
0
0.4
0.1
0
9
13
0.2
0.2
3.1
0.4
2.6
0
0
0
0
0.1
0
0
0
11
11.7
5
0.1
9.6
1.6
1.2
0.2
0
0.7
0
0
0
0
0
>5
12
2.7
8.9
7.6
2.3
2.7
0.1
1.7
0.8
0.1
0.2
0
0.1
0
0
6
9.9
0.1
0
1.6
0.1
1.3
0
0
0
0
0
0
0
0
8
8.8
2.9
0
6.9
0.7
0.6
0
0
0.3
0
0
0
0
0
>6
9.5
1.7
6.8
5.7
1.4
1.7
0
1.1
0.5
0
0
0
0.1
0
0
4
7.4
0
0
0.9
0
0.6
0
0
0
0
0
0
0
0
5.8
6.5
1.7
0
4.9
0.3
0.2
0
0
0.1
0
0
0
0
0
1
2
-------
2 5F-2 EXPOSURE MODELING RESULTS FOR ADJUSTED AIR QUALITY
3 In this section, we present the exposures estimated when considering the air quality
4 adjusted to just meeting the existing 63 NAAQS standard, as well as when considering potential
5 alternative standard levels (55, 60, 65, 70 ppb 8-hr) of the existing standard. We note that one
6 study area (Chicago) O^ ambient monitor design values were below that of the existing standard
7 during the 2008-2010, therefore APEX simulations could not be performed for that 3-year
8 period. We could not simulate just meeting a standard level of 60 ppb-8hr or below in the New
9 York study area, thus APEX simulations for these air quality scenarios could not be performed
10 for the New York study area.
11 First are presented three-paneled figures for each of the four exposure study groups of
12 interest (i.e. school-age children, asthmatic school-age children, asthmatic adults, older adults),
13 one panel of which was briefly summarized at the end of Chapter 5 in the key observation
14 section (all school-age children, 60 ppb-8hr benchmark). Presented for each of the three
15 exposure benchmarks (60 ppb-8hr, 70 ppb-8hr, 80 ppb-8hr) are the highest estimated percent
16 exposed while at moderate or greater exertion in each study area, considering just meeting the
17 existing and alternative standards (Figure 5F-7 to Figure 5F-10).
18 Exposures for the all school-age children study group were additionally characterized by
19 calculating the mean percent (averaged over the study years) experiencing at least one exposure
20 at or above each of the three benchmarks (60 ppb-8hr, 70 ppb-8hr, 80 ppb-8hr) while at
21 moderate or greater exertion (Figure 5F-11). Further, the maximum (Figure 5F-12) and mean
22 (Figure 5F-13) percent of all school-age children experiencing at least two exposures at or
23 above the three health effect benchmark levels are presented. Following these figures, Table
24 5F-2 provides the complete exposure output for all study areas, years, benchmark levels, and
25 adjusted air quality scenarios for all school-age children, the study group containing the greatest
26 percent and number of persons exposed in the REA.
27 And finally, the mean and maximum number of all school-age children and associated
28 person days with at least one exposure at or above each of the benchmark levels is provided in
29 Table 5F-3, by study area and air quality scenario. A similar but more visually pleasing
30 presentation is given in Figure 5F-14 and Figure 5F-15, providing the average number (and
31 person-days, respectively) of all four exposure study study groups experiencing at least one 8-hr
32 average exposure at or above 60 ppb across the 15 study areas considering each of the adjusted
33 air quality scenarios. Table 5F-4 contains the total number of persons experiencing at least one
34 or two 8-hour exposures in all study areas by year, base air quality and air quality adjusted to
35 just meeting the existing 75 ppb standard.
36
-------
4
5
6
7
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 60 ppb
standard level (ppb) I I 60 I I 65 i i 70 I I 75
standard level (ppb)
Figure 5F-7. Incremental increases in percent of all school-age children with at least one
exposure at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom
panel) using the maximum percent exposed for each study area, year 2006-2010 adjusted air
quality.
-------
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
I
|
3
] i
standard level (ppb)
4 Figure 5F-8. Incremental increases in percent of asthmatic school-age children with at least
5 one exposure at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr
6 (bottom panel) using the maximum percent exposed for each study area, year 2006-2010
7 adjusted air quality.
-------
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Percent of Asthmatic Adults (19-95) with at Least One 8-hr Daily Max Exposure > 60 ppb
standard level (ppb) I 1 60 I 1 65 i 1 70 I 1 75
0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8% 2.0% 2.!
Percent of Asthmatic Adults (19-95) with at Least One 8-hr Daily Max Exposure > 70 ppb
standard level (ppb) I I 60 ^H 65 ^M 70 I I 75
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
_| 1
D
standard level (ppb)
4 Figure 5F-9. Incremental increases in percent of asthmatic adults with at least one exposure at
5 or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom panel) using
6 the maximum percent exposed for each study area, year 2006-2010 adjusted air quality.
-------
4
5
6
7
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Percent of Older Adults (65-95) with at Least One 8-hr Daily Max Exposure > 60 ppb
standard level (ppb) I I 60 I I 65 i i 70 I I 75
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.80/
Percent of Older Adults (65-95) with at Least One 8-hr Daily Max Exposure > 70 ppb
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30%
Percent of Older Adults (65-95) with at Least One 8-hr Daily Max Exposure > 80 ppb
standard level (ppb) I I 60 [^H 65 ^H 70 I I 75
Figure 5F-10. Incremental increases in percent of all older adults with at least one exposure at
or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom panel) using
the maximum percent exposed for each study area, year 2006-2010 adjusted air quality.
-------
4
5
6
7
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 60 ppb
standard level (ppb) I I 60 I I 65 i i 70 I I 75
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
D
0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30% 0.35%
Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 80 ppb
standard level (ppb) I I 60 I^H 65 ^M 70 I I 75
Figure 5F-11. Incremental increases in percent of all school-age children with at least one
exposure at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom
panel) using the mean percent exposed for each study area, year 2006-2010 adjusted air quality.
-------
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 60 ppb
standard level (ppb) I 1 60 I 1 65 i 1 70 I 1 75
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
D 1
ID
0.00% 0.20% 0.40% 0.60% 0.80% 1.00% 1.20% 1.40% 1.60% 1.80% 2.00% 2.20°/(
Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 70 ppb
standard level (ppb) I I 60 ^m 65 ^H 70 I I 75
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
0.000% 0.010% 0.020% 0.030% 0.040% 0.050% 0.060% 0.070%
Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 80 ppb
standard level (ppb) I I 60 [^H 65 ^H 70 I I 75
4 Figure 5F-12. Incremental increases in percent of all school-age children with at least two
5 exposures at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom
6 panel) using the maximum percent exposed for each study area, year 2006-2010 adjusted air
7 quality.
-------
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 60 ppb
standard level (ppb) I I 60 I I 65 i i 70 I I 75
0.00% 0.10% 0.20% 0.30% 0.40% 0.50% 0.600/
Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 70 ppb
standard level (ppb) I I 60 ^H 65 ^M 70 I I 75
Atlanta
Baltimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
0.000% 0.002% 0.004% 0.006% 0.008% 0.010% 0.012% 0.014%
Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 80 ppb
standard level (ppb) I I 60 [^H 65 ^M 70 I I 75
4 Figure 5F-13. Incremental increases in percent of all school-age children with at least two
5 exposures at or above 60 ppb-8hr (top panel), 70 ppb-8hr (middle panel), or 80 ppb-8hr (bottom
6 panel) using the mean percent exposed for each study area, year 2006-2010 adjusted air quality.
-------
Fable 5F-2. Percent of all school-age children with Os exposures at or above 60, 70, and 80
3pb-8hr while at moderate or greater exertion, years 2006-2010, adjusted air quality.
Study Area
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Exposure
Benchmark
(ppb-8hr)
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
Air Quality
Scenario
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
Year
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.3
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1.2
0.8
0
0.8
0.2
0.3
0.1
0
0
0
0
0
0
0
0
0
0
0
4.8
4.2
0.5
3.3
1.4
2.2
0.5
0.2
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0.7
0
0.6
0.1
0.3
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0.2
0
0.1
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-24
-------
Study Area
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Exposure
Benchmark
(ppb-8hr)
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
Air Quality
Scenario
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
Year
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0.2
0
0
0
0
0
0
0
0
10.8
9.4
1.8
10
4.4
6.9
1.4
1
0.1
1.2
0.4
0.3
0.2
0.1
0
0.1
0
0
19.3
17.7
5.4
19.3
10.6
14
4.4
3.7
0.4
4.2
1.6
1.9
0.7
0.4
0
0.5
0.1
0.1
>2
0
0
0
0
0
0
0
0
0
0
3.3
2.8
0.2
3.1
0.8
1.8
0.1
0.1
0
0.1
0
0
0
0
0
0
0
0
8.9
7.9
1.2
8.7
3
5.5
0.7
0.6
0
0.7
0.1
0.2
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
1.2
0.9
0
1.1
0.2
0.5
0
0
0
0
0
0
0
0
0
0
0
0
4.5
3.9
0.3
4.5
1
2.5
0.1
0.1
0
0.1
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0.4
0.3
0
0.4
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
2.4
2.1
0.1
2.4
0.4
1.2
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0.1
0.1
0
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
1.4
1.1
0
1.3
0.1
0.6
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.7
0.5
0
0.7
0
0.3
0
0
0
0
0
0
0
0
0
0
0
0
5F-25
-------
Study Area
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Exposure
Benchmark
(ppb-8hr)
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
Air Quality
Scenario
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
Year
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.1
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
1
0.4
0.3
0.6
0
1.2
0
0
0
0
0
0
0
0
0
0
0
0
5.4
3.3
2.1
2.5
0.1
3.8
0.3
0.2
0.1
0.1
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.9
0.4
0.1
0.2
0
0.5
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0.1
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-26
-------
Study Area
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Boston
Boston
Exposure
Benchmark
(ppb-8hr)
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
Air Quality
Scenario
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
Year
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0.3
0
0
0
0
0
0
11.8
7.7
5.3
6.3
0.6
9.5
1.2
0.6
0.4
0.6
0
1
0.1
0.1
0
0
0
0.1
19
13.6
9.7
10.7
1.9
16.2
4
2
1.4
1.7
0.1
2.6
0.3
0.2
0.1
0.1
0
0.4
0
0.2
>2
0
0
0
0
0
0
0
0
3.7
1.8
0.8
1.2
0
2.5
0.1
0
0
0
0
0
0
0
0
0
0
0
8.4
5.1
2.5
3
0.2
6.4
0.5
0.2
0.1
0.1
0
0.2
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
1.2
0.5
0.2
0.3
0
0.7
0
0
0
0
0
0
0
0
0
0
0
0
4.1
2.1
0.8
1
0
2.8
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0.5
0.2
0
0.1
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
2.1
0.9
0.3
0.3
0
1.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0.2
0.1
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
1.1
0.4
0.1
0.1
0
0.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.5
0.2
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-27
-------
Study Area
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Exposure
Benchmark
(ppb-8hr)
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
Air Quality
Scenario
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
Year
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
1.7
0
0.3
1.3
0.4
0
0
0
0
0
0
0
0
0
0
0
0
1.2
6.7
0.8
2.9
4.6
2.5
0
0.5
0
0.1
0.2
0
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
1.1
0
0.3
0.5
0.2
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-28
-------
Study Area
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Exposure
Benchmark
(ppb-8hr)
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
Air Quality
Scenario
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
Year
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0
0
0
0
6.7
15.7
4.7
9.1
9
6.7
0.3
3.2
0.2
0.8
1.3
0.7
0
0.2
0
0.1
0
0
11.9
21.9
9.1
15.9
11.4
11.2
1.4
6.6
0.8
3.2
2.1
1.7
0
1
0.1
0.4
0.1
0.1
0.1
0.3
0
0
>2
0
0
0
0
0
0
1.2
5.5
0.7
2.1
1.8
1.1
0
0.4
0
0
0
0
0
0
0
0
0
0
3.1
9.7
2.1
5.5
2.8
3
0.1
1.1
0
0.3
0.1
0.1
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0.2
2
0.1
0.5
0.2
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0.9
4.6
0.5
2.1
0.6
0.9
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0.8
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
2.3
0.1
0.8
0.1
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1.1
0
0.3
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.5
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-29
-------
Study Area
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Exposure
Benchmark
(ppb-8hr)
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
Air Quality
Scenario
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
Year
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.3
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0.7
2.2
0
0.1
1.1
2
0
0.1
0
0
0.1
0
0
0
0
0
0
0
2.9
8.1
0.2
0.8
2.9
6.5
0.1
0.4
0
0
0.3
0.2
0
0
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.3
0
0
0.1
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0.3
1.8
0
0
0.4
1.5
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0
0
0.1
0.4
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0.1
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-30
-------
Study Area
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Exposure
Benchmark
(ppb-8hr)
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
70
70
70
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
Air Quality
Scenario
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(06-08)
75(06-08)
75(06-08)
75(06-08)
75(06-08)
75(06-08)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
Year
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2006
2007
2008
2006
2007
2008
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0
0
7.7
16
1.1
2.9
6.6
13.6
0.5
2.7
0
0
0.6
1
0
0.1
0
0
0.1
0
13.5
24.7
3
2.1
7.5
0
0
0.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>2
0
0
0
0
1.6
5.7
0.1
0.3
1.2
4.1
0
0.2
0
0
0
0.1
0
0
0
0
0
0
3.9
11.6
0.3
0.1
1.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0.4
2.1
0
0
0.2
1.3
0
0
0
0
0
0
0
0
0
0
0
0
1.2
6
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0.1
0.8
0
0
0
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0.4
3.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0.3
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.1
1.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-31
-------
Study Area
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Exposure
Benchmark
(ppb-8hr)
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
Air Quality
Scenario
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
Year
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0
0.1
0.4
0
0
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.5
3
1.2
0.6
0.7
0.5
0
0.2
0
0
0
0
0
0
0
0
0
0
3.1
9.3
5.3
4.1
1.9
2.2
0.1
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.5
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
2.6
0.9
0.6
0.2
0.3
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0.7
0.2
0.1
0
0.1
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-32
-------
Study Area
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Exposure
Benchmark
(ppb-8hr)
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
Air Quality
Scenario
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
Year
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.9
0.4
0.2
0.2
0.1
0
0
0
0
0
0
9.3
18
11.7
10.6
4.5
8.2
0.6
3.7
1.8
1.5
0.5
0.5
0
0.2
0.1
0
0
0
0.1
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>2
0
0
0
0
0
0
0
0
0
0
0
2.2
7.5
3.4
2.9
0.9
2
0
0.5
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0.6
3.4
1.2
1
0.2
0.6
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0.2
1.6
0.4
0.4
0.1
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0.7
0.1
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-33
-------
Study Area
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Exposure
Benchmark
(ppb-8hr)
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
Air Quality
Scenario
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
Year
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
1.9
1.1
0.1
0.1
0.7
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
7.6
2.6
0.7
0.7
3.1
1
0.2
0.3
0
0
0.1
0
0
0
0
0
0
0
16
6.4
3
3
8.4
3.7
1.5
1
0.1
>2
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0.1
0
0
0.4
0.1
0
0
0
0
0
0
0
0
0
0
0
0
7.1
1
0.4
0.4
1.9
0.5
0.1
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3.4
0.1
0.1
0.1
0.5
0.1
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1.7
0
0
0
0.1
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0
0
0
0
0
0
0
0
5F-34
-------
Study Area
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Exposure
Benchmark
(ppb-8hr)
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
Air Quality
Scenario
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
Year
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.1
0.6
0.1
0
0.1
0
0
0
0
22.9
10.9
6.7
7.9
14.9
8.3
4.5
1.9
0.3
0.5
2.2
0.6
0.2
0.3
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.5
>2
0
0
0
0
0
0
0
0
0
12.2
2.8
1.4
1.9
5.5
2
0.8
0.1
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
7.3
0.8
0.4
0.6
2.2
0.5
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
4.4
0.2
0.1
0.2
0.8
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
2.7
0
0
0
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
1.6
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-35
-------
Study Area
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Exposure
Benchmark
(ppb-8hr)
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
Air Quality
Scenario
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
Year
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.1
0.4
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4.4
2
2.8
9.5
3.1
3.3
0
0
0
0.4
0
0
0
0
0
0
0
0
12.9
7
8.9
18.9
7.8
9.5
0.7
0.2
0.4
1.7
0.4
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0.3
0.3
2.8
0.4
0.7
0
0
0
0
0
0
0
0
0
0
0
0
4.4
2
2.5
9.2
2
3.1
0
0
0
0.1
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0.1
0.1
1
0.1
0.2
0
0
0
0
0
0
0
0
0
0
0
0
1.8
0.7
0.9
5
0.6
1.2
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0.3
0.3
2.8
0.2
0.4
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0.1
0.1
1.5
0.1
0.2
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0.1
0.9
0
0.1
0
0
0
0
0
5F-36
-------
Study Area
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Exposure
Benchmark
(ppb-8hr)
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
Air Quality
Scenario
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
Year
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.2
0
0
0
0.1
0
0
21.3
13.8
16.7
25.6
12.5
16.3
2.9
0.9
1.2
4.1
1.2
1
0.1
0
0
0.5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0.7
0
>2
0
0
0
0
0
0
0
10.4
5.4
7.6
14.4
4.2
7.1
0.3
0.1
0
0.4
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
5.7
2.5
3.8
9.1
1.5
3.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
3.1
1.3
1.9
5.9
0.6
1.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
1.8
0.7
1
3.8
0.2
0.8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
1
0.4
0.6
2.5
0.1
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-37
-------
Study Area
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Exposure
Benchmark
(ppb-8hr)
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
Air Quality
Scenario
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
Year
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.4
0.3
0.2
0
0
0
0
0
0
0
0
0
0
0
0
2
4.6
0.7
3.6
2.8
3.1
0
0.2
0
0
0.1
0
0
0
0
0
0
0
4.9
10.3
2.5
9
6.9
8.7
0.2
0.9
0
0.9
0.5
0.5
0
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
1.1
0
0.5
0.3
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0.8
3.6
0.2
2.3
1.4
2.5
0
0.1
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.3
0
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.1
1.4
0
0.6
0.3
0.8
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.5
0
0.1
0.1
0.2
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0.1
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
5F-38
-------
Study Area
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Exposure
Benchmark
(ppb-8hr)
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
Air Quality
Scenario
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
Year
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0
0
0
10.6
19.1
6.7
15.6
12.8
16.8
1.4
4.2
0.3
3
2.2
2.6
0
0.2
0
0
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.3
0
0.1
0.2
0.7
>2
0
0
0
0
0
3
8.6
1.4
5.3
3.6
6.3
0
0.8
0
0.2
0.2
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0.9
4.3
0.3
1.8
1
2.6
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0.3
2.3
0.1
0.6
0.3
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0.1
1.3
0
0.2
0.1
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0.7
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-39
-------
Study Area
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Exposure
Benchmark
(ppb-8hr)
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
Air Quality
Scenario
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
Year
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.2
0
0
0
0
0
0
0
0
0
0
0
0
3
0.2
0.3
2.1
5.7
3.3
0.2
0
0
0
0.4
0
0
0
0
0
0
0
7
1.4
0.8
6.9
11.9
9
1
0
0
0.5
2.1
0.6
0.1
0
0
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0.6
0
0
0.2
0.7
0.3
0
0
0
0
0
0
0
0
0
0
0
0
1.9
0.1
0
1.4
2.9
2.2
0.1
0
0
0
0.1
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0.6
0
0
0.3
0.8
0.6
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0.2
0.2
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-40
-------
Study Area
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Exposure
Benchmark
(ppb-8hr)
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
Air Quality
Scenario
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
Year
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0.1
0
12
4.1
2.5
13.2
17.8
15.3
2.5
0.1
0.1
1.9
5.5
2.4
0.3
0
0
0.2
0.7
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
>2
0
0
0
4.1
0.7
0.2
4.4
6.3
5.6
0.4
0
0
0.1
0.6
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
1.7
0.2
0
1.5
2.4
2.2
0.1
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0.7
0
0
0.5
0.9
0.9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0.3
0
0
0.2
0.3
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0.1
0
0
0.1
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-41
-------
Study Area
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Exposure
Benchmark
(ppb-8hr)
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
Air Quality
Scenario
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
Year
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0
0
0
0
0
0
0
0
0
0.9
1.5
0.9
1.2
1.2
0.8
0
0
0
0
0
0
0
0
0
0
0
0
4.4
4.9
4.2
4.9
5
3.2
0
0.2
0
0
0
0
0
0
0
0
0
>2
0
0
0
0
0
0
0
0
0
0
0
0.2
0.3
0.3
0.3
0.3
0.2
0
0
0
0
0
0
0
0
0
0
0
0
1.6
1.6
1.5
1.8
1.8
1.3
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0.1
0.1
0.1
0.1
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0.8
0.8
0.9
0.9
0.7
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0.5
0.5
0.4
0.6
0.5
0.4
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.3
0.3
0.3
0.3
0.3
0.2
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0.2
0.2
0.2
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
5F-42
-------
Study Area
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Exposure
Benchmark
(ppb-8hr)
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
Air Quality
Scenario
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
Year
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
10.2
10.2
9.9
10.2
10
6.9
0.5
1
0.5
0.6
0.5
0.4
0
0.1
0
0
0
0
0
0
0
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
2.6
2
2.3
5.7
1.2
6.6
0.2
0
0.1
>2
0
4.5
4.3
4.3
4.5
4.4
2.9
0.1
0.1
0.1
0.1
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0.3
0.3
1.1
0.1
1.4
0
0
0
>3
0
2.5
2.4
2.4
2.5
2.5
1.7
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0.1
0.3
0
0.3
0
0
0
>4
0
1.6
1.4
1.5
1.6
1.5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0.1
0
0
0
>5
0
1.1
0.9
1
1.1
1
0.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0.7
0.7
0.7
0.7
0.7
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-43
-------
Study Area
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Exposure
Benchmark
(ppb-8hr)
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
Air Quality
Scenario
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
Year
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.4
0
0.5
0
0
0
0
0
0
9.1
8.5
7.8
17.2
5.4
19
1.5
0.4
0.7
3.4
0.3
3.7
0.1
0
0
0.3
0
0.3
0
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0.1
>2
0
0
0
0
0
0
0
0
0
1.9
2.2
1.7
6.3
0.9
8
0.1
0
0
0.4
0
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0.4
0.7
0.5
2.4
0.2
3.6
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0.1
0.2
0.2
1
0
1.8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0.1
0.1
0.4
0
0.9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-44
-------
Study Area
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Exposure
Benchmark
(ppb-8hr)
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
Air Quality
Scenario
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
Year
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.7
0.1
1
0
1.7
0
0
0
0
0
0.1
0
0
0
0
0
0
1.3
3.3
1
4.3
0
4.6
0
0.1
0
0.1
0
0.3
0
0
0
0
0
0
5.3
9.9
4.3
11.8
0.8
11.6
0.1
0.9
0.1
1.1
0
>2
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0.4
0
0.6
0
0.6
0
0
0
0
0
0
0
0
0
0
0
0
0.9
2.4
0.6
3.2
0
3.3
0
0.1
0
0.1
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0.7
0.1
1.1
0
1.1
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0.3
0
0.4
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0.1
0
0.1
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
5F-45
-------
Study Area
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Exposure
Benchmark
(ppb-8hr)
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
Air Quality
Scenario
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
Year
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
1.5
0
0
0
0
0
0.1
12.1
17.5
10
20.5
4
20.2
0.8
3
0.8
4.1
0
4.2
0
0.3
0
0.3
0
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.3
0.4
0.8
>2
0.1
0
0
0
0
0
0
3.7
6.6
2.4
8.6
0.4
8.7
0
0.3
0
0.5
0
0.5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
>3
0
0
0
0
0
0
0
1.3
2.7
0.7
3.9
0
4.3
0
0.1
0
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0.4
1.2
0.2
1.9
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0.2
0.6
0.1
0.8
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0.1
0.2
0
0.4
0
0.5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-46
-------
Study Area
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Exposure
Benchmark
(ppb-8hr)
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
Air Quality
Scenario
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
Year
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.8
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
4.7
1.6
4.3
3.8
2.2
0.9
0
0.2
0.2
0.2
0
0
0
0
0
0
0
0
10
3
8.3
7.6
5.9
2.2
0.4
0.5
0.9
0.8
0.1
0.1
0
>2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.9
0
0.9
0.7
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
3.4
0.3
2.4
2.1
1.3
0.3
0
0
0.1
0.1
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0.2
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1.3
0
0.9
0.8
0.3
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.5
0
0.4
0.3
0.1
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0.1
0.1
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0.1
0
0
0
0
0
0
0
0
0
0
5F-47
-------
Study Area
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Exposure
Benchmark
(ppb-8hr)
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
Air Quality
Scenario
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
Year
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0
0
0
16.5
6
13.5
12.8
11.2
4.5
2.6
1.1
2.7
2.3
1.1
0.5
0
0.2
0.2
0.2
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
1.5
0
0.4
0.4
>2
0
0
0
0
0
7.4
1.2
5.4
4.9
3.9
0.9
0.4
0
0.5
0.4
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
>3
0
0
0
0
0
3.7
0.3
2.5
2.2
1.5
0.2
0
0
0.1
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
2
0.1
1.2
1.1
0.6
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
1.1
0
0.7
0.6
0.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0.6
0
0.4
0.3
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-48
-------
Study Area
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Exposure
Benchmark
(ppb-8hr)
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
Air Quality
Scenario
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
Year
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.5
0
0
0
0
0
0
0
0
0
0
0
0
2.5
7.3
0.2
2.4
2.4
6.3
0
0.4
0
0.1
0.2
0.1
0
0
0
0
0
0
9.3
16.9
1.2
6
6.2
14.9
0.5
2.7
0
0.5
0.7
0.9
0
0.2
0
>2
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
2
0
0.2
0.1
1.1
0
0
0
0
0
0
0
0
0
0
0
0
2.3
7
0.1
1.1
0.9
5.2
0
0.3
0
0
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.6
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0.7
3.4
0
0.2
0.1
1.9
0
0.1
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
1.7
0
0.1
0
0.7
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0
0
0
0.2
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0
0
0
0.1
0
0
0
0
0
0
0
0
0
5F-49
-------
Study Area
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Exposure
Benchmark
(ppb-8hr)
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
Air Quality
Scenario
70(08-10)
70(08-10)
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
55(06-08)
55(06-08)
55(06-08)
55(08-10)
55(08-10)
55(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
Year
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0.1
0
18.1
25.8
3.5
9.5
10
21.1
2.9
8.1
0.2
1.1
1.4
3.6
0.1
1.1
0
0
0.2
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.6
0.1
0.1
0.3
0
0.6
0
>2
0
0
0
7.8
13.8
0.5
2.4
2.2
9.6
0.2
2.2
0
0
0
0.4
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>3
0
0
0
3.7
8.2
0.1
0.6
0.5
4.8
0
0.7
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
1.6
5
0
0.2
0.1
2.3
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0.8
3
0
0.1
0
1.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0.4
1.8
0
0
0
0.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5F-50
-------
Study Area
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Exposure
Benchmark
(ppb-8hr)
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
Air Quality
Scenario
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
60(06-08)
60(06-08)
60(06-08)
60(08-10)
60(08-10)
60(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
65(06-08)
65(06-08)
65(06-08)
65(08-10)
65(08-10)
65(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
70(08-10)
70(06-08)
70(06-08)
70(06-08)
70(08-10)
70(08-10)
Year
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0
0
0
0
0
0
0
0
0
0
0
2.4
1.8
0.9
3.4
0.1
5
0.1
0
0
0.2
0
0.2
0
0
0
0
0
0
6.7
6.5
3.4
9.2
1
12.5
0.7
0.1
0.2
1.3
0
1.4
0
0
0
0.1
0
>2
0
0
0
0
0
0
0
0
0
0
0
0.3
0.2
0.1
0.6
0
1.2
0
0
0
0
0
0
0
0
0
0
0
0
1.8
1.7
0.6
2.7
0.1
5
0
0
0
0.1
0
0.1
0
0
0
0
0
>3
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0.1
0
0.4
0
0
0
0
0
0
0
0
0
0
0
0
0.5
0.6
0.1
0.8
0
2.4
0
0
0
0
0
0
0
0
0
0
0
>4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0
0
0
0
0
0
0
0
0
0
0
0
0.2
0.2
0
0.2
0
1.3
0
0
0
0
0
0
0
0
0
0
0
>5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
0
0.1
0.1
0
0.1
0
0.7
0
0
0
0
0
0
0
0
0
0
0
>6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0
0
0
0
0
0
0
0
0
0
0
5F-51
-------
Study Area
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Exposure
Benchmark
(ppb-8hr)
80
60
60
60
60
60
60
70
70
70
70
70
70
80
80
80
80
80
80
Air Quality
Scenario
70(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
75(06-08)
75(06-08)
75(06-08)
75(08-10)
75(08-10)
75(08-10)
Year
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
2006
2007
2008
2008
2009
2010
% of all school-age children experiencing multiple exposures per
O3 season at or above benchmarks, adjusted air quality
>1
0.1
12.5
12.9
7.3
18.5
4.1
23.4
2
1.1
0.8
4.8
0.2
6
0.1
0
0
0.8
0
0.6
>2
0
4.6
4.9
1.8
8.2
0.6
12.5
0.2
0.1
0
0.8
0
1.4
0
0
0
0
0
0
>3
0
2.1
2.2
0.5
4.1
0.1
7.7
0
0
0
0.1
0
0.4
0
0
0
0
0
0
>4
0
1
1.1
0.1
2
0
4.9
0
0
0
0
0
0.1
0
0
0
0
0
0
>5
0
0.4
0.5
0
0.9
0
3.3
0
0
0
0
0
0
0
0
0
0
0
0
>6
0
0.2
0.3
0
0.4
0
2.1
0
0
0
0
0
0
0
0
0
0
0
0
Abbreviation indicates 8 hour standard level
simulated ambient concentrations just meeting
and three year averaging period. For example, 75(08-10) represents
the existing standard (75 ppb-8hr) using air quality years 2008-2010.
5F-52
-------
Table 5F-3. Mean and
63 season) with at least
exertion.
maximum number of all school-age children (and associated days per
one 63 exposure at or above 60 ppb-8hr while at moderate or greater
Study area
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Dallas
Dallas
Dallas
Dallas
Dallas
Dallas
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Air quality
scenario/
standard
level (ppb)
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
base
Mean
Number of
persons
255487
127378
64409
24933
5064
818
130826
61511
35864
15050
3109
346
177358
124529
81220
30411
6710
538
258923
260946
174401
79122
22578
3831
119740
59189
24462
6525
838
81
310037
141119
82161
32893
8561
846
129615
95296
57266
21348
1177
21
207174
Number of
days per year
827740
229661
90926
29770
5359
841
357038
103377
49008
17414
3218
346
331700
190220
108757
34767
7064
538
429030
449687
246130
99309
26014
4223
234478
88930
31229
7396
864
83
853320
247643
122988
41095
9159
860
328720
187450
91933
27215
1192
21
402320
Maximum
Number of
persons
364916
166169
93000
41687
10128
2522
186072
95781
59793
27416
5888
817
287713
198171
141821
60377
15506
1713
503935
47001 1
303354
153346
41894
5842
172100
104388
53736
17557
2195
188
452737
251505
175109
83162
20508
2913
178689
143603
106034
53234
2863
39
349520
Number of
days per year
1496000
325800
136700
50720
10840
2599
609600
179700
88270
33050
5998
817
699100
365900
220300
72810
16620
1713
1046000
922700
477300
198700
48110
6885
423200
183800
75140
20980
2289
189
1894000
588800
327400
117000
23050
2913
563800
367300
221600
78130
2928
39
922500
5F-53
-------
Study area
Detroit
Detroit
Detroit
Detroit
Detroit
Houston
Houston
Houston
Houston
Houston
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Washington
Washington
Washington
Washington
Washington
Washington
Air quality
scenario/
standard
level (ppb)
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
base
75
70
65
60
55
Mean
Number of
persons
143352
74557
29881
3329
82
254991
110832
64399
25986
2565
41
1244571
342236
159498
39327
1548
15
1148294
418702
125784
1602
388598
170184
87649
29333
7291
699
147074
47859
27070
12503
1744
0
122408
86067
53629
20760
2915
140
267667
127234
63711
22251
2645
0
Number of
days per year
230015
102732
35337
3457
82
574580
170062
84835
29950
2610
41
3978000
741990
290320
57289
1547
15
2740240
635750
153098
1880
1094200
279328
117594
33504
7693
699
469140
80022
37802
14970
1830
0
315640
162499
82013
25857
3100
143
771500
246782
97776
27721
2776
0
Maximum
Number of
persons
194330
104733
46936
6699
320
360732
173115
115161
55481
6888
236
1423198
368974
179329
54045
5831
75
1368877
729630
253458
3241
503583
252907
145466
56832
20486
2891
190752
76891
46556
22069
3892
0
202543
136172
89003
38381
7840
503
345115
226043
121074
48425
5578
0
Number of
days per year
374700
163400
61570
7225
320
1082000
270900
152500
63640
6947
236
4981000
814100
334200
72580
5831
75
3664000
1311000
324700
3704
1630000
459900
203900
65860
21890
2891
764900
148400
71980
27510
4296
0
689100
316800
160900
54050
8718
515
1141000
556700
219700
65920
5803
0
5F-54
-------
.
ro ,_
2,500,000
2,000,000
•a; J= L. 1,500,000
o o S
500,000
.
U 0- C
u ° -2
•S "> t
OJ ° V
M -M "-
-------
4,500,000
300,000
0
75ppb
70ppb 65ppb
Air Quality Standard Level
60ppb
• Atlanta
i Baltimore
I Boston
M Chicago
y Cleveland
• Dallas
• Denver
• Detroit
U Houston
hi Los Angeles
U New York
y Philadelphia
y Sacramento
U St. Louis
Washington
70ppb 65ppb
Air Quality Standard Level
60ppb
15 §
£ *t V
* o a
1
o" S
111
2
« -
M m
t
o s 18
o! s £
Q. ** \3
III
*S D. *-
a] u5 -a
Atlanta
altimore
Boston
Chicago
Cleveland
Dallas
Denver
Detroit
Houston
U Los Angeles
J New York
U Philadelphia
i Sacramento
i St. Louis
I Washington
70ppb 65ppb
Air Quality Standard Level
60ppb
J New York
U Philadelphia
y Sacramento
U St. Louis
j Washington
70ppb 65ppb
Air Quality Standard Level
60ppb
Figure 5F-15. Average total number of days in an Oj season where simulated persons experienced 8-hour exposures at or above 60
ppb considering the existing and alternative standards, year 2006-2010 adjusted air quality. All school-age children (top left), asthmatic
school-age children (top right), asthmatic adults (bottom left), older adults (bottom right).
5F-56
-------
Table 5F-4. Total number of persons experiencing at least one or two 8-hour exposures in all
study areas by year, base air quality and air quality adjusted to just meeting the existing 75 ppb
standard.
Exposure Study Group
Asthmatic School-age
Children
All School-age Children
Air Quality
Scenario (3-
year averaging
period)
75(2006-2008)
base
75(2006-2008)
Year
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
2006
2007
8-hr Average
Exposure
Benchmark
Level (ppb)
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
Number of Persons
Exposed Per O3 Season1
At least
once
254454
39422
2399
281062
51351
4236
155918
12603
428
160535
19889
1159
265139
45688
3981
664077
322187
116538
677867
311929
96028
523387
209734
59813
364614
115454
34482
504479
165488
30418
2434809
350957
20489
2624485
481637
45085
At least
twice
94876
4113
120
113215
8788
133
42881
713
13
49447
1532
39
110871
6737
143
381016
126882
26233
397000
113719
16731
280744
67291
11174
157977
33741
5779
263154
49314
3270
900706
38834
708
1045539
74715
992
lumbers of children are summed across urban case study areas in each year. Because Chicago does not have a
simulation of the existing standard for the 2008-2010 three-year standard averaging period, year 2008 study area
sums were based only the year 2008 simulations that used the 2006-2008 three-year standard averaging period.
5F-57
-------
Exposure Study Group
Air Quality
Scenario (3-
year averaging
period)
base
Year
2008
2009
2010
2006
2007
2008
2009
2010
8-hr Average
Exposure
Benchmark
Level (ppb)
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
60
70
80
Number of Persons
Exposed Per O3 Season1
At least
once
1492203
120656
3615
1529789
188071
12385
2439477
395217
27981
6294372
3104290
1140328
6307074
2926601
892814
4987568
2021415
597040
3553528
1191875
370166
4671276
1528455
273651
At least
twice
438479
10568
52
469352
16908
141
997079
50200
670
3653902
1265681
264098
3658057
1067241
149031
2672573
684051
132098
1574327
372643
63075
2417136
421992
27126
Numbers of children are summed across urban case study areas in each year. Because Chicago does not have a
simulation of the existing standard for the 2008-2010 three-year standard averaging period, year 2008 study area
sums were based only the year 2008 simulations that used the 2006-2008 thee-year standard averaging period.
5F-58
-------
1
2 Appendix 5-G
3
4 Targeted Evaluation of Exposure Model Input and Output Data
5
6 Table of Contents
7 5G-1 ANALYSIS OF TIME-LOCATON-ACTIVITY DATA 4
8 5G-1.1 Personal Attributes of Survey Participants in CHAD and Used by APEX 4
9 5G-1.2 Afternoon Time Spent Outdoors for CHAD Survey Participants 7
10 5G-1.3 Afternoon Time Spent Outdoors For ATUS Survey Participants 10
11 5G-1.4 Outdoor Time and Exertion Level of Asthmatics and Non-Asthmatics In CHAD
12 12
13 5G-2 CHARACTERIZATION OF FACTORS INFLUENCING HIGH EXPOSURES 17
14 5G-3 ANALYSIS OF APEX SIMULATED LONGITUDINAL ACTIVITY PATTERNS 26
15 5G-4 EXPOSURE RESULTS FOR ADDITIONAL AT-RISK POPULATIONS AND
16 LIFESTAGES, EXPOSURE SCENARIOS, AND AIR QUALITY INPUT DATA USED
17 32
18 5G-4.1 Exposure Estimated For All School-Age Children During Summer Months,
19 Neither Attending School nor Performing Paid Work 32
20 5G-4.2 Exposures Estimated For Adult Outdoor Workers During Summer Months 34
21 5G-4.3 Averting Behavior and Potential Impact to Exposure Estimates 42
22 5G-5 COMPARISON OF PERSONAL EXPOSURE MEASUREMENT AND APEX
23 MODELED EXPOSURES 47
24 5G-6 REFERENCES 50
25
26
5G-1
-------
l List of Tables
2 Table 5G-1. Personal attributes of survey participants within CHAD and used by APEX 6
3 Table 5G-2. Comparison of outdoor time expenditure and exertion level among asthmatic and
4 non-asthmatic diary days for CHAD diaries used by APEX 14
5 Table 5G-3. Percent of waking hours spent outdoors at an elevated activity level. A
6 comparison of CHAD with Shamoo et al. (1994) study asthmatics 15
7 Table 5G-4. Percent of waking hours spent outdoors at an elevated activity level: a comparison
8 of CHAD with EPRI (1992) study asthmatics 16
9 Table 5G-5. Percent of waking hours spent outdoors at an elevated activity level: a comparison
10 of CHAD with EPRI (1988) study asthmatics 16
11 Table 5G-6. Range of R fit statistics for ANOVA models used to evaluate daily maximum 8-
12 hour Os exposure concentrations stratified by study area, air quality scenario, and
13 exposure level 19
14 Table 5G-7. Range of R2 fit statistics for ANOVA models used to evaluate daily maximum 8-
15 hour Os exposure concentrations in Los Angeles stratified by age group, air
16 quality scenario, and exposure level 19
17 Table 5G-8. Distribution of days per week spent performing outdoor work considering the
18 BLS/O*NET data set and stratified by APEX/CHAD occupation groups 36
19 Table 5G-9. Personal attributes and mean time spent working outdoors for CHAD diaries
20 reporting at least two hours of outdoor work 37
21 Table 5G-10. Distribution of days per week spent performing outdoor work considering the
22 APEX simulated population and stratified by APEX/CHAD occupation and age
23 groups 41
24
25 List of Figures
26 Figure 5G-1. Participation rate in outdoor activities (top) and mean time spent outdoors (bottom)
27 for CHAD diaries having at least one minute outdoors (left) and CHAD diaries
28 having at least two hours outdoors (right) during the afternoon 8
29 Figure 5G-2. Participation rate in outdoor activities (top) and mean time spent outdoors (bottom)
30 for ATUS diaries having at least one minute outdoors (left) and ATUS diaries
31 having atleasttwo hours outdoors (right) 11
32 Figure 5G-3. Contribution of influential factors to daily maximum 8-hour ozone exposures using
33 base air quality (left), air quality adjusted to just meet the existing standard
34 (right), considering all person days (top) and those days where daily maximum 8-
35 hour exposure exceeded 50 ppb (bottom) in Boston 21
36 Figure 5G-4. Contribution of influential factors to daily maximum 8-hour ozone exposures using
37 base air quality (left), air quality adjusted to just meet the existing standard
38 (right), considering all person days (top) and those days where daily maximum 8-
39 hour exposure exceeded 50 ppb (bottom) in Atlanta 22
40 Figure 5G-5. Distributions of afternoon outdoor time expenditure and daily maximum 8-hour
41 ambient O?, concentrations for simulated Boston school-age children (ages 5 to
42 18) (left) and adults (ages 19 to 35) (right) using base air quality (top) and
43 concentrations adjusted to just meet the existing standard (bottom) for person
44 days having daily maximum 8-hour exposures either below or above 50 ppb 24
45 Figure 5G-6. Afternoon microenvironmental time (top) and activities performed during
46 afternoon time outdoors (bottom) for school-age children (left) and adults (right)
5G-2
-------
1 experiencing 8-hour daily maximum Os exposures > 50 ppb, Boston base air
2 quality, 2006 26
3 Figure 5G-7. Cumulative distribution of median time spent outdoors (top row), afternoon
4 outdoor participation > 1 minute/day (2n row), and afternoon outdoor
5 participation > 2 hours/day (3n row) for male and female school-age children in
6 Atlanta (left column), Boston (middle column) and Denver (right column) study
7 areas. Percent of school-age children with > 2 hours outdoors during afternoon
8 hours (4* row) and the number of particular CHAD study diary days used (bottom
9 row) for each exposure simulation day 30
10 Figure 5G-8. Cumulative distribution of median time spent outdoors (top row), afternoon
11 outdoor participation > 1 minute/day (2n row), and afternoon outdoor
12 participation > 2 hours/day (3n row) for male and female school-age children in
13 Houston (left column), Philadelphia (middle column) and Sacramento (right
14 column) study areas. Percent of school-age children with > 2 hours outdoors
15 during afternoon hours (4th row) and the number of particular CHAD study diary
16 days used (bottom row) for each exposure simulation day 31
17 Figure 5G-9. Comparison of the percent of all school-age children having daily maximum Os
18 concentration at or above 60 ppb-8hr (top), 70 ppb-8hr (middle), or 80 ppb-8hr
19 (bottom) during June, July, and August in Detroit 2007: using any available
20 CHAD diary ("All CHAD Diaries") or using CHAD diaries having no time spent
21 in school or performing paid work ("No School/Work Diaries") 33
22 Figure 5G-10. Percent of persons between age 19-35 (left) and 36-55 (right) experiencing
23 exposures at or above selected benchmark levels while at moderate or greater
24 exertion using an outdoor worker scenario-based approach (top) and a general
25 population-based approach (bottom) considering air quality adjusted to just meet
26 the existing standard in Atlanta, GA, Jun-Aug, 2006 42
27 Figure 5G-11. Distribution of daily personal O3 exposures (top row), outdoor time (2nd row
28 from top), ambient Os concentrations (3r row from top), and air exchange rate
29 (bottom row) for DEARS study participants (left column) and APEX simulated
30 individuals (right column) in Wayne County, MI, July-August 2006 49
31
32
5G-3
-------
1 This appendix presents the complete results of several targeted evaluations and exposure
2 simulations designed to provide additional insights to APEX input data or approaches used to
3 estimate exposures, algorithm and model performance evaluations, and estimated exposures for
4 additional exposure study groups and lifestages of interest.
5 5G-1 ANALYSIS OF TIME-LOCATON-ACTIVITY DATA
6 We first present an overview of the data currently available in the CHAD database used
7 by APEX, including comparison with the version of CHAD used to estimate exposures in the 1st
8 draft O^ REA. This is followed by an analysis of time spent outdoors - one of the most important
9 attributes influencing exposures at or above benchmark levels - using CHAD and recent time-
10 location-activity pattern data from the American Time Use Survey (ATUS). And finally, CHAD
11 diaries identified as coming from asthmatics are compared with that of non-asthmatics for
12 afternoon outdoor time and activity level as well as compared with available independent studies
13 of asthmatic activity patterns.
14 5G-1.1 Personal Attributes of Survey Participants in CHAD and Used by APEX
15 The survey participants whose diary data are within CHAD were asked a number of
16 questions regarding their personal attributes. The number and type of attributes present for
17 diaries in CHAD is driven largely by the original intent of the individual study. In our exposure
18 assessment, we have strict requirements to simulate individuals using several personal attributes,
19 namely age, sex, temperature (as a surrogate for seasonal variation in activity patterns), and day-
20 of-week. These attributes are considered as important drivers influencing daily activity patterns
21 (Graham & McCurdy, 2004) and when diaries do not have these particular attributes for a
22 particular day, they will not be used by APEX.
23 This APEX modeling requirement serves as an initial screen to the number of available
24 diaries in the complete CHAD master database (i.e., 54,373) and considering the age range of the
25 simulated exposure study groups (persons between the ages of 5 and 95), the actual number of
26 diary days having complete information and used by APEX in the 2n draft Oj, REA is 41,474.l
27 This represents an increase of about 8,700 diaries currently used by APEX compared with what
28 was used by APEX in the 1st draft Os REA. Additionally, there have been eight new study data
29 sets incorporated into CHAD and used in our current exposure assessment since the previous 63
30 NAAQS review conducted in 2007, most of which were from recently conducted activity pattern
1 Diaries from persons age 4 are included in this evaluation because they may be used in a simulation to represent a
person aged 5 due to the probabilistic nature of APEX. Typically, a diary matching the attributes of the simulated
individual has a greater probability of selection. Accommodations are allowed to increase the diary pool size (e.g.,
expand the age window of diaries available by value (one year) or percent (15%) of the simulated persons age.
5G-4
-------
1 studies (Appendix 5B, Section 5B-4). The diary data included from these new studies have more
2 than doubled the total activity pattern data used for 2007 63 exposure modeling and has
3 increased the number of children's diaries by about a factor of five.
4 Table 5G-1 presents a summary of the important personal attributes used by APEX in
5 creating activity patterns for simulated persons, along with other attributes of potential interest
6 (e.g., race/ethnicity). First, we compared the representation of several attributes in the current
7 CHAD used by APEX versus that used in the 1st draft 63 REA. Outside of increases in the
8 number of persons, the general distribution of diaries within the APEX diary selection attributes
9 (e.g., age, sex, temperature, day-of-week) is similar in both databases. Worth noting is the
10 number and percent of diaries from each of the three decades analyzed. Currently, the majority
11 of diaries (54%) from CHAD are taken from surveys conducted in the past decade, while the pre-
12 1990s represent less than 15% of the total diaries available by APEX.
13 While there may be other personal or situational attributes that affect daily time
14 expenditure, these are typically not included in our assessment to generate simulated individuals
15 simply because the response to the attribute is missing for most persons. For example, income
16 level is missing for just over 66% of the study participants and only about 30% of employed
17 workers (persons ages 19 to 64) reported their occupation (Table 5G-1). Missing response data
18 in CHAD results from either the study not having an income/occupation related survey question
19 or perhaps the participant refused to answer the question. Note also, when any attribute is added
20 to the development of a person's profile, the pool of diaries available for selection in simulating
21 an individual is reduced. This could lead to an increased repetition of diaries used for simulated
22 individuals, potentially artificially reducing variability in time expenditure. In addition, the
23 desired study group to be simulated may have too few diaries within a diary pool if most diaries
24 are missing the needed attribute, leading to a simulation failure. This is why personal attributes
25 are carefully selected and prioritized according to both their prevalence in CHAD and whether
26 attribute has a known significant influence on activity patterns.
27
5G-5
-------
1 Table 5G-1. Personal attributes of survey participants within CHAD and used by APEX.
Personal
Attribute
Age (years)
Sex
Daily
Maximum
Temperature
(°F)
Day of Week
Decade
Employed
(ages 1 9-64)
Occupation
(employed)
Income Group
Calendar
Month
Race/Ethnicity
Group Within
Attribute
4-18
19-34
35-50
51 -64
65+
Female
Male
84+
55-83
<55
Weekday
Weekend
1980s
1990s
2000s
No
Yes
Missing/Unknown
Known
Missing/Unknown
> 1.5 x Poverty
< 1.5x Poverty
Missing/Unknown
Calendar Months 5 -
9
Calendar Months 1 -
4, 10-12
Asian
Black
Hispanic
Native American
White
Other
Missing/Unknown
Current APEX CHAD
(41 ,474 total days)
person days
(n)
17680
4490
7238
6181
5885
21466
20008
13817
17827
9830
29031
12443
5999
12831
22644
4651
12755
503
3867
8888
10347
3713
27414
19151
22323
670
6993
2476
28
25009
770
5528
percent of
attribute
42.6
10.8
17.5
14.9
14.2
51.8
48.2
33.3
43
23.7
70.0
30.0
14.5
30.9
54.6
26.0
71.2
2.8
30.3
69.7
24.9
9.0
66.1
46.2
53.8
1.6
16.9
6.0
0.1
60.3
1.9
13.3
1st Draft APEX CHAD
(32,788 total days)
person days
(n)
14111
4001
5957
5016
3703
16840
15948
12113
13078
7597
23794
8994
6167
12390
14231
3747
11227
n/a
3012
8215
10416
3730
18642
16812
15976
349
4040
1339
7
19569
609
6875
percent of
attribute
43
12.2
18.2
15.3
11.3
51.4
48.6
36.9
39.9
23.2
72.6
27.4
18.8
37.8
43.4
25.0
75.0
n/a
26.8
73.2
31.8
11.4
56.9
51.3
48.7
1.1
12.3
4.1
0
59.7
1.9
21
5G-6
-------
1 5G-1.2 Afternoon Time Spent Outdoors for CHAD Survey Participants
2 There have been questions raised regarding the representativeness of the diaries from
3 studies conducted in the 1980s and whether there are any recognizable patterns in time
4 expenditure in the CHAD diaries across the time period when data were collected. Because time
5 spent outdoors is a significant factor influencing daily maximum 8-hour 63 exposures, we
6 evaluated the current collection of CHAD diaries used by APEX for two metrics: outdoor
7 participation rate and mean time spent outdoors. The participation rate is the percent of the
8 person-days having at least one minute outdoors, and because high 63 concentrations commonly
9 occur during the afternoon hours of summer months, we restricted the analysis to those times of
10 day (12 PM to 8 PM) and year (May through September). The same data set was used to
11 calculate a mean outdoor time, though the calculation was further restricted to person days
12 meeting an additional criterion: person-days having at least one minute outdoors and person-days
13 having at least 2-hours outdoors. Separating the data into these sub-groups give us insight to the
14 diaries most likely to be used in simulating a person that exceeds a selected benchmark level and
15 protects (to a limited degree) from study sample design bias (15-minute time block diaries versus
16 minute-by-minute event level diaries). Data were further stratified by five age groups (4-18, 19-
17 34, 35-50, 51-64, 65+) and three decades (1980s, 1990s, and 2000s) using the year the particular
18 activity pattern study was conducted. As a reminder, CHAD is composed of primarily cross-
19 sectional data, thus the trend evaluated over the three decades is changes (if any) in participation
20 rate and the time spent outdoors by the study group, not individuals.
21 Figure 5G-1 illustrates the trends in afternoon outdoor activity participation and mean
22 time expended outdoors, considering three decades, five age groups, and whether the total
23 afternoon time spent outdoors was at least one minute or two hours. Regardless of decade and
24 duration of time spent outdoors, participation in outdoor activities follows an expected pattern
25 considering age groups, that is, children tend to have the highest participation rate when
26 compared with the other age groups, while the oldest persons (aged 65 or greater) tend to have
27 the lowest participation rate (Figure 5G-1, top left panel). When considering decade and CHAD
28 diaries having at least one minute spent outdoors, the participation rate appears to have a non-
29 linear concave trend, whereas CHAD diaries collected during the 1990s exhibit the lowest
30 outdoor participation rate (ranging from about 40-70%) while much greater participation is found
31 with the CHAD diaries collected during the 1980s (80-90%) and 2000s (70-80%).
5G-7
-------
CHAD, > 1-minsoutdoortime, months May-Sep, hours 12-8PM
CHAD, > 1-min outdoortime, months May-Sep, hours 12-8PM
CHAD, > 2-hrs outdoortime, months May-Sep, hours 12-8PM
CHAD, > 2-hrs outdoortime, months May-Sep, hours 12-8PM
I 150
1990s
Decade
ages 4 to 18 -*-ages 19 to 34 -»-ages 35 to 50 -±-ages51 to 64
ages 65+
Figure 5G-1. Participation rate in outdoor activities (top) and mean time spent outdoors
(bottom) for CHAD diaries having at least one minute outdoors (left) and CHAD diaries having
at least two hours outdoors (right) during the afternoon.
It is possible that this observed pattern may be the result of the original study survey
design. All of the CHAD diaries collected during the 1980s used an 'event' level approach, that
is locations visited and activities performed were reported on a minute or longer basis and many
of those same diaries also used a 'contemporaneous diary' approach, that is, real-time data
reporting. The CHAD diaries collected during the 1990s used a mixture of event level and 15-
minute time block data, though mostly using a 'recall' approach, that is, participants were asked
about their activities performed the day before. It is likely that these diaries exhibit the lowest
outdoor participation rate due to the participant missing or ignoring short duration events (< 15
minutes) that may have occurred outdoors (e.g., outdoors in a parking lot and walking to their
vehicle). The CHAD diaries collected during the 2000s, while also a combination of studies that
used the event level and 15-minute time block approach did have a few studies using the real-
time diary approach, possibly responsible for the observed increase in outdoor participation rate
during this decade.
5G-8
-------
When restricting the data set to person days having at least two hours of afternoon time
spent outdoors, the above mentioned temporal pattern nearly disappears (Figure 5G-1, top right
panel) as most age groups exhibit little to no variability in outdoor time participation rate across
the three decades (-20-30% of person-days). However, the diaries from the 1980s for children
ages 4-18 indicate the highest outdoor participation rate (i.e., 50%) compared to all other age
groups and decade of collection. Most of these diaries (i.e., 96%) are from the California
Children's Study and Cincinnati Activity Patterns Study. To date it remains unexplained why
these two studies would have this unusual outdoor participation rate compared to the other
studies and decades and could simply be a function of the two study selecting particularly active
children by chance.2 When considering the entire pool of all diaries available for this age group
and used by APEX, these two studies contribute to about 19% of diaries having two or more
hours of time spent outdoors during the afternoon. This translates to a small difference in the
overall outdoor participation rate for diary pools that would include these earlier studies (39%)
compared to the participation rate excluding these studies (36%), with both values similar to the
findings reported recently by Marino et al. (2012) of 37.5% using a similar metric, though for
pre-school age children. Thus, when considering participation in outdoor activities and the
representativeness of the CHAD study data from the 1980s, it is unlikely that use of these older
diaries would adversely influence exposure model estimates.
When considering the mean time spent outdoors during the afternoon hours for
diaries having at least one minute recorded outdoors, the observed pattern is generally the
inverse of participation. This pattern would be consistent with the above proposed reasoning:
relatively more persons reporting shorter duration events leads to higher overall outdoor
participation rate coinciding with a decrease in the mean time spent outdoors (Figure 5G-1,
bottom left panel). In general, when restricting the data set to person-days that recorded at least
two hours outdoors during the afternoon (Figure 5G-1, bottom right panel), there is variability in
the amount of outdoor time over the three decades, with diaries from the 2000s exhibiting
perhaps the lowest range of mean outdoor time (190-220 min/day) compared with the 1980s
(210-240 min/day) and 1990s (212-258 min/day), a trend perhaps most notable trend when
considering the children's diaries (a decrease in time spent outdoor of about 30 minutes over the
period). However, the coefficient of variation (COV) for each of the age groups and across all
decades for the cross-sectional data was consistently about 40% (data not shown), supporting a
2 Restricting the data by any criterion will result in fewer diary days available that subset, thus any remaining or
newly observed trends (e.g., an apparent linear decline in participation by the diaries from persons aged 19-34)
should include an assessment of potential confounding factors. Given our current and other researcher's past
evaluations of the CHAD data (Graham and McCurdy, 2003; Isaacs et al., 2012; McCurdy and Graham, 2004) often
times, as is the case here, additional influential factors have not been measured and the observed phenomenon can
only be identified as study related.
5G-9
-------
general conclusion of no large differences in the mean time spent outdoors for this set of diaries
over the three decades of data collection. Thus, when considering all diaries having at least two
hours of afternoon outdoors time and the representativeness of the CHAD study data from the
1980s, inclusion of these earlier diaries is also unlikely to have a significant adverse influence on
exposure modeling outcomes.
5G-1.3 Afternoon Time Spent Outdoors For ATUS Survey Participants
We evaluated recent year (2002-2011) time expenditure data from the American Time
Use Survey (ATUS) (US BLS, 2012a). As was done with the CHAD data set, the purpose of the
evaluation was to evaluate trends in outdoor time over the period of time data were collected. A
few strengths of the ATUS data are (1) its recent and ongoing data collection efforts, (2) large
sample size (>120,000 diary days), (3) national representativeness, and (4) that varying diary
approaches would not be an influential or confounding factor in evaluating trends over time.
ATUS does however have a few noteworthy limitations when compared with the CHAD
data: (1) there are no survey participants under 15 years of age, (2) time spent at home locations
is neither distinguished as indoors or outdoors, and (3) missing or unknown location data can
comprise a significant portion of a persons' day (on average, about 40% (George and McCurdy,
2009)). To overcome the limitation afforded by the ambiguous home location, we identified
particular activity codes most likely to occur outdoors (e.g., participation in a sport) to better
approximate each ATUS individual's outdoor time expenditure. Missing several hours of
location and activity information can be problematic when modeling exposures, an issue that
renders the ATUS diaries generally unusable by APEX. However, the particular time of day the
missing data occurs is more accommodating to the purpose of this analysis. While most diaries
are missing location information for 6 or more hours per day, on average about 85% of the
missing time information occurs outside of the hours of interest here (i.e., 12 PM-8 PM), with
most missing time occurring between early morning (4 AM-9 AM) or late evening hours (10
PM-12 AM). Still though, we restricted the ATUS outdoor time analysis to diaries having no
more than 1-hour of missing afternoon time while also only retaining diaries from ATUS
identified metropolitan areas. Data were then stratified by the same five age groups as was done
for the CHAD data, though here the time trends were assessed over individual survey years.
Figure 5G-2 illustrates the results of the ATUS diary outdoor participation rates (top row) and
the mean time spent outdoors during afternoon hours (bottom row) for persons having at least
one minute of afternoon time spent outdoors (left column) or two hours or more outdoors (right
column).
5G-10
-------
Mean Afternoon OutdoorTime
Afternoon OutdoorTime Participation
ATUS, > 1-min outdoor time, months May-Sep, hours 12-8PM
&
~o
D
c
lioo
.fc^^ ,+-
i^^^g^r^p^i
2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
ATUS, > 1-min outdoor time, months May-Sep, hours 12-8PM
£.
(0
cc „-
30 -
— y/^^
^•^7 — i | |^ r~~—
2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
Afternoon OutdoorTime Participation
Mean Afternoon Outdoor Time
ATUS,> 2-hrsoutdoortime, months May-Sep, hours 12-8PM
90 -
E
" 40 -
10 -
^^..^^^^
J^*- ' « "T-T- r-i
2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
ATUS, > 2-hrs outdoor time, months May-Sep, hours 12-8PM
(0
-a
D
C
-g-ioo
^ ^^^^^^
2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
ages 15 to 18
-*-ages 19 to 34
-•-ages 35 to 50
-*-ages51to64
-•-ages 65+
Figure 5G-2. Participation rate in outdoor activities (top) and mean time spent outdoors
(bottom) for ATUS diaries having at least one minute outdoors (left) and ATUS diaries having at
least two hours outdoors (right).
5G-11
-------
1 Not surprisingly given the lack of distinction regarding time indoors and outdoors while
2 at home for ATUS participants as well as the diary approach used,3 the outdoor activity
3 participation rate for ATUS study subjects is lower than that of CHAD study subjects; about 30-
4 40% of ATUS person-days have at least one minute of outdoor time (Figure 5G-2, top left
5 panel). As was observed with the CHAD data, children (ATUS ages 15 to 18) are more likely to
6 participate in outdoor activities. The mean time outdoors for persons that reported any amount
7 of outdoor time is similar to the range indicated by CHAD diaries, generally between 100-150
8 minutes per day (Figure 5G-2, bottom left panel). When considering person-days having at least
9 2 hours of time spent outdoors, the range in ATUS diary outdoor participation rate (10-20%,
10 Figure 5G-2, top right panel)) is lower than that observed for the CHAD data (generally between
11 20-40%), while the range in mean time spent outdoors (190-240 minutes per day, Figure 5G-2,
12 top right panel) was similar to that of the CHAD data. Consistent also across the two studies is
13 the participation rate of children being greater than that of the other age groups. There are no
14 consistent trends over the nine year ATUS study period regarding either the participation rate or
15 the mean time spent outdoors for any of the age groups.
16 5G-1.4 Outdoor Time and Exertion Level of Asthmatics and Non-Asthmatics In
17 CHAD
18 Due to limited number of CHAD diaries with survey requested health information, all
19 CHAD diaries are assumed appropriate for any simulated individual (i.e., whether asthmatic,
20 non-asthmatic, or not indicated), provided they concur with age, sex, temperature, and day-of-
21 week selection criteria. In general, the assumption of modeling asthmatics similarly to healthy
22 individuals (i.e., using the same time-location-activity profiles) is supported by the findings of
23 van Gent et al. (2007), at least when considering children 7 to 10 years in age. These researchers
24 used three different activity-level measurement techniques; an accelerometer recording 1-minute
25 time intervals, a written diary considering 15-minute time blocks, and a categorical scale of
26 activity level. Based on analysis of 5-days of monitoring, van Gent et al. (2007) showed no
27 difference in the activity data collection methods used as well as no difference between asthmatic
28 children and healthy children when comparing their respective activity levels. Contrary to this,
29 an analysis of 2000 BRFSS data by Ford et al. (2003) indicated a statistically significant
30 difference between the percent of current asthmatics (30.9%) and non-asthmatics (27.8%)
31 characterized as inactive. In addition, these researchers found small but statistically significant
32 differences in the percent of asthmatic (26.6%) and non-asthmatic (28.1%) adults achieving
33 recommended levels of physical activity (i.e., either moderate or greater activity levels).
3 The ATUS time-use information was collected by subject recall of the prior day's activities and "conversational
interviewing" rather than asking scripted questions.
5G-12
-------
1 Note though, the salient issue is not just outdoor time and activity levels, but the
2 intersection of the two as well as recognizing the performance capabilities of persons with
3 asthma. A person's overall physical activity level is strongly linked with their time spent
4 outdoors and is considered an important correlate in encouraging increased physical activity
5 among children and adults alike (e.g., Sallis et al., 1998). In addition, introducing regular
6 exercise has been shown to improve physical fitness in asthmatic children, with statistically
7 significant increases in ventilation measures such as maximum minute ventilation rate (VEmax)
8 maximum oxygen uptake (VO2max) (e.g., van Vledhoven et al., 2001). Further, in other related
9 research, Santuz et al. (1997) indicated no statistically significant difference between asthmatic
10 and non-asthmatic children when comparing maximum exercise performance levels, provided
11 the individuals were conditioned through habitual exercise. Thus it appears that asthmatics
12 perform activities at elevated levels and do so in outdoor microenvironments in similar fashion to
13 non-asthmatics.
14 To provide further support to the assumption that any CHAD diary day can be used to
15 represent the asthmatic study groups regardless of the study participants' characterization of
16 having asthma or not, we first compared the amount of afternoon outdoor time and participation
17 in elevated exertion levels among asthmatics and non-asthmatics. Because six of the 19 studies
18 incorporated in CHAD reported whether the individual was asthmatic or non-asthmatic, we
19 categorized the data and results using three categories (i.e., asthmatic, non-asthmatic, not
20 classifiable). Afternoon hours were characterized as was done for above CHAD analyses, that is,
21 the time between 12 PM and 8 PM and only those persons that did spend some time outdoors
22 were retained. As is done by APEX in simulating individuals, level of exertion was estimated by
23 sampling from the specific METS distributions assigned for each person's activity performed.
24 Then, we selected for activities having a METS value of greater than 3 as times where a person
25 was at moderate or greater exertion levels (US DHHS, 1999). Afternoon outdoor time was then
26 stratified by exertion level, summed for two study groups of interest (children and adults), and
27 presented in percent form within Table 5G-2.
28 When considering CHAD diaries used by APEX in our simulations, about 18% of the
29 diaries are from either an asthmatic child or an asthmatic adult. Far fewer children's diaries are
30 from persons whose asthmatic status is unknown (12%) when compared to adults (30%) though
31 still, persons having unknown health status are a smaller proportion of the total available person -
32 days. On average, about 43% of all children spent some afternoon time outdoors while asthmatic
33 children have a higher participation rate (48.5%) when compared to non-asthmatic children
34 (41.2%). About half of the adults whose asthmatic condition was known did spend afternoon
35 time spent outdoors with participation rate generally similar for both asthmatic and non-
36 asthmatic adults. Outdoor participation rate for persons having unknown asthma status varied
5G-13
-------
1 from that of known persons; about 60% of the children's diaries and 31% of the adult diaries
2 indicate some afternoon time was spent outdoors.
3
4
Table 5G-2. Comparison of outdoor time expenditure and exertion level among asthmatic and
non-asthmatic diary days for CHAD diaries used by APEX.
Asthmatic?
Persons (n)
Outdoor Habitue (n)
Outdoor Habitue (%)
Percent of Afternoon
Hours Spent
Outdoors (%)
Percent of Afternoon
Outdoor Time at
Moderate or Greater
Exertion (%)
CHAD: Children (4 to 18)1
Yes
3,206
1,564
48.8
28.5
80.3
No
12,346
5,092
41.2
27.5
78.2
Unknown
2,128
1,267
59.5
28.9
79.2
CHAD: Adults (19 to 95)z
Yes
1,254
602
48.0
26.2
62.7
No
15,465
7,949
51.4
27.2
63.8
Unknown
7,075
2,176
30.8
22.2
60.3
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
CHAD studies for where a survey questionnaire response of whether or not child was asthmatic include CIN, ISR,
NHA, NHW, OAB, and SEA (see REA Table 5-3 for study names).
2 CHAD studies for where survey a questionnaire response of whether or not adult was asthmatic include CIN, EPA,
ISR, NHA, NHW, NSA, and SEA.
The amount of time spent outdoors by the persons that did so varied little across the two
study groups and three asthma categories. On average, diaries from children indicate
approximately 2Vi hours of afternoon time is spent outdoors, 80% of which is at a moderate or
greater exertion level, regardless of their asthma status. Slightly less afternoon time is spent
outdoors by adults (about 125-130 minutes) when compared with children whose asthma status
is known, though more notable is the lowered percent of afternoon time adults perform moderate
or greater exertion level activities (about 63%). As noted above regarding the reduced
participation rate for adults whose asthma status is unknown, diaries for these adults also have
about 20 fewer minutes of afternoon time spent outdoors compared with those persons whose
asthma status is known.
Outdoor time and activity levels of respective cohorts from three independent asthma
activity pattern studies were compared to CHAD diary days using similar metrics. To make the
CHAD data compatible with the independent asthma study data, the entire diary day was
evaluated, not just the afternoon hours, and all persons (not just outdoor habitue) were
considered. In addition and where possible, the demographics of the independent study
participants was used to select for the most representative CHAD diaries (e.g., person's age, sex,
month- or day-of-year, etc.). Table 5G-3, Table 5G-4, and Table 5G-5 summarize the data
reported from the three asthma activity pattern studies and the compatible results generated using
CHAD and the indicated asthma status of the study persons.
5G-14
-------
1
2
Table 5G-3. Percent of waking hours spent outdoors at an elevated activity level. A comparison
of CHAD with Shamoo et al. (1994) study asthmatics.
Study
Location
Time of Year
Asthmatic?
Person days
Mean age
(min-max)
Exertion Level
Low
Moderate
Strenuous
Shamoo (1994)1
Los Angeles
Summer
Months
(5-9)
Yes
336
Winter
Months
(11-3)
Yes
314
33
(18-50)
CHAD2
Any
Summer Months (May-Sep)
Yes
375
37.3
(18-50)
No
4,812
37.9
(18-50)
Unknown
1,049
35.2
(18-50)
Winter Months (Nov-Mar)
Yes
211
30.4
(18-50)
No
2,512
32.3
(18-50)
Unknown
1,998
34.3
(18-50)
Percent of Asthmatic Waking Hours Spent Outdoors at Given Exertion Level
8.5 6.0
1.9 1.7
0.2 0.2
4.6
6.2
1.1
5.6
6.9
1.2
5.4
4.6
1.0
2.0
2.0
0.6
2.1
2.7
0.7
1.4
2.0
0.5
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Based on number of minutes performing three self-rated activity levels for three locations per hour (indoor,
outdoor, in-vehicles) over seven days. Non-random sample of 49 subjects selected from voluntary clinical studies.
2 Combination of random and non random selection studies, national and city-specific, as well as varying diary
protocol (see REA Appendix 5B). The APEX CHAD file (n=41,474) was additionally screened for persons having
no sleep reported (n=141). Randomly sampled METS values from each activity-specific distribution were assigned
to each person's activities. Moderate and vigorous activity levels were selected based on activities having a METS
value of 3 to <6 and >6, respectively.
When considering the three independent asthma studies, the amount of time spent
outdoors at moderate activity level ranges from a low of approximately 2% to a high of about
11% of waking hours. The estimates of outdoor time associated with moderate activity level
using a similarly constructed cohort of CHAD diaries fall within that range (i.e., between 2.0 and
7.2%). A small but consistent trend of fewer minutes spent outdoors at moderate exertion was
observed for the CHAD asthmatic cohort when compared with the CHAD non-asthmatic group.
The general range in percent of outdoor time associated with strenuous activities using the
CHAD person days (0.4% to 7.6%) was greater when compared to asthmatic persons from the
three independent studies (0.2% to 3.3%).
We recognize that there are a number of differences that exist among the three asthmatic
studies used for comparison along with the use of CHAD diary data from either asthmatics, non-
asthmatics, or unclassified asthmatics that could contribute to variation in the time spent
outdoors at elevated activity levels. This would include: the diary/survey collection methods
used, the classification of activities performed and associated activity levels, the number of study
subjects, and sample selection methods. The particulars regarding how each of these were
addressed across the various studies is wide ranging and could potentially influence the results
generated here. However, based on the mostly comparable results observed in time spent
outdoors at moderate or greater exertion activity levels, we judge the use of a CHAD diary
5G-15
-------
1
2
3
4
5
6
7
regardless of a persons' asthma condition is reasonably justified based on the available data
analyzed.
Table 5G-4. Percent of waking hours spent outdoors at an elevated activity level: a comparison
of CHAD with EPRI (1992) study asthmatics.
Study
Location
Time of Year
Asthmatic?
Person days
Mean age
(min-max)
Exertion Level
Moderate
Strenuous
EPRI(1992)1
Cincinnati
August
(Wed-Sun)
Yes
408
26
(1 - 78)
CHAD
Any
July and August
(Thu-Sat)
Yes
711
17.9
(1 - 77)
No
3,085
34.9
(1 - 78)
Unknown
1,209
32.3
(1 - 78)
Percent of Asthmatic Waking Hours Spent
Outdoors at Given Exertion Level
11
3.3
5.0
7.6
7.2
3.7
7.2
2.6
Hour diary questionnaire form used up to three activities per hour. Random digit dialing and multiplicity sampling
were used. Three consecutive diary days were collected from 136 asthmatics, mostly Thu-Sat though some
Wednesday and Sunday data were included.
9
10
11
12
13
14
15
16
Table 5G-5. Percent of waking hours spent outdoors at an elevated activity level: a comparison
of CHAD with EPRI (1988) study asthmatics.
Study
Location
Time of Year
Asthmatic?
Person days
Mean age
(min-max)
Exertion Level
Moderate
Strenuous
EPRICI988)1
Los Angeles
April (Fri-Mon)
Yes
156
(18-37)2
CHAD
Any
April and May (Fri-Mon)
Yes
26.2
(18-39)
No
29.7
(18-40)
Unknown
30.9
(18-40)
Percent of Asthmatic Waking Hours Spent
Outdoors at Given Exertion Level
7
2.4
2.8
0.4
4.5
0.6
4.4
1.2
Hour diary questionnaire form used up to three activities per hour. Non-random sample of 26 mild/moderate, 26
moderate/severe asthmatics selected from voluntary clinical studies. Three consecutive diary days were collected
per person (either Fri-Sun or Sat-Mon).
2 General age range approximated from twenty-nine subjects noted by EPRI (1988) as from Linn et al. (1987).
5G-16
-------
1 5G-2 CHARACTERIZATION OF FACTORS INFLUENCING HIGH EXPOSURES
2 We investigated the factors that influence estimated exposures, with a focus on persons
3 experiencing the highest daily maximum 8-hour exposures six selected study areas - Atlanta,
4 Boston, Denver, Houston, Philadelphia, and Sacramento.4 This analysis required the generation
5 of detailed APEX output files having varying time intervals, that is, the daily, hourly, and
6 minute-by-minute (or events) files. Given that the size of these time-series files is dependent on
7 the number of persons simulated, we simulated 5,000 persons and restricted the analysis to a
8 single year (2006) to make this evaluation tractable.5 Both the base case (unadjusted or 'as is'
9 recent air quality conditions) and ambient 63 adjusted to just meet the existing standard (75 ppb-
10 8hr) air quality scenarios were evaluated in each of six study areas. All APEX conditions (e.g.,
11 ME descriptions, AERs, MET data) were consistent with the 200,000 person APEX simulations
12 that generated all of summary output discussed in the main body of this chapter.
13 We were interested in identifying the specific microenvironments and activities most
14 important to 63 exposure and evaluating their duration and particular times of the day people
15 were engaged in them. Because ambient 63 concentrations peak mainly during the afternoon
16 hours, we focused our microenvironmental time expenditure analysis on the hours between 12
17 PM and 8 PM. For every person and day of the exposure simulation, we aggregated the time
18 spent outdoors, indoors, near-roadways, and inside vehicles during these afternoon hours (i.e.,
19 the time of interest summed to 480 minutes per person day). Data from several APEX output
20 files were then combined to generate a single daily file for each person containing a variety of
21 personal attributes (e.g., age, sex), their daily maximum 8-hour ambient and exposure
22 concentrations, and the aforementioned time expenditure metrics.
23 We performed an analysis of variance (ANOVA) using SAS PROC GLM (SAS, 2012)
24 to determine the factors contributing the greatest to the observed variability in the dependent
25 variable, i.e., each person's daily maximum 8-hour 63 exposure concentrations. This analysis
26 was distinct for four age-groups of interest (i.e., 5-18, 19-35, 36-64, >65 years of age). The final
4 For the 1st draft O3 REA, this analysis was performed for four study areas: Atlanta, Denver, Los Angeles, and
Philadelphia. One important difference between the exposure simulations at that time compared with this 2nd draft
O3 REA was the air quality data input to APEX: ambient monitoring data were used for the 1st draft O3 REA along
with a quadratic approach for adjusting air quality to just meet the existing standard.
5 We recognize that there is year-to-year variability in ambient O3 concentrations and it is possible that fewer
persons simulated could result in differences in exposures compared to large-scale multi-year model simulations.
Based on a similar detailed evaluation performed for the Carbon Monoxide REA (US EPA, 2010), it is expected any
differences that exist between exposures estimated in a large simulation versus that using a smaller subset of persons
would be small and of limited importance to this particular evaluation.
5G-17
-------
1 statistical models6 included a total of seven explanatory variables: the main effects of (1) daily
2 maximum 8-hour ambient 63, (2 to 4) afternoon time spent outdoors, near-roads, and inside
3 vehicles,7 and (5) physical activity index (PAI), while also including interaction effects from (6)
4 afternoon time outdoors by daily maximum 8-hour ambient concentration, and (7) PAI by
5 afternoon time outdoors. Two conditions were considered: all person days of the simulation, and
o
6 only those days where a person's 8-hour maximum exposure concentration was >50 ppb.
7 Selected output from this ANOVA included parameter estimates for each variable, model R-
8 square statistic (R2), and Type III model sums of squares (SS3).9
r\
9 Model fits, as indicated by an R value, were reasonable across each of the study areas
10 (Table 5G-6). The selected factors explain about 40-80% of the total variability in 8-hour daily
11 maximum exposures. Model fits were best when using all person days of the simulation though
12 results were similar for both air quality scenarios. When considering only those days where
13 persons had 8-hour daily maximum 63 exposures >50 ppb, consistently less variability in
14 maximum exposure concentrations was explained by the factors included in each model, though
15 overall model fits were acceptable. Furthermore, the most robust models tended to be those
16 developed using either school-age children aged 5 to 18 or adults 19 to 35 years old (e.g., see
9
17 Table 5G-7 for Atlanta model R results stratified by age groups).
18 We evaluated the relative contribution each variable had on the total explained variability
in 9
19 using the SS3 in each respective model. As with the R statistics generated above, the percent
20 contribution results were separated into four exposure scenarios for each study area, with
21 estimates for Boston illustrated in Figure 5G-3. When considering all person days of the
22 simulation (top row), the daily maximum 8-hour ambient Os concentration variable contributes
23 the greatest to the explained model variance, consistently estimated to be about 85% across all
24 age groups and for either the base or existing standard air quality scenarios. The interaction of
25 this variable with afternoon outdoor time contributes an additional 7-10% to the explained
26 variance, indicating that both ambient concentration and time spent outdoors collectively
27 contribute to 90% or more of the explained model variance when evaluating all (e.g., high, mid-
28 range, and low level) daily maximum 8-hour O?, exposure concentrations. The main effect of
6 In this investigation, we also evaluated the influence of sex, work and home districts, meteorological zones, each
with varying statistical significance, though overall adding little to explaining variability beyond the variables
selected for the final ANOVA model.
7 Including indoor afternoon time creates a strict linear dependence among these four variables and generates biased
estimates, thus it was neither included nor needed in this analysis.
8 This exposure concentration was selected due to the reduced sample size needed for these simulations (i.e., 5,000
total persons), an issue of increasing importance when selecting for persons with the highest exposures.
9 In each of the ANOVA models constructed, type II = type III = type IV sums of squares.
10 Type III sums of squares (SS3) for a given effect are adjusted for all other effects evaluated in the model,
regardless of whether they contain the given effect or not. Thus, the S S3 for each variable represents the individual
effect sums of squares that sum to the total effect sums of squares (or the total model explained variance).
5G-18
-------
1 outdoor time contributed < 1% to the explained variance under these conditions as did
2 contributions from the other included variables, except for time spent near-roads (about a 5%
3 contribution). These results suggest that when considering the Boston exposure study groups
4 broadly, the daily maximum 8-hour ambient O?, concentration is the most important driver in
5 estimating population-based 63 exposures, nearly regardless of specific microenvironmental
6 locations where exposure might occur.
9
7 Table 5G-6. Range of R fit statistics for ANOVA models used to evaluate daily maximum 8-
8 hour Os exposure concentrations stratified by study area, air quality scenario, and exposure level.
Study Area
(O3 season)
Atlanta
(245 days)
Boston
(183 days)
Denver
(21 4 days)
Houston
(365 days)
Philadelphia
(21 4 days)
Sacramento
(365 days)
Base Case Model R2 (sample sizes)
All Person
Days
0.71 -0.78
(1,225,000)
0.64-0.70
(915,000)
0.61 -0.68
(1,070,000)
0.75-0.80
(1,825,000)
0.66-0.73
(1,070,000)
0.75-0.81
(1,825,000)
Person days with 8-hr daily
max exposure > 50 ppb
0.62-0.70
(43,646)
0.44-0.62
(23,496)
0.49-0.62
(24,850)
0.57-0.67
(17,779)
0.51 -0.67
(39,561)
0.50-0.71
(19,734)
Existing Standard Model R2 (sample sizes)
All Person
Days
0.67-0.74
(1,225,000)
0.58-0.65
(915,000)
0.55-0.62
(1,070,000)
0.65-0.71
(1,825,000)
0.57-0.64
(1,070,000)
0.68-0.74
(1,825,000)
Person days with 8-hr daily
max exposure > 50 ppb
0.61 -0.70
(18,758)
0.41 -0.59
(16,184)
0.47-0.61
(18,487)
0.38-0.65
(7,322)
0.48-0.66
(16,841)
0.48-0.73
(6,503)
10
11
12
Table 5G-7. Range of R fit statistics for ANOVA models used to evaluate daily maximum 8-
hour O3 exposure concentrations in Los Angeles stratified by age group, air quality scenario, and
exposure level.
Study Area
Atlanta
Age Group
(years)
5-18
19-35
36-64
>65
Base Case Model R2
All Person
Days
0.78
0.71
0.73
0.74
Person days with
8-hr daily max
exposure > 50 ppb
0.64
0.70
0.64
0.62
Existing Standard Model R2
All Person
Days
0.74
0.67
0.68
0.70
Person days with
8-hr daily max
exposure > 50 ppb
0.62
0.70
0.64
0.61
13
14
15
16
17
When considering only person days having daily maximum 8-hour Oi exposures > 50
ppb and for either air quality scenario in Boston, collectively the main effects of ambient
concentrations and outdoor time combined with their interaction similarly contribute to
approximately 85% of the total explained variance (Figure 5G-3, bottom row). However, the
5G-19
-------
1 main effect of the 8-hour daily maximum ambient Os concentration variable has a sharply lower
2 contribution (generally about 5-15%) along with greater contribution from the main effects
3 variable outdoor time (15-25% contribution) and its interaction with the ambient concentration
4 variable (40-60%). These results suggest that for highly exposed persons in Boston, the most
5 important influential factors are time spent outdoors corresponding with high daily maximum 8-
6 hour ambient Os concentrations.
7 Results for Atlanta (Figure 5G-4), were generally similar to Boston with notable
8 differences discussed here.n The contribution of the maximum 8-hour ambient Oj concentration
9 variable to the total explained variance (about 40-50%) was less than that observed in Boston
10 when considering all person days (Figure 5G-3 and Figure 5G-4, top rows), while the
11 contribution from the outdoor time/ambient Os interaction variable was greater in Atlanta (about
12 20-40% versus 10% in Boston).
13 This observed dissimilarity in the contribution by ambient concentrations and afternoon
14 outdoor time may be driven by the A/C prevalence rates and AER distributions used for each
15 study area.12 Boston has lower A/C prevalence though overall higher AERs (even when
16 considering mechanical ventilation), thus a greater contribution to exposure is expected from
17 ambient concentrations by infiltrating to indoor microenvironments and hence, reflected in the
18 strong main effects for the 8-hour daily maximum ambient Os concentration variable in Boston.
19 Afternoon time spent near Atlanta roads was estimated to contribute to about 20-30% of the total
20 explained variance when considering all person days and exposures, a value greater than that
21 estimated for Boston (generally about 5%) again possibly reflecting an increased importance of
22 this outdoor microenvironment in Atlanta (and Houston, Sacramento, not shown) relative to that
23 in Boston (and Philadelphia, not shown).
11 The discussion regarding the relative contribution of the variables to the total explained model variance also
extends to the other four study areas, whereas results for Philadelphia were generally similar to Boston, Sacramento
and Houston were similar to Atlanta, and Denver generally fell somewhere in between these extremes presented.
12 A/C prevalence is highest in Houston (99%), Atlanta (98%), Philadelphia (95%), and Sacramento (93%)
compared to Boston (86%) and Denver (67%). Boston and Philadelphia used the same (and highest) AER
distributions; Sacramento, Houston, and Atlanta used separate but similar (and lower) AER distributions, while
Denver AER distributions fall somewhere in between these two extremes (see Appendix B, Tables 5B-3 to 5B-5).
5G-20
-------
Boston, Base Air Quality,
All Person Days
75%
Model Explained Variance
Boston, Base Air Quality,
Person Days w/S-hr Exposures > 50 ppb
Model Explained Variance
Boston, Existing 75 ppb Standard,
Person Days w/S-hr Exposures > 50 ppb
Model Explained Variance
Boston, Existing75 ppb Standard,
All Person Days
Model Explained Variance
100%
5
6
1
I Max8-hr Ambient O3 •Time Outdoors 12-8PM
Max 8-hr Ambient O3*Time Outdoors 12-8PM • Physical Activity Index (PAI)
I Time Outdoors 12-8PM*PAI •Time Near Roads 12-8PM
Time In-Vehicles 12-8PM
Figure 5G-3. Contribution of influential factors to daily maximum 8-hour ozone exposures
using base air quality (left), air quality adjusted to just meet the existing standard (right),
considering all person days (top) and those days where daily maximum 8-hour exposure
exceeded 50 ppb (bottom) in Boston.
5G-21
-------
Atlanta, Base Air Quality,
All Person Days
75%
Model Explained Variance
Atlanta, Base AirQuality,
Person Days w/S-hr Exposures > 50 ppb
Model Explained Variance
Atlanta, Existing75 ppb Standard,
All Person Days
100%
Model Explained Variance
Atlanta, Existing 75 ppb Standard,
Person Days w/8-hr Exposures > 50 ppb
.
Model Explained Variance
I Max8-hr Ambient O3
Max8-hr Ambient O3*Time Outdoors 12-8PM
(Time Outdoors 12-8PM*PAI
Time In-Vehicles 12-8PM
• Time Outdoors 12-8PM
• Physical Activity Index (PAI)
Time Near Roads 12-8PM
5
6 Figure 5G-4. Contribution of influential factors to daily maximum 8-hour ozone exposures
7 using base air quality (left), air quality adjusted to just meet the existing standard (right),
8 considering all person days (top) and those days where daily maximum 8-hour exposure
9 exceeded 50 ppb (bottom) in Atlanta.
10 Because afternoon outdoor time expenditure and daily maximum 8-hour ambient Os
11 concentrations are an important determinant for high Oj, exposures regardless of air quality
12 scenario considered, we compared the distributions of these two variables using person days
13 where daily maximum 8-hour Os exposures were either below or above 50 ppb. Figure 5G-5
14 presents this comparison for Boston13 school-age children (ages 5 to 18) and adults (ages 19 to
15 35) and considering 2006 base air quality and air quality adjusted to just meet the existing Os 8-
16 hour standard. For school-age children that did not experience a daily maximum 8-hour
The overall features of the outdoor time and ambient concentration distributions illustrated by simulated persons
in Boston are similar to each of the other study areas (data not shown).
5G-22
-------
1 exposure at or above 50 ppb (e.g., top left panel, base air quality), over half of them did not
2 spend afternoon time spent outdoors, while just under 20% of them spent at least two hours of
3 their afternoon time spent outdoors, with fewer than 5% spending more than four hours of their
4 afternoon time outdoors. In addition, nearly 70% would have their daily maximum 8-hour
5 ambient concentrations below 50 ppb (please note, ambient is not exposure).
6 Not surprisingly, the distributions for both the outdoor time and ambient concentration
7 variables are shifted to the right of the figure for school-age children's person days where daily
8 maximum 8-hour exposures > 50 ppb (e.g., Figure 5G-1, top left panel, base air quality), as for
9 more than half of the days, highly exposed simulated individuals spend about 250 minutes
10 outdoors during the afternoon hours along with experiencing daily maximum 8-hour ambient Os
11 concentrations > 75 ppb.
12 By design, when air quality is simulated to just meet the existing standard (e.g., Figure
13 5G-5, bottom left panel), upper percentile ambient concentrations are reduced compared to those
14 comprising the base air quality such that the majority of ambient concentrations fall well below
15 the existing standard level of 75 ppb. Given so few occurrences of very high 8-hour ambient 63
16 concentrations for this air quality scenario, only those school-age children having a majority of
17 their time spent outdoors experienced the highest daily maximum 8-hour O^ exposure
18 concentrations (Figure 5G-5, bottom left panel, right-most solid line). For additional
19 completeness, we note the time and concentration distributions for adult person days (Figure
20 5G-5, right column) were similar with that estimated for school-age children.
5G-23
-------
Boston, Children 5-18 Years Old, Base Air Quality
Daily Maximum 8-Hour Ambient Ozone (ppb)
25 50 75 100 125
80 160 240 320 400
Total Afternoon Time Spent Outdoors (minutes)
Boston, Children 5-18 Years Old, Existing75 ppb Standard
Daily Maximum 8-Hour Ambient Ozone {ppb]
50 75 100 125
160 240 320 400
Total Afternoon Time Spent Outdoors (minutes)
Boston, Adults 19-35 Years Old, Base Air Quality
Daily Maximum 8-Hour Ambient Ozone {ppb]
25 50 75 100 125
80 160 240 320 400
Total Afternoon Time Spent Outdoors (minutes)
Boston, Adults 19-35 Years Old, Existing75 ppb Standard
Daily Maximum 8-Hour Ambient Ozone {ppb]
50 75 100 125
160 240 320 400
Total Afternoon Time Spent Outdoors (minutes)
-Out time: Max 8-hr Exposure < 50 ppb
-Max8-hr Amb: Max 8-hr Exposure < 50 ppb
^—Out time: Max 8-hr Exposure > 50 ppb
——•Max 8-hr Amb: Max 8-hr Exposure > 50 ppb
5
6 Figure 5G-5. Distributions of afternoon outdoor time expenditure and daily maximum 8-hour
7 ambient 63 concentrations for simulated Boston school-age children (ages 5 to 18) (left) and
8 adults (ages 19 to 35) (right) using base air quality (top) and concentrations adjusted to just meet
9 the existing standard (bottom) for person days having daily maximum 8-hour exposures either
10 below or above 50 ppb.
11
12 By definition, any 8-hour average exposure is time-averaged across all
13 microenvironmental concentrations; thus several different microenvironments may contribute to
14 each person's daily maximum level. Understandably based on the above analyses, the outdoor
15 microenvironment is most important for persons experiencing the highest 63 exposures, but we
16 are also interested in the percentage of time expenditure spent among detailed indoor, outdoor,
17 and vehicular locations people may inhabit during the afternoon. We summed the afternoon time
18 expended for highly exposed persons, considering a total of 12 microenvironments (i.e., 3
19 indoor, 5 outdoor, 2 near road, and 2 vehicular). As an example, Figure 5G-6 presents this
20 microenvironmental information for Boston school-age children (Figure 5G-6, top left panel) and
21 adults (Figure 5G-6, top right panel) for persons experiencing daily maximum 8-hour average Os
5G-24
-------
1 exposures > 50 ppb and considering base air quality conditions. On average, approximately 50%
2 of school-age children's total afternoon time is spent outdoors, of which half of this portion is
3 spent outdoors at home, with parks and other non-residential outdoor locations comprising the
4 remaining portion. Approximately 45% of the school-age children's afternoon time on high
5 exposure days is spent indoors, while just less than 10% of afternoon time is spent near-roads or
6 inside motor vehicles. Afternoon microenvironmental time expenditure for highly exposed
7 adults (ages 19-35) in Boston was generally similar with that estimated for school-age children
8 (Figure 5G-6, top right panel).
9 A person's activity level plays an important role in estimating the risk of adverse health
10 responses to inhaled ozone. As such, we evaluated the activities performed by highly exposed
11 individuals while they spent time outdoors during the afternoon hours. Note there are over 100
12 specific activity codes used in CHAD/APEX, though not all of these will be used in an exposure
13 modeling simulation depending on the particular diaries that are selected to represent the
14 simulated study group. We summed the time spent in each specific activity across all highly
15 exposed persons when spending afternoon time outdoors, ranked the activity sums, and identified
16 the top eleven activities performed. An aggregate of any remaining less often performed
17 activities was generated to complete this analysis of activity time expenditure.
18 Figure 5G-6 shows results for Boston school-age children (bottom left panel), indicating
19 that greater than half of the time highly exposed children spent outdoors specifically involves
20 performing a moderate or greater exertion level activity, such as a sporting activity. The same
21 type of analysis was done for highly exposed adults in Boston (Figure 5G-6, bottom right panel),
22 whereas about 30% of the outdoor time expenditure was spent engaged in a paid work related
23 activity (though not necessarily a high exertion level activity), about 15% of the time was spent
24 playing sports or other moderate or greater exertion level activity, with much of the remaining
25 specific activities associated with low exertion level (e.g., eating, sitting, visiting) or other less
26 frequently performed activities of variable exertion level. These results support our
27 identification of school-age children as an important exposure group, largely a result of the
28 combined outdoor time expenditure along with concomitantly performing moderate or high
29 exertion level activities. It is worth noting, one important group not directly assessed in the
30 general population-based exposure modeling and remaining as a limitation to the main body
31 REA results is outdoor workers. This exposure study group was explicitly modeled using a
32 scenario based approach and summarized in section 5G-4.2.
5G-25
-------
Afternoon Time Expenditure: Children 5-18, 8-hr Exposures > 50 ppb,
Vehicle (Cars and Boston, Base Air Quality
Light Duty Trucks)^ ^Vehicle Other (4MEs)
Outdoor (Restaurant_____5%
or Cafe) ~
<1% Outdoor (School_
grounds)
Outdoor (Park or Golf _/
.Indoor (Otherindot
9MEs)
ar-road (Within 10
yards of street)
Afternoon Outdoor Activities: Children 5-18, 8-hr Exposures > 50 ppb,
Boston, Base Air Quality
Travel, All
Othersportsand_-
active leisure
5% Play/OutdoorLeisi
AfternoonTime Expenditure: Adults 19-35, 8-hr Exposures>50 ppb,
Vehicle (Cars and Boston, Base Air Quality
Vehicle Other (4 MEs)
Outdoor (School
grounds)
Out door (Park orGolf
Indoor (Office
^building. Bank, Post
I ear-road (Within 10
yards of street)
Afternoon Outdoor Activities: Adults 19-35, 8-hr Exposures >50 ppb,
Boston, Base Air Quality
Repair/maintain car
Other entertainment
/social event:
5 Figure 5G-6. Afternoon microenvironmental time (top) and activities performed during
6 afternoon time outdoors (bottom) for school-age children (left) and adults (right) experiencing 8-
7 hour daily maximum Os exposures > 50 ppb, Boston base air quality, 2006.
8 5G-3 ANALYSIS OF APEX SIMULATED LONGITUDINAL ACTIVITY PATTERNS
9 IN SCHOOL-AGE CHILDREN
10 We evaluated the APEX approach used for linking together cross-sectional activity
11 pattern diaries to generate longitudinal profiles for our simulated individuals. Of particular
12 interest were how well variability in outdoor participation rate and the amount of time expended
13 were represented in our population-based exposure simulations. Our goal in developing the most
14 reasonable longitudinal profiles is to capture expected, important features of population activity
15 patterns, i.e., there is correlation within an individual's day-to-day activity patterns (though not
16 exactly repeated nor entirely random) and variability across the modeled study group in day-to-
17 day activity patterns (not every simulated individual in the study group does the same thing on
18 the same day). As a reminder, the longitudinal approach is probabilistic, though guided by key
19 variables influencing activity patterns (i.e., age, sex, day-of-week, commute time [employed
20 person only], daily maximum temperature, and in our application, considers within and between
21 variability in outdoor time expenditure). See REA Appendix 5B, section 5B-4.2.
5G-26
-------
1 We used the same event-level output data that was generated for the high exposure
2 analysis above (section 5G-2), which includes the same six study areas - Atlanta, Boston,
3 Denver, Houston, Philadelphia, and Sacramento - and focused the analysis on school-age
4 children (ages 5-18). Total time spent outdoors during the afternoon hours (12 PM-8 PM) was
5 calculated for each person-day of the simulation in each study area. Results of this analysis are
6 presented in five individual plots for each study area, though combined in a multi-panel display,
7 one per three study areas, designed to fit on a single page. The five individual plots generated
8 for each study area are described as follows.
9 1) Cumulative distribution summarizing each child's median time spent outdoors
10 across an Os season:14 We first selected simulated individuals within the age group
11 of interest (5-18) and then stratified these persons by sex. The median value (50*
12 percentile) of afternoon time spent outdoors was determined for each simulated
13 individual using all days in their study area's 63 season. This data set, comprised of
14 individual median values (in minutes) was ranked and plotted, stratified by sex.
15 2) Cumulative distribution summarizing each child's afternoon outdoor
16 participation (at least one minute/day) across an Os season: We subset school-age
17 children from the data set and stratified by sex. A categorical variable was developed
18 by assigning a numeric value of 1 when an individual spent at least one minute during
19 the afternoon hours outdoors on that given day. Then for each simulated individual,
20 outdoor participation was determined by summing this variable across the simulation
21 period (i.e., the number of days per 63 season the persons spent at least one minute
22 outdoor during the afternoon hours) and dividing by the total number of days in that
23 study area's 63 season. This data set, comprised of individual participation values
24 (provided as a percent) was ranked and plotted, stratified by sex.
25 3) Cumulative distribution summarizing each child's afternoon outdoor
26 participation (at least two hours/day) across an Os season: Calculated and
27 presented in the same manner as #2 above, only that the categorical variable was
28 assigned a numeric value of 1 if the simulated individual spent at least two hours
29 outdoor during the afternoon hours on that given day.
30 4) Daily time series of afternoon outdoor participation (at least two hours/day) by
31 study group across Os season: Using the categorical variable determined in #3, we
32 calculated the study group's outdoor participation for every day of each study area's
33 Os season. Data similar in fashion to our earlier analyses of outdoor time expenditure
34 (e.g., section 5G-2), only differing in that presented are day-to-day variability in
14 The number of days in an O3 season varies across the six study areas. See Table 5G-6.
5G-27
-------
1 outdoor participation for the simulated study group across each study area's O?,
2 season.
3 5) Daily time series of the number of study-specific CHAD diaries used across Os
4 season: The APEX daily file output can include the identity of the specific CHAD
5 diary used to simulate every individual's daily activity. For every day simulated, we
6 summed the number of CHAD diaries used to model the school age children's
7 activity patterns for each day, though stratified by CHAD study identifier (e.g., see
8 REA Appendix 5B, Table 5B-1). Plotted is the day-to-day variability in particular
9 CHAD study diaries used across each study area's Os season.
10
11 The results of this longitudinal activity pattern analysis in given in Figure 5G-7 and
12 Figure 5G-8. To begin, a few generalities regarding the features of each plot and where
13 consistency is exhibited across study areas. In general, simulated female school-age children
14 tend to spend less afternoon time outdoors than their male counterparts, consistent of course with
15 expectations and the data used to develop these simulated profiles (Graham and McCurdy,
16 2004). About half of the simulated study group spends about half of their days with no afternoon
17 time spent outdoors across their study area's ozone season, while about 10-20% spent just over 2
18 hours afternoon time spent outdoors for half of their days (top row of Figure 5G-7 and Figure
19 5G-8). Nearly every simulated individual participates in at least one afternoon outdoor activity
20 across the Os season and exhibits a mostly monotonic relationship (2n row of Figure 5G-7 and
21 Figure 5G-8), though when considering durations of 2 hours or more, longitudinal outdoor
22 participation drops dramatically (an non-linearly) for most persons comprising the study group
23 (3r row of Figure 5G-7 and Figure 5G-8). For more than half of simulated school age children,
24 only approximately 1 out of every 5 days was spent outdoors during the afternoon hours for at
25 least two hours, while a maximum value (around 3 of 5 days) was simulated for only about 10%
26 or fewer children comprising the study group. Study group participation in at least two hours of
27 afternoon time outdoors day-to-day ranges from about 5-40% across each study area's 63 season
28 (4 row of Figure 5G-7 and Figure 5G-8), though not surprisingly highest during typical summer
29 months (June through September). And finally, the majority of CHAD diaries that are used
30 come from the recently conducted ISR and OAB studies (bottom row of Figure 5G-7 and Figure
31 5G-8). This is also expected given that these two studies were designed to collect children's
32 activity patterns and contribute to the bulk of the children's diaries in CHAD. Worthy of note is
33 the shift in the source of diaries used across the calendar year; the contribution from the OAB
34 study increases during the summer months while that of the ISR wanes. This is because of the
35 days/seasons of the year the original study data were collected; most of the ISR data were
36 collected during non-summer months while the OAB study was conducted during peak Os
5G-28
-------
1 concentration days. While the use of these different studies in varying numbers over the
2 simulation period likely drives some of the observed variability in the outdoor participation, at
3 this time (and previously) staff treat the CHAD study data equally without bias, following our
4 initial screen of the CHAD master data base that selected for the most complete data available.
5 Variability in the five longitudinal display plots across the six study areas is evident,
6 though to a much smaller degree than that observed when considering the magnitude of the
7 within study area variability. While different lengths of each study areas' 63 season may negate
8 direct comparability of the distributions presented, it is reasonable to conclude that simulated
9 school-age children in the Atlanta, Houston, and Sacramento study areas had slightly overall
10 greater participation in outdoor activities and spent more time outdoors than counterparts in
11 Boston, Denver, and Philadelphia. That said, when considering the daily time series of
12 participation rates, on many summer days Philadelphia and Sacramento school-age participation
13 rates for males are as great or greater than participation rates observed most other study areas,
14 including study areas likely having considerably warmer summer temperatures (Figure 5G-7 and
15 Figure 5G-8, 4th row). It is possible that for study areas such as Atlanta, summertime maximum
16 daily temperatures exceed the range affording outdoor comfort, yielding slightly lower rates of
17 participation in outdoor activities.
18 Overall, the simulated longitudinal profiles indicate the method for linking together
19 cross-sectional diaries generates a diverse mixture of persons having variable, though expected,
20 activity patterns: a small fraction of the simulated population spend a limited amount of
21 afternoon time outdoors and occurring at a low frequency across an 63 season, a small fraction
22 consistently spends a greater amount (> 2 hours) of time outdoors and occurring at greater
23 frequency (e.g., 4/5 days per week), while the remaining simulated individuals fall somewhere in
24 between these two lower and upper bounds regarding participation and total time. While we are
25 not aware of a population database available to compare with these simulated results, we are
26 comfortable with the method performance in representing the intended variability in longitudinal
27 activity patterns
5G-29
-------
Atlanta, 2007
16
17
18
19
20
21
b °u
Q- en -
« DU
> 40 -
E ^o
3 ,n
-P-"|— *l
fSffi®3^
\^^£$&^^~"^
^^*ffl9^^
.wfaV20
^SJ-'tJ
-•-School-Age Children (Females)
oSchool-Age Children (Males)
Boston, 2007
Denver, 2007
50 -
«^»**
^P"^ ^
J^
^^**r&®£
g,«f$®2
-•-School-Age Children (Females)
<>School-Age Children (Males)
• 80
fe bU
•i 50-
> 40 ^
a 30 -
= -in
^^0
^^*^ J
^f^
^^
^.M^^DtSOt
>«^*^fflS*^*"^
rmCT^
013-'
AUVU CF
-^School-Age Children (Females)
-^School-Age Children (Males)
60 120 180 240
Median Afternoon Time Outdoors
(Minutes/Day)
60 120 180 240
Median Afternoon Time Outdoors
(Minutes/Day) 1 1
60 120 180
Median Afternoon Time Outdoors
(Minutes/Day)
90
60
50
20
- -"-School-Age Children (Females)
: oSchool-Age Children (Males) ^^
^3
A^
^
v^
,/J
r>"
^^
,**
$f^
jX^X
'v#^
iF
&
20% 40% 60% 80%
Afternoon Outdoor Participation
(Percent of Days > 1 Minute/Day)
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 1 Minute/Day) 12
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 1 Minute/Day)
« yU
g 70 -
.1 40
1 20
= 10
^/
- /^ 1
- f jf
-/y
7/
s**
V**' ' j$f&
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^School-Age Children (Females)
^School-Age Children (Males)
o, 90
g 70 -
'Z bu
.1 40
= on
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_/
s
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- fjf
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--School-Age Children
School-Age Children
(Females)
(Males)
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 2 Hours/Day)
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 2 Hours/Day) 1 3
20% 40% 60% 80%
Afternoon Outdoor Participation
(Percent of Days > 2 Hours/Day)
in CD r^ co O)
Exposure Simulation Date
£N^^^^^^^
^^^^^^^^
Exposure Simulation Date ^~ 14
5 5 5 5 5 5 5
Exposure Simulation Date
in CD r^
Exposure Simulation Date
10
in CD r^ co O)
Exposure Simulation Date
CD r^ co O)
Exposure Simulation Date
Figure 5G-7. Cumulative distribution of median time spent outdoors (top row), afternoon
outdoor participation > 1 minute/day (2n row), and afternoon outdoor participation > 2
hours/day (3n row) for male and female school-age children in Atlanta (left column), Boston
(middle column) and Denver (right column) study areas. Percent of school-age children with > 2
hours outdoors during afternoon hours (4th row) and the number of particular CHAD study diary
days used (bottom row) for each exposure simulation day.
5G-30
-------
Houston, 2007
• 80 -
a. 50
> 40 -
3 3° •
J
-tf^ ,
^__,^s^_
^0®
^J*^^
•*** ^3=5°*-
^^°
,*»^Q^
CJSS"15"^
Soix— -
-•-School-Age Children (Females)
oSchool-Age Children (Males)
Philadelphia, 2007
Sacramento, 2007
• 80
50 "
> 40 -
3 3° •
^f^
^stP^
*"^ „, f0f
ap£J
2^-
-^S^9
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^-School-Age Children (Females)
-^School-Age Children (Males)
• 80
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3 30 •
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**^ 0^^
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-•-School-Age Children (Females)
oSchool-Age Children (Males)
60 120 180 240
Median Afternoon Time Outdoors
(Minutes/Day)
60 120 180
Median Afternoon Time Outdoors
(Minutes/Day)
60 120 180
Median Afternoon Time Outdoors
(Minutes/Day)
20% 40%
Afternoon Outdoor Participation
(Percent of Days > 1 Minute/Day)
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 1 Minute/Day) 12
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 1 Minute/Day)
« yU
• ou
s 60
01
"S 30
3 nn
U n
^^^
^^ *$^
- f J^\
- //'
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r
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^School-Age Children (Males)
g 70 -
a.
01
~
_
/
E
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^^^
y
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1
^
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--School-Age Children
School-Age Children
(Females)
(Males)
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 2 Hours/Day)
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 2 Hours/Day) 1 3
20% 40% 60% 80% 100%
Afternoon Outdoor Participation
(Percent of Days > 2 Hours/Day)
<-CIN "ISR *OAB «SEA "SUP OCA[X] +NHAPS -Other
Exposure Simulation Date
^ Q
Exposure Simulation Date
Exposure Simulation Date
16 Figure 5G-8. Cumulative distribution of median time spent outdoors (top row), afternoon
17 outdoor participation > 1 minute/day (2n row), and afternoon outdoor participation > 2
18 hours/day (3n row) for male and female school-age children in Houston (left column),
19 Philadelphia (middle column) and Sacramento (right column) study areas. Percent of school-age
20 children with > 2 hours outdoors during afternoon hours (4th row) and the number of particular
21 CHAD study diary days used (bottom row) for each exposure simulation day.
5G-31
-------
1 5G-4 EXPOSURE RESULTS FOR ADDITIONAL AT-RISK POPULATIONS AND
2 LIFESTAGES, EXPOSURE SCENARIOS, AND AIR QUALITY INPUT DATA USED
3 This section includes results for three additional simulations designed to complement
4 exposures estimated using our general population-based modeling approach presented in the
5 main body of the REA. These simulations include (1) exposures estimated for school-aged
6 children during summer months only (section 5G-4.1), (2) adult outdoor worker exposures
7 (section 5G-4.2), and (3) exposures to school-age children and asthmatic school-age children
8 assuming a portion of these study groups exhibit averting behavior in response to high Oj,
9 concentration days (section 5G-4.3).
10 5G-4.1 Exposure Estimated For All School-Age Children During Summer Months,
11 Neither Attending School nor Performing Paid Work
12 A targeted simulation was performed for the Detroit study area during the months of June
13 through August 2007 to simulate summertime exposures by assuming all children were on a
14 traditional calendar year summer vacation. To do this, a subset of the CHAD diaries used by
15 APEX was created by including only those persons that did not have any time spent while at
16 school or time performing paid work. Even though the school children age range in our
17 exposure simulation is 5-18 years old, to maximize the number of diaries available for use by
18 APEX we expanded the CHAD diary selection to include children from 4-19 years old. In
19 considering these diary selection criteria, the resulting time location activity pattern data set input
20 to APEX had a total of 10,226 diaries having 379,524 event entries. All simulation conditions
21 were set identically to those set for the main REA exposure simulations in Detroit, though
22 75,000 children were explicitly simulated here for this targeted analysis. Four air quality
23 scenarios were considered: just meeting the existing 8-hour standard of 75 ppb, and at alternative
24 levels of 70, 65, and 60 ppb. The exposure results from these targeted simulations were
25 compared with identical APEX simulations run using all available CHAD diaries during the
26 same summer months (i.e., diary days that include locations visited and activities performed
27 from persons reporting either school time or paid work).
28 Figure 5G-9 contains the exposure results for this simulation ( "No School/Work
29 Diaries"} and for a nearly identical simulation that differed only in that is used all CHAD diaries
30 ( "all CHAD Diaries "). When restricting the CHAD diary pool to include only those diaries
31 having no time spent at school or performing paid work activities, there is about 1/3 or 33%
32 increase in the number of children at or above each of the selected benchmark levels, a
33 relationship also consistent when considering multiple exposures over the simulation period.
5G-32
-------
= 1 Exposure-All CHAD Diaries
= 1 Exposure -No School/Work Diaries
= 2 Exposures-All CHAD Diaries
= 2 Exposures-NoSchool/Work Diaries
= 3 Exposures-All CHAD Diaries
= 3 Exposure s-No School/Work Diaries
= 1 Exposure-All CHAD Diaries
= 1 Exposure -No School/Work Diaries
= 2 Exposures-All CHAD Diaries
= 2 Exposure s-No School/Work Diaries
= 3 Exposures-All CHAD Diaries
>= 3 Exposure s-No School/Work Diaries
0.0% 1.0% 2.0% 3.0% 4.0% 5
Percent of Children with 8-hr Daily Max Exposure > 70 ppb
= 1 Exposure-All CHAD Diaries
= 1 Exposure -No School/Work Diaries
= 2 Exposures-All CHAD Diaries
= 2 Exposure s-No School/Work Diaries
= 3 Exposures-All CHAD Diaries
= 3 Exposure s-No School/Work Diaries
0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30% 0.350/
Percent of Children with 8-hr Daily Max Exposure > 80 ppb
4
5
6
7
standard level (ppb)
] 60
] 65
70
I 75
Figure 5G-9. Comparison of the percent of all school-age children having daily maximum O^
concentration at or above 60 ppb-8hr (top), 70 ppb-8hr (middle), or 80 ppb-8hr (bottom) during
June, July, and August in Detroit 2007: using any available CHAD diary ("All CHAD Diaries")
or using CHAD diaries having no time spent in school or performing paid work ("No
School/Work Diaries").
5G-33
-------
1 5G-4.2 Exposures Estimated For Adult Outdoor Workers During Summer Months
2 A targeted APEX simulation was performed for the Atlanta study area to simulate
3 summertime exposures for two hypothetical adult outdoor worker study groups (ages 19-35 and
4 ages 35-54), using 2006 air quality just meeting the existing O^ standard. To do this, both the
5 daily and longitudinal activity patterns used by APEX needed to best reflect patterns expected
6 for adult outdoor workers (e.g., a standardized work schedule during weekdays) while also
7 capturing variability in those patterns across various occupation types and the overall simulated
8 adult outdoor worker study group. The development of reasonable time location activity pattern
9 data to be input to APEX was a complex undertaking, attempting to account for a number of
10 influential factors such as the distribution of adult outdoor workers, their varying occupation
11 types, the probabilities associated with performing outdoor work, the linking of this information
12 with the existing CHAD diaries and APEX METS distributions, all to be done within the existing
13 APEX model framework and capabilities.
14 First, the complete distribution of all employed persons' occupations was estimated using
15 data provided by the U.S. Bureau of Labor and Statistics (US BLS, 2012b).15 The information of
16 interest was obtained from the 2010 National Employment Matrix, data originally developed
17 from the Occupational Employment Statistics (OES) survey and based on the 2010/2000
18 Standard Occupational Classification (SOC) system. The three variables retained for our
19 purposes here included the SOC occupation titles and codes and the 2010 estimated number of
20 persons employed, covering over 750 occupation titles.
21 Second, the identification of occupations where workers spend time outdoors for at least
22 one or more days per week was determined using data from the Occupational Information
23 Network (O*NET).16 The O*NET was developed by a partnership of public and private
24 organizations17 via sponsorship by the US Department of Labor/Employment and Training
25 Administration (USDOL/ETA). A wealth of information is provided by the O*NET regarding
26 specific occupations including human interaction processes (e.g., amount of public speaking in a
27 particular job, the likelihood of encountering angry people), physical work conditions (e.g.,
28 approximate time spent standing, whether exposed to radiation), and structural job characteristics
29 (e.g., the degree of job automation, freedom to make decisions).
30 An advanced search of O*NET was performed using the web database. We first isolated
31 the data of interest here by work context and selected physical work conditions. Data tables for
32 two survey question responses were downloaded: the first consisted of persons responding to the
15 US employment data by SOC codes were obtained from: http://www.bls.gov/emp/#tables: Table 1.2 Employment
by occupation, 2010 and projected 2020.
16
17 ,
16 Additional information is available at http://www.onetonline.org.
Current O*NET partners include Research Triangle Institute (RTI), the Human Resources Research Organization
(HumRRO), North Carolina State University (NCSU), MCNC, and Maher & Maher.
5G-34
-------
1 question, "how often does this job require working outdoors, exposed to all weather conditions?"
2 and the second "how often does this job require working outdoors, under cover (e.g., structure
3 with roof but no walls)?". The tables contain the responses to these two questions, stratified by
4 the occupation names/codes, and rated using a context score ranging from 0 to 100. According
5 to the context scale provided by O*NET, occupations with a score of 75 were characterized as
6 having at least one day per week outdoors, while a score of 100 indicated that every day work
7 was performed outdoors by workers in that particular occupation, thus the greater the context
8 score, the greater the likelihood of outdoor work participation.
9 To start, there were 862 unique occupation codes with context scores for question #1 (i.e.,
10 exposed entirely to weather), 30 of which also contained context scores for question #2 (there
11 were no occupations with context scores for question #2 alone). Assuming ozone exposure
12 would be similar for outdoor workers whether under cover or totally exposed to weather, we
13 merged the data responses from the two questions by occupation code and assigned the highest
14 context score of the two responses to each occupation. Given the context scaling information
15 provided by O*NET, we then assumed the context scores 76-80, 81-85, 86-90, 91-95, and 96-
16 100 characterized occupations as having 1, 2, 3, 4, or 5 days per week outdoors. Then, mapping
17 of the O*NET occupations to the above described BLS SOC occupation data set was performed,
18 and was generally agreeable, with a few exceptions.18 Following additional processing, there
19 were 144 unique occupation titles having one to five days per week where work was performed
20 outdoors and the number of persons constituting each. See Attachment 1 for the final
21 O*NET/BLS mapping and additional data processing assumptions.
22 These 144 specific occupations then needed to be mapped to the occupation-related
23 activity codes used by APEX to generate METs in estimating energy expenditure. When CHAD
24 was developed in the late 1990s, occupation codes from the 1990 US Census were mapped to
25 twelve broad occupation categories19 and were assigned METs distributions based on the most
26 commonly performed activities associated with work tasks. In order to use the APEX/CHAD
27 METs database in its current format and integrate the newly developed 2010 BLS/O*NET data
28 set, the 1990 Census occupation codes were translated using two additional mapping files: a
29 1990 Census to 2000 Census code map file20 and a 2000 Census to SOC code map file.21 The
18 There were seven O*NET codes that did not directly correlate with the BLS SOC codes and several O*NET
occupations that were subcategories to broader BLS SOC codes.
19 The list of 1990 Census codes used to map the occupation titles to the APEX METs distributions are found at
https ://usa.ipums.org/usa/volii/99occup. shtml. The twelve occupation groups are Executive Administrative
Managerial (ADMIN), Administrative support (ADMSUP), Professional (PROF), Technical (TECH), Sales (SALE),
Protective Services (PROTECT), Service (SERV), Farming Forestry Fishing (FARM), Precision Production
(PREC), Machine Operators (MACH), Transportation (TRANS), and Laborers (LABOR). Household workers
(HSHLD), while an APEX/CHAD occupation group, do not have any work days where time is spent outdoors.
20 http ://www. census. gov/people/io/files/techtab02 .pdf.
21 http://www.census.gov/people/eeotabulation/documentation/occcategories.pdf
5G-35
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
mapping was generally agreeable among the various occupations and across the source files over
the years however, in characterizing several of the O*NET occupation codes to the broad
APEX/CHAD occupation groups, there were several instances where more than one broad
occupation group could be assigned to a single O*NET occupation. Professional judgement was
used to assign the most appropriate broad occupation category when there were multiple choices,
with decisions made to complete the processing of the 34 occupation titles given in Attachment 2
of this appendix. The final distribution of days per week spent performing outdoor work
considering the BLS/O*NET data set and stratified by APEX/CHAD occupation groups is
provided in Table 5G-8. This data set provides the target for developing an activity pattern data
set that reasonably reflects a distribution of outdoor workers properly weighted by occupation
groups along with assignment of an approximate number of days per week they spend outdoors.
Table 5G-8. Distribution of days per week spent performing outdoor work considering the
BLS/O*NET data set and stratified by APEX/CHAD occupation groups.
CHAD
Group
ADMIN
ADMSUP
FARM
LABOR
MACH
PREC
PROF
PROTEC
T
SALE
SERV
TECH
TRANS
TOTAL
BLS/O*NET Occupations
(n)
7
7
19
10
3
42
3
15
1
4
6
27
144
Number of Outdoor
Work Days per Week
(mean)
1.4
3.4
4.0
4.5
4.0
2.5
1.3
3
1.0
3.2
3.4
4.3
-
(min)
1
1
1
1
2
1
1
1
1
1
3
1
-
(max)
3
5
5
5
4
5
4
5
1
5
4
5
-
Percent of Employed Persons
Performing Outdoor Work
Outdoor
Workers only
5.1
4.6
18.3
9.3
0.5
25.1
0.2
7.7
2.4
1
3.1
22.7
100
All Workers
0.6
0.5
2.1
1.1
0.1
2.9
<0.1
0.9
0.3
0.1
0.4
2.7
11.7
Target for CHAD
Diary Outdoor Work
Days per Week
(based on
BLS/0*NET mean)
1
3
4
5
4
3
1
3
1
3
3
4
-
CHAD contains diary information from a number of persons who reported their time
spent at work, and is indicated by CHAD activity codes beginning with either '100' or ' 101'.
This, combined with outdoor location information (i.e., 60 location codes provided in
Attachment 3 of this appendix) and selected for where the total time working outdoors for the
day was > 2 hours, yielded 1,510 CHAD daily activity patterns potentially usable in representing
outdoor workers. We evaluated these diaries across a number of personal attributes and
calculated the mean time spent outdoors for each diary day (Table 5G-9). As expected, very few
diaries were from persons that identified their occupation, though where that information was
5G-36
-------
1 present, these diaries retained that specific identifier and used to simulate persons having that
2 particular occupation group. Because we were interested in generating a typical work week, that
3 is, work was performed during weekday days, only weekday diaries were used to develop the
4 target diary pool for the weekday schedule, giving a final pool of 1,403 usable diaries.
5 Table 5G-9. Personal attributes and mean time spent working outdoors for CHAD diaries
6 reporting at least two hours of outdoor work.
Category
CHAD
Occupation
Group
Age Range
(years)
Sex
Day of Week
Attribute
ADMIN
ADMSUP
FARM
LABOR
MACH
PREC
PROF
PROTECT
SALE
SERV
TECH
TRANS
X
15-18
19-24
25-34
35-44
45-54
55-64
65-74
75-84
>84
F
M
FRI
MON
SAT
SUN
THU
TUE
WED
Outdoor Worker
Diary days (n)
13
4
11
14
10
52
8
4
13
5
1
12
1363
19
78
155
364
384
360
137
11
2
349
1161
123
174
66
41
280
357
469
Mean Outdoor Work
Time (minutes)
264
336
478
469
320
416
307
402
290
291
290
328
344
327
420
443
359
347
306
286
243
475
290
365
349
379
399
434
341
342
328
8 We assumed outdoor worker diaries having missing occupation information could be
9 used to represent outdoor worker diaries having an assigned occupation and, in the absence of
10 any additional information to suggest stronger alternative, simply assigned CHAD occupation
11 groups to these diaries equally, though weighted by the appropriate days per week outdoor work
5G-37
-------
1 was performed by a particular occupation group. This is in part because, in performing an APEX
2 simulation, the options for developing a multiday diary profile can be either controlled by a key
3 variable (such as time spent outdoors), use the same diary to represent everyday of the simulated
4 person's exposure period, or be constructed by an entirely random sequence of diaries. Outdoor
5 time, a commonly used key variable to represent intra- and inter-personal variability activity
6 patterns over multiple days by APEX, in general is an unknown for each occupation group and
7 thus the key variable approach cannot be used to develop the longitudinal profiles. Rather than
8 use the same diary for each person day, we elected to use a random selection of diaries, though
9 having the selected diaries drawn from specific occupation groups developed from a large diary
10 pool weighted by the target number of days per week where outdoor work was performed (Table
11 5G-8). To clarify how this was done, an example follows using a single occupation group of
12 outdoor workers, i.e., those comprising the Transportation (TRANS) group.
13 According to information summarized in Table 5G-8, transportation-associated workers
14 (TRANS) were estimated to, on average, spend four days per week working outdoors. We
15 assigned the set of all available weekday outdoor worker diaries having 'missing' for their
16 occupation (n=l,363) as now having an occupation of 'TRANS'. We then replicated this new
17 set of outdoor worker diaries (including those few diaries having a known occupation, TRANS
18 or otherwise, to total 1,403 outdoor worker diaries) to generate a data set now having four
19 weekday days per person day. Thus from an APEX modeling perspective, all personal attributes
20 for persons in that pool are identical from one day to the next and have an equal likelihood of
21 selection. This four day by 1,403 person activity pattern data set, now principally comprised of
22 outdoor workers having 'TRANS' as their occupation, was then combined with a single weekday
23 by 1,403 person activity pattern data set, only that this particular one day data set, while still
24 derived from the same set of outdoor worker diaries, differs in that all of the paid work time
25 spent outdoors was changed to paid work time occurring within indoor locations for that one
26 work day. Then, when APEX constructs a longitudinal diary for any person with the 'TRANS'
27 occupation using completely random selection, all other personal attributes remaining the same,
28 the probability of selecting an outdoor work diary for any day is 0.8 while that of an indoor diary
29 is 0.2, appropriately reflecting, on average, the days per week that occupation group spends time
30 working outdoors. This process of building up the activity pattern diary pool was repeated for
31 each outdoor worker occupation group to reflect both the probability of performing either indoor
32 or outdoor work during weekdays. This collection of weekday diaries was then combined with
33 the pool of all remaining CHAD weekend diaries (n=13,953 person days), though where persons
34 had missing occupation information (n=12,561 person days), any one of the twelve occupation
5G-38
-------
1 groups was randomly assigned to a given weekend day. This complete outdoor worker activity
2 pattern set now totaled 98,133 person days22 having 3,656,560 activity events.
3 That said, it was soon apparent this input data set was too large for APEX to use when
4 error messages were generated upon model execution. For these outdoor worker simulations to
5 proceed, we determined the maximum size of the diary data set was approximately 42,000 diary
6 days. Our current response to this limitation was to first restrict the exposure simulations using a
7 tighter age range, thus permitting us to also limit the activity pattern input data set by similar age
8 ranges. Two age groups of outdoor workers were of interest for our exposure simulations: 19-35
9^
9 and 36-55 years old. To maximize the number of diaries used to model the 19-35 year olds, we
10 increased the range for APEX usable diaries from the large outdoor worker activity pattern data
11 set initially developed to include ages from 16 to 42 years old (n=30,657 person days). As a
12 reminder, only the activity patterns of 16-18 year olds characterized as outdoor workers (i.e.,
13 persons having > 2 hours of paid work occurring outdoors) were available to simulate adults ages
14 19 or above. All of the anthropometric attributes of these simulated adults (e.g., body mass,
15 resting metabolic rate, ventilation rate) were derived from their age appropriate data,
16 distributions, and/or equations. There were adequate numbers of diaries available to model the
17 36-55 year olds such that the age range for inclusion in that data set was restricted to those
18 between the ages of 36-54 (n=41,736 person days).
19 When modeling exposures using occupation groups, two additional input files were
20 needed by APEX. The first was simply a file containing the CHAD ID and the specific
21 occupation group identified for that person day. The second, a profile factors file, contains the
22 probabilities a simulated person in the model domain will have a particular occupation, a profile
23 variable that can be stratified by age, sex, and/or census tract (US EPA 2012a, b). Based on the
24 information we developed in Table 5G-8, we only assigned specific probabilities for each
25 particular occupation group, i.e., the percent of employed persons performing outdoor work
26 using the outdoor worker proportions equally across ages, sexes and tracts. An additional
27 modification to the APEX employment probabilities input file (Employment2000 043003.txt)
28 was also needed to generate exposures and appropriate output summary tables only for employed
29 persons. And finally, because of the generally limited number of diaries available in developing
30 the diary pool for the 19-35 year olds and to use as many of the diaries available, we lengthened
31 the age selection range (AgeCutPct = 30.0 and Age2Probab = 0.15) and only included two
32 temperature diary pools (<84 or >84) to have an adequate number of diaries available in each
33 diary pool for APEX to execute the desired simulation.
22 In summary, the weekday data set was developed assuming 12 occupations for 5 days per work week (or 35
outdoor days+ 25 indoor days) for each of the 1,403 diary days and added to the 13,953 weekend days.
23 Note from Table 5G-9, there are just over 250 diaries from persons aged 15-34.
5G-39
-------
1 Finally, a 10,000 person exposure simulation was performed for each age group of
2 outdoor workers in Atlanta for Jun 1 - Aug 30, 2006 using air quality just meeting the existing
3 standard. In addition, as a point of comparison for the longitudinal approaches developed here
4 and used for estimating general population-based exposures, identical simulations were
5 performed in Atlanta during the same time of year, air quality scenario, and age groups only
6 differing by using the approach described in our primary REA exposure simulations, i.e., using
7 outdoor time as the key variable in developing longitudinal profiles, sampling from all available
8 CHAD diaries, and not explicitly addressing simulated worker occupations and work performed
9 (structured schedules and associated METs values).24
10 We first summarized the outdoor work time performed by each simulated outdoor worker
11 during weekdays, stratified by particular occupation, to ascertain whether or not the exposure
12 simulation met our defined goal. In comparing the results of Table 5G-10 with those provided in
13 Table 5G-8 we see that the goal was met for both age groups of outdoor workers, i.e., the
14 longitudinal approach was structured correctly to reproduce the distribution of the outdoor
15 worker occupation groups and the number of days persons in a particular group spent working
16 outdoors. Estimated exposures are presented in (Figure 5G-10) for each of the two outdoor
17 worker study groups and considering either a longitudinal scenario-based approach designed
18 specifically to reflect an outdoor worker weekday schedule or using our general population-
19 based modeling approach. It is clear that when accounting for a structured schedule that includes
20 repeated occurrences of time spent outdoors for a specified study group, all while more
21 consistently performing work tasks that may be at or above moderate or greater exertion levels, a
22 greater percent of the study group experiences exposures at or above the selected health effect
23 benchmark levels than that estimated using our general population-based approach. The
24 differences between exposures estimated for the two longitudinal approaches become much
25 greater when considering the percent of persons experiencing multiple exposure days at or above
26 benchmark levels. For example, < 2% of the general population-based exposure group was
27 estimated to have two or more exposures at or above 60 ppb-8hr, while >17% of specifically
28 simulated outdoor workers were estimated to experience exposures at or above that same level.
29 In general, there was little difference in exposures estimated for the two age groups of outdoor
30 workers.
31
32
33
24 Because most CHAD IDs have unknown occupations, METs are sampled from a 'composite' triangular
distribution min, peak, max [1.2, 1.9, 5.6] developed from the METs distributions used for all occupation groups.
As such, exertion levels achieved by laborers, service, and transportation industries are not well represented using a
general population approach given their respective METs distributions triangular[3.6, 8.1, 13.8], triangular[1.6, 5.6,
8.4], and lognormal geometric mean, standard deviation [3.0, 1.5] truncated to min, max [1.3, 8.4].
5G-40
-------
1
2
Table 5G-10. Distribution of days per week spent performing outdoor work considering the
APEX simulated population and stratified by APEX/CHAD occupation and age groups.
4
5
6
7
8
9
10
11
12
13
14
15
16
Age
Group
19-35
36-55
CHAD Occupation
Group
ADMIN
ADMSUP
FARM
LABOR
MACH
PREC
PROF
PROTECT
SALE
SERV
TECH
TRANS
ADMIN
ADMSUP
FARM
LABOR
MACH
PREC
PROF
PROTECT
SALE
SERV
TECH
TRANS
Percent of Simulated
Outdoor Workers
5.0
4.6
19.1
9.5
0.5
24.3
0.2
7.8
2.4
1.1
3.2
22.4
5.4
4.7
17.5
9.6
0.6
24.8
0.3
7.5
2.5
0.9
3.1
23.2
Number of Outdoor Work Days
per Week
(mean)
1.2
3.0
3.9
4.9
4.0
3.0
1.4
3.0
1.2
3.0
3.0
4.0
1.1
3
3.9
4.9
3.9
3
1
3
1.1
3
3
3.9
(min)
0
2
3
5
3
2
1
2
0
2
2
3
0
2
3
5
3
2
0
2
0
2
2
3
(max)
2
4
5
5
5
4
3
4
2
4
4
5
2
4
5
5
4
4
2
4
2
4
4
5
Additional context regarding the estimate of the number of persons exposed between the
two approaches can added with the following. Approximately 30% of our outdoor worker study
group ages 19-55 was estimated to experience at least one exposure at or above 60 ppb-8hr while
at moderate or greater exertion. Assuming a 92% employment rate and that outdoor workers
constitute approximately 12% of the workforce (Table 5G-8), outdoor workers experiencing at
least one exposure at or above 60 ppb-8hr would comprise about 3.3% of a total simulated
population in that study area. For the same air quality scenario we estimated using the general
population-based approach, about 5-8% of the study group would experience exposures at or
above the same benchmark. To some extent, the general population-based approach will
simulate exposure profiles of outdoor workers (persons with frequent and above average time
spent working outdoors experiencing instances of high exposure concentrations at elevated
exertion levels) by applying our standard longitudinal diary selection method. Intuitively,
5G-41
-------
1
2
3
4
5
6
7
13
14
15
16
outdoor workers probably constitute a significant portion of the overall population-based study
group that could exceed benchmark levels though at this time it is unknown whether the portion
estimated here of 40-60% is accurate. It is however reasonable to conclude given the
comparative exposure results that the general population-based approach would tend to
underestimate the multiday exposures at or above selected benchmark levels experienced by
persons adhering to a more rigid outdoor work schedule.
Outdoor Worker Scenario-based Approach (ages 19-35)
Atlanta, 2006, Just Meet Existing75ppb Standard
123456
Number of Times Benchmark Exceeded (June-August 2006)
JLQ
General Population-based Approach (ages 19-35)
Atlanta, 2006, Just Meet Existing 75 ppb Standard
123456
Number of Times Benchmark Exceeded (June-August 2007)
Outdoor Worker Scenario-based Approach (ages 36-55)
Atlanta, 2006, Just Meet Existing 75 ppb Standard
i > | 25%
« no/
123456
Number of Times Bench mark Exceeded (June-August 2006)
General Population-based Approach (ages 36-55)
Atlanta, 2006, Just Meet Existing 75 ppb Standard
OJ OJ +J
•
123456
Number of Times Bench mark Exceeded (June-August 2006)
12
Figure 5G-10. Percent of persons between age 19-35 (left) and 36-55 (right) experiencing
exposures at or above selected benchmark levels while at moderate or greater exertion using an
outdoor worker scenario-based approach (top) and a general population-based approach (bottom)
considering air quality adjusted to just meet the existing standard in Atlanta, GA, Jun-Aug, 2006.
17 5G-4.3 Averting Behavior and Potential Impact to Exposure Estimates
18 A growing area of air pollution research involves evaluating the actions persons might
19 perform in response to high Os concentration days (ISA, section 4.1.1). Most commonly termed
20 averting behaviors, they can be broadly characterized as personal activities that either reduce
21 pollutant emissions or limit personal exposure levels. The latter topic is of particular interest in
22 this REA due to the potential negative impact it could have on Os concentration-response (C-R)
5G-42
-------
1 functions used to estimate health risk and on time expenditure and activity exertion levels
2 recorded in the CHAD diaries used by APEX to estimate 63 exposures. To this end, we have
3 performed an additional review of the available literature here beyond that summarized in the
4 ISA to include several recent technical reports that collected and/or evaluated averting behavior
5 data. Our purpose is to generate a few reasonable quantitative approximations that allow us to
6 better understand how averting behavior might affect our current population-based exposure and
7 risk estimates. We expect that the continued development and communication of air quality
8 information via all levels of environmental, health, and meteorological organizations will only
9 further increase awareness of air pollution, its associated health effects, and the recommended
10 actions to take to avoid exposure, thus making averting behaviors and participation rates an even
11 more important consideration in future Os exposure and risk assessments. The following is a
12 summary of our literature review, with details provided by Graham (2012). Later in this section,
13 preliminary results of an exposure simulation designed to account for averting behavior are
14 provided.
15 The first element considered in our evaluation is peoples' general perception of air
16 pollution and whether they were aware of alert notification systems. The prevalence of
17 awareness was variable; about 50% to 90% of survey study participants acknowledged or were
18 familiar with air quality systems (e.g., Blanken et al., 1991; KS DOH, 2006; Mansfield et al.,
19 2006; Semenza et al., 2008) and was dependent on several factors. In studies that considered a
20 persons' health status, e.g., asthmatics or parents of asthmatic children, there was a consistently
21 greater degree of awareness (approximately a few to 15 percentage points) when compared to
22 that of non-asthmatics. Residing in an urban area was also an important influential factor raising
23 awareness, as both the number of high air pollution events and their associated alerts are greater
24 when compared to rural areas. Of lesser importance, though remaining a statistically significant
25 influential variable, were several commonly correlated demographic attributes such as age,
26 education-level, and income-level, with each factor positively associated with awareness.
27 The second element considered in our evaluation was the type of averting behaviors
28 performed. For our purposes in this O^ REA, the most relevant studies were those evaluating
29 outdoor time expenditure, more specifically, the duration of outdoor events and the associated
30 exertion level of activities performed while outdoors. This is because both of these variables are
31 necessary to understanding Oj, exposure and associated adverse effects, and hence, in accurately
32 estimating human health risk.
33 As stated above regarding air quality awareness, asthmatics consistently indicated a
34 greater likelihood of performing averting behaviors compared to non-asthmatics - estimated to
35 differ by about a factor of two. This difference could be the combined effect of those persons
36 having been advised by health professional to avoid high air pollution events and them being
5G-43
-------
1 aware of alert notification systems. Based on the survey studies reviewed, we estimate that 30%
2 of asthmatics may reduce their outdoor activity level on alert days (e.g., KS DOH, 2006;
3 McDermott et al., 2006; Wen et al., 2009).25 An estimate of 15%, derived from reductions in
4 public attendance at outdoor events (Zivin and Neidell, 2009) would be consistent with our
5 estimate above when considering that the Zivin and Neidell (2009) study group is likely
6 comprised mainly of non-asthmatics. That said, both attenuation and the re-establishment of
7 averting behavior was apparent when considering a few to several days above high pollution
8 alert levels (either occurring over consecutive days or across an entire year) (McDermott et al.,
9 2006; Zivin and Neidell, 2009), suggesting that participation in averting behavior over a
10 multiday period for an individual is complex and likely best represented by a time and activity-
11 dependent function rather than a simple point estimate.
12 There were only a few studies offering quantitative estimates of durations of averting
13 behavior, either considering outdoor exertion level or outdoor time (Bresnahan et al., 1997;
14 Mansfield et.al, 2006, Neidell, 2010; Sexton, 2011). Each of these studies considered outdoor
15 time expenditure during the afternoon hours. Based on the studies reviewed, we estimate that
16 outdoor time/exertion during afternoon hours may be reduced by about 20-40 minutes in
17 response to an air quality alert notification. Generally requisite factors include: a high alert level
18 for the day (e.g., red or greater on the AQI), high Oj, concentrations (above the NAAQS), and
19 persons having a compromised health condition (e.g., asthmatic or elderly).
20 The third element considered in our preliminary evaluation was how to further define the
21 impact of averting behavior on modeled exposure estimates.26 As described in section REA
22 5.3.2, APEX uses time location activity data (diaries) from CHAD to estimate population-based
23 exposures. These diaries originate from a number of differing studies; some were generated as
24 part of an air pollution research study, some were collected during a summer/ozone season, while
25 some diary days may have corresponded with high O?, concentration and air quality alert days.
26 At this time, none of the diary days used by APEX have been specifically identified as
27 representing days where a person did or did not adjust their activity pattern reduce their
28 exposure. In considering the above discussion regarding the potential rate of participation and
29 averting actions performed, it is possible that some of the CHAD diary days express instances
30 where that selected individual may have reduced their time spent outdoors or reduced their
31 exertion level while outdoors. Currently, without having a personal identifier for averting
32 behavior in CHAD, the diaries are assigned to a simulated persons' day without directly
25 Many of these studies do not account for one important factor when using a recall questionnaire design: whether
the participant's stated response to air pollution is the same as the action they performed.
26 The discussion of another important effect of averting behavior is on concentration-response functions (more
relevant to the risk assessment in chapter 8). This is presented in the ISA (section 4.1.2).
5G-44
-------
97
1 considering ambient Oj, concentration levels . Therefore, it is possible that there are instances
2 where, on a given APEX simulation day, the simulated person may use a diary day from a person
3 that did engage in one or more types of averting behavior (e.g., a diary having less time than
4 usual spent outdoors in the afternoon), while for most other persons simulated on the same day
5 (or the same person on a different high concentration day) the diaries used are from persons that
6 did not actively engage in averting behavior. As a result, the effect of averting behavior may
7 already be incorporated into our exposure modeling, albeit to an unknown though likely small
8 degree,28 though definitely generating low-biased estimates of exposures (and reduced number of
9 persons at or above selected 8-h 03 benchmark concentrations) that would occur in the complete
10 absence of averting behavior.
11 With this in mind, we performed an APEX simulation to reflect the instance that a
12 fraction of a selected study group spends less time outdoors on high concentration ozone days.
13 First, a general APEX simulation was performed during June-August 2007 in Detroit to identify
14 a short time period where a high number of children/asthmatic children were estimated to be
15 exposed at or above the 8-hour Oj, benchmark levels of interest. To maintain a degree of
16 tractability in the simulations, the development of the new CHAD input data, and the analysis of
17 the exposure results, we restricted the exposure simulations to 5,000 total persons. One such
18 high exposure event occurred over a two-day period considering base year air quality - August
19 1-2, 2007. Because conditions in APEX simulations can be controlled by using an identical
20 random number seed, APEX daily files were output to explicitly identify all of the CHAD diaries
21 used for this two-day simulation.
22 The activity pattern data from the identified CHAD diaries used to simulate the two-day
23 exposure period were then used to generate a new activity pattern input data set, one adjusted for
24 the above estimated parameters used to reflect averting performed by the two exposure study
25 groups of interest. We did this after determining the following:
26 1) There were a total of 1,988 diaries used to simulate the maximum exposure day for
27 each person, obviously some CHAD diaries were used more than once to simulate
28 different people on their maximum exposure day. Note also, 48 diaries were used to
29 simulate both days for the same person, 37 of these occasions were for unique
30 individuals while for the remaining 11 instances the same diary was also used in
31 either two or three persons two-day simulation.
32 2) We calculated the total time spent outdoors by hour of day for the CHAD diaries used
33 by APEX for each persons' maximum exposure day, estimated the mean outdoor time
27 APEX uses maximum temperature in assigning diaries for a select day in an area, capturing some relevant
variability in O3 concentrations.
28 Neither the participation rate nor the duration of averting for simulated persons is being strictly controlled for by
APEX when simulating exposures.
5G-45
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
and number of persons by hour of day, and then stratified these results by the number
of times a diary was used in the simulation. Ideally, the most effective adjustment to
outdoor time considering averting would be applied to the afternoon hours. We
observed that at hour 15 (3 PM-4 PM), there were both a reasonable number of
CHAD diaries and diaries having an appropriate total time spent outdoors to achieve
our desired adjustment of 15% and 30% participation for children and asthmatics,
respectively, for on average 40 minutes in a day. This was determined using the
results in the following table. To simulate averting for children, we selected the
collection of diaries from the '2' and '3' times a diary was used category (n=766 total
diary days or 15.3% of all diaries used for each persons maximum exposure day) and
reduced all outdoor time events to 0 minutes spent outdoors during 3 PM-4 PM. To
simulate averting for asthmatic children, we selected the collection of diaries from the
' 1' through '5' times a diary was used category (n=l,518 total diary days or 30.4% of
all diaries used for each persons' maximum exposure day) and reduced all outdoor
time events to 0 minutes spent outdoors during 3 PM-4 PM.
Number of
times a select
diary was used
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
17
Number
of
persons
181
182
134
84
47
20
13
7
4
5
5
2
1
1
3
1
Mean
Outdoor
Time
43.2
43.4
44.6
45.6
40.6
48.4
44.1
45.4
39.5
48.0
40.6
35.0
5.0
45.0
60.0
60.0
Total
Diary
Days
181
364
402
336
235
120
91
56
36
50
55
24
13
14
45
17
3) Following the selection of the hour where outdoor time would be adjusted to reflect
averting, we evaluated whether diaries used for both simulation days per person and
when used for multiple persons for both simulation days would affect the targeted
reduction in outdoor time. Even considering the small number of instances identified
in #1) above (48 single diaries used for both days of the two day simulation per
person out of 5,000 possible cases), only two diaries had time spent outdoors during
5G-46
-------
1 the hours of 3 PM-4 PM where both days would be adjusted for averting, thus likely
2 having a negligible effect the meeting of our approximate averting goals.
O
4 Both the outdoor time adjusted CHAD and the standard CHAD input files were
5 separately used to simulate exposures to children and asthmatic children. Exposure results for
6 the four simulations (simulated averting vs. no averting for both children and asthmatic children)
7 are found in the main body of the REA.
8 5G-5 COMPARISON OF PERSONAL EXPOSURE MEASUREMENT AND APEX
9 MODELED EXPOSURES
10 A new evaluation of APEX was performed using a subset of personal Os exposure
11 measurements obtained from the Detroit Exposure and Aerosol Research Study (DEARS) (Meng
12 et. al, 2012). For five consecutive days, personal 63 outdoor concentrations along with daily
13 time-location activity diaries were collected from 36 study participants in Wayne County
14 Michigan during July and August 2006. The majority of participants were female (80%) having
15 a mean age of 40.6 (min 20, max 72); mean age for males was 41.4 (min 22, max 65). Rather
16 than using daily personal exposures estimated below the reported detection limit of 3 ppb (i.e., 0,
17 1 and 2 ppb), we approximated those falling below this level using a random assignment of
18 concentrations of 1 and 2 ppb.
19 An APEX simulation was performed considering these same geographic and temporal
20 features, followed with the sub-setting of APEX output data according to important personal
21 attributes of the DEARS study participants (specific 5-day collection study periods, age/sex
22 distributions, outdoor time, ambient concentrations, and air exchange rate). For both data sets
23 and considering the output variables independently, the median daily values for each study
24 participant attribute was generated using each individual's 5 person-days of data, then ranked
25 median values were plotted along with each individual's corresponding minimum and maximum
26 value. Distributions for four of these variables of (personal Os exposure, outdoor time, ambient
27 Oj, concentrations, and air exchange rate from each of the two data sets are presented in Figure
28 5G-11.
29 Distributions of time spent outdoors and ambient concentrations were similar by design
30 of the APEX population-based sample selection method. The upper percentiles the DEARS
31 participant AER distribution was greater than that of AER of APEX simulated persons. For
32 example, 40% of DEARS participants had a median value of two air exchanges per hour, while
33 the same rate was only observed for 5% of APEX simulated individuals. In contrast, over 50%
34 of APEX simulated individuals had median daily Os exposure concentrations above 10 ppb,
35 while only 3% of DEARS participants' median values exceeded 10 ppb. This difference in
5G-47
-------
1 exposure is surprising given the sharply higher residential indoor air exchange rate for the
2 DEARS participants (i.e., indoor microenvironmental exposures would be expected to have a
3 greater influence on total DEARS exposure compared with the APEX simulated exposures) all
4 while holding all other potential influential variables the same between the two data sets and is
5 subj ect to further investigation.
6
5G-48
-------
DEARS DATA
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3 O O O C
o
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60 120 180 240 300 360 420 480 540 6C
Daily Total Outdoor Time (minutes)
x
x
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+
f
/
yt
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-p50
max
5 10 15 20 25 30 35 40 45 50 55 6
Daily Mean Ambient 03 Concentration (ppb)
X
xy
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r
1
P
X
x X
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r : +
X
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+
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max
APEX DATA
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p50
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y
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10
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Daily Air ExchangeRate(hour1
•>nd
11 Figure 5G-11. Distribution of daily personal O3 exposures (top row), outdoor time (2na row
12 from top), ambient Os concentrations (3r row from top), and air exchange rate (bottom row) for
13 DEARS study participants (left column) and APEX simulated individuals (right column) in
14 Wayne County, MI, July-August 2006.
5G-49
-------
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9 spent in selected locations for 7-12-year old children. J Expo Anal Environ Epidemiol.
10 14(3):222-33.
11 Zivin, J. G., Neidell M. (2009). Days of haze: Environmental information disclosure and
12 intertemporal avoidance behavior. JEnvironmentEconManag. 58(2):119-128.
5G-57
-------
1 Attachment 1. Occupations estimated to have at least one day per week where work is performed outdoors.
Major BLS
SOC code
11
11
11
11
13
13
13
13
17
17
17
19
19
19
29
33
33
33
33
33
33
33
33
Major BLS SOC name
Management
Occupations
Management
Occupations
Management
Occupations
Management
Occupations
Business and Financial
Operations Occupations
Business and Financial
Operations Occupations
Business and Financial
Operations Occupations
Business and Financial
Operations Occupations
Architecture and
Engineering Occupations
Architecture and
Engineering Occupations
Architecture and
Engineering Occupations
Life, Physical, and Social
Science Occupations
Life, Physical, and Social
Science Occupations
Life, Physical, and Social
Science Occupations
Healthcare Practitioners
and Technical
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
BLS SOC name
Industrial Production Managers
Farmers, Ranchers, and Other
Agricultural Managers
Farmers, Ranchers, and Other
Agricultural Managers
Construction Managers
Insurance Appraisers, Auto
Damage
Compliance Officers
Farm Labor Contractors
Appraisers and Assessors of
Real Estate
Surveyors
Surveyors
Surveying and Mapping
Technicians
Conservation Scientists
Conservation Scientists
Forest and Conservation
Technicians
Emergency Medical
Technicians and Paramedics
First-Line Supervisors of Police
and Detectives
First-Line Supervisors of Fire
Fighting and Prevention
Workers
First-Line Supervisors of Fire
Fighting and Prevention
Workers
Firefighters
Firefighters
Fire Inspectors and
Investigators
Fire Inspectors and
Investigators
Forest Fire Inspectors and
Prevention Specialists
BLS SOC
code
11-0000
11-9013
11-9013
11-9021
13-0000
13-1041
13-1074
13-2021
17-0000
17-1022
17-3031
19-0000
19-1031
19-4093
29-0000
33-0000
33-1021
33-1021
33-2011
33-2011
33-2021
33-2021
33-2022
Employed
(1000's)
150.3
400.8
400.8
523.1
10.6
216.6
0.3
77.8
25.6
25.6
56.9
7.8
7.8
36.5
226.5
106.1
30
30
155.2
155.2
6.8
6.8
1.6
O*NET Code
11-3051.02
11-9013.01
11-9013.03
11-9021.00
13-1032.00
13-1041.04
13-1074.00
13-2021.02
17-1022.00
17-1022.01
17-3031.01
19-1031.01
19-1031.03
19-4093.00
29-2041.00
33-1012.00
33-1021.01
33-1021.02
33-2011.01
33-2011.02
33-2021.01
33-2021.02
33-2022.00
O*NET Name
Geothermal Production
Managers
Nursery and Greenhouse
Managers
Aquacultural Managers
Construction Managers
Insurance Appraisers, Auto
Damage
Government Property
Inspectors and
Investigators
Farm Labor Contractors
Appraisers, Real Estate
Surveyors
Geodetic Surveyors
Surveying Technicians
Soil and Water
Conservationists
Park Naturalists
Forest and Conservation
Technicians
Emergency Medical
Technicians and
Paramedics
First-Line Supervisors of
Police and Detectives
Municipal Fire Fighting and
Prevention Supervisors
Forest Fire Fighting and
Prevention Supervisors
Municipal Firefighters
Forest Firefighters
Fire Inspectors
Fire Investigators
Forest Fire Inspectors and
Prevention Specialists
Context
77
96
82
78
89
82
79
77
90
78
92
79
78
90
87
82
88
86
80
87
86
84
77
Out
days/wk
1
5
2
1
3
2
1
1
3
1
4
1
1
3
3
2
3
3
1
3
3
2
1
Comment
divided bis code 11-9013 (Farm/Ranch/Other Ag
Managers- 1202.5) by 3, possibly an
overestimate
divided bis code 11-9013 (Farm/Ranch/Other Ag
Managers- 1202.5) by 3, possibly an
overestimate
divided bis code 17-1022 (surveyors- 51. 2) by 2,
estimate should be ok
divided bis code 17-1022 (surveyors- 51. 2) by 2,
estimate should be ok
divided bis code 19-1031 (Conservation
Scientists- 23.4) by 3, possibly an overestimate
divided bis code 19-1031 (Conservation
Scientists- 23.4) by 3, possibly an overestimate
divided bis code 33-1021 (First-Line Supervisors
of Fire Fighting and Prevention Workers- 60.1) by
2, estimate should be ok
divided bis code 33-1021 (First-Line Supervisors
of Fire Fighting and Prevention Workers- 60.1) by
2, estimate should be ok
divided bis code 33-2011 (Firefighters- 310.4) by
2, estimate should be ok
divided bis code 33-2011 (Firefighters- 310.4) by
2, estimate should be ok
divided bis code 33-2021 (Fire Inspectors and
Investigators- 13.6) by 2, estimate should be ok
divided bis code 33-2021 (Fire Inspectors and
Investigators- 13.6) by 2, estimate should be ok
5G-58
-------
Major BLS
SOC code
33
33
33
33
33
33
33
33
33
33
37
37
37
37
37
39
39
43
43
43
45
Major BLS SOC name
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Protective Service
Occupations
Building and Grounds
Cleaning and
Maintenance Occupations
Building and Grounds
Cleaning and
Maintenance Occupations
Building and Grounds
Cleaning and
Maintenance Occupations
Building and Grounds
Cleaning and
Maintenance Occupations
Building and Grounds
Cleaning and
Maintenance Occupations
Personal Care and
Service Occupations
Personal Care and
Service Occupations
Office and Administrative
Support Occupations
Office and Administrative
Support Occupations
Office and Administrative
Support Occupations
Farming, Fishing, and
Forestry Occupations
BLS SOC name
Detectives and Criminal
Investigators
Detectives and Criminal
Investigators
Detectives and Criminal
Investigators
Fish and Game Wardens
Parking Enforcement Workers
Police and Sheriffs Patrol
Officers
Police and Sheriffs Patrol
Officers
Animal Control Workers
Private Detectives and
Investigators
Crossing Guards
First-Line Supervisors of
Landscaping, Lawn Service,
and Groundskeeping Workers
Pest Control Workers
Landscaping and
Groundskeeping Workers
Pesticide Handlers, Sprayers,
and Applicators, Vegetation
Tree Trimmers and Pruners
Baggage Porters and Bellhops
Couriers and Messengers
Meter Readers, Utilities
Postal Service Mail Carriers
First-Line Supervisors of
Farming, Fishing, and Forestry
Workers
BLS SOC
code
33-3021
33-3021
33-3021
33-3031
33-3041
33-3051
33-3051
33-9011
33-9021
33-9091
37-0000
37-2021
37-3011
37-3012
37-3013
39-0000
39-6011
43-0000
43-5041
43-5052
45-0000
Employed
(1000's)
23.9
23.9
23.9
7.6
9.8
332
332
15.5
34.7
69.3
202.9
68.4
1151.5
29.5
50.6
29.3
46
116.2
40.5
316.7
11.8
O*NET Code
33-3021.01
33-3021.03
33-3021.05
33-3031 .00
33-3041 .00
33-3051.01
33-3051 .03
33-901 1 .00
33-9021.00
33-9091 .00
37-1012.00
37-2021.00
37-3011.00
37-3012.00
37-3013.00
39-4031 .00
39-601 1 .00
43-5021.00
43-5041 .00
43-5052.00
45-1011.05
O*NET Name
Police Detectives
Criminal Investigators and
Special Agents
Immigration and Customs
Inspectors
Fish and Game Wardens
Parking Enforcement
Workers
Police Patrol Officers
Sheriffs and Deputy
Sheriffs
Animal Control Workers
Private Detectives and
Investigators
Crossing Guards
First-Line Supervisors of
Landscaping, Lawn
Service, and
Groundskeeping Workers
Pest Control Workers
Landscaping and
Groundskeeping Workers
Pesticide Handlers,
Sprayers, and Applicators,
Vegetation
Tree Trimmers and
Pruners
Morticians, Undertakers,
and Funeral Directors
Baggage Porters and
Bellhops
Couriers and Messengers
Meter Readers, Utilities
Postal Service Mail
Carriers
First-Line Supervisors of
Logging Workers
Context
87
76
85
96
95
91
88
87
76
100
94
98
99
89
99
77
82
89
99
97
95
Out
days/wk
3
1
2
5
4
4
3
3
1
5
4
5
5
3
5
1
2
3
5
5
4
Comment
divided bis code 33-3021 (Detectives and
Criminal Investigators- 119.4) by 5, possibly an
overestimate
divided bis code 33-3021 (Detectives and
Criminal Investigators- 119.4) by 5, possibly an
overestimate
divided bis code 33-3021 (Detectives and
Criminal Investigators- 119.4) by 5, possibly an
overestimate
divided bis code 33-3051 (Police and Sheriff
Patrol Officers- 663.9) by 2, estimate should be
ok
divided bis code 33-3051 (Police and Sheriff
Patrol Officers- 663.9) by 2, estimate should be
ok
used employment data from bis code 39-4831
(funeral dir., etc), estimate should be ok
divided bis code 45-1011 (First-Line Supervisors
of Farming, Fishing, and Forestry Workers- 47)
by 4, estimate should be ok
5G-59
-------
Major BLS
SOC code
45
45
45
45
45
45
45
45
45
45
45
45
45
45
47
47
47
47
47
47
47
47
47
Major BLS SOC name
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Farming, Fishing, and
Forestry Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
BLS SOC name
First-Line Supervisors of
Farming, Fishing, and Forestry
Workers
First-Line Supervisors of
Farming, Fishing, and Forestry
Workers
First-Line Supervisors of
Farming, Fishing, and Forestry
Workers
Animal Breeders
Fishers and Related Fishing
Workers
Forest and Conservation
Workers
Fallers
Logging Equipment Operators
Log Graders and Sealers
First-Line Supervisors of
Construction Trades and
Extraction Workers
Brickmasons and Blockmasons
Stonemasons
Carpenters
Carpenters
Cement Masons and Concrete
Finishers
Construction Laborers
Paving, Surfacing, and
Tamping Equipment Operators
Pile-Driver Operators
BLS SOC
code
45-1011
45-1011
45-1011
45-2021
45-2091
45-2092
45-2092
45-2093
45-3011
45-3021
45-4011
45-4021
45-4022
45-4023
47-0000
47-2021
47-2022
47-2031
47-2031
47-2051
47-2061
47-2071
47-2072
Employed
(1000's)
11.8
11.8
11.8
11.5
186.6
186.6
186.6
186.6
32
0.6
13.7
9.6
35.1
3.8
558.5
89.2
15.6
500.8
500.8
144.7
998.8
51.6
4.1
O*NET Code
45-1011.06
45-1011.07
45-1011.08
45-2021.00
45-2091 .00
45-2092.01
45-2092.02
45-2093.00
45-301 1 .00
45-3021.00
45-401 1 .00
45-4021.00
45-4022.00
45-4023.00
47-1011.00
47-2021.00
47-2022.00
47-2031.01
47-2031.02
47-2051.00
47-2061.00
47-2071.00
47-2072.00
O*NET Name
First-Line Supervisors of
Aquacultural Workers
First-Line Supervisors of
Agricultural Crop and
Horticultural Workers
First-Line Supervisors of
Animal Husbandry and
Animal Care Workers
Animal Breeders
Agricultural Equipment
Operators
Nursery Workers
Farmworkers and
Laborers, Crop
Farmworkers, Farm,
Ranch, and Aquacultural
Animals
Fishers and Related
Fishing Workers
Hunters and Trappers
Forest and Conservation
Workers
Fallers
Logging Equipment
Operators
Log Graders and Sealers
First-Line Supervisors of
Construction Trades and
Extraction Workers
Brickmasons and
Blockmasons
Stonemasons
Construction Carpenters
Rough Carpenters
Cement Masons and
Concrete Finishers
Construction Laborers
Paving, Surfacing, and
Tamping Equipment
Operators
Pile-Driver Operators
Context
87
87
84
91
95
76
98
83
93
99
83
99
95
81
83
99
92
78
83
100
97
94
100
Out
days/wk
3
3
2
4
4
1
5
2
4
5
2
5
4
2
2
5
4
1
2
5
5
4
5
Comment
divided bis code 45-1011 (First-Line Supervisors
of Farming, Fishing, and Forestry Workers- 47)
by 4, estimate should be ok
divided bis code 45-1011 (First-Line Supervisors
of Farming, Fishing, and Forestry Workers- 47)
by 4, estimate should be ok
divided bis code 45-1011 (First-Line Supervisors
of Farming, Fishing, and Forestry Workers- 47)
by 4, estimate should be ok
used employment data from bis code 45-2090
(misc ag- 746.4) divided by 4, possibly an
overestimate
used employment data from bis code 45-2090
(misc ag- 746.4) divided by 4, possibly an
overestimate
used employment data from bis code 45-2090
(misc ag- 746.4) divided by 4, possibly an
overestimate
used employment data from bis code 45-2090
(misc ag- 746.4) divided by 4, possibly an
overestimate
diff of parent bis 45-3000 (fish hunt- 32.6) and
sub 45-3011 (fishing- 32), estimate should be ok
divided bis code 47-2031 (Carpenters-1001.7) by
2, estimate should be ok
divided bis code 47-2031 (Carpenters- 1001.7) by
2, estimate should be ok
5G-60
-------
Major BLS
SOC code
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
47
Major BLS SOC name
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
Extraction Occupations
Construction and
BLS SOC name
Operating Engineers and Other
Construction Equipment
Operators
Glaziers
Insulation Workers, Floor,
Ceiling, and Wall
Pipelayers
Plumbers, Pipefitters, and
Steamfitters
Reinforcing Iron and Rebar
Workers
Roofers
Structural Iron and Steel
Workers
Helpers— Brickmasons,
Blockmasons, Stonemasons,
and Tile and Marble Setters
Helpers— Pipelayers, Plumbers,
Pipefitters, and Steamfitters
Helpers— Roofers
Construction and Building
Inspectors
Fence Erectors
Highway Maintenance Workers
Rail-Track Laying and
Maintenance Equipment
Operators
Septic Tank Servicers and
Sewer Pipe Cleaners
Seg mental Pavers
Derrick Operators, Oil and Gas
Rotary Drill Operators, Oil and
Gas
Service Unit Operators, Oil,
Gas, and Mining
Earth Drillers, Except Oil and
Gas
Explosives Workers, Ordnance
Handling Experts, and Blasters
Rock Splitters, Quarry
Roustabouts, Oil and Gas
BLS SOC
code
47-2073
47-2121
47-2131
47-2151
47-2152
47-2171
47-2181
47-2221
47-3011
47-3015
47-3016
47-4011
47-4031
47-4051
47-4061
47-4071
47-4091
47-5011
47-5012
47-5013
47-5021
47-5031
47-5051
47-5071
Employed
(1000's)
349.1
41.9
23.2
53.1
419.9
19.1
136.7
59.8
29.4
57.9
12.7
102.4
32.1
148.5
15
25.3
1.3
18.9
22.5
40.7
17.8
6.8
3.5
52.7
O*NET Code
47-2073.00
47-2121.00
47-2131.00
47-2151.00
47-2152.02
47-2171.00
47-2181.00
47-2221.00
47-3011.00
47-3015.00
47-3016.00
47-4011.00
47-4031 .00
47-4051 .00
47-4061 .00
47-4071.00
47-4091 .00
47-5011.00
47-5012.00
47-5013.00
47-5021.00
47-5031 .00
47-5051 .00
47-5071.00
O*NET Name
Operating Engineers and
Other Construction
Equipment Operators
Glaziers
Insulation Workers, Floor,
Ceiling, and Wall
Pipelayers
Plumbers
Reinforcing Iron and Rebar
Workers
Roofers
Structural Iron and Steel
Workers
Helpers— Brickmasons,
Blockmasons,
Stonemasons, and Tile and
Marble Setters
Helpers— Pipelayers,
Plumbers, Pipefitters, and
Steamfitters
Helpers— Roofers
Construction and Building
Inspectors
Fence Erectors
Highway Maintenance
Workers
Rail-Track Laying and
Maintenance Equipment
Operators
Septic Tank Servicers and
Sewer Pipe Cleaners
Seg mental Pavers
Derrick Operators, Oil and
Gas
Rotary Drill Operators, Oil
and Gas
Service Unit Operators, Oil,
Gas, and Mining
Earth Drillers, Except Oil
and Gas
Explosives Workers,
Ordnance Handling
Experts, and Blasters
Rock Splitters, Quarry
Roustabouts, Oil and Gas
Context
99
91
87
97
79
94
100
97
86
87
88
88
99
84
93
92
84
100
99
99
95
98
94
100
Out
days/wk
5
4
3
5
1
4
5
5
3
3
3
3
5
2
4
4
2
5
5
5
4
5
4
5
Comment
5G-61
-------
Major BLS
SOC code
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
49
Major BLS SOC name
Extraction Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
and Repair Occupations
Installation, Maintenance,
BLS SOC name
Telecommunications
Equipment Installers and
Repairers, Except Line
Installers
Electrical and Electronics
Repairers, Powerhouse,
Substation, and Relay
Automotive Glass Installers and
Repairers
Bus and Truck Mechanics and
Diesel Engine Specialists
Farm Equipment Mechanics
and Service Technicians
Mobile Heavy Equipment
Mechanics, Except Engines
Rail Car Repairers
Motorboat Mechanics and
Service Technicians
Mechanical Door Repairers
Control and Valve Installers
and Repairers, Except
Mechanical Door
Heating, Air Conditioning, and
Refrigeration Mechanics and
Installers
Heating, Air Conditioning, and
Refrigeration Mechanics and
Installers
Millwrights
Electrical Power-Line Installers
and Repairers
Telecommunications Line
Installers and Repairers
Coin, Vending, and Amusement
Machine Servicers and
Repairers
Commercial Divers
Locksmiths and Safe Repairers
Manufactured Building and
Mobile Home Installers
Riggers
Signal and Track Switch
Repairers
BLS SOC
code
49-0000
49-2095
49-3022
49-3031
49-3041
49-3042
49-3043
49-3051
49-9011
49-9012
49-9021
49-9021
49-9044
49-9051
49-9052
49-9091
49-9092
49-9094
49-9095
49-9096
49-9097
49-9099
Employed
(1000's)
194.9
23.4
18.1
242.2
32.9
124.6
21.7
20.8
12.8
43.8
133.9
133.9
36.5
108.4
160.6
39.1
3.8
25.7
7.8
15.2
7.1
143.6
O*NET Code
49-2022.00
49-2095.00
49-3022.00
49-3031 .00
49-3041 .00
49-3042.00
49-3043.00
49-3051 .00
49-901 1 .00
49-9012.00
49-9021.01
49-9021.02
49-9044.00
49-9051 .00
49-9052.00
49-9091 .00
49-9092.00
49-9094.00
49-9095.00
49-9096.00
49-9097.00
49-9099.01
O*NET Name
Telecommunications
Equipment Installers and
Repairers, Except Line
Installers
Electrical and Electronics
Repairers, Powerhouse,
Substation, and Relay
Automotive Glass Installers
and Repairers
Bus and Truck Mechanics
and Diesel Engine
Specialists
Farm Equipment
Mechanics and Service
Technicians
Mobile Heavy Equipment
Mechanics, Except
Engines
Rail Car Repairers
Motorboat Mechanics and
Service Technicians
Mechanical Door Repairers
Control and Valve Installers
and Repairers, Except
Mechanical Door
Heating and Air
Conditioning Mechanics
and Installers
Refrigeration Mechanics
and Installers
Millwrights
Electrical Power-Line
Installers and Repairers
Telecommunications Line
Installers and Repairers
Coin, Vending, and
Amusement Machine
Servicers and Repairers
Commercial Divers
Locksmiths and Safe
Repairers
Manufactured Building and
Mobile Home Installers
Riggers
Signal and Track Switch
Repairers
Geothermal Technicians
Context
79
92
88
83
83
78
81
86
93
88
84
94
77
95
94
85
92
87
86
86
96
94
Out
days/wk
1
4
3
2
2
1
2
3
4
3
2
4
1
4
4
2
4
3
3
3
5
4
Comment
divided bis code 49-9021 (Heating AC refrig
mech- 267.8) by 2, estimate should be ok
divided bis code 49-9021 (Heating AC refrig
mech- 267.8) by 2, estimate should be ok
used employment data from bis code 49-9799
5G-62
-------
Major BLS
SOC code
51
51
51
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
Major BLS SOC name
and Repair Occupations
Production Occupations
Production Occupations
Production Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
BLS SOC name
Water and Wastewater
Treatment Plant and System
Operators
Gas Plant Operators
Petroleum Pump System
Operators, Refinery Operators,
and Gaugers
Aircraft Cargo Handling
Supervisors
Commercial Pilots
Bus Drivers, Transit and
Intercity
Driver/Sales Workers
Heavy and Tractor-Trailer Truck
Drivers
Light Truck or Delivery Services
Drivers
Taxi Drivers and Chauffeurs
Locomotive Engineers
Locomotive Firers
Rail Yard Engineers, Dinkey
Operators, and Hostlers
Railroad Brake, Signal, and
Switch Operators
Railroad Conductors and
Yardmasters
Subway and Streetcar
Operators
Sailors and Marine Oilers
Captains, Mates, and Pilots of
Water Vessels
BLS SOC
code
51-0000
51-8092
51-8093
53-0000
53-2012
53-3021
53-3031
53-3032
53-3033
53-3041
53-4011
53-4012
53-4013
53-4021
53-4031
53-4041
53-5011
53-5021
Employed
(1000's)
110.7
13.7
44.2
6.3
32.7
186.3
406.6
1604.8
856
239.9
38.7
1.1
5.6
21.7
40.8
6.5
33.4
36.1
O*NET Code
51-8031.00
51-8092.00
51-8093.00
53-1011.00
53-2012.00
53-3021.00
53-3031 .00
53-3032.00
53-3033.00
53-3041 .00
53-401 1 .00
53-4012.00
53-4013.00
53-4021.00
53-4031 .00
53-4041 .00
53-501 1 .00
53-5021.02
O*NET Name
Water and Wastewater
Treatment Plant and
System Operators
Gas Plant Operators
Petroleum Pump System
Operators, Refinery
Operators, and Gaugers
Aircraft Cargo Handling
Supervisors
Commercial Pilots
Bus Drivers, Transit and
Intercity
Driver/Sales Workers
Heavy and Tractor-Trailer
Truck Drivers
Light Truck or Delivery
Services Drivers
Taxi Drivers and
Chauffeurs
Locomotive Engineers
Locomotive Firers
Rail Yard Engineers,
Dinkey Operators, and
Hostlers
Railroad Brake, Signal, and
Switch Operators
Railroad Conductors and
Yardmasters
Subway and Streetcar
Operators
Sailors and Marine Oilers
Mates- Ship, Boat, and
Barge
Context
93
92
93
77
92
83
76
100
100
78
86
86
84
100
87
77
99
95
Out
days/wk
4
4
4
1
4
2
1
5
5
1
3
3
2
5
3
1
5
4
Comment
(install, repair, other, etc.), possibly an
overestimate
5G-63
-------
Major BLS
SOC code
53
53
53
53
53
53
53
53
53
53
53
53
53
Major BLS SOC name
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
Transportation and
Material Moving
Occupations
BLS SOC name
Motorboat Operators
Ship Engineers
Parking Lot Attendants
Automotive and Watercraft
Service Attendants
Transportation Inspectors
Transportation Attendants,
Except Flight Attendants
Dredge Operators
Excavating and Loading
Machine and Dragline
Operators
Gas Compressor and Gas
Pumping Station Operators
Pump Operators, Except
Wellhead Pumpers
Wellhead Pumpers
Refuse and Recyclable Material
Collectors
Tank Car, Truck, and Ship
Loaders
BLS SOC
code
53-5022
53-5031
53-6021
53-6031
53-6051
53-6061
53-7031
53-7032
53-7071
53-7072
53-7073
53-7081
53-7121
Employed
(1000's)
3.1
10.1
125.1
86.3
27.4
24.8
2.1
61.5
4.5
10.8
15.1
139.9
10.4
O*NET Code
53-5022.00
53-5031 .00
53-6021.00
53-6031 .00
53-6051 .08
53-6061 .00
53-7031 .00
53-7032.00
53-7071.00
53-7072.00
53-7073.00
53-7081 .00
53-7121.00
O*NET Name
Motorboat Operators
Ship Engineers
Parking Lot Attendants
Automotive and Watercraft
Service Attendants
Freight and Cargo
Inspectors
Transportation Attendants,
Except Flight Attendants
Dredge Operators
Excavating and Loading
Machine and Dragline
Operators
Gas Compressor and Gas
Pumping Station Operators
Pump Operators, Except
Wellhead Pumpers
Wellhead Pumpers
Refuse and Recyclable
Material Collectors
Tank Car, Truck, and Ship
Loaders
Context
85
77
76
94
80
77
86
88
91
99
93
100
91
Out
days/wk
2
1
1
4
1
1
3
3
4
5
4
5
4
Comment
1
2
5G-64
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Attachment 2. Additional mapping
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
if
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
occ
~
occ
code
code
code
code
code~
code
code
code
code
code
code
code
code
code
code
code~
code
code
code
code
code
code
code~
code
code
code
code
code
code
code~
code
code
code
~
code
onet=
onet= '
onet='
onet= '
onet='
onet= '
onet= '
onet= '
onet= '
onet='
onet= '
onet='
onet= '
onet= '
onet= '
onet='
onet='
onet= '
onet='
onet= '
onet= '
onet= '
onet='
onet= '
onet= '
onet='
onet= '
onet='
onet= '
onet='
onet= '
onet= '
onet= '
onet='
' 13-1032
13-1041.
13-1074.
13-2021.
17-3031.
19-4093.
33-9011.
33-9021.
33-9091.
37-3013.
43-5021.
45-2021.
45-2093.
45-3011.
45-4011.
45-4023.
47-2061.
47-2071.
47-2073.
47-2121.
47-4011.
47-4051.
47-4061.
47-5071.
49-3022.
49-9091.
49-9094 .
49-9096.
49-9099.
53-1011.
53-5011.
53-5031.
53-6051.
53-3032 .
. 00 '
04 '
00'
02'
01'
00'
00 '
00'
00 '
00'
00 '
00'
00'
00 '
00'
00 '
00'
00 '
00'
00 '
00 '
00'
00 '
00'
00 '
00'
00 '
00'
01'
00 '
00'
00 '
08'
00'
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
then
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
Occ
, 7"
Occ
of O*NET occupation codes to CHAD/APEX METs occupation activity codes
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
name
• • ~
name
CHAD=
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
CHAD= '
' ADMSUP ' ; . •::'.., : ;
ADMSUP ' ; : . . : • . : . ' : . ;
ADMSUP ' ; : ' ;
ADMSUP ' ; . ,
TECH' ; , . ' • ' •'
TECH' ; . : ' . ' •'•'.;
PROTECT ' ;
PROTECT ' ; • ' . : . ' •
PROTECT' ;•..':' :. ;
FARM ' ; : . ;
ADMSUP ' ; ' . : ' ' . . : . ;
FARM ' ; ' : . ;
FARM ' ; . , , • , : ' . ;
FARM ' ; . : : ' . '
FARM ' ; . : ' .
FARM ' ; • ' : : ,
LABOR' ' . • ' : ;
TRANS ' ' : , . ' ' ' : , : :'::': : . ;
TRANS ' ' : : ' . : ' . ' ' : ' : : ;
PREC ' ; ' . ;
ADMIN' ' ' :•':'• :
LABOR ' ' '
LABOR ' ' • .'•:••' : ' :
LABOR' . : . , ' : ' . ;
PREC ' ; ' ' . . . . : : ' . ;
PREC' ; ' ' , :' :, : . ' ' .' ' ' . : • : ' . ;
PREC ' ; • . ' . : . ' : ' . ;
TRANS ' • ' : : ;
TECH' ; ' •'•'.;
TRANS ' ' ' : ' • . ' : ' .
TRANS ' , ' ' . :
TRANS ' , ' :
TRANS ' ' : ' . •. • . ;
TRANS' : ' . ;
5G-65
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-452/P-14-004c
Environmental Protection Air Quality Strategies and Standards Division February 2014
Agency Research Triangle Park, NC
------- |