Health Risk and Exposure Assessment 

for Ozone 

Final Report 

Chapter 5 Appendices 

 
This page left intentionally blank 
 

EPA-452/R-14-004c 

August 2014 

Health Risk and Exposure Assessment for Ozone 

Final Report 

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 

 
DISCLAIMER

This final 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. 

 

Questions related to this 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). 
CHAPTER 5 APPENDICES 

 Title Pages 

APPENDIX 5A: Description of the Air Pollutants Exposure Model 

(APEX) 

(5A-1 to 5A-22)

APPENDIX 5B: Inputs to the APEX Exposure Model (5B-1 to 5B-38)

APPENDIX 5C: Generation of Adult and Child Census-tract 

Level Asthma Prevalence using NHIS (2006-

2010) and US Census (2000) Data 

(5C-1 to 5C-80)

APPENDIX 5D: Variability Analysis and Uncertainty 

Characterization 

(5D-1 to 5D-12)

APPENDIX 5E: Updated Analysis of Air Exchange Rate Data: 

Memorandum from ICF International 

(5E-1 to 5E-25)

APPENDIX 5F: Detailed Exposure Results (5F-1 to 5F-55)

APPENDIX 5G: Targeted Evaluation of Exposure Model Input 

and Output Data 

(5G-1 to 5G-62)
This page left intentionally blank 
 5A-i 

APPENDIX 5A 

Description of the Air Pollutants Exposure Model (APEX) 

Table of Contents 

5A-1. OVERVIEW .............................................................................................................. 5A-1 

5A-2. MODEL INPUTS ...................................................................................................... 5A-3 

5A-3. DEMOGRAPHIC CHARACTERISTICS ................................................................. 5A-4 

5A-4. ATTRIBUTES OF INDIVIDUALS .......................................................................... 5A-4 

5A-5. CONSTRUCTION OF LONGITUDINAL DIARY SEQUENCE ............................ 5A-5 

5A-6. KEY PHYSIOLOGICAL PROCESSES MODELED ............................................... 5A-7 

5A-7. ESTIMATING MICROENVIRONMENTAL CONCENTRATIONS ..................... 5A-9 

5A-7.1. Mass Balance Model ......................................................................................... 5A-9 

5A-7.2. Factors Model ................................................................................................. 5A-16 

5A-8. EXPOSURE AND DOSE TIME SERIES CALCULATIONS ............................... 5A-17 

5A-9. MODEL OUTPUT ................................................................................................... 5A-19 

5A-10. REFERENCES ........................................................................................................ 5A-21 
 5A-ii 

List of Tables 

Table 5A-1. Ventilation coefficient parameter estimates (bi) and residuals distributions (ei) 

from Graham and McCurdy (2005)................................................. 5A-9

List of Figures 

Figure 5A-1. Illustration of the mass balance model used by APEX. .................................. 5A-10

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). ................................................................. 5A-18

Figure 5A-3. The percent of simulated children (ages 5-18) at or above 8-hour average O3

exposures while at moderate or greater exertion. ........................................... 5A-20 
 5A-1 

This Appendix briefly describes the EPAs Air Pollutants Exposure (APEX) model. 

5A-1. OVERVIEW 

APEX is the human inhalation exposure model within the Total Risk Integrated 

Methodology (TRIM) framework (US EPA 2012a,b). APEX is conceptually based on the 

probabilistic NAAQS Exposure Model (pNEM) that was used to estimate population exposures 

for the 1996 O3 NAAQS review (Johnson et al., 1996a; 1996b; 1996c). Since that time the 

model has been restructured, improved, and expanded to reflect conceptual advances in the 

science of exposure modeling and newer input data available for the model. Key improvements 

to algorithms include replacement of the cohort approach with a probabilistic sampling approach 

focused on individuals, accounting for fatigue and oxygen debt after exercise in the calculation 

of ventilation rates (Isaacs et al., 2008), and new approaches for construction of longitudinal 

activity patterns for simulated persons (Glen et al., 2008; Rosenbaum et al., 2008). Major 

improvements to data input to the model include updated air exchange rates (AERs), population 

census and commuting data, and the daily time-location-activities database. These 

improvements are described later in this and other Chapter 5 Appendices. 

APEX estimates human exposure to criteria and toxic air pollutants at local, urban, or 

regional scales using a stochastic, microenvironmental approach. That is, the model randomly 

selects data on a sample of hypothetical individuals in an actual population database and 

simulates each individual’s movements through time and space (e.g., at home, in vehicles) to 

estimate their exposure to the pollutant. APEX can assume people live and work in the same 

general area (i.e., that the ambient air quality is the same at home and at work) or optionally can 

model commuting and thus exposure at the work location for individuals who work. 

 The APEX model is a microenvironmental, longitudinal human exposure model for 

airborne pollutants. It is applied to a specified study area, which is typically a metropolitan area. 

The time period of the simulation is typically one year, but can easily be made either longer or 

shorter. APEX uses census data, such as gender and age, to generate the demographic 

characteristics of simulated individuals. It then assembles a composite activity diary to represent 

the sequence of activities and microenvironments that the individual experiences. Each 

microenvironment has a user-specified method for determining air quality. The inhalation 

exposure in each microenvironment is simply equal to the air concentration in that 

microenvironment. When coupled with breathing rate information and a physiological model, 

various measures of dose can also be calculated. 

The term microenvironment is intended to represent the immediate surroundings of an 

individual, in which the pollutant of interest is assumed to be well-mixed. Time is modeled as a 

sequence of discrete time steps called events. In APEX, the concentration in a microenvironment 
 5A-2 

may change between events. For each microenvironment, the user specifies the method of 

concentration calculation (either mass balance or regression factors, described later in this 

paper), the relationship of the microenvironment to the ambient air, and the strength of any 

pollutant sources specific to that microenvironment. Because the microenvironments that are 

relevant to exposure depend on the nature of the target chemical and APEX is designed to be 

applied to a wide range of chemicals, both the total number of microenvironments and the 

properties of each are free to be specified by the user. 

 The ambient air data are provided as input to the model in the form of time series at a 

list of specified locations. Typically, hourly air concentrations are used, although temporal 

resolutions as small as one minute may be used. The spatial range of applicability of a given 

ambient location is called an air district. Any number of air districts can be accommodated in a 

model run, subject only to computer hardware limitations. In principle, any microenvironment 

could be found within a given air district. Therefore, to estimate exposures as an individual 

engages in activities throughout the period it is necessary to determine both the 

microenvironment and the air district that apply for each event. 

An exposure event is determined by the time reported in the activity diary; during any 

event the district, microenvironment, ambient air quality, and breathing rate are assumed to 

remain fixed. Since the ambient air data change every hour, the maximum duration of an event 

is limited to one hour. The event duration may be less than this (as short as one minute) if the 

activity diary indicates that the individual changes microenvironments or activities performed 

within the hour. 

An APEX simulation includes the following steps: 

1. Characterize the study area - APEX selects sectors (e.g., census tracts) within a study area 

based on user-defined criteria and thus identifies the potentially exposed population and 

defines the air quality and weather input data required for the area. 

2. Generate simulated individuals - APEX stochastically generates a sample of simulated 

individuals based on the census data for the study area and human profile distribution data 

(such as age-specific employment probabilities). The user must specify the size of the 

sample. The larger the sample, the more representative it is of the population in the study 

area and the more stable the model results are (but also the longer the computing time). 

3. Construct a long-term sequence of activity events and determine breathing rates - APEX 

constructs an event sequence (activity pattern) spanning the period of simulation for each 

simulated person. The model then stochastically assigns breathing rates to each event, based 

on the type of activity and the physical characteristics of the simulated person. 

4. Calculate pollutant concentrations in microenvironments - APEX enables the user to define 

any microenvironment that individuals in a study area would visit. The model then 

calculates concentrations of each pollutant in each of the microenvironments. 
 5A-3 

5. Calculate pollutant exposures for each simulated individual - Microenvironmental 

concentrations are time weighted based on individuals’ events (i.e., time spent in the 

microenvironment) to produce a sequence of time-averaged exposures (or minute by minute 

time series) spanning the simulation period. 

6. Estimate dose - APEX can also calculate the dose time series for each of the simulated 

individuals based on the exposures and breathing rates for each event. For O3, the adverse 

health metric of interest is decrement in forced expiratory volume occurring in one second 

(FEV1). This algorithm responsible for combining the time series of APEX estimated 

exposure and breathing rates for individuals is discussed in greater detail in the main body of 

the HREA, Chapter 5. 

The model simulation continues until exposures are determined for the user-specified 

number of simulated individuals. APEX then calculates population exposure statistics (such as 

the number of exposures exceeding user-specified levels) for the entire simulation and writes out 

tables of distributions of these statistics. 

5A-2. MODEL INPUTS 

APEX requires certain inputs from the user. The user specifies the geographic area and 

the range of ages and age groups to be used for the simulation. Hourly (or shorter) ambient air 

quality and hourly temperature data must be furnished for the entire simulation period. Other 

hourly meteorological data (humidity, wind speed, wind direction, precipitation) can be used by 

the model to estimate microenvironmental concentrations, but are optional. 

In addition, most variables used in the model algorithms are represented by user-specified 

probability distributions which capture population variability. APEX provides great flexibility in 

defining model inputs and parameters, including options for the frequency of selecting new 

values from the probability distributions. The model also allows different distributions to be 

used at different times of day or on different days, and the distribution can depend conditionally 

on values of other parameters. The probability distributions available in APEX include beta, 

binary, Cauchy, discrete, exponential, extreme value, gamma, logistic, lognormal, loguniform, 

normal, off/on, Pareto, point (constant), triangle, uniform, Weibull, and nonparametric 

distributions. Minimum and maximum bounds can be specified for each distribution if a 

truncated distribution is appropriate. There are two options for handling truncation. The 

generated samples outside the truncation points can be set to the truncation limit; in this case, 

samples “stack up” at the truncation points. Alternatively, new random values can be selected, in 

which case the probability outside the limits is spread over the specified range, and thus the 

probabilities inside the truncation limits will be higher than the theoretical untruncated 

distribution. 
 5A-4 

5A-3. DEMOGRAPHIC CHARACTERISTICS 

The starting point for constructing a simulated individual is the population census 

database; this contains population counts for each combination of age, gender, race, and sector. 

The user may decide what spatial area is represented by a sector, but the default input file defines 

a sector as a census tract. Census tracts are variable in both geographic size and population 

number, though usually have between 1,500 and 8,000 persons. Currently, the default file 

contains population counts from the 2000 census for every census tract in the United States, thus 

the default file should be sufficient for most exposure modeling purposes. The combination of 

age, gender, race, and sector are selected first. The sector becomes the home sector for the 

individual, and the corresponding air district becomes the home district. The probabilistic 

selection of individuals is based on the sector population and demographic composition, and 

taken collectively, the set of simulated individuals constitutes a random sample from the study 

area. 

The second step in constructing a simulated individual is to determine their employment 

status. This is determined by a probability which is a function of age, gender, and home sector. 

An input file is provided which contains employment probabilities from the 2000 census for 

every combination of age (16 and over), gender, and census tract. APEX assumes that persons 

under age 16 do not commute. For persons who are determined to be workers, APEX then 

randomly selects a work sector, based on probabilities determined from the commuting matrix. 

The work sector is used to assign a work district for the individual that may differ from the home 

district, and thus different ambient air quality may be used when the individual is at work. 

The commuting matrix contains data on flows (number of individuals) traveling from a 

given home sector to a given work sector. Based on commuting data from the 2000 census, a 

commuting data base for the entire United States has been prepared. This permits the entire list 

of non-zero flows to be specified on one input file. Given a home sector, the number of 

destinations to which people commute varies anywhere from one to several hundred other tracts. 

5A-4. ATTRIBUTES OF INDIVIDUALS 

In addition to the above demographic information, each individual is assigned status and 

physiological attributes. The status variables are factors deemed important in estimating 

microenvironmental concentrations, and are specified by the user. Status variables can include, 

but are not limited to, people’s housing type, whether their home has air conditioning, whether 

they use a gas stove at home, whether the stove has a gas pilot light, and whether their car has air 

conditioning. Physiological variables are important when estimating pollutant specific dose. 

These variables could include height, weight, blood volume, pulmonary diffusion rate, resting 

metabolic rate, energy conversion factor (liters of oxygen per kilocalorie energy expended), 
 5A-5 

hemoglobin density in blood, maximum limit on metabolic equivalents of work (MET) ratios 

(see below), and endogenous CO production rate. All of these variables are treated 

probabilistically taking into account interdependencies where possible, and reflecting variability 

in the population. 

Two key personal attributes determined for each individual in this assessment are body 

mass (BM) and body surface area (BSA). Each simulated individual’s body mass was randomly 

sampled from age- and gender-specific body mass distributions generated from National Health 

and Nutrition Examination Survey (NHANES) data for the years 1999-2004.1

 Details in their 

development and the parameter values are provided by Isaacs and Smith (2005). Then age- and 

gender-specific body surface area can be estimated for each simulated individual. Briefly, the 

BSA calculation is based on logarithmic relationships developed by Burmaster (1998) that use 

body mass as an independent variable as follows: 

2.2781 0.6821 BSA e BM   Equation (5A-1) 

where, 

BSA = body surface area (m2) 

BM = body mass (kg) 

5A-5. CONSTRUCTION OF LONGITUDINAL DIARY SEQUENCE 

The activity diary determines the sequence of microenvironments visited by the 

simulated person. A longitudinal sequence of daily diaries must be constructed for each 

simulated individual to cover the entire simulation period. The default activity diaries in APEX 

are derived from those in the EPA's Consolidated Human Activity Database (CHAD) (US EPA, 

2000; 2002), although the user could provide area specific diaries if available. There are over 

53,000 CHAD diaries, each covering a 24 hour period, that have been compiled from several 

studies. CHAD is essentially a cross-sectional database that, for the most part, only has one 

diary per person. Therefore, APEX must assemble each longitudinal diary sequence for a 

simulated individual from many single-day diaries selected from a pool of similar people. 

APEX selects diaries from CHAD by matching gender and employment status, and by 

requiring that age falls within a user-specified range on either side of the age of the simulated 

individual. For example, if the user specifies plus or minus 20%, then for a 40 year old 

simulated individual, the available CHAD diaries are those from persons aged 32 to 48. Each 

 

1

 Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES studies were obtained 

from http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm. 
 5A-6 

simulated individual therefore has an age window of acceptable diaries; these windows can 

partially overlap those for other simulated individuals. This differs from a cohort-based 

approach, where the age windows are fixed and non-overlapping. The user may optionally 

request that APEX allow a decreased probability for selecting diaries from ages outside the 

primary age window, and also for selecting diaries from persons of missing gender, age, or 

employment status. These options allow the model to continue the simulation when diaries are 

not available within the primary window. 

The available CHAD diaries are classified into diary pools, based on the temperature and 

day of the week. The model will select diaries from the appropriate pool for days in the 

simulation having matching temperature and day type characteristics. The rules for defining 

these pools are specified by the user. For example, the user could request that all diaries from 

Monday to Friday be classified together, and Saturday and Sunday diaries in another class. 

Alternatively, the user could instead create more than two classes of weekdays, combine all 

seven days into one class, or split all seven days into separate classes. 

The temperature classification can be based either on daily maximum temperature, daily 

average temperature, or both. The user specifies both the ranges and numbers of temperatures 

classes. For example, the user might wish to create four temperature classes and set their ranges 

to below 50 °F, 50-69 °F, 70-84 °F, and above a daily maximum of 84 °F. Then day type and 

temperature classes are combined to create the diary pools. For example, if there are four 

temperature classes and two day type classes, then there will be eight diary pools. 

APEX then determines the day-type and the applicable temperature for each person’s 

simulated day. APEX allows multiple temperature stations to be used; the sectors are 

automatically mapped to the nearest temperature station. This may be important for study areas 

such as the greater Los Angeles area, where the inland desert sectors may have very different 

temperatures from the coastal sectors. For selected diaries, the temperature in the home sector of 

the simulated person is used. For each day of the simulation, the appropriate diary pool is 

identified and a CHAD dairy is randomly drawn. When a diary for every day in the simulation 

period has been selected, they are concatenated into a single longitudinal diary covering the 

entire simulation for that individual. APEX contains three algorithms for stochastically selecting 

diaries from the pools to create the longitudinal diary. The first method selects diaries at random 

after stratification by age, gender, and diary pool; the second method selects diaries based on 

metrics related to exposure (e.g., time spent outdoors) with the goal of creating longitudinal 

diaries with variance properties designated by the user (Glen et al., 2008); and the third method 

uses a clustering algorithm to obtain more realistic recurring behavioral patterns (Rosenbaum 

2008). 
 5A-7 

The final step in processing the activity diary is to map the CHAD location codes into the 

set of APEX microenvironments, supplied by the user as an input file. The user may define the 

number of microenvironments, from one up to the number of different CHAD location codes 

(which is currently 115). 

5A-6. KEY PHYSIOLOGICAL PROCESSES MODELED 

Ventilation is a general term describing the movement of air into and out of the lungs. 

The rate of ventilation is determined by the type of activity an individual performs which in turn 

is related to the amount of oxygen required to perform the activity. Minute or total ventilation 

rate is used to describe the volume of air moved in or out of the lungs per minute. Quantitatively, 

the volume of air breathed in per minute ( 

VI ) is slightly greater than the volume expired per 

minute (V E



). Clinically, however, this difference is not important, and by convention, the 

ventilation rate is always measured by the expired volume. 

The rate of oxygen consumption (V O2



) is related to the rate of energy usage in 

performing activities as follows: 

V O  EEECF 

2 Equation (5A-2) 

where, 

V O2



 = Oxygen consumption rate (liters O2/minute) 

EE = Energy expenditure (kcal/minute) 

ECF = Energy conversion factor (liters O2/kcal). 

The ECF shows little variation and typically, commonly a value between 0.20 and 0.21 is 

used to represent the conversion from energy units to oxygen consumption. APEX can randomly 

sample from a uniform distribution defined by these lower and upper bounds to estimate an ECF 

for each simulated individual. The activity-specific energy expenditure is highly variable and 

can be estimated using metabolic equivalents (METs), or the ratios of the rate of energy 

consumption for non-rest activities to the resting rate of energy consumption, as follows 

EE  MET RMR Equation (5A-3) 

where, 

EE = Energy expenditure (kcal/minute) 
 5A-8 

 MET = Metabolic equivalent of work (unitless) 

RMR = Resting metabolic rate (kcal/minute) 

APEX contains distributions of METs for all activities that might be performed by 

simulated individuals. APEX randomly samples from the various METs distributions to obtain 

values for every activity performed by each individual. Age- and gender-specific RMR are 

estimated once for each simulated individual using a linear regression model (see Johnson et al., 

2002) 2 as follows 

RMR [b0  b1 (BM )  ]F Equation (5A-4) 

where, 

RMR = Resting metabolic rate (kcal/min) 

bo = Regression intercept (MJ/day) 

b1 = Regression slope (MJ/day/kg) 

BM = body mass (kg) 

ε = randomly sampled error term, N{0, se)3 (MJ/day) 

F = Factor for converting MJ/day to kcal/min (0.166) 

Finally, Graham and McCurdy (2005) describe an approach to estimate V E



 using V O2



. 

In that report, a series of age- and gender-specific multiple linear regression equations were 

derived from data generated in 32 clinical exercise studies. The algorithm accounts for 

variability in ventilation rate due to variation in oxygen consumption, the variability within age 

groups, and both inter- and intra-personal and variability. The basic algorithm is 

E O b w V BM  b  b V BM  b  age  b gender  e  e  

2 3 2 0 1 ln( / ) ln( / ) ln (1 ) Equation (5A-5) 

where, 

ln = natural logarithm of variable 

V E / BM 

 = activity specific ventilation rate, body mass normalized (liter air/kg) 

bi = see below 

 

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 

V O2 / BM 

 = activity specific oxygen consumption rate, body mass normalized 

(liter/O2/kg) 

age = the age of the individual (years) 

gender = gender value (-1 for males and +1 for females) 

eb = randomly sampled error term for between persons N{0, se), (liter 

air/kg) 

ew = randomly sampled error term for within persons N{0, se), (liter 

air/kg) 

As indicated above, the random error (ε) is allocated to two variance components used to 

estimate the between-person (inter-individual variability) residuals distribution (eb) and withinperson (intra-individual variability) residuals distribution (ew). The regression parameters b0, b1, 

b2, and b3 are assumed to be constant over time for all simulated persons, eb 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 (bi) and residuals distributions (ei) 

from Graham and McCurdy (2005). 

Age 

group 

Regression Coefficients1 Random Error1

b0 b1 b2 b3 eb ew

<20 4.3675 1.0751 -0.2714 0.0479 0.0955 0.1117 

20-<34 3.7603 1.2491 0.1416 0.0533 0.1217 0.1296 

34-<61 3.2440 1.1464 0.1856 0.0380 0.1260 0.1152 

61+ 2.5826 1.0840 0.2766 -0.0208 0.1064 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): 
 5A-10 

 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. 

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment

Air

inflow

Indoor sources

Removal due to:

•Chemical reactions

•Deposition

•Filtration

Air

outflow

Microenvironment

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: 

  Cin Cout Cremoval Csource dt

dC t         Equation (5A-6) 

where, 

C(t) = Concentration in the microenvironment at time t 

C in = Rate of change in C(t) due to air entering the microenvironment 

C out = Rate of change in C(t) due to air leaving the microenvironment 

C removal = Rate of change in C(t) due to all internal removal processes 

C source = Rate of change in C(t) due to all internal source terms 
 5A-11 

Concentrations are calculated in the same units as the ambient air quality data, e.g., ppm, 

ppb, ppt, or μg/m3. In the following equations concentration is shown only in μg/m3 for brevity. 

The change in microenvironmental concentration due to influx of air, C in, is given by: 

in outdoor penetration Rair exchange C  C f   Equation (5A-7) 

where, 

Coutdoor = Ambient concentration at an outdoor microenvironment or outside an 

indoor microenvironment (μg/m3) 

fpenetration = Penetration factor (unitless) 

 Rair exchange = Air exchange rate (hr-1) 

Since the air pressure is approximately constant in microenvironments that are modeled 

in practice, the flow of outside air into the microenvironment is equal to that flowing out of the 

microenvironment, and this flow rate is given by the air exchange rate. The air exchange rate 

(hr-1) can be loosely interpreted as the number of times per hour the entire volume of air in the 

microenvironment is replaced. For some pollutants (especially particulate matter), the process of 

infiltration may remove a fraction of the pollutant from the outside air. The fraction that is 

retained in the air is given by the penetration factor fpenetration. 

A proximity factor (fproximity) and a local outdoor source term are used to account for 

differences in ambient concentrations between the geographic location represented by the 

ambient air quality data (e.g., a regional fixed-site monitor) and the geographic location of the 

microenvironment. That is, the outdoor air at a particular location may differ systematically 

from the concentration input to the model representing the air quality district. For example, a 

playground or house might be located next to a busy road in which case the air at the playground 

or outside the house would have elevated levels for mobile source pollutants such as carbon 

monoxide and benzene. The concentration in the air at an outdoor location or directly outside an 

indoor microenvironment (Coutdoor) is calculated as: 

outdoor proximityCambient CLocalOutdoorSources C  f  Equation (5A-8) 

where, 

Cambient = Ambient air district concentration (μg/m3) 
 5A-12 

fproximity = Proximity factor (unitless) 

 CLocalOutdoorSources = the contribution to the concentration at this location from local 

sources not represented by the ambient air district 

concentration (μg/m3

) 

During exploratory analyses, the user may examine how a microenvironment affects 

overall exposure by setting the microenvironment’s proximity or penetration factor to zero, thus 

effectively eliminating the specified microenvironment. 

Change in microenvironmental concentration due to outflux of air is calculated as the 

concentration in the microenvironment C(t) multiplied by the air exchange rate: 

C R C t out  air exchange   Equation (5A-9) 

The third term (C removal) in the mass balance calculation (Equation 5A-6) represents 

removal processes within the microenvironment. There are three such processes in general: 

chemical reaction, deposition, and filtration. Chemical reactions are significant for O3, for 

example, but not for carbon monoxide. The amount lost to chemical reactions will generally be 

proportional to the amount present, which in the absence of any other factors would result in an 

exponential decay in the concentration with time. Similarly, deposition rates are usually given 

by the product of a (constant) deposition velocity and a (time-varying) concentration, also 

resulting in an exponential decay. The third removal process is filtration, usually as part of a 

forced air circulation or HVAC system. Filtration will normally be more effective at removing 

particles than gases. In any case, filtration rates are also approximately proportional to 

concentration. Change in concentration due to deposition, filtration, and chemical degradation in 

a microenvironment is simulated based on the first-order equation: 

   

R C t

C R R R C t

removal

removal deposition filtration chemical

 

    

 Equation (5A-10) 

where, 

C removal = Change in microenvironmental concentration due to removal 

processes (μg/m3

/hr) 

Rdeposition = Removal rate of a pollutant from a microenvironment due to 

deposition (hr-1) 
 5A-13 

Rfiltration = Removal rate of a pollutant from a microenvironment due to 

filtration (hr-1) 

Rchemical = Removal rate of a pollutant from a microenvironment due to 

chemical degradation (hr-1) 

Rremoval = Removal rate of a pollutant from a microenvironment due to the 

combined effects of deposition, filtration, and chemical 

degradation (hr-1) 

The fourth term in the mass balance calculation represents pollutant sources within the 

microenvironment. This is the most complicated term, in part because several sources may be 

present. APEX allows two methods of specifying source strengths: emission sources and 

concentration sources. Either may be used for mass balance microenvironments, and both can be 

used within the same microenvironment. The source strength values are used to calculate the 

term C source (μg/m3

/hr). 

Emission sources are expressed as emission rates in units of μg/hr, irrespective of the 

units of concentration. To determine the rate of change of concentration associated with an 

emission source SE, it is divided by the volume of the microenvironment: 

V

S C E

source,SE   Equation (5A-11) 

where, 

C source,SE = Rate of change in C(t) due to the emission source SE (μg/m3/hr) 

SE = The emission rate (μg/hr) 

 V = The volume of the microenvironment (m3) 

Concentration sources (SC) however, are expressed in units of concentration. These must 

be the same units as used for the ambient concentration (e.g., μg/m3

). Concentration sources are 

normally used as additive terms for microenvironments using the factors model. Strictly 

speaking, they are somewhat inconsistent with the mass balance method, since concentrations 

should not be inputs but should be consequences of the dynamics of the system. Nevertheless, a 

suitable meaning can be found by determining the rate of change of concentration (C source) that 

would result in a mean increase of SC in the concentration, given constant parameters and 

equilibrium conditions, in this way: 

Assume that a microenvironment is always in contact with clean air (ambient = zero), and 

it contains one constant concentration source. Then the mean concentration over time in this 
 5A-14 

microenvironment from this source should be equal to SC. The mean source strength expressed 

in ppm/hr or μg/m3

/hr is the rate of change in concentration (C source,SC). In equilibrium, 

air exchange removal

source,SC

S R R

C

C

 

 Equation (5A-12) 

where, Cs is the mean increase in concentration over time in the microenvironment due to the 

source C source,SC . Thus, C source,SC can be expressed as 

Csource, SC  CS  Rmean  Equation (5A-13) 

where Rmean is the chemical removal rate. From Equation (5A-13), Rmean is the sum of the air 

exchange rate and the removal rate (Rair exchange + Rremoval) under equilibrium conditions. In 

general, however, the microenvironment will not be in equilibrium, but in such conditions there 

is no clear meaning to attach to C source,SC since there is no fixed emission rate that will lead to a 

fixed increase in concentration. The simplest solution is to use Rmean = Rair exchange + Rremoval. 

However, the user is given the option of specifically specifying Rmean (see discussion below). 

This may be used to generate a truly constant source strength C source,SC by making SC and Rmean

both constant in time. If this is not done, then Rmean is simply set to the sum of (Rair exchange + 

Rremoval). If these parameters change over time, then C source,SC also changes. Physically, the 

reason for this is that in order to maintain a fixed elevation of concentration over the base 

conditions, then the source emission rate would have to rise if the air exchange rate were to rise. 

Multiple emission and concentration sources within a single microenvironment are 

combined into the final total source term by combining Equations (5A-11) and (5A-13): 

   

    e nc

i 1

mean Si

n

i 1

source source,SE source,SC ESi R C

V

1 C C C Equation (5A-14) 

where, 

SEi = Emission source strength for emission source i (μg/hr, irrespective of 

the concentration units) 

SCi = Emission source strength for concentration source i (μg/m3) 

ne = Number of emission sources in the microenvironment 

nc = Number of concentration sources in the microenvironment 
 5A-15 

In Equations (5A-11) and (5A-14), if the units of air quality are ppm rather than μg/m3, 

1/V is replaced by f/V, where f = ppm / μg/m3 = gram molecular weight / 24.45. (24.45 is the 

volume (liters) of a mole of the gas at 25°C and 1 atmosphere pressure.) 

Equations (5A-7), (5A-9), (5A-10), and (5A-14) can now be combined with Equation (5A-6) to 

form the differential equation for the microenvironmental concentration C(t). Within the time 

period of a time step (at most 1 hour), C source and C in are assumed to be constant. Using 

C combined = C source + C in leads to: 

   

C - R C t

C - R C t R C t

dt

dC t

combined mean

combined air exchange removal







  Equation (5A-15) 

 

Solving this differential equation leads to: 

    ( ) 0 t t t 















    Rmean -

mean

combined

0

mean

combined e

R

C C

R

C C t   Equation (5A-16) 

where, 

C(t0) = Concentration of a pollutant in a microenvironment at the beginning of 

a time step (μg/m3

) 

C(t) = Concentration of a pollutant in a microenvironment at time t within the 

time step (μg/m3). 

Based on Equation (5A-16), the following three concentrations in a microenvironment 

are calculated: 

 

air exchange removal

source in

mean

combined

equil R R

C C

R

C C C t



     

  

 Equation (5A-17) 

    RmeanT C(t0 T Cequil C t0 Cequil e  )    Equation (5A-18) 

  R T

1 e C(t)dt C C(t C

T

C

mean

R

mean equil 0 equil

t T meanT

t

        ) 1 0

0

 Equation (5A-19) 
 5A-16 

where, 

Cequil = Concentration in a microenvironment (μg/m3) if t   (equilibrium 

state). 

C(t0) = Concentration in a microenvironment at the beginning of the time step 

(μg/m3) 

C(t0+T) = Concentration in a microenvironment at the end of the time step 

(μg/m3) 

 C mean = Mean concentration over the time step in a microenvironment (μg/m3) 

Rmean = Rair exchange + Rremoval (hr-1) 

At each time step of the simulation period, APEX uses Equations (5A-17), (5A-18), and 

(5A-19) to calculate the equilibrium, ending, and mean concentrations, respectively. The 

calculation continues to the next time step by using C(t0+T) for the previous hour as C(t0). 

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: 



 

nc

i 1

mean ambient proximity penetration SCi C C f f Equation (5A-20) 

where, 

Cmean = Mean concentration over the time step in a microenvironment (μg/m3) 

Cambient = The concentration in the ambient (outdoor) environment (μg/m3) 

fproximity = Proximity factor (unitless) 

fpenetration = Penetration factor (unitless) 

SCi = Mean air concentration resulting from source i (μg/m3) 

nc = Number of concentration sources in the microenvironment 

The user may specify distributions for proximity, penetration, and any concentration 

source terms. All of the parameters in Equation (5A-20) are evaluated for each time step, 
 5A-17 

although these values might remain constant for several time steps or even for the entire 

simulation. 

The ambient air quality data are supplied as time series over the simulation period at 

several locations across the modeled region. The other variables in the factors and mass balance 

equations are randomly drawn from user-specified distributions. The user also controls the 

frequency and pattern of these random draws. Within a single day, the user selects the number 

of random draws to be made and the hours to which they apply. Over the simulation, the same 

set of 24 hourly values may either be reused on a regular basis (for example, each winter 

weekday), or a new set of values may be drawn. The usage patterns may depend on day of the 

week, on month, or both. It is also possible to define different distributions that apply if specific 

conditions are met. The air exchange rate is typically modeled with one set of distributions for 

buildings with air conditioning and another set of distributions for those which do not. The 

choice of a distribution within a set typically depends on the outdoor temperature and possibly 

other variables. In total there are eleven such conditional variables which can be used to select 

the appropriate distributions for the variables in the mass balance or factors equations. 

For example, the hourly emissions of CO from a gas stove may be given by the product 

of three random variables: a binary on/off variable that indicates if the stove is used at all during 

that hour, a usage duration sampled from a continuous distribution, and an emission rate per 

minute of usage. The binary on/off variable may have a probability for on that varies by time of 

day and season of the year. The usage duration could be taken from a truncated normal or 

lognormal distribution that is resampled for each cooking event, while the emission rate could be 

sampled just once per stove. 

5A-8. EXPOSURE AND DOSE TIME SERIES CALCULATIONS 

The activity diaries provide the time sequence of microenvironments visited by the 

simulated individual and the activities performed by each individual. The pollutant 

concentration in the air in each microenvironment is assumed to be spatially uniform throughout 

the microenvironment and unchanging within each diary event and is calculated by either the 

factors or the mass balance method, as specified by the user. The exposure of the individual is 

given by the time sequence of airborne pollutant concentrations that are encountered in the 

microenvironments visited. Figure 5A-2 illustrates the exposures for one simulated 12-year old 

child over a 2-day period. On both days the child travels to and from school in an automobile, 

goes outside to a playground in the afternoon while at school, and spends time outside at home in 

the evening. 
 5A-18 

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). 

In addition to exposure, APEX models breathing rates based on the physiology of each 

individual and the exertion levels associated with the activities performed. For each activity type 

in CHAD, a distribution is provided for a corresponding normalized metabolic equivalent of 

work or METs (McCurdy, 2000). METs are derived by dividing the metabolic energy 

requirements for the specific activity by a person’s resting, or basal, metabolic rate. The MET 

ratios have less interpersonal variation than do the absolute energy expenditures. Based on age 

and gender, the resting metabolic rate, along with other physiological variables is determined for 

each individual as part of their anthropometric characteristics. Because the MET ratios are 

sampled independently from distributions for each diary event, it would be possible to produce 

time-series of MET ratios that are physiologically unrealistic. APEX employs a MET 

adjustment algorithm based on a modeled oxygen deficit to prevent such overestimation of MET 

and breathing rates (Isaacs et al., 2008). The relationship between the oxygen deficit and the 

applied limits on MET ratios are nonlinear and are derived from published data on work capacity 

and oxygen consumption. The resulting combination of microenvironmental concentration and 

breathing ventilation rates provides a time series of inhalation intake dose for most pollutants. 
 5A-19 

5A-9. MODEL OUTPUT 

APEX calculates the exposure and dose time series based on the events as listed on the 

activity diary with a minimum of one event per hour but usually more during waking hours. 

APEX can aggregate the event level exposure and dose time series to output hourly, daily, 

monthly, and annual averages. The types of output files are selected by the user, and can be as 

detailed as event-level data for each simulated individual (note, Figure 5A-2 was produced from 

the event output file). A set of summary tables are produced for a variety of exposure and dose 

measures. These include tables of person-minutes at various exposure levels, by 

microenvironment, a table of person-days at or above each average daily exposure level, and 

tables describing the distributions of exposures for different groups. An example of how APEX 

results can be depicted is given in 

, which shows the percent of children with at least one 8-hour average exposure at or above 

different exposure levels, concomitant with moderate or greater exertion. These are results from 

a simulation of O3 exposures for the greater Washington, D.C. metropolitan area for the year 

2002. From this graph ones sees, for example, that APEX estimates 30 percent of the children in 

this area experience exposures above 0.08 ppm-8hr while exercising, at least once during the 

year. 
 5A-20 

Figure 5A-3. The percent of simulated children (ages 5-18) at or above 8-hour average O3

exposures while at moderate or greater exertion. 
 5A-21 

5A-10. REFERENCES 

Burmaster, D.E. (1998). LogNormal distributions for skin area as a function of body weight. 

Risk Analysis. 18(1):27-32. 

Glen, G., Smith, L., Isaacs, K., McCurdy, T., Langstaff, J. (2008). A new method of 

longitudinal diary assembly for human exposure modeling. J Expos Sci Environ Epidem. 

18:299-311. 

Graham, S.E., McCurdy, T. (2005). Revised ventilation rate (VE) equations for use in 

inhalation-oriented exposure models. Report no. EPA/600/X-05/008 is Appendix A of US 

EPA (2009). Metabolically Derived Human Ventilation Rates: A Revised Approach Based 

Upon Oxygen Consumption Rates (Final Report). Report no. EPA/600/R-06/129F. 

Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543. 

Isaacs, K., Glen, G., McCurdy, T., Smith, L. (2008). Modeling energy expenditure and oxygen 

consumption in human exposure models: accounting for fatigue and EPOC. J Expos Sci 

Environ Epidemiol. 18:289-298. 

Isaacs, K., Smith, L. (2005). New Values for Physiological Parameters for the Exposure Model 

Input File Physiology.txt. Memorandum submitted to the U.S. Environmental Protection 

Agency under EPA Contract EP-D-05-065. NERL WA 10. Alion Science and Technology. 

Found in US EPA. (2009). Risk and Exposure Assessment to Support the Review of the 

SO2 Primary National Ambient Air Quality Standard. EPA-452/R-09-007. August 2009. 

Available at 

http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 

Johnson, T., Capel, J., McCoy, M. (1996a). Estimation of Ozone Exposures Experienced by 

Urban Residents Using a Probabilistic Version of NEM and 1990 Population Data. 

Prepared by IT Air Quality Services for the Office of Air Quality Planning and Standards, 

U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 

September. 

Johnson, T., Capel, J., Mozier, J., McCoy, M. (1996b). Estimation of Ozone Exposures 

Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of 

NEM. Prepared for the Air Quality Management Division under Contract No. 68-DO30094, April. 

Johnson, T., Capel, J., McCoy, M., Mozier, J. (1996c). Estimation of Ozone Exposures 

Experienced by Outdoor Workers in Nine Urban Areas Using a Probabilistic Version of 

NEM. Prepared for the Air Quality Management Division under Contract No. 68-DO30094, April. 

Johnson, T. (2002). A Guide to Selected Algorithms, Distributions, and Databases Used in 

Exposure Models Developed By the Office of Air Quality Planning and Standards. Revised 

Draft. Prepared for U.S. Environmental Protection Agency under EPA Grant No. 

CR827033. 

McCurdy, T. (2000). Conceptual basis for multi-route intake dose modeling using an energy 

expenditure approach. J Expo Anal Environ Epidemiol. 10:1-12. 
 5A-22 

McCurdy, T., Glen, G., Smith, L., Lakkadi, Y. (2000). The National Exposure Research 

Laboratory’s Consolidated Human Activity Database. J Exp Anal Environ Epidemiol. 

10:566-578. 

Rosenbaum, A. S. (2008). The Cluster-Markov algorithm in APEX. Memorandum prepared for 

Stephen Graham, John Langstaff. USEPA OAQPS by ICF International. 

Schofield, W. N. (1985). Predicting basal metabolic rate, new standards, and review of previous 

work. Hum Nutr Clin Nutr. 39C(S1):5-41. 

US EPA. (2002). Consolidated Human Activities Database (CHAD) Users Guide. Database and 

documentation available at: http://www.epa.gov/chadnet1/. 

US EPA. (2012a). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4.4) Volume I: User’s Guide. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. EPA-452/B-12-001a. Available at: 

http://www.epa.gov/ttn/fera/human_apex.html 

US EPA. (2012b). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4.4) Volume II: Technical Support 

Document. Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC. EPA-452/B-12-001b. Available at: 

http://www.epa.gov/ttn/fera/human_apex.html 
 5B-i 

APPENDIX 5B 

Inputs to the APEX Exposure Model 

Table of Contents 

5B-1 POPULATION DEMOGRAPHICS .......................................................................... 5B-1

5B-2 POPULATION COMMUTING PATTERNS ........................................................... 5B-2

5B-3 ASTHMA PREVALENCE RATES .......................................................................... 5B-3

5B-4 HUMAN ACTIVITY DATA .................................................................................... 5B-4

5B-4.1 CHAD Updates Since the 2007 Ozone NAAQS Review ................................. 5B-6

5B-4.2 Longitudinal Activity Pattern Methodology ..................................................... 5B-8

5B-5 PHYSIOLOGICAL AND METABOLIC EQUIVALENTS DATA ....................... 5B-11

5B-6 MICROENVIRONMENTS MODELED ................................................................ 5B-12

5B-6.1 Air Exchange Rates for Indoor Residential Microenvironments .................... 5B-15

5B-6.2 Air Conditioning Prevalence for Indoor Residential MicroEnvironments ..... 5B-17

5B-6.3 AER Distributions for Other Indoor Microenvironments ............................... 5B-19

5B-6.4 Proximity and Penetration Factors for In-vehicle and Near-Road 

Microenvironments ......................................................................................... 5B-20

5B-6.5 Proximity and Penetration Factors for Outdoor Microenvironments ............. 5B-21

5B-6.6 Ozone Decay and Deposition Rates ................................................................ 5B-21

5B-7 AMBIENT OZONE CONCENTRATIONS ............................................................ 5B-23

5B-8 METEOROLOGICAL DATA................................................................................. 5B-32

5B-9 CONDITIONAL VARIABLES ............................................................................... 5B-35

5B-10 REFERENCES ........................................................................................................ 5B-36
 5B-ii 

List of Tables 

Table 5B-1. Consolidated Human Activity Database (CHAD) study information and diarydays used by APEX. ......................................................................................... 5B-9

Table 5B-2. Microenvironments modeled and calculation method used. ........................... 5B-13

Table 5B-3. AERs for indoor residential microenvironments (ME-1) with A/C by study area 

and temperature. .............................................................................................. 5B-16

Table 5B-4. AERs for indoor residential microenvironments (ME-1) without A/C by study 

area and temperature. ...................................................................................... 5B-17

Table 5B-5. American Housing Survey A/C prevalence from Current Housing Reports (Table 

1-4) for selected urban areas. .......................................................................... 5B-18

Table 5B-6. Parameter values for distributions of penetration and proximity factors used for 

estimating in-vehicle microenvironmental concentrations. ............................ 5B-20

Table 5B-7. VMT fractions of interstate, urban, and local roads in the study areas used to 

select in-vehicle proximity factor distributions. ............................................. 5B-22

Table 5B-8. Identification of U.S. counties and the number of APEX air districts included each 

study area. ....................................................................................................... 5B-25

Table 5B-9. Ambient monitors used to define exposure modeling domain and the population 

modeled in each study area. ............................................................................ 5B-26

Table 5B-10. Study area meteorological stations, locations, and hours of missing data. ..... 5B-33

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. .............. 5B-28

Figure 5B-2. Illustration of APEX exposure modeling domains (2000 US Census tract 

centroids) for Cleveland, Dallas, Denver and Detroit study areas. ................. 5B-29

Figure 5B-3. Illustration of APEX exposure modeling domains (2000 US Census tract 

centroids) for Houston, Los Angeles, New York and Philadelphia study areas...

......................................................................................................................... 5B-30

Figure 5B-4. Illustration of APEX exposure modeling domains (2000 US Census tract 

centroids) for Sacramento, St. Louis and Washington DC study areas. ......... 5B-31
 5B-1 

 The APEX model inputs require extensive analysis and preparation to ensure the model 

outputs are appropriate as intended, reasonable, and relevant. This Appendix describes the 

preparation and the sources of data for the APEX input files. 

5B-1 POPULATION DEMOGRAPHICS 

 APEX accounts for important population characteristics in representing study area 

demographics. Population counts and employment probabilities by age and gender are used to 

develop representative profiles of hypothetical individuals for the simulation. For the main-body 

results of the O3 Health Risk and Exposure Assessment (HREA), we estimated population-based 

exposures using US Census tract-level population counts stratified by age in one-year 

increments, from birth to 99 years, and were obtained from the 2000 Census of Population and 

Housing Summary File 1 (SF1).1 The SF1 contains the 100-percent data, which is the 

information compiled from the questions asked of all people and about every housing unit. 

 Three standard APEX input files are used for the current O3 assessment: 

 pop_geo2000_011403.txt: census tract ID’s, their latitudes and longitudes 

 pop_fall2000_043003.txt: tract-level population counts for females by age 

 pop_fall2000_043003.txt: tract-level population counts for males by age 

Census tract employment rates were developed using the Employment Status: 2000-

Supplemental Tables.2

 The file input to APEX is stratified by gender and age group, so that each 

gender/age group combination is given an employment probability fraction (ranging from 0 to 1) 

within each census tract. The age groupings in this employment file are: 16-19, 20-21, 22-24, 

25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75. Children under 16 

years of age are assumed to not be employed.3

One standard APEX input file is used for the current O3 assessment: 

 Employment2000_043003.txt: census tract employment probabilities by age 

groups 

 

1 http://www.census.gov/census2000/sumfile1.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-2 

5B-2 POPULATION COMMUTING PATTERNS 

To more realistically simulate human behavior, APEX incorporates workplace patterns 

into the assessment by use of home-to-work commuting data. By design, commuting is only 

used for those simulated individuals who are employed (i.e., ≥ 16 years old). The commuting 

data were derived from the 2000 Census Transportation Planning Package (CTPP) Part 3- 

Journey-to-Work (JTW) files.4 These files contain counts of individuals commuting from home 

to work locations at varying geographic scales. These data were processed to calculate fractions 

for each tract-to-tract flow to create a national commuting flow file distributed with APEX. This 

database contains commuting data for each of the 50 states and Washington, D.C. Important 

processing and application assumptions include the following: 

 Commuting within the Home Tract: the APEX commuting database does not 

differentiate people that work at home from those that commute within their home tract. 

 Commuting Distance Cutoff: all persons in home-work flows up to 120 km are daily 

commuters and no persons in more widely separated flows commute daily, thus the list of 

destinations for each home tract was restricted to only those work tracts that are within 

120 km of the home tract.5

 Eliminated Records: tract-to-tract pairs that represented workers who either worked 

outside of the U.S. (9,631 tract pairs with 107,595 workers) or worked in an unknown 

location (120,830 tract pairs with 8,940,163 workers) were eliminated. An additional 515 

workers in the commuting database whose data were missing from the original files, 

possibly due to privacy concerns or errors, were also deleted. 

 Simulation of Leavers: we restricted the simulated population to those who do not 

commute to destinations outside the study area because we have not estimated ambient 

concentrations of O3 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-3 

An additional commuting input file was recently developed as a companion to the APEX 

commuting flow file. Also derived from the 2000 census are tract-level population counts of 

one-way commute times, and given in 13 time bins (in minutes): < 5, 5 to 9, 10 to 14, 15 to 19, 

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 

minutes commuting time). APEX uses these time bins to create a cumulative probability 

distribution of commuting times for each tract, which it then uses in conjunction with the 

distribution of commuting distances to assign a profile-level one-way commuting time variable 

to each employed person in the population. This commuting time profile variable is then used to 

select for CHAD diaries having appropriate commute times in their daily activity pattern (i.e., a 

total time spent in travel locations or activities before and after work activities) to represent the 

simulated individual. 

Two standard APEX input files are used for the current O3 assessment:

 Commuting2000_010505.txt: home/work census tract ID’s, cumulative 

probabilities of commuting to work tract from home tract, distances of home to 

work tract (km) 

 CommutingTimes2000_050610.txt: tract-level counts of all workers, commuters, 

and commute time bins 

5B-3 ASTHMA PREVALENCE RATES 

One of the important study group in the exposure assessment is asthmatic school-age 

children (ages 5-18). Modeling exposures for this study group with APEX requires the 

estimation of children’s asthma prevalence rates. The estimates are based on children’s asthma 

prevalence data from the 2006-2010 National Health Interview Survey (NHIS). Briefly, 2000 

US census tract level asthma prevalence was estimated for children (by single age years) and 

adults (by age groups), also stratified by gender and family income/poverty ratio (i.e., whether 

the family income was considered below or at/above the US Census estimate of poverty level for 

the given year). Given the significant differences in asthma prevalence by age, gender, region, 

and poverty status, the variability in the spatial distribution of poverty status across census tracts 

(and also stratified by age), and the spatial variability in local scale ambient concentrations of 

many air pollutants, the goal was to better represent the variability in population-based exposures 

when accounting for and modeling these newly refined attributes of this study group. A detailed 

description of how the NHIS data were processed for input to APEX is provided in Appendix 

5C. 

One standard APEX input file is used for the current O3 assessment:

 AsthmaPrevalence053112.txt: tract-level asthma prevalence by age (for ages <18) 

and age groups (for ages > 17) 
 5B-4 

5B-4 HUMAN ACTIVITY DATA 

Exposure models use human activity pattern data to predict and estimate exposure to 

pollutants. Different human activities, such as outdoor exercise, indoor reading, or driving, 

would lead to varying pollutant exposures. In addition, different human activities require 

different energy expenditures, and thus, higher exposure media consumption rates lead to higher 

doses received. To accurately model individuals and their exposure to pollutants, it is critical to 

have a firm understanding of the locations where people spend time and the activities performed 

in such locations. 

The Consolidated Human Activity Database (CHAD) provides time series data on human 

activities through a database system of collected human diaries, or daily time location activity 

logs (US EPA, 2002). The purpose of CHAD is to provide a basis for conducting multi-route, 

multi-media exposure assessments (McCurdy et al., 2000). The data contained within CHAD 

come from multiple surveys with somewhat variable study-specific structure (e.g., minute-byminute versus time-block averaged sequence of diary events), though common to all studies 

included, individuals provided information on their locations visited and activities performed for 

each survey day. Personal attribute data for these surveyed individuals, such as age and gender, 

are included in CHAD as well. The latest version of CHAD master (071113) contains data for 

54,373 person-days. 

The CHAD served as the primary source of time location activity pattern data and was 

processed to retain appropriate diary data for use by APEX. Diaries with missing personal 

attribute data (i.e., age, gender), missing diary day information (i.e., either daily mean/ maximum 

temperature, day-of-week), or having 3-hours or more of missing location and/or activity 

information are not used by APEX. For the latter case, CHAD diaries were evaluated for 

instances where a diary may contain enough information for the purposes of this exposure 

assessment allowing it to be adjusted to reduce the missing information to less than 3 hours on a 

given day. For example, the diary structure of the ozone averting behavior (OAB) study resulted 

in nearly all of the diary days (n=2,776) having no diary information between the hours of 8PM 

and midnight. In processing the CHAD data for this subset of diaries, the location was assumed 

by staff to be indoors at their residence and persons were engaged in a sleep activity. This 

substitution was judged by staff as a reasonable approximation based on the limited likelihood of 

a person’s highest O3 exposures occurring at this time of day, while still retaining the relevant 

activity pattern data of interest (e.g., locations visited and activities performed during the 

daytime hours). 

The following is a list of adjustments made to CHAD diary data where study specific 

structure was a factor in missing data or diary information was present in either CHAD location 

or activity codes to infer specific information where data were missing. 
 5B-5 

 OAB (a children’s study) missing location and activity events from 8PM – 12AM 

were set to ‘indoor residence’ and ‘sleep’; 

 BAL missing activity events at 8AM occurring indoors were set to ‘personal 

care’; 

 ISR missing activity events occurring when attending school were set to either 

‘attend K-12’ (ages 5-18) or ‘attend day-care’ (ages <5); 

 NSA (an adults study) missing activity events at 8PM - 12AM occurring indoor 

residences were set to ‘leisure, general’; 

 Locations missing for a number of staff judged outdoor activities6

 were set to 

‘outdoor, general’; 

 Locations missing for a number of staff judged indoor residential activities7

 were 

set to “indoor, residence”; and 

 Locations missing for a number of staff judged general indoor activities8

 were set 

to “indoor, other”. 

Three standard APEX input files are used for the current O3 assessment:

 CHADQuest_013013B.txt: personal (e.g., age, gender, employment status, county 

of residence, etc.) and day (e.g., daily maximum temperature, day-of-week) 

attribute meta data for each diary day 

 CHADEvents_013013B.txt: time sequence of locations visited and activities 

performed by individuals for each diary day 

 CHADSTATSOutdoor_013013B.txt: time spent outdoors for each diary day 

Table 5B-1 summarizes the studies and number of diary days used by APEX in this 

modeling analysis, providing over 41,000 diary-days of activity data (nearly 18,000 diary-days 

for ages 4-18) collected between 1982 and 2010. 

 

6 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-6 

5B-4.1 CHAD Updates Since the 2007 Ozone NAAQS Review 

 Since the time of the prior O3 NAAQS review conducted in 2007, there have been 

a number new data sets incorporated into CHAD and used in our current exposure assessment, 

most of which were from recently conducted studies. The data from these eight additional 

studies incorporated in CHAD and available for use by APEX have more than doubled the total 

activity pattern data used for O3 exposure modeling in 2007 and has increased the number of 

children diaries by a factor of five. The studies from which these new data were derived are 

briefly described below. 

 DEA. The diaries are from 2 seasons of the 6-season sampling period (2004-2007) used 

by EPA in the Detroit Exposure and Aerosol Research Study (DEARS) (Williams et al., 

2008). The intent was to obtain environmental samples and time use data for 10 days—5 

in each of 2 seasons per participant located in 6 areas in Wayne County, Michigan (in and 

around Detroit). A 15-minute block diary approach was used to collect activity data. 

Participants were all adults and activity data was collected from Tuesday through 

Saturday. Just over 300 diary-days from DEARS are used by APEX. 

 EPA. The diaries were collected as part of an ongoing longitudinal internal EPA study 

by EPA scientists, and in some cases, their families. This dataset contains two long-term 

longitudinal diaries: one by a 60 year-old-male in 1999-2000 (McCurdy and Graham, 

2003), and one by a 35 year old male in 2002. Additional longitudinal diaries were kept 

for a 35-year-old female and her infant daughter in 2008 (though the infant data are not 

used here). The remaining diaries are from a study of a group of 9 adults (Isaacs et al. 

2012). In this portion of the study, all subjects were studied for approximately 17 

consecutive days in each of 4 seasons in 2006 and 2007. Approximately of 1,400 diarydays are used by APEX. 

 ISR. The diaries are from phase I (1997), phase II (2002-03), and phase III (2007-08) of 

the University of Michigan's Panel Study of Income Dynamics (PSID), respectively 

(University of Michigan, 2012). Nationally representative activity pattern data from 

nearly 11,000 children ages 0-13 (phase I), ages 5-19 (phase II), and ages 10-19 (phase 

III) were added to the APEX activity pattern data. For each child, time use data were 

reported by primary care-givers, school teachers, and/or the children themselves on two 

nonconsecutive days in a single week, in no particular season, though mostly occurring 

during the spring and fall (phase I), winter (phase II), and spring, fall and winter (phase 

III) months. 

 NSA. The diaries were collected as part of the National-Scale Activity Survey (NSAS), 

an EPA-funded study of averting behavior related to air quality alerts (Knowledge 

Networks, 2009). Data were collected from about 1,200 adults aged 35-92 in seven 

metropolitan areas (Atlanta, St. Louis, Sacramento, Washington DC, Dallas, Houston, 

and Philadelphia). Data were collected over 1-15 (partially consecutive) days across the 

2009 ozone season, providing approximately 7,000 person days of data for use by APEX. 

 OAB. The diaries were collected in a study of children's activities on high and low ozone 

days during the 2002 ozone season (Mansfield et al., 2009). Children ages 2-12 from 35 

U.S. metropolitan areas having the worst O3 pollution were studied, and of whom, about 
 5B-7 

half of were asthmatics. Activity data were collected on 6 nonconsecutive days from 

each subject, with some subjects providing fewer days, providing nearly 2,200 persons 

days of data to APEX. 

 SEA. The diaries are from a particulate matter (PM) exposure study of susceptible study 

groups living in Seattle, WA between 1999 and 2002 (Liu et al., 2003). Two cohorts 

were studied: an older adult group with either chronic obstructive pulmonary disease 

(COPD) or coronary heart disease and a children’s group (ages 6-13) with asthma. 

Activity data were collected on 10 consecutive days from each subject, with some 

subjects providing fewer days. Over 1,600 adult diaries and more than 300 children 

diaries were included in the APEX activity pattern file. 

 SUP. The diaries are from the SUPERB study (Study of Use of Products and ExposureRelated Behaviors) undertaken by researchers from the University of California at Davis 

Bennett et al., 2012a; Hertz-Picciotto et al., 2012). The study focused on the use of 

household and personal care products from 47 California households, 30 with children 

(ages 1-18) living in 22 counties in northern California, and 17 with an older adult (>55 

y) living in 3 central California counties. Two days of activity data were obtained via the 

internet for each participant—a weekday and a weekend day. Approximately 2,500 

diary-days from SUPERB met appropriate criteria for use in APEX. 

 RTP. The diaries were collected in a panel study of PM exposure in the Research 

Triangle Park (RTP), NC area (Williams et al., 2003a, b). Two older adult cohorts (ages 

55-85) were studied: a cohort having implanted cardiac defibrillators living in Chapel 

Hill, NC and a second group of 30 people having controlled hypertension and residing in 

a low-to-moderate SES neighborhood in Raleigh, NC. Data were collected on 

approximately 8 consecutive days in 4 consecutive calendar seasons in 2000-2001. 

Approximately 900 diary-days were included from this study. 
 5B-8 

5B-4.2 Longitudinal Activity Pattern Methodology 

An important issue in this assessment is the approach used for creating an O3-season or 

year-long activity sequence for each simulated individual based on a largely cross-sectional 

activity database of 24-hour records. The typical subject in the time location activity studies in 

CHAD provided about two days of diary data. For this reason, the construction of a season-long 

activity sequence for each individual requires some combination of repeating the same data from 

one subject and using data from multiple subjects. The best approach would reasonably account 

for the day-to-day and week-to-week repetition of activities common to individuals (though 

recognizing even these diary sequences are not entirely correlated) while maintaining realistic 

variability among individuals comprising each study group. 

The method currently used in APEX for creating longitudinal diaries was designed to 

capture the tendency of individuals to repeat activities, based on reproducing realistic variation in 

a key diary variable, which is a user selected function of diary variables. For this O3 analysis, 

the key variable selected is the amount of time an individual spends outdoors each day, one of 

the most important determinants of exposure to high levels of O3. The actual diary construction 

method targets two statistics, a population diversity statistic (D) and a within-person 

autocorrelation statistic (A). The D statistic reflects the relative importance of within- and 

between-person variance in the key variable. The A statistic quantifies the lag-one (day-to-day) 

key variable autocorrelation. Further details regarding the longitudinal methodology can be 

found in US EPA (2013a, b). 

Desired D and A values for the key variable are selected by the user and set in the APEX 

parameters file, and the method algorithm constructs longitudinal diaries that preserve these 

parameters. Longitudinal diary data from a limited field study of children ages 7-12 (Geyh et al., 

2000; Xue et al., 2004) estimated values of approximately 0.2 for D and 0.2 for A. In the 

absence of data for estimating these statistics for younger children and others outside the study 

age range, and since APEX appears to underestimate repeated activities, values of 0.5 for D and 

0.2 for A are used for all ages.
 5B-9 

Table 5B-1. Consolidated Human Activity Database (CHAD) study information and diary-days used by AStudy Name (CHAD Abbreviation) 

Geographic Coverage Study Dates 

Study 

Subject

Ages 

APEX 

Diary-days 

(ages 4-94) 

APEX 

Diary-days 

(ages 4-18)

Diary Type, Time Format, Survey Design S

Baltimore Retirement Home Study (BAL) 

One building in Baltimore, MD 

1/1997 to 2/1997; 7/1998 to 8/1998 

72 - 93 304 0 Diary, 15 Minute 

Block, Panel 

W

California Youth Activity Patterns Study (CAY) 

California 10/1987 to 9/1988 12 - 17 182 182 Recall, Event, 

Random 

RW

California Adults Activity Patterns Study (CAA) 

California 10/1987 to 9/1988 18 - 94 1,555 36 Recall, Event, 

Random 

RW

California Children Activity Patterns Study (CAC) 

California 4/1989 to 2/1990 <1 - 11 1,195 771 Recall, Event, 

Random 

W

Cincinnati Activity Patterns Study (CIN) 

Cincinnati, OH metro. area 

3/1985 to 4/1985; 8/1985 

<1 - 86 2,449 727 Diary, Event, 

Random 

J

Detroit Exposure and Aerosol Research Study (DEA) 

Detroit, MI metro. area 

7/2005 to 8/2005; 7/2006 to 8/2006 

18 - 74 331 5 Recall, 15 Minute 

Block, Panel 

W

Denver CO Personal Exposure Study (DEN) 

Denver, CO metro. area 

11/1982 to 2/1983 

18 - 70 714 7 Diary, Event, 

Random 

Je

EPA Longitudinal Studies (EPA) 

RTP, NC 2/1999 to 2/2000; 

2/2002 to 8/2002; 7/2006 to 6/2008 

<1 - 60 1,417 0 Diary, Event, 

Panel 

Is

Los Angeles Ozone Exposure Study: Elementary School (LAE) 

Los Angeles, CA 

10/1989 10 - 12 50 50 Diary, Event, 

Panel 

S

Los Angeles Ozone Exposure Study: High School (LAH) 

Los Angeles, CA 

9/1990 to 10/1990 13 - 17 42 42 Diary, Event, 

Panel 

S
 5B-10 

Study Name (CHAD Abbreviation) 

Geographic Coverage Study Dates 

Study 

Subject

Ages 

APEX 

Diary-days 

(ages 4-94) 

APEX 

Diary-days 

(ages 4-18)

Diary Type, Time Format, Survey Design S

National Human Activity Pattern Study: Air (NHA) 

National 9/1992 to 10/1994 <1 - 93 4,329 693 Recall, Event, 

Random 

KT(

National Human Activity Pattern Study: Water (NHW) 

National 9/1992 to 10/1994 <1 - 93 4,329 745 Recall, Event, 

Random 

KT(

National-Scale Activity Survey (NSA) 

7 US metro. areas 

6/2009 to 9/2009 35 - 92 6,825 0 Recall, 15 Minute 

Block, Random 

K(2

Population Study of Income Dynamics PSID I (ISR) 

National 2/1997 to 12/1997 <1 - 13 4,978 3,507 Recall, 15 Minute 

Block, Random/Panel 

U(2

Population Study of Income Dynamics PSID II (ISR) 

National 1/2002 to 12/2003 5 - 19 4,800 4,793 Recall, 15 Minute 

Block, Random/Panel 

U(2

Population Study of Income Dynamics PSID III (ISR) 

National 10/2007 to 4/2008 10 - 19 2,650 2,614 Recall, 15 Minute 

Block, Random/Panel 

U(2

RTI Ozone Averting Behavior (OAB) 

35 US metro. areas 

7/2002 to 8/2003 2 - 12 2,872 2,187 Recall, 15 Minute 

Block, Random 

M2

RTP Panel (RTP) RTP, NC 6/2000 to 5/2001 55 - 85 871 0 Diary, 15 Minute 

Block, Panel 

W

Seattle (SEA) Seattle, WA 10/1999 to 3/2002 6 - 91 1,624 317 Diary, 15 Minute 

Block, Panel 

L

Study of Use of Products and Exposure Related Behavior (SUP) 

Broader Sacramento & San Francisco, CA Counties 

7/2006 to 3/2010 1 - 88 3,456 994 Recall, 15 Minute 

Block, Panel 

BH(2

Washington, D.C. (WAS) 

Wash. DC metro. area 

11/1982 to 2/1983 18 - 71 686 10 Diary, Event, 

Random 

HA

Totals 1982 - 2010 <1 - 94 41,474 17,680 
 5B-11 

5B-5 PHYSIOLOGICAL AND METABOLIC EQUIVALENTS DATA 

 APEX requires several physiological parameters to accurately model processes 

that affect pollutant intake rate for individuals. This is because differences in physiology may 

cause people with the same exposure and activity scenarios to have different pollutant intake 

levels. The physiological parameters file used by APEX contains individual data or data 

distributions stratified by age and gender for maximum ventilatory capacity (in terms of age- and 

gender-specific maximum oxygen consumption potential, NVO2max), body mass (BM), resting 

metabolic rate (RMR), body surface area (BSA), maximum oxygen deficits (MOXD) and 

associated recovery time (RECTIME), height, and oxygen consumption-to-ventilation rate 

relationships (ECF), among a few others not used for estimating O3 exposure and dose). 

APEX also uses an input file containing the metabolic equivalents for work (METS) to 

estimate the specific energy expended for each activity listed in the diary file. These METS 

values are commonly in the form of distributions and were originally derived as relative to an 

individual’s RMR. Some activities are specified as a single point value (for instance, sleep), 

while others, such as athletic endeavors or manual labor, are normally, lognormally, or otherwise 

statistically distributed. APEX samples from these distributions and calculates values to 

simulate the variable nature of activity levels among different people. These personal- and 

activity-level physiological variables are ultimately used to estimate ventilation rate (VE) and 

decrements in forced expiratory volume, in one second (dFEV1). 

Three standard APEX input files are used for the current O3 assessment:

 Physiology010213_threshold.txt: NVO2max, BM, RMR, BSA, MOXD, 

RECTIME, height, ECF, and dFEV1 distributions and equation coefficients, by 

sex and age groups 

 MET_Distributions_030612.txt: statistical form and parameters for METS 

distributions associated with each activity performed, some by age groups 

 Ventilation_121106.txt: distributions and equation coefficients to estimate 

individual activity- specific VE by sex and age groups 

 
 5B-12 

5B-6 MICROENVIRONMENTS MODELED 

 In APEX, exposure for simulated individuals occurs in microenvironments. For 

exposures to be accurately estimated, it is important maintain the spatial and temporal sequence 

of microenvironments persons inhabit and appropriately represent the time series of 

concentrations that occur within them. As discussed in Appendix 5A, the two methods available 

in APEX for calculating pollutant concentrations within microenvironments are a mass balance 

model and a transfer factor approach, each of which uses an appropriate ambient pollutant 

concentration to estimate the microenvironmental concentration. Table 5B-2 lists the 28 

microenvironments selected for this analysis and the exposure calculation method for each. The 

variables used and their associated parameters to calculate microenvironmental concentrations 

are described in subsequent subsections below. 

 The CHAD database has 115 locations codes, many of which go beyond the scale 

of the microenvironmental modeling (e.g., inside at residence in a bedroom). Therefore these 

more specific locations are aggregated by mapping these 115 location codes to the 28 modeled 

microenvironments. Further, all microenvironmental concentrations in this exposure assessment 

are estimated using an ambient concentration (section 5B-7), though these concentrations not 

only vary temporally but spatially, depending on the particular microenvironment. The mapping 

of locations to the 28 microenvironments also includes an identifier that designates what ambient 

concentration is used in the calculation of the microenvironmental concentration for each event. 

For this assessment, we used ambient concentration for each individual based on either their 

home (H), work (W), near work (NW), near home (NH), last (L, either NH or NW), other (O, 

average of all), or unknown (U, last ME determined) tracts. 

Multiple APEX ME input files are used for the current O3 assessment, varying by study 

area though given in one form. Only one ME mapping file is used:

 ME_descriptions_28MEs_O3_CSA[studyarea.]_[date].txt: defines calculation 

method, variables and their parameters used to estimate all microenvironmental 

concentrations 

 MicroEnv_Mapping_CHAD_to_APEX_28MEs_022613.txt: maps 115 CHAD 

locations to 28 APEX microenvironments and defines tract-level ambient 

concentrations to use for each location 
 5B-13 

Table 5B-2. Microenvironments modeled and calculation method used. 

Microenvironment (ME) 

AEPX 

ME 

Number Not Used In CHAD Study 

Calculation 

Method Variables1

Indoor – Residence 1 Mass 

balance 

AER & DE 

Indoor – Community 

Center or Auditorium 

2 Mass 

balance 

AER & DE 

Indoor – Restaurant 3 SEA Mass 

balance 

AER & DE 

Indoor – Hotel, Motel 4 BAL, CIN, DEN, LAE, LAH, 

OAB, SEA, WAS 

Mass 

balance 

AER & DE 

Indoor – Office building, 

Bank, Post office 

5 SEA Mass 

balance 

AER & DE 

Indoor – Bar, Night club, 

Café 

6 BAL, CAC, CAY, DEN, LAE, 

LAH, OAB, SEA, WAS 

Mass 

balance 

AER & DE 

Indoor – School 7 BAL, Mass 

balance 

AER & DE 

Indoor – Shopping mall, 

Non-grocery store 

8 LAE, LAH, SEA Mass 

balance 

AER & DE 

Indoor – Grocery store, 

Convenience store 

9 BAL, ISR, SEA Mass 

balance 

AER & DE 

Indoor – Metro-SubwayTrain station 10 BAL, CAA, CAC, CAY, CIN, 

DEA, DEN, LAE, LAH, NSA, 

OAB, RTP, SUP, WAS 

Mass 

balance 

AER & DE 

Indoor – Hospital, Medical 

care facility 

11 LAH Mass 

balance 

AER & DE 

Indoor – Industrial, 

factory, warehouse 

26 BAL, DEA, ISR, LAE, LAH, 

OAB, SEA, WAS 

Mass 

balance 

AER & DE 

Indoor – Other indoor 27 Mass 

balance 

AER & DE 

Outdoor – Residential 12 BAL, Factors None 

Outdoor – Park or Golf 

course 

14 BAL, CAA, CAY, SEA, WAS Factors None 

Outdoor – Restaurant or 

Café 

15 CAA, CAC, CAY, CIN, DEA, 

DEN, LAE, LAH, NSA, OAB, 

SEA, SUP, WAS 

Factors None 

Outdoor – School 

grounds 

16 BAL, CAC Factors None 

Outdoor – Boat 25 BAL, CAA, CAC, CAY, DEA, 

DEN, ISR, LAE, LAH, RTP, 

SEA, WAS 

Factors None 

Outdoor – Other outdoor 

non-residential 

13 Factors None 
 5B-14 

Microenvironment (ME) 

AEPX 

ME 

Number Not Used In CHAD Study 

Calculation 

Method Variables1

Near-road – MetroSubway-Train stop 17 BAL, CIN, DEA, DEN, ISR, 

LAE, LAH, WAS 

Factors PR 

Near-road – Within 10 

yards of street 

18 CAC, OAB, SEA, SUP Factors PR 

Near-road – Parking 

garage (covered or below 

ground) 

19 BAL, CAA, CAC, CAY, DEA, 

ISR, LAE, NSA, OAB, SEA 

Factors PR 

Near-road – Parking lot 

(open), Street parking 

20 CAA, CAC, CAY, ISR, OAB, 

SEA, SUP 

Factors PR 

Near-road – Service 

station 

21 BAL, LAH, OAB, SEA Factors PR 

Vehicle – Cars and Light 

Duty Trucks 

22 Factors PE & PR 

Vehicle – Heavy Duty 

Trucks 

28 BAL, CIN, DEA, DEN, EPA, 

ISR, LAE, LAH, NSA, OAB, 

RTP, SEA, SUP, WAS 

Factors PE & PR 

Vehicle – Bus 23 ISR Factors PE & PR 

Vehicle – Train, Subway 24 BAL, CAC, DEA, DEN, ISR, 

LAE, LAH, RTP, SEA 

Factors PE & PR 

1 AER = air exchange rate, DE = decay-deposition rate, PR = proximity factor, PE = penetration factor.
 5B-15 

5B-6.1 Air Exchange Rates for Indoor Residential Microenvironments 

Distributions of air exchange rates (AERs) for the indoor residential microenvironments 

(ME-1) were developed using data from several studies. The analysis of these data and the 

development of most of the distributions used in the modeling were originally described in detail 

in US EPA (2007) Appendix A, though recently updated by Cohen et al. (2012) and provided in 

Appendix 5E. 

The analyses indicated that the AER distributions for the residential microenvironments 

depend on the type of air conditioning (A/C) and on the outdoor temperature, among other 

variables for which we do not have sufficient data to estimate. These analyses demonstrate that 

the AER distributions vary greatly across cities, A/C types, and temperatures, so that the selected 

AER distributions for the modeled cities should also depend on these attributes. For example, 

the mean AER for residences with A/C ranges from 0.38 in Research Triangle Park, NC at 

temperatures > 25 oC upwards to 1.244 in New York, NY considering the same temperature bin. 

For each combination of A/C type, city, and temperature with a minimum of 11 AER 

values, exponential, lognormal, normal, and Weibull distributions were fit to the AER values and 

compared. Generally, the lognormal distribution was the best-fitting of the four distributions, 

and so, for consistency, the fitted lognormal distributions are used for all the cases. Los Angeles 

had an adequate number of samples and identifiers to distinguish the estimated AER 

distributions by central A/C and room unit A/C for the homes with A/C. 

There were a number of limitations in generating study-area specific AER stratified by 

temperature and A/C type. For example, AER data and derived 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 a result, 

city-specific AER distributions were used where possible; otherwise staff selected AER data 

from a similar city. Another important limitation of the analysis was that distributions were not 

able to be fitted to all of the temperature ranges due to limited number of available measurement 

data in these ranges. A description of how these limitations were addressed can be found in 

Appendix 5E. The AER distributions used for the exposure modeling are given in Table 5B-3 

(Residences with A/C) and Table 5B-4 (Residences without A/C). 

 
 5B-16 

Table 5B-3. AERs for indoor residential microenvironments (ME-1) with A/C by study 

area and temperature. 

Study Area 

Daily Mean 

Temperature 

(°C)

Lognormal Distribution 

{GM, GSD, min, max} Original AER Study Data 

Used 

Atlanta, Baltimore, 

Washington DC 

< 10 {0.962, 1.809, 0.1, 10} Research Triangle Park, 

NC 10 - 20 {0.562, 1.906, 0.1, 10} 

20 - 25 {0.397, 1.889, 0.1, 10} 

> 25 {0.380, 1.709, 0.1, 10} 

Boston, New York, 

Philadelphia 

< 10 {0.711, 2.108, 0.1, 10} New York, NY 

10 - 25 {1.139, 2.677, 0.1, 10} 

> 25 {1.244, 2.177, 0.1, 10} 

Chicago, Cleveland, 

Detroit 

< 10 {0.744, 1.982, 0.1, 10} Detroit, MI and New York, 

NY 10 - 20 {0.811, 2.653, 0.1, 10} 

20 - 25 {0.785, 2.817, 0.1, 10} 

> 25 {0.916, 2.671, 0.1, 10} 

Dallas, Houston < 20 {0.407, 2.113, 0.1, 10} Houston, TX 

20 - 25 {0.467, 1.938, 0.1, 10} 

25 - 30 {0.422, 2.258, 0.1, 10} 

> 30 {0.499, 1.717, 0.1, 10} 

Denver, St. Louis < 10 {0.921, 1.854, 0.1, 10} All Cities Outside of CA 

10 - 20 {0.573, 1.990, 0.1, 10} 

20 - 25 {0.530, 2.427, 0.1, 10} 

25 - 30 {0.527, 2.381, 0.1, 10} 

> 30 {0.609, 2.369, 0.1, 10} 

Los Angeles 

(Central A/C) 

< 20 {0.577, 1.897, 0.1, 10} Los Angeles, CA 

20 - 25 {1.084, 2.336, 0.1, 10} 

> 25 {0.861, 2.344, 0.1, 10} 

Los Angeles 

(Room Unit A/C) 

< 20 {0.672, 1.863, 0.1, 10} Los Angeles, CA 

20 - 25 {1.674, 2.223, 0.1, 10} 

> 25 {0.949, 1.644, 0.1, 10} 

Sacramento < 25 {0.503, 1.921, 0.1, 10} Sacramento, Riverside, 

>25 {0.830, 2.353, 0.1, 10} San Bernardino Counties 
 5B-17 

Table 5B-4. AERs for indoor residential microenvironments (ME-1) without A/C by study 

area and temperature. 

5B-6.2 Air Conditioning Prevalence for Indoor Residential MicroEnvironments 

 The selection of an AER distribution is conditioned on the presence or absence of 

A/C. We assigned this housing attribute to indoor residential microenvironments (ME-1) using 

A/C prevalence data from the American Housing Survey (AHS)9

. A/C prevalence is noted as 

distinct from usage rate, the latter represented by the AER distribution and dependent on 

temperature. The A/C prevalence data were assigned to our study areas where the AHS data best 

matched our exposure simulation years (Table 5B-5). Because we were able to stratify the AER 

distributions by three A/C types in Los Angeles, both the individual central and room unit values 

were used. In all other study areas, the sum of room unit and central A/C prevalence was used. 

 

9

 Available at: http://www.census.gov/housing/ahs/data/metro.html. 

Study Area 

Daily Mean 

Temperature 

(°C)

Lognormal Distribution 

{GM, GSD, min, max} Original AER Study Data 

Used 

Atlanta, Baltimore, 

Denver, St. Louis, 

Washington DC 

< 10 {0.923, 1.843, 0.1, 10} All Cities Outside of CA 

10 - 20 {0.951, 2.708, 0.1, 10} 

> 20 {1.575, 2.454, 0.1, 10} 

Boston, New York, 

Philadelphia 

< 10 {1.016, 2.138, 0.1, 10} New York, NY 

10 - 20 {0.791, 2.042, 0.1, 10} 

> 20 {1.606, 2.119, 0.1, 10} 

Chicago, Cleveland, 

Detroit 

< 0 {1.074, 1.772, 0.1, 10} Detroit, MI and New York, 

NY 0 - 10 {0.760, 1.747, 0.1, 10} 

10 - 20 {1.447, 2.950, 0.1, 10} 

20 - 25 {1.531, 2.472, 0.1, 10} 

> 25 {1.901, 2.524, 0.1, 10} 

Dallas, Houston < 10 {0.656, 1.679, 0.1, 10} Houston, TX 

10 - 20 {0.625, 2.916, 0.1, 10} 

> 20 {0.916, 2.451, 0.1, 10} 

Los Angeles < 20 {0.744, 2.057, 0.1, 10} Los Angeles, CA 

20 - 25 {1.448, 2.315, 0.1, 10} 

> 25 {0.856, 2.018, 0.1, 10} 

Sacramento < 10 {0.526, 3.192, 0.1, 10} Sacramento, Riverside, 

10 - 20 {0.665, 2.174, 0.1, 10} San Bernardino Counties 

20 - 25 {1.054, 1.711, 0.1, 10} 

> 25 {0.827, 2.265, 0.1, 10} 
 5B-18 

Table 5B-5. American Housing Survey A/C prevalence from Current Housing Reports (Table 1-4) for sele

Metropolitan 

Area Area1

Study 

Years 

Total Occupied 

Housing Units 

(x1000) 

Number of Occupied Housing Units (x1000) with: % of Occupie

Central 

A/C 

>1 Central 

A/C 

1 Room 

Unit 

2 Room 

Units 

3+ Room 

Units 

Central 

A/C 

Wi

U

Atlanta MA 2004 1595.8 1473.8 245.9 39.8 32.5 18.3 92 Baltimore MSA 2007 1012.3 785.4 44.4 55.7 72.4 65.4 78 Boston CMSA 2007 1057.1 291.5 20.3 259.4 198.7 156.0 28 Chicago PMSA 2009 3010.7 2050.6 116.2 412.0 265.1 124.4 68 Cleveland PMSA 2004 769.3 416.4 14.1 132.9 47.0 17.6 54 Dallas PMSA 2002 1235.3 1146.3 171.6 25.5 29.8 27.8 92.8 Denver MA 2004 855.7 425.1 16.8 123.8 20.3 3.9 50 Detroit PMSA 2009 1672.5 1194.3 46.5 192.3 82.8 29.2 71 Houston PMSA 2007 1872.0 1682.5 153.7 46.0 59.9 60.6 90 Los Angeles3 PMSA 2003 5152.4 2448.4 161.6 702.1 118.6 46.9 47.5 New York4 PMSA 2009 4493.3 872.4 38.2 1036.9 1184.1 812.6 19 Philadelphia PMSA 2009 1916.2 1095.9 52.3 197.9 260.8 265.8 57 Sacramento PMSA 2004 669.4 549.5 30.7 57.1 12.3 2.4 82 St. Louis MA 2004 1139.6 974.4 53.7 65.8 43.5 16.6 86 Washington, DC MA 2007 1949.1 1729.6 145.7 69.2 64.8 61.2 89 1 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 microenvironm3 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 

5B-6.3 AER Distributions for Other Indoor Microenvironments 

To estimate AER distributions for non-residential, indoor environments (e.g., offices, 

libraries), we obtained and analyzed two AER data sets: “Turk” (Turk et al., 1989); and “Persily” 

(Persily and Gorfain, 2004; Persily et al., 2005). The Turk data set includes 40 AER 

measurements from offices (25 values), schools (7 values), libraries (3 values), and multipurpose buildings (5 values), each measured using an SF6 tracer over two or four hours in 

different seasons of the year. The Persily data 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 US. This data base consists of 390 AER measurements in 96 large, mechanically ventilated 

offices. AERs were measured both by a volumetric method and by a CO2 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. 

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 relatively short period. 

The mean values are similar for the two studies, but the standard deviations are about twice as 

high for the Persily data. We fitted exponential, lognormal, normal, and Weibull distributions to 

the 96 office space average AER values from the more recent Persily data, and the best fitting of 

these was the lognormal. The fitted parameters for this distribution are a geometric mean of 

1.109, geometric standard deviation of 3.015, and bounded by the lower and upper values of the 

sample data set {0.07, 13.8}. These are used for AER distributions for several indoor nonresidential microenvironments (ME-2, ME-4, ME-5, ME-8, ME-9, ME-10, ME-11, ME-26) 

except for indoor schools (ME-7) and indoor restaurants, bars, night clubs, and cafés (ME-3 and 

ME-6). 

The AER distribution used for indoor schools (ME-7) is a discrete distribution {0.8, 1.3, 

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, 

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 

al. (2004). 
 5B-20 

The AER distribution used for indoor restaurants, bars, night clubs, and cafés (ME-3, 

ME-6) is a fitted lognormal distribution, having a geometric mean = 3.712, geometric standard 

deviation = 1.855 and bounded by the lower and upper values of the sample data set {1.46, 

9.07}. This distribution was developed using data from Bennett et al. (2012b), who measured 

these six values in restaurants (details on derivation provided in Appendix 5E). 

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 

Road type Arithmetic 

Mean 

Standard 

Deviation 

Lower 

Bound1

Upper 

Bound 

Penetration All 0.300 0.232 0.100 1.0 

Proximity Local 0.755 0.203 0.422 1.0 

Urban 0.754 0.243 0.355 1.0 

Interstate 0.364 0.165 0.093 1.0 

1 A 5th percentile value estimated using a normal approximation as Mean – 1.64 × standard deviation. 
 5B-21 

The Vehicle Miles of Travel (VMT) fractions10 provided by the U.S. Department of 

Transportation (DOT) are used to generate daily conditional variables that determine the 

selection of which proximity factor distributions are used to estimate in-vehicle 

microenvironmental concentrations (Table 5B-7). For local and interstate road types, the VMT 

for the same DOT categories were used. For urban roads, the VMT for all other DOT road types 

were summed (i.e., other freeways/expressways, other principal arterial, minor arterial, and 

collector). At the time of this writing, data were only available for four of our modeled years, 

2006-2008 and 2010. Staff assumed that values for 2009 would be best represented by averaging 

2008 and 2010. 

For all outdoors-near-road microenvironments (ME-17, ME-18, ME-19, ME-20, ME-21) 

we employed the distribution for local roads (i.e., a normal distribution {0.755, 0.203}, bounded 

by 0.422 and 1.0), based on the assumption that most of the outdoors-near-road ozone exposures 

will occur proximal to local roads. 

5B-6.5 Proximity and Penetration Factors for Outdoor Microenvironments 

All outdoor microenvironments (ME-12, ME-13, ME-14, ME-15, ME-16, ME-25) are 

assumed well represented by the census tract level O3 concentrations. Therefore, both the 

penetration factor and proximity factor for this microenvironment were set to equal 1. 

5B-6.6 Ozone Decay and Deposition Rates 

 A distribution for combined O3 decay and deposition rates was obtained from the 

analysis of measurements from a study by Lee et al. (1999). This study measured decay rates in 

the living rooms of 43 residences in Southern California. Measurements of decay rates in a 

second room were made in 24 of these residences. The 67 decay rates range from 0.95 to 8.05 

hour-1. A lognormal distribution was fit to the measurements from this study, yielding a 

geometric mean of 2.51 and a geometric standard deviation of 1.53. These values are 

constrained to lie between 0.95 and 8.05 hour-1. This distribution was used for all indoor 

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/policyinformation/statistics/2010/xls/hm71.xls
 5B-22 

Table 5B-7. VMT fractions of interstate, urban, and local roads in the study areas used to select in-vehicle distributions. 

Study Area 

2006 2007 2008 2009 

Interstate Urban Local Inter- state Urban Local Inter- state Urban Local Inter- state Urban Loca

Atlanta 0.34 0.46 0.20 0.34 0.47 0.19 0.32 0.45 0.23 0.31 0.44 0.2Baltimore 0.34 0.59 0.07 0.34 0.59 0.07 0.34 0.59 0.07 0.34 0.59 0.0Boston 0.32 0.55 0.13 0.32 0.55 0.13 0.32 0.54 0.14 0.32 0.54 0.1Chicago 0.30 0.58 0.12 0.30 0.58 0.12 0.31 0.57 0.12 0.30 0.57 0.1Cleveland 0.40 0.44 0.16 0.40 0.44 0.16 0.39 0.45 0.16 0.39 0.45 0.1Dallas 0.30 0.66 0.04 0.30 0.66 0.04 0.30 0.65 0.05 0.30 0.66 0.0Denver 0.23 0.67 0.10 0.24 0.66 0.10 0.25 0.65 0.10 0.25 0.65 0.1Detroit 0.26 0.64 0.10 0.25 0.65 0.10 0.24 0.66 0.10 0.25 0.65 0.1Houston 0.24 0.72 0.04 0.24 0.72 0.04 0.24 0.72 0.04 0.24 0.72 0.0Los Angeles 0.29 0.66 0.05 0.29 0.67 0.04 0.28 0.67 0.05 0.29 0.66 0.0New York 0.19 0.66 0.15 0.19 0.65 0.16 0.19 0.66 0.15 0.19 0.66 0.1Philadelphia 0.23 0.65 0.12 0.24 0.65 0.11 0.24 0.65 0.11 0.20 0.59 0.2Sacramento 0.25 0.72 0.03 0.25 0.70 0.05 0.23 0.69 0.08 0.23 0.69 0.0St. Louis 0.36 0.45 0.19 0.37 0.45 0.18 0.38 0.45 0.17 0.37 0.45 0.1Wash., DC 0.31 0.61 0.08 0.31 0.61 0.08 0.30 0.61 0.09 0.30 0.61 0.0

A few individual fractions have been adjusted to yield an annual sum of 1.00. 
 5B-23 

5B-7 AMBIENT OZONE CONCENTRATIONS 

To estimate exposure in this assessment, APEX requires hourly ambient O3

concentrations at a set of locations (or air districts) within study area. We used hourly ambient 

monitoring data along with a statistical approach (VNA) to better approximate spatial 

heterogeneity (where such heterogeneity might be present) across each study area (O3 HREA, 

Chapter 4). General processing steps performed to generate the final APEX ambient 

concentration input files that were used were as follows. 

After identifying the 15 study areas to be modeled in this assessment, staff defined a 

broad air quality modeling domain for each study area, specifically bounding where exposures 

were to be estimated. We evaluated 1) counties modeled in the previous 2007 O3 NAAQS 

review common to current study areas, 2) political/statistical county aggregations (MSA, 

PMSA), and 3) if the study area was designated as a non-attainment area (NAA), the counties 

that were part of the NAA list. A final list of counties was generated using this information 

(Table 5B-8), then hourly O3 concentrations were estimated at every census tract within the 

counties that comprised each study area (O3 HREA, Chapter 4). These data served as the air 

quality input to APEX with some exception (see below), though note also, not all of the 

estimated hourly concentrations would be used in the exposure simulation even if supplied to 

APEX. 

A 30 km radius of influence was used for each monitoring site within the above countylevel defined study. All census tracts that fell within the 30 km radius of each ambient monitor 

used to estimate the air quality concentration fields were selected, then any tracts/monitor radii 

that were largely outside of the urban core were removed, thus defining a final exposure 

modeling domain in each study area (Table 5B-8). 

Because APEX uses 2000 census population data and the air concentrations were 

modeled to 2010 census tracts, some of the air district locations differed slightly from that of the 

exposure tracts, resulting in different numbers of air districts when compared with the number of 

census tracts used in simulating exposures. This difference is expected to have a negligible 

effect on exposure and risk results because APEX always uses the air district nearest to the tract 

to be modeled, the distances between any two air district centroids within these urban study areas 

(census tract level) is expected to be small, and the concentration gradient across that said 

distance is also expected to not be significant. 
 5B-24 

Further, staff had computational difficulty in simulating the large number of tracts and air 

districts for the Los Angeles, New York, and Chicago study areas (number and size of arrays 

needed in APEX calculations was beyond the standard PC capabilities); based on simulations 

that ran to completion, the maximum number of air districts possible using a standard 32-bit PC 

was estimated as 1,900 – 2,000. Thus, to make the analysis more tractable for these study areas, 

first staff reduced the number of air districts originally modeled (i.e., all year 2010 US tracts in 

the broad county domain) to the number needed for the actual year 2000 census tracts in the 

exposure model domain (i.e., all tracts within 30 km of ambient monitors in the broad county 

domain). Using this approach, the number of air districts was reduced to the following: Chicago 

(1,882), Los Angeles (3,268), and New York (4,646). For Los Angeles and New York, the 

number of air districts was reduced to 2,000 and 1,900 using simple random sampling of these 

tracts using SAS’s SURVEYSELECT procedure; the number of air districts for Chicago 

remained at 1,882. While we estimated this number of districts would run on a standard PC, 

these three study areas would only run on a 64-bit PC. 

The final list of year 2000 census tract IDs where exposure was modeled is within the 

APEX control files. The final list of 2010 census tract IDs where ambient concentrations were 

estimated is within the APEX air districts files. Table 5B-9 contains the final list of counties, the 

number of US census tracts where exposures were estimated, the number air districts ultimately 

used from the air quality input files, and the population counts represented in each study area. 

The final list of year 2000 census tract IDs where exposure was modeled is within the APEX 

control files. The final list of 2010 census tract IDs where ambient concentrations were 

estimated is within the APEX air districts files. Figure 5B-1 through Figure 5B-4 illustrate the 

general exposure modeling domains (i.e., the selected census tract centroids falling within 30 km 

of a ambient monitor) for each of the 15 study areas. 

Multiple unique APEX input files are used for the current O3 assessment, varying by the 

air quality scenario, year, and study area, though generally in two forms:

 concsCSA[studyarea]S[scenario]P[std. avg. period]Y[year].txt: hourly 

concentrations for each tract, by study area, air quality scenario, standard 

averaging period, year 

 districtsCSA[studyarea]Y[year].txt: tract ID’s, latitudes and longitudes, start and 

stop dates of concentrations 
 5B-25 

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, Broomfield2, Clear Creek, Denver, Douglas, Elbert, Gilpin, Jefferson, Larimer, Park, 

Weld)

Detroit (MI: 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: JeffersonAll AREAS (Counties: 207 Exposure of 215 Air Quality) 1 italicized: in air quality domain 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 

Table 5B-9. Ambient monitors used to define exposure modeling domain and the population modeled in each 

Study Area (State Abbreviation: List of Monitors1) 

C

Tra

Ex

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; RI: 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, WI: 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 (MI: 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) 
 5B-27 

Study Area (State Abbreviation: List of Monitors1) 

C

Tra

Ex

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, 511790001, 515100009; WV: 540030003) All AREAS (324 ambient monitors) 21 A 30 km radius for monitors operating anytime during 2006-2010 was used to select census tracts in defining the exposure modeling dom
 5B-28 

Figure 5B-1. Illustration of APEX exposure modeling domains (2000 US Census tract 

centroids) for Atlanta, Boston, Baltimore and Chicago study areas. 
 5B-29 

1 

Figure 5B-2. Illustration of APEX exposure modeling domains (2000 US Census tract 

centroids) for Cleveland, Dallas, Denver and Detroit study areas. 
 5B-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. 
 5B-31 

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.11 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 

day, with each of the other monitors, and choosing the model which maximizes R2, 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 O3 assessment, varying by the 

year and study area, though generally in two forms:

 METdataCSA[studyarea]Y[year].txt: hourly temperature for each MET station, 

by study area and year 

 METlocsCSA[studyarea]Y[year].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 

Table 5B-10. Study area meteorological stations, locations, and hours of missing data. 

Study Area ISH ID1 Latitude Longitude 2006 2007 2008 2009 2010

Atlanta 

2217003813 32.683 -83.65 15 2 140 113 128

2219013874 33.633 -84.433 0 0 101 41 18

2219503888 33.767 -84.517 14 15 113 103 29

2225593842 32.517 -84.95 16 4 170 52 48

2227013864 33.917 -84.517 2502 1611 266 93 73

2228403892 32.616 -85.433 469 114 209 77 540

2228713871 33.583 -85.85 24 4 168 210 55

2311013873 33.95 -83.333 4 4 271 39 45

2320093801 34.35 -85.167 14 30 187 59 68

Baltimore 2406093721 39.167 -76.683 0 0 101 54 27

4594013705 38.817 -76.867 34 86 173 184 42

Boston 

2505464710 41.917 -71.5 275 71 362 741 315

2506014704 41.65 -70.517 46 84 126 54 95

2506454769 41.917 -70.733 5 10 259 143 285

2507014765 41.717 -71.433 0 1 94 51 45

2509014739 42.367 -71.017 0 2 97 41 34

2509594746 42.267 -71.883 34 15 128 61 53

4394514710 42.933 -71.433 3 10 103 49 55

Chicago 

2530094846 41.983 -87.917 2 1 126 44 21

2530594892 41.917 -88.25 71 12 170 97 64

2534014819 41.783 -87.75 0 1 127 44 23

2535014848 41.7 -86.333 3 0 91 46 18

2543094822 42.2 -89.1 1 0 96 43 18

Cleveland 

2521014895 40.917 -81.433 7 1 128 56 44

2524014820 41.4 -81.85 0 1 82 38 19

2524504853 41.517 -81.683 12 116 144 79 68

2525014852 41.25 -80.667 0 0 119 45 28

Dallas 

2258313960 32.85 -96.85 1 2 93 51 40

2259003927 32.9 -97.017 0 0 88 46 30

2259613961 32.817 -97.367 6 22 157 84 138

Denver 

2466093037 38.817 -104.717 2 2 108 110 71

2466693067 39.567 -104.85 2 1 104 53 45

2469523036 39.717 -104.75 32 41 103 52 32

2476824051 40.433 -104.633 72 393 876 134 140

2476994062 40.45 -105.017 19 532 314 381 164

2564024018 41.15 -104.8 1 7 129 46 66

2565003017 39.833 -104.65 0 2 91 44 40

Detroit 

2537094847 42.217 -83.35 1 1 106 40 27

2537514822 42.4 -83 17 110 226 104 122

2537614853 42.233 -83.533 1 7 148 107 78

2537714804 42.617 -82.833 11 27 236 59 94

2538404888 42.917 -82.533 40 140 94 32 55

2539014836 42.783 -84.583 1 3 148 70 37

2539514833 42.267 -84.467 4 35 329 49 144

2637014826 42.967 -83.75 0 0 116 52 74

Houston 2241012917 29.95 -94.017 5 24 119 67 27
 5B-34 

Study Area ISH ID1 Latitude Longitude 2006 2007 2008 2009 2010

2242012923 29.3 -94.8 126 19 2468 444 186

2243012960 30 -95.367 1 0 173 57 30

2243512918 29.65 -95.283 0 0 160 99 33

2244503904 30.583 -96.367 5 3 108 107 40

Los Angeles 

2286023119 33.9 -117.25 13 25 103 48 29

2288023152 34.2 -118.35 2 12 152 86 37

2295023174 33.933 -118.4 0 0 113 44 19

2297023129 33.833 -118.167 2 4 99 173 269

2381523161 34.85 -116.8 69 4 196 741 241

2381603159 34.733 -118.217 126 11 438 176 411

2383023187 34.75 -118.717 31 3 375 276 554

2391093111 34.117 -119.117 2046 16 265 77 792

2392623136 34.217 -119.083 21 47 311 218 139

4718703104 33.633 -116.167 99 18 134 161 60

4718823158 33.617 -114.717 2 15 400 54 159

New York 

2408454760 40.183 -74.067 251 475 566 183 794

2409614706 40.017 -74.6 66 62 131 83 121

2502014734 40.717 -74.183 1 0 105 47 22

2502594741 40.85 -74.067 0 2 129 70 31

2502964707 41.483 -73.133 554 292 569 842 703

2503014732 40.783 -73.883 0 0 73 38 17

2503504781 40.783 -73.1 2 0 174 138 18

2503614757 41.633 -73.883 14 5 406 388 139

2503794745 41.067 -73.717 8 0 119 93 34

2503814714 41.5 -74.1 565 851 1144 1159 1358

2504094702 41.183 -73.15 61 68 146 114 173

2505894793 41.167 -71.583 687 349 586 2970 127

2514554746 41.7 -74.8 1387 356 159 375 582

2517014737 40.65 -75.45 5 4 148 74 51

4486094789 40.65 -73.8 4 0 101 56 21

Philadelphia 

2407093730 39.45 -74.567 4 3 142 112 161

2407513735 39.367 -75.083 20 84 268 73 74

2408013739 39.867 -75.233 1 0 122 57 21

2408454760 40.183 -74.067 251 475 566 183 794

2408594732 40.083 -75.017 0 10 143 60 38

2408913781 39.667 -75.6 22 1 156 244 89

2409614706 40.017 -74.6 66 62 131 83 121

2517014737 40.65 -75.45 5 4 148 74 51

4596603726 39 -74.917 945 500 1299 1562 1855

Sacramento 

2483023232 38.5 -121.5 3 5 115 51 77

2483993225 38.7 -121.583 0 0 116 52 42

2584523225 39.3 -120.717 1 1 217 124 350

4516023202 38.267 -121.933 34 110 152 78 46

St. Louis 

2433813802 38.55 -89.85 40 55 129 68 41

2434013994 38.75 -90.367 1 0 98 46 27

2434503966 38.65 -90.65 5 33 189 62 41

2445493996 37.767 -90.4 186 24 74 134 402
 5B-35 

Study Area ISH ID1 Latitude Longitude 2006 2007 2008 2009 2010

Washington DC 

2403093738 38.933 -77.45 3 2 102 52 28

2403303706 38.267 -77.45 41 48 4587 145 36

2403513773 38.5 -77.3 177 113 1937 788 610

2403603710 38.717 -77.517 725 698 943 1631 97

2404013721 38.3 -76.417 1414 123 322 83 86

2405013743 38.867 -77.033 0 1 96 46 21

2405303717 39.15 -78.15 38 7 78 32 35

2405503714 39.083 -77.567 28 28 64 38 181

2406093721 39.167 -76.683 0 0 101 54 27

2417713734 39.4 -77.983 36 42 217 137 118

4594013705 38.817 -76.867 34 86 173 184 42

1

 From the Federal Climate Complex Integrated Surface Hourly (ISH) global database. 

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 O3 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 

conditioning 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 O3 assessment, varying by the 

year and study area:

 Functions_O3_CSA[studyarea]Y[year]_[date].txt: conditional variables and values used 

 
 5B-36 

5B-10 REFERENCES 

AHS. (2003). U.S. Bureau of the Census and U.S. Department of Housing and Urban 

Development. 2003 American Housing Survey (AHS): National Survey Data. Available 

at: http://www.census.gov/hhes/www/housing/ahs/ahs.html, and 

http://www.huduser.org/datasets/ahs.html

Akland, G. G., Hartwell, T. D., Johnson, T. R., Whitmore, R. W. (1985). Measuring human 

exposure to carbon monoxide in Washington, D. C. and Denver, Colorado during the 

winter of 1982-83. Environ Sci Technol. 19: 911-918. 

American Petroleum Institute. (1997). Sensitivity Testing of pNEM/O3 Exposure to Changes in 

the Model Algorithms. Health and Environmental Sciences Department. 

Bennett, D. H., Wu, X., Teague, C.H., Lee, K., Cassady, D. L., Ritz, B., Hertz-Picciotto, I. 

(2012a). Passive sampling methods to determine household and personal care product use. 

J Expos Sci Environ Epidem. 22: 148-160. 

Bennett, D. H, Fisk, W., Apte, M. G., Wu, X., Trout, A., Faulkner, D., Sulivan D. (2012b). 

Ventilation, Temperature, and HVAC Characteristics in Small and Medium Commercial 

Buildings (SMCBs) in California. Indoor Air. 22(4): 309-320. 

Geyh, A. S., Xue, J., Ozkaynak, H., Spengler, J. D. (2000). The Harvard Southern California 

chronic ozone exposure study: assessing ozone exposure of grade-school-age children in 

two southern California communities. Environ Health Perspect. 108: 265-270. 

Hartwell, T. D., Clayton, C. A., Ritchie, R. M., Whitmore, R. W., Zelon, H. S., Jones, S. M., 

Whitehurst, D. A. (1984). Study of Carbon Monoxide Exposure of Residents of 

Washington, DC and Denver, Colorado. Research Triangle Park, NC: U.S. Environmental 

Protection Agency, Office of Research and Development, Environmental Monitoring 

Systems Laboratory. EPA-600/4-84-031. 

Hertz-Picciotto, I., Cassady, D., Lee, K., Bennett, D. H., Ritz, B., Vogt, R. (2010). Study of Use 

of Products and Exposure-Related Behaviors (SUPERB): study design, methods, and 

demographic characteristics of cohorts. Environ Health. 9:54. 

Isaacs, K. K., McCurdy, T., Glen, G., Nysewander, M., Errickson, A., Forbes, S., Graham, S., 

McCurdy, L., Smith, L., Tulve, N., and Vallero, D. (2012). Statistical properties of 

longitudinal time-activity data for use in human exposure modeling. J Expos Sci Environ 

Epidemiol. 23(3): 328-336. 

Johnson, T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. 

Research Triangle Park, NC: U.S. Environmental Protection Agency, Environmental 

Monitoring Systems Laboratory. EPA-600/4-84-014. 

Johnson, T. (1989). Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric 

Power Research Institute. EPRI EN-6204. 

Johnson, T., Pakrasi, A., Wisbeth, A., Meiners, G., Ollison, W. (1995). Ozone exposures Within 

Motor Vehicles – Results of a Field Study in Cincinnati, Ohio. Proceedings 88th annual 

meeting and exposition of the Air & Waste Management Association, San Antonio, TX. 

June 18-23, 1995. Preprint paper 95-WA84A.02. 

Klepeis, N. E., Tsang, A. M., Behar, J. V. (1996). Analysis of the National Human Activity 

Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. 
 5B-37 

Washington, DC: U.S. Environmental Protection Agency, Office of Research and 

Development. EPA/600/R-96/074. 

Knowledge Networks. (2009). Field Report: National-Scale Activity Survey (NSAS). 

Conducted for Research Triangle Institute. Submitted to Carol Mansfield November 13, 

2009. 

Lee, K., Vallarino, J., Dumyahn, T., Ozkaynak, H., and Spengler, J. D. (1999). Ozone decay 

rates in residences. J Air Waste Manag Assoc. 49: 1238-1244. 

Liu, L.-J. S., Box, M., Kalman, D., Kaufman, J., Koenig J., Larson, T., Lumley, T., Sheppard, L., 

Wallace, L. (2003). Exposure assessment of particulate matter for susceptible populations 

in Seattle. Environ Health Persp. 111: 909-918. 

Mansfield, C., Houtven, G. V., Johnson, F. R., Yang, J.-C. (2009). Environmental Risks and 

Behavior: Do children spend less time outdoors when ozone pollution is high? ASSA 

annual meeting, January 5, 2009. Update of Houtven et al. (2003), using the OAB CHAD 

data set, and related to Mansfield et al. (2006). 

Mansfield, C., Johnson, F. R., Van Houtven, G. (2006). The missing piece: averting behavior for 

children’s ozone exposures. Resource Energy Econ. 28:215-228. 

McCurdy, T., Glen, G., Smith, L., Lakkadi, Y. (2000). The National Exposure Research 

Laboratory's Consolidated Human Activity Database. J Expo Anal Environ Epidemiol. 10: 

566-578. 

McCurdy, T., Graham, S. E. (2003). Using human activity data in exposure models: analysis of 

discriminating factors. J Expos Anal Environ Epidemiol. 13(4): 294-317. 

Persily, A., Gorfain, J. (2004). Analysis of Ventilation Data from the U.S. Environmental 

Protection Agency Building Assessment Survey and Evaluation (BASE) Study. National 

Institute of Standards and Technology, NISTIR 7145, December 2004. 

Persily, A., Gorfain, J., Brunner, G. (2005). Ventilation Design and Performance in U.S. Office 

Buildings. ASHRAE Journal. April 2005, 30-35. 

Robinson, J. P., Wiley, J. A., Piazza, T., Garrett, K., and Cirksena, K. (1989). Activity Patterns 

of California Residents and their Implications for Potential Exposure to Pollution. 

California Air Resources Board, Sacramento, CA. CARB-A6-177-33. 

Cohen, J., Mallya, H., Rosenbaum, A. (2012). Updated Analysis of Air Exchange Rate, ICF 

International Memo to John Langstaff. Available in Appendix 5E of O3 HREA. 

Shendell, D. G., Winer, A. M., Weker, R., Colome, S. D. (2004). Evidence of inadequate 

ventilation in portable classrooms: results of a pilot study in Los Angeles County. Indoor 

Air. 14:154–158. 

Spier, C. E., Little, D. E., Trim, S. C., Johnson, T. R., Linn, W. S., Hackney, J. D. (1992). 

Activity patterns in elementary and high school students exposed to oxidant pollution. J 

Expo Anal Environ Epidemiol. 2: 277-293. 

Tsang, A. M., Klepeis, N. E. (1996). Descriptive Statistics Tables from a Detailed Analysis of 

the National Human Activity Pattern Survey (NHAPS) Data. U.S. Environmental 

Protection Agency. EPA/600/R-96/148. 

Turk, B. H., Grimsrud, D. T., Brown, J. T., Geisling-Sobotka, K. L., Harrison, J., Prill, R. J. 

(1989). Commercial Building Ventilation Rates and Particle Concentrations. ASHRAE 

No. 3248. 
 5B-38 

US EPA. (2002). Consolidated Human Activities Database (CHAD) Users Guide. Database and 

documentation available at: http://www.epa.gov/chadnet1/

US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. Available at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html

US. EPA. (2012a). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4). Volume I: User's Guide. EPA-452/B12-001a. Available at: 

http://www.epa.gov/ttn/fera/data/apex/APEX45_UsersGuide_Vol1_Aug2012.pdf. 

US EPA. (2012b). Total Risk Integrated Methodology (TRIM) Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4) Volume II: Technical Support Document. 

EPA-452/B-12-001b. Available at: 

http://www.epa.gov/ttn/fera/data/apex/APEX45_UsersGuide_Vol2_Aug2012.pdf. 

University of Michigan. (2012). The Panel Study of Income Dynamics (PSID). Data and 

documentation available at http://psidonline.isr.umich.edu/

Wiley, J. A., Robinson, J. P., Piazza, T., Garrett, K., Cirksena, K., Cheng, Y.-T., Martin, G. 

(1991a). Activity Patterns of California Residents: Final Report. California Air Resources 

Board, Sacramento, CA. ARB/R93/487. Available from: NTIS, Springfield, VA., PB94-

108719. 

Wiley, J. A., Robinson, J. P., Cheng, Y.-T., Piazza, T., Stork, L., Pladsen, K. (1991b). Study of 

Children's Activity Patterns: Final Report. California Air Resources Board, Sacramento, 

CA. ARB-R-93/489. 

Williams, R., Suggs, J., Creason, J., Rodes, C., Lawless, P., Kwok, R., Zweidinger, R., Sheldon, 

L. (2000). The 1998 Baltimore particulate matter epidemiology-exposure study: Part 2. 

Personal exposure associated with an elderly population. J Expo Anal Environ Epidemiol. 

10(6): 533-543. 

Williams, R., Suggs J., Rea, A., Leovic, K., Vette, A., Croghan, C., Sheldon, L., Rodes, C., 

Thornburg, J., Ejire, A., Herbst, M., Sanders Jr., W. (2003a). The Research Triangle 

particulate panel study: PM mass concentrations relationships. Atmos Environ. 37:5349-

5363. 

Williams, R., Suggs, J., Rea, A., Sheldon, L., Rodes, C., Thornburg, J. (2003b). The Research 

Triangle particulate panel study: modeling ambient source contributions to personal and 

residential PM mass concentrations. Atmos Environ. 37:5365-5378. 

Williams, R., Rea, A., Vette, A., Croghan, C., Whitaker, D., Stevens, C., McDow, S., Fortmann, 

R., Sheldon, L., Wilson, H., Thornburg, J., Phillips, M., Lawless, P., Rodes, C., Daughtrey, 

H. (2008). The design and field implementation of the Detroit Exposure and Aerosol 

Research Study. J Expos Sci Environ Epidem. 19: 643-659. 

Xue, J., McCurdy, T., Spengler, J., Özkaynak, H. (2004). Understanding variability in time 

spent in selected locations for 7-12-year old children. J Expo Anal Environ Epidemiol. 

14(3): 222-33. 
5C-i 

APPENDIX 5C 

Generation of Adult and Child Census-tract Level Asthma 

Prevalence using NHIS (2006-2010) and US Census (2000) Data 

Table of Contents 

5C-1. OVERVIEW .............................................................................................................. 5C-1 

5C-2. RAW ASTHMA PREVALENCE DATA SET DESCRIPTION .............................. 5C-2 

5C-3. LOGISTIC MODELING APPROACH USED TO ESTIMATE ASTHMA 

PREVALENCE.......................................................................................................... 5C-4 

5C-4. APPLICATION OF LOESS SMOOTHER TO ASTHMA PREVALENCE 

ESTIMATION ........................................................................................................... 5C-8 

5C-5. CENSUS TRACT LEVEL POVERTY RATIO DATA SET DESCRIPTION AND 

PROCESSING ......................................................................................................... 5C-12 

5C-6. COMBINED CENSUS TRACT LEVEL POVERTY RATIO AND ASTHMA 

PREVALENCE DATA ........................................................................................... 5C-13 

5C-7. REFERENCES ........................................................................................................ 5C-14

 
5C-ii 

List of Tables 

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. ............ 5C-4

Table 5C-2. Example of alternative logistic models evaluated to estimate child asthma 

prevalence using the “EVER” asthma response variable and goodness of fit test 

results. ............................................................................................................... 5C-7

Table 5C-3. Top 20 model smoothing fits where residual standard error at or a value of 1.0.

............................................................................................ ......................5C-10

List of Figures 

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. ................ 5C-11

Figure 5C-2. Studentized residuals versus model predicted betas generated using a logistic 

model and using the children ‘EVER’ asthmatic data set, with smoothing set to 

0.6.................................................................................................................... 5C-12
5C-1 

5C-1 OVERVIEW 

This appendix describes the generation of our census tract level children and adult asthma 

prevalence data developed from the 2006-2010 National Health Interview Survey (NHIS) and 

census tract level poverty information from the 2000 US Census. The approach is, for the most 

part, a reapplication of work performed by Cohen and Rosenbaum (2005), though here we 

incorporated a few modifications as described below. Details regarding the earlier asthma 

prevalence work are documented in Appendix G of US EPA (2007). 

Briefly in the earlier asthma prevalence development work, Cohen and Rosenbaum 

(2005) calculated asthma prevalence for children aged 0 to 17 years for each age, gender, and 

four US regions using 2003 NHIS survey data. The four regions defined by NHIS were 

‘Midwest’, ‘Northeast’, ‘South’, and ‘West’. The asthma prevalence was defined as the 

probability of a ‘Yes’ response to the question “EVER been told that [the child] had asthma?”1

among those persons that responded either ‘Yes’ or ‘No’ to this question.2

 The responses were 

weighted to take into account the complex survey design of the NHIS.3 Standard errors and 

confidence intervals for the prevalence were calculated using a logistic model (PROC SURVEY 

LOGISTIC; SAS, 2012). A scatter-plot technique (LOESS SMOOTHER; SAS, 2012) was 

applied to smooth the prevalence curves and compute the standard errors and confidence 

intervals for the smoothed prevalence estimates. Logistic analysis of the raw and smoothed 

prevalence curves showed statistically significant differences in prevalence by gender and 

region, supporting their use as stratification variables in the final data set. These smoothed 

prevalence estimates were used as an input to EPA’s Air Pollution Exposure Model (APEX) to 

estimate air pollutant exposure in asthmatic children (US EPA, 2007; 2008; 2009). 

For the current asthma prevalence data set development, several years of recent NHIS 

survey data (2006-2010) were combined and used to calculate asthma prevalence. The current 

approach estimates asthma prevalence for children (by age in years) as was done previously by 

Cohen and Rosenbaum (2005) but now includes an estimate of adult asthma prevalence (by age 

groups). In addition, two sets of asthma prevalence for each adults and children were estimated 

here. The first data set, as was done previously, was based on responses to the question “EVER 

been told that [the child] had asthma”. The second data set was developed using the probability 

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 

regarding ever having asthma, specifically, do those persons “STILL have asthma?”4

 And 

finally, in addition to the nominal variables region and gender (and age and age groups), the 

asthma prevalence in this new analysis were further stratified by a family income/poverty ratio 

(i.e., whether the family income was considered below or at/above the US Census estimate of 

poverty level for the given year). 

These new asthma prevalence data sets were linked to the US census tract level poverty 

ratios probabilities (US Census, 2007), also stratified by age and age groups. Given 1) the 

significant differences in asthma prevalence by age, gender, region, and poverty status, 2) the 

variability in the spatial distribution of poverty status across census tracts, stratified by age, and 

3) the spatial variability in local scale ambient concentrations of many air pollutants, it is hoped 

that the variability in population exposures is now better represented when accounting for and 

modeling these newly refined attributes of this susceptible population.

5C-2 RAW ASTHMA PREVALENCE DATA SET DESCRIPTION 

In this section we describe the asthma prevalence data sets used and identify the variables 

retained for our final data set. First, raw data and associated documentation were downloaded 

from the Center for Disease Control (CDC) and Prevention’s National Health Interview Survey 

(NHIS) website.5 The ‘Sample Child’ and ‘Sample Adult’ files were selected because of the 

availability of person-level attributes of interest within these files, i.e., age in years (‘age_p’), 

gender (‘sex’), US geographic region (‘region’), coupled with the response to questions of 

whether or not the surveyed individual ever had and still has asthma. In total, five years of 

recent survey data were obtained, comprising over 50,000 children and 120,000 children for 

years 2006-2010 (Table 5C-1). 

Information regarding personal and family income and poverty ranking are also provided 

by the NHIS in separate files. Five files (‘INCIMPx.dat’) are available for each survey year, 

each containing either the actual responses (where recorded or provided by survey participant) or 

imputed values for the desired financial variable.6 For this current analysis, the ratio of income 

to poverty was used to develop a nominal variable: either the survey participant was below or 

at/above a selected poverty threshold. This was done in this manner to be consistent with data 

generated as part of a companion data set, i.e., census tract level poverty ratio probabilities 

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 

Given the changes in how income data were collected over the five year period of interest 

and the presence of imputed data, a data processing methodology was needed to conform each of 

the year’s data sets to a compatible nominal variable. Briefly, for survey years 2006-2008, 

poverty ratios (‘RAT_CATI’) are provided for each person as a categorical variable, ranging 

from <0.5 to 5.0 by increments of either 0.25 (for poverty ratios categories between <0.5 – 2.0) 

and 0.50 (for poverty ratios >5.0). For 2009 and 2010 data, the poverty ratio was provided as a 

continuous variable (‘POVRATI3’) rather than a categorical variable.7 

When considering the number of stratification variables, the level of asthma prevalence, 

and poverty distribution among the survey population, sample size was an important issue. For 

the adult data, there were insufficient numbers of persons available to stratify the data by single 

ages (for some years of age there were no survey persons). Therefore, the adult survey data were 

grouped as follows: ages 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, and, ≥75.8 To increase the 

number of persons within the age, gender, and four region groupings of our characterization of 

‘below poverty’ asthmatics persons, the poverty ratio threshold was selected as <1.5, therefore 

including persons that were within 50% above the poverty threshold. As there were five data 

sets containing variable imputed poverty ratios (as well as a non-varying values for where 

income information was reported) for each year, the method for determining whether a person 

was below or above the poverty threshold was as follows. If three or more of the five 

imputed/recorded values were <1.5, the person’s family income was categorized ‘below’ the 

poverty threshold, if three or more of the 5 values were ≥1.5, the person’s family income was 

categorized ‘above’ the poverty threshold. The person-level income files were then merged with 

the sample adult and child files using the ‘HHX’ (a household identifier), ‘FMX’ (a family 

identifier), and ‘FPX’ (an individual identifier) variables. Note, all persons within the sample 

adult and child files had corresponding financial survey data. 

Two asthma survey response variables were of interest in this analysis and were used to 

develop the two separate prevalence data sets for each children and adults. The response to the 

first question “Have you EVER been told by a doctor or other health professional that you [or 

your child] had asthma?” was recorded as variable name ‘CASHMEV’ for children and 

‘AASMEV’ for adults. Only persons having responses of either ‘Yes’ or ‘No’ to this question 

were retained to estimate the asthma prevalence. This assumes that the exclusion of those 

 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/poverty/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 

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 ‘CASSTILL’ 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 

(<0.5%). 

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 Number of Surveyed Persons 

Children 2010 2009 2008 2007 2006 TOTAL 

All Persons 11,277 11,156 8,815 9,417 9,837 50,502 

Yes/No Asthma 11,256 11,142 8,800 9,404 9,815 50,417 

Yes/No to Still Have + No Asthma 11,253 11,129 8,793 9,394 9,797 50,366 

 

Adults 2010 2009 2008 2007 2006 TOTAL 

All Persons 27,157 27,731 21,781 23,393 24,275 124,337 

Yes/No Asthma 27,157 27,715 21,766 23,372 24,242 124,252 

Yes/No to Still Have + No Asthma 27,113 27,686 21,726 23,349 24,208 124,082 

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 

appropriate. The two main issues with such a simplified approach are that the distributions of 
5C-5 

the estimated prevalence rates would not be well approximated by normal distributions and that 

the estimated confidence intervals based on a normal approximation would often extend outside 

the [0, 1] interval. A better approach for such survey data is to use a logistic transformation and 

fit the model: 

Prob(asthma) = exp(beta) / (1 + exp(beta) ), 

where, beta may depend on the explanatory variables for age, gender, poverty status, or region. 

This is equivalent to the model: 

Beta = logit {prob(asthma) } = log { prob(asthma) / [1 – prob(asthma)] } 

The distribution of the estimated values of beta is more closely approximated by a normal 

distribution than the distribution of the corresponding estimates of prob(asthma). By applying a 

logit transformation to the confidence intervals for beta, the corresponding confidence intervals 

for prob(asthma) will always be inside [0, 1]. Another advantage of the logistic modeling is that 

it can be used to compare alternative statistical models, such as models where the prevalence 

probability depends upon age, region, poverty status, and gender, or on age, region, poverty 

status but not gender. 

A variety of logistic models were fit and compared to use in estimating asthma 

prevalence, where the transformed probability variable beta is a given function of age, gender, 

poverty status, and region. I used the SAS procedure SURVEYLOGISTIC to fit the various 

logistic models, taking into account the NHIS survey weights and survey design (using both 

stratification and clustering options), as well as considering various combinations of the selected 

explanatory variables. 

As an example, Table 5C-2 lists the models fit and their log-likelihood goodness-of-fit 

measures using the sample child data and for the “EVER” asthma response variable. A total of 

32 models were fit, depending on the inclusion of selected explanatory variables and how age 

was considered in the model. The ‘Strata’ column lists the eight possible stratifications: no 

stratification, stratified by gender, by region, by poverty status, by region and gender, by region 

and poverty status, by gender and poverty status, and by region, gender and poverty status. For 

example, “5. region, gender” indicates that separate prevalence estimates were made for each 

combination of region and gender. As another example, “2. gender” means that separate 

prevalence estimates were made for each gender, so that for each gender, the prevalence is 

assumed to be the same for each region. Note the prevalence estimates are independently 
5C-6 

calculated for each stratum. The ‘Description’ column of Table 5C-2 indicates how beta 

depends upon the age: 

Linear in age Beta =  +  × age, where  and  vary with strata. 

Quadratic in age Beta =  +  × age +  × age2

, where   and  vary with strata. 

Cubic in age Beta =  +  × age +  × age2

 +  × age3, where , , , and  vary 

with the strata. 

f(age) Beta = arbitrary function of age, with different functions for 

different strata 

The category f(age) is equivalent to making age one of the stratification variables, and is 

also equivalent to making beta a polynomial of degree 16 in age (since the maximum age for 

children is 17), with coefficients that may vary with the strata. 

The fitted models are listed in order of complexity, where the simplest model (i.e., model 

1) is an unstratified linear model in age and the most complex model (model 32) has a 

prevalence that is an arbitrary function of age, gender, poverty status, and region. Model 32 is 

equivalent to calculating independent prevalence estimates for each of the 288 combinations of 

age, gender, poverty status, and region. 

Table 5C-2 also includes the -2 Log Likelihood statistic, a goodness-of-fit measure, and 

the associated degrees of freedom (DF), which is the total number of estimated parameters. Any 

two models can be compared using their -2 Log Likelihood values: models having lower values 

are preferred. If the first model is a special case of the second model, then the approximate 

statistical significance of the first model is estimated by comparing the difference in the -2 Log 

Likelihood values with a chi-squared random variable having r degrees of freedom, where r is 

the difference in the DF (hence a likelihood ratio test). For all pairs of models from Table 5C-2, 

all the differences in the -2 Log Likelihood statistic are at least 600,000 and thus significant at pvalues well below 1 percent. Based on its having the lowest -2 Log Likelihood value, the last 

model fit (model 32: retaining all explanatory variables and using f(age)) was preferred and used 

to estimate the asthma prevalence.9

 

 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 

Table 5C-2. Example of alternative logistic models evaluated to estimate child asthma 

prevalence using the “EVER” asthma response variable and goodness of fit test results. 

Model No. Description Strata - 2 Log Likelihood DF1

1 1. logit(prob) = linear in age 1. none 288740115.1 2

2 1. logit(prob) = linear in age 2. gender 287062346.4 4

3 1. logit(prob) = linear in age 3. region 288120804.1 8

4 1. logit(prob) = linear in age 4. poverty 287385013.1 4

5 1. logit(prob) = linear in age 5. region, gender 286367652.6 16

6 1. logit(prob) = linear in age 6. region, poverty 286283543.6 16

7 1. logit(prob) = linear in age 7. gender, poverty 285696164.7 8

8 1. logit(prob) = linear in age 8. region, gender, poverty 284477928.1 32

9 2. logit(prob) = quadratic in age 1. none 286862135.1 3

10 2. logit(prob) = quadratic in age 2. gender 285098650.6 6

11 2. logit(prob) = quadratic in age 3. region 286207721.5 12

12 2. logit(prob) = quadratic in age 4. poverty 285352164 6

13 2. logit(prob) = quadratic in age 5. region, gender 284330346.1 24

14 2. logit(prob) = quadratic in age 6. region, poverty 284182547.5 24

15 2. logit(prob) = quadratic in age 7. gender, poverty 283587631.7 12

16 2. logit(prob) = quadratic in age 8. region, gender, poverty 282241318.6 48

17 3. logit(prob) = cubic in age 1. none 286227019.6 4

18 3. logit(prob) = cubic in age 2. gender 284470413 8

19 3. logit(prob) = cubic in age 3. region 285546716.1 16

20 3. logit(prob) = cubic in age 4. poverty 284688169.9 8

21 3. logit(prob) = cubic in age 5. region, gender 283662673.5 32

22 3. logit(prob) = cubic in age 6. region, poverty 283404487.5 32

23 3. logit(prob) = cubic in age 7. gender, poverty 282890785.3 16

24 3. logit(prob) = cubic in age 8. region, gender, poverty 281407414.3 64

25 4. logit(prob) = f(age) 1. none 285821686.2 18

26 4. logit(prob) = f(age) 2. gender 283843266.2 36

27 4. logit(prob) = f(age) 3. region 284761522.8 72

28 4. logit(prob) = f(age) 4. poverty 284045849.2 36

29 4. logit(prob) = f(age) 5. region, gender 282099156.1 144

30 4. logit(prob) = f(age) 6. region, poverty 281929968.5 144

31 4. logit(prob) = f(age) 7. gender, poverty 281963915.7 72

32 4. logit(prob) = f(age) 8. region, gender, poverty 278655423.1 288

1

 model degrees of freedom.
5C-8 

 The SURVEYLOGISTIC procedure produces estimates of the beta values and their 95% 

confidence intervals for each combination of age, region, poverty status, and gender. By 

applying the inverse logit transformation, 

Prob(asthma) = exp( beta) / (1 + exp(beta) ), 

one can convert the beta values and associated 95% confidence intervals into predictions 

and 95% confidence intervals for the prevalence. The standard error for the prevalence was 

estimated as 

Std Error {Prob(asthma)} = Std Error (beta) × exp(- beta) / (1 + exp(beta) )2

which follows from the delta method (i.e., a first order Taylor series approximation). 

Estimated asthma prevalence using this approach and termed here as ‘unsmoothed’ are provided 

in Attachment A. Asthma prevalence for children is provided in Attachment A, Tables 5CA-1 

(‘EVER’ had Asthma) and 5CA-2 (‘STILL’ have asthma) while adult asthma prevalence is 

provided in Attachment A, Tables 5CA-3 (‘EVER’ had Asthma) and 5CA-4 (‘STILL’ have 

asthma). Graphical representation of each study group is also provided in a series of plots within 

Attachment A, Figures 5CA-1 to 5CA-4. The variables provided in the tabular presentation are: 

 Region 

 Gender 

 Age (in years) or Age_group (age categories) 

 Poverty Status 

 Prevalence = predicted prevalence 

 SE = standard error of predicted prevalence 

 LowerCI = lower bound of 95 % confidence interval for predicted prevalence 

 UpperCI = upper bound of 95 % confidence interval for predicted prevalence 

5C-4 APPLICATION OF LOESS SMOOTHER TO ASTHMA PREVALENCE 

ESTIMATION 

The estimated prevalence curves shows that the prevalence is not necessarily a smooth 

function of age. The linear, quadratic, and cubic functions of age modeled by 

SURVEYLOGISTIC were identified as a potential method for smoothing the curves, but they 

did not provide the best fit to the data. One reason for this might be due to the attempt to fit a 

global regression curve to all the age groups, which means that the predictions for age A are 

affected by data for very different ages. A local regression approach that separately fits a 
5C-9 

regression curve to each age A and its neighboring ages was used, giving a regression weight of 

1 to the age A, and lower weights to the neighboring ages using a tri-weight function: 

Weight = {1 – [ |age – A| / q ] 3}, where | age – A| <= q 

The parameter q defines the number of points in the neighborhood of the age A. Instead 

of calling q the smoothing parameter, SAS defines the smoothing parameter as the proportion of 

points in each neighborhood. A quadratic function of age to each age neighborhood was fit 

separately for each gender and region combination. These local regression curves were fit to the 

beta values, the logits of the asthma prevalence estimates, and then converted them back to 

estimated prevalence rates by applying the inverse logit function exp(beta) / (1 + exp(beta) ). In 

addition to the tri-weight variable, each beta value was assigned a weight of 

1 / [std error (beta)]2

, to account for their uncertainties. 

In this application of LOESS, weights of 1 / [std error (beta)] 2 were used such that 2 = 

1. The LOESS procedure estimates 2 from the weighted sum of squares. Because it is assumed 

2 = 1, the estimated standard errors are multiplied by 1 / estimated  and adjusted the widths of 

the confidence intervals by the same factor. 

One data issue was an overly influential point that needed to be adjusted to avoid 

imposing wild variation in the “smoothed” curves: for the West region, males, age 0, above 

poverty threshold, there were 249 children surveyed that all gave ‘No’ answers to the asthma 

question, leading to an estimated value of -14.203 for beta with a standard error of 0.09. In this 

case the raw probability of asthma equals zero, so the corresponding estimated beta would be 

negative infinity, but SAS’s software gives -14.203 instead. To reduce the excessive impact of 

this single data point, we replaced the estimated standard error by 4, which is approximately four 

times the maximum standard error for all other region, gender, poverty status, and age 

combinations. 

There are several potential values that can be selected for the smoothing parameter; the 

optimum value was determined by evaluating three regression diagnostics: the residual standard 

error, normal probability plots, and studentized residuals. To generate these statistics, the 

LOESS procedure was applied to estimated smoothed curves for beta, the logit of the prevalence, 

as a function of age, separately for each region, gender, and poverty classification. For the 

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 

smoothing parameter. This selected range of values was bounded using the following 

observations. With only 18 points (i.e., the number of ages), a smoothing parameter of 0.2 

cannot be used because the weight function assigns zero weights to all ages except age A, and a 

quadratic model cannot be uniquely fit to a single value. A smoothing parameter of 0.3 also 
5C-10 

cannot be used because that choice assigns a neighborhood of 5 points only (0.3 × 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 . As discussed above, the true value of  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 Asthma 

LOESS Smoothing Parameter

0.4 0.5 0.6 0.7 0.8 0.9 1.0

Children 

EVER 2 2 5 5 4 1 1 

STILL 2 3 4 2 3 3 3 

Adults 

EVER n/a n/a n/a n/a 6 6 8 

STILL n/a n/a n/a n/a 5 7 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 2

 = 1. To test this 

assumption, normal probability plots of the residuals were created for each smoothing parameter, 
5C-11 

combining all the studentized residuals across genders, regions, poverty status, and ages. These 

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. 

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. 

0.1 1 5 10 25 50 75 90 95 99 99.9

-3

-2

-1

0

1

2

3

student

Normal Percentiles
5C-12 

Figure 5C-2. Studentized residuals versus model predicted betas generated using a logistic 

model and using the children ‘EVER’ asthmatic data set, with smoothing set to 0.6. 

When considering both children asthma prevalence responses evaluated, the residual 

standard error (estimated values for sigma) suggests the choice of smoothing parameter as 0.6 to 

0.8. The normal probability plots of the studentized residuals suggest preference for smoothing 

at or above 0.6. The plots of residuals against smoothed predictions suggest the choices of 0.4 

through 0.6. We therefore chose the final value of 0.6 to use for smoothing the children’s asthma 

prevalence. For the adults, 0.9 was selected for smoothing. 

Smoothed asthma prevalence and associated graphical presentation are provided in 

Attachment C, following a similar format as the unsmoothed data provided in Attachment A. 

5C-5 CENSUS TRACT LEVEL POVERTY RATIO DATA SET DESCRIPTION AND 

PROCESSING 

This section describes the approach used to generate census tract level poverty ratios for 

all US census tracts, stratified by age and age groups where available. The data set generation 

involved primarily two types of data downloaded from the 2000 US Census, each are described 

below. 

First, individual state level SF3 geographic data (“geo”) .uf3 files and associated 

documentation were downloaded10 and, following import by SAS (SAS, 2012), were screened 

 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). 

student

-5

-4

-3

-2

-1

0

1

2

3

4

5

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevel

Midwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyL

Northeast-Female-BelowPovertyL Northeast-Male-AbovePovertyLev

Northeast-Male-BelowPovertyLev South-Female-AbovePovertyLevel

South-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel
5C-13 

for tract level information using the “sumlev” variable equal to ‘140’. For quality control 

purposes and ease of matching with the poverty level data, our geo data set retained the 

following variables: stusab, sumlev, logrecno, state, county, tract, name, latitude, and longitude. 

Second, the individual state level SF3 files (“30”) were downloaded, retaining the 

number of persons across the variable “PCT50” for all state “logrecno”.11 The data provided by 

the PCT50 variable is stratified by age or age groups (ages <5, 5, 6-11, 12-14, 15, 16-17, 18-24, 

25-34, 35-44, 45-54, 55-64, 65-74, and ≥75) and income/poverty ratios, given in increments of 

0.25. We calculated two new variables for each state logrecno using the number of persons from 

the PCT50 stratifications; the fraction of those persons having poverty ratios < 1.5 and ≥ 1.5 by 

summing the appropriate PCT50 variable and dividing by the total number of persons in that 

age/age group. Finally the poverty ratio data were combined with the above described census 

tract level geographic data using the “stusab” and “logrecno” variables. The final output was a 

single file containing relevant tract level poverty probabilities by age groups for all US census 

tracts (where available). 

5C-6 COMBINED CENSUS TRACT LEVEL POVERTY RATIO AND ASTHMA 

PREVALENCE DATA 

Because the prevalence data are stratified by standard US Census defined regions, 12 we 

first mapped the tract level poverty level data to an appropriate region based on the State. 

Further, as APEX requires the input data files to be complete, additional processing of the 

poverty probability file was needed. For where there was missing tract level poverty 

information,13 we substituted an age-specific value using the average for the particular county the 

tract was located within. The frequency of missing data substitution comprised 1.7% of the total 

poverty probability data set. The two data sets were merged and the final asthma prevalence was 

calculated using the following weighting scheme: 

Prevalence = round((pov_prob*prev_poor)+((1-pov_prob)*prev_notpoor),0.0001);

 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/stats10/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 

whereas each US census tract value now expresses a tract specific poverty-weighted 

prevalence, stratified by ages (children 0-17), age groups (adults), and two genders. These final 

prevalence data are found within the APEX asthmaprevalence.txt file. 

5C-7 REFERENCES 

Cohen, J., and Rosenbaum, A. (2005). Analysis of NHIS Asthma Prevalence Data. 

Memorandum to John Langstaff by ICF Incorporated. For US EPA Work Assignment 3-

08 under EPA contract 68D01052. 

SAS. (2012). SAS/STAT 9.2 User's Guide, Second Edition. Available at: 

http://support.sas.com/documentation/cdl/en/statug/63033/PDF/default/statug.pdf. 

US Census Bureau. (2007). 2000 Census of Population and Housing. Summary File 3 (SF3) 

Technical Documentation, available at: http://www.census.gov/prod/cen2000/doc/sf3.pdf. 

Individual SF3 files '30' (for income/poverty variables pct50) for each state were 

downloaded from: http://www2.census.gov/census_2000/datasets/Summary_File_3/. 

US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas (July 2007). 

Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA-452/R07-010. Available at: http://epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html. 

US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary 

National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November 

2008. Available at: 

http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 

US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009. 

Available at: 

http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf.
5C-15 

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 Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No Midwest Female Above Poverty 0 0.0018 0.0018 0.0002 0.0129 

No Midwest Female Above Poverty 1 0.0387 0.0233 0.0117 0.1208 

No Midwest Female Above Poverty 2 0.0367 0.0148 0.0165 0.0797 

No Midwest Female Above Poverty 3 0.0395 0.0186 0.0155 0.0972 

No Midwest Female Above Poverty 4 0.0815 0.0298 0.0390 0.1624 

No Midwest Female Above Poverty 5 0.0885 0.0207 0.0556 0.1382 

No Midwest Female Above Poverty 6 0.0438 0.0200 0.0176 0.1046 

No Midwest Female Above Poverty 7 0.1374 0.0277 0.0916 0.2010 

No Midwest Female Above Poverty 8 0.0820 0.0246 0.0450 0.1450 

No Midwest Female Above Poverty 9 0.1027 0.0220 0.0669 0.1545 

No Midwest Female Above Poverty 10 0.0995 0.0193 0.0675 0.1442 

No Midwest Female Above Poverty 11 0.1129 0.0277 0.0688 0.1797 

No Midwest Female Above Poverty 12 0.1752 0.0391 0.1112 0.2652 

No Midwest Female Above Poverty 13 0.1331 0.0256 0.0905 0.1916 

No Midwest Female Above Poverty 14 0.1944 0.0477 0.1173 0.3049 

No Midwest Female Above Poverty 15 0.1383 0.0302 0.0890 0.2086 

No Midwest Female Above Poverty 16 0.1731 0.0341 0.1160 0.2502 

No Midwest Female Above Poverty 17 0.1311 0.0256 0.0885 0.1898 

No Midwest Female Below Poverty 0 0.0564 0.0353 0.0160 0.1799 

No Midwest Female Below Poverty 1 0.0585 0.0197 0.0299 0.1112 

No Midwest Female Below Poverty 2 0.1256 0.0487 0.0567 0.2552 

No Midwest Female Below Poverty 3 0.1127 0.0419 0.0529 0.2240 

No Midwest Female Below Poverty 4 0.1746 0.0395 0.1100 0.2658 

No Midwest Female Below Poverty 5 0.1584 0.0447 0.0888 0.2664 

No Midwest Female Below Poverty 6 0.1229 0.0417 0.0616 0.2301 

No Midwest Female Below Poverty 7 0.0867 0.0353 0.0381 0.1851 

No Midwest Female Below Poverty 8 0.1523 0.0392 0.0902 0.2456 

No Midwest Female Below Poverty 9 0.2070 0.0486 0.1275 0.3182 

No Midwest Female Below Poverty 10 0.2293 0.1109 0.0800 0.5043 

No Midwest Female Below Poverty 11 0.1359 0.0470 0.0670 0.2562 

No Midwest Female Below Poverty 12 0.1501 0.0484 0.0774 0.2710 

No Midwest Female Below Poverty 13 0.1527 0.0380 0.0921 0.2427 

No Midwest Female Below Poverty 14 0.1197 0.0462 0.0544 0.2431 

No Midwest Female Below Poverty 15 0.2103 0.0760 0.0980 0.3949 

No Midwest Female Below Poverty 16 0.2054 0.0597 0.1121 0.3462 

No Midwest Female Below Poverty 17 0.1844 0.1134 0.0491 0.4976 

No Midwest Male Above Poverty 0 0.0061 0.0044 0.0015 0.0247 

No Midwest Male Above Poverty 1 0.0258 0.0178 0.0066 0.0957 

No Midwest Male Above Poverty 2 0.0848 0.0231 0.0491 0.1426 

No Midwest Male Above Poverty 3 0.0996 0.0261 0.0588 0.1636 

No Midwest Male Above Poverty 4 0.0876 0.0223 0.0527 0.1423 

No Midwest Male Above Poverty 5 0.1593 0.0313 0.1069 0.2306 

No Midwest Male Above Poverty 6 0.0977 0.0229 0.0611 0.1527 

No Midwest Male Above Poverty 7 0.1793 0.0313 0.1259 0.2489 

No Midwest Male Above Poverty 8 0.1503 0.0356 0.0930 0.2340 

No Midwest Male Above Poverty 9 0.1418 0.0265 0.0973 0.2021 

No Midwest Male Above Poverty 10 0.1569 0.0322 0.1035 0.2306 

No Midwest Male Above Poverty 11 0.1717 0.0371 0.1106 0.2568 

No Midwest Male Above Poverty 12 0.2054 0.0338 0.1470 0.2795 

No Midwest Male Above Poverty 13 0.1846 0.0358 0.1244 0.2650 

No Midwest Male Above Poverty 14 0.1671 0.0291 0.1175 0.2322 

No Midwest Male Above Poverty 15 0.1454 0.0356 0.0885 0.2297 

No Midwest Male Above Poverty 16 0.1557 0.0278 0.1087 0.2182 

No Midwest Male Above Poverty 17 0.1320 0.0233 0.0926 0.1848 

No Midwest Male Below Poverty 0 0.0293 0.0176 0.0089 0.0922 

No Midwest Male Below Poverty 1 0.1051 0.0376 0.0509 0.2047 

No Midwest Male Below Poverty 2 0.1786 0.0652 0.0835 0.3418 
5C-16 

Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No Midwest Male Below Poverty 3 0.2066 0.0513 0.1236 0.3247 

No Midwest Male Below Poverty 4 0.2770 0.0638 0.1703 0.4170 

No Midwest Male Below Poverty 5 0.2504 0.0499 0.1656 0.3600 

No Midwest Male Below Poverty 6 0.2186 0.0447 0.1436 0.3184 

No Midwest Male Below Poverty 7 0.2192 0.0456 0.1428 0.3211 

No Midwest Male Below Poverty 8 0.2902 0.0649 0.1806 0.4312 

No Midwest Male Below Poverty 9 0.1242 0.0437 0.0607 0.2374 

No Midwest Male Below Poverty 10 0.2897 0.0639 0.1815 0.4285 

No Midwest Male Below Poverty 11 0.2669 0.0613 0.1646 0.4021 

No Midwest Male Below Poverty 12 0.2589 0.1050 0.1068 0.5051 

No Midwest Male Below Poverty 13 0.2429 0.0693 0.1329 0.4017 

No Midwest Male Below Poverty 14 0.1470 0.0490 0.0742 0.2703 

No Midwest Male Below Poverty 15 0.1965 0.0509 0.1150 0.3151 

No Midwest Male Below Poverty 16 0.1855 0.0611 0.0935 0.3345 

No Midwest Male Below Poverty 17 0.3740 0.1042 0.1998 0.5884 

No Northeast Female Above Poverty 0 0.0055 0.0054 0.0008 0.0368 

No Northeast Female Above Poverty 1 0.0296 0.0164 0.0099 0.0854 

No Northeast Female Above Poverty 2 0.0697 0.0252 0.0337 0.1384 

No Northeast Female Above Poverty 3 0.0723 0.0250 0.0362 0.1394 

No Northeast Female Above Poverty 4 0.1142 0.0254 0.0731 0.1741 

No Northeast Female Above Poverty 5 0.1058 0.0296 0.0602 0.1793 

No Northeast Female Above Poverty 6 0.0933 0.0254 0.0541 0.1563 

No Northeast Female Above Poverty 7 0.1084 0.0251 0.0681 0.1682 

No Northeast Female Above Poverty 8 0.0780 0.0221 0.0442 0.1339 

No Northeast Female Above Poverty 9 0.1362 0.0374 0.0780 0.2272 

No Northeast Female Above Poverty 10 0.0979 0.0298 0.0530 0.1738 

No Northeast Female Above Poverty 11 0.1697 0.0382 0.1073 0.2578 

No Northeast Female Above Poverty 12 0.0535 0.0229 0.0228 0.1204 

No Northeast Female Above Poverty 13 0.0910 0.0273 0.0499 0.1604 

No Northeast Female Above Poverty 14 0.1500 0.0207 0.1138 0.1953 

No Northeast Female Above Poverty 15 0.1733 0.0355 0.1142 0.2541 

No Northeast Female Above Poverty 16 0.1884 0.0510 0.1077 0.3085 

No Northeast Female Above Poverty 17 0.1694 0.0395 0.1052 0.2613 

No Northeast Female Below Poverty 0 0.0315 0.0251 0.0064 0.1404 

No Northeast Female Below Poverty 1 0.1230 0.0576 0.0469 0.2852 

No Northeast Female Below Poverty 2 0.0703 0.0277 0.0319 0.1479 

No Northeast Female Below Poverty 3 0.1860 0.0555 0.1002 0.3193 

No Northeast Female Below Poverty 4 0.1666 0.0598 0.0791 0.3175 

No Northeast Female Below Poverty 5 0.2347 0.0636 0.1329 0.3802 

No Northeast Female Below Poverty 6 0.0682 0.0250 0.0327 0.1366 

No Northeast Female Below Poverty 7 0.0972 0.0362 0.0458 0.1944 

No Northeast Female Below Poverty 8 0.2049 0.0604 0.1107 0.3478 

No Northeast Female Below Poverty 9 0.1695 0.0698 0.0717 0.3505 

No Northeast Female Below Poverty 10 0.0988 0.0440 0.0400 0.2240 

No Northeast Female Below Poverty 11 0.2622 0.0734 0.1445 0.4277 

No Northeast Female Below Poverty 12 0.1377 0.0525 0.0629 0.2752 

No Northeast Female Below Poverty 13 0.3506 0.0762 0.2188 0.5100 

No Northeast Female Below Poverty 14 0.1869 0.0537 0.1031 0.3148 

No Northeast Female Below Poverty 15 0.1965 0.0534 0.1120 0.3217 

No Northeast Female Below Poverty 16 0.1986 0.0470 0.1221 0.3065 

No Northeast Female Below Poverty 17 0.1625 0.0602 0.0754 0.3158 

No Northeast Male Above Poverty 0 0.0256 0.0130 0.0094 0.0679 

No Northeast Male Above Poverty 1 0.0542 0.0231 0.0231 0.1218 

No Northeast Male Above Poverty 2 0.0635 0.0220 0.0318 0.1228 

No Northeast Male Above Poverty 3 0.0835 0.0232 0.0478 0.1418 

No Northeast Male Above Poverty 4 0.1378 0.0329 0.0849 0.2158 

No Northeast Male Above Poverty 5 0.1444 0.0357 0.0875 0.2291 

No Northeast Male Above Poverty 6 0.2175 0.0482 0.1376 0.3263 

No Northeast Male Above Poverty 7 0.2019 0.0343 0.1429 0.2774 

No Northeast Male Above Poverty 8 0.1878 0.0373 0.1252 0.2719 

No Northeast Male Above Poverty 9 0.1286 0.0342 0.0751 0.2115 

No Northeast Male Above Poverty 10 0.1879 0.0278 0.1394 0.2485 

No Northeast Male Above Poverty 11 0.2532 0.0420 0.1799 0.3439 

No Northeast Male Above Poverty 12 0.1801 0.0233 0.1388 0.2303 
5C-17 

Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No Northeast Male Above Poverty 13 0.1581 0.0340 0.1022 0.2366 

No Northeast Male Above Poverty 14 0.2043 0.0447 0.1303 0.3056 

No Northeast Male Above Poverty 15 0.1752 0.0287 0.1257 0.2387 

No Northeast Male Above Poverty 16 0.1798 0.0360 0.1195 0.2614 

No Northeast Male Above Poverty 17 0.1836 0.0282 0.1346 0.2454 

No Northeast Male Below Poverty 0 0.0375 0.0275 0.0087 0.1477 

No Northeast Male Below Poverty 1 0.1649 0.0506 0.0877 0.2887 

No Northeast Male Below Poverty 2 0.2200 0.0503 0.1371 0.3337 

No Northeast Male Below Poverty 3 0.1124 0.0445 0.0501 0.2330 

No Northeast Male Below Poverty 4 0.2651 0.0909 0.1262 0.4738 

No Northeast Male Below Poverty 5 0.2398 0.0651 0.1355 0.3885 

No Northeast Male Below Poverty 6 0.3209 0.0432 0.2427 0.4107 

No Northeast Male Below Poverty 7 0.2651 0.0572 0.1686 0.3908 

No Northeast Male Below Poverty 8 0.2905 0.0969 0.1401 0.5070 

No Northeast Male Below Poverty 9 0.3810 0.0773 0.2446 0.5392 

No Northeast Male Below Poverty 10 0.3382 0.1019 0.1732 0.5551 

No Northeast Male Below Poverty 11 0.2485 0.0708 0.1359 0.4102 

No Northeast Male Below Poverty 12 0.2819 0.0705 0.1656 0.4371 

No Northeast Male Below Poverty 13 0.2961 0.0685 0.1808 0.4448 

No Northeast Male Below Poverty 14 0.2876 0.0713 0.1695 0.4440 

No Northeast Male Below Poverty 15 0.2632 0.0661 0.1548 0.4107 

No Northeast Male Below Poverty 16 0.2407 0.0559 0.1483 0.3660 

No Northeast Male Below Poverty 17 0.3123 0.0734 0.1885 0.4701 

No South Female Above Poverty 0 0.0129 0.0080 0.0038 0.0427 

No South Female Above Poverty 1 0.0191 0.0084 0.0080 0.0447 

No South Female Above Poverty 2 0.0558 0.0147 0.0330 0.0928 

No South Female Above Poverty 3 0.0793 0.0200 0.0479 0.1286 

No South Female Above Poverty 4 0.0834 0.0184 0.0537 0.1273 

No South Female Above Poverty 5 0.0932 0.0222 0.0579 0.1467 

No South Female Above Poverty 6 0.1446 0.0226 0.1057 0.1948 

No South Female Above Poverty 7 0.1439 0.0248 0.1017 0.1996 

No South Female Above Poverty 8 0.1111 0.0194 0.0784 0.1550 

No South Female Above Poverty 9 0.1258 0.0222 0.0883 0.1762 

No South Female Above Poverty 10 0.0626 0.0154 0.0383 0.1005 

No South Female Above Poverty 11 0.1288 0.0210 0.0928 0.1759 

No South Female Above Poverty 12 0.1064 0.0182 0.0756 0.1478 

No South Female Above Poverty 13 0.1387 0.0222 0.1006 0.1881 

No South Female Above Poverty 14 0.1621 0.0243 0.1198 0.2156 

No South Female Above Poverty 15 0.1399 0.0169 0.1100 0.1763 

No South Female Above Poverty 16 0.1362 0.0253 0.0938 0.1938 

No South Female Above Poverty 17 0.1299 0.0197 0.0959 0.1737 

No South Female Below Poverty 0 0.0495 0.0216 0.0207 0.1137 

No South Female Below Poverty 1 0.0734 0.0210 0.0415 0.1268 

No South Female Below Poverty 2 0.0828 0.0207 0.0503 0.1336 

No South Female Below Poverty 3 0.0973 0.0271 0.0556 0.1649 

No South Female Below Poverty 4 0.1578 0.0372 0.0976 0.2450 

No South Female Below Poverty 5 0.1409 0.0300 0.0917 0.2103 

No South Female Below Poverty 6 0.1536 0.0381 0.0927 0.2439 

No South Female Below Poverty 7 0.1658 0.0332 0.1104 0.2414 

No South Female Below Poverty 8 0.1428 0.0302 0.0931 0.2126 

No South Female Below Poverty 9 0.2123 0.0413 0.1425 0.3042 

No South Female Below Poverty 10 0.1408 0.0347 0.0855 0.2233 

No South Female Below Poverty 11 0.2249 0.0466 0.1467 0.3288 

No South Female Below Poverty 12 0.1741 0.0519 0.0941 0.2997 

No South Female Below Poverty 13 0.1463 0.0296 0.0972 0.2142 

No South Female Below Poverty 14 0.2428 0.0437 0.1675 0.3382 

No South Female Below Poverty 15 0.1947 0.0399 0.1280 0.2847 

No South Female Below Poverty 16 0.1285 0.0344 0.0747 0.2122 

No South Female Below Poverty 17 0.1322 0.0323 0.0807 0.2092 

No South Male Above Poverty 0 0.0135 0.0065 0.0052 0.0342 

No South Male Above Poverty 1 0.0782 0.0162 0.0517 0.1165 

No South Male Above Poverty 2 0.1134 0.0190 0.0811 0.1563 

No South Male Above Poverty 3 0.1063 0.0211 0.0714 0.1554 

No South Male Above Poverty 4 0.1679 0.0303 0.1165 0.2360 
5C-18 

Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No South Male Above Poverty 5 0.1644 0.0226 0.1247 0.2136 

No South Male Above Poverty 6 0.1328 0.0212 0.0964 0.1802 

No South Male Above Poverty 7 0.1542 0.0270 0.1083 0.2148 

No South Male Above Poverty 8 0.1502 0.0224 0.1114 0.1994 

No South Male Above Poverty 9 0.1522 0.0232 0.1121 0.2033 

No South Male Above Poverty 10 0.1485 0.0240 0.1073 0.2018 

No South Male Above Poverty 11 0.1767 0.0255 0.1322 0.2323 

No South Male Above Poverty 12 0.1915 0.0236 0.1495 0.2419 

No South Male Above Poverty 13 0.1939 0.0255 0.1487 0.2487 

No South Male Above Poverty 14 0.1381 0.0196 0.1039 0.1813 

No South Male Above Poverty 15 0.1579 0.0246 0.1154 0.2122 

No South Male Above Poverty 16 0.1698 0.0193 0.1352 0.2110 

No South Male Above Poverty 17 0.1530 0.0240 0.1117 0.2061 

No South Male Below Poverty 0 0.0610 0.0181 0.0338 0.1076 

No South Male Below Poverty 1 0.1005 0.0206 0.0667 0.1488 

No South Male Below Poverty 2 0.1102 0.0225 0.0732 0.1626 

No South Male Below Poverty 3 0.1699 0.0324 0.1154 0.2431 

No South Male Below Poverty 4 0.1642 0.0288 0.1152 0.2285 

No South Male Below Poverty 5 0.2510 0.0485 0.1682 0.3572 

No South Male Below Poverty 6 0.2064 0.0339 0.1477 0.2808 

No South Male Below Poverty 7 0.1588 0.0309 0.1072 0.2290 

No South Male Below Poverty 8 0.2518 0.0503 0.1663 0.3622 

No South Male Below Poverty 9 0.2246 0.0381 0.1588 0.3078 

No South Male Below Poverty 10 0.2022 0.0368 0.1394 0.2839 

No South Male Below Poverty 11 0.1890 0.0344 0.1305 0.2658 

No South Male Below Poverty 12 0.2322 0.0383 0.1656 0.3153 

No South Male Below Poverty 13 0.2345 0.0454 0.1573 0.3345 

No South Male Below Poverty 14 0.2265 0.0489 0.1448 0.3361 

No South Male Below Poverty 15 0.1801 0.0371 0.1183 0.2645 

No South Male Below Poverty 16 0.1286 0.0303 0.0799 0.2005 

No South Male Below Poverty 17 0.1916 0.0297 0.1399 0.2566 

No West Female Above Poverty 0 0.0049 0.0037 0.0011 0.0216 

No West Female Above Poverty 1 0.0390 0.0202 0.0139 0.1048 

No West Female Above Poverty 2 0.0269 0.0097 0.0132 0.0541 

No West Female Above Poverty 3 0.0439 0.0153 0.0219 0.0858 

No West Female Above Poverty 4 0.0232 0.0079 0.0118 0.0450 

No West Female Above Poverty 5 0.0988 0.0294 0.0544 0.1730 

No West Female Above Poverty 6 0.0829 0.0223 0.0484 0.1384 

No West Female Above Poverty 7 0.1065 0.0281 0.0627 0.1752 

No West Female Above Poverty 8 0.0960 0.0280 0.0534 0.1666 

No West Female Above Poverty 9 0.1124 0.0296 0.0662 0.1846 

No West Female Above Poverty 10 0.0978 0.0285 0.0545 0.1695 

No West Female Above Poverty 11 0.1186 0.0188 0.0864 0.1606 

No West Female Above Poverty 12 0.1655 0.0352 0.1074 0.2463 

No West Female Above Poverty 13 0.0855 0.0196 0.0542 0.1324 

No West Female Above Poverty 14 0.1258 0.0278 0.0806 0.1911 

No West Female Above Poverty 15 0.1482 0.0213 0.1111 0.1949 

No West Female Above Poverty 16 0.1394 0.0254 0.0967 0.1969 

No West Female Above Poverty 17 0.2285 0.0375 0.1632 0.3101 

No West Female Below Poverty 0 0.0064 0.0064 0.0009 0.0441 

No West Female Below Poverty 1 0.0443 0.0195 0.0185 0.1025 

No West Female Below Poverty 2 0.0523 0.0220 0.0226 0.1166 

No West Female Below Poverty 3 0.0403 0.0140 0.0202 0.0788 

No West Female Below Poverty 4 0.0346 0.0177 0.0126 0.0919 

No West Female Below Poverty 5 0.0887 0.0372 0.0380 0.1934 

No West Female Below Poverty 6 0.1351 0.0432 0.0703 0.2439 

No West Female Below Poverty 7 0.1364 0.0360 0.0798 0.2234 

No West Female Below Poverty 8 0.1106 0.0244 0.0711 0.1682 

No West Female Below Poverty 9 0.1254 0.0405 0.0650 0.2283 

No West Female Below Poverty 10 0.0585 0.0204 0.0292 0.1137 

No West Female Below Poverty 11 0.0747 0.0264 0.0368 0.1460 

No West Female Below Poverty 12 0.0720 0.0279 0.0331 0.1496 

No West Female Below Poverty 13 0.1898 0.0591 0.0993 0.3323 

No West Female Below Poverty 14 0.1431 0.0431 0.0773 0.2495 
5C-19 

Appendix 5C, Attachment A, Table CA-1. Unsmoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No West Female Below Poverty 15 0.1168 0.0304 0.0692 0.1906 

No West Female Below Poverty 16 0.0814 0.0290 0.0398 0.1593 

No West Female Below Poverty 17 0.0637 0.0235 0.0305 0.1285 

No West Male Above Poverty 0 0.0000 0.0000 0.0000 0.0000 

No West Male Above Poverty 1 0.0244 0.0121 0.0092 0.0635 

No West Male Above Poverty 2 0.0517 0.0155 0.0285 0.0920 

No West Male Above Poverty 3 0.0601 0.0172 0.0339 0.1041 

No West Male Above Poverty 4 0.1698 0.0275 0.1224 0.2307 

No West Male Above Poverty 5 0.1236 0.0288 0.0772 0.1918 

No West Male Above Poverty 6 0.1376 0.0264 0.0934 0.1980 

No West Male Above Poverty 7 0.1288 0.0354 0.0738 0.2152 

No West Male Above Poverty 8 0.1018 0.0223 0.0657 0.1547 

No West Male Above Poverty 9 0.1884 0.0315 0.1342 0.2579 

No West Male Above Poverty 10 0.1604 0.0273 0.1138 0.2215 

No West Male Above Poverty 11 0.2121 0.0298 0.1596 0.2762 

No West Male Above Poverty 12 0.1833 0.0349 0.1244 0.2618 

No West Male Above Poverty 13 0.2105 0.0397 0.1431 0.2987 

No West Male Above Poverty 14 0.1475 0.0309 0.0966 0.2187 

No West Male Above Poverty 15 0.1641 0.0263 0.1188 0.2224 

No West Male Above Poverty 16 0.1958 0.0282 0.1463 0.2569 

No West Male Above Poverty 17 0.2113 0.0289 0.1602 0.2733 

No West Male Below Poverty 0 0.0135 0.0128 0.0020 0.0832 

No West Male Below Poverty 1 0.0812 0.0317 0.0370 0.1691 

No West Male Below Poverty 2 0.0417 0.0131 0.0224 0.0765 

No West Male Below Poverty 3 0.1182 0.0351 0.0647 0.2061 

No West Male Below Poverty 4 0.1349 0.0329 0.0823 0.2131 

No West Male Below Poverty 5 0.1562 0.0401 0.0926 0.2514 

No West Male Below Poverty 6 0.1853 0.0444 0.1133 0.2883 

No West Male Below Poverty 7 0.1484 0.0343 0.0928 0.2288 

No West Male Below Poverty 8 0.1549 0.0343 0.0988 0.2346 

No West Male Below Poverty 9 0.1275 0.0418 0.0654 0.2338 

No West Male Below Poverty 10 0.1742 0.0431 0.1049 0.2751 

No West Male Below Poverty 11 0.1909 0.0554 0.1046 0.3227 

No West Male Below Poverty 12 0.1678 0.0599 0.0800 0.3185 

No West Male Below Poverty 13 0.1793 0.0491 0.1021 0.2959 

No West Male Below Poverty 14 0.1919 0.0454 0.1180 0.2966 

No West Male Below Poverty 15 0.1410 0.0577 0.0606 0.2946 

No West Male Below Poverty 16 0.1863 0.0384 0.1223 0.2734 

No West Male Below Poverty 17 0.2030 0.0493 0.1229 0.3165 

 
5C-20 

Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No Midwest Female Above Poverty 0 0.0018 0.0018 0.0002 0.0129 

No Midwest Female Above Poverty 1 0.0387 0.0233 0.0117 0.1208 

No Midwest Female Above Poverty 2 0.0302 0.0135 0.0125 0.0715 

No Midwest Female Above Poverty 3 0.0395 0.0186 0.0155 0.0972 

No Midwest Female Above Poverty 4 0.0531 0.0214 0.0238 0.1142 

No Midwest Female Above Poverty 5 0.0617 0.0173 0.0354 0.1055 

No Midwest Female Above Poverty 6 0.0386 0.0192 0.0143 0.0999 

No Midwest Female Above Poverty 7 0.0801 0.0239 0.0442 0.1411 

No Midwest Female Above Poverty 8 0.0492 0.0151 0.0267 0.0888 

No Midwest Female Above Poverty 9 0.0789 0.0200 0.0476 0.1280 

No Midwest Female Above Poverty 10 0.0625 0.0162 0.0373 0.1029 

No Midwest Female Above Poverty 11 0.0856 0.0232 0.0498 0.1433 

No Midwest Female Above Poverty 12 0.1269 0.0357 0.0717 0.2145 

No Midwest Female Above Poverty 13 0.1089 0.0264 0.0669 0.1724 

No Midwest Female Above Poverty 14 0.1580 0.0478 0.0849 0.2751 

No Midwest Female Above Poverty 15 0.0863 0.0213 0.0526 0.1382 

No Midwest Female Above Poverty 16 0.1300 0.0319 0.0792 0.2062 

No Midwest Female Above Poverty 17 0.0989 0.0236 0.0613 0.1556 

No Midwest Female Below Poverty 0 0.0564 0.0353 0.0160 0.1799 

No Midwest Female Below Poverty 1 0.0486 0.0183 0.0229 0.1000 

No Midwest Female Below Poverty 2 0.0959 0.0434 0.0383 0.2206 

No Midwest Female Below Poverty 3 0.0697 0.0338 0.0263 0.1723 

No Midwest Female Below Poverty 4 0.1697 0.0387 0.1065 0.2594 

No Midwest Female Below Poverty 5 0.0819 0.0265 0.0428 0.1512 

No Midwest Female Below Poverty 6 0.0809 0.0357 0.0332 0.1840 

No Midwest Female Below Poverty 7 0.0680 0.0325 0.0261 0.1661 

No Midwest Female Below Poverty 8 0.1257 0.0346 0.0719 0.2105 

No Midwest Female Below Poverty 9 0.1394 0.0398 0.0779 0.2369 

No Midwest Female Below Poverty 10 0.1871 0.1071 0.0548 0.4777 

No Midwest Female Below Poverty 11 0.0726 0.0266 0.0349 0.1451 

No Midwest Female Below Poverty 12 0.1101 0.0452 0.0477 0.2340 

No Midwest Female Below Poverty 13 0.1258 0.0354 0.0711 0.2130 

No Midwest Female Below Poverty 14 0.0999 0.0435 0.0413 0.2226 

No Midwest Female Below Poverty 15 0.1648 0.0745 0.0640 0.3629 

No Midwest Female Below Poverty 16 0.1647 0.0576 0.0799 0.3094 

No Midwest Female Below Poverty 17 0.1747 0.1141 0.0429 0.4997 

No Midwest Male Above Poverty 0 0.0061 0.0044 0.0015 0.0247 

No Midwest Male Above Poverty 1 0.0214 0.0175 0.0042 0.1008 

No Midwest Male Above Poverty 2 0.0752 0.0222 0.0417 0.1319 

No Midwest Male Above Poverty 3 0.0692 0.0203 0.0385 0.1213 

No Midwest Male Above Poverty 4 0.0527 0.0201 0.0247 0.1090 

No Midwest Male Above Poverty 5 0.1293 0.0303 0.0805 0.2011 

No Midwest Male Above Poverty 6 0.0710 0.0193 0.0413 0.1193 

No Midwest Male Above Poverty 7 0.1369 0.0301 0.0878 0.2072 

No Midwest Male Above Poverty 8 0.1047 0.0299 0.0589 0.1793 

No Midwest Male Above Poverty 9 0.1096 0.0269 0.0669 0.1745 

No Midwest Male Above Poverty 10 0.1004 0.0281 0.0571 0.1704 

No Midwest Male Above Poverty 11 0.1340 0.0348 0.0791 0.2179 

No Midwest Male Above Poverty 12 0.1093 0.0242 0.0700 0.1665 

No Midwest Male Above Poverty 13 0.1029 0.0210 0.0684 0.1520 

No Midwest Male Above Poverty 14 0.1230 0.0236 0.0837 0.1771 

No Midwest Male Above Poverty 15 0.1007 0.0305 0.0548 0.1780 

No Midwest Male Above Poverty 16 0.1141 0.0268 0.0711 0.1780 

No Midwest Male Above Poverty 17 0.0644 0.0193 0.0354 0.1143 

No Midwest Male Below Poverty 0 0.0274 0.0175 0.0077 0.0925 

No Midwest Male Below Poverty 1 0.0892 0.0369 0.0386 0.1927 

No Midwest Male Below Poverty 2 0.1786 0.0652 0.0835 0.3418 

No Midwest Male Below Poverty 3 0.1620 0.0475 0.0888 0.2772 

No Midwest Male Below Poverty 4 0.2557 0.0634 0.1517 0.3974 

No Midwest Male Below Poverty 5 0.1914 0.0400 0.1248 0.2821 

No Midwest Male Below Poverty 6 0.1432 0.0333 0.0894 0.2215 

No Midwest Male Below Poverty 7 0.1788 0.0378 0.1162 0.2649 

No Midwest Male Below Poverty 8 0.2414 0.0604 0.1429 0.3780 

No Midwest Male Below Poverty 9 0.1114 0.0404 0.0533 0.2180 
5C-21 

Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No Midwest Male Below Poverty 10 0.2022 0.0624 0.1061 0.3511 

No Midwest Male Below Poverty 11 0.1731 0.0406 0.1072 0.2675 

No Midwest Male Below Poverty 12 0.2271 0.1064 0.0822 0.4908 

No Midwest Male Below Poverty 13 0.1627 0.0591 0.0767 0.3125 

No Midwest Male Below Poverty 14 0.0967 0.0413 0.0406 0.2129 

No Midwest Male Below Poverty 15 0.1509 0.0506 0.0757 0.2781 

No Midwest Male Below Poverty 16 0.1167 0.0490 0.0495 0.2512 

No Midwest Male Below Poverty 17 0.3301 0.1005 0.1683 0.5456 

No Northeast Female Above Poverty 0 0.0055 0.0054 0.0008 0.0368 

No Northeast Female Above Poverty 1 0.0296 0.0164 0.0099 0.0854 

No Northeast Female Above Poverty 2 0.0697 0.0252 0.0337 0.1384 

No Northeast Female Above Poverty 3 0.0470 0.0158 0.0240 0.0897 

No Northeast Female Above Poverty 4 0.0717 0.0199 0.0413 0.1218 

No Northeast Female Above Poverty 5 0.0642 0.0196 0.0349 0.1151 

No Northeast Female Above Poverty 6 0.0709 0.0254 0.0346 0.1398 

No Northeast Female Above Poverty 7 0.0697 0.0180 0.0416 0.1143 

No Northeast Female Above Poverty 8 0.0609 0.0209 0.0307 0.1171 

No Northeast Female Above Poverty 9 0.0996 0.0334 0.0507 0.1865 

No Northeast Female Above Poverty 10 0.0740 0.0260 0.0366 0.1439 

No Northeast Female Above Poverty 11 0.1028 0.0305 0.0565 0.1797 

No Northeast Female Above Poverty 12 0.0386 0.0187 0.0147 0.0975 

No Northeast Female Above Poverty 13 0.0187 0.0095 0.0069 0.0500 

No Northeast Female Above Poverty 14 0.0907 0.0181 0.0609 0.1330 

No Northeast Female Above Poverty 15 0.1270 0.0344 0.0733 0.2108 

No Northeast Female Above Poverty 16 0.0974 0.0267 0.0562 0.1636 

No Northeast Female Above Poverty 17 0.1239 0.0375 0.0671 0.2177 

No Northeast Female Below Poverty 0 0.0078 0.0078 0.0011 0.0541 

No Northeast Female Below Poverty 1 0.1230 0.0576 0.0469 0.2852 

No Northeast Female Below Poverty 2 0.0658 0.0272 0.0287 0.1436 

No Northeast Female Below Poverty 3 0.1700 0.0576 0.0842 0.3133 

No Northeast Female Below Poverty 4 0.1139 0.0456 0.0503 0.2376 

No Northeast Female Below Poverty 5 0.2219 0.0583 0.1282 0.3561 

No Northeast Female Below Poverty 6 0.0583 0.0290 0.0215 0.1484 

No Northeast Female Below Poverty 7 0.0495 0.0252 0.0179 0.1294 

No Northeast Female Below Poverty 8 0.0850 0.0368 0.0354 0.1903 

No Northeast Female Below Poverty 9 0.0652 0.0294 0.0264 0.1521 

No Northeast Female Below Poverty 10 0.0988 0.0440 0.0400 0.2240 

No Northeast Female Below Poverty 11 0.2587 0.0734 0.1416 0.4249 

No Northeast Female Below Poverty 12 0.0882 0.0426 0.0332 0.2146 

No Northeast Female Below Poverty 13 0.3162 0.0739 0.1913 0.4746 

No Northeast Female Below Poverty 14 0.1293 0.0372 0.0722 0.2209 

No Northeast Female Below Poverty 15 0.1798 0.0479 0.1039 0.2930 

No Northeast Female Below Poverty 16 0.1429 0.0381 0.0831 0.2348 

No Northeast Female Below Poverty 17 0.1133 0.0426 0.0527 0.2269 

No Northeast Male Above Poverty 0 0.0131 0.0101 0.0029 0.0574 

No Northeast Male Above Poverty 1 0.0505 0.0227 0.0206 0.1185 

No Northeast Male Above Poverty 2 0.0635 0.0220 0.0318 0.1228 

No Northeast Male Above Poverty 3 0.0582 0.0216 0.0277 0.1181 

No Northeast Male Above Poverty 4 0.1007 0.0281 0.0574 0.1705 

No Northeast Male Above Poverty 5 0.1245 0.0318 0.0742 0.2013 

No Northeast Male Above Poverty 6 0.1990 0.0511 0.1171 0.3177 

No Northeast Male Above Poverty 7 0.1240 0.0274 0.0795 0.1885 

No Northeast Male Above Poverty 8 0.1482 0.0321 0.0956 0.2227 

No Northeast Male Above Poverty 9 0.0980 0.0321 0.0506 0.1813 

No Northeast Male Above Poverty 10 0.0999 0.0216 0.0648 0.1509 

No Northeast Male Above Poverty 11 0.1805 0.0342 0.1229 0.2573 

No Northeast Male Above Poverty 12 0.1204 0.0211 0.0848 0.1682 

No Northeast Male Above Poverty 13 0.0855 0.0237 0.0491 0.1449 

No Northeast Male Above Poverty 14 0.1243 0.0351 0.0702 0.2108 

No Northeast Male Above Poverty 15 0.1249 0.0247 0.0839 0.1819 

No Northeast Male Above Poverty 16 0.1198 0.0283 0.0744 0.1872 

No Northeast Male Above Poverty 17 0.0690 0.0173 0.0418 0.1117 

No Northeast Male Below Poverty 0 0.0375 0.0275 0.0087 0.1477 

No Northeast Male Below Poverty 1 0.1649 0.0506 0.0877 0.2887 
5C-22 

Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No Northeast Male Below Poverty 2 0.1621 0.0496 0.0864 0.2835 

No Northeast Male Below Poverty 3 0.1015 0.0440 0.0420 0.2255 

No Northeast Male Below Poverty 4 0.2486 0.0909 0.1131 0.4621 

No Northeast Male Below Poverty 5 0.1479 0.0487 0.0753 0.2701 

No Northeast Male Below Poverty 6 0.2630 0.0391 0.1939 0.3463 

No Northeast Male Below Poverty 7 0.1707 0.0507 0.0926 0.2935 

No Northeast Male Below Poverty 8 0.2056 0.0966 0.0751 0.4521 

No Northeast Male Below Poverty 9 0.3343 0.0680 0.2162 0.4776 

No Northeast Male Below Poverty 10 0.2276 0.0786 0.1093 0.4145 

No Northeast Male Below Poverty 11 0.1643 0.0600 0.0770 0.3164 

No Northeast Male Below Poverty 12 0.1117 0.0389 0.0552 0.2132 

No Northeast Male Below Poverty 13 0.1931 0.0430 0.1223 0.2914 

No Northeast Male Below Poverty 14 0.1714 0.0664 0.0764 0.3410 

No Northeast Male Below Poverty 15 0.2043 0.0555 0.1162 0.3338 

No Northeast Male Below Poverty 16 0.1684 0.0501 0.0912 0.2901 

No Northeast Male Below Poverty 17 0.2140 0.0526 0.1286 0.3345 

No South Female Above Poverty 0 0.0129 0.0080 0.0038 0.0427 

No South Female Above Poverty 1 0.0144 0.0076 0.0051 0.0402 

No South Female Above Poverty 2 0.0452 0.0169 0.0215 0.0926 

No South Female Above Poverty 3 0.0675 0.0196 0.0379 0.1175 

No South Female Above Poverty 4 0.0540 0.0150 0.0311 0.0920 

No South Female Above Poverty 5 0.0572 0.0138 0.0354 0.0911 

No South Female Above Poverty 6 0.1002 0.0186 0.0692 0.1431 

No South Female Above Poverty 7 0.0894 0.0191 0.0584 0.1346 

No South Female Above Poverty 8 0.0762 0.0160 0.0502 0.1141 

No South Female Above Poverty 9 0.0969 0.0210 0.0627 0.1466 

No South Female Above Poverty 10 0.0473 0.0135 0.0269 0.0819 

No South Female Above Poverty 11 0.0847 0.0165 0.0576 0.1231 

No South Female Above Poverty 12 0.0768 0.0152 0.0518 0.1124 

No South Female Above Poverty 13 0.0700 0.0158 0.0447 0.1080 

No South Female Above Poverty 14 0.1059 0.0211 0.0711 0.1550 

No South Female Above Poverty 15 0.0930 0.0186 0.0624 0.1364 

No South Female Above Poverty 16 0.0702 0.0156 0.0451 0.1077 

No South Female Above Poverty 17 0.0867 0.0162 0.0597 0.1242 

No South Female Below Poverty 0 0.0404 0.0203 0.0149 0.1050 

No South Female Below Poverty 1 0.0613 0.0183 0.0338 0.1085 

No South Female Below Poverty 2 0.0704 0.0193 0.0408 0.1189 

No South Female Below Poverty 3 0.0812 0.0254 0.0434 0.1471 

No South Female Below Poverty 4 0.1404 0.0367 0.0826 0.2286 

No South Female Below Poverty 5 0.1276 0.0304 0.0789 0.1997 

No South Female Below Poverty 6 0.0792 0.0288 0.0381 0.1573 

No South Female Below Poverty 7 0.1262 0.0305 0.0775 0.1989 

No South Female Below Poverty 8 0.1185 0.0290 0.0724 0.1881 

No South Female Below Poverty 9 0.1147 0.0286 0.0694 0.1836 

No South Female Below Poverty 10 0.1038 0.0301 0.0579 0.1792 

No South Female Below Poverty 11 0.1461 0.0366 0.0879 0.2331 

No South Female Below Poverty 12 0.1299 0.0490 0.0600 0.2589 

No South Female Below Poverty 13 0.1013 0.0262 0.0602 0.1655 

No South Female Below Poverty 14 0.1699 0.0385 0.1071 0.2590 

No South Female Below Poverty 15 0.1591 0.0365 0.0998 0.2441 

No South Female Below Poverty 16 0.0633 0.0273 0.0267 0.1427 

No South Female Below Poverty 17 0.0975 0.0299 0.0526 0.1737 

No South Male Above Poverty 0 0.0044 0.0025 0.0014 0.0135 

No South Male Above Poverty 1 0.0700 0.0162 0.0442 0.1092 

No South Male Above Poverty 2 0.0911 0.0195 0.0595 0.1373 

No South Male Above Poverty 3 0.0962 0.0206 0.0627 0.1449 

No South Male Above Poverty 4 0.1230 0.0259 0.0805 0.1833 

No South Male Above Poverty 5 0.1321 0.0204 0.0970 0.1774 

No South Male Above Poverty 6 0.0999 0.0192 0.0681 0.1443 

No South Male Above Poverty 7 0.1114 0.0214 0.0758 0.1608 

No South Male Above Poverty 8 0.0946 0.0168 0.0664 0.1330 

No South Male Above Poverty 9 0.1108 0.0202 0.0770 0.1569 

No South Male Above Poverty 10 0.1010 0.0186 0.0699 0.1438 

No South Male Above Poverty 11 0.0946 0.0175 0.0655 0.1348 
5C-23 

Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No South Male Above Poverty 12 0.1340 0.0207 0.0983 0.1801 

No South Male Above Poverty 13 0.1122 0.0226 0.0750 0.1646 

No South Male Above Poverty 14 0.0713 0.0153 0.0466 0.1077 

No South Male Above Poverty 15 0.0899 0.0158 0.0635 0.1260 

No South Male Above Poverty 16 0.0871 0.0147 0.0623 0.1206 

No South Male Above Poverty 17 0.0700 0.0178 0.0421 0.1141 

No South Male Below Poverty 0 0.0477 0.0162 0.0242 0.0916 

No South Male Below Poverty 1 0.0859 0.0197 0.0544 0.1330 

No South Male Below Poverty 2 0.0820 0.0201 0.0503 0.1309 

No South Male Below Poverty 3 0.1434 0.0319 0.0914 0.2178 

No South Male Below Poverty 4 0.1320 0.0265 0.0881 0.1931 

No South Male Below Poverty 5 0.2314 0.0486 0.1498 0.3397 

No South Male Below Poverty 6 0.1395 0.0302 0.0902 0.2097 

No South Male Below Poverty 7 0.1207 0.0269 0.0771 0.1840 

No South Male Below Poverty 8 0.2064 0.0474 0.1285 0.3145 

No South Male Below Poverty 9 0.1364 0.0279 0.0903 0.2009 

No South Male Below Poverty 10 0.1473 0.0315 0.0956 0.2203 

No South Male Below Poverty 11 0.1390 0.0286 0.0917 0.2051 

No South Male Below Poverty 12 0.1673 0.0339 0.1109 0.2445 

No South Male Below Poverty 13 0.1684 0.0449 0.0975 0.2752 

No South Male Below Poverty 14 0.0936 0.0305 0.0485 0.1729 

No South Male Below Poverty 15 0.1379 0.0353 0.0820 0.2226 

No South Male Below Poverty 16 0.0816 0.0275 0.0415 0.1544 

No South Male Below Poverty 17 0.1057 0.0289 0.0609 0.1772 

No West Female Above Poverty 0 0.0013 0.0013 0.0002 0.0095 

No West Female Above Poverty 1 0.0353 0.0202 0.0113 0.1045 

No West Female Above Poverty 2 0.0159 0.0076 0.0062 0.0401 

No West Female Above Poverty 3 0.0284 0.0132 0.0113 0.0695 

No West Female Above Poverty 4 0.0183 0.0071 0.0085 0.0389 

No West Female Above Poverty 5 0.0689 0.0276 0.0308 0.1468 

No West Female Above Poverty 6 0.0477 0.0166 0.0239 0.0928 

No West Female Above Poverty 7 0.0469 0.0144 0.0255 0.0846 

No West Female Above Poverty 8 0.0756 0.0263 0.0376 0.1459 

No West Female Above Poverty 9 0.0686 0.0196 0.0388 0.1185 

No West Female Above Poverty 10 0.0791 0.0250 0.0420 0.1440 

No West Female Above Poverty 11 0.0763 0.0124 0.0553 0.1043 

No West Female Above Poverty 12 0.1023 0.0260 0.0614 0.1655 

No West Female Above Poverty 13 0.0571 0.0163 0.0323 0.0989 

No West Female Above Poverty 14 0.1012 0.0251 0.0615 0.1622 

No West Female Above Poverty 15 0.0923 0.0207 0.0590 0.1416 

No West Female Above Poverty 16 0.0787 0.0214 0.0458 0.1322 

No West Female Above Poverty 17 0.1303 0.0294 0.0827 0.1993 

No West Female Below Poverty 0 0.0064 0.0064 0.0009 0.0441 

No West Female Below Poverty 1 0.0443 0.0195 0.0185 0.1025 

No West Female Below Poverty 2 0.0249 0.0153 0.0074 0.0805 

No West Female Below Poverty 3 0.0372 0.0137 0.0179 0.0756 

No West Female Below Poverty 4 0.0114 0.0102 0.0020 0.0638 

No West Female Below Poverty 5 0.0491 0.0294 0.0148 0.1506 

No West Female Below Poverty 6 0.1016 0.0419 0.0440 0.2174 

No West Female Below Poverty 7 0.0908 0.0302 0.0464 0.1698 

No West Female Below Poverty 8 0.0874 0.0258 0.0484 0.1529 

No West Female Below Poverty 9 0.0839 0.0267 0.0443 0.1532 

No West Female Below Poverty 10 0.0275 0.0137 0.0103 0.0715 

No West Female Below Poverty 11 0.0339 0.0160 0.0133 0.0839 

No West Female Below Poverty 12 0.0551 0.0254 0.0219 0.1315 

No West Female Below Poverty 13 0.1028 0.0393 0.0474 0.2089 

No West Female Below Poverty 14 0.1312 0.0440 0.0662 0.2435 

No West Female Below Poverty 15 0.0630 0.0247 0.0288 0.1324 

No West Female Below Poverty 16 0.0758 0.0287 0.0354 0.1546 

No West Female Below Poverty 17 0.0328 0.0163 0.0122 0.0850 

No West Male Above Poverty 0 0.0000 0.0000 0.0000 0.0000 

No West Male Above Poverty 1 0.0039 0.0040 0.0005 0.0289 

No West Male Above Poverty 2 0.0305 0.0113 0.0147 0.0623 

No West Male Above Poverty 3 0.0384 0.0129 0.0197 0.0735 
5C-24 

Appendix 5C, Attachment A, Table CA-2. Unsmoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

No West Male Above Poverty 4 0.1363 0.0261 0.0927 0.1960 

No West Male Above Poverty 5 0.0933 0.0268 0.0523 0.1608 

No West Male Above Poverty 6 0.0803 0.0208 0.0478 0.1317 

No West Male Above Poverty 7 0.1014 0.0320 0.0537 0.1834 

No West Male Above Poverty 8 0.0537 0.0182 0.0273 0.1029 

No West Male Above Poverty 9 0.1120 0.0242 0.0726 0.1689 

No West Male Above Poverty 10 0.1202 0.0253 0.0788 0.1791 

No West Male Above Poverty 11 0.1333 0.0271 0.0885 0.1959 

No West Male Above Poverty 12 0.1258 0.0286 0.0796 0.1934 

No West Male Above Poverty 13 0.1039 0.0328 0.0549 0.1879 

No West Male Above Poverty 14 0.0873 0.0217 0.0531 0.1404 

No West Male Above Poverty 15 0.0881 0.0222 0.0532 0.1425 

No West Male Above Poverty 16 0.1066 0.0230 0.0692 0.1607 

No West Male Above Poverty 17 0.1364 0.0284 0.0897 0.2021 

No West Male Below Poverty 0 0.0135 0.0128 0.0020 0.0832 

No West Male Below Poverty 1 0.0812 0.0317 0.0370 0.1691 

No West Male Below Poverty 2 0.0308 0.0080 0.0185 0.0510 

No West Male Below Poverty 3 0.0944 0.0311 0.0486 0.1755 

No West Male Below Poverty 4 0.1056 0.0306 0.0588 0.1822 

No West Male Below Poverty 5 0.0856 0.0256 0.0471 0.1508 

No West Male Below Poverty 6 0.1277 0.0356 0.0726 0.2149 

No West Male Below Poverty 7 0.0943 0.0353 0.0443 0.1897 

No West Male Below Poverty 8 0.1282 0.0343 0.0746 0.2115 

No West Male Below Poverty 9 0.0883 0.0287 0.0459 0.1632 

No West Male Below Poverty 10 0.0697 0.0228 0.0363 0.1298 

No West Male Below Poverty 11 0.0954 0.0365 0.0440 0.1947 

No West Male Below Poverty 12 0.0759 0.0316 0.0329 0.1655 

No West Male Below Poverty 13 0.0600 0.0276 0.0239 0.1427 

No West Male Below Poverty 14 0.1457 0.0391 0.0844 0.2398 

No West Male Below Poverty 15 0.1099 0.0551 0.0394 0.2713 

No West Male Below Poverty 16 0.0957 0.0350 0.0458 0.1894 

No West Male Below Poverty 17 0.1136 0.0421 0.0534 0.2254 

 
5C-25 

Appendix 5C, Attachment A, Table CA-3. Unsmoothed prevalence for adults “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age_grp Prevalence SE LowerCI UpperCI 

No Midwest Female Above Poverty Level 18-24 0.1633 0.0154 0.1353 0.1958 

No Midwest Female Above Poverty Level 25-34 0.1347 0.0096 0.1169 0.1547 

No Midwest Female Above Poverty Level 35-44 0.1214 0.0084 0.1059 0.1389 

No Midwest Female Above Poverty Level 45-54 0.1157 0.0072 0.1022 0.1306 

No Midwest Female Above Poverty Level 55-64 0.1360 0.0103 0.1171 0.1575 

No Midwest Female Above Poverty Level 65-74 0.1104 0.0107 0.0910 0.1332 

No Midwest Female Above Poverty Level 75+ 0.0990 0.0095 0.0819 0.1193 

No Midwest Female Below Poverty Level 18-24 0.1990 0.0156 0.1701 0.2314 

No Midwest Female Below Poverty Level 25-34 0.1896 0.0177 0.1573 0.2268 

No Midwest Female Below Poverty Level 35-44 0.1789 0.0209 0.1415 0.2237 

No Midwest Female Below Poverty Level 45-54 0.1903 0.0180 0.1576 0.2281 

No Midwest Female Below Poverty Level 55-64 0.2760 0.0255 0.2289 0.3285 

No Midwest Female Below Poverty Level 65-74 0.1459 0.0205 0.1101 0.1908 

No Midwest Female Below Poverty Level 75+ 0.1295 0.0202 0.0948 0.1744 

No Midwest Male Above Poverty Level 18-24 0.1658 0.0158 0.1371 0.1990 

No Midwest Male Above Poverty Level 25-34 0.1254 0.0092 0.1085 0.1446 

No Midwest Male Above Poverty Level 35-44 0.0934 0.0083 0.0784 0.1109 

No Midwest Male Above Poverty Level 45-54 0.0659 0.0057 0.0555 0.0779 

No Midwest Male Above Poverty Level 55-64 0.0856 0.0086 0.0701 0.1040 

No Midwest Male Above Poverty Level 65-74 0.0884 0.0106 0.0697 0.1114 

No Midwest Male Above Poverty Level 75+ 0.0808 0.0110 0.0617 0.1050 

No Midwest Male Below Poverty Level 18-24 0.1672 0.0182 0.1345 0.2060 

No Midwest Male Below Poverty Level 25-34 0.1103 0.0156 0.0832 0.1447 

No Midwest Male Below Poverty Level 35-44 0.0945 0.0191 0.0632 0.1391 

No Midwest Male Below Poverty Level 45-54 0.1445 0.0204 0.1089 0.1893 

No Midwest Male Below Poverty Level 55-64 0.1623 0.0203 0.1263 0.2061 

No Midwest Male Below Poverty Level 65-74 0.1474 0.0307 0.0968 0.2182 

No Midwest Male Below Poverty Level 75+ 0.0830 0.0217 0.0492 0.1367 

No Northeast Female Above Poverty Level 18-24 0.1834 0.0199 0.1476 0.2256 

No Northeast Female Above Poverty Level 25-34 0.1375 0.0107 0.1178 0.1598 

No Northeast Female Above Poverty Level 35-44 0.1297 0.0109 0.1097 0.1527 

No Northeast Female Above Poverty Level 45-54 0.1209 0.0095 0.1034 0.1409 

No Northeast Female Above Poverty Level 55-64 0.1306 0.0106 0.1113 0.1528 

No Northeast Female Above Poverty Level 65-74 0.1244 0.0130 0.1010 0.1523 

No Northeast Female Above Poverty Level 75+ 0.0844 0.0101 0.0666 0.1064 

No Northeast Female Below Poverty Level 18-24 0.1642 0.0194 0.1296 0.2059 

No Northeast Female Below Poverty Level 25-34 0.1726 0.0170 0.1418 0.2084 

No Northeast Female Below Poverty Level 35-44 0.1771 0.0172 0.1459 0.2132 

No Northeast Female Below Poverty Level 45-54 0.2140 0.0204 0.1767 0.2567 

No Northeast Female Below Poverty Level 55-64 0.2174 0.0232 0.1753 0.2664 

No Northeast Female Below Poverty Level 65-74 0.1752 0.0186 0.1417 0.2147 

No Northeast Female Below Poverty Level 75+ 0.0941 0.0132 0.0712 0.1234 

No Northeast Male Above Poverty Level 18-24 0.1658 0.0223 0.1265 0.2142 

No Northeast Male Above Poverty Level 25-34 0.1262 0.0126 0.1034 0.1531 

No Northeast Male Above Poverty Level 35-44 0.0773 0.0094 0.0607 0.0980 

No Northeast Male Above Poverty Level 45-54 0.0976 0.0086 0.0820 0.1158 

No Northeast Male Above Poverty Level 55-64 0.0911 0.0096 0.0740 0.1117 

No Northeast Male Above Poverty Level 65-74 0.0926 0.0128 0.0704 0.1209 

No Northeast Male Above Poverty Level 75+ 0.0689 0.0127 0.0478 0.0982 

No Northeast Male Below Poverty Level 18-24 0.1753 0.0200 0.1395 0.2179 

No Northeast Male Below Poverty Level 25-34 0.1255 0.0178 0.0945 0.1648 

No Northeast Male Below Poverty Level 35-44 0.1317 0.0244 0.0909 0.1872 

No Northeast Male Below Poverty Level 45-54 0.1189 0.0162 0.0906 0.1545 

No Northeast Male Below Poverty Level 55-64 0.1681 0.0490 0.0923 0.2865 

No Northeast Male Below Poverty Level 65-74 0.1383 0.0313 0.0875 0.2118 

No Northeast Male Below Poverty Level 75+ 0.0943 0.0265 0.0536 0.1606 

No South Female Above Poverty Level 18-24 0.1501 0.0121 0.1279 0.1754 

No South Female Above Poverty Level 25-34 0.1290 0.0084 0.1134 0.1464 

No South Female Above Poverty Level 35-44 0.1050 0.0074 0.0914 0.1205 

No South Female Above Poverty Level 45-54 0.1163 0.0060 0.1051 0.1285 

No South Female Above Poverty Level 55-64 0.1279 0.0087 0.1119 0.1459 

No South Female Above Poverty Level 65-74 0.1231 0.0102 0.1044 0.1446 

No South Female Above Poverty Level 75+ 0.0939 0.0092 0.0773 0.1136 

No South Female Below Poverty Level 18-24 0.1511 0.0133 0.1269 0.1790 
5C-26 

Appendix 5C, Attachment A, Table CA-3. Unsmoothed prevalence for adults “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age_grp Prevalence SE LowerCI UpperCI 

No South Female Below Poverty Level 25-34 0.1336 0.0087 0.1175 0.1515 

No South Female Below Poverty Level 35-44 0.1452 0.0125 0.1224 0.1714 

No South Female Below Poverty Level 45-54 0.1622 0.0128 0.1386 0.1889 

No South Female Below Poverty Level 55-64 0.2039 0.0179 0.1711 0.2413 

No South Female Below Poverty Level 65-74 0.1616 0.0163 0.1321 0.1962 

No South Female Below Poverty Level 75+ 0.1127 0.0133 0.0891 0.1415 

No South Male Above Poverty Level 18-24 0.1438 0.0100 0.1253 0.1645 

No South Male Above Poverty Level 25-34 0.1095 0.0078 0.0952 0.1258 

No South Male Above Poverty Level 35-44 0.0890 0.0066 0.0769 0.1027 

No South Male Above Poverty Level 45-54 0.0704 0.0051 0.0610 0.0811 

No South Male Above Poverty Level 55-64 0.0782 0.0071 0.0654 0.0932 

No South Male Above Poverty Level 65-74 0.0789 0.0078 0.0649 0.0956 

No South Male Above Poverty Level 75+ 0.0893 0.0111 0.0698 0.1135 

No South Male Below Poverty Level 18-24 0.1473 0.0152 0.1199 0.1797 

No South Male Below Poverty Level 25-34 0.0914 0.0122 0.0701 0.1184 

No South Male Below Poverty Level 35-44 0.0972 0.0139 0.0732 0.1280 

No South Male Below Poverty Level 45-54 0.1062 0.0138 0.0821 0.1363 

No South Male Below Poverty Level 55-64 0.1068 0.0156 0.0799 0.1414 

No South Male Below Poverty Level 65-74 0.0966 0.0149 0.0710 0.1301 

No South Male Below Poverty Level 75+ 0.0702 0.0130 0.0486 0.1004 

No West Female Above Poverty Level 18-24 0.1595 0.0150 0.1323 0.1911 

No West Female Above Poverty Level 25-34 0.1387 0.0096 0.1209 0.1586 

No West Female Above Poverty Level 35-44 0.1368 0.0109 0.1168 0.1595 

No West Female Above Poverty Level 45-54 0.1431 0.0092 0.1261 0.1621 

No West Female Above Poverty Level 55-64 0.1478 0.0094 0.1303 0.1671 

No West Female Above Poverty Level 65-74 0.1541 0.0130 0.1302 0.1813 

No West Female Above Poverty Level 75+ 0.1231 0.0117 0.1020 0.1479 

No West Female Below Poverty Level 18-24 0.1522 0.0184 0.1195 0.1920 

No West Female Below Poverty Level 25-34 0.1191 0.0118 0.0978 0.1441 

No West Female Below Poverty Level 35-44 0.1466 0.0182 0.1145 0.1859 

No West Female Below Poverty Level 45-54 0.1874 0.0219 0.1483 0.2341 

No West Female Below Poverty Level 55-64 0.1747 0.0181 0.1419 0.2131 

No West Female Below Poverty Level 65-74 0.1318 0.0179 0.1005 0.1709 

No West Female Below Poverty Level 75+ 0.1370 0.0198 0.1027 0.1806 

No West Male Above Poverty Level 18-24 0.1499 0.0188 0.1167 0.1905 

No West Male Above Poverty Level 25-34 0.1304 0.0107 0.1108 0.1527 

No West Male Above Poverty Level 35-44 0.0984 0.0080 0.0837 0.1153 

No West Male Above Poverty Level 45-54 0.0944 0.0081 0.0796 0.1116 

No West Male Above Poverty Level 55-64 0.0917 0.0075 0.0780 0.1076 

No West Male Above Poverty Level 65-74 0.1168 0.0126 0.0943 0.1438 

No West Male Above Poverty Level 75+ 0.1208 0.0160 0.0928 0.1558 

No West Male Below Poverty Level 18-24 0.1589 0.0222 0.1201 0.2073 

No West Male Below Poverty Level 25-34 0.0846 0.0128 0.0626 0.1133 

No West Male Below Poverty Level 35-44 0.0760 0.0135 0.0535 0.1069 

No West Male Below Poverty Level 45-54 0.1422 0.0214 0.1052 0.1894 

No West Male Below Poverty Level 55-64 0.0979 0.0176 0.0684 0.1381 

No West Male Below Poverty Level 65-74 0.1349 0.0323 0.0831 0.2116 

No West Male Below Poverty Level 75+ 0.0937 0.0194 0.0620 0.1393 

 
5C-27 

Appendix 5C, Attachment A, Table CA-4. Unsmoothed prevalence for adults “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age_grp Prevalence SE LowerCI UpperCI 

No Midwest Female Above Poverty Level 18-24 0.1062 0.0133 0.0828 0.1354 

No Midwest Female Above Poverty Level 25-34 0.0859 0.0090 0.0699 0.1052 

No Midwest Female Above Poverty Level 35-44 0.0859 0.0081 0.0713 0.1031 

No Midwest Female Above Poverty Level 45-44 0.0858 0.0061 0.0746 0.0986 

No Midwest Female Above Poverty Level 55-64 0.0996 0.0090 0.0832 0.1188 

No Midwest Female Above Poverty Level 65-74 0.0755 0.0083 0.0608 0.0934 

No Midwest Female Above Poverty Level 75+ 0.0643 0.0073 0.0514 0.0802 

No Midwest Female Below Poverty Level 18-24 0.1306 0.0144 0.1049 0.1614 

No Midwest Female Below Poverty Level 25-34 0.1329 0.0143 0.1073 0.1634 

No Midwest Female Below Poverty Level 35-44 0.1354 0.0187 0.1027 0.1764 

No Midwest Female Below Poverty Level 45-44 0.1398 0.0166 0.1102 0.1757 

No Midwest Female Below Poverty Level 55-64 0.2110 0.0221 0.1709 0.2575 

No Midwest Female Below Poverty Level 65-74 0.1190 0.0180 0.0879 0.1590 

No Midwest Female Below Poverty Level 75+ 0.1029 0.0183 0.0722 0.1448 

No Midwest Male Above Poverty Level 18-24 0.0790 0.0125 0.0577 0.1071 

No Midwest Male Above Poverty Level 25-34 0.0599 0.0066 0.0482 0.0743 

No Midwest Male Above Poverty Level 35-44 0.0486 0.0063 0.0377 0.0625 

No Midwest Male Above Poverty Level 45-44 0.0447 0.0049 0.0360 0.0554 

No Midwest Male Above Poverty Level 55-64 0.0555 0.0059 0.0450 0.0683 

No Midwest Male Above Poverty Level 65-74 0.0524 0.0076 0.0394 0.0694 

No Midwest Male Above Poverty Level 75+ 0.0477 0.0088 0.0331 0.0682 

No Midwest Male Below Poverty Level 18-24 0.0938 0.0143 0.0693 0.1258 

No Midwest Male Below Poverty Level 25-34 0.0572 0.0137 0.0355 0.0908 

No Midwest Male Below Poverty Level 35-44 0.0731 0.0162 0.0470 0.1119 

No Midwest Male Below Poverty Level 45-44 0.0969 0.0208 0.0630 0.1461 

No Midwest Male Below Poverty Level 55-64 0.1350 0.0205 0.0997 0.1804 

No Midwest Male Below Poverty Level 65-74 0.1349 0.0294 0.0869 0.2035 

No Midwest Male Below Poverty Level 75+ 0.0643 0.0213 0.0332 0.1208 

No Northeast Female Above Poverty Level 18-24 0.1123 0.0148 0.0864 0.1447 

No Northeast Female Above Poverty Level 25-34 0.0917 0.0102 0.0735 0.1138 

No Northeast Female Above Poverty Level 35-44 0.0944 0.0092 0.0778 0.1141 

No Northeast Female Above Poverty Level 45-44 0.0858 0.0080 0.0714 0.1029 

No Northeast Female Above Poverty Level 55-64 0.0945 0.0086 0.0790 0.1127 

No Northeast Female Above Poverty Level 65-74 0.0898 0.0106 0.0711 0.1128 

No Northeast Female Above Poverty Level 75+ 0.0706 0.0098 0.0537 0.0924 

No Northeast Female Below Poverty Level 18-24 0.1232 0.0182 0.0918 0.1634 

No Northeast Female Below Poverty Level 25-34 0.1180 0.0147 0.0921 0.1499 

No Northeast Female Below Poverty Level 35-44 0.1265 0.0138 0.1018 0.1560 

No Northeast Female Below Poverty Level 45-44 0.1745 0.0185 0.1412 0.2137 

No Northeast Female Below Poverty Level 55-64 0.1744 0.0211 0.1369 0.2196 

No Northeast Female Below Poverty Level 65-74 0.1388 0.0148 0.1123 0.1704 

No Northeast Female Below Poverty Level 75+ 0.0488 0.0088 0.0341 0.0693 

No Northeast Male Above Poverty Level 18-24 0.0888 0.0161 0.0620 0.1257 

No Northeast Male Above Poverty Level 25-34 0.0655 0.0093 0.0495 0.0862 

No Northeast Male Above Poverty Level 35-44 0.0409 0.0061 0.0304 0.0547 

No Northeast Male Above Poverty Level 45-44 0.0564 0.0078 0.0429 0.0738 

No Northeast Male Above Poverty Level 55-64 0.0469 0.0085 0.0328 0.0667 

No Northeast Male Above Poverty Level 65-74 0.0641 0.0105 0.0463 0.0880 

No Northeast Male Above Poverty Level 75+ 0.0527 0.0110 0.0348 0.0789 

No Northeast Male Below Poverty Level 18-24 0.0780 0.0129 0.0562 0.1075 

No Northeast Male Below Poverty Level 25-34 0.0847 0.0171 0.0566 0.1248 

No Northeast Male Below Poverty Level 35-44 0.0795 0.0212 0.0467 0.1322 

No Northeast Male Below Poverty Level 45-44 0.0798 0.0196 0.0489 0.1275 

No Northeast Male Below Poverty Level 55-64 0.1322 0.0492 0.0617 0.2608 

No Northeast Male Below Poverty Level 65-74 0.1055 0.0296 0.0600 0.1789 

No Northeast Male Below Poverty Level 75+ 0.0758 0.0247 0.0395 0.1406 

No South Female Above Poverty Level 18-24 0.0893 0.0090 0.0732 0.1086 

No South Female Above Poverty Level 25-34 0.0731 0.0064 0.0615 0.0866 

No South Female Above Poverty Level 35-44 0.0689 0.0051 0.0595 0.0797 

No South Female Above Poverty Level 45-44 0.0716 0.0049 0.0626 0.0818 

No South Female Above Poverty Level 55-64 0.0865 0.0064 0.0747 0.1000 

No South Female Above Poverty Level 65-74 0.0914 0.0090 0.0753 0.1105 

No South Female Above Poverty Level 75+ 0.0599 0.0072 0.0473 0.0756 

No South Female Below Poverty Level 18-24 0.0996 0.0119 0.0786 0.1254 
5C-28 

Appendix 5C, Attachment A, Table CA-4. Unsmoothed prevalence for adults “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age_grp Prevalence SE LowerCI UpperCI 

No South Female Below Poverty Level 25-34 0.0867 0.0079 0.0725 0.1035 

No South Female Below Poverty Level 35-44 0.1152 0.0113 0.0948 0.1393 

No South Female Below Poverty Level 45-44 0.1369 0.0123 0.1144 0.1629 

No South Female Below Poverty Level 55-64 0.1780 0.0173 0.1467 0.2144 

No South Female Below Poverty Level 65-74 0.1303 0.0152 0.1033 0.1631 

No South Female Below Poverty Level 75+ 0.0895 0.0118 0.0689 0.1154 

No South Male Above Poverty Level 18-24 0.0608 0.0079 0.0471 0.0782 

No South Male Above Poverty Level 25-34 0.0471 0.0053 0.0377 0.0587 

No South Male Above Poverty Level 35-44 0.0451 0.0048 0.0365 0.0556 

No South Male Above Poverty Level 45-44 0.0359 0.0040 0.0288 0.0446 

No South Male Above Poverty Level 55-64 0.0413 0.0055 0.0317 0.0535 

No South Male Above Poverty Level 65-74 0.0441 0.0057 0.0342 0.0567 

No South Male Above Poverty Level 75+ 0.0636 0.0097 0.0470 0.0855 

No South Male Below Poverty Level 18-24 0.0617 0.0086 0.0468 0.0810 

No South Male Below Poverty Level 25-34 0.0344 0.0064 0.0239 0.0494 

No South Male Below Poverty Level 35-44 0.0488 0.0109 0.0314 0.0751 

No South Male Below Poverty Level 45-44 0.0800 0.0131 0.0579 0.1097 

No South Male Below Poverty Level 55-64 0.0676 0.0122 0.0473 0.0957 

No South Male Below Poverty Level 65-74 0.0687 0.0129 0.0473 0.0987 

No South Male Below Poverty Level 75+ 0.0331 0.0083 0.0202 0.0539 

No West Female Above Poverty Level 18-24 0.0908 0.0143 0.0663 0.1231 

No West Female Above Poverty Level 25-34 0.0819 0.0070 0.0691 0.0968 

No West Female Above Poverty Level 35-44 0.0994 0.0090 0.0830 0.1186 

No West Female Above Poverty Level 45-44 0.0937 0.0095 0.0766 0.1141 

No West Female Above Poverty Level 55-64 0.1013 0.0087 0.0854 0.1197 

No West Female Above Poverty Level 65-74 0.1103 0.0114 0.0898 0.1347 

No West Female Above Poverty Level 75+ 0.0783 0.0092 0.0621 0.0982 

No West Female Below Poverty Level 18-24 0.0901 0.0135 0.0669 0.1202 

No West Female Below Poverty Level 25-34 0.0861 0.0111 0.0667 0.1105 

No West Female Below Poverty Level 35-44 0.1081 0.0143 0.0831 0.1394 

No West Female Below Poverty Level 45-44 0.1391 0.0179 0.1075 0.1781 

No West Female Below Poverty Level 55-64 0.1293 0.0164 0.1005 0.1648 

No West Female Below Poverty Level 65-74 0.1053 0.0166 0.0770 0.1425 

No West Female Below Poverty Level 75+ 0.1061 0.0162 0.0782 0.1424 

No West Male Above Poverty Level 18-24 0.0620 0.0104 0.0445 0.0858 

No West Male Above Poverty Level 25-34 0.0528 0.0068 0.0410 0.0679 

No West Male Above Poverty Level 35-44 0.0582 0.0061 0.0473 0.0715 

No West Male Above Poverty Level 45-44 0.0499 0.0065 0.0386 0.0642 

No West Male Above Poverty Level 55-64 0.0542 0.0072 0.0416 0.0702 

No West Male Above Poverty Level 65-74 0.0756 0.0102 0.0579 0.0982 

No West Male Above Poverty Level 75+ 0.0711 0.0133 0.0491 0.1019 

No West Male Below Poverty Level 18-24 0.0741 0.0132 0.0520 0.1046 

No West Male Below Poverty Level 25-34 0.0457 0.0097 0.0301 0.0689 

No West Male Below Poverty Level 35-44 0.0344 0.0089 0.0207 0.0568 

No West Male Below Poverty Level 45-44 0.1119 0.0198 0.0786 0.1570 

No West Male Below Poverty Level 55-64 0.0528 0.0137 0.0316 0.0870 

No West Male Below Poverty Level 65-74 0.1159 0.0336 0.0644 0.1996 

No West Male Below Poverty Level 75+ 0.0442 0.0131 0.0246 0.0781 
5C-29 

Appendix 5C, Attachment A, Figure CA-1. Unsmoothed prevalence and confidence intervals for children ‘EV

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 1. Raw asthma 'EVER' prevalence rates a

region=Northeast pov_rat=Above Pov

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 1

Figure 1. Raw asthma 'EVER' prevalence rates 

region=Northeast pov_rat=Below P

gender Female 
5C-30 

Appendix 5C, Attachment A, Figure CA-1, cont. Unsmoothed prevalence and confidence intervals for childreasthma.

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Raw asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 1. Raw asthma 'EVER' prevalence rates an

region=West pov_rat=Above Povert

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 1. Raw asthma 'EVER' prevalence rates an

region=West pov_rat=Below Povert

gender Female 
5C-31 

Appendix 5C, Attachment A, Figure CA-2. Unsmoothed prevalence and confidence intervals for children ‘ST

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Raw asthma 'STILL' prevalence rates an

region=Northeast pov_rat=Above Pove

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Raw asthma 'STILL' prevalence rates an

region=Northeast pov_rat=Below Pove

gender Female 
5C-32 

Appendix 5C, Attachment A, Figure CA-2, cont. Unsmoothed prevalence and confidence intervals for childreasthma.

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Raw asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Raw asthma 'STILL' prevalence rates a

region=West pov_rat=Above Pover

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Raw asthma 'STILL' prevalence rates and 

region=West pov_rat=Below Poverty 

gender Female 
5C-33 

Appendix 5C, Attachment A, Figure CA-3. Unsmoothed prevalence and confidence intervals for adults ‘EVE

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Raw adult asthma 'EVER' prevalence rates 

region=Northeast pov_rat=Above Pov

gender Female 

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Raw adult asthma 'EVER' prevalence rates 

region=Northeast pov_rat=Below Pov

gender Female 
5C-34 

Appendix 5C, Attachment A, Figure CA-3, cont. Unsmoothed prevalence and confidence intervals for adults asthma.

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Raw adult asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Raw adult asthma 'EVER' prevalence rate

region=West pov_rat=Above Pover

gender Female 

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Raw adult asthma 'EVER' prevalence rat

region=West pov_rat=Below Pove

gender Female 
5C-35 

Appendix 5C, Attachment A, Figure CA-4. Unsmoothed prevalence and confidence intervals for adults ‘STIL

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 5

Figure 4. Raw adult asthma 'STILL' prevalence rates a

region=Northeast pov_rat=Above Poverty 

gender Female M

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 5

Figure 4. Raw adult asthma 'STILL' prevalence rates a

region=Northeast pov_rat=Below Poverty 

gender Female M
5C-36 

Appendix 5C, Attachment A, Figure CA-4, cont. Unsmoothed prevalence and confidence intervals for adults asthma.

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Raw adult asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 4. Raw adult asthma 'STILL' prevalence rate

region=West pov_rat=Above Povert

gender Female 

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 4. Raw adult asthma 'STILL' prevalence rates a

region=West pov_rat=Below Poverty 

gender Female 
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 Stratification Variable -2 log likelihood DF 

1. logit(prob) = linear in age 1. none 288740115.1 2

1. logit(prob) = linear in age 2. gender 287062346.4 4

1. logit(prob) = linear in age 3. region 288120804.1 8

1. logit(prob) = linear in age 4. poverty 287385013.1 4

1. logit(prob) = linear in age 5. region, gender 286367652.6 16

1. logit(prob) = linear in age 6. region, poverty 286283543.6 16

1. logit(prob) = linear in age 7. gender, poverty 285696164.7 8

1. logit(prob) = linear in age 8. region, gender, poverty 284477928.1 32

2. logit(prob) = quadratic in age 1. none 286862135.1 3

2. logit(prob) = quadratic in age 2. gender 285098650.6 6

2. logit(prob) = quadratic in age 3. region 286207721.5 12

2. logit(prob) = quadratic in age 4. poverty 285352164 6

2. logit(prob) = quadratic in age 5. region, gender 284330346.1 24

2. logit(prob) = quadratic in age 6. region, poverty 284182547.5 24

2. logit(prob) = quadratic in age 7. gender, poverty 283587631.7 12

2. logit(prob) = quadratic in age 8. region, gender, poverty 282241318.6 48

3. logit(prob) = cubic in age 1. none 286227019.6 4

3. logit(prob) = cubic in age 2. gender 284470413 8

3. logit(prob) = cubic in age 3. region 285546716.1 16

3. logit(prob) = cubic in age 4. poverty 284688169.9 8

3. logit(prob) = cubic in age 5. region, gender 283662673.5 32

3. logit(prob) = cubic in age 6. region, poverty 283404487.5 32

3. logit(prob) = cubic in age 7. gender, poverty 282890785.3 16

3. logit(prob) = cubic in age 8. region, gender, poverty 281407414.3 64

4. logit(prob) = f(age) 1. none 285821686.2 18

4. logit(prob) = f(age) 2. gender 283843266.2 36

4. logit(prob) = f(age) 3. region 284761522.8 72

4. logit(prob) = f(age) 4. poverty 284045849.2 36

4. logit(prob) = f(age) 5. region, gender 282099156.1 144

4. logit(prob) = f(age) 6. region, poverty 281929968.5 144

4. logit(prob) = f(age) 7. gender, poverty 281963915.7 72

4. logit(prob) = f(age) 8. region, gender, poverty 278655423.1 288

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 Stratification Variable -2 log likelihood DF 

1. logit(prob) = linear in age 1. none 181557347.7 2 

1. logit(prob) = linear in age 2. gender 180677544.6 4 

1. logit(prob) = linear in age 3. region 180947344.2 8 

1. logit(prob) = linear in age 4. poverty 180502490.5 4 

1. logit(prob) = linear in age 5. region, gender 179996184.8 16 

1. logit(prob) = linear in age 6. region, poverty 179517528 16 

1. logit(prob) = linear in age 7. gender, poverty 179637601.4 8 

1. logit(prob) = linear in age 8. region, gender, poverty 178567573.9 32 

2. logit(prob) = quadratic in age 1. none 180752073.1 3 

2. logit(prob) = quadratic in age 2. gender 179771977.6 6 

2. logit(prob) = quadratic in age 3. region 180088080.5 12 

2. logit(prob) = quadratic in age 4. poverty 179611530.4 6 

2. logit(prob) = quadratic in age 5. region, gender 179004935.6 24 

2. logit(prob) = quadratic in age 6. region, poverty 178519078.1 24 

2. logit(prob) = quadratic in age 7. gender, poverty 178640744.8 12 

2. logit(prob) = quadratic in age 8. region, gender, poverty 177414967.2 48 

3. logit(prob) = cubic in age 1. none 180247874.1 4 

3. logit(prob) = cubic in age 2. gender 179235170 8 

3. logit(prob) = cubic in age 3. region 179583725.1 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 Stratification Variable -2 log likelihood DF 

3. logit(prob) = cubic in age 4. poverty 179067549.2 8 

3. logit(prob) = cubic in age 5. region, gender 178407915.7 32 

3. logit(prob) = cubic in age 6. region, poverty 177897359.3 32 

3. logit(prob) = cubic in age 7. gender, poverty 178029240 16 

3. logit(prob) = cubic in age 8. region, gender, poverty 176642073.7 64 

4. logit(prob) = f(age) 1. none 179972765.3 18 

4. logit(prob) = f(age) 2. gender 178918713.8 36 

4. logit(prob) = f(age) 3. region 178852704.9 72 

4. logit(prob) = f(age) 4. poverty 178599743.4 36 

4. logit(prob) = f(age) 5. region, gender 177075815.4 144 

4. logit(prob) = f(age) 6. region, poverty 176418872.7 144 

4. logit(prob) = f(age) 7. gender, poverty 177422457.4 72 

4. logit(prob) = f(age) 8. region, gender, poverty 173888684.9 288 

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 Stratification Variable -2 log likelihood DF 

4. logit(prob) = f(age_grp) 1. none 825494282 7 

4. logit(prob) = f(age_grp) 2. gender 821614711.2 14 

4. logit(prob) = f(age_grp) 3. region 824598583.4 28 

4. logit(prob) = f(age_grp) 4. poverty 823443004.3 14 

4. logit(prob) = f(age_grp) 5. region, gender 820520390.7 56 

4. logit(prob) = f(age_grp) 6. region, poverty 821958349.1 56 

4. logit(prob) = f(age_grp) 7. gender, poverty 819560679.9 28 

4. logit(prob) = f(age_grp) 8. region, gender, poverty 817723710 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 Stratification Variable -2 log likelihood DF 

4. logit(prob) = f(age_grp) 1. none 600538044.1 7

4. logit(prob) = f(age_grp) 2. gender 594277797.3 14

4. logit(prob) = f(age_grp) 3. region 599561222.3 28

4. logit(prob) = f(age_grp) 4. poverty 597511872.6 14

4. logit(prob) = f(age_grp) 5. region, gender 593112157.6 56

4. logit(prob) = f(age_grp) 6. region, poverty 596008068.6 56

4. logit(prob) = f(age_grp) 7. gender, poverty 591394271.8 28

4. logit(prob) = f(age_grp) 8. region, gender, poverty 589398969.5 112

 
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 Gender Poverty Ratio Smoothing Parameter Residual Standard Error 

South Female Above Poverty Level 0.5 0.999919 

Northeast Female Above Poverty Level 0.7 1.00088 

South Male Above Poverty Level 0.6 1.003839 

Midwest Male Above Poverty Level 0.9 1.00548 

Midwest Male Below Poverty Level 0.8 1.010889 

South Female Above Poverty Level 0.8 1.012178 

South Male Above Poverty Level 0.5 0.982885 

Midwest Male Above Poverty Level 1 1.023284 

West Female Below Poverty Level 0.7 0.973279 

South Female Above Poverty Level 0.7 0.97298 

Midwest Female Above Poverty Level 0.7 1.028007 

Midwest Male Above Poverty Level 0.4 0.970948 

Midwest Male Above Poverty Level 0.8 0.965591 

Midwest Female Above Poverty Level 0.6 1.038233 

Northeast Female Above Poverty Level 0.4 0.961444 

South Male Above Poverty Level 0.7 1.040867 

South Female Above Poverty Level 0.6 0.954946 

Midwest Female Above Poverty Level 0.8 1.045107 

West Male Above Poverty Level 0.6 1.052418 

Northeast Female Above Poverty Level 0.6 0.946315 

South Female Below Poverty Level 0.5 0.945525 

Northeast Female Above Poverty Level 0.8 1.054556 

Midwest Male Above Poverty Level 0.7 0.940657 

Northeast Female Above Poverty Level 0.5 0.940383 

Midwest Male Below Poverty Level 0.9 1.063971 

West Female Below Poverty Level 0.8 1.066819 

West Male Above Poverty Level 0.5 1.067075 

South Female Above Poverty Level 0.9 1.067923 

South Female Below Poverty Level 0.4 0.930104 

Midwest Male Below Poverty Level 0.7 0.929292 

Midwest Female Above Poverty Level 0.9 1.072631 

South Male Below Poverty Level 0.6 0.927161 

Northeast Female Above Poverty Level 0.9 1.074984 

Midwest Male Above Poverty Level 0.5 0.917969 

South Male Below Poverty Level 0.7 0.912266 

South Female Above Poverty Level 0.4 1.089646 

Midwest Male Above Poverty Level 0.6 0.90827 

Midwest Male Below Poverty Level 0.4 0.906073 

Midwest Male Below Poverty Level 1 1.094737 

Midwest Female Above Poverty Level 0.5 1.096459 

South Male Above Poverty Level 0.8 1.099725 

South Male Below Poverty Level 0.5 0.898228 

Northeast Female Above Poverty Level 1 1.101884 

South Male Below Poverty Level 1 0.896985 

Midwest Female Above Poverty Level 1 1.103976 

West Male Below Poverty Level 0.4 0.894137 

South Male Below Poverty Level 0.8 0.893364 

South Female Below Poverty Level 0.6 0.891551 

South Male Below Poverty Level 0.9 0.890138 

West Female Below Poverty Level 0.9 1.111538 

South Male Above Poverty Level 0.4 0.885511 

West Male Above Poverty Level 0.4 1.115223 

South Female Below Poverty Level 0.7 0.86999 

Northeast Male Below Poverty Level 0.6 0.86934 

Midwest Male Below Poverty Level 0.6 0.86245 

Midwest Male Below Poverty Level 0.5 0.857982 

South Female Below Poverty Level 0.8 0.857778 

Northeast Male Below Poverty Level 0.5 0.857592 

West Female Below Poverty Level 0.6 0.852664 

West Female Below Poverty Level 1 1.147894 
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 Gender Poverty Ratio Smoothing Parameter Residual Standard Error 

South Female Below Poverty Level 1 0.849143 

South Female Below Poverty Level 0.9 0.847567 

Northeast Male Below Poverty Level 0.7 0.844668 

West Male Above Poverty Level 0.7 1.163749 

West Female Above Poverty Level 0.9 1.163943 

West Female Above Poverty Level 0.8 1.166005 

South Male Below Poverty Level 0.4 0.826195 

West Female Above Poverty Level 0.7 1.174564 

West Female Above Poverty Level 1 1.178045 

South Male Above Poverty Level 0.9 1.178803 

Northeast Male Below Poverty Level 0.8 0.820245 

South Female Above Poverty Level 1 1.182254 

West Female Above Poverty Level 0.6 1.187757 

Northeast Male Below Poverty Level 1 0.811815 

West Female Below Poverty Level 0.5 0.808706 

Northeast Male Below Poverty Level 0.9 0.805685 

West Male Below Poverty Level 1 0.804743 

Midwest Female Below Poverty Level 1 0.799988 

Northeast Male Above Poverty Level 1 0.799128 

Northeast Male Above Poverty Level 0.7 0.798212 

Midwest Female Above Poverty Level 0.4 1.20612 

West Male Below Poverty Level 0.5 0.793132 

Midwest Female Below Poverty Level 0.9 0.788082 

Northeast Male Above Poverty Level 0.6 0.78547 

South Male Above Poverty Level 1 1.216423 

Northeast Male Above Poverty Level 0.8 0.78144 

West Male Below Poverty Level 0.9 0.780843 

Northeast Male Above Poverty Level 0.9 0.779772 

West Female Above Poverty Level 0.5 1.224495 

Northeast Male Below Poverty Level 0.4 0.769037 

West Male Below Poverty Level 0.6 0.763027 

West Female Below Poverty Level 0.4 0.762134 

Midwest Female Below Poverty Level 0.8 0.758775 

West Male Below Poverty Level 0.8 0.756848 

West Male Below Poverty Level 0.7 0.752592 

Northeast Male Above Poverty Level 0.5 0.729776 

West Male Above Poverty Level 0.8 1.284153 

Northeast Female Below Poverty Level 0.8 1.292845 

Northeast Female Below Poverty Level 0.7 1.296274 

Northeast Female Below Poverty Level 0.9 1.308752 

Northeast Female Below Poverty Level 0.6 1.309671 

Midwest Female Below Poverty Level 0.7 0.688366 

Northeast Female Below Poverty Level 0.5 1.314991 

West Female Above Poverty Level 0.4 1.31595 

Northeast Female Below Poverty Level 1 1.327129 

West Male Above Poverty Level 0.9 1.35931 

Northeast Female Below Poverty Level 0.4 1.37577 

Northeast Male Above Poverty Level 0.4 0.618785 

Midwest Female Below Poverty Level 0.6 0.607758 

West Male Above Poverty Level 1 1.395061 

Midwest Female Below Poverty Level 0.5 0.541466 

Midwest Female Below Poverty Level 0.4 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 Gender Poverty Ratio 

Smoothing 

Parameter Residual Standard Error 

South Female Above Poverty Level 1 1.000117 

Northeast Male Above Poverty Level 0.9 1.000909 

Northeast Male Below Poverty Level 0.7 1.000993 

Northeast Male Below Poverty Level 0.9 0.997502 

Northeast Male Below Poverty Level 0.4 0.997275 

Midwest Male Above Poverty Level 0.7 0.996943 

Midwest Male Above Poverty Level 0.8 0.996544 

Midwest Female Above Poverty Level 1 1.003498 

Midwest Male Above Poverty Level 0.6 0.995815 

Northeast Male Below Poverty Level 0.8 0.995723 

South Male Below Poverty Level 0.6 1.007198 

Midwest Female Above Poverty Level 0.5 0.99235 

Northeast Male Above Poverty Level 0.5 1.008536 

South Female Above Poverty Level 0.4 0.99041 

Northeast Male Below Poverty Level 0.6 1.009859 

Northeast Male Below Poverty Level 0.5 1.01048 

Northeast Male Above Poverty Level 0.8 1.011028 

Midwest Male Above Poverty Level 0.9 1.011038 

South Female Above Poverty Level 0.6 1.013156 

Northeast Male Above Poverty Level 1 1.01445 

Northeast Male Below Poverty Level 1 1.016505 

Midwest Male Above Poverty Level 0.5 1.01692 

Midwest Female Above Poverty Level 0.9 0.979917 

Midwest Male Above Poverty Level 1 1.020707 

Northeast Male Above Poverty Level 0.7 1.021388 

Midwest Male Below Poverty Level 0.7 0.977074 

South Female Above Poverty Level 0.7 0.976479 

Northeast Male Above Poverty Level 0.6 1.024042 

South Male Below Poverty Level 1 0.975784 

West Male Below Poverty Level 0.9 1.025093 

South Male Below Poverty Level 0.5 1.026184 

South Male Below Poverty Level 0.7 0.971057 

South Female Above Poverty Level 0.8 0.965833 

South Female Above Poverty Level 0.9 0.965238 

West Male Below Poverty Level 0.8 1.03481 

South Male Below Poverty Level 0.9 0.964953 

West Female Below Poverty Level 0.7 1.036384 

West Female Above Poverty Level 1 1.040924 

South Male Below Poverty Level 0.8 0.957162 

West Female Above Poverty Level 0.9 1.044522 

Midwest Male Below Poverty Level 0.8 1.04601 

West Male Below Poverty Level 0.7 1.04802 

West Male Below Poverty Level 1 1.050309 

Midwest Female Above Poverty Level 0.8 0.946142 

Northeast Female Above Poverty Level 0.4 0.94543 

West Female Above Poverty Level 0.8 1.055218 

Midwest Female Above Poverty Level 0.6 0.938888 

West Male Above Poverty Level 0.7 1.063545 

South Female Above Poverty Level 0.5 1.063816 

Midwest Female Above Poverty Level 0.7 0.931681 

West Male Below Poverty Level 0.6 1.079146 

Midwest Male Above Poverty Level 0.4 1.080605 

Northeast Female Above Poverty Level 0.5 1.083479 

West Female Above Poverty Level 0.7 1.084472 

Midwest Male Below Poverty Level 0.9 1.084476 

Midwest Female Below Poverty Level 0.9 0.914962 

Midwest Female Below Poverty Level 1 0.913089 

South Male Below Poverty Level 0.4 1.087093 

Midwest Female Below Poverty Level 0.8 0.912722 
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 Gender Poverty Ratio 

Smoothing 

Parameter Residual Standard Error 

West Female Below Poverty Level 0.6 0.912605 

Midwest Male Below Poverty Level 0.6 0.907737 

Midwest Male Below Poverty Level 1 1.103127 

Northeast Female Above Poverty Level 0.6 1.103286 

South Male Above Poverty Level 0.4 1.112998 

Midwest Male Below Poverty Level 0.5 0.878223 

West Female Above Poverty Level 0.6 1.124127 

Midwest Female Below Poverty Level 0.7 0.875579 

Northeast Male Above Poverty Level 0.4 0.874469 

West Female Below Poverty Level 0.5 0.873529 

West Male Below Poverty Level 0.5 1.127032 

South Female Below Poverty Level 0.6 0.87206 

Midwest Male Below Poverty Level 0.4 0.869726 

Midwest Female Above Poverty Level 0.4 1.135372 

West Female Below Poverty Level 0.8 1.136048 

South Female Below Poverty Level 1 0.863066 

Northeast Female Above Poverty Level 0.7 1.140006 

South Female Below Poverty Level 0.5 0.858107 

Northeast Female Above Poverty Level 0.9 1.147352 

Northeast Female Above Poverty Level 1 1.148471 

West Male Below Poverty Level 0.4 1.152015 

Northeast Female Above Poverty Level 0.8 1.153553 

West Male Above Poverty Level 0.4 0.845979 

South Female Below Poverty Level 0.7 0.842335 

West Male Above Poverty Level 0.6 0.8413 

South Female Below Poverty Level 0.9 0.841106 

West Female Above Poverty Level 0.5 1.166931 

South Female Below Poverty Level 0.8 0.830955 

West Female Below Poverty Level 0.4 0.826586 

West Female Below Poverty Level 0.9 1.183444 

West Male Above Poverty Level 0.5 0.815615 

Midwest Female Below Poverty Level 0.6 0.802622 

West Female Below Poverty Level 1 1.20757 

Midwest Female Below Poverty Level 0.4 0.78769 

South Male Above Poverty Level 0.5 1.214019 

South Male Above Poverty Level 0.6 1.216661 

South Female Below Poverty Level 0.4 0.781555 

South Male Above Poverty Level 0.7 1.242272 

West Female Above Poverty Level 0.4 1.252141 

West Male Above Poverty Level 0.8 1.254244 

Midwest Female Below Poverty Level 0.5 0.742493 

South Male Above Poverty Level 0.8 1.294055 

Northeast Female Below Poverty Level 0.7 1.32003 

Northeast Female Below Poverty Level 0.6 1.355219 

West Male Above Poverty Level 0.9 1.356792 

South Male Above Poverty Level 0.9 1.365737 

Northeast Female Below Poverty Level 0.8 1.39015 

West Male Above Poverty Level 1 1.405599 

South Male Above Poverty Level 1 1.408469 

Northeast Female Below Poverty Level 0.5 1.431367 

Northeast Female Below Poverty Level 0.9 1.503674 

Northeast Female Below Poverty Level 1 1.574778 

Northeast Female Below Poverty Level 0.4 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 Gender Poverty Ratio Smoothing Parameter Residual Standard Error 

Midwest Female Above Poverty Level 1 0.983356 

South Female Below Poverty Level 1 1.040607 

West Female Below Poverty Level 0.9 1.044712 

West Male Above Poverty Level 0.8 0.937658 

South Female Above Poverty Level 1 1.06598 

Midwest Female Above Poverty Level 0.9 0.911278 

West Male Below Poverty Level 0.8 1.095844 

West Female Below Poverty Level 0.8 0.893319 

West Male Above Poverty Level 0.9 0.886119 

Northeast Female Above Poverty Level 1 0.875056 

West Male Above Poverty Level 1 0.858542 

Midwest Female Above Poverty Level 0.8 0.843191 

Northeast Male Above Poverty Level 0.8 1.177547 

South Male Below Poverty Level 1 0.813689 

Midwest Male Above Poverty Level 0.9 1.190978 

Midwest Male Below Poverty Level 1 0.785268 

South Female Above Poverty Level 0.9 0.77381 

Northeast Male Above Poverty Level 1 1.241548 

South Female Above Poverty Level 0.8 0.751726 

South Female Below Poverty Level 0.9 0.747912 

South Female Below Poverty Level 0.8 0.740577 

Northeast Male Below Poverty Level 1 0.732859 

West Female Below Poverty Level 1 1.275049 

South Male Above Poverty Level 0.9 0.708509 

South Male Above Poverty Level 1 0.706944 

Northeast Female Above Poverty Level 0.9 0.699107 

Northeast Male Above Poverty Level 0.9 1.301543 

Northeast Male Below Poverty Level 0.9 0.677309 

West Female Above Poverty Level 1 0.669638 

Northeast Female Below Poverty Level 1 0.662619 

Northeast Male Below Poverty Level 0.8 0.646318 

South Male Below Poverty Level 0.9 0.64328 

Midwest Male Above Poverty Level 1 1.395026 

West Female Above Poverty Level 0.8 0.597305 

South Male Below Poverty Level 0.8 0.58427 

West Female Above Poverty Level 0.9 0.567466 

Northeast Female Above Poverty Level 0.8 0.528031 

Midwest Male Below Poverty Level 0.9 0.49517 

West Male Below Poverty Level 0.9 1.523816 

West Male Below Poverty Level 1 1.537805 

South Male Above Poverty Level 0.8 0.400237 

Northeast Female Below Poverty Level 0.9 0.394894 

Northeast Female Below Poverty Level 0.8 0.362058 

Midwest Male Below Poverty Level 0.8 0.306085 

Midwest Male Above Poverty Level 0.8 0.169594 

Midwest Female Below Poverty Level 1 1.910643 

Midwest Female Below Poverty Level 0.9 1.920542 

Midwest Female Below Poverty Level 0.8 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 Gender Poverty Ratio Smoothing Parameter Residual Standard Error 

South Male Below Poverty Level 0.8 1.015193 

West Female Above Poverty Level 0.8 1.045714 

West Female Above Poverty Level 0.9 1.051807 

West Female Above Poverty Level 1 1.061488 

West Male Above Poverty Level 1 0.92928 

West Male Above Poverty Level 0.8 0.925921 

West Male Above Poverty Level 0.9 0.915895 

South Female Below Poverty Level 0.9 1.097531 

Midwest Female Above Poverty Level 1 0.89825 

Northeast Female Below Poverty Level 1 1.102905 

Midwest Female Above Poverty Level 0.9 0.876146 

South Female Below Poverty Level 0.8 1.128781 

Midwest Female Above Poverty Level 0.8 0.870507 

South Female Above Poverty Level 1 1.130393 

South Female Above Poverty Level 0.9 0.835583 

West Female Below Poverty Level 1 0.825684 

South Male Below Poverty Level 0.9 1.192655 

Midwest Male Below Poverty Level 1 0.788217 

Northeast Female Below Poverty Level 0.9 0.786205 

Northeast Male Above Poverty Level 1 1.21537 

South Female Below Poverty Level 1 1.23752 

South Male Above Poverty Level 0.9 0.748499 

South Male Above Poverty Level 0.8 0.717121 

West Female Below Poverty Level 0.9 0.670751 

South Male Above Poverty Level 1 0.664236 

Northeast Female Below Poverty Level 0.8 0.65848 

Northeast Female Above Poverty Level 1 0.653985 

Midwest Male Above Poverty Level 1 0.650735 

Northeast Female Above Poverty Level 0.9 0.630298 

Northeast Male Above Poverty Level 0.9 1.370134 

Northeast Male Above Poverty Level 0.8 1.375365 

Midwest Male Below Poverty Level 0.9 0.620174 

South Male Below Poverty Level 1 1.400273 

Northeast Male Below Poverty Level 1 0.581032 

South Female Above Poverty Level 0.8 0.568428 

Midwest Male Above Poverty Level 0.9 0.508247 

Midwest Male Below Poverty Level 0.8 0.503315 

Northeast Female Above Poverty Level 0.8 0.478186 

West Female Below Poverty Level 0.8 0.464598 

Northeast Male Below Poverty Level 0.9 0.453855 

Northeast Male Below Poverty Level 0.8 0.396203 

Midwest Female Below Poverty Level 1 1.616706 

Midwest Female Below Poverty Level 0.9 1.636938 

Midwest Male Above Poverty Level 0.8 0.295923 

Midwest Female Below Poverty Level 0.8 1.883863 

West Male Below Poverty Level 0.8 2.16547 

West Male Below Poverty Level 1 2.200364 

West Male Below Poverty Level 0.9 2.396381 
5C-45 

Appendix 5C, Attachment B, Figure 5CB-1. Normal probability plots of studentized residuals generated using logisti

asthmatic data set.

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=0.4

0.1 1 5 10 25 50 75 90 95 99 99.9

-3-2-1

012

student

Normal Percentiles

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=0.5

0.1 1 5 10 25 50 75 90 95 99 99.9

-3-2-1

012

student

Normal Percentiles

Normal probability plot of studentized resi

All genders, regions, poverty 

SmoothingParamete

0.1 1 5 10 25 50 

-3-2-1

012

student

Normal Perc

Normal probability plot of studentized resi

All genders, regions, poverty 

SmoothingParamete

0.1 1 5 10 25 50 

-3-2-1

0123

student

Normal Perc
5C-46 

Appendix 5C, Attachment B, Figure 5CB-1, cont. Normal probability plots of studentized residuals generated using l‘EVER’ asthmatic data set.

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=0.8

0.1 1 5 10 25 50 75 90 95 99 99.9

-3-2-1

0123

student

Normal Percentiles

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=1

0.1 1 5 10 25 50 75 90 95 99 99.9

-4-3-2-1

01234

student

Normal Percentiles

Normal probability plot of studentized resi

All genders, regions, poverty 

SmoothingParamete

0.1 1 5 10 25 50 

-4-3-2-1

01234

student

Normal Perc
5C-47 

Appendix 5C, Attachment B, Figure 5CB-2. Normal probability plots of studentized residuals generated using logisti‘STILL’ asthmatic data set.

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=0.4

0.1 1 5 10 25 50 75 90 95 99 99.9

-3-2-1

012

student

Normal Percentiles

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=0.5

0.1 1 5 10 25 50 75 90 95 99 99.9

-3-2-1

012

student

Normal Percentiles

Normal probability plot of studentized residua

All genders, regions, poverty rat

SmoothingParameter=

0.1 1 5 10 25 50 

-3-2-1

012

student

Normal Percent

Normal probability plot of studentized residua

All genders, regions, poverty rat

SmoothingParameter=

0.1 1 5 10 25 50 

-4-3-2-1

0123

student

Normal Percent
5C-48 

Appendix 5C, Attachment B, Figure 5CB-2, cont. Normal probability plots of studentized residuals generated using l

‘STILL’ asthmatic data set.

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=0.8

0.1 1 5 10 25 50 75 90 95 99 99.9

-4-3-2-1

01234

student

Normal Percentiles

Normal probability plot of studentized residuals by smoothing parameter

All genders, regions, poverty ratios combined

SmoothingParameter=0.9

0.1 1 5 10 25 50 75 90 95 99 99.9

-6-4-2

024

student

Normal Percentiles

Normal probability plot of studentized residua

All genders, regions, poverty rat

SmoothingParameter=

0.1 1 5 10 25 50 

-6-4-2

024

student

Normal Percent
5C-49 

Appendix 5C, Attachment B, Figure 5CB-3. Normal probability plots of studentized residuals generated using logisti

asthmatic data set. 

Normal probability plot of studentized residuals by smoothing parameter

Adults: All genders, regions, poverty ratios combined

SmoothingParameter=0.8

0.1 1 5 10 25 50 75 90 95 99 99.9

-2.0-1.5-1.0-0.5

0

0.51.01.5

student

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

-2-1

012

student

Normal Percentiles

Normal probability plot of studentized residua

Adults: All genders, regions, poverty 

SmoothingParameter=

0.1 1 5 10 25 50 

-2-1

0123

student

Normal Percent
5C-50 

Appendix 5C, Attachment B, Figure 5CB-4. Normal probability plots of studentized residuals generated using logisti

asthmatic data set.

Normal probability plot of studentized residuals by smoothing parameter

Adults Still: All genders, regions, poverty ratios combined

SmoothingParameter=0.8

0.1 1 5 10 25 50 75 90 95 99 99.9

-2.0-1.5-1.0-0.5

0

0.51.01.52.0

student

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

-3-2-1

012

student

Normal Percentiles

Normal probability plot of studentized residua

Adults Still: All genders, regions, pove

SmoothingParameter=

0.1 1 5 10 25 50 

-3-2-1

0123

student

Normal Percent
5C-51 

Appendix 5C, Attachment B, Figure 5CB-5. Studentized residuals generated using logistic model versus model predi

‘EVER’ asthmatic data set.

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of prevalence rates by smoothing parameter

SmoothingParameter=0.4

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of prevalence rates by smoothing parameter

SmoothingParameter=0.5

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted logit

-5.00000 -4.00000 -3.00000 

Studentized residual versus smoothed logits of preval

SmoothingParameter=

reggendpov 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

student

-5-4-3-2-1

012345

Predicted logit

-5.00000 -4.00000 -3.00000 

Studentized residual versus smoothed logits of preval

SmoothingParameter=

reggendpov 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
5C-52 

Appendix 5C, Attachment B, Figure 5CB-5, cont. Studentized residuals generated using logistic model versus model 

‘EVER’ asthmatic data set.

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of prevalence rates by smoothing parameter

SmoothingParameter=0.8

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevel

Midwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of prevalence rates by smoothing parameter

SmoothingParameter=0.9

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevel

Midwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted lo

-5.00000 -4.00000 -3.00000 

Studentized residual versus smoothed logits of pre

SmoothingParame

reggendpov 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
5C-53 

Appendix 5C, Attachment B, Figure 5CB-6. Studentized residuals generated using logistic model versus model predi

‘STILL’ asthmatic data set.

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter

SmoothingParameter=0.4

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter

SmoothingParameter=0.5

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted logit

-5.00000 -4.00000 -3.00000 

Studentized residual versus smoothed logits of still prev

SmoothingParameter=

reggendpov 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

student

-5-4-3-2-1

012345

Predicted logit

-5.00000 -4.00000 -3.00000 

Studentized residual versus smoothed logits of still prev

SmoothingParameter=

reggendpov 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
5C-54 

Appendix 5C, Attachment B, Figure 5CB-6, cont. Studentized residuals generated using logistic model versus model ‘STILL’ asthmatic data set.

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter

SmoothingParameter=0.8

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted logitprev

-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of still prevalence rates by smoothing parameter

SmoothingParameter=0.9

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-5-4-3-2-1

012345

Predicted logit

-5.00000 -4.00000 -3.00000 

Studentized residual versus smoothed logits of still prev

SmoothingParameter=

reggendpov 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
5C-55 

Appendix 5C, Attachment B, Figure 5CB-7. Studentized residuals generated using logistic model versus model predi

‘EVER’ asthmatic data set. 

student

-3-2-1

0123

Predicted logitprev

-3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameter

SmoothingParameter=0.8

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevel

Midwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-3-2-1

0123

Predicted logitprev

-3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameter

SmoothingParameter=0.9

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevel

Midwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-3-2-1

0123

Predicted lo

-3.00000 -2.00000 

Studentized residual versus smoothed logits of adult p

SmoothingParame

reggendpov 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
5C-56 

Appendix 5C, Attachment B, Figure 5CB-8. Studentized residuals generated using logistic model versus model predi

‘STILL’ asthmatic data set.

student

-3-2-1

0123

Predicted logitprev

-4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameter

SmoothingParameter=0.8

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevel

Midwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-3-2-1

0123

Predicted logitprev

-4.00000 -3.00000 -2.00000 -1.00000 0

Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameter

SmoothingParameter=0.9

reggendpov All: LOESS Smoothed Midwest-Female-AbovePovertyLev

Midwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevel

Midwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevel

South-Male-BelowPovertyLevel West-Female-AbovePovertyLevel

West-Female-BelowPovertyLevel West-Male-AbovePovertyLevel

West-Male-BelowPovertyLevel

student

-3-2-1

0123

Predicted lo

-4.00000 -3.00000 -2.000

Studentized residual versus smoothed logits of adult stil

SmoothingParame

reggendpov 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
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 Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes Midwest Female Above Poverty Level 0 0.0083 0.0050 0.0022 0.0310 

Yes Midwest Female Above Poverty Level 1 0.0179 0.0066 0.0079 0.0397 

Yes Midwest Female Above Poverty Level 2 0.0327 0.0076 0.0195 0.0541 

Yes Midwest Female Above Poverty Level 3 0.0509 0.0096 0.0336 0.0766 

Yes Midwest Female Above Poverty Level 4 0.0671 0.0122 0.0448 0.0993 

Yes Midwest Female Above Poverty Level 5 0.0854 0.0134 0.0602 0.1198 

Yes Midwest Female Above Poverty Level 6 0.0995 0.0141 0.0725 0.1351 

Yes Midwest Female Above Poverty Level 7 0.1041 0.0145 0.0765 0.1403 

Yes Midwest Female Above Poverty Level 8 0.1024 0.0132 0.0769 0.1352 

Yes Midwest Female Above Poverty Level 9 0.1020 0.0121 0.0784 0.1317 

Yes Midwest Female Above Poverty Level 10 0.1055 0.0127 0.0806 0.1369 

Yes Midwest Female Above Poverty Level 11 0.1192 0.0137 0.0922 0.1527 

Yes Midwest Female Above Poverty Level 12 0.1390 0.0163 0.1070 0.1787 

Yes Midwest Female Above Poverty Level 13 0.1529 0.0176 0.1182 0.1956 

Yes Midwest Female Above Poverty Level 14 0.1603 0.0176 0.1254 0.2026 

Yes Midwest Female Above Poverty Level 15 0.1597 0.0160 0.1277 0.1979 

Yes Midwest Female Above Poverty Level 16 0.1517 0.0161 0.1197 0.1903 

Yes Midwest Female Above Poverty Level 17 0.1374 0.0229 0.0945 0.1956 

Yes Midwest Female Below Poverty Level 0 0.0413 0.0168 0.0167 0.0985 

Yes Midwest Female Below Poverty Level 1 0.0706 0.0168 0.0416 0.1174 

Yes Midwest Female Below Poverty Level 2 0.1047 0.0173 0.0724 0.1491 

Yes Midwest Female Below Poverty Level 3 0.1356 0.0208 0.0962 0.1879 

Yes Midwest Female Below Poverty Level 4 0.1553 0.0237 0.1100 0.2146 

Yes Midwest Female Below Poverty Level 5 0.1488 0.0229 0.1053 0.2062 

Yes Midwest Female Below Poverty Level 6 0.1327 0.0228 0.0902 0.1910 

Yes Midwest Female Below Poverty Level 7 0.1341 0.0224 0.0920 0.1912 

Yes Midwest Female Below Poverty Level 8 0.1535 0.0239 0.1080 0.2136 

Yes Midwest Female Below Poverty Level 9 0.1729 0.0270 0.1215 0.2401 

Yes Midwest Female Below Poverty Level 10 0.1861 0.0311 0.1272 0.2640 

Yes Midwest Female Below Poverty Level 11 0.1691 0.0300 0.1131 0.2451 

Yes Midwest Female Below Poverty Level 12 0.1470 0.0247 0.1006 0.2097 

Yes Midwest Female Below Poverty Level 13 0.1439 0.0239 0.0990 0.2045 

Yes Midwest Female Below Poverty Level 14 0.1541 0.0244 0.1078 0.2156 

Yes Midwest Female Below Poverty Level 15 0.1707 0.0275 0.1186 0.2395 

Yes Midwest Female Below Poverty Level 16 0.1962 0.0427 0.1187 0.3065 

Yes Midwest Female Below Poverty Level 17 0.2323 0.0813 0.1002 0.4512 

Yes Midwest Male Above Poverty Level 0 0.0133 0.0066 0.0045 0.0391 

Yes Midwest Male Above Poverty Level 1 0.0313 0.0091 0.0164 0.0588 

Yes Midwest Male Above Poverty Level 2 0.0585 0.0102 0.0398 0.0851 

Yes Midwest Male Above Poverty Level 3 0.0898 0.0121 0.0666 0.1200 

Yes Midwest Male Above Poverty Level 4 0.1111 0.0145 0.0831 0.1471 

Yes Midwest Male Above Poverty Level 5 0.1256 0.0149 0.0964 0.1621 

Yes Midwest Male Above Poverty Level 6 0.1411 0.0158 0.1100 0.1793 

Yes Midwest Male Above Poverty Level 7 0.1496 0.0164 0.1171 0.1892 

Yes Midwest Male Above Poverty Level 8 0.1502 0.0161 0.1182 0.1891 

Yes Midwest Male Above Poverty Level 9 0.1542 0.0166 0.1211 0.1942 

Yes Midwest Male Above Poverty Level 10 0.1627 0.0173 0.1283 0.2041 

Yes Midwest Male Above Poverty Level 11 0.1760 0.0181 0.1397 0.2193 

Yes Midwest Male Above Poverty Level 12 0.1876 0.0186 0.1501 0.2319 

Yes Midwest Male Above Poverty Level 13 0.1847 0.0181 0.1483 0.2277 

Yes Midwest Male Above Poverty Level 14 0.1764 0.0170 0.1422 0.2167 

Yes Midwest Male Above Poverty Level 15 0.1641 0.0149 0.1341 0.1994 

Yes Midwest Male Above Poverty Level 16 0.1487 0.0144 0.1198 0.1833 

Yes Midwest Male Above Poverty Level 17 0.1318 0.0201 0.0937 0.1823 

Yes Midwest Male Below Poverty Level 0 0.0429 0.0176 0.0173 0.1026 

Yes Midwest Male Below Poverty Level 1 0.0908 0.0214 0.0536 0.1498 

Yes Midwest Male Below Poverty Level 2 0.1530 0.0235 0.1084 0.2118 
5C-58 

Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes Midwest Male Below Poverty Level 3 0.2110 0.0277 0.1566 0.2780 

Yes Midwest Male Below Poverty Level 4 0.2428 0.0303 0.1828 0.3150 

Yes Midwest Male Below Poverty Level 5 0.2458 0.0285 0.1888 0.3133 

Yes Midwest Male Below Poverty Level 6 0.2393 0.0270 0.1853 0.3033 

Yes Midwest Male Below Poverty Level 7 0.2261 0.0268 0.1729 0.2900 

Yes Midwest Male Below Poverty Level 8 0.2225 0.0290 0.1655 0.2924 

Yes Midwest Male Below Poverty Level 9 0.2354 0.0311 0.1741 0.3101 

Yes Midwest Male Below Poverty Level 10 0.2499 0.0339 0.1831 0.3311 

Yes Midwest Male Below Poverty Level 11 0.2553 0.0357 0.1852 0.3409 

Yes Midwest Male Below Poverty Level 12 0.2512 0.0377 0.1779 0.3423 

Yes Midwest Male Below Poverty Level 13 0.2149 0.0355 0.1473 0.3025 

Yes Midwest Male Below Poverty Level 14 0.1941 0.0308 0.1353 0.2703 

Yes Midwest Male Below Poverty Level 15 0.2027 0.0292 0.1462 0.2741 

Yes Midwest Male Below Poverty Level 16 0.2364 0.0390 0.1617 0.3320 

Yes Midwest Male Below Poverty Level 17 0.3045 0.0768 0.1652 0.4921 

Yes Northeast Female Above Poverty Level 0 0.0115 0.0066 0.0032 0.0402 

Yes Northeast Female Above Poverty Level 1 0.0278 0.0095 0.0131 0.0583 

Yes Northeast Female Above Poverty Level 2 0.0533 0.0108 0.0340 0.0827 

Yes Northeast Female Above Poverty Level 3 0.0823 0.0127 0.0584 0.1150 

Yes Northeast Female Above Poverty Level 4 0.1027 0.0152 0.0737 0.1413 

Yes Northeast Female Above Poverty Level 5 0.1066 0.0150 0.0777 0.1445 

Yes Northeast Female Above Poverty Level 6 0.1023 0.0143 0.0749 0.1383 

Yes Northeast Female Above Poverty Level 7 0.0979 0.0137 0.0715 0.1325 

Yes Northeast Female Above Poverty Level 8 0.1010 0.0144 0.0734 0.1375 

Yes Northeast Female Above Poverty Level 9 0.1146 0.0166 0.0828 0.1566 

Yes Northeast Female Above Poverty Level 10 0.1179 0.0171 0.0852 0.1611 

Yes Northeast Female Above Poverty Level 11 0.1170 0.0175 0.0836 0.1615 

Yes Northeast Female Above Poverty Level 12 0.1154 0.0164 0.0838 0.1568 

Yes Northeast Female Above Poverty Level 13 0.1246 0.0148 0.0955 0.1611 

Yes Northeast Female Above Poverty Level 14 0.1405 0.0148 0.1109 0.1765 

Yes Northeast Female Above Poverty Level 15 0.1551 0.0152 0.1245 0.1916 

Yes Northeast Female Above Poverty Level 16 0.1714 0.0209 0.1302 0.2223 

Yes Northeast Female Above Poverty Level 17 0.1883 0.0376 0.1189 0.2851 

Yes Northeast Female Below Poverty Level 0 0.0394 0.0211 0.0119 0.1222 

Yes Northeast Female Below Poverty Level 1 0.0754 0.0229 0.0383 0.1433 

Yes Northeast Female Below Poverty Level 2 0.1188 0.0229 0.0770 0.1789 

Yes Northeast Female Below Poverty Level 3 0.1539 0.0265 0.1043 0.2214 

Yes Northeast Female Below Poverty Level 4 0.1684 0.0295 0.1131 0.2432 

Yes Northeast Female Below Poverty Level 5 0.1503 0.0269 0.1003 0.2193 

Yes Northeast Female Below Poverty Level 6 0.1355 0.0245 0.0902 0.1987 

Yes Northeast Female Below Poverty Level 7 0.1263 0.0231 0.0836 0.1862 

Yes Northeast Female Below Poverty Level 8 0.1322 0.0257 0.0853 0.1993 

Yes Northeast Female Below Poverty Level 9 0.1583 0.0301 0.1029 0.2358 

Yes Northeast Female Below Poverty Level 10 0.1818 0.0342 0.1183 0.2689 

Yes Northeast Female Below Poverty Level 11 0.2030 0.0358 0.1355 0.2926 

Yes Northeast Female Below Poverty Level 12 0.2293 0.0359 0.1600 0.3172 

Yes Northeast Female Below Poverty Level 13 0.2437 0.0366 0.1726 0.3323 

Yes Northeast Female Below Poverty Level 14 0.2368 0.0335 0.1713 0.3179 

Yes Northeast Female Below Poverty Level 15 0.2188 0.0286 0.1625 0.2879 

Yes Northeast Female Below Poverty Level 16 0.1906 0.0298 0.1335 0.2645 

Yes Northeast Female Below Poverty Level 17 0.1572 0.0443 0.0822 0.2796 

Yes Northeast Male Above Poverty Level 0 0.0279 0.0107 0.0119 0.0639 

Yes Northeast Male Above Poverty Level 1 0.0444 0.0103 0.0265 0.0733 

Yes Northeast Male Above Poverty Level 2 0.0668 0.0106 0.0470 0.0940 

Yes Northeast Male Above Poverty Level 3 0.0948 0.0134 0.0692 0.1284 

Yes Northeast Male Above Poverty Level 4 0.1269 0.0174 0.0933 0.1702 

Yes Northeast Male Above Poverty Level 5 0.1665 0.0209 0.1257 0.2173 

Yes Northeast Male Above Poverty Level 6 0.1891 0.0207 0.1478 0.2387 

Yes Northeast Male Above Poverty Level 7 0.1901 0.0204 0.1494 0.2389 

Yes Northeast Male Above Poverty Level 8 0.1858 0.0189 0.1479 0.2307 

Yes Northeast Male Above Poverty Level 9 0.1873 0.0189 0.1494 0.2322 

Yes Northeast Male Above Poverty Level 10 0.1908 0.0180 0.1545 0.2333 

Yes Northeast Male Above Poverty Level 11 0.1926 0.0163 0.1595 0.2307 

Yes Northeast Male Above Poverty Level 12 0.1934 0.0168 0.1592 0.2329 
5C-59 

Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes Northeast Male Above Poverty Level 13 0.1847 0.0172 0.1499 0.2253 

Yes Northeast Male Above Poverty Level 14 0.1797 0.0168 0.1458 0.2195 

Yes Northeast Male Above Poverty Level 15 0.1781 0.0156 0.1465 0.2149 

Yes Northeast Male Above Poverty Level 16 0.1795 0.0162 0.1467 0.2178 

Yes Northeast Male Above Poverty Level 17 0.1838 0.0251 0.1350 0.2452 

Yes Northeast Male Below Poverty Level 0 0.0946 0.0396 0.0365 0.2240 

Yes Northeast Male Below Poverty Level 1 0.1345 0.0296 0.0817 0.2134 

Yes Northeast Male Below Poverty Level 2 0.1759 0.0264 0.1251 0.2416 

Yes Northeast Male Below Poverty Level 3 0.2132 0.0326 0.1503 0.2932 

Yes Northeast Male Below Poverty Level 4 0.2353 0.0361 0.1653 0.3236 

Yes Northeast Male Below Poverty Level 5 0.2638 0.0316 0.2004 0.3388 

Yes Northeast Male Below Poverty Level 6 0.2909 0.0305 0.2287 0.3621 

Yes Northeast Male Below Poverty Level 7 0.3169 0.0339 0.2475 0.3954 

Yes Northeast Male Below Poverty Level 8 0.3272 0.0405 0.2451 0.4214 

Yes Northeast Male Below Poverty Level 9 0.3238 0.0439 0.2356 0.4265 

Yes Northeast Male Below Poverty Level 10 0.3163 0.0429 0.2304 0.4169 

Yes Northeast Male Below Poverty Level 11 0.3022 0.0412 0.2199 0.3995 

Yes Northeast Male Below Poverty Level 12 0.2846 0.0388 0.2074 0.3769 

Yes Northeast Male Below Poverty Level 13 0.2779 0.0367 0.2048 0.3651 

Yes Northeast Male Below Poverty Level 14 0.2702 0.0343 0.2016 0.3518 

Yes Northeast Male Below Poverty Level 15 0.2698 0.0316 0.2062 0.3445 

Yes Northeast Male Below Poverty Level 16 0.2745 0.0349 0.2048 0.3573 

Yes Northeast Male Below Poverty Level 17 0.2843 0.0575 0.1760 0.4250 

Yes South Female Above Poverty Level 0 0.0137 0.0056 0.0056 0.0334 

Yes South Female Above Poverty Level 1 0.0266 0.0064 0.0156 0.0450 

Yes South Female Above Poverty Level 2 0.0453 0.0068 0.0325 0.0629 

Yes South Female Above Poverty Level 3 0.0687 0.0086 0.0522 0.0901 

Yes South Female Above Poverty Level 4 0.0928 0.0112 0.0710 0.1203 

Yes South Female Above Poverty Level 5 0.1142 0.0123 0.0900 0.1439 

Yes South Female Above Poverty Level 6 0.1298 0.0128 0.1042 0.1605 

Yes South Female Above Poverty Level 7 0.1333 0.0123 0.1085 0.1627 

Yes South Female Above Poverty Level 8 0.1231 0.0117 0.0996 0.1512 

Yes South Female Above Poverty Level 9 0.1095 0.0109 0.0877 0.1359 

Yes South Female Above Poverty Level 10 0.1033 0.0102 0.0830 0.1279 

Yes South Female Above Poverty Level 11 0.1086 0.0103 0.0881 0.1332 

Yes South Female Above Poverty Level 12 0.1212 0.0110 0.0991 0.1475 

Yes South Female Above Poverty Level 13 0.1368 0.0113 0.1138 0.1635 

Yes South Female Above Poverty Level 14 0.1437 0.0111 0.1210 0.1699 

Yes South Female Above Poverty Level 15 0.1448 0.0104 0.1235 0.1690 

Yes South Female Above Poverty Level 16 0.1395 0.0113 0.1166 0.1661 

Yes South Female Above Poverty Level 17 0.1283 0.0172 0.0952 0.1709 

Yes South Female Below Poverty Level 0 0.0496 0.0153 0.0250 0.0962 

Yes South Female Below Poverty Level 1 0.0682 0.0123 0.0458 0.1004 

Yes South Female Below Poverty Level 2 0.0893 0.0116 0.0670 0.1181 

Yes South Female Below Poverty Level 3 0.1111 0.0141 0.0838 0.1459 

Yes South Female Below Poverty Level 4 0.1319 0.0171 0.0987 0.1740 

Yes South Female Below Poverty Level 5 0.1473 0.0181 0.1120 0.1914 

Yes South Female Below Poverty Level 6 0.1553 0.0183 0.1193 0.1997 

Yes South Female Below Poverty Level 7 0.1592 0.0183 0.1231 0.2035 

Yes South Female Below Poverty Level 8 0.1650 0.0188 0.1277 0.2104 

Yes South Female Below Poverty Level 9 0.1766 0.0198 0.1374 0.2241 

Yes South Female Below Poverty Level 10 0.1825 0.0216 0.1398 0.2347 

Yes South Female Below Poverty Level 11 0.1805 0.0219 0.1373 0.2336 

Yes South Female Below Poverty Level 12 0.1837 0.0221 0.1401 0.2371 

Yes South Female Below Poverty Level 13 0.1932 0.0218 0.1499 0.2453 

Yes South Female Below Poverty Level 14 0.1891 0.0202 0.1487 0.2374 

Yes South Female Below Poverty Level 15 0.1760 0.0181 0.1398 0.2192 

Yes South Female Below Poverty Level 16 0.1560 0.0195 0.1178 0.2037 

Yes South Female Below Poverty Level 17 0.1298 0.0271 0.0810 0.2015 

Yes South Male Above Poverty Level 0 0.0335 0.0089 0.0186 0.0596 

Yes South Male Above Poverty Level 1 0.0629 0.0093 0.0453 0.0867 

Yes South Male Above Poverty Level 2 0.0985 0.0094 0.0797 0.1212 

Yes South Male Above Poverty Level 3 0.1306 0.0116 0.1073 0.1581 

Yes South Male Above Poverty Level 4 0.1472 0.0133 0.1204 0.1787 
5C-60 

Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes South Male Above Poverty Level 5 0.1523 0.0130 0.1259 0.1831 

Yes South Male Above Poverty Level 6 0.1539 0.0128 0.1278 0.1842 

Yes South Male Above Poverty Level 7 0.1485 0.0125 0.1231 0.1782 

Yes South Male Above Poverty Level 8 0.1461 0.0123 0.1212 0.1752 

Yes South Male Above Poverty Level 9 0.1517 0.0124 0.1265 0.1810 

Yes South Male Above Poverty Level 10 0.1639 0.0129 0.1375 0.1943 

Yes South Male Above Poverty Level 11 0.1772 0.0134 0.1496 0.2085 

Yes South Male Above Poverty Level 12 0.1794 0.0128 0.1530 0.2093 

Yes South Male Above Poverty Level 13 0.1752 0.0127 0.1491 0.2049 

Yes South Male Above Poverty Level 14 0.1705 0.0120 0.1458 0.1984 

Yes South Male Above Poverty Level 15 0.1652 0.0108 0.1428 0.1902 

Yes South Male Above Poverty Level 16 0.1600 0.0118 0.1358 0.1876 

Yes South Male Above Poverty Level 17 0.1562 0.0190 0.1189 0.2026 

Yes South Male Below Poverty Level 0 0.0629 0.0140 0.0383 0.1016 

Yes South Male Below Poverty Level 1 0.0922 0.0118 0.0694 0.1215 

Yes South Male Below Poverty Level 2 0.1253 0.0123 0.1008 0.1547 

Yes South Male Below Poverty Level 3 0.1578 0.0156 0.1265 0.1951 

Yes South Male Below Poverty Level 4 0.1852 0.0186 0.1479 0.2294 

Yes South Male Below Poverty Level 5 0.1975 0.0190 0.1592 0.2424 

Yes South Male Below Poverty Level 6 0.2038 0.0198 0.1639 0.2506 

Yes South Male Below Poverty Level 7 0.2087 0.0204 0.1675 0.2570 

Yes South Male Below Poverty Level 8 0.2078 0.0203 0.1669 0.2558 

Yes South Male Below Poverty Level 9 0.2080 0.0206 0.1664 0.2567 

Yes South Male Below Poverty Level 10 0.2122 0.0203 0.1711 0.2601 

Yes South Male Below Poverty Level 11 0.2137 0.0202 0.1727 0.2612 

Yes South Male Below Poverty Level 12 0.2192 0.0214 0.1759 0.2698 

Yes South Male Below Poverty Level 13 0.2199 0.0220 0.1755 0.2718 

Yes South Male Below Poverty Level 14 0.2059 0.0209 0.1639 0.2554 

Yes South Male Below Poverty Level 15 0.1946 0.0186 0.1571 0.2385 

Yes South Male Below Poverty Level 16 0.1827 0.0177 0.1471 0.2246 

Yes South Male Below Poverty Level 17 0.1709 0.0246 0.1235 0.2317 

Yes West Female Above Poverty Level 0 0.0131 0.0067 0.0042 0.0400 

Yes West Female Above Poverty Level 1 0.0188 0.0057 0.0096 0.0365 

Yes West Female Above Poverty Level 2 0.0264 0.0053 0.0171 0.0407 

Yes West Female Above Poverty Level 3 0.0361 0.0064 0.0245 0.0531 

Yes West Female Above Poverty Level 4 0.0469 0.0083 0.0317 0.0689 

Yes West Female Above Poverty Level 5 0.0647 0.0105 0.0451 0.0919 

Yes West Female Above Poverty Level 6 0.0857 0.0130 0.0611 0.1189 

Yes West Female Above Poverty Level 7 0.1008 0.0144 0.0733 0.1372 

Yes West Female Above Poverty Level 8 0.1032 0.0151 0.0746 0.1412 

Yes West Female Above Poverty Level 9 0.1063 0.0144 0.0786 0.1424 

Yes West Female Above Poverty Level 10 0.1166 0.0140 0.0893 0.1509 

Yes West Female Above Poverty Level 11 0.1181 0.0129 0.0927 0.1494 

Yes West Female Above Poverty Level 12 0.1196 0.0131 0.0938 0.1513 

Yes West Female Above Poverty Level 13 0.1202 0.0130 0.0945 0.1519 

Yes West Female Above Poverty Level 14 0.1241 0.0127 0.0987 0.1548 

Yes West Female Above Poverty Level 15 0.1389 0.0125 0.1136 0.1687 

Yes West Female Above Poverty Level 16 0.1665 0.0152 0.1358 0.2025 

Yes West Female Above Poverty Level 17 0.2118 0.0305 0.1525 0.2864 

Yes West Female Below Poverty Level 0 0.0250 0.0138 0.0073 0.0819 

Yes West Female Below Poverty Level 1 0.0309 0.0099 0.0152 0.0618 

Yes West Female Below Poverty Level 2 0.0387 0.0082 0.0243 0.0612 

Yes West Female Below Poverty Level 3 0.0488 0.0099 0.0312 0.0757 

Yes West Female Below Poverty Level 4 0.0602 0.0129 0.0374 0.0955 

Yes West Female Below Poverty Level 5 0.0843 0.0169 0.0538 0.1296 

Yes West Female Below Poverty Level 6 0.1143 0.0197 0.0776 0.1652 

Yes West Female Below Poverty Level 7 0.1295 0.0191 0.0930 0.1775 

Yes West Female Below Poverty Level 8 0.1195 0.0175 0.0861 0.1636 

Yes West Female Below Poverty Level 9 0.0950 0.0151 0.0666 0.1338 

Yes West Female Below Poverty Level 10 0.0786 0.0139 0.0530 0.1150 

Yes West Female Below Poverty Level 11 0.0812 0.0150 0.0537 0.1209 

Yes West Female Below Poverty Level 12 0.0979 0.0179 0.0651 0.1447 

Yes West Female Below Poverty Level 13 0.1278 0.0221 0.0866 0.1848 

Yes West Female Below Poverty Level 14 0.1324 0.0211 0.0925 0.1859 
5C-61 

Appendix 5C, Attachment C, Table 5CC-1. Smoothed prevalence for children “EVER” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes West Female Below Poverty Level 15 0.1188 0.0176 0.0853 0.1631 

Yes West Female Below Poverty Level 16 0.0917 0.0164 0.0615 0.1347 

Yes West Female Below Poverty Level 17 0.0600 0.0186 0.0300 0.1163 

Yes West Male Above Poverty Level 0 0.0057 0.0035 0.0014 0.0229 

Yes West Male Above Poverty Level 1 0.0191 0.0067 0.0084 0.0428 

Yes West Male Above Poverty Level 2 0.0479 0.0092 0.0306 0.0743 

Yes West Male Above Poverty Level 3 0.0903 0.0114 0.0673 0.1201 

Yes West Male Above Poverty Level 4 0.1300 0.0149 0.0993 0.1685 

Yes West Male Above Poverty Level 5 0.1437 0.0158 0.1110 0.1842 

Yes West Male Above Poverty Level 6 0.1374 0.0157 0.1050 0.1779 

Yes West Male Above Poverty Level 7 0.1290 0.0148 0.0985 0.1671 

Yes West Male Above Poverty Level 8 0.1365 0.0148 0.1058 0.1743 

Yes West Male Above Poverty Level 9 0.1560 0.0154 0.1236 0.1950 

Yes West Male Above Poverty Level 10 0.1794 0.0160 0.1454 0.2193 

Yes West Male Above Poverty Level 11 0.1980 0.0175 0.1608 0.2413 

Yes West Male Above Poverty Level 12 0.1948 0.0180 0.1566 0.2396 

Yes West Male Above Poverty Level 13 0.1818 0.0175 0.1449 0.2256 

Yes West Male Above Poverty Level 14 0.1771 0.0164 0.1423 0.2183 

Yes West Male Above Poverty Level 15 0.1801 0.0148 0.1484 0.2167 

Yes West Male Above Poverty Level 16 0.1897 0.0149 0.1577 0.2264 

Yes West Male Above Poverty Level 17 0.2081 0.0248 0.1567 0.2709 

Yes West Male Below Poverty Level 0 0.0258 0.0126 0.0087 0.0738 

Yes West Male Below Poverty Level 1 0.0442 0.0124 0.0237 0.0812 

Yes West Male Below Poverty Level 2 0.0700 0.0119 0.0479 0.1013 

Yes West Male Below Poverty Level 3 0.1005 0.0144 0.0729 0.1370 

Yes West Male Below Poverty Level 4 0.1323 0.0190 0.0959 0.1799 

Yes West Male Below Poverty Level 5 0.1609 0.0218 0.1186 0.2147 

Yes West Male Below Poverty Level 6 0.1663 0.0213 0.1247 0.2184 

Yes West Male Below Poverty Level 7 0.1582 0.0205 0.1182 0.2086 

Yes West Male Below Poverty Level 8 0.1536 0.0204 0.1140 0.2040 

Yes West Male Below Poverty Level 9 0.1543 0.0214 0.1128 0.2075 

Yes West Male Below Poverty Level 10 0.1630 0.0240 0.1168 0.2228 

Yes West Male Below Poverty Level 11 0.1746 0.0270 0.1230 0.2420 

Yes West Male Below Poverty Level 12 0.1828 0.0270 0.1306 0.2498 

Yes West Male Below Poverty Level 13 0.1809 0.0276 0.1280 0.2495 

Yes West Male Below Poverty Level 14 0.1800 0.0259 0.1298 0.2440 

Yes West Male Below Poverty Level 15 0.1828 0.0233 0.1371 0.2396 

Yes West Male Below Poverty Level 16 0.1881 0.0242 0.1405 0.2471 

Yes West Male Below Poverty Level 17 0.1964 0.0396 0.1234 0.2978 

 
5C-62 

Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes Midwest Female Above Poverty Level 0 0.0082 0.0051 0.0021 0.0319 

Yes Midwest Female Above Poverty Level 1 0.0168 0.0064 0.0073 0.0382 

Yes Midwest Female Above Poverty Level 2 0.0289 0.0070 0.0169 0.0490 

Yes Midwest Female Above Poverty Level 3 0.0420 0.0086 0.0267 0.0655 

Yes Midwest Female Above Poverty Level 4 0.0509 0.0103 0.0326 0.0788 

Yes Midwest Female Above Poverty Level 5 0.0573 0.0108 0.0378 0.0859 

Yes Midwest Female Above Poverty Level 6 0.0611 0.0109 0.0412 0.0897 

Yes Midwest Female Above Poverty Level 7 0.0624 0.0107 0.0427 0.0902 

Yes Midwest Female Above Poverty Level 8 0.0629 0.0100 0.0443 0.0886 

Yes Midwest Female Above Poverty Level 9 0.0663 0.0096 0.0481 0.0907 

Yes Midwest Female Above Poverty Level 10 0.0737 0.0108 0.0533 0.1012 

Yes Midwest Female Above Poverty Level 11 0.0889 0.0126 0.0649 0.1206 

Yes Midwest Female Above Poverty Level 12 0.1056 0.0151 0.0768 0.1435 

Yes Midwest Female Above Poverty Level 13 0.1157 0.0163 0.0845 0.1565 

Yes Midwest Female Above Poverty Level 14 0.1191 0.0160 0.0882 0.1588 

Yes Midwest Female Above Poverty Level 15 0.1177 0.0144 0.0896 0.1530 

Yes Midwest Female Above Poverty Level 16 0.1107 0.0143 0.0831 0.1461 

Yes Midwest Female Above Poverty Level 17 0.0999 0.0205 0.0632 0.1544 

Yes Midwest Female Below Poverty Level 0 0.0381 0.0164 0.0146 0.0956 

Yes Midwest Female Below Poverty Level 1 0.0620 0.0160 0.0349 0.1076 

Yes Midwest Female Below Poverty Level 2 0.0875 0.0160 0.0581 0.1295 

Yes Midwest Female Below Poverty Level 3 0.1079 0.0183 0.0738 0.1550 

Yes Midwest Female Below Poverty Level 4 0.1187 0.0202 0.0811 0.1704 

Yes Midwest Female Below Poverty Level 5 0.1117 0.0194 0.0758 0.1616 

Yes Midwest Female Below Poverty Level 6 0.0940 0.0188 0.0602 0.1439 

Yes Midwest Female Below Poverty Level 7 0.0974 0.0187 0.0634 0.1469 

Yes Midwest Female Below Poverty Level 8 0.1144 0.0205 0.0765 0.1676 

Yes Midwest Female Below Poverty Level 9 0.1237 0.0220 0.0830 0.1805 

Yes Midwest Female Below Poverty Level 10 0.1196 0.0237 0.0766 0.1821 

Yes Midwest Female Below Poverty Level 11 0.1074 0.0225 0.0672 0.1673 

Yes Midwest Female Below Poverty Level 12 0.1025 0.0199 0.0664 0.1551 

Yes Midwest Female Below Poverty Level 13 0.1096 0.0211 0.0712 0.1649 

Yes Midwest Female Below Poverty Level 14 0.1236 0.0229 0.0815 0.1830 

Yes Midwest Female Below Poverty Level 15 0.1412 0.0266 0.0924 0.2099 

Yes Midwest Female Below Poverty Level 16 0.1633 0.0413 0.0914 0.2746 

Yes Midwest Female Below Poverty Level 17 0.1906 0.0779 0.0722 0.4158 

Yes Midwest Male Above Poverty Level 0 0.0122 0.0064 0.0038 0.0384 

Yes Midwest Male Above Poverty Level 1 0.0268 0.0083 0.0135 0.0525 

Yes Midwest Male Above Poverty Level 2 0.0480 0.0091 0.0315 0.0725 

Yes Midwest Male Above Poverty Level 3 0.0710 0.0113 0.0500 0.1001 

Yes Midwest Male Above Poverty Level 4 0.0842 0.0134 0.0591 0.1187 

Yes Midwest Male Above Poverty Level 5 0.0934 0.0138 0.0673 0.1282 

Yes Midwest Male Above Poverty Level 6 0.1056 0.0144 0.0779 0.1416 

Yes Midwest Male Above Poverty Level 7 0.1117 0.0149 0.0829 0.1489 

Yes Midwest Male Above Poverty Level 8 0.1111 0.0152 0.0820 0.1489 

Yes Midwest Male Above Poverty Level 9 0.1138 0.0155 0.0840 0.1525 

Yes Midwest Male Above Poverty Level 10 0.1126 0.0153 0.0831 0.1507 

Yes Midwest Male Above Poverty Level 11 0.1108 0.0146 0.0826 0.1472 

Yes Midwest Male Above Poverty Level 12 0.1129 0.0137 0.0861 0.1466 

Yes Midwest Male Above Poverty Level 13 0.1139 0.0132 0.0880 0.1462 

Yes Midwest Male Above Poverty Level 14 0.1128 0.0127 0.0878 0.1438 

Yes Midwest Male Above Poverty Level 15 0.1054 0.0118 0.0822 0.1343 

Yes Midwest Male Above Poverty Level 16 0.0935 0.0133 0.0682 0.1269 

Yes Midwest Male Above Poverty Level 17 0.0782 0.0184 0.0462 0.1292 

Yes Midwest Male Below Poverty Level 0 0.0402 0.0177 0.0151 0.1028 

Yes Midwest Male Below Poverty Level 1 0.0824 0.0213 0.0463 0.1425 

Yes Midwest Male Below Poverty Level 2 0.1338 0.0225 0.0917 0.1911 

Yes Midwest Male Below Poverty Level 3 0.1774 0.0255 0.1282 0.2401 

Yes Midwest Male Below Poverty Level 4 0.1949 0.0267 0.1429 0.2601 

Yes Midwest Male Below Poverty Level 5 0.1867 0.0237 0.1402 0.2443 

Yes Midwest Male Below Poverty Level 6 0.1807 0.0222 0.1371 0.2344 

Yes Midwest Male Below Poverty Level 7 0.1734 0.0221 0.1301 0.2273 

Yes Midwest Male Below Poverty Level 8 0.1739 0.0248 0.1260 0.2350 

Yes Midwest Male Below Poverty Level 9 0.1814 0.0269 0.1297 0.2478 
5C-63 

Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes Midwest Male Below Poverty Level 10 0.1813 0.0282 0.1275 0.2514 

Yes Midwest Male Below Poverty Level 11 0.1749 0.0282 0.1214 0.2454 

Yes Midwest Male Below Poverty Level 12 0.1702 0.0298 0.1143 0.2457 

Yes Midwest Male Below Poverty Level 13 0.1499 0.0296 0.0959 0.2268 

Yes Midwest Male Below Poverty Level 14 0.1366 0.0269 0.0876 0.2066 

Yes Midwest Male Below Poverty Level 15 0.1484 0.0268 0.0987 0.2169 

Yes Midwest Male Below Poverty Level 16 0.1846 0.0359 0.1185 0.2761 

Yes Midwest Male Below Poverty Level 17 0.2590 0.0740 0.1306 0.4484 

Yes Northeast Female Above Poverty Level 0 0.0153 0.0089 0.0042 0.0537 

Yes Northeast Female Above Poverty Level 1 0.0281 0.0096 0.0132 0.0589 

Yes Northeast Female Above Poverty Level 2 0.0437 0.0090 0.0276 0.0683 

Yes Northeast Female Above Poverty Level 3 0.0584 0.0098 0.0402 0.0840 

Yes Northeast Female Above Poverty Level 4 0.0657 0.0112 0.0449 0.0950 

Yes Northeast Female Above Poverty Level 5 0.0668 0.0111 0.0461 0.0958 

Yes Northeast Female Above Poverty Level 6 0.0678 0.0111 0.0471 0.0967 

Yes Northeast Female Above Poverty Level 7 0.0696 0.0114 0.0482 0.0993 

Yes Northeast Female Above Poverty Level 8 0.0737 0.0124 0.0506 0.1062 

Yes Northeast Female Above Poverty Level 9 0.0840 0.0147 0.0569 0.1224 

Yes Northeast Female Above Poverty Level 10 0.0807 0.0144 0.0541 0.1187 

Yes Northeast Female Above Poverty Level 11 0.0710 0.0134 0.0466 0.1068 

Yes Northeast Female Above Poverty Level 12 0.0629 0.0116 0.0416 0.0938 

Yes Northeast Female Above Poverty Level 13 0.0680 0.0113 0.0469 0.0976 

Yes Northeast Female Above Poverty Level 14 0.0786 0.0117 0.0564 0.1085 

Yes Northeast Female Above Poverty Level 15 0.0913 0.0120 0.0681 0.1214 

Yes Northeast Female Above Poverty Level 16 0.1095 0.0165 0.0781 0.1513 

Yes Northeast Female Above Poverty Level 17 0.1328 0.0330 0.0753 0.2234 

Yes Northeast Female Below Poverty Level 0 0.0234 0.0142 0.0061 0.0856 

Yes Northeast Female Below Poverty Level 1 0.0564 0.0190 0.0266 0.1157 

Yes Northeast Female Below Poverty Level 2 0.1040 0.0219 0.0648 0.1627 

Yes Northeast Female Below Poverty Level 3 0.1466 0.0272 0.0964 0.2167 

Yes Northeast Female Below Poverty Level 4 0.1618 0.0304 0.1056 0.2400 

Yes Northeast Female Below Poverty Level 5 0.1441 0.0280 0.0928 0.2168 

Yes Northeast Female Below Poverty Level 6 0.1124 0.0238 0.0698 0.1761 

Yes Northeast Female Below Poverty Level 7 0.0751 0.0174 0.0447 0.1234 

Yes Northeast Female Below Poverty Level 8 0.0633 0.0157 0.0364 0.1078 

Yes Northeast Female Below Poverty Level 9 0.0838 0.0188 0.0507 0.1355 

Yes Northeast Female Below Poverty Level 10 0.1288 0.0270 0.0802 0.2004 

Yes Northeast Female Below Poverty Level 11 0.1778 0.0336 0.1154 0.2638 

Yes Northeast Female Below Poverty Level 12 0.2073 0.0349 0.1410 0.2941 

Yes Northeast Female Below Poverty Level 13 0.2063 0.0328 0.1435 0.2873 

Yes Northeast Female Below Poverty Level 14 0.1929 0.0287 0.1375 0.2637 

Yes Northeast Female Below Poverty Level 15 0.1703 0.0235 0.1248 0.2281 

Yes Northeast Female Below Poverty Level 16 0.1414 0.0234 0.0974 0.2009 

Yes Northeast Female Below Poverty Level 17 0.1108 0.0327 0.0567 0.2051 

Yes Northeast Male Above Poverty Level 0 0.0225 0.0108 0.0078 0.0633 

Yes Northeast Male Above Poverty Level 1 0.0368 0.0105 0.0195 0.0682 

Yes Northeast Male Above Poverty Level 2 0.0562 0.0104 0.0373 0.0838 

Yes Northeast Male Above Poverty Level 3 0.0797 0.0127 0.0559 0.1123 

Yes Northeast Male Above Poverty Level 4 0.1035 0.0162 0.0730 0.1449 

Yes Northeast Male Above Poverty Level 5 0.1289 0.0187 0.0931 0.1757 

Yes Northeast Male Above Poverty Level 6 0.1472 0.0190 0.1102 0.1938 

Yes Northeast Male Above Poverty Level 7 0.1423 0.0181 0.1070 0.1868 

Yes Northeast Male Above Poverty Level 8 0.1290 0.0163 0.0973 0.1690 

Yes Northeast Male Above Poverty Level 9 0.1251 0.0159 0.0943 0.1641 

Yes Northeast Male Above Poverty Level 10 0.1288 0.0155 0.0985 0.1668 

Yes Northeast Male Above Poverty Level 11 0.1262 0.0139 0.0989 0.1598 

Yes Northeast Male Above Poverty Level 12 0.1246 0.0139 0.0971 0.1584 

Yes Northeast Male Above Poverty Level 13 0.1230 0.0149 0.0939 0.1594 

Yes Northeast Male Above Poverty Level 14 0.1207 0.0144 0.0925 0.1560 

Yes Northeast Male Above Poverty Level 15 0.1114 0.0126 0.0868 0.1420 

Yes Northeast Male Above Poverty Level 16 0.0983 0.0124 0.0743 0.1291 

Yes Northeast Male Above Poverty Level 17 0.0823 0.0171 0.0518 0.1285 

Yes Northeast Male Below Poverty Level 0 0.0930 0.0402 0.0347 0.2262 

Yes Northeast Male Below Poverty Level 1 0.1202 0.0280 0.0710 0.1964 
5C-64 

Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes Northeast Male Below Poverty Level 2 0.1475 0.0256 0.0997 0.2130 

Yes Northeast Male Below Poverty Level 3 0.1714 0.0311 0.1134 0.2508 

Yes Northeast Male Below Poverty Level 4 0.1860 0.0335 0.1232 0.2708 

Yes Northeast Male Below Poverty Level 5 0.2060 0.0276 0.1519 0.2732 

Yes Northeast Male Below Poverty Level 6 0.2256 0.0276 0.1708 0.2919 

Yes Northeast Male Below Poverty Level 7 0.2496 0.0317 0.1866 0.3255 

Yes Northeast Male Below Poverty Level 8 0.2727 0.0387 0.1964 0.3653 

Yes Northeast Male Below Poverty Level 9 0.2579 0.0395 0.1810 0.3535 

Yes Northeast Male Below Poverty Level 10 0.2318 0.0366 0.1611 0.3216 

Yes Northeast Male Below Poverty Level 11 0.1902 0.0310 0.1311 0.2678 

Yes Northeast Male Below Poverty Level 12 0.1624 0.0268 0.1116 0.2302 

Yes Northeast Male Below Poverty Level 13 0.1641 0.0254 0.1155 0.2278 

Yes Northeast Male Below Poverty Level 14 0.1699 0.0251 0.1216 0.2323 

Yes Northeast Male Below Poverty Level 15 0.1797 0.0244 0.1321 0.2396 

Yes Northeast Male Below Poverty Level 16 0.1933 0.0276 0.1397 0.2612 

Yes Northeast Male Below Poverty Level 17 0.2097 0.0451 0.1274 0.3253 

Yes South Female Above Poverty Level 0 0.0131 0.0059 0.0048 0.0349 

Yes South Female Above Poverty Level 1 0.0228 0.0063 0.0124 0.0415 

Yes South Female Above Poverty Level 2 0.0352 0.0064 0.0236 0.0522 

Yes South Female Above Poverty Level 3 0.0495 0.0074 0.0355 0.0685 

Yes South Female Above Poverty Level 4 0.0633 0.0089 0.0464 0.0857 

Yes South Female Above Poverty Level 5 0.0740 0.0092 0.0561 0.0969 

Yes South Female Above Poverty Level 6 0.0826 0.0096 0.0638 0.1063 

Yes South Female Above Poverty Level 7 0.0888 0.0099 0.0695 0.1129 

Yes South Female Above Poverty Level 8 0.0860 0.0100 0.0666 0.1105 

Yes South Female Above Poverty Level 9 0.0791 0.0095 0.0606 0.1025 

Yes South Female Above Poverty Level 10 0.0747 0.0088 0.0576 0.0963 

Yes South Female Above Poverty Level 11 0.0736 0.0085 0.0570 0.0944 

Yes South Female Above Poverty Level 12 0.0776 0.0087 0.0606 0.0989 

Yes South Female Above Poverty Level 13 0.0851 0.0093 0.0669 0.1078 

Yes South Female Above Poverty Level 14 0.0871 0.0093 0.0688 0.1099 

Yes South Female Above Poverty Level 15 0.0876 0.0087 0.0702 0.1087 

Yes South Female Above Poverty Level 16 0.0859 0.0091 0.0681 0.1080 

Yes South Female Above Poverty Level 17 0.0819 0.0136 0.0567 0.1169 

Yes South Female Below Poverty Level 0 0.0396 0.0135 0.0186 0.0823 

Yes South Female Below Poverty Level 1 0.0573 0.0113 0.0371 0.0876 

Yes South Female Below Poverty Level 2 0.0772 0.0109 0.0564 0.1048 

Yes South Female Below Poverty Level 3 0.0963 0.0136 0.0704 0.1306 

Yes South Female Below Poverty Level 4 0.1120 0.0165 0.0805 0.1536 

Yes South Female Below Poverty Level 5 0.1206 0.0174 0.0874 0.1641 

Yes South Female Below Poverty Level 6 0.1219 0.0173 0.0888 0.1652 

Yes South Female Below Poverty Level 7 0.1152 0.0162 0.0842 0.1556 

Yes South Female Below Poverty Level 8 0.1131 0.0157 0.0829 0.1524 

Yes South Female Below Poverty Level 9 0.1190 0.0161 0.0880 0.1591 

Yes South Female Below Poverty Level 10 0.1208 0.0175 0.0874 0.1646 

Yes South Female Below Poverty Level 11 0.1195 0.0178 0.0857 0.1642 

Yes South Female Below Poverty Level 12 0.1275 0.0192 0.0910 0.1757 

Yes South Female Below Poverty Level 13 0.1405 0.0197 0.1026 0.1893 

Yes South Female Below Poverty Level 14 0.1394 0.0184 0.1037 0.1848 

Yes South Female Below Poverty Level 15 0.1296 0.0166 0.0973 0.1706 

Yes South Female Below Poverty Level 16 0.1136 0.0184 0.0791 0.1605 

Yes South Female Below Poverty Level 17 0.0923 0.0249 0.0503 0.1634 

Yes South Male Above Poverty Level 0 0.0228 0.0070 0.0116 0.0443 

Yes South Male Above Poverty Level 1 0.0476 0.0082 0.0325 0.0693 

Yes South Male Above Poverty Level 2 0.0793 0.0089 0.0619 0.1011 

Yes South Male Above Poverty Level 3 0.1076 0.0109 0.0859 0.1341 

Yes South Male Above Poverty Level 4 0.1193 0.0123 0.0949 0.1490 

Yes South Male Above Poverty Level 5 0.1194 0.0117 0.0960 0.1475 

Yes South Male Above Poverty Level 6 0.1145 0.0111 0.0924 0.1411 

Yes South Male Above Poverty Level 7 0.1071 0.0105 0.0861 0.1323 

Yes South Male Above Poverty Level 8 0.1011 0.0099 0.0813 0.1251 

Yes South Male Above Poverty Level 9 0.1000 0.0098 0.0806 0.1236 

Yes South Male Above Poverty Level 10 0.1059 0.0102 0.0855 0.1305 

Yes South Male Above Poverty Level 11 0.1122 0.0106 0.0910 0.1376 
5C-65 

Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes South Male Above Poverty Level 12 0.1103 0.0105 0.0893 0.1356 

Yes South Male Above Poverty Level 13 0.1052 0.0105 0.0843 0.1305 

Yes South Male Above Poverty Level 14 0.0983 0.0094 0.0795 0.1210 

Yes South Male Above Poverty Level 15 0.0899 0.0081 0.0737 0.1093 

Yes South Male Above Poverty Level 16 0.0811 0.0089 0.0636 0.1028 

Yes South Male Above Poverty Level 17 0.0727 0.0136 0.0479 0.1089 

Yes South Male Below Poverty Level 0 0.0499 0.0126 0.0285 0.0860 

Yes South Male Below Poverty Level 1 0.0749 0.0110 0.0542 0.1027 

Yes South Male Below Poverty Level 2 0.1033 0.0116 0.0805 0.1316 

Yes South Male Below Poverty Level 3 0.1305 0.0149 0.1012 0.1666 

Yes South Male Below Poverty Level 4 0.1519 0.0177 0.1171 0.1948 

Yes South Male Below Poverty Level 5 0.1595 0.0180 0.1240 0.2029 

Yes South Male Below Poverty Level 6 0.1598 0.0185 0.1234 0.2045 

Yes South Male Below Poverty Level 7 0.1540 0.0180 0.1186 0.1977 

Yes South Male Below Poverty Level 8 0.1466 0.0170 0.1130 0.1879 

Yes South Male Below Poverty Level 9 0.1457 0.0170 0.1122 0.1870 

Yes South Male Below Poverty Level 10 0.1504 0.0171 0.1167 0.1917 

Yes South Male Below Poverty Level 11 0.1508 0.0171 0.1171 0.1921 

Yes South Male Below Poverty Level 12 0.1506 0.0184 0.1146 0.1955 

Yes South Male Below Poverty Level 13 0.1470 0.0192 0.1097 0.1943 

Yes South Male Below Poverty Level 14 0.1345 0.0179 0.0999 0.1788 

Yes South Male Below Poverty Level 15 0.1215 0.0159 0.0907 0.1607 

Yes South Male Below Poverty Level 16 0.1080 0.0164 0.0770 0.1494 

Yes South Male Below Poverty Level 17 0.0948 0.0227 0.0555 0.1573 

Yes West Female Above Poverty Level 0 0.0077 0.0049 0.0019 0.0306 

Yes West Female Above Poverty Level 1 0.0122 0.0046 0.0053 0.0278 

Yes West Female Above Poverty Level 2 0.0181 0.0045 0.0105 0.0310 

Yes West Female Above Poverty Level 3 0.0248 0.0055 0.0153 0.0401 

Yes West Female Above Poverty Level 4 0.0305 0.0068 0.0186 0.0494 

Yes West Female Above Poverty Level 5 0.0382 0.0077 0.0245 0.0590 

Yes West Female Above Poverty Level 6 0.0482 0.0091 0.0318 0.0724 

Yes West Female Above Poverty Level 7 0.0573 0.0098 0.0393 0.0829 

Yes West Female Above Poverty Level 8 0.0628 0.0106 0.0432 0.0904 

Yes West Female Above Poverty Level 9 0.0697 0.0106 0.0497 0.0970 

Yes West Female Above Poverty Level 10 0.0768 0.0099 0.0577 0.1016 

Yes West Female Above Poverty Level 11 0.0786 0.0094 0.0603 0.1018 

Yes West Female Above Poverty Level 12 0.0808 0.0100 0.0615 0.1056 

Yes West Female Above Poverty Level 13 0.0829 0.0108 0.0621 0.1100 

Yes West Female Above Poverty Level 14 0.0845 0.0111 0.0632 0.1121 

Yes West Female Above Poverty Level 15 0.0908 0.0110 0.0694 0.1179 

Yes West Female Above Poverty Level 16 0.1016 0.0129 0.0766 0.1337 

Yes West Female Above Poverty Level 17 0.1180 0.0236 0.0753 0.1803 

Yes West Female Below Poverty Level 0 0.0244 0.0144 0.0066 0.0862 

Yes West Female Below Poverty Level 1 0.0270 0.0091 0.0128 0.0561 

Yes West Female Below Poverty Level 2 0.0306 0.0074 0.0179 0.0518 

Yes West Female Below Poverty Level 3 0.0354 0.0090 0.0201 0.0615 

Yes West Female Below Poverty Level 4 0.0407 0.0112 0.0221 0.0738 

Yes West Female Below Poverty Level 5 0.0577 0.0146 0.0328 0.0996 

Yes West Female Below Poverty Level 6 0.0807 0.0185 0.0483 0.1319 

Yes West Female Below Poverty Level 7 0.0954 0.0181 0.0624 0.1434 

Yes West Female Below Poverty Level 8 0.0876 0.0159 0.0583 0.1296 

Yes West Female Below Poverty Level 9 0.0648 0.0127 0.0419 0.0989 

Yes West Female Below Poverty Level 10 0.0495 0.0107 0.0306 0.0792 

Yes West Female Below Poverty Level 11 0.0473 0.0110 0.0282 0.0781 

Yes West Female Below Poverty Level 12 0.0606 0.0137 0.0366 0.0988 

Yes West Female Below Poverty Level 13 0.0845 0.0179 0.0526 0.1329 

Yes West Female Below Poverty Level 14 0.0931 0.0180 0.0603 0.1411 

Yes West Female Below Poverty Level 15 0.0846 0.0154 0.0562 0.1253 

Yes West Female Below Poverty Level 16 0.0629 0.0143 0.0379 0.1026 

Yes West Female Below Poverty Level 17 0.0376 0.0146 0.0158 0.0868 

Yes West Male Above Poverty Level 0 0.0007 0.0007 0.0001 0.0067 

Yes West Male Above Poverty Level 1 0.0052 0.0027 0.0014 0.0192 

Yes West Male Above Poverty Level 2 0.0225 0.0063 0.0112 0.0447 

Yes West Male Above Poverty Level 3 0.0596 0.0095 0.0398 0.0884 
5C-66 

Appendix 5C, Attachment C, Table 5CC-2. Smoothed prevalence for children “STILL” having asthma. 

Smoothed Region Gender Poverty Status Age Prevalence SE LowerCI UpperCI 

Yes West Male Above Poverty Level 4 0.0989 0.0140 0.0691 0.1397 

Yes West Male Above Poverty Level 5 0.1070 0.0147 0.0754 0.1496 

Yes West Male Above Poverty Level 6 0.0959 0.0141 0.0660 0.1372 

Yes West Male Above Poverty Level 7 0.0830 0.0126 0.0565 0.1203 

Yes West Male Above Poverty Level 8 0.0877 0.0124 0.0613 0.1239 

Yes West Male Above Poverty Level 9 0.1029 0.0135 0.0737 0.1419 

Yes West Male Above Poverty Level 10 0.1189 0.0140 0.0883 0.1584 

Yes West Male Above Poverty Level 11 0.1292 0.0153 0.0955 0.1724 

Yes West Male Above Poverty Level 12 0.1214 0.0154 0.0879 0.1653 

Yes West Male Above Poverty Level 13 0.1050 0.0139 0.0749 0.1452 

Yes West Male Above Poverty Level 14 0.0981 0.0127 0.0707 0.1346 

Yes West Male Above Poverty Level 15 0.0997 0.0116 0.0742 0.1327 

Yes West Male Above Poverty Level 16 0.1091 0.0128 0.0810 0.1454 

Yes West Male Above Poverty Level 17 0.1290 0.0231 0.0814 0.1984 

Yes West Male Below Poverty Level 0 0.0263 0.0130 0.0088 0.0761 

Yes West Male Below Poverty Level 1 0.0374 0.0101 0.0204 0.0673 

Yes West Male Below Poverty Level 2 0.0518 0.0086 0.0358 0.0742 

Yes West Male Below Poverty Level 3 0.0681 0.0105 0.0483 0.0952 

Yes West Male Below Poverty Level 4 0.0871 0.0143 0.0604 0.1240 

Yes West Male Below Poverty Level 5 0.1074 0.0173 0.0749 0.1517 

Yes West Male Below Poverty Level 6 0.1167 0.0183 0.0820 0.1635 

Yes West Male Below Poverty Level 7 0.1138 0.0186 0.0789 0.1615 

Yes West Male Below Poverty Level 8 0.1073 0.0177 0.0741 0.1529 

Yes West Male Below Poverty Level 9 0.0964 0.0164 0.0659 0.1389 

Yes West Male Below Poverty Level 10 0.0830 0.0149 0.0557 0.1221 

Yes West Male Below Poverty Level 11 0.0745 0.0151 0.0474 0.1152 

Yes West Male Below Poverty Level 12 0.0825 0.0165 0.0527 0.1268 

Yes West Male Below Poverty Level 13 0.1000 0.0197 0.0643 0.1524 

Yes West Male Below Poverty Level 14 0.1074 0.0200 0.0707 0.1600 

Yes West Male Below Poverty Level 15 0.1120 0.0193 0.0760 0.1620 

Yes West Male Below Poverty Level 16 0.1127 0.0222 0.0724 0.1714 

Yes West Male Below Poverty Level 17 0.1084 0.0340 0.0531 0.2088 

 
5C-67 

Appendix 5C, Attachment C, Table 5CC-3. Smoothed prevalence for adults “EVER” having asthma 

Smoothed Region Gender Poverty Status Age_group Prevalence SE LowerCI UpperCI 

Yes Midwest Female Above Poverty Level 18-24 0.1642 0.0141 0.1219 0.2176 

Yes Midwest Female Above Poverty Level 25-34 0.1341 0.0063 0.1142 0.1568 

Yes Midwest Female Above Poverty Level 35-44 0.1193 0.0058 0.1012 0.1402 

Yes Midwest Female Above Poverty Level 45-54 0.1204 0.0057 0.1025 0.1409 

Yes Midwest Female Above Poverty Level 55-64 0.1246 0.0066 0.1040 0.1486 

Yes Midwest Female Above Poverty Level 65-74 0.1165 0.0062 0.0971 0.1392 

Yes Midwest Female Above Poverty Level 75+ 0.0980 0.0089 0.0719 0.1322 

Yes Midwest Female Below Poverty Level 18-24 0.2014 0.0153 0.1531 0.2603 

Yes Midwest Female Below Poverty Level 25-34 0.1812 0.0114 0.1445 0.2248 

Yes Midwest Female Below Poverty Level 35-44 0.1782 0.0130 0.1370 0.2284 

Yes Midwest Female Below Poverty Level 45-54 0.2104 0.0146 0.1638 0.2662 

Yes Midwest Female Below Poverty Level 55-64 0.2295 0.0164 0.1770 0.2920 

Yes Midwest Female Below Poverty Level 65-74 0.1892 0.0145 0.1435 0.2453 

Yes Midwest Female Below Poverty Level 75+ 0.1176 0.0173 0.0690 0.1933 

Yes Midwest Male Above Poverty Level 18-24 0.1705 0.0149 0.1249 0.2284 

Yes Midwest Male Above Poverty Level 25-34 0.1209 0.0063 0.1008 0.1444 

Yes Midwest Male Above Poverty Level 35-44 0.0886 0.0053 0.0719 0.1087 

Yes Midwest Male Above Poverty Level 45-54 0.0727 0.0046 0.0583 0.0904 

Yes Midwest Male Above Poverty Level 55-64 0.0770 0.0054 0.0602 0.0980 

Yes Midwest Male Above Poverty Level 65-74 0.0828 0.0058 0.0647 0.1053 

Yes Midwest Male Above Poverty Level 75+ 0.0847 0.0106 0.0545 0.1292 

Yes Midwest Male Below Poverty Level 18-24 0.1654 0.0175 0.1122 0.2370 

Yes Midwest Male Below Poverty Level 25-34 0.1143 0.0109 0.0808 0.1593 

Yes Midwest Male Below Poverty Level 35-44 0.1066 0.0122 0.0703 0.1585 

Yes Midwest Male Below Poverty Level 45-54 0.1376 0.0146 0.0936 0.1979 

Yes Midwest Male Below Poverty Level 55-64 0.1643 0.0164 0.1141 0.2309 

Yes Midwest Male Below Poverty Level 65-74 0.1396 0.0160 0.0918 0.2068 

Yes Midwest Male Below Poverty Level 75+ 0.0853 0.0205 0.0353 0.1920 

Yes Northeast Female Above Poverty Level 18-24 0.1791 0.0176 0.1265 0.2474 

Yes Northeast Female Above Poverty Level 25-34 0.1423 0.0076 0.1183 0.1701 

Yes Northeast Female Above Poverty Level 35-44 0.1256 0.0072 0.1029 0.1525 

Yes Northeast Female Above Poverty Level 45-54 0.1246 0.0071 0.1024 0.1509 

Yes Northeast Female Above Poverty Level 55-64 0.1281 0.0076 0.1043 0.1565 

Yes Northeast Female Above Poverty Level 65-74 0.1151 0.0070 0.0934 0.1412 

Yes Northeast Female Above Poverty Level 75+ 0.0879 0.0098 0.0598 0.1273 

Yes Northeast Female Below Poverty Level 18-24 0.1646 0.0182 0.1104 0.2383 

Yes Northeast Female Below Poverty Level 25-34 0.1705 0.0110 0.1356 0.2123 

Yes Northeast Female Below Poverty Level 35-44 0.1842 0.0126 0.1442 0.2323 

Yes Northeast Female Below Poverty Level 45-54 0.2084 0.0143 0.1629 0.2627 

Yes Northeast Female Below Poverty Level 55-64 0.2180 0.0156 0.1684 0.2773 

Yes Northeast Female Below Poverty Level 65-74 0.1695 0.0118 0.1321 0.2149 

Yes Northeast Female Below Poverty Level 75+ 0.0960 0.0125 0.0603 0.1495 

Yes Northeast Male Above Poverty Level 18-24 0.1728 0.0210 0.1126 0.2560 

Yes Northeast Male Above Poverty Level 25-34 0.1163 0.0081 0.0914 0.1469 

Yes Northeast Male Above Poverty Level 35-44 0.0932 0.0070 0.0721 0.1197 

Yes Northeast Male Above Poverty Level 45-54 0.0901 0.0063 0.0710 0.1139 

Yes Northeast Male Above Poverty Level 55-64 0.0963 0.0072 0.0744 0.1237 

Yes Northeast Male Above Poverty Level 65-74 0.0874 0.0073 0.0656 0.1155 

Yes Northeast Male Above Poverty Level 75+ 0.0708 0.0118 0.0398 0.1229 

Yes Northeast Male Below Poverty Level 18-24 0.1734 0.0193 0.1138 0.2552 

Yes Northeast Male Below Poverty Level 25-34 0.1323 0.0138 0.0896 0.1911 

Yes Northeast Male Below Poverty Level 35-44 0.1182 0.0135 0.0772 0.1768 

Yes Northeast Male Below Poverty Level 45-54 0.1254 0.0144 0.0816 0.1879 

Yes Northeast Male Below Poverty Level 55-64 0.1361 0.0198 0.0786 0.2253 

Yes Northeast Male Below Poverty Level 65-74 0.1305 0.0195 0.0743 0.2191 

Yes Northeast Male Below Poverty Level 75+ 0.0988 0.0255 0.0373 0.2366 

Yes South Female Above Poverty Level 18-24 0.1533 0.0114 0.1185 0.1959 

Yes South Female Above Poverty Level 25-34 0.1235 0.0054 0.1065 0.1429 

Yes South Female Above Poverty Level 35-44 0.1114 0.0050 0.0956 0.1295 

Yes South Female Above Poverty Level 45-54 0.1149 0.0047 0.0998 0.1320 

Yes South Female Above Poverty Level 55-64 0.1261 0.0058 0.1077 0.1472 

Yes South Female Above Poverty Level 65-74 0.1188 0.0058 0.1004 0.1400 

Yes South Female Above Poverty Level 75+ 0.0959 0.0087 0.0701 0.1297 

Yes South Female Below Poverty Level 18-24 0.1491 0.0122 0.1107 0.1978 
5C-68 

Appendix 5C, Attachment C, Table 5CC-3. Smoothed prevalence for adults “EVER” having asthma 

Smoothed Region Gender Poverty Status Age_group Prevalence SE LowerCI UpperCI 

Yes South Female Below Poverty Level 25-34 0.1365 0.0066 0.1149 0.1614 

Yes South Female Below Poverty Level 35-44 0.1414 0.0078 0.1159 0.1714 

Yes South Female Below Poverty Level 45-54 0.1686 0.0097 0.1369 0.2059 

Yes South Female Below Poverty Level 55-64 0.1881 0.0115 0.1505 0.2324 

Yes South Female Below Poverty Level 65-74 0.1651 0.0101 0.1325 0.2039 

Yes South Female Below Poverty Level 75+ 0.1125 0.0124 0.0755 0.1644 

Yes South Male Above Poverty Level 18-24 0.1445 0.0095 0.1147 0.1805 

Yes South Male Above Poverty Level 25-34 0.1086 0.0050 0.0926 0.1269 

Yes South Male Above Poverty Level 35-44 0.0860 0.0044 0.0720 0.1025 

Yes South Male Above Poverty Level 45-54 0.0742 0.0040 0.0616 0.0891 

Yes South Male Above Poverty Level 55-64 0.0733 0.0045 0.0594 0.0902 

Yes South Male Above Poverty Level 65-74 0.0790 0.0048 0.0639 0.0974 

Yes South Male Above Poverty Level 75+ 0.0900 0.0102 0.0606 0.1316 

Yes South Male Below Poverty Level 18-24 0.1433 0.0144 0.1000 0.2013 

Yes South Male Below Poverty Level 25-34 0.1031 0.0087 0.0766 0.1376 

Yes South Male Below Poverty Level 35-44 0.0934 0.0090 0.0664 0.1300 

Yes South Male Below Poverty Level 45-54 0.1055 0.0101 0.0751 0.1462 

Yes South Male Below Poverty Level 55-64 0.1072 0.0108 0.0750 0.1510 

Yes South Male Below Poverty Level 65-74 0.0942 0.0092 0.0666 0.1314 

Yes South Male Below Poverty Level 75+ 0.0712 0.0123 0.0385 0.1279 

Yes West Female Above Poverty Level 18-24 0.1571 0.0135 0.1163 0.2089 

Yes West Female Above Poverty Level 25-34 0.1415 0.0067 0.1201 0.1660 

Yes West Female Above Poverty Level 35-44 0.1373 0.0070 0.1150 0.1631 

Yes West Female Above Poverty Level 45-54 0.1423 0.0067 0.1207 0.1670 

Yes West Female Above Poverty Level 55-64 0.1497 0.0071 0.1268 0.1758 

Yes West Female Above Poverty Level 65-74 0.1445 0.0070 0.1220 0.1704 

Yes West Female Above Poverty Level 75+ 0.1266 0.0112 0.0929 0.1702 

Yes West Female Below Poverty Level 18-24 0.1434 0.0164 0.0945 0.2117 

Yes West Female Below Poverty Level 25-34 0.1318 0.0092 0.1026 0.1678 

Yes West Female Below Poverty Level 35-44 0.1440 0.0117 0.1074 0.1903 

Yes West Female Below Poverty Level 45-54 0.1806 0.0144 0.1350 0.2374 

Yes West Female Below Poverty Level 55-64 0.1713 0.0136 0.1284 0.2248 

Yes West Female Below Poverty Level 65-74 0.1511 0.0117 0.1141 0.1974 

Yes West Female Below Poverty Level 75+ 0.1292 0.0177 0.0785 0.2054 

Yes West Male Above Poverty Level 18-24 0.1566 0.0173 0.1067 0.2240 

Yes West Male Above Poverty Level 25-34 0.1233 0.0069 0.1019 0.1485 

Yes West Male Above Poverty Level 35-44 0.1025 0.0060 0.0839 0.1247 

Yes West Male Above Poverty Level 45-54 0.0908 0.0054 0.0741 0.1107 

Yes West Male Above Poverty Level 55-64 0.0955 0.0059 0.0774 0.1174 

Yes West Male Above Poverty Level 65-74 0.1067 0.0068 0.0860 0.1318 

Yes West Male Above Poverty Level 75+ 0.1265 0.0152 0.0834 0.1871 

Yes West Male Below Poverty Level 18-24 0.1521 0.0204 0.0938 0.2373 

Yes West Male Below Poverty Level 25-34 0.0942 0.0095 0.0660 0.1327 

Yes West Male Below Poverty Level 35-44 0.0885 0.0102 0.0590 0.1308 

Yes West Male Below Poverty Level 45-54 0.1133 0.0130 0.0753 0.1670 

Yes West Male Below Poverty Level 55-64 0.1237 0.0156 0.0789 0.1888 

Yes West Male Below Poverty Level 65-74 0.1134 0.0142 0.0726 0.1727 

Yes West Male Below Poverty Level 75+ 0.0961 0.0190 0.0474 0.1849 

 
5C-69 

Appendix 5C, Attachment C, Table 5CC-4. Smoothed prevalence for adults “STILL” having asthma 

Smoothed Region Gender Poverty Status Age_group Prevalence SE LowerCI UpperCI 

Yes Midwest Female Above Poverty Level 18-24 0.1046 0.0121 0.0703 0.1528 

Yes Midwest Female Above Poverty Level 25-34 0.0888 0.0057 0.0714 0.1100 

Yes Midwest Female Above Poverty Level 35-44 0.0835 0.0052 0.0675 0.1030 

Yes Midwest Female Above Poverty Level 45-44 0.0893 0.0050 0.0738 0.1077 

Yes Midwest Female Above Poverty Level 55-64 0.0909 0.0057 0.0736 0.1118 

Yes Midwest Female Above Poverty Level 65-74 0.0811 0.0051 0.0654 0.1002 

Yes Midwest Female Above Poverty Level 75+ 0.0630 0.0067 0.0438 0.0898 

Yes Midwest Female Below Poverty Level 18-24 0.1327 0.0139 0.0907 0.1899 

Yes Midwest Female Below Poverty Level 25-34 0.1280 0.0095 0.0980 0.1656 

Yes Midwest Female Below Poverty Level 35-44 0.1315 0.0114 0.0961 0.1772 

Yes Midwest Female Below Poverty Level 45-44 0.1600 0.0134 0.1181 0.2132 

Yes Midwest Female Below Poverty Level 55-64 0.1777 0.0146 0.1318 0.2352 

Yes Midwest Female Below Poverty Level 65-74 0.1488 0.0128 0.1091 0.1998 

Yes Midwest Female Below Poverty Level 75+ 0.0940 0.0157 0.0513 0.1659 

Yes Midwest Male Above Poverty Level 18-24 0.0807 0.0115 0.0491 0.1299 

Yes Midwest Male Above Poverty Level 25-34 0.0584 0.0045 0.0448 0.0758 

Yes Midwest Male Above Poverty Level 35-44 0.0479 0.0040 0.0359 0.0637 

Yes Midwest Male Above Poverty Level 45-44 0.0472 0.0038 0.0358 0.0620 

Yes Midwest Male Above Poverty Level 55-64 0.0522 0.0042 0.0395 0.0687 

Yes Midwest Male Above Poverty Level 65-74 0.0528 0.0045 0.0393 0.0706 

Yes Midwest Male Above Poverty Level 75+ 0.0481 0.0081 0.0268 0.0847 

Yes Midwest Male Below Poverty Level 18-24 0.0912 0.0136 0.0542 0.1496 

Yes Midwest Male Below Poverty Level 25-34 0.0683 0.0091 0.0430 0.1067 

Yes Midwest Male Below Poverty Level 35-44 0.0694 0.0109 0.0402 0.1173 

Yes Midwest Male Below Poverty Level 45-44 0.1015 0.0141 0.0624 0.1610 

Yes Midwest Male Below Poverty Level 55-64 0.1338 0.0165 0.0866 0.2010 

Yes Midwest Male Below Poverty Level 65-74 0.1202 0.0161 0.0751 0.1869 

Yes Midwest Male Below Poverty Level 75+ 0.0709 0.0210 0.0250 0.1850 

Yes Northeast Female Above Poverty Level 18-24 0.1098 0.0134 0.0721 0.1638 

Yes Northeast Female Above Poverty Level 25-34 0.0965 0.0065 0.0765 0.1210 

Yes Northeast Female Above Poverty Level 35-44 0.0899 0.0063 0.0708 0.1136 

Yes Northeast Female Above Poverty Level 45-44 0.0901 0.0060 0.0718 0.1124 

Yes Northeast Female Above Poverty Level 55-64 0.0917 0.0062 0.0727 0.1151 

Yes Northeast Female Above Poverty Level 65-74 0.0862 0.0059 0.0681 0.1085 

Yes Northeast Female Above Poverty Level 75+ 0.0726 0.0093 0.0467 0.1110 

Yes Northeast Female Below Poverty Level 18-24 0.1212 0.0166 0.0744 0.1915 

Yes Northeast Female Below Poverty Level 25-34 0.1199 0.0093 0.0914 0.1559 

Yes Northeast Female Below Poverty Level 35-44 0.1338 0.0106 0.1013 0.1747 

Yes Northeast Female Below Poverty Level 45-44 0.1655 0.0127 0.1260 0.2143 

Yes Northeast Female Below Poverty Level 55-64 0.1824 0.0143 0.1381 0.2370 

Yes Northeast Female Below Poverty Level 65-74 0.1273 0.0098 0.0972 0.1650 

Yes Northeast Female Below Poverty Level 75+ 0.0529 0.0086 0.0300 0.0917 

Yes Northeast Male Above Poverty Level 18-24 0.0922 0.0154 0.0509 0.1616 

Yes Northeast Male Above Poverty Level 25-34 0.0600 0.0058 0.0428 0.0836 

Yes Northeast Male Above Poverty Level 35-44 0.0488 0.0050 0.0340 0.0696 

Yes Northeast Male Above Poverty Level 45-44 0.0483 0.0051 0.0334 0.0693 

Yes Northeast Male Above Poverty Level 55-64 0.0563 0.0065 0.0376 0.0834 

Yes Northeast Male Above Poverty Level 65-74 0.0576 0.0063 0.0393 0.0837 

Yes Northeast Male Above Poverty Level 75+ 0.0554 0.0106 0.0281 0.1062 

Yes Northeast Male Below Poverty Level 18-24 0.0791 0.0128 0.0430 0.1409 

Yes Northeast Male Below Poverty Level 25-34 0.0800 0.0119 0.0459 0.1360 

Yes Northeast Male Below Poverty Level 35-44 0.0805 0.0135 0.0427 0.1465 

Yes Northeast Male Below Poverty Level 45-44 0.0857 0.0162 0.0419 0.1672 

Yes Northeast Male Below Poverty Level 55-64 0.1064 0.0224 0.0475 0.2211 

Yes Northeast Male Below Poverty Level 65-74 0.1040 0.0200 0.0501 0.2035 

Yes Northeast Male Below Poverty Level 75+ 0.0771 0.0236 0.0241 0.2203 

Yes South Female Above Poverty Level 18-24 0.0891 0.0083 0.0649 0.1212 

Yes South Female Above Poverty Level 25-34 0.0735 0.0039 0.0615 0.0876 

Yes South Female Above Poverty Level 35-44 0.0684 0.0036 0.0571 0.0817 

Yes South Female Above Poverty Level 45-44 0.0732 0.0037 0.0617 0.0866 

Yes South Female Above Poverty Level 55-64 0.0846 0.0046 0.0705 0.1012 

Yes South Female Above Poverty Level 65-74 0.0817 0.0047 0.0674 0.0987 

Yes South Female Above Poverty Level 75+ 0.0641 0.0070 0.0443 0.0920 

Yes South Female Below Poverty Level 18-24 0.0948 0.0105 0.0641 0.1380 
5C-70 

Appendix 5C, Attachment C, Table 5CC-4. Smoothed prevalence for adults “STILL” having asthma 

Smoothed Region Gender Poverty Status Age_group Prevalence SE LowerCI UpperCI 

Yes South Female Below Poverty Level 25-34 0.0942 0.0059 0.0758 0.1166 

Yes South Female Below Poverty Level 35-44 0.1086 0.0073 0.0859 0.1365 

Yes South Female Below Poverty Level 45-44 0.1446 0.0095 0.1149 0.1806 

Yes South Female Below Poverty Level 55-64 0.1618 0.0112 0.1267 0.2043 

Yes South Female Below Poverty Level 65-74 0.1379 0.0095 0.1082 0.1742 

Yes South Female Below Poverty Level 75+ 0.0881 0.0109 0.0570 0.1337 

Yes South Male Above Poverty Level 18-24 0.0600 0.0073 0.0392 0.0907 

Yes South Male Above Poverty Level 25-34 0.0490 0.0035 0.0381 0.0629 

Yes South Male Above Poverty Level 35-44 0.0421 0.0033 0.0322 0.0550 

Yes South Male Above Poverty Level 45-44 0.0386 0.0031 0.0292 0.0510 

Yes South Male Above Poverty Level 55-64 0.0384 0.0034 0.0282 0.0520 

Yes South Male Above Poverty Level 65-74 0.0457 0.0038 0.0343 0.0607 

Yes South Male Above Poverty Level 75+ 0.0627 0.0089 0.0382 0.1013 

Yes South Male Below Poverty Level 18-24 0.0583 0.0080 0.0358 0.0937 

Yes South Male Below Poverty Level 25-34 0.0443 0.0053 0.0290 0.0672 

Yes South Male Below Poverty Level 35-44 0.0492 0.0067 0.0303 0.0790 

Yes South Male Below Poverty Level 45-44 0.0720 0.0090 0.0460 0.1112 

Yes South Male Below Poverty Level 55-64 0.0771 0.0096 0.0492 0.1188 

Yes South Male Below Poverty Level 65-74 0.0608 0.0075 0.0390 0.0937 

Yes South Male Below Poverty Level 75+ 0.0353 0.0082 0.0154 0.0787 

Yes West Female Above Poverty Level 18-24 0.0842 0.0115 0.0522 0.1328 

Yes West Female Above Poverty Level 25-34 0.0876 0.0054 0.0708 0.1080 

Yes West Female Above Poverty Level 35-44 0.0931 0.0062 0.0742 0.1163 

Yes West Female Above Poverty Level 45-44 0.0981 0.0065 0.0781 0.1226 

Yes West Female Above Poverty Level 55-64 0.1028 0.0067 0.0820 0.1281 

Yes West Female Above Poverty Level 65-74 0.0984 0.0061 0.0795 0.1213 

Yes West Female Above Poverty Level 75+ 0.0825 0.0090 0.0565 0.1189 

Yes West Female Below Poverty Level 18-24 0.0863 0.0121 0.0524 0.1387 

Yes West Female Below Poverty Level 25-34 0.0934 0.0078 0.0695 0.1243 

Yes West Female Below Poverty Level 35-44 0.1091 0.0100 0.0789 0.1489 

Yes West Female Below Poverty Level 45-44 0.1332 0.0120 0.0967 0.1806 

Yes West Female Below Poverty Level 55-64 0.1292 0.0120 0.0929 0.1770 

Yes West Female Below Poverty Level 65-74 0.1169 0.0104 0.0854 0.1580 

Yes West Female Below Poverty Level 75+ 0.1021 0.0148 0.0609 0.1662 

Yes West Male Above Poverty Level 18-24 0.0597 0.0092 0.0351 0.0998 

Yes West Male Above Poverty Level 25-34 0.0569 0.0046 0.0432 0.0745 

Yes West Male Above Poverty Level 35-44 0.0549 0.0045 0.0414 0.0723 

Yes West Male Above Poverty Level 45-44 0.0525 0.0046 0.0389 0.0704 

Yes West Male Above Poverty Level 55-64 0.0562 0.0053 0.0407 0.0770 

Yes West Male Above Poverty Level 65-74 0.0660 0.0058 0.0487 0.0889 

Yes West Male Above Poverty Level 75+ 0.0783 0.0131 0.0437 0.1364 

Yes West Male Below Poverty Level 18-24 0.0720 0.0125 0.0389 0.1295 

Yes West Male Below Poverty Level 25-34 0.0484 0.0068 0.0294 0.0787 

Yes West Male Below Poverty Level 35-44 0.0539 0.0084 0.0311 0.0919 

Yes West Male Below Poverty Level 45-44 0.0784 0.0115 0.0465 0.1293 

Yes West Male Below Poverty Level 55-64 0.0936 0.0155 0.0517 0.1635 

Yes West Male Below Poverty Level 65-74 0.0758 0.0129 0.0413 0.1350 

Yes West Male Below Poverty Level 75+ 0.0489 0.0136 0.0182 0.1250 
5C-71 

Appendix 5C, Attachment C, Figure 5CC-1. Smoothed prevalence and confidence intervals for children ‘EVE

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 1. Smoothed asthma 'EVER' prevalence rate

region=Northeast pov_rat=Above Pov

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 1. Smoothed asthma 'EVER' prevalence rate

region=Northeast pov_rat=Below Pov

gender Female 
5C-72 

Appendix 5C, Attachment C, Figure 5CC-1, cont. Smoothed prevalence and confidence intervals for children asthma.

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 1. Smoothed asthma 'EVER' prevalence rates 

region=West pov_rat=Above Povert

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 1. Smoothed asthma 'EVER' prevalence rates 

region=West pov_rat=Below Povert

gender Female 
5C-73 

Appendix 5C, Attachment C, Figure 5CC-2. Smoothed prevalence and confidence intervals for children ‘STIL

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Smoothed asthma 'STILL' prevalence rates 

region=Northeast pov_rat=Above Pove

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Smoothed asthma 'STILL' prevalence rates a

region=Northeast pov_rat=Below Pover

gender Female 
5C-74 

Appendix 5C, Attachment C, Figure 5CC-2, cont. Smoothed prevalence and confidence intervals for children asthma.

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Smoothed asthma 'STILL' prevalence rates a

region=West pov_rat=Above Poverty L

gender Female 

prev

0.00.10.20.30.40.50.6

age

0 1 2 3 4 5 6 7 8 9 10 

Figure 2. Smoothed asthma 'STILL' prevalence rat

region=West pov_rat=Below Pove

gender Female 
5C-75 

Appendix 5C, Attachment C, Figure 5CC-3. Smoothed prevalence and confidence intervals for Adults ‘EVER

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Smoothed adult asthma 'EVER' prevalence ra

region=Northeast pov_rat=Above Pov

gender Female 

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Smoothed adult asthma 'EVER' prevalence rat

region=Northeast pov_rat=Below Pove

gender Female 
5C-76 

Appendix 5C, Attachment C, Figure 5CC-3, cont. Smoothed prevalence and confidence intervals for Adults ‘

asthma.

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Smoothed adult asthma 'EVER' prevalence rate

region=West pov_rat=Above Poverty 

gender Female 

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 3. Smoothed adult asthma 'EVER' prevalence rate

region=West pov_rat=Below Poverty 

gender Female 
5C-77 

Appendix 5C, Attachment C, Figure 5CC-4. Smoothed prevalence and confidence intervals for Adults ‘STILL

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals

region=Midwest pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 4. Smoothed adult asthma 'STILL' prevalence ra

region=Northeast pov_rat=Above Pov

gender Female 

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 4. Smoothed adult asthma 'STILL' prevalence rat

region=Northeast pov_rat=Below Pove

gender Female 
5C-78 

Appendix 5C, Attachment C, Figure 5CC-4, cont. Smoothed prevalence and confidence intervals for Adults ‘

asthma.

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Above Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 55-64 65-74 75+

Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervals

region=South pov_rat=Below Poverty Level

gender Female Male

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 4. Smoothed adult asthma 'STILL' prevalence ra

region=West pov_rat=Above Povert

gender Female 

prev

0.00.10.20.30.4

age_grp

18-24 25-34 35-44 45-54 

Figure 4. Smoothed adult asthma 'STILL' prevalence rate

region=West pov_rat=Below Poverty 

gender Female 
 5D-i 

APPENDIX 5D 

Variability Analysis and Uncertainty Characterization

Table of Contents 

5D-1. OVERVIEW .............................................................................................................. 5D-1 

5D-2. TREATMENT OF VARIABILITY AND CO-VARIABILITY ............................... 5D-1 

5D-3. CHARACTERIZATION OF UNCERTAINTY........................................................ 5D-7 

5D-4. REFERENCES ........................................................................................................ 5D-10
 5D-ii 

List of Tables 

Table 5D-1. Components of exposure variability modeled by APEX. ................................. 5D-4

Table 5D-2. Important components of co-variability in exposure modeling. ....................... 5D-6
 5D-1 

5D-1. OVERVIEW 

An important issue associated with any population exposure or risk assessment is the 

characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in 

a population or variable of interest (e.g., residential air exchange rates). The degree of variability 

cannot be reduced through further research, only better characterized with additional 

measurement. Uncertainty refers to the lack of knowledge regarding the values of model input 

variables (i.e., parameter uncertainty), the physical systems or relationships used (i.e., use of 

input variables to estimate exposure or risk or model uncertainty), and in specifying the scenario 

that is consistent with purpose of the assessment (i.e., scenario uncertainty). Uncertainty is, 

ideally, reduced to the maximum extent possible through improved measurement of key 

parameters and iterative model refinement. The approaches used to assess variability and to 

characterize uncertainty in this HREA are discussed in the following two sections. The primary 

purpose of this characterization is to provide a summary of variability and uncertainty 

evaluations conducted to date regarding our O3 exposure assessments and APEX exposure 

modeling and to identify the most important elements of uncertainty in need of further 

characterization. Each section contains a concise tabular summary of the identified components 

and how, for elements of uncertainty, each source may affect the estimated exposures. 

5D-2. TREATMENT OF VARIABILITY AND CO-VARIABILITY 

The purpose for addressing variability in this HREA is to ensure that the estimates of 

exposure and risk reflect the variability of ambient O3 concentrations, population characteristics, 

associated O3 exposure and intake dose, and potential health risk across the study area and for 

the simulated at-risk populations. In this HREA, there are several algorithms that account for 

variability of input data when generating the number of estimated benchmark exceedances or 

health risk outputs. For example, variability may arise from differences in the population 

residing within census tracts (e.g., age distribution) and the activities that may affect population 

exposure to O3 and the resulting intake dose estimate (e.g., time spent outdoors, performing 

moderate or greater exertion level activities outdoors). A complete range of potential exposure 

levels and associated risk estimates can be generated when appropriately addressing variability in 

exposure and risk assessments; note however that the range of values obtained would be within 

the constraints of the input parameters, algorithms, or modeling system used, not necessarily the 

complete range of the true exposure or risk values. 

Where possible, staff identified and incorporated the observed variability in input data 

sets rather than employing standard default assumptions and/or using point estimates to describe 

model inputs. The details regarding variability distributions used in data inputs are described in 
 5D-2 

Appendix 5B, while details regarding the variability addressed within its algorithms and 

processes are found in the APEX TSD (US EPA, 2012). 

Briefly, APEX has been designed to account for variability in most of the input data, 

including the physiological variables that are important inputs to determining exertion levels and 

associated ventilation rates. APEX simulates individuals and then calculates O3 exposures for 

each of these simulated individuals. The individuals are selected to represent a random sample 

from a defined population. The collection of individuals represents the variability of the target 

population, and accounts for several types of variability, including demographic, physiological, 

and human behavior. In this assessment, we simulated 200,000 individuals to reasonably capture 

the variability expected in the population exposure distribution for each study area. APEX 

incorporates stochastic processes representing the natural variability of personal profile 

characteristics, activity patterns, and microenvironment parameters. In this way, APEX is able 

to represent much of the variability in the exposure estimates resulting from the variability of the 

factors effecting human exposure. 

We note also that correlations and non-linear relationships between variables input to the 

model can result in the model producing incorrect results if the inherent relationships between 

these variables are not preserved. That is why APEX is also designed to account for covariability, or linear and nonlinear correlation among the model inputs, provided that enough is 

known about these relationships to specify them. This is accomplished by providing inputs that 

enable the correlation to be modeled explicitly within APEX. For example, there is a non-linear 

relationship between the outdoor temperature and air exchange rate in homes. One factor that 

contributes to this non-linear relationship is that windows tend to be closed more often when 

temperatures are at either low or high extremes than when temperatures are moderate. This 

relationship is explicitly modeled in APEX by specifying different probability distributions of air 

exchange rates for different ambient temperatures. In any event, APEX models variability and 

co-variability in two ways: 

 Stochastically. The user provides APEX with probability distributions 

characterizing the variability of many input parameters. These are treated 

stochastically in the model and the estimated exposure distributions reflect this 

variability. For example, the rate of O3 removal in houses can depend on a 

number of factors which we are not able to explicitly model at this time, due to a 

lack of data. However, we can specify a distribution of removal rates which 

reflects observed variations in O3 decay. APEX randomly samples from this 

distribution to obtain values which are used in the mass balance model. Further, 

co-variability can be modeled stochastically through the use of conditional 

distributions. If two or more parameters are related, conditional distributions that 
 5D-3 

depend on the values of the related parameters are input to APEX. For example, 

the distribution of air exchange rates (AERs) in a house depends on the outdoor 

temperature and whether or not air conditioning (A/C) is in use. In this case, a set 

of AER distributions is provided to APEX for different ranges of temperatures 

and A/C use, and the selection of the distribution in APEX is driven by the 

temperature and A/C status at that time. The spatial variability of A/C prevalence 

is modeled by supplying APEX with A/C prevalence for each Census tract in the 

modeled area. 

 Explicitly. For some variables used in modeling exposure, APEX models 

variability and co-variability explicitly and not stochastically. For example, 

hourly-average ambient O3 concentrations and temperatures are used in model 

calculations. These are input to the model for every hour in the time period 

modeled at different spatial locations, and in this way the variability and covariability of hourly concentrations and temperatures are modeled explicitly. 

Important sources of the variability and co-variability accounted for by APEX and used 

for this exposure analysis are summarized in Table 5D-1 and Table 5D-2 below, respectively. 
 5D-4 

Table 5D-1. Components of exposure variability modeled by APEX. 

Component Variability Source Comment 

Simulated 

Individuals 

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). 

Activity patterns 

Data diaries are randomly selected from CHAD master 

(>38,000 diaries) using six diary pools stratified by two daytypes (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). 

Ambient Input 

Measured ambient O3

concentrations 

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 for titration by NO. 
 5D-5 

Component Variability Source Comment 

Microenvironments: 

Temporal Variability 

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. 

Air exchange rates 

Several lognormal distributions are sampled based on five 

daily mean temperature ranges, study area, and study-area 

specific A/C prevalence rates. 

Proximity factors for 

on- and near roads 

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. 

Physiological 

Factors and 

Algorithms 

Resting metabolic 

rate (RMR) 

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). 

Maximum normalized 

oxygen consumption 

rate (NVO2) 

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). 

Maximum oxygen 

debt (MOXD) 

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. 

Recovery time 

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). 

METS by activity 

Values randomly sampled from distributions developed for 

specific activities (a few are age-group specific) (McCurdy, 

2000; US EPA, 2002). 

Oxygen uptake per 

unit of energy 

expended (UCF) 

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). 

Body mass 

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). 

Height 

Values randomly sampled from distributions used are based 

on equations developed for each gender by Johnson (1998) 

using height and weight data from Brainard and Burmaster 

(1992) (also see Appendix B of 2010 CO REA). 

Body surface area 

Point estimates of exponential parameters used for 

calculating body surface area as a function of body mass 

(Burmaster, 1998) 
 5D-6 

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 postexercise oxygen consumption (EPOC) that may occur 

following vigorous exertion activities (Isaacs et al., 2007 and 

section 7.2 of APEX TSD). 

Table 5D-2. Important components of co-variability in exposure modeling. 

Type of Co-variability Modeled 

by 

APEX? 

Treatment in APEX / Comments 

Within-person correlations 1 Yes Sequence of activities performed, 

microenvironments visited, and general 

physiological parameters (body mass, height, 

ventilation rates). 

Between-person correlations No Judged as not important. 

Correlations between profile variables and 

microenvironment parameters 

Yes Profiles are assigned microenvironment 

parameters. 

Correlations between demographic 

variables (e.g., age, gender) and activities 

Yes Age and gender are used in activity diary selection. 

Correlations between activities and 

microenvironment parameters 

No Perhaps important, but do not have data. For 

example, frequency of opening windows when 

cooking or smoking tobacco products. 

Correlations among microenvironment 

parameters in the same microenvironment 

Yes Modeled with joint conditional variables. 

Correlations between demographic 

variables and air quality 

Yes Modeled with the spatially varying demographic 

variables and air quality input to APEX. 

Correlations between meteorological 

variables and activities 

Yes Temperature is used in activity diary selection. 

Correlations between meteorological 

variables and microenvironment 

parameters 

Yes The distributions of microenvironment parameters 

can be functions of temperature. 

Correlations between drive times in CHAD 

and commute distances traveled 

Yes CHAD diary selection is weighted by commute 

times for employed persons during weekdays. 

Consistency of occupation/school 

microenvironmental time and time spent 

commuting/busing for individuals from one 

working/school day to the next. 

No 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). 

1

 The term correlation is used to represent linear and nonlinear relationships. 
 5D-7 

5D-3. CHARACTERIZATION OF UNCERTAINTY 

While it may be possible to capture a range of exposure or risk values by accounting for 

variability inherent to influential factors, the true exposure or risk for any given individual within 

a study area is unknown, though can be estimated. To characterize health risks, exposure and 

risk assessors commonly use an iterative process of gathering data, developing models, and 

estimating exposures and risks, given the goals of the assessment, scale of the assessment 

performed, and limitations of the input data available. However, significant uncertainty often 

remains and emphasis is then placed on characterizing the nature of that uncertainty and its 

impact on exposure and risk estimates. 

In the final 2008 O3 NAAQS rule,1

 EPA staff performed such a characterization and at 

that time, identified the most important uncertainties affecting the exposure estimates. The key 

elements of uncertainty were 1) the modeling of human activity patterns over an O3 season, 2) 

the modeling of variations in ambient O3 concentrations near roadways, 3) the modeling of air 

exchange rates that affect the amount of O3 that penetrates indoors, and 4) the characterization of 

energy expenditure (and related ventilation rate estimates) for children engaged in various 

activities. Further, the primary findings of a quantitative Monte Carlo analysis also performed at 

that time indicated that the overall uncertainty of the APEX estimated exposure distributions was 

relatively small: the percent of children or asthmatic children with exposures above 0.06, 0.07, or 

0.08 ppm-8hr under moderate exertion have 95% were estimated by APEX to have uncertainty 

intervals of at most ±6 percentage points. Details for these previously identified uncertainties are 

discussed in the 2007 O3 Staff Paper (section 4.6) and in a technical memorandum describing the 

2007 O3 exposure modeling uncertainty analysis (Langstaff, 2007). 

The REA’s conducted for the most recent NO2 (US EPA, 2008), SO2 (US EPA, 2009), 

and CO (US EPA, 2010) NAAQS reviews also presented characterizations of the uncertainties 

associated with APEX exposure modeling (among other pollutant specific issues), albeit mainly 

qualitative evaluations. Conclusions drawn from all of these assessments regarding exposure 

modeling uncertainty have been integrated here, following the standard approach used by EPA 

staff since 2008 and outlined by WHO (2008) to identify, evaluate, and prioritize the most 

important uncertainties relevant to the estimated potential health effect endpoints used in this O3

HREA. Staff selected a mainly qualitative approach here supplemented by various model 

sensitivity analyses and input data evaluations, all complimentary to quantitative uncertainty 

characterizations conducted for 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 

The qualitative approach used here varies from that described by WHO (2008) in that a 

greater focus was placed on evaluating the direction and the magnitude2 of the uncertainty; that 

is, qualitatively rating how the source of uncertainty, in the presence of alternative information, 

may affect the estimated exposures and health risk results. In addition and consistent with the 

WHO (2008) guidance, staff discuss the uncertainty in the knowledge base (e.g., the accuracy of 

the data used, acknowledgement of data gaps) and decisions made where possible (e.g., selection 

of particular model forms), although qualitative ratings were assigned only to uncertainty 

regarding the knowledge base. 

First, staff identified the key aspects of the assessment approach that may contribute to 

uncertainty in the exposure and risk estimates and provided the rationale for their inclusion. 

Then, staff characterized the magnitude and direction of the influence on the assessment results 

for each of these identified sources of uncertainty. Consistent with the WHO (2008) guidance, 

staff subjectively scaled the overall impact of the uncertainty by considering the degree of 

uncertainty as implied by the relationship between the source of uncertainty and the exposure 

concentrations. 

Where the magnitude of uncertainty was rated low, it was judged that changes within the 

source of uncertainty would have only a small effect on the exposure results. For example, we 

have commonly employed statistical procedure to substitute missing concentration values to 

complete the meteorological data sets. Staff has consistently compared the air quality 

distributions and found negligible differences between the substituted data set and the one with 

missing values (e.g., Tables 5-13 through 5-16 of US EPA, 2010), primarily because of the 

infrequency of missing value substitutions needed to complete a data set. There is still 

uncertainty in the approach used, and there may be alternative, and possibly better, methods 

available to perform such a task. However, in this instance, staff judged that the quantitative 

comparison of the ambient concentration data sets indicates that there would likely be little 

influence on exposure estimates by the data substitution procedure used. 

A magnitude designation of moderate implies that a change within the source of 

uncertainty would likely have a moderate (or proportional) effect on the results. For example, 

the magnitude of uncertainty associated with using the quadratic approach to represent a 

hypothetical future air quality scenario was rated as low-moderate. While we do not have 

information regarding how the ambient O3 concentration distribution might look in the future, we 

do know however what the distribution might look like based on historical trends and the 

emission sources. These historical data and trends serve to generate algorithms used to adjust air 

quality. If these trends in observed concentrations and emissions were to remain constant in the 

future, then the magnitude of the impact to estimated exposures in this assessment would be 

 

2

 This is synonymous with the “level of uncertainty” discussed in WHO (2008), section 5.1.2.2. 
 5D-9 

judged as likely low or having negligible impact on the estimated exposures. However, if there 

are entirely new emission sources in the future or if the approach developed is not equally 

appropriate across the range of assessed study areas, the magnitude of influence might be judged 

as greater. For example, when comparing exposure estimates for one year that used three 

different 3-year periods to adjust that year’s air quality levels to just meet the current standard, 

staff observed mainly proportional differences (e.g., a factor of two or three) in the estimated 

number of persons exposed in more than half of the twelve study areas (Langstaff, 2007). 

Assuming that these types of ambient concentration adjustments could reflect the addition of a 

new or unaccounted for emission source in a particular study area, staff also judged the 

magnitude of influence in using the quadratic approach to adjust air quality data to represent a 

hypothetical future scenario as moderate. A characterization of high implies that a small change 

in the source would have a large effect on results, potentially an order of magnitude or more. 

This rating would be used where the model estimates were extremely sensitive to the identified 

source of uncertainty. 

In addition to characterizing the magnitude of uncertainty, staff also included the 

direction of influence, indicating how the source of uncertainty was judged to affect estimated 

exposures or risk estimates; either the estimated values were possibly over- or under-estimated. 

In the instance where the component of uncertainty can affect the assessment endpoint in either 

direction, the influence was judged as both. Staff characterized the direction of influence as 

unknown when there was no evidence available to judge the directional nature of uncertainty 

associated with the particular source. Staff also subjectively scaled the knowledge-base 

uncertainty associated with each identified source using a three-level scale: low indicated 

significant confidence in the data used and its applicability to the assessment endpoints, 

moderate implied that there were some limitations regarding consistency and completeness of 

the data used or scientific evidence presented, and high indicated the extent of the knowledgebase was extremely limited. 

The output of the uncertainty characterization is a summary describing, for each 

identified source of uncertainty, the magnitude of the impact and the direction of influence the 

uncertainty may have on the exposure and risk characterization results. 
 5D-10 

5D-4. REFERENCES 

Brainard J and Burmaster D. (1992). Bivariate distributions for height and weight of men and 

women in the United States. Risk Analysis. 12(2):267-275. 

Burmaster DE. (1998). Lognormal distributions for skin area as a function of body weight. Risk 

Analysis. 18(1):27-32. 

CDC. (2011). Summary Health Statistics for U.S. Adults: National Health Interview Survey, 

years 2006-10. U.S. Department of Health and Human Services, Hyattsville, MD. Data 

and documentation available at: http://www.cdc.gov/nchs/nhis.htm (accessed October 4, 

2011). 

Glen G, Smith L, Isaacs K, McCurdy T, Langstaff J. (2008). A new method of longitudinal 

diary assembly for human exposure modeling. J Expos Sci Environ Epidem. 18:299-311. 

Graham SE and T McCurdy. (2005). Revised ventilation rate (VE) equations for use in 

inhalation-oriented exposure models. Report no. EPA/600/X-05/008. Report is found 

within Appendix A of US EPA (2009). Metabolically Derived Human Ventilation Rates: 

A Revised Approach Based Upon Oxygen Consumption Rates (Final Report). Report no. 

EPA/600/R-06/129F. Appendix D contains “Response to peer-review comments on 

Appendix A”, prepared by S. Graham (US EPA). Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543

Issacs K and Smith L. (2005). New Values for Physiological Parameters for the Exposure Model 

Input File Physiology.txt. Technical memorandum to Tom McCurdy, NERL WA10. 

December 20, 2005. Provided in Appendix A of the CO REA (US EPA, 2010). 

Isaacs K, Glen G, McCurdy T., and Smith L. (2007). Modeling energy expenditure and oxygen 

consumption in human exposure models: Accounting for fatigue and EPOC. J Expos Sci 

Environ Epidemiol. 18(3):289-98. 

Johnson T. (1998). Memo No. 5: Equations for Converting Weight to Height Proposed for the 

1998 Version of pNEM/CO. Memorandum Submitted to U.S. Environmental Protection 

Agency. TRJ Environmental, Inc., 713 Shadylawn Road, Chapel Hill, North Carolina 

27514. 

Johnson T, Mihlan G, LaPointe J, Fletcher K, Capel J, Rosenbaum A, Cohen J, Stiefer P. (2000). 

Estimation of carbon monoxide exposures and associated carboxyhemoglobin levels for 

residents of Denver and Los Angeles using pNEM/CO. Appendices. EPA contract 68-D6-

0064. 

Langstaff JE. (2007). OAQPS Staff Memorandum to Ozone NAAQS Review Docket (OAR2005-0172). Subject: Analysis of Uncertainty in Ozone Population Exposure Modeling. 

[January 31, 2007]. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html

McCurdy T. (2000). Conceptual basis for multi-route intake dose modeling using an energy 

expenditure approach. J Expos Anal Environ Epidemiol. 10:1-12. 

Schofield WN. (1985). Predicting basal metabolic rate, new standards, and review of previous 

work. Hum Nutr Clin Nutr. 39C(S1):5-41. 

US Census Bureau. (2007a). Employment Status: 2000- Supplemental Tables. Available at: 

http://www.census.gov/population/www/cen2000/phc-t28.html. 
 5D-11 

US Census Bureau. (2007b). 2000 Census of Population and Housing. Summary File 3 (SF3) 

Technical Documentation, available at: http://www.census.gov/prod/cen2000/doc/sf3.pdf. 

Individual SF3 files '30' (for income/poverty variables pct49-pct51) for each state were 

downloaded from: http://www2.census.gov/census_2000/datasets/Summary_File_3/. 

US EPA. (2002). EPA’s Consolidated Human Activities Database. Available at: 

http://www.epa.gov/chad/. 

US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. Available at: http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html

US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary 

National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November 

2008. Available at: 

http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 

US EPA. (2009). Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009. 

Available 

athttp://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 

US EPA. (2010). Quantitative Risk and Exposure Assessment for Carbon Monoxide – 

Amended. EPA Office of Air Quality Planning and Standards. EPA-452/R-10-009. July 

2010. Available at: http://www.epa.gov/ttn/naaqs/standards/co/data/CO-REA-AmendedJuly2010.pdf

US EPA. (2012). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4.4) Volume I: User’s Guide. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. EPA-452/B-12-001a. Available at: 

http://www.epa.gov/ttn/fera/human_apex.html

WHO. (2008). Harmonization Project Document No. 6. Part 1: Guidance document on 

characterizing and communicating uncertainty in exposure assessment. Available at: 

http://www.who.int/ipcs/methods/harmonization/areas/exposure/en/. 
 5D-12 

This page left intentionally blank 
5E-i 

APPENDIX 5E 

Updated Analysis of Air Exchange Rate Data: Memorandum from 

ICF International 
5E-1 

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, MI; 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. 
5E-2 

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 twoday 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 

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.
5E-4 

Table 1. Summary of Studies of Residential Air Exchange Rates. 

Study/ 

Attribute Avol RTP Panel RIOPA TEACH Wilson 1984 Wilson 1991 

Locations 

Lancaster, Lake 

Gregory, 

Riverside, San 

Dimas. All in 

Southern CA 

Research 

Triangle Park, NC CA; NJ; TX Los Angeles, CA; New York City, NY Southern CA Southern CA 

Years 1994 2000; 2001 1999; 2000; 2001 1999; 2000 1984, 1985 1984 

Months/ 

Seasons 

Feb; Mar; Apr; 

May; Jun; Jul; 

Aug; Sep; Oct; 

Nov 

2000 (Jun; Jul; 

Aug; Sep; Oct; 

Nov), 2001 (Jan; 

Feb; Apr; May) 

1999 (July to 

Dec); 2000 (all 

months); 2001 

(Jan and Feb) 

1999 (Feb; Mar; 

Apr; Jul; Aug); 

2000 (Jan; Feb; 

Mar; Sep; Oct) 

Mar 1984, Jul 1984, Jan 

1985 Jan, Mar, Jul 

Homes with 

AER 

Measurements 

86 37 284 85 581 288 

Total AER 

Measurements 161 854 524 151 1,362 316 

Average AER 

Measurements 

per Home 

1.87 23.08 1.85 1.78 2.34 1.10 

AER 

Measurement 

Duration 

Not Available 24 hour 24 to 96 hours 

Sample time (hours) 

reported. Ranges 

from about 1 to 7 

days 

7 days 7 days 

AER 

Measurement 

Technique 

Not Available Perflourocarbon tracer PMCH tracer Perflourocarbon tracer Perflourocarbon tracer Perflourocarbon tracer 

Min AER (h-1) 0.01 0.02 0.08 0.12 0.03 0.01 

Max AER (h-1) 2.70 21.44 87.50 8.87 11.77 2.91 

Mean AER (h-1) 0.80 0.72 1.41 1.71 1.05 0.57 

Min Temp. (°C) -0.04 -2.18 -6.82 -1.36 11.00 3.00 

Max Temp. (°C) 36.25 30.81 32.50 32.00 28.00 25.00 

Air Conditioner 

Categories 

No A/C; Central 

or Room A/C; 

Swamp Cooler 

only; Swamp + 

[Central or 

Room] 

Central or Room 

A/C (Y/N) 

Window A/C 

(Y/N); Evap 

Coolers (Y/N) 

Central or Room 

A/C (Y/N) 

Central A/C (Y/N); Room 

A/C (Y/N); 

Central A/C (Y/N); Room 

A/C (Y/N); Swamp 

Cooler(Y/N) 
5E-5 

Study/ 

Attribute Avol RTP Panel RIOPA TEACH Wilson 1984 Wilson 1991 

Air Conditioner Measurements 

Made 

A/C use in 

minutes Not Available 

Duration 

measurements in 

Hrs and Mins 

Not Available Not Available Not Available 

Fan 

Categories Not available Fan (Y/N) Fan (Y/N) Not Available Not Available Not Available 

Fan 

Measurements 

Made 

Time on or off for 

various fan types 

during sampling 

was recorded, 

but not included 

in database 

provided. 

Not Available 

Duration 

measurements in 

Hours and 

Minutes 

Not Available Not Available Not Available 

Window Open/ 

Closed Data 

Duration open 

between times 

6am-12 pm; 

12pm - 6 pm; 

and 6pm - 6am 

Windows (open / 

closed along with 

duration open in 

inch-hours units 

Windows (Open / 

Closed) along 

with window 

open duration 

measurements 

Not Available Not Available Not Available 

Comments 

CA sample was 

a random sample 

of homes. NJ 

and TX homes 

were deliberately 

chosen to be 

near to ambient 

sources. 

Restricted to innercity homes with high 

school students. 

Contemporaneous 

temperature data 

obtained for these 

analyses from SCRAM 

and 

www.wunderground.com 

meteorological data. 

Contemporaneous 

temperature data 

obtained for these 

analyses from SCRAM 

and 

www.wunderground.com 

meteorological data. 
5E-6 

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 

 House_ID – Residence identifier 

 Measurement_ID – 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 
5E-7 

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 oC, 0 to 10 oC, 10 to 20 oC, 20 to 25 oC, 25 to 30 oC, 

30 to 40 oC (Values equal to the lower bounds are excluded from each interval). Also 
5E-8 

included were strata defined by study = “All” and/or city = “All,” and/or A/C type = “All” 

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 

 Deciles (Min, 10th, 20th ... 90th 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 oC to 1.73 for New York between 20 and 25 oC. The 

mean AER for residences without A/C ranges from 0.46 for San Francisco between 10 

and 20 oC upwards to 2.54 for Detroit between 25 and 30 oC. 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 oC, 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 Comparisons of Means.

Comparison 

Analysis 

Number 

Comparison 

Variable(s) 

“Groups 

Compared” 

Stratification 

Variable(s) 

(not missing in 

worksheet) 

Total 

Comparisons

Cases with significantly 

different means (5 % 

level) 

AER Log AER 

1 City Type of A/C AND 

Temp. Range 

12 8 9 

2 Temp. Range Study AND City 13 6 6 

3 Type of A/C Study AND City 16 6 6 

4 City Type of A/C 2 2 2 

5 City Temp. Range 6 6 6 

6 Type of A/C AND 

Temp. Range 

Study AND City 18 7 7 
5E-9 

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. Pvalues 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. 
5E-10 

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 pvalue for three standard goodness-of-fit tests: Kolmogorov-Smirnov (K-S), Cramer-VonMises (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 = -1 exp(-x/), where shape = mean = 

 Log-normal: density = {x(2)}-1 exp{ -(log x - )

2 / (22)}, where shape =  and 

scale = . Thus the geometric mean and geometric standard deviation are given 

by exp() and exp(), respectively. 

 Normal: density = {(2)}-1 exp{ -(x - )2 / (22)}, where mean =  and standard 

deviation = 

 Weibull: density = (c/) (x/)

c-1 exp{-(x/)c

}, where shape = c and scale = 

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 
5E-11 

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, MI: 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. 
5E-12 

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 nonCalifornia 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 

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 
5E-14 

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”. 
5E-15 

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 SURVEY AREA & YEAR PERCENTAGE 

Atlanta Atlanta, 2004 98.03, replace by 100* 

Baltimore Baltimore, 2007 96.70, replace by 100* 

Boston Boston, 2007 85.67 

Chicago Chicago, 2009 94.74 

Cleveland Cleveland, 2004 79.80 

Denver Denver, 2004 84.56 

Detroit Detroit, 2009 89.61 

Houston Houston, 2007 98.77 

New York New York, 2009 86.93 

Philadelphia Philadelphia, 2009 95.00 

Sacramento Sacramento, 2004 92.81 

St. Louis St. Louis, 2004 98.73 

Washington DC Washington DC, 2007 98.15, replace by 100* 

Research Triangle Park Charlotte NC, 2002 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 PERCENTAGE 

No A/C 33.89 

Room A/C 16.44 

Central A/C 49.68 

1 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 
5E-16 

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 CO2 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.sas7bdat 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 
5E-17 

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 nonrestaurants. 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, lognormal, 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 Variable Subgroup N Mean Std Dev Min 

25th

%ile Median 

75th

%ile Max 

Bennett AER All 40 1.622 1.649 0.300 0.705 1.035 1.890 9.070

Bennett AER 

Not 

restaurant 34 1.144 0.768 0.300 0.620 0.995 1.460 4.020

Bennett AER Restaurant 6 4.328 2.642 1.460 2.640 3.855 5.090 9.070

Persily AER All 96 1.962 2.325 0.071 0.501 1.080 2.756 13.824

Turk AER All 40 1.540 0.881 0.300 0.850 1.500 2.050 4.100

Bennett Log AER All 40 0.152 0.785 -1.204 -0.350 0.034 0.637 2.205

Bennett Log AER 

Not 

restaurant 34 -0.052 0.619 -1.204 -0.478 -0.005 0.378 1.391

Bennett Log AER Restaurant 6 1.312 0.618 0.378 0.971 1.344 1.627 2.205

Persily Log AER All 96 0.104 1.104 -2.642 -0.694 0.077 1.012 2.626

Turk Log AER All 40 0.254 0.639 -1.204 -0.164 0.405 0.715 1.411

 
5E-18 

Table 6. Best fitting restaurant AER distributions from the Bennett et al. (2011).

Scale Shape Mean Std_Dev Distribution

P-Value 

KolmogorovSmirnov 

P-Value 

Cramervon 

Mises 

P-Value 

AndersonDarling 

4.328 4.328 4.328 Exponential 0.25 0.23 0.25

1.312 0.618 4.492 3.062 Lognormal 0.15 0.50 0.50

 4.328 2.642 Normal 0.15 0.25 0.25

4.907 1.922 4.353 2.358 Weibull 0.25 0.25

Table 7. Best fitting non-restaurant AER distributions from the Bennett et al. 

(2011).

Scale Shape Mean Std_Dev Distribution

P-Value 

KolmogorovSmirnov 

P-Value 

Cramervon 

Mises 

P-Value 

AndersonDarling 

1.144 1.144 1.144 Exponential 0.00 0.00 0.00

-0.052 0.619 1.149 0.785 Lognormal 0.15 0.50 0.50

 1.144 0.768 Normal 0.01 0.01 0.01

1.290 1.654 1.153 0.716 Weibull 0.21 0.18

(For an explanation of the Kolmogorov-Smirnov, Cramer-von Mises, and AndersonDarling 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. 
5E-19 

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 outsidevehicle 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 outsidevehicle 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 site1

Road 

Type1

Number 

of 

cases1

Mean1 Standard 

Deviation1

25th

Percentile1

50th

Percentile1

75th

Percentile1

Estimated 

5th

Percentile2

Local 191 0.755 0.203 0.645 0.742 0.911 0.422 

Urban 299 0.754 0.243 0.585 0.722 0.896 0.355 

Interstate 241 0.364 0.165 0.232 0.369 0.484 0.093 

All 731 0.626 0.278 0.417 0.623 0.808 0.170 

1 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 × 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 
5E-20 

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 FederalAID 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 
5E-21 

(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.

 

FRACTION VMT BY ROAD 

TYPE 

FEDERAL-AID URBANIZED 

AREA INTERSTATE URBAN LOCAL 

Atlanta 0.32 0.45 0.23 

Baltimore 0.34 0.59 0.07 

Boston 0.32 0.54 0.14 

Chicago 0.31 0.57 0.12 

Cleveland 0.38 0.45 0.17 

Denver 0.25 0.65 0.10 

Detroit 0.24 0.66 0.10 

Houston 0.24 0.73 0.03 

Los Angeles 0.29 0.66 0.05 

New York 0.18 0.67 0.15 

Philadelphia 0.23 0.65 0.12 

Sacramento 0.24 0.69 0.07 

St. Louis 0.37 0.45 0.18 

Washington 0.30 0.62 0.08 

1http://www.fhwa.dot.gov/policyinformation/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 ozone1.

Ventilation1 Number 

of 

cases1

Mean1 Standard 

Deviation1

25th

Percentile1

50th

Percentile1

75th

Percentile1

Estimated 

5th

Percentile2

Vent Open 226 0.361 0.217 0.199 0.307 0.519 0.005 

Normal A/C 332 0.417 0.211 0.236 0.408 0.585 0.071 

Maximum 

A/C 

254 0.093 0.088 0.016 0.071 0.149 0.0003

All 812 0.300 0.232 0.117 0.251 0.463 0.0003

 

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 × 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 
5E-22 

estimate the ventilation type distributions for each modeled city. Furthermore, since the 

Cincinnati Ozone Study was scripted, the ventilation conditions may not represent realworld 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. 
5E-23 

References 

American Petroleum Institute (1997). Sensitivity testing of pNEM/O3 exposure to 

changes in the model algorithms. Health and Environmental Sciences Department. 

Avol, E. L., W. C. Navioli, and S. D. Colome (1998) Modeling ozone levels in and 

around southern California homes. Environ. Sci. Technol. 32, 463-468. 

Bennett, D. H, W. Fisk, M. G. Apte, X. Wu, A. Trout, D. Faulkner, D. Sulivan (2011). 

Ventilation, Temperature, and HVAC Characteristics in Small and Medium 

Commercial Buildings (SMCBs) in California. Journal submission.

Chilrud, S. N., D. Epstein, J. M. Ross, S. N. Sax, D. Pederson, J. D. Spengler, P. L. 

Kinney (2004). Elevated airborne exposures of teenagers to manganese, chromium, 

and iron from steel dust and New York City's subway system. Environ. Sci. Technol. 

38, 732-737. 

Colome, S.D., A. L. Wilson, Y. Tian (1993). California Residential Indoor Air Quality 

Study, Volume 1, Methodology and Descriptive Statistics. Report prepared for the 

Gas Research Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., 

Southern California Gas Co. 

Jakobi, G and Fabian, P. (1997). Indoor/outdoor concentrations of ozone and 

peroxyacetyl nitrate (PAN). Int. J. Biometeorol. 40: 162-165.. 

Johnson, T., A. Pakrasi, A. Wisbeth, G. Meiners, W. M. Ollison (1995). Ozone 

exposures within motor vehicles – results of a field study in Cincinnati, Ohio. 

Proceedings 88th annual meeting and exposition of the Air & Waste Management 

Association, June 18-23, 1995. San Antonio, TX. Preprint paper 95-WA84A.02. 

Kinney, P. L., S. N. Chillrud, S. Ramstrom, J. Ross, J. D. Spengler (2002). Exposures to 

multiple air toxics in New York City. Environ Health Perspect 110, 539-546. 

Liu, L.-J. S, P. Koutrakis, J. Leech, I. Broder, (1995) Assessment of ozone exposures in 

the greater metropolitan Toronto area. J. Air Waste Manage. Assoc. 45: 223-234 

Meng, Q. Y., B. J. Turpin, L. Korn, C. P. Weisel, M. Morandi, S. Colome, J. J. Zhang, T. 

Stock, D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giovanetti, W. Cui, J. Kwon, S. 

Alimokhtari, D. Shendell, J. Jones, C. Farrar, S. Maberti (2004). Influence of ambient 

(outdoor) sources on residential indoor and personal PM2.5 concentrations: Analyses 

of RIOPA data. Journal of Exposure Analysis and Environ Epidemiology. Preprint. 

Murray, D. M. and D. E. Burmaster (1995). Residential Air Exchange Rates in the 

United States: Empirical and Estimated Parametric Distributions by Season and 

Climatic Region. Risk Analysis, Vol. 15, No. 4, 459-465. 

Persily, A. and J. Gorfain.(2004). Analysis of ventilation data from the U.S. 

Environmental Protection Agency Building Assessment Survey and Evaluation 

(BASE) Study. National Institute of Standards and Technology, NISTIR 7145, 

December 2004. 

Persily, A., J. Gorfain, G. Brunner.(2005). Ventilation design and performance in U.S. 

office buildings. ASHRAE Journal. April 2005, 30-35. 

Sax, S. N., D. H. Bennett, S. N. Chillrud, P. L. Kinney, J. D. Spengler (2004). 

Differences in source emission rates of volatile organic compounds in inner-city 
5E-24 

residences of New York City and Los Angeles. Journal of Exposure Analysis and 

Environ Epidemiology. Preprint. 

Sheldon, L. (2007). The Detroit Exposure and Aerosol Research Study (DEARS). 

Presentation. 

Turk, B. H., D. T. Grimsrud, J. T. Brown, K. L. Geisling-Sobotka, J. Harrison, R. J. Prill 

(1989). Commercial building ventilation rates and particle concentrations. ASHRAE, 

No. 3248. 

Weschler, C. J. (2000) Ozone in indoor environments: concentration and chemistry. 

Indoor Air 10: 269-288. 

Weschler, C. J. and Shields, H. C. (2000) The influence of ventilation on reactions 

among indoor pollutants: modeling and experimental observations. Indoor Air. 10: 

92-100. 

Weisel, C. P., J. J. Zhang, B. J. Turpin, M. T. Morandi, S. Colome, T. H. Stock, D. M. 

Spektor, L. Korn, A. Winer, S. Alimokhtari, J. Kwon, K. Mohan, R. Harrington, R. 

Giovanetti, W. Cui, M. Afshar, S. Maberti, D. Shendell (2004). Relationship of Indoor, 

Outdoor and Personal Air (RIOPA) study; study design, methods and quality 

assurance / control results. Journal of Exposure Analysis and Environ Epidemiology. 

Preprint. 

Williams, R., J. Suggs, A. Rea, K. Leovic, A. Vette, C. Croghan, L. Sheldon, C. Rodes, 

J. Thornburg, A. Ejire, M. Herbst, W. Sanders Jr. (2003a). The Research Triangle 

Park particulate matter panel study: PM mass concentration relationships. Atmos 

Env 37, 5349-5363. 

Williams, R., J. Suggs, A. Rea, L. Sheldon, C. Rodes, J. Thornburg (2003b). The 

Research Triangle Park particulate patter panel study: modeling ambient source 

contribution to personal and residential PM mass concentrations. Atmos Env 37, 

5365-5378. 

Wilson, A. L., S. D. Colome, P. E. Baker, E. W. Becker (1986). Residential Indoor Air 

Quality Characterization Study of Nitrogen Dioxide, Phase I, Final Report. Prepared 

for Southern California Gas Company, Los Angeles. 

Wilson, A. L., S. D. Colome, Y. Tian (1994). California Residential Indoor Air Quality 

Study, Volume 2, Carbon Monoxide and Air Exchange Rate: A Univariate & 

Multivariate Analysis. Report prepared for the Gas Research Institute, Pacific Gas & 

Electric Co., San Diego Gas & Electric Co., Southern California Gas Co. 

Wilson, A. L., S. D. Colome, Y. Tian, P. E. Baker, E. W. Becker, D. W. Behrens, I. H. 

Billick, C. A. Garrison (1996). California residential air exchange rates and residence 

volumes. Journal of Exposure Analysis and Environ Epidemiology. Vol. 6, No. 3. 
5E-25 

This page left intentionally blank 
5F-i 

APPENDIX 5F 

Detailed Exposure Results 

Table of Contents 

5F-1 Exposure Modeling Results for Base Air Quality ...................................................... 5F-1

5F-2 Exposure Modeling Results for Adjusted Air Quality .............................................. 5F-14
5F-ii 

List of Tables 

Table 5F-1. Percent of all school-age children with O3 exposures at or above 60, 70, and 80 

ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality.

.......................................................................................................................... 5F-10

Table 5F-2. Percent of all school-age children with O3 exposures at or above 60, 70, and 80 

ppb-8hr while at moderate or greater exertion, years 2006-2010, adjusted air 

quality........................... .......................................................................... 5F-22

Table 5F-3. Mean and maximum number of all school-age children (and associated days per 

O3 season) with at least one daily maximum 8-hr average O3 exposure at or above 

60 ppb-8hr while at moderate or greater exertion. ........................................... 5F-51

Table 5F-4. Total number of school-age children and asthmatic school-age children 

experiencing at least one or two daily maximum 8-hr average O3 exposures in all 

study areas by year, base air quality and air quality adjusted to just meeting the 

existing 75 ppb standard. ................................................................................. 5F-53

Table 5F-5. Mean and maximum number of school-age children and asthmatic school-age 

children with at least one or two daily maximum 8-hr average O3 exposures at or 

above benchmark levels, all 15 urban study areas combined. ......................... 5F-55

List of Figures 

Figure 5F-1. Percent of all school-age children with at least one daily maximum 8-hr average 

O3 exposure at or above 60, 70, and 80 ppb while at moderate or greater exertion, 

years 2006-2010, base air quality. ..................................................................... 5F-4

Figure 5F-2. Percent of asthmatic school-age children with at least one daily maximum 8-hr 

average O3 exposure at or above 60, 70, and 80 ppb while at moderate or greater 

exertion, years 2006-2010, base air quality. ...................................................... 5F-5

Figure 5F-3. Percent of asthmatic adults with at least one daily maximum 8-hr average O3

exposure at or above 60, 70, and s 80 ppb while at moderate or greater exertion, 

years 2006-2010, base air quality. ..................................................................... 5F-6

Figure 5F-4. Percent of older adults with at least one daily maximum 8-hr average O3 exposure 

at or above 60, 70, and 80 ppb while at moderate or greater exertion, years 2006-

2010, base air quality. ........................................................................................ 5F-7

Figure 5F-5. Percent of school-age children with multiple daily maximum 8-hr average O3

exposures at or above 60 ppb per study area O3 season, while at moderate or 

greater exertion, years 2006-2010, base air quality. .......................................... 5F-8

Figure 5F-6. Percent of school-age children with multiple daily maximum 8-hr average O3

exposures at or above 70 ppb per study area O3 season, while at moderate or 

greater exertion, years 2006-2010, base air quality. .......................................... 5F-9

Figure 5F-7. Incremental increases in percent of all school-age children with at least one daily 

maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb 
5F-iii 

(middle panel), or 80 ppb (bottom panel) using the maximum percent exposed for 

each study area, year 2006-2010 adjusted air quality. ..................................... 5F-15

Figure 5F-8. Incremental increases in percent of asthmatic school-age children with at least one 

daily maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb 

(middle panel), or 80 ppb (bottom panel) using the maximum percent exposed for 

each study area, year 2006-2010 adjusted air quality. ..................................... 5F-16

Figure 5F-9. Incremental increases in percent of asthmatic adults with at least one daily 

maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb 

(middle panel), or 80 ppb (bottom panel) using the maximum percent exposed for 

each study area, year 2006-2010 adjusted air quality. ..................................... 5F-17

Figure 5F-10. Incremental increases in percent of all older adults with at least one daily 

maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb 

(middle panel), or 80 ppb (bottom panel) using the maximum percent exposed for 

each study area, year 2006-2010 adjusted air quality. ..................................... 5F-18

Figure 5F-11. Incremental increases in percent of all school-age children with at least one daily 

maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb 

(middle panel), or 80 ppb (bottom panel) using the mean percent exposed for each 

study area, year 2006-2010 adjusted air quality. ............................................. 5F-19

Figure 5F-12. Incremental increases in percent of all school-age children with at least two daily 

maximum 8-hr average O3 exposures at or above 60 ppb (top panel), 70 ppb 

(middle panel), or 80 ppb (bottom panel) using the maximum percent exposed for 

each study area, year 2006-2010 adjusted air quality. ..................................... 5F-20

Figure 5F-13. Incremental increases in percent of all school-age children with at least two daily 

maximum 8-hr average O3 exposures at or above 60 ppb (top panel), 70 ppb 

(middle panel), or 80 ppb (bottom panel) using the mean percent exposed for each 

study area, year 2006-2010 adjusted air quality. ............................................. 5F-21
5F-1 

This appendix contains the detailed results for the primary APEX simulations performed 

to estimate exposures associated with base air quality (section 5F-1) and for air quality just 

meeting the existing and alternative standard levels (section 5F-2).

5F-1 EXPOSURE MODELING RESULTS FOR BASE AIR QUALITY 

As described in the main body of the HREA, comprehensive multi-panel displays of 

exposure results are presented for each of the study groups of interest, i.e., all school-age 

children (ages 5 to 18), asthmatic school-age children, asthmatic adults (ages 19 to 95), and older 

adults (ages 65 to 95) (Figure 5F-1 to Figure 5F-4, respectively). Included in each display are 

the three benchmark levels (60, 70, and 80 ppb-8hr), the five years of air quality (2006-2010), for 

the 15 study areas. Modeled exposures in the 15 study areas and considering each benchmark 

level are presented on the same scale to allow for direct comparisons across the multi-panel 

display. The most notable patterns in the exposure results are described here using one study 

group (i.e., school-age children), as there is a general consistency in the year-to-year variability 

within each study area across all four study groups. Any deviation from the observed pattern 

will be discussed for the subsequent study group. Table 5F-1 is also provided and contains the 

complete exposure output for all study areas and years for school-age children. 

Figure 5F-1 presents the percent of school-age children experiencing at least one O3

exposure at or above the selected benchmark levels while at moderate or greater exertion. 

Consistent with the previously discussed observations regarding year-to-year variability in 

ambient concentrations (Chapter 4), most study areas have the greatest percent of school-age 

children experiencing concentrations at or above the three benchmark levels during 2006 or 2007 

along with having the lowest percent of school-age children exposed during 2009. Three 

Western U.S. study areas, Dallas, Los Angeles, and Sacramento, differ slightly from this pattern 

in that they exhibit a minimum percent of school-age children exposed during 2010, while in 

Houston and Chicago the minimum exposures occur during year 2008. In general, between 20 to 

40% of school-age children experience at least one daily maximum 8-hr average O3 exposure at 

or above 60 ppb, 10 to 20% experience at least one O3 exposure at or above 70 ppb, and 0 to 

10% experience at least one O3 exposure at or above 80 ppb, all while at moderate or greater 

exertion and considering the base air quality (2006-2010). 

The percent of asthmatic school-age children experiencing at least one daily maximum 8-

hr average O3 exposure at or above the selected benchmark levels while at moderate or greater 

exertion (Figure 5F-2) is virtually indistinguishable from that of all school-age children (Figure 

5F-1) regarding both the year-to-year pattern and percent of persons exposed. This is the result 

of having both simulated study groups use an identical time-location-activity diary pool to 

construct each simulated individual’s time series of activities performed and locations visited. 

Different however would be the relative number of asthmatic school-age children exposed in 
5F-2 

each study area if compared with non-asthmatic school-age children, as the asthma prevalence 

rates vary by U.S. location (HREA, Table 5-2) though on average are about 10% of all schoolage children. 

As mentioned above, the overall year-to-year pattern of exposure for asthmatic adults is 

similar to that observed for school-age children, though the percent of asthmatic adults 

experiencing exposures at or above the health effect benchmark levels is lower by a factor of 

about three or more (Figure 5F-3). Having a lower percent of asthmatic adults exposed is 

expected given that outdoor time expenditure is an important determinant of O3 exposure (HREA 

section 5.3.2) and that adults spend less time outdoors than children (REA section 5.3.1). In 

general, between 5 to 10% of asthmatic adults experience at least one daily maximum 8-hr 

average O3 exposure at or above 60 ppb, 0 to 5% experience at least one daily maximum 8-hr 

average O3 exposure at or above 70 ppb, and 0 to 2% experience at least one daily maximum 8-

hr average O3 exposure at or above 80 ppb, all while at moderate or greater exertion. 

While the percent of asthmatic adults exposed is much lower, the number of asthmatic 

adults at or above the exposure benchmarks is generally just below that estimated number of 

asthmatic school-age children. As an example, for year 2006 in Atlanta, approximately 44% of 

asthmatic school-age children (or about 37,000) were estimated to experience at least one daily 

maximum 8-hr average exposure at or above 60 ppb. Though a much smaller percent of 

asthmatic adults were estimated to experience a similar exposure for the same year (i.e., about 

16%), this is equivalent to nearly 31,000 asthmatic adults exposed, at least one time, to an 8-hr 

average O3 concentration at or above 60 ppb. 

The percent of older adults (ages 65 to 95) experiencing exposures at or above the 

selected benchmark levels (Figure 5F-4) is lower by a fewer percentage points when compared 

with the results for asthmatic adults. Again, older adults, on average, would tend to spend less 

time outdoors when compared with both adults and children (REA section 5.3.1), in addition to 

fewer older adults performing activities at moderate or greater exertion for extended periods of 

time, thus leading to fewer older adults exposed to concentrations of concern. In general, less 

than 10% of older adults experience at least one O3 exposure at or above 60 ppb-8hr, less than 

5% experience at least one O3 exposure at or above 70 ppb-8hr, and about 2% or less experience 

at least one O3 exposure at or above 80 ppb-8hr, all while at moderate or greater exertion 

considering base air quality. 

Given the similar year-to-year patterns of the single and multiple exposure occurrences 

and when considering any of the four study groups, we present the graphic multi-day exposure 

results here considering school-age children only. All multi-day exposure results are provided in 

Table 5F-1. Figure 5F-5 illustrates the percent of school-age children having multiple exposures 

at or above 60 ppb-8hr for each of the 15 study areas, considering base air quality (2006-2010). 
5F-3 

Depending on the year and study area, about 10 to 25% of school-age children could experience 

at least two exposures above the 60 ppb benchmark during the ozone season, while about 5 to 

10% could experience at least four. Most study areas and years are estimated to have fewer than 

5% of school-age children experience six or more exposures above 60 ppb considering the base 

air quality. When considering the multi-day exposures for school-age children at or above the 70 

ppb benchmark (Figure 5F-6), about 2 to 10% of school-age children could experience at least 

two exposures during the ozone season, while four or more exposures were generally limited to 

fewer than 4% of school-age children. 
5F-4 

Percent of All School-Age Children (5-18) with at Least One 8-hour Exposure At or 

Above Benchmark Level While at Moderate or Greater Exertion, Base Air Quality

Figure 5F-1. Percent of all school-age children with at least one daily maximum 8-hr 

average O3 exposure at or above 60, 70, and 80 ppb while at moderate or greater exertion, 

years 2006-2010, base air quality. 

Exposure 

Benchmark 

(ppb)
5F-5 

Percent of Asthmatic School-Age Children (5-18) with at Least One 8-hour Exposure 

At or Above Benchmark Level While at Moderate or Greater Exertion, Base Air Quality

Figure 5F-2. Percent of asthmatic school-age children with at least one daily maximum 8-

hr average O3 exposure at or above 60, 70, and 80 ppb while at moderate or greater 

exertion, years 2006-2010, base air quality. 

Exposure 

Benchmark 

(ppb)
5F-6 

Percent of Asthmatic Adults (19-95) with at Least One 8-hour Exposure At or Above 

Benchmark Level While at Moderate or Greater Exertion, Base Air Quality

Figure 5F-3. Percent of asthmatic adults with at least one daily maximum 8-hr average O3

exposure at or above 60, 70, and s 80 ppb while at moderate or greater exertion, years 

2006-2010, base air quality. 

Exposure 

Benchmark 

(ppb)
5F-7 

Percent of Older Adults (65-95) with at Least One 8-hour Exposure At or Above 

Benchmark Level While at Moderate or Greater Exertion, Base Air Quality

Figure 5F-4. Percent of all older adults with at least one daily maximum 8-hr average O3

exposure at or above 60, 70, and 80 ppb while at moderate or greater exertion, years 2006-

2010, base air quality. 

Exposure 

Benchmark 

(ppb)
5F-8 

Figure 5F-5. Percent of all school-age children with multiple daily maximum 8-hr average 

O3 exposures at or above 60 ppb per study area O3 season, while at moderate or greater 

exertion, years 2006-2010, base air quality. 

Number of 

Exceedances 

Per O3 Season

≥ 2

≥ 4

≥ 6
5F-9 

Figure 5F-6. Percent of all school-age children with multiple daily maximum 8-hr average 

O3 exposures at or above 70 ppb per study area O3 season, while at moderate or greater 

exertion, years 2006-2010, base air quality. 

Number of 

Exceedances 

Per O3 Season

≥ 2

≥ 4

≥ 6
5F-10 

Table 5F-1. Percent of all school-age children with O3 exposures at or above 60, 70, and 80 

ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality. 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) Year 

% of school-age children experiencing multiple exposures per O3 season 

at or above benchmarks, base air quality 

≥1 ≥2 ≥3 ≥4 ≥5 ≥6 

Atlanta 

60 

2006 42.4 28.9 21.9 17.2 13.8 11 

2007 41.8 28.5 21.5 16.8 13.3 10.5 

2008 27.3 15 9.2 5.7 3.6 2.3 

2009 16.7 6.7 2.9 1.3 0.5 0.2 

2010 20.3 10.1 5.6 3.2 1.8 1.1 

70 

2006 25.2 13 7.3 4.3 2.4 1.3 

2007 23.8 11.9 6.4 3.6 2 1.1 

2008 9.4 2.5 0.7 0.2 0.1 0 

2009 3.8 0.5 0.1 0 0 0 

2010 4.4 0.8 0.2 0 0 0 

80 

2006 10.6 2.7 0.8 0.2 0 0 

2007 9.3 2.4 0.7 0.2 0.1 0 

2008 1.8 0.1 0 0 0 0 

2009 0.5 0 0 0 0 0 

2010 0.4 0 0 0 0 0 

Baltimore 

60 

2006 36.8 23.5 16.6 12.2 9 6.7 

2007 29 16.5 10.9 7.2 5.1 3.7 

2008 22.6 11.1 6 3.6 2.2 1.3 

2009 10.1 2.7 0.8 0.3 0.1 0 

2010 31 18.6 12.5 8.6 5.8 4 

70 

2006 19 8.1 3.8 1.8 0.8 0.4 

2007 11.4 3.9 1.7 0.7 0.3 0.2 

2008 8.4 2.1 0.6 0.2 0.1 0 

2009 1.1 0.1 0 0 0 0 

2010 11.8 3.6 1.3 0.5 0.2 0.1 

80 

2006 5.8 0.9 0.1 0 0 0 

2007 2.7 0.4 0 0 0 0 

2008 2 0.1 0 0 0 0 

2009 0.1 0 0 0 0 0 

2010 2.5 0.2 0 0 0 0 

Boston 

60 

2006 22.7 9.6 4.5 2.1 0.9 0.4 

2007 31.8 17.5 10.8 6.8 4.2 2.6 

2008 18.3 7.1 2.8 1.2 0.5 0.2 

2009 12.3 3.2 0.8 0.1 0 0 

2010 12.9 4 1.3 0.4 0.1 0 

70 

2006 7.1 1.2 0.2 0 0 0 

2007 14.9 4.8 1.8 0.7 0.2 0.1 

2008 4.6 0.6 0.1 0 0 0 

2009 2.6 0.2 0 0 0 0 

2010 2.3 0.1 0 0 0 0 

80 

2006 1 0 0 0 0 0 

2007 4.8 0.6 0.1 0 0 0 

2008 0.6 0 0 0 0 0 

2009 0.1 0 0 0 0 0 

2010 0.1 0 0 0 0 0 

Chicago 

60 

2006 14.7 4.5 1.4 0.5 0.1 0 

2007 26.5 13.1 7.1 3.9 2.1 1.2 

2008 3.6 0.4 0.1 0 0 0 

2009 7.8 1.4 0.3 0 0 0 

2010 15.6 5 1.7 0.5 0.1 0 

70 

2006 2.7 0.2 0 0 0 0 

2007 9.2 2 0.4 0.1 0 0 

2008 0 0 0 0 0 0 

2009 0.8 0 0 0 0 0 

2010 1.5 0.1 0 0 0 0 

80 

2006 0.1 0 0 0 0 0 

2007 1.2 0 0 0 0 0 

2008 0 0 0 0 0 0 

2009 0.1 0 0 0 0 0 
5F-11 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) Year 

% of school-age children experiencing multiple exposures per O3 season 

at or above benchmarks, base air quality 

≥1 ≥2 ≥3 ≥4 ≥5 ≥6 

2010 0 0 0 0 0 0 

Cleveland 

60 

2006 21.4 9.1 4.2 1.8 0.8 0.3 

2007 29.7 16.4 10.2 6.4 3.9 2.5 

2008 21.5 9.7 4.7 2.2 1.1 0.5 

2009 10.2 2.6 0.8 0.2 0.1 0 

2010 20.6 8.9 4.3 2.1 1 0.4 

70 

2006 4.2 0.4 0.1 0 0 0 

2007 12 3.6 1.2 0.4 0.2 0.1 

2008 6 0.9 0.1 0 0 0 

2009 0.8 0 0 0 0 0 

2010 4.1 0.6 0.1 0 0 0 

80 

2006 0.2 0 0 0 0 0 

2007 2.5 0.2 0 0 0 0 

2008 0.6 0 0 0 0 0 

2009 0 0 0 0 0 0 

2010 0.3 0 0 0 0 0 

Dallas 

60 

2006 41.3 28.4 21.7 17.2 13.8 11 

2007 27.1 14.1 8 4.5 2.5 1.3 

2008 21.9 10.3 5.4 2.8 1.5 0.7 

2009 30 17 10.6 6.7 4.2 2.6 

2010 21.1 9.6 4.6 2.2 1 0.4 

70 

2006 22.1 11.1 6.1 3.4 1.9 1 

2007 10.4 2.3 0.5 0.1 0 0 

2008 5.6 0.9 0.2 0 0 0 

2009 12.2 3.6 1.1 0.3 0.1 0 

2010 5.3 0.9 0.2 0 0 0 

80 

2006 6.3 1.3 0.4 0.1 0 0 

2007 2.9 0.1 0 0 0 0 

2008 0.5 0 0 0 0 0 

2009 2.5 0.2 0 0 0 0 

2010 0.6 0 0 0 0 0 

Denver 

60 

2006 31.9 19.6 13.3 9.5 7 5.2 

2007 24.1 13 8.1 5.3 3.4 2.2 

2008 27.3 15.7 10.2 6.8 4.6 3.1 

2009 14 5.1 2 0.8 0.3 0.1 

2010 18.4 8.6 4.4 2.4 1.2 0.6 

70 

2006 11.7 4 1.5 0.6 0.2 0.1 

2007 5.2 1 0.3 0.1 0 0 

2008 5.3 0.7 0.1 0 0 0 

2009 1.6 0.1 0 0 0 0 

2010 1.6 0.1 0 0 0 0 

80 

2006 1.6 0.1 0 0 0 0 

2007 0.3 0 0 0 0 0 

2008 0.6 0 0 0 0 0 

2009 0 0 0 0 0 0 

2010 0 0 0 0 0 0 

Detroit 

60 

2006 22.3 9.1 4.1 1.7 0.7 0.3 

2007 34.4 20.1 13.1 8.5 5.6 3.6 

2008 15.6 5.3 1.8 0.6 0.2 0 

2009 12.8 3.6 1 0.3 0.1 0 

2010 16.8 6.3 2.6 1 0.4 0.2 

70 

2006 6.1 0.8 0.1 0 0 0 

2007 16.3 6.1 2.5 1 0.4 0.1 

2008 3 0.2 0 0 0 0 

2009 2.2 0.2 0 0 0 0 

2010 2.6 0.3 0 0 0 0 

80 

2006 0.8 0 0 0 0 0 

2007 4 0.6 0.1 0 0 0 

2008 0 0 0 0 0 0 

2009 0.1 0 0 0 0 0 

2010 0.1 0 0 0 0 0 

 
5F-12 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) Year 

% of school-age children experiencing multiple exposures per O3 season 

at or above benchmarks, base air quality 

≥1 ≥2 ≥3 ≥4 ≥5 ≥6 

 

Houston 

60 

2006 37.2 23 15.7 11 7.8 5.5 

2007 25.9 13.4 7.5 4.2 2.4 1.2 

2008 20.6 8.9 4.3 2 0.9 0.4 

2009 24.7 11 5.1 2.3 1 0.4 

2010 23.1 11.1 6 3.2 1.6 0.8 

70 

2006 20.5 8.6 4 1.9 0.9 0.4 

2007 9.6 2.2 0.6 0.1 0 0 

2008 5.5 0.8 0.1 0 0 0 

2009 11.4 2.2 0.4 0.1 0 0 

2010 7.8 1.5 0.3 0 0 0 

80 

2006 9.3 2.3 0.6 0.1 0 0 

2007 1.5 0.1 0 0 0 0 

2008 0.7 0 0 0 0 0 

2009 3.8 0.2 0 0 0 0 

2010 1.1 0 0 0 0 0 

Los Angeles 

60 

2006 39.3 24.1 16.9 12.6 9.6 7.4 

2007 35 20.1 13.1 9.4 6.9 5.2 

2008 38.8 23.6 16.5 12.2 9.2 7.2 

2009 35.4 20.4 13.8 10 7.5 5.7 

2010 23.4 12.2 7.6 5.1 3.7 2.7 

70 

2006 23.2 11.3 6.4 3.9 2.4 1.5 

2007 19.1 7.8 3.8 1.9 1 0.5 

2008 21.3 10 5.6 3.3 2.1 1.3 

2009 18.2 7.4 3.8 2 1.1 0.6 

2010 9.6 3.1 1.3 0.6 0.3 0.1 

80 

2006 11.8 3.9 1.5 0.5 0.2 0.1 

2007 8.2 1.8 0.4 0.1 0 0 

2008 9.8 3 1.1 0.5 0.2 0.1 

2009 7.4 1.5 0.4 0.1 0 0 

2010 2.2 0.3 0.1 0 0 0 

New York 

60 

2006 34.8 19.1 11.4 7 4.1 2.5 

2007 35.6 21.1 13.6 9 6.1 4 

2008 31.9 17.2 9.8 5.7 3.3 1.8 

2009 13.5 4 1.2 0.3 0.1 0 

2010 33.6 19.4 12.5 8.3 5.5 3.6 

70 

2006 18 5.9 2 0.6 0.2 0.1 

2007 16.1 5.6 2 0.7 0.2 0.1 

2008 13 3.4 0.9 0.2 0 0 

2009 1.8 0.1 0 0 0 0 

2010 12.8 3.8 1.2 0.3 0.1 0 

80 

2006 6.6 0.8 0.1 0 0 0 

2007 4.2 0.5 0.1 0 0 0 

2008 2.5 0.2 0 0 0 0 

2009 0 0 0 0 0 0 

2010 2.4 0.2 0 0 0 0 

Philadelphia 

60 

2006 36.6 22.4 15.4 10.8 7.6 5.4 

2007 40.9 26 18.1 13.2 9.8 7.1 

2008 34.1 19.8 12.7 8.4 5.5 3.7 

2009 12.7 3.7 1.1 0.3 0.1 0 

2010 33.5 19.8 13.1 8.9 6.2 4.3 

70 

2006 16.1 5.9 2.3 0.9 0.4 0.1 

2007 20.9 8.6 3.9 1.8 0.7 0.3 

2008 14.5 4.6 1.6 0.6 0.2 0.1 

2009 1 0 0 0 0 0 

2010 13.5 4.3 1.5 0.5 0.2 0.1 

80 

2006 3.4 0.4 0 0 0 0 

2007 7 1.1 0.2 0 0 0 

2008 3 0.3 0 0 0 0 

2009 0 0 0 0 0 0 

2010 2.7 0.3 0 0 0 0 

 
5F-13 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) Year 

% of school-age children experiencing multiple exposures per O3 season 

at or above benchmarks, base air quality 

≥1 ≥2 ≥3 ≥4 ≥5 ≥6 

 

Sacramento 

60 

2006 40.9 26.9 20 15.3 12 9.5 

2007 24.7 12.4 7.2 4.3 2.7 1.7 

2008 36.3 22.8 16.1 11.7 8.9 6.8 

2009 35 21.4 14.5 10.4 7.6 5.7 

2010 20.8 9.9 5.6 3.5 2.3 1.4 

70 

2006 22.8 11.7 6.8 4.2 2.7 1.7 

2007 9.2 2.3 0.7 0.2 0.1 0 

2008 19.1 8.8 4.7 2.7 1.7 1.1 

2009 16.7 6.8 3.2 1.7 0.8 0.5 

2010 7.4 2 0.7 0.3 0.1 0 

80 

2006 8.8 2.7 0.9 0.3 0.2 0 

2007 2.7 0.2 0 0 0 0 

2008 7.6 2.3 0.9 0.4 0.1 0.1 

2009 5.2 1 0.3 0.1 0 0 

2010 2 0.3 0 0 0 0 

St. Louis 

60 

2006 32.4 19.7 13.2 9 6 4 

2007 38.4 24.8 17.8 13 9.9 7.4 

2008 11 3 0.9 0.2 0.1 0 

2009 11.3 2.8 0.8 0.2 0 0 

2010 22.9 11 5.8 3.1 1.6 0.9 

70 

2006 12.7 3.9 1.3 0.4 0.1 0 

2007 20.9 9.9 5 2.6 1.3 0.6 

2008 1.4 0.1 0 0 0 0 

2009 1.9 0 0 0 0 0 

2010 4.9 0.7 0.1 0 0 0 

80 

2006 2.2 0.1 0 0 0 0 

2007 6.8 1.4 0.3 0.1 0 0 

2008 0 0 0 0 0 0 

2009 0.2 0 0 0 0 0 

2010 0.1 0 0 0 0 0 

Washington DC 

60 

2006 35.4 22.1 15.3 11 8 5.8 

2007 35.7 22.8 16.1 11.7 8.8 6.5 

2008 26.2 14.1 8.4 5 2.9 1.7 

2009 8.9 2 0.5 0.1 0 0 

2010 32.1 19.8 13.5 9.6 6.9 4.9 

70 

2006 18.4 7.7 3.5 1.6 0.7 0.3 

2007 16.6 6.4 2.7 1.2 0.6 0.2 

2008 10.3 2.7 0.7 0.2 0 0 

2009 0.7 0 0 0 0 0 

2010 12.5 4.2 1.6 0.7 0.3 0.1 

80 

2006 5.7 1 0.1 0 0 0 

2007 3.9 0.5 0.1 0 0 0 

2008 2.5 0.2 0 0 0 0 

2009 0.1 0 0 0 0 0 

2010 2.4 0.3 0 0 0 0 
5F-14 

5F-2 EXPOSURE MODELING RESULTS FOR ADJUSTED AIR QUALITY 

In this section, we present the exposures estimated when considering the air quality 

adjusted to just meeting the existing O3 NAAQS standard, as well as when considering potential 

alternative standard levels (55, 60, 65, 70 ppb 8-hr) of the existing standard. We note that one 

study area (Chicago) O3 ambient monitor design values were below that of the existing standard 

during the 2008-2010, therefore APEX simulations could not be performed for that 3-year 

period. We could not simulate just meeting a standard level of 60 ppb-8hr or below in the New 

York study area, thus APEX simulations for these air quality scenarios could not be performed 

for the New York study area. 

First are presented three-paneled figures for each of the four exposure study groups of 

interest (i.e. school-age children, asthmatic school-age children, asthmatic adults, older adults), 

one panel of which was briefly summarized at the end of Chapter 5 in the key observation 

section (all school-age children, 60 ppb-8hr benchmark). Presented for each of the three 

exposure benchmarks (60 ppb-8hr, 70 ppb-8hr, 80 ppb-8hr) are the highest estimated percent 

exposed while at moderate or greater exertion in each study area, considering just meeting the 

existing and alternative standards (Figure 5F-7 to Figure 5F-10). 

Exposures for the all school-age children study group were additionally characterized by 

calculating the mean percent (averaged over the study years) experiencing at least one exposure 

at or above each of the three benchmarks (60 ppb-8hr, 70 ppb-8hr, 80 ppb-8hr) while at 

moderate or greater exertion (Figure 5F-11). Further, the maximum (Figure 5F-12) and mean 

(Figure 5F-13) percent of all school-age children experiencing at least two exposures at or above 

the three health effect benchmark levels are presented. Following these figures, Table 5F-2 

provides the complete exposure output for all study areas, years, benchmark levels, and adjusted 

air quality scenarios for all school-age children, the study group containing the greatest percent 

and number of persons exposed in the HREA. 

And finally, the mean and maximum number of all school-age children and associated 

person days with at least one exposure at or above each of the benchmark levels is provided in 

Table 5F-3, by study area and air quality scenario. Table 5F-4 contains the 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. And finally, Table 

5F-5 contains the number of school-age children and asthmatic school-age children experiencing 

at least one or two exposures above the selected benchmark levels, summed for all 15 study 

areas, and representing all simulated air quality scenarios. 
5F-15 

Figure 5F-7. Incremental increases in percent of all school-age children with at least one 

daily maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb (middle 

panel), or 80 ppb (bottom panel) using the maximum percent exposed for each study area, 

year 2006-2010 adjusted air quality. 
5F-16 

Figure 5F-8. Incremental increases in percent of asthmatic school-age children with at 

least one daily maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb 

(middle panel), or 80 ppb (bottom panel) using the maximum percent exposed for each 

study area, year 2006-2010 adjusted air quality. 
5F-17 

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of Asthmatic Adults (19-95) with at Least One 8-hr Daily Max Exposure > 60 ppb

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of Asthmatic Adults (19-95) with at Least One 8-hr Daily Max Exposure > 70 ppb

0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8% 2.0% 2.2%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of Asthmatic Adults (19-95) with at Least One 8-hr Daily Max Exposure > 80 ppb

0.00% 0.02% 0.04% 0.06% 0.08% 0.10% 0.12% 0.14% 0.16% 0.18% 0.20% 0.22%

Figure 5F-9. Incremental increases in percent of asthmatic adults with at least one daily 

maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb (middle panel), 

or 80 ppb (bottom panel) using the maximum percent exposed for each study area, year 

2006-2010 adjusted air quality. 
5F-18 

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of Older Adults (65-95) with at Least One 8-hr Daily Max Exposure > 60 ppb

0% 1% 2% 3% 4% 5% 6% 7%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of Older Adults (65-95) with at Least One 8-hr Daily Max Exposure > 70 ppb

0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of Older Adults (65-95) with at Least One 8-hr Daily Max Exposure > 80 ppb

0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30%

Figure 5F-10. Incremental increases in percent of all older adults with at least one daily 

maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb (middle panel), 

or 80 ppb (bottom panel) using the maximum percent exposed for each study area, year 

2006-2010 adjusted air quality. 
5F-19 

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 60 ppb

0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 70 ppb

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 80 ppb

0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30% 0.35%

Figure 5F-11. Incremental increases in percent of all school-age children with at least one 

daily maximum 8-hr average O3 exposure at or above 60 ppb (top panel), 70 ppb (middle 

panel), or 80 ppb (bottom panel) using the mean percent exposed for each study area, year 

2006-2010 adjusted air quality. 
5F-20 

Figure 5F-12. Incremental increases in percent of all school-age children with at least two 

daily maximum 8-hr average O3 exposures at or above 60 ppb (top panel), 70 ppb (middle 

panel), or 80 ppb (bottom panel) using the maximum percent exposed for each study area, 

year 2006-2010 adjusted air quality. 
5F-21 

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 60 ppb

0% 1% 2% 3% 4% 5% 6% 7% 8%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 70 ppb

0.00% 0.10% 0.20% 0.30% 0.40% 0.50% 0.60%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 80 ppb

0.000% 0.002% 0.004% 0.006% 0.008% 0.010% 0.012% 0.014%

Figure 5F-13. Incremental increases in percent of all school-age children with at least two 

daily maximum 8-hr average O3 exposures at or above 60 ppb (top panel), 70 ppb (middle 

panel), or 80 ppb (bottom panel) using the mean percent exposed for each study area, year 

2006-2010 adjusted air quality.
 5F-22 

Table 5F-2. Percent of all school-age children with O3 exposures at or above 60, 70, and 80 

ppb-8hr while at moderate or greater exertion, years 2006-2010, adjusted air quality. 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Atlanta 60 55(06-08) 2006 0.3 0 0 0 0 0

Atlanta 60 55(06-08) 2007 0.2 0 0 0 0 0

Atlanta 60 55(06-08) 2008 0 0 0 0 0 0

Atlanta 60 55(08-10) 2008 0 0 0 0 0 0

Atlanta 60 55(08-10) 2009 0 0 0 0 0 0

Atlanta 60 55(08-10) 2010 0 0 0 0 0 0

Atlanta 70 55(06-08) 2006 0 0 0 0 0 0

Atlanta 70 55(06-08) 2007 0 0 0 0 0 0

Atlanta 70 55(06-08) 2008 0 0 0 0 0 0

Atlanta 70 55(08-10) 2008 0 0 0 0 0 0

Atlanta 70 55(08-10) 2009 0 0 0 0 0 0

Atlanta 70 55(08-10) 2010 0 0 0 0 0 0

Atlanta 80 55(06-08) 2006 0 0 0 0 0 0

Atlanta 80 55(06-08) 2007 0 0 0 0 0 0

Atlanta 80 55(06-08) 2008 0 0 0 0 0 0

Atlanta 80 55(08-10) 2008 0 0 0 0 0 0

Atlanta 80 55(08-10) 2009 0 0 0 0 0 0

Atlanta 80 55(08-10) 2010 0 0 0 0 0 0

Atlanta 60 60(06-08) 2006 1.2 0.1 0 0 0 0

Atlanta 60 60(06-08) 2007 0.8 0 0 0 0 0

Atlanta 60 60(06-08) 2008 0 0 0 0 0 0

Atlanta 60 60(08-10) 2008 0.8 0 0 0 0 0

Atlanta 60 60(08-10) 2009 0.2 0 0 0 0 0

Atlanta 60 60(08-10) 2010 0.3 0 0 0 0 0

Atlanta 70 60(06-08) 2006 0.1 0 0 0 0 0

Atlanta 70 60(06-08) 2007 0 0 0 0 0 0

Atlanta 70 60(06-08) 2008 0 0 0 0 0 0

Atlanta 70 60(08-10) 2008 0 0 0 0 0 0

Atlanta 70 60(08-10) 2009 0 0 0 0 0 0

Atlanta 70 60(08-10) 2010 0 0 0 0 0 0

Atlanta 80 60(06-08) 2006 0 0 0 0 0 0

Atlanta 80 60(06-08) 2007 0 0 0 0 0 0

Atlanta 80 60(06-08) 2008 0 0 0 0 0 0

Atlanta 80 60(08-10) 2008 0 0 0 0 0 0

Atlanta 80 60(08-10) 2009 0 0 0 0 0 0

Atlanta 80 60(08-10) 2010 0 0 0 0 0 0

Atlanta 60 65(06-08) 2006 4.8 0.8 0.2 0 0 0

Atlanta 60 65(06-08) 2007 4.2 0.7 0.2 0 0 0

Atlanta 60 65(06-08) 2008 0.5 0 0 0 0 0

Atlanta 60 65(08-10) 2008 3.3 0.6 0.1 0 0 0

Atlanta 60 65(08-10) 2009 1.4 0.1 0 0 0 0

Atlanta 60 65(08-10) 2010 2.2 0.3 0 0 0 0

Atlanta 70 65(06-08) 2006 0.5 0 0 0 0 0
 5F-23 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Atlanta 70 65(06-08) 2007 0.2 0 0 0 0 0

Atlanta 70 65(06-08) 2008 0 0 0 0 0 0

Atlanta 70 65(08-10) 2008 0.2 0 0 0 0 0

Atlanta 70 65(08-10) 2009 0 0 0 0 0 0

Atlanta 70 65(08-10) 2010 0 0 0 0 0 0

Atlanta 80 65(06-08) 2006 0 0 0 0 0 0

Atlanta 80 65(06-08) 2007 0 0 0 0 0 0

Atlanta 80 65(06-08) 2008 0 0 0 0 0 0

Atlanta 80 65(08-10) 2008 0 0 0 0 0 0

Atlanta 80 65(08-10) 2009 0 0 0 0 0 0

Atlanta 80 65(08-10) 2010 0 0 0 0 0 0

Atlanta 60 70(06-08) 2006 10.8 3.3 1.2 0.4 0.1 0

Atlanta 60 70(06-08) 2007 9.4 2.8 0.9 0.3 0.1 0

Atlanta 60 70(06-08) 2008 1.8 0.2 0 0 0 0

Atlanta 60 70(08-10) 2008 10 3.1 1.1 0.4 0.1 0.1

Atlanta 60 70(08-10) 2009 4.4 0.8 0.2 0 0 0

Atlanta 60 70(08-10) 2010 6.9 1.8 0.5 0.2 0.1 0

Atlanta 70 70(06-08) 2006 1.4 0.1 0 0 0 0

Atlanta 70 70(06-08) 2007 1 0.1 0 0 0 0

Atlanta 70 70(06-08) 2008 0.1 0 0 0 0 0

Atlanta 70 70(08-10) 2008 1.2 0.1 0 0 0 0

Atlanta 70 70(08-10) 2009 0.4 0 0 0 0 0

Atlanta 70 70(08-10) 2010 0.3 0 0 0 0 0

Atlanta 80 70(06-08) 2006 0.2 0 0 0 0 0

Atlanta 80 70(06-08) 2007 0.1 0 0 0 0 0

Atlanta 80 70(06-08) 2008 0 0 0 0 0 0

Atlanta 80 70(08-10) 2008 0.1 0 0 0 0 0

Atlanta 80 70(08-10) 2009 0 0 0 0 0 0

Atlanta 80 70(08-10) 2010 0 0 0 0 0 0

Atlanta 60 75(06-08) 2006 19.3 8.9 4.5 2.4 1.4 0.7

Atlanta 60 75(06-08) 2007 17.7 7.9 3.9 2.1 1.1 0.5

Atlanta 60 75(06-08) 2008 5.4 1.2 0.3 0.1 0 0

Atlanta 60 75(08-10) 2008 19.3 8.7 4.5 2.4 1.3 0.7

Atlanta 60 75(08-10) 2009 10.6 3 1 0.4 0.1 0

Atlanta 60 75(08-10) 2010 14 5.5 2.5 1.2 0.6 0.3

Atlanta 70 75(06-08) 2006 4.4 0.7 0.1 0 0 0

Atlanta 70 75(06-08) 2007 3.7 0.6 0.1 0 0 0

Atlanta 70 75(06-08) 2008 0.4 0 0 0 0 0

Atlanta 70 75(08-10) 2008 4.2 0.7 0.1 0 0 0

Atlanta 70 75(08-10) 2009 1.6 0.1 0 0 0 0

Atlanta 70 75(08-10) 2010 1.9 0.2 0 0 0 0

Atlanta 80 75(06-08) 2006 0.7 0 0 0 0 0

Atlanta 80 75(06-08) 2007 0.4 0 0 0 0 0

Atlanta 80 75(06-08) 2008 0 0 0 0 0 0

Atlanta 80 75(08-10) 2008 0.5 0 0 0 0 0

Atlanta 80 75(08-10) 2009 0.1 0 0 0 0 0
 5F-24 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Atlanta 80 75(08-10) 2010 0.1 0 0 0 0 0

Baltimore 60 55(06-08) 2006 0.1 0 0 0 0 0

Baltimore 60 55(06-08) 2007 0 0 0 0 0 0

Baltimore 60 55(06-08) 2008 0 0 0 0 0 0

Baltimore 60 55(08-10) 2008 0 0 0 0 0 0

Baltimore 60 55(08-10) 2009 0 0 0 0 0 0

Baltimore 60 55(08-10) 2010 0.2 0 0 0 0 0

Baltimore 70 55(06-08) 2006 0 0 0 0 0 0

Baltimore 70 55(06-08) 2007 0 0 0 0 0 0

Baltimore 70 55(06-08) 2008 0 0 0 0 0 0

Baltimore 70 55(08-10) 2008 0 0 0 0 0 0

Baltimore 70 55(08-10) 2009 0 0 0 0 0 0

Baltimore 70 55(08-10) 2010 0 0 0 0 0 0

Baltimore 80 55(06-08) 2006 0 0 0 0 0 0

Baltimore 80 55(06-08) 2007 0 0 0 0 0 0

Baltimore 80 55(06-08) 2008 0 0 0 0 0 0

Baltimore 80 55(08-10) 2008 0 0 0 0 0 0

Baltimore 80 55(08-10) 2009 0 0 0 0 0 0

Baltimore 80 55(08-10) 2010 0 0 0 0 0 0

Baltimore 60 60(06-08) 2006 1 0.1 0 0 0 0

Baltimore 60 60(06-08) 2007 0.4 0 0 0 0 0

Baltimore 60 60(06-08) 2008 0.3 0 0 0 0 0

Baltimore 60 60(08-10) 2008 0.6 0 0 0 0 0

Baltimore 60 60(08-10) 2009 0 0 0 0 0 0

Baltimore 60 60(08-10) 2010 1.2 0 0 0 0 0

Baltimore 70 60(06-08) 2006 0 0 0 0 0 0

Baltimore 70 60(06-08) 2007 0 0 0 0 0 0

Baltimore 70 60(06-08) 2008 0 0 0 0 0 0

Baltimore 70 60(08-10) 2008 0 0 0 0 0 0

Baltimore 70 60(08-10) 2009 0 0 0 0 0 0

Baltimore 70 60(08-10) 2010 0 0 0 0 0 0

Baltimore 80 60(06-08) 2006 0 0 0 0 0 0

Baltimore 80 60(06-08) 2007 0 0 0 0 0 0

Baltimore 80 60(06-08) 2008 0 0 0 0 0 0

Baltimore 80 60(08-10) 2008 0 0 0 0 0 0

Baltimore 80 60(08-10) 2009 0 0 0 0 0 0

Baltimore 80 60(08-10) 2010 0 0 0 0 0 0

Baltimore 60 65(06-08) 2006 5.4 0.9 0.2 0 0 0

Baltimore 60 65(06-08) 2007 3.3 0.4 0 0 0 0

Baltimore 60 65(06-08) 2008 2.1 0.1 0 0 0 0

Baltimore 60 65(08-10) 2008 2.5 0.2 0 0 0 0

Baltimore 60 65(08-10) 2009 0.1 0 0 0 0 0

Baltimore 60 65(08-10) 2010 3.8 0.5 0.1 0 0 0

Baltimore 70 65(06-08) 2006 0.3 0 0 0 0 0

Baltimore 70 65(06-08) 2007 0.2 0 0 0 0 0

Baltimore 70 65(06-08) 2008 0.1 0 0 0 0 0
 5F-25 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Baltimore 70 65(08-10) 2008 0.1 0 0 0 0 0

Baltimore 70 65(08-10) 2009 0 0 0 0 0 0

Baltimore 70 65(08-10) 2010 0.3 0 0 0 0 0

Baltimore 80 65(06-08) 2006 0 0 0 0 0 0

Baltimore 80 65(06-08) 2007 0 0 0 0 0 0

Baltimore 80 65(06-08) 2008 0 0 0 0 0 0

Baltimore 80 65(08-10) 2008 0 0 0 0 0 0

Baltimore 80 65(08-10) 2009 0 0 0 0 0 0

Baltimore 80 65(08-10) 2010 0 0 0 0 0 0

Baltimore 60 70(06-08) 2006 11.8 3.7 1.2 0.5 0.2 0.1

Baltimore 60 70(06-08) 2007 7.7 1.8 0.5 0.2 0.1 0

Baltimore 60 70(06-08) 2008 5.3 0.8 0.2 0 0 0

Baltimore 60 70(08-10) 2008 6.3 1.2 0.3 0.1 0 0

Baltimore 60 70(08-10) 2009 0.6 0 0 0 0 0

Baltimore 60 70(08-10) 2010 9.5 2.5 0.7 0.2 0.1 0

Baltimore 70 70(06-08) 2006 1.2 0.1 0 0 0 0

Baltimore 70 70(06-08) 2007 0.6 0 0 0 0 0

Baltimore 70 70(06-08) 2008 0.4 0 0 0 0 0

Baltimore 70 70(08-10) 2008 0.6 0 0 0 0 0

Baltimore 70 70(08-10) 2009 0 0 0 0 0 0

Baltimore 70 70(08-10) 2010 1 0 0 0 0 0

Baltimore 80 70(06-08) 2006 0.1 0 0 0 0 0

Baltimore 80 70(06-08) 2007 0.1 0 0 0 0 0

Baltimore 80 70(06-08) 2008 0 0 0 0 0 0

Baltimore 80 70(08-10) 2008 0 0 0 0 0 0

Baltimore 80 70(08-10) 2009 0 0 0 0 0 0

Baltimore 80 70(08-10) 2010 0.1 0 0 0 0 0

Baltimore 60 75(06-08) 2006 19 8.4 4.1 2.1 1.1 0.5

Baltimore 60 75(06-08) 2007 13.6 5.1 2.1 0.9 0.4 0.2

Baltimore 60 75(06-08) 2008 9.7 2.5 0.8 0.3 0.1 0

Baltimore 60 75(08-10) 2008 10.7 3 1 0.3 0.1 0

Baltimore 60 75(08-10) 2009 1.9 0.2 0 0 0 0

Baltimore 60 75(08-10) 2010 16.2 6.4 2.8 1.3 0.6 0.2

Baltimore 70 75(06-08) 2006 4 0.5 0.1 0 0 0

Baltimore 70 75(06-08) 2007 2 0.2 0 0 0 0

Baltimore 70 75(06-08) 2008 1.4 0.1 0 0 0 0

Baltimore 70 75(08-10) 2008 1.7 0.1 0 0 0 0

Baltimore 70 75(08-10) 2009 0.1 0 0 0 0 0

Baltimore 70 75(08-10) 2010 2.6 0.2 0 0 0 0

Baltimore 80 75(06-08) 2006 0.3 0 0 0 0 0

Baltimore 80 75(06-08) 2007 0.2 0 0 0 0 0

Baltimore 80 75(06-08) 2008 0.1 0 0 0 0 0

Baltimore 80 75(08-10) 2008 0.1 0 0 0 0 0

Baltimore 80 75(08-10) 2009 0 0 0 0 0 0

Baltimore 80 75(08-10) 2010 0.4 0 0 0 0 0

Boston 60 55(06-08) 2006 0 0 0 0 0 0
 5F-26 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Boston 60 55(06-08) 2007 0.2 0 0 0 0 0

Boston 60 55(06-08) 2008 0 0 0 0 0 0

Boston 60 55(08-10) 2008 0 0 0 0 0 0

Boston 60 55(08-10) 2009 0.1 0 0 0 0 0

Boston 60 55(08-10) 2010 0 0 0 0 0 0

Boston 70 55(06-08) 2006 0 0 0 0 0 0

Boston 70 55(06-08) 2007 0 0 0 0 0 0

Boston 70 55(06-08) 2008 0 0 0 0 0 0

Boston 70 55(08-10) 2008 0 0 0 0 0 0

Boston 70 55(08-10) 2009 0 0 0 0 0 0

Boston 70 55(08-10) 2010 0 0 0 0 0 0

Boston 80 55(06-08) 2006 0 0 0 0 0 0

Boston 80 55(06-08) 2007 0 0 0 0 0 0

Boston 80 55(06-08) 2008 0 0 0 0 0 0

Boston 80 55(08-10) 2008 0 0 0 0 0 0

Boston 80 55(08-10) 2009 0 0 0 0 0 0

Boston 80 55(08-10) 2010 0 0 0 0 0 0

Boston 60 60(06-08) 2006 0.1 0 0 0 0 0

Boston 60 60(06-08) 2007 1.7 0.1 0 0 0 0

Boston 60 60(06-08) 2008 0 0 0 0 0 0

Boston 60 60(08-10) 2008 0.3 0 0 0 0 0

Boston 60 60(08-10) 2009 1.3 0.1 0 0 0 0

Boston 60 60(08-10) 2010 0.4 0 0 0 0 0

Boston 70 60(06-08) 2006 0 0 0 0 0 0

Boston 70 60(06-08) 2007 0 0 0 0 0 0

Boston 70 60(06-08) 2008 0 0 0 0 0 0

Boston 70 60(08-10) 2008 0 0 0 0 0 0

Boston 70 60(08-10) 2009 0 0 0 0 0 0

Boston 70 60(08-10) 2010 0 0 0 0 0 0

Boston 80 60(06-08) 2006 0 0 0 0 0 0

Boston 80 60(06-08) 2007 0 0 0 0 0 0

Boston 80 60(06-08) 2008 0 0 0 0 0 0

Boston 80 60(08-10) 2008 0 0 0 0 0 0

Boston 80 60(08-10) 2009 0 0 0 0 0 0

Boston 80 60(08-10) 2010 0 0 0 0 0 0

Boston 60 65(06-08) 2006 1.2 0.1 0 0 0 0

Boston 60 65(06-08) 2007 6.7 1.1 0.2 0 0 0

Boston 60 65(06-08) 2008 0.8 0 0 0 0 0

Boston 60 65(08-10) 2008 2.9 0.3 0 0 0 0

Boston 60 65(08-10) 2009 4.6 0.5 0 0 0 0

Boston 60 65(08-10) 2010 2.5 0.2 0 0 0 0

Boston 70 65(06-08) 2006 0 0 0 0 0 0

Boston 70 65(06-08) 2007 0.5 0 0 0 0 0

Boston 70 65(06-08) 2008 0 0 0 0 0 0

Boston 70 65(08-10) 2008 0.1 0 0 0 0 0

Boston 70 65(08-10) 2009 0.2 0 0 0 0 0
 5F-27 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Boston 70 65(08-10) 2010 0 0 0 0 0 0

Boston 80 65(06-08) 2006 0 0 0 0 0 0

Boston 80 65(06-08) 2007 0 0 0 0 0 0

Boston 80 65(06-08) 2008 0 0 0 0 0 0

Boston 80 65(08-10) 2008 0 0 0 0 0 0

Boston 80 65(08-10) 2009 0 0 0 0 0 0

Boston 80 65(08-10) 2010 0 0 0 0 0 0

Boston 60 70(06-08) 2006 6.7 1.2 0.2 0 0 0

Boston 60 70(06-08) 2007 15.7 5.5 2 0.8 0.3 0.1

Boston 60 70(06-08) 2008 4.7 0.7 0.1 0 0 0

Boston 60 70(08-10) 2008 9.1 2.1 0.5 0.1 0 0

Boston 60 70(08-10) 2009 9 1.8 0.2 0 0 0

Boston 60 70(08-10) 2010 6.7 1.1 0.2 0 0 0

Boston 70 70(06-08) 2006 0.3 0 0 0 0 0

Boston 70 70(06-08) 2007 3.2 0.4 0 0 0 0

Boston 70 70(06-08) 2008 0.2 0 0 0 0 0

Boston 70 70(08-10) 2008 0.8 0 0 0 0 0

Boston 70 70(08-10) 2009 1.3 0 0 0 0 0

Boston 70 70(08-10) 2010 0.7 0 0 0 0 0

Boston 80 70(06-08) 2006 0 0 0 0 0 0

Boston 80 70(06-08) 2007 0.2 0 0 0 0 0

Boston 80 70(06-08) 2008 0 0 0 0 0 0

Boston 80 70(08-10) 2008 0.1 0 0 0 0 0

Boston 80 70(08-10) 2009 0 0 0 0 0 0

Boston 80 70(08-10) 2010 0 0 0 0 0 0

Boston 60 75(06-08) 2006 11.9 3.1 0.9 0.2 0 0

Boston 60 75(06-08) 2007 21.9 9.7 4.6 2.3 1.1 0.5

Boston 60 75(06-08) 2008 9.1 2.1 0.5 0.1 0 0

Boston 60 75(08-10) 2008 15.9 5.5 2.1 0.8 0.3 0.1

Boston 60 75(08-10) 2009 11.4 2.8 0.6 0.1 0 0

Boston 60 75(08-10) 2010 11.2 3 0.9 0.2 0.1 0

Boston 70 75(06-08) 2006 1.4 0.1 0 0 0 0

Boston 70 75(06-08) 2007 6.6 1.1 0.2 0 0 0

Boston 70 75(06-08) 2008 0.8 0 0 0 0 0

Boston 70 75(08-10) 2008 3.2 0.3 0 0 0 0

Boston 70 75(08-10) 2009 2.1 0.1 0 0 0 0

Boston 70 75(08-10) 2010 1.7 0.1 0 0 0 0

Boston 80 75(06-08) 2006 0 0 0 0 0 0

Boston 80 75(06-08) 2007 1 0 0 0 0 0

Boston 80 75(06-08) 2008 0.1 0 0 0 0 0

Boston 80 75(08-10) 2008 0.4 0 0 0 0 0

Boston 80 75(08-10) 2009 0.1 0 0 0 0 0

Boston 80 75(08-10) 2010 0.1 0 0 0 0 0

Chicago 60 55(06-08) 2006 0.1 0 0 0 0 0

Chicago 60 55(06-08) 2007 0.3 0 0 0 0 0

Chicago 60 55(06-08) 2008 0 0 0 0 0 0
 5F-28 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Chicago 60 55(08-10) 2008 0 0 0 0 0 0

Chicago 60 55(08-10) 2009 0.3 0 0 0 0 0

Chicago 60 55(08-10) 2010 0.3 0 0 0 0 0

Chicago 70 55(06-08) 2006 0 0 0 0 0 0

Chicago 70 55(06-08) 2007 0 0 0 0 0 0

Chicago 70 55(06-08) 2008 0 0 0 0 0 0

Chicago 70 55(08-10) 2008 0 0 0 0 0 0

Chicago 70 55(08-10) 2009 0 0 0 0 0 0

Chicago 70 55(08-10) 2010 0 0 0 0 0 0

Chicago 80 55(06-08) 2006 0 0 0 0 0 0

Chicago 80 55(06-08) 2007 0 0 0 0 0 0

Chicago 80 55(06-08) 2008 0 0 0 0 0 0

Chicago 80 55(08-10) 2008 0 0 0 0 0 0

Chicago 80 55(08-10) 2009 0 0 0 0 0 0

Chicago 80 55(08-10) 2010 0 0 0 0 0 0

Chicago 60 60(06-08) 2006 0.7 0 0 0 0 0

Chicago 60 60(06-08) 2007 2.2 0.3 0 0 0 0

Chicago 60 60(06-08) 2008 0 0 0 0 0 0

Chicago 60 60(08-10) 2008 0.1 0 0 0 0 0

Chicago 60 60(08-10) 2009 1.1 0.1 0 0 0 0

Chicago 60 60(08-10) 2010 2 0.3 0.1 0 0 0

Chicago 70 60(06-08) 2006 0 0 0 0 0 0

Chicago 70 60(06-08) 2007 0.1 0 0 0 0 0

Chicago 70 60(06-08) 2008 0 0 0 0 0 0

Chicago 70 60(08-10) 2008 0 0 0 0 0 0

Chicago 70 60(08-10) 2009 0.1 0 0 0 0 0

Chicago 70 60(08-10) 2010 0 0 0 0 0 0

Chicago 80 60(06-08) 2006 0 0 0 0 0 0

Chicago 80 60(06-08) 2007 0 0 0 0 0 0

Chicago 80 60(06-08) 2008 0 0 0 0 0 0

Chicago 80 60(08-10) 2008 0 0 0 0 0 0

Chicago 80 60(08-10) 2009 0 0 0 0 0 0

Chicago 80 60(08-10) 2010 0 0 0 0 0 0

Chicago 60 65(06-08) 2006 2.9 0.3 0 0 0 0

Chicago 60 65(06-08) 2007 8.1 1.8 0.4 0.1 0 0

Chicago 60 65(06-08) 2008 0.2 0 0 0 0 0

Chicago 60 65(08-10) 2008 0.8 0 0 0 0 0

Chicago 60 65(08-10) 2009 2.9 0.4 0.1 0 0 0

Chicago 60 65(08-10) 2010 6.5 1.5 0.4 0.1 0 0

Chicago 70 65(06-08) 2006 0.1 0 0 0 0 0

Chicago 70 65(06-08) 2007 0.4 0 0 0 0 0

Chicago 70 65(06-08) 2008 0 0 0 0 0 0

Chicago 70 65(08-10) 2008 0 0 0 0 0 0

Chicago 70 65(08-10) 2009 0.3 0 0 0 0 0

Chicago 70 65(08-10) 2010 0.2 0 0 0 0 0

Chicago 80 65(06-08) 2006 0 0 0 0 0 0
 5F-29 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Chicago 80 65(06-08) 2007 0 0 0 0 0 0

Chicago 80 65(06-08) 2008 0 0 0 0 0 0

Chicago 80 65(08-10) 2008 0 0 0 0 0 0

Chicago 80 65(08-10) 2009 0 0 0 0 0 0

Chicago 80 65(08-10) 2010 0 0 0 0 0 0

Chicago 60 70(06-08) 2006 7.7 1.6 0.4 0.1 0 0

Chicago 60 70(06-08) 2007 16 5.7 2.1 0.8 0.3 0.1

Chicago 60 70(06-08) 2008 1.1 0.1 0 0 0 0

Chicago 60 70(08-10) 2008 2.9 0.3 0 0 0 0

Chicago 60 70(08-10) 2009 6.6 1.2 0.2 0 0 0

Chicago 60 70(08-10) 2010 13.6 4.1 1.3 0.4 0.1 0

Chicago 70 70(06-08) 2006 0.5 0 0 0 0 0

Chicago 70 70(06-08) 2007 2.7 0.2 0 0 0 0

Chicago 70 70(06-08) 2008 0 0 0 0 0 0

Chicago 70 70(08-10) 2008 0 0 0 0 0 0

Chicago 70 70(08-10) 2009 0.6 0 0 0 0 0

Chicago 70 70(08-10) 2010 1 0.1 0 0 0 0

Chicago 80 70(06-08) 2006 0 0 0 0 0 0

Chicago 80 70(06-08) 2007 0.1 0 0 0 0 0

Chicago 80 70(06-08) 2008 0 0 0 0 0 0

Chicago 80 70(08-10) 2008 0 0 0 0 0 0

Chicago 80 70(08-10) 2009 0.1 0 0 0 0 0

Chicago 80 70(08-10) 2010 0 0 0 0 0 0

Chicago 60 75(06-08) 2006 13.5 3.9 1.2 0.4 0.1 0

Chicago 60 75(06-08) 2007 24.7 11.6 6 3.1 1.6 0.8

Chicago 60 75(06-08) 2008 3 0.3 0 0 0 0

Chicago 70 75(06-08) 2006 2.1 0.1 0 0 0 0

Chicago 70 75(06-08) 2007 7.5 1.3 0.2 0 0 0

Chicago 70 75(06-08) 2008 0 0 0 0 0 0

Chicago 80 75(06-08) 2006 0 0 0 0 0 0

Chicago 80 75(06-08) 2007 0.7 0 0 0 0 0

Chicago 80 75(06-08) 2008 0 0 0 0 0 0

Cleveland 60 55(06-08) 2006 0 0 0 0 0 0

Cleveland 60 55(06-08) 2007 0 0 0 0 0 0

Cleveland 60 55(06-08) 2008 0 0 0 0 0 0

Cleveland 60 55(08-10) 2008 0 0 0 0 0 0

Cleveland 60 55(08-10) 2009 0 0 0 0 0 0

Cleveland 60 55(08-10) 2010 0 0 0 0 0 0

Cleveland 70 55(06-08) 2006 0 0 0 0 0 0

Cleveland 70 55(06-08) 2007 0 0 0 0 0 0

Cleveland 70 55(06-08) 2008 0 0 0 0 0 0

Cleveland 70 55(08-10) 2008 0 0 0 0 0 0

Cleveland 70 55(08-10) 2009 0 0 0 0 0 0

Cleveland 70 55(08-10) 2010 0 0 0 0 0 0

Cleveland 80 55(06-08) 2006 0 0 0 0 0 0

Cleveland 80 55(06-08) 2007 0 0 0 0 0 0
 5F-30 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Cleveland 80 55(06-08) 2008 0 0 0 0 0 0

Cleveland 80 55(08-10) 2008 0 0 0 0 0 0

Cleveland 80 55(08-10) 2009 0 0 0 0 0 0

Cleveland 80 55(08-10) 2010 0 0 0 0 0 0

Cleveland 60 60(06-08) 2006 0.1 0 0 0 0 0

Cleveland 60 60(06-08) 2007 0.4 0 0 0 0 0

Cleveland 60 60(06-08) 2008 0 0 0 0 0 0

Cleveland 60 60(08-10) 2008 0 0 0 0 0 0

Cleveland 60 60(08-10) 2009 0.2 0 0 0 0 0

Cleveland 60 60(08-10) 2010 0.1 0 0 0 0 0

Cleveland 70 60(06-08) 2006 0 0 0 0 0 0

Cleveland 70 60(06-08) 2007 0 0 0 0 0 0

Cleveland 70 60(06-08) 2008 0 0 0 0 0 0

Cleveland 70 60(08-10) 2008 0 0 0 0 0 0

Cleveland 70 60(08-10) 2009 0 0 0 0 0 0

Cleveland 70 60(08-10) 2010 0 0 0 0 0 0

Cleveland 80 60(06-08) 2006 0 0 0 0 0 0

Cleveland 80 60(06-08) 2007 0 0 0 0 0 0

Cleveland 80 60(06-08) 2008 0 0 0 0 0 0

Cleveland 80 60(08-10) 2008 0 0 0 0 0 0

Cleveland 80 60(08-10) 2009 0 0 0 0 0 0

Cleveland 80 60(08-10) 2010 0 0 0 0 0 0

Cleveland 60 65(06-08) 2006 0.5 0 0 0 0 0

Cleveland 60 65(06-08) 2007 3 0.5 0.1 0 0 0

Cleveland 60 65(06-08) 2008 1.2 0.1 0 0 0 0

Cleveland 60 65(08-10) 2008 0.6 0 0 0 0 0

Cleveland 60 65(08-10) 2009 0.7 0 0 0 0 0

Cleveland 60 65(08-10) 2010 0.5 0 0 0 0 0

Cleveland 70 65(06-08) 2006 0 0 0 0 0 0

Cleveland 70 65(06-08) 2007 0.2 0 0 0 0 0

Cleveland 70 65(06-08) 2008 0 0 0 0 0 0

Cleveland 70 65(08-10) 2008 0 0 0 0 0 0

Cleveland 70 65(08-10) 2009 0 0 0 0 0 0

Cleveland 70 65(08-10) 2010 0 0 0 0 0 0

Cleveland 80 65(06-08) 2006 0 0 0 0 0 0

Cleveland 80 65(06-08) 2007 0 0 0 0 0 0

Cleveland 80 65(06-08) 2008 0 0 0 0 0 0

Cleveland 80 65(08-10) 2008 0 0 0 0 0 0

Cleveland 80 65(08-10) 2009 0 0 0 0 0 0

Cleveland 80 65(08-10) 2010 0 0 0 0 0 0

Cleveland 60 70(06-08) 2006 3.1 0.4 0.1 0 0 0

Cleveland 60 70(06-08) 2007 9.3 2.6 0.7 0.2 0.1 0

Cleveland 60 70(06-08) 2008 5.3 0.9 0.2 0 0 0

Cleveland 60 70(08-10) 2008 4.1 0.6 0.1 0 0 0

Cleveland 60 70(08-10) 2009 1.9 0.2 0 0 0 0

Cleveland 60 70(08-10) 2010 2.2 0.3 0.1 0 0 0
 5F-31 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Cleveland 70 70(06-08) 2006 0.1 0 0 0 0 0

Cleveland 70 70(06-08) 2007 0.9 0 0 0 0 0

Cleveland 70 70(06-08) 2008 0.4 0 0 0 0 0

Cleveland 70 70(08-10) 2008 0.2 0 0 0 0 0

Cleveland 70 70(08-10) 2009 0.2 0 0 0 0 0

Cleveland 70 70(08-10) 2010 0.1 0 0 0 0 0

Cleveland 80 70(06-08) 2006 0 0 0 0 0 0

Cleveland 80 70(06-08) 2007 0 0 0 0 0 0

Cleveland 80 70(06-08) 2008 0 0 0 0 0 0

Cleveland 80 70(08-10) 2008 0 0 0 0 0 0

Cleveland 80 70(08-10) 2009 0 0 0 0 0 0

Cleveland 80 70(08-10) 2010 0 0 0 0 0 0

Cleveland 60 75(06-08) 2006 9.3 2.2 0.6 0.2 0 0

Cleveland 60 75(06-08) 2007 18 7.5 3.4 1.6 0.7 0.3

Cleveland 60 75(06-08) 2008 11.7 3.4 1.2 0.4 0.1 0

Cleveland 60 75(08-10) 2008 10.6 2.9 1 0.4 0.1 0

Cleveland 60 75(08-10) 2009 4.5 0.9 0.2 0.1 0 0

Cleveland 60 75(08-10) 2010 8.2 2 0.6 0.2 0.1 0

Cleveland 70 75(06-08) 2006 0.6 0 0 0 0 0

Cleveland 70 75(06-08) 2007 3.7 0.5 0.1 0 0 0

Cleveland 70 75(06-08) 2008 1.8 0.2 0 0 0 0

Cleveland 70 75(08-10) 2008 1.5 0.1 0 0 0 0

Cleveland 70 75(08-10) 2009 0.5 0 0 0 0 0

Cleveland 70 75(08-10) 2010 0.5 0 0 0 0 0

Cleveland 80 75(06-08) 2006 0 0 0 0 0 0

Cleveland 80 75(06-08) 2007 0.2 0 0 0 0 0

Cleveland 80 75(06-08) 2008 0.1 0 0 0 0 0

Cleveland 80 75(08-10) 2008 0 0 0 0 0 0

Cleveland 80 75(08-10) 2009 0 0 0 0 0 0

Cleveland 80 75(08-10) 2010 0 0 0 0 0 0

Dallas 60 55(06-08) 2006 0.1 0 0 0 0 0

Dallas 60 55(06-08) 2007 0.3 0 0 0 0 0

Dallas 60 55(06-08) 2008 0 0 0 0 0 0

Dallas 60 55(08-10) 2008 0 0 0 0 0 0

Dallas 60 55(08-10) 2009 0 0 0 0 0 0

Dallas 60 55(08-10) 2010 0 0 0 0 0 0

Dallas 70 55(06-08) 2006 0 0 0 0 0 0

Dallas 70 55(06-08) 2007 0 0 0 0 0 0

Dallas 70 55(06-08) 2008 0 0 0 0 0 0

Dallas 70 55(08-10) 2008 0 0 0 0 0 0

Dallas 70 55(08-10) 2009 0 0 0 0 0 0

Dallas 70 55(08-10) 2010 0 0 0 0 0 0

Dallas 80 55(06-08) 2006 0 0 0 0 0 0

Dallas 80 55(06-08) 2007 0 0 0 0 0 0

Dallas 80 55(06-08) 2008 0 0 0 0 0 0

Dallas 80 55(08-10) 2008 0 0 0 0 0 0
 5F-32 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Dallas 80 55(08-10) 2009 0 0 0 0 0 0

Dallas 80 55(08-10) 2010 0 0 0 0 0 0

Dallas 60 60(06-08) 2006 1.9 0.2 0 0 0 0

Dallas 60 60(06-08) 2007 1.1 0 0 0 0 0

Dallas 60 60(06-08) 2008 0.1 0 0 0 0 0

Dallas 60 60(08-10) 2008 0.1 0 0 0 0 0

Dallas 60 60(08-10) 2009 0.7 0 0 0 0 0

Dallas 60 60(08-10) 2010 0.1 0 0 0 0 0

Dallas 70 60(06-08) 2006 0 0 0 0 0 0

Dallas 70 60(06-08) 2007 0.1 0 0 0 0 0

Dallas 70 60(06-08) 2008 0 0 0 0 0 0

Dallas 70 60(08-10) 2008 0 0 0 0 0 0

Dallas 70 60(08-10) 2009 0 0 0 0 0 0

Dallas 70 60(08-10) 2010 0 0 0 0 0 0

Dallas 80 60(06-08) 2006 0 0 0 0 0 0

Dallas 80 60(06-08) 2007 0 0 0 0 0 0

Dallas 80 60(06-08) 2008 0 0 0 0 0 0

Dallas 80 60(08-10) 2008 0 0 0 0 0 0

Dallas 80 60(08-10) 2009 0 0 0 0 0 0

Dallas 80 60(08-10) 2010 0 0 0 0 0 0

Dallas 60 65(06-08) 2006 7.6 2 0.7 0.2 0.1 0

Dallas 60 65(06-08) 2007 2.6 0.1 0 0 0 0

Dallas 60 65(06-08) 2008 0.7 0 0 0 0 0

Dallas 60 65(08-10) 2008 0.7 0 0 0 0 0

Dallas 60 65(08-10) 2009 3.1 0.4 0 0 0 0

Dallas 60 65(08-10) 2010 1 0.1 0 0 0 0

Dallas 70 65(06-08) 2006 0.2 0 0 0 0 0

Dallas 70 65(06-08) 2007 0.3 0 0 0 0 0

Dallas 70 65(06-08) 2008 0 0 0 0 0 0

Dallas 70 65(08-10) 2008 0 0 0 0 0 0

Dallas 70 65(08-10) 2009 0.1 0 0 0 0 0

Dallas 70 65(08-10) 2010 0 0 0 0 0 0

Dallas 80 65(06-08) 2006 0 0 0 0 0 0

Dallas 80 65(06-08) 2007 0 0 0 0 0 0

Dallas 80 65(06-08) 2008 0 0 0 0 0 0

Dallas 80 65(08-10) 2008 0 0 0 0 0 0

Dallas 80 65(08-10) 2009 0 0 0 0 0 0

Dallas 80 65(08-10) 2010 0 0 0 0 0 0

Dallas 60 70(06-08) 2006 16 7.1 3.4 1.7 0.8 0.4

Dallas 60 70(06-08) 2007 6.4 1 0.1 0 0 0

Dallas 60 70(06-08) 2008 3 0.4 0.1 0 0 0

Dallas 60 70(08-10) 2008 3 0.4 0.1 0 0 0

Dallas 60 70(08-10) 2009 8.4 1.9 0.5 0.1 0 0

Dallas 60 70(08-10) 2010 3.7 0.5 0.1 0 0 0

Dallas 70 70(06-08) 2006 1.5 0.1 0 0 0 0

Dallas 70 70(06-08) 2007 1 0 0 0 0 0
 5F-33 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Dallas 70 70(06-08) 2008 0.1 0 0 0 0 0

Dallas 70 70(08-10) 2008 0.1 0 0 0 0 0

Dallas 70 70(08-10) 2009 0.6 0 0 0 0 0

Dallas 70 70(08-10) 2010 0.1 0 0 0 0 0

Dallas 80 70(06-08) 2006 0 0 0 0 0 0

Dallas 80 70(06-08) 2007 0.1 0 0 0 0 0

Dallas 80 70(06-08) 2008 0 0 0 0 0 0

Dallas 80 70(08-10) 2008 0 0 0 0 0 0

Dallas 80 70(08-10) 2009 0 0 0 0 0 0

Dallas 80 70(08-10) 2010 0 0 0 0 0 0

Dallas 60 75(06-08) 2006 22.9 12.2 7.3 4.4 2.7 1.6

Dallas 60 75(06-08) 2007 10.9 2.8 0.8 0.2 0 0

Dallas 60 75(06-08) 2008 6.7 1.4 0.4 0.1 0 0

Dallas 60 75(08-10) 2008 7.9 1.9 0.6 0.2 0 0

Dallas 60 75(08-10) 2009 14.9 5.5 2.2 0.8 0.3 0.1

Dallas 60 75(08-10) 2010 8.3 2 0.5 0.2 0 0

Dallas 70 75(06-08) 2006 4.5 0.8 0.2 0.1 0 0

Dallas 70 75(06-08) 2007 1.9 0.1 0 0 0 0

Dallas 70 75(06-08) 2008 0.3 0 0 0 0 0

Dallas 70 75(08-10) 2008 0.5 0 0 0 0 0

Dallas 70 75(08-10) 2009 2.2 0.2 0 0 0 0

Dallas 70 75(08-10) 2010 0.6 0 0 0 0 0

Dallas 80 75(06-08) 2006 0.2 0 0 0 0 0

Dallas 80 75(06-08) 2007 0.3 0 0 0 0 0

Dallas 80 75(06-08) 2008 0 0 0 0 0 0

Dallas 80 75(08-10) 2008 0 0 0 0 0 0

Dallas 80 75(08-10) 2009 0.1 0 0 0 0 0

Dallas 80 75(08-10) 2010 0 0 0 0 0 0

Denver 60 55(06-08) 2006 0 0 0 0 0 0

Denver 60 55(06-08) 2007 0 0 0 0 0 0

Denver 60 55(06-08) 2008 0 0 0 0 0 0

Denver 60 55(08-10) 2008 0 0 0 0 0 0

Denver 60 55(08-10) 2009 0 0 0 0 0 0

Denver 60 55(08-10) 2010 0 0 0 0 0 0

Denver 70 55(06-08) 2006 0 0 0 0 0 0

Denver 70 55(06-08) 2007 0 0 0 0 0 0

Denver 70 55(06-08) 2008 0 0 0 0 0 0

Denver 70 55(08-10) 2008 0 0 0 0 0 0

Denver 70 55(08-10) 2009 0 0 0 0 0 0

Denver 70 55(08-10) 2010 0 0 0 0 0 0

Denver 80 55(06-08) 2006 0 0 0 0 0 0

Denver 80 55(06-08) 2007 0 0 0 0 0 0

Denver 80 55(06-08) 2008 0 0 0 0 0 0

Denver 80 55(08-10) 2008 0 0 0 0 0 0

Denver 80 55(08-10) 2009 0 0 0 0 0 0

Denver 80 55(08-10) 2010 0 0 0 0 0 0
 5F-34 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Denver 60 60(06-08) 2006 0.5 0 0 0 0 0

Denver 60 60(06-08) 2007 0.1 0 0 0 0 0

Denver 60 60(06-08) 2008 0.4 0 0 0 0 0

Denver 60 60(08-10) 2008 0.4 0 0 0 0 0

Denver 60 60(08-10) 2009 0 0 0 0 0 0

Denver 60 60(08-10) 2010 0 0 0 0 0 0

Denver 70 60(06-08) 2006 0 0 0 0 0 0

Denver 70 60(06-08) 2007 0 0 0 0 0 0

Denver 70 60(06-08) 2008 0 0 0 0 0 0

Denver 70 60(08-10) 2008 0 0 0 0 0 0

Denver 70 60(08-10) 2009 0 0 0 0 0 0

Denver 70 60(08-10) 2010 0 0 0 0 0 0

Denver 80 60(06-08) 2006 0 0 0 0 0 0

Denver 80 60(06-08) 2007 0 0 0 0 0 0

Denver 80 60(06-08) 2008 0 0 0 0 0 0

Denver 80 60(08-10) 2008 0 0 0 0 0 0

Denver 80 60(08-10) 2009 0 0 0 0 0 0

Denver 80 60(08-10) 2010 0 0 0 0 0 0

Denver 60 65(06-08) 2006 4.4 0.8 0.2 0 0 0

Denver 60 65(06-08) 2007 2 0.3 0.1 0 0 0

Denver 60 65(06-08) 2008 2.8 0.3 0.1 0 0 0

Denver 60 65(08-10) 2008 9.5 2.8 1 0.4 0.2 0.1

Denver 60 65(08-10) 2009 3.1 0.4 0.1 0 0 0

Denver 60 65(08-10) 2010 3.3 0.7 0.2 0.1 0 0

Denver 70 65(06-08) 2006 0 0 0 0 0 0

Denver 70 65(06-08) 2007 0 0 0 0 0 0

Denver 70 65(06-08) 2008 0 0 0 0 0 0

Denver 70 65(08-10) 2008 0.4 0 0 0 0 0

Denver 70 65(08-10) 2009 0 0 0 0 0 0

Denver 70 65(08-10) 2010 0 0 0 0 0 0

Denver 80 65(06-08) 2006 0 0 0 0 0 0

Denver 80 65(06-08) 2007 0 0 0 0 0 0

Denver 80 65(06-08) 2008 0 0 0 0 0 0

Denver 80 65(08-10) 2008 0 0 0 0 0 0

Denver 80 65(08-10) 2009 0 0 0 0 0 0

Denver 80 65(08-10) 2010 0 0 0 0 0 0

Denver 60 70(06-08) 2006 12.9 4.4 1.8 0.8 0.4 0.2

Denver 60 70(06-08) 2007 7 2 0.7 0.3 0.1 0

Denver 60 70(06-08) 2008 8.9 2.5 0.9 0.3 0.1 0.1

Denver 60 70(08-10) 2008 18.9 9.2 5 2.8 1.5 0.9

Denver 60 70(08-10) 2009 7.8 2 0.6 0.2 0.1 0

Denver 60 70(08-10) 2010 9.5 3.1 1.2 0.4 0.2 0.1

Denver 70 70(06-08) 2006 0.7 0 0 0 0 0

Denver 70 70(06-08) 2007 0.2 0 0 0 0 0

Denver 70 70(06-08) 2008 0.4 0 0 0 0 0

Denver 70 70(08-10) 2008 1.7 0.1 0 0 0 0
 5F-35 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Denver 70 70(08-10) 2009 0.4 0 0 0 0 0

Denver 70 70(08-10) 2010 0.2 0 0 0 0 0

Denver 80 70(06-08) 2006 0 0 0 0 0 0

Denver 80 70(06-08) 2007 0 0 0 0 0 0

Denver 80 70(06-08) 2008 0 0 0 0 0 0

Denver 80 70(08-10) 2008 0.1 0 0 0 0 0

Denver 80 70(08-10) 2009 0 0 0 0 0 0

Denver 80 70(08-10) 2010 0 0 0 0 0 0

Denver 60 75(06-08) 2006 21.3 10.4 5.7 3.1 1.8 1

Denver 60 75(06-08) 2007 13.8 5.4 2.5 1.3 0.7 0.4

Denver 60 75(06-08) 2008 16.7 7.6 3.8 1.9 1 0.6

Denver 60 75(08-10) 2008 25.6 14.4 9.1 5.9 3.8 2.5

Denver 60 75(08-10) 2009 12.5 4.2 1.5 0.6 0.2 0.1

Denver 60 75(08-10) 2010 16.3 7.1 3.4 1.7 0.8 0.4

Denver 70 75(06-08) 2006 2.9 0.3 0 0 0 0

Denver 70 75(06-08) 2007 0.9 0.1 0 0 0 0

Denver 70 75(06-08) 2008 1.2 0 0 0 0 0

Denver 70 75(08-10) 2008 4.1 0.4 0 0 0 0

Denver 70 75(08-10) 2009 1.2 0.1 0 0 0 0

Denver 70 75(08-10) 2010 1 0.1 0 0 0 0

Denver 80 75(06-08) 2006 0.1 0 0 0 0 0

Denver 80 75(06-08) 2007 0 0 0 0 0 0

Denver 80 75(06-08) 2008 0 0 0 0 0 0

Denver 80 75(08-10) 2008 0.5 0 0 0 0 0

Denver 80 75(08-10) 2009 0 0 0 0 0 0

Denver 80 75(08-10) 2010 0 0 0 0 0 0

Detroit 60 55(06-08) 2006 0 0 0 0 0 0

Detroit 60 55(06-08) 2007 0 0 0 0 0 0

Detroit 60 55(06-08) 2008 0 0 0 0 0 0

Detroit 60 55(08-10) 2008 0 0 0 0 0 0

Detroit 60 55(08-10) 2009 0 0 0 0 0 0

Detroit 60 55(08-10) 2010 0 0 0 0 0 0

Detroit 70 55(06-08) 2006 0 0 0 0 0 0

Detroit 70 55(06-08) 2007 0 0 0 0 0 0

Detroit 70 55(06-08) 2008 0 0 0 0 0 0

Detroit 70 55(08-10) 2008 0 0 0 0 0 0

Detroit 70 55(08-10) 2009 0 0 0 0 0 0

Detroit 70 55(08-10) 2010 0 0 0 0 0 0

Detroit 80 55(06-08) 2006 0 0 0 0 0 0

Detroit 80 55(06-08) 2007 0 0 0 0 0 0

Detroit 80 55(06-08) 2008 0 0 0 0 0 0

Detroit 80 55(08-10) 2008 0 0 0 0 0 0

Detroit 80 55(08-10) 2009 0 0 0 0 0 0

Detroit 80 55(08-10) 2010 0 0 0 0 0 0

Detroit 60 60(06-08) 2006 0.2 0 0 0 0 0

Detroit 60 60(06-08) 2007 0.7 0 0 0 0 0
 5F-36 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Detroit 60 60(06-08) 2008 0 0 0 0 0 0

Detroit 60 60(08-10) 2008 0.4 0 0 0 0 0

Detroit 60 60(08-10) 2009 0.3 0 0 0 0 0

Detroit 60 60(08-10) 2010 0.2 0 0 0 0 0

Detroit 70 60(06-08) 2006 0 0 0 0 0 0

Detroit 70 60(06-08) 2007 0 0 0 0 0 0

Detroit 70 60(06-08) 2008 0 0 0 0 0 0

Detroit 70 60(08-10) 2008 0 0 0 0 0 0

Detroit 70 60(08-10) 2009 0 0 0 0 0 0

Detroit 70 60(08-10) 2010 0 0 0 0 0 0

Detroit 80 60(06-08) 2006 0 0 0 0 0 0

Detroit 80 60(06-08) 2007 0 0 0 0 0 0

Detroit 80 60(06-08) 2008 0 0 0 0 0 0

Detroit 80 60(08-10) 2008 0 0 0 0 0 0

Detroit 80 60(08-10) 2009 0 0 0 0 0 0

Detroit 80 60(08-10) 2010 0 0 0 0 0 0

Detroit 60 65(06-08) 2006 2 0.1 0 0 0 0

Detroit 60 65(06-08) 2007 4.6 1.1 0.3 0.1 0 0

Detroit 60 65(06-08) 2008 0.7 0 0 0 0 0

Detroit 60 65(08-10) 2008 3.6 0.5 0.1 0 0 0

Detroit 60 65(08-10) 2009 2.8 0.3 0 0 0 0

Detroit 60 65(08-10) 2010 3.1 0.4 0.1 0 0 0

Detroit 70 65(06-08) 2006 0 0 0 0 0 0

Detroit 70 65(06-08) 2007 0.2 0 0 0 0 0

Detroit 70 65(06-08) 2008 0 0 0 0 0 0

Detroit 70 65(08-10) 2008 0 0 0 0 0 0

Detroit 70 65(08-10) 2009 0.1 0 0 0 0 0

Detroit 70 65(08-10) 2010 0 0 0 0 0 0

Detroit 80 65(06-08) 2006 0 0 0 0 0 0

Detroit 80 65(06-08) 2007 0 0 0 0 0 0

Detroit 80 65(06-08) 2008 0 0 0 0 0 0

Detroit 80 65(08-10) 2008 0 0 0 0 0 0

Detroit 80 65(08-10) 2009 0 0 0 0 0 0

Detroit 80 65(08-10) 2010 0 0 0 0 0 0

Detroit 60 70(06-08) 2006 4.9 0.8 0.1 0 0 0

Detroit 60 70(06-08) 2007 10.3 3.6 1.4 0.5 0.2 0.1

Detroit 60 70(06-08) 2008 2.5 0.2 0 0 0 0

Detroit 60 70(08-10) 2008 9 2.3 0.6 0.1 0 0

Detroit 60 70(08-10) 2009 6.9 1.4 0.3 0.1 0 0

Detroit 60 70(08-10) 2010 8.7 2.5 0.8 0.2 0.1 0

Detroit 70 70(06-08) 2006 0.2 0 0 0 0 0

Detroit 70 70(06-08) 2007 0.9 0.1 0 0 0 0

Detroit 70 70(06-08) 2008 0 0 0 0 0 0

Detroit 70 70(08-10) 2008 0.9 0 0 0 0 0

Detroit 70 70(08-10) 2009 0.5 0 0 0 0 0

Detroit 70 70(08-10) 2010 0.5 0 0 0 0 0
 5F-37 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Detroit 80 70(06-08) 2006 0 0 0 0 0 0

Detroit 80 70(06-08) 2007 0 0 0 0 0 0

Detroit 80 70(06-08) 2008 0 0 0 0 0 0

Detroit 80 70(08-10) 2008 0 0 0 0 0 0

Detroit 80 70(08-10) 2009 0 0 0 0 0 0

Detroit 80 70(08-10) 2010 0 0 0 0 0 0

Detroit 60 75(06-08) 2006 10.6 3 0.9 0.3 0.1 0

Detroit 60 75(06-08) 2007 19.1 8.6 4.3 2.3 1.3 0.7

Detroit 60 75(06-08) 2008 6.7 1.4 0.3 0.1 0 0

Detroit 60 75(08-10) 2008 15.6 5.3 1.8 0.6 0.2 0

Detroit 60 75(08-10) 2009 12.8 3.6 1 0.3 0.1 0

Detroit 60 75(08-10) 2010 16.8 6.3 2.6 1 0.4 0.2

Detroit 70 75(06-08) 2006 1.4 0 0 0 0 0

Detroit 70 75(06-08) 2007 4.2 0.8 0.2 0 0 0

Detroit 70 75(06-08) 2008 0.3 0 0 0 0 0

Detroit 70 75(08-10) 2008 3 0.2 0 0 0 0

Detroit 70 75(08-10) 2009 2.2 0.2 0 0 0 0

Detroit 70 75(08-10) 2010 2.6 0.3 0 0 0 0

Detroit 80 75(06-08) 2006 0 0 0 0 0 0

Detroit 80 75(06-08) 2007 0.2 0 0 0 0 0

Detroit 80 75(06-08) 2008 0 0 0 0 0 0

Detroit 80 75(08-10) 2008 0 0 0 0 0 0

Detroit 80 75(08-10) 2009 0.1 0 0 0 0 0

Detroit 80 75(08-10) 2010 0.1 0 0 0 0 0

Houston 60 55(06-08) 2006 0 0 0 0 0 0

Houston 60 55(06-08) 2007 0 0 0 0 0 0

Houston 60 55(06-08) 2008 0 0 0 0 0 0

Houston 60 55(08-10) 2008 0 0 0 0 0 0

Houston 60 55(08-10) 2009 0 0 0 0 0 0

Houston 60 55(08-10) 2010 0 0 0 0 0 0

Houston 70 55(06-08) 2006 0 0 0 0 0 0

Houston 70 55(06-08) 2007 0 0 0 0 0 0

Houston 70 55(06-08) 2008 0 0 0 0 0 0

Houston 70 55(08-10) 2008 0 0 0 0 0 0

Houston 70 55(08-10) 2009 0 0 0 0 0 0

Houston 70 55(08-10) 2010 0 0 0 0 0 0

Houston 80 55(06-08) 2006 0 0 0 0 0 0

Houston 80 55(06-08) 2007 0 0 0 0 0 0

Houston 80 55(06-08) 2008 0 0 0 0 0 0

Houston 80 55(08-10) 2008 0 0 0 0 0 0

Houston 80 55(08-10) 2009 0 0 0 0 0 0

Houston 80 55(08-10) 2010 0 0 0 0 0 0

Houston 60 60(06-08) 2006 0.3 0 0 0 0 0

Houston 60 60(06-08) 2007 0 0 0 0 0 0

Houston 60 60(06-08) 2008 0.1 0 0 0 0 0

Houston 60 60(08-10) 2008 0.2 0 0 0 0 0
 5F-38 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Houston 60 60(08-10) 2009 0.7 0 0 0 0 0

Houston 60 60(08-10) 2010 0.2 0 0 0 0 0

Houston 70 60(06-08) 2006 0 0 0 0 0 0

Houston 70 60(06-08) 2007 0 0 0 0 0 0

Houston 70 60(06-08) 2008 0 0 0 0 0 0

Houston 70 60(08-10) 2008 0 0 0 0 0 0

Houston 70 60(08-10) 2009 0 0 0 0 0 0

Houston 70 60(08-10) 2010 0 0 0 0 0 0

Houston 80 60(06-08) 2006 0 0 0 0 0 0

Houston 80 60(06-08) 2007 0 0 0 0 0 0

Houston 80 60(06-08) 2008 0 0 0 0 0 0

Houston 80 60(08-10) 2008 0 0 0 0 0 0

Houston 80 60(08-10) 2009 0 0 0 0 0 0

Houston 80 60(08-10) 2010 0 0 0 0 0 0

Houston 60 65(06-08) 2006 3 0.6 0.1 0 0 0

Houston 60 65(06-08) 2007 0.2 0 0 0 0 0

Houston 60 65(06-08) 2008 0.3 0 0 0 0 0

Houston 60 65(08-10) 2008 2.1 0.2 0 0 0 0

Houston 60 65(08-10) 2009 5.7 0.7 0.1 0 0 0

Houston 60 65(08-10) 2010 3.3 0.3 0 0 0 0

Houston 70 65(06-08) 2006 0.2 0 0 0 0 0

Houston 70 65(06-08) 2007 0 0 0 0 0 0

Houston 70 65(06-08) 2008 0 0 0 0 0 0

Houston 70 65(08-10) 2008 0 0 0 0 0 0

Houston 70 65(08-10) 2009 0.4 0 0 0 0 0

Houston 70 65(08-10) 2010 0 0 0 0 0 0

Houston 80 65(06-08) 2006 0 0 0 0 0 0

Houston 80 65(06-08) 2007 0 0 0 0 0 0

Houston 80 65(06-08) 2008 0 0 0 0 0 0

Houston 80 65(08-10) 2008 0 0 0 0 0 0

Houston 80 65(08-10) 2009 0 0 0 0 0 0

Houston 80 65(08-10) 2010 0 0 0 0 0 0

Houston 60 70(06-08) 2006 7 1.9 0.6 0.2 0.1 0

Houston 60 70(06-08) 2007 1.4 0.1 0 0 0 0

Houston 60 70(06-08) 2008 0.8 0 0 0 0 0

Houston 60 70(08-10) 2008 6.9 1.4 0.3 0 0 0

Houston 60 70(08-10) 2009 11.9 2.9 0.8 0.2 0 0

Houston 60 70(08-10) 2010 9 2.2 0.6 0.2 0 0

Houston 70 70(06-08) 2006 1 0.1 0 0 0 0

Houston 70 70(06-08) 2007 0 0 0 0 0 0

Houston 70 70(06-08) 2008 0 0 0 0 0 0

Houston 70 70(08-10) 2008 0.5 0 0 0 0 0

Houston 70 70(08-10) 2009 2.1 0.1 0 0 0 0

Houston 70 70(08-10) 2010 0.6 0 0 0 0 0

Houston 80 70(06-08) 2006 0.1 0 0 0 0 0

Houston 80 70(06-08) 2007 0 0 0 0 0 0
 5F-39 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Houston 80 70(06-08) 2008 0 0 0 0 0 0

Houston 80 70(08-10) 2008 0 0 0 0 0 0

Houston 80 70(08-10) 2009 0.1 0 0 0 0 0

Houston 80 70(08-10) 2010 0 0 0 0 0 0

Houston 60 75(06-08) 2006 12 4.1 1.7 0.7 0.3 0.1

Houston 60 75(06-08) 2007 4.1 0.7 0.2 0 0 0

Houston 60 75(06-08) 2008 2.5 0.2 0 0 0 0

Houston 60 75(08-10) 2008 13.2 4.4 1.5 0.5 0.2 0.1

Houston 60 75(08-10) 2009 17.8 6.3 2.4 0.9 0.3 0.1

Houston 60 75(08-10) 2010 15.3 5.6 2.2 0.9 0.3 0.1

Houston 70 75(06-08) 2006 2.5 0.4 0.1 0 0 0

Houston 70 75(06-08) 2007 0.1 0 0 0 0 0

Houston 70 75(06-08) 2008 0.1 0 0 0 0 0

Houston 70 75(08-10) 2008 1.9 0.1 0 0 0 0

Houston 70 75(08-10) 2009 5.5 0.6 0.1 0 0 0

Houston 70 75(08-10) 2010 2.4 0.2 0 0 0 0

Houston 80 75(06-08) 2006 0.3 0 0 0 0 0

Houston 80 75(06-08) 2007 0 0 0 0 0 0

Houston 80 75(06-08) 2008 0 0 0 0 0 0

Houston 80 75(08-10) 2008 0.2 0 0 0 0 0

Houston 80 75(08-10) 2009 0.7 0 0 0 0 0

Houston 80 75(08-10) 2010 0.1 0 0 0 0 0

Los Angeles 60 55(06-08) 2006 0 0 0 0 0 0

Los Angeles 60 55(06-08) 2007 0 0 0 0 0 0

Los Angeles 60 55(06-08) 2008 0 0 0 0 0 0

Los Angeles 60 55(08-10) 2008 0 0 0 0 0 0

Los Angeles 60 55(08-10) 2009 0 0 0 0 0 0

Los Angeles 60 55(08-10) 2010 0 0 0 0 0 0

Los Angeles 70 55(06-08) 2006 0 0 0 0 0 0

Los Angeles 70 55(06-08) 2007 0 0 0 0 0 0

Los Angeles 70 55(06-08) 2008 0 0 0 0 0 0

Los Angeles 70 55(08-10) 2008 0 0 0 0 0 0

Los Angeles 70 55(08-10) 2009 0 0 0 0 0 0

Los Angeles 70 55(08-10) 2010 0 0 0 0 0 0

Los Angeles 80 55(06-08) 2006 0 0 0 0 0 0

Los Angeles 80 55(06-08) 2007 0 0 0 0 0 0

Los Angeles 80 55(06-08) 2008 0 0 0 0 0 0

Los Angeles 80 55(08-10) 2008 0 0 0 0 0 0

Los Angeles 80 55(08-10) 2009 0 0 0 0 0 0

Los Angeles 80 55(08-10) 2010 0 0 0 0 0 0

Los Angeles 60 60(06-08) 2006 0 0 0 0 0 0

Los Angeles 60 60(06-08) 2007 0.2 0 0 0 0 0

Los Angeles 60 60(06-08) 2008 0 0 0 0 0 0

Los Angeles 60 60(08-10) 2008 0 0 0 0 0 0

Los Angeles 60 60(08-10) 2009 0 0 0 0 0 0

Los Angeles 60 60(08-10) 2010 0 0 0 0 0 0
 5F-40 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Los Angeles 70 60(06-08) 2006 0 0 0 0 0 0

Los Angeles 70 60(06-08) 2007 0 0 0 0 0 0

Los Angeles 70 60(06-08) 2008 0 0 0 0 0 0

Los Angeles 70 60(08-10) 2008 0 0 0 0 0 0

Los Angeles 70 60(08-10) 2009 0 0 0 0 0 0

Los Angeles 70 60(08-10) 2010 0 0 0 0 0 0

Los Angeles 80 60(06-08) 2006 0 0 0 0 0 0

Los Angeles 80 60(06-08) 2007 0 0 0 0 0 0

Los Angeles 80 60(06-08) 2008 0 0 0 0 0 0

Los Angeles 80 60(08-10) 2008 0 0 0 0 0 0

Los Angeles 80 60(08-10) 2009 0 0 0 0 0 0

Los Angeles 80 60(08-10) 2010 0 0 0 0 0 0

Los Angeles 60 65(06-08) 2006 0.9 0.2 0.1 0 0 0

Los Angeles 60 65(06-08) 2007 1.5 0.3 0.1 0 0 0

Los Angeles 60 65(06-08) 2008 0.9 0.3 0.1 0 0 0

Los Angeles 60 65(08-10) 2008 1.2 0.3 0.1 0.1 0 0

Los Angeles 60 65(08-10) 2009 1.2 0.3 0.1 0.1 0 0

Los Angeles 60 65(08-10) 2010 0.8 0.2 0.1 0 0 0

Los Angeles 70 65(06-08) 2006 0 0 0 0 0 0

Los Angeles 70 65(06-08) 2007 0 0 0 0 0 0

Los Angeles 70 65(06-08) 2008 0 0 0 0 0 0

Los Angeles 70 65(08-10) 2008 0 0 0 0 0 0

Los Angeles 70 65(08-10) 2009 0 0 0 0 0 0

Los Angeles 70 65(08-10) 2010 0 0 0 0 0 0

Los Angeles 80 65(06-08) 2006 0 0 0 0 0 0

Los Angeles 80 65(06-08) 2007 0 0 0 0 0 0

Los Angeles 80 65(06-08) 2008 0 0 0 0 0 0

Los Angeles 80 65(08-10) 2008 0 0 0 0 0 0

Los Angeles 80 65(08-10) 2009 0 0 0 0 0 0

Los Angeles 80 65(08-10) 2010 0 0 0 0 0 0

Los Angeles 60 70(06-08) 2006 4.4 1.6 0.8 0.5 0.3 0.2

Los Angeles 60 70(06-08) 2007 4.9 1.6 0.8 0.5 0.3 0.2

Los Angeles 60 70(06-08) 2008 4.2 1.5 0.8 0.4 0.3 0.2

Los Angeles 60 70(08-10) 2008 4.9 1.8 0.9 0.6 0.3 0.2

Los Angeles 60 70(08-10) 2009 5 1.8 0.9 0.5 0.3 0.2

Los Angeles 60 70(08-10) 2010 3.2 1.3 0.7 0.4 0.2 0.1

Los Angeles 70 70(06-08) 2006 0 0 0 0 0 0

Los Angeles 70 70(06-08) 2007 0.2 0 0 0 0 0

Los Angeles 70 70(06-08) 2008 0 0 0 0 0 0

Los Angeles 70 70(08-10) 2008 0 0 0 0 0 0

Los Angeles 70 70(08-10) 2009 0 0 0 0 0 0

Los Angeles 70 70(08-10) 2010 0 0 0 0 0 0

Los Angeles 80 70(06-08) 2006 0 0 0 0 0 0

Los Angeles 80 70(06-08) 2007 0 0 0 0 0 0

Los Angeles 80 70(06-08) 2008 0 0 0 0 0 0

Los Angeles 80 70(08-10) 2008 0 0 0 0 0 0
 5F-41 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Los Angeles 80 70(08-10) 2009 0 0 0 0 0 0

Los Angeles 80 70(08-10) 2010 0 0 0 0 0 0

Los Angeles 60 75(06-08) 2006 10.2 4.5 2.5 1.6 1.1 0.7

Los Angeles 60 75(06-08) 2007 10.2 4.3 2.4 1.4 0.9 0.7

Los Angeles 60 75(06-08) 2008 9.9 4.3 2.4 1.5 1 0.7

Los Angeles 60 75(08-10) 2008 10.2 4.5 2.5 1.6 1.1 0.7

Los Angeles 60 75(08-10) 2009 10 4.4 2.5 1.5 1 0.7

Los Angeles 60 75(08-10) 2010 6.9 2.9 1.7 1 0.7 0.4

Los Angeles 70 75(06-08) 2006 0.5 0.1 0 0 0 0

Los Angeles 70 75(06-08) 2007 1 0.1 0 0 0 0

Los Angeles 70 75(06-08) 2008 0.5 0.1 0 0 0 0

Los Angeles 70 75(08-10) 2008 0.6 0.1 0.1 0 0 0

Los Angeles 70 75(08-10) 2009 0.5 0.1 0 0 0 0

Los Angeles 70 75(08-10) 2010 0.4 0.1 0 0 0 0

Los Angeles 80 75(06-08) 2006 0 0 0 0 0 0

Los Angeles 80 75(06-08) 2007 0.1 0 0 0 0 0

Los Angeles 80 75(06-08) 2008 0 0 0 0 0 0

Los Angeles 80 75(08-10) 2008 0 0 0 0 0 0

Los Angeles 80 75(08-10) 2009 0 0 0 0 0 0

Los Angeles 80 75(08-10) 2010 0 0 0 0 0 0

New York 60 65(06-08) 2006 0 0 0 0 0 0

New York 60 65(06-08) 2007 0 0 0 0 0 0

New York 60 65(06-08) 2008 0 0 0 0 0 0

New York 60 65(08-10) 2008 0.1 0 0 0 0 0

New York 60 65(08-10) 2009 0 0 0 0 0 0

New York 60 65(08-10) 2010 0.1 0 0 0 0 0

New York 70 65(06-08) 2006 0 0 0 0 0 0

New York 70 65(06-08) 2007 0 0 0 0 0 0

New York 70 65(06-08) 2008 0 0 0 0 0 0

New York 70 65(08-10) 2008 0 0 0 0 0 0

New York 70 65(08-10) 2009 0 0 0 0 0 0

New York 70 65(08-10) 2010 0 0 0 0 0 0

New York 80 65(06-08) 2006 0 0 0 0 0 0

New York 80 65(06-08) 2007 0 0 0 0 0 0

New York 80 65(06-08) 2008 0 0 0 0 0 0

New York 80 65(08-10) 2008 0 0 0 0 0 0

New York 80 65(08-10) 2009 0 0 0 0 0 0

New York 80 65(08-10) 2010 0 0 0 0 0 0

New York 60 70(06-08) 2006 2.6 0.2 0 0 0 0

New York 60 70(06-08) 2007 2 0.3 0.1 0 0 0

New York 60 70(06-08) 2008 2.3 0.3 0.1 0 0 0

New York 60 70(08-10) 2008 5.7 1.1 0.3 0.1 0 0

New York 60 70(08-10) 2009 1.2 0.1 0 0 0 0

New York 60 70(08-10) 2010 6.6 1.4 0.3 0.1 0 0

New York 70 70(06-08) 2006 0.2 0 0 0 0 0

New York 70 70(06-08) 2007 0 0 0 0 0 0
 5F-42 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

New York 70 70(06-08) 2008 0.1 0 0 0 0 0

New York 70 70(08-10) 2008 0.4 0 0 0 0 0

New York 70 70(08-10) 2009 0 0 0 0 0 0

New York 70 70(08-10) 2010 0.5 0 0 0 0 0

New York 80 70(06-08) 2006 0 0 0 0 0 0

New York 80 70(06-08) 2007 0 0 0 0 0 0

New York 80 70(06-08) 2008 0 0 0 0 0 0

New York 80 70(08-10) 2008 0 0 0 0 0 0

New York 80 70(08-10) 2009 0 0 0 0 0 0

New York 80 70(08-10) 2010 0 0 0 0 0 0

New York 60 75(06-08) 2006 9.1 1.9 0.4 0.1 0 0

New York 60 75(06-08) 2007 8.5 2.2 0.7 0.2 0.1 0

New York 60 75(06-08) 2008 7.8 1.7 0.5 0.2 0.1 0

New York 60 75(08-10) 2008 17.2 6.3 2.4 1 0.4 0.2

New York 60 75(08-10) 2009 5.4 0.9 0.2 0 0 0

New York 60 75(08-10) 2010 19 8 3.6 1.8 0.9 0.4

New York 70 75(06-08) 2006 1.5 0.1 0 0 0 0

New York 70 75(06-08) 2007 0.4 0 0 0 0 0

New York 70 75(06-08) 2008 0.7 0 0 0 0 0

New York 70 75(08-10) 2008 3.4 0.4 0.1 0 0 0

New York 70 75(08-10) 2009 0.3 0 0 0 0 0

New York 70 75(08-10) 2010 3.7 0.4 0 0 0 0

New York 80 75(06-08) 2006 0.1 0 0 0 0 0

New York 80 75(06-08) 2007 0 0 0 0 0 0

New York 80 75(06-08) 2008 0 0 0 0 0 0

New York 80 75(08-10) 2008 0.3 0 0 0 0 0

New York 80 75(08-10) 2009 0 0 0 0 0 0

New York 80 75(08-10) 2010 0.3 0 0 0 0 0

Philadelphia 60 55(06-08) 2006 0 0 0 0 0 0

Philadelphia 60 55(06-08) 2007 0 0 0 0 0 0

Philadelphia 60 55(06-08) 2008 0 0 0 0 0 0

Philadelphia 60 55(08-10) 2008 0 0 0 0 0 0

Philadelphia 60 55(08-10) 2009 0 0 0 0 0 0

Philadelphia 60 55(08-10) 2010 0.2 0 0 0 0 0

Philadelphia 70 55(06-08) 2006 0 0 0 0 0 0

Philadelphia 70 55(06-08) 2007 0 0 0 0 0 0

Philadelphia 70 55(06-08) 2008 0 0 0 0 0 0

Philadelphia 70 55(08-10) 2008 0 0 0 0 0 0

Philadelphia 70 55(08-10) 2009 0 0 0 0 0 0

Philadelphia 70 55(08-10) 2010 0 0 0 0 0 0

Philadelphia 80 55(06-08) 2006 0 0 0 0 0 0

Philadelphia 80 55(06-08) 2007 0 0 0 0 0 0

Philadelphia 80 55(06-08) 2008 0 0 0 0 0 0

Philadelphia 80 55(08-10) 2008 0 0 0 0 0 0

Philadelphia 80 55(08-10) 2009 0 0 0 0 0 0

Philadelphia 80 55(08-10) 2010 0 0 0 0 0 0
 5F-43 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Philadelphia 60 60(06-08) 2006 0.1 0 0 0 0 0

Philadelphia 60 60(06-08) 2007 0.7 0 0 0 0 0

Philadelphia 60 60(06-08) 2008 0.1 0 0 0 0 0

Philadelphia 60 60(08-10) 2008 1 0 0 0 0 0

Philadelphia 60 60(08-10) 2009 0 0 0 0 0 0

Philadelphia 60 60(08-10) 2010 1.7 0.1 0 0 0 0

Philadelphia 70 60(06-08) 2006 0 0 0 0 0 0

Philadelphia 70 60(06-08) 2007 0 0 0 0 0 0

Philadelphia 70 60(06-08) 2008 0 0 0 0 0 0

Philadelphia 70 60(08-10) 2008 0 0 0 0 0 0

Philadelphia 70 60(08-10) 2009 0 0 0 0 0 0

Philadelphia 70 60(08-10) 2010 0.1 0 0 0 0 0

Philadelphia 80 60(06-08) 2006 0 0 0 0 0 0

Philadelphia 80 60(06-08) 2007 0 0 0 0 0 0

Philadelphia 80 60(06-08) 2008 0 0 0 0 0 0

Philadelphia 80 60(08-10) 2008 0 0 0 0 0 0

Philadelphia 80 60(08-10) 2009 0 0 0 0 0 0

Philadelphia 80 60(08-10) 2010 0 0 0 0 0 0

Philadelphia 60 65(06-08) 2006 1.3 0.1 0 0 0 0

Philadelphia 60 65(06-08) 2007 3.3 0.4 0.1 0 0 0

Philadelphia 60 65(06-08) 2008 1 0 0 0 0 0

Philadelphia 60 65(08-10) 2008 4.3 0.6 0.1 0 0 0

Philadelphia 60 65(08-10) 2009 0 0 0 0 0 0

Philadelphia 60 65(08-10) 2010 4.6 0.6 0.1 0 0 0

Philadelphia 70 65(06-08) 2006 0 0 0 0 0 0

Philadelphia 70 65(06-08) 2007 0.1 0 0 0 0 0

Philadelphia 70 65(06-08) 2008 0 0 0 0 0 0

Philadelphia 70 65(08-10) 2008 0.1 0 0 0 0 0

Philadelphia 70 65(08-10) 2009 0 0 0 0 0 0

Philadelphia 70 65(08-10) 2010 0.3 0 0 0 0 0

Philadelphia 80 65(06-08) 2006 0 0 0 0 0 0

Philadelphia 80 65(06-08) 2007 0 0 0 0 0 0

Philadelphia 80 65(06-08) 2008 0 0 0 0 0 0

Philadelphia 80 65(08-10) 2008 0 0 0 0 0 0

Philadelphia 80 65(08-10) 2009 0 0 0 0 0 0

Philadelphia 80 65(08-10) 2010 0 0 0 0 0 0

Philadelphia 60 70(06-08) 2006 5.3 0.9 0.2 0 0 0

Philadelphia 60 70(06-08) 2007 9.9 2.4 0.7 0.2 0.1 0

Philadelphia 60 70(06-08) 2008 4.3 0.6 0.1 0 0 0

Philadelphia 60 70(08-10) 2008 11.8 3.2 1.1 0.3 0.1 0

Philadelphia 60 70(08-10) 2009 0.8 0 0 0 0 0

Philadelphia 60 70(08-10) 2010 11.6 3.3 1.1 0.4 0.1 0.1

Philadelphia 70 70(06-08) 2006 0.1 0 0 0 0 0

Philadelphia 70 70(06-08) 2007 0.9 0.1 0 0 0 0

Philadelphia 70 70(06-08) 2008 0.1 0 0 0 0 0

Philadelphia 70 70(08-10) 2008 1.1 0.1 0 0 0 0
 5F-44 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Philadelphia 70 70(08-10) 2009 0 0 0 0 0 0

Philadelphia 70 70(08-10) 2010 1.5 0.1 0 0 0 0

Philadelphia 80 70(06-08) 2006 0 0 0 0 0 0

Philadelphia 80 70(06-08) 2007 0 0 0 0 0 0

Philadelphia 80 70(06-08) 2008 0 0 0 0 0 0

Philadelphia 80 70(08-10) 2008 0 0 0 0 0 0

Philadelphia 80 70(08-10) 2009 0 0 0 0 0 0

Philadelphia 80 70(08-10) 2010 0.1 0 0 0 0 0

Philadelphia 60 75(06-08) 2006 12.1 3.7 1.3 0.4 0.2 0.1

Philadelphia 60 75(06-08) 2007 17.5 6.6 2.7 1.2 0.6 0.2

Philadelphia 60 75(06-08) 2008 10 2.4 0.7 0.2 0.1 0

Philadelphia 60 75(08-10) 2008 20.5 8.6 3.9 1.9 0.8 0.4

Philadelphia 60 75(08-10) 2009 4 0.4 0 0 0 0

Philadelphia 60 75(08-10) 2010 20.2 8.7 4.3 2 1 0.5

Philadelphia 70 75(06-08) 2006 0.8 0 0 0 0 0

Philadelphia 70 75(06-08) 2007 3 0.3 0.1 0 0 0

Philadelphia 70 75(06-08) 2008 0.8 0 0 0 0 0

Philadelphia 70 75(08-10) 2008 4.1 0.5 0.1 0 0 0

Philadelphia 70 75(08-10) 2009 0 0 0 0 0 0

Philadelphia 70 75(08-10) 2010 4.2 0.5 0.1 0 0 0

Philadelphia 80 75(06-08) 2006 0 0 0 0 0 0

Philadelphia 80 75(06-08) 2007 0.3 0 0 0 0 0

Philadelphia 80 75(06-08) 2008 0 0 0 0 0 0

Philadelphia 80 75(08-10) 2008 0.3 0 0 0 0 0

Philadelphia 80 75(08-10) 2009 0 0 0 0 0 0

Philadelphia 80 75(08-10) 2010 0.4 0 0 0 0 0

Sacramento 60 55(06-08) 2006 0 0 0 0 0 0

Sacramento 60 55(06-08) 2007 0 0 0 0 0 0

Sacramento 60 55(06-08) 2008 0 0 0 0 0 0

Sacramento 60 55(08-10) 2008 0 0 0 0 0 0

Sacramento 60 55(08-10) 2009 0 0 0 0 0 0

Sacramento 60 55(08-10) 2010 0 0 0 0 0 0

Sacramento 70 55(06-08) 2006 0 0 0 0 0 0

Sacramento 70 55(06-08) 2007 0 0 0 0 0 0

Sacramento 70 55(06-08) 2008 0 0 0 0 0 0

Sacramento 70 55(08-10) 2008 0 0 0 0 0 0

Sacramento 70 55(08-10) 2009 0 0 0 0 0 0

Sacramento 70 55(08-10) 2010 0 0 0 0 0 0

Sacramento 80 55(06-08) 2006 0 0 0 0 0 0

Sacramento 80 55(06-08) 2007 0 0 0 0 0 0

Sacramento 80 55(06-08) 2008 0 0 0 0 0 0

Sacramento 80 55(08-10) 2008 0 0 0 0 0 0

Sacramento 80 55(08-10) 2009 0 0 0 0 0 0

Sacramento 80 55(08-10) 2010 0 0 0 0 0 0

Sacramento 60 60(06-08) 2006 0.3 0 0 0 0 0

Sacramento 60 60(06-08) 2007 0.4 0 0 0 0 0
 5F-45 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Sacramento 60 60(06-08) 2008 0.8 0.1 0 0 0 0

Sacramento 60 60(08-10) 2008 0.8 0.1 0 0 0 0

Sacramento 60 60(08-10) 2009 0.1 0 0 0 0 0

Sacramento 60 60(08-10) 2010 0.1 0 0 0 0 0

Sacramento 70 60(06-08) 2006 0 0 0 0 0 0

Sacramento 70 60(06-08) 2007 0 0 0 0 0 0

Sacramento 70 60(06-08) 2008 0 0 0 0 0 0

Sacramento 70 60(08-10) 2008 0 0 0 0 0 0

Sacramento 70 60(08-10) 2009 0 0 0 0 0 0

Sacramento 70 60(08-10) 2010 0 0 0 0 0 0

Sacramento 80 60(06-08) 2006 0 0 0 0 0 0

Sacramento 80 60(06-08) 2007 0 0 0 0 0 0

Sacramento 80 60(06-08) 2008 0 0 0 0 0 0

Sacramento 80 60(08-10) 2008 0 0 0 0 0 0

Sacramento 80 60(08-10) 2009 0 0 0 0 0 0

Sacramento 80 60(08-10) 2010 0 0 0 0 0 0

Sacramento 60 65(06-08) 2006 4.7 0.9 0.2 0 0 0

Sacramento 60 65(06-08) 2007 1.6 0 0 0 0 0

Sacramento 60 65(06-08) 2008 4.3 0.9 0.2 0.1 0 0

Sacramento 60 65(08-10) 2008 3.8 0.7 0.2 0 0 0

Sacramento 60 65(08-10) 2009 2.2 0.2 0 0 0 0

Sacramento 60 65(08-10) 2010 0.9 0.1 0 0 0 0

Sacramento 70 65(06-08) 2006 0 0 0 0 0 0

Sacramento 70 65(06-08) 2007 0.2 0 0 0 0 0

Sacramento 70 65(06-08) 2008 0.2 0 0 0 0 0

Sacramento 70 65(08-10) 2008 0.2 0 0 0 0 0

Sacramento 70 65(08-10) 2009 0 0 0 0 0 0

Sacramento 70 65(08-10) 2010 0 0 0 0 0 0

Sacramento 80 65(06-08) 2006 0 0 0 0 0 0

Sacramento 80 65(06-08) 2007 0 0 0 0 0 0

Sacramento 80 65(06-08) 2008 0 0 0 0 0 0

Sacramento 80 65(08-10) 2008 0 0 0 0 0 0

Sacramento 80 65(08-10) 2009 0 0 0 0 0 0

Sacramento 80 65(08-10) 2010 0 0 0 0 0 0

Sacramento 60 70(06-08) 2006 10 3.4 1.3 0.5 0.2 0.1

Sacramento 60 70(06-08) 2007 3 0.3 0 0 0 0

Sacramento 60 70(06-08) 2008 8.3 2.4 0.9 0.4 0.1 0.1

Sacramento 60 70(08-10) 2008 7.6 2.1 0.8 0.3 0.1 0

Sacramento 60 70(08-10) 2009 5.9 1.3 0.3 0.1 0 0

Sacramento 60 70(08-10) 2010 2.2 0.3 0 0 0 0

Sacramento 70 70(06-08) 2006 0.4 0 0 0 0 0

Sacramento 70 70(06-08) 2007 0.5 0 0 0 0 0

Sacramento 70 70(06-08) 2008 0.9 0.1 0 0 0 0

Sacramento 70 70(08-10) 2008 0.8 0.1 0 0 0 0

Sacramento 70 70(08-10) 2009 0.1 0 0 0 0 0

Sacramento 70 70(08-10) 2010 0.1 0 0 0 0 0
 5F-46 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Sacramento 80 70(06-08) 2006 0 0 0 0 0 0

Sacramento 80 70(06-08) 2007 0 0 0 0 0 0

Sacramento 80 70(06-08) 2008 0 0 0 0 0 0

Sacramento 80 70(08-10) 2008 0 0 0 0 0 0

Sacramento 80 70(08-10) 2009 0 0 0 0 0 0

Sacramento 80 70(08-10) 2010 0 0 0 0 0 0

Sacramento 60 75(06-08) 2006 16.5 7.4 3.7 2 1.1 0.6

Sacramento 60 75(06-08) 2007 6 1.2 0.3 0.1 0 0

Sacramento 60 75(06-08) 2008 13.5 5.4 2.5 1.2 0.7 0.4

Sacramento 60 75(08-10) 2008 12.8 4.9 2.2 1.1 0.6 0.3

Sacramento 60 75(08-10) 2009 11.2 3.9 1.5 0.6 0.3 0.1

Sacramento 60 75(08-10) 2010 4.5 0.9 0.2 0.1 0 0

Sacramento 70 75(06-08) 2006 2.6 0.4 0 0 0 0

Sacramento 70 75(06-08) 2007 1.1 0 0 0 0 0

Sacramento 70 75(06-08) 2008 2.7 0.5 0.1 0 0 0

Sacramento 70 75(08-10) 2008 2.3 0.4 0.1 0 0 0

Sacramento 70 75(08-10) 2009 1.1 0.1 0 0 0 0

Sacramento 70 75(08-10) 2010 0.5 0 0 0 0 0

Sacramento 80 75(06-08) 2006 0 0 0 0 0 0

Sacramento 80 75(06-08) 2007 0.2 0 0 0 0 0

Sacramento 80 75(06-08) 2008 0.2 0 0 0 0 0

Sacramento 80 75(08-10) 2008 0.2 0 0 0 0 0

Sacramento 80 75(08-10) 2009 0 0 0 0 0 0

Sacramento 80 75(08-10) 2010 0 0 0 0 0 0

St. Louis 60 55(06-08) 2006 0 0 0 0 0 0

St. Louis 60 55(06-08) 2007 0.1 0 0 0 0 0

St. Louis 60 55(06-08) 2008 0 0 0 0 0 0

St. Louis 60 55(08-10) 2008 0 0 0 0 0 0

St. Louis 60 55(08-10) 2009 0 0 0 0 0 0

St. Louis 60 55(08-10) 2010 0 0 0 0 0 0

St. Louis 70 55(06-08) 2006 0 0 0 0 0 0

St. Louis 70 55(06-08) 2007 0 0 0 0 0 0

St. Louis 70 55(06-08) 2008 0 0 0 0 0 0

St. Louis 70 55(08-10) 2008 0 0 0 0 0 0

St. Louis 70 55(08-10) 2009 0 0 0 0 0 0

St. Louis 70 55(08-10) 2010 0 0 0 0 0 0

St. Louis 80 55(06-08) 2006 0 0 0 0 0 0

St. Louis 80 55(06-08) 2007 0 0 0 0 0 0

St. Louis 80 55(06-08) 2008 0 0 0 0 0 0

St. Louis 80 55(08-10) 2008 0 0 0 0 0 0

St. Louis 80 55(08-10) 2009 0 0 0 0 0 0

St. Louis 80 55(08-10) 2010 0 0 0 0 0 0

St. Louis 60 60(06-08) 2006 0.1 0 0 0 0 0

St. Louis 60 60(06-08) 2007 1.5 0.1 0 0 0 0

St. Louis 60 60(06-08) 2008 0 0 0 0 0 0

St. Louis 60 60(08-10) 2008 0.4 0 0 0 0 0
 5F-47 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

St. Louis 60 60(08-10) 2009 0.4 0 0 0 0 0

St. Louis 60 60(08-10) 2010 0.5 0 0 0 0 0

St. Louis 70 60(06-08) 2006 0 0 0 0 0 0

St. Louis 70 60(06-08) 2007 0 0 0 0 0 0

St. Louis 70 60(06-08) 2008 0 0 0 0 0 0

St. Louis 70 60(08-10) 2008 0 0 0 0 0 0

St. Louis 70 60(08-10) 2009 0 0 0 0 0 0

St. Louis 70 60(08-10) 2010 0 0 0 0 0 0

St. Louis 80 60(06-08) 2006 0 0 0 0 0 0

St. Louis 80 60(06-08) 2007 0 0 0 0 0 0

St. Louis 80 60(06-08) 2008 0 0 0 0 0 0

St. Louis 80 60(08-10) 2008 0 0 0 0 0 0

St. Louis 80 60(08-10) 2009 0 0 0 0 0 0

St. Louis 80 60(08-10) 2010 0 0 0 0 0 0

St. Louis 60 65(06-08) 2006 2.5 0.2 0 0 0 0

St. Louis 60 65(06-08) 2007 7.3 2 0.6 0.2 0.1 0

St. Louis 60 65(06-08) 2008 0.2 0 0 0 0 0

St. Louis 60 65(08-10) 2008 2.4 0.2 0 0 0 0

St. Louis 60 65(08-10) 2009 2.4 0.1 0 0 0 0

St. Louis 60 65(08-10) 2010 6.3 1.1 0.2 0 0 0

St. Louis 70 65(06-08) 2006 0 0 0 0 0 0

St. Louis 70 65(06-08) 2007 0.4 0 0 0 0 0

St. Louis 70 65(06-08) 2008 0 0 0 0 0 0

St. Louis 70 65(08-10) 2008 0.1 0 0 0 0 0

St. Louis 70 65(08-10) 2009 0.2 0 0 0 0 0

St. Louis 70 65(08-10) 2010 0.1 0 0 0 0 0

St. Louis 80 65(06-08) 2006 0 0 0 0 0 0

St. Louis 80 65(06-08) 2007 0 0 0 0 0 0

St. Louis 80 65(06-08) 2008 0 0 0 0 0 0

St. Louis 80 65(08-10) 2008 0 0 0 0 0 0

St. Louis 80 65(08-10) 2009 0 0 0 0 0 0

St. Louis 80 65(08-10) 2010 0 0 0 0 0 0

St. Louis 60 70(06-08) 2006 9.3 2.3 0.7 0.2 0 0

St. Louis 60 70(06-08) 2007 16.9 7 3.4 1.7 0.8 0.4

St. Louis 60 70(06-08) 2008 1.2 0.1 0 0 0 0

St. Louis 60 70(08-10) 2008 6 1.1 0.2 0.1 0 0

St. Louis 60 70(08-10) 2009 6.2 0.9 0.1 0 0 0

St. Louis 60 70(08-10) 2010 14.9 5.2 1.9 0.7 0.2 0.1

St. Louis 70 70(06-08) 2006 0.5 0 0 0 0 0

St. Louis 70 70(06-08) 2007 2.7 0.3 0.1 0 0 0

St. Louis 70 70(06-08) 2008 0 0 0 0 0 0

St. Louis 70 70(08-10) 2008 0.5 0 0 0 0 0

St. Louis 70 70(08-10) 2009 0.7 0 0 0 0 0

St. Louis 70 70(08-10) 2010 0.9 0 0 0 0 0

St. Louis 80 70(06-08) 2006 0 0 0 0 0 0

St. Louis 80 70(06-08) 2007 0.2 0 0 0 0 0
 5F-48 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

St. Louis 80 70(06-08) 2008 0 0 0 0 0 0

St. Louis 80 70(08-10) 2008 0 0 0 0 0 0

St. Louis 80 70(08-10) 2009 0.1 0 0 0 0 0

St. Louis 80 70(08-10) 2010 0 0 0 0 0 0

St. Louis 60 75(06-08) 2006 18.1 7.8 3.7 1.6 0.8 0.4

St. Louis 60 75(06-08) 2007 25.8 13.8 8.2 5 3 1.8

St. Louis 60 75(06-08) 2008 3.5 0.5 0.1 0 0 0

St. Louis 60 75(08-10) 2008 9.5 2.4 0.6 0.2 0.1 0

St. Louis 60 75(08-10) 2009 10 2.2 0.5 0.1 0 0

St. Louis 60 75(08-10) 2010 21.1 9.6 4.8 2.3 1.2 0.6

St. Louis 70 75(06-08) 2006 2.9 0.2 0 0 0 0

St. Louis 70 75(06-08) 2007 8.1 2.2 0.7 0.2 0 0

St. Louis 70 75(06-08) 2008 0.2 0 0 0 0 0

St. Louis 70 75(08-10) 2008 1.1 0 0 0 0 0

St. Louis 70 75(08-10) 2009 1.4 0 0 0 0 0

St. Louis 70 75(08-10) 2010 3.6 0.4 0.1 0 0 0

St. Louis 80 75(06-08) 2006 0.1 0 0 0 0 0

St. Louis 80 75(06-08) 2007 1.1 0.1 0 0 0 0

St. Louis 80 75(06-08) 2008 0 0 0 0 0 0

St. Louis 80 75(08-10) 2008 0 0 0 0 0 0

St. Louis 80 75(08-10) 2009 0.2 0 0 0 0 0

St. Louis 80 75(08-10) 2010 0.1 0 0 0 0 0

Washington DC 60 55(06-08) 2006 0 0 0 0 0 0

Washington DC 60 55(06-08) 2007 0 0 0 0 0 0

Washington DC 60 55(06-08) 2008 0 0 0 0 0 0

Washington DC 60 55(08-10) 2008 0 0 0 0 0 0

Washington DC 60 55(08-10) 2009 0 0 0 0 0 0

Washington DC 60 55(08-10) 2010 0 0 0 0 0 0

Washington DC 70 55(06-08) 2006 0 0 0 0 0 0

Washington DC 70 55(06-08) 2007 0 0 0 0 0 0

Washington DC 70 55(06-08) 2008 0 0 0 0 0 0

Washington DC 70 55(08-10) 2008 0 0 0 0 0 0

Washington DC 70 55(08-10) 2009 0 0 0 0 0 0

Washington DC 70 55(08-10) 2010 0 0 0 0 0 0

Washington DC 80 55(06-08) 2006 0 0 0 0 0 0

Washington DC 80 55(06-08) 2007 0 0 0 0 0 0

Washington DC 80 55(06-08) 2008 0 0 0 0 0 0

Washington DC 80 55(08-10) 2008 0 0 0 0 0 0

Washington DC 80 55(08-10) 2009 0 0 0 0 0 0

Washington DC 80 55(08-10) 2010 0 0 0 0 0 0

Washington DC 60 60(06-08) 2006 0.6 0 0 0 0 0

Washington DC 60 60(06-08) 2007 0.1 0 0 0 0 0

Washington DC 60 60(06-08) 2008 0.1 0 0 0 0 0

Washington DC 60 60(08-10) 2008 0.3 0 0 0 0 0

Washington DC 60 60(08-10) 2009 0 0 0 0 0 0

Washington DC 60 60(08-10) 2010 0.6 0 0 0 0 0
 5F-49 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Washington DC 70 60(06-08) 2006 0 0 0 0 0 0

Washington DC 70 60(06-08) 2007 0 0 0 0 0 0

Washington DC 70 60(06-08) 2008 0 0 0 0 0 0

Washington DC 70 60(08-10) 2008 0 0 0 0 0 0

Washington DC 70 60(08-10) 2009 0 0 0 0 0 0

Washington DC 70 60(08-10) 2010 0 0 0 0 0 0

Washington DC 80 60(06-08) 2006 0 0 0 0 0 0

Washington DC 80 60(06-08) 2007 0 0 0 0 0 0

Washington DC 80 60(06-08) 2008 0 0 0 0 0 0

Washington DC 80 60(08-10) 2008 0 0 0 0 0 0

Washington DC 80 60(08-10) 2009 0 0 0 0 0 0

Washington DC 80 60(08-10) 2010 0 0 0 0 0 0

Washington DC 60 65(06-08) 2006 2.4 0.3 0.1 0 0 0

Washington DC 60 65(06-08) 2007 1.8 0.2 0 0 0 0

Washington DC 60 65(06-08) 2008 0.9 0.1 0 0 0 0

Washington DC 60 65(08-10) 2008 3.4 0.6 0.1 0 0 0

Washington DC 60 65(08-10) 2009 0.1 0 0 0 0 0

Washington DC 60 65(08-10) 2010 5 1.2 0.4 0.2 0.1 0

Washington DC 70 65(06-08) 2006 0.1 0 0 0 0 0

Washington DC 70 65(06-08) 2007 0 0 0 0 0 0

Washington DC 70 65(06-08) 2008 0 0 0 0 0 0

Washington DC 70 65(08-10) 2008 0.2 0 0 0 0 0

Washington DC 70 65(08-10) 2009 0 0 0 0 0 0

Washington DC 70 65(08-10) 2010 0.2 0 0 0 0 0

Washington DC 80 65(06-08) 2006 0 0 0 0 0 0

Washington DC 80 65(06-08) 2007 0 0 0 0 0 0

Washington DC 80 65(06-08) 2008 0 0 0 0 0 0

Washington DC 80 65(08-10) 2008 0 0 0 0 0 0

Washington DC 80 65(08-10) 2009 0 0 0 0 0 0

Washington DC 80 65(08-10) 2010 0 0 0 0 0 0

Washington DC 60 70(06-08) 2006 6.7 1.8 0.5 0.2 0.1 0

Washington DC 60 70(06-08) 2007 6.5 1.7 0.6 0.2 0.1 0

Washington DC 60 70(06-08) 2008 3.4 0.6 0.1 0 0 0

Washington DC 60 70(08-10) 2008 9.2 2.7 0.8 0.2 0.1 0

Washington DC 60 70(08-10) 2009 1 0.1 0 0 0 0

Washington DC 60 70(08-10) 2010 12.5 5 2.4 1.3 0.7 0.4

Washington DC 70 70(06-08) 2006 0.7 0 0 0 0 0

Washington DC 70 70(06-08) 2007 0.1 0 0 0 0 0

Washington DC 70 70(06-08) 2008 0.2 0 0 0 0 0

Washington DC 70 70(08-10) 2008 1.3 0.1 0 0 0 0

Washington DC 70 70(08-10) 2009 0 0 0 0 0 0

Washington DC 70 70(08-10) 2010 1.4 0.1 0 0 0 0

Washington DC 80 70(06-08) 2006 0 0 0 0 0 0

Washington DC 80 70(06-08) 2007 0 0 0 0 0 0

Washington DC 80 70(06-08) 2008 0 0 0 0 0 0

Washington DC 80 70(08-10) 2008 0.1 0 0 0 0 0
 5F-50 

Study Area 

Exposure 

Benchmark 

(ppb-8hr) 

Air Quality 

Scenario1 Year 

% of all school-age children experiencing multiple exposures per 

O3 season at or above benchmarks, adjusted air quality

≥1 ≥2 ≥3 ≥4 ≥5 ≥6

Washington DC 80 70(08-10) 2009 0 0 0 0 0 0

Washington DC 80 70(08-10) 2010 0.1 0 0 0 0 0

Washington DC 60 75(06-08) 2006 12.5 4.6 2.1 1 0.4 0.2

Washington DC 60 75(06-08) 2007 12.9 4.9 2.2 1.1 0.5 0.3

Washington DC 60 75(06-08) 2008 7.3 1.8 0.5 0.1 0 0

Washington DC 60 75(08-10) 2008 18.5 8.2 4.1 2 0.9 0.4

Washington DC 60 75(08-10) 2009 4.1 0.6 0.1 0 0 0

Washington DC 60 75(08-10) 2010 23.4 12.5 7.7 4.9 3.3 2.1

Washington DC 70 75(06-08) 2006 2 0.2 0 0 0 0

Washington DC 70 75(06-08) 2007 1.1 0.1 0 0 0 0

Washington DC 70 75(06-08) 2008 0.8 0 0 0 0 0

Washington DC 70 75(08-10) 2008 4.8 0.8 0.1 0 0 0

Washington DC 70 75(08-10) 2009 0.2 0 0 0 0 0

Washington DC 70 75(08-10) 2010 6 1.4 0.4 0.1 0 0

Washington DC 80 75(06-08) 2006 0.1 0 0 0 0 0

Washington DC 80 75(06-08) 2007 0 0 0 0 0 0

Washington DC 80 75(06-08) 2008 0 0 0 0 0 0

Washington DC 80 75(08-10) 2008 0.8 0 0 0 0 0

Washington DC 80 75(08-10) 2009 0 0 0 0 0 0

Washington DC 80 75(08-10) 2010 0.6 0 0 0 0 0

1

 Abbreviation indicates 8 hour standard level and three year averaging period. For example, 75(08-10) represents 

simulated ambient concentrations just meeting the existing standard (75 ppb-8hr) using air quality years 2008-2010. 

Values indicated as zero represent values of < 0.05% or actual zero (no one experienced an exposure. 
 5F-51 

Table 5F-3. Mean and maximum number of all school-age children (and associated days 

per O3 season) with at least one daily maximum 8-hr average O3 exposure at or above 60 

ppb-8hr while at moderate or greater exertion.

Study area 

Air quality 

scenario/ 

standard 

level (ppb) 

Mean Maximum 

Number of 

children 

Number of 

days per year 

Number of 

children 

Number of 

days per year 

Atlanta base 255487 827740 364916 1496000 

Atlanta 75 127378 229661 166169 325800 

Atlanta 70 64409 90926 93000 136700 

Atlanta 65 24933 29770 41687 50720 

Atlanta 60 5064 5359 10128 10840 

Atlanta 55 818 841 2522 2599 

Baltimore base 130826 357038 186072 609600 

Baltimore 75 61511 103377 95781 179700 

Baltimore 70 35864 49008 59793 88270 

Baltimore 65 15050 17414 27416 33050 

Baltimore 60 3109 3218 5888 5998 

Baltimore 55 346 346 817 817 

Boston base 177358 331700 287713 699100 

Boston 75 124529 190220 198171 365900 

Boston 70 81220 108757 141821 220300 

Boston 65 30411 34767 60377 72810 

Boston 60 6710 7064 15506 16620 

Boston 55 538 538 1713 1713 

Chicago base 258923 429030 503935 1046000 

Chicago 75 260946 449687 470011 922700 

Chicago 70 174401 246130 303354 477300 

Chicago 65 79122 99309 153346 198700 

Chicago 60 22578 26014 41894 48110 

Chicago 55 3831 4223 5842 6885 

Cleveland base 119740 234478 172100 423200 

Cleveland 75 59189 88930 104388 183800 

Cleveland 70 24462 31229 53736 75140 

Cleveland 65 6525 7396 17557 20980 

Cleveland 60 838 864 2195 2289 

Cleveland 55 81 83 188 189 

Dallas base 310037 853320 452737 1894000 

Dallas 75 141119 247643 251505 588800 

Dallas 70 82161 122988 175109 327400 

Dallas 65 32893 41095 83162 117000 

Dallas 60 8561 9159 20508 23050 

Dallas 55 846 860 2913 2913 

Denver base 129615 328720 178689 563800 

Denver 75 95296 187450 143603 367300 

Denver 70 57266 91933 106034 221600 

Denver 65 21348 27215 53234 78130 

Denver 60 1177 1192 2863 2928 

Denver 55 21 21 39 39 
 5F-52 

Study area 

Air quality 

scenario/ 

standard 

level (ppb) 

Mean Maximum 

Number of 

children 

Number of 

days per year 

Number of 

children 

Number of 

days per year 

Detroit base 207174 402320 349520 922500 

Detroit 75 143352 230015 194330 374700 

Detroit 70 74557 102732 104733 163400 

Detroit 65 29881 35337 46936 61570 

Detroit 60 3329 3457 6699 7225 

Detroit 55 82 82 320 320 

Houston base 254991 574580 360732 1082000 

Houston 75 110832 170062 173115 270900 

Houston 70 64399 84835 115161 152500 

Houston 65 25986 29950 55481 63640 

Houston 60 2565 2610 6888 6947 

Houston 55 41 41 236 236 

Los Angeles base 1244571 3978000 1423198 4981000 

Los Angeles 75 342236 741990 368974 814100 

Los Angeles 70 159498 290320 179329 334200 

Los Angeles 65 39327 57289 54045 72580 

Los Angeles 60 1548 1547 5831 5831 

Los Angeles 55 15 15 75 75 

New York base 1148294 2740240 1368877 3664000 

New York 75 418702 635750 729630 1311000 

New York 70 125784 153098 253458 324700 

New York 65 1602 1880 3241 3704 

Philadelphia base 388598 1094200 503583 1630000 

Philadelphia 75 170184 279328 252907 459900 

Philadelphia 70 87649 117594 145466 203900 

Philadelphia 65 29333 33504 56832 65860 

Philadelphia 60 7291 7693 20486 21890 

Philadelphia 55 699 699 2891 2891 

Sacramento base 147074 469140 190752 764900 

Sacramento 75 47859 80022 76891 148400 

Sacramento 70 27070 37802 46556 71980 

Sacramento 65 12503 14970 22069 27510 

Sacramento 60 1744 1830 3892 4296 

Sacramento 55 0 0 0 0 

St. Louis base 122408 315640 202543 689100 

St. Louis 75 86067 162499 136172 316800 

St. Louis 70 53629 82013 89003 160900 

St. Louis 65 20760 25857 38381 54050 

St. Louis 60 2915 3100 7840 8718 

St. Louis 55 140 143 503 515 

Washington base 267667 771500 345115 1141000 

Washington 75 127234 246782 226043 556700 

Washington 70 63711 97776 121074 219700 

Washington 65 22251 27721 48425 65920 

Washington 60 2645 2776 5578 5803 

Washington 55 0 0 0 0 
 5F-53 

Table 5F-4. Total number of school-age children and asthmatic school-age children 

experiencing at least one or two daily maximum 8-hr average O3 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 

Air Quality 

Scenario (3-

year averaging 

period) Year 

8-hr Average 

Exposure 

Benchmark 

Level (ppb) 

Number of Persons 

Exposed Per O3 Season1 

At least 

once 

At least 

twice 

Asthmatic School-age 

Children 

75(2006-2008) 

2006 

60 254454 94876

70 39422 4113

80 2399 120

2007 

60 281062 113215

70 51351 8788

80 4236 133

2008 

60 155918 42881

70 12603 713

80 428 13

2009 

60 160535 49447

70 19889 1532

80 1159 39

2010 

60 265139 110871

70 45688 6737

80 3981 143

base 

2006 

60 664077 381016

70 322187 126882

80 116538 26233

2007 

60 677867 397000

70 311929 113719

80 96028 16731

2008 

60 523387 280744

70 209734 67291

80 59813 11174

2009 

60 364614 157977

70 115454 33741

80 34482 5779

2010 

60 504479 263154

70 165488 49314

80 30418 3270

All School-age Children 75(2006-2008) 

2006 

60 2434809 900706

70 350957 38834

80 20489 708

2007 60 2624485 1045539

70 481637 74715

 

1

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 three-year standard averaging period. 
 5F-54 

Exposure Study Group 

Air Quality 

Scenario (3-

year averaging 

period) Year 

8-hr Average 

Exposure 

Benchmark 

Level (ppb) 

Number of Persons 

Exposed Per O3 Season1 

At least 

once 

At least 

twice 

80 45085 992

2008 

60 1492203 438479

70 120656 10568

80 3615 52

2009 

60 1529789 469352

70 188071 16908

80 12385 141

2010 

60 2439477 997079

70 395217 50200

80 27981 670

base 

2006 

60 6294372 3653902

70 3104290 1265681

80 1140328 264098

2007 

60 6307074 3658057

70 2926601 1067241

80 892814 149031

2008 

60 4987568 2672573

70 2021415 684051

80 597040 132098

2009 

60 3553528 1574327

70 1191875 372643

80 370166 63075

2010 

60 4671276 2417136

70 1528455 421992

80 273651 27126 1

 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-55 

Table 5F-5. Mean and maximum number of school-age children and asthmatic school-age 

children with at least one or two daily maximum 8-hr average O3 exposures at or above 

benchmark levels, all 15 urban study areas combined. 

Study Group 

(Total Simulated 

Population) 

Benchmark 

Level (ppb) 

Air Quality 

Scenario 

Number of People with 

at least One Exposure 

Number of People with 

at least Two Exposures

Mean Year Maximum 

year 

Mean Year Maximum 

year 

All School-age 

Children 

(19,049,557) 

60 

base 5,163,000 6,890,000 2,795,000 4,197,000 

75 2,316,000 3,588,000 865,000 1,643,000 

70 1,176,000 1,988,000 320,000 674,000 

65 392,000 762,000 67,000 161,000 

60 70,000 156,000 5,100 14,000 

55 7,500 18,000 400 1,100 

70 

base 2,155,000 3,548,000 762,000 1,470,000 

75 362,000 792,000 46,000 127,000 

70 94,000 236,000 5,400 18,000 

65 14,000 41,000 300 1,100 

60 1,400 4,500 0 0 

55 200 700 0 0 

80 

base 655,000 1,311,000 127,000 293,000 

75 27,000 79,000 600 2,300 

70 3,700 12,000 0 200 

65 300 1,300 0 0 

60 100 300 0 0 

55 0 0 0 0 

Asthmatic 

School-age 

Children 

(1,992,762) 

60 

base 547,000 738,000 296,000 448,000 

75 246,000 385,000 93,000 179,000 

70 126,000 214,000 35,000 74,000 

65 42,000 81,000 7,500 18,000 

60 7,700 17,000 700 2,100 

55 900 2,200 100 300 

70 

base 225,000 370,000 78,000 151,000 

75 40,000 88,000 5,300 15,000 

70 10,000 27,000 600 2,300 

65 1,700 4,500 100 300 

60 200 500 0 0 

55 0 0 0 0 

80 

base 67,000 135,000 13,000 29,000 

75 2,900 8,900 100 400 

70 300 1,100 0 0 

65 0 0 0 0 

60 0 0 0 0 

55 0 0 0 0 

For adjusted air quality, the average of the two values for year 2008 (i.e., the 2006-08 and 2008-10 three-year averaging periods) 

was first calculated within each study area; then the study area average calculated using all 5 simulated years (2006-2010). 

Values ≥ 10,000 were rounded to the nearest thousand, values <10,000 were rounded to the nearest 100. 
 5G-i 

APPENDIX 5G 

Targeted Evaluation of Exposure Model Input and Output Data 

Table of Contents 

5G-1 ANALYSIS OF TIME-LOCATON-ACTIVITY DATA .......................................... 5G-1

5G-1.1 Personal Attributes of Survey Participants in CHAD and Used by APEX ...... 5G-1

5G-1.2 Afternoon Time Spent Outdoors for CHAD Survey Participants .................... 5G-4

5G-1.3 Afternoon Time Spent Outdoors For ATUS Survey Participants .................... 5G-7

5G-1.4 Outdoor Time and Exertion Level of Asthmatics and Non-Asthmatics In 

CHAD... . ...........................................................................................5G-9

5G-2 CHARACTERIZATION OF FACTORS INFLUENCING HIGH EXPOSURES . 5G-14

5G-3 ANALYSIS OF APEX SIMULATED LONGITUDINAL ACTIVITY PATTERNS

 ..................................................................................................5G-23

5G-4 EXPOSURE RESULTS FOR ADDITIONAL AT-RISK POPULATIONS AND 

LIFESTAGES, EXPOSURE SCENARIOS, AND AIR QUALITY INPUT DATA 

USED ....................................................................................................................... 5G-29

5G-4.1 Exposure Estimated For All School-Age Children During Summer Months, 

Neither Attending School nor Performing Paid Work .................................... 5G-29

5G-4.2 Exposures Estimated For Adult Outdoor Workers During Summer Months . 5G-31

5G-4.3 Averting Behavior and Potential Impact to Exposure Estimates .................... 5G-39

5G-5 COMPARISON OF PERSONAL EXPOSURE MEASUREMENT AND APEX 

MODELED EXPOSURES ...................................................................................... 5G-44

5G-6 REFERENCES ........................................................................................................ 5G-47
 5G-ii 

List of Tables 

Table 5G-1. Personal attributes of survey participants within CHAD and used by APEX. . 5G-3

Table 5G-2. Comparison of outdoor time expenditure and exertion level among asthmatic and 

non-asthmatic diary days for CHAD diaries used by APEX. ......................... 5G-11

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. .............. 5G-12

Table 5G-4. Percent of waking hours spent outdoors at an elevated activity level: a comparison 

of CHAD with EPRI (1992) study asthmatics. ............................................... 5G-13

Table 5G-5. Percent of waking hours spent outdoors at an elevated activity level: a comparison 

of CHAD with EPRI (1988) study asthmatics. ............................................... 5G-13

Table 5G-6. Range of R2 fit statistics for ANOVA models used to evaluate daily maximum 8-

hour O3 exposure concentrations stratified by study area, air quality scenario, and 

exposure level. ................................................................................................ 5G-16

Table 5G-7. Range of R2 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. .............................................................. 5G-16

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. .... 5G-33

Table 5G-9. Personal attributes and mean time spent working outdoors for CHAD diaries 

reporting at least two hours of outdoor work. ................................................. 5G-34

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. ............................................................................................................. 5G-38

List of Figures 

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. .......... 5G-5

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-8

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-18

Figure 5G-4. 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 Atlanta. ..................................... 5G-19

Figure 5G-5. Distributions of afternoon outdoor time expenditure and daily maximum 8-hour 

ambient O3 concentrations for simulated Boston school-age children (ages 5 to 
 5G-iii 

18) (left) and adults (ages 19 to 35) (right) using base air quality (top) and 

concentrations adjusted to just meet the existing standard (bottom) for person 

days having daily maximum 8-hour exposures either below or above 50 ppb....

......................................................................................................................... 5G-21

Figure 5G-6. Afternoon microenvironmental time (top) and activities performed during 

afternoon time outdoors (bottom) for school-age children (left) and adults (right) 

experiencing 8-hour daily maximum O3 exposures ≥ 50 ppb, Boston base air 

quality, 2006. .................................................................................................. 5G-23

Figure 5G-7. Cumulative distribution of median time spent outdoors (top row), afternoon 

outdoor participation ≥ 1 minute/day (2nd row), and afternoon outdoor 

participation ≥ 2 hours/day (3nd 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-27

Figure 5G-8. Cumulative distribution of median time spent outdoors (top row), afternoon 

outdoor participation ≥ 1 minute/day (2nd row), and afternoon outdoor 

participation ≥ 2 hours/day (3nd row) for male and female school-age children in 

Houston (left column), Philadelphia (middle column) and Sacramento (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-28

Figure 5G-9. Comparison of the percent of all school-age children having daily maximum O3

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-30

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. .................................... 5G-39

Figure 5G-11. Distribution of daily personal O3 exposures (top row), outdoor time (2nd row from 

top), ambient O3 concentrations (3rd row from top), and air exchange rate (bottom 

row) for DEARS study participants (left column) and APEX simulated 

individuals (right column) in Wayne County, MI, July-August 2006. ........... 5G-46
 5G-1 

This Appendix presents the complete results of several targeted evaluations and exposure 

simulations designed to provide additional insights to APEX input data or approaches used to 

estimate exposures, algorithm and model performance evaluations, and estimated exposures for 

additional exposure study groups and lifestages of interest. 

5G-1 ANALYSIS OF TIME-LOCATON-ACTIVITY DATA 

 We first present an overview of the data currently available in the CHAD database used 

by APEX, including comparison with the version of CHAD used to estimate exposures in the 1st

draft O3 HREA. This is followed by an analysis of time spent outdoors - one of the most 

important attributes influencing exposures at or above benchmark levels - using CHAD and 

recent time-location-activity pattern data from the American Time Use Survey (ATUS). And 

finally, CHAD diaries identified as coming from asthmatics are compared with that of nonasthmatics for afternoon outdoor time and activity level as well as compared with available 

independent studies of asthmatic activity patterns. 

5G-1.1 Personal Attributes of Survey Participants in CHAD and Used by APEX 

 The survey participants whose diary data are within CHAD were asked a number of 

questions regarding their personal attributes. The number and type of attributes present for 

diaries in CHAD is driven largely by the original intent of the individual study. In our exposure 

assessment, we have strict requirements to simulate individuals using several personal attributes, 

namely age, sex, temperature (as a surrogate for seasonal variation in activity patterns), and dayof-week. These attributes are considered as important drivers influencing daily activity patterns 

(Graham & McCurdy, 2004) and when diaries do not have these particular attributes for a 

particular day, they will not be used by APEX. 

 This APEX modeling requirement serves as an initial screen to the number of available 

diaries in the complete CHAD master database (i.e., 54,373) and considering the age range of the 

simulated exposure study groups (persons between the ages of 5 and 95), the actual number of 

diary days having complete information and used by APEX in the final O3 HREA is 41,474.1

 

This represents an increase of about 8,700 diaries currently used by APEX compared with what 

was used by APEX in the 1st draft O3 HREA. Additionally, there have been eight new study data 

sets incorporated into CHAD and used in our current exposure assessment since the previous O3

NAAQS review conducted in 2007, most of which were from recently conducted activity pattern 

studies (Appendix 5B, Section 5B-4). The diary data included from these new studies have more 

 

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 person’s age. 
 5G-2 

than doubled the total activity pattern data used for 2007 O3 exposure modeling and has 

increased the number of children’s diaries by about a factor of five. 

 Table 5G-1 presents a summary of the important personal attributes used by APEX in 

creating activity patterns for simulated persons, along with other attributes of potential interest 

(e.g., race/ethnicity). First, we compared the representation of several attributes in the current 

CHAD used by APEX versus that used in the 1st draft O3 HREA. Outside of increases in the 

number of persons, the general distribution of diaries within the APEX diary selection attributes 

(e.g., age, sex, temperature, day-of-week) is similar in both databases. Worth noting is the 

number and percent of diaries from each of the three decades analyzed. Currently, the majority 

of diaries (54%) from CHAD are taken from surveys conducted in the past decade, while the pre1990s represent less than 15% of the total diaries available by APEX. 

 While there may be other personal or situational attributes that affect daily time 

expenditure, these are typically not included in our assessment to generate simulated individuals 

simply because the response to the attribute is missing for most persons. For example, income 

level is missing for just over 66% of the study participants and only about 30% of employed 

workers (persons ages 19 to 64) reported their occupation (Table 5G-1). Missing response data 

in CHAD results from either the study not having an income/occupation related survey question 

or perhaps the participant refused to answer the question. Note also, when any attribute is added 

to the development of a person’s profile, the pool of diaries available for selection in simulating 

an individual is reduced. This could lead to an increased repetition of diaries used for simulated 

individuals, potentially artificially reducing variability in time expenditure. In addition, the 

desired study group to be simulated may have too few diaries within a diary pool if most diaries 

are missing the needed attribute, leading to a simulation failure. This is why personal attributes 

are carefully selected and prioritized according to both their prevalence in CHAD and whether 

attribute has a known significant influence on activity patterns. 
 5G-3 

Table 5G-1. Personal attributes of survey participants within CHAD and used by APEX. 

Personal 

Attribute

Group Within 

Attribute

Current APEX CHAD 

(41,474 total days) 

1st Draft APEX CHAD 

(32,788 total days) 

person days 

(n) 

percent of 

attribute 

person days 

(n) 

percent of 

attribute 

Age (years) 

4 - 18 17680 42.6 14111 43 

19 - 34 4490 10.8 4001 12.2 

35 - 50 7238 17.5 5957 18.2 

51 - 64 6181 14.9 5016 15.3 

65+ 5885 14.2 3703 11.3 

Sex 

Female 21466 51.8 16840 51.4 

Male 20008 48.2 15948 48.6 

Daily 

Maximum 

Temperature 

(°F) 

 84+ 13817 33.3 12113 36.9 

55-83 17827 43 13078 39.9 

 <55 9830 23.7 7597 23.2 

Day of Week Weekday 29031 70.0 23794 72.6 

Weekend 12443 30.0 8994 27.4 

Decade 

1980s 5999 14.5 6167 18.8 

1990s 12831 30.9 12390 37.8 

2000s 22644 54.6 14231 43.4 

Employed 

(ages 19-64) 

No 4651 26.0 3747 25.0 

Yes 12755 71.2 11227 75.0 

Missing/Unknown 503 2.8 n/a n/a 

Occupation 

(employed) 

Known 3867 30.3 3012 26.8 

Missing/Unknown 8888 69.7 8215 73.2 

Income Group 

> 1.5 x Poverty 10347 24.9 10416 31.8 

≤ 1.5 x Poverty 3713 9.0 3730 11.4 

Missing/Unknown 27414 66.1 18642 56.9 

Calendar 

Month 

Calendar Months 5 - 

9 19151 46.2 16812 51.3 

Calendar Months 1 - 

4, 10 - 12 22323 53.8 15976 48.7 

Race/Ethnicity 

Asian 670 1.6 349 1.1 

Black 6993 16.9 4040 12.3 

Hispanic 2476 6.0 1339 4.1 

Native American 28 0.1 7 0 

White 25009 60.3 19569 59.7 

Other 770 1.9 609 1.9 

Missing/Unknown 5528 13.3 6875 21 
 5G-4 

5G-1.2 Afternoon Time Spent Outdoors for CHAD Survey Participants 

 There have been questions raised regarding the representativeness of the diaries from 

studies conducted in the 1980s and whether there are any recognizable patterns in time 

expenditure in the CHAD diaries across the time period when data were collected. Because time 

spent outdoors is a significant factor influencing daily maximum 8-hour O3 exposures, we 

evaluated the current collection of CHAD diaries used by APEX for two metrics: outdoor 

participation rate and mean time spent outdoors. The participation rate is the percent of the 

person-days having at least one minute outdoors, and because high O3 concentrations commonly 

occur during the afternoon hours of summer months, we restricted the analysis to those times of 

day (12 PM to 8 PM) and year (May through September). The same data set was used to 

calculate a mean outdoor time, though the calculation was further restricted to person days 

meeting an additional criterion: person-days having at least one minute outdoors and person-days 

having at least 2-hours outdoors. Separating the data into these sub-groups give us insight to the 

diaries most likely to be used in simulating a person that exceeds a selected benchmark level and 

protects (to a limited degree) from study sample design bias (15-minute time block diaries versus 

minute-by-minute event level diaries). Data were further stratified by five age groups (4-18, 19-

34, 35-50, 51-64, 65+) and three decades (1980s, 1990s, and 2000s) using the year the particular 

activity pattern study was conducted. As a reminder, CHAD is composed of primarily crosssectional data, thus the trend evaluated over the three decades is changes (if any) in participation 

rate and the time spent outdoors by the study group, not individuals. 

 Figure 5G-1 illustrates the trends in afternoon outdoor activity participation and mean 

time expended outdoors, considering three decades, five age groups, and whether the total 

afternoon time spent outdoors was at least one minute or two hours. Regardless of decade and 

duration of time spent outdoors, participation in outdoor activities follows an expected pattern 

considering age groups, that is, children tend to have the highest participation rate when 

compared with the other age groups, while the oldest persons (aged 65 or greater) tend to have 

the lowest participation rate (Figure 5G-1, top left panel). When considering decade and CHAD 

diaries having at least one minute spent outdoors, the participation rate appears to have a nonlinear concave trend, whereas CHAD diaries collected during the 1990s exhibit the lowest 

outdoor participation rate (ranging from about 40-70%) while much greater participation is found 

with the CHAD diaries collected during the 1980s (80-90%) and 2000s (70-80%). 
 5G-5 

0

10

20

30

40

50

60

70

80

90

100

1980s 1990s 2000s

Afternoon Outdoor Participation Rate (%)

Decade

CHAD, ≥ 1‐mins outdoortime, months May‐Sep, hours12‐8PM

0

50

100

150

200

250

300

1980s 1990s 2000s

Mean Afternoon Outdoor Time

(minutes/day)

Decade

CHAD, ≥ 1‐min outdoortime, months May‐Sep, hours 12‐8PM

0

10

20

30

40

50

60

70

80

90

100

1980s 1990s 2000s

Afternoon Outdoor Participation Rate (%)

Decade

CHAD, ≥ 2‐hrs outdoortime, months May‐Sep, hours12‐8PM

0

50

100

150

200

250

300

1980s 1990s 2000s

Mean Afternoon Outdoor Time

(minutes/day)

Decade

CHAD, ≥ 2‐hrs outdoortime, months May‐Sep, hours12‐8PM

0 100

1980s 1990s 2000s

Af

te

rn

o

o...

ages 4 toCHAD, 18  ≥ 1ages ‐mins19outdoor to 34 time, agesmonths 35 to 50May‐Sep, ageshours 51 to 6412‐8PMages 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-
 5G-6 

time diary approach, possibly responsible for the observed increase in outdoor participation rate 

during this decade. 

 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 

 

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-7 

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 

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-8 

0

50

100

150

200

250

300

2003 2004 2005 2006 2007 2008 2009 2010 2011

Mean Afternoon Outdoor Time

(minutes/day)

Year

ATUS, ≥ 1‐min outdoortime, months May‐Sep, hours 12‐8PM

0

10

20

30

40

50

60

70

80

90

100

2003 2004 2005 2006 2007 2008 2009 2010 2011

Afternoon Outdoor Time Participation

Rate (%)

Year

ATUS, ≥ 1‐min outdoortime, months May‐Sep, hours 12‐8PM

0

10

20

30

40

50

60

70

80

90

100

2003 2004 2005 2006 2007 2008 2009 2010 2011

Afternoon Outdoor Time Participation

Rate (%)

Year

ATUS, ≥ 2‐hrs outdoortime, months May‐Sep, hours12‐8PM

0

50

100

150

200

250

300

2003 2004 2005 2006 2007 2008 2009 2010 2011

Mean Afternoon Outdoor Time

(minutes/day)

Year

ATUS, ≥ 2‐hrs outdoortime, months May‐Sep, hours12‐8PM

M

50

ages 15 to 18 ages 19 to 34 ages 35 to 50 ages 51 to 64 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-9 

Not surprisingly given the lack of distinction regarding time indoors and outdoors while 

at home for ATUS participants as well as the diary approach used,3 the outdoor activity 

participation rate for ATUS study subjects is lower than that of CHAD study subjects; about 30-

40% of ATUS person-days have at least one minute of outdoor time (Figure 5G-2, top left 

panel). As was observed with the CHAD data, children (ATUS ages 15 to 18) are more likely to 

participate in outdoor activities. The mean time outdoors for persons that reported any amount 

of outdoor time is similar to the range indicated by CHAD diaries, generally between 100-150 

minutes per day (Figure 5G-2, bottom left panel). When considering person-days having at least 

2 hours of time spent outdoors, the range in ATUS diary outdoor participation rate (10-20%, 

Figure 5G-2, top right panel)) is lower than that observed for the CHAD data (generally between 

20-40%), while the range in mean time spent outdoors (190-240 minutes per day, Figure 5G-2, 

top right panel) was similar to that of the CHAD data. Consistent also across the two studies is 

the participation rate of children being greater than that of the other age groups. There are no 

consistent trends over the nine year ATUS study period regarding either the participation rate or 

the mean time spent outdoors for any of the age groups. 

5G-1.4 Outdoor Time and Exertion Level of Asthmatics and Non-Asthmatics in CHAD 

Due to limited number of CHAD diaries with survey requested health information, all 

CHAD diaries are assumed appropriate for any simulated individual (i.e., whether asthmatic, 

non-asthmatic, or not indicated), provided they concur with age, sex, temperature, and day-ofweek selection criteria. In general, the assumption of modeling asthmatics similarly to healthy 

individuals (i.e., using the same time-location-activity profiles) is supported by the findings of 

van Gent et al. (2007), at least when considering children 7 to 10 years in age. These researchers 

used three different activity-level measurement techniques; an accelerometer recording 1-minute 

time intervals, a written diary considering 15-minute time blocks, and a categorical scale of 

activity level. Based on analysis of 5-days of monitoring, van Gent et al. (2007) showed no 

difference in the activity data collection methods used as well as no difference between asthmatic 

children and healthy children when comparing their respective activity levels. Contrary to this, 

an analysis of 2000 BRFSS data by Ford et al. (2003) indicated a statistically significant 

difference between the percent of current asthmatics (30.9%) and non-asthmatics (27.8%) 

characterized as inactive. In addition, these researchers found small but statistically significant 

differences in the percent of asthmatic (26.6%) and non-asthmatic (28.1%) adults achieving 

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-10 

Note though, the salient issue is not just outdoor time and activity levels, but the 

intersection of the two as well as recognizing the performance capabilities of persons with 

asthma. A person’s overall physical activity level is strongly linked with their time spent 

outdoors and is considered an important correlate in encouraging increased physical activity 

among children and adults alike (e.g., Sallis et al., 1998). In addition, introducing regular 

exercise has been shown to improve physical fitness in asthmatic children, with statistically 

significant increases in ventilation measures such as maximum minute ventilation rate (VEmax) 

maximum oxygen uptake (VO2max) (e.g., van Vledhoven et al., 2001). Further, in other related 

research, Santuz et al. (1997) indicated no statistically significant difference between asthmatic 

and non-asthmatic children when comparing maximum exercise performance levels, provided 

the individuals were conditioned through habitual exercise. Thus it appears that asthmatics 

perform activities at elevated levels and do so in outdoor microenvironments in similar fashion to 

non-asthmatics. 

To provide further support to the assumption that any CHAD diary day can be used to 

represent the asthmatic study groups regardless of the study participants’ characterization of 

having asthma or not, we first compared the amount of afternoon outdoor time and participation 

in elevated exertion levels among asthmatics and non-asthmatics. Because six of the 19 studies 

incorporated in CHAD reported whether the individual was asthmatic or non-asthmatic, we 

categorized the data and results using three categories (i.e., asthmatic, non-asthmatic, not 

classifiable). Afternoon hours were characterized as was done for above CHAD analyses, that is, 

the time between 12 PM and 8 PM and only those persons that did spend some time outdoors 

were retained. As is done by APEX in simulating individuals, level of exertion was estimated by 

sampling from the specific METS distributions assigned for each person’s activity performed. 

Then, we selected for activities having a METS value of greater than 3 as times where a person 

was at moderate or greater exertion levels (US DHHS, 1999). Afternoon outdoor time was then 

stratified by exertion level, summed for two study groups of interest (children and adults), and 

presented in percent form within Table 5G-2. 

When considering CHAD diaries used by APEX in our simulations, about 18% of the 

diaries are from either an asthmatic child or an asthmatic adult. Far fewer children’s diaries are 

from persons whose asthmatic status is unknown (12%) when compared to adults (30%) though 

still, persons having unknown health status are a smaller proportion of the total available persondays. On average, about 43% of all children spent some afternoon time outdoors while asthmatic 

children have a higher participation rate (48.5%) when compared to non-asthmatic children 

(41.2%). About half of the adults whose asthmatic condition was known did spend afternoon 

time spent outdoors with participation rate generally similar for both asthmatic and nonasthmatic adults. Outdoor participation rate for persons having unknown asthma status varied 
 5G-11 

from that of known persons; about 60% of the children’s diaries and 31% of the adult diaries 

indicate some afternoon time was spent outdoors. 

Table 5G-2. Comparison of outdoor time expenditure and exertion level among asthmatic 

and non-asthmatic diary days for CHAD diaries used by APEX. 

CHAD: Children (4 to 18)1 CHAD: Adults (19 to 95)2

Asthmatic? Yes No Unknown Yes No Unknown 

Persons (n) 3,206 12,346 2,128 1,254 15,465 7,075 

 Outdoor Habitué (n) 1,564 5,092 1,267 602 7,949 2,176 

Outdoor Habitué (%) 48.8 41.2 59.5 48.0 51.4 30.8 

Percent of Afternoon 

Hours Spent 

Outdoors (%) 28.5 27.5 28.9 26.2 27.2 22.2 

Percent of Afternoon 

Outdoor Time at 

Moderate or Greater 

Exertion (%) 80.3 78.2 79.2 62.7 63.8 60.3 1

 CHAD studies for where a survey questionnaire response of whether or not child was asthmatic include CIN, ISR, 

NHA, NHW, OAB, and SEA (see HREA 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 21⁄4 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 habitué) 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-12 

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 Shamoo (1994)1 CHAD2

Location Los Angeles Any Diary 

Time of Year 

Summer 

Months 

(5-9) 

Winter 

Months 

(11-3) Summer Months (May-Sep) Winter Months (Nov-Mar) 

Asthmatic? Yes Yes Yes No Unknown Yes No Unknown

Person days 336 314 375 4,812 1,049 211 2,512 1,998 

Mean age 

(min-max) 

33 

(18 – 50) 

37.3 

(18 – 50)

37.9 

(18 – 50) 

35.2 

(18 – 50)

30.4 

(18 – 50) 

32.3 

(18 – 50) 

34.3 

(18 – 50)

Exertion Level Percent of Asthmatic Waking Hours Spent Outdoors at Given Exertion Level 

Low 8.5 6.0 4.6 5.6 5.4 2.0 2.1 1.4 

Moderate 1.9 1.7 6.2 6.9 4.6 2.0 2.7 2.0 

Strenuous 0.2 0.2 1.1 1.2 1.0 0.6 0.7 0.5 

1

 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 HREA 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, nonasthmatics, 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-13 

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 EPRI (1992)1 CHAD 

Location Cincinnati Any Diary 

Time of Year 

August 

(Wed-Sun) 

July and August 

(Thu-Sat) 

Asthmatic? Yes Yes No Unknown

Person days 408 711 3,085 1,209 

Mean age 

(min-max) 

26 

(1 – 78) 

17.9 

(1 – 77) 

34.9 

(1 – 78) 

32.3 

(1 – 78) 

Exertion Level 

Percent of Asthmatic Waking Hours Spent 

Outdoors at Given Exertion Level 

Moderate 11 5.0 7.2 7.2 

Strenuous 3.3 7.6 3.7 2.6 

1

 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. 

Table 5G-5. Percent of waking hours spent outdoors at an elevated activity level: a 

comparison of CHAD with EPRI (1988) study asthmatics. 

Study EPRI (1988)1 CHAD 

Location Los Angeles Any 

Time of Year April (Fri-Mon) April and May (Fri-Mon) 

Asthmatic? Yes Yes No Unknown

Person days 156 

Mean age 

(min-max) 

- 

(18 – 37)2

26.2 

(18 – 39) 

29.7 

(18 – 40)

30.9 

(18 – 40)

Exertion Level 

Percent of Asthmatic Waking Hours Spent 

Outdoors at Given Exertion Level 

Moderate 7 2.8 4.5 4.4 

Strenuous 2.4 0.4 0.6 1.2 

1

 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-14 

5G-2 CHARACTERIZATION OF FACTORS INFLUENCING HIGH EXPOSURES 

We investigated the factors that influence estimated exposures, with a focus on persons 

experiencing the highest daily maximum 8-hour exposures six selected study areas – Atlanta, 

Boston, Denver, Houston, Philadelphia, and Sacramento.4 This analysis required the generation 

of detailed APEX output files having varying time intervals, that is, the daily, hourly, and 

minute-by-minute (or events) files. Given that the size of these time-series files is dependent on 

the number of persons simulated, we simulated 5,000 persons and restricted the analysis to a 

single year (2006) to make this evaluation tractable.5 Both the base case (unadjusted or ‘as is’ 

recent air quality conditions) and ambient O3 adjusted to just meet the existing standard (75 ppb8hr) air quality scenarios were evaluated in each of six study areas. All APEX conditions (e.g., 

ME descriptions, AERs, MET data) were consistent with the 200,000 person APEX simulations 

that generated all of summary output discussed in the main body of this chapter. 

We were interested in identifying the specific microenvironments and activities most 

important to O3 exposure and evaluating their duration and particular times of the day people 

were engaged in them. Because ambient O3 concentrations peak mainly during the afternoon 

hours, we focused our microenvironmental time expenditure analysis on the hours between 12 

PM and 8 PM. For every person and day of the exposure simulation, we aggregated the time 

spent outdoors, indoors, near-roadways, and inside vehicles during these afternoon hours (i.e., 

the time of interest summed to 480 minutes per person day). Data from several APEX output 

files were then combined to generate a single daily file for each person containing a variety of 

personal attributes (e.g., age, sex), their daily maximum 8-hour ambient and exposure 

concentrations, and the aforementioned time expenditure metrics. 

 We performed an analysis of variance (ANOVA) using SAS PROC GLM (SAS, 2012) 

to determine the factors contributing the greatest to the observed variability in the dependent 

variable, i.e., each person’s daily maximum 8-hour O3 exposure concentrations. This analysis 

was distinct for four age-groups of interest (i.e., 5-18, 19-35, 36-64, ≥65 years of age). The final 

statistical models6 included a total of seven explanatory variables: the main effects of (1) daily 

 

4

 For the 1st draft O3 HREA, 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 final O3

HREA was the air quality data input to APEX: ambient monitoring data were used for the 1st draft O3 HREA 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. 

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. 
 5G-15 

maximum 8-hour ambient O3, (2 to 4) afternoon time spent outdoors, near-roads, and inside 

vehicles,7 and (5) physical activity index (PAI), while also including interaction effects from (6) 

afternoon time outdoors by daily maximum 8-hour ambient concentration, and (7) PAI by 

afternoon time outdoors. Two conditions were considered: all person days of the simulation, and 

only those days where a person’s 8-hour maximum exposure concentration was ≥50 ppb.8 

Selected output from this ANOVA included parameter estimates for each variable, model Rsquare statistic (R2

), and Type III model sums of squares (SS3).9

Model fits, as indicated by an R2 value, were reasonable across each of the study areas 

(Table 5G-6). The selected factors explain about 40-80% of the total variability in 8-hour daily 

maximum exposures. Model fits were best when using all person days of the simulation though 

results were similar for both air quality scenarios. When considering only those days where 

persons had 8-hour daily maximum O3 exposures ≥50 ppb, consistently less variability in 

maximum exposure concentrations was explained by the factors included in each model, though 

overall model fits were acceptable. Furthermore, the most robust models tended to be those 

developed using either school-age children aged 5 to 18 or adults 19 to 35 years old (e.g., see 

Table 5G-7 for Atlanta model R2 results stratified by age groups). 

We evaluated the relative contribution each variable had on the total explained variability 

using the SS3 in each respective model.10 As with the R2 statistics generated above, the percent 

contribution results were separated into four exposure scenarios for each study area, with 

estimates for Boston illustrated in Figure 5G-3. When considering all person days of the 

simulation (top row), the daily maximum 8-hour ambient O3 concentration variable contributes 

the greatest to the explained model variance, consistently estimated to be about 85% across all 

age groups and for either the base or existing standard air quality scenarios. The interaction of 

this variable with afternoon outdoor time contributes an additional 7-10% to the explained 

variance, indicating that both ambient concentration and time spent outdoors collectively 

contribute to 90% or more of the explained model variance when evaluating all (e.g., high, midrange, and low level) daily maximum 8-hour O3 exposure concentrations. The main effect of 

outdoor time contributed < 1% to the explained variance under these conditions as did 

contributions from the other included variables, except for time spent near-roads (about a 5% 

 

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 SS3 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-16 

contribution). These results suggest that when considering the Boston exposure study groups 

broadly, the daily maximum 8-hour ambient O3 concentration is the most important driver in 

estimating population-based O3 exposures, nearly regardless of specific microenvironmental 

locations where exposure might occur. 

Table 5G-6. Range of R2 fit statistics for ANOVA models used to evaluate daily maximum 

8-hour O3 exposure concentrations stratified by study area, air quality scenario, and 

exposure level. 

Study Area 

(O3 season) 

Base Case Model R2 (sample sizes) Existing Standard Model R2 (sample sizes) 

All Person 

Days 

Person days with 8-hr daily 

max exposure ≥ 50 ppb 

All Person 

Days 

Person days with 8-hr daily 

max exposure ≥ 50 ppb 

Atlanta 

(245 days) 

0.71 – 0.78 

(1,225,000) 

0.62 – 0.70 

(43,646) 

0.67 – 0.74 

(1,225,000) 

0.61 – 0.70 

(18,758) 

Boston 

(183 days) 

0.64 – 0.70 

(915,000) 

0.44 – 0.62 

(23,496) 

0.58 – 0.65 

(915,000) 

0.41 – 0.59 

(16,184) 

Denver 

(214 days) 

0.61 – 0.68 

(1,070,000) 

0.49 – 0.62 

(24,850) 

0.55 – 0.62 

(1,070,000) 

0.47 – 0.61 

(18,487) 

Houston 

(365 days) 

0.75 – 0.80 

(1,825,000) 

0.57 – 0.67 

(17,779) 

0.65 – 0.71 

(1,825,000) 

0.38 – 0.65 

(7,322) 

Philadelphia 

(214 days) 

0.66 – 0.73 

(1,070,000) 

0.51 – 0.67 

(39,561) 

0.57 – 0.64 

(1,070,000) 

0.48 – 0.66 

(16,841) 

Sacramento 

(365 days) 

0.75 – 0.81 

(1,825,000) 

0.50 – 0.71 

(19,734) 

0.68 – 0.74 

(1,825,000) 

0.48 – 0.73 

(6,503) 

Table 5G-7. Range of R2 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 

Age Group 

(years) 

Base Case Model R2 Existing Standard Model R2

All Person 

Days 

Person days with 

8-hr daily max 

exposure ≥ 50 ppb 

All Person 

Days 

Person days with 

8-hr daily max 

exposure ≥ 50 ppb 

Atlanta 5-18 0.78 0.64 0.74 0.62 

19-35 0.71 0.70 0.67 0.70 

36-64 0.73 0.64 0.68 0.64 

≥65 0.74 0.62 0.70 0.61 
 5G-17 

When considering only person days having daily maximum 8-hour O3 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 

main effect of the 8-hour daily maximum ambient O3 concentration variable has a sharply lower 

contribution (generally about 5-15%) along with greater contribution from the main effects 

variable outdoor time (15-25% contribution) and its interaction with the ambient concentration 

variable (40-60%). These results suggest that for highly exposed persons in Boston, the most 

important influential factors are time spent outdoors corresponding with high daily maximum 8-

hour ambient O3 concentrations. 

Results for Atlanta (Figure 5G-4), were generally similar to Boston with notable 

differences discussed here.11 The contribution of the maximum 8-hour ambient O3 concentration 

variable to the total explained variance (about 40-50%) was less than that observed in Boston 

when considering all person days (Figure 5G-3 and Figure 5G-4, top rows), while the 

contribution from the outdoor time/ambient O3 interaction variable was greater in Atlanta (about 

20-40% versus 10% in Boston). 

This observed dissimilarity in the contribution by ambient concentrations and afternoon 

outdoor time may be driven by the A/C prevalence rates and AER distributions used for each 

study area.12 Boston has lower A/C prevalence though overall higher AERs (even when 

considering mechanical ventilation), thus a greater contribution to exposure is expected from 

ambient concentrations by infiltrating to indoor microenvironments and hence, reflected in the 

strong main effects for the 8-hour daily maximum ambient O3 concentration variable in Boston. 

Afternoon time spent near Atlanta roads was estimated to contribute to about 20-30% of the total 

explained variance when considering all person days and exposures, a value greater than that 

estimated for Boston (generally about 5%) again possibly reflecting an increased importance of 

this outdoor microenvironment in Atlanta (and Houston, Sacramento, not shown) relative to that 

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-18 

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

Age Group

Boston, Base Air Quality,

All Person Days

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

AgeGroup

Boston,Base Air Quality,

Person Days w/8‐hr Exposures ≥ 50 ppb

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

Age Group

Boston, Existing 75 ppb Standard,

Person Days w/8‐hr Exposures ≥ 50 ppb

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

AgeGroup

Boston, Existing 75 ppb Standard,

All Person Days

0% 25% 50% 75% 100%

<      5

36 to 65

Model Explained Variance

Age

Group

Max 8‐hr Ambient O3 Atlanta, CurrentTimeStandard, Outdoors...12‐8PM

Max 8‐hr Ambient O3*Time Outdoors 12‐8PM PhysicalActivity Index (PAI)

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-19 

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

Age Group

Atlanta, Base Air Quality,

All Person Days

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

AgeGroup

Atlanta, Base Air Quality,

Person Days w/8‐hr Exposures ≥ 50 ppb

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

Age Group

Atlanta, Existing 75 ppb Standard,

All Person Days

0% 25% 50% 75% 100%

5 to 18

19 to 35

36 to 64

≥     65

Model Explained Variance

AgeGroup

Atlanta, Existing 75 ppb Standard,

Person Days w/8‐hr Exposures ≥ 50 ppb

0% 25% 50% 75% 100%

<      5

36 to 65

Model Explained Variance

Age

Group

Max 8‐hr Ambient O3 Atlanta, CurrentTimeStandard, Outdoors...12‐8PM

Max 8‐hr Ambient O3*Time Outdoors 12‐8PM PhysicalActivity Index (PAI)

Time Outdoors 12‐8PM*PAI Time Near Roads 12‐8PM

Time In‐Vehicles 12‐8PM

Figure 5G-4. 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 Atlanta. 

Because afternoon outdoor time expenditure and daily maximum 8-hour ambient O3

concentrations are an important determinant for high O3 exposures regardless of air quality 

scenario considered, we compared the distributions of these two variables using person days 

where daily maximum 8-hour O3 exposures were either below or above 50 ppb. Figure 5G-5 

presents this comparison for Boston13 school-age children (ages 5 to 18) and adults (ages 19 to 

35) and considering 2006 base air quality and air quality adjusted to just meet the existing O3 8-

hour standard. For school-age children that did not experience a daily maximum 8-hour 

 

13 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-20 

exposure at or above 50 ppb (e.g., top left panel, base air quality), over half of them did not 

spend afternoon time spent outdoors, while just under 20% of them spent at least two hours of 

their afternoon time spent outdoors, with fewer than 5% spending more than four hours of their 

afternoon time outdoors. In addition, nearly 70% would have their daily maximum 8-hour 

ambient concentrations below 50 ppb (please note, ambient is not exposure). 

Not surprisingly, the distributions for both the outdoor time and ambient concentration 

variables are shifted to the right of the figure for school-age children’s person days where daily 

maximum 8-hour exposures ≥ 50 ppb (e.g., Figure 5G-1, top left panel, base air quality), as for 

more than half of the days, highly exposed simulated individuals spend about 250 minutes 

outdoors during the afternoon hours along with experiencing daily maximum 8-hour ambient O3

concentrations ≥ 75 ppb. 

By design, when air quality is simulated to just meet the existing standard (e.g., Figure 

5G-5, bottom left panel), upper percentile ambient concentrations are reduced compared to those 

comprising the base air quality such that the majority of ambient concentrations fall well below 

the existing standard level of 75 ppb. Given so few occurrences of very high 8-hour ambient O3

concentrations for this air quality scenario, only those school-age children having a majority of 

their time spent outdoors experienced the highest daily maximum 8-hour O3 exposure 

concentrations (Figure 5G-5, bottom left panel, right-most solid line). For additional 

completeness, we note the time and concentration distributions for adult person days (Figure 

5G-5, right column) were similar with that estimated for school-age children.
 5G-21 

0

10

20

30

40

50

60

70

80

90

100

0 25 50 75 100 125 150

0

10

20

30

40

50

60

70

80

90

100

0 80 160 240 320 400 480

Percentile

Daily Maximum 8‐Hour AmbientOzone (ppb)

Percentile

Total Afternoon Time Spent Outdoors(minutes)

Boston, Children5‐18 YearsOld, Base Air Quality

0

10

20

30

40

50

60

70

80

90

100

0 25 50 75 100 125 150

0

10

20

30

40

50

60

70

80

90

100

0 80 160 240 320 400 480

Percentile

Daily Maximum 8‐Hour AmbientOzone (ppb)

Percentile

Total Afternoon Time Spent Outdoors(minutes)

Boston, Children5‐18 YearsOld, Existing 75 ppb Standard

0

10

20

30

40

50

60

70

80

90

100

0 25 50 75 100 125 150

0

10

20

30

40

50

60

70

80

90

100

0 80 160 240 320 400 480

Percentile

Daily Maximum 8‐Hour AmbientOzone (ppb)

Percentile

Total Afternoon Time Spent Outdoors(minutes)

Boston,Adults 19‐35 YearsOld, Base Air Quality

0

10

20

30

40

50

60

70

80

90

100

0 25 50 75 100 125 150

0

10

20

30

40

50

60

70

80

90

100

0 80 160 240 320 400 480

Percentile

Daily Maximum 8‐Hour AmbientOzone (ppb)

Percentile

Total Afternoon Time Spent Outdoors(minutes)

Boston,Adults 19‐35 YearsOld, Existing 75 ppb Standard

0

10

0

10 Out time: Max 8‐hr Exposure < 50 ppb Out time: Max 8‐hr Exposure ≥ 50 ppb

Max 8‐hr Amb: Max 8‐hr Exposure < 50 ppb Max 8‐hr Amb: Max 8‐hr Exposure  ≥ 50 ppb

Figure 5G-5. Distributions of afternoon outdoor time expenditure and daily maximum 8-

hour ambient O3 concentrations for simulated Boston school-age children (ages 5 to 18) 

(left) and adults (ages 19 to 35) (right) using base air quality (top) and concentrations 

adjusted to just meet the existing standard (bottom) for person days having daily 

maximum 8-hour exposures either below or above 50 ppb. 

By definition, any 8-hour average exposure is time-averaged across all 

microenvironmental concentrations; thus several different microenvironments may contribute to 

each person’s daily maximum level. Understandably based on the above analyses, the outdoor 

microenvironment is most important for persons experiencing the highest O3 exposures, but we 

are also interested in the percentage of time expenditure spent among detailed indoor, outdoor, 

and vehicular locations people may inhabit during the afternoon. We summed the afternoon time 

expended for highly exposed persons, considering a total of 12 microenvironments (i.e., 3 

indoor, 5 outdoor, 2 near road, and 2 vehicular). As an example, Figure 5G-6 presents this 

microenvironmental information for Boston school-age children (Figure 5G-6, top left panel) and 

adults (Figure 5G-6, top right panel) for persons experiencing daily maximum 8-hour average O3

exposures ≥ 50 ppb and considering base air quality conditions. On average, approximately 50% 
 5G-22 

of school-age children’s total afternoon time is spent outdoors, of which half of this portion is 

spent outdoors at home, with parks and other non-residential outdoor locations comprising the 

remaining portion. Approximately 45% of the school-age children’s afternoon time on high 

exposure days is spent indoors, while just less than 10% of afternoon time is spent near-roads or 

inside motor vehicles. Afternoon microenvironmental time expenditure for highly exposed 

adults (ages 19-35) in Boston was generally similar with that estimated for school-age children 

(Figure 5G-6, top right panel). 

A person’s activity level plays an important role in estimating the risk of adverse health 

responses to inhaled ozone. As such, we evaluated the activities performed by highly exposed 

individuals while they spent time outdoors during the afternoon hours. Note there are over 100 

specific activity codes used in CHAD/APEX, though not all of these will be used in an exposure 

modeling simulation depending on the particular diaries that are selected to represent the 

simulated study group. We summed the time spent in each specific activity across all highly 

exposed persons when spending afternoon time outdoors, ranked the activity sums, and identified 

the top eleven activities performed. An aggregate of any remaining less often performed 

activities was generated to complete this analysis of activity time expenditure. 

Figure 5G-6 shows results for Boston school-age children (bottom left panel), indicating 

that greater than half of the time highly exposed children spent outdoors specifically involves 

performing a moderate or greater exertion level activity, such as a sporting activity. The same 

type of analysis was done for highly exposed adults in Boston (Figure 5G-6, bottom right panel), 

whereas about 30% of the outdoor time expenditure was spent engaged in a paid work related 

activity (though not necessarily a high exertion level activity), about 15% of the time was spent 

playing sports or other moderate or greater exertion level activity, with much of the remaining 

specific activities associated with low exertion level (e.g., eating, sitting, visiting) or other less 

frequently performed activities of variable exertion level. These results support our 

identification of school-age children as an important exposure group, largely a result of the 

combined outdoor time expenditure along with concomitantly performing moderate or high 

exertion level activities. It is worth noting, one important group not directly assessed in the 

general population-based exposure modeling and remaining as a limitation to the main body 

HREA results is outdoor workers. This exposure study group was explicitly modeled using a 

scenario based approach and summarized in section 5G-4.2. 
 5G-23 

Indoor(Residence)

33%

Indoor(Otherindoor‐

9 MEs)

7%

Indoor(School)

5%

Near‐road (Within 10

yards ofstreet)

2% Near‐roadOther(4

MEs)

1%

Outdoor(Residential)

23%

Outdoor(Non‐

residential)

15%

Outdoor(Park or Golf

course)

6%

Outdoor(School

grounds)

3%

Outdoor(Restaurant

or Café)

<1%

Vehicle (Cars and

Light Duty Trucks)

5% Vehicle Other(4 MEs)

<1%

AfternoonTime Expenditure: Children5‐18, 8‐hr Exposures ≥ 50 ppb,

Boston, Base Air Quality

Indoor(Residence)

25%

Indoor(Otherindoor‐

11 MEs)

11% Indoor(Office

building, Bank, Post

office)

3%

Near‐road (Within 10

yards ofstreet)

2% Near‐roadOther(4

MEs)

2%

Outdoor(Residential)

16%

Outdoor(Non‐

Residential)

26%

Outdoor(Park or Golf

course)

7%

Outdoor(School

grounds)

1%

Outdoor(Restaurant

or Café)

<1%

Vehicle (Cars and

Light Duty Trucks)

7% Vehicle Other(4 MEs)

<1%

AfternoonTime Expenditure: Adults 19‐35, 8‐hr Exposures ≥50 ppb,

Boston, Base Air Quality

Leisure/Active

Leisure/Sports

33%

Participate in sports

18%

Play/Outdoor Leisure

8%

Othersports and

active leisure

5%

Play games

5%

Participate in passive

leisure

4%

Exercise/Walk

4%

UnspecifiedWork

3%

Play outdoors

2%

Eat

2%

Travel, All

2%

Other(67 activities)

14%

AfternoonOutdoor Activities: Children5‐18, 8‐hr Exposures ≥ 50 ppb,

Boston, Base Air Quality

Unspecified Work

32%

Passive,sitting

9%

Participate

in sports

9%

Outdoor chores

4%

Visit

4%

Eat

3%

Think and relax

2%

Exercise/Walk

4%

Other entertainment

/ social events

2%

Repair / maintain car

2%

Otherrepairs

2%

Other(80 activities)

27%

AfternoonOutdoor Activities: Adults 19‐35, 8‐hr Exposures ≥50 ppb,

Boston, Base Air Quality

Figure 5G-6. Afternoon microenvironmental time (top) and activities performed during 

afternoon time outdoors (bottom) for school-age children (left) and adults (right) 

experiencing 8-hour daily maximum O3 exposures ≥ 50 ppb, Boston base air quality, 2006. 

5G-3 ANALYSIS OF APEX SIMULATED LONGITUDINAL ACTIVITY PATTERNS 

IN SCHOOL-AGE CHILDREN 

We evaluated the APEX approach used for linking together cross-sectional activity 

pattern diaries to generate longitudinal profiles for our simulated individuals. Of particular 

interest were how well variability in outdoor participation rate and the amount of time expended 

were represented in our population-based exposure simulations. Our goal in developing the most 

reasonable longitudinal profiles is to capture expected, important features of population activity 

patterns, i.e., there is correlation within an individual’s day-to-day activity patterns (though not 

exactly repeated nor entirely random) and variability across the modeled study group in day-today activity patterns (not every simulated individual in the study group does the same thing on 

the same day). As a reminder, the longitudinal approach is probabilistic, though guided by key 

variables influencing activity patterns (i.e., age, sex, day-of-week, commute time [employed 

person only], daily maximum temperature, and in our application, considers within and between 

variability in outdoor time expenditure). See HREA Appendix 5B, section 5B-4.2. 
 5G-24 

We used the same event-level output data that was generated for the high exposure 

analysis above (section 5G-2), which includes the same six study areas – Atlanta, Boston, 

Denver, Houston, Philadelphia, and Sacramento – and focused the analysis on school-age 

children (ages 5-18). Total time spent outdoors during the afternoon hours (12 PM-8 PM) was 

calculated for each person-day of the simulation in each study area. Results of this analysis are 

presented in five individual plots for each study area, though combined in a multi-panel display, 

one per three study areas, designed to fit on a single page. The five individual plots generated 

for each study area are described as follows. 

1) Cumulative distribution summarizing each child’s median time spent outdoors 

across an O3 season:14 We first selected simulated individuals within the age group 

of interest (5-18) and then stratified these persons by sex. The median value (50th

percentile) of afternoon time spent outdoors was determined for each simulated 

individual using all days in their study area’s O3 season. This data set, comprised of 

individual median values (in minutes) was ranked and plotted, stratified by sex. 

2) Cumulative distribution summarizing each child’s afternoon outdoor 

participation (at least one minute/day) across an O3 season: We subset school-age 

children from the data set and stratified by sex. A categorical variable was developed 

by assigning a numeric value of 1 when an individual spent at least one minute during 

the afternoon hours outdoors on that given day. Then for each simulated individual, 

outdoor participation was determined by summing this variable across the simulation 

period (i.e., the number of days per O3 season the persons spent at least one minute 

outdoor during the afternoon hours) and dividing by the total number of days in that 

study area’s O3 season. This data set, comprised of individual participation values 

(provided as a percent) was ranked and plotted, stratified by sex. 

3) Cumulative distribution summarizing each child’s afternoon outdoor 

participation (at least two hours/day) across an O3 season: Calculated and 

presented in the same manner as #2 above, only that the categorical variable was 

assigned a numeric value of 1 if the simulated individual spent at least two hours 

outdoor during the afternoon hours on that given day. 

4) Daily time series of afternoon outdoor participation (at least two hours/day) by 

study group across O3 season: Using the categorical variable determined in #3, we 

calculated the study group’s outdoor participation for every day of each study area’s 

O3 season. Data similar in fashion to our earlier analyses of outdoor time expenditure 

(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-25 

outdoor participation for the simulated study group across each study area’s O3

season. 

5) Daily time series of the number of study-specific CHAD diaries used across O3

season: The APEX daily file output can include the identity of the specific CHAD 

diary used to simulate every individual’s daily activity. For every day simulated, we 

summed the number of CHAD diaries used to model the school age children’s 

activity patterns for each day, though stratified by CHAD study identifier (e.g., see 

HREA Appendix 5B, Table 5B-1). Plotted is the day-to-day variability in particular 

CHAD study diaries used across each study area’s O3 season. 

The results of this longitudinal activity pattern analysis in given in Figure 5G-7 and 

Figure 5G-8. To begin, a few generalities regarding the features of each plot and where 

consistency is exhibited across study areas. In general, simulated female school-age children 

tend to spend less afternoon time outdoors than their male counterparts, consistent of course with 

expectations and the data used to develop these simulated profiles (Graham and McCurdy, 

2004). About half of the simulated study group spends about half of their days with no afternoon 

time spent outdoors across their study area’s ozone season, while about 10-20% spent just over 2 

hours afternoon time spent outdoors for half of their days (top row of Figure 5G-7 and Figure 

5G-8). Nearly every simulated individual participates in at least one afternoon outdoor activity 

across the O3 season and exhibits a mostly monotonic relationship (2nd row of Figure 5G-7 and 

Figure 5G-8), though when considering durations of 2 hours or more, longitudinal outdoor 

participation drops dramatically (an non-linearly) for most persons comprising the study group 

(3rd row of Figure 5G-7 and Figure 5G-8). For more than half of simulated school age children, 

only approximately 1 out of every 5 days was spent outdoors during the afternoon hours for at 

least two hours, while a maximum value (around 3 of 5 days) was simulated for only about 10% 

or fewer children comprising the study group. Study group participation in at least two hours of 

afternoon time outdoors day-to-day ranges from about 5-40% across each study area’s O3 season 

(4th row of Figure 5G-7 and Figure 5G-8), though not surprisingly highest during typical summer 

months (June through September). And finally, the majority of CHAD diaries that are used 

come from the recently conducted ISR and OAB studies (bottom row of Figure 5G-7 and Figure 

5G-8). This is also expected given that these two studies were designed to collect children’s 

activity patterns and contribute to the bulk of the children’s diaries in CHAD. Worthy of note is 

the shift in the source of diaries used across the calendar year; the contribution from the OAB 

study increases during the summer months while that of the ISR wanes. This is because of the 

days/seasons of the year the original study data were collected; most of the ISR data were 

collected during non-summer months while the OAB study was conducted during peak O3
 5G-26 

concentration days. While the use of these different studies in varying numbers over the 

simulation period likely drives some of the observed variability in the outdoor participation, at 

this time (and previously) staff treat the CHAD study data equally without bias, following our 

initial screen of the CHAD master data base that selected for the most complete data available. 

Variability in the five longitudinal display plots across the six study areas is evident, 

though to a much smaller degree than that observed when considering the magnitude of the 

within study area variability. While different lengths of each study areas’ O3 season may negate 

direct comparability of the distributions presented, it is reasonable to conclude that simulated 

school-age children in the Atlanta, Houston, and Sacramento study areas had slightly overall 

greater participation in outdoor activities and spent more time outdoors than counterparts in 

Boston, Denver, and Philadelphia. That said, when considering the daily time series of 

participation rates, on many summer days Philadelphia and Sacramento school-age participation 

rates for males are as great or greater than participation rates observed most other study areas, 

including study areas likely having considerably warmer summer temperatures (Figure 5G-7 and 

Figure 5G-8, 4th row). It is possible that for study areas such as Atlanta, summertime maximum 

daily temperatures exceed the range affording outdoor comfort, yielding slightly lower rates of 

participation in outdoor activities. 

Overall, the simulated longitudinal profiles indicate the method for linking together 

cross-sectional diaries generates a diverse mixture of persons having variable, though expected, 

activity patterns: a small fraction of the simulated population spend a limited amount of 

afternoon time outdoors and occurring at a low frequency across an O3 season, a small fraction 

consistently spends a greater amount (> 2 hours) of time outdoors and occurring at greater 

frequency (e.g., 4/5 days per week), while the remaining simulated individuals fall somewhere in 

between these two lower and upper bounds regarding participation and total time. While we are 

not aware of a population database available to compare with these simulated results, we are 

comfortable with the method performance in representing the intended variability in longitudinal 

activity patterns
 5G-27 

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240

Cumulative Percentile

Median Afternoon Time Outdoors

(Minutes/Day)

Atlanta, 2007

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 1 Minute/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 2 Hours/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0%

20%

40%

60%

80%

100%

3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Afternoon Outdoor 

Participation (≥ 2 Hours/Day)

Exposure Simulation Date

,

School-Age Children (Females)

School-Age Children (Males)

0

200

400

600

800

1000

3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Number of CHAD Diary Days 

Used Per Day

Exposure Simulation Date

,

CIN ISR OAB SEA SUP CA[X] NHAPS Other

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240

Cumulative Percentile

Median Afternoon Time Outdoors

(Minutes/Day)

Boston, 2007

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 1 Minute/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 2 Hours/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0%

20%

40%

60%

80%

100%

3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Afternoon Outdoor 

Participation (≥ 2 Hours/Day)

Exposure Simulation Date

,

School-Age Children (Females)

School-Age Children (Males)

0

200

400

600

800

1000

3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Number of CHAD Diary Days 

Used Per Day

Exposure Simulation Date

,

CIN ISR OAB SEA SUP CA_studies NHAPS Other

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240

Cumulative Percentile

Median Afternoon Time Outdoors

(Minutes/Day)

Denver, 2007

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 1 Minute/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 2 Hours/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0%

20%

40%

60%

80%

100%

3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Afternoon Outdoor 

Participation (≥ 2 Hours/Day)

Exposure Simulation Date

,

School-Age Children (Females)

School-Age Children (Males)

0

200

400

600

800

1000

3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Number of CHAD Diary Days 

Used Per Day

Exposure Simulation Date

,

CIN ISR OAB SEA SUP CA_studies NHAPS Other

Figure 5G-7. Cumulative distribution of median time spent outdoors (top row), afternoon 

outdoor participation ≥ 1 minute/day (2nd row), and afternoon outdoor participation ≥ 2 

hours/day (3nd 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-28 

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240

Cumulative Percentile

Median Afternoon Time Outdoors

(Minutes/Day)

Houston, 2007

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 1 Minute/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 2 Hours/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0%

20%

40%

60%

80%

100%

1/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

11/1/2007

12/1/2007

Afternoon Outdoor 

Participation (≥ 2 Hours/Day)

Exposure Simulation Date

,

School-Age Children (Females)

School-Age Children (Males)

0

200

400

600

800

1000

1/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

11/1/2007

12/1/2007

Number of CHAD Diary Days 

Used Per Day

Exposure Simulation Date

,

CIN ISR OAB SEA SUP CA[X] NHAPS Other

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240

Cumulative Percentile

Median Afternoon Time Outdoors

(Minutes/Day)

Philadelphia, 2007

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 1 Minute/Day)

p ,

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 2 Hours/Day)

p ,

School-Age Children (Females)

School-Age Children (Males)

0%

20%

40%

60%

80%

100%

4/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Afternoon Outdoor 

Participation (≥ 2 Hours/Day)

Exposure Simulation Date

p ,

School-Age Children (Females)

School-Age Children (Males)

0

200

400

600

800

1000

4/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

Number of CHAD Diary Days 

Used Per Day

Exposure Simulation Date

p ,

CIN ISR OAB SEA SUP CA[X] NHAPS Other

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240

Cumulative Percentile

Median Afternoon Time Outdoors

(Minutes/Day)

Sacramento, 2007

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 1 Minute/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0

10

20

30

40

50

60

70

80

90

100

0% 20% 40% 60% 80% 100%

Cumulative Percentile

Afternoon Outdoor Participation

(Percent of Days ≥ 2 Hours/Day)

,

School-Age Children (Females)

School-Age Children (Males)

0%

20%

40%

60%

80%

100%

1/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

11/1/2007

12/1/2007

Afternoon Outdoor 

Participation (≥ 2 Hours/Day)

Exposure Simulation Date

,

School-Age Children (Females)

School-Age Children (Males)

0

200

400

600

800

1000

1/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/2007

10/1/2007

11/1/2007

12/1/2007

Number of CHAD Diary Days 

Used Per Day

Exposure Simulation Date

,

CIN ISR OAB SEA SUP CA[X] NHAPS Other

Figure 5G-8. Cumulative distribution of median time spent outdoors (top row), afternoon 

outdoor participation ≥ 1 minute/day (2nd row), and afternoon outdoor participation ≥ 2 

hours/day (3nd row) for male and female school-age children in Houston (left column), 

Philadelphia (middle column) and Sacramento (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-29 

5G-4 EXPOSURE RESULTS FOR ADDITIONAL AT-RISK POPULATIONS AND 

LIFESTAGES, EXPOSURE SCENARIOS, AND AIR QUALITY INPUT DATA 

USED 

This section includes results for three additional simulations designed to complement 

exposures estimated using our general population-based modeling approach presented in the 

main body of the HREA. These simulations include (1) exposures estimated for school-aged 

children during summer months only (section 5G-4.1), (2) adult outdoor worker exposures 

(section 5G-4.2), and (3) exposures to school-age children and asthmatic school-age children 

assuming a portion of these study groups exhibit averting behavior in response to high O3

concentration days (section 5G-4.3). 

5G-4.1 Exposure Estimated For All School-Age Children during Summer Months, 

neither Attending School nor Performing Paid Work 

A targeted simulation was performed for the Detroit study area during the months of June 

through August 2007 to simulate summertime exposures by assuming all children were on a 

traditional calendar year summer vacation. To do this, a subset of the CHAD diaries used by 

APEX was created by including only those persons that did not have any time spent while at 

school or time performing paid work. Even though the school children age range in our 

exposure simulation is 5-18 years old, to maximize the number of diaries available for use by 

APEX we expanded the CHAD diary selection to include children from 4-19 years old. In 

considering these diary selection criteria, the resulting time location activity pattern data set input 

to APEX had a total of 10,226 diaries having 379,524 event entries. All simulation conditions 

were set identically to those set for the main HREA exposure simulations in Detroit, though 

75,000 children were explicitly simulated here for this targeted analysis. Four air quality 

scenarios were considered: just meeting the existing 8-hour standard of 75 ppb, and at alternative 

levels of 70, 65, and 60 ppb. The exposure results from these targeted simulations were 

compared with identical APEX simulations run using all available CHAD diaries during the 

same summer months (i.e., diary days that include locations visited and activities performed 

from persons reporting either school time or paid work). 

Figure 5G-9 contains the exposure results for this simulation (“No School/Work 

Diaries”) and for a nearly identical simulation that differed only in that is used all CHAD diaries 

(“all CHAD Diaries”). When restricting the CHAD diary pool to include only those diaries 

having no time spent at school or performing paid work activities, there is about 1/3 or 33% 

increase in the number of children at or above each of the selected benchmark levels, a 

relationship also consistent when considering multiple exposures over the simulation period. 
 5G-30 

Figure 5G-9. Comparison of the percent of all school-age children having daily maximum 

O3 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-31 

5G-4.2 Exposures Estimated For Adult Outdoor Workers during Summer Months 

A targeted APEX simulation was performed for the Atlanta study area to simulate 

summertime exposures for two hypothetical adult outdoor worker study groups (ages 19-35 and 

ages 35-54), using 2006 air quality just meeting the existing O3 standard. To do this, both the 

daily and longitudinal activity patterns used by APEX needed to best reflect patterns expected 

for adult outdoor workers (e.g., a standardized work schedule during weekdays) while also 

capturing variability in those patterns across various occupation types and the overall simulated 

adult outdoor worker study group. The development of reasonable time location activity pattern 

data to be input to APEX was a complex undertaking, attempting to account for a number of 

influential factors such as the distribution of adult outdoor workers, their varying occupation 

types, the probabilities associated with performing outdoor work, the linking of this information 

with the existing CHAD diaries and APEX METS distributions, all to be done within the existing 

APEX model framework and capabilities. 

First, the complete distribution of all employed persons’ occupations was estimated using 

data provided by the U.S. Bureau of Labor and Statistics (US BLS, 2012b).15 The information of 

interest was obtained from the 2010 National Employment Matrix, data originally developed 

from the Occupational Employment Statistics (OES) survey and based on the 2010/2000 

Standard Occupational Classification (SOC) system. The three variables retained for our 

purposes here included the SOC occupation titles and codes and the 2010 estimated number of 

persons employed, covering over 750 occupation titles. 

Second, the identification of occupations where workers spend time outdoors for at least 

one or more days per week was determined using data from the Occupational Information 

Network (O*NET).16 The O*NET was developed by a partnership of public and private 

organizations17 via sponsorship by the US Department of Labor/Employment and Training 

Administration (USDOL/ETA). A wealth of information is provided by the O*NET regarding 

specific occupations including human interaction processes (e.g., amount of public speaking in a 

particular job, the likelihood of encountering angry people), physical work conditions (e.g., 

approximate time spent standing, whether exposed to radiation), and structural job characteristics 

(e.g., the degree of job automation, freedom to make decisions). 

An advanced search of O*NET was performed using the web database. We first isolated 

the data of interest here by work context and selected physical work conditions. Data tables for 

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 Additional information is available at http://www.onetonline.org. 17 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-32 

question, “how often does this job require working outdoors, exposed to all weather conditions?” 

and the second “how often does this job require working outdoors, under cover (e.g., structure 

with roof but no walls)?”. The tables contain the responses to these two questions, stratified by 

the occupation names/codes, and rated using a context score ranging from 0 to 100. According 

to the context scale provided by O*NET, occupations with a score of 75 were characterized as 

having at least one day per week outdoors, while a score of 100 indicated that every day work 

was performed outdoors by workers in that particular occupation, thus the greater the context 

score, the greater the likelihood of outdoor work participation. 

To start, there were 862 unique occupation codes with context scores for question #1 (i.e., 

exposed entirely to weather), 30 of which also contained context scores for question #2 (there 

were no occupations with context scores for question #2 alone). Assuming ozone exposure 

would be similar for outdoor workers whether under cover or totally exposed to weather, we 

merged the data responses from the two questions by occupation code and assigned the highest 

context score of the two responses to each occupation. Given the context scaling information 

provided by O*NET, we then assumed the context scores 76-80, 81-85, 86-90, 91-95, and 96-

100 characterized occupations as having 1, 2, 3, 4, or 5 days per week outdoors. Then, mapping 

of the O*NET occupations to the above described BLS SOC occupation data set was performed, 

and was generally agreeable, with a few exceptions.18 Following additional processing, there 

were 144 unique occupation titles having one to five days per week where work was performed 

outdoors and the number of persons constituting each. See Attachment 1 for the final 

O*NET/BLS mapping and additional data processing assumptions. 

These 144 specific occupations then needed to be mapped to the occupation-related 

activity codes used by APEX to generate METs in estimating energy expenditure. When CHAD 

was developed in the late 1990s, occupation codes from the 1990 US Census were mapped to 

twelve broad occupation categories19 and were assigned METs distributions based on the most 

commonly performed activities associated with work tasks. In order to use the APEX/CHAD 

METs database in its current format and integrate the newly developed 2010 BLS/O*NET data 

set, the 1990 Census occupation codes were translated using two additional mapping files: a 

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. 
 5G-33 

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 

BLS/O*NET Occupations 

Target for CHAD 

Diary Outdoor Work 

Days per Week 

(based on 

(n) BLS/O*NET mean) 

Number of Outdoor 

Work Days per Week

Percent of Employed Persons 

Performing Outdoor Work 

(mean) (min) (max)

Outdoor 

Workers only All Workers 

ADMIN 7 1.4 1 3 5.1 0.6 1 

ADMSUP 7 3.4 1 5 4.6 0.5 3 

FARM 19 4.0 1 5 18.3 2.1 4 

LABOR 10 4.5 1 5 9.3 1.1 5 

MACH 3 4.0 2 4 0.5 0.1 4 

PREC 42 2.5 1 5 25.1 2.9 3 

PROF 3 1.3 1 4 0.2 <0.1 1 

PROTEC

T 15 3 1 5 7.7 0.9 3 

SALE 1 1.0 1 1 2.4 0.3 1 

SERV 4 3.2 1 5 1 0.1 3 

TECH 6 3.4 3 4 3.1 0.4 3 

TRANS 27 4.3 1 5 22.7 2.7 4 

TOTAL 144 - - - 100 11.7 - 

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 

 

21 http://www.census.gov/people/eeotabulation/documentation/occcategories.pdf
 5G-34 

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 

present, these diaries retained that specific identifier and used to simulate persons having that 

particular occupation group. Because we were interested in generating a typical work week, that 

is, work was performed during weekday days, only weekday diaries were used to develop the 

target diary pool for the weekday schedule, giving a final pool of 1,403 usable diaries. 

Table 5G-9. Personal attributes and mean time spent working outdoors for CHAD diaries 

reporting at least two hours of outdoor work. 

Category Attribute 

Outdoor Worker 

Diary days (n) 

Mean Outdoor Work 

Time (minutes) 

CHAD 

Occupation 

Group 

ADMIN 13 264 

ADMSUP 4 336 

FARM 11 478 

LABOR 14 469 

MACH 10 320 

PREC 52 416 

PROF 8 307 

PROTECT 4 402 

SALE 13 290 

SERV 5 291 

TECH 1 290 

TRANS 12 328 

X 1363 344 

Age Range 

(years) 

15-18 19 327 

19-24 78 420 

25-34 155 443 

35-44 364 359 

45-54 384 347 

55-64 360 306 

65-74 137 286 

75-84 11 243 

> 84 2 475 

Sex F 349 290 

M 1161 365 

Day of Week 

FRI 123 349 

MON 174 379 

SAT 66 399 

SUN 41 434 

THU 280 341 

TUE 357 342 

WED 469 328 
 5G-35 

We assumed outdoor worker diaries having missing occupation information could be 

used to represent outdoor worker diaries having an assigned occupation and, in the absence of 

any additional information to suggest stronger alternative, simply assigned CHAD occupation 

groups to these diaries equally, though weighted by the appropriate days per week outdoor work 

was performed by a particular occupation group. This is in part because, in performing an APEX 

simulation, the options for developing a multiday diary profile can be either controlled by a key 

variable (such as time spent outdoors), use the same diary to represent every day of the simulated 

person’s exposure period, or be constructed by an entirely random sequence of diaries. Outdoor 

time, a commonly used key variable to represent intra- and inter-personal variability activity 

patterns over multiple days by APEX, in general is an unknown for each occupation group and 

thus the key variable approach cannot be used to develop the longitudinal profiles. Rather than 

use the same diary for each person day, we elected to use a random selection of diaries, though 

having the selected diaries drawn from specific occupation groups developed from a large diary 

pool weighted by the target number of days per week where outdoor work was performed (Table 

5G-8). To clarify how this was done, an example follows using a single occupation group of 

outdoor workers, i.e., those comprising the Transportation (TRANS) group. 

According to information summarized in Table 5G-8, transportation-associated workers 

(TRANS) were estimated to, on average, spend four days per week working outdoors. We 

assigned the set of all available weekday outdoor worker diaries having ‘missing’ for their 

occupation (n=1,363) as now having an occupation of ‘TRANS’. We then replicated this new 

set of outdoor worker diaries (including those few diaries having a known occupation, TRANS 

or otherwise, to total 1,403 outdoor worker diaries) to generate a data set now having four 

weekday days per person day. Thus from an APEX modeling perspective, all personal attributes 

for persons in that pool are identical from one day to the next and have an equal likelihood of 

selection. This four day by 1,403 person activity pattern data set, now principally comprised of 

outdoor workers having ‘TRANS’ as their occupation, was then combined with a single weekday 

by 1,403 person activity pattern data set, only that this particular one day data set, while still 

derived from the same set of outdoor worker diaries, differs in that all of the paid work time 

spent outdoors was changed to paid work time occurring within indoor locations for that one 

work day. Then, when APEX constructs a longitudinal diary for any person with the ‘TRANS’ 

occupation using completely random selection, all other personal attributes remaining the same, 

the probability of selecting an outdoor work diary for any day is 0.8 while that of an indoor diary 

is 0.2, appropriately reflecting, on average, the days per week that occupation group spends time 

working outdoors. This process of building up the activity pattern diary pool was repeated for 

each outdoor worker occupation group to reflect both the probability of performing either indoor 

or outdoor work during weekdays. This collection of weekday diaries was then combined with 
 5G-36 

the pool of all remaining CHAD weekend diaries (n=13,953 person days), though where persons 

had missing occupation information (n=12,561 person days), any one of the twelve occupation 

groups was randomly assigned to a given weekend day. This complete outdoor worker activity 

pattern set now totaled 98,133 person days22 having 3,656,560 activity events. 

That said, it was soon apparent this input data set was too large for APEX to use when 

error messages were generated upon model execution. For these outdoor worker simulations to 

proceed, we determined the maximum size of the diary data set was approximately 42,000 diary 

days. Our current response to this limitation was to first restrict the exposure simulations using a 

tighter age range, thus permitting us to also limit the activity pattern input data set by similar age 

ranges. Two age groups of outdoor workers were of interest for our exposure simulations: 19-35 

and 36-55 years old. To maximize the number of diaries used to model the 19-35 year olds,23 we 

increased the range for APEX usable diaries from the large outdoor worker activity pattern data 

set initially developed to include ages from 16 to 42 years old (n=30,657 person days). As a 

reminder, only the activity patterns of 16-18 year olds characterized as outdoor workers (i.e., 

persons having ≥ 2 hours of paid work occurring outdoors) were available to simulate adults ages 

19 or above. All of the anthropometric attributes of these simulated adults (e.g., body mass, 

resting metabolic rate, ventilation rate) were derived from their age appropriate data, 

distributions, and/or equations. There were adequate numbers of diaries available to model the 

36-55 year olds such that the age range for inclusion in that data set was restricted to those 

between the ages of 36-54 (n=41,736 person days). 

When modeling exposures using occupation groups, two additional input files were 

needed by APEX. The first was simply a file containing the CHAD ID and the specific 

occupation group identified for that person day. The second, a profile factors file, contains the 

probabilities a simulated person in the model domain will have a particular occupation, a profile 

variable that can be stratified by age, sex, and/or census tract (US EPA 2012a, b). Based on the 

information we developed in Table 5G-8, we only assigned specific probabilities for each 

particular occupation group, i.e., the percent of employed persons performing outdoor work 

using the outdoor worker proportions equally across ages, sexes and tracts. An additional 

modification to the APEX employment probabilities input file (Employment2000_043003.txt) 

was also needed to generate exposures and appropriate output summary tables only for employed 

persons. And finally, because of the generally limited number of diaries available in developing 

the diary pool for the 19-35 year olds and to use as many of the diaries available, we lengthened 

the age selection range (AgeCutPct = 30.0 and Age2Probab = 0.15) and only included two 

 

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-37 

temperature diary pools (<84 or ≥84) to have an adequate number of diaries available in each 

diary pool for APEX to execute the desired simulation. 

Finally, a 10,000 person exposure simulation was performed for each age group of 

outdoor workers in Atlanta for Jun 1- Aug 30, 2006 using air quality just meeting the existing 

standard. In addition, as a point of comparison for the longitudinal approaches developed here 

and used for estimating general population-based exposures, identical simulations were 

performed in Atlanta during the same time of year, air quality scenario, and age groups only 

differing by using the approach described in our primary HREA exposure simulations, i.e., using 

outdoor time as the key variable in developing longitudinal profiles, sampling from all available 

CHAD diaries, and not explicitly addressing simulated worker occupations and work performed 

(structured schedules and associated METs values).24 

We first summarized the outdoor work time performed by each simulated outdoor worker 

during weekdays, stratified by particular occupation, to ascertain whether or not the exposure 

simulation met our defined goal. In comparing the results of Table 5G-10 with those provided in 

Table 5G-8 we see that the goal was met for both age groups of outdoor workers, i.e., the 

longitudinal approach was structured correctly to reproduce the distribution of the outdoor 

worker occupation groups and the number of days persons in a particular group spent working 

outdoors. Estimated exposures are presented in (Figure 5G-10) for each of the two outdoor 

worker study groups and considering either a longitudinal scenario-based approach designed 

specifically to reflect an outdoor worker weekday schedule or using our general populationbased modeling approach. It is clear that when accounting for a structured schedule that includes 

repeated occurrences of time spent outdoors for a specified study group, all while more 

consistently performing work tasks that may be at or above moderate or greater exertion levels, a 

greater percent of the study group experiences exposures at or above the selected health effect 

benchmark levels than that estimated using our general population-based approach. The 

differences between exposures estimated for the two longitudinal approaches become much 

greater when considering the percent of persons experiencing multiple exposure days at or above 

benchmark levels. For example, ≤ 2% of the general population-based exposure group was 

estimated to have two or more exposures at or above 60 ppb-8hr, while >17% of specifically 

simulated outdoor workers were estimated to experience exposures at or above that same level. 

In general, there was little difference in exposures estimated for the two age groups of outdoor 

workers. 

 

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-38 

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. 

 

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 daily maximum 8-hr average exposure 

at or above 60 ppb 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 daily maximum 8-hr average exposure at or above 60 ppb 

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 

Age 

Group 

CHAD Occupation 

Group 

Percent of Simulated 

Outdoor Workers 

Number of Outdoor Work Days 

per Week 

(mean) (min) (max) 

19-35 

ADMIN 5.0 1.2 0 2 

ADMSUP 4.6 3.0 2 4 

FARM 19.1 3.9 3 5 

LABOR 9.5 4.9 5 5 

MACH 0.5 4.0 3 5 

PREC 24.3 3.0 2 4 

PROF 0.2 1.4 1 3 

PROTECT 7.8 3.0 2 4 

SALE 2.4 1.2 0 2 

SERV 1.1 3.0 2 4 

TECH 3.2 3.0 2 4 

TRANS 22.4 4.0 3 5 

36-55 

ADMIN 5.4 1.1 0 2 

ADMSUP 4.7 3 2 4 

FARM 17.5 3.9 3 5 

LABOR 9.6 4.9 5 5 

MACH 0.6 3.9 3 4 

PREC 24.8 3 2 4 

PROF 0.3 1 0 2 

PROTECT 7.5 3 2 4 

SALE 2.5 1.1 0 2 

SERV 0.9 3 2 4 

TECH 3.1 3 2 4 

TRANS 23.2 3.9 3 5 
 5G-39 

selection method. Intuitively, 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. 

0%

5%

10%

15%

20%

25%

30%

123456

Percentof Population Experiencing ExposuresAt or

Above Selected Benchmark Level While at

Moderate or Greater Exertion

Number of Times Benchmark Exceeded (June‐August 2006)

Outdoor Worker Scenario‐based Approach (ages 19‐35)

Atlanta, 2006,Just Meet Existing 75 ppb Standard

80 PPB

70 PPB

60 PPB

0%

2%

4%

6%

8%

10%

123456

Percentof Population Experiencing ExposuresAt or

Above Selected Benchmark Level While at

Moderate or Greater Exertion

Number of Times Benchmark Exceeded (June‐August 2007)

General Population‐basedApproach(ages 19‐35)

Atlanta, 2006, Just Meet Existing 75 ppb Standard

80 PPB

70 PPB

60 PPB

0%

5%

10%

15%

20%

25%

30%

123456

Percentof Population Experiencing ExposuresAt or

Above Selected Benchmark Level While at

Moderate or Greater Exertion

Number of Times Benchmark Exceeded (June‐August 2006)

Outdoor Worker Scenario‐based Approach (ages 36‐55)

Atlanta, 2006, Just Meet Existing 75 ppb Standard

80 PPB

70 PPB

60 PPB

0%

2%

4%

6%

8%

10%

123456

Percentof Population Experiencing ExposuresAt or

Above Selected Benchmark Level While at

Moderate or Greater Exertion

Number of Times Benchmark Exceeded (June‐August 2006)

General Population‐basedApproach(ages 36‐55)

Atlanta, 2006, Just Meet Existing 75 ppb Standard

80 PPB

70 PPB

60 PPB

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. 

5G-4.3 Averting Behavior and Potential Impact to Exposure Estimates 

A growing area of air pollution research involves evaluating the actions persons might 

perform in response to high O3 concentration days (ISA, section 4.1.1). Most commonly termed 

averting behaviors, they can be broadly characterized as personal activities that either reduce 
 5G-40 

pollutant emissions or limit personal exposure levels. The latter topic is of particular interest in 

this HREA due to the potential negative impact it could have on O3 concentration-response (CR) functions used to estimate health risk and on time expenditure and activity exertion levels 

recorded in the CHAD diaries used by APEX to estimate O3 exposures. To this end, we have 

performed an additional review of the available literature here beyond that summarized in the 

ISA to include several recent technical reports that collected and/or evaluated averting behavior 

data. Our purpose is to generate a few reasonable quantitative approximations that allow us to 

better understand how averting behavior might affect our current population-based exposure and 

risk estimates. We expect that the continued development and communication of air quality 

information via all levels of environmental, health, and meteorological organizations will only 

further increase awareness of air pollution, its associated health effects, and the recommended 

actions to take to avoid exposure, thus making averting behaviors and participation rates an even 

more important consideration in future O3 exposure and risk assessments. The following is a 

summary of our literature review, with details provided by Graham (2012). Later in this section, 

preliminary results of an exposure simulation designed to account for averting behavior are 

provided. 

The first element considered in our evaluation is peoples’ general perception of air 

pollution and whether they were aware of alert notification systems. The prevalence of 

awareness was variable; about 50% to 90% of survey study participants acknowledged or were 

familiar with air quality systems (e.g., Blanken et al., 1991; KS DOH, 2006; Mansfield et al., 

2006; Semenza et al., 2008) and was dependent on several factors. In studies that considered a 

persons’ health status, e.g., asthmatics or parents of asthmatic children, there was a consistently 

greater degree of awareness (approximately a few to 15 percentage points) when compared to 

that of non-asthmatics. Residing in an urban area was also an important influential factor raising 

awareness, as both the number of high air pollution events and their associated alerts are greater 

when compared to rural areas. Of lesser importance, though remaining a statistically significant 

influential variable, were several commonly correlated demographic attributes such as age, 

education-level, and income-level, with each factor positively associated with awareness. 

The second element considered in our evaluation was the type of averting behaviors 

performed. For our purposes in this O3 HREA, the most relevant studies were those evaluating 

outdoor time expenditure, more specifically, the duration of outdoor events and the associated 

exertion level of activities performed while outdoors. This is because both of these variables are 

necessary to understanding O3 exposure and associated adverse effects, and hence, in accurately 

estimating human health risk. 

As stated above regarding air quality awareness, asthmatics consistently indicated a 

greater likelihood of performing averting behaviors compared to non-asthmatics – estimated to 
 5G-41 

differ by about a factor of two. This difference could be the combined effect of those persons 

having been advised by health professional to avoid high air pollution events and them being 

aware of alert notification systems. Based on the survey studies reviewed, we estimate that 30% 

of asthmatics may reduce their outdoor activity level on alert days (e.g., KS DOH, 2006; 

McDermott et al., 2006; Wen et al., 2009).25 An estimate of 15%, derived from reductions in 

public attendance at outdoor events (Zivin and Neidell, 2009) would be consistent with our 

estimate above when considering that the Zivin and Neidell (2009) study group is likely 

comprised mainly of non-asthmatics. That said, both attenuation and the re-establishment of 

averting behavior was apparent when considering a few to several days above high pollution 

alert levels (either occurring over consecutive days or across an entire year) (McDermott et al., 

2006; Zivin and Neidell, 2009), suggesting that participation in averting behavior over a 

multiday period for an individual is complex and likely best represented by a time and activitydependent function rather than a simple point estimate. 

There were only a few studies offering quantitative estimates of durations of averting 

behavior, either considering outdoor exertion level or outdoor time (Bresnahan et al., 1997; 

Mansfield et.al, 2006, Neidell, 2010; Sexton, 2011). Each of these studies considered outdoor 

time expenditure during the afternoon hours. Based on the studies reviewed, we estimate that 

outdoor time/exertion during afternoon hours may be reduced by about 20-40 minutes in 

response to an air quality alert notification. Generally requisite factors include: a high alert level 

for the day (e.g., red or greater on the AQI), high O3 concentrations (above the NAAQS), and 

persons having a compromised health condition (e.g., asthmatic or elderly). 

The third element considered in our preliminary evaluation was how to further define the 

impact of averting behavior on modeled exposure estimates.26 As described in HREA section 

5.2.5, APEX uses time location activity data (diaries) from CHAD to estimate population-based 

exposures. These diaries originate from a number of differing studies; some were generated as 

part of an air pollution research study, some were collected during a summer/ozone season, while 

some diary days may have corresponded with high O3 concentration and air quality alert days. 

At this time, none of the diary days used by APEX have been specifically identified as 

representing days where a person did or did not adjust their activity pattern reduce their 

exposure. In considering the above discussion regarding the potential rate of participation and 

averting actions performed, it is possible that some of the CHAD diary days express instances 

where that selected individual may have reduced their time spent outdoors or reduced their 

exertion level while outdoors. Currently, without having a personal identifier for averting 

 

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-42 

behavior in CHAD, the diaries are assigned to a simulated persons’ day without directly 

considering ambient O3 concentration levels27. Therefore, it is possible that there are instances 

where, on a given APEX simulation day, the simulated person may use a diary day from a person 

that did engage in one or more types of averting behavior (e.g., a diary having less time than 

usual spent outdoors in the afternoon), while for most other persons simulated on the same day 

(or the same person on a different high concentration day) the diaries used are from persons that 

did not actively engage in averting behavior. As a result, the effect of averting behavior may 

already be incorporated into our exposure modeling, albeit to an unknown though likely small 

degree,28 though definitely generating low-biased estimates of exposures (and reduced number of 

persons at or above selected 8-h O3 benchmark concentrations) that would occur in the complete 

absence of averting behavior. 

With this in mind, we performed an APEX simulation to reflect the instance that a 

fraction of a selected study group spends less time outdoors on high concentration ozone days. 

First, a general APEX simulation was performed during June-August 2007 in Detroit to identify 

a short time period where a high number of children/asthmatic children were estimated to be 

exposed at or above the 8-hour O3 benchmark levels of interest. To maintain a degree of 

tractability in the simulations, the development of the new CHAD input data, and the analysis of 

the exposure results, we restricted the exposure simulations to 5,000 total persons. One such 

high exposure event occurred over a two-day period considering base year air quality – August 

1-2, 2007. Because conditions in APEX simulations can be controlled by using an identical 

random number seed, APEX daily files were output to explicitly identify all of the CHAD diaries 

used for this two-day simulation. 

The activity pattern data from the identified CHAD diaries used to simulate the two-day 

exposure period were then used to generate a new activity pattern input data set, one adjusted for 

the above estimated parameters used to reflect averting performed by the two exposure study 

groups of interest. We did this after determining the following: 

1) There were a total of 1,988 diaries used to simulate the maximum exposure day for 

each person, obviously some CHAD diaries were used more than once to simulate 

different people on their maximum exposure day. Note also, 48 diaries were used to 

simulate both days for the same person, 37 of these occasions were for unique 

individuals while for the remaining 11 instances the same diary was also used in 

either two or three persons two-day simulation. 

 

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-43 

2) We calculated the total time spent outdoors by hour of day for the CHAD diaries used 

by APEX for each persons’ maximum exposure day, estimated the mean outdoor time 

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 person’s 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=1,518 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 

Number 

of 

persons 

Mean 

Outdoor 

Time 

Total 

Diary 

Days 

1 181 43.2 181

2 182 43.4 364

3 134 44.6 402

4 84 45.6 336

5 47 40.6 235

6 20 48.4 120

7 13 44.1 91

8 7 45.4 56

9 4 39.5 36

10 5 48.0 50

11 5 40.6 55

12 2 35.0 24

13 1 5.0 13

14 1 45.0 14

15 3 60.0 45

17 1 60.0 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 
 5G-44 

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 

the hours of 3 PM-4 PM where both days would be adjusted for averting, thus likely 

having a negligible effect the meeting of our approximate averting goals. 

Both the outdoor time adjusted CHAD and the standard CHAD input files were 

separately used to simulate exposures to children and asthmatic children. Exposure results for 

the four simulations (simulated averting vs. no averting for both children and asthmatic children) 

are found in the main body of the HREA. 

5G-5 COMPARISON OF PERSONAL EXPOSURE MEASUREMENT AND APEX 

MODELED EXPOSURES 

A new evaluation of APEX was performed using a subset of personal O3 exposure 

measurements obtained from the Detroit Exposure and Aerosol Research Study (DEARS) (Meng 

et. al, 2012). For five consecutive days, personal O3 outdoor concentrations along with daily 

time-location activity diaries were collected from 36 study participants in Wayne County 

Michigan during July and August 2006. The majority of participants were female (80%) having 

a mean age of 40.6 (min 20, max 72); mean age for males was 41.4 (min 22, max 65). Rather 

than using daily personal exposures estimated below the reported detection limit of 3 ppb (i.e., 0, 

1 and 2 ppb), we approximated those falling below this level using a random assignment of 

concentrations of 1 and 2 ppb. 

An APEX simulation was performed considering these same geographic and temporal 

features, followed with the sub-setting of APEX output data according to important personal 

attributes of the DEARS study participants (specific 5-day collection study periods, age/sex 

distributions, outdoor time, ambient concentrations, and air exchange rate). For both data sets 

and considering the output variables independently, the median daily values for each study 

participant attribute was generated using each individual’s 5 person-days of data, then ranked 

median values were plotted along with each individual’s corresponding minimum and maximum 

value. Distributions for four of these variables of (personal O3 exposure, outdoor time, ambient 

O3 concentrations, and air exchange rate from each of the two data sets are presented in Figure 

5G-11. 

Distributions of time spent outdoors and ambient concentrations were similar by design 

of the APEX population-based sample selection method. The upper percentiles the DEARS 

participant AER distribution was greater than that of AER of APEX simulated persons. For 

example, 40% of DEARS participants had a median value of two air exchanges per hour, while 

the same rate was only observed for 5% of APEX simulated individuals. In contrast, over 50% 

of APEX simulated individuals had median daily O3 exposure concentrations above 10 ppb, 
 5G-45 

while only 3% of DEARS participants’ median values exceeded 10 ppb. This difference in 

exposure is surprising given the sharply higher residential indoor air exchange rate for the 

DEARS participants (i.e., indoor microenvironmental exposures would be expected to have a 

greater influence on total DEARS exposure compared with the APEX simulated exposures) all 

while holding all other potential influential variables the same between the two data sets and is 

subject to further investigation. 
 5G-46 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Cumulative Percent (%)

Daily Mean O3 Exposure (ppb)

p50

min

max

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240 300 360 420 480 540 600

Cumulative Percent (%)

Daily Total Outdoor Time (minutes)

p50

min

max

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35 40 45 50 55 60

Cumulative Percent (%)

Daily Mean Ambient O3 Concentration (ppb)

p50

min

max

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10

Cumulative Percent (%)

Daily Air Exchange Rate (hour-1)

p50

min

max

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Cumulative Percent (%)

Daily Mean O3 Exposure (ppb)

p50

min

max

0

10

20

30

40

50

60

70

80

90

100

0 60 120 180 240 300 360 420 480 540 600

Cumulative Percent (%)

Daily Total Outdoor Time (minutes)

p50

min

max

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35 40 45 50 55 60

Cumulative Percent (%)

Daily Mean Ambient O3 Concentration (ppb)

p50

min

max

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10

Cumulative Percent (%)

Daily Air Exchange Rate (hour-1)

p50

min

max

Figure 5G-11. Distribution of daily personal O3 exposures (top row), outdoor time (2nd row 

from top), ambient O3 concentrations (3rd row from top), and air exchange rate (bottom 

row) for DEARS study participants (left column) and APEX simulated individuals (right 

column) in Wayne County, MI, July-August 2006. 

DEARS DATA APEX DATA 
 5G-47 

5G-6 REFERENCES 

Ainsworth, B. E., Haskell, W. L., Leon, A. S., Jacobs, D. R., Jr., Montoye, H. J., Sallis, J. F., 

Paffenbarger, R. S., Jr. (1993). Compendium of physical activities: classification of energy 

costs of human physical activities. Med Sci Sports Exer. 25: 71-80. 

Arcus-Arth, A. and Blaisdell, J. (2007). Statistical distributions of daily breathing rates for 

narrow age groups of infants and children. Risk Analysis. 27(1):97-110. 

Akland, G. G., Hartwell, T. D., Johnson, T. R., Whitmore, R. W. (1985). Measuring human 

exposure to carbon monoxide in Washington, D. C. and Denver, Colorado during the 

winter of 1982-83. Environ Sci Technol. 19: 911-918. 

Blanken, P. D., Dillon, J., Wismann, G. (2001). The impact of an air quality advisory program 

on voluntary mobile source air pollution reduction. Atmos Environ. 35:2417-2421. 

Bresnahan, B. W., Dickie, M., Gerking, S. (1997). Averting behavior and air pollution. Land 

Econ. 73:340-357. 

Brochu, P., Ducre-Robitaille, J.-F., Brodeur, J. (2006a). Physiological daily inhalation rates for 

free-living individuals aged 1 month to 96 years, using data from doubly labeled water 

measurements: a proposal for air quality criteria, standard calculations and health risk 

assessment. Human and Ecological Risk Assessment. 12:675-701. 

Brochu, P., Ducre-Robitaille, J.-F., Brodeur, J. (2006b). Supplemental Material for 

Physiological daily inhalation rates for free-living individuals aged 1 month to 96 years, 

using data from doubly labeled water measurements: a proposal for air quality criteria, 

standard calculations and health risk assessment. Human and Ecological Risk Assessment. 

12:1-12. 

EPRI. (1988). A Study of Activity Patterns Among a Group of Los Angeles Asthmatics. 

Research Project 940-5. Electric Power Research Institute. Prepared by Roth Associates. 

November 1988. 

EPRI. (1992). A Survey of Daily Asthmatic Activity Patterns in Cincinnati. TR-101396. 

Research Project 940-05. Electric Power Research Institute. Prepared by Roth Associates. 

November 1992. 

Ford, E. S., Heath, G. W., Mannino, D. M., Redd, S. C. (2003). Leisure-time physical activity 

patterns among US adults with asthma. Chest. 124:432-437. 

Freedson, P. S. (1989). Field monitoring of physical activity in children. Pediatr Exerc Sci. 

 1:8-18. 

George, B. J., McCurdy, T. (2009). Investigating the American Time Use Survey from an 

exposure modeling perspective. JESEE. 21:92-105. 

Geyh, A. S., Xue, J., Özkaynak, H., and Spengler, J. D. (2000). The Harvard Southern 

California chronic ozone exposure study: assessing ozone exposure of grade-school-age 

children in two Southern California communities. Environ Health Persp. 108:265-270. 

Glen, G., Smith, L., Isaacs, K., McCurdy, T., Langstaff, J. (2008). A new method of longitudinal 

diary assembly for human exposure modeling. J Expos Sci Environ Epidem. 18:299-311. 

Graham, S. E., McCurdy, T. (2004). Developing meaningful cohorts for human exposure 

models. J Expo Anal Environ Epidemiol. 14: 23-43. 
 5G-48 

Graham, S. E., McCurdy, T. (2005). Revised ventilation rate (VE) equations for use in 

inhalation-oriented exposure models. Report no. EPA/600/X-05/008 is Appendix A of US 

EPA (2009b). Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543. 

Graham, S. (2009). Response to peer-review comments on Appendix A, prepared by S. Graham 

(US EPA). Response is Appendix D of US EPA (2009b). Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543. 

Graham, S. E. (2012). Comprehensive review of published averting behavior studies and 

available technical documents. Memo to Bryan Hubbell, Group Leader, Risk and Benefits 

Group, Office of Air Quality Planning and Standards. Docket # EPA-HQ-OAR-2008-

0699-0085. 

Hartwell, T. D., Clayton, C. A., Ritchie, R. M., Whitmore, R. W., Zelon, H. S., Jones, S. M., 

Whitehurst, D. A. (1984). Study of Carbon Monoxide Exposure of Residents of 

Washington, DC and Denver, Colorado. Research Triangle Park, NC: U.S. Environmental 

Protection Agency, Office of Research and Development, Environmental Monitoring 

Systems Laboratory. EPA-600/4-84-031. 

Houtven, G. V., Johnson, F. R., Mansfiled, C., Yang, J.-C., Pyles, A. (2003). Parental Averting 

Behavior With Respect to Ozone Alerts. For presentation at AEA/ASSA Meeting, San 

Diego CA, January 2004. 

Isaacs, K., Glen, G., McCurdy, T., Smith, L. (2008). Modeling energy expenditure and oxygen 

consumption in human exposure models: accounting for fatigue and EPOC. J Expos Sci 

Environ Epidemiol. 18: 289–298. 

Johnson, T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. 

Research Triangle Park, NC: U.S. Environmental Protection Agency, Environmental 

Monitoring Systems Laboratory. EPA-600/4-84-014. 

Johnson, T. (1989). Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric 

Power Research Institute. EPRI EN-6204. 

Johnson, T., Capel, J., McCoy, M. (1996a). Estimation of Ozone Exposures Experienced by 

Urban Residents Using a Probabilistic Version of NEM and 1990 Population Data. 

Prepared by International Technology Air Quality Services for Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. Contract no. 63-D-30094. April 1996. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr_td.html. 

Johnson, T., Capel, J., McCoy, M., Warnasch, J. (1996b). Estimation of Ozone Exposures 

Experienced by Outdoor Children in Nine Urban Areas Using a Probabilistic Version of 

NEM. Prepared by International Technology Air Quality Services for Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. Contract no. 63-D-30094. April 1996. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr_td.html

Johnson, T., Capel, J., McCoy, M., Warnasch, J. (1996c). Estimation of Ozone Exposures 

Experienced by Outdoor Workers in Nine Urban Areas Using a Probabilistic Version of 

NEM. 1996. Prepared by International Technology Air Quality Services for Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. Contract no. 63-D-30094. April, 1996. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr_td.html
 5G-49 

Klepeis, N. E., Tsang, A. M., Behar, J. V. (1996). Analysis of the National Human Activity 

Pattern Survey (NHAPS) Respondents from a Standpoint of Exposure Assessment. 

Washington, DC: U.S. Environmental Protection Agency, Office of Research and 

Development. EPA/600/R-96/074. 

Knowledge Networks. (2009). Field Report: National Scale Activity Survey (NSAS). 

Conducted for Research Triangle Institute. Submitted to Carol Mansfield November 13, 

2009. 

KS DOH. (2006). Environmental Factors, Outdoor Air Quality, and Activity Level. Results 

from 2005 Kansas Behavioral Risk Factor Surveillance System. Office of Health 

Promotion, Kansas Department of Health and Environment. Available at: 

http://www.kdheks.gov/brfss/PDF/cste_report_final.pdf 

Langstaff, J. E. (2007). OAQPS Staff Memorandum to Ozone NAAQS Review Docket (OAR2005-0172). Subject: Analysis of Uncertainty in Ozone Population Exposure Modeling. 

[January 31, 2007]. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html

Linn, W. S., Avol, E. L., Peng, R. C., Shamoo, D. A., Hackney, J. D. (1987). Replicated doseresponse study of sulfur dioxide effects in normal, atopic, and asthmatic volunteers. Am 

Rev Respir Dis. 136:1127-1134. 

Lioy, P.J. (1990). The analysis of total human exposure for exposure assessment: A multidiscipline science for examining human contact with contaminants. Environ Sci Technol. 

24: 938-945. 

Liu, L.-J. S., Box, M., Kalman, D., Kaufman, J., Koenig, J., Larson, T., Lumley, T., Sheppard, 

L., Wallace, L. (2003). Exposure assessment of particulate matter for susceptible 

populations in Seattle. Environ Health Persp. 111: 909-918. 

Mansfield, C., Houtven, G. V., Johnson, F. R., Yang, J.-C. (2009). Environmental Risks and 

Behavior: Do children spend less time outdoors when ozone pollution is high? ASSA 

annual meeting, January 5, 2009. Update of Houtven et al. (2003), using the OAB CHAD 

data set, and related to Mansfield et al. (2006). 

Mansfield, C., Johnson, F. R., Van Houtven, G. (2006). The missing piece: averting behavior for 

children’s ozone exposures. Resource Energy Econ. 28:215-228. 

Marino, A. J., Fletcher, E. N., Whitaker, R. C., Anderson, S. E. (2012). Amount and 

environmental predictors of outdoor playtime at home and school: Across-sectional 

analysis of a national sample of preschool-aged children attending Head Start. Health & 

Place. 18: 1224-1230. 

McCurdy, T. (2000). Conceptual basis for multi-route intake dose modeling using an energy 

expenditure approach. J Expo Anal Environ Epidemiol. 10: 1-12. 

McCurdy, T., Glen, G., Smith, L., Lakkadi, Y. (2000). The National Exposure Research 

Laboratory's Consolidated Human Activity Database. J Expo Anal Environ Epidemiol. 10: 

566-578. 

McDermott, M., Srivastava, R., Croskell, S. (2006). Awareness of and compliance with air 

pollution advisories: A comparison of parents of asthmatics with other parents. J Asthma. 

43:235-239. 
 5G-50 

Meng, Q., Williams, R., Pinto, J. P. (2012). Determinants of the associations between ambient 

concentrations and personal exposures to PM2.5, NO2, and O3 during DEARS. Atmos 

Environ. 63:109-116. 

Montoye, H. J., Kemper, H. C. G., Saris, W.H.N., Washburn, R.A. (1996). Measuring Physical 

Activity and Energy Expenditure. Champaign IL: Human Kinetics. 

National Research Council (1991). Human Exposure Assessment for Airborne Pollutants: 

Advances and Opportunities. Washington, DC: National Academy of Sciences. 

Neidell, M. (2010). Air quality warnings and outdoor activities: evidence from Southern 

California using a regression discontinuity approach design. J Epidemiol Community 

Health. 64:921-926. 

Richmond H., Palma, T, Langstaff, J., McCurdy, T., Glenn, G., Smith, L. (2002). Further 

Refinements and Testing of APEX (3.0): EPA's population exposure model for criteria and 

air toxic inhalation exposures. Joint meeting of the International Society of Exposure 

Analysis and International Society of Environmental Epidemiology, Vancouver, CAN. 

August 11-15, 2002. 

Robinson, J. P., Wiley, J. A., Piazza, T., Garrett, K., and Cirksena, K. (1989). Activity Patterns 

of California Residents and their Implications for Potential Exposure to Pollution. 

California Air Resources Board, Sacramento, CA. CARB-A6-177-33. 

Sallis, J. F., Bauman, A., Pratt, M. (1998). Environmental and policy interventions to promote 

physical activity. American Journal of Preventive Medicine. 15(4):379-397. 

Santuz, P., Baraldi, E., Filippone, M., Zacchello, F. (1997). Exercise performance in children 

with asthma: Is it different from that of healthy controls? Eur Respir J. 10:1254-1260. 

SAS (2012). SAS/STAT 9.2 User's Guide, Second Edition. Available at: 

http://support.sas.com/documentation/cdl/en/statug/63033/PDF/default/statug.pdf

Semenza J. C., Wilson, D. J., Parra J., Bontempo, B. D., Hart, M., Sailor, D. J., George, L. A. 

(2008). Public perception and behavior change in relationship to hot weather and air 

pollution. Environ Res. 107:401-411. 

Sexton, A. L. (2011). Responses to Air Quality Alerts: Do Americans Spend Less Time 

Outdoors? Available at: http://www.apec.umn.edu/prod/groups/cfans/@pub/@cfans/

@apec/documents/asset/cfans_asset_365645.pdf 

Shamoo, D. A., Linn, W. S., Peng, R.-C., Solomon, J. C., Webb, T. L., Hackney, J. D., Hong, H. 

(1994). Time-activity patterns and diurnal variation of respiratory status in a panel of 

asthmatics: implications for short-term air pollution effects. J Expo Anal Environ 

Epidemiol. 4(2):133-148. 

Spier, C. E., Little, D. E., Trim, S. C., Johnson, T. R., Linn, W. S., Hackney, J. D. (1992). 

Activity patterns in elementary and high school students exposed to oxidant pollution. J 

Expo Anal Environ Epidemiol. 2:277-293. 

Suh, H. (2008). Personal Communication, August 27, 2008, regarding personal exposure data 

for Atlanta. 

Tsang, A. M., Klepeis, N. E. (1996). Descriptive Statistics Tables from a Detailed Analysis of 

the National Human Activity Pattern Survey (NHAPS) Data. U.S. Environmental 

Protection Agency. EPA/600/R-96/148. 
 5G-51 

US BLS. (2012a). American Time Use Survey User’s Guide. Understanding ATUS 2003 to 

2011. United States Bureau of Labor Statistics (BLS). August 2012. Data and 

documentation available at: http://www.bls.gov/tus/

US BLS. (2012b). Employment outlook: 2010–2020. Overview of projections to 2020. 

Available at: http://www.bls.gov/opub/mlr/2012/01/art1full.pdf. 

US Census Bureau. (2007). 2000 Census of Population and Housing. Summary File 3 (SF3) 

Technical Documentation, available at: http://www.census.gov/prod/cen2000/doc/sf3.pdf. 

Individual SF3 files '30' (for income/poverty variables pct50) for each state were 

downloaded from: http://www2.census.gov/census_2000/datasets/Summary_File_3/. 

USDA. (2000). Continuing Survey of Food Intake by Individuals (CSFII) 1994-96, 1998. 

Agricultural Research Service, CD-ROM. Available at: 

http://www.ars.usda.gov/Services/docs.htm?docid=14531

US DHHS. (1999). Promoting physical activity: a guide for community action. Champaign, IL: 

Human Kinetics. Department of Health and Human Services, Public Health Service, 

Centers for Disease Control and Prevention, National Center for Chronic Disease 

Prevention and Health Promotion, Division of Nutrition and Physical Activity. See Table 

2-1 available at 

http://www.cdc.gov/nccdphp/dnpa/physical/pdf/PA_Intensity_table_2_1.pdf. 

US EPA. (1986). Air Quality Criteria for Ozone and Other Photochemical Oxidants. Office of 

Health and Environmental Assessment, Environmental Criteria and Assessment Office, 

U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-600/8-84-

020aF-eF. Available from: NTIS, Springfield, VA., PB87-142949. 

US EPA. (1996a). Review of National Ambient Air Quality Standards for Ozone: Assessment 

of Scientific and Technical Information - OAQPS Staff Paper. Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. EPA/452/R-96-007. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr_sp.html

US EPA . (1996b). Air Quality Criteria for Ozone and Related Photochemical Oxidants. Office 

of Research and Development, National Center for Environmental Assessment, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. EPA/600/P-93/004aF-cF. 

Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=2831

US EPA. (2002). Consolidated Human Activities Database (CHAD) Users Guide. Database and 

documentation available at: http://www.epa.gov/chadnet1/

US EPA . (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). 

National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Research Triangle Park, NC. EPA/600/R-05/004aF-cF. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923

US EPA. (2007a). Review of National Ambient Air Quality Standards for Ozone: Policy 

Assessment of Scientific and Technical Information - OAQPS Staff Paper. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. EPA-452/R-07-007. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_07_ozone_staff_paper.pdf

US EPA. (2007b). Ozone Population Exposure Analysis for Selected Urban Areas. Office of 

Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research 
 5G-52 

Triangle Park, NC. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_cr_td.html

US EPA. (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary 

National Ambient Air Quality Standard. EPA-452/R-08-008a. November 2008. Available 

at: http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf. 

US EPA . (2009a). Risk and Exposure Assessment to Support the Review of the SO2 Primary 

National Ambient Air Quality Standard. EPA-452/R-09-007. August 2009. Available at 

http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf. 

US EPA. (2009b). Metabolically derived human ventilation rates: a revised approach based 

upon oxygen consumption rates. National Center for Environmental Assessment, 

Washington, DC; EPA/600/R-06/129F. Available at: 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=202543

US EPA. (2010). Quantitative Risk and Exposure Assessment for Carbon Monoxide – 

Amended. EPA Office of Air Quality Planning and Standards. EPA-452/R-10-009. July 

2010. Available at: http://www.epa.gov/ttn/naaqs/standards/co/data/CO-REA-AmendedJuly2010.pdf

US EPA. (2011). Exposure Factors Handbook: 2011 Edition. National Center for Environmental 

Assessment, Washington, DC; EPA/600/R-09/052F. Available at: 

http://www.epa.gov/ncea/efh/pdfs/efh-complete.pdf

US EPA. (2012a). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4.4) Volume I: User’s Guide. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. EPA-452/B-12-001a. Available at: 

http://www.epa.gov/ttn/fera/human_apex.html

US EPA. (2012b). Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

Documentation (TRIM.Expo / APEX, Version 4.4) Volume II: Technical Support 

Document. Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC. EPA-452/B-12-001b. Available at: 

http://www.epa.gov/ttn/fera/human_apex.html

US EPA. (2013). Integrated Science Assessment of Ozone and Related Photochemical Oxidants. 

U.S. Environmental Protection Agency, National Center for Environmental Assessment, 

U.S. Environmental Protection Agency, Research Triangle Park, N. EPA 600/R-10/076F. 

Available at: http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download 

University of Michigan. (2012). The Panel Study of Income Dynamics (PSID). Data and 

documentation available at http://psidonline.isr.umich.edu. 

van Gent, R., van der Ent, K., van Essen-Zandvliet, L. E. M., Rovers, M. M., Kimpen, J. L. L., 

de Meer, G., Klijn, P. H. C. (2007). No Difference in physical activity in (un)diagnosed 

asthma and healthy controls. Pediatric Pulmonology. 42:1018-1023. 

van Veldhoven, N. H. M. J., Vermeer, A., Bogaard, J. M., Hessels, M. G. P., Wijnroks, L., 

Colland, V. T., van Essen-Zandvliet, E. E. M. (2001). Children with asthma and physical 

exercise: effects of an exercise programme. Clinical Rehabilitation. 15:360-370. 

Wen, X.-J., Balluz, L., Mokdad, A. (2009). Association between media alerts of Air Quality 

Index and change of outdoor activity among adult asthma in six states, BRFSS, 2005. J 

Comm Health. 34:40-46. 
 5G-53 

Wheeler, A., Zanobetti, A., Gold, D. R., Schwartz, J., Stone, P., Suh, H. H. (2006). The 

relationship between ambient air pollution and heart rate variability differs for individuals 

with heart and pulmonary disease. Environ Health Perspect. 114(4):560-6. 

Whitfield, R., Biller, W., Jusko, M., Keisler, J. (1996). A Probabilistic Assessment of Health 

Risks Associated with Short- and Long-Term Exposure to Tropospheric Ozone. Argonne 

National Laboratory, Argonne, IL. 

Wiley, J. A., Robinson, J. P., Piazza, T., Garrett, K., Cirksena, K., Cheng, Y.-T., Martin, G. 

(1991a). Activity Patterns of California Residents: Final Report. California Air Resources 

Board, Sacramento, CA. ARB/R93/487. Available from: NTIS, Springfield, VA., PB94-

108719. 

Wiley, J. A., Robinson, J. P., Cheng, Y.-T., Piazza, T., Stork, L., Pladsen, K. (1991b). Study of 

Children's Activity Patterns: Final Report. California Air Resources Board, Sacramento, 

CA. ARB-R-93/489. 

Williams, R., Suggs, J., Creason, J., Rodes, C., Lawless, P., Kwok, R., Zweidinger, R., Sheldon, 

L. (2000). The 1998 Baltimore particulate matter epidemiology-exposure study: Part 2. 

Personal exposure associated with an elderly population. J Expo Anal Environ Epidemiol. 

10(6): 533-543. 

Williams, R., Suggs J., Rea, A., Leovic, K., Vette, A., Croghan, C., Sheldon, L., Rodes, C., 

Thornburg, J., Ejire, A., Herbst, M., Sanders Jr., W. (2003a). The Research Triangle 

particulate panel study: PM mass concentrations relationships. Atmos Environ. 37:5349-

5363. 

Williams, R., Suggs, J., Rea, A., Sheldon, L., Rodes, C., Thornburg, J. (2003b). The Research 

Triangle particulate panel study: modeling ambient source contributions to personal and 

residential PM mass concentrations. Atmos Environ. 37:5365-5378. 

Xue, J., McCurdy, T., Spengler, J., Özkaynak, H. (2004). Understanding variability in time 

spent in selected locations for 7-12-year old children. J Expo Anal Environ Epidemiol. 

14(3):222-33. 

Zivin, J. G., Neidell M. (2009). Days of haze: Environmental information disclosure and 

intertemporal avoidance behavior. J Environment Econ Manag. 58(2):119-128.
 5G-54 

Attachment 1. Occupations estimated to have at least one day per week where work is performed outdoors

Major BLS SOC code Major BLS SOC name BLS SOC name 

BLS SOC code 

Employed (1000’s) O*NET Code O*NET Name Context 

Out days/wk 

11 

Management Occupations Industrial Production Managers 11-0000 150.3 11-3051.02 

Geothermal Production Managers 77 1 

11 

Management Occupations 

Farmers, Ranchers, and Other Agricultural Managers 11-9013 400.8 11-9013.01 

Nursery and Greenhouse Managers 96 5

11 

Management Occupations 

Farmers, Ranchers, and Other Agricultural Managers 11-9013 400.8 11-9013.03 Aquacultural Managers 82 2

11 

Management Occupations Construction Managers 11-9021 523.1 11-9021.00 Construction Managers 78 1 

13 

Business and Financial Operations Occupations 

Insurance Appraisers, Auto Damage 13-0000 10.6 13-1032.00 

Insurance Appraisers, Auto Damage 89 3 

13 

Business and Financial Operations Occupations Compliance Officers 13-1041 216.6 13-1041.04 

Government Property Inspectors and Investigators 82 2 

13 

Business and Financial Operations Occupations Farm Labor Contractors 13-1074 0.3 13-1074.00 Farm Labor Contractors 79 1 

13 

Business and Financial Operations Occupations 

Appraisers and Assessors of Real Estate 13-2021 77.8 13-2021.02 Appraisers, Real Estate 77 1 

17 

Architecture and Engineering Occupations Surveyors 17-0000 25.6 17-1022.00 Surveyors 90 3

17 

Architecture and Engineering Occupations Surveyors 17-1022 25.6 17-1022.01 Geodetic Surveyors 78 1

17 

Architecture and Engineering Occupations 

Surveying and Mapping Technicians 17-3031 56.9 17-3031.01 Surveying Technicians 92 4 

19 

Life, Physical, and Social Science Occupations Conservation Scientists 19-0000 7.8 19-1031.01 

Soil and Water Conservationists 79 1

19 

Life, Physical, and Social Science Occupations Conservation Scientists 19-1031 7.8 19-1031.03 Park Naturalists 78 1

19 

Life, Physical, and Social Science Occupations 

Forest and Conservation Technicians 19-4093 36.5 19-4093.00 

Forest and Conservation Technicians 90 3 

29 

Healthcare Practitioners and Technical Occupations 

Emergency Medical Technicians and Paramedics 29-0000 226.5 29-2041.00 

Emergency Medical Technicians and Paramedics 87 3 

33 

Protective Service Occupations 

First-Line Supervisors of Police and Detectives 33-0000 106.1 33-1012.00 

First-Line Supervisors of Police and Detectives 82 2 

33 

Protective Service Occupations 

First-Line Supervisors of Fire Fighting and Prevention Workers 33-1021 30 33-1021.01 

Municipal Fire Fighting and Prevention Supervisors 88 3

33 

Protective Service Occupations 

First-Line Supervisors of Fire Fighting and Prevention Workers 33-1021 30 33-1021.02 

Forest Fire Fighting and Prevention Supervisors 86 3

33 

Protective Service Occupations Firefighters 33-2011 155.2 33-2011.01 Municipal Firefighters 80 1

33 

Protective Service Occupations Firefighters 33-2011 155.2 33-2011.02 Forest Firefighters 87 3

33 

Protective Service Occupations 

Fire Inspectors and Investigators 33-2021 6.8 33-2021.01 Fire Inspectors 86 3

33 

Protective Service Occupations 

Fire Inspectors and Investigators 33-2021 6.8 33-2021.02 Fire Investigators 84 2

33 

Protective Service Occupations 

Forest Fire Inspectors and Prevention Specialists 33-2022 1.6 33-2022.00 

Forest Fire Inspectors and Prevention Specialists 77 1 
 5G-55 

Major BLS SOC code Major BLS SOC name BLS SOC name 

BLS SOC code 

Employed (1000’s) O*NET Code O*NET Name Context 

Out days/wk 

33 

Protective Service Occupations 

Detectives and Criminal Investigators 33-3021 23.9 33-3021.01 Police Detectives 87 3

33 

Protective Service Occupations 

Detectives and Criminal Investigators 33-3021 23.9 33-3021.03 

Criminal Investigators and Special Agents 76 1

33 

Protective Service Occupations 

Detectives and Criminal Investigators 33-3021 23.9 33-3021.05 

Immigration and Customs Inspectors 85 2

33 

Protective Service Occupations Fish and Game Wardens 33-3031 7.6 33-3031.00 Fish and Game Wardens 96 5 

33 

Protective Service Occupations Parking Enforcement Workers 33-3041 9.8 33-3041.00 

Parking Enforcement Workers 95 4 

33 

Protective Service Occupations 

Police and Sheriff's Patrol Officers 33-3051 332 33-3051.01 Police Patrol Officers 91 4

33 

Protective Service Occupations 

Police and Sheriff's Patrol Officers 33-3051 332 33-3051.03 

Sheriffs and Deputy Sheriffs 88 3

33 

Protective Service Occupations Animal Control Workers 33-9011 15.5 33-9011.00 Animal Control Workers 87 3 

33 

Protective Service Occupations 

Private Detectives and Investigators 33-9021 34.7 33-9021.00 

Private Detectives and Investigators 76 1 

33 

Protective Service Occupations Crossing Guards 33-9091 69.3 33-9091.00 Crossing Guards 100 5 

37 

Building and Grounds Cleaning and Maintenance Occupations 

First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers 37-0000 202.9 37-1012.00 

First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers 94 4 

37 

Building and Grounds Cleaning and Maintenance Occupations Pest Control Workers 37-2021 68.4 37-2021.00 Pest Control Workers 98 5 

37 

Building and Grounds Cleaning and Maintenance Occupations 

Landscaping and Groundskeeping Workers 37-3011 1151.5 37-3011.00 

Landscaping and Groundskeeping Workers 99 5 

37 

Building and Grounds Cleaning and Maintenance Occupations 

Pesticide Handlers, Sprayers, and Applicators, Vegetation 37-3012 29.5 37-3012.00 

Pesticide Handlers, Sprayers, and Applicators, Vegetation 89 3 

37 

Building and Grounds Cleaning and Maintenance Occupations Tree Trimmers and Pruners 37-3013 50.6 37-3013.00 

Tree Trimmers and Pruners 99 5 

39 

Personal Care and Service Occupations 39-0000 29.3 39-4031.00 

Morticians, Undertakers, and Funeral Directors 77 1

39 

Personal Care and Service Occupations Baggage Porters and Bellhops 39-6011 46 39-6011.00 

Baggage Porters and Bellhops 82 2 

43 

Office and Administrative Support Occupations Couriers and Messengers 43-0000 116.2 43-5021.00 Couriers and Messengers 89 3 

43 

Office and Administrative Support Occupations Meter Readers, Utilities 43-5041 40.5 43-5041.00 Meter Readers, Utilities 99 5 

43 

Office and Administrative Support Occupations Postal Service Mail Carriers 43-5052 316.7 43-5052.00 

Postal Service Mail Carriers 97 5 

45 

Farming, Fishing, and Forestry Occupations 

First-Line Supervisors of Farming, Fishing, and Forestry Workers 45-0000 11.8 45-1011.05 

First-Line Supervisors of Logging Workers 95 4
 5G-56 

Major BLS SOC code Major BLS SOC name BLS SOC name 

BLS SOC code 

Employed (1000’s) O*NET Code O*NET Name Context 

Out days/wk 

45 

Farming, Fishing, and Forestry Occupations 

First-Line Supervisors of Farming, Fishing, and Forestry Workers 45-1011 11.8 45-1011.06 

First-Line Supervisors of Aquacultural Workers 87 3

45 

Farming, Fishing, and Forestry Occupations 

First-Line Supervisors of Farming, Fishing, and Forestry Workers 45-1011 11.8 45-1011.07 

First-Line Supervisors of Agricultural Crop and Horticultural Workers 87 3

45 

Farming, Fishing, and Forestry Occupations 

First-Line Supervisors of Farming, Fishing, and Forestry Workers 45-1011 11.8 45-1011.08 

First-Line Supervisors of Animal Husbandry and Animal Care Workers 84 2

45 

Farming, Fishing, and Forestry Occupations Animal Breeders 45-2021 11.5 45-2021.00 Animal Breeders 91 4 

45 

Farming, Fishing, and Forestry Occupations 45-2091 186.6 45-2091.00 

Agricultural Equipment Operators 95 4

45 

Farming, Fishing, and Forestry Occupations 45-2092 186.6 45-2092.01 Nursery Workers 76 1

45 

Farming, Fishing, and Forestry Occupations 45-2092 186.6 45-2092.02 

Farmworkers and Laborers, Crop 98 5

45 

Farming, Fishing, and Forestry Occupations 45-2093 186.6 45-2093.00 

Farmworkers, Farm, Ranch, and Aquacultural Animals 83 2

45 

Farming, Fishing, and Forestry Occupations 

Fishers and Related Fishing Workers 45-3011 32 45-3011.00 

Fishers and Related Fishing Workers 93 4 

45 

Farming, Fishing, and Forestry Occupations 45-3021 0.6 45-3021.00 Hunters and Trappers 99 5

45 

Farming, Fishing, and Forestry Occupations 

Forest and Conservation Workers 45-4011 13.7 45-4011.00 

Forest and Conservation Workers 83 2 

45 

Farming, Fishing, and Forestry Occupations Fallers 45-4021 9.6 45-4021.00 Fallers 99 5 

45 

Farming, Fishing, and Forestry Occupations Logging Equipment Operators 45-4022 35.1 45-4022.00 

Logging Equipment Operators 95 4 

45 

Farming, Fishing, and Forestry Occupations Log Graders and Scalers 45-4023 3.8 45-4023.00 Log Graders and Scalers 81 2 

47 

Construction and Extraction Occupations 

First-Line Supervisors of Construction Trades and Extraction Workers 47-0000 558.5 47-1011.00 

First-Line Supervisors of Construction Trades and Extraction Workers 83 2 

47 

Construction and Extraction Occupations Brickmasons and Blockmasons 47-2021 89.2 47-2021.00 

Brickmasons and Blockmasons 99 5 

47 

Construction and Extraction Occupations Stonemasons 47-2022 15.6 47-2022.00 Stonemasons 92 4 

47 

Construction and Extraction Occupations Carpenters 47-2031 500.8 47-2031.01 Construction Carpenters 78 1

47 

Construction and Extraction Occupations Carpenters 47-2031 500.8 47-2031.02 Rough Carpenters 83 2

47 

Construction and Extraction Occupations 

Cement Masons and Concrete Finishers 47-2051 144.7 47-2051.00 

Cement Masons and Concrete Finishers 100 5 

47 

Construction and Extraction Occupations Construction Laborers 47-2061 998.8 47-2061.00 Construction Laborers 97 5 

47 

Construction and Extraction Occupations 

Paving, Surfacing, and Tamping Equipment Operators 47-2071 51.6 47-2071.00 

Paving, Surfacing, and Tamping Equipment Operators 94 4 

47 Construction and Pile-Driver Operators 47-2072 4.1 47-2072.00 Pile-Driver Operators 100 5 
 5G-57 

Major BLS SOC code Major BLS SOC name BLS SOC name 

BLS SOC code 

Employed (1000’s) O*NET Code O*NET Name Context 

Out days/wk 

Extraction Occupations 

47 

Construction and Extraction Occupations 

Operating Engineers and Other Construction Equipment Operators 47-2073 349.1 47-2073.00 

Operating Engineers and Other Construction Equipment Operators 99 5 

47 

Construction and Extraction Occupations Glaziers 47-2121 41.9 47-2121.00 Glaziers 91 4 

47 

Construction and Extraction Occupations 

Insulation Workers, Floor, Ceiling, and Wall 47-2131 23.2 47-2131.00 

Insulation Workers, Floor, Ceiling, and Wall 87 3 

47 

Construction and Extraction Occupations Pipelayers 47-2151 53.1 47-2151.00 Pipelayers 97 5 

47 

Construction and Extraction Occupations 

Plumbers, Pipefitters, and Steamfitters 47-2152 419.9 47-2152.02 Plumbers 79 1 

47 

Construction and Extraction Occupations 

Reinforcing Iron and Rebar Workers 47-2171 19.1 47-2171.00 

Reinforcing Iron and Rebar Workers 94 4 

47 

Construction and Extraction Occupations Roofers 47-2181 136.7 47-2181.00 Roofers 100 5 

47 

Construction and Extraction Occupations 

Structural Iron and Steel Workers 47-2221 59.8 47-2221.00 

Structural Iron and Steel Workers 97 5 

47 

Construction and Extraction Occupations 

Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters 47-3011 29.4 47-3011.00 

Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters 86 3 

47 

Construction and Extraction Occupations 

Helpers--Pipelayers, Plumbers, Pipefitters, and Steamfitters 47-3015 57.9 47-3015.00 

Helpers--Pipelayers, Plumbers, Pipefitters, and Steamfitters 87 3 

47 

Construction and Extraction Occupations Helpers--Roofers 47-3016 12.7 47-3016.00 Helpers--Roofers 88 3 

47 

Construction and Extraction Occupations 

Construction and Building Inspectors 47-4011 102.4 47-4011.00 

Construction and Building Inspectors 88 3 

47 

Construction and Extraction Occupations Fence Erectors 47-4031 32.1 47-4031.00 Fence Erectors 99 5 

47 

Construction and Extraction Occupations Highway Maintenance Workers 47-4051 148.5 47-4051.00 

Highway Maintenance Workers 84 2 

47 

Construction and Extraction Occupations 

Rail-Track Laying and Maintenance Equipment Operators 47-4061 15 47-4061.00 

Rail-Track Laying and Maintenance Equipment Operators 93 4 

47 

Construction and Extraction Occupations 

Septic Tank Servicers and Sewer Pipe Cleaners 47-4071 25.3 47-4071.00 

Septic Tank Servicers and Sewer Pipe Cleaners 92 4 

47 

Construction and Extraction Occupations Segmental Pavers 47-4091 1.3 47-4091.00 Segmental Pavers 84 2 

47 

Construction and Extraction Occupations Derrick Operators, Oil and Gas 47-5011 18.9 47-5011.00 

Derrick Operators, Oil and Gas 100 5 

47 

Construction and Extraction Occupations 

Rotary Drill Operators, Oil and Gas 47-5012 22.5 47-5012.00 

Rotary Drill Operators, Oil and Gas 99 5 

47 

Construction and Extraction Occupations 

Service Unit Operators, Oil, Gas, and Mining 47-5013 40.7 47-5013.00 

Service Unit Operators, Oil, Gas, and Mining 99 5 

47 

Construction and Extraction Occupations 

Earth Drillers, Except Oil and Gas 47-5021 17.8 47-5021.00 

Earth Drillers, Except Oil and Gas 95 4 

47 

Construction and Extraction Occupations 

Explosives Workers, Ordnance Handling Experts, and Blasters 47-5031 6.8 47-5031.00 

Explosives Workers, Ordnance Handling Experts, and Blasters 98 5 

47 

Construction and Extraction Occupations Rock Splitters, Quarry 47-5051 3.5 47-5051.00 Rock Splitters, Quarry 94 4 

47 Construction and Roustabouts, Oil and Gas 47-5071 52.7 47-5071.00 Roustabouts, Oil and Gas 100 5 
 5G-58 

Major BLS SOC code Major BLS SOC name BLS SOC name 

BLS SOC code 

Employed (1000’s) O*NET Code O*NET Name Context 

Out days/wk 

Extraction Occupations 

49 

Installation, Maintenance, and Repair Occupations 

Telecommunications Equipment Installers and Repairers, Except Line Installers 49-0000 194.9 49-2022.00 

Telecommunications Equipment Installers and Repairers, Except Line Installers 79 1 

49 

Installation, Maintenance, and Repair Occupations 

Electrical and Electronics Repairers, Powerhouse, Substation, and Relay 49-2095 23.4 49-2095.00 

Electrical and Electronics Repairers, Powerhouse, Substation, and Relay 92 4 

49 

Installation, Maintenance, and Repair Occupations 

Automotive Glass Installers and Repairers 49-3022 18.1 49-3022.00 

Automotive Glass Installers and Repairers 88 3 

49 

Installation, Maintenance, and Repair Occupations 

Bus and Truck Mechanics and Diesel Engine Specialists 49-3031 242.2 49-3031.00 

Bus and Truck Mechanics and Diesel Engine Specialists 83 2 

49 

Installation, Maintenance, and Repair Occupations 

Farm Equipment Mechanics and Service Technicians 49-3041 32.9 49-3041.00 

Farm Equipment Mechanics and Service Technicians 83 2 

49 

Installation, Maintenance, and Repair Occupations 

Mobile Heavy Equipment Mechanics, Except Engines 49-3042 124.6 49-3042.00 

Mobile Heavy Equipment Mechanics, Except Engines 78 1 

49 

Installation, Maintenance, and Repair Occupations Rail Car Repairers 49-3043 21.7 49-3043.00 Rail Car Repairers 81 2 

49 

Installation, Maintenance, and Repair Occupations 

Motorboat Mechanics and Service Technicians 49-3051 20.8 49-3051.00 

Motorboat Mechanics and Service Technicians 86 3 

49 

Installation, Maintenance, and Repair Occupations Mechanical Door Repairers 49-9011 12.8 49-9011.00 Mechanical Door Repairers 93 4 

49 

Installation, Maintenance, and Repair Occupations 

Control and Valve Installers and Repairers, Except Mechanical Door 49-9012 43.8 49-9012.00 

Control and Valve Installers and Repairers, Except Mechanical Door 88 3 

49 

Installation, Maintenance, and Repair Occupations 

Heating, Air Conditioning, and Refrigeration Mechanics and Installers 49-9021 133.9 49-9021.01 

Heating and Air Conditioning Mechanics and Installers 84 2

49 

Installation, Maintenance, and Repair Occupations 

Heating, Air Conditioning, and Refrigeration Mechanics and Installers 49-9021 133.9 49-9021.02 

Refrigeration Mechanics and Installers 94 4

49 

Installation, Maintenance, and Repair Occupations Millwrights 49-9044 36.5 49-9044.00 Millwrights 77 1 

49 

Installation, Maintenance, and Repair Occupations 

Electrical Power-Line Installers and Repairers 49-9051 108.4 49-9051.00 

Electrical Power-Line Installers and Repairers 95 4 

49 

Installation, Maintenance, and Repair Occupations 

Telecommunications Line Installers and Repairers 49-9052 160.6 49-9052.00 

Telecommunications Line Installers and Repairers 94 4 

49 

Installation, Maintenance, and Repair Occupations 

Coin, Vending, and Amusement Machine Servicers and Repairers 49-9091 39.1 49-9091.00 

Coin, Vending, and Amusement Machine Servicers and Repairers 85 2 

49 

Installation, Maintenance, and Repair Occupations Commercial Divers 49-9092 3.8 49-9092.00 Commercial Divers 92 4 

49 

Installation, Maintenance, and Repair Occupations Locksmiths and Safe Repairers 49-9094 25.7 49-9094.00 

Locksmiths and Safe Repairers 87 3 

49 

Installation, Maintenance, and Repair Occupations 

Manufactured Building and Mobile Home Installers 49-9095 7.8 49-9095.00 

Manufactured Building and Mobile Home Installers 86 3 

49 

Installation, Maintenance, and Repair Occupations Riggers 49-9096 15.2 49-9096.00 Riggers 86 3 

49 

Installation, Maintenance, and Repair Occupations 

Signal and Track Switch Repairers 49-9097 7.1 49-9097.00 

Signal and Track Switch Repairers 96 5 

49 Installation, Maintenance, 49-9099 143.6 49-9099.01 Geothermal Technicians 94 4 
 5G-59 

Major BLS SOC code Major BLS SOC name BLS SOC name 

BLS SOC code 

Employed (1000’s) O*NET Code O*NET Name Context 

Out days/wk 

and Repair Occupations 

51 Production Occupations 

Water and Wastewater Treatment Plant and System Operators 51-0000 110.7 51-8031.00 

Water and Wastewater Treatment Plant and System Operators 93 4 

51 Production Occupations Gas Plant Operators 51-8092 13.7 51-8092.00 Gas Plant Operators 92 4 51 Production Occupations 

Petroleum Pump System Operators, Refinery Operators, and Gaugers 51-8093 44.2 51-8093.00 

Petroleum Pump System Operators, Refinery Operators, and Gaugers 93 4 

53 

Transportation and Material Moving Occupations 

Aircraft Cargo Handling Supervisors 53-0000 6.3 53-1011.00 

Aircraft Cargo Handling Supervisors 77 1 

53 

Transportation and Material Moving Occupations Commercial Pilots 53-2012 32.7 53-2012.00 Commercial Pilots 92 4 

53 

Transportation and Material Moving Occupations 

Bus Drivers, Transit and Intercity 53-3021 186.3 53-3021.00 

Bus Drivers, Transit and Intercity 83 2 

53 

Transportation and Material Moving Occupations Driver/Sales Workers 53-3031 406.6 53-3031.00 Driver/Sales Workers 76 1 

53 

Transportation and Material Moving Occupations 

Heavy and Tractor-Trailer Truck Drivers 53-3032 1604.8 53-3032.00 

Heavy and Tractor-Trailer Truck Drivers 100 5 

53 

Transportation and Material Moving Occupations 

Light Truck or Delivery Services Drivers 53-3033 856 53-3033.00 

Light Truck or Delivery Services Drivers 100 5 

53 

Transportation and Material Moving Occupations Taxi Drivers and Chauffeurs 53-3041 239.9 53-3041.00 

Taxi Drivers and Chauffeurs 78 1 

53 

Transportation and Material Moving Occupations Locomotive Engineers 53-4011 38.7 53-4011.00 Locomotive Engineers 86 3 

53 

Transportation and Material Moving Occupations Locomotive Firers 53-4012 1.1 53-4012.00 Locomotive Firers 86 3 

53 

Transportation and Material Moving Occupations 

Rail Yard Engineers, Dinkey Operators, and Hostlers 53-4013 5.6 53-4013.00 

Rail Yard Engineers, Dinkey Operators, and Hostlers 84 2 

53 

Transportation and Material Moving Occupations 

Railroad Brake, Signal, and Switch Operators 53-4021 21.7 53-4021.00 

Railroad Brake, Signal, and Switch Operators 100 5 

53 

Transportation and Material Moving Occupations 

Railroad Conductors and Yardmasters 53-4031 40.8 53-4031.00 

Railroad Conductors and Yardmasters 87 3 

53 

Transportation and Material Moving Occupations 

Subway and Streetcar Operators 53-4041 6.5 53-4041.00 

Subway and Streetcar Operators 77 1 

53 

Transportation and Material Moving Occupations Sailors and Marine Oilers 53-5011 33.4 53-5011.00 Sailors and Marine Oilers 99 5 

53 

Transportation and Material Moving Occupations 

Captains, Mates, and Pilots of Water Vessels 53-5021 36.1 53-5021.02 

Mates- Ship, Boat, and Barge 95 4 
 5G-60 

Major BLS SOC code Major BLS SOC name BLS SOC name 

BLS SOC code 

Employed (1000’s) O*NET Code O*NET Name Context 

Out days/wk 

53 

Transportation and Material Moving Occupations Motorboat Operators 53-5022 3.1 53-5022.00 Motorboat Operators 85 2 

53 

Transportation and Material Moving Occupations Ship Engineers 53-5031 10.1 53-5031.00 Ship Engineers 77 1 

53 

Transportation and Material Moving Occupations Parking Lot Attendants 53-6021 125.1 53-6021.00 Parking Lot Attendants 76 1 

53 

Transportation and Material Moving Occupations 

Automotive and Watercraft Service Attendants 53-6031 86.3 53-6031.00 

Automotive and Watercraft Service Attendants 94 4 

53 

Transportation and Material Moving Occupations Transportation Inspectors 53-6051 27.4 53-6051.08 

Freight and Cargo Inspectors 80 1 

53 

Transportation and Material Moving Occupations 

Transportation Attendants, Except Flight Attendants 53-6061 24.8 53-6061.00 

Transportation Attendants, Except Flight Attendants 77 1 

53 

Transportation and Material Moving Occupations Dredge Operators 53-7031 2.1 53-7031.00 Dredge Operators 86 3 

53 

Transportation and Material Moving Occupations 

Excavating and Loading Machine and Dragline Operators 53-7032 61.5 53-7032.00 

Excavating and Loading Machine and Dragline Operators 88 3 

53 

Transportation and Material Moving Occupations 

Gas Compressor and Gas Pumping Station Operators 53-7071 4.5 53-7071.00 

Gas Compressor and Gas Pumping Station Operators 91 4 

53 

Transportation and Material Moving Occupations 

Pump Operators, Except Wellhead Pumpers 53-7072 10.8 53-7072.00 

Pump Operators, Except Wellhead Pumpers 99 5 

53 

Transportation and Material Moving Occupations Wellhead Pumpers 53-7073 15.1 53-7073.00 Wellhead Pumpers 93 4 

53 

Transportation and Material Moving Occupations 

Refuse and Recyclable Material Collectors 53-7081 139.9 53-7081.00 

Refuse and Recyclable Material Collectors 100 5 

53 

Transportation and Material Moving Occupations 

Tank Car, Truck, and Ship Loaders 53-7121 10.4 53-7121.00 

Tank Car, Truck, and Ship Loaders 91 4 
 5G-61 

Attachment 2. Additional mapping of O*NET occupation codes to CHAD/APEX METs occupation activif occ_code_onet='13-1032.00' then Occ_name_CHAD='ADMSUP';*Insurance Appraisers, Auto Damage;

if occ_code_onet='13-1041.04' then Occ_name_CHAD='ADMSUP';*Government Property Inspectors and Investigators;if occ_code_onet='13-1074.00' then Occ_name_CHAD='ADMSUP';*Farm Labor Contractors;if occ_code_onet='13-2021.02' then Occ_name_CHAD='ADMSUP';*Appraisers, Real Estate;if occ_code_onet='17-3031.01' then Occ_name_CHAD='TECH';*Surveying Technicians;if occ_code_onet='19-4093.00' then Occ_name_CHAD='TECH';*Forest and Conservation Technicians;if occ_code_onet='33-9011.00' then Occ_name_CHAD='PROTECT';*Animal Control Workers;if occ_code_onet='33-9021.00' then Occ_name_CHAD='PROTECT';*Private Detectives and Investigators;if occ_code_onet='33-9091.00' then Occ_name_CHAD='PROTECT';*Crossing Guards;if occ_code_onet='37-3013.00' then Occ_name_CHAD='FARM';*Tree Trimmers and Pruners;if occ_code_onet='43-5021.00' then Occ_name_CHAD='ADMSUP';*Couriers and Messengers;if occ_code_onet='45-2021.00' then Occ_name_CHAD='FARM';*Animal Breeders;if occ_code_onet='45-2093.00' then Occ_name_CHAD='FARM';*Farmworkers, Farm, Ranch, and Aquacultural Animals;if occ_code_onet='45-3011.00' then Occ_name_CHAD='FARM';*Fishers and Related Fishing Workers;if occ_code_onet='45-4011.00' then Occ_name_CHAD='FARM';*Forest and Conservation Workers;if occ_code_onet='45-4023.00' then Occ_name_CHAD='FARM';*Log Graders and Scalers;if occ_code_onet='47-2061.00' then Occ_name_CHAD='LABOR';*Construction Laborers;if occ_code_onet='47-2071.00' then Occ_name_CHAD='TRANS';*Paving, Surfacing, and Tamping Equipment Operators;if occ_code_onet='47-2073.00' then Occ_name_CHAD='TRANS';*Operating Engineers and Other Construction Equipment Oif occ_code_onet='47-2121.00' then Occ_name_CHAD='PREC';*Glaziers;if occ_code_onet='47-4011.00' then Occ_name_CHAD='ADMIN';*Construction and Building Inspectors;if occ_code_onet='47-4051.00' then Occ_name_CHAD='LABOR';*Highway Maintenance Workers;if occ_code_onet='47-4061.00' then Occ_name_CHAD='LABOR';*Rail-Track Laying and Maintenance Equipment Operators;if occ_code_onet='47-5071.00' then Occ_name_CHAD='LABOR';*Roustabouts, Oil and Gas;if occ_code_onet='49-3022.00' then Occ_name_CHAD='PREC';*Automotive Glass Installers and Repairers;if occ_code_onet='49-9091.00' then Occ_name_CHAD='PREC';*Coin, Vending, and Amusement Machine Servicers and Repaif occ_code_onet='49-9094.00' then Occ_name_CHAD='PREC';*Locksmiths and Safe Repairers;if occ_code_onet='49-9096.00' then Occ_name_CHAD='TRANS';*Riggers;if occ_code_onet='49-9099.01' then Occ_name_CHAD='TECH';*Geothermal Technicians;if occ_code_onet='53-1011.00' then Occ_name_CHAD='TRANS';*Aircraft Cargo Handling Supervisors;if occ_code_onet='53-5011.00' then Occ_name_CHAD='TRANS';*Sailors and Marine Oilers;if occ_code_onet='53-5031.00' then Occ_name_CHAD='TRANS';*Ship Engineers;if occ_code_onet='53-6051.08' then Occ_name_CHAD='TRANS';*Freight and Cargo Inspectors;*this following was mischaracterized as 'SALES'; if occ_code_onet='53-3032.00' then Occ_name_CHAD='TRANS'; *Heavy and Tractor-Trailer Truck Drivers;
 5G-62 

This page left intentionally blank 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication N